From 10e6953dc2534f33f9c098f4272238e591f35783 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Thu, 4 Jan 2024 16:12:20 -0800 Subject: [PATCH 001/254] small bug fixes --- flystar/align.py | 10 +++++----- flystar/analysis.py | 3 +++ flystar/match.py | 7 +++++-- flystar/plots.py | 25 +++++++++++++++++-------- flystar/transforms.py | 4 ++-- 5 files changed, 32 insertions(+), 17 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 3975510..e590436 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -304,7 +304,7 @@ def fit(self): self.ref_table.detections() ### Drop all stars that have 0 detections. - idx = np.where(self.ref_table['n_detect'] == 0)[0] + idx = np.where((self.ref_table['n_detect'] == 0) & (self.ref_table['ref_orig'] == False))[0] print(' *** Getting rid of {0:d} out of {1:d} junk sources'.format(len(idx), len(self.ref_table))) self.ref_table.remove_rows(idx) @@ -340,7 +340,7 @@ def fit(self): self.ref_table.detections() ### Drop all stars that have 0 detections. - idx = np.where(self.ref_table['n_detect'] == 0)[0] + idx = np.where((self.ref_table['n_detect'] == 0) & (self.ref_table['ref_orig'] == False))[0] print(' *** Getting rid of {0:d} out of {1:d} junk sources'.format(len(idx), len(self.ref_table))) self.ref_table.remove_rows(idx) @@ -1585,7 +1585,7 @@ def fit(self): self.match_and_transform(self.ref_mag_lim, self.dr_tol[nn], self.dm_tol[nn], self.outlier_tol[nn], self.trans_args[nn]) - + # Clean up the reference table # Find where stars are detected. self.ref_table.detections() @@ -1627,7 +1627,7 @@ def fit(self): self.ref_table.detections() ### Drop all stars that have 0 detections. - idx = np.where(self.ref_table['n_detect'] == 0)[0] + idx = np.where((self.ref_table['n_detect'] == 0) & (self.ref_table['ref_orig'] == False))[0] print(' *** Getting rid of {0:d} out of {1:d} junk sources'.format(len(idx), len(self.ref_table))) self.ref_table.remove_rows(idx) @@ -2445,7 +2445,7 @@ def find_transform(table1, table1_trans, table2, transModel=transforms.PolyTrans # calculate weights from *transformed* coords. This is where we use the # transformation object - if (table1_trans != None) and ('xe' in table1_trans.colnames): + if (table1_trans is not None) and ('xe' in table1_trans.colnames): x1e = table1_trans['xe'] y1e = table1_trans['ye'] diff --git a/flystar/analysis.py b/flystar/analysis.py index 85d7ef3..9f4f801 100644 --- a/flystar/analysis.py +++ b/flystar/analysis.py @@ -276,6 +276,9 @@ def rename_after_flystar(star_tab, label_dat_file, new_copy=True, dr_tol=0.05, d idx_lab, idx_star, dr, dm = match.match(x_lab, y_lab, m_lab, star_tab['x0'], star_tab['y0'], star_tab['m0'], dr_tol=dr_tol, dm_tol=dm_tol, verbose=verbose) + #print('idx_lab:') + #for iii in range(len(idx_lab)): + # print(label_tab["name"][idx_lab[iii]], star_tab["name"][idx_star[iii]]) print('Renaming {0:d} out of {1:d} stars'.format(len(idx_lab), len(star_tab))) diff --git a/flystar/match.py b/flystar/match.py index 67a04f3..ae1c86b 100644 --- a/flystar/match.py +++ b/flystar/match.py @@ -279,8 +279,11 @@ def match(x1, y1, m1, x2, y2, m2, dr_tol, dm_tol=None, verbose=True): idxs2 = np.ones(x1.size, dtype=int) * -1 # The matching will be done using a KDTree. - kdt = KDT(coords2, balanced_tree=False) - + #kdt = KDT(coords2, balanced_tree=False) + #KDTree handling of NaNs throws error in scipy v1.10.1 and newer. + #Replace NaNs in coords2 with zero (0). -SKT + kdt = KDT(np.where(np.isfinite(coords2), coords2, 0), balanced_tree=False) + # This returns the number of neighbors within the specified # radius. We will use this to find those stars that have no or one # match and deal with them easily. The more complicated conflict diff --git a/flystar/plots.py b/flystar/plots.py index 7abde6e..060c644 100755 --- a/flystar/plots.py +++ b/flystar/plots.py @@ -2361,6 +2361,10 @@ def plot_stars(tab, star_names, NcolMax=2, epoch_array = None, figsize=(15,25), epoch_array : None, array Array of the epoch indicies to plot. If None, plots all epochs. """ + + def rs(x): + return x.reshape(len(x)) + print( 'Creating residuals plots for star(s):' ) print( star_names ) @@ -2505,7 +2509,12 @@ def plot_stars(tab, star_names, NcolMax=2, epoch_array = None, figsize=(15,25), plt.plot(time, fitLineX + fitSigX, 'b--') plt.plot(time, fitLineX - fitSigX, 'b--') if not color_time: - plt.errorbar(time, x, yerr=xerr.reshape(len(xerr),), fmt='k.') + #print('x:',x) + #print('xerr:',xerr) + #print('xerr_reshaped:', xerr.reshape(len(xerr),)) + #plt.errorbar(time, x, yerr=xerr.reshape(len(xerr)), fmt='k.') + plt.errorbar(rs(time), rs(x), yerr=rs(xerr), fmt='k.') + #plt.errorbar(time, x, yerr=xerr, fmt='k.') else: norm = colors.Normalize(vmin=0, vmax=1, clip=True) mapper = cm.ScalarMappable(norm=norm, cmap='hsv') @@ -2537,7 +2546,7 @@ def plot_stars(tab, star_names, NcolMax=2, epoch_array = None, figsize=(15,25), plt.plot(time, fitLineY + fitSigY, 'b--') plt.plot(time, fitLineY - fitSigY, 'b--') if not color_time: - plt.errorbar(time, y, yerr=yerr.reshape(len(yerr),), fmt='k.') + plt.errorbar(rs(time), rs(y), yerr=rs(yerr), fmt='k.') else: norm = colors.Normalize(vmin=0, vmax=1, clip=True) mapper = cm.ScalarMappable(norm=norm, cmap='hsv') @@ -2567,7 +2576,7 @@ def plot_stars(tab, star_names, NcolMax=2, epoch_array = None, figsize=(15,25), plt.plot(time, fitLineM + fitSigM, 'g--') plt.plot(time, fitLineM - fitSigM, 'g--') if not color_time: - plt.errorbar(time, m, yerr=merr.reshape(len(merr),), fmt='k.') + plt.errorbar(rs(time), rs(m), yerr=rs(merr), fmt='k.') else: norm = colors.Normalize(vmin=0, vmax=1, clip=True) mapper = cm.ScalarMappable(norm=norm, cmap='hsv') @@ -2599,7 +2608,7 @@ def plot_stars(tab, star_names, NcolMax=2, epoch_array = None, figsize=(15,25), plt.plot(time, fitSigX*1e3, 'b--') plt.plot(time, -fitSigX*1e3, 'b--') if not color_time: - plt.errorbar(time, (x - fitLineX)*1e3, yerr=xerr.reshape(len(xerr),)*1e3, fmt='k.') + plt.errorbar(rs(time), rs(x - fitLineX)*1e3, yerr=rs(xerr)*1e3, fmt='k.') else: norm = colors.Normalize(vmin=0, vmax=1, clip=True) mapper = cm.ScalarMappable(norm=norm, cmap='hsv') @@ -2627,7 +2636,7 @@ def plot_stars(tab, star_names, NcolMax=2, epoch_array = None, figsize=(15,25), plt.plot(time, fitSigY*1e3, 'b--') plt.plot(time, -fitSigY*1e3, 'b--') if not color_time: - plt.errorbar(time, (y - fitLineY)*1e3, yerr=yerr.reshape(len(yerr),)*1e3, fmt='k.') + plt.errorbar(rs(time), rs(y - fitLineY)*1e3, yerr=rs(yerr)*1e3, fmt='k.') else: norm = colors.Normalize(vmin=0, vmax=1, clip=True) mapper = cm.ScalarMappable(norm=norm, cmap='hsv') @@ -2655,7 +2664,7 @@ def plot_stars(tab, star_names, NcolMax=2, epoch_array = None, figsize=(15,25), plt.plot(time, fitSigM*1e3, 'g--') plt.plot(time, -fitSigM*1e3, 'g--') if not color_time: - plt.errorbar(time, (m - fitLineM), yerr=merr.reshape(len(merr),), fmt='k.') + plt.errorbar(rs(time), rs(m - fitLineM), yerr=rs(merr), fmt='k.') else: norm = colors.Normalize(vmin=0, vmax=1, clip=True) mapper = cm.ScalarMappable(norm=norm, cmap='hsv') @@ -2682,8 +2691,8 @@ def plot_stars(tab, star_names, NcolMax=2, epoch_array = None, figsize=(15,25), paxes = plt.subplot(Nrows, Ncols, ind) if not color_time: - plt.errorbar(x,y, xerr=xerr.reshape(len(xerr),), - yerr=yerr.reshape(len(yerr),), fmt='k.') + plt.errorbar(rs(x),rs(y), xerr=rs(xerr), + yerr=rs(yerr), fmt='k.') else: sc = plt.scatter(x, y, s=0, c=dtime, vmin=0, vmax=1, cmap='hsv') clb = plt.colorbar(sc) diff --git a/flystar/transforms.py b/flystar/transforms.py index 0a1885a..ca2f7eb 100755 --- a/flystar/transforms.py +++ b/flystar/transforms.py @@ -288,7 +288,7 @@ def __init__(self, order, px, py, pxerr=None, pyerr=None, mag_offset=0.0): px_dict = PolyTransform.make_param_dict(px, self.poly_order, isY=False) py_dict = PolyTransform.make_param_dict(py, self.poly_order, isY=True) - fixed_params = {'c0_0': False, 'c1_0': True, 'c1_1': True} + fixed_params = {'c0_0': False, 'c1_0': True, 'c0_1': True} self.px = models.Polynomial2D(self.poly_order, **px_dict, fixed=fixed_params) self.py = models.Polynomial2D(self.poly_order, **py_dict, fixed=fixed_params) else: @@ -580,7 +580,7 @@ def derive_transform(cls, x, y, xref, yref, order, m=None, mref=None, init_gx = PolyTransform.make_param_dict(init_gx, poly_order, isY=False) init_gy = PolyTransform.make_param_dict(init_gy, poly_order, isY=True) - fixed_params = {'c0_0': False, 'c1_0': True, 'c1_1': True, 'c0_1': True} + fixed_params = {'c0_0': False, 'c1_0': True, 'c0_1': True} p_init_x = models.Polynomial2D(poly_order, **init_gx, fixed=fixed_params) p_init_y = models.Polynomial2D(poly_order, **init_gy, fixed=fixed_params) else: From 428c17da960521a1c116b74c90fb34d2d7cfe820 Mon Sep 17 00:00:00 2001 From: Jessica Lu Date: Tue, 13 Feb 2024 13:14:03 -0800 Subject: [PATCH 002/254] Adding some tests... not functional yet --- flystar/tests/test_align.py | 173 ++++++++++++++++++++++++++++++++++++ 1 file changed, 173 insertions(+) diff --git a/flystar/tests/test_align.py b/flystar/tests/test_align.py index 026a8b1..45741d7 100644 --- a/flystar/tests/test_align.py +++ b/flystar/tests/test_align.py @@ -214,6 +214,7 @@ def test_MosaicSelfRef_vel(): return + def test_MosaicToRef(): make_fake_starlists_poly1_vel(seed=42) @@ -273,7 +274,76 @@ def test_MosaicToRef(): assert np.any(np.not_equal(msc.ref_table['vx'], ref_list['vx'])) return msc + +def test_MosaicToRef_acc(): + make_fake_starlists_poly1_acc(seed=42) + ref_file = 'random_acc_ref.fits' + list_files = ['random_acc_0.fits', + 'random_acc_1.fits', + 'random_acc_2.fits', + 'random_acc_3.fits'] + + ref_list = Table.read(ref_file) + + # Convert velocities to arcsec/yr + ref_list['vx'] *= 1e-3 + ref_list['vy'] *= 1e-3 + ref_list['vxe'] *= 1e-3 + ref_list['vye'] *= 1e-3 + + # Convert accelerations to arcsec/yr**2 + ref_list['ax'] *= 1e-3 + ref_list['ay'] *= 1e-3 + ref_list['axe'] *= 1e-3 + ref_list['aye'] *= 1e-3 + + # Switch our list to a "increasing to the West" list. + ref_list['x0'] *= -1.0 + ref_list['vx'] *= -1.0 + ref_list['ax'] *= -1.0 + + lists = [starlists.StarList.read(lf) for lf in list_files] + + msc = align.MosaicToRef(ref_list, lists, iters=2, + dr_tol=[0.2, 0.1], dm_tol=[1, 0.5], + trans_class=transforms.PolyTransform, + trans_args={'order': 2}, + motion_class=motion_model.Accel, + use_vel=True, + update_ref_orig=False, verbose=False) + + msc.fit() + + # Check our status columns + assert 'use_in_trans' in msc.ref_table.colnames + assert 'used_in_trans' in msc.ref_table.colnames + assert 'ref_orig' in msc.ref_table.colnames + assert msc.ref_table['use_in_trans'].shape == msc.ref_table['x0'].shape + assert msc.ref_table['used_in_trans'].shape == msc.ref_table['x'].shape + + # The velocities should be almost the same as the input + # velocities since update_ref_orig == False. + np.testing.assert_almost_equal(msc.ref_table['vx'], ref_list['vx'], 5) + np.testing.assert_almost_equal(msc.ref_table['vy'], ref_list['vy'], 5) + + + ########## + # Align and let velocities be free. + ########## + msc.update_ref_orig = True + msc.fit() + + # The velocities should be almost the same (but not as close as before) + # as the input velocities since update_ref == False. + np.testing.assert_almost_equal(msc.ref_table['vx'], ref_list['vx'], 1) + np.testing.assert_almost_equal(msc.ref_table['vy'], ref_list['vy'], 1) + + # Also double check that they aren't exactly the same for the reference stars. + assert np.any(np.not_equal(msc.ref_table['vx'], ref_list['vx'])) + + return msc + def make_fake_starlists_shifts(): N_stars = 200 @@ -463,6 +533,109 @@ def make_fake_starlists_poly1_vel(seed=-1): new_lis.write('random_vel_{0:d}.fits'.format(ss), overwrite=True) return (xy_trans, mag_trans) + +def make_fake_starlists_poly1_acc(seed=-1): + # If seed >=0, then set random seed to that value + if seed >= 0: + np.random.seed(seed=seed) + + N_stars = 200 + + x0 = np.random.rand(N_stars) * 10.0 # arcsec (increasing to East) + y0 = np.random.rand(N_stars) * 10.0 # arcsec + x0e = np.random.randn(N_stars) * 5.0e-4 # arcsec + y0e = np.random.randn(N_stars) * 5.0e-4 # arcsec + vx = np.random.randn(N_stars) * 5.0 # mas / yr + vy = np.random.randn(N_stars) * 5.0 # mas / yr + vxe = np.random.randn(N_stars) * 0.1 # mas / yr + vye = np.random.randn(N_stars) * 0.1 # mas / yr + ax = np.random.randn(N_stars) * 0.5 # mas / yr^2 + ay = np.random.randn(N_stars) * 0.5 # mas / yr^2 + axe = np.random.randn(N_stars) * 0.01 # mas / yr^2 + aye = np.random.randn(N_stars) * 0.01 # mas / yr^2 + m0 = (np.random.rand(N_stars) * 8) + 9 # mag + m0e = np.random.randn(N_stars) * 0.05 # mag + t0 = np.ones(N_stars) * 2019.5 + + # Make all the errors positive + x0e = np.abs(x0e) + y0e = np.abs(y0e) + m0e = np.abs(m0e) + vxe = np.abs(vxe) + vye = np.abs(vye) + axe = np.abs(axe) + aye = np.abs(aye) + + name = ['star_{0:03d}'.format(ii) for ii in range(N_stars)] + + # Make an StarList + lis = starlists.StarList([name, m0, m0e, + x0, x0e, y0, y0e, + vx, vxe, vy, vye, + ax, axe, ay, aye, + t0], + names = ('name', 'm0', 'm0e', + 'x0', 'x0e', 'y0', 'y0e', + 'vx', 'vxe', 'vy', 'vye', + 'ax', 'axe', 'ay', 'aye', + 't0')) + + sdx = np.argsort(m0) + lis = lis[sdx] + + # Save original positions as reference (1st) list + # in a StarList format (with velocities). + lis.write('random_acc_ref.fits', overwrite=True) + + ########## + # Propogate to new times and distort. + ########## + # Make 4 new starlists with different epochs and transformations. + times = [2018.5, 2019.5, 2020.5, 2021.5] + xy_trans = [[[ 6.5, 0.99, 1e-5], [ 10.1, 1e-5, 0.99]], + [[100.3, 0.98, 1e-5], [ 50.5, 9e-6, 1.001]], + [[ 0.0, 1.00, 0.0], [ 0.0, 0.0, 1.0]], + [[250.0, 0.97, 2e-5], [-250.0, 1e-5, 1.001]]] + mag_trans = [0.1, 0.4, 0.0, -0.3] + + # Convert into pixels (undistorted) with the following info. + scale = 0.01 # arcsec / pix + shift = [1.0, 1.0] # pix + + for ss in range(len(times)): + dt = times[ss] - lis['t0'] + + x = lis['x0'] + (lis['vx']/1e3) * dt + (lis['ax']/1e3) * dt**2 + y = lis['y0'] + (lis['vy']/1e3) * dt + (lis['ay']/1e3) * dt**2 + t = np.ones(N_stars) * times[ss] + + # Convert into pixels + xp = (x / -scale) + shift[0] # -1 from switching to increasing to West (right) + yp = (y / scale) + shift[1] + xpe = lis['x0e'] / scale + ype = lis['y0e'] / scale + + # Distort the positions + trans = transforms.PolyTransform(1, xy_trans[ss][0], xy_trans[ss][1], mag_offset=mag_trans[ss]) + xd, yd = trans.evaluate(xp, yp) + md = trans.evaluate_mag(lis['m0']) + + # Perturb with small errors (0.1 pix) + xd += np.random.randn(N_stars) * 0.1 + yd += np.random.randn(N_stars) * 0.1 + md += np.random.randn(N_stars) * 0.02 + xde = xpe + yde = ype + mde = lis['m0e'] + + # Save the new list as a starlist. + new_lis = starlists.StarList([lis['name'], md, mde, xd, xde, yd, yde, t], + names=('name', 'm', 'me', 'x', 'xe', 'y', 'ye', 't')) + + new_lis.write('random_acc_{0:d}.fits'.format(ss), overwrite=True) + + return (xy_trans, mag_trans) + def test_MosaicToRef_hst_me(): """ From d7be289a07132653cb162305c7081dfee5e1c490 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Thu, 30 May 2024 16:47:37 -0700 Subject: [PATCH 003/254] minor bug fixes --- flystar/analysis.py | 61 +++++++++++++++++- flystar/plots.py | 152 ++++++++++++++++++++++++++++++++++++++++---- 2 files changed, 197 insertions(+), 16 deletions(-) diff --git a/flystar/analysis.py b/flystar/analysis.py index 9f4f801..9289343 100644 --- a/flystar/analysis.py +++ b/flystar/analysis.py @@ -54,6 +54,57 @@ def query_gaia(ra, dec, search_radius=30.0, table_name='gaiadr2'): return gaia +def check_gaia_parallaxes(ra,dec,search_radius=10.0,table_name='gaiadr3',target='(unnamed)', + file_ext=''): + """ + Query the Gaia database at the specified location + and with the specified search radius, and plot + parallaxes. + + Input + ---------- + ra : string + R.A. in hours in the format such as '17:45:40.3' + + dec : string + Dec. in degrees in the format such as '-29:00:28.0' + + search_radius : float + The search radius in arcseconds. + + Optional Input + -------------- + table_name : string + Options are 'gaiadr2' or 'gaiadr3' + """ + # Query Gaia + gaia = query_gaia(ra,dec,search_radius=search_radius,table_name=table_name) + # Set up reasonable histogram bins + plim0,plim1 = np.min(gaia['parallax']),np.max(gaia['parallax']) + pplim0,pplim1 = np.min(gaia['parallax']/gaia['parallax_error']),np.max(gaia['parallax']/gaia['parallax_error']) + binwidth = 1 + pbins = np.arange(np.floor(plim0),np.ceil(plim1)+binwidth,binwidth) + ppbins = np.arange(np.floor(pplim0),np.ceil(pplim1)+binwidth,binwidth) + # Find number where plx/plx_err>3 + p_perr = (gaia['parallax']/gaia['parallax_error']).compressed() + nppe3 = sum((p_perr>3).astype(int)) + nppen3 = sum((p_perr<-3).astype(int)) + print(table_name,'stars within',search_radius,'\" with plx/plx_err>3: ', nppe3, ' of ', len(gaia['parallax'])) + print(table_name,'stars within',search_radius,'\" with plx/plx_err<-3: ', nppen3, ' of ', len(gaia['parallax'])) + # Plot + plt.subplots(nrows=1,ncols=2,figsize=(12,6)) + plt.subplot(121) + plt.xlabel('parallax (mas)'); plt.ylabel('N stars') + plt.hist(gaia['parallax'],bins=pbins) + plt.yscale('log') + plt.title(table_name+' parallax histograms, '+str(search_radius)+'\" radius around '+target, loc='left') + plt.subplot(122) + plt.xlabel('parallax/parallax_error') + plt.hist(gaia['parallax']/gaia['parallax_error'],bins=ppbins) + plt.yscale('log') + plt.tight_layout() + plt.savefig('gaiaplx'+file_ext+'.png') + def prepare_gaia_for_flystar(gaia, ra, dec, targets_dict=None, match_dr_max=0.2): """ @@ -118,6 +169,13 @@ def prepare_gaia_for_flystar(gaia, ra, dec, targets_dict=None, match_dr_max=0.2) gaia_new['vy'][idx] = 0.0 gaia_new['vye'][idx] = 0.0 + #macy additions to try to fix wild magnitude values + gaia_new['ruwe'] = gaia['ruwe'] + try: + gaia_new = gaia_new[~gaia_new['m'].mask] + except: + print('no invalig mags') + gaia_new = gaia_new.filled() #convert masked colunms to regular columns if targets_dict != None: @@ -130,11 +188,10 @@ def prepare_gaia_for_flystar(gaia, ra, dec, targets_dict=None, match_dr_max=0.2) if dr[idx] < match_dr_max: gaia_new['name'][idx] = targ_name - print('Found match for: ', targ_name) + print('Found match for: ', targ_name, ' - ',gaia_new['source_id'][idx]) return gaia_new - def run_flystar(): test_file = '/u/jlu/work/microlens/OB150211/a_2018_10_19/a_ob150211_2018_10_19/lis/stars_matched2.fits' diff --git a/flystar/plots.py b/flystar/plots.py index 060c644..077f215 100755 --- a/flystar/plots.py +++ b/flystar/plots.py @@ -13,6 +13,8 @@ import math import astropy from astropy.io import ascii +from astropy.coordinates import SkyCoord +from astropy import units as u #################################################### # Code for making diagnostic plots for astrometry @@ -2221,23 +2223,23 @@ def plot_chi2_dist(tab, Ndetect, xlim=40, n_bins=50): return -def plot_chi2_dist_per_epoch(tab, Ndetect, xlim, ylim = [-1, 1], target_idx = 0): +def plot_chi2_dist_per_epoch(tab, Ndetect, mlim=[14,21], ylim = [-1, 1], target_idx = 0): """ tab = flystar table Ndetect = Number of epochs star detected in """ - diffX_arr = -99 * np.ones((len(tab['xe']), Ndetect)) - diffY_arr = -99 * np.ones((len(tab['xe']), Ndetect)) - errX_arr = -99 * np.ones((len(tab['xe']), Ndetect)) - errY_arr = -99 * np.ones((len(tab['xe']), Ndetect)) - sigX_arr = -99 * np.ones((len(tab['xe']), Ndetect)) - sigY_arr = -99 * np.ones((len(tab['xe']), Ndetect)) - m_arr = -99 * np.ones((len(tab['xe']), Ndetect)) + diffX_arr = np.nan * np.ones((len(tab['xe']), Ndetect)) + diffY_arr = np.nan * np.ones((len(tab['xe']), Ndetect)) + errX_arr = np.nan * np.ones((len(tab['xe']), Ndetect)) + errY_arr = np.nan * np.ones((len(tab['xe']), Ndetect)) + sigX_arr = np.nan * np.ones((len(tab['xe']), Ndetect)) + sigY_arr = np.nan * np.ones((len(tab['xe']), Ndetect)) + m_arr = np.nan * np.ones((len(tab['xe']), Ndetect)) for ii in range(len(tab['xe'])): # Ignore the NaNs fnd = np.argwhere(~np.isnan(tab['xe'][ii,:])) - if len(fnd) == Ndetect: + if len(fnd) == Ndetect and tab['use_in_trans'][ii]: time = tab['t'][ii, fnd] x = tab['x'][ii, fnd] y = tab['y'][ii, fnd] @@ -2280,14 +2282,17 @@ def plot_chi2_dist_per_epoch(tab, Ndetect, xlim, ylim = [-1, 1], target_idx = 0) if target_idx is not None: ax2.plot(m_arr[target_idx, ii], sigX_arr[target_idx, ii], 's', color='black', ms=5) ax2.plot(m_arr[target_idx, ii], sigY_arr[target_idx, ii], 'o', color='black', ms=5) - ax2.set_xlim(xlim[0], xlim[1]) + ax2.set_xlim(mlim[0], mlim[1]) ax2.set_ylim(-5, 5) ax2.axhline(y=0, color='black', alpha=0.9, zorder=1000) + ax2.axhline(y=np.nanmean(sigX_arr[:, ii]), color='tab:blue', alpha=0.9,linestyle='dotted', zorder=1001) + ax2.axhline(y=np.nanmean(sigY_arr[:, ii]), color='tab:orange', alpha=0.9,linestyle='dotted', zorder=1002) ax2.set_xlabel('mag') ax2.set_ylabel('sigma') ax2.set_title('Epoch {0}'.format(ii)) ax2.legend() + #print(errX_arr[:, ii]) ax3.errorbar(m_arr[:, ii], diffX_arr[:, ii]*1E3, yerr=errX_arr[:, ii]*1E3, marker='s', label = 'X', ls='none', color='tab:blue', alpha=0.4, ms=5) ax3.errorbar(m_arr[:, ii], diffY_arr[:, ii]*1E3, yerr=errY_arr[:, ii]*1E3, @@ -2297,13 +2302,134 @@ def plot_chi2_dist_per_epoch(tab, Ndetect, xlim, ylim = [-1, 1], target_idx = 0) marker='s', ls='none', color='black', ms=5) ax3.errorbar(m_arr[target_idx, ii], diffY_arr[target_idx, ii]*1E3, yerr=errY_arr[target_idx, ii]*1E3, marker='o', ls='none', color='black', ms=5) - ax3.set_xlim(xlim[0], xlim[1]) + ax3.set_xlim(mlim[0], mlim[1]) ax3.set_ylim(ylim[0], ylim[1]) ax3.axhline(y=0, color='black', alpha=0.9, zorder=1000) + ax3.axhline(y=np.nanmean(diffX_arr[:, ii]*1E3), color='tab:blue', alpha=0.9,linestyle='dotted', zorder=1001) + ax3.axhline(y=np.nanmean(diffY_arr[:, ii]*1E3), color='tab:orange', alpha=0.9,linestyle='dotted', zorder=1002) ax3.set_xlabel('mag') ax3.set_ylabel('residual (mas)') return + +def plot_chi2_ecliptic_per_epoch(tab, Ndetect,ra,dec, mlim=[14,21], ylim = [-1, 1], target_idx = 0): + """ + tab = flystar table + Ndetect = Number of epochs star detected in + """ + diffX_arr = -99 * np.ones((len(tab['xe']), Ndetect)) + diffY_arr = -99 * np.ones((len(tab['xe']), Ndetect)) + errX_arr = 99 * np.ones((len(tab['xe']), Ndetect)) + errY_arr = 99 * np.ones((len(tab['xe']), Ndetect)) + sigX_arr = -99 * np.ones((len(tab['xe']), Ndetect)) + sigY_arr = -99 * np.ones((len(tab['xe']), Ndetect)) + m_arr = -99 * np.ones((len(tab['xe']), Ndetect)) + + rad_to_as = 180/np.pi * 60 * 60 + deg_to_as = 60 * 60 + def eq_to_ec(ra,dec): + e = 23.446 * np.pi/180 + sinb = np.sin(dec)*np.cos(e) - np.cos(dec)*np.sin(e)*np.sin(ra) + cosb = np.cos(np.arcsin(sinb)) + cosg = np.cos(ra)*np.cos(dec)/cosb + sing = (np.sin(dec)*np.sin(e) + np.cos(dec)*np.cos(e)*np.sin(ra))/cosb + g,b = np.arctan2(sing,cosg)*180/np.pi,np.arcsin(sinb)*180/np.pi + g = 360+g + return g*deg_to_as,b*deg_to_as + coord0 = SkyCoord(ra=ra,dec=dec,unit=(u.hourangle, u.deg),frame='icrs') + + for ii in range(len(tab['xe'])): + # Ignore the NaNs + fnd = np.argwhere(~np.isnan(tab['xe'][ii,:])) + if len(fnd) == Ndetect and tab['use_in_trans'][ii]: + time = tab['t'][ii, fnd] + x = tab['x'][ii, fnd] + y = tab['y'][ii, fnd] + m = tab['m'][ii, fnd] + vx = tab['vx'][ii] + vy = tab['vy'][ii] + lambda_0,beta_0 = eq_to_ec((coord0.ra - tab['x0'][ii]*u.arcsec).radian, + (coord0.dec + tab['y0'][ii]*u.arcsec).radian) + x1 = coord0.ra - u.arcsec*x + y1 = coord0.dec + u.arcsec*y + ra_rad,dec_rad = x1.radian, y1.radian + lambda_obs,beta_obs = eq_to_ec(ra_rad,dec_rad) + x2 = coord0.ra - tab['x0'][ii]*u.arcsec - (time-tab['t0'][ii])*vx*u.arcsec + y2 = coord0.dec + tab['y0'][ii]*u.arcsec + (time-tab['t0'][ii])*vy*u.arcsec + ra_rad,dec_rad = x2.radian, y2.radian + lambda_pm,beta_pm = eq_to_ec(ra_rad,dec_rad) + + xerr = tab['xe'][ii, fnd] + yerr = tab['ye'][ii, fnd] + + dt = tab['t'][ii, fnd] - tab['t0'][ii] + fitLineX = lambda_pm + fitLineY = beta_pm + + diffX = lambda_obs - fitLineX + diffY = beta_obs - fitLineY + sigX = diffX / xerr + sigY = diffY / yerr + + diffX_arr[ii] = diffX.reshape(Ndetect,) + diffY_arr[ii] = diffY.reshape(Ndetect,) + errX_arr[ii] = xerr.reshape(Ndetect,) + errY_arr[ii] = yerr.reshape(Ndetect,) + sigX_arr[ii] = sigX.reshape(Ndetect,) + sigY_arr[ii] = sigY.reshape(Ndetect,) + m_arr[ii] = m.reshape(Ndetect,) + + ts_folded = tab['t'][0]%1 + i_sort = np.argsort(ts_folded) + print(ts_folded,i_sort) + for ii in i_sort: +# fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(15, 4), +# gridspec_kw={'width_ratios': [1, 2, 2]}) +# plt.subplots_adjust(wspace=0.5) +# ax1.hist(sigX_arr[:, ii], label = 'X', histtype='step', bins=np.linspace(-10, 10)) +# ax1.hist(sigY_arr[:, ii], label = 'Y', histtype='step', bins=np.linspace(-10, 10)) +# ax1.set_xlabel('sigma') +# ax1.legend() + + fig, (ax2, ax3) = plt.subplots(1, 2, figsize=(14, 4)) + plt.subplots_adjust(wspace=0.25) + + '''ax2.plot(m_arr[:, ii], sigX_arr[:, ii], 's', label = 'lambda', color='tab:blue', alpha=0.4, ms=5) + ax2.plot(m_arr[:, ii], sigY_arr[:, ii], 'o', label = 'beta', color='tab:orange', alpha=0.4, ms=5) + if target_idx is not None: + ax2.plot(m_arr[target_idx, ii], sigX_arr[target_idx, ii], 's', color='black', ms=5) + ax2.plot(m_arr[target_idx, ii], sigY_arr[target_idx, ii], 'o', color='black', ms=5) + ax2.set_xlim(mlim[0], mlim[1]) + ax2.set_ylim(-5, 5) + ax2.axhline(y=0, color='black', alpha=0.9, zorder=1000) + ax2.set_xlabel('mag') + ax2.set_ylabel('sigma')''' + ax2.set_title('Epoch {0}'.format(ii)+', phase='+str(tab['t'][0][ii]%1)[:5]) + + #print(errX_arr[:, ii]) + ax2.errorbar(m_arr[:, ii], diffX_arr[:, ii]*1E3, yerr=errX_arr[:, ii]*1E3, + marker='s', label = 'lambda', ls='none', color='tab:blue', alpha=0.4, ms=5) + ax3.errorbar(m_arr[:, ii], diffY_arr[:, ii]*1E3, yerr=errY_arr[:, ii]*1E3, + marker='o', label = 'beta', ls='none', color='tab:orange', alpha=0.4, ms=5) + if target_idx is not None: + #print('target',m_arr[target_idx, ii],diffX_arr[target_idx, ii]*1E3,diffY_arr[target_idx, ii]*1E3) + ax2.errorbar(m_arr[target_idx, ii], diffX_arr[target_idx, ii]*1E3, yerr=errX_arr[target_idx, ii]*1E3, + marker='s', ls='none', color='black', ms=5) + ax3.errorbar(m_arr[target_idx, ii], diffY_arr[target_idx, ii]*1E3, yerr=errY_arr[target_idx, ii]*1E3, + marker='o', ls='none', color='black', ms=5) + ax2.legend() + ax3.legend() + ax2.set_xlim(mlim[0], mlim[1]) + ax3.set_xlim(mlim[0], mlim[1]) + ax2.set_ylim(ylim[0], ylim[1]) + ax3.set_ylim(ylim[0], ylim[1]) + ax2.axhline(y=0, color='black', alpha=0.9, zorder=1000) + ax3.axhline(y=0, color='black', alpha=0.9, zorder=1000) + ax2.set_xlabel('mag') + ax2.set_ylabel('residual (mas)') + ax3.set_xlabel('mag') + ax3.set_ylabel('residual (mas)') + return def plot_chi2_dist_mag(tab, Ndetect, mlim=40, n_bins=30): """ @@ -2760,8 +2886,6 @@ def rs(x): return - - def plot_stars_nfilt(tab, star_names, NcolMax=2, epoch_array_list = None, color_list = None, figsize=(15,25), color_time=False, resTicRng=None): """ @@ -3319,7 +3443,7 @@ def plot_sky(stars_tab, label=label, picker=4) #for legend - if label is not '_nolegend_': + if label != '_nolegend_': line.set_label(str(label)) epochs_legend.append(line) From 295309b2304412e9b5357501cf4f882c4d941caf Mon Sep 17 00:00:00 2001 From: Jessica Lu Date: Tue, 11 Jun 2024 11:08:21 -0700 Subject: [PATCH 004/254] stub for new motion model --- flystar/motion_model.py | 128 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 128 insertions(+) create mode 100644 flystar/motion_model.py diff --git a/flystar/motion_model.py b/flystar/motion_model.py new file mode 100644 index 0000000..1333fa2 --- /dev/null +++ b/flystar/motion_model.py @@ -0,0 +1,128 @@ +from astropy.modeling import models, fitting +import numpy as np +from abc import ABC + +class MotionModel(ABC): + # Fit paramters: Shared fit parameters + fitter_param_names = [] + + # Fixed parameters: These are parameters that are required for the model, but are not + # fit quantities. For example, RA and Dec in a parallax model. + fixed_param_names = [] + + # Non-fit paramters: Custom paramters that will not be fit. + # These parameters should be derived from the fit parameters and + # they must exist as a variable on the model object + optional_param_names = [] + + def __init__(self, *args, **kwargs): + # Check that required phot_params are proper arrays. + # If not, then make them arrays of len(1). + for param in self.fitter_param_names: + param_var = getattr(self, param) + if not isinstance(param_var, (list, np.ndarray)): + setattr(self, param, np.array([param_var])) + + return + + def get_pos_at_time(self, t): + #return x, y + pass + + def fit_pos_at_time(self, t, x, y, xe, ye, update=True): + """ + Fit the input positions on the sky and errors + to determine new parameters for this motion model (MM). + Current MM parameters are used as the initial guess. + Best-fit parameters will be returned along with uncertainties + and updated if update=True. + """ + #return params, param_errors + pass + +class Linear(MotionModel): + """ + A 2D linear motion model for a star on the sky. + """ + fitter_param_names = ['x0', 'vx', 'y0', 'vy'] + + def __init__(x0, vx, y0, vy, t0): + self.x0 = x0 + self.vx = vx + self.y0 = y0 + self.vy = vy + self.t0 = t0 + + # Must call after setting parameters. + # This checks for proper parameter formatting. + super().__init__() + + self.poly_order = 1 + self.px = models.Polynomial1D(self.poly_order, c0=self.x0, c1=self.vx) + self.py = models.Polynomial1D(self.poly_order, c0=self.y0, c1=self.vy) + + return + + def get_pos_at_time(self, t): + dt = t - self.t0 + + x = self.px(dt) + y = self.py(dt) + + return x, y + + def fit_pos_at_time(self, t, x, y, xe, ye, update=False, fixed_t0=False): + fitter = fitting.LevMarLSQFitter() + + # Determine the new optimal t0. + # Unless t0 is fixed, calculate the t0 for the stars. + if fixed_t0 is False: + t_weight = 1.0 / np.hypot(xe, ye) + t0 = np.average(t, weights=t_weight) + elif fixed_t0 is True: + t0 = self.t0 + else: + t0 = fixed_t0 + + dt = t - t0 + + px_new = fitter(self.px, dt, x, weights=1/xe) + px_cov = fitter.fit_into['param_cov'] + + py_new = fitter(self.py, dt, y, weights=1/ye) + py_cov = fitter.fit_into['param_cov'] + + x0 = px.c0 + vx = px.c1 + y0 = py.c0 + vy = py.c1 + + px_param_errs = dict(zip(self.px.param_names, np.diag(px_cov)**0.5)) + py_param_errs = dict(zip(self.py.param_names, np.diag(py_cov)**0.5)) + + x0e = px_param_errors['c0'] + vxe = px_param_errors['c1'] + y0e = py_param_errors['c0'] + vye = py_param_errors['c1'] + + if update: + self.px = px_new + self.py = py_new + + self.x0 = self.px.c0 + self.vx = self.px.c1 + self.y0 = self.py.c0 + self.vy = self.py.c1 + + params = [x0, vx, y0, vy, t0] + param_errors = [x0e, vxe, y0e, vye] + + return params, param_errors + + + + + + + + From c69a630bfdfd400ae84923d32e0f38855f4264e6 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Mon, 17 Jun 2024 17:28:47 -0700 Subject: [PATCH 005/254] some initial basic implementation of MotionModel --- flystar/motion_model.py | 121 +++++++++++++++++++++++++++-------- flystar/startables.py | 137 +++++++++++++--------------------------- 2 files changed, 141 insertions(+), 117 deletions(-) diff --git a/flystar/motion_model.py b/flystar/motion_model.py index 1333fa2..1d4c8b9 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -29,7 +29,7 @@ def get_pos_at_time(self, t): #return x, y pass - def fit_pos_at_time(self, t, x, y, xe, ye, update=True): + def fit_motion_model(self, t, x, y, xe, ye, update=True): """ Fit the input positions on the sky and errors to determine new parameters for this motion model (MM). @@ -40,13 +40,53 @@ def fit_pos_at_time(self, t, x, y, xe, ye, update=True): #return params, param_errors pass +class Fixed(MotionModel): + """ + A non-moving motion model for a star on the sky. + """ + fitter_param_names = ['x0','y0'] + + def __init__(self, x0, y0, t0): + self.x0 = x0 + self.y0 = y0 + self.t0 = t0 + + # Must call after setting parameters. + # This checks for proper parameter formatting. + super().__init__() + + return + + def get_pos_at_time(self,t): + return self.x0, self.y0 + + def fit_motion_model(self, dt, x, y, xe, ye, update=False, fixed_t0=False): + # Handle single data point case + if len(x)==1: + return [x[0],y[0]],[xe[0],ye[0]] + + #TODO it seems like sometimes it's weighted by std and sometimes by var - confirm which to do here + x0 = np.average(x, weights=1/xe**2) + x0e = np.sqrt(np.average((x-x0)**2,weights=1/xe)) + y0 = np.average(y, weights=1/ye**2) + y0e = np.sqrt(np.average((y-y0)**2,weights=1/ye)) + + params = [x0, y0] + param_errors = [x0e, y0e] + + if update: + self.x0 = x0 + self.y0 = y0 + + return params, param_errors + class Linear(MotionModel): """ A 2D linear motion model for a star on the sky. """ fitter_param_names = ['x0', 'vx', 'y0', 'vy'] - def __init__(x0, vx, y0, vy, t0): + def __init__(self, x0, vx, y0, vy, t0): self.x0 = x0 self.vx = vx self.y0 = y0 @@ -71,39 +111,70 @@ def get_pos_at_time(self, t): return x, y - def fit_pos_at_time(self, t, x, y, xe, ye, update=False, fixed_t0=False): + def fit_motion_model(self, dt, x, y, xe, ye, update=False, bootstrap=False): fitter = fitting.LevMarLSQFitter() - # Determine the new optimal t0. - # Unless t0 is fixed, calculate the t0 for the stars. - if fixed_t0 is False: - t_weight = 1.0 / np.hypot(xe, ye) - t0 = np.average(t, weights=t_weight) - elif fixed_t0 is True: - t0 = self.t0 - else: - t0 = fixed_t0 - - dt = t - t0 + # Handle 2-data point case + # TODO is this the best way to handle this case ? + if len(x)==2: + x0 = np.average(x, weights=1.0/xe**2) + y0 = np.average(y, weights=1.0/ye**2) + dx = np.diff(x)[0] + dy = np.diff(y)[0] + dt_diff = np.diff(dt)[0] + x0e = np.abs(dx) / 2**0.5 + y0e = np.abs(dy) / 2**0.5 + vx = dx / dt_diff + vy = dy / dt_diff + vxe = 0.0 + vye = 0.0 + return [x0, vx, y0, vy],[x0e, vxe, y0e, vye] px_new = fitter(self.px, dt, x, weights=1/xe) - px_cov = fitter.fit_into['param_cov'] + px_cov = fitter.fit_info['param_cov'] py_new = fitter(self.py, dt, y, weights=1/ye) - py_cov = fitter.fit_into['param_cov'] + py_cov = fitter.fit_info['param_cov'] x0 = px.c0 vx = px.c1 y0 = py.c0 vy = py.c1 - - px_param_errs = dict(zip(self.px.param_names, np.diag(px_cov)**0.5)) - py_param_errs = dict(zip(self.py.param_names, np.diag(py_cov)**0.5)) - - x0e = px_param_errors['c0'] - vxe = px_param_errors['c1'] - y0e = py_param_errors['c0'] - vye = py_param_errors['c1'] + + # Run the bootstrap + if bootstrap > 0: + edx = np.arange(N_good, dtype=int) + + fit_x0_b = np.zeros(bootstrap, dtype=float) + fit_vx_b = np.zeros(bootstrap, dtype=float) + fit_y0_b = np.zeros(bootstrap, dtype=float) + fit_vy_b = np.zeros(bootstrap, dtype=float) + + for bb in range(bootstrap): + bdx = np.random.choice(edx, N_good) + + px_b = fitter(self.px, dt[bdx], x[bdx], weights=1/xe[bdx]) + px_b_cov = fitter.fit_info['param_cov'] + py_b = fitter(self.py, dt[bdx], y[bdx], weights=1/ye[bdx]) + py_b_cov = fitter.fit_info['param_cov'] + + fit_x0_b[bb] = px_b.c0 + fit_vx_b[bb] = px_b.c1 + fit_y0_b[bb] = py_b.c0 + fit_vy_b[bb] = py_b.c1 + + # Save the errors from the bootstrap + x0e = fit_x0_b.std() + vxe = fit_vx_b.std() + y0e = fit_y0_b.std() + vye = fit_vy_b.std() + else: + px_param_errs = dict(zip(self.px.param_names, np.diag(px_cov)**0.5)) + py_param_errs = dict(zip(self.py.param_names, np.diag(py_cov)**0.5)) + x0e = px_param_errors['c0'] + vxe = px_param_errors['c1'] + y0e = py_param_errors['c0'] + vye = py_param_errors['c1'] if update: self.px = px_new @@ -114,7 +185,7 @@ def fit_pos_at_time(self, t, x, y, xe, ye, update=False, fixed_t0=False): self.y0 = self.py.c0 self.vy = self.py.c1 - params = [x0, vx, y0, vy, t0] + params = [x0, vx, y0, vy] param_errors = [x0e, vxe, y0e, vye] return params, param_errors diff --git a/flystar/startables.py b/flystar/startables.py index 6641800..42ffbb8 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -7,6 +7,7 @@ import pdb import time import copy +import motion_model class StarTable(Table): @@ -29,6 +30,9 @@ class StarTable(Table): Optional table columns (input as keywords): ------------------------- + motion_model : 1D numpy.array with shape = N_stars + string indicating motion model type for each star + xe : 2D numpy.array with shape = (N_stars, N_lists) Position uncertainties of N_stars in each of N_lists in the x dimension. @@ -67,7 +71,7 @@ class StarTable(Table): print(t['name'][0:10]) # print the first 10 star names print(t['x'][0:10, 0]) # print x from the first epoch/list/column for the first 10 stars """ - def __init__(self, *args, ref_list=0, **kwargs): + def __init__(self, *args, ref_list=0, motion_model_default='linear', **kwargs): """ """ @@ -159,6 +163,10 @@ def __init__(self, *args, ref_list=0, **kwargs): self.add_column(Column(data=kwargs[arg], name=arg)) if arg == 'name_in_list': self['name_in_list'] = self['name_in_list'].astype('U20') + if arg == 'motion_model': + self['motion_model'] = self['motion_model'].astype('U20') + if 'motion_model' not in kwargs: + self['motion_model'] = np.repeat(motion_model_default, len(self['names'])) return @@ -603,9 +611,6 @@ def fit_velocities(self, bootstrap=0, fixed_t0=False, verbose=False, def fit_velocity_for_star(self, ss, bootstrap=False, fixed_t0=False, mask_val=None, mask_lists=False): - def poly_model(time, *params): - pos = np.polynomial.polynomial.polyval(time, params) - return pos # Make a mask of invalid (NaN) values and a user-specified invalid value. x = np.ma.masked_invalid(self['x'][ss, :].data) @@ -712,104 +717,52 @@ def poly_model(time, *params): if fixed_t0 is False: t_weight = 1.0 / np.hypot(xe, ye) t0 = np.average(t, weights=t_weight) + elif fixed_t0 is True: + t0 = self.t0 else: - t0 = fixed_t0[ss] + t0 = fixed_t0 dt = t - t0 self['t0'][ss] = t0 self['n_vfit'][ss] = N_good - - # Catch the case where all the times are identical - if (dt == dt[0]).all(): - wgt_x = (1.0/xe)**2 - wgt_y = (1.0/ye)**2 - - self['x0'][ss] = np.average(x, weights=wgt_x) - self['y0'][ss] = np.average(y, weights=wgt_y) - self['x0e'][ss] = np.sqrt(np.average((x - self['x0'][ss])**2, weights=wgt_x)) - self['y0e'][ss] = np.sqrt(np.average((y - self['y0'][ss])**2, weights=wgt_x)) - + + # OK next, we need to decide which motion_model to fit. + motion_model_assigned = self['motion_model'][ss] + if motion_model_assigned=='fixed' or N_good==1 or (dt == dt[0]).all(): + # Either 'fixed' is selected, or is required because + # of no time-domain data + motion_model_use = 'fixed' + elif motion_model_assigned=='linear' and N_good>1: + # If 'linear' is selected and enough data exists + # to model linear motion + motion_model_use = 'linear' + self['motion_model'][ss] = motion_model_use + + if motion_model_use=='fixed': + mod = motion_model.Fixed(x[0],y[0],t[0]) + params,param_errs = mod.fit_motion_model(dt, x, y, xe, ye) + self['x0'][ss] = params[0] + self['y0'][ss] = params[1] + self['x0e'][ss] = param_errs[0] + self['y0e'][ss] = param_errs[1] self['vx'][ss] = 0.0 self['vy'][ss] = 0.0 self['vxe'][ss] = 0.0 self['vye'][ss] = 0.0 - return - - # Catch the case where we have enough measurements to actually - # fit a velocity! - if N_good > 2: - vx_opt, vx_cov = curve_fit(poly_model, dt.compressed(), x.compressed(), p0=p0x, sigma=xe.compressed(), - absolute_sigma=True) - vy_opt, vy_cov = curve_fit(poly_model, dt.compressed(), y.compressed(), p0=p0y, sigma=ye.compressed(), - absolute_sigma=True) - - self['x0'][ss] = vx_opt[0] - self['vx'][ss] = vx_opt[1] - self['y0'][ss] = vy_opt[0] - self['vy'][ss] = vy_opt[1] - - # Run the bootstrap - if bootstrap > 0: - edx = np.arange(N_good, dtype=int) - - fit_x0_b = np.zeros(bootstrap, dtype=float) - fit_vx_b = np.zeros(bootstrap, dtype=float) - fit_y0_b = np.zeros(bootstrap, dtype=float) - fit_vy_b = np.zeros(bootstrap, dtype=float) - - for bb in range(bootstrap): - bdx = np.random.choice(edx, N_good) - - vx_opt_b, vx_cov_b = curve_fit(poly_model, dt[bdx].compressed(), x[bdx].compressed(), p0=vx_opt, sigma=xe[bdx].compressed(), - absolute_sigma=True) - vy_opt_b, vy_cov_b = curve_fit(poly_model, dt[bdx].compressed(), y[bdx].compressed(), p0=vy_opt, sigma=ye[bdx].compressed(), - absolute_sigma=True) - - fit_x0_b[bb] = vx_opt_b[0] - fit_vx_b[bb] = vx_opt_b[1] - fit_y0_b[bb] = vy_opt_b[0] - fit_vy_b[bb] = vy_opt_b[1] - - # Save the errors from the bootstrap - self['x0e'][ss] = fit_x0_b.std() - self['vxe'][ss] = fit_vx_b.std() - self['y0e'][ss] = fit_y0_b.std() - self['vye'][ss] = fit_vy_b.std() - else: - vx_err = np.sqrt(vx_cov.diagonal()) - vy_err = np.sqrt(vy_cov.diagonal()) - - self['x0e'][ss] = vx_err[0] - self['vxe'][ss] = vx_err[1] - self['y0e'][ss] = vy_err[0] - self['vye'][ss] = vy_err[1] - - elif N_good == 2: - # Note nough epochs to fit a velocity. - self['x0'][ss] = np.average(x, weights=1.0/xe**2) - self['y0'][ss] = np.average(y, weights=1.0/ye**2) - - dx = np.diff(x)[0] - dy = np.diff(y)[0] - dt_diff = np.diff(dt)[0] - - self['x0e'][ss] = np.abs(dx) / 2**0.5 - self['y0e'][ss] = np.abs(dy) / 2**0.5 - self['vx'][ss] = dx / dt_diff - self['vy'][ss] = dy / dt_diff - self['vxe'][ss] = 0.0 - self['vye'][ss] = 0.0 - - else: - # N_good == 1 case - self['n_vfit'][ss] = 1 - self['x0'][ss] = x - self['y0'][ss] = y - - if 'xe' in self.colnames: - self['x0e'] = xe - self['y0e'] = ye + elif motion_model_use=='linear': + mod = motion_model.Linear(x[0], (x[-1]-x[0])/(t[-1]-t[0]), + y[0], (y[-1]-y[0])/(t[-1]-t[0]), + t[0]) + params,param_errs = mod.fit_motion_model(dt, x, y, xe, ye, bootstrap=bootstrap) + self['x0'][ss] = params[0] + self['vx'][ss] = params[1] + self['y0'][ss] = params[2] + self['vy'][ss] = params[3] + self['x0e'][ss] = param_errs[0] + self['vxe'][ss] = param_errs[1] + self['y0e'][ss] = param_errs[2] + self['vye'][ss] = param_errs[3] return From ca19b994b3fd6327e163d254148f174de0440835 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Tue, 18 Jun 2024 14:47:16 -0700 Subject: [PATCH 006/254] motion model debugging --- flystar/align.py | 7 +------ flystar/conftest.py | 6 +++--- flystar/motion_model.py | 45 ++++++++++++++++++++++++----------------- flystar/startables.py | 42 +++++++++++--------------------------- 4 files changed, 42 insertions(+), 58 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 3975510..abdb54c 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -4,6 +4,7 @@ from flystar import plots from flystar.starlists import StarList from flystar.startables import StarTable +from flystar.motion_model import motion_model_col_names from astropy.table import Table, Column, vstack import datetime import copy @@ -13,12 +14,6 @@ import warnings from astropy.utils.exceptions import AstropyUserWarning -# Keep a list of columns that are "aggregated" motion model terms. -motion_model_col_names = ['x0', 'x0e', 'y0', 'y0e', - 'vx', 'vxe', 'vy', 'vye', - 'ax', 'axe', 'ay', 'aye', - 't0', 'm0', 'm0e', 'use_in_trans'] - class MosaicSelfRef(object): def __init__(self, list_of_starlists, ref_index=0, iters=2, dr_tol=[1, 1], dm_tol=[2, 1], diff --git a/flystar/conftest.py b/flystar/conftest.py index 672b273..d38dfc8 100644 --- a/flystar/conftest.py +++ b/flystar/conftest.py @@ -31,9 +31,9 @@ def pytest_configure(config): PYTEST_HEADER_MODULES.pop('Pandas', None) PYTEST_HEADER_MODULES['scikit-image'] = 'skimage' - from . import __version__ - packagename = os.path.basename(os.path.dirname(__file__)) - TESTED_VERSIONS[packagename] = __version__ + #from . import __version__ + #packagename = os.path.basename(os.path.dirname(__file__)) + #TESTED_VERSIONS[packagename] = __version__ # Uncomment the last two lines in this block to treat all DeprecationWarnings as # exceptions. For Astropy v2.0 or later, there are 2 additional keywords, diff --git a/flystar/motion_model.py b/flystar/motion_model.py index 1d4c8b9..2476eb2 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -2,6 +2,13 @@ import numpy as np from abc import ABC +# Keep a list of columns that are "aggregated" motion model terms. +motion_model_col_names = ['x0', 'x0e', 'y0', 'y0e', + 'vx', 'vxe', 'vy', 'vye', + 'ax', 'axe', 'ay', 'aye', + 't0', 'm0', 'm0e', + 'motion_model', 'use_in_trans'] + class MotionModel(ABC): # Fit paramters: Shared fit parameters fitter_param_names = [] @@ -111,7 +118,7 @@ def get_pos_at_time(self, t): return x, y - def fit_motion_model(self, dt, x, y, xe, ye, update=False, bootstrap=False): + def fit_motion_model(self, dt, x, y, xe, ye, update=False, bootstrap=0): fitter = fitting.LevMarLSQFitter() # Handle 2-data point case @@ -136,14 +143,14 @@ def fit_motion_model(self, dt, x, y, xe, ye, update=False, bootstrap=False): py_new = fitter(self.py, dt, y, weights=1/ye) py_cov = fitter.fit_info['param_cov'] - x0 = px.c0 - vx = px.c1 - y0 = py.c0 - vy = py.c1 + x0 = px_new.c0.value + vx = px_new.c1.value + y0 = py_new.c0.value + vy = py_new.c1.value # Run the bootstrap if bootstrap > 0: - edx = np.arange(N_good, dtype=int) + edx = np.arange(len(x), dtype=int) fit_x0_b = np.zeros(bootstrap, dtype=float) fit_vx_b = np.zeros(bootstrap, dtype=float) @@ -151,17 +158,17 @@ def fit_motion_model(self, dt, x, y, xe, ye, update=False, bootstrap=False): fit_vy_b = np.zeros(bootstrap, dtype=float) for bb in range(bootstrap): - bdx = np.random.choice(edx, N_good) + bdx = np.random.choice(edx, len(x)) px_b = fitter(self.px, dt[bdx], x[bdx], weights=1/xe[bdx]) px_b_cov = fitter.fit_info['param_cov'] py_b = fitter(self.py, dt[bdx], y[bdx], weights=1/ye[bdx]) py_b_cov = fitter.fit_info['param_cov'] - fit_x0_b[bb] = px_b.c0 - fit_vx_b[bb] = px_b.c1 - fit_y0_b[bb] = py_b.c0 - fit_vy_b[bb] = py_b.c1 + fit_x0_b[bb] = px_b.c0.value + fit_vx_b[bb] = px_b.c1.value + fit_y0_b[bb] = py_b.c0.value + fit_vy_b[bb] = py_b.c1.value # Save the errors from the bootstrap x0e = fit_x0_b.std() @@ -171,19 +178,19 @@ def fit_motion_model(self, dt, x, y, xe, ye, update=False, bootstrap=False): else: px_param_errs = dict(zip(self.px.param_names, np.diag(px_cov)**0.5)) py_param_errs = dict(zip(self.py.param_names, np.diag(py_cov)**0.5)) - x0e = px_param_errors['c0'] - vxe = px_param_errors['c1'] - y0e = py_param_errors['c0'] - vye = py_param_errors['c1'] + x0e = px_param_errs['c0'] + vxe = px_param_errs['c1'] + y0e = py_param_errs['c0'] + vye = py_param_errs['c1'] if update: self.px = px_new self.py = py_new - self.x0 = self.px.c0 - self.vx = self.px.c1 - self.y0 = self.py.c0 - self.vy = self.py.c1 + self.x0 = self.px.c0.value + self.vx = self.px.c1.value + self.y0 = self.py.c0.value + self.vy = self.py.c1.value params = [x0, vx, y0, vy] param_errors = [x0e, vxe, y0e, vye] diff --git a/flystar/startables.py b/flystar/startables.py index 42ffbb8..7a0f4a3 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -7,7 +7,7 @@ import pdb import time import copy -import motion_model +from flystar import motion_model class StarTable(Table): @@ -166,7 +166,7 @@ def __init__(self, *args, ref_list=0, motion_model_default='linear', **kwargs): if arg == 'motion_model': self['motion_model'] = self['motion_model'].astype('U20') if 'motion_model' not in kwargs: - self['motion_model'] = np.repeat(motion_model_default, len(self['names'])) + self['motion_model'] = np.repeat(motion_model_default, len(self['name'])) return @@ -549,31 +549,18 @@ def fit_velocities(self, bootstrap=0, fixed_t0=False, verbose=False, msg = 'Starting startable.fit_velocities for {0:d} stars with n={1:d} bootstrap' print(msg.format(N_stars, bootstrap)) + col_list_float = ['x0','vx','y0','vy','x0e','vxe','y0e','vye','t0'] + col_list_int = ['n_vfit'] + col_list = col_list_float+col_list_int # Clean/remove up old arrays. - if 'x0' in self.colnames: self.remove_column('x0') - if 'vx' in self.colnames: self.remove_column('vx') - if 'y0' in self.colnames: self.remove_column('y0') - if 'vy' in self.colnames: self.remove_column('vy') - if 'x0e' in self.colnames: self.remove_column('x0e') - if 'vxe' in self.colnames: self.remove_column('vxe') - if 'y0e' in self.colnames: self.remove_column('y0e') - if 'vye' in self.colnames: self.remove_column('vye') - if 't0' in self.colnames: self.remove_column('t0') - if 'n_vfit' in self.colnames: self.remove_column('n_vfit') + for col in col_list: + if col in self.colnames: self.remove_column(col) # Define output arrays for the best-fit parameters. - self.add_column(Column(data = np.zeros(N_stars, dtype=float), name = 'x0')) - self.add_column(Column(data = np.zeros(N_stars, dtype=float), name = 'vx')) - self.add_column(Column(data = np.zeros(N_stars, dtype=float), name = 'y0')) - self.add_column(Column(data = np.zeros(N_stars, dtype=float), name = 'vy')) - - self.add_column(Column(data = np.zeros(N_stars, dtype=float), name = 'x0e')) - self.add_column(Column(data = np.zeros(N_stars, dtype=float), name = 'vxe')) - self.add_column(Column(data = np.zeros(N_stars, dtype=float), name = 'y0e')) - self.add_column(Column(data = np.zeros(N_stars, dtype=float), name = 'vye')) - - self.add_column(Column(data = np.zeros(N_stars, dtype=float), name = 't0')) - self.add_column(Column(data = np.zeros(N_stars, dtype=int), name = 'n_vfit')) + for col in col_list_float: + self.add_column(Column(data = np.zeros(N_stars, dtype=float), name = col)) + for col in col_list_int: + self.add_column(Column(data = np.zeros(N_stars, dtype=int), name = col)) self.meta['n_vfit_bootstrap'] = bootstrap @@ -708,10 +695,6 @@ def fit_velocity_for_star(self, ss, bootstrap=False, fixed_t0=False, t = t[good] xe = xe[good] ye = ye[good] - - # np.polynomial ordering - p0x = np.array([x.mean(), 0.0]) - p0y = np.array([y.mean(), 0.0]) # Unless t0 is fixed, calculate the t0 for the stars. if fixed_t0 is False: @@ -720,9 +703,8 @@ def fit_velocity_for_star(self, ss, bootstrap=False, fixed_t0=False, elif fixed_t0 is True: t0 = self.t0 else: - t0 = fixed_t0 + t0 = fixed_t0[ss] dt = t - t0 - self['t0'][ss] = t0 self['n_vfit'][ss] = N_good From 1c3c7a6f61e79534e55945048a6c043dbca053fc Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Tue, 18 Jun 2024 16:27:19 -0700 Subject: [PATCH 007/254] motion model debugging --- flystar/startables.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/flystar/startables.py b/flystar/startables.py index 7a0f4a3..df1ea05 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -708,7 +708,7 @@ def fit_velocity_for_star(self, ss, bootstrap=False, fixed_t0=False, self['t0'][ss] = t0 self['n_vfit'][ss] = N_good - # OK next, we need to decide which motion_model to fit. + # Decide which motion_model to fit. motion_model_assigned = self['motion_model'][ss] if motion_model_assigned=='fixed' or N_good==1 or (dt == dt[0]).all(): # Either 'fixed' is selected, or is required because From 1bfea27c00d42e0f801b85d3ae4ed3cb9aa4790c Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Mon, 24 Jun 2024 16:17:00 -0700 Subject: [PATCH 008/254] continuing motion model work --- flystar/startables.py | 8 ++++---- flystar/tests/test_startable.py | 4 ++++ 2 files changed, 8 insertions(+), 4 deletions(-) diff --git a/flystar/startables.py b/flystar/startables.py index 90e50d7..9733600 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -854,14 +854,14 @@ def fit_velocities_all_detected(self, weighting='var', use_scipy=False, absolute valid_x = ~np.any(x.mask, axis=1) valid_y = ~np.any(y.mask, axis=1) detected_in_all_epochs = np.logical_and.reduce(( - valid_x, valid_y, valid_xe, valid_ye - )) + valid_x, valid_y, valid_xe, valid_ye)) else: detected_in_all_epochs = np.logical_and(valid_xe, valid_ye) # Fit velocities - vel_result = fit_velocity(self[detected_in_all_epochs], weighting=weighting, use_scipy=use_scipy, absolute_sigma=absolute_sigma, epoch_cols=epoch_cols, art_star=art_star) + vel_result = fit_velocity(self[detected_in_all_epochs], weighting=weighting, use_scipy=use_scipy, + absolute_sigma=absolute_sigma, epoch_cols=epoch_cols, art_star=art_star) vel_result = Table.from_pandas(vel_result) @@ -885,4 +885,4 @@ def fit_velocities_all_detected(self, weighting='var', use_scipy=False, absolute if return_result: return vel_result else: - return \ No newline at end of file + return diff --git a/flystar/tests/test_startable.py b/flystar/tests/test_startable.py index 4f8b654..fc90be6 100644 --- a/flystar/tests/test_startable.py +++ b/flystar/tests/test_startable.py @@ -301,6 +301,7 @@ def test_fit_velocities(): tab2 = tab[10000:10100] tab3 = tab[-100:] tab = table.vstack((tab1, tab2, tab3)) + tab.meta = tab1.meta tab.fit_velocities(verbose=True) @@ -359,6 +360,7 @@ def test_fit_velocities(): # Test bootstrap ########## tab_b = table.vstack((tab1, tab2, tab3)) + tab_b.meta = tab1.meta tab_b.fit_velocities(verbose=True, bootstrap=50) assert tab_b.meta['n_vfit_bootstrap'] == 50 @@ -418,6 +420,7 @@ def test_fit_velocities_1epoch(): tab2 = tab[10000:10100] tab3 = tab[-100:] tab_1 = table.vstack((tab1, tab2, tab3)) + tab_1.meta = tab1.meta tab_1.fit_velocities(verbose=False) @@ -461,6 +464,7 @@ def test_fit_velocities_2epoch(): tab2 = tab[10000:10100] tab3 = tab[-100:] tab_2 = table.vstack((tab1, tab2, tab3)) + tab_2.meta=tab1.meta tab_2.fit_velocities(verbose=False) From f05a628d29fa42d63ad74c339b24a3cbf38d3e73 Mon Sep 17 00:00:00 2001 From: Jessica Lu Date: Mon, 19 Aug 2024 12:49:00 -0700 Subject: [PATCH 009/254] Cleaning some bugs in test_startable for the new motion model --- flystar/motion_model.py | 10 +- flystar/startables.py | 171 +++++++++++++++++++++----------- flystar/tests/test_startable.py | 113 +++++++++++---------- 3 files changed, 177 insertions(+), 117 deletions(-) diff --git a/flystar/motion_model.py b/flystar/motion_model.py index 2476eb2..9d6806c 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -52,6 +52,7 @@ class Fixed(MotionModel): A non-moving motion model for a star on the sky. """ fitter_param_names = ['x0','y0'] + fixed_param_names = ['t0'] def __init__(self, x0, y0, t0): self.x0 = x0 @@ -92,8 +93,9 @@ class Linear(MotionModel): A 2D linear motion model for a star on the sky. """ fitter_param_names = ['x0', 'vx', 'y0', 'vy'] + fixed_param_names = ['t0'] - def __init__(self, x0, vx, y0, vy, t0): + def __init__(self, x0=0, vx=0, y0=0, vy=0, t0=2025): self.x0 = x0 self.vx = vx self.y0 = y0 @@ -199,7 +201,11 @@ def fit_motion_model(self, dt, x, y, xe, ye, update=False, bootstrap=0): - +def get_motion_model_param_names(motion_model_list, with_errors=False): + return list_of_parameters + +def get_all_motion_model_param_names(with_errors=False): + return list_of_parameters diff --git a/flystar/startables.py b/flystar/startables.py index 9733600..13d3267 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -73,7 +73,7 @@ class StarTable(Table): print(t['name'][0:10]) # print the first 10 star names print(t['x'][0:10, 0]) # print x from the first epoch/list/column for the first 10 stars """ - def __init__(self, *args, ref_list=0, motion_model_default='linear', **kwargs): + def __init__(self, *args, ref_list=0, motion_model_default='Linear', **kwargs): """ """ @@ -581,25 +581,52 @@ def fit_velocities(self, weighting='var', use_scipy=True, absolute_sigma=True, b msg = 'Starting startable.fit_velocities for {0:d} stars with n={1:d} bootstrap' print(msg.format(N_stars, bootstrap)) - col_list_float = ['x0','vx','y0','vy','x0e','vxe','y0e','vye','chi2_vx','chi2_vy','t0'] - col_list_int = ['n_vfit'] - col_list = col_list_float+col_list_int - # Clean/remove up old arrays. - for col in col_list: - if col in self.colnames: self.remove_column(col) - + # + # Fill table with all possible motion model parameter names as new + # columns. Make everything empty for now. + # + all_motion_models = np.unique(self['motion_model']).tolist() + if 'Fixed' not in all_motion_models: + all_motion_models.append('Fixed') + if 'Linear' not in all_motion_models: + all_motion_models.append('Linear') + + new_col_list = [] + for aa in range(len(all_motion_models)): + mmod = getattr(motion_model, all_motion_models[aa]) + + # Add fit parameters and errors. + param_names = mmod.fitter_param_names + param_err_names = [par + '_err' for par in param_names] + + new_col_list += param_names + new_col_list += param_err_names + + # Append goodness of fit metrics and t0. + new_col_list += ['chi2_x', 'chi2_y', 't0'] + + # Remove repeat column names. Returns a numpy array. + new_col_list = np.unique(new_col_list) + # Define output arrays for the best-fit parameters. - for col in col_list_float: - self.add_column(Column(data = np.zeros(N_stars, dtype=float), name = col)) - for col in col_list_int: - self.add_column(Column(data = np.zeros(N_stars, dtype=int), name = col)) + for col in new_col_list: + # Clean/remove up old arrays. + if col in self.colnames: self.remove_column(col) - self.meta['n_vfit_bootstrap'] = bootstrap + # Add column + self.add_column(Column(data = np.full(N_stars, np.nan, dtype=float), name = col)) + # Add a column to keep track of the number of points used in a fit. + self['n_fit'] = 0 + + # Preserve the number of bootstraps that will be run (if any). + self.meta['n_fit_bootstrap'] = bootstrap + # (FIXME: Do we need to catch the case where there's a single *unmasked* epoch?) # Catch the case when there is only a single epoch. Just return 0 velocity # and the same input position for the x0/y0. if self['x'].shape[1] == 1: + self['motion_model'] = 'Fixed' self['x0'] = self['x'][:,0] self['y0'] = self['y'][:,0] @@ -609,10 +636,10 @@ def fit_velocities(self, weighting='var', use_scipy=True, absolute_sigma=True, b self['t0'] = self.meta['list_times'][0] if 'xe' in self.colnames: - self['x0e'] = self['xe'][:,0] - self['y0e'] = self['ye'][:,0] + self['x0_err'] = self['xe'][:,0] + self['y0_err'] = self['ye'][:,0] - self['n_vfit'] = 1 + self['n_fit'] = 1 return @@ -620,22 +647,27 @@ def fit_velocities(self, weighting='var', use_scipy=True, absolute_sigma=True, b # This is slow; but robust. if show_progress: for ss in tqdm(range(N_stars)): - self.fit_velocity_for_star(ss, weighting=weighting, use_scipy=use_scipy, absolute_sigma=absolute_sigma, bootstrap=bootstrap, fixed_t0=fixed_t0, - mask_val=mask_val, mask_lists=mask_lists) + self.fit_velocity_for_star(ss, weighting=weighting, use_scipy=use_scipy, + absolute_sigma=absolute_sigma, bootstrap=bootstrap, fixed_t0=fixed_t0, + mask_val=mask_val, mask_lists=mask_lists) else: for ss in range(N_stars): - self.fit_velocity_for_star(ss, weighting=weighting, use_scipy=use_scipy, absolute_sigma=absolute_sigma, bootstrap=bootstrap, fixed_t0=fixed_t0, - mask_val=mask_val, mask_lists=mask_lists, ) + self.fit_velocity_for_star(ss, weighting=weighting, use_scipy=use_scipy, + absolute_sigma=absolute_sigma, bootstrap=bootstrap, fixed_t0=fixed_t0, + mask_val=mask_val, mask_lists=mask_lists) if verbose: stop_time = time.time() print('startable.fit_velocities runtime = {0:.0f} s for {1:d} stars'.format(stop_time - start_time, N_stars)) return - def fit_velocity_for_star(self, ss, weighting='var', use_scipy=True, absolute_sigma=True, bootstrap=False, fixed_t0=False, + def fit_velocity_for_star(self, ss, weighting='var', use_scipy=True, + absolute_sigma=True, bootstrap=False, fixed_t0=False, mask_val=None, mask_lists=False): + # # Make a mask of invalid (NaN) values and a user-specified invalid value. + # x = np.ma.masked_invalid(self['x'][ss, :].data) y = np.ma.masked_invalid(self['y'][ss, :].data) if mask_val: @@ -659,6 +691,9 @@ def fit_velocity_for_star(self, ss, weighting='var', use_scipy=True, absolute_si if not isinstance(mask_lists, list): raise RuntimeError('mask_lists needs to be a list.') + # + # Assign the appropriate positional errors + # if 'xe' in self.colnames: # Make a mask of invalid (NaN) values and a user-specified invalid value. xe = np.ma.masked_invalid(self['xe'][ss, :].data) @@ -706,7 +741,9 @@ def fit_velocity_for_star(self, ss, weighting='var', use_scipy=True, absolute_si if not isinstance(mask_lists, list): raise RuntimeError('mask_lists needs to be a list.') + # # Make a mask of invalid (NaN) values and a user-specified invalid value. + # if 't' in self.colnames: t = np.ma.masked_invalid(self['t'][ss, :].data) else: @@ -726,10 +763,13 @@ def fit_velocity_for_star(self, ss, weighting='var', use_scipy=True, absolute_si # Throw a warning if mask_lists is not a list if not isinstance(mask_lists, list): raise RuntimeError('mask_lists needs to be a list.') - + # For inconsistent masks, mask the star if any of the values are masked. new_mask = np.logical_or.reduce((t.mask, x.mask, y.mask, xe.mask, ye.mask)) + + # # Figure out where we have detections (as indicated by error columns) + # good = np.where((xe != 0) & (ye != 0) & np.isfinite(xe) & np.isfinite(ye) & np.isfinite(x) & np.isfinite(y) & ~new_mask)[0] @@ -746,8 +786,10 @@ def fit_velocity_for_star(self, ss, weighting='var', use_scipy=True, absolute_si t = t[good] xe = xe[good] ye = ye[good] - + + # # Unless t0 is fixed, calculate the t0 for the stars. + # if fixed_t0 is False: t_weight = 1.0 / np.hypot(xe, ye) t0 = np.average(t, weights=t_weight) @@ -757,46 +799,59 @@ def fit_velocity_for_star(self, ss, weighting='var', use_scipy=True, absolute_si t0 = fixed_t0[ss] dt = t - t0 self['t0'][ss] = t0 - self['n_vfit'][ss] = N_good - + self['n_fit'][ss] = N_good + + # # Decide which motion_model to fit. + # motion_model_assigned = self['motion_model'][ss] - if motion_model_assigned=='fixed' or N_good==1 or (dt == dt[0]).all(): + + # Override the motion model if we don't have enought data. + # TODO: Query the motion model object to see the minimum number of points needed. + if motion_model_assigned=='Fixed' or N_good==1 or (dt == dt[0]).all(): # Either 'fixed' is selected, or is required because # of no time-domain data - motion_model_use = 'fixed' - elif motion_model_assigned=='linear' and N_good>1: - # If 'linear' is selected and enough data exists + motion_model_use = 'Fixed' + elif motion_model_assigned=='Linear' and N_good>1: + # If 'Linear' is selected and enough data exists # to model linear motion - motion_model_use = 'linear' + motion_model_use = 'Linear' self['motion_model'][ss] = motion_model_use - if motion_model_use=='fixed': - mod = motion_model.Fixed(x[0],y[0],t[0]) - params,param_errs = mod.fit_motion_model(dt, x, y, xe, ye) - self['x0'][ss] = params[0] - self['y0'][ss] = params[1] - self['x0e'][ss] = param_errs[0] - self['y0e'][ss] = param_errs[1] - self['vx'][ss] = 0.0 - self['vy'][ss] = 0.0 - self['vxe'][ss] = 0.0 - self['vye'][ss] = 0.0 - return + # Instantiate the motion model object. + modClass = getattr(motion_model, motion_model_use) + + # Load up any prior information on parameters for this model. + param_dict = {} + for par in modClass.fitter_param_names: + if ~np.isnan(self[par][ss]): + param_dict[par] = self[par][ss] + + # Load fixed parameters, if needed. + for par in modClass.fixed_param_names: + if par not in self.colnames: + msg = f'fit_velocity_for_star: ' + msg += f'Missing fixed_params column {par} needed for motion model {motion_model_use}.' + + raise RuntimeException(msg) + + if self[par][ss] != np.nan: + param_dict[par] = self[par][ss] + + # Model object + mod = modClass(**param_dict) + + # Fit for the best parameters + params, param_errs = mod.fit_motion_model(dt, x, y, xe, ye, bootstrap=bootstrap) + + # Save parameters and errors to table. + for pp in range(len(modClass.fitter_param_names)): + par = modClass.fitter_param_names[pp] + par_err = par + '_err' + + self[par][ss] = params[pp] + self[par_err][ss] = param_errs[pp] - elif motion_model_use=='linear': - mod = motion_model.Linear(x[0], (x[-1]-x[0])/(t[-1]-t[0]), - y[0], (y[-1]-y[0])/(t[-1]-t[0]), - t[0]) - params,param_errs = mod.fit_motion_model(dt, x, y, xe, ye, bootstrap=bootstrap) - self['x0'][ss] = params[0] - self['vx'][ss] = params[1] - self['y0'][ss] = params[2] - self['vy'][ss] = params[3] - self['x0e'][ss] = param_errs[0] - self['vxe'][ss] = param_errs[1] - self['y0e'][ss] = param_errs[2] - self['vye'][ss] = param_errs[3] return @@ -866,11 +921,11 @@ def fit_velocities_all_detected(self, weighting='var', use_scipy=False, absolute # Add n_vfit - n_vfit = len(epoch_cols) - vel_result['n_vfit'] = n_vfit + n_fit = len(epoch_cols) + vel_result['n_fit'] = n_fit # Clean/remove up old arrays. - columns = [*vel_result.keys(), 'n_vfit'] + columns = [*vel_result.keys(), 'n_fit'] for column in columns: if column in self.colnames: self.remove_column(column) diff --git a/flystar/tests/test_startable.py b/flystar/tests/test_startable.py index fc90be6..a862b55 100644 --- a/flystar/tests/test_startable.py +++ b/flystar/tests/test_startable.py @@ -308,45 +308,45 @@ def test_fit_velocities(): # Test creation of new variables assert len(tab['vx']) == len(tab) assert len(tab['vy']) == len(tab) - assert len(tab['vxe']) == len(tab) - assert len(tab['vye']) == len(tab) - assert len(tab['n_vfit']) == len(tab) - assert tab.meta['n_vfit_bootstrap'] == 0 + assert len(tab['vx_err']) == len(tab) + assert len(tab['vy_err']) == len(tab) + assert len(tab['n_fit']) == len(tab) + assert tab.meta['n_fit_bootstrap'] == 0 # Test no-fit for stars with N<2 epochs. n_epochs = (tab['x'] >= 0).sum(axis=1) idx = np.where(n_epochs < 2)[0] assert (tab['vx'][idx] == 0).all() - assert (tab['vxe'][idx] == 0).all() - assert (tab['n_vfit'][idx] == 2).all() + assert (tab['vx_err'][idx] == 0).all() + assert (tab['n_fit'][idx] == 2).all() # Test that the velocity errors were calculated. - assert (tab['vxe'][0:100] > 0).all() - assert (tab['x0e'][0:100] > 0).all() - assert (tab['vye'][0:100] > 0).all() - assert (tab['y0e'][0:100] > 0).all() + assert (tab['vx_err'][0:100] > 0).all() + assert (tab['x0_err'][0:100] > 0).all() + assert (tab['vy_err'][0:100] > 0).all() + assert (tab['y0_err'][0:100] > 0).all() assert np.isfinite(tab['x0']).all() assert np.isfinite(tab['vx']).all() assert np.isfinite(tab['y0']).all() assert np.isfinite(tab['vy']).all() - assert np.isfinite(tab['x0e']).all() - assert np.isfinite(tab['vxe']).all() - assert np.isfinite(tab['y0e']).all() - assert np.isfinite(tab['vye']).all() + assert np.isfinite(tab['x0_err']).all() + assert np.isfinite(tab['vx_err']).all() + assert np.isfinite(tab['y0_err']).all() + assert np.isfinite(tab['vy_err']).all() ########## # Test running a second time. We should get the same results. ########## vx_orig = tab['vx'] x0_orig = tab['x0'] - vxe_orig = tab['vxe'] - x0e_orig = tab['x0e'] + vxe_orig = tab['vx_err'] + x0e_orig = tab['x0_err'] tab.fit_velocities(verbose=False) assert (vx_orig == tab['vx']).all() assert (x0_orig == tab['x0']).all() - assert (vxe_orig == tab['vxe']).all() - assert (x0e_orig == tab['x0e']).all() + assert (vxe_orig == tab['vx_err']).all() + assert (x0e_orig == tab['x0_err']).all() ########## # Test fixed_t0 functionality @@ -363,27 +363,27 @@ def test_fit_velocities(): tab_b.meta = tab1.meta tab_b.fit_velocities(verbose=True, bootstrap=50) - assert tab_b.meta['n_vfit_bootstrap'] == 50 - assert tab_b['x0e'][0] > tab['x0e'][0] - assert tab_b['vxe'][0] > tab['vxe'][0] - assert tab_b['y0e'][0] > tab['y0e'][0] - assert tab_b['vye'][0] > tab['vye'][0] + assert tab_b.meta['n_fit_bootstrap'] == 50 + assert tab_b['x0_err'][0] > tab['x0_err'][0] + assert tab_b['vx_err'][0] > tab['vx_err'][0] + assert tab_b['y0_err'][0] > tab['y0_err'][0] + assert tab_b['vy_err'][0] > tab['vy_err'][0] ########## # Test what happens with no velocity errors ########## - tab.remove_columns(['xe', 'ye', 'x0', 'y0', 'x0e', 'y0e', 'vx', 'vy', 'vxe', 'vye', 'n_vfit']) + tab.remove_columns(['xe', 'ye', 'x0', 'y0', 'x0_err', 'y0_err', 'vx', 'vy', 'vx_err', 'vy_err', 'n_fit']) tab.fit_velocities(verbose=False) assert len(tab['vx']) == len(tab) assert len(tab['vy']) == len(tab) - assert len(tab['vxe']) == len(tab) - assert len(tab['vye']) == len(tab) - assert len(tab['n_vfit']) == len(tab) - assert (tab['vxe'][0:100] > 0).all() - assert (tab['x0e'][0:100] > 0).all() - assert (tab['vye'][0:100] > 0).all() - assert (tab['y0e'][0:100] > 0).all() + assert len(tab['vx_err']) == len(tab) + assert len(tab['vy_err']) == len(tab) + assert len(tab['n_fit']) == len(tab) + assert (tab['vx_err'][0:100] > 0).all() + assert (tab['x0_err'][0:100] > 0).all() + assert (tab['vy_err'][0:100] > 0).all() + assert (tab['y0_err'][0:100] > 0).all() ######### # Test mask_list @@ -392,12 +392,12 @@ def test_fit_velocities(): tt.fit_velocities(bootstrap=0, verbose=False, mask_lists=[1]) assert np.arange(2.25, 48, 5) == pytest.approx(tt['x0'].data) assert np.arange(2.25, 48, 5) == pytest.approx(tt['y0'].data) - assert np.zeros(10) == pytest.approx(tt['x0e'].data) - assert np.zeros(10) == pytest.approx(tt['y0e'].data) + assert np.full(10, 0.05) == pytest.approx(tt['x0_err'].data) + assert np.full(10, 0.05) == pytest.approx(tt['y0_err'].data) assert np.ones(10) == pytest.approx(tt['vx'].data) assert np.ones(10) == pytest.approx(tt['vy'].data) - assert np.zeros(10) == pytest.approx(tt['vxe'].data) - assert np.zeros(10) == pytest.approx(tt['vye'].data) + assert np.full(10, 0.03380617) == pytest.approx(tt['vx_err'].data) + assert np.full(10, 0.03380617) == pytest.approx(tt['vy_err'].data) assert 2017.25 * np.ones(10) == pytest.approx(tt['t0'].data) # Test 5b: Things that should break the code. @@ -424,30 +424,29 @@ def test_fit_velocities_1epoch(): tab_1.fit_velocities(verbose=False) - assert 'n_vfit' in tab_1.colnames + assert 'n_fit' in tab_1.colnames assert 't0' in tab_1.colnames assert 'x0' in tab_1.colnames assert 'y0' in tab_1.colnames assert 'vx' in tab_1.colnames assert 'vy' in tab_1.colnames - assert 'x0e' in tab_1.colnames - assert 'y0e' in tab_1.colnames - assert 'vxe' in tab_1.colnames - assert 'vye' in tab_1.colnames + assert 'x0_err' in tab_1.colnames + assert 'y0_err' in tab_1.colnames + assert 'vx_err' in tab_1.colnames + assert 'vy_err' in tab_1.colnames - assert (tab_1['x0'] == tab_1['x'][:,0]).all() assert (tab_1['y0'] == tab_1['y'][:,0]).all() - assert (tab_1['x0e'] == tab_1['xe'][:,0]).all() - assert (tab_1['y0e'] == tab_1['ye'][:,0]).all() + assert (tab_1['x0_err'] == tab_1['xe'][:,0]).all() + assert (tab_1['y0_err'] == tab_1['ye'][:,0]).all() - assert(tab_1['vx'] == 0).all() - assert(tab_1['vy'] == 0).all() - assert(tab_1['vxe'] == 0).all() - assert(tab_1['vye'] == 0).all() + assert(np.isnan(tab_1['vx'])).all() + assert(np.isnan(tab_1['vy'])).all() + assert(np.isnan(tab_1['vx_err'])).all() + assert(np.isnan(tab_1['vy_err'])).all() assert(tab_1['t0'] == 2001.0).all() - assert(tab_1['n_vfit'] == 1).all() + assert(tab_1['n_fit'] == 1).all() return @@ -468,28 +467,28 @@ def test_fit_velocities_2epoch(): tab_2.fit_velocities(verbose=False) - assert 'n_vfit' in tab_2.colnames + assert 'n_fit' in tab_2.colnames assert 't0' in tab_2.colnames assert 'x0' in tab_2.colnames assert 'y0' in tab_2.colnames assert 'vx' in tab_2.colnames assert 'vy' in tab_2.colnames - assert 'x0e' in tab_2.colnames - assert 'y0e' in tab_2.colnames - assert 'vxe' in tab_2.colnames - assert 'vye' in tab_2.colnames + assert 'x0_err' in tab_2.colnames + assert 'y0_err' in tab_2.colnames + assert 'vx_err' in tab_2.colnames + assert 'vy_err' in tab_2.colnames # 2 detections np.testing.assert_almost_equal(tab_2['x0'][0], tab_2['x'][0,0], 1) - assert tab_2['n_vfit'][0] == 2 + assert tab_2['n_fit'][0] == 2 # 1 detection assert tab_2['x0'][100] == tab_2['x'][100, 0] - assert tab_2['n_vfit'][100] == 1 + assert tab_2['n_fit'][100] == 1 # 0 detections - assert tab_2['x0'][-1] == 0 - assert tab_2['n_vfit'][-1] == 0 + assert np.isnan(tab_2['x0'][-1]) + assert tab_2['n_fit'][-1] == 0 return From a0aea71e33f704be482ad3c57fa1a3e3eabe61d3 Mon Sep 17 00:00:00 2001 From: Jessica Lu Date: Mon, 19 Aug 2024 12:50:57 -0700 Subject: [PATCH 010/254] Cleaning some bugs in test_startable for the new motion model. all now. --- flystar/motion_model.py | 1 - 1 file changed, 1 deletion(-) diff --git a/flystar/motion_model.py b/flystar/motion_model.py index 9d6806c..2e58959 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -199,7 +199,6 @@ def fit_motion_model(self, dt, x, y, xe, ye, update=False, bootstrap=0): return params, param_errors - def get_motion_model_param_names(motion_model_list, with_errors=False): return list_of_parameters From 2785b49aaca2edcd6ca15932457c4db3da3a5458 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Tue, 20 Aug 2024 12:00:29 -0700 Subject: [PATCH 011/254] some progress on motion model --- flystar/align.py | 183 +++++++++++++++++------------------- flystar/motion_model.py | 44 ++++++--- flystar/startables.py | 38 ++------ flystar/tests/test_align.py | 19 ++-- 4 files changed, 137 insertions(+), 147 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index a19c2dd..08bacd6 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -4,7 +4,7 @@ from flystar import plots from flystar.starlists import StarList from flystar.startables import StarTable -from flystar.motion_model import motion_model_col_names +from flystar import motion_model from astropy.table import Table, Column, vstack import datetime import copy @@ -22,7 +22,9 @@ def __init__(self, list_of_starlists, ref_index=0, iters=2, init_order=1, mag_trans=True, mag_lim=None, weights=None, trans_input=None, trans_class=transforms.PolyTransform, - use_vel=False, calc_trans_inverse=False, + #TODO if we use both keywords, add a check for compatibility + use_motion=False, default_motion_model='Fixed', + calc_trans_inverse=False, init_guess_mode='miracle', iter_callback=None, verbose=True): @@ -75,7 +77,7 @@ def __init__(self, list_of_starlists, ref_index=0, iters=2, magnitudes in each list to bring them into a common magnitude system. This is essential for matching (with finite dm_tol) starlists of different filters or starlists that are not photometrically calibrated. Note that the final_table columns - of 'm', 'm0', and 'm0e' will contain the transformed magnitudes while the + of 'm', 'm0', and 'm0_err' will contain the transformed magnitudes while the final_table column 'm_orig' will contain the original un-transformed magnitudes. If mag_trans = False, then no such zeropoint offset it applied at any point. @@ -105,8 +107,9 @@ def = None. If not None, then this should contain an array or list of transform then the transformation argument (i.e. order) will be changed for every iteration in iters. - use_vel : boolean - If velocities are present in the reference list and use_vel == True, then during + TODO: update when decided + use_motion : boolean + If velocities are present in the reference list and use_motion == True, then during each iteration of the alignment, the reference list will be propogated in time using the velocity information. So all transformations will be derived w.r.t. the propogated positions. See also update_vel. @@ -177,7 +180,8 @@ def = None. If not None, then this should contain an array or list of transform self.trans_input = trans_input self.trans_class = trans_class self.calc_trans_inverse = calc_trans_inverse - self.use_vel = use_vel + self.use_motion = use_motion + self.default_motion_model = default_motion_model self.init_guess_mode = init_guess_mode self.iter_callback = iter_callback self.verbose = verbose @@ -252,10 +256,10 @@ def fit(self): x0e y0e m0e - vx (only if use_vel=True) - vy (only if use_vel=True) - vxe (only if use_vel=True) - vye (only if use_vel=True) + vx (only if use_motion=True) + vy (only if use_motion=True) + vxe (only if use_motion=True) + vye (only if use_motion=True) """ ########## @@ -560,6 +564,7 @@ def setup_ref_table_from_starlist(self, star_list): array in the original reference star list. """ col_arrays = {} + motion_model_col_names = motion_model.get_all_motion_model_param_names(with_errors=True) for col_name in star_list.colnames: if col_name == 'name': # The "name" column will be 1D; but we will also add a "name_in_list" column. @@ -612,7 +617,7 @@ def setup_ref_table_from_starlist(self, star_list): # just fill these tables with zeros. We need something # in these columns in order for the error propagation to # work later on. - new_err_cols = ['x0e', 'y0e', 'm0e'] + new_err_cols = ['x0_err', 'y0_err', 'm0_err'] orig_err_cols = ['xe', 'ye', 'me'] for ii in range(len(new_err_cols)): # If the orig col name (e.g. xe) is in the ref_table, but the new col name @@ -801,52 +806,35 @@ def update_ref_table_aggregates(self, n_boot=0): # In certain cases, we will NOT update these. if not self.update_ref_orig: ref_orig_idx = np.where(self.ref_table['ref_orig'] == True)[0] - x0_orig = self.ref_table['x0'][ref_orig_idx] - y0_orig = self.ref_table['y0'][ref_orig_idx] - m0_orig = self.ref_table['m0'][ref_orig_idx] - x0e_orig = self.ref_table['x0e'][ref_orig_idx] - y0e_orig = self.ref_table['y0e'][ref_orig_idx] - m0e_orig = self.ref_table['m0e'][ref_orig_idx] - - if self.use_vel: - vx_orig = self.ref_table['vx'][ref_orig_idx] - vy_orig = self.ref_table['vy'][ref_orig_idx] - vxe_orig = self.ref_table['vxe'][ref_orig_idx] - vye_orig = self.ref_table['vye'][ref_orig_idx] - t0_orig = self.ref_table['t0'][ref_orig_idx] + vals_orig = {} + vals_orig['m0'] = self.ref_table['m0'][ref_orig_idx] + vals_orig['m0_err'] = self.ref_table['m0_err'][ref_orig_idx] + motion_model_col_names = motion_model.get_motion_model_param_names(self.ref_table['motion_model'][ref_orig_idx], with_errors=True) + for mm in motion_model_col_names: + vals_orig[mm] = self.ref_table[mm][ref_orig_idx] - if self.use_vel: + #if self.use_motion: # Combine positions with a velocity fit. - self.ref_table.fit_velocities(bootstrap=n_boot, verbose=self.verbose) - - # Combine (transformed) magnitudes - if 'me' in self.ref_table.colnames: - weights_col = None - else: - weights_col = 'me' - - self.ref_table.combine_lists('m', weights_col=weights_col, ismag=True) + self.ref_table.fit_velocities(bootstrap=n_boot, verbose=self.verbose, default_motion_model=self.default_motion_model) + + # Combine (transformed) magnitudes + # TODO: how does this work? + if 'me' in self.ref_table.colnames: + weights_col = None else: + weights_col = 'me' + + self.ref_table.combine_lists('m', weights_col=weights_col, ismag=True) + '''else: weighted_xy = ('xe' in self.ref_table.colnames) and ('ye' in self.ref_table.colnames) weighted_m = ('me' in self.ref_table.colnames) - self.ref_table.combine_lists_xym(weighted_xy=weighted_xy, weighted_m=weighted_m) + self.ref_table.combine_lists_xym(weighted_xy=weighted_xy, weighted_m=weighted_m)''' # Replace the originals if we are supposed to keep them fixed. if not self.update_ref_orig: - self.ref_table['x0'][ref_orig_idx] = x0_orig - self.ref_table['y0'][ref_orig_idx] = y0_orig - self.ref_table['m0'][ref_orig_idx] = m0_orig - self.ref_table['x0e'][ref_orig_idx] = x0e_orig - self.ref_table['y0e'][ref_orig_idx] = y0e_orig - self.ref_table['m0e'][ref_orig_idx] = m0e_orig - - if self.use_vel: - self.ref_table['vx'][ref_orig_idx] = vx_orig - self.ref_table['vy'][ref_orig_idx] = vy_orig - self.ref_table['vxe'][ref_orig_idx] = vxe_orig - self.ref_table['vye'][ref_orig_idx] = vye_orig - self.ref_table['t0'][ref_orig_idx] = t0_orig + for val in vals_orig.keys(): + self.ref_table[val][ref_orig_idx] = vals_orig[val] return @@ -918,7 +906,7 @@ def match_lists(self, dr_tol, dm_tol): else: star_list_T.transform_xy(self.trans_list[ii]) - xref, yref = get_pos_at_time(star_list_T['t'][0], self.ref_table, use_vel=self.use_vel) # optional velocity propogation. + xref, yref = get_pos_at_time(star_list_T['t'][0], self.ref_table, use_motion=self.use_motion) # optional velocity propogation. mref = self.ref_table['m0'] idx_lis, idx_ref, dr, dm = match.match(star_list_T['x'], star_list_T['y'], star_list_T['m'], @@ -950,36 +938,36 @@ def get_ref_list_from_table(self, epoch): # Reference stars will be named. name = self.ref_table['name'] - if self.use_vel and ('vx' in self.ref_table.colnames): + if self.use_motion and ('vx' in self.ref_table.colnames): # First check if we should use velocities and if they exist. dt = epoch - self.ref_table['t0'] x = self.ref_table['x0'] + (self.ref_table['vx'] * dt) y = self.ref_table['y0'] + (self.ref_table['vy'] * dt) - xe = np.hypot(self.ref_table['x0e'], self.ref_table['vxe']*dt) - ye = np.hypot(self.ref_table['y0e'], self.ref_table['vye']*dt) + xe = np.hypot(self.ref_table['x0_err'], self.ref_table['vxe']*dt) + ye = np.hypot(self.ref_table['y0_err'], self.ref_table['vye']*dt) idx = np.where(np.isfinite(self.ref_table['vx']) == False)[0] x[idx] = self.ref_table['x0'][idx] y[idx] = self.ref_table['y0'][idx] - xe[idx] = self.ref_table['x0e'][idx] - ye[idx] = self.ref_table['y0e'][idx] + xe[idx] = self.ref_table['x0_err'][idx] + ye[idx] = self.ref_table['y0_err'][idx] else: # No velocities... just used average positions. x = self.ref_table['x0'] y = self.ref_table['y0'] - if 'x0e' in self.ref_table.colnames: - xe = self.ref_table['x0e'] - ye = self.ref_table['y0e'] + if 'x0_err' in self.ref_table.colnames: + xe = self.ref_table['x0_err'] + ye = self.ref_table['y0_err'] else: xe = None ye = None m = self.ref_table['m0'] - if 'm0e' in self.ref_table.colnames: - me = self.ref_table['m0e'] + if 'm0_err' in self.ref_table.colnames: + me = self.ref_table['m0_err'] else: me = None @@ -1221,11 +1209,11 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot xe=xe_trans_arr[:,ii,boot_idx], ye=ye_trans_arr[:,ii,boot_idx], me=me_trans_arr[:,ii,boot_idx], - t=np.tile(t_boot, (len(ref_table),1)) ) + t=np.tile(t_boot, (len(ref_table),1))) # Now, do proper motion calculation, making sure to fix t0 to the # orig value (so we can get a reasonable error on x0, y0) - star_table.fit_velocities(fixed_t0=t0_arr) + star_table.fit_velocities(fixed_t0=t0_arr, default_motion_model=self.default_motion_model) # Save proper motion fit results to output arrays x0_arr[:,ii] = star_table['x0'] @@ -1302,7 +1290,8 @@ def __init__(self, ref_list, list_of_starlists, iters=2, trans_class=transforms.PolyTransform, calc_trans_inverse=False, use_ref_new=False, - use_vel=False, update_ref_orig=False, + use_motion=False, default_motion_model='Fixed', + update_ref_orig=False, init_guess_mode='miracle', iter_callback=None, verbose=True): @@ -1352,7 +1341,7 @@ def __init__(self, ref_list, list_of_starlists, iters=2, magnitudes in each list to bring them into a common magnitude system. This is essential for matching (with finite dm_tol) starlists of different filters or starlists that are not photometrically calibrated. Note that the final_table columns - of 'm', 'm0', and 'm0e' will contain the transformed magnitudes while the + of 'm', 'm0', and 'm0_err' will contain the transformed magnitudes while the final_table column 'm_orig' will contain the original un-transformed magnitudes. If mag_trans = False, then no such zeropoint offset it applied at any point. @@ -1417,8 +1406,8 @@ def = None. If not None, then this should contain an array or list of transform If False, then the new stars will be carried, but not used in the transformation. We determine which stars to use through setting a boolean use_in_trans flag. - use_vel : boolean - If velocities are present in the reference list and use_vel == True, then during + use_motion : boolean + If velocities are present in the reference list and use_motion == True, then during each iteration of the alignment, the reference list will be propogated in time using the velocity information. So all transformations will be derived w.r.t. the propogated positions. See also update_vel. @@ -1439,7 +1428,7 @@ def = None. If not None, then this should contain an array or list of transform outlier_tol=[None], mag_lim=[13, 21], trans_class=transforms.PolyTransform, trans_args=[{'order': 1}], - use_vel=True, + use_motion=True, use_ref_new=False, update_ref_orig=False, mag_trans=False, @@ -1470,7 +1459,7 @@ def = None. If not None, then this should contain an array or list of transform init_order=init_order, mag_trans=mag_trans, mag_lim=mag_lim, weights=weights, trans_input=trans_input, trans_class=trans_class, - calc_trans_inverse=calc_trans_inverse, use_vel=use_vel, + calc_trans_inverse=calc_trans_inverse, use_motion=use_motion, init_guess_mode=init_guess_mode, iter_callback=iter_callback, verbose=verbose) @@ -1484,13 +1473,13 @@ def = None. If not None, then this should contain an array or list of transform if ('x' not in self.ref_list.colnames) and ('x0' in self.ref_list.colnames): self.ref_list['x'] = self.ref_list['x0'] self.ref_list['y'] = self.ref_list['y0'] - if ('xe' not in self.ref_list.colnames) and ('x0e' in self.ref_list.colnames): - self.ref_list['xe'] = self.ref_list['x0e'] - self.ref_list['ye'] = self.ref_list['y0e'] + if ('xe' not in self.ref_list.colnames) and ('x0_err' in self.ref_list.colnames): + self.ref_list['xe'] = self.ref_list['x0_err'] + self.ref_list['ye'] = self.ref_list['y0_err'] if ('m' not in self.ref_list.colnames) and ('m0' in self.ref_list.colnames): self.ref_list['m'] = self.ref_list['m0'] - if ('me' not in self.ref_list.colnames) and ('m0e' in self.ref_list.colnames): - self.ref_list['me'] = self.ref_list['m0e'] + if ('me' not in self.ref_list.colnames) and ('m0_err' in self.ref_list.colnames): + self.ref_list['me'] = self.ref_list['m0_err'] if ('t' not in self.ref_list.colnames) and ('t0' in self.ref_list.colnames): self.ref_list['t'] = self.ref_list['t0'] @@ -1515,10 +1504,10 @@ def fit(self): x0e y0e m0e - vx (only if use_vel=True) - vy (only if use_vel=True) - vxe (only if use_vel=True) - vye (only if use_vel=True) + vx (only if use_motion=True) + vy (only if use_motion=True) + vxe (only if use_motion=True) + vye (only if use_motion=True) """ # Create a log file of the parameters used in the fit. @@ -1537,7 +1526,8 @@ def fit(self): logger(_log, ' trans_class = ' + str(self.trans_class), self.verbose) logger(_log, ' calc_trans_inverse = ' + str(self.calc_trans_inverse), self.verbose) logger(_log, ' use_ref_new = ' + str(self.use_ref_new), self.verbose) - logger(_log, ' use_vel = ' + str(self.use_vel), self.verbose) + logger(_log, ' use_motion = ' + str(self.use_motion), self.verbose) + logger(_log, ' default_motion_model = ' + str(self.default_motion_model), self.verbose) logger(_log, ' update_ref_orig = ' + str(self.update_ref_orig), self.verbose) logger(_log, ' init_guess_mode = ' + str(self.init_guess_mode), self.verbose) logger(_log, ' iter_callback = ' + str(self.iter_callback), self.verbose) @@ -1670,6 +1660,7 @@ def setup_ref_table_from_starlist(star_list): array in the original reference star list. """ col_arrays = {} + motion_model_col_names = motion_model.get_all_motion_model_param_names(with_errors=True) for col_name in star_list.colnames: if col_name == 'name': # The "name" column will be 1D; but we will also add a "name_in_list" column. @@ -1704,7 +1695,7 @@ def setup_ref_table_from_starlist(star_list): # Make sure ref_table has the necessary x0, y0, m0 and associated # error columns. If they don't exist, then add them as a copy of # the original x,y,m etc columns. - new_cols_arr = ['x0', 'x0e', 'y0', 'y0e', 'm0', 'm0e'] + new_cols_arr = ['x0', 'x0_err', 'y0', 'y0_err', 'm0', 'm0_err'] orig_cols_arr = ['x', 'xe', 'y', 'ye', 'm', 'me'] assert len(new_cols_arr) == len(orig_cols_arr) ref_cols = ref_table.keys() @@ -1888,7 +1879,7 @@ def run_align_iter(catalog, trans_order=1, poly_deg=1, ref_mag_lim=19, ref_radiu calc_mag_avg_all_stars(d) tdx = np.where((d['name_0'] == 'OB120169') | (d['name_0'] == 'OB120169_L'))[0] - print(d[tdx]['name_0', 't0', 'mag', 'x0', 'vx', 'x0e', 'vxe', 'chi2x', 'y0', 'vy', 'y0e', 'vye', 'chi2y', 'dof']) + print(d[tdx]['name_0', 't0', 'mag', 'x0', 'vx', 'x0_err', 'vxe', 'chi2x', 'y0', 'vy', 'y0_err', 'vye', 'chi2y', 'dof']) ########## # Second iteration -- align everything to reference positions derived from iteration 1 @@ -2005,8 +1996,8 @@ def calc_transform_ref_poly(d, target_name, poly_deg, ref_mag_lim, ref_radius_li m_ref = d['mag'] x_ref = d['x0'] y_ref = d['y0'] - xe_ref = d['x0e'] - ye_ref = d['y0e'] + xe_ref = d['x0_err'] + ye_ref = d['y0_err'] # Calculate some quanitites we use for selecting reference stars. r_ref = np.hypot(x_ref - x_ref[tdx], y_ref - y_ref[tdx]) @@ -2198,8 +2189,8 @@ def calc_polyfit_all_stars(d, poly_deg, init_fig_idx=0): if poly_deg >= 0: d['x0'] = px_all[:, -1] d['y0'] = py_all[:, -1] - d['x0e'] = pxe_all[:, -1] - d['y0e'] = pye_all[:, -1] + d['x0_err'] = pxe_all[:, -1] + d['y0_err'] = pye_all[:, -1] if poly_deg >= 1: d['vx'] = px_all[:, -2] @@ -2735,8 +2726,8 @@ def transform_from_file(starlist, transFile): if vel: x0_orig = starlist['x0'] y0_orig = starlist['y0'] - x0e_orig = starlist['x0e'] - y0e_orig = starlist['y0e'] + x0e_orig = starlist['x0_err'] + y0e_orig = starlist['y0_err'] vx_orig = starlist['vx'] vy_orig = starlist['vy'] @@ -2847,8 +2838,8 @@ def transform_from_file(starlist, transFile): if vel: starlist_f['x0'] = x0_new starlist_f['y0'] = y0_new - starlist_f['x0e'] = x0e_new - starlist_f['y0e'] = y0e_new + starlist_f['x0_err'] = x0e_new + starlist_f['y0_err'] = y0e_new starlist_f['vx'] = vx_new starlist_f['vy'] = vy_new starlist_f['vxe'] = vxe_new @@ -2899,8 +2890,8 @@ def transform_from_object(starlist, transform): if vel: x0 = starlist_f['x0'] y0 = starlist_f['y0'] - x0e = starlist_f['x0e'] - y0e = starlist_f['y0e'] + x0e = starlist_f['x0_err'] + y0e = starlist_f['y0_err'] vx = starlist_f['vx'] vy = starlist_f['vy'] vxe = starlist_f['vxe'] @@ -2927,8 +2918,8 @@ def transform_from_object(starlist, transform): if vel: starlist_f['x0'] = x0_new starlist_f['y0'] = y0_new - starlist_f['x0e'] = x0e_new - starlist_f['y0e'] = y0e_new + starlist_f['x0_err'] = x0e_new + starlist_f['y0_err'] = y0e_new starlist_f['vx'] = vx_new starlist_f['vy'] = vy_new starlist_f['vxe'] = vxe_new @@ -3621,13 +3612,13 @@ def copy_and_rename_for_ref(star_list): if 'xe' in star_list.colnames: old_cols += ['xe'] - new_cols += ['x0e'] + new_cols += ['x0_err'] if 'ye' in star_list.colnames: old_cols += ['ye'] - new_cols += ['y0e'] + new_cols += ['y0_err'] if 'me' in star_list.colnames: old_cols += ['me'] - new_cols += ['m0e'] + new_cols += ['m0_err'] if 'w' in star_list.colnames: old_cols += ['w'] new_cols += ['w'] @@ -3776,7 +3767,7 @@ def get_weighting_scheme(weights, ref_list, star_list): return weight -def get_pos_at_time(t, starlist, use_vel=True): +def get_pos_at_time(t, starlist, use_motion=True): """ Take a starlist, check to see if it has velocity columns. If it does, then propogate the positions forward in time @@ -3790,7 +3781,7 @@ def get_pos_at_time(t, starlist, use_vel=True): but it should be in the same units as the 't0' column in starlist. """ - if use_vel and ('vx' in starlist.colnames) and ('vy' in starlist.colnames): + if use_motion and ('vx' in starlist.colnames) and ('vy' in starlist.colnames): dt = t - starlist['t0'] x = starlist['x0'] + (starlist['vx'] * dt) y = starlist['y0'] + (starlist['vy'] * dt) diff --git a/flystar/motion_model.py b/flystar/motion_model.py index 2e58959..6d69ac3 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -2,13 +2,6 @@ import numpy as np from abc import ABC -# Keep a list of columns that are "aggregated" motion model terms. -motion_model_col_names = ['x0', 'x0e', 'y0', 'y0e', - 'vx', 'vxe', 'vy', 'vye', - 'ax', 'axe', 'ay', 'aye', - 't0', 'm0', 'm0e', - 'motion_model', 'use_in_trans'] - class MotionModel(ABC): # Fit paramters: Shared fit parameters fitter_param_names = [] @@ -54,7 +47,7 @@ class Fixed(MotionModel): fitter_param_names = ['x0','y0'] fixed_param_names = ['t0'] - def __init__(self, x0, y0, t0): + def __init__(self, x0=0, y0=0, t0=2025.0): self.x0 = x0 self.y0 = y0 self.t0 = t0 @@ -68,7 +61,7 @@ def __init__(self, x0, y0, t0): def get_pos_at_time(self,t): return self.x0, self.y0 - def fit_motion_model(self, dt, x, y, xe, ye, update=False, fixed_t0=False): + def fit_motion_model(self, dt, x, y, xe, ye, update=False, bootstrap=0): # Handle single data point case if len(x)==1: return [x[0],y[0]],[xe[0],ye[0]] @@ -95,7 +88,7 @@ class Linear(MotionModel): fitter_param_names = ['x0', 'vx', 'y0', 'vy'] fixed_param_names = ['t0'] - def __init__(self, x0=0, vx=0, y0=0, vy=0, t0=2025): + def __init__(self, x0=0, vx=0, y0=0, vy=0, t0=2025.0): self.x0 = x0 self.vx = vx self.y0 = y0 @@ -124,7 +117,7 @@ def fit_motion_model(self, dt, x, y, xe, ye, update=False, bootstrap=0): fitter = fitting.LevMarLSQFitter() # Handle 2-data point case - # TODO is this the best way to handle this case ? + # TODO: is this the best way to handle this case ? if len(x)==2: x0 = np.average(x, weights=1.0/xe**2) y0 = np.average(y, weights=1.0/ye**2) @@ -141,7 +134,6 @@ def fit_motion_model(self, dt, x, y, xe, ye, update=False, bootstrap=0): px_new = fitter(self.px, dt, x, weights=1/xe) px_cov = fitter.fit_info['param_cov'] - py_new = fitter(self.py, dt, y, weights=1/ye) py_cov = fitter.fit_info['param_cov'] @@ -201,10 +193,34 @@ def fit_motion_model(self, dt, x, y, xe, ye, update=False, bootstrap=0): def get_motion_model_param_names(motion_model_list, with_errors=False): - return list_of_parameters + list_of_parameters = [] + all_motion_models = [eval(mm) for mm in np.unique(motion_model_list).tolist()] + for aa in range(len(all_motion_models)): + param_names = getattr(all_motion_models[aa], 'fitter_param_names') + param_fixed_names = getattr(all_motion_models[aa], 'fixed_param_names') + param_err_names = [par + '_err' for par in param_names] + + list_of_parameters += param_names + list_of_parameters += param_fixed_names + if with_errors: + list_of_parameters += param_err_names + + return np.unique(list_of_parameters).tolist() def get_all_motion_model_param_names(with_errors=False): - return list_of_parameters + list_of_parameters = [] + all_motion_models = MotionModel.__subclasses__() + for aa in range(len(all_motion_models)): + param_names = getattr(all_motion_models[aa], 'fitter_param_names') + param_fixed_names = getattr(all_motion_models[aa], 'fixed_param_names') + param_err_names = [par + '_err' for par in param_names] + + list_of_parameters += param_names + list_of_parameters += param_fixed_names + if with_errors: + list_of_parameters += param_err_names + + return np.unique(list_of_parameters).tolist() diff --git a/flystar/startables.py b/flystar/startables.py index 13d3267..32e2196 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -11,7 +11,6 @@ import copy from flystar import motion_model - class StarTable(Table): """ A StarTable is an astropy.Table with stars matched from multiple starlists. @@ -73,7 +72,7 @@ class StarTable(Table): print(t['name'][0:10]) # print the first 10 star names print(t['x'][0:10, 0]) # print x from the first epoch/list/column for the first 10 stars """ - def __init__(self, *args, ref_list=0, motion_model_default='Linear', **kwargs): + def __init__(self, *args, ref_list=0, **kwargs): """ """ @@ -167,8 +166,8 @@ def __init__(self, *args, ref_list=0, motion_model_default='Linear', **kwargs): self['name_in_list'] = self['name_in_list'].astype('U20') if arg == 'motion_model': self['motion_model'] = self['motion_model'].astype('U20') - if 'motion_model' not in kwargs: - self['motion_model'] = np.repeat(motion_model_default, len(self['name'])) + '''if 'motion_model' not in kwargs: + self['motion_model'] = np.repeat(default_motion_model, len(self['name']))''' return @@ -537,7 +536,7 @@ def detections(self): def fit_velocities(self, weighting='var', use_scipy=True, absolute_sigma=True, bootstrap=0, fixed_t0=False, verbose=False, - mask_val=None, mask_lists=False, show_progress=True): + mask_val=None, mask_lists=False, show_progress=True, default_motion_model='Linear'): """Fit velocities for all stars in the table and add to the columns 'vx', 'vxe', 'vy', 'vye', 'x0', 'x0e', 'y0', 'y0e'. Parameters @@ -585,34 +584,17 @@ def fit_velocities(self, weighting='var', use_scipy=True, absolute_sigma=True, b # Fill table with all possible motion model parameter names as new # columns. Make everything empty for now. # - all_motion_models = np.unique(self['motion_model']).tolist() - if 'Fixed' not in all_motion_models: - all_motion_models.append('Fixed') - if 'Linear' not in all_motion_models: - all_motion_models.append('Linear') - - new_col_list = [] - for aa in range(len(all_motion_models)): - mmod = getattr(motion_model, all_motion_models[aa]) - - # Add fit parameters and errors. - param_names = mmod.fitter_param_names - param_err_names = [par + '_err' for par in param_names] - - new_col_list += param_names - new_col_list += param_err_names - + if 'motion_model' not in self.colnames: + self['motion_model'] = default_motion_model + all_motion_models = np.unique(self['motion_model'].tolist() + ['Fixed','Linear']).tolist() + new_col_list = motion_model.get_motion_model_param_names(all_motion_models, with_errors=True) # Append goodness of fit metrics and t0. - new_col_list += ['chi2_x', 'chi2_y', 't0'] - - # Remove repeat column names. Returns a numpy array. - new_col_list = np.unique(new_col_list) + new_col_list += ['chi2_x', 'chi2_y'] # Define output arrays for the best-fit parameters. for col in new_col_list: # Clean/remove up old arrays. if col in self.colnames: self.remove_column(col) - # Add column self.add_column(Column(data = np.full(N_stars, np.nan, dtype=float), name = col)) @@ -625,7 +607,7 @@ def fit_velocities(self, weighting='var', use_scipy=True, absolute_sigma=True, b # (FIXME: Do we need to catch the case where there's a single *unmasked* epoch?) # Catch the case when there is only a single epoch. Just return 0 velocity # and the same input position for the x0/y0. - if self['x'].shape[1] == 1: + if (self['x'].shape[1] == 1): self['motion_model'] = 'Fixed' self['x0'] = self['x'][:,0] self['y0'] = self['y'][:,0] diff --git a/flystar/tests/test_align.py b/flystar/tests/test_align.py index 45741d7..4892acc 100644 --- a/flystar/tests/test_align.py +++ b/flystar/tests/test_align.py @@ -100,22 +100,23 @@ def test_MosaicSelfRef_vel_tconst(): msc = align.MosaicSelfRef(lists, ref_index=0, iters=2, dr_tol=[3, 3], dm_tol=[1, 1], trans_class=transforms.PolyTransform, - trans_args={'order': 2}, use_vel=True, + trans_args={'order': 2}, + use_motion=True, default_motion_model='Linear', verbose=False) msc.fit() # Check some of the output quantities on the final table. assert 'x0' in msc.ref_table.colnames - assert 'x0e' in msc.ref_table.colnames + assert 'x0_err' in msc.ref_table.colnames assert 'y0' in msc.ref_table.colnames - assert 'y0e' in msc.ref_table.colnames + assert 'y0_err' in msc.ref_table.colnames assert 'm0' in msc.ref_table.colnames - assert 'm0e' in msc.ref_table.colnames + assert 'm0_err' in msc.ref_table.colnames assert 'vx' in msc.ref_table.colnames - assert 'vxe' in msc.ref_table.colnames + assert 'vx_err' in msc.ref_table.colnames assert 'vy' in msc.ref_table.colnames - assert 'vye' in msc.ref_table.colnames + assert 'vy_err' in msc.ref_table.colnames assert 't0' in msc.ref_table.colnames # Check that we have some matched stars... should be at least 35 stars @@ -124,9 +125,9 @@ def test_MosaicSelfRef_vel_tconst(): assert len(idx) > 35 # Check that the transformation error isn't too big - assert (msc.ref_table['x0e'] < 3.0).all() # less than 1 pix - assert (msc.ref_table['y0e'] < 3.0).all() - assert (msc.ref_table['m0e'] < 1.0).all() # less than 0.5 mag + assert (msc.ref_table['x0_err'] < 3.0).all() # less than 1 pix + assert (msc.ref_table['y0_err'] < 3.0).all() + assert (msc.ref_table['m0_err'] < 1.0).all() # less than 0.5 mag # Check that the transformation lists aren't too wacky for ii in range(4): From 7c77150e852acb949c51d1aee9c52048cb6ed5ba Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Tue, 20 Aug 2024 17:01:30 -0700 Subject: [PATCH 012/254] checking in in-progress motion model work --- flystar/align.py | 60 +++++++++---------- flystar/match.py | 3 + flystar/motion_model.py | 85 ++++++++++++++++++++++---- flystar/startables.py | 102 +++++++++++++++++++++++++------- flystar/tests/test_align.py | 4 +- flystar/tests/test_startable.py | 4 +- 6 files changed, 191 insertions(+), 67 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 08bacd6..8968633 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -22,8 +22,9 @@ def __init__(self, list_of_starlists, ref_index=0, iters=2, init_order=1, mag_trans=True, mag_lim=None, weights=None, trans_input=None, trans_class=transforms.PolyTransform, - #TODO if we use both keywords, add a check for compatibility - use_motion=False, default_motion_model='Fixed', + # TODO: consider deleting use_vel fulll, for now I'm putting in + # a fallback so people can still use old code for now + use_vel=None, default_motion_model='Fixed', calc_trans_inverse=False, init_guess_mode='miracle', iter_callback=None, verbose=True): @@ -107,9 +108,9 @@ def = None. If not None, then this should contain an array or list of transform then the transformation argument (i.e. order) will be changed for every iteration in iters. - TODO: update when decided - use_motion : boolean - If velocities are present in the reference list and use_motion == True, then during + # TODO: update when decided + use_vel : boolean + If velocities are present in the reference list and use_vel == True, then during each iteration of the alignment, the reference list will be propogated in time using the velocity information. So all transformations will be derived w.r.t. the propogated positions. See also update_vel. @@ -179,9 +180,15 @@ def = None. If not None, then this should contain an array or list of transform self.weights = weights self.trans_input = trans_input self.trans_class = trans_class - self.calc_trans_inverse = calc_trans_inverse - self.use_motion = use_motion - self.default_motion_model = default_motion_model + self.calc_trans_inverse = calc_trans_inverse + # TODO: consider whether we want this fallback + if use_vel is None: + self.default_motion_model = default_motion_model + else: + if use_vel: + self.default_motion_model = 'Linear' + else: + self.default_motion_model = 'Fixed' self.init_guess_mode = init_guess_mode self.iter_callback = iter_callback self.verbose = verbose @@ -771,7 +778,8 @@ def update_ref_table_from_list(self, star_list, star_list_T, ii, idx_ref, idx_li self.ref_table['used_in_trans'][idx_ref_in_trans, ii] = True ### Add the unmatched stars and grow the size of the reference table. - self.ref_table, idx_lis_new, idx_ref_new = add_rows_for_new_stars(self.ref_table, star_list, idx_lis) + self.ref_table, idx_lis_new, idx_ref_new = add_rows_for_new_stars(self.ref_table, star_list, idx_lis, + default_motion_model=self.default_motion_model) if len(idx_ref_new) > 0: if self.verbose > 0: print(' Adding {0:d} new stars to the reference table.'.format(len(idx_ref_new))) @@ -809,7 +817,7 @@ def update_ref_table_aggregates(self, n_boot=0): vals_orig = {} vals_orig['m0'] = self.ref_table['m0'][ref_orig_idx] vals_orig['m0_err'] = self.ref_table['m0_err'][ref_orig_idx] - motion_model_col_names = motion_model.get_motion_model_param_names(self.ref_table['motion_model'][ref_orig_idx], with_errors=True) + motion_model_col_names = motion_model.get_list_motion_model_param_names(self.ref_table['motion_model'][ref_orig_idx], with_errors=True) for mm in motion_model_col_names: vals_orig[mm] = self.ref_table[mm][ref_orig_idx] @@ -906,7 +914,7 @@ def match_lists(self, dr_tol, dm_tol): else: star_list_T.transform_xy(self.trans_list[ii]) - xref, yref = get_pos_at_time(star_list_T['t'][0], self.ref_table, use_motion=self.use_motion) # optional velocity propogation. + xref, yref = get_pos_at_time(star_list_T['t'][0], self.ref_table) #, use_motion=self.use_motion) # optional velocity propogation. mref = self.ref_table['m0'] idx_lis, idx_ref, dr, dm = match.match(star_list_T['x'], star_list_T['y'], star_list_T['m'], @@ -938,20 +946,8 @@ def get_ref_list_from_table(self, epoch): # Reference stars will be named. name = self.ref_table['name'] - if self.use_motion and ('vx' in self.ref_table.colnames): - # First check if we should use velocities and if they exist. - dt = epoch - self.ref_table['t0'] - x = self.ref_table['x0'] + (self.ref_table['vx'] * dt) - y = self.ref_table['y0'] + (self.ref_table['vy'] * dt) - - xe = np.hypot(self.ref_table['x0_err'], self.ref_table['vxe']*dt) - ye = np.hypot(self.ref_table['y0_err'], self.ref_table['vye']*dt) - - idx = np.where(np.isfinite(self.ref_table['vx']) == False)[0] - x[idx] = self.ref_table['x0'][idx] - y[idx] = self.ref_table['y0'][idx] - xe[idx] = self.ref_table['x0_err'][idx] - ye[idx] = self.ref_table['y0_err'][idx] + if ('motion_model' in self.ref_table.colnames): + x,y,xe,ye = self.ref_table.get_star_positions_at_time(epoch) else: # No velocities... just used average positions. x = self.ref_table['x0'] @@ -1290,7 +1286,7 @@ def __init__(self, ref_list, list_of_starlists, iters=2, trans_class=transforms.PolyTransform, calc_trans_inverse=False, use_ref_new=False, - use_motion=False, default_motion_model='Fixed', + use_vel=None, default_motion_model='Fixed', update_ref_orig=False, init_guess_mode='miracle', iter_callback=None, @@ -1459,7 +1455,8 @@ def = None. If not None, then this should contain an array or list of transform init_order=init_order, mag_trans=mag_trans, mag_lim=mag_lim, weights=weights, trans_input=trans_input, trans_class=trans_class, - calc_trans_inverse=calc_trans_inverse, use_motion=use_motion, + calc_trans_inverse=calc_trans_inverse, use_vel=use_vel, + default_motion_model = default_motion_model, init_guess_mode=init_guess_mode, iter_callback=iter_callback, verbose=verbose) @@ -1526,7 +1523,7 @@ def fit(self): logger(_log, ' trans_class = ' + str(self.trans_class), self.verbose) logger(_log, ' calc_trans_inverse = ' + str(self.calc_trans_inverse), self.verbose) logger(_log, ' use_ref_new = ' + str(self.use_ref_new), self.verbose) - logger(_log, ' use_motion = ' + str(self.use_motion), self.verbose) + #logger(_log, ' use_vel = ' + str(self.use_vel), self.verbose) logger(_log, ' default_motion_model = ' + str(self.default_motion_model), self.verbose) logger(_log, ' update_ref_orig = ' + str(self.update_ref_orig), self.verbose) logger(_log, ' init_guess_mode = ' + str(self.init_guess_mode), self.verbose) @@ -1774,7 +1771,7 @@ def reset_ref_values(ref_table): return -def add_rows_for_new_stars(ref_table, star_list, idx_lis): +def add_rows_for_new_stars(ref_table, star_list, idx_lis, default_motion_model='Fixed'): """ For each star that is in star_list and NOT in idx_list, make a new row in the reference table. The values will be empty (None, NAN, etc.). @@ -1818,6 +1815,8 @@ def add_rows_for_new_stars(ref_table, star_list, idx_lis): new_col_empty = -1 elif ref_table[col_name].dtype == np.dtype('bool'): new_col_empty = False + elif col_name=='motion_model': + new_col_empty = default_motion_model else: new_col_empty = np.nan @@ -3767,7 +3766,8 @@ def get_weighting_scheme(weights, ref_list, star_list): return weight -def get_pos_at_time(t, starlist, use_motion=True): +# TODO: Are starlists supposed to hold velocities? We'll need to add a motion_model column if so +def get_pos_at_time(t, starlist,use_motion=True): """ Take a starlist, check to see if it has velocity columns. If it does, then propogate the positions forward in time diff --git a/flystar/match.py b/flystar/match.py index d40ccdb..96a9f77 100644 --- a/flystar/match.py +++ b/flystar/match.py @@ -277,6 +277,9 @@ def match(x1, y1, m1, x2, y2, m2, dr_tol, dm_tol=None, verbose=True): # have matches. idxs1 = np.ones(x1.size, dtype=int) * -1 idxs2 = np.ones(x1.size, dtype=int) * -1 + + print(coords2) + pdb.set_trace() # The matching will be done using a KDTree. kdt = KDT(coords2, balanced_tree=False) diff --git a/flystar/motion_model.py b/flystar/motion_model.py index 6d69ac3..14eda8a 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -1,8 +1,12 @@ from astropy.modeling import models, fitting import numpy as np from abc import ABC +import pdb class MotionModel(ABC): + # Number of data points required to fit model + n_pts_req = 0 + # Fit paramters: Shared fit parameters fitter_param_names = [] @@ -18,6 +22,7 @@ class MotionModel(ABC): def __init__(self, *args, **kwargs): # Check that required phot_params are proper arrays. # If not, then make them arrays of len(1). + # TODO: do we need this? for param in self.fitter_param_names: param_var = getattr(self, param) if not isinstance(param_var, (list, np.ndarray)): @@ -28,6 +33,14 @@ def __init__(self, *args, **kwargs): def get_pos_at_time(self, t): #return x, y pass + + def get_pos_err_at_time(self, t): + #return x_err, y_err + pass + + def get_batch_pos_at_time(self, t): + #return x, y, x_err, y_err + pass def fit_motion_model(self, t, x, y, xe, ye, update=True): """ @@ -44,14 +57,18 @@ class Fixed(MotionModel): """ A non-moving motion model for a star on the sky. """ + n_pts_req = 1 fitter_param_names = ['x0','y0'] fixed_param_names = ['t0'] - def __init__(self, x0=0, y0=0, t0=2025.0): + def __init__(self, x0=0, y0=0, t0=2025.0, + x0_err=0, y0_err=0): self.x0 = x0 self.y0 = y0 self.t0 = t0 - + self.x0_err = x0_err + self.y0_err = y0_err + # Must call after setting parameters. # This checks for proper parameter formatting. super().__init__() @@ -60,6 +77,15 @@ def __init__(self, x0=0, y0=0, t0=2025.0): def get_pos_at_time(self,t): return self.x0, self.y0 + + def get_pos_err_at_time(self,t): + return self.x0_err, self.y0_err + + def get_batch_pos_at_time(self,t, + x0=[],y0=[],t0=[], + x0_err=[], y0_err=[]): + return x0,y0,x0_err,y0_err + def fit_motion_model(self, dt, x, y, xe, ye, update=False, bootstrap=0): # Handle single data point case @@ -78,6 +104,8 @@ def fit_motion_model(self, dt, x, y, xe, ye, update=False, bootstrap=0): if update: self.x0 = x0 self.y0 = y0 + self.x0_err = x0e + self.y0_err = y0e return params, param_errors @@ -85,16 +113,22 @@ class Linear(MotionModel): """ A 2D linear motion model for a star on the sky. """ + n_pts_req = 2 fitter_param_names = ['x0', 'vx', 'y0', 'vy'] fixed_param_names = ['t0'] - def __init__(self, x0=0, vx=0, y0=0, vy=0, t0=2025.0): + def __init__(self, x0=0, vx=0, y0=0, vy=0, t0=2025.0, + x0_err=0, vx_err=0, y0_err=0, vy_err=0): self.x0 = x0 self.vx = vx self.y0 = y0 self.vy = vy self.t0 = t0 - + self.x0_err = x0_err + self.vx_err = vx_err + self.y0_err = y0_err + self.vy_err = vy_err + # Must call after setting parameters. # This checks for proper parameter formatting. super().__init__() @@ -112,6 +146,24 @@ def get_pos_at_time(self, t): y = self.py(dt) return x, y + + def get_pos_err_at_time(self, t): + dt = t - self.t0 + + x_err = np.hypot(self.x0_err, self.vx_err) + y_err = np.hypot(self.y0_err, self.vy_err) + + return x_err, y_err + + def get_batch_pos_at_time(self,t, + x0=[],vx=[], y0=[],vy=[], t0=[], + x0_err=[],vx_err=[], y0_err=[],vy_err=[]): + dt = t-t0 + x = x0 + dt*vx + y = y0 + dt*vy + x_err = np.hypot(x0_err, vx_err*dt) + y_err = np.hypot(y0_err, vy_err*dt) + return x,y,x_err,y_err def fit_motion_model(self, dt, x, y, xe, ye, update=False, bootstrap=0): fitter = fitting.LevMarLSQFitter() @@ -181,10 +233,15 @@ def fit_motion_model(self, dt, x, y, xe, ye, update=False, bootstrap=0): self.px = px_new self.py = py_new - self.x0 = self.px.c0.value - self.vx = self.px.c1.value - self.y0 = self.py.c0.value - self.vy = self.py.c1.value + self.x0 = x0 + self.vx = vx + self.y0 = y0 + self.vy = vy + + self.x0_err = x0e + self.vx_err = vxe + self.y0_err = y0e + self.vy_err = vye params = [x0, vx, y0, vy] param_errors = [x0e, vxe, y0e, vye] @@ -192,7 +249,15 @@ def fit_motion_model(self, dt, x, y, xe, ye, update=False, bootstrap=0): return params, param_errors -def get_motion_model_param_names(motion_model_list, with_errors=False): +def get_one_motion_model_param_names(motion_model_name, with_errors=True): + mod = eval(motion_model_name) + list_of_parameters = getattr(mod, 'fitter_param_names') + list_of_parameters += getattr(mod, 'fixed_param_names') + if with_errors: + list_of_parameters += [par + '_err' for par in getattr(mod, 'fitter_param_names')] + return list_of_parameters + +def get_list_motion_model_param_names(motion_model_list, with_errors=True): list_of_parameters = [] all_motion_models = [eval(mm) for mm in np.unique(motion_model_list).tolist()] for aa in range(len(all_motion_models)): @@ -207,7 +272,7 @@ def get_motion_model_param_names(motion_model_list, with_errors=False): return np.unique(list_of_parameters).tolist() -def get_all_motion_model_param_names(with_errors=False): +def get_all_motion_model_param_names(with_errors=True): list_of_parameters = [] all_motion_models = MotionModel.__subclasses__() for aa in range(len(all_motion_models)): diff --git a/flystar/startables.py b/flystar/startables.py index 32e2196..32d4138 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -533,7 +533,6 @@ def detections(self): self.add_column(Column(n_detect), name='n_detect') return - def fit_velocities(self, weighting='var', use_scipy=True, absolute_sigma=True, bootstrap=0, fixed_t0=False, verbose=False, mask_val=None, mask_lists=False, show_progress=True, default_motion_model='Linear'): @@ -586,9 +585,11 @@ def fit_velocities(self, weighting='var', use_scipy=True, absolute_sigma=True, b # if 'motion_model' not in self.colnames: self['motion_model'] = default_motion_model + all_motion_models = np.unique(self['motion_model'].tolist() + ['Fixed','Linear']).tolist() - new_col_list = motion_model.get_motion_model_param_names(all_motion_models, with_errors=True) + new_col_list = motion_model.get_list_motion_model_param_names(all_motion_models, with_errors=True) # Append goodness of fit metrics and t0. + # TODO: actually populate these columns new_col_list += ['chi2_x', 'chi2_y'] # Define output arrays for the best-fit parameters. @@ -605,8 +606,8 @@ def fit_velocities(self, weighting='var', use_scipy=True, absolute_sigma=True, b self.meta['n_fit_bootstrap'] = bootstrap # (FIXME: Do we need to catch the case where there's a single *unmasked* epoch?) - # Catch the case when there is only a single epoch. Just return 0 velocity - # and the same input position for the x0/y0. + # Catch the case when there is only a single epoch. Just return + # the same input position for the x0/y0. if (self['x'].shape[1] == 1): self['motion_model'] = 'Fixed' self['x0'] = self['x'][:,0] @@ -624,19 +625,28 @@ def fit_velocities(self, weighting='var', use_scipy=True, absolute_sigma=True, b self['n_fit'] = 1 return + + # TODO: this is not great - hack attempt at debugging + motion_model_dict = {} + for motion_model_name in np.unique(self['motion_model']): + motion_model_dict[motion_model_name] = getattr(motion_model, motion_model_name)() # STARS LOOP through the stars and work on them 1 at a time. # This is slow; but robust. if show_progress: for ss in tqdm(range(N_stars)): self.fit_velocity_for_star(ss, weighting=weighting, use_scipy=use_scipy, - absolute_sigma=absolute_sigma, bootstrap=bootstrap, fixed_t0=fixed_t0, - mask_val=mask_val, mask_lists=mask_lists) + absolute_sigma=absolute_sigma, bootstrap=bootstrap, + fixed_t0=fixed_t0, default_motion_model=default_motion_model, + mask_val=mask_val, mask_lists=mask_lists, + motion_model_dict=motion_model_dict) else: for ss in range(N_stars): self.fit_velocity_for_star(ss, weighting=weighting, use_scipy=use_scipy, - absolute_sigma=absolute_sigma, bootstrap=bootstrap, fixed_t0=fixed_t0, - mask_val=mask_val, mask_lists=mask_lists) + absolute_sigma=absolute_sigma, bootstrap=bootstrap, + fixed_t0=fixed_t0, default_motion_model=default_motion_model, + mask_val=mask_val, mask_lists=mask_lists, + motion_model_dict=motion_model_dict) if verbose: stop_time = time.time() print('startable.fit_velocities runtime = {0:.0f} s for {1:d} stars'.format(stop_time - start_time, N_stars)) @@ -645,7 +655,9 @@ def fit_velocities(self, weighting='var', use_scipy=True, absolute_sigma=True, b def fit_velocity_for_star(self, ss, weighting='var', use_scipy=True, absolute_sigma=True, bootstrap=False, fixed_t0=False, - mask_val=None, mask_lists=False): + default_motion_model='Linear', + mask_val=None, mask_lists=False, + motion_model_dict=None): # # Make a mask of invalid (NaN) values and a user-specified invalid value. @@ -786,18 +798,21 @@ def fit_velocity_for_star(self, ss, weighting='var', use_scipy=True, # # Decide which motion_model to fit. # - motion_model_assigned = self['motion_model'][ss] - - # Override the motion model if we don't have enought data. - # TODO: Query the motion model object to see the minimum number of points needed. - if motion_model_assigned=='Fixed' or N_good==1 or (dt == dt[0]).all(): - # Either 'fixed' is selected, or is required because - # of no time-domain data + motion_model_use = self['motion_model'][ss] + + # Go to default model if not enough points for assigned but enough for default + # TODO: think about whether we want other fallbacks besides the singular default and Fixed + if (N_good < getattr(motion_model, self['motion_model'][ss]).n_pts_req) and \ + (N_good >= getattr(motion_model, default_motion_model).n_pts_req): + motion_model_use = default_motion_model + # If not enough points for either, go to a fixed model + elif (N_good < getattr(motion_model, self['motion_model'][ss]).n_pts_req) and \ + (N_good < getattr(motion_model, default_motion_model).n_pts_req): motion_model_use = 'Fixed' - elif motion_model_assigned=='Linear' and N_good>1: - # If 'Linear' is selected and enough data exists - # to model linear motion - motion_model_use = 'Linear' + # If the points do not cover multiple times, go to a fixed model + if (dt == dt[0]).all(): + motion_model_use = 'Fixed' + self['motion_model'][ss] = motion_model_use # Instantiate the motion model object. @@ -825,6 +840,10 @@ def fit_velocity_for_star(self, ss, weighting='var', use_scipy=True, # Fit for the best parameters params, param_errs = mod.fit_motion_model(dt, x, y, xe, ye, bootstrap=bootstrap) + + # TODO: bad + #with modClass(**param_dict) as mod: + # params, param_errs = mod.fit_motion_model(dt, x, y, xe, ye, bootstrap=bootstrap) # Save parameters and errors to table. for pp in range(len(modClass.fitter_param_names)): @@ -833,10 +852,47 @@ def fit_velocity_for_star(self, ss, weighting='var', use_scipy=True, self[par][ss] = params[pp] self[par_err][ss] = param_errs[pp] - + return - - + + # New function, to use in align + def get_star_positions_at_time(self, t): + """ Get current x,y positions of each star according to its motion_model + Instead of looping through every star, we implement a faster calculation for Fixed and Linear models, + and loop through any stars with a more complex model + TODO: can add acceleration model to the ones with shortcuts + """ + # Start with empty arrays so we can fill them in batches + N_stars = len(self) + x = np.full(N_stars, np.nan, dtype=float) + y = np.full(N_stars, np.nan, dtype=float) + xe = np.full(N_stars, np.nan, dtype=float) + ye = np.full(N_stars, np.nan, dtype=float) + # Check which motion models we need + # use complex_mms to collect models besides Fixed and Linear + unique_mms = np.unique(self['motion_model']).tolist() + # Calculate current position in batches by motion model + for mm in unique_mms: + # Identify stars with this model & get class + idx = np.where(self['motion_model']==mm)[0] + modClass = getattr(motion_model, mm) + # Set up parameters + param_dict = {} + for par in modClass.fitter_param_names: + param_dict[par] = self[par][idx] + param_dict[par+'_err'] = self[par+'_err'][idx] + # Load fixed parameters, if needed. + for par in modClass.fixed_param_names: + if par not in self.colnames: + msg = f'fit_velocity_for_star: ' + msg += f'Missing fixed_params column {par} needed for motion model {motion_model_use}.' + raise RuntimeException(msg) + param_dict[par] = self[par][idx] + mod = modClass() + x[idx],y[idx],xe[idx],ye[idx] = mod.get_batch_pos_at_time(t,**param_dict) + return x,y,xe,ye + + def fit_velocities_all_detected(self, weighting='var', use_scipy=False, absolute_sigma=False, epoch_cols='all', mask_val=None, art_star=False, return_result=False): """Fit velocities for stars detected in all epochs specified by epoch_cols. Criterion: xe/ye error > 0 and finite, x/y not masked. diff --git a/flystar/tests/test_align.py b/flystar/tests/test_align.py index 4892acc..592dc69 100644 --- a/flystar/tests/test_align.py +++ b/flystar/tests/test_align.py @@ -8,7 +8,7 @@ import pylab as plt import pdb import datetime - +import pytest def test_MosaicSelfRef(): """ @@ -101,7 +101,7 @@ def test_MosaicSelfRef_vel_tconst(): dr_tol=[3, 3], dm_tol=[1, 1], trans_class=transforms.PolyTransform, trans_args={'order': 2}, - use_motion=True, default_motion_model='Linear', + default_motion_model='Linear', verbose=False) msc.fit() diff --git a/flystar/tests/test_startable.py b/flystar/tests/test_startable.py index a862b55..b03acf1 100644 --- a/flystar/tests/test_startable.py +++ b/flystar/tests/test_startable.py @@ -359,7 +359,7 @@ def test_fit_velocities(): ########## # Test bootstrap ########## - tab_b = table.vstack((tab1, tab2, tab3)) + '''tab_b = table.vstack((tab1, tab2, tab3)) tab_b.meta = tab1.meta tab_b.fit_velocities(verbose=True, bootstrap=50) @@ -367,7 +367,7 @@ def test_fit_velocities(): assert tab_b['x0_err'][0] > tab['x0_err'][0] assert tab_b['vx_err'][0] > tab['vx_err'][0] assert tab_b['y0_err'][0] > tab['y0_err'][0] - assert tab_b['vy_err'][0] > tab['vy_err'][0] + assert tab_b['vy_err'][0] > tab['vy_err'][0]''' ########## # Test what happens with no velocity errors From 860255f461b636dbf35478c8ebeb29edd33db93b Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Tue, 20 Aug 2024 17:41:06 -0700 Subject: [PATCH 013/254] minor bug fixes --- flystar/match.py | 4 +-- flystar/tests/test_align.py | 69 +++++++++++++++++++------------------ 2 files changed, 37 insertions(+), 36 deletions(-) diff --git a/flystar/match.py b/flystar/match.py index 96a9f77..591dd16 100644 --- a/flystar/match.py +++ b/flystar/match.py @@ -278,8 +278,8 @@ def match(x1, y1, m1, x2, y2, m2, dr_tol, dm_tol=None, verbose=True): idxs1 = np.ones(x1.size, dtype=int) * -1 idxs2 = np.ones(x1.size, dtype=int) * -1 - print(coords2) - pdb.set_trace() + # TODO: consider better solution for this + coords2=np.nan_to_num(coords2) # The matching will be done using a KDTree. kdt = KDT(coords2, balanced_tree=False) diff --git a/flystar/tests/test_align.py b/flystar/tests/test_align.py index 592dc69..6caab14 100644 --- a/flystar/tests/test_align.py +++ b/flystar/tests/test_align.py @@ -30,11 +30,11 @@ def test_MosaicSelfRef(): # Check some of the output quantities on the final table. assert 'x0' in msc.ref_table.colnames - assert 'x0e' in msc.ref_table.colnames + assert 'x0_err' in msc.ref_table.colnames assert 'y0' in msc.ref_table.colnames - assert 'y0e' in msc.ref_table.colnames + assert 'y0_err' in msc.ref_table.colnames assert 'm0' in msc.ref_table.colnames - assert 'm0e' in msc.ref_table.colnames + assert 'm0_err' in msc.ref_table.colnames assert 'use_in_trans' in msc.ref_table.colnames assert 'used_in_trans' in msc.ref_table.colnames assert 'ref_orig' in msc.ref_table.colnames @@ -46,13 +46,14 @@ def test_MosaicSelfRef(): # Check that we have some matched stars... should be at least 35 stars # that are detected in all 4 starlists. idx = np.where(msc.ref_table['n_detect'] == 4)[0] + print(len(idx)) assert len(idx) > 35 # Check that the transformation error isn't too big - assert (msc.ref_table['x0e'] < 3.0).all() # less than 1 pix - assert (msc.ref_table['y0e'] < 3.0).all() - #assert (msc.ref_table['m0e'] < 1.0).all() # less than 0.5 mag - assert (msc.ref_table['m0e'] < 1.5).all() # less than 0.5 mag + assert (msc.ref_table['x0_err'] < 3.0).all() # less than 1 pix + assert (msc.ref_table['y0_err'] < 3.0).all() + #assert (msc.ref_table['m0_err'] < 1.0).all() # less than 0.5 mag + assert (msc.ref_table['m0_err'] < 1.5).all() # less than 0.5 mag # Check that the transformation lists aren't too wacky for ii in range(4): @@ -62,8 +63,8 @@ def test_MosaicSelfRef(): # We didn't do any velocity fitting, so make sure nothing got created. assert 'vx' not in msc.ref_table.colnames assert 'vy' not in msc.ref_table.colnames - assert 'vxe' not in msc.ref_table.colnames - assert 'vye' not in msc.ref_table.colnames + assert 'vx_err' not in msc.ref_table.colnames + assert 'vy_err' not in msc.ref_table.colnames plt.clf() plt.plot(msc.ref_table['x'][:, 0], @@ -141,8 +142,8 @@ def test_MosaicSelfRef_vel_tconst(): assert (msc.ref_table['vx'] == 0).all() assert (msc.ref_table['vy'] == 0).all() - assert (msc.ref_table['vxe'] == 0).all() - assert (msc.ref_table['vye'] == 0).all() + assert (msc.ref_table['vx_err'] == 0).all() + assert (msc.ref_table['vy_err'] == 0).all() return @@ -181,15 +182,15 @@ def test_MosaicSelfRef_vel(): # Check some of the output quantities on the final table. assert 'x0' in msc.ref_table.colnames - assert 'x0e' in msc.ref_table.colnames + assert 'x0_err' in msc.ref_table.colnames assert 'y0' in msc.ref_table.colnames - assert 'y0e' in msc.ref_table.colnames + assert 'y0_err' in msc.ref_table.colnames assert 'm0' in msc.ref_table.colnames - assert 'm0e' in msc.ref_table.colnames + assert 'm0_err' in msc.ref_table.colnames assert 'vx' in msc.ref_table.colnames - assert 'vxe' in msc.ref_table.colnames + assert 'vx_err' in msc.ref_table.colnames assert 'vy' in msc.ref_table.colnames - assert 'vye' in msc.ref_table.colnames + assert 'vy_err' in msc.ref_table.colnames assert 't0' in msc.ref_table.colnames # Check that we have some matched stars... should be at least 35 stars @@ -198,9 +199,9 @@ def test_MosaicSelfRef_vel(): assert len(idx) > 35 # Check that the transformation error isn't too big - assert (msc.ref_table['x0e'] < 3.0).all() # less than 1 pix - assert (msc.ref_table['y0e'] < 3.0).all() - assert (msc.ref_table['m0e'] < 1.0).all() # less than 0.5 mag + assert (msc.ref_table['x0_err'] < 3.0).all() # less than 1 pix + assert (msc.ref_table['y0_err'] < 3.0).all() + assert (msc.ref_table['m0_err'] < 1.0).all() # less than 0.5 mag # Check that the transformation lists aren't too wacky for ii in range(4): @@ -230,8 +231,8 @@ def test_MosaicToRef(): # Convert velocities to arcsec/yr ref_list['vx'] *= 1e-3 ref_list['vy'] *= 1e-3 - ref_list['vxe'] *= 1e-3 - ref_list['vye'] *= 1e-3 + ref_list['vx_err'] *= 1e-3 + ref_list['vy_err'] *= 1e-3 # Switch our list to a "increasing to the West" list. ref_list['x0'] *= -1.0 @@ -290,8 +291,8 @@ def test_MosaicToRef_acc(): # Convert velocities to arcsec/yr ref_list['vx'] *= 1e-3 ref_list['vy'] *= 1e-3 - ref_list['vxe'] *= 1e-3 - ref_list['vye'] *= 1e-3 + ref_list['vx_err'] *= 1e-3 + ref_list['vy_err'] *= 1e-3 # Convert accelerations to arcsec/yr**2 ref_list['ax'] *= 1e-3 @@ -476,8 +477,8 @@ def make_fake_starlists_poly1_vel(seed=-1): # Make an StarList lis = starlists.StarList([name, m0, m0e, x0, x0e, y0, y0e, vx, vxe, vy, vye, t0], - names = ('name', 'm0', 'm0e', 'x0', 'x0e', 'y0', 'y0e', - 'vx', 'vxe', 'vy', 'vye', 't0')) + names = ('name', 'm0', 'm0_err', 'x0', 'x0_err', 'y0', 'y0_err', + 'vx', 'vx_err', 'vy', 'vy_err', 't0')) sdx = np.argsort(m0) lis = lis[sdx] @@ -511,8 +512,8 @@ def make_fake_starlists_poly1_vel(seed=-1): # Convert into pixels xp = (x / -scale) + shift[0] # -1 from switching to increasing to West (right) yp = (y / scale) + shift[1] - xpe = lis['x0e'] / scale - ype = lis['y0e'] / scale + xpe = lis['x0_err'] / scale + ype = lis['y0_err'] / scale # Distort the positions trans = transforms.PolyTransform(1, xy_trans[ss][0], xy_trans[ss][1], mag_offset=mag_trans[ss]) @@ -525,7 +526,7 @@ def make_fake_starlists_poly1_vel(seed=-1): md += np.random.randn(N_stars) * 0.02 xde = xpe yde = ype - mde = lis['m0e'] + mde = lis['m0_err'] # Save the new list as a starlist. new_lis = starlists.StarList([lis['name'], md, mde, xd, xde, yd, yde, t], @@ -575,9 +576,9 @@ def make_fake_starlists_poly1_acc(seed=-1): vx, vxe, vy, vye, ax, axe, ay, aye, t0], - names = ('name', 'm0', 'm0e', - 'x0', 'x0e', 'y0', 'y0e', - 'vx', 'vxe', 'vy', 'vye', + names = ('name', 'm0', 'm0_err', + 'x0', 'x0_err', 'y0', 'y0_err', + 'vx', 'vx_err', 'vy', 'vy_err', 'ax', 'axe', 'ay', 'aye', 't0')) @@ -613,8 +614,8 @@ def make_fake_starlists_poly1_acc(seed=-1): # Convert into pixels xp = (x / -scale) + shift[0] # -1 from switching to increasing to West (right) yp = (y / scale) + shift[1] - xpe = lis['x0e'] / scale - ype = lis['y0e'] / scale + xpe = lis['x0_err'] / scale + ype = lis['y0_err'] / scale # Distort the positions trans = transforms.PolyTransform(1, xy_trans[ss][0], xy_trans[ss][1], mag_offset=mag_trans[ss]) @@ -627,7 +628,7 @@ def make_fake_starlists_poly1_acc(seed=-1): md += np.random.randn(N_stars) * 0.02 xde = xpe yde = ype - mde = lis['m0e'] + mde = lis['m0_err'] # Save the new list as a starlist. new_lis = starlists.StarList([lis['name'], md, mde, xd, xde, yd, yde, t], From 343cf6461bd33f9c6f6dfe494577baeaf3e14898 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Wed, 21 Aug 2024 17:08:07 -0700 Subject: [PATCH 014/254] motion model progress' --- flystar/align.py | 70 ++++++++++++++++++------------------- flystar/starlists.py | 50 +++++++++++++------------- flystar/startables.py | 45 +++++++++++------------- flystar/tests/ref_vel.lis | 2 +- flystar/tests/test_align.py | 20 ++++++----- flystar/transforms.py | 8 ++--- 6 files changed, 97 insertions(+), 98 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 8968633..bd03d40 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -277,7 +277,6 @@ def fit(self): # w, w_orig (optiona) -- the input and output weights of stars in transform: 2D ########## self.ref_table = self.setup_ref_table_from_starlist(self.star_lists[self.ref_index]) - # Save the reference index to the meta data on the reference list. self.ref_table.meta['ref_list'] = self.ref_index @@ -402,7 +401,7 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar star_list_T.transform_xym(trans) # trimmed, transformed else: star_list_T.transform_xy(trans) - + # Match stars between the transformed, trimmed lists. idx1, idx2, dr, dm = match.match(star_list_T['x'], star_list_T['y'], star_list_T['m'], ref_list['x'], ref_list['y'], ref_list['m'], @@ -501,11 +500,12 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar ## Make plot, if desired plots.trans_positions(ref_list, ref_list[idx_ref], star_list_T, star_list_T[idx_lis], fileName='{0}'.format(star_list_T['t'][0])) - + ### Update the observed (but transformed) values in the reference table. self.update_ref_table_from_list(star_list, star_list_T, ii, idx_ref, idx_lis, idx2) - + ### Update the "average" values to be used as the reference frame for the next list. + print(self.ref_table.keys()) if self.update_ref_orig != 'periter': self.update_ref_table_aggregates() @@ -571,7 +571,7 @@ def setup_ref_table_from_starlist(self, star_list): array in the original reference star list. """ col_arrays = {} - motion_model_col_names = motion_model.get_all_motion_model_param_names(with_errors=True) + motion_model_col_names = motion_model.get_all_motion_model_param_names(with_errors=True) + ['m0','m0_err','use_in_trans'] for col_name in star_list.colnames: if col_name == 'name': # The "name" column will be 1D; but we will also add a "name_in_list" column. @@ -665,13 +665,12 @@ def setup_ref_table_from_starlist(self, star_list): # Keep track of whether this is an original reference star. col_ref_orig = Column(np.ones(len(ref_table), dtype=bool), name='ref_orig') ref_table.add_column(col_ref_orig) - # Now reset the original values to invalids... they will be filled in # at later times. Preserve content only in the columns: name, x0, y0, m0 (and 0e). # Note that these are all the 1D columsn. for col_name in ref_table.colnames: if len(ref_table[col_name].data.shape) == 2: # Find the 2D columns - ref_table._set_invalid_list_values(col_name, -1) + ref_table._set_invalid_list_values(col_name, -1) return ref_table @@ -817,7 +816,10 @@ def update_ref_table_aggregates(self, n_boot=0): vals_orig = {} vals_orig['m0'] = self.ref_table['m0'][ref_orig_idx] vals_orig['m0_err'] = self.ref_table['m0_err'][ref_orig_idx] - motion_model_col_names = motion_model.get_list_motion_model_param_names(self.ref_table['motion_model'][ref_orig_idx], with_errors=True) + motion_model_class_names = [self.default_motion_model] + if 'motion_model_used' in self.ref_table.keys(): + motion_model_class_names += self.ref_table['motion_model_used'][ref_orig_idx].tolist() + motion_model_col_names = motion_model.get_list_motion_model_param_names(motion_model_class_names, with_errors=True) for mm in motion_model_col_names: vals_orig[mm] = self.ref_table[mm][ref_orig_idx] @@ -838,12 +840,10 @@ def update_ref_table_aggregates(self, n_boot=0): weighted_m = ('me' in self.ref_table.colnames) self.ref_table.combine_lists_xym(weighted_xy=weighted_xy, weighted_m=weighted_m)''' - # Replace the originals if we are supposed to keep them fixed. if not self.update_ref_orig: for val in vals_orig.keys(): self.ref_table[val][ref_orig_idx] = vals_orig[val] - return def get_weights_for_lists(self, ref_list, star_list): @@ -946,7 +946,7 @@ def get_ref_list_from_table(self, epoch): # Reference stars will be named. name = self.ref_table['name'] - if ('motion_model' in self.ref_table.colnames): + if ('motion_model_used' in self.ref_table.colnames): x,y,xe,ye = self.ref_table.get_star_positions_at_time(epoch) else: # No velocities... just used average positions. @@ -1545,9 +1545,9 @@ def fit(self): self.ref_table['vx'] = self.ref_list['vx'] self.ref_table['vy'] = self.ref_list['vy'] self.ref_table['t0'] = self.ref_list['t0'] - if 'vxe' in self.ref_list.colnames: - self.ref_table['vxe'] = self.ref_list['vxe'] - self.ref_table['vye'] = self.ref_list['vye'] + if 'vx_err' in self.ref_list.colnames: + self.ref_table['vx_err'] = self.ref_list['vx_err'] + self.ref_table['vy_err'] = self.ref_list['vy_err'] ########## @@ -1710,7 +1710,7 @@ def setup_ref_table_from_starlist(star_list): if 'use_in_trans' not in ref_table.colnames: new_col = Column(np.ones(len(ref_table), dtype=bool), name='use_in_trans') ref_table.add_column(new_col) - + # Now reset the original values to invalids... they will be filled in # at later times. Preserve content only in the columns: name, x0, y0, m0 (and 0e). # Note that these are all the 1D columsn. @@ -1815,8 +1815,10 @@ def add_rows_for_new_stars(ref_table, star_list, idx_lis, default_motion_model=' new_col_empty = -1 elif ref_table[col_name].dtype == np.dtype('bool'): new_col_empty = False - elif col_name=='motion_model': + elif col_name=='motion_model_input': new_col_empty = default_motion_model + elif col_name=='motion_model_used': + new_col_empty = 'None' else: new_col_empty = np.nan @@ -1878,7 +1880,7 @@ def run_align_iter(catalog, trans_order=1, poly_deg=1, ref_mag_lim=19, ref_radiu calc_mag_avg_all_stars(d) tdx = np.where((d['name_0'] == 'OB120169') | (d['name_0'] == 'OB120169_L'))[0] - print(d[tdx]['name_0', 't0', 'mag', 'x0', 'vx', 'x0_err', 'vxe', 'chi2x', 'y0', 'vy', 'y0_err', 'vye', 'chi2y', 'dof']) + print(d[tdx]['name_0', 't0', 'mag', 'x0', 'vx', 'x0_err', 'vx_err', 'chi2x', 'y0', 'vy', 'y0_err', 'vy_err', 'chi2y', 'dof']) ########## # Second iteration -- align everything to reference positions derived from iteration 1 @@ -2021,8 +2023,8 @@ def calc_transform_ref_poly(d, target_name, poly_deg, ref_mag_lim, ref_radius_li if poly_deg >= 1: x_ref_ee += d['vx'] * dt y_ref_ee += d['vy'] * dt - xe_ref_ee = np.hypot(xe_ref_ee, d['vxe'] * dt) - ye_ref_ee = np.hypot(ye_ref_ee, d['vye'] * dt) + xe_ref_ee = np.hypot(xe_ref_ee, d['vx_err'] * dt) + ye_ref_ee = np.hypot(ye_ref_ee, d['vy_err'] * dt) if poly_deg >= 2: x_ref_ee += d['ax'] * dt @@ -2194,8 +2196,8 @@ def calc_polyfit_all_stars(d, poly_deg, init_fig_idx=0): if poly_deg >= 1: d['vx'] = px_all[:, -2] d['vy'] = py_all[:, -2] - d['vxe'] = pxe_all[:, -2] - d['vye'] = pye_all[:, -2] + d['vx_err'] = pxe_all[:, -2] + d['vy_err'] = pye_all[:, -2] if poly_deg >= 2: d['ax'] = px_all[:, -3] @@ -2730,8 +2732,8 @@ def transform_from_file(starlist, transFile): vx_orig = starlist['vx'] vy_orig = starlist['vy'] - vxe_orig = starlist['vxe'] - vye_orig = starlist['vye'] + vxe_orig = starlist['vx_err'] + vye_orig = starlist['vy_err'] # Read transFile trans = Table.read(transFile, format='ascii.commented_header', header_start=-1) @@ -2841,8 +2843,8 @@ def transform_from_file(starlist, transFile): starlist_f['y0_err'] = y0e_new starlist_f['vx'] = vx_new starlist_f['vy'] = vy_new - starlist_f['vxe'] = vxe_new - starlist_f['vye'] = vye_new + starlist_f['vx_err'] = vxe_new + starlist_f['vy_err'] = vye_new return starlist_f @@ -2893,8 +2895,8 @@ def transform_from_object(starlist, transform): y0e = starlist_f['y0_err'] vx = starlist_f['vx'] vy = starlist_f['vy'] - vxe = starlist_f['vxe'] - vye = starlist_f['vye'] + vxe = starlist_f['vx_err'] + vye = starlist_f['vy_err'] # calculate the transformed position and velocity @@ -2921,8 +2923,8 @@ def transform_from_object(starlist, transform): starlist_f['y0_err'] = y0e_new starlist_f['vx'] = vx_new starlist_f['vy'] = vy_new - starlist_f['vxe'] = vxe_new - starlist_f['vye'] = vye_new + starlist_f['vx_err'] = vxe_new + starlist_f['vy_err'] = vye_new return starlist_f @@ -3766,8 +3768,8 @@ def get_weighting_scheme(weights, ref_list, star_list): return weight -# TODO: Are starlists supposed to hold velocities? We'll need to add a motion_model column if so -def get_pos_at_time(t, starlist,use_motion=True): +# TODO: I think this is a startable, not a starlist +def get_pos_at_time(t, starlist): """ Take a starlist, check to see if it has velocity columns. If it does, then propogate the positions forward in time @@ -3781,10 +3783,8 @@ def get_pos_at_time(t, starlist,use_motion=True): but it should be in the same units as the 't0' column in starlist. """ - if use_motion and ('vx' in starlist.colnames) and ('vy' in starlist.colnames): - dt = t - starlist['t0'] - x = starlist['x0'] + (starlist['vx'] * dt) - y = starlist['y0'] + (starlist['vy'] * dt) + if 'motion_model_used' in starlist.colnames: + x,y,xe,ye = starlist.get_star_positions_at_time(t) else: if ('x0' in starlist.colnames) and ('y0' in starlist.colnames): x = starlist['x0'] diff --git a/flystar/starlists.py b/flystar/starlists.py index be49458..23df44f 100644 --- a/flystar/starlists.py +++ b/flystar/starlists.py @@ -209,12 +209,12 @@ def read_label(labelFile, prop_to_time=None, flipX=True): t_label.rename_column('col2', 'm') t_label.rename_column('col3', 'x0') t_label.rename_column('col4', 'y0') - t_label.rename_column('col5', 'x0e') - t_label.rename_column('col6', 'y0e') + t_label.rename_column('col5', 'x0_err') + t_label.rename_column('col6', 'y0_err') t_label.rename_column('col7', 'vx') t_label.rename_column('col8', 'vy') - t_label.rename_column('col9', 'vxe') - t_label.rename_column('col10','vye') + t_label.rename_column('col9', 'vx_err') + t_label.rename_column('col10','vy_err') t_label.rename_column('col11','t0') t_label.rename_column('col12','use') t_label.rename_column('col13','r0') @@ -222,23 +222,23 @@ def read_label(labelFile, prop_to_time=None, flipX=True): # Convert velocities from mas/yr to arcsec/year # t_label['vx'] *= 0.001 # t_label['vy'] *= 0.001 -# t_label['vxe'] *= 0.001 -# t_label['vye'] *= 0.001 +# t_label['vx_err'] *= 0.001 +# t_label['vy_err'] *= 0.001 t_label['vx'] = t_label['vx'] * 0.001 t_label['vy'] = t_label['vy'] * 0.001 - t_label['vxe'] = t_label['vxe'] * 0.001 - t_label['vye'] = t_label['vye'] * 0.001 + t_label['vx_err'] = t_label['vx_err'] * 0.001 + t_label['vy_err'] = t_label['vy_err'] * 0.001 # propogate to prop_to_time if prop_to_time is given if prop_to_time != None: x0 = t_label['x0'] - x0e = t_label['x0e'] + x0e = t_label['x0_err'] vx = t_label['vx'] - vxe = t_label['vxe'] + vxe = t_label['vx_err'] y0 = t_label['y0'] - y0e = t_label['y0e'] + y0e = t_label['y0_err'] vy = t_label['vy'] - vye = t_label['vye'] + vye = t_label['vy_err'] t0 = t_label['t0'] t_label['x'] = x0 + vx*(prop_to_time - t0) t_label['y'] = y0 + vy*(prop_to_time - t0) @@ -316,12 +316,12 @@ def read_label_accel(labelFile, prop_to_time=None, flipX=True): t_label.rename_column('col2', 'm') t_label.rename_column('col3', 'x0') t_label.rename_column('col4', 'y0') - t_label.rename_column('col5', 'x0e') - t_label.rename_column('col6', 'y0e') + t_label.rename_column('col5', 'x0_err') + t_label.rename_column('col6', 'y0_err') t_label.rename_column('col7', 'vx') t_label.rename_column('col8', 'vy') - t_label.rename_column('col9', 'vxe') - t_label.rename_column('col10','vye') + t_label.rename_column('col9', 'vx_err') + t_label.rename_column('col10','vy_err') t_label.rename_column('col11', 'ax') t_label.rename_column('col12', 'ay') t_label.rename_column('col13', 'axe') @@ -333,12 +333,12 @@ def read_label_accel(labelFile, prop_to_time=None, flipX=True): # Convert velocities from mas/yr to arcsec/year # t_label['vx'] *= 0.001 # t_label['vy'] *= 0.001 -# t_label['vxe'] *= 0.001 -# t_label['vye'] *= 0.001 +# t_label['vx_err'] *= 0.001 +# t_label['vy_err'] *= 0.001 t_label['vx'] = t_label['vx'] * 0.001 t_label['vy'] = t_label['vy'] * 0.001 - t_label['vxe'] = t_label['vxe'] * 0.001 - t_label['vye'] = t_label['vye'] * 0.001 + t_label['vx_err'] = t_label['vx_err'] * 0.001 + t_label['vy_err'] = t_label['vy_err'] * 0.001 t_label['ax'] = t_label['ax'] * 0.001 t_label['ay'] = t_label['ay'] * 0.001 @@ -348,15 +348,15 @@ def read_label_accel(labelFile, prop_to_time=None, flipX=True): # propogate to prop_to_time if prop_to_time is given if prop_to_time != None: x0 = t_label['x0'] - x0e = t_label['x0e'] + x0e = t_label['x0_err'] vx = t_label['vx'] - vxe = t_label['vxe'] + vxe = t_label['vx_err'] ax = t_label['ax'] axe = t_label['axe'] y0 = t_label['y0'] - y0e = t_label['y0e'] + y0e = t_label['y0_err'] vy = t_label['vy'] - vye = t_label['vye'] + vye = t_label['vy_err'] ay = t_label['ay'] aye = t_label['aye'] t0 = t_label['t0'] @@ -535,6 +535,8 @@ def __init__(self, *args, **kwargs): # Check all the arrays. arg_tab = ('x', 'y', 'm', 'xe', 'ye', 'me', 'corr') + #print(kwargs) + for arg_test in arg_tab: if arg_test in kwargs: if not isinstance(kwargs[arg_test], np.ndarray): diff --git a/flystar/startables.py b/flystar/startables.py index 32d4138..83f4b5e 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -164,8 +164,8 @@ def __init__(self, *args, ref_list=0, **kwargs): self.add_column(Column(data=kwargs[arg], name=arg)) if arg == 'name_in_list': self['name_in_list'] = self['name_in_list'].astype('U20') - if arg == 'motion_model': - self['motion_model'] = self['motion_model'].astype('U20') + if arg == 'motion_model_input': + self['motion_model_input'] = self['motion_model_input'].astype('U20') '''if 'motion_model' not in kwargs: self['motion_model'] = np.repeat(default_motion_model, len(self['name']))''' @@ -507,7 +507,7 @@ def combine_lists(self, col_name_in, weights_col=None, mask_val=None, # Save off our new AVG and STD into new columns with shape (N_stars). col_name_avg = col_name_in + '0' - col_name_std = col_name_in + '0e' + col_name_std = col_name_in + '0_err' if ismag: std = (2.5 / np.log(10)) * std / avg @@ -579,14 +579,17 @@ def fit_velocities(self, weighting='var', use_scipy=True, absolute_sigma=True, b msg = 'Starting startable.fit_velocities for {0:d} stars with n={1:d} bootstrap' print(msg.format(N_stars, bootstrap)) + # Set all to default_motion_model if none assigned already. + # Reset motion_model_used to the inputs for now -> will change as fits run + if 'motion_model_input' not in self.colnames: + self['motion_model_input'] = default_motion_model + self['motion_model_used'] = self['motion_model_input'] + # # Fill table with all possible motion model parameter names as new # columns. Make everything empty for now. # - if 'motion_model' not in self.colnames: - self['motion_model'] = default_motion_model - - all_motion_models = np.unique(self['motion_model'].tolist() + ['Fixed','Linear']).tolist() + all_motion_models = np.unique(self['motion_model_input'].tolist() + ['Fixed']+[default_motion_model]).tolist() new_col_list = motion_model.get_list_motion_model_param_names(all_motion_models, with_errors=True) # Append goodness of fit metrics and t0. # TODO: actually populate these columns @@ -609,7 +612,7 @@ def fit_velocities(self, weighting='var', use_scipy=True, absolute_sigma=True, b # Catch the case when there is only a single epoch. Just return # the same input position for the x0/y0. if (self['x'].shape[1] == 1): - self['motion_model'] = 'Fixed' + self['motion_model_used'] = 'Fixed' self['x0'] = self['x'][:,0] self['y0'] = self['y'][:,0] @@ -625,11 +628,6 @@ def fit_velocities(self, weighting='var', use_scipy=True, absolute_sigma=True, b self['n_fit'] = 1 return - - # TODO: this is not great - hack attempt at debugging - motion_model_dict = {} - for motion_model_name in np.unique(self['motion_model']): - motion_model_dict[motion_model_name] = getattr(motion_model, motion_model_name)() # STARS LOOP through the stars and work on them 1 at a time. # This is slow; but robust. @@ -638,15 +636,13 @@ def fit_velocities(self, weighting='var', use_scipy=True, absolute_sigma=True, b self.fit_velocity_for_star(ss, weighting=weighting, use_scipy=use_scipy, absolute_sigma=absolute_sigma, bootstrap=bootstrap, fixed_t0=fixed_t0, default_motion_model=default_motion_model, - mask_val=mask_val, mask_lists=mask_lists, - motion_model_dict=motion_model_dict) + mask_val=mask_val, mask_lists=mask_lists) else: for ss in range(N_stars): self.fit_velocity_for_star(ss, weighting=weighting, use_scipy=use_scipy, absolute_sigma=absolute_sigma, bootstrap=bootstrap, fixed_t0=fixed_t0, default_motion_model=default_motion_model, - mask_val=mask_val, mask_lists=mask_lists, - motion_model_dict=motion_model_dict) + mask_val=mask_val, mask_lists=mask_lists) if verbose: stop_time = time.time() print('startable.fit_velocities runtime = {0:.0f} s for {1:d} stars'.format(stop_time - start_time, N_stars)) @@ -656,8 +652,7 @@ def fit_velocities(self, weighting='var', use_scipy=True, absolute_sigma=True, b def fit_velocity_for_star(self, ss, weighting='var', use_scipy=True, absolute_sigma=True, bootstrap=False, fixed_t0=False, default_motion_model='Linear', - mask_val=None, mask_lists=False, - motion_model_dict=None): + mask_val=None, mask_lists=False): # # Make a mask of invalid (NaN) values and a user-specified invalid value. @@ -798,22 +793,22 @@ def fit_velocity_for_star(self, ss, weighting='var', use_scipy=True, # # Decide which motion_model to fit. # - motion_model_use = self['motion_model'][ss] + motion_model_use = self['motion_model_input'][ss] # Go to default model if not enough points for assigned but enough for default # TODO: think about whether we want other fallbacks besides the singular default and Fixed - if (N_good < getattr(motion_model, self['motion_model'][ss]).n_pts_req) and \ + if (N_good < getattr(motion_model, self['motion_model_input'][ss]).n_pts_req) and \ (N_good >= getattr(motion_model, default_motion_model).n_pts_req): motion_model_use = default_motion_model # If not enough points for either, go to a fixed model - elif (N_good < getattr(motion_model, self['motion_model'][ss]).n_pts_req) and \ + elif (N_good < getattr(motion_model, self['motion_model_input'][ss]).n_pts_req) and \ (N_good < getattr(motion_model, default_motion_model).n_pts_req): motion_model_use = 'Fixed' # If the points do not cover multiple times, go to a fixed model if (dt == dt[0]).all(): motion_model_use = 'Fixed' - self['motion_model'][ss] = motion_model_use + self['motion_model_used'][ss] = motion_model_use # Instantiate the motion model object. modClass = getattr(motion_model, motion_model_use) @@ -870,11 +865,11 @@ def get_star_positions_at_time(self, t): ye = np.full(N_stars, np.nan, dtype=float) # Check which motion models we need # use complex_mms to collect models besides Fixed and Linear - unique_mms = np.unique(self['motion_model']).tolist() + unique_mms = np.unique(self['motion_model_used']).tolist() # Calculate current position in batches by motion model for mm in unique_mms: # Identify stars with this model & get class - idx = np.where(self['motion_model']==mm)[0] + idx = np.where(self['motion_model_used']==mm)[0] modClass = getattr(motion_model, mm) # Set up parameters param_dict = {} diff --git a/flystar/tests/ref_vel.lis b/flystar/tests/ref_vel.lis index 4d223b0..fc191bb 100644 --- a/flystar/tests/ref_vel.lis +++ b/flystar/tests/ref_vel.lis @@ -1,4 +1,4 @@ -name x y m xe ye me t0 vx vy vxe vye +name x y m xe ye me t0 vx vy vx_err vy_err gaia_1150 -63.98457260029581 -30.67278228118061 13.628200000000001 0.00014609621924194742 0.00014585407086906515 0.0115 2010.5 0.0 0.0 0.1 0.1 gaia_1162 0.47637231898572985 -79.79611824529178 14.6439 0.00011419811781207949 0.00011415029792639667 0.0084 2010.5 0.0 0.0 0.1 0.1 gaia_1166 8.546170748636236 -47.35893234401765 14.696900000000001 0.00013283068515276605 0.00013260913293195234 0.0041 2010.5 0.0 0.0 0.1 0.1 diff --git a/flystar/tests/test_align.py b/flystar/tests/test_align.py index 6caab14..0ab2ab6 100644 --- a/flystar/tests/test_align.py +++ b/flystar/tests/test_align.py @@ -59,7 +59,6 @@ def test_MosaicSelfRef(): for ii in range(4): np.testing.assert_almost_equal(msc.trans_list[ii].px.c1_0, 1.0, 2) np.testing.assert_almost_equal(msc.trans_list[ii].py.c0_1, 1.0, 2) - # We didn't do any velocity fitting, so make sure nothing got created. assert 'vx' not in msc.ref_table.colnames assert 'vy' not in msc.ref_table.colnames @@ -82,6 +81,7 @@ def test_MosaicSelfRef(): plt.plot(msc.ref_table['x0'], msc.ref_table['y0'], 'k.', color='black', alpha=0.2) + return @@ -137,13 +137,14 @@ def test_MosaicSelfRef_vel_tconst(): # Check that the velocities aren't crazy... # they should be zero (since there is no time difference) - np.testing.assert_almost_equal(msc.ref_table['vx'], 0, 1) - np.testing.assert_almost_equal(msc.ref_table['vy'], 0, 1) + # TODO: is there a reason these were both happening? seemed like a duplicate assert + #np.testing.assert_almost_equal(msc.ref_table['vx'], 0, 1) + #np.testing.assert_almost_equal(msc.ref_table['vy'], 0, 1) - assert (msc.ref_table['vx'] == 0).all() - assert (msc.ref_table['vy'] == 0).all() - assert (msc.ref_table['vx_err'] == 0).all() - assert (msc.ref_table['vy_err'] == 0).all() + assert np.isnan(msc.ref_table['vx']).all() + assert np.isnan(msc.ref_table['vy']).all() + assert np.isnan(msc.ref_table['vx_err']).all() + assert np.isnan(msc.ref_table['vy_err']).all() return @@ -277,6 +278,7 @@ def test_MosaicToRef(): return msc +# TODO: Make this a valid test - it currently just runs with Linear def test_MosaicToRef_acc(): make_fake_starlists_poly1_acc(seed=42) @@ -311,11 +313,11 @@ def test_MosaicToRef_acc(): dr_tol=[0.2, 0.1], dm_tol=[1, 0.5], trans_class=transforms.PolyTransform, trans_args={'order': 2}, - motion_class=motion_model.Accel, - use_vel=True, + default_motion_model='Acceleration', update_ref_orig=False, verbose=False) msc.fit() + print(msc.ref_table['motion_model_input','motion_model_used']) # Check our status columns assert 'use_in_trans' in msc.ref_table.colnames diff --git a/flystar/transforms.py b/flystar/transforms.py index 0a1885a..7de327e 100755 --- a/flystar/transforms.py +++ b/flystar/transforms.py @@ -120,13 +120,13 @@ def evaluate_starlist(self, star_list): new_list['vy'] = vals[1] # Velocity errors (if they exist) - if 'vxe' in new_list.colnames: + if 'vx_err' in new_list.colnames: vals = self.evaluate_vel_error(star_list['x'], star_list['y'], star_list['vx'], star_list['vy'], star_list['xe'], star_list['ye'], - star_list['vxe'], star_list['vye']) - new_list['vxe'] = vals[0] - new_list['vye'] = vals[1] + star_list['vx_err'], star_list['vy_err']) + new_list['vx_err'] = vals[0] + new_list['vy_err'] = vals[1] return new_list From 7d13a070547dfd80f79d2fa5d8333fefb9f99545 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Wed, 21 Aug 2024 17:29:26 -0700 Subject: [PATCH 015/254] more motion model progress' --- flystar/align.py | 19 +++++++++---------- flystar/analysis.py | 34 +++++++++++++++++----------------- flystar/tests/test_align.py | 21 +++++++++++---------- 3 files changed, 37 insertions(+), 37 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index bd03d40..5d888c2 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -505,7 +505,6 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar self.update_ref_table_from_list(star_list, star_list_T, ii, idx_ref, idx_lis, idx2) ### Update the "average" values to be used as the reference frame for the next list. - print(self.ref_table.keys()) if self.update_ref_orig != 'periter': self.update_ref_table_aggregates() @@ -1054,10 +1053,10 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot 'me_boot', 2D column: bootstrap mag uncertainties due to transformation for each epoch If calc_vel_in_bootstrap: - 'x0e_boot', 1D column: bootstrap uncertainties in x0 for PM fit - 'y0e_boot', 1D column: bootstrap uncertainties in y0 for PM fit - 'vxe_boot', 1D column: bootstrap uncertainties in vx for PM fit - 'vye_boot', 1D column: bootstrap uncertainties in vy for PM fit + 'x0_err_boot', 1D column: bootstrap uncertainties in x0 for PM fit + 'y0_err_boot', 1D column: bootstrap uncertainties in y0 for PM fit + 'vx_err_boot', 1D column: bootstrap uncertainties in vx for PM fit + 'vy_err_boot', 1D column: bootstrap uncertainties in vy for PM fit For stars that fail boot_epochs_min criteria, np.nan is used """ @@ -1234,8 +1233,8 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot if calc_vel_in_bootstrap: x0_err_b = np.std(x0_arr, ddof=1, axis=1) y0_err_b = np.std(y0_arr, ddof=1, axis=1) - vx_err_b = np.std(vx_arr, ddof=1, axis=1) - vy_err_b = np.std(vy_arr, ddof=1, axis=1) + vx_err_b = np.nanstd(vx_arr, ddof=1, axis=1) + vy_err_b = np.nanstd(vy_arr, ddof=1, axis=1) else: x0_err_b = np.nan y0_err_b = np.nan @@ -1246,8 +1245,8 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot # hanging off of mosaic object. col_heads_2D = ['xe_boot', 'ye_boot', 'me_boot'] data_dict = {'xe_boot': x_err_b, 'ye_boot': y_err_b, 'me_boot': m_err_b, - 'x0e_boot': x0_err_b, 'y0e_boot': y0_err_b, - 'vxe_boot': vx_err_b, 'vye_boot': vy_err_b} + 'x0_err_boot': x0_err_b, 'y0_err_boot': y0_err_b, + 'vx_err_boot': vx_err_b, 'vy_err_boot': vy_err_b} for ff in col_heads_2D: col = Column(np.ones((len(self.ref_table), n_epochs)), name=ff) @@ -1258,7 +1257,7 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot # Now handle the velocities, if they were calculated if calc_vel_in_bootstrap: - col_heads_1D = [ 'x0e_boot', 'y0e_boot', 'vxe_boot', 'vye_boot'] + col_heads_1D = [ 'x0_err_boot', 'y0_err_boot', 'vx_err_boot', 'vy_err_boot'] for ff in col_heads_1D: col = Column(np.ones(len(self.ref_table)), name=ff) diff --git a/flystar/analysis.py b/flystar/analysis.py index 953461b..319ae01 100644 --- a/flystar/analysis.py +++ b/flystar/analysis.py @@ -90,14 +90,14 @@ def prepare_gaia_for_flystar(gaia, ra, dec, targets_dict=None, match_dr_max=0.2) gaia_new['x0'] = x * -1.0 gaia_new['y0'] = y - gaia_new['x0e'] = xe - gaia_new['y0e'] = ye + gaia_new['x0_err'] = xe + gaia_new['y0_err'] = ye # Also convert the velocities. Note that Gaia PM are already * cos(dec) gaia_new['vx'] = gaia['pmra'].data * -1.0 / 1e3 # asec/yr gaia_new['vy'] = gaia['pmdec'].data / 1e3 - gaia_new['vxe'] = gaia['pmra_error'].data / 1e3 - gaia_new['vye'] = gaia['pmdec_error'].data / 1e3 + gaia_new['vx_err'] = gaia['pmra_error'].data / 1e3 + gaia_new['vy_err'] = gaia['pmdec_error'].data / 1e3 gaia_new['t0'] = gaia['ref_epoch'].data gaia_new['source_id'] = gaia['source_id'].data.astype('S19') @@ -106,8 +106,8 @@ def prepare_gaia_for_flystar(gaia, ra, dec, targets_dict=None, match_dr_max=0.2) idx = np.where(gaia['pmdec'].mask == True)[0] gaia_new['vx'][idx] = 0.0 gaia_new['vy'][idx] = 0.0 - gaia_new['vxe'][idx] = 0.0 - gaia_new['vye'][idx] = 0.0 + gaia_new['vx_err'][idx] = 0.0 + gaia_new['vy_err'][idx] = 0.0 gaia_new['m'] = gaia['phot_g_mean_mag'] gaia_new['me'] = 1.09/gaia['phot_g_mean_flux_over_error'] @@ -117,9 +117,9 @@ def prepare_gaia_for_flystar(gaia, ra, dec, targets_dict=None, match_dr_max=0.2) # Set the velocities (and uncertainties) to zero if they aren't measured. idx = np.where(np.isnan(gaia_new['vx']) == True)[0] gaia_new['vx'][idx] = 0.0 - gaia_new['vxe'][idx] = 0.0 + gaia_new['vx_err'][idx] = 0.0 gaia_new['vy'][idx] = 0.0 - gaia_new['vye'][idx] = 0.0 + gaia_new['vy_err'][idx] = 0.0 gaia_new = gaia_new.filled() #convert masked colunms to regular columns @@ -317,12 +317,12 @@ def pick_good_ref_stars(star_tab, r_cut=None, m_cut=None, p_err_cut=None, pm_err print('pick_good_ref_stars: Use {0:d} stars after m<{1:.2f}.'.format(use.sum(), m_cut)) if p_err_cut is not None: - p_err = np.mean((star_tab['x0e'], star_tab['y0e']), axis=0) + p_err = np.mean((star_tab['x0_err'], star_tab['y0_err']), axis=0) use = use & (p_err < p_err_cut) print('pick_good_ref_stars: Use {0:d} stars after p_err<{1:.5f}.'.format(use.sum(), p_err_cut)) if pm_err_cut is not None: - pm_err = np.mean((star_tab['vxe'], star_tab['vye']), axis=0) + pm_err = np.mean((star_tab['vx_err'], star_tab['vy_err']), axis=0) use = use & (pm_err < pm_err_cut) print('pick_good_ref_stars: Use {0:d} stars after pm_err<{1:.5f}.'.format(use.sum(), pm_err_cut)) @@ -345,8 +345,8 @@ def startable_subset(tab, idx, mag_trans=True, mag_trans_orig=False): """ # Multiples: ['x', 'y', 'm', 'name_in_list', 'xe', 'ye', 'me', 't', # 'x_orig', 'y_orig', 'm_orig', 'xe_orig', 'ye_orig', 'me_orig', 'used_in_trans'] - # Single: ['name', 'm0', 'm0e', 'use_in_trans', 'ref_orig', 'n_detect', - # 'x0', 'vx', 'y0', 'vy', 'x0e', 'vxe', 'y0e', 'vye', 't0'] + # Single: ['name', 'm0', 'm0_err', 'use_in_trans', 'ref_orig', 'n_detect', + # 'x0', 'vx', 'y0', 'vy', 'x0_err', 'vx_err', 'y0_err', 'vy_err', 't0'] # Don't include n_vfit new_tab = startables.StarTable(name=tab['name'].data, @@ -365,16 +365,16 @@ def startable_subset(tab, idx, mag_trans=True, mag_trans_orig=False): me_orig=tab['me_orig'][:,idx].data, used_in_trans=tab['used_in_trans'][:,idx].data, m0=tab['m0'].data, - m0e=tab['m0e'].data, + m0e=tab['m0_err'].data, use_in_trans=tab['use_in_trans'].data, x0=tab['x0'].data, vx=tab['vx'].data, y0=tab['y0'].data, vy=tab['vy'].data, - x0e=tab['x0e'].data, - vxe=tab['vxe'].data, - y0e=tab['y0e'].data, - vye=tab['vye'].data, + x0e=tab['x0_err'].data, + vxe=tab['vx_err'].data, + y0e=tab['y0_err'].data, + vye=tab['vy_err'].data, t0=tab['t0'].data) new_tab.combine_lists('m', weights_col='me', sigma=3, ismag=True) diff --git a/flystar/tests/test_align.py b/flystar/tests/test_align.py index 0ab2ab6..c0b02bc 100644 --- a/flystar/tests/test_align.py +++ b/flystar/tests/test_align.py @@ -753,12 +753,13 @@ def test_bootstrap(): # Run bootstrap: no boot_epochs_min match1.calc_bootstrap_errors(n_boot=n_boot, boot_epochs_min=boot_epochs_min) - + print(match1.ref_table.keys()) # Make sure columns exist, and none of them are nan values assert np.sum(np.isnan(match1.ref_table['xe_boot'])) == 0 assert np.sum(np.isnan(match1.ref_table['ye_boot'])) == 0 - assert np.sum(np.isnan(match1.ref_table['vxe_boot'])) == 0 - assert np.sum(np.isnan(match1.ref_table['vye_boot'])) == 0 + print(match1.ref_table['vx_err_boot']) + assert np.sum(np.isnan(match1.ref_table['vx_err_boot'])) == 0 + assert np.sum(np.isnan(match1.ref_table['vy_err_boot'])) == 0 # Test 2: make sure boot_epochs_min is working # Eliminate some rows to list2, so some stars are only in 1 epoch. @@ -797,14 +798,14 @@ def test_bootstrap(): # For "good" stars: all bootstrap vals should be present assert np.sum(np.isnan(out['xe_boot'][good])) == 0 assert np.sum(np.isnan(out['ye_boot'][good])) == 0 - assert np.sum(np.isnan(out['vxe_boot'][good])) == 0 - assert np.sum(np.isnan(out['vye_boot'][good])) == 0 + assert np.sum(np.isnan(out['vx_err_boot'][good])) == 0 + assert np.sum(np.isnan(out['vy_err_boot'][good])) == 0 # For "bad" stars, all bootstrap vals should be nans assert np.sum(np.isfinite(out['xe_boot'][bad])) == 0 assert np.sum(np.isfinite(out['ye_boot'][bad])) == 0 - assert np.sum(np.isfinite(out['vxe_boot'][bad])) == 0 - assert np.sum(np.isfinite(out['vye_boot'][bad])) == 0 + assert np.sum(np.isfinite(out['vx_err_boot'][bad])) == 0 + assert np.sum(np.isfinite(out['vy_err_boot'][bad])) == 0 return @@ -869,15 +870,15 @@ def test_calc_vel_in_bootstrap(): assert 'xe_boot' in match_vel.ref_table.keys() assert np.sum(np.isnan(match_vel.ref_table['xe_boot'])) == 0 - assert 'vxe_boot' in match_vel.ref_table.keys() + assert 'vx_err_boot' in match_vel.ref_table.keys() assert np.sum(np.isnan(match_vel.ref_table['vxe_boot'])) == 0 # Run without calc_vel_in_bootstrap, make sure velocities are NOT calculated match.calc_bootstrap_errors(n_boot=n_boot, calc_vel_in_bootstrap=False) - assert 'xe_boot' in match.ref_table.keys() + assert 'x_e_boot' in match.ref_table.keys() assert np.sum(np.isnan(match.ref_table['xe_boot'])) == 0 - assert 'vxe_boot' not in match.ref_table.keys() + assert 'vx_err_boot' not in match.ref_table.keys() return From 461558d4487f9aab33ea93891b63822deddb3fc8 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Mon, 26 Aug 2024 15:35:32 -0700 Subject: [PATCH 016/254] test_align.py all running now --- flystar/align.py | 67 +++++++------ flystar/motion_model.py | 192 +++++++++++++++++++++++++++++++----- flystar/startables.py | 22 +---- flystar/tests/test_align.py | 40 ++++---- 4 files changed, 230 insertions(+), 91 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 5d888c2..85d7c7e 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -820,7 +820,8 @@ def update_ref_table_aggregates(self, n_boot=0): motion_model_class_names += self.ref_table['motion_model_used'][ref_orig_idx].tolist() motion_model_col_names = motion_model.get_list_motion_model_param_names(motion_model_class_names, with_errors=True) for mm in motion_model_col_names: - vals_orig[mm] = self.ref_table[mm][ref_orig_idx] + if mm in self.ref_table.keys(): + vals_orig[mm] = self.ref_table[mm][ref_orig_idx] #if self.use_motion: # Combine positions with a velocity fit. @@ -1086,11 +1087,18 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot xe_trans_arr = np.ones((len(ref_table['x']), n_boot, n_epochs)) * -999 ye_trans_arr = np.ones((len(ref_table['x']), n_boot, n_epochs)) * -999 me_trans_arr = np.ones((len(ref_table['x']), n_boot, n_epochs)) * -999 + + # Set up motion model parameters + motion_model_list = ['Fixed', self.default_motion_model] + if 'motion_model_used' in ref_table.keys(): + motion_model_list += ref_table['motion_model_used'].tolist() + elif 'motion_model_assigned' in ref_table.keys(): + motion_model_list += ref_table['motion_model_assigned'].tolist() + motion_col_list = motion_model.get_list_motion_model_param_names(np.unique(motion_model_list).tolist(), with_errors=False, with_fixed=False) if calc_vel_in_bootstrap: - x0_arr = np.ones((len(ref_table['x']), n_boot)) * -999 - y0_arr = np.ones((len(ref_table['x']), n_boot)) * -999 - vx_arr = np.ones((len(ref_table['x']), n_boot)) * -999 - vy_arr = np.ones((len(ref_table['x']), n_boot)) * -999 + motion_data = {} + for col in motion_col_list: + motion_data[col] = np.ones((len(ref_table['x']), n_boot)) * -999 ### IF MEMORY PROBLEMS HERE: ### DEFINE MEAN, STD VARIABLES AND BUILD THEM RATHER THAN SAVING FULL ARRAY @@ -1211,10 +1219,8 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot star_table.fit_velocities(fixed_t0=t0_arr, default_motion_model=self.default_motion_model) # Save proper motion fit results to output arrays - x0_arr[:,ii] = star_table['x0'] - y0_arr[:,ii] = star_table['y0'] - vx_arr[:,ii] = star_table['vx'] - vy_arr[:,ii] = star_table['vy'] + for col in motion_col_list: + motion_data[col][:,ii] = star_table[col] # Quick check to make sure bootstrap calc was valid: output t0 should be # same as input t0_arr, since we used fixed_t0 option @@ -1230,23 +1236,20 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot y_err_b = np.std(y_trans_arr, ddof=1, axis=1) m_err_b = np.std(m_trans_arr, ddof=1, axis=1) + motion_data_err = {} if calc_vel_in_bootstrap: - x0_err_b = np.std(x0_arr, ddof=1, axis=1) - y0_err_b = np.std(y0_arr, ddof=1, axis=1) - vx_err_b = np.nanstd(vx_arr, ddof=1, axis=1) - vy_err_b = np.nanstd(vy_arr, ddof=1, axis=1) + for col in motion_col_list: + motion_data_err[col] = np.nanstd(motion_data[col], ddof=1,axis=1) else: - x0_err_b = np.nan - y0_err_b = np.nan - vx_err_b = np.nan - vy_err_b = np.nan + for col in motion_col_list: + motion_data_err[col] = np.nan # Add summary statistics to *original* ref_table, i.e. ref_table # hanging off of mosaic object. col_heads_2D = ['xe_boot', 'ye_boot', 'me_boot'] - data_dict = {'xe_boot': x_err_b, 'ye_boot': y_err_b, 'me_boot': m_err_b, - 'x0_err_boot': x0_err_b, 'y0_err_boot': y0_err_b, - 'vx_err_boot': vx_err_b, 'vy_err_boot': vy_err_b} + data_dict = {'xe_boot': x_err_b, 'ye_boot': y_err_b, 'me_boot': m_err_b} + for col in motion_col_list: + data_dict[col+'_err_boot'] = motion_data_err[col] for ff in col_heads_2D: col = Column(np.ones((len(self.ref_table), n_epochs)), name=ff) @@ -1257,7 +1260,7 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot # Now handle the velocities, if they were calculated if calc_vel_in_bootstrap: - col_heads_1D = [ 'x0_err_boot', 'y0_err_boot', 'vx_err_boot', 'vy_err_boot'] + col_heads_1D = [col+'_err_boot' for col in motion_col_list] for ff in col_heads_1D: col = Column(np.ones(len(self.ref_table)), name=ff) @@ -3770,9 +3773,9 @@ def get_weighting_scheme(weights, ref_list, star_list): # TODO: I think this is a startable, not a starlist def get_pos_at_time(t, starlist): """ - Take a starlist, check to see if it has velocity columns. + Take a starlist, check to see if it has motion/velocity columns. If it does, then propogate the positions forward in time - to the desired epoch. If no velocities exist, then just + to the desired epoch. If no motion/velocities exist, then just use ['x0', 'y0'] or ['x', 'y'] Inputs @@ -3782,15 +3785,21 @@ def get_pos_at_time(t, starlist): but it should be in the same units as the 't0' column in starlist. """ + # Check for motion model if 'motion_model_used' in starlist.colnames: x,y,xe,ye = starlist.get_star_positions_at_time(t) + # If no motion model, check for velocities + elif ('vx' in starlist.colnames) and ('vy' in starlist.colnames) and ('x0' in starlist.colnames) and ('y0' in starlist.colnames): + x = starlist['x0'] + np.nan_to_num(starlist['vx'])*(t-starlist['t0']) + y = starlist['y0'] + np.nan_to_num(starlist['vy'])*(t-starlist['t0']) + # If no velocities, try fitted positon + elif ('x0' in starlist.colnames) and ('y0' in starlist.colnames): + x = starlist['x0'] + y = starlist['y0'] + # Otherwise, use measured position else: - if ('x0' in starlist.colnames) and ('y0' in starlist.colnames): - x = starlist['x0'] - y = starlist['y0'] - else: - x = starlist['x'] - y = starlist['y'] + x = starlist['x'] + y = starlist['y'] return (x, y) diff --git a/flystar/motion_model.py b/flystar/motion_model.py index 14eda8a..5035261 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -20,13 +20,11 @@ class MotionModel(ABC): optional_param_names = [] def __init__(self, *args, **kwargs): - # Check that required phot_params are proper arrays. - # If not, then make them arrays of len(1). # TODO: do we need this? - for param in self.fitter_param_names: + '''for param in self.fitter_param_names: param_var = getattr(self, param) if not isinstance(param_var, (list, np.ndarray)): - setattr(self, param, np.array([param_var])) + setattr(self, param, np.array([param_var]))''' return @@ -59,7 +57,7 @@ class Fixed(MotionModel): """ n_pts_req = 1 fitter_param_names = ['x0','y0'] - fixed_param_names = ['t0'] + fixed_param_names = [] def __init__(self, x0=0, y0=0, t0=2025.0, x0_err=0, y0_err=0): @@ -85,14 +83,13 @@ def get_batch_pos_at_time(self,t, x0=[],y0=[],t0=[], x0_err=[], y0_err=[]): return x0,y0,x0_err,y0_err - def fit_motion_model(self, dt, x, y, xe, ye, update=False, bootstrap=0): # Handle single data point case if len(x)==1: return [x[0],y[0]],[xe[0],ye[0]] - #TODO it seems like sometimes it's weighted by std and sometimes by var - confirm which to do here + #TODO: it seems like sometimes it's weighted by std and sometimes by var - confirm which to do here x0 = np.average(x, weights=1/xe**2) x0e = np.sqrt(np.average((x-x0)**2,weights=1/xe)) y0 = np.average(y, weights=1/ye**2) @@ -150,8 +147,8 @@ def get_pos_at_time(self, t): def get_pos_err_at_time(self, t): dt = t - self.t0 - x_err = np.hypot(self.x0_err, self.vx_err) - y_err = np.hypot(self.y0_err, self.vy_err) + x_err = np.hypot(self.x0_err, self.vx_err*dt) + y_err = np.hypot(self.y0_err, self.vy_err*dt) return x_err, y_err @@ -169,19 +166,21 @@ def fit_motion_model(self, dt, x, y, xe, ye, update=False, bootstrap=0): fitter = fitting.LevMarLSQFitter() # Handle 2-data point case - # TODO: is this the best way to handle this case ? + # TODO: is this the best way to handle this case ? Altered it to be consistent with t0 if len(x)==2: - x0 = np.average(x, weights=1.0/xe**2) - y0 = np.average(y, weights=1.0/ye**2) + ix = int(xe[0]>xe[1]) + iy = int(ye[0]>ye[1]) dx = np.diff(x)[0] dy = np.diff(y)[0] dt_diff = np.diff(dt)[0] - x0e = np.abs(dx) / 2**0.5 - y0e = np.abs(dy) / 2**0.5 vx = dx / dt_diff vy = dy / dt_diff - vxe = 0.0 - vye = 0.0 + x0 = x[ix]-vx*dt[ix] + y0 = y[iy]-vy*dt[iy] + vxe = np.hypot(*xe)/dt_diff + vye = np.hypot(*ye)/dt_diff + x0e = np.sqrt(xe[ix]**2 + (dt[ix]*vxe)**2) + y0e = np.sqrt(ye[iy]**2 + (dt[iy]*vye)**2) return [x0, vx, y0, vy],[x0e, vxe, y0e, vye] px_new = fitter(self.px, dt, x, weights=1/xe) @@ -248,40 +247,181 @@ def fit_motion_model(self, dt, x, y, xe, ye, update=False, bootstrap=0): return params, param_errors +class Acceleration(MotionModel): + """ + A 2D accelerating motion model for a star on the sky. + """ + n_pts_req = 4 # TODO: consider special case for 3 pts + fitter_param_names = ['x0', 'vx0', 'ax', 'y0', 'vy0', 'ay'] + fixed_param_names = ['t0'] + + def __init__(self, x0=0, vx0=0, ax=0, y0=0, vy0=0, ay=0, t0=2025.0, + x0_err=0, vx0_err=0, ax_err=0, y0_err=0, vy0_err=0, ay_err=0): + self.x0 = x0 + self.vx0 = vx0 + self.ax = ax + self.y0 = y0 + self.vy0 = vy0 + self.ay = ay + self.t0 = t0 + self.x0_err = x0_err + self.vx0_err = vx0_err + self.ax_err = ax_err + self.y0_err = y0_err + self.vy0_err = vy0_err + self.ay_err = ay_err + + # Must call after setting parameters. + # This checks for proper parameter formatting. + super().__init__() + + self.poly_order = 2 + self.px = models.Polynomial1D(self.poly_order, c0=self.x0, c1=self.vx0, c2=self.ax) + self.py = models.Polynomial1D(self.poly_order, c0=self.y0, c1=self.vy0, c2=self.ay) + + return + + def get_pos_at_time(self, t): + dt = t - self.t0 + x = self.px(dt) + y = self.py(dt) + return x, y + + def get_pos_err_at_time(self, t): + dt = t - self.t0 + x_err = np.sqrt(self.x0_err**2 + (self.vx_err*dt)**2 + (self.ax_err*dt**2)**2) + y_err = np.sqrt(self.y0_err**2 + (self.vy_err*dt)**2 + (self.ay_err*dt**2)**2) + return x_err, y_err + + def get_batch_pos_at_time(self,t, + x0=[],vx0=[],ax=[], y0=[],vy0=[],ay=[], t0=[], + x0_err=[],vx0_err=[],ax_err=[], y0_err=[],vy0_err=[],ay_err=[]): + dt = t-t0 + x = x0 + dt*vx0 + 0.5*dt**2*ax + y = y0 + dt*vy0 + 0.5*dt**2*ay + x_err = np.sqrt(x0_err**2 + (vx0_err*dt)**2 + (0.5*ax_err*dt**2)**2) + y_err = np.sqrt(y0_err**2 + (vy0_err*dt)**2 + (0.5*ay_err*dt**2)**2) + return x,y,x_err,y_err + + def fit_motion_model(self, dt, x, y, xe, ye, update=False, bootstrap=0): + fitter = fitting.LevMarLSQFitter() + + px_new = fitter(self.px, dt, x, weights=1/xe) + px_cov = fitter.fit_info['param_cov'] + py_new = fitter(self.py, dt, y, weights=1/ye) + py_cov = fitter.fit_info['param_cov'] + + x0 = px_new.c0.value + vx0 = px_new.c1.value + ax = px_new.c2.value + y0 = py_new.c0.value + vy0 = py_new.c1.value + ay = py_new.c2.value + + # Run the bootstrap + if bootstrap > 0: + edx = np.arange(len(x), dtype=int) + + fit_x0_b = np.zeros(bootstrap, dtype=float) + fit_vx0_b = np.zeros(bootstrap, dtype=float) + fit_ax_b = np.zeros(bootstrap, dtype=float) + fit_y0_b = np.zeros(bootstrap, dtype=float) + fit_vy0_b = np.zeros(bootstrap, dtype=float) + fit_ay_b = np.zeros(bootstrap, dtype=float) + + for bb in range(bootstrap): + bdx = np.random.choice(edx, len(x)) + + px_b = fitter(self.px, dt[bdx], x[bdx], weights=1/xe[bdx]) + px_b_cov = fitter.fit_info['param_cov'] + py_b = fitter(self.py, dt[bdx], y[bdx], weights=1/ye[bdx]) + py_b_cov = fitter.fit_info['param_cov'] + + fit_x0_b[bb] = px_b.c0.value + fit_vx0_b[bb] = px_b.c1.value + fit_ax_b[bb] = px_b.c2.value + fit_y0_b[bb] = py_b.c0.value + fit_vy0_b[bb] = py_b.c1.value + fit_ay_b[bb] = py_b.c2.value + + # Save the errors from the bootstrap + x0e = fit_x0_b.std() + vx0e = fit_vx0_b.std() + axe = fit_ax_b.std() + y0e = fit_y0_b.std() + vy0e = fit_vy0_b.std() + aye = fit_ay_b.std() + else: + px_param_errs = dict(zip(self.px.param_names, np.diag(px_cov)**0.5)) + py_param_errs = dict(zip(self.py.param_names, np.diag(py_cov)**0.5)) + x0e = px_param_errs['c0'] + vx0e = px_param_errs['c1'] + axe = px_param_errs['c2'] + y0e = py_param_errs['c0'] + vy0e = py_param_errs['c1'] + aye = py_param_errs['c2'] + + if update: + self.px = px_new + self.py = py_new + + self.x0 = x0 + self.vx0 = vx0 + self.ax = ax + self.y0 = y0 + self.vy0 = vy0 + self.ay = ay + + self.x0_err = x0e + self.vx0_err = vx0e + self.ax_err = axe + self.y0_err = y0e + self.vy0_err = vy0e + self.ay_err = aye + + params = [x0, vx0, ax, y0, vy0, ay] + param_errors = [x0e, vx0e, axe, y0e, vy0e, aye] + + return params, param_errors + -def get_one_motion_model_param_names(motion_model_name, with_errors=True): +def get_one_motion_model_param_names(motion_model_name, with_errors=True, with_fixed=True): mod = eval(motion_model_name) - list_of_parameters = getattr(mod, 'fitter_param_names') - list_of_parameters += getattr(mod, 'fixed_param_names') + list_of_parameters = [] + list_of_parameters += getattr(mod, 'fitter_param_names') + if with_fixed: + list_of_parameters += getattr(mod, 'fixed_param_names') if with_errors: - list_of_parameters += [par + '_err' for par in getattr(mod, 'fitter_param_names')] + list_of_parameters += [par+'_err' for par in getattr(mod, 'fitter_param_names')] return list_of_parameters -def get_list_motion_model_param_names(motion_model_list, with_errors=True): +def get_list_motion_model_param_names(motion_model_list, with_errors=True, with_fixed=True): list_of_parameters = [] all_motion_models = [eval(mm) for mm in np.unique(motion_model_list).tolist()] for aa in range(len(all_motion_models)): param_names = getattr(all_motion_models[aa], 'fitter_param_names') param_fixed_names = getattr(all_motion_models[aa], 'fixed_param_names') - param_err_names = [par + '_err' for par in param_names] + param_err_names = [par+'_err' for par in param_names] list_of_parameters += param_names - list_of_parameters += param_fixed_names + if with_fixed: + list_of_parameters += param_fixed_names if with_errors: list_of_parameters += param_err_names return np.unique(list_of_parameters).tolist() -def get_all_motion_model_param_names(with_errors=True): +def get_all_motion_model_param_names(with_errors=True, with_fixed=True): list_of_parameters = [] all_motion_models = MotionModel.__subclasses__() for aa in range(len(all_motion_models)): param_names = getattr(all_motion_models[aa], 'fitter_param_names') param_fixed_names = getattr(all_motion_models[aa], 'fixed_param_names') - param_err_names = [par + '_err' for par in param_names] + param_err_names = [par+'_err' for par in param_names] list_of_parameters += param_names - list_of_parameters += param_fixed_names + if with_fixed: + list_of_parameters += param_fixed_names if with_errors: list_of_parameters += param_err_names diff --git a/flystar/startables.py b/flystar/startables.py index 83f4b5e..b68b752 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -594,6 +594,8 @@ def fit_velocities(self, weighting='var', use_scipy=True, absolute_sigma=True, b # Append goodness of fit metrics and t0. # TODO: actually populate these columns new_col_list += ['chi2_x', 'chi2_y'] + if 't0' not in new_col_list: + new_col_list.append('t0') # Define output arrays for the best-fit parameters. for col in new_col_list: @@ -653,6 +655,7 @@ def fit_velocity_for_star(self, ss, weighting='var', use_scipy=True, absolute_sigma=True, bootstrap=False, fixed_t0=False, default_motion_model='Linear', mask_val=None, mask_lists=False): + # TODO: "weighting" is not used # # Make a mask of invalid (NaN) values and a user-specified invalid value. @@ -794,7 +797,6 @@ def fit_velocity_for_star(self, ss, weighting='var', use_scipy=True, # Decide which motion_model to fit. # motion_model_use = self['motion_model_input'][ss] - # Go to default model if not enough points for assigned but enough for default # TODO: think about whether we want other fallbacks besides the singular default and Fixed if (N_good < getattr(motion_model, self['motion_model_input'][ss]).n_pts_req) and \ @@ -810,7 +812,7 @@ def fit_velocity_for_star(self, ss, weighting='var', use_scipy=True, self['motion_model_used'][ss] = motion_model_use - # Instantiate the motion model object. + # Get the motion model object. modClass = getattr(motion_model, motion_model_use) # Load up any prior information on parameters for this model. @@ -835,16 +837,10 @@ def fit_velocity_for_star(self, ss, weighting='var', use_scipy=True, # Fit for the best parameters params, param_errs = mod.fit_motion_model(dt, x, y, xe, ye, bootstrap=bootstrap) - - # TODO: bad - #with modClass(**param_dict) as mod: - # params, param_errs = mod.fit_motion_model(dt, x, y, xe, ye, bootstrap=bootstrap) - # Save parameters and errors to table. for pp in range(len(modClass.fitter_param_names)): par = modClass.fitter_param_names[pp] par_err = par + '_err' - self[par][ss] = params[pp] self[par_err][ss] = param_errs[pp] @@ -873,15 +869,7 @@ def get_star_positions_at_time(self, t): modClass = getattr(motion_model, mm) # Set up parameters param_dict = {} - for par in modClass.fitter_param_names: - param_dict[par] = self[par][idx] - param_dict[par+'_err'] = self[par+'_err'][idx] - # Load fixed parameters, if needed. - for par in modClass.fixed_param_names: - if par not in self.colnames: - msg = f'fit_velocity_for_star: ' - msg += f'Missing fixed_params column {par} needed for motion model {motion_model_use}.' - raise RuntimeException(msg) + for par in motion_model.get_one_motion_model_param_names(mm,with_errors=True,with_fixed=True): param_dict[par] = self[par][idx] mod = modClass() x[idx],y[idx],xe[idx],ye[idx] = mod.get_batch_pos_at_time(t,**param_dict) diff --git a/flystar/tests/test_align.py b/flystar/tests/test_align.py index c0b02bc..9b99f06 100644 --- a/flystar/tests/test_align.py +++ b/flystar/tests/test_align.py @@ -278,7 +278,7 @@ def test_MosaicToRef(): return msc -# TODO: Make this a valid test - it currently just runs with Linear +# TODO: Make this a valid test def test_MosaicToRef_acc(): make_fake_starlists_poly1_acc(seed=42) @@ -289,12 +289,14 @@ def test_MosaicToRef_acc(): 'random_acc_3.fits'] ref_list = Table.read(ref_file) + print(ref_list.keys()) + print(ref_list) # Convert velocities to arcsec/yr - ref_list['vx'] *= 1e-3 - ref_list['vy'] *= 1e-3 - ref_list['vx_err'] *= 1e-3 - ref_list['vy_err'] *= 1e-3 + ref_list['vx0'] *= 1e-3 + ref_list['vy0'] *= 1e-3 + ref_list['vx0_err'] *= 1e-3 + ref_list['vy0_err'] *= 1e-3 # Convert accelerations to arcsec/yr**2 ref_list['ax'] *= 1e-3 @@ -304,7 +306,7 @@ def test_MosaicToRef_acc(): # Switch our list to a "increasing to the West" list. ref_list['x0'] *= -1.0 - ref_list['vx'] *= -1.0 + ref_list['vx0'] *= -1.0 ref_list['ax'] *= -1.0 lists = [starlists.StarList.read(lf) for lf in list_files] @@ -328,8 +330,8 @@ def test_MosaicToRef_acc(): # The velocities should be almost the same as the input # velocities since update_ref_orig == False. - np.testing.assert_almost_equal(msc.ref_table['vx'], ref_list['vx'], 5) - np.testing.assert_almost_equal(msc.ref_table['vy'], ref_list['vy'], 5) + np.testing.assert_almost_equal(msc.ref_table['vx0'], ref_list['vx0'], 5) + np.testing.assert_almost_equal(msc.ref_table['vy0'], ref_list['vy0'], 5) ########## @@ -340,11 +342,11 @@ def test_MosaicToRef_acc(): # The velocities should be almost the same (but not as close as before) # as the input velocities since update_ref == False. - np.testing.assert_almost_equal(msc.ref_table['vx'], ref_list['vx'], 1) - np.testing.assert_almost_equal(msc.ref_table['vy'], ref_list['vy'], 1) + np.testing.assert_almost_equal(msc.ref_table['vx0'], ref_list['vx0'], 1) + np.testing.assert_almost_equal(msc.ref_table['vy0'], ref_list['vy0'], 1) # Also double check that they aren't exactly the same for the reference stars. - assert np.any(np.not_equal(msc.ref_table['vx'], ref_list['vx'])) + assert np.any(np.not_equal(msc.ref_table['vx0'], ref_list['vx0'])) return msc @@ -580,7 +582,7 @@ def make_fake_starlists_poly1_acc(seed=-1): t0], names = ('name', 'm0', 'm0_err', 'x0', 'x0_err', 'y0', 'y0_err', - 'vx', 'vx_err', 'vy', 'vy_err', + 'vx0', 'vx0_err', 'vy0', 'vy0_err', 'ax', 'axe', 'ay', 'aye', 't0')) @@ -609,8 +611,8 @@ def make_fake_starlists_poly1_acc(seed=-1): for ss in range(len(times)): dt = times[ss] - lis['t0'] - x = lis['x0'] + (lis['vx']/1e3) * dt + (lis['ax']/1e3) * dt**2 - y = lis['y0'] + (lis['vy']/1e3) * dt + (lis['ay']/1e3) * dt**2 + x = lis['x0'] + (lis['vx0']/1e3) * dt + (lis['ax']/1e3) * dt**2 + y = lis['y0'] + (lis['vy0']/1e3) * dt + (lis['ay']/1e3) * dt**2 t = np.ones(N_stars) * times[ss] # Convert into pixels @@ -871,12 +873,12 @@ def test_calc_vel_in_bootstrap(): assert 'xe_boot' in match_vel.ref_table.keys() assert np.sum(np.isnan(match_vel.ref_table['xe_boot'])) == 0 assert 'vx_err_boot' in match_vel.ref_table.keys() - assert np.sum(np.isnan(match_vel.ref_table['vxe_boot'])) == 0 + assert np.sum(np.isnan(match_vel.ref_table['vx_err_boot'])) == 0 # Run without calc_vel_in_bootstrap, make sure velocities are NOT calculated match.calc_bootstrap_errors(n_boot=n_boot, calc_vel_in_bootstrap=False) - assert 'x_e_boot' in match.ref_table.keys() + assert 'xe_boot' in match.ref_table.keys() assert np.sum(np.isnan(match.ref_table['xe_boot'])) == 0 assert 'vx_err_boot' not in match.ref_table.keys() @@ -987,7 +989,7 @@ def test_MosaicToRef_mag_bug(): """ make_fake_starlists_poly1_vel() - ref_list = starlists.StarList.from_lis_file('random_0.lis', error=False) + ref_list = starlists.StarList.read('random_vel_0.fits') lists = [ref_list] msc = align.MosaicToRef(ref_list, lists, @@ -1031,8 +1033,8 @@ def test_masked_cols(): # Coordinates are arcsecs offset +x to the East. targets_dict = {'ob150029': [0.0, 0.0], - 'S11_15_3.9': [ 1.13982, 3.73524], - 'S13_13_4.5': [-4.42878, 0.03100] + 'S005': [1.1416, 3.7405], + 'S002': [-4.421, 0.027] } # Get gaia catalog stars. Note that this produces a masked column table From 03fb4837c812c8193f98b876fb71f4976b04387b Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Thu, 12 Sep 2024 11:08:38 -0700 Subject: [PATCH 017/254] motion model development; move some unused functions to align_old_functions.py --- flystar/align.py | 387 +-------------------------------- flystar/align_old_functions.py | 369 +++++++++++++++++++++++++++++++ flystar/motion_model.py | 39 +++- flystar/plots.py | 44 ++-- flystar/startables.py | 7 +- 5 files changed, 438 insertions(+), 408 deletions(-) create mode 100755 flystar/align_old_functions.py diff --git a/flystar/align.py b/flystar/align.py index 85d7c7e..704339f 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -22,13 +22,12 @@ def __init__(self, list_of_starlists, ref_index=0, iters=2, init_order=1, mag_trans=True, mag_lim=None, weights=None, trans_input=None, trans_class=transforms.PolyTransform, - # TODO: consider deleting use_vel fulll, for now I'm putting in + # TODO: consider deleting use_vel fully, for now I'm putting in # a fallback so people can still use old code for now use_vel=None, default_motion_model='Fixed', calc_trans_inverse=False, init_guess_mode='miracle', iter_callback=None, verbose=True): - """ Make a mosaic object by passing in a list of starlists and then running fit(). @@ -1542,15 +1541,14 @@ def fit(self): ########## self.ref_table = self.setup_ref_table_from_starlist(self.ref_list) - # copy over velocities if they exist in the reference list - if 'vx' in self.ref_list.colnames: - self.ref_table['vx'] = self.ref_list['vx'] - self.ref_table['vy'] = self.ref_list['vy'] - self.ref_table['t0'] = self.ref_list['t0'] - if 'vx_err' in self.ref_list.colnames: - self.ref_table['vx_err'] = self.ref_list['vx_err'] - self.ref_table['vy_err'] = self.ref_list['vy_err'] - + # copy over motion model parameters if they exist in the reference list + if 'motion_model_assigned' in self.ref_list.colnames: + self.ref_table['motion_model_assigned'] = self.ref_list['motion_model_assigned'] + if 'motion_model' in self.ref_list.colnames: + self.ref_table['motion_model_assigned'] = self.ref_list['motion_model'] + for param in motion_model.get_all_motion_model_param_names(with_fixed=True, with_errors=True): + if param in self.ref_list.colnames: + self.ref_table[param] = self.ref_list[param] ########## # @@ -1845,371 +1843,10 @@ def add_rows_for_new_stars(ref_table, star_list, idx_lis, default_motion_model=' return ref_table, idx_lis_new, idx_ref_new -def run_align_iter(catalog, trans_order=1, poly_deg=1, ref_mag_lim=19, ref_radius_lim=300): - # Load up data with matched stars. - d = Table.read(catalog) - - # Determine how many epochs there are. - N_epochs = len([n for n, c in enumerate(d.colnames) if c.startswith('name')]) - - # Determine how many stars there are. - N_stars = len(d) - - # Determine the reference epoch - ref = d.meta['L_REF'] - - # Figure out the number of free parameters for the specified - # poly2d order. - poly2d = models.Polynomial2D(trans_order) - N_par_trans_per_epoch = 2.0 * poly2d.get_num_coeff(2) # one poly2d for each dimension (X, Y) - N_par_trans = N_par_trans_per_epoch * N_epochs - - ########## - # First iteration -- align everything to REF epoch with zero velocities. - ########## - print('ALIGN_EPOCHS: run_align_iter() -- PASS 1') - ee_ref = d.meta['L_REF'] - - target_name = 'OB120169' - - trans1, used1 = calc_transform_ref_epoch(d, target_name, ee_ref, ref_mag_lim, ref_radius_lim) - - ########## - # Derive the velocity of each stars using the round 1 transforms. - ########## - calc_polyfit_all_stars(d, poly_deg, init_fig_idx=0) - - calc_mag_avg_all_stars(d) - - tdx = np.where((d['name_0'] == 'OB120169') | (d['name_0'] == 'OB120169_L'))[0] - print(d[tdx]['name_0', 't0', 'mag', 'x0', 'vx', 'x0_err', 'vx_err', 'chi2x', 'y0', 'vy', 'y0_err', 'vy_err', 'chi2y', 'dof']) - - ########## - # Second iteration -- align everything to reference positions derived from iteration 1 - ########## - print('ALIGN_EPOCHS: run_align_iter() -- PASS 2') - target_name = 'OB120169' - - trans2, used2 = calc_transform_ref_poly(d, target_name, poly_deg, ref_mag_lim, ref_radius_lim) - - ########## - # Derive the velocity of each stars using the round 1 transforms. - ########## - calc_polyfit_all_stars(d, poly_deg, init_fig_idx=4) - - ########## - # Save output - ########## - d.write(catalog.replace('.fits', '_aln.fits'), overwrite=True) - - return - -def calc_transform_ref_epoch(d, target_name, ee_ref, ref_mag_lim, ref_radius_lim): - # Determine how many epochs there are. - N_epochs = len([n for n, c in enumerate(d.colnames) if c.startswith('name')]) - - # output array - trans = [] - used = [] - - # Find the target - tdx = np.where(d['name_0'] == 'OB120169')[0][0] - - # Reference values - t_ref = d['t_{0:d}'.format(ee_ref)] - m_ref = d['m_{0:d}'.format(ee_ref)] - x_ref = d['x_{0:d}'.format(ee_ref)] - y_ref = d['y_{0:d}'.format(ee_ref)] - xe_ref = d['xe_{0:d}'.format(ee_ref)] - ye_ref = d['ye_{0:d}'.format(ee_ref)] - - # Calculate some quanitites we use for selecting reference stars. - r_ref = np.hypot(x_ref - x_ref[tdx], y_ref - y_ref[tdx]) - - # Loop through and align each epoch to the reference epoch. - for ee in range(N_epochs): - # Pull out the X, Y positions (and errors) for the two - # starlists we are going to align. - x_epo = d['x_{0:d}'.format(ee)] - y_epo = d['y_{0:d}'.format(ee)] - t_epo = d['t_{0:d}'.format(ee)] - xe_epo = d['xe_{0:d}'.format(ee)] - ye_epo = d['ye_{0:d}'.format(ee)] - - # Figure out the set of stars detected in both epochs. - idx = np.where((t_ref != 0) & (t_epo != 0) & (xe_ref != 0) & (xe_epo != 0))[0] - - # Find those in both epochs AND reference stars. This is [idx][rdx] - rdx = np.where((r_ref[idx] < ref_radius_lim) & (m_ref[idx] < ref_mag_lim))[0] - - # Average the positional errors together to get one weight per star. - xye_ref = (xe_ref + ye_ref) / 2.0 - xye_epo = (xe_epo + ye_epo) / 2.0 - xye_wgt = (xye_ref**2 + xye_epo**2)**0.5 - - # Calculate transform based on the matched stars - trans_tmp = transforms.PolyTransform(x_epo[idx][rdx], y_epo[idx][rdx], x_ref[idx][rdx], y_ref[idx][rdx], - weights=xye_wgt[idx][rdx], order=2) - - trans.append(trans_tmp) - - - # Apply thte transformation to the stars positions and errors: - xt_epo = np.zeros(len(d), dtype=float) - yt_epo = np.zeros(len(d), dtype=float) - xet_epo = np.zeros(len(d), dtype=float) - yet_epo = np.zeros(len(d), dtype=float) - - xt_epo[idx], xet_epo[idx], yt_epo[idx], yet_epo[idx] = trans_tmp.evaluate_errors(x_epo[idx], xe_epo[idx], - y_epo[idx], ye_epo[idx], - nsim=100) - - d['xt_{0:d}'.format(ee)] = xt_epo - d['yt_{0:d}'.format(ee)] = yt_epo - d['xet_{0:d}'.format(ee)] = xet_epo - d['yet_{0:d}'.format(ee)] = yet_epo - - # Record which stars we used in the transform. - used_tmp = np.zeros(len(d), dtype=bool) - used_tmp[idx[rdx]] = True - - used.append(used_tmp) +""" +Functions specific to OB120169 moved to align_old_functions,py +""" - if True: - plot_quiver_residuals(xt_epo, yt_epo, x_ref, y_ref, idx, rdx, 'Epoch: ' + str(ee)) - - used = np.array(used) - - return trans, used - - -def calc_transform_ref_poly(d, target_name, poly_deg, ref_mag_lim, ref_radius_lim): - # Determine how many epochs there are. - N_epochs = len([n for n, c in enumerate(d.colnames) if c.startswith('name')]) - - # output array - trans = [] - used = [] - - # Find the target - tdx = np.where(d['name_0'] == 'OB120169')[0][0] - - # Temporary Reference values - t_ref = d['t0'] - m_ref = d['mag'] - x_ref = d['x0'] - y_ref = d['y0'] - xe_ref = d['x0_err'] - ye_ref = d['y0_err'] - - # Calculate some quanitites we use for selecting reference stars. - r_ref = np.hypot(x_ref - x_ref[tdx], y_ref - y_ref[tdx]) - - for ee in range(N_epochs): - # Pull out the X, Y positions (and errors) for the two - # starlists we are going to align. - x_epo = d['x_{0:d}'.format(ee)] - y_epo = d['y_{0:d}'.format(ee)] - t_epo = d['t_{0:d}'.format(ee)] - xe_epo = d['xe_{0:d}'.format(ee)] - ye_epo = d['ye_{0:d}'.format(ee)] - - # Shift the reference position by the polyfit for each star. - dt = t_epo - t_ref - if poly_deg >= 0: - x_ref_ee = x_ref - y_ref_ee = y_ref - xe_ref_ee = x_ref - ye_ref_ee = y_ref - - if poly_deg >= 1: - x_ref_ee += d['vx'] * dt - y_ref_ee += d['vy'] * dt - xe_ref_ee = np.hypot(xe_ref_ee, d['vx_err'] * dt) - ye_ref_ee = np.hypot(ye_ref_ee, d['vy_err'] * dt) - - if poly_deg >= 2: - x_ref_ee += d['ax'] * dt - y_ref_ee += d['ay'] * dt - xe_ref_ee = np.hypot(xe_ref_ee, d['axe'] * dt) - ye_ref_ee = np.hypot(ye_ref_ee, d['aye'] * dt) - - # Figure out the set of stars detected in both. - idx = np.where((t_ref != 0) & (t_epo != 0) & (xe_ref != 0) & (xe_epo != 0))[0] - - # Find those in both AND reference stars. This is [idx][rdx] - rdx = np.where((r_ref[idx] < ref_radius_lim) & (m_ref[idx] < ref_mag_lim))[0] - - # Average the positional errors together to get one weight per star. - xye_ref = (xe_ref_ee + ye_ref_ee) / 2.0 - xye_epo = (xe_epo + ye_epo) / 2.0 - xye_wgt = (xye_ref**2 + xye_epo**2)**0.5 - - # Calculate transform based on the matched stars - trans_tmp = transforms.PolyTransform(x_epo[idx][rdx], y_epo[idx][rdx], x_ref_ee[idx][rdx], y_ref_ee[idx][rdx], - weights=xye_wgt[idx][rdx], order=2) - trans.append(trans_tmp) - - # Apply thte transformation to the stars positions and errors: - xt_epo = np.zeros(len(d), dtype=float) - yt_epo = np.zeros(len(d), dtype=float) - xet_epo = np.zeros(len(d), dtype=float) - yet_epo = np.zeros(len(d), dtype=float) - - xt_epo[idx], xet_epo[idx], yt_epo[idx], yet_epo[idx] = trans_tmp.evaluate_errors(x_epo[idx], xe_epo[idx], - y_epo[idx], ye_epo[idx], - nsim=100) - d['xt_{0:d}'.format(ee)] = xt_epo - d['yt_{0:d}'.format(ee)] = yt_epo - d['xet_{0:d}'.format(ee)] = xet_epo - d['yet_{0:d}'.format(ee)] = yet_epo - - # Record which stars we used in the transform. - used_tmp = np.zeros(len(d), dtype=bool) - used_tmp[idx[rdx]] = True - - used.append(used_tmp) - - if True: - plot_quiver_residuals(xt_epo, yt_epo, x_ref_ee, y_ref_ee, idx, rdx, 'Epoch: ' + str(ee)) - - used = np.array(used) - - return trans, used - -def calc_polyfit_all_stars(d, poly_deg, init_fig_idx=0): - # Determine how many stars there are. - N_stars = len(d) - - # Determine how many epochs there are. - N_epochs = len([n for n, c in enumerate(d.colnames) if c.startswith('name')]) - - # Setup some variables to save the results - t0_all = [] - px_all = [] - py_all = [] - pxe_all = [] - pye_all = [] - chi2x_all = [] - chi2y_all = [] - dof_all = [] - - # Get the time array, which is the same for all stars. - # Also, sort the time indices. - t = np.array([d['t_{0:d}'.format(ee)][0] for ee in range(N_epochs)]) - tdx = t.argsort() - t_sorted = t[tdx] - - # Run polyfit on each star. - for ss in range(N_stars): - # Get the x, y, xe, ye, and t arrays for this star. - xt = np.array([d['xt_{0:d}'.format(ee)][ss] for ee in range(N_epochs)]) - yt = np.array([d['yt_{0:d}'.format(ee)][ss] for ee in range(N_epochs)]) - xet = np.array([d['xet_{0:d}'.format(ee)][ss] for ee in range(N_epochs)]) - yet = np.array([d['yet_{0:d}'.format(ee)][ss] for ee in range(N_epochs)]) - t_tmp = np.array([d['t_{0:d}'.format(ee)][ss] for ee in range(N_epochs)]) - - # Sort these arrays. - xt_sorted = xt[tdx] - yt_sorted = yt[tdx] - xet_sorted = xet[tdx] - yet_sorted = yet[tdx] - t_tmp_sorted = t_tmp[tdx] - - # Get only the detected epochs. - edx = np.where(t_tmp_sorted != 0)[0] - - # Calculate the weighted t0 (using the transformed errors). - weight_for_t0 = 1.0 / np.hypot(xet_sorted, yet_sorted) - t0 = np.average(t_sorted[edx], weights=weight_for_t0[edx]) - - # for ee in edx: - # print('{0:8.3f} {1:10.5f} {2:10.5f} {3:8.5f} {4:8.5f}'.format(t[ee], xt[ee], yt[ee], xet[ee], yet[ee])) - # pdb.set_trace() - - # Run polyfit - dt = t_sorted - t0 - px, covx = np.polyfit(dt[edx], xt_sorted[edx], poly_deg, w=1./xet_sorted[edx], cov=True) - py, covy = np.polyfit(dt[edx], yt_sorted[edx], poly_deg, w=1./yet_sorted[edx], cov=True) - - pxe = np.sqrt(np.diag(covx)) - pye = np.sqrt(np.diag(covy)) - - - x_mod = np.polyval(px, dt[edx]) - y_mod = np.polyval(py, dt[edx]) - chi2x = np.sum( ((x_mod - xt_sorted[edx]) / xet_sorted[edx])**2 ) - chi2y = np.sum( ((y_mod - yt_sorted[edx]) / yet_sorted[edx])**2 ) - dof = len(edx) - (poly_deg + 1) - - # Save results: - t0_all.append(t0) - px_all.append(px) - py_all.append(py) - pxe_all.append(pxe) - pye_all.append(pye) - chi2x_all.append(chi2x) - chi2y_all.append(chi2y) - dof_all.append(dof) - - if d[ss]['name_0'] in ['OB120169', 'OB120169_L']: - gs = GridSpec(3, 2) # 3 rows, 1 column - fig = plt.figure(ss + 1 + init_fig_idx, figsize=(12, 8)) - a0 = fig.add_subplot(gs[0:2, 0]) - a1 = fig.add_subplot(gs[2, 0]) - a2 = fig.add_subplot(gs[0:2, 1]) - a3 = fig.add_subplot(gs[2, 1]) - - a0.errorbar(t_sorted[edx], xt_sorted[edx], yerr=xet_sorted[edx], fmt='ro') - a0.plot(t_sorted[edx], x_mod, 'k-') - a0.set_title(d[ss]['name_0'] + ' X') - a1.errorbar(t_sorted[edx], xt_sorted[edx] - x_mod, yerr=xet_sorted[edx], fmt='ro') - a1.axhline(0, linestyle='--') - a1.set_xlabel('Time (yrs)') - a2.errorbar(t_sorted[edx], yt_sorted[edx], yerr=yet_sorted[edx], fmt='ro') - a2.plot(t_sorted[edx], y_mod, 'k-') - a2.set_title(d[ss]['name_0'] + ' Y') - a3.errorbar(t_sorted[edx], yt_sorted[edx] - y_mod, yerr=yet_sorted[edx], fmt='ro') - a3.axhline(0, linestyle='--') - a3.set_xlabel('Time (yrs)') - - - - t0_all = np.array(t0_all) - px_all = np.array(px_all) - py_all = np.array(py_all) - pxe_all = np.array(pxe_all) - pye_all = np.array(pye_all) - chi2x_all = np.array(chi2x_all) - chi2y_all = np.array(chi2y_all) - dof_all = np.array(dof_all) - - # Done with all the stars... recast as numpy arrays and save to output table. - d['t0'] = t0_all - d['chi2x'] = chi2x_all - d['chi2y'] = chi2y_all - d['dof'] = dof_all - if poly_deg >= 0: - d['x0'] = px_all[:, -1] - d['y0'] = py_all[:, -1] - d['x0_err'] = pxe_all[:, -1] - d['y0_err'] = pye_all[:, -1] - - if poly_deg >= 1: - d['vx'] = px_all[:, -2] - d['vy'] = py_all[:, -2] - d['vx_err'] = pxe_all[:, -2] - d['vy_err'] = pye_all[:, -2] - - if poly_deg >= 2: - d['ax'] = px_all[:, -3] - d['ay'] = py_all[:, -3] - d['axe'] = pxe_all[:, -3] - d['aye'] = pye_all[:, -3] - - pdb.set_trace() - - return def calc_mag_avg_all_stars(d): # Determine how many stars there are. diff --git a/flystar/align_old_functions.py b/flystar/align_old_functions.py new file mode 100755 index 0000000..46aae42 --- /dev/null +++ b/flystar/align_old_functions.py @@ -0,0 +1,369 @@ +""" +Old functions iwht things hard-coded for OB120169 +""" + +def run_align_iter(catalog, trans_order=1, poly_deg=1, ref_mag_lim=19, ref_radius_lim=300): + # Load up data with matched stars. + d = Table.read(catalog) + + # Determine how many epochs there are. + N_epochs = len([n for n, c in enumerate(d.colnames) if c.startswith('name')]) + + # Determine how many stars there are. + N_stars = len(d) + + # Determine the reference epoch + ref = d.meta['L_REF'] + + # Figure out the number of free parameters for the specified + # poly2d order. + poly2d = models.Polynomial2D(trans_order) + N_par_trans_per_epoch = 2.0 * poly2d.get_num_coeff(2) # one poly2d for each dimension (X, Y) + N_par_trans = N_par_trans_per_epoch * N_epochs + + ########## + # First iteration -- align everything to REF epoch with zero velocities. + ########## + print('ALIGN_EPOCHS: run_align_iter() -- PASS 1') + ee_ref = d.meta['L_REF'] + + target_name = 'OB120169' + + trans1, used1 = calc_transform_ref_epoch(d, target_name, ee_ref, ref_mag_lim, ref_radius_lim) + + ########## + # Derive the velocity of each stars using the round 1 transforms. + ########## + calc_polyfit_all_stars(d, poly_deg, init_fig_idx=0) + + calc_mag_avg_all_stars(d) + + tdx = np.where((d['name_0'] == 'OB120169') | (d['name_0'] == 'OB120169_L'))[0] + print(d[tdx]['name_0', 't0', 'mag', 'x0', 'vx', 'x0_err', 'vx_err', 'chi2x', 'y0', 'vy', 'y0_err', 'vy_err', 'chi2y', 'dof']) + + ########## + # Second iteration -- align everything to reference positions derived from iteration 1 + ########## + print('ALIGN_EPOCHS: run_align_iter() -- PASS 2') + target_name = 'OB120169' + + trans2, used2 = calc_transform_ref_poly(d, target_name, poly_deg, ref_mag_lim, ref_radius_lim) + + ########## + # Derive the velocity of each stars using the round 1 transforms. + ########## + calc_polyfit_all_stars(d, poly_deg, init_fig_idx=4) + + ########## + # Save output + ########## + d.write(catalog.replace('.fits', '_aln.fits'), overwrite=True) + + return + +def calc_transform_ref_epoch(d, target_name, ee_ref, ref_mag_lim, ref_radius_lim): + # Determine how many epochs there are. + N_epochs = len([n for n, c in enumerate(d.colnames) if c.startswith('name')]) + + # output array + trans = [] + used = [] + + # Find the target + tdx = np.where(d['name_0'] == 'OB120169')[0][0] + + # Reference values + t_ref = d['t_{0:d}'.format(ee_ref)] + m_ref = d['m_{0:d}'.format(ee_ref)] + x_ref = d['x_{0:d}'.format(ee_ref)] + y_ref = d['y_{0:d}'.format(ee_ref)] + xe_ref = d['xe_{0:d}'.format(ee_ref)] + ye_ref = d['ye_{0:d}'.format(ee_ref)] + + # Calculate some quanitites we use for selecting reference stars. + r_ref = np.hypot(x_ref - x_ref[tdx], y_ref - y_ref[tdx]) + + # Loop through and align each epoch to the reference epoch. + for ee in range(N_epochs): + # Pull out the X, Y positions (and errors) for the two + # starlists we are going to align. + x_epo = d['x_{0:d}'.format(ee)] + y_epo = d['y_{0:d}'.format(ee)] + t_epo = d['t_{0:d}'.format(ee)] + xe_epo = d['xe_{0:d}'.format(ee)] + ye_epo = d['ye_{0:d}'.format(ee)] + + # Figure out the set of stars detected in both epochs. + idx = np.where((t_ref != 0) & (t_epo != 0) & (xe_ref != 0) & (xe_epo != 0))[0] + + # Find those in both epochs AND reference stars. This is [idx][rdx] + rdx = np.where((r_ref[idx] < ref_radius_lim) & (m_ref[idx] < ref_mag_lim))[0] + + # Average the positional errors together to get one weight per star. + xye_ref = (xe_ref + ye_ref) / 2.0 + xye_epo = (xe_epo + ye_epo) / 2.0 + xye_wgt = (xye_ref**2 + xye_epo**2)**0.5 + + # Calculate transform based on the matched stars + trans_tmp = transforms.PolyTransform(x_epo[idx][rdx], y_epo[idx][rdx], x_ref[idx][rdx], y_ref[idx][rdx], + weights=xye_wgt[idx][rdx], order=2) + + trans.append(trans_tmp) + + + # Apply thte transformation to the stars positions and errors: + xt_epo = np.zeros(len(d), dtype=float) + yt_epo = np.zeros(len(d), dtype=float) + xet_epo = np.zeros(len(d), dtype=float) + yet_epo = np.zeros(len(d), dtype=float) + + xt_epo[idx], xet_epo[idx], yt_epo[idx], yet_epo[idx] = trans_tmp.evaluate_errors(x_epo[idx], xe_epo[idx], + y_epo[idx], ye_epo[idx], + nsim=100) + + d['xt_{0:d}'.format(ee)] = xt_epo + d['yt_{0:d}'.format(ee)] = yt_epo + d['xet_{0:d}'.format(ee)] = xet_epo + d['yet_{0:d}'.format(ee)] = yet_epo + + # Record which stars we used in the transform. + used_tmp = np.zeros(len(d), dtype=bool) + used_tmp[idx[rdx]] = True + + used.append(used_tmp) + + if True: + plot_quiver_residuals(xt_epo, yt_epo, x_ref, y_ref, idx, rdx, 'Epoch: ' + str(ee)) + + used = np.array(used) + + return trans, used + +def calc_transform_ref_poly(d, target_name, poly_deg, ref_mag_lim, ref_radius_lim): + # Determine how many epochs there are. + N_epochs = len([n for n, c in enumerate(d.colnames) if c.startswith('name')]) + + # output array + trans = [] + used = [] + + # Find the target + tdx = np.where(d['name_0'] == 'OB120169')[0][0] + + # Temporary Reference values + t_ref = d['t0'] + m_ref = d['mag'] + x_ref = d['x0'] + y_ref = d['y0'] + xe_ref = d['x0_err'] + ye_ref = d['y0_err'] + + # Calculate some quanitites we use for selecting reference stars. + r_ref = np.hypot(x_ref - x_ref[tdx], y_ref - y_ref[tdx]) + + for ee in range(N_epochs): + # Pull out the X, Y positions (and errors) for the two + # starlists we are going to align. + x_epo = d['x_{0:d}'.format(ee)] + y_epo = d['y_{0:d}'.format(ee)] + t_epo = d['t_{0:d}'.format(ee)] + xe_epo = d['xe_{0:d}'.format(ee)] + ye_epo = d['ye_{0:d}'.format(ee)] + + # Shift the reference position by the polyfit for each star. + dt = t_epo - t_ref + if poly_deg >= 0: + x_ref_ee = x_ref + y_ref_ee = y_ref + xe_ref_ee = x_ref + ye_ref_ee = y_ref + + if poly_deg >= 1: + x_ref_ee += d['vx'] * dt + y_ref_ee += d['vy'] * dt + xe_ref_ee = np.hypot(xe_ref_ee, d['vx_err'] * dt) + ye_ref_ee = np.hypot(ye_ref_ee, d['vy_err'] * dt) + + if poly_deg >= 2: + x_ref_ee += d['ax'] * dt + y_ref_ee += d['ay'] * dt + xe_ref_ee = np.hypot(xe_ref_ee, d['axe'] * dt) + ye_ref_ee = np.hypot(ye_ref_ee, d['aye'] * dt) + + # Figure out the set of stars detected in both. + idx = np.where((t_ref != 0) & (t_epo != 0) & (xe_ref != 0) & (xe_epo != 0))[0] + + # Find those in both AND reference stars. This is [idx][rdx] + rdx = np.where((r_ref[idx] < ref_radius_lim) & (m_ref[idx] < ref_mag_lim))[0] + + # Average the positional errors together to get one weight per star. + xye_ref = (xe_ref_ee + ye_ref_ee) / 2.0 + xye_epo = (xe_epo + ye_epo) / 2.0 + xye_wgt = (xye_ref**2 + xye_epo**2)**0.5 + + # Calculate transform based on the matched stars + trans_tmp = transforms.PolyTransform(x_epo[idx][rdx], y_epo[idx][rdx], x_ref_ee[idx][rdx], y_ref_ee[idx][rdx], + weights=xye_wgt[idx][rdx], order=2) + trans.append(trans_tmp) + + # Apply thte transformation to the stars positions and errors: + xt_epo = np.zeros(len(d), dtype=float) + yt_epo = np.zeros(len(d), dtype=float) + xet_epo = np.zeros(len(d), dtype=float) + yet_epo = np.zeros(len(d), dtype=float) + + xt_epo[idx], xet_epo[idx], yt_epo[idx], yet_epo[idx] = trans_tmp.evaluate_errors(x_epo[idx], xe_epo[idx], + y_epo[idx], ye_epo[idx], + nsim=100) + d['xt_{0:d}'.format(ee)] = xt_epo + d['yt_{0:d}'.format(ee)] = yt_epo + d['xet_{0:d}'.format(ee)] = xet_epo + d['yet_{0:d}'.format(ee)] = yet_epo + + # Record which stars we used in the transform. + used_tmp = np.zeros(len(d), dtype=bool) + used_tmp[idx[rdx]] = True + + used.append(used_tmp) + + if True: + plot_quiver_residuals(xt_epo, yt_epo, x_ref_ee, y_ref_ee, idx, rdx, 'Epoch: ' + str(ee)) + + used = np.array(used) + + return trans, used + +def calc_polyfit_all_stars(d, poly_deg, init_fig_idx=0): + # Determine how many stars there are. + N_stars = len(d) + + # Determine how many epochs there are. + N_epochs = len([n for n, c in enumerate(d.colnames) if c.startswith('name')]) + + # Setup some variables to save the results + t0_all = [] + px_all = [] + py_all = [] + pxe_all = [] + pye_all = [] + chi2x_all = [] + chi2y_all = [] + dof_all = [] + + # Get the time array, which is the same for all stars. + # Also, sort the time indices. + t = np.array([d['t_{0:d}'.format(ee)][0] for ee in range(N_epochs)]) + tdx = t.argsort() + t_sorted = t[tdx] + + # Run polyfit on each star. + for ss in range(N_stars): + # Get the x, y, xe, ye, and t arrays for this star. + xt = np.array([d['xt_{0:d}'.format(ee)][ss] for ee in range(N_epochs)]) + yt = np.array([d['yt_{0:d}'.format(ee)][ss] for ee in range(N_epochs)]) + xet = np.array([d['xet_{0:d}'.format(ee)][ss] for ee in range(N_epochs)]) + yet = np.array([d['yet_{0:d}'.format(ee)][ss] for ee in range(N_epochs)]) + t_tmp = np.array([d['t_{0:d}'.format(ee)][ss] for ee in range(N_epochs)]) + + # Sort these arrays. + xt_sorted = xt[tdx] + yt_sorted = yt[tdx] + xet_sorted = xet[tdx] + yet_sorted = yet[tdx] + t_tmp_sorted = t_tmp[tdx] + + # Get only the detected epochs. + edx = np.where(t_tmp_sorted != 0)[0] + + # Calculate the weighted t0 (using the transformed errors). + weight_for_t0 = 1.0 / np.hypot(xet_sorted, yet_sorted) + t0 = np.average(t_sorted[edx], weights=weight_for_t0[edx]) + + # for ee in edx: + # print('{0:8.3f} {1:10.5f} {2:10.5f} {3:8.5f} {4:8.5f}'.format(t[ee], xt[ee], yt[ee], xet[ee], yet[ee])) + # pdb.set_trace() + + # Run polyfit + dt = t_sorted - t0 + px, covx = np.polyfit(dt[edx], xt_sorted[edx], poly_deg, w=1./xet_sorted[edx], cov=True) + py, covy = np.polyfit(dt[edx], yt_sorted[edx], poly_deg, w=1./yet_sorted[edx], cov=True) + + pxe = np.sqrt(np.diag(covx)) + pye = np.sqrt(np.diag(covy)) + + + x_mod = np.polyval(px, dt[edx]) + y_mod = np.polyval(py, dt[edx]) + chi2x = np.sum( ((x_mod - xt_sorted[edx]) / xet_sorted[edx])**2 ) + chi2y = np.sum( ((y_mod - yt_sorted[edx]) / yet_sorted[edx])**2 ) + dof = len(edx) - (poly_deg + 1) + + # Save results: + t0_all.append(t0) + px_all.append(px) + py_all.append(py) + pxe_all.append(pxe) + pye_all.append(pye) + chi2x_all.append(chi2x) + chi2y_all.append(chi2y) + dof_all.append(dof) + + if d[ss]['name_0'] in ['OB120169', 'OB120169_L']: + gs = GridSpec(3, 2) # 3 rows, 1 column + fig = plt.figure(ss + 1 + init_fig_idx, figsize=(12, 8)) + a0 = fig.add_subplot(gs[0:2, 0]) + a1 = fig.add_subplot(gs[2, 0]) + a2 = fig.add_subplot(gs[0:2, 1]) + a3 = fig.add_subplot(gs[2, 1]) + + a0.errorbar(t_sorted[edx], xt_sorted[edx], yerr=xet_sorted[edx], fmt='ro') + a0.plot(t_sorted[edx], x_mod, 'k-') + a0.set_title(d[ss]['name_0'] + ' X') + a1.errorbar(t_sorted[edx], xt_sorted[edx] - x_mod, yerr=xet_sorted[edx], fmt='ro') + a1.axhline(0, linestyle='--') + a1.set_xlabel('Time (yrs)') + a2.errorbar(t_sorted[edx], yt_sorted[edx], yerr=yet_sorted[edx], fmt='ro') + a2.plot(t_sorted[edx], y_mod, 'k-') + a2.set_title(d[ss]['name_0'] + ' Y') + a3.errorbar(t_sorted[edx], yt_sorted[edx] - y_mod, yerr=yet_sorted[edx], fmt='ro') + a3.axhline(0, linestyle='--') + a3.set_xlabel('Time (yrs)') + + + + t0_all = np.array(t0_all) + px_all = np.array(px_all) + py_all = np.array(py_all) + pxe_all = np.array(pxe_all) + pye_all = np.array(pye_all) + chi2x_all = np.array(chi2x_all) + chi2y_all = np.array(chi2y_all) + dof_all = np.array(dof_all) + + # Done with all the stars... recast as numpy arrays and save to output table. + d['t0'] = t0_all + d['chi2x'] = chi2x_all + d['chi2y'] = chi2y_all + d['dof'] = dof_all + if poly_deg >= 0: + d['x0'] = px_all[:, -1] + d['y0'] = py_all[:, -1] + d['x0_err'] = pxe_all[:, -1] + d['y0_err'] = pye_all[:, -1] + + if poly_deg >= 1: + d['vx'] = px_all[:, -2] + d['vy'] = py_all[:, -2] + d['vx_err'] = pxe_all[:, -2] + d['vy_err'] = pye_all[:, -2] + + if poly_deg >= 2: + d['ax'] = px_all[:, -3] + d['ay'] = py_all[:, -3] + d['axe'] = pxe_all[:, -3] + d['aye'] = pye_all[:, -3] + + pdb.set_trace() + + return + diff --git a/flystar/motion_model.py b/flystar/motion_model.py index 5035261..960af5d 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -6,6 +6,8 @@ class MotionModel(ABC): # Number of data points required to fit model n_pts_req = 0 + # Degrees of freedom for model + dof = 0 # Fit paramters: Shared fit parameters fitter_param_names = [] @@ -50,12 +52,24 @@ def fit_motion_model(self, t, x, y, xe, ye, update=True): """ #return params, param_errors pass + + def get_chi2(self,dt,x,y,xe,ye): + """ + Get the chi^2 value for the current MM and + the input data. + """ + # TODO: confirm whether we want reduced chi^2 or anything special - maybe kwarg option + x_pred,y_pred = self.get_pos_at_time(dt) + chi2x = np.sum((x-x_pred)**2 / xe**2) + chi2y = np.sum((y-y_pred)**2 / ye**2) + return chi2x,chi2y class Fixed(MotionModel): """ A non-moving motion model for a star on the sky. """ n_pts_req = 1 + dof=1 fitter_param_names = ['x0','y0'] fixed_param_names = [] @@ -111,6 +125,7 @@ class Linear(MotionModel): A 2D linear motion model for a star on the sky. """ n_pts_req = 2 + dof=2 fitter_param_names = ['x0', 'vx', 'y0', 'vy'] fixed_param_names = ['t0'] @@ -136,20 +151,14 @@ def __init__(self, x0=0, vx=0, y0=0, vy=0, t0=2025.0, return - def get_pos_at_time(self, t): - dt = t - self.t0 - + def get_pos_at_time(self, dt): x = self.px(dt) y = self.py(dt) - return x, y - def get_pos_err_at_time(self, t): - dt = t - self.t0 - + def get_pos_err_at_time(self, dt): x_err = np.hypot(self.x0_err, self.vx_err*dt) y_err = np.hypot(self.y0_err, self.vy_err*dt) - return x_err, y_err def get_batch_pos_at_time(self,t, @@ -252,6 +261,7 @@ class Acceleration(MotionModel): A 2D accelerating motion model for a star on the sky. """ n_pts_req = 4 # TODO: consider special case for 3 pts + dof=3 fitter_param_names = ['x0', 'vx0', 'ax', 'y0', 'vy0', 'ay'] fixed_param_names = ['t0'] @@ -384,7 +394,10 @@ def fit_motion_model(self, dt, x, y, xe, ye, update=False, bootstrap=0): return params, param_errors - +""" +Get all the motion model parameters for a given motion_model_name. +Optionally, include fixed and error parameters (included by default). +""" def get_one_motion_model_param_names(motion_model_name, with_errors=True, with_fixed=True): mod = eval(motion_model_name) list_of_parameters = [] @@ -395,6 +408,10 @@ def get_one_motion_model_param_names(motion_model_name, with_errors=True, with_f list_of_parameters += [par+'_err' for par in getattr(mod, 'fitter_param_names')] return list_of_parameters +""" +Get all the motion model parameters for all models given in motion_model_list. +Optionally, include fixed and error parameters (included by default). +""" def get_list_motion_model_param_names(motion_model_list, with_errors=True, with_fixed=True): list_of_parameters = [] all_motion_models = [eval(mm) for mm in np.unique(motion_model_list).tolist()] @@ -411,6 +428,10 @@ def get_list_motion_model_param_names(motion_model_list, with_errors=True, with_ return np.unique(list_of_parameters).tolist() +""" +Get all the motion model parameters for all models defined in this module. +Optionally, include fixed and error parameters (included by default). +""" def get_all_motion_model_param_names(with_errors=True, with_fixed=True): list_of_parameters = [] all_motion_models = MotionModel.__subclasses__() diff --git a/flystar/plots.py b/flystar/plots.py index c675170..e04cd68 100755 --- a/flystar/plots.py +++ b/flystar/plots.py @@ -518,8 +518,8 @@ def vel_diff_err_hist(ref_mat, starlist_mat, nbins=25, bin_width=None, vxlim=Non diff_vx = ref_mat['vx'] - starlist_mat['vx'] diff_vy = ref_mat['vy'] - starlist_mat['vy'] - vx_err = np.hypot(ref_mat['vxe'], starlist_mat['vxe']) - vy_err = np.hypot(ref_mat['vye'], starlist_mat['vye']) + vx_err = np.hypot(ref_mat['vx_err'], starlist_mat['vx_err']) + vy_err = np.hypot(ref_mat['vy_err'], starlist_mat['vy_err']) ratio_vx = diff_vx / vx_err ratio_vy = diff_vy / vy_err @@ -589,10 +589,10 @@ def residual_vpd(ref_mat, starlist_trans_mat, pscale=None): # Error calculation depends on if we are converting to mas/yr if pscale != None: - xerr_frac = np.hypot((ref_mat['vxe'] / ref_mat['vx']), - (starlist_trans_mat['vxe'] / starlist_trans_mat['vx'])) - yerr_frac = np.hypot((ref_mat['vye'] / ref_mat['vy']), - (starlist_trans_mat['vye'] / starlist_trans_mat['vy'])) + xerr_frac = np.hypot((ref_mat['vx_err'] / ref_mat['vx']), + (starlist_trans_mat['vx_err'] / starlist_trans_mat['vx'])) + yerr_frac = np.hypot((ref_mat['vy_err'] / ref_mat['vy']), + (starlist_trans_mat['vy_err'] / starlist_trans_mat['vy'])) # Now apply the plate scale to convert to mas/yr diff_x *= pscale @@ -600,8 +600,8 @@ def residual_vpd(ref_mat, starlist_trans_mat, pscale=None): xerr = diff_x * xerr_frac yerr = diff_y * yerr_frac else: - xerr = np.hypot(ref_mat['vxe'], starlist_trans_mat['vxe']) - yerr = np.hypot(ref_mat['vye'], starlist_trans_mat['vye']) + xerr = np.hypot(ref_mat['vx_err'], starlist_trans_mat['vx_err']) + yerr = np.hypot(ref_mat['vy_err'], starlist_trans_mat['vy_err']) # Plotting py.figure(figsize=(10,10)) @@ -1044,8 +1044,8 @@ def plot_gaia(gaia): def plot_pm_error(tab): plt.figure(figsize=(6,6)) plt.clf() - plt.semilogy(tab['m0'], tab['vxe']*1e3, 'r.', label=r'$\sigma_{\mu_{\alpha *}}$', alpha=0.4) - plt.semilogy(tab['m0'], tab['vye']*1e3, 'b.', label=r'$\sigma_{\mu_{\delta}}$', alpha=0.4) + plt.semilogy(tab['m0'], tab['vx_err']*1e3, 'r.', label=r'$\sigma_{\mu_{\alpha *}}$', alpha=0.4) + plt.semilogy(tab['m0'], tab['vy_err']*1e3, 'b.', label=r'$\sigma_{\mu_{\delta}}$', alpha=0.4) plt.legend() plt.xlabel('Mag') plt.ylabel('PM Error (mas/yr)') @@ -1055,7 +1055,7 @@ def plot_pm_error(tab): def plot_mag_error(tab): plt.figure(figsize=(6,6)) plt.clf() - plt.semilogy(tab['m0'], tab['m0e'], 'r.', alpha=0.4) + plt.semilogy(tab['m0'], tab['m0_err'], 'r.', alpha=0.4) plt.legend() plt.xlabel('Mag') plt.ylabel('Mag Error (mag)') @@ -1216,7 +1216,7 @@ def plot_quiver_residuals_with_orig_all_epochs(tab, trans_list, unit='arcsec', s scale=scale_orig, plotlim=plotlim) plot_mag_scatter(tab['m'][:, ee], - tab['m0'], tab['m0e'], + tab['m0'], tab['m0_err'], tab['x'][:, ee], tab['y'][:, ee], tab['xe'][:, ee], tab['ye'][:, ee], xt_mod, yt_mod, @@ -1226,7 +1226,7 @@ def plot_quiver_residuals_with_orig_all_epochs(tab, trans_list, unit='arcsec', s cte_fit=cte_fit, mlim=mlim) plot_y_scatter(tab['m'][:, ee], - tab['m0'], tab['m0e'], + tab['m0'], tab['m0_err'], tab['x'][:, ee], tab['y'][:, ee], tab['xe'][:, ee], tab['ye'][:, ee], xt_mod, yt_mod, @@ -2322,7 +2322,7 @@ def plot_chi2_dist_mag(tab, Ndetect, mlim=40, n_bins=30): m = tab['m'][ii, fnd] merr = tab['me'][ii, fnd] m0 = tab['m0'][ii] - m0err = tab['m0e'][ii] + m0err = tab['m0_err'][ii] diff_m = m0 - m sig_m = diff_m/merr @@ -2410,11 +2410,11 @@ def plot_stars(tab, star_names, NcolMax=2, epoch_array = None, figsize=(15,25), fitLineX = tab['x0'][ii] + (tab['vx'][ii] * dt) fitLineY = tab['y0'][ii] + (tab['vy'][ii] * dt) - fitSigX = np.hypot(tab['x0e'][ii], tab['vxe'][ii]*dt) - fitSigY = np.hypot(tab['y0e'][ii], tab['vye'][ii]*dt) + fitSigX = np.hypot(tab['x0_err'][ii], tab['vx_err'][ii]*dt) + fitSigY = np.hypot(tab['y0_err'][ii], tab['vy_err'][ii]*dt) fitLineM = np.repeat(tab['m0'][ii], len(dt)).reshape(len(dt),1) - fitSigM = np.repeat(tab['m0e'][ii], len(dt)).reshape(len(dt),1) + fitSigM = np.repeat(tab['m0_err'][ii], len(dt)).reshape(len(dt),1) diffX = x - fitLineX diffY = y - fitLineY @@ -2815,11 +2815,11 @@ def plot_stars_nfilt(tab, star_names, NcolMax=2, epoch_array_list = None, color_ fitLineX = tab['x0'][ii] + (tab['vx'][ii] * dt) fitLineY = tab['y0'][ii] + (tab['vy'][ii] * dt) - fitSigX = np.hypot(tab['x0e'][ii], tab['vxe'][ii]*dt) - fitSigY = np.hypot(tab['y0e'][ii], tab['vye'][ii]*dt) + fitSigX = np.hypot(tab['x0_err'][ii], tab['vx_err'][ii]*dt) + fitSigY = np.hypot(tab['y0_err'][ii], tab['vy_err'][ii]*dt) fitLineM = np.repeat(tab['m0'][ii], len(dt)).reshape(len(dt),1) - fitSigM = np.repeat(tab['m0e'][ii], len(dt)).reshape(len(dt),1) + fitSigM = np.repeat(tab['m0_err'][ii], len(dt)).reshape(len(dt),1) diffX = x - fitLineX diffY = y - fitLineY @@ -3166,8 +3166,8 @@ def plot_errors_vs_r_m(star_tab, vmax_perr=0.75, vmax_pmerr=0.75): two axis (as is used in pick_good_ref_stars()). """ r = np.hypot(star_tab['x0'], star_tab['y0']) - p_err = np.mean((star_tab['x0e'], star_tab['y0e']), axis=0) * 1e3 - pm_err = np.mean((star_tab['vxe'], star_tab['vye']), axis=0) * 1e3 + p_err = np.mean((star_tab['x0_err'], star_tab['y0_err']), axis=0) * 1e3 + pm_err = np.mean((star_tab['vx_err'], star_tab['vy_err']), axis=0) * 1e3 plt.figure(figsize=(12, 6)) plt.clf() diff --git a/flystar/startables.py b/flystar/startables.py index b68b752..95a21bf 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -592,7 +592,6 @@ def fit_velocities(self, weighting='var', use_scipy=True, absolute_sigma=True, b all_motion_models = np.unique(self['motion_model_input'].tolist() + ['Fixed']+[default_motion_model]).tolist() new_col_list = motion_model.get_list_motion_model_param_names(all_motion_models, with_errors=True) # Append goodness of fit metrics and t0. - # TODO: actually populate these columns new_col_list += ['chi2_x', 'chi2_y'] if 't0' not in new_col_list: new_col_list.append('t0') @@ -836,7 +835,11 @@ def fit_velocity_for_star(self, ss, weighting='var', use_scipy=True, mod = modClass(**param_dict) # Fit for the best parameters - params, param_errs = mod.fit_motion_model(dt, x, y, xe, ye, bootstrap=bootstrap) + params, param_errs = mod.fit_motion_model(dt, x, y, xe, ye, bootstrap=bootstrap, update=True) + chi2_x,chi2_y = mod.get_chi2(dt,x,y,xe,ye) + self['chi2_x'][ss]=chi2_x + self['chi2_y'][ss]=chi2_y + # Save parameters and errors to table. for pp in range(len(modClass.fitter_param_names)): par = modClass.fitter_param_names[pp] From 64876f0b00319da20299941f6849e230a8f35b90 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Thu, 26 Sep 2024 10:46:41 -0700 Subject: [PATCH 018/254] work in progress for motion model --- flystar/align.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 704339f..07e71b7 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -1842,12 +1842,10 @@ def add_rows_for_new_stars(ref_table, star_list, idx_lis, default_motion_model=' return ref_table, idx_lis_new, idx_ref_new - """ Functions specific to OB120169 moved to align_old_functions,py """ - def calc_mag_avg_all_stars(d): # Determine how many stars there are. N_stars = len(d) From d41ac6d882a0a7636f78a663f0b4d95d700de41b Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Tue, 22 Oct 2024 12:06:56 -0700 Subject: [PATCH 019/254] small updates to transforms --- flystar/align.py | 558 ++++++--------------------------- flystar/align_old_functions.py | 461 ++++++++++++++++++++++++++- 2 files changed, 549 insertions(+), 470 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 07e71b7..1609719 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -565,7 +565,7 @@ def setup_ref_table_from_starlist(self, star_list): """ Start with the reference list.... this will change and grow over time, so make a copy that we will keep updating. - The reference table will contain one columne for every named + The reference table will contain one column for every named array in the original reference star list. """ col_arrays = {} @@ -2320,6 +2320,8 @@ def write_transform(transform, starlist, reference, N_trans, deltaMag=0, restric return +# Transform_from_file original version moved to align_old_functions.py +# This version makes the transFile an object and uses transform_from_object def transform_from_file(starlist, transFile): """ Apply transformation from transFile to starlist. Returns astropy table with @@ -2327,8 +2329,7 @@ def transform_from_file(starlist, transFile): positions/position errors, plus velocities and velocity errors if they are present in starlist. - WARNING: THIS CODE WILL NOT WORK FOR LEGENDRE POLYNOMIAL - TRANSFORMS + WARNING: THIS CODE WORKS FOR POLYTRANSFORM Parameters: ---------- @@ -2344,153 +2345,31 @@ def transform_from_file(starlist, transFile): ------ Copy of starlist astropy table with transformed coordinates. """ - # Make a copy of starlist. This is what we will eventually modify with - # the transformed coordinates - starlist_f = copy.deepcopy(starlist) - - # Check to see if velocities are present in starlist. If so, we will - # need to transform these as well as positions - vel = False - keys = list(starlist.keys()) - if 'vx' in keys: - vel = True - - # Extract needed information from starlist - x_orig = starlist['x'] - y_orig = starlist['y'] - xe_orig = starlist['xe'] - ye_orig = starlist['ye'] - - if vel: - x0_orig = starlist['x0'] - y0_orig = starlist['y0'] - x0e_orig = starlist['x0_err'] - y0e_orig = starlist['y0_err'] - - vx_orig = starlist['vx'] - vy_orig = starlist['vy'] - vxe_orig = starlist['vx_err'] - vye_orig = starlist['vy_err'] - - # Read transFile - trans = Table.read(transFile, format='ascii.commented_header', header_start=-1) - Xcoeff = trans['Xcoeff'] - Ycoeff = trans['Ycoeff'] - - #-----------------------------------------------# - # General equation for applying the transform - #-----------------------------------------------# - #""" + # Make transform object + trans_table = Table.read(transFile, format='ascii.commented_header', header_start=-1) + Xcoeff = trans_table['Xcoeff'] + Ycoeff = trans_table['Ycoeff'] # First determine the order based on the number of terms # Comes from Nterms = (N+1)*(N+2) / 2. order = (np.sqrt(1 + 8*len(Xcoeff)) - 3) / 2. - if order%1 != 0: print( 'Incorrect number of coefficients for polynomial') print( 'Stopping') return order = int(order) - - # Position transformation - x_new, y_new = transform_pos_from_file(Xcoeff, Ycoeff, order, x_orig, - y_orig) + # Do transform + transform = transforms.PolyTransform(order, Xcoeff, Ycoeff) + return transform_from_object(starlist, transform) - if vel: - x0_new, y0_new = transform_pos_from_file(Xcoeff, Ycoeff, order, x0_orig, - y0_orig) - - # Position error transformation - xe_new, ye_new = transform_poserr_from_file(Xcoeff, Ycoeff, order, xe_orig, - ye_orig, x_orig, y_orig) - - if vel: - x0e_new, y0e_new = transform_poserr_from_file(Xcoeff, Ycoeff, order, x0e_orig, - y0e_orig, x0_orig, y0_orig) - - if vel: - # Velocity transformation - vx_new, vy_new = transform_vel_from_file(Xcoeff, Ycoeff, order, vx_orig, - vy_orig, x_orig, y_orig) - - # Velocity error transformation - vxe_new, vye_new = transform_velerr_from_file(Xcoeff, Ycoeff, order, - vxe_orig, vye_orig, - vx_orig, vy_orig, - xe_orig, ye_orig, - x_orig, y_orig) - - #----------------------------------------# - # Hard coded example: old but functional - #----------------------------------------# - """ - # How the transformation is applied depends on the type of transform. - # This can be determined by the length of Xcoeff, Ycoeff - if len(Xcoeff) == 3: - x_new = Xcoeff[0] + Xcoeff[1] * x_orig + Xcoeff[2] * y_orig - y_new = Ycoeff[0] + Ycoeff[1] * x_orig + Ycoeff[2] * y_orig - xe_new = np.sqrt( (Xcoeff[1] * xe_orig)**2 + (Xcoeff[2] * ye_orig)**2 ) - ye_new = np.sqrt( (Ycoeff[1] * xe_orig)**2 + (Ycoeff[2] * ye_orig)**2 ) - - if vel: - vx_new = Xcoeff[1] * vx_orig + Xcoeff[2] * vy_orig - vy_new = Ycoeff[1] * vx_orig + Ycoeff[2] * vy_orig - vxe_new = np.sqrt( (Xcoeff[1] * vxe_orig)**2 + (Xcoeff[2] * vye_orig)**2 ) - vye_new = np.sqrt( (Ycoeff[1] * vxe_orig)**2 + (Ycoeff[2] * vye_orig)**2 ) - - elif len(Xcoeff) == 6: - x_new = Xcoeff[0] + Xcoeff[1]*x_orig + Xcoeff[3]*x_orig**2 + Xcoeff[2]*y_orig + \ - Xcoeff[5]*y_orig**2. + Xcoeff[4]*x_orig*y_orig - - y_new = Ycoeff[0] + Ycoeff[1]*x_orig + Ycoeff[3]*x_orig**2 + Ycoeff[2]*y_orig + \ - Ycoeff[5]*y_orig**2. + Ycoeff[4]*x_orig*y_orig - - xe_new = np.sqrt( (Xcoeff[1] + 2*Xcoeff[3]*x_orig + Xcoeff[4]*y_orig)**2 * xe_orig**2 + \ - (Xcoeff[2] + 2*Xcoeff[5]*y_orig + Xcoeff[4]*x_orig)**2 * ye_orig**2 ) - - ye_new = np.sqrt( (Ycoeff[1] + 2*Ycoeff[3]*x_orig + Ycoeff[4]*y_orig)**2 * xe_orig**2 + \ - (Ycoeff[2] + 2*Ycoeff[5]*y_orig + Ycoeff[4]*x_orig)**2 * ye_orig**2 ) - - if vel: - vx_new = Xcoeff[1]*vx_orig + 2*Xcoeff[3]*x_orig*vx_orig + Xcoeff[2]*vy_orig + \ - 2.*Xcoeff[5]*y_orig*vy_orig + Xcoeff[4]*(x_orig*vy_orig + vx_orig*y_orig) - - vy_new = Ycoeff[1]*vx_orig + 2*Ycoeff[3]*x_orig*vx_orig + Ycoeff[2]*vy_orig + \ - 2.*Ycoeff[5]*y_orig*vy_orig + Ycoeff[4]*(x_orig*vy_orig + vx_orig*y_orig) - - vxe_new = np.sqrt( (Xcoeff[1] + 2*Xcoeff[3]*x_orig + Xcoeff[4]*y_orig)**2 * vxe_orig**2 + \ - (Xcoeff[2] + 2*Xcoeff[5]*y_orig + Xcoeff[4]*x_orig)**2 * vye_orig**2 + \ - (2*Xcoeff[3]*vx_orig + Xcoeff[4]*vy_orig)**2 * xe_orig**2 + \ - (2*Xcoeff[5]*vy_orig + Xcoeff[4]*vx_orig)**2 * ye_orig**2 ) - - vye_new = np.sqrt( (Ycoeff[1] + 2*Ycoeff[3]*x_orig + Ycoeff[4]*y_orig)**2 * vxe_orig**2 + \ - (Ycoeff[2] + 2*Ycoeff[5]*y_orig + Ycoeff[4]*x_orig)**2 * vye_orig**2 + \ - (2*Ycoeff[3]*vx_orig + Ycoeff[4]*vy_orig)**2 * xe_orig**2 + \ - (2*Ycoeff[5]*vy_orig + Ycoeff[4]*vx_orig)**2 * ye_orig**2 ) - """ - #Update transformed coords to copy of astropy table - starlist_f['x'] = x_new - starlist_f['y'] = y_new - starlist_f['xe'] = xe_new - starlist_f['ye'] = ye_new - if vel: - starlist_f['x0'] = x0_new - starlist_f['y0'] = y0_new - starlist_f['x0_err'] = x0e_new - starlist_f['y0_err'] = y0e_new - starlist_f['vx'] = vx_new - starlist_f['vy'] = vy_new - starlist_f['vx_err'] = vxe_new - starlist_f['vy_err'] = vye_new - - return starlist_f - def transform_from_object(starlist, transform): """ Apply transformation to starlist. Returns astropy table with transformed positions/position errors, velocities and velocity errors - if they are present in starlits + if they are present in starlits. If a more complex motion_model is + implemented, the motion parameters are set to nan, as we need the full time + series to refit. Parameters: ---------- @@ -2512,61 +2391,87 @@ def transform_from_object(starlist, transform): starlist_f = copy.deepcopy(starlist) keys = list(starlist.keys()) - # Check to see if velocities are present in starlist. If so, we will - # need to transform these as well as positions - vel = 'vx' in keys - err = 'xe' in keys + # Check to see if velocities or motion_model are present in starlist. + vel = ('vx' in keys)and ~("motion_model_assigned" in keys) + mot = ("motion_model_assigned" in keys) + # If the only motion models used are Fixed and Linear, we can still use the older, faster code. + if mot: + motion_models_unique = list(np.unique(starlist_f['motion_model_assigned'])) + if 'Linear' in motion_models_unique: + motion_models_unique.remove('Linear') + if 'Fixed' in motion_models_unique: + motion_models_unique.remove('Fixed') + if len(motion_models_unique)==0: + vel=True + mot=False - # Extract needed information from starlist - x = starlist_f['x'] - y = starlist_f['y'] - - if err: - xe = starlist_f['xe'] - ye = starlist_f['ye'] - + # Prior code before motion_model implementation + # Can still be used as shortcut for Linear+Fixed motion_model only if vel: - x0 = starlist_f['x0'] - y0 = starlist_f['y0'] - x0e = starlist_f['x0_err'] - y0e = starlist_f['y0_err'] - vx = starlist_f['vx'] - vy = starlist_f['vy'] - vxe = starlist_f['vx_err'] - vye = starlist_f['vy_err'] - - # calculate the transformed position and velocity - - # (x_new, y_new, xe_new, ye_new) in (x,y) - x_new, y_new, xe_new, ye_new = position_transform_from_object(x, y, xe, ye, transform) - - - if vel: - # (x0_new, y0_new, x0e_new, y0e_new) in (x0, y0, x0e, y0e) - x0_new, y0_new, x0e_new, y0e_new = position_transform_from_object(x0, y0, x0e, y0e, transform) - # (vx_new, vy_new, vxe_new, vye_new) in (x0, y0, x0e, y0e, vx, vy, vxe, vye) - vx_new, vy_new, vxe_new, vye_new = velocity_transform_from_object(x0, y0, x0e, y0e, vx, vy, vxe, vye, transform) - - # update transformed coords to copy of astropy table - starlist_f['x'] = x_new - starlist_f['y'] = y_new - starlist_f['xe'] = xe_new - starlist_f['ye'] = ye_new - - if vel: - starlist_f['x0'] = x0_new - starlist_f['y0'] = y0_new - starlist_f['x0_err'] = x0e_new - starlist_f['y0_err'] = y0e_new - starlist_f['vx'] = vx_new - starlist_f['vy'] = vy_new - starlist_f['vx_err'] = vxe_new - starlist_f['vy_err'] = vye_new + err = 'xe' in keys - return starlist_f + # Extract needed information from starlist + x = starlist_f['x'] + y = starlist_f['y'] + if err: + xe = starlist_f['xe'] + ye = starlist_f['ye'] + if vel: + x0 = starlist_f['x0'] + y0 = starlist_f['y0'] + x0e = starlist_f['x0_err'] + y0e = starlist_f['y0_err'] + vx = starlist_f['vx'] + vy = starlist_f['vy'] + vxe = starlist_f['vx_err'] + vye = starlist_f['vy_err'] + + # calculate the transformed position and velocity + + # (x_new, y_new, xe_new, ye_new) in (x,y) + x_new, y_new, xe_new, ye_new = position_transform_from_object(x, y, xe, ye, transform) + + if vel: + # (x0_new, y0_new, x0e_new, y0e_new) in (x0, y0, x0e, y0e) + x0_new, y0_new, x0e_new, y0e_new = position_transform_from_object(x0, y0, x0e, y0e, transform) + # (vx_new, vy_new, vxe_new, vye_new) in (x0, y0, x0e, y0e, vx, vy, vxe, vye) + vx_new, vy_new, vxe_new, vye_new = velocity_transform_from_object(x0, y0, x0e, y0e, vx, vy, vxe, vye, transform) + + # update transformed coords to copy of astropy table + starlist_f['x'] = x_new + starlist_f['y'] = y_new + starlist_f['xe'] = xe_new + starlist_f['ye'] = ye_new + + if vel: + starlist_f['x0'] = x0_new + starlist_f['y0'] = y0_new + starlist_f['x0_err'] = x0e_new + starlist_f['y0_err'] = y0e_new + starlist_f['vx'] = vx_new + starlist_f['vy'] = vy_new + starlist_f['vx_err'] = vxe_new + starlist_f['vy_err'] = vye_new + + # For more complicated motion_models, + # We can't easily transform them, set the values to nans and refit later + if mot: + # Transform positions + x_new, y_new, xe_new, ye_new = position_transform_from_object(x, y, xe, ye, transform) + starlist_f['x'] = x_new + starlist_f['y'] = y_new + starlist_f['xe'] = xe_new + starlist_f['ye'] = ye_new + + motion_model_params = motion_model.get_all_motion_model_param_names() + for param in motion_model_params: + if param in keys: + starlist_f[param] = np.nan + + return starlist_f def position_transform_from_object(x, y, xe, ye, transform): @@ -2815,291 +2720,6 @@ def velocity_transform_from_object(x0, y0, x0e, y0e, vx, vy, vxe, vye, transform return vx_new, vy_new, vxe_new, vye_new -def transform_pos_from_file(Xcoeff, Ycoeff, order, x_orig, y_orig): - """ - Given the read-in coefficients from transform_from_file, apply the - transformation to the observed positions. This is generalized to - work with any order polynomial transform. - - WARNING: THIS CODE WILL NOT WORK FOR LEGENDRE POLYNOMIAL - TRANSFORMS - - Parameters: - ---------- - Xcoeff: Array - Array with the coefficients of the X pos transformation - - Ycoeff: Array - Array with the coefficients of the Y pos transformation - - order: int - Order of transformation - - x_orig: array - Array with the original X positions - - y_orig: array - Array with the original Y positions - - Output: - ------ - x_new: array - Transformed X positions - - y_new: array - Transformed Y positions - - """ - idx = 0 # coeff index - x_new = 0.0 - y_new = 0.0 - for i in range(order+1): - for j in range(i+1): - x_new += Xcoeff[idx] * x_orig**(i-j) * y_orig**j - y_new += Ycoeff[idx] * x_orig**(i-j) * y_orig**j - - idx += 1 - - return x_new, y_new - -def transform_poserr_from_file(Xcoeff, Ycoeff, order, xe_orig, ye_orig, x_orig, y_orig): - """ - Given the read-in coefficients from transform_from_file, apply the - transformation to the observed position errors. This is generalized to - work with any order transform. - - WARNING: THIS CODE WILL NOT WORK FOR LEGENDRE POLYNOMIAL - TRANSFORMS - - Parameters: - ---------- - Xcoeff: Array - Array with the coefficients of the X pos transformation - - Ycoeff: Array - Array with the coefficients of the Y pos transformation - - order: int - Order of transformation - - xe_orig: array - Array with the original X position errs - - ye_orig: array - Array with the original Y position errs - - x_orig: array - Array with the original X positions - - y_orig: array - Array with the original Y positions - - Output: - ------ - xe_new: array - Transformed X position errs - - ye_new: array - Transformed Y position errs - """ - idx = 0 # coeff index - xe_new_tmp1 = 0.0 - ye_new_tmp1 = 0.0 - xe_new_tmp2 = 0.0 - ye_new_tmp2 = 0.0 - - # First loop: dx'/dx - for i in range(order+1): - for j in range(i+1): - xe_new_tmp1 += Xcoeff[idx] * (i - j) * x_orig**(i-j-1) * y_orig**j - ye_new_tmp1 += Ycoeff[idx] * (i - j) * x_orig**(i-j-1) * y_orig**j - - idx += 1 - - # Second loop: dy'/dy - idx = 0 # coeff index - for i in range(order+1): - for j in range(i+1): - xe_new_tmp2 += Xcoeff[idx] * (j) * x_orig**(i-j) * y_orig**(j-1) - ye_new_tmp2 += Ycoeff[idx] * (j) * x_orig**(i-j) * y_orig**(j-1) - - idx += 1 - # Take square root for xe/ye_new - xe_new = np.sqrt((xe_new_tmp1 * xe_orig)**2 + (xe_new_tmp2 * ye_orig)**2) - ye_new = np.sqrt((ye_new_tmp1 * ye_orig)**2 + (ye_new_tmp2 * ye_orig)**2) - - return xe_new, ye_new - -def transform_vel_from_file(Xcoeff, Ycoeff, order, vx_orig, vy_orig, x_orig, y_orig): - """ - Given the read-in coefficients from transform_from_file, apply the - transformation to the observed proper motions. This is generalized to - work with any order transform. - - WARNING: THIS CODE WILL NOT WORK FOR LEGENDRE POLYNOMIAL - TRANSFORMS - - Parameters: - ---------- - Xcoeff: Array - Array with the coefficients of the X pos transformation - - Ycoeff: Array - Array with the coefficients of the Y pos transformation - - order: int - Order of transformation - - vx_orig: array - Array with the original X proper motions - - vy_orig: array - Array with the original Y proper motions - - x_orig: array - Array with the original X positions - - y_orig: array - Array with the original Y positions - - Output: - ------ - vx_new: array - Transformed X proper motions - - vy_new: array - Transformed Y proper motions - """ - idx = 0 # coeff index - vx_new = 0.0 - vy_new = 0.0 - # First loop: dx'/dx - for i in range(order+1): - for j in range(i+1): - vx_new += Xcoeff[idx] * (i - j) * x_orig**(i-j-1) * y_orig**j * vx_orig - vy_new += Ycoeff[idx] * (i - j) * x_orig**(i-j-1) * y_orig**j * vx_orig - - idx += 1 - # Second loop: dy'/dy - idx = 0 # coeff index - for i in range(order+1): - for j in range(i+1): - vx_new += Xcoeff[idx] * (j) * x_orig**(i-j) * y_orig**(j-1) * vy_orig - vy_new += Ycoeff[idx] * (j) * x_orig**(i-j) * y_orig**(j-1) * vy_orig - - idx += 1 - - return vx_new, vy_new - -def transform_velerr_from_file(Xcoeff, Ycoeff, order, vxe_orig, vye_orig, vx_orig, - vy_orig, xe_orig, ye_orig, x_orig, y_orig): - """ - Given the read-in coefficients from transform_from_file, apply the - transformation to the observed proper motion errors. This is generalized to - work with any order transform. - - WARNING: THIS CODE WILL NOT WORK FOR LEGENDRE POLYNOMIAL - TRANSFORMS - - Parameters: - ---------- - Xcoeff: Array - Array with the coefficients of the X pos transformation - - Ycoeff: Array - Array with the coefficients of the Y pos transformation - - order: int - Order of transformation - - vxe_orig: array - Array with the original X proper motion errs - - vye_orig: array - Array with the original Y proper motion errs - - vx_orig: array - Array with the original X proper motions - - vy_orig: array - Array with the original Y proper motions - - xe_orig: array - Array with the original X position errs - - ye_orig: array - Array with the original Y position errs - - x_orig: array - Array with the original X positions - - y_orig: array - Array with the original Y positions - - Output: - ------ - vxe_new: array - Transformed X proper motion errs - - vye_new: array - Transformed Y proper motion errs - """ - idx = 0 - vxe_new_tmp1 = 0.0 - vye_new_tmp1 = 0.0 - vxe_new_tmp2 = 0.0 - vye_new_tmp2 = 0.0 - vxe_new_tmp3 = 0.0 - vye_new_tmp3 = 0.0 - vxe_new_tmp4 = 0.0 - vye_new_tmp4 = 0.0 - - - # First loop: dvx' / dx - for i in range(order+1): - for j in range(i+1): - vxe_new_tmp1 += Xcoeff[idx] * (i-j) * (i-j-1) * x_orig**(i-j-2) * y_orig**j * vx_orig - vxe_new_tmp1 += Xcoeff[idx] * (j) * (i-j) * x_orig**(i-j-1) * y_orig**(j-1) * vy_orig - vye_new_tmp1 += Ycoeff[idx] * (i-j) * (i-j-1) * x_orig**(i-j-2) * y_orig**j * vx_orig - vye_new_tmp1 += Ycoeff[idx] * (j) * (i-j) * x_orig**(i-j-1) * y_orig**(j-1) * vy_orig - - idx += 1 - - # Second loop: dvx' / dy - idx = 0 - for i in range(order+1): - for j in range(i+1): - vxe_new_tmp2 += Xcoeff[idx] * (i-j) * (j) * x_orig**(i-j-1) * y_orig**(j-1) * vx_orig - vxe_new_tmp2 += Xcoeff[idx] * (j) * (j-1) * x_orig**(i-j-1) * y_orig**(j-2) * vy_orig - vye_new_tmp2 += Ycoeff[idx] * (i-j) * (j) * x_orig**(i-j-1) * y_orig**(j-1) * vx_orig - vye_new_tmp2 += Ycoeff[idx] * (j) * (j-1) * x_orig**(i-j-1) * y_orig**(j-2) * vy_orig - - idx += 1 - - # Third loop: dvx' / dvx - idx = 0 - for i in range(order+1): - for j in range(i+1): - vxe_new_tmp3 += Xcoeff[idx] * (i-j) * x_orig**(i-j-1) * y_orig**j - vye_new_tmp3 += Ycoeff[idx] * (i-j) * x_orig**(i-j-1) * y_orig**j - - idx += 1 - - # Fourth loop: dvx' / dvy - idx = 0 - for i in range(order+1): - for j in range(i+1): - vxe_new_tmp4 += Xcoeff[idx] * (j) * x_orig**(i-j) * y_orig**(j-1) - vye_new_tmp4 += Ycoeff[idx] * (j) * x_orig**(i-j) * y_orig**(j-1) - - idx += 1 - - vxe_new = np.sqrt((vxe_new_tmp1 * xe_orig)**2 + (vxe_new_tmp2 * ye_orig)**2 + \ - (vxe_new_tmp3 * vxe_orig)**2 + (vxe_new_tmp4 * vye_orig)**2) - vye_new = np.sqrt((vye_new_tmp1 * xe_orig)**2 + (vye_new_tmp2 * ye_orig)**2 + \ - (vye_new_tmp3 * vxe_orig)**2 + (vye_new_tmp4 * vye_orig)**2) - - return vxe_new, vye_new def check_iter_tolerances(iters, dr_tol, dm_tol, outlier_tol): diff --git a/flystar/align_old_functions.py b/flystar/align_old_functions.py index 46aae42..9bae670 100755 --- a/flystar/align_old_functions.py +++ b/flystar/align_old_functions.py @@ -1,5 +1,464 @@ """ -Old functions iwht things hard-coded for OB120169 +Old functions that are only referenced in examples and template +""" +def transform_from_file(starlist, transFile): + """ + Apply transformation from transFile to starlist. Returns astropy table with + added columns with the transformed coordinates. NOTE: Transforms + positions/position errors, plus velocities and velocity errors if they + are present in starlist. + + WARNING: THIS CODE WILL NOT WORK FOR LEGENDRE POLYNOMIAL + TRANSFORMS + + Parameters: + ---------- + starlist: astropy table + Starlist we want to apply the transformation too. Must already + have standard column headers + + transFile: ascii file + File with the transformation coefficients. Assumed to be output of + write_transform, with coefficients specified as code documents + + Output: + ------ + Copy of starlist astropy table with transformed coordinates. + """ + # Make a copy of starlist. This is what we will eventually modify with + # the transformed coordinates + starlist_f = copy.deepcopy(starlist) + + # Check to see if velocities are present in starlist. If so, we will + # need to transform these as well as positions + vel = False + keys = list(starlist.keys()) + if 'vx' in keys: + vel = True + + # Extract needed information from starlist + x_orig = starlist['x'] + y_orig = starlist['y'] + xe_orig = starlist['xe'] + ye_orig = starlist['ye'] + + if vel: + x0_orig = starlist['x0'] + y0_orig = starlist['y0'] + x0e_orig = starlist['x0_err'] + y0e_orig = starlist['y0_err'] + + vx_orig = starlist['vx'] + vy_orig = starlist['vy'] + vxe_orig = starlist['vx_err'] + vye_orig = starlist['vy_err'] + + # Read transFile + trans = Table.read(transFile, format='ascii.commented_header', header_start=-1) + Xcoeff = trans['Xcoeff'] + Ycoeff = trans['Ycoeff'] + + #-----------------------------------------------# + # General equation for applying the transform + #-----------------------------------------------# + #""" + # First determine the order based on the number of terms + # Comes from Nterms = (N+1)*(N+2) / 2. + order = (np.sqrt(1 + 8*len(Xcoeff)) - 3) / 2. + + if order%1 != 0: + print( 'Incorrect number of coefficients for polynomial') + print( 'Stopping') + return + order = int(order) + + # Position transformation + x_new, y_new = transform_pos_from_file(Xcoeff, Ycoeff, order, x_orig, + y_orig) + + if vel: + x0_new, y0_new = transform_pos_from_file(Xcoeff, Ycoeff, order, x0_orig, + y0_orig) + + # Position error transformation + xe_new, ye_new = transform_poserr_from_file(Xcoeff, Ycoeff, order, xe_orig, + ye_orig, x_orig, y_orig) + + if vel: + x0e_new, y0e_new = transform_poserr_from_file(Xcoeff, Ycoeff, order, x0e_orig, + y0e_orig, x0_orig, y0_orig) + + if vel: + # Velocity transformation + vx_new, vy_new = transform_vel_from_file(Xcoeff, Ycoeff, order, vx_orig, + vy_orig, x_orig, y_orig) + + # Velocity error transformation + vxe_new, vye_new = transform_velerr_from_file(Xcoeff, Ycoeff, order, + vxe_orig, vye_orig, + vx_orig, vy_orig, + xe_orig, ye_orig, + x_orig, y_orig) + + #----------------------------------------# + # Hard coded example: old but functional + #----------------------------------------# + """ + # How the transformation is applied depends on the type of transform. + # This can be determined by the length of Xcoeff, Ycoeff + if len(Xcoeff) == 3: + x_new = Xcoeff[0] + Xcoeff[1] * x_orig + Xcoeff[2] * y_orig + y_new = Ycoeff[0] + Ycoeff[1] * x_orig + Ycoeff[2] * y_orig + xe_new = np.sqrt( (Xcoeff[1] * xe_orig)**2 + (Xcoeff[2] * ye_orig)**2 ) + ye_new = np.sqrt( (Ycoeff[1] * xe_orig)**2 + (Ycoeff[2] * ye_orig)**2 ) + + if vel: + vx_new = Xcoeff[1] * vx_orig + Xcoeff[2] * vy_orig + vy_new = Ycoeff[1] * vx_orig + Ycoeff[2] * vy_orig + vxe_new = np.sqrt( (Xcoeff[1] * vxe_orig)**2 + (Xcoeff[2] * vye_orig)**2 ) + vye_new = np.sqrt( (Ycoeff[1] * vxe_orig)**2 + (Ycoeff[2] * vye_orig)**2 ) + + elif len(Xcoeff) == 6: + x_new = Xcoeff[0] + Xcoeff[1]*x_orig + Xcoeff[3]*x_orig**2 + Xcoeff[2]*y_orig + \ + Xcoeff[5]*y_orig**2. + Xcoeff[4]*x_orig*y_orig + + y_new = Ycoeff[0] + Ycoeff[1]*x_orig + Ycoeff[3]*x_orig**2 + Ycoeff[2]*y_orig + \ + Ycoeff[5]*y_orig**2. + Ycoeff[4]*x_orig*y_orig + + xe_new = np.sqrt( (Xcoeff[1] + 2*Xcoeff[3]*x_orig + Xcoeff[4]*y_orig)**2 * xe_orig**2 + \ + (Xcoeff[2] + 2*Xcoeff[5]*y_orig + Xcoeff[4]*x_orig)**2 * ye_orig**2 ) + + ye_new = np.sqrt( (Ycoeff[1] + 2*Ycoeff[3]*x_orig + Ycoeff[4]*y_orig)**2 * xe_orig**2 + \ + (Ycoeff[2] + 2*Ycoeff[5]*y_orig + Ycoeff[4]*x_orig)**2 * ye_orig**2 ) + + if vel: + vx_new = Xcoeff[1]*vx_orig + 2*Xcoeff[3]*x_orig*vx_orig + Xcoeff[2]*vy_orig + \ + 2.*Xcoeff[5]*y_orig*vy_orig + Xcoeff[4]*(x_orig*vy_orig + vx_orig*y_orig) + + vy_new = Ycoeff[1]*vx_orig + 2*Ycoeff[3]*x_orig*vx_orig + Ycoeff[2]*vy_orig + \ + 2.*Ycoeff[5]*y_orig*vy_orig + Ycoeff[4]*(x_orig*vy_orig + vx_orig*y_orig) + + vxe_new = np.sqrt( (Xcoeff[1] + 2*Xcoeff[3]*x_orig + Xcoeff[4]*y_orig)**2 * vxe_orig**2 + \ + (Xcoeff[2] + 2*Xcoeff[5]*y_orig + Xcoeff[4]*x_orig)**2 * vye_orig**2 + \ + (2*Xcoeff[3]*vx_orig + Xcoeff[4]*vy_orig)**2 * xe_orig**2 + \ + (2*Xcoeff[5]*vy_orig + Xcoeff[4]*vx_orig)**2 * ye_orig**2 ) + + vye_new = np.sqrt( (Ycoeff[1] + 2*Ycoeff[3]*x_orig + Ycoeff[4]*y_orig)**2 * vxe_orig**2 + \ + (Ycoeff[2] + 2*Ycoeff[5]*y_orig + Ycoeff[4]*x_orig)**2 * vye_orig**2 + \ + (2*Ycoeff[3]*vx_orig + Ycoeff[4]*vy_orig)**2 * xe_orig**2 + \ + (2*Ycoeff[5]*vy_orig + Ycoeff[4]*vx_orig)**2 * ye_orig**2 ) + """ + #Update transformed coords to copy of astropy table + starlist_f['x'] = x_new + starlist_f['y'] = y_new + starlist_f['xe'] = xe_new + starlist_f['ye'] = ye_new + + if vel: + starlist_f['x0'] = x0_new + starlist_f['y0'] = y0_new + starlist_f['x0_err'] = x0e_new + starlist_f['y0_err'] = y0e_new + starlist_f['vx'] = vx_new + starlist_f['vy'] = vy_new + starlist_f['vx_err'] = vxe_new + starlist_f['vy_err'] = vye_new + + return starlist_f + +def transform_pos_from_file(Xcoeff, Ycoeff, order, x_orig, y_orig): + """ + Given the read-in coefficients from transform_from_file, apply the + transformation to the observed positions. This is generalized to + work with any order polynomial transform. + + WARNING: THIS CODE WILL NOT WORK FOR LEGENDRE POLYNOMIAL + TRANSFORMS + + Parameters: + ---------- + Xcoeff: Array + Array with the coefficients of the X pos transformation + + Ycoeff: Array + Array with the coefficients of the Y pos transformation + + order: int + Order of transformation + + x_orig: array + Array with the original X positions + + y_orig: array + Array with the original Y positions + + Output: + ------ + x_new: array + Transformed X positions + + y_new: array + Transformed Y positions + + """ + idx = 0 # coeff index + x_new = 0.0 + y_new = 0.0 + for i in range(order+1): + for j in range(i+1): + x_new += Xcoeff[idx] * x_orig**(i-j) * y_orig**j + y_new += Ycoeff[idx] * x_orig**(i-j) * y_orig**j + + idx += 1 + + return x_new, y_new + +def transform_poserr_from_file(Xcoeff, Ycoeff, order, xe_orig, ye_orig, x_orig, y_orig): + """ + Given the read-in coefficients from transform_from_file, apply the + transformation to the observed position errors. This is generalized to + work with any order transform. + + WARNING: THIS CODE WILL NOT WORK FOR LEGENDRE POLYNOMIAL + TRANSFORMS + + Parameters: + ---------- + Xcoeff: Array + Array with the coefficients of the X pos transformation + + Ycoeff: Array + Array with the coefficients of the Y pos transformation + + order: int + Order of transformation + + xe_orig: array + Array with the original X position errs + + ye_orig: array + Array with the original Y position errs + + x_orig: array + Array with the original X positions + + y_orig: array + Array with the original Y positions + + Output: + ------ + xe_new: array + Transformed X position errs + + ye_new: array + Transformed Y position errs + """ + idx = 0 # coeff index + xe_new_tmp1 = 0.0 + ye_new_tmp1 = 0.0 + xe_new_tmp2 = 0.0 + ye_new_tmp2 = 0.0 + + # First loop: dx'/dx + for i in range(order+1): + for j in range(i+1): + xe_new_tmp1 += Xcoeff[idx] * (i - j) * x_orig**(i-j-1) * y_orig**j + ye_new_tmp1 += Ycoeff[idx] * (i - j) * x_orig**(i-j-1) * y_orig**j + + idx += 1 + + # Second loop: dy'/dy + idx = 0 # coeff index + for i in range(order+1): + for j in range(i+1): + xe_new_tmp2 += Xcoeff[idx] * (j) * x_orig**(i-j) * y_orig**(j-1) + ye_new_tmp2 += Ycoeff[idx] * (j) * x_orig**(i-j) * y_orig**(j-1) + + idx += 1 + # Take square root for xe/ye_new + xe_new = np.sqrt((xe_new_tmp1 * xe_orig)**2 + (xe_new_tmp2 * ye_orig)**2) + ye_new = np.sqrt((ye_new_tmp1 * ye_orig)**2 + (ye_new_tmp2 * ye_orig)**2) + + return xe_new, ye_new + +def transform_vel_from_file(Xcoeff, Ycoeff, order, vx_orig, vy_orig, x_orig, y_orig): + """ + Given the read-in coefficients from transform_from_file, apply the + transformation to the observed proper motions. This is generalized to + work with any order transform. + + WARNING: THIS CODE WILL NOT WORK FOR LEGENDRE POLYNOMIAL + TRANSFORMS + + Parameters: + ---------- + Xcoeff: Array + Array with the coefficients of the X pos transformation + + Ycoeff: Array + Array with the coefficients of the Y pos transformation + + order: int + Order of transformation + + vx_orig: array + Array with the original X proper motions + + vy_orig: array + Array with the original Y proper motions + + x_orig: array + Array with the original X positions + + y_orig: array + Array with the original Y positions + + Output: + ------ + vx_new: array + Transformed X proper motions + + vy_new: array + Transformed Y proper motions + """ + idx = 0 # coeff index + vx_new = 0.0 + vy_new = 0.0 + # First loop: dx'/dx + for i in range(order+1): + for j in range(i+1): + vx_new += Xcoeff[idx] * (i - j) * x_orig**(i-j-1) * y_orig**j * vx_orig + vy_new += Ycoeff[idx] * (i - j) * x_orig**(i-j-1) * y_orig**j * vx_orig + + idx += 1 + # Second loop: dy'/dy + idx = 0 # coeff index + for i in range(order+1): + for j in range(i+1): + vx_new += Xcoeff[idx] * (j) * x_orig**(i-j) * y_orig**(j-1) * vy_orig + vy_new += Ycoeff[idx] * (j) * x_orig**(i-j) * y_orig**(j-1) * vy_orig + + idx += 1 + + return vx_new, vy_new + +def transform_velerr_from_file(Xcoeff, Ycoeff, order, vxe_orig, vye_orig, vx_orig, + vy_orig, xe_orig, ye_orig, x_orig, y_orig): + """ + Given the read-in coefficients from transform_from_file, apply the + transformation to the observed proper motion errors. This is generalized to + work with any order transform. + + WARNING: THIS CODE WILL NOT WORK FOR LEGENDRE POLYNOMIAL + TRANSFORMS + + Parameters: + ---------- + Xcoeff: Array + Array with the coefficients of the X pos transformation + + Ycoeff: Array + Array with the coefficients of the Y pos transformation + + order: int + Order of transformation + + vxe_orig: array + Array with the original X proper motion errs + + vye_orig: array + Array with the original Y proper motion errs + + vx_orig: array + Array with the original X proper motions + + vy_orig: array + Array with the original Y proper motions + + xe_orig: array + Array with the original X position errs + + ye_orig: array + Array with the original Y position errs + + x_orig: array + Array with the original X positions + + y_orig: array + Array with the original Y positions + + Output: + ------ + vxe_new: array + Transformed X proper motion errs + + vye_new: array + Transformed Y proper motion errs + """ + idx = 0 + vxe_new_tmp1 = 0.0 + vye_new_tmp1 = 0.0 + vxe_new_tmp2 = 0.0 + vye_new_tmp2 = 0.0 + vxe_new_tmp3 = 0.0 + vye_new_tmp3 = 0.0 + vxe_new_tmp4 = 0.0 + vye_new_tmp4 = 0.0 + + + # First loop: dvx' / dx + for i in range(order+1): + for j in range(i+1): + vxe_new_tmp1 += Xcoeff[idx] * (i-j) * (i-j-1) * x_orig**(i-j-2) * y_orig**j * vx_orig + vxe_new_tmp1 += Xcoeff[idx] * (j) * (i-j) * x_orig**(i-j-1) * y_orig**(j-1) * vy_orig + vye_new_tmp1 += Ycoeff[idx] * (i-j) * (i-j-1) * x_orig**(i-j-2) * y_orig**j * vx_orig + vye_new_tmp1 += Ycoeff[idx] * (j) * (i-j) * x_orig**(i-j-1) * y_orig**(j-1) * vy_orig + + idx += 1 + + # Second loop: dvx' / dy + idx = 0 + for i in range(order+1): + for j in range(i+1): + vxe_new_tmp2 += Xcoeff[idx] * (i-j) * (j) * x_orig**(i-j-1) * y_orig**(j-1) * vx_orig + vxe_new_tmp2 += Xcoeff[idx] * (j) * (j-1) * x_orig**(i-j-1) * y_orig**(j-2) * vy_orig + vye_new_tmp2 += Ycoeff[idx] * (i-j) * (j) * x_orig**(i-j-1) * y_orig**(j-1) * vx_orig + vye_new_tmp2 += Ycoeff[idx] * (j) * (j-1) * x_orig**(i-j-1) * y_orig**(j-2) * vy_orig + + idx += 1 + + # Third loop: dvx' / dvx + idx = 0 + for i in range(order+1): + for j in range(i+1): + vxe_new_tmp3 += Xcoeff[idx] * (i-j) * x_orig**(i-j-1) * y_orig**j + vye_new_tmp3 += Ycoeff[idx] * (i-j) * x_orig**(i-j-1) * y_orig**j + + idx += 1 + + # Fourth loop: dvx' / dvy + idx = 0 + for i in range(order+1): + for j in range(i+1): + vxe_new_tmp4 += Xcoeff[idx] * (j) * x_orig**(i-j) * y_orig**(j-1) + vye_new_tmp4 += Ycoeff[idx] * (j) * x_orig**(i-j) * y_orig**(j-1) + + idx += 1 + + vxe_new = np.sqrt((vxe_new_tmp1 * xe_orig)**2 + (vxe_new_tmp2 * ye_orig)**2 + \ + (vxe_new_tmp3 * vxe_orig)**2 + (vxe_new_tmp4 * vye_orig)**2) + vye_new = np.sqrt((vye_new_tmp1 * xe_orig)**2 + (vye_new_tmp2 * ye_orig)**2 + \ + (vye_new_tmp3 * vxe_orig)**2 + (vye_new_tmp4 * vye_orig)**2) + + return vxe_new, vye_new + + + + + + +""" +Old functions with things hard-coded for OB120169 """ def run_align_iter(catalog, trans_order=1, poly_deg=1, ref_mag_lim=19, ref_radius_lim=300): From dbdbde4e54a388945a05b91abdee4a38f1a7a376 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Mon, 2 Dec 2024 14:21:07 -0800 Subject: [PATCH 020/254] progress on parallax model, exception in transforms for complex motion --- flystar/align.py | 3 +- flystar/motion_model.py | 115 ++++++++++++-- flystar/parallax.py | 322 ++++++++++++++++++++++++++++++++++++++++ flystar/transforms.py | 23 ++- 4 files changed, 445 insertions(+), 18 deletions(-) create mode 100755 flystar/parallax.py diff --git a/flystar/align.py b/flystar/align.py index 1609719..4c39707 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -1996,7 +1996,6 @@ def transform_and_match(table1, table2, transform, dr_tol=1.0, dm_tol=None, verb y2 = table2['y'] m2 = table2['m'] - # Transform x, y coordinates from starlist 1 into starlist 2 x1t, y1t = transform.evaluate(x1, y1) @@ -2394,7 +2393,7 @@ def transform_from_object(starlist, transform): # Check to see if velocities or motion_model are present in starlist. vel = ('vx' in keys)and ~("motion_model_assigned" in keys) mot = ("motion_model_assigned" in keys) - # If the only motion models used are Fixed and Linear, we can still use the older, faster code. + # If the only motion models used are Fixed and Linear, we can still transform velocities. if mot: motion_models_unique = list(np.unique(starlist_f['motion_model_assigned'])) if 'Linear' in motion_models_unique: diff --git a/flystar/motion_model.py b/flystar/motion_model.py index 960af5d..f9da0cf 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -2,6 +2,9 @@ import numpy as np from abc import ABC import pdb +from flystar import parallax +from astropy.time import Time +from scipy.optimize import minimize class MotionModel(ABC): # Number of data points required to fit model @@ -52,17 +55,6 @@ def fit_motion_model(self, t, x, y, xe, ye, update=True): """ #return params, param_errors pass - - def get_chi2(self,dt,x,y,xe,ye): - """ - Get the chi^2 value for the current MM and - the input data. - """ - # TODO: confirm whether we want reduced chi^2 or anything special - maybe kwarg option - x_pred,y_pred = self.get_pos_at_time(dt) - chi2x = np.sum((x-x_pred)**2 / xe**2) - chi2y = np.sum((y-y_pred)**2 / ye**2) - return chi2x,chi2y class Fixed(MotionModel): """ @@ -98,7 +90,7 @@ def get_batch_pos_at_time(self,t, x0_err=[], y0_err=[]): return x0,y0,x0_err,y0_err - def fit_motion_model(self, dt, x, y, xe, ye, update=False, bootstrap=0): + def fit_motion_model(self, dt, x, y, xe, ye, update=True, bootstrap=0): # Handle single data point case if len(x)==1: return [x[0],y[0]],[xe[0],ye[0]] @@ -171,7 +163,7 @@ def get_batch_pos_at_time(self,t, y_err = np.hypot(y0_err, vy_err*dt) return x,y,x_err,y_err - def fit_motion_model(self, dt, x, y, xe, ye, update=False, bootstrap=0): + def fit_motion_model(self, dt, x, y, xe, ye, update=True, bootstrap=0): fitter = fitting.LevMarLSQFitter() # Handle 2-data point case @@ -313,7 +305,7 @@ def get_batch_pos_at_time(self,t, y_err = np.sqrt(y0_err**2 + (vy0_err*dt)**2 + (0.5*ay_err*dt**2)**2) return x,y,x_err,y_err - def fit_motion_model(self, dt, x, y, xe, ye, update=False, bootstrap=0): + def fit_motion_model(self, dt, x, y, xe, ye, update=True, bootstrap=0): fitter = fitting.LevMarLSQFitter() px_new = fitter(self.px, dt, x, weights=1/xe) @@ -394,6 +386,101 @@ def fit_motion_model(self, dt, x, y, xe, ye, update=False, bootstrap=0): return params, param_errors +class Parallax(MotionModel): + """ + Motion model for linear proper motion + parallax + """ + n_pts_req = 5 + # TODO: did we count dofs properly in previous ones? (incl. x0 and y0) + dof=5 + fitter_param_names = ['x0', 'vx', 'y0', 'vy', 'pi'] + fixed_param_names = ['t0', 'RA','Dec','obs'] + + def __init__(self, x0=0, vx=0, y0=0, vy=0, t0=2025.0, + x0_err=0, vx_err=0, y0_err=0, vy_err=0, + pi=0, pi_err=0, + RA=None, Dec=None, obs='earth'): + self.x0 = x0 + self.vx = vx + self.y0 = y0 + self.vy = vy + self.t0 = t0 + self.x0_err = x0_err + self.vx_err = vx_err + self.y0_err = y0_err + self.vy_err = vy_err + self.pi = pi + self.pi_err = pi_err + self.RA = RA + self.Dec = Dec + self.obs = obs + return + + def get_pos_at_time(self, t): + t_mjd = Time(t, format='decimalyear', scale='utc').mjd + pvec = parallax.parallax_in_direction(self.RA, self.Dec, t_mjd, obsLocation=self.obs).T + # TODO: need to confirm x-e orientation + x = self.x0 + self.vx*(t-self.t0) + self.pi*pvec[0] + y = self.y0 + self.vy*(t-self.t0) + self.pi*pvec[1] + return x, y + + def get_pos_err_at_time(self, t): + t_mjd = Time(t, format='decimalyear', scale='utc').mjd + pvec = parallax.parallax_in_direction(self.RA, self.Dec, t_mjd, obsLocation=self.obs).T + x_err = np.sqrt(self.y0_err**2 + ((t-self.t0)*self.vx_err)**2 + (self.pi_err*pvec[0])**2) + y_err = np.sqrt(self.x0_err**2 + ((t-self.t0)*self.vy_err)**2 + (self.pi_err*pvec[1])**2) + return x_err, y_err + + def get_batch_pos_at_time(self, t): + #return x, y, x_err, y_err + pass + + def fit_motion_model(self, t, x, y, xe, ye, update=True,method='Nelder-Mead'): + t_mjd = Time(t, format='decimalyear', scale='utc').mjd + pvec = parallax.parallax_in_direction(self.RA, self.Dec, t_mjd, obsLocation=self.obs).T + def fit_func(params): + x0,y0, vx,vy, pi = params + x_res = x0 + vx*(t-self.t0) + pi*pvec[0] + y_res = y0 + vy*(t-self.t0) + pi*pvec[1] + chi2 = np.sum((x-x_res)**2/xe**2 + (y-y_res)**2/ye**2) + return chi2 + # Initial guesses, x0,y0 as x,y averages; + # vx,vy as average velocity if first and last points are perfectly measured; + # pi for 10 pc disance + res = minimize(fit_func, x0=[np.mean(x),np.mean(y), (x[-1]-x[0])/(t[-1]-t[0]),(y[-1]-y[0])/(t[-1]-t[0]), 1], + method = method) + print(res) + if res.success: + x0,y0,vx,vy,pi = res.x + if update: + self.x0 = x0 + self.y0=y0 + self.vx=vx + self.vy=vy + self.pi=pi + return res + else: + print('failed') + if update: + self.x0 = 0 + self.y0=0 + self.vx=0 + self.vy=0 + self.pi=0 + + def get_chi2(self,dt,x,y,xe,ye): + """ + Get the chi^2 value for the current MM and + the input data. + """ + # TODO: confirm whether we want reduced chi^2 or anything special - maybe kwarg option + x_pred,y_pred = self.get_pos_at_time(dt) + chi2x = np.sum((x-x_pred)**2 / xe**2) + chi2y = np.sum((y-y_pred)**2 / ye**2) + return chi2x,chi2y + + + """ Get all the motion model parameters for a given motion_model_name. Optionally, include fixed and error parameters (included by default). diff --git a/flystar/parallax.py b/flystar/parallax.py new file mode 100755 index 0000000..fff5026 --- /dev/null +++ b/flystar/parallax.py @@ -0,0 +1,322 @@ +import math + +import numpy as np +from joblib import Memory +import os +from astropy import units, units as u +from astropy.coordinates import SkyCoord, get_body_barycentric, get_body_barycentric_posvel, solar_system_ephemeris, \ + CartesianRepresentation +from astropy.time import Time + +# Use the JPL ephemerides. +solar_system_ephemeris.set('jpl') + +# Setup a parallax cache +try: + cache_dir = os.environ['PARALLAX_CACHE_DIR'] +except: + cache_dir = os.path.dirname(__file__) + '/parallax_cache/' +cache_memory = Memory(cache_dir, verbose=0, bytes_limit='1G') +# Default cache size is 1 GB +cache_memory.reduce_size() + +@cache_memory.cache() +def parallax_in_direction(RA, Dec, mjd, obsLocation='earth'): + """ + | R.A. in degrees. (J2000) + | Dec. in degrees. (J2000) + | MJD + + Equations following MulensModel. + """ + #print('parallax_in_direction: len(t) = ', len(mjd)) + + # Munge inputs into astropy format. + times = Time(mjd + 2400000.5, format='jd', scale='tdb') + coord = SkyCoord(RA, Dec, unit=(units.deg, units.deg)) + + direction = coord.cartesian.xyz.value + north = np.array([0., 0., 1.]) + _east_projected = np.cross(north, direction) / np.linalg.norm(np.cross(north, direction)) + _north_projected = np.cross(direction, _east_projected) / np.linalg.norm(np.cross(direction, _east_projected)) + + obs_pos = get_observer_barycentric(obsLocation, times) + sun_pos = get_body_barycentric(body='sun', time=times) + + sun_obs_pos = sun_pos - obs_pos + + pos = sun_obs_pos.xyz.T.to(units.au) + + e = np.dot(pos, _east_projected) + n = np.dot(pos, _north_projected) + + pvec = np.array([e.value, n.value]).T + + return pvec + + +def dparallax_dt_in_direction(RA, Dec, mjd, obsLocation='earth'): + """ + R.A. in degrees. (J2000) + Dec. in degrees. (J2000) + MJD + + Equations following MulensModel. + Time derivative --> units are yr^-1 + + """ + # print('parallax_in_direction: len(t) = ', len(mjd)) + # Munge inputs into astropy format. + times = Time(mjd + 2400000.5, format='jd', scale='tdb') + coord = SkyCoord(RA, Dec, unit=(units.deg, units.deg)) + + direction = coord.cartesian.xyz.value + north = np.array([0., 0., 1.]) + _east_projected = np.cross(north, direction) / np.linalg.norm(np.cross(north, direction)) + _north_projected = np.cross(direction, _east_projected) / np.linalg.norm(np.cross(direction, _east_projected)) + + obs_posvel = get_observer_barycentric(obsLocation, times, velocity=True)[1] + sun_posvel = get_body_barycentric_posvel('Sun', times)[1] + sun_obs_vel = sun_posvel - obs_posvel + vel = sun_obs_vel.xyz.T.to(units.au / units.year) + + e = np.dot(vel, _east_projected) + n = np.dot(vel, _north_projected) + + dpvec_dt = np.array([e.value, n.value]).T + + return dpvec_dt + + +def get_observer_barycentric(body, times, min_ephem_step=1, velocity=False): + """ + Get the barycentric position of a satellite or other Solar System body + using JPL emphemerides through the Horizon app. + + The ephemeris is queried at a decimated time step set by min_ephem_step + (def=1 day) that must be 1 day or larger. The positions + (and optionally velocities) are then interpolated onto the desired + time array. + + Inputs + ------ + body : str + The name of the Solar System body. Must use the JPL Horizon + naming scheme. + + times : astropy.time.Time array + Array of times (astropy.time.core.Time) objects at which to + fetch the position of the specified Solar System body. + + Optional Inputs + --------------- + min_ephem_step : int + Minimum time step to query JPL in days. Must not be <1 and must + be in integer days. + + veloctiy : bool + If true, return both position and velocity vectors over time. + + Return + ------ + coord : astropy.coordinates.CartesianRepresentation + The xyz coordinates in the plane of the Solar System at the + input times. + """ + + if body in solar_system_ephemeris.bodies: + if velocity: + obs_pos, obs_vel = get_body_barycentric_posvel(body=body, time=times) + else: + obs_pos = get_body_barycentric(body=body, time=times) + else: + # Figure out a cadence for the ephemerides, not smaller than 1 day. + dt = np.median(np.diff(times)).jd + if dt < min_ephem_step: + dt = min_ephem_step + + # Get the date range, add some padding on each side. + t_min = times.min() + t_max = times.max() + t_min.format = 'iso' + t_max.format = 'iso' + t_min = str(t_min - dt*u.day).split()[0] + t_max = str(t_max + dt*u.day).split()[0] + step = f'{dt:.0f}d' + + # Fetch the Horizons ephemeris. + from astroquery.jplhorizons import Horizons + obj = Horizons(id=body, epochs={'start':t_min, 'stop':t_max, 'step':step}) + obj_data = obj.vectors() + + ephem_jd = obj_data['datetime_jd'] + + # Interpolate to the actual time array. + obj_x_at_t = np.interp(times.jd, ephem_jd, obj_data['x'].to('km')) * u.km + obj_y_at_t = np.interp(times.jd, ephem_jd, obj_data['y'].to('km')) * u.km + obj_z_at_t = np.interp(times.jd, ephem_jd, obj_data['z'].to('km')) * u.km + + if velocity: + obj_vx_at_t = np.interp(times.jd, ephem_jd, obj_data['vx'].to('km/s')) * u.km / u.s + obj_vy_at_t = np.interp(times.jd, ephem_jd, obj_data['vy'].to('km/s')) * u.km / u.s + obj_vz_at_t = np.interp(times.jd, ephem_jd, obj_data['vz'].to('km/s')) * u.km / u.s + + obs_vel = CartesianRepresentation(obj_vx_at_t, obj_vy_at_t, obj_vz_at_t) + + obs_pos = CartesianRepresentation(obj_x_at_t, obj_y_at_t, obj_z_at_t) + + if velocity: + return (obs_pos, obs_vel) + else: + return obs_pos + + +def sun_position(mjd, radians=False): + """ + + NAME: + SUNPOS + + PURPOSE: + To compute the RA and Dec of the Sun at a given date. + + INPUTS: + mjd - The modified Julian date of the day (and time), scalar or vector + + OUTPUTS: + ra: + | The right ascension of the sun at that date in DEGREES + | double precision, same number of elements as jd + dec: + The declination of the sun at that date in DEGREES + elong: + Ecliptic longitude of the sun at that date in DEGREES. + obliquity: + the obliquity of the ecliptic, in DEGREES + + OPTIONAL INPUT KEYWORD: + RADIAN [def=False] - If this keyword is set to True, then all output variables + are given in Radians rather than Degrees + + NOTES: + Patrick Wallace (Rutherford Appleton Laboratory, UK) has tested the + accuracy of a C adaptation of the sunpos.pro code and found the + following results. From 1900-2100 SUNPOS gave 7.3 arcsec maximum + error, 2.6 arcsec RMS. Over the shorter interval 1950-2050 the figures + were 6.4 arcsec max, 2.2 arcsec RMS. + + The returned RA and Dec are in the given date's equinox. + + Procedure was extensively revised in May 1996, and the new calling + sequence is incompatible with the old one. + METHOD: + Uses a truncated version of Newcomb's Sun. Adapted from the IDL + routine SUN_POS by CD Pike, which was adapted from a FORTRAN routine + by B. Emerson (RGO). + EXAMPLE: + (1) Find the apparent RA and Dec of the Sun on May 1, 1982 + + | IDL> jdcnv, 1982, 5, 1,0 ,jd ;Find Julian date jd = 2445090.5 + | IDL> sunpos, jd, ra, dec + | IDL> print,adstring(ra,dec,2) + | 02 31 32.61 +14 54 34.9 + + The Astronomical Almanac gives 02 31 32.58 +14 54 34.9 so the error + in SUNPOS for this case is < 0.5". + + (2) Find the apparent RA and Dec of the Sun for every day in 1997 + + | IDL> jdcnv, 1997,1,1,0, jd ;Julian date on Jan 1, 1997 + | IDL> sunpos, jd+ dindgen(365), ra, dec ;RA and Dec for each day + + MODIFICATION HISTORY: + + * Written by Michael R. Greason, STX, 28 October 1988. + * Accept vector arguments, W. Landsman - April,1989 + * Eliminated negative right ascensions - MRG, Hughes STX, 6 May 1992. + * Rewritten using the 1993 Almanac. Keywords added. MRG, HSTX, 10 February 1994. + * Major rewrite, improved accuracy, always return values in degrees - W. Landsman May, 1996 + * Added /RADIAN keyword; W. Landsman; August, 1997 + * Converted to IDL V5.0; W. Landsman; September 1997 + * Converted to python; J. R. Lu; August 2016 + """ + # form time in Julian centuries from 1900.0 + t_obj = Time(mjd, format='mjd') + t = (t_obj.jd - 2415020.0) / 36525.0 + + # form sun's mean longitude + l = (279.696678 + ((36000.768925 * t) % 360.0)) * 3600.0 + + # allow for ellipticity of the orbit (equation of centre) + # using the Earth's mean anomaly ME + me = 358.475844 + ((35999.049750 * t) % 360.0) + ellcor = (6910.1 - 17.2 * t) * np.sin(np.radians(me)) + 72.3 * np.sin( + np.radians(2.0 * me)) + l = l + ellcor + + # allow for the Venus perturbations using the mean anomaly of Venus MV + mv = 212.603219 + ((58517.803875 * t) % 360.0) + vencorr = 4.8 * np.cos(np.radians(299.1017 + mv - me)) + \ + 5.5 * np.cos(np.radians(148.3133 + 2.0 * mv - 2.0 * me)) + \ + 2.5 * np.cos(np.radians(315.9433 + 2.0 * mv - 3.0 * me)) + \ + 1.6 * np.cos(np.radians(345.2533 + 3.0 * mv - 4.0 * me)) + \ + 1.0 * np.cos(np.radians(318.1500 + 3.0 * mv - 5.0 * me)) + l += vencorr + + # Allow for the Mars perturbations using the mean anomaly of Mars MM + mm = 319.529425 + ((19139.858500 * t) % 360.0) + marscorr = 2.0 * np.cos(np.radians(343.8883 - 2.0 * mm + 2.0 * me)) + \ + 1.8 * np.cos(np.radians(200.4017 - 2.0 * mm + me)) + l += marscorr + + # Allow for the Jupiter perturbations using the mean anomaly of Jupiter MJ + mj = 225.328328 + ((3034.6920239 * t) % 360.0) + jupcorr = 7.2 * np.cos(np.radians(179.5317 - mj + me)) + \ + 2.6 * np.cos(np.radians(263.2167 - mj)) + \ + 2.7 * np.cos(np.radians(87.1450 - 2.0 * mj + 2.0 * me)) + \ + 1.6 * np.cos(np.radians(109.4933 - 2.0 * mj + me)) + l += jupcorr + + # Allow for the Moons perturbations using the mean elongation of + # the Moon from the Sun D + d = 350.7376814 + ((445267.11422 * t) % 360.0) + mooncorr = 6.5 * np.sin(np.radians(d)) + l += mooncorr + + # Allow for long period terms + longterm = + 6.4 * np.sin(np.radians(231.19 + 20.20 * t)) + l += longterm + l = (l + 2592000.0) % 1296000.0 + longmed = l / 3600.0 + + # Allow for Aberration + l -= 20.5 + + # Allow for Nutation using the longitude of the Moons mean node OMEGA + omega = 259.183275 - ((1934.142008 * t) % 360.0) + l -= 17.2 * np.sin(np.radians(omega)) + + # Form the True Obliquity + oblt = 23.452294 - 0.0130125 * t + ( + 9.2 * np.cos(np.radians(omega))) / 3600.0 + + # Form Right Ascension and Declination + l = l / 3600.0 + l_rad = np.radians(l) + oblt_rad = np.radians(oblt) + ra = np.arctan2(np.sin(l_rad) * np.cos(oblt_rad), np.cos(l_rad)) + + if (len(ra) > 1): + neg = np.where(ra < 0.0)[0] + ra[neg] = ra[neg] + 2.0 * math.pi + + dec = np.arcsin(np.sin(l_rad) * np.sin(oblt_rad)) + + if radians: + oblt = oblt_rad + longmed = np.radians(longmed) + else: + ra = np.degrees(ra) + dec = np.degrees(dec) + + return ra, dec, longmed, oblt diff --git a/flystar/transforms.py b/flystar/transforms.py index 7de327e..1ed7cd0 100755 --- a/flystar/transforms.py +++ b/flystar/transforms.py @@ -6,6 +6,7 @@ import collections import re import pdb +from flystar import motion_model class Transform2D(object): ''' @@ -112,8 +113,26 @@ def evaluate_starlist(self, star_list): new_list['xe'] = vals[0] new_list['ye'] = vals[1] - # Velocities (if they exist) - if 'vx' in new_list.colnames: + # Velocities (if they exist and no more complex motion model used) + complex_motion_model = ('motion_model_assigned' in new_list.colnames) + if complex_motion_model: + # If the only motion models used are Fixed and Linear, we can still transform velocities. + motion_models_unique = list(np.unique(starlist_f['motion_model_assigned'])) + if 'Linear' in motion_models_unique: + motion_models_unique.remove('Linear') + if 'Fixed' in motion_models_unique: + motion_models_unique.remove('Fixed') + if len(motion_models_unique)==0: + complex_motion_model=False + # Cannot transform more complex motion models - set values to nan + if complex_motion_model: + motion_params = motion_model.get_list_motion_model_param_names(new_list['motion_model_assigned'], with_errors=True, with_fixed=False) + for param in motion_params: + if param in new_list.colnames: + new_list[param] = np.nan + + if ('vx' in new_list.colnames) and (not complex_motion_model): + # For velocity only, no problem vals = self.evaluate_vel(star_list['x'], star_list['y'], star_list['vx'], star_list['vy']) new_list['vx'] = vals[0] From 34c2dcc48296c6d52f6f46e516a7e33ae093e3c3 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Mon, 2 Dec 2024 14:31:00 -0800 Subject: [PATCH 021/254] progress on parallax model, exception in transforms for complex motion --- flystar/align.py | 93 +++++++++++++++++++++--------------------------- 1 file changed, 40 insertions(+), 53 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 4c39707..6ac1dfa 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -2406,65 +2406,52 @@ def transform_from_object(starlist, transform): # Prior code before motion_model implementation # Can still be used as shortcut for Linear+Fixed motion_model only + err = 'xe' in keys + + # Extract needed information from starlist + x = starlist_f['x'] + y = starlist_f['y'] + + if err: + xe = starlist_f['xe'] + ye = starlist_f['ye'] + if vel: - err = 'xe' in keys + x0 = starlist_f['x0'] + y0 = starlist_f['y0'] + x0e = starlist_f['x0_err'] + y0e = starlist_f['y0_err'] + vx = starlist_f['vx'] + vy = starlist_f['vy'] + vxe = starlist_f['vx_err'] + vye = starlist_f['vy_err'] - # Extract needed information from starlist - x = starlist_f['x'] - y = starlist_f['y'] - - if err: - xe = starlist_f['xe'] - ye = starlist_f['ye'] - - if vel: - x0 = starlist_f['x0'] - y0 = starlist_f['y0'] - x0e = starlist_f['x0_err'] - y0e = starlist_f['y0_err'] - vx = starlist_f['vx'] - vy = starlist_f['vy'] - vxe = starlist_f['vx_err'] - vye = starlist_f['vy_err'] - - # calculate the transformed position and velocity - - # (x_new, y_new, xe_new, ye_new) in (x,y) - x_new, y_new, xe_new, ye_new = position_transform_from_object(x, y, xe, ye, transform) + # calculate the transformed position and velocity + x_new, y_new, xe_new, ye_new = position_transform_from_object(x, y, xe, ye, transform) - - if vel: - # (x0_new, y0_new, x0e_new, y0e_new) in (x0, y0, x0e, y0e) - x0_new, y0_new, x0e_new, y0e_new = position_transform_from_object(x0, y0, x0e, y0e, transform) - # (vx_new, vy_new, vxe_new, vye_new) in (x0, y0, x0e, y0e, vx, vy, vxe, vye) - vx_new, vy_new, vxe_new, vye_new = velocity_transform_from_object(x0, y0, x0e, y0e, vx, vy, vxe, vye, transform) - - # update transformed coords to copy of astropy table - starlist_f['x'] = x_new - starlist_f['y'] = y_new - starlist_f['xe'] = xe_new - starlist_f['ye'] = ye_new - - if vel: - starlist_f['x0'] = x0_new - starlist_f['y0'] = y0_new - starlist_f['x0_err'] = x0e_new - starlist_f['y0_err'] = y0e_new - starlist_f['vx'] = vx_new - starlist_f['vy'] = vy_new - starlist_f['vx_err'] = vxe_new - starlist_f['vy_err'] = vye_new + if vel: + x0_new, y0_new, x0e_new, y0e_new = position_transform_from_object(x0, y0, x0e, y0e, transform) + vx_new, vy_new, vxe_new, vye_new = velocity_transform_from_object(x0, y0, x0e, y0e, vx, vy, vxe, vye, transform) + + # update transformed coords to copy of astropy table + starlist_f['x'] = x_new + starlist_f['y'] = y_new + starlist_f['xe'] = xe_new + starlist_f['ye'] = ye_new + + if vel: + starlist_f['x0'] = x0_new + starlist_f['y0'] = y0_new + starlist_f['x0_err'] = x0e_new + starlist_f['y0_err'] = y0e_new + starlist_f['vx'] = vx_new + starlist_f['vy'] = vy_new + starlist_f['vx_err'] = vxe_new + starlist_f['vy_err'] = vye_new # For more complicated motion_models, - # We can't easily transform them, set the values to nans and refit later + # we can't easily transform them, set the values to nans and refit later. if mot: - # Transform positions - x_new, y_new, xe_new, ye_new = position_transform_from_object(x, y, xe, ye, transform) - starlist_f['x'] = x_new - starlist_f['y'] = y_new - starlist_f['xe'] = xe_new - starlist_f['ye'] = ye_new - motion_model_params = motion_model.get_all_motion_model_param_names() for param in motion_model_params: if param in keys: From 58ae5ab850878d1280d4403847f806b0ef403fc6 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Mon, 2 Dec 2024 17:30:04 -0800 Subject: [PATCH 022/254] parallax fitter working --- flystar/align.py | 4 +--- flystar/motion_model.py | 48 ++++++++++++++++++++--------------------- 2 files changed, 24 insertions(+), 28 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 6ac1dfa..ab0fd06 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -2511,10 +2511,9 @@ def position_transform_from_object(x, y, xe, ye, transform): for j in range(1, N+2-i): sub = int(2*N + 2 + j + (2*N+2-i) * (i-1)/2.) y_new += Ycoeff[sub] * (x**i) * (y**j) - """ - THIS IS WRONG BELOW! + THIS IS WRONG BELOW! - NOTE: I don't think this is wrong any more Currently doing: ((A + B + C) * xe)**2 @@ -2659,7 +2658,6 @@ def velocity_transform_from_object(x0, y0, x0e, y0e, vx, vy, vxe, vye, transform vxe_new = np.sqrt((temp1*x0e)**2 + (temp2*y0e)**2 + (temp3*vxe)**2 + (temp4*vye)**2) - vye_new = 0 temp1 = 0 temp2 = 0 diff --git a/flystar/motion_model.py b/flystar/motion_model.py index f9da0cf..18436a4 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -4,7 +4,7 @@ import pdb from flystar import parallax from astropy.time import Time -from scipy.optimize import minimize +from scipy.optimize import minimize,curve_fit class MotionModel(ABC): # Number of data points required to fit model @@ -438,35 +438,33 @@ def get_batch_pos_at_time(self, t): def fit_motion_model(self, t, x, y, xe, ye, update=True,method='Nelder-Mead'): t_mjd = Time(t, format='decimalyear', scale='utc').mjd pvec = parallax.parallax_in_direction(self.RA, self.Dec, t_mjd, obsLocation=self.obs).T - def fit_func(params): - x0,y0, vx,vy, pi = params + def fit_func(t, x0,y0, vx,vy, pi): x_res = x0 + vx*(t-self.t0) + pi*pvec[0] y_res = y0 + vy*(t-self.t0) + pi*pvec[1] - chi2 = np.sum((x-x_res)**2/xe**2 + (y-y_res)**2/ye**2) - return chi2 + diff = (x-x_res)**2/xe**2 + (y-y_res)**2/ye**2 + return diff # Initial guesses, x0,y0 as x,y averages; # vx,vy as average velocity if first and last points are perfectly measured; # pi for 10 pc disance - res = minimize(fit_func, x0=[np.mean(x),np.mean(y), (x[-1]-x[0])/(t[-1]-t[0]),(y[-1]-y[0])/(t[-1]-t[0]), 1], - method = method) - print(res) - if res.success: - x0,y0,vx,vy,pi = res.x - if update: - self.x0 = x0 - self.y0=y0 - self.vx=vx - self.vy=vy - self.pi=pi - return res - else: - print('failed') - if update: - self.x0 = 0 - self.y0=0 - self.vx=0 - self.vy=0 - self.pi=0 + res = curve_fit(fit_func, t, np.zeros(len(t)), + p0=[np.mean(x),np.mean(y), (x[-1]-x[0])/(t[-1]-t[0]),(y[-1]-y[0])/(t[-1]-t[0]), 1]) + x0,y0,vx,vy,pi = res[0] + x0_err,y0_err,vx_err,vy_err,pi_err = np.sqrt(np.diag(res[1])) + if update: + self.x0 = x0 + self.y0=y0 + self.vx=vx + self.vy=vy + self.pi=pi + self.x0_err=x0_err + self.y0_err=y0_err + self.vx_err=vx_err + self.vy_err=vy_err + self.pi_err=pi_err + params = [x0, vx, y0, vy, pi] + param_errors = [x0_err, vx_err, y0_err, vy_err, pi_err] + return params, param_errors + def get_chi2(self,dt,x,y,xe,ye): """ From db08bdc69b8c4f0cad87901b349c965cc3c43aff Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Tue, 3 Dec 2024 17:16:14 -0800 Subject: [PATCH 023/254] check in parallax progress, PA working --- flystar/motion_model.py | 42 +++++++++++++++++++++++++---------------- flystar/parallax.py | 14 ++++++++++---- 2 files changed, 36 insertions(+), 20 deletions(-) diff --git a/flystar/motion_model.py b/flystar/motion_model.py index 18436a4..0d097ff 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -4,7 +4,7 @@ import pdb from flystar import parallax from astropy.time import Time -from scipy.optimize import minimize,curve_fit +from scipy.optimize import curve_fit class MotionModel(ABC): # Number of data points required to fit model @@ -389,17 +389,21 @@ def fit_motion_model(self, dt, x, y, xe, ye, update=True, bootstrap=0): class Parallax(MotionModel): """ Motion model for linear proper motion + parallax + + Requires RA, Dec, and PA parameters (degrees) for parallax calculation. + RA, Dec in J2000 + PA is counterclockwise offset between North and the image y-axis. + Optional obs parameter describing observer location, default is 'earth'. """ - n_pts_req = 5 - # TODO: did we count dofs properly in previous ones? (incl. x0 and y0) - dof=5 + n_pts_req = 4 + dof=3 fitter_param_names = ['x0', 'vx', 'y0', 'vy', 'pi'] - fixed_param_names = ['t0', 'RA','Dec','obs'] + fixed_param_names = ['t0', 'RA','Dec','PA','obs'] def __init__(self, x0=0, vx=0, y0=0, vy=0, t0=2025.0, x0_err=0, vx_err=0, y0_err=0, vy_err=0, pi=0, pi_err=0, - RA=None, Dec=None, obs='earth'): + RA=None, Dec=None, PA=None, obs='earth'): self.x0 = x0 self.vx = vx self.y0 = y0 @@ -413,12 +417,13 @@ def __init__(self, x0=0, vx=0, y0=0, vy=0, t0=2025.0, self.pi_err = pi_err self.RA = RA self.Dec = Dec + self.PA = PA self.obs = obs return def get_pos_at_time(self, t): t_mjd = Time(t, format='decimalyear', scale='utc').mjd - pvec = parallax.parallax_in_direction(self.RA, self.Dec, t_mjd, obsLocation=self.obs).T + pvec = parallax.parallax_in_direction(self.RA, self.Dec, t_mjd, obsLocation=self.obs, PA=self.PA).T # TODO: need to confirm x-e orientation x = self.x0 + self.vx*(t-self.t0) + self.pi*pvec[0] y = self.y0 + self.vy*(t-self.t0) + self.pi*pvec[1] @@ -426,18 +431,26 @@ def get_pos_at_time(self, t): def get_pos_err_at_time(self, t): t_mjd = Time(t, format='decimalyear', scale='utc').mjd - pvec = parallax.parallax_in_direction(self.RA, self.Dec, t_mjd, obsLocation=self.obs).T + pvec = parallax.parallax_in_direction(self.RA, self.Dec, t_mjd, obsLocation=self.obs, PA=self.PA).T x_err = np.sqrt(self.y0_err**2 + ((t-self.t0)*self.vx_err)**2 + (self.pi_err*pvec[0])**2) y_err = np.sqrt(self.x0_err**2 + ((t-self.t0)*self.vy_err)**2 + (self.pi_err*pvec[1])**2) return x_err, y_err - def get_batch_pos_at_time(self, t): - #return x, y, x_err, y_err - pass + def get_batch_pos_at_time(self, t, + x0=[],vx=[], y0=[],vy=[], pi=[], t0=[], + x0_err=[],vx_err=[], y0_err=[],vy_err=[], pi_err=[]): + t_mjd = Time(t, format='decimalyear', scale='utc').mjd + pvec = parallax.parallax_in_direction(self.RA, self.Dec, t_mjd, obsLocation=self.obs, PA=self.PA).T + dt = t-t0 + x = x0 + dt*vx + pi*pvec[0] + y = y0 + dt*vy + pi*pvec[1] + x_err = np.sqrt(x0_err**2 + (vx_err*dt)**2 + (pi_err*pvec[0])**2) + y_err = np.sqrt(y0_err**2 + (vy_err*dt)**2 + (pi_err*pvec[1])**2) + return x,y,x_err,y_err - def fit_motion_model(self, t, x, y, xe, ye, update=True,method='Nelder-Mead'): + def fit_motion_model(self, t, x, y, xe, ye, update=True): t_mjd = Time(t, format='decimalyear', scale='utc').mjd - pvec = parallax.parallax_in_direction(self.RA, self.Dec, t_mjd, obsLocation=self.obs).T + pvec = parallax.parallax_in_direction(self.RA, self.Dec, t_mjd, obsLocation=self.obs, PA=self.PA).T def fit_func(t, x0,y0, vx,vy, pi): x_res = x0 + vx*(t-self.t0) + pi*pvec[0] y_res = y0 + vy*(t-self.t0) + pi*pvec[1] @@ -464,7 +477,6 @@ def fit_func(t, x0,y0, vx,vy, pi): params = [x0, vx, y0, vy, pi] param_errors = [x0_err, vx_err, y0_err, vy_err, pi_err] return params, param_errors - def get_chi2(self,dt,x,y,xe,ye): """ @@ -477,8 +489,6 @@ def get_chi2(self,dt,x,y,xe,ye): chi2y = np.sum((y-y_pred)**2 / ye**2) return chi2x,chi2y - - """ Get all the motion model parameters for a given motion_model_name. Optionally, include fixed and error parameters (included by default). diff --git a/flystar/parallax.py b/flystar/parallax.py index fff5026..88360cc 100755 --- a/flystar/parallax.py +++ b/flystar/parallax.py @@ -21,11 +21,12 @@ cache_memory.reduce_size() @cache_memory.cache() -def parallax_in_direction(RA, Dec, mjd, obsLocation='earth'): +def parallax_in_direction(RA, Dec, mjd, obsLocation='earth', PA=0): """ | R.A. in degrees. (J2000) | Dec. in degrees. (J2000) | MJD + | PA in degrees. (counterclockwise misalignment of North and image y-axis) Equations following MulensModel. """ @@ -49,9 +50,14 @@ def parallax_in_direction(RA, Dec, mjd, obsLocation='earth'): e = np.dot(pos, _east_projected) n = np.dot(pos, _north_projected) - - pvec = np.array([e.value, n.value]).T - + + # Rotate frame e,n->x,y accounting for PA + PA_rad = np.pi/180.0 * PA + x = -e.value*np.cos(PA_rad) + n.value*np.sin(PA_rad) + y = e.value*np.sin(PA_rad) + n.value*np.cos(PA_rad) + + pvec = np.array([x, y]).T + return pvec From cdc58964c136fe178059adef53d6d41195f538c1 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Wed, 4 Dec 2024 15:00:17 -0800 Subject: [PATCH 024/254] fix documentation error --- flystar/motion_model.py | 81 ++++++++++++++++++++++++----------------- flystar/parallax.py | 2 +- flystar/startables.py | 4 -- 3 files changed, 48 insertions(+), 39 deletions(-) diff --git a/flystar/motion_model.py b/flystar/motion_model.py index 0d097ff..329e234 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -55,6 +55,17 @@ def fit_motion_model(self, t, x, y, xe, ye, update=True): """ #return params, param_errors pass + + def get_chi2(self,t,x,y,xe,ye): + """ + Get the chi^2 value for the current MM and + the input data. + """ + # TODO: confirm whether we want reduced chi^2 or anything special - maybe kwarg option + x_pred,y_pred = self.get_pos_at_time(t) + chi2x = np.sum((x-x_pred)**2 / xe**2) + chi2y = np.sum((y-y_pred)**2 / ye**2) + return chi2x,chi2y class Fixed(MotionModel): """ @@ -80,26 +91,35 @@ def __init__(self, x0=0, y0=0, t0=2025.0, return def get_pos_at_time(self,t): - return self.x0, self.y0 + if hasattr(t, "__len__"): + return np.repeat(self.x0, len(t)), np.repeat(self.y0, len(t)) + else: + return self.x0, self.y0 def get_pos_err_at_time(self,t): - return self.x0_err, self.y0_err + if hasattr(t, "__len__"): + return np.repeat(self.x0_err, len(t)), np.repeat(self.y0_err, len(t)) + else: + return self.x0_err, self.y0_err def get_batch_pos_at_time(self,t, x0=[],y0=[],t0=[], x0_err=[], y0_err=[]): - return x0,y0,x0_err,y0_err + if hasattr(t, "__len__"): + return np.repeat(x0[:,np.newaxis],len(t),axis=1), np.repeat(y0[:,np.newaxis],len(t),axis=1), np.repeat(x0_err[:,np.newaxis],len(t),axis=1), np.repeat(y0_err[:,np.newaxis],len(t),axis=1) + else: + return x0,y0,x0_err,y0_err - def fit_motion_model(self, dt, x, y, xe, ye, update=True, bootstrap=0): + def fit_motion_model(self, t, x, y, xe, ye, update=True, bootstrap=0): # Handle single data point case if len(x)==1: return [x[0],y[0]],[xe[0],ye[0]] #TODO: it seems like sometimes it's weighted by std and sometimes by var - confirm which to do here x0 = np.average(x, weights=1/xe**2) - x0e = np.sqrt(np.average((x-x0)**2,weights=1/xe)) + x0e = np.sqrt(np.average((x-x0)**2,weights=1/xe**2)) y0 = np.average(y, weights=1/ye**2) - y0e = np.sqrt(np.average((y-y0)**2,weights=1/ye)) + y0e = np.sqrt(np.average((y-y0)**2,weights=1/ye**2)) params = [x0, y0] param_errors = [x0e, y0e] @@ -142,25 +162,30 @@ def __init__(self, x0=0, vx=0, y0=0, vy=0, t0=2025.0, self.py = models.Polynomial1D(self.poly_order, c0=self.y0, c1=self.vy) return - - def get_pos_at_time(self, dt): - x = self.px(dt) - y = self.py(dt) - return x, y - def get_pos_err_at_time(self, dt): - x_err = np.hypot(self.x0_err, self.vx_err*dt) - y_err = np.hypot(self.y0_err, self.vy_err*dt) - return x_err, y_err + def get_pos_at_time(self, t): + dt = t-self.t0 + return self.x0 + self.vx*dt, self.y0 + self.vy*dt - def get_batch_pos_at_time(self,t, + def get_pos_err_at_time(self, t): + dt = t-self.t0 + return np.hypot(self.x0_err, self.vx_err*dt), np.hypot(self.y0_err, self.vy_err*dt) + + def get_batch_pos_at_time(self, t, x0=[],vx=[], y0=[],vy=[], t0=[], x0_err=[],vx_err=[], y0_err=[],vy_err=[]): - dt = t-t0 - x = x0 + dt*vx - y = y0 + dt*vy - x_err = np.hypot(x0_err, vx_err*dt) - y_err = np.hypot(y0_err, vy_err*dt) + if hasattr(t, "__len__"): + dt = t-t0[:,np.newaxis] + x = x0[:,np.newaxis] + dt*vx[:,np.newaxis] + y = y0[:,np.newaxis] + dt*vy[:,np.newaxis] + x_err = np.hypot(x0_err[:,np.newaxis], vx_err[:,np.newaxis]*dt) + y_err = np.hypot(y0_err[:,np.newaxis], vy_err[:,np.newaxis]*dt) + else: + dt = t-t0 + x = x0 + dt*vx + y = y0 + dt*vy + x_err = np.hypot(x0_err, vx_err*dt) + y_err = np.hypot(y0_err, vy_err*dt) return x,y,x_err,y_err def fit_motion_model(self, dt, x, y, xe, ye, update=True, bootstrap=0): @@ -392,7 +417,7 @@ class Parallax(MotionModel): Requires RA, Dec, and PA parameters (degrees) for parallax calculation. RA, Dec in J2000 - PA is counterclockwise offset between North and the image y-axis. + PA is counterclockwise offset of the image y-axis from North. Optional obs parameter describing observer location, default is 'earth'. """ n_pts_req = 4 @@ -424,7 +449,6 @@ def __init__(self, x0=0, vx=0, y0=0, vy=0, t0=2025.0, def get_pos_at_time(self, t): t_mjd = Time(t, format='decimalyear', scale='utc').mjd pvec = parallax.parallax_in_direction(self.RA, self.Dec, t_mjd, obsLocation=self.obs, PA=self.PA).T - # TODO: need to confirm x-e orientation x = self.x0 + self.vx*(t-self.t0) + self.pi*pvec[0] y = self.y0 + self.vy*(t-self.t0) + self.pi*pvec[1] return x, y @@ -478,17 +502,6 @@ def fit_func(t, x0,y0, vx,vy, pi): param_errors = [x0_err, vx_err, y0_err, vy_err, pi_err] return params, param_errors - def get_chi2(self,dt,x,y,xe,ye): - """ - Get the chi^2 value for the current MM and - the input data. - """ - # TODO: confirm whether we want reduced chi^2 or anything special - maybe kwarg option - x_pred,y_pred = self.get_pos_at_time(dt) - chi2x = np.sum((x-x_pred)**2 / xe**2) - chi2y = np.sum((y-y_pred)**2 / ye**2) - return chi2x,chi2y - """ Get all the motion model parameters for a given motion_model_name. Optionally, include fixed and error parameters (included by default). diff --git a/flystar/parallax.py b/flystar/parallax.py index 88360cc..4c60ae7 100755 --- a/flystar/parallax.py +++ b/flystar/parallax.py @@ -26,7 +26,7 @@ def parallax_in_direction(RA, Dec, mjd, obsLocation='earth', PA=0): | R.A. in degrees. (J2000) | Dec. in degrees. (J2000) | MJD - | PA in degrees. (counterclockwise misalignment of North and image y-axis) + | PA in degrees. (counterclockwise offset of the image y-axis from North) Equations following MulensModel. """ diff --git a/flystar/startables.py b/flystar/startables.py index 95a21bf..a9ed67d 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -655,7 +655,6 @@ def fit_velocity_for_star(self, ss, weighting='var', use_scipy=True, default_motion_model='Linear', mask_val=None, mask_lists=False): # TODO: "weighting" is not used - # # Make a mask of invalid (NaN) values and a user-specified invalid value. # @@ -670,7 +669,6 @@ def fit_velocity_for_star(self, ss, weighting='var', use_scipy=True, if not np.ma.is_masked(y): y.mask = np.zeros_like(y.data, dtype=bool) - if mask_lists is not False: # Remove a list if isinstance(mask_lists, list): @@ -681,7 +679,6 @@ def fit_velocity_for_star(self, ss, weighting='var', use_scipy=True, # Throw a warning if mask_lists is not a list if not isinstance(mask_lists, list): raise RuntimeError('mask_lists needs to be a list.') - # # Assign the appropriate positional errors # @@ -854,7 +851,6 @@ def get_star_positions_at_time(self, t): """ Get current x,y positions of each star according to its motion_model Instead of looping through every star, we implement a faster calculation for Fixed and Linear models, and loop through any stars with a more complex model - TODO: can add acceleration model to the ones with shortcuts """ # Start with empty arrays so we can fill them in batches N_stars = len(self) From 37393be93439d6c87b47ec5cd45942035ef12444 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Thu, 12 Dec 2024 17:08:12 -0800 Subject: [PATCH 025/254] check in progress on motion model testing --- flystar/align.py | 52 ++++-- flystar/motion_model.py | 58 ++++--- flystar/startables.py | 20 ++- flystar/tests/test_align.py | 106 ++++++++++++- flystar/tests/test_motion_model.py | 243 +++++++++++++++++++++++++++++ flystar/transforms.py | 6 +- 6 files changed, 445 insertions(+), 40 deletions(-) create mode 100755 flystar/tests/test_motion_model.py diff --git a/flystar/align.py b/flystar/align.py index ab0fd06..f8374f8 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -27,6 +27,7 @@ def __init__(self, list_of_starlists, ref_index=0, iters=2, use_vel=None, default_motion_model='Fixed', calc_trans_inverse=False, init_guess_mode='miracle', iter_callback=None, + position_angle=None, RA=None, Dec=None, observer_location='earth', verbose=True): """ Make a mosaic object by passing in a list of starlists and then running fit(). @@ -129,7 +130,16 @@ def = None. If not None, then this should contain an array or list of transform iter_callback : None or function A function to call (that accepts a StarTable object and an iteration number) - at the end of every iteration. This can be used for plotting or printing state. + at the end of every iteration. This can be used for plotting or printing state. + + position_angle, RA, Dec : float (degrees) + [Only required if using Parallax motion model] + position_angle: clockwise angular offset of image y-axis from North + RA, Dec: J2000.0 coordinates of your image center + These values are assumed to be the same for all starlists + + observer_location: str + Only used for parallax motion models, default is 'earth' verbose : int (0 to 9, inclusive) Controls the verbosity of print statements. (0 least, 9 most verbose). @@ -180,6 +190,11 @@ def = None. If not None, then this should contain an array or list of transform self.trans_input = trans_input self.trans_class = trans_class self.calc_trans_inverse = calc_trans_inverse + self.position_angle=position_angle + self.RA=RA + self.Dec=Dec + self.observer_location=observer_location + # TODO: consider whether we want this fallback if use_vel is None: self.default_motion_model = default_motion_model @@ -587,7 +602,7 @@ def setup_ref_table_from_starlist(self, star_list): col_arrays[new_col_name] = new_col_data # Use the columns from the ref list to make the ref_table. - ref_table = StarTable(**col_arrays) + ref_table = StarTable(**col_arrays, position_angle=self.position_angle, RA=self.RA, Dec=self.Dec) # Make new columns to hold original values. These will be copies # of the old columns and will only include x, y, m, xe, ye, me. @@ -1091,8 +1106,8 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot motion_model_list = ['Fixed', self.default_motion_model] if 'motion_model_used' in ref_table.keys(): motion_model_list += ref_table['motion_model_used'].tolist() - elif 'motion_model_assigned' in ref_table.keys(): - motion_model_list += ref_table['motion_model_assigned'].tolist() + elif 'motion_model_input' in ref_table.keys(): + motion_model_list += ref_table['motion_model_input'].tolist() motion_col_list = motion_model.get_list_motion_model_param_names(np.unique(motion_model_list).tolist(), with_errors=False, with_fixed=False) if calc_vel_in_bootstrap: motion_data = {} @@ -1291,6 +1306,8 @@ def __init__(self, ref_list, list_of_starlists, iters=2, update_ref_orig=False, init_guess_mode='miracle', iter_callback=None, + position_angle=None, RA=None, Dec=None, + observer_location='earth', verbose=True): """ @@ -1416,7 +1433,16 @@ def = None. If not None, then this should contain an array or list of transform iter_callback : None or function A function to call (that accepts a StarTable object and an iteration number) - at the end of every iteration. This can be used for plotting or printing state. + at the end of every iteration. This can be used for plotting or printing state. + + position_angle, RA, Dec : float (degrees) + Only required if using Parallax motion model + position_angle: clockwise angular offset of image y-axis from North + RA, Dec: J2000.0 coordinates of your image center + These values are assumed to be the same for all starlists + + observer_location: str + Only used for parallax motion models, default is 'earth' Example ---------- @@ -1460,6 +1486,8 @@ def = None. If not None, then this should contain an array or list of transform default_motion_model = default_motion_model, init_guess_mode=init_guess_mode, iter_callback=iter_callback, + position_angle=position_angle, RA=RA, Dec=Dec, + observer_location=observer_location, verbose=verbose) self.ref_list = copy.deepcopy(ref_list) @@ -1542,10 +1570,10 @@ def fit(self): self.ref_table = self.setup_ref_table_from_starlist(self.ref_list) # copy over motion model parameters if they exist in the reference list - if 'motion_model_assigned' in self.ref_list.colnames: - self.ref_table['motion_model_assigned'] = self.ref_list['motion_model_assigned'] + if 'motion_model_input' in self.ref_list.colnames: + self.ref_table['motion_model_input'] = self.ref_list['motion_model_input'] if 'motion_model' in self.ref_list.colnames: - self.ref_table['motion_model_assigned'] = self.ref_list['motion_model'] + self.ref_table['motion_model_input'] = self.ref_list['motion_model'] for param in motion_model.get_all_motion_model_param_names(with_fixed=True, with_errors=True): if param in self.ref_list.colnames: self.ref_table[param] = self.ref_list[param] @@ -1675,7 +1703,7 @@ def setup_ref_table_from_starlist(star_list): col_arrays[new_col_name] = new_col_data # Use the columns from the ref list to make the ref_table. - ref_table = StarTable(**col_arrays) + ref_table = StarTable(**col_arrays, position_angle=self.position_angle, RA=self.RA, Dec=self.Dec) # Make new columns to hold original values. These will be copies # of the old columns and will only include x, y, m, xe, ye, me. @@ -2391,11 +2419,11 @@ def transform_from_object(starlist, transform): keys = list(starlist.keys()) # Check to see if velocities or motion_model are present in starlist. - vel = ('vx' in keys)and ~("motion_model_assigned" in keys) - mot = ("motion_model_assigned" in keys) + vel = ('vx' in keys)and ~("motion_model_input" in keys) + mot = ("motion_model_input" in keys) # If the only motion models used are Fixed and Linear, we can still transform velocities. if mot: - motion_models_unique = list(np.unique(starlist_f['motion_model_assigned'])) + motion_models_unique = list(np.unique(starlist_f['motion_model_input'])) if 'Linear' in motion_models_unique: motion_models_unique.remove('Linear') if 'Fixed' in motion_models_unique: diff --git a/flystar/motion_model.py b/flystar/motion_model.py index 329e234..a6726d3 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -77,7 +77,7 @@ class Fixed(MotionModel): fixed_param_names = [] def __init__(self, x0=0, y0=0, t0=2025.0, - x0_err=0, y0_err=0): + x0_err=0, y0_err=0, **kwargs): self.x0 = x0 self.y0 = y0 self.t0 = t0 @@ -142,7 +142,7 @@ class Linear(MotionModel): fixed_param_names = ['t0'] def __init__(self, x0=0, vx=0, y0=0, vy=0, t0=2025.0, - x0_err=0, vx_err=0, y0_err=0, vy_err=0): + x0_err=0, vx_err=0, y0_err=0, vy_err=0, **kwargs): self.x0 = x0 self.vx = vx self.y0 = y0 @@ -173,7 +173,7 @@ def get_pos_err_at_time(self, t): def get_batch_pos_at_time(self, t, x0=[],vx=[], y0=[],vy=[], t0=[], - x0_err=[],vx_err=[], y0_err=[],vy_err=[]): + x0_err=[],vx_err=[], y0_err=[],vy_err=[], **kwargs): if hasattr(t, "__len__"): dt = t-t0[:,np.newaxis] x = x0[:,np.newaxis] + dt*vx[:,np.newaxis] @@ -283,7 +283,7 @@ class Acceleration(MotionModel): fixed_param_names = ['t0'] def __init__(self, x0=0, vx0=0, ax=0, y0=0, vy0=0, ay=0, t0=2025.0, - x0_err=0, vx0_err=0, ax_err=0, y0_err=0, vy0_err=0, ay_err=0): + x0_err=0, vx0_err=0, ax_err=0, y0_err=0, vy0_err=0, ay_err=0, **kwargs): self.x0 = x0 self.vx0 = vx0 self.ax = ax @@ -316,18 +316,25 @@ def get_pos_at_time(self, t): def get_pos_err_at_time(self, t): dt = t - self.t0 - x_err = np.sqrt(self.x0_err**2 + (self.vx_err*dt)**2 + (self.ax_err*dt**2)**2) - y_err = np.sqrt(self.y0_err**2 + (self.vy_err*dt)**2 + (self.ay_err*dt**2)**2) + x_err = np.sqrt(self.x0_err**2 + (self.vx0_err*dt)**2 + (0.5*self.ax_err*dt**2)**2) + y_err = np.sqrt(self.y0_err**2 + (self.vy0_err*dt)**2 + (0.5*self.ay_err*dt**2)**2) return x_err, y_err def get_batch_pos_at_time(self,t, x0=[],vx0=[],ax=[], y0=[],vy0=[],ay=[], t0=[], - x0_err=[],vx0_err=[],ax_err=[], y0_err=[],vy0_err=[],ay_err=[]): - dt = t-t0 - x = x0 + dt*vx0 + 0.5*dt**2*ax - y = y0 + dt*vy0 + 0.5*dt**2*ay - x_err = np.sqrt(x0_err**2 + (vx0_err*dt)**2 + (0.5*ax_err*dt**2)**2) - y_err = np.sqrt(y0_err**2 + (vy0_err*dt)**2 + (0.5*ay_err*dt**2)**2) + x0_err=[],vx0_err=[],ax_err=[], y0_err=[],vy0_err=[],ay_err=[], **kwargs): + if hasattr(t, "__len__"): + dt = t-t0[:,np.newaxis] + x = x0[:,np.newaxis] + dt*vx0[:,np.newaxis] + 0.5*dt**2*ax[:,np.newaxis] + y = y0[:,np.newaxis] + dt*vy0[:,np.newaxis] + 0.5*dt**2*ay[:,np.newaxis] + x_err = np.sqrt(x0_err[:,np.newaxis]**2 + (vx0_err[:,np.newaxis]*dt)**2 + (0.5*ax_err[:,np.newaxis]*dt**2)**2) + y_err = np.sqrt(y0_err[:,np.newaxis]**2 + (vy0_err[:,np.newaxis]*dt)**2 + (0.5*ay_err[:,np.newaxis]*dt**2)**2) + else: + dt = t-t0 + x = x0 + dt*vx0 + 0.5*dt**2*ax + y = y0 + dt*vy0 + 0.5*dt**2*ay + x_err = np.sqrt(x0_err**2 + (vx0_err*dt)**2 + (0.5*ax_err*dt**2)**2) + y_err = np.sqrt(y0_err**2 + (vy0_err*dt)**2 + (0.5*ay_err*dt**2)**2) return x,y,x_err,y_err def fit_motion_model(self, dt, x, y, xe, ye, update=True, bootstrap=0): @@ -428,7 +435,7 @@ class Parallax(MotionModel): def __init__(self, x0=0, vx=0, y0=0, vy=0, t0=2025.0, x0_err=0, vx_err=0, y0_err=0, vy_err=0, pi=0, pi_err=0, - RA=None, Dec=None, PA=None, obs='earth'): + RA=None, Dec=None, PA=None, obs='earth', **kwargs): self.x0 = x0 self.vx = vx self.y0 = y0 @@ -462,14 +469,27 @@ def get_pos_err_at_time(self, t): def get_batch_pos_at_time(self, t, x0=[],vx=[], y0=[],vy=[], pi=[], t0=[], - x0_err=[],vx_err=[], y0_err=[],vy_err=[], pi_err=[]): + x0_err=[],vx_err=[], y0_err=[],vy_err=[], pi_err=[], **kwargs): t_mjd = Time(t, format='decimalyear', scale='utc').mjd pvec = parallax.parallax_in_direction(self.RA, self.Dec, t_mjd, obsLocation=self.obs, PA=self.PA).T - dt = t-t0 - x = x0 + dt*vx + pi*pvec[0] - y = y0 + dt*vy + pi*pvec[1] - x_err = np.sqrt(x0_err**2 + (vx_err*dt)**2 + (pi_err*pvec[0])**2) - y_err = np.sqrt(y0_err**2 + (vy_err*dt)**2 + (pi_err*pvec[1])**2) + if hasattr(t, "__len__"): + dt = t-t0[:,np.newaxis] + x = x0[:,np.newaxis] + dt*vx[:,np.newaxis] + pi[:,np.newaxis]*pvec[0] + y = y0[:,np.newaxis] + dt*vy[:,np.newaxis] + pi[:,np.newaxis]*pvec[1] + try: + x_err = np.sqrt(x0_err[:,np.newaxis]**2 + (vx_err[:,np.newaxis]*dt)**2 + (pi_err[:,np.newaxis]*pvec[0])**2) + y_err = np.sqrt(y0_err[:,np.newaxis]**2 + (vy_err[:,np.newaxis]*dt)**2 + (pi_err[:,np.newaxis]*pvec[1])**2) + except: + x_err,y_err = [],[] + else: + dt = t-t0 + x = x0 + dt*vx + pi*pvec[0] + y = y0 + dt*vy + pi*pvec[1] + try: + x_err = np.sqrt(x0_err**2 + (vx_err*dt)**2 + (pi_err*pvec[0])**2) + y_err = np.sqrt(y0_err**2 + (vy_err*dt)**2 + (pi_err*pvec[1])**2) + except: + x_err,y_err = [],[] return x,y,x_err,y_err def fit_motion_model(self, t, x, y, xe, ye, update=True): diff --git a/flystar/startables.py b/flystar/startables.py index a9ed67d..20f4263 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -60,7 +60,18 @@ class StarTable(Table): ref_list : int Specify which list is the reference list (if any). - + + position_angle: float (degree) + required for parallax motion model + clockwise angular offset between image y-axis and North + + RA, Dec: float (degrees) + required for parallax motion model + image position coordinates + + observer_location: string + only used by parallax motion model + default is 'earth' Examples -------------------------- @@ -72,7 +83,7 @@ class StarTable(Table): print(t['name'][0:10]) # print the first 10 star names print(t['x'][0:10, 0]) # print x from the first epoch/list/column for the first 10 stars """ - def __init__(self, *args, ref_list=0, **kwargs): + def __init__(self, *args, ref_list=0, position_angle=None, RA=None, Dec=None, observer_location='earth', **kwargs): """ """ @@ -150,7 +161,8 @@ def __init__(self, *args, ref_list=0, **kwargs): Table.__init__(self, (kwargs['name'], kwargs['x'], kwargs['y'], kwargs['m']), names=('name', 'x', 'y', 'm')) self['name'] = self['name'].astype('U20') - self.meta = {'n_stars': n_stars, 'n_lists': n_lists, 'ref_list': ref_list} + self.meta = {'n_stars': n_stars, 'n_lists': n_lists, 'ref_list': ref_list, + 'position_angle': position_angle, 'RA': RA, 'Dec':Dec, 'observer_location':observer_location} for meta_arg in meta_tab: if meta_arg in kwargs: @@ -829,7 +841,7 @@ def fit_velocity_for_star(self, ss, weighting='var', use_scipy=True, param_dict[par] = self[par][ss] # Model object - mod = modClass(**param_dict) + mod = modClass(**param_dict, PA=self.meta['position_angle'], RA=self.meta['RA'], Dec=self.meta['Dec'], obs=self.meta['observer_location']) # Fit for the best parameters params, param_errs = mod.fit_motion_model(dt, x, y, xe, ye, bootstrap=bootstrap, update=True) diff --git a/flystar/tests/test_align.py b/flystar/tests/test_align.py index 9b99f06..5b78653 100644 --- a/flystar/tests/test_align.py +++ b/flystar/tests/test_align.py @@ -3,6 +3,7 @@ from flystar import startables from flystar import transforms from flystar import analysis +from flystar import motion_model from astropy.table import Table import numpy as np import pylab as plt @@ -534,7 +535,7 @@ def make_fake_starlists_poly1_vel(seed=-1): # Save the new list as a starlist. new_lis = starlists.StarList([lis['name'], md, mde, xd, xde, yd, yde, t], - names=('name', 'm', 'me', 'x', 'xe', 'y', 'ye', 't')) + names=('name', 'm', 'm_err', 'x', 'x_err', 'y', 'y_err', 't')) new_lis.write('random_vel_{0:d}.fits'.format(ss), overwrite=True) @@ -583,7 +584,7 @@ def make_fake_starlists_poly1_acc(seed=-1): names = ('name', 'm0', 'm0_err', 'x0', 'x0_err', 'y0', 'y0_err', 'vx0', 'vx0_err', 'vy0', 'vy0_err', - 'ax', 'axe', 'ay', 'aye', + 'ax', 'ax_err', 'ay', 'ay_err', 't0')) sdx = np.argsort(m0) @@ -641,6 +642,107 @@ def make_fake_starlists_poly1_acc(seed=-1): new_lis.write('random_acc_{0:d}.fits'.format(ss), overwrite=True) return (xy_trans, mag_trans) + +def make_fake_starlists_poly1_par(seed=-1): + # If seed >=0, then set random seed to that value + if seed >= 0: + np.random.seed(seed=seed) + + N_stars = 200 + + x0 = np.random.rand(N_stars) * 10.0 # arcsec (increasing to East) + y0 = np.random.rand(N_stars) * 10.0 # arcsec + x0e = np.random.randn(N_stars) * 5.0e-4 # arcsec + y0e = np.random.randn(N_stars) * 5.0e-4 # arcsec + vx = np.random.randn(N_stars) * 5.0 # mas / yr + vy = np.random.randn(N_stars) * 5.0 # mas / yr + vxe = np.random.randn(N_stars) * 0.1 # mas / yr + vye = np.random.randn(N_stars) * 0.1 # mas / yr + pi = np.random.randn(N_stars) * 0.5 # mas + pie = np.random.randn(N_stars) * 0.01 # mas + m0 = (np.random.rand(N_stars) * 8) + 9 # mag + m0e = np.random.randn(N_stars) * 0.05 # mag + t0 = np.ones(N_stars) * 2019.5 + + # Make all the errors positive + x0e = np.abs(x0e) + y0e = np.abs(y0e) + m0e = np.abs(m0e) + vxe = np.abs(vxe) + vye = np.abs(vye) + pie = np.abs(pie) + + name = ['star_{0:03d}'.format(ii) for ii in range(N_stars)] + + # Make an StarList + lis = starlists.StarList([name, m0, m0e, + x0, x0e, y0, y0e, + vx, vxe, vy, vye, + pi, pie, + t0], + names = ('name', 'm0', 'm0_err', + 'x0', 'x0_err', 'y0', 'y0_err', + 'vx', 'vx_err', 'vy', 'vy_err', + 'pi', 'pi_err', + 't0')) + + sdx = np.argsort(m0) + lis = lis[sdx] + + # Save original positions as reference (1st) list + # in a StarList format (with velocities). + lis.write('random_par_ref.fits', overwrite=True) + + ########## + # Propogate to new times and distort. + ########## + # Make 4 new starlists with different epochs and transformations. + times = [2018.5, 2019.5, 2020.5, 2021.5] + xy_trans = [[[ 6.5, 0.99, 1e-5], [ 10.1, 1e-5, 0.99]], + [[100.3, 0.98, 1e-5], [ 50.5, 9e-6, 1.001]], + [[ 0.0, 1.00, 0.0], [ 0.0, 0.0, 1.0]], + [[250.0, 0.97, 2e-5], [-250.0, 1e-5, 1.001]]] + mag_trans = [0.1, 0.4, 0.0, -0.3] + + # Convert into pixels (undistorted) with the following info. + scale = 0.01 # arcsec / pix + shift = [1.0, 1.0] # pix + + for ss in range(len(times)): + dt = times[ss] - lis['t0'] + + par_mod = motion_model.Parallax(PA=0,RA=18.0, Dec=-30.0) + par_mod_dat = par_mod.get_batch_pos_at_time(dt+lis['t0'], x0=lis['x0'],vx=lis['vx']/1e3, pi=lis['pi'], + y0=lis['y0'], vy=lis['vy']/1e3, t0=lis['t0']) + x,y = par_mod_dat[0], par_mod_dat[1] + t = np.ones(N_stars) * times[ss] + + # Convert into pixels + xp = (x / -scale) + shift[0] # -1 from switching to increasing to West (right) + yp = (y / scale) + shift[1] + xpe = lis['x0_err'] / scale + ype = lis['y0_err'] / scale + + # Distort the positions + trans = transforms.PolyTransform(1, xy_trans[ss][0], xy_trans[ss][1], mag_offset=mag_trans[ss]) + xd, yd = trans.evaluate(xp, yp) + md = trans.evaluate_mag(lis['m0']) + + # Perturb with small errors (0.1 pix) + xd += np.random.randn(N_stars) * 0.1 + yd += np.random.randn(N_stars) * 0.1 + md += np.random.randn(N_stars) * 0.02 + xde = xpe + yde = ype + mde = lis['m0_err'] + + # Save the new list as a starlist. + new_lis = starlists.StarList([lis['name'], md, mde, xd, xde, yd, yde, t], + names=('name', 'm', 'me', 'x', 'xe', 'y', 'ye', 't')) + + new_lis.write('random_par_{0:d}.fits'.format(ss), overwrite=True) + + return (xy_trans, mag_trans) def test_MosaicToRef_hst_me(): diff --git a/flystar/tests/test_motion_model.py b/flystar/tests/test_motion_model.py new file mode 100755 index 0000000..d221a29 --- /dev/null +++ b/flystar/tests/test_motion_model.py @@ -0,0 +1,243 @@ +from flystar import motion_model +import numpy as np +import pytest + +def within_error(true_val, fit_val, fit_err, n_sigma=1): + return (true_val < (fit_val+fit_err)) & (true_val> (fit_val-fit_err)) + +def test_Fixed(): + # Test handling of a single star + true_params = {'x0': 1.0, 'y0':0.5, 'x0_err':0.1, 'y0_err':0.1} + mod_true = motion_model.Fixed(**true_params) + param_list = mod_true.fitter_param_names + # Confirm return of proper values for single t and array t + x_t, y_t = mod_true.get_pos_at_time(0.0) + assert x_t==true_params['x0'] + assert y_t==true_params['y0'] + x_t, y_t = mod_true.get_pos_at_time([0.0,mod_true.t0,10000]) + assert (x_t==true_params['x0']).all() + assert (y_t==true_params['y0']).all() + x_err_t, y_err_t = mod_true.get_pos_err_at_time(0.0) + assert x_err_t==true_params['x0_err'] + assert y_err_t==true_params['y0_err'] + x_err_t, y_err_t = mod_true.get_pos_err_at_time([0.0,mod_true.t0,10000]) + assert (x_err_t==true_params['x0_err']).all() + assert (y_err_t==true_params['y0_err']).all() + + # Check behavior of get_batch_pos_at_time + x0_batch = np.random.uniform(-2.0,2.0, 50) + y0_batch = np.random.uniform(-2.0,2.0, 50) + x0_err_batch = np.repeat(0.1, 50) + y0_err_batch = np.repeat(0.1, 50) + # Single epoch + t_batch=2020.0 + x_t_batch, y_t_batch, x_err_t_batch, y_err_t_batch = mod_true.get_batch_pos_at_time(t_batch, + x0=x0_batch, y0=y0_batch, x0_err=x0_err_batch, y0_err=y0_err_batch) + assert (x_t_batch==x0_batch).all() + assert (y_t_batch==y0_batch).all() + assert (x_err_t_batch==x0_err_batch).all() + assert (y_err_t_batch==y0_err_batch).all() + # Multiple times + t_batch = np.arange(2015.0,2025.0, 0.5) + x_t_batch, y_t_batch, x_err_t_batch, y_err_t_batch = mod_true.get_batch_pos_at_time(t_batch, + x0=x0_batch, y0=y0_batch, x0_err=x0_err_batch, y0_err=y0_err_batch) + assert (x_t_batch==np.array([np.repeat(x0_batch_i, len(t_batch)) for x0_batch_i in x0_batch])).all() + assert (y_t_batch==np.array([np.repeat(y0_batch_i, len(t_batch)) for y0_batch_i in y0_batch])).all() + assert (x_err_t_batch==np.array([np.repeat(x0_err_batch_i, len(t_batch)) for x0_err_batch_i in x0_err_batch])).all() + assert (y_err_t_batch==np.array([np.repeat(y0_err_batch_i, len(t_batch)) for y0_err_batch_i in y0_err_batch])).all() + + # Test fitter + t = np.arange(2015.0,2025.0, 0.5) + # Get values from model and add scatter + x_true, y_true = mod_true.get_pos_at_time(t) + x_true_err, y_true_err = mod_true.get_pos_err_at_time(t) + x_sim = np.random.normal(x_true, x_true_err) + y_sim = np.random.normal(y_true, y_true_err) + # Run fit + mod_fit = motion_model.Fixed() + params, param_errs = mod_fit.fit_motion_model(t, x_sim,y_sim, x_true_err, y_true_err) + # Confirm true value is within error bar of fit value + assert [within_error(true_params[param_list[i]], params[i], param_errs[i]) for i in range(len(params))] + + +def test_Linear(): + # Test handling of a single star + true_params = {'x0': 1.0, 'y0':0.5, 'x0_err':0.1, 'y0_err':0.1, + 'vx':0.2, 'vy':0.5, 'vx_err':0.05, 'vy_err':0.05} + mod_true = motion_model.Linear(**true_params) + param_list = mod_true.fitter_param_names + # Confirm return of proper values for single t=t0 and array t + x_t, y_t = mod_true.get_pos_at_time(mod_true.t0) + assert x_t==true_params['x0'] + assert y_t==true_params['y0'] + t_arr = np.array([2010.0,mod_true.t0,2030.0]) + x_t, y_t = mod_true.get_pos_at_time(t_arr) + assert (x_t==(true_params['x0'] + (t_arr-mod_true.t0)*true_params['vx'])).all() + assert (y_t==(true_params['y0'] + (t_arr-mod_true.t0)*true_params['vy'])).all() + x_err_t, y_err_t = mod_true.get_pos_err_at_time(mod_true.t0) + assert x_err_t==true_params['x0_err'] + assert y_err_t==true_params['y0_err'] + x_err_t, y_err_t = mod_true.get_pos_err_at_time(t_arr) + assert (x_err_t==np.hypot(true_params['x0_err'],(t_arr-mod_true.t0)*true_params['vx_err'])).all() + assert (y_err_t==np.hypot(true_params['y0_err'],(t_arr-mod_true.t0)*true_params['vy_err'])).all() + + # Check behavior of get_batch_pos_at_time + x0_batch = np.random.uniform(-2.0,2.0, 50) + y0_batch = np.random.uniform(-2.0,2.0, 50) + vx_batch = np.random.uniform(-2.0,2.0, 50) + vy_batch = np.random.uniform(-2.0,2.0, 50) + x0_err_batch = np.repeat(0.1, 50) + y0_err_batch = np.repeat(0.1, 50) + vx_err_batch = np.repeat(0.05, 50) + vy_err_batch = np.repeat(0.05, 50) + t0_batch = np.repeat(2025.0,50) + # Single epoch + t_batch=2020.0 + x_t_batch, y_t_batch, x_err_t_batch, y_err_t_batch = mod_true.get_batch_pos_at_time(t_batch, + x0=x0_batch, y0=y0_batch, x0_err=x0_err_batch, y0_err=y0_err_batch, + vx=vx_batch, vy=vy_batch, vx_err=vx_err_batch, vy_err=vy_err_batch, + t0=t0_batch) + assert (x_t_batch==(x0_batch+(t_batch-t0_batch)*vx_batch)).all() + assert (y_t_batch==(y0_batch+(t_batch-t0_batch)*vy_batch)).all() + assert (x_err_t_batch==np.hypot(x0_err_batch, (t_batch-t0_batch)*vx_err_batch)).all() + assert (y_err_t_batch==np.hypot(y0_err_batch, (t_batch-t0_batch)*vy_err_batch)).all() + # Multiple times + t_batch = np.arange(2015.0,2025.0, 0.5) + x_t_batch, y_t_batch, x_err_t_batch, y_err_t_batch = mod_true.get_batch_pos_at_time(t_batch, + x0=x0_batch, y0=y0_batch, x0_err=x0_err_batch, y0_err=y0_err_batch, + vx=vx_batch, vy=vy_batch, vx_err=vx_err_batch, vy_err=vy_err_batch, + t0=t0_batch) + assert (x_t_batch==np.array([x0_batch[i] + (t_batch-t0_batch[i])*vx_batch[i] for i in range(len(x0_batch))])).all() + assert (y_t_batch==np.array([y0_batch[i] + (t_batch-t0_batch[i])*vy_batch[i] for i in range(len(x0_batch))])).all() + assert (x_err_t_batch==np.array([np.hypot(x0_err_batch[i], (t_batch-t0_batch[i])*vx_err_batch[i]) for i in range(len(x0_batch))])).all() + assert (y_err_t_batch==np.array([np.hypot(y0_err_batch[i], (t_batch-t0_batch[i])*vy_err_batch[i]) for i in range(len(x0_batch))])).all() + + # Test fitter + t = np.arange(2015.0,2025.0, 0.5) + # Get values from model and add scatter + x_true, y_true = mod_true.get_pos_at_time(t) + x_true_err, y_true_err = mod_true.get_pos_err_at_time(t) + x_sim = np.random.normal(x_true, x_true_err) + y_sim = np.random.normal(y_true, y_true_err) + # Run fit + mod_fit = motion_model.Linear() + params, param_errs = mod_fit.fit_motion_model(t, x_sim,y_sim, x_true_err, y_true_err) + # Confirm true value is within error bar of fit value + assert [within_error(true_params[param_list[i]], params[i], param_errs[i]) for i in range(len(params))] + + +def test_Acceleration(): + # Test handling of a single star + true_params = {'x0': 1.0, 'y0':0.5, 'x0_err':0.1, 'y0_err':0.1, + 'vx0':0.2, 'vy0':0.5, 'vx0_err':0.05, 'vy0_err':0.05, + 'ax':0.1, 'ay':-0.1, 'ax_err':0.02, 'ay_err':0.02} + mod_true = motion_model.Acceleration(**true_params) + param_list = mod_true.fitter_param_names + # Confirm return of proper values for single t=t0 and array t + x_t, y_t = mod_true.get_pos_at_time(mod_true.t0) + assert x_t==true_params['x0'] + assert y_t==true_params['y0'] + t_arr = np.array([2010.0,mod_true.t0,2030.0]) + x_t, y_t = mod_true.get_pos_at_time(t_arr) + assert (x_t==(true_params['x0'] + (t_arr-mod_true.t0)*true_params['vx0'] + (t_arr-mod_true.t0)**2*true_params['ax'])).all() + assert (y_t==(true_params['y0'] + (t_arr-mod_true.t0)*true_params['vy0'] + (t_arr-mod_true.t0)**2*true_params['ay'])).all() + x_err_t, y_err_t = mod_true.get_pos_err_at_time(mod_true.t0) + assert x_err_t==true_params['x0_err'] + assert y_err_t==true_params['y0_err'] + x_err_t, y_err_t = mod_true.get_pos_err_at_time(t_arr) + assert (x_err_t==np.sqrt(true_params['x0_err']**2 + ((t_arr-mod_true.t0)*true_params['vx0_err'])**2 + + (0.5*(t_arr-mod_true.t0)**2*true_params['ax_err'])**2)).all() + assert (y_err_t==np.sqrt(true_params['y0_err']**2 + ((t_arr-mod_true.t0)*true_params['vy0_err'])**2 + + (0.5*(t_arr-mod_true.t0)**2*true_params['ay_err'])**2)).all() + + # Check behavior of get_batch_pos_at_time + x0_batch = np.random.uniform(-2.0,2.0, 50) + y0_batch = np.random.uniform(-2.0,2.0, 50) + vx0_batch = np.random.uniform(-2.0,2.0, 50) + vy0_batch = np.random.uniform(-2.0,2.0, 50) + ax_batch = np.random.uniform(-1.0,1.0, 50) + ay_batch = np.random.uniform(-1.0,1.0, 50) + x0_err_batch = np.repeat(0.1, 50) + y0_err_batch = np.repeat(0.1, 50) + vx0_err_batch = np.repeat(0.05, 50) + vy0_err_batch = np.repeat(0.05, 50) + ax_err_batch = np.repeat(0.02, 50) + ay_err_batch = np.repeat(0.02, 50) + t0_batch = np.repeat(2025.0,50) + # Single epoch + t_batch=2020.0 + x_t_batch, y_t_batch, x_err_t_batch, y_err_t_batch = mod_true.get_batch_pos_at_time(t_batch, + x0=x0_batch, y0=y0_batch, x0_err=x0_err_batch, y0_err=y0_err_batch, + vx0=vx0_batch, vy0=vy0_batch, vx0_err=vx0_err_batch, vy0_err=vy0_err_batch, + ax=ax_batch, ay=ay_batch, ax_err=ax_err_batch, ay_err=ay_err_batch, + t0=t0_batch) + assert (x_t_batch==(x0_batch + (t_batch-t0_batch)*vx0_batch + 0.5*(t_batch-t0_batch)**2*ax_batch)).all() + assert (y_t_batch==(y0_batch + (t_batch-t0_batch)*vy0_batch + 0.5*(t_batch-t0_batch)**2*ay_batch)).all() + assert (x_err_t_batch==np.sqrt(x0_err_batch**2 + ((t_batch-t0_batch)*vx0_err_batch)**2 + + (0.5*(t_batch-t0_batch)**2*ax_err_batch)**2)).all() + assert (y_err_t_batch==np.sqrt(y0_err_batch**2 + ((t_batch-t0_batch)*vy0_err_batch)**2 + + (0.5*(t_batch-t0_batch)**2*ay_err_batch)**2)).all() + # Multiple times + t_batch = np.arange(2015.0,2025.0, 0.5) + x_t_batch, y_t_batch, x_err_t_batch, y_err_t_batch = mod_true.get_batch_pos_at_time(t_batch, + x0=x0_batch, y0=y0_batch, x0_err=x0_err_batch, y0_err=y0_err_batch, + vx0=vx0_batch, vy0=vy0_batch, vx0_err=vx0_err_batch, vy0_err=vy0_err_batch, + ax=ax_batch, ay=ay_batch, ax_err=ax_err_batch, ay_err=ay_err_batch, + t0=t0_batch) + assert (x_t_batch==np.array([x0_batch[i] + (t_batch-t0_batch[i])*vx0_batch[i] + 0.5*(t_batch-t0_batch[i])**2*ax_batch[i] for i in range(len(x0_batch))])).all() + assert (y_t_batch==np.array([y0_batch[i] + (t_batch-t0_batch[i])*vy0_batch[i] + 0.5*(t_batch-t0_batch[i])**2*ay_batch[i] for i in range(len(x0_batch))])).all() + assert (x_err_t_batch==np.array([np.sqrt(x0_err_batch[i]**2 + ((t_batch-t0_batch[i])*vx0_err_batch[i])**2 + (0.5*(t_batch-t0_batch[i])**2*ax_err_batch[i])**2) for i in range(len(x0_batch))])).all() + assert (y_err_t_batch==np.array([np.sqrt(y0_err_batch[i]**2 + ((t_batch-t0_batch[i])*vy0_err_batch[i])**2 + (0.5*(t_batch-t0_batch[i])**2*ay_err_batch[i])**2) for i in range(len(x0_batch))])).all() + + # Test fitter + t = np.arange(2015.0,2025.0, 0.5) + # Get values from model and add scatter + x_true, y_true = mod_true.get_pos_at_time(t) + x_true_err, y_true_err = mod_true.get_pos_err_at_time(t) + x_sim = np.random.normal(x_true, x_true_err) + y_sim = np.random.normal(y_true, y_true_err) + # Run fit + mod_fit = motion_model.Acceleration() + params, param_errs = mod_fit.fit_motion_model(t, x_sim,y_sim, x_true_err, y_true_err) + # Confirm true value is within error bar of fit value + assert [within_error(true_params[param_list[i]], params[i], param_errs[i]) for i in range(len(params))] + +#@pytest.mark.skip(reason="not written") +def test_Parallax(): + # Test handling of a single star + true_params = {'x0': 1.0, 'y0':-0.5, 'x0_err':0.1, 'y0_err':0.1, + 'vx':-0.2, 'vy':0.5, 'vx_err':0.05, 'vy_err':0.05, + 'pi':0.5, 'RA':17.76, 'Dec':-28.933, 'PA':0} + mod_true = motion_model.Parallax(**true_params) + param_list = mod_true.fitter_param_names + + # Test fitter + t = np.arange(2015.0,2025.0, 0.5) + # Get values from model and add scatter + x_true, y_true = mod_true.get_pos_at_time(t) + x_true_err, y_true_err = mod_true.get_pos_err_at_time(t) + x_sim = np.random.normal(x_true, x_true_err) + y_sim = np.random.normal(y_true, y_true_err) + # Run fit + mod_fit = motion_model.Parallax(RA=17.76, Dec=-28.933, PA=0) + params, param_errs = mod_fit.fit_motion_model(t, x_sim,y_sim, x_true_err, y_true_err) + # Confirm true value is within error bar of fit value + assert [within_error(true_params[param_list[i]], params[i], param_errs[i]) for i in range(len(params))] + + +def test_Parallax_PA(): + # Set PA=0 model + x0, y0 = 2.0, -1.0 + vx, vy = 0.2, 0.5 + RA, Dec = 17.76, -28.933 + pi = 0.5 + mod_pa0 = motion_model.Parallax(x0=x0,vx=vx,y0=y0,vy=vy,pi=pi, + RA=RA,Dec=Dec,t0=2020.0, PA=0) + # Set PA=90 model with equivalent parameters in that frame + mod_pa90 = motion_model.Parallax(x0=y0,vx=vy,y0=-x0,vy=-vx,pi=pi, + RA=RA,Dec=Dec,t0=2020.0, PA=90) + t_set = np.arange(2018,2024,0.01) + dat_pa0 = mod_pa0.get_pos_at_time(t_set) + dat_pa90 = mod_pa90.get_pos_at_time(t_set) + assert (np.abs(dat_pa0[0]-(-dat_pa90[1]))<1e-10).all() + assert (np.abs(dat_pa0[1]-(dat_pa90[0]))<1e-10).all() diff --git a/flystar/transforms.py b/flystar/transforms.py index 1ed7cd0..2bb80d3 100755 --- a/flystar/transforms.py +++ b/flystar/transforms.py @@ -114,10 +114,10 @@ def evaluate_starlist(self, star_list): new_list['ye'] = vals[1] # Velocities (if they exist and no more complex motion model used) - complex_motion_model = ('motion_model_assigned' in new_list.colnames) + complex_motion_model = ('motion_model_input' in new_list.colnames) if complex_motion_model: # If the only motion models used are Fixed and Linear, we can still transform velocities. - motion_models_unique = list(np.unique(starlist_f['motion_model_assigned'])) + motion_models_unique = list(np.unique(starlist_f['motion_model_input'])) if 'Linear' in motion_models_unique: motion_models_unique.remove('Linear') if 'Fixed' in motion_models_unique: @@ -126,7 +126,7 @@ def evaluate_starlist(self, star_list): complex_motion_model=False # Cannot transform more complex motion models - set values to nan if complex_motion_model: - motion_params = motion_model.get_list_motion_model_param_names(new_list['motion_model_assigned'], with_errors=True, with_fixed=False) + motion_params = motion_model.get_list_motion_model_param_names(new_list['motion_model_input'], with_errors=True, with_fixed=False) for param in motion_params: if param in new_list.colnames: new_list[param] = np.nan From 90496535bf8e0d08ee7798633431994ea082ef80 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Fri, 13 Dec 2024 16:01:23 -0800 Subject: [PATCH 026/254] motion model testing progress --- flystar/tests/test_align.py | 94 ++++++++++++++++++++++++++++++------- 1 file changed, 78 insertions(+), 16 deletions(-) diff --git a/flystar/tests/test_align.py b/flystar/tests/test_align.py index 5b78653..6cd7abe 100644 --- a/flystar/tests/test_align.py +++ b/flystar/tests/test_align.py @@ -69,19 +69,19 @@ def test_MosaicSelfRef(): plt.clf() plt.plot(msc.ref_table['x'][:, 0], msc.ref_table['y'][:, 0], - 'k+', color='red', mec='red', mfc='none') + '+', color='red', mec='red', mfc='none') plt.plot(msc.ref_table['x'][:, 1], msc.ref_table['y'][:, 1], - 'kx', color='blue', mec='blue', mfc='none') + 'x', color='blue', mec='blue', mfc='none') plt.plot(msc.ref_table['x'][:, 2], msc.ref_table['y'][:, 2], - 'ko', color='cyan', mec='cyan', mfc='none') + 'o', color='cyan', mec='cyan', mfc='none') plt.plot(msc.ref_table['x'][:, 3], msc.ref_table['y'][:, 3], - 'k^', color='green', mec='green', mfc='none') + '^', color='green', mec='green', mfc='none') plt.plot(msc.ref_table['x0'], msc.ref_table['y0'], - 'k.', color='black', alpha=0.2) + '.', color='black', alpha=0.2) return @@ -217,6 +217,66 @@ def test_MosaicSelfRef_vel(): 'k.', color='black', alpha=0.2) return + +'''def test_MosaicSelfRef_acc(): + """ + Cross-match and align 4 starlists using the OO version of mosaic lists. + + """ + make_fake_starlists_poly1_acc(seed=42) + + ref_file = 'random_acc_ref.fits' + ref_list = Table.read(ref_file) + names = ref_list["name"] + list_files = ['random_acc_0.fits', + 'random_acc_1.fits', + 'random_acc_2.fits', + 'random_acc_3.fits'] + lists = [starlists.StarList.read(lf) for lf in list_files] + + ########## + # Test instantiation and basic fitting. + ########## + msc = align.MosaicSelfRef(lists, ref_index=0, iters=3, + dr_tol=[5, 3, 3], dm_tol=[1, 1, 0.5], outlier_tol=None, + trans_class=transforms.PolyTransform, + trans_args={'order': 2}, default_motion_model='Acceleration', + verbose=False) + + msc.fit() + + # Check some of the output quantities on the final table. + assert 'x0' in msc.ref_table.colnames + assert 'x0_err' in msc.ref_table.colnames + assert 'y0' in msc.ref_table.colnames + assert 'y0_err' in msc.ref_table.colnames + assert 'm0' in msc.ref_table.colnames + assert 'm0_err' in msc.ref_table.colnames + assert 'vx0' in msc.ref_table.colnames + assert 'vx0_err' in msc.ref_table.colnames + assert 'vy0' in msc.ref_table.colnames + assert 'vy0_err' in msc.ref_table.colnames + assert 'ax' in msc.ref_table.colnames + assert 'ax_err' in msc.ref_table.colnames + assert 'ay' in msc.ref_table.colnames + assert 'ay_err' in msc.ref_table.colnames + assert 't0' in msc.ref_table.colnames + + # Check that we have some matched stars... should be at least 35 stars + # that are detected in all 4 starlists. + idx = np.where(msc.ref_table['n_detect'] == 4)[0] + assert len(idx) > 35 + + # Check that the transformation error isn't too big + assert (msc.ref_table['x0_err'] < 3.0).all() # less than 1 pix + assert (msc.ref_table['y0_err'] < 3.0).all() + assert (msc.ref_table['m0_err'] < 1.0).all() # less than 0.5 mag + + # Check fit quality + for param in ['x0','y0','vx0','vy0','ax','ay']: + #np.testing.assert_almost_equal(msc.ref_table[param][:len(ref_list)], ref_list[param], 2) + print(param,np.transpose([msc.ref_table[param][:len(ref_list)], ref_list[param]])) + return''' def test_MosaicToRef(): @@ -246,7 +306,7 @@ def test_MosaicToRef(): dr_tol=[0.2, 0.1], dm_tol=[1, 0.5], trans_class=transforms.PolyTransform, trans_args={'order': 2}, use_vel=True, - update_ref_orig=False, verbose=False) + update_ref_orig=False, verbose=True) msc.fit() @@ -262,7 +322,6 @@ def test_MosaicToRef(): np.testing.assert_almost_equal(msc.ref_table['vx'], ref_list['vx'], 5) np.testing.assert_almost_equal(msc.ref_table['vy'], ref_list['vy'], 5) - ########## # Align and let velocities be free. ########## @@ -279,7 +338,6 @@ def test_MosaicToRef(): return msc -# TODO: Make this a valid test def test_MosaicToRef_acc(): make_fake_starlists_poly1_acc(seed=42) @@ -302,8 +360,8 @@ def test_MosaicToRef_acc(): # Convert accelerations to arcsec/yr**2 ref_list['ax'] *= 1e-3 ref_list['ay'] *= 1e-3 - ref_list['axe'] *= 1e-3 - ref_list['aye'] *= 1e-3 + ref_list['ax_err'] *= 1e-3 + ref_list['ay_err'] *= 1e-3 # Switch our list to a "increasing to the West" list. ref_list['x0'] *= -1.0 @@ -598,12 +656,16 @@ def make_fake_starlists_poly1_acc(seed=-1): # Propogate to new times and distort. ########## # Make 4 new starlists with different epochs and transformations. - times = [2018.5, 2019.5, 2020.5, 2021.5] + times = [2018.5, 2019.0, 2019.5, 2020.0, 2020.5, 2021.0, 2021.5, 2022.0] xy_trans = [[[ 6.5, 0.99, 1e-5], [ 10.1, 1e-5, 0.99]], [[100.3, 0.98, 1e-5], [ 50.5, 9e-6, 1.001]], - [[ 0.0, 1.00, 0.0], [ 0.0, 0.0, 1.0]], - [[250.0, 0.97, 2e-5], [-250.0, 1e-5, 1.001]]] - mag_trans = [0.1, 0.4, 0.0, -0.3] + [[ 0.0, 1.00, 0.0], [ 0.0, 0.0, 1.000]], + [[250.0, 0.97, 2e-5], [-250.0, 1e-5, 1.001]], + [[ 50.0, 1.01, 1e-5], [ -31.0, 1e-5, 1.000]], + [[ 78.0, 0.98, 0.0 ], [ 45.0, 9e-6, 1.001]], + [[-13.0, 0.99, 1e-5], [ 150, 2e-5, 1.002]], + [[ 94.0, 1.00, 9e-6], [-182.0, 0.0, 0.99]]] + mag_trans = [0.1, 0.4, 0.0, -0.3, 0.2, 0.0, -0.1, -0.3] # Convert into pixels (undistorted) with the following info. scale = 0.01 # arcsec / pix @@ -612,8 +674,8 @@ def make_fake_starlists_poly1_acc(seed=-1): for ss in range(len(times)): dt = times[ss] - lis['t0'] - x = lis['x0'] + (lis['vx0']/1e3) * dt + (lis['ax']/1e3) * dt**2 - y = lis['y0'] + (lis['vy0']/1e3) * dt + (lis['ay']/1e3) * dt**2 + x = lis['x0'] + (lis['vx0']/1e3) * dt + 0.5*(lis['ax']/1e3) * dt**2 + y = lis['y0'] + (lis['vy0']/1e3) * dt + 0.5*(lis['ay']/1e3) * dt**2 t = np.ones(N_stars) * times[ss] # Convert into pixels From be831f3af84fdf285c3e11f79425f32281b5d410 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Tue, 17 Dec 2024 14:53:47 -0800 Subject: [PATCH 027/254] motion model testing progress --- flystar/align.py | 23 +-- flystar/motion_model.py | 202 +++++++++++-------------- flystar/startables.py | 29 ++-- flystar/tests/test_align.py | 233 ++++++++++++++++++++++------- flystar/tests/test_motion_model.py | 31 +++- flystar/transforms.py | 11 +- 6 files changed, 334 insertions(+), 195 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index f8374f8..2d697b5 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -521,7 +521,7 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar ### Update the "average" values to be used as the reference frame for the next list. if self.update_ref_orig != 'periter': self.update_ref_table_aggregates() - + # Print out some metrics if self.verbose > 0: msg1 = ' {0:2s} (mean and std) for {1:10s}: {2:8.5f} +/- {3:8.5f}' @@ -837,23 +837,27 @@ def update_ref_table_aggregates(self, n_boot=0): if mm in self.ref_table.keys(): vals_orig[mm] = self.ref_table[mm][ref_orig_idx] - #if self.use_motion: - # Combine positions with a velocity fit. + #if 'motion_model_input' in self.ref_table.keys(): + # Combine positions with a velocity fit. + if 'vx' in self.ref_table.keys(): + print('before:',self.ref_table['vx'][:10]) self.ref_table.fit_velocities(bootstrap=n_boot, verbose=self.verbose, default_motion_model=self.default_motion_model) + print(np.unique(self.ref_table['motion_model_used'])) + if 'vx' in self.ref_table.keys(): + print('after:', self.ref_table['vx'][:10]) # Combine (transformed) magnitudes - # TODO: how does this work? if 'me' in self.ref_table.colnames: weights_col = None else: weights_col = 'me' self.ref_table.combine_lists('m', weights_col=weights_col, ismag=True) - '''else: - weighted_xy = ('xe' in self.ref_table.colnames) and ('ye' in self.ref_table.colnames) - weighted_m = ('me' in self.ref_table.colnames) + #else: + # weighted_xy = ('xe' in self.ref_table.colnames) and ('ye' in self.ref_table.colnames) + # weighted_m = ('me' in self.ref_table.colnames) - self.ref_table.combine_lists_xym(weighted_xy=weighted_xy, weighted_m=weighted_m)''' + # self.ref_table.combine_lists_xym(weighted_xy=weighted_xy, weighted_m=weighted_m) # Replace the originals if we are supposed to keep them fixed. if not self.update_ref_orig: for val in vals_orig.keys(): @@ -962,6 +966,7 @@ def get_ref_list_from_table(self, epoch): if ('motion_model_used' in self.ref_table.colnames): x,y,xe,ye = self.ref_table.get_star_positions_at_time(epoch) + #print('ref star pos',epoch,x,y) else: # No velocities... just used average positions. x = self.ref_table['x0'] @@ -3037,7 +3042,7 @@ def get_weighting_scheme(weights, ref_list, star_list): return weight -# TODO: I think this is a startable, not a starlist +# TODO: I think this is a startable, not a starlist, at least as currently used def get_pos_at_time(t, starlist): """ Take a starlist, check to see if it has motion/velocity columns. diff --git a/flystar/motion_model.py b/flystar/motion_model.py index a6726d3..ff9fbd5 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -5,6 +5,7 @@ from flystar import parallax from astropy.time import Time from scipy.optimize import curve_fit +import warnings class MotionModel(ABC): # Number of data points required to fit model @@ -18,6 +19,8 @@ class MotionModel(ABC): # Fixed parameters: These are parameters that are required for the model, but are not # fit quantities. For example, RA and Dec in a parallax model. fixed_param_names = [] + # TODO: for values that are for the full data set, not per star - are we happy with this method? + fixed_meta_data = [] # Non-fit paramters: Custom paramters that will not be fit. # These parameters should be derived from the fit parameters and @@ -44,8 +47,21 @@ def get_pos_err_at_time(self, t): def get_batch_pos_at_time(self, t): #return x, y, x_err, y_err pass + + def run_fit(self, t, x, y, xe, ye, update=True, weighting='var'): + # Run a single fit (used both for overall fit + bootstrap iterations) + pass + + def get_weights(self, xe, ye, weighting='var'): + if weighting=='std': + return 1./xe, 1./ye + elif weighting=='var': + return 1./xe**2, 1./ye**2 + else: + warnings.warn("Invalid weighting, using default weighting scheme var.", UserWarning) + return 1./xe**2, 1./ye**2 - def fit_motion_model(self, t, x, y, xe, ye, update=True): + def fit_motion_model(self, t, x, y, xe, ye, update=True, bootstrap=0, weighting='var'): """ Fit the input positions on the sky and errors to determine new parameters for this motion model (MM). @@ -53,10 +69,26 @@ def fit_motion_model(self, t, x, y, xe, ye, update=True): Best-fit parameters will be returned along with uncertainties and updated if update=True. """ - #return params, param_errors - pass + params, param_errs = self.run_fit(t, x, y, xe, ye, weighting=weighting, update=True) + + if bootstrap>0 and len(x)>(self.n_pts_req): + edx = np.arange(len(x), dtype=int) + bb_params = [] + for bb in range(bootstrap): + bdx = np.random.choice(edx, len(x)) + params_bdx, param_errs_bdx = self.run_fit(t[bdx], x[bdx], y[bdx], xe[bdx], ye[bdx], weighting=weighting, update=False) + bb_params.append(params_bdx) + + # Save the errors from the bootstrap + param_errs = np.std(bb_params, axis=0) + + if update: + for i in range(len(self.fitter_param_names)): + setattr(self, self.fitter_param_names[i]+'_err', param_errs[i]) + + return params, param_errs - def get_chi2(self,t,x,y,xe,ye): + def get_chi2(self,t,x,y,xe,ye,reduced=False): """ Get the chi^2 value for the current MM and the input data. @@ -65,6 +97,11 @@ def get_chi2(self,t,x,y,xe,ye): x_pred,y_pred = self.get_pos_at_time(t) chi2x = np.sum((x-x_pred)**2 / xe**2) chi2y = np.sum((y-y_pred)**2 / ye**2) + if reduced: + if len(t)==self.dof: + chi2x, chi2y = 0,0 + else: + chi2x, chi2y = chi2x/(len(x)-self.dof), chi2y/(len(x)-self.dof) return chi2x,chi2y class Fixed(MotionModel): @@ -110,16 +147,16 @@ def get_batch_pos_at_time(self,t, else: return x0,y0,x0_err,y0_err - def fit_motion_model(self, t, x, y, xe, ye, update=True, bootstrap=0): + def run_fit(self, t, x, y, xe, ye, update=True, weighting='var'): # Handle single data point case if len(x)==1: return [x[0],y[0]],[xe[0],ye[0]] - #TODO: it seems like sometimes it's weighted by std and sometimes by var - confirm which to do here - x0 = np.average(x, weights=1/xe**2) - x0e = np.sqrt(np.average((x-x0)**2,weights=1/xe**2)) - y0 = np.average(y, weights=1/ye**2) - y0e = np.sqrt(np.average((y-y0)**2,weights=1/ye**2)) + x_wt, y_wt = self.get_weights(xe,ye, weighting=weighting) + x0 = np.average(x, weights=x_wt) + x0e = np.sqrt(np.average((x-x0)**2,weights=x_wt)) + y0 = np.average(y, weights=y_wt) + y0e = np.sqrt(np.average((y-y0)**2,weights=y_wt)) params = [x0, y0] param_errors = [x0e, y0e] @@ -188,91 +225,62 @@ def get_batch_pos_at_time(self, t, y_err = np.hypot(y0_err, vy_err*dt) return x,y,x_err,y_err - def fit_motion_model(self, dt, x, y, xe, ye, update=True, bootstrap=0): + def run_fit(self, t, x, y, xe, ye, update=True, weighting='var'): fitter = fitting.LevMarLSQFitter() - + dt = t-self.t0 # Handle 2-data point case - # TODO: is this the best way to handle this case ? Altered it to be consistent with t0 if len(x)==2: ix = int(xe[0]>xe[1]) iy = int(ye[0]>ye[1]) dx = np.diff(x)[0] dy = np.diff(y)[0] - dt_diff = np.diff(dt)[0] - vx = dx / dt_diff - vy = dy / dt_diff - x0 = x[ix]-vx*dt[ix] - y0 = y[iy]-vy*dt[iy] - vxe = np.hypot(*xe)/dt_diff - vye = np.hypot(*ye)/dt_diff + t_diff = np.diff(t)[0] + vx = dx / t_diff + vy = dy / t_diff + x0 = x[ix]+vx*(-dt[ix]) + y0 = y[ix]+vy*(-dt[ix]) + vxe = np.hypot(*xe)/t_diff + vye = np.hypot(*ye)/t_diff x0e = np.sqrt(xe[ix]**2 + (dt[ix]*vxe)**2) y0e = np.sqrt(ye[iy]**2 + (dt[iy]*vye)**2) return [x0, vx, y0, vy],[x0e, vxe, y0e, vye] - px_new = fitter(self.px, dt, x, weights=1/xe) + x_wt, y_wt = self.get_weights(xe,ye, weighting=weighting) + px_new = fitter(self.px, dt, x, weights=x_wt) px_cov = fitter.fit_info['param_cov'] - py_new = fitter(self.py, dt, y, weights=1/ye) + py_new = fitter(self.py, dt, y, weights=y_wt) py_cov = fitter.fit_info['param_cov'] x0 = px_new.c0.value vx = px_new.c1.value y0 = py_new.c0.value vy = py_new.c1.value - - # Run the bootstrap - if bootstrap > 0: - edx = np.arange(len(x), dtype=int) - fit_x0_b = np.zeros(bootstrap, dtype=float) - fit_vx_b = np.zeros(bootstrap, dtype=float) - fit_y0_b = np.zeros(bootstrap, dtype=float) - fit_vy_b = np.zeros(bootstrap, dtype=float) - - for bb in range(bootstrap): - bdx = np.random.choice(edx, len(x)) - - px_b = fitter(self.px, dt[bdx], x[bdx], weights=1/xe[bdx]) - px_b_cov = fitter.fit_info['param_cov'] - py_b = fitter(self.py, dt[bdx], y[bdx], weights=1/ye[bdx]) - py_b_cov = fitter.fit_info['param_cov'] + px_param_errs = dict(zip(self.px.param_names, np.diag(px_cov)**0.5)) + py_param_errs = dict(zip(self.py.param_names, np.diag(py_cov)**0.5)) + x0e = px_param_errs['c0'] + vxe = px_param_errs['c1'] + y0e = py_param_errs['c0'] + vye = py_param_errs['c1'] - fit_x0_b[bb] = px_b.c0.value - fit_vx_b[bb] = px_b.c1.value - fit_y0_b[bb] = py_b.c0.value - fit_vy_b[bb] = py_b.c1.value - - # Save the errors from the bootstrap - x0e = fit_x0_b.std() - vxe = fit_vx_b.std() - y0e = fit_y0_b.std() - vye = fit_vy_b.std() - else: - px_param_errs = dict(zip(self.px.param_names, np.diag(px_cov)**0.5)) - py_param_errs = dict(zip(self.py.param_names, np.diag(py_cov)**0.5)) - x0e = px_param_errs['c0'] - vxe = px_param_errs['c1'] - y0e = py_param_errs['c0'] - vye = py_param_errs['c1'] + params = [x0, vx, y0, vy] + param_errors = [x0e, vxe, y0e, vye] if update: self.px = px_new self.py = py_new - self.x0 = x0 self.vx = vx self.y0 = y0 self.vy = vy - self.x0_err = x0e self.vx_err = vxe self.y0_err = y0e self.vy_err = vye - - params = [x0, vx, y0, vy] - param_errors = [x0e, vxe, y0e, vye] return params, param_errors - + + class Acceleration(MotionModel): """ A 2D accelerating motion model for a star on the sky. @@ -337,12 +345,14 @@ def get_batch_pos_at_time(self,t, y_err = np.sqrt(y0_err**2 + (vy0_err*dt)**2 + (0.5*ay_err*dt**2)**2) return x,y,x_err,y_err - def fit_motion_model(self, dt, x, y, xe, ye, update=True, bootstrap=0): + def run_fit(self, t, x, y, xe, ye, update=True, weighting='var'): fitter = fitting.LevMarLSQFitter() + dt = t-self.t0 + x_wt, y_wt = self.get_weights(xe,ye, weighting=weighting) - px_new = fitter(self.px, dt, x, weights=1/xe) + px_new = fitter(self.px, dt, x, weights=x_wt) px_cov = fitter.fit_info['param_cov'] - py_new = fitter(self.py, dt, y, weights=1/ye) + py_new = fitter(self.py, dt, y, weights=y_wt) py_cov = fitter.fit_info['param_cov'] x0 = px_new.c0.value @@ -351,61 +361,25 @@ def fit_motion_model(self, dt, x, y, xe, ye, update=True, bootstrap=0): y0 = py_new.c0.value vy0 = py_new.c1.value ay = py_new.c2.value - - # Run the bootstrap - if bootstrap > 0: - edx = np.arange(len(x), dtype=int) - - fit_x0_b = np.zeros(bootstrap, dtype=float) - fit_vx0_b = np.zeros(bootstrap, dtype=float) - fit_ax_b = np.zeros(bootstrap, dtype=float) - fit_y0_b = np.zeros(bootstrap, dtype=float) - fit_vy0_b = np.zeros(bootstrap, dtype=float) - fit_ay_b = np.zeros(bootstrap, dtype=float) - - for bb in range(bootstrap): - bdx = np.random.choice(edx, len(x)) - - px_b = fitter(self.px, dt[bdx], x[bdx], weights=1/xe[bdx]) - px_b_cov = fitter.fit_info['param_cov'] - py_b = fitter(self.py, dt[bdx], y[bdx], weights=1/ye[bdx]) - py_b_cov = fitter.fit_info['param_cov'] - - fit_x0_b[bb] = px_b.c0.value - fit_vx0_b[bb] = px_b.c1.value - fit_ax_b[bb] = px_b.c2.value - fit_y0_b[bb] = py_b.c0.value - fit_vy0_b[bb] = py_b.c1.value - fit_ay_b[bb] = py_b.c2.value - - # Save the errors from the bootstrap - x0e = fit_x0_b.std() - vx0e = fit_vx0_b.std() - axe = fit_ax_b.std() - y0e = fit_y0_b.std() - vy0e = fit_vy0_b.std() - aye = fit_ay_b.std() - else: - px_param_errs = dict(zip(self.px.param_names, np.diag(px_cov)**0.5)) - py_param_errs = dict(zip(self.py.param_names, np.diag(py_cov)**0.5)) - x0e = px_param_errs['c0'] - vx0e = px_param_errs['c1'] - axe = px_param_errs['c2'] - y0e = py_param_errs['c0'] - vy0e = py_param_errs['c1'] - aye = py_param_errs['c2'] + + px_param_errs = dict(zip(self.px.param_names, np.diag(px_cov)**0.5)) + py_param_errs = dict(zip(self.py.param_names, np.diag(py_cov)**0.5)) + x0e = px_param_errs['c0'] + vx0e = px_param_errs['c1'] + axe = px_param_errs['c2'] + y0e = py_param_errs['c0'] + vy0e = py_param_errs['c1'] + aye = py_param_errs['c2'] if update: self.px = px_new self.py = py_new - self.x0 = x0 self.vx0 = vx0 self.ax = ax self.y0 = y0 self.vy0 = vy0 self.ay = ay - self.x0_err = x0e self.vx0_err = vx0e self.ax_err = axe @@ -425,12 +399,13 @@ class Parallax(MotionModel): Requires RA, Dec, and PA parameters (degrees) for parallax calculation. RA, Dec in J2000 PA is counterclockwise offset of the image y-axis from North. - Optional obs parameter describing observer location, default is 'earth'. + Optional obs parameter describes observer location, default is 'earth'. """ n_pts_req = 4 dof=3 fitter_param_names = ['x0', 'vx', 'y0', 'vy', 'pi'] - fixed_param_names = ['t0', 'RA','Dec','PA','obs'] + fixed_param_names = ['t0'] + fixed_meta_data = ['RA','Dec','PA','obs'] def __init__(self, x0=0, vx=0, y0=0, vy=0, t0=2025.0, x0_err=0, vx_err=0, y0_err=0, vy_err=0, @@ -492,13 +467,14 @@ def get_batch_pos_at_time(self, t, x_err,y_err = [],[] return x,y,x_err,y_err - def fit_motion_model(self, t, x, y, xe, ye, update=True): + def run_fit(self, t, x, y, xe, ye, update=True, weighting='var'): t_mjd = Time(t, format='decimalyear', scale='utc').mjd pvec = parallax.parallax_in_direction(self.RA, self.Dec, t_mjd, obsLocation=self.obs, PA=self.PA).T + x_wt, y_wt = self.get_weights(xe,ye, weighting=weighting) def fit_func(t, x0,y0, vx,vy, pi): x_res = x0 + vx*(t-self.t0) + pi*pvec[0] y_res = y0 + vy*(t-self.t0) + pi*pvec[1] - diff = (x-x_res)**2/xe**2 + (y-y_res)**2/ye**2 + diff = (x-x_res)**2 * x_wt + (y-y_res)**2 * y_wt return diff # Initial guesses, x0,y0 as x,y averages; # vx,vy as average velocity if first and last points are perfectly measured; diff --git a/flystar/startables.py b/flystar/startables.py index 20f4263..dea27dc 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -178,8 +178,8 @@ def __init__(self, *args, ref_list=0, position_angle=None, RA=None, Dec=None, ob self['name_in_list'] = self['name_in_list'].astype('U20') if arg == 'motion_model_input': self['motion_model_input'] = self['motion_model_input'].astype('U20') - '''if 'motion_model' not in kwargs: - self['motion_model'] = np.repeat(default_motion_model, len(self['name']))''' + #if 'motion_model_input' not in kwargs: + # self['motion_model_input'] = np.repeat(self.default_motion_model, len(self['name'])) return @@ -247,7 +247,7 @@ def _add_list_data_from_starlist(self, starlist): # Meta table entries with a size that matches the n_lists size are the ones # that need a new value. We have to add something... whatever was passed in or None - if isinstance(self.meta[tab_key], collections.abc.Iterable) and (len(self.meta[tab_key]) == self.meta['n_lists']): + if isinstance(self.meta[tab_key], collections.abc.Iterable) and (len(self.meta[tab_key]) == self.meta['n_lists']) and (not isinstance(self.meta[tab_key], str)): # If we find the key in the starlists' meta argument, then add the new values. # Otherwise, add "None". @@ -304,7 +304,7 @@ def _add_list_data_from_keywords(self, **kwargs): for key in self.meta.keys(): # Meta table entries with a size that matches the n_lists size are the ones # that need a new value. We have to add something... whatever was passed in or None - if isinstance(self.meta[key], collections.abc.Iterable) and (len(self.meta[key]) == self.meta['n_lists']): + if isinstance(self.meta[key], collections.abc.Iterable) and (len(self.meta[key]) == self.meta['n_lists']) and (not isinstance(self.meta[key], str)): # If we find the key is the passed in meta argument, then add the new values. # Otherwise, add "None". if 'meta' in kwargs: @@ -641,7 +641,7 @@ def fit_velocities(self, weighting='var', use_scipy=True, absolute_sigma=True, b self['n_fit'] = 1 return - + # STARS LOOP through the stars and work on them 1 at a time. # This is slow; but robust. if show_progress: @@ -670,6 +670,11 @@ def fit_velocity_for_star(self, ss, weighting='var', use_scipy=True, # # Make a mask of invalid (NaN) values and a user-specified invalid value. # + + if ss==0: + print('first star') + print(' x', self['x'][ss, :].data) + x = np.ma.masked_invalid(self['x'][ss, :].data) y = np.ma.masked_invalid(self['y'][ss, :].data) if mask_val: @@ -797,7 +802,6 @@ def fit_velocity_for_star(self, ss, weighting='var', use_scipy=True, t0 = self.t0 else: t0 = fixed_t0[ss] - dt = t - t0 self['t0'][ss] = t0 self['n_fit'][ss] = N_good @@ -815,7 +819,7 @@ def fit_velocity_for_star(self, ss, weighting='var', use_scipy=True, (N_good < getattr(motion_model, default_motion_model).n_pts_req): motion_model_use = 'Fixed' # If the points do not cover multiple times, go to a fixed model - if (dt == dt[0]).all(): + if (t == t[0]).all(): motion_model_use = 'Fixed' self['motion_model_used'][ss] = motion_model_use @@ -829,8 +833,9 @@ def fit_velocity_for_star(self, ss, weighting='var', use_scipy=True, if ~np.isnan(self[par][ss]): param_dict[par] = self[par][ss] + # TODO: this doesn't match how we actually handle ra,dec,pa - need to adjust # Load fixed parameters, if needed. - for par in modClass.fixed_param_names: + '''for par in modClass.fixed_param_names: if par not in self.colnames: msg = f'fit_velocity_for_star: ' msg += f'Missing fixed_params column {par} needed for motion model {motion_model_use}.' @@ -838,14 +843,14 @@ def fit_velocity_for_star(self, ss, weighting='var', use_scipy=True, raise RuntimeException(msg) if self[par][ss] != np.nan: - param_dict[par] = self[par][ss] + param_dict[par] = self[par][ss]''' # Model object mod = modClass(**param_dict, PA=self.meta['position_angle'], RA=self.meta['RA'], Dec=self.meta['Dec'], obs=self.meta['observer_location']) # Fit for the best parameters - params, param_errs = mod.fit_motion_model(dt, x, y, xe, ye, bootstrap=bootstrap, update=True) - chi2_x,chi2_y = mod.get_chi2(dt,x,y,xe,ye) + params, param_errs = mod.fit_motion_model(t, x, y, xe, ye, bootstrap=bootstrap, update=True) + chi2_x,chi2_y = mod.get_chi2(t,x,y,xe,ye) self['chi2_x'][ss]=chi2_x self['chi2_y'][ss]=chi2_y @@ -882,7 +887,7 @@ def get_star_positions_at_time(self, t): param_dict = {} for par in motion_model.get_one_motion_model_param_names(mm,with_errors=True,with_fixed=True): param_dict[par] = self[par][idx] - mod = modClass() + mod = modClass(RA=self.meta['RA'], Dec=self.meta['Dec'], PA=self.meta['position_angle'], obs=self.meta['observer_location']) x[idx],y[idx],xe[idx],ye[idx] = mod.get_batch_pos_at_time(t,**param_dict) return x,y,xe,ye diff --git a/flystar/tests/test_align.py b/flystar/tests/test_align.py index 6cd7abe..6926ec1 100644 --- a/flystar/tests/test_align.py +++ b/flystar/tests/test_align.py @@ -137,11 +137,7 @@ def test_MosaicSelfRef_vel_tconst(): np.testing.assert_almost_equal(msc.trans_list[ii].py.c0_1, 1.0, 2) # Check that the velocities aren't crazy... - # they should be zero (since there is no time difference) - # TODO: is there a reason these were both happening? seemed like a duplicate assert - #np.testing.assert_almost_equal(msc.ref_table['vx'], 0, 1) - #np.testing.assert_almost_equal(msc.ref_table['vy'], 0, 1) - + # they should be non-existent (since there is no time difference) assert np.isnan(msc.ref_table['vx']).all() assert np.isnan(msc.ref_table['vy']).all() assert np.isnan(msc.ref_table['vx_err']).all() @@ -177,7 +173,7 @@ def test_MosaicSelfRef_vel(): msc = align.MosaicSelfRef(lists, ref_index=0, iters=3, dr_tol=[5, 3, 3], dm_tol=[1, 1, 0.5], outlier_tol=None, trans_class=transforms.PolyTransform, - trans_args={'order': 2}, use_vel=True, + trans_args={'order': 2}, default_motion_model='Linear', verbose=False) msc.fit() @@ -280,13 +276,83 @@ def test_MosaicSelfRef_vel(): def test_MosaicToRef(): + make_fake_starlists_poly1(seed=42) + + ref_file = 'random_ref.fits' + list_files = ['random_0.fits', + 'random_1.fits', + 'random_2.fits', + 'random_3.fits', + 'random_4.fits', + 'random_5.fits', + 'random_6.fits', + 'random_7.fits'] + + ref_list = Table.read(ref_file) + + # Switch our list to a "increasing to the West" list. + ref_list['x0'] *= -1.0 + + lists = [starlists.StarList.read(lf) for lf in list_files] + + msc = align.MosaicToRef(ref_list, lists, iters=2, + dr_tol=[0.2, 0.1], dm_tol=[1, 0.5], + trans_class=transforms.PolyTransform, + trans_args={'order': 2}, default_motion_model='Fixed', + update_ref_orig=False, verbose=True) + + msc.fit() + + # Check our status columns + assert 'use_in_trans' in msc.ref_table.colnames + assert 'used_in_trans' in msc.ref_table.colnames + assert 'ref_orig' in msc.ref_table.colnames + assert msc.ref_table['use_in_trans'].shape == msc.ref_table['x0'].shape + assert msc.ref_table['used_in_trans'].shape == msc.ref_table['x'].shape + + # The velocities should be almost the same as the input + # velocities since update_ref_orig == False. + for i,star in enumerate(ref_list["name"]): + if star in msc.ref_table["name"]: + ii = np.where(msc.ref_table["name"]==star)[0][0] + np.testing.assert_almost_equal(msc.ref_table['x0'][ii], ref_list['x0'][i], 5) + np.testing.assert_almost_equal(msc.ref_table['y0'][ii], ref_list['y0'][i], 5) + + ########## + # Align and let velocities be free. + ########## + msc.update_ref_orig = True + msc.fit() + + # The velocities should be almost the same (but not as close as before) + # as the input velocities since update_ref == False. + for i,star in enumerate(ref_list["name"]): + if star in msc.ref_table["name"]: + ii = np.where(msc.ref_table["name"]==star)[0][0] + np.testing.assert_almost_equal(msc.ref_table['x0'][ii], ref_list['x0'][i], 1) + np.testing.assert_almost_equal(msc.ref_table['y0'][ii], ref_list['y0'][i], 1) + + # Also double check that they aren't exactly the same for the reference stars. + #assert np.any(np.not_equal(msc.ref_table['vx'], ref_list['vx'])) + for i,star in enumerate(ref_list["name"]): + if star in msc.ref_table["name"]: + ii = np.where(msc.ref_table["name"]==star)[0][0] + assert np.not_equal(msc.ref_table['x0'][ii], ref_list['x0'][i]) + + return msc + +def test_MosaicToRef_vel(): make_fake_starlists_poly1_vel(seed=42) ref_file = 'random_vel_ref.fits' list_files = ['random_vel_0.fits', 'random_vel_1.fits', 'random_vel_2.fits', - 'random_vel_3.fits'] + 'random_vel_3.fits', + 'random_vel_4.fits', + 'random_vel_5.fits', + 'random_vel_6.fits', + 'random_vel_7.fits'] ref_list = Table.read(ref_file) @@ -305,7 +371,7 @@ def test_MosaicToRef(): msc = align.MosaicToRef(ref_list, lists, iters=2, dr_tol=[0.2, 0.1], dm_tol=[1, 0.5], trans_class=transforms.PolyTransform, - trans_args={'order': 2}, use_vel=True, + trans_args={'order': 2}, default_motion_model='Linear', update_ref_orig=False, verbose=True) msc.fit() @@ -319,8 +385,11 @@ def test_MosaicToRef(): # The velocities should be almost the same as the input # velocities since update_ref_orig == False. - np.testing.assert_almost_equal(msc.ref_table['vx'], ref_list['vx'], 5) - np.testing.assert_almost_equal(msc.ref_table['vy'], ref_list['vy'], 5) + for i,star in enumerate(ref_list["name"]): + if star in msc.ref_table["name"]: + ii = np.where(msc.ref_table["name"]==star)[0][0] + np.testing.assert_almost_equal(msc.ref_table['vx'][ii], ref_list['vx'][i], 5) + np.testing.assert_almost_equal(msc.ref_table['vy'][ii], ref_list['vy'][i], 5) ########## # Align and let velocities be free. @@ -330,11 +399,18 @@ def test_MosaicToRef(): # The velocities should be almost the same (but not as close as before) # as the input velocities since update_ref == False. - np.testing.assert_almost_equal(msc.ref_table['vx'], ref_list['vx'], 1) - np.testing.assert_almost_equal(msc.ref_table['vy'], ref_list['vy'], 1) + for i,star in enumerate(ref_list["name"]): + if star in msc.ref_table["name"]: + ii = np.where(msc.ref_table["name"]==star)[0][0] + np.testing.assert_almost_equal(msc.ref_table['vx'][ii], ref_list['vx'][i], 1) + np.testing.assert_almost_equal(msc.ref_table['vy'][ii], ref_list['vy'][i], 1) # Also double check that they aren't exactly the same for the reference stars. - assert np.any(np.not_equal(msc.ref_table['vx'], ref_list['vx'])) + #assert np.any(np.not_equal(msc.ref_table['vx'], ref_list['vx'])) + for i,star in enumerate(ref_list["name"]): + if star in msc.ref_table["name"]: + ii = np.where(msc.ref_table["name"]==star)[0][0] + assert np.not_equal(msc.ref_table['vx'][ii], ref_list['vx'][i]) return msc @@ -345,7 +421,11 @@ def test_MosaicToRef_acc(): list_files = ['random_acc_0.fits', 'random_acc_1.fits', 'random_acc_2.fits', - 'random_acc_3.fits'] + 'random_acc_3.fits', + 'random_acc_4.fits', + 'random_acc_5.fits', + 'random_acc_6.fits', + 'random_acc_7.fits'] ref_list = Table.read(ref_file) print(ref_list.keys()) @@ -371,14 +451,13 @@ def test_MosaicToRef_acc(): lists = [starlists.StarList.read(lf) for lf in list_files] msc = align.MosaicToRef(ref_list, lists, iters=2, - dr_tol=[0.2, 0.1], dm_tol=[1, 0.5], + dr_tol=[0.4, 0.2], dm_tol=[1, 0.5], trans_class=transforms.PolyTransform, trans_args={'order': 2}, default_motion_model='Acceleration', update_ref_orig=False, verbose=False) msc.fit() - print(msc.ref_table['motion_model_input','motion_model_used']) # Check our status columns assert 'use_in_trans' in msc.ref_table.colnames @@ -389,9 +468,11 @@ def test_MosaicToRef_acc(): # The velocities should be almost the same as the input # velocities since update_ref_orig == False. - np.testing.assert_almost_equal(msc.ref_table['vx0'], ref_list['vx0'], 5) - np.testing.assert_almost_equal(msc.ref_table['vy0'], ref_list['vy0'], 5) - + for i,star in enumerate(ref_list["name"]): + if star in msc.ref_table["name"]: + ii = np.where(msc.ref_table["name"]==star)[0][0] + np.testing.assert_almost_equal(msc.ref_table['vx0'][ii], ref_list['vx0'][i], 5) + np.testing.assert_almost_equal(msc.ref_table['vy0'][ii], ref_list['vy0'][i], 5) ########## # Align and let velocities be free. @@ -401,8 +482,11 @@ def test_MosaicToRef_acc(): # The velocities should be almost the same (but not as close as before) # as the input velocities since update_ref == False. - np.testing.assert_almost_equal(msc.ref_table['vx0'], ref_list['vx0'], 1) - np.testing.assert_almost_equal(msc.ref_table['vy0'], ref_list['vy0'], 1) + for i,star in enumerate(ref_list["name"]): + if star in msc.ref_table["name"]: + ii = np.where(msc.ref_table["name"]==star)[0][0] + np.testing.assert_almost_equal(msc.ref_table['vx0'][ii], ref_list['vx0'][i], 1) + np.testing.assert_almost_equal(msc.ref_table['vy0'][ii], ref_list['vy0'][i], 1) # Also double check that they aren't exactly the same for the reference stars. assert np.any(np.not_equal(msc.ref_table['vx0'], ref_list['vx0'])) @@ -463,51 +547,85 @@ def make_fake_starlists_poly1(seed=-1): np.random.seed(seed=seed) N_stars = 200 - x = np.random.rand(N_stars) * 1000 - y = np.random.rand(N_stars) * 1000 - m = (np.random.rand(N_stars) * 8) + 9 - - sdx = np.argsort(m) - x = x[sdx] - y = y[sdx] - m = m[sdx] + + x0 = np.random.rand(N_stars) * 10.0 # arcsec (increasing to East) + y0 = np.random.rand(N_stars) * 10.0 # arcsec + x0e = np.random.randn(N_stars) * 5.0e-4 # arcsec + y0e = np.random.randn(N_stars) * 5.0e-4 # arcsec + m0 = (np.random.rand(N_stars) * 8) + 9 # mag + m0e = np.random.randn(N_stars) * 0.05 # mag + t0 = np.ones(N_stars) * 2019.5 + + # Make all the errors positive + x0e = np.abs(x0e) + y0e = np.abs(y0e) + m0e = np.abs(m0e) name = ['star_{0:03d}'.format(ii) for ii in range(N_stars)] - # Save original positions as reference (1st) list. - fmt = '{0:10s} {1:5.2f} 2015.0 {2:9.4f} {3:9.4f} 0 0 0 0\n' - _out = open('random_0.lis', 'w') - for ii in range(N_stars): - _out.write(fmt.format(name[ii], m[ii], x[ii], y[ii])) - _out.close() + # Make an StarList + lis = starlists.StarList([name, m0, m0e, x0, x0e, y0, y0e, t0], + names = ('name', 'm0', 'm0_err', 'x0', 'x0_err', 'y0', 'y0_err', 't0')) + + sdx = np.argsort(m0) + lis = lis[sdx] + # Save original positions as reference (1st) list + # in a StarList format (with velocities). + lis.write('random_ref.fits', overwrite=True) ########## # Shifts ########## # Make 4 new starlists with different shifts. - transforms = [[[ 6.5, 0.99, 1e-5], [ 10.1, 1e-5, 0.99]], - [[100.3, 0.98, 1e-5], [ 50.5, 9e-6, 1.001]], - [[-30.0, 1.00, 1e-5], [-100.7, 2e-5, 0.999]], - [[250.0, 0.97, 2e-5], [-250.0, 1e-5, 1.001]]] + times = [2018.5, 2019.0, 2019.5, 2020.0, 2020.5, 2021.0, 2021.5, 2022.0] + xy_trans = [[[ 6.5, 0.99, 1e-5], [ 10.1, 1e-5, 0.99]], + [[100.3, 0.98, 1e-5], [ 50.5, 9e-6, 1.001]], + [[ 0.0, 1.00, 0.0], [ 0.0, 0.0, 1.0]], + [[250.0, 0.97, 2e-5], [-250.0, 1e-5, 1.001]], + [[ 50.0, 1.01, 1e-5], [ -31.0, 1e-5, 1.000]], + [[ 78.0, 0.98, 0.0 ], [ 45.0, 9e-6, 1.001]], + [[-13.0, 0.99, 1e-5], [ 150, 2e-5, 1.002]], + [[ 94.0, 1.00, 9e-6], [-182.0, 0.0, 0.99]]] + mag_trans = [0.1, 0.4, 0.0, -0.3, 0.2, 0.0, -0.1, -0.3] + + # Convert into pixels (undistorted) with the following info. + scale = 0.01 # arcsec / pix + shift = [1.0, 1.0] # pix - for ss in range(len(shifts)): - #transforms.PolyTransform2D(1, transforms[ss]) - xnew = x - shifts[ss][0] - ynew = y - shifts[ss][1] + for ss in range(len(times)): + dt = times[ss] - lis['t0'] + + x = lis['x0'] + y = lis['y0'] + t = np.ones(N_stars) * times[ss] + + # Convert into pixels + xp = (x / -scale) + shift[0] # -1 from switching to increasing to West (right) + yp = (y / scale) + shift[1] + xpe = lis['x0_err'] / scale + ype = lis['y0_err'] / scale + + # Distort the positions + trans = transforms.PolyTransform(1, xy_trans[ss][0], xy_trans[ss][1], mag_offset=mag_trans[ss]) + xd, yd = trans.evaluate(xp, yp) + md = trans.evaluate_mag(lis['m0']) # Perturb with small errors (0.1 pix) - xnew += np.random.randn(N_stars) * 0.1 - ynew += np.random.randn(N_stars) * 0.1 + xd += np.random.randn(N_stars) * 0.1 + yd += np.random.randn(N_stars) * 0.1 + md += np.random.randn(N_stars) * 0.02 + xde = xpe + yde = ype + mde = lis['m0_err'] - mnew = m + np.random.randn(N_stars) * 0.05 + # Save the new list as a starlist. + new_lis = starlists.StarList([lis['name'], md, mde, xd, xde, yd, yde, t], + names=('name', 'm', 'me', 'x', 'xe', 'y', 'ye', 't')) - _out = open('random_shift_{0:d}.lis'.format(ss+1), 'w') - for ii in range(N_stars): - _out.write(fmt.format(name[ii], mnew[ii], xnew[ii], ynew[ii])) - _out.close() + new_lis.write('random_{0:d}.fits'.format(ss), overwrite=True) - return shifts + return (xy_trans,mag_trans) def make_fake_starlists_poly1_vel(seed=-1): @@ -554,12 +672,16 @@ def make_fake_starlists_poly1_vel(seed=-1): # Propogate to new times and distort. ########## # Make 4 new starlists with different epochs and transformations. - times = [2018.5, 2019.5, 2020.5, 2021.5] + times = [2018.5, 2019.0, 2019.5, 2020.0, 2020.5, 2021.0, 2021.5, 2022.0] xy_trans = [[[ 6.5, 0.99, 1e-5], [ 10.1, 1e-5, 0.99]], [[100.3, 0.98, 1e-5], [ 50.5, 9e-6, 1.001]], [[ 0.0, 1.00, 0.0], [ 0.0, 0.0, 1.0]], - [[250.0, 0.97, 2e-5], [-250.0, 1e-5, 1.001]]] - mag_trans = [0.1, 0.4, 0.0, -0.3] + [[250.0, 0.97, 2e-5], [-250.0, 1e-5, 1.001]], + [[ 50.0, 1.01, 1e-5], [ -31.0, 1e-5, 1.000]], + [[ 78.0, 0.98, 0.0 ], [ 45.0, 9e-6, 1.001]], + [[-13.0, 0.99, 1e-5], [ 150, 2e-5, 1.002]], + [[ 94.0, 1.00, 9e-6], [-182.0, 0.0, 0.99]]] + mag_trans = [0.1, 0.4, 0.0, -0.3, 0.2, 0.0, -0.1, -0.3] # Convert into pixels (undistorted) with the following info. scale = 0.01 # arcsec / pix @@ -582,6 +704,7 @@ def make_fake_starlists_poly1_vel(seed=-1): trans = transforms.PolyTransform(1, xy_trans[ss][0], xy_trans[ss][1], mag_offset=mag_trans[ss]) xd, yd = trans.evaluate(xp, yp) md = trans.evaluate_mag(lis['m0']) + print([xd-xp]) # Perturb with small errors (0.1 pix) xd += np.random.randn(N_stars) * 0.1 @@ -593,7 +716,7 @@ def make_fake_starlists_poly1_vel(seed=-1): # Save the new list as a starlist. new_lis = starlists.StarList([lis['name'], md, mde, xd, xde, yd, yde, t], - names=('name', 'm', 'm_err', 'x', 'x_err', 'y', 'y_err', 't')) + names=('name', 'm', 'me', 'x', 'xe', 'y', 'ye', 't')) new_lis.write('random_vel_{0:d}.fits'.format(ss), overwrite=True) diff --git a/flystar/tests/test_motion_model.py b/flystar/tests/test_motion_model.py index d221a29..4fe703f 100755 --- a/flystar/tests/test_motion_model.py +++ b/flystar/tests/test_motion_model.py @@ -122,6 +122,35 @@ def test_Linear(): # Run fit mod_fit = motion_model.Linear() params, param_errs = mod_fit.fit_motion_model(t, x_sim,y_sim, x_true_err, y_true_err) + print(param_errs) + # Confirm true value is within error bar of fit value + assert [within_error(true_params[param_list[i]], params[i], param_errs[i]) for i in range(len(params))] + + # Test fitter with bootstrap + t = np.arange(2015.0,2025.0, 0.5) + # Get values from model and add scatter + x_true, y_true = mod_true.get_pos_at_time(t) + x_true_err, y_true_err = mod_true.get_pos_err_at_time(t) + x_sim = np.random.normal(x_true, x_true_err) + y_sim = np.random.normal(y_true, y_true_err) + # Run fit + mod_fit = motion_model.Linear() + params, param_errs = mod_fit.fit_motion_model(t, x_sim,y_sim, x_true_err, y_true_err,bootstrap=10) + print(param_errs) + # Confirm true value is within error bar of fit value + assert [within_error(true_params[param_list[i]], params[i], param_errs[i]) for i in range(len(params))] + + # Test fitter for 2 pts + t = np.array([2015.0,2025.0]) + # Get values from model and add scatter + x_true, y_true = mod_true.get_pos_at_time(t) + x_true_err, y_true_err = mod_true.get_pos_err_at_time(t) + x_sim = np.random.normal(x_true, x_true_err) + y_sim = np.random.normal(y_true, y_true_err) + # Run fit + mod_fit = motion_model.Linear() + params, param_errs = mod_fit.fit_motion_model(t, x_sim,y_sim, x_true_err, y_true_err) + print(param_errs) # Confirm true value is within error bar of fit value assert [within_error(true_params[param_list[i]], params[i], param_errs[i]) for i in range(len(params))] @@ -223,7 +252,7 @@ def test_Parallax(): params, param_errs = mod_fit.fit_motion_model(t, x_sim,y_sim, x_true_err, y_true_err) # Confirm true value is within error bar of fit value assert [within_error(true_params[param_list[i]], params[i], param_errs[i]) for i in range(len(params))] - + def test_Parallax_PA(): # Set PA=0 model diff --git a/flystar/transforms.py b/flystar/transforms.py index 2bb80d3..b4f7a2d 100755 --- a/flystar/transforms.py +++ b/flystar/transforms.py @@ -273,14 +273,14 @@ def __init__(self, order, px, py, pxerr=None, pyerr=None, mag_offset=0.0): Parameters ---------- - px : list or array [a0, a1, a2, ...] + order : int + The order of the transformation. 0 = 2 free parameters, 1 = 6 free parameters. + + px : list or array [a0, a1, a2, ...] coefficients to transform input x coordinates into output x' coordinates. py : list or array [b0, b1, b2, ...] coefficients to transform input y coordinates into output y' coordinates. - - order : int - The order of the transformation. 0 = 2 free parameters, 1 = 6 free parameters. pxerr : array or list array or list of errors of the coefficients to transform input x coordinates @@ -331,7 +331,7 @@ def make_param_dict(initial_param, order, isY=False): a0 + a1*x + a2*y + a3*x^2 + a4*x*y + a5*y^2 + a6*x^3 + a7*x^2*y + a8*x*y^2 + a9*y^3 - and conver this into a dictionary where: + and convert this into a dictionary where: c0_0 = a0 c1_0 = a1 @@ -611,6 +611,7 @@ def derive_transform(cls, x, y, xref, yref, order, m=None, mref=None, fit_p = fitting.LinearLSQFitter() + #pdb.set_trace() px = fit_p(p_init_x, x, y, xref, weights=weights) py = fit_p(p_init_y, x, y, yref, weights=weights) From fea26125c58f484430f7ca2c3b714c11325c1745 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Wed, 18 Dec 2024 10:09:13 -0800 Subject: [PATCH 028/254] check in testing progress --- flystar/align.py | 12 +++---- flystar/startables.py | 22 +++++++++--- flystar/tests/test_align.py | 69 +++++++++++++++++-------------------- 3 files changed, 53 insertions(+), 50 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 2d697b5..477989a 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -646,7 +646,6 @@ def setup_ref_table_from_starlist(self, star_list): # Some munging to convert data shape from (N,1) to (N,), # since these are all 1D cols vals = np.transpose(np.array(ref_table[orig_err_cols[ii]]))[0] - # Now add to ref_table new_col = Column(vals, name=new_err_cols[ii]) ref_table.add_column(new_col) @@ -837,14 +836,13 @@ def update_ref_table_aggregates(self, n_boot=0): if mm in self.ref_table.keys(): vals_orig[mm] = self.ref_table[mm][ref_orig_idx] - #if 'motion_model_input' in self.ref_table.keys(): # Combine positions with a velocity fit. - if 'vx' in self.ref_table.keys(): - print('before:',self.ref_table['vx'][:10]) + #if 'vx' in self.ref_table.keys(): + #print('before:',self.ref_table['vx'][:10]) self.ref_table.fit_velocities(bootstrap=n_boot, verbose=self.verbose, default_motion_model=self.default_motion_model) - print(np.unique(self.ref_table['motion_model_used'])) - if 'vx' in self.ref_table.keys(): - print('after:', self.ref_table['vx'][:10]) + #print(np.unique(self.ref_table['motion_model_used'])) + #if 'vx' in self.ref_table.keys(): + #print('after:', self.ref_table['vx'][:10]) # Combine (transformed) magnitudes if 'me' in self.ref_table.colnames: diff --git a/flystar/startables.py b/flystar/startables.py index dea27dc..f6f6382 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -578,6 +578,15 @@ def fit_velocities(self, weighting='var', use_scipy=True, absolute_sigma=True, b KeyError If there's not time information in the table """ + + ss=0 + print('first star') + print(' x', self['x'][ss, :].data) + print(' y', self['y'][ss, :].data) + if 'vx' in self.keys(): + print(' vx', self['vx'][ss]) + print(' vy', self['vy'][ss]) + if weighting not in ['var', 'std']: raise ValueError(f"fit_velocities: Weighting must either be 'var' or 'std', not {weighting}!") @@ -612,7 +621,7 @@ def fit_velocities(self, weighting='var', use_scipy=True, absolute_sigma=True, b for col in new_col_list: # Clean/remove up old arrays. if col in self.colnames: self.remove_column(col) - # Add column + # Add column #TODO: is this good for filling??? self.add_column(Column(data = np.full(N_stars, np.nan, dtype=float), name = col)) # Add a column to keep track of the number of points used in a fit. @@ -671,10 +680,6 @@ def fit_velocity_for_star(self, ss, weighting='var', use_scipy=True, # Make a mask of invalid (NaN) values and a user-specified invalid value. # - if ss==0: - print('first star') - print(' x', self['x'][ss, :].data) - x = np.ma.masked_invalid(self['x'][ss, :].data) y = np.ma.masked_invalid(self['y'][ss, :].data) if mask_val: @@ -783,6 +788,7 @@ def fit_velocity_for_star(self, ss, weighting='var', use_scipy=True, # Catch the case where there is NO good data. if N_good == 0: + #self['motion_model_used'][ss] = 'None' return # Everything below has N_good >= 1 @@ -854,6 +860,8 @@ def fit_velocity_for_star(self, ss, weighting='var', use_scipy=True, self['chi2_x'][ss]=chi2_x self['chi2_y'][ss]=chi2_y + #print('N good', N_good, motion_model_use, params) + # Save parameters and errors to table. for pp in range(len(modClass.fitter_param_names)): par = modClass.fitter_param_names[pp] @@ -878,10 +886,12 @@ def get_star_positions_at_time(self, t): # Check which motion models we need # use complex_mms to collect models besides Fixed and Linear unique_mms = np.unique(self['motion_model_used']).tolist() + print(list(self['motion_model_used'])) # Calculate current position in batches by motion model for mm in unique_mms: # Identify stars with this model & get class idx = np.where(self['motion_model_used']==mm)[0] + print(mm,'idx',idx) modClass = getattr(motion_model, mm) # Set up parameters param_dict = {} @@ -889,6 +899,8 @@ def get_star_positions_at_time(self, t): param_dict[par] = self[par][idx] mod = modClass(RA=self.meta['RA'], Dec=self.meta['Dec'], PA=self.meta['position_angle'], obs=self.meta['observer_location']) x[idx],y[idx],xe[idx],ye[idx] = mod.get_batch_pos_at_time(t,**param_dict) + print('all x',x) + print('nans:', np.sum(np.isnan(x))) return x,y,xe,ye diff --git a/flystar/tests/test_align.py b/flystar/tests/test_align.py index 6926ec1..602ae9c 100644 --- a/flystar/tests/test_align.py +++ b/flystar/tests/test_align.py @@ -58,8 +58,8 @@ def test_MosaicSelfRef(): # Check that the transformation lists aren't too wacky for ii in range(4): - np.testing.assert_almost_equal(msc.trans_list[ii].px.c1_0, 1.0, 2) - np.testing.assert_almost_equal(msc.trans_list[ii].py.c0_1, 1.0, 2) + np.testing.assert_allclose(msc.trans_list[ii].px.c1_0, 1.0, rtol=1e-2) + np.testing.assert_allclose(msc.trans_list[ii].py.c0_1, 1.0, rtol=1e-2) # We didn't do any velocity fitting, so make sure nothing got created. assert 'vx' not in msc.ref_table.colnames assert 'vy' not in msc.ref_table.colnames @@ -133,8 +133,8 @@ def test_MosaicSelfRef_vel_tconst(): # Check that the transformation lists aren't too wacky for ii in range(4): - np.testing.assert_almost_equal(msc.trans_list[ii].px.c1_0, 1.0, 2) - np.testing.assert_almost_equal(msc.trans_list[ii].py.c0_1, 1.0, 2) + np.testing.assert_allclose(msc.trans_list[ii].px.c1_0, 1.0, rtol=1e-2) + np.testing.assert_allclose(msc.trans_list[ii].py.c0_1, 1.0, rtol=1e-2) # Check that the velocities aren't crazy... # they should be non-existent (since there is no time difference) @@ -203,9 +203,8 @@ def test_MosaicSelfRef_vel(): # Check that the transformation lists aren't too wacky for ii in range(4): - np.testing.assert_almost_equal(msc.trans_list[ii].px.c1_0, 1.0, 2) - np.testing.assert_almost_equal(msc.trans_list[ii].py.c0_1, 1.0, 2) - + np.testing.assert_allclose(msc.trans_list[ii].px.c1_0, 1.0, rtol=1e-2) + np.testing.assert_allclose(msc.trans_list[ii].py.c0_1, 1.0, rtol=1e-2) plt.clf() plt.plot(msc.ref_table['vx'], @@ -299,7 +298,7 @@ def test_MosaicToRef(): dr_tol=[0.2, 0.1], dm_tol=[1, 0.5], trans_class=transforms.PolyTransform, trans_args={'order': 2}, default_motion_model='Fixed', - update_ref_orig=False, verbose=True) + update_ref_orig=False, verbose=False) msc.fit() @@ -312,11 +311,8 @@ def test_MosaicToRef(): # The velocities should be almost the same as the input # velocities since update_ref_orig == False. - for i,star in enumerate(ref_list["name"]): - if star in msc.ref_table["name"]: - ii = np.where(msc.ref_table["name"]==star)[0][0] - np.testing.assert_almost_equal(msc.ref_table['x0'][ii], ref_list['x0'][i], 5) - np.testing.assert_almost_equal(msc.ref_table['y0'][ii], ref_list['y0'][i], 5) + np.testing.assert_allclose(msc.ref_table['x0'], ref_list['x0'], rtol=1e-5) + np.testing.assert_allclose(msc.ref_table['y0'], ref_list['y0'], rtol=1e-5) ########## # Align and let velocities be free. @@ -326,18 +322,12 @@ def test_MosaicToRef(): # The velocities should be almost the same (but not as close as before) # as the input velocities since update_ref == False. - for i,star in enumerate(ref_list["name"]): - if star in msc.ref_table["name"]: - ii = np.where(msc.ref_table["name"]==star)[0][0] - np.testing.assert_almost_equal(msc.ref_table['x0'][ii], ref_list['x0'][i], 1) - np.testing.assert_almost_equal(msc.ref_table['y0'][ii], ref_list['y0'][i], 1) + np.testing.assert_allclose(msc.ref_table['x0'], ref_list['x0'], rtol=1e-1) + np.testing.assert_allclose(msc.ref_table['y0'], ref_list['y0'], rtol=1e-1) # Also double check that they aren't exactly the same for the reference stars. - #assert np.any(np.not_equal(msc.ref_table['vx'], ref_list['vx'])) - for i,star in enumerate(ref_list["name"]): - if star in msc.ref_table["name"]: - ii = np.where(msc.ref_table["name"]==star)[0][0] - assert np.not_equal(msc.ref_table['x0'][ii], ref_list['x0'][i]) + assert np.not_equal(msc.ref_table['x0'], ref_list['x0']).all() + assert np.not_equal(msc.ref_table['y0'], ref_list['y0']).all() return msc @@ -367,13 +357,13 @@ def test_MosaicToRef_vel(): ref_list['vx'] *= -1.0 lists = [starlists.StarList.read(lf) for lf in list_files] + print(ref_list[['x0','vx']]) msc = align.MosaicToRef(ref_list, lists, iters=2, dr_tol=[0.2, 0.1], dm_tol=[1, 0.5], trans_class=transforms.PolyTransform, trans_args={'order': 2}, default_motion_model='Linear', - update_ref_orig=False, verbose=True) - + update_ref_orig=False, verbose=False) msc.fit() # Check our status columns @@ -388,22 +378,25 @@ def test_MosaicToRef_vel(): for i,star in enumerate(ref_list["name"]): if star in msc.ref_table["name"]: ii = np.where(msc.ref_table["name"]==star)[0][0] - np.testing.assert_almost_equal(msc.ref_table['vx'][ii], ref_list['vx'][i], 5) - np.testing.assert_almost_equal(msc.ref_table['vy'][ii], ref_list['vy'][i], 5) + np.testing.assert_allclose(msc.ref_table['vx'][ii], ref_list['vx'][i], rtol=1e-5) + np.testing.assert_allclose(msc.ref_table['vy'][ii], ref_list['vy'][i], rtol=1e-5) ########## # Align and let velocities be free. ########## msc.update_ref_orig = True msc.fit() + + print(msc.ref_table[['name','vx']][:10]) + print(ref_list['name','vx'][:10]) # The velocities should be almost the same (but not as close as before) # as the input velocities since update_ref == False. for i,star in enumerate(ref_list["name"]): if star in msc.ref_table["name"]: ii = np.where(msc.ref_table["name"]==star)[0][0] - np.testing.assert_almost_equal(msc.ref_table['vx'][ii], ref_list['vx'][i], 1) - np.testing.assert_almost_equal(msc.ref_table['vy'][ii], ref_list['vy'][i], 1) + np.testing.assert_allclose(msc.ref_table['vx'][ii], ref_list['vx'][i], rtol=1e-1) + np.testing.assert_allclose(msc.ref_table['vy'][ii], ref_list['vy'][i], rtol=1e-1) # Also double check that they aren't exactly the same for the reference stars. #assert np.any(np.not_equal(msc.ref_table['vx'], ref_list['vx'])) @@ -471,8 +464,8 @@ def test_MosaicToRef_acc(): for i,star in enumerate(ref_list["name"]): if star in msc.ref_table["name"]: ii = np.where(msc.ref_table["name"]==star)[0][0] - np.testing.assert_almost_equal(msc.ref_table['vx0'][ii], ref_list['vx0'][i], 5) - np.testing.assert_almost_equal(msc.ref_table['vy0'][ii], ref_list['vy0'][i], 5) + np.testing.assert_allclose(msc.ref_table['vx0'][ii], ref_list['vx0'][i], rtol=1e-5) + np.testing.assert_allclose(msc.ref_table['vy0'][ii], ref_list['vy0'][i], rtol=1e-5) ########## # Align and let velocities be free. @@ -485,8 +478,8 @@ def test_MosaicToRef_acc(): for i,star in enumerate(ref_list["name"]): if star in msc.ref_table["name"]: ii = np.where(msc.ref_table["name"]==star)[0][0] - np.testing.assert_almost_equal(msc.ref_table['vx0'][ii], ref_list['vx0'][i], 1) - np.testing.assert_almost_equal(msc.ref_table['vy0'][ii], ref_list['vy0'][i], 1) + np.testing.assert_allclose(msc.ref_table['vx0'][ii], ref_list['vx0'][i], rtol=1e-1) + np.testing.assert_allclose(msc.ref_table['vy0'][ii], ref_list['vy0'][i], rtol=1e-1) # Also double check that they aren't exactly the same for the reference stars. assert np.any(np.not_equal(msc.ref_table['vx0'], ref_list['vx0'])) @@ -677,11 +670,11 @@ def make_fake_starlists_poly1_vel(seed=-1): [[100.3, 0.98, 1e-5], [ 50.5, 9e-6, 1.001]], [[ 0.0, 1.00, 0.0], [ 0.0, 0.0, 1.0]], [[250.0, 0.97, 2e-5], [-250.0, 1e-5, 1.001]], - [[ 50.0, 1.01, 1e-5], [ -31.0, 1e-5, 1.000]], - [[ 78.0, 0.98, 0.0 ], [ 45.0, 9e-6, 1.001]], - [[-13.0, 0.99, 1e-5], [ 150, 2e-5, 1.002]], - [[ 94.0, 1.00, 9e-6], [-182.0, 0.0, 0.99]]] - mag_trans = [0.1, 0.4, 0.0, -0.3, 0.2, 0.0, -0.1, -0.3] + [[ 50.0, 1.00, 0.0], [ -31.0, 0.0, 1.000]], + [[ 78.0, 1.00, 0.0 ], [ 45.0, 0.0, 1.00]], + [[-13.0, 1.00, 0.0], [ 150, 0.0, 1.00]], + [[ 94.0, 1.00, 0.0], [-182.0, 0.0, 1.00]]] + mag_trans = [0.1, 0.4, 0.0, -0.3, 0.0, 0.0, 0.0, 0.0] # Convert into pixels (undistorted) with the following info. scale = 0.01 # arcsec / pix From a9e3f9e367f1e370abc38677155a5272a8082310 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Wed, 18 Dec 2024 10:30:57 -0800 Subject: [PATCH 029/254] test debugging --- flystar/analysis.py | 12 ++++++------ flystar/conftest.py | 4 ++-- flystar/tests/test_align.py | 24 +++++++++++++++++------- 3 files changed, 25 insertions(+), 15 deletions(-) diff --git a/flystar/analysis.py b/flystar/analysis.py index e0696fc..931723f 100644 --- a/flystar/analysis.py +++ b/flystar/analysis.py @@ -173,13 +173,13 @@ def prepare_gaia_for_flystar(gaia, ra, dec, targets_dict=None, match_dr_max=0.2) gaia_new['vye'][idx] = 0.0 #macy additions to try to fix wild magnitude values - gaia_new['ruwe'] = gaia['ruwe'] - try: - gaia_new = gaia_new[~gaia_new['m'].mask] - except: - print('no invalig mags') + #gaia_new['ruwe'] = gaia['ruwe'] + #try: + # gaia_new = gaia_new[~gaia_new['m'].mask] + #except: + # print('no invalig mags') - gaia_new = gaia_new.filled() #convert masked colunms to regular columns + #gaia_new = gaia_new.filled() #convert masked colunms to regular columns if targets_dict != None: for targ_name, targ_coo in targets_dict.items(): diff --git a/flystar/conftest.py b/flystar/conftest.py index 672b273..da164b5 100644 --- a/flystar/conftest.py +++ b/flystar/conftest.py @@ -31,9 +31,9 @@ def pytest_configure(config): PYTEST_HEADER_MODULES.pop('Pandas', None) PYTEST_HEADER_MODULES['scikit-image'] = 'skimage' - from . import __version__ + #from . import __version__ packagename = os.path.basename(os.path.dirname(__file__)) - TESTED_VERSIONS[packagename] = __version__ + #TESTED_VERSIONS[packagename] = __version__ # Uncomment the last two lines in this block to treat all DeprecationWarnings as # exceptions. For Astropy v2.0 or later, there are 2 additional keywords, diff --git a/flystar/tests/test_align.py b/flystar/tests/test_align.py index 026a8b1..ef8f55f 100644 --- a/flystar/tests/test_align.py +++ b/flystar/tests/test_align.py @@ -254,9 +254,8 @@ def test_MosaicToRef(): # The velocities should be almost the same as the input # velocities since update_ref_orig == False. - np.testing.assert_almost_equal(msc.ref_table['vx'], ref_list['vx'], 5) - np.testing.assert_almost_equal(msc.ref_table['vy'], ref_list['vy'], 5) - + np.testing.assert_allclose(msc.ref_table['vx'], ref_list['vx'], rtol=1e-5) + np.testing.assert_allclose(msc.ref_table['vy'], ref_list['vy'], rtol=1e-5) ########## # Align and let velocities be free. @@ -266,8 +265,8 @@ def test_MosaicToRef(): # The velocities should be almost the same (but not as close as before) # as the input velocities since update_ref == False. - np.testing.assert_almost_equal(msc.ref_table['vx'], ref_list['vx'], 1) - np.testing.assert_almost_equal(msc.ref_table['vy'], ref_list['vy'], 1) + np.testing.assert_allclose(msc.ref_table['vx'], ref_list['vx'], rtol=1e-1) + np.testing.assert_allclose(msc.ref_table['vy'], ref_list['vy'], rtol=1e-1) # Also double check that they aren't exactly the same for the reference stars. assert np.any(np.not_equal(msc.ref_table['vx'], ref_list['vx'])) @@ -419,12 +418,23 @@ def make_fake_starlists_poly1_vel(seed=-1): # Propogate to new times and distort. ########## # Make 4 new starlists with different epochs and transformations. - times = [2018.5, 2019.5, 2020.5, 2021.5] + '''times = [2018.5, 2019.5, 2020.5, 2021.5] xy_trans = [[[ 6.5, 0.99, 1e-5], [ 10.1, 1e-5, 0.99]], [[100.3, 0.98, 1e-5], [ 50.5, 9e-6, 1.001]], [[ 0.0, 1.00, 0.0], [ 0.0, 0.0, 1.0]], [[250.0, 0.97, 2e-5], [-250.0, 1e-5, 1.001]]] - mag_trans = [0.1, 0.4, 0.0, -0.3] + mag_trans = [0.1, 0.4, 0.0, -0.3]''' + + times = [2018.5, 2019.0, 2019.5, 2020.0, 2020.5, 2021.0, 2021.5, 2022.0] + xy_trans = [[[ 6.5, 0.99, 1e-5], [ 10.1, 1e-5, 0.99]], + [[100.3, 0.98, 1e-5], [ 50.5, 9e-6, 1.001]], + [[ 0.0, 1.00, 0.0], [ 0.0, 0.0, 1.0]], + [[250.0, 0.97, 2e-5], [-250.0, 1e-5, 1.001]], + [[ 50.0, 1.00, 0.0], [ -31.0, 0.0, 1.000]], + [[ 78.0, 1.00, 0.0 ], [ 45.0, 0.0, 1.00]], + [[-13.0, 1.00, 0.0], [ 150, 0.0, 1.00]], + [[ 94.0, 1.00, 0.0], [-182.0, 0.0, 1.00]]] + mag_trans = [0.1, 0.4, 0.0, -0.3, 0.0, 0.0, 0.0, 0.0] # Convert into pixels (undistorted) with the following info. scale = 0.01 # arcsec / pix From 4eb7cacee07a475e4b9ea2b14fa8aba649e4d173 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Wed, 18 Dec 2024 14:34:31 -0800 Subject: [PATCH 030/254] motion model debugging --- flystar/motion_model.py | 119 +++++++++++------------------ flystar/tests/test_motion_model.py | 4 +- 2 files changed, 47 insertions(+), 76 deletions(-) diff --git a/flystar/motion_model.py b/flystar/motion_model.py index ff9fbd5..37f1b6e 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -1,4 +1,3 @@ -from astropy.modeling import models, fitting import numpy as np from abc import ABC import pdb @@ -150,13 +149,14 @@ def get_batch_pos_at_time(self,t, def run_fit(self, t, x, y, xe, ye, update=True, weighting='var'): # Handle single data point case if len(x)==1: - return [x[0],y[0]],[xe[0],ye[0]] + x0,y0,x0e,y0e = x[0],y[0],xe[0],ye[0] - x_wt, y_wt = self.get_weights(xe,ye, weighting=weighting) - x0 = np.average(x, weights=x_wt) - x0e = np.sqrt(np.average((x-x0)**2,weights=x_wt)) - y0 = np.average(y, weights=y_wt) - y0e = np.sqrt(np.average((y-y0)**2,weights=y_wt)) + else: + x_wt, y_wt = self.get_weights(xe,ye, weighting=weighting) + x0 = np.average(x, weights=x_wt) + x0e = np.sqrt(np.average((x-x0)**2,weights=x_wt)) + y0 = np.average(y, weights=y_wt) + y0e = np.sqrt(np.average((y-y0)**2,weights=y_wt)) params = [x0, y0] param_errors = [x0e, y0e] @@ -168,7 +168,7 @@ def run_fit(self, t, x, y, xe, ye, update=True, weighting='var'): self.y0_err = y0e return params, param_errors - + class Linear(MotionModel): """ A 2D linear motion model for a star on the sky. @@ -193,10 +193,6 @@ def __init__(self, x0=0, vx=0, y0=0, vy=0, t0=2025.0, # Must call after setting parameters. # This checks for proper parameter formatting. super().__init__() - - self.poly_order = 1 - self.px = models.Polynomial1D(self.poly_order, c0=self.x0, c1=self.vx) - self.py = models.Polynomial1D(self.poly_order, c0=self.y0, c1=self.vy) return @@ -226,49 +222,39 @@ def get_batch_pos_at_time(self, t, return x,y,x_err,y_err def run_fit(self, t, x, y, xe, ye, update=True, weighting='var'): - fitter = fitting.LevMarLSQFitter() dt = t-self.t0 + x_wt, y_wt = self.get_weights(xe,ye, weighting=weighting) + # Handle 2-data point case if len(x)==2: - ix = int(xe[0]>xe[1]) - iy = int(ye[0]>ye[1]) dx = np.diff(x)[0] dy = np.diff(y)[0] - t_diff = np.diff(t)[0] - vx = dx / t_diff - vy = dy / t_diff - x0 = x[ix]+vx*(-dt[ix]) - y0 = y[ix]+vy*(-dt[ix]) - vxe = np.hypot(*xe)/t_diff - vye = np.hypot(*ye)/t_diff - x0e = np.sqrt(xe[ix]**2 + (dt[ix]*vxe)**2) - y0e = np.sqrt(ye[iy]**2 + (dt[iy]*vye)**2) - return [x0, vx, y0, vy],[x0e, vxe, y0e, vye] + dt_diff = np.diff(dt)[0] + vx = dx / dt_diff + vy = dy / dt_diff + x0 = np.average(x, weights=x_wt) + y0 = np.average(y, weights=y_wt) + vxe = 0.0 + vye = 0.0 + x0e = np.abs(dx) / 2**0.5 + y0e = np.abs(dy) /2 **0.5 - x_wt, y_wt = self.get_weights(xe,ye, weighting=weighting) - px_new = fitter(self.px, dt, x, weights=x_wt) - px_cov = fitter.fit_info['param_cov'] - py_new = fitter(self.py, dt, y, weights=y_wt) - py_cov = fitter.fit_info['param_cov'] - - x0 = px_new.c0.value - vx = px_new.c1.value - y0 = py_new.c0.value - vy = py_new.c1.value - - px_param_errs = dict(zip(self.px.param_names, np.diag(px_cov)**0.5)) - py_param_errs = dict(zip(self.py.param_names, np.diag(py_cov)**0.5)) - x0e = px_param_errs['c0'] - vxe = px_param_errs['c1'] - y0e = py_param_errs['c0'] - vye = py_param_errs['c1'] + else: + def linear(t, c0, c1): + return c0 + c1*t + x_opt, x_cov = curve_fit(linear, dt, x, p0=np.array([x.mean(),0.0]), sigma=1/x_wt, absolute_sigma=True) + y_opt, y_cov = curve_fit(linear, dt, y, p0=np.array([y.mean(),0.0]), sigma=1/y_wt, absolute_sigma=True) + x0 = x_opt[0] + vx = x_opt[1] + y0 = y_opt[0] + vy = y_opt[1] + x0e, vxe = np.sqrt(x_cov.diagonal()) + y0e, vye = np.sqrt(y_cov.diagonal()) params = [x0, vx, y0, vy] param_errors = [x0e, vxe, y0e, vye] if update: - self.px = px_new - self.py = py_new self.x0 = x0 self.vx = vx self.y0 = y0 @@ -309,17 +295,12 @@ def __init__(self, x0=0, vx0=0, ax=0, y0=0, vy0=0, ay=0, t0=2025.0, # Must call after setting parameters. # This checks for proper parameter formatting. super().__init__() - - self.poly_order = 2 - self.px = models.Polynomial1D(self.poly_order, c0=self.x0, c1=self.vx0, c2=self.ax) - self.py = models.Polynomial1D(self.poly_order, c0=self.y0, c1=self.vy0, c2=self.ay) - return def get_pos_at_time(self, t): dt = t - self.t0 - x = self.px(dt) - y = self.py(dt) + x = self.x0 + self.vx0*dt + 0.5*self.ax*dt**2 + y = self.y0 + self.vy0*dt + 0.5*self.ay*dt**2 return x, y def get_pos_err_at_time(self, t): @@ -346,34 +327,24 @@ def get_batch_pos_at_time(self,t, return x,y,x_err,y_err def run_fit(self, t, x, y, xe, ye, update=True, weighting='var'): - fitter = fitting.LevMarLSQFitter() dt = t-self.t0 x_wt, y_wt = self.get_weights(xe,ye, weighting=weighting) - px_new = fitter(self.px, dt, x, weights=x_wt) - px_cov = fitter.fit_info['param_cov'] - py_new = fitter(self.py, dt, y, weights=y_wt) - py_cov = fitter.fit_info['param_cov'] - - x0 = px_new.c0.value - vx0 = px_new.c1.value - ax = px_new.c2.value - y0 = py_new.c0.value - vy0 = py_new.c1.value - ay = py_new.c2.value - - px_param_errs = dict(zip(self.px.param_names, np.diag(px_cov)**0.5)) - py_param_errs = dict(zip(self.py.param_names, np.diag(py_cov)**0.5)) - x0e = px_param_errs['c0'] - vx0e = px_param_errs['c1'] - axe = px_param_errs['c2'] - y0e = py_param_errs['c0'] - vy0e = py_param_errs['c1'] - aye = py_param_errs['c2'] + def accel(t, c0,c1,c2): + return c0 + c1*t + 0.5*c2*t**2 + x_opt, x_cov = curve_fit(accel, dt, x, p0=np.array([x.mean(),0.0,0.0]), sigma=1/x_wt, absolute_sigma=True) + y_opt, y_cov = curve_fit(accel, dt, y, p0=np.array([y.mean(),0.0,0.0]), sigma=1/y_wt, absolute_sigma=True) + x0 = x_opt[0] + y0 = y_opt[0] + vx0 = x_opt[1] + vy0 = y_opt[1] + ax = x_opt[2] + ay = y_opt[2] + + x0e, vx0e, axe = np.sqrt(x_cov.diagonal()) + y0e, vy0e, aye = np.sqrt(y_cov.diagonal()) if update: - self.px = px_new - self.py = py_new self.x0 = x0 self.vx0 = vx0 self.ax = ax diff --git a/flystar/tests/test_motion_model.py b/flystar/tests/test_motion_model.py index 4fe703f..94474a7 100755 --- a/flystar/tests/test_motion_model.py +++ b/flystar/tests/test_motion_model.py @@ -168,8 +168,8 @@ def test_Acceleration(): assert y_t==true_params['y0'] t_arr = np.array([2010.0,mod_true.t0,2030.0]) x_t, y_t = mod_true.get_pos_at_time(t_arr) - assert (x_t==(true_params['x0'] + (t_arr-mod_true.t0)*true_params['vx0'] + (t_arr-mod_true.t0)**2*true_params['ax'])).all() - assert (y_t==(true_params['y0'] + (t_arr-mod_true.t0)*true_params['vy0'] + (t_arr-mod_true.t0)**2*true_params['ay'])).all() + assert (x_t==(true_params['x0'] + (t_arr-mod_true.t0)*true_params['vx0'] + 0.5*(t_arr-mod_true.t0)**2*true_params['ax'])).all() + assert (y_t==(true_params['y0'] + (t_arr-mod_true.t0)*true_params['vy0'] + 0.5*(t_arr-mod_true.t0)**2*true_params['ay'])).all() x_err_t, y_err_t = mod_true.get_pos_err_at_time(mod_true.t0) assert x_err_t==true_params['x0_err'] assert y_err_t==true_params['y0_err'] From 93e52bfbaa13abd91d20f8e0738289fb213c7d0b Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Wed, 18 Dec 2024 15:54:15 -0800 Subject: [PATCH 031/254] motion model debugging --- flystar/motion_model.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/flystar/motion_model.py b/flystar/motion_model.py index 37f1b6e..18f59b9 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -232,12 +232,12 @@ def run_fit(self, t, x, y, xe, ye, update=True, weighting='var'): dt_diff = np.diff(dt)[0] vx = dx / dt_diff vy = dy / dt_diff - x0 = np.average(x, weights=x_wt) - y0 = np.average(y, weights=y_wt) + x0 = x[0] - dt[0]*vx + y0 = y[0] - dt[0]*vy vxe = 0.0 vye = 0.0 x0e = np.abs(dx) / 2**0.5 - y0e = np.abs(dy) /2 **0.5 + y0e = np.abs(dy) / 2**0.5 else: def linear(t, c0, c1): From 64649b85b49c536b8d99e57d2e9a2f16970792ac Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Thu, 19 Dec 2024 10:51:11 -0800 Subject: [PATCH 032/254] testing --- flystar/tests/test_startable.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/flystar/tests/test_startable.py b/flystar/tests/test_startable.py index b03acf1..4bce597 100644 --- a/flystar/tests/test_startable.py +++ b/flystar/tests/test_startable.py @@ -479,7 +479,7 @@ def test_fit_velocities_2epoch(): assert 'vy_err' in tab_2.colnames # 2 detections - np.testing.assert_almost_equal(tab_2['x0'][0], tab_2['x'][0,0], 1) + np.testing.assert_allclose(tab_2['x0'][0], tab_2['x'][0,0], rtol=1e-1) assert tab_2['n_fit'][0] == 2 # 1 detection From 425c2b386c7974afd4c25fe32f5f028be63d1566 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Thu, 19 Dec 2024 11:16:17 -0800 Subject: [PATCH 033/254] resolved a bug, resolving much of the linear fit disagreement --- flystar/startables.py | 19 ++----------------- flystar/tests/test_align.py | 22 +++++----------------- flystar/tests/test_startable.py | 3 ++- 3 files changed, 9 insertions(+), 35 deletions(-) diff --git a/flystar/startables.py b/flystar/startables.py index f6f6382..333a907 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -578,15 +578,6 @@ def fit_velocities(self, weighting='var', use_scipy=True, absolute_sigma=True, b KeyError If there's not time information in the table """ - - ss=0 - print('first star') - print(' x', self['x'][ss, :].data) - print(' y', self['y'][ss, :].data) - if 'vx' in self.keys(): - print(' vx', self['vx'][ss]) - print(' vy', self['vy'][ss]) - if weighting not in ['var', 'std']: raise ValueError(f"fit_velocities: Weighting must either be 'var' or 'std', not {weighting}!") @@ -835,7 +826,7 @@ def fit_velocity_for_star(self, ss, weighting='var', use_scipy=True, # Load up any prior information on parameters for this model. param_dict = {} - for par in modClass.fitter_param_names: + for par in modClass.fitter_param_names+modClass.fixed_param_names: if ~np.isnan(self[par][ss]): param_dict[par] = self[par][ss] @@ -859,9 +850,7 @@ def fit_velocity_for_star(self, ss, weighting='var', use_scipy=True, chi2_x,chi2_y = mod.get_chi2(t,x,y,xe,ye) self['chi2_x'][ss]=chi2_x self['chi2_y'][ss]=chi2_y - - #print('N good', N_good, motion_model_use, params) - + # Save parameters and errors to table. for pp in range(len(modClass.fitter_param_names)): par = modClass.fitter_param_names[pp] @@ -886,12 +875,10 @@ def get_star_positions_at_time(self, t): # Check which motion models we need # use complex_mms to collect models besides Fixed and Linear unique_mms = np.unique(self['motion_model_used']).tolist() - print(list(self['motion_model_used'])) # Calculate current position in batches by motion model for mm in unique_mms: # Identify stars with this model & get class idx = np.where(self['motion_model_used']==mm)[0] - print(mm,'idx',idx) modClass = getattr(motion_model, mm) # Set up parameters param_dict = {} @@ -899,8 +886,6 @@ def get_star_positions_at_time(self, t): param_dict[par] = self[par][idx] mod = modClass(RA=self.meta['RA'], Dec=self.meta['Dec'], PA=self.meta['position_angle'], obs=self.meta['observer_location']) x[idx],y[idx],xe[idx],ye[idx] = mod.get_batch_pos_at_time(t,**param_dict) - print('all x',x) - print('nans:', np.sum(np.isnan(x))) return x,y,xe,ye diff --git a/flystar/tests/test_align.py b/flystar/tests/test_align.py index 602ae9c..8d6edb2 100644 --- a/flystar/tests/test_align.py +++ b/flystar/tests/test_align.py @@ -375,35 +375,23 @@ def test_MosaicToRef_vel(): # The velocities should be almost the same as the input # velocities since update_ref_orig == False. - for i,star in enumerate(ref_list["name"]): - if star in msc.ref_table["name"]: - ii = np.where(msc.ref_table["name"]==star)[0][0] - np.testing.assert_allclose(msc.ref_table['vx'][ii], ref_list['vx'][i], rtol=1e-5) - np.testing.assert_allclose(msc.ref_table['vy'][ii], ref_list['vy'][i], rtol=1e-5) + np.testing.assert_allclose(msc.ref_table['vx'], ref_list['vx'], rtol=1e-5) + np.testing.assert_allclose(msc.ref_table['vy'], ref_list['vy'], rtol=1e-5) ########## # Align and let velocities be free. ########## msc.update_ref_orig = True msc.fit() - - print(msc.ref_table[['name','vx']][:10]) - print(ref_list['name','vx'][:10]) # The velocities should be almost the same (but not as close as before) # as the input velocities since update_ref == False. - for i,star in enumerate(ref_list["name"]): - if star in msc.ref_table["name"]: - ii = np.where(msc.ref_table["name"]==star)[0][0] - np.testing.assert_allclose(msc.ref_table['vx'][ii], ref_list['vx'][i], rtol=1e-1) - np.testing.assert_allclose(msc.ref_table['vy'][ii], ref_list['vy'][i], rtol=1e-1) + np.testing.assert_allclose(msc.ref_table['vx'], ref_list['vx'], rtol=1e-1) + np.testing.assert_allclose(msc.ref_table['vy'], ref_list['vy'], rtol=1e-1) # Also double check that they aren't exactly the same for the reference stars. #assert np.any(np.not_equal(msc.ref_table['vx'], ref_list['vx'])) - for i,star in enumerate(ref_list["name"]): - if star in msc.ref_table["name"]: - ii = np.where(msc.ref_table["name"]==star)[0][0] - assert np.not_equal(msc.ref_table['vx'][ii], ref_list['vx'][i]) + assert np.not_equal(msc.ref_table['vx'], ref_list['vx']).any() return msc diff --git a/flystar/tests/test_startable.py b/flystar/tests/test_startable.py index 4bce597..66421d9 100644 --- a/flystar/tests/test_startable.py +++ b/flystar/tests/test_startable.py @@ -479,7 +479,8 @@ def test_fit_velocities_2epoch(): assert 'vy_err' in tab_2.colnames # 2 detections - np.testing.assert_allclose(tab_2['x0'][0], tab_2['x'][0,0], rtol=1e-1) + print(tab1.meta) + np.testing.assert_almost_equal(tab_2['x0'][0], tab_2['x'][0,0], 1) assert tab_2['n_fit'][0] == 2 # 1 detection From 40a1bf269a9b3a5af5ce5c57cd702448942b177d Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Thu, 19 Dec 2024 12:06:55 -0800 Subject: [PATCH 034/254] Linear motion model matching Main branch use_vel results now --- flystar/align.py | 3 +-- flystar/motion_model.py | 13 +++++++------ flystar/startables.py | 1 + flystar/tests/test_align.py | 1 + 4 files changed, 10 insertions(+), 8 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 477989a..cdb051a 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -50,7 +50,6 @@ def __init__(self, list_of_starlists, ref_index=0, iters=2, for those stars not trimmed out by the other criteria. - Optional Parameters ---------- ref_index : int @@ -602,7 +601,7 @@ def setup_ref_table_from_starlist(self, star_list): col_arrays[new_col_name] = new_col_data # Use the columns from the ref list to make the ref_table. - ref_table = StarTable(**col_arrays, position_angle=self.position_angle, RA=self.RA, Dec=self.Dec) + ref_table = StarTable(**col_arrays, position_angle=self.position_angle, RA=self.RA, Dec=self.Dec, observer_location=self.observer_location) # Make new columns to hold original values. These will be copies # of the old columns and will only include x, y, m, xe, ye, me. diff --git a/flystar/motion_model.py b/flystar/motion_model.py index 18f59b9..c18c24f 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -112,7 +112,7 @@ class Fixed(MotionModel): fitter_param_names = ['x0','y0'] fixed_param_names = [] - def __init__(self, x0=0, y0=0, t0=2025.0, + def __init__(self, x0=0, y0=0, t0=None, x0_err=0, y0_err=0, **kwargs): self.x0 = x0 self.y0 = y0 @@ -178,7 +178,7 @@ class Linear(MotionModel): fitter_param_names = ['x0', 'vx', 'y0', 'vy'] fixed_param_names = ['t0'] - def __init__(self, x0=0, vx=0, y0=0, vy=0, t0=2025.0, + def __init__(self, x0=0, vx=0, y0=0, vy=0, t0=None, x0_err=0, vx_err=0, y0_err=0, vy_err=0, **kwargs): self.x0 = x0 self.vx = vx @@ -232,8 +232,9 @@ def run_fit(self, t, x, y, xe, ye, update=True, weighting='var'): dt_diff = np.diff(dt)[0] vx = dx / dt_diff vy = dy / dt_diff - x0 = x[0] - dt[0]*vx - y0 = y[0] - dt[0]*vy + # TODO: this does not align with how t0 works..... + x0 = np.average(x, weights=x_wt) #x[0] - dt[0]*vx + y0 = np.average(y, weights=y_wt) #y[0] - dt[0]*vy vxe = 0.0 vye = 0.0 x0e = np.abs(dx) / 2**0.5 @@ -276,7 +277,7 @@ class Acceleration(MotionModel): fitter_param_names = ['x0', 'vx0', 'ax', 'y0', 'vy0', 'ay'] fixed_param_names = ['t0'] - def __init__(self, x0=0, vx0=0, ax=0, y0=0, vy0=0, ay=0, t0=2025.0, + def __init__(self, x0=0, vx0=0, ax=0, y0=0, vy0=0, ay=0, t0=None, x0_err=0, vx0_err=0, ax_err=0, y0_err=0, vy0_err=0, ay_err=0, **kwargs): self.x0 = x0 self.vx0 = vx0 @@ -378,7 +379,7 @@ class Parallax(MotionModel): fixed_param_names = ['t0'] fixed_meta_data = ['RA','Dec','PA','obs'] - def __init__(self, x0=0, vx=0, y0=0, vy=0, t0=2025.0, + def __init__(self, x0=0, vx=0, y0=0, vy=0, t0=None, x0_err=0, vx_err=0, y0_err=0, vy_err=0, pi=0, pi_err=0, RA=None, Dec=None, PA=None, obs='earth', **kwargs): diff --git a/flystar/startables.py b/flystar/startables.py index 333a907..ab08665 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -843,6 +843,7 @@ def fit_velocity_for_star(self, ss, weighting='var', use_scipy=True, param_dict[par] = self[par][ss]''' # Model object + #print(motion_model_use,param_dict) mod = modClass(**param_dict, PA=self.meta['position_angle'], RA=self.meta['RA'], Dec=self.meta['Dec'], obs=self.meta['observer_location']) # Fit for the best parameters diff --git a/flystar/tests/test_align.py b/flystar/tests/test_align.py index 8d6edb2..5964274 100644 --- a/flystar/tests/test_align.py +++ b/flystar/tests/test_align.py @@ -194,6 +194,7 @@ def test_MosaicSelfRef_vel(): # Check that we have some matched stars... should be at least 35 stars # that are detected in all 4 starlists. idx = np.where(msc.ref_table['n_detect'] == 4)[0] + print(idx) assert len(idx) > 35 # Check that the transformation error isn't too big From fa6c1ff01aa5e6b48b3351a2e0d632487216adb3 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Thu, 19 Dec 2024 12:26:29 -0800 Subject: [PATCH 035/254] post-testing cleanup --- flystar/align.py | 15 +------- flystar/startables.py | 1 - flystar/tests/test_align.py | 73 +------------------------------------ 3 files changed, 4 insertions(+), 85 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index cdb051a..3ab42ee 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -48,7 +48,7 @@ def __init__(self, list_of_starlists, ref_index=0, iters=2, star_list['w'] * ref_list['w'] * weight_from_keyword (see the weights parameter) - for those stars not trimmed out by the other criteria. + for those stars not trimmed out by the other criteria. Optional Parameters ---------- @@ -836,25 +836,15 @@ def update_ref_table_aggregates(self, n_boot=0): vals_orig[mm] = self.ref_table[mm][ref_orig_idx] # Combine positions with a velocity fit. - #if 'vx' in self.ref_table.keys(): - #print('before:',self.ref_table['vx'][:10]) self.ref_table.fit_velocities(bootstrap=n_boot, verbose=self.verbose, default_motion_model=self.default_motion_model) - #print(np.unique(self.ref_table['motion_model_used'])) - #if 'vx' in self.ref_table.keys(): - #print('after:', self.ref_table['vx'][:10]) # Combine (transformed) magnitudes if 'me' in self.ref_table.colnames: weights_col = None else: weights_col = 'me' - self.ref_table.combine_lists('m', weights_col=weights_col, ismag=True) - #else: - # weighted_xy = ('xe' in self.ref_table.colnames) and ('ye' in self.ref_table.colnames) - # weighted_m = ('me' in self.ref_table.colnames) - - # self.ref_table.combine_lists_xym(weighted_xy=weighted_xy, weighted_m=weighted_m) + # Replace the originals if we are supposed to keep them fixed. if not self.update_ref_orig: for val in vals_orig.keys(): @@ -963,7 +953,6 @@ def get_ref_list_from_table(self, epoch): if ('motion_model_used' in self.ref_table.colnames): x,y,xe,ye = self.ref_table.get_star_positions_at_time(epoch) - #print('ref star pos',epoch,x,y) else: # No velocities... just used average positions. x = self.ref_table['x0'] diff --git a/flystar/startables.py b/flystar/startables.py index ab08665..333a907 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -843,7 +843,6 @@ def fit_velocity_for_star(self, ss, weighting='var', use_scipy=True, param_dict[par] = self[par][ss]''' # Model object - #print(motion_model_use,param_dict) mod = modClass(**param_dict, PA=self.meta['position_angle'], RA=self.meta['RA'], Dec=self.meta['Dec'], obs=self.meta['observer_location']) # Fit for the best parameters diff --git a/flystar/tests/test_align.py b/flystar/tests/test_align.py index 5964274..de8e72d 100644 --- a/flystar/tests/test_align.py +++ b/flystar/tests/test_align.py @@ -47,8 +47,7 @@ def test_MosaicSelfRef(): # Check that we have some matched stars... should be at least 35 stars # that are detected in all 4 starlists. idx = np.where(msc.ref_table['n_detect'] == 4)[0] - print(len(idx)) - assert len(idx) > 35 + assert len(idx) > 35 # Check that the transformation error isn't too big assert (msc.ref_table['x0_err'] < 3.0).all() # less than 1 pix @@ -194,8 +193,7 @@ def test_MosaicSelfRef_vel(): # Check that we have some matched stars... should be at least 35 stars # that are detected in all 4 starlists. idx = np.where(msc.ref_table['n_detect'] == 4)[0] - print(idx) - assert len(idx) > 35 + assert len(idx) > 35 # Check that the transformation error isn't too big assert (msc.ref_table['x0_err'] < 3.0).all() # less than 1 pix @@ -213,67 +211,6 @@ def test_MosaicSelfRef_vel(): 'k.', color='black', alpha=0.2) return - -'''def test_MosaicSelfRef_acc(): - """ - Cross-match and align 4 starlists using the OO version of mosaic lists. - - """ - make_fake_starlists_poly1_acc(seed=42) - - ref_file = 'random_acc_ref.fits' - ref_list = Table.read(ref_file) - names = ref_list["name"] - list_files = ['random_acc_0.fits', - 'random_acc_1.fits', - 'random_acc_2.fits', - 'random_acc_3.fits'] - lists = [starlists.StarList.read(lf) for lf in list_files] - - ########## - # Test instantiation and basic fitting. - ########## - msc = align.MosaicSelfRef(lists, ref_index=0, iters=3, - dr_tol=[5, 3, 3], dm_tol=[1, 1, 0.5], outlier_tol=None, - trans_class=transforms.PolyTransform, - trans_args={'order': 2}, default_motion_model='Acceleration', - verbose=False) - - msc.fit() - - # Check some of the output quantities on the final table. - assert 'x0' in msc.ref_table.colnames - assert 'x0_err' in msc.ref_table.colnames - assert 'y0' in msc.ref_table.colnames - assert 'y0_err' in msc.ref_table.colnames - assert 'm0' in msc.ref_table.colnames - assert 'm0_err' in msc.ref_table.colnames - assert 'vx0' in msc.ref_table.colnames - assert 'vx0_err' in msc.ref_table.colnames - assert 'vy0' in msc.ref_table.colnames - assert 'vy0_err' in msc.ref_table.colnames - assert 'ax' in msc.ref_table.colnames - assert 'ax_err' in msc.ref_table.colnames - assert 'ay' in msc.ref_table.colnames - assert 'ay_err' in msc.ref_table.colnames - assert 't0' in msc.ref_table.colnames - - # Check that we have some matched stars... should be at least 35 stars - # that are detected in all 4 starlists. - idx = np.where(msc.ref_table['n_detect'] == 4)[0] - assert len(idx) > 35 - - # Check that the transformation error isn't too big - assert (msc.ref_table['x0_err'] < 3.0).all() # less than 1 pix - assert (msc.ref_table['y0_err'] < 3.0).all() - assert (msc.ref_table['m0_err'] < 1.0).all() # less than 0.5 mag - - # Check fit quality - for param in ['x0','y0','vx0','vy0','ax','ay']: - #np.testing.assert_almost_equal(msc.ref_table[param][:len(ref_list)], ref_list[param], 2) - print(param,np.transpose([msc.ref_table[param][:len(ref_list)], ref_list[param]])) - return''' - def test_MosaicToRef(): make_fake_starlists_poly1(seed=42) @@ -358,7 +295,6 @@ def test_MosaicToRef_vel(): ref_list['vx'] *= -1.0 lists = [starlists.StarList.read(lf) for lf in list_files] - print(ref_list[['x0','vx']]) msc = align.MosaicToRef(ref_list, lists, iters=2, dr_tol=[0.2, 0.1], dm_tol=[1, 0.5], @@ -410,8 +346,6 @@ def test_MosaicToRef_acc(): 'random_acc_7.fits'] ref_list = Table.read(ref_file) - print(ref_list.keys()) - print(ref_list) # Convert velocities to arcsec/yr ref_list['vx0'] *= 1e-3 @@ -686,7 +620,6 @@ def make_fake_starlists_poly1_vel(seed=-1): trans = transforms.PolyTransform(1, xy_trans[ss][0], xy_trans[ss][1], mag_offset=mag_trans[ss]) xd, yd = trans.evaluate(xp, yp) md = trans.evaluate_mag(lis['m0']) - print([xd-xp]) # Perturb with small errors (0.1 pix) xd += np.random.randn(N_stars) * 0.1 @@ -1024,11 +957,9 @@ def test_bootstrap(): # Run bootstrap: no boot_epochs_min match1.calc_bootstrap_errors(n_boot=n_boot, boot_epochs_min=boot_epochs_min) - print(match1.ref_table.keys()) # Make sure columns exist, and none of them are nan values assert np.sum(np.isnan(match1.ref_table['xe_boot'])) == 0 assert np.sum(np.isnan(match1.ref_table['ye_boot'])) == 0 - print(match1.ref_table['vx_err_boot']) assert np.sum(np.isnan(match1.ref_table['vx_err_boot'])) == 0 assert np.sum(np.isnan(match1.ref_table['vy_err_boot'])) == 0 From bdf0177019d97c91d572fd0b3bf61ea4d6d13d57 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Thu, 19 Dec 2024 13:23:47 -0800 Subject: [PATCH 036/254] testing --- flystar/tests/test_align.py | 32 +++++++++++++++++++------------- flystar/tests/test_startable.py | 4 ++-- 2 files changed, 21 insertions(+), 15 deletions(-) diff --git a/flystar/tests/test_align.py b/flystar/tests/test_align.py index de8e72d..c59bbe0 100644 --- a/flystar/tests/test_align.py +++ b/flystar/tests/test_align.py @@ -384,11 +384,13 @@ def test_MosaicToRef_acc(): # The velocities should be almost the same as the input # velocities since update_ref_orig == False. + i_orig, i_fit = [],[] for i,star in enumerate(ref_list["name"]): if star in msc.ref_table["name"]: - ii = np.where(msc.ref_table["name"]==star)[0][0] - np.testing.assert_allclose(msc.ref_table['vx0'][ii], ref_list['vx0'][i], rtol=1e-5) - np.testing.assert_allclose(msc.ref_table['vy0'][ii], ref_list['vy0'][i], rtol=1e-5) + i_fit.append(np.where(msc.ref_table["name"]==star)[0][0]) + i_orig.append(i) + np.testing.assert_allclose(msc.ref_table['ax'][i_fit], ref_list['ax'][i_orig], rtol=1e-5) + np.testing.assert_allclose(msc.ref_table['ay'][i_fit], ref_list['ay'][i_orig], rtol=1e-5) ########## # Align and let velocities be free. @@ -398,14 +400,18 @@ def test_MosaicToRef_acc(): # The velocities should be almost the same (but not as close as before) # as the input velocities since update_ref == False. + i_orig, i_fit = [],[] for i,star in enumerate(ref_list["name"]): if star in msc.ref_table["name"]: - ii = np.where(msc.ref_table["name"]==star)[0][0] - np.testing.assert_allclose(msc.ref_table['vx0'][ii], ref_list['vx0'][i], rtol=1e-1) - np.testing.assert_allclose(msc.ref_table['vy0'][ii], ref_list['vy0'][i], rtol=1e-1) + ix_fit = np.where(msc.ref_table["name"]==star)[0][0] + if ~np.isnan(msc.ref_table['ax'][ix_fit]): + i_orig.append(i) + i_fit.append(ix_fit) + np.testing.assert_allclose(msc.ref_table['ax'][i_fit], ref_list['ax'][i_orig], rtol=1e-1) + np.testing.assert_allclose(msc.ref_table['ay'][i_fit], ref_list['ay'][i_orig], rtol=1e-1) # Also double check that they aren't exactly the same for the reference stars. - assert np.any(np.not_equal(msc.ref_table['vx0'], ref_list['vx0'])) + assert np.any(np.not_equal(msc.ref_table['ax'], ref_list['ax'])) return msc @@ -591,13 +597,13 @@ def make_fake_starlists_poly1_vel(seed=-1): times = [2018.5, 2019.0, 2019.5, 2020.0, 2020.5, 2021.0, 2021.5, 2022.0] xy_trans = [[[ 6.5, 0.99, 1e-5], [ 10.1, 1e-5, 0.99]], [[100.3, 0.98, 1e-5], [ 50.5, 9e-6, 1.001]], - [[ 0.0, 1.00, 0.0], [ 0.0, 0.0, 1.0]], + [[ 0.0, 1.00, 0.0], [ 0.0, 0.0, 1.000]], [[250.0, 0.97, 2e-5], [-250.0, 1e-5, 1.001]], - [[ 50.0, 1.00, 0.0], [ -31.0, 0.0, 1.000]], - [[ 78.0, 1.00, 0.0 ], [ 45.0, 0.0, 1.00]], - [[-13.0, 1.00, 0.0], [ 150, 0.0, 1.00]], - [[ 94.0, 1.00, 0.0], [-182.0, 0.0, 1.00]]] - mag_trans = [0.1, 0.4, 0.0, -0.3, 0.0, 0.0, 0.0, 0.0] + [[ 50.0, 1.01, 1e-5], [ -31.0, 1e-5, 1.000]], + [[ 78.0, 0.98, 0.0 ], [ 45.0, 9e-6, 1.001]], + [[-13.0, 0.99, 1e-5], [ 150, 2e-5, 1.002]], + [[ 94.0, 1.00, 9e-6], [-182.0, 0.0, 0.99]]] + mag_trans = [0.1, 0.4, 0.0, -0.3, 0.2, 0.0, -0.1, -0.3] # Convert into pixels (undistorted) with the following info. scale = 0.01 # arcsec / pix diff --git a/flystar/tests/test_startable.py b/flystar/tests/test_startable.py index 66421d9..b5c816c 100644 --- a/flystar/tests/test_startable.py +++ b/flystar/tests/test_startable.py @@ -359,7 +359,7 @@ def test_fit_velocities(): ########## # Test bootstrap ########## - '''tab_b = table.vstack((tab1, tab2, tab3)) + tab_b = table.vstack((tab1, tab2, tab3)) tab_b.meta = tab1.meta tab_b.fit_velocities(verbose=True, bootstrap=50) @@ -367,7 +367,7 @@ def test_fit_velocities(): assert tab_b['x0_err'][0] > tab['x0_err'][0] assert tab_b['vx_err'][0] > tab['vx_err'][0] assert tab_b['y0_err'][0] > tab['y0_err'][0] - assert tab_b['vy_err'][0] > tab['vy_err'][0]''' + assert tab_b['vy_err'][0] > tab['vy_err'][0] ########## # Test what happens with no velocity errors From d3331d6a644a0ecaa94539dee349c991697e8d6c Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Thu, 19 Dec 2024 14:34:26 -0800 Subject: [PATCH 037/254] error scaling in curve_fit --- flystar/motion_model.py | 19 ++++++++----------- flystar/startables.py | 2 +- flystar/tests/test_startable.py | 1 + 3 files changed, 10 insertions(+), 12 deletions(-) diff --git a/flystar/motion_model.py b/flystar/motion_model.py index c18c24f..e9f996b 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -243,17 +243,12 @@ def run_fit(self, t, x, y, xe, ye, update=True, weighting='var'): else: def linear(t, c0, c1): return c0 + c1*t - x_opt, x_cov = curve_fit(linear, dt, x, p0=np.array([x.mean(),0.0]), sigma=1/x_wt, absolute_sigma=True) - y_opt, y_cov = curve_fit(linear, dt, y, p0=np.array([y.mean(),0.0]), sigma=1/y_wt, absolute_sigma=True) - x0 = x_opt[0] - vx = x_opt[1] - y0 = y_opt[0] - vy = y_opt[1] + x_opt, x_cov = curve_fit(linear, dt, x, p0=np.array([x.mean(),0.0]), sigma=1/np.sqrt(x_wt), absolute_sigma=True) + y_opt, y_cov = curve_fit(linear, dt, y, p0=np.array([y.mean(),0.0]), sigma=1/np.sqrt(y_wt), absolute_sigma=True) + x0, vx = x_opt + y0, vy = y_opt x0e, vxe = np.sqrt(x_cov.diagonal()) y0e, vye = np.sqrt(y_cov.diagonal()) - - params = [x0, vx, y0, vy] - param_errors = [x0e, vxe, y0e, vye] if update: self.x0 = x0 @@ -265,6 +260,8 @@ def linear(t, c0, c1): self.y0_err = y0e self.vy_err = vye + params = [x0, vx, y0, vy] + param_errors = [x0e, vxe, y0e, vye] return params, param_errors @@ -333,8 +330,8 @@ def run_fit(self, t, x, y, xe, ye, update=True, weighting='var'): def accel(t, c0,c1,c2): return c0 + c1*t + 0.5*c2*t**2 - x_opt, x_cov = curve_fit(accel, dt, x, p0=np.array([x.mean(),0.0,0.0]), sigma=1/x_wt, absolute_sigma=True) - y_opt, y_cov = curve_fit(accel, dt, y, p0=np.array([y.mean(),0.0,0.0]), sigma=1/y_wt, absolute_sigma=True) + x_opt, x_cov = curve_fit(accel, dt, x, p0=np.array([x.mean(),0.0,0.0]), sigma=1/x_wt**0.5, absolute_sigma=True) + y_opt, y_cov = curve_fit(accel, dt, y, p0=np.array([y.mean(),0.0,0.0]), sigma=1/y_wt**0.5, absolute_sigma=True) x0 = x_opt[0] y0 = y_opt[0] vx0 = x_opt[1] diff --git a/flystar/startables.py b/flystar/startables.py index 333a907..74873c1 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -846,7 +846,7 @@ def fit_velocity_for_star(self, ss, weighting='var', use_scipy=True, mod = modClass(**param_dict, PA=self.meta['position_angle'], RA=self.meta['RA'], Dec=self.meta['Dec'], obs=self.meta['observer_location']) # Fit for the best parameters - params, param_errs = mod.fit_motion_model(t, x, y, xe, ye, bootstrap=bootstrap, update=True) + params, param_errs = mod.fit_motion_model(t, x, y, xe, ye, bootstrap=bootstrap, update=True, weighting=weighting) chi2_x,chi2_y = mod.get_chi2(t,x,y,xe,ye) self['chi2_x'][ss]=chi2_x self['chi2_y'][ss]=chi2_y diff --git a/flystar/tests/test_startable.py b/flystar/tests/test_startable.py index b5c816c..feda0d6 100644 --- a/flystar/tests/test_startable.py +++ b/flystar/tests/test_startable.py @@ -389,6 +389,7 @@ def test_fit_velocities(): # Test mask_list ######### # Test 5a: Masked + print("Testing Masked List") tt.fit_velocities(bootstrap=0, verbose=False, mask_lists=[1]) assert np.arange(2.25, 48, 5) == pytest.approx(tt['x0'].data) assert np.arange(2.25, 48, 5) == pytest.approx(tt['y0'].data) From 44a67e49a11436c0c6ad0edb91c82609d2946471 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Fri, 20 Dec 2024 12:17:32 -0800 Subject: [PATCH 038/254] improve motion model fitting --- flystar/motion_model.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/flystar/motion_model.py b/flystar/motion_model.py index e9f996b..ddf965f 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -443,13 +443,13 @@ def run_fit(self, t, x, y, xe, ye, update=True, weighting='var'): def fit_func(t, x0,y0, vx,vy, pi): x_res = x0 + vx*(t-self.t0) + pi*pvec[0] y_res = y0 + vy*(t-self.t0) + pi*pvec[1] - diff = (x-x_res)**2 * x_wt + (y-y_res)**2 * y_wt - return diff + return np.append(x_res, y_res) # Initial guesses, x0,y0 as x,y averages; # vx,vy as average velocity if first and last points are perfectly measured; # pi for 10 pc disance - res = curve_fit(fit_func, t, np.zeros(len(t)), - p0=[np.mean(x),np.mean(y), (x[-1]-x[0])/(t[-1]-t[0]),(y[-1]-y[0])/(t[-1]-t[0]), 1]) + res = curve_fit(fit_func, t, np.append(x,y), + p0=[np.mean(x),np.mean(y), (x[-1]-x[0])/(t[-1]-t[0]),(y[-1]-y[0])/(t[-1]-t[0]), 1], + sigma = 1.0/np.append(x_wt,y_wt)) x0,y0,vx,vy,pi = res[0] x0_err,y0_err,vx_err,vy_err,pi_err = np.sqrt(np.diag(res[1])) if update: From d35e3c64495f7567ae5bd2eb6d6111955ff26e54 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Fri, 20 Dec 2024 12:37:22 -0800 Subject: [PATCH 039/254] modify plx fitting --- flystar/motion_model.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/flystar/motion_model.py b/flystar/motion_model.py index ddf965f..45d6469 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -441,13 +441,14 @@ def run_fit(self, t, x, y, xe, ye, update=True, weighting='var'): pvec = parallax.parallax_in_direction(self.RA, self.Dec, t_mjd, obsLocation=self.obs, PA=self.PA).T x_wt, y_wt = self.get_weights(xe,ye, weighting=weighting) def fit_func(t, x0,y0, vx,vy, pi): - x_res = x0 + vx*(t-self.t0) + pi*pvec[0] - y_res = y0 + vy*(t-self.t0) + pi*pvec[1] + use_t = t[:int(len(t)/2)] + x_res = x0 + vx*(use_t-self.t0) + pi*pvec[0] + y_res = y0 + vy*(use_t-self.t0) + pi*pvec[1] return np.append(x_res, y_res) # Initial guesses, x0,y0 as x,y averages; # vx,vy as average velocity if first and last points are perfectly measured; # pi for 10 pc disance - res = curve_fit(fit_func, t, np.append(x,y), + res = curve_fit(fit_func, np.append(t,t), np.append(x,y), p0=[np.mean(x),np.mean(y), (x[-1]-x[0])/(t[-1]-t[0]),(y[-1]-y[0])/(t[-1]-t[0]), 1], sigma = 1.0/np.append(x_wt,y_wt)) x0,y0,vx,vy,pi = res[0] From 5d46b09df34a76842acaf7ff997d5ce80852e11b Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Fri, 20 Dec 2024 15:47:29 -0800 Subject: [PATCH 040/254] plot updates for motion model --- flystar/motion_model.py | 12 ++++++++---- flystar/plots.py | 26 ++++++++++++++++++++------ 2 files changed, 28 insertions(+), 10 deletions(-) diff --git a/flystar/motion_model.py b/flystar/motion_model.py index 45d6469..44f2c00 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -400,15 +400,19 @@ def __init__(self, x0=0, vx=0, y0=0, vy=0, t0=None, def get_pos_at_time(self, t): t_mjd = Time(t, format='decimalyear', scale='utc').mjd pvec = parallax.parallax_in_direction(self.RA, self.Dec, t_mjd, obsLocation=self.obs, PA=self.PA).T - x = self.x0 + self.vx*(t-self.t0) + self.pi*pvec[0] - y = self.y0 + self.vy*(t-self.t0) + self.pi*pvec[1] + pvec_x = np.reshape(pvec[0], t.shape) + pvec_y = np.reshape(pvec[1], t.shape) + x = self.x0 + self.vx*(t-self.t0) + self.pi*pvec_x + y = self.y0 + self.vy*(t-self.t0) + self.pi*pvec_y return x, y def get_pos_err_at_time(self, t): t_mjd = Time(t, format='decimalyear', scale='utc').mjd pvec = parallax.parallax_in_direction(self.RA, self.Dec, t_mjd, obsLocation=self.obs, PA=self.PA).T - x_err = np.sqrt(self.y0_err**2 + ((t-self.t0)*self.vx_err)**2 + (self.pi_err*pvec[0])**2) - y_err = np.sqrt(self.x0_err**2 + ((t-self.t0)*self.vy_err)**2 + (self.pi_err*pvec[1])**2) + pvec_x = np.reshape(pvec[0], t.shape) + pvec_y = np.reshape(pvec[1], t.shape) + x_err = np.sqrt(self.y0_err**2 + ((t-self.t0)*self.vx_err)**2 + (self.pi_err*pvec_x)**2) + y_err = np.sqrt(self.x0_err**2 + ((t-self.t0)*self.vy_err)**2 + (self.pi_err*pvec_y)**2) return x_err, y_err def get_batch_pos_at_time(self, t, diff --git a/flystar/plots.py b/flystar/plots.py index b3f69b8..070767e 100755 --- a/flystar/plots.py +++ b/flystar/plots.py @@ -1,4 +1,4 @@ -from flystar import analysis +from flystar import analysis, motion_model import pylab as py import pylab as plt import numpy as np @@ -2481,7 +2481,8 @@ def plot_chi2_dist_mag(tab, Ndetect, mlim=40, n_bins=30): return -def plot_stars(tab, star_names, NcolMax=2, epoch_array = None, figsize=(15,25), color_time=False): +def plot_stars(tab, star_names, NcolMax=2, epoch_array = None, figsize=(15,25), color_time=False, + position_angle=None, RA=None, Dec=None, observer_location='earth'): """ Plot a set of stars positions, flux and residuals over time. @@ -2537,11 +2538,22 @@ def rs(x): merr = tab['me'][ii, fnd] dt = tab['t'][ii, fnd] - tab['t0'][ii] - fitLineX = tab['x0'][ii] + (tab['vx'][ii] * dt) - fitLineY = tab['y0'][ii] + (tab['vy'][ii] * dt) + + if 'motion_model_used' not in tab.keys(): + fitLineX = tab['x0'][ii] + (tab['vx'][ii] * dt) + fitLineY = tab['y0'][ii] + (tab['vy'][ii] * dt) - fitSigX = np.hypot(tab['x0_err'][ii], tab['vx_err'][ii]*dt) - fitSigY = np.hypot(tab['y0_err'][ii], tab['vy_err'][ii]*dt) + fitSigX = np.hypot(tab['x0_err'][ii], tab['vx_err'][ii]*dt) + fitSigY = np.hypot(tab['y0_err'][ii], tab['vy_err'][ii]*dt) + else: + motion_model_str = tab['motion_model_used'][ii] + modClass = getattr(motion_model, motion_model_str) + param_dict = {} + for par in modClass.fitter_param_names+modClass.fixed_param_names: + param_dict[par] = tab[par][ii] + mod = modClass(**param_dict, PA=position_angle, RA=RA, Dec=Dec, obs=observer_location) + fitLineX, fitLineY = mod.get_pos_at_time(time) + fitSigX, fitSigY = mod.get_pos_err_at_time(time) fitLineM = np.repeat(tab['m0'][ii], len(dt)).reshape(len(dt),1) fitSigM = np.repeat(tab['m0_err'][ii], len(dt)).reshape(len(dt),1) @@ -2582,6 +2594,8 @@ def rs(x): (chi2_red_y, chi2_y, dof)) print( '\tM Chi^2 = %5.2f (%6.2f for %2d dof)' % (chi2_red_m, chi2_m, dofM)) + if 'motion_model_used' in tab.keys(): + print('\tMotion model:', tab['motion_model_used'][ii]) tmin = time.min() tmax = time.max() From 6b9b77573202f9a6c3ec212a984e66e3a21ae829 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Mon, 13 Jan 2025 09:51:19 -0800 Subject: [PATCH 041/254] minor bug fixes --- flystar/align.py | 4 ++-- flystar/match.py | 3 --- flystar/motion_model.py | 14 +++++++++++++- flystar/startables.py | 1 + 4 files changed, 16 insertions(+), 6 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 85d45d0..ed22926 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -324,7 +324,7 @@ def fit(self): self.ref_table.detections() ### Drop all stars that have 0 detections. - idx = np.where((self.ref_table['n_detect'] == 0) & (self.ref_table['ref_orig'] == False))[0] + idx = np.where((self.ref_table['n_detect'] == 0))[0] print(' *** Getting rid of {0:d} out of {1:d} junk sources'.format(len(idx), len(self.ref_table))) self.ref_table.remove_rows(idx) @@ -360,7 +360,7 @@ def fit(self): self.ref_table.detections() ### Drop all stars that have 0 detections. - idx = np.where((self.ref_table['n_detect'] == 0) & (self.ref_table['ref_orig'] == False))[0] + idx = np.where((self.ref_table['n_detect'] == 0))[0] print(' *** Getting rid of {0:d} out of {1:d} junk sources'.format(len(idx), len(self.ref_table))) self.ref_table.remove_rows(idx) diff --git a/flystar/match.py b/flystar/match.py index b959d85..c2b41c6 100644 --- a/flystar/match.py +++ b/flystar/match.py @@ -277,9 +277,6 @@ def match(x1, y1, m1, x2, y2, m2, dr_tol, dm_tol=None, verbose=True): # have matches. idxs1 = np.ones(x1.size, dtype=int) * -1 idxs2 = np.ones(x1.size, dtype=int) * -1 - - # TODO: consider better solution for this - coords2=np.nan_to_num(coords2) # The matching will be done using a KDTree. #kdt = KDT(coords2, balanced_tree=False) diff --git a/flystar/motion_model.py b/flystar/motion_model.py index 44f2c00..5878cfa 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -59,6 +59,15 @@ def get_weights(self, xe, ye, weighting='var'): else: warnings.warn("Invalid weighting, using default weighting scheme var.", UserWarning) return 1./xe**2, 1./ye**2 + + def scale_errors(self, errs, weighting='var'): + if weighting=='std': + return errs**2 + elif weighting=='var': + return errs + else: + warnings.warn("Invalid weighting, using default weighting scheme var.", UserWarning) + return errs def fit_motion_model(self, t, x, y, xe, ye, update=True, bootstrap=0, weighting='var'): """ @@ -249,6 +258,7 @@ def linear(t, c0, c1): y0, vy = y_opt x0e, vxe = np.sqrt(x_cov.diagonal()) y0e, vye = np.sqrt(y_cov.diagonal()) + x0e, vxe, y0e, vye = self.scale_errors([x0e, vxe, y0e, vye], weighting=weighting) if update: self.x0 = x0 @@ -341,6 +351,7 @@ def accel(t, c0,c1,c2): x0e, vx0e, axe = np.sqrt(x_cov.diagonal()) y0e, vy0e, aye = np.sqrt(y_cov.diagonal()) + x0e, vxe, axe, y0e, vye, aye = self.scale_errors([x0e, vxe, axe, y0e, vye, aye], weighting=weighting) if update: self.x0 = x0 @@ -456,7 +467,8 @@ def fit_func(t, x0,y0, vx,vy, pi): p0=[np.mean(x),np.mean(y), (x[-1]-x[0])/(t[-1]-t[0]),(y[-1]-y[0])/(t[-1]-t[0]), 1], sigma = 1.0/np.append(x_wt,y_wt)) x0,y0,vx,vy,pi = res[0] - x0_err,y0_err,vx_err,vy_err,pi_err = np.sqrt(np.diag(res[1])) + x0_err,y0_err,vx_err,vy_err,pi_err = self.scale_errors(np.sqrt(np.diag(res[1])), weighting=weighting) + if update: self.x0 = x0 self.y0=y0 diff --git a/flystar/startables.py b/flystar/startables.py index 74873c1..e03ece7 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -780,6 +780,7 @@ def fit_velocity_for_star(self, ss, weighting='var', use_scipy=True, # Catch the case where there is NO good data. if N_good == 0: #self['motion_model_used'][ss] = 'None' + self['n_fit'][ss] = N_good return # Everything below has N_good >= 1 From 622beda7b22e51b987e0c8e9fc8e891e27dcc3ae Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Mon, 13 Jan 2025 14:39:02 -0800 Subject: [PATCH 042/254] continued testing --- flystar/tests/test_align.py | 208 ++++++++++++++++++++++++++++++++---- 1 file changed, 185 insertions(+), 23 deletions(-) diff --git a/flystar/tests/test_align.py b/flystar/tests/test_align.py index e936254..1a306c2 100644 --- a/flystar/tests/test_align.py +++ b/flystar/tests/test_align.py @@ -269,6 +269,72 @@ def test_MosaicToRef(): return msc +def test_MosaicToRef_p0_vel(): + make_fake_starlists_poly0_vel(seed=42) + + ref_file = 'random_vel_ref.fits' + list_files = ['random_vel_p0_0.fits', + 'random_vel_p0_1.fits', + 'random_vel_p0_2.fits', + 'random_vel_p0_3.fits'] + #'random_vel_4.fits', + #'random_vel_5.fits', + #'random_vel_6.fits', + #'random_vel_7.fits'] + + ref_list = Table.read(ref_file) + + # Convert velocities to arcsec/yr + ref_list['vx'] *= 1e-3 + ref_list['vy'] *= 1e-3 + ref_list['vx_err'] *= 1e-3 + ref_list['vy_err'] *= 1e-3 + + # Switch our list to a "increasing to the West" list. + ref_list['x0'] *= -1.0 + ref_list['vx'] *= -1.0 + + lists = [starlists.StarList.read(lf) for lf in list_files] + + msc = align.MosaicToRef(ref_list, lists, iters=2, + dr_tol=[0.2, 0.1], dm_tol=[1, 0.5], + outlier_tol=[None, None], + trans_class=transforms.PolyTransform, + trans_args={'order': 1}, default_motion_model='Linear', + update_ref_orig=False, verbose=False) + msc.fit() + + # Check our status columns + assert 'use_in_trans' in msc.ref_table.colnames + assert 'used_in_trans' in msc.ref_table.colnames + assert 'ref_orig' in msc.ref_table.colnames + assert msc.ref_table['use_in_trans'].shape == msc.ref_table['x0'].shape + assert msc.ref_table['used_in_trans'].shape == msc.ref_table['x'].shape + + # The velocities should be almost the same as the input + # velocities since update_ref_orig == False. + assert (msc.ref_table['name']==ref_list['name']).all() + np.testing.assert_allclose(msc.ref_table['vx'], ref_list['vx'], rtol=1e-5) + np.testing.assert_allclose(msc.ref_table['vy'], ref_list['vy'], rtol=1e-5) + + ########## + # Align and let velocities be free. + ########## + msc.update_ref_orig = True + msc.fit() + + # The velocities should be almost the same (but not as close as before) + # as the input velocities since update_ref == True. + assert (msc.ref_table['name']==ref_list['name']).all() + np.testing.assert_allclose(msc.ref_table['vx'], ref_list['vx'], rtol=1e-1) + np.testing.assert_allclose(msc.ref_table['vy'], ref_list['vy'], rtol=1e-1) + + # Also double check that they aren't exactly the same for the reference stars. + #assert np.any(np.not_equal(msc.ref_table['vx'], ref_list['vx'])) + assert np.not_equal(msc.ref_table['vx'], ref_list['vx']).any() + + return msc + def test_MosaicToRef_vel(): make_fake_starlists_poly1_vel(seed=42) @@ -276,11 +342,11 @@ def test_MosaicToRef_vel(): list_files = ['random_vel_0.fits', 'random_vel_1.fits', 'random_vel_2.fits', - 'random_vel_3.fits', - 'random_vel_4.fits', - 'random_vel_5.fits', - 'random_vel_6.fits', - 'random_vel_7.fits'] + 'random_vel_3.fits'] + #'random_vel_4.fits', + #'random_vel_5.fits', + #'random_vel_6.fits', + #'random_vel_7.fits'] ref_list = Table.read(ref_file) @@ -298,8 +364,9 @@ def test_MosaicToRef_vel(): msc = align.MosaicToRef(ref_list, lists, iters=2, dr_tol=[0.2, 0.1], dm_tol=[1, 0.5], + outlier_tol=[None, None], trans_class=transforms.PolyTransform, - trans_args={'order': 2}, default_motion_model='Linear', + trans_args={'order': 1}, default_motion_model='Linear', update_ref_orig=False, verbose=False) msc.fit() @@ -312,6 +379,7 @@ def test_MosaicToRef_vel(): # The velocities should be almost the same as the input # velocities since update_ref_orig == False. + assert (msc.ref_table['name']==ref_list['name']).all() np.testing.assert_allclose(msc.ref_table['vx'], ref_list['vx'], rtol=1e-5) np.testing.assert_allclose(msc.ref_table['vy'], ref_list['vy'], rtol=1e-5) @@ -322,7 +390,8 @@ def test_MosaicToRef_vel(): msc.fit() # The velocities should be almost the same (but not as close as before) - # as the input velocities since update_ref == False. + # as the input velocities since update_ref == True. + assert (msc.ref_table['name']==ref_list['name']).all() np.testing.assert_allclose(msc.ref_table['vx'], ref_list['vx'], rtol=1e-1) np.testing.assert_allclose(msc.ref_table['vy'], ref_list['vy'], rtol=1e-1) @@ -549,6 +618,99 @@ def make_fake_starlists_poly1(seed=-1): return (xy_trans,mag_trans) +def make_fake_starlists_poly0_vel(seed=-1): + # If seed >=0, then set random seed to that value + if seed >= 0: + np.random.seed(seed=seed) + + N_stars = 200 + + x0 = np.random.rand(N_stars) * 10.0 # arcsec (increasing to East) + y0 = np.random.rand(N_stars) * 10.0 # arcsec + x0e = np.ones(N_stars) * 1.0e-4 # arcsec + y0e = np.ones(N_stars) * 1.0e-4 # arcsec + vx = np.random.randn(N_stars) * 5.0 # mas / yr + vy = np.random.randn(N_stars) * 5.0 # mas / yr + vxe = np.ones(N_stars) * 0.05 # mas / yr + vye = np.ones(N_stars) * 0.05 # mas / yr + m0 = (np.random.rand(N_stars) * 8) + 9 # mag + m0e = np.random.randn(N_stars) * 0.05 # mag + t0 = np.ones(N_stars) * 2019.5 + + # Make all the errors positive + x0e = np.abs(x0e) + y0e = np.abs(y0e) + m0e = np.abs(m0e) + vxe = np.abs(vxe) + vye = np.abs(vye) + + name = ['star_{0:03d}'.format(ii) for ii in range(N_stars)] + + # Make an StarList + lis = starlists.StarList([name, m0, m0e, x0, x0e, y0, y0e, vx, vxe, vy, vye, t0], + names = ('name', 'm0', 'm0_err', 'x0', 'x0_err', 'y0', 'y0_err', + 'vx', 'vx_err', 'vy', 'vy_err', 't0')) + + sdx = np.argsort(m0) + lis = lis[sdx] + + # Save original positions as reference (1st) list + # in a StarList format (with velocities). + lis.write('random_vel_ref.fits', overwrite=True) + + ########## + # Propogate to new times and distort. + ########## + # Make 4 new starlists with different epochs and transformations. + times = [2018.5, 2019.0, 2019.5, 2020.0, 2020.5, 2021.0, 2021.5, 2022.0] + xy_trans = [[[ 6.5], [ 10.1]], + [[100.3], [ 50.5]], + [[ 0.0], [ 0.0]], + [[250.0], [-250.0]], + [[ 50.0], [ -31.0]], + [[ 78.0], [ 45.0]], + [[-13.0], [ 150]], + [[ 94.0], [-182.0]]] + mag_trans = [0.1, 0.4, 0.0, -0.3, 0.2, 0.0, -0.1, -0.3] + + # Convert into pixels (undistorted) with the following info. + scale = 0.01 # arcsec / pix + shift = [1.0, 1.0] # pix + + for ss in range(len(times)): + dt = times[ss] - lis['t0'] + + x = lis['x0'] + (lis['vx']/1e3) * dt + y = lis['y0'] + (lis['vy']/1e3) * dt + t = np.ones(N_stars) * times[ss] + + # Convert into pixels + xp = (x / -scale) + shift[0] # -1 from switching to increasing to West (right) + yp = (y / scale) + shift[1] + xpe = lis['x0_err'] / scale + ype = lis['y0_err'] / scale + + # Distort the positions + trans = transforms.PolyTransform(0, xy_trans[ss][0], xy_trans[ss][1], mag_offset=mag_trans[ss]) + xd, yd = trans.evaluate(xp, yp) + md = trans.evaluate_mag(lis['m0']) + + # Perturb with small errors (0.1 pix) + xd += np.random.randn(N_stars) * xpe + yd += np.random.randn(N_stars) * ype + md += np.random.randn(N_stars) * 0.02 + xde = xpe + yde = ype + mde = lis['m0_err'] + + # Save the new list as a starlist. + new_lis = starlists.StarList([lis['name'], md, mde, xd, xde, yd, yde, t], + names=('name', 'm', 'me', 'x', 'xe', 'y', 'ye', 't')) + + new_lis.write('random_vel_p0_{0:d}.fits'.format(ss), overwrite=True) + + return (xy_trans, mag_trans) + def make_fake_starlists_poly1_vel(seed=-1): # If seed >=0, then set random seed to that value @@ -559,12 +721,12 @@ def make_fake_starlists_poly1_vel(seed=-1): x0 = np.random.rand(N_stars) * 10.0 # arcsec (increasing to East) y0 = np.random.rand(N_stars) * 10.0 # arcsec - x0e = np.random.randn(N_stars) * 5.0e-4 # arcsec - y0e = np.random.randn(N_stars) * 5.0e-4 # arcsec + x0e = np.ones(N_stars) * 1.0e-4 # arcsec + y0e = np.ones(N_stars) * 1.0e-4 # arcsec vx = np.random.randn(N_stars) * 5.0 # mas / yr vy = np.random.randn(N_stars) * 5.0 # mas / yr - vxe = np.random.randn(N_stars) * 0.1 # mas / yr - vye = np.random.randn(N_stars) * 0.1 # mas / yr + vxe = np.ones(N_stars) * 0.05 # mas / yr + vye = np.ones(N_stars) * 0.05 # mas / yr m0 = (np.random.rand(N_stars) * 8) + 9 # mag m0e = np.random.randn(N_stars) * 0.05 # mag t0 = np.ones(N_stars) * 2019.5 @@ -598,7 +760,7 @@ def make_fake_starlists_poly1_vel(seed=-1): xy_trans = [[[ 6.5, 0.99, 1e-5], [ 10.1, 1e-5, 0.99]], [[100.3, 0.98, 1e-5], [ 50.5, 9e-6, 1.001]], [[ 0.0, 1.00, 0.0], [ 0.0, 0.0, 1.000]], - [[250.0, 0.97, 2e-5], [-250.0, 1e-5, 1.001]], + [[250.0, 1.01, 2e-5], [-250.0, 1e-5, 0.98]], [[ 50.0, 1.01, 1e-5], [ -31.0, 1e-5, 1.000]], [[ 78.0, 0.98, 0.0 ], [ 45.0, 9e-6, 1.001]], [[-13.0, 0.99, 1e-5], [ 150, 2e-5, 1.002]], @@ -627,9 +789,9 @@ def make_fake_starlists_poly1_vel(seed=-1): xd, yd = trans.evaluate(xp, yp) md = trans.evaluate_mag(lis['m0']) - # Perturb with small errors (0.1 pix) - xd += np.random.randn(N_stars) * 0.1 - yd += np.random.randn(N_stars) * 0.1 + # Perturb with small errors (0.1 mas) + xd += np.random.randn(N_stars) * xpe + yd += np.random.randn(N_stars) * ype md += np.random.randn(N_stars) * 0.02 xde = xpe yde = ype @@ -652,16 +814,16 @@ def make_fake_starlists_poly1_acc(seed=-1): x0 = np.random.rand(N_stars) * 10.0 # arcsec (increasing to East) y0 = np.random.rand(N_stars) * 10.0 # arcsec - x0e = np.random.randn(N_stars) * 5.0e-4 # arcsec - y0e = np.random.randn(N_stars) * 5.0e-4 # arcsec + x0e = np.ones(N_stars) * 1.0e-4 # arcsec + y0e = np.ones(N_stars) * 1.0e-4 # arcsec vx = np.random.randn(N_stars) * 5.0 # mas / yr vy = np.random.randn(N_stars) * 5.0 # mas / yr - vxe = np.random.randn(N_stars) * 0.1 # mas / yr - vye = np.random.randn(N_stars) * 0.1 # mas / yr + vxe = np.ones(N_stars) * 0.1 # mas / yr + vye = np.ones(N_stars) * 0.1 # mas / yr ax = np.random.randn(N_stars) * 0.5 # mas / yr^2 ay = np.random.randn(N_stars) * 0.5 # mas / yr^2 - axe = np.random.randn(N_stars) * 0.01 # mas / yr^2 - aye = np.random.randn(N_stars) * 0.01 # mas / yr^2 + axe = np.ones(N_stars) * 0.01 # mas / yr^2 + aye = np.ones(N_stars) * 0.01 # mas / yr^2 m0 = (np.random.rand(N_stars) * 8) + 9 # mag m0e = np.random.randn(N_stars) * 0.05 # mag t0 = np.ones(N_stars) * 2019.5 @@ -734,8 +896,8 @@ def make_fake_starlists_poly1_acc(seed=-1): md = trans.evaluate_mag(lis['m0']) # Perturb with small errors (0.1 pix) - xd += np.random.randn(N_stars) * 0.1 - yd += np.random.randn(N_stars) * 0.1 + xd += np.random.randn(N_stars) * xpe + yd += np.random.randn(N_stars) * ype md += np.random.randn(N_stars) * 0.02 xde = xpe yde = ype From ab6b1beceade04e128147faf7a0e1e192edf81a9 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Mon, 13 Jan 2025 15:35:25 -0800 Subject: [PATCH 043/254] undo accidental comment --- flystar/analysis.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/flystar/analysis.py b/flystar/analysis.py index 78aacab..aba9046 100644 --- a/flystar/analysis.py +++ b/flystar/analysis.py @@ -179,7 +179,7 @@ def prepare_gaia_for_flystar(gaia, ra, dec, targets_dict=None, match_dr_max=0.2) #except: # print('no invalig mags') - #gaia_new = gaia_new.filled() #convert masked colunms to regular columns + gaia_new = gaia_new.filled() #convert masked colunms to regular columns if targets_dict != None: for targ_name, targ_coo in targets_dict.items(): From 07d126534fdce927df4ffcad96248fbebabe3270 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Mon, 13 Jan 2025 16:14:53 -0800 Subject: [PATCH 044/254] update gaia to dr3 --- flystar/analysis.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/flystar/analysis.py b/flystar/analysis.py index aba9046..11d30de 100644 --- a/flystar/analysis.py +++ b/flystar/analysis.py @@ -21,7 +21,7 @@ # the new StarTable and StarList format. ################################################## -def query_gaia(ra, dec, search_radius=30.0, table_name='gaiadr2'): +def query_gaia(ra, dec, search_radius=30.0, table_name='gaiadr3'): """ Query the Gaia database at the specified location and with the specified search radius From f7fa74ee478b1486645643423aca8f1432d78bb5 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Mon, 13 Jan 2025 16:45:00 -0800 Subject: [PATCH 045/254] tiny bug fix --- flystar/motion_model.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/flystar/motion_model.py b/flystar/motion_model.py index 5878cfa..e747ab8 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -77,7 +77,7 @@ def fit_motion_model(self, t, x, y, xe, ye, update=True, bootstrap=0, weighting= Best-fit parameters will be returned along with uncertainties and updated if update=True. """ - params, param_errs = self.run_fit(t, x, y, xe, ye, weighting=weighting, update=True) + params, param_errs = self.run_fit(t, x, y, xe, ye, weighting=weighting, update=update) if bootstrap>0 and len(x)>(self.n_pts_req): edx = np.arange(len(x), dtype=int) From 0cad524d84837c14688c788aedc7ee17870bc8d0 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Tue, 14 Jan 2025 09:02:33 -0800 Subject: [PATCH 046/254] test bug fix --- flystar/tests/test_motion_model.py | 20 +++++++++++--------- 1 file changed, 11 insertions(+), 9 deletions(-) diff --git a/flystar/tests/test_motion_model.py b/flystar/tests/test_motion_model.py index 94474a7..91439d4 100755 --- a/flystar/tests/test_motion_model.py +++ b/flystar/tests/test_motion_model.py @@ -2,8 +2,9 @@ import numpy as np import pytest -def within_error(true_val, fit_val, fit_err, n_sigma=1): - return (true_val < (fit_val+fit_err)) & (true_val> (fit_val-fit_err)) +def within_error(true_val, fit_val, fit_err, n_sigma=2): + print('True', true_val, 'Fit', fit_val, 'Fit err', fit_err) + return (true_val < (fit_val+fit_err*n_sigma)) & (true_val> (fit_val-fit_err*n_sigma)) def test_Fixed(): # Test handling of a single star @@ -63,7 +64,8 @@ def test_Fixed(): def test_Linear(): # Test handling of a single star true_params = {'x0': 1.0, 'y0':0.5, 'x0_err':0.1, 'y0_err':0.1, - 'vx':0.2, 'vy':0.5, 'vx_err':0.05, 'vy_err':0.05} + 'vx':0.2, 'vy':0.5, 'vx_err':0.05, 'vy_err':0.05, + 't0':2025.0} mod_true = motion_model.Linear(**true_params) param_list = mod_true.fitter_param_names # Confirm return of proper values for single t=t0 and array t @@ -120,11 +122,11 @@ def test_Linear(): x_sim = np.random.normal(x_true, x_true_err) y_sim = np.random.normal(y_true, y_true_err) # Run fit - mod_fit = motion_model.Linear() + mod_fit = motion_model.Linear(t0=true_params['t0']) params, param_errs = mod_fit.fit_motion_model(t, x_sim,y_sim, x_true_err, y_true_err) print(param_errs) # Confirm true value is within error bar of fit value - assert [within_error(true_params[param_list[i]], params[i], param_errs[i]) for i in range(len(params))] + assert np.all([within_error(true_params[param_list[i]], params[i], param_errs[i]) for i in range(len(params))]) # Test fitter with bootstrap t = np.arange(2015.0,2025.0, 0.5) @@ -134,11 +136,11 @@ def test_Linear(): x_sim = np.random.normal(x_true, x_true_err) y_sim = np.random.normal(y_true, y_true_err) # Run fit - mod_fit = motion_model.Linear() + mod_fit = motion_model.Linear(t0=true_params['t0']) params, param_errs = mod_fit.fit_motion_model(t, x_sim,y_sim, x_true_err, y_true_err,bootstrap=10) print(param_errs) # Confirm true value is within error bar of fit value - assert [within_error(true_params[param_list[i]], params[i], param_errs[i]) for i in range(len(params))] + assert np.all([within_error(true_params[param_list[i]], params[i], param_errs[i]) for i in range(len(params))]) # Test fitter for 2 pts t = np.array([2015.0,2025.0]) @@ -148,11 +150,11 @@ def test_Linear(): x_sim = np.random.normal(x_true, x_true_err) y_sim = np.random.normal(y_true, y_true_err) # Run fit - mod_fit = motion_model.Linear() + mod_fit = motion_model.Linear(t0=true_params['t0']) params, param_errs = mod_fit.fit_motion_model(t, x_sim,y_sim, x_true_err, y_true_err) print(param_errs) # Confirm true value is within error bar of fit value - assert [within_error(true_params[param_list[i]], params[i], param_errs[i]) for i in range(len(params))] + assert np.all([within_error(true_params[param_list[i]], params[i], param_errs[i]) for i in range(len(params))]) def test_Acceleration(): From 2a4124ca1081ce97dbdd34b3f0803f98466dd634 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Tue, 14 Jan 2025 10:29:25 -0800 Subject: [PATCH 047/254] more motion model debugging --- flystar/align.py | 39 ++++++++++++++++++++++++++------------- flystar/match.py | 2 +- 2 files changed, 27 insertions(+), 14 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index ed22926..fda9040 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -454,6 +454,7 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar **trans_args, m=star_list_orig_trim['m'][idx1], mref=ref_list['m'][idx2], weights=weight, mag_trans=self.mag_trans) + print(trans.px, trans.py) # Save the final transformation. self.trans_list[ii] = trans @@ -583,7 +584,7 @@ def setup_ref_table_from_starlist(self, star_list): array in the original reference star list. """ col_arrays = {} - motion_model_col_names = motion_model.get_all_motion_model_param_names(with_errors=True) + ['m0','m0_err','use_in_trans'] + motion_model_col_names = motion_model.get_all_motion_model_param_names(with_errors=True, with_fixed=True) + ['m0','m0_err','use_in_trans', 'motion_model_input', 'motion_model_used'] for col_name in star_list.colnames: if col_name == 'name': # The "name" column will be 1D; but we will also add a "name_in_list" column. @@ -682,6 +683,11 @@ def setup_ref_table_from_starlist(self, star_list): for col_name in ref_table.colnames: if len(ref_table[col_name].data.shape) == 2: # Find the 2D columns ref_table._set_invalid_list_values(col_name, -1) + + if 'motion_model_input' not in ref_table.colnames: + ref_table.add_column(Column(np.repeat(self.default_motion_model, len(ref_table)), name='motion_model_input')) + if 'motion_model_used' not in ref_table.colnames: + ref_table.add_column(Column(np.repeat('Fixed', len(ref_table)), name='motion_model_used')) return ref_table @@ -830,20 +836,27 @@ def update_ref_table_aggregates(self, n_boot=0): motion_model_class_names = [self.default_motion_model] if 'motion_model_used' in self.ref_table.keys(): motion_model_class_names += self.ref_table['motion_model_used'][ref_orig_idx].tolist() - motion_model_col_names = motion_model.get_list_motion_model_param_names(motion_model_class_names, with_errors=True) + motion_model_col_names = motion_model.get_list_motion_model_param_names(motion_model_class_names, with_errors=True, with_fixed=True) for mm in motion_model_col_names: if mm in self.ref_table.keys(): vals_orig[mm] = self.ref_table[mm][ref_orig_idx] - - # Combine positions with a velocity fit. - self.ref_table.fit_velocities(bootstrap=n_boot, verbose=self.verbose, default_motion_model=self.default_motion_model) - - # Combine (transformed) magnitudes - if 'me' in self.ref_table.colnames: - weights_col = None + # Figure out whether motion fits are necessary + all_fixed = np.all(self.ref_table['motion_model_input']=='Fixed') + if all_fixed: + weighted_xy = ('xe' in self.ref_table.colnames) and ('ye' in self.ref_table.colnames) + weighted_m = ('me' in self.ref_table.colnames) + + self.ref_table.combine_lists_xym(weighted_xy=weighted_xy, weighted_m=weighted_m) else: - weights_col = 'me' - self.ref_table.combine_lists('m', weights_col=weights_col, ismag=True) + # Combine positions with a velocity fit. + self.ref_table.fit_velocities(bootstrap=n_boot, verbose=self.verbose, default_motion_model=self.default_motion_model) + + # Combine (transformed) magnitudes + if 'me' in self.ref_table.colnames: + weights_col = None + else: + weights_col = 'me' + self.ref_table.combine_lists('m', weights_col=weights_col, ismag=True) # Replace the originals if we are supposed to keep them fixed. if not self.update_ref_orig: @@ -1636,7 +1649,7 @@ def fit(self): self.ref_table.detections() ### Drop all stars that have 0 detections. - idx = np.where((self.ref_table['n_detect'] == 0) & (self.ref_table['ref_orig'] == False))[0] + idx = np.where((self.ref_table['n_detect'] == 0))[0] print(' *** Getting rid of {0:d} out of {1:d} junk sources'.format(len(idx), len(self.ref_table))) self.ref_table.remove_rows(idx) @@ -1837,7 +1850,7 @@ def add_rows_for_new_stars(ref_table, star_list, idx_lis, default_motion_model=' elif col_name=='motion_model_input': new_col_empty = default_motion_model elif col_name=='motion_model_used': - new_col_empty = 'None' + new_col_empty = 'Fixed' else: new_col_empty = np.nan diff --git a/flystar/match.py b/flystar/match.py index c2b41c6..b086d32 100644 --- a/flystar/match.py +++ b/flystar/match.py @@ -15,7 +15,7 @@ def miracle_match_briteN(xin1, yin1, min1, xin2, yin2, min2, Nbrite, Nbins_vmax=200, Nbins_angle=360,verbose=False): """ Take two input starlists and select the brightest stars from - each. Then performa a triangle matching algorithm along the lines of + each. Then perform a triangle matching algorithm along the lines of Groth 1986. For every possible triangle (combination of 3 stars) in a starlist, From 5a0a408ec8ab7919495d7fb254993949e3a17c04 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Tue, 14 Jan 2025 11:36:45 -0800 Subject: [PATCH 048/254] more motion model debugging --- flystar/startables.py | 38 ++++++++++++++++++++++++++++---------- 1 file changed, 28 insertions(+), 10 deletions(-) diff --git a/flystar/startables.py b/flystar/startables.py index e03ece7..0fb7eb4 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -873,20 +873,38 @@ def get_star_positions_at_time(self, t): y = np.full(N_stars, np.nan, dtype=float) xe = np.full(N_stars, np.nan, dtype=float) ye = np.full(N_stars, np.nan, dtype=float) + + # TODO: probably worth some additional testing here # Check which motion models we need # use complex_mms to collect models besides Fixed and Linear - unique_mms = np.unique(self['motion_model_used']).tolist() + unique_mms = np.unique(self['motion_model_input']).tolist() # Calculate current position in batches by motion model for mm in unique_mms: - # Identify stars with this model & get class - idx = np.where(self['motion_model_used']==mm)[0] - modClass = getattr(motion_model, mm) - # Set up parameters - param_dict = {} - for par in motion_model.get_one_motion_model_param_names(mm,with_errors=True,with_fixed=True): - param_dict[par] = self[par][idx] - mod = modClass(RA=self.meta['RA'], Dec=self.meta['Dec'], PA=self.meta['position_angle'], obs=self.meta['observer_location']) - x[idx],y[idx],xe[idx],ye[idx] = mod.get_batch_pos_at_time(t,**param_dict) + try: + # Identify stars with this model & get class + idx = np.where(self['motion_model_input']==mm)[0] + modClass = getattr(motion_model, mm) + # Set up parameters + param_dict = {} + for par in motion_model.get_one_motion_model_param_names(mm,with_errors=True,with_fixed=True): + param_dict[par] = self[par][idx] + mod = modClass(RA=self.meta['RA'], Dec=self.meta['Dec'], PA=self.meta['position_angle'], obs=self.meta['observer_location']) + x[idx],y[idx],xe[idx],ye[idx] = mod.get_batch_pos_at_time(t,**param_dict) + except: + pass + if np.isnan(x).any(): + re_calc = np.where(np.isnan(x))[0] + for idx in re_calc: + mm = self['motion_model_used'][idx] + modClass = getattr(motion_model, mm) + # Set up parameters + param_dict = {} + for par in motion_model.get_one_motion_model_param_names(mm,with_errors=True,with_fixed=True): + param_dict[par] = self[par][idx] + mod = modClass(**param_dict, RA=self.meta['RA'], Dec=self.meta['Dec'], PA=self.meta['position_angle'], obs=self.meta['observer_location']) + x[idx],y[idx] = mod.get_pos_at_time(t) + xe[idx],ye[idx] = mod.get_pos_err_at_time(t) + return x,y,xe,ye From ab93f998e27b9166c82f9a67ff55c6bddc0d59f8 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Tue, 14 Jan 2025 16:35:04 -0800 Subject: [PATCH 049/254] debugging with real data --- flystar/align.py | 23 +++++++---------------- flystar/startables.py | 18 ++++++++++-------- 2 files changed, 17 insertions(+), 24 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index fda9040..15c6288 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -454,7 +454,6 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar **trans_args, m=star_list_orig_trim['m'][idx1], mref=ref_list['m'][idx2], weights=weight, mag_trans=self.mag_trans) - print(trans.px, trans.py) # Save the final transformation. self.trans_list[ii] = trans @@ -833,7 +832,7 @@ def update_ref_table_aggregates(self, n_boot=0): vals_orig = {} vals_orig['m0'] = self.ref_table['m0'][ref_orig_idx] vals_orig['m0_err'] = self.ref_table['m0_err'][ref_orig_idx] - motion_model_class_names = [self.default_motion_model] + motion_model_class_names = self.ref_table['motion_model_input'].tolist() if 'motion_model_used' in self.ref_table.keys(): motion_model_class_names += self.ref_table['motion_model_used'][ref_orig_idx].tolist() motion_model_col_names = motion_model.get_list_motion_model_param_names(motion_model_class_names, with_errors=True, with_fixed=True) @@ -938,6 +937,7 @@ def match_lists(self, dr_tol, dm_tol): idx_lis, idx_ref, dr, dm = match.match(star_list_T['x'], star_list_T['y'], star_list_T['m'], xref, yref, mref, dr_tol=dr_tol, dm_tol=dm_tol, verbose=self.verbose) + if self.verbose > 0: fmt = 'Matched {0:5d} out of {1:5d} stars in list {2:2d} [dr = {3:7.4f} +/- {4:6.4f}, dm = {5:5.2f} +/- {6:4.2f}' print(fmt.format(len(idx_lis), len(star_list_T), ii, dr.mean(), dr.std(), dm.mean(), dm.std())) @@ -1572,15 +1572,6 @@ def fit(self): # w, w_orig (optiona) -- the input and output weights of stars in transform: 2D ########## self.ref_table = self.setup_ref_table_from_starlist(self.ref_list) - - # copy over motion model parameters if they exist in the reference list - if 'motion_model_input' in self.ref_list.colnames: - self.ref_table['motion_model_input'] = self.ref_list['motion_model_input'] - if 'motion_model' in self.ref_list.colnames: - self.ref_table['motion_model_input'] = self.ref_list['motion_model'] - for param in motion_model.get_all_motion_model_param_names(with_fixed=True, with_errors=True): - if param in self.ref_list.colnames: - self.ref_table[param] = self.ref_list[param] ########## # @@ -1773,7 +1764,7 @@ def copy_over_values(ref_table, star_list, star_list_T, idx_epoch, idx_ref, idx_ idx_ref : list or array The indices into the ref_table where values are copied to. idx_lis : list or array - The indices into the star_list or star_lsit_T where values are copied from. + The indices into the star_list or star_list_T where values are copied from. """ for col_name in ref_table.colnames: if col_name in star_list_T.colnames: @@ -3041,7 +3032,7 @@ def get_weighting_scheme(weights, ref_list, star_list): return weight -# TODO: I think this is a startable, not a starlist, at least as currently used +# TODO: This is sometimes run on a startable, not a starlist, at least as currently used def get_pos_at_time(t, starlist): """ Take a starlist, check to see if it has motion/velocity columns. @@ -3060,9 +3051,9 @@ def get_pos_at_time(t, starlist): if 'motion_model_used' in starlist.colnames: x,y,xe,ye = starlist.get_star_positions_at_time(t) # If no motion model, check for velocities - elif ('vx' in starlist.colnames) and ('vy' in starlist.colnames) and ('x0' in starlist.colnames) and ('y0' in starlist.colnames): - x = starlist['x0'] + np.nan_to_num(starlist['vx'])*(t-starlist['t0']) - y = starlist['y0'] + np.nan_to_num(starlist['vy'])*(t-starlist['t0']) + elif ('vx' in starlist.colnames) and ('vy' in starlist.colnames): + x = starlist['x0'] + np.nan_to_num(starlist['vx'])*np.nan_to_num(t-starlist['t0']) + y = starlist['y0'] + np.nan_to_num(starlist['vy'])*np.nan_to_num(t-starlist['t0']) # If no velocities, try fitted positon elif ('x0' in starlist.colnames) and ('y0' in starlist.colnames): x = starlist['x0'] diff --git a/flystar/startables.py b/flystar/startables.py index 0fb7eb4..be5b76d 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -864,8 +864,6 @@ def fit_velocity_for_star(self, ss, weighting='var', use_scipy=True, # New function, to use in align def get_star_positions_at_time(self, t): """ Get current x,y positions of each star according to its motion_model - Instead of looping through every star, we implement a faster calculation for Fixed and Linear models, - and loop through any stars with a more complex model """ # Start with empty arrays so we can fill them in batches N_stars = len(self) @@ -894,17 +892,21 @@ def get_star_positions_at_time(self, t): pass if np.isnan(x).any(): re_calc = np.where(np.isnan(x))[0] - for idx in re_calc: - mm = self['motion_model_used'][idx] + unique_mms = np.unique(self['motion_model_used'][re_calc]).tolist() + # Calculate current position in batches by motion model + for mm in unique_mms: + # Identify stars with this model & get class + idx_0 = np.where(self['motion_model_used']==mm)[0] + idx = np.intersect1d(re_calc, idx_0) modClass = getattr(motion_model, mm) # Set up parameters param_dict = {} for par in motion_model.get_one_motion_model_param_names(mm,with_errors=True,with_fixed=True): param_dict[par] = self[par][idx] - mod = modClass(**param_dict, RA=self.meta['RA'], Dec=self.meta['Dec'], PA=self.meta['position_angle'], obs=self.meta['observer_location']) - x[idx],y[idx] = mod.get_pos_at_time(t) - xe[idx],ye[idx] = mod.get_pos_err_at_time(t) - + mod = modClass(RA=self.meta['RA'], Dec=self.meta['Dec'], PA=self.meta['position_angle'], obs=self.meta['observer_location']) + x[idx],y[idx],xe[idx],ye[idx] = mod.get_batch_pos_at_time(t,**param_dict) + + #print(x) return x,y,xe,ye From 6d2e6f6cb5916d030836cc78cee798a906b350e9 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Tue, 14 Jan 2025 16:51:16 -0800 Subject: [PATCH 050/254] still debugging.... --- flystar/align.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/flystar/align.py b/flystar/align.py index 15c6288..278b369 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -931,7 +931,11 @@ def match_lists(self, dr_tol, dm_tol): else: star_list_T.transform_xy(self.trans_list[ii]) + print('star list T x:',star_list_T['x']) + print('ref table x0', self.ref_table['x0']) + print('ref table vx', self.ref_table['vx']) xref, yref = get_pos_at_time(star_list_T['t'][0], self.ref_table) #, use_motion=self.use_motion) # optional velocity propogation. + print('x ref:',xref) mref = self.ref_table['m0'] idx_lis, idx_ref, dr, dm = match.match(star_list_T['x'], star_list_T['y'], star_list_T['m'], From c6f5bfd58ef66fe94370110399668f3a6a25fce9 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Tue, 14 Jan 2025 17:06:19 -0800 Subject: [PATCH 051/254] debugging - temporary solution --- flystar/align.py | 11 ++++------- 1 file changed, 4 insertions(+), 7 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 278b369..75dda0c 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -931,11 +931,7 @@ def match_lists(self, dr_tol, dm_tol): else: star_list_T.transform_xy(self.trans_list[ii]) - print('star list T x:',star_list_T['x']) - print('ref table x0', self.ref_table['x0']) - print('ref table vx', self.ref_table['vx']) xref, yref = get_pos_at_time(star_list_T['t'][0], self.ref_table) #, use_motion=self.use_motion) # optional velocity propogation. - print('x ref:',xref) mref = self.ref_table['m0'] idx_lis, idx_ref, dr, dm = match.match(star_list_T['x'], star_list_T['y'], star_list_T['m'], @@ -3055,9 +3051,10 @@ def get_pos_at_time(t, starlist): if 'motion_model_used' in starlist.colnames: x,y,xe,ye = starlist.get_star_positions_at_time(t) # If no motion model, check for velocities - elif ('vx' in starlist.colnames) and ('vy' in starlist.colnames): - x = starlist['x0'] + np.nan_to_num(starlist['vx'])*np.nan_to_num(t-starlist['t0']) - y = starlist['y0'] + np.nan_to_num(starlist['vy'])*np.nan_to_num(t-starlist['t0']) + #TODO: This is a hacky temporary solution, need to make motion-model compatible + if ('vx' in starlist.colnames) and ('vy' in starlist.colnames): + x = starlist['x0'] + starlist['vx']*(t-starlist['t0']) + y = starlist['y0'] + starlist['vy']*(t-starlist['t0']) # If no velocities, try fitted positon elif ('x0' in starlist.colnames) and ('y0' in starlist.colnames): x = starlist['x0'] From 8278d23059d1ae316f310a55693d8d6fe5a04d3c Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Tue, 14 Jan 2025 17:11:00 -0800 Subject: [PATCH 052/254] align.py --- flystar/startables.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/flystar/startables.py b/flystar/startables.py index be5b76d..427c3e1 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -862,7 +862,7 @@ def fit_velocity_for_star(self, ss, weighting='var', use_scipy=True, return # New function, to use in align - def get_star_positions_at_time(self, t): + def get_star_positions_at_time(self, t, allow_alt_models=True): """ Get current x,y positions of each star according to its motion_model """ # Start with empty arrays so we can fill them in batches @@ -890,7 +890,7 @@ def get_star_positions_at_time(self, t): x[idx],y[idx],xe[idx],ye[idx] = mod.get_batch_pos_at_time(t,**param_dict) except: pass - if np.isnan(x).any(): + if np.isnan(x).any() and allow_alt_models: re_calc = np.where(np.isnan(x))[0] unique_mms = np.unique(self['motion_model_used'][re_calc]).tolist() # Calculate current position in batches by motion model From 62daecc3e36f72ac9df9aab9fd31768097d6daf1 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Tue, 14 Jan 2025 17:11:56 -0800 Subject: [PATCH 053/254] debugged issue mb19284 brought up --- flystar/align.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 75dda0c..c0153e3 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -3049,10 +3049,9 @@ def get_pos_at_time(t, starlist): """ # Check for motion model if 'motion_model_used' in starlist.colnames: - x,y,xe,ye = starlist.get_star_positions_at_time(t) + x,y,xe,ye = starlist.get_star_positions_at_time(t, allow_alt_models=False) # If no motion model, check for velocities - #TODO: This is a hacky temporary solution, need to make motion-model compatible - if ('vx' in starlist.colnames) and ('vy' in starlist.colnames): + elif ('vx' in starlist.colnames) and ('vy' in starlist.colnames): x = starlist['x0'] + starlist['vx']*(t-starlist['t0']) y = starlist['y0'] + starlist['vy']*(t-starlist['t0']) # If no velocities, try fitted positon From 6acd56ac1a484b8b3e9583aaeaf1e3f37ceccec9 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Thu, 16 Jan 2025 16:59:18 -0800 Subject: [PATCH 054/254] motion model application to plots --- flystar/plots.py | 72 ++++++++++++++++++++++++++---------------------- 1 file changed, 39 insertions(+), 33 deletions(-) diff --git a/flystar/plots.py b/flystar/plots.py index 070767e..9c933ce 100755 --- a/flystar/plots.py +++ b/flystar/plots.py @@ -1,4 +1,4 @@ -from flystar import analysis, motion_model +from flystar import analysis, motion_model, startable import pylab as py import pylab as plt import numpy as np @@ -1072,8 +1072,7 @@ def plot_mean_residuals_by_epoch(tab): """ # Predicted model positions at each epoch dt = tab['t'] - tab['t0'][:, np.newaxis] - xt_mod = tab['x0'][:, np.newaxis] + tab['vx'][:, np.newaxis] * dt - yt_mod = tab['y0'][:, np.newaxis] + tab['vy'][:, np.newaxis] * dt + xt_mod, yt_mod, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'], allow_alt_models=False) # Residuals dx = tab['x'] - xt_mod @@ -1129,11 +1128,13 @@ def plot_quiver_residuals_all_epochs(tab, unit='arcsec', scale=None, plotlim=Non n_good = np.zeros(len(tab), dtype=int) dr_ref = np.zeros(len(tab), dtype=float) n_ref = np.zeros(len(tab), dtype=int) + + xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'], allow_alt_models=False) for ee in range(tab['x'].shape[1]): dt = tab['t'][:, ee] - tab['t0'] - xt_mod = tab['x0'] + tab['vx'] * dt - yt_mod = tab['y0'] + tab['vy'] * dt + xt_mod = xt_mod_all[ee] + yt_mod = yt_mod_all[ee] good_idx = np.where(np.isfinite(tab['x'][:, ee]) == True)[0] ref_idx = np.where(tab[good_idx]['used_in_trans'][:, ee] == True)[0] @@ -1193,11 +1194,13 @@ def plot_quiver_residuals_with_orig_all_epochs(tab, trans_list, unit='arcsec', s n_good = np.zeros(len(tab), dtype=int) dr_ref = np.zeros(len(tab), dtype=float) n_ref = np.zeros(len(tab), dtype=int) + + xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'], allow_alt_models=False) for ee in range(tab['x'].shape[1]): dt = tab['t'][:, ee] - tab['t0'] - xt_mod = tab['x0'] + tab['vx'] * dt - yt_mod = tab['y0'] + tab['vy'] * dt + xt_mod = xt_mod_all[ee] + yt_mod = yt_mod_all[ee] good_idx = np.where(np.isfinite(tab['x'][:, ee]) == True)[0] ref_idx = np.where(tab[good_idx]['used_in_trans'][:, ee] == True)[0] @@ -1302,14 +1305,15 @@ def plot_mag_scatter_multi_trans_all_epochs(tab_list, trans_list_list, unit='arc da_list = [] ntrans = len(tab_list) + xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'], allow_alt_models=False) for mm in range(ntrans): tab = tab_list[mm] trans_list = trans_list_list[mm] for ee in range(tab['x'].shape[1]): dt = tab['t'][:, ee] - tab['t0'] - xt_mod = tab['x0'] + tab['vx'] * dt - yt_mod = tab['y0'] + tab['vy'] * dt + xt_mod = xt_mod_all[ee] + yt_mod = yt_mod_all[ee] good_idx = np.where(np.isfinite(tab['x'][:, ee]) == True)[0] ref_idx = np.where(tab[good_idx]['used_in_trans'][:, ee] == True)[0] @@ -1854,11 +1858,12 @@ def plot_quiver_residuals_magcolor_all_epochs(tab, unit='arcsec', scale=None, pl idx = np.where((tab['m0'] < lower_mag) & (tab['m0'] > upper_mag))[0] tab = tab[idx] + xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'], allow_alt_models=False) for ee in range(tab['x'].shape[1]): dt = tab['t'][:, ee] - tab['t0'] - xt_mod = tab['x0'] + tab['vx'] * dt - yt_mod = tab['y0'] + tab['vy'] * dt + xt_mod = xt_mod_all[ee] + yt_mod = yt_mod_all[ee] mag = tab['m0'] good_idx = np.where(np.isfinite(tab['x'][:, ee]) == True)[0] @@ -2159,6 +2164,8 @@ def plot_chi2_dist(tab, Ndetect, xlim=40, n_bins=50): chi2_x_list = [] chi2_y_list = [] fnd_list = [] # Number of non-NaN error measurements + + xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'], allow_alt_models=False) for ii in range(len(tab)): # Ignore the NaNs @@ -2172,8 +2179,8 @@ def plot_chi2_dist(tab, Ndetect, xlim=40, n_bins=50): yerr = tab['ye'][ii, fnd] dt = tab['t'][ii, fnd] - tab['t0'][ii] - fitLineX = tab['x0'][ii] + (tab['vx'][ii] * dt) - fitLineY = tab['y0'][ii] + (tab['vy'][ii] * dt) + fitLineX = xt_mod_all[ee] + fitLineY = yt_mod_all[ee] diffX = x - fitLineX diffY = y - fitLineY @@ -2236,6 +2243,8 @@ def plot_chi2_dist_per_epoch(tab, Ndetect, mlim=[14,21], ylim = [-1, 1], target_ sigX_arr = np.nan * np.ones((len(tab['xe']), Ndetect)) sigY_arr = np.nan * np.ones((len(tab['xe']), Ndetect)) m_arr = np.nan * np.ones((len(tab['xe']), Ndetect)) + + xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'], allow_alt_models=False) for ii in range(len(tab['xe'])): # Ignore the NaNs @@ -2250,8 +2259,8 @@ def plot_chi2_dist_per_epoch(tab, Ndetect, mlim=[14,21], ylim = [-1, 1], target_ yerr = tab['ye'][ii, fnd] dt = tab['t'][ii, fnd] - tab['t0'][ii] - fitLineX = tab['x0'][ii] + (tab['vx'][ii] * dt) - fitLineY = tab['y0'][ii] + (tab['vy'][ii] * dt) + fitLineX = xt_mod_all[ee] + fitLineY = yt_mod_all[ee] diffX = x - fitLineX diffY = y - fitLineY @@ -2313,6 +2322,7 @@ def plot_chi2_dist_per_epoch(tab, Ndetect, mlim=[14,21], ylim = [-1, 1], target_ return +# TODO: update for motion model def plot_chi2_ecliptic_per_epoch(tab, Ndetect,ra,dec, mlim=[14,21], ylim = [-1, 1], target_idx = 0): """ tab = flystar table @@ -2510,6 +2520,8 @@ def rs(x): x = tab['x0'] y = tab['y0'] r = np.hypot(x, y) + xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'], allow_alt_models=False) + for i in range(Nstars): starName = star_names[i] @@ -2539,21 +2551,12 @@ def rs(x): dt = tab['t'][ii, fnd] - tab['t0'][ii] - if 'motion_model_used' not in tab.keys(): - fitLineX = tab['x0'][ii] + (tab['vx'][ii] * dt) - fitLineY = tab['y0'][ii] + (tab['vy'][ii] * dt) + fitLineX = xt_mod_all[ee] + fitLineY = yt_mod_all[ee] + + fitSigX = xt_mod_err[ee] + fitSigY = yt_mod_err[ee] - fitSigX = np.hypot(tab['x0_err'][ii], tab['vx_err'][ii]*dt) - fitSigY = np.hypot(tab['y0_err'][ii], tab['vy_err'][ii]*dt) - else: - motion_model_str = tab['motion_model_used'][ii] - modClass = getattr(motion_model, motion_model_str) - param_dict = {} - for par in modClass.fitter_param_names+modClass.fixed_param_names: - param_dict[par] = tab[par][ii] - mod = modClass(**param_dict, PA=position_angle, RA=RA, Dec=Dec, obs=observer_location) - fitLineX, fitLineY = mod.get_pos_at_time(time) - fitSigX, fitSigY = mod.get_pos_err_at_time(time) fitLineM = np.repeat(tab['m0'][ii], len(dt)).reshape(len(dt),1) fitSigM = np.repeat(tab['m0_err'][ii], len(dt)).reshape(len(dt),1) @@ -2915,6 +2918,9 @@ def plot_stars_nfilt(tab, star_names, NcolMax=2, epoch_array_list = None, color_ print( 'Creating residuals plots for star(s):' ) print( star_names ) + xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'], allow_alt_models=False) + + Nstars = len(star_names) Ncols = 3 * np.min([Nstars, NcolMax]) if Nstars <= Ncols/3: @@ -2959,11 +2965,11 @@ def plot_stars_nfilt(tab, star_names, NcolMax=2, epoch_array_list = None, color_ merr = tab['me'][ii, fnd] dt = tab['t'][ii, fnd] - tab['t0'][ii] - fitLineX = tab['x0'][ii] + (tab['vx'][ii] * dt) - fitLineY = tab['y0'][ii] + (tab['vy'][ii] * dt) + fitLineX = xt_mod_all[ee] + fitLineY = yt_mod_all[ee] - fitSigX = np.hypot(tab['x0_err'][ii], tab['vx_err'][ii]*dt) - fitSigY = np.hypot(tab['y0_err'][ii], tab['vy_err'][ii]*dt) + fitSigX = xt_mod_err[ee] + fitSigY = yt_mod_err[ee] fitLineM = np.repeat(tab['m0'][ii], len(dt)).reshape(len(dt),1) fitSigM = np.repeat(tab['m0_err'][ii], len(dt)).reshape(len(dt),1) From 70430b87f9bd505e2d4eac187539923c6d833b44 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Fri, 17 Jan 2025 17:28:40 -0800 Subject: [PATCH 055/254] debugging periter, adding atend update_ref_orig options --- flystar/align.py | 18 ++++++++++-------- flystar/motion_model.py | 2 +- flystar/plots.py | 2 +- 3 files changed, 12 insertions(+), 10 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index c0153e3..13331ed 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -346,7 +346,8 @@ def fit(self): print("**********") self.match_lists(self.dr_tol[-1], self.dm_tol[-1]) - self.update_ref_table_aggregates() + # Hard-coded not to keep ref values for MosaicSelfRef + self.update_ref_table_aggregates(False) ########## # Clean up output table. @@ -518,8 +519,8 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar self.update_ref_table_from_list(star_list, star_list_T, ii, idx_ref, idx_lis, idx2) ### Update the "average" values to be used as the reference frame for the next list. - if self.update_ref_orig != 'periter': - self.update_ref_table_aggregates() + keep_ref_orig = (self.update_ref_orig==False) or (self.update_ref_orig=='atend') or (self.update_ref_orig=='periter' and ii<(len(self.star_lists)-1)) + self.update_ref_table_aggregates(keep_ref_orig) # Print out some metrics if self.verbose > 0: @@ -815,19 +816,19 @@ def update_ref_table_from_list(self, star_list, star_list_T, ii, idx_ref, idx_li return - def update_ref_table_aggregates(self, n_boot=0): + def update_ref_table_aggregates(self, keep_ref_orig, n_boot=0): """ Average positions or fit velocities. Average magnitudes. Calculate bootstrap errors if desired. - Update the use_in_trans values as needed. + Update the use_in_trans values as needed. TODO: ???? Updates aggregate columns in self.ref_table in place. """ # Keep track of the original reference values. # In certain cases, we will NOT update these. - if not self.update_ref_orig: + if keep_ref_orig: ref_orig_idx = np.where(self.ref_table['ref_orig'] == True)[0] vals_orig = {} vals_orig['m0'] = self.ref_table['m0'][ref_orig_idx] @@ -858,7 +859,7 @@ def update_ref_table_aggregates(self, n_boot=0): self.ref_table.combine_lists('m', weights_col=weights_col, ismag=True) # Replace the originals if we are supposed to keep them fixed. - if not self.update_ref_orig: + if keep_ref_orig: for val in vals_orig.keys(): self.ref_table[val][ref_orig_idx] = vals_orig[val] return @@ -1626,7 +1627,8 @@ def fit(self): print("**********") self.match_lists(self.dr_tol[-1], self.dm_tol[-1]) - self.update_ref_table_aggregates() + keep_ref_orig = (self.update_ref_orig==False) + self.update_ref_table_aggregates(keep_ref_orig) ########## # Clean up output table. diff --git a/flystar/motion_model.py b/flystar/motion_model.py index e747ab8..6479bbc 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -351,7 +351,7 @@ def accel(t, c0,c1,c2): x0e, vx0e, axe = np.sqrt(x_cov.diagonal()) y0e, vy0e, aye = np.sqrt(y_cov.diagonal()) - x0e, vxe, axe, y0e, vye, aye = self.scale_errors([x0e, vxe, axe, y0e, vye, aye], weighting=weighting) + x0e, vx0e, axe, y0e, vy0e, aye = self.scale_errors([x0e, vx0e, axe, y0e, vy0e, aye], weighting=weighting) if update: self.x0 = x0 diff --git a/flystar/plots.py b/flystar/plots.py index 9c933ce..e211e8e 100755 --- a/flystar/plots.py +++ b/flystar/plots.py @@ -1,4 +1,4 @@ -from flystar import analysis, motion_model, startable +from flystar import analysis, motion_model, startables import pylab as py import pylab as plt import numpy as np From bba22e39fd566fa6c2b8cff14c9e5722208503f6 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Tue, 21 Jan 2025 16:50:37 -0800 Subject: [PATCH 056/254] reset input model option for fit_velocitis --- flystar/startables.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/flystar/startables.py b/flystar/startables.py index 427c3e1..71c314c 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -547,7 +547,8 @@ def detections(self): return def fit_velocities(self, weighting='var', use_scipy=True, absolute_sigma=True, bootstrap=0, fixed_t0=False, verbose=False, - mask_val=None, mask_lists=False, show_progress=True, default_motion_model='Linear'): + mask_val=None, mask_lists=False, show_progress=True, default_motion_model='Linear', + reassign_motion_model=False): """Fit velocities for all stars in the table and add to the columns 'vx', 'vxe', 'vy', 'vye', 'x0', 'x0e', 'y0', 'y0e'. Parameters @@ -593,7 +594,7 @@ def fit_velocities(self, weighting='var', use_scipy=True, absolute_sigma=True, b # Set all to default_motion_model if none assigned already. # Reset motion_model_used to the inputs for now -> will change as fits run - if 'motion_model_input' not in self.colnames: + if ('motion_model_input' not in self.colnames) or reassign_motion_model: self['motion_model_input'] = default_motion_model self['motion_model_used'] = self['motion_model_input'] From 8625d4fc35dddc151da929bebc0a8a35297d4846 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Tue, 21 Jan 2025 17:07:27 -0800 Subject: [PATCH 057/254] only fit stars if needed --- flystar/align.py | 6 +++++- flystar/startables.py | 10 +++++++--- 2 files changed, 12 insertions(+), 4 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 13331ed..a86ee19 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -840,6 +840,9 @@ def update_ref_table_aggregates(self, keep_ref_orig, n_boot=0): for mm in motion_model_col_names: if mm in self.ref_table.keys(): vals_orig[mm] = self.ref_table[mm][ref_orig_idx] + fit_star_idxs = np.where(self.ref_table['ref_orig'] == False)[0] + else: + fit_star_idxs = None # Figure out whether motion fits are necessary all_fixed = np.all(self.ref_table['motion_model_input']=='Fixed') if all_fixed: @@ -849,7 +852,8 @@ def update_ref_table_aggregates(self, keep_ref_orig, n_boot=0): self.ref_table.combine_lists_xym(weighted_xy=weighted_xy, weighted_m=weighted_m) else: # Combine positions with a velocity fit. - self.ref_table.fit_velocities(bootstrap=n_boot, verbose=self.verbose, default_motion_model=self.default_motion_model) + self.ref_table.fit_velocities(bootstrap=n_boot, verbose=self.verbose, + default_motion_model=self.default_motion_model, select_stars=fit_star_idxs) # Combine (transformed) magnitudes if 'me' in self.ref_table.colnames: diff --git a/flystar/startables.py b/flystar/startables.py index 71c314c..7104618 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -548,7 +548,7 @@ def detections(self): def fit_velocities(self, weighting='var', use_scipy=True, absolute_sigma=True, bootstrap=0, fixed_t0=False, verbose=False, mask_val=None, mask_lists=False, show_progress=True, default_motion_model='Linear', - reassign_motion_model=False): + reassign_motion_model=False, select_stars=None): """Fit velocities for all stars in the table and add to the columns 'vx', 'vxe', 'vy', 'vye', 'x0', 'x0e', 'y0', 'y0e'. Parameters @@ -643,16 +643,20 @@ def fit_velocities(self, weighting='var', use_scipy=True, absolute_sigma=True, b return + # Only fit selected stars, if list given + fit_star_idxs = range(N_stars) + if select_stars is not None: + fit_star_idxs = select_stars # STARS LOOP through the stars and work on them 1 at a time. # This is slow; but robust. if show_progress: - for ss in tqdm(range(N_stars)): + for ss in tqdm(fit_star_idxs): self.fit_velocity_for_star(ss, weighting=weighting, use_scipy=use_scipy, absolute_sigma=absolute_sigma, bootstrap=bootstrap, fixed_t0=fixed_t0, default_motion_model=default_motion_model, mask_val=mask_val, mask_lists=mask_lists) else: - for ss in range(N_stars): + for ss in range(fit_star_idxs): self.fit_velocity_for_star(ss, weighting=weighting, use_scipy=use_scipy, absolute_sigma=absolute_sigma, bootstrap=bootstrap, fixed_t0=fixed_t0, default_motion_model=default_motion_model, From 35c08d3d1ea4548d8caf9e219452af27918bdc1d Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Wed, 22 Jan 2025 16:39:43 -0800 Subject: [PATCH 058/254] more motion model debugging, with plots and parallax --- flystar/analysis.py | 35 ++++++----------------------- flystar/motion_model.py | 9 ++++---- flystar/plots.py | 49 ++++++++++++++++++++--------------------- flystar/startables.py | 19 +++++++++++----- 4 files changed, 49 insertions(+), 63 deletions(-) diff --git a/flystar/analysis.py b/flystar/analysis.py index 11d30de..4aac894 100644 --- a/flystar/analysis.py +++ b/flystar/analysis.py @@ -18,7 +18,7 @@ ################################################## # New codes for velocity support in FlyStar and using -# the new StarTable and StarList format. +# the new StarTable and StarList format. ################################################## def query_gaia(ra, dec, search_radius=30.0, table_name='gaiadr3'): @@ -409,33 +409,12 @@ def startable_subset(tab, idx, mag_trans=True, mag_trans_orig=False): # 'x0', 'vx', 'y0', 'vy', 'x0_err', 'vx_err', 'y0_err', 'vy_err', 't0'] # Don't include n_vfit - new_tab = startables.StarTable(name=tab['name'].data, - x=tab['x'][:,idx].data, - y=tab['y'][:,idx].data, - m=tab['m'][:,idx].data, - xe=tab['xe'][:,idx].data, - ye=tab['ye'][:,idx].data, - me=tab['me'][:,idx].data, - t=tab['t'][:,idx].data, - x_orig=tab['x_orig'][:,idx].data, - y_orig=tab['y_orig'][:,idx].data, - m_orig=tab['m_orig'][:,idx].data, - xe_orig=tab['xe_orig'][:,idx].data, - ye_orig=tab['ye_orig'][:,idx].data, - me_orig=tab['me_orig'][:,idx].data, - used_in_trans=tab['used_in_trans'][:,idx].data, - m0=tab['m0'].data, - m0e=tab['m0_err'].data, - use_in_trans=tab['use_in_trans'].data, - x0=tab['x0'].data, - vx=tab['vx'].data, - y0=tab['y0'].data, - vy=tab['vy'].data, - x0e=tab['x0_err'].data, - vxe=tab['vx_err'].data, - y0e=tab['y0_err'].data, - vye=tab['vy_err'].data, - t0=tab['t0'].data) + new_tab = copy.deepcopy(tab) + #new_tab.remove_column('n_fit') + new_tab.remove_column('n_detect') + for col in ['x','y','m','xe','ye','me','t','x_orig','y_orig','m_orig', + 'xe_orig','ye_orig','me_orig','used_in_trans']: + new_tab[col] = tab[col][:,idx] new_tab.combine_lists('m', weights_col='me', sigma=3, ismag=True) diff --git a/flystar/motion_model.py b/flystar/motion_model.py index 6479bbc..4a18509 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -101,7 +101,6 @@ def get_chi2(self,t,x,y,xe,ye,reduced=False): Get the chi^2 value for the current MM and the input data. """ - # TODO: confirm whether we want reduced chi^2 or anything special - maybe kwarg option x_pred,y_pred = self.get_pos_at_time(t) chi2x = np.sum((x-x_pred)**2 / xe**2) chi2y = np.sum((y-y_pred)**2 / ye**2) @@ -433,11 +432,11 @@ def get_batch_pos_at_time(self, t, pvec = parallax.parallax_in_direction(self.RA, self.Dec, t_mjd, obsLocation=self.obs, PA=self.PA).T if hasattr(t, "__len__"): dt = t-t0[:,np.newaxis] - x = x0[:,np.newaxis] + dt*vx[:,np.newaxis] + pi[:,np.newaxis]*pvec[0] - y = y0[:,np.newaxis] + dt*vy[:,np.newaxis] + pi[:,np.newaxis]*pvec[1] + x = x0[:,np.newaxis] + dt*vx[:,np.newaxis] + pi[:,np.newaxis]*pvec[0].T + y = y0[:,np.newaxis] + dt*vy[:,np.newaxis] + pi[:,np.newaxis]*pvec[1].T try: - x_err = np.sqrt(x0_err[:,np.newaxis]**2 + (vx_err[:,np.newaxis]*dt)**2 + (pi_err[:,np.newaxis]*pvec[0])**2) - y_err = np.sqrt(y0_err[:,np.newaxis]**2 + (vy_err[:,np.newaxis]*dt)**2 + (pi_err[:,np.newaxis]*pvec[1])**2) + x_err = np.sqrt(x0_err[:,np.newaxis]**2 + (vx_err[:,np.newaxis]*dt)**2 + (pi_err[:,np.newaxis]*pvec[0].T)**2) + y_err = np.sqrt(y0_err[:,np.newaxis]**2 + (vy_err[:,np.newaxis]*dt)**2 + (pi_err[:,np.newaxis]*pvec[1].T)**2) except: x_err,y_err = [],[] else: diff --git a/flystar/plots.py b/flystar/plots.py index e211e8e..2e335a4 100755 --- a/flystar/plots.py +++ b/flystar/plots.py @@ -2165,12 +2165,12 @@ def plot_chi2_dist(tab, Ndetect, xlim=40, n_bins=50): chi2_y_list = [] fnd_list = [] # Number of non-NaN error measurements - xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'], allow_alt_models=False) + i_all_detected = np.where(~np.any(np.isnan(tab['t']),axis=1))[0][0] + xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], allow_alt_models=True) for ii in range(len(tab)): # Ignore the NaNs fnd = np.argwhere(~np.isnan(tab['xe'][ii,:])) -# fnd = np.where(tab['xe'][ii, :] > 0)[0] fnd_list.append(len(fnd)) x = tab['x'][ii, fnd] @@ -2178,9 +2178,8 @@ def plot_chi2_dist(tab, Ndetect, xlim=40, n_bins=50): xerr = tab['xe'][ii, fnd] yerr = tab['ye'][ii, fnd] - dt = tab['t'][ii, fnd] - tab['t0'][ii] - fitLineX = xt_mod_all[ee] - fitLineY = yt_mod_all[ee] + fitLineX = xt_mod_all[ii, fnd] + fitLineY = yt_mod_all[ii,fnd] diffX = x - fitLineX diffY = y - fitLineY @@ -2198,7 +2197,7 @@ def plot_chi2_dist(tab, Ndetect, xlim=40, n_bins=50): idx = np.where(fnd == Ndetect)[0] # Fitting position and velocity... so subtract 2 to get Ndof - Ndof = Ndetect - 2 + Ndof = Ndetect - tab['dof'][i_all_detected] chi2_xaxis = np.linspace(0, xlim, xlim*3) chi2_bins = np.linspace(0, xlim, n_bins) @@ -2206,7 +2205,7 @@ def plot_chi2_dist(tab, Ndetect, xlim=40, n_bins=50): plt.clf() plt.hist(x[idx], bins=chi2_bins, histtype='step', label='X', density=True) plt.hist(y[idx], bins=chi2_bins, histtype='step', label='Y', density=True) - plt.plot(chi2_xaxis, chi2.pdf(chi2_xaxis, Ndof), 'r-', alpha=0.6, + plt.plot(chi2_xaxis, chi2.pdf(chi2_xaxis, Ndof), 'r-', alpha=0.6, label='$\chi^2$ ' + str(Ndof) + ' dof') plt.title('$N_{epoch} = $' + str(Ndetect) + ', $N_{dof} = $' + str(Ndof)) plt.xlim(0, xlim) @@ -2244,8 +2243,9 @@ def plot_chi2_dist_per_epoch(tab, Ndetect, mlim=[14,21], ylim = [-1, 1], target_ sigY_arr = np.nan * np.ones((len(tab['xe']), Ndetect)) m_arr = np.nan * np.ones((len(tab['xe']), Ndetect)) - xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'], allow_alt_models=False) - + i_all_detected = np.where(~np.any(np.isnan(tab['t']),axis=1))[0][0] + xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], allow_alt_models=True) + for ii in range(len(tab['xe'])): # Ignore the NaNs fnd = np.argwhere(~np.isnan(tab['xe'][ii,:])) @@ -2258,9 +2258,8 @@ def plot_chi2_dist_per_epoch(tab, Ndetect, mlim=[14,21], ylim = [-1, 1], target_ xerr = tab['xe'][ii, fnd] yerr = tab['ye'][ii, fnd] - dt = tab['t'][ii, fnd] - tab['t0'][ii] - fitLineX = xt_mod_all[ee] - fitLineY = yt_mod_all[ee] + fitLineX = xt_mod_all[ii, fnd] + fitLineY = yt_mod_all[ii, fnd] diffX = x - fitLineX diffY = y - fitLineY @@ -2442,7 +2441,7 @@ def eq_to_ec(ra,dec): ax3.set_ylabel('residual (mas)') return -def plot_chi2_dist_mag(tab, Ndetect, mlim=40, n_bins=30): +def plot_chi2_dist_mag(tab, Ndetect, xlim=40, n_bins=30): """ tab = flystar table Ndetect = Number of epochs star detected in @@ -2473,16 +2472,16 @@ def plot_chi2_dist_mag(tab, Ndetect, mlim=40, n_bins=30): # Fitting mean magnitude... so subtract 1 to get Ndof Ndof = Ndetect - 1 - chi2_maxis = np.linspace(0, mlim, mlim*3) - chi2_bins = np.linspace(0, mlim, n_bins) + chi2_maxis = np.linspace(0, xlim, xlim*3) + chi2_bins = np.linspace(0, xlim, n_bins) plt.figure(figsize=(6,4)) plt.clf() - plt.hist(chi2_m[idx], bins=np.arange(mlim*10), histtype='step', density=True) + plt.hist(chi2_m[idx], bins=np.arange(xlim*10), histtype='step', density=True) plt.plot(chi2_maxis, chi2.pdf(chi2_maxis, Ndof), 'r-', alpha=0.6, label='$\chi^2$ ' + str(Ndof) + ' dof') plt.title('$N_{epoch} = $' + str(Ndetect) + ', $N_{dof} = $' + str(Ndof)) - plt.xlim(0, mlim) + plt.xlim(0, xlim) plt.legend() print('Mean reduced chi^2: (Ndetect = {0:d} of {1:d})'.format(len(idx), len(tab))) @@ -2551,11 +2550,11 @@ def rs(x): dt = tab['t'][ii, fnd] - tab['t0'][ii] - fitLineX = xt_mod_all[ee] - fitLineY = yt_mod_all[ee] + fitLineX = xt_mod_all[ii] + fitLineY = yt_mod_all[ii] - fitSigX = xt_mod_err[ee] - fitSigY = yt_mod_err[ee] + fitSigX = xt_mod_err[ii] + fitSigY = yt_mod_err[ii] fitLineM = np.repeat(tab['m0'][ii], len(dt)).reshape(len(dt),1) @@ -2965,11 +2964,11 @@ def plot_stars_nfilt(tab, star_names, NcolMax=2, epoch_array_list = None, color_ merr = tab['me'][ii, fnd] dt = tab['t'][ii, fnd] - tab['t0'][ii] - fitLineX = xt_mod_all[ee] - fitLineY = yt_mod_all[ee] + fitLineX = xt_mod_all[ii] + fitLineY = yt_mod_all[ii] - fitSigX = xt_mod_err[ee] - fitSigY = yt_mod_err[ee] + fitSigX = xt_mod_err[ii] + fitSigY = yt_mod_err[ii] fitLineM = np.repeat(tab['m0'][ii], len(dt)).reshape(len(dt),1) fitSigM = np.repeat(tab['m0_err'][ii], len(dt)).reshape(len(dt),1) diff --git a/flystar/startables.py b/flystar/startables.py index 7104618..b4ec991 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -605,7 +605,7 @@ def fit_velocities(self, weighting='var', use_scipy=True, absolute_sigma=True, b all_motion_models = np.unique(self['motion_model_input'].tolist() + ['Fixed']+[default_motion_model]).tolist() new_col_list = motion_model.get_list_motion_model_param_names(all_motion_models, with_errors=True) # Append goodness of fit metrics and t0. - new_col_list += ['chi2_x', 'chi2_y'] + new_col_list += ['chi2_x', 'chi2_y', 'dof'] if 't0' not in new_col_list: new_col_list.append('t0') @@ -640,6 +640,7 @@ def fit_velocities(self, weighting='var', use_scipy=True, absolute_sigma=True, b self['y0_err'] = self['ye'][:,0] self['n_fit'] = 1 + self['dof'] = 1 return @@ -786,6 +787,7 @@ def fit_velocity_for_star(self, ss, weighting='var', use_scipy=True, if N_good == 0: #self['motion_model_used'][ss] = 'None' self['n_fit'][ss] = N_good + self['dof'][ss] = 0 return # Everything below has N_good >= 1 @@ -856,6 +858,7 @@ def fit_velocity_for_star(self, ss, weighting='var', use_scipy=True, chi2_x,chi2_y = mod.get_chi2(t,x,y,xe,ye) self['chi2_x'][ss]=chi2_x self['chi2_y'][ss]=chi2_y + self['dof'][ss] = mod.dof # Save parameters and errors to table. for pp in range(len(modClass.fitter_param_names)): @@ -872,10 +875,16 @@ def get_star_positions_at_time(self, t, allow_alt_models=True): """ # Start with empty arrays so we can fill them in batches N_stars = len(self) - x = np.full(N_stars, np.nan, dtype=float) - y = np.full(N_stars, np.nan, dtype=float) - xe = np.full(N_stars, np.nan, dtype=float) - ye = np.full(N_stars, np.nan, dtype=float) + if hasattr(t, "__len__"): + x = np.full((N_stars,len(t)), np.nan, dtype=float) + y = np.full((N_stars,len(t)), np.nan, dtype=float) + xe = np.full((N_stars,len(t)), np.nan, dtype=float) + ye = np.full((N_stars,len(t)), np.nan, dtype=float) + else: + x = np.full(N_stars, np.nan, dtype=float) + y = np.full(N_stars, np.nan, dtype=float) + xe = np.full(N_stars, np.nan, dtype=float) + ye = np.full(N_stars, np.nan, dtype=float) # TODO: probably worth some additional testing here # Check which motion models we need From fecf8af8d115fee53f115807185a88c075e6afb5 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Wed, 22 Jan 2025 17:19:53 -0800 Subject: [PATCH 059/254] weird pyplot reshape thing --- flystar/plots.py | 36 +++++++++++++++++++----------------- 1 file changed, 19 insertions(+), 17 deletions(-) diff --git a/flystar/plots.py b/flystar/plots.py index 2e335a4..dacebf2 100755 --- a/flystar/plots.py +++ b/flystar/plots.py @@ -2916,9 +2916,11 @@ def plot_stars_nfilt(tab, star_names, NcolMax=2, epoch_array_list = None, color_ """ print( 'Creating residuals plots for star(s):' ) print( star_names ) + def rs(x): + return x.reshape(len(x)) - xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'], allow_alt_models=False) - + i_all_detected = np.where(~np.any(np.isnan(tab['t']),axis=1))[0][0] + xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], allow_alt_models=True) Nstars = len(star_names) Ncols = 3 * np.min([Nstars, NcolMax]) @@ -2963,15 +2965,14 @@ def plot_stars_nfilt(tab, star_names, NcolMax=2, epoch_array_list = None, color_ yerr = tab['ye'][ii, fnd] merr = tab['me'][ii, fnd] - dt = tab['t'][ii, fnd] - tab['t0'][ii] - fitLineX = xt_mod_all[ii] - fitLineY = yt_mod_all[ii] + fitLineX = xt_mod_all[ii, fnd] + fitLineY = yt_mod_all[ii, fnd] - fitSigX = xt_mod_err[ii] - fitSigY = yt_mod_err[ii] + fitSigX = xt_mod_err[ii, fnd] + fitSigY = yt_mod_err[ii, fnd] - fitLineM = np.repeat(tab['m0'][ii], len(dt)).reshape(len(dt),1) - fitSigM = np.repeat(tab['m0_err'][ii], len(dt)).reshape(len(dt),1) + fitLineM = np.repeat(tab['m0'][ii], len(time)).reshape(len(time),1) + fitSigM = np.repeat(tab['m0_err'][ii], len(time)).reshape(len(time),1) diffX = x - fitLineX diffY = y - fitLineY @@ -3063,8 +3064,9 @@ def plot_stars_nfilt(tab, star_names, NcolMax=2, epoch_array_list = None, color_ plt.plot(time, fitLineX, 'b-') plt.plot(time, fitLineX + fitSigX, 'b--') plt.plot(time, fitLineX - fitSigX, 'b--') + print(np.shape(xerr.reshape(len(xerr),))) if not color_time: - plt.errorbar(time, x, yerr=xerr.reshape(len(xerr),), marker='.', color=color, ls='none') + plt.errorbar(rs(time), rs(x), yerr=rs(xerr), marker='.', color=color, ls='none') else: norm = colors.Normalize(vmin=0, vmax=1, clip=True) mapper = cm.ScalarMappable(norm=norm, cmap='hsv') @@ -3096,7 +3098,7 @@ def plot_stars_nfilt(tab, star_names, NcolMax=2, epoch_array_list = None, color_ plt.plot(time, fitLineY + fitSigY, 'b--') plt.plot(time, fitLineY - fitSigY, 'b--') if not color_time: - plt.errorbar(time, y, yerr=yerr.reshape(len(yerr),), marker='.', color=color, ls='none') + plt.errorbar(rs(time), rs(y), yerr=rs(yerr), marker='.', color=color, ls='none') else: norm = colors.Normalize(vmin=0, vmax=1, clip=True) mapper = cm.ScalarMappable(norm=norm, cmap='hsv') @@ -3126,7 +3128,7 @@ def plot_stars_nfilt(tab, star_names, NcolMax=2, epoch_array_list = None, color_ plt.plot(time, fitLineM + fitSigM, 'g--') plt.plot(time, fitLineM - fitSigM, 'g--') if not color_time: - plt.errorbar(time, m, yerr=merr.reshape(len(merr),), marker='.', color=color, ls='none') + plt.errorbar(rs(time), rs(m), yerr=rs(merr), marker='.', color=color, ls='none') else: norm = colors.Normalize(vmin=0, vmax=1, clip=True) mapper = cm.ScalarMappable(norm=norm, cmap='hsv') @@ -3158,7 +3160,7 @@ def plot_stars_nfilt(tab, star_names, NcolMax=2, epoch_array_list = None, color_ plt.plot(time, fitSigX*1e3, 'b--') plt.plot(time, -fitSigX*1e3, 'b--') if not color_time: - plt.errorbar(time, (x - fitLineX)*1e3, yerr=xerr.reshape(len(xerr),)*1e3, marker='.', color=color, ls='none') + plt.errorbar(rs(time), rs(x - fitLineX)*1e3, yerr=rs(xerr)*1e3, marker='.', color=color, ls='none') else: norm = colors.Normalize(vmin=0, vmax=1, clip=True) mapper = cm.ScalarMappable(norm=norm, cmap='hsv') @@ -3186,7 +3188,7 @@ def plot_stars_nfilt(tab, star_names, NcolMax=2, epoch_array_list = None, color_ plt.plot(time, fitSigY*1e3, 'b--') plt.plot(time, -fitSigY*1e3, 'b--') if not color_time: - plt.errorbar(time, (y - fitLineY)*1e3, yerr=yerr.reshape(len(yerr),)*1e3, marker='.', color=color, ls='none') + plt.errorbar(rs(time), rs(y - fitLineY)*1e3, yerr=rs(yerr)*1e3, marker='.', color=color, ls='none') else: norm = colors.Normalize(vmin=0, vmax=1, clip=True) mapper = cm.ScalarMappable(norm=norm, cmap='hsv') @@ -3214,7 +3216,7 @@ def plot_stars_nfilt(tab, star_names, NcolMax=2, epoch_array_list = None, color_ plt.plot(time, fitSigM*1e3, 'g--') plt.plot(time, -fitSigM*1e3, 'g--') if not color_time: - plt.errorbar(time, (m - fitLineM), yerr=merr.reshape(len(merr),), marker='.', color=color, ls='none') + plt.errorbar(rs(time), rs(m - fitLineM), yerr=rs(merr), marker='.', color=color, ls='none') else: norm = colors.Normalize(vmin=0, vmax=1, clip=True) mapper = cm.ScalarMappable(norm=norm, cmap='hsv') @@ -3241,8 +3243,8 @@ def plot_stars_nfilt(tab, star_names, NcolMax=2, epoch_array_list = None, color_ paxes = plt.subplot(Nrows, Ncols, ind) if not color_time: - plt.errorbar(x,y, xerr=xerr.reshape(len(xerr),), - yerr=yerr.reshape(len(yerr),), marker='.', color=color, ls='none') + plt.errorbar(rs(x),rs(y), xerr=rs(xerr), + yerr=rs(yerr), marker='.', color=color, ls='none') else: sc = plt.scatter(x, y, s=0, c=dtime, vmin=0, vmax=1, cmap='hsv') clb = plt.colorbar(sc) From 59828d68d04e09a2358ecc9e8e2786f5c97a9dbd Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Wed, 29 Jan 2025 13:58:18 -0800 Subject: [PATCH 060/254] motion model testing updates --- flystar/tests/test_align.py | 8 ++++---- flystar/tests/test_motion_model.py | 20 +++++++++++--------- 2 files changed, 15 insertions(+), 13 deletions(-) diff --git a/flystar/tests/test_align.py b/flystar/tests/test_align.py index 1a306c2..e0e1533 100644 --- a/flystar/tests/test_align.py +++ b/flystar/tests/test_align.py @@ -255,7 +255,7 @@ def test_MosaicToRef(): ########## # Align and let velocities be free. ########## - msc.update_ref_orig = True + msc.update_ref_orig = 'periter' msc.fit() # The velocities should be almost the same (but not as close as before) @@ -320,7 +320,7 @@ def test_MosaicToRef_p0_vel(): ########## # Align and let velocities be free. ########## - msc.update_ref_orig = True + msc.update_ref_orig = 'periter' msc.fit() # The velocities should be almost the same (but not as close as before) @@ -386,7 +386,7 @@ def test_MosaicToRef_vel(): ########## # Align and let velocities be free. ########## - msc.update_ref_orig = True + msc.update_ref_orig = 'periter' msc.fit() # The velocities should be almost the same (but not as close as before) @@ -464,7 +464,7 @@ def test_MosaicToRef_acc(): ########## # Align and let velocities be free. ########## - msc.update_ref_orig = True + msc.update_ref_orig = 'periter' msc.fit() # The velocities should be almost the same (but not as close as before) diff --git a/flystar/tests/test_motion_model.py b/flystar/tests/test_motion_model.py index 91439d4..facb2af 100755 --- a/flystar/tests/test_motion_model.py +++ b/flystar/tests/test_motion_model.py @@ -2,8 +2,8 @@ import numpy as np import pytest -def within_error(true_val, fit_val, fit_err, n_sigma=2): - print('True', true_val, 'Fit', fit_val, 'Fit err', fit_err) +def within_error(true_val, fit_val, fit_err, n_sigma=3): + #print('True', true_val, 'Fit', fit_val, 'Fit err', fit_err) return (true_val < (fit_val+fit_err*n_sigma)) & (true_val> (fit_val-fit_err*n_sigma)) def test_Fixed(): @@ -58,7 +58,7 @@ def test_Fixed(): mod_fit = motion_model.Fixed() params, param_errs = mod_fit.fit_motion_model(t, x_sim,y_sim, x_true_err, y_true_err) # Confirm true value is within error bar of fit value - assert [within_error(true_params[param_list[i]], params[i], param_errs[i]) for i in range(len(params))] + assert np.all([within_error(true_params[param_list[i]], params[i], param_errs[i]) for i in range(len(params))]) def test_Linear(): @@ -161,7 +161,8 @@ def test_Acceleration(): # Test handling of a single star true_params = {'x0': 1.0, 'y0':0.5, 'x0_err':0.1, 'y0_err':0.1, 'vx0':0.2, 'vy0':0.5, 'vx0_err':0.05, 'vy0_err':0.05, - 'ax':0.1, 'ay':-0.1, 'ax_err':0.02, 'ay_err':0.02} + 'ax':0.1, 'ay':-0.1, 'ax_err':0.02, 'ay_err':0.02, + 't0':2025.0} mod_true = motion_model.Acceleration(**true_params) param_list = mod_true.fitter_param_names # Confirm return of proper values for single t=t0 and array t @@ -228,17 +229,18 @@ def test_Acceleration(): x_sim = np.random.normal(x_true, x_true_err) y_sim = np.random.normal(y_true, y_true_err) # Run fit - mod_fit = motion_model.Acceleration() + mod_fit = motion_model.Acceleration(t0=mod_true.t0) params, param_errs = mod_fit.fit_motion_model(t, x_sim,y_sim, x_true_err, y_true_err) # Confirm true value is within error bar of fit value - assert [within_error(true_params[param_list[i]], params[i], param_errs[i]) for i in range(len(params))] + assert np.all([within_error(true_params[param_list[i]], params[i], param_errs[i]) for i in range(len(params))]) #@pytest.mark.skip(reason="not written") def test_Parallax(): # Test handling of a single star true_params = {'x0': 1.0, 'y0':-0.5, 'x0_err':0.1, 'y0_err':0.1, 'vx':-0.2, 'vy':0.5, 'vx_err':0.05, 'vy_err':0.05, - 'pi':0.5, 'RA':17.76, 'Dec':-28.933, 'PA':0} + 'pi':0.5, 'RA':17.76, 'Dec':-28.933, 'PA':0, + 't0':2020.0} mod_true = motion_model.Parallax(**true_params) param_list = mod_true.fitter_param_names @@ -250,10 +252,10 @@ def test_Parallax(): x_sim = np.random.normal(x_true, x_true_err) y_sim = np.random.normal(y_true, y_true_err) # Run fit - mod_fit = motion_model.Parallax(RA=17.76, Dec=-28.933, PA=0) + mod_fit = motion_model.Parallax(RA=17.76, Dec=-28.933, PA=0, t0=mod_true.t0) params, param_errs = mod_fit.fit_motion_model(t, x_sim,y_sim, x_true_err, y_true_err) # Confirm true value is within error bar of fit value - assert [within_error(true_params[param_list[i]], params[i], param_errs[i]) for i in range(len(params))] + assert np.all([within_error(true_params[param_list[i]], params[i], param_errs[i]) for i in range(len(params))]) def test_Parallax_PA(): From a9f58a208d67847eaae597dfef672c3825e3f88c Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Wed, 26 Mar 2025 12:39:52 -0700 Subject: [PATCH 061/254] motion model debugging --- flystar/align.py | 24 ++++++------------------ flystar/startables.py | 1 - 2 files changed, 6 insertions(+), 19 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index a86ee19..1adce21 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -276,10 +276,7 @@ def fit(self): x0e y0e m0e - vx (only if use_motion=True) - vy (only if use_motion=True) - vxe (only if use_motion=True) - vye (only if use_motion=True) + additional motion_model columns """ ########## @@ -836,6 +833,7 @@ def update_ref_table_aggregates(self, keep_ref_orig, n_boot=0): motion_model_class_names = self.ref_table['motion_model_input'].tolist() if 'motion_model_used' in self.ref_table.keys(): motion_model_class_names += self.ref_table['motion_model_used'][ref_orig_idx].tolist() + vals_orig['motion_model_used'] = self.ref_table['motion_model_used'][ref_orig_idx] motion_model_col_names = motion_model.get_list_motion_model_param_names(motion_model_class_names, with_errors=True, with_fixed=True) for mm in motion_model_col_names: if mm in self.ref_table.keys(): @@ -936,7 +934,7 @@ def match_lists(self, dr_tol, dm_tol): else: star_list_T.transform_xy(self.trans_list[ii]) - xref, yref = get_pos_at_time(star_list_T['t'][0], self.ref_table) #, use_motion=self.use_motion) # optional velocity propogation. + xref, yref = get_pos_at_time(star_list_T['t'][0], self.ref_table) mref = self.ref_table['m0'] idx_lis, idx_ref, dr, dm = match.match(star_list_T['x'], star_list_T['y'], star_list_T['m'], @@ -970,7 +968,7 @@ def get_ref_list_from_table(self, epoch): name = self.ref_table['name'] if ('motion_model_used' in self.ref_table.colnames): - x,y,xe,ye = self.ref_table.get_star_positions_at_time(epoch) + x,y,xe,ye = self.ref_table.get_star_positions_at_time(epoch, allow_alt_models=True) else: # No velocities... just used average positions. x = self.ref_table['x0'] @@ -1427,13 +1425,7 @@ def = None. If not None, then this should contain an array or list of transform necessarily want to use these in the reference frame in subsequent passes. If True, then the new stars will be used in later passes/iterations. If False, then the new stars will be carried, but not used in the transformation. - We determine which stars to use through setting a boolean use_in_trans flag. - - use_motion : boolean - If velocities are present in the reference list and use_motion == True, then during - each iteration of the alignment, the reference list will be propogated in time - using the velocity information. So all transformations will be derived w.r.t. - the propogated positions. See also update_vel. + We determine which stars to use through setting a boolean use_in_trans flag. init_guess_mode : string If no initial transformations are passed in via the trans_input keyword, then we have @@ -1460,7 +1452,6 @@ def = None. If not None, then this should contain an array or list of transform outlier_tol=[None], mag_lim=[13, 21], trans_class=transforms.PolyTransform, trans_args=[{'order': 1}], - use_motion=True, use_ref_new=False, update_ref_orig=False, mag_trans=False, @@ -1539,10 +1530,7 @@ def fit(self): x0e y0e m0e - vx (only if use_motion=True) - vy (only if use_motion=True) - vxe (only if use_motion=True) - vye (only if use_motion=True) + addl. motion_model parameters """ # Create a log file of the parameters used in the fit. diff --git a/flystar/startables.py b/flystar/startables.py index b4ec991..c54ddc4 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -920,7 +920,6 @@ def get_star_positions_at_time(self, t, allow_alt_models=True): mod = modClass(RA=self.meta['RA'], Dec=self.meta['Dec'], PA=self.meta['position_angle'], obs=self.meta['observer_location']) x[idx],y[idx],xe[idx],ye[idx] = mod.get_batch_pos_at_time(t,**param_dict) - #print(x) return x,y,xe,ye From bed88d26a8402de65aa73f86071871bf0dac7f83 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Thu, 27 Mar 2025 14:02:43 -0700 Subject: [PATCH 062/254] adding motion model to plots --- flystar/plots.py | 44 +++++++++++++++++++++++--------------------- 1 file changed, 23 insertions(+), 21 deletions(-) diff --git a/flystar/plots.py b/flystar/plots.py index dacebf2..c51b5be 100755 --- a/flystar/plots.py +++ b/flystar/plots.py @@ -1071,9 +1071,9 @@ def plot_mean_residuals_by_epoch(tab): the size of the mean residual. """ # Predicted model positions at each epoch - dt = tab['t'] - tab['t0'][:, np.newaxis] - xt_mod, yt_mod, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'], allow_alt_models=False) - + i_all_detected = np.where(~np.any(np.isnan(tab['t']),axis=1))[0][0] + xt_mod, yt_mod, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], allow_alt_models=True) + # Residuals dx = tab['x'] - xt_mod dy = tab['y'] - yt_mod @@ -1129,12 +1129,12 @@ def plot_quiver_residuals_all_epochs(tab, unit='arcsec', scale=None, plotlim=Non dr_ref = np.zeros(len(tab), dtype=float) n_ref = np.zeros(len(tab), dtype=int) - xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'], allow_alt_models=False) - + i_all_detected = np.where(~np.any(np.isnan(tab['t']),axis=1))[0][0] + xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], allow_alt_models=True) + for ee in range(tab['x'].shape[1]): - dt = tab['t'][:, ee] - tab['t0'] - xt_mod = xt_mod_all[ee] - yt_mod = yt_mod_all[ee] + xt_mod = xt_mod_all[:,ee] + yt_mod = yt_mod_all[:,ee] good_idx = np.where(np.isfinite(tab['x'][:, ee]) == True)[0] ref_idx = np.where(tab[good_idx]['used_in_trans'][:, ee] == True)[0] @@ -1195,8 +1195,9 @@ def plot_quiver_residuals_with_orig_all_epochs(tab, trans_list, unit='arcsec', s dr_ref = np.zeros(len(tab), dtype=float) n_ref = np.zeros(len(tab), dtype=int) - xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'], allow_alt_models=False) - + i_all_detected = np.where(~np.any(np.isnan(tab['t']),axis=1))[0][0] + xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], allow_alt_models=True) + for ee in range(tab['x'].shape[1]): dt = tab['t'][:, ee] - tab['t0'] xt_mod = xt_mod_all[ee] @@ -1305,8 +1306,9 @@ def plot_mag_scatter_multi_trans_all_epochs(tab_list, trans_list_list, unit='arc da_list = [] ntrans = len(tab_list) - xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'], allow_alt_models=False) - + i_all_detected = np.where(~np.any(np.isnan(tab['t']),axis=1))[0][0] + xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], allow_alt_models=True) + for mm in range(ntrans): tab = tab_list[mm] trans_list = trans_list_list[mm] @@ -1858,8 +1860,9 @@ def plot_quiver_residuals_magcolor_all_epochs(tab, unit='arcsec', scale=None, pl idx = np.where((tab['m0'] < lower_mag) & (tab['m0'] > upper_mag))[0] tab = tab[idx] - xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'], allow_alt_models=False) - + i_all_detected = np.where(~np.any(np.isnan(tab['t']),axis=1))[0][0] + xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], allow_alt_models=True) + for ee in range(tab['x'].shape[1]): dt = tab['t'][:, ee] - tab['t0'] xt_mod = xt_mod_all[ee] @@ -2519,8 +2522,8 @@ def rs(x): x = tab['x0'] y = tab['y0'] r = np.hypot(x, y) - xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'], allow_alt_models=False) - + i_all_detected = np.where(~np.any(np.isnan(tab['t']),axis=1))[0][0] + xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], allow_alt_models=True) for i in range(Nstars): starName = star_names[i] @@ -2550,12 +2553,11 @@ def rs(x): dt = tab['t'][ii, fnd] - tab['t0'][ii] - fitLineX = xt_mod_all[ii] - fitLineY = yt_mod_all[ii] - - fitSigX = xt_mod_err[ii] - fitSigY = yt_mod_err[ii] + fitLineX = xt_mod_all[ii, fnd] + fitLineY = yt_mod_all[ii, fnd] + fitSigX = xt_mod_err[ii, fnd] + fitSigY = yt_mod_err[ii, fnd] fitLineM = np.repeat(tab['m0'][ii], len(dt)).reshape(len(dt),1) fitSigM = np.repeat(tab['m0_err'][ii], len(dt)).reshape(len(dt),1) From 5cb692ae5b83e8ff6e3debec78bf61f03a6f08db Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Tue, 15 Apr 2025 17:25:56 -0700 Subject: [PATCH 063/254] debugging --- flystar/align.py | 8 ++++---- flystar/analysis.py | 23 ++++++++++++++++++----- flystar/match.py | 2 +- flystar/startables.py | 2 ++ 4 files changed, 25 insertions(+), 10 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 1adce21..bf7f7ef 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -684,7 +684,7 @@ def setup_ref_table_from_starlist(self, star_list): if 'motion_model_input' not in ref_table.colnames: ref_table.add_column(Column(np.repeat(self.default_motion_model, len(ref_table)), name='motion_model_input')) if 'motion_model_used' not in ref_table.colnames: - ref_table.add_column(Column(np.repeat('Fixed', len(ref_table)), name='motion_model_used')) + ref_table.add_column(Column(np.repeat(self.default_motion_model, len(ref_table)), name='motion_model_used')) return ref_table @@ -1609,7 +1609,7 @@ def fit(self): # # Re-do all matching given final transformations. # No trimming this time. - # First rest the reference table 2D values. + # First reset the reference table 2D values. ########## self.reset_ref_values(exclude=['used_in_trans']) @@ -1634,7 +1634,7 @@ def fit(self): self.ref_table.detections() ### Drop all stars that have 0 detections. - idx = np.where((self.ref_table['n_detect'] == 0))[0] + idx = np.where((self.ref_table['n_detect'] == 0) & (self.ref_table['ref_orig'] == False))[0] print(' *** Getting rid of {0:d} out of {1:d} junk sources'.format(len(idx), len(self.ref_table))) self.ref_table.remove_rows(idx) @@ -3043,7 +3043,7 @@ def get_pos_at_time(t, starlist): """ # Check for motion model if 'motion_model_used' in starlist.colnames: - x,y,xe,ye = starlist.get_star_positions_at_time(t, allow_alt_models=False) + x,y,xe,ye = starlist.get_star_positions_at_time(t, allow_alt_models=True) # If no motion model, check for velocities elif ('vx' in starlist.colnames) and ('vy' in starlist.colnames): x = starlist['x0'] + starlist['vx']*(t-starlist['t0']) diff --git a/flystar/analysis.py b/flystar/analysis.py index 4aac894..f29143e 100644 --- a/flystar/analysis.py +++ b/flystar/analysis.py @@ -182,16 +182,29 @@ def prepare_gaia_for_flystar(gaia, ra, dec, targets_dict=None, match_dr_max=0.2) gaia_new = gaia_new.filled() #convert masked colunms to regular columns if targets_dict != None: - for targ_name, targ_coo in targets_dict.items(): - dx = gaia_new['x0'] - (targ_coo[0] * -1.0) - dy = gaia_new['y0'] - targ_coo[1] +# for targ_name, targ_coo in targets_dict.items(): +# dx = gaia_new['x0'] - (targ_coo[0] * -1.0) +# dy = gaia_new['y0'] - targ_coo[1] +# dr = np.hypot(dx, dy) +# +# idx = dr.argmin() +# +# if dr[idx] < match_dr_max: +# gaia_new['name'][idx] = targ_name +# print('Found match for: ', targ_name, ' - ',gaia_new['source_id'][idx]) + targ_names = [x for x in targets_dict] + targ_xs = np.array([targets_dict[x][0] for x in targets_dict]) + targ_ys = np.array([targets_dict[x][1] for x in targets_dict]) + for i_gaia in range(len(gaia_new)): + dx = gaia_new['x0'][i_gaia] - (targ_xs * -1.0) + dy = gaia_new['y0'][i_gaia] - targ_ys dr = np.hypot(dx, dy) idx = dr.argmin() if dr[idx] < match_dr_max: - gaia_new['name'][idx] = targ_name - print('Found match for: ', targ_name, ' - ',gaia_new['source_id'][idx]) + gaia_new['name'][i_gaia] = targ_names[idx] + print('Found match for: ', targ_names[idx], ' - ',gaia_new['source_id'][i_gaia]) return gaia_new diff --git a/flystar/match.py b/flystar/match.py index b086d32..ba43f58 100644 --- a/flystar/match.py +++ b/flystar/match.py @@ -392,7 +392,7 @@ def match(x1, y1, m1, x2, y2, m2, dr_tol, dm_tol=None, verbose=True): keep[dups[dm_min]] = True else: if verbose: - print(' confused, dropping') + print(' confused, dropping star at',x2[idxs2[dups]][0],y2[idxs2[dups]][0]) # Clean up the duplicates diff --git a/flystar/startables.py b/flystar/startables.py index c54ddc4..2739252 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -178,6 +178,8 @@ def __init__(self, *args, ref_list=0, position_angle=None, RA=None, Dec=None, ob self['name_in_list'] = self['name_in_list'].astype('U20') if arg == 'motion_model_input': self['motion_model_input'] = self['motion_model_input'].astype('U20') + if arg == 'motion_model_used': + self['motion_model_used'] = self['motion_model_used'].astype('U20') #if 'motion_model_input' not in kwargs: # self['motion_model_input'] = np.repeat(self.default_motion_model, len(self['name'])) From b81c221f47e7cbdab38ee02c1c50c5f4347fcf4a Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Thu, 17 Apr 2025 09:56:40 -0700 Subject: [PATCH 064/254] n_good=2 case for linear fitting --- flystar/motion_model.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/flystar/motion_model.py b/flystar/motion_model.py index 4a18509..63ee866 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -240,14 +240,14 @@ def run_fit(self, t, x, y, xe, ye, update=True, weighting='var'): dt_diff = np.diff(dt)[0] vx = dx / dt_diff vy = dy / dt_diff - # TODO: this does not align with how t0 works..... - x0 = np.average(x, weights=x_wt) #x[0] - dt[0]*vx - y0 = np.average(y, weights=y_wt) #y[0] - dt[0]*vy - vxe = 0.0 - vye = 0.0 - x0e = np.abs(dx) / 2**0.5 - y0e = np.abs(dy) / 2**0.5 - + # TODO: still not sure about the error handling here + x0 = x[0] - dt[0]*vx # np.average(x, weights=x_wt) # + y0 = y[0] - dt[0]*vy # np.average(y, weights=y_wt) # + x0e = np.abs(dx) / 2**0.5 # np.sqrt(np.sum(xe**2)/2) # + y0e = np.abs(dy) / 2**0.5 # np.sqrt(np.sum(ye**2)/2) # + vxe = 0.0 #np.abs(vx) * np.sqrt(np.sum(xe**2/x**2)) + vye = 0.0 #np.abs(vy) * np.sqrt(np.sum(ye**2/y**2)) + else: def linear(t, c0, c1): return c0 + c1*t From a7ffa817828be9bca3e224460a84ae564514ef15 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Thu, 17 Apr 2025 12:31:34 -0700 Subject: [PATCH 065/254] continuous model plotting (display improved for parallax) --- flystar/plots.py | 26 ++++++++++++++------------ 1 file changed, 14 insertions(+), 12 deletions(-) diff --git a/flystar/plots.py b/flystar/plots.py index c51b5be..079498b 100755 --- a/flystar/plots.py +++ b/flystar/plots.py @@ -2523,7 +2523,9 @@ def rs(x): y = tab['y0'] r = np.hypot(x, y) i_all_detected = np.where(~np.any(np.isnan(tab['t']),axis=1))[0][0] + cont_times = np.arange(np.min(tab['t'][i_all_detected]), np.max(tab['t'][i_all_detected]), 0.01) xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], allow_alt_models=True) + xt_cont_all, yt_cont_all, xt_cont_err, yt_cont_err = tab.get_star_positions_at_time(cont_times, allow_alt_models=True) for i in range(Nstars): starName = star_names[i] @@ -2650,9 +2652,9 @@ def rs(x): ind = int((row-1)*Ncols + col) paxes = plt.subplot(Nrows, Ncols, ind) - plt.plot(time, fitLineX, 'b-') - plt.plot(time, fitLineX + fitSigX, 'b--') - plt.plot(time, fitLineX - fitSigX, 'b--') + plt.plot(cont_times, xt_cont_all[ii], 'b-') + plt.plot(cont_times, xt_cont_all[ii] + xt_cont_err[ii], 'b--') + plt.plot(cont_times, xt_cont_all[ii] - xt_cont_err[ii], 'b--') if not color_time: #print('x:',x) #print('xerr:',xerr) @@ -2687,9 +2689,9 @@ def rs(x): ind = int((row-1)*Ncols + col) paxes = plt.subplot(Nrows, Ncols, ind) - plt.plot(time, fitLineY, 'b-') - plt.plot(time, fitLineY + fitSigY, 'b--') - plt.plot(time, fitLineY - fitSigY, 'b--') + plt.plot(cont_times, yt_cont_all[ii], 'b-') + plt.plot(cont_times, yt_cont_all[ii] + yt_cont_err[ii], 'b--') + plt.plot(cont_times, yt_cont_all[ii] - yt_cont_err[ii], 'b--') if not color_time: plt.errorbar(rs(time), rs(y), yerr=rs(yerr), fmt='k.') else: @@ -2750,8 +2752,8 @@ def rs(x): paxes = plt.subplot(Nrows, Ncols, ind) plt.plot(time, np.zeros(len(time)), 'b-') - plt.plot(time, fitSigX*1e3, 'b--') - plt.plot(time, -fitSigX*1e3, 'b--') + plt.plot(cont_times, xt_cont_err[ii]*1e3, 'b--') + plt.plot(cont_times, -xt_cont_err[ii]*1e3, 'b--') if not color_time: plt.errorbar(rs(time), rs(x - fitLineX)*1e3, yerr=rs(xerr)*1e3, fmt='k.') else: @@ -2778,8 +2780,8 @@ def rs(x): paxes = plt.subplot(Nrows, Ncols, ind) plt.plot(time, np.zeros(len(time)), 'b-') - plt.plot(time, fitSigY*1e3, 'b--') - plt.plot(time, -fitSigY*1e3, 'b--') + plt.plot(cont_times, yt_cont_err[ii]*1e3, 'b--') + plt.plot(cont_times, -yt_cont_err[ii]*1e3, 'b--') if not color_time: plt.errorbar(rs(time), rs(y - fitLineY)*1e3, yerr=rs(yerr)*1e3, fmt='k.') else: @@ -2854,8 +2856,8 @@ def rs(x): paxes.xaxis.set_major_formatter(FormatStrFormatter('%.3f')) plt.xlabel('X (asec)', fontsize=fontsize1) plt.ylabel('Y (asec)', fontsize=fontsize1) - plt.plot(fitLineX, fitLineY, 'b-') - + plt.plot(xt_cont_all[ii], yt_cont_all[ii], 'b-') + ########## # X, Y Histogram of Residuals ########## From 8cda1d5e9270e4fdc220b129e326b48a3f999611 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Wed, 7 May 2025 12:54:13 -0700 Subject: [PATCH 066/254] bootstrap improvements --- flystar/motion_model.py | 56 ++++++++++++++++++++++++++++------------- flystar/plots.py | 12 ++++++++- 2 files changed, 50 insertions(+), 18 deletions(-) diff --git a/flystar/motion_model.py b/flystar/motion_model.py index 63ee866..70abe5f 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -6,6 +6,8 @@ from scipy.optimize import curve_fit import warnings +plx_vector_cached = None + class MotionModel(ABC): # Number of data points required to fit model n_pts_req = 0 @@ -32,7 +34,6 @@ def __init__(self, *args, **kwargs): param_var = getattr(self, param) if not isinstance(param_var, (list, np.ndarray)): setattr(self, param, np.array([param_var]))''' - return def get_pos_at_time(self, t): @@ -84,7 +85,9 @@ def fit_motion_model(self, t, x, y, xe, ye, update=True, bootstrap=0, weighting= bb_params = [] for bb in range(bootstrap): bdx = np.random.choice(edx, len(x)) - params_bdx, param_errs_bdx = self.run_fit(t[bdx], x[bdx], y[bdx], xe[bdx], ye[bdx], weighting=weighting, update=False) + while len(np.unique(bdx)) Date: Fri, 9 May 2025 17:05:16 -0700 Subject: [PATCH 067/254] new per_filter plotting functions for chi2 distribution --- flystar/plots.py | 134 +++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 134 insertions(+) diff --git a/flystar/plots.py b/flystar/plots.py index 758a50b..aaf1e20 100755 --- a/flystar/plots.py +++ b/flystar/plots.py @@ -2232,6 +2232,85 @@ def plot_chi2_dist(tab, Ndetect, xlim=40, n_bins=50): return +def plot_chi2_dist_per_filter(tab, Ndetect, xlim=40, n_bins=50, filter=None): + """ + tab = flystar table + Ndetect = Number of epochs star detected in + """ + chi2_x_list = [] + chi2_y_list = [] + fnd_list = [] # Number of non-NaN error measurements + + i_all_detected = np.where(~np.any(np.isnan(tab['t']),axis=1))[0][0] + xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], allow_alt_models=True) + + for ii in range(len(tab)): + # Ignore the NaNs + fnd = np.argwhere(~np.isnan(tab['xe'][ii,:])) + fnd_list.append(len(fnd)) + + x = tab['x'][ii, fnd] + y = tab['y'][ii, fnd] + xerr = tab['xe'][ii, fnd] + yerr = tab['ye'][ii, fnd] + + fitLineX = xt_mod_all[ii, fnd] + fitLineY = yt_mod_all[ii,fnd] + + diffX = x - fitLineX + diffY = y - fitLineY + sigX = diffX / xerr + sigY = diffY / yerr + + chi2_x = np.sum(sigX**2) + chi2_y = np.sum(sigY**2) + chi2_x_list.append(chi2_x) + chi2_y_list.append(chi2_y) + + x = np.array(chi2_x_list) + y = np.array(chi2_y_list) + fnd = np.array(fnd_list) + + idx = np.where(fnd == Ndetect)[0] + # Fitting position and velocity... so subtract 2 to get Ndof + Ndof = Ndetect - tab['dof'][i_all_detected] + chi2_xaxis = np.linspace(0, xlim, xlim*3) + chi2_bins = np.linspace(0, xlim, n_bins) + + plt.figure(figsize=(6,4)) + plt.clf() + plt.hist(x[idx], bins=chi2_bins, histtype='stepfilled', label='RA', density=True, color='skyblue', alpha=0.8, edgecolor='k') + plt.hist(y[idx], bins=chi2_bins, histtype='stepfilled', label='DEC', density=True, color='orange', alpha=0.8, edgecolor='k') + plt.plot(chi2_xaxis, chi2.pdf(chi2_xaxis, Ndof), 'r-', alpha=0.6, + label='$\chi^2$ ' + str(Ndof) + ' dof') + #plt.title('$N_{epoch} = $' + str(Ndetect) + ', $N_{dof} = $' + str(Ndof)) + plt.title(str(filter)+' (N = '+str(len(chi2_x_list))+')', fontsize=22) + plt.xlim(0, xlim) + plt.ylabel(r'PDF', fontsize=28) + plt.legend(fontsize=20) + + plt.tick_params(labelsize=20, direction='in', right=True, top=True) + + plt.savefig(str(filter)+'_chi2_dist.png', dpi=400) + + chi2red_x = x / (fnd - 2) + chi2red_y = y / (fnd - 2) + chi2red_t = (x + y) / (2.0 * (fnd - 2)) + + print('Mean reduced chi^2: (Ndetect = {0:d} of {1:d})'.format(len(idx), len(tab))) + fmt = ' {0:s} = {1:.1f} for N_detect and {2:.1f} for all' + med_chi2red_x_f = np.median(chi2red_x[idx]) + med_chi2red_x_a = np.median(chi2red_x) + med_chi2red_y_f = np.median(chi2red_y[idx]) + med_chi2red_y_a = np.median(chi2red_y) + med_chi2red_t_f = np.median(chi2red_t[idx]) + med_chi2red_t_a = np.median(chi2red_t) + print(fmt.format(' X', med_chi2red_x_f, med_chi2red_x_a)) + print(fmt.format(' Y', med_chi2red_y_f, med_chi2red_y_a)) + print(fmt.format('Tot', med_chi2red_t_f, med_chi2red_t_a)) + + return + def plot_chi2_dist_per_epoch(tab, Ndetect, mlim=[14,21], ylim = [-1, 1], target_idx = 0): """ @@ -2493,6 +2572,61 @@ def plot_chi2_dist_mag(tab, Ndetect, xlim=40, n_bins=30): return +def plot_chi2_dist_mag_per_filter(tab, Ndetect, mlim=40, n_bins=30, xlim=40, file_name=None, filter=None): + """ + tab = flystar table + Ndetect = Number of epochs star detected in + """ + chi2_m_list = [] + fnd_list = [] # Number of non-NaN error measurements + + for ii in range(len(tab['me'])): + # Ignore the NaNs + fnd = np.argwhere(~np.isnan(tab['me'][ii,:])) + fnd_list.append(len(fnd)) + + m = tab['m'][ii, fnd] + merr = tab['me'][ii, fnd] + m0 = tab['m0'][ii] + m0err = tab['m0_err'][ii] + + diff_m = m0 - m + sig_m = diff_m/merr + + chi2_m = np.sum(sig_m**2) + chi2_m_list.append(chi2_m) + + chi2_m = np.array(chi2_m_list) + fnd = np.array(fnd_list) + + idx = np.where(fnd == Ndetect)[0] + + # Fitting mean magnitude... so subtract 1 to get Ndof + Ndof = Ndetect - 1 + chi2_maxis = np.linspace(0, xlim, xlim*3) + chi2_bins = np.linspace(0, xlim, n_bins) + + plt.figure(figsize=(6,4)) + plt.clf() + plt.hist(chi2_m[idx], bins=np.arange(xlim*10), label='mag', histtype='stepfilled', density=True, color='green', alpha=0.7, edgecolor='k') + plt.plot(chi2_maxis, chi2.pdf(chi2_maxis, Ndof), 'r-', alpha=0.6, + label='$\chi^2$ ' + str(Ndof) + ' dof') + #plt.title('$N_{epoch} = $' + str(Ndetect) + ', $N_{dof} = $' + str(Ndof)) + plt.xlim(0, xlim) + plt.xlabel(r'$\chi^{2}$', fontsize=28) + plt.ylabel(r'PDF', fontsize=28) + plt.legend(fontsize=20) + + plt.tick_params(labelsize=20, direction='in', right=True, top=True) + + plt.savefig(str(filter)+'_chi2_dist_mag.png', dpi=400) + + print('Mean reduced chi^2: (Ndetect = {0:d} of {1:d})'.format(len(idx), len(tab))) + fmt = ' {0:s} = {1:.1f} for N_detect and {2:.1f} for all' + print(fmt.format('M', np.median(chi2_m[idx] / (fnd[idx] - 2)), np.median(chi2_m / (fnd - 2)))) + + return + def plot_stars(tab, star_names, NcolMax=2, epoch_array = None, figsize=(15,25), color_time=False, position_angle=None, RA=None, Dec=None, observer_location='earth'): """ From 29506c4ddbf346fc34d59c56c2e849bc9e77373c Mon Sep 17 00:00:00 2001 From: skterry Date: Wed, 14 May 2025 11:28:16 -0700 Subject: [PATCH 068/254] add optional print flag for plot_stars_nfilt() --- flystar/plots.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/flystar/plots.py b/flystar/plots.py index aaf1e20..8211e21 100755 --- a/flystar/plots.py +++ b/flystar/plots.py @@ -3042,7 +3042,7 @@ def rs(x): return def plot_stars_nfilt(tab, star_names, NcolMax=2, epoch_array_list = None, color_list = None, - figsize=(15,25), color_time=False, resTicRng=None): + figsize=(15,25), color_time=False, resTicRng=None, save_name=None): """ Plot a set of stars positions, flux and residuals over time. @@ -3445,7 +3445,8 @@ def rs(x): plt.subplots_adjust(wspace=0.6, hspace=0.6, left = 0.08, bottom = 0.05, right=0.95, top=0.90) # plt.savefig(rootDir+'plots/plotStar_all.png') plt.show() - + if save_name is not None: + plt.savefig(save_name + '.png') plt.show() return From c40fdeaee867e77e169bc150b142046d8d53fdf0 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Tue, 3 Jun 2025 12:29:23 -0700 Subject: [PATCH 069/254] parallax handling tweaks --- flystar/analysis.py | 15 ++++++++++++--- flystar/parallax.py | 2 +- flystar/plots.py | 4 +++- 3 files changed, 16 insertions(+), 5 deletions(-) diff --git a/flystar/analysis.py b/flystar/analysis.py index f29143e..d16fd89 100644 --- a/flystar/analysis.py +++ b/flystar/analysis.py @@ -109,7 +109,7 @@ def check_gaia_parallaxes(ra,dec,search_radius=10.0,table_name='gaiadr3',target= plt.savefig('gaiaplx'+file_ext+'.png') -def prepare_gaia_for_flystar(gaia, ra, dec, targets_dict=None, match_dr_max=0.2): +def prepare_gaia_for_flystar(gaia, ra, dec, targets_dict=None, match_dr_max=0.2, pi_err_limit=0.4): """ Take a Gaia table (from astroquery) and produce a new table with a tangential projection and shift such that the origin is centered on the target of interest. @@ -162,8 +162,8 @@ def prepare_gaia_for_flystar(gaia, ra, dec, targets_dict=None, match_dr_max=0.2) gaia_new['m'] = gaia['phot_g_mean_mag'] gaia_new['me'] = 1.09/gaia['phot_g_mean_flux_over_error'] - gaia_new['parallax'] = gaia['parallax'] - gaia_new['parallax_error'] = gaia['parallax_error'] + gaia_new['pi'] = gaia['parallax'].data*1e-3 + gaia_new['pi_err'] = gaia['parallax_error'].data*1e-3 # Set the velocities (and uncertainties) to zero if they aren't measured. idx = np.where(np.isnan(gaia_new['vx']) == True)[0] @@ -171,6 +171,15 @@ def prepare_gaia_for_flystar(gaia, ra, dec, targets_dict=None, match_dr_max=0.2) gaia_new['vx_err'][idx] = 0.0 gaia_new['vy'][idx] = 0.0 gaia_new['vy_err'][idx] = 0.0 + + # Cut out stars with high plx error and set motion models + idx = np.where((gaia_new['pi_err']>(pi_err_limit/1e3)) | (gaia['parallax'].mask == True))[0] + print(gaia_new['pi_err']) + print(idx) + gaia_new['pi'][idx] = 0.0 + gaia_new['pi_err'][idx] = 0.0 + gaia_new['motion_model_used'] = 'Parallax' + gaia_new['motion_model_used'][idx] = 'Linear' #macy additions to try to fix wild magnitude values #gaia_new['ruwe'] = gaia['ruwe'] diff --git a/flystar/parallax.py b/flystar/parallax.py index 4c60ae7..27cd632 100755 --- a/flystar/parallax.py +++ b/flystar/parallax.py @@ -16,7 +16,7 @@ cache_dir = os.environ['PARALLAX_CACHE_DIR'] except: cache_dir = os.path.dirname(__file__) + '/parallax_cache/' -cache_memory = Memory(cache_dir, verbose=0, bytes_limit='1G') +cache_memory = Memory(cache_dir, verbose=0) # Default cache size is 1 GB cache_memory.reduce_size() diff --git a/flystar/plots.py b/flystar/plots.py index 758a50b..d094e64 100755 --- a/flystar/plots.py +++ b/flystar/plots.py @@ -3345,9 +3345,11 @@ def plot_errors_vs_r_m(star_tab, vmax_perr=0.75, vmax_pmerr=0.75): return -def plot_plxs(star_tab): +def plot_plxs(star_tab, target_idx=0): fig,ax = plt.subplots(nrows=1,ncols=2,figsize=(10,5)) ax[0].errorbar(star_tab['m0'],star_tab['pi']*1e3, yerr=star_tab['pi_err']*1e3,marker='.',linestyle='none') + if target_idx is not None: + ax[0].errorbar(star_tab['m0'][target_idx],star_tab['pi'][target_idx]*1e3, yerr=star_tab['pi_err'][target_idx]*1e3,marker='*',linestyle='none', color='orange', markersize=10) ax[0].axhline(0, c='gray') ax[0].set_ylabel('Plx (mas)') ax[0].set_xlabel('Mag') From 8d91bf8e82887a1edccdee20d25db2259f9e4236 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Tue, 3 Jun 2025 15:23:01 -0700 Subject: [PATCH 070/254] add mm input to gaia table --- flystar/analysis.py | 1 + 1 file changed, 1 insertion(+) diff --git a/flystar/analysis.py b/flystar/analysis.py index d16fd89..39b97c6 100644 --- a/flystar/analysis.py +++ b/flystar/analysis.py @@ -178,6 +178,7 @@ def prepare_gaia_for_flystar(gaia, ra, dec, targets_dict=None, match_dr_max=0.2, print(idx) gaia_new['pi'][idx] = 0.0 gaia_new['pi_err'][idx] = 0.0 + gaia_new['motion_model_input'] = 'Parallax' gaia_new['motion_model_used'] = 'Parallax' gaia_new['motion_model_used'][idx] = 'Linear' From 3139bee092e1a743f8c06d5553fd1f188a82d268 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Mon, 16 Jun 2025 16:44:03 -0700 Subject: [PATCH 071/254] remove extra prints) --- flystar/analysis.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/flystar/analysis.py b/flystar/analysis.py index 39b97c6..f048e5b 100644 --- a/flystar/analysis.py +++ b/flystar/analysis.py @@ -174,8 +174,6 @@ def prepare_gaia_for_flystar(gaia, ra, dec, targets_dict=None, match_dr_max=0.2, # Cut out stars with high plx error and set motion models idx = np.where((gaia_new['pi_err']>(pi_err_limit/1e3)) | (gaia['parallax'].mask == True))[0] - print(gaia_new['pi_err']) - print(idx) gaia_new['pi'][idx] = 0.0 gaia_new['pi_err'][idx] = 0.0 gaia_new['motion_model_input'] = 'Parallax' From 82f50fda215eaafded90c28092c78c259e02dfe8 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Mon, 16 Jun 2025 16:57:57 -0700 Subject: [PATCH 072/254] gaia motion model editable by keyword --- flystar/analysis.py | 19 ++++++++++++++----- 1 file changed, 14 insertions(+), 5 deletions(-) diff --git a/flystar/analysis.py b/flystar/analysis.py index f048e5b..94d14b6 100644 --- a/flystar/analysis.py +++ b/flystar/analysis.py @@ -109,7 +109,7 @@ def check_gaia_parallaxes(ra,dec,search_radius=10.0,table_name='gaiadr3',target= plt.savefig('gaiaplx'+file_ext+'.png') -def prepare_gaia_for_flystar(gaia, ra, dec, targets_dict=None, match_dr_max=0.2, pi_err_limit=0.4): +def prepare_gaia_for_flystar(gaia, ra, dec, targets_dict=None, match_dr_max=0.2, pi_err_limit=0.4, default_motion_model='Linear'): """ Take a Gaia table (from astroquery) and produce a new table with a tangential projection and shift such that the origin is centered on the target of interest. @@ -176,10 +176,19 @@ def prepare_gaia_for_flystar(gaia, ra, dec, targets_dict=None, match_dr_max=0.2, idx = np.where((gaia_new['pi_err']>(pi_err_limit/1e3)) | (gaia['parallax'].mask == True))[0] gaia_new['pi'][idx] = 0.0 gaia_new['pi_err'][idx] = 0.0 - gaia_new['motion_model_input'] = 'Parallax' - gaia_new['motion_model_used'] = 'Parallax' - gaia_new['motion_model_used'][idx] = 'Linear' - + if default_motion_model=='Parallax': + gaia_new['motion_model_input'] = 'Parallax' + gaia_new['motion_model_used'] = 'Parallax' + gaia_new['motion_model_used'][idx] = 'Linear' + elif default_motion_model=='Linear': + gaia_new['motion_model_input'] = 'Linear' + gaia_new['motion_model_used'] = 'Linear' + elif default_motion_model=='Fixed': + gaia_new['motion_model_input'] = 'Fixed' + gaia_new['motion_model_used'] = 'Fixed' + else: + print("Invalid motion model",default_motion_model,"- none assigned") + #macy additions to try to fix wild magnitude values #gaia_new['ruwe'] = gaia['ruwe'] #try: From 4b467c8fe132aa638ef5d54af406e9a34a2a2a66 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Tue, 17 Jun 2025 17:15:27 -0700 Subject: [PATCH 073/254] reference frame shift function --- flystar/startables.py | 21 ++++++++++++++++++++- 1 file changed, 20 insertions(+), 1 deletion(-) diff --git a/flystar/startables.py b/flystar/startables.py index 2739252..4d48336 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -1,5 +1,6 @@ from astropy.table import Table, Column, hstack from astropy.stats import sigma_clipping +from astropy.time import Time from scipy.optimize import curve_fit from flystar.fit_velocity import linear_fit, calc_chi2, linear, fit_velocity from tqdm import tqdm @@ -9,7 +10,7 @@ import pdb import time import copy -from flystar import motion_model +from flystar import motion_model, parallax class StarTable(Table): """ @@ -1011,3 +1012,21 @@ def fit_velocities_all_detected(self, weighting='var', use_scipy=False, absolute return vel_result else: return + + def shift_reference_frame(self, delta_vx=0.0, delta_vy=0.0, delta_pi=0.0): + if delta_vx==0.0 and delta_vy==0.0 and delta_pi==0.0: + print("No shifts input, reference frame unchanged.") + return + self['vx'] += delta_vx + self['x'] += delta_vx*(self['t']-self['t0'][:, np.newaxis]) + self['vy'] += delta_vy + self['y'] += delta_vy*(self['t']-self['t0'][:, np.newaxis]) + if delta_pi!=0.0: + t_all = self['t'][np.where(~np.any(np.isnan(self['t']), axis=1))[0][0]] + t_mjd = Time(t_all, format='decimalyear', scale='utc').mjd + pvec = parallax.parallax_in_direction(self.meta['RA'], self.meta['Dec'], t_mjd, + obsLocation=self.meta['observer_location'], PA=self.meta['position_angle']) + self['pi'] += delta_pi + self['x'] += delta_pi*pvec[:,0] + self['y'] += delta_pi*pvec[:,1] + return From 5e1711debf638b55fab9b350b4a70046909ab99e Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Tue, 17 Jun 2025 17:24:27 -0700 Subject: [PATCH 074/254] reference frame shift function --- flystar/startables.py | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/flystar/startables.py b/flystar/startables.py index 4d48336..3c1fe6e 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -1014,8 +1014,22 @@ def fit_velocities_all_detected(self, weighting='var', use_scipy=False, absolute return def shift_reference_frame(self, delta_vx=0.0, delta_vy=0.0, delta_pi=0.0): + """ + After completing an alignment, shift from your relative reference frame to + the absolute frame using either Gaia or a Galactic model. + + Parameters + ---------- + delta_vx : float, optional + velocity shift in x-direction (as/yr) + delta_vy : float, optional + velocity shift in y-direction (as/yr) + delta_pi : float, optional + parallax shift (as) + """ if delta_vx==0.0 and delta_vy==0.0 and delta_pi==0.0: print("No shifts input, reference frame unchanged.") + print("Specify delta_vx, delta_vy, and/or delta_pi to perform a reference frame shift.") return self['vx'] += delta_vx self['x'] += delta_vx*(self['t']-self['t0'][:, np.newaxis]) From 0d7cd36e146c442df1e24554c1f89675c6748d0c Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Wed, 18 Jun 2025 11:32:50 -0700 Subject: [PATCH 075/254] debug calc_bootstrap_errors for motion model --- flystar/align.py | 5 ++++- flystar/plots.py | 33 ++++++++++++++++++++++++--------- flystar/startables.py | 4 +++- 3 files changed, 31 insertions(+), 11 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index bf7f7ef..b4759bc 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -1233,7 +1233,10 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot xe=xe_trans_arr[:,ii,boot_idx], ye=ye_trans_arr[:,ii,boot_idx], me=me_trans_arr[:,ii,boot_idx], - t=np.tile(t_boot, (len(ref_table),1))) + t=np.tile(t_boot, (len(ref_table),1)), + RA=self.RA, Dec=self.Dec, + position_angle=self.position_angle, + observer_location=self.observer_location) # Now, do proper motion calculation, making sure to fix t0 to the # orig value (so we can get a reasonable error on x0, y0) diff --git a/flystar/plots.py b/flystar/plots.py index 2aa0833..95b1eee 100755 --- a/flystar/plots.py +++ b/flystar/plots.py @@ -2159,7 +2159,7 @@ def plot_quiver_residuals_orig_angle_xy(x_t, y_t, x_ref, y_ref, good_idx, ref_id return -def plot_chi2_dist(tab, Ndetect, xlim=40, n_bins=50): +def plot_chi2_dist(tab, Ndetect, xlim=40, n_bins=50, boot_err=False): """ tab = flystar table Ndetect = Number of epochs star detected in @@ -2178,8 +2178,12 @@ def plot_chi2_dist(tab, Ndetect, xlim=40, n_bins=50): x = tab['x'][ii, fnd] y = tab['y'][ii, fnd] - xerr = tab['xe'][ii, fnd] - yerr = tab['ye'][ii, fnd] + if boot_err: + xerr = tab['xe_boot'][ii, fnd] + yerr = tab['ye_boot'][ii, fnd] + else: + xerr = tab['xe'][ii, fnd] + yerr = tab['ye'][ii, fnd] fitLineX = xt_mod_all[ii, fnd] fitLineY = yt_mod_all[ii,fnd] @@ -2232,7 +2236,7 @@ def plot_chi2_dist(tab, Ndetect, xlim=40, n_bins=50): return -def plot_chi2_dist_per_filter(tab, Ndetect, xlim=40, n_bins=50, filter=None): +def plot_chi2_dist_per_filter(tab, Ndetect, xlim=40, n_bins=50, filter=None, boot_err=False): """ tab = flystar table Ndetect = Number of epochs star detected in @@ -2251,8 +2255,12 @@ def plot_chi2_dist_per_filter(tab, Ndetect, xlim=40, n_bins=50, filter=None): x = tab['x'][ii, fnd] y = tab['y'][ii, fnd] - xerr = tab['xe'][ii, fnd] - yerr = tab['ye'][ii, fnd] + if boot_err: + xerr = tab['xe_boot'][ii, fnd] + yerr = tab['ye_boot'][ii, fnd] + else: + xerr = tab['xe'][ii, fnd] + yerr = tab['ye'][ii, fnd] fitLineX = xt_mod_all[ii, fnd] fitLineY = yt_mod_all[ii,fnd] @@ -2312,7 +2320,7 @@ def plot_chi2_dist_per_filter(tab, Ndetect, xlim=40, n_bins=50, filter=None): return -def plot_chi2_dist_per_epoch(tab, Ndetect, mlim=[14,21], ylim = [-1, 1], target_idx = 0): +def plot_chi2_dist_per_epoch(tab, Ndetect, mlim=[14,21], ylim = [-1, 1], target_idx = 0, boot_err=False): """ tab = flystar table Ndetect = Number of epochs star detected in @@ -2337,6 +2345,10 @@ def plot_chi2_dist_per_epoch(tab, Ndetect, mlim=[14,21], ylim = [-1, 1], target_ y = tab['y'][ii, fnd] m = tab['m'][ii, fnd] + if boot_err: + xerr = tab['xe_boot'][ii, fnd] + yerr = tab['ye_boot'][ii, fnd] + else: xerr = tab['xe'][ii, fnd] yerr = tab['ye'][ii, fnd] @@ -2523,7 +2535,7 @@ def eq_to_ec(ra,dec): ax3.set_ylabel('residual (mas)') return -def plot_chi2_dist_mag(tab, Ndetect, xlim=40, n_bins=30): +def plot_chi2_dist_mag(tab, Ndetect, xlim=40, n_bins=30, boot_err=False): """ tab = flystar table Ndetect = Number of epochs star detected in @@ -2537,7 +2549,10 @@ def plot_chi2_dist_mag(tab, Ndetect, xlim=40, n_bins=30): fnd_list.append(len(fnd)) m = tab['m'][ii, fnd] - merr = tab['me'][ii, fnd] + if boot_err: + merr = tab['me_boot'][ii, fnd] + else: + merr = tab['me'][ii, fnd] m0 = tab['m0'][ii] m0err = tab['m0_err'][ii] diff --git a/flystar/startables.py b/flystar/startables.py index 3c1fe6e..66447ff 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -1016,7 +1016,9 @@ def fit_velocities_all_detected(self, weighting='var', use_scipy=False, absolute def shift_reference_frame(self, delta_vx=0.0, delta_vy=0.0, delta_pi=0.0): """ After completing an alignment, shift from your relative reference frame to - the absolute frame using either Gaia or a Galactic model. + the absolute frame using either Gaia or a Galactic model. This modified the + motion model fit parameters as well as the time series astrometry, assuming + zero error on the shift values. Parameters ---------- From 80a6930fac728423315db27c268b3edaa5dc641c Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Fri, 20 Jun 2025 11:57:04 -0700 Subject: [PATCH 076/254] debug motion_model_used for MosaicSelfRef --- flystar/align.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index b4759bc..c6dd39e 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -286,7 +286,7 @@ def fit(self): # x_orig, y_orig, m_orig, (opt. errors) -- the transformed errors for the lists: 2D # w, w_orig (optiona) -- the input and output weights of stars in transform: 2D ########## - self.ref_table = self.setup_ref_table_from_starlist(self.star_lists[self.ref_index]) + self.ref_table = self.setup_ref_table_from_starlist(self.star_lists[self.ref_index],motion_model_used='Fixed') # Save the reference index to the meta data on the reference list. self.ref_table.meta['ref_list'] = self.ref_index @@ -573,7 +573,7 @@ def setup_trans_info(self): return - def setup_ref_table_from_starlist(self, star_list): + def setup_ref_table_from_starlist(self, star_list, motion_model_used=None): """ Start with the reference list.... this will change and grow over time, so make a copy that we will keep updating. @@ -684,7 +684,10 @@ def setup_ref_table_from_starlist(self, star_list): if 'motion_model_input' not in ref_table.colnames: ref_table.add_column(Column(np.repeat(self.default_motion_model, len(ref_table)), name='motion_model_input')) if 'motion_model_used' not in ref_table.colnames: - ref_table.add_column(Column(np.repeat(self.default_motion_model, len(ref_table)), name='motion_model_used')) + if motion_model_used is None: + ref_table.add_column(Column(np.repeat(self.default_motion_model, len(ref_table)), name='motion_model_used')) + else: + ref_table.add_column(Column(np.repeat(motion_model_used, len(ref_table)), name='motion_model_used')) return ref_table From d777c6191365957b5058d6c974935d1b885e29b0 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Thu, 26 Jun 2025 12:06:05 -0700 Subject: [PATCH 077/254] check in motion model progress --- flystar/align.py | 1 + flystar/match.py | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/flystar/align.py b/flystar/align.py index c6dd39e..f6e0953 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -1295,6 +1295,7 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot col[idx_good] = data_dict[ff] self.ref_table.add_column(col) + pdb.set_trace() print('===============================') print('Done with bootstrap') diff --git a/flystar/match.py b/flystar/match.py index ba43f58..d7c391e 100644 --- a/flystar/match.py +++ b/flystar/match.py @@ -345,7 +345,7 @@ def match(x1, y1, m1, x2, y2, m2, dr_tol, dm_tol=None, verbose=True): # Double check that "min" choice is still within our # detla-mag tolerence. - dm_tmp = np.array([dm.T[dm_min[I]][I] for I in np.lib.index_tricks.ndindex(dm_min.shape)]) + dm_tmp = np.array([dm.T[dm_min[I]][I] for I in np.ndindex(dm_min.shape)]) keep = (dm_min == dr_min) & (dm_tmp < dm_tol) else: From c36b71687d9f9c03e22310442de3a3d9b6183b4d Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Thu, 26 Jun 2025 17:01:32 -0700 Subject: [PATCH 078/254] debug matching issue in calc_bootstrap_errors --- flystar/align.py | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index f6e0953..0d198c2 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -1078,10 +1078,7 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot 'me_boot', 2D column: bootstrap mag uncertainties due to transformation for each epoch If calc_vel_in_bootstrap: - 'x0_err_boot', 1D column: bootstrap uncertainties in x0 for PM fit - 'y0_err_boot', 1D column: bootstrap uncertainties in y0 for PM fit - 'vx_err_boot', 1D column: bootstrap uncertainties in vx for PM fit - 'vy_err_boot', 1D column: bootstrap uncertainties in vy for PM fit + '_err_boot', 1D column: bootstrap uncertainties in for motion model fit For stars that fail boot_epochs_min criteria, np.nan is used """ @@ -1137,6 +1134,8 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot for jj in range(n_epochs): # Extract bootstrap sample of matched reference stars good = np.where(~np.isnan(ref_table['x_orig'][idx_ref][:,jj])) + # TODO: consider confirming we reach some threshold of unique time values here? + # TODO: Like, grab n_pts needed for the default motion model maybe samp_idx = np.random.choice(good[0], len(good[0]), replace=True) # Get reference star positions in particular epoch from ref_list. @@ -1147,7 +1146,7 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot # Then, use these to build reference starlist for the alignment idx_tmp = [] for ff in range(len(samp_idx)): - name_tmp = ref_table['name'][samp_idx[ff]] + name_tmp = ref_table['name'][idx_ref][samp_idx[ff]] foo = np.where(ref_orig['name'] == name_tmp)[0][0] idx_tmp.append(foo) @@ -1192,6 +1191,8 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot self.trans_args[0]['order'], m=starlist_boot['m'], mref=ref_boot['m'], weights=weight, mag_trans=self.mag_trans) + #print(jj) + #pdb.set_trace() # Apply transformation to *all* orig positions in this epoch. Need to make a new # FLYSTAR starlist object with the original positions for this. We don't @@ -1295,7 +1296,7 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot col[idx_good] = data_dict[ff] self.ref_table.add_column(col) - pdb.set_trace() + #pdb.set_trace() print('===============================') print('Done with bootstrap') From e2176e5d3003bf4cd5e387053837a9f6fb3ac6d2 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Tue, 15 Jul 2025 15:19:09 -0700 Subject: [PATCH 079/254] check in debug attempts --- flystar/align.py | 27 ++++++++++++++++++---- flystar/analysis.py | 4 ++++ flystar/plots.py | 54 ++++++++++++++++++++++++++----------------- flystar/transforms.py | 4 ++-- 4 files changed, 61 insertions(+), 28 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 0d198c2..8452649 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -844,6 +844,7 @@ def update_ref_table_aggregates(self, keep_ref_orig, n_boot=0): fit_star_idxs = np.where(self.ref_table['ref_orig'] == False)[0] else: fit_star_idxs = None + #pdb.set_trace() # Figure out whether motion fits are necessary all_fixed = np.all(self.ref_table['motion_model_input']=='Fixed') if all_fixed: @@ -862,11 +863,13 @@ def update_ref_table_aggregates(self, keep_ref_orig, n_boot=0): else: weights_col = 'me' self.ref_table.combine_lists('m', weights_col=weights_col, ismag=True) - + #pdb.set_trace() # Replace the originals if we are supposed to keep them fixed. if keep_ref_orig: for val in vals_orig.keys(): self.ref_table[val][ref_orig_idx] = vals_orig[val] + + #pdb.set_trace() return def get_weights_for_lists(self, ref_list, star_list): @@ -1134,8 +1137,6 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot for jj in range(n_epochs): # Extract bootstrap sample of matched reference stars good = np.where(~np.isnan(ref_table['x_orig'][idx_ref][:,jj])) - # TODO: consider confirming we reach some threshold of unique time values here? - # TODO: Like, grab n_pts needed for the default motion model maybe samp_idx = np.random.choice(good[0], len(good[0]), replace=True) # Get reference star positions in particular epoch from ref_list. @@ -1227,6 +1228,8 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot # for each star, if desired. Draw a full-sample bootstrap over the epochs # for each star, and then run it through the startable fit_velocities machinery if calc_vel_in_bootstrap: + # TODO: consider confirming we reach some threshold of unique time values here? + # TODO: Like, grab n_pts needed for the default motion model maybe boot_idx = np.random.choice(np.arange(0, n_epochs, 1), size=n_epochs) t_boot = t_arr[boot_idx] @@ -1263,6 +1266,7 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot x_err_b = np.std(x_trans_arr, ddof=1, axis=1) y_err_b = np.std(y_trans_arr, ddof=1, axis=1) m_err_b = np.std(m_trans_arr, ddof=1, axis=1) + #pdb.set_trace() motion_data_err = {} if calc_vel_in_bootstrap: @@ -1285,6 +1289,19 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot col[idx_good] = data_dict[ff] self.ref_table.add_column(col) + + # Calculate chi^2 with bootstrap positional errors + x_pred, y_pred, _, _ = self.ref_table.get_star_positions_at_time(t_arr, allow_alt_models=True) + xe_comb = np.hypot(self.ref_table['xe'], self.ref_table['xe_boot']) + ye_comb = np.hypot(self.ref_table['ye'], self.ref_table['ye_boot']) + data_dict['chi2_x_boot'] = np.nansum((self.ref_table['x']-x_pred)**2/(xe_comb)**2,axis=1) + data_dict['chi2_y_boot'] = np.nansum((self.ref_table['y']-y_pred)**2/(ye_comb)**2,axis=1) + for ff in ['chi2_x_boot', 'chi2_y_boot']: + col = Column(np.ones(len(self.ref_table)), name=ff) + col.fill(np.nan) + + col[idx_good] = data_dict[ff][idx_good] + self.ref_table.add_column(col) # Now handle the velocities, if they were calculated if calc_vel_in_bootstrap: @@ -1297,7 +1314,7 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot col[idx_good] = data_dict[ff] self.ref_table.add_column(col) #pdb.set_trace() - + print('===============================') print('Done with bootstrap') print('===============================') @@ -1605,7 +1622,7 @@ def fit(self): self.ref_table.detections() ### Drop all stars that have 0 detections. - idx = np.where((self.ref_table['n_detect'] == 0) & (self.ref_table['ref_orig'] == False))[0] + idx = np.where((self.ref_table['n_detect'] == 0))[0] # & (self.ref_table['ref_orig'] == False))[0] if self.verbose > 0: print(' *** Getting rid of {0:d} out of {1:d} junk sources'.format(len(idx), len(self.ref_table))) self.ref_table.remove_rows(idx) diff --git a/flystar/analysis.py b/flystar/analysis.py index 94d14b6..50fe06a 100644 --- a/flystar/analysis.py +++ b/flystar/analysis.py @@ -180,12 +180,16 @@ def prepare_gaia_for_flystar(gaia, ra, dec, targets_dict=None, match_dr_max=0.2, gaia_new['motion_model_input'] = 'Parallax' gaia_new['motion_model_used'] = 'Parallax' gaia_new['motion_model_used'][idx] = 'Linear' + #gaia_new['dof'] = 3 + #gaia_new['dof'][idx] = 2 elif default_motion_model=='Linear': gaia_new['motion_model_input'] = 'Linear' gaia_new['motion_model_used'] = 'Linear' + #gaia_new['dof'] = 2 elif default_motion_model=='Fixed': gaia_new['motion_model_input'] = 'Fixed' gaia_new['motion_model_used'] = 'Fixed' + #gaia_new['dof'] = 1 else: print("Invalid motion model",default_motion_model,"- none assigned") diff --git a/flystar/plots.py b/flystar/plots.py index 95b1eee..b4832b4 100755 --- a/flystar/plots.py +++ b/flystar/plots.py @@ -2179,8 +2179,8 @@ def plot_chi2_dist(tab, Ndetect, xlim=40, n_bins=50, boot_err=False): x = tab['x'][ii, fnd] y = tab['y'][ii, fnd] if boot_err: - xerr = tab['xe_boot'][ii, fnd] - yerr = tab['ye_boot'][ii, fnd] + xerr = np.hypot(tab['xe_boot'][ii, fnd], tab['xe'][ii, fnd]) + yerr = np.hypot(tab['ye_boot'][ii, fnd], tab['ye'][ii, fnd]) else: xerr = tab['xe'][ii, fnd] yerr = tab['ye'][ii, fnd] @@ -2256,8 +2256,8 @@ def plot_chi2_dist_per_filter(tab, Ndetect, xlim=40, n_bins=50, filter=None, boo x = tab['x'][ii, fnd] y = tab['y'][ii, fnd] if boot_err: - xerr = tab['xe_boot'][ii, fnd] - yerr = tab['ye_boot'][ii, fnd] + xerr = np.hypot(tab['xe_boot'][ii, fnd], tab['xe'][ii, fnd]) + yerr = np.hypot(tab['ye_boot'][ii, fnd], tab['ye'][ii, fnd]) else: xerr = tab['xe'][ii, fnd] yerr = tab['ye'][ii, fnd] @@ -2274,6 +2274,7 @@ def plot_chi2_dist_per_filter(tab, Ndetect, xlim=40, n_bins=50, filter=None, boo chi2_y = np.sum(sigY**2) chi2_x_list.append(chi2_x) chi2_y_list.append(chi2_y) + #pdb.set_trace() x = np.array(chi2_x_list) y = np.array(chi2_y_list) @@ -2281,9 +2282,10 @@ def plot_chi2_dist_per_filter(tab, Ndetect, xlim=40, n_bins=50, filter=None, boo idx = np.where(fnd == Ndetect)[0] # Fitting position and velocity... so subtract 2 to get Ndof - Ndof = Ndetect - tab['dof'][i_all_detected] + Ndof = Ndetect - 1 #tab['dof'][i_all_detected] chi2_xaxis = np.linspace(0, xlim, xlim*3) chi2_bins = np.linspace(0, xlim, n_bins) + #pdb.set_trace() plt.figure(figsize=(6,4)) plt.clf() @@ -2345,12 +2347,12 @@ def plot_chi2_dist_per_epoch(tab, Ndetect, mlim=[14,21], ylim = [-1, 1], target_ y = tab['y'][ii, fnd] m = tab['m'][ii, fnd] - if boot_err: - xerr = tab['xe_boot'][ii, fnd] - yerr = tab['ye_boot'][ii, fnd] - else: - xerr = tab['xe'][ii, fnd] - yerr = tab['ye'][ii, fnd] + if boot_err: + xerr = np.hypot(tab['xe_boot'][ii, fnd], tab['xe'][ii, fnd]) + yerr = np.hypot(tab['ye_boot'][ii, fnd], tab['ye'][ii, fnd]) + else: + xerr = tab['xe'][ii, fnd] + yerr = tab['ye'][ii, fnd] fitLineX = xt_mod_all[ii, fnd] fitLineY = yt_mod_all[ii, fnd] @@ -2550,7 +2552,7 @@ def plot_chi2_dist_mag(tab, Ndetect, xlim=40, n_bins=30, boot_err=False): m = tab['m'][ii, fnd] if boot_err: - merr = tab['me_boot'][ii, fnd] + merr = np.hypot(tab['me_boot'][ii, fnd], tab['me'][ii, fnd]) else: merr = tab['me'][ii, fnd] m0 = tab['m0'][ii] @@ -2643,7 +2645,7 @@ def plot_chi2_dist_mag_per_filter(tab, Ndetect, mlim=40, n_bins=30, xlim=40, fil return def plot_stars(tab, star_names, NcolMax=2, epoch_array = None, figsize=(15,25), color_time=False, - position_angle=None, RA=None, Dec=None, observer_location='earth'): + position_angle=None, RA=None, Dec=None, observer_location='earth', boot_err=False): """ Plot a set of stars positions, flux and residuals over time. @@ -2698,9 +2700,14 @@ def rs(x): y = tab['y'][ii, fnd] m = tab['m'][ii, fnd] - xerr = tab['xe'][ii, fnd] - yerr = tab['ye'][ii, fnd] - merr = tab['me'][ii, fnd] + if boot_err: + xerr = np.hypot(tab['xe'][ii, fnd], tab['xe_boot'][ii, fnd]) + yerr = np.hypot(tab['ye'][ii, fnd], tab['ye_boot'][ii, fnd]) + merr = np.hypot(tab['me'][ii, fnd], tab['me_boot'][ii, fnd]) + else: + xerr = tab['xe'][ii, fnd] + yerr = tab['ye'][ii, fnd] + merr = tab['me'][ii, fnd] dt = tab['t'][ii, fnd] - tab['t0'][ii] @@ -3057,7 +3064,7 @@ def rs(x): return def plot_stars_nfilt(tab, star_names, NcolMax=2, epoch_array_list = None, color_list = None, - figsize=(15,25), color_time=False, resTicRng=None, save_name=None): + figsize=(15,25), color_time=False, resTicRng=None, save_name=None, boot_err=False): """ Plot a set of stars positions, flux and residuals over time. @@ -3114,9 +3121,14 @@ def rs(x): y = tab['y'][ii, fnd] m = tab['m'][ii, fnd] - xerr = tab['xe'][ii, fnd] - yerr = tab['ye'][ii, fnd] - merr = tab['me'][ii, fnd] + if boot_err: + xerr = np.hypot(tab['xe'][ii, fnd], tab['xe_boot'][ii, fnd]) + yerr = np.hypot(tab['ye'][ii, fnd], tab['ye_boot'][ii, fnd]) + merr = np.hypot(tab['me'][ii, fnd], tab['me_boot'][ii, fnd]) + else: + xerr = tab['xe'][ii, fnd] + yerr = tab['ye'][ii, fnd] + merr = tab['me'][ii, fnd] fitLineX = xt_mod_all[ii, fnd] fitLineY = yt_mod_all[ii, fnd] @@ -3503,7 +3515,7 @@ def plot_plxs(star_tab, target_idx=0): ax[0].axhline(0, c='gray') ax[0].set_ylabel('Plx (mas)') ax[0].set_xlabel('Mag') - ax[1].hist(star_tab['pi']/star_tab['pi_err'], bins=range(-20,20)) + ax[1].hist(star_tab['pi']/star_tab['pi_err'], bins=range(-10,10)) ax[1].set_ylabel('N stars') ax[1].set_xlabel('Plx/Plx_err') plt.tight_layout() diff --git a/flystar/transforms.py b/flystar/transforms.py index 8264aa6..6cc865a 100755 --- a/flystar/transforms.py +++ b/flystar/transforms.py @@ -307,7 +307,7 @@ def __init__(self, order, px, py, pxerr=None, pyerr=None, mag_offset=0.0): px_dict = PolyTransform.make_param_dict(px, self.poly_order, isY=False) py_dict = PolyTransform.make_param_dict(py, self.poly_order, isY=True) - fixed_params = {'c0_0': False, 'c1_0': True, 'c0_1': True} + fixed_params = {'c0_0': False, 'c1_0': True, 'c0_1': True} #, 'c1_1':True} self.px = models.Polynomial2D(self.poly_order, **px_dict, fixed=fixed_params) self.py = models.Polynomial2D(self.poly_order, **py_dict, fixed=fixed_params) else: @@ -599,7 +599,7 @@ def derive_transform(cls, x, y, xref, yref, order, m=None, mref=None, init_gx = PolyTransform.make_param_dict(init_gx, poly_order, isY=False) init_gy = PolyTransform.make_param_dict(init_gy, poly_order, isY=True) - fixed_params = {'c0_0': False, 'c1_0': True, 'c0_1': True} + fixed_params = {'c0_0': False, 'c1_0': True, 'c0_1': True} #, 'c1_1':True} p_init_x = models.Polynomial2D(poly_order, **init_gx, fixed=fixed_params) p_init_y = models.Polynomial2D(poly_order, **init_gy, fixed=fixed_params) else: From a40ad280c9524bf85eceea4378bea60670845066 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Thu, 17 Jul 2025 13:49:00 -0700 Subject: [PATCH 080/254] dof handling --- flystar/plots.py | 21 +++++++++++++-------- 1 file changed, 13 insertions(+), 8 deletions(-) diff --git a/flystar/plots.py b/flystar/plots.py index b4832b4..83d759e 100755 --- a/flystar/plots.py +++ b/flystar/plots.py @@ -2205,6 +2205,7 @@ def plot_chi2_dist(tab, Ndetect, xlim=40, n_bins=50, boot_err=False): idx = np.where(fnd == Ndetect)[0] # Fitting position and velocity... so subtract 2 to get Ndof Ndof = Ndetect - tab['dof'][i_all_detected] + print(i_all_detected) chi2_xaxis = np.linspace(0, xlim, xlim*3) chi2_bins = np.linspace(0, xlim, n_bins) @@ -2218,9 +2219,9 @@ def plot_chi2_dist(tab, Ndetect, xlim=40, n_bins=50, boot_err=False): plt.xlim(0, xlim) plt.legend() - chi2red_x = x / (fnd - 2) - chi2red_y = y / (fnd - 2) - chi2red_t = (x + y) / (2.0 * (fnd - 2)) + chi2red_x = x / Ndof + chi2red_y = y / Ndof + chi2red_t = (x + y) / (2.0 * Ndof) print('Mean reduced chi^2: (Ndetect = {0:d} of {1:d})'.format(len(idx), len(tab))) fmt = ' {0:s} = {1:.1f} for N_detect and {2:.1f} for all' @@ -2274,17 +2275,21 @@ def plot_chi2_dist_per_filter(tab, Ndetect, xlim=40, n_bins=50, filter=None, boo chi2_y = np.sum(sigY**2) chi2_x_list.append(chi2_x) chi2_y_list.append(chi2_y) - #pdb.set_trace() + #print(fitLineX, x, xerr) + #pdb.set_trace() x = np.array(chi2_x_list) y = np.array(chi2_y_list) fnd = np.array(fnd_list) + idx = np.where(fnd == Ndetect)[0] # Fitting position and velocity... so subtract 2 to get Ndof - Ndof = Ndetect - 1 #tab['dof'][i_all_detected] + Ndof = Ndetect - tab['dof'][i_all_detected] + print(i_all_detected) chi2_xaxis = np.linspace(0, xlim, xlim*3) chi2_bins = np.linspace(0, xlim, n_bins) + print(x[idx]) #pdb.set_trace() plt.figure(figsize=(6,4)) @@ -2303,9 +2308,9 @@ def plot_chi2_dist_per_filter(tab, Ndetect, xlim=40, n_bins=50, filter=None, boo plt.savefig(str(filter)+'_chi2_dist.png', dpi=400) - chi2red_x = x / (fnd - 2) - chi2red_y = y / (fnd - 2) - chi2red_t = (x + y) / (2.0 * (fnd - 2)) + chi2red_x = x / Ndof + chi2red_y = y / Ndof + chi2red_t = (x + y) / (2.0 * Ndof) print('Mean reduced chi^2: (Ndetect = {0:d} of {1:d})'.format(len(idx), len(tab))) fmt = ' {0:s} = {1:.1f} for N_detect and {2:.1f} for all' From 85f78c42d1576e8026c2961c280c4728f61f58fc Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Mon, 28 Jul 2025 10:35:35 -0700 Subject: [PATCH 081/254] implementation of motion_model_dict to store motion model metadata --- flystar/align.py | 99 +++++------- flystar/motion_model.py | 293 +++++++++++++----------------------- flystar/startables.py | 105 +++++-------- flystar/tests/test_align.py | 16 +- 4 files changed, 186 insertions(+), 327 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 8452649..14926d9 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -22,12 +22,10 @@ def __init__(self, list_of_starlists, ref_index=0, iters=2, init_order=1, mag_trans=True, mag_lim=None, weights=None, trans_input=None, trans_class=transforms.PolyTransform, - # TODO: consider deleting use_vel fully, for now I'm putting in - # a fallback so people can still use old code for now - use_vel=None, default_motion_model='Fixed', calc_trans_inverse=False, init_guess_mode='miracle', iter_callback=None, - position_angle=None, RA=None, Dec=None, observer_location='earth', + default_motion_model='Fixed', + motion_model_dict = {}, verbose=True): """ Make a mosaic object by passing in a list of starlists and then running fit(). @@ -107,13 +105,6 @@ def = None. If not None, then this should contain an array or list of transform then the transformation argument (i.e. order) will be changed for every iteration in iters. - # TODO: update when decided - use_vel : boolean - If velocities are present in the reference list and use_vel == True, then during - each iteration of the alignment, the reference list will be propogated in time - using the velocity information. So all transformations will be derived w.r.t. - the propogated positions. See also update_vel. - calc_trans_inverse: boolean If true, then calculate the inverse transformation (from reference to starlist) in addition to the normal transformation (from starlist to reference). The inverse @@ -131,14 +122,11 @@ def = None. If not None, then this should contain an array or list of transform A function to call (that accepts a StarTable object and an iteration number) at the end of every iteration. This can be used for plotting or printing state. - position_angle, RA, Dec : float (degrees) - [Only required if using Parallax motion model] - position_angle: clockwise angular offset of image y-axis from North - RA, Dec: J2000.0 coordinates of your image center - These values are assumed to be the same for all starlists + default_motion_model : string + Name of motion model to use for new or unassigned stars - observer_location: str - Only used for parallax motion models, default is 'earth' + motion_model_dict : None or dict + Dict of motion model name keys (strings) and corresponding MotionModel object values verbose : int (0 to 9, inclusive) Controls the verbosity of print statements. (0 least, 9 most verbose). @@ -189,19 +177,8 @@ def = None. If not None, then this should contain an array or list of transform self.trans_input = trans_input self.trans_class = trans_class self.calc_trans_inverse = calc_trans_inverse - self.position_angle=position_angle - self.RA=RA - self.Dec=Dec - self.observer_location=observer_location - - # TODO: consider whether we want this fallback - if use_vel is None: - self.default_motion_model = default_motion_model - else: - if use_vel: - self.default_motion_model = 'Linear' - else: - self.default_motion_model = 'Fixed' + self.motion_model_dict = motion_model_dict + self.default_motion_model = default_motion_model self.init_guess_mode = init_guess_mode self.iter_callback = iter_callback self.verbose = verbose @@ -233,6 +210,10 @@ def = None. If not None, then this should contain an array or list of transform # is passed in, replicate for all star lists, all loop iterations. ########## self.setup_trans_info() + + # Make sure the motion models are ready + self.motion_model_dict = motion_model.validate_motion_model_dict(self.motion_model_dict, + StarTable(), self.default_motion_model) return @@ -402,7 +383,7 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar # Only use "use_in_trans" reference stars, even for initial guessing. keepers = np.where(ref_list['use_in_trans'] == True)[0] - trans = trans_initial_guess(ref_list[keepers], star_list_orig_trim, self.trans_args[0], + trans = trans_initial_guess(ref_list[keepers], star_list_orig_trim, self.trans_args[0], self.motion_model_dict, mode=self.init_guess_mode, order=self.init_order, verbose=self.verbose, @@ -599,7 +580,7 @@ def setup_ref_table_from_starlist(self, star_list, motion_model_used=None): col_arrays[new_col_name] = new_col_data # Use the columns from the ref list to make the ref_table. - ref_table = StarTable(**col_arrays, position_angle=self.position_angle, RA=self.RA, Dec=self.Dec, observer_location=self.observer_location) + ref_table = StarTable(**col_arrays) # Make new columns to hold original values. These will be copies # of the old columns and will only include x, y, m, xe, ye, me. @@ -855,7 +836,8 @@ def update_ref_table_aggregates(self, keep_ref_orig, n_boot=0): else: # Combine positions with a velocity fit. self.ref_table.fit_velocities(bootstrap=n_boot, verbose=self.verbose, - default_motion_model=self.default_motion_model, select_stars=fit_star_idxs) + default_motion_model=self.default_motion_model, select_stars=fit_star_idxs, + motion_model_dict=self.motion_model_dict) # Combine (transformed) magnitudes if 'me' in self.ref_table.colnames: @@ -940,7 +922,7 @@ def match_lists(self, dr_tol, dm_tol): else: star_list_T.transform_xy(self.trans_list[ii]) - xref, yref = get_pos_at_time(star_list_T['t'][0], self.ref_table) + xref, yref = get_pos_at_time(star_list_T['t'][0], self.ref_table, self.motion_model_dict) mref = self.ref_table['m0'] idx_lis, idx_ref, dr, dm = match.match(star_list_T['x'], star_list_T['y'], star_list_T['m'], @@ -974,7 +956,7 @@ def get_ref_list_from_table(self, epoch): name = self.ref_table['name'] if ('motion_model_used' in self.ref_table.colnames): - x,y,xe,ye = self.ref_table.get_star_positions_at_time(epoch, allow_alt_models=True) + x,y,xe,ye = self.ref_table.get_star_positions_at_time(epoch, self.motion_model_dict, allow_alt_models=True) else: # No velocities... just used average positions. x = self.ref_table['x0'] @@ -1240,14 +1222,11 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot xe=xe_trans_arr[:,ii,boot_idx], ye=ye_trans_arr[:,ii,boot_idx], me=me_trans_arr[:,ii,boot_idx], - t=np.tile(t_boot, (len(ref_table),1)), - RA=self.RA, Dec=self.Dec, - position_angle=self.position_angle, - observer_location=self.observer_location) + t=np.tile(t_boot, (len(ref_table),1))) # Now, do proper motion calculation, making sure to fix t0 to the # orig value (so we can get a reasonable error on x0, y0) - star_table.fit_velocities(fixed_t0=t0_arr, default_motion_model=self.default_motion_model) + star_table.fit_velocities(fixed_t0=t0_arr, default_motion_model=self.default_motion_model, motion_model_dict=self.motion_model_dict) # Save proper motion fit results to output arrays for col in motion_col_list: @@ -1291,7 +1270,7 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot self.ref_table.add_column(col) # Calculate chi^2 with bootstrap positional errors - x_pred, y_pred, _, _ = self.ref_table.get_star_positions_at_time(t_arr, allow_alt_models=True) + x_pred, y_pred, _, _ = self.ref_table.get_star_positions_at_time(t_arr, self.motion_model_dict, allow_alt_models=True) xe_comb = np.hypot(self.ref_table['xe'], self.ref_table['xe_boot']) ye_comb = np.hypot(self.ref_table['ye'], self.ref_table['ye_boot']) data_dict['chi2_x_boot'] = np.nansum((self.ref_table['x']-x_pred)**2/(xe_comb)**2,axis=1) @@ -1334,12 +1313,11 @@ def __init__(self, ref_list, list_of_starlists, iters=2, trans_class=transforms.PolyTransform, calc_trans_inverse=False, use_ref_new=False, - use_vel=None, default_motion_model='Fixed', update_ref_orig=False, init_guess_mode='miracle', iter_callback=None, - position_angle=None, RA=None, Dec=None, - observer_location='earth', + default_motion_model='Fixed', + motion_model_dict={}, verbose=True): """ @@ -1461,14 +1439,11 @@ def = None. If not None, then this should contain an array or list of transform A function to call (that accepts a StarTable object and an iteration number) at the end of every iteration. This can be used for plotting or printing state. - position_angle, RA, Dec : float (degrees) - Only required if using Parallax motion model - position_angle: clockwise angular offset of image y-axis from North - RA, Dec: J2000.0 coordinates of your image center - These values are assumed to be the same for all starlists + default_motion_model : string + Name of motion model to use for new or unassigned stars - observer_location: str - Only used for parallax motion models, default is 'earth' + motion_model_dict : None or dict + Dict of motion model name keys (strings) and corresponding MotionModel object values Example ---------- @@ -1507,12 +1482,11 @@ def = None. If not None, then this should contain an array or list of transform init_order=init_order, mag_trans=mag_trans, mag_lim=mag_lim, weights=weights, trans_input=trans_input, trans_class=trans_class, - calc_trans_inverse=calc_trans_inverse, use_vel=use_vel, + calc_trans_inverse=calc_trans_inverse, default_motion_model = default_motion_model, init_guess_mode=init_guess_mode, iter_callback=iter_callback, - position_angle=position_angle, RA=RA, Dec=Dec, - observer_location=observer_location, + motion_model_dict=motion_model_dict, verbose=verbose) self.ref_list = copy.deepcopy(ref_list) @@ -1533,6 +1507,10 @@ def = None. If not None, then this should contain an array or list of transform self.ref_list['me'] = self.ref_list['m0_err'] if ('t' not in self.ref_list.colnames) and ('t0' in self.ref_list.colnames): self.ref_list['t'] = self.ref_list['t0'] + + # Make sure the motion models are ready + self.motion_model_dict = motion_model.validate_motion_model_dict(self.motion_model_dict, + self.ref_list, self.default_motion_model) return @@ -1574,7 +1552,6 @@ def fit(self): logger(_log, ' trans_class = ' + str(self.trans_class), self.verbose) logger(_log, ' calc_trans_inverse = ' + str(self.calc_trans_inverse), self.verbose) logger(_log, ' use_ref_new = ' + str(self.use_ref_new), self.verbose) - #logger(_log, ' use_vel = ' + str(self.use_vel), self.verbose) logger(_log, ' default_motion_model = ' + str(self.default_motion_model), self.verbose) logger(_log, ' update_ref_orig = ' + str(self.update_ref_orig), self.verbose) logger(_log, ' init_guess_mode = ' + str(self.init_guess_mode), self.verbose) @@ -1717,7 +1694,7 @@ def setup_ref_table_from_starlist(star_list): col_arrays[new_col_name] = new_col_data # Use the columns from the ref list to make the ref_table. - ref_table = StarTable(**col_arrays, position_angle=self.position_angle, RA=self.RA, Dec=self.Dec) + ref_table = StarTable(**col_arrays) # Make new columns to hold original values. These will be copies # of the old columns and will only include x, y, m, xe, ye, me. @@ -2775,7 +2752,7 @@ def check_trans_input(list_of_starlists, trans_input, mag_trans): return -def trans_initial_guess(ref_list, star_list, trans_args, mode='miracle', +def trans_initial_guess(ref_list, star_list, trans_args, motion_model_dict, mode='miracle', ignore_contains='star', verbose=True, n_req_match=3, mag_trans=True, order=1): """ @@ -2813,7 +2790,7 @@ def trans_initial_guess(ref_list, star_list, trans_args, mode='miracle', # If there are velocities in the reference list, use them. # We assume velocities are in the same units as the positions. - xref, yref = get_pos_at_time(star_list['t'][0], ref_list) + xref, yref = get_pos_at_time(star_list['t'][0], ref_list, motion_model_dict) if 'm' in ref_list.colnames: mref = ref_list['m'] else: @@ -3052,7 +3029,7 @@ def get_weighting_scheme(weights, ref_list, star_list): return weight # TODO: This is sometimes run on a startable, not a starlist, at least as currently used -def get_pos_at_time(t, starlist): +def get_pos_at_time(t, starlist, motion_model_dict): """ Take a starlist, check to see if it has motion/velocity columns. If it does, then propogate the positions forward in time @@ -3068,7 +3045,7 @@ def get_pos_at_time(t, starlist): """ # Check for motion model if 'motion_model_used' in starlist.colnames: - x,y,xe,ye = starlist.get_star_positions_at_time(t, allow_alt_models=True) + x,y,xe,ye = starlist.get_star_positions_at_time(t, motion_model_dict, allow_alt_models=True) # If no motion model, check for velocities elif ('vx' in starlist.colnames) and ('vy' in starlist.colnames): x = starlist['x0'] + starlist['vx']*(t-starlist['t0']) diff --git a/flystar/motion_model.py b/flystar/motion_model.py index 70abe5f..5aca90a 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -6,13 +6,11 @@ from scipy.optimize import curve_fit import warnings -plx_vector_cached = None - class MotionModel(ABC): # Number of data points required to fit model n_pts_req = 0 # Degrees of freedom for model - dof = 0 + n_params = 0 # Fit paramters: Shared fit parameters fitter_param_names = [] @@ -20,7 +18,6 @@ class MotionModel(ABC): # Fixed parameters: These are parameters that are required for the model, but are not # fit quantities. For example, RA and Dec in a parallax model. fixed_param_names = [] - # TODO: for values that are for the full data set, not per star - are we happy with this method? fixed_meta_data = [] # Non-fit paramters: Custom paramters that will not be fit. @@ -36,19 +33,15 @@ def __init__(self, *args, **kwargs): setattr(self, param, np.array([param_var]))''' return - def get_pos_at_time(self, t): + def get_pos_at_time(self, params, t): #return x, y pass - def get_pos_err_at_time(self, t): - #return x_err, y_err - pass - def get_batch_pos_at_time(self, t): #return x, y, x_err, y_err pass - def run_fit(self, t, x, y, xe, ye, update=True, weighting='var'): + def run_fit(self, t, x, y, xe, ye, t0, weighting='var'): # Run a single fit (used both for overall fit + bootstrap iterations) pass @@ -70,15 +63,13 @@ def scale_errors(self, errs, weighting='var'): warnings.warn("Invalid weighting, using default weighting scheme var.", UserWarning) return errs - def fit_motion_model(self, t, x, y, xe, ye, update=True, bootstrap=0, weighting='var'): + def fit_motion_model(self, t, x, y, xe, ye, t0, bootstrap=0, weighting='var'): """ Fit the input positions on the sky and errors to determine new parameters for this motion model (MM). - Current MM parameters are used as the initial guess. - Best-fit parameters will be returned along with uncertainties - and updated if update=True. + Best-fit parameters will be returned along with uncertainties. """ - params, param_errs = self.run_fit(t, x, y, xe, ye, weighting=weighting, update=update) + params, param_errs = self.run_fit(t, x, y, xe, ye, t0, weighting=weighting) if bootstrap>0 and len(x)>(self.n_pts_req): edx = np.arange(len(x), dtype=int) @@ -87,31 +78,28 @@ def fit_motion_model(self, t, x, y, xe, ye, update=True, bootstrap=0, weighting= bdx = np.random.choice(edx, len(x)) while len(np.unique(bdx))0: + raise ValueError(f"Cannot use {mm} motion model without required metadata. Please initialize with required metadata and provide in motion_model_dict.") + else: + motion_model_dict[mm] = mm_obj() + warnings.warn(f"Using default model/fitter for {mm}.", UserWarning) + + return motion_model_dict + """ Get all the motion model parameters for a given motion_model_name. Optionally, include fixed and error parameters (included by default). @@ -559,6 +473,3 @@ def get_all_motion_model_param_names(with_errors=True, with_fixed=True): return np.unique(list_of_parameters).tolist() - - - diff --git a/flystar/startables.py b/flystar/startables.py index 66447ff..618dba3 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -61,18 +61,6 @@ class StarTable(Table): ref_list : int Specify which list is the reference list (if any). - - position_angle: float (degree) - required for parallax motion model - clockwise angular offset between image y-axis and North - - RA, Dec: float (degrees) - required for parallax motion model - image position coordinates - - observer_location: string - only used by parallax motion model - default is 'earth' Examples -------------------------- @@ -84,7 +72,7 @@ class StarTable(Table): print(t['name'][0:10]) # print the first 10 star names print(t['x'][0:10, 0]) # print x from the first epoch/list/column for the first 10 stars """ - def __init__(self, *args, ref_list=0, position_angle=None, RA=None, Dec=None, observer_location='earth', **kwargs): + def __init__(self, *args, ref_list=0, **kwargs): """ """ @@ -162,8 +150,7 @@ def __init__(self, *args, ref_list=0, position_angle=None, RA=None, Dec=None, ob Table.__init__(self, (kwargs['name'], kwargs['x'], kwargs['y'], kwargs['m']), names=('name', 'x', 'y', 'm')) self['name'] = self['name'].astype('U20') - self.meta = {'n_stars': n_stars, 'n_lists': n_lists, 'ref_list': ref_list, - 'position_angle': position_angle, 'RA': RA, 'Dec':Dec, 'observer_location':observer_location} + self.meta = {'n_stars': n_stars, 'n_lists': n_lists, 'ref_list': ref_list} for meta_arg in meta_tab: if meta_arg in kwargs: @@ -549,19 +536,15 @@ def detections(self): return - def fit_velocities(self, weighting='var', use_scipy=True, absolute_sigma=True, bootstrap=0, fixed_t0=False, verbose=False, + def fit_velocities(self, weighting='var', bootstrap=0, fixed_t0=False, verbose=False, mask_val=None, mask_lists=False, show_progress=True, default_motion_model='Linear', - reassign_motion_model=False, select_stars=None): + reassign_motion_model=False, select_stars=None, motion_model_dict={}): """Fit velocities for all stars in the table and add to the columns 'vx', 'vxe', 'vy', 'vye', 'x0', 'x0e', 'y0', 'y0e'. Parameters ---------- weighting : str, optional Weight by variance 'var' or standard deviation 'std', by default 'var' - use_scipy : bool, optional - Use scipy.curve_fit (recommended for large number of epochs, but may return inf or nan) or analytic fitting from flystar.fit_velocity.linear_fit (recommended for a few epochs), by default True - absolute_sigma : bool, optional - Absolute sigma or not. See https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.curve_fit.html for details, by default True bootstrap : int, optional Calculate uncertain using bootstraping or not, by default 0 fixed_t0 : bool or array-like, optional @@ -600,6 +583,8 @@ def fit_velocities(self, weighting='var', use_scipy=True, absolute_sigma=True, b if ('motion_model_input' not in self.colnames) or reassign_motion_model: self['motion_model_input'] = default_motion_model self['motion_model_used'] = self['motion_model_input'] + + motion_model_dict = motion_model.validate_motion_model_dict(motion_model_dict, self, default_motion_model) # # Fill table with all possible motion model parameter names as new @@ -608,7 +593,7 @@ def fit_velocities(self, weighting='var', use_scipy=True, absolute_sigma=True, b all_motion_models = np.unique(self['motion_model_input'].tolist() + ['Fixed']+[default_motion_model]).tolist() new_col_list = motion_model.get_list_motion_model_param_names(all_motion_models, with_errors=True) # Append goodness of fit metrics and t0. - new_col_list += ['chi2_x', 'chi2_y', 'dof'] + new_col_list += ['chi2_x', 'chi2_y', 'n_params'] if 't0' not in new_col_list: new_col_list.append('t0') @@ -643,7 +628,7 @@ def fit_velocities(self, weighting='var', use_scipy=True, absolute_sigma=True, b self['y0_err'] = self['ye'][:,0] self['n_fit'] = 1 - self['dof'] = 1 + self['n_params'] = 1 return @@ -655,14 +640,12 @@ def fit_velocities(self, weighting='var', use_scipy=True, absolute_sigma=True, b # This is slow; but robust. if show_progress: for ss in tqdm(fit_star_idxs): - self.fit_velocity_for_star(ss, weighting=weighting, use_scipy=use_scipy, - absolute_sigma=absolute_sigma, bootstrap=bootstrap, + self.fit_velocity_for_star(ss, motion_model_dict, weighting=weighting, bootstrap=bootstrap, fixed_t0=fixed_t0, default_motion_model=default_motion_model, mask_val=mask_val, mask_lists=mask_lists) else: for ss in range(fit_star_idxs): - self.fit_velocity_for_star(ss, weighting=weighting, use_scipy=use_scipy, - absolute_sigma=absolute_sigma, bootstrap=bootstrap, + self.fit_velocity_for_star(ss, motion_model_dict, weighting=weighting, bootstrap=bootstrap, fixed_t0=fixed_t0, default_motion_model=default_motion_model, mask_val=mask_val, mask_lists=mask_lists) if verbose: @@ -671,8 +654,7 @@ def fit_velocities(self, weighting='var', use_scipy=True, absolute_sigma=True, b return - def fit_velocity_for_star(self, ss, weighting='var', use_scipy=True, - absolute_sigma=True, bootstrap=False, fixed_t0=False, + def fit_velocity_for_star(self, ss, motion_model_dict, weighting='var', bootstrap=False, fixed_t0=False, default_motion_model='Linear', mask_val=None, mask_lists=False): # TODO: "weighting" is not used @@ -790,7 +772,7 @@ def fit_velocity_for_star(self, ss, weighting='var', use_scipy=True, if N_good == 0: #self['motion_model_used'][ss] = 'None' self['n_fit'][ss] = N_good - self['dof'][ss] = 0 + self['n_params'][ss] = 0 return # Everything below has N_good >= 1 @@ -819,12 +801,12 @@ def fit_velocity_for_star(self, ss, weighting='var', use_scipy=True, motion_model_use = self['motion_model_input'][ss] # Go to default model if not enough points for assigned but enough for default # TODO: think about whether we want other fallbacks besides the singular default and Fixed - if (N_good < getattr(motion_model, self['motion_model_input'][ss]).n_pts_req) and \ - (N_good >= getattr(motion_model, default_motion_model).n_pts_req): + if (N_good < motion_model_dict[motion_model_use].n_pts_req) and \ + (N_good >= motion_model_dict[default_motion_model].n_pts_req): motion_model_use = default_motion_model # If not enough points for either, go to a fixed model - elif (N_good < getattr(motion_model, self['motion_model_input'][ss]).n_pts_req) and \ - (N_good < getattr(motion_model, default_motion_model).n_pts_req): + elif (N_good < motion_model_dict[motion_model_use].n_pts_req) and \ + (N_good < motion_model_dict[default_motion_model].n_pts_req): motion_model_use = 'Fixed' # If the points do not cover multiple times, go to a fixed model if (t == t[0]).all(): @@ -832,40 +814,31 @@ def fit_velocity_for_star(self, ss, weighting='var', use_scipy=True, self['motion_model_used'][ss] = motion_model_use - # Get the motion model object. - modClass = getattr(motion_model, motion_model_use) - - # Load up any prior information on parameters for this model. - param_dict = {} - for par in modClass.fitter_param_names+modClass.fixed_param_names: - if ~np.isnan(self[par][ss]): - param_dict[par] = self[par][ss] - - # TODO: this doesn't match how we actually handle ra,dec,pa - need to adjust - # Load fixed parameters, if needed. - '''for par in modClass.fixed_param_names: - if par not in self.colnames: - msg = f'fit_velocity_for_star: ' - msg += f'Missing fixed_params column {par} needed for motion model {motion_model_use}.' - - raise RuntimeException(msg) - - if self[par][ss] != np.nan: - param_dict[par] = self[par][ss]''' +# # Get the motion model object. +# modClass = motion_model_dict[motion_model_use] +# +# # Load up any prior information on parameters for this model. +# param_dict = {} +# for par in modClass.fitter_param_names+modClass.fixed_param_names: +# if ~np.isnan(self[par][ss]): +# param_dict[par] = self[par][ss] # Model object - mod = modClass(**param_dict, PA=self.meta['position_angle'], RA=self.meta['RA'], Dec=self.meta['Dec'], obs=self.meta['observer_location']) + mod = motion_model_dict[motion_model_use] + print(mod) + fixed_params = [self[par][ss] for par in mod.fixed_param_names] # Fit for the best parameters - params, param_errs = mod.fit_motion_model(t, x, y, xe, ye, bootstrap=bootstrap, update=True, weighting=weighting) - chi2_x,chi2_y = mod.get_chi2(t,x,y,xe,ye) + params, param_errs = mod.fit_motion_model(t, x, y, xe, ye, t0, bootstrap=bootstrap, + weighting=weighting) + chi2_x,chi2_y = mod.get_chi2(params,fixed_params, t,x,y,xe,ye) self['chi2_x'][ss]=chi2_x self['chi2_y'][ss]=chi2_y - self['dof'][ss] = mod.dof + self['n_params'][ss] = mod.n_params # Save parameters and errors to table. - for pp in range(len(modClass.fitter_param_names)): - par = modClass.fitter_param_names[pp] + for pp in range(len(mod.fitter_param_names)): + par = mod.fitter_param_names[pp] par_err = par + '_err' self[par][ss] = params[pp] self[par_err][ss] = param_errs[pp] @@ -873,7 +846,7 @@ def fit_velocity_for_star(self, ss, weighting='var', use_scipy=True, return # New function, to use in align - def get_star_positions_at_time(self, t, allow_alt_models=True): + def get_star_positions_at_time(self, t, motion_model_dict, allow_alt_models=True): """ Get current x,y positions of each star according to its motion_model """ # Start with empty arrays so we can fill them in batches @@ -898,12 +871,11 @@ def get_star_positions_at_time(self, t, allow_alt_models=True): try: # Identify stars with this model & get class idx = np.where(self['motion_model_input']==mm)[0] - modClass = getattr(motion_model, mm) + mod = motion_model_dict[mm] # Set up parameters param_dict = {} - for par in motion_model.get_one_motion_model_param_names(mm,with_errors=True,with_fixed=True): + for par in mod.fitter_param_names + mod.fixed_param_names + [pm+'_err' for pm in mod.fitter_param_names]: param_dict[par] = self[par][idx] - mod = modClass(RA=self.meta['RA'], Dec=self.meta['Dec'], PA=self.meta['position_angle'], obs=self.meta['observer_location']) x[idx],y[idx],xe[idx],ye[idx] = mod.get_batch_pos_at_time(t,**param_dict) except: pass @@ -920,7 +892,7 @@ def get_star_positions_at_time(self, t, allow_alt_models=True): param_dict = {} for par in motion_model.get_one_motion_model_param_names(mm,with_errors=True,with_fixed=True): param_dict[par] = self[par][idx] - mod = modClass(RA=self.meta['RA'], Dec=self.meta['Dec'], PA=self.meta['position_angle'], obs=self.meta['observer_location']) + mod = modClass() x[idx],y[idx],xe[idx],ye[idx] = mod.get_batch_pos_at_time(t,**param_dict) return x,y,xe,ye @@ -1040,8 +1012,7 @@ def shift_reference_frame(self, delta_vx=0.0, delta_vy=0.0, delta_pi=0.0): if delta_pi!=0.0: t_all = self['t'][np.where(~np.any(np.isnan(self['t']), axis=1))[0][0]] t_mjd = Time(t_all, format='decimalyear', scale='utc').mjd - pvec = parallax.parallax_in_direction(self.meta['RA'], self.meta['Dec'], t_mjd, - obsLocation=self.meta['observer_location'], PA=self.meta['position_angle']) + pvec = parallax.parallax_in_direction() self['pi'] += delta_pi self['x'] += delta_pi*pvec[:,0] self['y'] += delta_pi*pvec[:,1] diff --git a/flystar/tests/test_align.py b/flystar/tests/test_align.py index e0e1533..d94d34c 100644 --- a/flystar/tests/test_align.py +++ b/flystar/tests/test_align.py @@ -1069,7 +1069,7 @@ def test_MosaicToRef_hst_me(): outlier_tol=[None], mag_lim=[13, 21], trans_class=transforms.PolyTransform, trans_args=[{'order': 1}], - use_vel=False, + default_motion_model='Fixed', use_ref_new=False, update_ref_orig=False, mag_trans=False, @@ -1121,7 +1121,7 @@ def test_bootstrap(): mag_lim=mag_lim, ref_mag_lim=ref_mag_lim, weights=weights, - use_vel=True, + default_motion_model='Linear', use_ref_new=False, update_ref_orig=False, init_guess_mode='name', @@ -1155,7 +1155,7 @@ def test_bootstrap(): mag_lim=mag_lim, ref_mag_lim=ref_mag_lim, weights=weights, - use_vel=True, + default_motion_model='Linear', use_ref_new=False, update_ref_orig=False, init_guess_mode='name', @@ -1233,7 +1233,7 @@ def test_calc_vel_in_bootstrap(): mag_lim=mag_lim, ref_mag_lim=ref_mag_lim, weights=weights, - use_vel=True, + default_motion_model='Linear', use_ref_new=False, update_ref_orig=False, init_guess_mode='name', @@ -1300,7 +1300,7 @@ def test_transform_xym(): mag_lim=mag_lim, ref_mag_lim=ref_mag_lim, weights=weights, - use_vel=False, + default_motion_model='Fixed', use_ref_new=False, update_ref_orig=False, init_guess_mode='name', @@ -1335,7 +1335,7 @@ def test_transform_xym(): mag_lim=mag_lim, ref_mag_lim=ref_mag_lim, weights=weights, - use_vel=False, + default_motion_model='Fixed', use_ref_new=False, update_ref_orig=False, init_guess_mode='name', @@ -1378,7 +1378,7 @@ def test_MosaicToRef_mag_bug(): outlier_tol=None, trans_class=transforms.PolyTransform, trans_args=[{'order': 1}], - use_vel=False, + default_motion_model='Fixed', use_ref_new=False, update_ref_orig=False, verbose=True) @@ -1441,7 +1441,7 @@ def test_masked_cols(): dr_tol=[0.2, 0.1], dm_tol=[1, 1], trans_class=transforms.PolyTransform, trans_args=[{'order': 1}, {'order': 1}], - use_vel=True, + default_motion_model='Linear', use_ref_new=False, update_ref_orig=False, mag_trans=True, From 3c3e0ea532349b12b567fef5d528bbc121bf0fad Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Wed, 30 Jul 2025 15:27:00 -0700 Subject: [PATCH 082/254] debug motion model rework --- flystar/analysis.py | 8 ++--- flystar/motion_model.py | 32 +++++++++++++++----- flystar/plots.py | 65 ++++++++++++++++++++++++----------------- flystar/startables.py | 10 +++---- 4 files changed, 72 insertions(+), 43 deletions(-) diff --git a/flystar/analysis.py b/flystar/analysis.py index 50fe06a..81ab3f4 100644 --- a/flystar/analysis.py +++ b/flystar/analysis.py @@ -180,16 +180,16 @@ def prepare_gaia_for_flystar(gaia, ra, dec, targets_dict=None, match_dr_max=0.2, gaia_new['motion_model_input'] = 'Parallax' gaia_new['motion_model_used'] = 'Parallax' gaia_new['motion_model_used'][idx] = 'Linear' - #gaia_new['dof'] = 3 - #gaia_new['dof'][idx] = 2 + gaia_new['n_params'] = 3 + gaia_new['n_params'][idx] = 2 elif default_motion_model=='Linear': gaia_new['motion_model_input'] = 'Linear' gaia_new['motion_model_used'] = 'Linear' - #gaia_new['dof'] = 2 + gaia_new['n_params'] = 2 elif default_motion_model=='Fixed': gaia_new['motion_model_input'] = 'Fixed' gaia_new['motion_model_used'] = 'Fixed' - #gaia_new['dof'] = 1 + gaia_new['n_params'] = 1 else: print("Invalid motion model",default_motion_model,"- none assigned") diff --git a/flystar/motion_model.py b/flystar/motion_model.py index 5aca90a..2af4cae 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -74,15 +74,21 @@ def fit_motion_model(self, t, x, y, xe, ye, t0, bootstrap=0, weighting='var'): if bootstrap>0 and len(x)>(self.n_pts_req): edx = np.arange(len(x), dtype=int) bb_params = [] + bb_params_errs = [] for bb in range(bootstrap): bdx = np.random.choice(edx, len(x)) while len(np.unique(bdx))2: # Catch case where bootstrap sends only 2 unique epochs + _,idx=np.unique(dt, return_index=True) + dt = dt[idx] + x = x[idx] + y = y[idx] + xe = xe[idx] + ye = ye[idx] dx = np.diff(x)[0] dy = np.diff(y)[0] dt_diff = np.diff(dt)[0] @@ -319,24 +332,27 @@ def __init__(self, RA, Dec, PA=0.0, obs='earth', **kwargs): self.plx_vector_cached = None return + def get_parallax_vector(self, t_mjd): + return parallax.parallax_in_direction(self.RA, self.Dec, t_mjd, obsLocation=self.obs, PA=self.PA) + def get_pos_at_time(self, fit_params, fixed_params, t): fit_params_dict = dict(zip(self.fitter_param_names, fit_params)) fixed_params_dict = dict(zip(self.fixed_param_names, fixed_params)) dt = t-fixed_params_dict['t0'] t_mjd = Time(t, format='decimalyear', scale='utc').mjd - pvec = parallax.parallax_in_direction(self.RA, self.Dec, t_mjd, obsLocation=self.obs, PA=self.PA).T + pvec = self.get_parallax_vector(t_mjd).T pvec_x = np.reshape(pvec[0], t.shape) pvec_y = np.reshape(pvec[1], t.shape) - x = fit_params_dict['x0'] + fit_params_dict['vx']*dt + self.pi*pvec_x - y = fit_params_dict['y0'] + fit_params_dict['vy']*dt + self.pi*pvec_y + x = fit_params_dict['x0'] + fit_params_dict['vx']*dt + fit_params_dict['pi']*pvec_x + y = fit_params_dict['y0'] + fit_params_dict['vy']*dt + fit_params_dict['pi']*pvec_y return x, y def get_batch_pos_at_time(self, t, x0=[],vx=[], y0=[],vy=[], pi=[], t0=[], x0_err=[],vx_err=[], y0_err=[],vy_err=[], pi_err=[], **kwargs): t_mjd = Time(t, format='decimalyear', scale='utc').mjd - pvec = parallax.parallax_in_direction(self.RA, self.Dec, t_mjd, obsLocation=self.obs, PA=self.PA).T + pvec = self.get_parallax_vector(t_mjd).T if hasattr(t, "__len__"): dt = t-t0[:,np.newaxis] x = x0[:,np.newaxis] + dt*vx[:,np.newaxis] + pi[:,np.newaxis]*pvec[0].T @@ -369,7 +385,7 @@ def run_fit(self, t, x, y, xe, ye, t0, weighting='var', params_guess=None): pvec = [self.plx_vector_cached[1][pvec_idxs], self.plx_vector_cached[2][pvec_idxs]] recalc_plx = False if recalc_plx: - pvec = parallax.parallax_in_direction(self.RA, self.Dec, t_mjd, obsLocation=self.obs, PA=self.PA).T + pvec = self.get_parallax_vector(t_mjd).T self.plx_vector_cached = [t_mjd, pvec[0], pvec[1]] x_wt, y_wt = self.get_weights(xe,ye, weighting=weighting) def fit_func(t, x0,vx, y0,vy, pi): @@ -399,7 +415,9 @@ def fit_func(t, x0,vx, y0,vy, pi): """ def validate_motion_model_dict(motion_model_dict, startable, default_motion_model): # Collect names of all motion models that might get used. - all_motion_model_names = ['Fixed', default_motion_model] + all_motion_model_names = ['Fixed'] + if default_motion_model is not None: + all_motion_model_names.append(default_motion_model) if 'motion_model_input' in startable.columns: all_motion_model_names += np.unique(startable['motion_model_input']).tolist() if 'motion_model_input' in startable.columns: diff --git a/flystar/plots.py b/flystar/plots.py index 83d759e..61df7c1 100755 --- a/flystar/plots.py +++ b/flystar/plots.py @@ -1064,15 +1064,16 @@ def plot_mag_error(tab): return -def plot_mean_residuals_by_epoch(tab): +def plot_mean_residuals_by_epoch(tab, motion_model_dict={}): """ Plot mean position and magnitude residuals vs. epoch. Note we are plotting the mean( |dx} ) to see the size of the mean residual. """ # Predicted model positions at each epoch + motion_model_dict = motion_model.validate_motion_model_dict(motion_model_dict, tab, None) i_all_detected = np.where(~np.any(np.isnan(tab['t']),axis=1))[0][0] - xt_mod, yt_mod, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], allow_alt_models=True) + xt_mod, yt_mod, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], motion_model_dict, allow_alt_models=True) # Residuals dx = tab['x'] - xt_mod @@ -1121,7 +1122,7 @@ def plot_mean_residuals_by_epoch(tab): return -def plot_quiver_residuals_all_epochs(tab, unit='arcsec', scale=None, plotlim=None): +def plot_quiver_residuals_all_epochs(tab, motion_model_dict={}, unit='arcsec', scale=None, plotlim=None): # Keep track of the residuals for averaging. dr_good = np.zeros(len(tab), dtype=float) @@ -1129,8 +1130,9 @@ def plot_quiver_residuals_all_epochs(tab, unit='arcsec', scale=None, plotlim=Non dr_ref = np.zeros(len(tab), dtype=float) n_ref = np.zeros(len(tab), dtype=int) + motion_model_dict = motion_model.validate_motion_model_dict(motion_model_dict, tab, None) i_all_detected = np.where(~np.any(np.isnan(tab['t']),axis=1))[0][0] - xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], allow_alt_models=True) + xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], motion_model_dict, allow_alt_models=True) for ee in range(tab['x'].shape[1]): xt_mod = xt_mod_all[:,ee] @@ -1187,7 +1189,7 @@ def plot_quiver_residuals_all_epochs(tab, unit='arcsec', scale=None, plotlim=Non return -def plot_quiver_residuals_with_orig_all_epochs(tab, trans_list, unit='arcsec', scale=None, plotlim=None, scale_orig=None, cte_fit=None, mlim=15): +def plot_quiver_residuals_with_orig_all_epochs(tab, trans_list, motion_model_dict={}, unit='arcsec', scale=None, plotlim=None, scale_orig=None, cte_fit=None, mlim=15): # Keep track of the residuals for averaging. dr_good = np.zeros(len(tab), dtype=float) @@ -1195,8 +1197,9 @@ def plot_quiver_residuals_with_orig_all_epochs(tab, trans_list, unit='arcsec', s dr_ref = np.zeros(len(tab), dtype=float) n_ref = np.zeros(len(tab), dtype=int) + motion_model_dict = motion_model.validate_motion_model_dict(motion_model_dict, tab, None) i_all_detected = np.where(~np.any(np.isnan(tab['t']),axis=1))[0][0] - xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], allow_alt_models=True) + xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], motion_model_dict, allow_alt_models=True) for ee in range(tab['x'].shape[1]): dt = tab['t'][:, ee] - tab['t0'] @@ -1293,7 +1296,7 @@ def plot_quiver_residuals_with_orig_all_epochs(tab, trans_list, unit='arcsec', s return -def plot_mag_scatter_multi_trans_all_epochs(tab_list, trans_list_list, unit='arcsec', scale=None, plotlim=None, scale_orig=None): +def plot_mag_scatter_multi_trans_all_epochs(tab_list, trans_list_list, motion_model_dict={}, unit='arcsec', scale=None, plotlim=None, scale_orig=None): m_t_list = [] x_t_list = [] y_t_list = [] @@ -1306,8 +1309,9 @@ def plot_mag_scatter_multi_trans_all_epochs(tab_list, trans_list_list, unit='arc da_list = [] ntrans = len(tab_list) + motion_model_dict = motion_model.validate_motion_model_dict(motion_model_dict, tab, None) i_all_detected = np.where(~np.any(np.isnan(tab['t']),axis=1))[0][0] - xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], allow_alt_models=True) + xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], motion_model_dict, allow_alt_models=True) for mm in range(ntrans): tab = tab_list[mm] @@ -1850,7 +1854,7 @@ def plot_quiver_residuals(x_t, y_t, x_ref, y_ref, good_idx, ref_idx, title, return (dx, dy) -def plot_quiver_residuals_magcolor_all_epochs(tab, unit='arcsec', scale=None, plotlim=None, lower_mag=18, upper_mag=13): +def plot_quiver_residuals_magcolor_all_epochs(tab, motion_model_dict={}, unit='arcsec', scale=None, plotlim=None, lower_mag=18, upper_mag=13): # Keep track of the residuals for averaging. dr_good = np.zeros(len(tab), dtype=float) n_good = np.zeros(len(tab), dtype=int) @@ -1860,8 +1864,9 @@ def plot_quiver_residuals_magcolor_all_epochs(tab, unit='arcsec', scale=None, pl idx = np.where((tab['m0'] < lower_mag) & (tab['m0'] > upper_mag))[0] tab = tab[idx] + motion_model_dict = motion_model.validate_motion_model_dict(motion_model_dict, tab, None) i_all_detected = np.where(~np.any(np.isnan(tab['t']),axis=1))[0][0] - xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], allow_alt_models=True) + xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], motion_model_dict, allow_alt_models=True) for ee in range(tab['x'].shape[1]): dt = tab['t'][:, ee] - tab['t0'] @@ -2159,7 +2164,7 @@ def plot_quiver_residuals_orig_angle_xy(x_t, y_t, x_ref, y_ref, good_idx, ref_id return -def plot_chi2_dist(tab, Ndetect, xlim=40, n_bins=50, boot_err=False): +def plot_chi2_dist(tab, Ndetect, motion_model_dict={}, xlim=40, n_bins=50, boot_err=False): """ tab = flystar table Ndetect = Number of epochs star detected in @@ -2168,8 +2173,9 @@ def plot_chi2_dist(tab, Ndetect, xlim=40, n_bins=50, boot_err=False): chi2_y_list = [] fnd_list = [] # Number of non-NaN error measurements + motion_model_dict = motion_model.validate_motion_model_dict(motion_model_dict, tab, None) i_all_detected = np.where(~np.any(np.isnan(tab['t']),axis=1))[0][0] - xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], allow_alt_models=True) + xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], motion_model_dict, allow_alt_models=True) for ii in range(len(tab)): # Ignore the NaNs @@ -2204,8 +2210,9 @@ def plot_chi2_dist(tab, Ndetect, xlim=40, n_bins=50, boot_err=False): idx = np.where(fnd == Ndetect)[0] # Fitting position and velocity... so subtract 2 to get Ndof - Ndof = Ndetect - tab['dof'][i_all_detected] - print(i_all_detected) + n_params = np.nanmean(tab['n_params'][idx]) + Ndof = Ndetect - n_params + print(f"Ndof={Ndof}, Ndetect={Ndetect}, Nparams={n_params}") chi2_xaxis = np.linspace(0, xlim, xlim*3) chi2_bins = np.linspace(0, xlim, n_bins) @@ -2237,7 +2244,7 @@ def plot_chi2_dist(tab, Ndetect, xlim=40, n_bins=50, boot_err=False): return -def plot_chi2_dist_per_filter(tab, Ndetect, xlim=40, n_bins=50, filter=None, boot_err=False): +def plot_chi2_dist_per_filter(tab, Ndetect, motion_model_dict={}, xlim=40, n_bins=50, filter=None, boot_err=False): """ tab = flystar table Ndetect = Number of epochs star detected in @@ -2246,8 +2253,9 @@ def plot_chi2_dist_per_filter(tab, Ndetect, xlim=40, n_bins=50, filter=None, boo chi2_y_list = [] fnd_list = [] # Number of non-NaN error measurements + motion_model_dict = motion_model.validate_motion_model_dict(motion_model_dict, tab, None) i_all_detected = np.where(~np.any(np.isnan(tab['t']),axis=1))[0][0] - xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], allow_alt_models=True) + xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], motion_model_dict, allow_alt_models=True) for ii in range(len(tab)): # Ignore the NaNs @@ -2284,9 +2292,10 @@ def plot_chi2_dist_per_filter(tab, Ndetect, xlim=40, n_bins=50, filter=None, boo idx = np.where(fnd == Ndetect)[0] - # Fitting position and velocity... so subtract 2 to get Ndof - Ndof = Ndetect - tab['dof'][i_all_detected] - print(i_all_detected) + # Fitting position and velocity... so subtract n_params to get Ndof + n_params = np.nanmean(tab['n_params'][idx]) + Ndof = Ndetect - n_params + print(f"Ndof={Ndof}, Ndetect={Ndetect}, Nparams={n_params}") chi2_xaxis = np.linspace(0, xlim, xlim*3) chi2_bins = np.linspace(0, xlim, n_bins) print(x[idx]) @@ -2327,7 +2336,7 @@ def plot_chi2_dist_per_filter(tab, Ndetect, xlim=40, n_bins=50, filter=None, boo return -def plot_chi2_dist_per_epoch(tab, Ndetect, mlim=[14,21], ylim = [-1, 1], target_idx = 0, boot_err=False): +def plot_chi2_dist_per_epoch(tab, Ndetect, motion_model_dict={}, mlim=[14,21], ylim = [-1, 1], target_idx = 0, boot_err=False): """ tab = flystar table Ndetect = Number of epochs star detected in @@ -2340,8 +2349,9 @@ def plot_chi2_dist_per_epoch(tab, Ndetect, mlim=[14,21], ylim = [-1, 1], target_ sigY_arr = np.nan * np.ones((len(tab['xe']), Ndetect)) m_arr = np.nan * np.ones((len(tab['xe']), Ndetect)) + motion_model_dict = motion_model.validate_motion_model_dict(motion_model_dict, tab, None) i_all_detected = np.where(~np.any(np.isnan(tab['t']),axis=1))[0][0] - xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], allow_alt_models=True) + xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], motion_model_dict, allow_alt_models=True) for ii in range(len(tab['xe'])): # Ignore the NaNs @@ -2649,8 +2659,7 @@ def plot_chi2_dist_mag_per_filter(tab, Ndetect, mlim=40, n_bins=30, xlim=40, fil return -def plot_stars(tab, star_names, NcolMax=2, epoch_array = None, figsize=(15,25), color_time=False, - position_angle=None, RA=None, Dec=None, observer_location='earth', boot_err=False): +def plot_stars(tab, star_names, motion_model_dict={}, NcolMax=2, epoch_array = None, figsize=(15,25), color_time=False, boot_err=False): """ Plot a set of stars positions, flux and residuals over time. @@ -2678,10 +2687,11 @@ def rs(x): x = tab['x0'] y = tab['y0'] r = np.hypot(x, y) + motion_model_dict = motion_model.validate_motion_model_dict(motion_model_dict, tab, None) i_all_detected = np.where(~np.any(np.isnan(tab['t']),axis=1))[0][0] cont_times = np.arange(np.min(tab['t'][i_all_detected]), np.max(tab['t'][i_all_detected]), 0.01) - xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], allow_alt_models=True) - xt_cont_all, yt_cont_all, xt_cont_err, yt_cont_err = tab.get_star_positions_at_time(cont_times, allow_alt_models=True) + xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], motion_model_dict, allow_alt_models=True) + xt_cont_all, yt_cont_all, xt_cont_err, yt_cont_err = tab.get_star_positions_at_time(cont_times, motion_model_dict, allow_alt_models=True) for i in range(Nstars): starName = star_names[i] @@ -3068,7 +3078,7 @@ def rs(x): return -def plot_stars_nfilt(tab, star_names, NcolMax=2, epoch_array_list = None, color_list = None, +def plot_stars_nfilt(tab, star_names, motion_model_dict={}, NcolMax=2, epoch_array_list = None, color_list = None, figsize=(15,25), color_time=False, resTicRng=None, save_name=None, boot_err=False): """ Plot a set of stars positions, flux and residuals over time. @@ -3084,8 +3094,9 @@ def plot_stars_nfilt(tab, star_names, NcolMax=2, epoch_array_list = None, color_ def rs(x): return x.reshape(len(x)) + motion_model_dict = motion_model.validate_motion_model_dict(motion_model_dict, tab, None) i_all_detected = np.where(~np.any(np.isnan(tab['t']),axis=1))[0][0] - xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], allow_alt_models=True) + xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], motion_model_dict, allow_alt_models=True) Nstars = len(star_names) Ncols = 3 * np.min([Nstars, NcolMax]) diff --git a/flystar/startables.py b/flystar/startables.py index 618dba3..aef2bd9 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -825,7 +825,6 @@ def fit_velocity_for_star(self, ss, motion_model_dict, weighting='var', bootstra # Model object mod = motion_model_dict[motion_model_use] - print(mod) fixed_params = [self[par][ss] for par in mod.fixed_param_names] # Fit for the best parameters @@ -887,12 +886,11 @@ def get_star_positions_at_time(self, t, motion_model_dict, allow_alt_models=True # Identify stars with this model & get class idx_0 = np.where(self['motion_model_used']==mm)[0] idx = np.intersect1d(re_calc, idx_0) - modClass = getattr(motion_model, mm) + mod = motion_model_dict[mm] # Set up parameters param_dict = {} for par in motion_model.get_one_motion_model_param_names(mm,with_errors=True,with_fixed=True): param_dict[par] = self[par][idx] - mod = modClass() x[idx],y[idx],xe[idx],ye[idx] = mod.get_batch_pos_at_time(t,**param_dict) return x,y,xe,ye @@ -985,7 +983,8 @@ def fit_velocities_all_detected(self, weighting='var', use_scipy=False, absolute else: return - def shift_reference_frame(self, delta_vx=0.0, delta_vy=0.0, delta_pi=0.0): + def shift_reference_frame(self, delta_vx=0.0, delta_vy=0.0, delta_pi=0.0, + motion_model_dict={}): """ After completing an alignment, shift from your relative reference frame to the absolute frame using either Gaia or a Galactic model. This modified the @@ -1001,6 +1000,7 @@ def shift_reference_frame(self, delta_vx=0.0, delta_vy=0.0, delta_pi=0.0): delta_pi : float, optional parallax shift (as) """ + motion_model_dict = motion_model.validate_motion_model_dict(motion_model_dict, self, None) if delta_vx==0.0 and delta_vy==0.0 and delta_pi==0.0: print("No shifts input, reference frame unchanged.") print("Specify delta_vx, delta_vy, and/or delta_pi to perform a reference frame shift.") @@ -1012,7 +1012,7 @@ def shift_reference_frame(self, delta_vx=0.0, delta_vy=0.0, delta_pi=0.0): if delta_pi!=0.0: t_all = self['t'][np.where(~np.any(np.isnan(self['t']), axis=1))[0][0]] t_mjd = Time(t_all, format='decimalyear', scale='utc').mjd - pvec = parallax.parallax_in_direction() + pvec = motion_model_dict['Parallax'].get_parallax_vector(t_mjd) self['pi'] += delta_pi self['x'] += delta_pi*pvec[:,0] self['y'] += delta_pi*pvec[:,1] From 0c64e5c5a950d539704178766d7298818f12ffe6 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Wed, 30 Jul 2025 15:41:34 -0700 Subject: [PATCH 083/254] improve plx caching --- flystar/motion_model.py | 37 ++++++++++++++++++++++--------------- flystar/startables.py | 4 ++-- 2 files changed, 24 insertions(+), 17 deletions(-) diff --git a/flystar/motion_model.py b/flystar/motion_model.py index 2af4cae..1a5b6f2 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -333,7 +333,25 @@ def __init__(self, RA, Dec, PA=0.0, obs='earth', **kwargs): return def get_parallax_vector(self, t_mjd): - return parallax.parallax_in_direction(self.RA, self.Dec, t_mjd, obsLocation=self.obs, PA=self.PA) + recalc_plx = True + if self.plx_vector_cached is not None: + if hasattr(t_mjd, "__len__"): + if list(t_mjd) == list(self.plx_vector_cached[0]): + pvec = self.plx_vector_cached[1:] + recalc_plx = False + elif all([t_mjd_i in self.plx_vector_cached[0] for t_mjd_i in t_mjd]): + pvec_idxs = [np.argwhere(self.plx_vector_cached[0]==t_mjd_i)[0][0] for t_mjd_i in t_mjd] + pvec = [self.plx_vector_cached[1][pvec_idxs], self.plx_vector_cached[2][pvec_idxs]] + recalc_plx = False + elif t_mjd in self.plx_vector_cached[0]: + idx = np.where(t_mjd==self.plx_vector_cached[0])[0][0] + pvec = np.array([self.plx_vector_cached[1][idx], self.plx_vector_cached[2][idx]]) + recalc_plx = False + if recalc_plx: + pvec = parallax.parallax_in_direction(self.RA, self.Dec, t_mjd, obsLocation=self.obs, PA=self.PA).T + if hasattr(t_mjd, "__len__"): + self.plx_vector_cached = [t_mjd, pvec[0], pvec[1]] + return pvec def get_pos_at_time(self, fit_params, fixed_params, t): fit_params_dict = dict(zip(self.fitter_param_names, fit_params)) @@ -341,7 +359,7 @@ def get_pos_at_time(self, fit_params, fixed_params, t): dt = t-fixed_params_dict['t0'] t_mjd = Time(t, format='decimalyear', scale='utc').mjd - pvec = self.get_parallax_vector(t_mjd).T + pvec = self.get_parallax_vector(t_mjd) pvec_x = np.reshape(pvec[0], t.shape) pvec_y = np.reshape(pvec[1], t.shape) x = fit_params_dict['x0'] + fit_params_dict['vx']*dt + fit_params_dict['pi']*pvec_x @@ -352,7 +370,7 @@ def get_batch_pos_at_time(self, t, x0=[],vx=[], y0=[],vy=[], pi=[], t0=[], x0_err=[],vx_err=[], y0_err=[],vy_err=[], pi_err=[], **kwargs): t_mjd = Time(t, format='decimalyear', scale='utc').mjd - pvec = self.get_parallax_vector(t_mjd).T + pvec = self.get_parallax_vector(t_mjd) if hasattr(t, "__len__"): dt = t-t0[:,np.newaxis] x = x0[:,np.newaxis] + dt*vx[:,np.newaxis] + pi[:,np.newaxis]*pvec[0].T @@ -375,18 +393,7 @@ def get_batch_pos_at_time(self, t, def run_fit(self, t, x, y, xe, ye, t0, weighting='var', params_guess=None): t_mjd = Time(t, format='decimalyear', scale='utc').mjd - recalc_plx = True - if self.plx_vector_cached is not None: - if list(t_mjd) == list(self.plx_vector_cached[0]): - pvec = self.plx_vector_cached[1:] - recalc_plx = False - elif all([t_mjd_i in self.plx_vector_cached[0] for t_mjd_i in t_mjd]): - pvec_idxs = [np.argwhere(self.plx_vector_cached[0]==t_mjd_i)[0][0] for t_mjd_i in t_mjd] - pvec = [self.plx_vector_cached[1][pvec_idxs], self.plx_vector_cached[2][pvec_idxs]] - recalc_plx = False - if recalc_plx: - pvec = self.get_parallax_vector(t_mjd).T - self.plx_vector_cached = [t_mjd, pvec[0], pvec[1]] + pvec = self.get_parallax_vector(t_mjd) x_wt, y_wt = self.get_weights(xe,ye, weighting=weighting) def fit_func(t, x0,vx, y0,vy, pi): use_t = t[:int(len(t)/2)] diff --git a/flystar/startables.py b/flystar/startables.py index aef2bd9..b0d6164 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -1014,6 +1014,6 @@ def shift_reference_frame(self, delta_vx=0.0, delta_vy=0.0, delta_pi=0.0, t_mjd = Time(t_all, format='decimalyear', scale='utc').mjd pvec = motion_model_dict['Parallax'].get_parallax_vector(t_mjd) self['pi'] += delta_pi - self['x'] += delta_pi*pvec[:,0] - self['y'] += delta_pi*pvec[:,1] + self['x'] += delta_pi*pvec[0] + self['y'] += delta_pi*pvec[1] return From bc57c60526179f2f3e0ea702f60342e9d873be2f Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Thu, 7 Aug 2025 12:11:18 -0700 Subject: [PATCH 084/254] fit_velocities_all_detected to motion_model first pass --- flystar/fit_velocity.py | 203 ---------------------------------------- flystar/motion_model.py | 29 +++++- flystar/startables.py | 59 +++++++++--- 3 files changed, 73 insertions(+), 218 deletions(-) delete mode 100755 flystar/fit_velocity.py diff --git a/flystar/fit_velocity.py b/flystar/fit_velocity.py deleted file mode 100755 index 45d55d0..0000000 --- a/flystar/fit_velocity.py +++ /dev/null @@ -1,203 +0,0 @@ -from tqdm import tqdm -import numpy as np -import pandas as pd - -def linear(x, k, b): - return k*x + b - -def linear_fit(x, y, sigma=None, absolute_sigma=True): - """Weighted linear regression (See https://en.wikipedia.org/wiki/Weighted_least_squares#Solution). Recommended for low-dimension, non-degenerate data. Otherwise, please use scipy.curve_fit. - - Parameters - ---------- - x : array-like - x data - y : array-like - y data - sigma : array-like, optional - Weighted by 1/sigma**2. If not provided, weight = 1, by default None - absolute_sigma : bool, optional - If True (default), sigma is used in an absolute sense and the estimated parameter uncertainty reflects these absolute values. If False, only the relative magnitudes of the sigma values matter, by default True - - Returns - ------- - result : dictionary - Dictionary with keys 'slope', 'e_slope', 'intercept', 'e_intercept', and 'chi2' if return_chi2=True. - """ - x = np.array(x) - y = np.array(y) - if sigma is None: - sigma = np.ones_like(x) - else: - sigma = np.array(sigma) - - X = np.vander(x, 2) - W = np.diag(1/sigma**2) - XTWX = X.T @ W @ X - pcov = np.linalg.inv(XTWX) # Covariance Matrix - popt = pcov @ X.T @ W @ y # Linear Solution - perr = np.sqrt(np.diag(pcov)) # Uncertainty of Linear Solution - - residual = y - X @ popt - chi2 = residual.T @ W @ residual - - if not absolute_sigma: - reduced_chi2 = chi2/(len(x) - 2) - perr *= reduced_chi2**0.5 - - result = { - 'slope': popt[0], - 'intercept': popt[1], - 'e_slope': perr[0], - 'e_intercept': perr[1], - 'chi2': chi2 - } - - return result - - -def calc_chi2(x, y, sigma, slope, intercept): - popt = np.array([slope, intercept]) - X = np.vander(x, 2) - W = np.diag(1/sigma**2) - residual = y - X @ popt - return residual.T @ W @ residual - - -def fit_velocity(startable, weighting='var', use_scipy=False, absolute_sigma=True, epoch_cols='all', art_star=False): - """Fit proper motion with weighted linear regression equations (see https://en.wikipedia.org/wiki/Weighted_least_squares#Solution). - Assumes that all data are valid. - - Parameters - ---------- - startable : StarTable - StarTable object - weighting : str, optional - Weighting by variance (1/ye**2) or standard deviation (1/ye), by default 'var' - use_scipy : bool, optional - Use scipy.curve_fit or flystar.fit_velocity.linear_fit, by default False - epoch_cols : str or list of intergers, optional - List of indicies of columns to use. If 'all', use all columns, by default 'all' - art_star : bool, optional - Artificial star catalog or not. If True, use startable['x'][:, epoch_ols, 1] as the location, by default False. - - Returns - ------- - result : pd.DataFrame - Proper motion dataframe with keys vx, vxe, vy, vye, x0, x0e, y0, y0e - - Raises - ------ - ValueError - If weighting is neither 'std' nor 'var' - """ - if weighting not in ['std', 'var']: - raise ValueError(f"Weighting must be either 'std' or 'var', not '{weighting}'.") - if epoch_cols is None: - epoch_cols = np.arange(len(startable.meta['YEARS'])) # use all cols if not specified - - N = len(startable) - vx = np.zeros(N) - vy = np.zeros(N) - vxe = np.zeros(N) - vye = np.zeros(N) - x0 = np.zeros(N) - y0 = np.zeros(N) - x0e = np.zeros(N) - y0e = np.zeros(N) - chi2_vx = np.zeros(N) - chi2_vy = np.zeros(N) - t0 = np.zeros(N) - - time = np.array(startable.meta['YEARS'])[epoch_cols] - - if not art_star: - x_arr = startable['x'][:, epoch_cols] - y_arr = startable['y'][:, epoch_cols] - else: - x_arr = startable['x'][:, epoch_cols, 1] - y_arr = startable['y'][:, epoch_cols, 1] - - xe_arr = startable['xe'][:, epoch_cols] - ye_arr = startable['ye'][:, epoch_cols] - - if weighting=='std': - sigma_x_arr = np.abs(xe_arr)**0.5 - sigma_y_arr = np.abs(ye_arr)**0.5 - elif weighting=='var': - sigma_x_arr = xe_arr - sigma_y_arr = ye_arr - - # For each star - for i in tqdm(range(len(startable))): - x = x_arr[i] - y = y_arr[i] - xe = xe_arr[i] - ye = ye_arr[i] - sigma_x = sigma_x_arr[i] - sigma_y = sigma_y_arr[i] - - t_weight = 1. / np.hypot(xe, ye) - t0[i] = np.average(time, weights=t_weight) - dt = time - t0[i] - - if use_scipy: - p0x = np.array([0., x.mean()]) - p0y = np.array([0., y.mean()]) - - # Use scipy.curve_fit to fit for velocity - vx_opt, vx_cov = curve_fit(linear, dt, x, p0=p0x, sigma=sigma_x, absolute_sigma=absolute_sigma) - vy_opt, vy_cov = curve_fit(linear, dt, y, p0=p0y, sigma=sigma_y, absolute_sigma=absolute_sigma) - - vx[i] = vx_opt[0] - vy[i] = vy_opt[0] - x0[i] = vx_opt[1] - y0[i] = vy_opt[1] - vxe[i], x0e[i] = np.sqrt(vx_cov.diagonal()) - vye[i], y0e[i] = np.sqrt(vy_cov.diagonal()) - chi2_vx[i] = calc_chi2(dt, x, sigma_x, *vx_opt) - chi2_vy[i] = calc_chi2(dt, y, sigma_y, *vy_opt) - - else: - vx_result = linear_fit(dt, x, sigma=sigma_x, absolute_sigma=absolute_sigma) - vy_result = linear_fit(dt, y, sigma=sigma_y, absolute_sigma=absolute_sigma) - - vx[i] = vx_result['slope'] - vxe[i] = vx_result['e_slope'] - x0[i] = vx_result['intercept'] - x0e[i] = vx_result['e_intercept'] - chi2_vx[i] = vx_result['chi2'] - - vy[i] = vy_result['slope'] - vye[i] = vy_result['e_slope'] - y0[i] = vy_result['intercept'] - y0e[i] = vy_result['e_intercept'] - chi2_vy[i] = vy_result['chi2'] - - result = pd.DataFrame({ - 'vx': vx, 'vy': vy, - 'vxe': vxe, 'vye': vye, - 'x0': x0, 'y0': y0, - 'x0e': x0e, 'y0e': y0e, - 'chi2_vx': chi2_vx, - 'chi2_vy': chi2_vy, - 't0': t0 - }) - return result - - -# Test -if __name__=='__main__': - from scipy.optimize import curve_fit - - x = np.array([1,2,3,4]) - y = np.array([1,2,5,6]) - sigma = np.array([.4,.2,.1,.3]) - - for absolute_sigma in [True, False]: - result = linear_fit(x, y, sigma=sigma, absolute_sigma=absolute_sigma) - popt, pcov = curve_fit(linear, x, y, sigma=sigma, absolute_sigma=absolute_sigma) - perr = np.sqrt(np.diag(pcov)) - print(f'Absolute Sigma = {absolute_sigma}') - print(f"linear_fit: slope = {result['slope']:.3f} ± {result['e_slope']:.3f}, intercept = {result['intercept']:.3f} ± {result['e_intercept']:.3f}, chi2={result['chi2']:.3f}") - print(f'curve_fit: slope = {popt[0]:.3f} ± {perr[0]:.3f}, intercept = {popt[1]:.3f} ± {perr[1]:.3f}, chi2={calc_chi2(x, y, sigma, *popt):.3f}\n') \ No newline at end of file diff --git a/flystar/motion_model.py b/flystar/motion_model.py index 1a5b6f2..2429a0b 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -97,7 +97,6 @@ def get_chi2(self,fit_params,fixed_params, t,x,y,xe,ye,reduced=False): Get the chi^2 value for the current MM and the input data. """ - # TODO: fix this function - no more get_pos_at_time x_pred,y_pred = self.get_pos_at_time(fit_params,fixed_params, t) chi2x = np.sum((x-x_pred)**2 / xe**2) chi2y = np.sum((y-y_pred)**2 / ye**2) @@ -227,15 +226,37 @@ def run_fit(self, t, x, y, xe, ye, t0, weighting='var', params_guess=None): if self.use_scipy: def linear(t, c0, c1): return c0 + c1*t - x_opt, x_cov = curve_fit(linear, dt, x, p0=np.array(params_guess[:2]), sigma=1/np.sqrt(x_wt), absolute_sigma=True) - y_opt, y_cov = curve_fit(linear, dt, y, p0=np.array(params_guess[2:]), sigma=1/np.sqrt(y_wt), absolute_sigma=True) + x_opt, x_cov = curve_fit(linear, dt, x, p0=np.array(params_guess[:2]), sigma=1/np.sqrt(x_wt), absolute_sigma=self.absolute_sigma) + y_opt, y_cov = curve_fit(linear, dt, y, p0=np.array(params_guess[2:]), sigma=1/np.sqrt(y_wt), absolute_sigma=self.absolute_sigma) x0, vx = x_opt y0, vy = y_opt x0e, vxe = np.sqrt(x_cov.diagonal()) y0e, vye = np.sqrt(y_cov.diagonal()) x0e, vxe, y0e, vye = self.scale_errors([x0e, vxe, y0e, vye], weighting=weighting) else: - raise ValueError("Option use_scipy=False is not yet implemented for the Linear motion model.") + # Use https://en.wikipedia.org/wiki/Weighted_least_squares#Solution scheme + x = np.array(x) + y = np.array(y) + t = np.array(t) + X_mat_t = np.vander(t, 2) + # x calculation + W_mat_x = np.diag(x_wt) + XTWX_mat_x = X_mat_t.T @ W_mat_x @ X_mat_t + pcov_x = np.linalg.inv(XTWX_mat_x) # Covariance Matrix + popt_x = pcov_x @ X_mat_t.T @ W_mat_x @ x # Linear Solution + perr_x = np.sqrt(np.diag(pcov_x)) # Uncertainty of Linear Solution + # y calculation + W_mat_y = np.diag(y_wt) + XTWX_mat_y = X_mat_t.T @ W_mat_y @ X_mat_t + pcov_y = np.linalg.inv(XTWX_mat_y) # Covariance Matrix + popt_y = pcov_y @ X_mat_t.T @ W_mat_y @ y # Linear Solution + perr_y = np.sqrt(np.diag(pcov_y)) # Uncertainty of Linear Solution + # prepare values to return + x0, vx = popt_x[1], popt_x[0] + y0, vy = popt_y[1], popt_y[0] + x0e, vxe = perr_x[1], perr_x[0] + y0e, vye = perr_y[1], perr_y[0] + x0e, vxe, y0e, vye = self.scale_errors([x0e, vxe, y0e, vye], weighting=weighting) params = [x0, vx, y0, vy] param_errors = [x0e, vxe, y0e, vye] diff --git a/flystar/startables.py b/flystar/startables.py index b0d6164..ce7e5bf 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -2,7 +2,7 @@ from astropy.stats import sigma_clipping from astropy.time import Time from scipy.optimize import curve_fit -from flystar.fit_velocity import linear_fit, calc_chi2, linear, fit_velocity +#from flystar.fit_velocity import linear_fit, calc_chi2, linear, fit_velocity from tqdm import tqdm import numpy as np import warnings @@ -10,7 +10,7 @@ import pdb import time import copy -from flystar import motion_model, parallax +from flystar import motion_model class StarTable(Table): """ @@ -896,18 +896,16 @@ def get_star_positions_at_time(self, t, motion_model_dict, allow_alt_models=True return x,y,xe,ye - def fit_velocities_all_detected(self, weighting='var', use_scipy=False, absolute_sigma=False, epoch_cols='all', mask_val=None, art_star=False, return_result=False): + def fit_velocities_all_detected(self, motion_model_to_fit, weighting='var', epoch_cols='all', mask_val=None, art_star=False, return_result=False): """Fit velocities for stars detected in all epochs specified by epoch_cols. Criterion: xe/ye error > 0 and finite, x/y not masked. Parameters ---------- + motion_model_to_fit : MotionModel + motion model object to use for fitting all stars weighting : str, optional Variance weighting('var') or standard deviation weighting ('std'), by default 'var' - use_scipy : bool, optional - Use scipy.curve_fit or flystar.fit_velocity.fit_velocity, by default False - absolute_sigma : bool, optional - Absolute sigma or rescaled sigma, by default False epoch_cols : str or list of intergers, optional List of epoch column indices used for fitting velocity, by default 'all' mask_val : float, optional @@ -954,12 +952,51 @@ def fit_velocities_all_detected(self, weighting='var', use_scipy=False, absolute else: detected_in_all_epochs = np.logical_and(valid_xe, valid_ye) + # START FORMER FIT VELOCITY FUNCITON + if epoch_cols is None: + epoch_cols = np.arange(len(startable.meta['YEARS'])) # use all cols if not specified` + + N = len(startable) + fit_params = motion_model_to_fit.fitter_param_names + param_data = {p: np.zeros(N) for p in fit_params} + param_data.update({p+'_err': np.zeros(N) for p in fit_params}) + param_data.update({p: np.zeros(N) for p in motion_model_to_fit.fixed_param_names}) + param_data['chi2_x'] = np.zeros(N) + param_data['chi2_y'] = np.zeros(N) + + time = np.array(startable.meta['YEARS'])[epoch_cols] + + if not art_star: + x_arr = startable['x'][:, epoch_cols] + y_arr = startable['y'][:, epoch_cols] + else: + x_arr = startable['x'][:, epoch_cols, 1] + y_arr = startable['y'][:, epoch_cols, 1] - # Fit velocities - vel_result = fit_velocity(self[detected_in_all_epochs], weighting=weighting, use_scipy=use_scipy, - absolute_sigma=absolute_sigma, epoch_cols=epoch_cols, art_star=art_star) - vel_result = Table.from_pandas(vel_result) + xe_arr = startable['xe'][:, epoch_cols] + ye_arr = startable['ye'][:, epoch_cols] + + # For each star + for i in tqdm(range(len(startable))): + x = x_arr[i] + y = y_arr[i] + xe = xe_arr[i] + ye = ye_arr[i] + t0 = np.average(time, weights=1. / np.hypot(xe, ye)) + + # Run fit and record results + params, param_errs = motion_model_to_fit.fit_motion_model(time, x, y, xe, ye, t0, weighting=weighting) + if 't0' in motion_model_to_fit.fixed_param_names: + param_data['t0'][i] = t0 + for j, param in fit_params: + param_data[param][i] = params[j] + param_data[param+'_err'][i] = param_errs[j] + chi2x, chi2y = motion_model_to_fit.get_chi2([params], [t0], time, x, y, xe, ye) + param_data['chi2_x'][i] = chi2x + param_data['chi2_y'][i] = chi2y + vel_result = Table.from_pandas(pd.DataFrame(param_data)) + # END FUNCTION # Add n_vfit n_fit = len(epoch_cols) From f73d9c50ae0f155fbc85c759afda6e8389531300 Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Thu, 7 Aug 2025 16:32:14 -0700 Subject: [PATCH 085/254] Added test catalog for testing linear models --- flystar/tests/test_linear_vel.fits | 2911 ++++++++++++++++++++++++++++ 1 file changed, 2911 insertions(+) create mode 100644 flystar/tests/test_linear_vel.fits diff --git a/flystar/tests/test_linear_vel.fits b/flystar/tests/test_linear_vel.fits new file mode 100644 index 0000000..ae56198 --- /dev/null +++ b/flystar/tests/test_linear_vel.fits @@ -0,0 +1,2911 @@ +SIMPLE = T / conforms to FITS standard BITPIX = 8 / array data type NAXIS = 0 / number of array dimensions EXTEND = T END XTENSION= 'BINTABLE' / binary table extension BITPIX = 8 / array data type NAXIS = 2 / number of array dimensions NAXIS1 = 632 / length of dimension 1 NAXIS2 = 2000 / length of dimension 2 PCOUNT = 0 / number of group parameters GCOUNT = 1 / number of groups TFIELDS = 21 / number of table fields TTYPE1 = 'name ' TFORM1 = 'K ' TTYPE2 = 'x ' TFORM2 = '12D ' TDIM2 = '(2,6) ' TTYPE3 = 'y ' TFORM3 = '12D ' TDIM3 = '(2,6) ' TTYPE4 = 'm ' TFORM4 = '12D ' TDIM4 = '(2,6) ' TTYPE5 = 'xe ' TFORM5 = '6D ' TDIM5 = '(6) ' TTYPE6 = 'ye ' TFORM6 = '6D ' TDIM6 = '(6) ' TTYPE7 = 'me ' TFORM7 = '6D ' TDIM7 = '(6) ' TTYPE8 = 'n ' TFORM8 = '6D ' TDIM8 = '(6) ' TTYPE9 = 'det ' TFORM9 = '6D ' TDIM9 = '(6) ' TTYPE10 = 'vx ' TFORM10 = 'D ' TTYPE11 = 'vy ' TFORM11 = 'D ' TTYPE12 = 'vxe ' TFORM12 = 'D ' TTYPE13 = 'vye ' TFORM13 = 'D ' TTYPE14 = 'x0 ' TFORM14 = 'D ' TTYPE15 = 'y0 ' TFORM15 = 'D ' TTYPE16 = 'x0e ' TFORM16 = 'D ' TTYPE17 = 'y0e ' TFORM17 = 'D ' TTYPE18 = 'chi2_vx ' TFORM18 = 'D ' TTYPE19 = 'chi2_vy ' TFORM19 = 'D ' TTYPE20 = 't0 ' TFORM20 = 'D ' TTYPE21 = 'n_vfit ' TFORM21 = 'D ' EPNAMES = '2005_F814W_F1' EPNAMES = '2010_F125W_F3' EPNAMES = '2010_F139M_F2' EPNAMES = '2010_F160W_F1' EPNAMES = '2013_F160W_F1' EPNAMES = '2015_F160W_F1' ZPOINTS = 32.6783 ZPOINTS = 25.2305 ZPOINTS = 23.2835 ZPOINTS = 24.5698 ZPOINTS = 24.5698 ZPOINTS = 24.5698 YEARS = 2005.485 YEARS = 2010.652 YEARS = 2010.652 YEARS = 2010.652 YEARS = 2013.199 YEARS = 2015.148 HIERARCH DATE PRODUCED = '2025-06-30' HIERARCH INSTRUMENT = 'ACSWFC ' HIERARCH INSTRUMENT = 'WFC3IR ' HIERARCH INSTRUMENT = 'WFC3IR ' HIERARCH INSTRUMENT = 'WFC3IR ' HIERARCH INSTRUMENT = 'WFC3IR ' HIERARCH INSTRUMENT = 'WFC3IR ' END @Ÿ ˆ1&éy@Ÿ ›c+Ø(@Ÿ ˆ1&éy@Ÿ “4U‡*@Ÿ ˆ1&éy@Ÿ ¥ÆàOS@Ÿ ˆ1&éy@Ÿ…ÍÅ•Ü@Ÿ ˆ1&éy@Ÿ èÁ Î!@Ÿ ˆ1&éy@Ÿ]H/Ò@¢nzáG®@¢ns2ph@¢nzáG®@¢nˆ:ötä@¢nzáG®@¢mÐüùI±@¢nzáG®@¢mÓÐùòm@¢nzáG®@¢nbö3›@¢nzáG®@¢nsþ¤ÿŠ@8‚ò䎊@8m¥1›Š@4›¥ãSø@3¹Ã!dÏÎ@3š~ùÛ"Ñ@3Q@䩤@2èè§æ›@2h4¥àûZ@2èè§æ›@2•ÄŠÉRd@2èè§æ›@2–&îEK?¤hja¢ÖQ?¤ƒ´Æ*àä?¢iœy‘Ï?ÁÛÆÀï?» +Ld¢?²OU=°6i?¿/nI|Áâ?˜Š·¤‰ÿ?•`«²lñ??ºþ!æàg?Àî’”ï '?»Ï‡Ê1ñ?¶å¿#ý ?–ãjo¼ð?ƒ /±‘OÈ?‘‰©Þ¥e?£ü.Eôv?£ì¶Ñ [\@@"@@"@4@.?ð?ð?ð?ð?ð?ð?“EkŸ€?m¥•oÜ?zÁ•ºS ?œT 8O@ŸËÌÃ@¢n?ð/Ç? ? 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chi2x, chi2y = motion_model_to_fit.get_chi2([params], [t0], time, x, y, xe, ye) + chi2x, chi2y = motion_model_to_fit.get_chi2(params, [t0], time, x, y, xe, ye) param_data['chi2_x'][i] = chi2x param_data['chi2_y'][i] = chi2y From 13a523a430a3abda10c7830142b497dd04f3f8fc Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Thu, 7 Aug 2025 16:42:38 -0700 Subject: [PATCH 087/254] Fix undefined startable --- flystar/motion_model.py | 6 +++--- flystar/startables.py | 30 +++++++++++++++--------------- 2 files changed, 18 insertions(+), 18 deletions(-) diff --git a/flystar/motion_model.py b/flystar/motion_model.py index 2429a0b..dddcb4e 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -91,13 +91,13 @@ def fit_motion_model(self, t, x, y, xe, ye, t0, bootstrap=0, weighting='var'): param_errs[inf_errs] = 0.0 return params, param_errs - - def get_chi2(self,fit_params,fixed_params, t,x,y,xe,ye,reduced=False): + + def get_chi2(self, fit_params, fixed_params, t, x, y, xe, ye, reduced=False): """ Get the chi^2 value for the current MM and the input data. """ - x_pred,y_pred = self.get_pos_at_time(fit_params,fixed_params, t) + x_pred, y_pred = self.get_pos_at_time(fit_params, fixed_params, t) chi2x = np.sum((x-x_pred)**2 / xe**2) chi2y = np.sum((y-y_pred)**2 / ye**2) if reduced: diff --git a/flystar/startables.py b/flystar/startables.py index ce7e5bf..8bbcc86 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -903,7 +903,7 @@ def fit_velocities_all_detected(self, motion_model_to_fit, weighting='var', epoc Parameters ---------- motion_model_to_fit : MotionModel - motion model object to use for fitting all stars + Motion model object to use for fitting all stars weighting : str, optional Variance weighting('var') or standard deviation weighting ('std'), by default 'var' epoch_cols : str or list of intergers, optional @@ -954,9 +954,9 @@ def fit_velocities_all_detected(self, motion_model_to_fit, weighting='var', epoc # START FORMER FIT VELOCITY FUNCITON if epoch_cols is None: - epoch_cols = np.arange(len(startable.meta['YEARS'])) # use all cols if not specified` - - N = len(startable) + epoch_cols = np.arange(len(self.meta['YEARS'])) # use all cols if not specified` + + N = len(self) fit_params = motion_model_to_fit.fitter_param_names param_data = {p: np.zeros(N) for p in fit_params} param_data.update({p+'_err': np.zeros(N) for p in fit_params}) @@ -964,20 +964,20 @@ def fit_velocities_all_detected(self, motion_model_to_fit, weighting='var', epoc param_data['chi2_x'] = np.zeros(N) param_data['chi2_y'] = np.zeros(N) - time = np.array(startable.meta['YEARS'])[epoch_cols] + time = np.array(self.meta['YEARS'])[epoch_cols] if not art_star: - x_arr = startable['x'][:, epoch_cols] - y_arr = startable['y'][:, epoch_cols] + x_arr = self['x'][:, epoch_cols] + y_arr = self['y'][:, epoch_cols] else: - x_arr = startable['x'][:, epoch_cols, 1] - y_arr = startable['y'][:, epoch_cols, 1] - - xe_arr = startable['xe'][:, epoch_cols] - ye_arr = startable['ye'][:, epoch_cols] - + x_arr = self['x'][:, epoch_cols, 1] + y_arr = self['y'][:, epoch_cols, 1] + + xe_arr = self['xe'][:, epoch_cols] + ye_arr = self['ye'][:, epoch_cols] + # For each star - for i in tqdm(range(len(startable))): + for i in tqdm(range(N)): x = x_arr[i] y = y_arr[i] xe = xe_arr[i] @@ -988,7 +988,7 @@ def fit_velocities_all_detected(self, motion_model_to_fit, weighting='var', epoc params, param_errs = motion_model_to_fit.fit_motion_model(time, x, y, xe, ye, t0, weighting=weighting) if 't0' in motion_model_to_fit.fixed_param_names: param_data['t0'][i] = t0 - for j, param in fit_params: + for j, param in enumerate(fit_params): param_data[param][i] = params[j] param_data[param+'_err'][i] = param_errs[j] chi2x, chi2y = motion_model_to_fit.get_chi2([params], [t0], time, x, y, xe, ye) From c29daaad7d7c16bdf5754b13a717ae32f008aafe Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Thu, 7 Aug 2025 17:51:49 -0700 Subject: [PATCH 088/254] Added test function for fit_velocities_detected_all --- flystar/startables.py | 6 ++-- ...linear_vel.fits => test_all_detected.fits} | 0 flystar/tests/test_startable.py | 28 +++++++++++++++++++ 3 files changed, 32 insertions(+), 2 deletions(-) rename flystar/tests/{test_linear_vel.fits => test_all_detected.fits} (100%) diff --git a/flystar/startables.py b/flystar/startables.py index ad26d24..8b1d40f 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -1,4 +1,4 @@ -from astropy.table import Table, Column, hstack +from astropy.table import Table, Column, MaskedColumn, hstack from astropy.stats import sigma_clipping from astropy.time import Time from scipy.optimize import curve_fit @@ -11,6 +11,7 @@ import time import copy from flystar import motion_model +import pandas as pd class StarTable(Table): """ @@ -1010,7 +1011,8 @@ def fit_velocities_all_detected(self, motion_model_to_fit, weighting='var', epoc # Update self for column in columns: column_array = np.ma.zeros(N_stars) - column_array[detected_in_all_epochs] = vel_result[column] + print(vel_result[column]) + column_array = MaskedColumn(vel_result[column], dtype=float) column_array[~detected_in_all_epochs] = np.nan column_array.mask = ~detected_in_all_epochs self[column] = column_array diff --git a/flystar/tests/test_linear_vel.fits b/flystar/tests/test_all_detected.fits similarity index 100% rename from flystar/tests/test_linear_vel.fits rename to flystar/tests/test_all_detected.fits diff --git a/flystar/tests/test_startable.py b/flystar/tests/test_startable.py index feda0d6..0bc7f1a 100644 --- a/flystar/tests/test_startable.py +++ b/flystar/tests/test_startable.py @@ -494,6 +494,34 @@ def test_fit_velocities_2epoch(): return +def test_fit_velocities_all_detected(): + """ + Test the fit_velocities function when all stars are detected in all epochs. + """ + tab = StarTable.read(test_dir + '/test_all_detected.fits') + tab_orig = tab.copy() + + epochs = ['2005_F814W', '2010_F160W', '2013_F160W', '2015_F160W'] + epoch_cols = [['_'.join(_.split('_')[:2]) for _ in tab.meta['EPNAMES']].index(epoch) for epoch in epochs] + + mm = Linear(use_scipy=False, absolute_sigma=False) + tab.fit_velocities_all_detected( + weighting='var', + motion_model_to_fit=mm, + ) + + # Check that the output table has the expected columns + for col in ['n_fit', 't0', 'x0', 'y0', 'vx', 'vy', 'x0_err', 'y0_err', 'vx_err', 'vy_err']: + assert col in tab.colnames + + # Check that the fitted values match the original values + np.testing.assert_almost_equal(tab['x0'], tab_orig['x0']) + np.testing.assert_almost_equal(tab['y0'], tab_orig['y0']) + np.testing.assert_almost_equal(tab['vx'], tab_orig['vx']) + np.testing.assert_almost_equal(tab['vy'], tab_orig['vy']) + + return + def make_star_table(): # User input cat_file = test_dir + '/test_catalog.fits' From bf89b6f85a6051aca2701349afa32bfc6d13d3f5 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Fri, 8 Aug 2025 13:33:22 -0700 Subject: [PATCH 089/254] motion model import --- flystar/tests/test_startable.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/flystar/tests/test_startable.py b/flystar/tests/test_startable.py index 0bc7f1a..ad1db21 100644 --- a/flystar/tests/test_startable.py +++ b/flystar/tests/test_startable.py @@ -2,6 +2,7 @@ from astropy import table from flystar.startables import StarTable from flystar.starlists import StarList +from flystar import motion_model import numpy as np import pytest import os @@ -504,7 +505,7 @@ def test_fit_velocities_all_detected(): epochs = ['2005_F814W', '2010_F160W', '2013_F160W', '2015_F160W'] epoch_cols = [['_'.join(_.split('_')[:2]) for _ in tab.meta['EPNAMES']].index(epoch) for epoch in epochs] - mm = Linear(use_scipy=False, absolute_sigma=False) + mm = motion_model.Linear(use_scipy=False, absolute_sigma=False) tab.fit_velocities_all_detected( weighting='var', motion_model_to_fit=mm, From b252fbb0c58ff1eae7fb3a4917dae48f7372df4f Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Fri, 8 Aug 2025 15:25:20 -0700 Subject: [PATCH 090/254] Fix test_fit_velocities_all_detected --- flystar/motion_model.py | 4 ++-- flystar/startables.py | 1 - flystar/tests/test_startable.py | 11 +++++++++-- 3 files changed, 11 insertions(+), 5 deletions(-) diff --git a/flystar/motion_model.py b/flystar/motion_model.py index dddcb4e..2ba6846 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -237,8 +237,8 @@ def linear(t, c0, c1): # Use https://en.wikipedia.org/wiki/Weighted_least_squares#Solution scheme x = np.array(x) y = np.array(y) - t = np.array(t) - X_mat_t = np.vander(t, 2) + dt = np.array(dt) + X_mat_t = np.vander(dt, 2) # x calculation W_mat_x = np.diag(x_wt) XTWX_mat_x = X_mat_t.T @ W_mat_x @ X_mat_t diff --git a/flystar/startables.py b/flystar/startables.py index 8b1d40f..a5bb496 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -1011,7 +1011,6 @@ def fit_velocities_all_detected(self, motion_model_to_fit, weighting='var', epoc # Update self for column in columns: column_array = np.ma.zeros(N_stars) - print(vel_result[column]) column_array = MaskedColumn(vel_result[column], dtype=float) column_array[~detected_in_all_epochs] = np.nan column_array.mask = ~detected_in_all_epochs diff --git a/flystar/tests/test_startable.py b/flystar/tests/test_startable.py index 0bc7f1a..8cbb478 100644 --- a/flystar/tests/test_startable.py +++ b/flystar/tests/test_startable.py @@ -1,5 +1,6 @@ from astropy.table import Table from astropy import table +from flystar import motion_model from flystar.startables import StarTable from flystar.starlists import StarList import numpy as np @@ -500,14 +501,17 @@ def test_fit_velocities_all_detected(): """ tab = StarTable.read(test_dir + '/test_all_detected.fits') tab_orig = tab.copy() + # tab = tab[:1] epochs = ['2005_F814W', '2010_F160W', '2013_F160W', '2015_F160W'] epoch_cols = [['_'.join(_.split('_')[:2]) for _ in tab.meta['EPNAMES']].index(epoch) for epoch in epochs] - - mm = Linear(use_scipy=False, absolute_sigma=False) + + mm = motion_model.Linear(use_scipy=False, absolute_sigma=False) tab.fit_velocities_all_detected( weighting='var', motion_model_to_fit=mm, + epoch_cols=epoch_cols, + art_star=True ) # Check that the output table has the expected columns @@ -517,8 +521,11 @@ def test_fit_velocities_all_detected(): # Check that the fitted values match the original values np.testing.assert_almost_equal(tab['x0'], tab_orig['x0']) np.testing.assert_almost_equal(tab['y0'], tab_orig['y0']) + np.testing.assert_almost_equal(tab['t0'], tab_orig['t0']) np.testing.assert_almost_equal(tab['vx'], tab_orig['vx']) np.testing.assert_almost_equal(tab['vy'], tab_orig['vy']) + np.testing.assert_almost_equal(tab['vxe'], tab_orig['vxe']) + np.testing.assert_almost_equal(tab['vye'], tab_orig['vye']) return From 454e49735b4448ccdb1219e9a5cc7d63c93f5a39 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Thu, 14 Aug 2025 12:20:29 -0700 Subject: [PATCH 091/254] move fitter options into fit_velocities functions, not motion_model objects --- flystar/align.py | 3 +++ flystar/motion_model.py | 42 +++++++++++++++++++++++++++-------------- flystar/startables.py | 22 ++++++++++++--------- 3 files changed, 44 insertions(+), 23 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 14926d9..811fabe 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -2434,6 +2434,9 @@ def transform_from_object(starlist, transform): if err: xe = starlist_f['xe'] ye = starlist_f['ye'] + else: + xe = np.zeros(len(starlist_f)) + ye = np.zeros(len(starlist_f)) if vel: x0 = starlist_f['x0'] diff --git a/flystar/motion_model.py b/flystar/motion_model.py index 2ba6846..9777152 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -41,7 +41,8 @@ def get_batch_pos_at_time(self, t): #return x, y, x_err, y_err pass - def run_fit(self, t, x, y, xe, ye, t0, weighting='var'): + def run_fit(self, t, x, y, xe, ye, t0, weighting='var', + use_scipy=True, absolute_sigma=True): # Run a single fit (used both for overall fit + bootstrap iterations) pass @@ -63,13 +64,15 @@ def scale_errors(self, errs, weighting='var'): warnings.warn("Invalid weighting, using default weighting scheme var.", UserWarning) return errs - def fit_motion_model(self, t, x, y, xe, ye, t0, bootstrap=0, weighting='var'): + def fit_motion_model(self, t, x, y, xe, ye, t0, bootstrap=0, weighting='var', + use_scipy=True, absolute_sigma=True): """ Fit the input positions on the sky and errors to determine new parameters for this motion model (MM). Best-fit parameters will be returned along with uncertainties. """ - params, param_errs = self.run_fit(t, x, y, xe, ye, t0, weighting=weighting) + params, param_errs = self.run_fit(t, x, y, xe, ye, t0, weighting=weighting, + use_scipy=use_scipy, absolute_sigma=absolute_sigma) if bootstrap>0 and len(x)>(self.n_pts_req): edx = np.arange(len(x), dtype=int) @@ -79,7 +82,9 @@ def fit_motion_model(self, t, x, y, xe, ye, t0, bootstrap=0, weighting='var'): bdx = np.random.choice(edx, len(x)) while len(np.unique(bdx)) 0 and finite, x/y not masked. @@ -986,7 +989,8 @@ def fit_velocities_all_detected(self, motion_model_to_fit, weighting='var', epoc t0 = np.average(time, weights=1. / np.hypot(xe, ye)) # Run fit and record results - params, param_errs = motion_model_to_fit.fit_motion_model(time, x, y, xe, ye, t0, weighting=weighting) + params, param_errs = motion_model_to_fit.fit_motion_model(time, x, y, xe, ye, t0, weighting=weighting, + use_scipy=use_scipy, absolute_sigma=absolute_sigma) if 't0' in motion_model_to_fit.fixed_param_names: param_data['t0'][i] = t0 for j, param in enumerate(fit_params): From dd50d1878bb948952cbdf818d2d0b52b4d47c130 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Thu, 14 Aug 2025 12:25:19 -0700 Subject: [PATCH 092/254] move fitter settings for fit_velocities_all_detected test --- flystar/tests/test_startable.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/flystar/tests/test_startable.py b/flystar/tests/test_startable.py index f405101..9962c05 100644 --- a/flystar/tests/test_startable.py +++ b/flystar/tests/test_startable.py @@ -507,9 +507,10 @@ def test_fit_velocities_all_detected(): epochs = ['2005_F814W', '2010_F160W', '2013_F160W', '2015_F160W'] epoch_cols = [['_'.join(_.split('_')[:2]) for _ in tab.meta['EPNAMES']].index(epoch) for epoch in epochs] - mm = motion_model.Linear(use_scipy=False, absolute_sigma=False) + mm = motion_model.Linear() tab.fit_velocities_all_detected( weighting='var', + use_scipy=False, absolute_sigma=False, motion_model_to_fit=mm, epoch_cols=epoch_cols, art_star=True From 1bf24d8a1d6799d54598c2a775abc84772002054 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Thu, 14 Aug 2025 17:52:18 -0700 Subject: [PATCH 093/254] change motion_model tests for updates --- flystar/tests/test_motion_model.py | 182 +++++++++++++++-------------- 1 file changed, 93 insertions(+), 89 deletions(-) diff --git a/flystar/tests/test_motion_model.py b/flystar/tests/test_motion_model.py index facb2af..a5d8fdb 100755 --- a/flystar/tests/test_motion_model.py +++ b/flystar/tests/test_motion_model.py @@ -9,21 +9,19 @@ def within_error(true_val, fit_val, fit_err, n_sigma=3): def test_Fixed(): # Test handling of a single star true_params = {'x0': 1.0, 'y0':0.5, 'x0_err':0.1, 'y0_err':0.1} - mod_true = motion_model.Fixed(**true_params) - param_list = mod_true.fitter_param_names + mod = motion_model.Fixed() + param_list = mod.fitter_param_names + fixed_param_list = mod.fixed_param_names # Confirm return of proper values for single t and array t - x_t, y_t = mod_true.get_pos_at_time(0.0) + x_t, y_t = mod.get_pos_at_time([true_params[p] for p in param_list], + [true_params[p] for p in fixed_param_list], 0.0) assert x_t==true_params['x0'] assert y_t==true_params['y0'] - x_t, y_t = mod_true.get_pos_at_time([0.0,mod_true.t0,10000]) + x_t, y_t = mod.get_pos_at_time([true_params[p] for p in param_list], + [true_params[p] for p in fixed_param_list], + [0.0,2025.0,10000]) assert (x_t==true_params['x0']).all() assert (y_t==true_params['y0']).all() - x_err_t, y_err_t = mod_true.get_pos_err_at_time(0.0) - assert x_err_t==true_params['x0_err'] - assert y_err_t==true_params['y0_err'] - x_err_t, y_err_t = mod_true.get_pos_err_at_time([0.0,mod_true.t0,10000]) - assert (x_err_t==true_params['x0_err']).all() - assert (y_err_t==true_params['y0_err']).all() # Check behavior of get_batch_pos_at_time x0_batch = np.random.uniform(-2.0,2.0, 50) @@ -32,7 +30,7 @@ def test_Fixed(): y0_err_batch = np.repeat(0.1, 50) # Single epoch t_batch=2020.0 - x_t_batch, y_t_batch, x_err_t_batch, y_err_t_batch = mod_true.get_batch_pos_at_time(t_batch, + x_t_batch, y_t_batch, x_err_t_batch, y_err_t_batch = mod.get_batch_pos_at_time(t_batch, x0=x0_batch, y0=y0_batch, x0_err=x0_err_batch, y0_err=y0_err_batch) assert (x_t_batch==x0_batch).all() assert (y_t_batch==y0_batch).all() @@ -40,7 +38,7 @@ def test_Fixed(): assert (y_err_t_batch==y0_err_batch).all() # Multiple times t_batch = np.arange(2015.0,2025.0, 0.5) - x_t_batch, y_t_batch, x_err_t_batch, y_err_t_batch = mod_true.get_batch_pos_at_time(t_batch, + x_t_batch, y_t_batch, x_err_t_batch, y_err_t_batch = mod.get_batch_pos_at_time(t_batch, x0=x0_batch, y0=y0_batch, x0_err=x0_err_batch, y0_err=y0_err_batch) assert (x_t_batch==np.array([np.repeat(x0_batch_i, len(t_batch)) for x0_batch_i in x0_batch])).all() assert (y_t_batch==np.array([np.repeat(y0_batch_i, len(t_batch)) for y0_batch_i in y0_batch])).all() @@ -50,13 +48,15 @@ def test_Fixed(): # Test fitter t = np.arange(2015.0,2025.0, 0.5) # Get values from model and add scatter - x_true, y_true = mod_true.get_pos_at_time(t) - x_true_err, y_true_err = mod_true.get_pos_err_at_time(t) - x_sim = np.random.normal(x_true, x_true_err) - y_sim = np.random.normal(y_true, y_true_err) + x_true, y_true = mod.get_pos_at_time([true_params[p] for p in param_list], + [true_params[p] for p in fixed_param_list], t) + x_sim = np.random.normal(x_true, true_params['x0_err']) + y_sim = np.random.normal(y_true, true_params['y0_err']) # Run fit - mod_fit = motion_model.Fixed() - params, param_errs = mod_fit.fit_motion_model(t, x_sim,y_sim, x_true_err, y_true_err) + params, param_errs = mod.fit_motion_model(t, x_sim,y_sim, + np.ones(len(t))*true_params['x0_err'], + np.ones(len(t))*true_params['y0_err'], + np.nan) # Confirm true value is within error bar of fit value assert np.all([within_error(true_params[param_list[i]], params[i], param_errs[i]) for i in range(len(params))]) @@ -66,22 +66,21 @@ def test_Linear(): true_params = {'x0': 1.0, 'y0':0.5, 'x0_err':0.1, 'y0_err':0.1, 'vx':0.2, 'vy':0.5, 'vx_err':0.05, 'vy_err':0.05, 't0':2025.0} - mod_true = motion_model.Linear(**true_params) - param_list = mod_true.fitter_param_names + mod = motion_model.Linear() + param_list = mod.fitter_param_names + fixed_param_list = mod.fixed_param_names # Confirm return of proper values for single t=t0 and array t - x_t, y_t = mod_true.get_pos_at_time(mod_true.t0) + x_t, y_t = mod.get_pos_at_time([true_params[p] for p in param_list], + [true_params[p] for p in fixed_param_list], + true_params['t0']) assert x_t==true_params['x0'] assert y_t==true_params['y0'] - t_arr = np.array([2010.0,mod_true.t0,2030.0]) - x_t, y_t = mod_true.get_pos_at_time(t_arr) - assert (x_t==(true_params['x0'] + (t_arr-mod_true.t0)*true_params['vx'])).all() - assert (y_t==(true_params['y0'] + (t_arr-mod_true.t0)*true_params['vy'])).all() - x_err_t, y_err_t = mod_true.get_pos_err_at_time(mod_true.t0) - assert x_err_t==true_params['x0_err'] - assert y_err_t==true_params['y0_err'] - x_err_t, y_err_t = mod_true.get_pos_err_at_time(t_arr) - assert (x_err_t==np.hypot(true_params['x0_err'],(t_arr-mod_true.t0)*true_params['vx_err'])).all() - assert (y_err_t==np.hypot(true_params['y0_err'],(t_arr-mod_true.t0)*true_params['vy_err'])).all() + t_arr = np.array([2010.0,true_params['t0'],2030.0]) + x_t, y_t = mod.get_pos_at_time([true_params[p] for p in param_list], + [true_params[p] for p in fixed_param_list], + t_arr) + assert (x_t==(true_params['x0'] + (t_arr-true_params['t0'])*true_params['vx'])).all() + assert (y_t==(true_params['y0'] + (t_arr-true_params['t0'])*true_params['vy'])).all() # Check behavior of get_batch_pos_at_time x0_batch = np.random.uniform(-2.0,2.0, 50) @@ -95,7 +94,7 @@ def test_Linear(): t0_batch = np.repeat(2025.0,50) # Single epoch t_batch=2020.0 - x_t_batch, y_t_batch, x_err_t_batch, y_err_t_batch = mod_true.get_batch_pos_at_time(t_batch, + x_t_batch, y_t_batch, x_err_t_batch, y_err_t_batch = mod.get_batch_pos_at_time(t_batch, x0=x0_batch, y0=y0_batch, x0_err=x0_err_batch, y0_err=y0_err_batch, vx=vx_batch, vy=vy_batch, vx_err=vx_err_batch, vy_err=vy_err_batch, t0=t0_batch) @@ -105,7 +104,7 @@ def test_Linear(): assert (y_err_t_batch==np.hypot(y0_err_batch, (t_batch-t0_batch)*vy_err_batch)).all() # Multiple times t_batch = np.arange(2015.0,2025.0, 0.5) - x_t_batch, y_t_batch, x_err_t_batch, y_err_t_batch = mod_true.get_batch_pos_at_time(t_batch, + x_t_batch, y_t_batch, x_err_t_batch, y_err_t_batch = mod.get_batch_pos_at_time(t_batch, x0=x0_batch, y0=y0_batch, x0_err=x0_err_batch, y0_err=y0_err_batch, vx=vx_batch, vy=vy_batch, vx_err=vx_err_batch, vy_err=vy_err_batch, t0=t0_batch) @@ -117,13 +116,13 @@ def test_Linear(): # Test fitter t = np.arange(2015.0,2025.0, 0.5) # Get values from model and add scatter - x_true, y_true = mod_true.get_pos_at_time(t) - x_true_err, y_true_err = mod_true.get_pos_err_at_time(t) - x_sim = np.random.normal(x_true, x_true_err) - y_sim = np.random.normal(y_true, y_true_err) + x_true, y_true = mod.get_pos_at_time([true_params[p] for p in param_list], + [true_params[p] for p in fixed_param_list],t) + x_sim = np.random.normal(x_true, 0.05) + y_sim = np.random.normal(y_true, 0.05) # Run fit - mod_fit = motion_model.Linear(t0=true_params['t0']) - params, param_errs = mod_fit.fit_motion_model(t, x_sim,y_sim, x_true_err, y_true_err) + params, param_errs = mod.fit_motion_model(t, x_sim,y_sim, + np.repeat(0.05, len(t)), np.repeat(0.05,len(t)), true_params['t0']) print(param_errs) # Confirm true value is within error bar of fit value assert np.all([within_error(true_params[param_list[i]], params[i], param_errs[i]) for i in range(len(params))]) @@ -131,30 +130,34 @@ def test_Linear(): # Test fitter with bootstrap t = np.arange(2015.0,2025.0, 0.5) # Get values from model and add scatter - x_true, y_true = mod_true.get_pos_at_time(t) - x_true_err, y_true_err = mod_true.get_pos_err_at_time(t) + x_true, y_true = mod.get_pos_at_time([true_params[p] for p in param_list], + [true_params[p] for p in fixed_param_list],t) + x_true_err, y_true_err = np.repeat(0.05,len(t)), np.repeat(0.05,len(t)) x_sim = np.random.normal(x_true, x_true_err) y_sim = np.random.normal(y_true, y_true_err) # Run fit - mod_fit = motion_model.Linear(t0=true_params['t0']) - params, param_errs = mod_fit.fit_motion_model(t, x_sim,y_sim, x_true_err, y_true_err,bootstrap=10) + params, param_errs = mod.fit_motion_model(t, x_sim,y_sim, x_true_err, y_true_err, true_params['t0'],bootstrap=10) print(param_errs) # Confirm true value is within error bar of fit value assert np.all([within_error(true_params[param_list[i]], params[i], param_errs[i]) for i in range(len(params))]) - # Test fitter for 2 pts - t = np.array([2015.0,2025.0]) - # Get values from model and add scatter - x_true, y_true = mod_true.get_pos_at_time(t) - x_true_err, y_true_err = mod_true.get_pos_err_at_time(t) - x_sim = np.random.normal(x_true, x_true_err) - y_sim = np.random.normal(y_true, y_true_err) - # Run fit - mod_fit = motion_model.Linear(t0=true_params['t0']) - params, param_errs = mod_fit.fit_motion_model(t, x_sim,y_sim, x_true_err, y_true_err) - print(param_errs) - # Confirm true value is within error bar of fit value - assert np.all([within_error(true_params[param_list[i]], params[i], param_errs[i]) for i in range(len(params))]) +# # Test fitter for 2 pts +# t = np.array([2015.0,2025.0]) +# # Get values from model and add scatter +# x_true, y_true = mod.get_pos_at_time([true_params[p] for p in param_list], +# [true_params[p] for p in fixed_param_list],t) +# x_true_err, y_true_err = np.repeat(0.05,len(t)), np.repeat(0.05,len(t)) +# x_sim = np.random.normal(x_true, x_true_err) +# y_sim = np.random.normal(y_true, y_true_err) +# # Run fit +# mod_fit = motion_model.Linear(t0=true_params['t0']) +# params, param_errs = mod_fit.fit_motion_model(t, x_sim,y_sim, x_true_err, y_true_err, true_params['t0']) +# print("DJSKBGJ",param_list) +# print([true_params[p] for p in param_list]) +# print(params) +# print(param_errs) +# # Confirm true value is within error bar of fit value +# assert np.all([within_error(true_params[param_list[i]], params[i], param_errs[i]) for i in range(len(params),2)]) def test_Acceleration(): @@ -163,24 +166,21 @@ def test_Acceleration(): 'vx0':0.2, 'vy0':0.5, 'vx0_err':0.05, 'vy0_err':0.05, 'ax':0.1, 'ay':-0.1, 'ax_err':0.02, 'ay_err':0.02, 't0':2025.0} - mod_true = motion_model.Acceleration(**true_params) - param_list = mod_true.fitter_param_names + mod = motion_model.Acceleration() + param_list = mod.fitter_param_names + fixed_param_list = mod.fixed_param_names # Confirm return of proper values for single t=t0 and array t - x_t, y_t = mod_true.get_pos_at_time(mod_true.t0) + x_t, y_t = mod.get_pos_at_time([true_params[p] for p in param_list], + [true_params[p] for p in fixed_param_list], + true_params['t0']) assert x_t==true_params['x0'] assert y_t==true_params['y0'] - t_arr = np.array([2010.0,mod_true.t0,2030.0]) - x_t, y_t = mod_true.get_pos_at_time(t_arr) - assert (x_t==(true_params['x0'] + (t_arr-mod_true.t0)*true_params['vx0'] + 0.5*(t_arr-mod_true.t0)**2*true_params['ax'])).all() - assert (y_t==(true_params['y0'] + (t_arr-mod_true.t0)*true_params['vy0'] + 0.5*(t_arr-mod_true.t0)**2*true_params['ay'])).all() - x_err_t, y_err_t = mod_true.get_pos_err_at_time(mod_true.t0) - assert x_err_t==true_params['x0_err'] - assert y_err_t==true_params['y0_err'] - x_err_t, y_err_t = mod_true.get_pos_err_at_time(t_arr) - assert (x_err_t==np.sqrt(true_params['x0_err']**2 + ((t_arr-mod_true.t0)*true_params['vx0_err'])**2 + - (0.5*(t_arr-mod_true.t0)**2*true_params['ax_err'])**2)).all() - assert (y_err_t==np.sqrt(true_params['y0_err']**2 + ((t_arr-mod_true.t0)*true_params['vy0_err'])**2 + - (0.5*(t_arr-mod_true.t0)**2*true_params['ay_err'])**2)).all() + t_arr = np.array([2010.0,true_params['t0'],2030.0]) + x_t, y_t = mod.get_pos_at_time([true_params[p] for p in param_list], + [true_params[p] for p in fixed_param_list], + t_arr) + assert (x_t==(true_params['x0'] + (t_arr-true_params['t0'])*true_params['vx0'] + 0.5*(t_arr-true_params['t0'])**2*true_params['ax'])).all() + assert (y_t==(true_params['y0'] + (t_arr-true_params['t0'])*true_params['vy0'] + 0.5*(t_arr-true_params['t0'])**2*true_params['ay'])).all() # Check behavior of get_batch_pos_at_time x0_batch = np.random.uniform(-2.0,2.0, 50) @@ -198,7 +198,7 @@ def test_Acceleration(): t0_batch = np.repeat(2025.0,50) # Single epoch t_batch=2020.0 - x_t_batch, y_t_batch, x_err_t_batch, y_err_t_batch = mod_true.get_batch_pos_at_time(t_batch, + x_t_batch, y_t_batch, x_err_t_batch, y_err_t_batch = mod.get_batch_pos_at_time(t_batch, x0=x0_batch, y0=y0_batch, x0_err=x0_err_batch, y0_err=y0_err_batch, vx0=vx0_batch, vy0=vy0_batch, vx0_err=vx0_err_batch, vy0_err=vy0_err_batch, ax=ax_batch, ay=ay_batch, ax_err=ax_err_batch, ay_err=ay_err_batch, @@ -211,7 +211,7 @@ def test_Acceleration(): (0.5*(t_batch-t0_batch)**2*ay_err_batch)**2)).all() # Multiple times t_batch = np.arange(2015.0,2025.0, 0.5) - x_t_batch, y_t_batch, x_err_t_batch, y_err_t_batch = mod_true.get_batch_pos_at_time(t_batch, + x_t_batch, y_t_batch, x_err_t_batch, y_err_t_batch = mod.get_batch_pos_at_time(t_batch, x0=x0_batch, y0=y0_batch, x0_err=x0_err_batch, y0_err=y0_err_batch, vx0=vx0_batch, vy0=vy0_batch, vx0_err=vx0_err_batch, vy0_err=vy0_err_batch, ax=ax_batch, ay=ay_batch, ax_err=ax_err_batch, ay_err=ay_err_batch, @@ -224,13 +224,17 @@ def test_Acceleration(): # Test fitter t = np.arange(2015.0,2025.0, 0.5) # Get values from model and add scatter - x_true, y_true = mod_true.get_pos_at_time(t) - x_true_err, y_true_err = mod_true.get_pos_err_at_time(t) + x_true, y_true = mod.get_pos_at_time([true_params[p] for p in param_list], + [true_params[p] for p in fixed_param_list],t) + x_true_err = np.sqrt(true_params['x0_err']**2 + ((t-true_params['t0'])*true_params['vx0_err'])**2 + + (0.5*(t-true_params['t0'])**2*true_params['ax_err'])**2) + y_true_err = np.sqrt(true_params['y0_err']**2 + ((t-true_params['t0'])*true_params['vy0_err'])**2 + + (0.5*(t-true_params['t0'])**2*true_params['ay_err'])**2) x_sim = np.random.normal(x_true, x_true_err) y_sim = np.random.normal(y_true, y_true_err) # Run fit - mod_fit = motion_model.Acceleration(t0=mod_true.t0) - params, param_errs = mod_fit.fit_motion_model(t, x_sim,y_sim, x_true_err, y_true_err) + mod_fit = motion_model.Acceleration(t0=true_params['t0']) + params, param_errs = mod_fit.fit_motion_model(t, x_sim,y_sim, x_true_err, y_true_err, true_params['t0']) # Confirm true value is within error bar of fit value assert np.all([within_error(true_params[param_list[i]], params[i], param_errs[i]) for i in range(len(params))]) @@ -241,19 +245,21 @@ def test_Parallax(): 'vx':-0.2, 'vy':0.5, 'vx_err':0.05, 'vy_err':0.05, 'pi':0.5, 'RA':17.76, 'Dec':-28.933, 'PA':0, 't0':2020.0} - mod_true = motion_model.Parallax(**true_params) - param_list = mod_true.fitter_param_names + mod = motion_model.Parallax(**{'RA':17.76, 'Dec':-28.933, 'PA':0}) + param_list = mod.fitter_param_names + fixed_param_list = mod.fixed_param_names + print(param_list) # Test fitter t = np.arange(2015.0,2025.0, 0.5) # Get values from model and add scatter - x_true, y_true = mod_true.get_pos_at_time(t) - x_true_err, y_true_err = mod_true.get_pos_err_at_time(t) + x_true, y_true = mod.get_pos_at_time([true_params[p] for p in param_list], + [true_params[p] for p in fixed_param_list],t) + x_true_err, y_true_err = np.repeat(0.1,len(t)), np.repeat(0.1,len(t)) x_sim = np.random.normal(x_true, x_true_err) y_sim = np.random.normal(y_true, y_true_err) # Run fit - mod_fit = motion_model.Parallax(RA=17.76, Dec=-28.933, PA=0, t0=mod_true.t0) - params, param_errs = mod_fit.fit_motion_model(t, x_sim,y_sim, x_true_err, y_true_err) + params, param_errs = mod.fit_motion_model(t, x_sim,y_sim, x_true_err, y_true_err, true_params['t0']) # Confirm true value is within error bar of fit value assert np.all([within_error(true_params[param_list[i]], params[i], param_errs[i]) for i in range(len(params))]) @@ -264,13 +270,11 @@ def test_Parallax_PA(): vx, vy = 0.2, 0.5 RA, Dec = 17.76, -28.933 pi = 0.5 - mod_pa0 = motion_model.Parallax(x0=x0,vx=vx,y0=y0,vy=vy,pi=pi, - RA=RA,Dec=Dec,t0=2020.0, PA=0) + mod_pa0 = motion_model.Parallax(RA=RA,Dec=Dec, PA=0) # Set PA=90 model with equivalent parameters in that frame - mod_pa90 = motion_model.Parallax(x0=y0,vx=vy,y0=-x0,vy=-vx,pi=pi, - RA=RA,Dec=Dec,t0=2020.0, PA=90) + mod_pa90 = motion_model.Parallax(RA=RA,Dec=Dec,t0=2020.0, PA=90) t_set = np.arange(2018,2024,0.01) - dat_pa0 = mod_pa0.get_pos_at_time(t_set) - dat_pa90 = mod_pa90.get_pos_at_time(t_set) + dat_pa0 = mod_pa0.get_pos_at_time([x0,vx,y0,vy,pi],[2020.0],t_set) + dat_pa90 = mod_pa90.get_pos_at_time([y0,vy,-x0,-vx,pi],[2020.0],t_set) assert (np.abs(dat_pa0[0]-(-dat_pa90[1]))<1e-10).all() assert (np.abs(dat_pa0[1]-(dat_pa90[0]))<1e-10).all() From ed803320da47915ea77964ae79bf51bef3610fe4 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Wed, 27 Aug 2025 15:09:58 -0700 Subject: [PATCH 094/254] remove some unecessary fitting --- flystar/align.py | 39 +++++++++++++++++++++++++-------------- 1 file changed, 25 insertions(+), 14 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 811fabe..36f7cf5 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -325,7 +325,7 @@ def fit(self): self.match_lists(self.dr_tol[-1], self.dm_tol[-1]) # Hard-coded not to keep ref values for MosaicSelfRef - self.update_ref_table_aggregates(False) + self.update_ref_table_aggregates() ########## # Clean up output table. @@ -498,7 +498,15 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar ### Update the "average" values to be used as the reference frame for the next list. keep_ref_orig = (self.update_ref_orig==False) or (self.update_ref_orig=='atend') or (self.update_ref_orig=='periter' and ii<(len(self.star_lists)-1)) - self.update_ref_table_aggregates(keep_ref_orig) + if keep_ref_orig and ii<(len(self.star_lists)-1): + keep_orig = np.where(self.ref_table['ref_orig'] | np.isnan(self.ref_table['x'][:,ii]))[0] + elif keep_ref_orig: + keep_orig = np.where(self.ref_table['ref_orig'])[0] + elif ii<(len(self.star_lists)-1): + keep_orig = np.where(np.isnan(self.ref_table['x'][:,ii]))[0] + else: + keep_orig=None + self.update_ref_table_aggregates(keep_orig=keep_orig) # Print out some metrics if self.verbose > 0: @@ -797,7 +805,7 @@ def update_ref_table_from_list(self, star_list, star_list_T, ii, idx_ref, idx_li return - def update_ref_table_aggregates(self, keep_ref_orig, n_boot=0): + def update_ref_table_aggregates(self, keep_orig=None, n_boot=0): """ Average positions or fit velocities. Average magnitudes. @@ -809,20 +817,19 @@ def update_ref_table_aggregates(self, keep_ref_orig, n_boot=0): """ # Keep track of the original reference values. # In certain cases, we will NOT update these. - if keep_ref_orig: - ref_orig_idx = np.where(self.ref_table['ref_orig'] == True)[0] + if keep_orig is not None: vals_orig = {} - vals_orig['m0'] = self.ref_table['m0'][ref_orig_idx] - vals_orig['m0_err'] = self.ref_table['m0_err'][ref_orig_idx] + vals_orig['m0'] = self.ref_table['m0'][keep_orig] + vals_orig['m0_err'] = self.ref_table['m0_err'][keep_orig] motion_model_class_names = self.ref_table['motion_model_input'].tolist() if 'motion_model_used' in self.ref_table.keys(): - motion_model_class_names += self.ref_table['motion_model_used'][ref_orig_idx].tolist() - vals_orig['motion_model_used'] = self.ref_table['motion_model_used'][ref_orig_idx] + motion_model_class_names += self.ref_table['motion_model_used'][keep_orig].tolist() + vals_orig['motion_model_used'] = self.ref_table['motion_model_used'][keep_orig] motion_model_col_names = motion_model.get_list_motion_model_param_names(motion_model_class_names, with_errors=True, with_fixed=True) for mm in motion_model_col_names: if mm in self.ref_table.keys(): - vals_orig[mm] = self.ref_table[mm][ref_orig_idx] - fit_star_idxs = np.where(self.ref_table['ref_orig'] == False)[0] + vals_orig[mm] = self.ref_table[mm][keep_orig] + fit_star_idxs = [idx for idx in range(len(self.ref_table)) if idx not in keep_orig] else: fit_star_idxs = None #pdb.set_trace() @@ -847,9 +854,9 @@ def update_ref_table_aggregates(self, keep_ref_orig, n_boot=0): self.ref_table.combine_lists('m', weights_col=weights_col, ismag=True) #pdb.set_trace() # Replace the originals if we are supposed to keep them fixed. - if keep_ref_orig: + if keep_orig is not None: for val in vals_orig.keys(): - self.ref_table[val][ref_orig_idx] = vals_orig[val] + self.ref_table[val][keep_orig] = vals_orig[val] #pdb.set_trace() return @@ -1622,7 +1629,11 @@ def fit(self): self.match_lists(self.dr_tol[-1], self.dm_tol[-1]) keep_ref_orig = (self.update_ref_orig==False) - self.update_ref_table_aggregates(keep_ref_orig) + if keep_ref_orig: + keep_orig = np.where(self.ref_table['ref_orig'])[0] + else: + keep_orig=None + self.update_ref_table_aggregates(keep_orig=keep_orig) ########## # Clean up output table. From 1c80e66bef296e1c26756b6349c16d24a48bd8db Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Thu, 28 Aug 2025 11:24:22 -0700 Subject: [PATCH 095/254] remove fit_velocities.py --- flystar/startables.py | 1 - 1 file changed, 1 deletion(-) diff --git a/flystar/startables.py b/flystar/startables.py index 0a41859..a56a79f 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -2,7 +2,6 @@ from astropy.stats import sigma_clipping from astropy.time import Time from scipy.optimize import curve_fit -#from flystar.fit_velocity import linear_fit, calc_chi2, linear, fit_velocity from tqdm import tqdm import numpy as np import warnings From 495690b65d5602f6dac045a776e0db851563f560 Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Thu, 28 Aug 2025 11:42:31 -0700 Subject: [PATCH 096/254] Add select_idx function and single epoch case --- flystar/startables.py | 133 +++++++++++++++++++++++------------------- 1 file changed, 73 insertions(+), 60 deletions(-) diff --git a/flystar/startables.py b/flystar/startables.py index 0a41859..ff08dfc 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -547,7 +547,7 @@ def fit_velocities(self, weighting='var', use_scipy=True, absolute_sigma=True, b weighting : str, optional Weight by variance 'var' or standard deviation 'std', by default 'var' bootstrap : int, optional - Calculate uncertain using bootstraping or not, by default 0 + Calculate uncertainty using bootstraping or not, by default 0 fixed_t0 : bool or array-like, optional Fix the t0 in dt = time - t0 if user provides an array with the same length of the table, or automatically calculate t0 = np.average(time, weights=1/np.hypot(xe, ye)) if False, by default False verbose : bool, optional @@ -754,7 +754,7 @@ def fit_velocity_for_star(self, ss, motion_model_dict, weighting='var', use_scip if isinstance(mask_lists, list): if all(isinstance(item, int) for item in mask_lists): t.mask[mask_lists] = True - + # Throw a warning if mask_lists is not a list if not isinstance(mask_lists, list): raise RuntimeError('mask_lists needs to be a list.') @@ -899,8 +899,8 @@ def get_star_positions_at_time(self, t, motion_model_dict, allow_alt_models=True return x,y,xe,ye - def fit_velocities_all_detected(self, motion_model_to_fit, weighting='var', use_scipy=True, absolute_sigma=True, - default_motion_model='Linear', epoch_cols='all', mask_val=None, art_star=False, return_result=False): + def fit_velocities_all_detected(self, motion_model_to_fit, weighting='var', use_scipy=True, absolute_sigma=True, times=None, + select_stars=None, epoch_cols='all', mask_val=None, art_star=False, return_result=False): """Fit velocities for stars detected in all epochs specified by epoch_cols. Criterion: xe/ye error > 0 and finite, x/y not masked. @@ -910,6 +910,8 @@ def fit_velocities_all_detected(self, motion_model_to_fit, weighting='var', use_ Motion model object to use for fitting all stars weighting : str, optional Variance weighting('var') or standard deviation weighting ('std'), by default 'var' + select_idx : array-like, optional + Indices of stars to select for fitting, by default None (fit all detected stars) epoch_cols : str or list of intergers, optional List of epoch column indices used for fitting velocity, by default 'all' mask_val : float, optional @@ -926,100 +928,111 @@ def fit_velocities_all_detected(self, motion_model_to_fit, weighting='var', use_ """ N_stars = len(self) - + if select_stars is None: + select_stars = np.arange(N_stars) + else: + select_stars = np.asarray(select_stars) + if epoch_cols == 'all': epoch_cols = np.arange(np.shape(self['x'])[1]) - + # Artificial Star if art_star: - detected_in_all_epochs = np.all(self['det'][:, epoch_cols], axis=1) - + detected_in_all_epochs = np.all(self['det'][select_stars, :][:, epoch_cols], axis=1) + # Observation Star else: - valid_xe = np.all(self['xe'][:, epoch_cols]!=0, axis=1) & np.all(np.isfinite(self['xe'][:, epoch_cols]), axis=1) - valid_ye = np.all(self['ye'][:, epoch_cols]!=0, axis=1) & np.all(np.isfinite(self['ye'][:, epoch_cols]), axis=1) - + valid_xe = np.all(self['xe'][select_stars, :][:, epoch_cols]!=0, axis=1) & np.all(np.isfinite(self['xe'][select_stars, :][:, epoch_cols]), axis=1) + valid_ye = np.all(self['ye'][select_stars, :][:, epoch_cols]!=0, axis=1) & np.all(np.isfinite(self['ye'][select_stars, :][:, epoch_cols]), axis=1) + if mask_val: - x = np.ma.masked_values(self['x'][:, epoch_cols], mask_val) - y = np.ma.masked_values(self['y'][:, epoch_cols], mask_val) - + x = np.ma.masked_values(self['x'][select_stars, :][:, epoch_cols], mask_val) + y = np.ma.masked_values(self['y'][select_stars, :][:, epoch_cols], mask_val) + # If no mask, convert x.mask to list if not np.ma.is_masked(x): - x.mask = np.zeros_like(self['x'][:, epoch_cols].data, dtype=bool) + x.mask = np.zeros_like(self['x'][select_stars, :][:, epoch_cols].data, dtype=bool) if not np.ma.is_masked(y): - y.mask = np.zeros_like(self['y'][:, epoch_cols].data, dtype=bool) - + y.mask = np.zeros_like(self['y'][select_stars, :][:, epoch_cols].data, dtype=bool) + valid_x = ~np.any(x.mask, axis=1) valid_y = ~np.any(y.mask, axis=1) detected_in_all_epochs = np.logical_and.reduce(( valid_x, valid_y, valid_xe, valid_ye)) else: detected_in_all_epochs = np.logical_and(valid_xe, valid_ye) - - # START FORMER FIT VELOCITY FUNCITON - if epoch_cols is None: - epoch_cols = np.arange(len(self.meta['YEARS'])) # use all cols if not specified` - N = len(self) + N = len(self['x'][select_stars, :]) fit_params = motion_model_to_fit.fitter_param_names param_data = {p: np.zeros(N) for p in fit_params} param_data.update({p+'_err': np.zeros(N) for p in fit_params}) param_data.update({p: np.zeros(N) for p in motion_model_to_fit.fixed_param_names}) param_data['chi2_x'] = np.zeros(N) param_data['chi2_y'] = np.zeros(N) + + if times is None: + if 'YEARS' in self.meta: + times = np.array(self.meta['YEARS'])[epoch_cols] + elif 't' in self.colnames: + times = self['t'][0, epoch_cols] + else: + raise ValueError("No valid time column found.") - time = np.array(self.meta['YEARS'])[epoch_cols] - if not art_star: - x_arr = self['x'][:, epoch_cols] - y_arr = self['y'][:, epoch_cols] + x_arr = self['x'][select_stars, :][:, epoch_cols] + y_arr = self['y'][select_stars, :][:, epoch_cols] else: - x_arr = self['x'][:, epoch_cols, 1] - y_arr = self['y'][:, epoch_cols, 1] - - xe_arr = self['xe'][:, epoch_cols] - ye_arr = self['ye'][:, epoch_cols] - - # For each star - for i in tqdm(range(N)): - x = x_arr[i] - y = y_arr[i] - xe = xe_arr[i] - ye = ye_arr[i] - t0 = np.average(time, weights=1. / np.hypot(xe, ye)) - - # Run fit and record results - params, param_errs = motion_model_to_fit.fit_motion_model(time, x, y, xe, ye, t0, weighting=weighting, - use_scipy=use_scipy, absolute_sigma=absolute_sigma) - if 't0' in motion_model_to_fit.fixed_param_names: - param_data['t0'][i] = t0 - for j, param in enumerate(fit_params): - param_data[param][i] = params[j] - param_data[param+'_err'][i] = param_errs[j] - chi2x, chi2y = motion_model_to_fit.get_chi2(params, [t0], time, x, y, xe, ye) - param_data['chi2_x'][i] = chi2x - param_data['chi2_y'][i] = chi2y + x_arr = self['x'][select_stars, :][:, epoch_cols, 1] + y_arr = self['y'][select_stars, :][:, epoch_cols, 1] + + xe_arr = self['xe'][select_stars, :][:, epoch_cols] + ye_arr = self['ye'][select_stars, :][:, epoch_cols] + # Only fit for >1 epochs, otherwise all velocities will be 0 + if len(epoch_cols) > 1: + # For each star + for i in tqdm(range(N)): + x = x_arr[i] + y = y_arr[i] + xe = xe_arr[i] + ye = ye_arr[i] + t0 = np.average(times, weights=1. / np.hypot(xe, ye)) + + # Run fit and record results + params, param_errs = motion_model_to_fit.fit_motion_model( + times, x, y, xe, ye, t0, weighting=weighting, + use_scipy=use_scipy, absolute_sigma=absolute_sigma + ) + if 't0' in motion_model_to_fit.fixed_param_names: + param_data['t0'][i] = t0 + for j, param in enumerate(fit_params): + param_data[param][i] = params[j] + param_data[f'{param}_err'][i] = param_errs[j] + chi2x, chi2y = motion_model_to_fit.get_chi2(params, [t0], times, x, y, xe, ye) + param_data['chi2_x'][i] = chi2x + param_data['chi2_y'][i] = chi2y + vel_result = Table.from_pandas(pd.DataFrame(param_data)) - # END FUNCTION - + # Add n_vfit n_fit = len(epoch_cols) vel_result['n_fit'] = n_fit - + # Clean/remove up old arrays. columns = [*vel_result.keys(), 'n_fit'] for column in columns: if column in self.colnames: self.remove_column(column) - + # Update self for column in columns: - column_array = np.ma.zeros(N_stars) - column_array = MaskedColumn(vel_result[column], dtype=float) - column_array[~detected_in_all_epochs] = np.nan - column_array.mask = ~detected_in_all_epochs + column_array = MaskedColumn(np.ma.zeros(N_stars), dtype=float, name=column) + column_array[select_stars] = vel_result[column] + column_array[select_stars][~detected_in_all_epochs] = np.nan + column_array.mask[select_stars] = ~detected_in_all_epochs + # Mask unselected indices + column_array.mask[~np.isin(np.arange(N_stars), select_stars)] = True self[column] = column_array - + if return_result: return vel_result else: From fef5025204c02983ce0d711dc0d7f9c9e0ba5b38 Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Thu, 28 Aug 2025 11:42:59 -0700 Subject: [PATCH 097/254] Default t0 to 0 --- flystar/motion_model.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/flystar/motion_model.py b/flystar/motion_model.py index 9777152..bf79502 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -41,7 +41,7 @@ def get_batch_pos_at_time(self, t): #return x, y, x_err, y_err pass - def run_fit(self, t, x, y, xe, ye, t0, weighting='var', + def run_fit(self, t, x, y, xe, ye, t0=0, weighting='var', use_scipy=True, absolute_sigma=True): # Run a single fit (used both for overall fit + bootstrap iterations) pass @@ -64,14 +64,14 @@ def scale_errors(self, errs, weighting='var'): warnings.warn("Invalid weighting, using default weighting scheme var.", UserWarning) return errs - def fit_motion_model(self, t, x, y, xe, ye, t0, bootstrap=0, weighting='var', + def fit_motion_model(self, t, x, y, xe, ye, t0=0, bootstrap=0, weighting='var', use_scipy=True, absolute_sigma=True): """ Fit the input positions on the sky and errors to determine new parameters for this motion model (MM). Best-fit parameters will be returned along with uncertainties. """ - params, param_errs = self.run_fit(t, x, y, xe, ye, t0, weighting=weighting, + params, param_errs = self.run_fit(t, x, y, xe, ye, t0=t0, weighting=weighting, use_scipy=use_scipy, absolute_sigma=absolute_sigma) if bootstrap>0 and len(x)>(self.n_pts_req): @@ -82,7 +82,7 @@ def fit_motion_model(self, t, x, y, xe, ye, t0, bootstrap=0, weighting='var', bdx = np.random.choice(edx, len(x)) while len(np.unique(bdx)) Date: Thu, 28 Aug 2025 12:04:27 -0700 Subject: [PATCH 098/254] Nothing but code format change --- flystar/align.py | 21 ++++++++++++++------- 1 file changed, 14 insertions(+), 7 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 36f7cf5..7ccfff6 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -841,10 +841,15 @@ def update_ref_table_aggregates(self, keep_orig=None, n_boot=0): self.ref_table.combine_lists_xym(weighted_xy=weighted_xy, weighted_m=weighted_m) else: - # Combine positions with a velocity fit. - self.ref_table.fit_velocities(bootstrap=n_boot, verbose=self.verbose, - default_motion_model=self.default_motion_model, select_stars=fit_star_idxs, - motion_model_dict=self.motion_model_dict) + + + self.ref_table.fit_velocities( + bootstrap=n_boot, + verbose=self.verbose, + default_motion_model=self.default_motion_model, + select_stars=fit_star_idxs, + motion_model_dict=self.motion_model_dict + ) # Combine (transformed) magnitudes if 'me' in self.ref_table.colnames: @@ -857,8 +862,6 @@ def update_ref_table_aggregates(self, keep_orig=None, n_boot=0): if keep_orig is not None: for val in vals_orig.keys(): self.ref_table[val][keep_orig] = vals_orig[val] - - #pdb.set_trace() return def get_weights_for_lists(self, ref_list, star_list): @@ -1233,7 +1236,11 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot # Now, do proper motion calculation, making sure to fix t0 to the # orig value (so we can get a reasonable error on x0, y0) - star_table.fit_velocities(fixed_t0=t0_arr, default_motion_model=self.default_motion_model, motion_model_dict=self.motion_model_dict) + star_table.fit_velocities( + fixed_t0=t0_arr, + default_motion_model=self.default_motion_model, + motion_model_dict=self.motion_model_dict + ) # Save proper motion fit results to output arrays for col in motion_col_list: From 0ce481497eaf4feed8c68f324348320a35fac91a Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Fri, 29 Aug 2025 14:33:39 -0700 Subject: [PATCH 099/254] Add use_scipy, absolute_sigma, and save_path in align.py --- flystar/align.py | 50 +++++++++++++++++++++++++++++++++++++++++++----- 1 file changed, 45 insertions(+), 5 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 7ccfff6..e67e635 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -12,6 +12,7 @@ import pdb import time import warnings +import pickle from astropy.utils.exceptions import AstropyUserWarning class MosaicSelfRef(object): @@ -26,6 +27,9 @@ def __init__(self, list_of_starlists, ref_index=0, iters=2, init_guess_mode='miracle', iter_callback=None, default_motion_model='Fixed', motion_model_dict = {}, + use_scipy=True, + absolute_sigma=False, + save_path=None, verbose=True): """ Make a mosaic object by passing in a list of starlists and then running fit(). @@ -121,13 +125,22 @@ def = None. If not None, then this should contain an array or list of transform iter_callback : None or function A function to call (that accepts a StarTable object and an iteration number) at the end of every iteration. This can be used for plotting or printing state. - + default_motion_model : string Name of motion model to use for new or unassigned stars - + motion_model_dict : None or dict Dict of motion model name keys (strings) and corresponding MotionModel object values + use_scipy : bool, optional + If True, use scipy.optimize.curve_fit for velocity fitting. If False, use linear algebra fitting, by default True. + + absolute_sigma : bool, optional + If True, the velocity fit will use absolute errors in the data. If False, relative errors will be used, by default False. + + save_path : str, optional + Path to save the MosaicSelfRef object as a pickle file. + verbose : int (0 to 9, inclusive) Controls the verbosity of print statements. (0 least, 9 most verbose). For backwards compatibility, 0 = False, 9 = True. @@ -178,9 +191,12 @@ def = None. If not None, then this should contain an array or list of transform self.trans_class = trans_class self.calc_trans_inverse = calc_trans_inverse self.motion_model_dict = motion_model_dict + self.use_scipy = use_scipy + self.absolute_sigma = absolute_sigma self.default_motion_model = default_motion_model self.init_guess_mode = init_guess_mode self.iter_callback = iter_callback + self.save_path = save_path self.verbose = verbose # For backwards compatibility. @@ -346,6 +362,9 @@ def fit(self): if self.iter_callback != None: self.iter_callback(self.ref_table, nn) + if self.save_path: + with open(self.save_path, 'wb') as file: + pickle.dump(self, file) return def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_args): @@ -845,6 +864,8 @@ def update_ref_table_aggregates(self, keep_orig=None, n_boot=0): self.ref_table.fit_velocities( bootstrap=n_boot, + use_scipy=self.use_scipy, + absolute_sigma=self.absolute_sigma, verbose=self.verbose, default_motion_model=self.default_motion_model, select_stars=fit_star_idxs, @@ -1239,7 +1260,9 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot star_table.fit_velocities( fixed_t0=t0_arr, default_motion_model=self.default_motion_model, - motion_model_dict=self.motion_model_dict + motion_model_dict=self.motion_model_dict, + use_scipy=self.use_scipy, + absolute_sigma=self.absolute_sigma ) # Save proper motion fit results to output arrays @@ -1332,6 +1355,9 @@ def __init__(self, ref_list, list_of_starlists, iters=2, iter_callback=None, default_motion_model='Fixed', motion_model_dict={}, + use_scipy=True, + absolute_sigma=False, + save_path=None, verbose=True): """ @@ -1459,6 +1485,15 @@ def = None. If not None, then this should contain an array or list of transform motion_model_dict : None or dict Dict of motion model name keys (strings) and corresponding MotionModel object values + use_scipy : bool, optional + If True, use scipy.optimize.curve_fit for velocity fitting. If False, use linear algebra fitting, by default True. + + absolute_sigma : bool, optional + If True, the velocity fit will use absolute errors in the data. If False, relative errors will be used, by default False. + + save_path : str, optional + Path to save the MosaicToRef object as a pickle file. + Example ---------- msc = align.MosaicToRef(my_gaia, list_of_starlists, iters=1, @@ -1501,8 +1536,9 @@ def = None. If not None, then this should contain an array or list of transform init_guess_mode=init_guess_mode, iter_callback=iter_callback, motion_model_dict=motion_model_dict, - verbose=verbose) - + verbose=verbose, use_scipy=use_scipy, + absolute_sigma=absolute_sigma, save_path=save_path) + self.ref_list = copy.deepcopy(ref_list) self.ref_mag_lim = ref_mag_lim self.update_ref_orig = update_ref_orig @@ -1660,6 +1696,10 @@ def fit(self): if self.iter_callback != None: self.iter_callback(self.ref_table, nn) + + if self.save_path: + with open(self.save_path, 'wb') as file: + pickle.dump(self, file) return def get_all_epochs(t): From c062a35b65690db692c6d1be7f8419472975fcad Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Fri, 29 Aug 2025 14:34:22 -0700 Subject: [PATCH 100/254] Reverting the t0 to be required parameter --- flystar/motion_model.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/flystar/motion_model.py b/flystar/motion_model.py index bf79502..268f7da 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -41,7 +41,7 @@ def get_batch_pos_at_time(self, t): #return x, y, x_err, y_err pass - def run_fit(self, t, x, y, xe, ye, t0=0, weighting='var', + def run_fit(self, t, x, y, xe, ye, t0, weighting='var', use_scipy=True, absolute_sigma=True): # Run a single fit (used both for overall fit + bootstrap iterations) pass @@ -64,7 +64,7 @@ def scale_errors(self, errs, weighting='var'): warnings.warn("Invalid weighting, using default weighting scheme var.", UserWarning) return errs - def fit_motion_model(self, t, x, y, xe, ye, t0=0, bootstrap=0, weighting='var', + def fit_motion_model(self, t, x, y, xe, ye, t0, bootstrap=0, weighting='var', use_scipy=True, absolute_sigma=True): """ Fit the input positions on the sky and errors @@ -111,7 +111,7 @@ def get_chi2(self, fit_params, fixed_params, t, x, y, xe, ye, reduced=False): else: chi2x, chi2y = chi2x/(len(x)-self.n_params), chi2y/(len(x)-self.n_params) return chi2x,chi2y - + class Fixed(MotionModel): """ A non-moving motion model for a star on the sky. @@ -142,7 +142,7 @@ def get_batch_pos_at_time(self,t, else: return x0,y0,x0_err,y0_err - def run_fit(self, t, x, y, xe, ye, t0=0, weighting='var', params_guess=None, + def run_fit(self, t, x, y, xe, ye, t0, weighting='var', params_guess=None, use_scipy=True, absolute_sigma=True): if not use_scipy: Warning("Fixed model has no non-scipy fitter option. Running with scipy.") @@ -200,7 +200,7 @@ def get_batch_pos_at_time(self, t, x0=[],vx=[], y0=[],vy=[], t0=[], y_err = np.hypot(y0_err, vy_err*dt) return x,y,x_err,y_err - def run_fit(self, t, x, y, xe, ye, t0=0, weighting='var', params_guess=None, + def run_fit(self, t, x, y, xe, ye, t0, weighting='var', params_guess=None, use_scipy=True, absolute_sigma=True): dt = t-t0 x_wt, y_wt = self.get_weights(xe,ye, weighting=weighting) From 55ca61aa2ca08fa6326ff3feff2ac14c73967e1f Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Fri, 29 Aug 2025 14:35:15 -0700 Subject: [PATCH 101/254] Removing the all detected requirement for time array --- flystar/plots.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/flystar/plots.py b/flystar/plots.py index 61df7c1..be6f0fe 100755 --- a/flystar/plots.py +++ b/flystar/plots.py @@ -1131,9 +1131,9 @@ def plot_quiver_residuals_all_epochs(tab, motion_model_dict={}, unit='arcsec', s n_ref = np.zeros(len(tab), dtype=int) motion_model_dict = motion_model.validate_motion_model_dict(motion_model_dict, tab, None) - i_all_detected = np.where(~np.any(np.isnan(tab['t']),axis=1))[0][0] - xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], motion_model_dict, allow_alt_models=True) - + complete_times = np.array([np.unique(col[~np.isnan(col)])[0] for col in tab['t'].T]) + xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(complete_times, motion_model_dict, allow_alt_models=True) + for ee in range(tab['x'].shape[1]): xt_mod = xt_mod_all[:,ee] yt_mod = yt_mod_all[:,ee] From 1f349e7e9200c35f484a6f25e244ae4ef697c3d2 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Fri, 29 Aug 2025 14:44:59 -0700 Subject: [PATCH 102/254] merging --- flystar/align.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 2ce0df7..53d12cc 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -824,7 +824,7 @@ def update_ref_table_from_list(self, star_list, star_list_T, ii, idx_ref, idx_li return - def update_ref_table_aggregates(self, keep_orig=None, n_boot=0): #, weighting='var', use_scipy=True, absolute_sigma=False, show_progress=True + def update_ref_table_aggregates(self, keep_orig=None, n_boot=0): """ Average positions or fit velocities. Average magnitudes. @@ -1083,9 +1083,6 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot 'var' or 'std' weighting for velocity fitting, by default 'var'. If 'var', use the variance of the residuals to weight the fit. If 'std', use the standard deviation of the residuals to weight the fit. - use_scipy: boolean - If True, use scipy.optimize.curve_fit to fit the velocity. If False, use flystar.fit_velocity.linear_fit, by default True. - absolute_sigma: boolean If True, use the absolute sigma in the velocity fitting. If False, use the relative sigma, by default False. From c696977b236566e7c757d76ab7a74c98d049e920 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Fri, 29 Aug 2025 14:57:09 -0700 Subject: [PATCH 103/254] fitter keyword debug --- flystar/align.py | 14 +++++++++++--- flystar/startables.py | 2 +- 2 files changed, 12 insertions(+), 4 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 53d12cc..d26809b 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -187,6 +187,9 @@ def = None. If not None, then this should contain an array or list of transform self.mag_trans = mag_trans self.mag_lim = mag_lim self.weights = weights + self.weighting = 'var' + if self.weights is not None: + self.weighting=self.weights.split(',')[-1] self.trans_input = trans_input self.trans_class = trans_class self.calc_trans_inverse = calc_trans_inverse @@ -860,10 +863,15 @@ def update_ref_table_aggregates(self, keep_orig=None, n_boot=0): self.ref_table.combine_lists_xym(weighted_xy=weighted_xy, weighted_m=weighted_m) else: # Combine positions with a velocity fit. - self.ref_table.fit_velocities(bootstrap=n_boot, verbose=self.verbose, show_progress=(self.verbose>0), - default_motion_model=self.default_motion_model, select_stars=fit_star_idxs, + self.ref_table.fit_velocities(bootstrap=n_boot, + verbose=self.verbose, + show_progress=(self.verbose>0), + default_motion_model=self.default_motion_model, + select_stars=fit_star_idxs, motion_model_dict=self.motion_model_dict, - weighting=self.weighting, use_scipy=self.use_scipy, absolute_sigma=self.absolute_sigma) + weighting=self.weighting, + use_scipy=self.use_scipy, + absolute_sigma=self.absolute_sigma) # Combine (transformed) magnitudes if 'me' in self.ref_table.colnames: diff --git a/flystar/startables.py b/flystar/startables.py index b83d6c4..a0bf3e3 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -660,7 +660,7 @@ def fit_velocities(self, weighting='var', use_scipy=True, absolute_sigma=True, b fixed_t0=fixed_t0, default_motion_model=default_motion_model, mask_val=mask_val, mask_lists=mask_lists) else: - for ss in range(fit_star_idxs): + for ss in fit_star_idxs: self.fit_velocity_for_star(ss, motion_model_dict, weighting=weighting, bootstrap=bootstrap, use_scipy=use_scipy, absolute_sigma=absolute_sigma, fixed_t0=fixed_t0, default_motion_model=default_motion_model, From c8d818658299e882a268627b7116cd96799a5523 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Thu, 4 Sep 2025 17:50:13 -0700 Subject: [PATCH 104/254] debug weighting keywords --- flystar/align.py | 46 ++++++++++++++++++++++--------------- flystar/motion_model.py | 2 +- flystar/tests/test_align.py | 18 +++++++-------- 3 files changed, 37 insertions(+), 29 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index d26809b..7b00ba0 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -21,7 +21,7 @@ def __init__(self, list_of_starlists, ref_index=0, iters=2, outlier_tol=[None, None], trans_args=[{'order': 2}, {'order': 2}], init_order=1, - mag_trans=True, mag_lim=None, weights=None, + mag_trans=True, mag_lim=None, trans_weights=None, vel_weights='var', trans_input=None, trans_class=transforms.PolyTransform, calc_trans_inverse=False, init_guess_mode='miracle', iter_callback=None, @@ -89,11 +89,15 @@ def __init__(self, list_of_starlists, ref_index=0, iters=2, separately for each list and each iteration, you need to pass in a 2D array that has shape (N_lists, 2). - weights : str + trans_weights : str Either None (def), 'both,var', 'list,var', or 'ref,var' depending on whether you want to weight by the positional uncertainties (variances) in the individual starlists, or also with the uncertainties in the reference frame itself. Note weighting only works when there are positional uncertainties availabe. Other options include 'both,std', 'list,std', 'list,var'. + + vel_weights : str + Either 'var' (def) or 'std', depending on whether you want to weight the motion model + fits by the variance or standard deviation of the position data trans_input : array or list of transform objects def = None. If not None, then this should contain an array or list of transform @@ -186,10 +190,8 @@ def = None. If not None, then this should contain an array or list of transform self.init_order = init_order self.mag_trans = mag_trans self.mag_lim = mag_lim - self.weights = weights - self.weighting = 'var' - if self.weights is not None: - self.weighting=self.weights.split(',')[-1] + self.trans_weights = trans_weights + self.vel_weights = vel_weights self.trans_input = trans_input self.trans_class = trans_class self.calc_trans_inverse = calc_trans_inverse @@ -869,7 +871,7 @@ def update_ref_table_aggregates(self, keep_orig=None, n_boot=0): default_motion_model=self.default_motion_model, select_stars=fit_star_idxs, motion_model_dict=self.motion_model_dict, - weighting=self.weighting, + weighting=self.vel_weights, use_scipy=self.use_scipy, absolute_sigma=self.absolute_sigma) @@ -901,18 +903,18 @@ def get_weights_for_lists(self, ref_list, star_list): var_xlis = 0.0 var_ylis = 0.0 - if self.weights != None: - if self.weights == 'both,var': + if self.trans_weights != None: + if self.trans_weights == 'both,var': weight = 1.0 / (var_xref + var_xlis + var_yref + var_ylis) - if self.weights == 'both,std': + if self.trans_weights == 'both,std': weight = 1.0 / np.sqrt(var_xref + var_xlis + var_yref + var_ylis) - if self.weights == 'ref,var': + if self.trans_weights == 'ref,var': weight = 1.0 / (var_xref + var_yref) - if self.weights == 'ref,std': + if self.trans_weights == 'ref,std': weight = 1.0 / np.sqrt(var_xref + var_yref) - if self.weights == 'list,var': + if self.trans_weights == 'list,var': weight = 1.0 / (var_xlis + var_ylis) - if self.weights == 'list,std': + if self.trans_weights == 'list,std': weight = 1.0 / np.sqrt(var_xlis, var_ylis) else: weight = None @@ -1193,7 +1195,7 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot # Calculate weights based on weights keyword. If weights desired, will need to # make starlist objects for this - if self.weights != None: + if self.trans_weights != None: # In order for weights calculation to work, we need to apply a transformation # to the star_list_T so it is in the same units as ref_boot. So, we'll apply # the final transformation for the epoch to get close enough for the @@ -1354,7 +1356,7 @@ def __init__(self, ref_list, list_of_starlists, iters=2, trans_args=[{'order': 2}, {'order': 2}], init_order=1, mag_trans=True, mag_lim=None, ref_mag_lim=None, - weights=None, + trans_weights=None, vel_weights='var', trans_input=None, trans_class=transforms.PolyTransform, calc_trans_inverse=False, @@ -1428,11 +1430,15 @@ def __init__(self, ref_list, list_of_starlists, iters=2, If different from None, it indicates the minimum and maximum magnitude on the reference catalog for finding the transformations. - weights : str + trans_weights : str Either None (def), 'both,var', 'list,var', or 'ref,var' depending on whether you want to weight by the positional uncertainties (variances) in the individual starlists, or also with the uncertainties in the reference frame itself. Note weighting only works when there are positional uncertainties availabe. Other options include 'both,std', 'list,std', 'list,var'. + + vel_weights : str + Either 'var' (def) or 'std', depending on whether you want to weight the motion model + fits by the variance or standard deviation of the position data trans_input : array or list of transform objects def = None. If not None, then this should contain an array or list of transform @@ -1538,7 +1544,8 @@ def = None. If not None, then this should contain an array or list of transform dr_tol=dr_tol, dm_tol=dm_tol, outlier_tol=outlier_tol, trans_args=trans_args, init_order=init_order, - mag_trans=mag_trans, mag_lim=mag_lim, weights=weights, + mag_trans=mag_trans, mag_lim=mag_lim, + trans_weights=trans_weights, vel_weights=vel_weights, trans_input=trans_input, trans_class=trans_class, calc_trans_inverse=calc_trans_inverse, default_motion_model = default_motion_model, @@ -1606,7 +1613,8 @@ def fit(self): logger(_log, ' mag_trans = ' + str(self.mag_trans), self.verbose) logger(_log, ' mag_lim = ' + str(self.mag_lim), self.verbose) logger(_log, ' ref_mag_lim = ' + str(self.ref_mag_lim), self.verbose) - logger(_log, ' weights = ' + str(self.weights), self.verbose) + logger(_log, ' trans_weights = ' + str(self.trans_weights), self.verbose) + logger(_log, ' vel_weights = ' + str(self.vel_weights), self.verbose) logger(_log, ' trans_input = ' + str(self.trans_input), self.verbose) logger(_log, ' trans_class = ' + str(self.trans_class), self.verbose) logger(_log, ' calc_trans_inverse = ' + str(self.calc_trans_inverse), self.verbose) diff --git a/flystar/motion_model.py b/flystar/motion_model.py index 268f7da..ebf4c46 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -57,7 +57,7 @@ def get_weights(self, xe, ye, weighting='var'): def scale_errors(self, errs, weighting='var'): if weighting=='std': - return errs**2 + return np.array(errs)**2 elif weighting=='var': return errs else: diff --git a/flystar/tests/test_align.py b/flystar/tests/test_align.py index d94d34c..6a63a58 100644 --- a/flystar/tests/test_align.py +++ b/flystar/tests/test_align.py @@ -1073,7 +1073,7 @@ def test_MosaicToRef_hst_me(): use_ref_new=False, update_ref_orig=False, mag_trans=False, - weights='both,std', + trans_weights='both,std', init_guess_mode='miracle', verbose=False) msc.fit() tab = msc.ref_table @@ -1106,7 +1106,7 @@ def test_bootstrap(): outlier_tol = None mag_lim = None ref_mag_lim = None - weights = 'both,var' + trans_weights = 'both,var' mag_trans = False n_boot = 15 @@ -1120,7 +1120,7 @@ def test_bootstrap(): mag_trans=mag_trans, mag_lim=mag_lim, ref_mag_lim=ref_mag_lim, - weights=weights, + trans_weights=trans_weights, default_motion_model='Linear', use_ref_new=False, update_ref_orig=False, @@ -1154,7 +1154,7 @@ def test_bootstrap(): mag_trans=mag_trans, mag_lim=mag_lim, ref_mag_lim=ref_mag_lim, - weights=weights, + trans_weights=trans_weights, default_motion_model='Linear', use_ref_new=False, update_ref_orig=False, @@ -1218,7 +1218,7 @@ def test_calc_vel_in_bootstrap(): outlier_tol = None mag_lim = None ref_mag_lim = None - weights = 'both,var' + trans_weights = 'both,var' mag_trans = False n_boot = 15 @@ -1232,7 +1232,7 @@ def test_calc_vel_in_bootstrap(): mag_trans=mag_trans, mag_lim=mag_lim, ref_mag_lim=ref_mag_lim, - weights=weights, + trans_weights=trans_weights, default_motion_model='Linear', use_ref_new=False, update_ref_orig=False, @@ -1286,7 +1286,7 @@ def test_transform_xym(): outlier_tol = None mag_lim = None ref_mag_lim = None - weights = 'both,var' + trans_weights = 'both,var' n_boot = 15 mag_trans = False @@ -1299,7 +1299,7 @@ def test_transform_xym(): mag_trans=mag_trans, mag_lim=mag_lim, ref_mag_lim=ref_mag_lim, - weights=weights, + trans_weights=trans_weights, default_motion_model='Fixed', use_ref_new=False, update_ref_orig=False, @@ -1334,7 +1334,7 @@ def test_transform_xym(): mag_trans=mag_trans, mag_lim=mag_lim, ref_mag_lim=ref_mag_lim, - weights=weights, + trans_weights=trans_weights, default_motion_model='Fixed', use_ref_new=False, update_ref_orig=False, From 4e70fbe95cb7a00fa52f64d15a3800a8896a402a Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Tue, 9 Sep 2025 20:10:00 -0700 Subject: [PATCH 105/254] Minor format change --- flystar/tests/test_startable.py | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/flystar/tests/test_startable.py b/flystar/tests/test_startable.py index 9962c05..cf3be71 100644 --- a/flystar/tests/test_startable.py +++ b/flystar/tests/test_startable.py @@ -554,9 +554,15 @@ def make_star_table(): starlist_names = np.array(['file1', 'file2', 'file3', 'file4', 'file5', 'file6', 'file7', 'file8']) # Generate the startable - startable = StarTable(name=name_in, x=x_in, y=y_in, m=m_in, xe=xe_in, ye=ye_in, me=me_in, n=n_in, - ref_list=1, - list_times=starlist_times, list_names=starlist_names) + startable = StarTable( + name=name_in, + x=x_in, y=y_in, m=m_in, + xe=xe_in, ye=ye_in, me=me_in, + n=n_in, + ref_list=1, + list_times=starlist_times, + list_names=starlist_names + ) return startable From bd7ac5ea06e5e7b0fbeed2c0026fdc0016896ee2 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Fri, 12 Sep 2025 18:49:34 -0700 Subject: [PATCH 106/254] debug motion model validation --- flystar/motion_model.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/flystar/motion_model.py b/flystar/motion_model.py index ebf4c46..66acce6 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -462,7 +462,7 @@ def validate_motion_model_dict(motion_model_dict, startable, default_motion_mode all_motion_model_names.append(default_motion_model) if 'motion_model_input' in startable.columns: all_motion_model_names += np.unique(startable['motion_model_input']).tolist() - if 'motion_model_input' in startable.columns: + if 'motion_model_used' in startable.columns: all_motion_model_names += np.unique(startable['motion_model_used']).tolist() all_motion_model_names = np.unique(all_motion_model_names) From 243b56f675d0757623861ec4d270b311691153a9 Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Wed, 17 Sep 2025 00:00:27 -0700 Subject: [PATCH 107/254] Remove 2-epoch linear fitting as scipy/matrix already handles it; Fixed absolute sigma in Linear model --- flystar/motion_model.py | 117 ++++++++++++++++++++-------------------- 1 file changed, 59 insertions(+), 58 deletions(-) diff --git a/flystar/motion_model.py b/flystar/motion_model.py index ebf4c46..39e2573 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -3,7 +3,7 @@ import pdb from flystar import parallax from astropy.time import Time -from scipy.optimize import curve_fit +from scipy.optimize import curve_fit, OptimizeWarning import warnings class MotionModel(ABC): @@ -207,64 +207,65 @@ def run_fit(self, t, x, y, xe, ye, t0, weighting='var', params_guess=None, if params_guess is None: params_guess = [x.mean(),0.0,y.mean(),0.0] - # Handle 2-data point case - if len(np.unique(dt))==2: - if len(x)>2: # Catch case where bootstrap sends only 2 unique epochs - _,idx=np.unique(dt, return_index=True) - dt = dt[idx] - x = x[idx] - y = y[idx] - xe = xe[idx] - ye = ye[idx] - dx = np.diff(x)[0] - dy = np.diff(y)[0] - dt_diff = np.diff(dt)[0] - vx = dx / dt_diff - vy = dy / dt_diff - # TODO: still not sure about the error handling here - x0 = x[0] - dt[0]*vx # np.average(x, weights=x_wt) # - y0 = y[0] - dt[0]*vy # np.average(y, weights=y_wt) # - x0e = np.abs(dx) / 2**0.5 # np.sqrt(np.sum(xe**2)/2) # - y0e = np.abs(dy) / 2**0.5 # np.sqrt(np.sum(ye**2)/2) # - vxe = 0.0 #np.abs(vx) * np.sqrt(np.sum(xe**2/x**2)) - vye = 0.0 #np.abs(vy) * np.sqrt(np.sum(ye**2/y**2)) - + if use_scipy: + def linear(t, c0, c1): + return c0 + c1*t + x_opt, x_cov = curve_fit(linear, dt, x, p0=np.array(params_guess[:2]), sigma=1/np.sqrt(x_wt), absolute_sigma=absolute_sigma) + y_opt, y_cov = curve_fit(linear, dt, y, p0=np.array(params_guess[2:]), sigma=1/np.sqrt(y_wt), absolute_sigma=absolute_sigma) + x0, vx = x_opt + y0, vy = y_opt + x0e, vxe = np.sqrt(x_cov.diagonal()) + y0e, vye = np.sqrt(y_cov.diagonal()) + x0e, vxe, y0e, vye = self.scale_errors([x0e, vxe, y0e, vye], weighting=weighting) else: - if use_scipy: - def linear(t, c0, c1): - return c0 + c1*t - x_opt, x_cov = curve_fit(linear, dt, x, p0=np.array(params_guess[:2]), sigma=1/np.sqrt(x_wt), absolute_sigma=absolute_sigma) - y_opt, y_cov = curve_fit(linear, dt, y, p0=np.array(params_guess[2:]), sigma=1/np.sqrt(y_wt), absolute_sigma=absolute_sigma) - x0, vx = x_opt - y0, vy = y_opt - x0e, vxe = np.sqrt(x_cov.diagonal()) - y0e, vye = np.sqrt(y_cov.diagonal()) - x0e, vxe, y0e, vye = self.scale_errors([x0e, vxe, y0e, vye], weighting=weighting) - else: - # Use https://en.wikipedia.org/wiki/Weighted_least_squares#Solution scheme - x = np.array(x) - y = np.array(y) - dt = np.array(dt) - X_mat_t = np.vander(dt, 2) - # x calculation - W_mat_x = np.diag(x_wt) - XTWX_mat_x = X_mat_t.T @ W_mat_x @ X_mat_t - pcov_x = np.linalg.inv(XTWX_mat_x) # Covariance Matrix - popt_x = pcov_x @ X_mat_t.T @ W_mat_x @ x # Linear Solution - perr_x = np.sqrt(np.diag(pcov_x)) # Uncertainty of Linear Solution - # y calculation - W_mat_y = np.diag(y_wt) - XTWX_mat_y = X_mat_t.T @ W_mat_y @ X_mat_t - pcov_y = np.linalg.inv(XTWX_mat_y) # Covariance Matrix - popt_y = pcov_y @ X_mat_t.T @ W_mat_y @ y # Linear Solution - perr_y = np.sqrt(np.diag(pcov_y)) # Uncertainty of Linear Solution - # prepare values to return - x0, vx = popt_x[1], popt_x[0] - y0, vy = popt_y[1], popt_y[0] - x0e, vxe = perr_x[1], perr_x[0] - y0e, vye = perr_y[1], perr_y[0] - x0e, vxe, y0e, vye = self.scale_errors([x0e, vxe, y0e, vye], weighting=weighting) - + # Use https://en.wikipedia.org/wiki/Weighted_least_squares#Solution scheme + x = np.array(x) + y = np.array(y) + dt = np.array(dt) + X_mat_t = np.vander(dt, 2) + # x calculation + W_mat_x = np.diag(x_wt) + XTWX_mat_x = X_mat_t.T @ W_mat_x @ X_mat_t + pcov_x = np.linalg.inv(XTWX_mat_x) # Covariance Matrix + popt_x = pcov_x @ X_mat_t.T @ W_mat_x @ x # Linear Solution + perr_x = np.sqrt(np.diag(pcov_x)) # Uncertainty of Linear Solution + # y calculation + W_mat_y = np.diag(y_wt) + XTWX_mat_y = X_mat_t.T @ W_mat_y @ X_mat_t + pcov_y = np.linalg.inv(XTWX_mat_y) # Covariance Matrix + popt_y = pcov_y @ X_mat_t.T @ W_mat_y @ y # Linear Solution + perr_y = np.sqrt(np.diag(pcov_y)) # Uncertainty of Linear Solution + # prepare values to return + vx, x0 = popt_x + vy, y0 = popt_y + vxe, x0e = perr_x + vye, y0e = perr_y + x0e, vxe, y0e, vye = self.scale_errors([x0e, vxe, y0e, vye], weighting=weighting) + + residual_x = x - X_mat_t @ popt_x + residual_y = y - X_mat_t @ popt_y + + chi2_x = residual_x.T @ W_mat_x @ residual_x + chi2_y = residual_y.T @ W_mat_y @ residual_y + + if not absolute_sigma: + degree_of_freedom = len(x) - 2 + if degree_of_freedom > 0: + reduced_chi2_x = chi2_x/(len(x) - 2) + reduced_chi2_y = chi2_y/(len(x) - 2) + x0e *= reduced_chi2_x**0.5 + y0e *= reduced_chi2_y**0.5 + vxe *= reduced_chi2_x**0.5 + vye *= reduced_chi2_y**0.5 + else: + warnings.warn( + "Degree of freedom <= 0. Covariance of the parameters could not be estimated. Setting parameter uncertainties to infinity.", + OptimizeWarning, stacklevel=2 + ) + x0e *= np.inf + y0e *= np.inf + vxe *= np.inf + vye *= np.inf params = [x0, vx, y0, vy] param_errors = [x0e, vxe, y0e, vye] return params, param_errors From 138546437d9a8adf352d15b7e2f4800c181c0c6c Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Wed, 17 Sep 2025 00:13:30 -0700 Subject: [PATCH 108/254] Add testing function for both absolute_sigma True and False --- flystar/tests/test_motion_model.py | 219 +++++++++++++++++++++++++++++ 1 file changed, 219 insertions(+) diff --git a/flystar/tests/test_motion_model.py b/flystar/tests/test_motion_model.py index a5d8fdb..2fa1b57 100755 --- a/flystar/tests/test_motion_model.py +++ b/flystar/tests/test_motion_model.py @@ -1,6 +1,8 @@ from flystar import motion_model import numpy as np import pytest +import matplotlib.pyplot as plt +from scipy.optimize import curve_fit def within_error(true_val, fit_val, fit_err, n_sigma=3): #print('True', true_val, 'Fit', fit_val, 'Fit err', fit_err) @@ -278,3 +280,220 @@ def test_Parallax_PA(): dat_pa90 = mod_pa90.get_pos_at_time([y0,vy,-x0,-vx,pi],[2020.0],t_set) assert (np.abs(dat_pa0[0]-(-dat_pa90[1]))<1e-10).all() assert (np.abs(dat_pa0[1]-(dat_pa90[0]))<1e-10).all() + + +def test_Linear_fit_vs_scipy(): + # Compare Linear fit results to scipy curve_fit results + t = np.array([0, 1., 2.2, 3.5, 5.]) + + x = np.array([ + [0., 0.5, 2.1, 3.2, 6.0], # Increasing 5 Epochs + [10.0, 8.9, 9.2, 7.4, 7.0], # Decreasing 5 Epochs + [2.5, np.nan, 5.2, np.nan, 5.0], # 3 Epochs + [np.nan, 6.2, np.nan, np.nan, 9.2], # 2 Epochs + # [np.nan, 2.0, np.nan, np.nan, np.nan], # 1 Epoch + # [np.nan, np.nan, np.nan, np.nan, np.nan] # All NaNs + ]) + + y = np.array([ + [10.2, 8.5, 9.1, 12.2, 13.0], # Increasing 5 Epochs + [8.0, 9.9, 8.2, 7.4, 7.0], # Decreasing 5 Epochs + [5.2, np.nan, 4.7, np.nan, 6.0], # 3 Epochs + [np.nan, 1.2, np.nan, np.nan, 3.2], # 2 Epochs + # [np.nan, 2.0, np.nan, np.nan, np.nan], # 1 Epoch + # [np.nan, np.nan, np.nan, np.nan, np.nan] # All NaNs + ]) + + xe = np.array([ + [0.2, 0.5, 0.3, 0.4, 0.6], + [0.5, 0.2, 0.7, 0.3, 0.2], + [0.5, np.nan, 0.6, np.nan, 0.3], + [np.nan, 0.6, np.nan, np.nan, 0.3], + # [np.nan, 0.4, np.nan, np.nan, np.nan], + # [np.nan, np.nan, np.nan, np.nan, np.nan] + ]) + + ye = np.array([ + [0.3, 0.2, 0.5, 0.2, 0.4], + [0.2, 0.5, 0.6, 0.4, 0.2], + [0.7, np.nan, 0.5, np.nan, 0.2], + [np.nan, 0.4, np.nan, np.nan, 0.5], + # [np.nan, 0.5, np.nan, np.nan, np.nan], + # [np.nan, np.nan, np.nan, np.nan, np.nan] + ]) + + x = np.ma.masked_invalid(x) + y = np.ma.masked_invalid(y) + xe = np.ma.masked_invalid(xe) + ye = np.ma.masked_invalid(ye) + mask = np.ma.getmaskarray(x) | np.ma.getmaskarray(y) | np.ma.getmaskarray(xe) | np.ma.getmaskarray(ye) + + # tab = StarTable({ + # 'x': x, + # 'y': y, + # 'xe': xe, + # 'ye': ye + # }) + # tab.meta['LIST_TIMES'] = t + # tab.fit_velocities(use_scipy=True, absolute_sigma=True) + + # Plot data + N = x.shape[0] + fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 6)) + for i in range(N): + line_mask = ~np.isnan(x[i]) & ~mask[i] + ax1.errorbar(t[line_mask], x[i][line_mask], yerr=xe[i][line_mask], fmt='o-', label=f'Line {i}') + ax2.errorbar(t[line_mask], y[i][line_mask], yerr=ye[i][line_mask], fmt='o-', label=f'Line {i}') + ax1.set_xlabel('Time') + ax1.set_ylabel('Position') + ax1.legend() + ax1.set_title('X vs Time') + ax2.set_xlabel('Time') + ax2.set_ylabel('Position') + ax2.legend() + ax2.set_title('Y vs Time') + plt.show() + + N = len(x) + t0 = np.average(np.broadcast_to(t, x.shape), weights=1./np.hypot(xe, ye), axis=1) + dt = np.zeros_like(x) + + # velfit + # vx_velfit = np.zeros(N) + # vxe_velfit = np.zeros(N) + # vy_velfit = np.zeros(N) + # vye_velfit = np.zeros(N) + # x0_velfit = np.zeros(N) + # x0e_velfit = np.zeros(N) + # y0_velfit = np.zeros(N) + # y0e_velfit = np.zeros(N) + + # scipy + vx_scipy = np.zeros(N) + vxe_scipy = np.zeros(N) + vy_scipy = np.zeros(N) + vye_scipy = np.zeros(N) + x0_scipy = np.zeros(N) + x0e_scipy = np.zeros(N) + y0_scipy = np.zeros(N) + y0e_scipy = np.zeros(N) + + # motion_model + mm = motion_model.Linear() + + vx_mm_scipy = np.zeros(N) + vxe_mm_scipy = np.zeros(N) + vy_mm_scipy = np.zeros(N) + vye_mm_scipy = np.zeros(N) + x0_mm_scipy = np.zeros(N) + x0e_mm_scipy = np.zeros(N) + y0_mm_scipy = np.zeros(N) + y0e_mm_scipy = np.zeros(N) + + vx_mm = np.zeros(N) + vxe_mm = np.zeros(N) + vy_mm = np.zeros(N) + vye_mm = np.zeros(N) + x0_mm = np.zeros(N) + x0e_mm = np.zeros(N) + y0_mm = np.zeros(N) + y0e_mm = np.zeros(N) + + def linear(t, c0, c1): + return c0 + c1*t + + # Absolute sigma + for absolute_sigma in [True, False]: + for i in range(N): + dt[i] = t - t0[i] + + # # velfit.linear_fit + # vx_velfit_results = linear_fit(dt[i][~mask[i]], x[i][~mask[i]], sigma=xe[i][~mask[i]], absolute_sigma=absolute_sigma) + # vy_velfit_results = linear_fit(dt[i][~mask[i]], y[i][~mask[i]], sigma=ye[i][~mask[i]], absolute_sigma=absolute_sigma) + + # vx_velfit[i] = vx_velfit_results['slope'] + # vxe_velfit[i] = vx_velfit_results['e_slope'] + # vy_velfit[i] = vy_velfit_results['slope'] + # vye_velfit[i] = vy_velfit_results['e_slope'] + # x0_velfit[i] = vx_velfit_results['intercept'] + # x0e_velfit[i] = vx_velfit_results['e_intercept'] + # y0_velfit[i] = vy_velfit_results['intercept'] + # y0e_velfit[i] = vy_velfit_results['e_intercept'] + + # scipy.curve_fit + p0x = np.array([0., x[i][~mask[i]].mean()]) + p0y = np.array([0., y[i][~mask[i]].mean()]) + popt_x, pcov_x = curve_fit(linear, dt[i][~mask[i]], x[i][~mask[i]], p0=p0x, sigma=xe[i][~mask[i]], absolute_sigma=absolute_sigma) + vx_scipy[i], vxe_scipy[i] = popt_x[1], np.sqrt(pcov_x[1, 1]) + x0_scipy[i], x0e_scipy[i] = popt_x[0], np.sqrt(pcov_x[0, 0]) + popt_y, pcov_y = curve_fit(linear, dt[i][~mask[i]], y[i][~mask[i]], p0=p0y, sigma=ye[i][~mask[i]], absolute_sigma=absolute_sigma) + vy_scipy[i], vye_scipy[i] = popt_y[1], np.sqrt(pcov_y[1, 1]) + y0_scipy[i], y0e_scipy[i] = popt_y[0], np.sqrt(pcov_y[0, 0]) + + # motion_model without scipy + params, param_errs = mm.fit_motion_model( + t[~mask[i]], x[i][~mask[i]], y[i][~mask[i]], + xe[i][~mask[i]], ye[i][~mask[i]], t0[i], + weighting='var', + use_scipy=False, + absolute_sigma=absolute_sigma + ) + vx_mm[i] = params[mm.fitter_param_names.index('vx')] + vy_mm[i] = params[mm.fitter_param_names.index('vy')] + vxe_mm[i] = param_errs[mm.fitter_param_names.index('vx')] + vye_mm[i] = param_errs[mm.fitter_param_names.index('vy')] + x0_mm[i] = params[mm.fitter_param_names.index('x0')] + y0_mm[i] = params[mm.fitter_param_names.index('y0')] + x0e_mm[i] = param_errs[mm.fitter_param_names.index('x0')] + y0e_mm[i] = param_errs[mm.fitter_param_names.index('y0')] + + # motion_model with scipy + params, param_errs = mm.fit_motion_model( + t[~mask[i]], x[i][~mask[i]], y[i][~mask[i]], + xe[i][~mask[i]], ye[i][~mask[i]], t0[i], + weighting='var', + use_scipy=True, + absolute_sigma=absolute_sigma + ) + vx_mm_scipy[i] = params[mm.fitter_param_names.index('vx')] + vy_mm_scipy[i] = params[mm.fitter_param_names.index('vy')] + vxe_mm_scipy[i] = param_errs[mm.fitter_param_names.index('vx')] + vye_mm_scipy[i] = param_errs[mm.fitter_param_names.index('vy')] + x0_mm_scipy[i] = params[mm.fitter_param_names.index('x0')] + y0_mm_scipy[i] = params[mm.fitter_param_names.index('y0')] + x0e_mm_scipy[i] = param_errs[mm.fitter_param_names.index('x0')] + y0e_mm_scipy[i] = param_errs[mm.fitter_param_names.index('y0')] + + rtol = 1e-5 + # np.testing.assert_allclose(vx_velfit, vx_scipy, rtol=rtol) + # np.testing.assert_allclose(vxe_velfit, vxe_scipy, rtol=rtol) + # np.testing.assert_allclose(vy_velfit, vy_scipy, rtol=rtol) + # np.testing.assert_allclose(vye_velfit, vye_scipy, rtol=rtol) + # np.testing.assert_allclose(x0_velfit, x0_scipy, rtol=rtol) + # np.testing.assert_allclose(x0e_velfit, x0e_scipy, rtol=rtol) + # np.testing.assert_allclose(y0_velfit, y0_scipy, rtol=rtol) + # np.testing.assert_allclose(y0e_velfit, y0e_scipy, rtol=rtol) + # np.testing.assert_allclose(vx_velfit, vx_mm, rtol=rtol) + # np.testing.assert_allclose(vxe_velfit, vxe_mm, rtol=rtol) + # np.testing.assert_allclose(vy_velfit, vy_mm, rtol=rtol) + # np.testing.assert_allclose(vye_velfit, vye_mm, rtol=rtol) + # np.testing.assert_allclose(x0_velfit, x0_mm, rtol=rtol) + # np.testing.assert_allclose(x0e_velfit, x0e_mm, rtol=rtol) + # np.testing.assert_allclose(y0_velfit, y0_mm, rtol=rtol) + # np.testing.assert_allclose(y0e_velfit, y0e_mm, rtol=rtol) + np.testing.assert_allclose(vx_scipy, vx_mm, rtol=rtol) + np.testing.assert_allclose(vxe_scipy, vxe_mm, rtol=rtol) + np.testing.assert_allclose(vy_scipy, vy_mm, rtol=rtol) + np.testing.assert_allclose(vye_scipy, vye_mm, rtol=rtol) + np.testing.assert_allclose(x0_scipy, x0_mm, rtol=rtol) + np.testing.assert_allclose(x0e_scipy, x0e_mm, rtol=rtol) + np.testing.assert_allclose(y0_scipy, y0_mm, rtol=rtol) + np.testing.assert_allclose(y0e_scipy, y0e_mm, rtol=rtol) + np.testing.assert_allclose(vx_scipy, vx_mm_scipy, rtol=rtol) + np.testing.assert_allclose(vxe_scipy, vxe_mm_scipy, rtol=rtol) + np.testing.assert_allclose(vy_scipy, vy_mm_scipy, rtol=rtol) + np.testing.assert_allclose(vye_scipy, vye_mm_scipy, rtol=rtol) + np.testing.assert_allclose(x0_scipy, x0_mm_scipy, rtol=rtol) + np.testing.assert_allclose(x0e_scipy, x0e_mm_scipy, rtol=rtol) + np.testing.assert_allclose(y0_scipy, y0_mm_scipy, rtol=rtol) + np.testing.assert_allclose(y0e_scipy, y0e_mm_scipy, rtol=rtol) From d7884e85dc250e44bc2394f732c21d53a9e22e3a Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Wed, 17 Sep 2025 00:14:14 -0700 Subject: [PATCH 109/254] Simple cleanup --- flystar/startables.py | 13 +++---------- 1 file changed, 3 insertions(+), 10 deletions(-) diff --git a/flystar/startables.py b/flystar/startables.py index a0bf3e3..422f9a7 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -828,7 +828,7 @@ def fit_velocity_for_star(self, ss, motion_model_dict, weighting='var', use_scip # If the points do not cover multiple times, go to a fixed model if (t == t[0]).all(): motion_model_use = 'Fixed' - + self['motion_model_used'][ss] = motion_model_use # # Get the motion model object. @@ -960,15 +960,8 @@ def fit_velocities_all_detected(self, motion_model_to_fit, weighting='var', use_ valid_ye = np.all(self['ye'][select_stars, :][:, epoch_cols]!=0, axis=1) & np.all(np.isfinite(self['ye'][select_stars, :][:, epoch_cols]), axis=1) if mask_val: - x = np.ma.masked_values(self['x'][select_stars, :][:, epoch_cols], mask_val) - y = np.ma.masked_values(self['y'][select_stars, :][:, epoch_cols], mask_val) - - # If no mask, convert x.mask to list - if not np.ma.is_masked(x): - x.mask = np.zeros_like(self['x'][select_stars, :][:, epoch_cols].data, dtype=bool) - if not np.ma.is_masked(y): - y.mask = np.zeros_like(self['y'][select_stars, :][:, epoch_cols].data, dtype=bool) - + x = np.ma.masked_values(self['x'][select_stars, :][:, epoch_cols], mask_val, shrink=False) + y = np.ma.masked_values(self['y'][select_stars, :][:, epoch_cols], mask_val, shrink=False) valid_x = ~np.any(x.mask, axis=1) valid_y = ~np.any(y.mask, axis=1) detected_in_all_epochs = np.logical_and.reduce(( From 200d7e70653702411533cce2bc1ed50006eaa8bb Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Wed, 17 Sep 2025 10:09:56 -0700 Subject: [PATCH 110/254] Revert 2 epoch case changes --- flystar/motion_model.py | 138 +++++++++++++++++++++++----------------- 1 file changed, 80 insertions(+), 58 deletions(-) diff --git a/flystar/motion_model.py b/flystar/motion_model.py index 549b46a..c5642d6 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -207,65 +207,87 @@ def run_fit(self, t, x, y, xe, ye, t0, weighting='var', params_guess=None, if params_guess is None: params_guess = [x.mean(),0.0,y.mean(),0.0] - if use_scipy: - def linear(t, c0, c1): - return c0 + c1*t - x_opt, x_cov = curve_fit(linear, dt, x, p0=np.array(params_guess[:2]), sigma=1/np.sqrt(x_wt), absolute_sigma=absolute_sigma) - y_opt, y_cov = curve_fit(linear, dt, y, p0=np.array(params_guess[2:]), sigma=1/np.sqrt(y_wt), absolute_sigma=absolute_sigma) - x0, vx = x_opt - y0, vy = y_opt - x0e, vxe = np.sqrt(x_cov.diagonal()) - y0e, vye = np.sqrt(y_cov.diagonal()) - x0e, vxe, y0e, vye = self.scale_errors([x0e, vxe, y0e, vye], weighting=weighting) + # Handle 2-data point case + if len(np.unique(dt))==2: + if len(x)>2: # Catch case where bootstrap sends only 2 unique epochs + _,idx=np.unique(dt, return_index=True) + dt = dt[idx] + x = x[idx] + y = y[idx] + xe = xe[idx] + ye = ye[idx] + dx = np.diff(x)[0] + dy = np.diff(y)[0] + dt_diff = np.diff(dt)[0] + vx = dx / dt_diff + vy = dy / dt_diff + # TODO: still not sure about the error handling here + x0 = x[0] - dt[0]*vx # np.average(x, weights=x_wt) # + y0 = y[0] - dt[0]*vy # np.average(y, weights=y_wt) # + x0e = np.abs(dx) / 2**0.5 # np.sqrt(np.sum(xe**2)/2) # + y0e = np.abs(dy) / 2**0.5 # np.sqrt(np.sum(ye**2)/2) # + vxe = 0.0 #np.abs(vx) * np.sqrt(np.sum(xe**2/x**2)) + vye = 0.0 #np.abs(vy) * np.sqrt(np.sum(ye**2/y**2)) else: - # Use https://en.wikipedia.org/wiki/Weighted_least_squares#Solution scheme - x = np.array(x) - y = np.array(y) - dt = np.array(dt) - X_mat_t = np.vander(dt, 2) - # x calculation - W_mat_x = np.diag(x_wt) - XTWX_mat_x = X_mat_t.T @ W_mat_x @ X_mat_t - pcov_x = np.linalg.inv(XTWX_mat_x) # Covariance Matrix - popt_x = pcov_x @ X_mat_t.T @ W_mat_x @ x # Linear Solution - perr_x = np.sqrt(np.diag(pcov_x)) # Uncertainty of Linear Solution - # y calculation - W_mat_y = np.diag(y_wt) - XTWX_mat_y = X_mat_t.T @ W_mat_y @ X_mat_t - pcov_y = np.linalg.inv(XTWX_mat_y) # Covariance Matrix - popt_y = pcov_y @ X_mat_t.T @ W_mat_y @ y # Linear Solution - perr_y = np.sqrt(np.diag(pcov_y)) # Uncertainty of Linear Solution - # prepare values to return - vx, x0 = popt_x - vy, y0 = popt_y - vxe, x0e = perr_x - vye, y0e = perr_y - x0e, vxe, y0e, vye = self.scale_errors([x0e, vxe, y0e, vye], weighting=weighting) - - residual_x = x - X_mat_t @ popt_x - residual_y = y - X_mat_t @ popt_y - - chi2_x = residual_x.T @ W_mat_x @ residual_x - chi2_y = residual_y.T @ W_mat_y @ residual_y - - if not absolute_sigma: - degree_of_freedom = len(x) - 2 - if degree_of_freedom > 0: - reduced_chi2_x = chi2_x/(len(x) - 2) - reduced_chi2_y = chi2_y/(len(x) - 2) - x0e *= reduced_chi2_x**0.5 - y0e *= reduced_chi2_y**0.5 - vxe *= reduced_chi2_x**0.5 - vye *= reduced_chi2_y**0.5 - else: - warnings.warn( - "Degree of freedom <= 0. Covariance of the parameters could not be estimated. Setting parameter uncertainties to infinity.", - OptimizeWarning, stacklevel=2 - ) - x0e *= np.inf - y0e *= np.inf - vxe *= np.inf - vye *= np.inf + if use_scipy: + def linear(t, c0, c1): + return c0 + c1*t + x_opt, x_cov = curve_fit(linear, dt, x, p0=np.array(params_guess[:2]), sigma=1/np.sqrt(x_wt), absolute_sigma=absolute_sigma) + y_opt, y_cov = curve_fit(linear, dt, y, p0=np.array(params_guess[2:]), sigma=1/np.sqrt(y_wt), absolute_sigma=absolute_sigma) + x0, vx = x_opt + y0, vy = y_opt + x0e, vxe = np.sqrt(x_cov.diagonal()) + y0e, vye = np.sqrt(y_cov.diagonal()) + x0e, vxe, y0e, vye = self.scale_errors([x0e, vxe, y0e, vye], weighting=weighting) + else: + # Use https://en.wikipedia.org/wiki/Weighted_least_squares#Solution scheme + x = np.array(x) + y = np.array(y) + dt = np.array(dt) + X_mat_t = np.vander(dt, 2) + # x calculation + W_mat_x = np.diag(x_wt) + XTWX_mat_x = X_mat_t.T @ W_mat_x @ X_mat_t + pcov_x = np.linalg.inv(XTWX_mat_x) # Covariance Matrix + popt_x = pcov_x @ X_mat_t.T @ W_mat_x @ x # Linear Solution + perr_x = np.sqrt(np.diag(pcov_x)) # Uncertainty of Linear Solution + # y calculation + W_mat_y = np.diag(y_wt) + XTWX_mat_y = X_mat_t.T @ W_mat_y @ X_mat_t + pcov_y = np.linalg.inv(XTWX_mat_y) # Covariance Matrix + popt_y = pcov_y @ X_mat_t.T @ W_mat_y @ y # Linear Solution + perr_y = np.sqrt(np.diag(pcov_y)) # Uncertainty of Linear Solution + # prepare values to return + vx, x0 = popt_x + vy, y0 = popt_y + vxe, x0e = perr_x + vye, y0e = perr_y + x0e, vxe, y0e, vye = self.scale_errors([x0e, vxe, y0e, vye], weighting=weighting) + + residual_x = x - X_mat_t @ popt_x + residual_y = y - X_mat_t @ popt_y + + chi2_x = residual_x.T @ W_mat_x @ residual_x + chi2_y = residual_y.T @ W_mat_y @ residual_y + + if not absolute_sigma: + degree_of_freedom = len(x) - 2 + if degree_of_freedom > 0: + reduced_chi2_x = chi2_x/(len(x) - 2) + reduced_chi2_y = chi2_y/(len(x) - 2) + x0e *= reduced_chi2_x**0.5 + y0e *= reduced_chi2_y**0.5 + vxe *= reduced_chi2_x**0.5 + vye *= reduced_chi2_y**0.5 + else: + warnings.warn( + "Degree of freedom <= 0. Covariance of the parameters could not be estimated. Setting parameter uncertainties to infinity.", + OptimizeWarning, stacklevel=2 + ) + x0e *= np.inf + y0e *= np.inf + vxe *= np.inf + vye *= np.inf params = [x0, vx, y0, vy] param_errors = [x0e, vxe, y0e, vye] return params, param_errors From 5a859dbeefbe43427cb7026a1bd49816b89fe1aa Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Wed, 24 Sep 2025 18:17:01 -0700 Subject: [PATCH 111/254] Fix r string import warning --- flystar/plots.py | 12 ++++++------ flystar/starlists.py | 4 ++-- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/flystar/plots.py b/flystar/plots.py index be6f0fe..d6a8d40 100755 --- a/flystar/plots.py +++ b/flystar/plots.py @@ -262,7 +262,7 @@ def pos_diff_err_hist(ref_mat, starlist_mat, transform, nbins=25, bin_width=None py.plot(x, norm.pdf(x,mean,sigma), 'g-', linewidth=2) # Annotate reduced chi-sqared values in plot: with outliers - xstr = '$\chi^2_r$ = {0}'.format(np.round(chi_sq_red, decimals=3)) + xstr = r'$\chi^2_r$ = {0}'.format(np.round(chi_sq_red, decimals=3)) py.annotate(xstr, xy=(0.3, 0.77), xycoords='figure fraction', color='black') txt = r'$\nu$ = 2*{0} - {1} = {2}'.format(len(diff_x), num_mod_params, deg_freedom) @@ -273,7 +273,7 @@ def pos_diff_err_hist(ref_mat, starlist_mat, transform, nbins=25, bin_width=None py.annotate(xstr3, xy=(0.25, 0.80), xycoords='figure fraction', color='black') # Annotate reduced chi-sqared values in plot: without outliers - xstr = '$\chi^2_r$ = {0}'.format(np.round(chi_sq_red_good, decimals=3)) + xstr = r'$\chi^2_r$ = {0}'.format(np.round(chi_sq_red_good, decimals=3)) py.annotate(xstr, xy=(0.7, 0.8), xycoords='figure fraction', color='black') txt = r'$\nu$ = 2*{0} - {1} = {2}'.format(len(good[0]), num_mod_params, deg_freedom_good) @@ -2221,7 +2221,7 @@ def plot_chi2_dist(tab, Ndetect, motion_model_dict={}, xlim=40, n_bins=50, boot_ plt.hist(x[idx], bins=chi2_bins, histtype='step', label='X', density=True) plt.hist(y[idx], bins=chi2_bins, histtype='step', label='Y', density=True) plt.plot(chi2_xaxis, chi2.pdf(chi2_xaxis, Ndof), 'r-', alpha=0.6, - label='$\chi^2$ ' + str(Ndof) + ' dof') + label=r'$\chi^2$ ' + str(Ndof) + ' dof') plt.title('$N_{epoch} = $' + str(Ndetect) + ', $N_{dof} = $' + str(Ndof)) plt.xlim(0, xlim) plt.legend() @@ -2306,7 +2306,7 @@ def plot_chi2_dist_per_filter(tab, Ndetect, motion_model_dict={}, xlim=40, n_bin plt.hist(x[idx], bins=chi2_bins, histtype='stepfilled', label='RA', density=True, color='skyblue', alpha=0.8, edgecolor='k') plt.hist(y[idx], bins=chi2_bins, histtype='stepfilled', label='DEC', density=True, color='orange', alpha=0.8, edgecolor='k') plt.plot(chi2_xaxis, chi2.pdf(chi2_xaxis, Ndof), 'r-', alpha=0.6, - label='$\chi^2$ ' + str(Ndof) + ' dof') + label=r'$\chi^2$ ' + str(Ndof) + ' dof') #plt.title('$N_{epoch} = $' + str(Ndetect) + ', $N_{dof} = $' + str(Ndof)) plt.title(str(filter)+' (N = '+str(len(chi2_x_list))+')', fontsize=22) plt.xlim(0, xlim) @@ -2593,7 +2593,7 @@ def plot_chi2_dist_mag(tab, Ndetect, xlim=40, n_bins=30, boot_err=False): plt.clf() plt.hist(chi2_m[idx], bins=np.arange(xlim*10), histtype='step', density=True) plt.plot(chi2_maxis, chi2.pdf(chi2_maxis, Ndof), 'r-', alpha=0.6, - label='$\chi^2$ ' + str(Ndof) + ' dof') + label=r'$\chi^2$ ' + str(Ndof) + ' dof') plt.title('$N_{epoch} = $' + str(Ndetect) + ', $N_{dof} = $' + str(Ndof)) plt.xlim(0, xlim) plt.legend() @@ -2642,7 +2642,7 @@ def plot_chi2_dist_mag_per_filter(tab, Ndetect, mlim=40, n_bins=30, xlim=40, fil plt.clf() plt.hist(chi2_m[idx], bins=np.arange(xlim*10), label='mag', histtype='stepfilled', density=True, color='green', alpha=0.7, edgecolor='k') plt.plot(chi2_maxis, chi2.pdf(chi2_maxis, Ndof), 'r-', alpha=0.6, - label='$\chi^2$ ' + str(Ndof) + ' dof') + label=r'$\chi^2$ ' + str(Ndof) + ' dof') #plt.title('$N_{epoch} = $' + str(Ndetect) + ', $N_{dof} = $' + str(Ndof)) plt.xlim(0, xlim) plt.xlabel(r'$\chi^{2}$', fontsize=28) diff --git a/flystar/starlists.py b/flystar/starlists.py index 23df44f..f1f3278 100644 --- a/flystar/starlists.py +++ b/flystar/starlists.py @@ -421,7 +421,7 @@ def read_starlist(starlistFile, error=True): starlist astropy table. containing: name, m, x, y, xe, ye, t """ - t_ref = Table.read(starlistFile, format='ascii', delimiter='\s') + t_ref = Table.read(starlistFile, format='ascii', delimiter=r'\s') # Check if this already has column names: cols = t_ref.colnames @@ -624,7 +624,7 @@ def from_lis_file(cls, filename, error=True, fvu_file=None): ------ starlists.StarList() object (subclass of Astropy Table). """ - t_ref = Table.read(filename, format='ascii', delimiter='\s') + t_ref = Table.read(filename, format='ascii', delimiter=r'\s') # Check if this already has column names: cols = t_ref.colnames From 4c85f80b7e9a74234ac3238bf75970f430709b4f Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Thu, 23 Oct 2025 14:32:45 -0700 Subject: [PATCH 112/254] catch up --- flystar/plots.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/flystar/plots.py b/flystar/plots.py index be6f0fe..707d6e6 100755 --- a/flystar/plots.py +++ b/flystar/plots.py @@ -2336,7 +2336,7 @@ def plot_chi2_dist_per_filter(tab, Ndetect, motion_model_dict={}, xlim=40, n_bin return -def plot_chi2_dist_per_epoch(tab, Ndetect, motion_model_dict={}, mlim=[14,21], ylim = [-1, 1], target_idx = 0, boot_err=False): +def plot_chi2_dist_per_epoch(tab, Ndetect, mlim=[14,21], ylim = [-1, 1], target_idx = 0, motion_model_dict={}, boot_err=False): """ tab = flystar table Ndetect = Number of epochs star detected in From 010e6b1148d70b560fbba3f64106b3f2982b99fb Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Fri, 24 Oct 2025 08:08:38 -0700 Subject: [PATCH 113/254] decrease memory useage in bootstrap --- flystar/align.py | 82 +++++++++++++++++++++++++++++++----------------- 1 file changed, 53 insertions(+), 29 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 7b00ba0..03f557b 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -1127,15 +1127,15 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot else: idx_good = np.arange(0, len(ref_table), 1) idx_ref = np.where(ref_table['use_in_trans'] == True) - - # Initialize output arrays - x_trans_arr = np.ones((len(ref_table['x']), n_boot, n_epochs)) * -999 - y_trans_arr = np.ones((len(ref_table['x']), n_boot, n_epochs)) * -999 - m_trans_arr = np.ones((len(ref_table['x']), n_boot, n_epochs)) * -999 - xe_trans_arr = np.ones((len(ref_table['x']), n_boot, n_epochs)) * -999 - ye_trans_arr = np.ones((len(ref_table['x']), n_boot, n_epochs)) * -999 - me_trans_arr = np.ones((len(ref_table['x']), n_boot, n_epochs)) * -999 - + + # Initialize sums for output + x_boot_sum = np.zeros((len(ref_table['x']), n_epochs)) + x2_boot_sum = np.zeros((len(ref_table['x']), n_epochs)) + y_boot_sum = np.zeros((len(ref_table['x']), n_epochs)) + y2_boot_sum = np.zeros((len(ref_table['x']), n_epochs)) + m_boot_sum = np.zeros((len(ref_table['x']), n_epochs)) + m2_boot_sum = np.zeros((len(ref_table['x']), n_epochs)) + # Set up motion model parameters motion_model_list = ['Fixed', self.default_motion_model] if 'motion_model_used' in ref_table.keys(): @@ -1144,9 +1144,11 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot motion_model_list += ref_table['motion_model_input'].tolist() motion_col_list = motion_model.get_list_motion_model_param_names(np.unique(motion_model_list).tolist(), with_errors=False, with_fixed=False) if calc_vel_in_bootstrap: - motion_data = {} + motion_boot_sum = {} + motion2_boot_sum = {} for col in motion_col_list: - motion_data[col] = np.ones((len(ref_table['x']), n_boot)) * -999 + motion_boot_sum[col] = np.zeros((len(ref_table['x']))) + motion2_boot_sum[col] = np.zeros((len(ref_table['x']))) ### IF MEMORY PROBLEMS HERE: ### DEFINE MEAN, STD VARIABLES AND BUILD THEM RATHER THAN SAVING FULL ARRAY @@ -1158,6 +1160,15 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot # reference stars. Use a loop for each epoch here, so we # can handle case where different reference stars are used # in different epochs + + # Initialize data arrays + x_trans_arr = np.ones((len(ref_table['x']), n_epochs)) * -999 + y_trans_arr = np.ones((len(ref_table['x']), n_epochs)) * -999 + m_trans_arr = np.ones((len(ref_table['x']), n_epochs)) * -999 + xe_trans_arr = np.ones((len(ref_table['x']), n_epochs)) * -999 + ye_trans_arr = np.ones((len(ref_table['x']), n_epochs)) * -999 + me_trans_arr = np.ones((len(ref_table['x']), n_epochs)) * -999 + for jj in range(n_epochs): # Extract bootstrap sample of matched reference stars good = np.where(~np.isnan(ref_table['x_orig'][idx_ref][:,jj])) @@ -1236,13 +1247,20 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot starlist_T.transform_xy(trans) # Add output to pos arrays - x_trans_arr[:,ii,jj] = starlist_T['x'] - y_trans_arr[:,ii,jj] = starlist_T['y'] - m_trans_arr[:,ii,jj] = starlist_T['m'] - xe_trans_arr[:,ii,jj] = starlist_T['xe'] - ye_trans_arr[:,ii,jj] = starlist_T['ye'] - me_trans_arr[:,ii,jj] = starlist_T['me'] - + x_trans_arr[:,jj] = starlist_T['x'] + y_trans_arr[:,jj] = starlist_T['y'] + m_trans_arr[:,jj] = starlist_T['m'] + xe_trans_arr[:,jj] = starlist_T['xe'] + ye_trans_arr[:,jj] = starlist_T['ye'] + me_trans_arr[:,jj] = starlist_T['me'] + + x_boot_sum += x_trans_arr + x2_boot_sum += x_trans_arr**2 + y_boot_sum += y_trans_arr + y2_boot_sum += y_trans_arr**2 + m_boot_sum += m_trans_arr + m2_boot_sum += m_trans_arr**2 + t2 = time.time() #print('=================================================') #print('Time to do {0} epochs: {1}s'.format(n_epochs, t2-t1)) @@ -1258,12 +1276,12 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot t_boot = t_arr[boot_idx] star_table = StarTable(name=ref_table['name'], - x=x_trans_arr[:,ii,boot_idx], - y=y_trans_arr[:,ii,boot_idx], - m=m_trans_arr[:,ii,boot_idx], - xe=xe_trans_arr[:,ii,boot_idx], - ye=ye_trans_arr[:,ii,boot_idx], - me=me_trans_arr[:,ii,boot_idx], + x=x_trans_arr[:,boot_idx], + y=y_trans_arr[:,boot_idx], + m=m_trans_arr[:,boot_idx], + xe=xe_trans_arr[:,boot_idx], + ye=ye_trans_arr[:,boot_idx], + me=me_trans_arr[:,boot_idx], t=np.tile(t_boot, (len(ref_table),1))) # Now, do proper motion calculation, making sure to fix t0 to the @@ -1278,7 +1296,8 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot # Save proper motion fit results to output arrays for col in motion_col_list: - motion_data[col][:,ii] = star_table[col] + motion_boot_sum[col] += star_table[col] + motion2_boot_sum[col] += star_table[col]**2 # Quick check to make sure bootstrap calc was valid: output t0 should be # same as input t0_arr, since we used fixed_t0 option @@ -1290,15 +1309,20 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot #print('=================================================') # Calculate the bootstrap error values. - x_err_b = np.std(x_trans_arr, ddof=1, axis=1) - y_err_b = np.std(y_trans_arr, ddof=1, axis=1) - m_err_b = np.std(m_trans_arr, ddof=1, axis=1) + x_boot_mean = x_boot_sum/n_boot + x_err_b = np.sqrt((x2_boot_sum - 2*x_boot_mean*x_boot_sum + n_boot*x_boot_mean**2)/n_boot) + y_boot_mean = y_boot_sum/n_boot + y_err_b = np.sqrt((y2_boot_sum - 2*y_boot_mean*y_boot_sum + n_boot*y_boot_mean**2)/n_boot) + m_boot_mean = m_boot_sum/n_boot + m_err_b = np.sqrt((m2_boot_sum - 2*m_boot_mean*m_boot_sum + n_boot*m_boot_mean**2)/n_boot) #pdb.set_trace() motion_data_err = {} if calc_vel_in_bootstrap: for col in motion_col_list: - motion_data_err[col] = np.nanstd(motion_data[col], ddof=1,axis=1) + mot_boot_mean = motion_boot_sum[col]/n_boot + motion_data_err[col] = np.sqrt((motion2_boot_sum[col] - + 2*mot_boot_mean*motion_boot_sum[col] + n_boot*mot_boot_mean**2)/n_boot) else: for col in motion_col_list: motion_data_err[col] = np.nan From 6e50b6c247b44661d65a4a99c3f27af4d117d046 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Fri, 24 Oct 2025 09:15:15 -0700 Subject: [PATCH 114/254] debug bootstrap vel --- flystar/align.py | 11 ++++++++--- flystar/tests/test_align.py | 1 + 2 files changed, 9 insertions(+), 3 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 03f557b..f394879 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -1149,6 +1149,8 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot for col in motion_col_list: motion_boot_sum[col] = np.zeros((len(ref_table['x']))) motion2_boot_sum[col] = np.zeros((len(ref_table['x']))) + motion_boot_min_epochs = np.max([self.motion_model_dict[mod].n_pts_req + for mod in np.unique(motion_model_list)]) ### IF MEMORY PROBLEMS HERE: ### DEFINE MEAN, STD VARIABLES AND BUILD THEM RATHER THAN SAVING FULL ARRAY @@ -1270,9 +1272,10 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot # for each star, if desired. Draw a full-sample bootstrap over the epochs # for each star, and then run it through the startable fit_velocities machinery if calc_vel_in_bootstrap: - # TODO: consider confirming we reach some threshold of unique time values here? - # TODO: Like, grab n_pts needed for the default motion model maybe boot_idx = np.random.choice(np.arange(0, n_epochs, 1), size=n_epochs) + while len(np.unique(boot_idx)) < motion_boot_min_epochs: + boot_idx = np.random.choice(np.arange(0, n_epochs, 1), size=n_epochs) + print(boot_idx) t_boot = t_arr[boot_idx] star_table = StarTable(name=ref_table['name'], @@ -1298,6 +1301,8 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot for col in motion_col_list: motion_boot_sum[col] += star_table[col] motion2_boot_sum[col] += star_table[col]**2 + print(t_boot) + print(star_table[['vx','x0']]) # Quick check to make sure bootstrap calc was valid: output t0 should be # same as input t0_arr, since we used fixed_t0 option @@ -1315,7 +1320,7 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot y_err_b = np.sqrt((y2_boot_sum - 2*y_boot_mean*y_boot_sum + n_boot*y_boot_mean**2)/n_boot) m_boot_mean = m_boot_sum/n_boot m_err_b = np.sqrt((m2_boot_sum - 2*m_boot_mean*m_boot_sum + n_boot*m_boot_mean**2)/n_boot) - #pdb.set_trace() + pdb.set_trace() motion_data_err = {} if calc_vel_in_bootstrap: diff --git a/flystar/tests/test_align.py b/flystar/tests/test_align.py index 6a63a58..9b65eb6 100644 --- a/flystar/tests/test_align.py +++ b/flystar/tests/test_align.py @@ -1141,6 +1141,7 @@ def test_bootstrap(): assert np.sum(np.isnan(match1.ref_table['ye_boot'])) == 0 assert np.sum(np.isnan(match1.ref_table['vx_err_boot'])) == 0 assert np.sum(np.isnan(match1.ref_table['vy_err_boot'])) == 0 + #pdb.set_trace() # Test 2: make sure boot_epochs_min is working # Eliminate some rows to list2, so some stars are only in 1 epoch. From 8d68cbf5a128594e86bd0b485bfec8637575a10f Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Fri, 24 Oct 2025 09:26:02 -0700 Subject: [PATCH 115/254] debug bootstrap vel --- flystar/align.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index f394879..87995e1 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -1260,8 +1260,9 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot x2_boot_sum += x_trans_arr**2 y_boot_sum += y_trans_arr y2_boot_sum += y_trans_arr**2 - m_boot_sum += m_trans_arr - m2_boot_sum += m_trans_arr**2 + if self.mag_trans: + m_boot_sum += m_trans_arr + m2_boot_sum += m_trans_arr**2 t2 = time.time() #print('=================================================') From b28f8a0e6dbe3b4e2292a159e9aa6ff661c877c0 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Fri, 24 Oct 2025 09:44:01 -0700 Subject: [PATCH 116/254] finalize bootstrap changes --- .gitignore | 3 +++ flystar/align.py | 4 ---- 2 files changed, 3 insertions(+), 4 deletions(-) diff --git a/.gitignore b/.gitignore index b56c81e..b291312 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,6 @@ +*.png +*random*.fits +*.DS_Store *.pyc .ipynb_checkpoints/ diff --git a/flystar/align.py b/flystar/align.py index 87995e1..f5f0742 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -1276,7 +1276,6 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot boot_idx = np.random.choice(np.arange(0, n_epochs, 1), size=n_epochs) while len(np.unique(boot_idx)) < motion_boot_min_epochs: boot_idx = np.random.choice(np.arange(0, n_epochs, 1), size=n_epochs) - print(boot_idx) t_boot = t_arr[boot_idx] star_table = StarTable(name=ref_table['name'], @@ -1302,8 +1301,6 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot for col in motion_col_list: motion_boot_sum[col] += star_table[col] motion2_boot_sum[col] += star_table[col]**2 - print(t_boot) - print(star_table[['vx','x0']]) # Quick check to make sure bootstrap calc was valid: output t0 should be # same as input t0_arr, since we used fixed_t0 option @@ -1321,7 +1318,6 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot y_err_b = np.sqrt((y2_boot_sum - 2*y_boot_mean*y_boot_sum + n_boot*y_boot_mean**2)/n_boot) m_boot_mean = m_boot_sum/n_boot m_err_b = np.sqrt((m2_boot_sum - 2*m_boot_mean*m_boot_sum + n_boot*m_boot_mean**2)/n_boot) - pdb.set_trace() motion_data_err = {} if calc_vel_in_bootstrap: From ce82de209494a270b4d5d72255d21aa8ca3a9963 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Mon, 3 Nov 2025 17:43:27 -0800 Subject: [PATCH 117/254] minor cleanup --- flystar/align.py | 6 ++++-- flystar/motion_model.py | 11 +++++------ flystar/parallax.py | 3 +++ 3 files changed, 12 insertions(+), 8 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index f5f0742..994a3b1 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -137,10 +137,12 @@ def = None. If not None, then this should contain an array or list of transform Dict of motion model name keys (strings) and corresponding MotionModel object values use_scipy : bool, optional - If True, use scipy.optimize.curve_fit for velocity fitting. If False, use linear algebra fitting, by default True. + If True, use scipy.optimize.curve_fit for velocity fitting. If False, use linear + algebra fitting of posible, by default True. absolute_sigma : bool, optional - If True, the velocity fit will use absolute errors in the data. If False, relative errors will be used, by default False. + If True, the velocity fit will use absolute errors in the data. If False, relative + errors will be used, by default False. save_path : str, optional Path to save the MosaicSelfRef object as a pickle file. diff --git a/flystar/motion_model.py b/flystar/motion_model.py index 66acce6..e24067b 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -430,20 +430,19 @@ def run_fit(self, t, x, y, xe, ye, t0, weighting='var', params_guess=None, t_mjd = Time(t, format='decimalyear', scale='utc').mjd pvec = self.get_parallax_vector(t_mjd) x_wt, y_wt = self.get_weights(xe,ye, weighting=weighting) - def fit_func(t, x0,vx, y0,vy, pi): - use_t = t[:int(len(t)/2)] + def fit_func(use_t, x0,vx, y0,vy, pi): x_res = x0 + vx*(use_t-t0) + pi*pvec[0] y_res = y0 + vy*(use_t-t0) + pi*pvec[1] - return np.append(x_res, y_res) + return np.hstack([x_res, y_res]) # Initial guesses, x0,y0 as x,y averages; # vx,vy as average velocity if first and last points are perfectly measured; # pi for 10 pc disance if params_guess is None: idx_first, idx_last = np.argmin(t), np.argmax(t) params_guess = [x.mean(),(x[idx_last]-x[idx_first])/(t[idx_last]-t[idx_first]), - y.mean(),(y[idx_last]-y[idx_first])/(t[idx_last]-t[idx_first]), 1] - res = curve_fit(fit_func, np.append(t,t), np.append(x,y), - p0=params_guess, sigma = 1.0/np.append(x_wt,y_wt)) + y.mean(),(y[idx_last]-y[idx_first])/(t[idx_last]-t[idx_first]), 0.1] + res = curve_fit(fit_func, t, np.hstack([x,y]), + p0=params_guess, sigma = 1.0/np.hstack([x_wt,y_wt])) x0,vx,y0,vy,pi = res[0] x0_err,vx_err,y0_err,vy_err,pi_err = self.scale_errors(np.sqrt(np.diag(res[1])), weighting=weighting) diff --git a/flystar/parallax.py b/flystar/parallax.py index 27cd632..b4b3a1b 100755 --- a/flystar/parallax.py +++ b/flystar/parallax.py @@ -1,3 +1,6 @@ +# Parallax calculation module for motion models involving parallax +# Adapted from BAGLE's parallax.py + import math import numpy as np From 253c52db5b0337944237a490a4850a67aa1b950e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Fri, 7 Nov 2025 12:42:39 -0800 Subject: [PATCH 118/254] Clean up: Remove scale_errors; Update init functions; Update Linear model error calculations --- flystar/motion_model.py | 370 +++++++++++++++++++++------------------- 1 file changed, 199 insertions(+), 171 deletions(-) diff --git a/flystar/motion_model.py b/flystar/motion_model.py index c5642d6..3c5069c 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -41,8 +41,13 @@ def get_batch_pos_at_time(self, t): #return x, y, x_err, y_err pass - def run_fit(self, t, x, y, xe, ye, t0, weighting='var', - use_scipy=True, absolute_sigma=True): + def run_fit( + self, t, x, y, xe, ye, t0, + weighting='var', + use_scipy=True, + absolute_sigma=True, + fill_value=np.inf + ): # Run a single fit (used both for overall fit + bootstrap iterations) pass @@ -54,37 +59,43 @@ def get_weights(self, xe, ye, weighting='var'): else: warnings.warn("Invalid weighting, using default weighting scheme var.", UserWarning) return 1./xe**2, 1./ye**2 - - def scale_errors(self, errs, weighting='var'): - if weighting=='std': - return np.array(errs)**2 - elif weighting=='var': - return errs - else: - warnings.warn("Invalid weighting, using default weighting scheme var.", UserWarning) - return errs - def fit_motion_model(self, t, x, y, xe, ye, t0, bootstrap=0, weighting='var', - use_scipy=True, absolute_sigma=True): + def fit_motion_model( + self, t, x, y, xe, ye, t0, + bootstrap=0, + weighting='var', + use_scipy=True, + absolute_sigma=True, + fill_value=np.inf, + rng=None + ): """ Fit the input positions on the sky and errors to determine new parameters for this motion model (MM). Best-fit parameters will be returned along with uncertainties. """ - params, param_errs = self.run_fit(t, x, y, xe, ye, t0=t0, weighting=weighting, - use_scipy=use_scipy, absolute_sigma=absolute_sigma) - - if bootstrap>0 and len(x)>(self.n_pts_req): + params, param_errs, chi2x, chi2y = self.run_fit( + t, x, y, xe, ye, t0=t0, + weighting=weighting, + use_scipy=use_scipy, + absolute_sigma=absolute_sigma, + fill_value=fill_value + ) + + if bootstrap > 0 and len(x) > (self.n_pts_req): edx = np.arange(len(x), dtype=int) bb_params = [] bb_params_errs = [] for bb in range(bootstrap): - bdx = np.random.choice(edx, len(x)) - while len(np.unique(bdx)) 0: + reduced_chi2x = chi2x / degree_of_freedom + reduced_chi2y = chi2y / degree_of_freedom + + param_errors[0] *= reduced_chi2x**0.5 + param_errors[1] *= reduced_chi2y**0.5 + else: + warnings.warn( + f'Degree of freedom <= 0. Covariance of the parameters could not be estimated. Setting parameter uncertainties to fill value {fill_value}.', + OptimizeWarning, stacklevel=2 + ) + param_errors = np.full_like(param_errors, fill_value) + + return params, param_errors, chi2x, chi2y + class Linear(MotionModel): """ A 2D linear motion model for a star on the sky. """ - n_pts_req = 2 - n_params=2 - fitter_param_names = ['x0', 'vx', 'y0', 'vy'] - fixed_param_names = ['t0'] - def __init__(self, **kwargs): # Must call after setting parameters. # This checks for proper parameter formatting. super().__init__() + self.n_pts_req = 2 + self.n_params = 2 + self.fitter_param_names = ['x0', 'vx', 'y0', 'vy'] + self.fixed_param_names = ['t0'] return def get_pos_at_time(self, fit_params, fixed_params, t): @@ -183,7 +216,7 @@ def get_pos_at_time(self, fit_params, fixed_params, t): fixed_params_dict = dict(zip(self.fixed_param_names, fixed_params)) dt = t-fixed_params_dict['t0'] return fit_params_dict['x0'] + fit_params_dict['vx']*dt, fit_params_dict['y0'] + fit_params_dict['vy']*dt - + def get_batch_pos_at_time(self, t, x0=[],vx=[], y0=[],vy=[], t0=[], x0_err=[],vx_err=[], y0_err=[],vy_err=[], **kwargs): if hasattr(t, "__len__"): @@ -200,113 +233,98 @@ def get_batch_pos_at_time(self, t, x0=[],vx=[], y0=[],vy=[], t0=[], y_err = np.hypot(y0_err, vy_err*dt) return x,y,x_err,y_err - def run_fit(self, t, x, y, xe, ye, t0, weighting='var', params_guess=None, - use_scipy=True, absolute_sigma=True): - dt = t-t0 - x_wt, y_wt = self.get_weights(xe,ye, weighting=weighting) + def run_fit( + self, t, x, y, xe, ye, t0, + weighting='var', + use_scipy=True, + absolute_sigma=True, + params_guess=None, + fill_value=np.inf + ): + dt = t - t0 + x_wt, y_wt = self.get_weights(xe, ye, weighting=weighting) if params_guess is None: - params_guess = [x.mean(),0.0,y.mean(),0.0] - - # Handle 2-data point case - if len(np.unique(dt))==2: - if len(x)>2: # Catch case where bootstrap sends only 2 unique epochs - _,idx=np.unique(dt, return_index=True) - dt = dt[idx] - x = x[idx] - y = y[idx] - xe = xe[idx] - ye = ye[idx] - dx = np.diff(x)[0] - dy = np.diff(y)[0] - dt_diff = np.diff(dt)[0] - vx = dx / dt_diff - vy = dy / dt_diff - # TODO: still not sure about the error handling here - x0 = x[0] - dt[0]*vx # np.average(x, weights=x_wt) # - y0 = y[0] - dt[0]*vy # np.average(y, weights=y_wt) # - x0e = np.abs(dx) / 2**0.5 # np.sqrt(np.sum(xe**2)/2) # - y0e = np.abs(dy) / 2**0.5 # np.sqrt(np.sum(ye**2)/2) # - vxe = 0.0 #np.abs(vx) * np.sqrt(np.sum(xe**2/x**2)) - vye = 0.0 #np.abs(vy) * np.sqrt(np.sum(ye**2/y**2)) + params_guess = [x.mean(), 0., y.mean(), 0.] + + if use_scipy: + def linear(t, c0, c1): + return c0 + c1*t + x_opt, x_cov = curve_fit(linear, dt, x, p0=np.array(params_guess[:2]), sigma=1/np.sqrt(x_wt), absolute_sigma=absolute_sigma) + y_opt, y_cov = curve_fit(linear, dt, y, p0=np.array(params_guess[2:]), sigma=1/np.sqrt(y_wt), absolute_sigma=absolute_sigma) + x0, vx = x_opt + y0, vy = y_opt + x0e, vxe = np.sqrt(x_cov.diagonal()) + y0e, vye = np.sqrt(y_cov.diagonal()) + params = np.array([x0, vx, y0, vy]) + param_errors = np.array([x0e, vxe, y0e, vye]) + chi2_x, chi2_y = self.get_chi2(params, [t0], t, x, y, xe, ye) + else: - if use_scipy: - def linear(t, c0, c1): - return c0 + c1*t - x_opt, x_cov = curve_fit(linear, dt, x, p0=np.array(params_guess[:2]), sigma=1/np.sqrt(x_wt), absolute_sigma=absolute_sigma) - y_opt, y_cov = curve_fit(linear, dt, y, p0=np.array(params_guess[2:]), sigma=1/np.sqrt(y_wt), absolute_sigma=absolute_sigma) - x0, vx = x_opt - y0, vy = y_opt - x0e, vxe = np.sqrt(x_cov.diagonal()) - y0e, vye = np.sqrt(y_cov.diagonal()) - x0e, vxe, y0e, vye = self.scale_errors([x0e, vxe, y0e, vye], weighting=weighting) - else: - # Use https://en.wikipedia.org/wiki/Weighted_least_squares#Solution scheme - x = np.array(x) - y = np.array(y) - dt = np.array(dt) - X_mat_t = np.vander(dt, 2) - # x calculation - W_mat_x = np.diag(x_wt) - XTWX_mat_x = X_mat_t.T @ W_mat_x @ X_mat_t - pcov_x = np.linalg.inv(XTWX_mat_x) # Covariance Matrix - popt_x = pcov_x @ X_mat_t.T @ W_mat_x @ x # Linear Solution - perr_x = np.sqrt(np.diag(pcov_x)) # Uncertainty of Linear Solution - # y calculation - W_mat_y = np.diag(y_wt) - XTWX_mat_y = X_mat_t.T @ W_mat_y @ X_mat_t - pcov_y = np.linalg.inv(XTWX_mat_y) # Covariance Matrix - popt_y = pcov_y @ X_mat_t.T @ W_mat_y @ y # Linear Solution - perr_y = np.sqrt(np.diag(pcov_y)) # Uncertainty of Linear Solution - # prepare values to return - vx, x0 = popt_x - vy, y0 = popt_y - vxe, x0e = perr_x - vye, y0e = perr_y - x0e, vxe, y0e, vye = self.scale_errors([x0e, vxe, y0e, vye], weighting=weighting) - - residual_x = x - X_mat_t @ popt_x - residual_y = y - X_mat_t @ popt_y - - chi2_x = residual_x.T @ W_mat_x @ residual_x - chi2_y = residual_y.T @ W_mat_y @ residual_y - - if not absolute_sigma: - degree_of_freedom = len(x) - 2 - if degree_of_freedom > 0: - reduced_chi2_x = chi2_x/(len(x) - 2) - reduced_chi2_y = chi2_y/(len(x) - 2) - x0e *= reduced_chi2_x**0.5 - y0e *= reduced_chi2_y**0.5 - vxe *= reduced_chi2_x**0.5 - vye *= reduced_chi2_y**0.5 - else: - warnings.warn( - "Degree of freedom <= 0. Covariance of the parameters could not be estimated. Setting parameter uncertainties to infinity.", - OptimizeWarning, stacklevel=2 - ) - x0e *= np.inf - y0e *= np.inf - vxe *= np.inf - vye *= np.inf - params = [x0, vx, y0, vy] - param_errors = [x0e, vxe, y0e, vye] - return params, param_errors - - + # Use https://en.wikipedia.org/wiki/Weighted_least_squares#Solution scheme + x = np.array(x) + y = np.array(y) + dt = np.array(dt) + X_mat_t = np.vander(dt, 2) + # x calculation + W_mat_x = np.diag(x_wt) + XTWX_mat_x = X_mat_t.T @ W_mat_x @ X_mat_t + pcov_x = np.linalg.inv(XTWX_mat_x) # Covariance Matrix + popt_x = pcov_x @ X_mat_t.T @ W_mat_x @ x # Linear Solution + perr_x = np.sqrt(np.diag(pcov_x)) # Uncertainty of Linear Solution + # y calculation + W_mat_y = np.diag(y_wt) + XTWX_mat_y = X_mat_t.T @ W_mat_y @ X_mat_t + pcov_y = np.linalg.inv(XTWX_mat_y) # Covariance Matrix + popt_y = pcov_y @ X_mat_t.T @ W_mat_y @ y # Linear Solution + perr_y = np.sqrt(np.diag(pcov_y)) # Uncertainty of Linear Solution + # prepare values to return + vx, x0 = popt_x + vy, y0 = popt_y + vxe, x0e = perr_x + vye, y0e = perr_y + + # Does not use get_chi2 to accelerate calculation + residual_x = x - X_mat_t @ popt_x + residual_y = y - X_mat_t @ popt_y + + + chi2_x = residual_x.T @ W_mat_x @ residual_x + chi2_y = residual_y.T @ W_mat_y @ residual_y + + params = np.array([x0, vx, y0, vy]) + param_errors = np.array([x0e, vxe, y0e, vye]) + + if not absolute_sigma: + degree_of_freedom = len(x) - 2 + if degree_of_freedom > 0: + reduced_chi2_x = chi2_x/(len(x) - 2) + reduced_chi2_y = chi2_y/(len(x) - 2) + + param_errors[0:2] *= reduced_chi2_x**0.5 + param_errors[2:4] *= reduced_chi2_y**0.5 + + else: + warnings.warn( + f'Degree of freedom <= 0. Covariance of the parameters could not be estimated. Setting parameter uncertainties to fill value {fill_value}.', + OptimizeWarning, stacklevel=2 + ) + param_errors = np.full_like(param_errors, fill_value) + + return params, param_errors, chi2_x, chi2_y + class Acceleration(MotionModel): """ A 2D accelerating motion model for a star on the sky. """ - n_pts_req = 4 # TODO: consider special case for 3 pts - n_params=3 - fitter_param_names = ['x0', 'vx0', 'ax', 'y0', 'vy0', 'ay'] - fixed_param_names = ['t0'] - def __init__(self, x0=0, vx0=0, ax=0, y0=0, vy0=0, ay=0, t0=None, x0_err=0, vx0_err=0, ax_err=0, y0_err=0, vy0_err=0, ay_err=0, **kwargs): # Must call after setting parameters. # This checks for proper parameter formatting. super().__init__() + self.n_pts_req = 4 # TODO: consider special case for 3 pts + self.n_params = 3 + self.fitter_param_names = ['x0', 'vx0', 'ax', 'y0', 'vy0', 'ay'] + self.fixed_param_names = ['t0'] return def get_pos_at_time(self, fit_params, fixed_params, t): @@ -334,18 +352,24 @@ def get_batch_pos_at_time(self,t, y_err = np.sqrt(y0_err**2 + (vy0_err*dt)**2 + (0.5*ay_err*dt**2)**2) return x,y,x_err,y_err - def run_fit(self, t, x, y, xe, ye, t0, weighting='var', params_guess=None, - use_scipy=True, absolute_sigma=True): + def run_fit( + self, t, x, y, xe, ye, t0, + weighting='var', + use_scipy=True, + absolute_sigma=True, + params_guess=None, + fill_value=np.inf + ): if not use_scipy: Warning("Acceleration model has no non-scipy fitter option. Running with scipy.") - dt = t-t0 + dt = t - t0 x_wt, y_wt = self.get_weights(xe,ye, weighting=weighting) if params_guess is None: - params_guess = [x.mean(),0.0,0.0,y.mean(),0.0,0.0] - - def accel(t, c0,c1,c2): + params_guess = [x.mean(), 0., 0., y.mean(), 0., 0.] + + def accel(t, c0, c1, c2): return c0 + c1*t + 0.5*c2*t**2 - + x_opt, x_cov = curve_fit(accel, dt, x, p0=np.array(params_guess[:3]), sigma=1/x_wt**0.5, absolute_sigma=True) y_opt, y_cov = curve_fit(accel, dt, y, p0=np.array(params_guess[3:]), sigma=1/y_wt**0.5, absolute_sigma=True) x0 = x_opt[0] @@ -354,14 +378,13 @@ def accel(t, c0,c1,c2): vy0 = y_opt[1] ax = x_opt[2] ay = y_opt[2] - + x0e, vx0e, axe = np.sqrt(x_cov.diagonal()) y0e, vy0e, aye = np.sqrt(y_cov.diagonal()) - x0e, vx0e, axe, y0e, vy0e, aye = self.scale_errors([x0e, vx0e, axe, y0e, vy0e, aye], weighting=weighting) params = [x0, vx0, ax, y0, vy0, ay] param_errors = [x0e, vx0e, axe, y0e, vy0e, aye] - + return params, param_errors class Parallax(MotionModel): @@ -373,18 +396,17 @@ class Parallax(MotionModel): Optional PA is counterclockwise offset of the image y-axis from North. Optional obs parameter describes observer location, default is 'earth'. """ - n_pts_req = 4 - n_params=3 - fitter_param_names = ['x0', 'vx', 'y0', 'vy', 'pi'] - fixed_param_names = ['t0'] - fixed_meta_data = ['RA','Dec','PA','obs'] - def __init__(self, RA, Dec, PA=0.0, obs='earth', **kwargs): self.RA = RA self.Dec = Dec self.PA = PA self.obs = obs self.plx_vector_cached = None + self.n_pts_req = 4 + self.n_params = 3 + self.fitter_param_names = ['x0', 'vx', 'y0', 'vy', 'pi'] + self.fixed_param_names = ['t0'] + self.fixed_meta_data = ['RA','Dec','PA','obs'] return def get_parallax_vector(self, t_mjd): @@ -411,7 +433,7 @@ def get_parallax_vector(self, t_mjd): def get_pos_at_time(self, fit_params, fixed_params, t): fit_params_dict = dict(zip(self.fitter_param_names, fit_params)) fixed_params_dict = dict(zip(self.fixed_param_names, fixed_params)) - dt = t-fixed_params_dict['t0'] + dt = t - fixed_params_dict['t0'] t_mjd = Time(t, format='decimalyear', scale='utc').mjd pvec = self.get_parallax_vector(t_mjd) @@ -420,7 +442,7 @@ def get_pos_at_time(self, fit_params, fixed_params, t): x = fit_params_dict['x0'] + fit_params_dict['vx']*dt + fit_params_dict['pi']*pvec_x y = fit_params_dict['y0'] + fit_params_dict['vy']*dt + fit_params_dict['pi']*pvec_y return x, y - + def get_batch_pos_at_time(self, t, x0=[],vx=[], y0=[],vy=[], pi=[], t0=[], x0_err=[],vx_err=[], y0_err=[],vy_err=[], pi_err=[], **kwargs): @@ -446,8 +468,14 @@ def get_batch_pos_at_time(self, t, x_err,y_err = [],[] return x,y,x_err,y_err - def run_fit(self, t, x, y, xe, ye, t0, weighting='var', params_guess=None, - use_scipy=True, absolute_sigma=True): + def run_fit( + self, t, x, y, xe, ye, t0, + weighting='var', + use_scipy=True, + absolute_sigma=True, + params_guess=None, + fill_value=np.inf + ): if not use_scipy: Warning("Parallax model has no non-scipy fitter option. Running with scipy.") t_mjd = Time(t, format='decimalyear', scale='utc').mjd @@ -467,8 +495,8 @@ def fit_func(t, x0,vx, y0,vy, pi): y.mean(),(y[idx_last]-y[idx_first])/(t[idx_last]-t[idx_first]), 1] res = curve_fit(fit_func, np.append(t,t), np.append(x,y), p0=params_guess, sigma = 1.0/np.append(x_wt,y_wt)) - x0,vx,y0,vy,pi = res[0] - x0_err,vx_err,y0_err,vy_err,pi_err = self.scale_errors(np.sqrt(np.diag(res[1])), weighting=weighting) + x0, vx, y0, vy, pi = res[0] + x0_err, vx_err, y0_err, vy_err, pi_err = np.sqrt(np.diag(res[1])) params = [x0, vx, y0, vy, pi] param_errors = [x0_err, vx_err, y0_err, vy_err, pi_err] @@ -498,7 +526,7 @@ def validate_motion_model_dict(motion_model_dict, startable, default_motion_mode raise ValueError(f"Cannot use {mm} motion model without required metadata. Please initialize with required metadata and provide in motion_model_dict.") else: motion_model_dict[mm] = mm_obj() - warnings.warn(f"Using default model/fitter for {mm}.", UserWarning) + # warnings.warn(f"Using default model/fitter for {mm}.", UserWarning) return motion_model_dict From 0ba32c91bb052d2f6fa924e4c10c936962aa2220 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Fri, 7 Nov 2025 12:47:53 -0800 Subject: [PATCH 119/254] Rename py to plt; Add save path; Fix unclosed figure --- flystar/plots.py | 531 ++++++++++++++++++++++++----------------------- 1 file changed, 268 insertions(+), 263 deletions(-) diff --git a/flystar/plots.py b/flystar/plots.py index d6a8d40..d2dea16 100755 --- a/flystar/plots.py +++ b/flystar/plots.py @@ -1,9 +1,8 @@ from flystar import analysis, motion_model, startables -import pylab as py -import pylab as plt import numpy as np import matplotlib.mlab as mlab import matplotlib +import matplotlib.pyplot as plt from matplotlib import colors import matplotlib.cm as cm from scipy.stats import chi2 @@ -23,8 +22,8 @@ #################################################### -def trans_positions(ref, ref_mat, starlist, starlist_mat, xlim=None, ylim=None, fileName=None, - equal_axis=True, root='./'): +def trans_positions(ref, ref_mat, starlist, starlist_mat, xlim=None, ylim=None, + equal_axis=True, save_path=None, show_plot=True): """ Plot positions of stars in reference list and the transformed starlist, in reference list coordinates. Stars used in the transformation are @@ -55,31 +54,37 @@ def trans_positions(ref, ref_mat, starlist, starlist_mat, xlim=None, ylim=None, equal_axis: boolean If true, make axes equal. True by default + + save_path: string + Path to save the figure to. Default is None + show_plot: boolean + If true, show the plot. Default is True + """ - py.figure(figsize=(10,10)) - py.clf() - py.plot(ref['x'], ref['y'], 'g+', ms=5, label='Reference') - py.plot(starlist['x'], starlist['y'], 'rx', ms=5, label='starlist') - py.plot(ref_mat['x'], ref_mat['y'], color='skyblue', marker='s', ms=10, alpha=0.3, + plt.figure(figsize=(10,10)) + plt.clf() + plt.plot(ref['x'], ref['y'], 'g+', ms=5, label='Reference') + plt.plot(starlist['x'], starlist['y'], 'rx', ms=5, label='starlist') + plt.plot(ref_mat['x'], ref_mat['y'], color='skyblue', marker='s', ms=10, alpha=0.3, linestyle='None', label='Matched Reference') - py.plot(starlist_mat['x'], starlist_mat['y'], color='darkblue', marker='s', ms=5, alpha=0.3, + plt.plot(starlist_mat['x'], starlist_mat['y'], color='darkblue', marker='s', ms=5, alpha=0.3, linestyle='None', label='Matched starlist') - py.xlabel('X position (Reference Coords)') - py.ylabel('Y position (Reference Coords)') - py.legend(numpoints=1) - py.title('Label.dat Positions After Transformation') + plt.xlabel('X position (Reference Coords)') + plt.ylabel('Y position (Reference Coords)') + plt.legend(numpoints=1) + plt.title('Label.dat Positions After Transformation') if xlim != None: - py.axis([xlim[0], xlim[1], ylim[0], ylim[1]]) + plt.axis([xlim[0], xlim[1], ylim[0], ylim[1]]) if equal_axis: - py.axis('equal') - if fileName!=None: - #py.savefig(root + fileName[3:8] + 'Transformed_positions_' + '.png') - py.savefig(root + 'Transformed_positions_{0}'.format(fileName) + '.png') - else: - py.savefig(root + 'Transformed_positions.png') + plt.axis('equal') + + if save_path: + plt.savefig(save_path) + if show_plot: + plt.show() - py.close() + plt.close() return @@ -121,22 +126,22 @@ def pos_diff_hist(ref_mat, starlist_mat, nbins=25, bin_width=None, xlim=None, fi bins = np.arange(min_range, max_range+bin_width, bin_width) - py.figure(figsize=(10,10)) - py.clf() - py.hist(diff_x, histtype='step', bins=bins, color='blue', label='X') - py.hist(diff_y, histtype='step', bins=bins, color='red', label='Y') - py.xlabel('Reference Position - starlist Position') - py.ylabel('N stars') - py.title('Position Differences for matched stars') + plt.figure(figsize=(10,10)) + plt.clf() + plt.hist(diff_x, histtype='step', bins=bins, color='blue', label='X') + plt.hist(diff_y, histtype='step', bins=bins, color='red', label='Y') + plt.xlabel('Reference Position - starlist Position') + plt.ylabel('N stars') + plt.title('Position Differences for matched stars') if xlim != None: - py.xlim([xlim[0], xlim[1]]) - py.legend() + plt.xlim([xlim[0], xlim[1]]) + plt.legend() if fileName != None: - py.savefig(root + fileName[3:8] + 'Positions_hist_' + '.png') + plt.savefig(root + fileName[3:8] + 'Positions_hist_' + '.png') else: - py.savefig(root + 'Positions_hist.png') + plt.savefig(root + 'Positions_hist.png') - py.close() + plt.close() return def pos_diff_err_hist(ref_mat, starlist_mat, transform, nbins=25, bin_width=None, errs='both', xlim=None, @@ -248,51 +253,51 @@ def pos_diff_err_hist(ref_mat, starlist_mat, transform, nbins=25, bin_width=None bins = np.arange(min_range, max_range+bin_width, bin_width) - py.figure(figsize=(10,10)) - py.clf() - n_x, bins_x, p = py.hist(ratio_x, histtype='step', bins=bins, color='blue', + plt.figure(figsize=(10,10)) + plt.clf() + n_x, bins_x, p = plt.hist(ratio_x, histtype='step', bins=bins, color='blue', label='X', density=True, linewidth=2) - n_y, bins_y, p = py.hist(ratio_y, histtype='step', bins=bins, color='red', + n_y, bins_y, p = plt.hist(ratio_y, histtype='step', bins=bins, color='red', label='Y', density=True, linewidth=2) # Overplot a Gaussian, as well mean = 0 sigma = 1 x = np.arange(-6, 6, 0.1) - py.plot(x, norm.pdf(x,mean,sigma), 'g-', linewidth=2) + plt.plot(x, norm.pdf(x,mean,sigma), 'g-', linewidth=2) # Annotate reduced chi-sqared values in plot: with outliers xstr = r'$\chi^2_r$ = {0}'.format(np.round(chi_sq_red, decimals=3)) - py.annotate(xstr, xy=(0.3, 0.77), xycoords='figure fraction', color='black') + plt.annotate(xstr, xy=(0.3, 0.77), xycoords='figure fraction', color='black') txt = r'$\nu$ = 2*{0} - {1} = {2}'.format(len(diff_x), num_mod_params, deg_freedom) - py.annotate(txt, xy=(0.25,0.74), xycoords='figure fraction', color='black') + plt.annotate(txt, xy=(0.25,0.74), xycoords='figure fraction', color='black') xstr2 = 'With Outliers' xstr3 = '{0} with +/- {1}+ sigma'.format(len(ratio_x) - len(good[0]), outlier) - py.annotate(xstr2, xy=(0.29, 0.83), xycoords='figure fraction', color='black') - py.annotate(xstr3, xy=(0.25, 0.80), xycoords='figure fraction', color='black') + plt.annotate(xstr2, xy=(0.29, 0.83), xycoords='figure fraction', color='black') + plt.annotate(xstr3, xy=(0.25, 0.80), xycoords='figure fraction', color='black') # Annotate reduced chi-sqared values in plot: without outliers xstr = r'$\chi^2_r$ = {0}'.format(np.round(chi_sq_red_good, decimals=3)) - py.annotate(xstr, xy=(0.7, 0.8), xycoords='figure fraction', color='black') + plt.annotate(xstr, xy=(0.7, 0.8), xycoords='figure fraction', color='black') txt = r'$\nu$ = 2*{0} - {1} = {2}'.format(len(good[0]), num_mod_params, deg_freedom_good) - py.annotate(txt, xy=(0.65,0.77), xycoords='figure fraction', color='black') + plt.annotate(txt, xy=(0.65,0.77), xycoords='figure fraction', color='black') xstr2 = 'Without Outliers' - py.annotate(xstr2, xy=(0.67, 0.83), xycoords='figure fraction', color='black') + plt.annotate(xstr2, xy=(0.67, 0.83), xycoords='figure fraction', color='black') - py.xlabel('(Ref Pos - TransStarlist Pos) / Ast. Error') - py.ylabel('N stars (normalized)') - py.title('Position Residuals for Matched Stars') + plt.xlabel('(Ref Pos - TransStarlist Pos) / Ast. Error') + plt.ylabel('N stars (normalized)') + plt.title('Position Residuals for Matched Stars') if xlim != None: - py.xlim([xlim[0], xlim[1]]) - py.legend() + plt.xlim([xlim[0], xlim[1]]) + plt.legend() if fileName != None: - py.savefig(root + fileName[3:8] + 'Positions_err_ratio_hist_' + '.png') + plt.savefig(root + fileName[3:8] + 'Positions_err_ratio_hist_' + '.png') else: - py.savefig(root + 'Positions_err_ratio_hist.png') + plt.savefig(root + 'Positions_err_ratio_hist.png') - py.close() + plt.close() return @@ -319,18 +324,18 @@ def mag_diff_hist(ref_mat, starlist_mat, bins=25, fileName=None, root='./'): bad2 = np.where(bad == True) diff_m = np.delete(diff_m, bad2) - py.figure(figsize=(10,10)) - py.clf() - py.hist(diff_m, bins=bins) - py.xlabel('Reference Mag - TransStarlist Mag') - py.ylabel('N stars') - py.title('Magnitude Difference for matched stars') + plt.figure(figsize=(10,10)) + plt.clf() + plt.hist(diff_m, bins=bins) + plt.xlabel('Reference Mag - TransStarlist Mag') + plt.ylabel('N stars') + plt.title('Magnitude Difference for matched stars') if fileName != None: - py.savefig(root + fileName[3:8] + 'Magnitude_hist_' + '.png') + plt.savefig(root + fileName[3:8] + 'Magnitude_hist_' + '.png') else: - py.savefig(root + 'Magnitude_hist.png') + plt.savefig(root + 'Magnitude_hist.png') - py.close() + plt.close() return def pos_diff_quiver(ref_mat, starlist_mat, qscale=10, keyLength=0.2, xlim=None, ylim=None, @@ -411,35 +416,35 @@ def pos_diff_quiver(ref_mat, starlist_mat, qscale=10, keyLength=0.2, xlim=None, s = len(xpos) - py.figure(figsize=(10,10)) - py.clf() - q = py.quiver(xpos, ypos, diff_x, diff_y, scale=qscale) + plt.figure(figsize=(10,10)) + plt.clf() + q = plt.quiver(xpos, ypos, diff_x, diff_y, scale=qscale) fmt = '{0} ref units'.format(keyLength) - #py.quiverkey(q, 0.2, 0.92, keyLength, fmt, coordinates='figure', color='black') + #plt.quiverkey(q, 0.2, 0.92, keyLength, fmt, coordinates='figure', color='black') # Make our reference arrow a different color - q2 = py.quiver(xpos[s-2:s], ypos[s-2:s], diff_x[s-2:s], diff_y[s-2:s], scale=qscale, color='red') + q2 = plt.quiver(xpos[s-2:s], ypos[s-2:s], diff_x[s-2:s], diff_y[s-2:s], scale=qscale, color='red') # Annotate our reference quiver arrow - py.annotate(fmt, xy=(xpos[-1]-2, ypos[-1]+0.5), color='red') - py.xlabel('X Position (Reference coords)') - py.ylabel('Y Position (Reference coords)') + plt.annotate(fmt, xy=(xpos[-1]-2, ypos[-1]+0.5), color='red') + plt.xlabel('X Position (Reference coords)') + plt.ylabel('Y Position (Reference coords)') if xlim != None: - py.axis([xlim[0], ylim[1], ylim[0], ylim[1]]) + plt.axis([xlim[0], ylim[1], ylim[0], ylim[1]]) if sigma: if fileName != None: - py.title('(Reference - Transformed Starlist positions) / sigma') - py.savefig(root + fileName[3:8] + 'Positions_quiver_sigma_' + '.png') + plt.title('(Reference - Transformed Starlist positions) / sigma') + plt.savefig(root + fileName[3:8] + 'Positions_quiver_sigma_' + '.png') else: - py.title('(Reference - Transformed Starlist positions) / sigma') - py.savefig(root + 'Positions_quiver_sigma.png') + plt.title('(Reference - Transformed Starlist positions) / sigma') + plt.savefig(root + 'Positions_quiver_sigma.png') else: if fileName != None: - py.title('Reference - Transformed Starlist positions') - py.savefig(root + fileName[3:8] + 'Positions_quiver_' + '.png') + plt.title('Reference - Transformed Starlist positions') + plt.savefig(root + fileName[3:8] + 'Positions_quiver_' + '.png') else: - py.title('Reference - Transformed Starlist positions') - py.savefig(root + 'Positions_quiver.png') + plt.title('Reference - Transformed Starlist positions') + plt.savefig(root + 'Positions_quiver.png') - py.close() + plt.close() return def vpd(ref, starlist_trans, vxlim, vylim): @@ -472,17 +477,17 @@ def vpd(ref, starlist_trans, vxlim, vylim): trans_vx = starlist_trans['vx'] trans_vy = starlist_trans['vy'] - py.figure(figsize=(10,10)) - py.clf() - py.plot(trans_vx, trans_vy, 'k.', ms=8, label='Transformed', alpha=0.4) - py.plot(ref_vx, ref_vy, 'r.', ms=8, label='Reference', alpha=0.4) - py.xlabel('Vx (Reference units)') - py.ylabel('Vy (Reference units)') + plt.figure(figsize=(10,10)) + plt.clf() + plt.plot(trans_vx, trans_vy, 'k.', ms=8, label='Transformed', alpha=0.4) + plt.plot(ref_vx, ref_vy, 'r.', ms=8, label='Reference', alpha=0.4) + plt.xlabel('Vx (Reference units)') + plt.ylabel('Vy (Reference units)') if vxlim != None: - py.axis([vxlim[0], vylim[1], vylim[0], vylim[1]]) - py.title('Reference and Transformed Proper Motions') - py.legend() - py.savefig('Transformed_velocities.png') + plt.axis([vxlim[0], vylim[1], vylim[0], vylim[1]]) + plt.title('Reference and Transformed Proper Motions') + plt.legend() + plt.savefig('Transformed_velocities.png') return @@ -538,27 +543,27 @@ def vel_diff_err_hist(ref_mat, starlist_mat, nbins=25, bin_width=None, vxlim=Non sigma = 1 x = np.arange(-6, 6, 0.1) - py.figure(figsize=(20,10)) - py.subplot(121) - py.subplots_adjust(left=0.1) - py.hist(ratio_vx, bins=xbins, histtype='step', color='black', density=True, + plt.figure(figsize=(20,10)) + plt.subplot(121) + plt.subplots_adjust(left=0.1) + plt.hist(ratio_vx, bins=xbins, histtype='step', color='black', density=True, linewidth=2) - py.plot(x, norm.pdf(x,mean,sigma), 'r-', linewidth=2) - py.xlabel('(Ref Vx - Trans Vx) / Vxe') - py.ylabel('N_stars') - py.title('Vx Residuals, Matched') + plt.plot(x, norm.pdf(x,mean,sigma), 'r-', linewidth=2) + plt.xlabel('(Ref Vx - Trans Vx) / Vxe') + plt.ylabel('N_stars') + plt.title('Vx Residuals, Matched') if vxlim != None: - py.xlim([vxlim[0], vxlim[1]]) - py.subplot(122) - py.hist(ratio_vy, bins=ybins, histtype='step', color='black', density=True, + plt.xlim([vxlim[0], vxlim[1]]) + plt.subplot(122) + plt.hist(ratio_vy, bins=ybins, histtype='step', color='black', density=True, linewidth=2) - py.plot(x, norm.pdf(x,mean,sigma), 'r-', linewidth=2) - py.xlabel('(Ref Vy - Trans Vy) / Vye') - py.ylabel('N_stars') - py.title('Vy Residuals, Matched') + plt.plot(x, norm.pdf(x,mean,sigma), 'r-', linewidth=2) + plt.xlabel('(Ref Vy - Trans Vy) / Vye') + plt.ylabel('N_stars') + plt.title('Vy Residuals, Matched') if vylim != None: - py.xlim([vylim[0], vylim[1]]) - py.savefig('Vel_err_ratio_dist.png') + plt.xlim([vylim[0], vylim[1]]) + plt.savefig('Vel_err_ratio_dist.png') return @@ -606,17 +611,17 @@ def residual_vpd(ref_mat, starlist_trans_mat, pscale=None): yerr = np.hypot(ref_mat['vy_err'], starlist_trans_mat['vy_err']) # Plotting - py.figure(figsize=(10,10)) - py.clf() - py.errorbar(diff_x, diff_y, xerr=xerr, yerr=yerr, fmt='k.', ms=8, alpha=0.5) + plt.figure(figsize=(10,10)) + plt.clf() + plt.errorbar(diff_x, diff_y, xerr=xerr, yerr=yerr, fmt='k.', ms=8, alpha=0.5) if pscale != None: - py.xlabel('Reference_vx - Transformed_vx (mas/yr)') - py.ylabel('Reference_vy - Transformed_vy (mas/yr)') + plt.xlabel('Reference_vx - Transformed_vx (mas/yr)') + plt.ylabel('Reference_vy - Transformed_vy (mas/yr)') else: - py.xlabel('Reference_vx - Transformed_vx (reference coords)') - py.ylabel('Reference_vy - Transformed_vy (reference coords)') - py.title('Proper Motion Residuals') - py.savefig('resid_vpd.png') + plt.xlabel('Reference_vx - Transformed_vx (reference coords)') + plt.ylabel('Reference_vy - Transformed_vy (reference coords)') + plt.title('Proper Motion Residuals') + plt.savefig('resid_vpd.png') return @@ -636,8 +641,8 @@ def plotStar(starNames, rootDir='./', align='align/align_d_rms_1000_abs_t', else: Nrows = math.ceil(Nstars / (Ncols / 2)) * 3 - py.close('all') - py.figure(2, figsize=figsize) + plt.close('all') + plt.figure(2, figsize=figsize) names = s.getArray('name') mag = s.getArray('mag') x = s.getArray('x') @@ -746,7 +751,7 @@ def plotStar(starNames, rootDir='./', align='align/align_d_rms_1000_abs_t', t0 = int(np.floor(np.min(time))) tO = int(np.ceil(np.max(time))) - dateTicLoc = py.MultipleLocator(3) + dateTicLoc = plt.MultipleLocator(3) dateTicRng = [t0-1, tO+1] dateTics = np.arange(t0, tO+1) DateTicsLabel = dateTics-2000 @@ -754,7 +759,7 @@ def plotStar(starNames, rootDir='./', align='align/align_d_rms_1000_abs_t', # See if we are using MJD instead. if time[0] > 50000: print('MJD') - dateTicLoc = py.MultipleLocator(1000) + dateTicLoc = plt.MultipleLocator(1000) t0 = int(np.round(np.min(time), 50)) tO = int(np.round(np.max(time), 50)) dateTicRng = [t0-200, tO+200] @@ -779,121 +784,121 @@ def plotStar(starNames, rootDir='./', align='align/align_d_rms_1000_abs_t', ind = (row-1)*Ncols + col - paxes = py.subplot(Nrows, Ncols, ind) - py.plot(time, fitLineX, 'b-') - py.plot(time, fitLineX + fitSigX, 'b--') - py.plot(time, fitLineX - fitSigX, 'b--') - py.errorbar(time, x, yerr=xerr, fmt='k.') - rng = py.axis() - py.ylim(np.min(x-xerr-0.1),np.max(x+xerr+0.1)) - py.xlabel('Date - 2000 (yrs)', fontsize=fontsize1) + paxes = plt.subplot(Nrows, Ncols, ind) + plt.plot(time, fitLineX, 'b-') + plt.plot(time, fitLineX + fitSigX, 'b--') + plt.plot(time, fitLineX - fitSigX, 'b--') + plt.errorbar(time, x, yerr=xerr, fmt='k.') + rng = plt.axis() + plt.ylim(np.min(x-xerr-0.1),np.max(x+xerr+0.1)) + plt.xlabel('Date - 2000 (yrs)', fontsize=fontsize1) if time[0] > 50000: - py.xlabel('Date (MJD)', fontsize=fontsize1) - py.ylabel('X (pix)', fontsize=fontsize1) + plt.xlabel('Date (MJD)', fontsize=fontsize1) + plt.ylabel('X (pix)', fontsize=fontsize1) paxes.xaxis.set_major_formatter(fmtX) paxes.get_xaxis().set_major_locator(dateTicLoc) paxes.yaxis.set_major_formatter(fmtY) paxes.tick_params(axis='both', which='major', labelsize=fontsize1) - py.yticks(np.arange(np.min(x-xerr-0.1), np.max(x+xerr+0.1), 0.2)) - py.xticks(dateTics, DateTicsLabel) - py.xlim(np.min(dateTics), np.max(dateTics)) - py.annotate(starName,xy=(1.0,1.1), xycoords='axes fraction', fontsize=12, color='red') + plt.yticks(np.arange(np.min(x-xerr-0.1), np.max(x+xerr+0.1), 0.2)) + plt.xticks(dateTics, DateTicsLabel) + plt.xlim(np.min(dateTics), np.max(dateTics)) + plt.annotate(starName,xy=(1.0,1.1), xycoords='axes fraction', fontsize=12, color='red') col = col + 1 ind = (row-1)*Ncols + col - paxes = py.subplot(Nrows, Ncols, ind) - py.plot(time, fitLineY, 'b-') - py.plot(time, fitLineY + fitSigY, 'b--') - py.plot(time, fitLineY - fitSigY, 'b--') - py.errorbar(time, y, yerr=yerr, fmt='k.') - rng = py.axis() - py.axis(dateTicRng + [rng[2], rng[3]], fontsize=fontsize1) - py.xlabel('Date - 2000 (yrs)', fontsize=fontsize1) + paxes = plt.subplot(Nrows, Ncols, ind) + plt.plot(time, fitLineY, 'b-') + plt.plot(time, fitLineY + fitSigY, 'b--') + plt.plot(time, fitLineY - fitSigY, 'b--') + plt.errorbar(time, y, yerr=yerr, fmt='k.') + rng = plt.axis() + plt.axis(dateTicRng + [rng[2], rng[3]], fontsize=fontsize1) + plt.xlabel('Date - 2000 (yrs)', fontsize=fontsize1) if time[0] > 50000: - py.xlabel('Date (MJD)', fontsize=fontsize1) - py.ylabel('Y (pix)', fontsize=fontsize1) + plt.xlabel('Date (MJD)', fontsize=fontsize1) + plt.ylabel('Y (pix)', fontsize=fontsize1) #paxes.get_xaxis().set_major_locator(dateTicLoc) paxes.xaxis.set_major_formatter(fmtX) paxes.get_xaxis().set_major_locator(dateTicLoc) paxes.yaxis.set_major_formatter(fmtY) paxes.tick_params(axis='both', which='major', labelsize=12) - py.ylim(np.min(y-yerr-0.1),np.max(y+yerr+0.1)) - py.yticks(np.arange(np.min(y-yerr-0.1), np.max(y+yerr+0.1), 0.2)) - py.xticks(dateTics, DateTicsLabel) - py.xlim(np.min(dateTics), np.max(dateTics)) + plt.ylim(np.min(y-yerr-0.1),np.max(y+yerr+0.1)) + plt.yticks(np.arange(np.min(y-yerr-0.1), np.max(y+yerr+0.1), 0.2)) + plt.xticks(dateTics, DateTicsLabel) + plt.xlim(np.min(dateTics), np.max(dateTics)) row = row + 1 col = col - 1 ind = (row-1)*Ncols + col - paxes = py.subplot(Nrows, Ncols, ind) - py.plot(time, np.zeros(len(time)), 'b-') - py.plot(time, fitSigX, 'b--') - py.plot(time, -fitSigX, 'b--') - py.errorbar(time, x - fitLineX, yerr=xerr, fmt='k.') - py.axis(dateTicRng + resTicRng, fontsize=fontsize1) - py.xlabel('Date - 2000 (yrs)', fontsize=fontsize1) + paxes = plt.subplot(Nrows, Ncols, ind) + plt.plot(time, np.zeros(len(time)), 'b-') + plt.plot(time, fitSigX, 'b--') + plt.plot(time, -fitSigX, 'b--') + plt.errorbar(time, x - fitLineX, yerr=xerr, fmt='k.') + plt.axis(dateTicRng + resTicRng, fontsize=fontsize1) + plt.xlabel('Date - 2000 (yrs)', fontsize=fontsize1) if time[0] > 50000: - py.xlabel('Date (MJD)', fontsize=fontsize1) - py.ylabel('X Residuals (pix)', fontsize=fontsize1) + plt.xlabel('Date (MJD)', fontsize=fontsize1) + plt.ylabel('X Residuals (pix)', fontsize=fontsize1) paxes.get_xaxis().set_major_locator(dateTicLoc) paxes.xaxis.set_major_formatter(fmtX) paxes.tick_params(axis='both', which='major', labelsize=fontsize1) - py.xticks(dateTics, DateTicsLabel) - py.xlim(np.min(dateTics), np.max(dateTics)) + plt.xticks(dateTics, DateTicsLabel) + plt.xlim(np.min(dateTics), np.max(dateTics)) col = col + 1 ind = (row-1)*Ncols + col - paxes = py.subplot(Nrows, Ncols, ind) - py.plot(time, np.zeros(len(time)), 'b-') - py.plot(time, fitSigY, 'b--') - py.plot(time, -fitSigY, 'b--') - py.errorbar(time, y - fitLineY, yerr=yerr, fmt='k.') - py.axis(dateTicRng + resTicRng, fontsize=fontsize1) - py.xlabel('Date -2000 (yrs)', fontsize=fontsize1) + paxes = plt.subplot(Nrows, Ncols, ind) + plt.plot(time, np.zeros(len(time)), 'b-') + plt.plot(time, fitSigY, 'b--') + plt.plot(time, -fitSigY, 'b--') + plt.errorbar(time, y - fitLineY, yerr=yerr, fmt='k.') + plt.axis(dateTicRng + resTicRng, fontsize=fontsize1) + plt.xlabel('Date -2000 (yrs)', fontsize=fontsize1) if time[0] > 50000: - py.xlabel('Date (MJD)', fontsize=fontsize1) - py.ylabel('Y Residuals (pix)', fontsize=fontsize1) + plt.xlabel('Date (MJD)', fontsize=fontsize1) + plt.ylabel('Y Residuals (pix)', fontsize=fontsize1) paxes.get_xaxis().set_major_locator(dateTicLoc) paxes.xaxis.set_major_formatter(fmtX) paxes.tick_params(axis='both', which='major', labelsize=fontsize1) - py.xticks(dateTics, DateTicsLabel) - py.xlim(np.min(dateTics), np.max(dateTics)) + plt.xticks(dateTics, DateTicsLabel) + plt.xlim(np.min(dateTics), np.max(dateTics)) row = row + 1 col = col - 1 ind = (row-1)*Ncols + col - paxes = py.subplot(Nrows, Ncols, ind) - py.errorbar(x,y, xerr=xerr, yerr=yerr, fmt='k.') - py.yticks(np.arange(np.min(y-yerr-0.1), np.max(y+yerr+0.1), 0.2)) - py.xticks(np.arange(np.min(x-xerr-0.1), np.max(x+xerr+0.1), 0.2), rotation = 270) - py.axis('equal') + paxes = plt.subplot(Nrows, Ncols, ind) + plt.errorbar(x,y, xerr=xerr, yerr=yerr, fmt='k.') + plt.yticks(np.arange(np.min(y-yerr-0.1), np.max(y+yerr+0.1), 0.2)) + plt.xticks(np.arange(np.min(x-xerr-0.1), np.max(x+xerr+0.1), 0.2), rotation = 270) + plt.axis('equal') paxes.tick_params(axis='both', which='major', labelsize=fontsize1) paxes.yaxis.set_major_formatter(FormatStrFormatter('%.2f')) paxes.xaxis.set_major_formatter(FormatStrFormatter('%.2f')) - py.xlabel('X (pix)', fontsize=fontsize1) - py.ylabel('Y (pix)', fontsize=fontsize1) - py.plot(fitLineX, fitLineY, 'b-') + plt.xlabel('X (pix)', fontsize=fontsize1) + plt.ylabel('Y (pix)', fontsize=fontsize1) + plt.plot(fitLineX, fitLineY, 'b-') col = col + 1 ind = (row-1)*Ncols + col bins = np.arange(-7.5, 7.5, 1) - paxes = py.subplot(Nrows, Ncols, ind) + paxes = plt.subplot(Nrows, Ncols, ind) id = np.where(diffY < 0)[0] sig[id] = -1.*sig[id] - (n, b, p) = py.hist(sigX, bins, histtype='stepfilled', color='b', label='X') - py.setp(p, 'facecolor', 'b') - (n, b, p) = py.hist(sigY, bins, histtype='step', color='r', label='Y') - py.axis([-7, 7, 0, 8], fontsize=10) - py.legend() - py.xlabel('Residuals (sigma)', fontsize=fontsize1) - py.ylabel('Number of Epochs', fontsize=fontsize1) + (n, b, p) = plt.hist(sigX, bins, histtype='stepfilled', color='b', label='X') + plt.setp(p, 'facecolor', 'b') + (n, b, p) = plt.hist(sigY, bins, histtype='step', color='r', label='Y') + plt.axis([-7, 7, 0, 8], fontsize=10) + plt.legend() + plt.xlabel('Residuals (sigma)', fontsize=fontsize1) + plt.ylabel('Number of Epochs', fontsize=fontsize1) ########## # @@ -901,9 +906,9 @@ def plotStar(starNames, rootDir='./', align='align/align_d_rms_1000_abs_t', # ########## if (radial == True): - py.clf() + plt.clf() - dateTicLoc = py.MultipleLocator(3) + dateTicLoc = plt.MultipleLocator(3) maxErr = np.array([rerr, terr]).max() resTicRng = [-3*maxErr, 3*maxErr] @@ -912,83 +917,83 @@ def plotStar(starNames, rootDir='./', align='align/align_d_rms_1000_abs_t', fmtX = FormatStrFormatter('%5i') fmtY = FormatStrFormatter('%6.2f') - paxes = py.subplot(3,2,1) - py.plot(time, fitLineR, 'b-') - py.plot(time, fitLineR + fitSigR, 'b--') - py.plot(time, fitLineR - fitSigR, 'b--') - py.errorbar(time, r, yerr=rerr, fmt='k.') - rng = py.axis() - py.axis(dateTicRng + [rng[2], rng[3]]) - py.xlabel('Date (yrs)') - py.ylabel('R (pix)') + paxes = plt.subplot(3,2,1) + plt.plot(time, fitLineR, 'b-') + plt.plot(time, fitLineR + fitSigR, 'b--') + plt.plot(time, fitLineR - fitSigR, 'b--') + plt.errorbar(time, r, yerr=rerr, fmt='k.') + rng = plt.axis() + plt.axis(dateTicRng + [rng[2], rng[3]]) + plt.xlabel('Date (yrs)') + plt.ylabel('R (pix)') paxes.xaxis.set_major_formatter(fmtX) paxes.get_xaxis().set_major_locator(dateTicLoc) paxes.yaxis.set_major_formatter(fmtY) - paxes = py.subplot(3, 2, 2) - py.plot(time, fitLineT, 'b-') - py.plot(time, fitLineT + fitSigT, 'b--') - py.plot(time, fitLineT - fitSigT, 'b--') - py.errorbar(time, t, yerr=terr, fmt='k.') - rng = py.axis() - py.axis(dateTicRng + [rng[2], rng[3]]) - py.xlabel('Date (yrs)') - py.ylabel('T (pix)') + paxes = plt.subplot(3, 2, 2) + plt.plot(time, fitLineT, 'b-') + plt.plot(time, fitLineT + fitSigT, 'b--') + plt.plot(time, fitLineT - fitSigT, 'b--') + plt.errorbar(time, t, yerr=terr, fmt='k.') + rng = plt.axis() + plt.axis(dateTicRng + [rng[2], rng[3]]) + plt.xlabel('Date (yrs)') + plt.ylabel('T (pix)') paxes.xaxis.set_major_formatter(fmtX) paxes.get_xaxis().set_major_locator(dateTicLoc) paxes.yaxis.set_major_formatter(fmtY) - paxes = py.subplot(3, 2, 3) - py.plot(time, np.zeros(len(time)), 'b-') - py.plot(time, fitSigR, 'b--') - py.plot(time, -fitSigR, 'b--') - py.errorbar(time, r - fitLineR, yerr=rerr, fmt='k.') - py.axis(dateTicRng + resTicRng) - py.xlabel('Date (yrs)') - py.ylabel('R Residuals (pix)') + paxes = plt.subplot(3, 2, 3) + plt.plot(time, np.zeros(len(time)), 'b-') + plt.plot(time, fitSigR, 'b--') + plt.plot(time, -fitSigR, 'b--') + plt.errorbar(time, r - fitLineR, yerr=rerr, fmt='k.') + plt.axis(dateTicRng + resTicRng) + plt.xlabel('Date (yrs)') + plt.ylabel('R Residuals (pix)') paxes.get_xaxis().set_major_locator(dateTicLoc) - paxes = py.subplot(3, 2, 4) - py.plot(time, np.zeros(len(time)), 'b-') - py.plot(time, fitSigT, 'b--') - py.plot(time, -fitSigT, 'b--') - py.errorbar(time, t - fitLineT, yerr=terr, fmt='k.') - py.axis(dateTicRng + resTicRng) - py.xlabel('Date (yrs)') - py.ylabel('T Residuals (pix)') + paxes = plt.subplot(3, 2, 4) + plt.plot(time, np.zeros(len(time)), 'b-') + plt.plot(time, fitSigT, 'b--') + plt.plot(time, -fitSigT, 'b--') + plt.errorbar(time, t - fitLineT, yerr=terr, fmt='k.') + plt.axis(dateTicRng + resTicRng) + plt.xlabel('Date (yrs)') + plt.ylabel('T Residuals (pix)') paxes.get_xaxis().set_major_locator(dateTicLoc) bins = np.arange(-7, 7, 1) - py.subplot(3, 2, 5) - (n, b, p) = py.hist(sigR, bins) - py.setp(p, 'facecolor', 'k') - py.axis([-5, 5, 0, 20]) - py.xlabel('T Residuals (sigma)') - py.ylabel('Number of Epochs') - - py.subplot(3, 2, 6) - (n, b, p) = py.hist(sigT, bins) - py.axis([-5, 5, 0, 20]) - py.setp(p, 'facecolor', 'k') - py.xlabel('Y Residuals (sigma)') - py.ylabel('Number of Epochs') - - py.subplots_adjust(wspace=0.4, hspace=0.4, right=0.95, top=0.95) - py.savefig(rootDir+'plots/plotStarRadial_' + starName + '.png') - py.show() + plt.subplot(3, 2, 5) + (n, b, p) = plt.hist(sigR, bins) + plt.setp(p, 'facecolor', 'k') + plt.axis([-5, 5, 0, 20]) + plt.xlabel('T Residuals (sigma)') + plt.ylabel('Number of Epochs') + + plt.subplot(3, 2, 6) + (n, b, p) = plt.hist(sigT, bins) + plt.axis([-5, 5, 0, 20]) + plt.setp(p, 'facecolor', 'k') + plt.xlabel('Y Residuals (sigma)') + plt.ylabel('Number of Epochs') + + plt.subplots_adjust(wspace=0.4, hspace=0.4, right=0.95, top=0.95) + plt.savefig(rootDir+'plots/plotStarRadial_' + starName + '.png') + plt.show() title = rootDir.split('/')[-2] - py.suptitle(title, x=0.5, y=0.97) + plt.suptitle(title, x=0.5, y=0.97) if Nstars == 1: - py.subplots_adjust(wspace=0.4, hspace=0.4, left = 0.15, bottom = 0.1, right=0.9, top=0.9) - py.savefig(rootDir+'plots/plotStar_' + starName + '.png') + plt.subplots_adjust(wspace=0.4, hspace=0.4, left = 0.15, bottom = 0.1, right=0.9, top=0.9) + plt.savefig(rootDir+'plots/plotStar_' + starName + '.png') else: - py.subplots_adjust(wspace=0.6, hspace=0.6, left = 0.08, bottom = 0.05, right=0.95, top=0.90) - py.savefig(rootDir+'plots/plotStar_all.png') - py.show() + plt.subplots_adjust(wspace=0.6, hspace=0.6, left = 0.08, bottom = 0.05, right=0.95, top=0.90) + plt.savefig(rootDir+'plots/plotStar_all.png') + plt.show() - py.show() + plt.show() print('Fubar') @@ -1051,7 +1056,7 @@ def plot_pm_error(tab): plt.legend() plt.xlabel('Mag') plt.ylabel('PM Error (mas/yr)') - + plt.show() return def plot_mag_error(tab): @@ -3607,8 +3612,8 @@ def plot_sky(stars_tab, foo = cnorm(yearsInt[ee]) colorList.append( cmap(cnorm(yearsInt[ee])) ) - py.close(2) - fig = py.figure(2, figsize=(13,10)) + plt.close(2) + fig = plt.figure(2, figsize=(13,10)) previousYear = 0.0 @@ -3646,13 +3651,13 @@ def plot_sky(stars_tab, label = '_nolegend_' if plot_errors: - (line, foo1, foo2) = py.errorbar(x, y, xerr=xe, yerr=ye, + (line, foo1, foo2) = plt.errorbar(x, y, xerr=xe, yerr=ye, color=colorList[ee], fmt='^', markeredgecolor=colorList[ee], markerfacecolor=colorList[ee], label=label, picker=4) else: - (line, foo1, foo2) = py.errorbar(x, y, xerr=None, yerr=None, + (line, foo1, foo2) = plt.errorbar(x, y, xerr=None, yerr=None, color=colorList[ee], fmt='^', markeredgecolor=colorList[ee], markerfacecolor=colorList[ee], @@ -3670,19 +3675,19 @@ def plot_sky(stars_tab, point_labels[line] = points_info foo = PrintSelected(point_labels, fig, stars_tab, mag_range, manual_print=manual_print) - py.connect('pick_event', foo) + plt.connect('pick_event', foo) xlo = xcenter + (range) xhi = xcenter - (range) ylo = ycenter - (range) yhi = ycenter + (range) - py.axis('equal') - py.axis([xlo, xhi, ylo, yhi]) - py.xlabel('R.A. Offset from Sgr A* (arcsec)') - py.ylabel('Dec. Offset from Sgr A* (arcsec)') + plt.axis('equal') + plt.axis([xlo, xhi, ylo, yhi]) + plt.xlabel('R.A. Offset from Sgr A* (arcsec)') + plt.ylabel('Dec. Offset from Sgr A* (arcsec)') - py.legend(handles=epochs_legend, numpoints=1, loc='lower left', fontsize=12) + plt.legend(handles=epochs_legend, numpoints=1, loc='lower left', fontsize=12) if show_names: xpos = stars_tab['x0'] @@ -3690,16 +3695,16 @@ def plot_sky(stars_tab, goodind = np.where((xpos <= xlo) & (xpos >= xhi) & (ypos >= ylo) & (ypos <= yhi))[0] for ind in goodind: - py.text(xpos[ind], ypos[ind], stars_tab['name'][ind], size=10) + plt.text(xpos[ind], ypos[ind], stars_tab['name'][ind], size=10) if saveplot: - py.show(block=0) + plt.show(block=0) if (center_star != None): - py.savefig('plot_sky_' + center_star + '.png') + plt.savefig('plot_sky_' + center_star + '.png') else: - py.savefig('plot_sky.png') + plt.savefig('plot_sky.png') else: - py.show() + plt.show() return From f0e478cf0b0396f850e694771e3af6a969c2c1dc Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Fri, 7 Nov 2025 12:51:14 -0800 Subject: [PATCH 120/254] Minor fix: Add assertion message; Add times into ref_table meta data; Update trans_positions --- flystar/align.py | 19 ++++++++++++------- 1 file changed, 12 insertions(+), 7 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 7b00ba0..152ceac 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -241,15 +241,15 @@ def = None. If not None, then this should contain an array or list of transform def fix_iterable_conditions(self): if not np.iterable(self.dr_tol): self.dr_tol = np.repeat(self.dr_tol, self.iters) - assert len(self.dr_tol) == self.iters + assert len(self.dr_tol) == self.iters, f'len(dr_tol)={len(self.dr_tol)} != iters={self.iters}' if not np.iterable(self.dm_tol): self.dm_tol = np.repeat(self.dm_tol, self.iters) - assert len(self.dm_tol) == self.iters + assert len(self.dm_tol) == self.iters, f'len(dm_tol)={len(self.dm_tol)} != iters={self.iters}' if not np.iterable(self.outlier_tol): self.outlier_tol = np.repeat(self.outlier_tol, self.iters) - assert len(self.outlier_tol) == self.iters + assert len(self.outlier_tol) == self.iters, f'len(outlier_tol)={len(self.outlier_tol)} != iters={self.iters}' if self.mag_lim is None: self.mag_lim = np.repeat([[None, None]], len(self.star_lists), axis=0) @@ -367,6 +367,10 @@ def fit(self): if self.iter_callback != None: self.iter_callback(self.ref_table, nn) + # Add times into ref_table meta data + complete_times = np.array([np.unique(col[~np.isnan(col)])[0] for col in self.ref_table['t'].T]) + self.ref_table.meta['LIST_TIMES'] = complete_times + if self.save_path: with open(self.save_path, 'wb') as file: pickle.dump(self, file) @@ -504,7 +508,7 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar dy=(star_t['y'] - star_r['y']) * 1e3, dm=(star_t['m'] - star_r['m']), xo=star_s['x'], yo=star_s['y'], mo=star_s['m'])) - + idx_lis, idx_ref, dr, dm = match.match(star_list_T['x'], star_list_T['y'], star_list_T['m'], ref_list['x'], ref_list['y'], ref_list['m'], dr_tol=dr_tol, dm_tol=dm_tol, verbose=self.verbose) @@ -515,7 +519,8 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar ## Make plot, if desired plots.trans_positions(ref_list, ref_list[idx_ref], star_list_T, star_list_T[idx_lis], - fileName='{0}'.format(star_list_T['t'][0])) + save_path=f"{self.save_path}/Transformed_Positions_{star_list_T['t'][0]}.png" if self.save_path else None, + show_plot=False) ### Update the observed (but transformed) values in the reference table. self.update_ref_table_from_list(star_list, star_list_T, ii, idx_ref, idx_lis, idx2) @@ -1424,7 +1429,7 @@ def __init__(self, ref_list, list_of_starlists, iters=2, If different from None, it indicates the minimum and maximum magnitude on the catalogs for finding the transformations. Note, if you want specify the mag_lim separately for each list and each iteration, you need to pass in a 2D array that - has shape (N_lists, 2). + has shape (N_lists, N_iters). ref_mag_lim : array If different from None, it indicates the minimum and maximum magnitude @@ -2485,7 +2490,7 @@ def transform_from_object(starlist, transform): keys = list(starlist.keys()) # Check to see if velocities or motion_model are present in starlist. - vel = ('vx' in keys)and ~("motion_model_input" in keys) + vel = ('vx' in keys) and ("motion_model_input" not in keys) mot = ("motion_model_input" in keys) # If the only motion models used are Fixed and Linear, we can still transform velocities. if mot: From 7b010034e7257ed3c6e64599c687e376ad7c92a0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Fri, 7 Nov 2025 12:52:02 -0800 Subject: [PATCH 121/254] Minor fix: Update startables accordingly with motion model --- flystar/startables.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/flystar/startables.py b/flystar/startables.py index 422f9a7..8f7a397 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -845,13 +845,13 @@ def fit_velocity_for_star(self, ss, motion_model_dict, weighting='var', use_scip fixed_params = [self[par][ss] for par in mod.fixed_param_names] # Fit for the best parameters - params, param_errs = mod.fit_motion_model(t, x, y, xe, ye, t0, bootstrap=bootstrap, + params, param_errs, chi2_x, chi2_y = mod.fit_motion_model(t, x, y, xe, ye, t0, bootstrap=bootstrap, weighting=weighting, use_scipy=use_scipy, absolute_sigma=absolute_sigma) - chi2_x,chi2_y = mod.get_chi2(params,fixed_params, t,x,y,xe,ye) + # chi2_x,chi2_y = mod.get_chi2(params,fixed_params, t,x,y,xe,ye) self['chi2_x'][ss]=chi2_x self['chi2_y'][ss]=chi2_y self['n_params'][ss] = mod.n_params - + # Save parameters and errors to table. for pp in range(len(mod.fitter_param_names)): par = mod.fitter_param_names[pp] From 289150ddf5b054e539e8e56df0b2de16d1092730 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Fri, 7 Nov 2025 13:48:53 -0800 Subject: [PATCH 122/254] Revert class meta definitions --- flystar/motion_model.py | 58 +++++++++++++++++++++++++---------------- 1 file changed, 36 insertions(+), 22 deletions(-) diff --git a/flystar/motion_model.py b/flystar/motion_model.py index 3c5069c..27d84af 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -129,14 +129,15 @@ class Fixed(MotionModel): A non-moving motion model for a star on the sky. """ + n_pts_req = 1 + n_params = 1 + fitter_param_names = ['x0','y0'] + fixed_param_names = [] + def __init__(self, **kwargs): # Must call after setting parameters. # This checks for proper parameter formatting. super().__init__() - self.n_pts_req = 1 - self.n_params = 1 - self.fitter_param_names = ['x0','y0'] - self.fixed_param_names = [] return def get_pos_at_time(self, fit_params, fixed_params, t): @@ -200,17 +201,19 @@ class Linear(MotionModel): """ A 2D linear motion model for a star on the sky. """ + + n_pts_req = 2 + n_params = 2 + fitter_param_names = ['x0', 'vx', 'y0', 'vy'] + fixed_param_names = ['t0'] + def __init__(self, **kwargs): # Must call after setting parameters. # This checks for proper parameter formatting. super().__init__() - self.n_pts_req = 2 - self.n_params = 2 - self.fitter_param_names = ['x0', 'vx', 'y0', 'vy'] - self.fixed_param_names = ['t0'] return - + def get_pos_at_time(self, fit_params, fixed_params, t): fit_params_dict = dict(zip(self.fitter_param_names, fit_params)) fixed_params_dict = dict(zip(self.fixed_param_names, fixed_params)) @@ -316,15 +319,16 @@ class Acceleration(MotionModel): """ A 2D accelerating motion model for a star on the sky. """ + n_pts_req = 4 # TODO: consider special case for 3 pts + n_params = 3 + fitter_param_names = ['x0', 'vx0', 'ax', 'y0', 'vy0', 'ay'] + fixed_param_names = ['t0'] + def __init__(self, x0=0, vx0=0, ax=0, y0=0, vy0=0, ay=0, t0=None, x0_err=0, vx0_err=0, ax_err=0, y0_err=0, vy0_err=0, ay_err=0, **kwargs): # Must call after setting parameters. # This checks for proper parameter formatting. super().__init__() - self.n_pts_req = 4 # TODO: consider special case for 3 pts - self.n_params = 3 - self.fitter_param_names = ['x0', 'vx0', 'ax', 'y0', 'vy0', 'ay'] - self.fixed_param_names = ['t0'] return def get_pos_at_time(self, fit_params, fixed_params, t): @@ -396,17 +400,19 @@ class Parallax(MotionModel): Optional PA is counterclockwise offset of the image y-axis from North. Optional obs parameter describes observer location, default is 'earth'. """ + + n_pts_req = 4 + n_params = 3 + fitter_param_names = ['x0', 'vx', 'y0', 'vy', 'pi'] + fixed_param_names = ['t0'] + fixed_meta_data = ['RA','Dec','PA','obs'] + def __init__(self, RA, Dec, PA=0.0, obs='earth', **kwargs): self.RA = RA self.Dec = Dec self.PA = PA self.obs = obs self.plx_vector_cached = None - self.n_pts_req = 4 - self.n_params = 3 - self.fitter_param_names = ['x0', 'vx', 'y0', 'vy', 'pi'] - self.fixed_param_names = ['t0'] - self.fixed_meta_data = ['RA','Dec','PA','obs'] return def get_parallax_vector(self, t_mjd): @@ -549,11 +555,19 @@ def get_one_motion_model_param_names(motion_model_name, with_errors=True, with_f Optionally, include fixed and error parameters (included by default). """ def get_list_motion_model_param_names(motion_model_list, with_errors=True, with_fixed=True): + motion_model_map = { + 'Fixed': Fixed, + 'Linear': Linear, + 'Acceleration': Acceleration, + 'Parallax': Parallax + } + list_of_parameters = [] - all_motion_models = [eval(mm) for mm in np.unique(motion_model_list).tolist()] - for aa in range(len(all_motion_models)): - param_names = getattr(all_motion_models[aa], 'fitter_param_names') - param_fixed_names = getattr(all_motion_models[aa], 'fixed_param_names') + # all_motion_models = [eval(mm) for mm in np.unique(motion_model_list).tolist()] + for mm in range(len(motion_model_list)): + motion_model = motion_model_map[motion_model_list[mm]] + param_names = motion_model.fitter_param_names + param_fixed_names = motion_model.fixed_param_names param_err_names = [par+'_err' for par in param_names] list_of_parameters += param_names From 443c4bdff1ddbe09f980acc7a8d563d69998a178 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Tue, 11 Nov 2025 20:03:44 -0800 Subject: [PATCH 123/254] Add get_sigma --- flystar/motion_model.py | 32 +++++++++++++++++++++----------- 1 file changed, 21 insertions(+), 11 deletions(-) diff --git a/flystar/motion_model.py b/flystar/motion_model.py index fd9f0cd..e38d6df 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -50,7 +50,16 @@ def run_fit( ): # Run a single fit (used both for overall fit + bootstrap iterations) pass - + + def get_sigma(self, xe, ye, weighting='var'): + if weighting=='std': + return xe**0.5, ye**0.5 + elif weighting=='var': + return xe, ye + else: + warnings.warn("Invalid weighting, using default weighting scheme var.", UserWarning) + return xe, ye + def get_weights(self, xe, ye, weighting='var'): if weighting=='std': return 1./xe, 1./ye @@ -74,7 +83,7 @@ def fit_motion_model( to determine new parameters for this motion model (MM). Best-fit parameters will be returned along with uncertainties. """ - params, param_errs, chi2x, chi2y = self.run_fit( + params, param_errs, chi2_x, chi2_y = self.run_fit( t, x, y, xe, ye, t0=t0, weighting=weighting, use_scipy=use_scipy, @@ -82,6 +91,7 @@ def fit_motion_model( fill_value=fill_value ) + # Bootstrap errors if bootstrap > 0 and len(x) > (self.n_pts_req): edx = np.arange(len(x), dtype=int) bb_params = [] @@ -101,12 +111,12 @@ def fit_motion_model( # Save the errors from the bootstrap param_errs = np.std(bb_params, axis=0) - + # Account for odd case inf_errs = [np.all(arr==np.inf) for arr in np.transpose(np.array(bb_params_errs))] param_errs[inf_errs] = 0.0 - return params, param_errs, chi2x, chi2y + return params, param_errs, chi2_x, chi2_y def get_chi2(self, fit_params, fixed_params, t, x, y, xe, ye, reduced=False): """ @@ -217,7 +227,7 @@ def __init__(self, **kwargs): def get_pos_at_time(self, fit_params, fixed_params, t): fit_params_dict = dict(zip(self.fitter_param_names, fit_params)) fixed_params_dict = dict(zip(self.fixed_param_names, fixed_params)) - dt = t-fixed_params_dict['t0'] + dt = t - fixed_params_dict['t0'] return fit_params_dict['x0'] + fit_params_dict['vx']*dt, fit_params_dict['y0'] + fit_params_dict['vy']*dt def get_batch_pos_at_time(self, t, x0=[],vx=[], y0=[],vy=[], t0=[], @@ -234,7 +244,7 @@ def get_batch_pos_at_time(self, t, x0=[],vx=[], y0=[],vy=[], t0=[], y = y0 + dt*vy x_err = np.hypot(x0_err, vx_err*dt) y_err = np.hypot(y0_err, vy_err*dt) - return x,y,x_err,y_err + return x, y, x_err, y_err def run_fit( self, t, x, y, xe, ye, t0, @@ -245,15 +255,15 @@ def run_fit( fill_value=np.inf ): dt = t - t0 - x_wt, y_wt = self.get_weights(xe, ye, weighting=weighting) + sigma_x, sigma_y = self.get_sigma(xe, ye, weighting=weighting) if params_guess is None: params_guess = [x.mean(), 0., y.mean(), 0.] if use_scipy: def linear(t, c0, c1): return c0 + c1*t - x_opt, x_cov = curve_fit(linear, dt, x, p0=np.array(params_guess[:2]), sigma=1/np.sqrt(x_wt), absolute_sigma=absolute_sigma) - y_opt, y_cov = curve_fit(linear, dt, y, p0=np.array(params_guess[2:]), sigma=1/np.sqrt(y_wt), absolute_sigma=absolute_sigma) + x_opt, x_cov = curve_fit(linear, dt, x, p0=np.array(params_guess[:2]), sigma=sigma_x, absolute_sigma=absolute_sigma) + y_opt, y_cov = curve_fit(linear, dt, y, p0=np.array(params_guess[2:]), sigma=sigma_y, absolute_sigma=absolute_sigma) x0, vx = x_opt y0, vy = y_opt x0e, vxe = np.sqrt(x_cov.diagonal()) @@ -269,13 +279,13 @@ def linear(t, c0, c1): dt = np.array(dt) X_mat_t = np.vander(dt, 2) # x calculation - W_mat_x = np.diag(x_wt) + W_mat_x = np.diag(1 / sigma_x**2) XTWX_mat_x = X_mat_t.T @ W_mat_x @ X_mat_t pcov_x = np.linalg.inv(XTWX_mat_x) # Covariance Matrix popt_x = pcov_x @ X_mat_t.T @ W_mat_x @ x # Linear Solution perr_x = np.sqrt(np.diag(pcov_x)) # Uncertainty of Linear Solution # y calculation - W_mat_y = np.diag(y_wt) + W_mat_y = np.diag(1 / sigma_y**2) XTWX_mat_y = X_mat_t.T @ W_mat_y @ X_mat_t pcov_y = np.linalg.inv(XTWX_mat_y) # Covariance Matrix popt_y = pcov_y @ X_mat_t.T @ W_mat_y @ y # Linear Solution From a1ef471d1339816be94876531af6c4a88d1f04c1 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Tue, 11 Nov 2025 20:15:31 -0800 Subject: [PATCH 124/254] Revert back to get_weight --- flystar/motion_model.py | 21 ++++++--------------- 1 file changed, 6 insertions(+), 15 deletions(-) diff --git a/flystar/motion_model.py b/flystar/motion_model.py index e38d6df..90d1442 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -50,16 +50,7 @@ def run_fit( ): # Run a single fit (used both for overall fit + bootstrap iterations) pass - - def get_sigma(self, xe, ye, weighting='var'): - if weighting=='std': - return xe**0.5, ye**0.5 - elif weighting=='var': - return xe, ye - else: - warnings.warn("Invalid weighting, using default weighting scheme var.", UserWarning) - return xe, ye - + def get_weights(self, xe, ye, weighting='var'): if weighting=='std': return 1./xe, 1./ye @@ -255,15 +246,15 @@ def run_fit( fill_value=np.inf ): dt = t - t0 - sigma_x, sigma_y = self.get_sigma(xe, ye, weighting=weighting) + x_wt, y_wt = self.get_weights(xe, ye, weighting=weighting) if params_guess is None: params_guess = [x.mean(), 0., y.mean(), 0.] if use_scipy: def linear(t, c0, c1): return c0 + c1*t - x_opt, x_cov = curve_fit(linear, dt, x, p0=np.array(params_guess[:2]), sigma=sigma_x, absolute_sigma=absolute_sigma) - y_opt, y_cov = curve_fit(linear, dt, y, p0=np.array(params_guess[2:]), sigma=sigma_y, absolute_sigma=absolute_sigma) + x_opt, x_cov = curve_fit(linear, dt, x, p0=np.array(params_guess[:2]), sigma=1/x_wt**0.5, absolute_sigma=absolute_sigma) + y_opt, y_cov = curve_fit(linear, dt, y, p0=np.array(params_guess[2:]), sigma=1/y_wt**0.5, absolute_sigma=absolute_sigma) x0, vx = x_opt y0, vy = y_opt x0e, vxe = np.sqrt(x_cov.diagonal()) @@ -279,13 +270,13 @@ def linear(t, c0, c1): dt = np.array(dt) X_mat_t = np.vander(dt, 2) # x calculation - W_mat_x = np.diag(1 / sigma_x**2) + W_mat_x = np.diag(x_wt) XTWX_mat_x = X_mat_t.T @ W_mat_x @ X_mat_t pcov_x = np.linalg.inv(XTWX_mat_x) # Covariance Matrix popt_x = pcov_x @ X_mat_t.T @ W_mat_x @ x # Linear Solution perr_x = np.sqrt(np.diag(pcov_x)) # Uncertainty of Linear Solution # y calculation - W_mat_y = np.diag(1 / sigma_y**2) + W_mat_y = np.diag(y_wt) XTWX_mat_y = X_mat_t.T @ W_mat_y @ X_mat_t pcov_y = np.linalg.inv(XTWX_mat_y) # Covariance Matrix popt_y = pcov_y @ X_mat_t.T @ W_mat_y @ y # Linear Solution From 1a7481d45e3ad44035c4bf2a8ca7dafaac5c9ba8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Wed, 12 Nov 2025 23:34:02 -0800 Subject: [PATCH 125/254] Major Update: Update data fitting logic: Compare observed data points with model parameters. If n_obs < n_params, return fill value for the parameters and infinity for the uncertainties; Otherwise, calculate parameters normally. If n_obs = n_params and absolute_sigma=False, return infinity for uncertainties (Same behavior as scipy curve fit). --- flystar/motion_model.py | 506 +++++++++++++++++++++++++++------------- 1 file changed, 343 insertions(+), 163 deletions(-) diff --git a/flystar/motion_model.py b/flystar/motion_model.py index 90d1442..939cd0b 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -7,8 +7,6 @@ import warnings class MotionModel(ABC): - # Number of data points required to fit model - n_pts_req = 0 # Degrees of freedom for model n_params = 0 @@ -33,7 +31,7 @@ def __init__(self, *args, **kwargs): setattr(self, param, np.array([param_var]))''' return - def get_pos_at_time(self, params, t): + def get_pos_at_time(self, fit_params, fixed_params, t): #return x, y pass @@ -46,10 +44,11 @@ def run_fit( weighting='var', use_scipy=True, absolute_sigma=True, - fill_value=np.inf + fill_value=np.nan, + verbose=True ): # Run a single fit (used both for overall fit + bootstrap iterations) - pass + return np.full(self.n_params, fill_value), np.full(self.n_params, np.inf), np.nan, np.nan def get_weights(self, xe, ye, weighting='var'): if weighting=='std': @@ -66,36 +65,74 @@ def fit_motion_model( weighting='var', use_scipy=True, absolute_sigma=True, - fill_value=np.inf, - rng=None + fill_value=np.nan, + verbose=True, + seed=None ): - """ - Fit the input positions on the sky and errors - to determine new parameters for this motion model (MM). - Best-fit parameters will be returned along with uncertainties. + """Fit stellar motion parameters + + Parameters + ---------- + t : array-like + Times of measurements + x : array-like + x-coordinates + y : array-like + y-coordinates + xe : array-like + Uncertainty of x + ye : array-like + Uncertainty of y + t0 : array-like + Reference time for fitting, i.e. dt = t - t0 will be used in fitting + bootstrap : int, optional + Bootstrapping uncertainties, by default 0 + weighting : str, optional + Use standard error weighting ('std': w=1/xe, 1/ye) or variance weighting ('var': w=1/xe**2, 1/ye**2), by default 'var' + use_scipy : bool, optional + Use scipy for optmization. Otherwise, use linear algebraic solution (Linear model only), which is faster for < 300 epochs, by default True + absolute_sigma : bool, optional + Absolute sigma. See scipy.optimize.curve_fit for details, by default True + fill_value : float, optional + Fill value for parameters when not enough data points to fit model, by default np.nan + verbose : bool, optional + Print warning messages, by default True + seed : int, optional + Seed for the random number generator, by default None + Returns + ------- + params, params_err, chi2_x, chi2_y + Parameters, uncertainties, and chi squares. The corresponding parameter names are in self.fitter_param_names. """ params, param_errs, chi2_x, chi2_y = self.run_fit( t, x, y, xe, ye, t0=t0, weighting=weighting, use_scipy=use_scipy, absolute_sigma=absolute_sigma, - fill_value=fill_value + fill_value=fill_value, + verbose=verbose ) - + if seed is not None: + rng = np.random.default_rng(seed) + else: + rng = np.random.default_rng() + # Bootstrap errors - if bootstrap > 0 and len(x) > (self.n_pts_req): - edx = np.arange(len(x), dtype=int) + n_obs = len(t) + if bootstrap > 0 and n_obs > (self.n_params): + edx = np.arange(n_obs, dtype=int) bb_params = [] bb_params_errs = [] for bb in range(bootstrap): - bdx = np.random.choice(edx, len(x), replace=False) + bdx = rng.choice(edx, n_obs, replace=False) params_bdx, param_errs_bdx, chi2x_bdx, chi2y_bdx = self.run_fit( t[bdx], x[bdx], y[bdx], xe[bdx], ye[bdx], t0=t0, weighting=weighting, use_scipy=use_scipy, absolute_sigma=absolute_sigma, params_guess=params, - fill_value=fill_value + fill_value=fill_value, + verbose=verbose ) bb_params.append(params_bdx) bb_params_errs.append(param_errs_bdx) @@ -125,12 +162,51 @@ def get_chi2(self, fit_params, fixed_params, t, x, y, xe, ye, reduced=False): chi2x, chi2y = chi2x / dof, chi2y / dof return chi2x, chi2y +class Empty(MotionModel): + n_params = 0 + fitter_param_names = [] + fixed_param_names = [] + + def __init__(self, **kwargs): + """Empty motion model, returns nan for values and inf for uncertainties. + """ + super().__init__() + return + + def get_pos_at_time(self, fit_params, fixed_params, t): + if hasattr(t, "__len__"): + return np.full(len(t), np.nan), np.full(len(t), np.nan) + else: + return np.nan, np.nan + + def get_batch_pos_at_time(self,t, + x0=[],y0=[],t0=[], + x0_err=[], y0_err=[]): + if hasattr(t, "__len__"): + return np.full((len(x0), len(t)), np.nan), np.full((len(y0), len(t)), np.nan), np.full((len(x0), len(t)), np.nan), np.full((len(y0), len(t)), np.nan) + else: + return np.nan, np.nan, np.nan, np.nan + + def run_fit( + self, t, x, y, xe, ye, t0, + weighting='var', + use_scipy=True, + absolute_sigma=True, + fill_value=np.nan, + verbose=True + ): + if verbose: + warnings.warn(f"Empty data cannot be fit. Setting parameters to {fill_value} and uncertainties to np.inf.", OptimizeWarning, stacklevel=2) + params = np.full(self.n_params, fill_value) + param_errors = np.full(self.n_params, np.inf) + return params, param_errors, np.nan, np.nan + + class Fixed(MotionModel): """ A non-moving motion model for a star on the sky. """ - n_pts_req = 1 n_params = 1 fitter_param_names = ['x0','y0'] fixed_param_names = [] @@ -154,7 +230,7 @@ def get_batch_pos_at_time(self,t, if hasattr(t, "__len__"): return np.repeat(x0[:,np.newaxis],len(t),axis=1), np.repeat(y0[:,np.newaxis],len(t),axis=1), np.repeat(x0_err[:,np.newaxis],len(t),axis=1), np.repeat(y0_err[:,np.newaxis],len(t),axis=1) else: - return x0,y0,x0_err,y0_err + return x0, y0, x0_err, y0_err def run_fit( self, t, x, y, xe, ye, t0, @@ -162,26 +238,40 @@ def run_fit( use_scipy=True, absolute_sigma=True, params_guess=None, - fill_value=np.inf + fill_value=np.nan, + verbose=True ): - if not use_scipy: - Warning("Fixed model has no non-scipy fitter option. Running with scipy.") - # Handle single data point case - if len(x)==1: - x0, y0, x0e, y0e = x[0], y[0], xe[0], ye[0] - - else: - x_wt, y_wt = self.get_weights(xe, ye, weighting=weighting) - x0 = np.average(x, weights=x_wt) - x0e = np.sqrt(np.average((x - x0)**2, weights=x_wt)) - y0 = np.average(y, weights=y_wt) - y0e = np.sqrt(np.average((y - y0)**2, weights=y_wt)) + if verbose and (not use_scipy): + warnings.warn("Fixed model has no non-scipy fitter option. Running with scipy.") + + n_obs = len(t) + degree_of_freedom = n_obs - self.n_params + # Not enough data points to fit model + if degree_of_freedom < 0: + if verbose: + warnings.warn( + f'Not enough data points to fit model. Setting parameters to {fill_value} and uncertainties to np.inf.', + OptimizeWarning, stacklevel=2 + ) + params = np.full(self.n_params, fill_value) + param_errors = np.full(self.n_params, np.inf) + return params, param_errors, np.nan, np.nan + + # degree_of_freedom >= 0 + # Calculate weighted average position + x_wt, y_wt = self.get_weights(xe, ye, weighting=weighting) + x_wt_norm = x_wt / np.sum(x_wt) + y_wt_norm = y_wt / np.sum(y_wt) + x0 = np.average(x, weights=x_wt) + x0e = (np.sum(x_wt_norm**2 * xe**2))**0.5 / n_obs # Error propagation + y0 = np.average(y, weights=y_wt) + y0e = (np.sum(y_wt_norm**2 * ye**2))**0.5 / n_obs # Error propagation params = np.array([x0, y0]) param_errors = np.array([x0e, y0e]) chi2x, chi2y = self.get_chi2(params, [], t, x, y, xe, ye) - degree_of_freedom = len(x) - 1 + if not absolute_sigma: if degree_of_freedom > 0: reduced_chi2x = chi2x / degree_of_freedom @@ -190,11 +280,13 @@ def run_fit( param_errors[0] *= reduced_chi2x**0.5 param_errors[1] *= reduced_chi2y**0.5 else: + # degree_of_freedom == 0, as < 0 case already handled above warnings.warn( - f'Degree of freedom <= 0. Covariance of the parameters could not be estimated. Setting parameter uncertainties to fill value {fill_value}.', + f'Degree of freedom < 0. Covariance of the parameters could not be estimated. Setting parameter uncertainties to fill value np.inf.', OptimizeWarning, stacklevel=2 ) - param_errors = np.full_like(param_errors, fill_value) + # Set parameter uncertainties to np.inf, same behavior as scipy.optimize.curve_fit + param_errors = np.full_like(param_errors, np.inf) return params, param_errors, chi2x, chi2y @@ -202,8 +294,6 @@ class Linear(MotionModel): """ A 2D linear motion model for a star on the sky. """ - - n_pts_req = 2 n_params = 2 fitter_param_names = ['x0', 'vx', 'y0', 'vy'] fixed_param_names = ['t0'] @@ -224,13 +314,13 @@ def get_pos_at_time(self, fit_params, fixed_params, t): def get_batch_pos_at_time(self, t, x0=[],vx=[], y0=[],vy=[], t0=[], x0_err=[],vx_err=[], y0_err=[],vy_err=[], **kwargs): if hasattr(t, "__len__"): - dt = t-t0[:,np.newaxis] + dt = t - t0[:,np.newaxis] x = x0[:,np.newaxis] + dt*vx[:,np.newaxis] y = y0[:,np.newaxis] + dt*vy[:,np.newaxis] x_err = np.hypot(x0_err[:,np.newaxis], vx_err[:,np.newaxis]*dt) y_err = np.hypot(y0_err[:,np.newaxis], vy_err[:,np.newaxis]*dt) else: - dt = t-t0 + dt = t - t0 x = x0 + dt*vx y = y0 + dt*vy x_err = np.hypot(x0_err, vx_err*dt) @@ -243,8 +333,23 @@ def run_fit( use_scipy=True, absolute_sigma=True, params_guess=None, - fill_value=np.inf + fill_value=np.nan, + verbose=True ): + n_obs = len(t) + degree_of_freedom = n_obs - self.n_params + # Not enough data points to fit model + if degree_of_freedom < 0: + if verbose: + warnings.warn( + f'Not enough data points to fit model. Setting parameters to {fill_value} and uncertainties to np.inf.', + OptimizeWarning, stacklevel=2 + ) + params = np.full(self.n_params, fill_value) + param_errors = np.full(self.n_params, np.inf) + return params, param_errors, np.nan, np.nan + + # degree_of_freedom >= 0 dt = t - t0 x_wt, y_wt = self.get_weights(xe, ye, weighting=weighting) if params_guess is None: @@ -290,8 +395,7 @@ def linear(t, c0, c1): # Does not use get_chi2 to accelerate calculation residual_x = x - X_mat_t @ popt_x residual_y = y - X_mat_t @ popt_y - - + chi2_x = residual_x.T @ W_mat_x @ residual_x chi2_y = residual_y.T @ W_mat_y @ residual_y @@ -299,20 +403,21 @@ def linear(t, c0, c1): param_errors = np.array([x0e, vxe, y0e, vye]) if not absolute_sigma: - degree_of_freedom = len(x) - 2 if degree_of_freedom > 0: - reduced_chi2_x = chi2_x/(len(x) - 2) - reduced_chi2_y = chi2_y/(len(x) - 2) + reduced_chi2_x = chi2_x / degree_of_freedom + reduced_chi2_y = chi2_y / degree_of_freedom param_errors[0:2] *= reduced_chi2_x**0.5 param_errors[2:4] *= reduced_chi2_y**0.5 else: + # degree_of_freedom == 0, as < 0 case already handled above warnings.warn( - f'Degree of freedom <= 0. Covariance of the parameters could not be estimated. Setting parameter uncertainties to fill value {fill_value}.', + f'Degree of freedom < 0. Covariance of the parameters could not be estimated. Setting parameter uncertainties to fill value np.inf.', OptimizeWarning, stacklevel=2 ) - param_errors = np.full_like(param_errors, fill_value) + # Set parameter uncertainties to np.inf, same behavior as scipy.optimize.curve_fit + param_errors = np.full_like(param_errors, np.inf) return params, param_errors, chi2_x, chi2_y @@ -320,7 +425,6 @@ class Acceleration(MotionModel): """ A 2D accelerating motion model for a star on the sky. """ - n_pts_req = 4 # TODO: consider special case for 3 pts n_params = 3 fitter_param_names = ['x0', 'vx0', 'ax', 'y0', 'vy0', 'ay'] fixed_param_names = ['t0'] @@ -335,7 +439,7 @@ def __init__(self, x0=0, vx0=0, ax=0, y0=0, vy0=0, ay=0, t0=None, def get_pos_at_time(self, fit_params, fixed_params, t): fit_params_dict = dict(zip(self.fitter_param_names, fit_params)) fixed_params_dict = dict(zip(self.fixed_param_names, fixed_params)) - dt = t-fixed_params_dict['t0'] + dt = t - fixed_params_dict['t0'] x = fit_params_dict['x0'] + fit_params_dict['vx0']*dt + 0.5*fit_params_dict['ax']*dt**2 y = fit_params_dict['y0'] + fit_params_dict['vy0']*dt + 0.5*fit_params_dict['ay']*dt**2 return x, y @@ -344,18 +448,18 @@ def get_batch_pos_at_time(self,t, x0=[],vx0=[],ax=[], y0=[],vy0=[],ay=[], t0=[], x0_err=[],vx0_err=[],ax_err=[], y0_err=[],vy0_err=[],ay_err=[], **kwargs): if hasattr(t, "__len__"): - dt = t-t0[:,np.newaxis] - x = x0[:,np.newaxis] + dt*vx0[:,np.newaxis] + 0.5*dt**2*ax[:,np.newaxis] - y = y0[:,np.newaxis] + dt*vy0[:,np.newaxis] + 0.5*dt**2*ay[:,np.newaxis] - x_err = np.sqrt(x0_err[:,np.newaxis]**2 + (vx0_err[:,np.newaxis]*dt)**2 + (0.5*ax_err[:,np.newaxis]*dt**2)**2) - y_err = np.sqrt(y0_err[:,np.newaxis]**2 + (vy0_err[:,np.newaxis]*dt)**2 + (0.5*ay_err[:,np.newaxis]*dt**2)**2) + dt = t - t0[:,np.newaxis] + x = x0[:, np.newaxis] + dt * vx0[:, np.newaxis] + 0.5 * ax[:, np.newaxis] * dt**2 + y = y0[:, np.newaxis] + dt * vy0[:, np.newaxis] + 0.5 * ay[:, np.newaxis] * dt**2 + x_err = np.sqrt(x0_err[:, np.newaxis]**2 + (vx0_err[:, np.newaxis]*dt)**2 + (0.5*ax_err[:, np.newaxis]*dt**2)**2) + y_err = np.sqrt(y0_err[:, np.newaxis]**2 + (vy0_err[:, np.newaxis]*dt)**2 + (0.5*ay_err[:, np.newaxis]*dt**2)**2) else: - dt = t-t0 - x = x0 + dt*vx0 + 0.5*dt**2*ax - y = y0 + dt*vy0 + 0.5*dt**2*ay - x_err = np.sqrt(x0_err**2 + (vx0_err*dt)**2 + (0.5*ax_err*dt**2)**2) - y_err = np.sqrt(y0_err**2 + (vy0_err*dt)**2 + (0.5*ay_err*dt**2)**2) - return x,y,x_err,y_err + dt = t - t0 + x = x0 + dt * vx0 + 0.5 * ax * dt**2 + y = y0 + dt * vy0 + 0.5 * ay * dt**2 + x_err = np.sqrt(x0_err**2 + (vx0_err * dt)**2 + (0.5 * ax_err * dt**2)**2) + y_err = np.sqrt(y0_err**2 + (vy0_err * dt)**2 + (0.5 * ay_err * dt**2)**2) + return x, y, x_err, y_err def run_fit( self, t, x, y, xe, ye, t0, @@ -363,34 +467,50 @@ def run_fit( use_scipy=True, absolute_sigma=True, params_guess=None, - fill_value=np.inf + fill_value=np.nan, + verbose=True ): if not use_scipy: - Warning("Acceleration model has no non-scipy fitter option. Running with scipy.") + if verbose: + warnings.warn("Acceleration model has no non-scipy fitter option. Running with scipy.") + + n_obs = len(t) + degree_of_freedom = n_obs - self.n_params + # Not enough data points to fit model + if degree_of_freedom < 0: + if verbose: + warnings.warn( + f'Not enough data points to fit model. Setting parameters to {fill_value} and uncertainties to np.inf.', + OptimizeWarning, stacklevel=2 + ) + params = np.full(self.n_params, fill_value) + param_errors = np.full(self.n_params, np.inf) + return params, param_errors, np.nan, np.nan + + # degree_of_freedom >= 0 dt = t - t0 x_wt, y_wt = self.get_weights(xe,ye, weighting=weighting) if params_guess is None: - params_guess = [x.mean(), 0., 0., y.mean(), 0., 0.] + # Initial guess for velocity: + idx_first, idx_last = np.argmin(t), np.argmax(t) + t_span = t[idx_last] - t[idx_first] + params_guess = [x.mean(), (x[idx_last] - x[idx_first]) / t_span, 0., y.mean(), (y[idx_last] - y[idx_first]) / t_span, 0.] def accel(t, c0, c1, c2): return c0 + c1*t + 0.5*c2*t**2 - x_opt, x_cov = curve_fit(accel, dt, x, p0=np.array(params_guess[:3]), sigma=1/x_wt**0.5, absolute_sigma=True) - y_opt, y_cov = curve_fit(accel, dt, y, p0=np.array(params_guess[3:]), sigma=1/y_wt**0.5, absolute_sigma=True) - x0 = x_opt[0] - y0 = y_opt[0] - vx0 = x_opt[1] - vy0 = y_opt[1] - ax = x_opt[2] - ay = y_opt[2] - + x_opt, x_cov = curve_fit(accel, dt, x, p0=np.array(params_guess[:3]), sigma=1/x_wt**0.5, absolute_sigma=absolute_sigma) + y_opt, y_cov = curve_fit(accel, dt, y, p0=np.array(params_guess[3:]), sigma=1/y_wt**0.5, absolute_sigma=absolute_sigma) + x0, vx0, ax = x_opt + y0, vy0, ay = y_opt x0e, vx0e, axe = np.sqrt(x_cov.diagonal()) y0e, vy0e, aye = np.sqrt(y_cov.diagonal()) - params = [x0, vx0, ax, y0, vy0, ay] - param_errors = [x0e, vx0e, axe, y0e, vy0e, aye] + params = np.array([x0, vx0, ax, y0, vy0, ay]) + param_errors = np.array([x0e, vx0e, axe, y0e, vy0e, aye]) + chi2_x, chi2_y = self.get_chi2(params, [t0], t, x, y, xe, ye) - return params, param_errors + return params, param_errors, chi2_x, chi2_y class Parallax(MotionModel): """ @@ -401,8 +521,6 @@ class Parallax(MotionModel): Optional PA is counterclockwise offset of the image y-axis from North. Optional obs parameter describes observer location, default is 'earth'. """ - - n_pts_req = 4 n_params = 3 fitter_param_names = ['x0', 'vx', 'y0', 'vy', 'pi'] fixed_param_names = ['t0'] @@ -473,7 +591,7 @@ def get_batch_pos_at_time(self, t, y_err = np.sqrt(y0_err**2 + (vy_err*dt)**2 + (pi_err*pvec[1])**2) except: x_err,y_err = [],[] - return x,y,x_err,y_err + return x, y, x_err, y_err def run_fit( self, t, x, y, xe, ye, t0, @@ -481,10 +599,27 @@ def run_fit( use_scipy=True, absolute_sigma=True, params_guess=None, - fill_value=np.inf + fill_value=np.nan, + verbose=True ): if not use_scipy: - Warning("Parallax model has no non-scipy fitter option. Running with scipy.") + if verbose: + warnings.warn("Parallax model has no non-scipy fitter option. Running with scipy.", UserWarning) + + n_obs = len(t) + degree_of_freedom = n_obs - self.n_params + # Not enough data points to fit model + if degree_of_freedom < 0: + if verbose: + warnings.warn( + f'Not enough data points to fit model. Setting parameters to {fill_value} and uncertainties to np.inf.', + OptimizeWarning, stacklevel=2 + ) + params = np.full(self.n_params, fill_value) + param_errors = np.full(self.n_params, np.inf) + return params, param_errors + + # degree_of_freedom >= 0 t_mjd = Time(t, format='decimalyear', scale='utc').mjd pvec = self.get_parallax_vector(t_mjd) x_wt, y_wt = self.get_weights(xe,ye, weighting=weighting) @@ -494,107 +629,152 @@ def fit_func(use_t, x0,vx, y0,vy, pi): return np.hstack([x_res, y_res]) # Initial guesses, x0,y0 as x,y averages; # vx,vy as average velocity if first and last points are perfectly measured; - # pi for 10 pc disance + # pi for 10 pc distance if params_guess is None: idx_first, idx_last = np.argmin(t), np.argmax(t) - params_guess = [x.mean(),(x[idx_last]-x[idx_first])/(t[idx_last]-t[idx_first]), - y.mean(),(y[idx_last]-y[idx_first])/(t[idx_last]-t[idx_first]), 0.1] - res = curve_fit(fit_func, t, np.hstack([x,y]), - p0=params_guess, sigma = 1.0/np.hstack([x_wt,y_wt])) + t_span = t[idx_last] - t[idx_first] + params_guess = [ + x.mean(), (x[idx_last] - x[idx_first]) / t_span, + y.mean(), (y[idx_last] - y[idx_first]) / t_span, + 0.1 + ] + res = curve_fit( + fit_func, t, np.hstack([x,y]), + p0=params_guess, sigma = 1.0/np.hstack([x_wt,y_wt]), + absolute_sigma=absolute_sigma + ) x0, vx, y0, vy, pi = res[0] x0_err, vx_err, y0_err, vy_err, pi_err = np.sqrt(np.diag(res[1])) - params = [x0, vx, y0, vy, pi] - param_errors = [x0_err, vx_err, y0_err, vy_err, pi_err] - return params, param_errors + params = np.array([x0, vx, y0, vy, pi]) + param_errors = np.array([x0_err, vx_err, y0_err, vy_err, pi_err]) + chi2_x, chi2_y = self.get_chi2(params, [t0], t, x, y, xe, ye) -""" -Check that everything is set up properly for motion models to run and their -required metadata. -""" -def validate_motion_model_dict(motion_model_dict, startable, default_motion_model): - # Collect names of all motion models that might get used. - all_motion_model_names = ['Fixed'] - if default_motion_model is not None: - all_motion_model_names.append(default_motion_model) - if 'motion_model_input' in startable.columns: - all_motion_model_names += np.unique(startable['motion_model_input']).tolist() - if 'motion_model_used' in startable.columns: - all_motion_model_names += np.unique(startable['motion_model_used']).tolist() - all_motion_model_names = np.unique(all_motion_model_names) - - # Check whether all motion models are in the dict, and if not, try to add them - # here or raise an error. - for mm in all_motion_model_names: - if mm not in motion_model_dict: - mm_obj = eval(mm) - if len(mm_obj.fixed_meta_data)>0: - raise ValueError(f"Cannot use {mm} motion model without required metadata. Please initialize with required metadata and provide in motion_model_dict.") - else: - motion_model_dict[mm] = mm_obj() - # warnings.warn(f"Using default model/fitter for {mm}.", UserWarning) + return params, param_errors, chi2_x, chi2_y - return motion_model_dict - -""" -Get all the motion model parameters for a given motion_model_name. -Optionally, include fixed and error parameters (included by default). -""" -def get_one_motion_model_param_names(motion_model_name, with_errors=True, with_fixed=True): - mod = eval(motion_model_name) - list_of_parameters = [] - list_of_parameters += getattr(mod, 'fitter_param_names') - if with_fixed: - list_of_parameters += getattr(mod, 'fixed_param_names') - if with_errors: - list_of_parameters += [par+'_err' for par in getattr(mod, 'fitter_param_names')] - return list_of_parameters -""" -Get all the motion model parameters for all models given in motion_model_list. -Optionally, include fixed and error parameters (included by default). -""" -def get_list_motion_model_param_names(motion_model_list, with_errors=True, with_fixed=True): +def validate_motion_models(motion_models, startable, default_motion_model): + """Validate that all the unique motion models in startable and default_motion_model are in the motion_models. If not, add available models to the list. + + Parameters + ---------- + motion_models : list of MotionModels + List of MotionModels that are expected to encompass all the motion models + startable : StarTable + Star table that possibly contains 'motion_model_input' and 'motion_model_used' + default_motion_model : MotionModel + Default MotionModel + """ motion_model_map = { 'Fixed': Fixed, 'Linear': Linear, 'Acceleration': Acceleration, 'Parallax': Parallax } + # Collect names of all motion models that might get used. + all_motion_model_names = set() + all_motion_model_names.add('Fixed') + if default_motion_model is not None: + all_motion_model_names.add(default_motion_model.__name__) + if 'motion_model_input' in startable.colnames: + all_motion_model_names.update(startable['motion_model_input'].tolist()) + if 'motion_model_used' in startable.colnames: + all_motion_model_names.update(startable['motion_model_used'].tolist()) + + # Check whether all motion models are in the list, and if not, raise an error. + all_motion_models = [motion_model_map[mm] for mm in all_motion_model_names] + for mm in all_motion_models: + if mm not in motion_models: + if len(mm.fixed_meta_data) > 0: + raise ValueError(f"Cannot use {mm} motion model without required metadata. Please initialize with required metadata and provide in motion_models.") + else: + motion_models.append(mm) + warnings.warn(f"{mm} not found in motion_models list. Added default instance.", UserWarning) + + return motion_models + + +def get_one_motion_model_param_names(motion_model, with_errors=True, with_fixed=True): + """Get all the motion model parameters + + Parameters + ---------- + motion_model : MotionModel + MotionModel instance + with_errors : bool, optional + Add uncertainty names with '_err' suffix or not, by default True + with_fixed : bool, optional + Add fixed param names with '_fixed' suffix or not, by default True + Returns + ------- + list + List of all parameter names for the motion model + """ list_of_parameters = [] - # all_motion_models = [eval(mm) for mm in np.unique(motion_model_list).tolist()] - for mm in range(len(motion_model_list)): - motion_model = motion_model_map[motion_model_list[mm]] - param_names = motion_model.fitter_param_names - param_fixed_names = motion_model.fixed_param_names - param_err_names = [par+'_err' for par in param_names] - - list_of_parameters += param_names - if with_fixed: - list_of_parameters += param_fixed_names - if with_errors: - list_of_parameters += param_err_names - return np.unique(list_of_parameters).tolist() + def list_add(name): + if name not in list_of_parameters: + list_of_parameters.append(name) + + for param in motion_model.fitter_param_names: + # Fitter params + list_add(param) + # Error params + if with_errors: + list_add(param + '_err') + # Fixed params + if with_fixed: + for param in motion_model.fixed_param_names: + list_add(param) + return list_of_parameters + -""" -Get all the motion model parameters for all models defined in this module. -Optionally, include fixed and error parameters (included by default). -""" -def get_all_motion_model_param_names(with_errors=True, with_fixed=True): +def get_list_motion_model_param_names(motion_model_list, with_errors=True, with_fixed=True): + """Get all the motion model parameters + + Parameters + ---------- + motion_model_list : list + List of MotionModels + with_errors : bool, optional + Add uncertainty names with '_err' suffix or not, by default True + with_fixed : bool, optional + Add fixed param names with '_fixed' suffix or not, by default True + + Returns + ------- + list + List of all unique parameter names across all motion models + """ list_of_parameters = [] - all_motion_models = MotionModel.__subclasses__() - for aa in range(len(all_motion_models)): - param_names = getattr(all_motion_models[aa], 'fitter_param_names') - param_fixed_names = getattr(all_motion_models[aa], 'fixed_param_names') - param_err_names = [par+'_err' for par in param_names] - list_of_parameters += param_names + def list_add(name): + if name not in list_of_parameters: + list_of_parameters.append(name) + + for mm in motion_model_list: + for param in mm.fitter_param_names: + # Fitter params + list_add(param) + # Error params + if with_errors: + list_add(param + '_err') + # Fixed params if with_fixed: - list_of_parameters += param_fixed_names - if with_errors: - list_of_parameters += param_err_names - - return np.unique(list_of_parameters).tolist() - + for param in mm.fixed_param_names: + list_add(param) + return list(list_of_parameters) + + +def get_all_motion_model_names(with_errors=True, with_fixed=True): + return get_list_motion_model_param_names(MotionModel.__subclasses__(), with_errors=with_errors, with_fixed=with_fixed) + +def motion_model_map(): + return { + 'Empty': Empty, + 'Fixed': Fixed, + 'Linear': Linear, + 'Acceleration': Acceleration, + 'Parallax': Parallax + } \ No newline at end of file From 77820b2ab6d28cbe519bfb7edd90a9473c6514ca Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Wed, 12 Nov 2025 23:37:32 -0800 Subject: [PATCH 126/254] Rewrite startable fit velocity function for acceleration and the new motion models. Update fitting logic: Provide multiple motion models, use the available model with the most number of parameters to fit according to the observed data points (i.e., choose the model with n_obs >= n_params). Removed motion_model_input and default_motion_model for clarity. --- flystar/startables.py | 296 ++++++++++++++++++++++++++++++++++-------- 1 file changed, 243 insertions(+), 53 deletions(-) diff --git a/flystar/startables.py b/flystar/startables.py index 8f7a397..60b8310 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -11,78 +11,74 @@ import copy from flystar import motion_model import pandas as pd +from flystar.motion_model import Empty, Fixed, Linear class StarTable(Table): - """ - A StarTable is an astropy.Table with stars matched from multiple starlists. + def __init__(self, *args, ref_list=0, **kwargs): + """ + A StarTable is an astropy.Table with stars matched from multiple starlists. - Required table columns (input as keywords): - ------------------------- - name : 1D numpy.array with shape = N_stars - List of unique names for each of the stars in the table. + Required table columns (input as keywords): + ------------------------- + name : 1D numpy.array with shape = N_stars + List of unique names for each of the stars in the table. - x : 2D numpy.array with shape = (N_stars, N_lists) - Positions of N_stars in each of N_lists in the x dimension. + x : 2D numpy.array with shape = (N_stars, N_lists) + Positions of N_stars in each of N_lists in the x dimension. - y : 2D numpy.array with shape = (N_stars, N_lists) - Positions of N_stars in each of N_lists in the y dimension. + y : 2D numpy.array with shape = (N_stars, N_lists) + Positions of N_stars in each of N_lists in the y dimension. - m : 2D numpy.array with shape = (N_stars, N_lists) - Magnitudes of N_stars in each of N_lists. + m : 2D numpy.array with shape = (N_stars, N_lists) + Magnitudes of N_stars in each of N_lists. - Optional table columns (input as keywords): - ------------------------- - motion_model : 1D numpy.array with shape = N_stars - string indicating motion model type for each star - - xe : 2D numpy.array with shape = (N_stars, N_lists) - Position uncertainties of N_stars in each of N_lists in the x dimension. + Optional table columns (input as keywords): + ------------------------- + motion_model : 1D numpy.array with shape = N_stars + string indicating motion model type for each star + + xe : 2D numpy.array with shape = (N_stars, N_lists) + Position uncertainties of N_stars in each of N_lists in the x dimension. - ye : 2D numpy.array with shape = (N_stars, N_lists) - Position uncertainties of N_stars in each of N_lists in the y dimension. + ye : 2D numpy.array with shape = (N_stars, N_lists) + Position uncertainties of N_stars in each of N_lists in the y dimension. - me : 2D numpy.array with shape = (N_stars, N_lists) - Magnitude uncertainties of N_stars in each of N_lists. + me : 2D numpy.array with shape = (N_stars, N_lists) + Magnitude uncertainties of N_stars in each of N_lists. - ep_name : 2D numpy.array with shape = (N_stars, N_lists) - Names in each epoch for each of N_stars in each of N_lists. This is - useful for tracking purposes. - - corr : 2D numpy.array with shape = (N_stars, N_lists) - Fitting correlation for each of N_stars in each of N_lists. + ep_name : 2D numpy.array with shape = (N_stars, N_lists) + Names in each epoch for each of N_stars in each of N_lists. This is + useful for tracking purposes. + + corr : 2D numpy.array with shape = (N_stars, N_lists) + Fitting correlation for each of N_stars in each of N_lists. - Optional table meta data - ------------------------- - list_names : list of strings - List of names, one for each of the starlists. + Optional table meta data + ------------------------- + list_names : list of strings + List of names, one for each of the starlists. - list_times : list of integers or floats - List of times/dates for each starlist. + list_times : list of integers or floats + List of times/dates for each starlist. - ref_list : int - Specify which list is the reference list (if any). + ref_list : int + Specify which list is the reference list (if any). - Examples - -------------------------- + Examples + -------------------------- - t = startables.StarTable(name=name, x=x, y=y, m=m) + t = startables.StarTable(name=name, x=x, y=y, m=m) - # Access the data: - print(t) - print(t['name'][0:10]) # print the first 10 star names - print(t['x'][0:10, 0]) # print x from the first epoch/list/column for the first 10 stars - """ - def __init__(self, *args, ref_list=0, **kwargs): - """ + # Access the data: + print(t) + print(t['name'][0:10]) # print the first 10 star names + print(t['x'][0:10, 0]) # print x from the first epoch/list/column for the first 10 stars """ # Check if the required arguments are present arg_req = ('name', 'x', 'y', 'm') - - found_all_required = True - for arg_test in arg_req: - if arg_test not in kwargs: - found_all_required = False + + found_all_required = all(arg in kwargs for arg in arg_req) if not found_all_required: if len(args) > 1: # If there are no arguments, it's because the @@ -151,7 +147,7 @@ def __init__(self, *args, ref_list=0, **kwargs): names=('name', 'x', 'y', 'm')) self['name'] = self['name'].astype('U20') self.meta = {'n_stars': n_stars, 'n_lists': n_lists, 'ref_list': ref_list} - + for meta_arg in meta_tab: if meta_arg in kwargs: self.meta[meta_arg] = kwargs[meta_arg] @@ -536,6 +532,200 @@ def detections(self): return + def fit_velocities_new( + self, + motion_models=['Empty', 'Fixed', 'Linear'], + weighting='var', + use_scipy=False, + absolute_sigma=True, + bootstrap=0, + fixed_t0=False, + verbose=True, + mask_value=None, + fill_value=np.nan, + show_progress=True + ): + """Fit velocity for star table + + Parameters + ---------- + motion_models : list, optional + Motion models name to use. + If multiple models are supplied, prioritize the model with the most parameters to fit. + If multiple models have the same number of parameters, raise AssertionError: not sure which to use. + When not enough data points, use the model with just enough parameters to fit, by default ['Empty, 'Fixed', 'Linear'] + weighting : str, optional + Uncertainty weighting, 'std' for weight=1/xe(ye) or 'var' for weight=1/xe(ye)**2, by default 'var' + use_scipy : bool, optional + Use scipy.optimize.curve_fit or algebraic solution (for Linear model only), by default False + absolute_sigma : bool, optional + Use absolute sigma or not, see scipy curve_fit for details, by default True + bootstrap : int, optional + Number of bootstrap for uncertainty resampling, by default 0 + fixed_t0 : bool or float, optional + If provided, use the fixed t0. Otherwise, use average t weighted by 1/np.hypot(xe, ye), by default False + verbose : bool, optional + Print verbose messages or not, by default True + mask_value : float, optional + Values to mask in data, by default None + fill_value : float, optional + Fill value when there is not enough data points to fit, by default np.nan + show_progress : bool, optional + Show progress bar or not, by default True + + Raises + ------ + ValueError + If weighting is not 'var' or 'std'. + KeyError + If time values are not found in the table or meta. + KeyError + If required columns 'x' and 'y' are missing in the table. + """ + ########################### + ####### Check Params ###### + ########################### + if weighting not in ['var', 'std']: + raise ValueError(f"fit_velocities: Weighting must either be 'var' or 'std', not {weighting}!") + + if ('t' not in self.colnames) and ('LIST_TIMES' not in self.meta): + raise KeyError("fit_velocities: Failed to access time values. No 't' column in table, no 'LIST_TIMES' in meta.") + + # Check if we have the required columns + if not all([_ in self.colnames for _ in ['x', 'y']]): + raise KeyError(f"fit_velocities: Missing required columns in the table: {', '.join(['x', 'y'])}!") + + # Convert motion models from strings to classes + motion_model_map = motion_model.motion_model_map() + if 'Empty' not in motion_models: + motion_models.insert(0, 'Empty') # Ensure Empty model is always included + motion_models = [motion_model_map[mm] for mm in motion_models] + + ########################### + ####### Prepare Data ###### + ########################### + # Prepare data for fitting + N_stars = len(self) + x_data = np.ma.masked_invalid(self['x'].data, copy=True) + y_data = np.ma.masked_invalid(self['y'].data, copy=True) + xe_data = np.ma.masked_invalid(self['xe'].data, copy=True) if 'xe' in self.colnames else None + ye_data = np.ma.masked_invalid(self['ye'].data, copy=True) if 'ye' in self.colnames else None + # t_data: 2d array with shape (N_stars, N_epochs) + # t0: 1d array with shape (N_stars,) + if 't' in self.colnames: + t_data = copy.deepcopy(self['t'].data) + t0 = np.average(t_data, axis=1, weights=1/np.hypot(xe_data, ye_data)) if not fixed_t0 else np.ones(N_stars)*fixed_t0 + else: + t_data = copy.deepcopy(np.array(self.meta['LIST_TIMES'])) + t_data = np.broadcast_to(t_data, x_data.shape) + t0 = np.average(t_data, axis=1, weights=1/np.hypot(xe_data, ye_data)) if not fixed_t0 else np.ones(N_stars)*fixed_t0 + if mask_value: + x_data = np.ma.masked_values(x_data, mask_value) + y_data = np.ma.masked_values(y_data, mask_value) + if xe_data is not None: + xe_data = np.ma.masked_values(xe_data, mask_value) + if ye_data is not None: + ye_data = np.ma.masked_values(ye_data, mask_value) + + # Calculate mask array + xy_mask = (~x_data.mask) & (~y_data.mask) + self['n_obs'] = xy_mask.sum(axis=1) + + # Convert to lists of arrays for faster access during fitting + t_stars = [np.array(t_data[i][xy_mask[i]]) for i in range(N_stars)] + x_stars = [np.array(x_data[i][xy_mask[i]]) for i in range(N_stars)] + y_stars = [np.array(y_data[i][xy_mask[i]]) for i in range(N_stars)] + xe_stars = [np.array(xe_data[i][xy_mask[i]]) if xe_data is not None else None for i in range(N_stars)] + ye_stars = [np.array(ye_data[i][xy_mask[i]]) if ye_data is not None else None for i in range(N_stars)] + + + ########################### + ####### Determine MM ###### + ########################### + mm_n_params = np.sort([mm.n_params for mm in motion_models]) + # Assert that motion model n_params are unique and sorted + assert len(mm_n_params) == len(set(mm_n_params)), "fit_velocities: Provided motion model n_params are not unique! Cannot decide which motion model to use based on n_obs." + + # Select motion model based on n_obs + mm_digitized = np.digitize( + x=self['n_obs'], + bins=mm_n_params + ) - 1 # -1 to convert to 0-based index + self['motion_model'] = np.array([motion_models[d].__name__ for d in mm_digitized]) + + # Fill table with all possible motion model parameter names as new columns. + new_col_list = motion_model.get_list_motion_model_param_names(motion_models, with_errors=True) + new_col_list += ['chi2_x', 'chi2_y', 'n_params'] + if 't0' not in new_col_list: + new_col_list.append('t0') + + # Replace old columns if they exist + for col in new_col_list: + if col.endswith('_err'): + self.add_column( + Column(data=np.full(N_stars, np.inf, dtype=float), name=col), + rename_duplicate=True + ) + else: + self.add_column( + Column(data=np.full(N_stars, np.nan, dtype=float), name=col), + rename_duplicate=True + ) + + # Add a column to keep track of the number of points used in a fit and number of bootstrap used. + self['n_bootstrap'] = bootstrap + + ########################### + ######### FITTING ######### + ########################### + unique_motion_models, unique_inv_indices = np.unique(self['motion_model'], return_inverse=True) + indices_by_motion_model = {key: np.flatnonzero(unique_inv_indices == k) for k, key in enumerate(unique_motion_models)} + + for unique_motion_model, unique_index in indices_by_motion_model.items(): + # Create motion model instance + motion_model_instance = motion_model_map[unique_motion_model]() + # Initialize arrays to store results + n_stars_this_model = len(unique_index) + n_params = len(motion_model_instance.fitter_param_names) + + params_array = np.full((n_stars_this_model, n_params), fill_value, dtype=float) + param_errs_array = np.full((n_stars_this_model, n_params), np.inf, dtype=float) + chi2_x_array = np.full(n_stars_this_model, np.nan, dtype=float) + chi2_y_array = np.full(n_stars_this_model, np.nan, dtype=float) + + for idx, i_star in enumerate(tqdm(unique_index, disable=not show_progress, desc=f"Fitting motion model {unique_motion_model}")): + # Fit the star + params, param_errs, chi2_x, chi2_y = motion_model_instance.fit_motion_model( + t=t_stars[i_star], + x=x_stars[i_star], + y=y_stars[i_star], + xe=xe_stars[i_star], + ye=ye_stars[i_star], + t0=t0[i_star], + weighting=weighting, + use_scipy=use_scipy, + absolute_sigma=absolute_sigma, + bootstrap=bootstrap, + fill_value=fill_value, + verbose=verbose + ) + # Store results to arrays + params_array[idx] = params + param_errs_array[idx] = param_errs + chi2_x_array[idx] = chi2_x + chi2_y_array[idx] = chi2_y + + # Store results back to the table + param_names = motion_model_instance.fitter_param_names + for j, param_name in enumerate(param_names): + self[param_name][unique_index] = params_array[:, j] + self[param_name + '_err'][unique_index] = param_errs_array[:, j] + self['chi2_x'][unique_index] = chi2_x_array + self['chi2_y'][unique_index] = chi2_y_array + self['n_params'][unique_index] = n_params + self['t0'][unique_index] = t0[unique_index] + return + def fit_velocities(self, weighting='var', use_scipy=True, absolute_sigma=True, bootstrap=0, fixed_t0=False, verbose=False, mask_val=None, mask_lists=False, show_progress=True, default_motion_model='Linear', reassign_motion_model=False, select_stars=None, motion_model_dict={}): From 043cdf1386c90320fa1b29f9b50ec8d23778630b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Sun, 16 Nov 2025 20:22:18 -0800 Subject: [PATCH 127/254] Add support for motion_model_input --- flystar/motion_model.py | 44 +++++++++++----- flystar/startables.py | 113 +++++++++++++++++++++++++++++----------- 2 files changed, 114 insertions(+), 43 deletions(-) diff --git a/flystar/motion_model.py b/flystar/motion_model.py index 939cd0b..4849214 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -7,12 +7,12 @@ import warnings class MotionModel(ABC): - # Degrees of freedom for model - n_params = 0 - # Fit paramters: Shared fit parameters fitter_param_names = [] + # Number of fit parameters/required observations in each direction + n_params = int(np.ceil(len(fitter_param_names) / 2)) + # Fixed parameters: These are parameters that are required for the model, but are not # fit quantities. For example, RA and Dec in a parallax model. fixed_param_names = [] @@ -22,6 +22,7 @@ class MotionModel(ABC): # These parameters should be derived from the fit parameters and # they must exist as a variable on the model object optional_param_names = [] + name = "MotionModel" def __init__(self, *args, **kwargs): # TODO: do we need this? @@ -59,7 +60,7 @@ def get_weights(self, xe, ye, weighting='var'): warnings.warn("Invalid weighting, using default weighting scheme var.", UserWarning) return 1./xe**2, 1./ye**2 - def fit_motion_model( + def fit( self, t, x, y, xe, ye, t0, bootstrap=0, weighting='var', @@ -163,9 +164,11 @@ def get_chi2(self, fit_params, fixed_params, t, x, y, xe, ye, reduced=False): return chi2x, chi2y class Empty(MotionModel): - n_params = 0 fitter_param_names = [] fixed_param_names = [] + name = "Empty" + # Number of fit parameters/required observations in each direction + n_params = int(np.ceil(len(fitter_param_names) / 2)) def __init__(self, **kwargs): """Empty motion model, returns nan for values and inf for uncertainties. @@ -207,9 +210,12 @@ class Fixed(MotionModel): A non-moving motion model for a star on the sky. """ - n_params = 1 fitter_param_names = ['x0','y0'] fixed_param_names = [] + # Number of fit parameters/required observations in each direction + n_params = int(np.ceil(len(fitter_param_names) / 2)) + + name = "Fixed" def __init__(self, **kwargs): # Must call after setting parameters. @@ -294,10 +300,14 @@ class Linear(MotionModel): """ A 2D linear motion model for a star on the sky. """ - n_params = 2 fitter_param_names = ['x0', 'vx', 'y0', 'vy'] fixed_param_names = ['t0'] + # Number of fit parameters/required observations in each direction + n_params = int(np.ceil(len(fitter_param_names) / 2)) + + name = "Linear" + def __init__(self, **kwargs): # Must call after setting parameters. @@ -348,7 +358,7 @@ def run_fit( params = np.full(self.n_params, fill_value) param_errors = np.full(self.n_params, np.inf) return params, param_errors, np.nan, np.nan - + # degree_of_freedom >= 0 dt = t - t0 x_wt, y_wt = self.get_weights(xe, ye, weighting=weighting) @@ -425,9 +435,12 @@ class Acceleration(MotionModel): """ A 2D accelerating motion model for a star on the sky. """ - n_params = 3 fitter_param_names = ['x0', 'vx0', 'ax', 'y0', 'vy0', 'ay'] fixed_param_names = ['t0'] + name = "Acceleration" + + # Number of fit parameters/required observations in each direction + n_params = int(np.ceil(len(fitter_param_names) / 2)) def __init__(self, x0=0, vx0=0, ax=0, y0=0, vy0=0, ay=0, t0=None, x0_err=0, vx0_err=0, ax_err=0, y0_err=0, vy0_err=0, ay_err=0, **kwargs): @@ -521,10 +534,13 @@ class Parallax(MotionModel): Optional PA is counterclockwise offset of the image y-axis from North. Optional obs parameter describes observer location, default is 'earth'. """ - n_params = 3 fitter_param_names = ['x0', 'vx', 'y0', 'vy', 'pi'] fixed_param_names = ['t0'] fixed_meta_data = ['RA','Dec','PA','obs'] + name = "Parallax" + + # Number of fit parameters/required observations in each direction + n_params = int(np.ceil(len(fitter_param_names) / 2)) def __init__(self, RA, Dec, PA=0.0, obs='earth', **kwargs): self.RA = RA @@ -771,10 +787,14 @@ def get_all_motion_model_names(with_errors=True, with_fixed=True): return get_list_motion_model_param_names(MotionModel.__subclasses__(), with_errors=with_errors, with_fixed=with_fixed) def motion_model_map(): - return { + mm_map = { 'Empty': Empty, 'Fixed': Fixed, 'Linear': Linear, 'Acceleration': Acceleration, 'Parallax': Parallax - } \ No newline at end of file + } + + # Sort by n_params + mm_map = dict(sorted(mm_map.items(), key=lambda item: item[1].n_params)) + return mm_map \ No newline at end of file diff --git a/flystar/startables.py b/flystar/startables.py index 60b8310..08461a1 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -532,9 +532,9 @@ def detections(self): return - def fit_velocities_new( + def fit_motion_model( self, - motion_models=['Empty', 'Fixed', 'Linear'], + motion_models=[Empty(), Fixed(), Linear()], weighting='var', use_scipy=False, absolute_sigma=True, @@ -549,11 +549,19 @@ def fit_velocities_new( Parameters ---------- - motion_models : list, optional - Motion models name to use. - If multiple models are supplied, prioritize the model with the most parameters to fit. - If multiple models have the same number of parameters, raise AssertionError: not sure which to use. - When not enough data points, use the model with just enough parameters to fit, by default ['Empty, 'Fixed', 'Linear'] + motion_models : list of MotionModel, optional + Motion models to use. + Empty() and Fixed() models are always added automatically for stars with n_fit = 0 or 1. + The behavior is as follows: + 1. If 'motion_model_input' column is NOT in table: + - Use the most complex model that has enough parameters to fit the data (n_fit >= n_params). + - If multiple models are supplied, prioritize the model with the most parameters to fit. + - If multiple models have the same number of parameters, raise AssertionError: not sure which to use. + 2. If 'motion_model_input' column is in table: + - Use the model specified in the 'motion_model_input' column. + - If motion model requires initialization parameters, an instance of the motion model must be provided in motion_models list, i.e., motion_models=[Parallax(RA=0, DEC=0)]. + - If not enough data points to fit the specified model, use the most complex model that has enough parameters to fit the data (n_fit >= n_params) among the provided motion_models and 'motion_model_input'. + The actual used motion model is stored in the 'motion_model_used' column. The default motion_models are [Empty(), Fixed(), Linear()]. weighting : str, optional Uncertainty weighting, 'std' for weight=1/xe(ye) or 'var' for weight=1/xe(ye)**2, by default 'var' use_scipy : bool, optional @@ -595,11 +603,38 @@ def fit_velocities_new( if not all([_ in self.colnames for _ in ['x', 'y']]): raise KeyError(f"fit_velocities: Missing required columns in the table: {', '.join(['x', 'y'])}!") - # Convert motion models from strings to classes - motion_model_map = motion_model.motion_model_map() - if 'Empty' not in motion_models: - motion_models.insert(0, 'Empty') # Ensure Empty model is always included - motion_models = [motion_model_map[mm] for mm in motion_models] + # Always add Empty and Fixed in motion models + mm_names = [mm.name for mm in motion_models] + if 'Fixed' not in mm_names: + motion_models.insert(0, Fixed()) + if 'Empty' not in mm_names: + motion_models.insert(0, Empty()) + mm_names = [mm.name for mm in motion_models] + + # Construct motion models if motion_model_input column exists + all_mm_map = motion_model.motion_model_map() + if 'motion_model_input' in self.colnames: + input_mm_names = np.unique(self['motion_model_input']) + assert all([name in all_mm_map.keys() for name in input_mm_names]), \ + f"fit_velocities: Unknown motion model name(s) in 'motion_model_input' column. Available motion models are: {', '.join(all_mm_map.keys())}." + for mm_name in input_mm_names: + if mm_name not in mm_names: + try: + motion_models.append(all_mm_map[mm_name]()) + except Exception as e: + raise ValueError(f"fit_velocities: An instance of motion model {mm_name} with initialization parameters is missing in motion_models: {e}") + + # Sort motion models by n_params + motion_models = sorted(motion_models, key=lambda mm: mm.n_params) + + input_mm_map = {mm.name: mm for mm in motion_models} + + mm_n_params = np.sort([mm.n_params for mm in motion_models]) + if 'motion_model_input' not in self.colnames: + # If motion_model_input column is not provided, assert that motion model n_params are unique and sorted + # Otherwise the fitter does not know which motion model to use based on n_obs + assert len(mm_n_params) == len(set(mm_n_params)), "fit_velocities: Provided motion model n_params are not unique! Cannot decide which motion model to use based on n_obs. Please provide unique motion_models or a 'motion_model_input' column." + ########################### ####### Prepare Data ###### @@ -610,6 +645,7 @@ def fit_velocities_new( y_data = np.ma.masked_invalid(self['y'].data, copy=True) xe_data = np.ma.masked_invalid(self['xe'].data, copy=True) if 'xe' in self.colnames else None ye_data = np.ma.masked_invalid(self['ye'].data, copy=True) if 'ye' in self.colnames else None + # t_data: 2d array with shape (N_stars, N_epochs) # t0: 1d array with shape (N_stars,) if 't' in self.colnames: @@ -619,6 +655,7 @@ def fit_velocities_new( t_data = copy.deepcopy(np.array(self.meta['LIST_TIMES'])) t_data = np.broadcast_to(t_data, x_data.shape) t0 = np.average(t_data, axis=1, weights=1/np.hypot(xe_data, ye_data)) if not fixed_t0 else np.ones(N_stars)*fixed_t0 + if mask_value: x_data = np.ma.masked_values(x_data, mask_value) y_data = np.ma.masked_values(y_data, mask_value) @@ -629,7 +666,7 @@ def fit_velocities_new( # Calculate mask array xy_mask = (~x_data.mask) & (~y_data.mask) - self['n_obs'] = xy_mask.sum(axis=1) + self['n_fit'] = xy_mask.sum(axis=1) # Convert to lists of arrays for faster access during fitting t_stars = [np.array(t_data[i][xy_mask[i]]) for i in range(N_stars)] @@ -642,19 +679,31 @@ def fit_velocities_new( ########################### ####### Determine MM ###### ########################### - mm_n_params = np.sort([mm.n_params for mm in motion_models]) - # Assert that motion model n_params are unique and sorted - assert len(mm_n_params) == len(set(mm_n_params)), "fit_velocities: Provided motion model n_params are not unique! Cannot decide which motion model to use based on n_obs." + if 'motion_model_input' in self.colnames: + # Determine which motion model to use based on motion_model_input column + # If n_fit < required n_params for the input motion model, use the most complicated motion model with n_fit >= n_params + motion_model_names = np.unique(self['motion_model_input']) + required_params = [all_mm_map[mm_name].n_params for mm_name in self['motion_model_input']] + mm_digitized = np.digitize( + x=np.minimum(np.array(self['n_fit']), required_params), + bins=mm_n_params + ) - 1 # Convert to 0-based index + + else: + mm_digitized = np.digitize( + x=np.array(self['n_fit']), + bins=mm_n_params + ) - 1 # Convert to 0-based index + + self['motion_model_used'] = np.array([motion_models[d].name for d in mm_digitized]) - # Select motion model based on n_obs - mm_digitized = np.digitize( - x=self['n_obs'], - bins=mm_n_params - ) - 1 # -1 to convert to 0-based index - self['motion_model'] = np.array([motion_models[d].__name__ for d in mm_digitized]) + ############################ + ####### Prepare Table ###### + ############################ # Fill table with all possible motion model parameter names as new columns. - new_col_list = motion_model.get_list_motion_model_param_names(motion_models, with_errors=True) + motion_model_used = [all_mm_map[name] for name in np.unique(self['motion_model_used'])] + new_col_list = motion_model.get_list_motion_model_param_names(motion_model_used, with_errors=True) new_col_list += ['chi2_x', 'chi2_y', 'n_params'] if 't0' not in new_col_list: new_col_list.append('t0') @@ -668,7 +717,7 @@ def fit_velocities_new( ) else: self.add_column( - Column(data=np.full(N_stars, np.nan, dtype=float), name=col), + Column(data=np.full(N_stars, fill_value, dtype=float), name=col), rename_duplicate=True ) @@ -678,12 +727,13 @@ def fit_velocities_new( ########################### ######### FITTING ######### ########################### - unique_motion_models, unique_inv_indices = np.unique(self['motion_model'], return_inverse=True) + unique_motion_models, unique_inv_indices = np.unique(self['motion_model_used'], return_inverse=True) indices_by_motion_model = {key: np.flatnonzero(unique_inv_indices == k) for k, key in enumerate(unique_motion_models)} - + + start_time = time.time() for unique_motion_model, unique_index in indices_by_motion_model.items(): # Create motion model instance - motion_model_instance = motion_model_map[unique_motion_model]() + motion_model_instance = input_mm_map[unique_motion_model] # Initialize arrays to store results n_stars_this_model = len(unique_index) n_params = len(motion_model_instance.fitter_param_names) @@ -695,7 +745,7 @@ def fit_velocities_new( for idx, i_star in enumerate(tqdm(unique_index, disable=not show_progress, desc=f"Fitting motion model {unique_motion_model}")): # Fit the star - params, param_errs, chi2_x, chi2_y = motion_model_instance.fit_motion_model( + params, param_errs, chi2_x, chi2_y = motion_model_instance.fit( t=t_stars[i_star], x=x_stars[i_star], y=y_stars[i_star], @@ -709,7 +759,7 @@ def fit_velocities_new( fill_value=fill_value, verbose=verbose ) - # Store results to arrays + # print(f'{params_array.shape=}, {idx=}, {params=}') params_array[idx] = params param_errs_array[idx] = param_errs chi2_x_array[idx] = chi2_x @@ -722,10 +772,11 @@ def fit_velocities_new( self[param_name + '_err'][unique_index] = param_errs_array[:, j] self['chi2_x'][unique_index] = chi2_x_array self['chi2_y'][unique_index] = chi2_y_array - self['n_params'][unique_index] = n_params + self['n_params'][unique_index] = motion_model_instance.n_params self['t0'][unique_index] = t0[unique_index] return + def fit_velocities(self, weighting='var', use_scipy=True, absolute_sigma=True, bootstrap=0, fixed_t0=False, verbose=False, mask_val=None, mask_lists=False, show_progress=True, default_motion_model='Linear', reassign_motion_model=False, select_stars=None, motion_model_dict={}): @@ -1100,7 +1151,7 @@ def get_star_positions_at_time(self, t, motion_model_dict, allow_alt_models=True param_dict[par] = self[par][idx] x[idx],y[idx],xe[idx],ye[idx] = mod.get_batch_pos_at_time(t,**param_dict) - return x,y,xe,ye + return x, y, xe, ye def fit_velocities_all_detected(self, motion_model_to_fit, weighting='var', use_scipy=True, absolute_sigma=True, times=None, From 82585b478d9dd89d8db145a99d7189f357ec3c2d Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Tue, 18 Nov 2025 15:04:00 -0800 Subject: [PATCH 128/254] attempt to fix plot_stars_nfilt --- flystar/plots.py | 25 +++++++++++++++---------- 1 file changed, 15 insertions(+), 10 deletions(-) diff --git a/flystar/plots.py b/flystar/plots.py index 707d6e6..7553a8d 100755 --- a/flystar/plots.py +++ b/flystar/plots.py @@ -3112,6 +3112,11 @@ def rs(x): x = tab['x0'] y = tab['y0'] r = np.hypot(x, y) + motion_model_dict = motion_model.validate_motion_model_dict(motion_model_dict, tab, None) + i_all_detected = np.where(~np.any(np.isnan(tab['t']),axis=1))[0][0] + cont_times = np.arange(np.min(tab['t'][i_all_detected]), np.max(tab['t'][i_all_detected]), 0.01) + xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], motion_model_dict, allow_alt_models=True) + xt_cont_all, yt_cont_all, xt_cont_err, yt_cont_err = tab.get_star_positions_at_time(cont_times, motion_model_dict, allow_alt_models=True) for i in range(Nstars): for ea, epoch_array in enumerate(epoch_array_list): @@ -3242,9 +3247,9 @@ def rs(x): ind = int((row-1)*Ncols + col) paxes = plt.subplot(Nrows, Ncols, ind) - plt.plot(time, fitLineX, 'b-') - plt.plot(time, fitLineX + fitSigX, 'b--') - plt.plot(time, fitLineX - fitSigX, 'b--') + plt.plot(cont_times, xt_cont_all[ii], 'b-') + plt.plot(cont_times, xt_cont_all[ii] + xt_cont_err[ii], 'b--') + plt.plot(cont_times, xt_cont_all[ii] - xt_cont_err[ii], 'b--') print(np.shape(xerr.reshape(len(xerr),))) if not color_time: plt.errorbar(rs(time), rs(x), yerr=rs(xerr), marker='.', color=color, ls='none') @@ -3275,9 +3280,9 @@ def rs(x): ind = int((row-1)*Ncols + col) paxes = plt.subplot(Nrows, Ncols, ind) - plt.plot(time, fitLineY, 'b-') - plt.plot(time, fitLineY + fitSigY, 'b--') - plt.plot(time, fitLineY - fitSigY, 'b--') + plt.plot(cont_times, yt_cont_all[ii], 'b-') + plt.plot(cont_times, yt_cont_all[ii] + yt_cont_err[ii], 'b--') + plt.plot(cont_times, yt_cont_all[ii] - yt_cont_err[ii], 'b--') if not color_time: plt.errorbar(rs(time), rs(y), yerr=rs(yerr), marker='.', color=color, ls='none') else: @@ -3338,8 +3343,8 @@ def rs(x): paxes = plt.subplot(Nrows, Ncols, ind) plt.plot(time, np.zeros(len(time)), 'b-') - plt.plot(time, fitSigX*1e3, 'b--') - plt.plot(time, -fitSigX*1e3, 'b--') + plt.plot(cont_times, xt_cont_err[ii]*1e3, 'b--') + plt.plot(cont_times, -xt_cont_err[ii]*1e3, 'b--') if not color_time: plt.errorbar(rs(time), rs(x - fitLineX)*1e3, yerr=rs(xerr)*1e3, marker='.', color=color, ls='none') else: @@ -3366,8 +3371,8 @@ def rs(x): paxes = plt.subplot(Nrows, Ncols, ind) plt.plot(time, np.zeros(len(time)), 'b-') - plt.plot(time, fitSigY*1e3, 'b--') - plt.plot(time, -fitSigY*1e3, 'b--') + plt.plot(cont_times, yt_cont_err[ii]*1e3, 'b--') + plt.plot(cont_times, -yt_cont_err[ii]*1e3, 'b--') if not color_time: plt.errorbar(rs(time), rs(y - fitLineY)*1e3, yerr=rs(yerr)*1e3, marker='.', color=color, ls='none') else: From 5ec18cb6847b97562f8936c33890cf3b3bb7e8f5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Thu, 20 Nov 2025 19:05:15 -0800 Subject: [PATCH 129/254] Passed testing for fit_motion_model. Merged get_pos_at_time and get_batch_pos at_time into model; Renamed get_ functions into calc_ functions --- flystar/motion_model.py | 568 +++++++++++++++++++---------- flystar/parallax.py | 43 ++- flystar/startables.py | 17 +- flystar/tests/test_align.py | 2 +- flystar/tests/test_motion_model.py | 288 +++++++++------ flystar/tests/test_startable.py | 12 +- 6 files changed, 599 insertions(+), 331 deletions(-) diff --git a/flystar/motion_model.py b/flystar/motion_model.py index 4849214..5a542e0 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -8,10 +8,10 @@ class MotionModel(ABC): # Fit paramters: Shared fit parameters - fitter_param_names = [] + fit_param_names = [] # Number of fit parameters/required observations in each direction - n_params = int(np.ceil(len(fitter_param_names) / 2)) + n_params = int(np.ceil(len(fit_param_names) / 2)) # Fixed parameters: These are parameters that are required for the model, but are not # fit quantities. For example, RA and Dec in a parallax model. @@ -25,21 +25,16 @@ class MotionModel(ABC): name = "MotionModel" def __init__(self, *args, **kwargs): - # TODO: do we need this? - '''for param in self.fitter_param_names: - param_var = getattr(self, param) - if not isinstance(param_var, (list, np.ndarray)): - setattr(self, param, np.array([param_var]))''' return + + def model_fit(self, dt): + return np.full_like(dt, np.nan) + + def model(self, t, fit_params, fixed_params=None, fit_param_errs=None): + if fit_param_errs is None: + return np.full_like(t, np.nan), np.full_like(t, np.nan) + return np.full_like(t, np.nan), np.full_like(t, np.nan), np.full_like(t, np.inf), np.full_like(t, np.inf) - def get_pos_at_time(self, fit_params, fixed_params, t): - #return x, y - pass - - def get_batch_pos_at_time(self, t): - #return x, y, x_err, y_err - pass - def run_fit( self, t, x, y, xe, ye, t0, weighting='var', @@ -103,7 +98,7 @@ def fit( Returns ------- params, params_err, chi2_x, chi2_y - Parameters, uncertainties, and chi squares. The corresponding parameter names are in self.fitter_param_names. + Parameters, uncertainties, and chi squares. The corresponding parameter names are in self.fit_param_names. """ params, param_errs, chi2_x, chi2_y = self.run_fit( t, x, y, xe, ye, t0=t0, @@ -147,48 +142,41 @@ def fit( return params, param_errs, chi2_x, chi2_y - def get_chi2(self, fit_params, fixed_params, t, x, y, xe, ye, reduced=False): + def calc_chi2(self, fit_params, fixed_params, t, x, y, xe, ye, reduced=False): """ Get the chi^2 value for the current MM and the input data. """ - x_pred, y_pred = self.get_pos_at_time(fit_params, fixed_params, t) + x_pred, y_pred = self.model(t, fit_params, fixed_params) chi2x = np.sum((x - x_pred)**2 / xe**2) chi2y = np.sum((y - y_pred)**2 / ye**2) if reduced: if len(t) == self.n_params: - chi2x, chi2y = 0, 0 + return np.inf, np.inf else: - dof = len(x) - self.n_params - chi2x, chi2y = chi2x / dof, chi2y / dof + degree_of_freedom = len(x) - self.n_params + chi2x, chi2y = chi2x / degree_of_freedom, chi2y / degree_of_freedom return chi2x, chi2y class Empty(MotionModel): - fitter_param_names = [] + fit_param_names = [] fixed_param_names = [] name = "Empty" # Number of fit parameters/required observations in each direction - n_params = int(np.ceil(len(fitter_param_names) / 2)) + n_params = int(np.ceil(len(fit_param_names) / 2)) def __init__(self, **kwargs): """Empty motion model, returns nan for values and inf for uncertainties. """ super().__init__() return - - def get_pos_at_time(self, fit_params, fixed_params, t): - if hasattr(t, "__len__"): - return np.full(len(t), np.nan), np.full(len(t), np.nan) - else: - return np.nan, np.nan + + def model_fit(self, dt): + return np.full_like(dt, np.nan) - def get_batch_pos_at_time(self,t, - x0=[],y0=[],t0=[], - x0_err=[], y0_err=[]): - if hasattr(t, "__len__"): - return np.full((len(x0), len(t)), np.nan), np.full((len(y0), len(t)), np.nan), np.full((len(x0), len(t)), np.nan), np.full((len(y0), len(t)), np.nan) - else: - return np.nan, np.nan, np.nan, np.nan + def model(self, t, fit_params, fixed_params, fixed_param_errs=None): + t = np.atleast_1d(t) + return np.full_like(t, np.nan), np.full_like(t, np.nan), np.full_like(t, np.inf), np.full_like(t, np.inf) def run_fit( self, t, x, y, xe, ye, t0, @@ -210,10 +198,10 @@ class Fixed(MotionModel): A non-moving motion model for a star on the sky. """ - fitter_param_names = ['x0','y0'] + fit_param_names = ['x0','y0'] fixed_param_names = [] # Number of fit parameters/required observations in each direction - n_params = int(np.ceil(len(fitter_param_names) / 2)) + n_params = int(np.ceil(len(fit_param_names) / 2)) name = "Fixed" @@ -222,22 +210,78 @@ def __init__(self, **kwargs): # This checks for proper parameter formatting. super().__init__() return + + def model_fit(self, dt, x0): + """Fit function for Fixed motion model + + Parameters + ---------- + dt : array-like + Time offset, shape (N_times,) + x0 : float or array-like + Average positions, scalar or shape (N_stars,) - def get_pos_at_time(self, fit_params, fixed_params, t): - fit_params_dict = dict(zip(self.fitter_param_names, fit_params)) - if hasattr(t, "__len__"): - return np.repeat(fit_params_dict['x0'], len(t)), np.repeat(fit_params_dict['y0'], len(t)) - else: - return fit_params_dict['x0'], fit_params_dict['y0'] - - def get_batch_pos_at_time(self,t, - x0=[],y0=[],t0=[], - x0_err=[], y0_err=[]): - if hasattr(t, "__len__"): - return np.repeat(x0[:,np.newaxis],len(t),axis=1), np.repeat(y0[:,np.newaxis],len(t),axis=1), np.repeat(x0_err[:,np.newaxis],len(t),axis=1), np.repeat(y0_err[:,np.newaxis],len(t),axis=1) - else: - return x0, y0, x0_err, y0_err - + Returns + ------- + x : array-like + Predicted positions, shape (N_times,) if scalar x0, else (N_stars, N_times) + """ + dt = np.atleast_1d(dt) + x0 = np.asarray(x0) + return np.broadcast_to(x0[:, np.newaxis], (x0.shape[0], dt.shape[0])) if x0.ndim > 0 else np.full_like(dt, x0) + + def model(self, t, fit_params, fixed_params=None, fit_param_errs=None): + """Predicted positions (and uncertainties, if fit_param_errs is provided) at time t of Fixed model. + + Parameters + ---------- + t : float or array-like + Time array, shape (N_times,) + fit_params : array-like + Fit parameters x0, y0 in shape (N_params,) or (N_stars, N_params) + fixed_params : array-like, optional + Not applicable for Fixed, by default None + fit_param_errs : array-like, optional + Uncertainties for x0, y0 in shape (N_params,) or (N_stars, N_params), by default None + + Returns + ------- + x, y (, xe, ye) + Predicted position (and uncertainties) of Fixed model, shape (N_stars, N_times), or (N_times,) if N_stars=1, or (N_stars,) if N_times=1 + """ + t = np.atleast_1d(t) + fit_params = np.atleast_2d(fit_params) # (N_stars, N_params) + + N_stars = fit_params.shape[0] if fit_params.ndim > 1 else 1 + N_times = len(t) + x0, y0 = fit_params.T # Each shape (N_stars,) + + # Return results in (N_stars, N_times) shape + x = self.model_fit(t, x0) # Shape (N_stars, N_times) + y = self.model_fit(t, y0) # Shape (N_stars, N_times) + + if N_stars == 1 or N_times == 1: + # If only one star, return flattened arrays + x = x.flatten() + y = y.flatten() + + if fit_param_errs is None: + return x, y + + fit_param_errs = np.atleast_2d(fit_param_errs) # (N_stars, N_params) + x0_err, y0_err = fit_param_errs.T + + # Return results in (N_stars, N_times) shape + x_err = np.broadcast_to(x0_err[:, np.newaxis], (N_stars, N_times)) + y_err = np.broadcast_to(y0_err[:, np.newaxis], (N_stars, N_times)) + + if N_stars == 1 or N_times == 1: + # If only one star, return flattened arrays + x_err = x_err.flatten() + y_err = y_err.flatten() + + return x, y, x_err, y_err + def run_fit( self, t, x, y, xe, ye, t0, weighting='var', @@ -262,22 +306,22 @@ def run_fit( params = np.full(self.n_params, fill_value) param_errors = np.full(self.n_params, np.inf) return params, param_errors, np.nan, np.nan - + # degree_of_freedom >= 0 # Calculate weighted average position x_wt, y_wt = self.get_weights(xe, ye, weighting=weighting) x_wt_norm = x_wt / np.sum(x_wt) y_wt_norm = y_wt / np.sum(y_wt) x0 = np.average(x, weights=x_wt) - x0e = (np.sum(x_wt_norm**2 * xe**2))**0.5 / n_obs # Error propagation + x0e = (np.sum(x_wt_norm**2 * xe**2))**0.5 # Error propagation y0 = np.average(y, weights=y_wt) - y0e = (np.sum(y_wt_norm**2 * ye**2))**0.5 / n_obs # Error propagation + y0e = (np.sum(y_wt_norm**2 * ye**2))**0.5 # Error propagation params = np.array([x0, y0]) param_errors = np.array([x0e, y0e]) - - chi2x, chi2y = self.get_chi2(params, [], t, x, y, xe, ye) - + + chi2x, chi2y = self.calc_chi2(params, [], t, x, y, xe, ye) + if not absolute_sigma: if degree_of_freedom > 0: reduced_chi2x = chi2x / degree_of_freedom @@ -300,41 +344,89 @@ class Linear(MotionModel): """ A 2D linear motion model for a star on the sky. """ - fitter_param_names = ['x0', 'vx', 'y0', 'vy'] + fit_param_names = ['x0', 'vx', 'y0', 'vy'] fixed_param_names = ['t0'] # Number of fit parameters/required observations in each direction - n_params = int(np.ceil(len(fitter_param_names) / 2)) + n_params = int(np.ceil(len(fit_param_names) / 2)) name = "Linear" def __init__(self, **kwargs): - # Must call after setting parameters. # This checks for proper parameter formatting. super().__init__() return - def get_pos_at_time(self, fit_params, fixed_params, t): - fit_params_dict = dict(zip(self.fitter_param_names, fit_params)) - fixed_params_dict = dict(zip(self.fixed_param_names, fixed_params)) - dt = t - fixed_params_dict['t0'] - return fit_params_dict['x0'] + fit_params_dict['vx']*dt, fit_params_dict['y0'] + fit_params_dict['vy']*dt - - def get_batch_pos_at_time(self, t, x0=[],vx=[], y0=[],vy=[], t0=[], - x0_err=[],vx_err=[], y0_err=[],vy_err=[], **kwargs): - if hasattr(t, "__len__"): - dt = t - t0[:,np.newaxis] - x = x0[:,np.newaxis] + dt*vx[:,np.newaxis] - y = y0[:,np.newaxis] + dt*vy[:,np.newaxis] - x_err = np.hypot(x0_err[:,np.newaxis], vx_err[:,np.newaxis]*dt) - y_err = np.hypot(y0_err[:,np.newaxis], vy_err[:,np.newaxis]*dt) - else: - dt = t - t0 - x = x0 + dt*vx - y = y0 + dt*vy - x_err = np.hypot(x0_err, vx_err*dt) - y_err = np.hypot(y0_err, vy_err*dt) + def model_fit(self, dt, x0, v): + """Linear motion model fit function + + Parameters + ---------- + dt : array-like + Time offset, shape (N_times,) + x0 : float or array-like + Initial position, shape (N_stars,) or scalar + v : float or array-like + Velocity, shape (N_stars,) or scalar + + Returns + ------- + x : array-like + Predicted position(s) + """ + return x0 + v * dt + + def model(self, t, fit_params, fixed_params, fit_param_errs=None): + """Model positions (and uncertainties, if fit_param_errs is provided) at time t of Linear model. + + Parameters + ---------- + t : float or array-like + Time(s) at which to evaluate the model + fit_params : array-like + Fit parameters x0, vx, y0, vy in shape (N_params,) or (N_stars, N_params) + fixed_params : array-like + Fixed parameters t0 in shape (1,) or (N_stars, 1) + fit_param_errs : array-like, optional + Uncertainties of fit parameters in shape (N_params,) or (N_stars, N_params), by default None + + Returns + ------- + x, y (, xe, ye) + Predicted positions (and uncertainties, if fit_param_errs is provided) with shape (N_stars, N_times), or (N_times,) if N_stars=1, or (N_stars,) if N_times=1 + """ + t = np.atleast_1d(t) + fit_params = np.atleast_2d(fit_params) # (N_stars, N_params) + + N_stars = fit_params.shape[0] if fit_params.ndim > 1 else 1 + N_times = len(t) + + x0, vx, y0, vy = fit_params.T # Each shape (N_stars,) + t0 = np.atleast_1d(fixed_params[0]) # Shape (N_stars,) or (1,) + + dt = t[np.newaxis, :] - t0[:, np.newaxis] # Shape (N_stars, N_times) + + x = self.model_fit(dt, x0[:, np.newaxis], vx[:, np.newaxis]) # Shape (N_stars, N_times) + y = self.model_fit(dt, y0[:, np.newaxis], vy[:, np.newaxis]) # Shape (N_stars, N_times) + + if N_stars == 1 or N_times == 1: + # If only one star, return flattened arrays + x = x.flatten() + y = y.flatten() + + if fit_param_errs is None: + return x, y + + fit_param_errs = np.atleast_2d(fit_param_errs) # (N_stars, N_params) + x0_err, vx_err, y0_err, vy_err = fit_param_errs.T # Each shape (N_stars,) + x_err = np.hypot(x0_err[:, np.newaxis], vx_err[:, np.newaxis] * dt) # Shape (N_stars, N_times) + y_err = np.hypot(y0_err[:, np.newaxis], vy_err[:, np.newaxis] * dt) # Shape (N_stars, N_times) + + if N_stars == 1 or N_times == 1: + # If only one star, return flattened arrays + x_err = x_err.flatten() + y_err = y_err.flatten() return x, y, x_err, y_err def run_fit( @@ -366,17 +458,15 @@ def run_fit( params_guess = [x.mean(), 0., y.mean(), 0.] if use_scipy: - def linear(t, c0, c1): - return c0 + c1*t - x_opt, x_cov = curve_fit(linear, dt, x, p0=np.array(params_guess[:2]), sigma=1/x_wt**0.5, absolute_sigma=absolute_sigma) - y_opt, y_cov = curve_fit(linear, dt, y, p0=np.array(params_guess[2:]), sigma=1/y_wt**0.5, absolute_sigma=absolute_sigma) + x_opt, x_cov = curve_fit(self.model_fit, dt, x, p0=np.array(params_guess[:2]), sigma=1/x_wt**0.5, absolute_sigma=absolute_sigma) + y_opt, y_cov = curve_fit(self.model_fit, dt, y, p0=np.array(params_guess[2:]), sigma=1/y_wt**0.5, absolute_sigma=absolute_sigma) x0, vx = x_opt y0, vy = y_opt x0e, vxe = np.sqrt(x_cov.diagonal()) y0e, vye = np.sqrt(y_cov.diagonal()) params = np.array([x0, vx, y0, vy]) param_errors = np.array([x0e, vxe, y0e, vye]) - chi2_x, chi2_y = self.get_chi2(params, [t0], t, x, y, xe, ye) + chi2_x, chi2_y = self.calc_chi2(params, [t0], t, x, y, xe, ye) else: # Use https://en.wikipedia.org/wiki/Weighted_least_squares#Solution scheme @@ -435,45 +525,94 @@ class Acceleration(MotionModel): """ A 2D accelerating motion model for a star on the sky. """ - fitter_param_names = ['x0', 'vx0', 'ax', 'y0', 'vy0', 'ay'] + fit_param_names = ['x0', 'vx0', 'ax', 'y0', 'vy0', 'ay'] fixed_param_names = ['t0'] name = "Acceleration" # Number of fit parameters/required observations in each direction - n_params = int(np.ceil(len(fitter_param_names) / 2)) - - def __init__(self, x0=0, vx0=0, ax=0, y0=0, vy0=0, ay=0, t0=None, - x0_err=0, vx0_err=0, ax_err=0, y0_err=0, vy0_err=0, ay_err=0, **kwargs): + n_params = int(np.ceil(len(fit_param_names) / 2)) + + def __init__(self): # Must call after setting parameters. # This checks for proper parameter formatting. super().__init__() return + + def model_fit(self, t, x0, v0, a): + """Model positions at time t of Acceleration model. + + Parameters + ---------- + t : float or array-like + Time(s) at which to evaluate the model + x0 : float or array-like + Initial position(s) + v0 : float or array-like + Initial velocity(ies) + a : float or array-like + Acceleration(s) + + Returns + ------- + float or array-like + Model positions at time t of Acceleration model + """ + return x0 + v0*t + 0.5*a*t**2 + + def model(self, t, fit_params, fixed_params, fit_param_errs=None): + """Model positions (and uncertainties, if fit_param_errs is provided) at time t of Acceleration model. + + Parameters + ---------- + t : float or array-like + Time(s) at which to evaluate the model + fit_params : array-like + Fit parameters with shape (N_stars, N_params) or (N_params,) + fixed_params : array-like + Fixed parameters with shape (N_stars, N_fixed_params) or (N_fixed_params,) + fit_param_errs : array-like, optional + Fit parameter uncertainties with shape (N_stars, N_params) or (N_params,), by default None + + Returns + ------- + x, y (, xe, ye) + Predicted positions (and uncertainties, if fit_param_errs is provided) with shape (N_stars, N_times), or (N_times,) if N_stars=1, or (N_stars,) if N_times=1 + """ + t = np.atleast_1d(t) + fit_params = np.atleast_2d(fit_params) # (N_stars, N_params) + + N_stars = fit_params.shape[0] if fit_params.ndim > 1 else 1 + N_times = len(t) - def get_pos_at_time(self, fit_params, fixed_params, t): - fit_params_dict = dict(zip(self.fitter_param_names, fit_params)) - fixed_params_dict = dict(zip(self.fixed_param_names, fixed_params)) - dt = t - fixed_params_dict['t0'] - x = fit_params_dict['x0'] + fit_params_dict['vx0']*dt + 0.5*fit_params_dict['ax']*dt**2 - y = fit_params_dict['y0'] + fit_params_dict['vy0']*dt + 0.5*fit_params_dict['ay']*dt**2 - return x, y + x0, vx0, ax, y0, vy0, ay = fit_params.T # Each shape (N_stars,) + t0 = np.atleast_1d(fixed_params[0]) # Shape (N_stars,) or (1,) - def get_batch_pos_at_time(self,t, - x0=[],vx0=[],ax=[], y0=[],vy0=[],ay=[], t0=[], - x0_err=[],vx0_err=[],ax_err=[], y0_err=[],vy0_err=[],ay_err=[], **kwargs): - if hasattr(t, "__len__"): - dt = t - t0[:,np.newaxis] - x = x0[:, np.newaxis] + dt * vx0[:, np.newaxis] + 0.5 * ax[:, np.newaxis] * dt**2 - y = y0[:, np.newaxis] + dt * vy0[:, np.newaxis] + 0.5 * ay[:, np.newaxis] * dt**2 - x_err = np.sqrt(x0_err[:, np.newaxis]**2 + (vx0_err[:, np.newaxis]*dt)**2 + (0.5*ax_err[:, np.newaxis]*dt**2)**2) - y_err = np.sqrt(y0_err[:, np.newaxis]**2 + (vy0_err[:, np.newaxis]*dt)**2 + (0.5*ay_err[:, np.newaxis]*dt**2)**2) - else: - dt = t - t0 - x = x0 + dt * vx0 + 0.5 * ax * dt**2 - y = y0 + dt * vy0 + 0.5 * ay * dt**2 - x_err = np.sqrt(x0_err**2 + (vx0_err * dt)**2 + (0.5 * ax_err * dt**2)**2) - y_err = np.sqrt(y0_err**2 + (vy0_err * dt)**2 + (0.5 * ay_err * dt**2)**2) + dt = t[np.newaxis, :] - t0[:, np.newaxis] # Shape (N_stars, N_times) + + x = self.model_fit(dt, x0[:, np.newaxis], vx0[:, np.newaxis], ax[:, np.newaxis]) # Shape (N_stars, N_times) + y = self.model_fit(dt, y0[:, np.newaxis], vy0[:, np.newaxis], ay[:, np.newaxis]) # Shape (N_stars, N_times) + + if N_stars == 1 or N_times == 1: + # If only one star, return flattened arrays + x = x.flatten() + y = y.flatten() + + if fit_param_errs is None: + return x, y + + fit_param_errs = np.atleast_2d(fit_param_errs) # (N_stars, N_params) + x0_err, vx0_err, ax_err, y0_err, vy0_err, ay_err = fit_param_errs.T + x_err = np.sqrt(x0_err[:, np.newaxis]**2 + (vx0_err[:, np.newaxis] * dt)**2 + (0.5 * ax_err[:, np.newaxis] * dt**2)**2) # Shape (N_stars, N_times) + y_err = np.sqrt(y0_err[:, np.newaxis]**2 + (vy0_err[:, np.newaxis] * dt)**2 + (0.5 * ay_err[:, np.newaxis] * dt**2)**2) # Shape (N_stars, N_times) + + if N_stars == 1 or N_times == 1: + # If only one star, return flattened arrays + x_err = x_err.flatten() + y_err = y_err.flatten() + return x, y, x_err, y_err + def run_fit( self, t, x, y, xe, ye, t0, weighting='var', @@ -509,11 +648,8 @@ def run_fit( t_span = t[idx_last] - t[idx_first] params_guess = [x.mean(), (x[idx_last] - x[idx_first]) / t_span, 0., y.mean(), (y[idx_last] - y[idx_first]) / t_span, 0.] - def accel(t, c0, c1, c2): - return c0 + c1*t + 0.5*c2*t**2 - - x_opt, x_cov = curve_fit(accel, dt, x, p0=np.array(params_guess[:3]), sigma=1/x_wt**0.5, absolute_sigma=absolute_sigma) - y_opt, y_cov = curve_fit(accel, dt, y, p0=np.array(params_guess[3:]), sigma=1/y_wt**0.5, absolute_sigma=absolute_sigma) + x_opt, x_cov = curve_fit(self.model_fit, dt, x, p0=np.array(params_guess[:3]), sigma=1/x_wt**0.5, absolute_sigma=absolute_sigma) + y_opt, y_cov = curve_fit(self.model_fit, dt, y, p0=np.array(params_guess[3:]), sigma=1/y_wt**0.5, absolute_sigma=absolute_sigma) x0, vx0, ax = x_opt y0, vy0, ay = y_opt x0e, vx0e, axe = np.sqrt(x_cov.diagonal()) @@ -521,7 +657,7 @@ def accel(t, c0, c1, c2): params = np.array([x0, vx0, ax, y0, vy0, ay]) param_errors = np.array([x0e, vx0e, axe, y0e, vy0e, aye]) - chi2_x, chi2_y = self.get_chi2(params, [t0], t, x, y, xe, ye) + chi2_x, chi2_y = self.calc_chi2(params, [t0], t, x, y, xe, ye) return params, param_errors, chi2_x, chi2_y @@ -534,81 +670,135 @@ class Parallax(MotionModel): Optional PA is counterclockwise offset of the image y-axis from North. Optional obs parameter describes observer location, default is 'earth'. """ - fitter_param_names = ['x0', 'vx', 'y0', 'vy', 'pi'] + fit_param_names = ['x0', 'vx', 'y0', 'vy', 'pi'] fixed_param_names = ['t0'] fixed_meta_data = ['RA','Dec','PA','obs'] name = "Parallax" # Number of fit parameters/required observations in each direction - n_params = int(np.ceil(len(fitter_param_names) / 2)) - - def __init__(self, RA, Dec, PA=0.0, obs='earth', **kwargs): - self.RA = RA - self.Dec = Dec - self.PA = PA + n_params = int(np.ceil(len(fit_param_names) / 2)) + + def __init__(self, ra, dec, pa=0., obs='earth'): + super().__init__() + self.ra = ra + self.dec = dec + self.pa = pa self.obs = obs self.plx_vector_cached = None return - - def get_parallax_vector(self, t_mjd): - recalc_plx = True + + def calc_parallax_vector(self, t_mjd): + """Calculate parallax vector of shape (2, N_times) + + Parameters + ---------- + t_mjd : array-like + Time array in mjd + + Returns + ------- + pvec + Parallax vector of shape (2, N_times) + """ if self.plx_vector_cached is not None: - if hasattr(t_mjd, "__len__"): - if list(t_mjd) == list(self.plx_vector_cached[0]): - pvec = self.plx_vector_cached[1:] - recalc_plx = False - elif all([t_mjd_i in self.plx_vector_cached[0] for t_mjd_i in t_mjd]): - pvec_idxs = [np.argwhere(self.plx_vector_cached[0]==t_mjd_i)[0][0] for t_mjd_i in t_mjd] - pvec = [self.plx_vector_cached[1][pvec_idxs], self.plx_vector_cached[2][pvec_idxs]] - recalc_plx = False - elif t_mjd in self.plx_vector_cached[0]: - idx = np.where(t_mjd==self.plx_vector_cached[0])[0][0] - pvec = np.array([self.plx_vector_cached[1][idx], self.plx_vector_cached[2][idx]]) - recalc_plx = False - if recalc_plx: - pvec = parallax.parallax_in_direction(self.RA, self.Dec, t_mjd, obsLocation=self.obs, PA=self.PA).T - if hasattr(t_mjd, "__len__"): - self.plx_vector_cached = [t_mjd, pvec[0], pvec[1]] + t_mjd = np.atleast_1d(t_mjd) + t_mjd_cached = self.plx_vector_cached[0] + if np.allclose(t_mjd, t_mjd_cached): + # If cached values match input times, return cached values + return self.plx_vector_cached[1] + + elif all(np.isin(t_mjd, t_mjd_cached)): + # If all input times are in cached values, return those + # Calculate pvec_idxs such that t_mjd_cached[ pvec_idxs ] == t_mjd + pvec_idxs = np.array([np.where(t_mjd_cached==t_mjd_i)[0][0] for t_mjd_i in t_mjd]) + pvec = self.plx_vector_cached[1][:, pvec_idxs] + return pvec + + pvec = parallax.parallax_in_direction(self.ra, self.dec, t_mjd, obsLocation=self.obs, PA=self.pa).T + self.plx_vector_cached = [t_mjd, pvec] return pvec + + def model_fit(self, dt, x0, vx, y0, vy, pi): + """Model positions at time t of Parallax model. + + Parameters + ---------- + dt : float or array-like + Time(s) at which to evaluate the model + x0 : float or array-like + Initial position(s) + vx : float or array-like + Velocity(ies) + y0 : float or array-like + Initial position(s) + vy : float or array-like + Velocity(ies) + pi : float or array-like + Parallax factor(s) + + Returns + ------- + 2d array + Model positions at time t of Parallax model, shape (2, N_times) + """ + x_res = x0 + vx*dt + pi * self.pvec[0] + y_res = y0 + vy*dt + pi * self.pvec[1] + return np.vstack([x_res, y_res]) + + + def model(self, t, fit_params, fixed_params, fit_param_errs=None): + """Model positions (and uncertainties, if fit_param_errs is provided) at time t of Parallax model. + + Parameters + ---------- + t : float or array-like + Times at which to evaluate the model + fit_params : array-like + Fit parameters for the model + fixed_params : array-like + Fixed parameters for the model + fit_param_errs : array-like, optional + Uncertainties in fit parameters, by default None + + Returns + ------- + x, y (, xe, ye) + Predicted positions (and uncertainties, if fit_param_errs is provided) with shape (N_stars, N_times), or (N_times,) if N_stars=1, or (N_stars,) if N_times=1 + """ + t = np.atleast_1d(t) + fit_params = np.atleast_2d(fit_params) # (N_stars, N_params) - def get_pos_at_time(self, fit_params, fixed_params, t): - fit_params_dict = dict(zip(self.fitter_param_names, fit_params)) - fixed_params_dict = dict(zip(self.fixed_param_names, fixed_params)) - dt = t - fixed_params_dict['t0'] + N_stars = fit_params.shape[0] if fit_params.ndim > 1 else 1 + N_times = len(t) + + x0, vx, y0, vy, pi = fit_params.T # Each shape (N_stars,) + t0 = np.atleast_1d(fixed_params[0]) # Shape (N_stars,) or (1,) + + dt = t[np.newaxis, :] - t0[:, np.newaxis] # Shape (N_stars, N_times) + t_mjd = Time(t, format='decimalyear', scale='utc').mjd # Shape (N_times,) + self.pvec = self.calc_parallax_vector(t_mjd) # Shape (2, N_times) + x, y = self.model_fit(dt, x0[:, np.newaxis], vx[:, np.newaxis], y0[:, np.newaxis], vy[:, np.newaxis], pi[:, np.newaxis]) # Shape (N_stars, N_times) + + if N_stars == 1 or N_times == 1: + # If only one star, return flattened arrays + x = x.flatten() + y = y.flatten() + + if fit_param_errs is None: + return x, y + + fit_param_errs = np.atleast_2d(fit_param_errs) # (N_stars, N_params) + x0_err, vx_err, y0_err, vy_err, pi_err = fit_param_errs.T + x_err = np.sqrt(x0_err[:, np.newaxis]**2 + (vx_err[:, np.newaxis] * dt)**2 + (pi_err[:, np.newaxis] * self.pvec[0][np.newaxis, :])**2) # Shape (N_stars, N_times) + y_err = np.sqrt(y0_err[:, np.newaxis]**2 + (vy_err[:, np.newaxis] * dt)**2 + (pi_err[:, np.newaxis] * self.pvec[1][np.newaxis, :])**2) # Shape (N_stars, N_times) - t_mjd = Time(t, format='decimalyear', scale='utc').mjd - pvec = self.get_parallax_vector(t_mjd) - pvec_x = np.reshape(pvec[0], t.shape) - pvec_y = np.reshape(pvec[1], t.shape) - x = fit_params_dict['x0'] + fit_params_dict['vx']*dt + fit_params_dict['pi']*pvec_x - y = fit_params_dict['y0'] + fit_params_dict['vy']*dt + fit_params_dict['pi']*pvec_y - return x, y - - def get_batch_pos_at_time(self, t, - x0=[],vx=[], y0=[],vy=[], pi=[], t0=[], - x0_err=[],vx_err=[], y0_err=[],vy_err=[], pi_err=[], **kwargs): - t_mjd = Time(t, format='decimalyear', scale='utc').mjd - pvec = self.get_parallax_vector(t_mjd) - if hasattr(t, "__len__"): - dt = t-t0[:,np.newaxis] - x = x0[:,np.newaxis] + dt*vx[:,np.newaxis] + pi[:,np.newaxis]*pvec[0].T - y = y0[:,np.newaxis] + dt*vy[:,np.newaxis] + pi[:,np.newaxis]*pvec[1].T - try: - x_err = np.sqrt(x0_err[:,np.newaxis]**2 + (vx_err[:,np.newaxis]*dt)**2 + (pi_err[:,np.newaxis]*pvec[0].T)**2) - y_err = np.sqrt(y0_err[:,np.newaxis]**2 + (vy_err[:,np.newaxis]*dt)**2 + (pi_err[:,np.newaxis]*pvec[1].T)**2) - except: - x_err,y_err = [],[] - else: - dt = t-t0 - x = x0 + dt*vx + pi*pvec[0] - y = y0 + dt*vy + pi*pvec[1] - try: - x_err = np.sqrt(x0_err**2 + (vx_err*dt)**2 + (pi_err*pvec[0])**2) - y_err = np.sqrt(y0_err**2 + (vy_err*dt)**2 + (pi_err*pvec[1])**2) - except: - x_err,y_err = [],[] + if N_stars == 1 or N_times == 1: + # If only one star, return flattened arrays + x_err = x_err.flatten() + y_err = y_err.flatten() return x, y, x_err, y_err + def run_fit( self, t, x, y, xe, ye, t0, weighting='var', @@ -622,6 +812,7 @@ def run_fit( if verbose: warnings.warn("Parallax model has no non-scipy fitter option. Running with scipy.", UserWarning) + t = np.atleast_1d(t) n_obs = len(t) degree_of_freedom = n_obs - self.n_params # Not enough data points to fit model @@ -637,12 +828,9 @@ def run_fit( # degree_of_freedom >= 0 t_mjd = Time(t, format='decimalyear', scale='utc').mjd - pvec = self.get_parallax_vector(t_mjd) + self.pvec = self.get_parallax_vector(t_mjd) x_wt, y_wt = self.get_weights(xe,ye, weighting=weighting) - def fit_func(use_t, x0,vx, y0,vy, pi): - x_res = x0 + vx*(use_t-t0) + pi*pvec[0] - y_res = y0 + vy*(use_t-t0) + pi*pvec[1] - return np.hstack([x_res, y_res]) + # Initial guesses, x0,y0 as x,y averages; # vx,vy as average velocity if first and last points are perfectly measured; # pi for 10 pc distance @@ -655,7 +843,7 @@ def fit_func(use_t, x0,vx, y0,vy, pi): 0.1 ] res = curve_fit( - fit_func, t, np.hstack([x,y]), + self.model_fit, t - t0, np.vstack([x, y]), p0=params_guess, sigma = 1.0/np.hstack([x_wt,y_wt]), absolute_sigma=absolute_sigma ) @@ -664,7 +852,7 @@ def fit_func(use_t, x0,vx, y0,vy, pi): params = np.array([x0, vx, y0, vy, pi]) param_errors = np.array([x0_err, vx_err, y0_err, vy_err, pi_err]) - chi2_x, chi2_y = self.get_chi2(params, [t0], t, x, y, xe, ye) + chi2_x, chi2_y = self.calc_chi2(params, [t0], t, x, y, xe, ye) return params, param_errors, chi2_x, chi2_y @@ -733,7 +921,7 @@ def list_add(name): if name not in list_of_parameters: list_of_parameters.append(name) - for param in motion_model.fitter_param_names: + for param in motion_model.fit_param_names: # Fitter params list_add(param) # Error params @@ -770,7 +958,7 @@ def list_add(name): list_of_parameters.append(name) for mm in motion_model_list: - for param in mm.fitter_param_names: + for param in mm.fit_param_names: # Fitter params list_add(param) # Error params diff --git a/flystar/parallax.py b/flystar/parallax.py index b4b3a1b..3f7602b 100755 --- a/flystar/parallax.py +++ b/flystar/parallax.py @@ -24,20 +24,32 @@ cache_memory.reduce_size() @cache_memory.cache() -def parallax_in_direction(RA, Dec, mjd, obsLocation='earth', PA=0): +def parallax_in_direction(ra, dec, mjd, obsLocation='earth', PA=0.): """ - | R.A. in degrees. (J2000) - | Dec. in degrees. (J2000) - | MJD - | PA in degrees. (counterclockwise offset of the image y-axis from North) - - Equations following MulensModel. + Calculate the parallax vector in a given direction following MulensModel. + + Parameters + ---------- + RA : float + Right Ascension in degrees. (J2000) + Dec : float + Declination in degrees. (J2000) + mjd : float or array-like + Modified Julian Date. + obsLocation : str, optional + Observer location, by default 'earth'. + PA : float, optional + Position angle in degrees (counterclockwise offset of the image y-axis from North), by default 0. + + Returns + ------- + pvec : ndarray + Parallax vector components, shape of (N, 2), where N is the number of mjd entries. """ - #print('parallax_in_direction: len(t) = ', len(mjd)) - # Munge inputs into astropy format. - times = Time(mjd + 2400000.5, format='jd', scale='tdb') - coord = SkyCoord(RA, Dec, unit=(units.deg, units.deg)) + # times = Time(mjd + 2400000.5, format='jd', scale='tdb') + times = Time(mjd, format='mjd', scale='tdb') # convert to TDB + coord = SkyCoord(ra, dec, unit=(units.deg, units.deg)) direction = coord.cartesian.xyz.value north = np.array([0., 0., 1.]) @@ -58,13 +70,12 @@ def parallax_in_direction(RA, Dec, mjd, obsLocation='earth', PA=0): PA_rad = np.pi/180.0 * PA x = -e.value*np.cos(PA_rad) + n.value*np.sin(PA_rad) y = e.value*np.sin(PA_rad) + n.value*np.cos(PA_rad) - pvec = np.array([x, y]).T - + return pvec -def dparallax_dt_in_direction(RA, Dec, mjd, obsLocation='earth'): +def dparallax_dt_in_direction(ra, dec, mjd, obsLocation='earth'): """ R.A. in degrees. (J2000) Dec. in degrees. (J2000) @@ -76,8 +87,8 @@ def dparallax_dt_in_direction(RA, Dec, mjd, obsLocation='earth'): """ # print('parallax_in_direction: len(t) = ', len(mjd)) # Munge inputs into astropy format. - times = Time(mjd + 2400000.5, format='jd', scale='tdb') - coord = SkyCoord(RA, Dec, unit=(units.deg, units.deg)) + times = Time(mjd, format='mjd', scale='tdb') + coord = SkyCoord(ra, dec, unit=(units.deg, units.deg)) direction = coord.cartesian.xyz.value north = np.array([0., 0., 1.]) diff --git a/flystar/startables.py b/flystar/startables.py index 08461a1..661382f 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -730,19 +730,19 @@ def fit_motion_model( unique_motion_models, unique_inv_indices = np.unique(self['motion_model_used'], return_inverse=True) indices_by_motion_model = {key: np.flatnonzero(unique_inv_indices == k) for k, key in enumerate(unique_motion_models)} - start_time = time.time() for unique_motion_model, unique_index in indices_by_motion_model.items(): # Create motion model instance motion_model_instance = input_mm_map[unique_motion_model] # Initialize arrays to store results n_stars_this_model = len(unique_index) - n_params = len(motion_model_instance.fitter_param_names) + n_params = len(motion_model_instance.fit_param_names) params_array = np.full((n_stars_this_model, n_params), fill_value, dtype=float) param_errs_array = np.full((n_stars_this_model, n_params), np.inf, dtype=float) chi2_x_array = np.full(n_stars_this_model, np.nan, dtype=float) chi2_y_array = np.full(n_stars_this_model, np.nan, dtype=float) + # Expensive for loop! Prepare everything beforehand to speed up. for idx, i_star in enumerate(tqdm(unique_index, disable=not show_progress, desc=f"Fitting motion model {unique_motion_model}")): # Fit the star params, param_errs, chi2_x, chi2_y = motion_model_instance.fit( @@ -759,14 +759,13 @@ def fit_motion_model( fill_value=fill_value, verbose=verbose ) - # print(f'{params_array.shape=}, {idx=}, {params=}') params_array[idx] = params param_errs_array[idx] = param_errs chi2_x_array[idx] = chi2_x chi2_y_array[idx] = chi2_y # Store results back to the table - param_names = motion_model_instance.fitter_param_names + param_names = motion_model_instance.fit_param_names for j, param_name in enumerate(param_names): self[param_name][unique_index] = params_array[:, j] self[param_name + '_err'][unique_index] = param_errs_array[:, j] @@ -1077,7 +1076,7 @@ def fit_velocity_for_star(self, ss, motion_model_dict, weighting='var', use_scip # # # Load up any prior information on parameters for this model. # param_dict = {} -# for par in modClass.fitter_param_names+modClass.fixed_param_names: +# for par in modClass.fit_param_names+modClass.fixed_param_names: # if ~np.isnan(self[par][ss]): # param_dict[par] = self[par][ss] @@ -1094,8 +1093,8 @@ def fit_velocity_for_star(self, ss, motion_model_dict, weighting='var', use_scip self['n_params'][ss] = mod.n_params # Save parameters and errors to table. - for pp in range(len(mod.fitter_param_names)): - par = mod.fitter_param_names[pp] + for pp in range(len(mod.fit_param_names)): + par = mod.fit_param_names[pp] par_err = par + '_err' self[par][ss] = params[pp] self[par_err][ss] = param_errs[pp] @@ -1131,7 +1130,7 @@ def get_star_positions_at_time(self, t, motion_model_dict, allow_alt_models=True mod = motion_model_dict[mm] # Set up parameters param_dict = {} - for par in mod.fitter_param_names + mod.fixed_param_names + [pm+'_err' for pm in mod.fitter_param_names]: + for par in mod.fit_param_names + mod.fixed_param_names + [pm+'_err' for pm in mod.fit_param_names]: param_dict[par] = self[par][idx] x[idx],y[idx],xe[idx],ye[idx] = mod.get_batch_pos_at_time(t,**param_dict) except: @@ -1211,7 +1210,7 @@ def fit_velocities_all_detected(self, motion_model_to_fit, weighting='var', use_ detected_in_all_epochs = np.logical_and(valid_xe, valid_ye) N = len(self['x'][select_stars, :]) - fit_params = motion_model_to_fit.fitter_param_names + fit_params = motion_model_to_fit.fit_param_names param_data = {p: np.zeros(N) for p in fit_params} param_data.update({p+'_err': np.zeros(N) for p in fit_params}) param_data.update({p: np.zeros(N) for p in motion_model_to_fit.fixed_param_names}) diff --git a/flystar/tests/test_align.py b/flystar/tests/test_align.py index 9b65eb6..195a67b 100644 --- a/flystar/tests/test_align.py +++ b/flystar/tests/test_align.py @@ -990,7 +990,7 @@ def make_fake_starlists_poly1_par(seed=-1): for ss in range(len(times)): dt = times[ss] - lis['t0'] - par_mod = motion_model.Parallax(PA=0,RA=18.0, Dec=-30.0) + par_mod = motion_model.Parallax(pa=0,ra=18.0, dec=-30.0) par_mod_dat = par_mod.get_batch_pos_at_time(dt+lis['t0'], x0=lis['x0'],vx=lis['vx']/1e3, pi=lis['pi'], y0=lis['y0'], vy=lis['vy']/1e3, t0=lis['t0']) x,y = par_mod_dat[0], par_mod_dat[1] diff --git a/flystar/tests/test_motion_model.py b/flystar/tests/test_motion_model.py index 2fa1b57..ccbba68 100755 --- a/flystar/tests/test_motion_model.py +++ b/flystar/tests/test_motion_model.py @@ -1,27 +1,35 @@ from flystar import motion_model import numpy as np -import pytest import matplotlib.pyplot as plt from scipy.optimize import curve_fit def within_error(true_val, fit_val, fit_err, n_sigma=3): #print('True', true_val, 'Fit', fit_val, 'Fit err', fit_err) - return (true_val < (fit_val+fit_err*n_sigma)) & (true_val> (fit_val-fit_err*n_sigma)) + # return (true_val < (fit_val + fit_err*n_sigma)) & (true_val > (fit_val - fit_err*n_sigma)) + return np.abs(true_val - fit_val) <= n_sigma*fit_err def test_Fixed(): # Test handling of a single star true_params = {'x0': 1.0, 'y0':0.5, 'x0_err':0.1, 'y0_err':0.1} mod = motion_model.Fixed() - param_list = mod.fitter_param_names + param_list = mod.fit_param_names fixed_param_list = mod.fixed_param_names # Confirm return of proper values for single t and array t - x_t, y_t = mod.get_pos_at_time([true_params[p] for p in param_list], - [true_params[p] for p in fixed_param_list], 0.0) + # x_t, y_t = mod.get_pos_at_time([true_params[p] for p in param_list], + # [true_params[p] for p in fixed_param_list], 0.0) + x_t, y_t = mod.model( + 0.0, + fit_params=np.array([true_params['x0'], true_params['y0']]).T + ) assert x_t==true_params['x0'] assert y_t==true_params['y0'] - x_t, y_t = mod.get_pos_at_time([true_params[p] for p in param_list], - [true_params[p] for p in fixed_param_list], - [0.0,2025.0,10000]) + # x_t, y_t = mod.get_pos_at_time([true_params[p] for p in param_list], + # [true_params[p] for p in fixed_param_list], + # [0.0,2025.0,10000]) + x_t, y_t = mod.model( + [0.0,2025.0,10000], + fit_params=np.array([true_params['x0'], true_params['y0']]).T + ) assert (x_t==true_params['x0']).all() assert (y_t==true_params['y0']).all() @@ -32,16 +40,26 @@ def test_Fixed(): y0_err_batch = np.repeat(0.1, 50) # Single epoch t_batch=2020.0 - x_t_batch, y_t_batch, x_err_t_batch, y_err_t_batch = mod.get_batch_pos_at_time(t_batch, - x0=x0_batch, y0=y0_batch, x0_err=x0_err_batch, y0_err=y0_err_batch) + # x_t_batch, y_t_batch, x_err_t_batch, y_err_t_batch = mod.get_batch_pos_at_time(t_batch, + # x0=x0_batch, y0=y0_batch, x0_err=x0_err_batch, y0_err=y0_err_batch) + x_t_batch, y_t_batch, x_err_t_batch, y_err_t_batch = mod.model( + t_batch, + fit_params=np.array([x0_batch, y0_batch]).T, + fit_param_errs=np.array([x0_err_batch, y0_err_batch]).T + ) assert (x_t_batch==x0_batch).all() assert (y_t_batch==y0_batch).all() assert (x_err_t_batch==x0_err_batch).all() assert (y_err_t_batch==y0_err_batch).all() # Multiple times t_batch = np.arange(2015.0,2025.0, 0.5) - x_t_batch, y_t_batch, x_err_t_batch, y_err_t_batch = mod.get_batch_pos_at_time(t_batch, - x0=x0_batch, y0=y0_batch, x0_err=x0_err_batch, y0_err=y0_err_batch) + # x_t_batch, y_t_batch, x_err_t_batch, y_err_t_batch = mod.get_batch_pos_at_time(t_batch, + # x0=x0_batch, y0=y0_batch, x0_err=x0_err_batch, y0_err=y0_err_batch) + x_t_batch, y_t_batch, x_err_t_batch, y_err_t_batch = mod.model( + t_batch, + fit_params=np.array([x0_batch, y0_batch]).T, + fit_param_errs=np.array([x0_err_batch, y0_err_batch]).T + ) assert (x_t_batch==np.array([np.repeat(x0_batch_i, len(t_batch)) for x0_batch_i in x0_batch])).all() assert (y_t_batch==np.array([np.repeat(y0_batch_i, len(t_batch)) for y0_batch_i in y0_batch])).all() assert (x_err_t_batch==np.array([np.repeat(x0_err_batch_i, len(t_batch)) for x0_err_batch_i in x0_err_batch])).all() @@ -50,15 +68,22 @@ def test_Fixed(): # Test fitter t = np.arange(2015.0,2025.0, 0.5) # Get values from model and add scatter - x_true, y_true = mod.get_pos_at_time([true_params[p] for p in param_list], - [true_params[p] for p in fixed_param_list], t) + # x_true, y_true = mod.get_pos_at_time([true_params[p] for p in param_list], + # [true_params[p] for p in fixed_param_list], t) + x_true, y_true = mod.model( + t, + fit_params=np.array([true_params['x0'], true_params['y0']]) + ) x_sim = np.random.normal(x_true, true_params['x0_err']) y_sim = np.random.normal(y_true, true_params['y0_err']) # Run fit - params, param_errs = mod.fit_motion_model(t, x_sim,y_sim, - np.ones(len(t))*true_params['x0_err'], - np.ones(len(t))*true_params['y0_err'], - np.nan) + params, param_errs, _ , _ = mod.fit( + t, + x_sim,y_sim, + xe=np.ones(len(t))*true_params['x0_err'], + ye=np.ones(len(t))*true_params['y0_err'], + t0=np.nan + ) # Confirm true value is within error bar of fit value assert np.all([within_error(true_params[param_list[i]], params[i], param_errs[i]) for i in range(len(params))]) @@ -69,21 +94,25 @@ def test_Linear(): 'vx':0.2, 'vy':0.5, 'vx_err':0.05, 'vy_err':0.05, 't0':2025.0} mod = motion_model.Linear() - param_list = mod.fitter_param_names + param_list = mod.fit_param_names fixed_param_list = mod.fixed_param_names # Confirm return of proper values for single t=t0 and array t - x_t, y_t = mod.get_pos_at_time([true_params[p] for p in param_list], - [true_params[p] for p in fixed_param_list], - true_params['t0']) + x_t, y_t = mod.model( + t=true_params['t0'], + fit_params=np.array([true_params[p] for p in param_list]).T, + fixed_params=np.array([true_params[p] for p in fixed_param_list]).T + ) assert x_t==true_params['x0'] assert y_t==true_params['y0'] t_arr = np.array([2010.0,true_params['t0'],2030.0]) - x_t, y_t = mod.get_pos_at_time([true_params[p] for p in param_list], - [true_params[p] for p in fixed_param_list], - t_arr) + x_t, y_t = mod.model( + t=t_arr, + fit_params=np.array([true_params[p] for p in param_list]).T, + fixed_params=np.array([true_params[p] for p in fixed_param_list]).T + ) assert (x_t==(true_params['x0'] + (t_arr-true_params['t0'])*true_params['vx'])).all() assert (y_t==(true_params['y0'] + (t_arr-true_params['t0'])*true_params['vy'])).all() - + # Check behavior of get_batch_pos_at_time x0_batch = np.random.uniform(-2.0,2.0, 50) y0_batch = np.random.uniform(-2.0,2.0, 50) @@ -96,20 +125,24 @@ def test_Linear(): t0_batch = np.repeat(2025.0,50) # Single epoch t_batch=2020.0 - x_t_batch, y_t_batch, x_err_t_batch, y_err_t_batch = mod.get_batch_pos_at_time(t_batch, - x0=x0_batch, y0=y0_batch, x0_err=x0_err_batch, y0_err=y0_err_batch, - vx=vx_batch, vy=vy_batch, vx_err=vx_err_batch, vy_err=vy_err_batch, - t0=t0_batch) + x_t_batch, y_t_batch, x_err_t_batch, y_err_t_batch = mod.model( + t=t_batch, + fit_params=np.array([x0_batch, vx_batch, y0_batch, vy_batch]).T, + fit_param_errs=np.array([x0_err_batch, vx_err_batch, y0_err_batch, vy_err_batch]).T, + fixed_params=t0_batch + ) assert (x_t_batch==(x0_batch+(t_batch-t0_batch)*vx_batch)).all() assert (y_t_batch==(y0_batch+(t_batch-t0_batch)*vy_batch)).all() assert (x_err_t_batch==np.hypot(x0_err_batch, (t_batch-t0_batch)*vx_err_batch)).all() assert (y_err_t_batch==np.hypot(y0_err_batch, (t_batch-t0_batch)*vy_err_batch)).all() # Multiple times t_batch = np.arange(2015.0,2025.0, 0.5) - x_t_batch, y_t_batch, x_err_t_batch, y_err_t_batch = mod.get_batch_pos_at_time(t_batch, - x0=x0_batch, y0=y0_batch, x0_err=x0_err_batch, y0_err=y0_err_batch, - vx=vx_batch, vy=vy_batch, vx_err=vx_err_batch, vy_err=vy_err_batch, - t0=t0_batch) + x_t_batch, y_t_batch, x_err_t_batch, y_err_t_batch = mod.model( + t=t_batch, + fit_params=np.array([x0_batch, vx_batch, y0_batch, vy_batch]).T, + fit_param_errs=np.array([x0_err_batch, vx_err_batch, y0_err_batch, vy_err_batch]).T, + fixed_params=t0_batch + ) assert (x_t_batch==np.array([x0_batch[i] + (t_batch-t0_batch[i])*vx_batch[i] for i in range(len(x0_batch))])).all() assert (y_t_batch==np.array([y0_batch[i] + (t_batch-t0_batch[i])*vy_batch[i] for i in range(len(x0_batch))])).all() assert (x_err_t_batch==np.array([np.hypot(x0_err_batch[i], (t_batch-t0_batch[i])*vx_err_batch[i]) for i in range(len(x0_batch))])).all() @@ -118,13 +151,24 @@ def test_Linear(): # Test fitter t = np.arange(2015.0,2025.0, 0.5) # Get values from model and add scatter - x_true, y_true = mod.get_pos_at_time([true_params[p] for p in param_list], - [true_params[p] for p in fixed_param_list],t) + # x_true, y_true = mod.get_pos_at_time([true_params[p] for p in param_list], + # [true_params[p] for p in fixed_param_list],t) + x_true, y_true = mod.model( + t=t, + fit_params=np.array([true_params[p] for p in param_list]).T, + fixed_params=np.array([true_params[p] for p in fixed_param_list]).T + ) x_sim = np.random.normal(x_true, 0.05) y_sim = np.random.normal(y_true, 0.05) # Run fit - params, param_errs = mod.fit_motion_model(t, x_sim,y_sim, - np.repeat(0.05, len(t)), np.repeat(0.05,len(t)), true_params['t0']) + params, param_errs, _, _ = mod.fit( + t=t, + x=x_sim, + y=y_sim, + xe=np.repeat(0.05, len(t)), + ye=np.repeat(0.05,len(t)), + t0=true_params['t0'] + ) print(param_errs) # Confirm true value is within error bar of fit value assert np.all([within_error(true_params[param_list[i]], params[i], param_errs[i]) for i in range(len(params))]) @@ -138,11 +182,11 @@ def test_Linear(): x_sim = np.random.normal(x_true, x_true_err) y_sim = np.random.normal(y_true, y_true_err) # Run fit - params, param_errs = mod.fit_motion_model(t, x_sim,y_sim, x_true_err, y_true_err, true_params['t0'],bootstrap=10) + params, param_errs = mod.fit(t, x_sim,y_sim, x_true_err, y_true_err, true_params['t0'],bootstrap=10) print(param_errs) # Confirm true value is within error bar of fit value assert np.all([within_error(true_params[param_list[i]], params[i], param_errs[i]) for i in range(len(params))]) - + # # Test fitter for 2 pts # t = np.array([2015.0,2025.0]) # # Get values from model and add scatter @@ -169,20 +213,24 @@ def test_Acceleration(): 'ax':0.1, 'ay':-0.1, 'ax_err':0.02, 'ay_err':0.02, 't0':2025.0} mod = motion_model.Acceleration() - param_list = mod.fitter_param_names + param_list = mod.fit_param_names fixed_param_list = mod.fixed_param_names # Confirm return of proper values for single t=t0 and array t - x_t, y_t = mod.get_pos_at_time([true_params[p] for p in param_list], - [true_params[p] for p in fixed_param_list], - true_params['t0']) - assert x_t==true_params['x0'] - assert y_t==true_params['y0'] - t_arr = np.array([2010.0,true_params['t0'],2030.0]) - x_t, y_t = mod.get_pos_at_time([true_params[p] for p in param_list], - [true_params[p] for p in fixed_param_list], - t_arr) - assert (x_t==(true_params['x0'] + (t_arr-true_params['t0'])*true_params['vx0'] + 0.5*(t_arr-true_params['t0'])**2*true_params['ax'])).all() - assert (y_t==(true_params['y0'] + (t_arr-true_params['t0'])*true_params['vy0'] + 0.5*(t_arr-true_params['t0'])**2*true_params['ay'])).all() + x_t, y_t = mod.model( + t=true_params['t0'], + fit_params=np.array([true_params[p] for p in param_list]).T, + fixed_params=np.array([true_params[p] for p in fixed_param_list]).T + ) + np.testing.assert_allclose(x_t, true_params['x0']) + np.testing.assert_allclose(y_t, true_params['y0']) + t_arr = np.array([2010.0, true_params['t0'], 2030.0]) + x_t, y_t = mod.model( + t=t_arr, + fit_params=np.array([true_params[p] for p in param_list]).T, + fixed_params=np.array([true_params[p] for p in fixed_param_list]).T + ) + np.testing.assert_allclose(x_t, true_params['x0'] + (t_arr-true_params['t0'])*true_params['vx0'] + 0.5*(t_arr-true_params['t0'])**2*true_params['ax']) + np.testing.assert_allclose(y_t, true_params['y0'] + (t_arr-true_params['t0'])*true_params['vy0'] + 0.5*(t_arr-true_params['t0'])**2*true_params['ay']) # Check behavior of get_batch_pos_at_time x0_batch = np.random.uniform(-2.0,2.0, 50) @@ -200,43 +248,56 @@ def test_Acceleration(): t0_batch = np.repeat(2025.0,50) # Single epoch t_batch=2020.0 - x_t_batch, y_t_batch, x_err_t_batch, y_err_t_batch = mod.get_batch_pos_at_time(t_batch, - x0=x0_batch, y0=y0_batch, x0_err=x0_err_batch, y0_err=y0_err_batch, - vx0=vx0_batch, vy0=vy0_batch, vx0_err=vx0_err_batch, vy0_err=vy0_err_batch, - ax=ax_batch, ay=ay_batch, ax_err=ax_err_batch, ay_err=ay_err_batch, - t0=t0_batch) - assert (x_t_batch==(x0_batch + (t_batch-t0_batch)*vx0_batch + 0.5*(t_batch-t0_batch)**2*ax_batch)).all() - assert (y_t_batch==(y0_batch + (t_batch-t0_batch)*vy0_batch + 0.5*(t_batch-t0_batch)**2*ay_batch)).all() - assert (x_err_t_batch==np.sqrt(x0_err_batch**2 + ((t_batch-t0_batch)*vx0_err_batch)**2 + - (0.5*(t_batch-t0_batch)**2*ax_err_batch)**2)).all() - assert (y_err_t_batch==np.sqrt(y0_err_batch**2 + ((t_batch-t0_batch)*vy0_err_batch)**2 + - (0.5*(t_batch-t0_batch)**2*ay_err_batch)**2)).all() + x_t_batch, y_t_batch, x_err_t_batch, y_err_t_batch = mod.model( + t=t_batch, + fit_params=np.array([x0_batch, vx0_batch, ax_batch, y0_batch, vy0_batch, ay_batch]).T, + fit_param_errs=np.array([x0_err_batch, vx0_err_batch, ax_err_batch, y0_err_batch, vy0_err_batch, ay_err_batch]).T, + fixed_params=t0_batch + ) + np.testing.assert_allclose(x_t_batch, x0_batch + (t_batch-t0_batch)*vx0_batch + 0.5*(t_batch-t0_batch)**2*ax_batch) + np.testing.assert_allclose(y_t_batch, y0_batch + (t_batch-t0_batch)*vy0_batch + 0.5*(t_batch-t0_batch)**2*ay_batch) + np.testing.assert_allclose(x_err_t_batch, np.sqrt(x0_err_batch**2 + ((t_batch-t0_batch)*vx0_err_batch)**2 + + (0.5*(t_batch-t0_batch)**2*ax_err_batch)**2)) + np.testing.assert_allclose(y_err_t_batch, np.sqrt(y0_err_batch**2 + ((t_batch-t0_batch)*vy0_err_batch)**2 + + (0.5*(t_batch-t0_batch)**2*ay_err_batch)**2)) + # Multiple times t_batch = np.arange(2015.0,2025.0, 0.5) - x_t_batch, y_t_batch, x_err_t_batch, y_err_t_batch = mod.get_batch_pos_at_time(t_batch, - x0=x0_batch, y0=y0_batch, x0_err=x0_err_batch, y0_err=y0_err_batch, - vx0=vx0_batch, vy0=vy0_batch, vx0_err=vx0_err_batch, vy0_err=vy0_err_batch, - ax=ax_batch, ay=ay_batch, ax_err=ax_err_batch, ay_err=ay_err_batch, - t0=t0_batch) - assert (x_t_batch==np.array([x0_batch[i] + (t_batch-t0_batch[i])*vx0_batch[i] + 0.5*(t_batch-t0_batch[i])**2*ax_batch[i] for i in range(len(x0_batch))])).all() - assert (y_t_batch==np.array([y0_batch[i] + (t_batch-t0_batch[i])*vy0_batch[i] + 0.5*(t_batch-t0_batch[i])**2*ay_batch[i] for i in range(len(x0_batch))])).all() - assert (x_err_t_batch==np.array([np.sqrt(x0_err_batch[i]**2 + ((t_batch-t0_batch[i])*vx0_err_batch[i])**2 + (0.5*(t_batch-t0_batch[i])**2*ax_err_batch[i])**2) for i in range(len(x0_batch))])).all() - assert (y_err_t_batch==np.array([np.sqrt(y0_err_batch[i]**2 + ((t_batch-t0_batch[i])*vy0_err_batch[i])**2 + (0.5*(t_batch-t0_batch[i])**2*ay_err_batch[i])**2) for i in range(len(x0_batch))])).all() - + x_t_batch, y_t_batch, x_err_t_batch, y_err_t_batch = mod.model( + t=t_batch, + fit_params=np.array([x0_batch, vx0_batch, ax_batch, y0_batch, vy0_batch, ay_batch]).T, + fit_param_errs=np.array([x0_err_batch, vx0_err_batch, ax_err_batch, y0_err_batch, vy0_err_batch, ay_err_batch]).T, + fixed_params=t0_batch + ) + np.testing.assert_allclose(x_t_batch, np.array([x0_batch[i] + (t_batch-t0_batch[i])*vx0_batch[i] + 0.5*(t_batch-t0_batch[i])**2*ax_batch[i] for i in range(len(x0_batch))])) + np.testing.assert_allclose(y_t_batch, np.array([y0_batch[i] + (t_batch-t0_batch[i])*vy0_batch[i] + 0.5*(t_batch-t0_batch[i])**2*ay_batch[i] for i in range(len(x0_batch))])) + np.testing.assert_allclose(x_err_t_batch, np.array([np.sqrt(x0_err_batch[i]**2 + ((t_batch-t0_batch[i])*vx0_err_batch[i])**2 + (0.5*(t_batch-t0_batch[i])**2*ax_err_batch[i])**2) for i in range(len(x0_batch))])) + np.testing.assert_allclose(y_err_t_batch, np.array([np.sqrt(y0_err_batch[i]**2 + ((t_batch-t0_batch[i])*vy0_err_batch[i])**2 + (0.5*(t_batch-t0_batch[i])**2*ay_err_batch[i])**2) for i in range(len(x0_batch))])) + # Test fitter t = np.arange(2015.0,2025.0, 0.5) # Get values from model and add scatter - x_true, y_true = mod.get_pos_at_time([true_params[p] for p in param_list], - [true_params[p] for p in fixed_param_list],t) - x_true_err = np.sqrt(true_params['x0_err']**2 + ((t-true_params['t0'])*true_params['vx0_err'])**2 + - (0.5*(t-true_params['t0'])**2*true_params['ax_err'])**2) - y_true_err = np.sqrt(true_params['y0_err']**2 + ((t-true_params['t0'])*true_params['vy0_err'])**2 + - (0.5*(t-true_params['t0'])**2*true_params['ay_err'])**2) + x_true, y_true = mod.model( + t=t, + fit_params=np.array([true_params[p] for p in param_list]).T, + fixed_params=np.array([true_params[p] for p in fixed_param_list]).T + ) + x_true_err = np.sqrt(true_params['x0_err']**2 + ((t - true_params['t0']) * true_params['vx0_err'])**2 + + (0.5*(t - true_params['t0'])**2 * true_params['ax_err'])**2) + y_true_err = np.sqrt(true_params['y0_err']**2 + ((t - true_params['t0']) * true_params['vy0_err'])**2 + + (0.5*(t - true_params['t0'])**2 * true_params['ay_err'])**2) x_sim = np.random.normal(x_true, x_true_err) y_sim = np.random.normal(y_true, y_true_err) # Run fit - mod_fit = motion_model.Acceleration(t0=true_params['t0']) - params, param_errs = mod_fit.fit_motion_model(t, x_sim,y_sim, x_true_err, y_true_err, true_params['t0']) + mod_fit = motion_model.Acceleration() + params, param_errs, _, _ = mod_fit.fit( + t=t, + x=x_sim, + y=y_sim, + xe=x_true_err, + ye=y_true_err, + t0=true_params['t0'] + ) # Confirm true value is within error bar of fit value assert np.all([within_error(true_params[param_list[i]], params[i], param_errs[i]) for i in range(len(params))]) @@ -248,7 +309,7 @@ def test_Parallax(): 'pi':0.5, 'RA':17.76, 'Dec':-28.933, 'PA':0, 't0':2020.0} mod = motion_model.Parallax(**{'RA':17.76, 'Dec':-28.933, 'PA':0}) - param_list = mod.fitter_param_names + param_list = mod.fit_param_names fixed_param_list = mod.fixed_param_names print(param_list) @@ -261,7 +322,7 @@ def test_Parallax(): x_sim = np.random.normal(x_true, x_true_err) y_sim = np.random.normal(y_true, y_true_err) # Run fit - params, param_errs = mod.fit_motion_model(t, x_sim,y_sim, x_true_err, y_true_err, true_params['t0']) + params, param_errs = mod.fit(t, x_sim,y_sim, x_true_err, y_true_err, true_params['t0']) # Confirm true value is within error bar of fit value assert np.all([within_error(true_params[param_list[i]], params[i], param_errs[i]) for i in range(len(params))]) @@ -270,16 +331,25 @@ def test_Parallax_PA(): # Set PA=0 model x0, y0 = 2.0, -1.0 vx, vy = 0.2, 0.5 - RA, Dec = 17.76, -28.933 + ra, dec = 17.76, -28.933 pi = 0.5 - mod_pa0 = motion_model.Parallax(RA=RA,Dec=Dec, PA=0) + mod_pa0 = motion_model.Parallax(ra=ra, dec=dec, pa=0) # Set PA=90 model with equivalent parameters in that frame - mod_pa90 = motion_model.Parallax(RA=RA,Dec=Dec,t0=2020.0, PA=90) - t_set = np.arange(2018,2024,0.01) - dat_pa0 = mod_pa0.get_pos_at_time([x0,vx,y0,vy,pi],[2020.0],t_set) - dat_pa90 = mod_pa90.get_pos_at_time([y0,vy,-x0,-vx,pi],[2020.0],t_set) - assert (np.abs(dat_pa0[0]-(-dat_pa90[1]))<1e-10).all() - assert (np.abs(dat_pa0[1]-(dat_pa90[0]))<1e-10).all() + mod_pa90 = motion_model.Parallax(ra=ra, dec=dec, pa=90) + + t_set = np.arange(2018, 2024, 0.01) + dat_pa0 = mod_pa0.model( + t = t_set, + fit_params = np.array([x0, vx, y0, vy, pi]).T, + fixed_params = [2020.0] + ) + dat_pa90 = mod_pa90.model( + t = t_set, + fit_params = np.array([y0, vy, -x0, -vx, pi]).T, + fixed_params = [2020.0] + ) + np.testing.assert_allclose(dat_pa0[0], -dat_pa90[1], atol=1e-10) + np.testing.assert_allclose(dat_pa0[1], dat_pa90[0], atol=1e-10) def test_Linear_fit_vs_scipy(): @@ -431,38 +501,38 @@ def linear(t, c0, c1): y0_scipy[i], y0e_scipy[i] = popt_y[0], np.sqrt(pcov_y[0, 0]) # motion_model without scipy - params, param_errs = mm.fit_motion_model( + params, param_errs = mm.fit( t[~mask[i]], x[i][~mask[i]], y[i][~mask[i]], xe[i][~mask[i]], ye[i][~mask[i]], t0[i], weighting='var', use_scipy=False, absolute_sigma=absolute_sigma ) - vx_mm[i] = params[mm.fitter_param_names.index('vx')] - vy_mm[i] = params[mm.fitter_param_names.index('vy')] - vxe_mm[i] = param_errs[mm.fitter_param_names.index('vx')] - vye_mm[i] = param_errs[mm.fitter_param_names.index('vy')] - x0_mm[i] = params[mm.fitter_param_names.index('x0')] - y0_mm[i] = params[mm.fitter_param_names.index('y0')] - x0e_mm[i] = param_errs[mm.fitter_param_names.index('x0')] - y0e_mm[i] = param_errs[mm.fitter_param_names.index('y0')] + vx_mm[i] = params[mm.fit_param_names.index('vx')] + vy_mm[i] = params[mm.fit_param_names.index('vy')] + vxe_mm[i] = param_errs[mm.fit_param_names.index('vx')] + vye_mm[i] = param_errs[mm.fit_param_names.index('vy')] + x0_mm[i] = params[mm.fit_param_names.index('x0')] + y0_mm[i] = params[mm.fit_param_names.index('y0')] + x0e_mm[i] = param_errs[mm.fit_param_names.index('x0')] + y0e_mm[i] = param_errs[mm.fit_param_names.index('y0')] # motion_model with scipy - params, param_errs = mm.fit_motion_model( + params, param_errs = mm.fit( t[~mask[i]], x[i][~mask[i]], y[i][~mask[i]], xe[i][~mask[i]], ye[i][~mask[i]], t0[i], weighting='var', use_scipy=True, absolute_sigma=absolute_sigma ) - vx_mm_scipy[i] = params[mm.fitter_param_names.index('vx')] - vy_mm_scipy[i] = params[mm.fitter_param_names.index('vy')] - vxe_mm_scipy[i] = param_errs[mm.fitter_param_names.index('vx')] - vye_mm_scipy[i] = param_errs[mm.fitter_param_names.index('vy')] - x0_mm_scipy[i] = params[mm.fitter_param_names.index('x0')] - y0_mm_scipy[i] = params[mm.fitter_param_names.index('y0')] - x0e_mm_scipy[i] = param_errs[mm.fitter_param_names.index('x0')] - y0e_mm_scipy[i] = param_errs[mm.fitter_param_names.index('y0')] + vx_mm_scipy[i] = params[mm.fit_param_names.index('vx')] + vy_mm_scipy[i] = params[mm.fit_param_names.index('vy')] + vxe_mm_scipy[i] = param_errs[mm.fit_param_names.index('vx')] + vye_mm_scipy[i] = param_errs[mm.fit_param_names.index('vy')] + x0_mm_scipy[i] = params[mm.fit_param_names.index('x0')] + y0_mm_scipy[i] = params[mm.fit_param_names.index('y0')] + x0e_mm_scipy[i] = param_errs[mm.fit_param_names.index('x0')] + y0e_mm_scipy[i] = param_errs[mm.fit_param_names.index('y0')] rtol = 1e-5 # np.testing.assert_allclose(vx_velfit, vx_scipy, rtol=rtol) diff --git a/flystar/tests/test_startable.py b/flystar/tests/test_startable.py index cf3be71..1b8e5cb 100644 --- a/flystar/tests/test_startable.py +++ b/flystar/tests/test_startable.py @@ -305,7 +305,7 @@ def test_fit_velocities(): tab = table.vstack((tab1, tab2, tab3)) tab.meta = tab1.meta - tab.fit_velocities(verbose=True) + tab.fit_motion_model(verbose=True) # Test creation of new variables assert len(tab['vx']) == len(tab) @@ -313,7 +313,7 @@ def test_fit_velocities(): assert len(tab['vx_err']) == len(tab) assert len(tab['vy_err']) == len(tab) assert len(tab['n_fit']) == len(tab) - assert tab.meta['n_fit_bootstrap'] == 0 + assert tab.meta['n_bootstrap'] == 0 # Test no-fit for stars with N<2 epochs. n_epochs = (tab['x'] >= 0).sum(axis=1) @@ -365,7 +365,7 @@ def test_fit_velocities(): tab_b.meta = tab1.meta tab_b.fit_velocities(verbose=True, bootstrap=50) - assert tab_b.meta['n_fit_bootstrap'] == 50 + assert tab_b.meta['n_bootstrap'] == 50 assert tab_b['x0_err'][0] > tab['x0_err'][0] assert tab_b['vx_err'][0] > tab['vx_err'][0] assert tab_b['y0_err'][0] > tab['y0_err'][0] @@ -559,10 +559,10 @@ def make_star_table(): x=x_in, y=y_in, m=m_in, xe=xe_in, ye=ye_in, me=me_in, n=n_in, - ref_list=1, - list_times=starlist_times, - list_names=starlist_names + ref_list=1 ) + startable.meta['LIST_TIMES'] = starlist_times + startable.meta['LIST_NAMES'] = starlist_names return startable From 69af17bda6054274d3813fb6906c36ad931c7901 Mon Sep 17 00:00:00 2001 From: skterry Date: Sat, 22 Nov 2025 01:08:47 -0500 Subject: [PATCH 130/254] update startable_subset() with bootstrap errors --- flystar/analysis.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/flystar/analysis.py b/flystar/analysis.py index 81ab3f4..3121458 100644 --- a/flystar/analysis.py +++ b/flystar/analysis.py @@ -438,7 +438,8 @@ def startable_subset(tab, idx, mag_trans=True, mag_trans_orig=False): combined astrometry + uncombined photometry table. """ # Multiples: ['x', 'y', 'm', 'name_in_list', 'xe', 'ye', 'me', 't', - # 'x_orig', 'y_orig', 'm_orig', 'xe_orig', 'ye_orig', 'me_orig', 'used_in_trans'] + # 'x_orig', 'y_orig', 'm_orig', 'xe_orig', 'ye_orig', 'me_orig', 'used_in_trans', + # 'xe_boot','ye_boot','me_boot'] # Single: ['name', 'm0', 'm0_err', 'use_in_trans', 'ref_orig', 'n_detect', # 'x0', 'vx', 'y0', 'vy', 'x0_err', 'vx_err', 'y0_err', 'vy_err', 't0'] # Don't include n_vfit @@ -446,8 +447,8 @@ def startable_subset(tab, idx, mag_trans=True, mag_trans_orig=False): new_tab = copy.deepcopy(tab) #new_tab.remove_column('n_fit') new_tab.remove_column('n_detect') - for col in ['x','y','m','xe','ye','me','t','x_orig','y_orig','m_orig', - 'xe_orig','ye_orig','me_orig','used_in_trans']: + for col in ['x','y','m','name_in_list','xe','ye','me','t','x_orig','y_orig','m_orig', + 'xe_orig','ye_orig','me_orig','used_in_trans','xe_boot','ye_boot','me_boot']: new_tab[col] = tab[col][:,idx] new_tab.combine_lists('m', weights_col='me', sigma=3, ismag=True) From e0e0c2c13573ba5d199aa3019a0df2d14fa67b4e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Tue, 25 Nov 2025 15:54:47 -0800 Subject: [PATCH 131/254] MotionModel acceleration with passed testings. Merged get_pos_at_time and get_batch_pos_at_time into model; Replaced MotionModel.fit_motion_model with MotionModel.fit; Rewrote and accelerated StarTable.fit_velocities with StarTable.fit_motion_model --- flystar/motion_model.py | 479 +++-- flystar/parallax.py | 41 +- flystar/startables.py | 119 +- flystar/tests/test_all_detected.fits | 2911 -------------------------- flystar/tests/test_motion_model.py | 411 +--- flystar/tests/test_startable.py | 182 +- 6 files changed, 595 insertions(+), 3548 deletions(-) delete mode 100644 flystar/tests/test_all_detected.fits diff --git a/flystar/motion_model.py b/flystar/motion_model.py index 5a542e0..5ccb6ee 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -1,6 +1,5 @@ import numpy as np from abc import ABC -import pdb from flystar import parallax from astropy.time import Time from scipy.optimize import curve_fit, OptimizeWarning @@ -36,17 +35,19 @@ def model(self, t, fit_params, fixed_params=None, fit_param_errs=None): return np.full_like(t, np.nan), np.full_like(t, np.nan), np.full_like(t, np.inf), np.full_like(t, np.inf) def run_fit( - self, t, x, y, xe, ye, t0, + self, t, x, y, xe, ye, + fixed_params_dict=None, weighting='var', use_scipy=True, absolute_sigma=True, + params_guess=None, fill_value=np.nan, verbose=True ): # Run a single fit (used both for overall fit + bootstrap iterations) return np.full(self.n_params, fill_value), np.full(self.n_params, np.inf), np.nan, np.nan - def get_weights(self, xe, ye, weighting='var'): + def calc_weights(self, xe, ye, weighting='var'): if weighting=='std': return 1./xe, 1./ye elif weighting=='var': @@ -56,12 +57,15 @@ def get_weights(self, xe, ye, weighting='var'): return 1./xe**2, 1./ye**2 def fit( - self, t, x, y, xe, ye, t0, - bootstrap=0, + self, t, x, y, xe, ye, + fixed_params_dict=None, weighting='var', use_scipy=True, absolute_sigma=True, fill_value=np.nan, + params_guess=None, + return_chi2=False, + bootstrap=0, verbose=True, seed=None ): @@ -79,10 +83,8 @@ def fit( Uncertainty of x ye : array-like Uncertainty of y - t0 : array-like - Reference time for fitting, i.e. dt = t - t0 will be used in fitting - bootstrap : int, optional - Bootstrapping uncertainties, by default 0 + fixed_params_dict : dict, optional + Dictionary of fixed parameters, see each motion model's fixed_param_names for details, by default None weighting : str, optional Use standard error weighting ('std': w=1/xe, 1/ye) or variance weighting ('var': w=1/xe**2, 1/ye**2), by default 'var' use_scipy : bool, optional @@ -91,6 +93,12 @@ def fit( Absolute sigma. See scipy.optimize.curve_fit for details, by default True fill_value : float, optional Fill value for parameters when not enough data points to fit model, by default np.nan + params_guess : array-like, optional + Initial guess for the fit parameters used in scipy curve_fit, by default None + return_chi2 : bool, optional + Return chi^2 values along with parameters and uncertainties in params, param_errs, chi2_x, chi2_y, by default False + bootstrap : int, optional + Bootstrapping uncertainties, by default 0 verbose : bool, optional Print warning messages, by default True seed : int, optional @@ -100,39 +108,66 @@ def fit( params, params_err, chi2_x, chi2_y Parameters, uncertainties, and chi squares. The corresponding parameter names are in self.fit_param_names. """ - params, param_errs, chi2_x, chi2_y = self.run_fit( - t, x, y, xe, ye, t0=t0, + fit_result = self.run_fit( + t, x, y, xe, ye, + fixed_params_dict=fixed_params_dict, weighting=weighting, use_scipy=use_scipy, absolute_sigma=absolute_sigma, fill_value=fill_value, + params_guess=params_guess, + return_chi2=return_chi2, verbose=verbose ) - if seed is not None: - rng = np.random.default_rng(seed) + + if return_chi2: + params, param_errs, chi2_x, chi2_y = fit_result else: - rng = np.random.default_rng() + params, param_errs = fit_result + # Bootstrap errors n_obs = len(t) + if bootstrap > 0 and n_obs > (self.n_params): + # Use m out of n bootstrap to ensure enough unique points + m = np.max([self.n_params, int(len(t) * 0.8)]) + rng = np.random.default_rng(seed) edx = np.arange(n_obs, dtype=int) + # Precompute All Bootstrap Draws at Once + bdx_all = rng.choice(edx, size=(bootstrap, m), replace=True) + + # Count unique indices per bootstrap sample + uniq_counts = np.apply_along_axis(lambda x: len(np.unique(x)), 1, bdx_all) + + # Identify invalid samples + bad = uniq_counts < self.n_params + n_bad = bad.sum() + + while n_bad > 0: + # Resample only bad rows + bdx_all[bad] = rng.choice(edx, size=(n_bad, m), replace=True) + uniq_counts = np.apply_along_axis(lambda x: len(np.unique(x)), 1, bdx_all) + bad = uniq_counts < self.n_params + n_bad = bad.sum() + bb_params = [] bb_params_errs = [] - for bb in range(bootstrap): - bdx = rng.choice(edx, n_obs, replace=False) - params_bdx, param_errs_bdx, chi2x_bdx, chi2y_bdx = self.run_fit( - t[bdx], x[bdx], y[bdx], xe[bdx], ye[bdx], t0=t0, + for bdx in bdx_all: + params_bdx, param_errs_bdx = self.run_fit( + t[bdx], x[bdx], y[bdx], xe[bdx], ye[bdx], + fixed_params_dict=fixed_params_dict, weighting=weighting, use_scipy=use_scipy, absolute_sigma=absolute_sigma, params_guess=params, fill_value=fill_value, + return_chi2=False, verbose=verbose ) bb_params.append(params_bdx) bb_params_errs.append(param_errs_bdx) - + # Save the errors from the bootstrap param_errs = np.std(bb_params, axis=0) @@ -140,14 +175,18 @@ def fit( inf_errs = [np.all(arr==np.inf) for arr in np.transpose(np.array(bb_params_errs))] param_errs[inf_errs] = 0.0 - return params, param_errs, chi2_x, chi2_y + if return_chi2: + return params, param_errs, chi2_x, chi2_y + else: + return params, param_errs + - def calc_chi2(self, fit_params, fixed_params, t, x, y, xe, ye, reduced=False): + def calc_chi2(self, t, x, y, xe, ye, fit_params, fixed_params_dict=None, reduced=False): """ Get the chi^2 value for the current MM and the input data. """ - x_pred, y_pred = self.model(t, fit_params, fixed_params) + x_pred, y_pred = self.model(t, fit_params, fixed_params_dict) chi2x = np.sum((x - x_pred)**2 / xe**2) chi2y = np.sum((y - y_pred)**2 / ye**2) if reduced: @@ -174,23 +213,86 @@ def __init__(self, **kwargs): def model_fit(self, dt): return np.full_like(dt, np.nan) - def model(self, t, fit_params, fixed_params, fixed_param_errs=None): + def model(self, t, fit_params, fixed_params_dict, fixed_param_errs=None): + """Predicted positions (and uncertainties, if fit_param_errs is provided) at time t of Empty model. + + Parameters + ---------- + t : float or array-like + Time array, shape (N_times,) + fit_params : array-like + Fit parameters, shape (N_params,) or (N_stars, N_params) + fixed_params_dict : dict + Dictionary of fixed parameters, not applicable for Empty model + fixed_param_errs : array-like, optional + Uncertainties for fixed parameters, not applicable for Empty model, by default None + + Returns + ------- + x, y (, xe, ye) + Predicted position (and uncertainties) of Empty model, shape (N_times,) + """ + t = np.atleast_1d(t) + if fixed_param_errs is None: + return np.full_like(t, np.nan), np.full_like(t, np.nan) return np.full_like(t, np.nan), np.full_like(t, np.nan), np.full_like(t, np.inf), np.full_like(t, np.inf) def run_fit( - self, t, x, y, xe, ye, t0, + self, t, x, y, xe, ye, + fixed_params_dict=None, weighting='var', use_scipy=True, absolute_sigma=True, fill_value=np.nan, + params_guess=None, + return_chi2=False, verbose=True ): + """Fit stellar motion parameters + + Parameters + ---------- + t : float or array-like + Time array, shape (N_times,) + x : array-like + Observed x positions, shape (N_times,) + y : array-like + Observed y positions, shape (N_times,) + xe : array-like + Observed uncertainties in x positions, shape (N_times,) + ye : array-like + Observed uncertainties in y positions, shape (N_times,) + fixed_params_dict : dict, optional + Dictionary of fixed parameters, not applicable for Empty model, by default None + weighting : str, optional + Weighting scheme to use, 'var' or 'std', by default 'var' + use_scipy : bool, optional + Whether to use scipy.optimize for fitting, by default True + absolute_sigma : bool, optional + Whether to treat sigma as absolute, by default True + fill_value : float, optional + Value to fill parameters with when fitting is not possible, by default np.nan + params_guess : array-like, optional + Initial guess for parameters, by default None + return_chi2 : bool, optional + Whether to return chi-squared value, by default False + verbose : bool, optional + Whether to print verbose output, by default True + + Returns + ------- + params, param_errors (, chi2_x, chi2_y) + Fitted parameters, their uncertainties, and optionally chi-squared values + """ if verbose: warnings.warn(f"Empty data cannot be fit. Setting parameters to {fill_value} and uncertainties to np.inf.", OptimizeWarning, stacklevel=2) params = np.full(self.n_params, fill_value) param_errors = np.full(self.n_params, np.inf) - return params, param_errors, np.nan, np.nan + if return_chi2: + return params, param_errors, np.nan, np.nan + else: + return params, param_errors class Fixed(MotionModel): @@ -230,7 +332,7 @@ def model_fit(self, dt, x0): x0 = np.asarray(x0) return np.broadcast_to(x0[:, np.newaxis], (x0.shape[0], dt.shape[0])) if x0.ndim > 0 else np.full_like(dt, x0) - def model(self, t, fit_params, fixed_params=None, fit_param_errs=None): + def model(self, t, fit_params, fixed_params_dict=None, fit_param_errs=None): """Predicted positions (and uncertainties, if fit_param_errs is provided) at time t of Fixed model. Parameters @@ -238,8 +340,8 @@ def model(self, t, fit_params, fixed_params=None, fit_param_errs=None): t : float or array-like Time array, shape (N_times,) fit_params : array-like - Fit parameters x0, y0 in shape (N_params,) or (N_stars, N_params) - fixed_params : array-like, optional + x0, y0 in shape (N_params,) or (N_stars, N_params) + fixed_params_dict : dict, optional Not applicable for Fixed, by default None fit_param_errs : array-like, optional Uncertainties for x0, y0 in shape (N_params,) or (N_stars, N_params), by default None @@ -283,12 +385,14 @@ def model(self, t, fit_params, fixed_params=None, fit_param_errs=None): return x, y, x_err, y_err def run_fit( - self, t, x, y, xe, ye, t0, + self, t, x, y, xe, ye, + fixed_params_dict=None, weighting='var', use_scipy=True, absolute_sigma=True, - params_guess=None, fill_value=np.nan, + params_guess=None, + return_chi2=False, verbose=True ): if verbose and (not use_scipy): @@ -309,7 +413,7 @@ def run_fit( # degree_of_freedom >= 0 # Calculate weighted average position - x_wt, y_wt = self.get_weights(xe, ye, weighting=weighting) + x_wt, y_wt = self.calc_weights(xe, ye, weighting=weighting) x_wt_norm = x_wt / np.sum(x_wt) y_wt_norm = y_wt / np.sum(y_wt) x0 = np.average(x, weights=x_wt) @@ -320,7 +424,8 @@ def run_fit( params = np.array([x0, y0]) param_errors = np.array([x0e, y0e]) - chi2x, chi2y = self.calc_chi2(params, [], t, x, y, xe, ye) + if (not absolute_sigma) or return_chi2: + chi2x, chi2y = self.calc_chi2(t, x, y, xe, ye, params) if not absolute_sigma: if degree_of_freedom > 0: @@ -338,7 +443,10 @@ def run_fit( # Set parameter uncertainties to np.inf, same behavior as scipy.optimize.curve_fit param_errors = np.full_like(param_errors, np.inf) - return params, param_errors, chi2x, chi2y + if return_chi2: + return params, param_errors, chi2x, chi2y + else: + return params, param_errors class Linear(MotionModel): """ @@ -349,7 +457,6 @@ class Linear(MotionModel): # Number of fit parameters/required observations in each direction n_params = int(np.ceil(len(fit_param_names) / 2)) - name = "Linear" def __init__(self, **kwargs): @@ -377,7 +484,7 @@ def model_fit(self, dt, x0, v): """ return x0 + v * dt - def model(self, t, fit_params, fixed_params, fit_param_errs=None): + def model(self, t, fit_params, fixed_params_dict, fit_param_errs=None): """Model positions (and uncertainties, if fit_param_errs is provided) at time t of Linear model. Parameters @@ -385,9 +492,9 @@ def model(self, t, fit_params, fixed_params, fit_param_errs=None): t : float or array-like Time(s) at which to evaluate the model fit_params : array-like - Fit parameters x0, vx, y0, vy in shape (N_params,) or (N_stars, N_params) - fixed_params : array-like - Fixed parameters t0 in shape (1,) or (N_stars, 1) + x0, vx, y0, vy in shape (N_params,) or (N_stars, N_params) + fixed_params_dict : dict + t0, shape (1,) or (N_stars,) fit_param_errs : array-like, optional Uncertainties of fit parameters in shape (N_params,) or (N_stars, N_params), by default None @@ -396,6 +503,8 @@ def model(self, t, fit_params, fixed_params, fit_param_errs=None): x, y (, xe, ye) Predicted positions (and uncertainties, if fit_param_errs is provided) with shape (N_stars, N_times), or (N_times,) if N_stars=1, or (N_stars,) if N_times=1 """ + assert 't0' in fixed_params_dict, "Fixed parameter t0 is required for Linear model." + t = np.atleast_1d(t) fit_params = np.atleast_2d(fit_params) # (N_stars, N_params) @@ -403,7 +512,7 @@ def model(self, t, fit_params, fixed_params, fit_param_errs=None): N_times = len(t) x0, vx, y0, vy = fit_params.T # Each shape (N_stars,) - t0 = np.atleast_1d(fixed_params[0]) # Shape (N_stars,) or (1,) + t0 = np.atleast_1d(fixed_params_dict['t0']) # Shape (N_stars,) or (1,) dt = t[np.newaxis, :] - t0[:, np.newaxis] # Shape (N_stars, N_times) @@ -430,14 +539,24 @@ def model(self, t, fit_params, fixed_params, fit_param_errs=None): return x, y, x_err, y_err def run_fit( - self, t, x, y, xe, ye, t0, + self, t, x, y, xe, ye, + fixed_params_dict, weighting='var', use_scipy=True, absolute_sigma=True, - params_guess=None, fill_value=np.nan, + params_guess=None, + return_chi2=False, verbose=True ): + assert 't0' in fixed_params_dict, "Fixed parameter t0 is required for Linear model." + t0 = fixed_params_dict['t0'] + t = np.atleast_1d(t) + x = np.atleast_1d(x) + y = np.atleast_1d(y) + xe = np.atleast_1d(xe) + ye = np.atleast_1d(ye) + n_obs = len(t) degree_of_freedom = n_obs - self.n_params # Not enough data points to fit model @@ -449,11 +568,14 @@ def run_fit( ) params = np.full(self.n_params, fill_value) param_errors = np.full(self.n_params, np.inf) - return params, param_errors, np.nan, np.nan + if return_chi2: + return params, param_errors, np.nan, np.nan + else: + return params, param_errors # degree_of_freedom >= 0 dt = t - t0 - x_wt, y_wt = self.get_weights(xe, ye, weighting=weighting) + x_wt, y_wt = self.calc_weights(xe, ye, weighting=weighting) if params_guess is None: params_guess = [x.mean(), 0., y.mean(), 0.] @@ -466,60 +588,65 @@ def run_fit( y0e, vye = np.sqrt(y_cov.diagonal()) params = np.array([x0, vx, y0, vy]) param_errors = np.array([x0e, vxe, y0e, vye]) - chi2_x, chi2_y = self.calc_chi2(params, [t0], t, x, y, xe, ye) - - else: - # Use https://en.wikipedia.org/wiki/Weighted_least_squares#Solution scheme - x = np.array(x) - y = np.array(y) - dt = np.array(dt) - X_mat_t = np.vander(dt, 2) - # x calculation - W_mat_x = np.diag(x_wt) - XTWX_mat_x = X_mat_t.T @ W_mat_x @ X_mat_t - pcov_x = np.linalg.inv(XTWX_mat_x) # Covariance Matrix - popt_x = pcov_x @ X_mat_t.T @ W_mat_x @ x # Linear Solution - perr_x = np.sqrt(np.diag(pcov_x)) # Uncertainty of Linear Solution - # y calculation - W_mat_y = np.diag(y_wt) - XTWX_mat_y = X_mat_t.T @ W_mat_y @ X_mat_t - pcov_y = np.linalg.inv(XTWX_mat_y) # Covariance Matrix - popt_y = pcov_y @ X_mat_t.T @ W_mat_y @ y # Linear Solution - perr_y = np.sqrt(np.diag(pcov_y)) # Uncertainty of Linear Solution - # prepare values to return - vx, x0 = popt_x - vy, y0 = popt_y - vxe, x0e = perr_x - vye, y0e = perr_y - - # Does not use get_chi2 to accelerate calculation + if return_chi2: + chi2_x, chi2_y = self.calc_chi2(t, x, y, xe, ye, params, fixed_params_dict) + return params, param_errors, chi2_x, chi2_y + else: + return params, param_errors + + # Linear algebraic solution + # Use https://en.wikipedia.org/wiki/Weighted_least_squares#Solution_scheme + X_mat_t = np.vander(dt, 2) + # x calculation + W_mat_x = np.diag(x_wt) + XTWX_mat_x = X_mat_t.T @ W_mat_x @ X_mat_t + pcov_x = np.linalg.inv(XTWX_mat_x) # Covariance Matrix + popt_x = pcov_x @ X_mat_t.T @ W_mat_x @ x # Linear Solution + perr_x = np.sqrt(np.diag(pcov_x)) # Uncertainty of Linear Solution + # y calculation + W_mat_y = np.diag(y_wt) + XTWX_mat_y = X_mat_t.T @ W_mat_y @ X_mat_t + pcov_y = np.linalg.inv(XTWX_mat_y) # Covariance Matrix + popt_y = pcov_y @ X_mat_t.T @ W_mat_y @ y # Linear Solution + perr_y = np.sqrt(np.diag(pcov_y)) # Uncertainty of Linear Solution + # prepare values to return + vx, x0 = popt_x + vy, y0 = popt_y + vxe, x0e = perr_x + vye, y0e = perr_y + + params = np.array([x0, vx, y0, vy]) + param_errors = np.array([x0e, vxe, y0e, vye]) + + # Does not use get_chi2 to accelerate calculation + if return_chi2 or (not absolute_sigma): residual_x = x - X_mat_t @ popt_x residual_y = y - X_mat_t @ popt_y chi2_x = residual_x.T @ W_mat_x @ residual_x chi2_y = residual_y.T @ W_mat_y @ residual_y - params = np.array([x0, vx, y0, vy]) - param_errors = np.array([x0e, vxe, y0e, vye]) + if not absolute_sigma: + if degree_of_freedom > 0: + reduced_chi2_x = chi2_x / degree_of_freedom + reduced_chi2_y = chi2_y / degree_of_freedom + + param_errors[0:2] *= reduced_chi2_x**0.5 + param_errors[2:4] *= reduced_chi2_y**0.5 - if not absolute_sigma: - if degree_of_freedom > 0: - reduced_chi2_x = chi2_x / degree_of_freedom - reduced_chi2_y = chi2_y / degree_of_freedom - - param_errors[0:2] *= reduced_chi2_x**0.5 - param_errors[2:4] *= reduced_chi2_y**0.5 - - else: - # degree_of_freedom == 0, as < 0 case already handled above - warnings.warn( - f'Degree of freedom < 0. Covariance of the parameters could not be estimated. Setting parameter uncertainties to fill value np.inf.', - OptimizeWarning, stacklevel=2 - ) - # Set parameter uncertainties to np.inf, same behavior as scipy.optimize.curve_fit - param_errors = np.full_like(param_errors, np.inf) - - return params, param_errors, chi2_x, chi2_y + else: + # degree_of_freedom == 0, as < 0 case already handled above + warnings.warn( + f'Degree of freedom < 0. Covariance of the parameters could not be estimated. Setting parameter uncertainties to fill value np.inf.', + OptimizeWarning, stacklevel=2 + ) + # Set parameter uncertainties to np.inf, same behavior as scipy.optimize.curve_fit + param_errors = np.full_like(param_errors, np.inf) + + if return_chi2: + return params, param_errors, chi2_x, chi2_y + else: + return params, param_errors class Acceleration(MotionModel): """ @@ -559,7 +686,7 @@ def model_fit(self, t, x0, v0, a): """ return x0 + v0*t + 0.5*a*t**2 - def model(self, t, fit_params, fixed_params, fit_param_errs=None): + def model(self, t, fit_params, fixed_params_dict, fit_param_errs=None): """Model positions (and uncertainties, if fit_param_errs is provided) at time t of Acceleration model. Parameters @@ -567,9 +694,9 @@ def model(self, t, fit_params, fixed_params, fit_param_errs=None): t : float or array-like Time(s) at which to evaluate the model fit_params : array-like - Fit parameters with shape (N_stars, N_params) or (N_params,) - fixed_params : array-like - Fixed parameters with shape (N_stars, N_fixed_params) or (N_fixed_params,) + x0, vx0, ax, y0, vy0, ay in shape (N_params,) or (N_stars, N_params) + fixed_params_dict : dict + t0, shape (1,) or (N_stars,) fit_param_errs : array-like, optional Fit parameter uncertainties with shape (N_stars, N_params) or (N_params,), by default None @@ -578,6 +705,8 @@ def model(self, t, fit_params, fixed_params, fit_param_errs=None): x, y (, xe, ye) Predicted positions (and uncertainties, if fit_param_errs is provided) with shape (N_stars, N_times), or (N_times,) if N_stars=1, or (N_stars,) if N_times=1 """ + assert 't0' in fixed_params_dict, "Fixed parameter t0 is required for Acceleration model." + t = np.atleast_1d(t) fit_params = np.atleast_2d(fit_params) # (N_stars, N_params) @@ -585,7 +714,7 @@ def model(self, t, fit_params, fixed_params, fit_param_errs=None): N_times = len(t) x0, vx0, ax, y0, vy0, ay = fit_params.T # Each shape (N_stars,) - t0 = np.atleast_1d(fixed_params[0]) # Shape (N_stars,) or (1,) + t0 = np.atleast_1d(fixed_params_dict['t0']) # Shape (N_stars,) or (1,) dt = t[np.newaxis, :] - t0[:, np.newaxis] # Shape (N_stars, N_times) @@ -614,18 +743,28 @@ def model(self, t, fit_params, fixed_params, fit_param_errs=None): def run_fit( - self, t, x, y, xe, ye, t0, + self, t, x, y, xe, ye, + fixed_params_dict, weighting='var', use_scipy=True, absolute_sigma=True, params_guess=None, fill_value=np.nan, + return_chi2=False, verbose=True ): + assert 't0' in fixed_params_dict, "Fixed parameter t0 is required for Acceleration model." + t0 = fixed_params_dict['t0'] + t = np.atleast_1d(t) + x = np.atleast_1d(x) + y = np.atleast_1d(y) + xe = np.atleast_1d(xe) + ye = np.atleast_1d(ye) + if not use_scipy: if verbose: warnings.warn("Acceleration model has no non-scipy fitter option. Running with scipy.") - + n_obs = len(t) degree_of_freedom = n_obs - self.n_params # Not enough data points to fit model @@ -637,11 +776,14 @@ def run_fit( ) params = np.full(self.n_params, fill_value) param_errors = np.full(self.n_params, np.inf) - return params, param_errors, np.nan, np.nan + if return_chi2: + return params, param_errors, np.nan, np.nan + else: + return params, param_errors # degree_of_freedom >= 0 dt = t - t0 - x_wt, y_wt = self.get_weights(xe,ye, weighting=weighting) + x_wt, y_wt = self.calc_weights(xe,ye, weighting=weighting) if params_guess is None: # Initial guess for velocity: idx_first, idx_last = np.argmin(t), np.argmax(t) @@ -657,9 +799,11 @@ def run_fit( params = np.array([x0, vx0, ax, y0, vy0, ay]) param_errors = np.array([x0e, vx0e, axe, y0e, vy0e, aye]) - chi2_x, chi2_y = self.calc_chi2(params, [t0], t, x, y, xe, ye) - - return params, param_errors, chi2_x, chi2_y + if return_chi2: + chi2_x, chi2_y = self.calc_chi2(t, x, y, xe, ye, params, fixed_params_dict) + return params, param_errors, chi2_x, chi2_y + else: + return params, param_errors class Parallax(MotionModel): """ @@ -671,29 +815,32 @@ class Parallax(MotionModel): Optional obs parameter describes observer location, default is 'earth'. """ fit_param_names = ['x0', 'vx', 'y0', 'vy', 'pi'] - fixed_param_names = ['t0'] - fixed_meta_data = ['RA','Dec','PA','obs'] + fixed_param_names = ['t0', 'ra', 'dec', 'pa', 'obsLocation'] name = "Parallax" # Number of fit parameters/required observations in each direction n_params = int(np.ceil(len(fit_param_names) / 2)) - def __init__(self, ra, dec, pa=0., obs='earth'): + def __init__(self): super().__init__() - self.ra = ra - self.dec = dec - self.pa = pa - self.obs = obs - self.plx_vector_cached = None + self.plx_vector_cached = None # Cache for parallax vector return - def calc_parallax_vector(self, t_mjd): + def calc_parallax_vector(self, t_mjd, ra, dec, pa=0., obsLocation='earth'): """Calculate parallax vector of shape (2, N_times) Parameters ---------- t_mjd : array-like Time array in mjd + ra : float or array-like + Right ascension(s) in degrees + dec : float or array-like + Declination(s) in degrees + pa : float or array-like, optional + Position angle(s) of image y-axis from North in degrees, by default 0. + obsLocation : str, optional + Observer location, by default 'earth' Returns ------- @@ -710,11 +857,11 @@ def calc_parallax_vector(self, t_mjd): elif all(np.isin(t_mjd, t_mjd_cached)): # If all input times are in cached values, return those # Calculate pvec_idxs such that t_mjd_cached[ pvec_idxs ] == t_mjd - pvec_idxs = np.array([np.where(t_mjd_cached==t_mjd_i)[0][0] for t_mjd_i in t_mjd]) + pvec_idxs = np.array([np.where(t_mjd_cached == t_mjd_i)[0][0] for t_mjd_i in t_mjd]) pvec = self.plx_vector_cached[1][:, pvec_idxs] return pvec - pvec = parallax.parallax_in_direction(self.ra, self.dec, t_mjd, obsLocation=self.obs, PA=self.pa).T + pvec = parallax.parallax_in_direction(ra, dec, t_mjd, obsLocation=obsLocation, pa=pa) self.plx_vector_cached = [t_mjd, pvec] return pvec @@ -738,15 +885,20 @@ def model_fit(self, dt, x0, vx, y0, vy, pi): Returns ------- - 2d array - Model positions at time t of Parallax model, shape (2, N_times) + x_res, y_res : array-like + Model positions at time t of Parallax model """ - x_res = x0 + vx*dt + pi * self.pvec[0] - y_res = y0 + vy*dt + pi * self.pvec[1] - return np.vstack([x_res, y_res]) - + # x0, vx, y0, vy, pi are all shape (N_stars, N_times) + x_res = x0 + vx * dt + pi * self.pvec[0] + y_res = y0 + vy * dt + pi * self.pvec[1] + return x_res, y_res + + def _model_fit(self, dt, x0, vx, y0, vy, pi): + """Wrapper for model_fit to return concatenated results for scipy fitting.""" + x_res, y_res = self.model_fit(dt, x0, vx, y0, vy, pi) + return np.hstack([x_res, y_res]) # Shape (N_stars, 2*N_times) - def model(self, t, fit_params, fixed_params, fit_param_errs=None): + def model(self, t, fit_params, fixed_params_dict, fit_param_errs=None): """Model positions (and uncertainties, if fit_param_errs is provided) at time t of Parallax model. Parameters @@ -754,9 +906,13 @@ def model(self, t, fit_params, fixed_params, fit_param_errs=None): t : float or array-like Times at which to evaluate the model fit_params : array-like - Fit parameters for the model - fixed_params : array-like - Fixed parameters for the model + x0, vx, y0, vy, pi in shape (N_params,) or (N_stars, N_params) + fixed_params : dict + - t0, shape (N_stars,) or (1,). + - ra, shape (N_stars,) or (1,). + - dec, shape (N_stars,) or (1,). + - pa, optional, shape (N_stars,) or (1,), by default 0. + - obsLocation, optional,shape (N_stars,) or (1,), by default 'earth' fit_param_errs : array-like, optional Uncertainties in fit parameters, by default None @@ -767,17 +923,22 @@ def model(self, t, fit_params, fixed_params, fit_param_errs=None): """ t = np.atleast_1d(t) fit_params = np.atleast_2d(fit_params) # (N_stars, N_params) - N_stars = fit_params.shape[0] if fit_params.ndim > 1 else 1 N_times = len(t) x0, vx, y0, vy, pi = fit_params.T # Each shape (N_stars,) - t0 = np.atleast_1d(fixed_params[0]) # Shape (N_stars,) or (1,) + t0 = np.atleast_1d(fixed_params_dict['t0']) # Shape (N_stars,) or (1,) + ra = np.atleast_1d(fixed_params_dict['ra']) + dec = np.atleast_1d(fixed_params_dict['dec']) + pa = np.atleast_1d(fixed_params_dict.get('pa', 0.0)) + obsLocation = fixed_params_dict.get('obsLocation', 'earth') dt = t[np.newaxis, :] - t0[:, np.newaxis] # Shape (N_stars, N_times) t_mjd = Time(t, format='decimalyear', scale='utc').mjd # Shape (N_times,) - self.pvec = self.calc_parallax_vector(t_mjd) # Shape (2, N_times) - x, y = self.model_fit(dt, x0[:, np.newaxis], vx[:, np.newaxis], y0[:, np.newaxis], vy[:, np.newaxis], pi[:, np.newaxis]) # Shape (N_stars, N_times) + self.pvec = self.calc_parallax_vector(t_mjd, ra, dec, pa=pa, obsLocation=obsLocation) # Shape (2, N_times) + xy = self.model_fit(dt, x0[:, np.newaxis], vx[:, np.newaxis], y0[:, np.newaxis], vy[:, np.newaxis], pi[:, np.newaxis]) # Shape (N_stars, N_times) + x = xy[:, :N_times] # Shape (N_stars, N_times) + y = xy[:, N_times:] # Shape (N_stars, N_times) if N_stars == 1 or N_times == 1: # If only one star, return flattened arrays @@ -800,21 +961,30 @@ def model(self, t, fit_params, fixed_params, fit_param_errs=None): def run_fit( - self, t, x, y, xe, ye, t0, + self, t, x, y, xe, ye, + fixed_params_dict, weighting='var', use_scipy=True, absolute_sigma=True, params_guess=None, fill_value=np.nan, + return_chi2=False, verbose=True ): if not use_scipy: if verbose: warnings.warn("Parallax model has no non-scipy fitter option. Running with scipy.", UserWarning) - + + assert all([k in fixed_params_dict for k in ['t0', 'ra', 'dec']]), "Parallax model requires 't0', 'ra', and 'dec' in fixed_params." t = np.atleast_1d(t) - n_obs = len(t) - degree_of_freedom = n_obs - self.n_params + t0 = np.atleast_1d(fixed_params_dict['t0']) + ra = np.atleast_1d(fixed_params_dict['ra']) + dec = np.atleast_1d(fixed_params_dict['dec']) + pa = np.atleast_1d(fixed_params_dict.get('pa', 0.0)) + obsLocation = fixed_params_dict.get('obsLocation', 'earth') + + n_fit = len(t) + degree_of_freedom = n_fit - self.n_params # Not enough data points to fit model if degree_of_freedom < 0: if verbose: @@ -824,12 +994,15 @@ def run_fit( ) params = np.full(self.n_params, fill_value) param_errors = np.full(self.n_params, np.inf) - return params, param_errors - + if return_chi2: + return params, param_errors, np.nan, np.nan + else: + return params, param_errors + # degree_of_freedom >= 0 t_mjd = Time(t, format='decimalyear', scale='utc').mjd - self.pvec = self.get_parallax_vector(t_mjd) - x_wt, y_wt = self.get_weights(xe,ye, weighting=weighting) + self.pvec = self.calc_parallax_vector(t_mjd, ra, dec, pa=pa, obsLocation=obsLocation) # Shape (2, N_times) + x_wt, y_wt = self.calc_weights(xe, ye, weighting=weighting) # Initial guesses, x0,y0 as x,y averages; # vx,vy as average velocity if first and last points are perfectly measured; @@ -842,20 +1015,22 @@ def run_fit( y.mean(), (y[idx_last] - y[idx_first]) / t_span, 0.1 ] - res = curve_fit( - self.model_fit, t - t0, np.vstack([x, y]), - p0=params_guess, sigma = 1.0/np.hstack([x_wt,y_wt]), + popt, pcov = curve_fit( + self._model_fit, t - t0, np.hstack([x, y]), + p0=params_guess, sigma=np.hstack([x_wt, y_wt]), absolute_sigma=absolute_sigma ) - x0, vx, y0, vy, pi = res[0] - x0_err, vx_err, y0_err, vy_err, pi_err = np.sqrt(np.diag(res[1])) + x0, vx, y0, vy, pi = popt + x0_err, vx_err, y0_err, vy_err, pi_err = np.sqrt(pcov.diagonal()) params = np.array([x0, vx, y0, vy, pi]) param_errors = np.array([x0_err, vx_err, y0_err, vy_err, pi_err]) - chi2_x, chi2_y = self.calc_chi2(params, [t0], t, x, y, xe, ye) - return params, param_errors, chi2_x, chi2_y - + if return_chi2: + chi2_x, chi2_y = self.calc_chi2(t, x, y, xe, ye, params, fixed_params_dict) + return params, param_errors, chi2_x, chi2_y + else: + return params, param_errors def validate_motion_models(motion_models, startable, default_motion_model): """Validate that all the unique motion models in startable and default_motion_model are in the motion_models. If not, add available models to the list. @@ -869,12 +1044,7 @@ def validate_motion_models(motion_models, startable, default_motion_model): default_motion_model : MotionModel Default MotionModel """ - motion_model_map = { - 'Fixed': Fixed, - 'Linear': Linear, - 'Acceleration': Acceleration, - 'Parallax': Parallax - } + motion_model_map = motion_model_map() # Collect names of all motion models that might get used. all_motion_model_names = set() all_motion_model_names.add('Fixed') @@ -975,14 +1145,9 @@ def get_all_motion_model_names(with_errors=True, with_fixed=True): return get_list_motion_model_param_names(MotionModel.__subclasses__(), with_errors=with_errors, with_fixed=with_fixed) def motion_model_map(): - mm_map = { - 'Empty': Empty, - 'Fixed': Fixed, - 'Linear': Linear, - 'Acceleration': Acceleration, - 'Parallax': Parallax - } - + mm_map = dict( + [(mm.__name__, mm()) for mm in MotionModel.__subclasses__()] + ) # Sort by n_params mm_map = dict(sorted(mm_map.items(), key=lambda item: item[1].n_params)) return mm_map \ No newline at end of file diff --git a/flystar/parallax.py b/flystar/parallax.py index 3f7602b..2bd352a 100755 --- a/flystar/parallax.py +++ b/flystar/parallax.py @@ -23,16 +23,16 @@ # Default cache size is 1 GB cache_memory.reduce_size() -@cache_memory.cache() -def parallax_in_direction(ra, dec, mjd, obsLocation='earth', PA=0.): +# @cache_memory.cache() +def parallax_in_direction(ra, dec, mjd, obsLocation='earth', pa=0.): """ Calculate the parallax vector in a given direction following MulensModel. Parameters ---------- - RA : float + RA : float or array-like Right Ascension in degrees. (J2000) - Dec : float + Dec : float or array-like Declination in degrees. (J2000) mjd : float or array-like Modified Julian Date. @@ -44,33 +44,42 @@ def parallax_in_direction(ra, dec, mjd, obsLocation='earth', PA=0.): Returns ------- pvec : ndarray - Parallax vector components, shape of (N, 2), where N is the number of mjd entries. + Parallax vector components, shape of (2, N) or (2,), where N is the number of stars. """ # Munge inputs into astropy format. # times = Time(mjd + 2400000.5, format='jd', scale='tdb') + ra = np.atleast_1d(ra) + dec = np.atleast_1d(dec) + mjd = np.atleast_1d(mjd) times = Time(mjd, format='mjd', scale='tdb') # convert to TDB coord = SkyCoord(ra, dec, unit=(units.deg, units.deg)) - direction = coord.cartesian.xyz.value + directions = coord.cartesian.xyz.value.T # Shape (N_stars, 3) north = np.array([0., 0., 1.]) - _east_projected = np.cross(north, direction) / np.linalg.norm(np.cross(north, direction)) - _north_projected = np.cross(direction, _east_projected) / np.linalg.norm(np.cross(direction, _east_projected)) + # Cross product of each star with north vector + _east_projected = np.cross(north, directions) + _east_projected /= np.linalg.norm(_east_projected, axis=1)[:, np.newaxis] # Shape (N_stars, 3) + _north_projected = np.cross(directions, _east_projected) + _north_projected /= np.linalg.norm(_north_projected, axis=1)[:, np.newaxis] # Shape (N_stars, 3) obs_pos = get_observer_barycentric(obsLocation, times) sun_pos = get_body_barycentric(body='sun', time=times) sun_obs_pos = sun_pos - obs_pos - pos = sun_obs_pos.xyz.T.to(units.au) + pos = sun_obs_pos.xyz.T.to(units.au).value # Shape (N_stars, 3) + + e = np.einsum('ij,ij->i', pos, _east_projected) # Shape (N_stars,) + n = np.einsum('ij,ij->i', pos, _north_projected) # Shape (N_stars,) - e = np.dot(pos, _east_projected) - n = np.dot(pos, _north_projected) - # Rotate frame e,n->x,y accounting for PA - PA_rad = np.pi/180.0 * PA - x = -e.value*np.cos(PA_rad) + n.value*np.sin(PA_rad) - y = e.value*np.sin(PA_rad) + n.value*np.cos(PA_rad) - pvec = np.array([x, y]).T + pa = np.deg2rad(pa) + x = -e * np.cos(pa) + n * np.sin(pa) + y = e * np.sin(pa) + n * np.cos(pa) + pvec = np.array([x, y]) # Shape (2, N_stars) + + if pvec.shape[1] == 1: + pvec = pvec.flatten() return pvec diff --git a/flystar/startables.py b/flystar/startables.py index 661382f..dd44178 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -125,7 +125,7 @@ def __init__(self, *args, ref_list=0, **kwargs): # We have to have special handling of meta-data (i.e. info that has # dimensions of n_lists). - meta_tab = ('LIST_TIMES', 'LIST_NAMES') + meta_tab = ('list_times', 'list_names') meta_type = ((float, int), str) for mm in range(len(meta_tab)): meta_test = meta_tab[mm] @@ -154,7 +154,7 @@ def __init__(self, *args, ref_list=0, **kwargs): del kwargs[meta_arg] for arg in kwargs: - if arg in ['name', 'x', 'y', 'm']: + if arg in ['name', 'x', 'y', 'm', 'list_times', 'list_names']: continue else: self.add_column(Column(data=kwargs[arg], name=arg)) @@ -218,7 +218,7 @@ def _add_list_data_from_starlist(self, starlist): else: # Add junk data it if wasn't input self._set_invalid_list_values(col_name, -1) - + ########## # Update the table meta-data. Remember that entries are lists not numpy arrays. ########## @@ -246,10 +246,10 @@ def _add_list_data_from_starlist(self, starlist): # Update the n_lists meta keyword. self.meta['n_lists'] += 1 - + return - - + + def _add_list_data_from_keywords(self, **kwargs): # # Check if the required arguments are present # arg_req = ('x', 'y', 'm') @@ -534,14 +534,15 @@ def detections(self): def fit_motion_model( self, - motion_models=[Empty(), Fixed(), Linear()], + motion_models=[Empty, Fixed, Linear], + fixed_params_dict=None, weighting='var', use_scipy=False, absolute_sigma=True, bootstrap=0, - fixed_t0=False, verbose=True, mask_value=None, + mask_lists=None, fill_value=np.nan, show_progress=True ): @@ -551,17 +552,21 @@ def fit_motion_model( ---------- motion_models : list of MotionModel, optional Motion models to use. - Empty() and Fixed() models are always added automatically for stars with n_fit = 0 or 1. + Empty and Fixed models are always added automatically for stars with n_fit = 0 or 1. The behavior is as follows: 1. If 'motion_model_input' column is NOT in table: - Use the most complex model that has enough parameters to fit the data (n_fit >= n_params). - If multiple models are supplied, prioritize the model with the most parameters to fit. - If multiple models have the same number of parameters, raise AssertionError: not sure which to use. - 2. If 'motion_model_input' column is in table: + 2. If 'motion_model_input' column IS in table: - Use the model specified in the 'motion_model_input' column. - - If motion model requires initialization parameters, an instance of the motion model must be provided in motion_models list, i.e., motion_models=[Parallax(RA=0, DEC=0)]. - - If not enough data points to fit the specified model, use the most complex model that has enough parameters to fit the data (n_fit >= n_params) among the provided motion_models and 'motion_model_input'. - The actual used motion model is stored in the 'motion_model_used' column. The default motion_models are [Empty(), Fixed(), Linear()]. + - If not enough data points to fit the specified model, use the most complex model in any 'motion_model_input' column that has enough parameters to fit the data (n_fit >= n_params) among the provided motion_models and 'motion_model_input'. + The actual used motion model is stored in the 'motion_model_used' column. The default motion_models are [Empty, Fixed, Linear]. + fixed_params_dict : dict, optional + Dictionary of fixed parameters for motion models, e.g., {'t0': 0., 'ra': np.array([...]), 'dec': np.array([...])}. + - Scalar values are used for all stars, array values should have length = N_stars. + - t0 is automatically calculated as np.average(t, weights=1/np.hypot(xe, ye)) if not provided. + - The keys should match the fixed parameter names in the motion models. See MotionModel class for details, by default None weighting : str, optional Uncertainty weighting, 'std' for weight=1/xe(ye) or 'var' for weight=1/xe(ye)**2, by default 'var' use_scipy : bool, optional @@ -570,12 +575,12 @@ def fit_motion_model( Use absolute sigma or not, see scipy curve_fit for details, by default True bootstrap : int, optional Number of bootstrap for uncertainty resampling, by default 0 - fixed_t0 : bool or float, optional - If provided, use the fixed t0. Otherwise, use average t weighted by 1/np.hypot(xe, ye), by default False verbose : bool, optional Print verbose messages or not, by default True mask_value : float, optional Values to mask in data, by default None + mask_lists : list of int, optional + Indices of lists to mask/exclude from fitting, by default None fill_value : float, optional Fill value when there is not enough data points to fit, by default np.nan show_progress : bool, optional @@ -596,19 +601,24 @@ def fit_motion_model( if weighting not in ['var', 'std']: raise ValueError(f"fit_velocities: Weighting must either be 'var' or 'std', not {weighting}!") - if ('t' not in self.colnames) and ('LIST_TIMES' not in self.meta): - raise KeyError("fit_velocities: Failed to access time values. No 't' column in table, no 'LIST_TIMES' in meta.") + if ('t' not in self.colnames) and ('list_times' not in self.meta): + raise KeyError("fit_velocities: Failed to access time values. No 't' column in table, no 'list_times' in meta.") # Check if we have the required columns if not all([_ in self.colnames for _ in ['x', 'y']]): raise KeyError(f"fit_velocities: Missing required columns in the table: {', '.join(['x', 'y'])}!") + # Check fixed_params_dict is a dict + if fixed_params_dict is not None: + if not isinstance(fixed_params_dict, dict): + raise ValueError("fit_velocities: fixed_params_dict must be a dictionary!") + # Always add Empty and Fixed in motion models mm_names = [mm.name for mm in motion_models] if 'Fixed' not in mm_names: - motion_models.insert(0, Fixed()) + motion_models.insert(0, Fixed) if 'Empty' not in mm_names: - motion_models.insert(0, Empty()) + motion_models.insert(0, Empty) mm_names = [mm.name for mm in motion_models] # Construct motion models if motion_model_input column exists @@ -643,19 +653,52 @@ def fit_motion_model( N_stars = len(self) x_data = np.ma.masked_invalid(self['x'].data, copy=True) y_data = np.ma.masked_invalid(self['y'].data, copy=True) - xe_data = np.ma.masked_invalid(self['xe'].data, copy=True) if 'xe' in self.colnames else None - ye_data = np.ma.masked_invalid(self['ye'].data, copy=True) if 'ye' in self.colnames else None + xe_data = np.ma.masked_invalid(self['xe'].data, copy=True) if 'xe' in self.colnames else np.ones_like(x_data) + ye_data = np.ma.masked_invalid(self['ye'].data, copy=True) if 'ye' in self.colnames else np.ones_like(y_data) + + if mask_lists is not None: + x_data.mask[:, mask_lists] = True + y_data.mask[:, mask_lists] = True + xe_data.mask[:, mask_lists] = True + ye_data.mask[:, mask_lists] = True # t_data: 2d array with shape (N_stars, N_epochs) # t0: 1d array with shape (N_stars,) if 't' in self.colnames: t_data = copy.deepcopy(self['t'].data) - t0 = np.average(t_data, axis=1, weights=1/np.hypot(xe_data, ye_data)) if not fixed_t0 else np.ones(N_stars)*fixed_t0 else: - t_data = copy.deepcopy(np.array(self.meta['LIST_TIMES'])) + t_data = copy.deepcopy(np.array(self.meta['list_times'])) t_data = np.broadcast_to(t_data, x_data.shape) - t0 = np.average(t_data, axis=1, weights=1/np.hypot(xe_data, ye_data)) if not fixed_t0 else np.ones(N_stars)*fixed_t0 + + # Add default t0 if not provided in fixed_params_dict + if fixed_params_dict is None: + weights = 1/np.hypot(xe_data, ye_data) if xe_data is not None else None + t0 = np.average(t_data, axis=1, weights=weights) + fixed_params_dict = {'t0': t0} + elif 't0' not in fixed_params_dict: + weights = 1/np.hypot(xe_data, ye_data) if xe_data is not None else None + fixed_params_dict['t0'] = np.average(t_data, axis=1, weights=weights) + else: + if np.ndim(fixed_params_dict['t0']) == 0: + fixed_params_dict['t0'] = np.full(N_stars, fixed_params_dict['t0']) + t0 = fixed_params_dict['t0'] + + # Prepare fixed_params_dict for each star + # This avoids checking types and slicing inside the fitting loop + fixed_params_stars = [{} for _ in range(N_stars)] + # Identify array parameters (length N_stars) and scalar parameters + array_params = {k: v for k, v in fixed_params_dict.items() if np.ndim(v) > 0 and len(v) == N_stars} + scalar_params = {k: v for k, v in fixed_params_dict.items() if k not in array_params} + + # Construct list of dicts for each star + # Using list comprehension for speed + fixed_params_stars = [ + {**scalar_params, **{k: v[i] for k, v in array_params.items()}} + for i in range(N_stars) + ] + + # Apply mask_value if provided if mask_value: x_data = np.ma.masked_values(x_data, mask_value) y_data = np.ma.masked_values(y_data, mask_value) @@ -664,6 +707,7 @@ def fit_motion_model( if ye_data is not None: ye_data = np.ma.masked_values(ye_data, mask_value) + # Calculate mask array xy_mask = (~x_data.mask) & (~y_data.mask) self['n_fit'] = xy_mask.sum(axis=1) @@ -682,7 +726,6 @@ def fit_motion_model( if 'motion_model_input' in self.colnames: # Determine which motion model to use based on motion_model_input column # If n_fit < required n_params for the input motion model, use the most complicated motion model with n_fit >= n_params - motion_model_names = np.unique(self['motion_model_input']) required_params = [all_mm_map[mm_name].n_params for mm_name in self['motion_model_input']] mm_digitized = np.digitize( x=np.minimum(np.array(self['n_fit']), required_params), @@ -694,7 +737,8 @@ def fit_motion_model( x=np.array(self['n_fit']), bins=mm_n_params ) - 1 # Convert to 0-based index - + + # Assign motion models to stars self['motion_model_used'] = np.array([motion_models[d].name for d in mm_digitized]) @@ -722,7 +766,8 @@ def fit_motion_model( ) # Add a column to keep track of the number of points used in a fit and number of bootstrap used. - self['n_bootstrap'] = bootstrap + self.meta['n_bootstrap'] = bootstrap + ########################### ######### FITTING ######### @@ -730,9 +775,10 @@ def fit_motion_model( unique_motion_models, unique_inv_indices = np.unique(self['motion_model_used'], return_inverse=True) indices_by_motion_model = {key: np.flatnonzero(unique_inv_indices == k) for k, key in enumerate(unique_motion_models)} + # Expensive for loop! Prepare everything beforehand to speed up. for unique_motion_model, unique_index in indices_by_motion_model.items(): # Create motion model instance - motion_model_instance = input_mm_map[unique_motion_model] + motion_model_instance = input_mm_map[unique_motion_model]() # Initialize arrays to store results n_stars_this_model = len(unique_index) n_params = len(motion_model_instance.fit_param_names) @@ -751,12 +797,13 @@ def fit_motion_model( y=y_stars[i_star], xe=xe_stars[i_star], ye=ye_stars[i_star], - t0=t0[i_star], + fixed_params_dict=fixed_params_stars[i_star], weighting=weighting, use_scipy=use_scipy, absolute_sigma=absolute_sigma, bootstrap=bootstrap, fill_value=fill_value, + return_chi2=True, verbose=verbose ) params_array[idx] = params @@ -808,8 +855,8 @@ def fit_velocities(self, weighting='var', use_scipy=True, absolute_sigma=True, b if weighting not in ['var', 'std']: raise ValueError(f"fit_velocities: Weighting must either be 'var' or 'std', not {weighting}!") - if ('t' not in self.colnames) and ('LIST_TIMES' not in self.meta): - raise KeyError("fit_velocities: Failed to access time values. No 't' column in table, no 'LIST_TIMES' in meta.") + if ('t' not in self.colnames) and ('list_times' not in self.meta): + raise KeyError("fit_velocities: Failed to access time values. No 't' column in table, no 'list_times' in meta.") # Check if we have the required columns if not all([_ in self.colnames for _ in ['x', 'y']]): @@ -827,9 +874,9 @@ def fit_velocities(self, weighting='var', use_scipy=True, absolute_sigma=True, b if ('motion_model_input' not in self.colnames) or reassign_motion_model: self['motion_model_input'] = default_motion_model self['motion_model_used'] = self['motion_model_input'] - + motion_model_dict = motion_model.validate_motion_model_dict(motion_model_dict, self, default_motion_model) - + # # Fill table with all possible motion model parameter names as new # columns. Make everything empty for now. @@ -864,7 +911,7 @@ def fit_velocities(self, weighting='var', use_scipy=True, absolute_sigma=True, b if 't' in self.colnames: self['t0'] = self['t'] else: - self['t0'] = self.meta['LIST_TIMES'][0] + self['t0'] = self.meta['list_times'][0] if 'xe' in self.colnames: self['x0_err'] = self['xe'] self['y0_err'] = self['ye'] @@ -879,7 +926,7 @@ def fit_velocities(self, weighting='var', use_scipy=True, absolute_sigma=True, b if 't' in self.colnames: self['t0'] = self['t'][:, 0] else: - self['t0'] = self.meta['LIST_TIMES'][0] + self['t0'] = self.meta['list_times'][0] if 'xe' in self.colnames: self['x0_err'] = self['xe'][:,0] self['y0_err'] = self['ye'][:,0] @@ -996,7 +1043,7 @@ def fit_velocity_for_star(self, ss, motion_model_dict, weighting='var', use_scip if 't' in self.colnames: t = np.ma.masked_invalid(self['t'][ss, :].data) else: - t = np.ma.masked_invalid(self.meta['LIST_TIMES']) + t = np.ma.masked_invalid(self.meta['list_times']) if mask_val: t = np.ma.masked_values(t, mask_val) diff --git a/flystar/tests/test_all_detected.fits b/flystar/tests/test_all_detected.fits deleted file mode 100644 index ae56198..0000000 --- a/flystar/tests/test_all_detected.fits +++ /dev/null @@ -1,2911 +0,0 @@ -SIMPLE = T / conforms to FITS standard BITPIX = 8 / array data type NAXIS = 0 / number of array dimensions EXTEND = T END XTENSION= 'BINTABLE' / binary table extension BITPIX = 8 / array data type NAXIS = 2 / number of array dimensions NAXIS1 = 632 / length of dimension 1 NAXIS2 = 2000 / length of dimension 2 PCOUNT = 0 / number of group parameters GCOUNT = 1 / number of groups TFIELDS = 21 / number of table fields TTYPE1 = 'name ' TFORM1 = 'K ' TTYPE2 = 'x ' TFORM2 = '12D ' TDIM2 = '(2,6) ' TTYPE3 = 'y ' TFORM3 = '12D ' TDIM3 = '(2,6) ' TTYPE4 = 'm ' TFORM4 = '12D ' TDIM4 = '(2,6) ' TTYPE5 = 'xe ' TFORM5 = '6D ' TDIM5 = '(6) ' TTYPE6 = 'ye ' TFORM6 = '6D ' TDIM6 = '(6) ' TTYPE7 = 'me ' TFORM7 = '6D ' TDIM7 = '(6) ' TTYPE8 = 'n ' TFORM8 = '6D ' TDIM8 = '(6) ' TTYPE9 = 'det ' TFORM9 = '6D ' TDIM9 = '(6) ' TTYPE10 = 'vx ' TFORM10 = 'D ' TTYPE11 = 'vy ' TFORM11 = 'D ' TTYPE12 = 'vxe ' TFORM12 = 'D ' TTYPE13 = 'vye ' TFORM13 = 'D ' TTYPE14 = 'x0 ' TFORM14 = 'D ' TTYPE15 = 'y0 ' TFORM15 = 'D ' TTYPE16 = 'x0e ' TFORM16 = 'D ' TTYPE17 = 'y0e ' TFORM17 = 'D ' TTYPE18 = 'chi2_vx ' TFORM18 = 'D ' TTYPE19 = 'chi2_vy ' TFORM19 = 'D ' TTYPE20 = 't0 ' TFORM20 = 'D ' TTYPE21 = 'n_vfit ' TFORM21 = 'D ' EPNAMES = '2005_F814W_F1' EPNAMES = '2010_F125W_F3' EPNAMES = '2010_F139M_F2' EPNAMES = '2010_F160W_F1' EPNAMES = '2013_F160W_F1' EPNAMES = '2015_F160W_F1' ZPOINTS = 32.6783 ZPOINTS = 25.2305 ZPOINTS = 23.2835 ZPOINTS = 24.5698 ZPOINTS = 24.5698 ZPOINTS = 24.5698 YEARS = 2005.485 YEARS = 2010.652 YEARS = 2010.652 YEARS = 2010.652 YEARS = 2013.199 YEARS = 2015.148 HIERARCH DATE PRODUCED = '2025-06-30' HIERARCH INSTRUMENT = 'ACSWFC ' HIERARCH INSTRUMENT = 'WFC3IR ' HIERARCH INSTRUMENT = 'WFC3IR ' HIERARCH INSTRUMENT = 'WFC3IR ' HIERARCH INSTRUMENT = 'WFC3IR ' HIERARCH INSTRUMENT = 'WFC3IR ' END @Ÿ ˆ1&éy@Ÿ ›c+Ø(@Ÿ ˆ1&éy@Ÿ “4U‡*@Ÿ ˆ1&éy@Ÿ ¥ÆàOS@Ÿ ˆ1&éy@Ÿ…ÍÅ•Ü@Ÿ ˆ1&éy@Ÿ èÁ Î!@Ÿ ˆ1&éy@Ÿ]H/Ò@¢nzáG®@¢ns2ph@¢nzáG®@¢nˆ:ötä@¢nzáG®@¢mÐüùI±@¢nzáG®@¢mÓÐùòm@¢nzáG®@¢nbö3›@¢nzáG®@¢nsþ¤ÿŠ@8‚ò䎊@8m¥1›Š@4›¥ãSø@3¹Ã!dÏÎ@3š~ùÛ"Ñ@3Q@䩤@2èè§æ›@2h4¥àûZ@2èè§æ›@2•ÄŠÉRd@2èè§æ›@2–&îEK?¤hja¢ÖQ?¤ƒ´Æ*àä?¢iœy‘Ï?ÁÛÆÀï?» -Ld¢?²OU=°6i?¿/nI|Áâ?˜Š·¤‰ÿ?•`«²lñ??ºþ!æàg?Àî’”ï '?»Ï‡Ê1ñ?¶å¿#ý ?–ãjo¼ð?ƒ /±‘OÈ?‘‰©Þ¥e?£ü.Eôv?£ì¶Ñ [\@@"@@"@4@.?ð?ð?ð?ð?ð?ð?“EkŸ€?m¥•oÜ?zÁ•ºS ?œT 8O@ŸËÌÃ@¢n?ð/Ç? ? 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fit_err*n_sigma)) return np.abs(true_val - fit_val) <= n_sigma*fit_err def test_Fixed(): @@ -76,16 +74,31 @@ def test_Fixed(): ) x_sim = np.random.normal(x_true, true_params['x0_err']) y_sim = np.random.normal(y_true, true_params['y0_err']) + xe = np.ones_like(t)*true_params['x0_err'] + ye = np.ones_like(t)*true_params['y0_err'] # Run fit - params, param_errs, _ , _ = mod.fit( + params, param_errs = mod.fit( t, x_sim,y_sim, - xe=np.ones(len(t))*true_params['x0_err'], - ye=np.ones(len(t))*true_params['y0_err'], - t0=np.nan + xe=xe, + ye=ye ) + + x_wt = 1. / xe**2 + y_wt = 1. / ye**2 + x_wt_norm = x_wt / np.sum(x_wt) + y_wt_norm = y_wt / np.sum(y_wt) + x_mean = np.average(x_sim, weights=x_wt) + y_mean = np.average(y_sim, weights=y_wt) + x_std = (np.sum(x_wt_norm**2 * xe**2))**0.5 + y_std = (np.sum(y_wt_norm**2 * ye**2))**0.5 + # Confirm true value is within error bar of fit value assert np.all([within_error(true_params[param_list[i]], params[i], param_errs[i]) for i in range(len(params))]) + np.testing.assert_allclose(params[0], x_mean, atol=1e-5) + np.testing.assert_allclose(params[1], y_mean, atol=1e-5) + np.testing.assert_allclose(param_errs[0], x_std, atol=1e-5) + np.testing.assert_allclose(param_errs[1], y_std, atol=1e-5) def test_Linear(): @@ -95,12 +108,11 @@ def test_Linear(): 't0':2025.0} mod = motion_model.Linear() param_list = mod.fit_param_names - fixed_param_list = mod.fixed_param_names # Confirm return of proper values for single t=t0 and array t x_t, y_t = mod.model( t=true_params['t0'], fit_params=np.array([true_params[p] for p in param_list]).T, - fixed_params=np.array([true_params[p] for p in fixed_param_list]).T + fixed_params_dict={'t0': true_params['t0']} ) assert x_t==true_params['x0'] assert y_t==true_params['y0'] @@ -108,7 +120,7 @@ def test_Linear(): x_t, y_t = mod.model( t=t_arr, fit_params=np.array([true_params[p] for p in param_list]).T, - fixed_params=np.array([true_params[p] for p in fixed_param_list]).T + fixed_params_dict={'t0': true_params['t0']} ) assert (x_t==(true_params['x0'] + (t_arr-true_params['t0'])*true_params['vx'])).all() assert (y_t==(true_params['y0'] + (t_arr-true_params['t0'])*true_params['vy'])).all() @@ -129,25 +141,28 @@ def test_Linear(): t=t_batch, fit_params=np.array([x0_batch, vx_batch, y0_batch, vy_batch]).T, fit_param_errs=np.array([x0_err_batch, vx_err_batch, y0_err_batch, vy_err_batch]).T, - fixed_params=t0_batch + fixed_params_dict={'t0': t0_batch} ) - assert (x_t_batch==(x0_batch+(t_batch-t0_batch)*vx_batch)).all() - assert (y_t_batch==(y0_batch+(t_batch-t0_batch)*vy_batch)).all() - assert (x_err_t_batch==np.hypot(x0_err_batch, (t_batch-t0_batch)*vx_err_batch)).all() - assert (y_err_t_batch==np.hypot(y0_err_batch, (t_batch-t0_batch)*vy_err_batch)).all() + + + np.testing.assert_allclose(x_t_batch, (x0_batch+(t_batch-t0_batch)*vx_batch), atol=1e-5) + np.testing.assert_allclose(y_t_batch, (y0_batch+(t_batch-t0_batch)*vy_batch), atol=1e-5) + np.testing.assert_allclose(x_err_t_batch, np.hypot(x0_err_batch, (t_batch-t0_batch)*vx_err_batch), atol=1e-5) + np.testing.assert_allclose(y_err_t_batch, np.hypot(y0_err_batch, (t_batch-t0_batch)*vy_err_batch), atol=1e-5) + # Multiple times t_batch = np.arange(2015.0,2025.0, 0.5) x_t_batch, y_t_batch, x_err_t_batch, y_err_t_batch = mod.model( t=t_batch, fit_params=np.array([x0_batch, vx_batch, y0_batch, vy_batch]).T, fit_param_errs=np.array([x0_err_batch, vx_err_batch, y0_err_batch, vy_err_batch]).T, - fixed_params=t0_batch + fixed_params_dict={'t0': t0_batch} ) - assert (x_t_batch==np.array([x0_batch[i] + (t_batch-t0_batch[i])*vx_batch[i] for i in range(len(x0_batch))])).all() - assert (y_t_batch==np.array([y0_batch[i] + (t_batch-t0_batch[i])*vy_batch[i] for i in range(len(x0_batch))])).all() - assert (x_err_t_batch==np.array([np.hypot(x0_err_batch[i], (t_batch-t0_batch[i])*vx_err_batch[i]) for i in range(len(x0_batch))])).all() - assert (y_err_t_batch==np.array([np.hypot(y0_err_batch[i], (t_batch-t0_batch[i])*vy_err_batch[i]) for i in range(len(x0_batch))])).all() - + np.testing.assert_allclose(x_t_batch, np.array([x0_batch[i] + (t_batch-t0_batch[i])*vx_batch[i] for i in range(len(x0_batch))]), atol=1e-5) + np.testing.assert_allclose(y_t_batch, np.array([y0_batch[i] + (t_batch-t0_batch[i])*vy_batch[i] for i in range(len(x0_batch))]), atol=1e-5) + np.testing.assert_allclose(x_err_t_batch, np.array([np.hypot(x0_err_batch[i], (t_batch-t0_batch[i])*vx_err_batch[i]) for i in range(len(x0_batch))]), atol=1e-5) + np.testing.assert_allclose(y_err_t_batch, np.array([np.hypot(y0_err_batch[i], (t_batch-t0_batch[i])*vy_err_batch[i]) for i in range(len(x0_batch))]), atol=1e-5) + # Test fitter t = np.arange(2015.0,2025.0, 0.5) # Get values from model and add scatter @@ -156,55 +171,73 @@ def test_Linear(): x_true, y_true = mod.model( t=t, fit_params=np.array([true_params[p] for p in param_list]).T, - fixed_params=np.array([true_params[p] for p in fixed_param_list]).T + fixed_params_dict={'t0': true_params['t0']} ) x_sim = np.random.normal(x_true, 0.05) y_sim = np.random.normal(y_true, 0.05) # Run fit - params, param_errs, _, _ = mod.fit( - t=t, - x=x_sim, - y=y_sim, - xe=np.repeat(0.05, len(t)), - ye=np.repeat(0.05,len(t)), - t0=true_params['t0'] - ) - print(param_errs) - # Confirm true value is within error bar of fit value - assert np.all([within_error(true_params[param_list[i]], params[i], param_errs[i]) for i in range(len(params))]) - + xe = np.ones_like(t)*0.05 + ye = np.ones_like(t)*0.05 + + def linear(t, x0, vx): + return x0 + vx * t + + for absolute_sigma in [True, False]: + for weighting in ['std', 'var']: + for use_scipy in [True, False]: + params, param_errs = mod.fit( + t=t, + x=x_sim, + y=y_sim, + xe=xe, + ye=ye, + fixed_params_dict={'t0': true_params['t0']}, + weighting=weighting, + use_scipy=use_scipy, + absolute_sigma=absolute_sigma + ) + + # Scipy + xe_scipy = xe**0.5 if weighting=='std' else xe + ye_scipy = ye**0.5 if weighting=='std' else ye + x_popt, x_pcov = curve_fit( + linear, + t - true_params['t0'], + x_sim, + sigma=xe_scipy, + absolute_sigma=absolute_sigma, + p0=[np.mean(x_sim), 0.0] + ) + y_popt, y_pcov = curve_fit( + linear, + t - true_params['t0'], + y_sim, + sigma=ye_scipy, + absolute_sigma=absolute_sigma, + p0=[np.mean(y_sim), 0.0] + ) + np.testing.assert_allclose(params[:2], x_popt, atol=1e-5) + np.testing.assert_allclose(param_errs[:2], np.sqrt(np.diag(x_pcov)), atol=1e-5) + np.testing.assert_allclose(params[2:], y_popt, atol=1e-5) + np.testing.assert_allclose(param_errs[2:], np.sqrt(np.diag(y_pcov)), atol=1e-5) + # Test fitter with bootstrap - t = np.arange(2015.0,2025.0, 0.5) + t = np.arange(2015.0, 2025.0, 0.5) # Get values from model and add scatter - x_true, y_true = mod.get_pos_at_time([true_params[p] for p in param_list], - [true_params[p] for p in fixed_param_list],t) - x_true_err, y_true_err = np.repeat(0.05,len(t)), np.repeat(0.05,len(t)) + x_true, y_true = mod.model( + t=t, + fit_params=np.array([true_params[p] for p in param_list]).T, + fixed_params_dict={'t0': true_params['t0']} + ) + x_true_err, y_true_err = np.ones_like(t)*0.05, np.ones_like(t)*0.05 x_sim = np.random.normal(x_true, x_true_err) y_sim = np.random.normal(y_true, y_true_err) # Run fit - params, param_errs = mod.fit(t, x_sim,y_sim, x_true_err, y_true_err, true_params['t0'],bootstrap=10) + params, param_errs = mod.fit(t, x_sim, y_sim, x_true_err, y_true_err, fixed_params_dict={'t0': true_params['t0']}, bootstrap=10) print(param_errs) # Confirm true value is within error bar of fit value assert np.all([within_error(true_params[param_list[i]], params[i], param_errs[i]) for i in range(len(params))]) -# # Test fitter for 2 pts -# t = np.array([2015.0,2025.0]) -# # Get values from model and add scatter -# x_true, y_true = mod.get_pos_at_time([true_params[p] for p in param_list], -# [true_params[p] for p in fixed_param_list],t) -# x_true_err, y_true_err = np.repeat(0.05,len(t)), np.repeat(0.05,len(t)) -# x_sim = np.random.normal(x_true, x_true_err) -# y_sim = np.random.normal(y_true, y_true_err) -# # Run fit -# mod_fit = motion_model.Linear(t0=true_params['t0']) -# params, param_errs = mod_fit.fit_motion_model(t, x_sim,y_sim, x_true_err, y_true_err, true_params['t0']) -# print("DJSKBGJ",param_list) -# print([true_params[p] for p in param_list]) -# print(params) -# print(param_errs) -# # Confirm true value is within error bar of fit value -# assert np.all([within_error(true_params[param_list[i]], params[i], param_errs[i]) for i in range(len(params),2)]) - def test_Acceleration(): # Test handling of a single star @@ -214,12 +247,11 @@ def test_Acceleration(): 't0':2025.0} mod = motion_model.Acceleration() param_list = mod.fit_param_names - fixed_param_list = mod.fixed_param_names # Confirm return of proper values for single t=t0 and array t x_t, y_t = mod.model( t=true_params['t0'], fit_params=np.array([true_params[p] for p in param_list]).T, - fixed_params=np.array([true_params[p] for p in fixed_param_list]).T + fixed_params_dict={'t0': true_params['t0']} ) np.testing.assert_allclose(x_t, true_params['x0']) np.testing.assert_allclose(y_t, true_params['y0']) @@ -227,7 +259,7 @@ def test_Acceleration(): x_t, y_t = mod.model( t=t_arr, fit_params=np.array([true_params[p] for p in param_list]).T, - fixed_params=np.array([true_params[p] for p in fixed_param_list]).T + fixed_params_dict={'t0': true_params['t0']} ) np.testing.assert_allclose(x_t, true_params['x0'] + (t_arr-true_params['t0'])*true_params['vx0'] + 0.5*(t_arr-true_params['t0'])**2*true_params['ax']) np.testing.assert_allclose(y_t, true_params['y0'] + (t_arr-true_params['t0'])*true_params['vy0'] + 0.5*(t_arr-true_params['t0'])**2*true_params['ay']) @@ -252,7 +284,7 @@ def test_Acceleration(): t=t_batch, fit_params=np.array([x0_batch, vx0_batch, ax_batch, y0_batch, vy0_batch, ay_batch]).T, fit_param_errs=np.array([x0_err_batch, vx0_err_batch, ax_err_batch, y0_err_batch, vy0_err_batch, ay_err_batch]).T, - fixed_params=t0_batch + fixed_params_dict={'t0': t0_batch} ) np.testing.assert_allclose(x_t_batch, x0_batch + (t_batch-t0_batch)*vx0_batch + 0.5*(t_batch-t0_batch)**2*ax_batch) np.testing.assert_allclose(y_t_batch, y0_batch + (t_batch-t0_batch)*vy0_batch + 0.5*(t_batch-t0_batch)**2*ay_batch) @@ -267,7 +299,7 @@ def test_Acceleration(): t=t_batch, fit_params=np.array([x0_batch, vx0_batch, ax_batch, y0_batch, vy0_batch, ay_batch]).T, fit_param_errs=np.array([x0_err_batch, vx0_err_batch, ax_err_batch, y0_err_batch, vy0_err_batch, ay_err_batch]).T, - fixed_params=t0_batch + fixed_params_dict={'t0': t0_batch} ) np.testing.assert_allclose(x_t_batch, np.array([x0_batch[i] + (t_batch-t0_batch[i])*vx0_batch[i] + 0.5*(t_batch-t0_batch[i])**2*ax_batch[i] for i in range(len(x0_batch))])) np.testing.assert_allclose(y_t_batch, np.array([y0_batch[i] + (t_batch-t0_batch[i])*vy0_batch[i] + 0.5*(t_batch-t0_batch[i])**2*ay_batch[i] for i in range(len(x0_batch))])) @@ -280,7 +312,7 @@ def test_Acceleration(): x_true, y_true = mod.model( t=t, fit_params=np.array([true_params[p] for p in param_list]).T, - fixed_params=np.array([true_params[p] for p in fixed_param_list]).T + fixed_params_dict={'t0': true_params['t0']} ) x_true_err = np.sqrt(true_params['x0_err']**2 + ((t - true_params['t0']) * true_params['vx0_err'])**2 + (0.5*(t - true_params['t0'])**2 * true_params['ax_err'])**2) @@ -290,280 +322,65 @@ def test_Acceleration(): y_sim = np.random.normal(y_true, y_true_err) # Run fit mod_fit = motion_model.Acceleration() - params, param_errs, _, _ = mod_fit.fit( + params, param_errs = mod_fit.fit( t=t, x=x_sim, y=y_sim, xe=x_true_err, ye=y_true_err, - t0=true_params['t0'] + fixed_params_dict={'t0': true_params['t0']} ) # Confirm true value is within error bar of fit value assert np.all([within_error(true_params[param_list[i]], params[i], param_errs[i]) for i in range(len(params))]) - + #@pytest.mark.skip(reason="not written") def test_Parallax(): # Test handling of a single star true_params = {'x0': 1.0, 'y0':-0.5, 'x0_err':0.1, 'y0_err':0.1, 'vx':-0.2, 'vy':0.5, 'vx_err':0.05, 'vy_err':0.05, - 'pi':0.5, 'RA':17.76, 'Dec':-28.933, 'PA':0, - 't0':2020.0} - mod = motion_model.Parallax(**{'RA':17.76, 'Dec':-28.933, 'PA':0}) + 'pi':0.5, 'ra':17.76, 'dec':-28.933, 'pa':0, + 't0':2020.0, 'obsLocation': 'earth'} + mod = motion_model.Parallax() param_list = mod.fit_param_names fixed_param_list = mod.fixed_param_names - print(param_list) - + # Test fitter t = np.arange(2015.0,2025.0, 0.5) # Get values from model and add scatter - x_true, y_true = mod.get_pos_at_time([true_params[p] for p in param_list], - [true_params[p] for p in fixed_param_list],t) - x_true_err, y_true_err = np.repeat(0.1,len(t)), np.repeat(0.1,len(t)) + x_true, y_true = mod.model( + t=t, + fit_params=np.array([true_params[p] for p in param_list]).T, + fixed_params_dict={p: true_params[p] for p in fixed_param_list} + ) + x_true_err, y_true_err = np.ones_like(t)*true_params['x0_err'], np.ones_like(t)*true_params['y0_err'] x_sim = np.random.normal(x_true, x_true_err) y_sim = np.random.normal(y_true, y_true_err) # Run fit - params, param_errs = mod.fit(t, x_sim,y_sim, x_true_err, y_true_err, true_params['t0']) + params, param_errs = mod.fit(t, x_sim,y_sim, x_true_err, y_true_err, fixed_params_dict={p: true_params[p] for p in fixed_param_list}) # Confirm true value is within error bar of fit value assert np.all([within_error(true_params[param_list[i]], params[i], param_errs[i]) for i in range(len(params))]) - def test_Parallax_PA(): # Set PA=0 model x0, y0 = 2.0, -1.0 vx, vy = 0.2, 0.5 ra, dec = 17.76, -28.933 pi = 0.5 - mod_pa0 = motion_model.Parallax(ra=ra, dec=dec, pa=0) + mod_pa0 = motion_model.Parallax() # Set PA=90 model with equivalent parameters in that frame - mod_pa90 = motion_model.Parallax(ra=ra, dec=dec, pa=90) + mod_pa90 = motion_model.Parallax() t_set = np.arange(2018, 2024, 0.01) + t0 = 2020.0 dat_pa0 = mod_pa0.model( t = t_set, fit_params = np.array([x0, vx, y0, vy, pi]).T, - fixed_params = [2020.0] + fixed_params_dict = {'t0': t0, 'ra': ra, 'dec': dec, 'pa': 0} ) dat_pa90 = mod_pa90.model( t = t_set, fit_params = np.array([y0, vy, -x0, -vx, pi]).T, - fixed_params = [2020.0] + fixed_params_dict = {'t0': t0, 'ra': ra, 'dec': dec, 'pa': 90} ) np.testing.assert_allclose(dat_pa0[0], -dat_pa90[1], atol=1e-10) - np.testing.assert_allclose(dat_pa0[1], dat_pa90[0], atol=1e-10) - - -def test_Linear_fit_vs_scipy(): - # Compare Linear fit results to scipy curve_fit results - t = np.array([0, 1., 2.2, 3.5, 5.]) - - x = np.array([ - [0., 0.5, 2.1, 3.2, 6.0], # Increasing 5 Epochs - [10.0, 8.9, 9.2, 7.4, 7.0], # Decreasing 5 Epochs - [2.5, np.nan, 5.2, np.nan, 5.0], # 3 Epochs - [np.nan, 6.2, np.nan, np.nan, 9.2], # 2 Epochs - # [np.nan, 2.0, np.nan, np.nan, np.nan], # 1 Epoch - # [np.nan, np.nan, np.nan, np.nan, np.nan] # All NaNs - ]) - - y = np.array([ - [10.2, 8.5, 9.1, 12.2, 13.0], # Increasing 5 Epochs - [8.0, 9.9, 8.2, 7.4, 7.0], # Decreasing 5 Epochs - [5.2, np.nan, 4.7, np.nan, 6.0], # 3 Epochs - [np.nan, 1.2, np.nan, np.nan, 3.2], # 2 Epochs - # [np.nan, 2.0, np.nan, np.nan, np.nan], # 1 Epoch - # [np.nan, np.nan, np.nan, np.nan, np.nan] # All NaNs - ]) - - xe = np.array([ - [0.2, 0.5, 0.3, 0.4, 0.6], - [0.5, 0.2, 0.7, 0.3, 0.2], - [0.5, np.nan, 0.6, np.nan, 0.3], - [np.nan, 0.6, np.nan, np.nan, 0.3], - # [np.nan, 0.4, np.nan, np.nan, np.nan], - # [np.nan, np.nan, np.nan, np.nan, np.nan] - ]) - - ye = np.array([ - [0.3, 0.2, 0.5, 0.2, 0.4], - [0.2, 0.5, 0.6, 0.4, 0.2], - [0.7, np.nan, 0.5, np.nan, 0.2], - [np.nan, 0.4, np.nan, np.nan, 0.5], - # [np.nan, 0.5, np.nan, np.nan, np.nan], - # [np.nan, np.nan, np.nan, np.nan, np.nan] - ]) - - x = np.ma.masked_invalid(x) - y = np.ma.masked_invalid(y) - xe = np.ma.masked_invalid(xe) - ye = np.ma.masked_invalid(ye) - mask = np.ma.getmaskarray(x) | np.ma.getmaskarray(y) | np.ma.getmaskarray(xe) | np.ma.getmaskarray(ye) - - # tab = StarTable({ - # 'x': x, - # 'y': y, - # 'xe': xe, - # 'ye': ye - # }) - # tab.meta['LIST_TIMES'] = t - # tab.fit_velocities(use_scipy=True, absolute_sigma=True) - - # Plot data - N = x.shape[0] - fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 6)) - for i in range(N): - line_mask = ~np.isnan(x[i]) & ~mask[i] - ax1.errorbar(t[line_mask], x[i][line_mask], yerr=xe[i][line_mask], fmt='o-', label=f'Line {i}') - ax2.errorbar(t[line_mask], y[i][line_mask], yerr=ye[i][line_mask], fmt='o-', label=f'Line {i}') - ax1.set_xlabel('Time') - ax1.set_ylabel('Position') - ax1.legend() - ax1.set_title('X vs Time') - ax2.set_xlabel('Time') - ax2.set_ylabel('Position') - ax2.legend() - ax2.set_title('Y vs Time') - plt.show() - - N = len(x) - t0 = np.average(np.broadcast_to(t, x.shape), weights=1./np.hypot(xe, ye), axis=1) - dt = np.zeros_like(x) - - # velfit - # vx_velfit = np.zeros(N) - # vxe_velfit = np.zeros(N) - # vy_velfit = np.zeros(N) - # vye_velfit = np.zeros(N) - # x0_velfit = np.zeros(N) - # x0e_velfit = np.zeros(N) - # y0_velfit = np.zeros(N) - # y0e_velfit = np.zeros(N) - - # scipy - vx_scipy = np.zeros(N) - vxe_scipy = np.zeros(N) - vy_scipy = np.zeros(N) - vye_scipy = np.zeros(N) - x0_scipy = np.zeros(N) - x0e_scipy = np.zeros(N) - y0_scipy = np.zeros(N) - y0e_scipy = np.zeros(N) - - # motion_model - mm = motion_model.Linear() - - vx_mm_scipy = np.zeros(N) - vxe_mm_scipy = np.zeros(N) - vy_mm_scipy = np.zeros(N) - vye_mm_scipy = np.zeros(N) - x0_mm_scipy = np.zeros(N) - x0e_mm_scipy = np.zeros(N) - y0_mm_scipy = np.zeros(N) - y0e_mm_scipy = np.zeros(N) - - vx_mm = np.zeros(N) - vxe_mm = np.zeros(N) - vy_mm = np.zeros(N) - vye_mm = np.zeros(N) - x0_mm = np.zeros(N) - x0e_mm = np.zeros(N) - y0_mm = np.zeros(N) - y0e_mm = np.zeros(N) - - def linear(t, c0, c1): - return c0 + c1*t - - # Absolute sigma - for absolute_sigma in [True, False]: - for i in range(N): - dt[i] = t - t0[i] - - # # velfit.linear_fit - # vx_velfit_results = linear_fit(dt[i][~mask[i]], x[i][~mask[i]], sigma=xe[i][~mask[i]], absolute_sigma=absolute_sigma) - # vy_velfit_results = linear_fit(dt[i][~mask[i]], y[i][~mask[i]], sigma=ye[i][~mask[i]], absolute_sigma=absolute_sigma) - - # vx_velfit[i] = vx_velfit_results['slope'] - # vxe_velfit[i] = vx_velfit_results['e_slope'] - # vy_velfit[i] = vy_velfit_results['slope'] - # vye_velfit[i] = vy_velfit_results['e_slope'] - # x0_velfit[i] = vx_velfit_results['intercept'] - # x0e_velfit[i] = vx_velfit_results['e_intercept'] - # y0_velfit[i] = vy_velfit_results['intercept'] - # y0e_velfit[i] = vy_velfit_results['e_intercept'] - - # scipy.curve_fit - p0x = np.array([0., x[i][~mask[i]].mean()]) - p0y = np.array([0., y[i][~mask[i]].mean()]) - popt_x, pcov_x = curve_fit(linear, dt[i][~mask[i]], x[i][~mask[i]], p0=p0x, sigma=xe[i][~mask[i]], absolute_sigma=absolute_sigma) - vx_scipy[i], vxe_scipy[i] = popt_x[1], np.sqrt(pcov_x[1, 1]) - x0_scipy[i], x0e_scipy[i] = popt_x[0], np.sqrt(pcov_x[0, 0]) - popt_y, pcov_y = curve_fit(linear, dt[i][~mask[i]], y[i][~mask[i]], p0=p0y, sigma=ye[i][~mask[i]], absolute_sigma=absolute_sigma) - vy_scipy[i], vye_scipy[i] = popt_y[1], np.sqrt(pcov_y[1, 1]) - y0_scipy[i], y0e_scipy[i] = popt_y[0], np.sqrt(pcov_y[0, 0]) - - # motion_model without scipy - params, param_errs = mm.fit( - t[~mask[i]], x[i][~mask[i]], y[i][~mask[i]], - xe[i][~mask[i]], ye[i][~mask[i]], t0[i], - weighting='var', - use_scipy=False, - absolute_sigma=absolute_sigma - ) - vx_mm[i] = params[mm.fit_param_names.index('vx')] - vy_mm[i] = params[mm.fit_param_names.index('vy')] - vxe_mm[i] = param_errs[mm.fit_param_names.index('vx')] - vye_mm[i] = param_errs[mm.fit_param_names.index('vy')] - x0_mm[i] = params[mm.fit_param_names.index('x0')] - y0_mm[i] = params[mm.fit_param_names.index('y0')] - x0e_mm[i] = param_errs[mm.fit_param_names.index('x0')] - y0e_mm[i] = param_errs[mm.fit_param_names.index('y0')] - - # motion_model with scipy - params, param_errs = mm.fit( - t[~mask[i]], x[i][~mask[i]], y[i][~mask[i]], - xe[i][~mask[i]], ye[i][~mask[i]], t0[i], - weighting='var', - use_scipy=True, - absolute_sigma=absolute_sigma - ) - vx_mm_scipy[i] = params[mm.fit_param_names.index('vx')] - vy_mm_scipy[i] = params[mm.fit_param_names.index('vy')] - vxe_mm_scipy[i] = param_errs[mm.fit_param_names.index('vx')] - vye_mm_scipy[i] = param_errs[mm.fit_param_names.index('vy')] - x0_mm_scipy[i] = params[mm.fit_param_names.index('x0')] - y0_mm_scipy[i] = params[mm.fit_param_names.index('y0')] - x0e_mm_scipy[i] = param_errs[mm.fit_param_names.index('x0')] - y0e_mm_scipy[i] = param_errs[mm.fit_param_names.index('y0')] - - rtol = 1e-5 - # np.testing.assert_allclose(vx_velfit, vx_scipy, rtol=rtol) - # np.testing.assert_allclose(vxe_velfit, vxe_scipy, rtol=rtol) - # np.testing.assert_allclose(vy_velfit, vy_scipy, rtol=rtol) - # np.testing.assert_allclose(vye_velfit, vye_scipy, rtol=rtol) - # np.testing.assert_allclose(x0_velfit, x0_scipy, rtol=rtol) - # np.testing.assert_allclose(x0e_velfit, x0e_scipy, rtol=rtol) - # np.testing.assert_allclose(y0_velfit, y0_scipy, rtol=rtol) - # np.testing.assert_allclose(y0e_velfit, y0e_scipy, rtol=rtol) - # np.testing.assert_allclose(vx_velfit, vx_mm, rtol=rtol) - # np.testing.assert_allclose(vxe_velfit, vxe_mm, rtol=rtol) - # np.testing.assert_allclose(vy_velfit, vy_mm, rtol=rtol) - # np.testing.assert_allclose(vye_velfit, vye_mm, rtol=rtol) - # np.testing.assert_allclose(x0_velfit, x0_mm, rtol=rtol) - # np.testing.assert_allclose(x0e_velfit, x0e_mm, rtol=rtol) - # np.testing.assert_allclose(y0_velfit, y0_mm, rtol=rtol) - # np.testing.assert_allclose(y0e_velfit, y0e_mm, rtol=rtol) - np.testing.assert_allclose(vx_scipy, vx_mm, rtol=rtol) - np.testing.assert_allclose(vxe_scipy, vxe_mm, rtol=rtol) - np.testing.assert_allclose(vy_scipy, vy_mm, rtol=rtol) - np.testing.assert_allclose(vye_scipy, vye_mm, rtol=rtol) - np.testing.assert_allclose(x0_scipy, x0_mm, rtol=rtol) - np.testing.assert_allclose(x0e_scipy, x0e_mm, rtol=rtol) - np.testing.assert_allclose(y0_scipy, y0_mm, rtol=rtol) - np.testing.assert_allclose(y0e_scipy, y0e_mm, rtol=rtol) - np.testing.assert_allclose(vx_scipy, vx_mm_scipy, rtol=rtol) - np.testing.assert_allclose(vxe_scipy, vxe_mm_scipy, rtol=rtol) - np.testing.assert_allclose(vy_scipy, vy_mm_scipy, rtol=rtol) - np.testing.assert_allclose(vye_scipy, vye_mm_scipy, rtol=rtol) - np.testing.assert_allclose(x0_scipy, x0_mm_scipy, rtol=rtol) - np.testing.assert_allclose(x0e_scipy, x0e_mm_scipy, rtol=rtol) - np.testing.assert_allclose(y0_scipy, y0_mm_scipy, rtol=rtol) - np.testing.assert_allclose(y0e_scipy, y0e_mm_scipy, rtol=rtol) + np.testing.assert_allclose(dat_pa0[1], dat_pa90[0], atol=1e-10) \ No newline at end of file diff --git a/flystar/tests/test_startable.py b/flystar/tests/test_startable.py index 1b8e5cb..804d4e7 100644 --- a/flystar/tests/test_startable.py +++ b/flystar/tests/test_startable.py @@ -41,9 +41,14 @@ def test_StarTable_init1(): starlist_names = np.array(['file1', 'file2', 'file3', 'file4', 'file5', 'file6', 'file7', 'file8']) # Generate the startable - startable = StarTable(name=name_in, x=x_in, y=y_in, m=m_in, xe=xe_in, ye=ye_in, me=me_in, - ref_list=1, - list_times=starlist_times, list_names=starlist_names) + startable = StarTable( + name=name_in, + x=x_in, y=y_in, m=m_in, + xe=xe_in, ye=ye_in, me=me_in, + ref_list=1, + list_times=starlist_times, + list_names=starlist_names + ) # Now put in some assertions to make sure all our startable columns # have the right dimensions. @@ -57,7 +62,7 @@ def test_StarTable_init1(): assert len(startable['name']) == N_stars assert startable.meta['list_times'][0] == starlist_times[0] assert type(startable) == StarTable - + return def test_StarTable_init2(): @@ -102,7 +107,7 @@ def test_combine_lists(): t.combine_lists('x', mask_val=-100000) assert t['x0'][0] == x_avg_0 assert t['x0'][-1] == pytest.approx(2108.855, 0.001) - + # Test 4: weighted average of x. x_wgt_0 = 1.0 / t['xe'][0, :]**2 x_avg_0 = np.average(t['x'][0, :], weights=x_wgt_0) @@ -204,8 +209,12 @@ def test_add_starlist(): assert t.meta['n_lists'] == 9 # Test 2: Add as starlist rather than with keywords. - starlist = StarList(name=t_orig['name'], x=x_new, y=y_new, m=m_new, - xe=xe_new, ye=ye_new, me=me_new, list_time=2001.0, list_name='A.lis') + starlist = StarList( + name=t_orig['name'], + x=x_new, y=y_new, m=m_new, + xe=xe_new, ye=ye_new, me=me_new, + list_time=2001.0, list_name='A.lis' + ) t = make_star_table() t.add_starlist(starlist=starlist) @@ -257,7 +266,7 @@ def test_get_starlist(): assert t['x'][0,2] == t_list['x'][0] assert type(t_list) == StarList assert len(t_list['x'].shape) == 1 - + return @@ -305,7 +314,7 @@ def test_fit_velocities(): tab = table.vstack((tab1, tab2, tab3)) tab.meta = tab1.meta - tab.fit_motion_model(verbose=True) + tab.fit_motion_model(verbose=True, mask_value=-100000.) # Test creation of new variables assert len(tab['vx']) == len(tab) @@ -323,18 +332,11 @@ def test_fit_velocities(): assert (tab['n_fit'][idx] == 2).all() # Test that the velocity errors were calculated. - assert (tab['vx_err'][0:100] > 0).all() - assert (tab['x0_err'][0:100] > 0).all() - assert (tab['vy_err'][0:100] > 0).all() - assert (tab['y0_err'][0:100] > 0).all() - assert np.isfinite(tab['x0']).all() - assert np.isfinite(tab['vx']).all() - assert np.isfinite(tab['y0']).all() - assert np.isfinite(tab['vy']).all() - assert np.isfinite(tab['x0_err']).all() - assert np.isfinite(tab['vx_err']).all() - assert np.isfinite(tab['y0_err']).all() - assert np.isfinite(tab['vy_err']).all() + assert (~(tab['vx_err'][0:100] < 0)).all() + assert (~(tab['x0_err'][0:100] < 0)).all() + assert (~(tab['vy_err'][0:100] < 0)).all() + assert (~(tab['y0_err'][0:100] < 0)).all() + ########## # Test running a second time. We should get the same results. @@ -343,28 +345,27 @@ def test_fit_velocities(): x0_orig = tab['x0'] vxe_orig = tab['vx_err'] x0e_orig = tab['x0_err'] - tab.fit_velocities(verbose=False) - - assert (vx_orig == tab['vx']).all() - assert (x0_orig == tab['x0']).all() - assert (vxe_orig == tab['vx_err']).all() - assert (x0e_orig == tab['x0_err']).all() + tab.fit_motion_model(verbose=False, mask_value=-100000.) + + np.testing.assert_allclose(tab['vx'], vx_orig) + np.testing.assert_allclose(tab['x0'], x0_orig) + np.testing.assert_allclose(tab['vx_err'], vxe_orig) + np.testing.assert_allclose(tab['x0_err'], x0e_orig) ########## # Test fixed_t0 functionality ########## fixed_t0 = tab['t0'] + np.random.normal(size=len(tab)) - tab.fit_velocities(fixed_t0=fixed_t0) - - assert(np.sum(abs(tab['t0'] - fixed_t0)) == 0) + tab.fit_motion_model(verbose=False, mask_value=-100000., fixed_params_dict={'t0': fixed_t0}) + np.testing.assert_allclose(tab['t0'], fixed_t0) ########## # Test bootstrap ########## tab_b = table.vstack((tab1, tab2, tab3)) tab_b.meta = tab1.meta - tab_b.fit_velocities(verbose=True, bootstrap=50) - + tab_b.fit_motion_model(verbose=True, bootstrap=50) + assert tab_b.meta['n_bootstrap'] == 50 assert tab_b['x0_err'][0] > tab['x0_err'][0] assert tab_b['vx_err'][0] > tab['vx_err'][0] @@ -375,83 +376,36 @@ def test_fit_velocities(): # Test what happens with no velocity errors ########## tab.remove_columns(['xe', 'ye', 'x0', 'y0', 'x0_err', 'y0_err', 'vx', 'vy', 'vx_err', 'vy_err', 'n_fit']) - tab.fit_velocities(verbose=False) + tab.fit_motion_model(verbose=False) assert len(tab['vx']) == len(tab) assert len(tab['vy']) == len(tab) assert len(tab['vx_err']) == len(tab) assert len(tab['vy_err']) == len(tab) assert len(tab['n_fit']) == len(tab) - assert (tab['vx_err'][0:100] > 0).all() - assert (tab['x0_err'][0:100] > 0).all() - assert (tab['vy_err'][0:100] > 0).all() - assert (tab['y0_err'][0:100] > 0).all() + assert (~(tab['vx_err'][0:100] < 0)).all() + assert (~(tab['x0_err'][0:100] < 0)).all() + assert (~(tab['vy_err'][0:100] < 0)).all() + assert (~(tab['y0_err'][0:100] < 0)).all() ######### # Test mask_list ######### # Test 5a: Masked print("Testing Masked List") - tt.fit_velocities(bootstrap=0, verbose=False, mask_lists=[1]) - assert np.arange(2.25, 48, 5) == pytest.approx(tt['x0'].data) - assert np.arange(2.25, 48, 5) == pytest.approx(tt['y0'].data) - assert np.full(10, 0.05) == pytest.approx(tt['x0_err'].data) - assert np.full(10, 0.05) == pytest.approx(tt['y0_err'].data) - assert np.ones(10) == pytest.approx(tt['vx'].data) - assert np.ones(10) == pytest.approx(tt['vy'].data) - assert np.full(10, 0.03380617) == pytest.approx(tt['vx_err'].data) - assert np.full(10, 0.03380617) == pytest.approx(tt['vy_err'].data) - assert 2017.25 * np.ones(10) == pytest.approx(tt['t0'].data) - - # Test 5b: Things that should break the code. - with pytest.raises(RuntimeError): - tt.fit_velocities(bootstrap=0, verbose=False, mask_lists=np.arange(2)) - with pytest.raises(RuntimeError): - tt.fit_velocities(bootstrap=0, verbose=False, mask_lists=True) + tt.fit_motion_model(verbose=False, mask_lists=[1]) + np.testing.assert_allclose(np.arange(2.25, 48, 5), tt['x0'].data) + np.testing.assert_allclose(np.arange(2.25, 48, 5), tt['y0'].data) + np.testing.assert_allclose(np.full(10, 0.05), tt['x0_err'].data) + np.testing.assert_allclose(np.full(10, 0.05), tt['y0_err'].data) + np.testing.assert_allclose(np.ones(10), tt['vx'].data) + np.testing.assert_allclose(np.ones(10), tt['vy'].data) + np.testing.assert_allclose(np.full(10, 0.03380617), tt['vx_err'].data) + np.testing.assert_allclose(np.full(10, 0.03380617), tt['vy_err'].data) + np.testing.assert_allclose(2017.25 * np.ones(10), tt['t0'].data) return -def test_fit_velocities_1epoch(): - ########## - # Test: only 1 epoch - ########## - tab = make_star_table_1epoch() - - # We don't need the entire table... lets just - # pull a small subset for faster testing. - tab1 = tab[0:100] - tab2 = tab[10000:10100] - tab3 = tab[-100:] - tab_1 = table.vstack((tab1, tab2, tab3)) - tab_1.meta = tab1.meta - - tab_1.fit_velocities(verbose=False) - - assert 'n_fit' in tab_1.colnames - assert 't0' in tab_1.colnames - assert 'x0' in tab_1.colnames - assert 'y0' in tab_1.colnames - assert 'vx' in tab_1.colnames - assert 'vy' in tab_1.colnames - assert 'x0_err' in tab_1.colnames - assert 'y0_err' in tab_1.colnames - assert 'vx_err' in tab_1.colnames - assert 'vy_err' in tab_1.colnames - - assert (tab_1['x0'] == tab_1['x'][:,0]).all() - assert (tab_1['y0'] == tab_1['y'][:,0]).all() - assert (tab_1['x0_err'] == tab_1['xe'][:,0]).all() - assert (tab_1['y0_err'] == tab_1['ye'][:,0]).all() - - assert(np.isnan(tab_1['vx'])).all() - assert(np.isnan(tab_1['vy'])).all() - assert(np.isnan(tab_1['vx_err'])).all() - assert(np.isnan(tab_1['vy_err'])).all() - - assert(tab_1['t0'] == 2001.0).all() - assert(tab_1['n_fit'] == 1).all() - - return def test_fit_velocities_2epoch(): @@ -468,7 +422,7 @@ def test_fit_velocities_2epoch(): tab_2 = table.vstack((tab1, tab2, tab3)) tab_2.meta=tab1.meta - tab_2.fit_velocities(verbose=False) + tab_2.fit_motion_model(verbose=False, mask_value=-100000.) assert 'n_fit' in tab_2.colnames assert 't0' in tab_2.colnames @@ -496,40 +450,6 @@ def test_fit_velocities_2epoch(): return -def test_fit_velocities_all_detected(): - """ - Test the fit_velocities function when all stars are detected in all epochs. - """ - tab = StarTable.read(test_dir + '/test_all_detected.fits') - tab_orig = tab.copy() - # tab = tab[:1] - - epochs = ['2005_F814W', '2010_F160W', '2013_F160W', '2015_F160W'] - epoch_cols = [['_'.join(_.split('_')[:2]) for _ in tab.meta['EPNAMES']].index(epoch) for epoch in epochs] - - mm = motion_model.Linear() - tab.fit_velocities_all_detected( - weighting='var', - use_scipy=False, absolute_sigma=False, - motion_model_to_fit=mm, - epoch_cols=epoch_cols, - art_star=True - ) - - # Check that the output table has the expected columns - for col in ['n_fit', 't0', 'x0', 'y0', 'vx', 'vy', 'x0_err', 'y0_err', 'vx_err', 'vy_err']: - assert col in tab.colnames - - # Check that the fitted values match the original values - np.testing.assert_almost_equal(tab['x0'], tab_orig['x0']) - np.testing.assert_almost_equal(tab['y0'], tab_orig['y0']) - np.testing.assert_almost_equal(tab['t0'], tab_orig['t0']) - np.testing.assert_almost_equal(tab['vx'], tab_orig['vx']) - np.testing.assert_almost_equal(tab['vy'], tab_orig['vy']) - np.testing.assert_almost_equal(tab['vxe'], tab_orig['vxe']) - np.testing.assert_almost_equal(tab['vye'], tab_orig['vye']) - - return def make_star_table(): # User input @@ -561,8 +481,8 @@ def make_star_table(): n=n_in, ref_list=1 ) - startable.meta['LIST_TIMES'] = starlist_times - startable.meta['LIST_NAMES'] = starlist_names + startable.meta['list_times'] = starlist_times + startable.meta['list_names'] = starlist_names return startable From 2b0bccba67f93e025c38a0f3c49cdc18c7ddbfee Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Thu, 4 Dec 2025 11:56:35 -0800 Subject: [PATCH 132/254] Remove unecessary plx functions and add motion model documentation --- flystar/motion_model.py | 77 +++++++++++------ flystar/parallax.py | 182 ---------------------------------------- 2 files changed, 52 insertions(+), 207 deletions(-) diff --git a/flystar/motion_model.py b/flystar/motion_model.py index e24067b..9ab80b7 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -26,27 +26,47 @@ class MotionModel(ABC): optional_param_names = [] def __init__(self, *args, **kwargs): - # TODO: do we need this? - '''for param in self.fitter_param_names: - param_var = getattr(self, param) - if not isinstance(param_var, (list, np.ndarray)): - setattr(self, param, np.array([param_var]))''' + """ + Make a motion model object. This object defines the fitter and fixed parameters, + and if needed stores metadata such as RA and Dec for Parallax, + for the given motion model and contains functions to fit these values to data + and apply the values to compute expected positions at given times. Each instance + corresponds to a given motion model, not an individual star, and thus the fit + values are only input/returned in functions and not stored in the object. + """ return def get_pos_at_time(self, params, t): + """ + Position calculator for a single star using a given motion model and input + model parameters and times. + """ #return x, y pass def get_batch_pos_at_time(self, t): + """ + Position calculator for a set of stars using a given motion model and input + model parameters and times. + """ #return x, y, x_err, y_err pass def run_fit(self, t, x, y, xe, ye, t0, weighting='var', use_scipy=True, absolute_sigma=True): + """ + Run a single fit of the data to the motion model and return the best parameters. + This function is used by the overall fit_motion_model function once for a basic fit + or several times for a bootstrap fit. + """ # Run a single fit (used both for overall fit + bootstrap iterations) pass def get_weights(self, xe, ye, weighting='var'): + """ + Get the weights for each data point for fitting. Options are 'var' (default) + and 'std'. + """ if weighting=='std': return 1./xe, 1./ye elif weighting=='var': @@ -56,6 +76,9 @@ def get_weights(self, xe, ye, weighting='var'): return 1./xe**2, 1./ye**2 def scale_errors(self, errs, weighting='var'): + """ + Rescale the fit result errors as needed, according to the weighting scheme used. + """ if weighting=='std': return np.array(errs)**2 elif weighting=='var': @@ -70,6 +93,7 @@ def fit_motion_model(self, t, x, y, xe, ye, t0, bootstrap=0, weighting='var', Fit the input positions on the sky and errors to determine new parameters for this motion model (MM). Best-fit parameters will be returned along with uncertainties. + Optionally, bootstrap error estimation can be performed. """ params, param_errs = self.run_fit(t, x, y, xe, ye, t0=t0, weighting=weighting, use_scipy=use_scipy, absolute_sigma=absolute_sigma) @@ -99,8 +123,7 @@ def fit_motion_model(self, t, x, y, xe, ye, t0, bootstrap=0, weighting='var', def get_chi2(self, fit_params, fixed_params, t, x, y, xe, ye, reduced=False): """ - Get the chi^2 value for the current MM and - the input data. + Get the chi^2 value for the input motion model parameters and data. """ x_pred, y_pred = self.get_pos_at_time(fit_params, fixed_params, t) chi2x = np.sum((x-x_pred)**2 / xe**2) @@ -345,8 +368,7 @@ class Parallax(MotionModel): """ Motion model for linear proper motion + parallax - Requires RA, Dec, and PA parameters (degrees) for parallax calculation. - RA, Dec in J2000 + Requires RA & Dec (J2000) for parallax calculation. Optional PA is counterclockwise offset of the image y-axis from North. Optional obs parameter describes observer location, default is 'earth'. """ @@ -450,11 +472,13 @@ def fit_func(use_t, x0,vx, y0,vy, pi): param_errors = [x0_err, vx_err, y0_err, vy_err, pi_err] return params, param_errors -""" -Check that everything is set up properly for motion models to run and their -required metadata. -""" + def validate_motion_model_dict(motion_model_dict, startable, default_motion_model): + """ + Check that everything is set up properly for motion models to run and their + required metadata. + """ + # Collect names of all motion models that might get used. all_motion_model_names = ['Fixed'] if default_motion_model is not None: @@ -478,11 +502,12 @@ def validate_motion_model_dict(motion_model_dict, startable, default_motion_mode return motion_model_dict -""" -Get all the motion model parameters for a given motion_model_name. -Optionally, include fixed and error parameters (included by default). -""" + def get_one_motion_model_param_names(motion_model_name, with_errors=True, with_fixed=True): + """ + Get all the motion model parameters for a given motion_model_name. + Optionally, include fixed and error parameters (included by default). + """ mod = eval(motion_model_name) list_of_parameters = [] list_of_parameters += getattr(mod, 'fitter_param_names') @@ -492,11 +517,12 @@ def get_one_motion_model_param_names(motion_model_name, with_errors=True, with_f list_of_parameters += [par+'_err' for par in getattr(mod, 'fitter_param_names')] return list_of_parameters -""" -Get all the motion model parameters for all models given in motion_model_list. -Optionally, include fixed and error parameters (included by default). -""" + def get_list_motion_model_param_names(motion_model_list, with_errors=True, with_fixed=True): + """ + Get all the motion model parameters for all models given in motion_model_list. + Optionally, include fixed and error parameters (included by default). + """ list_of_parameters = [] all_motion_models = [eval(mm) for mm in np.unique(motion_model_list).tolist()] for aa in range(len(all_motion_models)): @@ -512,11 +538,12 @@ def get_list_motion_model_param_names(motion_model_list, with_errors=True, with_ return np.unique(list_of_parameters).tolist() -""" -Get all the motion model parameters for all models defined in this module. -Optionally, include fixed and error parameters (included by default). -""" + def get_all_motion_model_param_names(with_errors=True, with_fixed=True): + """ + Get all the motion model parameters for all models defined in this module. + Optionally, include fixed and error parameters (included by default). + """ list_of_parameters = [] all_motion_models = MotionModel.__subclasses__() for aa in range(len(all_motion_models)): diff --git a/flystar/parallax.py b/flystar/parallax.py index b4b3a1b..4792ec6 100755 --- a/flystar/parallax.py +++ b/flystar/parallax.py @@ -64,39 +64,6 @@ def parallax_in_direction(RA, Dec, mjd, obsLocation='earth', PA=0): return pvec -def dparallax_dt_in_direction(RA, Dec, mjd, obsLocation='earth'): - """ - R.A. in degrees. (J2000) - Dec. in degrees. (J2000) - MJD - - Equations following MulensModel. - Time derivative --> units are yr^-1 - - """ - # print('parallax_in_direction: len(t) = ', len(mjd)) - # Munge inputs into astropy format. - times = Time(mjd + 2400000.5, format='jd', scale='tdb') - coord = SkyCoord(RA, Dec, unit=(units.deg, units.deg)) - - direction = coord.cartesian.xyz.value - north = np.array([0., 0., 1.]) - _east_projected = np.cross(north, direction) / np.linalg.norm(np.cross(north, direction)) - _north_projected = np.cross(direction, _east_projected) / np.linalg.norm(np.cross(direction, _east_projected)) - - obs_posvel = get_observer_barycentric(obsLocation, times, velocity=True)[1] - sun_posvel = get_body_barycentric_posvel('Sun', times)[1] - sun_obs_vel = sun_posvel - obs_posvel - vel = sun_obs_vel.xyz.T.to(units.au / units.year) - - e = np.dot(vel, _east_projected) - n = np.dot(vel, _north_projected) - - dpvec_dt = np.array([e.value, n.value]).T - - return dpvec_dt - - def get_observer_barycentric(body, times, min_ephem_step=1, velocity=False): """ Get the barycentric position of a satellite or other Solar System body @@ -180,152 +147,3 @@ def get_observer_barycentric(body, times, min_ephem_step=1, velocity=False): return obs_pos -def sun_position(mjd, radians=False): - """ - - NAME: - SUNPOS - - PURPOSE: - To compute the RA and Dec of the Sun at a given date. - - INPUTS: - mjd - The modified Julian date of the day (and time), scalar or vector - - OUTPUTS: - ra: - | The right ascension of the sun at that date in DEGREES - | double precision, same number of elements as jd - dec: - The declination of the sun at that date in DEGREES - elong: - Ecliptic longitude of the sun at that date in DEGREES. - obliquity: - the obliquity of the ecliptic, in DEGREES - - OPTIONAL INPUT KEYWORD: - RADIAN [def=False] - If this keyword is set to True, then all output variables - are given in Radians rather than Degrees - - NOTES: - Patrick Wallace (Rutherford Appleton Laboratory, UK) has tested the - accuracy of a C adaptation of the sunpos.pro code and found the - following results. From 1900-2100 SUNPOS gave 7.3 arcsec maximum - error, 2.6 arcsec RMS. Over the shorter interval 1950-2050 the figures - were 6.4 arcsec max, 2.2 arcsec RMS. - - The returned RA and Dec are in the given date's equinox. - - Procedure was extensively revised in May 1996, and the new calling - sequence is incompatible with the old one. - METHOD: - Uses a truncated version of Newcomb's Sun. Adapted from the IDL - routine SUN_POS by CD Pike, which was adapted from a FORTRAN routine - by B. Emerson (RGO). - EXAMPLE: - (1) Find the apparent RA and Dec of the Sun on May 1, 1982 - - | IDL> jdcnv, 1982, 5, 1,0 ,jd ;Find Julian date jd = 2445090.5 - | IDL> sunpos, jd, ra, dec - | IDL> print,adstring(ra,dec,2) - | 02 31 32.61 +14 54 34.9 - - The Astronomical Almanac gives 02 31 32.58 +14 54 34.9 so the error - in SUNPOS for this case is < 0.5". - - (2) Find the apparent RA and Dec of the Sun for every day in 1997 - - | IDL> jdcnv, 1997,1,1,0, jd ;Julian date on Jan 1, 1997 - | IDL> sunpos, jd+ dindgen(365), ra, dec ;RA and Dec for each day - - MODIFICATION HISTORY: - - * Written by Michael R. Greason, STX, 28 October 1988. - * Accept vector arguments, W. Landsman - April,1989 - * Eliminated negative right ascensions - MRG, Hughes STX, 6 May 1992. - * Rewritten using the 1993 Almanac. Keywords added. MRG, HSTX, 10 February 1994. - * Major rewrite, improved accuracy, always return values in degrees - W. Landsman May, 1996 - * Added /RADIAN keyword; W. Landsman; August, 1997 - * Converted to IDL V5.0; W. Landsman; September 1997 - * Converted to python; J. R. Lu; August 2016 - """ - # form time in Julian centuries from 1900.0 - t_obj = Time(mjd, format='mjd') - t = (t_obj.jd - 2415020.0) / 36525.0 - - # form sun's mean longitude - l = (279.696678 + ((36000.768925 * t) % 360.0)) * 3600.0 - - # allow for ellipticity of the orbit (equation of centre) - # using the Earth's mean anomaly ME - me = 358.475844 + ((35999.049750 * t) % 360.0) - ellcor = (6910.1 - 17.2 * t) * np.sin(np.radians(me)) + 72.3 * np.sin( - np.radians(2.0 * me)) - l = l + ellcor - - # allow for the Venus perturbations using the mean anomaly of Venus MV - mv = 212.603219 + ((58517.803875 * t) % 360.0) - vencorr = 4.8 * np.cos(np.radians(299.1017 + mv - me)) + \ - 5.5 * np.cos(np.radians(148.3133 + 2.0 * mv - 2.0 * me)) + \ - 2.5 * np.cos(np.radians(315.9433 + 2.0 * mv - 3.0 * me)) + \ - 1.6 * np.cos(np.radians(345.2533 + 3.0 * mv - 4.0 * me)) + \ - 1.0 * np.cos(np.radians(318.1500 + 3.0 * mv - 5.0 * me)) - l += vencorr - - # Allow for the Mars perturbations using the mean anomaly of Mars MM - mm = 319.529425 + ((19139.858500 * t) % 360.0) - marscorr = 2.0 * np.cos(np.radians(343.8883 - 2.0 * mm + 2.0 * me)) + \ - 1.8 * np.cos(np.radians(200.4017 - 2.0 * mm + me)) - l += marscorr - - # Allow for the Jupiter perturbations using the mean anomaly of Jupiter MJ - mj = 225.328328 + ((3034.6920239 * t) % 360.0) - jupcorr = 7.2 * np.cos(np.radians(179.5317 - mj + me)) + \ - 2.6 * np.cos(np.radians(263.2167 - mj)) + \ - 2.7 * np.cos(np.radians(87.1450 - 2.0 * mj + 2.0 * me)) + \ - 1.6 * np.cos(np.radians(109.4933 - 2.0 * mj + me)) - l += jupcorr - - # Allow for the Moons perturbations using the mean elongation of - # the Moon from the Sun D - d = 350.7376814 + ((445267.11422 * t) % 360.0) - mooncorr = 6.5 * np.sin(np.radians(d)) - l += mooncorr - - # Allow for long period terms - longterm = + 6.4 * np.sin(np.radians(231.19 + 20.20 * t)) - l += longterm - l = (l + 2592000.0) % 1296000.0 - longmed = l / 3600.0 - - # Allow for Aberration - l -= 20.5 - - # Allow for Nutation using the longitude of the Moons mean node OMEGA - omega = 259.183275 - ((1934.142008 * t) % 360.0) - l -= 17.2 * np.sin(np.radians(omega)) - - # Form the True Obliquity - oblt = 23.452294 - 0.0130125 * t + ( - 9.2 * np.cos(np.radians(omega))) / 3600.0 - - # Form Right Ascension and Declination - l = l / 3600.0 - l_rad = np.radians(l) - oblt_rad = np.radians(oblt) - ra = np.arctan2(np.sin(l_rad) * np.cos(oblt_rad), np.cos(l_rad)) - - if (len(ra) > 1): - neg = np.where(ra < 0.0)[0] - ra[neg] = ra[neg] + 2.0 * math.pi - - dec = np.arcsin(np.sin(l_rad) * np.sin(oblt_rad)) - - if radians: - oblt = oblt_rad - longmed = np.radians(longmed) - else: - ra = np.degrees(ra) - dec = np.degrees(dec) - - return ra, dec, longmed, oblt From 9aa4da73c0994f64f620cce515da38db6946f3d0 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Tue, 9 Dec 2025 14:36:33 -0800 Subject: [PATCH 133/254] missing tab fix --- flystar/motion_model.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/flystar/motion_model.py b/flystar/motion_model.py index 9ab80b7..0b86d07 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -26,14 +26,14 @@ class MotionModel(ABC): optional_param_names = [] def __init__(self, *args, **kwargs): - """ - Make a motion model object. This object defines the fitter and fixed parameters, - and if needed stores metadata such as RA and Dec for Parallax, - for the given motion model and contains functions to fit these values to data - and apply the values to compute expected positions at given times. Each instance - corresponds to a given motion model, not an individual star, and thus the fit - values are only input/returned in functions and not stored in the object. - """ + """ + Make a motion model object. This object defines the fitter and fixed parameters, + and if needed stores metadata such as RA and Dec for Parallax, + for the given motion model and contains functions to fit these values to data + and apply the values to compute expected positions at given times. Each instance + corresponds to a given motion model, not an individual star, and thus the fit + values are only input/returned in functions and not stored in the object. + """ return def get_pos_at_time(self, params, t): From f932e230684738777337c778e1d8ce0643911a7e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Wed, 10 Dec 2025 00:33:26 -0800 Subject: [PATCH 134/254] Add Parallax test plot --- flystar/motion_model.py | 25 ++++---------- flystar/tests/test_motion_model.py | 52 +++++++++++++++++------------- 2 files changed, 37 insertions(+), 40 deletions(-) diff --git a/flystar/motion_model.py b/flystar/motion_model.py index 5ccb6ee..42a5e54 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -135,21 +135,10 @@ def fit( rng = np.random.default_rng(seed) edx = np.arange(n_obs, dtype=int) # Precompute All Bootstrap Draws at Once - bdx_all = rng.choice(edx, size=(bootstrap, m), replace=True) - - # Count unique indices per bootstrap sample - uniq_counts = np.apply_along_axis(lambda x: len(np.unique(x)), 1, bdx_all) - - # Identify invalid samples - bad = uniq_counts < self.n_params - n_bad = bad.sum() - - while n_bad > 0: - # Resample only bad rows - bdx_all[bad] = rng.choice(edx, size=(n_bad, m), replace=True) - uniq_counts = np.apply_along_axis(lambda x: len(np.unique(x)), 1, bdx_all) - bad = uniq_counts < self.n_params - n_bad = bad.sum() + # Ensure there are enough unique points in each bootstrap sample + bdx_unique = rng.choice(edx, size=(bootstrap, self.n_params), replace=False) + bdx_extra = rng.choice(edx, size=(bootstrap, m - self.n_params), replace=True) + bdx_all = np.hstack((bdx_unique, bdx_extra)) bb_params = [] bb_params_errs = [] @@ -936,9 +925,9 @@ def model(self, t, fit_params, fixed_params_dict, fit_param_errs=None): dt = t[np.newaxis, :] - t0[:, np.newaxis] # Shape (N_stars, N_times) t_mjd = Time(t, format='decimalyear', scale='utc').mjd # Shape (N_times,) self.pvec = self.calc_parallax_vector(t_mjd, ra, dec, pa=pa, obsLocation=obsLocation) # Shape (2, N_times) - xy = self.model_fit(dt, x0[:, np.newaxis], vx[:, np.newaxis], y0[:, np.newaxis], vy[:, np.newaxis], pi[:, np.newaxis]) # Shape (N_stars, N_times) - x = xy[:, :N_times] # Shape (N_stars, N_times) - y = xy[:, N_times:] # Shape (N_stars, N_times) + x, y = self.model_fit(dt, x0[:, np.newaxis], vx[:, np.newaxis], y0[:, np.newaxis], vy[:, np.newaxis], pi[:, np.newaxis]) # Shape (N_stars, N_times) + # x = xy[:, :N_times] # Shape (N_stars, N_times) + # y = xy[:, N_times:] # Shape (N_stars, N_times) if N_stars == 1 or N_times == 1: # If only one star, return flattened arrays diff --git a/flystar/tests/test_motion_model.py b/flystar/tests/test_motion_model.py index 0215dbe..b3bb4e8 100755 --- a/flystar/tests/test_motion_model.py +++ b/flystar/tests/test_motion_model.py @@ -11,19 +11,13 @@ def test_Fixed(): true_params = {'x0': 1.0, 'y0':0.5, 'x0_err':0.1, 'y0_err':0.1} mod = motion_model.Fixed() param_list = mod.fit_param_names - fixed_param_list = mod.fixed_param_names # Confirm return of proper values for single t and array t - # x_t, y_t = mod.get_pos_at_time([true_params[p] for p in param_list], - # [true_params[p] for p in fixed_param_list], 0.0) x_t, y_t = mod.model( 0.0, fit_params=np.array([true_params['x0'], true_params['y0']]).T ) assert x_t==true_params['x0'] assert y_t==true_params['y0'] - # x_t, y_t = mod.get_pos_at_time([true_params[p] for p in param_list], - # [true_params[p] for p in fixed_param_list], - # [0.0,2025.0,10000]) x_t, y_t = mod.model( [0.0,2025.0,10000], fit_params=np.array([true_params['x0'], true_params['y0']]).T @@ -31,15 +25,13 @@ def test_Fixed(): assert (x_t==true_params['x0']).all() assert (y_t==true_params['y0']).all() - # Check behavior of get_batch_pos_at_time + # Check behavior of model x0_batch = np.random.uniform(-2.0,2.0, 50) y0_batch = np.random.uniform(-2.0,2.0, 50) x0_err_batch = np.repeat(0.1, 50) y0_err_batch = np.repeat(0.1, 50) # Single epoch t_batch=2020.0 - # x_t_batch, y_t_batch, x_err_t_batch, y_err_t_batch = mod.get_batch_pos_at_time(t_batch, - # x0=x0_batch, y0=y0_batch, x0_err=x0_err_batch, y0_err=y0_err_batch) x_t_batch, y_t_batch, x_err_t_batch, y_err_t_batch = mod.model( t_batch, fit_params=np.array([x0_batch, y0_batch]).T, @@ -51,8 +43,6 @@ def test_Fixed(): assert (y_err_t_batch==y0_err_batch).all() # Multiple times t_batch = np.arange(2015.0,2025.0, 0.5) - # x_t_batch, y_t_batch, x_err_t_batch, y_err_t_batch = mod.get_batch_pos_at_time(t_batch, - # x0=x0_batch, y0=y0_batch, x0_err=x0_err_batch, y0_err=y0_err_batch) x_t_batch, y_t_batch, x_err_t_batch, y_err_t_batch = mod.model( t_batch, fit_params=np.array([x0_batch, y0_batch]).T, @@ -66,8 +56,6 @@ def test_Fixed(): # Test fitter t = np.arange(2015.0,2025.0, 0.5) # Get values from model and add scatter - # x_true, y_true = mod.get_pos_at_time([true_params[p] for p in param_list], - # [true_params[p] for p in fixed_param_list], t) x_true, y_true = mod.model( t, fit_params=np.array([true_params['x0'], true_params['y0']]) @@ -125,7 +113,7 @@ def test_Linear(): assert (x_t==(true_params['x0'] + (t_arr-true_params['t0'])*true_params['vx'])).all() assert (y_t==(true_params['y0'] + (t_arr-true_params['t0'])*true_params['vy'])).all() - # Check behavior of get_batch_pos_at_time + # Check behavior of model x0_batch = np.random.uniform(-2.0,2.0, 50) y0_batch = np.random.uniform(-2.0,2.0, 50) vx_batch = np.random.uniform(-2.0,2.0, 50) @@ -166,8 +154,6 @@ def test_Linear(): # Test fitter t = np.arange(2015.0,2025.0, 0.5) # Get values from model and add scatter - # x_true, y_true = mod.get_pos_at_time([true_params[p] for p in param_list], - # [true_params[p] for p in fixed_param_list],t) x_true, y_true = mod.model( t=t, fit_params=np.array([true_params[p] for p in param_list]).T, @@ -234,7 +220,6 @@ def linear(t, x0, vx): y_sim = np.random.normal(y_true, y_true_err) # Run fit params, param_errs = mod.fit(t, x_sim, y_sim, x_true_err, y_true_err, fixed_params_dict={'t0': true_params['t0']}, bootstrap=10) - print(param_errs) # Confirm true value is within error bar of fit value assert np.all([within_error(true_params[param_list[i]], params[i], param_errs[i]) for i in range(len(params))]) @@ -264,7 +249,7 @@ def test_Acceleration(): np.testing.assert_allclose(x_t, true_params['x0'] + (t_arr-true_params['t0'])*true_params['vx0'] + 0.5*(t_arr-true_params['t0'])**2*true_params['ax']) np.testing.assert_allclose(y_t, true_params['y0'] + (t_arr-true_params['t0'])*true_params['vy0'] + 0.5*(t_arr-true_params['t0'])**2*true_params['ay']) - # Check behavior of get_batch_pos_at_time + # Check behavior of model x0_batch = np.random.uniform(-2.0,2.0, 50) y0_batch = np.random.uniform(-2.0,2.0, 50) vx0_batch = np.random.uniform(-2.0,2.0, 50) @@ -342,21 +327,44 @@ def test_Parallax(): 't0':2020.0, 'obsLocation': 'earth'} mod = motion_model.Parallax() param_list = mod.fit_param_names - fixed_param_list = mod.fixed_param_names - + fixed_params_dict = { + 't0': true_params['t0'], + 'ra': true_params['ra'], + 'dec': true_params['dec'], + 'pa': true_params['pa'], + 'obsLocation': true_params['obsLocation'] + } + # Test fitter t = np.arange(2015.0,2025.0, 0.5) # Get values from model and add scatter x_true, y_true = mod.model( t=t, fit_params=np.array([true_params[p] for p in param_list]).T, - fixed_params_dict={p: true_params[p] for p in fixed_param_list} + fixed_params_dict=fixed_params_dict ) x_true_err, y_true_err = np.ones_like(t)*true_params['x0_err'], np.ones_like(t)*true_params['y0_err'] x_sim = np.random.normal(x_true, x_true_err) y_sim = np.random.normal(y_true, y_true_err) # Run fit - params, param_errs = mod.fit(t, x_sim,y_sim, x_true_err, y_true_err, fixed_params_dict={p: true_params[p] for p in fixed_param_list}) + params, param_errs = mod.fit(t, x_sim,y_sim, x_true_err, y_true_err, fixed_params_dict=fixed_params_dict) + + x_model, y_model = mod.model( + t=t, + fit_params=params, + fixed_params_dict=fixed_params_dict + ) + fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5)) + ax1.plot(t, x_true, 'k-', label='True x') + ax1.errorbar(t, x_sim, yerr=x_true_err, fmt='ro', label='Sim x') + ax1.plot(t, x_model, 'r-', label='Model x') + ax1.set_xlabel('t') + ax1.set_ylabel('x') + ax1.legend() + ax2.plot(t, y_true, 'k-', label='True x') + ax2.errorbar(t, y_sim, yerr=x_true_err, fmt='ro', label='Sim x') + ax2.plot(t, y_model, 'r-', label='Model x') + ax2.set_xlabel('t') # Confirm true value is within error bar of fit value assert np.all([within_error(true_params[param_list[i]], params[i], param_errs[i]) for i in range(len(params))]) From be254926a2116bfa4aa8ba1336929309ce38eed8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Mon, 15 Dec 2025 06:46:35 -0800 Subject: [PATCH 135/254] Partial Update of align and test_align --- flystar/align.py | 264 ++++++++++++++++++++---------------- flystar/tests/test_align.py | 79 +++++------ 2 files changed, 185 insertions(+), 158 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 8215246..ca9bc93 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -1,10 +1,10 @@ import numpy as np -from flystar import match -from flystar import transforms -from flystar import plots -from flystar.starlists import StarList -from flystar.startables import StarTable -from flystar import motion_model +from . import match +from . import transforms +from . import plots +from .starlists import StarList +from .startables import StarTable +from . import motion_model from astropy.table import Table, Column, vstack import datetime import copy @@ -21,12 +21,12 @@ def __init__(self, list_of_starlists, ref_index=0, iters=2, outlier_tol=[None, None], trans_args=[{'order': 2}, {'order': 2}], init_order=1, - mag_trans=True, mag_lim=None, trans_weights=None, vel_weights='var', + mag_trans=True, mag_lim=None, trans_weighting=None, vel_weighting='var', trans_input=None, trans_class=transforms.PolyTransform, calc_trans_inverse=False, init_guess_mode='miracle', iter_callback=None, - default_motion_model='Fixed', - motion_model_dict = {}, + motion_models=['Empty', 'Fixed'], + fixed_params_dict = None, use_scipy=True, absolute_sigma=False, save_path=None, @@ -89,13 +89,13 @@ def __init__(self, list_of_starlists, ref_index=0, iters=2, separately for each list and each iteration, you need to pass in a 2D array that has shape (N_lists, 2). - trans_weights : str + trans_weighting : str Either None (def), 'both,var', 'list,var', or 'ref,var' depending on whether you want to weight by the positional uncertainties (variances) in the individual starlists, or also with the uncertainties in the reference frame itself. Note weighting only works when there are positional uncertainties availabe. Other options include 'both,std', 'list,std', 'list,var'. - vel_weights : str + vel_weighting : str Either 'var' (def) or 'std', depending on whether you want to weight the motion model fits by the variance or standard deviation of the position data @@ -130,11 +130,11 @@ def = None. If not None, then this should contain an array or list of transform A function to call (that accepts a StarTable object and an iteration number) at the end of every iteration. This can be used for plotting or printing state. - default_motion_model : string - Name of motion model to use for new or unassigned stars + motion_models : list of MotionModel or str, optional + Motion models or their names to use for new or unassigned stars - motion_model_dict : None or dict - Dict of motion model name keys (strings) and corresponding MotionModel object values + fixed_params_dict : None or dict + Dictionary of motion model fixed parameters use_scipy : bool, optional If True, use scipy.optimize.curve_fit for velocity fitting. If False, use linear @@ -192,20 +192,34 @@ def = None. If not None, then this should contain an array or list of transform self.init_order = init_order self.mag_trans = mag_trans self.mag_lim = mag_lim - self.trans_weights = trans_weights - self.vel_weights = vel_weights + self.trans_weighting = trans_weighting + self.vel_weighting = vel_weighting self.trans_input = trans_input self.trans_class = trans_class self.calc_trans_inverse = calc_trans_inverse - self.motion_model_dict = motion_model_dict self.use_scipy = use_scipy self.absolute_sigma = absolute_sigma - self.default_motion_model = default_motion_model + self.fixed_params_dict = fixed_params_dict self.init_guess_mode = init_guess_mode self.iter_callback = iter_callback self.save_path = save_path self.verbose = verbose + all_mm_map = motion_model.motion_model_map() + if all(isinstance(mm, str) for mm in motion_models): + mm_names = motion_models + motion_models = [all_mm_map[mm] for mm in motion_models] + else: + mm_names = [mm.name for mm in motion_models] + if 'Empty' not in mm_names: + motion_models.append(all_mm_map['Empty']) + if 'Fixed' not in mm_names: + motion_models.append(all_mm_map['Fixed']) + + # Sort by increasing n_params + motion_models = sorted(motion_models, key=lambda mm: mm.n_params) + self.motion_models = motion_models + # For backwards compatibility. if self.verbose is True: self.verbose = 9 @@ -235,8 +249,8 @@ def = None. If not None, then this should contain an array or list of transform self.setup_trans_info() # Make sure the motion models are ready - self.motion_model_dict = motion_model.validate_motion_model_dict(self.motion_model_dict, - StarTable(), self.default_motion_model) + # self.motion_model_dict = motion_model.validate_motion_model_dict(self.motion_model_dict, + # StarTable(), self.default_motion_model) return @@ -397,6 +411,7 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar print(" **********") star_list = self.star_lists[ii] + pdb.set_trance() ref_list = self.get_ref_list_from_table(star_list['t'][0]) trans = self.trans_list[ii] @@ -413,11 +428,15 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar # Only use "use_in_trans" reference stars, even for initial guessing. keepers = np.where(ref_list['use_in_trans'] == True)[0] - trans = trans_initial_guess(ref_list[keepers], star_list_orig_trim, self.trans_args[0], self.motion_model_dict, - mode=self.init_guess_mode, - order=self.init_order, - verbose=self.verbose, - mag_trans=self.mag_trans) + trans = trans_initial_guess( + ref_list[keepers], + star_list_orig_trim, + self.trans_args[0], + mode=self.init_guess_mode, + order=self.init_order, + verbose=self.verbose, + mag_trans=self.mag_trans + ) if self.mag_trans: star_list_T.transform_xym(trans) # trimmed, transformed @@ -700,14 +719,14 @@ def setup_ref_table_from_starlist(self, star_list, motion_model_used=None): for col_name in ref_table.colnames: if len(ref_table[col_name].data.shape) == 2: # Find the 2D columns ref_table._set_invalid_list_values(col_name, -1) - + if 'motion_model_input' not in ref_table.colnames: - ref_table.add_column(Column(np.repeat(self.default_motion_model, len(ref_table)), name='motion_model_input')) - if 'motion_model_used' not in ref_table.colnames: - if motion_model_used is None: - ref_table.add_column(Column(np.repeat(self.default_motion_model, len(ref_table)), name='motion_model_used')) - else: - ref_table.add_column(Column(np.repeat(motion_model_used, len(ref_table)), name='motion_model_used')) + ref_table.add_column(Column(np.repeat(self.motion_models[-1].name, len(ref_table)), name='motion_model_input')) + # if 'motion_model_used' not in ref_table.colnames: + # if motion_model_used is None: + # ref_table.add_column(Column(np.repeat(self.default_motion_model, len(ref_table)), name='motion_model_used')) + # else: + # ref_table.add_column(Column(np.repeat(motion_model_used, len(ref_table)), name='motion_model_used')) return ref_table @@ -807,35 +826,34 @@ def update_ref_table_from_list(self, star_list, star_list_T, ii, idx_ref, idx_li if ((self.ref_table['x'].shape[1] != len(self.star_lists)) and (ii != self.ref_index) and (ii >= self.ref_table['x'].shape[1])): - + self.ref_table.add_starlist() - + copy_over_values(self.ref_table, star_list, star_list_T, ii, idx_ref, idx_lis) self.ref_table['used_in_trans'][idx_ref_in_trans, ii] = True ### Add the unmatched stars and grow the size of the reference table. - self.ref_table, idx_lis_new, idx_ref_new = add_rows_for_new_stars(self.ref_table, star_list, idx_lis, - default_motion_model=self.default_motion_model) + self.ref_table, idx_lis_new, idx_ref_new = add_rows_for_new_stars(self.ref_table, star_list, idx_lis) if len(idx_ref_new) > 0: if self.verbose > 0: print(' Adding {0:d} new stars to the reference table.'.format(len(idx_ref_new))) - + copy_over_values(self.ref_table, star_list, star_list_T, ii, idx_ref_new, idx_lis_new) # Copy the single-epoch values to the aggregate (only for new stars). self.ref_table['x0'][idx_ref_new] = star_list_T['x'][idx_lis_new] self.ref_table['y0'][idx_ref_new] = star_list_T['y'][idx_lis_new] self.ref_table['m0'][idx_ref_new] = star_list_T['m'][idx_lis_new] - + self.ref_table['name'] = update_old_and_new_names(self.ref_table, ii, idx_ref_new) if self.use_ref_new == True: self.ref_table['use_in_trans'][idx_ref_new] = True else: self.ref_table['use_in_trans'][idx_ref_new] = False - + return - + def update_ref_table_aggregates(self, keep_orig=None, n_boot=0): """ Average positions or fit velocities. @@ -852,7 +870,9 @@ def update_ref_table_aggregates(self, keep_orig=None, n_boot=0): vals_orig = {} vals_orig['m0'] = self.ref_table['m0'][keep_orig] vals_orig['m0_err'] = self.ref_table['m0_err'][keep_orig] - motion_model_class_names = self.ref_table['motion_model_input'].tolist() + motion_model_class_names = [] + if 'motion_model_input' in self.ref_table.keys(): + motion_model_class_names += self.ref_table['motion_model_input'].tolist() if 'motion_model_used' in self.ref_table.keys(): motion_model_class_names += self.ref_table['motion_model_used'][keep_orig].tolist() vals_orig['motion_model_used'] = self.ref_table['motion_model_used'][keep_orig] @@ -863,24 +883,23 @@ def update_ref_table_aggregates(self, keep_orig=None, n_boot=0): fit_star_idxs = [idx for idx in range(len(self.ref_table)) if idx not in keep_orig] else: fit_star_idxs = None - #pdb.set_trace() + # Figure out whether motion fits are necessary - all_fixed = np.all(self.ref_table['motion_model_input']=='Fixed') - if all_fixed: + if ('motion_model_input' in self.ref_table.keys()) and np.all(self.ref_table['motion_model_input']=='Fixed'): weighted_xy = ('xe' in self.ref_table.colnames) and ('ye' in self.ref_table.colnames) weighted_m = ('me' in self.ref_table.colnames) self.ref_table.combine_lists_xym(weighted_xy=weighted_xy, weighted_m=weighted_m) else: # Combine positions with a velocity fit. - self.ref_table.fit_velocities(bootstrap=n_boot, - verbose=self.verbose, - show_progress=(self.verbose>0), - default_motion_model=self.default_motion_model, - select_stars=fit_star_idxs, - motion_model_dict=self.motion_model_dict, - weighting=self.vel_weights, - use_scipy=self.use_scipy, - absolute_sigma=self.absolute_sigma) + self.ref_table.fit_motion_model( + motion_models=self.motion_models, + fixed_params_dict=self.fixed_params_dict, + weighting=self.vel_weighting, + use_scipy=self.use_scipy, + absolute_sigma=self.absolute_sigma, + bootstrap=n_boot, + verbose=self.verbose + ) # Combine (transformed) magnitudes if 'me' in self.ref_table.colnames: @@ -910,18 +929,18 @@ def get_weights_for_lists(self, ref_list, star_list): var_xlis = 0.0 var_ylis = 0.0 - if self.trans_weights != None: - if self.trans_weights == 'both,var': + if self.trans_weighting != None: + if self.trans_weighting == 'both,var': weight = 1.0 / (var_xref + var_xlis + var_yref + var_ylis) - if self.trans_weights == 'both,std': + if self.trans_weighting == 'both,std': weight = 1.0 / np.sqrt(var_xref + var_xlis + var_yref + var_ylis) - if self.trans_weights == 'ref,var': + if self.trans_weighting == 'ref,var': weight = 1.0 / (var_xref + var_yref) - if self.trans_weights == 'ref,std': + if self.trans_weighting == 'ref,std': weight = 1.0 / np.sqrt(var_xref + var_yref) - if self.trans_weights == 'list,var': + if self.trans_weighting == 'list,var': weight = 1.0 / (var_xlis + var_ylis) - if self.trans_weights == 'list,std': + if self.trans_weighting == 'list,std': weight = 1.0 / np.sqrt(var_xlis, var_ylis) else: weight = None @@ -963,7 +982,7 @@ def match_lists(self, dr_tol, dm_tol): else: star_list_T.transform_xy(self.trans_list[ii]) - xref, yref = get_pos_at_time(star_list_T['t'][0], self.ref_table, self.motion_model_dict) + xref, yref = infer_positions(star_list_T['t'][0], self.ref_table) mref = self.ref_table['m0'] idx_lis, idx_ref, dr, dm = match.match(star_list_T['x'], star_list_T['y'], star_list_T['m'], @@ -997,7 +1016,8 @@ def get_ref_list_from_table(self, epoch): name = self.ref_table['name'] if ('motion_model_used' in self.ref_table.colnames): - x,y,xe,ye = self.ref_table.get_star_positions_at_time(epoch, self.motion_model_dict, allow_alt_models=True) + print(f'{epoch=}, {epoch.shape=}') + x, y, xe, ye = self.ref_table.infer_positions(epoch) else: # No velocities... just used average positions. x = self.ref_table['x0'] @@ -1144,20 +1164,24 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot m2_boot_sum = np.zeros((len(ref_table['x']), n_epochs)) # Set up motion model parameters - motion_model_list = ['Fixed', self.default_motion_model] if 'motion_model_used' in ref_table.keys(): - motion_model_list += ref_table['motion_model_used'].tolist() + motion_model_list = np.unique(ref_table['motion_model_used']).tolist() elif 'motion_model_input' in ref_table.keys(): - motion_model_list += ref_table['motion_model_input'].tolist() - motion_col_list = motion_model.get_list_motion_model_param_names(np.unique(motion_model_list).tolist(), with_errors=False, with_fixed=False) + motion_model_list = np.unique(ref_table['motion_model_input']).tolist() + + all_mm_map = motion_model.motion_model_map() + motion_model_list = [all_mm_map[mm_name] for mm_name in motion_model_list] + + motion_col_list = motion_model.get_list_motion_model_param_names(motion_model_list, with_errors=False, with_fixed=False) if calc_vel_in_bootstrap: motion_boot_sum = {} motion2_boot_sum = {} for col in motion_col_list: motion_boot_sum[col] = np.zeros((len(ref_table['x']))) motion2_boot_sum[col] = np.zeros((len(ref_table['x']))) - motion_boot_min_epochs = np.max([self.motion_model_dict[mod].n_pts_req - for mod in np.unique(motion_model_list)]) + + all_mm_map = motion_model.motion_model_map() + motion_boot_min_epochs = np.max([all_mm_map[mm].n_params for mm in motion_model_list]) ### IF MEMORY PROBLEMS HERE: ### DEFINE MEAN, STD VARIABLES AND BUILD THEM RATHER THAN SAVING FULL ARRAY @@ -1215,7 +1239,7 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot # Calculate weights based on weights keyword. If weights desired, will need to # make starlist objects for this - if self.trans_weights != None: + if self.trans_weighting != None: # In order for weights calculation to work, we need to apply a transformation # to the star_list_T so it is in the same units as ref_boot. So, we'll apply # the final transformation for the epoch to get close enough for the @@ -1296,10 +1320,16 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot # Now, do proper motion calculation, making sure to fix t0 to the # orig value (so we can get a reasonable error on x0, y0) - star_table.fit_velocities( - fixed_t0=t0_arr, - default_motion_model=self.default_motion_model, - motion_model_dict=self.motion_model_dict, + if self.fixed_params_dict is None: + fixed_params_dict = {'t0': t0_arr} + elif 't0' not in self.fixed_params_dict.keys(): + fixed_params_dict = self.fixed_params_dict.copy() + fixed_params_dict['t0'] = t0_arr + + star_table.fit_motion_model( + motion_models=self.motion_models, + fixed_params_dict=fixed_params_dict, + weighting=self.vel_weighting, use_scipy=self.use_scipy, absolute_sigma=self.absolute_sigma ) @@ -1351,7 +1381,7 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot self.ref_table.add_column(col) # Calculate chi^2 with bootstrap positional errors - x_pred, y_pred, _, _ = self.ref_table.get_star_positions_at_time(t_arr, self.motion_model_dict, allow_alt_models=True) + x_pred, y_pred, _, _ = self.ref_table.infer_positions(t_arr) xe_comb = np.hypot(self.ref_table['xe'], self.ref_table['xe_boot']) ye_comb = np.hypot(self.ref_table['ye'], self.ref_table['ye_boot']) data_dict['chi2_x_boot'] = np.nansum((self.ref_table['x']-x_pred)**2/(xe_comb)**2,axis=1) @@ -1373,7 +1403,6 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot col[idx_good] = data_dict[ff] self.ref_table.add_column(col) - #pdb.set_trace() print('===============================') print('Done with bootstrap') @@ -1389,7 +1418,7 @@ def __init__(self, ref_list, list_of_starlists, iters=2, trans_args=[{'order': 2}, {'order': 2}], init_order=1, mag_trans=True, mag_lim=None, ref_mag_lim=None, - trans_weights=None, vel_weights='var', + trans_weighting=None, vel_weighting='var', trans_input=None, trans_class=transforms.PolyTransform, calc_trans_inverse=False, @@ -1397,8 +1426,8 @@ def __init__(self, ref_list, list_of_starlists, iters=2, update_ref_orig=False, init_guess_mode='miracle', iter_callback=None, - default_motion_model='Fixed', - motion_model_dict={}, + motion_models=['Empty', 'Fixed'], + fixed_params_dict=None, use_scipy=True, absolute_sigma=False, save_path=None, @@ -1463,13 +1492,13 @@ def __init__(self, ref_list, list_of_starlists, iters=2, If different from None, it indicates the minimum and maximum magnitude on the reference catalog for finding the transformations. - trans_weights : str + trans_weighting : str Either None (def), 'both,var', 'list,var', or 'ref,var' depending on whether you want to weight by the positional uncertainties (variances) in the individual starlists, or also with the uncertainties in the reference frame itself. Note weighting only works when there are positional uncertainties availabe. Other options include 'both,std', 'list,std', 'list,var'. - vel_weights : str + vel_weighting : str Either 'var' (def) or 'std', depending on whether you want to weight the motion model fits by the variance or standard deviation of the position data @@ -1529,9 +1558,12 @@ def = None. If not None, then this should contain an array or list of transform default_motion_model : string Name of motion model to use for new or unassigned stars - - motion_model_dict : None or dict - Dict of motion model name keys (strings) and corresponding MotionModel object values + + motion_models : list of str or MotionModel objects + List of motion model names (strings) or MotionModel objects to use + + fixed_params_dict : None or dict + Dictionary of fixed parameters for motion models use_scipy : bool, optional If True, use scipy.optimize.curve_fit for velocity fitting. If False, use linear algebra fitting, by default True. @@ -1578,13 +1610,13 @@ def = None. If not None, then this should contain an array or list of transform outlier_tol=outlier_tol, trans_args=trans_args, init_order=init_order, mag_trans=mag_trans, mag_lim=mag_lim, - trans_weights=trans_weights, vel_weights=vel_weights, + trans_weighting=trans_weighting, vel_weighting=vel_weighting, trans_input=trans_input, trans_class=trans_class, calc_trans_inverse=calc_trans_inverse, - default_motion_model = default_motion_model, init_guess_mode=init_guess_mode, iter_callback=iter_callback, - motion_model_dict=motion_model_dict, + motion_models=motion_models, + fixed_params_dict=fixed_params_dict, verbose=verbose, use_scipy=use_scipy, absolute_sigma=absolute_sigma, save_path=save_path) @@ -1606,10 +1638,10 @@ def = None. If not None, then this should contain an array or list of transform self.ref_list['me'] = self.ref_list['m0_err'] if ('t' not in self.ref_list.colnames) and ('t0' in self.ref_list.colnames): self.ref_list['t'] = self.ref_list['t0'] - + # Make sure the motion models are ready - self.motion_model_dict = motion_model.validate_motion_model_dict(self.motion_model_dict, - self.ref_list, self.default_motion_model) + # self.motion_model_dict = motion_model.validate_motion_model_dict(self.motion_model_dict, + # self.ref_list, self.default_motion_model) return @@ -1646,13 +1678,13 @@ def fit(self): logger(_log, ' mag_trans = ' + str(self.mag_trans), self.verbose) logger(_log, ' mag_lim = ' + str(self.mag_lim), self.verbose) logger(_log, ' ref_mag_lim = ' + str(self.ref_mag_lim), self.verbose) - logger(_log, ' trans_weights = ' + str(self.trans_weights), self.verbose) - logger(_log, ' vel_weights = ' + str(self.vel_weights), self.verbose) + logger(_log, ' trans_weighting = ' + str(self.trans_weighting), self.verbose) + logger(_log, ' vel_weighting = ' + str(self.vel_weighting), self.verbose) logger(_log, ' trans_input = ' + str(self.trans_input), self.verbose) logger(_log, ' trans_class = ' + str(self.trans_class), self.verbose) logger(_log, ' calc_trans_inverse = ' + str(self.calc_trans_inverse), self.verbose) logger(_log, ' use_ref_new = ' + str(self.use_ref_new), self.verbose) - logger(_log, ' default_motion_model = ' + str(self.default_motion_model), self.verbose) + logger(_log, ' motion_models = ' + str([mm.name for mm in self.motion_models]), self.verbose) logger(_log, ' update_ref_orig = ' + str(self.update_ref_orig), self.verbose) logger(_log, ' init_guess_mode = ' + str(self.init_guess_mode), self.verbose) logger(_log, ' iter_callback = ' + str(self.iter_callback), self.verbose) @@ -1687,13 +1719,13 @@ def fit(self): print('Starting iter {0:d} with ref_table shape:'.format(nn), self.ref_table['x'].shape) print("**********") print("**********") - + # ALL the action is in here. Match and transform the stack of starlists. # This updates trans objects and the ref_table. self.match_and_transform(self.ref_mag_lim, self.dr_tol[nn], self.dm_tol[nn], self.outlier_tol[nn], self.trans_args[nn]) - + # Clean up the reference table # Find where stars are detected. self.ref_table.detections() @@ -1898,7 +1930,7 @@ def reset_ref_values(ref_table): return -def add_rows_for_new_stars(ref_table, star_list, idx_lis, default_motion_model='Fixed'): +def add_rows_for_new_stars(ref_table, star_list, idx_lis): """ For each star that is in star_list and NOT in idx_list, make a new row in the reference table. The values will be empty (None, NAN, etc.). @@ -1943,12 +1975,12 @@ def add_rows_for_new_stars(ref_table, star_list, idx_lis, default_motion_model=' elif ref_table[col_name].dtype == np.dtype('bool'): new_col_empty = False elif col_name=='motion_model_input': - new_col_empty = default_motion_model + new_col_empty = 'Empty' elif col_name=='motion_model_used': - new_col_empty = 'Fixed' + new_col_empty = 'Empty' else: new_col_empty = np.nan - + if len(ref_table[col_name].shape) == 1: new_col_shape = len(idx_lis_new) else: @@ -1966,7 +1998,7 @@ def add_rows_for_new_stars(ref_table, star_list, idx_lis, default_motion_model=' ref_table = vstack([ref_table, ref_table_new]) idx_ref_new = np.arange(last_star_idx, len(ref_table)) - + return ref_table, idx_lis_new, idx_ref_new """ @@ -2863,7 +2895,7 @@ def check_trans_input(list_of_starlists, trans_input, mag_trans): return -def trans_initial_guess(ref_list, star_list, trans_args, motion_model_dict, mode='miracle', +def trans_initial_guess(ref_list, star_list, trans_args, mode='miracle', ignore_contains='star', verbose=True, n_req_match=3, mag_trans=True, order=1): """ @@ -2901,7 +2933,7 @@ def trans_initial_guess(ref_list, star_list, trans_args, motion_model_dict, mode # If there are velocities in the reference list, use them. # We assume velocities are in the same units as the positions. - xref, yref = get_pos_at_time(star_list['t'][0], ref_list, motion_model_dict) + xref, yref = infer_positions(star_list['t'][0], ref_list) if 'm' in ref_list.colnames: mref = ref_list['m'] else: @@ -3140,9 +3172,9 @@ def get_weighting_scheme(weights, ref_list, star_list): return weight # TODO: This is sometimes run on a startable, not a starlist, at least as currently used -def get_pos_at_time(t, starlist, motion_model_dict): +def infer_positions(t, startable): """ - Take a starlist, check to see if it has motion/velocity columns. + Take a startable, check to see if it has motion/velocity columns. If it does, then propogate the positions forward in time to the desired epoch. If no motion/velocities exist, then just use ['x0', 'y0'] or ['x', 'y'] @@ -3155,21 +3187,23 @@ def get_pos_at_time(t, starlist, motion_model_dict): as the 't0' column in starlist. """ # Check for motion model - if 'motion_model_used' in starlist.colnames: - x,y,xe,ye = starlist.get_star_positions_at_time(t, motion_model_dict, allow_alt_models=True) + if 'motion_model_used' in startable.colnames: + x, y, xe, ye = startable.infer_positions(t) + # If no motion model, check for velocities - elif ('vx' in starlist.colnames) and ('vy' in starlist.colnames): - x = starlist['x0'] + starlist['vx']*(t-starlist['t0']) - y = starlist['y0'] + starlist['vy']*(t-starlist['t0']) + elif ('vx' in startable.colnames) and ('vy' in startable.colnames): + x = startable['x0'] + startable['vx'] * (t - startable['t0']) + y = startable['y0'] + startable['vy'] * (t - startable['t0']) + # If no velocities, try fitted positon - elif ('x0' in starlist.colnames) and ('y0' in starlist.colnames): - x = starlist['x0'] - y = starlist['y0'] + elif ('x0' in startable.colnames) and ('y0' in startable.colnames): + x = startable['x0'] + y = startable['y0'] # Otherwise, use measured position else: - x = starlist['x'] - y = starlist['y'] - + x = startable['x'] + y = startable['y'] + return (x, y) def logger(logfile, message, verbose = 9): diff --git a/flystar/tests/test_align.py b/flystar/tests/test_align.py index 195a67b..cc4de2a 100644 --- a/flystar/tests/test_align.py +++ b/flystar/tests/test_align.py @@ -6,10 +6,8 @@ from flystar import motion_model from astropy.table import Table import numpy as np -import pylab as plt +import matplotlib.pyplot as plt import pdb -import datetime -import pytest def test_MosaicSelfRef(): """ @@ -28,7 +26,7 @@ def test_MosaicSelfRef(): trans_args={'order': 2}) msc.fit() - + # Check some of the output quantities on the final table. assert 'x0' in msc.ref_table.colnames assert 'x0_err' in msc.ref_table.colnames @@ -42,7 +40,6 @@ def test_MosaicSelfRef(): assert msc.ref_table['use_in_trans'].shape == msc.ref_table['x0'].shape assert msc.ref_table['used_in_trans'].shape == msc.ref_table['x'].shape - # Check that we have some matched stars... should be at least 35 stars # that are detected in all 4 starlists. @@ -50,11 +47,11 @@ def test_MosaicSelfRef(): assert len(idx) > 35 # Check that the transformation error isn't too big - assert (msc.ref_table['x0_err'] < 3.0).all() # less than 1 pix - assert (msc.ref_table['y0_err'] < 3.0).all() - #assert (msc.ref_table['m0_err'] < 1.0).all() # less than 0.5 mag - assert (msc.ref_table['m0_err'] < 1.5).all() # less than 0.5 mag - + valid_err = np.isfinite(msc.ref_table['x0_err']) & np.isfinite(msc.ref_table['y0_err']) & np.isfinite(msc.ref_table['m0_err']) + assert (msc.ref_table['x0_err'][valid_err] < 3.0).all() # less than 1 pix + assert (msc.ref_table['y0_err'][valid_err] < 3.0).all() + #assert (msc.ref_table['m0_err'][valid_err] < 1.0).all() # less than 0.5 mag + assert (msc.ref_table['m0_err'][valid_err] < 1.5).all() # less than 0.5 mag # Check that the transformation lists aren't too wacky for ii in range(4): np.testing.assert_allclose(msc.trans_list[ii].px.c1_0, 1.0, rtol=1e-2) @@ -81,7 +78,7 @@ def test_MosaicSelfRef(): plt.plot(msc.ref_table['x0'], msc.ref_table['y0'], '.', color='black', alpha=0.2) - + return @@ -102,11 +99,11 @@ def test_MosaicSelfRef_vel_tconst(): dr_tol=[3, 3], dm_tol=[1, 1], trans_class=transforms.PolyTransform, trans_args={'order': 2}, - default_motion_model='Linear', + motion_models=['Empty', 'Fixed', 'Linear'], verbose=False) msc.fit() - + # Check some of the output quantities on the final table. assert 'x0' in msc.ref_table.colnames assert 'x0_err' in msc.ref_table.colnames @@ -126,21 +123,16 @@ def test_MosaicSelfRef_vel_tconst(): assert len(idx) > 35 # Check that the transformation error isn't too big - assert (msc.ref_table['x0_err'] < 3.0).all() # less than 1 pix - assert (msc.ref_table['y0_err'] < 3.0).all() - assert (msc.ref_table['m0_err'] < 1.0).all() # less than 0.5 mag - + valid_err = np.isfinite(msc.ref_table['x0_err']) & np.isfinite(msc.ref_table['y0_err']) & np.isfinite(msc.ref_table['m0_err']) + assert (msc.ref_table['x0_err'][valid_err] < 3.0).all() # less than 1 pix + assert (msc.ref_table['y0_err'][valid_err] < 3.0).all() + assert (msc.ref_table['m0_err'][valid_err] < 1.0).all() # less than 0.5 mag + # Check that the transformation lists aren't too wacky for ii in range(4): np.testing.assert_allclose(msc.trans_list[ii].px.c1_0, 1.0, rtol=1e-2) np.testing.assert_allclose(msc.trans_list[ii].py.c0_1, 1.0, rtol=1e-2) - # Check that the velocities aren't crazy... - # they should be non-existent (since there is no time difference) - assert np.isnan(msc.ref_table['vx']).all() - assert np.isnan(msc.ref_table['vy']).all() - assert np.isnan(msc.ref_table['vx_err']).all() - assert np.isnan(msc.ref_table['vy_err']).all() return @@ -172,7 +164,7 @@ def test_MosaicSelfRef_vel(): msc = align.MosaicSelfRef(lists, ref_index=0, iters=3, dr_tol=[5, 3, 3], dm_tol=[1, 1, 0.5], outlier_tol=None, trans_class=transforms.PolyTransform, - trans_args={'order': 2}, default_motion_model='Linear', + trans_args={'order': 2}, motion_models=['Empty', 'Fixed', 'Linear'], verbose=False) msc.fit() @@ -196,10 +188,11 @@ def test_MosaicSelfRef_vel(): assert len(idx) > 35 # Check that the transformation error isn't too big - assert (msc.ref_table['x0_err'] < 3.0).all() # less than 1 pix - assert (msc.ref_table['y0_err'] < 3.0).all() - assert (msc.ref_table['m0_err'] < 1.0).all() # less than 0.5 mag - + valid_err = np.isfinite(msc.ref_table['x0_err']) & np.isfinite(msc.ref_table['y0_err']) & np.isfinite(msc.ref_table['m0_err']) + assert (msc.ref_table['x0_err'][valid_err] < 3.0).all() # less than 1 pix + assert (msc.ref_table['y0_err'][valid_err] < 3.0).all() + assert (msc.ref_table['m0_err'][valid_err] < 1.0).all() # less than 0.5 mag + # Check that the transformation lists aren't too wacky for ii in range(4): np.testing.assert_allclose(msc.trans_list[ii].px.c1_0, 1.0, rtol=1e-2) @@ -214,7 +207,7 @@ def test_MosaicSelfRef_vel(): def test_MosaicToRef(): make_fake_starlists_poly1(seed=42) - + ref_file = 'random_ref.fits' list_files = ['random_0.fits', 'random_1.fits', @@ -235,7 +228,7 @@ def test_MosaicToRef(): msc = align.MosaicToRef(ref_list, lists, iters=2, dr_tol=[0.2, 0.1], dm_tol=[1, 0.5], trans_class=transforms.PolyTransform, - trans_args={'order': 2}, default_motion_model='Fixed', + trans_args={'order': 2}, motion_models=['Empty', 'Fixed'], update_ref_orig=False, verbose=False) msc.fit() @@ -300,7 +293,7 @@ def test_MosaicToRef_p0_vel(): dr_tol=[0.2, 0.1], dm_tol=[1, 0.5], outlier_tol=[None, None], trans_class=transforms.PolyTransform, - trans_args={'order': 1}, default_motion_model='Linear', + trans_args={'order': 1}, motion_models=['Empty', 'Fixed', 'Linear'], update_ref_orig=False, verbose=False) msc.fit() @@ -326,18 +319,18 @@ def test_MosaicToRef_p0_vel(): # The velocities should be almost the same (but not as close as before) # as the input velocities since update_ref == True. assert (msc.ref_table['name']==ref_list['name']).all() - np.testing.assert_allclose(msc.ref_table['vx'], ref_list['vx'], rtol=1e-1) - np.testing.assert_allclose(msc.ref_table['vy'], ref_list['vy'], rtol=1e-1) + np.testing.assert_allclose(msc.ref_table['vx'], ref_list['vx'], atol=1e-2) + np.testing.assert_allclose(msc.ref_table['vy'], ref_list['vy'], atol=1e-2) # Also double check that they aren't exactly the same for the reference stars. #assert np.any(np.not_equal(msc.ref_table['vx'], ref_list['vx'])) assert np.not_equal(msc.ref_table['vx'], ref_list['vx']).any() - + return msc def test_MosaicToRef_vel(): make_fake_starlists_poly1_vel(seed=42) - + ref_file = 'random_vel_ref.fits' list_files = ['random_vel_0.fits', 'random_vel_1.fits', @@ -359,14 +352,14 @@ def test_MosaicToRef_vel(): # Switch our list to a "increasing to the West" list. ref_list['x0'] *= -1.0 ref_list['vx'] *= -1.0 - + lists = [starlists.StarList.read(lf) for lf in list_files] msc = align.MosaicToRef(ref_list, lists, iters=2, dr_tol=[0.2, 0.1], dm_tol=[1, 0.5], outlier_tol=[None, None], trans_class=transforms.PolyTransform, - trans_args={'order': 1}, default_motion_model='Linear', + trans_args={'order': 1}, motion_models=['Empty', 'Fixed', 'Linear'], update_ref_orig=False, verbose=False) msc.fit() @@ -392,8 +385,8 @@ def test_MosaicToRef_vel(): # The velocities should be almost the same (but not as close as before) # as the input velocities since update_ref == True. assert (msc.ref_table['name']==ref_list['name']).all() - np.testing.assert_allclose(msc.ref_table['vx'], ref_list['vx'], rtol=1e-1) - np.testing.assert_allclose(msc.ref_table['vy'], ref_list['vy'], rtol=1e-1) + np.testing.assert_allclose(msc.ref_table['vx'], ref_list['vx'], atol=1e-2) + np.testing.assert_allclose(msc.ref_table['vy'], ref_list['vy'], atol=1e-2) # Also double check that they aren't exactly the same for the reference stars. #assert np.any(np.not_equal(msc.ref_table['vx'], ref_list['vx'])) @@ -403,7 +396,7 @@ def test_MosaicToRef_vel(): def test_MosaicToRef_acc(): make_fake_starlists_poly1_acc(seed=42) - + ref_file = 'random_acc_ref.fits' list_files = ['random_acc_0.fits', 'random_acc_1.fits', @@ -427,19 +420,19 @@ def test_MosaicToRef_acc(): ref_list['ay'] *= 1e-3 ref_list['ax_err'] *= 1e-3 ref_list['ay_err'] *= 1e-3 - + # Switch our list to a "increasing to the West" list. ref_list['x0'] *= -1.0 ref_list['vx0'] *= -1.0 ref_list['ax'] *= -1.0 - + lists = [starlists.StarList.read(lf) for lf in list_files] msc = align.MosaicToRef(ref_list, lists, iters=2, dr_tol=[0.4, 0.2], dm_tol=[1, 0.5], trans_class=transforms.PolyTransform, trans_args={'order': 2}, - default_motion_model='Acceleration', + motion_models=['Acceleration'], update_ref_orig=False, verbose=False) msc.fit() From 6de890b85ae0870668d49708330e9a266072d841 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Mon, 15 Dec 2025 17:28:05 -0800 Subject: [PATCH 136/254] test debugging --- flystar/startables.py | 15 +++++++++------ flystar/tests/test_align.py | 14 +++++++------- 2 files changed, 16 insertions(+), 13 deletions(-) diff --git a/flystar/startables.py b/flystar/startables.py index a0bf3e3..c12976e 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -129,7 +129,7 @@ def __init__(self, *args, ref_list=0, **kwargs): # We have to have special handling of meta-data (i.e. info that has # dimensions of n_lists). - meta_tab = ('LIST_TIMES', 'LIST_NAMES') + meta_tab = ('list_times', 'list_names') meta_type = ((float, int), str) for mm in range(len(meta_tab)): meta_test = meta_tab[mm] @@ -156,6 +156,9 @@ def __init__(self, *args, ref_list=0, **kwargs): if meta_arg in kwargs: self.meta[meta_arg] = kwargs[meta_arg] del kwargs[meta_arg] + elif meta_arg.upper() in kwargs: + self.meta[meta_arg] = kwargs[meta_arg.upper()] + del kwargs[meta_arg] for arg in kwargs: if arg in ['name', 'x', 'y', 'm']: @@ -568,8 +571,8 @@ def fit_velocities(self, weighting='var', use_scipy=True, absolute_sigma=True, b if weighting not in ['var', 'std']: raise ValueError(f"fit_velocities: Weighting must either be 'var' or 'std', not {weighting}!") - if ('t' not in self.colnames) and ('LIST_TIMES' not in self.meta): - raise KeyError("fit_velocities: Failed to access time values. No 't' column in table, no 'LIST_TIMES' in meta.") + if ('t' not in self.colnames) and ('list_times' not in self.meta): + raise KeyError("fit_velocities: Failed to access time values. No 't' column in table, no 'list_times' in meta.") # Check if we have the required columns if not all([_ in self.colnames for _ in ['x', 'y']]): @@ -624,7 +627,7 @@ def fit_velocities(self, weighting='var', use_scipy=True, absolute_sigma=True, b if 't' in self.colnames: self['t0'] = self['t'] else: - self['t0'] = self.meta['LIST_TIMES'][0] + self['t0'] = self.meta['list_times'][0] if 'xe' in self.colnames: self['x0_err'] = self['xe'] self['y0_err'] = self['ye'] @@ -639,7 +642,7 @@ def fit_velocities(self, weighting='var', use_scipy=True, absolute_sigma=True, b if 't' in self.colnames: self['t0'] = self['t'][:, 0] else: - self['t0'] = self.meta['LIST_TIMES'][0] + self['t0'] = self.meta['list_times'][0] if 'xe' in self.colnames: self['x0_err'] = self['xe'][:,0] self['y0_err'] = self['ye'][:,0] @@ -756,7 +759,7 @@ def fit_velocity_for_star(self, ss, motion_model_dict, weighting='var', use_scip if 't' in self.colnames: t = np.ma.masked_invalid(self['t'][ss, :].data) else: - t = np.ma.masked_invalid(self.meta['LIST_TIMES']) + t = np.ma.masked_invalid(self.meta['list_times']) if mask_val: t = np.ma.masked_values(t, mask_val) diff --git a/flystar/tests/test_align.py b/flystar/tests/test_align.py index 9b65eb6..2d6b0dc 100644 --- a/flystar/tests/test_align.py +++ b/flystar/tests/test_align.py @@ -326,8 +326,8 @@ def test_MosaicToRef_p0_vel(): # The velocities should be almost the same (but not as close as before) # as the input velocities since update_ref == True. assert (msc.ref_table['name']==ref_list['name']).all() - np.testing.assert_allclose(msc.ref_table['vx'], ref_list['vx'], rtol=1e-1) - np.testing.assert_allclose(msc.ref_table['vy'], ref_list['vy'], rtol=1e-1) + assert np.max(np.abs(msc.ref_table['vx']-ref_list['vx']))<3e-4 + assert np.max(np.abs(msc.ref_table['vy']-ref_list['vy']))<3e-4 # Also double check that they aren't exactly the same for the reference stars. #assert np.any(np.not_equal(msc.ref_table['vx'], ref_list['vx'])) @@ -392,8 +392,8 @@ def test_MosaicToRef_vel(): # The velocities should be almost the same (but not as close as before) # as the input velocities since update_ref == True. assert (msc.ref_table['name']==ref_list['name']).all() - np.testing.assert_allclose(msc.ref_table['vx'], ref_list['vx'], rtol=1e-1) - np.testing.assert_allclose(msc.ref_table['vy'], ref_list['vy'], rtol=1e-1) + np.testing.assert_allclose(msc.ref_table['vx'], ref_list['vx'], rtol=1e-1, atol=3e-4) + np.testing.assert_allclose(msc.ref_table['vy'], ref_list['vy'], rtol=1e-1, atol=3e-4) # Also double check that they aren't exactly the same for the reference stars. #assert np.any(np.not_equal(msc.ref_table['vx'], ref_list['vx'])) @@ -476,11 +476,11 @@ def test_MosaicToRef_acc(): if ~np.isnan(msc.ref_table['ax'][ix_fit]): i_orig.append(i) i_fit.append(ix_fit) - np.testing.assert_allclose(msc.ref_table['ax'][i_fit], ref_list['ax'][i_orig], rtol=1e-1) - np.testing.assert_allclose(msc.ref_table['ay'][i_fit], ref_list['ay'][i_orig], rtol=1e-1) + np.testing.assert_allclose(msc.ref_table['ax'][i_fit], ref_list['ax'][i_orig], rtol=1e-1, atol=3e-4) + np.testing.assert_allclose(msc.ref_table['ay'][i_fit], ref_list['ay'][i_orig], rtol=1e-1, atol=3e-4) # Also double check that they aren't exactly the same for the reference stars. - assert np.any(np.not_equal(msc.ref_table['ax'], ref_list['ax'])) + assert np.any(np.not_equal(msc.ref_table['ax'][:200], ref_list['ax'][:200])) return msc From a19b10678e4c38b90c16265ebe657a6e0abf3c18 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Tue, 16 Dec 2025 04:16:42 -0800 Subject: [PATCH 137/254] Passed startable tests; Accelerated imports with relative import --- flystar/analysis.py | 13 +- flystar/match.py | 5 +- flystar/motion_model.py | 34 +- flystar/parallax.py | 24 +- flystar/plots.py | 8 +- flystar/startables.py | 573 +++++++------------------------- flystar/tests/test_startable.py | 173 +++++----- 7 files changed, 241 insertions(+), 589 deletions(-) diff --git a/flystar/analysis.py b/flystar/analysis.py index 81ab3f4..9ac826f 100644 --- a/flystar/analysis.py +++ b/flystar/analysis.py @@ -1,17 +1,15 @@ import numpy as np import pylab as plt -from flystar import starlists -from flystar import startables -from flystar import align -from flystar import match -from flystar import transforms +from . import starlists +from . import startables +from . import align +from . import match +from . import transforms from astropy import table from astropy.table import Table, Column from astropy.coordinates import SkyCoord from astropy import units as u from astropy.wcs import WCS -from astroquery.gaia import Gaia -from astroquery.mast import Observations, Catalogs import pdb, copy import math from scipy.stats import f @@ -42,6 +40,7 @@ def query_gaia(ra, dec, search_radius=30.0, table_name='gaiadr3'): table_name : string Options are 'gaiadr2' or 'gaiaedr3' """ + from astroquery.gaia import Gaia target_coords = SkyCoord(ra, dec, unit=(u.hourangle, u.deg), frame='icrs') ra = target_coords.ra.degree dec = target_coords.dec.degree diff --git a/flystar/match.py b/flystar/match.py index d7c391e..4cd5e36 100644 --- a/flystar/match.py +++ b/flystar/match.py @@ -1,5 +1,7 @@ import numpy as np -from flystar import starlists, transforms, startables, align +from . import starlists +from . import transforms +from . import startables from collections import Counter from scipy.spatial import cKDTree as KDT from astropy.table import Column, Table @@ -526,6 +528,7 @@ def generic_match(sl1, sl2, init_mode='triangle', Startable of the two matched catalogs """ + from . import align # Check the input StarLists and transform them into astropy Tables if not isinstance(sl1, starlists.StarList): diff --git a/flystar/motion_model.py b/flystar/motion_model.py index 42a5e54..6741c65 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -538,8 +538,7 @@ def run_fit( return_chi2=False, verbose=True ): - assert 't0' in fixed_params_dict, "Fixed parameter t0 is required for Linear model." - t0 = fixed_params_dict['t0'] + t0 = fixed_params_dict.get('t0', np.average(t, weights=1./np.hypot(xe, ye))) t = np.atleast_1d(t) x = np.atleast_1d(x) y = np.atleast_1d(y) @@ -742,8 +741,7 @@ def run_fit( return_chi2=False, verbose=True ): - assert 't0' in fixed_params_dict, "Fixed parameter t0 is required for Acceleration model." - t0 = fixed_params_dict['t0'] + t0 = fixed_params_dict.get('t0', np.average(t, weights=1./np.hypot(xe, ye))) t = np.atleast_1d(t) x = np.atleast_1d(x) y = np.atleast_1d(y) @@ -839,7 +837,7 @@ def calc_parallax_vector(self, t_mjd, ra, dec, pa=0., obsLocation='earth'): if self.plx_vector_cached is not None: t_mjd = np.atleast_1d(t_mjd) t_mjd_cached = self.plx_vector_cached[0] - if np.allclose(t_mjd, t_mjd_cached): + if np.array_equal(t_mjd, t_mjd_cached): # If cached values match input times, return cached values return self.plx_vector_cached[1] @@ -901,7 +899,7 @@ def model(self, t, fit_params, fixed_params_dict, fit_param_errs=None): - ra, shape (N_stars,) or (1,). - dec, shape (N_stars,) or (1,). - pa, optional, shape (N_stars,) or (1,), by default 0. - - obsLocation, optional,shape (N_stars,) or (1,), by default 'earth' + - obsLocation, optional, string, by default 'earth' fit_param_errs : array-like, optional Uncertainties in fit parameters, by default None @@ -910,6 +908,9 @@ def model(self, t, fit_params, fixed_params_dict, fit_param_errs=None): x, y (, xe, ye) Predicted positions (and uncertainties, if fit_param_errs is provided) with shape (N_stars, N_times), or (N_times,) if N_stars=1, or (N_stars,) if N_times=1 """ + + assert all([_ in fixed_params_dict for _ in ['t0', 'ra', 'dec']]), "Fixed parameters t0, ra, and dec are required for Parallax model." + t = np.atleast_1d(t) fit_params = np.atleast_2d(fit_params) # (N_stars, N_params) N_stars = fit_params.shape[0] if fit_params.ndim > 1 else 1 @@ -922,12 +923,13 @@ def model(self, t, fit_params, fixed_params_dict, fit_param_errs=None): pa = np.atleast_1d(fixed_params_dict.get('pa', 0.0)) obsLocation = fixed_params_dict.get('obsLocation', 'earth') + # TODO: vectorize parallax.parallax_in_direction to handle multiple obsLocation? + assert type(obsLocation) == str, "obsLocation must be a single string for all stars at this time." + dt = t[np.newaxis, :] - t0[:, np.newaxis] # Shape (N_stars, N_times) t_mjd = Time(t, format='decimalyear', scale='utc').mjd # Shape (N_times,) self.pvec = self.calc_parallax_vector(t_mjd, ra, dec, pa=pa, obsLocation=obsLocation) # Shape (2, N_times) x, y = self.model_fit(dt, x0[:, np.newaxis], vx[:, np.newaxis], y0[:, np.newaxis], vy[:, np.newaxis], pi[:, np.newaxis]) # Shape (N_stars, N_times) - # x = xy[:, :N_times] # Shape (N_stars, N_times) - # y = xy[:, N_times:] # Shape (N_stars, N_times) if N_stars == 1 or N_times == 1: # If only one star, return flattened arrays @@ -1068,14 +1070,18 @@ def get_one_motion_model_param_names(motion_model, with_errors=True, with_fixed= Add uncertainty names with '_err' suffix or not, by default True with_fixed : bool, optional Add fixed param names with '_fixed' suffix or not, by default True - + Returns ------- list List of all parameter names for the motion model """ - list_of_parameters = [] + if isinstance(motion_model, str): + all_mm_map = motion_model_map() + motion_model = all_mm_map[motion_model] + list_of_parameters = [] + def list_add(name): if name not in list_of_parameters: list_of_parameters.append(name) @@ -1112,6 +1118,10 @@ def get_list_motion_model_param_names(motion_model_list, with_errors=True, with_ """ list_of_parameters = [] + if len(motion_model_list) > 0 and isinstance(motion_model_list[0], str): + all_mm_map = motion_model_map() + motion_model_list = [all_mm_map[mm_name] for mm_name in motion_model_list] + def list_add(name): if name not in list_of_parameters: list_of_parameters.append(name) @@ -1130,12 +1140,12 @@ def list_add(name): return list(list_of_parameters) -def get_all_motion_model_names(with_errors=True, with_fixed=True): +def get_all_motion_model_param_names(with_errors=True, with_fixed=True): return get_list_motion_model_param_names(MotionModel.__subclasses__(), with_errors=with_errors, with_fixed=with_fixed) def motion_model_map(): mm_map = dict( - [(mm.__name__, mm()) for mm in MotionModel.__subclasses__()] + [(mm.__name__, mm) for mm in MotionModel.__subclasses__()] ) # Sort by n_params mm_map = dict(sorted(mm_map.items(), key=lambda item: item[1].n_params)) diff --git a/flystar/parallax.py b/flystar/parallax.py index 2bd352a..1a6dcf1 100755 --- a/flystar/parallax.py +++ b/flystar/parallax.py @@ -44,7 +44,7 @@ def parallax_in_direction(ra, dec, mjd, obsLocation='earth', pa=0.): Returns ------- pvec : ndarray - Parallax vector components, shape of (2, N) or (2,), where N is the number of stars. + Parallax vector components, shape of (2, N_stars, N_times), or (2, N_stars) if N_times=1, or (2, N_times) if N_stars=1. """ # Munge inputs into astropy format. # times = Time(mjd + 2400000.5, format='jd', scale='tdb') @@ -62,24 +62,24 @@ def parallax_in_direction(ra, dec, mjd, obsLocation='earth', pa=0.): _north_projected = np.cross(directions, _east_projected) _north_projected /= np.linalg.norm(_north_projected, axis=1)[:, np.newaxis] # Shape (N_stars, 3) - obs_pos = get_observer_barycentric(obsLocation, times) - sun_pos = get_body_barycentric(body='sun', time=times) + obs_pos = get_observer_barycentric(obsLocation, times) # Shape (N_times,) + sun_pos = get_body_barycentric(body='sun', time=times) # Shape (N_times,) sun_obs_pos = sun_pos - obs_pos - pos = sun_obs_pos.xyz.T.to(units.au).value # Shape (N_stars, 3) + pos = sun_obs_pos.xyz.T.to(units.au).value # Shape (N_times, 3) - e = np.einsum('ij,ij->i', pos, _east_projected) # Shape (N_stars,) - n = np.einsum('ij,ij->i', pos, _north_projected) # Shape (N_stars,) + e = np.einsum('ti,si->st', pos, _east_projected) # Shape (N_stars, N_times) + n = np.einsum('ti,si->st', pos, _north_projected) # Shape (N_stars, N_times) # Rotate frame e,n->x,y accounting for PA - pa = np.deg2rad(pa) - x = -e * np.cos(pa) + n * np.sin(pa) - y = e * np.sin(pa) + n * np.cos(pa) - pvec = np.array([x, y]) # Shape (2, N_stars) + pa = np.deg2rad(pa) # shape (N_stars,) + x = -e * np.cos(pa[:, np.newaxis]) + n * np.sin(pa[:, np.newaxis]) # Shape (N_stars, N_times) + y = e * np.sin(pa[:, np.newaxis]) + n * np.cos(pa[:, np.newaxis]) # Shape (N_stars, N_times) + pvec = np.array([x, y]) # Shape (2, N_stars, N_times) - if pvec.shape[1] == 1: - pvec = pvec.flatten() + if pvec.shape[1] == 1 or pvec.shape[2] == 1: + pvec = pvec.reshape(2, -1) # Shape (2, N_stars) or (2, N_times) return pvec diff --git a/flystar/plots.py b/flystar/plots.py index 8df9d51..8a2127c 100755 --- a/flystar/plots.py +++ b/flystar/plots.py @@ -1,4 +1,4 @@ -from flystar import analysis, motion_model, startables +from . import motion_model, startables import numpy as np import matplotlib.mlab as mlab import matplotlib @@ -193,6 +193,7 @@ def pos_diff_err_hist(ref_mat, starlist_mat, transform, nbins=25, bin_width=None an outlier. """ + from . import analysis diff_x = ref_mat['x'] - starlist_mat['x'] diff_y = ref_mat['y'] - starlist_mat['y'] @@ -1069,16 +1070,15 @@ def plot_mag_error(tab): return -def plot_mean_residuals_by_epoch(tab, motion_model_dict={}): +def plot_mean_residuals_by_epoch(tab): """ Plot mean position and magnitude residuals vs. epoch. Note we are plotting the mean( |dx} ) to see the size of the mean residual. """ # Predicted model positions at each epoch - motion_model_dict = motion_model.validate_motion_model_dict(motion_model_dict, tab, None) i_all_detected = np.where(~np.any(np.isnan(tab['t']),axis=1))[0][0] - xt_mod, yt_mod, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], motion_model_dict, allow_alt_models=True) + xt_mod, yt_mod, xt_mod_err, yt_mod_err = tab.predict_positions(tab['t'][i_all_detected]) # Residuals dx = tab['x'] - xt_mod diff --git a/flystar/startables.py b/flystar/startables.py index dd44178..bdcb880 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -227,7 +227,7 @@ def _add_list_data_from_starlist(self, starlist): lis_meta_keys = list(starlist.meta.keys()) # append 's' to the end to pluralize the input starlist. lis_meta_keys_plural = [lis_meta_key + 's' for lis_meta_key in lis_meta_keys] - + for kk in range(len(tab_meta_keys)): tab_key = tab_meta_keys[kk] @@ -237,9 +237,9 @@ def _add_list_data_from_starlist(self, starlist): # If we find the key in the starlists' meta argument, then add the new values. # Otherwise, add "None". - idx = np.where(lis_meta_keys_plural == tab_key)[0] - if len(idx) > 0: - lis_key = lis_meta_keys[idx[0]] + idx = lis_meta_keys_plural.index(tab_key) if tab_key in lis_meta_keys_plural else None + if idx is not None: + lis_key = lis_meta_keys[idx] self.meta[tab_key] = np.append(self.meta[tab_key], [starlist.meta[lis_key]]) else: self._append_invalid_meta_values(tab_key) @@ -550,7 +550,7 @@ def fit_motion_model( Parameters ---------- - motion_models : list of MotionModel, optional + motion_models : list of MotionModel or str, optional Motion models to use. Empty and Fixed models are always added automatically for stars with n_fit = 0 or 1. The behavior is as follows: @@ -612,9 +612,16 @@ def fit_motion_model( if fixed_params_dict is not None: if not isinstance(fixed_params_dict, dict): raise ValueError("fit_velocities: fixed_params_dict must be a dictionary!") - + + # Convert motion_models to MotionModel objects if they are strings: + all_mm_map = motion_model.motion_model_map() + if all(isinstance(mm, str) for mm in motion_models): + mm_names = motion_models + motion_models = [all_mm_map[mm] for mm in motion_models] + else: + mm_names = [mm.name for mm in motion_models] + # Always add Empty and Fixed in motion models - mm_names = [mm.name for mm in motion_models] if 'Fixed' not in mm_names: motion_models.insert(0, Fixed) if 'Empty' not in mm_names: @@ -622,17 +629,13 @@ def fit_motion_model( mm_names = [mm.name for mm in motion_models] # Construct motion models if motion_model_input column exists - all_mm_map = motion_model.motion_model_map() if 'motion_model_input' in self.colnames: input_mm_names = np.unique(self['motion_model_input']) assert all([name in all_mm_map.keys() for name in input_mm_names]), \ f"fit_velocities: Unknown motion model name(s) in 'motion_model_input' column. Available motion models are: {', '.join(all_mm_map.keys())}." for mm_name in input_mm_names: if mm_name not in mm_names: - try: - motion_models.append(all_mm_map[mm_name]()) - except Exception as e: - raise ValueError(f"fit_velocities: An instance of motion model {mm_name} with initialization parameters is missing in motion_models: {e}") + motion_models.append(all_mm_map[mm_name]) # Sort motion models by n_params motion_models = sorted(motion_models, key=lambda mm: mm.n_params) @@ -673,15 +676,16 @@ def fit_motion_model( # Add default t0 if not provided in fixed_params_dict if fixed_params_dict is None: weights = 1/np.hypot(xe_data, ye_data) if xe_data is not None else None - t0 = np.average(t_data, axis=1, weights=weights) - fixed_params_dict = {'t0': t0} + fixed_params_dict = {'t0': np.average(t_data, axis=1, weights=weights)} + elif 't0' not in fixed_params_dict: weights = 1/np.hypot(xe_data, ye_data) if xe_data is not None else None fixed_params_dict['t0'] = np.average(t_data, axis=1, weights=weights) else: if np.ndim(fixed_params_dict['t0']) == 0: fixed_params_dict['t0'] = np.full(N_stars, fixed_params_dict['t0']) - t0 = fixed_params_dict['t0'] + + t0 = fixed_params_dict['t0'] # Prepare fixed_params_dict for each star @@ -739,7 +743,7 @@ def fit_motion_model( ) - 1 # Convert to 0-based index # Assign motion models to stars - self['motion_model_used'] = np.array([motion_models[d].name for d in mm_digitized]) + self['motion_model_used'] = np.array([motion_models[d].name for d in mm_digitized], dtype='U20') ############################ @@ -747,8 +751,9 @@ def fit_motion_model( ############################ # Fill table with all possible motion model parameter names as new columns. motion_model_used = [all_mm_map[name] for name in np.unique(self['motion_model_used'])] - new_col_list = motion_model.get_list_motion_model_param_names(motion_model_used, with_errors=True) + new_col_list = motion_model.get_list_motion_model_param_names(motion_model_used, with_errors=True, with_fixed=False) new_col_list += ['chi2_x', 'chi2_y', 'n_params'] + if 't0' not in new_col_list: new_col_list.append('t0') @@ -765,6 +770,32 @@ def fit_motion_model( rename_duplicate=True ) + # Add fixed parameter columns if they do not exist + fixed_param_names = [] + for mm in motion_model_used: + for param in mm.fixed_param_names: + if param not in fixed_param_names: + fixed_param_names.append(param) + if 't0' in fixed_param_names: + fixed_param_names.remove('t0') + + # Add fixed parameter columns + for param in fixed_param_names: + coldata = np.array([fixed_params_stars[i][param] for i in range(N_stars)]) + if param in self.colnames: + # If the column already exists, check if the data are the same + if np.allclose(self[param], coldata, equal_nan=True): + # Same data, skip + continue + else: + # Different data, add with _mm suffix to avoid name conflict + colname = param + '_mm' + else: + colname = param + + self.add_column(Column(data=coldata, name=colname)) + + # Add a column to keep track of the number of points used in a fit and number of bootstrap used. self.meta['n_bootstrap'] = bootstrap @@ -822,332 +853,87 @@ def fit_motion_model( self['t0'][unique_index] = t0[unique_index] return - - def fit_velocities(self, weighting='var', use_scipy=True, absolute_sigma=True, bootstrap=0, - fixed_t0=False, verbose=False, mask_val=None, mask_lists=False, show_progress=True, - default_motion_model='Linear', reassign_motion_model=False, select_stars=None, motion_model_dict={}): - """Fit velocities for all stars in the table and add to the columns 'vx', 'vxe', 'vy', 'vye', 'x0', 'x0e', 'y0', 'y0e'. + def infer_positions(self, times, fill_value=np.nan): + """Infer star positions at given times using fitted motion models. Parameters ---------- - weighting : str, optional - Weight by variance 'var' or standard deviation 'std', by default 'var' - bootstrap : int, optional - Calculate uncertainty using bootstraping or not, by default 0 - fixed_t0 : bool or array-like, optional - Fix the t0 in dt = time - t0 if user provides an array with the same length of the table, or automatically calculate t0 = np.average(time, weights=1/np.hypot(xe, ye)) if False, by default False - verbose : bool, optional - Output verbose information or not, by default False - mask_val : float, optional - Value that needs to be masked in the data, e.g. -100000, by default None - mask_lists : list, optional - Columns that needs to be masked, by default False - show_progress : bool, optional - Show progress bar or not, by default True + times : array_like + Times at which to predict positions. + fill_value : float, optional + Value to use for missing data, by default np.nan - Raises - ------ - ValueError - If weighting is neither 'var' or 'std' - KeyError - If there's not time information in the table + Returns + ------- + x, y, xe, ye : ndarray + Arrays of predicted x, y positions and their uncertainties xe, ye, with shape (N_stars, N_times) or (N_stars,) if N_times=1, or (N_times,) if N_stars=1, or scalar. """ - if weighting not in ['var', 'std']: - raise ValueError(f"fit_velocities: Weighting must either be 'var' or 'std', not {weighting}!") - - if ('t' not in self.colnames) and ('list_times' not in self.meta): - raise KeyError("fit_velocities: Failed to access time values. No 't' column in table, no 'list_times' in meta.") - - # Check if we have the required columns - if not all([_ in self.colnames for _ in ['x', 'y']]): - raise KeyError(f"fit_velocities: Missing required columns in the table: {', '.join(['x', 'y'])}!") - - N_stars = len(self) + assert 'motion_model_used' in self.colnames, \ + "infer_positions: 'motion_model_used' column not found in the table. Please run fit_motion_model() first." - if verbose: - start_time = time.time() - msg = 'Starting startable.fit_velocities for {0:d} stars with n={1:d} bootstrap' - print(msg.format(N_stars, bootstrap)) - - # Set all to default_motion_model if none assigned already. - # Reset motion_model_used to the inputs for now -> will change as fits run - if ('motion_model_input' not in self.colnames) or reassign_motion_model: - self['motion_model_input'] = default_motion_model - self['motion_model_used'] = self['motion_model_input'] - - motion_model_dict = motion_model.validate_motion_model_dict(motion_model_dict, self, default_motion_model) - - # - # Fill table with all possible motion model parameter names as new - # columns. Make everything empty for now. - # - all_motion_models = np.unique(self['motion_model_input'].tolist() + ['Fixed']+[default_motion_model]).tolist() - new_col_list = motion_model.get_list_motion_model_param_names(all_motion_models, with_errors=True) - # Append goodness of fit metrics and t0. - new_col_list += ['chi2_x', 'chi2_y', 'n_params'] - if 't0' not in new_col_list: - new_col_list.append('t0') + N_stars = len(self) + times = np.atleast_1d(times) + N_times = len(times) + + if (N_stars > 1) and (N_times > 1): + x_pred = np.full((N_stars, N_times), fill_value, dtype=float) + y_pred = np.full((N_stars, N_times), fill_value, dtype=float) + xe_pred = np.full((N_stars, N_times), np.inf, dtype=float) + ye_pred = np.full((N_stars, N_times), np.inf, dtype=float) + elif N_stars==1: + x_pred = np.full(N_times, fill_value, dtype=float) + y_pred = np.full(N_times, fill_value, dtype=float) + xe_pred = np.full(N_times, np.inf, dtype=float) + ye_pred = np.full(N_times, np.inf, dtype=float) + else: + x_pred = np.full(N_stars, fill_value, dtype=float) + y_pred = np.full(N_stars, fill_value, dtype=float) + xe_pred = np.full(N_stars, np.inf, dtype=float) + ye_pred = np.full(N_stars, np.inf, dtype=float) - # Define output arrays for the best-fit parameters. - for col in new_col_list: - # Clean/remove up old arrays. - if col in self.colnames: self.remove_column(col) - # Add column #TODO: is this good for filling??? - self.add_column(Column(data = np.full(N_stars, np.nan, dtype=float), name = col)) - # Add a column to keep track of the number of points used in a fit. - self['n_fit'] = 0 - - # Preserve the number of bootstraps that will be run (if any). - self.meta['n_fit_bootstrap'] = bootstrap - - # (FIXME: Do we need to catch the case where there's a single *unmasked* epoch?) - # Catch the case when there is only a single epoch. Just return 0 velocity - # and the same input position for the x0/y0. - if len(self['x'].shape) == 1: - self['motion_model_used'] = 'Fixed' - self['x0'] = self['x'] - self['y0'] = self['y'] - if 't' in self.colnames: - self['t0'] = self['t'] - else: - self['t0'] = self.meta['list_times'][0] - if 'xe' in self.colnames: - self['x0_err'] = self['xe'] - self['y0_err'] = self['ye'] - self['n_fit'] = 1 - self['n_params'] = 1 - return - - if (self['x'].shape[1] == 1): - self['motion_model_used'] = 'Fixed' - self['x0'] = self['x'][:,0] - self['y0'] = self['y'][:,0] - if 't' in self.colnames: - self['t0'] = self['t'][:, 0] - else: - self['t0'] = self.meta['list_times'][0] - if 'xe' in self.colnames: - self['x0_err'] = self['xe'][:,0] - self['y0_err'] = self['ye'][:,0] - self['n_fit'] = 1 - self['n_params'] = 1 - return - - # Only fit selected stars, if list given - fit_star_idxs = range(N_stars) - if select_stars is not None: - fit_star_idxs = select_stars - # STARS LOOP through the stars and work on them 1 at a time. - # This is slow; but robust. - if show_progress: - for ss in tqdm(fit_star_idxs): - self.fit_velocity_for_star(ss, motion_model_dict, weighting=weighting, bootstrap=bootstrap, - use_scipy=use_scipy, absolute_sigma=absolute_sigma, - fixed_t0=fixed_t0, default_motion_model=default_motion_model, - mask_val=mask_val, mask_lists=mask_lists) - else: - for ss in fit_star_idxs: - self.fit_velocity_for_star(ss, motion_model_dict, weighting=weighting, bootstrap=bootstrap, - use_scipy=use_scipy, absolute_sigma=absolute_sigma, - fixed_t0=fixed_t0, default_motion_model=default_motion_model, - mask_val=mask_val, mask_lists=mask_lists) - if verbose: - stop_time = time.time() - print('startable.fit_velocities runtime = {0:.0f} s for {1:d} stars'.format(stop_time - start_time, N_stars)) + unique_motion_models, unique_inv_indices = np.unique(self['motion_model_used'], return_inverse=True) + indices_by_motion_model = {key: np.flatnonzero(unique_inv_indices == k) for k, key in enumerate(unique_motion_models)} - return - - def fit_velocity_for_star(self, ss, motion_model_dict, weighting='var', use_scipy=True, absolute_sigma=True, - bootstrap=False, fixed_t0=False, mask_val=None, mask_lists=False, - default_motion_model='Linear'): - # TODO: "weighting" is not used - # - # Make a mask of invalid (NaN) values and a user-specified invalid value. - # + # Prepare fit_params, fixed_params, fit_param_errs for each star - x = np.ma.masked_invalid(self['x'][ss, :].data) - y = np.ma.masked_invalid(self['y'][ss, :].data) - if mask_val: - x = np.ma.masked_values(x, mask_val) - y = np.ma.masked_values(y, mask_val) - # If no mask, convert x.mask to list - if not np.ma.is_masked(x): - x.mask = np.zeros_like(x.data, dtype=bool) - if not np.ma.is_masked(y): - y.mask = np.zeros_like(y.data, dtype=bool) - - if mask_lists is not False: - # Remove a list - if isinstance(mask_lists, list): - if all(isinstance(item, int) for item in mask_lists): - x.mask[mask_lists] = True - y.mask[mask_lists] = True - - # Throw a warning if mask_lists is not a list - if not isinstance(mask_lists, list): - raise RuntimeError('mask_lists needs to be a list.') - # - # Assign the appropriate positional errors - # - if 'xe' in self.colnames: - # Make a mask of invalid (NaN) values and a user-specified invalid value. - xe = np.ma.masked_invalid(self['xe'][ss, :].data) - ye = np.ma.masked_invalid(self['ye'][ss, :].data) - - # Catch the case where we have positions but no errors for - # some of the entries... we need to "fill in" reasonable - # weights for these... just use the average weights over - # all the other epochs. - pos_no_err = np.where((np.isfinite(x) & np.isfinite(y)) & - (np.isfinite(xe) == False) & (np.isfinite(ye) == False))[0] - pos_with_err = np.where((np.isfinite(x) & np.isfinite(y)) & - (np.isfinite(xe) & np.isfinite(ye)))[0] - - if len(pos_with_err) > 0: - xe[pos_no_err] = xe[pos_with_err].mean() - ye[pos_no_err] = ye[pos_with_err].mean() - else: - xe[pos_no_err] = 1.0 - ye[pos_no_err] = 1.0 - else: - N_epochs = len(x) - xe = np.ones(N_epochs, dtype=float) - ye = np.ones(N_epochs, dtype=float) - xe = np.ma.masked_invalid(xe) - ye = np.ma.masked_invalid(xe) - - if mask_val: - xe = np.ma.masked_values(xe, mask_val) - ye = np.ma.masked_values(ye, mask_val) - # If no mask, convert xe.mask to list - if not np.ma.is_masked(xe): - xe.mask = np.zeros_like(xe.data, dtype=bool) - if not np.ma.is_masked(ye): - ye.mask = np.zeros_like(ye.data, dtype=bool) + for unique_motion_model, unique_index in indices_by_motion_model.items(): + # Create motion model instance + motion_model_instance = motion_model.motion_model_map()[unique_motion_model]() + # Prepare parameters for prediction + fit_params = np.array([ + self[param_name][unique_index] for param_name in motion_model_instance.fit_param_names + ]).T # shape (N_stars_this_model, N_params) + + fit_param_errs = np.array([ + self[param_name + '_err'][unique_index] for param_name in motion_model_instance.fit_param_names + ]).T # shape (N_stars_this_model, N_params) + + fixed_params = {} + for param_name in motion_model_instance.fixed_param_names: + col_name = param_name + if param_name + '_mm' in self.colnames: + col_name = param_name + '_mm' + fixed_params[param_name] = self[col_name][unique_index] - if mask_lists is not False: - # Remove a list - if isinstance(mask_lists, list): - if all(isinstance(item, int) for item in mask_lists): - xe.mask[mask_lists] = True - ye.mask[mask_lists] = True - - # Throw a warning if mask_lists is not a list - if not isinstance(mask_lists, list): - raise RuntimeError('mask_lists needs to be a list.') + # TODO: vectorize obsLocation handling in motion models + if (param_name == 'obsLocation'): + assert np.unique(fixed_params[param_name]).size == 1, \ + "infer_positions: obsLocation fixed parameter has different values for different stars. Vectorized handling not implemented yet." + fixed_params[param_name] = fixed_params[param_name][0] - # - # Make a mask of invalid (NaN) values and a user-specified invalid value. - # - if 't' in self.colnames: - t = np.ma.masked_invalid(self['t'][ss, :].data) - else: - t = np.ma.masked_invalid(self.meta['list_times']) + # Predict positions + x, y, xe, ye = motion_model_instance.model( + times, fit_params, fixed_params, fit_param_errs + ) + x_pred[unique_index] = x + y_pred[unique_index] = y + xe_pred[unique_index] = xe + ye_pred[unique_index] = ye - if mask_val: - t = np.ma.masked_values(t, mask_val) - if not np.ma.is_masked(t): - t.mask = np.zeros_like(t.data, dtype=bool) - - if mask_lists is not False: - # Remove a list - if isinstance(mask_lists, list): - if all(isinstance(item, int) for item in mask_lists): - t.mask[mask_lists] = True + return x_pred, y_pred, xe_pred, ye_pred - # Throw a warning if mask_lists is not a list - if not isinstance(mask_lists, list): - raise RuntimeError('mask_lists needs to be a list.') - # For inconsistent masks, mask the star if any of the values are masked. - new_mask = np.logical_or.reduce((t.mask, x.mask, y.mask, xe.mask, ye.mask)) - - # - # Figure out where we have detections (as indicated by error columns) - # - good = np.where((xe != 0) & (ye != 0) & - np.isfinite(xe) & np.isfinite(ye) & - np.isfinite(x) & np.isfinite(y) & ~new_mask)[0] - - N_good = len(good) - - # Catch the case where there is NO good data. - if N_good == 0: - #self['motion_model_used'][ss] = 'None' - self['n_fit'][ss] = N_good - self['n_params'][ss] = 0 - return - - # Everything below has N_good >= 1 - x = x[good] - y = y[good] - t = t[good] - xe = xe[good] - ye = ye[good] - - # - # Unless t0 is fixed, calculate the t0 for the stars. - # - if fixed_t0 is False: - t_weight = 1.0 / np.hypot(xe, ye) - t0 = np.average(t, weights=t_weight) - elif fixed_t0 is True: - t0 = self.t0 - else: - t0 = fixed_t0[ss] - self['t0'][ss] = t0 - self['n_fit'][ss] = N_good - - # - # Decide which motion_model to fit. - # - motion_model_use = self['motion_model_input'][ss] - # Go to default model if not enough points for assigned but enough for default - # TODO: think about whether we want other fallbacks besides the singular default and Fixed - if (N_good < motion_model_dict[motion_model_use].n_pts_req) and \ - (N_good >= motion_model_dict[default_motion_model].n_pts_req): - motion_model_use = default_motion_model - # If not enough points for either, go to a fixed model - elif (N_good < motion_model_dict[motion_model_use].n_pts_req) and \ - (N_good < motion_model_dict[default_motion_model].n_pts_req): - motion_model_use = 'Fixed' - # If the points do not cover multiple times, go to a fixed model - if (t == t[0]).all(): - motion_model_use = 'Fixed' - - self['motion_model_used'][ss] = motion_model_use - -# # Get the motion model object. -# modClass = motion_model_dict[motion_model_use] -# -# # Load up any prior information on parameters for this model. -# param_dict = {} -# for par in modClass.fit_param_names+modClass.fixed_param_names: -# if ~np.isnan(self[par][ss]): -# param_dict[par] = self[par][ss] - - # Model object - mod = motion_model_dict[motion_model_use] - fixed_params = [self[par][ss] for par in mod.fixed_param_names] - - # Fit for the best parameters - params, param_errs, chi2_x, chi2_y = mod.fit_motion_model(t, x, y, xe, ye, t0, bootstrap=bootstrap, - weighting=weighting, use_scipy=use_scipy, absolute_sigma=absolute_sigma) - # chi2_x,chi2_y = mod.get_chi2(params,fixed_params, t,x,y,xe,ye) - self['chi2_x'][ss]=chi2_x - self['chi2_y'][ss]=chi2_y - self['n_params'][ss] = mod.n_params - - # Save parameters and errors to table. - for pp in range(len(mod.fit_param_names)): - par = mod.fit_param_names[pp] - par_err = par + '_err' - self[par][ss] = params[pp] - self[par_err][ss] = param_errs[pp] - - return - # New function, to use in align def get_star_positions_at_time(self, t, motion_model_dict, allow_alt_models=True): """ Get current x,y positions of each star according to its motion_model @@ -1200,137 +986,6 @@ def get_star_positions_at_time(self, t, motion_model_dict, allow_alt_models=True return x, y, xe, ye - def fit_velocities_all_detected(self, motion_model_to_fit, weighting='var', use_scipy=True, absolute_sigma=True, times=None, - select_stars=None, epoch_cols='all', mask_val=None, art_star=False, return_result=False): - """Fit velocities for stars detected in all epochs specified by epoch_cols. - Criterion: xe/ye error > 0 and finite, x/y not masked. - - Parameters - ---------- - motion_model_to_fit : MotionModel - Motion model object to use for fitting all stars - weighting : str, optional - Variance weighting('var') or standard deviation weighting ('std'), by default 'var' - select_idx : array-like, optional - Indices of stars to select for fitting, by default None (fit all detected stars) - epoch_cols : str or list of intergers, optional - List of epoch column indices used for fitting velocity, by default 'all' - mask_val : float, optional - Values in x, y to be masked - art_star : bool, optional - Artificial star or observation star catalog. If artificial star, use 'det' column to select stars detected in all epochs, by default False - return_result : bool, optional - Return the velocity results or not, by default False - - Returns - ------- - vel_result : astropy Table - Astropy Table with velocity results - """ - - N_stars = len(self) - if select_stars is None: - select_stars = np.arange(N_stars) - else: - select_stars = np.asarray(select_stars) - - if epoch_cols == 'all': - epoch_cols = np.arange(np.shape(self['x'])[1]) - - # Artificial Star - if art_star: - detected_in_all_epochs = np.all(self['det'][select_stars, :][:, epoch_cols], axis=1) - - # Observation Star - else: - valid_xe = np.all(self['xe'][select_stars, :][:, epoch_cols]!=0, axis=1) & np.all(np.isfinite(self['xe'][select_stars, :][:, epoch_cols]), axis=1) - valid_ye = np.all(self['ye'][select_stars, :][:, epoch_cols]!=0, axis=1) & np.all(np.isfinite(self['ye'][select_stars, :][:, epoch_cols]), axis=1) - - if mask_val: - x = np.ma.masked_values(self['x'][select_stars, :][:, epoch_cols], mask_val, shrink=False) - y = np.ma.masked_values(self['y'][select_stars, :][:, epoch_cols], mask_val, shrink=False) - valid_x = ~np.any(x.mask, axis=1) - valid_y = ~np.any(y.mask, axis=1) - detected_in_all_epochs = np.logical_and.reduce(( - valid_x, valid_y, valid_xe, valid_ye)) - else: - detected_in_all_epochs = np.logical_and(valid_xe, valid_ye) - - N = len(self['x'][select_stars, :]) - fit_params = motion_model_to_fit.fit_param_names - param_data = {p: np.zeros(N) for p in fit_params} - param_data.update({p+'_err': np.zeros(N) for p in fit_params}) - param_data.update({p: np.zeros(N) for p in motion_model_to_fit.fixed_param_names}) - param_data['chi2_x'] = np.zeros(N) - param_data['chi2_y'] = np.zeros(N) - - if times is None: - if 'YEARS' in self.meta: - times = np.array(self.meta['YEARS'])[epoch_cols] - elif 't' in self.colnames: - times = self['t'][0, epoch_cols] - else: - raise ValueError("No valid time column found.") - - if not art_star: - x_arr = self['x'][select_stars, :][:, epoch_cols] - y_arr = self['y'][select_stars, :][:, epoch_cols] - else: - x_arr = self['x'][select_stars, :][:, epoch_cols, 1] - y_arr = self['y'][select_stars, :][:, epoch_cols, 1] - - xe_arr = self['xe'][select_stars, :][:, epoch_cols] - ye_arr = self['ye'][select_stars, :][:, epoch_cols] - - # Only fit for >1 epochs, otherwise all velocities will be 0 - if len(epoch_cols) > 1: - # For each star - for i in tqdm(range(N)): - x = x_arr[i] - y = y_arr[i] - xe = xe_arr[i] - ye = ye_arr[i] - t0 = np.average(times, weights=1. / np.hypot(xe, ye)) - - # Run fit and record results - params, param_errs = motion_model_to_fit.fit_motion_model( - times, x, y, xe, ye, t0, weighting=weighting, - use_scipy=use_scipy, absolute_sigma=absolute_sigma - ) - if 't0' in motion_model_to_fit.fixed_param_names: - param_data['t0'][i] = t0 - for j, param in enumerate(fit_params): - param_data[param][i] = params[j] - param_data[f'{param}_err'][i] = param_errs[j] - chi2x, chi2y = motion_model_to_fit.get_chi2(params, [t0], times, x, y, xe, ye) - param_data['chi2_x'][i] = chi2x - param_data['chi2_y'][i] = chi2y - - vel_result = Table.from_pandas(pd.DataFrame(param_data)) - - # Add n_vfit - n_fit = len(epoch_cols) - vel_result['n_fit'] = n_fit - - # Clean/remove up old arrays. - columns = [*vel_result.keys(), 'n_fit'] - for column in columns: - if column in self.colnames: self.remove_column(column) - - # Update self - for column in columns: - column_array = MaskedColumn(np.ma.zeros(N_stars), dtype=float, name=column) - column_array[select_stars] = vel_result[column] - column_array[select_stars][~detected_in_all_epochs] = np.nan - column_array.mask[select_stars] = ~detected_in_all_epochs - # Mask unselected indices - column_array.mask[~np.isin(np.arange(N_stars), select_stars)] = True - self[column] = column_array - - if return_result: - return vel_result - else: - return def shift_reference_frame(self, delta_vx=0.0, delta_vy=0.0, delta_pi=0.0, motion_model_dict={}): diff --git a/flystar/tests/test_startable.py b/flystar/tests/test_startable.py index 804d4e7..f9cd97c 100644 --- a/flystar/tests/test_startable.py +++ b/flystar/tests/test_startable.py @@ -82,7 +82,6 @@ def test_StarTable_init2(): assert len(tab) == len(list1) - return def test_combine_lists(): @@ -175,39 +174,37 @@ def test_add_starlist(): t.add_starlist(x=x_new, y=y_new, m=m_new, xe=xe_new, ye=ye_new, me=me_new, meta={'list_times': t_new}) - assert len(t) == len(t_orig) + np.testing.assert_equal(len(t), len(t_orig)) expected_shape = np.array(t_orig['x'].shape) expected_shape[1] += 1 - - assert len(t['x'].shape) == len(expected_shape) - assert t['x'].shape[0] == expected_shape[0] + + np.testing.assert_equal(len(t['x'].shape), len(expected_shape)) + np.testing.assert_equal(t['x'].shape[0], expected_shape[0]) assert t['x'].shape[1] == expected_shape[1] - assert len(t['y'].shape) == len(expected_shape) - assert t['y'].shape[0] == expected_shape[0] + np.testing.assert_equal(len(t['y'].shape), len(expected_shape)) + np.testing.assert_equal(t['y'].shape[0], expected_shape[0]) assert t['y'].shape[1] == expected_shape[1] - assert len(t['m'].shape) == len(expected_shape) - assert t['m'].shape[0] == expected_shape[0] + np.testing.assert_equal(len(t['m'].shape), len(expected_shape)) + np.testing.assert_equal(t['m'].shape[0], expected_shape[0]) assert t['m'].shape[1] == expected_shape[1] - assert len(t['xe'].shape) == len(expected_shape) - assert t['xe'].shape[0] == expected_shape[0] - assert t['xe'].shape[1] == expected_shape[1] - - assert len(t['ye'].shape) == len(expected_shape) - assert t['ye'].shape[0] == expected_shape[0] - assert t['ye'].shape[1] == expected_shape[1] + np.testing.assert_equal(len(t['xe'].shape), len(expected_shape)) + np.testing.assert_equal(t['xe'].shape[0], expected_shape[0]) + np.testing.assert_equal(t['xe'].shape[1], expected_shape[1]) - assert len(t['me'].shape) == len(expected_shape) - assert t['me'].shape[0] == expected_shape[0] - assert t['me'].shape[1] == expected_shape[1] - - assert len(t['name']) == len(t_orig['name']) - assert len(t.meta['list_times']) == expected_shape[1] - assert t.meta['n_lists'] == 9 + np.testing.assert_equal(len(t['ye'].shape), len(expected_shape)) + np.testing.assert_equal(t['ye'].shape[0], expected_shape[0]) + np.testing.assert_equal(t['ye'].shape[1], expected_shape[1]) + np.testing.assert_equal(len(t['me'].shape), len(expected_shape)) + np.testing.assert_equal(t['me'].shape[0], expected_shape[0]) + np.testing.assert_equal(t['me'].shape[1], expected_shape[1]) + np.testing.assert_equal(len(t['name']), len(t_orig['name'])) + np.testing.assert_equal(len(t.meta['list_times']), expected_shape[1]) + np.testing.assert_equal(t.meta['n_lists'], 9) # Test 2: Add as starlist rather than with keywords. starlist = StarList( name=t_orig['name'], @@ -219,38 +216,37 @@ def test_add_starlist(): t = make_star_table() t.add_starlist(starlist=starlist) - assert len(t) == len(t_orig) + np.testing.assert_equal(len(t), len(t_orig)) expected_shape = np.array(t_orig['x'].shape) expected_shape[1] += 1 - - assert len(t['x'].shape) == len(expected_shape) - assert t['x'].shape[0] == expected_shape[0] - assert t['x'].shape[1] == expected_shape[1] - assert len(t['y'].shape) == len(expected_shape) - assert t['y'].shape[0] == expected_shape[0] - assert t['y'].shape[1] == expected_shape[1] + np.testing.assert_equal(len(t['x'].shape), len(expected_shape)) + np.testing.assert_equal(t['x'].shape[0], expected_shape[0]) + np.testing.assert_equal(t['x'].shape[1], expected_shape[1]) - assert len(t['m'].shape) == len(expected_shape) - assert t['m'].shape[0] == expected_shape[0] - assert t['m'].shape[1] == expected_shape[1] + np.testing.assert_equal(len(t['y'].shape), len(expected_shape)) + np.testing.assert_equal(t['y'].shape[0], expected_shape[0]) + np.testing.assert_equal(t['y'].shape[1], expected_shape[1]) - assert len(t['xe'].shape) == len(expected_shape) - assert t['xe'].shape[0] == expected_shape[0] - assert t['xe'].shape[1] == expected_shape[1] + np.testing.assert_equal(len(t['m'].shape), len(expected_shape)) + np.testing.assert_equal(t['m'].shape[0], expected_shape[0]) + np.testing.assert_equal(t['m'].shape[1], expected_shape[1]) - assert len(t['ye'].shape) == len(expected_shape) - assert t['ye'].shape[0] == expected_shape[0] - assert t['ye'].shape[1] == expected_shape[1] + np.testing.assert_equal(len(t['xe'].shape), len(expected_shape)) + np.testing.assert_equal(t['xe'].shape[0], expected_shape[0]) + np.testing.assert_equal(t['xe'].shape[1], expected_shape[1]) + np.testing.assert_equal(len(t['ye'].shape), len(expected_shape)) + np.testing.assert_equal(t['ye'].shape[0], expected_shape[0]) + np.testing.assert_equal(t['ye'].shape[1], expected_shape[1]) - assert len(t['me'].shape) == len(expected_shape) - assert t['me'].shape[0] == expected_shape[0] - assert t['me'].shape[1] == expected_shape[1] + np.testing.assert_equal(len(t['me'].shape), len(expected_shape)) + np.testing.assert_equal(t['me'].shape[0], expected_shape[0]) + np.testing.assert_equal(t['me'].shape[1], expected_shape[1]) - assert len(t['name']) == len(t_orig['name']) - assert len(t.meta['list_times']) == expected_shape[1] - assert t.meta['n_lists'] == 9 + np.testing.assert_equal(len(t['name']), len(t_orig['name'])) + np.testing.assert_equal(len(t.meta['list_times']), expected_shape[1]) + np.testing.assert_equal(t.meta['n_lists'], 9) return @@ -298,11 +294,11 @@ def test_combine_1col(): t.combine_lists('x', weights_col='xe') - assert t['x0'][0] == t['x'][0] + np.testing.assert_equal(t['x0'][0], t['x'][0]) return -def test_fit_velocities(): +def test_fit_motion_models(): tab = make_star_table() tt = make_tiny_star_table() @@ -317,26 +313,25 @@ def test_fit_velocities(): tab.fit_motion_model(verbose=True, mask_value=-100000.) # Test creation of new variables - assert len(tab['vx']) == len(tab) - assert len(tab['vy']) == len(tab) - assert len(tab['vx_err']) == len(tab) - assert len(tab['vy_err']) == len(tab) - assert len(tab['n_fit']) == len(tab) - assert tab.meta['n_bootstrap'] == 0 + np.testing.assert_equal(len(tab['vx']), len(tab)) + np.testing.assert_equal(len(tab['vy']), len(tab)) + np.testing.assert_equal(len(tab['vx_err']), len(tab)) + np.testing.assert_equal(len(tab['vy_err']), len(tab)) + np.testing.assert_equal(len(tab['n_fit']), len(tab)) + np.testing.assert_equal(tab.meta['n_bootstrap'], 0) # Test no-fit for stars with N<2 epochs. n_epochs = (tab['x'] >= 0).sum(axis=1) idx = np.where(n_epochs < 2)[0] - assert (tab['vx'][idx] == 0).all() - assert (tab['vx_err'][idx] == 0).all() - assert (tab['n_fit'][idx] == 2).all() + np.testing.assert_equal((tab['vx'][idx] == 0).all(), True) + np.testing.assert_equal((tab['vx_err'][idx] == 0).all(), True) + np.testing.assert_equal((tab['n_fit'][idx] == 2).all(), True) # Test that the velocity errors were calculated. - assert (~(tab['vx_err'][0:100] < 0)).all() - assert (~(tab['x0_err'][0:100] < 0)).all() - assert (~(tab['vy_err'][0:100] < 0)).all() - assert (~(tab['y0_err'][0:100] < 0)).all() - + np.testing.assert_equal((~(tab['vx_err'][0:100] < 0)).all(), True) + np.testing.assert_equal((~(tab['x0_err'][0:100] < 0)).all(), True) + np.testing.assert_equal((~(tab['vy_err'][0:100] < 0)).all(), True) + np.testing.assert_equal((~(tab['y0_err'][0:100] < 0)).all(), True) ########## # Test running a second time. We should get the same results. @@ -366,11 +361,11 @@ def test_fit_velocities(): tab_b.meta = tab1.meta tab_b.fit_motion_model(verbose=True, bootstrap=50) - assert tab_b.meta['n_bootstrap'] == 50 - assert tab_b['x0_err'][0] > tab['x0_err'][0] - assert tab_b['vx_err'][0] > tab['vx_err'][0] - assert tab_b['y0_err'][0] > tab['y0_err'][0] - assert tab_b['vy_err'][0] > tab['vy_err'][0] + np.testing.assert_equal(tab_b.meta['n_bootstrap'], 50) + np.testing.assert_array_less(tab['x0_err'][0], tab_b['x0_err'][0]) + np.testing.assert_array_less(tab['vx_err'][0], tab_b['vx_err'][0]) + np.testing.assert_array_less(tab['y0_err'][0], tab_b['y0_err'][0]) + np.testing.assert_array_less(tab['vy_err'][0], tab_b['vy_err'][0]) ########## # Test what happens with no velocity errors @@ -378,15 +373,15 @@ def test_fit_velocities(): tab.remove_columns(['xe', 'ye', 'x0', 'y0', 'x0_err', 'y0_err', 'vx', 'vy', 'vx_err', 'vy_err', 'n_fit']) tab.fit_motion_model(verbose=False) - assert len(tab['vx']) == len(tab) - assert len(tab['vy']) == len(tab) - assert len(tab['vx_err']) == len(tab) - assert len(tab['vy_err']) == len(tab) - assert len(tab['n_fit']) == len(tab) - assert (~(tab['vx_err'][0:100] < 0)).all() - assert (~(tab['x0_err'][0:100] < 0)).all() - assert (~(tab['vy_err'][0:100] < 0)).all() - assert (~(tab['y0_err'][0:100] < 0)).all() + np.testing.assert_equal(len(tab['vx']), len(tab)) + np.testing.assert_equal(len(tab['vy']), len(tab)) + np.testing.assert_equal(len(tab['vx_err']), len(tab)) + np.testing.assert_equal(len(tab['vy_err']), len(tab)) + np.testing.assert_equal(len(tab['n_fit']), len(tab)) + np.testing.assert_equal((~(tab['vx_err'][0:100] < 0)).all(), True) + np.testing.assert_equal((~(tab['x0_err'][0:100] < 0)).all(), True) + np.testing.assert_equal((~(tab['vy_err'][0:100] < 0)).all(), True) + np.testing.assert_equal((~(tab['y0_err'][0:100] < 0)).all(), True) ######### # Test mask_list @@ -408,7 +403,6 @@ def test_fit_velocities(): def test_fit_velocities_2epoch(): - ########## # Test: only 2 epoch2 ########## @@ -424,30 +418,21 @@ def test_fit_velocities_2epoch(): tab_2.fit_motion_model(verbose=False, mask_value=-100000.) - assert 'n_fit' in tab_2.colnames - assert 't0' in tab_2.colnames - assert 'x0' in tab_2.colnames - assert 'y0' in tab_2.colnames - assert 'vx' in tab_2.colnames - assert 'vy' in tab_2.colnames - assert 'x0_err' in tab_2.colnames - assert 'y0_err' in tab_2.colnames - assert 'vx_err' in tab_2.colnames - assert 'vy_err' in tab_2.colnames + assert all([_ in tab_2.colnames for _ in ['n_fit', 't0', 'x0', 'y0', 'vx', 'vy', 'x0_err', 'y0_err', 'vx_err', 'vy_err']]) # 2 detections print(tab1.meta) np.testing.assert_almost_equal(tab_2['x0'][0], tab_2['x'][0,0], 1) - assert tab_2['n_fit'][0] == 2 + np.testing.assert_equal(tab_2['n_fit'][0], 2) # 1 detection - assert tab_2['x0'][100] == tab_2['x'][100, 0] - assert tab_2['n_fit'][100] == 1 - + np.testing.assert_equal(tab_2['x0'][100], tab_2['x'][100, 0]) + np.testing.assert_equal(tab_2['n_fit'][100], 1) + # 0 detections - assert np.isnan(tab_2['x0'][-1]) - assert tab_2['n_fit'][-1] == 0 - + np.testing.assert_equal(np.isnan(tab_2['x0'][-1]), True) + np.testing.assert_equal(tab_2['n_fit'][-1], 0) + return From 48321cbc93e1c38afd18be389b01344f51c6b638 Mon Sep 17 00:00:00 2001 From: Wei Lingfeng Date: Tue, 16 Dec 2025 20:19:14 +0800 Subject: [PATCH 138/254] Resolve undefined variables and cleaned imports --- flystar/align.py | 45 +++++++++++++++------------------ flystar/analysis.py | 6 +---- flystar/examples.py | 10 ++------ flystar/match.py | 8 ++---- flystar/motion_model.py | 7 +++-- flystar/tests/test_startable.py | 1 - flystar/transforms.py | 19 +++++++------- 7 files changed, 41 insertions(+), 55 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index ca9bc93..53db62f 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -1,10 +1,7 @@ import numpy as np -from . import match -from . import transforms -from . import plots +from . import match, transforms, plots, motion_model from .starlists import StarList from .startables import StarTable -from . import motion_model from astropy.table import Table, Column, vstack import datetime import copy @@ -411,7 +408,6 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar print(" **********") star_list = self.star_lists[ii] - pdb.set_trance() ref_list = self.get_ref_list_from_table(star_list['t'][0]) trans = self.trans_list[ii] @@ -880,9 +876,9 @@ def update_ref_table_aggregates(self, keep_orig=None, n_boot=0): for mm in motion_model_col_names: if mm in self.ref_table.keys(): vals_orig[mm] = self.ref_table[mm][keep_orig] - fit_star_idxs = [idx for idx in range(len(self.ref_table)) if idx not in keep_orig] - else: - fit_star_idxs = None + # fit_star_idxs = [idx for idx in range(len(self.ref_table)) if idx not in keep_orig] + # else: + # fit_star_idxs = None # Figure out whether motion fits are necessary if ('motion_model_input' in self.ref_table.keys()) and np.all(self.ref_table['motion_model_input']=='Fixed'): @@ -2331,10 +2327,10 @@ def find_transform_new(table1_mat, table2_mat, if transInit != None: table1T_mat = table1_mat.copy() - table1T_mat = transform_by_object(table1T_mat, transInit) + table1T_mat = transform_from_object(table1T_mat, transInit) - x1e = table1T_mag['xe'] - y1e = table1T_mag['ye'] + x1e = table1T_mat['xe'] + y1e = table1T_mat['ye'] # Calculate weights as to user specification if weights == 'both': @@ -2419,8 +2415,7 @@ def write_transform(transform, starlist, reference, N_trans, deltaMag=0, restric Xcoeff = transform.px.parameters Ycoeff = transform.py.parameters else: - print(( '{0} not yet supported!'.format(transType))) - return + raise Exception(f'{trans_name} not yet supported!') # Write output _out = open(outFile, 'w') @@ -2437,7 +2432,7 @@ def write_transform(transform, starlist, reference, N_trans, deltaMag=0, restric _out.write('## N_trans: {0}\n'.format(N_trans)) _out.write('## Delta Mag: {0}\n'.format(deltaMag)) _out.write('{0:16s} {1:16s}\n'.format('# Xcoeff', 'Ycoeff')) - + # Write the coefficients such that the orders are together as defined in # documentation. This is a pain because PolyTransform output is weird. # (see astropy Polynomial2D documentation) @@ -2648,7 +2643,7 @@ def position_transform_from_object(x, y, xe, ye, transform): order = transform.order else: txt = 'Transform not yet supported by position_transform_from_object' - raise StandardError(txt) + raise Exception(txt) # How the transformation is applied depends on the type of transform. # This can be determined by the length of Xcoeff, Ycoeff @@ -2747,7 +2742,7 @@ def velocity_transform_from_object(x0, y0, x0e, y0e, vx, vy, vxe, vye, transform order = transform.order else: txt = 'Transform not yet supported by velocity_transform_from_object' - raise StandardError(txt) + raise Exception(txt) # How the transformation is applied depends on the type of transform. # This can be determined by the length of Xcoeff, Ycoeff @@ -2939,13 +2934,15 @@ def trans_initial_guess(ref_list, star_list, trans_args, mode='miracle', else: mref = ref_list['m0'] - N, x1m, y1m, m1m, x2m, y2m, m2m = match.miracle_match_briteN(star_list['x'], - star_list['y'], - star_list['m'], - xref, - yref, - mref, - briteN) + N, x1m, y1m, m1m, x2m, y2m, m2m = match.miracle_match_briteN( + star_list['x'], + star_list['y'], + star_list['m'], + xref, + yref, + mref, + briteN + ) err_msg = 'Failed to find more than '+str(n_req_match) err_msg += ' (only ' + str(len(x1m)) + ') matches, giving up.' @@ -3066,7 +3063,7 @@ def outlier_rejection_indices(star_list, ref_list, outlier_tol, verbose=True): """ # Optionally propogate the reference positions forward in time. xref, yref = get_pos_in_time(star_list['t'][0], ref_list) - + # Residuals x_resid_on_old_trans = star_list['x'] - xref y_resid_on_old_trans = star_list['y'] - yref diff --git a/flystar/analysis.py b/flystar/analysis.py index 81ab3f4..ab16f77 100644 --- a/flystar/analysis.py +++ b/flystar/analysis.py @@ -1,10 +1,6 @@ import numpy as np import pylab as plt -from flystar import starlists -from flystar import startables -from flystar import align -from flystar import match -from flystar import transforms +from . import starlists, match from astropy import table from astropy.table import Table, Column from astropy.coordinates import SkyCoord diff --git a/flystar/examples.py b/flystar/examples.py index 8059562..65723ec 100644 --- a/flystar/examples.py +++ b/flystar/examples.py @@ -1,11 +1,5 @@ -from flystar import transforms -from flystar import match -from flystar import align -from flystar import starlists -from flystar import plots import numpy as np -import copy -import pdb +from . import transforms, match, align, starlists, plots def align_example(labelFile, reference, transModel=transforms.four_paramNW, order=1, N_loop=2, @@ -83,7 +77,7 @@ def align_example(labelFile, reference, transModel=transforms.four_paramNW, orde trans, N_trans = align.find_transform(label[idx_label], label_trans[idx_label], - starlist_mat[idx_starlist], + starlist[idx_starlist], transModel=transModel, order=order, weights=weights) diff --git a/flystar/match.py b/flystar/match.py index d7c391e..9bda523 100644 --- a/flystar/match.py +++ b/flystar/match.py @@ -1,14 +1,10 @@ import numpy as np -from flystar import starlists, transforms, startables, align +from . import starlists, transforms, startables, align from collections import Counter from scipy.spatial import cKDTree as KDT -from astropy.table import Column, Table +from astropy.table import Column import itertools import copy -import scipy.signal -from scipy.spatial import distance -import math -import pdb def miracle_match_briteN(xin1, yin1, min1, xin2, yin2, min2, Nbrite, diff --git a/flystar/motion_model.py b/flystar/motion_model.py index 42a5e54..e85d7a7 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -1098,7 +1098,7 @@ def get_list_motion_model_param_names(motion_model_list, with_errors=True, with_ Parameters ---------- - motion_model_list : list + motion_model_list : list of MotionModels or str List of MotionModels with_errors : bool, optional Add uncertainty names with '_err' suffix or not, by default True @@ -1116,7 +1116,10 @@ def list_add(name): if name not in list_of_parameters: list_of_parameters.append(name) + mm_map = motion_model_map() for mm in motion_model_list: + if isinstance(mm, str): + mm = mm_map[mm] for param in mm.fit_param_names: # Fitter params list_add(param) @@ -1130,7 +1133,7 @@ def list_add(name): return list(list_of_parameters) -def get_all_motion_model_names(with_errors=True, with_fixed=True): +def get_all_motion_model_param_names(with_errors=True, with_fixed=True): return get_list_motion_model_param_names(MotionModel.__subclasses__(), with_errors=with_errors, with_fixed=with_fixed) def motion_model_map(): diff --git a/flystar/tests/test_startable.py b/flystar/tests/test_startable.py index 804d4e7..b0ee1b9 100644 --- a/flystar/tests/test_startable.py +++ b/flystar/tests/test_startable.py @@ -3,7 +3,6 @@ from flystar import motion_model from flystar.startables import StarTable from flystar.starlists import StarList -from flystar import motion_model import numpy as np import pytest import os diff --git a/flystar/transforms.py b/flystar/transforms.py index 6cc865a..8cb9525 100755 --- a/flystar/transforms.py +++ b/flystar/transforms.py @@ -5,8 +5,9 @@ from astropy.table import Table import collections import re -import pdb -from flystar import motion_model +import copy +import datetime +from . import motion_model class Transform2D(object): ''' @@ -220,7 +221,7 @@ def evaluate(self, x, y): yn = self.py[0] + self.py[1]*x + self.py[2]*y return xn, yn - def evaluate_error(self, x, y): + def evaluate_error(self, x, y, xe, ye): """ Transform positional uncertainties. @@ -245,7 +246,7 @@ def evaluate_error(self, x, y): """ xe_new = np.hypot(self.px[1] * xe, self.px[2] * ye) - xe_new = np.hpyot(self.px[1] * xe, self.px[2] * ye) + ye_new = np.hpyot(self.px[1] * xe, self.px[2] * ye) return xe_new, ye_new @@ -666,7 +667,7 @@ def from_file(cls, trans_file): return trans_obj - def to_file(self, trans_file): + def to_file(self, transform, outFile): """ Given a transformation object, write out the coefficients in a text file (readable by java align). Outfile name is specified by user. @@ -677,9 +678,9 @@ def to_file(self, trans_file): Parameters: ---------- - trans_file : str - The name of the output file to save the coefficients and meta data to. - This file can be read back in with + transform : PolyTransform + The transformation object containing the coefficients and meta data to save. + This object can be recreated with trans_obj = PolyTransfrom.from_file(trans_file). @@ -695,7 +696,7 @@ def to_file(self, trans_file): # Write output _out = open(outFile, 'w') - + # Write the header. DO NOT CHANGE, HARDCODED IN JAVA ALIGN _out.write('## Date: {0}\n'.format(datetime.date.today()) ) _out.write('## File: {0}, Reference: {1}\n'.format(starlist, reference) ) From f0e8884deb5ccbc68a897648f6f204ce1a57c453 Mon Sep 17 00:00:00 2001 From: Wei Lingfeng Date: Tue, 16 Dec 2025 20:57:17 +0800 Subject: [PATCH 139/254] Fix bootstrap sampling strategy and 100% passed startable test --- flystar/motion_model.py | 10 ++++++++-- flystar/tests/test_startable.py | 4 ++-- 2 files changed, 10 insertions(+), 4 deletions(-) diff --git a/flystar/motion_model.py b/flystar/motion_model.py index 60c213b..11f6b16 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -136,8 +136,14 @@ def fit( edx = np.arange(n_obs, dtype=int) # Precompute All Bootstrap Draws at Once # Ensure there are enough unique points in each bootstrap sample - bdx_unique = rng.choice(edx, size=(bootstrap, self.n_params), replace=False) - bdx_extra = rng.choice(edx, size=(bootstrap, m - self.n_params), replace=True) + bdx_unique = np.stack([ + rng.choice(edx, size=self.n_params, replace=False) + for _ in range(bootstrap) + ]) + bdx_extra = np.stack([ + rng.choice(edx, size=self.n_params, replace=True) + for _ in range(bootstrap) + ]) bdx_all = np.hstack((bdx_unique, bdx_extra)) bb_params = [] diff --git a/flystar/tests/test_startable.py b/flystar/tests/test_startable.py index 70ec18a..5580daf 100644 --- a/flystar/tests/test_startable.py +++ b/flystar/tests/test_startable.py @@ -340,7 +340,7 @@ def test_fit_motion_models(): vxe_orig = tab['vx_err'] x0e_orig = tab['x0_err'] tab.fit_motion_model(verbose=False, mask_value=-100000.) - + np.testing.assert_allclose(tab['vx'], vx_orig) np.testing.assert_allclose(tab['x0'], x0_orig) np.testing.assert_allclose(tab['vx_err'], vxe_orig) @@ -423,7 +423,7 @@ def test_fit_velocities_2epoch(): print(tab1.meta) np.testing.assert_almost_equal(tab_2['x0'][0], tab_2['x'][0,0], 1) np.testing.assert_equal(tab_2['n_fit'][0], 2) - + # 1 detection np.testing.assert_equal(tab_2['x0'][100], tab_2['x'][100, 0]) np.testing.assert_equal(tab_2['n_fit'][100], 1) From a1ff8f345ac5c661ca6aa9f4b6e3beb0e53e7a82 Mon Sep 17 00:00:00 2001 From: Wei Lingfeng Date: Sat, 20 Dec 2025 15:24:37 +0800 Subject: [PATCH 140/254] Midway save of align --- flystar/align.py | 179 ++++++++++++++++++++++++------------ flystar/match.py | 2 +- flystar/motion_model.py | 59 ++---------- flystar/startables.py | 8 +- flystar/tests/test_align.py | 104 ++++++++++----------- flystar/transforms.py | 2 +- 6 files changed, 185 insertions(+), 169 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 53db62f..3a3160d 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -23,7 +23,7 @@ def __init__(self, list_of_starlists, ref_index=0, iters=2, calc_trans_inverse=False, init_guess_mode='miracle', iter_callback=None, motion_models=['Empty', 'Fixed'], - fixed_params_dict = None, + fixed_params_dict=None, use_scipy=True, absolute_sigma=False, save_path=None, @@ -301,7 +301,7 @@ def fit(self): # x_orig, y_orig, m_orig, (opt. errors) -- the transformed errors for the lists: 2D # w, w_orig (optiona) -- the input and output weights of stars in transform: 2D ########## - self.ref_table = self.setup_ref_table_from_starlist(self.star_lists[self.ref_index],motion_model_used='Fixed') + self.ref_table = self.setup_ref_table_from_starlist(self.star_lists[self.ref_index]) # Save the reference index to the meta data on the reference list. self.ref_table.meta['ref_list'] = self.ref_index @@ -423,11 +423,10 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar if trans is None: # Only use "use_in_trans" reference stars, even for initial guessing. keepers = np.where(ref_list['use_in_trans'] == True)[0] - trans = trans_initial_guess( - ref_list[keepers], - star_list_orig_trim, - self.trans_args[0], + ref_list[keepers], + star_list_orig_trim, + self.trans_args[0], mode=self.init_guess_mode, order=self.init_order, verbose=self.verbose, @@ -603,12 +602,12 @@ def setup_trans_info(self): # Add inverse trans list, if desired if self.calc_trans_inverse: - trans_list_inverse = [None for ii in range(N_lists)] + trans_list_inverse = [None] * N_lists self.trans_list_inverse = trans_list_inverse return - def setup_ref_table_from_starlist(self, star_list, motion_model_used=None): + def setup_ref_table_from_starlist(self, star_list): """ Start with the reference list.... this will change and grow over time, so make a copy that we will keep updating. @@ -616,7 +615,7 @@ def setup_ref_table_from_starlist(self, star_list, motion_model_used=None): array in the original reference star list. """ col_arrays = {} - motion_model_col_names = motion_model.get_all_motion_model_param_names(with_errors=True, with_fixed=True) + ['m0','m0_err','use_in_trans', 'motion_model_input', 'motion_model_used'] + motion_model_col_names = motion_model.motion_model_param_names(self.motion_models, with_errors=True, with_fixed=True) + ['m0','m0_err','use_in_trans', 'motion_model_input', 'motion_model_used'] for col_name in star_list.colnames: if col_name == 'name': # The "name" column will be 1D; but we will also add a "name_in_list" column. @@ -829,7 +828,12 @@ def update_ref_table_from_list(self, star_list, star_list_T, ii, idx_ref, idx_li self.ref_table['used_in_trans'][idx_ref_in_trans, ii] = True ### Add the unmatched stars and grow the size of the reference table. - self.ref_table, idx_lis_new, idx_ref_new = add_rows_for_new_stars(self.ref_table, star_list, idx_lis) + self.ref_table, idx_lis_new, idx_ref_new = add_rows_for_new_stars( + self.ref_table, + star_list, + idx_lis, + motion_model=self.motion_models[-1].name + ) if len(idx_ref_new) > 0: if self.verbose > 0: print(' Adding {0:d} new stars to the reference table.'.format(len(idx_ref_new))) @@ -862,7 +866,7 @@ def update_ref_table_aggregates(self, keep_orig=None, n_boot=0): """ # Keep track of the original reference values. # In certain cases, we will NOT update these. - if keep_orig is not None: + if (keep_orig is not None) and (len(keep_orig) > 0): vals_orig = {} vals_orig['m0'] = self.ref_table['m0'][keep_orig] vals_orig['m0_err'] = self.ref_table['m0_err'][keep_orig] @@ -872,22 +876,41 @@ def update_ref_table_aggregates(self, keep_orig=None, n_boot=0): if 'motion_model_used' in self.ref_table.keys(): motion_model_class_names += self.ref_table['motion_model_used'][keep_orig].tolist() vals_orig['motion_model_used'] = self.ref_table['motion_model_used'][keep_orig] - motion_model_col_names = motion_model.get_list_motion_model_param_names(motion_model_class_names, with_errors=True, with_fixed=True) + motion_model_col_names = motion_model.motion_model_param_names(motion_model_class_names, with_errors=True, with_fixed=True) for mm in motion_model_col_names: if mm in self.ref_table.keys(): vals_orig[mm] = self.ref_table[mm][keep_orig] - # fit_star_idxs = [idx for idx in range(len(self.ref_table)) if idx not in keep_orig] - # else: - # fit_star_idxs = None + fit_star_idxs = np.array([idx for idx in range(len(self.ref_table)) if idx not in keep_orig], dtype=int) + else: + fit_star_idxs = None # Figure out whether motion fits are necessary if ('motion_model_input' in self.ref_table.keys()) and np.all(self.ref_table['motion_model_input']=='Fixed'): weighted_xy = ('xe' in self.ref_table.colnames) and ('ye' in self.ref_table.colnames) weighted_m = ('me' in self.ref_table.colnames) self.ref_table.combine_lists_xym(weighted_xy=weighted_xy, weighted_m=weighted_m) + elif fit_star_idxs is None: + self.ref_table.fit_motion_model( + motion_models=self.motion_models, + fixed_params_dict=self.fixed_params_dict, + weighting=self.vel_weighting, + use_scipy=self.use_scipy, + absolute_sigma=self.absolute_sigma, + bootstrap=n_boot, + verbose=self.verbose + ) + # Combine (transformed) magnitudes + if 'me' in self.ref_table.colnames: + weights_col = None + else: + weights_col = 'me' + self.ref_table.combine_lists('m', weights_col=weights_col, ismag=True) + else: # Combine positions with a velocity fit. - self.ref_table.fit_motion_model( + update_ref_table = self.ref_table[fit_star_idxs] + keep_ref_table = self.ref_table[keep_orig] + update_ref_table.fit_motion_model( motion_models=self.motion_models, fixed_params_dict=self.fixed_params_dict, weighting=self.vel_weighting, @@ -897,14 +920,33 @@ def update_ref_table_aggregates(self, keep_orig=None, n_boot=0): verbose=self.verbose ) + # Determine motion models for keep_ref_table + pdb.set_trace() + if 'motion_model_used' not in keep_ref_table.colnames: + all_mm_map = motion_model.motion_model_map() + mm_n_params = np.sort([mm.n_params for mm in self.motion_models]) + required_params = np.array([all_mm_map[mm_name].n_params for mm_name in keep_ref_table['motion_model_input']]) + mm_digitized = np.digitize( + x=np.minimum(np.array(keep_ref_table['n_detect']), required_params), + bins=mm_n_params + ) - 1 + keep_ref_table['motion_model_used'] = np.array([self.motion_models[d].name for d in mm_digitized]) + + # Merge back into the full ref_table + new_ref_table = vstack([keep_ref_table, update_ref_table]) + self.ref_table = new_ref_table.copy() + self.ref_table[keep_orig] = new_ref_table[0:len(keep_orig)] + self.ref_table[fit_star_idxs] = new_ref_table[len(keep_orig):] + # Combine (transformed) magnitudes if 'me' in self.ref_table.colnames: weights_col = None else: weights_col = 'me' self.ref_table.combine_lists('m', weights_col=weights_col, ismag=True) + # Replace the originals if we are supposed to keep them fixed. - if keep_orig is not None: + if (keep_orig is not None) and (len(keep_orig) > 0): for val in vals_orig.keys(): self.ref_table[val][keep_orig] = vals_orig[val] @@ -977,14 +1019,14 @@ def match_lists(self, dr_tol, dm_tol): star_list_T.transform_xym(self.trans_list[ii]) else: star_list_T.transform_xy(self.trans_list[ii]) - + xref, yref = infer_positions(star_list_T['t'][0], self.ref_table) mref = self.ref_table['m0'] idx_lis, idx_ref, dr, dm = match.match(star_list_T['x'], star_list_T['y'], star_list_T['m'], xref, yref, mref, dr_tol=dr_tol, dm_tol=dm_tol, verbose=self.verbose) - + if self.verbose > 0: fmt = 'Matched {0:5d} out of {1:5d} stars in list {2:2d} [dr = {3:7.4f} +/- {4:6.4f}, dm = {5:5.2f} +/- {6:4.2f}' print(fmt.format(len(idx_lis), len(star_list_T), ii, dr.mean(), dr.std(), dm.mean(), dm.std())) @@ -1012,7 +1054,6 @@ def get_ref_list_from_table(self, epoch): name = self.ref_table['name'] if ('motion_model_used' in self.ref_table.colnames): - print(f'{epoch=}, {epoch.shape=}') x, y, xe, ye = self.ref_table.infer_positions(epoch) else: # No velocities... just used average positions. @@ -1158,17 +1199,19 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot y2_boot_sum = np.zeros((len(ref_table['x']), n_epochs)) m_boot_sum = np.zeros((len(ref_table['x']), n_epochs)) m2_boot_sum = np.zeros((len(ref_table['x']), n_epochs)) - + # Set up motion model parameters if 'motion_model_used' in ref_table.keys(): motion_model_list = np.unique(ref_table['motion_model_used']).tolist() elif 'motion_model_input' in ref_table.keys(): motion_model_list = np.unique(ref_table['motion_model_input']).tolist() - all_mm_map = motion_model.motion_model_map() - motion_model_list = [all_mm_map[mm_name] for mm_name in motion_model_list] + if 'Empty' not in motion_model_list: + motion_model_list.append('Empty') + if 'Fixed' not in motion_model_list: + motion_model_list.append('Fixed') - motion_col_list = motion_model.get_list_motion_model_param_names(motion_model_list, with_errors=False, with_fixed=False) + motion_col_list = motion_model.motion_model_param_names(motion_model_list, with_errors=False, with_fixed=False) if calc_vel_in_bootstrap: motion_boot_sum = {} motion2_boot_sum = {} @@ -1177,7 +1220,8 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot motion2_boot_sum[col] = np.zeros((len(ref_table['x']))) all_mm_map = motion_model.motion_model_map() - motion_boot_min_epochs = np.max([all_mm_map[mm].n_params for mm in motion_model_list]) + motion_model_list = [all_mm_map[mm_name] for mm_name in motion_model_list] + motion_boot_min_epochs = np.max([mm.n_params for mm in motion_model_list]) ### IF MEMORY PROBLEMS HERE: ### DEFINE MEAN, STD VARIABLES AND BUILD THEM RATHER THAN SAVING FULL ARRAY @@ -1257,7 +1301,6 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot m=starlist_boot['m'], mref=ref_boot['m'], weights=weight, mag_trans=self.mag_trans) #print(jj) - #pdb.set_trace() # Apply transformation to *all* orig positions in this epoch. Need to make a new # FLYSTAR starlist object with the original positions for this. We don't @@ -1337,7 +1380,7 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot # Quick check to make sure bootstrap calc was valid: output t0 should be # same as input t0_arr, since we used fixed_t0 option - assert np.sum(abs(star_table['t0'] - t0_arr) == 0) + np.testing.assert_array_equal(star_table['t0'], t0_arr) #t3 = time.time() #print('=================================================') @@ -1376,7 +1419,19 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot col[idx_good] = data_dict[ff] self.ref_table.add_column(col) - # Calculate chi^2 with bootstrap positional errors + # # Calculate chi^2 with bootstrap positional errors + # # Determine which motion model to use: + # motion_model_list = sorted(motion_model_list, key=lambda mm: mm.n_params) + # mm_n_params = np.sort([mm.n_params for mm in motion_model_list]) + + # required_params = [all_mm_map[mm_name].n_params for mm_name in self.ref_table['motion_model_input']] + # mm_digitized = np.digitize( + # x=np.minimum(np.array(self.ref_table['n_detect']), required_params), + # bins=mm_n_params + # ) - 1 + # self.ref_table['motion_model_used'] = np.array([motion_model_list[d].name for d in mm_digitized], dtype='U20') + + x_pred, y_pred, _, _ = self.ref_table.infer_positions(t_arr) xe_comb = np.hypot(self.ref_table['xe'], self.ref_table['xe_boot']) ye_comb = np.hypot(self.ref_table['ye'], self.ref_table['ye_boot']) @@ -1551,9 +1606,6 @@ def = None. If not None, then this should contain an array or list of transform iter_callback : None or function A function to call (that accepts a StarTable object and an iteration number) at the end of every iteration. This can be used for plotting or printing state. - - default_motion_model : string - Name of motion model to use for new or unassigned stars motion_models : list of str or MotionModel objects List of motion model names (strings) or MotionModel objects to use @@ -1702,7 +1754,6 @@ def fit(self): # ########## for nn in range(self.iters): - # If we are on subsequent iterations, remove matching results from the # prior iteration. This leaves aggregated (1D) columns alone. if nn > 0: @@ -1749,11 +1800,10 @@ def fit(self): print("**********") self.match_lists(self.dr_tol[-1], self.dm_tol[-1]) - keep_ref_orig = (self.update_ref_orig==False) - if keep_ref_orig: - keep_orig = np.where(self.ref_table['ref_orig'])[0] - else: + if self.update_ref_orig: keep_orig=None + else: + keep_orig = np.where(self.ref_table['ref_orig'])[0] self.update_ref_table_aggregates(keep_orig=keep_orig) ########## @@ -1804,7 +1854,7 @@ def get_all_epochs(t): return all_epochs -def setup_ref_table_from_starlist(star_list): +def setup_ref_table_from_starlist(star_list, motion_models): """ Start with the reference list.... this will change and grow over time, so make a copy that we will keep updating. @@ -1812,7 +1862,7 @@ def setup_ref_table_from_starlist(star_list): array in the original reference star list. """ col_arrays = {} - motion_model_col_names = motion_model.get_all_motion_model_param_names(with_errors=True) + motion_model_col_names = motion_model.motion_model_param_names(motion_models, with_errors=True) for col_name in star_list.colnames: if col_name == 'name': # The "name" column will be 1D; but we will also add a "name_in_list" column. @@ -1820,7 +1870,7 @@ def setup_ref_table_from_starlist(star_list): new_col_name = "name_in_list" else: new_col_name = col_name - + # Make every column's 2D arrays except "name" and those # columns used for the motion model. if col_name in motion_model_col_names: @@ -1856,7 +1906,7 @@ def setup_ref_table_from_starlist(star_list): if not new_cols_arr[ii] in ref_cols: # Some munging to convert data shape from (N,1) to (N,), # since these are all 1D cols - vals = np.transpose(np.array(ref_table[orig_cols_arr[ii]]))[0] + vals =np.array(ref_table[orig_cols_arr[ii]]).flatten() # Now add to ref_table new_col = Column(vals, name=new_cols_arr[ii]) @@ -1926,7 +1976,7 @@ def reset_ref_values(ref_table): return -def add_rows_for_new_stars(ref_table, star_list, idx_lis): +def add_rows_for_new_stars(ref_table, star_list, idx_lis, motion_model='Fixed'): """ For each star that is in star_list and NOT in idx_list, make a new row in the reference table. The values will be empty (None, NAN, etc.). @@ -1935,13 +1985,13 @@ def add_rows_for_new_stars(ref_table, star_list, idx_lis): ---------- ref_table : StarTable The reference table that the rows will be added to. - star_list : StarList The starlist that will be used to estimate how many new stars there are. - idx_lis : array or list The indices of the non-new stars (those that matched already). The complement of this array will be used as the new stars. + motion_model : str + The motion model to assign to the new stars. Returns ---------- @@ -1957,8 +2007,9 @@ def add_rows_for_new_stars(ref_table, star_list, idx_lis): idx_lis_orig = np.arange(len(star_list)) idx_lis_new = np.array(list(set(idx_lis_orig) - set(idx_lis))) + N_newstars = len(idx_lis_new) - if len(idx_lis_new) > 0: + if N_newstars > 0: col_arrays = {} for col_name in ref_table.colnames: @@ -1971,16 +2022,16 @@ def add_rows_for_new_stars(ref_table, star_list, idx_lis): elif ref_table[col_name].dtype == np.dtype('bool'): new_col_empty = False elif col_name=='motion_model_input': - new_col_empty = 'Empty' + new_col_empty = motion_model elif col_name=='motion_model_used': - new_col_empty = 'Empty' + new_col_empty = 'Fixed' else: new_col_empty = np.nan - + if len(ref_table[col_name].shape) == 1: - new_col_shape = len(idx_lis_new) + new_col_shape = N_newstars else: - new_col_shape = [len(idx_lis_new), ref_table[col_name].shape[1]] + new_col_shape = [N_newstars, ref_table[col_name].shape[1]] new_col_data = Column(data=np.tile(new_col_empty, new_col_shape), name=col_name, dtype=ref_table[col_name].dtype) @@ -2609,7 +2660,7 @@ def transform_from_object(starlist, transform): # For more complicated motion_models, # we can't easily transform them, set the values to nans and refit later. if mot: - motion_model_params = motion_model.get_all_motion_model_param_names() + motion_model_params = motion_model.motion_model_param_names() for param in motion_model_params: if param in keys: starlist_f[param] = np.nan @@ -2933,7 +2984,7 @@ def trans_initial_guess(ref_list, star_list, trans_args, mode='miracle', mref = ref_list['m'] else: mref = ref_list['m0'] - + N, x1m, y1m, m1m, x2m, y2m, m2m = match.miracle_match_briteN( star_list['x'], star_list['y'], @@ -2943,10 +2994,9 @@ def trans_initial_guess(ref_list, star_list, trans_args, mode='miracle', mref, briteN ) - - err_msg = 'Failed to find more than '+str(n_req_match) - err_msg += ' (only ' + str(len(x1m)) + ') matches, giving up.' - assert len(x1m) >= n_req_match, err_msg + + assert len(x1m) >= n_req_match, \ + f'Failed to find more than {n_req_match} (only {len(x1m)}) matches, giving up.' if verbose > 1: print('initial_guess: {0:d} stars matched between starlist and reference list'.format(N)) @@ -2965,12 +3015,12 @@ def trans_initial_guess(ref_list, star_list, trans_args, mode='miracle', trans.mag_offset = np.mean(m2m - m1m) else: trans.mag_offset = 0 - + if verbose > 1: print('init guess: ', trans.px.parameters, trans.py.parameters) warnings.filterwarnings('default', category=AstropyUserWarning) - + return trans @@ -3062,7 +3112,7 @@ def outlier_rejection_indices(star_list, ref_list, outlier_tol, verbose=True): The indicies of the stars to keep. """ # Optionally propogate the reference positions forward in time. - xref, yref = get_pos_in_time(star_list['t'][0], ref_list) + xref, yref = infer_positions(star_list['t'][0], ref_list) # Residuals x_resid_on_old_trans = star_list['x'] - xref @@ -3176,12 +3226,19 @@ def infer_positions(t, startable): to the desired epoch. If no motion/velocities exist, then just use ['x0', 'y0'] or ['x', 'y'] - Inputs + Parameters ---------- t_array : float The time to propogate to. Usually in decimal years; but it should be in the same units as the 't0' column in starlist. + startable : StarTable + Startable that needs to be inferred. + + Returns + ------- + x, y : tuple + Inferred position at time t """ # Check for motion model if 'motion_model_used' in startable.colnames: @@ -3201,10 +3258,10 @@ def infer_positions(t, startable): x = startable['x'] y = startable['y'] - return (x, y) + return x, y def logger(logfile, message, verbose = 9): if verbose > 4: print(message) logfile.write(message + '\n') - return + return \ No newline at end of file diff --git a/flystar/match.py b/flystar/match.py index 56710a9..f564cd3 100644 --- a/flystar/match.py +++ b/flystar/match.py @@ -1,5 +1,5 @@ import numpy as np -from . import starlists, transforms, startables, align +from . import starlists, transforms, startables from collections import Counter from scipy.spatial import cKDTree as KDT from astropy.table import Column diff --git a/flystar/motion_model.py b/flystar/motion_model.py index 11f6b16..1ba2dcf 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -1065,53 +1065,13 @@ def validate_motion_models(motion_models, startable, default_motion_model): return motion_models -def get_one_motion_model_param_names(motion_model, with_errors=True, with_fixed=True): - """Get all the motion model parameters +def motion_model_param_names(motion_models, with_errors=True, with_fixed=True): + """Get the motion model parameter names from a list of MotionModels. Parameters ---------- - motion_model : MotionModel - MotionModel instance - with_errors : bool, optional - Add uncertainty names with '_err' suffix or not, by default True - with_fixed : bool, optional - Add fixed param names with '_fixed' suffix or not, by default True - - Returns - ------- - list - List of all parameter names for the motion model - """ - if isinstance(motion_model, str): - all_mm_map = motion_model_map() - motion_model = all_mm_map[motion_model] - - list_of_parameters = [] - - def list_add(name): - if name not in list_of_parameters: - list_of_parameters.append(name) - - for param in motion_model.fit_param_names: - # Fitter params - list_add(param) - # Error params - if with_errors: - list_add(param + '_err') - # Fixed params - if with_fixed: - for param in motion_model.fixed_param_names: - list_add(param) - return list_of_parameters - - -def get_list_motion_model_param_names(motion_model_list, with_errors=True, with_fixed=True): - """Get all the motion model parameters - - Parameters - ---------- - motion_model_list : list of MotionModels or str - List of MotionModels + motion_models : MotionModel, str, or list of MotionModels/strings. + Motion model to query parameter names from. If str, should be the name of a MotionModel class. with_errors : bool, optional Add uncertainty names with '_err' suffix or not, by default True with_fixed : bool, optional @@ -1127,9 +1087,10 @@ def get_list_motion_model_param_names(motion_model_list, with_errors=True, with_ def list_add(name): if name not in list_of_parameters: list_of_parameters.append(name) - + + motion_models = np.atleast_1d(motion_models) mm_map = motion_model_map() - for mm in motion_model_list: + for mm in motion_models: if isinstance(mm, str): mm = mm_map[mm] for param in mm.fit_param_names: @@ -1142,11 +1103,11 @@ def list_add(name): if with_fixed: for param in mm.fixed_param_names: list_add(param) - return list(list_of_parameters) + return list_of_parameters -def get_all_motion_model_param_names(with_errors=True, with_fixed=True): - return get_list_motion_model_param_names(MotionModel.__subclasses__(), with_errors=with_errors, with_fixed=with_fixed) +def all_motion_model_param_names(with_errors=True, with_fixed=True): + return motion_model_param_names(MotionModel.__subclasses__(), with_errors=with_errors, with_fixed=with_fixed) def motion_model_map(): mm_map = dict( diff --git a/flystar/startables.py b/flystar/startables.py index bdcb880..861775e 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -730,7 +730,7 @@ def fit_motion_model( if 'motion_model_input' in self.colnames: # Determine which motion model to use based on motion_model_input column # If n_fit < required n_params for the input motion model, use the most complicated motion model with n_fit >= n_params - required_params = [all_mm_map[mm_name].n_params for mm_name in self['motion_model_input']] + required_params = np.array([all_mm_map[mm_name].n_params for mm_name in self['motion_model_input']]) mm_digitized = np.digitize( x=np.minimum(np.array(self['n_fit']), required_params), bins=mm_n_params @@ -751,7 +751,7 @@ def fit_motion_model( ############################ # Fill table with all possible motion model parameter names as new columns. motion_model_used = [all_mm_map[name] for name in np.unique(self['motion_model_used'])] - new_col_list = motion_model.get_list_motion_model_param_names(motion_model_used, with_errors=True, with_fixed=False) + new_col_list = motion_model.motion_model_param_names(motion_model_used, with_errors=True, with_fixed=False) new_col_list += ['chi2_x', 'chi2_y', 'n_params'] if 't0' not in new_col_list: @@ -810,9 +810,10 @@ def fit_motion_model( for unique_motion_model, unique_index in indices_by_motion_model.items(): # Create motion model instance motion_model_instance = input_mm_map[unique_motion_model]() + param_names = motion_model_instance.fit_param_names # Initialize arrays to store results n_stars_this_model = len(unique_index) - n_params = len(motion_model_instance.fit_param_names) + n_params = len(param_names) params_array = np.full((n_stars_this_model, n_params), fill_value, dtype=float) param_errs_array = np.full((n_stars_this_model, n_params), np.inf, dtype=float) @@ -843,7 +844,6 @@ def fit_motion_model( chi2_y_array[idx] = chi2_y # Store results back to the table - param_names = motion_model_instance.fit_param_names for j, param_name in enumerate(param_names): self[param_name][unique_index] = params_array[:, j] self[param_name + '_err'][unique_index] = param_errs_array[:, j] diff --git a/flystar/tests/test_align.py b/flystar/tests/test_align.py index cc4de2a..ea76ee8 100644 --- a/flystar/tests/test_align.py +++ b/flystar/tests/test_align.py @@ -259,12 +259,12 @@ def test_MosaicToRef(): # Also double check that they aren't exactly the same for the reference stars. assert np.not_equal(msc.ref_table['x0'], ref_list['x0']).all() assert np.not_equal(msc.ref_table['y0'], ref_list['y0']).all() - + return msc def test_MosaicToRef_p0_vel(): make_fake_starlists_poly0_vel(seed=42) - + ref_file = 'random_vel_ref.fits' list_files = ['random_vel_p0_0.fits', 'random_vel_p0_1.fits', @@ -286,7 +286,8 @@ def test_MosaicToRef_p0_vel(): # Switch our list to a "increasing to the West" list. ref_list['x0'] *= -1.0 ref_list['vx'] *= -1.0 - + ref_list['motion_model_used'] = 'Linear' + lists = [starlists.StarList.read(lf) for lf in list_files] msc = align.MosaicToRef(ref_list, lists, iters=2, @@ -391,7 +392,7 @@ def test_MosaicToRef_vel(): # Also double check that they aren't exactly the same for the reference stars. #assert np.any(np.not_equal(msc.ref_table['vx'], ref_list['vx'])) assert np.not_equal(msc.ref_table['vx'], ref_list['vx']).any() - + return msc def test_MosaicToRef_acc(): @@ -469,12 +470,13 @@ def test_MosaicToRef_acc(): if ~np.isnan(msc.ref_table['ax'][ix_fit]): i_orig.append(i) i_fit.append(ix_fit) - np.testing.assert_allclose(msc.ref_table['ax'][i_fit], ref_list['ax'][i_orig], rtol=1e-1) - np.testing.assert_allclose(msc.ref_table['ay'][i_fit], ref_list['ay'][i_orig], rtol=1e-1) + # Accelerations all too small, rtol doesn't work well here. + np.testing.assert_allclose(msc.ref_table['ax'][i_fit], ref_list['ax'][i_orig], atol=1e-3) + np.testing.assert_allclose(msc.ref_table['ay'][i_fit], ref_list['ay'][i_orig], atol=1e-3) # Also double check that they aren't exactly the same for the reference stars. - assert np.any(np.not_equal(msc.ref_table['ax'], ref_list['ax'])) - + assert np.any(np.not_equal(msc.ref_table['ax'][i_fit], ref_list['ax'][i_orig])) + return msc @@ -483,12 +485,12 @@ def make_fake_starlists_shifts(): x = np.random.rand(N_stars) * 1000 y = np.random.rand(N_stars) * 1000 m = (np.random.rand(N_stars) * 8) + 9 - + sdx = np.argsort(m) x = x[sdx] y = y[sdx] m = m[sdx] - + name = ['star_{0:03d}'.format(ii) for ii in range(N_stars)] # Save original positions as reference (1st) list. @@ -1016,7 +1018,7 @@ def make_fake_starlists_poly1_par(seed=-1): return (xy_trans, mag_trans) - + def test_MosaicToRef_hst_me(): """ Test Casey's issue with 'me' not getting propogated @@ -1025,30 +1027,30 @@ def test_MosaicToRef_hst_me(): Use data from MB10-364 microlensing target for the test. """ # Target RA and Dec (MOA data download) - ra = '17:57:05.401' - dec = '-34:27:05.01' - + # ra = '17:57:05.401' + # dec = '-34:27:05.01' + # Load up a Gaia catalog (queried around the RA/Dec above) my_gaia = Table.read('mb10364_data/my_gaia.fits') my_gaia['me'] = 0.01 - + # Gather the list of starlists. For first pass, don't modify the starlists. # Loop through the observations and read them in, in prep for alignment with Gaia epochs = [2011.83, 2012.73, 2013.81] starlist_names = ['mb10364_data/2011_10_31_F606W_MATCHUP_XYMEEE_final.calib', 'mb10364_data/2012_09_25_F606W_MATCHUP_XYMEEE_final.calib', 'mb10364_data/2013_10_24_F606W_MATCHUP_XYMEEE_final.calib'] - + list_of_starlists = [] - + # Just using the F606W filters first. for ee in range(len(starlist_names)): lis = starlists.StarList.from_lis_file(starlist_names[ee]) - + # # Add additive error term. MAYBE YOU DON'T NEED THIS # lis['xe'] = np.hypot(lis['xe'], 0.01) # Adding 0.01 pix (0.1 mas) in quadrature. # lis['ye'] = np.hypot(lis['ye'], 0.01) - + lis['t'] = epochs[ee] # Lets dump the faint stars. @@ -1056,23 +1058,21 @@ def test_MosaicToRef_hst_me(): lis = lis[idx] list_of_starlists.append(lis) - + msc = align.MosaicToRef(my_gaia, list_of_starlists, iters=1, dr_tol=[0.1], dm_tol=[5], outlier_tol=[None], mag_lim=[13, 21], trans_class=transforms.PolyTransform, trans_args=[{'order': 1}], - default_motion_model='Fixed', + motion_models=['Empty', 'Fixed'], use_ref_new=False, update_ref_orig=False, mag_trans=False, - trans_weights='both,std', + trans_weighting='both,std', init_guess_mode='miracle', verbose=False) msc.fit() - tab = msc.ref_table - - assert 'me' in tab.colnames + assert 'me' in msc.ref_table.colnames return def test_bootstrap(): @@ -1099,7 +1099,7 @@ def test_bootstrap(): outlier_tol = None mag_lim = None ref_mag_lim = None - trans_weights = 'both,var' + trans_weighting = 'both,var' mag_trans = False n_boot = 15 @@ -1113,8 +1113,8 @@ def test_bootstrap(): mag_trans=mag_trans, mag_lim=mag_lim, ref_mag_lim=ref_mag_lim, - trans_weights=trans_weights, - default_motion_model='Linear', + trans_weighting=trans_weighting, + motion_models=['Linear'], use_ref_new=False, update_ref_orig=False, init_guess_mode='name', @@ -1134,7 +1134,6 @@ def test_bootstrap(): assert np.sum(np.isnan(match1.ref_table['ye_boot'])) == 0 assert np.sum(np.isnan(match1.ref_table['vx_err_boot'])) == 0 assert np.sum(np.isnan(match1.ref_table['vy_err_boot'])) == 0 - #pdb.set_trace() # Test 2: make sure boot_epochs_min is working # Eliminate some rows to list2, so some stars are only in 1 epoch. @@ -1148,8 +1147,8 @@ def test_bootstrap(): mag_trans=mag_trans, mag_lim=mag_lim, ref_mag_lim=ref_mag_lim, - trans_weights=trans_weights, - default_motion_model='Linear', + trans_weighting=trans_weighting, + motion_models=['Linear'], use_ref_new=False, update_ref_orig=False, init_guess_mode='name', @@ -1171,10 +1170,10 @@ def test_bootstrap(): assert len(good[0]) > 0 # For "good" stars: all bootstrap vals should be present - assert np.sum(np.isnan(out['xe_boot'][good])) == 0 - assert np.sum(np.isnan(out['ye_boot'][good])) == 0 - assert np.sum(np.isnan(out['vx_err_boot'][good])) == 0 - assert np.sum(np.isnan(out['vy_err_boot'][good])) == 0 + assert np.sum(~np.isfinite(out['xe_boot'][good])) == 0 + assert np.sum(~np.isfinite(out['ye_boot'][good])) == 0 + assert np.sum(~np.isfinite(out['vx_err_boot'][good])) == 0 + assert np.sum(~np.isfinite(out['vy_err_boot'][good])) == 0 # For "bad" stars, all bootstrap vals should be nans assert np.sum(np.isfinite(out['xe_boot'][bad])) == 0 @@ -1193,7 +1192,7 @@ def test_calc_vel_in_bootstrap(): """ import copy - + # Define match parameters ref = Table.read('ref_vel.lis', format='ascii') @@ -1212,7 +1211,7 @@ def test_calc_vel_in_bootstrap(): outlier_tol = None mag_lim = None ref_mag_lim = None - trans_weights = 'both,var' + trans_weighting = 'both,var' mag_trans = False n_boot = 15 @@ -1226,8 +1225,8 @@ def test_calc_vel_in_bootstrap(): mag_trans=mag_trans, mag_lim=mag_lim, ref_mag_lim=ref_mag_lim, - trans_weights=trans_weights, - default_motion_model='Linear', + trans_weighting=trans_weighting, + motion_models=['Linear'], use_ref_new=False, update_ref_orig=False, init_guess_mode='name', @@ -1280,7 +1279,7 @@ def test_transform_xym(): outlier_tol = None mag_lim = None ref_mag_lim = None - trans_weights = 'both,var' + trans_weighting = 'both,var' n_boot = 15 mag_trans = False @@ -1293,8 +1292,8 @@ def test_transform_xym(): mag_trans=mag_trans, mag_lim=mag_lim, ref_mag_lim=ref_mag_lim, - trans_weights=trans_weights, - default_motion_model='Fixed', + trans_weighting=trans_weighting, + motion_models=['Fixed'], use_ref_new=False, update_ref_orig=False, init_guess_mode='name', @@ -1328,7 +1327,7 @@ def test_transform_xym(): mag_trans=mag_trans, mag_lim=mag_lim, ref_mag_lim=ref_mag_lim, - trans_weights=trans_weights, + trans_weighting=trans_weighting, default_motion_model='Fixed', use_ref_new=False, update_ref_orig=False, @@ -1372,7 +1371,7 @@ def test_MosaicToRef_mag_bug(): outlier_tol=None, trans_class=transforms.PolyTransform, trans_args=[{'order': 1}], - default_motion_model='Fixed', + motion_models=['Fixed'], use_ref_new=False, update_ref_orig=False, verbose=True) @@ -1400,15 +1399,16 @@ def test_masked_cols(): """ # Get gaia reference stars using analysis.py # around a test location. - target = 'ob150029' + # target = 'ob150029' ra = '17:59:46.60' dec = '-28:38:41.8' # Coordinates are arcsecs offset +x to the East. - targets_dict = {'ob150029': [0.0, 0.0], - 'S005': [1.1416, 3.7405], - 'S002': [-4.421, 0.027] - } + targets_dict = { + 'ob150029': [0.0, 0.0], + 'S005': [1.1416, 3.7405], + 'S002': [-4.421, 0.027] + } # Get gaia catalog stars. Note that this produces a masked column table search_rad = 10.0 # arcsec @@ -1418,7 +1418,7 @@ def test_masked_cols(): assert isinstance(my_gaia, Table) # Let's make sure the entire align runs, just to be safe - + # Get starlists to align to gaia epochs = ['15jun07','16jul14', '17may21'] @@ -1427,7 +1427,6 @@ def test_masked_cols(): for ee in range(len(epochs)): lis_file = 'mag' + epochs[ee] + '_ob150029_kp_rms_named.lis' lis = starlists.StarList.from_lis_file(lis_file) - list_of_starlists.append(lis) # Run the align @@ -1435,12 +1434,11 @@ def test_masked_cols(): dr_tol=[0.2, 0.1], dm_tol=[1, 1], trans_class=transforms.PolyTransform, trans_args=[{'order': 1}, {'order': 1}], - default_motion_model='Linear', + motion_models=['Linear'], use_ref_new=False, update_ref_orig=False, mag_trans=True, init_guess_mode='name', verbose=True) msc.fit() - return diff --git a/flystar/transforms.py b/flystar/transforms.py index 8cb9525..968ccfa 100755 --- a/flystar/transforms.py +++ b/flystar/transforms.py @@ -127,7 +127,7 @@ def evaluate_starlist(self, star_list): complex_motion_model=False # Cannot transform more complex motion models - set values to nan if complex_motion_model: - motion_params = motion_model.get_list_motion_model_param_names(new_list['motion_model_input'], with_errors=True, with_fixed=False) + motion_params = motion_model.motion_model_param_names(new_list['motion_model_input'], with_errors=True, with_fixed=False) for param in motion_params: if param in new_list.colnames: new_list[param] = np.nan From 04fd8eae9b72e1e9d75c006517db0ca3573e6caf Mon Sep 17 00:00:00 2001 From: Wei Lingfeng Date: Sat, 20 Dec 2025 18:46:35 +0800 Subject: [PATCH 141/254] Add select_stars functionality in fit_motion_model --- flystar/align.py | 48 ++++++------------------------------- flystar/startables.py | 17 +++++++++++-- flystar/tests/test_align.py | 1 - 3 files changed, 22 insertions(+), 44 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 3a3160d..b4c03c4 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -834,6 +834,7 @@ def update_ref_table_from_list(self, star_list, star_list_T, ii, idx_ref, idx_li idx_lis, motion_model=self.motion_models[-1].name ) + pdb.set_trace() if len(idx_ref_new) > 0: if self.verbose > 0: print(' Adding {0:d} new stars to the reference table.'.format(len(idx_ref_new))) @@ -889,55 +890,19 @@ def update_ref_table_aggregates(self, keep_orig=None, n_boot=0): weighted_xy = ('xe' in self.ref_table.colnames) and ('ye' in self.ref_table.colnames) weighted_m = ('me' in self.ref_table.colnames) self.ref_table.combine_lists_xym(weighted_xy=weighted_xy, weighted_m=weighted_m) - elif fit_star_idxs is None: - self.ref_table.fit_motion_model( - motion_models=self.motion_models, - fixed_params_dict=self.fixed_params_dict, - weighting=self.vel_weighting, - use_scipy=self.use_scipy, - absolute_sigma=self.absolute_sigma, - bootstrap=n_boot, - verbose=self.verbose - ) - # Combine (transformed) magnitudes - if 'me' in self.ref_table.colnames: - weights_col = None - else: - weights_col = 'me' - self.ref_table.combine_lists('m', weights_col=weights_col, ismag=True) else: - # Combine positions with a velocity fit. - update_ref_table = self.ref_table[fit_star_idxs] - keep_ref_table = self.ref_table[keep_orig] - update_ref_table.fit_motion_model( + self.ref_table.fit_motion_model( motion_models=self.motion_models, fixed_params_dict=self.fixed_params_dict, weighting=self.vel_weighting, use_scipy=self.use_scipy, absolute_sigma=self.absolute_sigma, + select_stars=fit_star_idxs, bootstrap=n_boot, verbose=self.verbose ) - # Determine motion models for keep_ref_table - pdb.set_trace() - if 'motion_model_used' not in keep_ref_table.colnames: - all_mm_map = motion_model.motion_model_map() - mm_n_params = np.sort([mm.n_params for mm in self.motion_models]) - required_params = np.array([all_mm_map[mm_name].n_params for mm_name in keep_ref_table['motion_model_input']]) - mm_digitized = np.digitize( - x=np.minimum(np.array(keep_ref_table['n_detect']), required_params), - bins=mm_n_params - ) - 1 - keep_ref_table['motion_model_used'] = np.array([self.motion_models[d].name for d in mm_digitized]) - - # Merge back into the full ref_table - new_ref_table = vstack([keep_ref_table, update_ref_table]) - self.ref_table = new_ref_table.copy() - self.ref_table[keep_orig] = new_ref_table[0:len(keep_orig)] - self.ref_table[fit_star_idxs] = new_ref_table[len(keep_orig):] - # Combine (transformed) magnitudes if 'me' in self.ref_table.colnames: weights_col = None @@ -1949,6 +1914,7 @@ def copy_over_values(ref_table, star_list, star_list_T, idx_epoch, idx_ref, idx_ The indices into the star_list or star_list_T where values are copied from. """ for col_name in ref_table.colnames: + if col_name=='x': pdb.set_trace() if col_name in star_list_T.colnames: if col_name == 'name': ref_table['name_in_list'][idx_ref, idx_epoch] = star_list_T[col_name][list(idx_lis)] @@ -1976,7 +1942,7 @@ def reset_ref_values(ref_table): return -def add_rows_for_new_stars(ref_table, star_list, idx_lis, motion_model='Fixed'): +def add_rows_for_new_stars(ref_table, star_list, idx_list, motion_model='Fixed'): """ For each star that is in star_list and NOT in idx_list, make a new row in the reference table. The values will be empty (None, NAN, etc.). @@ -1987,7 +1953,7 @@ def add_rows_for_new_stars(ref_table, star_list, idx_lis, motion_model='Fixed'): The reference table that the rows will be added to. star_list : StarList The starlist that will be used to estimate how many new stars there are. - idx_lis : array or list + idx_list : array or list The indices of the non-new stars (those that matched already). The complement of this array will be used as the new stars. motion_model : str @@ -2006,7 +1972,7 @@ def add_rows_for_new_stars(ref_table, star_list, idx_lis, motion_model='Fixed'): last_star_idx = len(ref_table) idx_lis_orig = np.arange(len(star_list)) - idx_lis_new = np.array(list(set(idx_lis_orig) - set(idx_lis))) + idx_lis_new = np.array(list(set(idx_lis_orig) - set(idx_list))) N_newstars = len(idx_lis_new) if N_newstars > 0: diff --git a/flystar/startables.py b/flystar/startables.py index 861775e..25e15dd 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -539,6 +539,7 @@ def fit_motion_model( weighting='var', use_scipy=False, absolute_sigma=True, + select_stars=None, bootstrap=0, verbose=True, mask_value=None, @@ -573,6 +574,8 @@ def fit_motion_model( Use scipy.optimize.curve_fit or algebraic solution (for Linear model only), by default False absolute_sigma : bool, optional Use absolute sigma or not, see scipy curve_fit for details, by default True + select_stars : list of int, optional + Indices of stars to fit, by default None (fit all stars) bootstrap : int, optional Number of bootstrap for uncertainty resampling, by default 0 verbose : bool, optional @@ -757,8 +760,11 @@ def fit_motion_model( if 't0' not in new_col_list: new_col_list.append('t0') - # Replace old columns if they exist + # Add new columns if they do not exist for col in new_col_list: + if col in self.colnames: + # Keep old data if the column already exists + continue if col.endswith('_err'): self.add_column( Column(data=np.full(N_stars, np.inf, dtype=float), name=col), @@ -804,7 +810,14 @@ def fit_motion_model( ######### FITTING ######### ########################### unique_motion_models, unique_inv_indices = np.unique(self['motion_model_used'], return_inverse=True) - indices_by_motion_model = {key: np.flatnonzero(unique_inv_indices == k) for k, key in enumerate(unique_motion_models)} + if select_stars is not None: + select_stars = np.asarray(select_stars) + if select_stars.dtype == bool: + select_stars = np.flatnonzero(select_stars) + indices_by_motion_model = {key: np.intersect1d(select_stars, np.flatnonzero(unique_inv_indices == k)) for k, key in enumerate(unique_motion_models)} + else: + indices_by_motion_model = {key: np.flatnonzero(unique_inv_indices == k) for k, key in enumerate(unique_motion_models)} + # Expensive for loop! Prepare everything beforehand to speed up. for unique_motion_model, unique_index in indices_by_motion_model.items(): diff --git a/flystar/tests/test_align.py b/flystar/tests/test_align.py index ea76ee8..3e55e2c 100644 --- a/flystar/tests/test_align.py +++ b/flystar/tests/test_align.py @@ -286,7 +286,6 @@ def test_MosaicToRef_p0_vel(): # Switch our list to a "increasing to the West" list. ref_list['x0'] *= -1.0 ref_list['vx'] *= -1.0 - ref_list['motion_model_used'] = 'Linear' lists = [starlists.StarList.read(lf) for lf in list_files] From 87d7cc444f6cff384e7ce52cb987e9b4b187b430 Mon Sep 17 00:00:00 2001 From: Wei Lingfeng Date: Sun, 21 Dec 2025 11:57:30 +0800 Subject: [PATCH 142/254] Passed all tests! --- flystar/align.py | 24 +- flystar/analysis.py | 2 +- flystar/startables.py | 14 +- flystar/tests/test_align.ipynb | 366 ------- flystar/tests/test_align.py | 1588 ++++++++++++++++--------------- flystar/tests/test_startable.py | 2 +- 6 files changed, 829 insertions(+), 1167 deletions(-) delete mode 100644 flystar/tests/test_align.ipynb diff --git a/flystar/align.py b/flystar/align.py index b4c03c4..2820ae5 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -616,6 +616,8 @@ def setup_ref_table_from_starlist(self, star_list): """ col_arrays = {} motion_model_col_names = motion_model.motion_model_param_names(self.motion_models, with_errors=True, with_fixed=True) + ['m0','m0_err','use_in_trans', 'motion_model_input', 'motion_model_used'] + if 't0' not in motion_model_col_names: + motion_model_col_names.insert(0, 't0') for col_name in star_list.colnames: if col_name == 'name': # The "name" column will be 1D; but we will also add a "name_in_list" column. @@ -657,7 +659,7 @@ def setup_ref_table_from_starlist(self, star_list): if not new_cols_arr[ii] in ref_cols: # Some munging to convert data shape from (N,1) to (N,), # since these are all 1D cols - vals = np.transpose(np.array(ref_table[orig_cols_arr[ii]]))[0] + vals = np.array(ref_table[orig_cols_arr[ii]]).flatten() # Now add to ref_table new_col = Column(vals, name=new_cols_arr[ii]) @@ -717,11 +719,14 @@ def setup_ref_table_from_starlist(self, star_list): if 'motion_model_input' not in ref_table.colnames: ref_table.add_column(Column(np.repeat(self.motion_models[-1].name, len(ref_table)), name='motion_model_input')) - # if 'motion_model_used' not in ref_table.colnames: - # if motion_model_used is None: - # ref_table.add_column(Column(np.repeat(self.default_motion_model, len(ref_table)), name='motion_model_used')) - # else: - # ref_table.add_column(Column(np.repeat(motion_model_used, len(ref_table)), name='motion_model_used')) + if 'motion_model_used' not in ref_table.colnames: + # Order self.motion_models by decreasing n_params + sorted_mms = sorted(self.motion_models, key=lambda mm: mm.n_params, reverse=True) + # Save the most complex motion model that can infer the positions with the existing columns. + for mm in sorted_mms: + if all([_ in ref_table.colnames for _ in mm.fit_param_names]) and all([_ in ref_table.colnames for _ in mm.fixed_param_names]): + ref_table.add_column(Column(np.repeat(mm.name, len(ref_table)), name='motion_model_used')) + break return ref_table @@ -834,7 +839,7 @@ def update_ref_table_from_list(self, star_list, star_list_T, ii, idx_ref, idx_li idx_lis, motion_model=self.motion_models[-1].name ) - pdb.set_trace() + if len(idx_ref_new) > 0: if self.verbose > 0: print(' Adding {0:d} new stars to the reference table.'.format(len(idx_ref_new))) @@ -1880,10 +1885,10 @@ def setup_ref_table_from_starlist(star_list, motion_models): if 'use_in_trans' not in ref_table.colnames: new_col = Column(np.ones(len(ref_table), dtype=bool), name='use_in_trans') ref_table.add_column(new_col) - + # Now reset the original values to invalids... they will be filled in # at later times. Preserve content only in the columns: name, x0, y0, m0 (and 0e). - # Note that these are all the 1D columsn. + # Note that these are all the 1D columns. for col_name in ref_table.colnames: if len(ref_table[col_name].data.shape) == 2: # Find the 2D columns ref_table._set_invalid_list_values(col_name, -1) @@ -1914,7 +1919,6 @@ def copy_over_values(ref_table, star_list, star_list_T, idx_epoch, idx_ref, idx_ The indices into the star_list or star_list_T where values are copied from. """ for col_name in ref_table.colnames: - if col_name=='x': pdb.set_trace() if col_name in star_list_T.colnames: if col_name == 'name': ref_table['name_in_list'][idx_ref, idx_epoch] = star_list_T[col_name][list(idx_lis)] diff --git a/flystar/analysis.py b/flystar/analysis.py index f502375..ceca739 100644 --- a/flystar/analysis.py +++ b/flystar/analysis.py @@ -44,7 +44,7 @@ def query_gaia(ra, dec, search_radius=30.0, table_name='gaiadr3'): search_radius *= u.arcsec Gaia.ROW_LIMIT = 50000 - gaia_job = Gaia.cone_search_async(target_coords, search_radius, table_name = table_name + '.gaia_source') + gaia_job = Gaia.cone_search_async(target_coords, radius=search_radius, table_name=table_name + '.gaia_source') gaia = gaia_job.get_results() #Change new 'SOURCE_ID' column header back to lowercase 'source_id' so all subsequent functions still work: diff --git a/flystar/startables.py b/flystar/startables.py index 25e15dd..3de41cc 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -602,19 +602,19 @@ def fit_motion_model( ####### Check Params ###### ########################### if weighting not in ['var', 'std']: - raise ValueError(f"fit_velocities: Weighting must either be 'var' or 'std', not {weighting}!") + raise ValueError(f"fit_motion_model: Weighting must either be 'var' or 'std', not {weighting}!") if ('t' not in self.colnames) and ('list_times' not in self.meta): - raise KeyError("fit_velocities: Failed to access time values. No 't' column in table, no 'list_times' in meta.") + raise KeyError("fit_motion_model: Failed to access time values. No 't' column in table, no 'list_times' in meta.") # Check if we have the required columns if not all([_ in self.colnames for _ in ['x', 'y']]): - raise KeyError(f"fit_velocities: Missing required columns in the table: {', '.join(['x', 'y'])}!") + raise KeyError(f"fit_motion_model: Missing required columns in the table: {', '.join(['x', 'y'])}!") # Check fixed_params_dict is a dict if fixed_params_dict is not None: if not isinstance(fixed_params_dict, dict): - raise ValueError("fit_velocities: fixed_params_dict must be a dictionary!") + raise ValueError("fit_motion_model: fixed_params_dict must be a dictionary!") # Convert motion_models to MotionModel objects if they are strings: all_mm_map = motion_model.motion_model_map() @@ -635,7 +635,7 @@ def fit_motion_model( if 'motion_model_input' in self.colnames: input_mm_names = np.unique(self['motion_model_input']) assert all([name in all_mm_map.keys() for name in input_mm_names]), \ - f"fit_velocities: Unknown motion model name(s) in 'motion_model_input' column. Available motion models are: {', '.join(all_mm_map.keys())}." + f"fit_motion_model: Unknown motion model name(s) in 'motion_model_input' column. Available motion models are: {', '.join(all_mm_map.keys())}." for mm_name in input_mm_names: if mm_name not in mm_names: motion_models.append(all_mm_map[mm_name]) @@ -649,7 +649,7 @@ def fit_motion_model( if 'motion_model_input' not in self.colnames: # If motion_model_input column is not provided, assert that motion model n_params are unique and sorted # Otherwise the fitter does not know which motion model to use based on n_obs - assert len(mm_n_params) == len(set(mm_n_params)), "fit_velocities: Provided motion model n_params are not unique! Cannot decide which motion model to use based on n_obs. Please provide unique motion_models or a 'motion_model_input' column." + assert len(mm_n_params) == len(set(mm_n_params)), "fit_motion_model: Provided motion model n_params are not unique! Cannot decide which motion model to use based on n_obs. Please provide unique motion_models or a 'motion_model_input' column." ########################### @@ -814,6 +814,8 @@ def fit_motion_model( select_stars = np.asarray(select_stars) if select_stars.dtype == bool: select_stars = np.flatnonzero(select_stars) + else: + select_stars = np.asarray(select_stars, dtype=int) indices_by_motion_model = {key: np.intersect1d(select_stars, np.flatnonzero(unique_inv_indices == k)) for k, key in enumerate(unique_motion_models)} else: indices_by_motion_model = {key: np.flatnonzero(unique_inv_indices == k) for k, key in enumerate(unique_motion_models)} diff --git a/flystar/tests/test_align.ipynb b/flystar/tests/test_align.ipynb deleted file mode 100644 index 02442b9..0000000 --- a/flystar/tests/test_align.ipynb +++ /dev/null @@ -1,366 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Notebook for Running Align Tests" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "from flystar.tests import test_align\n", - "from flystar import starlists\n", - "from astropy.table import Table" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test: make_fake_starlists_poly1_vel\n", - "\n", - "Just make sure the tables look sensible and are in the right units." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " name m0 m0e ... vye t0 \n", - "-------- ----------------- -------------------- ... ------------------- ------\n", - "star_155 9.106905292995506 0.054167528156861204 ... 0.1564397531527286 2019.5\n", - "star_113 9.153031462110043 0.0421090989942197 ... 0.08128628950126615 2019.5\n", - "star_077 9.16547870263162 0.02021147759307802 ... 0.05907352582911862 2019.5\n", - "star_069 9.169817788300977 0.027788213230369625 ... 0.04965351499764548 2019.5\n", - "star_037 9.173200786855755 0.007665400875860144 ... 0.22723357600795704 2019.5\n", - " name m me ... ye t \n", - "-------- ----------------- -------------------- ... -------------------- ------\n", - "star_155 9.198437965086988 0.054167528156861204 ... 0.02649499466969545 2018.5\n", - "star_113 9.257333243243941 0.0421090989942197 ... 0.02606700846524875 2018.5\n", - "star_077 9.252158908537464 0.02021147759307802 ... 0.04250920654497108 2018.5\n", - "star_069 9.267901667333167 0.027788213230369625 ... 0.042689240225924296 2018.5\n", - "star_037 9.276780126418494 0.007665400875860144 ... 0.03592203011554212 2018.5\n", - " name m me ... ye t \n", - "-------- ----------------- -------------------- ... -------------------- ------\n", - "star_155 9.478887659623185 0.054167528156861204 ... 0.02649499466969545 2019.5\n", - "star_113 9.569878576042546 0.0421090989942197 ... 0.02606700846524875 2019.5\n", - "star_077 9.575998150724095 0.02021147759307802 ... 0.04250920654497108 2019.5\n", - "star_069 9.593581807234129 0.027788213230369625 ... 0.042689240225924296 2019.5\n", - "star_037 9.553127108740597 0.007665400875860144 ... 0.03592203011554212 2019.5\n", - "['name', 'm0', 'm0e', 'x0', 'x0e', 'y0', 'y0e', 'vx', 'vxe', 'vy', 'vye', 't0']\n", - "['name', 'm', 'me', 'x', 'xe', 'y', 'ye', 't']\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jlu/code/python/flystar/flystar/starlists.py:386: UserWarning: The StarList class requires a arguments('name', 'x', 'y', 'm')\n", - " warnings.warn(err_msg, UserWarning)\n" - ] - } - ], - "source": [ - "test_align.make_fake_starlists_poly1_vel()\n", - "\n", - "ref = Table.read('random_vel_ref.fits')\n", - "lis0 = Table.read('random_vel_0.fits')\n", - "lis1 = Table.read('random_vel_1.fits')\n", - "\n", - "print(ref[0:5])\n", - "print(lis0[0:5])\n", - "print(lis1[0:5])\n", - "\n", - "print(ref.colnames)\n", - "print(lis0.colnames)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## test_align_vel\n", - "\n", - "Make sure it runs, make some plots along the way, etc." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jlu/code/python/flystar/flystar/starlists.py:386: UserWarning: The StarList class requires a arguments('name', 'x', 'y', 'm')\n", - " warnings.warn(err_msg, UserWarning)\n", - "/Users/jlu/code/python/flystar/flystar/starlists.py:386: UserWarning: The StarList class requires a arguments('name', 'x', 'y', 'm')\n", - " warnings.warn(err_msg, UserWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " \n", - "**********\n", - "**********\n", - "Starting iter 0 with ref_table shape: (200, 1)\n", - "**********\n", - "**********\n", - " \n", - " **********\n", - " Matching catalog 1 / 4 in iteration 0 with 200 stars\n", - " **********\n", - "initial_guess: 50 stars matched between starlist and reference list\n", - "initial_guess: [-6.05144456e+00 1.01098279e+00 -2.50608887e-04] [-1.07161761e+01 4.89226304e-05 1.01096529e+00]\n", - " Found 0 duplicates out of 196 matches\n", - "In Loop 0 found 196 matches\n", - " Found 0 duplicates out of 196 matches\n", - " \n", - " **********\n", - " Matching catalog 2 / 4 in iteration 0 with 200 stars\n", - " **********\n", - "initial_guess: 49 stars matched between starlist and reference list\n", - "initial_guess: [-1.02158015e+02 1.02080743e+00 -1.45081519e-04] [-5.07779471e+01 -2.60729494e-05 9.99423500e-01]\n", - " Found 0 duplicates out of 200 matches\n", - "In Loop 0 found 200 matches\n", - " Found 0 duplicates out of 200 matches\n", - " \n", - " **********\n", - " Matching catalog 3 / 4 in iteration 0 with 200 stars\n", - " **********\n", - "initial_guess: 50 stars matched between starlist and reference list\n", - "initial_guess: [-2.14220566e-10 1.00000000e+00 -2.24089697e-16] [2.50622339e-10 0.00000000e+00 1.00000000e+00]\n", - " Found 0 duplicates out of 200 matches\n", - "In Loop 0 found 200 matches\n", - " Found 0 duplicates out of 200 matches\n", - " \n", - " **********\n", - " Matching catalog 4 / 4 in iteration 0 with 200 stars\n", - " **********\n", - "initial_guess: 50 stars matched between starlist and reference list\n", - "initial_guess: [-2.57803428e+02 1.03052409e+00 -5.28390832e-05] [ 2.49886631e+02 -6.00884405e-05 9.98642952e-01]\n", - " Found 0 duplicates out of 200 matches\n", - "In Loop 0 found 200 matches\n", - " Found 0 duplicates out of 200 matches\n", - " \n", - "**********\n", - "**********\n", - "Starting iter 1 with ref_table shape: (204, 4)\n", - "**********\n", - "**********\n", - " \n", - " **********\n", - " Matching catalog 1 / 4 in iteration 1 with 200 stars\n", - " **********\n", - " Found 0 duplicates out of 199 matches\n", - "In Loop 1 found 199 matches\n", - " Found 0 duplicates out of 199 matches\n", - " \n", - " **********\n", - " Matching catalog 2 / 4 in iteration 1 with 200 stars\n", - " **********\n", - " Found 0 duplicates out of 198 matches\n", - "In Loop 1 found 198 matches\n", - " Found 0 duplicates out of 199 matches\n", - " \n", - " **********\n", - " Matching catalog 3 / 4 in iteration 1 with 200 stars\n", - " **********\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jlu/code/python/flystar/flystar/starlists.py:386: UserWarning: The StarList class requires a arguments('name', 'x', 'y', 'm')\n", - " warnings.warn(err_msg, UserWarning)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Found 0 duplicates out of 200 matches\n", - "In Loop 1 found 200 matches\n", - " Found 0 duplicates out of 200 matches\n", - " \n", - " **********\n", - " Matching catalog 4 / 4 in iteration 1 with 200 stars\n", - " **********\n", - " Found 0 duplicates out of 200 matches\n", - "In Loop 1 found 200 matches\n", - " Found 0 duplicates out of 200 matches\n", - "**********\n", - "Final Matching\n", - "**********\n", - " Found 0 duplicates out of 199 matches\n", - "Matched 199 out of 200 stars in list 0\n", - " Found 0 duplicates out of 199 matches\n", - "Matched 199 out of 200 stars in list 1\n", - " Found 0 duplicates out of 200 matches\n", - "Matched 200 out of 200 stars in list 2\n", - " Found 0 duplicates out of 199 matches\n", - "Matched 199 out of 200 stars in list 3\n", - "\n", - " Preparing the reference table...\n" - ] - } - ], - "source": [ - "test_align.test_mosaic_lists_vel()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> /Users/jlu/code/python/flystar/flystar/align.py(3244)apply_mag_lim()\n", - "-> star_list_T.restrict_by_value(**conditions)\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "(Pdb) conditions\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'m0_min': None, 'm0_max': None}\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "(Pdb) type(star_list_T)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "(Pdb) type(ref_list)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "*** NameError: name 'ref_list' is not defined\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "(Pdb) ref_list\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "*** NameError: name 'ref_list' is not defined\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "(Pdb) u\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> /Users/jlu/code/python/flystar/flystar/align.py(991)mosaic_lists()\n", - "-> ref_list_T = apply_mag_lim(ref_list, mag_lim[ref_index])\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "(Pdb) type(ref_list)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "name": "stdin", - "output_type": "stream", - "text": [ - "(Pdb) q\n" - ] - } - ], - "source": [ - "import pdb\n", - "pdb.pm()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.7" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/flystar/tests/test_align.py b/flystar/tests/test_align.py index 3e55e2c..080ded3 100644 --- a/flystar/tests/test_align.py +++ b/flystar/tests/test_align.py @@ -470,442 +470,510 @@ def test_MosaicToRef_acc(): i_orig.append(i) i_fit.append(ix_fit) # Accelerations all too small, rtol doesn't work well here. - np.testing.assert_allclose(msc.ref_table['ax'][i_fit], ref_list['ax'][i_orig], atol=1e-3) - np.testing.assert_allclose(msc.ref_table['ay'][i_fit], ref_list['ay'][i_orig], atol=1e-3) + atol = 3e-4 + np.testing.assert_allclose(msc.ref_table['ax'][i_fit], ref_list['ax'][i_orig], atol=atol) + np.testing.assert_allclose(msc.ref_table['ay'][i_fit], ref_list['ay'][i_orig], atol=atol) + + ax_min = np.min(ref_list['ax'][i_orig]) + ax_max = np.max(ref_list['ax'][i_orig]) + ay_min = np.min(ref_list['ay'][i_orig]) + ay_max = np.max(ref_list['ay'][i_orig]) + fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5)) + ax1.plot(ref_list['ax'][i_orig], msc.ref_table['ax'][i_fit], '.') + ax1.plot([ax_min, ax_max], [ax_min, ax_max], color='C3') + ax1.plot([ax_min, ax_max], [ax_min - atol, ax_max - atol], ls='--', color='C3') + ax1.plot([ax_min, ax_max], [ax_min + atol, ax_max + atol], ls='--', color='C3') + ax1.set_xlabel('Input ax') + ax1.set_ylabel('Ref Table ax') + ax1.set_title('Acceleration in X') + + ax2.plot(ref_list['ay'][i_orig], msc.ref_table['ay'][i_fit], '.') + ax2.plot([ay_min, ay_max], [ay_min, ay_max], color='C3') + ax2.plot([ay_min, ay_max], [ay_min - atol, ay_max - atol], ls='--', color='C3') + ax2.plot([ay_min, ay_max], [ay_min + atol, ay_max + atol], ls='--', color='C3') + ax2.set_xlabel('Input ay') + ax2.set_ylabel('Ref Table ay') + ax2.set_title('Acceleration in Y') + plt.tight_layout() + plt.show() # Also double check that they aren't exactly the same for the reference stars. assert np.any(np.not_equal(msc.ref_table['ax'][i_fit], ref_list['ax'][i_orig])) return msc +def test_MosaicToRef_hst_me(): + """ + Test Casey's issue with 'me' not getting propogated + from the input starlists to the output table. -def make_fake_starlists_shifts(): - N_stars = 200 - x = np.random.rand(N_stars) * 1000 - y = np.random.rand(N_stars) * 1000 - m = (np.random.rand(N_stars) * 8) + 9 + Use data from MB10-364 microlensing target for the test. + """ + # Target RA and Dec (MOA data download) + # ra = '17:57:05.401' + # dec = '-34:27:05.01' - sdx = np.argsort(m) - x = x[sdx] - y = y[sdx] - m = m[sdx] + # Load up a Gaia catalog (queried around the RA/Dec above) + my_gaia = Table.read('mb10364_data/my_gaia.fits') + my_gaia['me'] = 0.01 - name = ['star_{0:03d}'.format(ii) for ii in range(N_stars)] + # Gather the list of starlists. For first pass, don't modify the starlists. + # Loop through the observations and read them in, in prep for alignment with Gaia + epochs = [2011.83, 2012.73, 2013.81] + starlist_names = ['mb10364_data/2011_10_31_F606W_MATCHUP_XYMEEE_final.calib', + 'mb10364_data/2012_09_25_F606W_MATCHUP_XYMEEE_final.calib', + 'mb10364_data/2013_10_24_F606W_MATCHUP_XYMEEE_final.calib'] - # Save original positions as reference (1st) list. - fmt = '{0:10s} {1:5.2f} 2015.0 {2:9.4f} {3:9.4f} 0 0 0 0\n' - _out = open('random_0.lis', 'w') - for ii in range(N_stars): - _out.write(fmt.format(name[ii], m[ii], x[ii], y[ii])) - _out.close() + list_of_starlists = [] + # Just using the F606W filters first. + for ee in range(len(starlist_names)): + lis = starlists.StarList.from_lis_file(starlist_names[ee]) - ########## - # Shifts - ########## - # Make 4 new starlists with different shifts. - shifts = [[ 6.5, 10.1], - [100.3, 50.5], - [-30.0,-100.7], - [250.0,-250.0]] + # # Add additive error term. MAYBE YOU DON'T NEED THIS + # lis['xe'] = np.hypot(lis['xe'], 0.01) # Adding 0.01 pix (0.1 mas) in quadrature. + # lis['ye'] = np.hypot(lis['ye'], 0.01) - for ss in range(len(shifts)): - xnew = x - shifts[ss][0] - ynew = y - shifts[ss][1] + lis['t'] = epochs[ee] - # Perturb with small errors (0.1 pix) - xnew += np.random.randn(N_stars) * 0.1 - ynew += np.random.randn(N_stars) * 0.1 + # Lets dump the faint stars. + idx = np.where(lis['m'] < 20.0)[0] + lis = lis[idx] - mnew = m + np.random.randn(N_stars) * 0.05 + list_of_starlists.append(lis) - _out = open('random_shift_{0:d}.lis'.format(ss+1), 'w') - for ii in range(N_stars): - _out.write(fmt.format(name[ii], mnew[ii], xnew[ii], ynew[ii])) - _out.close() + msc = align.MosaicToRef(my_gaia, list_of_starlists, iters=1, + dr_tol=[0.1], dm_tol=[5], + outlier_tol=[None], mag_lim=[13, 21], + trans_class=transforms.PolyTransform, + trans_args=[{'order': 1}], + motion_models=['Empty', 'Fixed'], + use_ref_new=False, + update_ref_orig=False, + mag_trans=False, + trans_weighting='both,std', + init_guess_mode='miracle', verbose=False) + msc.fit() - return shifts + assert 'me' in msc.ref_table.colnames + return -def make_fake_starlists_poly1(seed=-1): - # If seed >=0, then set random seed to that value - if seed >= 0: - np.random.seed(seed=seed) - - N_stars = 200 +def test_bootstrap(): + """ + Test to make sure calc_bootstrap_error() call is working + properly (e.g., only called when user calls calc_bootstrap_error, + n_boot param for calc_bootstrap_error only, boot_epochs_min working, + etc.) + """ + # Read in starlists for MosaicToRef + ref = Table.read('ref_vel.lis', format='ascii') + list1 = Table.read('E.lis', format='ascii') + list2 = Table.read('F.lis', format='ascii') - x0 = np.random.rand(N_stars) * 10.0 # arcsec (increasing to East) - y0 = np.random.rand(N_stars) * 10.0 # arcsec - x0e = np.random.randn(N_stars) * 5.0e-4 # arcsec - y0e = np.random.randn(N_stars) * 5.0e-4 # arcsec - m0 = (np.random.rand(N_stars) * 8) + 9 # mag - m0e = np.random.randn(N_stars) * 0.05 # mag - t0 = np.ones(N_stars) * 2019.5 + list1 = starlists.StarList.from_table(list1) + list2 = starlists.StarList.from_table(list2) + + # Set parameters for alignment + transModel = transforms.PolyTransform + trans_args = {'order':2} + N_loop = 1 + dr_tol = 0.08 + dm_tol = 99 + outlier_tol = None + mag_lim = None + ref_mag_lim = None + trans_weighting = 'both,var' + mag_trans = False - # Make all the errors positive - x0e = np.abs(x0e) - y0e = np.abs(y0e) - m0e = np.abs(m0e) - - name = ['star_{0:03d}'.format(ii) for ii in range(N_stars)] + n_boot = 15 + boot_epochs_min=-1 - # Make an StarList - lis = starlists.StarList([name, m0, m0e, x0, x0e, y0, y0e, t0], - names = ('name', 'm0', 'm0_err', 'x0', 'x0_err', 'y0', 'y0_err', 't0')) - - sdx = np.argsort(m0) - lis = lis[sdx] + # Run FLYSTAR, no bootstraps yet! + match1 = align.MosaicToRef(ref, [list1, list2], iters=N_loop, dr_tol=dr_tol, + dm_tol=dm_tol, outlier_tol=outlier_tol, + trans_class=transModel, + trans_args=trans_args, + mag_trans=mag_trans, + mag_lim=mag_lim, + ref_mag_lim=ref_mag_lim, + trans_weighting=trans_weighting, + motion_models=['Linear'], + use_ref_new=False, + update_ref_orig=False, + init_guess_mode='name', + verbose=False) + match1.fit() - # Save original positions as reference (1st) list - # in a StarList format (with velocities). - lis.write('random_ref.fits', overwrite=True) + # Make sure no bootstrap columns exist + assert 'xe_boot' not in match1.ref_table.keys() + assert 'ye_boot' not in match1.ref_table.keys() + assert 'vxe_boot' not in match1.ref_table.keys() + assert 'vye_boot' not in match1.ref_table.keys() - ########## - # Shifts - ########## - # Make 4 new starlists with different shifts. - times = [2018.5, 2019.0, 2019.5, 2020.0, 2020.5, 2021.0, 2021.5, 2022.0] - xy_trans = [[[ 6.5, 0.99, 1e-5], [ 10.1, 1e-5, 0.99]], - [[100.3, 0.98, 1e-5], [ 50.5, 9e-6, 1.001]], - [[ 0.0, 1.00, 0.0], [ 0.0, 0.0, 1.0]], - [[250.0, 0.97, 2e-5], [-250.0, 1e-5, 1.001]], - [[ 50.0, 1.01, 1e-5], [ -31.0, 1e-5, 1.000]], - [[ 78.0, 0.98, 0.0 ], [ 45.0, 9e-6, 1.001]], - [[-13.0, 0.99, 1e-5], [ 150, 2e-5, 1.002]], - [[ 94.0, 1.00, 9e-6], [-182.0, 0.0, 0.99]]] - mag_trans = [0.1, 0.4, 0.0, -0.3, 0.2, 0.0, -0.1, -0.3] - - # Convert into pixels (undistorted) with the following info. - scale = 0.01 # arcsec / pix - shift = [1.0, 1.0] # pix + # Run bootstrap: no boot_epochs_min + match1.calc_bootstrap_errors(n_boot=n_boot, boot_epochs_min=boot_epochs_min) + # Make sure columns exist, and none of them are nan values + assert np.sum(np.isnan(match1.ref_table['xe_boot'])) == 0 + assert np.sum(np.isnan(match1.ref_table['ye_boot'])) == 0 + assert np.sum(np.isnan(match1.ref_table['vx_err_boot'])) == 0 + assert np.sum(np.isnan(match1.ref_table['vy_err_boot'])) == 0 - for ss in range(len(times)): - dt = times[ss] - lis['t0'] - - x = lis['x0'] - y = lis['y0'] - t = np.ones(N_stars) * times[ss] + # Test 2: make sure boot_epochs_min is working + # Eliminate some rows to list2, so some stars are only in 1 epoch. + # Rerun align. Some stars should only be detected in 1 epoch + list3 = list2[0:60] - # Convert into pixels - xp = (x / -scale) + shift[0] # -1 from switching to increasing to West (right) - yp = (y / scale) + shift[1] - xpe = lis['x0_err'] / scale - ype = lis['y0_err'] / scale + match2 = align.MosaicToRef(ref, [list1, list3], iters=N_loop, dr_tol=dr_tol, + dm_tol=dm_tol, outlier_tol=outlier_tol, + trans_class=transModel, + trans_args=trans_args, + mag_trans=mag_trans, + mag_lim=mag_lim, + ref_mag_lim=ref_mag_lim, + trans_weighting=trans_weighting, + motion_models=['Linear'], + use_ref_new=False, + update_ref_orig=False, + init_guess_mode='name', + verbose=False) + match2.fit() - # Distort the positions - trans = transforms.PolyTransform(1, xy_trans[ss][0], xy_trans[ss][1], mag_offset=mag_trans[ss]) - xd, yd = trans.evaluate(xp, yp) - md = trans.evaluate_mag(lis['m0']) + # Now run_calc_bootstrap_error, with boot_epochs_min engaged + boot_epochs_min2 = 2 + match2.calc_bootstrap_errors(n_boot=n_boot, boot_epochs_min=boot_epochs_min2) - # Perturb with small errors (0.1 pix) - xd += np.random.randn(N_stars) * 0.1 - yd += np.random.randn(N_stars) * 0.1 - md += np.random.randn(N_stars) * 0.02 - xde = xpe - yde = ype - mde = lis['m0_err'] + # Make sure boot_epochs_min cut worked as intended + out = match2.ref_table + bad = np.where( (out['n_detect'] == 1) & (out['use_in_trans'] == False) ) + good = np.where(out['n_detect'] == 2) - # Save the new list as a starlist. - new_lis = starlists.StarList([lis['name'], md, mde, xd, xde, yd, yde, t], - names=('name', 'm', 'me', 'x', 'xe', 'y', 'ye', 't')) + # Some stars must exist in both "good" and "bad" criteria, + # otherwise this test isn't as useful as intended. + assert len(bad[0]) > 0 + assert len(good[0]) > 0 - new_lis.write('random_{0:d}.fits'.format(ss), overwrite=True) + # For "good" stars: all bootstrap vals should be present + assert np.sum(~np.isfinite(out['xe_boot'][good])) == 0 + assert np.sum(~np.isfinite(out['ye_boot'][good])) == 0 + assert np.sum(~np.isfinite(out['vx_err_boot'][good])) == 0 + assert np.sum(~np.isfinite(out['vy_err_boot'][good])) == 0 - return (xy_trans,mag_trans) + # For "bad" stars, all bootstrap vals should be nans + assert np.sum(np.isfinite(out['xe_boot'][bad])) == 0 + assert np.sum(np.isfinite(out['ye_boot'][bad])) == 0 + assert np.sum(np.isfinite(out['vx_err_boot'][bad])) == 0 + assert np.sum(np.isfinite(out['vy_err_boot'][bad])) == 0 -def make_fake_starlists_poly0_vel(seed=-1): - # If seed >=0, then set random seed to that value - if seed >= 0: - np.random.seed(seed=seed) - - N_stars = 200 + return - x0 = np.random.rand(N_stars) * 10.0 # arcsec (increasing to East) - y0 = np.random.rand(N_stars) * 10.0 # arcsec - x0e = np.ones(N_stars) * 1.0e-4 # arcsec - y0e = np.ones(N_stars) * 1.0e-4 # arcsec - vx = np.random.randn(N_stars) * 5.0 # mas / yr - vy = np.random.randn(N_stars) * 5.0 # mas / yr - vxe = np.ones(N_stars) * 0.05 # mas / yr - vye = np.ones(N_stars) * 0.05 # mas / yr - m0 = (np.random.rand(N_stars) * 8) + 9 # mag - m0e = np.random.randn(N_stars) * 0.05 # mag - t0 = np.ones(N_stars) * 2019.5 - - # Make all the errors positive - x0e = np.abs(x0e) - y0e = np.abs(y0e) - m0e = np.abs(m0e) - vxe = np.abs(vxe) - vye = np.abs(vye) +def test_calc_vel_in_bootstrap(): + """ + Check calc_vel_in_bootstrap performance in calc_bootstrap_errors() - name = ['star_{0:03d}'.format(ii) for ii in range(N_stars)] + Only calculate velocity bootstrap (e.g., bootstrap over epochs and + calculating proper motions) if calc_vel_in_bootstrap=True. - # Make an StarList - lis = starlists.StarList([name, m0, m0e, x0, x0e, y0, y0e, vx, vxe, vy, vye, t0], - names = ('name', 'm0', 'm0_err', 'x0', 'x0_err', 'y0', 'y0_err', - 'vx', 'vx_err', 'vy', 'vy_err', 't0')) + """ + import copy + + # Define match parameters + ref = Table.read('ref_vel.lis', format='ascii') + + list1 = Table.read('E.lis', format='ascii') + list2 = Table.read('F.lis', format='ascii') + + list1 = starlists.StarList.from_table(list1) + list2 = starlists.StarList.from_table(list2) + + # Set parameters for alignment + transModel = transforms.PolyTransform + trans_args = {'order':2} + N_loop = 1 + dr_tol = 0.08 + dm_tol = 99 + outlier_tol = None + mag_lim = None + ref_mag_lim = None + trans_weighting = 'both,var' + mag_trans = False + + n_boot = 15 + boot_epochs_min=-1 + + # Run match + match = align.MosaicToRef(ref, [list1, list2], iters=N_loop, dr_tol=dr_tol, + dm_tol=dm_tol, outlier_tol=outlier_tol, + trans_class=transModel, + trans_args=trans_args, + mag_trans=mag_trans, + mag_lim=mag_lim, + ref_mag_lim=ref_mag_lim, + trans_weighting=trans_weighting, + motion_models=['Linear'], + use_ref_new=False, + update_ref_orig=False, + init_guess_mode='name', + verbose=False) + match.fit() + + # Make 2 copies of match object: one to test + # each case of calc_vel_in_bootstrap + match_vel = copy.deepcopy(match) + + # Run calc_bootstrap_error function with calc_vel_in_bootstrap=True. + # Make sure bootstrap velocity errors are calculated and valid + n_boot = 50 + match_vel.calc_bootstrap_errors(n_boot=n_boot, calc_vel_in_bootstrap=True) + + assert 'xe_boot' in match_vel.ref_table.keys() + assert np.sum(np.isnan(match_vel.ref_table['xe_boot'])) == 0 + assert 'vx_err_boot' in match_vel.ref_table.keys() + assert np.sum(np.isnan(match_vel.ref_table['vx_err_boot'])) == 0 + + # Run without calc_vel_in_bootstrap, make sure velocities are NOT calculated + match.calc_bootstrap_errors(n_boot=n_boot, calc_vel_in_bootstrap=False) + + assert 'xe_boot' in match.ref_table.keys() + assert np.sum(np.isnan(match.ref_table['xe_boot'])) == 0 + assert 'vx_err_boot' not in match.ref_table.keys() - sdx = np.argsort(m0) - lis = lis[sdx] + return - # Save original positions as reference (1st) list - # in a StarList format (with velocities). - lis.write('random_vel_ref.fits', overwrite=True) +def test_transform_xym(): + """ + Test to make sure transforms are being done to mags only + if mag_trans = True. This can cause subtle bugs + otherwise + """ + #---Align 1: self.mag_Trans = False---# + ref = Table.read('ref_vel.lis', format='ascii') + list1 = Table.read('E.lis', format='ascii') + list2 = Table.read('F.lis', format='ascii') + + list1 = starlists.StarList.from_table(list1) + list2 = starlists.StarList.from_table(list2) + + # Set parameters for alignment + transModel = transforms.PolyTransform + trans_args = {'order':2} + N_loop = 1 + dr_tol = 0.08 + dm_tol = 99 + outlier_tol = None + mag_lim = None + ref_mag_lim = None + trans_weighting = 'both,var' + n_boot = 15 + + mag_trans = False + + # Run FLYSTAR, with bootstraps + match1 = align.MosaicToRef(ref, [list1, list2], iters=N_loop, dr_tol=dr_tol, + dm_tol=dm_tol, outlier_tol=outlier_tol, + trans_class=transModel, + trans_args=trans_args, + mag_trans=mag_trans, + mag_lim=mag_lim, + ref_mag_lim=ref_mag_lim, + trans_weighting=trans_weighting, + motion_models=['Fixed'], + use_ref_new=False, + update_ref_orig=False, + init_guess_mode='name', + verbose=False) + + match1.fit() + match1.calc_bootstrap_errors(n_boot=n_boot) + + # Make sure all transformations have mag_offset = 0 + trans_list = match1.trans_list + + for ii in trans_list: + assert ii.mag_offset == 0 + + # Check that no mag transformation has been applied to m col in ref_table + tab1 = match1.ref_table + assert np.all(tab1['m'] == tab1['m_orig']) - ########## - # Propogate to new times and distort. - ########## - # Make 4 new starlists with different epochs and transformations. - times = [2018.5, 2019.0, 2019.5, 2020.0, 2020.5, 2021.0, 2021.5, 2022.0] - xy_trans = [[[ 6.5], [ 10.1]], - [[100.3], [ 50.5]], - [[ 0.0], [ 0.0]], - [[250.0], [-250.0]], - [[ 50.0], [ -31.0]], - [[ 78.0], [ 45.0]], - [[-13.0], [ 150]], - [[ 94.0], [-182.0]]] - mag_trans = [0.1, 0.4, 0.0, -0.3, 0.2, 0.0, -0.1, -0.3] + # Check me_boost == 0 or really small (should be the case + # since we don't transform mags) + assert np.isclose(np.max(tab1['me_boot']), 0, rtol=10**-5) + print('Done mag_trans = False case') - # Convert into pixels (undistorted) with the following info. - scale = 0.01 # arcsec / pix - shift = [1.0, 1.0] # pix + #---Align 2: self.mag_Trans = True---# + # Repeat, this time with mag_trans = False + mag_trans = True + match2 = align.MosaicToRef(ref, [list1, list2], iters=N_loop, dr_tol=dr_tol, + dm_tol=dm_tol, outlier_tol=outlier_tol, + trans_class=transModel, + trans_args=trans_args, + mag_trans=mag_trans, + mag_lim=mag_lim, + ref_mag_lim=ref_mag_lim, + trans_weighting=trans_weighting, + motion_models=['Fixed'], + use_ref_new=False, + update_ref_orig=False, + init_guess_mode='name', + verbose=False) + + match2.fit() + match2.calc_bootstrap_errors(n_boot=n_boot) + + + # Make sure all transformations have correct mag offset + trans_list2 = match2.trans_list + + for ii in trans_list2: + assert ii.mag_offset > 20 + + # Make sure final table mags have transform applied (i.e, + tab2 = match2.ref_table + assert np.all(tab2['m'] != tab2['m_orig']) - for ss in range(len(times)): - dt = times[ss] - lis['t0'] - - x = lis['x0'] + (lis['vx']/1e3) * dt - y = lis['y0'] + (lis['vy']/1e3) * dt - t = np.ones(N_stars) * times[ss] + # Check me_boost > 0 + assert np.min(tab2['me_boot']) > 10**-3 - # Convert into pixels - xp = (x / -scale) + shift[0] # -1 from switching to increasing to West (right) - yp = (y / scale) + shift[1] - xpe = lis['x0_err'] / scale - ype = lis['y0_err'] / scale + print('Done mag_trans = True case') + + return - # Distort the positions - trans = transforms.PolyTransform(0, xy_trans[ss][0], xy_trans[ss][1], mag_offset=mag_trans[ss]) - xd, yd = trans.evaluate(xp, yp) - md = trans.evaluate_mag(lis['m0']) +def test_MosaicToRef_mag_bug(): + """ + Bug found by Tuan Do on 2020-04-12. + """ + make_fake_starlists_poly1_vel() - # Perturb with small errors (0.1 pix) - xd += np.random.randn(N_stars) * xpe - yd += np.random.randn(N_stars) * ype - md += np.random.randn(N_stars) * 0.02 - xde = xpe - yde = ype - mde = lis['m0_err'] + ref_list = starlists.StarList.read('random_vel_0.fits') + lists = [ref_list] - # Save the new list as a starlist. - new_lis = starlists.StarList([lis['name'], md, mde, xd, xde, yd, yde, t], - names=('name', 'm', 'me', 'x', 'xe', 'y', 'ye', 't')) + msc = align.MosaicToRef(ref_list, lists, + mag_trans=True, + iters=1, + dr_tol=[0.2], dm_tol=[1], + outlier_tol=None, + trans_class=transforms.PolyTransform, + trans_args=[{'order': 1}], + motion_models=['Fixed'], + use_ref_new=False, + update_ref_orig=False, + verbose=True) - new_lis.write('random_vel_p0_{0:d}.fits'.format(ss), overwrite=True) + msc.fit() - return (xy_trans, mag_trans) + out_tab = msc.ref_table + # The issue is that in the initial guess with + # mag_trans = True + # somehow the transformed magnitudes are nan. + # This causes zero matches to occur. + assert len(out_tab) == len(ref_list) -def make_fake_starlists_poly1_vel(seed=-1): - # If seed >=0, then set random seed to that value - if seed >= 0: - np.random.seed(seed=seed) - - N_stars = 200 + return - x0 = np.random.rand(N_stars) * 10.0 # arcsec (increasing to East) - y0 = np.random.rand(N_stars) * 10.0 # arcsec - x0e = np.ones(N_stars) * 1.0e-4 # arcsec - y0e = np.ones(N_stars) * 1.0e-4 # arcsec - vx = np.random.randn(N_stars) * 5.0 # mas / yr - vy = np.random.randn(N_stars) * 5.0 # mas / yr - vxe = np.ones(N_stars) * 0.05 # mas / yr - vye = np.ones(N_stars) * 0.05 # mas / yr - m0 = (np.random.rand(N_stars) * 8) + 9 # mag - m0e = np.random.randn(N_stars) * 0.05 # mag - t0 = np.ones(N_stars) * 2019.5 +def test_masked_cols(): + """ + Test to make sure analysis.prepare_gaia_for_flystar + produces an astropy.table.Table, NOT a masked column + table. MosaicToRef cannot handle masked column tables. - # Make all the errors positive - x0e = np.abs(x0e) - y0e = np.abs(y0e) - m0e = np.abs(m0e) - vxe = np.abs(vxe) - vye = np.abs(vye) - - name = ['star_{0:03d}'.format(ii) for ii in range(N_stars)] + Also make sure this example works, since we use it for the examples + jupyter notebook. + """ + # Get gaia reference stars using analysis.py + # around a test location. + # target = 'ob150029' + ra = '17:59:46.60' + dec = '-28:38:41.8' - # Make an StarList - lis = starlists.StarList([name, m0, m0e, x0, x0e, y0, y0e, vx, vxe, vy, vye, t0], - names = ('name', 'm0', 'm0_err', 'x0', 'x0_err', 'y0', 'y0_err', - 'vx', 'vx_err', 'vy', 'vy_err', 't0')) - - sdx = np.argsort(m0) - lis = lis[sdx] + # Coordinates are arcsecs offset +x to the East. + targets_dict = { + 'ob150029': [0.0, 0.0], + 'S005': [1.1416, 3.7405], + 'S002': [-4.421, 0.027] + } - # Save original positions as reference (1st) list - # in a StarList format (with velocities). - lis.write('random_vel_ref.fits', overwrite=True) - - ########## - # Propogate to new times and distort. - ########## - # Make 4 new starlists with different epochs and transformations. - times = [2018.5, 2019.0, 2019.5, 2020.0, 2020.5, 2021.0, 2021.5, 2022.0] - xy_trans = [[[ 6.5, 0.99, 1e-5], [ 10.1, 1e-5, 0.99]], - [[100.3, 0.98, 1e-5], [ 50.5, 9e-6, 1.001]], - [[ 0.0, 1.00, 0.0], [ 0.0, 0.0, 1.000]], - [[250.0, 1.01, 2e-5], [-250.0, 1e-5, 0.98]], - [[ 50.0, 1.01, 1e-5], [ -31.0, 1e-5, 1.000]], - [[ 78.0, 0.98, 0.0 ], [ 45.0, 9e-6, 1.001]], - [[-13.0, 0.99, 1e-5], [ 150, 2e-5, 1.002]], - [[ 94.0, 1.00, 9e-6], [-182.0, 0.0, 0.99]]] - mag_trans = [0.1, 0.4, 0.0, -0.3, 0.2, 0.0, -0.1, -0.3] + # Get gaia catalog stars. Note that this produces a masked column table + search_rad = 10.0 # arcsec + gaia = analysis.query_gaia(ra, dec, search_radius=search_rad) + my_gaia = analysis.prepare_gaia_for_flystar(gaia, ra, dec, targets_dict=targets_dict) - # Convert into pixels (undistorted) with the following info. - scale = 0.01 # arcsec / pix - shift = [1.0, 1.0] # pix - - for ss in range(len(times)): - dt = times[ss] - lis['t0'] - - x = lis['x0'] + (lis['vx']/1e3) * dt - y = lis['y0'] + (lis['vy']/1e3) * dt - t = np.ones(N_stars) * times[ss] + assert isinstance(my_gaia, Table) - # Convert into pixels - xp = (x / -scale) + shift[0] # -1 from switching to increasing to West (right) - yp = (y / scale) + shift[1] - xpe = lis['x0_err'] / scale - ype = lis['y0_err'] / scale + # Let's make sure the entire align runs, just to be safe - # Distort the positions - trans = transforms.PolyTransform(1, xy_trans[ss][0], xy_trans[ss][1], mag_offset=mag_trans[ss]) - xd, yd = trans.evaluate(xp, yp) - md = trans.evaluate_mag(lis['m0']) + # Get starlists to align to gaia + epochs = ['15jun07','16jul14', '17may21'] - # Perturb with small errors (0.1 mas) - xd += np.random.randn(N_stars) * xpe - yd += np.random.randn(N_stars) * ype - md += np.random.randn(N_stars) * 0.02 - xde = xpe - yde = ype - mde = lis['m0_err'] + list_of_starlists = [] - # Save the new list as a starlist. - new_lis = starlists.StarList([lis['name'], md, mde, xd, xde, yd, yde, t], - names=('name', 'm', 'me', 'x', 'xe', 'y', 'ye', 't')) + for ee in range(len(epochs)): + lis_file = 'mag' + epochs[ee] + '_ob150029_kp_rms_named.lis' + lis = starlists.StarList.from_lis_file(lis_file) + list_of_starlists.append(lis) - new_lis.write('random_vel_{0:d}.fits'.format(ss), overwrite=True) + # Run the align + msc = align.MosaicToRef(my_gaia, list_of_starlists, iters=2, + dr_tol=[0.2, 0.1], dm_tol=[1, 1], + trans_class=transforms.PolyTransform, + trans_args=[{'order': 1}, {'order': 1}], + motion_models=['Linear'], + use_ref_new=False, + update_ref_orig=False, + mag_trans=True, + init_guess_mode='name', verbose=True) - return (xy_trans, mag_trans) + msc.fit() + return -def make_fake_starlists_poly1_acc(seed=-1): - # If seed >=0, then set random seed to that value - if seed >= 0: - np.random.seed(seed=seed) - +def make_fake_starlists_shifts(): N_stars = 200 + x = np.random.rand(N_stars) * 1000 + y = np.random.rand(N_stars) * 1000 + m = (np.random.rand(N_stars) * 8) + 9 - x0 = np.random.rand(N_stars) * 10.0 # arcsec (increasing to East) - y0 = np.random.rand(N_stars) * 10.0 # arcsec - x0e = np.ones(N_stars) * 1.0e-4 # arcsec - y0e = np.ones(N_stars) * 1.0e-4 # arcsec - vx = np.random.randn(N_stars) * 5.0 # mas / yr - vy = np.random.randn(N_stars) * 5.0 # mas / yr - vxe = np.ones(N_stars) * 0.1 # mas / yr - vye = np.ones(N_stars) * 0.1 # mas / yr - ax = np.random.randn(N_stars) * 0.5 # mas / yr^2 - ay = np.random.randn(N_stars) * 0.5 # mas / yr^2 - axe = np.ones(N_stars) * 0.01 # mas / yr^2 - aye = np.ones(N_stars) * 0.01 # mas / yr^2 - m0 = (np.random.rand(N_stars) * 8) + 9 # mag - m0e = np.random.randn(N_stars) * 0.05 # mag - t0 = np.ones(N_stars) * 2019.5 + sdx = np.argsort(m) + x = x[sdx] + y = y[sdx] + m = m[sdx] - # Make all the errors positive - x0e = np.abs(x0e) - y0e = np.abs(y0e) - m0e = np.abs(m0e) - vxe = np.abs(vxe) - vye = np.abs(vye) - axe = np.abs(axe) - aye = np.abs(aye) - name = ['star_{0:03d}'.format(ii) for ii in range(N_stars)] - # Make an StarList - lis = starlists.StarList([name, m0, m0e, - x0, x0e, y0, y0e, - vx, vxe, vy, vye, - ax, axe, ay, aye, - t0], - names = ('name', 'm0', 'm0_err', - 'x0', 'x0_err', 'y0', 'y0_err', - 'vx0', 'vx0_err', 'vy0', 'vy0_err', - 'ax', 'ax_err', 'ay', 'ay_err', - 't0')) - - sdx = np.argsort(m0) - lis = lis[sdx] + # Save original positions as reference (1st) list. + fmt = '{0:10s} {1:5.2f} 2015.0 {2:9.4f} {3:9.4f} 0 0 0 0\n' + _out = open('random_0.lis', 'w') + for ii in range(N_stars): + _out.write(fmt.format(name[ii], m[ii], x[ii], y[ii])) + _out.close() + - # Save original positions as reference (1st) list - # in a StarList format (with velocities). - lis.write('random_acc_ref.fits', overwrite=True) - ########## - # Propogate to new times and distort. + # Shifts ########## - # Make 4 new starlists with different epochs and transformations. - times = [2018.5, 2019.0, 2019.5, 2020.0, 2020.5, 2021.0, 2021.5, 2022.0] - xy_trans = [[[ 6.5, 0.99, 1e-5], [ 10.1, 1e-5, 0.99]], - [[100.3, 0.98, 1e-5], [ 50.5, 9e-6, 1.001]], - [[ 0.0, 1.00, 0.0], [ 0.0, 0.0, 1.000]], - [[250.0, 0.97, 2e-5], [-250.0, 1e-5, 1.001]], - [[ 50.0, 1.01, 1e-5], [ -31.0, 1e-5, 1.000]], - [[ 78.0, 0.98, 0.0 ], [ 45.0, 9e-6, 1.001]], - [[-13.0, 0.99, 1e-5], [ 150, 2e-5, 1.002]], - [[ 94.0, 1.00, 9e-6], [-182.0, 0.0, 0.99]]] - mag_trans = [0.1, 0.4, 0.0, -0.3, 0.2, 0.0, -0.1, -0.3] - - # Convert into pixels (undistorted) with the following info. - scale = 0.01 # arcsec / pix - shift = [1.0, 1.0] # pix - - for ss in range(len(times)): - dt = times[ss] - lis['t0'] - - x = lis['x0'] + (lis['vx0']/1e3) * dt + 0.5*(lis['ax']/1e3) * dt**2 - y = lis['y0'] + (lis['vy0']/1e3) * dt + 0.5*(lis['ay']/1e3) * dt**2 - t = np.ones(N_stars) * times[ss] - - # Convert into pixels - xp = (x / -scale) + shift[0] # -1 from switching to increasing to West (right) - yp = (y / scale) + shift[1] - xpe = lis['x0_err'] / scale - ype = lis['y0_err'] / scale + # Make 4 new starlists with different shifts. + shifts = [[ 6.5, 10.1], + [100.3, 50.5], + [-30.0,-100.7], + [250.0,-250.0]] - # Distort the positions - trans = transforms.PolyTransform(1, xy_trans[ss][0], xy_trans[ss][1], mag_offset=mag_trans[ss]) - xd, yd = trans.evaluate(xp, yp) - md = trans.evaluate_mag(lis['m0']) + for ss in range(len(shifts)): + xnew = x - shifts[ss][0] + ynew = y - shifts[ss][1] # Perturb with small errors (0.1 pix) - xd += np.random.randn(N_stars) * xpe - yd += np.random.randn(N_stars) * ype - md += np.random.randn(N_stars) * 0.02 - xde = xpe - yde = ype - mde = lis['m0_err'] + xnew += np.random.randn(N_stars) * 0.1 + ynew += np.random.randn(N_stars) * 0.1 - # Save the new list as a starlist. - new_lis = starlists.StarList([lis['name'], md, mde, xd, xde, yd, yde, t], - names=('name', 'm', 'me', 'x', 'xe', 'y', 'ye', 't')) + mnew = m + np.random.randn(N_stars) * 0.05 - new_lis.write('random_acc_{0:d}.fits'.format(ss), overwrite=True) + _out = open('random_shift_{0:d}.lis'.format(ss+1), 'w') + for ii in range(N_stars): + _out.write(fmt.format(name[ii], mnew[ii], xnew[ii], ynew[ii])) + _out.close() - return (xy_trans, mag_trans) - -def make_fake_starlists_poly1_par(seed=-1): + return shifts + +def make_fake_starlists_poly1(seed=-1): # If seed >=0, then set random seed to that value if seed >= 0: np.random.seed(seed=seed) @@ -916,12 +984,6 @@ def make_fake_starlists_poly1_par(seed=-1): y0 = np.random.rand(N_stars) * 10.0 # arcsec x0e = np.random.randn(N_stars) * 5.0e-4 # arcsec y0e = np.random.randn(N_stars) * 5.0e-4 # arcsec - vx = np.random.randn(N_stars) * 5.0 # mas / yr - vy = np.random.randn(N_stars) * 5.0 # mas / yr - vxe = np.random.randn(N_stars) * 0.1 # mas / yr - vye = np.random.randn(N_stars) * 0.1 # mas / yr - pi = np.random.randn(N_stars) * 0.5 # mas - pie = np.random.randn(N_stars) * 0.01 # mas m0 = (np.random.rand(N_stars) * 8) + 9 # mag m0e = np.random.randn(N_stars) * 0.05 # mag t0 = np.ones(N_stars) * 2019.5 @@ -929,65 +991,45 @@ def make_fake_starlists_poly1_par(seed=-1): # Make all the errors positive x0e = np.abs(x0e) y0e = np.abs(y0e) - m0e = np.abs(m0e) - vxe = np.abs(vxe) - vye = np.abs(vye) - pie = np.abs(pie) - - name = ['star_{0:03d}'.format(ii) for ii in range(N_stars)] - - # Make an StarList - lis = starlists.StarList([name, m0, m0e, - x0, x0e, y0, y0e, - vx, vxe, vy, vye, - pi, pie, - t0], - names = ('name', 'm0', 'm0_err', - 'x0', 'x0_err', 'y0', 'y0_err', - 'vx', 'vx_err', 'vy', 'vy_err', - 'pi', 'pi_err', - 't0')) + m0e = np.abs(m0e) + + name = ['star_{0:03d}'.format(ii) for ii in range(N_stars)] + + # Make an StarList + lis = starlists.StarList([name, m0, m0e, x0, x0e, y0, y0e, t0], + names = ('name', 'm0', 'm0_err', 'x0', 'x0_err', 'y0', 'y0_err', 't0')) sdx = np.argsort(m0) lis = lis[sdx] # Save original positions as reference (1st) list # in a StarList format (with velocities). - lis.write('random_par_ref.fits', overwrite=True) - + lis.write('random_ref.fits', overwrite=True) + ########## - # Propogate to new times and distort. + # Shifts ########## - # Make 4 new starlists with different epochs and transformations. - '''times = [2018.5, 2019.5, 2020.5, 2021.5] - xy_trans = [[[ 6.5, 0.99, 1e-5], [ 10.1, 1e-5, 0.99]], - [[100.3, 0.98, 1e-5], [ 50.5, 9e-6, 1.001]], - [[ 0.0, 1.00, 0.0], [ 0.0, 0.0, 1.0]], - [[250.0, 0.97, 2e-5], [-250.0, 1e-5, 1.001]]] - mag_trans = [0.1, 0.4, 0.0, -0.3]''' - + # Make 4 new starlists with different shifts. times = [2018.5, 2019.0, 2019.5, 2020.0, 2020.5, 2021.0, 2021.5, 2022.0] xy_trans = [[[ 6.5, 0.99, 1e-5], [ 10.1, 1e-5, 0.99]], [[100.3, 0.98, 1e-5], [ 50.5, 9e-6, 1.001]], [[ 0.0, 1.00, 0.0], [ 0.0, 0.0, 1.0]], [[250.0, 0.97, 2e-5], [-250.0, 1e-5, 1.001]], - [[ 50.0, 1.00, 0.0], [ -31.0, 0.0, 1.000]], - [[ 78.0, 1.00, 0.0 ], [ 45.0, 0.0, 1.00]], - [[-13.0, 1.00, 0.0], [ 150, 0.0, 1.00]], - [[ 94.0, 1.00, 0.0], [-182.0, 0.0, 1.00]]] - mag_trans = [0.1, 0.4, 0.0, -0.3, 0.0, 0.0, 0.0, 0.0] - + [[ 50.0, 1.01, 1e-5], [ -31.0, 1e-5, 1.000]], + [[ 78.0, 0.98, 0.0 ], [ 45.0, 9e-6, 1.001]], + [[-13.0, 0.99, 1e-5], [ 150, 2e-5, 1.002]], + [[ 94.0, 1.00, 9e-6], [-182.0, 0.0, 0.99]]] + mag_trans = [0.1, 0.4, 0.0, -0.3, 0.2, 0.0, -0.1, -0.3] + # Convert into pixels (undistorted) with the following info. scale = 0.01 # arcsec / pix shift = [1.0, 1.0] # pix - + for ss in range(len(times)): dt = times[ss] - lis['t0'] - par_mod = motion_model.Parallax(pa=0,ra=18.0, dec=-30.0) - par_mod_dat = par_mod.get_batch_pos_at_time(dt+lis['t0'], x0=lis['x0'],vx=lis['vx']/1e3, pi=lis['pi'], - y0=lis['y0'], vy=lis['vy']/1e3, t0=lis['t0']) - x,y = par_mod_dat[0], par_mod_dat[1] + x = lis['x0'] + y = lis['y0'] t = np.ones(N_stars) * times[ss] # Convert into pixels @@ -1013,431 +1055,411 @@ def make_fake_starlists_poly1_par(seed=-1): new_lis = starlists.StarList([lis['name'], md, mde, xd, xde, yd, yde, t], names=('name', 'm', 'me', 'x', 'xe', 'y', 'ye', 't')) - new_lis.write('random_par_{0:d}.fits'.format(ss), overwrite=True) - - return (xy_trans, mag_trans) - - -def test_MosaicToRef_hst_me(): - """ - Test Casey's issue with 'me' not getting propogated - from the input starlists to the output table. - - Use data from MB10-364 microlensing target for the test. - """ - # Target RA and Dec (MOA data download) - # ra = '17:57:05.401' - # dec = '-34:27:05.01' - - # Load up a Gaia catalog (queried around the RA/Dec above) - my_gaia = Table.read('mb10364_data/my_gaia.fits') - my_gaia['me'] = 0.01 - - # Gather the list of starlists. For first pass, don't modify the starlists. - # Loop through the observations and read them in, in prep for alignment with Gaia - epochs = [2011.83, 2012.73, 2013.81] - starlist_names = ['mb10364_data/2011_10_31_F606W_MATCHUP_XYMEEE_final.calib', - 'mb10364_data/2012_09_25_F606W_MATCHUP_XYMEEE_final.calib', - 'mb10364_data/2013_10_24_F606W_MATCHUP_XYMEEE_final.calib'] - - list_of_starlists = [] - - # Just using the F606W filters first. - for ee in range(len(starlist_names)): - lis = starlists.StarList.from_lis_file(starlist_names[ee]) - - # # Add additive error term. MAYBE YOU DON'T NEED THIS - # lis['xe'] = np.hypot(lis['xe'], 0.01) # Adding 0.01 pix (0.1 mas) in quadrature. - # lis['ye'] = np.hypot(lis['ye'], 0.01) - - lis['t'] = epochs[ee] - - # Lets dump the faint stars. - idx = np.where(lis['m'] < 20.0)[0] - lis = lis[idx] - - list_of_starlists.append(lis) - - msc = align.MosaicToRef(my_gaia, list_of_starlists, iters=1, - dr_tol=[0.1], dm_tol=[5], - outlier_tol=[None], mag_lim=[13, 21], - trans_class=transforms.PolyTransform, - trans_args=[{'order': 1}], - motion_models=['Empty', 'Fixed'], - use_ref_new=False, - update_ref_orig=False, - mag_trans=False, - trans_weighting='both,std', - init_guess_mode='miracle', verbose=False) - msc.fit() - - assert 'me' in msc.ref_table.colnames - return + new_lis.write('random_{0:d}.fits'.format(ss), overwrite=True) -def test_bootstrap(): - """ - Test to make sure calc_bootstrap_error() call is working - properly (e.g., only called when user calls calc_bootstrap_error, - n_boot param for calc_bootstrap_error only, boot_epochs_min working, - etc.) - """ - # Read in starlists for MosaicToRef - ref = Table.read('ref_vel.lis', format='ascii') - list1 = Table.read('E.lis', format='ascii') - list2 = Table.read('F.lis', format='ascii') + return (xy_trans,mag_trans) - list1 = starlists.StarList.from_table(list1) - list2 = starlists.StarList.from_table(list2) +def make_fake_starlists_poly0_vel(seed=-1): + # If seed >=0, then set random seed to that value + if seed >= 0: + np.random.seed(seed=seed) - # Set parameters for alignment - transModel = transforms.PolyTransform - trans_args = {'order':2} - N_loop = 1 - dr_tol = 0.08 - dm_tol = 99 - outlier_tol = None - mag_lim = None - ref_mag_lim = None - trans_weighting = 'both,var' - mag_trans = False - - n_boot = 15 - boot_epochs_min=-1 - - # Run FLYSTAR, no bootstraps yet! - match1 = align.MosaicToRef(ref, [list1, list2], iters=N_loop, dr_tol=dr_tol, - dm_tol=dm_tol, outlier_tol=outlier_tol, - trans_class=transModel, - trans_args=trans_args, - mag_trans=mag_trans, - mag_lim=mag_lim, - ref_mag_lim=ref_mag_lim, - trans_weighting=trans_weighting, - motion_models=['Linear'], - use_ref_new=False, - update_ref_orig=False, - init_guess_mode='name', - verbose=False) - match1.fit() - - # Make sure no bootstrap columns exist - assert 'xe_boot' not in match1.ref_table.keys() - assert 'ye_boot' not in match1.ref_table.keys() - assert 'vxe_boot' not in match1.ref_table.keys() - assert 'vye_boot' not in match1.ref_table.keys() - - # Run bootstrap: no boot_epochs_min - match1.calc_bootstrap_errors(n_boot=n_boot, boot_epochs_min=boot_epochs_min) - # Make sure columns exist, and none of them are nan values - assert np.sum(np.isnan(match1.ref_table['xe_boot'])) == 0 - assert np.sum(np.isnan(match1.ref_table['ye_boot'])) == 0 - assert np.sum(np.isnan(match1.ref_table['vx_err_boot'])) == 0 - assert np.sum(np.isnan(match1.ref_table['vy_err_boot'])) == 0 - - # Test 2: make sure boot_epochs_min is working - # Eliminate some rows to list2, so some stars are only in 1 epoch. - # Rerun align. Some stars should only be detected in 1 epoch - list3 = list2[0:60] - - match2 = align.MosaicToRef(ref, [list1, list3], iters=N_loop, dr_tol=dr_tol, - dm_tol=dm_tol, outlier_tol=outlier_tol, - trans_class=transModel, - trans_args=trans_args, - mag_trans=mag_trans, - mag_lim=mag_lim, - ref_mag_lim=ref_mag_lim, - trans_weighting=trans_weighting, - motion_models=['Linear'], - use_ref_new=False, - update_ref_orig=False, - init_guess_mode='name', - verbose=False) - match2.fit() - - # Now run_calc_bootstrap_error, with boot_epochs_min engaged - boot_epochs_min2 = 2 - match2.calc_bootstrap_errors(n_boot=n_boot, boot_epochs_min=boot_epochs_min2) - - # Make sure boot_epochs_min cut worked as intended - out = match2.ref_table - bad = np.where( (out['n_detect'] == 1) & (out['use_in_trans'] == False) ) - good = np.where(out['n_detect'] == 2) - - # Some stars must exist in both "good" and "bad" criteria, - # otherwise this test isn't as useful as intended. - assert len(bad[0]) > 0 - assert len(good[0]) > 0 + N_stars = 200 - # For "good" stars: all bootstrap vals should be present - assert np.sum(~np.isfinite(out['xe_boot'][good])) == 0 - assert np.sum(~np.isfinite(out['ye_boot'][good])) == 0 - assert np.sum(~np.isfinite(out['vx_err_boot'][good])) == 0 - assert np.sum(~np.isfinite(out['vy_err_boot'][good])) == 0 + x0 = np.random.rand(N_stars) * 10.0 # arcsec (increasing to East) + y0 = np.random.rand(N_stars) * 10.0 # arcsec + x0e = np.ones(N_stars) * 1.0e-4 # arcsec + y0e = np.ones(N_stars) * 1.0e-4 # arcsec + vx = np.random.randn(N_stars) * 5.0 # mas / yr + vy = np.random.randn(N_stars) * 5.0 # mas / yr + vxe = np.ones(N_stars) * 0.05 # mas / yr + vye = np.ones(N_stars) * 0.05 # mas / yr + m0 = (np.random.rand(N_stars) * 8) + 9 # mag + m0e = np.random.randn(N_stars) * 0.05 # mag + t0 = np.ones(N_stars) * 2019.5 - # For "bad" stars, all bootstrap vals should be nans - assert np.sum(np.isfinite(out['xe_boot'][bad])) == 0 - assert np.sum(np.isfinite(out['ye_boot'][bad])) == 0 - assert np.sum(np.isfinite(out['vx_err_boot'][bad])) == 0 - assert np.sum(np.isfinite(out['vy_err_boot'][bad])) == 0 + # Make all the errors positive + x0e = np.abs(x0e) + y0e = np.abs(y0e) + m0e = np.abs(m0e) + vxe = np.abs(vxe) + vye = np.abs(vye) + + name = ['star_{0:03d}'.format(ii) for ii in range(N_stars)] - return + # Make an StarList + lis = starlists.StarList([name, m0, m0e, x0, x0e, y0, y0e, vx, vxe, vy, vye, t0], + names = ('name', 'm0', 'm0_err', 'x0', 'x0_err', 'y0', 'y0_err', + 'vx', 'vx_err', 'vy', 'vy_err', 't0')) + + sdx = np.argsort(m0) + lis = lis[sdx] -def test_calc_vel_in_bootstrap(): - """ - Check calc_vel_in_bootstrap performance in calc_bootstrap_errors() + # Save original positions as reference (1st) list + # in a StarList format (with velocities). + lis.write('random_vel_ref.fits', overwrite=True) - Only calculate velocity bootstrap (e.g., bootstrap over epochs and - calculating proper motions) if calc_vel_in_bootstrap=True. + ########## + # Propogate to new times and distort. + ########## + # Make 4 new starlists with different epochs and transformations. + times = [2018.5, 2019.0, 2019.5, 2020.0, 2020.5, 2021.0, 2021.5, 2022.0] + xy_trans = [[[ 6.5], [ 10.1]], + [[100.3], [ 50.5]], + [[ 0.0], [ 0.0]], + [[250.0], [-250.0]], + [[ 50.0], [ -31.0]], + [[ 78.0], [ 45.0]], + [[-13.0], [ 150]], + [[ 94.0], [-182.0]]] + mag_trans = [0.1, 0.4, 0.0, -0.3, 0.2, 0.0, -0.1, -0.3] - """ - import copy + # Convert into pixels (undistorted) with the following info. + scale = 0.01 # arcsec / pix + shift = [1.0, 1.0] # pix + + for ss in range(len(times)): + dt = times[ss] - lis['t0'] + + x = lis['x0'] + (lis['vx']/1e3) * dt + y = lis['y0'] + (lis['vy']/1e3) * dt + t = np.ones(N_stars) * times[ss] - # Define match parameters - ref = Table.read('ref_vel.lis', format='ascii') + # Convert into pixels + xp = (x / -scale) + shift[0] # -1 from switching to increasing to West (right) + yp = (y / scale) + shift[1] + xpe = lis['x0_err'] / scale + ype = lis['y0_err'] / scale - list1 = Table.read('E.lis', format='ascii') - list2 = Table.read('F.lis', format='ascii') + # Distort the positions + trans = transforms.PolyTransform(0, xy_trans[ss][0], xy_trans[ss][1], mag_offset=mag_trans[ss]) + xd, yd = trans.evaluate(xp, yp) + md = trans.evaluate_mag(lis['m0']) - list1 = starlists.StarList.from_table(list1) - list2 = starlists.StarList.from_table(list2) - - # Set parameters for alignment - transModel = transforms.PolyTransform - trans_args = {'order':2} - N_loop = 1 - dr_tol = 0.08 - dm_tol = 99 - outlier_tol = None - mag_lim = None - ref_mag_lim = None - trans_weighting = 'both,var' - mag_trans = False + # Perturb with small errors (0.1 pix) + xd += np.random.randn(N_stars) * xpe + yd += np.random.randn(N_stars) * ype + md += np.random.randn(N_stars) * 0.02 + xde = xpe + yde = ype + mde = lis['m0_err'] - n_boot = 15 - boot_epochs_min=-1 + # Save the new list as a starlist. + new_lis = starlists.StarList([lis['name'], md, mde, xd, xde, yd, yde, t], + names=('name', 'm', 'me', 'x', 'xe', 'y', 'ye', 't')) - # Run match - match = align.MosaicToRef(ref, [list1, list2], iters=N_loop, dr_tol=dr_tol, - dm_tol=dm_tol, outlier_tol=outlier_tol, - trans_class=transModel, - trans_args=trans_args, - mag_trans=mag_trans, - mag_lim=mag_lim, - ref_mag_lim=ref_mag_lim, - trans_weighting=trans_weighting, - motion_models=['Linear'], - use_ref_new=False, - update_ref_orig=False, - init_guess_mode='name', - verbose=False) - match.fit() + new_lis.write('random_vel_p0_{0:d}.fits'.format(ss), overwrite=True) - # Make 2 copies of match object: one to test - # each case of calc_vel_in_bootstrap - match_vel = copy.deepcopy(match) + return (xy_trans, mag_trans) - # Run calc_bootstrap_error function with calc_vel_in_bootstrap=True. - # Make sure bootstrap velocity errors are calculated and valid - n_boot = 50 - match_vel.calc_bootstrap_errors(n_boot=n_boot, calc_vel_in_bootstrap=True) - assert 'xe_boot' in match_vel.ref_table.keys() - assert np.sum(np.isnan(match_vel.ref_table['xe_boot'])) == 0 - assert 'vx_err_boot' in match_vel.ref_table.keys() - assert np.sum(np.isnan(match_vel.ref_table['vx_err_boot'])) == 0 +def make_fake_starlists_poly1_vel(seed=-1): + # If seed >=0, then set random seed to that value + if seed >= 0: + np.random.seed(seed=seed) + + N_stars = 200 - # Run without calc_vel_in_bootstrap, make sure velocities are NOT calculated - match.calc_bootstrap_errors(n_boot=n_boot, calc_vel_in_bootstrap=False) + x0 = np.random.rand(N_stars) * 10.0 # arcsec (increasing to East) + y0 = np.random.rand(N_stars) * 10.0 # arcsec + x0e = np.ones(N_stars) * 1.0e-4 # arcsec + y0e = np.ones(N_stars) * 1.0e-4 # arcsec + vx = np.random.randn(N_stars) * 5.0 # mas / yr + vy = np.random.randn(N_stars) * 5.0 # mas / yr + vxe = np.ones(N_stars) * 0.05 # mas / yr + vye = np.ones(N_stars) * 0.05 # mas / yr + m0 = (np.random.rand(N_stars) * 8) + 9 # mag + m0e = np.random.randn(N_stars) * 0.05 # mag + t0 = np.ones(N_stars) * 2019.5 - assert 'xe_boot' in match.ref_table.keys() - assert np.sum(np.isnan(match.ref_table['xe_boot'])) == 0 - assert 'vx_err_boot' not in match.ref_table.keys() + # Make all the errors positive + x0e = np.abs(x0e) + y0e = np.abs(y0e) + m0e = np.abs(m0e) + vxe = np.abs(vxe) + vye = np.abs(vye) - return - -def test_transform_xym(): - """ - Test to make sure transforms are being done to mags only - if mag_trans = True. This can cause subtle bugs - otherwise - """ - #---Align 1: self.mag_Trans = False---# - ref = Table.read('ref_vel.lis', format='ascii') - list1 = Table.read('E.lis', format='ascii') - list2 = Table.read('F.lis', format='ascii') + name = ['star_{0:03d}'.format(ii) for ii in range(N_stars)] - list1 = starlists.StarList.from_table(list1) - list2 = starlists.StarList.from_table(list2) + # Make an StarList + lis = starlists.StarList([name, m0, m0e, x0, x0e, y0, y0e, vx, vxe, vy, vye, t0], + names = ('name', 'm0', 'm0_err', 'x0', 'x0_err', 'y0', 'y0_err', + 'vx', 'vx_err', 'vy', 'vy_err', 't0')) - # Set parameters for alignment - transModel = transforms.PolyTransform - trans_args = {'order':2} - N_loop = 1 - dr_tol = 0.08 - dm_tol = 99 - outlier_tol = None - mag_lim = None - ref_mag_lim = None - trans_weighting = 'both,var' - n_boot = 15 + sdx = np.argsort(m0) + lis = lis[sdx] - mag_trans = False + # Save original positions as reference (1st) list + # in a StarList format (with velocities). + lis.write('random_vel_ref.fits', overwrite=True) + + ########## + # Propogate to new times and distort. + ########## + # Make 4 new starlists with different epochs and transformations. + times = [2018.5, 2019.0, 2019.5, 2020.0, 2020.5, 2021.0, 2021.5, 2022.0] + xy_trans = [[[ 6.5, 0.99, 1e-5], [ 10.1, 1e-5, 0.99]], + [[100.3, 0.98, 1e-5], [ 50.5, 9e-6, 1.001]], + [[ 0.0, 1.00, 0.0], [ 0.0, 0.0, 1.000]], + [[250.0, 1.01, 2e-5], [-250.0, 1e-5, 0.98]], + [[ 50.0, 1.01, 1e-5], [ -31.0, 1e-5, 1.000]], + [[ 78.0, 0.98, 0.0 ], [ 45.0, 9e-6, 1.001]], + [[-13.0, 0.99, 1e-5], [ 150, 2e-5, 1.002]], + [[ 94.0, 1.00, 9e-6], [-182.0, 0.0, 0.99]]] + mag_trans = [0.1, 0.4, 0.0, -0.3, 0.2, 0.0, -0.1, -0.3] - # Run FLYSTAR, with bootstraps - match1 = align.MosaicToRef(ref, [list1, list2], iters=N_loop, dr_tol=dr_tol, - dm_tol=dm_tol, outlier_tol=outlier_tol, - trans_class=transModel, - trans_args=trans_args, - mag_trans=mag_trans, - mag_lim=mag_lim, - ref_mag_lim=ref_mag_lim, - trans_weighting=trans_weighting, - motion_models=['Fixed'], - use_ref_new=False, - update_ref_orig=False, - init_guess_mode='name', - verbose=False) + # Convert into pixels (undistorted) with the following info. + scale = 0.01 # arcsec / pix + shift = [1.0, 1.0] # pix + + for ss in range(len(times)): + dt = times[ss] - lis['t0'] + + x = lis['x0'] + (lis['vx']/1e3) * dt + y = lis['y0'] + (lis['vy']/1e3) * dt + t = np.ones(N_stars) * times[ss] - match1.fit() - match1.calc_bootstrap_errors(n_boot=n_boot) + # Convert into pixels + xp = (x / -scale) + shift[0] # -1 from switching to increasing to West (right) + yp = (y / scale) + shift[1] + xpe = lis['x0_err'] / scale + ype = lis['y0_err'] / scale - # Make sure all transformations have mag_offset = 0 - trans_list = match1.trans_list + # Distort the positions + trans = transforms.PolyTransform(1, xy_trans[ss][0], xy_trans[ss][1], mag_offset=mag_trans[ss]) + xd, yd = trans.evaluate(xp, yp) + md = trans.evaluate_mag(lis['m0']) - for ii in trans_list: - assert ii.mag_offset == 0 + # Perturb with small errors (0.1 mas) + xd += np.random.randn(N_stars) * xpe + yd += np.random.randn(N_stars) * ype + md += np.random.randn(N_stars) * 0.02 + xde = xpe + yde = ype + mde = lis['m0_err'] - # Check that no mag transformation has been applied to m col in ref_table - tab1 = match1.ref_table - assert np.all(tab1['m'] == tab1['m_orig']) - - # Check me_boost == 0 or really small (should be the case - # since we don't transform mags) - assert np.isclose(np.max(tab1['me_boot']), 0, rtol=10**-5) - print('Done mag_trans = False case') + # Save the new list as a starlist. + new_lis = starlists.StarList([lis['name'], md, mde, xd, xde, yd, yde, t], + names=('name', 'm', 'me', 'x', 'xe', 'y', 'ye', 't')) - #---Align 2: self.mag_Trans = True---# - # Repeat, this time with mag_trans = False - mag_trans = True - match2 = align.MosaicToRef(ref, [list1, list2], iters=N_loop, dr_tol=dr_tol, - dm_tol=dm_tol, outlier_tol=outlier_tol, - trans_class=transModel, - trans_args=trans_args, - mag_trans=mag_trans, - mag_lim=mag_lim, - ref_mag_lim=ref_mag_lim, - trans_weighting=trans_weighting, - default_motion_model='Fixed', - use_ref_new=False, - update_ref_orig=False, - init_guess_mode='name', - verbose=False) + new_lis.write('random_vel_{0:d}.fits'.format(ss), overwrite=True) - match2.fit() - match2.calc_bootstrap_errors(n_boot=n_boot) + return (xy_trans, mag_trans) +def make_fake_starlists_poly1_acc(seed=-1): + # If seed >=0, then set random seed to that value + if seed >= 0: + np.random.seed(seed=seed) + + N_stars = 200 - # Make sure all transformations have correct mag offset - trans_list2 = match2.trans_list + x0 = np.random.rand(N_stars) * 10.0 # arcsec (increasing to East) + y0 = np.random.rand(N_stars) * 10.0 # arcsec + x0e = np.ones(N_stars) * 1.0e-4 # arcsec + y0e = np.ones(N_stars) * 1.0e-4 # arcsec + vx = np.random.randn(N_stars) * 5.0 # mas / yr + vy = np.random.randn(N_stars) * 5.0 # mas / yr + vxe = np.ones(N_stars) * 0.1 # mas / yr + vye = np.ones(N_stars) * 0.1 # mas / yr + ax = np.random.randn(N_stars) * 0.5 # mas / yr^2 + ay = np.random.randn(N_stars) * 0.5 # mas / yr^2 + axe = np.ones(N_stars) * 0.01 # mas / yr^2 + aye = np.ones(N_stars) * 0.01 # mas / yr^2 + m0 = (np.random.rand(N_stars) * 8) + 9 # mag + m0e = np.random.randn(N_stars) * 0.05 # mag + t0 = np.ones(N_stars) * 2019.5 - for ii in trans_list2: - assert ii.mag_offset > 20 + # Make all the errors positive + x0e = np.abs(x0e) + y0e = np.abs(y0e) + m0e = np.abs(m0e) + vxe = np.abs(vxe) + vye = np.abs(vye) + axe = np.abs(axe) + aye = np.abs(aye) + + name = ['star_{0:03d}'.format(ii) for ii in range(N_stars)] - # Make sure final table mags have transform applied (i.e, - tab2 = match2.ref_table - assert np.all(tab2['m'] != tab2['m_orig']) + # Make an StarList + lis = starlists.StarList([name, m0, m0e, + x0, x0e, y0, y0e, + vx, vxe, vy, vye, + ax, axe, ay, aye, + t0], + names = ('name', 'm0', 'm0_err', + 'x0', 'x0_err', 'y0', 'y0_err', + 'vx0', 'vx0_err', 'vy0', 'vy0_err', + 'ax', 'ax_err', 'ay', 'ay_err', + 't0')) - # Check me_boost > 0 - assert np.min(tab2['me_boot']) > 10**-3 + sdx = np.argsort(m0) + lis = lis[sdx] - print('Done mag_trans = True case') - - return + # Save original positions as reference (1st) list + # in a StarList format (with velocities). + lis.write('random_acc_ref.fits', overwrite=True) + + ########## + # Propogate to new times and distort. + ########## + # Make 4 new starlists with different epochs and transformations. + times = [2018.5, 2019.0, 2019.5, 2020.0, 2020.5, 2021.0, 2021.5, 2022.0] + xy_trans = [[[ 6.5, 0.99, 1e-5], [ 10.1, 1e-5, 0.99]], + [[100.3, 0.98, 1e-5], [ 50.5, 9e-6, 1.001]], + [[ 0.0, 1.00, 0.0], [ 0.0, 0.0, 1.000]], + [[250.0, 0.97, 2e-5], [-250.0, 1e-5, 1.001]], + [[ 50.0, 1.01, 1e-5], [ -31.0, 1e-5, 1.000]], + [[ 78.0, 0.98, 0.0 ], [ 45.0, 9e-6, 1.001]], + [[-13.0, 0.99, 1e-5], [ 150, 2e-5, 1.002]], + [[ 94.0, 1.00, 9e-6], [-182.0, 0.0, 0.99]]] + mag_trans = [0.1, 0.4, 0.0, -0.3, 0.2, 0.0, -0.1, -0.3] -def test_MosaicToRef_mag_bug(): - """ - Bug found by Tuan Do on 2020-04-12. - """ - make_fake_starlists_poly1_vel() + # Convert into pixels (undistorted) with the following info. + scale = 0.01 # arcsec / pix + shift = [1.0, 1.0] # pix + + for ss in range(len(times)): + dt = times[ss] - lis['t0'] + + x = lis['x0'] + (lis['vx0']/1e3) * dt + 0.5*(lis['ax']/1e3) * dt**2 + y = lis['y0'] + (lis['vy0']/1e3) * dt + 0.5*(lis['ay']/1e3) * dt**2 + t = np.ones(N_stars) * times[ss] - ref_list = starlists.StarList.read('random_vel_0.fits') - lists = [ref_list] + # Convert into pixels + xp = (x / -scale) + shift[0] # -1 from switching to increasing to West (right) + yp = (y / scale) + shift[1] + xpe = lis['x0_err'] / scale + ype = lis['y0_err'] / scale - msc = align.MosaicToRef(ref_list, lists, - mag_trans=True, - iters=1, - dr_tol=[0.2], dm_tol=[1], - outlier_tol=None, - trans_class=transforms.PolyTransform, - trans_args=[{'order': 1}], - motion_models=['Fixed'], - use_ref_new=False, - update_ref_orig=False, - verbose=True) + # Distort the positions + trans = transforms.PolyTransform(1, xy_trans[ss][0], xy_trans[ss][1], mag_offset=mag_trans[ss]) + xd, yd = trans.evaluate(xp, yp) + md = trans.evaluate_mag(lis['m0']) - msc.fit() + # Perturb with small errors (0.1 pix) + xd += np.random.randn(N_stars) * xpe + yd += np.random.randn(N_stars) * ype + md += np.random.randn(N_stars) * 0.02 + xde = xpe + yde = ype + mde = lis['m0_err'] - out_tab = msc.ref_table + # Save the new list as a starlist. + new_lis = starlists.StarList([lis['name'], md, mde, xd, xde, yd, yde, t], + names=('name', 'm', 'me', 'x', 'xe', 'y', 'ye', 't')) - # The issue is that in the initial guess with - # mag_trans = True - # somehow the transformed magnitudes are nan. - # This causes zero matches to occur. - assert len(out_tab) == len(ref_list) + new_lis.write('random_acc_{0:d}.fits'.format(ss), overwrite=True) - return + return (xy_trans, mag_trans) + +def make_fake_starlists_poly1_par(seed=-1): + # If seed >=0, then set random seed to that value + if seed >= 0: + np.random.seed(seed=seed) + + N_stars = 200 -def test_masked_cols(): - """ - Test to make sure analysis.prepare_gaia_for_flystar - produces an astropy.table.Table, NOT a masked column - table. MosaicToRef cannot handle masked column tables. + x0 = np.random.rand(N_stars) * 10.0 # arcsec (increasing to East) + y0 = np.random.rand(N_stars) * 10.0 # arcsec + x0e = np.random.randn(N_stars) * 5.0e-4 # arcsec + y0e = np.random.randn(N_stars) * 5.0e-4 # arcsec + vx = np.random.randn(N_stars) * 5.0 # mas / yr + vy = np.random.randn(N_stars) * 5.0 # mas / yr + vxe = np.random.randn(N_stars) * 0.1 # mas / yr + vye = np.random.randn(N_stars) * 0.1 # mas / yr + pi = np.random.randn(N_stars) * 0.5 # mas + pie = np.random.randn(N_stars) * 0.01 # mas + m0 = (np.random.rand(N_stars) * 8) + 9 # mag + m0e = np.random.randn(N_stars) * 0.05 # mag + t0 = np.ones(N_stars) * 2019.5 - Also make sure this example works, since we use it for the examples - jupyter notebook. - """ - # Get gaia reference stars using analysis.py - # around a test location. - # target = 'ob150029' - ra = '17:59:46.60' - dec = '-28:38:41.8' + # Make all the errors positive + x0e = np.abs(x0e) + y0e = np.abs(y0e) + m0e = np.abs(m0e) + vxe = np.abs(vxe) + vye = np.abs(vye) + pie = np.abs(pie) + + name = ['star_{0:03d}'.format(ii) for ii in range(N_stars)] - # Coordinates are arcsecs offset +x to the East. - targets_dict = { - 'ob150029': [0.0, 0.0], - 'S005': [1.1416, 3.7405], - 'S002': [-4.421, 0.027] - } + # Make an StarList + lis = starlists.StarList([name, m0, m0e, + x0, x0e, y0, y0e, + vx, vxe, vy, vye, + pi, pie, + t0], + names = ('name', 'm0', 'm0_err', + 'x0', 'x0_err', 'y0', 'y0_err', + 'vx', 'vx_err', 'vy', 'vy_err', + 'pi', 'pi_err', + 't0')) + + sdx = np.argsort(m0) + lis = lis[sdx] - # Get gaia catalog stars. Note that this produces a masked column table - search_rad = 10.0 # arcsec - gaia = analysis.query_gaia(ra, dec, search_radius=search_rad) - my_gaia = analysis.prepare_gaia_for_flystar(gaia, ra, dec, targets_dict=targets_dict) + # Save original positions as reference (1st) list + # in a StarList format (with velocities). + lis.write('random_par_ref.fits', overwrite=True) + + ########## + # Propogate to new times and distort. + ########## + # Make 4 new starlists with different epochs and transformations. + '''times = [2018.5, 2019.5, 2020.5, 2021.5] + xy_trans = [[[ 6.5, 0.99, 1e-5], [ 10.1, 1e-5, 0.99]], + [[100.3, 0.98, 1e-5], [ 50.5, 9e-6, 1.001]], + [[ 0.0, 1.00, 0.0], [ 0.0, 0.0, 1.0]], + [[250.0, 0.97, 2e-5], [-250.0, 1e-5, 1.001]]] + mag_trans = [0.1, 0.4, 0.0, -0.3]''' + + times = [2018.5, 2019.0, 2019.5, 2020.0, 2020.5, 2021.0, 2021.5, 2022.0] + xy_trans = [[[ 6.5, 0.99, 1e-5], [ 10.1, 1e-5, 0.99]], + [[100.3, 0.98, 1e-5], [ 50.5, 9e-6, 1.001]], + [[ 0.0, 1.00, 0.0], [ 0.0, 0.0, 1.0]], + [[250.0, 0.97, 2e-5], [-250.0, 1e-5, 1.001]], + [[ 50.0, 1.00, 0.0], [ -31.0, 0.0, 1.000]], + [[ 78.0, 1.00, 0.0 ], [ 45.0, 0.0, 1.00]], + [[-13.0, 1.00, 0.0], [ 150, 0.0, 1.00]], + [[ 94.0, 1.00, 0.0], [-182.0, 0.0, 1.00]]] + mag_trans = [0.1, 0.4, 0.0, -0.3, 0.0, 0.0, 0.0, 0.0] - assert isinstance(my_gaia, Table) + # Convert into pixels (undistorted) with the following info. + scale = 0.01 # arcsec / pix + shift = [1.0, 1.0] # pix + + for ss in range(len(times)): + dt = times[ss] - lis['t0'] + + par_mod = motion_model.Parallax(pa=0,ra=18.0, dec=-30.0) + par_mod_dat = par_mod.get_batch_pos_at_time(dt+lis['t0'], x0=lis['x0'],vx=lis['vx']/1e3, pi=lis['pi'], + y0=lis['y0'], vy=lis['vy']/1e3, t0=lis['t0']) + x,y = par_mod_dat[0], par_mod_dat[1] + t = np.ones(N_stars) * times[ss] - # Let's make sure the entire align runs, just to be safe + # Convert into pixels + xp = (x / -scale) + shift[0] # -1 from switching to increasing to West (right) + yp = (y / scale) + shift[1] + xpe = lis['x0_err'] / scale + ype = lis['y0_err'] / scale - # Get starlists to align to gaia - epochs = ['15jun07','16jul14', '17may21'] + # Distort the positions + trans = transforms.PolyTransform(1, xy_trans[ss][0], xy_trans[ss][1], mag_offset=mag_trans[ss]) + xd, yd = trans.evaluate(xp, yp) + md = trans.evaluate_mag(lis['m0']) - list_of_starlists = [] + # Perturb with small errors (0.1 pix) + xd += np.random.randn(N_stars) * 0.1 + yd += np.random.randn(N_stars) * 0.1 + md += np.random.randn(N_stars) * 0.02 + xde = xpe + yde = ype + mde = lis['m0_err'] - for ee in range(len(epochs)): - lis_file = 'mag' + epochs[ee] + '_ob150029_kp_rms_named.lis' - lis = starlists.StarList.from_lis_file(lis_file) - list_of_starlists.append(lis) + # Save the new list as a starlist. + new_lis = starlists.StarList([lis['name'], md, mde, xd, xde, yd, yde, t], + names=('name', 'm', 'me', 'x', 'xe', 'y', 'ye', 't')) - # Run the align - msc = align.MosaicToRef(my_gaia, list_of_starlists, iters=2, - dr_tol=[0.2, 0.1], dm_tol=[1, 1], - trans_class=transforms.PolyTransform, - trans_args=[{'order': 1}, {'order': 1}], - motion_models=['Linear'], - use_ref_new=False, - update_ref_orig=False, - mag_trans=True, - init_guess_mode='name', verbose=True) + new_lis.write('random_par_{0:d}.fits'.format(ss), overwrite=True) - msc.fit() - return + return (xy_trans, mag_trans) \ No newline at end of file diff --git a/flystar/tests/test_startable.py b/flystar/tests/test_startable.py index 5580daf..4475970 100644 --- a/flystar/tests/test_startable.py +++ b/flystar/tests/test_startable.py @@ -401,7 +401,7 @@ def test_fit_motion_models(): return -def test_fit_velocities_2epoch(): +def test_fit_motion_model_2epoch(): ########## # Test: only 2 epoch2 ########## From a685011dd0ba2205bc8cf3e3c185532f95a628e5 Mon Sep 17 00:00:00 2001 From: Wei Lingfeng Date: Sun, 21 Dec 2025 13:32:53 +0800 Subject: [PATCH 143/254] Merge with upstream mm_rework branch --- flystar/analysis.py | 7 +- flystar/motion_model.py | 9 +- flystar/parallax.py | 186 +----------------------------------- flystar/tests/test_align.py | 8 +- 4 files changed, 16 insertions(+), 194 deletions(-) diff --git a/flystar/analysis.py b/flystar/analysis.py index ceca739..55094e5 100644 --- a/flystar/analysis.py +++ b/flystar/analysis.py @@ -433,7 +433,8 @@ def startable_subset(tab, idx, mag_trans=True, mag_trans_orig=False): combined astrometry + uncombined photometry table. """ # Multiples: ['x', 'y', 'm', 'name_in_list', 'xe', 'ye', 'me', 't', - # 'x_orig', 'y_orig', 'm_orig', 'xe_orig', 'ye_orig', 'me_orig', 'used_in_trans'] + # 'x_orig', 'y_orig', 'm_orig', 'xe_orig', 'ye_orig', 'me_orig', 'used_in_trans', + # 'xe_boot','ye_boot','me_boot'] # Single: ['name', 'm0', 'm0_err', 'use_in_trans', 'ref_orig', 'n_detect', # 'x0', 'vx', 'y0', 'vy', 'x0_err', 'vx_err', 'y0_err', 'vy_err', 't0'] # Don't include n_vfit @@ -441,8 +442,8 @@ def startable_subset(tab, idx, mag_trans=True, mag_trans_orig=False): new_tab = copy.deepcopy(tab) #new_tab.remove_column('n_fit') new_tab.remove_column('n_detect') - for col in ['x','y','m','xe','ye','me','t','x_orig','y_orig','m_orig', - 'xe_orig','ye_orig','me_orig','used_in_trans']: + for col in ['x','y','m','name_in_list','xe','ye','me','t','x_orig','y_orig','m_orig', + 'xe_orig','ye_orig','me_orig','used_in_trans','xe_boot','ye_boot','me_boot']: new_tab[col] = tab[col][:,idx] new_tab.combine_lists('m', weights_col='me', sigma=3, ismag=True) diff --git a/flystar/motion_model.py b/flystar/motion_model.py index 1ba2dcf..9a79c89 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -24,6 +24,12 @@ class MotionModel(ABC): name = "MotionModel" def __init__(self, *args, **kwargs): + """ + Make a motion model object. This object defines the fit and fixed parameters, + and contains functions to fit the model to data and infer positions at given times. + Each instance corresponds to a given motion model, not an individual star, + and thus the fit values are only input/returned in functions, not stored in the object. + """ return def model_fit(self, dt): @@ -802,8 +808,7 @@ class Parallax(MotionModel): """ Motion model for linear proper motion + parallax - Requires RA, Dec, and PA parameters (degrees) for parallax calculation. - RA, Dec in J2000 + Requires RA and Dec J2000 (degrees) for parallax calculation. Optional PA is counterclockwise offset of the image y-axis from North. Optional obs parameter describes observer location, default is 'earth'. """ diff --git a/flystar/parallax.py b/flystar/parallax.py index 1a6dcf1..a4f0f8c 100755 --- a/flystar/parallax.py +++ b/flystar/parallax.py @@ -84,39 +84,6 @@ def parallax_in_direction(ra, dec, mjd, obsLocation='earth', pa=0.): return pvec -def dparallax_dt_in_direction(ra, dec, mjd, obsLocation='earth'): - """ - R.A. in degrees. (J2000) - Dec. in degrees. (J2000) - MJD - - Equations following MulensModel. - Time derivative --> units are yr^-1 - - """ - # print('parallax_in_direction: len(t) = ', len(mjd)) - # Munge inputs into astropy format. - times = Time(mjd, format='mjd', scale='tdb') - coord = SkyCoord(ra, dec, unit=(units.deg, units.deg)) - - direction = coord.cartesian.xyz.value - north = np.array([0., 0., 1.]) - _east_projected = np.cross(north, direction) / np.linalg.norm(np.cross(north, direction)) - _north_projected = np.cross(direction, _east_projected) / np.linalg.norm(np.cross(direction, _east_projected)) - - obs_posvel = get_observer_barycentric(obsLocation, times, velocity=True)[1] - sun_posvel = get_body_barycentric_posvel('Sun', times)[1] - sun_obs_vel = sun_posvel - obs_posvel - vel = sun_obs_vel.xyz.T.to(units.au / units.year) - - e = np.dot(vel, _east_projected) - n = np.dot(vel, _north_projected) - - dpvec_dt = np.array([e.value, n.value]).T - - return dpvec_dt - - def get_observer_barycentric(body, times, min_ephem_step=1, velocity=False): """ Get the barycentric position of a satellite or other Solar System body @@ -197,155 +164,4 @@ def get_observer_barycentric(body, times, min_ephem_step=1, velocity=False): if velocity: return (obs_pos, obs_vel) else: - return obs_pos - - -def sun_position(mjd, radians=False): - """ - - NAME: - SUNPOS - - PURPOSE: - To compute the RA and Dec of the Sun at a given date. - - INPUTS: - mjd - The modified Julian date of the day (and time), scalar or vector - - OUTPUTS: - ra: - | The right ascension of the sun at that date in DEGREES - | double precision, same number of elements as jd - dec: - The declination of the sun at that date in DEGREES - elong: - Ecliptic longitude of the sun at that date in DEGREES. - obliquity: - the obliquity of the ecliptic, in DEGREES - - OPTIONAL INPUT KEYWORD: - RADIAN [def=False] - If this keyword is set to True, then all output variables - are given in Radians rather than Degrees - - NOTES: - Patrick Wallace (Rutherford Appleton Laboratory, UK) has tested the - accuracy of a C adaptation of the sunpos.pro code and found the - following results. From 1900-2100 SUNPOS gave 7.3 arcsec maximum - error, 2.6 arcsec RMS. Over the shorter interval 1950-2050 the figures - were 6.4 arcsec max, 2.2 arcsec RMS. - - The returned RA and Dec are in the given date's equinox. - - Procedure was extensively revised in May 1996, and the new calling - sequence is incompatible with the old one. - METHOD: - Uses a truncated version of Newcomb's Sun. Adapted from the IDL - routine SUN_POS by CD Pike, which was adapted from a FORTRAN routine - by B. Emerson (RGO). - EXAMPLE: - (1) Find the apparent RA and Dec of the Sun on May 1, 1982 - - | IDL> jdcnv, 1982, 5, 1,0 ,jd ;Find Julian date jd = 2445090.5 - | IDL> sunpos, jd, ra, dec - | IDL> print,adstring(ra,dec,2) - | 02 31 32.61 +14 54 34.9 - - The Astronomical Almanac gives 02 31 32.58 +14 54 34.9 so the error - in SUNPOS for this case is < 0.5". - - (2) Find the apparent RA and Dec of the Sun for every day in 1997 - - | IDL> jdcnv, 1997,1,1,0, jd ;Julian date on Jan 1, 1997 - | IDL> sunpos, jd+ dindgen(365), ra, dec ;RA and Dec for each day - - MODIFICATION HISTORY: - - * Written by Michael R. Greason, STX, 28 October 1988. - * Accept vector arguments, W. Landsman - April,1989 - * Eliminated negative right ascensions - MRG, Hughes STX, 6 May 1992. - * Rewritten using the 1993 Almanac. Keywords added. MRG, HSTX, 10 February 1994. - * Major rewrite, improved accuracy, always return values in degrees - W. Landsman May, 1996 - * Added /RADIAN keyword; W. Landsman; August, 1997 - * Converted to IDL V5.0; W. Landsman; September 1997 - * Converted to python; J. R. Lu; August 2016 - """ - # form time in Julian centuries from 1900.0 - t_obj = Time(mjd, format='mjd') - t = (t_obj.jd - 2415020.0) / 36525.0 - - # form sun's mean longitude - l = (279.696678 + ((36000.768925 * t) % 360.0)) * 3600.0 - - # allow for ellipticity of the orbit (equation of centre) - # using the Earth's mean anomaly ME - me = 358.475844 + ((35999.049750 * t) % 360.0) - ellcor = (6910.1 - 17.2 * t) * np.sin(np.radians(me)) + 72.3 * np.sin( - np.radians(2.0 * me)) - l = l + ellcor - - # allow for the Venus perturbations using the mean anomaly of Venus MV - mv = 212.603219 + ((58517.803875 * t) % 360.0) - vencorr = 4.8 * np.cos(np.radians(299.1017 + mv - me)) + \ - 5.5 * np.cos(np.radians(148.3133 + 2.0 * mv - 2.0 * me)) + \ - 2.5 * np.cos(np.radians(315.9433 + 2.0 * mv - 3.0 * me)) + \ - 1.6 * np.cos(np.radians(345.2533 + 3.0 * mv - 4.0 * me)) + \ - 1.0 * np.cos(np.radians(318.1500 + 3.0 * mv - 5.0 * me)) - l += vencorr - - # Allow for the Mars perturbations using the mean anomaly of Mars MM - mm = 319.529425 + ((19139.858500 * t) % 360.0) - marscorr = 2.0 * np.cos(np.radians(343.8883 - 2.0 * mm + 2.0 * me)) + \ - 1.8 * np.cos(np.radians(200.4017 - 2.0 * mm + me)) - l += marscorr - - # Allow for the Jupiter perturbations using the mean anomaly of Jupiter MJ - mj = 225.328328 + ((3034.6920239 * t) % 360.0) - jupcorr = 7.2 * np.cos(np.radians(179.5317 - mj + me)) + \ - 2.6 * np.cos(np.radians(263.2167 - mj)) + \ - 2.7 * np.cos(np.radians(87.1450 - 2.0 * mj + 2.0 * me)) + \ - 1.6 * np.cos(np.radians(109.4933 - 2.0 * mj + me)) - l += jupcorr - - # Allow for the Moons perturbations using the mean elongation of - # the Moon from the Sun D - d = 350.7376814 + ((445267.11422 * t) % 360.0) - mooncorr = 6.5 * np.sin(np.radians(d)) - l += mooncorr - - # Allow for long period terms - longterm = + 6.4 * np.sin(np.radians(231.19 + 20.20 * t)) - l += longterm - l = (l + 2592000.0) % 1296000.0 - longmed = l / 3600.0 - - # Allow for Aberration - l -= 20.5 - - # Allow for Nutation using the longitude of the Moons mean node OMEGA - omega = 259.183275 - ((1934.142008 * t) % 360.0) - l -= 17.2 * np.sin(np.radians(omega)) - - # Form the True Obliquity - oblt = 23.452294 - 0.0130125 * t + ( - 9.2 * np.cos(np.radians(omega))) / 3600.0 - - # Form Right Ascension and Declination - l = l / 3600.0 - l_rad = np.radians(l) - oblt_rad = np.radians(oblt) - ra = np.arctan2(np.sin(l_rad) * np.cos(oblt_rad), np.cos(l_rad)) - - if (len(ra) > 1): - neg = np.where(ra < 0.0)[0] - ra[neg] = ra[neg] + 2.0 * math.pi - - dec = np.arcsin(np.sin(l_rad) * np.sin(oblt_rad)) - - if radians: - oblt = oblt_rad - longmed = np.radians(longmed) - else: - ra = np.degrees(ra) - dec = np.degrees(dec) - - return ra, dec, longmed, oblt + return obs_pos \ No newline at end of file diff --git a/flystar/tests/test_align.py b/flystar/tests/test_align.py index 080ded3..ad082cb 100644 --- a/flystar/tests/test_align.py +++ b/flystar/tests/test_align.py @@ -319,8 +319,8 @@ def test_MosaicToRef_p0_vel(): # The velocities should be almost the same (but not as close as before) # as the input velocities since update_ref == True. assert (msc.ref_table['name']==ref_list['name']).all() - np.testing.assert_allclose(msc.ref_table['vx'], ref_list['vx'], atol=1e-2) - np.testing.assert_allclose(msc.ref_table['vy'], ref_list['vy'], atol=1e-2) + np.testing.assert_allclose(msc.ref_table['vx'], ref_list['vx'], rtol=1e-1, atol=3e-4) + np.testing.assert_allclose(msc.ref_table['vy'], ref_list['vy'], rtol=1e-1, atol=3e-4) # Also double check that they aren't exactly the same for the reference stars. #assert np.any(np.not_equal(msc.ref_table['vx'], ref_list['vx'])) @@ -385,8 +385,8 @@ def test_MosaicToRef_vel(): # The velocities should be almost the same (but not as close as before) # as the input velocities since update_ref == True. assert (msc.ref_table['name']==ref_list['name']).all() - np.testing.assert_allclose(msc.ref_table['vx'], ref_list['vx'], atol=1e-2) - np.testing.assert_allclose(msc.ref_table['vy'], ref_list['vy'], atol=1e-2) + np.testing.assert_allclose(msc.ref_table['vx'], ref_list['vx'], rtol=1e-1, atol=3e-4) + np.testing.assert_allclose(msc.ref_table['vy'], ref_list['vy'], rtol=1e-1, atol=3e-4) # Also double check that they aren't exactly the same for the reference stars. #assert np.any(np.not_equal(msc.ref_table['vx'], ref_list['vx'])) From f75a55febe8bca3a61bb792571d821069a716cd2 Mon Sep 17 00:00:00 2001 From: Wei Lingfeng Date: Mon, 22 Dec 2025 23:48:07 +0800 Subject: [PATCH 144/254] Added Motion Model Example Notebook; Revert Acceleration Velocity Names --- .../examples/motion_model_example.ipynb | 1363 +++++++++++++++++ flystar/motion_model.py | 94 +- flystar/startables.py | 39 +- flystar/tests/test_align.py | 10 +- 4 files changed, 1458 insertions(+), 48 deletions(-) create mode 100644 docs/flystar/examples/motion_model_example.ipynb diff --git a/docs/flystar/examples/motion_model_example.ipynb b/docs/flystar/examples/motion_model_example.ipynb new file mode 100644 index 0000000..413b616 --- /dev/null +++ b/docs/flystar/examples/motion_model_example.ipynb @@ -0,0 +1,1363 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "333cd262", + "metadata": {}, + "source": [ + "# Motion Model Examples" + ] + }, + { + "cell_type": "markdown", + "id": "9251851e", + "metadata": {}, + "source": [ + "# Table of Contents" + ] + }, + { + "cell_type": "markdown", + "id": "1e4364ed", + "metadata": {}, + "source": [ + "# Table of Contents\n", + "- [1. Motion Model](#1-motion-model)\n", + " - [1.1. Example: Linear Model Fit](#11-example-linear-model-fit)\n", + " - [1.2. Example: Acceleration Model Fit](#12-example-acceleration-model-fit)\n", + " - [1.3. Example: Parallax Model Fit](#13-example-parallax-model-fit)\n", + "- [2. Fit Motion Model in StarTable](#2-fit-motion-model-in-startable)\n", + " - [2.1. Example: Default Fitting](#21-example-default-fitting)\n", + " - [2.2 Example: Specify Motion Models](#22-example-specify-motion-models)\n", + " - [2.3. Example: Specify the `motion_model_input` Column](#23-example-specify-the-motion_model_input-column)\n", + " - [2.4. Example: Infer Positions](#24-example-infer-positions)\n", + " - [2.5. Speed Test](#25-speed-test)\n" + ] + }, + { + "cell_type": "markdown", + "id": "4bd92a9d", + "metadata": {}, + "source": [ + "# 1. Motion Model" + ] + }, + { + "cell_type": "markdown", + "id": "0d084c38", + "metadata": {}, + "source": [ + "Summary of currently implemented motion models" + ] + }, + { + "cell_type": "markdown", + "id": "faddd6d8", + "metadata": {}, + "source": [ + "| Motion Model | n_params | params | fixed_params | model | Description |\n", + "|--------------|----------|--------------------------------------------|-------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n", + "| Empty | 0 | NA | NA | $x(t) = $ NaN / fill_value
$x_e(t) = $ Inf | |\n", + "| Fixed | 1 | $x_0$
$y_0$ | NA | $x(t) = $ np.average($x$, weights=$x_{wt}$) | $x_{wt} = 1/xe^2$ if weighting='var'
$x_{wt} = 1/\\|xe\\|$ if weighting = 'std' |\n", + "| Linear | 2 | $x_0, v_x$
$y_0, v_y$ | optional: $t_0 =$ np.average($t, 1/\\sqrt{x_e^2 + y_e^2}$) | $x(t) = x_0 + v_x * (t - t_0)$ | |\n", + "| Acceleration | 3 | $x_0, v_{x0}, a_x$
$y_0, v_{y0}, a_y$ | optional: $t_0 =$ np.average($t, 1/\\sqrt{x_e^2 + y_e^2}$) | $x(t) = x_0 + v_{x0} * (t - t_0) + 1/2 * a_x * (t - t_0)^2$ | |\n", + "| Parallax | 3 | $x_0, v_x, pi$
$y_0, v_y$ | required: ra, dec
optional: $t_0 =$ np.average($t, 1/\\sqrt{x_e^2 + y_e^2}$); $pa=0$; obsLocation='earth' | $x(t) = x_0 + v_x * (t - t_0) + pvec * (t - t_0)$ | pvec is the parallax vector calculated based on ra, dec, pa, and obsLocation.
Only supports the same obsLocation for all stars in StarTable.fit_motion_model right now. |" + ] + }, + { + "cell_type": "markdown", + "id": "6fdc98af", + "metadata": {}, + "source": [ + "Examples on using `flystar.MotionModel`:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "51c963a1", + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "markdown", + "id": "473b0674", + "metadata": {}, + "source": [ + "Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "ce4edb88", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from flystar import motion_model\n", + "from flystar.startables import StarTable\n", + "from flystar.motion_model import Empty, Fixed, Linear, Acceleration, Parallax" + ] + }, + { + "cell_type": "markdown", + "id": "8c0e8559", + "metadata": {}, + "source": [ + "Prepare data" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "86b6319d", + "metadata": {}, + "outputs": [], + "source": [ + "t = np.array([0, 1., 2.2, 3.5, 5.]) + 2025.0\n", + "x = np.array([0., 0.5, 2.1, 3.2, 8.0])\n", + "y = np.array([10.2, 8.5, 9.1, 10.5, 13.0])\n", + "xe = np.array([0.2, 0.5, 0.3, 0.4, 0.6])\n", + "ye = np.array([0.3, 0.2, 0.5, 0.2, 0.4])\n", + "t_test = np.linspace(2025.0, 2030.0, 100) # Test times for model evaluation" + ] + }, + { + "cell_type": "markdown", + "id": "b1a87102", + "metadata": {}, + "source": [ + "## 1.1. Example: Linear Model Fit" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "0926c0a8", + "metadata": {}, + "outputs": [], + "source": [ + "mm = Linear()\n", + "params, param_errs = mm.fit(t, x, y, xe, ye)" + ] + }, + { + "cell_type": "markdown", + "id": "1fad1962", + "metadata": {}, + "source": [ + "Evaluate model at time t:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "840693ae", + "metadata": {}, + "outputs": [], + "source": [ + "x_model, y_model = mm.model(t, params)" + ] + }, + { + "cell_type": "markdown", + "id": "42fbd575", + "metadata": {}, + "source": [ + "Or if uncertainties of parameters is provided at the same time, the model will return the model uncertainties as well:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "8fcbdc5d", + "metadata": {}, + "outputs": [], + "source": [ + "x_model, y_model, xe_model, ye_model = mm.model(t, params, param_errs)" + ] + }, + { + "cell_type": "markdown", + "id": "6f9954ef", + "metadata": {}, + "source": [ + "Note that we did not provide the `fixed_params_dict` parameter in the `model` function, so the MotionModel will use the saved self.fixed_params_dict. One can also specify the fixed_params_dict as:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "6752e477", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'t0': np.float64(2027.0454838983064)}" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mm.fixed_params_dict" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "eba675c8", + "metadata": {}, + "outputs": [], + "source": [ + "x_model, y_model, xe_model, ye_model = mm.model(t_test, params, param_errs, mm.fixed_params_dict)" + ] + }, + { + "cell_type": "markdown", + "id": "a2acbe90", + "metadata": {}, + "source": [ + "Define a helper function to visualize result" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "7dba325f", + "metadata": {}, + "outputs": [], + "source": [ + "def visualize_fit(t, x, y, xe, ye, x_model, y_model, xe_model, ye_model, mm_name, t_test=None):\n", + " if t_test is None:\n", + " t_test = t\n", + " x = np.atleast_2d(x)\n", + " y = np.atleast_2d(y)\n", + " xe = np.atleast_2d(xe)\n", + " ye = np.atleast_2d(ye)\n", + " x_model = np.atleast_2d(x_model)\n", + " y_model = np.atleast_2d(y_model)\n", + " xe_model = np.atleast_2d(xe_model)\n", + " ye_model = np.atleast_2d(ye_model)\n", + " \n", + " N_cases = x.shape[0]\n", + " fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))\n", + " for i in range(N_cases):\n", + " l0 = ax1.errorbar(t, x[i], yerr=xe[i], fmt='o', color=f'C{i%10}', label='Data')\n", + " l1, = ax1.plot(t_test, x_model[i], label=f'{mm_name} Fit')\n", + " l2 = ax1.fill_between(t_test, x_model[i] - xe_model[i], x_model[i] + xe_model[i], color=f'C{i%10}', alpha=0.3, label='Model Uncertainty')\n", + "\n", + " r0 = ax2.errorbar(t, y[i], yerr=ye[i], fmt='o', color=f'C{i%10}', label='Data')\n", + " r1, = ax2.plot(t_test, y_model[i], label=f'{mm_name} Fit')\n", + " r2 = ax2.fill_between(t_test, y_model[i] - ye_model[i], y_model[i] + ye_model[i], color=f'C{i%10}', alpha=0.3, label='Model Uncertainty')\n", + " ax1.set_xlabel('Time')\n", + " ax1.set_ylabel('X Position')\n", + " ax1.set_title(f'{mm_name} Motion Model Fit')\n", + " ax1.legend(\n", + " [l0, (l1, l2)], \n", + " ['Data', 'Model Fit'],\n", + " )\n", + " \n", + " ax2.set_xlabel('Time')\n", + " ax2.set_ylabel('Y Position')\n", + " ax2.set_title(f'{mm_name} Motion Model Fit')\n", + " ax2.legend(\n", + " [r0, (r1, r2)], \n", + " ['Data', 'Model Fit'],\n", + " )\n", + " plt.tight_layout()\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "ad03fc67", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "visualize_fit(t, x, y, xe, ye, x_model, y_model, xe_model, ye_model, mm.name, t_test)" + ] + }, + { + "cell_type": "markdown", + "id": "98d3e2c4", + "metadata": {}, + "source": [ + "## 1.2. Example: Acceleration Model Fit" + ] + }, + { + "cell_type": "markdown", + "id": "ede486e5", + "metadata": {}, + "source": [ + "Upon further inspection, acceleration model seems to be a better representation of the data" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "0a0d9d1f", + "metadata": {}, + "outputs": [], + "source": [ + "mm = Acceleration()\n", + "params, param_errs = mm.fit(t, x, y, xe, ye)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "b3d63417", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x_model, y_model, xe_model, ye_model = mm.model(t_test, params, param_errs)\n", + "visualize_fit(t, x, y, xe, ye, x_model, y_model, xe_model, ye_model, mm.name, t_test)" + ] + }, + { + "cell_type": "markdown", + "id": "9d1f63b4", + "metadata": {}, + "source": [ + "Moreover, `MotionModel.model` is fully vectorized, and can infer positions of multiple stars at multiple times, and the resulting inferred positions has shape (N_stars, N_times). See the example below:" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "d1e406c5", + "metadata": {}, + "outputs": [], + "source": [ + "t = np.array([0, 1., 2.2, 3.5, 5.]) + 2025.0\n", + "\n", + "xs = np.array([\n", + " [0., 0.5, 2.1, 3.2, 8.0],\n", + " [10.0, 8.9, 9.2, 7.4, 7.0],\n", + " [2.5, 6.2, 5.2, 3.2, 5.0]\n", + "])\n", + "\n", + "ys = np.array([\n", + " [10.2, 8.5, 9.1, 10.5, 13.0],\n", + " [8.0, 9.9, 8.2, 7.4, 7.0],\n", + " [5.2, 6.2, 4.7, 3.2, 6.0]\n", + "])\n", + "\n", + "xes = np.array([\n", + " [0.2, 0.5, 0.3, 0.4, 0.6],\n", + " [0.5, 0.2, 0.7, 0.3, 0.2],\n", + " [0.5, 0.7, 0.6, 0.4, 0.3]\n", + "])\n", + "\n", + "yes = np.array([\n", + " [0.3, 0.2, 0.5, 0.2, 0.4],\n", + " [0.2, 0.5, 0.6, 0.4, 0.2],\n", + " [0.4, 0.2, 0.3, 0.4, 0.5]\n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "4adebbe8", + "metadata": {}, + "outputs": [], + "source": [ + "params = []\n", + "param_errs = []\n", + "for xi, yi, xei, yei in zip(xs, ys, xes, yes):\n", + " p, pe = mm.fit(t, xi, yi, xei, yei)\n", + " params.append(p)\n", + " param_errs.append(pe)" + ] + }, + { + "cell_type": "markdown", + "id": "4e0424df", + "metadata": {}, + "source": [ + "Once we have the params and param errors, we can infer the model positions at any given time." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "95745baa", + "metadata": {}, + "outputs": [], + "source": [ + "x_model, y_model, xe_model, ye_model = mm.model(t_test, params, param_errs)" + ] + }, + { + "cell_type": "markdown", + "id": "06fdca50", + "metadata": {}, + "source": [ + "The inferred positions should have shape (N_stars, N_times):" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "54206834", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(3, 100)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_model.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "e6a4e42e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "visualize_fit(t, xs, ys, xes, yes, x_model, y_model, xe_model, ye_model, mm.name, t_test)" + ] + }, + { + "cell_type": "markdown", + "id": "f7ae3e7f", + "metadata": {}, + "source": [ + "## 1.3. Example: Parallax Model Fit" + ] + }, + { + "cell_type": "markdown", + "id": "08eceab5", + "metadata": {}, + "source": [ + "Parallax model requires some fixed parameters: `ra`, `dec`, `pa`, `obsLocation`, and `t0`.\n", + "- `ra` and `dec` are required parameters. \n", + "- `pa = 0` by default\n", + "- `obsLocation = 'earth'` by default\n", + "- `t0 = np.average(t, 1./np.hypot(xe, ye))` by default\n", + "\n", + "We need to provide the fixed parameters in the `fixed_params_dict`:" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "018fc13a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/weilingfeng/Software/miniconda3/envs/main/lib/python3.13/site-packages/erfa/core.py:133: ErfaWarning: ERFA function \"dtf2d\" yielded 1 of \"dubious year (Note 6)\"\n", + " warn(f'ERFA function \"{func_name}\" yielded {wmsg}', ErfaWarning)\n", + "/Users/weilingfeng/Software/miniconda3/envs/main/lib/python3.13/site-packages/erfa/core.py:133: ErfaWarning: ERFA function \"dtf2d\" yielded 2 of \"dubious year (Note 6)\"\n", + " warn(f'ERFA function \"{func_name}\" yielded {wmsg}', ErfaWarning)\n", + "/Users/weilingfeng/Software/miniconda3/envs/main/lib/python3.13/site-packages/erfa/core.py:133: ErfaWarning: ERFA function \"utctai\" yielded 2 of \"dubious year (Note 3)\"\n", + " warn(f'ERFA function \"{func_name}\" yielded {wmsg}', ErfaWarning)\n", + "/Users/weilingfeng/Software/miniconda3/envs/main/lib/python3.13/site-packages/erfa/core.py:133: ErfaWarning: ERFA function \"utctai\" yielded 1 of \"dubious year (Note 3)\"\n", + " warn(f'ERFA function \"{func_name}\" yielded {wmsg}', ErfaWarning)\n", + "/Users/weilingfeng/Software/miniconda3/envs/main/lib/python3.13/site-packages/erfa/core.py:133: ErfaWarning: ERFA function \"taiutc\" yielded 1 of \"dubious year (Note 4)\"\n", + " warn(f'ERFA function \"{func_name}\" yielded {wmsg}', ErfaWarning)\n" + ] + } + ], + "source": [ + "mm = Parallax()\n", + "fixed_params_dict = {'ra': 0., 'dec': 10., 'pa': 0., 'obsLocation': 'earth'}\n", + "params, param_errs = mm.fit(t, x, y, xe, ye, fixed_params_dict)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "73dafb1f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/weilingfeng/Software/miniconda3/envs/main/lib/python3.13/site-packages/erfa/core.py:133: ErfaWarning: ERFA function \"dtf2d\" yielded 20 of \"dubious year (Note 6)\"\n", + " warn(f'ERFA function \"{func_name}\" yielded {wmsg}', ErfaWarning)\n", + "/Users/weilingfeng/Software/miniconda3/envs/main/lib/python3.13/site-packages/erfa/core.py:133: ErfaWarning: ERFA function \"dtf2d\" yielded 40 of \"dubious year (Note 6)\"\n", + " warn(f'ERFA function \"{func_name}\" yielded {wmsg}', ErfaWarning)\n", + "/Users/weilingfeng/Software/miniconda3/envs/main/lib/python3.13/site-packages/erfa/core.py:133: ErfaWarning: ERFA function \"utctai\" yielded 40 of \"dubious year (Note 3)\"\n", + " warn(f'ERFA function \"{func_name}\" yielded {wmsg}', ErfaWarning)\n", + "/Users/weilingfeng/Software/miniconda3/envs/main/lib/python3.13/site-packages/erfa/core.py:133: ErfaWarning: ERFA function \"utctai\" yielded 20 of \"dubious year (Note 3)\"\n", + " warn(f'ERFA function \"{func_name}\" yielded {wmsg}', ErfaWarning)\n", + "/Users/weilingfeng/Software/miniconda3/envs/main/lib/python3.13/site-packages/erfa/core.py:133: ErfaWarning: ERFA function \"taiutc\" yielded 20 of \"dubious year (Note 4)\"\n", + " warn(f'ERFA function \"{func_name}\" yielded {wmsg}', ErfaWarning)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x_model, y_model, xe_model, ye_model = mm.model(t_test, params, param_errs)\n", + "visualize_fit(t, x, y, xe, ye, x_model, y_model, xe_model, ye_model, mm.name, t_test)" + ] + }, + { + "cell_type": "markdown", + "id": "5be8fb7e", + "metadata": {}, + "source": [ + "# 2. Fit Motion Model in StarTable" + ] + }, + { + "cell_type": "markdown", + "id": "3bd8dec7", + "metadata": {}, + "source": [ + "Examples on `flystar.StarTable.fit_motion_model`. Prepare the data with invalid values:" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "aa698e86", + "metadata": {}, + "outputs": [], + "source": [ + "t = np.array([0, 1., 2.2, 3.5, 5.]) + 2025.0\n", + "\n", + "x = np.array([\n", + " [0., 0.5, 2.1, 3.2, 8.0], # Increasing 5 Epochs\n", + " [10.0, 8.9, 9.2, 7.4, 7.0], # Decreasing 5 Epochs\n", + " [2.5, np.nan, 5.2, np.nan, 5.0], # 3 Epochs\n", + " [np.nan, 6.2, np.nan, np.nan, 9.2], # 2 Epochs\n", + " [np.nan, 2.0, np.nan, np.nan, np.nan], # 1 Epoch\n", + " [np.nan, np.nan, np.nan, np.nan, np.nan] # All NaNs\n", + "])\n", + "\n", + "y = np.array([\n", + " [10.2, 8.5, 9.1, 10.5, 13.0], # Increasing 5 Epochs\n", + " [8.0, 9.9, 8.2, 7.4, 7.0], # Decreasing 5 Epochs\n", + " [5.2, np.nan, 4.7, np.nan, 6.0], # 3 Epochs\n", + " [np.nan, 1.2, np.nan, np.nan, 3.2], # 2 Epochs\n", + " [np.nan, 2.0, np.nan, np.nan, np.nan], # 1 Epoch\n", + " [np.nan, np.nan, np.nan, np.nan, np.nan] # All NaNs\n", + "])\n", + "\n", + "xe = np.array([\n", + " [0.2, 0.5, 0.3, 0.4, 0.6],\n", + " [0.5, 0.2, 0.7, 0.3, 0.2],\n", + " [0.5, np.nan, 0.6, np.nan, 0.3],\n", + " [np.nan, 0.6, np.nan, np.nan, 0.3],\n", + " [np.nan, 0.4, np.nan, np.nan, np.nan],\n", + " [np.nan, np.nan, np.nan, np.nan, np.nan]\n", + "])\n", + "\n", + "ye = np.array([\n", + " [0.3, 0.2, 0.5, 0.2, 0.4],\n", + " [0.2, 0.5, 0.6, 0.4, 0.2],\n", + " [0.7, np.nan, 0.5, np.nan, 0.2],\n", + " [np.nan, 0.4, np.nan, np.nan, 0.5],\n", + " [np.nan, 0.5, np.nan, np.nan, np.nan],\n", + " [np.nan, np.nan, np.nan, np.nan, np.nan]\n", + "])\n", + "\n", + "x = np.ma.masked_invalid(x)\n", + "y = np.ma.masked_invalid(y)\n", + "xe = np.ma.masked_invalid(xe)\n", + "ye = np.ma.masked_invalid(ye)\n", + "mask = np.ma.getmaskarray(x) | np.ma.getmaskarray(y) | np.ma.getmaskarray(xe) | np.ma.getmaskarray(ye)\n", + "\n", + "tab = StarTable({\n", + " 'x': x,\n", + " 'y': y,\n", + " 'xe': xe,\n", + " 'ye': ye\n", + "})\n", + "tab.meta['list_times'] = t" + ] + }, + { + "cell_type": "markdown", + "id": "9201897f", + "metadata": {}, + "source": [ + "There are a 2 ways to specify the desired motion models:\n", + "1. Let MotionModel automatically determine which motion model to use among the given `motion_models` list based on the number of valid observations. MotionModel will choose the motion model that has enough observations, i.e. $n_\\text{fit} \\geq n_\\text{params}$. \n", + "2. Specify a motion model for each star in the `motion_model_input` column. In case there is not enough observations, MotionModel will \"downgrade\" to a model with less parameters until $n_\\text{fit} \\geq n_\\text{params}$ among all the unique motion models specified in the column.\n", + "\n", + "Note that when `absolute_sigma=False` and `n_fit == n_params`, we don't have enough degree of freedom to rescale the uncertainties, so the uncertainties will be set to infinity -- the same behavior as `scipy.optimize.curve_fit`.
By default `motion_models = [Empty, Fixed, Linear]`. `Empty` and `Fixed` will always be added in the list to handle 0 and 1 point cases. See examples below for details. Let's start with the most basic usage." + ] + }, + { + "cell_type": "markdown", + "id": "e58f429d", + "metadata": {}, + "source": [ + "## 2.1. Example: Default Fitting" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "02642d3b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Fitting motion model Empty: 0%| | 0/1 [00:00StarTable length=6\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
n_fitn_requiredmotion_model_used
int64int64str20
52Linear
52Linear
32Linear
22Linear
12Fixed
02Empty
" + ], + "text/plain": [ + "\n", + "n_fit n_required motion_model_used\n", + "int64 int64 str20 \n", + "----- ---------- -----------------\n", + " 5 2 Linear\n", + " 5 2 Linear\n", + " 3 2 Linear\n", + " 2 2 Linear\n", + " 1 2 Fixed\n", + " 0 2 Empty" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tab['n_required'] = 2\n", + "tab[['n_fit', 'n_required', 'motion_model_used']]" + ] + }, + { + "cell_type": "markdown", + "id": "20470c6e", + "metadata": {}, + "source": [ + "Next, let's try `absolute_sigma=False`. As mentioned above, we don't have enough degree of freedom to rescale the uncertainties for the forth star. In this case, the parameter uncertainties will be set to infinity, which is the same behavior as `scipy.optimize.curve_fit`. The same `OptmizieWarning` as in `scipy` will be raised." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "26b11593", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Fitting motion model Empty: 0%| | 0/1 [00:00\n", + "0.2398025689409276\n", + "0.07197698078673948\n", + "0.26723109004421475\n", + "inf\n", + "inf\n", + "inf\n", + "" + ], + "text/plain": [ + "\n", + " 0.2398025689409276\n", + "0.07197698078673948\n", + "0.26723109004421475\n", + " inf\n", + " inf\n", + " inf" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tab['vx_err']" + ] + }, + { + "cell_type": "markdown", + "id": "241ab6d6", + "metadata": {}, + "source": [ + "## 2.2. Example: Specify Motion Models" + ] + }, + { + "cell_type": "markdown", + "id": "220922c5", + "metadata": {}, + "source": [ + "Alternatively, one can specify a list of motion models to use, and the function will also automatically determine which model to use for each star depending on the valid observed epochs. In the following example, we specify `Acceleration` model, but **the function will always implicitly add `Empty` and `Fixed`** to handle the 0 or 1 epoch stars." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "a596c8e8", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Fitting motion model Acceleration: 0%| | 0/3 [00:00StarTable length=6\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
n_fitmotion_model_used
int64str20
5Acceleration
5Acceleration
3Acceleration
2Fixed
1Fixed
0Empty
" + ], + "text/plain": [ + "\n", + "n_fit motion_model_used\n", + "int64 str20 \n", + "----- -----------------\n", + " 5 Acceleration\n", + " 5 Acceleration\n", + " 3 Acceleration\n", + " 2 Fixed\n", + " 1 Fixed\n", + " 0 Empty" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tab[['n_fit', 'motion_model_used']]" + ] + }, + { + "cell_type": "markdown", + "id": "188290a9", + "metadata": {}, + "source": [ + "## 2.3. Example: Specify the `motion_model_input` Column" + ] + }, + { + "cell_type": "markdown", + "id": "99624463", + "metadata": {}, + "source": [ + "One can also specify a motion model for each star as a column in the star table. However, the function will \"downgrade\" the model to one with fewer parameters until $n_\\text{fit} \\geq n_\\text{params}$:" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "04db5f9e", + "metadata": {}, + "outputs": [], + "source": [ + "ra = np.zeros(len(x))\n", + "dec = np.zeros(len(x))\n", + "pa = np.zeros(len(x))\n", + "\n", + "motion_model_input = [\n", + " 'Acceleration', # Will use Acceleration\n", + " 'Parallax', # Will use Parallax\n", + " 'Linear', # Will use Linear\n", + " 'Acceleration', # Will use Linear, as n_fit = 2 < 3\n", + " 'Linear', # Will use Fixed, as n_fit = 1 < 2\n", + " 'Fixed' # Will use Empty, as n_fit = 0 < 1\n", + "]\n", + "tab = StarTable({\n", + " 'x': x,\n", + " 'y': y,\n", + " 'xe': xe,\n", + " 'ye': ye,\n", + " 'ra': ra,\n", + " 'dec': dec,\n", + " 'pa': pa,\n", + " 'motion_model_input': motion_model_input\n", + "})\n", + "tab.meta['list_times'] = t" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "2b61fbcf", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Fitting motion model Acceleration: 0%| | 0/1 [00:00StarTable length=6\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
n_fitn_requiredmotion_model_inputmotion_model_used
int64int64str12str12
53AccelerationAcceleration
53ParallaxParallax
32LinearLinear
23AccelerationLinear
12LinearFixed
01FixedEmpty
" + ], + "text/plain": [ + "\n", + "n_fit n_required motion_model_input motion_model_used\n", + "int64 int64 str12 str12 \n", + "----- ---------- ------------------ -----------------\n", + " 5 3 Acceleration Acceleration\n", + " 5 3 Parallax Parallax\n", + " 3 2 Linear Linear\n", + " 2 3 Acceleration Linear\n", + " 1 2 Linear Fixed\n", + " 0 1 Fixed Empty" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_mm_map = motion_model.motion_model_map()\n", + "tab['n_required'] = np.array([all_mm_map[mm].n_params for mm in tab['motion_model_input']], dtype=int)\n", + "tab[['n_fit', 'n_required', 'motion_model_input', 'motion_model_used']]" + ] + }, + { + "cell_type": "markdown", + "id": "d4f96fcb", + "metadata": {}, + "source": [ + "## 2.4. Example: Infer Positions" + ] + }, + { + "cell_type": "markdown", + "id": "c660ec98", + "metadata": {}, + "source": [ + "Continuing from the previous example: Once we fit the motion models and the parameters are added into the table, we can infer the positions at arbitrary times with `StarTable.infer_positions`" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "095be28f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/weilingfeng/Software/miniconda3/envs/main/lib/python3.13/site-packages/erfa/core.py:133: ErfaWarning: ERFA function \"dtf2d\" yielded 20 of \"dubious year (Note 6)\"\n", + " warn(f'ERFA function \"{func_name}\" yielded {wmsg}', ErfaWarning)\n", + "/Users/weilingfeng/Software/miniconda3/envs/main/lib/python3.13/site-packages/erfa/core.py:133: ErfaWarning: ERFA function \"dtf2d\" yielded 40 of \"dubious year (Note 6)\"\n", + " warn(f'ERFA function \"{func_name}\" yielded {wmsg}', ErfaWarning)\n", + "/Users/weilingfeng/Software/miniconda3/envs/main/lib/python3.13/site-packages/erfa/core.py:133: ErfaWarning: ERFA function \"utctai\" yielded 40 of \"dubious year (Note 3)\"\n", + " warn(f'ERFA function \"{func_name}\" yielded {wmsg}', ErfaWarning)\n", + "/Users/weilingfeng/Software/miniconda3/envs/main/lib/python3.13/site-packages/erfa/core.py:133: ErfaWarning: ERFA function \"utctai\" yielded 20 of \"dubious year (Note 3)\"\n", + " warn(f'ERFA function \"{func_name}\" yielded {wmsg}', ErfaWarning)\n", + "/Users/weilingfeng/Software/miniconda3/envs/main/lib/python3.13/site-packages/erfa/core.py:133: ErfaWarning: ERFA function \"taiutc\" yielded 20 of \"dubious year (Note 4)\"\n", + " warn(f'ERFA function \"{func_name}\" yielded {wmsg}', ErfaWarning)\n" + ] + } + ], + "source": [ + "x_model, y_model, xe_model, ye_model = tab.infer_positions(t_test)" + ] + }, + { + "cell_type": "markdown", + "id": "a4df5458", + "metadata": {}, + "source": [ + "As in `MotionModel.model`, `StarTable.infer_positions` is also vectorized and returns positions and uncertainties in shapes of $(N_\\text{stars}, N_\\text{times})$" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "2f7e8b7a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(6, 100)" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_model.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "7aab0868", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "visualize_fit(t, x, y, xe, ye, x_model, y_model, xe_model, ye_model, mm.name, t_test)" + ] + }, + { + "cell_type": "markdown", + "id": "12bb0136", + "metadata": {}, + "source": [ + "## 2.5. Speed Test" + ] + }, + { + "cell_type": "markdown", + "id": "43fd87c5", + "metadata": {}, + "source": [ + "Speed test for the most commonly used Linear model. As the `use_scipy=False` option for the Linear model uses the [matrix multiplication solution](https://en.wikipedia.org/wiki/Weighted_least_squares#Solution), it is extremely fast at fewer epochs: " + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "de576a47", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting 10 epochs...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Fitting motion model Linear: 100%|██████████| 10000/10000 [00:01<00:00, 6350.75it/s]\n", + "Fitting motion model Linear: 100%|██████████| 10000/10000 [00:00<00:00, 25802.05it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting 31 epochs...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Fitting motion model Linear: 100%|██████████| 10000/10000 [00:01<00:00, 6184.77it/s]\n", + "Fitting motion model Linear: 100%|██████████| 10000/10000 [00:00<00:00, 23908.79it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting 100 epochs...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Fitting motion model Linear: 100%|██████████| 10000/10000 [00:01<00:00, 6347.19it/s]\n", + "Fitting motion model Linear: 100%|██████████| 10000/10000 [00:00<00:00, 14309.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting 316 epochs...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Fitting motion model Linear: 100%|██████████| 10000/10000 [00:01<00:00, 5023.37it/s]\n", + "Fitting motion model Linear: 100%|██████████| 10000/10000 [00:03<00:00, 3288.47it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting 1000 epochs...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Fitting motion model Linear: 100%|██████████| 10000/10000 [00:02<00:00, 4314.91it/s]\n", + "Fitting motion model Linear: 100%|██████████| 10000/10000 [01:19<00:00, 125.47it/s]\n" + ] + } + ], + "source": [ + "import time\n", + "N = 10000\n", + "dims = np.logspace(1, 3, 5, dtype=int)\n", + "rng = np.random.default_rng(42)\n", + "\n", + "scipy_times = []\n", + "analytic_times = []\n", + "\n", + "for dim in dims:\n", + " print(f'Fitting {dim} epochs...')\n", + " t = np.linspace(2025.0, 2030.0, dim)\n", + " x = rng.random((N, dim))\n", + " y = rng.random((N, dim))\n", + " xe = rng.uniform(0, 0.2, size=(N, dim))\n", + " ye = rng.uniform(0, 0.2, size=(N, dim))\n", + " tab = StarTable({\n", + " 'x': x,\n", + " 'y': y,\n", + " 'xe': xe,\n", + " 'ye': ye\n", + " })\n", + " tab.meta['list_times'] = t\n", + " \n", + " start = time.time()\n", + " tab.fit_motion_model(use_scipy=True)\n", + " end = time.time()\n", + " scipy_times.append(end - start)\n", + " \n", + " start = time.time()\n", + " tab.fit_motion_model(use_scipy=False)\n", + " end = time.time()\n", + " analytic_times.append(end - start)\n", + "\n", + "scipy_times = np.array(scipy_times)\n", + "analytic_times = np.array(analytic_times)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "3d2a8457", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "280" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Collect memory garbage data\n", + "import gc\n", + "gc.collect()" + ] + }, + { + "cell_type": "markdown", + "id": "06442faf", + "metadata": {}, + "source": [ + "Let's visualize the performance:" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "03d53769", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "ax.plot(dims, N / scipy_times, marker='o', label='Scipy Curve Fit')\n", + "ax.plot(dims, N / analytic_times, marker='o', color='C3', label='Motion Model Analytic')\n", + "ax.set_xscale('log')\n", + "ax.set_xlabel('Number of Epochs')\n", + "ax.set_ylabel('Stars Fit per Second')\n", + "ax.set_title(f'Motion Model Fitting Performance of {N} Stars')\n", + "ax.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "ea672ab4", + "metadata": {}, + "source": [ + "It can be seen that for epochs < 200, the analytic solution is faster than scipy, and vice versa for > 300 epochs." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "main", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/flystar/motion_model.py b/flystar/motion_model.py index 7a28690..b8cde55 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -35,7 +35,7 @@ def __init__(self, *args, **kwargs): def model_fit(self, dt): return np.full_like(dt, np.nan) - def model(self, t, fit_params, fixed_params=None, fit_param_errs=None): + def model(self, t, fit_params, fit_param_errs=None, fixed_params=None): if fit_param_errs is None: return np.full_like(t, np.nan), np.full_like(t, np.nan) return np.full_like(t, np.nan), np.full_like(t, np.nan), np.full_like(t, np.inf), np.full_like(t, np.inf) @@ -114,6 +114,8 @@ def fit( params, params_err, chi2_x, chi2_y Parameters, uncertainties, and chi squares. The corresponding parameter names are in self.fit_param_names. """ + assert np.ndim(t) == np.ndim(x) == np.ndim(y) == np.ndim(xe) == np.ndim(ye) == 1, "Input arrays must be 1D! Motion model can only fit individual stars" + assert len(t) == len(x) == len(y) == len(xe) == len(ye), "Input arrays must have the same length!" fit_result = self.run_fit( t, x, y, xe, ye, fixed_params_dict=fixed_params_dict, @@ -186,7 +188,7 @@ def calc_chi2(self, t, x, y, xe, ye, fit_params, fixed_params_dict=None, reduced """ Get the chi^2 value for the input motion model parameters and data. """ - x_pred, y_pred = self.model(t, fit_params, fixed_params_dict) + x_pred, y_pred = self.model(t, fit_params, fixed_params_dict=fixed_params_dict) chi2x = np.sum((x - x_pred)**2 / xe**2) chi2y = np.sum((y - y_pred)**2 / ye**2) if reduced: @@ -213,7 +215,7 @@ def __init__(self, **kwargs): def model_fit(self, dt): return np.full_like(dt, np.nan) - def model(self, t, fit_params, fixed_params_dict, fixed_param_errs=None): + def model(self, t, fit_params, fit_param_errs=None, fixed_params_dict=None): """Predicted positions (and uncertainties, if fit_param_errs is provided) at time t of Empty model. Parameters @@ -222,10 +224,10 @@ def model(self, t, fit_params, fixed_params_dict, fixed_param_errs=None): Time array, shape (N_times,) fit_params : array-like Fit parameters, shape (N_params,) or (N_stars, N_params) - fixed_params_dict : dict - Dictionary of fixed parameters, not applicable for Empty model - fixed_param_errs : array-like, optional - Uncertainties for fixed parameters, not applicable for Empty model, by default None + fit_param_errs : array-like, optional + Uncertainties for fit parameters, not applicable for Empty model, by default None + fixed_params_dict : dict, optional + Not applicable for Empty model, by default None Returns ------- @@ -234,7 +236,7 @@ def model(self, t, fit_params, fixed_params_dict, fixed_param_errs=None): """ t = np.atleast_1d(t) - if fixed_param_errs is None: + if fit_param_errs is None: return np.full_like(t, np.nan), np.full_like(t, np.nan) return np.full_like(t, np.nan), np.full_like(t, np.nan), np.full_like(t, np.inf), np.full_like(t, np.inf) @@ -285,6 +287,7 @@ def run_fit( params, param_errors (, chi2_x, chi2_y) Fitted parameters, their uncertainties, and optionally chi-squared values """ + self.fixed_params_dict = fixed_params_dict if verbose: warnings.warn(f"Empty data cannot be fit. Setting parameters to {fill_value} and uncertainties to np.inf.", OptimizeWarning, stacklevel=2) params = np.full(self.n_params, fill_value) @@ -332,7 +335,7 @@ def model_fit(self, dt, x0): x0 = np.asarray(x0) return np.broadcast_to(x0[:, np.newaxis], (x0.shape[0], dt.shape[0])) if x0.ndim > 0 else np.full_like(dt, x0) - def model(self, t, fit_params, fixed_params_dict=None, fit_param_errs=None): + def model(self, t, fit_params, fit_param_errs=None, fixed_params_dict=None): """Predicted positions (and uncertainties, if fit_param_errs is provided) at time t of Fixed model. Parameters @@ -341,16 +344,18 @@ def model(self, t, fit_params, fixed_params_dict=None, fit_param_errs=None): Time array, shape (N_times,) fit_params : array-like x0, y0 in shape (N_params,) or (N_stars, N_params) - fixed_params_dict : dict, optional - Not applicable for Fixed, by default None fit_param_errs : array-like, optional Uncertainties for x0, y0 in shape (N_params,) or (N_stars, N_params), by default None + fixed_params_dict : dict, optional + Not applicable for Fixed, by default None + Returns ------- x, y (, xe, ye) Predicted position (and uncertainties) of Fixed model, shape (N_stars, N_times), or (N_times,) if N_stars=1, or (N_stars,) if N_times=1 """ + self.fixed_params_dict = fixed_params_dict t = np.atleast_1d(t) fit_params = np.atleast_2d(fit_params) # (N_stars, N_params) @@ -484,7 +489,7 @@ def model_fit(self, dt, x0, v): """ return x0 + v * dt - def model(self, t, fit_params, fixed_params_dict, fit_param_errs=None): + def model(self, t, fit_params, fit_param_errs=None, fixed_params_dict=None): """Model positions (and uncertainties, if fit_param_errs is provided) at time t of Linear model. Parameters @@ -493,16 +498,18 @@ def model(self, t, fit_params, fixed_params_dict, fit_param_errs=None): Time(s) at which to evaluate the model fit_params : array-like x0, vx, y0, vy in shape (N_params,) or (N_stars, N_params) - fixed_params_dict : dict - t0, shape (1,) or (N_stars,) fit_param_errs : array-like, optional Uncertainties of fit parameters in shape (N_params,) or (N_stars, N_params), by default None + fixed_params_dict : dict + t0, shape (1,) or (N_stars,) Returns ------- x, y (, xe, ye) Predicted positions (and uncertainties, if fit_param_errs is provided) with shape (N_stars, N_times), or (N_times,) if N_stars=1, or (N_stars,) if N_times=1 """ + if fixed_params_dict is None: + fixed_params_dict = self.fixed_params_dict assert 't0' in fixed_params_dict, "Fixed parameter t0 is required for Linear model." t = np.atleast_1d(t) @@ -540,7 +547,7 @@ def model(self, t, fit_params, fixed_params_dict, fit_param_errs=None): def run_fit( self, t, x, y, xe, ye, - fixed_params_dict, + fixed_params_dict=None, weighting='var', use_scipy=True, absolute_sigma=True, @@ -549,7 +556,13 @@ def run_fit( return_chi2=False, verbose=True ): - t0 = fixed_params_dict.get('t0', np.average(t, weights=1./np.hypot(xe, ye))) + if fixed_params_dict is None: + fixed_params_dict = {} + if 't0' not in fixed_params_dict: + # Default t0 to weighted average time + fixed_params_dict['t0'] = np.average(t, weights=1./np.hypot(xe, ye)) + self.fixed_params_dict = fixed_params_dict + t0 = np.atleast_1d(fixed_params_dict['t0']) t = np.atleast_1d(t) x = np.atleast_1d(x) y = np.atleast_1d(y) @@ -592,7 +605,7 @@ def run_fit( return params, param_errors, chi2_x, chi2_y else: return params, param_errors - + # Linear algebraic solution # Use https://en.wikipedia.org/wiki/Weighted_least_squares#Solution_scheme X_mat_t = np.vander(dt, 2) @@ -685,7 +698,7 @@ def model_fit(self, t, x0, v0, a): """ return x0 + v0*t + 0.5*a*t**2 - def model(self, t, fit_params, fixed_params_dict, fit_param_errs=None): + def model(self, t, fit_params, fit_param_errs=None, fixed_params_dict=None): """Model positions (and uncertainties, if fit_param_errs is provided) at time t of Acceleration model. Parameters @@ -693,17 +706,19 @@ def model(self, t, fit_params, fixed_params_dict, fit_param_errs=None): t : float or array-like Time(s) at which to evaluate the model fit_params : array-like - x0, vx0, ax, y0, vy0, ay in shape (N_params,) or (N_stars, N_params) - fixed_params_dict : dict - t0, shape (1,) or (N_stars,) + x0, vx, ax, y0, vy, ay in shape (N_params,) or (N_stars, N_params) fit_param_errs : array-like, optional Fit parameter uncertainties with shape (N_stars, N_params) or (N_params,), by default None + fixed_params_dict : dict + t0, shape (1,) or (N_stars,) Returns ------- x, y (, xe, ye) Predicted positions (and uncertainties, if fit_param_errs is provided) with shape (N_stars, N_times), or (N_times,) if N_stars=1, or (N_stars,) if N_times=1 """ + if fixed_params_dict is None: + fixed_params_dict = self.fixed_params_dict assert 't0' in fixed_params_dict, "Fixed parameter t0 is required for Acceleration model." t = np.atleast_1d(t) @@ -743,7 +758,7 @@ def model(self, t, fit_params, fixed_params_dict, fit_param_errs=None): def run_fit( self, t, x, y, xe, ye, - fixed_params_dict, + fixed_params_dict=None, weighting='var', use_scipy=True, absolute_sigma=True, @@ -752,7 +767,13 @@ def run_fit( return_chi2=False, verbose=True ): - t0 = fixed_params_dict.get('t0', np.average(t, weights=1./np.hypot(xe, ye))) + if fixed_params_dict is None: + fixed_params_dict = {} + if 't0' not in fixed_params_dict: + # Default t0 to weighted average time + fixed_params_dict['t0'] = np.average(t, weights=1./np.hypot(xe, ye)) + self.fixed_params_dict = fixed_params_dict + t0 = np.atleast_1d(fixed_params_dict['t0']) t = np.atleast_1d(t) x = np.atleast_1d(x) y = np.atleast_1d(y) @@ -895,7 +916,7 @@ def _model_fit(self, dt, x0, vx, y0, vy, pi): x_res, y_res = self.model_fit(dt, x0, vx, y0, vy, pi) return np.hstack([x_res, y_res]) # Shape (N_stars, 2*N_times) - def model(self, t, fit_params, fixed_params_dict, fit_param_errs=None): + def model(self, t, fit_params, fit_param_errs=None, fixed_params_dict=None): """Model positions (and uncertainties, if fit_param_errs is provided) at time t of Parallax model. Parameters @@ -904,21 +925,22 @@ def model(self, t, fit_params, fixed_params_dict, fit_param_errs=None): Times at which to evaluate the model fit_params : array-like x0, vx, y0, vy, pi in shape (N_params,) or (N_stars, N_params) + fit_param_errs : array-like, optional + Uncertainties in fit parameters, by default None fixed_params : dict - t0, shape (N_stars,) or (1,). - ra, shape (N_stars,) or (1,). - dec, shape (N_stars,) or (1,). - pa, optional, shape (N_stars,) or (1,), by default 0. - obsLocation, optional, string, by default 'earth' - fit_param_errs : array-like, optional - Uncertainties in fit parameters, by default None Returns ------- x, y (, xe, ye) Predicted positions (and uncertainties, if fit_param_errs is provided) with shape (N_stars, N_times), or (N_times,) if N_stars=1, or (N_stars,) if N_times=1 """ - + if fixed_params_dict is None: + fixed_params_dict = self.fixed_params_dict assert all([_ in fixed_params_dict for _ in ['t0', 'ra', 'dec']]), "Fixed parameters t0, ra, and dec are required for Parallax model." t = np.atleast_1d(t) @@ -934,7 +956,10 @@ def model(self, t, fit_params, fixed_params_dict, fit_param_errs=None): obsLocation = fixed_params_dict.get('obsLocation', 'earth') # TODO: vectorize parallax.parallax_in_direction to handle multiple obsLocation? - assert type(obsLocation) == str, "obsLocation must be a single string for all stars at this time." + + assert (type(obsLocation) == str) or (np.unique(obsLocation).size == 1), "obsLocation must be a single string for all stars at this time." + if type(obsLocation) != str: + obsLocation = np.unique(obsLocation)[0] dt = t[np.newaxis, :] - t0[:, np.newaxis] # Shape (N_stars, N_times) t_mjd = Time(t, format='decimalyear', scale='utc').mjd # Shape (N_times,) @@ -975,14 +1000,21 @@ def run_fit( if not use_scipy: if verbose: warnings.warn("Parallax model has no non-scipy fitter option. Running with scipy.", UserWarning) - - assert all([k in fixed_params_dict for k in ['t0', 'ra', 'dec']]), "Parallax model requires 't0', 'ra', and 'dec' in fixed_params." + + assert all([k in fixed_params_dict for k in ['ra', 'dec']]), "Parallax model requires 'ra' and 'dec' in fixed_params." t = np.atleast_1d(t) + + if 't0' not in fixed_params_dict: + # Default t0 to weighted average time + fixed_params_dict['t0'] = np.average(t, weights=1./np.hypot(xe, ye)) + if 'obsLocation' not in fixed_params_dict: + fixed_params_dict['obsLocation'] = 'earth' + self.fixed_params_dict = fixed_params_dict t0 = np.atleast_1d(fixed_params_dict['t0']) ra = np.atleast_1d(fixed_params_dict['ra']) dec = np.atleast_1d(fixed_params_dict['dec']) pa = np.atleast_1d(fixed_params_dict.get('pa', 0.0)) - obsLocation = fixed_params_dict.get('obsLocation', 'earth') + obsLocation = fixed_params_dict['obsLocation'] n_fit = len(t) degree_of_freedom = n_fit - self.n_params diff --git a/flystar/startables.py b/flystar/startables.py index 2bb062c..b25f8c5 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -538,7 +538,7 @@ def detections(self): def fit_motion_model( self, - motion_models=[Empty, Fixed, Linear], + motion_models=None, fixed_params_dict=None, weighting='var', use_scipy=False, @@ -556,7 +556,7 @@ def fit_motion_model( Parameters ---------- motion_models : list of MotionModel or str, optional - Motion models to use. + Motion models to use, by default Empty, Fixed and Linear. Empty and Fixed models are always added automatically for stars with n_fit = 0 or 1. The behavior is as follows: 1. If 'motion_model_input' column is NOT in table: @@ -619,8 +619,12 @@ def fit_motion_model( if fixed_params_dict is not None: if not isinstance(fixed_params_dict, dict): raise ValueError("fit_motion_model: fixed_params_dict must be a dictionary!") - + # Convert motion_models to MotionModel objects if they are strings: + if motion_models is None: + # Setting the default to None to avoid mutable default argument issue + # See https://stackoverflow.com/questions/15189245/assigning-class-variable-as-default-value-to-class-method-argument + motion_models = [Empty, Fixed, Linear] all_mm_map = motion_model.motion_model_map() if all(isinstance(mm, str) for mm in motion_models): mm_names = motion_models @@ -653,7 +657,8 @@ def fit_motion_model( if 'motion_model_input' not in self.colnames: # If motion_model_input column is not provided, assert that motion model n_params are unique and sorted # Otherwise the fitter does not know which motion model to use based on n_obs - assert len(mm_n_params) == len(set(mm_n_params)), "fit_motion_model: Provided motion model n_params are not unique! Cannot decide which motion model to use based on n_obs. Please provide unique motion_models or a 'motion_model_input' column." + assert len(mm_n_params) == len(set(mm_n_params)), \ + f"fit_motion_model: Provided motion model n_params are not unique! Motion Models are: {[_.name for _ in motion_models]} Cannot decide which motion model to use based on n_obs. Please provide unique motion_models or a 'motion_model_input' column." ########################### @@ -684,7 +689,6 @@ def fit_motion_model( if fixed_params_dict is None: weights = 1/np.hypot(xe_data, ye_data) if xe_data is not None else None fixed_params_dict = {'t0': np.average(t_data, axis=1, weights=weights)} - elif 't0' not in fixed_params_dict: weights = 1/np.hypot(xe_data, ye_data) if xe_data is not None else None fixed_params_dict['t0'] = np.average(t_data, axis=1, weights=weights) @@ -692,8 +696,7 @@ def fit_motion_model( if np.ndim(fixed_params_dict['t0']) == 0: fixed_params_dict['t0'] = np.full(N_stars, fixed_params_dict['t0']) - t0 = fixed_params_dict['t0'] - + t0 = fixed_params_dict['t0'] # Prepare fixed_params_dict for each star # This avoids checking types and slicing inside the fitting loop @@ -734,24 +737,35 @@ def fit_motion_model( ########################### ####### Determine MM ###### ########################### + n_fit = np.array(self['n_fit']) if 'motion_model_input' in self.colnames: # Determine which motion model to use based on motion_model_input column # If n_fit < required n_params for the input motion model, use the most complicated motion model with n_fit >= n_params required_params = np.array([all_mm_map[mm_name].n_params for mm_name in self['motion_model_input']]) + reassign_mm = n_fit < required_params + mm_digitized = np.digitize( - x=np.minimum(np.array(self['n_fit']), required_params), + x=n_fit[reassign_mm], bins=mm_n_params ) - 1 # Convert to 0-based index + # Assign motion models to stars + self['motion_model_used'] = self['motion_model_input'] + self['motion_model_used'][reassign_mm] = np.array([motion_models[d].name for d in mm_digitized], dtype='U20') + else: mm_digitized = np.digitize( - x=np.array(self['n_fit']), + x=n_fit, bins=mm_n_params ) - 1 # Convert to 0-based index - # Assign motion models to stars - self['motion_model_used'] = np.array([motion_models[d].name for d in mm_digitized], dtype='U20') + # Assign motion models to stars + self['motion_model_used'] = np.array([motion_models[d].name for d in mm_digitized], dtype='U20') + # Add default obsLocation if not provided in fixed_params_dict + mm_used = np.unique(self['motion_model_used'].name) + if 'Parallax' in mm_used and 'obsLocation' not in fixed_params_dict: + fixed_params_dict['obsLocation'] = 'earth' ############################ ####### Prepare Table ###### @@ -786,6 +800,7 @@ def fit_motion_model( for param in mm.fixed_param_names: if param not in fixed_param_names: fixed_param_names.append(param) + # Remove t0 from fixed_param_names as it will be saved during fitting if 't0' in fixed_param_names: fixed_param_names.remove('t0') @@ -943,7 +958,7 @@ def infer_positions(self, times, fill_value=np.nan): # Predict positions x, y, xe, ye = motion_model_instance.model( - times, fit_params, fixed_params, fit_param_errs + times, fit_params, fit_param_errs, fixed_params ) x_pred[unique_index] = x y_pred[unique_index] = y diff --git a/flystar/tests/test_align.py b/flystar/tests/test_align.py index ad082cb..c5ae094 100644 --- a/flystar/tests/test_align.py +++ b/flystar/tests/test_align.py @@ -410,10 +410,10 @@ def test_MosaicToRef_acc(): ref_list = Table.read(ref_file) # Convert velocities to arcsec/yr - ref_list['vx0'] *= 1e-3 - ref_list['vy0'] *= 1e-3 - ref_list['vx0_err'] *= 1e-3 - ref_list['vy0_err'] *= 1e-3 + ref_list['vx'] *= 1e-3 + ref_list['vy'] *= 1e-3 + ref_list['vx_err'] *= 1e-3 + ref_list['vy_err'] *= 1e-3 # Convert accelerations to arcsec/yr**2 ref_list['ax'] *= 1e-3 @@ -423,7 +423,7 @@ def test_MosaicToRef_acc(): # Switch our list to a "increasing to the West" list. ref_list['x0'] *= -1.0 - ref_list['vx0'] *= -1.0 + ref_list['vx'] *= -1.0 ref_list['ax'] *= -1.0 lists = [starlists.StarList.read(lf) for lf in list_files] From 141be48ffefc4fc27640bc487daa12ff505a0502 Mon Sep 17 00:00:00 2001 From: Matt Hosek Date: Thu, 8 Jan 2026 14:27:08 -0800 Subject: [PATCH 145/254] calc_bootstrap_errors ref star selection bugfix, plus removing unnecessary loop --- flystar/align.py | 58 ++++++++++++++++++++++++++---------------------- 1 file changed, 32 insertions(+), 26 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 994a3b1..5d067fe 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -1128,7 +1128,8 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot t0_arr = t0_arr[idx_good] else: idx_good = np.arange(0, len(ref_table), 1) - idx_ref = np.where(ref_table['use_in_trans'] == True) + + #idx_ref = np.where(ref_table['use_in_trans'] == True) # Initialize sums for output x_boot_sum = np.zeros((len(ref_table['x']), n_epochs)) @@ -1174,40 +1175,45 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot me_trans_arr = np.ones((len(ref_table['x']), n_epochs)) * -999 for jj in range(n_epochs): - # Extract bootstrap sample of matched reference stars - good = np.where(~np.isnan(ref_table['x_orig'][idx_ref][:,jj])) + # Extract bootstrap sample of matched reference stars for this epoch + #good = np.where(~np.isnan(ref_table['x_orig'][idx_ref][:,jj])) + good = np.where(ref_table['used_in_trans'][:,jj] == True) samp_idx = np.random.choice(good[0], len(good[0]), replace=True) # Get reference star positions in particular epoch from ref_list. t_epoch = t_arr[jj] ref_orig = self.get_ref_list_from_table(t_epoch) - # Get idx of reference stars in bootstrap sample in the ref_orig. - # Then, use these to build reference starlist for the alignment - idx_tmp = [] - for ff in range(len(samp_idx)): - name_tmp = ref_table['name'][idx_ref][samp_idx[ff]] - foo = np.where(ref_orig['name'] == name_tmp)[0][0] - idx_tmp.append(foo) - - ref_boot = StarList(name=ref_orig['name'][idx_tmp], - x=ref_orig['x'][idx_tmp], - y=ref_orig['y'][idx_tmp], - m=ref_orig['m'][idx_tmp], - xe=ref_orig['xe'][idx_tmp], - ye=ref_orig['ye'][idx_tmp], - me=ref_orig['me'][idx_tmp]) + ## Get idx of reference stars in bootstrap sample in the ref_orig. + ## Then, use these to build reference starlist for the alignment + #idx_tmp = [] + #for ff in range(len(samp_idx)): + # name_tmp = ref_table['name'][idx_ref][samp_idx[ff]] + # foo = np.where(ref_orig['name'] == name_tmp)[0][0] + # idx_tmp.append(foo) + + ref_boot = StarList(name=ref_orig['name'][samp_idx], + x=ref_orig['x'][samp_idx], + y=ref_orig['y'][samp_idx], + m=ref_orig['m'][samp_idx], + xe=ref_orig['xe'][samp_idx], + ye=ref_orig['ye'][samp_idx], + me=ref_orig['me'][samp_idx]) # Now build star list with original positions of the reference stars # in the bootstrap sample - starlist_boot = StarList(name=ref_table['name'][idx_ref][samp_idx], - x=ref_table['x_orig'][:,jj][idx_ref][samp_idx], - y=ref_table['y_orig'][:,jj][idx_ref][samp_idx], - m=ref_table['m_orig'][:,jj][idx_ref][samp_idx], - xe=ref_table['xe_orig'][:,jj][idx_ref][samp_idx], - ye=ref_table['ye_orig'][:,jj][idx_ref][samp_idx], - me=ref_table['me_orig'][:,jj][idx_ref][samp_idx]) - + starlist_boot = StarList(name=ref_table['name'][samp_idx], + x=ref_table['x_orig'][:,jj][samp_idx], + y=ref_table['y_orig'][:,jj][samp_idx], + m=ref_table['m_orig'][:,jj][samp_idx], + xe=ref_table['xe_orig'][:,jj][samp_idx], + ye=ref_table['ye_orig'][:,jj][samp_idx], + me=ref_table['me_orig'][:,jj][samp_idx]) + + # Sanity check: makes sure names match between ref_boot and starlist_boot, + # since they need to line up + assert np.all(ref_boot['name'] == starlist_boot['name']) + # Calculate weights based on weights keyword. If weights desired, will need to # make starlist objects for this if self.trans_weights != None: From a34161da7e33827ce77bc02ce93391284e3e27e9 Mon Sep 17 00:00:00 2001 From: Matt Hosek Date: Thu, 8 Jan 2026 14:55:25 -0800 Subject: [PATCH 146/254] reintroducing requirement that ref star actually be matched in epoch --- flystar/align.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/flystar/align.py b/flystar/align.py index 5d067fe..c62a2a0 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -1177,7 +1177,7 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot for jj in range(n_epochs): # Extract bootstrap sample of matched reference stars for this epoch #good = np.where(~np.isnan(ref_table['x_orig'][idx_ref][:,jj])) - good = np.where(ref_table['used_in_trans'][:,jj] == True) + good = np.where( (ref_table['used_in_trans'][:,jj] == True) & (~np.isnan(ref_table['x_orig'][:,jj])) ) samp_idx = np.random.choice(good[0], len(good[0]), replace=True) # Get reference star positions in particular epoch from ref_list. From 066309299b2e517e67b3c2c4f6854811f672b3c7 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Fri, 9 Jan 2026 10:43:52 -0800 Subject: [PATCH 147/254] minor debugs and new helper functions --- flystar/align.py | 2 + flystar/plots.py | 92 +++++++++++++++++++++++++++++++++++++++++-- flystar/startables.py | 35 ++++++++++++++++ 3 files changed, 125 insertions(+), 4 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 994a3b1..b87f44d 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -1288,6 +1288,8 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot ye=ye_trans_arr[:,boot_idx], me=me_trans_arr[:,boot_idx], t=np.tile(t_boot, (len(ref_table),1))) + if 'motion_model_used' in ref_table.columns: + star_table['motion_model_input'] = ref_table['motion_model_used'] # Now, do proper motion calculation, making sure to fix t0 to the # orig value (so we can get a reasonable error on x0, y0) diff --git a/flystar/plots.py b/flystar/plots.py index 7553a8d..d077571 100755 --- a/flystar/plots.py +++ b/flystar/plots.py @@ -2212,7 +2212,12 @@ def plot_chi2_dist(tab, Ndetect, motion_model_dict={}, xlim=40, n_bins=50, boot_ # Fitting position and velocity... so subtract 2 to get Ndof n_params = np.nanmean(tab['n_params'][idx]) Ndof = Ndetect - n_params - print(f"Ndof={Ndof}, Ndetect={Ndetect}, Nparams={n_params}") + if len(np.unique(tab['n_params'][idx]))>1: + print("** Warning: using average Ndof for multiple motion models. **") + print("** Consider using plot_chi2_reduced_dist. **") + print(f"Ndof={Ndof:.2f}, Ndetect={Ndetect}, Nparams={n_params:.2f}") + else: + print(f"Ndof={Ndof}, Ndetect={Ndetect}, Nparams={n_params}") chi2_xaxis = np.linspace(0, xlim, xlim*3) chi2_bins = np.linspace(0, xlim, n_bins) @@ -2221,8 +2226,8 @@ def plot_chi2_dist(tab, Ndetect, motion_model_dict={}, xlim=40, n_bins=50, boot_ plt.hist(x[idx], bins=chi2_bins, histtype='step', label='X', density=True) plt.hist(y[idx], bins=chi2_bins, histtype='step', label='Y', density=True) plt.plot(chi2_xaxis, chi2.pdf(chi2_xaxis, Ndof), 'r-', alpha=0.6, - label='$\chi^2$ ' + str(Ndof) + ' dof') - plt.title('$N_{epoch} = $' + str(Ndetect) + ', $N_{dof} = $' + str(Ndof)) + label='$\chi^2$ ' + str(round(Ndof,2)) + ' dof') + plt.title('$N_{epoch} = $' + str(Ndetect) + ', $N_{dof} = $' + str(round(Ndof,2))) plt.xlim(0, xlim) plt.legend() @@ -2244,6 +2249,85 @@ def plot_chi2_dist(tab, Ndetect, motion_model_dict={}, xlim=40, n_bins=50, boot_ return +def plot_chi2_reduced_dist(tab, Ndetect, motion_model_dict={}, xlim=8, n_bins=50, boot_err=False): + """ + tab = flystar table + Ndetect = Number of epochs star detected in + """ + chi2_x_list = [] + chi2_y_list = [] + fnd_list = [] # Number of non-NaN error measurements + + motion_model_dict = motion_model.validate_motion_model_dict(motion_model_dict, tab, None) + i_all_detected = np.where(~np.any(np.isnan(tab['t']),axis=1))[0][0] + xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], motion_model_dict, allow_alt_models=True) + + for ii in range(len(tab)): + # Ignore the NaNs + fnd = np.argwhere(~np.isnan(tab['xe'][ii,:])) + fnd_list.append(len(fnd)) + + x = tab['x'][ii, fnd] + y = tab['y'][ii, fnd] + if boot_err: + xerr = np.hypot(tab['xe_boot'][ii, fnd], tab['xe'][ii, fnd]) + yerr = np.hypot(tab['ye_boot'][ii, fnd], tab['ye'][ii, fnd]) + else: + xerr = tab['xe'][ii, fnd] + yerr = tab['ye'][ii, fnd] + + fitLineX = xt_mod_all[ii, fnd] + fitLineY = yt_mod_all[ii,fnd] + + diffX = x - fitLineX + diffY = y - fitLineY + sigX = diffX / xerr + sigY = diffY / yerr + + chi2_x = np.sum(sigX**2) + chi2_y = np.sum(sigY**2) + chi2_x_list.append(chi2_x) + chi2_y_list.append(chi2_y) + + x = np.array(chi2_x_list) + y = np.array(chi2_y_list) + fnd = np.array(fnd_list) + + idx = np.where(fnd == Ndetect)[0] + n_params = tab['n_params'] + Ndof = Ndetect - n_params + print("Reduced chi2 for Ndetect="+str(Ndetect)) + chi2_bins = np.linspace(0, xlim, n_bins) + + plt.figure(figsize=(6,4)) + plt.clf() + plt.hist(x[idx]/Ndof[idx], bins=chi2_bins, histtype='step', label='X', density=True, color='tab:blue') + plt.hist(y[idx]/Ndof[idx], bins=chi2_bins, histtype='step', label='Y', density=True, color='tab:orange') + plt.axvline(np.median(x[idx]/Ndof[idx]), color='tab:blue', linestyle='--', label='X median') + plt.axvline(np.median(y[idx]/Ndof[idx]), color='tab:orange', linestyle='--', label='Y median') + plt.title('Reduced chi2, $N_{epoch} = $' + str(Ndetect)) + plt.xlim(0, xlim) + plt.legend() + + chi2red_x = x / Ndof + chi2red_y = y / Ndof + chi2red_t = (x + y) / (2.0 * Ndof + 1*(tab['motion_model_used']=='Parallax')) + + print('Mean reduced chi^2: (Ndetect = {0:d} of {1:d})'.format(len(idx), len(tab))) + fmt = ' {0:s} = {1:.1f} for N_detect and {2:.1f} for all' + med_chi2red_x_f = np.median(chi2red_x[idx]) + med_chi2red_x_a = np.median(chi2red_x) + med_chi2red_y_f = np.median(chi2red_y[idx]) + med_chi2red_y_a = np.median(chi2red_y) + med_chi2red_t_f = np.median(chi2red_t[idx]) + med_chi2red_t_a = np.median(chi2red_t) + print(fmt.format(' X', med_chi2red_x_f, med_chi2red_x_a)) + print(fmt.format(' Y', med_chi2red_y_f, med_chi2red_y_a)) + print(fmt.format('Tot', med_chi2red_t_f, med_chi2red_t_a)) + + return + + def plot_chi2_dist_per_filter(tab, Ndetect, motion_model_dict={}, xlim=40, n_bins=50, filter=None, boot_err=False): """ tab = flystar table @@ -2756,7 +2840,7 @@ def rs(x): chi2_y = np.sum(sigY**2) chi2_m = np.sum(sigM**2) - dof = len(x) - 2 + dof = int(tab['n_fit'][ii]-tab['n_params'][ii]) dofM = len(m) - 1 chi2_red_x = chi2_x / dof diff --git a/flystar/startables.py b/flystar/startables.py index a0bf3e3..767799a 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -1086,3 +1086,38 @@ def shift_reference_frame(self, delta_vx=0.0, delta_vy=0.0, delta_pi=0.0, self['x'] += delta_pi*pvec[0] self['y'] += delta_pi*pvec[1] return + +def shift_reference_frame(table, delta_vx=0.0, delta_vy=0.0, delta_pi=0.0, + motion_model_dict={}): + """ + After completing an alignment, shift from your relative reference frame to + the absolute frame using either Gaia or a Galactic model. This modified the + motion model fit parameters as well as the time series astrometry, assuming + zero error on the shift values. + + Parameters + ---------- + delta_vx : float, optional + velocity shift in x-direction (as/yr) + delta_vy : float, optional + velocity shift in y-direction (as/yr) + delta_pi : float, optional + parallax shift (as) + """ + motion_model_dict = motion_model.validate_motion_model_dict(motion_model_dict, table, None) + if delta_vx==0.0 and delta_vy==0.0 and delta_pi==0.0: + print("No shifts input, reference frame unchanged.") + print("Specify delta_vx, delta_vy, and/or delta_pi to perform a reference frame shift.") + return + table['vx'] += delta_vx + table['x'] += delta_vx*(table['t']-table['t0'][:, np.newaxis]) + table['vy'] += delta_vy + table['y'] += delta_vy*(table['t']-table['t0'][:, np.newaxis]) + if delta_pi!=0.0: + t_all = table['t'][np.where(~np.any(np.isnan(table['t']), axis=1))[0][0]] + t_mjd = Time(t_all, format='decimalyear', scale='utc').mjd + pvec = motion_model_dict['Parallax'].get_parallax_vector(t_mjd) + table['pi'] += delta_pi + table['x'] += delta_pi*pvec[0] + table['y'] += delta_pi*pvec[1] + return table From 4a7e77c9a56667083d1bee700836d9e7836aeb20 Mon Sep 17 00:00:00 2001 From: Matt Hosek Date: Fri, 9 Jan 2026 12:07:46 -0800 Subject: [PATCH 148/254] commenting out --doctest-rst which crashes testing --- setup.cfg | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.cfg b/setup.cfg index 0bf235e..d851345 100644 --- a/setup.cfg +++ b/setup.cfg @@ -37,7 +37,7 @@ testpaths = "flystar" "docs" astropy_header = true doctest_plus = enabled text_file_format = rst -addopts = --doctest-rst +#addopts = --doctest-rst [coverage:run] omit = From 5368750a4d57340707f3e8419cf573d801d459fd Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Mon, 12 Jan 2026 12:26:13 -0800 Subject: [PATCH 149/254] plotting fixes for multiple motion models --- flystar/plots.py | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/flystar/plots.py b/flystar/plots.py index d077571..2d65b2c 100755 --- a/flystar/plots.py +++ b/flystar/plots.py @@ -2301,10 +2301,10 @@ def plot_chi2_reduced_dist(tab, Ndetect, motion_model_dict={}, xlim=8, n_bins=50 plt.figure(figsize=(6,4)) plt.clf() - plt.hist(x[idx]/Ndof[idx], bins=chi2_bins, histtype='step', label='X', density=True, color='tab:blue') - plt.hist(y[idx]/Ndof[idx], bins=chi2_bins, histtype='step', label='Y', density=True, color='tab:orange') - plt.axvline(np.median(x[idx]/Ndof[idx]), color='tab:blue', linestyle='--', label='X median') - plt.axvline(np.median(y[idx]/Ndof[idx]), color='tab:orange', linestyle='--', label='Y median') + plt.hist(x[idx]/Ndof[idx], bins=chi2_bins, histtype='step', label='X', density=True) + plt.hist(y[idx]/Ndof[idx], bins=chi2_bins, histtype='step', label='Y', density=True) + plt.axvline(np.median(x[idx]/Ndof[idx]), color='C0', linestyle='--', label='X median') + plt.axvline(np.median(y[idx]/Ndof[idx]), color='C1', linestyle='--', label='Y median') plt.title('Reduced chi2, $N_{epoch} = $' + str(Ndetect)) plt.xlim(0, xlim) plt.legend() @@ -2840,7 +2840,7 @@ def rs(x): chi2_y = np.sum(sigY**2) chi2_m = np.sum(sigM**2) - dof = int(tab['n_fit'][ii]-tab['n_params'][ii]) + dof = (len(tab['x'][ii])-tab['n_params'][ii]).astype(int) dofM = len(m) - 1 chi2_red_x = chi2_x / dof @@ -3624,6 +3624,7 @@ def plot_plxs(star_tab, target_idx=0): ax[1].set_ylabel('N stars') ax[1].set_xlabel('Plx/Plx_err') plt.tight_layout() + ax[0].set_ylim(-5,5) def plot_sky(stars_tab, plot_errors=False, center_star=None, range=0.4, From 91eec87662ca460c037d0bfd28e3ca11835bcc2b Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Mon, 12 Jan 2026 14:02:00 -0800 Subject: [PATCH 150/254] debug bootstrap indexing --- flystar/align.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index a2e6c56..a0fff82 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -973,7 +973,7 @@ def match_lists(self, dr_tol, dm_tol): return - def get_ref_list_from_table(self, epoch): + def get_ref_list_from_table(self, epoch, select_idxs=None): """ Convert the averaged quantites in self.ref_table into a StarList object appropriate for the specified epoch. @@ -1030,6 +1030,9 @@ def get_ref_list_from_table(self, epoch): if me is not None: ref_list['me'] = me + if select_idxs is not None: + ref_list = ref_list[select_idxs] + return ref_list def reset_ref_values(self, exclude=None): @@ -1182,7 +1185,7 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot # Get reference star positions in particular epoch from ref_list. t_epoch = t_arr[jj] - ref_orig = self.get_ref_list_from_table(t_epoch) + ref_orig = self.get_ref_list_from_table(t_epoch, select_idxs=idx_good) ## Get idx of reference stars in bootstrap sample in the ref_orig. ## Then, use these to build reference starlist for the alignment From c8ac551a732365aab07832351638580516a9341a Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Mon, 12 Jan 2026 14:09:33 -0800 Subject: [PATCH 151/254] simplify bug fix --- flystar/align.py | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index a0fff82..7e4e82f 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -973,7 +973,7 @@ def match_lists(self, dr_tol, dm_tol): return - def get_ref_list_from_table(self, epoch, select_idxs=None): + def get_ref_list_from_table(self, epoch): """ Convert the averaged quantites in self.ref_table into a StarList object appropriate for the specified epoch. @@ -1030,9 +1030,6 @@ def get_ref_list_from_table(self, epoch, select_idxs=None): if me is not None: ref_list['me'] = me - if select_idxs is not None: - ref_list = ref_list[select_idxs] - return ref_list def reset_ref_values(self, exclude=None): @@ -1185,7 +1182,7 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot # Get reference star positions in particular epoch from ref_list. t_epoch = t_arr[jj] - ref_orig = self.get_ref_list_from_table(t_epoch, select_idxs=idx_good) + ref_orig = self.get_ref_list_from_table(t_epoch)[idx_good] ## Get idx of reference stars in bootstrap sample in the ref_orig. ## Then, use these to build reference starlist for the alignment From 93afbb675358843b049be8f25e69a4510ef5fc13 Mon Sep 17 00:00:00 2001 From: Macy Huston Date: Wed, 14 Jan 2026 16:21:45 -0800 Subject: [PATCH 152/254] allow bootstrap to optionally update positional errors --- flystar/align.py | 17 +++++++++++++++-- 1 file changed, 15 insertions(+), 2 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 7e4e82f..db37954 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -1050,7 +1050,7 @@ def reset_ref_values(self, exclude=None): return - def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_bootstrap=True, weighting='var', use_scipy=True, absolute_sigma=False, show_progress=True): + def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_bootstrap=True, weighting='var', use_scipy=True, absolute_sigma=False, show_progress=True, update_errors=False): """ Function to calculate bootstrap errors for the transformations as well as the proper motions. For each iteration, this will: @@ -1098,10 +1098,12 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot absolute_sigma: boolean If True, use the absolute sigma in the velocity fitting. If False, use the relative sigma, by default False. + update_errors: boolean + If True, save the starlist errors as xe_list, bootstrap errors as xe_boot, and their quad sum as xe (and likewise for ye and me). If False (default), leave the starlist errors in place as xe and bootstrap errors as xe_boot. Output: ------ - Seven new columns will be added to self.ref_table: + New columns will be added to self.ref_table: 'xe_boot', 2D column: bootstrap x pos uncertainties due to transformation for each epoch 'ye_boot', 2D column: bootstrap y pos uncertainties due to transformation for each epoch 'me_boot', 2D column: bootstrap mag uncertainties due to transformation for each epoch @@ -1382,6 +1384,17 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot print('Done with bootstrap') print('===============================') + if update_errors: + self.ref_table['xe_list'] = self.ref_table['xe'] + self.ref_table['ye_list'] = self.ref_table['ye'] + self.ref_table['me_list'] = self.ref_table['me'] + self.ref_table['xe'] = np.hypot(self.ref_table['xe_list'], self.ref_table['xe_boot']) + self.ref_table['ye'] = np.hypot(self.ref_table['ye_list'], self.ref_table['ye_boot']) + self.ref_table['me'] = np.hypot(self.ref_table['me_list'], self.ref_table['me_boot']) + print("Saved starlist errors to xe_list and added xe_boot to xe in quadrature.") + print("The same was done for ye and me.") + + return From 5a049fe02cd2db035d708b17325b879d599c8b2a Mon Sep 17 00:00:00 2001 From: Wei Lingfeng Date: Fri, 16 Jan 2026 00:17:36 +0900 Subject: [PATCH 153/254] Fix bootstrap index size error --- flystar/motion_model.py | 5 ++--- flystar/tests/test_align.py | 2 +- 2 files changed, 3 insertions(+), 4 deletions(-) diff --git a/flystar/motion_model.py b/flystar/motion_model.py index b8cde55..b69f8f2 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -138,8 +138,6 @@ def fit( n_obs = len(t) if bootstrap > 0 and n_obs > (self.n_params): - # Use m out of n bootstrap to ensure enough unique points - m = np.max([self.n_params, int(len(t) * 0.8)]) rng = np.random.default_rng(seed) edx = np.arange(n_obs, dtype=int) # Precompute All Bootstrap Draws at Once @@ -148,8 +146,9 @@ def fit( rng.choice(edx, size=self.n_params, replace=False) for _ in range(bootstrap) ]) + # Draw with replacement for the rest bdx_extra = np.stack([ - rng.choice(edx, size=self.n_params, replace=True) + rng.choice(edx, size=n_obs - self.n_params, replace=True) for _ in range(bootstrap) ]) bdx_all = np.hstack((bdx_unique, bdx_extra)) diff --git a/flystar/tests/test_align.py b/flystar/tests/test_align.py index c5ae094..3937ea6 100644 --- a/flystar/tests/test_align.py +++ b/flystar/tests/test_align.py @@ -572,7 +572,7 @@ def test_bootstrap(): list1 = starlists.StarList.from_table(list1) list2 = starlists.StarList.from_table(list2) - + # Set parameters for alignment transModel = transforms.PolyTransform trans_args = {'order':2} From d87766dae14d1976fde04bec2254ad4fbfd35cb2 Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Wed, 21 Jan 2026 17:50:56 -0800 Subject: [PATCH 154/254] Simplify n_params calculation --- flystar/motion_model.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/flystar/motion_model.py b/flystar/motion_model.py index b69f8f2..5b34582 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -10,7 +10,7 @@ class MotionModel(ABC): fit_param_names = [] # Number of fit parameters/required observations in each direction - n_params = int(np.ceil(len(fit_param_names) / 2)) + n_params = int((len(fit_param_names) + 1) / 2) # Fixed parameters: These are parameters that are required for the model, but are not # fit quantities. For example, RA and Dec in a parallax model. @@ -203,7 +203,7 @@ class Empty(MotionModel): fixed_param_names = [] name = "Empty" # Number of fit parameters/required observations in each direction - n_params = int(np.ceil(len(fit_param_names) / 2)) + n_params = int((len(fit_param_names) + 1) / 2) def __init__(self, **kwargs): """Empty motion model, returns nan for values and inf for uncertainties. @@ -305,7 +305,7 @@ class Fixed(MotionModel): fit_param_names = ['x0','y0'] fixed_param_names = [] # Number of fit parameters/required observations in each direction - n_params = int(np.ceil(len(fit_param_names) / 2)) + n_params = int((len(fit_param_names) + 1) / 2) name = "Fixed" @@ -460,7 +460,7 @@ class Linear(MotionModel): fixed_param_names = ['t0'] # Number of fit parameters/required observations in each direction - n_params = int(np.ceil(len(fit_param_names) / 2)) + n_params = int((len(fit_param_names) + 1) / 2) name = "Linear" def __init__(self, **kwargs): @@ -668,7 +668,7 @@ class Acceleration(MotionModel): name = "Acceleration" # Number of fit parameters/required observations in each direction - n_params = int(np.ceil(len(fit_param_names) / 2)) + n_params = int((len(fit_param_names) + 1) / 2) def __init__(self): # Must call after setting parameters. @@ -836,7 +836,7 @@ class Parallax(MotionModel): name = "Parallax" # Number of fit parameters/required observations in each direction - n_params = int(np.ceil(len(fit_param_names) / 2)) + n_params = int((len(fit_param_names) + 1) / 2) def __init__(self): super().__init__() From 2170fbcd0a52b5be7bbc8819b9bcd78ec97aa672 Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Mon, 26 Jan 2026 10:51:33 -0800 Subject: [PATCH 155/254] Reorder parameters of MosaicSelf/ToRef --- flystar/align.py | 101 +++++++++++++++++++++++++++++++---------------- 1 file changed, 66 insertions(+), 35 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 2820ae5..d25e903 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -13,21 +13,37 @@ from astropy.utils.exceptions import AstropyUserWarning class MosaicSelfRef(object): - def __init__(self, list_of_starlists, ref_index=0, iters=2, - dr_tol=[1, 1], dm_tol=[2, 1], - outlier_tol=[None, None], - trans_args=[{'order': 2}, {'order': 2}], - init_order=1, - mag_trans=True, mag_lim=None, trans_weighting=None, vel_weighting='var', - trans_input=None, trans_class=transforms.PolyTransform, - calc_trans_inverse=False, - init_guess_mode='miracle', iter_callback=None, - motion_models=['Empty', 'Fixed'], - fixed_params_dict=None, - use_scipy=True, - absolute_sigma=False, - save_path=None, - verbose=True): + def __init__( + self, + list_of_starlists, + # Alignment tolerance parameters + ref_index=0, + iters=2, + dr_tol=[1, 1], + dm_tol=[2, 1], + outlier_tol=[None, None], + # Transformation parameters + trans_class=transforms.PolyTransform, + trans_args=[{'order': 2}, {'order': 2}], + trans_input=None, + trans_weighting=None, + init_order=1, + init_guess_mode='miracle', + calc_trans_inverse=False, + # Magnitude parameters + mag_trans=True, + mag_lim=None, + # Motion model parameters + motion_models=['Empty', 'Fixed'], + fixed_params_dict=None, + vel_weighting='var', + use_scipy=True, + absolute_sigma=False, + # Advanced options + iter_callback=None, + save_path=None, + verbose=True + ): """ Make a mosaic object by passing in a list of starlists and then running fit(). @@ -1433,26 +1449,41 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot class MosaicToRef(MosaicSelfRef): - def __init__(self, ref_list, list_of_starlists, iters=2, - dr_tol=[1, 1], dm_tol=[2, 1], - outlier_tol=[None, None], - trans_args=[{'order': 2}, {'order': 2}], - init_order=1, - mag_trans=True, mag_lim=None, ref_mag_lim=None, - trans_weighting=None, vel_weighting='var', - trans_input=None, - trans_class=transforms.PolyTransform, - calc_trans_inverse=False, - use_ref_new=False, - update_ref_orig=False, - init_guess_mode='miracle', - iter_callback=None, - motion_models=['Empty', 'Fixed'], - fixed_params_dict=None, - use_scipy=True, - absolute_sigma=False, - save_path=None, - verbose=True): + def __init__( + self, + ref_list, + list_of_starlists, + # Alignment tolerance parameters + iters=2, + dr_tol=[1, 1], + dm_tol=[2, 1], + outlier_tol=[None, None], + # Reference behavior (MosiacToRef specific) + use_ref_new=False, + update_ref_orig=False, + # Transformation parameters + trans_class=transforms.PolyTransform, + trans_args=[{'order': 2}, {'order': 2}], + trans_input=None, + trans_weighting=None, + init_order=1, + init_guess_mode='miracle', + calc_trans_inverse=False, + # Magnitude parameters + mag_trans=True, + mag_lim=None, + ref_mag_lim=None, + # Motion model parameters + motion_models=['Empty', 'Fixed'], + fixed_params_dict=None, + vel_weighting='var', + use_scipy=True, + absolute_sigma=False, + # Advanced options + iter_callback=None, + save_path=None, + verbose=True + ): """ Required Parameters From 1422171954f234061bc1ece1dfa70fb2b96131cc Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Mon, 26 Jan 2026 10:55:12 -0800 Subject: [PATCH 156/254] Update type comparison method --- flystar/motion_model.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/flystar/motion_model.py b/flystar/motion_model.py index 5b34582..7010a4e 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -956,8 +956,8 @@ def model(self, t, fit_params, fit_param_errs=None, fixed_params_dict=None): # TODO: vectorize parallax.parallax_in_direction to handle multiple obsLocation? - assert (type(obsLocation) == str) or (np.unique(obsLocation).size == 1), "obsLocation must be a single string for all stars at this time." - if type(obsLocation) != str: + assert isinstance(obsLocation, str) or (np.unique(obsLocation).size == 1), "obsLocation must be a single string for all stars at this time." + if not isinstance(obsLocation, str): obsLocation = np.unique(obsLocation)[0] dt = t[np.newaxis, :] - t0[:, np.newaxis] # Shape (N_stars, N_times) From 93f4d44754b84ca6ec211a45475e8a34676b20a0 Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Tue, 27 Jan 2026 17:54:19 -0800 Subject: [PATCH 157/254] Add save path for log file --- flystar/align.py | 43 ++++++++++++++++++++++--------------------- 1 file changed, 22 insertions(+), 21 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index d304d4b..400b6e1 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -1738,27 +1738,28 @@ def fit(self): """ # Create a log file of the parameters used in the fit. - with open('MosaicToRef_input_params.log', 'w',) as _log: - logger(_log, 'Parameters used for fit: ', self.verbose) - logger(_log, '------------------------- ', self.verbose) - logger(_log, ' dr_tol = ' + str(self.dr_tol), self.verbose) - logger(_log, ' dm_tol = ' + str(self.dm_tol), self.verbose) - logger(_log, ' outlier_tol = ' + str(self.outlier_tol), self.verbose) - logger(_log, ' trans_args = ' + str(self.trans_args), self.verbose) - logger(_log, ' mag_trans = ' + str(self.mag_trans), self.verbose) - logger(_log, ' mag_lim = ' + str(self.mag_lim), self.verbose) - logger(_log, ' ref_mag_lim = ' + str(self.ref_mag_lim), self.verbose) - logger(_log, ' trans_weighting = ' + str(self.trans_weighting), self.verbose) - logger(_log, ' vel_weighting = ' + str(self.vel_weighting), self.verbose) - logger(_log, ' trans_input = ' + str(self.trans_input), self.verbose) - logger(_log, ' trans_class = ' + str(self.trans_class), self.verbose) - logger(_log, ' calc_trans_inverse = ' + str(self.calc_trans_inverse), self.verbose) - logger(_log, ' use_ref_new = ' + str(self.use_ref_new), self.verbose) - logger(_log, ' motion_models = ' + str([mm.name for mm in self.motion_models]), self.verbose) - logger(_log, ' update_ref_orig = ' + str(self.update_ref_orig), self.verbose) - logger(_log, ' init_guess_mode = ' + str(self.init_guess_mode), self.verbose) - logger(_log, ' iter_callback = ' + str(self.iter_callback), self.verbose) - logger(_log, '-------------------------\n', self.verbose) + if self.save_path is not None: + with open(f'{self.save_path}/MosaicToRef_input_params.log', 'w',) as _log: + logger(_log, 'Parameters used for fit: ', self.verbose) + logger(_log, '------------------------- ', self.verbose) + logger(_log, ' dr_tol = ' + str(self.dr_tol), self.verbose) + logger(_log, ' dm_tol = ' + str(self.dm_tol), self.verbose) + logger(_log, ' outlier_tol = ' + str(self.outlier_tol), self.verbose) + logger(_log, ' trans_args = ' + str(self.trans_args), self.verbose) + logger(_log, ' mag_trans = ' + str(self.mag_trans), self.verbose) + logger(_log, ' mag_lim = ' + str(self.mag_lim), self.verbose) + logger(_log, ' ref_mag_lim = ' + str(self.ref_mag_lim), self.verbose) + logger(_log, ' trans_weighting = ' + str(self.trans_weighting), self.verbose) + logger(_log, ' vel_weighting = ' + str(self.vel_weighting), self.verbose) + logger(_log, ' trans_input = ' + str(self.trans_input), self.verbose) + logger(_log, ' trans_class = ' + str(self.trans_class), self.verbose) + logger(_log, ' calc_trans_inverse = ' + str(self.calc_trans_inverse), self.verbose) + logger(_log, ' use_ref_new = ' + str(self.use_ref_new), self.verbose) + logger(_log, ' motion_models = ' + str([mm.name for mm in self.motion_models]), self.verbose) + logger(_log, ' update_ref_orig = ' + str(self.update_ref_orig), self.verbose) + logger(_log, ' init_guess_mode = ' + str(self.init_guess_mode), self.verbose) + logger(_log, ' iter_callback = ' + str(self.iter_callback), self.verbose) + logger(_log, '-------------------------\n', self.verbose) ########## From 9251d9c1e5be6e9b531fa37ca5696ab57d8258bd Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Thu, 29 Jan 2026 23:22:04 -0800 Subject: [PATCH 158/254] Fix mag_lim error in align --- flystar/align.py | 6 +++--- flystar/starlists.py | 20 ++++++++++---------- 2 files changed, 13 insertions(+), 13 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 400b6e1..f7ff27e 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -282,7 +282,7 @@ def fix_iterable_conditions(self): if self.mag_lim is None: self.mag_lim = np.repeat([[None, None]], len(self.star_lists), axis=0) - elif (len(self.mag_lim) == 2): + elif (len(self.mag_lim) == 2) and (np.ndim(self.mag_lim) == 1): self.mag_lim = np.repeat([self.mag_lim], len(self.star_lists), axis=0) assert len(self.mag_lim) == len(self.star_lists) @@ -2979,8 +2979,8 @@ def trans_initial_guess(ref_list, star_list, trans_args, mode='miracle', if mode == 'name': # First trim the two lists down to only those that don't contain # the "ignore_contains" string. - idx_r = np.flatnonzero(np.char.find(ref_list['name'], ignore_contains) == -1) - idx_s = np.flatnonzero(np.char.find(star_list['name'], ignore_contains) == -1) + idx_r = np.flatnonzero(np.char.find(ref_list['name'].astype(str), ignore_contains) == -1) + idx_s = np.flatnonzero(np.char.find(star_list['name'].astype(str), ignore_contains) == -1) # Match the star names name_matches, ndx_r, ndx_s = np.intersect1d(ref_list['name'][idx_r], diff --git a/flystar/starlists.py b/flystar/starlists.py index f1f3278..8387226 100644 --- a/flystar/starlists.py +++ b/flystar/starlists.py @@ -737,21 +737,21 @@ def restrict_by_value(self, **kwargs): # Loop through all conditions and build up # an array of indicies of rows to remove. remove_flag = np.zeros(len(self), dtype=bool) - - for kwarg in kwargs: - if kwargs[kwarg] is not None: + + for key, value in kwargs.items(): + if value is not None: # Get the name of the column to act on and # whether the condition is min or max. - kwarg_split = kwarg.split('_') - + key_split = key.split('_') + # Support column names such as x_0. - col = '_'.join(kwarg_split[:-1]) + col = '_'.join(key_split[:-1]) - if kwarg_split[-1] == 'min': - remove_flag = np.logical_or(remove_flag, self[col] <= kwargs[kwarg]) + if key_split[-1] == 'min': + remove_flag = np.logical_or(remove_flag, self[col] <= value) - if kwarg_split[-1] == 'max': - remove_flag = np.logical_or(remove_flag, self[col] >= kwargs[kwarg]) + if key_split[-1] == 'max': + remove_flag = np.logical_or(remove_flag, self[col] >= value) rem_idx = np.where(remove_flag == True)[0] From 4980483a62766e7dfc829193bc387e037a707312 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Sun, 1 Feb 2026 20:45:35 -0800 Subject: [PATCH 159/254] Update save path: Must ends with pkl --- flystar/align.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/flystar/align.py b/flystar/align.py index 2820ae5..ce3a184 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -201,6 +201,12 @@ def = None. If not None, then this should contain an array or list of transform self.iter_callback = iter_callback self.save_path = save_path self.verbose = verbose + + # Setup save_path: + if self.save_path: + assert self.save_path.endswith('.pkl'), 'Save_path must end with .pkl' + if not os.path.exists(os.path.dirname(self.save_path)): + os.makedirs(os.path.dirname(self.save_path)) all_mm_map = motion_model.motion_model_map() if all(isinstance(mm, str) for mm in motion_models): @@ -535,7 +541,7 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar ## Make plot, if desired plots.trans_positions(ref_list, ref_list[idx_ref], star_list_T, star_list_T[idx_lis], - save_path=f"{self.save_path}/Transformed_Positions_{star_list_T['t'][0]}.png" if self.save_path else None, + save_path=f"{os.path.dirname(self.save_path)}/Transformed_Positions_{star_list_T['t'][0]}.png" if self.save_path else None, show_plot=False) ### Update the observed (but transformed) values in the reference table. From dcf8d135f19cf38fdd60382d5f61d7213bbfedf8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Tue, 3 Feb 2026 13:30:25 -0800 Subject: [PATCH 160/254] Fix string warning message --- flystar/plots.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/flystar/plots.py b/flystar/plots.py index 112934c..8b9587d 100755 --- a/flystar/plots.py +++ b/flystar/plots.py @@ -2231,7 +2231,7 @@ def plot_chi2_dist(tab, Ndetect, motion_model_dict={}, xlim=40, n_bins=50, boot_ plt.hist(x[idx], bins=chi2_bins, histtype='step', label='X', density=True) plt.hist(y[idx], bins=chi2_bins, histtype='step', label='Y', density=True) plt.plot(chi2_xaxis, chi2.pdf(chi2_xaxis, Ndof), 'r-', alpha=0.6, - label='$\chi^2$ ' + str(round(Ndof,2)) + ' dof') + label=r'$\chi^2$ ' + str(round(Ndof,2)) + ' dof') plt.title('$N_{epoch} = $' + str(Ndetect) + ', $N_{dof} = $' + str(round(Ndof,2))) plt.xlim(0, xlim) plt.legend() From 89eaee578f81c1e064b8f978946054abf2793a30 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Sat, 7 Feb 2026 17:31:19 -0800 Subject: [PATCH 161/254] Update motion model used for keep_orig stars in align; Update chi2 after ref table has been created in align; Added save path in plots; Moved all testing data files under flystar/test/test_data; Removed trailing spaces --- .../examples/motion_model_example.ipynb | 84 +++--- flystar/align.py | 66 ++++- flystar/analysis.py | 3 +- flystar/plots.py | 263 +++++++++-------- flystar/startables.py | 9 +- flystar/tests/test_align.py | 268 +++++++++--------- flystar/tests/{ => test_data}/A.lis | 0 flystar/tests/{ => test_data}/B.lis | 0 flystar/tests/{ => test_data}/C.lis | 0 flystar/tests/{ => test_data}/D.lis | 0 flystar/tests/{ => test_data}/E.lis | 0 flystar/tests/{ => test_data}/F.lis | 0 flystar/tests/{ => test_data}/coveragerc | 0 flystar/tests/{ => test_data}/ref.lis | 0 flystar/tests/{ => test_data}/ref_vel.lis | 0 .../tests/{ => test_data}/test_catalog.fits | 0 16 files changed, 383 insertions(+), 310 deletions(-) rename flystar/tests/{ => test_data}/A.lis (100%) rename flystar/tests/{ => test_data}/B.lis (100%) rename flystar/tests/{ => test_data}/C.lis (100%) rename flystar/tests/{ => test_data}/D.lis (100%) rename flystar/tests/{ => test_data}/E.lis (100%) rename flystar/tests/{ => test_data}/F.lis (100%) rename flystar/tests/{ => test_data}/coveragerc (100%) rename flystar/tests/{ => test_data}/ref.lis (100%) rename flystar/tests/{ => test_data}/ref_vel.lis (100%) rename flystar/tests/{ => test_data}/test_catalog.fits (100%) diff --git a/docs/flystar/examples/motion_model_example.ipynb b/docs/flystar/examples/motion_model_example.ipynb index 413b616..4877b5c 100644 --- a/docs/flystar/examples/motion_model_example.ipynb +++ b/docs/flystar/examples/motion_model_example.ipynb @@ -93,7 +93,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 2, "id": "ce4edb88", "metadata": {}, "outputs": [], @@ -115,7 +115,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 3, "id": "86b6319d", "metadata": {}, "outputs": [], @@ -138,7 +138,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 4, "id": "0926c0a8", "metadata": {}, "outputs": [], @@ -157,7 +157,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 5, "id": "840693ae", "metadata": {}, "outputs": [], @@ -175,7 +175,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 6, "id": "8fcbdc5d", "metadata": {}, "outputs": [], @@ -193,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 7, "id": "6752e477", "metadata": {}, "outputs": [ @@ -203,7 +203,7 @@ "{'t0': np.float64(2027.0454838983064)}" ] }, - "execution_count": 15, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -214,7 +214,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 8, "id": "eba675c8", "metadata": {}, "outputs": [], @@ -232,7 +232,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 9, "id": "7dba325f", "metadata": {}, "outputs": [], @@ -280,13 +280,13 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 10, "id": "ad03fc67", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -317,7 +317,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 11, "id": "0a0d9d1f", "metadata": {}, "outputs": [], @@ -328,13 +328,13 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 12, "id": "b3d63417", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -358,7 +358,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 13, "id": "d1e406c5", "metadata": {}, "outputs": [], @@ -392,7 +392,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 14, "id": "4adebbe8", "metadata": {}, "outputs": [], @@ -415,7 +415,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 15, "id": "95745baa", "metadata": {}, "outputs": [], @@ -433,7 +433,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 16, "id": "54206834", "metadata": {}, "outputs": [ @@ -443,7 +443,7 @@ "(3, 100)" ] }, - "execution_count": 32, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -454,13 +454,13 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 17, "id": "e6a4e42e", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -497,7 +497,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 18, "id": "018fc13a", "metadata": {}, "outputs": [ @@ -505,15 +505,15 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/weilingfeng/Software/miniconda3/envs/main/lib/python3.13/site-packages/erfa/core.py:133: ErfaWarning: ERFA function \"dtf2d\" yielded 1 of \"dubious year (Note 6)\"\n", + "/home/weilingfeng/Software/miniconda3/envs/main/lib/python3.12/site-packages/erfa/core.py:133: ErfaWarning: ERFA function \"dtf2d\" yielded 1 of \"dubious year (Note 6)\"\n", " warn(f'ERFA function \"{func_name}\" yielded {wmsg}', ErfaWarning)\n", - "/Users/weilingfeng/Software/miniconda3/envs/main/lib/python3.13/site-packages/erfa/core.py:133: ErfaWarning: ERFA function \"dtf2d\" yielded 2 of \"dubious year (Note 6)\"\n", + "/home/weilingfeng/Software/miniconda3/envs/main/lib/python3.12/site-packages/erfa/core.py:133: ErfaWarning: ERFA function \"dtf2d\" yielded 2 of \"dubious year (Note 6)\"\n", " warn(f'ERFA function \"{func_name}\" yielded {wmsg}', ErfaWarning)\n", - "/Users/weilingfeng/Software/miniconda3/envs/main/lib/python3.13/site-packages/erfa/core.py:133: ErfaWarning: ERFA function \"utctai\" yielded 2 of \"dubious year (Note 3)\"\n", + "/home/weilingfeng/Software/miniconda3/envs/main/lib/python3.12/site-packages/erfa/core.py:133: ErfaWarning: ERFA function \"utctai\" yielded 2 of \"dubious year (Note 3)\"\n", " warn(f'ERFA function \"{func_name}\" yielded {wmsg}', ErfaWarning)\n", - "/Users/weilingfeng/Software/miniconda3/envs/main/lib/python3.13/site-packages/erfa/core.py:133: ErfaWarning: ERFA function \"utctai\" yielded 1 of \"dubious year (Note 3)\"\n", + "/home/weilingfeng/Software/miniconda3/envs/main/lib/python3.12/site-packages/erfa/core.py:133: ErfaWarning: ERFA function \"utctai\" yielded 1 of \"dubious year (Note 3)\"\n", " warn(f'ERFA function \"{func_name}\" yielded {wmsg}', ErfaWarning)\n", - "/Users/weilingfeng/Software/miniconda3/envs/main/lib/python3.13/site-packages/erfa/core.py:133: ErfaWarning: ERFA function \"taiutc\" yielded 1 of \"dubious year (Note 4)\"\n", + "/home/weilingfeng/Software/miniconda3/envs/main/lib/python3.12/site-packages/erfa/core.py:133: ErfaWarning: ERFA function \"taiutc\" yielded 1 of \"dubious year (Note 4)\"\n", " warn(f'ERFA function \"{func_name}\" yielded {wmsg}', ErfaWarning)\n" ] } @@ -526,7 +526,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 19, "id": "73dafb1f", "metadata": {}, "outputs": [ @@ -534,21 +534,21 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/weilingfeng/Software/miniconda3/envs/main/lib/python3.13/site-packages/erfa/core.py:133: ErfaWarning: ERFA function \"dtf2d\" yielded 20 of \"dubious year (Note 6)\"\n", + "/home/weilingfeng/Software/miniconda3/envs/main/lib/python3.12/site-packages/erfa/core.py:133: ErfaWarning: ERFA function \"dtf2d\" yielded 20 of \"dubious year (Note 6)\"\n", " warn(f'ERFA function \"{func_name}\" yielded {wmsg}', ErfaWarning)\n", - "/Users/weilingfeng/Software/miniconda3/envs/main/lib/python3.13/site-packages/erfa/core.py:133: ErfaWarning: ERFA function \"dtf2d\" yielded 40 of \"dubious year (Note 6)\"\n", + "/home/weilingfeng/Software/miniconda3/envs/main/lib/python3.12/site-packages/erfa/core.py:133: ErfaWarning: ERFA function \"dtf2d\" yielded 40 of \"dubious year (Note 6)\"\n", " warn(f'ERFA function \"{func_name}\" yielded {wmsg}', ErfaWarning)\n", - "/Users/weilingfeng/Software/miniconda3/envs/main/lib/python3.13/site-packages/erfa/core.py:133: ErfaWarning: ERFA function \"utctai\" yielded 40 of \"dubious year (Note 3)\"\n", + "/home/weilingfeng/Software/miniconda3/envs/main/lib/python3.12/site-packages/erfa/core.py:133: ErfaWarning: ERFA function \"utctai\" yielded 40 of \"dubious year (Note 3)\"\n", " warn(f'ERFA function \"{func_name}\" yielded {wmsg}', ErfaWarning)\n", - "/Users/weilingfeng/Software/miniconda3/envs/main/lib/python3.13/site-packages/erfa/core.py:133: ErfaWarning: ERFA function \"utctai\" yielded 20 of \"dubious year (Note 3)\"\n", + "/home/weilingfeng/Software/miniconda3/envs/main/lib/python3.12/site-packages/erfa/core.py:133: ErfaWarning: ERFA function \"utctai\" yielded 20 of \"dubious year (Note 3)\"\n", " warn(f'ERFA function \"{func_name}\" yielded {wmsg}', ErfaWarning)\n", - "/Users/weilingfeng/Software/miniconda3/envs/main/lib/python3.13/site-packages/erfa/core.py:133: ErfaWarning: ERFA function \"taiutc\" yielded 20 of \"dubious year (Note 4)\"\n", + "/home/weilingfeng/Software/miniconda3/envs/main/lib/python3.12/site-packages/erfa/core.py:133: ErfaWarning: ERFA function \"taiutc\" yielded 20 of \"dubious year (Note 4)\"\n", " warn(f'ERFA function \"{func_name}\" yielded {wmsg}', ErfaWarning)\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -580,7 +580,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 20, "id": "aa698e86", "metadata": {}, "outputs": [], @@ -660,7 +660,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 21, "id": "02642d3b", "metadata": {}, "outputs": [ @@ -668,13 +668,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "Fitting motion model Empty: 0%| | 0/1 [00:00 0): + # Determine motion_model_used for keep_orig stars + # Filter possible motion models based on available columns + motion_models_possible = [] + for mm in self.motion_models: + required_columns = mm.fit_param_names + mm.fixed_param_names + if all(col in self.ref_table.colnames for col in required_columns): + motion_models_possible.append((mm, required_columns)) + + # Check if values are finite for required columns + motion_model_used = [] + for k in keep_orig: + for mm, req in motion_models_possible[::-1]: + if all(np.isfinite(self.ref_table[k][col]) for col in req): + motion_model_used.append(mm.name) + break + + # Assign the determined motion models + self.ref_table['motion_model_used'][keep_orig] = motion_model_used # Combine (transformed) magnitudes if 'me' in self.ref_table.colnames: @@ -1442,7 +1473,7 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot for ff in ['chi2_x_boot', 'chi2_y_boot']: col = Column(np.ones(len(self.ref_table)), name=ff) col.fill(np.nan) - + col[idx_good] = data_dict[ff][idx_good] self.ref_table.add_column(col) @@ -1721,7 +1752,7 @@ def = None. If not None, then this should contain an array or list of transform return - + def fit(self): """ Using the current parameter settings, match and transform all the lists @@ -1745,7 +1776,7 @@ def fit(self): """ # Create a log file of the parameters used in the fit. if self.save_path is not None: - with open(f'{self.save_path}/MosaicToRef_input_params.log', 'w',) as _log: + with open(f'{os.path.dirname(self.save_path)}/MosaicToRef_input_params.log', 'w',) as _log: logger(_log, 'Parameters used for fit: ', self.verbose) logger(_log, '------------------------- ', self.verbose) logger(_log, ' dr_tol = ' + str(self.dr_tol), self.verbose) @@ -1854,6 +1885,21 @@ def fit(self): if self.iter_callback != None: self.iter_callback(self.ref_table, nn) + # Add times into ref_table meta data + all_epochs = get_all_epochs(self.ref_table) + self.ref_table.meta['list_times'] = all_epochs + + # Update chi2 values in ref table, as motion_model_used may have changed + x_inferred, y_inferred, _, _ = self.ref_table.infer_positions(all_epochs) + chi2_x_2d = ((self.ref_table['x'] - x_inferred) / self.ref_table['xe'])**2 + chi2_y_2d = ((self.ref_table['y'] - y_inferred) / self.ref_table['ye'])**2 + chi2_x = np.nansum(chi2_x_2d, axis=1) + chi2_y = np.nansum(chi2_y_2d, axis=1) + chi2_x[~np.isfinite(chi2_x_2d).any(axis=1)] = np.nan + chi2_y[~np.isfinite(chi2_y_2d).any(axis=1)] = np.nan + self.ref_table['chi2_x'] = chi2_x + self.ref_table['chi2_y'] = chi2_y + if self.save_path: with open(self.save_path, 'wb') as file: pickle.dump(self, file) diff --git a/flystar/analysis.py b/flystar/analysis.py index 55094e5..c955c2b 100644 --- a/flystar/analysis.py +++ b/flystar/analysis.py @@ -48,7 +48,8 @@ def query_gaia(ra, dec, search_radius=30.0, table_name='gaiadr3'): gaia = gaia_job.get_results() #Change new 'SOURCE_ID' column header back to lowercase 'source_id' so all subsequent functions still work: - gaia['SOURCE_ID'].name = 'source_id' + if 'SOURCE_ID' in gaia.colnames: + gaia.rename_column('SOURCE_ID', 'source_id') return gaia diff --git a/flystar/plots.py b/flystar/plots.py index 8b9587d..4728e60 100755 --- a/flystar/plots.py +++ b/flystar/plots.py @@ -1049,25 +1049,29 @@ def plot_gaia(gaia): return -def plot_pm_error(tab): - plt.figure(figsize=(6,6)) - plt.clf() - plt.semilogy(tab['m0'], tab['vx_err']*1e3, 'r.', label=r'$\sigma_{\mu_{\alpha *}}$', alpha=0.4) - plt.semilogy(tab['m0'], tab['vy_err']*1e3, 'b.', label=r'$\sigma_{\mu_{\delta}}$', alpha=0.4) - plt.legend() - plt.xlabel('Mag') - plt.ylabel('PM Error (mas/yr)') +def plot_pm_error(tab, save_path=None): + fig, ax = plt.subplots(1, 1, figsize=(6, 6)) + ax.semilogy(tab['m0'], tab['vx_err']*1e3, color='C0', marker='.', ls='none', ms=3, label=r'$\sigma_{\mu_{\alpha *}}$', alpha=0.3) + ax.semilogy(tab['m0'], tab['vy_err']*1e3, color='C3', marker='.', ls='none', ms=3, label=r'$\sigma_{\mu_{\delta}}$', alpha=0.3) + ax.legend() + ax.set_xlabel('Mag') + ax.set_ylabel('PM Error (mas/yr)') + plt.tight_layout() + if save_path is not None: + plt.savefig(save_path) plt.show() return -def plot_mag_error(tab): - plt.figure(figsize=(6,6)) - plt.clf() - plt.semilogy(tab['m0'], tab['m0_err'], 'r.', alpha=0.4) - plt.legend() - plt.xlabel('Mag') - plt.ylabel('Mag Error (mag)') - +def plot_mag_error(tab, save_path=None): + fig, ax = plt.subplots(1, 1, figsize=(6, 6)) + ax.semilogy(tab['m0'], tab['m0_err'], color='C0', marker='.', ls='none', alpha=0.4) + ax.legend() + ax.set_xlabel('Mag') + ax.set_ylabel('Mag Error (mag)') + plt.tight_layout() + if save_path is not None: + plt.savefig(save_path) + plt.show() return def plot_mean_residuals_by_epoch(tab): @@ -1127,17 +1131,18 @@ def plot_mean_residuals_by_epoch(tab): return -def plot_quiver_residuals_all_epochs(tab, motion_model_dict={}, unit='arcsec', scale=None, plotlim=None): +def plot_quiver_residuals_all_epochs(tab, unit='arcsec', scale=None, plotlim=None, save_path=None): # Keep track of the residuals for averaging. dr_good = np.zeros(len(tab), dtype=float) n_good = np.zeros(len(tab), dtype=int) dr_ref = np.zeros(len(tab), dtype=float) n_ref = np.zeros(len(tab), dtype=int) - - motion_model_dict = motion_model.validate_motion_model_dict(motion_model_dict, tab, None) + + # motion_model_dict = motion_model.validate_motion_model_dict(motion_model_dict, tab, None) complete_times = np.array([np.unique(col[~np.isnan(col)])[0] for col in tab['t'].T]) - xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(complete_times, motion_model_dict, allow_alt_models=True) + # xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(complete_times, motion_model_dict, allow_alt_models=True) + xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.infer_positions(complete_times) for ee in range(tab['x'].shape[1]): xt_mod = xt_mod_all[:,ee] @@ -1146,11 +1151,19 @@ def plot_quiver_residuals_all_epochs(tab, motion_model_dict={}, unit='arcsec', s good_idx = np.where(np.isfinite(tab['x'][:, ee]) == True)[0] ref_idx = np.where(tab[good_idx]['used_in_trans'][:, ee] == True)[0] - dx, dy = plot_quiver_residuals(tab['x'][:, ee], tab['y'][:, ee], - xt_mod, yt_mod, - good_idx, ref_idx, - 'Epoch {0:d}'.format(ee), - unit=unit, scale=scale, plotlim=plotlim) + dx, dy = plot_quiver_residuals( + tab['x'][:, ee], + tab['y'][:, ee], + xt_mod, + yt_mod, + good_idx, + ref_idx, + 'Epoch {0:d}'.format(ee), + unit=unit, + scale=scale, + plotlim=plotlim, + save_path=f'{save_path}/Quiver_Residual_{ee}.pdf' if save_path else None + ) # Building up average dr for a set of stars. dr = np.hypot(dx, dy) @@ -1169,8 +1182,8 @@ def plot_quiver_residuals_all_epochs(tab, motion_model_dict={}, unit='arcsec', s idx = np.where(n_ref > 0)[0] dr_ref_avg[idx] = dr_ref[idx] / n_ref[idx] - hdr = '{name:>16s} {mag:>5s} {dr:>6s} {x:>6s} {y:>6s} {r:>6s}' - fmt = '{name:16s} {mag:5.2f} {dr:6.4f} {x:6.3f} {y:6.3f} {r:6.3f}' + # hdr = '{name:>16s} {mag:>5s} {dr:>6s} {x:>6s} {y:>6s} {r:>6s}' + # fmt = '{name:16s} {mag:5.2f} {dr:6.4f} {x:6.3f} {y:6.3f} {r:6.3f}' # print() # print('##########') @@ -1194,7 +1207,7 @@ def plot_quiver_residuals_all_epochs(tab, motion_model_dict={}, unit='arcsec', s return -def plot_quiver_residuals_with_orig_all_epochs(tab, trans_list, motion_model_dict={}, unit='arcsec', scale=None, plotlim=None, scale_orig=None, cte_fit=None, mlim=15): +def plot_quiver_residuals_with_orig_all_epochs(tab, trans_list, unit='arcsec', scale=None, plotlim=None, scale_orig=None, cte_fit=None, mlim=15, save_path=None): # Keep track of the residuals for averaging. dr_good = np.zeros(len(tab), dtype=float) @@ -1202,15 +1215,16 @@ def plot_quiver_residuals_with_orig_all_epochs(tab, trans_list, motion_model_dic dr_ref = np.zeros(len(tab), dtype=float) n_ref = np.zeros(len(tab), dtype=int) - motion_model_dict = motion_model.validate_motion_model_dict(motion_model_dict, tab, None) + # motion_model_dict = motion_model.validate_motion_model_dict(motion_model_dict, tab, None) i_all_detected = np.where(~np.any(np.isnan(tab['t']),axis=1))[0][0] - xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], motion_model_dict, allow_alt_models=True) - + # xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], motion_model_dict, allow_alt_models=True) + xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.infer_positions(tab['t'][i_all_detected]) + for ee in range(tab['x'].shape[1]): dt = tab['t'][:, ee] - tab['t0'] xt_mod = xt_mod_all[ee] yt_mod = yt_mod_all[ee] - + good_idx = np.where(np.isfinite(tab['x'][:, ee]) == True)[0] ref_idx = np.where(tab[good_idx]['used_in_trans'][:, ee] == True)[0] @@ -1220,14 +1234,14 @@ def plot_quiver_residuals_with_orig_all_epochs(tab, trans_list, motion_model_dic xt_mod, yt_mod, good_idx, ref_idx, 'Epoch {0:d}'.format(ee), - unit=unit, scale=scale, plotlim=plotlim) + unit=unit, scale=scale, plotlim=plotlim, save_path=f'{save_path}/Quiver_Residual_{ee}.pdf' if save_path else None) plot_quiver_residuals_orig(tab['x'][:, ee], tab['y'][:, ee], xt_mod, yt_mod, good_idx, ref_idx, tab['x_orig'][:, ee], tab['y_orig'][:, ee], da, 'Epoch {0:d}'.format(ee), - scale=scale_orig, plotlim=plotlim) + scale=scale_orig, plotlim=plotlim, save_path=f'{save_path}/Quiver_Residual_Orig_{ee}.pdf' if save_path else None) plot_mag_scatter(tab['m'][:, ee], tab['m0'], tab['m0_err'], @@ -1237,7 +1251,7 @@ def plot_quiver_residuals_with_orig_all_epochs(tab, trans_list, motion_model_dic good_idx, ref_idx, 'Epoch {0:d}'.format(ee), da=da, xorig=tab['x_orig'][:, ee], yorig=tab['y_orig'][:, ee], - cte_fit=cte_fit, mlim=mlim) + cte_fit=cte_fit, mlim=mlim, save_path=f'{save_path}/Mag_Scatter_{ee}.pdf' if save_path else None) plot_y_scatter(tab['m'][:, ee], tab['m0'], tab['m0_err'], @@ -1247,7 +1261,7 @@ def plot_quiver_residuals_with_orig_all_epochs(tab, trans_list, motion_model_dic good_idx, ref_idx, 'Epoch {0:d}'.format(ee), da=da, xorig=tab['x_orig'][:, ee], yorig=tab['y_orig'][:, ee], - cte_fit=cte_fit, mlim=mlim) + cte_fit=cte_fit, mlim=mlim, save_path=f'{save_path}/Y_Scatter_{ee}.pdf' if save_path else None) # plot_quiver_residuals_orig_angle_xy(tab['x'][:, ee], tab['y'][:, ee], # xt_mod, yt_mod, @@ -1258,7 +1272,7 @@ def plot_quiver_residuals_with_orig_all_epochs(tab, trans_list, motion_model_dic # plot_quiver_residuals_vs_pos_err(dx, dy, good_idx, ref_idx, # 1e3 * tab['xe'][:, ee], 1e3 * tab['ye'][:, ee], # 'positional err (mas)', 'Epoch {0:d}'.format(ee), da=da) - + # Building up average dr for a set of stars. dr = np.hypot(dx, dy) @@ -1271,13 +1285,13 @@ def plot_quiver_residuals_with_orig_all_epochs(tab, trans_list, motion_model_dic dr_good_avg = np.zeros(len(tab), dtype=float) idx = np.where(n_good > 0)[0] dr_good_avg[idx] = dr_good[idx] / n_good[idx] - + dr_ref_avg = np.zeros(len(tab), dtype=float) idx = np.where(n_ref > 0)[0] dr_ref_avg[idx] = dr_ref[idx] / n_ref[idx] - hdr = '{name:>16s} {mag:>5s} {dr:>6s} {x:>6s} {y:>6s} {r:>6s}' - fmt = '{name:16s} {mag:5.2f} {dr:6.4f} {x:6.3f} {y:6.3f} {r:6.3f}' + # hdr = '{name:>16s} {mag:>5s} {dr:>6s} {x:>6s} {y:>6s} {r:>6s}' + # fmt = '{name:16s} {mag:5.2f} {dr:6.4f} {x:6.3f} {y:6.3f} {r:6.3f}' # print() # print('##########') @@ -1301,7 +1315,7 @@ def plot_quiver_residuals_with_orig_all_epochs(tab, trans_list, motion_model_dic return -def plot_mag_scatter_multi_trans_all_epochs(tab_list, trans_list_list, motion_model_dict={}, unit='arcsec', scale=None, plotlim=None, scale_orig=None): +def plot_mag_scatter_multi_trans_all_epochs(tab_list, trans_list_list, unit='arcsec', scale=None, plotlim=None, scale_orig=None): m_t_list = [] x_t_list = [] y_t_list = [] @@ -1314,9 +1328,10 @@ def plot_mag_scatter_multi_trans_all_epochs(tab_list, trans_list_list, motion_mo da_list = [] ntrans = len(tab_list) - motion_model_dict = motion_model.validate_motion_model_dict(motion_model_dict, tab, None) + # motion_model_dict = motion_model.validate_motion_model_dict(motion_model_dict, tab, None) i_all_detected = np.where(~np.any(np.isnan(tab['t']),axis=1))[0][0] - xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], motion_model_dict, allow_alt_models=True) + # xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], motion_model_dict, allow_alt_models=True) + xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.infer_positions(tab['t'][i_all_detected]) for mm in range(ntrans): tab = tab_list[mm] @@ -1790,7 +1805,7 @@ def T_line(m, a, b): def plot_quiver_residuals(x_t, y_t, x_ref, y_ref, good_idx, ref_idx, title, - unit='pixel', scale=None, plotlim=None): + unit='pixel', scale=None, plotlim=None, save_path=None): """ unit : str 'pixel' or 'arcsec' @@ -1827,23 +1842,24 @@ def plot_quiver_residuals(x_t, y_t, x_ref, y_ref, good_idx, ref_idx, title, unit2 = 'mas' - plt.figure(101, figsize=(6,6)) - plt.clf() - q = plt.quiver(x_ref[good_idx], y_ref[good_idx], dx[good_idx], dy[good_idx], + fig, ax = plt.subplots(1, 1, figsize=(6, 6)) + q = ax.quiver(x_ref[good_idx], y_ref[good_idx], dx[good_idx], dy[good_idx], color='black', scale=quiv_scale, angles='xy', alpha=0.5) - plt.quiver(x_ref[good_idx][ref_idx], y_ref[good_idx][ref_idx], dx[good_idx][ref_idx], dy[good_idx][ref_idx], + ax.quiver(x_ref[good_idx][ref_idx], y_ref[good_idx][ref_idx], dx[good_idx][ref_idx], dy[good_idx][ref_idx], color='red', scale=quiv_scale, angles='xy') - plt.quiverkey(q, 0.5, 0.85, quiv_label_val, quiv_label, + ax.quiverkey(q, 0.5, 0.85, quiv_label_val, quiv_label, coordinates='figure', labelpos='E', color='green') - plt.xlabel('X (ref ' + unit + ')') - plt.ylabel('Y (ref ' + unit + ')') - plt.title(title) - plt.axis('equal') + ax.set_xlabel('X (ref ' + unit + ')') + ax.set_ylabel('Y (ref ' + unit + ')') + ax.set_title(title) + ax.axis('equal') if plotlim is not None: - plt.xlim(-1 * plotlim, plotlim) - plt.ylim(-1 * plotlim, plotlim) + ax.set_xlim(-1 * plotlim, plotlim) + ax.set_ylim(-1 * plotlim, plotlim) + plt.tight_layout() + if save_path: + plt.savefig(save_path) plt.show() - plt.pause(1) str_fmt = 'Residuals (mean, std): dx = {0:7.3f} +/- {1:7.3f} {5:s} dy = {2:7.3f} +/- {3:7.3f} {5:s} for {4:s} stars' if len(ref_idx) > 1: @@ -1859,7 +1875,7 @@ def plot_quiver_residuals(x_t, y_t, x_ref, y_ref, good_idx, ref_idx, title, return (dx, dy) -def plot_quiver_residuals_magcolor_all_epochs(tab, motion_model_dict={}, unit='arcsec', scale=None, plotlim=None, lower_mag=18, upper_mag=13): +def plot_quiver_residuals_magcolor_all_epochs(tab, unit='arcsec', scale=None, plotlim=None, lower_mag=18, upper_mag=13): # Keep track of the residuals for averaging. dr_good = np.zeros(len(tab), dtype=float) n_good = np.zeros(len(tab), dtype=int) @@ -1869,9 +1885,10 @@ def plot_quiver_residuals_magcolor_all_epochs(tab, motion_model_dict={}, unit='a idx = np.where((tab['m0'] < lower_mag) & (tab['m0'] > upper_mag))[0] tab = tab[idx] - motion_model_dict = motion_model.validate_motion_model_dict(motion_model_dict, tab, None) + # motion_model_dict = motion_model.validate_motion_model_dict(motion_model_dict, tab, None) i_all_detected = np.where(~np.any(np.isnan(tab['t']),axis=1))[0][0] - xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], motion_model_dict, allow_alt_models=True) + # xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], motion_model_dict, allow_alt_models=True) + xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.infer_positions(tab['t'][i_all_detected]) for ee in range(tab['x'].shape[1]): dt = tab['t'][:, ee] - tab['t0'] @@ -1959,39 +1976,36 @@ def plot_quiver_residuals_magcolor(x_t, y_t, x_ref, y_ref, mag, good_idx, ref_id # norm=norm, # orientation='horizontal') - plt.figure(101, figsize=(6,6)) - plt.clf() - q = plt.quiver(x_ref[good_idx], y_ref[good_idx], dx[good_idx], dy[good_idx], + fig, ax=plt.subplots(1, 1, figsize=(6, 6)) + q = ax.quiver(x_ref[good_idx], y_ref[good_idx], dx[good_idx], dy[good_idx], color=cm(norm(mag[good_idx])), scale=quiv_scale, angles='xy', alpha=0.8) - plt.quiverkey(q, 0.5, 0.85, quiv_label_val, quiv_label, + ax.quiverkey(q, 0.5, 0.85, quiv_label_val, quiv_label, coordinates='figure', labelpos='E', color='green') - plt.colorbar(sm) - plt.xlabel('X (ref ' + unit + ')') - plt.ylabel('Y (ref ' + unit + ')') - plt.title(title + ', Good') - plt.axis('equal') + fig.colorbar(sm, ax=ax) + ax.set_xlabel('X (ref ' + unit + ')') + ax.set_ylabel('Y (ref ' + unit + ')') + ax.set_title(title + ', Good') + ax.axis('equal') if plotlim is not None: - plt.xlim(-1 * plotlim, plotlim) - plt.ylim(-1 * plotlim, plotlim) + ax.set_xlim(-1 * plotlim, plotlim) + ax.set_ylim(-1 * plotlim, plotlim) + plt.tight_layout() plt.show() - plt.pause(1) - plt.figure(102, figsize=(6,6)) - plt.clf() - q = plt.quiver(x_ref[good_idx][ref_idx], y_ref[good_idx][ref_idx], dx[good_idx][ref_idx], dy[good_idx][ref_idx], + fig, ax = plt.subplots(1, 1, figsize=(6,6)) + q = ax.quiver(x_ref[good_idx][ref_idx], y_ref[good_idx][ref_idx], dx[good_idx][ref_idx], dy[good_idx][ref_idx], color=cm(norm(mag[good_idx][ref_idx])), scale=quiv_scale, angles='xy', alpha=0.8) - plt.quiverkey(q, 0.5, 0.85, quiv_label_val, quiv_label, + ax.quiverkey(q, 0.5, 0.85, quiv_label_val, quiv_label, coordinates='figure', labelpos='E', color='green') - plt.colorbar(sm) - plt.xlabel('X (ref ' + unit + ')') - plt.ylabel('Y (ref ' + unit + ')') - plt.title(title + ', Ref') - plt.axis('equal') + fig.colorbar(sm, ax=ax) + ax.set_xlabel('X (ref ' + unit + ')') + ax.set_ylabel('Y (ref ' + unit + ')') + ax.set_title(title + ', Ref') + ax.axis('equal') if plotlim is not None: - plt.xlim(-1 * plotlim, plotlim) - plt.ylim(-1 * plotlim, plotlim) + ax.set_xlim(-1 * plotlim, plotlim) + ax.set_ylim(-1 * plotlim, plotlim) plt.show() - plt.pause(1) str_fmt = 'Residuals (mean, std): dx = {0:7.3f} +/- {1:7.3f} {5:s} dy = {2:7.3f} +/- {3:7.3f} {5:s} for {4:s} stars' if len(ref_idx) > 1: @@ -2010,7 +2024,7 @@ def plot_quiver_residuals_magcolor(x_t, y_t, x_ref, y_ref, mag, good_idx, ref_id def plot_quiver_residuals_orig(x_t, y_t, x_ref, y_ref, good_idx, ref_idx, x_orig, y_orig, da, title, - scale=None, plotlim=None): + scale=None, plotlim=None, save_path=None): """ unit : str 'pixel' or 'arcsec' @@ -2033,8 +2047,8 @@ def plot_quiver_residuals_orig(x_t, y_t, x_ref, y_ref, good_idx, ref_idx, dy /= 0.04 # Residual modulus - r_good = np.hypot(dx[good_idx], dy[good_idx]) - r_ref = np.hypot(dx[good_idx][ref_idx], dy[good_idx][ref_idx]) + # r_good = np.hypot(dx[good_idx], dy[good_idx]) + # r_ref = np.hypot(dx[good_idx][ref_idx], dy[good_idx][ref_idx]) # Residual angle agood = angle_from_xy(dx[good_idx], dy[good_idx]) @@ -2050,21 +2064,23 @@ def plot_quiver_residuals_orig(x_t, y_t, x_ref, y_ref, good_idx, ref_idx, dx_ref_new, dy_ref_new = rotate(dx[good_idx][ref_idx], dy[good_idx][ref_idx], -da) print('Rotation angle between HST and Gaia (deg): ', da) - plt.figure(102, figsize=(6,6)) - plt.clf() - q = plt.quiver(x_orig[good_idx], y_orig[good_idx], dx_good_new, dy_good_new, + fig, ax = plt.subplots(1, 1, figsize=(6, 6)) + q = ax.quiver(x_orig[good_idx], y_orig[good_idx], dx_good_new, dy_good_new, color='black', scale=scale, angles='xy', alpha=0.5) - plt.quiver(x_orig[good_idx][ref_idx], y_orig[good_idx][ref_idx], dx_ref_new, dy_ref_new, + ax.quiver(x_orig[good_idx][ref_idx], y_orig[good_idx][ref_idx], dx_ref_new, dy_ref_new, color='red', scale=scale, angles='xy') - plt.quiverkey(q, 0.5, 0.85, 0.3, '0.3 pix', + ax.quiverkey(q, 0.5, 0.85, 0.3, '0.3 pix', coordinates='figure', labelpos='E', color='green') - plt.xlabel('X (ref pix)') - plt.ylabel('Y (ref pix)') - plt.title(title) - plt.axis('equal') + ax.set_xlabel('X (ref pix)') + ax.set_ylabel('Y (ref pix)') + ax.set_title(title) + ax.axis('equal') if plotlim is not None: - plt.xlim(-1 * plotlim, plotlim) - plt.ylim(-1 * plotlim, plotlim) + ax.set_xlim(-1 * plotlim, plotlim) + ax.set_ylim(-1 * plotlim, plotlim) + plt.tight_layout() + if save_path: + plt.savefig(save_path) plt.show() plt.pause(1) @@ -2169,7 +2185,7 @@ def plot_quiver_residuals_orig_angle_xy(x_t, y_t, x_ref, y_ref, good_idx, ref_id return -def plot_chi2_dist(tab, Ndetect, motion_model_dict={}, xlim=40, n_bins=50, boot_err=False): +def plot_chi2_dist(tab, Ndetect, xlim=40, n_bins=50, boot_err=False): """ tab = flystar table Ndetect = Number of epochs star detected in @@ -2178,9 +2194,10 @@ def plot_chi2_dist(tab, Ndetect, motion_model_dict={}, xlim=40, n_bins=50, boot_ chi2_y_list = [] fnd_list = [] # Number of non-NaN error measurements - motion_model_dict = motion_model.validate_motion_model_dict(motion_model_dict, tab, None) + # motion_model_dict = motion_model.validate_motion_model_dict(motion_model_dict, tab, None) i_all_detected = np.where(~np.any(np.isnan(tab['t']),axis=1))[0][0] - xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], motion_model_dict, allow_alt_models=True) + # xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], motion_model_dict, allow_alt_models=True) + xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.infer_positions(tab['t'][i_all_detected]) for ii in range(len(tab)): # Ignore the NaNs @@ -2254,7 +2271,7 @@ def plot_chi2_dist(tab, Ndetect, motion_model_dict={}, xlim=40, n_bins=50, boot_ return -def plot_chi2_reduced_dist(tab, Ndetect, motion_model_dict={}, xlim=8, n_bins=50, boot_err=False): +def plot_chi2_reduced_dist(tab, Ndetect, xlim=8, n_bins=50, boot_err=False): """ tab = flystar table Ndetect = Number of epochs star detected in @@ -2263,9 +2280,10 @@ def plot_chi2_reduced_dist(tab, Ndetect, motion_model_dict={}, xlim=8, n_bins=50 chi2_y_list = [] fnd_list = [] # Number of non-NaN error measurements - motion_model_dict = motion_model.validate_motion_model_dict(motion_model_dict, tab, None) + # motion_model_dict = motion_model.validate_motion_model_dict(motion_model_dict, tab, None) i_all_detected = np.where(~np.any(np.isnan(tab['t']),axis=1))[0][0] - xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], motion_model_dict, allow_alt_models=True) + # xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], motion_model_dict, allow_alt_models=True) + xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.infer_positions(tab['t'][i_all_detected]) for ii in range(len(tab)): # Ignore the NaNs @@ -2333,7 +2351,7 @@ def plot_chi2_reduced_dist(tab, Ndetect, motion_model_dict={}, xlim=8, n_bins=50 return -def plot_chi2_dist_per_filter(tab, Ndetect, motion_model_dict={}, xlim=40, n_bins=50, filter=None, boot_err=False): +def plot_chi2_dist_per_filter(tab, Ndetect, xlim=40, n_bins=50, filter=None, boot_err=False): """ tab = flystar table Ndetect = Number of epochs star detected in @@ -2342,9 +2360,10 @@ def plot_chi2_dist_per_filter(tab, Ndetect, motion_model_dict={}, xlim=40, n_bin chi2_y_list = [] fnd_list = [] # Number of non-NaN error measurements - motion_model_dict = motion_model.validate_motion_model_dict(motion_model_dict, tab, None) + # motion_model_dict = motion_model.validate_motion_model_dict(motion_model_dict, tab, None) i_all_detected = np.where(~np.any(np.isnan(tab['t']),axis=1))[0][0] - xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], motion_model_dict, allow_alt_models=True) + # xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], motion_model_dict, allow_alt_models=True) + xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.infer_positions(tab['t'][i_all_detected]) for ii in range(len(tab)): # Ignore the NaNs @@ -2425,7 +2444,7 @@ def plot_chi2_dist_per_filter(tab, Ndetect, motion_model_dict={}, xlim=40, n_bin return -def plot_chi2_dist_per_epoch(tab, Ndetect, mlim=[14,21], ylim = [-1, 1], target_idx = 0, motion_model_dict={}, boot_err=False): +def plot_chi2_dist_per_epoch(tab, Ndetect, mlim=[14, 21], ylim=[-1, 1], target_idx=0, boot_err=False): """ tab = flystar table Ndetect = Number of epochs star detected in @@ -2438,9 +2457,10 @@ def plot_chi2_dist_per_epoch(tab, Ndetect, mlim=[14,21], ylim = [-1, 1], target_ sigY_arr = np.nan * np.ones((len(tab['xe']), Ndetect)) m_arr = np.nan * np.ones((len(tab['xe']), Ndetect)) - motion_model_dict = motion_model.validate_motion_model_dict(motion_model_dict, tab, None) + # motion_model_dict = motion_model.validate_motion_model_dict(motion_model_dict, tab, None) i_all_detected = np.where(~np.any(np.isnan(tab['t']),axis=1))[0][0] - xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], motion_model_dict, allow_alt_models=True) + # xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], motion_model_dict, allow_alt_models=True) + xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.infer_positions(tab['t'][i_all_detected]) for ii in range(len(tab['xe'])): # Ignore the NaNs @@ -2748,7 +2768,7 @@ def plot_chi2_dist_mag_per_filter(tab, Ndetect, mlim=40, n_bins=30, xlim=40, fil return -def plot_stars(tab, star_names, motion_model_dict={}, NcolMax=2, epoch_array = None, figsize=(15,25), color_time=False, boot_err=False): +def plot_stars(tab, star_names, NcolMax=2, epoch_array = None, figsize=(15,25), color_time=False, boot_err=False): """ Plot a set of stars positions, flux and residuals over time. @@ -2776,11 +2796,13 @@ def rs(x): x = tab['x0'] y = tab['y0'] r = np.hypot(x, y) - motion_model_dict = motion_model.validate_motion_model_dict(motion_model_dict, tab, None) + # motion_model_dict = motion_model.validate_motion_model_dict(motion_model_dict, tab, None) i_all_detected = np.where(~np.any(np.isnan(tab['t']),axis=1))[0][0] cont_times = np.arange(np.min(tab['t'][i_all_detected]), np.max(tab['t'][i_all_detected]), 0.01) - xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], motion_model_dict, allow_alt_models=True) - xt_cont_all, yt_cont_all, xt_cont_err, yt_cont_err = tab.get_star_positions_at_time(cont_times, motion_model_dict, allow_alt_models=True) + # xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], motion_model_dict, allow_alt_models=True) + xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.infer_positions(tab['t'][i_all_detected]) + # xt_cont_all, yt_cont_all, xt_cont_err, yt_cont_err = tab.get_star_positions_at_time(cont_times, motion_model_dict, allow_alt_models=True) + xt_cont_all, yt_cont_all, xt_cont_err, yt_cont_err = tab.infer_positions(cont_times) for i in range(Nstars): starName = star_names[i] @@ -3167,7 +3189,7 @@ def rs(x): return -def plot_stars_nfilt(tab, star_names, motion_model_dict={}, NcolMax=2, epoch_array_list = None, color_list = None, +def plot_stars_nfilt(tab, star_names, NcolMax=2, epoch_array_list = None, color_list = None, figsize=(15,25), color_time=False, resTicRng=None, save_name=None, boot_err=False): """ Plot a set of stars positions, flux and residuals over time. @@ -3183,9 +3205,10 @@ def plot_stars_nfilt(tab, star_names, motion_model_dict={}, NcolMax=2, epoch_arr def rs(x): return x.reshape(len(x)) - motion_model_dict = motion_model.validate_motion_model_dict(motion_model_dict, tab, None) + # motion_model_dict = motion_model.validate_motion_model_dict(motion_model_dict, tab, None) i_all_detected = np.where(~np.any(np.isnan(tab['t']),axis=1))[0][0] - xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], motion_model_dict, allow_alt_models=True) + # xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], motion_model_dict, allow_alt_models=True) + xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.infer_positions(tab['t'][i_all_detected]) Nstars = len(star_names) Ncols = 3 * np.min([Nstars, NcolMax]) @@ -3201,11 +3224,13 @@ def rs(x): x = tab['x0'] y = tab['y0'] r = np.hypot(x, y) - motion_model_dict = motion_model.validate_motion_model_dict(motion_model_dict, tab, None) + # motion_model_dict = motion_model.validate_motion_model_dict(motion_model_dict, tab, None) i_all_detected = np.where(~np.any(np.isnan(tab['t']),axis=1))[0][0] cont_times = np.arange(np.min(tab['t'][i_all_detected]), np.max(tab['t'][i_all_detected]), 0.01) - xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], motion_model_dict, allow_alt_models=True) - xt_cont_all, yt_cont_all, xt_cont_err, yt_cont_err = tab.get_star_positions_at_time(cont_times, motion_model_dict, allow_alt_models=True) + # xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], motion_model_dict, allow_alt_models=True) + xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.infer_positions(tab['t'][i_all_detected]) + # xt_cont_all, yt_cont_all, xt_cont_err, yt_cont_err = tab.get_star_positions_at_time(cont_times, motion_model_dict, allow_alt_models=True) + xt_cont_all, yt_cont_all, xt_cont_err, yt_cont_err = tab.infer_positions(cont_times) for i in range(Nstars): for ea, epoch_array in enumerate(epoch_array_list): diff --git a/flystar/startables.py b/flystar/startables.py index 0952e15..d934bac 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -893,7 +893,7 @@ def infer_positions(self, times, fill_value=np.nan): Parameters ---------- times : array_like - Times at which to predict positions. + Times at which to predict positions. Scalar, or (N_times,) array, or (N_stars, N_times) array. fill_value : float, optional Value to use for missing data, by default np.nan @@ -928,9 +928,8 @@ def infer_positions(self, times, fill_value=np.nan): unique_motion_models, unique_inv_indices = np.unique(self['motion_model_used'], return_inverse=True) indices_by_motion_model = {key: np.flatnonzero(unique_inv_indices == k) for k, key in enumerate(unique_motion_models)} - + # Prepare fit_params, fixed_params, fit_param_errs for each star - for unique_motion_model, unique_index in indices_by_motion_model.items(): # Create motion model instance motion_model_instance = motion_model.motion_model_map()[unique_motion_model]() @@ -949,8 +948,8 @@ def infer_positions(self, times, fill_value=np.nan): if param_name + '_mm' in self.colnames: col_name = param_name + '_mm' fixed_params[param_name] = self[col_name][unique_index] - - # TODO: vectorize obsLocation handling in motion models + + # TODO: vectorize obsLocation handling in motion models? if (param_name == 'obsLocation'): assert np.unique(fixed_params[param_name]).size == 1, \ "infer_positions: obsLocation fixed parameter has different values for different stars. Vectorized handling not implemented yet." diff --git a/flystar/tests/test_align.py b/flystar/tests/test_align.py index 3937ea6..d55e81e 100644 --- a/flystar/tests/test_align.py +++ b/flystar/tests/test_align.py @@ -13,7 +13,7 @@ def test_MosaicSelfRef(): """ Cross-match and align 4 starlists using the OO version of mosaic lists. """ - list_files = ['A.lis', 'B.lis', 'C.lis', 'D.lis'] + list_files = ['test_data/A.lis', 'test_data/B.lis', 'test_data/C.lis', 'test_data/D.lis'] lists = [starlists.StarList.from_lis_file(lf) for lf in list_files] ########## @@ -86,9 +86,9 @@ def test_MosaicSelfRef_vel_tconst(): """ Cross-match and align 4 starlists using the OO version of mosaic lists. The 4 lists are all taken at the same time (so 0 velocities should result). - + """ - list_files = ['A.lis', 'B.lis', 'C.lis', 'D.lis'] + list_files = ['test_data/A.lis', 'test_data/B.lis', 'test_data/C.lis', 'test_data/D.lis'] lists = [starlists.StarList.from_lis_file(lf) for lf in list_files] ########## @@ -120,7 +120,7 @@ def test_MosaicSelfRef_vel_tconst(): # Check that we have some matched stars... should be at least 35 stars # that are detected in all 4 starlists. idx = np.where(msc.ref_table['n_detect'] == 4)[0] - assert len(idx) > 35 + assert len(idx) > 35 # Check that the transformation error isn't too big valid_err = np.isfinite(msc.ref_table['x0_err']) & np.isfinite(msc.ref_table['y0_err']) & np.isfinite(msc.ref_table['m0_err']) @@ -139,22 +139,21 @@ def test_MosaicSelfRef_vel_tconst(): def test_MosaicSelfRef_vel(): """ Cross-match and align 4 starlists using the OO version of mosaic lists. - """ - list_files = ['A.lis', 'B.lis', 'C.lis', 'D.lis'] + list_files = ['test_data/A.lis', 'test_data/B.lis', 'test_data/C.lis', 'test_data/D.lis'] lists = [starlists.StarList.from_lis_file(lf) for lf in list_files] # Modify the times so that we get velocities out. - lists[0].meta['list_time'] = 2001.4 + lists[0].meta['list_times'] = 2001.4 lists[0]['t'] = 2001.4 - - lists[1].meta['list_time'] = 2002.4 + + lists[1].meta['list_times'] = 2002.4 lists[1]['t'] = 2002.4 - - lists[2].meta['list_time'] = 2003.4 + + lists[2].meta['list_times'] = 2003.4 lists[2]['t'] = 2003.4 - - lists[3].meta['list_time'] = 2004.4 + + lists[3].meta['list_times'] = 2004.4 lists[3]['t'] = 2004.4 @@ -168,7 +167,7 @@ def test_MosaicSelfRef_vel(): verbose=False) msc.fit() - + # Check some of the output quantities on the final table. assert 'x0' in msc.ref_table.colnames assert 'x0_err' in msc.ref_table.colnames @@ -197,7 +196,7 @@ def test_MosaicSelfRef_vel(): for ii in range(4): np.testing.assert_allclose(msc.trans_list[ii].px.c1_0, 1.0, rtol=1e-2) np.testing.assert_allclose(msc.trans_list[ii].py.c0_1, 1.0, rtol=1e-2) - + plt.clf() plt.plot(msc.ref_table['vx'], msc.ref_table['vy'], @@ -208,21 +207,21 @@ def test_MosaicSelfRef_vel(): def test_MosaicToRef(): make_fake_starlists_poly1(seed=42) - ref_file = 'random_ref.fits' - list_files = ['random_0.fits', - 'random_1.fits', - 'random_2.fits', - 'random_3.fits', - 'random_4.fits', - 'random_5.fits', - 'random_6.fits', - 'random_7.fits'] + ref_file = 'test_data/random_ref.fits' + list_files = ['test_data/random_0.fits', + 'test_data/random_1.fits', + 'test_data/random_2.fits', + 'test_data/random_3.fits', + 'test_data/random_4.fits', + 'test_data/random_5.fits', + 'test_data/random_6.fits', + 'test_data/random_7.fits'] ref_list = Table.read(ref_file) # Switch our list to a "increasing to the West" list. ref_list['x0'] *= -1.0 - + lists = [starlists.StarList.read(lf) for lf in list_files] msc = align.MosaicToRef(ref_list, lists, iters=2, @@ -265,15 +264,15 @@ def test_MosaicToRef(): def test_MosaicToRef_p0_vel(): make_fake_starlists_poly0_vel(seed=42) - ref_file = 'random_vel_ref.fits' - list_files = ['random_vel_p0_0.fits', - 'random_vel_p0_1.fits', - 'random_vel_p0_2.fits', - 'random_vel_p0_3.fits'] - #'random_vel_4.fits', - #'random_vel_5.fits', - #'random_vel_6.fits', - #'random_vel_7.fits'] + ref_file = 'test_data/random_vel_ref.fits' + list_files = ['test_data/random_vel_p0_0.fits', + 'test_data/random_vel_p0_1.fits', + 'test_data/random_vel_p0_2.fits', + 'test_data/random_vel_p0_3.fits'] + #'test_data/random_vel_4.fits', + #'test_data/random_vel_5.fits', + #'test_data/random_vel_6.fits', + #'test_data/random_vel_7.fits'] ref_list = Table.read(ref_file) @@ -331,15 +330,15 @@ def test_MosaicToRef_p0_vel(): def test_MosaicToRef_vel(): make_fake_starlists_poly1_vel(seed=42) - ref_file = 'random_vel_ref.fits' - list_files = ['random_vel_0.fits', - 'random_vel_1.fits', - 'random_vel_2.fits', - 'random_vel_3.fits'] - #'random_vel_4.fits', - #'random_vel_5.fits', - #'random_vel_6.fits', - #'random_vel_7.fits'] + ref_file = 'test_data/random_vel_ref.fits' + list_files = ['test_data/random_vel_0.fits', + 'test_data/random_vel_1.fits', + 'test_data/random_vel_2.fits', + 'test_data/random_vel_3.fits'] + #'test_data/random_vel_4.fits', + #'test_data/random_vel_5.fits', + #'test_data/random_vel_6.fits', + #'test_data/random_vel_7.fits'] ref_list = Table.read(ref_file) @@ -370,14 +369,14 @@ def test_MosaicToRef_vel(): assert msc.ref_table['use_in_trans'].shape == msc.ref_table['x0'].shape assert msc.ref_table['used_in_trans'].shape == msc.ref_table['x'].shape - # The velocities should be almost the same as the input + # The velocities should be almost the same as the input # velocities since update_ref_orig == False. assert (msc.ref_table['name']==ref_list['name']).all() np.testing.assert_allclose(msc.ref_table['vx'], ref_list['vx'], rtol=1e-5) np.testing.assert_allclose(msc.ref_table['vy'], ref_list['vy'], rtol=1e-5) ########## - # Align and let velocities be free. + # Align and let velocities be free. ########## msc.update_ref_orig = 'periter' msc.fit() @@ -397,23 +396,23 @@ def test_MosaicToRef_vel(): def test_MosaicToRef_acc(): make_fake_starlists_poly1_acc(seed=42) - ref_file = 'random_acc_ref.fits' - list_files = ['random_acc_0.fits', - 'random_acc_1.fits', - 'random_acc_2.fits', - 'random_acc_3.fits', - 'random_acc_4.fits', - 'random_acc_5.fits', - 'random_acc_6.fits', - 'random_acc_7.fits'] + ref_file = 'test_data/random_acc_ref.fits' + list_files = ['test_data/random_acc_0.fits', + 'test_data/random_acc_1.fits', + 'test_data/random_acc_2.fits', + 'test_data/random_acc_3.fits', + 'test_data/random_acc_4.fits', + 'test_data/random_acc_5.fits', + 'test_data/random_acc_6.fits', + 'test_data/random_acc_7.fits'] ref_list = Table.read(ref_file) # Convert velocities to arcsec/yr - ref_list['vx'] *= 1e-3 - ref_list['vy'] *= 1e-3 - ref_list['vx_err'] *= 1e-3 - ref_list['vy_err'] *= 1e-3 + ref_list['vx0'] *= 1e-3 + ref_list['vy0'] *= 1e-3 + ref_list['vx0_err'] *= 1e-3 + ref_list['vy0_err'] *= 1e-3 # Convert accelerations to arcsec/yr**2 ref_list['ax'] *= 1e-3 @@ -423,7 +422,7 @@ def test_MosaicToRef_acc(): # Switch our list to a "increasing to the West" list. ref_list['x0'] *= -1.0 - ref_list['vx'] *= -1.0 + ref_list['vx0'] *= -1.0 ref_list['ax'] *= -1.0 lists = [starlists.StarList.read(lf) for lf in list_files] @@ -444,7 +443,7 @@ def test_MosaicToRef_acc(): assert msc.ref_table['use_in_trans'].shape == msc.ref_table['x0'].shape assert msc.ref_table['used_in_trans'].shape == msc.ref_table['x'].shape - # The velocities should be almost the same as the input + # The velocities should be almost the same as the input # velocities since update_ref_orig == False. i_orig, i_fit = [],[] for i,star in enumerate(ref_list["name"]): @@ -455,7 +454,7 @@ def test_MosaicToRef_acc(): np.testing.assert_allclose(msc.ref_table['ay'][i_fit], ref_list['ay'][i_orig], rtol=1e-5) ########## - # Align and let velocities be free. + # Align and let velocities be free. ########## msc.update_ref_orig = 'periter' msc.fit() @@ -486,7 +485,7 @@ def test_MosaicToRef_acc(): ax1.set_xlabel('Input ax') ax1.set_ylabel('Ref Table ax') ax1.set_title('Acceleration in X') - + ax2.plot(ref_list['ay'][i_orig], msc.ref_table['ay'][i_fit], '.') ax2.plot([ay_min, ay_max], [ay_min, ay_max], color='C3') ax2.plot([ay_min, ay_max], [ay_min - atol, ay_max - atol], ls='--', color='C3') @@ -504,10 +503,10 @@ def test_MosaicToRef_acc(): def test_MosaicToRef_hst_me(): """ - Test Casey's issue with 'me' not getting propogated + Test Casey's issue with 'me' not getting propogated from the input starlists to the output table. - Use data from MB10-364 microlensing target for the test. + Use data from MB10-364 microlensing target for the test. """ # Target RA and Dec (MOA data download) # ra = '17:57:05.401' @@ -517,6 +516,10 @@ def test_MosaicToRef_hst_me(): my_gaia = Table.read('mb10364_data/my_gaia.fits') my_gaia['me'] = 0.01 + my_gaia.rename_columns( + ['x0e', 'y0e'], + ['x0_err', 'y0_err'] + ) # Gather the list of starlists. For first pass, don't modify the starlists. # Loop through the observations and read them in, in prep for alignment with Gaia epochs = [2011.83, 2012.73, 2013.81] @@ -560,15 +563,15 @@ def test_MosaicToRef_hst_me(): def test_bootstrap(): """ - Test to make sure calc_bootstrap_error() call is working + Test to make sure calc_bootstrap_error() call is working properly (e.g., only called when user calls calc_bootstrap_error, n_boot param for calc_bootstrap_error only, boot_epochs_min working, etc.) """ # Read in starlists for MosaicToRef - ref = Table.read('ref_vel.lis', format='ascii') - list1 = Table.read('E.lis', format='ascii') - list2 = Table.read('F.lis', format='ascii') + ref = Table.read('test_data/ref_vel.lis', format='ascii') + list1 = Table.read('test_data/E.lis', format='ascii') + list2 = Table.read('test_data/F.lis', format='ascii') list1 = starlists.StarList.from_table(list1) list2 = starlists.StarList.from_table(list2) @@ -669,22 +672,21 @@ def test_bootstrap(): def test_calc_vel_in_bootstrap(): """ Check calc_vel_in_bootstrap performance in calc_bootstrap_errors() - - Only calculate velocity bootstrap (e.g., bootstrap over epochs and + + Only calculate velocity bootstrap (e.g., bootstrap over epochs and calculating proper motions) if calc_vel_in_bootstrap=True. """ import copy # Define match parameters - ref = Table.read('ref_vel.lis', format='ascii') - - list1 = Table.read('E.lis', format='ascii') - list2 = Table.read('F.lis', format='ascii') + ref = Table.read('test_data/ref_vel.lis', format='ascii') + list1 = Table.read('test_data/E.lis', format='ascii') + list2 = Table.read('test_data/F.lis', format='ascii') list1 = starlists.StarList.from_table(list1) list2 = starlists.StarList.from_table(list2) - + # Set parameters for alignment transModel = transforms.PolyTransform trans_args = {'order':2} @@ -736,19 +738,19 @@ def test_calc_vel_in_bootstrap(): assert 'xe_boot' in match.ref_table.keys() assert np.sum(np.isnan(match.ref_table['xe_boot'])) == 0 assert 'vx_err_boot' not in match.ref_table.keys() - + return def test_transform_xym(): """ Test to make sure transforms are being done to mags only - if mag_trans = True. This can cause subtle bugs + if mag_trans = True. This can cause subtle bugs otherwise """ #---Align 1: self.mag_Trans = False---# - ref = Table.read('ref_vel.lis', format='ascii') - list1 = Table.read('E.lis', format='ascii') - list2 = Table.read('F.lis', format='ascii') + ref = Table.read('test_data/ref_vel.lis', format='ascii') + list1 = Table.read('test_data/E.lis', format='ascii') + list2 = Table.read('test_data/F.lis', format='ascii') list1 = starlists.StarList.from_table(list1) list2 = starlists.StarList.from_table(list2) @@ -785,7 +787,7 @@ def test_transform_xym(): match1.fit() match1.calc_bootstrap_errors(n_boot=n_boot) - # Make sure all transformations have mag_offset = 0 + # Make sure all transformations have mag_offset = 0 trans_list = match1.trans_list for ii in trans_list: @@ -794,7 +796,7 @@ def test_transform_xym(): # Check that no mag transformation has been applied to m col in ref_table tab1 = match1.ref_table assert np.all(tab1['m'] == tab1['m_orig']) - + # Check me_boost == 0 or really small (should be the case # since we don't transform mags) assert np.isclose(np.max(tab1['me_boot']), 0, rtol=10**-5) @@ -827,15 +829,15 @@ def test_transform_xym(): for ii in trans_list2: assert ii.mag_offset > 20 - # Make sure final table mags have transform applied (i.e, + # Make sure final table mags have transform applied (i.e, tab2 = match2.ref_table assert np.all(tab2['m'] != tab2['m_orig']) - + # Check me_boost > 0 assert np.min(tab2['me_boot']) > 10**-3 print('Done mag_trans = True case') - + return def test_MosaicToRef_mag_bug(): @@ -847,9 +849,9 @@ def test_MosaicToRef_mag_bug(): ref_list = starlists.StarList.read('random_vel_0.fits') lists = [ref_list] - msc = align.MosaicToRef(ref_list, lists, + msc = align.MosaicToRef(ref_list, lists, mag_trans=True, - iters=1, + iters=1, dr_tol=[0.2], dm_tol=[1], outlier_tol=None, trans_class=transforms.PolyTransform, @@ -874,7 +876,7 @@ def test_MosaicToRef_mag_bug(): def test_masked_cols(): """ Test to make sure analysis.prepare_gaia_for_flystar - produces an astropy.table.Table, NOT a masked column + produces an astropy.table.Table, NOT a masked column table. MosaicToRef cannot handle masked column tables. Also make sure this example works, since we use it for the examples @@ -916,10 +918,10 @@ def test_masked_cols(): msc = align.MosaicToRef(my_gaia, list_of_starlists, iters=2, dr_tol=[0.2, 0.1], dm_tol=[1, 1], trans_class=transforms.PolyTransform, - trans_args=[{'order': 1}, {'order': 1}], + trans_args=[{'order': 1}, {'order': 1}], motion_models=['Linear'], use_ref_new=False, - update_ref_orig=False, + update_ref_orig=False, mag_trans=True, init_guess_mode='name', verbose=True) @@ -941,7 +943,7 @@ def make_fake_starlists_shifts(): # Save original positions as reference (1st) list. fmt = '{0:10s} {1:5.2f} 2015.0 {2:9.4f} {3:9.4f} 0 0 0 0\n' - _out = open('random_0.lis', 'w') + _out = open('test_data/random_0.lis', 'w') for ii in range(N_stars): _out.write(fmt.format(name[ii], m[ii], x[ii], y[ii])) _out.close() @@ -966,7 +968,7 @@ def make_fake_starlists_shifts(): mnew = m + np.random.randn(N_stars) * 0.05 - _out = open('random_shift_{0:d}.lis'.format(ss+1), 'w') + _out = open('test_data/random_shift_{0:d}.lis'.format(ss+1), 'w') for ii in range(N_stars): _out.write(fmt.format(name[ii], mnew[ii], xnew[ii], ynew[ii])) _out.close() @@ -977,7 +979,7 @@ def make_fake_starlists_poly1(seed=-1): # If seed >=0, then set random seed to that value if seed >= 0: np.random.seed(seed=seed) - + N_stars = 200 x0 = np.random.rand(N_stars) * 10.0 # arcsec (increasing to East) @@ -992,19 +994,19 @@ def make_fake_starlists_poly1(seed=-1): x0e = np.abs(x0e) y0e = np.abs(y0e) m0e = np.abs(m0e) - + name = ['star_{0:03d}'.format(ii) for ii in range(N_stars)] # Make an StarList lis = starlists.StarList([name, m0, m0e, x0, x0e, y0, y0e, t0], names = ('name', 'm0', 'm0_err', 'x0', 'x0_err', 'y0', 'y0_err', 't0')) - + sdx = np.argsort(m0) lis = lis[sdx] # Save original positions as reference (1st) list # in a StarList format (with velocities). - lis.write('random_ref.fits', overwrite=True) + lis.write('test_data/random_ref.fits', overwrite=True) ########## # Shifts @@ -1020,14 +1022,14 @@ def make_fake_starlists_poly1(seed=-1): [[-13.0, 0.99, 1e-5], [ 150, 2e-5, 1.002]], [[ 94.0, 1.00, 9e-6], [-182.0, 0.0, 0.99]]] mag_trans = [0.1, 0.4, 0.0, -0.3, 0.2, 0.0, -0.1, -0.3] - + # Convert into pixels (undistorted) with the following info. scale = 0.01 # arcsec / pix shift = [1.0, 1.0] # pix for ss in range(len(times)): dt = times[ss] - lis['t0'] - + x = lis['x0'] y = lis['y0'] t = np.ones(N_stars) * times[ss] @@ -1055,7 +1057,7 @@ def make_fake_starlists_poly1(seed=-1): new_lis = starlists.StarList([lis['name'], md, mde, xd, xde, yd, yde, t], names=('name', 'm', 'me', 'x', 'xe', 'y', 'ye', 't')) - new_lis.write('random_{0:d}.fits'.format(ss), overwrite=True) + new_lis.write('test_data/random_{0:d}.fits'.format(ss), overwrite=True) return (xy_trans,mag_trans) @@ -1063,7 +1065,7 @@ def make_fake_starlists_poly0_vel(seed=-1): # If seed >=0, then set random seed to that value if seed >= 0: np.random.seed(seed=seed) - + N_stars = 200 x0 = np.random.rand(N_stars) * 10.0 # arcsec (increasing to East) @@ -1084,21 +1086,21 @@ def make_fake_starlists_poly0_vel(seed=-1): m0e = np.abs(m0e) vxe = np.abs(vxe) vye = np.abs(vye) - + name = ['star_{0:03d}'.format(ii) for ii in range(N_stars)] # Make an StarList lis = starlists.StarList([name, m0, m0e, x0, x0e, y0, y0e, vx, vxe, vy, vye, t0], names = ('name', 'm0', 'm0_err', 'x0', 'x0_err', 'y0', 'y0_err', 'vx', 'vx_err', 'vy', 'vy_err', 't0')) - + sdx = np.argsort(m0) lis = lis[sdx] # Save original positions as reference (1st) list # in a StarList format (with velocities). - lis.write('random_vel_ref.fits', overwrite=True) - + lis.write('test_data/random_vel_ref.fits', overwrite=True) + ########## # Propogate to new times and distort. ########## @@ -1117,10 +1119,10 @@ def make_fake_starlists_poly0_vel(seed=-1): # Convert into pixels (undistorted) with the following info. scale = 0.01 # arcsec / pix shift = [1.0, 1.0] # pix - + for ss in range(len(times)): dt = times[ss] - lis['t0'] - + x = lis['x0'] + (lis['vx']/1e3) * dt y = lis['y0'] + (lis['vy']/1e3) * dt t = np.ones(N_stars) * times[ss] @@ -1148,7 +1150,7 @@ def make_fake_starlists_poly0_vel(seed=-1): new_lis = starlists.StarList([lis['name'], md, mde, xd, xde, yd, yde, t], names=('name', 'm', 'me', 'x', 'xe', 'y', 'ye', 't')) - new_lis.write('random_vel_p0_{0:d}.fits'.format(ss), overwrite=True) + new_lis.write('test_data/random_vel_p0_{0:d}.fits'.format(ss), overwrite=True) return (xy_trans, mag_trans) @@ -1157,7 +1159,7 @@ def make_fake_starlists_poly1_vel(seed=-1): # If seed >=0, then set random seed to that value if seed >= 0: np.random.seed(seed=seed) - + N_stars = 200 x0 = np.random.rand(N_stars) * 10.0 # arcsec (increasing to East) @@ -1178,21 +1180,21 @@ def make_fake_starlists_poly1_vel(seed=-1): m0e = np.abs(m0e) vxe = np.abs(vxe) vye = np.abs(vye) - + name = ['star_{0:03d}'.format(ii) for ii in range(N_stars)] # Make an StarList lis = starlists.StarList([name, m0, m0e, x0, x0e, y0, y0e, vx, vxe, vy, vye, t0], names = ('name', 'm0', 'm0_err', 'x0', 'x0_err', 'y0', 'y0_err', 'vx', 'vx_err', 'vy', 'vy_err', 't0')) - + sdx = np.argsort(m0) lis = lis[sdx] # Save original positions as reference (1st) list # in a StarList format (with velocities). - lis.write('random_vel_ref.fits', overwrite=True) - + lis.write('test_data/random_vel_ref.fits', overwrite=True) + ########## # Propogate to new times and distort. ########## @@ -1211,10 +1213,10 @@ def make_fake_starlists_poly1_vel(seed=-1): # Convert into pixels (undistorted) with the following info. scale = 0.01 # arcsec / pix shift = [1.0, 1.0] # pix - + for ss in range(len(times)): dt = times[ss] - lis['t0'] - + x = lis['x0'] + (lis['vx']/1e3) * dt y = lis['y0'] + (lis['vy']/1e3) * dt t = np.ones(N_stars) * times[ss] @@ -1242,7 +1244,7 @@ def make_fake_starlists_poly1_vel(seed=-1): new_lis = starlists.StarList([lis['name'], md, mde, xd, xde, yd, yde, t], names=('name', 'm', 'me', 'x', 'xe', 'y', 'ye', 't')) - new_lis.write('random_vel_{0:d}.fits'.format(ss), overwrite=True) + new_lis.write('test_data/random_vel_{0:d}.fits'.format(ss), overwrite=True) return (xy_trans, mag_trans) @@ -1250,7 +1252,7 @@ def make_fake_starlists_poly1_acc(seed=-1): # If seed >=0, then set random seed to that value if seed >= 0: np.random.seed(seed=seed) - + N_stars = 200 x0 = np.random.rand(N_stars) * 10.0 # arcsec (increasing to East) @@ -1277,7 +1279,7 @@ def make_fake_starlists_poly1_acc(seed=-1): vye = np.abs(vye) axe = np.abs(axe) aye = np.abs(aye) - + name = ['star_{0:03d}'.format(ii) for ii in range(N_stars)] # Make an StarList @@ -1291,14 +1293,14 @@ def make_fake_starlists_poly1_acc(seed=-1): 'vx0', 'vx0_err', 'vy0', 'vy0_err', 'ax', 'ax_err', 'ay', 'ay_err', 't0')) - + sdx = np.argsort(m0) lis = lis[sdx] # Save original positions as reference (1st) list # in a StarList format (with velocities). - lis.write('random_acc_ref.fits', overwrite=True) - + lis.write('test_data/random_acc_ref.fits', overwrite=True) + ########## # Propogate to new times and distort. ########## @@ -1317,10 +1319,10 @@ def make_fake_starlists_poly1_acc(seed=-1): # Convert into pixels (undistorted) with the following info. scale = 0.01 # arcsec / pix shift = [1.0, 1.0] # pix - + for ss in range(len(times)): dt = times[ss] - lis['t0'] - + x = lis['x0'] + (lis['vx0']/1e3) * dt + 0.5*(lis['ax']/1e3) * dt**2 y = lis['y0'] + (lis['vy0']/1e3) * dt + 0.5*(lis['ay']/1e3) * dt**2 t = np.ones(N_stars) * times[ss] @@ -1348,15 +1350,15 @@ def make_fake_starlists_poly1_acc(seed=-1): new_lis = starlists.StarList([lis['name'], md, mde, xd, xde, yd, yde, t], names=('name', 'm', 'me', 'x', 'xe', 'y', 'ye', 't')) - new_lis.write('random_acc_{0:d}.fits'.format(ss), overwrite=True) + new_lis.write('test_data/random_acc_{0:d}.fits'.format(ss), overwrite=True) return (xy_trans, mag_trans) - + def make_fake_starlists_poly1_par(seed=-1): # If seed >=0, then set random seed to that value if seed >= 0: np.random.seed(seed=seed) - + N_stars = 200 x0 = np.random.rand(N_stars) * 10.0 # arcsec (increasing to East) @@ -1380,7 +1382,7 @@ def make_fake_starlists_poly1_par(seed=-1): vxe = np.abs(vxe) vye = np.abs(vye) pie = np.abs(pie) - + name = ['star_{0:03d}'.format(ii) for ii in range(N_stars)] # Make an StarList @@ -1394,14 +1396,14 @@ def make_fake_starlists_poly1_par(seed=-1): 'vx', 'vx_err', 'vy', 'vy_err', 'pi', 'pi_err', 't0')) - + sdx = np.argsort(m0) lis = lis[sdx] # Save original positions as reference (1st) list # in a StarList format (with velocities). - lis.write('random_par_ref.fits', overwrite=True) - + lis.write('test_data/random_par_ref.fits', overwrite=True) + ########## # Propogate to new times and distort. ########## @@ -1412,7 +1414,7 @@ def make_fake_starlists_poly1_par(seed=-1): [[ 0.0, 1.00, 0.0], [ 0.0, 0.0, 1.0]], [[250.0, 0.97, 2e-5], [-250.0, 1e-5, 1.001]]] mag_trans = [0.1, 0.4, 0.0, -0.3]''' - + times = [2018.5, 2019.0, 2019.5, 2020.0, 2020.5, 2021.0, 2021.5, 2022.0] xy_trans = [[[ 6.5, 0.99, 1e-5], [ 10.1, 1e-5, 0.99]], [[100.3, 0.98, 1e-5], [ 50.5, 9e-6, 1.001]], @@ -1427,10 +1429,10 @@ def make_fake_starlists_poly1_par(seed=-1): # Convert into pixels (undistorted) with the following info. scale = 0.01 # arcsec / pix shift = [1.0, 1.0] # pix - + for ss in range(len(times)): dt = times[ss] - lis['t0'] - + par_mod = motion_model.Parallax(pa=0,ra=18.0, dec=-30.0) par_mod_dat = par_mod.get_batch_pos_at_time(dt+lis['t0'], x0=lis['x0'],vx=lis['vx']/1e3, pi=lis['pi'], y0=lis['y0'], vy=lis['vy']/1e3, t0=lis['t0']) @@ -1460,6 +1462,6 @@ def make_fake_starlists_poly1_par(seed=-1): new_lis = starlists.StarList([lis['name'], md, mde, xd, xde, yd, yde, t], names=('name', 'm', 'me', 'x', 'xe', 'y', 'ye', 't')) - new_lis.write('random_par_{0:d}.fits'.format(ss), overwrite=True) + new_lis.write('test_data/random_par_{0:d}.fits'.format(ss), overwrite=True) return (xy_trans, mag_trans) \ No newline at end of file diff --git a/flystar/tests/A.lis b/flystar/tests/test_data/A.lis similarity index 100% rename from flystar/tests/A.lis rename to flystar/tests/test_data/A.lis diff --git a/flystar/tests/B.lis b/flystar/tests/test_data/B.lis similarity index 100% rename from flystar/tests/B.lis rename to flystar/tests/test_data/B.lis diff --git a/flystar/tests/C.lis b/flystar/tests/test_data/C.lis similarity index 100% rename from flystar/tests/C.lis rename to flystar/tests/test_data/C.lis diff --git a/flystar/tests/D.lis b/flystar/tests/test_data/D.lis similarity index 100% rename from flystar/tests/D.lis rename to flystar/tests/test_data/D.lis diff --git a/flystar/tests/E.lis b/flystar/tests/test_data/E.lis similarity index 100% rename from flystar/tests/E.lis rename to flystar/tests/test_data/E.lis diff --git a/flystar/tests/F.lis b/flystar/tests/test_data/F.lis similarity index 100% rename from flystar/tests/F.lis rename to flystar/tests/test_data/F.lis diff --git a/flystar/tests/coveragerc b/flystar/tests/test_data/coveragerc similarity index 100% rename from flystar/tests/coveragerc rename to flystar/tests/test_data/coveragerc diff --git a/flystar/tests/ref.lis b/flystar/tests/test_data/ref.lis similarity index 100% rename from flystar/tests/ref.lis rename to flystar/tests/test_data/ref.lis diff --git a/flystar/tests/ref_vel.lis b/flystar/tests/test_data/ref_vel.lis similarity index 100% rename from flystar/tests/ref_vel.lis rename to flystar/tests/test_data/ref_vel.lis diff --git a/flystar/tests/test_catalog.fits b/flystar/tests/test_data/test_catalog.fits similarity index 100% rename from flystar/tests/test_catalog.fits rename to flystar/tests/test_data/test_catalog.fits From 316e79d0e58f2acdaf74caee1de45a9cc8c1eec4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Sun, 8 Feb 2026 19:35:42 -0800 Subject: [PATCH 162/254] Fixed testing errors (Except general_match); Fixed parallax align error; motion_model_used column does not need to be set separately outside of the motion model fit function --- .gitignore | 1 + flystar/align.py | 523 ++++++++++++++++++----------- flystar/match.py | 16 +- flystar/motion_model.py | 107 ++++-- flystar/parallax.py | 18 +- flystar/startables.py | 68 ++-- flystar/tests/test_align.py | 6 +- flystar/tests/test_match.py | 7 +- flystar/tests/test_motion_model.py | 16 +- flystar/tests/test_starlist.py | 8 +- flystar/tests/test_startable.py | 18 +- flystar/tests/test_transforms.py | 4 +- 12 files changed, 489 insertions(+), 303 deletions(-) diff --git a/.gitignore b/.gitignore index b291312..17d4b90 100644 --- a/.gitignore +++ b/.gitignore @@ -1,5 +1,6 @@ *.png *random*.fits +*random*.lis *.DS_Store *.pyc .ipynb_checkpoints/ diff --git a/flystar/align.py b/flystar/align.py index ed41e8e..975fe05 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -11,6 +11,7 @@ import warnings import pickle from astropy.utils.exceptions import AstropyUserWarning +from .motion_model import Empty, Fixed class MosaicSelfRef(object): def __init__( @@ -234,7 +235,7 @@ def = None. If not None, then this should contain an array or list of transform motion_models.append(all_mm_map['Empty']) if 'Fixed' not in mm_names: motion_models.append(all_mm_map['Fixed']) - + # Sort by increasing n_params motion_models = sorted(motion_models, key=lambda mm: mm.n_params) self.motion_models = motion_models @@ -407,16 +408,16 @@ def fit(self): all_epochs = get_all_epochs(self.ref_table) self.ref_table.meta['list_times'] = all_epochs - # Update chi2 values in ref table, as motion_model_used may have changed - x_inferred, y_inferred, _, _ = self.ref_table.infer_positions(all_epochs) - chi2_x_2d = ((self.ref_table['x'] - x_inferred) / self.ref_table['xe'])**2 - chi2_y_2d = ((self.ref_table['y'] - y_inferred) / self.ref_table['ye'])**2 - chi2_x = np.nansum(chi2_x_2d, axis=1) - chi2_y = np.nansum(chi2_y_2d, axis=1) - chi2_x[~np.isfinite(chi2_x_2d).any(axis=1)] = np.nan - chi2_y[~np.isfinite(chi2_y_2d).any(axis=1)] = np.nan - self.ref_table['chi2_x'] = chi2_x - self.ref_table['chi2_y'] = chi2_y + # # Update chi2 values in ref table, as motion_model_used may have changed + # x_inferred, y_inferred, _, _ = self.ref_table.infer_positions(all_epochs) + # chi2_x_2d = ((self.ref_table['x'] - x_inferred) / self.ref_table['xe'])**2 + # chi2_y_2d = ((self.ref_table['y'] - y_inferred) / self.ref_table['ye'])**2 + # chi2_x = np.nansum(chi2_x_2d, axis=1) + # chi2_y = np.nansum(chi2_y_2d, axis=1) + # chi2_x[~np.isfinite(chi2_x_2d).any(axis=1)] = np.nan + # chi2_y[~np.isfinite(chi2_y_2d).any(axis=1)] = np.nan + # self.ref_table['chi2_x'] = chi2_x + # self.ref_table['chi2_y'] = chi2_y if self.save_path: with open(self.save_path, 'wb') as file: @@ -456,11 +457,12 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar ### Initial match and transform: 1st order (if we haven't already). if trans is None: # Only use "use_in_trans" reference stars, even for initial guessing. - keepers = np.where(ref_list['use_in_trans'] == True)[0] + keepers = ref_list['use_in_trans'] trans = trans_initial_guess( ref_list[keepers], star_list_orig_trim, self.trans_args[0], + motion_models=self.motion_models, mode=self.init_guess_mode, order=self.init_order, verbose=self.verbose, @@ -485,18 +487,18 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar keepers = self.outlier_rejection_indices(star_list_T[idx1], ref_list[idx2], outlier_tol) if self.verbose > 1: - print( ' Rejected ', len(idx1) - len(keepers), ' outliers.' ) + print( ' Rejected ', len(idx1) - sum(keepers), ' outliers.' ) idx1 = idx1[keepers] idx2 = idx2[keepers] # Only use stars specified by "use_in_trans" column. if 'use_in_trans' in ref_list.colnames: - keepers = np.where(ref_list[idx2]['use_in_trans'] == True)[0] + keepers = ref_list[idx2]['use_in_trans'] if self.verbose > 1: - print( ' Rejected ', len(idx1) - len(keepers), ' with use_in_trans=False.' ) - + print( ' Rejected ', len(idx1) - sum(keepers), ' with use_in_trans=False.' ) + idx1 = idx1[keepers] idx2 = idx2[keepers] @@ -578,15 +580,15 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar ### Update the "average" values to be used as the reference frame for the next list. keep_ref_orig = (self.update_ref_orig==False) or (self.update_ref_orig=='atend') or (self.update_ref_orig=='periter' and ii<(len(self.star_lists) - 1)) if keep_ref_orig and ii < (len(self.star_lists) - 1): - keep_orig = np.where(self.ref_table['ref_orig'] | np.isnan(self.ref_table['x'][:,ii]))[0] + keep_orig = self.ref_table['ref_orig'] | np.isnan(self.ref_table['x'][:,ii]) elif keep_ref_orig: - keep_orig = np.where(self.ref_table['ref_orig'])[0] + keep_orig = self.ref_table['ref_orig'] elif ii < (len(self.star_lists) - 1): - keep_orig = np.where(np.isnan(self.ref_table['x'][:,ii]))[0] + keep_orig = np.isnan(self.ref_table['x'][:,ii]) else: keep_orig=None self.update_ref_table_aggregates(keep_orig=keep_orig) - + # Print out some metrics if self.verbose > 0: msg1 = ' {0:2s} (mean and std) for {1:10s}: {2:8.5f} +/- {3:8.5f}' @@ -754,14 +756,15 @@ def setup_ref_table_from_starlist(self, star_list): if 'motion_model_input' not in ref_table.colnames: ref_table.add_column(Column(np.repeat(self.motion_models[-1].name, len(ref_table)), name='motion_model_input')) # FIXME: Why do we need to set motion_model_used here before fitting? - if 'motion_model_used' not in ref_table.colnames: - # Order self.motion_models by decreasing n_params - sorted_mms = sorted(self.motion_models, key=lambda mm: mm.n_params, reverse=True) - # Save the most complex motion model that can infer the positions with the existing columns. - for mm in sorted_mms: - if all([_ in ref_table.colnames for _ in mm.fit_param_names]) and all([_ in ref_table.colnames for _ in mm.fixed_param_names]): - ref_table.add_column(Column(np.repeat(mm.name, len(ref_table)), name='motion_model_used')) - break + # if 'motion_model_used' not in ref_table.colnames: + # # Order self.motion_models by decreasing n_params + # sorted_mms = sorted(self.motion_models, key=lambda mm: mm.n_params, reverse=True) + # # Save the most complex motion model that can infer the positions with the existing columns. + # for mm in sorted_mms: + # if all([_ in ref_table.colnames for _ in mm.fit_param_names]) and all([_ in ref_table.colnames for _ in mm.fixed_param_names]): + # ref_table.add_column(Column(np.repeat(mm.name, len(ref_table)), name='motion_model_used')) + # break + return ref_table def apply_mag_lim_via_use_in_trans(self, ref_list, ref_mag_lim): @@ -811,8 +814,8 @@ def outlier_rejection_indices(self, star_list, ref_list, outlier_tol, verbose=Tr Returns ---------- - keepers : nd.array - The indicies of the stars to keep. + keepers : boolean array + The boolean array of the stars to keep. """ # Optionally propogate the reference positions forward in time. xref = ref_list['x'] @@ -824,11 +827,11 @@ def outlier_rejection_indices(self, star_list, ref_list, outlier_tol, verbose=Tr resid_on_old_trans = np.hypot(x_resid_on_old_trans, y_resid_on_old_trans) threshold = outlier_tol * resid_on_old_trans.std() - keepers = np.where(resid_on_old_trans < threshold)[0] + keepers = resid_on_old_trans < threshold if verbose: msg = ' Outlier Rejection: Keeping {0:d} of {1:d}' - print(msg.format(len(keepers), len(resid_on_old_trans))) + print(msg.format(sum(keepers), len(resid_on_old_trans))) return keepers @@ -895,21 +898,29 @@ def update_ref_table_from_list(self, star_list, star_list_T, ii, idx_ref, idx_li return def update_ref_table_aggregates(self, keep_orig=None, n_boot=0): - """ - Average positions or fit velocities. + """ Average positions or fit velocities. Average magnitudes. Calculate bootstrap errors if desired. Update the use_in_trans values as needed. TODO: ???? Updates aggregate columns in self.ref_table in place. + + + Parameters + ---------- + keep_orig : array-like of bool, optional + Boolean array indicating which stars to keep original values for, by default None + n_boot : int, optional + Number of bootstrap iterations, by default 0 """ # Keep track of the original reference values. # In certain cases, we will NOT update these. - if (keep_orig is not None) and (len(keep_orig) > 0): + if (keep_orig is not None) and (sum(keep_orig) > 0): vals_orig = {} vals_orig['m0'] = self.ref_table['m0'][keep_orig] vals_orig['m0_err'] = self.ref_table['m0_err'][keep_orig] + # Collect all motion model parameter names motion_model_class_names = [] if 'motion_model_input' in self.ref_table.keys(): motion_model_class_names += self.ref_table['motion_model_input'].tolist() @@ -918,18 +929,25 @@ def update_ref_table_aggregates(self, keep_orig=None, n_boot=0): vals_orig['motion_model_used'] = self.ref_table['motion_model_used'][keep_orig] motion_model_col_names = motion_model.motion_model_param_names(motion_model_class_names, with_errors=True, with_fixed=True) for mm in motion_model_col_names: - if mm in self.ref_table.keys(): + if f'{mm}_mm' in self.ref_table.keys(): + vals_orig[mm] = self.ref_table[mm][keep_orig] + elif mm in self.ref_table.keys(): vals_orig[mm] = self.ref_table[mm][keep_orig] - fit_star_idxs = np.array([idx for idx in range(len(self.ref_table)) if idx not in keep_orig], dtype=int) + fit_star_idxs = ~keep_orig else: fit_star_idxs = None - # Figure out whether motion fits are necessary if ('motion_model_input' in self.ref_table.keys()) and np.all(self.ref_table['motion_model_input']=='Fixed'): + self.ref_table.fit_motion_model( + motion_models=['Fixed'], + weighting=self.vel_weighting, + use_scipy=self.use_scipy, + absolute_sigma=self.absolute_sigma, + verbose=self.verbose + ) weighted_xy = ('xe' in self.ref_table.colnames) and ('ye' in self.ref_table.colnames) weighted_m = ('me' in self.ref_table.colnames) self.ref_table.combine_lists_xym(weighted_xy=weighted_xy, weighted_m=weighted_m) - else: self.ref_table.fit_motion_model( motion_models=self.motion_models, @@ -941,7 +959,7 @@ def update_ref_table_aggregates(self, keep_orig=None, n_boot=0): bootstrap=n_boot, verbose=self.verbose ) - if (keep_orig is not None) and (len(keep_orig) > 0): + if (keep_orig is not None) and (sum(keep_orig) > 0): # Determine motion_model_used for keep_orig stars # Filter possible motion models based on available columns motion_models_possible = [] @@ -949,12 +967,12 @@ def update_ref_table_aggregates(self, keep_orig=None, n_boot=0): required_columns = mm.fit_param_names + mm.fixed_param_names if all(col in self.ref_table.colnames for col in required_columns): motion_models_possible.append((mm, required_columns)) - - # Check if values are finite for required columns + + # Check if values are finite for required columns in possible motion models motion_model_used = [] - for k in keep_orig: + for k in np.where(keep_orig)[0]: for mm, req in motion_models_possible[::-1]: - if all(np.isfinite(self.ref_table[k][col]) for col in req): + if all(np.isfinite(self.ref_table[k][col]) for col in req if self.ref_table[col].dtype.kind in 'f'): motion_model_used.append(mm.name) break @@ -969,7 +987,7 @@ def update_ref_table_aggregates(self, keep_orig=None, n_boot=0): self.ref_table.combine_lists('m', weights_col=weights_col, ismag=True) # Replace the originals if we are supposed to keep them fixed. - if (keep_orig is not None) and (len(keep_orig) > 0): + if (keep_orig is not None) and (sum(keep_orig) > 0): for val in vals_orig.keys(): self.ref_table[val][keep_orig] = vals_orig[val] @@ -1021,6 +1039,10 @@ def get_weights_for_lists(self, ref_list, star_list): # Fix bad weights: weight[bad] = 0.0 + if weight is not None and np.all(weight == 0.0): + # Catch the case where all weights were bad. + weight = None + return weight @@ -1043,7 +1065,7 @@ def match_lists(self, dr_tol, dm_tol): else: star_list_T.transform_xy(self.trans_list[ii]) - xref, yref = infer_positions(star_list_T['t'][0], self.ref_table) + xref, yref = infer_positions(star_list_T['t'][0], self.ref_table, self.motion_models) mref = self.ref_table['m0'] idx_lis, idx_ref, dr, dm = match.match(star_list_T['x'], star_list_T['y'], star_list_T['m'], @@ -1075,23 +1097,50 @@ def get_ref_list_from_table(self, epoch): """ # Reference stars will be named. name = self.ref_table['name'] - + # Calculate x, y, xe, ye + # x, y, xe, ye = infer_positions(epoch, self.ref_table, self.motion_models, return_errors=True) if ('motion_model_used' in self.ref_table.colnames): x, y, xe, ye = self.ref_table.infer_positions(epoch) else: - # No velocities... just used average positions. - x = self.ref_table['x0'] - y = self.ref_table['y0'] - - if 'x0_err' in self.ref_table.colnames: - xe = self.ref_table['x0_err'] - ye = self.ref_table['y0_err'] - else: - xe = None - ye = None + # Otherwise, infer positions using the most complex motion model with the existing columns, until it reaches Fixed or Empty + for mm in self.motion_models[::-1]: + required_columns = mm.fit_param_names + mm.fixed_param_names + if all([param in self.ref_table.colnames for param in required_columns]): + # Check if the values are finite for non-string columns in the required columns for this motion model. If not, skip to the next motion model. + if not all([np.isfinite(self.ref_table[param]).all() for param in required_columns if self.ref_table[param].dtype.kind in 'if']): + continue + + # If we have error columns for all fit parameters, then use them in the model inference. Otherwise, just use the fit parameters without errors. + if all([f'{param}_err' in self.ref_table.colnames for param in mm.fit_param_names]) and all([np.isfinite(self.ref_table[f'{param}_err']).all() for param in mm.fit_param_names]): + x, y, xe, ye = mm().model( + t=epoch, + fit_params=np.array([self.ref_table[param] for param in mm.fit_param_names]).T, + fit_param_errs=np.array([self.ref_table[f'{param}_err'] for param in mm.fit_param_names]).T, + fixed_params_dict={param: self.ref_table[param] for param in mm.fixed_param_names} + ) + else: + x, y = mm().model( + t=epoch, + fit_params=np.array([self.ref_table[param] for param in mm.fit_param_names]).T, + fixed_params_dict={param: self.ref_table[param] for param in mm.fixed_param_names} + ) + xe = None + ye = None + break + + # # No velocities... just used average positions. + # x = self.ref_table['x0'] + # y = self.ref_table['y0'] + + # if 'x0_err' in self.ref_table.colnames: + # xe = self.ref_table['x0_err'] + # ye = self.ref_table['y0_err'] + # else: + # xe = None + # ye = None m = self.ref_table['m0'] - + if 'm0_err' in self.ref_table.colnames: me = self.ref_table['m0_err'] else: @@ -1732,6 +1781,14 @@ def = None. If not None, then this should contain an array or list of transform self.update_ref_orig = update_ref_orig self.use_ref_new = use_ref_new + # If motion_model_used in columns but params columns are missing, raise a warning and remove motion_model_used column to avoid confusion. + if 'motion_model_used' in self.ref_list.colnames: + motion_model_params = motion_model.motion_model_param_names(np.unique(self.ref_list['motion_model_used']), with_errors=False, with_fixed=True) + missing_params = [param for param in motion_model_params if param not in self.ref_list.colnames] + if len(missing_params) > 0: + warnings.warn("Warning: 'motion_model_used' column found in ref_list, but the following motion model parameter columns are missing: " + ", ".join(missing_params) + ". Removing 'motion_model_used' column to avoid confusion.") + self.ref_list.remove_column('motion_model_used') + # Do some temporary clean up of the reference list. if ('x' not in self.ref_list.colnames) and ('x0' in self.ref_list.colnames): self.ref_list['x'] = self.ref_list['x0'] @@ -1828,7 +1885,7 @@ def fit(self): print("**********") # ALL the action is in here. Match and transform the stack of starlists. - # This updates trans objects and the ref_table. + # This updates trans objects and the ref_table. self.match_and_transform(self.ref_mag_lim, self.dr_tol[nn], self.dm_tol[nn], self.outlier_tol[nn], self.trans_args[nn]) @@ -1863,7 +1920,7 @@ def fit(self): if self.update_ref_orig: keep_orig=None else: - keep_orig = np.where(self.ref_table['ref_orig'])[0] + keep_orig = self.ref_table['ref_orig'] self.update_ref_table_aggregates(keep_orig=keep_orig) ########## @@ -1905,6 +1962,99 @@ def fit(self): pickle.dump(self, file) return +# TODO: This is sometimes run on a startable, not a starlist, at least as currently used +def infer_positions(t, startable, motion_models=None, return_errors=False): + """ + Take a startable, check to see if it has motion/velocity columns. + If it does, then propagate the positions forward in time + to the desired epoch. If no motion/velocities exist, then just + use ['x0', 'y0'] or ['x', 'y'] + + Parameters + ---------- + t : float + The time to propagate to. Usually in decimal years; + but it should be in the same units + as the 't0' column in starlist. + startable : StarTable + Startable that needs to be inferred. + motion_models : list of MotionModel classes or strings + The motion models to check for in the startable + return_errors : boolean + Whether to return the inferred position errors. If True, then the function returns x, y, xe, ye. If False, then it just returns x, y, by default False. + + Returns + ------- + x, y, (xe, ye) : tuple + Inferred position (and errors) at time t + """ + if ('motion_model_used' in startable.colnames): + x, y, xe, ye = startable.infer_positions(t) + if return_errors: + return x, y, xe, ye + else: + return x, y + + # Convert motion_models from strings to MotionModel classes if needed. + if motion_models is None: + # Setting the default to None to avoid mutable default argument issue + # See https://stackoverflow.com/questions/15189245/assigning-class-variable-as-default-value-to-class-method-argument + motion_models = [Empty, Fixed] + all_mm_map = motion_model.motion_model_map() + if all(isinstance(mm, str) for mm in motion_models): + mm_names = motion_models + motion_models = [all_mm_map[mm] for mm in motion_models] + else: + mm_names = [mm.name for mm in motion_models] + + # Always add Empty and Fixed in motion models + if 'Fixed' not in mm_names: + motion_models.insert(0, Fixed) + if 'Empty' not in mm_names: + motion_models.insert(0, Empty) + + # Otherwise, infer positions using the most complex motion model with the existing columns, until it reaches Fixed or Empty + # Sort motion models inversely by mm.n_params + motion_models = sorted(motion_models, key=lambda mm: mm.n_params, reverse=True) + for mm in motion_models: + if mm.name == 'Empty': + x = startable['x'] + y = startable['y'] + return x, y + + required_columns = mm.fit_param_names + mm.fixed_param_names + if all([param in startable.colnames for param in required_columns]): + # Check if the values are finite for non-string columns in the required columns for this motion model. If not, skip to the next motion model. + if not all([np.isfinite(startable[param]).all() for param in required_columns if startable[param].dtype.kind in 'if']): + continue + + # If we have error columns for all fit parameters, then use them in the model inference. Otherwise, just use the fit parameters without errors. + x, y = mm().model( + t=t, + fit_params=np.array([startable[param] for param in mm.fit_param_names]).T, + fixed_params_dict={param: startable[param] for param in mm.fixed_param_names} + ) + xe = None + ye = None + break + + return x, y + + # # If no motion model, check for velocities + # elif ('vx' in startable.colnames) and ('vy' in startable.colnames) and (np.isfinite(startable['vx']).all()) and (np.isfinite(startable['vy']).all()): + # x = startable['x0'] + startable['vx'] * (t - startable['t0']) + # y = startable['y0'] + startable['vy'] * (t - startable['t0']) + + # # If no velocities, try fitted positon + # elif ('x0' in startable.colnames) and ('y0' in startable.colnames) and (np.isfinite(startable['x0']).all()) and (np.isfinite(startable['y0']).all()): + # x = startable['x0'] + # y = startable['y0'] + # # Otherwise, use measured position + # else: + # x = startable['x'] + # y = startable['y'] + # return x, y + def get_all_epochs(t): """ Helper function to get times of all epochs from a ref table. @@ -2804,7 +2954,7 @@ def position_transform_from_object(x, y, xe, ye, transform): Should be doing: ((A**2 + B**2 + C**2) * xe**2) """ - + # xe_new & ye_new in (x,y,xe,ye) xe_new = 0 temp1 = 0 @@ -3016,7 +3166,7 @@ def check_trans_input(list_of_starlists, trans_input, mag_trans): return -def trans_initial_guess(ref_list, star_list, trans_args, mode='miracle', +def trans_initial_guess(ref_list, star_list, trans_args, motion_models=None, mode='miracle', ignore_contains='star', verbose=True, n_req_match=3, mag_trans=True, order=1): """ @@ -3027,7 +3177,8 @@ def trans_initial_guess(ref_list, star_list, trans_args, mode='miracle', is just blind triangle matching on the brightest 50 stars. """ warnings.filterwarnings('ignore', category=AstropyUserWarning) - + if motion_models is None: + motion_models = [] if mode == 'name': # First trim the two lists down to only those that don't contain # the "ignore_contains" string. @@ -3054,7 +3205,7 @@ def trans_initial_guess(ref_list, star_list, trans_args, mode='miracle', # If there are velocities in the reference list, use them. # We assume velocities are in the same units as the positions. - xref, yref = infer_positions(star_list['t'][0], ref_list) + xref, yref = infer_positions(star_list['t'][0], ref_list, motion_models) if 'm' in ref_list.colnames: mref = ref_list['m'] else: @@ -3156,72 +3307,75 @@ def copy_and_rename_for_ref(star_list): return ref_list -def outlier_rejection_indices(star_list, ref_list, outlier_tol, verbose=True): - """ - Determine the outliers based on the residual positions between two different - starlists and some threshold (in sigma). Return the indices of the stars - to keep (that shouldn't be rejected as outliers). - - Note that we assume that the star_list and ref_list are already transformed and - matched. - - Parameters - ---------- - star_list : StarList - starlist with 'x', 'y' - - ref_list : StarList - starlist with 'x0', 'y0' - - outlier_tol : float - Number of sigma inside which we keep stars and outside of which we - reject stars as outliers. - - Optional Parameters - -------------------- - verbose : boolean - - Returns - ---------- - keepers : nd.array - The indicies of the stars to keep. - """ - # Optionally propogate the reference positions forward in time. - xref, yref = infer_positions(star_list['t'][0], ref_list) - - # Residuals - x_resid_on_old_trans = star_list['x'] - xref - y_resid_on_old_trans = star_list['y'] - yref - resid_on_old_trans = np.hypot(x_resid_on_old_trans, y_resid_on_old_trans) - - threshold = outlier_tol * resid_on_old_trans.std() - keepers = np.where(resid_on_old_trans < threshold)[0] - - if verbose > 0: - msg = ' Outlier Rejection: Keeping {0:d} of {1:d}' - print(msg.format(len(keepers), len(resid_on_old_trans))) +# def outlier_rejection_indices(star_list, ref_list, outlier_tol, motion_models, verbose=True): +# """ +# Determine the outliers based on the residual positions between two different +# starlists and some threshold (in sigma). Return the indices of the stars +# to keep (that shouldn't be rejected as outliers). + +# Note that we assume that the star_list and ref_list are already transformed and +# matched. + +# Parameters +# ---------- +# star_list : StarList +# starlist with 'x', 'y' + +# ref_list : StarList +# starlist with 'x0', 'y0' + +# outlier_tol : float +# Number of sigma inside which we keep stars and outside of which we +# reject stars as outliers. + +# motion_models : list of motion_model objects +# The motion models used in the star_list. This is needed to propogate the reference positions forward in time to the epoch of the star_list. + +# Optional Parameters +# -------------------- +# verbose : boolean + +# Returns +# ---------- +# keepers : boolean array +# The boolean array of the stars to keep. +# """ +# # Optionally propogate the reference positions forward in time. +# xref, yref = infer_positions(star_list['t'][0], ref_list, motion_models) + +# # Residuals +# x_resid_on_old_trans = star_list['x'] - xref +# y_resid_on_old_trans = star_list['y'] - yref +# resid_on_old_trans = np.hypot(x_resid_on_old_trans, y_resid_on_old_trans) + +# threshold = outlier_tol * resid_on_old_trans.std() +# keepers = resid_on_old_trans < threshold + +# if verbose > 0: +# msg = ' Outlier Rejection: Keeping {0:d} of {1:d}' +# print(msg.format(sum(keepers), len(resid_on_old_trans))) - return keepers - -def setup_trans_info(trans_input, trans_args, N_lists, iters): - """ Setup transformation info into a usable format. - - trans_input : list or None - trans_args : dict or None - N_lists : int - iters : int - """ - trans_list = [None for ii in range(N_lists)] - if trans_input != None: - trans_list = [trans_input[ii] for ii in range(N_lists)] - - # Keep a list of trans_args, one for each starlist. If only - # a single is passed in, replicate for all star lists, all loop iterations. - if type(trans_args) == dict: - tmp = trans_args - trans_args = [tmp for ii in range(iters)] +# return keepers + +# def setup_trans_info(trans_input, trans_args, N_lists, iters): +# """ Setup transformation info into a usable format. + +# trans_input : list or None +# trans_args : dict or None +# N_lists : int +# iters : int +# """ +# trans_list = [None for ii in range(N_lists)] +# if trans_input != None: +# trans_list = [trans_input[ii] for ii in range(N_lists)] + +# # Keep a list of trans_args, one for each starlist. If only +# # a single is passed in, replicate for all star lists, all loop iterations. +# if type(trans_args) == dict: +# tmp = trans_args +# trans_args = [tmp for ii in range(iters)] - return trans_list, trans_args +# return trans_list, trans_args def apply_mag_lim(star_list, mag_lim): """ Apply a magnitude limit to the list. If no magnitude limit is @@ -3255,85 +3409,44 @@ def apply_mag_lim(star_list, mag_lim): return star_list_T -def get_weighting_scheme(weights, ref_list, star_list): - if 'xe' in ref_list.colnames: - var_xref = ref_list['xe']**2 - var_yref = ref_list['ye']**2 - else: - var_xref = 0.0 - var_yref = 0.0 - - if 'xe' in star_list.colnames: - var_xlis = star_list['xe']**2 - var_ylis = star_list['ye']**2 - else: - var_xlis = 0.0 - var_ylis = 0.0 - - if weights != None: - if weights == 'both,var': - weight = 1.0 / (var_xref + var_xlis + var_yref + var_ylis) - if weights == 'both,std': - weight = 1.0 / np.sqrt(var_xref + var_xlis + var_yref + var_ylis) - if weights == 'ref,var': - weight = 1.0 / (var_xref + var_yref) - if weights == 'ref,std': - weight = 1.0 / np.sqrt(var_xref + var_yref) - if weights == 'list,var': - weight = 1.0 / (var_xlis, var_ylis) - if weights == 'list,std': - weight = 1.0 / np.sqrt(var_xlis, var_ylis) - else: - weight = None - - # One last check to make sure we had weights at all. - # Technically, this is mis-use; but lets handle it anyhow. - if ('xe' not in ref_list.colnames) and ('ye' not in star_list.colnames): - weight = None - - return weight - -# TODO: This is sometimes run on a startable, not a starlist, at least as currently used -def infer_positions(t, startable): - """ - Take a startable, check to see if it has motion/velocity columns. - If it does, then propogate the positions forward in time - to the desired epoch. If no motion/velocities exist, then just - use ['x0', 'y0'] or ['x', 'y'] - - Parameters - ---------- - t_array : float - The time to propogate to. Usually in decimal years; - but it should be in the same units - as the 't0' column in starlist. - startable : StarTable - Startable that needs to be inferred. - - Returns - ------- - x, y : tuple - Inferred position at time t - """ - # Check for motion model - if 'motion_model_used' in startable.colnames: - x, y, xe, ye = startable.infer_positions(t) - - # If no motion model, check for velocities - elif ('vx' in startable.colnames) and ('vy' in startable.colnames): - x = startable['x0'] + startable['vx'] * (t - startable['t0']) - y = startable['y0'] + startable['vy'] * (t - startable['t0']) +# def get_weighting_scheme(weights, ref_list, star_list): +# if 'xe' in ref_list.colnames: +# var_xref = ref_list['xe']**2 +# var_yref = ref_list['ye']**2 +# else: +# var_xref = 0.0 +# var_yref = 0.0 - # If no velocities, try fitted positon - elif ('x0' in startable.colnames) and ('y0' in startable.colnames): - x = startable['x0'] - y = startable['y0'] - # Otherwise, use measured position - else: - x = startable['x'] - y = startable['y'] +# if 'xe' in star_list.colnames: +# var_xlis = star_list['xe']**2 +# var_ylis = star_list['ye']**2 +# else: +# var_xlis = 0.0 +# var_ylis = 0.0 + +# if weights != None: +# if weights == 'both,var': +# weight = 1.0 / (var_xref + var_xlis + var_yref + var_ylis) +# if weights == 'both,std': +# weight = 1.0 / np.sqrt(var_xref + var_xlis + var_yref + var_ylis) +# if weights == 'ref,var': +# weight = 1.0 / (var_xref + var_yref) +# if weights == 'ref,std': +# weight = 1.0 / np.sqrt(var_xref + var_yref) +# if weights == 'list,var': +# weight = 1.0 / (var_xlis, var_ylis) +# if weights == 'list,std': +# weight = 1.0 / np.sqrt(var_xlis, var_ylis) +# else: +# weight = None + +# # One last check to make sure we had weights at all. +# # Technically, this is mis-use; but lets handle it anyhow. +# if ('xe' not in ref_list.colnames) and ('ye' not in star_list.colnames): +# weight = None + +# return weight - return x, y def logger(logfile, message, verbose = 9): if verbose > 4: diff --git a/flystar/match.py b/flystar/match.py index f564cd3..ca7672a 100644 --- a/flystar/match.py +++ b/flystar/match.py @@ -1,7 +1,7 @@ import numpy as np from . import starlists, transforms, startables from collections import Counter -from scipy.spatial import cKDTree as KDT +from scipy.spatial import KDTree as KDT from astropy.table import Column import itertools import copy @@ -279,7 +279,7 @@ def match(x1, y1, m1, x2, y2, m2, dr_tol, dm_tol=None, verbose=True): #KDTree handling of NaNs throws error in scipy v1.10.1 and newer. #Replace NaNs in coords2 with zero (0). -SKT kdt = KDT(np.where(np.isfinite(coords2), coords2, 0), balanced_tree=False) - + # This returns the number of neighbors within the specified # radius. We will use this to find those stars that have no or one # match and deal with them easily. The more complicated conflict @@ -289,7 +289,6 @@ def match(x1, y1, m1, x2, y2, m2, dr_tol, dm_tol=None, verbose=True): # What is the largest number of matches we have for a given star? Nmatch_max = Nmatch.max() - # Loop through and handle all the different numbers of matches. # This turns out to be the most efficient so we can use numpy # array operations. Remember, skip the Nmatch=0 objects... they @@ -302,7 +301,7 @@ def match(x1, y1, m1, x2, y2, m2, dr_tol, dm_tol=None, verbose=True): if nn == 1: i2_nn = np.array([i2_match[mm][0] for mm in i1_nn]) - if dm_tol != None: + if dm_tol is not None: dm = np.abs(m1[i1_nn] - m2[i2_nn]) keep = dm < dm_tol idxs1[i1_nn[keep]] = i1_nn[keep] @@ -326,7 +325,7 @@ def match(x1, y1, m1, x2, y2, m2, dr_tol, dm_tol=None, verbose=True): dr = np.abs(x1_nn - x2_nn, y1_nn - y2_nn) dm = np.abs(m1_nn - m2_nn) - if dm_tol != None: + if dm_tol is not None: # Don't even consider stars that exceed our # delta-mag threshold. dr_msk = np.ma.masked_where(dm > dm_tol, dr) @@ -542,7 +541,7 @@ def generic_match(sl1, sl2, init_mode='triangle', y_min=xy_match[6], y_max=xy_match[7]) sl1_cut.restrict_by_value(m_min=m_match[0], m_max=m_match[1]) sl2_cut.restrict_by_value(m_min=m_match[2], m_max=m_match[3]) - + # Find the transformation # TODO: test 'initial_align' with StarList input transf = align.initial_align(sl1_cut, sl2_cut, briteN=n_bright, @@ -579,7 +578,10 @@ def generic_match(sl1, sl2, init_mode='triangle', # sl2_idx, sl1_idx = align.transform_and_match(sl2_match, sl1_match, transf, # dr_tol=order_dr[i_loop][1], # verbose=verbose) - + import matplotlib.pyplot as plt + plt.plot(sl1_match['x'], sl1_match['y'], 'x', ms=10) + plt.plot(sl2_match['x'], sl2_match['y'], 'o') + plt.show() sl2_idx, sl1_idx = align.transform_and_match(sl2_match, sl1_match, transf, dr_tol=order_dr[1], verbose=verbose) diff --git a/flystar/motion_model.py b/flystar/motion_model.py index 7010a4e..bf53d3f 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -31,11 +31,37 @@ def __init__(self, *args, **kwargs): and thus the fit values are only input/returned in functions, not stored in the object. """ return - + + def _check_param_dimensions(self, fit_params, fit_params_errs, fixed_params_dict): + """Check that parameters is either a scalar or length of N_stars + + Parameters + ---------- + fit_params: array-like + Fit parameters, shape (N_params,) or (N_stars, N_params) + fit_params_errs: array-like + Errors of fit parameters, shape (N_params,) or (N_stars, N_params) + fixed_params_dict : dict + Dictionary of fixed parameters + """ + N_stars = fit_params.shape[0] if fit_params.ndim > 1 else 1 + if fit_params_errs is not None: + assert fit_params_errs.shape == fit_params.shape, "fit_params and fit_params_errs must have the same shape!" + + if fixed_params_dict is not None: + for key, value in fixed_params_dict.items(): + # assert key in fixed_params_dict, f"Missing fixed parameter {key} in fixed_params_dict!" + value = fixed_params_dict[key] + if np.isscalar(value): + continue + else: + assert len(value) == N_stars, f"Length of fixed parameter {key} must be either 1 or N_stars={N_stars}!" + def model_fit(self, dt): return np.full_like(dt, np.nan) - - def model(self, t, fit_params, fit_param_errs=None, fixed_params=None): + + def model(self, t, fit_params, fit_param_errs=None, fixed_params_dict=None): + self._check_param_dimensions(fit_params, fit_param_errs, fixed_params_dict) if fit_param_errs is None: return np.full_like(t, np.nan), np.full_like(t, np.nan) return np.full_like(t, np.nan), np.full_like(t, np.nan), np.full_like(t, np.inf), np.full_like(t, np.inf) @@ -111,8 +137,8 @@ def fit( Seed for the random number generator, by default None Returns ------- - params, params_err, chi2_x, chi2_y - Parameters, uncertainties, and chi squares. The corresponding parameter names are in self.fit_param_names. + params, params_err(, chi2_x, chi2_y) + Parameters, uncertainties, and chi squares if return_chi2 is True. The corresponding parameter names are in self.fit_param_names. """ assert np.ndim(t) == np.ndim(x) == np.ndim(y) == np.ndim(xe) == np.ndim(ye) == 1, "Input arrays must be 1D! Motion model can only fit individual stars" assert len(t) == len(x) == len(y) == len(xe) == len(ye), "Input arrays must have the same length!" @@ -233,7 +259,7 @@ def model(self, t, fit_params, fit_param_errs=None, fixed_params_dict=None): x, y (, xe, ye) Predicted position (and uncertainties) of Empty model, shape (N_times,) """ - + self._check_param_dimensions(fit_params, fit_param_errs, fixed_params_dict) t = np.atleast_1d(t) if fit_param_errs is None: return np.full_like(t, np.nan), np.full_like(t, np.nan) @@ -357,6 +383,7 @@ def model(self, t, fit_params, fit_param_errs=None, fixed_params_dict=None): self.fixed_params_dict = fixed_params_dict t = np.atleast_1d(t) fit_params = np.atleast_2d(fit_params) # (N_stars, N_params) + self._check_param_dimensions(fit_params, fit_param_errs, fixed_params_dict) N_stars = fit_params.shape[0] if fit_params.ndim > 1 else 1 N_times = len(t) @@ -510,6 +537,7 @@ def model(self, t, fit_params, fit_param_errs=None, fixed_params_dict=None): if fixed_params_dict is None: fixed_params_dict = self.fixed_params_dict assert 't0' in fixed_params_dict, "Fixed parameter t0 is required for Linear model." + self._check_param_dimensions(fit_params, fit_param_errs, fixed_params_dict) t = np.atleast_1d(t) fit_params = np.atleast_2d(fit_params) # (N_stars, N_params) @@ -719,6 +747,7 @@ def model(self, t, fit_params, fit_param_errs=None, fixed_params_dict=None): if fixed_params_dict is None: fixed_params_dict = self.fixed_params_dict assert 't0' in fixed_params_dict, "Fixed parameter t0 is required for Acceleration model." + self._check_param_dimensions(fit_params, fit_param_errs, fixed_params_dict) t = np.atleast_1d(t) fit_params = np.atleast_2d(fit_params) # (N_stars, N_params) @@ -840,11 +869,12 @@ class Parallax(MotionModel): def __init__(self): super().__init__() - self.plx_vector_cached = None # Cache for parallax vector + self.pvec_cached = None # Cache for parallax vector + self.t_mjd_cached = None # Cache for times corresponding to cached parallax vector return def calc_parallax_vector(self, t_mjd, ra, dec, pa=0., obsLocation='earth'): - """Calculate parallax vector of shape (2, N_times) + """Calculate parallax vector of shape (N_stars, 2, N_times) Parameters ---------- @@ -862,24 +892,26 @@ def calc_parallax_vector(self, t_mjd, ra, dec, pa=0., obsLocation='earth'): Returns ------- pvec - Parallax vector of shape (2, N_times) + Parallax vector of shape (N_stars, 2, N_times) """ - if self.plx_vector_cached is not None: + if self.pvec_cached is not None: t_mjd = np.atleast_1d(t_mjd) - t_mjd_cached = self.plx_vector_cached[0] + t_mjd_cached = self.t_mjd_cached if np.array_equal(t_mjd, t_mjd_cached): # If cached values match input times, return cached values - return self.plx_vector_cached[1] + return self.pvec_cached elif all(np.isin(t_mjd, t_mjd_cached)): # If all input times are in cached values, return those # Calculate pvec_idxs such that t_mjd_cached[ pvec_idxs ] == t_mjd pvec_idxs = np.array([np.where(t_mjd_cached == t_mjd_i)[0][0] for t_mjd_i in t_mjd]) - pvec = self.plx_vector_cached[1][:, pvec_idxs] + pvec = self.pvec_cached[:, :, pvec_idxs] return pvec - pvec = parallax.parallax_in_direction(ra, dec, t_mjd, obsLocation=obsLocation, pa=pa) - self.plx_vector_cached = [t_mjd, pvec] + pvec = parallax.parallax_in_direction(ra, dec, t_mjd, obsLocation=obsLocation, pa=pa) # Shape (N_stars, 2, N_times) + # self.plx_vector_cached = [t_mjd, pvec] + self.t_mjd_cached = t_mjd + self.pvec_cached = pvec return pvec def model_fit(self, dt, x0, vx, y0, vy, pi): @@ -902,18 +934,21 @@ def model_fit(self, dt, x0, vx, y0, vy, pi): Returns ------- - x_res, y_res : array-like - Model positions at time t of Parallax model + x_result, y_result : array-like + Model positions at time t of Parallax model, shape (N_stars, N_times) """ # x0, vx, y0, vy, pi are all shape (N_stars, N_times) - x_res = x0 + vx * dt + pi * self.pvec[0] - y_res = y0 + vy * dt + pi * self.pvec[1] - return x_res, y_res - + x_result = x0 + vx * dt + pi * self.pvec[:, 0, :] # Parallax contribution in x direction + y_result = y0 + vy * dt + pi * self.pvec[:, 1, :] # Parallax contribution in y direction + return x_result, y_result + def _model_fit(self, dt, x0, vx, y0, vy, pi): """Wrapper for model_fit to return concatenated results for scipy fitting.""" - x_res, y_res = self.model_fit(dt, x0, vx, y0, vy, pi) - return np.hstack([x_res, y_res]) # Shape (N_stars, 2*N_times) + x_result, y_result = self.model_fit(dt, x0, vx, y0, vy, pi) + # scipy.optimize.curve_fit expects a 1D output array with the same length + # as the input ydata. For single-star fits, intermediate broadcasting can + # yield arrays with shape (1, N_times); flatten to avoid M=1 interpretation. + return np.hstack([np.ravel(x_result), np.ravel(y_result)]) # Shape (2*N_times,) def model(self, t, fit_params, fit_param_errs=None, fixed_params_dict=None): """Model positions (and uncertainties, if fit_param_errs is provided) at time t of Parallax model. @@ -941,6 +976,7 @@ def model(self, t, fit_params, fit_param_errs=None, fixed_params_dict=None): if fixed_params_dict is None: fixed_params_dict = self.fixed_params_dict assert all([_ in fixed_params_dict for _ in ['t0', 'ra', 'dec']]), "Fixed parameters t0, ra, and dec are required for Parallax model." + self._check_param_dimensions(fit_params, fit_param_errs, fixed_params_dict) t = np.atleast_1d(t) fit_params = np.atleast_2d(fit_params) # (N_stars, N_params) @@ -955,14 +991,13 @@ def model(self, t, fit_params, fit_param_errs=None, fixed_params_dict=None): obsLocation = fixed_params_dict.get('obsLocation', 'earth') # TODO: vectorize parallax.parallax_in_direction to handle multiple obsLocation? - assert isinstance(obsLocation, str) or (np.unique(obsLocation).size == 1), "obsLocation must be a single string for all stars at this time." if not isinstance(obsLocation, str): obsLocation = np.unique(obsLocation)[0] dt = t[np.newaxis, :] - t0[:, np.newaxis] # Shape (N_stars, N_times) t_mjd = Time(t, format='decimalyear', scale='utc').mjd # Shape (N_times,) - self.pvec = self.calc_parallax_vector(t_mjd, ra, dec, pa=pa, obsLocation=obsLocation) # Shape (2, N_times) + self.pvec = self.calc_parallax_vector(t_mjd, ra, dec, pa=pa, obsLocation=obsLocation) # Shape (N_stars, 2, N_times) x, y = self.model_fit(dt, x0[:, np.newaxis], vx[:, np.newaxis], y0[:, np.newaxis], vy[:, np.newaxis], pi[:, np.newaxis]) # Shape (N_stars, N_times) if N_stars == 1 or N_times == 1: @@ -975,9 +1010,9 @@ def model(self, t, fit_params, fit_param_errs=None, fixed_params_dict=None): fit_param_errs = np.atleast_2d(fit_param_errs) # (N_stars, N_params) x0_err, vx_err, y0_err, vy_err, pi_err = fit_param_errs.T - x_err = np.sqrt(x0_err[:, np.newaxis]**2 + (vx_err[:, np.newaxis] * dt)**2 + (pi_err[:, np.newaxis] * self.pvec[0][np.newaxis, :])**2) # Shape (N_stars, N_times) - y_err = np.sqrt(y0_err[:, np.newaxis]**2 + (vy_err[:, np.newaxis] * dt)**2 + (pi_err[:, np.newaxis] * self.pvec[1][np.newaxis, :])**2) # Shape (N_stars, N_times) - + x_err = np.sqrt(x0_err[:, np.newaxis]**2 + (vx_err[:, np.newaxis] * dt)**2 + (pi_err[:, np.newaxis] * self.pvec[:, 0, :])**2) # Shape (N_stars, N_times) + y_err = np.sqrt(y0_err[:, np.newaxis]**2 + (vy_err[:, np.newaxis] * dt)**2 + (pi_err[:, np.newaxis] * self.pvec[:, 1, :])**2) # Shape (N_stars, N_times) + if N_stars == 1 or N_times == 1: # If only one star, return flattened arrays x_err = x_err.flatten() @@ -1042,14 +1077,24 @@ def run_fit( if params_guess is None: idx_first, idx_last = np.argmin(t), np.argmax(t) t_span = t[idx_last] - t[idx_first] - params_guess = [ + params_guess = np.array([ x.mean(), (x[idx_last] - x[idx_first]) / t_span, y.mean(), (y[idx_last] - y[idx_first]) / t_span, 0.1 - ] + ]) + + # Convert weights to 1-sigma uncertainties for curve_fit. + # calc_weights returns w = 1/sigma^2 for 'var' and w = 1/sigma for 'std'. + if weighting == 'std': + sigma_x = 1.0 / x_wt + sigma_y = 1.0 / y_wt + else: + sigma_x = 1.0 / np.sqrt(x_wt) + sigma_y = 1.0 / np.sqrt(y_wt) + popt, pcov = curve_fit( self._model_fit, t - t0, np.hstack([x, y]), - p0=params_guess, sigma=np.hstack([x_wt, y_wt]), + p0=params_guess, sigma=np.hstack([sigma_x, sigma_y]), absolute_sigma=absolute_sigma ) x0, vx, y0, vy, pi = popt diff --git a/flystar/parallax.py b/flystar/parallax.py index a4f0f8c..1605060 100755 --- a/flystar/parallax.py +++ b/flystar/parallax.py @@ -44,15 +44,16 @@ def parallax_in_direction(ra, dec, mjd, obsLocation='earth', pa=0.): Returns ------- pvec : ndarray - Parallax vector components, shape of (2, N_stars, N_times), or (2, N_stars) if N_times=1, or (2, N_times) if N_stars=1. + Parallax vector components, shape of (N_stars, 2, N_times), where the second dimension corresponds to the x or y components. """ # Munge inputs into astropy format. # times = Time(mjd + 2400000.5, format='jd', scale='tdb') ra = np.atleast_1d(ra) dec = np.atleast_1d(dec) mjd = np.atleast_1d(mjd) + pa = np.atleast_1d(pa) times = Time(mjd, format='mjd', scale='tdb') # convert to TDB - coord = SkyCoord(ra, dec, unit=(units.deg, units.deg)) + coord = SkyCoord(ra, dec, unit=(units.deg, units.deg)) # Shape (N_stars,) directions = coord.cartesian.xyz.value.T # Shape (N_stars, 3) north = np.array([0., 0., 1.]) @@ -68,19 +69,18 @@ def parallax_in_direction(ra, dec, mjd, obsLocation='earth', pa=0.): sun_obs_pos = sun_pos - obs_pos pos = sun_obs_pos.xyz.T.to(units.au).value # Shape (N_times, 3) + # Broadcast pos to (N_stars, 3, N_times) and take dot product with east and north unit vectors to get components in those directions. + pos = np.broadcast_to(pos.T, (directions.shape[0], 3, pos.shape[0])) # Shape (N_stars, 3, N_times) - e = np.einsum('ti,si->st', pos, _east_projected) # Shape (N_stars, N_times) - n = np.einsum('ti,si->st', pos, _north_projected) # Shape (N_stars, N_times) + e = np.einsum('sdt,sd->st', pos, _east_projected) # Shape (N_stars, N_times) + n = np.einsum('sdt,sd->st', pos, _north_projected) # Shape (N_stars, N_times) # Rotate frame e,n->x,y accounting for PA pa = np.deg2rad(pa) # shape (N_stars,) x = -e * np.cos(pa[:, np.newaxis]) + n * np.sin(pa[:, np.newaxis]) # Shape (N_stars, N_times) y = e * np.sin(pa[:, np.newaxis]) + n * np.cos(pa[:, np.newaxis]) # Shape (N_stars, N_times) - pvec = np.array([x, y]) # Shape (2, N_stars, N_times) - - if pvec.shape[1] == 1 or pvec.shape[2] == 1: - pvec = pvec.reshape(2, -1) # Shape (2, N_stars) or (2, N_times) - + # pvec Shape (N_stars, 2, N_times) + pvec = np.stack((x, y), axis=1) return pvec diff --git a/flystar/startables.py b/flystar/startables.py index d934bac..f586a50 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -12,6 +12,7 @@ from flystar import motion_model import pandas as pd from flystar.motion_model import Empty, Fixed, Linear +from pandas.api.types import is_string_dtype class StarTable(Table): def __init__(self, *args, ref_list=0, **kwargs): @@ -615,6 +616,9 @@ def fit_motion_model( if not all([_ in self.colnames for _ in ['x', 'y']]): raise KeyError(f"fit_motion_model: Missing required columns in the table: {', '.join(['x', 'y'])}!") + # Make a copy of fixed_params_dict to avoid modifying the original one outside the function + fixed_params_dict = copy.deepcopy(fixed_params_dict) + # Check fixed_params_dict is a dict if fixed_params_dict is not None: if not isinstance(fixed_params_dict, dict): @@ -631,7 +635,7 @@ def fit_motion_model( motion_models = [all_mm_map[mm] for mm in motion_models] else: mm_names = [mm.name for mm in motion_models] - + # Always add Empty and Fixed in motion models if 'Fixed' not in mm_names: motion_models.insert(0, Fixed) @@ -670,6 +674,15 @@ def fit_motion_model( y_data = np.ma.masked_invalid(self['y'].data, copy=True) xe_data = np.ma.masked_invalid(self['xe'].data, copy=True) if 'xe' in self.colnames else np.ones_like(x_data) ye_data = np.ma.masked_invalid(self['ye'].data, copy=True) if 'ye' in self.colnames else np.ones_like(y_data) + + if np.ndim(x_data) == 1: + x_data = x_data[:, np.newaxis] + if np.ndim(y_data) == 1: + y_data = y_data[:, np.newaxis] + if np.ndim(xe_data) == 1: + xe_data = xe_data[:, np.newaxis] + if np.ndim(ye_data) == 1: + ye_data = ye_data[:, np.newaxis] if mask_lists is not None: x_data.mask[:, mask_lists] = True @@ -696,7 +709,7 @@ def fit_motion_model( if np.ndim(fixed_params_dict['t0']) == 0: fixed_params_dict['t0'] = np.full(N_stars, fixed_params_dict['t0']) - t0 = fixed_params_dict['t0'] + t0 = fixed_params_dict['t0'] # Prepare fixed_params_dict for each star # This avoids checking types and slicing inside the fitting loop @@ -808,8 +821,12 @@ def fit_motion_model( for param in fixed_param_names: coldata = np.array([fixed_params_stars[i][param] for i in range(N_stars)]) if param in self.colnames: + if is_string_dtype(self[param]): + if np.array_equal(self[param], coldata): + # Same data, skip + continue # If the column already exists, check if the data are the same - if np.allclose(self[param], coldata, equal_nan=True): + elif np.allclose(self[param], coldata, equal_nan=True): # Same data, skip continue else: @@ -818,7 +835,7 @@ def fit_motion_model( else: colname = param - self.add_column(Column(data=coldata, name=colname)) + self.add_column(Column(data=coldata, name=colname), rename_duplicate=True) # Add a column to keep track of the number of points used in a fit and number of bootstrap used. @@ -855,27 +872,28 @@ def fit_motion_model( chi2_y_array = np.full(n_stars_this_model, np.nan, dtype=float) # Expensive for loop! Prepare everything beforehand to speed up. - for idx, i_star in enumerate(tqdm(unique_index, disable=not show_progress, desc=f"Fitting motion model {unique_motion_model}")): - # Fit the star - params, param_errs, chi2_x, chi2_y = motion_model_instance.fit( - t=t_stars[i_star], - x=x_stars[i_star], - y=y_stars[i_star], - xe=xe_stars[i_star], - ye=ye_stars[i_star], - fixed_params_dict=fixed_params_stars[i_star], - weighting=weighting, - use_scipy=use_scipy, - absolute_sigma=absolute_sigma, - bootstrap=bootstrap, - fill_value=fill_value, - return_chi2=True, - verbose=verbose - ) - params_array[idx] = params - param_errs_array[idx] = param_errs - chi2_x_array[idx] = chi2_x - chi2_y_array[idx] = chi2_y + if len(unique_index) > 0: + for idx, i_star in enumerate(tqdm(unique_index, disable=not show_progress, desc=f"Fitting motion model {unique_motion_model}")): + # Fit the star + params, param_errs, chi2_x, chi2_y = motion_model_instance.fit( + t=t_stars[i_star], + x=x_stars[i_star], + y=y_stars[i_star], + xe=xe_stars[i_star], + ye=ye_stars[i_star], + fixed_params_dict=fixed_params_stars[i_star], + weighting=weighting, + use_scipy=use_scipy, + absolute_sigma=absolute_sigma, + bootstrap=bootstrap, + fill_value=fill_value, + return_chi2=True, + verbose=verbose + ) + params_array[idx] = params + param_errs_array[idx] = param_errs + chi2_x_array[idx] = chi2_x + chi2_y_array[idx] = chi2_y # Store results back to the table for j, param_name in enumerate(param_names): diff --git a/flystar/tests/test_align.py b/flystar/tests/test_align.py index d55e81e..4561f3a 100644 --- a/flystar/tests/test_align.py +++ b/flystar/tests/test_align.py @@ -846,7 +846,7 @@ def test_MosaicToRef_mag_bug(): """ make_fake_starlists_poly1_vel() - ref_list = starlists.StarList.read('random_vel_0.fits') + ref_list = starlists.StarList.read('test_data/random_vel_0.fits') lists = [ref_list] msc = align.MosaicToRef(ref_list, lists, @@ -911,7 +911,7 @@ def test_masked_cols(): for ee in range(len(epochs)): lis_file = 'mag' + epochs[ee] + '_ob150029_kp_rms_named.lis' - lis = starlists.StarList.from_lis_file(lis_file) + lis = starlists.StarList.from_lis_file(f'test_data/{lis_file}') list_of_starlists.append(lis) # Run the align @@ -1464,4 +1464,4 @@ def make_fake_starlists_poly1_par(seed=-1): new_lis.write('test_data/random_par_{0:d}.fits'.format(ss), overwrite=True) - return (xy_trans, mag_trans) \ No newline at end of file + return (xy_trans, mag_trans) diff --git a/flystar/tests/test_match.py b/flystar/tests/test_match.py index 594f0b9..1022f6d 100644 --- a/flystar/tests/test_match.py +++ b/flystar/tests/test_match.py @@ -164,7 +164,7 @@ def test_generic_match(): n2 = np.array(['S11', 'S12', 'S13', 'S14', 'S15', 'S16', 'S17', 'S18', 'S19']) - + list1 = Table([n1, x1, y1, m1], names=('name', 'x', 'y', 'm')) list2 = Table([n2, x2, y2, m2], @@ -180,4 +180,7 @@ def test_generic_match(): m_match=(None, None, None, None), sigma_match=None, n_bright=8, verbose=True) - +if __name__ == '__main__': + # test_match_duplicates() + # test_miracle_match_briteN() + test_generic_match() \ No newline at end of file diff --git a/flystar/tests/test_motion_model.py b/flystar/tests/test_motion_model.py index b3bb4e8..7a90288 100755 --- a/flystar/tests/test_motion_model.py +++ b/flystar/tests/test_motion_model.py @@ -355,16 +355,20 @@ def test_Parallax(): fixed_params_dict=fixed_params_dict ) fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5)) - ax1.plot(t, x_true, 'k-', label='True x') - ax1.errorbar(t, x_sim, yerr=x_true_err, fmt='ro', label='Sim x') - ax1.plot(t, x_model, 'r-', label='Model x') + ax1.plot(t, x_model, color='C3', lw=2, label='Model x') + ax1.plot(t, x_true, color='C0', ls='--', label='True x') + ax1.errorbar(t, x_sim, yerr=x_true_err, fmt='o', color='C0', label='Sim x') ax1.set_xlabel('t') ax1.set_ylabel('x') ax1.legend() - ax2.plot(t, y_true, 'k-', label='True x') - ax2.errorbar(t, y_sim, yerr=x_true_err, fmt='ro', label='Sim x') - ax2.plot(t, y_model, 'r-', label='Model x') + ax2.plot(t, y_model, color='C3', lw=2, label='Model y') + ax2.plot(t, y_true, color='C0', ls='--', label='True y') + ax2.errorbar(t, y_sim, yerr=y_true_err, fmt='o', color='C0', label='Sim y') ax2.set_xlabel('t') + ax2.set_ylabel('y') + ax2.legend() + plt.tight_layout() + plt.show() # Confirm true value is within error bar of fit value assert np.all([within_error(true_params[param_list[i]], params[i], param_errs[i]) for i in range(len(params))]) diff --git a/flystar/tests/test_starlist.py b/flystar/tests/test_starlist.py index 5113c43..c8ab2bb 100644 --- a/flystar/tests/test_starlist.py +++ b/flystar/tests/test_starlist.py @@ -2,15 +2,15 @@ from flystar.starlists import StarList import os, pdb -test_dir = os.path.dirname(__file__) +test_dir = os.path.dirname(__file__) + '/' def make_star_list(): # User input - cat_file = test_dir + '/A.lis' + cat_file = test_dir + 'test_data/A.lis' # Read and arrange the test input - cat_tab = Table.read(cat_file, format='ascii', delimiter='\s') + cat_tab = Table.read(cat_file, format='ascii', delimiter=r'\s') # Copy columns from the input file. # Note that all of these inputs will be numpy arrays. @@ -25,7 +25,7 @@ def make_star_list(): # Name is a unique name for each star and is a 1D array. starlist_time = 2011.1 - starlist_name = 'A.lis' + starlist_name = 'test_data/A.lis' # Generate the starlist starlist = StarList(name=name_in, x=x_in, y=y_in, m=m_in, xe=xe_in, diff --git a/flystar/tests/test_startable.py b/flystar/tests/test_startable.py index 4475970..f99ee38 100644 --- a/flystar/tests/test_startable.py +++ b/flystar/tests/test_startable.py @@ -8,14 +8,14 @@ import os import pdb -test_dir = os.path.dirname(__file__) +test_dir = os.path.dirname(__file__) + '/' def test_StarTable_init1(): """ Test creation of new StarTable. """ # User input - cat_file = test_dir + '/test_catalog.fits' + cat_file = test_dir + 'test_data/test_catalog.fits' # Read and arrange the test input cat_tab = Table.read(cat_file) @@ -71,8 +71,8 @@ def test_StarTable_init2(): Also double check that we can add a second list to it using add_starlist and we can get_starlist() as well. """ - list_file1 = 'A.lis' - list_file2 = 'B.lis' + list_file1 = 'test_data/A.lis' + list_file2 = 'test_data/B.lis' list1 = StarList.from_lis_file(list_file1) list2 = StarList.from_lis_file(list_file2) @@ -267,7 +267,7 @@ def test_get_starlist(): def test_combine_1col(): # User input - cat_file = test_dir + '/test_catalog.fits' + cat_file = test_dir + 'test_data/test_catalog.fits' # Read and arrange the test input cat_tab = Table.read(cat_file) @@ -437,7 +437,7 @@ def test_fit_motion_model_2epoch(): def make_star_table(): # User input - cat_file = test_dir + '/test_catalog.fits' + cat_file = test_dir + 'test_data/test_catalog.fits' # Read and arrange the test input cat_tab = Table.read(cat_file) @@ -472,7 +472,7 @@ def make_star_table(): def make_star_table_1epoch(): # User input - cat_file = test_dir + '/test_catalog.fits' + cat_file = test_dir + 'test_data/test_catalog.fits' # Read and arrange the test input cat_tab = Table.read(cat_file) @@ -500,8 +500,8 @@ def make_star_table_1epoch(): return startable def make_star_table_2epoch(): - # User inpup - cat_file = test_dir + '/test_catalog.fits' + # User input + cat_file = test_dir + 'test_data/test_catalog.fits' # Read and arrange the test input cat_tab = Table.read(cat_file) diff --git a/flystar/tests/test_transforms.py b/flystar/tests/test_transforms.py index ea7c423..11338ad 100644 --- a/flystar/tests/test_transforms.py +++ b/flystar/tests/test_transforms.py @@ -22,9 +22,9 @@ def compare_evaluate_errors(): xe = np.abs(np.random.randn(100) * 0.1) ye = np.abs(np.random.randn(100) * 0.1) - xe_new1 = foo._evaluate_error2(x, y, xe, ye, foo.px.parameters) + # xe_new1 = foo._evaluate_error2(x, y, xe, ye, foo.px.parameters) - xe_new2, ye_new2 = foo._evaluate_error(x, y, xe, ye) + xe_new2, ye_new2 = foo.evaluate_error(x, y, xe, ye) # BROKEN From 2aedc4d4dad7f6aaaf09f992aac5ae6faea7a8d6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Sun, 8 Feb 2026 19:42:05 -0800 Subject: [PATCH 163/254] Restore commented functions; Restore update chi squares --- flystar/align.py | 226 +++++++++++++++++++++++------------------------ 1 file changed, 113 insertions(+), 113 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 975fe05..b630675 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -408,16 +408,16 @@ def fit(self): all_epochs = get_all_epochs(self.ref_table) self.ref_table.meta['list_times'] = all_epochs - # # Update chi2 values in ref table, as motion_model_used may have changed - # x_inferred, y_inferred, _, _ = self.ref_table.infer_positions(all_epochs) - # chi2_x_2d = ((self.ref_table['x'] - x_inferred) / self.ref_table['xe'])**2 - # chi2_y_2d = ((self.ref_table['y'] - y_inferred) / self.ref_table['ye'])**2 - # chi2_x = np.nansum(chi2_x_2d, axis=1) - # chi2_y = np.nansum(chi2_y_2d, axis=1) - # chi2_x[~np.isfinite(chi2_x_2d).any(axis=1)] = np.nan - # chi2_y[~np.isfinite(chi2_y_2d).any(axis=1)] = np.nan - # self.ref_table['chi2_x'] = chi2_x - # self.ref_table['chi2_y'] = chi2_y + # Update chi2 values in ref table, as motion_model_used may have changed + x_inferred, y_inferred, _, _ = self.ref_table.infer_positions(all_epochs) + chi2_x_2d = ((self.ref_table['x'] - x_inferred) / self.ref_table['xe'])**2 + chi2_y_2d = ((self.ref_table['y'] - y_inferred) / self.ref_table['ye'])**2 + chi2_x = np.nansum(chi2_x_2d, axis=1) + chi2_y = np.nansum(chi2_y_2d, axis=1) + chi2_x[~np.isfinite(chi2_x_2d).any(axis=1)] = np.nan + chi2_y[~np.isfinite(chi2_y_2d).any(axis=1)] = np.nan + self.ref_table['chi2_x'] = chi2_x + self.ref_table['chi2_y'] = chi2_y if self.save_path: with open(self.save_path, 'wb') as file: @@ -3307,75 +3307,75 @@ def copy_and_rename_for_ref(star_list): return ref_list -# def outlier_rejection_indices(star_list, ref_list, outlier_tol, motion_models, verbose=True): -# """ -# Determine the outliers based on the residual positions between two different -# starlists and some threshold (in sigma). Return the indices of the stars -# to keep (that shouldn't be rejected as outliers). - -# Note that we assume that the star_list and ref_list are already transformed and -# matched. - -# Parameters -# ---------- -# star_list : StarList -# starlist with 'x', 'y' - -# ref_list : StarList -# starlist with 'x0', 'y0' - -# outlier_tol : float -# Number of sigma inside which we keep stars and outside of which we -# reject stars as outliers. - -# motion_models : list of motion_model objects -# The motion models used in the star_list. This is needed to propogate the reference positions forward in time to the epoch of the star_list. - -# Optional Parameters -# -------------------- -# verbose : boolean - -# Returns -# ---------- -# keepers : boolean array -# The boolean array of the stars to keep. -# """ -# # Optionally propogate the reference positions forward in time. -# xref, yref = infer_positions(star_list['t'][0], ref_list, motion_models) - -# # Residuals -# x_resid_on_old_trans = star_list['x'] - xref -# y_resid_on_old_trans = star_list['y'] - yref -# resid_on_old_trans = np.hypot(x_resid_on_old_trans, y_resid_on_old_trans) - -# threshold = outlier_tol * resid_on_old_trans.std() -# keepers = resid_on_old_trans < threshold - -# if verbose > 0: -# msg = ' Outlier Rejection: Keeping {0:d} of {1:d}' -# print(msg.format(sum(keepers), len(resid_on_old_trans))) +def outlier_rejection_indices(star_list, ref_list, outlier_tol, motion_models, verbose=True): + """ + Determine the outliers based on the residual positions between two different + starlists and some threshold (in sigma). Return the indices of the stars + to keep (that shouldn't be rejected as outliers). + + Note that we assume that the star_list and ref_list are already transformed and + matched. + + Parameters + ---------- + star_list : StarList + starlist with 'x', 'y' + + ref_list : StarList + starlist with 'x0', 'y0' + + outlier_tol : float + Number of sigma inside which we keep stars and outside of which we + reject stars as outliers. + + motion_models : list of motion_model objects + The motion models used in the star_list. This is needed to propogate the reference positions forward in time to the epoch of the star_list. + + Optional Parameters + -------------------- + verbose : boolean + + Returns + ---------- + keepers : boolean array + The boolean array of the stars to keep. + """ + # Optionally propogate the reference positions forward in time. + xref, yref = infer_positions(star_list['t'][0], ref_list, motion_models) + + # Residuals + x_resid_on_old_trans = star_list['x'] - xref + y_resid_on_old_trans = star_list['y'] - yref + resid_on_old_trans = np.hypot(x_resid_on_old_trans, y_resid_on_old_trans) + + threshold = outlier_tol * resid_on_old_trans.std() + keepers = resid_on_old_trans < threshold + + if verbose > 0: + msg = ' Outlier Rejection: Keeping {0:d} of {1:d}' + print(msg.format(sum(keepers), len(resid_on_old_trans))) -# return keepers - -# def setup_trans_info(trans_input, trans_args, N_lists, iters): -# """ Setup transformation info into a usable format. - -# trans_input : list or None -# trans_args : dict or None -# N_lists : int -# iters : int -# """ -# trans_list = [None for ii in range(N_lists)] -# if trans_input != None: -# trans_list = [trans_input[ii] for ii in range(N_lists)] - -# # Keep a list of trans_args, one for each starlist. If only -# # a single is passed in, replicate for all star lists, all loop iterations. -# if type(trans_args) == dict: -# tmp = trans_args -# trans_args = [tmp for ii in range(iters)] + return keepers + +def setup_trans_info(trans_input, trans_args, N_lists, iters): + """ Setup transformation info into a usable format. + + trans_input : list or None + trans_args : dict or None + N_lists : int + iters : int + """ + trans_list = [None for ii in range(N_lists)] + if trans_input != None: + trans_list = [trans_input[ii] for ii in range(N_lists)] + + # Keep a list of trans_args, one for each starlist. If only + # a single is passed in, replicate for all star lists, all loop iterations. + if type(trans_args) == dict: + tmp = trans_args + trans_args = [tmp for ii in range(iters)] -# return trans_list, trans_args + return trans_list, trans_args def apply_mag_lim(star_list, mag_lim): """ Apply a magnitude limit to the list. If no magnitude limit is @@ -3409,43 +3409,43 @@ def apply_mag_lim(star_list, mag_lim): return star_list_T -# def get_weighting_scheme(weights, ref_list, star_list): -# if 'xe' in ref_list.colnames: -# var_xref = ref_list['xe']**2 -# var_yref = ref_list['ye']**2 -# else: -# var_xref = 0.0 -# var_yref = 0.0 +def get_weighting_scheme(weights, ref_list, star_list): + if 'xe' in ref_list.colnames: + var_xref = ref_list['xe']**2 + var_yref = ref_list['ye']**2 + else: + var_xref = 0.0 + var_yref = 0.0 -# if 'xe' in star_list.colnames: -# var_xlis = star_list['xe']**2 -# var_ylis = star_list['ye']**2 -# else: -# var_xlis = 0.0 -# var_ylis = 0.0 - -# if weights != None: -# if weights == 'both,var': -# weight = 1.0 / (var_xref + var_xlis + var_yref + var_ylis) -# if weights == 'both,std': -# weight = 1.0 / np.sqrt(var_xref + var_xlis + var_yref + var_ylis) -# if weights == 'ref,var': -# weight = 1.0 / (var_xref + var_yref) -# if weights == 'ref,std': -# weight = 1.0 / np.sqrt(var_xref + var_yref) -# if weights == 'list,var': -# weight = 1.0 / (var_xlis, var_ylis) -# if weights == 'list,std': -# weight = 1.0 / np.sqrt(var_xlis, var_ylis) -# else: -# weight = None - -# # One last check to make sure we had weights at all. -# # Technically, this is mis-use; but lets handle it anyhow. -# if ('xe' not in ref_list.colnames) and ('ye' not in star_list.colnames): -# weight = None - -# return weight + if 'xe' in star_list.colnames: + var_xlis = star_list['xe']**2 + var_ylis = star_list['ye']**2 + else: + var_xlis = 0.0 + var_ylis = 0.0 + + if weights != None: + if weights == 'both,var': + weight = 1.0 / (var_xref + var_xlis + var_yref + var_ylis) + if weights == 'both,std': + weight = 1.0 / np.sqrt(var_xref + var_xlis + var_yref + var_ylis) + if weights == 'ref,var': + weight = 1.0 / (var_xref + var_yref) + if weights == 'ref,std': + weight = 1.0 / np.sqrt(var_xref + var_yref) + if weights == 'list,var': + weight = 1.0 / (var_xlis, var_ylis) + if weights == 'list,std': + weight = 1.0 / np.sqrt(var_xlis, var_ylis) + else: + weight = None + + # One last check to make sure we had weights at all. + # Technically, this is mis-use; but lets handle it anyhow. + if ('xe' not in ref_list.colnames) and ('ye' not in star_list.colnames): + weight = None + + return weight def logger(logfile, message, verbose = 9): From 8bf14a97fe95c4a2b1725287154c1686748faff3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Mon, 9 Feb 2026 11:08:05 -0800 Subject: [PATCH 164/254] Temporarily added parallax test --- flystar/align.py | 2 +- flystar/tests/test_align.py | 25 +++++++++++++++++++++++++ 2 files changed, 26 insertions(+), 1 deletion(-) diff --git a/flystar/align.py b/flystar/align.py index b630675..11f8c21 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -1553,7 +1553,7 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot return - + class MosaicToRef(MosaicSelfRef): def __init__( diff --git a/flystar/tests/test_align.py b/flystar/tests/test_align.py index 4561f3a..a6ad646 100644 --- a/flystar/tests/test_align.py +++ b/flystar/tests/test_align.py @@ -1465,3 +1465,28 @@ def make_fake_starlists_poly1_par(seed=-1): new_lis.write('test_data/random_par_{0:d}.fits'.format(ss), overwrite=True) return (xy_trans, mag_trans) + +if __name__ == '__main__': + import pickle + import matplotlib.pyplot as plt + with open('test_data/my_gaia.pkl', 'rb') as f: + my_gaia = pickle.load(f) + with open('test_data/list_of_starlists.pkl', 'rb') as f: + list_of_starlists = pickle.load(f) + ra_deg, dec_deg = 18.0, -30.0 + msc = align.MosaicToRef(my_gaia, list_of_starlists, iters=3, + dr_tol=[0.2, 0.1, 0.08], dm_tol=[5,5,5], + outlier_tol=[None, None, 3], mag_lim=[6, 20], + trans_class=transforms.PolyTransform, + trans_args=[{'order': 1}, {'order': 1}, {'order': 1}], + motion_models=['Empty','Fixed','Linear','Parallax'], + fixed_params_dict = {'ra':ra_deg, 'dec':dec_deg, 'pa':0.0, 'obsLocation':'earth'}, + use_ref_new=True, + update_ref_orig=False, + mag_trans=True, + trans_weighting='both,std', + init_guess_mode='name', verbose=3) + msc.fit() + for i in range(msc.ref_table['x'].shape[1]): + plt.scatter(msc.ref_table['x'][:, i], msc.ref_table['y'][:, i]) + plt.show() \ No newline at end of file From 2a4a6118133cac4e266fd95df8df0a17aaca7c8c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Tue, 10 Feb 2026 16:56:02 -0800 Subject: [PATCH 165/254] Midpoint test commit --- flystar/align.py | 97 +++++++++++-------- flystar/startables.py | 21 +++- flystar/tests/test_align.py | 81 ++++++++-------- ...011_10_31_F606W_MATCHUP_XYMEEE_final.calib | 0 ...012_09_25_F606W_MATCHUP_XYMEEE_final.calib | 0 ...013_10_24_F606W_MATCHUP_XYMEEE_final.calib | 0 .../{ => test_data}/mb10364_data/my_gaia.fits | 0 7 files changed, 114 insertions(+), 85 deletions(-) rename flystar/tests/{ => test_data}/mb10364_data/2011_10_31_F606W_MATCHUP_XYMEEE_final.calib (100%) rename flystar/tests/{ => test_data}/mb10364_data/2012_09_25_F606W_MATCHUP_XYMEEE_final.calib (100%) rename flystar/tests/{ => test_data}/mb10364_data/2013_10_24_F606W_MATCHUP_XYMEEE_final.calib (100%) rename flystar/tests/{ => test_data}/mb10364_data/my_gaia.fits (100%) diff --git a/flystar/align.py b/flystar/align.py index 11f8c21..20cc2ee 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -12,6 +12,7 @@ import pickle from astropy.utils.exceptions import AstropyUserWarning from .motion_model import Empty, Fixed +from tqdm import tqdm class MosaicSelfRef(object): def __init__( @@ -463,11 +464,14 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar star_list_orig_trim, self.trans_args[0], motion_models=self.motion_models, + fixed_params_dict=self.fixed_params_dict, mode=self.init_guess_mode, order=self.init_order, verbose=self.verbose, mag_trans=self.mag_trans ) + if np.isnan(trans.px.parameters).any() or np.isnan(trans.py.parameters).any(): + raise ValueError(f"Initial transformation contains NaN parameters. trans.px={trans.px.parameters}, trans.py={trans.py.parameters}.") if self.mag_trans: star_list_T.transform_xym(trans) # trimmed, transformed @@ -484,8 +488,7 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar # Outlier rejection if outlier_tol != None: - keepers = self.outlier_rejection_indices(star_list_T[idx1], ref_list[idx2], - outlier_tol) + keepers = self.outlier_rejection_indices(star_list_T[idx1], ref_list[idx2], outlier_tol, verbose=self.verbose) if self.verbose > 1: print( ' Rejected ', len(idx1) - sum(keepers), ' outliers.' ) @@ -495,7 +498,7 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar # Only use stars specified by "use_in_trans" column. if 'use_in_trans' in ref_list.colnames: keepers = ref_list[idx2]['use_in_trans'] - + if self.verbose > 1: print( ' Rejected ', len(idx1) - sum(keepers), ' with use_in_trans=False.' ) @@ -570,10 +573,10 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar '. If match count is low, check dr_tol, dm_tol.' ) ## Make plot, if desired - plots.trans_positions(ref_list, ref_list[idx_ref], star_list_T, star_list_T[idx_lis], - save_path=f"{os.path.dirname(self.save_path)}/Transformed_Positions_{star_list_T['t'][0]}.png" if self.save_path else None, - show_plot=False) - + if self.save_path: + plots.trans_positions(ref_list, ref_list[idx_ref], star_list_T, star_list_T[idx_lis], + save_path=f"{os.path.dirname(self.save_path)}/Transformed_Positions_{star_list_T['t'][0]}.png", + show_plot=False) ### Update the observed (but transformed) values in the reference table. self.update_ref_table_from_list(star_list, star_list_T, ii, idx_ref, idx_lis, idx2) @@ -740,7 +743,7 @@ def setup_ref_table_from_starlist(self, star_list): # Make sure we have a column to indicate whether each star # IS USED in the transformation. This will be 2D if 'used_in_trans' not in ref_table.colnames: - new_col = Column(np.zeros([len(ref_table),1], dtype=bool), name='used_in_trans') + new_col = Column(np.zeros([len(ref_table), 1], dtype=bool), name='used_in_trans') ref_table.add_column(new_col) # Keep track of whether this is an original reference star. @@ -780,8 +783,7 @@ def apply_mag_lim_via_use_in_trans(self, ref_list, ref_mag_lim): else: mcol = 'm' - no_use = np.where((ref_list[mcol] < ref_mag_lim[0]) | - (ref_list[mcol] >= ref_mag_lim[1])) + no_use = (ref_list[mcol] < ref_mag_lim[0]) | (ref_list[mcol] >= ref_mag_lim[1]) ref_list['use_in_trans'][no_use] = False @@ -965,14 +967,19 @@ def update_ref_table_aggregates(self, keep_orig=None, n_boot=0): motion_models_possible = [] for mm in self.motion_models: required_columns = mm.fit_param_names + mm.fixed_param_names - if all(col in self.ref_table.colnames for col in required_columns): + if all(col in self.ref_table.colnames or col in self.fixed_params_dict.keys() for col in required_columns): motion_models_possible.append((mm, required_columns)) # Check if values are finite for required columns in possible motion models motion_model_used = [] for k in np.where(keep_orig)[0]: for mm, req in motion_models_possible[::-1]: - if all(np.isfinite(self.ref_table[k][col]) for col in req if self.ref_table[col].dtype.kind in 'f'): + # if all(np.isfinite(self.ref_table[k][col]) for col in req if self.ref_table[col].dtype.kind in 'f'): + req_col_in_table = [col for col in req if col in self.ref_table.colnames] + req_col_in_dict = [col for col in req if (self.fixed_params_dict is not None) and (col in self.fixed_params_dict.keys())] + # If requested column in table/fixed_params dict is numeric, check if values are finite. + if all(np.isfinite(self.ref_table[col][k]) for col in req_col_in_table if np.issubdtype(self.ref_table[col].dtype, np.number)) \ + and all(np.isfinite(self.fixed_params_dict[col]) for col in req_col_in_dict if np.issubdtype(np.array(self.fixed_params_dict[col]).dtype, np.number)): motion_model_used.append(mm.name) break @@ -1020,7 +1027,7 @@ def get_weights_for_lists(self, ref_list, star_list): if self.trans_weighting == 'list,var': weight = 1.0 / (var_xlis + var_ylis) if self.trans_weighting == 'list,std': - weight = 1.0 / np.sqrt(var_xlis, var_ylis) + weight = 1.0 / np.sqrt(var_xlis + var_ylis) else: weight = None @@ -1065,7 +1072,7 @@ def match_lists(self, dr_tol, dm_tol): else: star_list_T.transform_xy(self.trans_list[ii]) - xref, yref = infer_positions(star_list_T['t'][0], self.ref_table, self.motion_models) + xref, yref = infer_positions(star_list_T['t'][0], self.ref_table, self.motion_models, self.fixed_params_dict) mref = self.ref_table['m0'] idx_lis, idx_ref, dr, dm = match.match(star_list_T['x'], star_list_T['y'], star_list_T['m'], @@ -1098,18 +1105,17 @@ def get_ref_list_from_table(self, epoch): # Reference stars will be named. name = self.ref_table['name'] # Calculate x, y, xe, ye - # x, y, xe, ye = infer_positions(epoch, self.ref_table, self.motion_models, return_errors=True) - if ('motion_model_used' in self.ref_table.colnames): - x, y, xe, ye = self.ref_table.infer_positions(epoch) + if 'motion_model_used' in self.ref_table.colnames: + x, y, xe, ye = self.ref_table.infer_positions(epoch, fixed_params_dict=self.fixed_params_dict) else: # Otherwise, infer positions using the most complex motion model with the existing columns, until it reaches Fixed or Empty for mm in self.motion_models[::-1]: required_columns = mm.fit_param_names + mm.fixed_param_names - if all([param in self.ref_table.colnames for param in required_columns]): + if all([param in self.ref_table.colnames or param in self.fixed_params_dict.keys() for param in required_columns]): # Check if the values are finite for non-string columns in the required columns for this motion model. If not, skip to the next motion model. if not all([np.isfinite(self.ref_table[param]).all() for param in required_columns if self.ref_table[param].dtype.kind in 'if']): continue - + print(f"Inferring positions using motion model {mm.name}.") # If we have error columns for all fit parameters, then use them in the model inference. Otherwise, just use the fit parameters without errors. if all([f'{param}_err' in self.ref_table.colnames for param in mm.fit_param_names]) and all([np.isfinite(self.ref_table[f'{param}_err']).all() for param in mm.fit_param_names]): x, y, xe, ye = mm().model( @@ -1302,8 +1308,7 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot ### DEFINE MEAN, STD VARIABLES AND BUILD THEM RATHER THAN SAVING FULL ARRAY ### DECREASE PRECISION ON ARRAYS (32 bit instead of 64: dtype=np.float32) ### AT SOME POINT, NEED TO CONVERT BACK (LOOK UP HOW TO DO THIS CAREFULLY) - t1 = time.time() - for ii in range(n_boot): + for ii in tqdm(range(n_boot), desc='Bootstrap iterations', disable=not show_progress): # Recalculate transformations using bootstrap sample of # reference stars. Use a loop for each epoch here, so we # can handle case where different reference stars are used @@ -1514,7 +1519,7 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot # self.ref_table['motion_model_used'] = np.array([motion_model_list[d].name for d in mm_digitized], dtype='U20') - x_pred, y_pred, _, _ = self.ref_table.infer_positions(t_arr) + x_pred, y_pred, _, _ = self.ref_table.infer_positions(t_arr, fixed_params_dict=self.fixed_params_dict) xe_comb = np.hypot(self.ref_table['xe'], self.ref_table['xe_boot']) ye_comb = np.hypot(self.ref_table['ye'], self.ref_table['ye_boot']) data_dict['chi2_x_boot'] = np.nansum((self.ref_table['x']-x_pred)**2/(xe_comb)**2,axis=1) @@ -1784,7 +1789,7 @@ def = None. If not None, then this should contain an array or list of transform # If motion_model_used in columns but params columns are missing, raise a warning and remove motion_model_used column to avoid confusion. if 'motion_model_used' in self.ref_list.colnames: motion_model_params = motion_model.motion_model_param_names(np.unique(self.ref_list['motion_model_used']), with_errors=False, with_fixed=True) - missing_params = [param for param in motion_model_params if param not in self.ref_list.colnames] + missing_params = [param for param in motion_model_params if (param not in self.ref_list.colnames) and (f'{param}_err' not in self.ref_list.colnames) and (param not in self.fixed_params_dict.keys())] if len(missing_params) > 0: warnings.warn("Warning: 'motion_model_used' column found in ref_list, but the following motion model parameter columns are missing: " + ", ".join(missing_params) + ". Removing 'motion_model_used' column to avoid confusion.") self.ref_list.remove_column('motion_model_used') @@ -1947,7 +1952,7 @@ def fit(self): self.ref_table.meta['list_times'] = all_epochs # Update chi2 values in ref table, as motion_model_used may have changed - x_inferred, y_inferred, _, _ = self.ref_table.infer_positions(all_epochs) + x_inferred, y_inferred, _, _ = self.ref_table.infer_positions(all_epochs, fixed_params_dict=self.fixed_params_dict) chi2_x_2d = ((self.ref_table['x'] - x_inferred) / self.ref_table['xe'])**2 chi2_y_2d = ((self.ref_table['y'] - y_inferred) / self.ref_table['ye'])**2 chi2_x = np.nansum(chi2_x_2d, axis=1) @@ -1963,7 +1968,7 @@ def fit(self): return # TODO: This is sometimes run on a startable, not a starlist, at least as currently used -def infer_positions(t, startable, motion_models=None, return_errors=False): +def infer_positions(t, startable, motion_models=None, fixed_params_dict=None, return_errors=False): """ Take a startable, check to see if it has motion/velocity columns. If it does, then propagate the positions forward in time @@ -1989,7 +1994,7 @@ def infer_positions(t, startable, motion_models=None, return_errors=False): Inferred position (and errors) at time t """ if ('motion_model_used' in startable.colnames): - x, y, xe, ye = startable.infer_positions(t) + x, y, xe, ye = startable.infer_positions(t, fixed_params_dict=fixed_params_dict) if return_errors: return x, y, xe, ye else: @@ -2016,6 +2021,7 @@ def infer_positions(t, startable, motion_models=None, return_errors=False): # Otherwise, infer positions using the most complex motion model with the existing columns, until it reaches Fixed or Empty # Sort motion models inversely by mm.n_params motion_models = sorted(motion_models, key=lambda mm: mm.n_params, reverse=True) + pdb.set_trace() for mm in motion_models: if mm.name == 'Empty': x = startable['x'] @@ -2034,8 +2040,6 @@ def infer_positions(t, startable, motion_models=None, return_errors=False): fit_params=np.array([startable[param] for param in mm.fit_param_names]).T, fixed_params_dict={param: startable[param] for param in mm.fixed_param_names} ) - xe = None - ye = None break return x, y @@ -3166,9 +3170,19 @@ def check_trans_input(list_of_starlists, trans_input, mag_trans): return -def trans_initial_guess(ref_list, star_list, trans_args, motion_models=None, mode='miracle', - ignore_contains='star', verbose=True, n_req_match=3, - mag_trans=True, order=1): +def trans_initial_guess( + ref_list, + star_list, + trans_args, + motion_models=None, + fixed_params_dict=None, + mode='miracle', + ignore_contains='star', + verbose=True, + n_req_match=3, + mag_trans=True, + order=1 +): """ Take two starlists and perform an initial matching and transformation. @@ -3190,7 +3204,7 @@ def trans_initial_guess(ref_list, star_list, trans_args, motion_models=None, mod star_list['name'][idx_s], assume_unique=True, return_indices=True) - + x1m = star_list['x'][idx_s][ndx_s] y1m = star_list['y'][idx_s][ndx_s] m1m = star_list['m'][idx_s][ndx_s] @@ -3205,7 +3219,7 @@ def trans_initial_guess(ref_list, star_list, trans_args, motion_models=None, mod # If there are velocities in the reference list, use them. # We assume velocities are in the same units as the positions. - xref, yref = infer_positions(star_list['t'][0], ref_list, motion_models) + xref, yref = infer_positions(star_list['t'][0], ref_list, motion_models, fixed_params_dict=fixed_params_dict) if 'm' in ref_list.colnames: mref = ref_list['m'] else: @@ -3307,7 +3321,7 @@ def copy_and_rename_for_ref(star_list): return ref_list -def outlier_rejection_indices(star_list, ref_list, outlier_tol, motion_models, verbose=True): +def outlier_rejection_indices(star_list, ref_list, outlier_tol, motion_models, fixed_params_dict=None, verbose=True): """ Determine the outliers based on the residual positions between two different starlists and some threshold (in sigma). Return the indices of the stars @@ -3320,20 +3334,17 @@ def outlier_rejection_indices(star_list, ref_list, outlier_tol, motion_models, v ---------- star_list : StarList starlist with 'x', 'y' - ref_list : StarList starlist with 'x0', 'y0' - outlier_tol : float Number of sigma inside which we keep stars and outside of which we reject stars as outliers. - motion_models : list of motion_model objects - The motion models used in the star_list. This is needed to propogate the reference positions forward in time to the epoch of the star_list. - - Optional Parameters - -------------------- - verbose : boolean + The motion models to use in the star_list + fixed_params_dict : dict or None, optional + Dictionary of fixed parameters for motion models, by default None + verbose : boolean, optional + If True, print information about the outlier rejection process, by default True Returns ---------- @@ -3341,7 +3352,7 @@ def outlier_rejection_indices(star_list, ref_list, outlier_tol, motion_models, v The boolean array of the stars to keep. """ # Optionally propogate the reference positions forward in time. - xref, yref = infer_positions(star_list['t'][0], ref_list, motion_models) + xref, yref = infer_positions(star_list['t'][0], ref_list, motion_models, fixed_params_dict=fixed_params_dict) # Residuals x_resid_on_old_trans = star_list['x'] - xref diff --git a/flystar/startables.py b/flystar/startables.py index f586a50..1590be4 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -905,13 +905,18 @@ def fit_motion_model( self['t0'][unique_index] = t0[unique_index] return - def infer_positions(self, times, fill_value=np.nan): + def infer_positions(self, times, fixed_params_dict=None, fill_value=np.nan): """Infer star positions at given times using fitted motion models. Parameters ---------- times : array_like Times at which to predict positions. Scalar, or (N_times,) array, or (N_stars, N_times) array. + fixed_params_dict : None or dict, optional + Dictionary of fixed parameters to use for prediction. + If not provided, will try to look for fixed parameters in the table columns. + If fixed params are found in both the table and the fixed_params_dict, the values in the table will be used and the fixed_params_dict values will be ignored, + by default None fill_value : float, optional Value to use for missing data, by default np.nan @@ -943,7 +948,7 @@ def infer_positions(self, times, fill_value=np.nan): xe_pred = np.full(N_stars, np.inf, dtype=float) ye_pred = np.full(N_stars, np.inf, dtype=float) - + # Calculate the dictionary of {motion_model: indices of stars with this motion model} for faster access during prediction unique_motion_models, unique_inv_indices = np.unique(self['motion_model_used'], return_inverse=True) indices_by_motion_model = {key: np.flatnonzero(unique_inv_indices == k) for k, key in enumerate(unique_motion_models)} @@ -962,12 +967,20 @@ def infer_positions(self, times, fill_value=np.nan): fixed_params = {} for param_name in motion_model_instance.fixed_param_names: - col_name = param_name + col_name = copy.deepcopy(param_name) + # If column not in table, check if it's provided in fixed_params_dict. If not, raise error. If provided, use the value from fixed_params_dict for all stars. + if (col_name not in self.colnames) and (f'{col_name}_mm' not in self.colnames): + if col_name in fixed_params_dict: + fixed_params[param_name] = fixed_params_dict[col_name] + continue + else: + raise KeyError(f"infer_positions: Fixed parameter '{param_name}' not found in table columns or fixed_params_dict. Please provide the value for this parameter in fixed_params_dict or add a column named '{param_name}' to the table.") + + # If original table has column and fit_motion_model added the column with _mm suffix, use the _mm column for prediction. if param_name + '_mm' in self.colnames: col_name = param_name + '_mm' fixed_params[param_name] = self[col_name][unique_index] - # TODO: vectorize obsLocation handling in motion models? if (param_name == 'obsLocation'): assert np.unique(fixed_params[param_name]).size == 1, \ "infer_positions: obsLocation fixed parameter has different values for different stars. Vectorized handling not implemented yet." diff --git a/flystar/tests/test_align.py b/flystar/tests/test_align.py index a6ad646..d37fd17 100644 --- a/flystar/tests/test_align.py +++ b/flystar/tests/test_align.py @@ -513,7 +513,7 @@ def test_MosaicToRef_hst_me(): # dec = '-34:27:05.01' # Load up a Gaia catalog (queried around the RA/Dec above) - my_gaia = Table.read('mb10364_data/my_gaia.fits') + my_gaia = Table.read('test_data/mb10364_data/my_gaia.fits') my_gaia['me'] = 0.01 my_gaia.rename_columns( @@ -523,9 +523,9 @@ def test_MosaicToRef_hst_me(): # Gather the list of starlists. For first pass, don't modify the starlists. # Loop through the observations and read them in, in prep for alignment with Gaia epochs = [2011.83, 2012.73, 2013.81] - starlist_names = ['mb10364_data/2011_10_31_F606W_MATCHUP_XYMEEE_final.calib', - 'mb10364_data/2012_09_25_F606W_MATCHUP_XYMEEE_final.calib', - 'mb10364_data/2013_10_24_F606W_MATCHUP_XYMEEE_final.calib'] + starlist_names = ['test_data/mb10364_data/2011_10_31_F606W_MATCHUP_XYMEEE_final.calib', + 'test_data/mb10364_data/2012_09_25_F606W_MATCHUP_XYMEEE_final.calib', + 'test_data/mb10364_data/2013_10_24_F606W_MATCHUP_XYMEEE_final.calib'] list_of_starlists = [] @@ -545,17 +545,21 @@ def test_MosaicToRef_hst_me(): list_of_starlists.append(lis) - msc = align.MosaicToRef(my_gaia, list_of_starlists, iters=1, - dr_tol=[0.1], dm_tol=[5], - outlier_tol=[None], mag_lim=[13, 21], - trans_class=transforms.PolyTransform, - trans_args=[{'order': 1}], - motion_models=['Empty', 'Fixed'], - use_ref_new=False, - update_ref_orig=False, - mag_trans=False, - trans_weighting='both,std', - init_guess_mode='miracle', verbose=False) + msc = align.MosaicToRef( + my_gaia, list_of_starlists, iters=1, + dr_tol=[0.1], dm_tol=[5], + outlier_tol=[None], mag_lim=[13, 21], + trans_class=transforms.PolyTransform, + trans_args=[{'order': 1}], + motion_models=['Empty', 'Fixed'], + use_ref_new=False, + update_ref_orig=False, + mag_trans=False, + trans_weighting='both,std', + init_guess_mode='miracle', + save_path='test_data/mb10364_data/test_MosaicToRef_hst_me.pkl', + verbose=False + ) msc.fit() assert 'me' in msc.ref_table.colnames @@ -1467,26 +1471,27 @@ def make_fake_starlists_poly1_par(seed=-1): return (xy_trans, mag_trans) if __name__ == '__main__': - import pickle - import matplotlib.pyplot as plt - with open('test_data/my_gaia.pkl', 'rb') as f: - my_gaia = pickle.load(f) - with open('test_data/list_of_starlists.pkl', 'rb') as f: - list_of_starlists = pickle.load(f) - ra_deg, dec_deg = 18.0, -30.0 - msc = align.MosaicToRef(my_gaia, list_of_starlists, iters=3, - dr_tol=[0.2, 0.1, 0.08], dm_tol=[5,5,5], - outlier_tol=[None, None, 3], mag_lim=[6, 20], - trans_class=transforms.PolyTransform, - trans_args=[{'order': 1}, {'order': 1}, {'order': 1}], - motion_models=['Empty','Fixed','Linear','Parallax'], - fixed_params_dict = {'ra':ra_deg, 'dec':dec_deg, 'pa':0.0, 'obsLocation':'earth'}, - use_ref_new=True, - update_ref_orig=False, - mag_trans=True, - trans_weighting='both,std', - init_guess_mode='name', verbose=3) - msc.fit() - for i in range(msc.ref_table['x'].shape[1]): - plt.scatter(msc.ref_table['x'][:, i], msc.ref_table['y'][:, i]) - plt.show() \ No newline at end of file + test_MosaicToRef_hst_me() + # import pickle + # import matplotlib.pyplot as plt + # with open('test_data/my_gaia.pkl', 'rb') as f: + # my_gaia = pickle.load(f) + # with open('test_data/list_of_starlists.pkl', 'rb') as f: + # list_of_starlists = pickle.load(f) + # ra_deg, dec_deg = 18.0, -30.0 + # msc = align.MosaicToRef(my_gaia, list_of_starlists, iters=3, + # dr_tol=[0.2, 0.1, 0.08], dm_tol=[5,5,5], + # outlier_tol=[None, None, 3], mag_lim=[6, 20], + # trans_class=transforms.PolyTransform, + # trans_args=[{'order': 1}, {'order': 1}, {'order': 1}], + # motion_models=['Empty','Fixed','Linear','Parallax'], + # fixed_params_dict = {'ra':ra_deg, 'dec':dec_deg, 'pa':0.0, 'obsLocation':'earth'}, + # use_ref_new=True, + # update_ref_orig=False, + # mag_trans=True, + # trans_weighting='both,std', + # init_guess_mode='name', verbose=3) + # msc.fit() + # for i in range(msc.ref_table['x'].shape[1]): + # plt.scatter(msc.ref_table['x'][:, i], msc.ref_table['y'][:, i]) + # plt.show() \ No newline at end of file diff --git a/flystar/tests/mb10364_data/2011_10_31_F606W_MATCHUP_XYMEEE_final.calib b/flystar/tests/test_data/mb10364_data/2011_10_31_F606W_MATCHUP_XYMEEE_final.calib similarity index 100% rename from flystar/tests/mb10364_data/2011_10_31_F606W_MATCHUP_XYMEEE_final.calib rename to flystar/tests/test_data/mb10364_data/2011_10_31_F606W_MATCHUP_XYMEEE_final.calib diff --git a/flystar/tests/mb10364_data/2012_09_25_F606W_MATCHUP_XYMEEE_final.calib b/flystar/tests/test_data/mb10364_data/2012_09_25_F606W_MATCHUP_XYMEEE_final.calib similarity index 100% rename from flystar/tests/mb10364_data/2012_09_25_F606W_MATCHUP_XYMEEE_final.calib rename to flystar/tests/test_data/mb10364_data/2012_09_25_F606W_MATCHUP_XYMEEE_final.calib diff --git a/flystar/tests/mb10364_data/2013_10_24_F606W_MATCHUP_XYMEEE_final.calib b/flystar/tests/test_data/mb10364_data/2013_10_24_F606W_MATCHUP_XYMEEE_final.calib similarity index 100% rename from flystar/tests/mb10364_data/2013_10_24_F606W_MATCHUP_XYMEEE_final.calib rename to flystar/tests/test_data/mb10364_data/2013_10_24_F606W_MATCHUP_XYMEEE_final.calib diff --git a/flystar/tests/mb10364_data/my_gaia.fits b/flystar/tests/test_data/mb10364_data/my_gaia.fits similarity index 100% rename from flystar/tests/mb10364_data/my_gaia.fits rename to flystar/tests/test_data/mb10364_data/my_gaia.fits From f979af7fa22ef46fdfc465ad4366e1c9d3f391f3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Tue, 17 Feb 2026 23:25:04 -0800 Subject: [PATCH 166/254] Add motion_model_for_new_star used in add_rows_for_new_stars; Fixed motion_model_used column problem: Now it will only be determined by motion model fit, and the column will be removed if provided in the reference list --- flystar/align.py | 392 ++++++++++++++++++++---------------- flystar/tests/test_align.py | 117 +++++------ 2 files changed, 267 insertions(+), 242 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 20cc2ee..9ec4a2f 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -18,7 +18,7 @@ class MosaicSelfRef(object): def __init__( self, list_of_starlists, - # Alignment tolerance parameters + # Alignment parameters ref_index=0, iters=2, dr_tol=[1, 1], @@ -37,6 +37,7 @@ def __init__( mag_lim=None, # Motion model parameters motion_models=['Empty', 'Fixed'], + motion_model_for_new_star=None, fixed_params_dict=None, vel_weighting='var', use_scipy=True, @@ -50,7 +51,7 @@ def __init__( Make a mosaic object by passing in a list of starlists and then running fit(). Required Parameters - ---------- + ------------------- list_of_starlists : array of StarList objects An array or list of flystar.starlists.StarList objects (which are Astropy Tables). There should be one for each starlist and they must contain 'x', 'y', and 'm' columns. @@ -68,7 +69,7 @@ def __init__( for those stars not trimmed out by the other criteria. Optional Parameters - ---------- + ------------------- ref_index : int The index of the reference epoch. (default = 0). Note that this is the reference list only for the first iteration. Subsequent iterations will utilize the sigma-clipped @@ -89,35 +90,6 @@ def __init__( The outlier tolerance (in units of sigma) for rejecting outlier stars. This is a list of tol values, one for each iteration of matching/transformation. - mag_trans : boolean - If true, this will also calculate and (temporarily) apply a zeropoint offset to - magnitudes in each list to bring them into a common magnitude system. This is - essential for matching (with finite dm_tol) starlists of different filters or - starlists that are not photometrically calibrated. Note that the final_table columns - of 'm', 'm0', and 'm0_err' will contain the transformed magnitudes while the - final_table column 'm_orig' will contain the original un-transformed magnitudes. - If mag_trans = False, then no such zeropoint offset it applied at any point. - - mag_lim : array - If different from None, it indicates the minimum and maximum magnitude - on the catalogs for finding the transformations. Note, if you want specify the mag_lim - separately for each list and each iteration, you need to pass in a 2D array that - has shape (N_lists, 2). - - trans_weighting : str - Either None (def), 'both,var', 'list,var', or 'ref,var' depending on whether you want - to weight by the positional uncertainties (variances) in the individual starlists, or also with - the uncertainties in the reference frame itself. Note weighting only works when there - are positional uncertainties availabe. Other options include 'both,std', 'list,std', 'list,var'. - - vel_weighting : str - Either 'var' (def) or 'std', depending on whether you want to weight the motion model - fits by the variance or standard deviation of the position data - - trans_input : array or list of transform objects - def = None. If not None, then this should contain an array or list of transform - objects that will be used as the initial guess in the alignment and matching. - trans_class : transforms.Transform2D object (or subclass) The transform class that will be used to when deriving the optimal transformation parameters between each list and the reference list. @@ -128,28 +100,59 @@ def = None. If not None, then this should contain an array or list of transform then the transformation argument (i.e. order) will be changed for every iteration in iters. - calc_trans_inverse: boolean - If true, then calculate the inverse transformation (from reference to starlist) - in addition to the normal transformation (from starlist to reference). The inverse - calculation is calculated by switching the order to the positions in match_and_transform. - The inverse transformations are saved in self.trans_list_inverse. + trans_input : array or list of transform objects + def = None. If not None, then this should contain an array or list of transform + objects that will be used as the initial guess in the alignment and matching. - self.trans_list_inverse doesn't exist if calc_trans_inverse == False + trans_weighting : str + Either None (def), 'both,var', 'list,var', or 'ref,var' depending on whether you want + to weight by the positional uncertainties (variances) in the individual starlists, or also with + the uncertainties in the reference frame itself. Note weighting only works when there + are positional uncertainties availabe. Other options include 'both,std', 'list,std', 'list,var'. + + init_order : int + The order of the initial transformation used for the first iteration. init_guess_mode : string If no initial transformations are passed in via the trans_input keyword, then we have to make the initial transformation and matching blindly. We can do this in a couple of different ways. Options are 'miracle' or 'name' (see trans_initial_guess() for more details). - iter_callback : None or function - A function to call (that accepts a StarTable object and an iteration number) - at the end of every iteration. This can be used for plotting or printing state. + calc_trans_inverse: boolean + If true, then calculate the inverse transformation (from reference to starlist) + in addition to the normal transformation (from starlist to reference). The inverse + calculation is calculated by switching the order to the positions in match_and_transform. + The inverse transformations are saved in self.trans_list_inverse. + self.trans_list_inverse doesn't exist if calc_trans_inverse == False + + mag_trans : boolean + If true, this will also calculate and (temporarily) apply a zeropoint offset to + magnitudes in each list to bring them into a common magnitude system. This is + essential for matching (with finite dm_tol) starlists of different filters or + starlists that are not photometrically calibrated. Note that the final_table columns + of 'm', 'm0', and 'm0_err' will contain the transformed magnitudes while the + final_table column 'm_orig' will contain the original un-transformed magnitudes. + If mag_trans = False, then no such zeropoint offset it applied at any point. + + mag_lim : array + If different from None, it indicates the minimum and maximum magnitude + on the catalogs for finding the transformations. Note, if you want specify the mag_lim + separately for each list and each iteration, you need to pass in a 2D array that + has shape (N_lists, 2). motion_models : list of MotionModel or str, optional Motion models or their names to use for new or unassigned stars + motion_model_for_new_star : str or MotionModel, optional + Motion model or its name for newly added stars in the ref table. Used in add_rows_for_new_stars(). + If None, the most complex motion model in motion_models will be used, by default None. + fixed_params_dict : None or dict - Dictionary of motion model fixed parameters + Dictionary of motion model fixed parameters, e.g., ra, dec, pa, obsLocation, t0, etc. See motion_model classes for details. + + vel_weighting : str + Either 'var' (def) or 'std', depending on whether you want to weight the motion model + fits by the variance or standard deviation of the position data use_scipy : bool, optional If True, use scipy.optimize.curve_fit for velocity fitting. If False, use linear @@ -159,16 +162,20 @@ def = None. If not None, then this should contain an array or list of transform If True, the velocity fit will use absolute errors in the data. If False, relative errors will be used, by default False. + iter_callback : None or function + A function to call (that accepts a StarTable object and an iteration number) + at the end of every iteration. This can be used for plotting or printing state. + save_path : str, optional Path to save the MosaicSelfRef object as a pickle file. - - verbose : int (0 to 9, inclusive) + + verbose : bool or int (0 to 9, inclusive) Controls the verbosity of print statements. (0 least, 9 most verbose). For backwards compatibility, 0 = False, 9 = True. (Note: technically right now no checks on whether the number is an integer or not...) Example - ---------- + ------- msc = align.MosaicToRef(list_of_starlists, iters=1, dr_tol=[0.1], dm_tol=[5], outlier_tol=[None], mag_lim=[13, 21], @@ -179,7 +186,7 @@ def = None. If not None, then this should contain an array or list of transform msc.fit() # Access a list of all the transformation parameters: - trans_list = msc.trans_list + trans_list = msc.trans_list # Access the fully-combined reference table. stars_table = msc.ref_table @@ -193,8 +200,7 @@ def = None. If not None, then this should contain an array or list of transform # Overplot the best-fit proper motion. times = stars_table['t'][0, :] plt.errorbar(times, stars_table['x'][0, :], yerr=stars_table['xe'][0, :]) - plt.axhline(stars_table['x0'][0] + stars_table['vx'][0]*(times - stars_table['t0'][0])) - + plt.axhline(stars_table['x0'][0] + stars_table['vx'][0]*(times - stars_table['t0'][0])) """ self.star_lists = list_of_starlists @@ -219,7 +225,7 @@ def = None. If not None, then this should contain an array or list of transform self.iter_callback = iter_callback self.save_path = save_path self.verbose = verbose - + # Setup save_path: if self.save_path: assert self.save_path.endswith('.pkl'), 'Save_path must end with .pkl' @@ -228,6 +234,7 @@ def = None. If not None, then this should contain an array or list of transform all_mm_map = motion_model.motion_model_map() if all(isinstance(mm, str) for mm in motion_models): + assert all(mm in all_mm_map.keys() for mm in motion_models), f"All motion model names must be in {list(all_mm_map.keys())}" mm_names = motion_models motion_models = [all_mm_map[mm] for mm in motion_models] else: @@ -241,6 +248,12 @@ def = None. If not None, then this should contain an array or list of transform motion_models = sorted(motion_models, key=lambda mm: mm.n_params) self.motion_models = motion_models + if motion_model_for_new_star is None: + self.motion_model_for_new_star = self.motion_models[-1] + elif isinstance(motion_model_for_new_star, str): + assert motion_model_for_new_star in all_mm_map.keys(), f"motion_model_for_new_star must be in {list(all_mm_map.keys())}" + self.motion_model_for_new_star = all_mm_map[motion_model_for_new_star] + # For backwards compatibility. if self.verbose is True: self.verbose = 9 @@ -268,11 +281,6 @@ def = None. If not None, then this should contain an array or list of transform # is passed in, replicate for all star lists, all loop iterations. ########## self.setup_trans_info() - - # Make sure the motion models are ready - # self.motion_model_dict = motion_model.validate_motion_model_dict(self.motion_model_dict, - # StarTable(), self.default_motion_model) - return def fix_iterable_conditions(self): @@ -549,7 +557,7 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar ml='m_lis_T', mr='m_ref', dx='dx_mpix', dy='dy_mpix', dm='dm', xo='x_orig', yo='y_orig', mo='m_orig')) - + fmt = '{nr:20s} {n:20s} {xl:9.5f} {xr:9.5f} {yl:9.5f} {yr:9.5f} {ml:6.2f} {mr:6.2f} ' fmt += '{dx:7.2f} {dy:7.2f} {dm:6.2f} {xo:9.5f} {yo:9.5f} {mo:6.2f}' for foo in range(len(idx1)): @@ -567,7 +575,7 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar idx_lis, idx_ref, dr, dm = match.match(star_list_T['x'], star_list_T['y'], star_list_T['m'], ref_list['x'], ref_list['y'], ref_list['m'], dr_tol=dr_tol, dm_tol=dm_tol, verbose=self.verbose) - + if self.verbose > 1: print( ' Match 2: After trans, found ', len(idx_lis), ' matches out of ', len(star_list_T), '. If match count is low, check dr_tol, dm_tol.' ) @@ -654,6 +662,11 @@ def setup_ref_table_from_starlist(self, star_list): array in the original reference star list. """ col_arrays = {} + # Remove motion_model_used if present, as it can only be determined by fit_motion_model function + if 'motion_model_used' in star_list.colnames: + warnings.warn("The input reference star list contains a 'motion_model_used' column. This column will be removed and re-calculated by the fit_motion_model function.") + star_list.remove_column('motion_model_used') + motion_model_col_names = motion_model.motion_model_param_names(self.motion_models, with_errors=True, with_fixed=True) + ['m0','m0_err','use_in_trans', 'motion_model_input', 'motion_model_used'] if 't0' not in motion_model_col_names: motion_model_col_names.insert(0, 't0') @@ -876,7 +889,7 @@ def update_ref_table_from_list(self, star_list, star_list_T, ii, idx_ref, idx_li self.ref_table, star_list, idx_lis, - motion_model=self.motion_models[-1].name + motion_model_name=self.motion_model_for_new_star.name ) if len(idx_ref_new) > 0: @@ -961,31 +974,6 @@ def update_ref_table_aggregates(self, keep_orig=None, n_boot=0): bootstrap=n_boot, verbose=self.verbose ) - if (keep_orig is not None) and (sum(keep_orig) > 0): - # Determine motion_model_used for keep_orig stars - # Filter possible motion models based on available columns - motion_models_possible = [] - for mm in self.motion_models: - required_columns = mm.fit_param_names + mm.fixed_param_names - if all(col in self.ref_table.colnames or col in self.fixed_params_dict.keys() for col in required_columns): - motion_models_possible.append((mm, required_columns)) - - # Check if values are finite for required columns in possible motion models - motion_model_used = [] - for k in np.where(keep_orig)[0]: - for mm, req in motion_models_possible[::-1]: - # if all(np.isfinite(self.ref_table[k][col]) for col in req if self.ref_table[col].dtype.kind in 'f'): - req_col_in_table = [col for col in req if col in self.ref_table.colnames] - req_col_in_dict = [col for col in req if (self.fixed_params_dict is not None) and (col in self.fixed_params_dict.keys())] - # If requested column in table/fixed_params dict is numeric, check if values are finite. - if all(np.isfinite(self.ref_table[col][k]) for col in req_col_in_table if np.issubdtype(self.ref_table[col].dtype, np.number)) \ - and all(np.isfinite(self.fixed_params_dict[col]) for col in req_col_in_dict if np.issubdtype(np.array(self.fixed_params_dict[col]).dtype, np.number)): - motion_model_used.append(mm.name) - break - - # Assign the determined motion models - self.ref_table['motion_model_used'][keep_orig] = motion_model_used - # Combine (transformed) magnitudes if 'me' in self.ref_table.colnames: weights_col = None @@ -993,6 +981,33 @@ def update_ref_table_aggregates(self, keep_orig=None, n_boot=0): weights_col = 'me' self.ref_table.combine_lists('m', weights_col=weights_col, ismag=True) + # if (keep_orig is not None) and (sum(keep_orig) > 0): + # Determine motion_model_used for keep_orig stars + # Filter possible motion models based on available columns + motion_models_possible = [] + for mm in self.motion_models: + required_columns = mm.fit_param_names + mm.fixed_param_names + if all(col in self.ref_table.colnames or (self.fixed_params_dict is not None and col in self.fixed_params_dict.keys()) for col in required_columns): + motion_models_possible.append((mm, required_columns)) + + # Check if values are finite for required columns in possible motion models + motion_model_used = [] + # for k in np.where(keep_orig)[0]: + for k in range(len(self.ref_table)): + for mm, req in motion_models_possible[::-1]: + # if all(np.isfinite(self.ref_table[k][col]) for col in req if self.ref_table[col].dtype.kind in 'f'): + req_col_in_table = [col for col in req if col in self.ref_table.colnames] + req_col_in_dict = [col for col in req if (self.fixed_params_dict is not None) and (col in self.fixed_params_dict.keys())] + # If requested column in table/fixed_params dict is numeric, check if values are finite. + if all(np.isfinite(self.ref_table[col][k]) for col in req_col_in_table if np.issubdtype(self.ref_table[col].dtype, np.number)) \ + and all(np.isfinite(self.fixed_params_dict[col]) for col in req_col_in_dict if np.issubdtype(np.array(self.fixed_params_dict[col]).dtype, np.number)): + motion_model_used.append(mm.name) + break + + # Assign the determined motion models + # self.ref_table['motion_model_used'][keep_orig] = motion_model_used + self.ref_table['motion_model_used'] = motion_model_used + # Replace the originals if we are supposed to keep them fixed. if (keep_orig is not None) and (sum(keep_orig) > 0): for val in vals_orig.keys(): @@ -1111,10 +1126,17 @@ def get_ref_list_from_table(self, epoch): # Otherwise, infer positions using the most complex motion model with the existing columns, until it reaches Fixed or Empty for mm in self.motion_models[::-1]: required_columns = mm.fit_param_names + mm.fixed_param_names - if all([param in self.ref_table.colnames or param in self.fixed_params_dict.keys() for param in required_columns]): + if all([param in self.ref_table.colnames or (self.fixed_params_dict is not None and param in self.fixed_params_dict.keys()) for param in required_columns]): # Check if the values are finite for non-string columns in the required columns for this motion model. If not, skip to the next motion model. + + if any([param not in self.ref_table.colnames for param in required_columns]): + # If any required column is missing, skip to the next motion model. + continue + if not all([np.isfinite(self.ref_table[param]).all() for param in required_columns if self.ref_table[param].dtype.kind in 'if']): + # If any required column has non-finite values, skip to the next motion model. continue + print(f"Inferring positions using motion model {mm.name}.") # If we have error columns for all fit parameters, then use them in the model inference. Otherwise, just use the fit parameters without errors. if all([f'{param}_err' in self.ref_table.colnames for param in mm.fit_param_names]) and all([np.isfinite(self.ref_table[f'{param}_err']).all() for param in mm.fit_param_names]): @@ -1565,7 +1587,7 @@ def __init__( self, ref_list, list_of_starlists, - # Alignment tolerance parameters + # Alignment parameters iters=2, dr_tol=[1, 1], dm_tol=[2, 1], @@ -1587,6 +1609,7 @@ def __init__( ref_mag_lim=None, # Motion model parameters motion_models=['Empty', 'Fixed'], + motion_model_for_new_star=None, fixed_params_dict=None, vel_weighting='var', use_scipy=True, @@ -1599,7 +1622,7 @@ def __init__( """ Required Parameters - ---------- + ------------------- ref_list : StarList object Can optionally have velocities. All starlists will be aligned to this one. @@ -1637,38 +1660,24 @@ def __init__( The outlier tolerance (in units of sigma) for rejecting outlier stars. This is a list of tol values, one for each iteration of matching/transformation. - mag_trans : boolean - If true, this will also calculate and (temporarily) apply a zeropoint offset to - magnitudes in each list to bring them into a common magnitude system. This is - essential for matching (with finite dm_tol) starlists of different filters or - starlists that are not photometrically calibrated. Note that the final_table columns - of 'm', 'm0', and 'm0_err' will contain the transformed magnitudes while the - final_table column 'm_orig' will contain the original un-transformed magnitudes. - If mag_trans = False, then no such zeropoint offset it applied at any point. - - mag_lim : array - If different from None, it indicates the minimum and maximum magnitude - on the catalogs for finding the transformations. Note, if you want specify the mag_lim - separately for each list and each iteration, you need to pass in a 2D array that - has shape (N_lists, N_iters). + use_ref_new : boolean + Each pass, new stars are matched and added to the ref_table. However, we don't + necessarily want to use these in the reference frame in subsequent passes. + If True, then the new stars will be used in later passes/iterations. + If False, then the new stars will be carried, but not used in the transformation. + We determine which stars to use through setting a boolean use_in_trans flag. - ref_mag_lim : array - If different from None, it indicates the minimum and maximum magnitude - on the reference catalog for finding the transformations. + update_ref_orig : boolean or str + Should we update the reference values (position, velocity, t0) after each starlist + is transformed in each iteration? - trans_weighting : str - Either None (def), 'both,var', 'list,var', or 'ref,var' depending on whether you want - to weight by the positional uncertainties (variances) in the individual starlists, or also with - the uncertainties in the reference frame itself. Note weighting only works when there - are positional uncertainties availabe. Other options include 'both,std', 'list,std', 'list,var'. - - vel_weighting : str - Either 'var' (def) or 'std', depending on whether you want to weight the motion model - fits by the variance or standard deviation of the position data + False if you want to get into an absolute reference frame and are using Gaia data. + True if you want to use the reference list as more of an initial guess. + 'periter' if you want to align all the starlists, then calculate the velocity. - trans_input : array or list of transform objects - def = None. If not None, then this should contain an array or list of transform - objects that will be used as the initial guess in the alignment and matching. + Note that this only impacts the stars that are in the original reference list... the + newly identified stars that end up in ref_table will always be updated; but not always + used for transformation fitting. trans_class : transforms.Transform2D object (or subclass) The transform class that will be used to when deriving the optimal @@ -1680,51 +1689,64 @@ def = None. If not None, then this should contain an array or list of transform then the transformation argument (i.e. order) will be changed for every iteration in iters. + trans_input : array or list of transform objects + def = None. If not None, then this should contain an array or list of transform + objects that will be used as the initial guess in the alignment and matching. + + trans_weighting : str + Either None (def), 'both,var', 'list,var', or 'ref,var' depending on whether you want + to weight by the positional uncertainties (variances) in the individual starlists, or also with + the uncertainties in the reference frame itself. Note weighting only works when there + are positional uncertainties availabe. Other options include 'both,std', 'list,std', 'list,var'. + init_order: int Polynomial transformation order to use for initial guess transformation. Order=1 should be used in most cases, but sometimes higher order is needed + init_guess_mode : string + If no initial transformations are passed in via the trans_input keyword, then we have + to make the initial transformation and matching blindly. We can do this in a couple of + different ways. Options are 'miracle' or 'name' (see trans_initial_guess() for more details). + calc_trans_inverse: boolean If true, then calculate the inverse transformation (from reference to starlist) in addition to the normal transformation (from starlist to reference). The inverse calculation is calculated by switching the order to the positions in match_and_transform. The inverse transformations are saved in self.trans_list_inverse. - self.trans_list_inverse doesn't exist if calc_trans_inverse == False - update_ref_orig : boolean or str - Should we update the reference values (position, velocity, t0) after each starlist - is transformed in each iteration? - - False if you want to get into an absolute reference frame and are using Gaia data. - True if you want to use the reference list as more of an initial guess. - 'periter' if you want to align all the starlists, then calculate the velocity. - - Note that this only impacts the stars that are in the original reference list... the - newly identified stars that end up in ref_table will always be updated; but not always - used for transformation fitting. - - use_ref_new : boolean - Each pass, new stars are matched and added to the ref_table. However, we don't - necessarily want to use these in the reference frame in subsequent passes. - If True, then the new stars will be used in later passes/iterations. - If False, then the new stars will be carried, but not used in the transformation. - We determine which stars to use through setting a boolean use_in_trans flag. + mag_trans : boolean + If true, this will also calculate and (temporarily) apply a zeropoint offset to + magnitudes in each list to bring them into a common magnitude system. This is + essential for matching (with finite dm_tol) starlists of different filters or + starlists that are not photometrically calibrated. Note that the final_table columns + of 'm', 'm0', and 'm0_err' will contain the transformed magnitudes while the + final_table column 'm_orig' will contain the original un-transformed magnitudes. + If mag_trans = False, then no such zeropoint offset it applied at any point. - init_guess_mode : string - If no initial transformations are passed in via the trans_input keyword, then we have - to make the initial transformation and matching blindly. We can do this in a couple of - different ways. Options are 'miracle' or 'name' (see trans_initial_guess() for more details). + mag_lim : array + If different from None, it indicates the minimum and maximum magnitude + on the catalogs for finding the transformations. Note, if you want specify the mag_lim + separately for each list and each iteration, you need to pass in a 2D array that + has shape (N_lists, N_iters). - iter_callback : None or function - A function to call (that accepts a StarTable object and an iteration number) - at the end of every iteration. This can be used for plotting or printing state. + ref_mag_lim : array + If different from None, it indicates the minimum and maximum magnitude + on the reference catalog for finding the transformations. motion_models : list of str or MotionModel objects List of motion model names (strings) or MotionModel objects to use + motion_model_for_new_star : str or MotionModel, optional + Motion model or its name for newly added stars in the ref table. Used in add_rows_for_new_stars(). + If None, the most complex motion model in motion_models will be used, by default None. + fixed_params_dict : None or dict Dictionary of fixed parameters for motion models + + vel_weighting : str + Either 'var' (def) or 'std', depending on whether you want to weight the motion model + fits by the variance or standard deviation of the position data use_scipy : bool, optional If True, use scipy.optimize.curve_fit for velocity fitting. If False, use linear algebra fitting, by default True. @@ -1732,11 +1754,20 @@ def = None. If not None, then this should contain an array or list of transform absolute_sigma : bool, optional If True, the velocity fit will use absolute errors in the data. If False, relative errors will be used, by default False. + iter_callback : None or function + A function to call (that accepts a StarTable object and an iteration number) + at the end of every iteration. This can be used for plotting or printing state. + save_path : str, optional Path to save the MosaicToRef object as a pickle file. + verbose : bool or int (0 to 9, inclusive) + Controls the verbosity of print statements. (0 least, 9 most verbose). + For backwards compatibility, 0 = False, 9 = True. + (Note: technically right now no checks on whether the number is an integer or not...) + Example - ---------- + ------- msc = align.MosaicToRef(my_gaia, list_of_starlists, iters=1, dr_tol=[0.1], dm_tol=[5], outlier_tol=[None], mag_lim=[13, 21], @@ -1766,20 +1797,37 @@ def = None. If not None, then this should contain an array or list of transform plt.errorbar(times, stars_table['x'][0, :], yerr=stars_table['xe'][0, :]) plt.axhline(stars_table['x0'][0] + stars_table['vx'][0]*(times - stars_table['t0'][0])) """ - super().__init__(list_of_starlists, ref_index=-1, iters=iters, - dr_tol=dr_tol, dm_tol=dm_tol, - outlier_tol=outlier_tol, trans_args=trans_args, - init_order=init_order, - mag_trans=mag_trans, mag_lim=mag_lim, - trans_weighting=trans_weighting, vel_weighting=vel_weighting, - trans_input=trans_input, trans_class=trans_class, - calc_trans_inverse=calc_trans_inverse, - init_guess_mode=init_guess_mode, - iter_callback=iter_callback, - motion_models=motion_models, - fixed_params_dict=fixed_params_dict, - verbose=verbose, use_scipy=use_scipy, - absolute_sigma=absolute_sigma, save_path=save_path) + super().__init__( + list_of_starlists, + # Alignment parameters + ref_index=-1, + iters=iters, + dr_tol=dr_tol, + dm_tol=dm_tol, + outlier_tol=outlier_tol, + # Transformation parameters + trans_class=trans_class, + trans_args=trans_args, + trans_input=trans_input, + trans_weighting=trans_weighting, + init_order=init_order, + init_guess_mode=init_guess_mode, + calc_trans_inverse=calc_trans_inverse, + # Magnitude parameters + mag_trans=mag_trans, + mag_lim=mag_lim, + # Motion model parameters + motion_models=motion_models, + motion_model_for_new_star=motion_model_for_new_star, + fixed_params_dict=fixed_params_dict, + vel_weighting=vel_weighting, + use_scipy=use_scipy, + absolute_sigma=absolute_sigma, + # Advanced options + iter_callback=iter_callback, + save_path=save_path, + verbose=verbose + ) self.ref_list = copy.deepcopy(ref_list) self.ref_mag_lim = ref_mag_lim @@ -1787,12 +1835,17 @@ def = None. If not None, then this should contain an array or list of transform self.use_ref_new = use_ref_new # If motion_model_used in columns but params columns are missing, raise a warning and remove motion_model_used column to avoid confusion. + # if 'motion_model_used' in self.ref_list.colnames: + # motion_model_params = motion_model.motion_model_param_names(np.unique(self.ref_list['motion_model_used']), with_errors=False, with_fixed=True) + # missing_params = [param for param in motion_model_params if (param not in self.ref_list.colnames) and (f'{param}_err' not in self.ref_list.colnames) and (param not in self.fixed_params_dict.keys())] + # if len(missing_params) > 0: + # warnings.warn("Warning: 'motion_model_used' column found in ref_list, but the following motion model parameter columns are missing: " + ", ".join(missing_params) + ". Removing 'motion_model_used' column to avoid confusion.") + # self.ref_list.remove_column('motion_model_used') + + # If motion_model_used in columns, remove it and raise a warning, since it will only be determined after the fit. if 'motion_model_used' in self.ref_list.colnames: - motion_model_params = motion_model.motion_model_param_names(np.unique(self.ref_list['motion_model_used']), with_errors=False, with_fixed=True) - missing_params = [param for param in motion_model_params if (param not in self.ref_list.colnames) and (f'{param}_err' not in self.ref_list.colnames) and (param not in self.fixed_params_dict.keys())] - if len(missing_params) > 0: - warnings.warn("Warning: 'motion_model_used' column found in ref_list, but the following motion model parameter columns are missing: " + ", ".join(missing_params) + ". Removing 'motion_model_used' column to avoid confusion.") - self.ref_list.remove_column('motion_model_used') + warnings.warn("Warning: 'motion_model_used' column found in ref_list. This column will be determined after the fit, so it is being removed from the input ref_list to avoid confusion.") + self.ref_list.remove_column('motion_model_used') # Do some temporary clean up of the reference list. if ('x' not in self.ref_list.colnames) and ('x0' in self.ref_list.colnames): @@ -1808,10 +1861,6 @@ def = None. If not None, then this should contain an array or list of transform if ('t' not in self.ref_list.colnames) and ('t0' in self.ref_list.colnames): self.ref_list['t'] = self.ref_list['t0'] - # Make sure the motion models are ready - # self.motion_model_dict = motion_model.validate_motion_model_dict(self.motion_model_dict, - # self.ref_list, self.default_motion_model) - return @@ -2021,7 +2070,6 @@ def infer_positions(t, startable, motion_models=None, fixed_params_dict=None, re # Otherwise, infer positions using the most complex motion model with the existing columns, until it reaches Fixed or Empty # Sort motion models inversely by mm.n_params motion_models = sorted(motion_models, key=lambda mm: mm.n_params, reverse=True) - pdb.set_trace() for mm in motion_models: if mm.name == 'Empty': x = startable['x'] @@ -2205,7 +2253,7 @@ def reset_ref_values(ref_table): return -def add_rows_for_new_stars(ref_table, star_list, idx_list, motion_model='Fixed'): +def add_rows_for_new_stars(ref_table, star_list, idx_list, motion_model_name='Fixed'): """ For each star that is in star_list and NOT in idx_list, make a new row in the reference table. The values will be empty (None, NAN, etc.). @@ -2219,8 +2267,8 @@ def add_rows_for_new_stars(ref_table, star_list, idx_list, motion_model='Fixed') idx_list : array or list The indices of the non-new stars (those that matched already). The complement of this array will be used as the new stars. - motion_model : str - The motion model to assign to the new stars. + motion_model_name : str + The motion model name to assign to the new stars. Returns ---------- @@ -2251,9 +2299,9 @@ def add_rows_for_new_stars(ref_table, star_list, idx_list, motion_model='Fixed') elif ref_table[col_name].dtype == np.dtype('bool'): new_col_empty = False elif col_name=='motion_model_input': - new_col_empty = motion_model + new_col_empty = motion_model_name elif col_name=='motion_model_used': - new_col_empty = 'Fixed' + new_col_empty = 'Empty' else: new_col_empty = np.nan @@ -3343,12 +3391,12 @@ def outlier_rejection_indices(star_list, ref_list, outlier_tol, motion_models, f The motion models to use in the star_list fixed_params_dict : dict or None, optional Dictionary of fixed parameters for motion models, by default None - verbose : boolean, optional + verbose : bool, optional If True, print information about the outlier rejection process, by default True Returns ---------- - keepers : boolean array + keepers : bool array The boolean array of the stars to keep. """ # Optionally propogate the reference positions forward in time. diff --git a/flystar/tests/test_align.py b/flystar/tests/test_align.py index d37fd17..e88cb79 100644 --- a/flystar/tests/test_align.py +++ b/flystar/tests/test_align.py @@ -1,3 +1,4 @@ +import flystar from flystar import align from flystar import starlists from flystar import startables @@ -9,11 +10,13 @@ import matplotlib.pyplot as plt import pdb +test_data_path = f'{flystar.__path__[0]}/tests/test_data' + def test_MosaicSelfRef(): """ Cross-match and align 4 starlists using the OO version of mosaic lists. """ - list_files = ['test_data/A.lis', 'test_data/B.lis', 'test_data/C.lis', 'test_data/D.lis'] + list_files = [f'{test_data_path}/{f}' for f in ['A.lis', 'B.lis', 'C.lis', 'D.lis']] lists = [starlists.StarList.from_lis_file(lf) for lf in list_files] ########## @@ -88,7 +91,7 @@ def test_MosaicSelfRef_vel_tconst(): The 4 lists are all taken at the same time (so 0 velocities should result). """ - list_files = ['test_data/A.lis', 'test_data/B.lis', 'test_data/C.lis', 'test_data/D.lis'] + list_files = [f'{test_data_path}/{f}' for f in ['A.lis', 'B.lis', 'C.lis', 'D.lis']] lists = [starlists.StarList.from_lis_file(lf) for lf in list_files] ########## @@ -140,7 +143,7 @@ def test_MosaicSelfRef_vel(): """ Cross-match and align 4 starlists using the OO version of mosaic lists. """ - list_files = ['test_data/A.lis', 'test_data/B.lis', 'test_data/C.lis', 'test_data/D.lis'] + list_files = [f'{test_data_path}/{f}' for f in ['A.lis', 'B.lis', 'C.lis', 'D.lis']] lists = [starlists.StarList.from_lis_file(lf) for lf in list_files] # Modify the times so that we get velocities out. @@ -207,15 +210,8 @@ def test_MosaicSelfRef_vel(): def test_MosaicToRef(): make_fake_starlists_poly1(seed=42) - ref_file = 'test_data/random_ref.fits' - list_files = ['test_data/random_0.fits', - 'test_data/random_1.fits', - 'test_data/random_2.fits', - 'test_data/random_3.fits', - 'test_data/random_4.fits', - 'test_data/random_5.fits', - 'test_data/random_6.fits', - 'test_data/random_7.fits'] + ref_file = f'{test_data_path}/random_ref.fits' + list_files = [f'{test_data_path}/random_{i}.fits' for i in range(8)] ref_list = Table.read(ref_file) @@ -264,15 +260,8 @@ def test_MosaicToRef(): def test_MosaicToRef_p0_vel(): make_fake_starlists_poly0_vel(seed=42) - ref_file = 'test_data/random_vel_ref.fits' - list_files = ['test_data/random_vel_p0_0.fits', - 'test_data/random_vel_p0_1.fits', - 'test_data/random_vel_p0_2.fits', - 'test_data/random_vel_p0_3.fits'] - #'test_data/random_vel_4.fits', - #'test_data/random_vel_5.fits', - #'test_data/random_vel_6.fits', - #'test_data/random_vel_7.fits'] + ref_file = f'{test_data_path}/random_vel_ref.fits' + list_files = [f'{test_data_path}/random_vel_p0_{i}.fits' for i in range(4)] ref_list = Table.read(ref_file) @@ -330,15 +319,8 @@ def test_MosaicToRef_p0_vel(): def test_MosaicToRef_vel(): make_fake_starlists_poly1_vel(seed=42) - ref_file = 'test_data/random_vel_ref.fits' - list_files = ['test_data/random_vel_0.fits', - 'test_data/random_vel_1.fits', - 'test_data/random_vel_2.fits', - 'test_data/random_vel_3.fits'] - #'test_data/random_vel_4.fits', - #'test_data/random_vel_5.fits', - #'test_data/random_vel_6.fits', - #'test_data/random_vel_7.fits'] + ref_file = f'{test_data_path}/random_vel_ref.fits' + list_files = [f'{test_data_path}/random_vel_{i}.fits' for i in range(4)] ref_list = Table.read(ref_file) @@ -396,15 +378,8 @@ def test_MosaicToRef_vel(): def test_MosaicToRef_acc(): make_fake_starlists_poly1_acc(seed=42) - ref_file = 'test_data/random_acc_ref.fits' - list_files = ['test_data/random_acc_0.fits', - 'test_data/random_acc_1.fits', - 'test_data/random_acc_2.fits', - 'test_data/random_acc_3.fits', - 'test_data/random_acc_4.fits', - 'test_data/random_acc_5.fits', - 'test_data/random_acc_6.fits', - 'test_data/random_acc_7.fits'] + ref_file = f'{test_data_path}/random_acc_ref.fits' + list_files = [f'{test_data_path}/random_acc_{i}.fits' for i in range(8)] ref_list = Table.read(ref_file) @@ -513,7 +488,7 @@ def test_MosaicToRef_hst_me(): # dec = '-34:27:05.01' # Load up a Gaia catalog (queried around the RA/Dec above) - my_gaia = Table.read('test_data/mb10364_data/my_gaia.fits') + my_gaia = Table.read(f'{test_data_path}/mb10364_data/my_gaia.fits') my_gaia['me'] = 0.01 my_gaia.rename_columns( @@ -523,9 +498,9 @@ def test_MosaicToRef_hst_me(): # Gather the list of starlists. For first pass, don't modify the starlists. # Loop through the observations and read them in, in prep for alignment with Gaia epochs = [2011.83, 2012.73, 2013.81] - starlist_names = ['test_data/mb10364_data/2011_10_31_F606W_MATCHUP_XYMEEE_final.calib', - 'test_data/mb10364_data/2012_09_25_F606W_MATCHUP_XYMEEE_final.calib', - 'test_data/mb10364_data/2013_10_24_F606W_MATCHUP_XYMEEE_final.calib'] + starlist_names = [f'{test_data_path}/mb10364_data/2011_10_31_F606W_MATCHUP_XYMEEE_final.calib', + f'{test_data_path}/mb10364_data/2012_09_25_F606W_MATCHUP_XYMEEE_final.calib', + f'{test_data_path}/mb10364_data/2013_10_24_F606W_MATCHUP_XYMEEE_final.calib'] list_of_starlists = [] @@ -557,7 +532,7 @@ def test_MosaicToRef_hst_me(): mag_trans=False, trans_weighting='both,std', init_guess_mode='miracle', - save_path='test_data/mb10364_data/test_MosaicToRef_hst_me.pkl', + # save_path=f'{test_data_path}/mb10364_data/test_MosaicToRef_hst_me.pkl', verbose=False ) msc.fit() @@ -573,9 +548,9 @@ def test_bootstrap(): etc.) """ # Read in starlists for MosaicToRef - ref = Table.read('test_data/ref_vel.lis', format='ascii') - list1 = Table.read('test_data/E.lis', format='ascii') - list2 = Table.read('test_data/F.lis', format='ascii') + ref = Table.read(f'{test_data_path}/ref_vel.lis', format='ascii') + list1 = Table.read(f'{test_data_path}/E.lis', format='ascii') + list2 = Table.read(f'{test_data_path}/F.lis', format='ascii') list1 = starlists.StarList.from_table(list1) list2 = starlists.StarList.from_table(list2) @@ -684,9 +659,9 @@ def test_calc_vel_in_bootstrap(): import copy # Define match parameters - ref = Table.read('test_data/ref_vel.lis', format='ascii') - list1 = Table.read('test_data/E.lis', format='ascii') - list2 = Table.read('test_data/F.lis', format='ascii') + ref = Table.read(f'{test_data_path}/ref_vel.lis', format='ascii') + list1 = Table.read(f'{test_data_path}/E.lis', format='ascii') + list2 = Table.read(f'{test_data_path}/F.lis', format='ascii') list1 = starlists.StarList.from_table(list1) list2 = starlists.StarList.from_table(list2) @@ -752,9 +727,9 @@ def test_transform_xym(): otherwise """ #---Align 1: self.mag_Trans = False---# - ref = Table.read('test_data/ref_vel.lis', format='ascii') - list1 = Table.read('test_data/E.lis', format='ascii') - list2 = Table.read('test_data/F.lis', format='ascii') + ref = Table.read(f'{test_data_path}/ref_vel.lis', format='ascii') + list1 = Table.read(f'{test_data_path}/E.lis', format='ascii') + list2 = Table.read(f'{test_data_path}/F.lis', format='ascii') list1 = starlists.StarList.from_table(list1) list2 = starlists.StarList.from_table(list2) @@ -915,7 +890,7 @@ def test_masked_cols(): for ee in range(len(epochs)): lis_file = 'mag' + epochs[ee] + '_ob150029_kp_rms_named.lis' - lis = starlists.StarList.from_lis_file(f'test_data/{lis_file}') + lis = starlists.StarList.from_lis_file(f'{test_data_path}/{lis_file}') list_of_starlists.append(lis) # Run the align @@ -947,7 +922,7 @@ def make_fake_starlists_shifts(): # Save original positions as reference (1st) list. fmt = '{0:10s} {1:5.2f} 2015.0 {2:9.4f} {3:9.4f} 0 0 0 0\n' - _out = open('test_data/random_0.lis', 'w') + _out = open(f'{test_data_path}/random_0.lis', 'w') for ii in range(N_stars): _out.write(fmt.format(name[ii], m[ii], x[ii], y[ii])) _out.close() @@ -972,7 +947,7 @@ def make_fake_starlists_shifts(): mnew = m + np.random.randn(N_stars) * 0.05 - _out = open('test_data/random_shift_{0:d}.lis'.format(ss+1), 'w') + _out = open(f'{test_data_path}/random_shift_{ss+1}.lis', 'w') for ii in range(N_stars): _out.write(fmt.format(name[ii], mnew[ii], xnew[ii], ynew[ii])) _out.close() @@ -1010,7 +985,7 @@ def make_fake_starlists_poly1(seed=-1): # Save original positions as reference (1st) list # in a StarList format (with velocities). - lis.write('test_data/random_ref.fits', overwrite=True) + lis.write(f'{test_data_path}/random_ref.fits', overwrite=True) ########## # Shifts @@ -1061,7 +1036,7 @@ def make_fake_starlists_poly1(seed=-1): new_lis = starlists.StarList([lis['name'], md, mde, xd, xde, yd, yde, t], names=('name', 'm', 'me', 'x', 'xe', 'y', 'ye', 't')) - new_lis.write('test_data/random_{0:d}.fits'.format(ss), overwrite=True) + new_lis.write(f'{test_data_path}/random_{ss}.fits', overwrite=True) return (xy_trans,mag_trans) @@ -1091,7 +1066,7 @@ def make_fake_starlists_poly0_vel(seed=-1): vxe = np.abs(vxe) vye = np.abs(vye) - name = ['star_{0:03d}'.format(ii) for ii in range(N_stars)] + name = [f'star_{ii:03d}' for ii in range(N_stars)] # Make an StarList lis = starlists.StarList([name, m0, m0e, x0, x0e, y0, y0e, vx, vxe, vy, vye, t0], @@ -1154,7 +1129,7 @@ def make_fake_starlists_poly0_vel(seed=-1): new_lis = starlists.StarList([lis['name'], md, mde, xd, xde, yd, yde, t], names=('name', 'm', 'me', 'x', 'xe', 'y', 'ye', 't')) - new_lis.write('test_data/random_vel_p0_{0:d}.fits'.format(ss), overwrite=True) + new_lis.write(f'{test_data_path}/random_vel_p0_{ss}.fits', overwrite=True) return (xy_trans, mag_trans) @@ -1185,7 +1160,7 @@ def make_fake_starlists_poly1_vel(seed=-1): vxe = np.abs(vxe) vye = np.abs(vye) - name = ['star_{0:03d}'.format(ii) for ii in range(N_stars)] + name = [f'star_{ii:03d}' for ii in range(N_stars)] # Make an StarList lis = starlists.StarList([name, m0, m0e, x0, x0e, y0, y0e, vx, vxe, vy, vye, t0], @@ -1197,7 +1172,7 @@ def make_fake_starlists_poly1_vel(seed=-1): # Save original positions as reference (1st) list # in a StarList format (with velocities). - lis.write('test_data/random_vel_ref.fits', overwrite=True) + lis.write(f'{test_data_path}/random_vel_ref.fits', overwrite=True) ########## # Propogate to new times and distort. @@ -1248,7 +1223,7 @@ def make_fake_starlists_poly1_vel(seed=-1): new_lis = starlists.StarList([lis['name'], md, mde, xd, xde, yd, yde, t], names=('name', 'm', 'me', 'x', 'xe', 'y', 'ye', 't')) - new_lis.write('test_data/random_vel_{0:d}.fits'.format(ss), overwrite=True) + new_lis.write(f'{test_data_path}/random_vel_{ss}.fits', overwrite=True) return (xy_trans, mag_trans) @@ -1303,7 +1278,7 @@ def make_fake_starlists_poly1_acc(seed=-1): # Save original positions as reference (1st) list # in a StarList format (with velocities). - lis.write('test_data/random_acc_ref.fits', overwrite=True) + lis.write(f'{test_data_path}/random_acc_ref.fits', overwrite=True) ########## # Propogate to new times and distort. @@ -1354,7 +1329,7 @@ def make_fake_starlists_poly1_acc(seed=-1): new_lis = starlists.StarList([lis['name'], md, mde, xd, xde, yd, yde, t], names=('name', 'm', 'me', 'x', 'xe', 'y', 'ye', 't')) - new_lis.write('test_data/random_acc_{0:d}.fits'.format(ss), overwrite=True) + new_lis.write(f'{test_data_path}/random_acc_{ss}.fits', overwrite=True) return (xy_trans, mag_trans) @@ -1406,7 +1381,7 @@ def make_fake_starlists_poly1_par(seed=-1): # Save original positions as reference (1st) list # in a StarList format (with velocities). - lis.write('test_data/random_par_ref.fits', overwrite=True) + lis.write(f'{test_data_path}/random_par_ref.fits', overwrite=True) ########## # Propogate to new times and distort. @@ -1466,19 +1441,21 @@ def make_fake_starlists_poly1_par(seed=-1): new_lis = starlists.StarList([lis['name'], md, mde, xd, xde, yd, yde, t], names=('name', 'm', 'me', 'x', 'xe', 'y', 'ye', 't')) - new_lis.write('test_data/random_par_{0:d}.fits'.format(ss), overwrite=True) + new_lis.write(f'{test_data_path}/random_par_{ss}.fits', overwrite=True) return (xy_trans, mag_trans) if __name__ == '__main__': - test_MosaicToRef_hst_me() + test_MosaicSelfRef() + # test_MosaicToRef_hst_me() # import pickle # import matplotlib.pyplot as plt - # with open('test_data/my_gaia.pkl', 'rb') as f: + # with open(f'{test_data_path}/my_gaia.pkl', 'rb') as f: # my_gaia = pickle.load(f) - # with open('test_data/list_of_starlists.pkl', 'rb') as f: + # with open(f'{test_data_path}/list_of_starlists.pkl', 'rb') as f: # list_of_starlists = pickle.load(f) # ra_deg, dec_deg = 18.0, -30.0 + # my_gaia.remove_column('motion_model_used') # msc = align.MosaicToRef(my_gaia, list_of_starlists, iters=3, # dr_tol=[0.2, 0.1, 0.08], dm_tol=[5,5,5], # outlier_tol=[None, None, 3], mag_lim=[6, 20], From 0686a9a6972bc12d70fd50308fce9eeb57ccf33e Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Wed, 25 Feb 2026 17:40:31 -0800 Subject: [PATCH 167/254] Trim trailing spaces --- flystar/align.py | 464 ++++++++++++++++---------------- flystar/analysis.py | 102 ++++---- flystar/archive_io.py | 6 +- flystar/examples.py | 48 ++-- flystar/match.py | 104 ++++---- flystar/motion_model.py | 122 ++++----- flystar/plots.py | 538 +++++++++++++++++++------------------- flystar/starlists.py | 100 +++---- flystar/startables.py | 120 ++++----- flystar/stitch_method2.py | 34 +-- flystar/template.py | 44 ++-- 11 files changed, 841 insertions(+), 841 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 9ec4a2f..d698e99 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -21,19 +21,19 @@ def __init__( # Alignment parameters ref_index=0, iters=2, - dr_tol=[1, 1], + dr_tol=[1, 1], dm_tol=[2, 1], outlier_tol=[None, None], # Transformation parameters trans_class=transforms.PolyTransform, trans_args=[{'order': 2}, {'order': 2}], - trans_input=None, + trans_input=None, trans_weighting=None, init_order=1, - init_guess_mode='miracle', + init_guess_mode='miracle', calc_trans_inverse=False, # Magnitude parameters - mag_trans=True, + mag_trans=True, mag_lim=None, # Motion model parameters motion_models=['Empty', 'Fixed'], @@ -44,11 +44,11 @@ def __init__( absolute_sigma=False, # Advanced options iter_callback=None, - save_path=None, + save_path=None, verbose=True ): """ - Make a mosaic object by passing in a list of starlists and then running fit(). + Make a mosaic object by passing in a list of starlists and then running fit(). Required Parameters ------------------- @@ -59,10 +59,10 @@ def __init__( Note that there is an optional weights column called 'w'. If this column exists in any of the lists, it will be queried to determine if an individual star can be used to derive the transformations between starlists. This is the most flexible way - to allow you to determine, as a function of time and star, which ones are good enough - in the transformation. Note that just because it can be used (i.e. w_in=1), - doesn't meant that it will be used. The mag limits and outliers still take precedence. - Note also that the weights that go into the transformation are + to allow you to determine, as a function of time and star, which ones are good enough + in the transformation. Note that just because it can be used (i.e. w_in=1), + doesn't meant that it will be used. The mag limits and outliers still take precedence. + Note also that the weights that go into the transformation are star_list['w'] * ref_list['w'] * weight_from_keyword (see the weights parameter) @@ -72,11 +72,11 @@ def __init__( ------------------- ref_index : int The index of the reference epoch. (default = 0). Note that this is the reference - list only for the first iteration. Subsequent iterations will utilize the sigma-clipped - mean of the positions from all the starlists. + list only for the first iteration. Subsequent iterations will utilize the sigma-clipped + mean of the positions from all the starlists. iters : int - The number of iterations used in the matching and transformation. TO DO: INNER/OUTER? + The number of iterations used in the matching and transformation. TO DO: INNER/OUTER? dr_tol : list or array The delta-radius (dr) tolerance for matching in units of the reference coordinate system. @@ -84,25 +84,25 @@ def __init__( dm_tol : list or array The delta-magnitude (dm) tolerance for matching in units of the reference coordinate system. - This is a list of dm values, one for each iteration of matching/transformation. + This is a list of dm values, one for each iteration of matching/transformation. outlier_tol : list or array - The outlier tolerance (in units of sigma) for rejecting outlier stars. + The outlier tolerance (in units of sigma) for rejecting outlier stars. This is a list of tol values, one for each iteration of matching/transformation. trans_class : transforms.Transform2D object (or subclass) The transform class that will be used to when deriving the optimal - transformation parameters between each list and the reference list. + transformation parameters between each list and the reference list. trans_args : dictionary - A dictionary (or a list of dictionaries) containing any extra keywords that are needed - in the transformation object. For instance, "order". Note that if a list is passed in, + A dictionary (or a list of dictionaries) containing any extra keywords that are needed + in the transformation object. For instance, "order". Note that if a list is passed in, then the transformation argument (i.e. order) will be changed for every iteration in iters. trans_input : array or list of transform objects def = None. If not None, then this should contain an array or list of transform - objects that will be used as the initial guess in the alignment and matching. + objects that will be used as the initial guess in the alignment and matching. trans_weighting : str Either None (def), 'both,var', 'list,var', or 'ref,var' depending on whether you want @@ -115,7 +115,7 @@ def = None. If not None, then this should contain an array or list of transform init_guess_mode : string If no initial transformations are passed in via the trans_input keyword, then we have - to make the initial transformation and matching blindly. We can do this in a couple of + to make the initial transformation and matching blindly. We can do this in a couple of different ways. Options are 'miracle' or 'name' (see trans_initial_guess() for more details). calc_trans_inverse: boolean @@ -126,13 +126,13 @@ def = None. If not None, then this should contain an array or list of transform self.trans_list_inverse doesn't exist if calc_trans_inverse == False mag_trans : boolean - If true, this will also calculate and (temporarily) apply a zeropoint offset to - magnitudes in each list to bring them into a common magnitude system. This is - essential for matching (with finite dm_tol) starlists of different filters or - starlists that are not photometrically calibrated. Note that the final_table columns + If true, this will also calculate and (temporarily) apply a zeropoint offset to + magnitudes in each list to bring them into a common magnitude system. This is + essential for matching (with finite dm_tol) starlists of different filters or + starlists that are not photometrically calibrated. Note that the final_table columns of 'm', 'm0', and 'm0_err' will contain the transformed magnitudes while the - final_table column 'm_orig' will contain the original un-transformed magnitudes. - If mag_trans = False, then no such zeropoint offset it applied at any point. + final_table column 'm_orig' will contain the original un-transformed magnitudes. + If mag_trans = False, then no such zeropoint offset it applied at any point. mag_lim : array If different from None, it indicates the minimum and maximum magnitude @@ -192,9 +192,9 @@ def = None. If not None, then this should contain an array or list of transform stars_table = msc.ref_table # Plot the magnitude of the first star vs. time: - # Overplot the mean magnitude. + # Overplot the mean magnitude. plt.plot(stars_table['t'][0, :], stars_table['m'][0, :], 'k.') - plt.axhline(stars_table['m0'][0]) + plt.axhline(stars_table['m0'][0]) # Plot the X position of the first star vs. time: # Overplot the best-fit proper motion. @@ -259,7 +259,7 @@ def = None. If not None, then this should contain an array or list of transform self.verbose = 9 if self.verbose is False: self.verbose = 0 - + self.N_lists = len(self.star_lists) # Hard-coded values: @@ -303,18 +303,18 @@ def fix_iterable_conditions(self): assert len(self.mag_lim) == len(self.star_lists) return - - + + def fit(self): """ Using the current parameter settings, match and transform all the lists to a reference position. Note in the first pass, the reference position is just the specified input reference starlist. In subsequent iterations, - this is updated. + this is updated. The ultimate outcome is the creation of self.ref_table. This reference table will contain "averaged" quantites as well as a big 2D array of all - the matched original and transformed quantities. + the matched original and transformed quantities. Averaged columns on ref_table: x0 @@ -343,8 +343,8 @@ def fit(self): # ########## for nn in range(self.iters): - - # If we are on subsequent iterations, remove matching results from the + + # If we are on subsequent iterations, remove matching results from the # prior iteration. This leaves aggregated (1D) columns alone. if nn > 0: self.reset_ref_values() @@ -358,7 +358,7 @@ def fit(self): print("**********") # ALL the action is in here. Match and transform the stack of starlists. - # This updates trans objects and the ref_table. + # This updates trans objects and the ref_table. self.match_and_transform(self.mag_lim[self.ref_index], self.dr_tol[nn], self.dm_tol[nn], self.outlier_tol[nn], self.trans_args[nn]) @@ -374,13 +374,13 @@ def fit(self): if self.iter_callback != None: self.iter_callback(self.ref_table, nn) - + ########## # # Re-do all matching given final transformations. # No trimming this time. - # First rest the reference table 2D values. + # First rest the reference table 2D values. ########## self.reset_ref_values(exclude=['used_in_trans']) @@ -395,13 +395,13 @@ def fit(self): ########## # Clean up output table. - # + # ########## # Find where stars are detected. if self.verbose > 0: print('') print(' Preparing the reference table...') - + self.ref_table.detections() ### Drop all stars that have 0 detections. @@ -484,7 +484,7 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar if self.mag_trans: star_list_T.transform_xym(trans) # trimmed, transformed else: - star_list_T.transform_xy(trans) + star_list_T.transform_xy(trans) # Match stars between the transformed, trimmed lists. idx1, idx2, dr, dm = match.match(star_list_T['x'], star_list_T['y'], star_list_T['m'], @@ -499,7 +499,7 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar keepers = self.outlier_rejection_indices(star_list_T[idx1], ref_list[idx2], outlier_tol, verbose=self.verbose) if self.verbose > 1: print( ' Rejected ', len(idx1) - sum(keepers), ' outliers.' ) - + idx1 = idx1[keepers] idx2 = idx2[keepers] @@ -516,10 +516,10 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar # Determine weights in the fit. weight = self.get_weights_for_lists(ref_list[idx2], star_list_T[idx1]) - # Derive the best-fit transformation parameters. + # Derive the best-fit transformation parameters. if self.verbose > 1: print( ' Using ', len(idx1), ' stars in transformation.' ) - trans = self.trans_class.derive_transform(star_list_orig_trim['x'][idx1], star_list_orig_trim['y'][idx1], + trans = self.trans_class.derive_transform(star_list_orig_trim['x'][idx1], star_list_orig_trim['y'][idx1], ref_list['x'][idx2], ref_list['y'][idx2], **trans_args, m=star_list_orig_trim['m'][idx1], mref=ref_list['m'][idx2], @@ -567,7 +567,7 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar print(fmt.format(nr=star_r['name'], n=star_s['name'], xl=star_t['x'], xr=star_r['x'], yl=star_t['y'], yr=star_r['y'], ml=star_t['m'], mr=star_r['m'], - dx=(star_t['x'] - star_r['x']) * 1e3, + dx=(star_t['x'] - star_r['x']) * 1e3, dy=(star_t['y'] - star_r['y']) * 1e3, dm=(star_t['m'] - star_r['m']), xo=star_s['x'], yo=star_s['y'], mo=star_s['m'])) @@ -610,7 +610,7 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar # Calculate the residuals just for those used in the transformation used = np.where(self.ref_table['used_in_trans'][:, ii] == True)[0] used_good = used[ np.where(np.isin(used, idx_ref) == True)[0] ] - + dr_u = np.hypot(self.ref_table['x'][used_good, ii] - ref_list['x'][used_good], self.ref_table['y'][used_good, ii] - ref_list['y'][used_good]) dm_u = np.abs(self.ref_table['m'][used_good, ii] - ref_list['m'][used_good]) @@ -620,7 +620,7 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar print(' Dropped {0:d} matches after transform.'.format(len(used) - len(used_good))) return - + def setup_trans_info(self): """ Setup transformation info into a usable format. @@ -633,7 +633,7 @@ def setup_trans_info(self): trans_args = self.trans_args N_lists = len(self.star_lists) iters = self.iters - + trans_list = [None for ii in range(N_lists)] if trans_input != None: trans_list = [trans_input[ii] for ii in range(N_lists)] @@ -655,7 +655,7 @@ def setup_trans_info(self): return def setup_ref_table_from_starlist(self, star_list): - """ + """ Start with the reference list.... this will change and grow over time, so make a copy that we will keep updating. The reference table will contain one column for every named @@ -688,10 +688,10 @@ def setup_ref_table_from_starlist(self, star_list): # Use the columns from the ref list to make the ref_table. ref_table = StarTable(**col_arrays) - + # Make new columns to hold original values. These will be copies # of the old columns and will only include x, y, m, xe, ye, me. - # The columns we have already created will hold transformed values. + # The columns we have already created will hold transformed values. trans_col_names = ['x', 'y', 'm', 'xe', 'ye', 'me', 'w'] for tt in range(len(trans_col_names)): old_name = trans_col_names[tt] @@ -703,7 +703,7 @@ def setup_ref_table_from_starlist(self, star_list): # Make sure ref_table has the necessary x0, y0, m0 and associated # error columns. If they don't exist, then add them as a copy of - # the original x,y,m etc columns. + # the original x,y,m etc columns. new_cols_arr = ['x0', 'y0', 'm0'] orig_cols_arr = ['x', 'y', 'm'] ref_cols = ref_table.keys() @@ -739,14 +739,14 @@ def setup_ref_table_from_starlist(self, star_list): # new_err_cols as an array of zeros vals = np.zeros(len(ref_table)) new_col = Column(vals, name=new_err_cols[ii]) - ref_table.add_column(new_col) + ref_table.add_column(new_col) # Final check: ref_table should now have x0, y0, m0, x0e, y0e, and m0e columns # This is necessary for later steps, even if the columns are just zeros. final_new_cols = np.concatenate((new_cols_arr, new_err_cols)) for ii in final_new_cols: assert ii in ref_table.keys() - + # Make sure we have a column to indicate whether each star # CAN BE USED in the transformation. This will be 1D if 'use_in_trans' not in ref_table.colnames: @@ -758,7 +758,7 @@ def setup_ref_table_from_starlist(self, star_list): if 'used_in_trans' not in ref_table.colnames: new_col = Column(np.zeros([len(ref_table), 1], dtype=bool), name='used_in_trans') ref_table.add_column(new_col) - + # Keep track of whether this is an original reference star. col_ref_orig = Column(np.ones(len(ref_table), dtype=bool), name='ref_orig') ref_table.add_column(col_ref_orig) @@ -784,8 +784,8 @@ def setup_ref_table_from_starlist(self, star_list): return ref_table def apply_mag_lim_via_use_in_trans(self, ref_list, ref_mag_lim): - """Set the use_in_trans flag to False for any star in the - star list that falls beyond the magnitude limits. + """Set the use_in_trans flag to False for any star in the + star list that falls beyond the magnitude limits. This should really only be applied to reference star lists. """ @@ -799,17 +799,17 @@ def apply_mag_lim_via_use_in_trans(self, ref_list, ref_mag_lim): no_use = (ref_list[mcol] < ref_mag_lim[0]) | (ref_list[mcol] >= ref_mag_lim[1]) ref_list['use_in_trans'][no_use] = False - + return def outlier_rejection_indices(self, star_list, ref_list, outlier_tol, verbose=True): """ Determine the outliers based on the residual positions between two different - starlists and some threshold (in sigma). Return the indices of the stars - to keep (that shouldn't be rejected as outliers). + starlists and some threshold (in sigma). Return the indices of the stars + to keep (that shouldn't be rejected as outliers). Note that we assume that the star_list and ref_list are already transformed and - matched. + matched. Parameters ---------- @@ -820,8 +820,8 @@ def outlier_rejection_indices(self, star_list, ref_list, outlier_tol, verbose=Tr starlist with 'x0', 'y0' outlier_tol : float - Number of sigma inside which we keep stars and outside of which we - reject stars as outliers. + Number of sigma inside which we keep stars and outside of which we + reject stars as outliers. Optional Parameters -------------------- @@ -830,7 +830,7 @@ def outlier_rejection_indices(self, star_list, ref_list, outlier_tol, verbose=Tr Returns ---------- keepers : boolean array - The boolean array of the stars to keep. + The boolean array of the stars to keep. """ # Optionally propogate the reference positions forward in time. xref = ref_list['x'] @@ -855,7 +855,7 @@ def update_ref_table_from_list(self, star_list, star_list_T, ii, idx_ref, idx_li Inputs ---------- star_list : StarList - The original star list. + The original star list. star_list_T : StarList The original star list now transformed into the reference coordinate system. @@ -870,7 +870,7 @@ def update_ref_table_from_list(self, star_list, star_list_T, ii, idx_ref, idx_li The indices of the matched targets in the origin starlist (epoch). idx_ref_in_trans : np.array dtype=int - The indices in the reference table (self.ref_table). + The indices in the reference table (self.ref_table). """ ### Update the reference table for matched stars. # Add the matched stars to the reference table. @@ -886,8 +886,8 @@ def update_ref_table_from_list(self, star_list, star_list_T, ii, idx_ref, idx_li ### Add the unmatched stars and grow the size of the reference table. self.ref_table, idx_lis_new, idx_ref_new = add_rows_for_new_stars( - self.ref_table, - star_list, + self.ref_table, + star_list, idx_lis, motion_model_name=self.motion_model_for_new_star.name ) @@ -1014,7 +1014,7 @@ def update_ref_table_aggregates(self, keep_orig=None, n_boot=0): self.ref_table[val][keep_orig] = vals_orig[val] return - + def get_weights_for_lists(self, ref_list, star_list): if 'xe' in ref_list.colnames: var_xref = ref_list['xe']**2 @@ -1022,7 +1022,7 @@ def get_weights_for_lists(self, ref_list, star_list): else: var_xref = 0.0 var_yref = 0.0 - + if 'xe' in star_list.colnames: var_xlis = star_list['xe']**2 var_ylis = star_list['ye']**2 @@ -1067,14 +1067,14 @@ def get_weights_for_lists(self, ref_list, star_list): return weight - + def match_lists(self, dr_tol, dm_tol): """ Using the existing trans objects, match all the starlists to the - reference starlist (self.ref_table), propogated to the appropriate epoch. + reference starlist (self.ref_table), propogated to the appropriate epoch. No trimming of stars. - No new transformations derived. + No new transformations derived. The resulting matched values will be used to update self.ref_table """ @@ -1105,7 +1105,7 @@ def match_lists(self, dr_tol, dm_tol): def get_ref_list_from_table(self, epoch): """ Convert the averaged quantites in self.ref_table into a StarList object - appropriate for the specified epoch. + appropriate for the specified epoch. Columns in resulting reference list will include: name @@ -1117,7 +1117,7 @@ def get_ref_list_from_table(self, epoch): me (optional) use_in_trans (optional) """ - # Reference stars will be named. + # Reference stars will be named. name = self.ref_table['name'] # Calculate x, y, xe, ye if 'motion_model_used' in self.ref_table.colnames: @@ -1136,7 +1136,7 @@ def get_ref_list_from_table(self, epoch): if not all([np.isfinite(self.ref_table[param]).all() for param in required_columns if self.ref_table[param].dtype.kind in 'if']): # If any required column has non-finite values, skip to the next motion model. continue - + print(f"Inferring positions using motion model {mm.name}.") # If we have error columns for all fit parameters, then use them in the model inference. Otherwise, just use the fit parameters without errors. if all([f'{param}_err' in self.ref_table.colnames for param in mm.fit_param_names]) and all([np.isfinite(self.ref_table[f'{param}_err']).all() for param in mm.fit_param_names]): @@ -1198,32 +1198,32 @@ def reset_ref_values(self, exclude=None): """ Reset all the 2D arrays in the reference table. This is the action we take at the beginning of each new iteration. We don't preserve matching - results from the prior iterations. + results from the prior iterations. """ # All 2D columns should be reset. for col_name in self.ref_table.colnames: if (exclude != None) and (col_name in exclude): continue - + if len(self.ref_table[col_name].data.shape) == 2: # Find the 2D columns # Loop through epochs for this array. for cc in range(self.ref_table[col_name].shape[1]): self.ref_table._set_invalid_list_values(col_name, cc) return - + def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_bootstrap=True, weighting='var', use_scipy=True, absolute_sigma=False, show_progress=True, update_errors=False): """ Function to calculate bootstrap errors for the transformations as well as the proper motions. For each iteration, this will: - 1) Draw full-size bootstrap w/replacement sample from reference stars in + 1) Draw full-size bootstrap w/replacement sample from reference stars in ref_table and re-calculate the transformations for each epoch 2) Apply transformation to all stars in each epoch If calc_vel_in_bootstraps: 3) For each star, draw full-size boostrap sample w/replacement from epochs 4) Calculate proper motion for each star using resampled epochs - + The saved outputs will be: x_trans, y_trans, m_trans (transformed postions/mags), as well as the proper motion fit parameters. @@ -1237,39 +1237,39 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot MosaicToRef object after the complete match_and_transform process n_boot: int, must be greater than 0 - Number of bootstrap iterations when calculating transformations and the proper motion. + Number of bootstrap iterations when calculating transformations and the proper motion. PM bootstrap is only done for final proper motion calculation (e.g., not for each iteration of the starlist for matching) boot_epochs_min: int or -1 - In order to be included in bootstrap analysis, non-reference stars must be detected in - at least boot_epochs_min epochs. If boot_epochs_min = -1, then all stars will + In order to be included in bootstrap analysis, non-reference stars must be detected in + at least boot_epochs_min epochs. If boot_epochs_min = -1, then all stars will be included in the analysis, regardless of the number of epochs detected. For stars that fail boot_epochs_min criteria, np.nan is used calc_vel_in_bootstrap: boolean - If true, do bootstrap sample w/ replacement over the epochs and calculate + If true, do bootstrap sample w/ replacement over the epochs and calculate stellar proper motions, as well as the bootstrap over reference stars - to calculate positional alignment errors. If false, only + to calculate positional alignment errors. If false, only calculate position alignment errors. - + weighting: str 'var' or 'std' weighting for velocity fitting, by default 'var'. If 'var', use the variance of the residuals to weight the fit. If 'std', use the standard deviation of the residuals to weight the fit. - + absolute_sigma: boolean If True, use the absolute sigma in the velocity fitting. If False, use the relative sigma, by default False. - + update_errors: boolean If True, save the starlist errors as xe_list, bootstrap errors as xe_boot, and their quad sum as xe (and likewise for ye and me). If False (default), leave the starlist errors in place as xe and bootstrap errors as xe_boot. - + Output: ------ New columns will be added to self.ref_table: 'xe_boot', 2D column: bootstrap x pos uncertainties due to transformation for each epoch 'ye_boot', 2D column: bootstrap y pos uncertainties due to transformation for each epoch 'me_boot', 2D column: bootstrap mag uncertainties due to transformation for each epoch - + If calc_vel_in_bootstrap: '_err_boot', 1D column: bootstrap uncertainties in for motion model fit @@ -1292,9 +1292,9 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot t0_arr = t0_arr[idx_good] else: idx_good = np.arange(0, len(ref_table), 1) - + #idx_ref = np.where(ref_table['use_in_trans'] == True) - + # Initialize sums for output x_boot_sum = np.zeros((len(ref_table['x']), n_epochs)) x2_boot_sum = np.zeros((len(ref_table['x']), n_epochs)) @@ -1321,7 +1321,7 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot for col in motion_col_list: motion_boot_sum[col] = np.zeros((len(ref_table['x']))) motion2_boot_sum[col] = np.zeros((len(ref_table['x']))) - + all_mm_map = motion_model.motion_model_map() motion_model_list = [all_mm_map[mm_name] for mm_name in motion_model_list] motion_boot_min_epochs = np.max([mm.n_params for mm in motion_model_list]) @@ -1335,7 +1335,7 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot # reference stars. Use a loop for each epoch here, so we # can handle case where different reference stars are used # in different epochs - + # Initialize data arrays x_trans_arr = np.ones((len(ref_table['x']), n_epochs)) * -999 y_trans_arr = np.ones((len(ref_table['x']), n_epochs)) * -999 @@ -1343,13 +1343,13 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot xe_trans_arr = np.ones((len(ref_table['x']), n_epochs)) * -999 ye_trans_arr = np.ones((len(ref_table['x']), n_epochs)) * -999 me_trans_arr = np.ones((len(ref_table['x']), n_epochs)) * -999 - + for jj in range(n_epochs): # Extract bootstrap sample of matched reference stars for this epoch #good = np.where(~np.isnan(ref_table['x_orig'][idx_ref][:,jj])) good = np.where( (ref_table['used_in_trans'][:,jj] == True) & (~np.isnan(ref_table['x_orig'][:,jj])) ) samp_idx = np.random.choice(good[0], len(good[0]), replace=True) - + # Get reference star positions in particular epoch from ref_list. t_epoch = t_arr[jj] ref_orig = self.get_ref_list_from_table(t_epoch)[idx_good] @@ -1383,7 +1383,7 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot # Sanity check: makes sure names match between ref_boot and starlist_boot, # since they need to line up assert np.all(ref_boot['name'] == starlist_boot['name']) - + # Calculate weights based on weights keyword. If weights desired, will need to # make starlist objects for this if self.trans_weighting != None: @@ -1396,11 +1396,11 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot starlist_boot_T.transform_xym(self.trans_list[jj]) else: starlist_boot_T.transform_xy(self.trans_list[jj]) - + weight = self.get_weights_for_lists(ref_boot, starlist_boot_T) else: weight = None - + # Recalculate transformation trans = self.trans_class.derive_transform(starlist_boot['x'], starlist_boot['y'], ref_boot['x'], ref_boot['y'], @@ -1424,7 +1424,7 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot starlist_T.transform_xym(trans) else: starlist_T.transform_xy(trans) - + # Add output to pos arrays x_trans_arr[:,jj] = starlist_T['x'] y_trans_arr[:,jj] = starlist_T['y'] @@ -1432,7 +1432,7 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot xe_trans_arr[:,jj] = starlist_T['xe'] ye_trans_arr[:,jj] = starlist_T['ye'] me_trans_arr[:,jj] = starlist_T['me'] - + x_boot_sum += x_trans_arr x2_boot_sum += x_trans_arr**2 y_boot_sum += y_trans_arr @@ -1440,12 +1440,12 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot if self.mag_trans: m_boot_sum += m_trans_arr m2_boot_sum += m_trans_arr**2 - + t2 = time.time() #print('=================================================') #print('Time to do {0} epochs: {1}s'.format(n_epochs, t2-t1)) #print('=================================================') - + # Finally, calculate proper motions for this bootstrap iteration # for each star, if desired. Draw a full-sample bootstrap over the epochs # for each star, and then run it through the startable fit_velocities machinery @@ -1454,7 +1454,7 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot while len(np.unique(boot_idx)) < motion_boot_min_epochs: boot_idx = np.random.choice(np.arange(0, n_epochs, 1), size=n_epochs) t_boot = t_arr[boot_idx] - + star_table = StarTable(name=ref_table['name'], x=x_trans_arr[:,boot_idx], y=y_trans_arr[:,boot_idx], @@ -1520,14 +1520,14 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot data_dict = {'xe_boot': x_err_b, 'ye_boot': y_err_b, 'me_boot': m_err_b} for col in motion_col_list: data_dict[col+'_err_boot'] = motion_data_err[col] - + for ff in col_heads_2D: col = Column(np.ones((len(self.ref_table), n_epochs)), name=ff) col.fill(np.nan) - + col[idx_good] = data_dict[ff] self.ref_table.add_column(col) - + # # Calculate chi^2 with bootstrap positional errors # # Determine which motion model to use: # motion_model_list = sorted(motion_model_list, key=lambda mm: mm.n_params) @@ -1556,18 +1556,18 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot # Now handle the velocities, if they were calculated if calc_vel_in_bootstrap: col_heads_1D = [col+'_err_boot' for col in motion_col_list] - + for ff in col_heads_1D: col = Column(np.ones(len(self.ref_table)), name=ff) col.fill(np.nan) - + col[idx_good] = data_dict[ff] self.ref_table.add_column(col) - + print('===============================') print('Done with bootstrap') print('===============================') - + if update_errors: self.ref_table['xe_list'] = self.ref_table['xe'] self.ref_table['ye_list'] = self.ref_table['ye'] @@ -1578,18 +1578,18 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot print("Saved starlist errors to xe_list and added xe_boot to xe in quadrature.") print("The same was done for ye and me.") - + return class MosaicToRef(MosaicSelfRef): def __init__( - self, - ref_list, - list_of_starlists, + self, + ref_list, + list_of_starlists, # Alignment parameters iters=2, - dr_tol=[1, 1], + dr_tol=[1, 1], dm_tol=[2, 1], outlier_tol=[None, None], # Reference behavior (MosiacToRef specific) @@ -1604,8 +1604,8 @@ def __init__( init_guess_mode='miracle', calc_trans_inverse=False, # Magnitude parameters - mag_trans=True, - mag_lim=None, + mag_trans=True, + mag_lim=None, ref_mag_lim=None, # Motion model parameters motion_models=['Empty', 'Fixed'], @@ -1624,7 +1624,7 @@ def __init__( Required Parameters ------------------- ref_list : StarList object - Can optionally have velocities. All starlists will be aligned to this one. + Can optionally have velocities. All starlists will be aligned to this one. list_of_starlists : array of StarList objects An array or list of flystar.starlists.StarList objects (which are Astropy Tables). @@ -1633,20 +1633,20 @@ def __init__( Note that there is an optional weights column called 'w'. If this column exists in any of the lists, it will be queried to determine if an individual star can be used to derive the transformations between starlists. This is the most flexible way - to allow you to determine, as a function of time and star, which ones are good enough - in the transformation. Note that just because it can be used (i.e. w_in=1), - doesn't meant that it will be used. The mag limits and outliers still take precedence. - Note also that the weights that go into the transformation are + to allow you to determine, as a function of time and star, which ones are good enough + in the transformation. Note that just because it can be used (i.e. w_in=1), + doesn't meant that it will be used. The mag limits and outliers still take precedence. + Note also that the weights that go into the transformation are star_list['w'] * ref_list['w'] * weight_from_keyword (see the weights parameter) - for those stars not trimmed out by the other criteria. + for those stars not trimmed out by the other criteria. Optional Parameters ---------- iters : int - The number of iterations used in the matching and transformation. TO DO: INNER/OUTER? + The number of iterations used in the matching and transformation. TO DO: INNER/OUTER? dr_tol : list or array The delta-radius (dr) tolerance for matching in units of the reference coordinate system. @@ -1654,24 +1654,24 @@ def __init__( dm_tol : list or array The delta-magnitude (dm) tolerance for matching in units of the reference coordinate system. - This is a list of dm values, one for each iteration of matching/transformation. + This is a list of dm values, one for each iteration of matching/transformation. outlier_tol : list or array - The outlier tolerance (in units of sigma) for rejecting outlier stars. + The outlier tolerance (in units of sigma) for rejecting outlier stars. This is a list of tol values, one for each iteration of matching/transformation. use_ref_new : boolean - Each pass, new stars are matched and added to the ref_table. However, we don't - necessarily want to use these in the reference frame in subsequent passes. + Each pass, new stars are matched and added to the ref_table. However, we don't + necessarily want to use these in the reference frame in subsequent passes. If True, then the new stars will be used in later passes/iterations. If False, then the new stars will be carried, but not used in the transformation. We determine which stars to use through setting a boolean use_in_trans flag. update_ref_orig : boolean or str Should we update the reference values (position, velocity, t0) after each starlist - is transformed in each iteration? + is transformed in each iteration? - False if you want to get into an absolute reference frame and are using Gaia data. + False if you want to get into an absolute reference frame and are using Gaia data. True if you want to use the reference list as more of an initial guess. 'periter' if you want to align all the starlists, then calculate the velocity. @@ -1681,17 +1681,17 @@ def __init__( trans_class : transforms.Transform2D object (or subclass) The transform class that will be used to when deriving the optimal - transformation parameters between each list and the reference list. + transformation parameters between each list and the reference list. trans_args : dictionary - A dictionary (or a list of dictionaries) containing any extra keywords that are needed - in the transformation object. For instance, "order". Note that if a list is passed in, + A dictionary (or a list of dictionaries) containing any extra keywords that are needed + in the transformation object. For instance, "order". Note that if a list is passed in, then the transformation argument (i.e. order) will be changed for every iteration in iters. trans_input : array or list of transform objects def = None. If not None, then this should contain an array or list of transform - objects that will be used as the initial guess in the alignment and matching. + objects that will be used as the initial guess in the alignment and matching. trans_weighting : str Either None (def), 'both,var', 'list,var', or 'ref,var' depending on whether you want @@ -1705,7 +1705,7 @@ def = None. If not None, then this should contain an array or list of transform init_guess_mode : string If no initial transformations are passed in via the trans_input keyword, then we have - to make the initial transformation and matching blindly. We can do this in a couple of + to make the initial transformation and matching blindly. We can do this in a couple of different ways. Options are 'miracle' or 'name' (see trans_initial_guess() for more details). calc_trans_inverse: boolean @@ -1716,13 +1716,13 @@ def = None. If not None, then this should contain an array or list of transform self.trans_list_inverse doesn't exist if calc_trans_inverse == False mag_trans : boolean - If true, this will also calculate and (temporarily) apply a zeropoint offset to - magnitudes in each list to bring them into a common magnitude system. This is - essential for matching (with finite dm_tol) starlists of different filters or - starlists that are not photometrically calibrated. Note that the final_table columns + If true, this will also calculate and (temporarily) apply a zeropoint offset to + magnitudes in each list to bring them into a common magnitude system. This is + essential for matching (with finite dm_tol) starlists of different filters or + starlists that are not photometrically calibrated. Note that the final_table columns of 'm', 'm0', and 'm0_err' will contain the transformed magnitudes while the - final_table column 'm_orig' will contain the original un-transformed magnitudes. - If mag_trans = False, then no such zeropoint offset it applied at any point. + final_table column 'm_orig' will contain the original un-transformed magnitudes. + If mag_trans = False, then no such zeropoint offset it applied at any point. mag_lim : array If different from None, it indicates the minimum and maximum magnitude @@ -1743,7 +1743,7 @@ def = None. If not None, then this should contain an array or list of transform fixed_params_dict : None or dict Dictionary of fixed parameters for motion models - + vel_weighting : str Either 'var' (def) or 'std', depending on whether you want to weight the motion model fits by the variance or standard deviation of the position data @@ -1765,7 +1765,7 @@ def = None. If not None, then this should contain an array or list of transform Controls the verbosity of print statements. (0 least, 9 most verbose). For backwards compatibility, 0 = False, 9 = True. (Note: technically right now no checks on whether the number is an integer or not...) - + Example ------- msc = align.MosaicToRef(my_gaia, list_of_starlists, iters=1, @@ -1781,30 +1781,30 @@ def = None. If not None, then this should contain an array or list of transform msc.fit() # Access a list of all the transformation parameters: - trans_list = msc.trans_list + trans_list = msc.trans_list # Access the fully-combined reference table. stars_table = msc.ref_table # Plot the magnitude of the first star vs. time: - # Overplot the mean magnitude. + # Overplot the mean magnitude. plt.plot(stars_table['t'][0, :], stars_table['m'][0, :], 'k.') - plt.axhline(stars_table['m0'][0]) + plt.axhline(stars_table['m0'][0]) # Plot the X position of the first star vs. time: # Overplot the best-fit proper motion. times = stars_table['t'][0, :] plt.errorbar(times, stars_table['x'][0, :], yerr=stars_table['xe'][0, :]) - plt.axhline(stars_table['x0'][0] + stars_table['vx'][0]*(times - stars_table['t0'][0])) + plt.axhline(stars_table['x0'][0] + stars_table['vx'][0]*(times - stars_table['t0'][0])) """ super().__init__( - list_of_starlists, + list_of_starlists, # Alignment parameters - ref_index=-1, + ref_index=-1, iters=iters, - dr_tol=dr_tol, + dr_tol=dr_tol, dm_tol=dm_tol, - outlier_tol=outlier_tol, + outlier_tol=outlier_tol, # Transformation parameters trans_class=trans_class, trans_args=trans_args, @@ -1869,11 +1869,11 @@ def fit(self): Using the current parameter settings, match and transform all the lists to a reference position. Note in the first pass, the reference position is just the specified input reference starlist. In subsequent iterations, - this is (optionally) updated. + this is (optionally) updated. The ultimate outcome is the creation of self.ref_table. This reference table will contain "averaged" quantites as well as a big 2D array of all - the matched original and transformed quantities. + the matched original and transformed quantities. Averaged columns on ref_table: x0 @@ -1925,7 +1925,7 @@ def fit(self): # ########## for nn in range(self.iters): - # If we are on subsequent iterations, remove matching results from the + # If we are on subsequent iterations, remove matching results from the # prior iteration. This leaves aggregated (1D) columns alone. if nn > 0: self.reset_ref_values() @@ -1979,13 +1979,13 @@ def fit(self): ########## # Clean up output table. - # + # ########## # Find where stars are detected. if self.verbose > 0: print('') print(' Preparing the reference table...') - + self.ref_table.detections() ### Drop all stars that have 0 detections. @@ -2020,14 +2020,14 @@ def fit(self): def infer_positions(t, startable, motion_models=None, fixed_params_dict=None, return_errors=False): """ Take a startable, check to see if it has motion/velocity columns. - If it does, then propagate the positions forward in time + If it does, then propagate the positions forward in time to the desired epoch. If no motion/velocities exist, then just use ['x0', 'y0'] or ['x', 'y'] Parameters ---------- t : float - The time to propagate to. Usually in decimal years; + The time to propagate to. Usually in decimal years; but it should be in the same units as the 't0' column in starlist. startable : StarTable @@ -2089,7 +2089,7 @@ def infer_positions(t, startable, motion_models=None, fixed_params_dict=None, re fixed_params_dict={param: startable[param] for param in mm.fixed_param_names} ) break - + return x, y # # If no motion model, check for velocities @@ -2110,9 +2110,9 @@ def infer_positions(t, startable, motion_models=None, fixed_params_dict=None, re def get_all_epochs(t): """ Helper function to get times of all epochs from a ref table. - This is required because our previous approach - of simply taking the time array of the star with the most detections - fails for mosaicked catalogs, because it is then possible that + This is required because our previous approach + of simply taking the time array of the star with the most detections + fails for mosaicked catalogs, because it is then possible that no star is detected in all fields. """ nepochs = len(t['t'][0]) @@ -2129,10 +2129,10 @@ def get_all_epochs(t): all_epochs = np.array(all_epochs) return all_epochs - + def setup_ref_table_from_starlist(star_list, motion_models): - """ + """ Start with the reference list.... this will change and grow over time, so make a copy that we will keep updating. The reference table will contain one columne for every named @@ -2161,7 +2161,7 @@ def setup_ref_table_from_starlist(star_list, motion_models): # Make new columns to hold original values. These will be copies # of the old columns and will only include x, y, m, xe, ye, me. - # The columns we have already created will hold transformed values. + # The columns we have already created will hold transformed values. trans_col_names = ['x', 'y', 'm', 'xe', 'ye', 'me', 'w'] for tt in range(len(trans_col_names)): old_name = trans_col_names[tt] @@ -2173,7 +2173,7 @@ def setup_ref_table_from_starlist(star_list, motion_models): # Make sure ref_table has the necessary x0, y0, m0 and associated # error columns. If they don't exist, then add them as a copy of - # the original x,y,m etc columns. + # the original x,y,m etc columns. new_cols_arr = ['x0', 'x0_err', 'y0', 'y0_err', 'm0', 'm0_err'] orig_cols_arr = ['x', 'xe', 'y', 'ye', 'm', 'me'] assert len(new_cols_arr) == len(orig_cols_arr) @@ -2188,7 +2188,7 @@ def setup_ref_table_from_starlist(star_list, motion_models): # Now add to ref_table new_col = Column(vals, name=new_cols_arr[ii]) ref_table.add_column(new_col) - + if 'use_in_trans' not in ref_table.colnames: new_col = Column(np.ones(len(ref_table), dtype=bool), name='use_in_trans') ref_table.add_column(new_col) @@ -2198,7 +2198,7 @@ def setup_ref_table_from_starlist(star_list, motion_models): # Note that these are all the 1D columns. for col_name in ref_table.colnames: if len(ref_table[col_name].data.shape) == 2: # Find the 2D columns - ref_table._set_invalid_list_values(col_name, -1) + ref_table._set_invalid_list_values(col_name, -1) return ref_table @@ -2208,14 +2208,14 @@ def copy_over_values(ref_table, star_list, star_list_T, idx_epoch, idx_ref, idx_ into the reference table we carry around and that is the final output product. Copy only those values for stars that match. - Copy all columns that are in both ref_table and star_list_T. + Copy all columns that are in both ref_table and star_list_T. Copy all columns that are also in star_list but copy them into _orig. Parameters ---------- ref_table : StarTable The table we will be copying values into. Note the columns with the appropriate - names and dimensions must already exist. + names and dimensions must already exist. star_list : StarList The astropy table to copy values from. These should be untransformed (orig) values. star_list_T : StarList @@ -2242,7 +2242,7 @@ def reset_ref_values(ref_table): """ Reset all the 2D arrays in the reference table. This is the action we take at the beginning of each new iteration. We don't preserve matching - results from the prior iterations. + results from the prior iterations. """ # All 2D columns should be reset. for col_name in ref_table.colnames: @@ -2250,13 +2250,13 @@ def reset_ref_values(ref_table): # Loop through epochs for this array. for cc in range(ref_table[col_name].shape[1]): ref_table._set_invalid_list_values(col_name, cc) - + return def add_rows_for_new_stars(ref_table, star_list, idx_list, motion_model_name='Fixed'): """ - For each star that is in star_list and NOT in idx_list, make a - new row in the reference table. The values will be empty (None, NAN, etc.). + For each star that is in star_list and NOT in idx_list, make a + new row in the reference table. The values will be empty (None, NAN, etc.). Parameters ---------- @@ -2277,7 +2277,7 @@ def add_rows_for_new_stars(ref_table, star_list, idx_list, motion_model_name='Fi idx_lis_new : list The list of indices into the star_list object for the "new" stars. idx_ref_new : list - The list of indices into the ref_table object for the "new" stars. + The list of indices into the ref_table object for the "new" stars. """ last_star_idx = len(ref_table) @@ -2304,7 +2304,7 @@ def add_rows_for_new_stars(ref_table, star_list, idx_list, motion_model_name='Fi new_col_empty = 'Empty' else: new_col_empty = np.nan - + if len(ref_table[col_name].shape) == 1: new_col_shape = N_newstars else: @@ -2330,7 +2330,7 @@ def add_rows_for_new_stars(ref_table, star_list, idx_list, motion_model_name='Fi """ def calc_mag_avg_all_stars(d): - # Determine how many stars there are. + # Determine how many stars there are. N_stars = len(d) # Determine how many epochs there are. @@ -2372,7 +2372,7 @@ def initial_align(table1, table2, briteN=100, y: y position xe: error in x position ye: error in y position - + vx: proper motion in x direction vy proper motion in y direction vxe: error in x proper motion @@ -2380,11 +2380,11 @@ def initial_align(table1, table2, briteN=100, m: magnitude me: magnitude error - + t0: linear motion time zero point use: specify use in transformation - + Parameters: ---------- @@ -2412,7 +2412,7 @@ def initial_align(table1, table2, briteN=100, Output: ------ Transformation object - + """ # Extract necessary information from tables (x, y, m) x1 = table1['x'] @@ -2460,7 +2460,7 @@ def transform_and_match(table1, table2, transform, dr_tol=1.0, dm_tol=None, verb starlist file positions. -transform: transformation object - + -verbose: bool, optional Prints on screen information on the matching @@ -2531,7 +2531,7 @@ def find_transform(table1, table1_trans, table2, transModel=transforms.PolyTrans if weights=='starlist', we only use postion error in transformed starlist. if weights=='reference', we only use position error in reference starlist. if weights==None, we don't use weights. - + verbose: bool (default=True) Prints on screen information on the matching @@ -2547,7 +2547,7 @@ def find_transform(table1, table1_trans, table2, transModel=transforms.PolyTrans (transModel != transforms.LegTransform) ): print(( '{0} not supported yet!'.format(transModel))) return - + # Extract *untransformed* coordinates from starlist 1 # and the matching coordinates from starlist 2 x1 = table1['x'] @@ -2625,7 +2625,7 @@ def find_transform_new(table1_mat, table2_mat, if weights = 'both' or 'starlist' then the positions in table 1 are first transformed using the transInit object. This is necessary if the plate scales are very different between the table 1 and the reference list. - + verbose: bool (default=True) Prints on screen information on the matching @@ -2638,7 +2638,7 @@ def find_transform_new(table1_mat, table2_mat, if ( (transModel != transforms.four_paramNW) & (transModel != transforms.PolyTransform) ): print(( '{0} not supported yet!'.format(transModel))) return - + # Extract *untransformed* coordinates from starlist 1 # and the matching coordinates from starlist 2 x1 = table1_mat['x'] @@ -2726,7 +2726,7 @@ def write_transform(transform, starlist, reference, N_trans, deltaMag=0, restric outFile: string (default: 'outTrans.txt') Name of output text file - + Output: ------ txt file with the file name outFile @@ -2734,7 +2734,7 @@ def write_transform(transform, starlist, reference, N_trans, deltaMag=0, restric # Extract info about transformation trans_name = transform.__class__.__name__ trans_order = transform.order - + # Extract X, Y coefficients from transform if trans_name == 'four_paramNW': Xcoeff = transform.px @@ -2744,10 +2744,10 @@ def write_transform(transform, starlist, reference, N_trans, deltaMag=0, restric Ycoeff = transform.py.parameters else: raise Exception(f'{trans_name} not yet supported!') - + # Write output _out = open(outFile, 'w') - + # Write the header. DO NOT CHANGE, HARDCODED IN JAVA ALIGN _out.write('## Date: {0}\n'.format(datetime.date.today()) ) _out.write('## File: {0}, Reference: {1}\n'.format(starlist, reference) ) @@ -2771,12 +2771,12 @@ def write_transform(transform, starlist, reference, N_trans, deltaMag=0, restric # CODE TO GET INDICIES N = trans_order - 1 idx_list = list() - + # when trans_order=1, N=0 idx_list.append(0) idx_list.append(1) idx_list.append(N+2) - + if trans_order >= 2: for k in range(2, N+2): idx_list.append(k) @@ -2797,7 +2797,7 @@ def write_transform(transform, starlist, reference, N_trans, deltaMag=0, restric _out.close() - + return @@ -2811,7 +2811,7 @@ def transform_from_file(starlist, transFile): are present in starlist. WARNING: THIS CODE WORKS FOR POLYTRANSFORM - + Parameters: ---------- starlist: astropy table @@ -2841,8 +2841,8 @@ def transform_from_file(starlist, transFile): # Do transform transform = transforms.PolyTransform(order, Xcoeff, Ycoeff) return transform_from_object(starlist, transform) - - + + def transform_from_object(starlist, transform): """ @@ -2851,7 +2851,7 @@ def transform_from_object(starlist, transform): if they are present in starlits. If a more complex motion_model is implemented, the motion parameters are set to nan, as we need the full time series to refit. - + Parameters: ---------- starlist: astropy table @@ -2885,11 +2885,11 @@ def transform_from_object(starlist, transform): if len(motion_models_unique)==0: vel=True mot=False - + # Prior code before motion_model implementation # Can still be used as shortcut for Linear+Fixed motion_model only err = 'xe' in keys - + # Extract needed information from starlist x = starlist_f['x'] y = starlist_f['y'] @@ -2910,7 +2910,7 @@ def transform_from_object(starlist, transform): vy = starlist_f['vy'] vxe = starlist_f['vx_err'] vye = starlist_f['vy_err'] - + # calculate the transformed position and velocity x_new, y_new, xe_new, ye_new = position_transform_from_object(x, y, xe, ye, transform) @@ -2923,7 +2923,7 @@ def transform_from_object(starlist, transform): starlist_f['y'] = y_new starlist_f['xe'] = xe_new starlist_f['ye'] = ye_new - + if vel: starlist_f['x0'] = x0_new starlist_f['y0'] = y0_new @@ -2933,7 +2933,7 @@ def transform_from_object(starlist, transform): starlist_f['vy'] = vy_new starlist_f['vx_err'] = vxe_new starlist_f['vy_err'] = vye_new - + # For more complicated motion_models, # we can't easily transform them, set the values to nans and refit later. if mot: @@ -2941,7 +2941,7 @@ def transform_from_object(starlist, transform): for param in motion_model_params: if param in keys: starlist_f[param] = np.nan - + return starlist_f @@ -2954,7 +2954,7 @@ def position_transform_from_object(x, y, xe, ye, transform): - x, y: original position - xe, ye: original position error - transform: transformation object from astropy.modeling.models.polynomial2D - + Outpus: - x_new, y_new: transformed position - xe_new, ye_new: transformed position error @@ -2972,7 +2972,7 @@ def position_transform_from_object(x, y, xe, ye, transform): else: txt = 'Transform not yet supported by position_transform_from_object' raise Exception(txt) - + # How the transformation is applied depends on the type of transform. # This can be determined by the length of Xcoeff, Ycoeff N = order - 1 @@ -2996,7 +2996,7 @@ def position_transform_from_object(x, y, xe, ye, transform): for j in range(1, N+2-i): sub = int(2*N + 2 + j + (2*N+2-i) * (i-1)/2.) y_new += Ycoeff[sub] * (x**i) * (y**j) - + """ THIS IS WRONG BELOW! - NOTE: I don't think this is wrong any more @@ -3054,11 +3054,11 @@ def velocity_transform_from_object(x0, y0, x0e, y0e, vx, vy, vxe, vye, transform - x0, y0, x0e, y0e: original position and position error - vx, vy, vxe, vye: original velocity and velocity error - transform: transformation object from astropy.modeling.models.polynomial2D - + Outpus: - vx_new, vy_new, vxe_new, vye_new: transformed velocity and velocity error """ - + # Read transformation: Extract X, Y coefficients from transform if transform.__class__.__name__ == 'four_paramNW': Xcoeff = transform.px @@ -3071,7 +3071,7 @@ def velocity_transform_from_object(x0, y0, x0e, y0e, vx, vy, vxe, vye, transform else: txt = 'Transform not yet supported by velocity_transform_from_object' raise Exception(txt) - + # How the transformation is applied depends on the type of transform. # This can be determined by the length of Xcoeff, Ycoeff N = order - 1 @@ -3132,7 +3132,7 @@ def velocity_transform_from_object(x0, y0, x0e, y0e, vx, vy, vxe, vye, transform for i in range(1, N+1): for j in range(1, N+2-i): sub = 2*N + 2 + j + (2*N+2-i) * (i-1)/2. - temp3 += i * Xcoeff[int(sub)] * (x0**(i-1)) * (y0**j) + temp3 += i * Xcoeff[int(sub)] * (x0**(i-1)) * (y0**j) for j in range(1, N+2): temp4 += j * Xcoeff[N+1+j] * (y0**(j-1)) @@ -3175,7 +3175,7 @@ def velocity_transform_from_object(x0, y0, x0e, y0e, vx, vy, vxe, vye, transform for i in range(1, N+1): for j in range(1, N+2-i): sub = 2*N + 2 + j + (2*N+2-i) * (i-1)/2. - temp3 += i * Ycoeff[int(sub)] * (x0**(i-1)) * (y0**j) + temp3 += i * Ycoeff[int(sub)] * (x0**(i-1)) * (y0**j) for j in range(1, N+2): temp4 += j * Ycoeff[N+1+j] * (y0**(j-1)) @@ -3206,7 +3206,7 @@ def check_trans_input(list_of_starlists, trans_input, mag_trans): if trans_input != None: assert len(trans_input) == len(list_of_starlists) - if mag_trans: + if mag_trans: for ii in range(len(trans_input)): if trans_input[ii] != None: try: @@ -3215,7 +3215,7 @@ def check_trans_input(list_of_starlists, trans_input, mag_trans): print('Missing trans.mag_offset on trans_input[{0:d}].'.format(ii)) print('Setting mag_offset = 0 and dm_tol[0] = 100 and hoping for the best!!') trans_input[ii].mag_offset = 0.0 - + return def trans_initial_guess( @@ -3236,7 +3236,7 @@ def trans_initial_guess( This function will grow with time to handle difference types of initial guess transformations (triangle matching, match by name, etc.). For now it - is just blind triangle matching on the brightest 50 stars. + is just blind triangle matching on the brightest 50 stars. """ warnings.filterwarnings('ignore', category=AstropyUserWarning) if motion_models is None: @@ -3330,15 +3330,15 @@ def update_old_and_new_names(ref_table, list_index, idx_ref_new): all_names = old_names.astype('U{0:d}'.format(new_name_len_max)) else: all_names = old_names - + all_names[idx_ref_new] = new_names - + return all_names def copy_and_rename_for_ref(star_list): """ Make a deep copy of the starlist and rename the columns to include - "0". This only applies to x, y, m and xe, ye, me (if they exist) + "0". This only applies to x, y, m and xe, ye, me (if they exist) columns. Input @@ -3361,7 +3361,7 @@ def copy_and_rename_for_ref(star_list): if 'w' in star_list.colnames: old_cols += ['w'] new_cols += ['w'] - + ref_list = copy.deepcopy(star_list) for ii in range(len(old_cols)): @@ -3372,11 +3372,11 @@ def copy_and_rename_for_ref(star_list): def outlier_rejection_indices(star_list, ref_list, outlier_tol, motion_models, fixed_params_dict=None, verbose=True): """ Determine the outliers based on the residual positions between two different - starlists and some threshold (in sigma). Return the indices of the stars - to keep (that shouldn't be rejected as outliers). + starlists and some threshold (in sigma). Return the indices of the stars + to keep (that shouldn't be rejected as outliers). Note that we assume that the star_list and ref_list are already transformed and - matched. + matched. Parameters ---------- @@ -3385,8 +3385,8 @@ def outlier_rejection_indices(star_list, ref_list, outlier_tol, motion_models, f ref_list : StarList starlist with 'x0', 'y0' outlier_tol : float - Number of sigma inside which we keep stars and outside of which we - reject stars as outliers. + Number of sigma inside which we keep stars and outside of which we + reject stars as outliers. motion_models : list of motion_model objects The motion models to use in the star_list fixed_params_dict : dict or None, optional @@ -3397,7 +3397,7 @@ def outlier_rejection_indices(star_list, ref_list, outlier_tol, motion_models, f Returns ---------- keepers : bool array - The boolean array of the stars to keep. + The boolean array of the stars to keep. """ # Optionally propogate the reference positions forward in time. xref, yref = infer_positions(star_list['t'][0], ref_list, motion_models, fixed_params_dict=fixed_params_dict) @@ -3413,7 +3413,7 @@ def outlier_rejection_indices(star_list, ref_list, outlier_tol, motion_models, f if verbose > 0: msg = ' Outlier Rejection: Keeping {0:d} of {1:d}' print(msg.format(sum(keepers), len(resid_on_old_trans))) - + return keepers def setup_trans_info(trans_input, trans_args, N_lists, iters): @@ -3433,12 +3433,12 @@ def setup_trans_info(trans_input, trans_args, N_lists, iters): if type(trans_args) == dict: tmp = trans_args trans_args = [tmp for ii in range(iters)] - + return trans_list, trans_args def apply_mag_lim(star_list, mag_lim): - """ Apply a magnitude limit to the list. If no magnitude limit is - specified, then return a copy of the list. This works on a + """ Apply a magnitude limit to the list. If no magnitude limit is + specified, then return a copy of the list. This works on a reference list (with 'm0') or a star_list ('m') with 'm0' taking priority. @@ -3457,7 +3457,7 @@ def apply_mag_lim(star_list, mag_lim): mcol = 'm' conditions = {} - + cond_key = '{0:s}_min'.format(mcol) conditions[cond_key] = mag_lim[0] @@ -3475,7 +3475,7 @@ def get_weighting_scheme(weights, ref_list, star_list): else: var_xref = 0.0 var_yref = 0.0 - + if 'xe' in star_list.colnames: var_xlis = star_list['xe']**2 var_ylis = star_list['ye']**2 diff --git a/flystar/analysis.py b/flystar/analysis.py index c955c2b..0a1397c 100644 --- a/flystar/analysis.py +++ b/flystar/analysis.py @@ -29,7 +29,7 @@ def query_gaia(ra, dec, search_radius=30.0, table_name='gaiadr3'): Dec. in degrees in the format such as '-29:00:28.0' search_radius : float - The search radius in arcseconds. + The search radius in arcseconds. Optional Input -------------- @@ -103,13 +103,13 @@ def check_gaia_parallaxes(ra,dec,search_radius=10.0,table_name='gaiadr3',target= plt.yscale('log') plt.tight_layout() plt.savefig('gaiaplx'+file_ext+'.png') - + def prepare_gaia_for_flystar(gaia, ra, dec, targets_dict=None, match_dr_max=0.2, pi_err_limit=0.4, default_motion_model='Linear'): """ Take a Gaia table (from astroquery) and produce a new table with a tangential projection - and shift such that the origin is centered on the target of interest. - Convert everything into arcseconds and name columns such that they are + and shift such that the origin is centered on the target of interest. + Convert everything into arcseconds and name columns such that they are ready for FlyStar input. Inputs @@ -126,7 +126,7 @@ def prepare_gaia_for_flystar(gaia, ra, dec, targets_dict=None, match_dr_max=0.2, target_coords = SkyCoord(ra, dec, unit=(u.hourangle, u.deg), frame='icrs') ra = target_coords.ra.degree # in decimal degrees dec = target_coords.dec.degree # in decimal degrees - + cos_dec = np.cos(np.radians(dec)) x = (gaia['ra'] - ra) * cos_dec * 3600.0 # arcsec y = (gaia['dec'] - dec) * 3600.0 # arcsec @@ -145,7 +145,7 @@ def prepare_gaia_for_flystar(gaia, ra, dec, targets_dict=None, match_dr_max=0.2, gaia_new['vy'] = gaia['pmdec'].data / 1e3 gaia_new['vx_err'] = gaia['pmra_error'].data / 1e3 gaia_new['vy_err'] = gaia['pmdec_error'].data / 1e3 - + gaia_new['t0'] = gaia['ref_epoch'].data gaia_new['source_id'] = gaia['source_id'].data.astype('S19') @@ -155,7 +155,7 @@ def prepare_gaia_for_flystar(gaia, ra, dec, targets_dict=None, match_dr_max=0.2, gaia_new['vy'][idx] = 0.0 gaia_new['vx_err'][idx] = 0.0 gaia_new['vy_err'][idx] = 0.0 - + gaia_new['m'] = gaia['phot_g_mean_mag'] gaia_new['me'] = 1.09/gaia['phot_g_mean_flux_over_error'] gaia_new['pi'] = gaia['parallax'].data*1e-3 @@ -167,7 +167,7 @@ def prepare_gaia_for_flystar(gaia, ra, dec, targets_dict=None, match_dr_max=0.2, gaia_new['vx_err'][idx] = 0.0 gaia_new['vy'][idx] = 0.0 gaia_new['vy_err'][idx] = 0.0 - + # Cut out stars with high plx error and set motion models idx = np.where((gaia_new['pi_err']>(pi_err_limit/1e3)) | (gaia['parallax'].mask == True))[0] gaia_new['pi'][idx] = 0.0 @@ -188,7 +188,7 @@ def prepare_gaia_for_flystar(gaia, ra, dec, targets_dict=None, match_dr_max=0.2, gaia_new['n_params'] = 1 else: print("Invalid motion model",default_motion_model,"- none assigned") - + #macy additions to try to fix wild magnitude values #gaia_new['ruwe'] = gaia['ruwe'] #try: @@ -224,9 +224,9 @@ def prepare_gaia_for_flystar(gaia, ra, dec, targets_dict=None, match_dr_max=0.2, print('Found match for: ', targ_names[idx], ' - ',gaia_new['source_id'][i_gaia]) return gaia_new - + def run_flystar(): - + test_file = '/u/jlu/work/microlens/OB150211/a_2018_10_19/a_ob150211_2018_10_19/lis/stars_matched2.fits' t = Table.read(test_file) @@ -258,39 +258,39 @@ def run_flystar(): ym_t = y0 + vy * (t - t0) # Model distorted positions - - + + return def project_gaia(gaia, epoch, ra, dec): """ Take the Gaia measurements, forward them in time, and then convert them into a tangential projection. - + Inputs ---------- epoch : float (year) The decimal year to project the measurement to. Note that we use 365.25 days per year. - + ra : float (deg) The right ascension (J2000) in decimal degrees of the center of the field. - + dec : float (deg) The declination (J2000) in decimal degrees of the center of the field. - + """ t0 = gaia['ref_epoch'] x0 = (gaia['ra'] - ra) * np.cos(np.radians(dec)) * 3600.0 # Arcsec y0 = (gaia['dec'] - dec) * 3600.0 x0e = gaia['ra_error'] / 1.0e3 # arcsec, already in alpha* (multiplied by cos(delta)) y0e = gaia['dec_error'] / 1.0e3 # arcsec - - + + vx = gaia['pmra'] / 1.0e3 # arcsec / yr - vy = gaia['pmdec'] / 1.0e3 + vy = gaia['pmdec'] / 1.0e3 vxe = gaia['pmra_error'] / 1.0e3 # arcsec / yr vye = gaia['pmdec_error'] / 1.0e3 - + # Modify any vx/vy, etc. that are zero and make a regular (unmasked) numpy array. vx[vx.mask] = 0.0 vy[vy.mask] = 0.0 @@ -300,29 +300,29 @@ def project_gaia(gaia, epoch, ra, dec): vy = np.array(vy) vxe = np.array(vxe) vye = np.array(vye) - + dt = epoch - t0 x_now = (x0 + (vx * dt)) * -1.0 # Switch to a left-handed coordinate system, like detector pixels. y_now = (y0 + (vy * dt)) xe_now = np.hypot(x0e, vxe*dt) ye_now = np.hypot(y0e, vye*dt) - + # Format as a starlist - gaia_lis = starlists.StarList(name=gaia['source_id'], + gaia_lis = starlists.StarList(name=gaia['source_id'], x=x_now, y=y_now, m=gaia['phot_g_mean_mag'], xe=xe_now, ye=ye_now, me=1.0/gaia['phot_g_mean_flux_over_error']) - + # Duplicate columns to 'x_avg', etc. Needed for initial guessing. gaia_lis['x_avg'] = gaia_lis['x'] gaia_lis['y_avg'] = gaia_lis['y'] - gaia_lis['m_avg'] = gaia_lis['m'] - + gaia_lis['m_avg'] = gaia_lis['m'] + return gaia_lis def rename_after_flystar(star_tab, label_dat_file, new_copy=True, dr_tol=0.05, dm_tol=0.3, verbose=False): """ - Take a StarTable output from FlyStar MosaicToRef that has been + Take a StarTable output from FlyStar MosaicToRef that has been aligned into R.A. and Dec. (usually by way of Gaia). Align the output to a label.dat file for this source and rename everything. @@ -350,20 +350,20 @@ def rename_after_flystar(star_tab, label_dat_file, new_copy=True, dr_tol=0.05, d x_lab[ndx_lab[ii]], star_tab['x0'][ndx_star[ii]], y_lab[ndx_lab[ii]], star_tab['y0'][ndx_star[ii]], m_lab[ndx_lab[ii]], star_tab['m0'][ndx_star[ii]])) - + print('Temporary shift transformations: ') print(' dm = {0:8.4f} +/- {1:8.4f}'.format(dm.mean(), dm.std())) print(' dx = {0:8.4f} +/- {1:8.4f}'.format(dx.mean(), dx.std())) print(' dy = {0:8.4f} +/- {1:8.4f}'.format(dy.mean(), dy.std())) - + m_lab = label_tab['m'] + dm.mean() x_lab += dx.mean() y_lab += dy.mean() - + # Now that we are in a common coordinate and magnitude # system, lets match the whole lists by coordinates. - idx_lab, idx_star, dr, dm = match.match(x_lab, y_lab, m_lab, + idx_lab, idx_star, dr, dm = match.match(x_lab, y_lab, m_lab, star_tab['x0'], star_tab['y0'], star_tab['m0'], dr_tol=dr_tol, dm_tol=dm_tol, verbose=verbose) #print('idx_lab:') @@ -371,7 +371,7 @@ def rename_after_flystar(star_tab, label_dat_file, new_copy=True, dr_tol=0.05, d # print(label_tab["name"][idx_lab[iii]], star_tab["name"][idx_star[iii]]) print('Renaming {0:d} out of {1:d} stars'.format(len(idx_lab), len(star_tab))) - + # Make a copy of the table, UNLESS, the user specifies. if new_copy: new_tab = copy.deepcopy(star_tab) @@ -381,9 +381,9 @@ def rename_after_flystar(star_tab, label_dat_file, new_copy=True, dr_tol=0.05, d # copy over the original names... don't overwrite (this could mean data loss) if 'name_orig' not in new_tab.colnames: new_tab.add_column(Column(star_tab['name'].data, name='name_orig')) - + new_tab['name'][idx_star] = label_tab[idx_lab]['name'] - + return new_tab def pick_good_ref_stars(star_tab, r_cut=None, m_cut=None, p_err_cut=None, pm_err_cut=None, name_cut=None, reset=True): @@ -428,9 +428,9 @@ def pick_good_ref_stars(star_tab, r_cut=None, m_cut=None, p_err_cut=None, pm_err def startable_subset(tab, idx, mag_trans=True, mag_trans_orig=False): """ - Input is MosaicToRef table from alignment of multiple filters, + Input is MosaicToRef table from alignment of multiple filters, such that the astrometry is combined but the photometry is not. - This function is used to separate out a selected filter from the + This function is used to separate out a selected filter from the combined astrometry + uncombined photometry table. """ # Multiples: ['x', 'y', 'm', 'name_in_list', 'xe', 'ye', 'me', 't', @@ -462,7 +462,7 @@ def startable_subset(tab, idx, mag_trans=True, mag_trans_orig=False): # Update the original table. if mag_trans_orig: tab['m'][:,idx[ii]] += mag_offset - + return new_tab @@ -472,13 +472,13 @@ def startable_subset(tab, idx, mag_trans=True, mag_trans_orig=False): def calc_chi2(ref_mat, starlist_mat, transform, errs='both'): """ - calculate the chi2 and reduced chi2 of the position + calculate the chi2 and reduced chi2 of the position between two matched starlists. Input: ref_mat: astropy table Reference starlist only containing matched stars that were used in the transformation. Standard column headers are assumed. - + starlist_mat: astropy table Transformed starlist only containing the matched stars used in the transformation. Standard column headers are assumed. @@ -518,7 +518,7 @@ def calc_chi2(ref_mat, starlist_mat, transform, errs='both'): elif errs == 'starlist': xerr = starlist_mat['xe'] yerr = starlist_mat['ye'] - + # For both X and Y, calculate chi-square. Combine arrays to get combined # chi-square @@ -526,11 +526,11 @@ def calc_chi2(ref_mat, starlist_mat, transform, errs='both'): chi_sq_y = diff_y**2. / yerr**2. chi_sq = np.append(chi_sq_x, chi_sq_y) - + # Calculate degrees of freedom in transformation num_mod_params = calc_nparam(transform) deg_freedom = len(chi_sq) - num_mod_params - + # Calculate reduced chi-square chi_sq = np.sum(chi_sq) chi_sq_red = chi_sq / deg_freedom @@ -547,7 +547,7 @@ def calc_nparam(transformation): nparam = 4 elif transformation.__class__.__name__ == 'PolyTransform': order = transformation.order - nparam = (order+1) * (order+2) + nparam = (order+1) * (order+2) return nparam def calc_F(red_chi2_1, red_chi2_2, v1, v2): @@ -568,24 +568,24 @@ def calc_F(red_chi2_1, red_chi2_2, v1, v2): for 1st order polynomial fitting: x' = a0 + a1*x + a2*y y' = b0 + b1*x + b2*y - v1 = 2*N1 - 2*3 (2*: because x and y direction) + v1 = 2*N1 - 2*3 (2*: because x and y direction) red_chi2_1 = chi2/v1 for 2nd order polynomial fitting: x' = a0 + a1*x + a2*y + a3*x**2 + a4*y**2 + a5*x*y y' = b0 + b1*x + b2*y + b3*x**2 + b4*y**2 + b5*x*y - v1 = 2*N1 - 2*6 + v1 = 2*N1 - 2*6 red_chi2_2 = chi2/v2 calc_F(red_chi2_1, red_chi2_2, v1, v2) - + ***Note*** - * make sure the first model is the simple model + * make sure the first model is the simple model and the second model is the more complicated model - * the return value represents the probability that + * the return value represents the probability that the first model is better than the second model, in other words, the small P means the more colicated model is needed. the large P means the simple model is good enough. - * normally, the P value will increase from model1->model2, to - model2->model3, to model3->model4. The user can decide a + * normally, the P value will increase from model1->model2, to + model2->model3, to model3->model4. The user can decide a critical value (eg, 0.7) to find the proper model. """ diff --git a/flystar/archive_io.py b/flystar/archive_io.py index 88de5cb..2177e40 100755 --- a/flystar/archive_io.py +++ b/flystar/archive_io.py @@ -1,9 +1,9 @@ import pickle -# Need to add these functions to a utility .py file rather than storing them in general structure. +# Need to add these functions to a utility .py file rather than storing them in general structure. def open_archive(file_name): """ - Helper function to open archived files. + Helper function to open archived files. """ with open(file_name, 'rb') as file_archive: file_dict = pickle.load(file_archive) @@ -11,7 +11,7 @@ def open_archive(file_name): def save_archive(file_name, save_data): """ - Helper function to archive a file. + Helper function to archive a file. """ with open(file_name, 'wb') as outfile: pickle.dump(save_data, outfile, protocol=pickle.HIGHEST_PROTOCOL) diff --git a/flystar/examples.py b/flystar/examples.py index 65723ec..0165cb3 100644 --- a/flystar/examples.py +++ b/flystar/examples.py @@ -32,7 +32,7 @@ def align_example(labelFile, reference, transModel=transforms.four_paramNW, orde dr_tol: float (default = 1.0) The search radius for the matching algorithm, in the same units as the starlist file positions. - + dm_tol: float or None If float, sets the maximum magnitude difference allowed in matching between label.dat and starlist. Note that this should be set to @@ -48,10 +48,10 @@ def align_example(labelFile, reference, transModel=transforms.four_paramNW, orde outFile: string (default = 'outTrans.txt') Name of output ascii file which contains the transform parameters. - + Output: ------ - + """ # Read in label.dat file and reference starlist, changing columns to their # standard column headers/epochs/orientations @@ -66,7 +66,7 @@ def align_example(labelFile, reference, transModel=transforms.four_paramNW, orde # Apply intial transformation to label.dat (for error weighting purposes below) label_trans = align.transform_from_object(label, trans) - + # Use transformation to match starlists, then recalculate transformation. # Iterate on this as many times as desired for i in range(N_loop): @@ -74,7 +74,7 @@ def align_example(labelFile, reference, transModel=transforms.four_paramNW, orde trans, dr_tol=dr_tol, dm_tol=dm_tol) - + trans, N_trans = align.find_transform(label[idx_label], label_trans[idx_label], starlist[idx_starlist], @@ -85,14 +85,14 @@ def align_example(labelFile, reference, transModel=transforms.four_paramNW, orde # Write final transform in java align format print('Write transform to {0}'.format(outFile)) align.write_transform(trans, labelFile, reference, N_trans, outFile=outFile) - + # Test transform: apply final transformation to label.dat label_trans2 = align.transform(label, outFile) # Make diagnostic plots - + return - + def align_Arches(labelFile, reference, transModel=transforms.four_paramNW, order=1, N_loop=2, dr_tol=1.0, dm_tol=None, briteN=100, weights=None, restrict=False, @@ -125,7 +125,7 @@ def align_Arches(labelFile, reference, transModel=transforms.four_paramNW, order dr_tol: float (default = 1.0) The search radius for the matching algorithm, in the same units as the starlist file positions. - + dm_tol: float or None (default = None) If float, sets the maximum magnitude difference allowed in matching between label.dat and starlist. Note that this should be set to @@ -137,7 +137,7 @@ def align_Arches(labelFile, reference, transModel=transforms.four_paramNW, order weights: string (default=None) if weights=='both', we use both position error and velocity error in transformed - starlist and reference starlist as uncertanties. And weights is the reciprocal + starlist and reference starlist as uncertanties. And weights is the reciprocal of this uncertanty. if weights=='starlist', we only use postion error and velocity error in transformed starlist as uncertainty. @@ -150,7 +150,7 @@ def align_Arches(labelFile, reference, transModel=transforms.four_paramNW, order outFile: string (default = 'outTrans.txt') Name of output ascii file which contains the transform parameters. - + Output: ------ outFile is written containing the tranformation coefficients @@ -164,11 +164,11 @@ def align_Arches(labelFile, reference, transModel=transforms.four_paramNW, order positions and the label.dat positions after transformation. -Positions_quiver.png: Quiver plot showing the difference between reference - positions and transformed label.dat positions as a function of location. - + positions and transformed label.dat positions as a function of location. + -Magnitude_hist.png: Histogram of the difference between the reference list magnitude and label.dat magnitude for matched stars. - + """ # Read in label.dat file and reference starlist, changing columns to their # standard column headers/epochs/orientations @@ -186,10 +186,10 @@ def align_Arches(labelFile, reference, transModel=transforms.four_paramNW, order # Perform blind matching of 100 brightest stars and calculate initial transform trans = align.initial_align(label_r, starlist, briteN, transformModel=transModel, order=order) - + # Apply transformation to label.dat file, for weighting purposes. label_trans = align.transform_from_object(label, trans) - + # Use transformation to match starlists, then recalculate transformation. # Iterate on this as many times as desired for i in range(N_loop): @@ -217,7 +217,7 @@ def align_Arches(labelFile, reference, transModel=transforms.four_paramNW, order print('Write transform to {0}'.format(outFile)) align.write_transform(trans, labelFile, reference, N_trans, deltaMag=delta_m, restrict=restrict, weights=weights, outFile=outFile) - + # Test transform: apply to label.dat, make diagnostic plots label_trans2 = align.transform_from_file(label, outFile) @@ -235,7 +235,7 @@ def align_Arches(labelFile, reference, transModel=transforms.four_paramNW, order label_trans2[idx_label], xlim=xlim, ylim=ylim) # Histogram of difference in transformed and reference positions for - # matched stars + # matched stars plots.pos_diff_hist(starlist[idx_starlist], label_trans2[idx_label]) # Histogram of difference in transformed and reference positions for @@ -244,7 +244,7 @@ def align_Arches(labelFile, reference, transModel=transforms.four_paramNW, order plots.pos_diff_err_hist(starlist[idx_starlist], label_trans2[idx_label], trans, errs='both', bin_width=0.5, xlim=[-6,6]) - # Histogram of difference in the magnitudes for the matched stars + # Histogram of difference in the magnitudes for the matched stars plots.mag_diff_hist(starlist[idx_starlist], label_trans2[idx_label]) # Quiver plot showing difference between transformed and reference @@ -254,7 +254,7 @@ def align_Arches(labelFile, reference, transModel=transforms.four_paramNW, order ylim=ylim, outlier_reject=None) print('Done with plots') - print('Done with plots') + print('Done with plots') return @@ -268,7 +268,7 @@ def align_gc(starFile, refFile, transModel=transforms.PolyTransform, order=1, N_ Parameters: ----------- starFile: string - Starlist we would like to transform into the reference frame, eg:label.dat + Starlist we would like to transform into the reference frame, eg:label.dat refFile: string Starlist that defines the reference frame. @@ -306,7 +306,7 @@ def align_gc(starFile, refFile, transModel=transforms.PolyTransform, order=1, N_ """ #---------------------------------------------- - # Read in starlist and reference + # Read in starlist and reference #---------------------------------------------- # starlist has postion & postion err ref = starlists.read_starlist(refFile, error=True) @@ -394,7 +394,7 @@ def align_starlists(starlist, ref, transModel=transforms.PolyTransform, order=2, Parameters: ----------- starlist: Table - Starlist we would like to transform into the reference frame, eg:label.dat + Starlist we would like to transform into the reference frame, eg:label.dat ref: Table Starlist that defines the reference frame. @@ -427,7 +427,7 @@ def align_starlists(starlist, ref, transModel=transforms.PolyTransform, order=2, outFile: string('outTrans.txt') the name of the output transformation file """ - + #-------------------------------------------------- # Initial transformation with brightest briteN stars #-------------------------------------------------- diff --git a/flystar/match.py b/flystar/match.py index ca7672a..3637b8c 100644 --- a/flystar/match.py +++ b/flystar/match.py @@ -26,7 +26,7 @@ def miracle_match_briteN(xin1, yin1, min1, xin2, yin2, min2, Nbrite, and brightness uncertainties, the more bigger the bin sizes should really be. But this isn't well tested. """ - + if verbose: print( '') print( ' miracle_match_briteN: use brightest {0}'.format(Nbrite)) @@ -51,7 +51,7 @@ def miracle_match_briteN(xin1, yin1, min1, xin2, yin2, min2, Nbrite, print( ' miracle_match_briteN: ') x1, y1, m1 = order_by_brite(xin1, yin1, min1, Nbrite, verbose=verbose) x2, y2, m2 = order_by_brite(xin2, yin2, min2, Nbrite, verbose=verbose) - + #################### # # Triangle Matching @@ -107,7 +107,7 @@ def miracle_match_briteN(xin1, yin1, min1, xin2, yin2, min2, Nbrite, idx2_vmax_hist = idx2_vmax_hist[good_idx2] idx2_angl_hist = idx2_angl_hist[good_idx2] - + ########## # Possible Matches ########## @@ -121,7 +121,7 @@ def miracle_match_briteN(xin1, yin1, min1, xin2, yin2, min2, Nbrite, # Now vote for all stars in the triangles that have possible matches (same vmax, angle) # between the first and second lists. votes = np.zeros((Nbrite, Nbrite)) - + matches = np.where(stars_in1_matches2[:,0] >= 0)[0] match_stars1 = stars_in1_matches2[matches,:] match_stars2 = stars_in_tri2[matches,:] @@ -134,7 +134,7 @@ def miracle_match_briteN(xin1, yin1, min1, xin2, yin2, min2, Nbrite, add_votes(votes, match_stars1[:,0], match_stars2[:,0]) add_votes(votes, match_stars1[:,1], match_stars2[:,1]) add_votes(votes, match_stars1[:,2], match_stars2[:,2]) - + ########## # Find matching triangles with most votes (and that pass threshold) ########## @@ -203,8 +203,8 @@ def match(x1, y1, m1, x2, y2, m2, dr_tol, dm_tol=None, verbose=True): if one is found that is the best match in both brightness and positional offsets (closest in both), then the match is made. Otherwise, their is a conflict and no match is returned for the star. - - + + Parameters x1 : array-like X coordinate in the first catalog @@ -226,9 +226,9 @@ def match(x1, y1, m1, x2, y2, m2, dr_tol, dm_tol=None, verbose=True): How close in delta-magnitude a match has to be to count as a match. If None, then any delta-magnitude is allowed. verbose : bool or int, optional - Prints on screen information on the matching. Higher verbose values + Prints on screen information on the matching. Higher verbose values (up to 9) provide more detail. - + Returns ------- idx1 : int array @@ -241,27 +241,27 @@ def match(x1, y1, m1, x2, y2, m2, dr_tol, dm_tol=None, verbose=True): Distance between the matches. dm : float array Delta-mag between the matches. (m1 - m2) - + """ - + x1 = np.array(x1, copy=False) y1 = np.array(y1, copy=False) m1 = np.array(m1, copy=False) x2 = np.array(x2, copy=False) y2 = np.array(y2, copy=False) m2 = np.array(m2, copy=False) - + if x1.shape != y1.shape: raise ValueError('x1 and y1 do not match!') if x2.shape != y2.shape: raise ValueError('x2 and y2 do not match!') - + # Setup coords1 pairs and coords 2 pairs # this is equivalent to, but faster than just doing np.array([x1, y1]) coords1 = np.empty((x1.size, 2)) coords1[:, 0] = x1 coords1[:, 1] = y1 - + # this is equivalent to, but faster than just doing np.array([x1, y1]) coords2 = np.empty((x2.size, 2)) coords2[:, 0] = x2 @@ -374,7 +374,7 @@ def match(x1, y1, m1, x2, y2, m2, dr_tol, dm_tol=None, verbose=True): # Assume the duplicates are confused first... see if we # can resolve the confusion below. keep[dups] = False - + dm_dups = m1[idxs1[dups]] - m2[idxs2[dups]] dr_dups = np.hypot(x1[idxs1[dups]] - x2[idxs2[dups]], y1[idxs1[dups]] - y2[idxs2[dups]]) @@ -395,12 +395,12 @@ def match(x1, y1, m1, x2, y2, m2, dr_tol, dm_tol=None, verbose=True): idxs2 = idxs2[keep] dr = dr[keep] dm = dm[keep] - + return idxs1, idxs2, dr, dm def calc_triangles_vmax_angle(x, y): idx = np.arange(len(x), dtype=np.int16) - + # Option 1 -- this takes 0.217 seconds for 50 objects # t1 = time.time() # combo_iter1 = itertools.combinations(idx1, 3) @@ -409,53 +409,53 @@ def calc_triangles_vmax_angle(x, y): # print( 'Finished Option 1: ', t2 - t1) # print( combo_idx1_1.shape) # print( combo_idx1_1) - + # Option 2 -- this takes 0.016 seconds for 50 objects combo_iter = itertools.combinations(idx, 3) combo_dt = np.dtype('i2,i2,i2') combo_idx_tmp = np.fromiter(combo_iter, dtype=combo_dt) combo_idx = combo_idx_tmp.view(np.int16).reshape(-1, 3) - + ii0 = combo_idx[:,0] ii1 = combo_idx[:,1] ii2 = combo_idx[:,2] - + dxab = x[ii1] - x[ii0] dyab = y[ii1] - y[ii0] dxac = x[ii2] - x[ii0] dyac = y[ii2] - y[ii0] - + dab = np.hypot(dxab, dyab) dac = np.hypot(dxac, dyac) - + dmax = np.max([dab, dac], axis=0) dmin = np.min([dab, dac], axis=0) - + vmax = dmin ** 2 / dmax ** 2 vmax[dab < dac] *= -1 - + vdprod = dxab * dxac + dyab * dyac vcprod = dxab * dyac - dyab * dxac - + angle = np.degrees( np.arctan2( vdprod, vcprod) ) angle[angle < 0] += 360.0 angle[angle > 360] -= 360.0 - + return combo_idx, vmax, angle def add_votes(votes, match1, match2): # Construct a histogram of how often a bin is matched... then add the delta flat_idx = np.ravel_multi_index((match1, match2), dims=votes.shape) - + # extract the unique indices and their position unique_idx, idx_idx = np.unique(flat_idx, return_inverse=True) - + # aggregate the repeated indices deltas = np.bincount(idx_idx) - + # Sum them to the array votes.flat[unique_idx] += deltas - + return @@ -512,7 +512,7 @@ def generic_match(sl1, sl2, init_mode='triangle', init_mode verbose : bool, optional Prints on screen information on the matching - + Returns ------- transf : Transform2D @@ -522,16 +522,16 @@ def generic_match(sl1, sl2, init_mode='triangle', """ from . import align - + # Check the input StarLists and transform them into astropy Tables if not isinstance(sl1, starlists.StarList): raise TypeError("The first catalog has to be a StarList") if not isinstance(sl2, starlists.StarList): raise TypeError("The second catalog has to be a StarList") - + # Find the initial transformation if init_mode == 'triangle': # Blind triangles method - + # Prepare the reduced starlists for matching sl1_cut = copy.deepcopy(sl1) sl2_cut = copy.deepcopy(sl2) @@ -546,16 +546,16 @@ def generic_match(sl1, sl2, init_mode='triangle', # TODO: test 'initial_align' with StarList input transf = align.initial_align(sl1_cut, sl2_cut, briteN=n_bright, transformModel=model, order=order_dr[0]) #order_dr[i_loop][0] ? - + elif init_mode == 'match_name': # Name match sl1_idx_init, sl2_idx_init, _ = starlists.restrict_by_name(sl1, sl2) transf = model(sl2['x'][sl2_idx_init], sl2['y'][sl2_idx_init], sl1['x'][sl1_idx_init], sl1['y'][sl1_idx_init], order=int(order_dr[0][0])) - + elif init_mode == 'load': # Load a transformation file transf = transforms.Transform2D.from_file(kwargs['transf_file']) - + else: # None of the above raise TypeError("Unrecognized initial matching method") @@ -564,16 +564,16 @@ def generic_match(sl1, sl2, init_mode='triangle', sl2_match = copy.deepcopy(sl2) sl1_match.restrict_by_value(m_min=m_match[0], m_max=m_match[1]) sl2_match.restrict_by_value(m_min=m_match[2], m_max=m_match[3]) - + # Refine the transformation if sigma_match: order_dr_len = len(order_dr) - + for i_loop in range(sigma_match[1]): order_dr = np.vstack((np.array(order_dr), np.array(order_dr[-1]))) - + for i_loop in range(len(order_dr)): - + # Transform and match the catalog to the reference frame # sl2_idx, sl1_idx = align.transform_and_match(sl2_match, sl1_match, transf, # dr_tol=order_dr[i_loop][1], @@ -588,7 +588,7 @@ def generic_match(sl1, sl2, init_mode='triangle', # Transform the catalog to the reference frame sl2_transf_match = align.transform_from_object(sl2_match, transf) - + # Sigma-rejection if sigma_match and (i_loop >= order_dr_len): resid = np.sqrt((sl1_match['x'][sl1_idx] - @@ -597,29 +597,29 @@ def generic_match(sl1, sl2, init_mode='triangle', sl2_transf_match['y'][sl2_idx])**2) sl1_idx = sl1_idx[resid <= (sigma_match[0] * np.std(resid))] sl2_idx = sl2_idx[resid <= (sigma_match[0] * np.std(resid))] - + # Test section to observe the matching catalogs before refining the transformation """ from matplotlib import pyplot - + _, axarr = pyplot.subplots(nrows=1, ncols=1, figsize=(10,10)) axarr.scatter(sl1_match['x'][sl1_idx], sl1_match['y'][sl1_idx]) xlim = axarr.get_xlim() ylim = axarr.get_ylim() - + _, axarr = pyplot.subplots(nrows=1, ncols=1, figsize=(10, 10)) axarr.scatter(sl2_transf_match['x'][sl2_idx], sl2_transf_match['y'][sl2_idx]) axarr.set_xlim(xlim) axarr.set_ylim(ylim) """ - + # Find a better transformation transf, _ = align.find_transform(sl2_match[sl2_idx], sl2_transf_match[sl2_idx], sl1_match[sl1_idx], transModel=model, order=order_dr[0], verbose=verbose) # order=int(order_dr[i_loop][0]), verbose=verbose) - + # This section was used for testing transformations with normalized # coordinates. Only several catalogs had reduced residuals when using # high order polynomials (>3), some of them became unstable @@ -638,15 +638,15 @@ def generic_match(sl1, sl2, init_mode='triangle', sl1_match_norm, transModel=model, order=poly_order, verbose=verbose) c_exp = np.zeros(len(transf.px._parameters)) - + for i_c in range(len(transf.px._parameters)): c_exp[i_c] = int(transf.px._param_names[i_c][1:].split('_')[0]) +\ int(transf.px._param_names[i_c][1:].split('_')[1]) - + c_corr = mm ** (1 - c_exp) transf.px._parameters = transf.px._parameters * c_corr transf.py._parameters = transf.py._parameters * c_corr""" - + # Do the final transformation and matching using sl2_idx, sl1_idx = align.transform_and_match(sl2, sl1, transf, dr_tol=dr_final, verbose=verbose) @@ -661,10 +661,10 @@ def generic_match(sl1, sl2, init_mode='triangle', # ep_name=np.column_stack((np.array(sl1['name'][sl1_idx]), np.array(sl2_transf['name'][sl2_idx]))), # list_times=[sl1.meta['list_time'], sl2.meta['list_time']], # list_names=[sl1.meta['list_name'], sl2.meta['list_name']]) - + for col in sl1.colnames: if col in sl2.colnames: if col not in ['name', 'x', 'y', 'm']: st.add_column(Column(np.column_stack((np.array(sl1[col][sl1_idx]),np.array(sl2_transf[col][sl2_idx]))), name=col)) - + return transf, st diff --git a/flystar/motion_model.py b/flystar/motion_model.py index bf53d3f..de76c46 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -12,7 +12,7 @@ class MotionModel(ABC): # Number of fit parameters/required observations in each direction n_params = int((len(fit_param_names) + 1) / 2) - # Fixed parameters: These are parameters that are required for the model, but are not + # Fixed parameters: These are parameters that are required for the model, but are not # fit quantities. For example, RA and Dec in a parallax model. fixed_param_names = [] fixed_meta_data = [] @@ -26,15 +26,15 @@ class MotionModel(ABC): def __init__(self, *args, **kwargs): """ Make a motion model object. This object defines the fit and fixed parameters, - and contains functions to fit the model to data and infer positions at given times. - Each instance corresponds to a given motion model, not an individual star, + and contains functions to fit the model to data and infer positions at given times. + Each instance corresponds to a given motion model, not an individual star, and thus the fit values are only input/returned in functions, not stored in the object. """ return def _check_param_dimensions(self, fit_params, fit_params_errs, fixed_params_dict): """Check that parameters is either a scalar or length of N_stars - + Parameters ---------- fit_params: array-like @@ -67,11 +67,11 @@ def model(self, t, fit_params, fit_param_errs=None, fixed_params_dict=None): return np.full_like(t, np.nan), np.full_like(t, np.nan), np.full_like(t, np.inf), np.full_like(t, np.inf) def run_fit( - self, t, x, y, xe, ye, + self, t, x, y, xe, ye, fixed_params_dict=None, weighting='var', - use_scipy=True, - absolute_sigma=True, + use_scipy=True, + absolute_sigma=True, params_guess=None, fill_value=np.nan, verbose=True @@ -89,15 +89,15 @@ def calc_weights(self, xe, ye, weighting='var'): return 1./xe**2, 1./ye**2 def fit( - self, t, x, y, xe, ye, + self, t, x, y, xe, ye, fixed_params_dict=None, weighting='var', - use_scipy=True, + use_scipy=True, absolute_sigma=True, fill_value=np.nan, params_guess=None, return_chi2=False, - bootstrap=0, + bootstrap=0, verbose=True, seed=None ): @@ -143,22 +143,22 @@ def fit( assert np.ndim(t) == np.ndim(x) == np.ndim(y) == np.ndim(xe) == np.ndim(ye) == 1, "Input arrays must be 1D! Motion model can only fit individual stars" assert len(t) == len(x) == len(y) == len(xe) == len(ye), "Input arrays must have the same length!" fit_result = self.run_fit( - t, x, y, xe, ye, + t, x, y, xe, ye, fixed_params_dict=fixed_params_dict, weighting=weighting, - use_scipy=use_scipy, + use_scipy=use_scipy, absolute_sigma=absolute_sigma, fill_value=fill_value, params_guess=params_guess, return_chi2=return_chi2, verbose=verbose ) - + if return_chi2: params, param_errs, chi2_x, chi2_y = fit_result else: params, param_errs = fit_result - + # Bootstrap errors n_obs = len(t) @@ -183,12 +183,12 @@ def fit( bb_params_errs = [] for bdx in bdx_all: params_bdx, param_errs_bdx = self.run_fit( - t[bdx], x[bdx], y[bdx], xe[bdx], ye[bdx], + t[bdx], x[bdx], y[bdx], xe[bdx], ye[bdx], fixed_params_dict=fixed_params_dict, - weighting=weighting, - use_scipy=use_scipy, - absolute_sigma=absolute_sigma, - params_guess=params, + weighting=weighting, + use_scipy=use_scipy, + absolute_sigma=absolute_sigma, + params_guess=params, fill_value=fill_value, return_chi2=False, verbose=verbose @@ -236,7 +236,7 @@ def __init__(self, **kwargs): """ super().__init__() return - + def model_fit(self, dt): return np.full_like(dt, np.nan) @@ -266,10 +266,10 @@ def model(self, t, fit_params, fit_param_errs=None, fixed_params_dict=None): return np.full_like(t, np.nan), np.full_like(t, np.nan), np.full_like(t, np.inf), np.full_like(t, np.inf) def run_fit( - self, t, x, y, xe, ye, + self, t, x, y, xe, ye, fixed_params_dict=None, - weighting='var', - use_scipy=True, + weighting='var', + use_scipy=True, absolute_sigma=True, fill_value=np.nan, params_guess=None, @@ -327,7 +327,7 @@ class Fixed(MotionModel): """ A non-moving motion model for a star on the sky. """ - + fit_param_names = ['x0','y0'] fixed_param_names = [] # Number of fit parameters/required observations in each direction @@ -350,7 +350,7 @@ def model_fit(self, dt, x0): Time offset, shape (N_times,) x0 : float or array-like Average positions, scalar or shape (N_stars,) - + Returns ------- x : array-like @@ -416,10 +416,10 @@ def model(self, t, fit_params, fit_param_errs=None, fixed_params_dict=None): return x, y, x_err, y_err def run_fit( - self, t, x, y, xe, ye, + self, t, x, y, xe, ye, fixed_params_dict=None, - weighting='var', - use_scipy=True, + weighting='var', + use_scipy=True, absolute_sigma=True, fill_value=np.nan, params_guess=None, @@ -549,7 +549,7 @@ def model(self, t, fit_params, fit_param_errs=None, fixed_params_dict=None): t0 = np.atleast_1d(fixed_params_dict['t0']) # Shape (N_stars,) or (1,) dt = t[np.newaxis, :] - t0[:, np.newaxis] # Shape (N_stars, N_times) - + x = self.model_fit(dt, x0[:, np.newaxis], vx[:, np.newaxis]) # Shape (N_stars, N_times) y = self.model_fit(dt, y0[:, np.newaxis], vy[:, np.newaxis]) # Shape (N_stars, N_times) @@ -560,12 +560,12 @@ def model(self, t, fit_params, fit_param_errs=None, fixed_params_dict=None): if fit_param_errs is None: return x, y - + fit_param_errs = np.atleast_2d(fit_param_errs) # (N_stars, N_params) x0_err, vx_err, y0_err, vy_err = fit_param_errs.T # Each shape (N_stars,) x_err = np.hypot(x0_err[:, np.newaxis], vx_err[:, np.newaxis] * dt) # Shape (N_stars, N_times) y_err = np.hypot(y0_err[:, np.newaxis], vy_err[:, np.newaxis] * dt) # Shape (N_stars, N_times) - + if N_stars == 1 or N_times == 1: # If only one star, return flattened arrays x_err = x_err.flatten() @@ -573,10 +573,10 @@ def model(self, t, fit_params, fit_param_errs=None, fixed_params_dict=None): return x, y, x_err, y_err def run_fit( - self, t, x, y, xe, ye, + self, t, x, y, xe, ye, fixed_params_dict=None, - weighting='var', - use_scipy=True, + weighting='var', + use_scipy=True, absolute_sigma=True, fill_value=np.nan, params_guess=None, @@ -595,7 +595,7 @@ def run_fit( y = np.atleast_1d(y) xe = np.atleast_1d(xe) ye = np.atleast_1d(ye) - + n_obs = len(t) degree_of_freedom = n_obs - self.n_params # Not enough data points to fit model @@ -669,7 +669,7 @@ def run_fit( if degree_of_freedom > 0: reduced_chi2_x = chi2_x / degree_of_freedom reduced_chi2_y = chi2_y / degree_of_freedom - + param_errors[0:2] *= reduced_chi2_x**0.5 param_errors[2:4] *= reduced_chi2_y**0.5 @@ -703,7 +703,7 @@ def __init__(self): # This checks for proper parameter formatting. super().__init__() return - + def model_fit(self, t, x0, v0, a): """Model positions at time t of Acceleration model. @@ -745,7 +745,7 @@ def model(self, t, fit_params, fit_param_errs=None, fixed_params_dict=None): Predicted positions (and uncertainties, if fit_param_errs is provided) with shape (N_stars, N_times), or (N_times,) if N_stars=1, or (N_stars,) if N_times=1 """ if fixed_params_dict is None: - fixed_params_dict = self.fixed_params_dict + fixed_params_dict = self.fixed_params_dict assert 't0' in fixed_params_dict, "Fixed parameter t0 is required for Acceleration model." self._check_param_dimensions(fit_params, fit_param_errs, fixed_params_dict) @@ -754,28 +754,28 @@ def model(self, t, fit_params, fit_param_errs=None, fixed_params_dict=None): N_stars = fit_params.shape[0] if fit_params.ndim > 1 else 1 N_times = len(t) - + x0, vx0, ax, y0, vy0, ay = fit_params.T # Each shape (N_stars,) t0 = np.atleast_1d(fixed_params_dict['t0']) # Shape (N_stars,) or (1,) - + dt = t[np.newaxis, :] - t0[:, np.newaxis] # Shape (N_stars, N_times) - + x = self.model_fit(dt, x0[:, np.newaxis], vx0[:, np.newaxis], ax[:, np.newaxis]) # Shape (N_stars, N_times) y = self.model_fit(dt, y0[:, np.newaxis], vy0[:, np.newaxis], ay[:, np.newaxis]) # Shape (N_stars, N_times) - + if N_stars == 1 or N_times == 1: # If only one star, return flattened arrays x = x.flatten() y = y.flatten() - + if fit_param_errs is None: return x, y - + fit_param_errs = np.atleast_2d(fit_param_errs) # (N_stars, N_params) x0_err, vx0_err, ax_err, y0_err, vy0_err, ay_err = fit_param_errs.T x_err = np.sqrt(x0_err[:, np.newaxis]**2 + (vx0_err[:, np.newaxis] * dt)**2 + (0.5 * ax_err[:, np.newaxis] * dt**2)**2) # Shape (N_stars, N_times) y_err = np.sqrt(y0_err[:, np.newaxis]**2 + (vy0_err[:, np.newaxis] * dt)**2 + (0.5 * ay_err[:, np.newaxis] * dt**2)**2) # Shape (N_stars, N_times) - + if N_stars == 1 or N_times == 1: # If only one star, return flattened arrays x_err = x_err.flatten() @@ -785,11 +785,11 @@ def model(self, t, fit_params, fit_param_errs=None, fixed_params_dict=None): def run_fit( - self, t, x, y, xe, ye, + self, t, x, y, xe, ye, fixed_params_dict=None, - weighting='var', - use_scipy=True, - absolute_sigma=True, + weighting='var', + use_scipy=True, + absolute_sigma=True, params_guess=None, fill_value=np.nan, return_chi2=False, @@ -855,7 +855,7 @@ def run_fit( class Parallax(MotionModel): """ Motion model for linear proper motion + parallax - + Requires RA and Dec J2000 (degrees) for parallax calculation. Optional PA is counterclockwise offset of the image y-axis from North. Optional obs parameter describes observer location, default is 'earth'. @@ -863,7 +863,7 @@ class Parallax(MotionModel): fit_param_names = ['x0', 'vx', 'y0', 'vy', 'pi'] fixed_param_names = ['t0', 'ra', 'dec', 'pa', 'obsLocation'] name = "Parallax" - + # Number of fit parameters/required observations in each direction n_params = int((len(fit_param_names) + 1) / 2) @@ -1021,12 +1021,12 @@ def model(self, t, fit_params, fit_param_errs=None, fixed_params_dict=None): def run_fit( - self, t, x, y, xe, ye, + self, t, x, y, xe, ye, fixed_params_dict, - weighting='var', - use_scipy=True, - absolute_sigma=True, - params_guess=None, + weighting='var', + use_scipy=True, + absolute_sigma=True, + params_guess=None, fill_value=np.nan, return_chi2=False, verbose=True @@ -1034,7 +1034,7 @@ def run_fit( if not use_scipy: if verbose: warnings.warn("Parallax model has no non-scipy fitter option. Running with scipy.", UserWarning) - + assert all([k in fixed_params_dict for k in ['ra', 'dec']]), "Parallax model requires 'ra' and 'dec' in fixed_params." t = np.atleast_1d(t) @@ -1079,7 +1079,7 @@ def run_fit( t_span = t[idx_last] - t[idx_first] params_guess = np.array([ x.mean(), (x[idx_last] - x[idx_first]) / t_span, - y.mean(), (y[idx_last] - y[idx_first]) / t_span, + y.mean(), (y[idx_last] - y[idx_first]) / t_span, 0.1 ]) @@ -1102,7 +1102,7 @@ def run_fit( params = np.array([x0, vx, y0, vy, pi]) param_errors = np.array([x0_err, vx_err, y0_err, vy_err, pi_err]) - + if return_chi2: chi2_x, chi2_y = self.calc_chi2(t, x, y, xe, ye, params, fixed_params_dict) return params, param_errors, chi2_x, chi2_y @@ -1121,7 +1121,7 @@ def motion_model_param_names(motion_models, with_errors=True, with_fixed=True): Add uncertainty names with '_err' suffix or not, by default True with_fixed : bool, optional Add fixed param names with '_fixed' suffix or not, by default True - + Returns ------- list @@ -1132,7 +1132,7 @@ def motion_model_param_names(motion_models, with_errors=True, with_fixed=True): def list_add(name): if name not in list_of_parameters: list_of_parameters.append(name) - + motion_models = np.atleast_1d(motion_models) mm_map = motion_model_map() for mm in motion_models: diff --git a/flystar/plots.py b/flystar/plots.py index 4728e60..e92f5ff 100755 --- a/flystar/plots.py +++ b/flystar/plots.py @@ -22,7 +22,7 @@ #################################################### -def trans_positions(ref, ref_mat, starlist, starlist_mat, xlim=None, ylim=None, +def trans_positions(ref, ref_mat, starlist, starlist_mat, xlim=None, ylim=None, equal_axis=True, save_path=None, show_plot=True): """ Plot positions of stars in reference list and the transformed starlist, @@ -50,14 +50,14 @@ def trans_positions(ref, ref_mat, starlist, starlist_mat, xlim=None, ylim=None, If not None, sets the xmin and xmax limit of the plot ylim: None or list/array [ymin, ymax] - If not None, sets the ymin and ymax limit of the plot + If not None, sets the ymin and ymax limit of the plot equal_axis: boolean If true, make axes equal. True by default save_path: string Path to save the figure to. Default is None - + show_plot: boolean If true, show the plot. Default is True @@ -78,7 +78,7 @@ def trans_positions(ref, ref_mat, starlist, starlist_mat, xlim=None, ylim=None, plt.axis([xlim[0], xlim[1], ylim[0], ylim[1]]) if equal_axis: plt.axis('equal') - + if save_path: plt.savefig(save_path) if show_plot: @@ -98,10 +98,10 @@ def pos_diff_hist(ref_mat, starlist_mat, nbins=25, bin_width=None, xlim=None, fi ref_mat: astropy table Reference starlist only containing matched stars that were used in the transformation. Standard column headers are assumed. - + starlist_mat: astropy table Transformed starlist only containing the matched stars used in - the transformation. Standard column headers are assumed. + the transformation. Standard column headers are assumed. nbins: int Number of bins used in histogram, regardless of data range. This is @@ -113,7 +113,7 @@ def pos_diff_hist(ref_mat, starlist_mat, nbins=25, bin_width=None, xlim=None, fi xlim: None or [xmin, xmax] If not none, set the X range of the plot - + """ diff_x = ref_mat['x'] - starlist_mat['x'] diff_y = ref_mat['y'] - starlist_mat['y'] @@ -125,7 +125,7 @@ def pos_diff_hist(ref_mat, starlist_mat, nbins=25, bin_width=None, xlim=None, fi max_range = max([max(diff_x), max(diff_y)]) bins = np.arange(min_range, max_range+bin_width, bin_width) - + plt.figure(figsize=(10,10)) plt.clf() plt.hist(diff_x, histtype='step', bins=bins, color='blue', label='X') @@ -159,7 +159,7 @@ def pos_diff_err_hist(ref_mat, starlist_mat, transform, nbins=25, bin_width=None ref_mat: astropy table Reference starlist only containing matched stars that were used in the transformation. Standard column headers are assumed. - + starlist_mat: astropy table Transformed starlist only containing the matched stars used in the transformation. Standard column headers are assumed. @@ -190,8 +190,8 @@ def pos_diff_err_hist(ref_mat, starlist_mat, transform, nbins=25, bin_width=None outlier: float (default = 10) Defines how many sigma away from 0 a star must be in order to be considered - an outlier. - + an outlier. + """ from . import analysis diff_x = ref_mat['x'] - starlist_mat['x'] @@ -207,7 +207,7 @@ def pos_diff_err_hist(ref_mat, starlist_mat, transform, nbins=25, bin_width=None elif errs == 'starlist': xerr = starlist_mat['xe'] yerr = starlist_mat['ye'] - + # Calculate ratio between differences and the combined error. This is # what we will plot ratio_x = diff_x / xerr @@ -215,7 +215,7 @@ def pos_diff_err_hist(ref_mat, starlist_mat, transform, nbins=25, bin_width=None # Identify non-outliers, within +/- sigma away from 0 good = np.where( (np.abs(ratio_x) < outlier) & (np.abs(ratio_y) < outlier) ) - + """ # For both X and Y, calculate chi-square. Combine arrays to get combined # chi-square @@ -223,11 +223,11 @@ def pos_diff_err_hist(ref_mat, starlist_mat, transform, nbins=25, bin_width=None chi_sq_y = diff_y**2. / yerr**2. chi_sq = np.append(chi_sq_x, chi_sq_y) - + # Calculate degrees of freedom in transformation num_mod_params = calc_nparam(transform) deg_freedom = len(chi_sq) - num_mod_params - + # Calculate reduced chi-square chi_sq_red = np.sum(chi_sq) / deg_freedom """ @@ -239,13 +239,13 @@ def pos_diff_err_hist(ref_mat, starlist_mat, transform, nbins=25, bin_width=None starlist_mat[good], transform, errs=errs) - + num_mod_params = analysis.calc_nparam(transform) #-------------------------------------------# # Plotting #-------------------------------------------# - + # Set the binning as per user input bins = nbins if bin_width != None: @@ -253,7 +253,7 @@ def pos_diff_err_hist(ref_mat, starlist_mat, transform, nbins=25, bin_width=None max_range = max([max(ratio_x), max(ratio_y)]) bins = np.arange(min_range, max_range+bin_width, bin_width) - + plt.figure(figsize=(10,10)) plt.clf() n_x, bins_x, p = plt.hist(ratio_x, histtype='step', bins=bins, color='blue', @@ -266,7 +266,7 @@ def pos_diff_err_hist(ref_mat, starlist_mat, transform, nbins=25, bin_width=None sigma = 1 x = np.arange(-6, 6, 0.1) plt.plot(x, norm.pdf(x,mean,sigma), 'g-', linewidth=2) - + # Annotate reduced chi-sqared values in plot: with outliers xstr = r'$\chi^2_r$ = {0}'.format(np.round(chi_sq_red, decimals=3)) plt.annotate(xstr, xy=(0.3, 0.77), xycoords='figure fraction', color='black') @@ -277,7 +277,7 @@ def pos_diff_err_hist(ref_mat, starlist_mat, transform, nbins=25, bin_width=None xstr3 = '{0} with +/- {1}+ sigma'.format(len(ratio_x) - len(good[0]), outlier) plt.annotate(xstr2, xy=(0.29, 0.83), xycoords='figure fraction', color='black') plt.annotate(xstr3, xy=(0.25, 0.80), xycoords='figure fraction', color='black') - + # Annotate reduced chi-sqared values in plot: without outliers xstr = r'$\chi^2_r$ = {0}'.format(np.round(chi_sq_red_good, decimals=3)) plt.annotate(xstr, xy=(0.7, 0.8), xycoords='figure fraction', color='black') @@ -286,7 +286,7 @@ def pos_diff_err_hist(ref_mat, starlist_mat, transform, nbins=25, bin_width=None plt.annotate(txt, xy=(0.65,0.77), xycoords='figure fraction', color='black') xstr2 = 'Without Outliers' plt.annotate(xstr2, xy=(0.67, 0.83), xycoords='figure fraction', color='black') - + plt.xlabel('(Ref Pos - TransStarlist Pos) / Ast. Error') plt.ylabel('N stars (normalized)') plt.title('Position Residuals for Matched Stars') @@ -312,10 +312,10 @@ def mag_diff_hist(ref_mat, starlist_mat, bins=25, fileName=None, root='./'): ref_mat: astropy table Reference starlist only containing matched stars that were used in the transformation. Standard column headers are assumed. - + starlist_mat: astropy table Transformed starlist only containing the matched stars used in - the transformation. Standard column headers are assumed. + the transformation. Standard column headers are assumed. """ diff_m = ref_mat['m'] - starlist_mat['m'] @@ -324,7 +324,7 @@ def mag_diff_hist(ref_mat, starlist_mat, bins=25, fileName=None, root='./'): bad = np.isnan(diff_m) bad2 = np.where(bad == True) diff_m = np.delete(diff_m, bad2) - + plt.figure(figsize=(10,10)) plt.clf() plt.hist(diff_m, bins=bins) @@ -350,7 +350,7 @@ def pos_diff_quiver(ref_mat, starlist_mat, qscale=10, keyLength=0.2, xlim=None, ref_mat: astropy table Reference starlist only containing matched stars that were used in the transformation. Standard column headers are assumed. - + starlist_mat: astropy table Transformed starlist only containing the matched stars used in the transformation. Standard column headers are assumed. @@ -395,7 +395,7 @@ def pos_diff_quiver(ref_mat, starlist_mat, qscale=10, keyLength=0.2, xlim=None, diff_y = diff_y[good] xpos = xpos[good] ypos = ypos[good] - + # Divide differences by reference error, if desired if sigma: @@ -416,7 +416,7 @@ def pos_diff_quiver(ref_mat, starlist_mat, qscale=10, keyLength=0.2, xlim=None, diff_y = np.append(diff_y, 0) s = len(xpos) - + plt.figure(figsize=(10,10)) plt.clf() q = plt.quiver(xpos, ypos, diff_x, diff_y, scale=qscale) @@ -470,7 +470,7 @@ def vpd(ref, starlist_trans, vxlim, vylim): If not None, sets the vxmin and vxmax limit of the plot vylim: None or list/array [vymin, vymax] - If not None, sets the vymin and vymax limit of the plot + If not None, sets the vymin and vymax limit of the plot """ # Extract velocities ref_vx = ref['vx'] @@ -513,7 +513,7 @@ def vel_diff_err_hist(ref_mat, starlist_mat, nbins=25, bin_width=None, vxlim=Non bin_width: None or float If float, sets the width of the bins used in the histograms. Will override nbins - + vxlim: None or [vx_min, vx_max] If not none, set the X axis of the Vx plot by defining the minimum and maximum values @@ -525,7 +525,7 @@ def vel_diff_err_hist(ref_mat, starlist_mat, nbins=25, bin_width=None, vxlim=Non # Will produce 2-panel plot: Vx resid and Vy resid diff_vx = ref_mat['vx'] - starlist_mat['vx'] diff_vy = ref_mat['vy'] - starlist_mat['vy'] - + vx_err = np.hypot(ref_mat['vx_err'], starlist_mat['vx_err']) vy_err = np.hypot(ref_mat['vy_err'], starlist_mat['vy_err']) @@ -543,7 +543,7 @@ def vel_diff_err_hist(ref_mat, starlist_mat, nbins=25, bin_width=None, vxlim=Non mean = 0 sigma = 1 x = np.arange(-6, 6, 0.1) - + plt.figure(figsize=(20,10)) plt.subplot(121) plt.subplots_adjust(left=0.1) @@ -632,7 +632,7 @@ def plotStar(starNames, rootDir='./', align='align/align_d_rms_1000_abs_t', print( 'Creating residuals plots for star(s):' ) print( starNames ) - + s = starset.StarSet(rootDir + align) s.loadPolyfit(rootDir + poly, accel=0, arcsec=0) Nstars = len(starNames) @@ -649,11 +649,11 @@ def plotStar(starNames, rootDir='./', align='align/align_d_rms_1000_abs_t', x = s.getArray('x') y = s.getArray('y') r = np.hypot(x,y) - + for i in range(Nstars): - + starName = starNames[i] - + ii = names.index(starName) star = s.stars[ii] @@ -734,9 +734,9 @@ def plotStar(starNames, rootDir='./', align='align/align_d_rms_1000_abs_t', idx = np.where(abs(sig) > 4) print( 'Star: ', starName ) - print( '\tX Chi^2 = %5.2f (%6.2f for %2d dof)' % + print( '\tX Chi^2 = %5.2f (%6.2f for %2d dof)' % (fitx.chi2red, fitx.chi2, fitx.dof)) - print( '\tY Chi^2 = %5.2f (%6.2f for %2d dof)' % + print( '\tY Chi^2 = %5.2f (%6.2f for %2d dof)' % (fity.chi2red, fity.chi2, fity.dof)) # print( 'X Outliers: ', time[idxX] ) # print( 'Y Outliers: ', time[idxY] ) @@ -751,7 +751,7 @@ def plotStar(starNames, rootDir='./', align='align/align_d_rms_1000_abs_t', t0 = int(np.floor(np.min(time))) tO = int(np.ceil(np.max(time))) - + dateTicLoc = plt.MultipleLocator(3) dateTicRng = [t0-1, tO+1] dateTics = np.arange(t0, tO+1) @@ -781,8 +781,8 @@ def plotStar(starNames, rootDir='./', align='align/align_d_rms_1000_abs_t', row = 1 else: col = 1 + 2*(i % (Ncols/2)) - row = 1 + 3*(i//(Ncols/2)) - + row = 1 + 3*(i//(Ncols/2)) + ind = (row-1)*Ncols + col paxes = plt.subplot(Nrows, Ncols, ind) @@ -791,7 +791,7 @@ def plotStar(starNames, rootDir='./', align='align/align_d_rms_1000_abs_t', plt.plot(time, fitLineX - fitSigX, 'b--') plt.errorbar(time, x, yerr=xerr, fmt='k.') rng = plt.axis() - plt.ylim(np.min(x-xerr-0.1),np.max(x+xerr+0.1)) + plt.ylim(np.min(x-xerr-0.1),np.max(x+xerr+0.1)) plt.xlabel('Date - 2000 (yrs)', fontsize=fontsize1) if time[0] > 50000: plt.xlabel('Date (MJD)', fontsize=fontsize1) @@ -884,7 +884,7 @@ def plotStar(starNames, rootDir='./', align='align/align_d_rms_1000_abs_t', paxes.xaxis.set_major_formatter(FormatStrFormatter('%.2f')) plt.xlabel('X (pix)', fontsize=fontsize1) plt.ylabel('Y (pix)', fontsize=fontsize1) - plt.plot(fitLineX, fitLineY, 'b-') + plt.plot(fitLineX, fitLineY, 'b-') col = col + 1 ind = (row-1)*Ncols + col @@ -892,7 +892,7 @@ def plotStar(starNames, rootDir='./', align='align/align_d_rms_1000_abs_t', bins = np.arange(-7.5, 7.5, 1) paxes = plt.subplot(Nrows, Ncols, ind) id = np.where(diffY < 0)[0] - sig[id] = -1.*sig[id] + sig[id] = -1.*sig[id] (n, b, p) = plt.hist(sigX, bins, histtype='stepfilled', color='b', label='X') plt.setp(p, 'facecolor', 'b') (n, b, p) = plt.hist(sigY, bins, histtype='step', color='r', label='Y') @@ -987,7 +987,7 @@ def plotStar(starNames, rootDir='./', align='align/align_d_rms_1000_abs_t', plt.suptitle(title, x=0.5, y=0.97) if Nstars == 1: - plt.subplots_adjust(wspace=0.4, hspace=0.4, left = 0.15, bottom = 0.1, right=0.9, top=0.9) + plt.subplots_adjust(wspace=0.4, hspace=0.4, left = 0.15, bottom = 0.1, right=0.9, top=0.9) plt.savefig(rootDir+'plots/plotStar_' + starName + '.png') else: plt.subplots_adjust(wspace=0.6, hspace=0.6, left = 0.08, bottom = 0.05, right=0.95, top=0.90) @@ -996,12 +996,12 @@ def plotStar(starNames, rootDir='./', align='align/align_d_rms_1000_abs_t', plt.show() print('Fubar') - + ################################################## # New codes for velocity support in FlyStar and using -# the new StarTable and StarList format. +# the new StarTable and StarList format. ################################################## def plot_pm(tab): @@ -1011,7 +1011,7 @@ def plot_pm(tab): q = plt.quiver(tab['x0'].data, tab['y0'].data, tab['vx'].data*1e3, tab['vy'].data*1e3, scale=1e2, angles='xy') - plt.quiverkey(q, 0.5, 0.8, 10, '10 mas/yr', color='red', + plt.quiverkey(q, 0.5, 0.8, 10, '10 mas/yr', color='red', coordinates='figure', labelpos='E') plt.xlabel(r'$\Delta \alpha$ (")') plt.ylabel(r'$\Delta \delta$ (")') @@ -1028,7 +1028,7 @@ def plot_gaia(gaia): d_ra_tan = (ra_tan - ra_tan_mean) * cos_dec * 3600.0 d_de_tan = (de_tan - de_tan_mean) * 3600.0 - + pmra = gaia['pmra'] pmdec = gaia['pmdec'] plt.figure(figsize=(6,6)) @@ -1037,7 +1037,7 @@ def plot_gaia(gaia): q = plt.quiver(d_ra_tan.data, d_de_tan.data, pmra.data, pmdec.data, scale=1e2, angles='xy') - plt.quiverkey(q, 0.5, 0.8, 10, '10 mas/yr', color='red', + plt.quiverkey(q, 0.5, 0.8, 10, '10 mas/yr', color='red', coordinates='figure', labelpos='E') plt.xlabel(r'$\Delta \alpha \cos \delta$ ('')') plt.ylabel(r'$\Delta \delta$ ('')') @@ -1045,7 +1045,7 @@ def plot_gaia(gaia): fmt = r'[$\alpha$, $\delta$] = [{0:8.3f}$^\circ$, {1:8.3f}$^\circ$]' plt.title(fmt.format(ra_tan_mean, de_tan_mean)) plt.gca().invert_xaxis() - + return @@ -1083,7 +1083,7 @@ def plot_mean_residuals_by_epoch(tab): # Predicted model positions at each epoch i_all_detected = np.where(~np.any(np.isnan(tab['t']),axis=1))[0][0] xt_mod, yt_mod, xt_mod_err, yt_mod_err = tab.predict_positions(tab['t'][i_all_detected]) - + # Residuals dx = tab['x'] - xt_mod dy = tab['y'] - yt_mod @@ -1128,7 +1128,7 @@ def plot_mean_residuals_by_epoch(tab): plt.axhline(0, ls='--', color='black') plt.xlabel('Time (yr)') plt.ylabel('Mag Residuals') - + return def plot_quiver_residuals_all_epochs(tab, unit='arcsec', scale=None, plotlim=None, save_path=None): @@ -1147,20 +1147,20 @@ def plot_quiver_residuals_all_epochs(tab, unit='arcsec', scale=None, plotlim=Non for ee in range(tab['x'].shape[1]): xt_mod = xt_mod_all[:,ee] yt_mod = yt_mod_all[:,ee] - + good_idx = np.where(np.isfinite(tab['x'][:, ee]) == True)[0] ref_idx = np.where(tab[good_idx]['used_in_trans'][:, ee] == True)[0] dx, dy = plot_quiver_residuals( - tab['x'][:, ee], - tab['y'][:, ee], - xt_mod, - yt_mod, - good_idx, + tab['x'][:, ee], + tab['y'][:, ee], + xt_mod, + yt_mod, + good_idx, ref_idx, - 'Epoch {0:d}'.format(ee), - unit=unit, - scale=scale, + 'Epoch {0:d}'.format(ee), + unit=unit, + scale=scale, plotlim=plotlim, save_path=f'{save_path}/Quiver_Residual_{ee}.pdf' if save_path else None ) @@ -1177,7 +1177,7 @@ def plot_quiver_residuals_all_epochs(tab, unit='arcsec', scale=None, plotlim=Non dr_good_avg = np.zeros(len(tab), dtype=float) idx = np.where(n_good > 0)[0] dr_good_avg[idx] = dr_good[idx] / n_good[idx] - + dr_ref_avg = np.zeros(len(tab), dtype=float) idx = np.where(n_ref > 0)[0] dr_ref_avg[idx] = dr_ref[idx] / n_ref[idx] @@ -1203,7 +1203,7 @@ def plot_quiver_residuals_all_epochs(tab, unit='arcsec', scale=None, plotlim=Non # if (dr_ref_avg[rr] > 0): # print(fmt.format(name=tab['name'][rr], mag=tab['m0'][rr], dr=dr_ref_avg[rr], # x=tab['x0'][rr], y=tab['y0'][rr], r=np.hypot(tab['x0'][rr], tab['y0'][rr]))) - + return @@ -1214,7 +1214,7 @@ def plot_quiver_residuals_with_orig_all_epochs(tab, trans_list, unit='arcsec', s n_good = np.zeros(len(tab), dtype=int) dr_ref = np.zeros(len(tab), dtype=float) n_ref = np.zeros(len(tab), dtype=int) - + # motion_model_dict = motion_model.validate_motion_model_dict(motion_model_dict, tab, None) i_all_detected = np.where(~np.any(np.isnan(tab['t']),axis=1))[0][0] # xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], motion_model_dict, allow_alt_models=True) @@ -1230,47 +1230,47 @@ def plot_quiver_residuals_with_orig_all_epochs(tab, trans_list, unit='arcsec', s da = calc_da(trans_list[ee]) - dx, dy = plot_quiver_residuals(tab['x'][:, ee], tab['y'][:, ee], - xt_mod, yt_mod, + dx, dy = plot_quiver_residuals(tab['x'][:, ee], tab['y'][:, ee], + xt_mod, yt_mod, good_idx, ref_idx, - 'Epoch {0:d}'.format(ee), + 'Epoch {0:d}'.format(ee), unit=unit, scale=scale, plotlim=plotlim, save_path=f'{save_path}/Quiver_Residual_{ee}.pdf' if save_path else None) - plot_quiver_residuals_orig(tab['x'][:, ee], tab['y'][:, ee], - xt_mod, yt_mod, + plot_quiver_residuals_orig(tab['x'][:, ee], tab['y'][:, ee], + xt_mod, yt_mod, good_idx, ref_idx, tab['x_orig'][:, ee], tab['y_orig'][:, ee], da, - 'Epoch {0:d}'.format(ee), + 'Epoch {0:d}'.format(ee), scale=scale_orig, plotlim=plotlim, save_path=f'{save_path}/Quiver_Residual_Orig_{ee}.pdf' if save_path else None) - plot_mag_scatter(tab['m'][:, ee], + plot_mag_scatter(tab['m'][:, ee], tab['m0'], tab['m0_err'], - tab['x'][:, ee], tab['y'][:, ee], + tab['x'][:, ee], tab['y'][:, ee], tab['xe'][:, ee], tab['ye'][:, ee], - xt_mod, yt_mod, + xt_mod, yt_mod, good_idx, ref_idx, 'Epoch {0:d}'.format(ee), da=da, xorig=tab['x_orig'][:, ee], yorig=tab['y_orig'][:, ee], cte_fit=cte_fit, mlim=mlim, save_path=f'{save_path}/Mag_Scatter_{ee}.pdf' if save_path else None) - plot_y_scatter(tab['m'][:, ee], + plot_y_scatter(tab['m'][:, ee], tab['m0'], tab['m0_err'], - tab['x'][:, ee], tab['y'][:, ee], + tab['x'][:, ee], tab['y'][:, ee], tab['xe'][:, ee], tab['ye'][:, ee], - xt_mod, yt_mod, + xt_mod, yt_mod, good_idx, ref_idx, 'Epoch {0:d}'.format(ee), da=da, xorig=tab['x_orig'][:, ee], yorig=tab['y_orig'][:, ee], cte_fit=cte_fit, mlim=mlim, save_path=f'{save_path}/Y_Scatter_{ee}.pdf' if save_path else None) # plot_quiver_residuals_orig_angle_xy(tab['x'][:, ee], tab['y'][:, ee], -# xt_mod, yt_mod, +# xt_mod, yt_mod, # good_idx, ref_idx, # tab['x_orig'][:, ee], tab['y_orig'][:, ee], da, # 'Epoch {0:d}'.format(ee)) # # plot_quiver_residuals_vs_pos_err(dx, dy, good_idx, ref_idx, -# 1e3 * tab['xe'][:, ee], 1e3 * tab['ye'][:, ee], +# 1e3 * tab['xe'][:, ee], 1e3 * tab['ye'][:, ee], # 'positional err (mas)', 'Epoch {0:d}'.format(ee), da=da) # Building up average dr for a set of stars. @@ -1311,7 +1311,7 @@ def plot_quiver_residuals_with_orig_all_epochs(tab, trans_list, unit='arcsec', s # if (dr_ref_avg[rr] > 0): # print(fmt.format(name=tab['name'][rr], mag=tab['m0'][rr], dr=dr_ref_avg[rr], # x=tab['x0'][rr], y=tab['y0'][rr], r=np.hypot(tab['x0'][rr], tab['y0'][rr]))) - + return @@ -1319,12 +1319,12 @@ def plot_mag_scatter_multi_trans_all_epochs(tab_list, trans_list_list, unit='arc m_t_list = [] x_t_list = [] y_t_list = [] - xe_t_list = [] + xe_t_list = [] ye_t_list = [] x_ref_list = [] - y_ref_list = [] - good_idx_list = [] - ref_idx_list =[] + y_ref_list = [] + good_idx_list = [] + ref_idx_list =[] da_list = [] ntrans = len(tab_list) @@ -1332,7 +1332,7 @@ def plot_mag_scatter_multi_trans_all_epochs(tab_list, trans_list_list, unit='arc i_all_detected = np.where(~np.any(np.isnan(tab['t']),axis=1))[0][0] # xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], motion_model_dict, allow_alt_models=True) xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.infer_positions(tab['t'][i_all_detected]) - + for mm in range(ntrans): tab = tab_list[mm] trans_list = trans_list_list[mm] @@ -1340,7 +1340,7 @@ def plot_mag_scatter_multi_trans_all_epochs(tab_list, trans_list_list, unit='arc dt = tab['t'][:, ee] - tab['t0'] xt_mod = xt_mod_all[ee] yt_mod = yt_mod_all[ee] - + good_idx = np.where(np.isfinite(tab['x'][:, ee]) == True)[0] ref_idx = np.where(tab[good_idx]['used_in_trans'][:, ee] == True)[0] @@ -1349,19 +1349,19 @@ def plot_mag_scatter_multi_trans_all_epochs(tab_list, trans_list_list, unit='arc m_t_list.append(tab['m'][:, ee]) x_t_list.append(tab['x'][:, ee]) y_t_list.append(tab['y'][:, ee]) - xe_t_list.append(tab['xe'][:, ee]) + xe_t_list.append(tab['xe'][:, ee]) ye_t_list.append(tab['ye'][:, ee]) x_ref_list.append(xt_mod) y_ref_list.append(yt_mod) - good_idx_list.append(good_idx) - ref_idx_list.append(ref_idx) + good_idx_list.append(good_idx) + ref_idx_list.append(ref_idx) da_list.append(da) for ee in range(tab_list[0]['x'].shape[1]): - plot_mag_scatter_multi_trans(m_t_list[ee::ntrans], x_t_list[ee::ntrans], y_t_list[ee::ntrans], - xe_t_list[ee::ntrans], ye_t_list[ee::ntrans], x_ref_list[ee::ntrans], y_ref_list[ee::ntrans], + plot_mag_scatter_multi_trans(m_t_list[ee::ntrans], x_t_list[ee::ntrans], y_t_list[ee::ntrans], + xe_t_list[ee::ntrans], ye_t_list[ee::ntrans], x_ref_list[ee::ntrans], y_ref_list[ee::ntrans], good_idx_list[ee::ntrans], ref_idx_list[ee::ntrans], 'Epoch {0:d}'.format(ee), da_list[ee::ntrans]) - + return @@ -1383,7 +1383,7 @@ def calc_da(trans_list): c01 = trans_list.px.parameters[c01_idx] c10 = trans_list.px.parameters[c10_idx] da = np.degrees(np.arctan2(-c01, c10)) - + return da @@ -1391,7 +1391,7 @@ def plot_mag_scatter(m_t, m0, m0e, x_t, y_t, xe_t, ye_t, x_ref, y_ref, good_idx, # Residual dx = (x_t - x_ref) dy = (y_t - y_ref) - + # Magnitude mgood = m_t[good_idx] mref = m_t[good_idx][ref_idx] @@ -1488,7 +1488,7 @@ def plot_y_scatter(m_t, m0, m0e, x_t, y_t, xe_t, ye_t, x_ref, y_ref, good_idx, r # Residual dx = (x_t - x_ref) dy = (y_t - y_ref) - + # Magnitude mgood = m_t[good_idx] mref = m_t[good_idx][ref_idx] @@ -1599,23 +1599,23 @@ def plot_y_scatter(m_t, m0, m0e, x_t, y_t, xe_t, ye_t, x_ref, y_ref, good_idx, r if cte_fit=='power': idx = np.where(mgood > mlim)[0] gpopt, gpcov = curve_fit(T_cte_y, mgood[idx], ygood[idx], maxfev=100000) - + marr = np.linspace(13, 24, 1000) - + # Corrected values ygood_new = ygood - T_cte_y(mgood, *gpopt) yref_new = yref - T_cte_y(mref, *gpopt) - + agood = angle_from_xy(xgood, ygood) % 360 rgood = np.hypot(xgood, ygood) aref = angle_from_xy(xref, yref) % 360 rref = np.hypot(xref, yref) - + agood_new = angle_from_xy(xgood, ygood_new) % 360 rgood_new = np.hypot(xgood, ygood_new) aref_new = angle_from_xy(xref, yref_new) % 360 rref_new = np.hypot(xref, yref_new) - + fig, ax = plt.subplots(4, 2, figsize=(12,12), sharex=True, sharey='row', num=105) plt.subplots_adjust(hspace=0.01, wspace=0.01) ax[0,0].scatter(mgood, ygood, color='black', alpha=0.3, s=2) @@ -1625,24 +1625,24 @@ def plot_y_scatter(m_t, m0, m0e, x_t, y_t, xe_t, ye_t, x_ref, y_ref, good_idx, r ax[0,0].axhline(y=0) ax[0,0].plot(marr, T_cte_y(marr, *gpopt), 'k-') ax[0,0].set_title('No correction') - + ax[0,1].scatter(mgood, ygood_new, color='black', alpha=0.3, s=2) ax[0,1].scatter(mref, yref_new, color='red', alpha=0.3, s=2) ax[0,1].set_ylim(-0.01, 0.01) ax[0,1].axhline(y=0) ax[0,1].set_title('Corrected') - + ax[1,0].scatter(mgood, ygood/yegood, color='black', alpha=0.3, s=2) ax[1,0].scatter(mref, yref/yeref, color='red', alpha=0.3, s=2) ax[1,0].set_ylabel('Res/Pos Err, y') ax[1,0].set_ylim(-10, 10) ax[1,0].axhline(y=0) - + ax[1,1].scatter(mgood, ygood_new/yegood, color='black', alpha=0.3, s=2) ax[1,1].scatter(mref, yref_new/yeref, color='red', alpha=0.3, s=2) ax[1,1].set_ylim(-10, 10) ax[1,1].axhline(y=0) - + ax[2,0].scatter(mgood, rgood, color='black', alpha=0.3, s=2) ax[2,0].scatter(mref, rref, color='red', alpha=0.3, s=2) ax[2,0].set_ylabel('Modulus (arcsec)') @@ -1651,7 +1651,7 @@ def plot_y_scatter(m_t, m0, m0e, x_t, y_t, xe_t, ye_t, x_ref, y_ref, good_idx, r ax[2,0].set_ylim(1e-6, 1.1 * np.max(np.concatenate([rgood.data, rref.data]))) else: ax[2,0].set_ylim(1e-6, 1.1 * np.max(np.concatenate([rgood, rref]))) - + ax[2,1].scatter(mgood, rgood_new, color='black', alpha=0.3, s=2) ax[2,1].scatter(mref, rref_new, color='red', alpha=0.3, s=2) ax[2,1].set_yscale('log') @@ -1659,12 +1659,12 @@ def plot_y_scatter(m_t, m0, m0e, x_t, y_t, xe_t, ye_t, x_ref, y_ref, good_idx, r ax[2,1].set_ylim(1e-6, 1.1 * np.max(np.concatenate([rgood_new.data, rref_new.data]))) else: ax[2,1].set_ylim(1e-6, 1.1 * np.max(np.concatenate([rgood_new, rref_new]))) - + ax[3,0].scatter(mgood, agood, color='black', alpha=0.3, s=2) ax[3,0].scatter(mref, aref, color='red', alpha=0.3, s=2) ax[3,0].set_ylabel('Angle (deg)') ax[3,0].set_xlabel('mag') - + ax[3,1].scatter(mgood, agood_new, color='black', alpha=0.3, s=2) ax[3,1].scatter(mref, aref_new, color='red', alpha=0.3, s=2) ax[3,1].set_xlabel('mag') @@ -1678,7 +1678,7 @@ def plot_y_scatter(m_t, m0, m0e, x_t, y_t, xe_t, ye_t, x_ref, y_ref, good_idx, r gpopt1, gpcov1 = curve_fit(T_line, mgood[idx1], ygood[idx1], maxfev=100000) gpopt2, gpcov2 = curve_fit(T_cte_y, mgood[idx2], ygood[idx2], maxfev=100000) - + marr1 = np.linspace(13, 18.5, 1000) marr2 = np.linspace(18.5, 24, 1000) @@ -1706,7 +1706,7 @@ def plot_y_scatter(m_t, m0, m0e, x_t, y_t, xe_t, ye_t, x_ref, y_ref, good_idx, r xeref2 = xeref[idx2r] yeref1 = yeref[idx1r] yeref2 = yeref[idx2r] - + # Corrected values ygood_new1 = ygood1 - T_line(mgood1, *gpopt1) yref_new1 = yref1 - T_line(mref1, *gpopt1) @@ -1732,7 +1732,7 @@ def plot_y_scatter(m_t, m0, m0e, x_t, y_t, xe_t, ye_t, x_ref, y_ref, good_idx, r rgood_new2 = np.hypot(xgood2, ygood_new2) aref_new2 = angle_from_xy(xref2, yref_new2) % 360 rref_new2 = np.hypot(xref2, yref_new2) - + fig, ax = plt.subplots(4, 2, figsize=(12,12), sharex=True, sharey='row', num=105) plt.subplots_adjust(hspace=0.01, wspace=0.01) ax[0,0].scatter(mgood, ygood, color='black', alpha=0.3, s=2) @@ -1743,7 +1743,7 @@ def plot_y_scatter(m_t, m0, m0e, x_t, y_t, xe_t, ye_t, x_ref, y_ref, good_idx, r ax[0,0].plot(marr1, T_line(marr1, *gpopt1), 'b-') ax[0,0].plot(marr2, T_cte_y(marr2, *gpopt2), 'b-') ax[0,0].set_title('No correction') - + ax[0,1].scatter(mgood1, ygood_new1, color='black', alpha=0.3, s=2) ax[0,1].scatter(mref1, yref_new1, color='red', alpha=0.3, s=2) ax[0,1].scatter(mgood2, ygood_new2, color='black', alpha=0.3, s=2) @@ -1751,20 +1751,20 @@ def plot_y_scatter(m_t, m0, m0e, x_t, y_t, xe_t, ye_t, x_ref, y_ref, good_idx, r ax[0,1].set_ylim(-0.01, 0.01) ax[0,1].axhline(y=0) ax[0,1].set_title('Corrected') - + ax[1,0].scatter(mgood, ygood/yegood, color='black', alpha=0.3, s=2) ax[1,0].scatter(mref, yref/yeref, color='red', alpha=0.3, s=2) ax[1,0].set_ylabel('Res/Pos Err, y') ax[1,0].set_ylim(-10, 10) ax[1,0].axhline(y=0) - + ax[1,1].scatter(mgood1, ygood_new1/yegood1, color='black', alpha=0.3, s=2) ax[1,1].scatter(mref1, yref_new1/yeref1, color='red', alpha=0.3, s=2) ax[1,1].scatter(mgood2, ygood_new2/yegood2, color='black', alpha=0.3, s=2) ax[1,1].scatter(mref2, yref_new2/yeref2, color='red', alpha=0.3, s=2) ax[1,1].set_ylim(-10, 10) ax[1,1].axhline(y=0) - + ax[2,0].scatter(mgood, rgood, color='black', alpha=0.3, s=2) ax[2,0].scatter(mref, rref, color='red', alpha=0.3, s=2) ax[2,0].set_ylabel('Modulus (arcsec)') @@ -1773,7 +1773,7 @@ def plot_y_scatter(m_t, m0, m0e, x_t, y_t, xe_t, ye_t, x_ref, y_ref, good_idx, r ax[2,0].set_ylim(1e-6, 1.1 * np.max(np.concatenate([rgood.data, rref.data]))) else: ax[2,0].set_ylim(1e-6, 1.1 * np.max(np.concatenate([rgood, rref]))) - + ax[2,1].scatter(mgood1, rgood_new1, color='black', alpha=0.3, s=2) ax[2,1].scatter(mref1, rref_new1, color='red', alpha=0.3, s=2) ax[2,1].scatter(mgood2, rgood_new2, color='black', alpha=0.3, s=2) @@ -1783,18 +1783,18 @@ def plot_y_scatter(m_t, m0, m0e, x_t, y_t, xe_t, ye_t, x_ref, y_ref, good_idx, r ax[2,1].set_ylim(1e-6, 1.1 * np.max(np.concatenate([rgood_new.data2, rref_new.data2]))) else: ax[2,1].set_ylim(1e-6, 1.1 * np.max(np.concatenate([rgood_new2, rref_new2]))) - + ax[3,0].scatter(mgood, agood, color='black', alpha=0.3, s=2) ax[3,0].scatter(mref, aref, color='red', alpha=0.3, s=2) ax[3,0].set_ylabel('Angle (deg)') ax[3,0].set_xlabel('mag') - + ax[3,1].scatter(mgood1, agood_new1, color='black', alpha=0.3, s=2) ax[3,1].scatter(mref1, aref_new1, color='red', alpha=0.3, s=2) ax[3,1].scatter(mgood2, agood_new2, color='black', alpha=0.3, s=2) ax[3,1].scatter(mref2, aref_new2, color='red', alpha=0.3, s=2) ax[3,1].set_xlabel('mag') - + def T_cte_y(m, A, m0, alpha, m1): base = m/m0 @@ -1804,16 +1804,16 @@ def T_line(m, a, b): return a + m*b -def plot_quiver_residuals(x_t, y_t, x_ref, y_ref, good_idx, ref_idx, title, +def plot_quiver_residuals(x_t, y_t, x_ref, y_ref, good_idx, ref_idx, title, unit='pixel', scale=None, plotlim=None, save_path=None): """ unit : str 'pixel' or 'arcsec' The pixel units of the input values. Note, if arcsec, then the values will be - converted to milli-arcsec for plotting when appropriate. + converted to milli-arcsec for plotting when appropriate. scale : float - The quiver scale. If none, then default units will be used appropriate to the unit. + The quiver scale. If none, then default units will be used appropriate to the unit. plotlim : float (positive) Sets the size of the plotted figure. If None, then default is used. @@ -1868,7 +1868,7 @@ def plot_quiver_residuals(x_t, y_t, x_ref, y_ref, good_idx, ref_idx, title, else: print(str_fmt.format(dx[good_idx][ref_idx].mean(), 0.0, dy[good_idx][ref_idx].mean(), 0.0, 'REF', unit2)) - + print(str_fmt.format(dx[good_idx].mean(), dx[good_idx].std(), dy[good_idx].mean(), dy[good_idx].std(), 'GOOD', unit2)) @@ -1882,14 +1882,14 @@ def plot_quiver_residuals_magcolor_all_epochs(tab, unit='arcsec', scale=None, pl dr_ref = np.zeros(len(tab), dtype=float) n_ref = np.zeros(len(tab), dtype=int) - idx = np.where((tab['m0'] < lower_mag) & + idx = np.where((tab['m0'] < lower_mag) & (tab['m0'] > upper_mag))[0] tab = tab[idx] # motion_model_dict = motion_model.validate_motion_model_dict(motion_model_dict, tab, None) i_all_detected = np.where(~np.any(np.isnan(tab['t']),axis=1))[0][0] # xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], motion_model_dict, allow_alt_models=True) xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.infer_positions(tab['t'][i_all_detected]) - + for ee in range(tab['x'].shape[1]): dt = tab['t'][:, ee] - tab['t0'] xt_mod = xt_mod_all[ee] @@ -1899,10 +1899,10 @@ def plot_quiver_residuals_magcolor_all_epochs(tab, unit='arcsec', scale=None, pl good_idx = np.where(np.isfinite(tab['x'][:, ee]) == True)[0] ref_idx = np.where(tab[good_idx]['used_in_trans'][:, ee] == True)[0] - dx, dy = plot_quiver_residuals_magcolor(tab['x'][:, ee], tab['y'][:, ee], + dx, dy = plot_quiver_residuals_magcolor(tab['x'][:, ee], tab['y'][:, ee], xt_mod, yt_mod, mag, good_idx, ref_idx, - 'Epoch {0:d}'.format(ee), + 'Epoch {0:d}'.format(ee), unit=unit, scale=scale, plotlim=plotlim) # Building up average dr for a set of stars. @@ -1917,7 +1917,7 @@ def plot_quiver_residuals_magcolor_all_epochs(tab, unit='arcsec', scale=None, pl dr_good_avg = np.zeros(len(tab), dtype=float) idx = np.where(n_good > 0)[0] dr_good_avg[idx] = dr_good[idx] / n_good[idx] - + dr_ref_avg = np.zeros(len(tab), dtype=float) idx = np.where(n_ref > 0)[0] dr_ref_avg[idx] = dr_ref[idx] / n_ref[idx] @@ -1927,16 +1927,16 @@ def plot_quiver_residuals_magcolor_all_epochs(tab, unit='arcsec', scale=None, pl -def plot_quiver_residuals_magcolor(x_t, y_t, x_ref, y_ref, mag, good_idx, ref_idx, title, +def plot_quiver_residuals_magcolor(x_t, y_t, x_ref, y_ref, mag, good_idx, ref_idx, title, unit='pixel', scale=None, plotlim=None): """ unit : str 'pixel' or 'arcsec' The pixel units of the input values. Note, if arcsec, then the values will be - converted to milli-arcsec for plotting when appropriate. + converted to milli-arcsec for plotting when appropriate. scale : float - The quiver scale. If none, then default units will be used appropriate to the unit. + The quiver scale. If none, then default units will be used appropriate to the unit. plotlim : float (positive) Sets the size of the plotted figure. If None, then default is used. @@ -1971,7 +1971,7 @@ def plot_quiver_residuals_magcolor(x_t, y_t, x_ref, y_ref, mag, good_idx, ref_id # cmap = mpl.cm.cool # norm = mpl.colors.Normalize(vmin=np.min(mag), vmax=np.max(mag)) -# +# # cb1 = mpl.colorbar.ColorbarBase(ax, cmap=cmap, # norm=norm, # orientation='horizontal') @@ -2014,7 +2014,7 @@ def plot_quiver_residuals_magcolor(x_t, y_t, x_ref, y_ref, mag, good_idx, ref_id else: print(str_fmt.format(dx[good_idx][ref_idx].mean(), 0.0, dy[good_idx][ref_idx].mean(), 0.0, 'REF', unit2)) - + print(str_fmt.format(dx[good_idx].mean(), dx[good_idx].std(), dy[good_idx].mean(), dy[good_idx].std(), 'GOOD', unit2)) @@ -2022,17 +2022,17 @@ def plot_quiver_residuals_magcolor(x_t, y_t, x_ref, y_ref, mag, good_idx, ref_id return (dx, dy) -def plot_quiver_residuals_orig(x_t, y_t, x_ref, y_ref, good_idx, ref_idx, - x_orig, y_orig, da, title, +def plot_quiver_residuals_orig(x_t, y_t, x_ref, y_ref, good_idx, ref_idx, + x_orig, y_orig, da, title, scale=None, plotlim=None, save_path=None): """ unit : str 'pixel' or 'arcsec' The pixel units of the input values. Note, if arcsec, then the values will be - converted to milli-arcsec for plotting when appropriate. + converted to milli-arcsec for plotting when appropriate. scale : float - The quiver scale. If none, then default units will be used appropriate to the unit. + The quiver scale. If none, then default units will be used appropriate to the unit. plotlim : float (positive) Sets the size of the plotted figure. If None, then default is used. @@ -2094,11 +2094,11 @@ def plot_quiver_residuals_orig(x_t, y_t, x_ref, y_ref, good_idx, ref_idx, # ax1.hist(aref ,color='red', histtype = 'step', # alpha=0.8, bins = 36, density=True) # ax1.set_xlabel('Quiver angle (degrees), HST camera') -# -# ax2.scatter(x_orig[good_idx], y_orig[good_idx], +# +# ax2.scatter(x_orig[good_idx], y_orig[good_idx], # s=5e3 * r_good**2, alpha=0.3, color='black') -# ax2.scatter(x_orig[good_idx][ref_idx], y_orig[good_idx][ref_idx], -# s=5e3 * r_ref**2, alpha=0.5, color='red') +# ax2.scatter(x_orig[good_idx][ref_idx], y_orig[good_idx][ref_idx], +# s=5e3 * r_ref**2, alpha=0.5, color='red') # ax2.set_xlabel('X (orig pix)') # ax2.set_ylabel('Y (orig pix)') # plt.title(title) @@ -2124,16 +2124,16 @@ def rotate(x, y, theta): return xnew, ynew -def plot_quiver_residuals_orig_angle_xy(x_t, y_t, x_ref, y_ref, good_idx, ref_idx, +def plot_quiver_residuals_orig_angle_xy(x_t, y_t, x_ref, y_ref, good_idx, ref_idx, x_orig, y_orig, da, title, scale=None, plotlim=None): """ unit : str 'pixel' or 'arcsec' The pixel units of the input values. Note, if arcsec, then the values will be - converted to milli-arcsec for plotting when appropriate. + converted to milli-arcsec for plotting when appropriate. scale : float - The quiver scale. If none, then default units will be used appropriate to the unit. + The quiver scale. If none, then default units will be used appropriate to the unit. plotlim : float (positive) Sets the size of the plotted figure. If None, then default is used. @@ -2141,7 +2141,7 @@ def plot_quiver_residuals_orig_angle_xy(x_t, y_t, x_ref, y_ref, good_idx, ref_id """ dx = (x_t - x_ref) dy = (y_t - y_ref) - + # Residual modulus r_good = np.hypot(dx[good_idx], dy[good_idx]) r_ref = np.hypot(dx[good_idx][ref_idx], dy[good_idx][ref_idx]) @@ -2193,17 +2193,17 @@ def plot_chi2_dist(tab, Ndetect, xlim=40, n_bins=50, boot_err=False): chi2_x_list = [] chi2_y_list = [] fnd_list = [] # Number of non-NaN error measurements - + # motion_model_dict = motion_model.validate_motion_model_dict(motion_model_dict, tab, None) i_all_detected = np.where(~np.any(np.isnan(tab['t']),axis=1))[0][0] # xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], motion_model_dict, allow_alt_models=True) xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.infer_positions(tab['t'][i_all_detected]) for ii in range(len(tab)): - # Ignore the NaNs + # Ignore the NaNs fnd = np.argwhere(~np.isnan(tab['xe'][ii,:])) fnd_list.append(len(fnd)) - + x = tab['x'][ii, fnd] y = tab['y'][ii, fnd] if boot_err: @@ -2220,7 +2220,7 @@ def plot_chi2_dist(tab, Ndetect, xlim=40, n_bins=50, boot_err=False): diffY = y - fitLineY sigX = diffX / xerr sigY = diffY / yerr - + chi2_x = np.sum(sigX**2) chi2_y = np.sum(sigY**2) chi2_x_list.append(chi2_x) @@ -2229,7 +2229,7 @@ def plot_chi2_dist(tab, Ndetect, xlim=40, n_bins=50, boot_err=False): x = np.array(chi2_x_list) y = np.array(chi2_y_list) fnd = np.array(fnd_list) - + idx = np.where(fnd == Ndetect)[0] # Fitting position and velocity... so subtract 2 to get Ndof n_params = np.nanmean(tab['n_params'][idx]) @@ -2256,7 +2256,7 @@ def plot_chi2_dist(tab, Ndetect, xlim=40, n_bins=50, boot_err=False): chi2red_x = x / Ndof chi2red_y = y / Ndof chi2red_t = (x + y) / (2.0 * Ndof) - + print('Mean reduced chi^2: (Ndetect = {0:d} of {1:d})'.format(len(idx), len(tab))) fmt = ' {0:s} = {1:.1f} for N_detect and {2:.1f} for all' med_chi2red_x_f = np.median(chi2red_x[idx]) @@ -2279,7 +2279,7 @@ def plot_chi2_reduced_dist(tab, Ndetect, xlim=8, n_bins=50, boot_err=False): chi2_x_list = [] chi2_y_list = [] fnd_list = [] # Number of non-NaN error measurements - + # motion_model_dict = motion_model.validate_motion_model_dict(motion_model_dict, tab, None) i_all_detected = np.where(~np.any(np.isnan(tab['t']),axis=1))[0][0] # xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], motion_model_dict, allow_alt_models=True) @@ -2289,7 +2289,7 @@ def plot_chi2_reduced_dist(tab, Ndetect, xlim=8, n_bins=50, boot_err=False): # Ignore the NaNs fnd = np.argwhere(~np.isnan(tab['xe'][ii,:])) fnd_list.append(len(fnd)) - + x = tab['x'][ii, fnd] y = tab['y'][ii, fnd] if boot_err: @@ -2306,7 +2306,7 @@ def plot_chi2_reduced_dist(tab, Ndetect, xlim=8, n_bins=50, boot_err=False): diffY = y - fitLineY sigX = diffX / xerr sigY = diffY / yerr - + chi2_x = np.sum(sigX**2) chi2_y = np.sum(sigY**2) chi2_x_list.append(chi2_x) @@ -2315,7 +2315,7 @@ def plot_chi2_reduced_dist(tab, Ndetect, xlim=8, n_bins=50, boot_err=False): x = np.array(chi2_x_list) y = np.array(chi2_y_list) fnd = np.array(fnd_list) - + idx = np.where(fnd == Ndetect)[0] n_params = tab['n_params'] Ndof = Ndetect - n_params @@ -2335,7 +2335,7 @@ def plot_chi2_reduced_dist(tab, Ndetect, xlim=8, n_bins=50, boot_err=False): chi2red_x = x / Ndof chi2red_y = y / Ndof chi2red_t = (x + y) / (2.0 * Ndof + 1*(tab['motion_model_used']=='Parallax')) - + print('Mean reduced chi^2: (Ndetect = {0:d} of {1:d})'.format(len(idx), len(tab))) fmt = ' {0:s} = {1:.1f} for N_detect and {2:.1f} for all' med_chi2red_x_f = np.median(chi2red_x[idx]) @@ -2359,17 +2359,17 @@ def plot_chi2_dist_per_filter(tab, Ndetect, xlim=40, n_bins=50, filter=None, boo chi2_x_list = [] chi2_y_list = [] fnd_list = [] # Number of non-NaN error measurements - + # motion_model_dict = motion_model.validate_motion_model_dict(motion_model_dict, tab, None) i_all_detected = np.where(~np.any(np.isnan(tab['t']),axis=1))[0][0] # xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], motion_model_dict, allow_alt_models=True) xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.infer_positions(tab['t'][i_all_detected]) for ii in range(len(tab)): - # Ignore the NaNs + # Ignore the NaNs fnd = np.argwhere(~np.isnan(tab['xe'][ii,:])) fnd_list.append(len(fnd)) - + x = tab['x'][ii, fnd] y = tab['y'][ii, fnd] if boot_err: @@ -2386,7 +2386,7 @@ def plot_chi2_dist_per_filter(tab, Ndetect, xlim=40, n_bins=50, filter=None, boo diffY = y - fitLineY sigX = diffX / xerr sigY = diffY / yerr - + chi2_x = np.sum(sigX**2) chi2_y = np.sum(sigY**2) chi2_x_list.append(chi2_x) @@ -2397,8 +2397,8 @@ def plot_chi2_dist_per_filter(tab, Ndetect, xlim=40, n_bins=50, filter=None, boo x = np.array(chi2_x_list) y = np.array(chi2_y_list) fnd = np.array(fnd_list) - - + + idx = np.where(fnd == Ndetect)[0] # Fitting position and velocity... so subtract n_params to get Ndof n_params = np.nanmean(tab['n_params'][idx]) @@ -2428,7 +2428,7 @@ def plot_chi2_dist_per_filter(tab, Ndetect, xlim=40, n_bins=50, filter=None, boo chi2red_x = x / Ndof chi2red_y = y / Ndof chi2red_t = (x + y) / (2.0 * Ndof) - + print('Mean reduced chi^2: (Ndetect = {0:d} of {1:d})'.format(len(idx), len(tab))) fmt = ' {0:s} = {1:.1f} for N_detect and {2:.1f} for all' med_chi2red_x_f = np.median(chi2red_x[idx]) @@ -2456,16 +2456,16 @@ def plot_chi2_dist_per_epoch(tab, Ndetect, mlim=[14, 21], ylim=[-1, 1], target_i sigX_arr = np.nan * np.ones((len(tab['xe']), Ndetect)) sigY_arr = np.nan * np.ones((len(tab['xe']), Ndetect)) m_arr = np.nan * np.ones((len(tab['xe']), Ndetect)) - + # motion_model_dict = motion_model.validate_motion_model_dict(motion_model_dict, tab, None) i_all_detected = np.where(~np.any(np.isnan(tab['t']),axis=1))[0][0] # xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], motion_model_dict, allow_alt_models=True) xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.infer_positions(tab['t'][i_all_detected]) - + for ii in range(len(tab['xe'])): - # Ignore the NaNs + # Ignore the NaNs fnd = np.argwhere(~np.isnan(tab['xe'][ii,:])) - if len(fnd) == Ndetect and tab['use_in_trans'][ii]: + if len(fnd) == Ndetect and tab['use_in_trans'][ii]: time = tab['t'][ii, fnd] x = tab['x'][ii, fnd] y = tab['y'][ii, fnd] @@ -2480,7 +2480,7 @@ def plot_chi2_dist_per_epoch(tab, Ndetect, mlim=[14, 21], ylim=[-1, 1], target_i fitLineX = xt_mod_all[ii, fnd] fitLineY = yt_mod_all[ii, fnd] - + diffX = x - fitLineX diffY = y - fitLineY sigX = diffX / xerr @@ -2489,7 +2489,7 @@ def plot_chi2_dist_per_epoch(tab, Ndetect, mlim=[14, 21], ylim=[-1, 1], target_i diffX_arr[ii] = diffX.reshape(Ndetect,) diffY_arr[ii] = diffY.reshape(Ndetect,) errX_arr[ii] = xerr.reshape(Ndetect,) - errY_arr[ii] = yerr.reshape(Ndetect,) + errY_arr[ii] = yerr.reshape(Ndetect,) sigX_arr[ii] = sigX.reshape(Ndetect,) sigY_arr[ii] = sigY.reshape(Ndetect,) m_arr[ii] = m.reshape(Ndetect,) @@ -2522,14 +2522,14 @@ def plot_chi2_dist_per_epoch(tab, Ndetect, mlim=[14, 21], ylim=[-1, 1], target_i ax2.legend() #print(errX_arr[:, ii]) - ax3.errorbar(m_arr[:, ii], diffX_arr[:, ii]*1E3, yerr=errX_arr[:, ii]*1E3, + ax3.errorbar(m_arr[:, ii], diffX_arr[:, ii]*1E3, yerr=errX_arr[:, ii]*1E3, marker='s', label = 'X', ls='none', color='tab:blue', alpha=0.4, ms=5) - ax3.errorbar(m_arr[:, ii], diffY_arr[:, ii]*1E3, yerr=errY_arr[:, ii]*1E3, + ax3.errorbar(m_arr[:, ii], diffY_arr[:, ii]*1E3, yerr=errY_arr[:, ii]*1E3, marker='o', label = 'Y', ls='none', color='tab:orange', alpha=0.4, ms=5) if target_idx is not None: - ax3.errorbar(m_arr[target_idx, ii], diffX_arr[target_idx, ii]*1E3, yerr=errX_arr[target_idx, ii]*1E3, + ax3.errorbar(m_arr[target_idx, ii], diffX_arr[target_idx, ii]*1E3, yerr=errX_arr[target_idx, ii]*1E3, marker='s', ls='none', color='black', ms=5) - ax3.errorbar(m_arr[target_idx, ii], diffY_arr[target_idx, ii]*1E3, yerr=errY_arr[target_idx, ii]*1E3, + ax3.errorbar(m_arr[target_idx, ii], diffY_arr[target_idx, ii]*1E3, yerr=errY_arr[target_idx, ii]*1E3, marker='o', ls='none', color='black', ms=5) ax3.set_xlim(mlim[0], mlim[1]) ax3.set_ylim(ylim[0], ylim[1]) @@ -2540,7 +2540,7 @@ def plot_chi2_dist_per_epoch(tab, Ndetect, mlim=[14, 21], ylim=[-1, 1], target_i ax3.set_ylabel('residual (mas)') return - + # TODO: update for motion model def plot_chi2_ecliptic_per_epoch(tab, Ndetect,ra,dec, mlim=[14,21], ylim = [-1, 1], target_idx = 0): """ @@ -2554,7 +2554,7 @@ def plot_chi2_ecliptic_per_epoch(tab, Ndetect,ra,dec, mlim=[14,21], ylim = [-1, sigX_arr = -99 * np.ones((len(tab['xe']), Ndetect)) sigY_arr = -99 * np.ones((len(tab['xe']), Ndetect)) m_arr = -99 * np.ones((len(tab['xe']), Ndetect)) - + rad_to_as = 180/np.pi * 60 * 60 deg_to_as = 60 * 60 def eq_to_ec(ra,dec): @@ -2595,7 +2595,7 @@ def eq_to_ec(ra,dec): dt = tab['t'][ii, fnd] - tab['t0'][ii] fitLineX = lambda_pm fitLineY = beta_pm - + diffX = lambda_obs - fitLineX diffY = beta_obs - fitLineY sigX = diffX / xerr @@ -2670,10 +2670,10 @@ def plot_chi2_dist_mag(tab, Ndetect, xlim=40, n_bins=30, boot_err=False): fnd_list = [] # Number of non-NaN error measurements for ii in range(len(tab['me'])): - # Ignore the NaNs + # Ignore the NaNs fnd = np.argwhere(~np.isnan(tab['me'][ii,:])) fnd_list.append(len(fnd)) - + m = tab['m'][ii, fnd] if boot_err: merr = np.hypot(tab['me_boot'][ii, fnd], tab['me'][ii, fnd]) @@ -2684,7 +2684,7 @@ def plot_chi2_dist_mag(tab, Ndetect, xlim=40, n_bins=30, boot_err=False): diff_m = m0 - m sig_m = diff_m/merr - + chi2_m = np.sum(sig_m**2) chi2_m_list.append(chi2_m) @@ -2701,7 +2701,7 @@ def plot_chi2_dist_mag(tab, Ndetect, xlim=40, n_bins=30, boot_err=False): plt.figure(figsize=(6,4)) plt.clf() plt.hist(chi2_m[idx], bins=np.arange(xlim*10), histtype='step', density=True) - plt.plot(chi2_maxis, chi2.pdf(chi2_maxis, Ndof), 'r-', alpha=0.6, + plt.plot(chi2_maxis, chi2.pdf(chi2_maxis, Ndof), 'r-', alpha=0.6, label=r'$\chi^2$ ' + str(Ndof) + ' dof') plt.title('$N_{epoch} = $' + str(Ndetect) + ', $N_{dof} = $' + str(Ndof)) plt.xlim(0, xlim) @@ -2710,7 +2710,7 @@ def plot_chi2_dist_mag(tab, Ndetect, xlim=40, n_bins=30, boot_err=False): print('Mean reduced chi^2: (Ndetect = {0:d} of {1:d})'.format(len(idx), len(tab))) fmt = ' {0:s} = {1:.1f} for N_detect and {2:.1f} for all' print(fmt.format('M', np.median(chi2_m[idx] / (fnd[idx] - 2)), np.median(chi2_m / (fnd - 2)))) - + return def plot_chi2_dist_mag_per_filter(tab, Ndetect, mlim=40, n_bins=30, xlim=40, file_name=None, filter=None): @@ -2722,10 +2722,10 @@ def plot_chi2_dist_mag_per_filter(tab, Ndetect, mlim=40, n_bins=30, xlim=40, fil fnd_list = [] # Number of non-NaN error measurements for ii in range(len(tab['me'])): - # Ignore the NaNs + # Ignore the NaNs fnd = np.argwhere(~np.isnan(tab['me'][ii,:])) fnd_list.append(len(fnd)) - + m = tab['m'][ii, fnd] merr = tab['me'][ii, fnd] m0 = tab['m0'][ii] @@ -2733,7 +2733,7 @@ def plot_chi2_dist_mag_per_filter(tab, Ndetect, mlim=40, n_bins=30, xlim=40, fil diff_m = m0 - m sig_m = diff_m/merr - + chi2_m = np.sum(sig_m**2) chi2_m_list.append(chi2_m) @@ -2750,7 +2750,7 @@ def plot_chi2_dist_mag_per_filter(tab, Ndetect, mlim=40, n_bins=30, xlim=40, fil plt.figure(figsize=(6,4)) plt.clf() plt.hist(chi2_m[idx], bins=np.arange(xlim*10), label='mag', histtype='stepfilled', density=True, color='green', alpha=0.7, edgecolor='k') - plt.plot(chi2_maxis, chi2.pdf(chi2_maxis, Ndof), 'r-', alpha=0.6, + plt.plot(chi2_maxis, chi2.pdf(chi2_maxis, Ndof), 'r-', alpha=0.6, label=r'$\chi^2$ ' + str(Ndof) + ' dof') #plt.title('$N_{epoch} = $' + str(Ndetect) + ', $N_{dof} = $' + str(Ndof)) plt.xlim(0, xlim) @@ -2765,23 +2765,23 @@ def plot_chi2_dist_mag_per_filter(tab, Ndetect, mlim=40, n_bins=30, xlim=40, fil print('Mean reduced chi^2: (Ndetect = {0:d} of {1:d})'.format(len(idx), len(tab))) fmt = ' {0:s} = {1:.1f} for N_detect and {2:.1f} for all' print(fmt.format('M', np.median(chi2_m[idx] / (fnd[idx] - 2)), np.median(chi2_m / (fnd - 2)))) - + return def plot_stars(tab, star_names, NcolMax=2, epoch_array = None, figsize=(15,25), color_time=False, boot_err=False): """ - Plot a set of stars positions, flux and residuals over time. + Plot a set of stars positions, flux and residuals over time. epoch_array : None, array Array of the epoch indicies to plot. If None, plots all epochs. """ - + def rs(x): return x.reshape(len(x)) - + print( 'Creating residuals plots for star(s):' ) print( star_names ) - + Nstars = len(star_names) Ncols = 3 * np.min([Nstars, NcolMax]) if Nstars <= Ncols/3: @@ -2803,10 +2803,10 @@ def rs(x): xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.infer_positions(tab['t'][i_all_detected]) # xt_cont_all, yt_cont_all, xt_cont_err, yt_cont_err = tab.get_star_positions_at_time(cont_times, motion_model_dict, allow_alt_models=True) xt_cont_all, yt_cont_all, xt_cont_err, yt_cont_err = tab.infer_positions(cont_times) - + for i in range(Nstars): starName = star_names[i] - + try: ii = np.where(tab['name'] == starName)[0][0] except IndexError: @@ -2821,7 +2821,7 @@ def rs(x): fnd = fnd.reshape(len(fnd),1) time = tab['t'][ii, fnd] - dtime = time.data % 1 + dtime = time.data % 1 x = tab['x'][ii, fnd] y = tab['y'][ii, fnd] m = tab['m'][ii, fnd] @@ -2836,7 +2836,7 @@ def rs(x): merr = tab['me'][ii, fnd] dt = tab['t'][ii, fnd] - tab['t0'][ii] - + fitLineX = xt_mod_all[ii, fnd] fitLineY = yt_mod_all[ii, fnd] @@ -2873,14 +2873,14 @@ def rs(x): chi2_red_x = chi2_x / dof chi2_red_y = chi2_y / dof chi2_red_m = chi2_m / dofM - + print( 'Star: ', starName ) - print( '\tX Chi^2 = %5.2f (%6.2f for %2d dof)' % + print( '\tX Chi^2 = %5.2f (%6.2f for %2d dof)' % (chi2_red_x, chi2_x, dof)) - print( '\tY Chi^2 = %5.2f (%6.2f for %2d dof)' % + print( '\tY Chi^2 = %5.2f (%6.2f for %2d dof)' % (chi2_red_y, chi2_y, dof)) - print( '\tM Chi^2 = %5.2f (%6.2f for %2d dof)' % + print( '\tM Chi^2 = %5.2f (%6.2f for %2d dof)' % (chi2_red_m, chi2_m, dofM)) if 'motion_model_used' in tab.keys(): print('\tMotion model:', tab['motion_model_used'][ii]) @@ -2929,7 +2929,7 @@ def rs(x): row = 1 else: col = 1 + 3*(i % (Ncols/3)) - row = 1 + 3*(i//(Ncols/3)) + row = 1 + 3*(i//(Ncols/3)) ind = int((row-1)*Ncols + col) @@ -3023,7 +3023,7 @@ def rs(x): paxes.xaxis.set_major_formatter(fmtX) paxes.yaxis.set_major_formatter(fmtM) paxes.tick_params(axis='both', which='major', labelsize=12) - + ########## # X residuals vs time @@ -3139,7 +3139,7 @@ def rs(x): plt.xlabel('X (asec)', fontsize=fontsize1) plt.ylabel('Y (asec)', fontsize=fontsize1) plt.plot(xt_cont_all[ii], yt_cont_all[ii], 'b-') - + ########## # X, Y Histogram of Residuals ########## @@ -3149,7 +3149,7 @@ def rs(x): bins = np.arange(-7.5, 7.5, 1) paxes = plt.subplot(Nrows, Ncols, ind) id = np.where(diffY < 0)[0] - sig[id] = -1.*sig[id] + sig[id] = -1.*sig[id] (n, b, p) = plt.hist(sigX, bins, histtype='stepfilled', color='b', label='X') plt.setp(p, 'facecolor', 'b') (n, b, p) = plt.hist(sigY, bins, histtype='step', color='r', label='Y') @@ -3175,10 +3175,10 @@ def rs(x): plt.xlabel('Residuals (sigma)', fontsize=fontsize1) plt.ylabel('Number of Epochs', fontsize=fontsize1) paxes.tick_params(axis='both', which='major', labelsize=fontsize1) - + if Nstars == 1: - plt.subplots_adjust(wspace=0.4, hspace=0.4, left = 0.15, bottom = 0.1, right=0.9, top=0.9) + plt.subplots_adjust(wspace=0.4, hspace=0.4, left = 0.15, bottom = 0.1, right=0.9, top=0.9) # plt.savefig(rootDir+'plots/plotStar_' + starName + '.png') else: plt.subplots_adjust(wspace=0.6, hspace=0.6, left = 0.08, bottom = 0.05, right=0.95, top=0.90) @@ -3192,7 +3192,7 @@ def rs(x): def plot_stars_nfilt(tab, star_names, NcolMax=2, epoch_array_list = None, color_list = None, figsize=(15,25), color_time=False, resTicRng=None, save_name=None, boot_err=False): """ - Plot a set of stars positions, flux and residuals over time. + Plot a set of stars positions, flux and residuals over time. epoch_array : None, array Array of the epoch indicies to plot. If None, plots all epochs. @@ -3204,12 +3204,12 @@ def plot_stars_nfilt(tab, star_names, NcolMax=2, epoch_array_list = None, color_ print( star_names ) def rs(x): return x.reshape(len(x)) - + # motion_model_dict = motion_model.validate_motion_model_dict(motion_model_dict, tab, None) i_all_detected = np.where(~np.any(np.isnan(tab['t']),axis=1))[0][0] # xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.get_star_positions_at_time(tab['t'][i_all_detected], motion_model_dict, allow_alt_models=True) xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.infer_positions(tab['t'][i_all_detected]) - + Nstars = len(star_names) Ncols = 3 * np.min([Nstars, NcolMax]) if Nstars <= Ncols/3: @@ -3231,31 +3231,31 @@ def rs(x): xt_mod_all, yt_mod_all, xt_mod_err, yt_mod_err = tab.infer_positions(tab['t'][i_all_detected]) # xt_cont_all, yt_cont_all, xt_cont_err, yt_cont_err = tab.get_star_positions_at_time(cont_times, motion_model_dict, allow_alt_models=True) xt_cont_all, yt_cont_all, xt_cont_err, yt_cont_err = tab.infer_positions(cont_times) - + for i in range(Nstars): for ea, epoch_array in enumerate(epoch_array_list): color=color_list[ea] starName = star_names[i] - + try: ii = np.where(tab['name'] == starName)[0][0] except IndexError: print("!! %s is not in this list"%starName) continue - + # Ignore the NaNs fnd = np.argwhere(~np.isnan(tab['xe'][ii,:])) - + if epoch_array is not None: fnd = np.intersect1d(fnd, epoch_array) fnd = fnd.reshape(len(fnd),1) - + time = tab['t'][ii, fnd] - dtime = time.data % 1 + dtime = time.data % 1 x = tab['x'][ii, fnd] y = tab['y'][ii, fnd] m = tab['m'][ii, fnd] - + if boot_err: xerr = np.hypot(tab['xe'][ii, fnd], tab['xe_boot'][ii, fnd]) yerr = np.hypot(tab['ye'][ii, fnd], tab['ye_boot'][ii, fnd]) @@ -3264,16 +3264,16 @@ def rs(x): xerr = tab['xe'][ii, fnd] yerr = tab['ye'][ii, fnd] merr = tab['me'][ii, fnd] - + fitLineX = xt_mod_all[ii, fnd] fitLineY = yt_mod_all[ii, fnd] - + fitSigX = xt_mod_err[ii, fnd] fitSigY = yt_mod_err[ii, fnd] - + fitLineM = np.repeat(tab['m0'][ii], len(time)).reshape(len(time),1) fitSigM = np.repeat(tab['m0_err'][ii], len(time)).reshape(len(time),1) - + diffX = x - fitLineX diffY = y - fitLineY diffM = m - fitLineM @@ -3283,42 +3283,42 @@ def rs(x): sigY = diffY / yerr sigM = diffM / merr sig = diff / rerr - + # Determine if there are points that are more than 4 sigma off idxX = np.where(abs(sigX) > 4) idxY = np.where(abs(sigY) > 4) idxM = np.where(abs(sigM) > 4) idx = np.where(abs(sig) > 4) - + # Calculate chi^2 metrics chi2_x = np.sum(sigX**2) chi2_y = np.sum(sigY**2) chi2_m = np.sum(sigM**2) - + dof = len(x) - 2 dofM = len(m) - 1 - + chi2_red_x = chi2_x / dof chi2_red_y = chi2_y / dof chi2_red_m = chi2_m / dofM - - + + print( 'Star: ', starName ) - print( '\tX Chi^2 = %5.2f (%6.2f for %2d dof)' % + print( '\tX Chi^2 = %5.2f (%6.2f for %2d dof)' % (chi2_red_x, chi2_x, dof)) - print( '\tY Chi^2 = %5.2f (%6.2f for %2d dof)' % + print( '\tY Chi^2 = %5.2f (%6.2f for %2d dof)' % (chi2_red_y, chi2_y, dof)) - print( '\tM Chi^2 = %5.2f (%6.2f for %2d dof)' % + print( '\tM Chi^2 = %5.2f (%6.2f for %2d dof)' % (chi2_red_m, chi2_m, dofM)) - + tmin = time.min() tmax = time.max() - + dateTicLoc = plt.MultipleLocator(3) dateTicRng = [np.floor(tmin), np.ceil(tmax)] dateTics = np.arange(np.floor(tmin), np.ceil(tmax)+0.1) DateTicsLabel = dateTics - + # See if we are using MJD instead. if time[0] > 50000: print('MJD') @@ -3328,12 +3328,12 @@ def rs(x): dateTicRng = [tmin-200, tmax+200] dateTics = np.arange(dateTicRng[0], dateTicRng[-1]+500, 1000) DateTicsLabel = dateTics - - + + maxErr = np.array([(diffX-xerr)*1e3, (diffX+xerr)*1e3, (diffY-yerr)*1e3, (diffY+yerr)*1e3]).max() maxErrM = np.array([(diffM - merr), (diffM + merr)]).max() - + if maxErr > 2: maxErr = 2.0 if maxErrM > 1.0: @@ -3341,13 +3341,13 @@ def rs(x): if resTicRng == None: resTicRng = [-1.1*maxErr, 1.1*maxErr] resTicRngM = [-1.1*maxErrM, 1.1*maxErrM] - + from matplotlib.ticker import FormatStrFormatter fmtX = FormatStrFormatter('%5i') fmtY = FormatStrFormatter('%6.3f') fmtM = FormatStrFormatter('%5.2f') fontsize1 = 10 - + ########## # X vs time ########## @@ -3356,10 +3356,10 @@ def rs(x): row = 1 else: col = 1 + 3*(i % (Ncols/3)) - row = 1 + 3*(i//(Ncols/3)) - + row = 1 + 3*(i//(Ncols/3)) + ind = int((row-1)*Ncols + col) - + paxes = plt.subplot(Nrows, Ncols, ind) plt.plot(cont_times, xt_cont_all[ii], 'b-') plt.plot(cont_times, xt_cont_all[ii] + xt_cont_err[ii], 'b--') @@ -3385,14 +3385,14 @@ def rs(x): paxes.yaxis.set_major_formatter(fmtY) paxes.tick_params(axis='both', which='major', labelsize=fontsize1) plt.annotate(starName, xy=(1.0,1.1), xycoords='axes fraction', fontsize=12, color='red') - - + + ########## # Y vs time ########## col = col + 1 ind = int((row-1)*Ncols + col) - + paxes = plt.subplot(Nrows, Ncols, ind) plt.plot(cont_times, yt_cont_all[ii], 'b-') plt.plot(cont_times, yt_cont_all[ii] + yt_cont_err[ii], 'b--') @@ -3416,13 +3416,13 @@ def rs(x): paxes.xaxis.set_major_formatter(fmtX) paxes.yaxis.set_major_formatter(fmtY) paxes.tick_params(axis='both', which='major', labelsize=fontsize1) - + ########## # M vs time ########## col = col + 1 ind = int((row - 1)*Ncols + col) - + paxes = plt.subplot(Nrows, Ncols, ind) plt.plot(time, fitLineM, 'g-') plt.plot(time, fitLineM + fitSigM, 'g--') @@ -3446,15 +3446,15 @@ def rs(x): paxes.xaxis.set_major_formatter(fmtX) paxes.yaxis.set_major_formatter(fmtM) paxes.tick_params(axis='both', which='major', labelsize=12) - - + + ########## # X residuals vs time ########## row = row + 1 col = col - 2 ind = int((row-1)*Ncols + col) - + paxes = plt.subplot(Nrows, Ncols, ind) plt.plot(time, np.zeros(len(time)), 'b-') plt.plot(cont_times, xt_cont_err[ii]*1e3, 'b--') @@ -3476,13 +3476,13 @@ def rs(x): plt.ylabel('X Residuals (mas)', fontsize=fontsize1) paxes.xaxis.set_major_formatter(fmtX) paxes.tick_params(axis='both', which='major', labelsize=fontsize1) - + ########## # Y residuals vs time ########## col = col + 1 ind = int((row-1)*Ncols + col) - + paxes = plt.subplot(Nrows, Ncols, ind) plt.plot(time, np.zeros(len(time)), 'b-') plt.plot(cont_times, yt_cont_err[ii]*1e3, 'b--') @@ -3504,13 +3504,13 @@ def rs(x): plt.ylabel('Y Residuals (mas)', fontsize=fontsize1) paxes.xaxis.set_major_formatter(fmtX) paxes.tick_params(axis='both', which='major', labelsize=fontsize1) - + ########## # M residuals vs time ########## col = col + 1 ind = int((row-1)*Ncols + col) - + paxes = plt.subplot(Nrows, Ncols, ind) plt.plot(time, np.zeros(len(time)), 'g-') plt.plot(time, fitSigM*1e3, 'g--') @@ -3532,15 +3532,15 @@ def rs(x): plt.ylabel('m Residuals (mag)', fontsize=fontsize1) paxes.xaxis.set_major_formatter(fmtX) paxes.tick_params(axis='both', which='major', labelsize=fontsize1) - - + + ########## # X vs. Y ########## row = row + 1 col = col - 2 ind = int((row-1)*Ncols + col) - + paxes = plt.subplot(Nrows, Ncols, ind) if not color_time: plt.errorbar(rs(x),rs(y), xerr=rs(xerr), @@ -3561,18 +3561,18 @@ def rs(x): paxes.xaxis.set_major_formatter(FormatStrFormatter('%.3f')) plt.xlabel('X (asec)', fontsize=fontsize1) plt.ylabel('Y (asec)', fontsize=fontsize1) - plt.plot(fitLineX, fitLineY, 'b-') - + plt.plot(fitLineX, fitLineY, 'b-') + ########## # X, Y Histogram of Residuals ########## col = col + 1 ind = int((row-1)*Ncols + col) - + bins = np.arange(-7.5, 7.5, 1) paxes = plt.subplot(Nrows, Ncols, ind) id = np.where(diffY < 0)[0] - sig[id] = -1.*sig[id] + sig[id] = -1.*sig[id] (n, b, p) = plt.hist(sigX, bins, histtype='stepfilled', color='b', label='X') plt.setp(p, 'facecolor', 'b') (n, b, p) = plt.hist(sigY, bins, histtype='step', color='r', label='Y') @@ -3582,13 +3582,13 @@ def rs(x): plt.xlabel('Residuals (sigma)', fontsize=fontsize1) plt.ylabel('Number of Epochs', fontsize=fontsize1) paxes.tick_params(axis='both', which='major', labelsize=fontsize1) - + ########## # M Histogram of Residuals ########## col = col + 1 ind = int((row-1)*Ncols + col) - + bins = np.arange(-7.5, 7.5, 1) paxes = plt.subplot(Nrows, Ncols, ind) (n, b, p) = plt.hist(sigM, bins, histtype='stepfilled', color='g', label='m') @@ -3598,10 +3598,10 @@ def rs(x): plt.xlabel('Residuals (sigma)', fontsize=fontsize1) plt.ylabel('Number of Epochs', fontsize=fontsize1) paxes.tick_params(axis='both', which='major', labelsize=fontsize1) - + if Nstars == 1: - plt.subplots_adjust(wspace=0.4, hspace=0.4, left = 0.15, bottom = 0.1, right=0.9, top=0.9) + plt.subplots_adjust(wspace=0.4, hspace=0.4, left = 0.15, bottom = 0.1, right=0.9, top=0.9) # plt.savefig(rootDir+'plots/plotStar_' + starName + '.png') else: plt.subplots_adjust(wspace=0.6, hspace=0.6, left = 0.08, bottom = 0.05, right=0.95, top=0.90) @@ -3616,9 +3616,9 @@ def rs(x): def plot_errors_vs_r_m(star_tab, vmax_perr=0.75, vmax_pmerr=0.75): """ - Plot the positional errors and the proper motion errors as a function of radius - and magnitude. The positional an proper motion errors will be the mean in the - two axis (as is used in pick_good_ref_stars()). + Plot the positional errors and the proper motion errors as a function of radius + and magnitude. The positional an proper motion errors will be the mean in the + two axis (as is used in pick_good_ref_stars()). """ r = np.hypot(star_tab['x0'], star_tab['y0']) p_err = np.mean((star_tab['x0_err'], star_tab['y0_err']), axis=0) * 1e3 @@ -3627,7 +3627,7 @@ def plot_errors_vs_r_m(star_tab, vmax_perr=0.75, vmax_pmerr=0.75): plt.figure(figsize=(12, 6)) plt.clf() plt.subplots_adjust(wspace=0.4) - + plt.subplot(1, 2, 1) plt.scatter(star_tab['m0'], r, c=p_err, s=8, vmin=0, vmax=vmax_perr) plt.colorbar(label='Pos Err (mas)') @@ -3655,7 +3655,7 @@ def plot_plxs(star_tab, target_idx=0): ax[1].set_xlabel('Plx/Plx_err') plt.tight_layout() ax[0].set_ylim(-5,5) - + def plot_sky(stars_tab, plot_errors=False, center_star=None, range=0.4, xcenter=0, ycenter=0, show_names=False, saveplot=False, @@ -3669,8 +3669,8 @@ def plot_sky(stars_tab, Parameters ---------- stars_tab : flystar.startables.StarTable - The StarTable containining 'x', 'y', 't', 'xe', 'ye', columns etc. - for plotting, where each of these columns is a 2D array of + The StarTable containining 'x', 'y', 't', 'xe', 'ye', columns etc. + for plotting, where each of these columns is a 2D array of [star_index, epoch_index]. @@ -3715,7 +3715,7 @@ def plot_sky(stars_tab, good_t = np.isfinite(stars_tab['t']) epochs = np.unique(stars_tab['t'][good_t]) assert len(epochs) == stars_tab['t'].shape[1] - + yearsInt = np.floor(epochs).astype('int') # Set up a color scheme @@ -3822,8 +3822,8 @@ def plot_sky(stars_tab, plt.show() return - - + + class PrintSelected(object): def __init__(self, points_info, fig, tab, mag_range, manual_print=False): self.points_info = points_info diff --git a/flystar/starlists.py b/flystar/starlists.py index 8387226..f872516 100644 --- a/flystar/starlists.py +++ b/flystar/starlists.py @@ -31,7 +31,7 @@ def restrict_by_name(table1, table2): name1 = table1['name'] name2 = table2['name'] - + Name = np.intersect1d(name1, name2) # trim out stars begin with 'star' idx = [] @@ -67,7 +67,7 @@ def restrict_by_area(table1, area, exclude=False): exclude: boolean (default=False) If true, *exclude* the stars that fall within the given area. If false, then only return stars that fall within the given area - + Output: ------ array of indicies corresponding to stars which are within the designated @@ -76,7 +76,7 @@ def restrict_by_area(table1, area, exclude=False): # Extract star coordinates xpos = table1['x'] ypos = table1['y'] - + # Extract desired coordinate ranges x_range = area[0] y_range = area[1] @@ -89,7 +89,7 @@ def restrict_by_area(table1, area, exclude=False): else: good = np.where( ( (xpos < x_range[0]) | (xpos > x_range[1]) ) & ( (ypos < y_range[0]) | (ypos > y_range[1]) ) ) - + return good[0] def restrict_by_use(label_mat, starlist_mat, idx_label, idx_starlist): @@ -114,7 +114,7 @@ def restrict_by_use(label_mat, starlist_mat, idx_label, idx_starlist): idx_starlist: array of indicies Indicies of the matched stars in the starlist. - + Output: ------- idx_label_f: array of indicies in the label catalog that fulfill the restrict @@ -122,15 +122,15 @@ def restrict_by_use(label_mat, starlist_mat, idx_label, idx_starlist): idx_starlist_f: array of indicies in the starlist that fulfill the restrict condition - - + + label_trim: astropy table label table with only use > 2 stars starlist_trim: astropy table reference table with only stars that correspond to use > 2 stars in the label_mat table. - + """ print( 'Restrict option activated') @@ -151,7 +151,7 @@ def restrict_by_use(label_mat, starlist_mat, idx_label, idx_starlist): print( 'Restrict option activated') print(( 'Keeping {0} of {1} stars'.format(len(idx_restrict), len(label_mat)))) - + return idx_label_f, idx_starlist_f @@ -188,7 +188,7 @@ def read_label(labelFile, prop_to_time=None, flipX=True): If true, multiply the x positions and velocities by -1.0. This is useful when label.dat has +x to the east, while reference starlist has +x to the west. - + #OLD# tref: reference epoch that label.dat is converted to. Output: @@ -196,11 +196,11 @@ def read_label(labelFile, prop_to_time=None, flipX=True): labelFile: astropy.table. containing name, m, x0, y0, x0e, y0e, vx, vy, vxe, vye, t0, use, r0, (if prop_to_time: x, y, xe, ye, t) - + x and y is in arcsec, converted to tref epoch, *(-1) so it increases to west - + vx, vy, vxe, vye is converted to arcsec/yr """ @@ -248,7 +248,7 @@ def read_label(labelFile, prop_to_time=None, flipX=True): t_label['y'].format = '.5f' t_label['xe'].format = '.5f' t_label['ye'].format = '.5f' - + # flip the x axis if flipX is True if flipX == True: t_label['x0'] = t_label['x0'] * (-1.0) @@ -295,7 +295,7 @@ def read_label_accel(labelFile, prop_to_time=None, flipX=True): If true, multiply the x positions and velocities by -1.0. This is useful when label.dat has +x to the east, while reference starlist has +x to the west. - + #OLD# tref: reference epoch that label.dat is converted to. Output: @@ -303,11 +303,11 @@ def read_label_accel(labelFile, prop_to_time=None, flipX=True): labelFile: astropy.table. containing name, m, x0, y0, x0e, y0e, vx, vy, vxe, vye, t0, use, r0, (if prop_to_time: x, y, xe, ye, t) - + x and y is in arcsec, converted to tref epoch, *(-1) so it increases to west - + vx, vy, vxe, vye is converted to arcsec/yr """ @@ -411,11 +411,11 @@ def read_starlist(starlistFile, error=True): col7: corr col8: N_frames col9: ? (left as default) - + error: boolean (default=True) If true, assumes starlist has error columns. This significantly changes the order of the columns. - + Output: ------ starlist astropy table. @@ -425,7 +425,7 @@ def read_starlist(starlistFile, error=True): # Check if this already has column names: cols = t_ref.colnames - + if cols[0] != 'col1': t_ref['name'] = t_ref['name'].astype(str) return t_ref @@ -436,7 +436,7 @@ def read_starlist(starlistFile, error=True): t_ref.rename_column(cols[2], 't') t_ref.rename_column(cols[3], 'x') t_ref.rename_column(cols[4], 'y') - + if error==True: t_ref.rename_column(cols[5], 'xe') t_ref.rename_column(cols[6], 'ye') @@ -449,7 +449,7 @@ def read_starlist(starlistFile, error=True): t_ref.rename_column(cols[6], 'corr') t_ref.rename_column(cols[7], 'N_frames') t_ref.rename_column(cols[8], 'flux') - + return t_ref @@ -481,7 +481,7 @@ class StarList(Table): me : 1D numpy.array with shape = N_stars Magnitude uncertainties of N_stars. - + corr : 1D numpy.array with shape = N_stars Fitting correlation of N_stars. @@ -495,7 +495,7 @@ class StarList(Table): """ - + def __init__(self, *args, **kwargs): """ """ @@ -503,7 +503,7 @@ def __init__(self, *args, **kwargs): arg_req = ('name', 'x', 'y', 'm') found_all_required = True - + for arg_test in arg_req: if arg_test not in kwargs: found_all_required = False @@ -583,7 +583,7 @@ def __init__(self, *args, **kwargs): self.add_column(MaskedColumn(data=kwargs[arg], name=arg)) else: self.add_column(Column(data=kwargs[arg], name=arg)) - + return @classmethod @@ -641,7 +641,7 @@ def from_lis_file(cls, filename, error=True, fvu_file=None): t_ref.rename_column(cols[2], 't') t_ref.rename_column(cols[3], 'x') t_ref.rename_column(cols[4], 'y') - + if error==True: t_ref.rename_column(cols[5], 'xe') t_ref.rename_column(cols[6], 'ye') @@ -654,7 +654,7 @@ def from_lis_file(cls, filename, error=True, fvu_file=None): t_ref.rename_column(cols[6], 'corr') t_ref.rename_column(cols[7], 'N_frames') t_ref.rename_column(cols[8], 'flux') - + if ('me' not in cols) and ('snr' in cols) and (error == True): t_ref['me'] = 1.0 / t_ref['snr'] @@ -667,16 +667,16 @@ def from_lis_file(cls, filename, error=True, fvu_file=None): msg = 'Star list and metric list have different lengths.\n' msg += '\t len(stars) = {0:d}\n' msg += '\t len(fvu) = {1:d}\n' - + raise RuntimeError(msg.format(len(t_ref), len(t_fvu))) - - t_ref = astropy.table.hstack([t_ref, t_fvu]) + + t_ref = astropy.table.hstack([t_ref, t_fvu]) return cls.from_table(t_ref) def to_lis_file(self, filename): _out = open(filename, 'w') - + hdr = '{name:13s} {mag:>6s} {year:>8s} ' hdr += '{x:>9s} {y:>9s} {xe:>9s} {ye:>9s} ' hdr += '{snr:>20s} {corr:>6s} {nimg:>8s} {flux:>20s}\n' @@ -684,7 +684,7 @@ def to_lis_file(self, filename): _out.write(hdr.format(name='# name', mag='m', year='t', x='x', y='y', xe='xe', ye='ye', snr='snr', corr='corr', nimg='N_frames', flux='flux')) - + fmt = '{name:13s} {mag:6.3f} {year:8.3f} ' fmt += '{x:9.3f} {y:9.3f} {xe:9.3f} {ye:9.3f} ' @@ -697,10 +697,10 @@ def to_lis_file(self, filename): flux=self['flux'][ss])) _out.close() - + return - - + + @classmethod def from_table(cls, table): """ @@ -709,7 +709,7 @@ def from_table(cls, table): will be added to the new StarList object that is returned. """ starlist = cls(name=table['name'], x=table['x'], y=table['y'], m=table['m'], meta=table.meta) - + for col in table.colnames: if col in ['name', 'x', 'y', 'm']: continue @@ -721,10 +721,10 @@ def from_table(cls, table): def fubar(self): print('This is in StarList') return - + def restrict_by_value(self, **kwargs): """ - Restrict a table to any min/max range of column values. For instance, + Restrict a table to any min/max range of column values. For instance, to restrict to only stars between 10 <= m <= 15, use: starlist.restrict_by_value(m_min=10, m_max=15) @@ -732,10 +732,10 @@ def restrict_by_value(self, **kwargs): where 'm' was the column name. This function acts on self, so the rows are removed - forever. + forever. """ # Loop through all conditions and build up - # an array of indicies of rows to remove. + # an array of indicies of rows to remove. remove_flag = np.zeros(len(self), dtype=bool) for key, value in kwargs.items(): @@ -743,20 +743,20 @@ def restrict_by_value(self, **kwargs): # Get the name of the column to act on and # whether the condition is min or max. key_split = key.split('_') - - # Support column names such as x_0. + + # Support column names such as x_0. col = '_'.join(key_split[:-1]) if key_split[-1] == 'min': remove_flag = np.logical_or(remove_flag, self[col] <= value) - + if key_split[-1] == 'max': remove_flag = np.logical_or(remove_flag, self[col] >= value) rem_idx = np.where(remove_flag == True)[0] - + self.remove_rows(rem_idx) - + return def transform_xym(self, trans): @@ -769,7 +769,7 @@ def transform_xym(self, trans): self.transform_xy(trans) self.transform_m(trans) - + return def transform_xy(self, trans): @@ -781,7 +781,7 @@ def transform_xy(self, trans): """ if trans == None: return - + x_T, y_T = trans.evaluate(self['x'], self['y']) self['x'] = x_T self['y'] = y_T @@ -792,7 +792,7 @@ def transform_xy(self, trans): self['ye'] = ye_T return - + def transform_m(self, trans): """ Apply a transformation (instance of flystar.transforms.Transform2D) @@ -802,14 +802,14 @@ def transform_m(self, trans): """ if trans == None: return - + m_T = trans.evaluate_mag(self['m']) self['m'] = m_T if 'me' in self.colnames: me_T = trans.evaluate_magerror(self['m'], self['me']) self['me'] = me_T - + return diff --git a/flystar/startables.py b/flystar/startables.py index 1590be4..b114fec 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -37,7 +37,7 @@ def __init__(self, *args, ref_list=0, **kwargs): ------------------------- motion_model : 1D numpy.array with shape = N_stars string indicating motion model type for each star - + xe : 2D numpy.array with shape = (N_stars, N_lists) Position uncertainties of N_stars in each of N_lists in the x dimension. @@ -50,7 +50,7 @@ def __init__(self, *args, ref_list=0, **kwargs): ep_name : 2D numpy.array with shape = (N_stars, N_lists) Names in each epoch for each of N_stars in each of N_lists. This is useful for tracking purposes. - + corr : 2D numpy.array with shape = (N_stars, N_lists) Fitting correlation for each of N_stars in each of N_lists. @@ -75,7 +75,7 @@ def __init__(self, *args, ref_list=0, **kwargs): print(t['name'][0:10]) # print the first 10 star names print(t['x'][0:10, 0]) # print x from the first epoch/list/column for the first 10 stars """ - + # Check if the required arguments are present arg_req = ('name', 'x', 'y', 'm') @@ -149,7 +149,7 @@ def __init__(self, *args, ref_list=0, **kwargs): names=('name', 'x', 'y', 'm')) self['name'] = self['name'].astype('U20') self.meta = {'n_stars': n_stars, 'n_lists': n_lists, 'ref_list': ref_list} - + for meta_arg in meta_tab: if meta_arg in kwargs: self.meta[meta_arg] = kwargs[meta_arg] @@ -173,12 +173,12 @@ def __init__(self, *args, ref_list=0, **kwargs): # self['motion_model_input'] = np.repeat(self.default_motion_model, len(self['name'])) return - + def add_starlist(self, **kwargs): """ - Add data from a new list to an existing StarTable. + Add data from a new list to an existing StarTable. Note, you can pass in the data via a StarList object or - via a series of keywords with a 1D array on each. + via a series of keywords with a 1D array on each. In either case, the number of stars must already match the existing number of stars in the StarTable. @@ -214,15 +214,15 @@ def _add_list_data_from_starlist(self, starlist): old_type = self[col_name].info.dtype new_data = np.empty((old_data.shape[0], old_data.shape[1] + 1), dtype=old_type) new_data[:, :-1] = old_data - + # Save the new data array (with both old and new data in it) to the table. - self[col_name] = new_data - + self[col_name] = new_data + if (col_name in starlist.colnames): # Add data if it was input self[col_name][:, -1] = starlist[col_name] else: # Add junk data it if wasn't input self._set_invalid_list_values(col_name, -1) - + ########## # Update the table meta-data. Remember that entries are lists not numpy arrays. @@ -258,12 +258,12 @@ def _add_list_data_from_starlist(self, starlist): def _add_list_data_from_keywords(self, **kwargs): # # Check if the required arguments are present # arg_req = ('x', 'y', 'm') - + # for arg_test in arg_req: # if arg_test not in kwargs: # err_msg = "Added lists require a '{0:s}' argument" # raise TypeError(err_msg.format(arg_test)) - + # # If we have errors, we need them in both dimensions. # if ('xe' in kwargs) ^ ('ye' in kwargs): # raise TypeError("Added lists with errors require both 'xe' and" + @@ -281,15 +281,15 @@ def _add_list_data_from_keywords(self, **kwargs): old_type = self[col_name].info.dtype new_data = np.empty((old_data.shape[0], old_data.shape[1] + 1), dtype=old_type) new_data[:, :-1] = old_data - + # Save the new data array (with both old and new data in it) to the table. self[col_name] = new_data - + if (col_name in kwargs): # Add data if it was input self[col_name][:, -1] = kwargs[col_name] else: # Add junk data it if wasn't input self._set_invalid_list_values(col_name, -1) - + # Update the table meta-data. Remember that entries are lists not numpy arrays. for key in self.meta.keys(): @@ -309,7 +309,7 @@ def _add_list_data_from_keywords(self, **kwargs): # Update the n_lists meta keyword. self.meta['n_lists'] += 1 - + return def _set_invalid_list_values(self, col_name, col_idx): @@ -323,7 +323,7 @@ def _set_invalid_list_values(self, col_name, col_idx): self[col_name][:, col_idx] = np.nan else: self[col_name][:, col_idx] = None - + return def _set_invalid_star_values(self, col_name, row_idx): @@ -337,13 +337,13 @@ def _set_invalid_star_values(self, col_name, row_idx): self[col_name][row_idx] = np.nan else: self[col_name][row_idx] = None - + return - + def _append_invalid_meta_values(self, key): """ - For an existing meta keyword that is a list (already known), - add an invalid value depending on the type. + For an existing meta keyword that is a list (already known), + add an invalid value depending on the type. """ if issubclass(type(self.meta[key][0]), np.integer): self.meta[key] = np.append(self.meta[key], [-1]) @@ -359,11 +359,11 @@ def _append_invalid_meta_values(self, key): warnings.warn(err_msg, UserWarning) return - - + + def get_starlist(self, list_index): """ - Return a StarList object for the specified list_index or epoch. + Return a StarList object for the specified list_index or epoch. Parameters ---------- @@ -383,16 +383,16 @@ def get_starlist(self, list_index): col_req_dict[col_name] = self[col_name] starlist = StarList(**col_req_dict) - + for col_name in self.colnames: if col_name in col_req_names: pass - + if len(self[col_name].data.shape) == 2: # Find the 2D columns starlist[col_name] = self[col_name][:, list_index] else: starlist[col_name] = self[col_name] - + return starlist @@ -402,7 +402,7 @@ def combine_lists_xym(self, weighted_xy=True, weighted_m=True, mask_lists=False, direction. For 'x', 'y' this means calculating the average position with outlier rejection. Optionally, weight by the 'xe' and 'ye' individual uncertainties. Optionally, use sigma clipping. - "mask_lists" is a list with the indices of starlists that are + "mask_lists" is a list with the indices of starlists that are excluded from the combination. Also, count the number of times a star is found in starlists. """ @@ -419,11 +419,11 @@ def combine_lists_xym(self, weighted_xy=True, weighted_m=True, mask_lists=False, weights_colm = 'me' else: weights_colm = None - + self.combine_lists('x', weights_col=weights_colx, mask_lists=mask_lists, sigma=sigma) self.combine_lists('y', weights_col=weights_coly, mask_lists=mask_lists, sigma=sigma) self.combine_lists('m', weights_col=weights_colm, mask_lists=mask_lists, sigma=sigma, ismag=True) - + return def combine_lists(self, col_name_in, weights_col=None, mask_val=None, @@ -437,10 +437,10 @@ def combine_lists(self, col_name_in, weights_col=None, mask_val=None, 0e -- the std (with outlier rejection) Masking of NaN values is also performed. - - "mask_lists" is a list with the indices of starlists that are + + "mask_lists" is a list with the indices of starlists that are excluded from the combination. - + A flag can be stored in the metadata to record if the average was weighted or not. """ @@ -454,31 +454,31 @@ def combine_lists(self, col_name_in, weights_col=None, mask_val=None, val_2d = np.ma.masked_invalid(val_2d) if mask_val: val_2d = np.ma.masked_values(val_2d, mask_val) - + if mask_lists is not False: # Remove a list if isinstance(mask_lists, list): if all(isinstance(item, int) for item in mask_lists): val_2d.mask[:, mask_lists] = True - + # Throw a warning if mask_lists is not a list if not isinstance(mask_lists, list): raise RuntimeError('mask_lists needs to be a list.') # Decide if we are going to have weights (before we # do the expensive sigma clipping routine). Note that - # if we have only 1 column to average, then we can't do weighting. + # if we have only 1 column to average, then we can't do weighting. if (weights_col and weights_col in self.colnames) and (val_2d.shape[1] > 1): err_2d = self[weights_col].data - + if ismag: # Convert to flux error err_2d = err_2d * val_2d * np.log(10) / 2.5 - + np.seterr(divide='ignore') wgt_2d = np.ma.masked_invalid(1.0 / err_2d**2) np.seterr(divide='warn') - + if meta_add: self.meta[col_name_in + '0'] = 'weighted' else: @@ -493,7 +493,7 @@ def combine_lists(self, col_name_in, weights_col=None, mask_val=None, warnings.filterwarnings('default', category=RuntimeWarning) else: val_2d_clip = val_2d - + # Calculate the (weighted) mean and standard deviation along # the N_lists direction (axis=1). if wgt_2d is not None: @@ -521,7 +521,7 @@ def combine_lists(self, col_name_in, weights_col=None, mask_val=None, else: self.add_column(Column(data=avg.data, name=col_name_avg)) self.add_column(Column(data=std.data, name=col_name_std)) - + return def detections(self): @@ -529,25 +529,25 @@ def detections(self): Find where stars are detected. # """ n_detect = np.sum(~np.isnan(self['x']), axis=1) - + if 'n_detect' in self.colnames: self['n_detect'] = n_detect else: self.add_column(Column(n_detect), name='n_detect') - + return - + def fit_motion_model( - self, + self, motion_models=None, fixed_params_dict=None, - weighting='var', - use_scipy=False, - absolute_sigma=True, + weighting='var', + use_scipy=False, + absolute_sigma=True, select_stars=None, bootstrap=0, - verbose=True, - mask_value=None, + verbose=True, + mask_value=None, mask_lists=None, fill_value=np.nan, show_progress=True @@ -562,7 +562,7 @@ def fit_motion_model( The behavior is as follows: 1. If 'motion_model_input' column is NOT in table: - Use the most complex model that has enough parameters to fit the data (n_fit >= n_params). - - If multiple models are supplied, prioritize the model with the most parameters to fit. + - If multiple models are supplied, prioritize the model with the most parameters to fit. - If multiple models have the same number of parameters, raise AssertionError: not sure which to use. 2. If 'motion_model_input' column IS in table: - Use the model specified in the 'motion_model_input' column. @@ -674,7 +674,7 @@ def fit_motion_model( y_data = np.ma.masked_invalid(self['y'].data, copy=True) xe_data = np.ma.masked_invalid(self['xe'].data, copy=True) if 'xe' in self.colnames else np.ones_like(x_data) ye_data = np.ma.masked_invalid(self['ye'].data, copy=True) if 'ye' in self.colnames else np.ones_like(y_data) - + if np.ndim(x_data) == 1: x_data = x_data[:, np.newaxis] if np.ndim(y_data) == 1: @@ -697,7 +697,7 @@ def fit_motion_model( else: t_data = copy.deepcopy(np.array(self.meta['list_times'])) t_data = np.broadcast_to(t_data, x_data.shape) - + # Add default t0 if not provided in fixed_params_dict if fixed_params_dict is None: weights = 1/np.hypot(xe_data, ye_data) if xe_data is not None else None @@ -771,7 +771,7 @@ def fit_motion_model( x=n_fit, bins=mm_n_params ) - 1 # Convert to 0-based index - + # Assign motion models to stars self['motion_model_used'] = np.array([motion_models[d].name for d in mm_digitized], dtype='U20') @@ -913,7 +913,7 @@ def infer_positions(self, times, fixed_params_dict=None, fill_value=np.nan): times : array_like Times at which to predict positions. Scalar, or (N_times,) array, or (N_stars, N_times) array. fixed_params_dict : None or dict, optional - Dictionary of fixed parameters to use for prediction. + Dictionary of fixed parameters to use for prediction. If not provided, will try to look for fixed parameters in the table columns. If fixed params are found in both the table and the fixed_params_dict, the values in the table will be used and the fixed_params_dict values will be ignored, by default None @@ -931,7 +931,7 @@ def infer_positions(self, times, fixed_params_dict=None, fill_value=np.nan): N_stars = len(self) times = np.atleast_1d(times) N_times = len(times) - + if (N_stars > 1) and (N_times > 1): x_pred = np.full((N_stars, N_times), fill_value, dtype=float) y_pred = np.full((N_stars, N_times), fill_value, dtype=float) @@ -1048,7 +1048,7 @@ def get_star_positions_at_time(self, t, motion_model_dict, allow_alt_models=True x[idx],y[idx],xe[idx],ye[idx] = mod.get_batch_pos_at_time(t,**param_dict) return x, y, xe, ye - + def shift_reference_frame(self, delta_vx=0.0, delta_vy=0.0, delta_pi=0.0, @@ -1058,7 +1058,7 @@ def shift_reference_frame(self, delta_vx=0.0, delta_vy=0.0, delta_pi=0.0, the absolute frame using either Gaia or a Galactic model. This modified the motion model fit parameters as well as the time series astrometry, assuming zero error on the shift values. - + Parameters ---------- delta_vx : float, optional @@ -1093,7 +1093,7 @@ def shift_reference_frame(table, delta_vx=0.0, delta_vy=0.0, delta_pi=0.0, the absolute frame using either Gaia or a Galactic model. This modified the motion model fit parameters as well as the time series astrometry, assuming zero error on the shift values. - + Parameters ---------- delta_vx : float, optional diff --git a/flystar/stitch_method2.py b/flystar/stitch_method2.py index 8cab361..f9aa4e0 100644 --- a/flystar/stitch_method2.py +++ b/flystar/stitch_method2.py @@ -42,7 +42,7 @@ def align_starlists(starlist, ref, transModel=transforms.PolyTransform, order=2, if weights==None, we don't use weights. """ - + #-------------------------------------------------- # Initial transformation with brightest briteN stars #-------------------------------------------------- @@ -98,7 +98,7 @@ def weighted_mean(df,x,xe,frames_in_use): # error = xe or ye # all_frames = e.g. ['A', 'B', 'C', ...] - + cols_x=["{0}_{1}".format(x,f) for f in frames_in_use] # columns for x_* e.g. ['x_A', 'x_B', 'x_C', ....] cols_xe=["{0}_{1}".format(xe,f) for f in frames_in_use] # columns for xe_* e.g. ['xe_A', 'xe_B', 'xe_C', ....] @@ -120,11 +120,11 @@ def weighted_mean(df,x,xe,frames_in_use): xe_master.append(array_xe[i][mask][0]) else: rows_to_drop.append(i) - + df=df.drop(rows_to_drop) df[x]=np.array(x_master) df[xe]=np.array(xe_master) - + return df @@ -132,12 +132,12 @@ def normal_mean(df,x,frames_in_use): cols_x=["{0}_{1}".format(x,f) for f in frames_in_use] df[x]=df[cols_x].mean(axis=1) - + return df def stitch(all_starlists, name_initial_ref, N_iter=5, corr_thresh=0.8, outMaster='./master.lis'): - + # all_starslist: the list of the names of all starlists e.g. ['A', 'B', 'C', ... ] # name_initial_ref: the name of the reference that you use in the very first match. # corr_thresh : threshold for correlation values. @@ -149,11 +149,11 @@ def stitch(all_starlists, name_initial_ref, N_iter=5, corr_thresh=0.8, outMaste input_starslists.remove(name_initial_ref) for name_starlist in input_starslists: - + starlist=starlists.read_starlist('{0}.lis'.format(name_starlist)) if 'ref' not in locals(): ref=starlists.read_starlist('{0}.lis'.format(name_initial_ref)) - + #------------ Choose good stars to use for a trans object -------------------- @@ -162,14 +162,14 @@ def stitch(all_starlists, name_initial_ref, N_iter=5, corr_thresh=0.8, outMaste # Select the very first 11 columns (i.e. the master reference) consistent with those of the starlist. # Table -> dataframe -> Table, which lets us avoid the following error: 'MaskedColumn' object has no attribute '_mask' - + ref_for_align=ref_for_align.to_pandas() - + ref_for_align=Table.from_pandas(ref_for_align[starlist_for_align.colnames]) _,_,_,trans=align_starlists(starlist_for_align,ref_for_align,order=2,dr_tol=1,N_loop=15) - + #------------ Transform the whole starlist using the trans object and match with the reference ------------- starlist_transformed=align.transform_from_object(starlist,trans) @@ -183,7 +183,7 @@ def stitch(all_starlists, name_initial_ref, N_iter=5, corr_thresh=0.8, outMaste #-------------Convert the astropy talbes into dataframes --------------------- df_ref=ref.to_pandas() df_starlist_transformed=starlist_transformed.to_pandas() - + #-------------Columns 11-21 contain the measurments for the initial reference-------------- colnames=starlist.colnames @@ -199,11 +199,11 @@ def stitch(all_starlists, name_initial_ref, N_iter=5, corr_thresh=0.8, outMaste for col in colnames: df_ref['{0}_{1}'.format(col,name_starlist)]=np.nan df_ref.loc[idx_ref_matched,'{0}_{1}'.format(col,name_starlist)]= np.array(df_starlist_transformed.loc[idx_starlist_transformed_matched,col]) - + else: for col in colnames: - + df_ref.insert(len(df_ref.columns),'{0}_{1}'.format(col,name_starlist),np.nan) df_ref.loc[idx_ref_matched,'{0}_{1}'.format(col,name_starlist)]= np.array(df_starlist_transformed.loc[idx_starlist_transformed_matched,col]) @@ -218,7 +218,7 @@ def stitch(all_starlists, name_initial_ref, N_iter=5, corr_thresh=0.8, outMaste #-------------- Figure out which frames are currently included in the master frame ----------- frames_in_use=sorted(set([column[-1] for column in columns if (column[-1] in all_starlists)])) - + #-------------- Average the measurements ------------- for col in colnames: if (col!='name') and (col!='x') and (col!='y') and (col!='xe') and (col!='ye') and (col!='N_frames'): @@ -226,7 +226,7 @@ def stitch(all_starlists, name_initial_ref, N_iter=5, corr_thresh=0.8, outMaste df_comb=weighted_mean(df_comb,'x','xe',frames_in_use) df_comb=weighted_mean(df_comb,'y','ye',frames_in_use) - + #-------------Recalculate 'N_frames' for the master frame -> N_frames = the number of input starlists containing the star----------- # N_frames = the number of notnull columns at each row in the master frame divided by the number of columns in an input starlist, then minus one. # The "minus one" at the end accounts for the very first columns, i.e. master columns, that contain the averaged values of all the input starlists. @@ -244,7 +244,7 @@ def stitch(all_starlists, name_initial_ref, N_iter=5, corr_thresh=0.8, outMaste #-------------- Convert the final dataframe back into an astropy table ------ ref=Table.from_pandas(df_comb) - + ref.write(outMaster,format='ascii.commented_header', header_start=-1, overwrite=True) return diff --git a/flystar/template.py b/flystar/template.py index c714f9d..1373799 100644 --- a/flystar/template.py +++ b/flystar/template.py @@ -8,28 +8,28 @@ import pdb -def align_template(labelFile, reference, transModel=transforms.PolyTransform, order=1, N_loop=2, +def align_template(labelFile, reference, transModel=transforms.PolyTransform, order=1, N_loop=2, dr_tol=1.0, dm_tol=None, briteN=100, weights='both', restrict=False, outFile='outTrans.txt'): """ Base example of how to use the flystar code. Assumes we are transforming a label.dat into a reference starlist. - + Parameters: ----------- labelFile: ascii file Starlist we would like to transform into the reference frame. For this code, we expect a label.dat file - + reference: ascii file Starlist that defines the reference frame - + transModel: transformation class (default: transforms.polyTransform) Defines which transformation model to use. Both the four-parameter and polynomial transformations are supported - + order: int (default=1) Order of the polynomial transformation. Only used for polynomial transform - + N_loop: int (default=2) How many times to iterate on the transformation calculation. Ideally, each iteration adds more stars and thus a better transform, to some @@ -39,11 +39,11 @@ def align_template(labelFile, reference, transModel=transforms.PolyTransform, or the distance tolerance for matching two stars in align.transform_and_match dm_tol: float (defalut=None) - the magnitude tolerance for matching two stars in align.trnasform_and_match + the magnitude tolerance for matching two stars in align.trnasform_and_match briteN: int (default=100) the number of stars used in blind matching - + weights: string (default='both') if weights=='both', we use both position error in transformed starlist and reference starlist as uncertanty. And weights is the reciprocal of this uncertanty. @@ -66,7 +66,7 @@ def align_template(labelFile, reference, transModel=transforms.PolyTransform, or tref = starlist['t'][0] # label.dat has position & position err and velocity & velocity error label = starlists.read_label(labelFile, prop_to_time=tref, flipX=True) - + #-------------------------------------------------- # Initial transformation with brightest briteN stars @@ -79,21 +79,21 @@ def align_template(labelFile, reference, transModel=transforms.PolyTransform, or # and calculate initial transform label_ini = label[idx_ini_label] starlist_ini = starlist[idx_ini_starlist] - + trans = align.initial_align(label_ini, starlist_ini, briteN=briteN, transformModel=transModel, order=order) - + # apply the initial transform to label.dat # this is used for future weights calculation label_trans_ini = align.transform_from_object(label, trans) - + #------------------------------------------------------------------------ # Use transformation to match starlists, then recalculate transformation. #------------------------------------------------------------------------ # Iterate on this as many times as desired for i in range(N_loop): - # apply the transformation to label.dat and + # apply the transformation to label.dat and # matched the transformed label with starlist. idx_label, idx_starlist = align.transform_and_match(label, starlist, trans, dr_tol=dr_tol, dm_tol=dm_tol) @@ -101,17 +101,17 @@ def align_template(labelFile, reference, transModel=transforms.PolyTransform, or if restrict: label_match = label[idx_label] starlist_match = starlist[idx_starlist] - idx_label, idx_starlist = stalists.restrict_by_use(label_match, starlist_match, + idx_label, idx_starlist = stalists.restrict_by_use(label_match, starlist_match, idx_label, idx_starlist) - + # use the matched stars to calculate new transformation label_match = label[idx_label] starlist_match = starlist[idx_starlist] label_ini_match = label_trans_ini[idx_label] - trans, N_trans = align.find_transform(label_match, label_ini_match, starlist_match, + trans, N_trans = align.find_transform(label_match, label_ini_match, starlist_match, transModel=transModel, order=order, weights = weights) - + #--------------------------------------------- # Write final transform in java align format @@ -121,7 +121,7 @@ def align_template(labelFile, reference, transModel=transforms.PolyTransform, or # write the transformation coefficients to 'outTrans.txt' align.write_transform(trans, labelFile, reference, N_trans, deltaMag=delta_m, restrict=restrict, weights=weights, outFile=outFile) - + #----------------------------------------------------------- # Test transform: apply to label.dat, make diagnostic plots @@ -129,11 +129,11 @@ def align_template(labelFile, reference, transModel=transforms.PolyTransform, or # apply the final transformation to label.dat label_trans = align.transform_from_object(label, trans) label_trans_match = label_trans[idx_label] - + # postion map with every star in starlist and transformed label. # both matched and unmatched stars. plots.trans_positions( starlist, starlist_match, label_trans, label_trans_match) - + # position difference histogram for matched stars. plots.pos_diff_hist( starlist_match, label_trans_match) @@ -146,6 +146,6 @@ def align_template(labelFile, reference, transModel=transforms.PolyTransform, or # quiver plot of postion residules plots.pos_diff_quiver( starlist_match, label_trans_match) - + return - + From ed8eea0304449ca185a79331c2ad5b11590d193d Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Wed, 11 Mar 2026 23:29:02 -0700 Subject: [PATCH 168/254] Update test data paths --- flystar/tests/test_align.py | 8 ++++---- flystar/tests/test_starlist.py | 7 ++++--- flystar/tests/test_startable.py | 13 +++++++------ 3 files changed, 15 insertions(+), 13 deletions(-) diff --git a/flystar/tests/test_align.py b/flystar/tests/test_align.py index e88cb79..d93ba8e 100644 --- a/flystar/tests/test_align.py +++ b/flystar/tests/test_align.py @@ -492,8 +492,8 @@ def test_MosaicToRef_hst_me(): my_gaia['me'] = 0.01 my_gaia.rename_columns( - ['x0e', 'y0e'], - ['x0_err', 'y0_err'] + ['x0e', 'y0e', 'vxe', 'vye'], + ['x0_err', 'y0_err', 'vx_err', 'vy_err'] ) # Gather the list of starlists. For first pass, don't modify the starlists. # Loop through the observations and read them in, in prep for alignment with Gaia @@ -825,7 +825,7 @@ def test_MosaicToRef_mag_bug(): """ make_fake_starlists_poly1_vel() - ref_list = starlists.StarList.read('test_data/random_vel_0.fits') + ref_list = starlists.StarList.read(f'{test_data_path}/random_vel_0.fits') lists = [ref_list] msc = align.MosaicToRef(ref_list, lists, @@ -1078,7 +1078,7 @@ def make_fake_starlists_poly0_vel(seed=-1): # Save original positions as reference (1st) list # in a StarList format (with velocities). - lis.write('test_data/random_vel_ref.fits', overwrite=True) + lis.write(f'{test_data_path}/random_vel_ref.fits', overwrite=True) ########## # Propogate to new times and distort. diff --git a/flystar/tests/test_starlist.py b/flystar/tests/test_starlist.py index c8ab2bb..568c26d 100644 --- a/flystar/tests/test_starlist.py +++ b/flystar/tests/test_starlist.py @@ -1,13 +1,14 @@ from astropy.table import Table from flystar.starlists import StarList import os, pdb +import flystar -test_dir = os.path.dirname(__file__) + '/' +test_data_path = f'{flystar.__path__[0]}/tests/test_data' def make_star_list(): # User input - cat_file = test_dir + 'test_data/A.lis' + cat_file = f'{test_data_path}/A.lis' # Read and arrange the test input cat_tab = Table.read(cat_file, format='ascii', delimiter=r'\s') @@ -25,7 +26,7 @@ def make_star_list(): # Name is a unique name for each star and is a 1D array. starlist_time = 2011.1 - starlist_name = 'test_data/A.lis' + starlist_name = f'{test_data_path}/A.lis' # Generate the starlist starlist = StarList(name=name_in, x=x_in, y=y_in, m=m_in, xe=xe_in, diff --git a/flystar/tests/test_startable.py b/flystar/tests/test_startable.py index f99ee38..fbb0a34 100644 --- a/flystar/tests/test_startable.py +++ b/flystar/tests/test_startable.py @@ -7,15 +7,16 @@ import pytest import os import pdb +import flystar -test_dir = os.path.dirname(__file__) + '/' +test_data_path = f'{flystar.__path__[0]}/tests/test_data' def test_StarTable_init1(): """ Test creation of new StarTable. """ # User input - cat_file = test_dir + 'test_data/test_catalog.fits' + cat_file = f'{test_data_path}/test_catalog.fits' # Read and arrange the test input cat_tab = Table.read(cat_file) @@ -267,7 +268,7 @@ def test_get_starlist(): def test_combine_1col(): # User input - cat_file = test_dir + 'test_data/test_catalog.fits' + cat_file = f'{test_data_path}/test_catalog.fits' # Read and arrange the test input cat_tab = Table.read(cat_file) @@ -437,7 +438,7 @@ def test_fit_motion_model_2epoch(): def make_star_table(): # User input - cat_file = test_dir + 'test_data/test_catalog.fits' + cat_file = f'{test_data_path}/test_catalog.fits' # Read and arrange the test input cat_tab = Table.read(cat_file) @@ -472,7 +473,7 @@ def make_star_table(): def make_star_table_1epoch(): # User input - cat_file = test_dir + 'test_data/test_catalog.fits' + cat_file = f'{test_data_path}/test_catalog.fits' # Read and arrange the test input cat_tab = Table.read(cat_file) @@ -501,7 +502,7 @@ def make_star_table_1epoch(): def make_star_table_2epoch(): # User input - cat_file = test_dir + 'test_data/test_catalog.fits' + cat_file = f'{test_data_path}/test_catalog.fits' # Read and arrange the test input cat_tab = Table.read(cat_file) From 4f816f2a61dd3806c67e25ee06e25d534698c597 Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Wed, 11 Mar 2026 23:30:49 -0700 Subject: [PATCH 169/254] Slightly accelerated fit motion model preparation --- flystar/startables.py | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/flystar/startables.py b/flystar/startables.py index b114fec..b1d3a2a 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -675,6 +675,7 @@ def fit_motion_model( xe_data = np.ma.masked_invalid(self['xe'].data, copy=True) if 'xe' in self.colnames else np.ones_like(x_data) ye_data = np.ma.masked_invalid(self['ye'].data, copy=True) if 'ye' in self.colnames else np.ones_like(y_data) + # Ensure data is 2D for consistent indexing, even if we have only one list/epoch (shape (N_stars, 1) instead of (N_stars,)) if np.ndim(x_data) == 1: x_data = x_data[:, np.newaxis] if np.ndim(y_data) == 1: @@ -736,16 +737,16 @@ def fit_motion_model( # Calculate mask array - xy_mask = (~x_data.mask) & (~y_data.mask) + xy_mask = ~ (x_data.mask | y_data.mask) self['n_fit'] = xy_mask.sum(axis=1) # Convert to lists of arrays for faster access during fitting - t_stars = [np.array(t_data[i][xy_mask[i]]) for i in range(N_stars)] - x_stars = [np.array(x_data[i][xy_mask[i]]) for i in range(N_stars)] - y_stars = [np.array(y_data[i][xy_mask[i]]) for i in range(N_stars)] - xe_stars = [np.array(xe_data[i][xy_mask[i]]) if xe_data is not None else None for i in range(N_stars)] - ye_stars = [np.array(ye_data[i][xy_mask[i]]) if ye_data is not None else None for i in range(N_stars)] - + idx = [np.flatnonzero(xy_mask[i]) for i in range(N_stars)] + t_stars = [np.array(t_data[i][idx[i]]) for i in range(N_stars)] + x_stars = [np.array(x_data[i][idx[i]]) for i in range(N_stars)] + y_stars = [np.array(y_data[i][idx[i]]) for i in range(N_stars)] + xe_stars = [np.array(xe_data[i][idx[i]]) for i in range(N_stars)] if xe_data is not None else [None]*N_stars + ye_stars = [np.array(ye_data[i][idx[i]]) for i in range(N_stars)] if ye_data is not None else [None]*N_stars ########################### ####### Determine MM ###### From 0a4c276b9e8d70d4be6fe6cb7f48c0ae9365ed37 Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Wed, 11 Mar 2026 23:35:48 -0700 Subject: [PATCH 170/254] Added determine motion model function used in get_ref_list_from_table and update_ref_table_aggregates; Fixed the data dimensions when N_times=1 --- flystar/align.py | 197 +++++++++++++++++++++++++++++++---------------- 1 file changed, 130 insertions(+), 67 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index d698e99..6f596a2 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -419,6 +419,11 @@ def fit(self): # Update chi2 values in ref table, as motion_model_used may have changed x_inferred, y_inferred, _, _ = self.ref_table.infer_positions(all_epochs) + # Ensure x_inferred and y_inferred is 2D for chi2 calculation + if x_inferred.ndim == 1: + x_inferred = x_inferred[:, np.newaxis] + if y_inferred.ndim == 1: + y_inferred = y_inferred[:, np.newaxis] chi2_x_2d = ((self.ref_table['x'] - x_inferred) / self.ref_table['xe'])**2 chi2_y_2d = ((self.ref_table['y'] - y_inferred) / self.ref_table['ye'])**2 chi2_x = np.nansum(chi2_x_2d, axis=1) @@ -662,14 +667,8 @@ def setup_ref_table_from_starlist(self, star_list): array in the original reference star list. """ col_arrays = {} - # Remove motion_model_used if present, as it can only be determined by fit_motion_model function - if 'motion_model_used' in star_list.colnames: - warnings.warn("The input reference star list contains a 'motion_model_used' column. This column will be removed and re-calculated by the fit_motion_model function.") - star_list.remove_column('motion_model_used') - - motion_model_col_names = motion_model.motion_model_param_names(self.motion_models, with_errors=True, with_fixed=True) + ['m0','m0_err','use_in_trans', 'motion_model_input', 'motion_model_used'] - if 't0' not in motion_model_col_names: - motion_model_col_names.insert(0, 't0') + + motion_model_col_names = motion_model.all_motion_model_param_names(with_errors=True, with_fixed=True) + ['m0','m0_err','use_in_trans', 'motion_model_input', 'motion_model_used'] for col_name in star_list.colnames: if col_name == 'name': # The "name" column will be 1D; but we will also add a "name_in_list" column. @@ -745,7 +744,7 @@ def setup_ref_table_from_starlist(self, star_list): # This is necessary for later steps, even if the columns are just zeros. final_new_cols = np.concatenate((new_cols_arr, new_err_cols)) for ii in final_new_cols: - assert ii in ref_table.keys() + assert ii in ref_table.keys(), f"ref_table is missing necessary column {ii}." # Make sure we have a column to indicate whether each star # CAN BE USED in the transformation. This will be 1D @@ -984,29 +983,31 @@ def update_ref_table_aggregates(self, keep_orig=None, n_boot=0): # if (keep_orig is not None) and (sum(keep_orig) > 0): # Determine motion_model_used for keep_orig stars # Filter possible motion models based on available columns - motion_models_possible = [] - for mm in self.motion_models: - required_columns = mm.fit_param_names + mm.fixed_param_names - if all(col in self.ref_table.colnames or (self.fixed_params_dict is not None and col in self.fixed_params_dict.keys()) for col in required_columns): - motion_models_possible.append((mm, required_columns)) - - # Check if values are finite for required columns in possible motion models - motion_model_used = [] - # for k in np.where(keep_orig)[0]: - for k in range(len(self.ref_table)): - for mm, req in motion_models_possible[::-1]: - # if all(np.isfinite(self.ref_table[k][col]) for col in req if self.ref_table[col].dtype.kind in 'f'): - req_col_in_table = [col for col in req if col in self.ref_table.colnames] - req_col_in_dict = [col for col in req if (self.fixed_params_dict is not None) and (col in self.fixed_params_dict.keys())] - # If requested column in table/fixed_params dict is numeric, check if values are finite. - if all(np.isfinite(self.ref_table[col][k]) for col in req_col_in_table if np.issubdtype(self.ref_table[col].dtype, np.number)) \ - and all(np.isfinite(self.fixed_params_dict[col]) for col in req_col_in_dict if np.issubdtype(np.array(self.fixed_params_dict[col]).dtype, np.number)): - motion_model_used.append(mm.name) - break + motion_model_used = determine_motion_model(self.ref_table, self.motion_models, self.fixed_params_dict) + + # motion_models_possible = [] + # for mm in self.motion_models: + # required_columns = mm.fit_param_names + mm.fixed_param_names + # if all(col in self.ref_table.colnames or (self.fixed_params_dict is not None and col in self.fixed_params_dict.keys()) for col in required_columns): + # motion_models_possible.append((mm, required_columns)) + + # # Check if values are finite for required columns in possible motion models + # motion_model_used = [] + # # for k in np.where(keep_orig)[0]: + # for k in range(len(self.ref_table)): + # for mm, req in motion_models_possible[::-1]: + # # if all(np.isfinite(self.ref_table[k][col]) for col in req if self.ref_table[col].dtype.kind in 'f'): + # req_col_in_table = [col for col in req if col in self.ref_table.colnames] + # req_col_in_dict = [col for col in req if (self.fixed_params_dict is not None) and (col in self.fixed_params_dict.keys())] + # # If requested column in table/fixed_params dict is numeric, check if values are finite. + # if all(np.isfinite(self.ref_table[col][k]) for col in req_col_in_table if np.issubdtype(self.ref_table[col].dtype, np.number)) \ + # and all(np.isfinite(self.fixed_params_dict[col]) for col in req_col_in_dict if np.issubdtype(np.array(self.fixed_params_dict[col]).dtype, np.number)): + # motion_model_used.append(mm.name) + # break # Assign the determined motion models # self.ref_table['motion_model_used'][keep_orig] = motion_model_used - self.ref_table['motion_model_used'] = motion_model_used + self.ref_table['motion_model_used'] = Column(motion_model_used, name='motion_model_used', dtype='U15') # Replace the originals if we are supposed to keep them fixed. if (keep_orig is not None) and (sum(keep_orig) > 0): @@ -1120,41 +1121,45 @@ def get_ref_list_from_table(self, epoch): # Reference stars will be named. name = self.ref_table['name'] # Calculate x, y, xe, ye - if 'motion_model_used' in self.ref_table.colnames: - x, y, xe, ye = self.ref_table.infer_positions(epoch, fixed_params_dict=self.fixed_params_dict) - else: - # Otherwise, infer positions using the most complex motion model with the existing columns, until it reaches Fixed or Empty - for mm in self.motion_models[::-1]: - required_columns = mm.fit_param_names + mm.fixed_param_names - if all([param in self.ref_table.colnames or (self.fixed_params_dict is not None and param in self.fixed_params_dict.keys()) for param in required_columns]): - # Check if the values are finite for non-string columns in the required columns for this motion model. If not, skip to the next motion model. - - if any([param not in self.ref_table.colnames for param in required_columns]): - # If any required column is missing, skip to the next motion model. - continue - - if not all([np.isfinite(self.ref_table[param]).all() for param in required_columns if self.ref_table[param].dtype.kind in 'if']): - # If any required column has non-finite values, skip to the next motion model. - continue - - print(f"Inferring positions using motion model {mm.name}.") - # If we have error columns for all fit parameters, then use them in the model inference. Otherwise, just use the fit parameters without errors. - if all([f'{param}_err' in self.ref_table.colnames for param in mm.fit_param_names]) and all([np.isfinite(self.ref_table[f'{param}_err']).all() for param in mm.fit_param_names]): - x, y, xe, ye = mm().model( - t=epoch, - fit_params=np.array([self.ref_table[param] for param in mm.fit_param_names]).T, - fit_param_errs=np.array([self.ref_table[f'{param}_err'] for param in mm.fit_param_names]).T, - fixed_params_dict={param: self.ref_table[param] for param in mm.fixed_param_names} - ) - else: - x, y = mm().model( - t=epoch, - fit_params=np.array([self.ref_table[param] for param in mm.fit_param_names]).T, - fixed_params_dict={param: self.ref_table[param] for param in mm.fixed_param_names} - ) - xe = None - ye = None - break + + if 'motion_model_used' not in self.ref_table.colnames: + motion_model_used = determine_motion_model(self.ref_table) + self.ref_table['motion_model_used'] = Column(motion_model_used, name='motion_model_used', dtype='U15') + + x, y, xe, ye = self.ref_table.infer_positions(epoch, fixed_params_dict=self.fixed_params_dict) + # else: + # # Otherwise, infer positions using the most complex motion model with the existing columns, until it reaches Fixed or Empty + # for mm in self.motion_models[::-1]: + # required_columns = mm.fit_param_names + mm.fixed_param_names + # if all([(param in self.ref_table.colnames) or (self.fixed_params_dict is not None and param in self.fixed_params_dict.keys()) for param in required_columns]): + # # Check if the values are finite for non-string columns in the required columns for this motion model. If not, skip to the next motion model. + + # if any([param not in self.ref_table.colnames for param in required_columns]): + # # If any required column is missing, skip to the next motion model. + # continue + + # if not all([np.isfinite(self.ref_table[param]).all() for param in required_columns if self.ref_table[param].dtype.kind in 'if']): + # # If any required column has non-finite values, skip to the next motion model. + # continue + + # print(f"Inferring positions using motion model {mm.name}.") + # # If we have error columns for all fit parameters, then use them in the model inference. Otherwise, just use the fit parameters without errors. + # if all([f'{param}_err' in self.ref_table.colnames for param in mm.fit_param_names]) and all([np.isfinite(self.ref_table[f'{param}_err']).all() for param in mm.fit_param_names]): + # x, y, xe, ye = mm().model( + # t=epoch, + # fit_params=np.array([self.ref_table[param] for param in mm.fit_param_names]).T, + # fit_param_errs=np.array([self.ref_table[f'{param}_err'] for param in mm.fit_param_names]).T, + # fixed_params_dict={param: self.ref_table[param] for param in mm.fixed_param_names} + # ) + # else: + # x, y = mm().model( + # t=epoch, + # fit_params=np.array([self.ref_table[param] for param in mm.fit_param_names]).T, + # fixed_params_dict={param: self.ref_table[param] for param in mm.fixed_param_names} + # ) + # xe = None + # ye = None + # break # # No velocities... just used average positions. # x = self.ref_table['x0'] @@ -2002,6 +2007,11 @@ def fit(self): # Update chi2 values in ref table, as motion_model_used may have changed x_inferred, y_inferred, _, _ = self.ref_table.infer_positions(all_epochs, fixed_params_dict=self.fixed_params_dict) + # Convert x_inferred and y_inferred to 2D arrays if they are 1D (i.e. if only one epoch), so that the chi2 calculation works correctly. + if x_inferred.ndim == 1: + x_inferred = x_inferred[:, np.newaxis] + if y_inferred.ndim == 1: + y_inferred = y_inferred[:, np.newaxis] chi2_x_2d = ((self.ref_table['x'] - x_inferred) / self.ref_table['xe'])**2 chi2_y_2d = ((self.ref_table['y'] - y_inferred) / self.ref_table['ye'])**2 chi2_x = np.nansum(chi2_x_2d, axis=1) @@ -2107,6 +2117,55 @@ def infer_positions(t, startable, motion_models=None, fixed_params_dict=None, re # y = startable['y'] # return x, y +def determine_motion_model(startable, motion_models=None, fixed_params_dict=None): + """Determine motion model used in star table based on the finite model parameter columns + + Parameters + ---------- + startable : startable + Startable with motion model parameter columns + motion_models : list of MotionModel or str, optional + List of motion model classes or their names to select from. + If None, all available motion models will be considered, by default None + fixed_params_dict : dict, optional + Dictionary of fixed parameters, by default None + + Returns + ------- + motion_model_used : list + List of motion model used for each star + """ + + if motion_models is None: + motion_models = motion_model.MotionModel.__subclasses__() + elif all(isinstance(mm, str) for mm in motion_models): + all_mm_map = motion_model.motion_model_map() + motion_models = [all_mm_map[mm] for mm in motion_models] + + if fixed_params_dict is None: + fixed_params_dict = {} + + motion_models_possible = [] + for mm in motion_models: + required_columns = mm.fit_param_names + mm.fixed_param_names + req_col_in_table = [col for col in required_columns if (col in startable.colnames)] + req_col_in_dict = [col for col in required_columns if (col in fixed_params_dict.keys())] + if all((col in startable.colnames) or (col in fixed_params_dict.keys()) for col in required_columns): + motion_models_possible.append((mm, req_col_in_table, req_col_in_dict)) + + # Check if values are finite for required columns in possible motion models + motion_model_used = [] + + for k in tqdm(range(len(startable)), desc='Determining motion model for each star'): + for mm, req_col_in_table, req_col_in_dict in motion_models_possible[::-1]: + # If requested column in table/fixed_params dict is numeric, check if values are finite. + if all(np.isfinite(startable[col][k]) for col in req_col_in_table if np.issubdtype(startable[col].dtype, np.number)) \ + and all(np.isfinite(fixed_params_dict[col]) for col in req_col_in_dict if np.issubdtype(np.array(fixed_params_dict[col]).dtype, np.number)): + motion_model_used.append(mm.name) + break + return motion_model_used + + def get_all_epochs(t): """ Helper function to get times of all epochs from a ref table. @@ -2310,8 +2369,12 @@ def add_rows_for_new_stars(ref_table, star_list, idx_list, motion_model_name='Fi else: new_col_shape = [N_newstars, ref_table[col_name].shape[1]] - new_col_data = Column(data=np.tile(new_col_empty, new_col_shape), - name=col_name, dtype=ref_table[col_name].dtype) + new_col_data = Column( + data=np.tile(new_col_empty, new_col_shape), + name=col_name, + dtype=ref_table[col_name].dtype + ) + col_arrays[new_col_name] = new_col_data ref_table_new = StarTable(**col_arrays) @@ -3318,7 +3381,7 @@ def update_old_and_new_names(ref_table, list_index, idx_ref_new): new_name_len_max = 0 for ss in idx_ref_new: - new_name = '{0:3d}_{1:s}'.format(list_index, ref_table['name_in_list'][ss, list_index]) + new_name = f"{list_index:3d}_{str(ref_table['name_in_list'][ss, list_index]):s}" new_names.append(new_name) new_name_len_max = max(new_name_len_max, len(new_name)) From 646a9570a25899fb71f2e948828ca13e2fbc57ef Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Wed, 11 Mar 2026 23:50:26 -0700 Subject: [PATCH 171/254] Passed parallax fit --- flystar/tests/test_align.py | 50 ++++++++++++++++++------------------- 1 file changed, 24 insertions(+), 26 deletions(-) diff --git a/flystar/tests/test_align.py b/flystar/tests/test_align.py index d93ba8e..14d1ab1 100644 --- a/flystar/tests/test_align.py +++ b/flystar/tests/test_align.py @@ -1446,29 +1446,27 @@ def make_fake_starlists_poly1_par(seed=-1): return (xy_trans, mag_trans) if __name__ == '__main__': - test_MosaicSelfRef() - # test_MosaicToRef_hst_me() - # import pickle - # import matplotlib.pyplot as plt - # with open(f'{test_data_path}/my_gaia.pkl', 'rb') as f: - # my_gaia = pickle.load(f) - # with open(f'{test_data_path}/list_of_starlists.pkl', 'rb') as f: - # list_of_starlists = pickle.load(f) - # ra_deg, dec_deg = 18.0, -30.0 - # my_gaia.remove_column('motion_model_used') - # msc = align.MosaicToRef(my_gaia, list_of_starlists, iters=3, - # dr_tol=[0.2, 0.1, 0.08], dm_tol=[5,5,5], - # outlier_tol=[None, None, 3], mag_lim=[6, 20], - # trans_class=transforms.PolyTransform, - # trans_args=[{'order': 1}, {'order': 1}, {'order': 1}], - # motion_models=['Empty','Fixed','Linear','Parallax'], - # fixed_params_dict = {'ra':ra_deg, 'dec':dec_deg, 'pa':0.0, 'obsLocation':'earth'}, - # use_ref_new=True, - # update_ref_orig=False, - # mag_trans=True, - # trans_weighting='both,std', - # init_guess_mode='name', verbose=3) - # msc.fit() - # for i in range(msc.ref_table['x'].shape[1]): - # plt.scatter(msc.ref_table['x'][:, i], msc.ref_table['y'][:, i]) - # plt.show() \ No newline at end of file + import pickle + import matplotlib.pyplot as plt + with open(f'{test_data_path}/my_gaia.pkl', 'rb') as f: + my_gaia = pickle.load(f) + with open(f'{test_data_path}/list_of_starlists.pkl', 'rb') as f: + list_of_starlists = pickle.load(f) + ra_deg, dec_deg = 18.0, -30.0 + my_gaia.remove_column('motion_model_used') + msc = align.MosaicToRef(my_gaia, list_of_starlists, iters=3, + dr_tol=[0.2, 0.1, 0.08], dm_tol=[5,5,5], + outlier_tol=[None, None, 3], mag_lim=[6, 20], + trans_class=transforms.PolyTransform, + trans_args=[{'order': 1}, {'order': 1}, {'order': 1}], + motion_models=['Empty','Fixed','Linear','Parallax'], + fixed_params_dict = {'ra':ra_deg, 'dec':dec_deg, 'pa':0.0, 'obsLocation':'earth'}, + use_ref_new=True, + update_ref_orig=False, + mag_trans=True, + trans_weighting='both,std', + init_guess_mode='name', verbose=3) + msc.fit() + for i in range(msc.ref_table['x'].shape[1]): + plt.scatter(msc.ref_table['x'][:, i], msc.ref_table['y'][:, i]) + plt.show() \ No newline at end of file From 3927e66d87cfcec76899de625d0d93217fe17e6c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Wed, 18 Mar 2026 17:51:22 -0700 Subject: [PATCH 172/254] Add number of iterations in align output information --- flystar/align.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 6f596a2..8c98550 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -361,7 +361,7 @@ def fit(self): # This updates trans objects and the ref_table. self.match_and_transform(self.mag_lim[self.ref_index], self.dr_tol[nn], self.dm_tol[nn], self.outlier_tol[nn], - self.trans_args[nn]) + self.trans_args[nn], nn) # Clean up the reference table # Find where stars are detected. @@ -438,7 +438,7 @@ def fit(self): pickle.dump(self, file) return - def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_args): + def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_args, nn=None): """ Given some reference list of positions, loop through all the starlists transform and match them. @@ -449,6 +449,8 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar msg2 = ' {0:8s} < {1:0.3f}' print(" ") print(" **********") + if nn is not None: + print(f" Iteration {nn+1} / {self.iters}") print(msg.format((ii + 1), len(self.star_lists), len(self.star_lists[ii]))) print(msg2.format('dr', dr_tol)) print(msg2.format('|dm|', dm_tol)) @@ -1947,7 +1949,7 @@ def fit(self): # This updates trans objects and the ref_table. self.match_and_transform(self.ref_mag_lim, self.dr_tol[nn], self.dm_tol[nn], self.outlier_tol[nn], - self.trans_args[nn]) + self.trans_args[nn], nn) # Clean up the reference table # Find where stars are detected. From 6a94841f3d9a3828f134a31c60a37faf2fc29e13 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Wed, 18 Mar 2026 17:52:08 -0700 Subject: [PATCH 173/254] n_fit now requires unique times as well --- flystar/startables.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/flystar/startables.py b/flystar/startables.py index b1d3a2a..53d4539 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -738,7 +738,11 @@ def fit_motion_model( # Calculate mask array xy_mask = ~ (x_data.mask | y_data.mask) - self['n_fit'] = xy_mask.sum(axis=1) + # Calculate n_fit: unique times & unmasked x y values + self['n_fit'] = np.array([ + len(set(t_data[i][xy_mask[i]])) + for i in range(N_stars) + ]) # Convert to lists of arrays for faster access during fitting idx = [np.flatnonzero(xy_mask[i]) for i in range(N_stars)] From f5102b1843078fdab3f8d74c3516530dad215dcc Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Wed, 25 Mar 2026 13:08:21 -0700 Subject: [PATCH 174/254] Added seed for test function --- flystar/tests/test_align.py | 46 ++++++++++++++++++------------------- 1 file changed, 23 insertions(+), 23 deletions(-) diff --git a/flystar/tests/test_align.py b/flystar/tests/test_align.py index 14d1ab1..1281b1e 100644 --- a/flystar/tests/test_align.py +++ b/flystar/tests/test_align.py @@ -823,7 +823,7 @@ def test_MosaicToRef_mag_bug(): """ Bug found by Tuan Do on 2020-04-12. """ - make_fake_starlists_poly1_vel() + make_fake_starlists_poly1_vel(seed=42) ref_list = starlists.StarList.read(f'{test_data_path}/random_vel_0.fits') lists = [ref_list] @@ -1448,25 +1448,25 @@ def make_fake_starlists_poly1_par(seed=-1): if __name__ == '__main__': import pickle import matplotlib.pyplot as plt - with open(f'{test_data_path}/my_gaia.pkl', 'rb') as f: - my_gaia = pickle.load(f) - with open(f'{test_data_path}/list_of_starlists.pkl', 'rb') as f: - list_of_starlists = pickle.load(f) - ra_deg, dec_deg = 18.0, -30.0 - my_gaia.remove_column('motion_model_used') - msc = align.MosaicToRef(my_gaia, list_of_starlists, iters=3, - dr_tol=[0.2, 0.1, 0.08], dm_tol=[5,5,5], - outlier_tol=[None, None, 3], mag_lim=[6, 20], - trans_class=transforms.PolyTransform, - trans_args=[{'order': 1}, {'order': 1}, {'order': 1}], - motion_models=['Empty','Fixed','Linear','Parallax'], - fixed_params_dict = {'ra':ra_deg, 'dec':dec_deg, 'pa':0.0, 'obsLocation':'earth'}, - use_ref_new=True, - update_ref_orig=False, - mag_trans=True, - trans_weighting='both,std', - init_guess_mode='name', verbose=3) - msc.fit() - for i in range(msc.ref_table['x'].shape[1]): - plt.scatter(msc.ref_table['x'][:, i], msc.ref_table['y'][:, i]) - plt.show() \ No newline at end of file + # with open(f'{test_data_path}/my_gaia.pkl', 'rb') as f: + # my_gaia = pickle.load(f) + # with open(f'{test_data_path}/list_of_starlists.pkl', 'rb') as f: + # list_of_starlists = pickle.load(f) + # ra_deg, dec_deg = 18.0, -30.0 + # my_gaia.remove_column('motion_model_used') + # msc = align.MosaicToRef(my_gaia, list_of_starlists, iters=3, + # dr_tol=[0.2, 0.1, 0.08], dm_tol=[5,5,5], + # outlier_tol=[None, None, 3], mag_lim=[6, 20], + # trans_class=transforms.PolyTransform, + # trans_args=[{'order': 1}, {'order': 1}, {'order': 1}], + # motion_models=['Empty','Fixed','Linear','Parallax'], + # fixed_params_dict = {'ra':ra_deg, 'dec':dec_deg, 'pa':0.0, 'obsLocation':'earth'}, + # use_ref_new=True, + # update_ref_orig=False, + # mag_trans=True, + # trans_weighting='both,std', + # init_guess_mode='name', verbose=3) + # msc.fit() + # for i in range(msc.ref_table['x'].shape[1]): + # plt.scatter(msc.ref_table['x'][:, i], msc.ref_table['y'][:, i]) + # plt.show() \ No newline at end of file From ab85b4d071ed1d0e2216b29b8b444dedec38a067 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Wed, 25 Mar 2026 13:09:23 -0700 Subject: [PATCH 175/254] Added mag offset to verbose output --- flystar/align.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 8c98550..6855dcc 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -3351,7 +3351,7 @@ def trans_initial_guess( assert len(x1m) >= n_req_match, \ f'Failed to find more than {n_req_match} (only {len(x1m)}) matches, giving up.' if verbose > 1: - print('initial_guess: {0:d} stars matched between starlist and reference list'.format(N)) + print('Initial_guess: {0:d} stars matched between starlist and reference list'.format(N)) # Calculate position transformation based on matches if ('order' in trans_args) and (trans_args['order'] == 0): @@ -3370,7 +3370,10 @@ def trans_initial_guess( trans.mag_offset = 0 if verbose > 1: - print('init guess: ', trans.px.parameters, trans.py.parameters) + print('Initial guess:') + print(f'{trans.px.parameters=}') + print(f'{trans.py.parameters=}') + print(f'{trans.mag_offset=}') warnings.filterwarnings('default', category=AstropyUserWarning) From aaadaa5e890746a657f322409c0ef2269f6efdd1 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Wed, 25 Mar 2026 13:10:00 -0700 Subject: [PATCH 176/254] Minor fix of variable names in docstring --- flystar/starlists.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/flystar/starlists.py b/flystar/starlists.py index f872516..7a3ad13 100644 --- a/flystar/starlists.py +++ b/flystar/starlists.py @@ -645,18 +645,18 @@ def from_lis_file(cls, filename, error=True, fvu_file=None): if error==True: t_ref.rename_column(cols[5], 'xe') t_ref.rename_column(cols[6], 'ye') - t_ref.rename_column(cols[7], 'snr') + t_ref.rename_column(cols[7], 'me') t_ref.rename_column(cols[8], 'corr') t_ref.rename_column(cols[9], 'N_frames') t_ref.rename_column(cols[10], 'flux') else: - t_ref.rename_column(cols[5], 'snr') + t_ref.rename_column(cols[5], 'me') t_ref.rename_column(cols[6], 'corr') t_ref.rename_column(cols[7], 'N_frames') t_ref.rename_column(cols[8], 'flux') - if ('me' not in cols) and ('snr' in cols) and (error == True): - t_ref['me'] = 1.0 / t_ref['snr'] + # if ('me' not in cols) and ('snr' in cols) and (error == True): + # t_ref['me'] = 1.0 / t_ref['snr'] if fvu_file is not None: t_fvu = Table.read(fvu_file, format='ascii.no_header') From e097536f6119fec5292f8bb76ede97736129790a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Wed, 25 Mar 2026 13:10:45 -0700 Subject: [PATCH 177/254] Masking close to 0 values in xe ye in fit motion model to avoid infinite weight --- flystar/startables.py | 21 +++++++++++++++++---- 1 file changed, 17 insertions(+), 4 deletions(-) diff --git a/flystar/startables.py b/flystar/startables.py index 53d4539..615b9d0 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -672,8 +672,19 @@ def fit_motion_model( N_stars = len(self) x_data = np.ma.masked_invalid(self['x'].data, copy=True) y_data = np.ma.masked_invalid(self['y'].data, copy=True) - xe_data = np.ma.masked_invalid(self['xe'].data, copy=True) if 'xe' in self.colnames else np.ones_like(x_data) - ye_data = np.ma.masked_invalid(self['ye'].data, copy=True) if 'ye' in self.colnames else np.ones_like(y_data) + xe_data = np.ma.masked_invalid(self['xe'].data, copy=True) if 'xe' in self.colnames else None + ye_data = np.ma.masked_invalid(self['ye'].data, copy=True) if 'ye' in self.colnames else None + # Mask out close to 0 values + if xe_data is not None: + xe_data.mask[np.isclose(xe_data, 0)] = True + if ye_data is not None: + ye_data.mask[np.isclose(ye_data, 0)] = True + + # If all of xe and ye is masked for a star, effectively no uncertainties provided, fill with 1. + if (xe_data is not None) and (ye_data is not None): + fill_with_one = np.all(xe_data.mask, axis=1) & np.all(ye_data.mask, axis=1) + xe_data[fill_with_one] = 1. + ye_data[fill_with_one] = 1. # Ensure data is 2D for consistent indexing, even if we have only one list/epoch (shape (N_stars, 1) instead of (N_stars,)) if np.ndim(x_data) == 1: @@ -701,10 +712,10 @@ def fit_motion_model( # Add default t0 if not provided in fixed_params_dict if fixed_params_dict is None: - weights = 1/np.hypot(xe_data, ye_data) if xe_data is not None else None + weights = 1/np.hypot(xe_data, ye_data) if (xe_data is not None) and (ye_data is not None) else None fixed_params_dict = {'t0': np.average(t_data, axis=1, weights=weights)} elif 't0' not in fixed_params_dict: - weights = 1/np.hypot(xe_data, ye_data) if xe_data is not None else None + weights = 1/np.hypot(xe_data, ye_data) if (xe_data is not None) and (ye_data is not None) else None fixed_params_dict['t0'] = np.average(t_data, axis=1, weights=weights) else: if np.ndim(fixed_params_dict['t0']) == 0: @@ -738,6 +749,8 @@ def fit_motion_model( # Calculate mask array xy_mask = ~ (x_data.mask | y_data.mask) + if (xe_data is not None) and (ye_data is not None): + xy_mask = xy_mask & (~ (xe_data.mask | ye_data.mask)) # Calculate n_fit: unique times & unmasked x y values self['n_fit'] = np.array([ len(set(t_data[i][xy_mask[i]])) From c85d2af27074b303ade2d5c29adf3ef885514fbf Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Wed, 25 Mar 2026 13:12:06 -0700 Subject: [PATCH 178/254] Revert "Minor fix of variable names in docstring" This reverts commit aaadaa5e890746a657f322409c0ef2269f6efdd1. --- flystar/starlists.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/flystar/starlists.py b/flystar/starlists.py index 7a3ad13..f872516 100644 --- a/flystar/starlists.py +++ b/flystar/starlists.py @@ -645,18 +645,18 @@ def from_lis_file(cls, filename, error=True, fvu_file=None): if error==True: t_ref.rename_column(cols[5], 'xe') t_ref.rename_column(cols[6], 'ye') - t_ref.rename_column(cols[7], 'me') + t_ref.rename_column(cols[7], 'snr') t_ref.rename_column(cols[8], 'corr') t_ref.rename_column(cols[9], 'N_frames') t_ref.rename_column(cols[10], 'flux') else: - t_ref.rename_column(cols[5], 'me') + t_ref.rename_column(cols[5], 'snr') t_ref.rename_column(cols[6], 'corr') t_ref.rename_column(cols[7], 'N_frames') t_ref.rename_column(cols[8], 'flux') - # if ('me' not in cols) and ('snr' in cols) and (error == True): - # t_ref['me'] = 1.0 / t_ref['snr'] + if ('me' not in cols) and ('snr' in cols) and (error == True): + t_ref['me'] = 1.0 / t_ref['snr'] if fvu_file is not None: t_fvu = Table.read(fvu_file, format='ascii.no_header') From 82462bc90aefd6c355d4c74c5e1bf27253632023 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Wed, 25 Mar 2026 13:14:28 -0700 Subject: [PATCH 179/254] Minor fix of variable names in docstring --- flystar/motion_model.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/flystar/motion_model.py b/flystar/motion_model.py index de76c46..7834e55 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -137,7 +137,7 @@ def fit( Seed for the random number generator, by default None Returns ------- - params, params_err(, chi2_x, chi2_y) + params, param_errs(, chi2_x, chi2_y) Parameters, uncertainties, and chi squares if return_chi2 is True. The corresponding parameter names are in self.fit_param_names. """ assert np.ndim(t) == np.ndim(x) == np.ndim(y) == np.ndim(xe) == np.ndim(ye) == 1, "Input arrays must be 1D! Motion model can only fit individual stars" From 93fa222c6bfd9761c330ecf2199ac753062abb09 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Sun, 29 Mar 2026 19:12:29 -0700 Subject: [PATCH 180/254] Added singular matrix handling; Optimized warning message printing --- flystar/motion_model.py | 82 ++++++++++++++++++++++++++--------------- 1 file changed, 53 insertions(+), 29 deletions(-) diff --git a/flystar/motion_model.py b/flystar/motion_model.py index 7834e55..6a48a80 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -120,7 +120,7 @@ def fit( weighting : str, optional Use standard error weighting ('std': w=1/xe, 1/ye) or variance weighting ('var': w=1/xe**2, 1/ye**2), by default 'var' use_scipy : bool, optional - Use scipy for optmization. Otherwise, use linear algebraic solution (Linear model only), which is faster for < 300 epochs, by default True + Use scipy for optimization. Otherwise, use linear algebraic solution (Linear model only), which is faster for < 300 epochs, by default True absolute_sigma : bool, optional Absolute sigma. See scipy.optimize.curve_fit for details, by default True fill_value : float, optional @@ -142,6 +142,10 @@ def fit( """ assert np.ndim(t) == np.ndim(x) == np.ndim(y) == np.ndim(xe) == np.ndim(ye) == 1, "Input arrays must be 1D! Motion model can only fit individual stars" assert len(t) == len(x) == len(y) == len(xe) == len(ye), "Input arrays must have the same length!" + + if not verbose: + warnings.filterwarnings("ignore", category=OptimizeWarning) + fit_result = self.run_fit( t, x, y, xe, ye, fixed_params_dict=fixed_params_dict, @@ -203,6 +207,9 @@ def fit( inf_errs = [np.all(arr==np.inf) for arr in np.transpose(np.array(bb_params_errs))] param_errs[inf_errs] = 0.0 + if not verbose: + warnings.resetwarnings() + if return_chi2: return params, param_errs, chi2_x, chi2_y else: @@ -433,11 +440,10 @@ def run_fit( degree_of_freedom = n_obs - self.n_params # Not enough data points to fit model if degree_of_freedom < 0: - if verbose: - warnings.warn( - f'Not enough data points to fit model. Setting parameters to {fill_value} and uncertainties to np.inf.', - OptimizeWarning, stacklevel=2 - ) + warnings.warn( + f'Not enough data points to fit model. Setting parameters to {fill_value} and uncertainties to np.inf.', + OptimizeWarning, stacklevel=2 + ) params = np.full(self.n_params, fill_value) param_errors = np.full(self.n_params, np.inf) return params, param_errors, np.nan, np.nan @@ -468,7 +474,7 @@ def run_fit( else: # degree_of_freedom == 0, as < 0 case already handled above warnings.warn( - f'Degree of freedom < 0. Covariance of the parameters could not be estimated. Setting parameter uncertainties to fill value np.inf.', + f'Degree of freedom < 0. Covariance of the parameters could not be estimated. Setting parameter uncertainties to np.inf.', OptimizeWarning, stacklevel=2 ) # Set parameter uncertainties to np.inf, same behavior as scipy.optimize.curve_fit @@ -600,11 +606,10 @@ def run_fit( degree_of_freedom = n_obs - self.n_params # Not enough data points to fit model if degree_of_freedom < 0: - if verbose: - warnings.warn( - f'Not enough data points to fit model. Setting parameters to {fill_value} and uncertainties to np.inf.', - OptimizeWarning, stacklevel=2 - ) + warnings.warn( + f'Not enough data points to fit model. Setting parameters to {fill_value} and uncertainties to np.inf.', + OptimizeWarning, stacklevel=2 + ) params = np.full(self.n_params, fill_value) param_errors = np.full(self.n_params, np.inf) if return_chi2: @@ -636,18 +641,39 @@ def run_fit( # Linear algebraic solution # Use https://en.wikipedia.org/wiki/Weighted_least_squares#Solution_scheme X_mat_t = np.vander(dt, 2) + # x calculation W_mat_x = np.diag(x_wt) - XTWX_mat_x = X_mat_t.T @ W_mat_x @ X_mat_t - pcov_x = np.linalg.inv(XTWX_mat_x) # Covariance Matrix + XTWX_mat_x = X_mat_t.T @ W_mat_x @ X_mat_t # Shape (2, 2) + pcov_x = np.linalg.pinv(XTWX_mat_x) # Covariance Matrix popt_x = pcov_x @ X_mat_t.T @ W_mat_x @ x # Linear Solution - perr_x = np.sqrt(np.diag(pcov_x)) # Uncertainty of Linear Solution + + # Singular matrix (not enough unique times): Fill uncertainty with Inf. + if np.linalg.matrix_rank(XTWX_mat_x) < 2: + warnings.warn( + f'Singular matrix. Covariance of the parameters could not be estimated. Setting parameter uncertainties to np.inf.', + OptimizeWarning, stacklevel=2 + ) + perr_x = np.full_like(popt_x, np.inf) + else: + perr_x = np.sqrt(np.diag(pcov_x)) # Uncertainty of Linear Solution + # y calculation W_mat_y = np.diag(y_wt) - XTWX_mat_y = X_mat_t.T @ W_mat_y @ X_mat_t - pcov_y = np.linalg.inv(XTWX_mat_y) # Covariance Matrix + XTWX_mat_y = X_mat_t.T @ W_mat_y @ X_mat_t # Shape (2, 2) + pcov_y = np.linalg.pinv(XTWX_mat_y) # Covariance Matrix popt_y = pcov_y @ X_mat_t.T @ W_mat_y @ y # Linear Solution - perr_y = np.sqrt(np.diag(pcov_y)) # Uncertainty of Linear Solution + + # Singular matrix (not enough unique times): Fill uncertainty with Inf. + if np.linalg.matrix_rank(XTWX_mat_y) < 2: + warnings.warn( + f'Singular matrix. Covariance of the parameters could not be estimated. Setting parameter uncertainties to np.inf.', + OptimizeWarning, stacklevel=2 + ) + perr_y = np.full_like(popt_y, np.inf) + else: + perr_y = np.sqrt(np.diag(pcov_y)) # Uncertainty of Linear Solution + # prepare values to return vx, x0 = popt_x vy, y0 = popt_y @@ -676,7 +702,7 @@ def run_fit( else: # degree_of_freedom == 0, as < 0 case already handled above warnings.warn( - f'Degree of freedom < 0. Covariance of the parameters could not be estimated. Setting parameter uncertainties to fill value np.inf.', + f'Degree of freedom < 0. Covariance of the parameters could not be estimated. Setting parameter uncertainties to np.inf.', OptimizeWarning, stacklevel=2 ) # Set parameter uncertainties to np.inf, same behavior as scipy.optimize.curve_fit @@ -816,11 +842,10 @@ def run_fit( degree_of_freedom = n_obs - self.n_params # Not enough data points to fit model if degree_of_freedom < 0: - if verbose: - warnings.warn( - f'Not enough data points to fit model. Setting parameters to {fill_value} and uncertainties to np.inf.', - OptimizeWarning, stacklevel=2 - ) + warnings.warn( + f'Not enough data points to fit model. Setting parameters to {fill_value} and uncertainties to np.inf.', + OptimizeWarning, stacklevel=2 + ) params = np.full(self.n_params, fill_value) param_errors = np.full(self.n_params, np.inf) if return_chi2: @@ -1054,11 +1079,10 @@ def run_fit( degree_of_freedom = n_fit - self.n_params # Not enough data points to fit model if degree_of_freedom < 0: - if verbose: - warnings.warn( - f'Not enough data points to fit model. Setting parameters to {fill_value} and uncertainties to np.inf.', - OptimizeWarning, stacklevel=2 - ) + warnings.warn( + f'Not enough data points to fit model. Setting parameters to {fill_value} and uncertainties to np.inf.', + OptimizeWarning, stacklevel=2 + ) params = np.full(self.n_params, fill_value) param_errors = np.full(self.n_params, np.inf) if return_chi2: From 3d611b76e218513e315b72519911f70cde448467 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Sun, 29 Mar 2026 19:16:05 -0700 Subject: [PATCH 181/254] Removed unecessary fixed motion model fit; Fixed bootstrap unmatched data shape; Optimized verbose control of output messages --- flystar/align.py | 72 +++++++++++++++++++++++++++--------------------- 1 file changed, 41 insertions(+), 31 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 6855dcc..9a0d734 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -255,10 +255,10 @@ def = None. If not None, then this should contain an array or list of transform self.motion_model_for_new_star = all_mm_map[motion_model_for_new_star] # For backwards compatibility. - if self.verbose is True: - self.verbose = 9 - if self.verbose is False: - self.verbose = 0 + # if self.verbose is True: + # self.verbose = 9 + # if self.verbose is False: + # self.verbose = 0 self.N_lists = len(self.star_lists) @@ -369,7 +369,8 @@ def fit(self): ### Drop all stars that have 0 detections. idx = np.where((self.ref_table['n_detect'] == 0))[0] - print(' *** Getting rid of {0:d} out of {1:d} junk sources'.format(len(idx), len(self.ref_table))) + if self.verbose: + print(' *** Getting rid of {0:d} out of {1:d} junk sources'.format(len(idx), len(self.ref_table))) self.ref_table.remove_rows(idx) if self.iter_callback != None: @@ -406,7 +407,8 @@ def fit(self): ### Drop all stars that have 0 detections. idx = np.where((self.ref_table['n_detect'] == 0))[0] - print(' *** Getting rid of {0:d} out of {1:d} junk sources'.format(len(idx), len(self.ref_table))) + if self.verbose: + print(f' *** Getting rid of {len(idx):d} out of {len(self.ref_table):d} junk sources') self.ref_table.remove_rows(idx) if self.iter_callback != None: @@ -436,6 +438,9 @@ def fit(self): if self.save_path: with open(self.save_path, 'wb') as file: pickle.dump(self, file) + print('================================') + print(f'Done with fit()') + print('================================') return def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_args, nn=None): @@ -954,13 +959,13 @@ def update_ref_table_aggregates(self, keep_orig=None, n_boot=0): fit_star_idxs = None if ('motion_model_input' in self.ref_table.keys()) and np.all(self.ref_table['motion_model_input']=='Fixed'): - self.ref_table.fit_motion_model( - motion_models=['Fixed'], - weighting=self.vel_weighting, - use_scipy=self.use_scipy, - absolute_sigma=self.absolute_sigma, - verbose=self.verbose - ) + # self.ref_table.fit_motion_model( + # motion_models=['Fixed'], + # weighting=self.vel_weighting, + # use_scipy=self.use_scipy, + # absolute_sigma=self.absolute_sigma, + # verbose=self.verbose + # ) weighted_xy = ('xe' in self.ref_table.colnames) and ('ye' in self.ref_table.colnames) weighted_m = ('me' in self.ref_table.colnames) self.ref_table.combine_lists_xym(weighted_xy=weighted_xy, weighted_m=weighted_m) @@ -1219,7 +1224,7 @@ def reset_ref_values(self, exclude=None): return - def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_bootstrap=True, weighting='var', use_scipy=True, absolute_sigma=False, show_progress=True, update_errors=False): + def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_bootstrap=True, update_errors=False, verbose=True): """ Function to calculate bootstrap errors for the transformations as well as the proper motions. For each iteration, this will: @@ -1260,16 +1265,12 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot to calculate positional alignment errors. If false, only calculate position alignment errors. - weighting: str - 'var' or 'std' weighting for velocity fitting, by default 'var'. If 'var', use the variance of the residuals to weight the fit. - If 'std', use the standard deviation of the residuals to weight the fit. - - absolute_sigma: boolean - If True, use the absolute sigma in the velocity fitting. If False, use the relative sigma, by default False. - update_errors: boolean If True, save the starlist errors as xe_list, bootstrap errors as xe_boot, and their quad sum as xe (and likewise for ye and me). If False (default), leave the starlist errors in place as xe and bootstrap errors as xe_boot. + verbose: boolean + Print verbose information or not, by default True + Output: ------ New columns will be added to self.ref_table: @@ -1283,7 +1284,7 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot For stars that fail boot_epochs_min criteria, np.nan is used """ # First, assert than n_boot > 0 - assert n_boot > 0 + assert n_boot > 0, f'{n_boot=} is not possive!' ref_table = copy.deepcopy(self.ref_table) n_epochs = len(ref_table['x'][0]) @@ -1337,7 +1338,7 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot ### DEFINE MEAN, STD VARIABLES AND BUILD THEM RATHER THAN SAVING FULL ARRAY ### DECREASE PRECISION ON ARRAYS (32 bit instead of 64: dtype=np.float32) ### AT SOME POINT, NEED TO CONVERT BACK (LOOK UP HOW TO DO THIS CAREFULLY) - for ii in tqdm(range(n_boot), desc='Bootstrap iterations', disable=not show_progress): + for ii in tqdm(range(n_boot), desc='Bootstrap iterations', disable=not verbose): # Recalculate transformations using bootstrap sample of # reference stars. Use a loop for each epoch here, so we # can handle case where different reference stars are used @@ -1448,7 +1449,7 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot m_boot_sum += m_trans_arr m2_boot_sum += m_trans_arr**2 - t2 = time.time() + # t2 = time.time() #print('=================================================') #print('Time to do {0} epochs: {1}s'.format(n_epochs, t2-t1)) #print('=================================================') @@ -1486,7 +1487,8 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot fixed_params_dict=fixed_params_dict, weighting=self.vel_weighting, use_scipy=self.use_scipy, - absolute_sigma=self.absolute_sigma + absolute_sigma=self.absolute_sigma, + verbose=self.verbose ) # Save proper motion fit results to output arrays @@ -1549,6 +1551,10 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot x_pred, y_pred, _, _ = self.ref_table.infer_positions(t_arr, fixed_params_dict=self.fixed_params_dict) + if np.ndim(x_pred) == 1: + x_pred = x_pred[:, np.newaxis] + if np.ndim(y_pred) == 1: + y_pred = y_pred[:, np.newaxis] xe_comb = np.hypot(self.ref_table['xe'], self.ref_table['xe_boot']) ye_comb = np.hypot(self.ref_table['ye'], self.ref_table['ye_boot']) data_dict['chi2_x_boot'] = np.nansum((self.ref_table['x']-x_pred)**2/(xe_comb)**2,axis=1) @@ -1571,9 +1577,10 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot col[idx_good] = data_dict[ff] self.ref_table.add_column(col) - print('===============================') - print('Done with bootstrap') - print('===============================') + if verbose: + print('===============================') + print('Done with bootstrap') + print('===============================') if update_errors: self.ref_table['xe_list'] = self.ref_table['xe'] @@ -1990,14 +1997,14 @@ def fit(self): ########## # Find where stars are detected. if self.verbose > 0: - print('') print(' Preparing the reference table...') self.ref_table.detections() ### Drop all stars that have 0 detections. idx = np.where((self.ref_table['n_detect'] == 0) & (self.ref_table['ref_orig'] == False))[0] - print(' *** Getting rid of {0:d} out of {1:d} junk sources'.format(len(idx), len(self.ref_table))) + if self.verbose: + print(' *** Getting rid of {0:d} out of {1:d} junk sources'.format(len(idx), len(self.ref_table))) self.ref_table.remove_rows(idx) if self.iter_callback != None: @@ -2026,6 +2033,9 @@ def fit(self): if self.save_path: with open(self.save_path, 'wb') as file: pickle.dump(self, file) + print('================================') + print(f'Done with fit()') + print('================================') return # TODO: This is sometimes run on a startable, not a starlist, at least as currently used @@ -2158,7 +2168,7 @@ def determine_motion_model(startable, motion_models=None, fixed_params_dict=None # Check if values are finite for required columns in possible motion models motion_model_used = [] - for k in tqdm(range(len(startable)), desc='Determining motion model for each star'): + for k in range(len(startable)): for mm, req_col_in_table, req_col_in_dict in motion_models_possible[::-1]: # If requested column in table/fixed_params dict is numeric, check if values are finite. if all(np.isfinite(startable[col][k]) for col in req_col_in_table if np.issubdtype(startable[col].dtype, np.number)) \ From 942e148bdfb414fe575912057d21bc63c09b6095 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Sun, 29 Mar 2026 19:18:04 -0700 Subject: [PATCH 182/254] Merged show_progress into verbose; Changed to construct list of parameters for each motion model, instead of all motion models for memory efficiency --- flystar/startables.py | 46 +++++++++++++++++++++++++------------------ 1 file changed, 27 insertions(+), 19 deletions(-) diff --git a/flystar/startables.py b/flystar/startables.py index 615b9d0..cfdbd4b 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -549,8 +549,7 @@ def fit_motion_model( verbose=True, mask_value=None, mask_lists=None, - fill_value=np.nan, - show_progress=True + fill_value=np.nan ): """Fit velocity for star table @@ -591,8 +590,6 @@ def fit_motion_model( Indices of lists to mask/exclude from fitting, by default None fill_value : float, optional Fill value when there is not enough data points to fit, by default np.nan - show_progress : bool, optional - Show progress bar or not, by default True Raises ------ @@ -681,6 +678,7 @@ def fit_motion_model( ye_data.mask[np.isclose(ye_data, 0)] = True # If all of xe and ye is masked for a star, effectively no uncertainties provided, fill with 1. + # Note that this automatically turn the mask to False for these stars if (xe_data is not None) and (ye_data is not None): fill_with_one = np.all(xe_data.mask, axis=1) & np.all(ye_data.mask, axis=1) xe_data[fill_with_one] = 1. @@ -751,20 +749,19 @@ def fit_motion_model( xy_mask = ~ (x_data.mask | y_data.mask) if (xe_data is not None) and (ye_data is not None): xy_mask = xy_mask & (~ (xe_data.mask | ye_data.mask)) + + # Calculate n_fit: unmasked x y values + # This will be used to determine which motion model to use for each star. + # Note that we don't require unique times here + # as scipy.curve_fit and Linear algebra can fit non-unique times. + # self['n_fit'] = np.sum(xy_mask, axis=1) + # Calculate n_fit: unique times & unmasked x y values self['n_fit'] = np.array([ len(set(t_data[i][xy_mask[i]])) for i in range(N_stars) ]) - # Convert to lists of arrays for faster access during fitting - idx = [np.flatnonzero(xy_mask[i]) for i in range(N_stars)] - t_stars = [np.array(t_data[i][idx[i]]) for i in range(N_stars)] - x_stars = [np.array(x_data[i][idx[i]]) for i in range(N_stars)] - y_stars = [np.array(y_data[i][idx[i]]) for i in range(N_stars)] - xe_stars = [np.array(xe_data[i][idx[i]]) for i in range(N_stars)] if xe_data is not None else [None]*N_stars - ye_stars = [np.array(ye_data[i][idx[i]]) for i in range(N_stars)] if ye_data is not None else [None]*N_stars - ########################### ####### Determine MM ###### ########################### @@ -785,6 +782,7 @@ def fit_motion_model( self['motion_model_used'][reassign_mm] = np.array([motion_models[d].name for d in mm_digitized], dtype='U20') else: + # If motion_model_input column is not provided, use the most complicated model in motion_models with n_fit >= n_params. mm_digitized = np.digitize( x=n_fit, bins=mm_n_params @@ -874,8 +872,10 @@ def fit_motion_model( else: indices_by_motion_model = {key: np.flatnonzero(unique_inv_indices == k) for k, key in enumerate(unique_motion_models)} + # Unmasked indices for each star: + unmasked_idx = [np.flatnonzero(xy_mask[i]) for i in range(N_stars)] - # Expensive for loop! Prepare everything beforehand to speed up. + # For each motion model for unique_motion_model, unique_index in indices_by_motion_model.items(): # Create motion model instance motion_model_instance = input_mm_map[unique_motion_model]() @@ -889,16 +889,24 @@ def fit_motion_model( chi2_x_array = np.full(n_stars_this_model, np.nan, dtype=float) chi2_y_array = np.full(n_stars_this_model, np.nan, dtype=float) + # Prepare data as lists of arrays for faster access during fitting + t_stars = [np.array(t_data[i][unmasked_idx[i]]) for i in unique_index] + x_stars = [np.array(x_data[i][unmasked_idx[i]]) for i in unique_index] + y_stars = [np.array(y_data[i][unmasked_idx[i]]) for i in unique_index] + xe_stars = [np.array(xe_data[i][unmasked_idx[i]]) for i in unique_index] if xe_data is not None else [None]*n_stars_this_model + ye_stars = [np.array(ye_data[i][unmasked_idx[i]]) for i in unique_index] if ye_data is not None else [None]*n_stars_this_model + + # For each star # Expensive for loop! Prepare everything beforehand to speed up. if len(unique_index) > 0: - for idx, i_star in enumerate(tqdm(unique_index, disable=not show_progress, desc=f"Fitting motion model {unique_motion_model}")): + for idx, i_star in enumerate(tqdm(unique_index, disable=not verbose, desc=f"Fitting motion model {unique_motion_model}")): # Fit the star params, param_errs, chi2_x, chi2_y = motion_model_instance.fit( - t=t_stars[i_star], - x=x_stars[i_star], - y=y_stars[i_star], - xe=xe_stars[i_star], - ye=ye_stars[i_star], + t=t_stars[idx], + x=x_stars[idx], + y=y_stars[idx], + xe=xe_stars[idx], + ye=ye_stars[idx], fixed_params_dict=fixed_params_stars[i_star], weighting=weighting, use_scipy=use_scipy, From c9b54a5c2c49aefe0430eaa882b0cfb579843188 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Sun, 29 Mar 2026 19:19:04 -0700 Subject: [PATCH 183/254] Adjusted import orders --- flystar/tests/test_startable.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/flystar/tests/test_startable.py b/flystar/tests/test_startable.py index fbb0a34..64e0681 100644 --- a/flystar/tests/test_startable.py +++ b/flystar/tests/test_startable.py @@ -1,13 +1,13 @@ +import os +import pdb +import pytest +import flystar +import numpy as np from astropy.table import Table from astropy import table from flystar import motion_model from flystar.startables import StarTable from flystar.starlists import StarList -import numpy as np -import pytest -import os -import pdb -import flystar test_data_path = f'{flystar.__path__[0]}/tests/test_data' From 5332543174315d79a15165f7c69dc3e469940e59 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Sun, 29 Mar 2026 19:20:20 -0700 Subject: [PATCH 184/254] Removed velocity assertion in align.ref_table if all epochs are the same: Fixed model will be used instead of assuming 0 velocities --- flystar/tests/test_align.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/flystar/tests/test_align.py b/flystar/tests/test_align.py index 1281b1e..ca82077 100644 --- a/flystar/tests/test_align.py +++ b/flystar/tests/test_align.py @@ -114,10 +114,11 @@ def test_MosaicSelfRef_vel_tconst(): assert 'y0_err' in msc.ref_table.colnames assert 'm0' in msc.ref_table.colnames assert 'm0_err' in msc.ref_table.colnames - assert 'vx' in msc.ref_table.colnames - assert 'vx_err' in msc.ref_table.colnames - assert 'vy' in msc.ref_table.colnames - assert 'vy_err' in msc.ref_table.colnames + # Since they are in the same epoch, no velocity information can be inferred + # assert 'vx' in msc.ref_table.colnames + # assert 'vx_err' in msc.ref_table.colnames + # assert 'vy' in msc.ref_table.colnames + # assert 'vy_err' in msc.ref_table.colnames assert 't0' in msc.ref_table.colnames # Check that we have some matched stars... should be at least 35 stars From f982b5b47fb2aea2b92ea65fa7642c7909dce667 Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Fri, 24 Apr 2026 18:14:04 -0700 Subject: [PATCH 185/254] Rename matplotlib imports --- flystar/plots.py | 146 +++++++++++++++++++++++++++++++++++++---------- 1 file changed, 116 insertions(+), 30 deletions(-) diff --git a/flystar/plots.py b/flystar/plots.py index e92f5ff..16ec8b1 100755 --- a/flystar/plots.py +++ b/flystar/plots.py @@ -1,20 +1,20 @@ -from . import motion_model, startables +import pdb +import math +import astropy +import matplotlib import numpy as np import matplotlib.mlab as mlab -import matplotlib import matplotlib.pyplot as plt -from matplotlib import colors -import matplotlib.cm as cm +import matplotlib.colors as mcolors +from matplotlib import cm from scipy.stats import chi2 -from scipy.optimize import curve_fit from scipy.stats import norm -import pdb -import math -import astropy -from astropy.table import Table +from scipy.optimize import curve_fit from astropy.io import ascii -from astropy.coordinates import SkyCoord from astropy import units as u +from astropy.table import Table +from astropy.coordinates import SkyCoord +from . import motion_model, startables #################################################### # Code for making diagnostic plots for astrometry @@ -39,7 +39,7 @@ def trans_positions(ref, ref_mat, starlist, starlist_mat, xlim=None, ylim=None, transformation. Standard column headers are assumed. starlist: astropy table - Transformed starist with the reference starlist coordinates. + Transformed starlist with the reference starlist coordinates. Standard column headers are assumed starlist_mat: astropy table @@ -1964,13 +1964,13 @@ def plot_quiver_residuals_magcolor(x_t, y_t, x_ref, y_ref, mag, good_idx, ref_id quiv_label_val = 1.0 unit2 = 'mas' - norm = matplotlib.colors.Normalize() + norm = mcolors.Normalize() norm.autoscale(mag) - cm = matplotlib.cm.viridis - sm = matplotlib.cm.ScalarMappable(cmap=cm, norm=norm) + cmap = matplotlib.colormaps['viridis'] + sm = matplotlib.cm.ScalarMappable(cmap=cmap, norm=norm) # cmap = mpl.cm.cool -# norm = mpl.colors.Normalize(vmin=np.min(mag), vmax=np.max(mag)) +# norm = mpl.mcolors.Normalize(vmin=np.min(mag), vmax=np.max(mag)) # # cb1 = mpl.colorbar.ColorbarBase(ax, cmap=cmap, # norm=norm, @@ -2945,7 +2945,7 @@ def rs(x): plt.errorbar(rs(time), rs(x), yerr=rs(xerr), fmt='k.') #plt.errorbar(time, x, yerr=xerr, fmt='k.') else: - norm = colors.Normalize(vmin=0, vmax=1, clip=True) + norm = mcolors.Normalize(vmin=0, vmax=1, clip=True) mapper = cm.ScalarMappable(norm=norm, cmap='hsv') time_color = np.array([(mapper.to_rgba(v)) for v in dtime]) for xx, yy, ee, color in zip(time, x, xerr, time_color): @@ -2977,7 +2977,7 @@ def rs(x): if not color_time: plt.errorbar(rs(time), rs(y), yerr=rs(yerr), fmt='k.') else: - norm = colors.Normalize(vmin=0, vmax=1, clip=True) + norm = mcolors.Normalize(vmin=0, vmax=1, clip=True) mapper = cm.ScalarMappable(norm=norm, cmap='hsv') time_color = np.array([(mapper.to_rgba(v)) for v in dtime]) for xx, yy, ee, color in zip(time, y, yerr, time_color): @@ -3007,7 +3007,7 @@ def rs(x): if not color_time: plt.errorbar(rs(time), rs(m), yerr=rs(merr), fmt='k.') else: - norm = colors.Normalize(vmin=0, vmax=1, clip=True) + norm = mcolors.Normalize(vmin=0, vmax=1, clip=True) mapper = cm.ScalarMappable(norm=norm, cmap='hsv') time_color = np.array([(mapper.to_rgba(v)) for v in dtime]) for xx, yy, ee, color in zip(time, m, merr, time_color): @@ -3039,7 +3039,7 @@ def rs(x): if not color_time: plt.errorbar(rs(time), rs(x - fitLineX)*1e3, yerr=rs(xerr)*1e3, fmt='k.') else: - norm = colors.Normalize(vmin=0, vmax=1, clip=True) + norm = mcolors.Normalize(vmin=0, vmax=1, clip=True) mapper = cm.ScalarMappable(norm=norm, cmap='hsv') time_color = np.array([(mapper.to_rgba(v)) for v in dtime]) for xx, yy, ee, color in zip(time, (x - fitLineX)*1e3, xerr*1e3, time_color): @@ -3067,7 +3067,7 @@ def rs(x): if not color_time: plt.errorbar(rs(time), rs(y - fitLineY)*1e3, yerr=rs(yerr)*1e3, fmt='k.') else: - norm = colors.Normalize(vmin=0, vmax=1, clip=True) + norm = mcolors.Normalize(vmin=0, vmax=1, clip=True) mapper = cm.ScalarMappable(norm=norm, cmap='hsv') time_color = np.array([(mapper.to_rgba(v)) for v in dtime]) for xx, yy, ee, color in zip(time, (y - fitLineY)*1e3, yerr*1e3, time_color): @@ -3095,7 +3095,7 @@ def rs(x): if not color_time: plt.errorbar(rs(time), rs(m - fitLineM), yerr=rs(merr), fmt='k.') else: - norm = colors.Normalize(vmin=0, vmax=1, clip=True) + norm = mcolors.Normalize(vmin=0, vmax=1, clip=True) mapper = cm.ScalarMappable(norm=norm, cmap='hsv') time_color = np.array([(mapper.to_rgba(v)) for v in dtime]) for xx, yy, ee, color in zip(time, (m - fitLineM), merr, time_color): @@ -3126,7 +3126,7 @@ def rs(x): sc = plt.scatter(x, y, s=0, c=dtime, vmin=0, vmax=1, cmap='hsv') clb = plt.colorbar(sc) clb.ax.tick_params(labelsize=fontsize1) - norm = colors.Normalize(vmin=0, vmax=1, clip=True) + norm = mcolors.Normalize(vmin=0, vmax=1, clip=True) mapper = cm.ScalarMappable(norm=norm, cmap='hsv') time_color = np.array([(mapper.to_rgba(v)) for v in dtime]) for xx, yy, eexx, eeyy, color in zip(x, y, xerr, yerr, time_color): @@ -3368,7 +3368,7 @@ def rs(x): if not color_time: plt.errorbar(rs(time), rs(x), yerr=rs(xerr), marker='.', color=color, ls='none') else: - norm = colors.Normalize(vmin=0, vmax=1, clip=True) + norm = mcolors.Normalize(vmin=0, vmax=1, clip=True) mapper = cm.ScalarMappable(norm=norm, cmap='hsv') time_color = np.array([(mapper.to_rgba(v)) for v in dtime]) for xx, yy, ee, color in zip(time, x, xerr, time_color): @@ -3400,7 +3400,7 @@ def rs(x): if not color_time: plt.errorbar(rs(time), rs(y), yerr=rs(yerr), marker='.', color=color, ls='none') else: - norm = colors.Normalize(vmin=0, vmax=1, clip=True) + norm = mcolors.Normalize(vmin=0, vmax=1, clip=True) mapper = cm.ScalarMappable(norm=norm, cmap='hsv') time_color = np.array([(mapper.to_rgba(v)) for v in dtime]) for xx, yy, ee, color in zip(time, y, yerr, time_color): @@ -3430,7 +3430,7 @@ def rs(x): if not color_time: plt.errorbar(rs(time), rs(m), yerr=rs(merr), marker='.', color=color, ls='none') else: - norm = colors.Normalize(vmin=0, vmax=1, clip=True) + norm = mcolors.Normalize(vmin=0, vmax=1, clip=True) mapper = cm.ScalarMappable(norm=norm, cmap='hsv') time_color = np.array([(mapper.to_rgba(v)) for v in dtime]) for xx, yy, ee, color in zip(time, m, merr, time_color): @@ -3462,7 +3462,7 @@ def rs(x): if not color_time: plt.errorbar(rs(time), rs(x - fitLineX)*1e3, yerr=rs(xerr)*1e3, marker='.', color=color, ls='none') else: - norm = colors.Normalize(vmin=0, vmax=1, clip=True) + norm = mcolors.Normalize(vmin=0, vmax=1, clip=True) mapper = cm.ScalarMappable(norm=norm, cmap='hsv') time_color = np.array([(mapper.to_rgba(v)) for v in dtime]) for xx, yy, ee, color in zip(time, (x - fitLineX)*1e3, xerr*1e3, time_color): @@ -3490,7 +3490,7 @@ def rs(x): if not color_time: plt.errorbar(rs(time), rs(y - fitLineY)*1e3, yerr=rs(yerr)*1e3, marker='.', color=color, ls='none') else: - norm = colors.Normalize(vmin=0, vmax=1, clip=True) + norm = mcolors.Normalize(vmin=0, vmax=1, clip=True) mapper = cm.ScalarMappable(norm=norm, cmap='hsv') time_color = np.array([(mapper.to_rgba(v)) for v in dtime]) for xx, yy, ee, color in zip(time, (y - fitLineY)*1e3, yerr*1e3, time_color): @@ -3518,7 +3518,7 @@ def rs(x): if not color_time: plt.errorbar(rs(time), rs(m - fitLineM), yerr=rs(merr), marker='.', color=color, ls='none') else: - norm = colors.Normalize(vmin=0, vmax=1, clip=True) + norm = mcolors.Normalize(vmin=0, vmax=1, clip=True) mapper = cm.ScalarMappable(norm=norm, cmap='hsv') time_color = np.array([(mapper.to_rgba(v)) for v in dtime]) for xx, yy, ee, color in zip(time, (m - fitLineM), merr, time_color): @@ -3549,7 +3549,7 @@ def rs(x): sc = plt.scatter(x, y, s=0, c=dtime, vmin=0, vmax=1, cmap='hsv') clb = plt.colorbar(sc) clb.ax.tick_params(labelsize=fontsize1) - norm = colors.Normalize(vmin=0, vmax=1, clip=True) + norm = mcolors.Normalize(vmin=0, vmax=1, clip=True) mapper = cm.ScalarMappable(norm=norm, cmap='hsv') time_color = np.array([(mapper.to_rgba(v)) for v in dtime]) for xx, yy, eexx, eeyy, color in zip(x, y, xerr, yerr, time_color): @@ -3719,7 +3719,7 @@ def plot_sky(stars_tab, yearsInt = np.floor(epochs).astype('int') # Set up a color scheme - cnorm = colors.Normalize(stars_tab['t'][0, :].min(), stars_tab['t'][0, :].max() + 1) + cnorm = mcolors.Normalize(stars_tab['t'][0, :].min(), stars_tab['t'][0, :].max() + 1) cmap = plt.cm.gist_ncar colorList = [] @@ -3891,3 +3891,89 @@ def __call__(self, event): self.fig.canvas.draw() return + + +def plotly_stars(x, y, m=None, marker_size=3, color='C0', alpha=0.7, label='starlist', fig=None, figsize=(600, 600), show=True): + """Plot stars with plotly in interactive html format + + Parameters + ---------- + x : array-like + x positions + y : array-like + y positions + m : array-like, optional + magnitude to be added in hover label, by default None + marker_size : int, optional + Size of marker, by default 10 + color : str or array, optional + Color of marker, either a string (e.g., 'red') or rgba array, by default 'C0' + alpha : float, optional + Opacity of marker color, by default 0.7 + label : str, optional + Label for the star list, by default 'starlist' + fig : plotly.graph_objects.Figure object, optional + Figure if the stars are to be added to an exisiting plot, by default None + figsize : tuple, optional + Figure size, by default (600, 600) + show : bool, optional + Show figure or not, by default True + + Returns + ------- + fig : plotly.graph_objects.Figure + Figure object + """ + import plotly.graph_objects as go + x = np.asarray(x) + y = np.asarray(y) + hover_template = 'x: %{x:.3f}
y: %{y:.3f}' + + + if isinstance(color, str) and color.startswith('C') and color[1:].isdigit(): + color = mcolors.to_rgba(color, alpha=alpha) + color = f'rgba({color[0]*255}, {color[1]*255}, {color[2]*255}, {color[3]:.2f})' + + if m is not None: + m = np.asarray(m) + hover_template += '
m: %{customdata:.2f}' + fig_data = go.Scatter( + x=x, + y=y, + mode='markers', + marker=dict( + size=marker_size, + color=color + ), + hovertemplate=hover_template, + customdata=m, # Add magnitude to hover data + name=label + ) + else: + fig_data = go.Scatter( + x=x, + y=y, + mode='markers', + marker=dict( + size=marker_size, + color=color + ), + hovertemplate=hover_template, + name=label + ) + + if fig is None: + fig = go.Figure(data=[fig_data]) + else: + fig.add_trace(fig_data) + + fig.update_layout( + xaxis_title='x', + yaxis_title='y', + xaxis=dict(scaleanchor='y', scaleratio=1), # Ensure equal aspect ratio + width=figsize[0], + height=figsize[1] + ) + if show: + fig.show() + return fig \ No newline at end of file From 4f22e99b704032a69270cfa9457b7a3329e8c313 Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Fri, 24 Apr 2026 18:15:27 -0700 Subject: [PATCH 186/254] Update prompt message when length < Nbrite --- flystar/match.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/flystar/match.py b/flystar/match.py index 3637b8c..c4cad4a 100644 --- a/flystar/match.py +++ b/flystar/match.py @@ -38,10 +38,11 @@ def miracle_match_briteN(xin1, yin1, min1, xin2, yin2, min2, Nbrite, nin2 = len(xin2) if (nin1 < Nbrite) or (nin2 < Nbrite): - print(( 'You need at least {0} to '.format(Nbrite))) - print( 'find the matches...') - print(( 'NIN1: ', nin1)) - print(( 'NIN2: ', nin2)) + print(f'WARNING: You need at least {Nbrite} to find the matches...') + print(f'NIN1: {nin1}') + print(f'NIN2: {nin2}') + # Nbrite = min(nin1, nin2) + # print(f'Updating Nbrite to {Nbrite}...') return (0, None, None, None, None, None, None) # Take the Nbrite brightest stars from each list and order by brightness. From 4bac0853ecb95a404f604b2a749158f863b07408 Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Fri, 24 Apr 2026 18:21:02 -0700 Subject: [PATCH 187/254] Add dimension check and update dimension manipulation for input starlists; --- flystar/align.py | 14 ++++++++++---- 1 file changed, 10 insertions(+), 4 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 9a0d734..ad06d84 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -226,6 +226,11 @@ def = None. If not None, then this should contain an array or list of transform self.save_path = save_path self.verbose = verbose + # Check x and y are 1d + for ii in range(len(self.star_lists)): + if self.star_lists[ii]['x'].ndim != 1 or self.star_lists[ii]['y'].ndim != 1: + raise ValueError(f"StarList at index {ii} has x and y that are not 1D. x.ndim={self.star_lists[ii]['x'].ndim}, y.ndim={self.star_lists[ii]['y'].ndim}. Please flatten these columns to be 1D.") + # Setup save_path: if self.save_path: assert self.save_path.endswith('.pkl'), 'Save_path must end with .pkl' @@ -684,12 +689,13 @@ def setup_ref_table_from_starlist(self, star_list): else: new_col_name = col_name - # Make every column's 2D arrays except "name" and those + # Make every column's 2D arrays per star except "name" and those # columns used for the motion model. if col_name in motion_model_col_names: col_arrays[new_col_name] = star_list[col_name].data else: - new_col_data = np.array([star_list[col_name].data]).T + new_col_data = star_list[col_name].data[:, None] + # new_col_data = np.array([star_list[col_name].data]).T col_arrays[new_col_name] = new_col_data # Use the columns from the ref list to make the ref_table. @@ -1557,8 +1563,8 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot y_pred = y_pred[:, np.newaxis] xe_comb = np.hypot(self.ref_table['xe'], self.ref_table['xe_boot']) ye_comb = np.hypot(self.ref_table['ye'], self.ref_table['ye_boot']) - data_dict['chi2_x_boot'] = np.nansum((self.ref_table['x']-x_pred)**2/(xe_comb)**2,axis=1) - data_dict['chi2_y_boot'] = np.nansum((self.ref_table['y']-y_pred)**2/(ye_comb)**2,axis=1) + data_dict['chi2_x_boot'] = np.nansum((self.ref_table['x'] - x_pred)**2 / xe_comb**2, axis=1) + data_dict['chi2_y_boot'] = np.nansum((self.ref_table['y'] - y_pred)**2 / ye_comb**2, axis=1) for ff in ['chi2_x_boot', 'chi2_y_boot']: col = Column(np.ones(len(self.ref_table)), name=ff) col.fill(np.nan) From 9beff67b597745c9815386c9ac99db2e65e31254 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Fri, 24 Apr 2026 18:36:14 -0700 Subject: [PATCH 188/254] Updated outlier_tol default behavior; Added prefix_name for saved files: PREFIX_input.log, MosaicSelfRef/MosaicToRef.pkl, PREFIX_ref_table.pkl; Added n_fit to ref_table --- flystar/align.py | 281 ++++++++++++++++++++++++++++++++--------------- 1 file changed, 190 insertions(+), 91 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 9a0d734..e8a1888 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -1,18 +1,19 @@ +import os +import pdb +import copy +import time +import pickle +import warnings +import datetime import numpy as np +from tqdm import tqdm from . import match, transforms, plots, motion_model from .starlists import StarList from .startables import StarTable +from .motion_model import Empty, Fixed from astropy.table import Table, Column, vstack -import datetime -import copy -import os -import pdb -import time -import warnings -import pickle from astropy.utils.exceptions import AstropyUserWarning -from .motion_model import Empty, Fixed -from tqdm import tqdm + class MosaicSelfRef(object): def __init__( @@ -23,7 +24,7 @@ def __init__( iters=2, dr_tol=[1, 1], dm_tol=[2, 1], - outlier_tol=[None, None], + outlier_tol=None, # Transformation parameters trans_class=transforms.PolyTransform, trans_args=[{'order': 2}, {'order': 2}], @@ -45,6 +46,7 @@ def __init__( # Advanced options iter_callback=None, save_path=None, + prefix_name='msr', verbose=True ): """ @@ -89,6 +91,7 @@ def __init__( outlier_tol : list or array The outlier tolerance (in units of sigma) for rejecting outlier stars. This is a list of tol values, one for each iteration of matching/transformation. + If not provided, will be None for each iteration. trans_class : transforms.Transform2D object (or subclass) The transform class that will be used to when deriving the optimal @@ -169,6 +172,9 @@ def = None. If not None, then this should contain an array or list of transform save_path : str, optional Path to save the MosaicSelfRef object as a pickle file. + prefix_name : str, optional + Prefix for the file names, including PREFIX_input.log, PREFIX.pkl, PREFIX_ref_table.fits. + verbose : bool or int (0 to 9, inclusive) Controls the verbosity of print statements. (0 least, 9 most verbose). For backwards compatibility, 0 = False, 9 = True. @@ -176,20 +182,20 @@ def = None. If not None, then this should contain an array or list of transform Example ------- - msc = align.MosaicToRef(list_of_starlists, iters=1, + mtr = align.MosaicToRef(list_of_starlists, iters=1, dr_tol=[0.1], dm_tol=[5], outlier_tol=[None], mag_lim=[13, 21], trans_class=transforms.PolyTransform, trans_args=[{'order': 1}], weights='both,std', init_guess_mode='miracle', verbose=False) - msc.fit() + mtr.fit() # Access a list of all the transformation parameters: - trans_list = msc.trans_list + trans_list = mtr.trans_list # Access the fully-combined reference table. - stars_table = msc.ref_table + stars_table = mtr.ref_table # Plot the magnitude of the first star vs. time: # Overplot the mean magnitude. @@ -208,7 +214,7 @@ def = None. If not None, then this should contain an array or list of transform self.iters = iters self.dr_tol = dr_tol self.dm_tol = dm_tol - self.outlier_tol = outlier_tol + # self.outlier_tol = outlier_tol self.trans_args = trans_args self.init_order = init_order self.mag_trans = mag_trans @@ -224,13 +230,13 @@ def = None. If not None, then this should contain an array or list of transform self.init_guess_mode = init_guess_mode self.iter_callback = iter_callback self.save_path = save_path + self.prefix_name = prefix_name self.verbose = verbose - # Setup save_path: - if self.save_path: - assert self.save_path.endswith('.pkl'), 'Save_path must end with .pkl' - if not os.path.exists(os.path.dirname(self.save_path)): - os.makedirs(os.path.dirname(self.save_path)) + if outlier_tol is None: + self.outlier_tol = [None] * self.iters + else: + self.outlier_tol = outlier_tol all_mm_map = motion_model.motion_model_map() if all(isinstance(mm, str) for mm in motion_models): @@ -326,6 +332,45 @@ def fit(self): additional motion_model columns """ + # Setup save_path: + if self.save_path: + if not os.path.exists(os.path.dirname(self.save_path)): + os.makedirs(os.path.dirname(self.save_path)) + + # Save input params + input_filename = f'{self.prefix_name}_input.txt' + input_dict = { + 'ref_index': self.ref_index, + 'iters': self.iters, + 'dr_tol': self.dr_tol, + 'dm_tol': self.dm_tol, + 'outlier_tol': self.outlier_tol, + 'trans_class': self.trans_class, + 'trans_args': self.trans_args, + 'trans_input': self.trans_input, + 'trans_weighting': self.trans_weighting, + 'init_order': self.init_order, + 'init_guess_mode': self.init_guess_mode, + 'calc_trans_inverse': self.calc_trans_inverse, + 'mag_trans': self.mag_trans, + 'mag_lim': self.mag_lim, + 'motion_models': self.motion_models, + 'fixed_params_dict': self.fixed_params_dict, + 'vel_weighting': self.vel_weighting, + 'use_scipy': self.use_scipy, + 'absolute_sigma': self.absolute_sigma, + 'iter_callback': self.iter_callback, + 'save_path': self.save_path, + 'prefix_name': self.prefix_name, + 'verbose': self.verbose + } + if self.save_path is not None: + if not os.path.exists(self.save_path): + os.makedirs(self.save_path) + with open(os.path.join(self.save_path, input_filename), 'w') as file: + for key, value in input_dict.items(): + file.write(f'{key}:\t{value}\n') + ########## # Setup a reference table to store data. It will contain: # x0, y0, m0 -- the running average of positions: 1D @@ -435,12 +480,18 @@ def fit(self): self.ref_table['chi2_x'] = chi2_x self.ref_table['chi2_y'] = chi2_y - if self.save_path: - with open(self.save_path, 'wb') as file: + if self.save_path is not None: + filename = f'{self.prefix_name}.pkl' + with open(os.path.join(self.save_path, filename), 'wb') as file: pickle.dump(self, file) - print('================================') - print(f'Done with fit()') - print('================================') + # Using pickle here because nan in a fits file is auto-converted to a masked value in astropy.io.fits.open() + filename = f'{self.prefix_name}_ref_table.pkl' + with open(os.path.join(self.save_path, filename), 'wb') as file: + pickle.dump(self.ref_table, file) + + print('===================================') + print('========== Done with fit ==========') + print('===================================') return def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_args, nn=None): @@ -713,15 +764,14 @@ def setup_ref_table_from_starlist(self, star_list): new_cols_arr = ['x0', 'y0', 'm0'] orig_cols_arr = ['x', 'y', 'm'] ref_cols = ref_table.keys() - for ii in range(len(new_cols_arr)): - if not new_cols_arr[ii] in ref_cols: + for new_col, orig_col in zip(new_cols_arr, orig_cols_arr): + if new_col not in ref_cols: # Some munging to convert data shape from (N,1) to (N,), # since these are all 1D cols - vals = np.array(ref_table[orig_cols_arr[ii]]).flatten() + vals = np.array(ref_table[orig_col]).flatten() # Now add to ref_table - new_col = Column(vals, name=new_cols_arr[ii]) - ref_table.add_column(new_col) + ref_table.add_column(vals, name=new_col) # Do the same thing for the x0e, y0e, m0e columns, but # ONLY IF THEY ALREADY EXIST IN REF_TABLE! Otherwise, @@ -730,22 +780,20 @@ def setup_ref_table_from_starlist(self, star_list): # work later on. new_err_cols = ['x0_err', 'y0_err', 'm0_err'] orig_err_cols = ['xe', 'ye', 'me'] - for ii in range(len(new_err_cols)): + for new_err_col, orig_err_col in zip(new_err_cols, orig_err_cols): # If the orig col name (e.g. xe) is in the ref_table, but the new col name # (e.g. x0e) doesn't exist, then add the x0e column as a duplicate of xe. - if (orig_err_cols[ii] in ref_cols) & (not new_err_cols[ii] in ref_cols): + if (orig_err_col in ref_cols) and (new_err_col not in ref_cols): # Some munging to convert data shape from (N,1) to (N,), # since these are all 1D cols - vals = np.transpose(np.array(ref_table[orig_err_cols[ii]]))[0] + vals = np.transpose(np.array(ref_table[orig_err_col]))[0] # Now add to ref_table - new_col = Column(vals, name=new_err_cols[ii]) - ref_table.add_column(new_col) - elif (not orig_err_cols[ii] in ref_cols) & (not new_err_cols[ii] in ref_cols): + ref_table.add_column(vals, name=new_err_col) + elif (orig_err_col not in ref_cols) and (new_err_col not in ref_cols): # If neither the orig_err_col or new_err_col is in the ref_table, put in the # new_err_cols as an array of zeros vals = np.zeros(len(ref_table)) - new_col = Column(vals, name=new_err_cols[ii]) - ref_table.add_column(new_col) + ref_table.add_column(vals, name=new_err_col) # Final check: ref_table should now have x0, y0, m0, x0e, y0e, and m0e columns # This is necessary for later steps, even if the columns are just zeros. @@ -756,18 +804,15 @@ def setup_ref_table_from_starlist(self, star_list): # Make sure we have a column to indicate whether each star # CAN BE USED in the transformation. This will be 1D if 'use_in_trans' not in ref_table.colnames: - new_col = Column(np.ones(len(ref_table), dtype=bool), name='use_in_trans') - ref_table.add_column(new_col) + ref_table.add_column(np.ones(len(ref_table), dtype=bool), name='use_in_trans') # Make sure we have a column to indicate whether each star # IS USED in the transformation. This will be 2D if 'used_in_trans' not in ref_table.colnames: - new_col = Column(np.zeros([len(ref_table), 1], dtype=bool), name='used_in_trans') - ref_table.add_column(new_col) + ref_table.add_column(np.zeros([len(ref_table), 1], dtype=bool), name='used_in_trans') # Keep track of whether this is an original reference star. - col_ref_orig = Column(np.ones(len(ref_table), dtype=bool), name='ref_orig') - ref_table.add_column(col_ref_orig) + ref_table.add_column(np.ones(len(ref_table), dtype=bool), name='ref_orig') # Now reset the original values to invalids... they will be filled in # at later times. Preserve content only in the columns: name, x0, y0, m0 (and 0e). # Note that these are all the 1D columsn. @@ -776,7 +821,7 @@ def setup_ref_table_from_starlist(self, star_list): ref_table._set_invalid_list_values(col_name, -1) if 'motion_model_input' not in ref_table.colnames: - ref_table.add_column(Column(np.repeat(self.motion_models[-1].name, len(ref_table)), name='motion_model_input')) + ref_table.add_column(np.repeat(self.motion_models[-1].name, len(ref_table)), name='motion_model_input') # FIXME: Why do we need to set motion_model_used here before fitting? # if 'motion_model_used' not in ref_table.colnames: # # Order self.motion_models by decreasing n_params @@ -784,7 +829,7 @@ def setup_ref_table_from_starlist(self, star_list): # # Save the most complex motion model that can infer the positions with the existing columns. # for mm in sorted_mms: # if all([_ in ref_table.colnames for _ in mm.fit_param_names]) and all([_ in ref_table.colnames for _ in mm.fixed_param_names]): - # ref_table.add_column(Column(np.repeat(mm.name, len(ref_table)), name='motion_model_used')) + # ref_table.add_column(np.repeat(mm.name, len(ref_table)), name='motion_model_used') # break return ref_table @@ -990,7 +1035,7 @@ def update_ref_table_aggregates(self, keep_orig=None, n_boot=0): # if (keep_orig is not None) and (sum(keep_orig) > 0): # Determine motion_model_used for keep_orig stars # Filter possible motion models based on available columns - motion_model_used = determine_motion_model(self.ref_table, self.motion_models, self.fixed_params_dict) + motion_model_used, n_fit = determine_motion_model(self.ref_table, self.motion_models, self.fixed_params_dict) # motion_models_possible = [] # for mm in self.motion_models: @@ -1014,7 +1059,8 @@ def update_ref_table_aggregates(self, keep_orig=None, n_boot=0): # Assign the determined motion models # self.ref_table['motion_model_used'][keep_orig] = motion_model_used - self.ref_table['motion_model_used'] = Column(motion_model_used, name='motion_model_used', dtype='U15') + self.ref_table['motion_model_used'] = Column(motion_model_used, name='motion_model_used', dtype='U20') + self.ref_table['n_fit'] = Column(n_fit, name='n_fit', dtype=int) # Replace the originals if we are supposed to keep them fixed. if (keep_orig is not None) and (sum(keep_orig) > 0): @@ -1130,8 +1176,9 @@ def get_ref_list_from_table(self, epoch): # Calculate x, y, xe, ye if 'motion_model_used' not in self.ref_table.colnames: - motion_model_used = determine_motion_model(self.ref_table) - self.ref_table['motion_model_used'] = Column(motion_model_used, name='motion_model_used', dtype='U15') + motion_model_used, n_fit = determine_motion_model(self.ref_table) + self.ref_table['motion_model_used'] = Column(motion_model_used, name='motion_model_used', dtype='U20') + self.ref_table['n_fit'] = Column(n_fit, name='n_fit', dtype=int) x, y, xe, ye = self.ref_table.infer_positions(epoch, fixed_params_dict=self.fixed_params_dict) # else: @@ -1605,7 +1652,7 @@ def __init__( iters=2, dr_tol=[1, 1], dm_tol=[2, 1], - outlier_tol=[None, None], + outlier_tol=None, # Reference behavior (MosiacToRef specific) use_ref_new=False, update_ref_orig=False, @@ -1631,6 +1678,7 @@ def __init__( # Advanced options iter_callback=None, save_path=None, + prefix_name='mtr', verbose=True ): @@ -1782,7 +1830,7 @@ def = None. If not None, then this should contain an array or list of transform Example ------- - msc = align.MosaicToRef(my_gaia, list_of_starlists, iters=1, + mtr = align.MosaicToRef(my_gaia, list_of_starlists, iters=1, dr_tol=[0.1], dm_tol=[5], outlier_tol=[None], mag_lim=[13, 21], trans_class=transforms.PolyTransform, @@ -1792,13 +1840,13 @@ def = None. If not None, then this should contain an array or list of transform mag_trans=False, weights='both,std', init_guess_mode='miracle', verbose=False) - msc.fit() + mtr.fit() # Access a list of all the transformation parameters: - trans_list = msc.trans_list + trans_list = mtr.trans_list # Access the fully-combined reference table. - stars_table = msc.ref_table + stars_table = mtr.ref_table # Plot the magnitude of the first star vs. time: # Overplot the mean magnitude. @@ -1840,6 +1888,7 @@ def = None. If not None, then this should contain an array or list of transform # Advanced options iter_callback=iter_callback, save_path=save_path, + prefix_name=prefix_name, verbose=verbose ) @@ -1900,28 +1949,70 @@ def fit(self): """ # Create a log file of the parameters used in the fit. + # Setup save_path: + if self.save_path: + if not os.path.exists(os.path.dirname(self.save_path)): + os.makedirs(os.path.dirname(self.save_path)) + + # Save input params + input_filename = f'{self.prefix_name}_input.txt' + input_dict = { + 'iters': self.iters, + 'dr_tol': self.dr_tol, + 'dm_tol': self.dm_tol, + 'outlier_tol': self.outlier_tol, + 'use_ref_new': self.use_ref_new, + 'update_ref_orig': self.update_ref_orig, + 'trans_class': self.trans_class, + 'trans_args': self.trans_args, + 'trans_input': self.trans_input, + 'trans_weighting': self.trans_weighting, + 'init_order': self.init_order, + 'init_guess_mode': self.init_guess_mode, + 'calc_trans_inverse': self.calc_trans_inverse, + 'mag_trans': self.mag_trans, + 'mag_lim': self.mag_lim, + 'ref_mag_lim': self.ref_mag_lim, + 'motion_models': self.motion_models, + 'fixed_params_dict': self.fixed_params_dict, + 'vel_weighting': self.vel_weighting, + 'use_scipy': self.use_scipy, + 'absolute_sigma': self.absolute_sigma, + 'iter_callback': self.iter_callback, + 'save_path': self.save_path, + 'prefix_name': self.prefix_name, + 'verbose': self.verbose + } if self.save_path is not None: - with open(f'{os.path.dirname(self.save_path)}/MosaicToRef_input_params.log', 'w',) as _log: - logger(_log, 'Parameters used for fit: ', self.verbose) - logger(_log, '------------------------- ', self.verbose) - logger(_log, ' dr_tol = ' + str(self.dr_tol), self.verbose) - logger(_log, ' dm_tol = ' + str(self.dm_tol), self.verbose) - logger(_log, ' outlier_tol = ' + str(self.outlier_tol), self.verbose) - logger(_log, ' trans_args = ' + str(self.trans_args), self.verbose) - logger(_log, ' mag_trans = ' + str(self.mag_trans), self.verbose) - logger(_log, ' mag_lim = ' + str(self.mag_lim), self.verbose) - logger(_log, ' ref_mag_lim = ' + str(self.ref_mag_lim), self.verbose) - logger(_log, ' trans_weighting = ' + str(self.trans_weighting), self.verbose) - logger(_log, ' vel_weighting = ' + str(self.vel_weighting), self.verbose) - logger(_log, ' trans_input = ' + str(self.trans_input), self.verbose) - logger(_log, ' trans_class = ' + str(self.trans_class), self.verbose) - logger(_log, ' calc_trans_inverse = ' + str(self.calc_trans_inverse), self.verbose) - logger(_log, ' use_ref_new = ' + str(self.use_ref_new), self.verbose) - logger(_log, ' motion_models = ' + str([mm.name for mm in self.motion_models]), self.verbose) - logger(_log, ' update_ref_orig = ' + str(self.update_ref_orig), self.verbose) - logger(_log, ' init_guess_mode = ' + str(self.init_guess_mode), self.verbose) - logger(_log, ' iter_callback = ' + str(self.iter_callback), self.verbose) - logger(_log, '-------------------------\n', self.verbose) + if not os.path.exists(self.save_path): + os.makedirs(self.save_path) + with open(os.path.join(self.save_path, input_filename), 'w') as file: + for key, value in input_dict.items(): + file.write(f'{key}:\t{value}\n') + + + # if self.save_path is not None: + # with open(f'{os.path.dirname(self.save_path)}/MosaicToRef_input_params.log', 'w',) as _log: + # logger(_log, 'Parameters used for fit: ', self.verbose) + # logger(_log, '------------------------- ', self.verbose) + # logger(_log, ' dr_tol = ' + str(self.dr_tol), self.verbose) + # logger(_log, ' dm_tol = ' + str(self.dm_tol), self.verbose) + # logger(_log, ' outlier_tol = ' + str(self.outlier_tol), self.verbose) + # logger(_log, ' trans_args = ' + str(self.trans_args), self.verbose) + # logger(_log, ' mag_trans = ' + str(self.mag_trans), self.verbose) + # logger(_log, ' mag_lim = ' + str(self.mag_lim), self.verbose) + # logger(_log, ' ref_mag_lim = ' + str(self.ref_mag_lim), self.verbose) + # logger(_log, ' trans_weighting = ' + str(self.trans_weighting), self.verbose) + # logger(_log, ' vel_weighting = ' + str(self.vel_weighting), self.verbose) + # logger(_log, ' trans_input = ' + str(self.trans_input), self.verbose) + # logger(_log, ' trans_class = ' + str(self.trans_class), self.verbose) + # logger(_log, ' calc_trans_inverse = ' + str(self.calc_trans_inverse), self.verbose) + # logger(_log, ' use_ref_new = ' + str(self.use_ref_new), self.verbose) + # logger(_log, ' motion_models = ' + str([mm.name for mm in self.motion_models]), self.verbose) + # logger(_log, ' update_ref_orig = ' + str(self.update_ref_orig), self.verbose) + # logger(_log, ' init_guess_mode = ' + str(self.init_guess_mode), self.verbose) + # logger(_log, ' iter_callback = ' + str(self.iter_callback), self.verbose) + # logger(_log, '-------------------------\n', self.verbose) ########## @@ -2002,7 +2093,7 @@ def fit(self): self.ref_table.detections() ### Drop all stars that have 0 detections. - idx = np.where((self.ref_table['n_detect'] == 0) & (self.ref_table['ref_orig'] == False))[0] + idx = np.where((self.ref_table['n_detect'] == 0))[0] # & (self.ref_table['ref_orig'] == False))[0] if self.verbose: print(' *** Getting rid of {0:d} out of {1:d} junk sources'.format(len(idx), len(self.ref_table))) self.ref_table.remove_rows(idx) @@ -2030,12 +2121,18 @@ def fit(self): self.ref_table['chi2_x'] = chi2_x self.ref_table['chi2_y'] = chi2_y - if self.save_path: - with open(self.save_path, 'wb') as file: + if self.save_path is not None: + filename = f'{self.prefix_name}.pkl' + with open(os.path.join(self.save_path, filename), 'wb') as file: pickle.dump(self, file) - print('================================') - print(f'Done with fit()') - print('================================') + # Using pickle here because nan in a fits file is auto-converted to a masked value in astropy.io.fits.open() + filename = f'{self.prefix_name}_ref_table.pkl' + with open(os.path.join(self.save_path, filename), 'wb') as file: + pickle.dump(self.ref_table, file) + + print('===================================') + print('========== Done with fit ==========') + print('===================================') return # TODO: This is sometimes run on a startable, not a starlist, at least as currently used @@ -2146,6 +2243,8 @@ def determine_motion_model(startable, motion_models=None, fixed_params_dict=None ------- motion_model_used : list List of motion model used for each star + n_fit : list + List of number of number of observations used to fit for each star """ if motion_models is None: @@ -2167,6 +2266,7 @@ def determine_motion_model(startable, motion_models=None, fixed_params_dict=None # Check if values are finite for required columns in possible motion models motion_model_used = [] + n_fit = [] for k in range(len(startable)): for mm, req_col_in_table, req_col_in_dict in motion_models_possible[::-1]: @@ -2174,8 +2274,9 @@ def determine_motion_model(startable, motion_models=None, fixed_params_dict=None if all(np.isfinite(startable[col][k]) for col in req_col_in_table if np.issubdtype(startable[col].dtype, np.number)) \ and all(np.isfinite(fixed_params_dict[col]) for col in req_col_in_dict if np.issubdtype(np.array(fixed_params_dict[col]).dtype, np.number)): motion_model_used.append(mm.name) + n_fit.append(mm.n_params) break - return motion_model_used + return motion_model_used, n_fit def get_all_epochs(t): @@ -2198,7 +2299,7 @@ def get_all_epochs(t): all_epochs.append(t['t'][good,ii]) - all_epochs = np.array(all_epochs) + # all_epochs = np.array(all_epochs) return all_epochs @@ -2250,19 +2351,17 @@ def setup_ref_table_from_starlist(star_list, motion_models): assert len(new_cols_arr) == len(orig_cols_arr) ref_cols = ref_table.keys() - for ii in range(len(new_cols_arr)): - if not new_cols_arr[ii] in ref_cols: + for new_col, orig_col in zip(new_cols_arr, orig_cols_arr): + if new_col not in ref_cols: # Some munging to convert data shape from (N,1) to (N,), # since these are all 1D cols - vals =np.array(ref_table[orig_cols_arr[ii]]).flatten() + vals = np.array(ref_table[orig_col]).flatten() # Now add to ref_table - new_col = Column(vals, name=new_cols_arr[ii]) - ref_table.add_column(new_col) + ref_table.add_column(vals, name=new_col) if 'use_in_trans' not in ref_table.colnames: - new_col = Column(np.ones(len(ref_table), dtype=bool), name='use_in_trans') - ref_table.add_column(new_col) + ref_table.add_column(np.ones(len(ref_table), dtype=bool), name='use_in_trans') # Now reset the original values to invalids... they will be filled in # at later times. Preserve content only in the columns: name, x0, y0, m0 (and 0e). From 14059722f143f4c651b5bf6f24e04467491def81 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Fri, 24 Apr 2026 18:53:10 -0700 Subject: [PATCH 189/254] Convert combine_lists into mask lists to avoid minus .data; Optimized mask_lists performance in fit_motion_model: only use the desired columns instead of masking --- flystar/startables.py | 161 +++++++++++++++++++++++------------------- 1 file changed, 87 insertions(+), 74 deletions(-) diff --git a/flystar/startables.py b/flystar/startables.py index cfdbd4b..64b4b0b 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -1,5 +1,5 @@ from astropy.table import Table, Column, MaskedColumn, hstack -from astropy.stats import sigma_clipping +from astropy.stats import sigma_clip from astropy.time import Time from scipy.optimize import curve_fit from tqdm import tqdm @@ -13,7 +13,7 @@ import pandas as pd from flystar.motion_model import Empty, Fixed, Linear from pandas.api.types import is_string_dtype - +from collections.abc import Iterable class StarTable(Table): def __init__(self, *args, ref_list=0, **kwargs): """ @@ -102,9 +102,9 @@ def __init__(self, *args, ref_list=0, **kwargs): # Check if the type and size of the arguments are correct. # Name checking: type and shape if (not isinstance(kwargs['name'], np.ndarray)) or (len(kwargs['name']) != n_stars): - err_msg = "The '{0:s}' argument has to be a numpy array " - err_msg += "with length = {1:d}" - raise TypeError(err_msg.format('name', n_stars)) + err_msg = f"The 'name' argument has to be a numpy array, not {type(kwargs['name'])};" + err_msg += f"Its length should be {n_stars}, not {len(kwargs['name'])}." + raise TypeError(err_msg) # Check all the 2D arrays. arg_tab = ('x', 'y', 'm', 'xe', 'ye', 'me', 'name_in_list') @@ -112,17 +112,17 @@ def __init__(self, *args, ref_list=0, **kwargs): for arg_test in arg_tab: if arg_test in kwargs: if not isinstance(kwargs[arg_test], np.ndarray): - err_msg = "The '{0:s}' argument has to be a numpy array" - raise TypeError(err_msg.format(arg_test)) + err_msg = f"The '{arg_test}' argument has to be a numpy array, not {type(kwargs[arg_test])}" + raise TypeError(err_msg) if kwargs[arg_test].shape != (n_stars, n_lists): - err_msg = "The '{0:s}' argument has to have shape = ({1:d}, {2:d})" - raise TypeError(err_msg.format(arg_test, n_stars, n_lists)) + err_msg = f"The '{arg_test}' argument has to have shape = ({n_stars}, {n_lists})" + raise TypeError(err_msg) # Check that the reference list is specified. if ref_list not in range(n_lists): - err_msg = "The 'ref_list' argument has to be an integer between 0 and {0:d}" - raise IndexError(err_msg.format(n_lists-1)) + err_msg = f"The 'ref_list' argument has to be an integer between 0 and {n_lists-1}" + raise IndexError(err_msg) # We have to have special handling of meta-data (i.e. info that has # dimensions of n_lists). @@ -135,13 +135,12 @@ def __init__(self, *args, ref_list=0, **kwargs): if meta_test in kwargs: if len(kwargs[meta_test]) != n_lists: - err_msg = "The '{0:s}' argument has to have length = {1:d}" - raise ValueError(err_msg.format(meta_test, n_lists)) + err_msg = f"The '{meta_test}' argument has to have length = {n_lists}" + raise ValueError(err_msg) if not all(isinstance(tt, meta_type_test) for tt in kwargs[meta_test]): - err_msg = "The '{0:s}' argument has to be a list of {1:s}." - raise TypeError(err_msg.format(meta_test, str(meta_type_test))) - + err_msg = f"The '{meta_test}' argument has to be a list of {str(meta_type_test)}." + raise TypeError(err_msg) ##### # Create the startable ##### @@ -238,7 +237,7 @@ def _add_list_data_from_starlist(self, starlist): # Meta table entries with a size that matches the n_lists size are the ones # that need a new value. We have to add something... whatever was passed in or None - if isinstance(self.meta[tab_key], collections.abc.Iterable) and (len(self.meta[tab_key]) == self.meta['n_lists']) and (not isinstance(self.meta[tab_key], str)): + if isinstance(self.meta[tab_key], Iterable) and (len(self.meta[tab_key]) == self.meta['n_lists']) and (not isinstance(self.meta[tab_key], str)): # If we find the key in the starlists' meta argument, then add the new values. # Otherwise, add "None". @@ -295,7 +294,7 @@ def _add_list_data_from_keywords(self, **kwargs): for key in self.meta.keys(): # Meta table entries with a size that matches the n_lists size are the ones # that need a new value. We have to add something... whatever was passed in or None - if isinstance(self.meta[key], collections.abc.Iterable) and (len(self.meta[key]) == self.meta['n_lists']) and (not isinstance(self.meta[key], str)): + if isinstance(self.meta[key], Iterable) and (len(self.meta[key]) == self.meta['n_lists']) and (not isinstance(self.meta[key], str)): # If we find the key is the passed in meta argument, then add the new values. # Otherwise, add "None". if 'meta' in kwargs: @@ -396,7 +395,7 @@ def get_starlist(self, list_index): return starlist - def combine_lists_xym(self, weighted_xy=True, weighted_m=True, mask_lists=False, sigma=3): + def combine_lists_xym(self, weighted_xy=True, weighted_m=True, mask_lists=None, sigma=3): """ For x, y and m columns in the table, collapse along the lists direction. For 'x', 'y' this means calculating the average position with @@ -427,7 +426,7 @@ def combine_lists_xym(self, weighted_xy=True, weighted_m=True, mask_lists=False, return def combine_lists(self, col_name_in, weights_col=None, mask_val=None, - mask_lists=False, meta_add=True, ismag=False, sigma=3): + mask_lists=None, meta_add=True, ismag=False, sigma=3): """ For the specified column (col_name_in), collapse along the starlists direction and calculated the average value, with outlier rejection. @@ -444,68 +443,71 @@ def combine_lists(self, col_name_in, weights_col=None, mask_val=None, A flag can be stored in the metadata to record if the average was weighted or not. """ - # Get the array we are going to combine. Make a copy so we don't mod it. - val_2d = copy.deepcopy( self[col_name_in].data ) + if mask_lists is not None: + # Extract list of indices that we want to keep (i.e. not mask) + mask_lists = np.atleast_1d(mask_lists) + assert mask_lists.dtype == int, "mask_lists needs to be a list of integers." + list_indices = np.array([i for i in np.arange(self[col_name_in].data.shape[1]) if i not in mask_lists]) + else: + # Use all indices + list_indices = np.arange(self[col_name_in].data.shape[1]) + + val_2d = np.ma.masked_invalid(self[col_name_in].data[:, list_indices]) if ismag: # Convert to flux. - val_2d = 10**(-val_2d / 2.5) + val_2d = 10**(-0.4 * val_2d) # Make a mask of invalid (NaN) values and a user-specified invalid value. - val_2d = np.ma.masked_invalid(val_2d) + if mask_val: val_2d = np.ma.masked_values(val_2d, mask_val) - if mask_lists is not False: - # Remove a list - if isinstance(mask_lists, list): - if all(isinstance(item, int) for item in mask_lists): - val_2d.mask[:, mask_lists] = True - - # Throw a warning if mask_lists is not a list - if not isinstance(mask_lists, list): - raise RuntimeError('mask_lists needs to be a list.') + # Figure out which ones are outliers. Returns a masked array. + if sigma: + # with warnings.catch_warnings(): + # warnings.filterwarnings('ignore', category=RuntimeWarning) + val_2d_clip = sigma_clip(val_2d, sigma=sigma, maxiters=5, axis=1) + else: + val_2d_clip = val_2d # Decide if we are going to have weights (before we # do the expensive sigma clipping routine). Note that # if we have only 1 column to average, then we can't do weighting. if (weights_col and weights_col in self.colnames) and (val_2d.shape[1] > 1): - err_2d = self[weights_col].data + err_2d = np.ma.masked_invalid(self[weights_col].data[:, list_indices]) if ismag: # Convert to flux error - err_2d = err_2d * val_2d * np.log(10) / 2.5 + err_2d = 0.4 * np.log(10) * val_2d * err_2d + + # Unify masks + unified_mask = val_2d_clip.mask | err_2d.mask + val_2d_clip.mask = unified_mask + err_2d.mask = unified_mask - np.seterr(divide='ignore') - wgt_2d = np.ma.masked_invalid(1.0 / err_2d**2) - np.seterr(divide='warn') + # Inverse variance weights minimize the propagated uncertainty + wgt_2d = np.ma.masked_invalid(1. / err_2d**2) + + # Calculate the weighted mean and uncertainty + avg = np.ma.average(val_2d_clip, weights=wgt_2d, axis=1) + std = np.ma.sqrt(1 / np.ma.sum(wgt_2d, axis=1)) if meta_add: self.meta[col_name_in + '0'] = 'weighted' else: wgt_2d = None + # Calculate the weighted mean and uncertainty + avg = np.ma.mean(val_2d_clip, axis=1) + std = np.ma.std(val_2d_clip, axis=1) / np.sqrt(len(list_indices)) # Standard error of the mean + if meta_add: self.meta[col_name_in + '0'] = 'not_weighted' - # Figure out which ones are outliers. Returns a masked array. - if sigma: - warnings.filterwarnings('ignore', category=RuntimeWarning) - val_2d_clip = sigma_clipping.sigma_clip(val_2d, sigma=sigma, maxiters=5, axis=1) - warnings.filterwarnings('default', category=RuntimeWarning) - else: - val_2d_clip = val_2d - - # Calculate the (weighted) mean and standard deviation along - # the N_lists direction (axis=1). - if wgt_2d is not None: - avg = np.ma.average(val_2d_clip, weights=wgt_2d, axis=1) - std = np.sqrt(np.ma.average((val_2d_clip.T - avg).T**2, weights=wgt_2d, axis=1)) - else: - avg = np.ma.mean(val_2d_clip, axis=1) - std = np.ma.std(val_2d_clip, axis=1) + # FIXME: What does this part do? # To Do: bring the previous uncertainties of stars that are detected # in only one input frame. if (weights_col and weights_col in self.colnames) and (val_2d.shape[1] > 1): - mask_for_singles = ((~np.isnan(val_2d_clip)).sum(axis=1)==1) + mask_for_singles = ((np.isfinite(val_2d_clip)).sum(axis=1)==1) std[mask_for_singles]=np.nanmean(err_2d[mask_for_singles], axis=1) # Save off our new AVG and STD into new columns with shape (N_stars). @@ -513,14 +515,19 @@ def combine_lists(self, col_name_in, weights_col=None, mask_val=None, col_name_std = col_name_in + '0_err' if ismag: - std = (2.5 / np.log(10)) * std / avg + std = 2.5 / np.log(10) * std / avg avg = -2.5 * np.ma.log10(avg) + + # Fill mask with nan or inf + avg = avg.filled(np.nan) + std = std.filled(np.inf) + if col_name_avg in self.colnames: - self[col_name_avg] = avg.data - self[col_name_std] = std.data + self[col_name_avg] = avg + self[col_name_std] = std else: - self.add_column(Column(data=avg.data, name=col_name_avg)) - self.add_column(Column(data=std.data, name=col_name_std)) + self.add_column(Column(data=avg, name=col_name_avg)) + self.add_column(Column(data=std, name=col_name_std)) return @@ -533,7 +540,7 @@ def detections(self): if 'n_detect' in self.colnames: self['n_detect'] = n_detect else: - self.add_column(Column(n_detect), name='n_detect') + self.add_column(Column(data=n_detect, name='n_detect')) return @@ -542,7 +549,7 @@ def fit_motion_model( motion_models=None, fixed_params_dict=None, weighting='var', - use_scipy=False, + use_scipy=True, absolute_sigma=True, select_stars=None, bootstrap=0, @@ -667,11 +674,17 @@ def fit_motion_model( ########################### # Prepare data for fitting N_stars = len(self) - x_data = np.ma.masked_invalid(self['x'].data, copy=True) - y_data = np.ma.masked_invalid(self['y'].data, copy=True) - xe_data = np.ma.masked_invalid(self['xe'].data, copy=True) if 'xe' in self.colnames else None - ye_data = np.ma.masked_invalid(self['ye'].data, copy=True) if 'ye' in self.colnames else None - # Mask out close to 0 values + N_times = self['x'].data.shape[1] + if mask_lists is not None: + list_indices = np.array([i for i in range(N_times) if i not in mask_lists]) + else: + list_indices = np.arange(N_times) + x_data = np.ma.masked_invalid(self['x'].data[:, list_indices], copy=True) + y_data = np.ma.masked_invalid(self['y'].data[:, list_indices], copy=True) + xe_data = np.ma.masked_invalid(self['xe'].data[:, list_indices], copy=True) if 'xe' in self.colnames else None + ye_data = np.ma.masked_invalid(self['ye'].data[:, list_indices], copy=True) if 'ye' in self.colnames else None + + # Mask out close to 0 values to avoid infinite weights if xe_data is not None: xe_data.mask[np.isclose(xe_data, 0)] = True if ye_data is not None: @@ -694,18 +707,18 @@ def fit_motion_model( if np.ndim(ye_data) == 1: ye_data = ye_data[:, np.newaxis] - if mask_lists is not None: - x_data.mask[:, mask_lists] = True - y_data.mask[:, mask_lists] = True - xe_data.mask[:, mask_lists] = True - ye_data.mask[:, mask_lists] = True + # if mask_lists is not None: + # x_data.mask[:, mask_lists] = True + # y_data.mask[:, mask_lists] = True + # xe_data.mask[:, mask_lists] = True + # ye_data.mask[:, mask_lists] = True # t_data: 2d array with shape (N_stars, N_epochs) # t0: 1d array with shape (N_stars,) if 't' in self.colnames: - t_data = copy.deepcopy(self['t'].data) + t_data = copy.deepcopy(self['t'].data[:, list_indices]) else: - t_data = copy.deepcopy(np.array(self.meta['list_times'])) + t_data = copy.deepcopy(np.array(self.meta['list_times']))[list_indices] t_data = np.broadcast_to(t_data, x_data.shape) # Add default t0 if not provided in fixed_params_dict From 938dbb26ecb701e01f0e3d7d3f4b05548284006b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Fri, 24 Apr 2026 18:54:15 -0700 Subject: [PATCH 190/254] Remove main function --- flystar/tests/test_align.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/flystar/tests/test_align.py b/flystar/tests/test_align.py index ca82077..5b4fc66 100644 --- a/flystar/tests/test_align.py +++ b/flystar/tests/test_align.py @@ -1446,9 +1446,9 @@ def make_fake_starlists_poly1_par(seed=-1): return (xy_trans, mag_trans) -if __name__ == '__main__': - import pickle - import matplotlib.pyplot as plt +# if __name__ == '__main__': + # import pickle + # import matplotlib.pyplot as plt # with open(f'{test_data_path}/my_gaia.pkl', 'rb') as f: # my_gaia = pickle.load(f) # with open(f'{test_data_path}/list_of_starlists.pkl', 'rb') as f: From 7012551b19787e165b02ca6aaccab2ef4591845b Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Sun, 26 Apr 2026 16:50:11 -0700 Subject: [PATCH 191/254] Added verbose control over done with fit message --- flystar/align.py | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 466270c..3f58ba9 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -494,9 +494,10 @@ def fit(self): with open(os.path.join(self.save_path, filename), 'wb') as file: pickle.dump(self.ref_table, file) - print('===================================') - print('========== Done with fit ==========') - print('===================================') + if self.verbose > 0: + print('===================================') + print('========== Done with fit ==========') + print('===================================') return def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_args, nn=None): @@ -746,7 +747,6 @@ def setup_ref_table_from_starlist(self, star_list): col_arrays[new_col_name] = star_list[col_name].data else: new_col_data = star_list[col_name].data[:, None] - # new_col_data = np.array([star_list[col_name].data]).T col_arrays[new_col_name] = new_col_data # Use the columns from the ref list to make the ref_table. @@ -2136,9 +2136,10 @@ def fit(self): with open(os.path.join(self.save_path, filename), 'wb') as file: pickle.dump(self.ref_table, file) - print('===================================') - print('========== Done with fit ==========') - print('===================================') + if self.verbose > 0: + print('===================================') + print('========== Done with fit ==========') + print('===================================') return # TODO: This is sometimes run on a startable, not a starlist, at least as currently used From 66dc63177d89e33e5586c924b5421daea7e12b96 Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Sun, 26 Apr 2026 17:31:41 -0700 Subject: [PATCH 192/254] Revert get all epochs to numpy array; Changed default mask_lists to None instead of False --- flystar/align.py | 4 ++-- flystar/tests/test_startable.py | 15 ++++++++++----- 2 files changed, 12 insertions(+), 7 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 3f58ba9..0abe28a 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -467,7 +467,7 @@ def fit(self): # Add times into ref_table meta data # complete_times = np.array([np.unique(col[~np.isnan(col)])[0] for col in self.ref_table['t'].T]) all_epochs = get_all_epochs(self.ref_table) - self.ref_table.meta['list_times'] = all_epochs + self.ref_table.meta['list_times'] = list(all_epochs) # Update chi2 values in ref table, as motion_model_used may have changed x_inferred, y_inferred, _, _ = self.ref_table.infer_positions(all_epochs) @@ -2306,7 +2306,7 @@ def get_all_epochs(t): all_epochs.append(t['t'][good,ii]) - # all_epochs = np.array(all_epochs) + all_epochs = np.array(all_epochs) return all_epochs diff --git a/flystar/tests/test_startable.py b/flystar/tests/test_startable.py index 64e0681..e743b74 100644 --- a/flystar/tests/test_startable.py +++ b/flystar/tests/test_startable.py @@ -121,14 +121,14 @@ def test_combine_lists(): # Test 5: make sure mask_list is working. ########## # Test 5ai: Non-masked, weighted_m=False - tt.combine_lists_xym(weighted_xy=True, weighted_m=False, mask_lists=False) + tt.combine_lists_xym(weighted_xy=True, weighted_m=False, mask_lists=None) assert np.arange(1.8, 38, 4) == pytest.approx(tt['x0'].data) assert np.arange(1.8, 38, 4) == pytest.approx(tt['y0'].data) avg_m = -2.5 * np.log10((4 * 10**-0.4 + 1)/5) assert avg_m * np.ones(10) == pytest.approx(tt['m0'].data) # Test 5aii: Non-masked, weighted_m=True - tt.combine_lists_xym(weighted_xy=True, weighted_m=True, mask_lists=False) + tt.combine_lists_xym(weighted_xy=True, weighted_m=True, mask_lists=None) assert np.arange(1.8, 38, 4) == pytest.approx(tt['x0'].data) assert np.arange(1.8, 38, 4) == pytest.approx(tt['y0'].data) avg_m_weight = 0.9391744564422395 @@ -147,9 +147,10 @@ def test_combine_lists(): assert np.ones(10) == pytest.approx(tt['m0'].data) # Test 5c: Things that should break the code. - with pytest.raises(RuntimeError): - t.combine_lists_xym(weighted_xy=True, weighted_m=True, mask_lists=np.arange(2)) - with pytest.raises(RuntimeError): + # with pytest.raises(RuntimeError): + # This would not break the code anymore + # t.combine_lists_xym(weighted_xy=True, weighted_m=True, mask_lists=np.arange(2)) + with pytest.raises(AssertionError): t.combine_lists_xym(weighted_xy=True, weighted_m=True, mask_lists=True) return @@ -556,3 +557,7 @@ def make_tiny_star_table(): xe=xe_in, ye=ye_in, me=me_in) return startable + + +if __name__ == "__main__": + test_fit_motion_models() From 0faeb879b9ebe5bc8a84c8b2b2d86ea00703eab7 Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Sun, 26 Apr 2026 19:00:21 -0700 Subject: [PATCH 193/254] Fix dr calculation in match --- flystar/match.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/flystar/match.py b/flystar/match.py index c4cad4a..8d13439 100644 --- a/flystar/match.py +++ b/flystar/match.py @@ -323,7 +323,7 @@ def match(x1, y1, m1, x2, y2, m2, dr_tol, dm_tol=None, verbose=True): x2_nn = x2[i2_tmp] y2_nn = y2[i2_tmp] m2_nn = m2[i2_tmp] - dr = np.abs(x1_nn - x2_nn, y1_nn - y2_nn) + dr = np.hypot(x1_nn - x2_nn, y1_nn - y2_nn) dm = np.abs(m1_nn - m2_nn) if dm_tol is not None: From abbf98de883da8cf417db0413ab66eb3a6ddf01e Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Sun, 26 Apr 2026 19:00:48 -0700 Subject: [PATCH 194/254] Fix xe and ye when not provided --- flystar/startables.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/flystar/startables.py b/flystar/startables.py index 64b4b0b..bbe7e4f 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -681,8 +681,8 @@ def fit_motion_model( list_indices = np.arange(N_times) x_data = np.ma.masked_invalid(self['x'].data[:, list_indices], copy=True) y_data = np.ma.masked_invalid(self['y'].data[:, list_indices], copy=True) - xe_data = np.ma.masked_invalid(self['xe'].data[:, list_indices], copy=True) if 'xe' in self.colnames else None - ye_data = np.ma.masked_invalid(self['ye'].data[:, list_indices], copy=True) if 'ye' in self.colnames else None + xe_data = np.ma.masked_invalid(self['xe'].data[:, list_indices], copy=True) if 'xe' in self.colnames else np.ones_like(x_data) + ye_data = np.ma.masked_invalid(self['ye'].data[:, list_indices], copy=True) if 'ye' in self.colnames else np.ones_like(y_data) # Mask out close to 0 values to avoid infinite weights if xe_data is not None: From 594657a25a04e13d965e0d21cb84fd2f7c9fa2b7 Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Tue, 28 Apr 2026 18:31:33 -0700 Subject: [PATCH 195/254] Changed plotly scatter to scattergl for faster plotting --- flystar/plots.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/flystar/plots.py b/flystar/plots.py index 16ec8b1..71ba07d 100755 --- a/flystar/plots.py +++ b/flystar/plots.py @@ -3937,7 +3937,7 @@ def plotly_stars(x, y, m=None, marker_size=3, color='C0', alpha=0.7, label='star if m is not None: m = np.asarray(m) hover_template += '
m: %{customdata:.2f}' - fig_data = go.Scatter( + fig_data = go.Scattergl( x=x, y=y, mode='markers', @@ -3950,7 +3950,7 @@ def plotly_stars(x, y, m=None, marker_size=3, color='C0', alpha=0.7, label='star name=label ) else: - fig_data = go.Scatter( + fig_data = go.Scattergl( x=x, y=y, mode='markers', From 01f3e7ea4a29e5a7181382afb0226f636c797d7e Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Wed, 29 Apr 2026 18:32:23 -0700 Subject: [PATCH 196/254] Add symbol control in plotly_stars --- flystar/plots.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/flystar/plots.py b/flystar/plots.py index 71ba07d..eaf1400 100755 --- a/flystar/plots.py +++ b/flystar/plots.py @@ -3893,7 +3893,7 @@ def __call__(self, event): return -def plotly_stars(x, y, m=None, marker_size=3, color='C0', alpha=0.7, label='starlist', fig=None, figsize=(600, 600), show=True): +def plotly_stars(x, y, m=None, marker_size=3, color='C0', alpha=0.7, symbol='circle', label='starlist', fig=None, figsize=(600, 600), show=True): """Plot stars with plotly in interactive html format Parameters @@ -3910,6 +3910,8 @@ def plotly_stars(x, y, m=None, marker_size=3, color='C0', alpha=0.7, label='star Color of marker, either a string (e.g., 'red') or rgba array, by default 'C0' alpha : float, optional Opacity of marker color, by default 0.7 + symbol : str, optional + Marker symbol, by default 'circle' label : str, optional Label for the star list, by default 'starlist' fig : plotly.graph_objects.Figure object, optional From 0655987c66ad74c46def2bf6d5d6e06e9eebd7c6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Thu, 30 Apr 2026 17:15:02 -0700 Subject: [PATCH 197/254] Changed to png with dpi=300 for faster viewing; Fixed marker symbol for plotly_stars; Added show_plot control for quiver residual plots --- flystar/plots.py | 111 +++++++++++++++++++++++++---------------------- 1 file changed, 58 insertions(+), 53 deletions(-) diff --git a/flystar/plots.py b/flystar/plots.py index eaf1400..8ee8a13 100755 --- a/flystar/plots.py +++ b/flystar/plots.py @@ -80,11 +80,11 @@ def trans_positions(ref, ref_mat, starlist, starlist_mat, xlim=None, ylim=None, plt.axis('equal') if save_path: - plt.savefig(save_path) + plt.savefig(save_path, dpi=300) if show_plot: plt.show() - - plt.close() + else: + plt.close() return @@ -137,9 +137,9 @@ def pos_diff_hist(ref_mat, starlist_mat, nbins=25, bin_width=None, xlim=None, fi plt.xlim([xlim[0], xlim[1]]) plt.legend() if fileName != None: - plt.savefig(root + fileName[3:8] + 'Positions_hist_' + '.png') + plt.savefig(root + fileName[3:8] + 'Positions_hist_' + '.png', dpi=300) else: - plt.savefig(root + 'Positions_hist.png') + plt.savefig(root + 'Positions_hist.png', dpi=300) plt.close() return @@ -274,7 +274,7 @@ def pos_diff_err_hist(ref_mat, starlist_mat, transform, nbins=25, bin_width=None deg_freedom) plt.annotate(txt, xy=(0.25,0.74), xycoords='figure fraction', color='black') xstr2 = 'With Outliers' - xstr3 = '{0} with +/- {1}+ sigma'.format(len(ratio_x) - len(good[0]), outlier) + xstr3 = '{0} with ± {1}+ sigma'.format(len(ratio_x) - len(good[0]), outlier) plt.annotate(xstr2, xy=(0.29, 0.83), xycoords='figure fraction', color='black') plt.annotate(xstr3, xy=(0.25, 0.80), xycoords='figure fraction', color='black') @@ -294,9 +294,9 @@ def pos_diff_err_hist(ref_mat, starlist_mat, transform, nbins=25, bin_width=None plt.xlim([xlim[0], xlim[1]]) plt.legend() if fileName != None: - plt.savefig(root + fileName[3:8] + 'Positions_err_ratio_hist_' + '.png') + plt.savefig(root + fileName[3:8] + 'Positions_err_ratio_hist_' + '.png', dpi=300) else: - plt.savefig(root + 'Positions_err_ratio_hist.png') + plt.savefig(root + 'Positions_err_ratio_hist.png', dpi=300) plt.close() return @@ -332,9 +332,9 @@ def mag_diff_hist(ref_mat, starlist_mat, bins=25, fileName=None, root='./'): plt.ylabel('N stars') plt.title('Magnitude Difference for matched stars') if fileName != None: - plt.savefig(root + fileName[3:8] + 'Magnitude_hist_' + '.png') + plt.savefig(root + fileName[3:8] + 'Magnitude_hist_' + '.png', dpi=300) else: - plt.savefig(root + 'Magnitude_hist.png') + plt.savefig(root + 'Magnitude_hist.png', dpi=300) plt.close() return @@ -433,17 +433,17 @@ def pos_diff_quiver(ref_mat, starlist_mat, qscale=10, keyLength=0.2, xlim=None, if sigma: if fileName != None: plt.title('(Reference - Transformed Starlist positions) / sigma') - plt.savefig(root + fileName[3:8] + 'Positions_quiver_sigma_' + '.png') + plt.savefig(root + fileName[3:8] + 'Positions_quiver_sigma_' + '.png', dpi=300) else: plt.title('(Reference - Transformed Starlist positions) / sigma') - plt.savefig(root + 'Positions_quiver_sigma.png') + plt.savefig(root + 'Positions_quiver_sigma.png', dpi=300) else: if fileName != None: plt.title('Reference - Transformed Starlist positions') - plt.savefig(root + fileName[3:8] + 'Positions_quiver_' + '.png') + plt.savefig(root + fileName[3:8] + 'Positions_quiver_' + '.png', dpi=300) else: plt.title('Reference - Transformed Starlist positions') - plt.savefig(root + 'Positions_quiver.png') + plt.savefig(root + 'Positions_quiver.png', dpi=300) plt.close() return @@ -488,7 +488,7 @@ def vpd(ref, starlist_trans, vxlim, vylim): plt.axis([vxlim[0], vylim[1], vylim[0], vylim[1]]) plt.title('Reference and Transformed Proper Motions') plt.legend() - plt.savefig('Transformed_velocities.png') + plt.savefig('Transformed_velocities.png', dpi=300) return @@ -564,7 +564,7 @@ def vel_diff_err_hist(ref_mat, starlist_mat, nbins=25, bin_width=None, vxlim=Non plt.title('Vy Residuals, Matched') if vylim != None: plt.xlim([vylim[0], vylim[1]]) - plt.savefig('Vel_err_ratio_dist.png') + plt.savefig('Vel_err_ratio_dist.png', dpi=300) return @@ -622,7 +622,7 @@ def residual_vpd(ref_mat, starlist_trans_mat, pscale=None): plt.xlabel('Reference_vx - Transformed_vx (reference coords)') plt.ylabel('Reference_vy - Transformed_vy (reference coords)') plt.title('Proper Motion Residuals') - plt.savefig('resid_vpd.png') + plt.savefig('resid_vpd.png', dpi=300) return @@ -980,7 +980,7 @@ def plotStar(starNames, rootDir='./', align='align/align_d_rms_1000_abs_t', plt.ylabel('Number of Epochs') plt.subplots_adjust(wspace=0.4, hspace=0.4, right=0.95, top=0.95) - plt.savefig(rootDir+'plots/plotStarRadial_' + starName + '.png') + plt.savefig(rootDir+'plots/plotStarRadial_' + starName + '.png', dpi=300) plt.show() title = rootDir.split('/')[-2] @@ -988,10 +988,10 @@ def plotStar(starNames, rootDir='./', align='align/align_d_rms_1000_abs_t', if Nstars == 1: plt.subplots_adjust(wspace=0.4, hspace=0.4, left = 0.15, bottom = 0.1, right=0.9, top=0.9) - plt.savefig(rootDir+'plots/plotStar_' + starName + '.png') + plt.savefig(rootDir+'plots/plotStar_' + starName + '.png', dpi=300) else: plt.subplots_adjust(wspace=0.6, hspace=0.6, left = 0.08, bottom = 0.05, right=0.95, top=0.90) - plt.savefig(rootDir+'plots/plotStar_all.png') + plt.savefig(rootDir+'plots/plotStar_all.png', dpi=300) plt.show() plt.show() @@ -1058,7 +1058,7 @@ def plot_pm_error(tab, save_path=None): ax.set_ylabel('PM Error (mas/yr)') plt.tight_layout() if save_path is not None: - plt.savefig(save_path) + plt.savefig(save_path, dpi=300) plt.show() return @@ -1070,7 +1070,7 @@ def plot_mag_error(tab, save_path=None): ax.set_ylabel('Mag Error (mag)') plt.tight_layout() if save_path is not None: - plt.savefig(save_path) + plt.savefig(save_path, dpi=300) plt.show() return @@ -1131,7 +1131,7 @@ def plot_mean_residuals_by_epoch(tab): return -def plot_quiver_residuals_all_epochs(tab, unit='arcsec', scale=None, plotlim=None, save_path=None): +def plot_quiver_residuals_all_epochs(tab, unit='arcsec', scale=None, plotlim=None, save_path=None, show_plot=True): # Keep track of the residuals for averaging. dr_good = np.zeros(len(tab), dtype=float) @@ -1162,7 +1162,8 @@ def plot_quiver_residuals_all_epochs(tab, unit='arcsec', scale=None, plotlim=Non unit=unit, scale=scale, plotlim=plotlim, - save_path=f'{save_path}/Quiver_Residual_{ee}.pdf' if save_path else None + show_plot=show_plot, + save_path=f'{save_path}/Quiver_Residual_{ee}.png' if save_path else None ) # Building up average dr for a set of stars. @@ -1207,7 +1208,7 @@ def plot_quiver_residuals_all_epochs(tab, unit='arcsec', scale=None, plotlim=Non return -def plot_quiver_residuals_with_orig_all_epochs(tab, trans_list, unit='arcsec', scale=None, plotlim=None, scale_orig=None, cte_fit=None, mlim=15, save_path=None): +def plot_quiver_residuals_with_orig_all_epochs(tab, trans_list, unit='arcsec', scale=None, plotlim=None, scale_orig=None, cte_fit=None, mlim=15, show_plot=True, save_path=None): # Keep track of the residuals for averaging. dr_good = np.zeros(len(tab), dtype=float) @@ -1234,14 +1235,14 @@ def plot_quiver_residuals_with_orig_all_epochs(tab, trans_list, unit='arcsec', s xt_mod, yt_mod, good_idx, ref_idx, 'Epoch {0:d}'.format(ee), - unit=unit, scale=scale, plotlim=plotlim, save_path=f'{save_path}/Quiver_Residual_{ee}.pdf' if save_path else None) + unit=unit, scale=scale, plotlim=plotlim, show_plot=show_plot, save_path=f'{save_path}/Quiver_Residual_{ee}.png' if save_path else None) plot_quiver_residuals_orig(tab['x'][:, ee], tab['y'][:, ee], xt_mod, yt_mod, good_idx, ref_idx, tab['x_orig'][:, ee], tab['y_orig'][:, ee], da, 'Epoch {0:d}'.format(ee), - scale=scale_orig, plotlim=plotlim, save_path=f'{save_path}/Quiver_Residual_Orig_{ee}.pdf' if save_path else None) + scale=scale_orig, plotlim=plotlim, show_plot=show_plot, save_path=f'{save_path}/Quiver_Residual_Orig_{ee}.png' if save_path else None) plot_mag_scatter(tab['m'][:, ee], tab['m0'], tab['m0_err'], @@ -1251,7 +1252,7 @@ def plot_quiver_residuals_with_orig_all_epochs(tab, trans_list, unit='arcsec', s good_idx, ref_idx, 'Epoch {0:d}'.format(ee), da=da, xorig=tab['x_orig'][:, ee], yorig=tab['y_orig'][:, ee], - cte_fit=cte_fit, mlim=mlim, save_path=f'{save_path}/Mag_Scatter_{ee}.pdf' if save_path else None) + cte_fit=cte_fit, mlim=mlim, show_plot=show_plot, save_path=f'{save_path}/Mag_Scatter_{ee}.png' if save_path else None) plot_y_scatter(tab['m'][:, ee], tab['m0'], tab['m0_err'], @@ -1261,7 +1262,7 @@ def plot_quiver_residuals_with_orig_all_epochs(tab, trans_list, unit='arcsec', s good_idx, ref_idx, 'Epoch {0:d}'.format(ee), da=da, xorig=tab['x_orig'][:, ee], yorig=tab['y_orig'][:, ee], - cte_fit=cte_fit, mlim=mlim, save_path=f'{save_path}/Y_Scatter_{ee}.pdf' if save_path else None) + cte_fit=cte_fit, mlim=mlim, show_plot=show_plot, save_path=f'{save_path}/Y_Scatter_{ee}.png' if save_path else None) # plot_quiver_residuals_orig_angle_xy(tab['x'][:, ee], tab['y'][:, ee], # xt_mod, yt_mod, @@ -1805,7 +1806,7 @@ def T_line(m, a, b): def plot_quiver_residuals(x_t, y_t, x_ref, y_ref, good_idx, ref_idx, title, - unit='pixel', scale=None, plotlim=None, save_path=None): + unit='pixel', scale=None, plotlim=None, save_path=None, show_plot=True): """ unit : str 'pixel' or 'arcsec' @@ -1858,18 +1859,21 @@ def plot_quiver_residuals(x_t, y_t, x_ref, y_ref, good_idx, ref_idx, title, ax.set_ylim(-1 * plotlim, plotlim) plt.tight_layout() if save_path: - plt.savefig(save_path) - plt.show() + plt.savefig(save_path, dpi=300) + if show_plot: + plt.show() + else: + plt.close() - str_fmt = 'Residuals (mean, std): dx = {0:7.3f} +/- {1:7.3f} {5:s} dy = {2:7.3f} +/- {3:7.3f} {5:s} for {4:s} stars' + str_fmt = '{0:s}: Residuals (mean, std): dx = {1:7.3f} ± {2:7.3f} {6:s} dy = {3:7.3f} ± {4:7.3f} {6:s} for {5:s} stars' if len(ref_idx) > 1: - print(str_fmt.format(dx[good_idx][ref_idx].mean(), dx[good_idx][ref_idx].std(), + print(str_fmt.format(title, dx[good_idx][ref_idx].mean(), dx[good_idx][ref_idx].std(), dy[good_idx][ref_idx].mean(), dy[good_idx][ref_idx].std(), 'REF', unit2)) else: - print(str_fmt.format(dx[good_idx][ref_idx].mean(), 0.0, + print(str_fmt.format(title, dx[good_idx][ref_idx].mean(), 0.0, dy[good_idx][ref_idx].mean(), 0.0, 'REF', unit2)) - print(str_fmt.format(dx[good_idx].mean(), dx[good_idx].std(), + print(str_fmt.format(title, dx[good_idx].mean(), dx[good_idx].std(), dy[good_idx].mean(), dy[good_idx].std(), 'GOOD', unit2)) @@ -2080,7 +2084,7 @@ def plot_quiver_residuals_orig(x_t, y_t, x_ref, y_ref, good_idx, ref_idx, ax.set_ylim(-1 * plotlim, plotlim) plt.tight_layout() if save_path: - plt.savefig(save_path) + plt.savefig(save_path, dpi=300) plt.show() plt.pause(1) @@ -2423,7 +2427,7 @@ def plot_chi2_dist_per_filter(tab, Ndetect, xlim=40, n_bins=50, filter=None, boo plt.tick_params(labelsize=20, direction='in', right=True, top=True) - plt.savefig(str(filter)+'_chi2_dist.png', dpi=400) + plt.savefig(str(filter)+'_chi2_dist.png', dpi=300) chi2red_x = x / Ndof chi2red_y = y / Ndof @@ -2760,7 +2764,7 @@ def plot_chi2_dist_mag_per_filter(tab, Ndetect, mlim=40, n_bins=30, xlim=40, fil plt.tick_params(labelsize=20, direction='in', right=True, top=True) - plt.savefig(str(filter)+'_chi2_dist_mag.png', dpi=400) + plt.savefig(str(filter)+'_chi2_dist_mag.png', dpi=300) print('Mean reduced chi^2: (Ndetect = {0:d} of {1:d})'.format(len(idx), len(tab))) fmt = ' {0:s} = {1:.1f} for N_detect and {2:.1f} for all' @@ -3179,10 +3183,10 @@ def rs(x): if Nstars == 1: plt.subplots_adjust(wspace=0.4, hspace=0.4, left = 0.15, bottom = 0.1, right=0.9, top=0.9) - # plt.savefig(rootDir+'plots/plotStar_' + starName + '.png') + # plt.savefig(rootDir+'plots/plotStar_' + starName + '.png', dpi=300) else: plt.subplots_adjust(wspace=0.6, hspace=0.6, left = 0.08, bottom = 0.05, right=0.95, top=0.90) - # plt.savefig(rootDir+'plots/plotStar_all.png') + # plt.savefig(rootDir+'plots/plotStar_all.png', dpi=300) plt.show() plt.show() @@ -3602,13 +3606,13 @@ def rs(x): if Nstars == 1: plt.subplots_adjust(wspace=0.4, hspace=0.4, left = 0.15, bottom = 0.1, right=0.9, top=0.9) - # plt.savefig(rootDir+'plots/plotStar_' + starName + '.png') + # plt.savefig(rootDir+'plots/plotStar_' + starName + '.png', dpi=300) else: plt.subplots_adjust(wspace=0.6, hspace=0.6, left = 0.08, bottom = 0.05, right=0.95, top=0.90) - # plt.savefig(rootDir+'plots/plotStar_all.png') + # plt.savefig(rootDir+'plots/plotStar_all.png', dpi=300) plt.show() if save_name is not None: - plt.savefig(save_name + '.png') + plt.savefig(save_name + '.png', dpi=300) plt.show() return @@ -3815,9 +3819,9 @@ def plot_sky(stars_tab, if saveplot: plt.show(block=0) if (center_star != None): - plt.savefig('plot_sky_' + center_star + '.png') + plt.savefig('plot_sky_' + center_star + '.png', dpi=300) else: - plt.savefig('plot_sky.png') + plt.savefig('plot_sky.png', dpi=300) else: plt.show() @@ -3893,7 +3897,7 @@ def __call__(self, event): return -def plotly_stars(x, y, m=None, marker_size=3, color='C0', alpha=0.7, symbol='circle', label='starlist', fig=None, figsize=(600, 600), show=True): +def plotly_stars(x, y, m=None, marker_size=3, color=None, alpha=0.7, symbol='circle', label='starlist', fig=None, figsize=(700, 700), show=True): """Plot stars with plotly in interactive html format Parameters @@ -3906,8 +3910,8 @@ def plotly_stars(x, y, m=None, marker_size=3, color='C0', alpha=0.7, symbol='cir magnitude to be added in hover label, by default None marker_size : int, optional Size of marker, by default 10 - color : str or array, optional - Color of marker, either a string (e.g., 'red') or rgba array, by default 'C0' + color : array or str, optional + Color of marker, either a string (e.g., 'red') or rgba array, by default None alpha : float, optional Opacity of marker color, by default 0.7 symbol : str, optional @@ -3917,7 +3921,7 @@ def plotly_stars(x, y, m=None, marker_size=3, color='C0', alpha=0.7, symbol='cir fig : plotly.graph_objects.Figure object, optional Figure if the stars are to be added to an exisiting plot, by default None figsize : tuple, optional - Figure size, by default (600, 600) + Figure size, by default (700, 700) show : bool, optional Show figure or not, by default True @@ -3931,7 +3935,6 @@ def plotly_stars(x, y, m=None, marker_size=3, color='C0', alpha=0.7, symbol='cir y = np.asarray(y) hover_template = 'x: %{x:.3f}
y: %{y:.3f}' - if isinstance(color, str) and color.startswith('C') and color[1:].isdigit(): color = mcolors.to_rgba(color, alpha=alpha) color = f'rgba({color[0]*255}, {color[1]*255}, {color[2]*255}, {color[3]:.2f})' @@ -3945,7 +3948,8 @@ def plotly_stars(x, y, m=None, marker_size=3, color='C0', alpha=0.7, symbol='cir mode='markers', marker=dict( size=marker_size, - color=color + color=color, + symbol=symbol ), hovertemplate=hover_template, customdata=m, # Add magnitude to hover data @@ -3958,7 +3962,8 @@ def plotly_stars(x, y, m=None, marker_size=3, color='C0', alpha=0.7, symbol='cir mode='markers', marker=dict( size=marker_size, - color=color + color=color, + symbol=symbol ), hovertemplate=hover_template, name=label From 33c1dfabe65c75cfbe7e05735acf519a33d8d3cc Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Thu, 30 Apr 2026 17:19:32 -0700 Subject: [PATCH 198/254] Added required_fixed_param_names and optional_fixed_params for motion models --- flystar/motion_model.py | 38 ++++++++++++++++++++++++++------------ 1 file changed, 26 insertions(+), 12 deletions(-) diff --git a/flystar/motion_model.py b/flystar/motion_model.py index 6a48a80..e9b778f 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -6,6 +6,8 @@ import warnings class MotionModel(ABC): + name = "MotionModel" + # Fit paramters: Shared fit parameters fit_param_names = [] @@ -15,13 +17,14 @@ class MotionModel(ABC): # Fixed parameters: These are parameters that are required for the model, but are not # fit quantities. For example, RA and Dec in a parallax model. fixed_param_names = [] + required_fixed_param_names = [] + optional_fixed_params = {} + fixed_meta_data = [] # Non-fit paramters: Custom paramters that will not be fit. # These parameters should be derived from the fit parameters and # they must exist as a variable on the model object - optional_param_names = [] - name = "MotionModel" def __init__(self, *args, **kwargs): """ @@ -232,9 +235,12 @@ def calc_chi2(self, t, x, y, xe, ye, fit_params, fixed_params_dict=None, reduced return chi2x, chi2y class Empty(MotionModel): + name = "Empty" fit_param_names = [] fixed_param_names = [] - name = "Empty" + required_fixed_param_names = [] + optional_fixed_params = {} + # Number of fit parameters/required observations in each direction n_params = int((len(fit_param_names) + 1) / 2) @@ -334,14 +340,15 @@ class Fixed(MotionModel): """ A non-moving motion model for a star on the sky. """ - + name = "Fixed" fit_param_names = ['x0','y0'] fixed_param_names = [] + required_fixed_param_names = [] + optional_fixed_params = {} + # Number of fit parameters/required observations in each direction n_params = int((len(fit_param_names) + 1) / 2) - name = "Fixed" - def __init__(self, **kwargs): # Must call after setting parameters. # This checks for proper parameter formatting. @@ -489,12 +496,14 @@ class Linear(MotionModel): """ A 2D linear motion model for a star on the sky. """ + name = "Linear" fit_param_names = ['x0', 'vx', 'y0', 'vy'] - fixed_param_names = ['t0'] + required_fixed_param_names = ['t0'] + optional_fixed_params = {} + fixed_param_names = required_fixed_param_names + list(optional_fixed_params.keys()) # Number of fit parameters/required observations in each direction n_params = int((len(fit_param_names) + 1) / 2) - name = "Linear" def __init__(self, **kwargs): # Must call after setting parameters. @@ -717,9 +726,11 @@ class Acceleration(MotionModel): """ A 2D accelerating motion model for a star on the sky. """ - fit_param_names = ['x0', 'vx0', 'ax', 'y0', 'vy0', 'ay'] - fixed_param_names = ['t0'] name = "Acceleration" + fit_param_names = ['x0', 'vx0', 'ax', 'y0', 'vy0', 'ay'] + required_fixed_param_names = ['t0'] + optional_fixed_params = {} + fixed_param_names = required_fixed_param_names + list(optional_fixed_params.keys()) # Number of fit parameters/required observations in each direction n_params = int((len(fit_param_names) + 1) / 2) @@ -885,9 +896,12 @@ class Parallax(MotionModel): Optional PA is counterclockwise offset of the image y-axis from North. Optional obs parameter describes observer location, default is 'earth'. """ - fit_param_names = ['x0', 'vx', 'y0', 'vy', 'pi'] - fixed_param_names = ['t0', 'ra', 'dec', 'pa', 'obsLocation'] name = "Parallax" + fit_param_names = ['x0', 'vx', 'y0', 'vy', 'pi'] + required_fixed_param_names = ['t0', 'ra', 'dec'] + optional_fixed_params = {'pa': 0., 'obsLocation': 'earth'} + fixed_param_names = required_fixed_param_names + list(optional_fixed_params.keys()) + # Number of fit parameters/required observations in each direction n_params = int((len(fit_param_names) + 1) / 2) From 0a9c5ce47fec4639ec2aeafb224df23fd2246bff Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Thu, 30 Apr 2026 17:21:41 -0700 Subject: [PATCH 199/254] Update fixed params dict construction in fit_motion_model: If not specified in fixed_params_dict, will try to look for them in the table columns; For optional fixed params, will use the default value if not provided --- flystar/startables.py | 62 ++++++++++++++++++++++++++++++++----------- 1 file changed, 47 insertions(+), 15 deletions(-) diff --git a/flystar/startables.py b/flystar/startables.py index bbe7e4f..85de68b 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -734,20 +734,6 @@ def fit_motion_model( t0 = fixed_params_dict['t0'] - # Prepare fixed_params_dict for each star - # This avoids checking types and slicing inside the fitting loop - fixed_params_stars = [{} for _ in range(N_stars)] - # Identify array parameters (length N_stars) and scalar parameters - array_params = {k: v for k, v in fixed_params_dict.items() if np.ndim(v) > 0 and len(v) == N_stars} - scalar_params = {k: v for k, v in fixed_params_dict.items() if k not in array_params} - - # Construct list of dicts for each star - # Using list comprehension for speed - fixed_params_stars = [ - {**scalar_params, **{k: v[i] for k, v in array_params.items()}} - for i in range(N_stars) - ] - # Apply mask_value if provided if mask_value: x_data = np.ma.masked_values(x_data, mask_value) @@ -775,6 +761,7 @@ def fit_motion_model( for i in range(N_stars) ]) + ########################### ####### Determine MM ###### ########################### @@ -809,11 +796,56 @@ def fit_motion_model( if 'Parallax' in mm_used and 'obsLocation' not in fixed_params_dict: fixed_params_dict['obsLocation'] = 'earth' + + ############################ + # Prepare Fixed Parameters # + ############################ + # If required fixed params in self but not provided in fixed_params_dict, add them to fixed_params_dict + motion_model_used = [all_mm_map[name] for name in np.unique(self['motion_model_used'])] + raise_key_error = False + missing_params = [] + for mm in motion_model_used: + # Check required fixed parameters + for param in mm.required_fixed_param_names: + if param not in fixed_params_dict: + if param in self.colnames: + fixed_params_dict[param] = self[param].data + else: + raise_key_error = True + missing_params.append(f"'{param}'") + + # Check optional fixed parameters + # Set to default value if not provided in fixed_params_dict or in self + for param, value in mm.optional_fixed_params.items(): + if param not in fixed_params_dict: + if param in self.colnames: + fixed_params_dict[param] = self[param].data + else: + fixed_params_dict[param] = value + + if raise_key_error: + raise KeyError(f"fit_motion_model: Missing required fixed parameter(s) for the motion models used: {', '.join(missing_params)}! Please provide them in fixed_params_dict or as columns in the table.") + + + # Prepare fixed_params_dict for each star + # This avoids checking types and slicing inside the fitting loop + fixed_params_stars = [{} for _ in range(N_stars)] + # Identify array parameters (length N_stars) and scalar parameters + array_params = {k: v for k, v in fixed_params_dict.items() if np.ndim(v) > 0 and len(v) == N_stars} + scalar_params = {k: v for k, v in fixed_params_dict.items() if k not in array_params} + + # Construct list of dicts for each star + # Using list comprehension for speed + fixed_params_stars = [ + {**scalar_params, **{k: v[i] for k, v in array_params.items()}} + for i in range(N_stars) + ] + + ############################ ####### Prepare Table ###### ############################ # Fill table with all possible motion model parameter names as new columns. - motion_model_used = [all_mm_map[name] for name in np.unique(self['motion_model_used'])] new_col_list = motion_model.motion_model_param_names(motion_model_used, with_errors=True, with_fixed=False) new_col_list += ['chi2_x', 'chi2_y', 'n_params'] From d8269901f6026b944cdfb3b2ee262b8a56c62728 Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Thu, 30 Apr 2026 17:31:39 -0700 Subject: [PATCH 200/254] Unify run_fit parameters for motion models --- flystar/motion_model.py | 59 ++++++++++++++++++++++------------------- 1 file changed, 31 insertions(+), 28 deletions(-) diff --git a/flystar/motion_model.py b/flystar/motion_model.py index e9b778f..378eac6 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -77,10 +77,13 @@ def run_fit( absolute_sigma=True, params_guess=None, fill_value=np.nan, + return_chi2=False, verbose=True ): # Run a single fit (used both for overall fit + bootstrap iterations) - return np.full(self.n_params, fill_value), np.full(self.n_params, np.inf), np.nan, np.nan + if return_chi2: + return np.full(self.n_params, fill_value), np.full(self.n_params, np.inf), np.nan, np.nan + return np.full(self.n_params, fill_value), np.full(self.n_params, np.inf) def calc_weights(self, xe, ye, weighting='var'): if weighting=='std': @@ -279,15 +282,15 @@ def model(self, t, fit_params, fit_param_errs=None, fixed_params_dict=None): return np.full_like(t, np.nan), np.full_like(t, np.nan), np.full_like(t, np.inf), np.full_like(t, np.inf) def run_fit( - self, t, x, y, xe, ye, - fixed_params_dict=None, - weighting='var', - use_scipy=True, - absolute_sigma=True, - fill_value=np.nan, - params_guess=None, - return_chi2=False, - verbose=True + self, t, x, y, xe, ye, + fixed_params_dict=None, + weighting='var', + use_scipy=True, + absolute_sigma=True, + params_guess=None, + fill_value=np.nan, + return_chi2=False, + verbose=True ): """Fit stellar motion parameters @@ -430,15 +433,15 @@ def model(self, t, fit_params, fit_param_errs=None, fixed_params_dict=None): return x, y, x_err, y_err def run_fit( - self, t, x, y, xe, ye, - fixed_params_dict=None, - weighting='var', - use_scipy=True, - absolute_sigma=True, - fill_value=np.nan, - params_guess=None, - return_chi2=False, - verbose=True + self, t, x, y, xe, ye, + fixed_params_dict=None, + weighting='var', + use_scipy=True, + absolute_sigma=True, + params_guess=None, + fill_value=np.nan, + return_chi2=False, + verbose=True ): if verbose and (not use_scipy): warnings.warn("Fixed model has no non-scipy fitter option. Running with scipy.") @@ -588,15 +591,15 @@ def model(self, t, fit_params, fit_param_errs=None, fixed_params_dict=None): return x, y, x_err, y_err def run_fit( - self, t, x, y, xe, ye, - fixed_params_dict=None, - weighting='var', - use_scipy=True, - absolute_sigma=True, - fill_value=np.nan, - params_guess=None, - return_chi2=False, - verbose=True + self, t, x, y, xe, ye, + fixed_params_dict=None, + weighting='var', + use_scipy=True, + absolute_sigma=True, + params_guess=None, + fill_value=np.nan, + return_chi2=False, + verbose=True ): if fixed_params_dict is None: fixed_params_dict = {} From 6caf11e78c9e5b230e03aabf843d93d61e5188f3 Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Thu, 30 Apr 2026 19:49:25 -0700 Subject: [PATCH 201/254] Added star name option in plotly stars --- flystar/plots.py | 57 ++++++++++++++++++++++++------------------------ 1 file changed, 29 insertions(+), 28 deletions(-) diff --git a/flystar/plots.py b/flystar/plots.py index 8ee8a13..7213174 100755 --- a/flystar/plots.py +++ b/flystar/plots.py @@ -3897,7 +3897,7 @@ def __call__(self, event): return -def plotly_stars(x, y, m=None, marker_size=3, color=None, alpha=0.7, symbol='circle', label='starlist', fig=None, figsize=(700, 700), show=True): +def plotly_stars(x, y, m=None, star_name=None, marker_size=3, color=None, alpha=0.7, symbol='circle', label='starlist', fig=None, figsize=(700, 700), show=True): """Plot stars with plotly in interactive html format Parameters @@ -3908,6 +3908,8 @@ def plotly_stars(x, y, m=None, marker_size=3, color=None, alpha=0.7, symbol='cir y positions m : array-like, optional magnitude to be added in hover label, by default None + star_name : array-like, optional + Star names to be added in hover label, by default None marker_size : int, optional Size of marker, by default 10 color : array or str, optional @@ -3939,35 +3941,34 @@ def plotly_stars(x, y, m=None, marker_size=3, color=None, alpha=0.7, symbol='cir color = mcolors.to_rgba(color, alpha=alpha) color = f'rgba({color[0]*255}, {color[1]*255}, {color[2]*255}, {color[3]:.2f})' + customdata = [] + if star_name is not None: + hover_template = 'name: %{customdata[0]}
' + hover_template + customdata.append(star_name) + if m is not None: m = np.asarray(m) - hover_template += '
m: %{customdata:.2f}' - fig_data = go.Scattergl( - x=x, - y=y, - mode='markers', - marker=dict( - size=marker_size, - color=color, - symbol=symbol - ), - hovertemplate=hover_template, - customdata=m, # Add magnitude to hover data - name=label - ) - else: - fig_data = go.Scattergl( - x=x, - y=y, - mode='markers', - marker=dict( - size=marker_size, - color=color, - symbol=symbol - ), - hovertemplate=hover_template, - name=label - ) + m_idx = len(customdata) + hover_template += f'
m: %{{customdata[{m_idx}]:.2f}}' + customdata.append(m) + + if customdata: + customdata = np.column_stack(customdata) + hover_template += '' + + fig_data = go.Scattergl( + x=x, + y=y, + mode='markers', + marker=dict( + size=marker_size, + color=color, + symbol=symbol + ), + customdata=customdata, + hovertemplate=hover_template, + name=label + ) if fig is None: fig = go.Figure(data=[fig_data]) From 62f92484ed2b34e25237e3c1760714a1d4e6d391 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Fri, 22 May 2026 05:31:58 -0700 Subject: [PATCH 202/254] Utilized scipy curve_fit fvec to accelerate chi2 calculation; Added support for 2d time arrays (if length==len(stars), assumes a corresponding relation instead of returning a 2D) --- flystar/align.py | 8 +-- flystar/motion_model.py | 125 +++++++++++++++++++++++++++++++--------- 2 files changed, 102 insertions(+), 31 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 0abe28a..ecaff32 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -1285,7 +1285,7 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot 1) Draw full-size bootstrap w/replacement sample from reference stars in ref_table and re-calculate the transformations for each epoch 2) Apply transformation to all stars in each epoch - If calc_vel_in_bootstraps: + If calc_vel_in_bootstrap: 3) For each star, draw full-size boostrap sample w/replacement from epochs 4) Calculate proper motion for each star using resampled epochs @@ -1631,9 +1631,9 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot self.ref_table.add_column(col) if verbose: - print('===============================') - print('Done with bootstrap') - print('===============================') + print('===================================') + print('======= Done with bootstrap =======') + print('===================================') if update_errors: self.ref_table['xe_list'] = self.ref_table['xe'] diff --git a/flystar/motion_model.py b/flystar/motion_model.py index 378eac6..46c9b2e 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -222,7 +222,28 @@ def fit( return params, param_errs - def calc_chi2(self, t, x, y, xe, ye, fit_params, fixed_params_dict=None, reduced=False): + # def calc_chi2(self, dt, x, y, x_wt, y_wt, popt_x, popt_y, reduced=False, parallax=False): + # X_mat_t = np.vander(dt, 2) + # residual_x = x - X_mat_t @ popt_x + # residual_y = y - X_mat_t @ popt_y + + # W_mat_x = np.diag(x_wt) + # W_mat_y = np.diag(y_wt) + + # chi2_x = residual_x.T @ W_mat_x @ residual_x + # chi2_y = residual_y.T @ W_mat_y @ residual_y + + # if reduced: + # if len(dt) == self.n_params: + # return np.inf, np.inf + # if not parallax: + # degree_of_freedom = len(x) - self.n_params + # else: + # degree_of_freedom = 2*len(x) - len(self.fit_param_names) + # chi2_x, chi2_y = chi2_x / degree_of_freedom, chi2_y / degree_of_freedom + # return chi2_x, chi2_y + + def calc_chi2(self, t, x, y, xe, ye, fit_params, fixed_params_dict=None, reduced=False, parallax=False): """ Get the chi^2 value for the input motion model parameters and data. """ @@ -232,9 +253,11 @@ def calc_chi2(self, t, x, y, xe, ye, fit_params, fixed_params_dict=None, reduced if reduced: if len(t) == self.n_params: return np.inf, np.inf - else: + if not parallax: degree_of_freedom = len(x) - self.n_params - chi2x, chi2y = chi2x / degree_of_freedom, chi2y / degree_of_freedom + else: + degree_of_freedom = 2*len(x) - len(self.fit_param_names) + chi2x, chi2y = chi2x / degree_of_freedom, chi2y / degree_of_freedom return chi2x, chi2y class Empty(MotionModel): @@ -276,10 +299,24 @@ def model(self, t, fit_params, fit_param_errs=None, fixed_params_dict=None): Predicted position (and uncertainties) of Empty model, shape (N_times,) """ self._check_param_dimensions(fit_params, fit_param_errs, fixed_params_dict) + t = np.atleast_1d(t) + fit_params = np.atleast_2d(fit_params) # (N_stars, N_params) + + N_stars = fit_params.shape[0] + N_times = len(t) + + if N_times == N_stars or N_times == 1 or N_stars == 1: + # Assume each time corresponds to each star, so N_times = 1 + x = np.full(N_stars, np.nan) + y = np.full(N_stars, np.nan) + else: + x = np.full((N_stars, N_times), np.nan) + y = np.full((N_stars, N_times), np.nan) + if fit_param_errs is None: - return np.full_like(t, np.nan), np.full_like(t, np.nan) - return np.full_like(t, np.nan), np.full_like(t, np.nan), np.full_like(t, np.inf), np.full_like(t, np.inf) + return x, y + return x, y, np.full_like(x, np.inf), np.full_like(y, np.inf) def run_fit( self, t, x, y, xe, ye, @@ -373,9 +410,7 @@ def model_fit(self, dt, x0): x : array-like Predicted positions, shape (N_times,) if scalar x0, else (N_stars, N_times) """ - dt = np.atleast_1d(dt) - x0 = np.asarray(x0) - return np.broadcast_to(x0[:, np.newaxis], (x0.shape[0], dt.shape[0])) if x0.ndim > 0 else np.full_like(dt, x0) + return x0 + np.zeros_like(x0) * dt def model(self, t, fit_params, fit_param_errs=None, fixed_params_dict=None): """Predicted positions (and uncertainties, if fit_param_errs is provided) at time t of Fixed model. @@ -402,13 +437,22 @@ def model(self, t, fit_params, fit_param_errs=None, fixed_params_dict=None): fit_params = np.atleast_2d(fit_params) # (N_stars, N_params) self._check_param_dimensions(fit_params, fit_param_errs, fixed_params_dict) - N_stars = fit_params.shape[0] if fit_params.ndim > 1 else 1 + N_stars = fit_params.shape[0] N_times = len(t) x0, y0 = fit_params.T # Each shape (N_stars,) + # FIXME: Do we want this assumption? + if N_times == N_stars: + # Assume each time corresponds to each star, so N_times = 1 + dt = t[:, np.newaxis] # Shape (N_stars, 1) + N_times = 1 + else: + # Else, calculate each time for each star + dt = t[np.newaxis, :] - np.zeros(N_stars)[:, np.newaxis] # Shape (N_stars, N_times) + # Return results in (N_stars, N_times) shape - x = self.model_fit(t, x0) # Shape (N_stars, N_times) - y = self.model_fit(t, y0) # Shape (N_stars, N_times) + x = self.model_fit(t, x0[:, np.newaxis]) # Shape (N_stars, N_times) + y = self.model_fit(t, y0[:, np.newaxis]) # Shape (N_stars, N_times) if N_stars == 1 or N_times == 1: # If only one star, return flattened arrays @@ -560,13 +604,19 @@ def model(self, t, fit_params, fit_param_errs=None, fixed_params_dict=None): t = np.atleast_1d(t) fit_params = np.atleast_2d(fit_params) # (N_stars, N_params) - N_stars = fit_params.shape[0] if fit_params.ndim > 1 else 1 + N_stars = fit_params.shape[0] N_times = len(t) x0, vx, y0, vy = fit_params.T # Each shape (N_stars,) t0 = np.atleast_1d(fixed_params_dict['t0']) # Shape (N_stars,) or (1,) - dt = t[np.newaxis, :] - t0[:, np.newaxis] # Shape (N_stars, N_times) + if N_times == N_stars: + # Assume each time corresponds to each star, so N_times = 1 + dt = t - t0 # Shape (N_stars,) + dt = dt[:, np.newaxis] # Shape (N_stars, 1) + N_times = 1 + else: + dt = t[np.newaxis, :] - t0[:, np.newaxis] # Shape (N_stars, N_times) x = self.model_fit(dt, x0[:, np.newaxis], vx[:, np.newaxis]) # Shape (N_stars, N_times) y = self.model_fit(dt, y0[:, np.newaxis], vy[:, np.newaxis]) # Shape (N_stars, N_times) @@ -636,8 +686,8 @@ def run_fit( params_guess = [x.mean(), 0., y.mean(), 0.] if use_scipy: - x_opt, x_cov = curve_fit(self.model_fit, dt, x, p0=np.array(params_guess[:2]), sigma=1/x_wt**0.5, absolute_sigma=absolute_sigma) - y_opt, y_cov = curve_fit(self.model_fit, dt, y, p0=np.array(params_guess[2:]), sigma=1/y_wt**0.5, absolute_sigma=absolute_sigma) + x_opt, x_cov, x_info, x_msg, x_ier = curve_fit(self.model_fit, dt, x, p0=np.array(params_guess[:2]), sigma=1/x_wt**0.5, absolute_sigma=absolute_sigma, full_output=True) + y_opt, y_cov, y_info, y_msg, y_ier = curve_fit(self.model_fit, dt, y, p0=np.array(params_guess[2:]), sigma=1/y_wt**0.5, absolute_sigma=absolute_sigma, full_output=True) x0, vx = x_opt y0, vy = y_opt x0e, vxe = np.sqrt(x_cov.diagonal()) @@ -645,7 +695,9 @@ def run_fit( params = np.array([x0, vx, y0, vy]) param_errors = np.array([x0e, vxe, y0e, vye]) if return_chi2: - chi2_x, chi2_y = self.calc_chi2(t, x, y, xe, ye, params, fixed_params_dict) + # chi2_x, chi2_y = self.calc_chi2(t, x, y, xe, ye, params, fixed_params_dict) + chi2_x = np.sum(x_info['fvec']**2) + chi2_y = np.sum(y_info['fvec']**2) return params, param_errors, chi2_x, chi2_y else: return params, param_errors @@ -792,13 +844,19 @@ def model(self, t, fit_params, fit_param_errs=None, fixed_params_dict=None): t = np.atleast_1d(t) fit_params = np.atleast_2d(fit_params) # (N_stars, N_params) - N_stars = fit_params.shape[0] if fit_params.ndim > 1 else 1 + N_stars = fit_params.shape[0] N_times = len(t) x0, vx0, ax, y0, vy0, ay = fit_params.T # Each shape (N_stars,) t0 = np.atleast_1d(fixed_params_dict['t0']) # Shape (N_stars,) or (1,) - dt = t[np.newaxis, :] - t0[:, np.newaxis] # Shape (N_stars, N_times) + if N_times == N_stars: + # Assume each time corresponds to each star, so N_times = 1 + dt = t - t0 # Shape (N_stars,) + dt = dt[:, np.newaxis] # Shape (N_stars, 1) + N_times = 1 + else: + dt = t[np.newaxis, :] - t0[:, np.newaxis] # Shape (N_stars, N_times) x = self.model_fit(dt, x0[:, np.newaxis], vx0[:, np.newaxis], ax[:, np.newaxis]) # Shape (N_stars, N_times) y = self.model_fit(dt, y0[:, np.newaxis], vy0[:, np.newaxis], ay[:, np.newaxis]) # Shape (N_stars, N_times) @@ -876,8 +934,8 @@ def run_fit( t_span = t[idx_last] - t[idx_first] params_guess = [x.mean(), (x[idx_last] - x[idx_first]) / t_span, 0., y.mean(), (y[idx_last] - y[idx_first]) / t_span, 0.] - x_opt, x_cov = curve_fit(self.model_fit, dt, x, p0=np.array(params_guess[:3]), sigma=1/x_wt**0.5, absolute_sigma=absolute_sigma) - y_opt, y_cov = curve_fit(self.model_fit, dt, y, p0=np.array(params_guess[3:]), sigma=1/y_wt**0.5, absolute_sigma=absolute_sigma) + x_opt, x_cov, x_info, x_msg, x_ier = curve_fit(self.model_fit, dt, x, p0=np.array(params_guess[:3]), sigma=1/x_wt**0.5, absolute_sigma=absolute_sigma, return_full=True) + y_opt, y_cov, y_info, y_msg, y_ier = curve_fit(self.model_fit, dt, y, p0=np.array(params_guess[3:]), sigma=1/y_wt**0.5, absolute_sigma=absolute_sigma, return_full=True) x0, vx0, ax = x_opt y0, vy0, ay = y_opt x0e, vx0e, axe = np.sqrt(x_cov.diagonal()) @@ -886,7 +944,9 @@ def run_fit( params = np.array([x0, vx0, ax, y0, vy0, ay]) param_errors = np.array([x0e, vx0e, axe, y0e, vy0e, aye]) if return_chi2: - chi2_x, chi2_y = self.calc_chi2(t, x, y, xe, ye, params, fixed_params_dict) + # chi2_x, chi2_y = self.calc_chi2(t, x, y, xe, ye, params, fixed_params_dict) + chi2_x = np.sum(x_info['fvec']**2) + chi2_y = np.sum(y_info['fvec']**2) return params, param_errors, chi2_x, chi2_y else: return params, param_errors @@ -904,7 +964,7 @@ class Parallax(MotionModel): required_fixed_param_names = ['t0', 'ra', 'dec'] optional_fixed_params = {'pa': 0., 'obsLocation': 'earth'} fixed_param_names = required_fixed_param_names + list(optional_fixed_params.keys()) - + # Number of fit parameters/required observations in each direction n_params = int((len(fit_param_names) + 1) / 2) @@ -1022,7 +1082,8 @@ def model(self, t, fit_params, fit_param_errs=None, fixed_params_dict=None): t = np.atleast_1d(t) fit_params = np.atleast_2d(fit_params) # (N_stars, N_params) - N_stars = fit_params.shape[0] if fit_params.ndim > 1 else 1 + + N_stars = fit_params.shape[0] N_times = len(t) x0, vx, y0, vy, pi = fit_params.T # Each shape (N_stars,) @@ -1037,7 +1098,15 @@ def model(self, t, fit_params, fit_param_errs=None, fixed_params_dict=None): if not isinstance(obsLocation, str): obsLocation = np.unique(obsLocation)[0] - dt = t[np.newaxis, :] - t0[:, np.newaxis] # Shape (N_stars, N_times) + + if N_times == N_stars: + # Assume each time corresponds to each star, so N_times = 1 + dt = t - t0 # Shape (N_stars,) + dt = dt[:, np.newaxis] # Shape (N_stars, 1) + N_times = 1 + else: + dt = t[np.newaxis, :] - t0[:, np.newaxis] # Shape (N_stars, N_times) + t_mjd = Time(t, format='decimalyear', scale='utc').mjd # Shape (N_times,) self.pvec = self.calc_parallax_vector(t_mjd, ra, dec, pa=pa, obsLocation=obsLocation) # Shape (N_stars, 2, N_times) x, y = self.model_fit(dt, x0[:, np.newaxis], vx[:, np.newaxis], y0[:, np.newaxis], vy[:, np.newaxis], pi[:, np.newaxis]) # Shape (N_stars, N_times) @@ -1133,10 +1202,10 @@ def run_fit( sigma_x = 1.0 / np.sqrt(x_wt) sigma_y = 1.0 / np.sqrt(y_wt) - popt, pcov = curve_fit( + popt, pcov, infodict, mesg, ier = curve_fit( self._model_fit, t - t0, np.hstack([x, y]), p0=params_guess, sigma=np.hstack([sigma_x, sigma_y]), - absolute_sigma=absolute_sigma + absolute_sigma=absolute_sigma, full_output=True ) x0, vx, y0, vy, pi = popt x0_err, vx_err, y0_err, vy_err, pi_err = np.sqrt(pcov.diagonal()) @@ -1145,7 +1214,9 @@ def run_fit( param_errors = np.array([x0_err, vx_err, y0_err, vy_err, pi_err]) if return_chi2: - chi2_x, chi2_y = self.calc_chi2(t, x, y, xe, ye, params, fixed_params_dict) + # chi2_x, chi2_y = self.calc_chi2(t, x, y, xe, ye, params, fixed_params_dict) + chi2_x = np.sum(infodict['fvec'][:len(t)]**2) + chi2_y = np.sum(infodict['fvec'][len(t):]**2) return params, param_errors, chi2_x, chi2_y else: return params, param_errors From 3f2ce00033c506e7c6f2d9fa7404d9f9df4555cb Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Fri, 22 May 2026 05:33:22 -0700 Subject: [PATCH 203/254] Fixed infer positions dimension handling --- flystar/startables.py | 39 ++++++++++++++++++++++++--------------- 1 file changed, 24 insertions(+), 15 deletions(-) diff --git a/flystar/startables.py b/flystar/startables.py index 85de68b..976f646 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -1003,21 +1003,10 @@ def infer_positions(self, times, fixed_params_dict=None, fill_value=np.nan): times = np.atleast_1d(times) N_times = len(times) - if (N_stars > 1) and (N_times > 1): - x_pred = np.full((N_stars, N_times), fill_value, dtype=float) - y_pred = np.full((N_stars, N_times), fill_value, dtype=float) - xe_pred = np.full((N_stars, N_times), np.inf, dtype=float) - ye_pred = np.full((N_stars, N_times), np.inf, dtype=float) - elif N_stars==1: - x_pred = np.full(N_times, fill_value, dtype=float) - y_pred = np.full(N_times, fill_value, dtype=float) - xe_pred = np.full(N_times, np.inf, dtype=float) - ye_pred = np.full(N_times, np.inf, dtype=float) - else: - x_pred = np.full(N_stars, fill_value, dtype=float) - y_pred = np.full(N_stars, fill_value, dtype=float) - xe_pred = np.full(N_stars, np.inf, dtype=float) - ye_pred = np.full(N_stars, np.inf, dtype=float) + x_pred = np.full((N_stars, N_times), fill_value, dtype=float) + y_pred = np.full((N_stars, N_times), fill_value, dtype=float) + xe_pred = np.full((N_stars, N_times), np.inf, dtype=float) + ye_pred = np.full((N_stars, N_times), np.inf, dtype=float) # Calculate the dictionary of {motion_model: indices of stars with this motion model} for faster access during prediction unique_motion_models, unique_inv_indices = np.unique(self['motion_model_used'], return_inverse=True) @@ -1058,14 +1047,34 @@ def infer_positions(self, times, fixed_params_dict=None, fill_value=np.nan): fixed_params[param_name] = fixed_params[param_name][0] # Predict positions + # shape = (N_stars_this_model, N_times) or (N_stars_this_model,) if N_times=1 or (N_times,) if N_stars_this_model=1 or scalar x, y, xe, ye = motion_model_instance.model( times, fit_params, fit_param_errs, fixed_params ) + if N_stars==1 and N_times > 1: + # Reshape (N_times,) to (1, N_times) + x = x[np.newaxis, :] + y = y[np.newaxis, :] + xe = xe[np.newaxis, :] + ye = ye[np.newaxis, :] + elif N_times==1 and N_stars > 1: + # Reshape (N_stars,) to (N_stars, 1) + x = x[:, np.newaxis] + y = y[:, np.newaxis] + xe = xe[:, np.newaxis] + ye = ye[:, np.newaxis] + x_pred[unique_index] = x y_pred[unique_index] = y xe_pred[unique_index] = xe ye_pred[unique_index] = ye + if N_stars==1 or N_times==1: + # Reshape back to 1D array or scalar + x_pred = x_pred.flatten() + y_pred = y_pred.flatten() + xe_pred = xe_pred.flatten() + ye_pred = ye_pred.flatten() return x_pred, y_pred, xe_pred, ye_pred From b41896d4f391979daa1ad122852a0bd49eefc81f Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Tue, 16 Jun 2026 16:39:28 -0700 Subject: [PATCH 204/254] Fix wrong parameter name in curve_fit --- flystar/motion_model.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/flystar/motion_model.py b/flystar/motion_model.py index 46c9b2e..57f5b78 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -934,8 +934,8 @@ def run_fit( t_span = t[idx_last] - t[idx_first] params_guess = [x.mean(), (x[idx_last] - x[idx_first]) / t_span, 0., y.mean(), (y[idx_last] - y[idx_first]) / t_span, 0.] - x_opt, x_cov, x_info, x_msg, x_ier = curve_fit(self.model_fit, dt, x, p0=np.array(params_guess[:3]), sigma=1/x_wt**0.5, absolute_sigma=absolute_sigma, return_full=True) - y_opt, y_cov, y_info, y_msg, y_ier = curve_fit(self.model_fit, dt, y, p0=np.array(params_guess[3:]), sigma=1/y_wt**0.5, absolute_sigma=absolute_sigma, return_full=True) + x_opt, x_cov, x_info, x_msg, x_ier = curve_fit(self.model_fit, dt, x, p0=np.array(params_guess[:3]), sigma=1/x_wt**0.5, absolute_sigma=absolute_sigma, full_output=True) + y_opt, y_cov, y_info, y_msg, y_ier = curve_fit(self.model_fit, dt, y, p0=np.array(params_guess[3:]), sigma=1/y_wt**0.5, absolute_sigma=absolute_sigma, full_output=True) x0, vx0, ax = x_opt y0, vy0, ay = y_opt x0e, vx0e, axe = np.sqrt(x_cov.diagonal()) From 2f64d7da3896ee7b500a1da4af85ca3ffc8450a7 Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Tue, 16 Jun 2026 16:43:58 -0700 Subject: [PATCH 205/254] Added starlist_vertices function to find briteN stars only in overlapping regions; Added briteN control in MosaicSelfRef and MosaicToRef --- flystar/align.py | 98 +++++++++++++++++++++++++++++-------- flystar/match.py | 123 +++++++++++++++++++++++++++++++++++++++++------ 2 files changed, 186 insertions(+), 35 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index ecaff32..d9127e0 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -1,4 +1,5 @@ import os +import gc import pdb import copy import time @@ -6,6 +7,7 @@ import warnings import datetime import numpy as np +import matplotlib.pyplot as plt from tqdm import tqdm from . import match, transforms, plots, motion_model from .starlists import StarList @@ -19,12 +21,14 @@ class MosaicSelfRef(object): def __init__( self, list_of_starlists, + starlist_vertices=None, # Alignment parameters ref_index=0, iters=2, dr_tol=[1, 1], dm_tol=[2, 1], outlier_tol=None, + briteN=None, # Transformation parameters trans_class=transforms.PolyTransform, trans_args=[{'order': 2}, {'order': 2}], @@ -72,6 +76,11 @@ def __init__( Optional Parameters ------------------- + starlist_vertices : list or array + A list or array of polygon vertices coordinates for each starlist. Initial guess will only use stars in overlapping regions defined by these polygons. + If not provided, will be None and will use minimum bounding box of the starlist positions. + Shape of (N_lists, N_vertices, 2) in the format of [[x1, y1], [x2, y2], ..., [xN, yN]] for each starlist, by default None + ref_index : int The index of the reference epoch. (default = 0). Note that this is the reference list only for the first iteration. Subsequent iterations will utilize the sigma-clipped @@ -92,6 +101,10 @@ def __init__( The outlier tolerance (in units of sigma) for rejecting outlier stars. This is a list of tol values, one for each iteration of matching/transformation. If not provided, will be None for each iteration. + + briteN : int + If init_guess_mode is 'miracle', this is the number of brightest stars to use in the miracle match. + Default is min(50, len(star_list)). trans_class : transforms.Transform2D object (or subclass) The transform class that will be used to when deriving the optimal @@ -210,11 +223,12 @@ def = None. If not None, then this should contain an array or list of transform """ self.star_lists = list_of_starlists + self.starlist_vertices = starlist_vertices self.ref_index = ref_index self.iters = iters self.dr_tol = dr_tol self.dm_tol = dm_tol - # self.outlier_tol = outlier_tol + self.briteN = briteN self.trans_args = trans_args self.init_order = init_order self.mag_trans = mag_trans @@ -233,6 +247,12 @@ def = None. If not None, then this should contain an array or list of transform self.prefix_name = prefix_name self.verbose = verbose + if self.starlist_vertices is not None: + import shapely + self.reflist_polygon = shapely.make_valid(shapely.Polygon(self.starlist_vertices[self.ref_index])) + else: + self.reflist_polygon = None + # Check x and y are 1d for ii in range(len(self.star_lists)): if self.star_lists[ii]['x'].ndim != 1 or self.star_lists[ii]['y'].ndim != 1: @@ -505,6 +525,8 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar Given some reference list of positions, loop through all the starlists transform and match them. """ + if self.starlist_vertices is not None: + import shapely for ii in range(len(self.star_lists)): if self.verbose > 0: msg = ' Matching catalog {0} / {1} with {2:d} stars' @@ -537,15 +559,19 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar # Only use "use_in_trans" reference stars, even for initial guessing. keepers = ref_list['use_in_trans'] trans = trans_initial_guess( - ref_list[keepers], - star_list_orig_trim, - self.trans_args[0], - motion_models=self.motion_models, - fixed_params_dict=self.fixed_params_dict, + ref_list=ref_list[keepers], + star_list=star_list_orig_trim, + trans_args=self.trans_args[0], mode=self.init_guess_mode, order=self.init_order, - verbose=self.verbose, - mag_trans=self.mag_trans + briteN=self.briteN, + polygon_reflist=self.reflist_polygon, + polygon_starlist=shapely.Polygon(self.starlist_vertices[ii]) if self.starlist_vertices is not None else None, + buffer=dr_tol, + motion_models=self.motion_models, + fixed_params_dict=self.fixed_params_dict, + mag_trans=self.mag_trans, + verbose=self.verbose ) if np.isnan(trans.px.parameters).any() or np.isnan(trans.py.parameters).any(): raise ValueError(f"Initial transformation contains NaN parameters. trans.px={trans.px.parameters}, trans.py={trans.py.parameters}.") @@ -652,7 +678,7 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar ## Make plot, if desired if self.save_path: plots.trans_positions(ref_list, ref_list[idx_ref], star_list_T, star_list_T[idx_lis], - save_path=f"{os.path.dirname(self.save_path)}/Transformed_Positions_{star_list_T['t'][0]}.png", + save_path=os.path.join(self.save_path, f"Transformed_Positions_{ii}_{star_list_T['t'][0]}.png"), show_plot=False) ### Update the observed (but transformed) values in the reference table. self.update_ref_table_from_list(star_list, star_list_T, ii, idx_ref, idx_lis, idx2) @@ -669,6 +695,10 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar keep_orig=None self.update_ref_table_aggregates(keep_orig=keep_orig) + # Update ref list polygon + if self.starlist_vertices is not None: + self.reflist_polygon = shapely.make_valid(self.reflist_polygon.union(shapely.Polygon(self.starlist_vertices[ii]))) + # Print out some metrics if self.verbose > 0: msg1 = ' {0:2s} (mean and std) for {1:10s}: {2:8.5f} +/- {3:8.5f}' @@ -687,7 +717,7 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar print(msg1.format('dm', 'trans stars', dm_u.mean(), dm_u.std())) print(' Used {0:d} trans ref stars.'.format(len(used))) print(' Dropped {0:d} matches after transform.'.format(len(used) - len(used_good))) - + gc.collect() # clean up memory after each iteration return def setup_trans_info(self): @@ -1155,7 +1185,7 @@ def match_lists(self, dr_tol, dm_tol): dr_tol=dr_tol, dm_tol=dm_tol, verbose=self.verbose) if self.verbose > 0: - fmt = 'Matched {0:5d} out of {1:5d} stars in list {2:2d} [dr = {3:7.4f} +/- {4:6.4f}, dm = {5:5.2f} +/- {6:4.2f}' + fmt = 'Matched {0:5d} out of {1:5d} stars in list {2:2d} [dr = {3:7.4f} ± {4:6.4f}, dm = {5:5.2f} ± {6:4.2f}]' print(fmt.format(len(idx_lis), len(star_list_T), ii, dr.mean(), dr.std(), dm.mean(), dm.std())) copy_over_values(self.ref_table, self.star_lists[ii], star_list_T, ii, idx_ref, idx_lis) @@ -1654,11 +1684,13 @@ def __init__( self, ref_list, list_of_starlists, + starlist_vertices=None, # Alignment parameters iters=2, dr_tol=[1, 1], dm_tol=[2, 1], outlier_tol=None, + briteN=None, # Reference behavior (MosiacToRef specific) use_ref_new=False, update_ref_orig=False, @@ -1713,6 +1745,11 @@ def __init__( Optional Parameters ---------- + starlist_vertices : list or array + A list or array of polygon vertices coordinates for each starlist. Initial guess will only use stars in overlapping regions defined by these polygons. + If not provided, will be None and will use minimum bounding box of the starlist positions. + Shape of (N_lists, N_vertices, 2) in the format of [[x1, y1], [x2, y2], ..., [xN, yN]] for each starlist, by default None + iters : int The number of iterations used in the matching and transformation. TO DO: INNER/OUTER? @@ -1728,6 +1765,10 @@ def __init__( The outlier tolerance (in units of sigma) for rejecting outlier stars. This is a list of tol values, one for each iteration of matching/transformation. + briteN : int + If init_guess_mode is 'miracle', this is the number of brightest stars to use in the miracle match. + Default is min(50, len(star_list)). + use_ref_new : boolean Each pass, new stars are matched and added to the ref_table. However, we don't necessarily want to use these in the reference frame in subsequent passes. @@ -1867,12 +1908,14 @@ def = None. If not None, then this should contain an array or list of transform """ super().__init__( list_of_starlists, + starlist_vertices=starlist_vertices, # Alignment parameters ref_index=-1, iters=iters, dr_tol=dr_tol, dm_tol=dm_tol, outlier_tol=outlier_tol, + briteN=briteN, # Transformation parameters trans_class=trans_class, trans_args=trans_args, @@ -3403,14 +3446,18 @@ def trans_initial_guess( ref_list, star_list, trans_args, + mode='miracle', + order=1, + briteN=None, + n_req_match=3, + polygon_reflist=None, + polygon_starlist=None, + buffer=0, motion_models=None, fixed_params_dict=None, - mode='miracle', ignore_contains='star', - verbose=True, - n_req_match=3, mag_trans=True, - order=1 + verbose=True ): """ Take two starlists and perform an initial matching and transformation. @@ -3444,7 +3491,10 @@ def trans_initial_guess( else: # Default is miracle match. - briteN = min(50, len(star_list)) + if briteN is None: + briteN = min(50, len(star_list)) + else: + assert (type(briteN) == int) and (briteN > 0), f'briteN must be a positive integer, but got {briteN}.' # If there are velocities in the reference list, use them. # We assume velocities are in the same units as the positions. @@ -3461,11 +3511,21 @@ def trans_initial_guess( xref, yref, mref, - briteN + briteN, + polygon_reflist, + polygon_starlist, + buffer=buffer ) - assert len(x1m) >= n_req_match, \ - f'Failed to find more than {n_req_match} (only {len(x1m)}) matches, giving up.' + if len(x1m) < n_req_match: + fig, ax = plt.subplots() + ax.scatter(star_list['x'], star_list['y'], s=1, label='star_list') + ax.scatter(xref, yref, s=1, label='ref_list') + ax.legend() + ax.set_aspect('equal') + plt.show() + raise AssertionError(f'Failed to find more than {n_req_match} (only {len(x1m)}) matches, giving up.') + if verbose > 1: print('Initial_guess: {0:d} stars matched between starlist and reference list'.format(N)) diff --git a/flystar/match.py b/flystar/match.py index 8d13439..b3a4271 100644 --- a/flystar/match.py +++ b/flystar/match.py @@ -1,19 +1,20 @@ +import copy +import itertools import numpy as np +import matplotlib.pyplot as plt from . import starlists, transforms, startables from collections import Counter -from scipy.spatial import KDTree as KDT from astropy.table import Column -import itertools -import copy +from scipy.spatial import KDTree as KDT -def miracle_match_briteN(xin1, yin1, min1, xin2, yin2, min2, Nbrite, - Nbins_vmax=200, Nbins_angle=360,verbose=False): +def miracle_match_briteN(xin1, yin1, min1, xin2, yin2, min2, Nbrite, + polygon1=None, polygon2=None, buffer=0, Nbins_vmax=200, Nbins_angle=360,verbose=False): """ Take two input starlists and select the brightest stars from each. Then perform a triangle matching algorithm along the lines of Groth 1986. - + For every possible triangle (combination of 3 stars) in a starlist, compute the ratio of two sides and the angle between those sides. These quantities are invariant under scale and rotation transformations. @@ -33,17 +34,108 @@ def miracle_match_briteN(xin1, yin1, min1, xin2, yin2, min2, Nbrite, print( ' miracle_match_briteN: ') print( ' miracle_match_briteN: ') + xin1 = np.array(xin1) + yin1 = np.array(yin1) + min1 = np.array(min1) + xin2 = np.array(xin2) + yin2 = np.array(yin2) + min2 = np.array(min2) + + if polygon1 is not None and polygon2 is not None: + import shapely + points1 = shapely.points(xin1, yin1) + points2 = shapely.points(xin2, yin2) + overlap = polygon1.intersection(polygon2).buffer(buffer) + in_poly1 = shapely.contains(overlap, points1) + in_poly2 = shapely.contains(overlap, points2) + xin1 = xin1[in_poly1] + yin1 = yin1[in_poly1] + min1 = min1[in_poly1] + xin2 = xin2[in_poly2] + yin2 = yin2[in_poly2] + min2 = min2[in_poly2] + # else: + # # Only look for matches within overlapping minimum-bounding-boxes of the 2 lists + # valid1 = (np.isfinite(xin1)) & (np.isfinite(yin1)) & (np.isfinite(min1)) + # valid2 = (np.isfinite(xin2)) & (np.isfinite(yin2)) & (np.isfinite(min2)) + # if (sum(valid1) < Nbrite) or (sum(valid2) < Nbrite): + # raise ValueError( + # f'Not enough valid stars to find matches! Need at least {Nbrite} valid stars.\n' + + # f'Valid stars in list 1: {sum(valid1)}\n' + + # f'Valid stars in list 2: {sum(valid2)}\n' + # ) + + # xin1 = xin1[valid1] + # yin1 = yin1[valid1] + # min1 = min1[valid1] + # xin2 = xin2[valid2] + # yin2 = yin2[valid2] + # min2 = min2[valid2] + + # xmin1, xmax1 = np.min(xin1), np.max(xin1) + # ymin1, ymax1 = np.min(yin1), np.max(yin1) + # xmin2, xmax2 = np.min(xin2), np.max(xin2) + # ymin2, ymax2 = np.min(yin2), np.max(yin2) + + # # Find the overlapping minimum bounding box + # x_overlap = (max(xmin1, xmin2), min(xmax1, xmax2)) + # y_overlap = (max(ymin1, ymin2), min(ymax1, ymax2)) + # if x_overlap[0] >= x_overlap[1] or y_overlap[0] >= y_overlap[1]: + # fig, ax = plt.subplots() + # ax.scatter(xin1, yin1, s=1, label='List 1') + # ax.scatter(xin2, yin2, s=1, label='List 2') + # ax.set_aspect('equal') + # ax.legend() + # plt.show() + # raise ValueError('The two star lists do not have an overlapping region!') + + # # Select overlapping regions + # in_overlap1 = (xin1 >= x_overlap[0]) & (xin1 <= x_overlap[1]) & (yin1 >= y_overlap[0]) & (yin1 <= y_overlap[1]) + # in_overlap2 = (xin2 >= x_overlap[0]) & (xin2 <= x_overlap[1]) & (yin2 >= y_overlap[0]) & (yin2 <= y_overlap[1]) + # if sum(in_overlap1) < Nbrite or sum(in_overlap2) < Nbrite: + # raise ValueError( + # 'Not enough stars in the overlapping region to find matches!\n' + + # f'Stars in overlap for list 1: {sum(in_overlap1)}\n' + + # f'Stars in overlap for list 2: {sum(in_overlap2)}\n' + # ) + + # from matplotlib.patches import Rectangle + # fig, ax = plt.subplots() + # polygon1 = Rectangle((xmin1, ymin1), xmax1-xmin1, ymax1-ymin1, fill=True, edgecolor='C0', facecolor='C0', alpha=0.5, label='MBB List 1') + # polygon2 = Rectangle((xmin2, ymin2), xmax2-xmin2, ymax2-ymin2, fill=True, edgecolor='C2', facecolor='C2', alpha=0.5, label='MBB List 2') + # polygon_overlap = Rectangle((x_overlap[0], y_overlap[0]), x_overlap[1]-x_overlap[0], y_overlap[1]-y_overlap[0], fill=True, edgecolor='red', facecolor='C3', alpha=0.5, label='Overlap Region') + # ax.scatter(xin1, yin1, s=1, label='List 1') + # ax.scatter(xin2, yin2, s=1, label='List 2') + # ax.add_patch(polygon1) + # ax.add_patch(polygon2) + # ax.add_patch(polygon_overlap) + # ax.set_aspect('equal') + # ax.legend() + # plt.show() + + # xin1 = xin1[in_overlap1] + # yin1 = yin1[in_overlap1] + # min1 = min1[in_overlap1] + # xin2 = xin2[in_overlap2] + # yin2 = yin2[in_overlap2] + # min2 = min2[in_overlap2] + # Get/check the lengths of the two starlists nin1 = len(xin1) nin2 = len(xin2) if (nin1 < Nbrite) or (nin2 < Nbrite): - print(f'WARNING: You need at least {Nbrite} to find the matches...') - print(f'NIN1: {nin1}') - print(f'NIN2: {nin2}') - # Nbrite = min(nin1, nin2) - # print(f'Updating Nbrite to {Nbrite}...') - return (0, None, None, None, None, None, None) + raise ValueError( + f'Not enough stars in the overlapping region to find matches! Need at least {Nbrite} valid stars.\n' + + f'Stars in overlap for list 1: {nin1}\n' + + f'Stars in overlap for list 2: {nin2}\n' + ) + # print(f'WARNING: You need at least {Nbrite} to find the matches...') + # print(f'NIN1: {nin1}') + # print(f'NIN2: {nin2}') + # # Nbrite = min(nin1, nin2) + # # print(f'Updating Nbrite to {Nbrite}...') + # return (0, None, None, None, None, None, None) # Take the Nbrite brightest stars from each list and order by brightness. if verbose: @@ -108,7 +200,6 @@ def miracle_match_briteN(xin1, yin1, min1, xin2, yin2, min2, Nbrite, idx2_vmax_hist = idx2_vmax_hist[good_idx2] idx2_angl_hist = idx2_angl_hist[good_idx2] - ########## # Possible Matches ########## @@ -163,7 +254,6 @@ def miracle_match_briteN(xin1, yin1, min1, xin2, yin2, min2, Nbrite, x1_mat = x1[votes_sdx[0, good]] y1_mat = y1[votes_sdx[0, good]] m1_mat = m1[votes_sdx[0, good]] - return len(x1_mat), x1_mat, y1_mat, m1_mat, x2_mat, y2_mat, m2_mat @@ -387,7 +477,7 @@ def match(x1, y1, m1, x2, y2, m2, dr_tol, dm_tol=None, verbose=True): if dm_min == dr_min: keep[dups[dm_min]] = True else: - if verbose: + if verbose > 3: print(' confused, dropping star at',x2[idxs2[dups]][0],y2[idxs2[dups]][0]) @@ -580,9 +670,10 @@ def generic_match(sl1, sl2, init_mode='triangle', # dr_tol=order_dr[i_loop][1], # verbose=verbose) import matplotlib.pyplot as plt + plt.clf() plt.plot(sl1_match['x'], sl1_match['y'], 'x', ms=10) plt.plot(sl2_match['x'], sl2_match['y'], 'o') - plt.show() + sl2_idx, sl1_idx = align.transform_and_match(sl2_match, sl1_match, transf, dr_tol=order_dr[1], verbose=verbose) From c106631defb9a3cc75d20cb46107ce18c6e0da6a Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Tue, 16 Jun 2026 16:44:59 -0700 Subject: [PATCH 206/254] Update from_lis_file column from snr to me (NEED CONFIRMATION) --- flystar/starlists.py | 72 +++++++++++++++++++++----------------------- 1 file changed, 34 insertions(+), 38 deletions(-) diff --git a/flystar/starlists.py b/flystar/starlists.py index f872516..fcca92c 100644 --- a/flystar/starlists.py +++ b/flystar/starlists.py @@ -454,50 +454,45 @@ def read_starlist(starlistFile, error=True): class StarList(Table): - """ - A StarList is an astropy.Table with star catalog from a single image. - - Required table columns (input as keywords): - ------------------------- - name : 1D numpy.array with shape = N_stars - List of names of the stars in the table. - - x : 1D numpy.array with shape = N_stars - Positions of N_stars in the x dimension. - - y : 1D numpy.array with shape = N_stars - Positions of N_stars in the y dimension. + def __init__(self, *args, **kwargs): + """ + A StarList is an astropy.Table with star catalog from a single image. - m : 1D numpy.array with shape = N_stars - Magnitudes of N_stars. + Required table columns (input as keywords): + ------------------------- + name : 1D numpy.array with shape = N_stars + List of names of the stars in the table. - Optional table columns (input as keywords): - ------------------------- - xe : 1D numpy.array with shape = N_stars - Position uncertainties of N_stars in the x dimension. + x : 1D numpy.array with shape = N_stars + Positions of N_stars in the x dimension. - ye : 1D numpy.array with shape = N_stars - Position uncertainties of N_stars in the y dimension. + y : 1D numpy.array with shape = N_stars + Positions of N_stars in the y dimension. - me : 1D numpy.array with shape = N_stars - Magnitude uncertainties of N_stars. + m : 1D numpy.array with shape = N_stars + Magnitudes of N_stars. - corr : 1D numpy.array with shape = N_stars - Fitting correlation of N_stars. + Optional table columns (input as keywords): + ------------------------- + xe : 1D numpy.array with shape = N_stars + Position uncertainties of N_stars in the x dimension. - Optional table meta data - ------------------------- - list_name : str - Name of the starlist. + ye : 1D numpy.array with shape = N_stars + Position uncertainties of N_stars in the y dimension. - list_time : int or float - Time/date of the starlist. + me : 1D numpy.array with shape = N_stars + Magnitude uncertainties of N_stars. + corr : 1D numpy.array with shape = N_stars + Fitting correlation of N_stars. - """ + Optional table meta data + ------------------------- + list_name : str + Name of the starlist. - def __init__(self, *args, **kwargs): - """ + list_time : int or float + Time/date of the starlist. """ # Check if the required arguments are present arg_req = ('name', 'x', 'y', 'm') @@ -526,6 +521,7 @@ def __init__(self, *args, **kwargs): # Check if the type and size of the arguments are correct. # Name checking: type and shape + kwargs['name'] = np.asarray(kwargs['name']) if (not isinstance(kwargs['name'], np.ndarray)) or ( len(kwargs['name']) != n_stars): err_msg = "The '{0:s}' argument has to be a numpy array " @@ -645,18 +641,18 @@ def from_lis_file(cls, filename, error=True, fvu_file=None): if error==True: t_ref.rename_column(cols[5], 'xe') t_ref.rename_column(cols[6], 'ye') - t_ref.rename_column(cols[7], 'snr') + t_ref.rename_column(cols[7], 'me') t_ref.rename_column(cols[8], 'corr') t_ref.rename_column(cols[9], 'N_frames') t_ref.rename_column(cols[10], 'flux') else: - t_ref.rename_column(cols[5], 'snr') + t_ref.rename_column(cols[5], 'me') t_ref.rename_column(cols[6], 'corr') t_ref.rename_column(cols[7], 'N_frames') t_ref.rename_column(cols[8], 'flux') - if ('me' not in cols) and ('snr' in cols) and (error == True): - t_ref['me'] = 1.0 / t_ref['snr'] + # if ('me' not in cols) and ('snr' in cols) and (error == True): + # t_ref['me'] = 1.0 / t_ref['snr'] if fvu_file is not None: t_fvu = Table.read(fvu_file, format='ascii.no_header') From dbe04624155252fd5233043e9e14ae24093c6868 Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Tue, 16 Jun 2026 16:46:19 -0700 Subject: [PATCH 207/254] Fixed test functions after fixing match.match dr calculation --- flystar/tests/test_align.py | 36 ++++++++++++------------------ flystar/tests/test_motion_model.py | 3 ++- 2 files changed, 16 insertions(+), 23 deletions(-) mode change 100755 => 100644 flystar/tests/test_motion_model.py diff --git a/flystar/tests/test_align.py b/flystar/tests/test_align.py index 5b4fc66..faa0ff2 100644 --- a/flystar/tests/test_align.py +++ b/flystar/tests/test_align.py @@ -1,14 +1,9 @@ +import pdb import flystar -from flystar import align -from flystar import starlists -from flystar import startables -from flystar import transforms -from flystar import analysis -from flystar import motion_model -from astropy.table import Table import numpy as np import matplotlib.pyplot as plt -import pdb +from astropy.table import Table +from flystar import align, starlists, transforms, analysis, motion_model test_data_path = f'{flystar.__path__[0]}/tests/test_data' @@ -81,8 +76,6 @@ def test_MosaicSelfRef(): plt.plot(msc.ref_table['x0'], msc.ref_table['y0'], '.', color='black', alpha=0.2) - - return def test_MosaicSelfRef_vel_tconst(): @@ -136,8 +129,6 @@ def test_MosaicSelfRef_vel_tconst(): for ii in range(4): np.testing.assert_allclose(msc.trans_list[ii].px.c1_0, 1.0, rtol=1e-2) np.testing.assert_allclose(msc.trans_list[ii].py.c0_1, 1.0, rtol=1e-2) - - return def test_MosaicSelfRef_vel(): @@ -165,7 +156,7 @@ def test_MosaicSelfRef_vel(): # Test instantiation and basic fitting. ########## msc = align.MosaicSelfRef(lists, ref_index=0, iters=3, - dr_tol=[5, 3, 3], dm_tol=[1, 1, 0.5], outlier_tol=None, + dr_tol=[5, 3, 3], dm_tol=[1, 1, 0.5], outlier_tol=None, briteN=30, trans_class=transforms.PolyTransform, trans_args={'order': 2}, motion_models=['Empty', 'Fixed', 'Linear'], verbose=False) @@ -188,7 +179,7 @@ def test_MosaicSelfRef_vel(): # Check that we have some matched stars... should be at least 35 stars # that are detected in all 4 starlists. idx = np.where(msc.ref_table['n_detect'] == 4)[0] - assert len(idx) > 35 + assert len(idx) >= 35, f"Expected at least 35 stars detected in all 4 starlists, but only found {len(idx)}" # Check that the transformation error isn't too big valid_err = np.isfinite(msc.ref_table['x0_err']) & np.isfinite(msc.ref_table['y0_err']) & np.isfinite(msc.ref_table['m0_err']) @@ -198,14 +189,15 @@ def test_MosaicSelfRef_vel(): # Check that the transformation lists aren't too wacky for ii in range(4): - np.testing.assert_allclose(msc.trans_list[ii].px.c1_0, 1.0, rtol=1e-2) - np.testing.assert_allclose(msc.trans_list[ii].py.c0_1, 1.0, rtol=1e-2) + np.testing.assert_allclose(msc.trans_list[ii].px.c1_0, 1.0, rtol=2e-2) + np.testing.assert_allclose(msc.trans_list[ii].py.c0_1, 1.0, rtol=2e-2) plt.clf() plt.plot(msc.ref_table['vx'], msc.ref_table['vy'], 'k.', color='black', alpha=0.2) + return def test_MosaicToRef(): @@ -256,7 +248,7 @@ def test_MosaicToRef(): assert np.not_equal(msc.ref_table['x0'], ref_list['x0']).all() assert np.not_equal(msc.ref_table['y0'], ref_list['y0']).all() - return msc + return def test_MosaicToRef_p0_vel(): make_fake_starlists_poly0_vel(seed=42) @@ -315,7 +307,7 @@ def test_MosaicToRef_p0_vel(): #assert np.any(np.not_equal(msc.ref_table['vx'], ref_list['vx'])) assert np.not_equal(msc.ref_table['vx'], ref_list['vx']).any() - return msc + return def test_MosaicToRef_vel(): make_fake_starlists_poly1_vel(seed=42) @@ -374,7 +366,7 @@ def test_MosaicToRef_vel(): #assert np.any(np.not_equal(msc.ref_table['vx'], ref_list['vx'])) assert np.not_equal(msc.ref_table['vx'], ref_list['vx']).any() - return msc + return def test_MosaicToRef_acc(): make_fake_starlists_poly1_acc(seed=42) @@ -453,6 +445,8 @@ def test_MosaicToRef_acc(): ax_max = np.max(ref_list['ax'][i_orig]) ay_min = np.min(ref_list['ay'][i_orig]) ay_max = np.max(ref_list['ay'][i_orig]) + + plt.clf() fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5)) ax1.plot(ref_list['ax'][i_orig], msc.ref_table['ax'][i_fit], '.') ax1.plot([ax_min, ax_max], [ax_min, ax_max], color='C3') @@ -470,12 +464,10 @@ def test_MosaicToRef_acc(): ax2.set_ylabel('Ref Table ay') ax2.set_title('Acceleration in Y') plt.tight_layout() - plt.show() # Also double check that they aren't exactly the same for the reference stars. assert np.any(np.not_equal(msc.ref_table['ax'][i_fit], ref_list['ax'][i_orig])) - - return msc + return def test_MosaicToRef_hst_me(): """ diff --git a/flystar/tests/test_motion_model.py b/flystar/tests/test_motion_model.py old mode 100755 new mode 100644 index 7a90288..5bd4ad2 --- a/flystar/tests/test_motion_model.py +++ b/flystar/tests/test_motion_model.py @@ -354,6 +354,7 @@ def test_Parallax(): fit_params=params, fixed_params_dict=fixed_params_dict ) + plt.clf() fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5)) ax1.plot(t, x_model, color='C3', lw=2, label='Model x') ax1.plot(t, x_true, color='C0', ls='--', label='True x') @@ -368,7 +369,7 @@ def test_Parallax(): ax2.set_ylabel('y') ax2.legend() plt.tight_layout() - plt.show() + # Confirm true value is within error bar of fit value assert np.all([within_error(true_params[param_list[i]], params[i], param_errs[i]) for i in range(len(params))]) From 33b35d26c55e8931fe40c00985408f13a6c9ddd2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Tue, 16 Jun 2026 16:55:11 -0700 Subject: [PATCH 208/254] Save bootstrap results automatically after calc_bootstrap_errors --- flystar/align.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/flystar/align.py b/flystar/align.py index ecaff32..3ec88f6 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -1645,6 +1645,11 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot print("Saved starlist errors to xe_list and added xe_boot to xe in quadrature.") print("The same was done for ye and me.") + if self.save_path is not None: + with open(os.path.join(self.save_path, self.prefix_name+'_bootstrap.pkl'), 'wb') as file: + pickle.dump(self, file) + with open(os.path.join(self.save_path, self.prefix_name+'_ref_table_bootstrap.pkl'), 'wb') as file: + pickle.dump(self.ref_table, file) return From 6431e52a75d44f443ac633bce8e7df48efe5126e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Tue, 16 Jun 2026 16:56:13 -0700 Subject: [PATCH 209/254] Add actual shape of arg_test in StarList init error message --- flystar/starlists.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/flystar/starlists.py b/flystar/starlists.py index f872516..7ebe7c2 100644 --- a/flystar/starlists.py +++ b/flystar/starlists.py @@ -544,8 +544,8 @@ def __init__(self, *args, **kwargs): raise TypeError(err_msg.format(arg_test)) if kwargs[arg_test].shape != (n_stars,): - err_msg = "The '{0:s}' argument has to have shape = ({1:d},)" - raise TypeError(err_msg.format(arg_test, n_stars)) + err_msg = "The '{0:s}' argument has to have shape = ({1:d},), but has shape = {2}" + raise TypeError(err_msg.format(arg_test, n_stars, kwargs[arg_test].shape)) # We have to have special handling of meta-data meta_tab = ('list_time', 'list_name') @@ -645,18 +645,18 @@ def from_lis_file(cls, filename, error=True, fvu_file=None): if error==True: t_ref.rename_column(cols[5], 'xe') t_ref.rename_column(cols[6], 'ye') - t_ref.rename_column(cols[7], 'snr') + t_ref.rename_column(cols[7], 'me') t_ref.rename_column(cols[8], 'corr') t_ref.rename_column(cols[9], 'N_frames') t_ref.rename_column(cols[10], 'flux') else: - t_ref.rename_column(cols[5], 'snr') + t_ref.rename_column(cols[5], 'me') t_ref.rename_column(cols[6], 'corr') t_ref.rename_column(cols[7], 'N_frames') t_ref.rename_column(cols[8], 'flux') - if ('me' not in cols) and ('snr' in cols) and (error == True): - t_ref['me'] = 1.0 / t_ref['snr'] + # if ('me' not in cols) and ('snr' in cols) and (error == True): + # t_ref['me'] = 1.0 / t_ref['snr'] if fvu_file is not None: t_fvu = Table.read(fvu_file, format='ascii.no_header') From 9df7b009f714bb2c7453057e44042f5f4372b2c1 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Tue, 16 Jun 2026 19:30:05 -0700 Subject: [PATCH 210/254] Changed fit_motion_model to fit_motion_models; Changed n_params to requried_params --- flystar/motion_model.py | 48 ++++++++++++++++++--------------- flystar/startables.py | 32 +++++++++++----------- flystar/tests/test_startable.py | 14 +++++----- 3 files changed, 50 insertions(+), 44 deletions(-) diff --git a/flystar/motion_model.py b/flystar/motion_model.py index 57f5b78..f75528d 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -12,7 +12,8 @@ class MotionModel(ABC): fit_param_names = [] # Number of fit parameters/required observations in each direction - n_params = int((len(fit_param_names) + 1) / 2) + n_params = len(fit_param_names) + required_epochs = int((n_params + 1) / 2) # Fixed parameters: These are parameters that are required for the model, but are not # fit quantities. For example, RA and Dec in a parallax model. @@ -173,18 +174,18 @@ def fit( # Bootstrap errors n_obs = len(t) - if bootstrap > 0 and n_obs > (self.n_params): + if (bootstrap > 0) and (n_obs > self.required_epochs): rng = np.random.default_rng(seed) edx = np.arange(n_obs, dtype=int) # Precompute All Bootstrap Draws at Once # Ensure there are enough unique points in each bootstrap sample bdx_unique = np.stack([ - rng.choice(edx, size=self.n_params, replace=False) + rng.choice(edx, size=self.required_epochs, replace=False) for _ in range(bootstrap) ]) # Draw with replacement for the rest bdx_extra = np.stack([ - rng.choice(edx, size=n_obs - self.n_params, replace=True) + rng.choice(edx, size=n_obs - self.required_epochs, replace=True) for _ in range(bootstrap) ]) bdx_all = np.hstack((bdx_unique, bdx_extra)) @@ -234,10 +235,10 @@ def fit( # chi2_y = residual_y.T @ W_mat_y @ residual_y # if reduced: - # if len(dt) == self.n_params: + # if len(dt) == self.required_epochs: # return np.inf, np.inf # if not parallax: - # degree_of_freedom = len(x) - self.n_params + # degree_of_freedom = len(x) - self.required_epochs # else: # degree_of_freedom = 2*len(x) - len(self.fit_param_names) # chi2_x, chi2_y = chi2_x / degree_of_freedom, chi2_y / degree_of_freedom @@ -251,10 +252,10 @@ def calc_chi2(self, t, x, y, xe, ye, fit_params, fixed_params_dict=None, reduced chi2x = np.sum((x - x_pred)**2 / xe**2) chi2y = np.sum((y - y_pred)**2 / ye**2) if reduced: - if len(t) == self.n_params: + if len(t) == self.required_epochs: return np.inf, np.inf if not parallax: - degree_of_freedom = len(x) - self.n_params + degree_of_freedom = len(x) - self.required_epochs else: degree_of_freedom = 2*len(x) - len(self.fit_param_names) chi2x, chi2y = chi2x / degree_of_freedom, chi2y / degree_of_freedom @@ -268,7 +269,8 @@ class Empty(MotionModel): optional_fixed_params = {} # Number of fit parameters/required observations in each direction - n_params = int((len(fit_param_names) + 1) / 2) + n_params = len(fit_param_names) + required_epochs = int((n_params + 1) / 2) def __init__(self, **kwargs): """Empty motion model, returns nan for values and inf for uncertainties. @@ -387,7 +389,8 @@ class Fixed(MotionModel): optional_fixed_params = {} # Number of fit parameters/required observations in each direction - n_params = int((len(fit_param_names) + 1) / 2) + n_params = len(fit_param_names) + required_epochs = int((n_params + 1) / 2) def __init__(self, **kwargs): # Must call after setting parameters. @@ -491,7 +494,7 @@ def run_fit( warnings.warn("Fixed model has no non-scipy fitter option. Running with scipy.") n_obs = len(t) - degree_of_freedom = n_obs - self.n_params + degree_of_freedom = n_obs - self.required_epochs # Not enough data points to fit model if degree_of_freedom < 0: warnings.warn( @@ -550,7 +553,8 @@ class Linear(MotionModel): fixed_param_names = required_fixed_param_names + list(optional_fixed_params.keys()) # Number of fit parameters/required observations in each direction - n_params = int((len(fit_param_names) + 1) / 2) + n_params = len(fit_param_names) + required_epochs = int((n_params + 1) / 2) def __init__(self, **kwargs): # Must call after setting parameters. @@ -665,7 +669,7 @@ def run_fit( ye = np.atleast_1d(ye) n_obs = len(t) - degree_of_freedom = n_obs - self.n_params + degree_of_freedom = n_obs - self.required_epochs # Not enough data points to fit model if degree_of_freedom < 0: warnings.warn( @@ -787,8 +791,9 @@ class Acceleration(MotionModel): optional_fixed_params = {} fixed_param_names = required_fixed_param_names + list(optional_fixed_params.keys()) - # Number of fit parameters/required observations in each direction - n_params = int((len(fit_param_names) + 1) / 2) + # Number of required observations in each direction + n_params = len(fit_param_names) + required_epochs = int((n_params + 1) / 2) def __init__(self): # Must call after setting parameters. @@ -911,7 +916,7 @@ def run_fit( warnings.warn("Acceleration model has no non-scipy fitter option. Running with scipy.") n_obs = len(t) - degree_of_freedom = n_obs - self.n_params + degree_of_freedom = n_obs - self.required_epochs # Not enough data points to fit model if degree_of_freedom < 0: warnings.warn( @@ -966,8 +971,9 @@ class Parallax(MotionModel): fixed_param_names = required_fixed_param_names + list(optional_fixed_params.keys()) - # Number of fit parameters/required observations in each direction - n_params = int((len(fit_param_names) + 1) / 2) + # Number of required observations in each direction + n_params = len(fit_param_names) + required_epochs = int((n_params + 1) / 2) def __init__(self): super().__init__() @@ -1162,7 +1168,7 @@ def run_fit( obsLocation = fixed_params_dict['obsLocation'] n_fit = len(t) - degree_of_freedom = n_fit - self.n_params + degree_of_freedom = n_fit - self.required_epochs # Not enough data points to fit model if degree_of_freedom < 0: warnings.warn( @@ -1291,6 +1297,6 @@ def motion_model_map(): mm_map = dict( [(mm.__name__, mm) for mm in MotionModel.__subclasses__()] ) - # Sort by n_params - mm_map = dict(sorted(mm_map.items(), key=lambda item: item[1].n_params)) + # Sort by required epochs + mm_map = dict(sorted(mm_map.items(), key=lambda item: item[1].required_epochs)) return mm_map \ No newline at end of file diff --git a/flystar/startables.py b/flystar/startables.py index 976f646..dc847aa 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -544,7 +544,7 @@ def detections(self): return - def fit_motion_model( + def fit_motion_models( self, motion_models=None, fixed_params_dict=None, @@ -567,12 +567,12 @@ def fit_motion_model( Empty and Fixed models are always added automatically for stars with n_fit = 0 or 1. The behavior is as follows: 1. If 'motion_model_input' column is NOT in table: - - Use the most complex model that has enough parameters to fit the data (n_fit >= n_params). + - Use the most complex model that has enough parameters to fit the data (n_fit >= required_epochs). - If multiple models are supplied, prioritize the model with the most parameters to fit. - If multiple models have the same number of parameters, raise AssertionError: not sure which to use. 2. If 'motion_model_input' column IS in table: - Use the model specified in the 'motion_model_input' column. - - If not enough data points to fit the specified model, use the most complex model in any 'motion_model_input' column that has enough parameters to fit the data (n_fit >= n_params) among the provided motion_models and 'motion_model_input'. + - If not enough data points to fit the specified model, use the most complex model in any 'motion_model_input' column that has enough parameters to fit the data (n_fit >= required_epochs) among the provided motion_models and 'motion_model_input'. The actual used motion model is stored in the 'motion_model_used' column. The default motion_models are [Empty, Fixed, Linear]. fixed_params_dict : dict, optional Dictionary of fixed parameters for motion models, e.g., {'t0': 0., 'ra': np.array([...]), 'dec': np.array([...])}. @@ -656,17 +656,17 @@ def fit_motion_model( if mm_name not in mm_names: motion_models.append(all_mm_map[mm_name]) - # Sort motion models by n_params - motion_models = sorted(motion_models, key=lambda mm: mm.n_params) + # Sort motion models by required epochs + motion_models = sorted(motion_models, key=lambda mm: mm.required_epochs) input_mm_map = {mm.name: mm for mm in motion_models} - mm_n_params = np.sort([mm.n_params for mm in motion_models]) + mm_required_epochs = np.sort([mm.required_epochs for mm in motion_models]) if 'motion_model_input' not in self.colnames: - # If motion_model_input column is not provided, assert that motion model n_params are unique and sorted + # If motion_model_input column is not provided, assert that motion model required_epochs are unique and sorted # Otherwise the fitter does not know which motion model to use based on n_obs - assert len(mm_n_params) == len(set(mm_n_params)), \ - f"fit_motion_model: Provided motion model n_params are not unique! Motion Models are: {[_.name for _ in motion_models]} Cannot decide which motion model to use based on n_obs. Please provide unique motion_models or a 'motion_model_input' column." + assert len(mm_required_epochs) == len(set(mm_required_epochs)), \ + f"fit_motion_models: Provided motion model required_epochs are not unique! Motion Models are: {[_.name for _ in motion_models]}" + '\n' + "Cannot decide which motion model to use based on n_obs. Please provide unique motion_models or a 'motion_model_input' column." ########################### @@ -768,13 +768,13 @@ def fit_motion_model( n_fit = np.array(self['n_fit']) if 'motion_model_input' in self.colnames: # Determine which motion model to use based on motion_model_input column - # If n_fit < required n_params for the input motion model, use the most complicated motion model with n_fit >= n_params - required_params = np.array([all_mm_map[mm_name].n_params for mm_name in self['motion_model_input']]) + # If n_fit < required_epochs for the input motion model, use the most complicated motion model with n_fit >= required_epochs + required_params = np.array([all_mm_map[mm_name].required_epochs for mm_name in self['motion_model_input']]) reassign_mm = n_fit < required_params mm_digitized = np.digitize( x=n_fit[reassign_mm], - bins=mm_n_params + bins=mm_required_epochs ) - 1 # Convert to 0-based index # Assign motion models to stars @@ -782,10 +782,10 @@ def fit_motion_model( self['motion_model_used'][reassign_mm] = np.array([motion_models[d].name for d in mm_digitized], dtype='U20') else: - # If motion_model_input column is not provided, use the most complicated model in motion_models with n_fit >= n_params. + # If motion_model_input column is not provided, use the most complicated model in motion_models with n_fit >= required_epochs. mm_digitized = np.digitize( x=n_fit, - bins=mm_n_params + bins=mm_required_epochs ) - 1 # Convert to 0-based index # Assign motion models to stars @@ -847,7 +847,7 @@ def fit_motion_model( ############################ # Fill table with all possible motion model parameter names as new columns. new_col_list = motion_model.motion_model_param_names(motion_model_used, with_errors=True, with_fixed=False) - new_col_list += ['chi2_x', 'chi2_y', 'n_params'] + new_col_list += ['chi2_x', 'chi2_y', 'required_epochs'] if 't0' not in new_col_list: new_col_list.append('t0') @@ -972,7 +972,7 @@ def fit_motion_model( self[param_name + '_err'][unique_index] = param_errs_array[:, j] self['chi2_x'][unique_index] = chi2_x_array self['chi2_y'][unique_index] = chi2_y_array - self['n_params'][unique_index] = motion_model_instance.n_params + self['required_epochs'][unique_index] = motion_model_instance.required_epochs self['t0'][unique_index] = t0[unique_index] return diff --git a/flystar/tests/test_startable.py b/flystar/tests/test_startable.py index e743b74..f8e24de 100644 --- a/flystar/tests/test_startable.py +++ b/flystar/tests/test_startable.py @@ -311,7 +311,7 @@ def test_fit_motion_models(): tab = table.vstack((tab1, tab2, tab3)) tab.meta = tab1.meta - tab.fit_motion_model(verbose=True, mask_value=-100000.) + tab.fit_motion_models(verbose=True, mask_value=-100000.) # Test creation of new variables np.testing.assert_equal(len(tab['vx']), len(tab)) @@ -341,7 +341,7 @@ def test_fit_motion_models(): x0_orig = tab['x0'] vxe_orig = tab['vx_err'] x0e_orig = tab['x0_err'] - tab.fit_motion_model(verbose=False, mask_value=-100000.) + tab.fit_motion_models(verbose=False, mask_value=-100000.) np.testing.assert_allclose(tab['vx'], vx_orig) np.testing.assert_allclose(tab['x0'], x0_orig) @@ -352,7 +352,7 @@ def test_fit_motion_models(): # Test fixed_t0 functionality ########## fixed_t0 = tab['t0'] + np.random.normal(size=len(tab)) - tab.fit_motion_model(verbose=False, mask_value=-100000., fixed_params_dict={'t0': fixed_t0}) + tab.fit_motion_models(verbose=False, mask_value=-100000., fixed_params_dict={'t0': fixed_t0}) np.testing.assert_allclose(tab['t0'], fixed_t0) ########## @@ -360,7 +360,7 @@ def test_fit_motion_models(): ########## tab_b = table.vstack((tab1, tab2, tab3)) tab_b.meta = tab1.meta - tab_b.fit_motion_model(verbose=True, bootstrap=50) + tab_b.fit_motion_models(verbose=True, bootstrap=50) np.testing.assert_equal(tab_b.meta['n_bootstrap'], 50) np.testing.assert_array_less(tab['x0_err'][0], tab_b['x0_err'][0]) @@ -372,7 +372,7 @@ def test_fit_motion_models(): # Test what happens with no velocity errors ########## tab.remove_columns(['xe', 'ye', 'x0', 'y0', 'x0_err', 'y0_err', 'vx', 'vy', 'vx_err', 'vy_err', 'n_fit']) - tab.fit_motion_model(verbose=False) + tab.fit_motion_models(verbose=False) np.testing.assert_equal(len(tab['vx']), len(tab)) np.testing.assert_equal(len(tab['vy']), len(tab)) @@ -389,7 +389,7 @@ def test_fit_motion_models(): ######### # Test 5a: Masked print("Testing Masked List") - tt.fit_motion_model(verbose=False, mask_lists=[1]) + tt.fit_motion_models(verbose=False, mask_lists=[1]) np.testing.assert_allclose(np.arange(2.25, 48, 5), tt['x0'].data) np.testing.assert_allclose(np.arange(2.25, 48, 5), tt['y0'].data) np.testing.assert_allclose(np.full(10, 0.05), tt['x0_err'].data) @@ -417,7 +417,7 @@ def test_fit_motion_model_2epoch(): tab_2 = table.vstack((tab1, tab2, tab3)) tab_2.meta=tab1.meta - tab_2.fit_motion_model(verbose=False, mask_value=-100000.) + tab_2.fit_motion_models(verbose=False, mask_value=-100000.) assert all([_ in tab_2.colnames for _ in ['n_fit', 't0', 'x0', 'y0', 'vx', 'vy', 'x0_err', 'y0_err', 'vx_err', 'vy_err']]) From fcb50586eddb5c9bf8221c141a96d1e7f5665519 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Tue, 16 Jun 2026 19:31:23 -0700 Subject: [PATCH 211/254] Added reflist_vertex for MosaicToRef; Removed motion_model_for_new_star --- flystar/align.py | 70 +++++++++++++++++++++++++++--------------------- 1 file changed, 40 insertions(+), 30 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 7d10083..23a3ebe 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -28,7 +28,6 @@ def __init__( dr_tol=[1, 1], dm_tol=[2, 1], outlier_tol=None, - briteN=None, # Transformation parameters trans_class=transforms.PolyTransform, trans_args=[{'order': 2}, {'order': 2}], @@ -36,13 +35,14 @@ def __init__( trans_weighting=None, init_order=1, init_guess_mode='miracle', + briteN=None, calc_trans_inverse=False, # Magnitude parameters mag_trans=True, mag_lim=None, # Motion model parameters motion_models=['Empty', 'Fixed'], - motion_model_for_new_star=None, + # motion_model_for_new_star=None, fixed_params_dict=None, vel_weighting='var', use_scipy=True, @@ -78,7 +78,6 @@ def __init__( ------------------- starlist_vertices : list or array A list or array of polygon vertices coordinates for each starlist. Initial guess will only use stars in overlapping regions defined by these polygons. - If not provided, will be None and will use minimum bounding box of the starlist positions. Shape of (N_lists, N_vertices, 2) in the format of [[x1, y1], [x2, y2], ..., [xN, yN]] for each starlist, by default None ref_index : int @@ -101,10 +100,6 @@ def __init__( The outlier tolerance (in units of sigma) for rejecting outlier stars. This is a list of tol values, one for each iteration of matching/transformation. If not provided, will be None for each iteration. - - briteN : int - If init_guess_mode is 'miracle', this is the number of brightest stars to use in the miracle match. - Default is min(50, len(star_list)). trans_class : transforms.Transform2D object (or subclass) The transform class that will be used to when deriving the optimal @@ -134,6 +129,10 @@ def = None. If not None, then this should contain an array or list of transform to make the initial transformation and matching blindly. We can do this in a couple of different ways. Options are 'miracle' or 'name' (see trans_initial_guess() for more details). + briteN : int + If init_guess_mode is 'miracle', this is the number of brightest stars to use in the miracle match. + Default is min(50, len(star_list)). + calc_trans_inverse: boolean If true, then calculate the inverse transformation (from reference to starlist) in addition to the normal transformation (from starlist to reference). The inverse @@ -228,7 +227,6 @@ def = None. If not None, then this should contain an array or list of transform self.iters = iters self.dr_tol = dr_tol self.dm_tol = dm_tol - self.briteN = briteN self.trans_args = trans_args self.init_order = init_order self.mag_trans = mag_trans @@ -242,6 +240,7 @@ def = None. If not None, then this should contain an array or list of transform self.absolute_sigma = absolute_sigma self.fixed_params_dict = fixed_params_dict self.init_guess_mode = init_guess_mode + self.briteN = briteN self.iter_callback = iter_callback self.save_path = save_path self.prefix_name = prefix_name @@ -276,14 +275,14 @@ def = None. If not None, then this should contain an array or list of transform motion_models.append(all_mm_map['Fixed']) # Sort by increasing n_params - motion_models = sorted(motion_models, key=lambda mm: mm.n_params) + motion_models = sorted(motion_models, key=lambda mm: mm.required_epochs) self.motion_models = motion_models - if motion_model_for_new_star is None: - self.motion_model_for_new_star = self.motion_models[-1] - elif isinstance(motion_model_for_new_star, str): - assert motion_model_for_new_star in all_mm_map.keys(), f"motion_model_for_new_star must be in {list(all_mm_map.keys())}" - self.motion_model_for_new_star = all_mm_map[motion_model_for_new_star] + # if motion_model_for_new_star is None: + # self.motion_model_for_new_star = self.motion_models[-1] + # elif isinstance(motion_model_for_new_star, str): + # assert motion_model_for_new_star in all_mm_map.keys(), f"motion_model_for_new_star must be in {list(all_mm_map.keys())}" + # self.motion_model_for_new_star = all_mm_map[motion_model_for_new_star] # For backwards compatibility. # if self.verbose is True: @@ -976,7 +975,8 @@ def update_ref_table_from_list(self, star_list, star_list_T, ii, idx_ref, idx_li self.ref_table, star_list, idx_lis, - motion_model_name=self.motion_model_for_new_star.name + # motion_model_name=self.motion_model_for_new_star.name + motion_model_name=self.motion_models[-1].name ) if len(idx_ref_new) > 0: @@ -1040,7 +1040,7 @@ def update_ref_table_aggregates(self, keep_orig=None, n_boot=0): fit_star_idxs = None if ('motion_model_input' in self.ref_table.keys()) and np.all(self.ref_table['motion_model_input']=='Fixed'): - # self.ref_table.fit_motion_model( + # self.ref_table.fit_motion_models( # motion_models=['Fixed'], # weighting=self.vel_weighting, # use_scipy=self.use_scipy, @@ -1051,7 +1051,7 @@ def update_ref_table_aggregates(self, keep_orig=None, n_boot=0): weighted_m = ('me' in self.ref_table.colnames) self.ref_table.combine_lists_xym(weighted_xy=weighted_xy, weighted_m=weighted_m) else: - self.ref_table.fit_motion_model( + self.ref_table.fit_motion_models( motion_models=self.motion_models, fixed_params_dict=self.fixed_params_dict, weighting=self.vel_weighting, @@ -1415,7 +1415,7 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot all_mm_map = motion_model.motion_model_map() motion_model_list = [all_mm_map[mm_name] for mm_name in motion_model_list] - motion_boot_min_epochs = np.max([mm.n_params for mm in motion_model_list]) + motion_boot_min_epochs = np.max([mm.required_epochs for mm in motion_model_list]) ### IF MEMORY PROBLEMS HERE: ### DEFINE MEAN, STD VARIABLES AND BUILD THEM RATHER THAN SAVING FULL ARRAY @@ -1565,7 +1565,7 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot fixed_params_dict = self.fixed_params_dict.copy() fixed_params_dict['t0'] = t0_arr - star_table.fit_motion_model( + star_table.fit_motion_models( motion_models=self.motion_models, fixed_params_dict=fixed_params_dict, weighting=self.vel_weighting, @@ -1689,13 +1689,13 @@ def __init__( self, ref_list, list_of_starlists, + reflist_vertex=None, starlist_vertices=None, # Alignment parameters iters=2, dr_tol=[1, 1], dm_tol=[2, 1], outlier_tol=None, - briteN=None, # Reference behavior (MosiacToRef specific) use_ref_new=False, update_ref_orig=False, @@ -1706,6 +1706,7 @@ def __init__( trans_weighting=None, init_order=1, init_guess_mode='miracle', + briteN=None, calc_trans_inverse=False, # Magnitude parameters mag_trans=True, @@ -1713,7 +1714,7 @@ def __init__( ref_mag_lim=None, # Motion model parameters motion_models=['Empty', 'Fixed'], - motion_model_for_new_star=None, + # motion_model_for_new_star=None, fixed_params_dict=None, vel_weighting='var', use_scipy=True, @@ -1750,9 +1751,12 @@ def __init__( Optional Parameters ---------- + reflist_vertex : array + An array of polygon vertices coordinates for the reference starlist. Initial guess will only use stars in overlapping regions defined by these polygons. + Shape of (N_vertices, 2) in the format of [[x1, y1], [x2, y2], ..., [xN, yN]] for the reference starlist, by default None + starlist_vertices : list or array A list or array of polygon vertices coordinates for each starlist. Initial guess will only use stars in overlapping regions defined by these polygons. - If not provided, will be None and will use minimum bounding box of the starlist positions. Shape of (N_lists, N_vertices, 2) in the format of [[x1, y1], [x2, y2], ..., [xN, yN]] for each starlist, by default None iters : int @@ -1770,10 +1774,6 @@ def __init__( The outlier tolerance (in units of sigma) for rejecting outlier stars. This is a list of tol values, one for each iteration of matching/transformation. - briteN : int - If init_guess_mode is 'miracle', this is the number of brightest stars to use in the miracle match. - Default is min(50, len(star_list)). - use_ref_new : boolean Each pass, new stars are matched and added to the ref_table. However, we don't necessarily want to use these in the reference frame in subsequent passes. @@ -1822,6 +1822,10 @@ def = None. If not None, then this should contain an array or list of transform to make the initial transformation and matching blindly. We can do this in a couple of different ways. Options are 'miracle' or 'name' (see trans_initial_guess() for more details). + briteN : int + If init_guess_mode is 'miracle', this is the number of brightest stars to use in the miracle match. + Default is min(50, len(star_list)). + calc_trans_inverse: boolean If true, then calculate the inverse transformation (from reference to starlist) in addition to the normal transformation (from starlist to reference). The inverse @@ -1913,14 +1917,12 @@ def = None. If not None, then this should contain an array or list of transform """ super().__init__( list_of_starlists, - starlist_vertices=starlist_vertices, # Alignment parameters ref_index=-1, iters=iters, dr_tol=dr_tol, dm_tol=dm_tol, outlier_tol=outlier_tol, - briteN=briteN, # Transformation parameters trans_class=trans_class, trans_args=trans_args, @@ -1928,13 +1930,14 @@ def = None. If not None, then this should contain an array or list of transform trans_weighting=trans_weighting, init_order=init_order, init_guess_mode=init_guess_mode, + briteN=briteN, calc_trans_inverse=calc_trans_inverse, # Magnitude parameters mag_trans=mag_trans, mag_lim=mag_lim, # Motion model parameters motion_models=motion_models, - motion_model_for_new_star=motion_model_for_new_star, + # motion_model_for_new_star=motion_model_for_new_star, fixed_params_dict=fixed_params_dict, vel_weighting=vel_weighting, use_scipy=use_scipy, @@ -1946,11 +1949,18 @@ def = None. If not None, then this should contain an array or list of transform verbose=verbose ) + self.starlist_vertices = starlist_vertices self.ref_list = copy.deepcopy(ref_list) self.ref_mag_lim = ref_mag_lim self.update_ref_orig = update_ref_orig self.use_ref_new = use_ref_new + if reflist_vertex is not None: + import shapely + self.reflist_polygon = shapely.make_valid(shapely.Polygon(reflist_vertex)) + else: + self.reflist_polygon = None + # If motion_model_used in columns but params columns are missing, raise a warning and remove motion_model_used column to avoid confusion. # if 'motion_model_used' in self.ref_list.colnames: # motion_model_params = motion_model.motion_model_param_names(np.unique(self.ref_list['motion_model_used']), with_errors=False, with_fixed=True) @@ -2243,7 +2253,7 @@ def infer_positions(t, startable, motion_models=None, fixed_params_dict=None, re # Otherwise, infer positions using the most complex motion model with the existing columns, until it reaches Fixed or Empty # Sort motion models inversely by mm.n_params - motion_models = sorted(motion_models, key=lambda mm: mm.n_params, reverse=True) + motion_models = sorted(motion_models, key=lambda mm: mm.required_epochs, reverse=True) for mm in motion_models: if mm.name == 'Empty': x = startable['x'] From cf29f752d090560c811865b9e9e55f848cef88bf Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Wed, 17 Jun 2026 12:54:05 -0700 Subject: [PATCH 212/254] Update variable names in example notebook --- .../examples/motion_model_example.ipynb | 2 +- flystar/tests/test_align.py | 48 +++++++++---------- 2 files changed, 25 insertions(+), 25 deletions(-) diff --git a/docs/flystar/examples/motion_model_example.ipynb b/docs/flystar/examples/motion_model_example.ipynb index 4877b5c..b3de4b9 100644 --- a/docs/flystar/examples/motion_model_example.ipynb +++ b/docs/flystar/examples/motion_model_example.ipynb @@ -55,7 +55,7 @@ "id": "faddd6d8", "metadata": {}, "source": [ - "| Motion Model | n_params | params | fixed_params | model | Description |\n", + "| Motion Model | required params | fit_params | fixed_params | model | Description |\n", "|--------------|----------|--------------------------------------------|-------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n", "| Empty | 0 | NA | NA | $x(t) = $ NaN / fill_value
$x_e(t) = $ Inf | |\n", "| Fixed | 1 | $x_0$
$y_0$ | NA | $x(t) = $ np.average($x$, weights=$x_{wt}$) | $x_{wt} = 1/xe^2$ if weighting='var'
$x_{wt} = 1/\\|xe\\|$ if weighting = 'std' |\n", diff --git a/flystar/tests/test_align.py b/flystar/tests/test_align.py index faa0ff2..5f24208 100644 --- a/flystar/tests/test_align.py +++ b/flystar/tests/test_align.py @@ -1439,27 +1439,27 @@ def make_fake_starlists_poly1_par(seed=-1): return (xy_trans, mag_trans) # if __name__ == '__main__': - # import pickle - # import matplotlib.pyplot as plt - # with open(f'{test_data_path}/my_gaia.pkl', 'rb') as f: - # my_gaia = pickle.load(f) - # with open(f'{test_data_path}/list_of_starlists.pkl', 'rb') as f: - # list_of_starlists = pickle.load(f) - # ra_deg, dec_deg = 18.0, -30.0 - # my_gaia.remove_column('motion_model_used') - # msc = align.MosaicToRef(my_gaia, list_of_starlists, iters=3, - # dr_tol=[0.2, 0.1, 0.08], dm_tol=[5,5,5], - # outlier_tol=[None, None, 3], mag_lim=[6, 20], - # trans_class=transforms.PolyTransform, - # trans_args=[{'order': 1}, {'order': 1}, {'order': 1}], - # motion_models=['Empty','Fixed','Linear','Parallax'], - # fixed_params_dict = {'ra':ra_deg, 'dec':dec_deg, 'pa':0.0, 'obsLocation':'earth'}, - # use_ref_new=True, - # update_ref_orig=False, - # mag_trans=True, - # trans_weighting='both,std', - # init_guess_mode='name', verbose=3) - # msc.fit() - # for i in range(msc.ref_table['x'].shape[1]): - # plt.scatter(msc.ref_table['x'][:, i], msc.ref_table['y'][:, i]) - # plt.show() \ No newline at end of file +# import pickle +# import matplotlib.pyplot as plt +# with open(f'{test_data_path}/my_gaia.pkl', 'rb') as f: +# my_gaia = pickle.load(f) +# with open(f'{test_data_path}/list_of_starlists.pkl', 'rb') as f: +# list_of_starlists = pickle.load(f) +# ra_deg, dec_deg = 18.0, -30.0 +# my_gaia.remove_column('motion_model_used') +# msc = align.MosaicToRef(my_gaia, list_of_starlists, iters=3, +# dr_tol=[0.2, 0.1, 0.08], dm_tol=[5,5,5], +# outlier_tol=[None, None, 3], mag_lim=[6, 20], +# trans_class=transforms.PolyTransform, +# trans_args=[{'order': 1}, {'order': 1}, {'order': 1}], +# motion_models=['Linear','Parallax'], +# fixed_params_dict = {'ra':ra_deg, 'dec':dec_deg, 'pa':0.0, 'obsLocation':'earth'}, +# use_ref_new=True, +# update_ref_orig=False, +# mag_trans=True, +# trans_weighting='both,std', +# init_guess_mode='name', verbose=3) +# msc.fit() +# for i in range(msc.ref_table['x'].shape[1]): +# plt.scatter(msc.ref_table['x'][:, i], msc.ref_table['y'][:, i]) +# plt.show() \ No newline at end of file From a7d1e6be981f2c4234e0dbe6345743116a1a143d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Thu, 18 Jun 2026 00:08:28 -0700 Subject: [PATCH 213/254] Fix duplicate columns; Fix plotting error --- flystar/align.py | 54 +++++++++++++++++++++++++------------ flystar/analysis.py | 4 +++ flystar/plots.py | 10 +++---- flystar/startables.py | 7 +++-- flystar/tests/test_align.py | 52 ++++++++++++++++++----------------- 5 files changed, 78 insertions(+), 49 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 23a3ebe..53a5413 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -976,7 +976,8 @@ def update_ref_table_from_list(self, star_list, star_list_T, ii, idx_ref, idx_li star_list, idx_lis, # motion_model_name=self.motion_model_for_new_star.name - motion_model_name=self.motion_models[-1].name + motion_model_name=self.motion_models[-1].name, + fixed_params_dict=self.fixed_params_dict ) if len(idx_ref_new) > 0: @@ -1029,6 +1030,8 @@ def update_ref_table_aggregates(self, keep_orig=None, n_boot=0): if 'motion_model_used' in self.ref_table.keys(): motion_model_class_names += self.ref_table['motion_model_used'][keep_orig].tolist() vals_orig['motion_model_used'] = self.ref_table['motion_model_used'][keep_orig] + vals_orig['required_epochs'] = self.ref_table['required_epochs'][keep_orig] + # vals_orig['n_fit'] = self.ref_table['n_fit'][keep_orig] motion_model_col_names = motion_model.motion_model_param_names(motion_model_class_names, with_errors=True, with_fixed=True) for mm in motion_model_col_names: if f'{mm}_mm' in self.ref_table.keys(): @@ -1071,7 +1074,7 @@ def update_ref_table_aggregates(self, keep_orig=None, n_boot=0): # if (keep_orig is not None) and (sum(keep_orig) > 0): # Determine motion_model_used for keep_orig stars # Filter possible motion models based on available columns - motion_model_used, n_fit = determine_motion_model(self.ref_table, self.motion_models, self.fixed_params_dict) + motion_model_used, required_epochs = determine_motion_model(self.ref_table, self.motion_models, self.fixed_params_dict) # motion_models_possible = [] # for mm in self.motion_models: @@ -1096,7 +1099,8 @@ def update_ref_table_aggregates(self, keep_orig=None, n_boot=0): # Assign the determined motion models # self.ref_table['motion_model_used'][keep_orig] = motion_model_used self.ref_table['motion_model_used'] = Column(motion_model_used, name='motion_model_used', dtype='U20') - self.ref_table['n_fit'] = Column(n_fit, name='n_fit', dtype=int) + # self.ref_table['n_fit'] = Column(required_epochs, name='n_fit', dtype=int) + self.ref_table['required_epochs'] = Column(required_epochs, name='required_epochs', dtype=int) # Replace the originals if we are supposed to keep them fixed. if (keep_orig is not None) and (sum(keep_orig) > 0): @@ -1212,9 +1216,10 @@ def get_ref_list_from_table(self, epoch): # Calculate x, y, xe, ye if 'motion_model_used' not in self.ref_table.colnames: - motion_model_used, n_fit = determine_motion_model(self.ref_table) + motion_model_used, required_epochs = determine_motion_model(self.ref_table) self.ref_table['motion_model_used'] = Column(motion_model_used, name='motion_model_used', dtype='U20') - self.ref_table['n_fit'] = Column(n_fit, name='n_fit', dtype=int) + # self.ref_table['n_fit'] = Column(required_epochs, name='n_fit', dtype=int) + self.ref_table['required_epochs'] = Column(required_epochs, name='required_epochs', dtype=int) x, y, xe, ye = self.ref_table.infer_positions(epoch, fixed_params_dict=self.fixed_params_dict) # else: @@ -2308,8 +2313,8 @@ def determine_motion_model(startable, motion_models=None, fixed_params_dict=None ------- motion_model_used : list List of motion model used for each star - n_fit : list - List of number of number of observations used to fit for each star + required_epochs : list + List of required epochs for each star """ if motion_models is None: @@ -2331,7 +2336,7 @@ def determine_motion_model(startable, motion_models=None, fixed_params_dict=None # Check if values are finite for required columns in possible motion models motion_model_used = [] - n_fit = [] + required_epochs = [] for k in range(len(startable)): for mm, req_col_in_table, req_col_in_dict in motion_models_possible[::-1]: @@ -2339,9 +2344,9 @@ def determine_motion_model(startable, motion_models=None, fixed_params_dict=None if all(np.isfinite(startable[col][k]) for col in req_col_in_table if np.issubdtype(startable[col].dtype, np.number)) \ and all(np.isfinite(fixed_params_dict[col]) for col in req_col_in_dict if np.issubdtype(np.array(fixed_params_dict[col]).dtype, np.number)): motion_model_used.append(mm.name) - n_fit.append(mm.n_params) + required_epochs.append(mm.required_epochs) break - return motion_model_used, n_fit + return motion_model_used, required_epochs def get_all_epochs(t): @@ -2488,7 +2493,7 @@ def reset_ref_values(ref_table): return -def add_rows_for_new_stars(ref_table, star_list, idx_list, motion_model_name='Fixed'): +def add_rows_for_new_stars(ref_table, star_list, idx_list, motion_model_name='Fixed', fixed_params_dict=None): """ For each star that is in star_list and NOT in idx_list, make a new row in the reference table. The values will be empty (None, NAN, etc.). @@ -2504,6 +2509,8 @@ def add_rows_for_new_stars(ref_table, star_list, idx_list, motion_model_name='Fi of this array will be used as the new stars. motion_model_name : str The motion model name to assign to the new stars. + fixed_params_dict : dict + The default fixed parameters to assign to the new stars. Returns ---------- @@ -2521,22 +2528,35 @@ def add_rows_for_new_stars(ref_table, star_list, idx_list, motion_model_name='Fi idx_lis_new = np.array(list(set(idx_lis_orig) - set(idx_list))) N_newstars = len(idx_lis_new) + mm_map = motion_model.motion_model_map() + mm = mm_map[motion_model_name] + + # Add optional fixed params default values into fixed params dict, prioritizing values in fixed_params_dict + if fixed_params_dict is not None: + fixed_params_dict.update({k: v for k, v in mm.optional_fixed_params.items() if k not in fixed_params_dict}) + else: + fixed_params_dict = mm.optional_fixed_params.copy() + if N_newstars > 0: col_arrays = {} for col_name in ref_table.colnames: new_col_name = col_name - - if ref_table[col_name].dtype == np.dtype('float'): + + if col_name in fixed_params_dict.keys(): + new_col_empty = fixed_params_dict[col_name] + elif col_name=='required_epochs': + new_col_empty = mm.required_epochs + elif col_name=='motion_model_input': + new_col_empty = motion_model_name + elif col_name=='motion_model_used': + new_col_empty = 'Empty' + elif ref_table[col_name].dtype == np.dtype('float'): new_col_empty = np.nan elif ref_table[col_name].dtype == np.dtype('int'): new_col_empty = -1 elif ref_table[col_name].dtype == np.dtype('bool'): new_col_empty = False - elif col_name=='motion_model_input': - new_col_empty = motion_model_name - elif col_name=='motion_model_used': - new_col_empty = 'Empty' else: new_col_empty = np.nan diff --git a/flystar/analysis.py b/flystar/analysis.py index 0a1397c..7deaa36 100644 --- a/flystar/analysis.py +++ b/flystar/analysis.py @@ -186,6 +186,10 @@ def prepare_gaia_for_flystar(gaia, ra, dec, targets_dict=None, match_dr_max=0.2, gaia_new['motion_model_input'] = 'Fixed' gaia_new['motion_model_used'] = 'Fixed' gaia_new['n_params'] = 1 + elif default_motion_model=='Empty': + gaia_new['motion_model_input'] = 'Empty' + gaia_new['motion_model_used'] = 'Empty' + gaia_new['n_params'] = 0 else: print("Invalid motion model",default_motion_model,"- none assigned") diff --git a/flystar/plots.py b/flystar/plots.py index 7213174..083676d 100755 --- a/flystar/plots.py +++ b/flystar/plots.py @@ -2236,9 +2236,9 @@ def plot_chi2_dist(tab, Ndetect, xlim=40, n_bins=50, boot_err=False): idx = np.where(fnd == Ndetect)[0] # Fitting position and velocity... so subtract 2 to get Ndof - n_params = np.nanmean(tab['n_params'][idx]) + n_params = np.nanmean(tab['required_epochs'][idx]) Ndof = Ndetect - n_params - if len(np.unique(tab['n_params'][idx]))>1: + if len(np.unique(tab['required_epochs'][idx]))>1: print("** Warning: using average Ndof for multiple motion models. **") print("** Consider using plot_chi2_reduced_dist. **") print(f"Ndof={Ndof:.2f}, Ndetect={Ndetect}, Nparams={n_params:.2f}") @@ -2321,7 +2321,7 @@ def plot_chi2_reduced_dist(tab, Ndetect, xlim=8, n_bins=50, boot_err=False): fnd = np.array(fnd_list) idx = np.where(fnd == Ndetect)[0] - n_params = tab['n_params'] + n_params = tab['required_epochs'] Ndof = Ndetect - n_params print("Reduced chi2 for Ndetect="+str(Ndetect)) chi2_bins = np.linspace(0, xlim, n_bins) @@ -2405,7 +2405,7 @@ def plot_chi2_dist_per_filter(tab, Ndetect, xlim=40, n_bins=50, filter=None, boo idx = np.where(fnd == Ndetect)[0] # Fitting position and velocity... so subtract n_params to get Ndof - n_params = np.nanmean(tab['n_params'][idx]) + n_params = np.nanmean(tab['required_epochs'][idx]) Ndof = Ndetect - n_params print(f"Ndof={Ndof}, Ndetect={Ndetect}, Nparams={n_params}") chi2_xaxis = np.linspace(0, xlim, xlim*3) @@ -2871,7 +2871,7 @@ def rs(x): chi2_y = np.sum(sigY**2) chi2_m = np.sum(sigM**2) - dof = (len(tab['x'][ii])-tab['n_params'][ii]).astype(int) + dof = (len(tab['x'][ii])-tab['required_epochs'][ii]).astype(int) dofM = len(m) - 1 chi2_red_x = chi2_x / dof diff --git a/flystar/startables.py b/flystar/startables.py index dc847aa..d4377fa 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -535,7 +535,7 @@ def detections(self): """ Find where stars are detected. # """ - n_detect = np.sum(~np.isnan(self['x']), axis=1) + n_detect = np.sum(np.isfinite(self['x']) & np.isfinite(self['y']), axis=1) if 'n_detect' in self.colnames: self['n_detect'] = n_detect @@ -972,8 +972,11 @@ def fit_motion_models( self[param_name + '_err'][unique_index] = param_errs_array[:, j] self['chi2_x'][unique_index] = chi2_x_array self['chi2_y'][unique_index] = chi2_y_array - self['required_epochs'][unique_index] = motion_model_instance.required_epochs self['t0'][unique_index] = t0[unique_index] + + # Update required_epochs regardless of selections + for mm in motion_model_used: + self['required_epochs'][self['motion_model_used'] == mm.name] = mm.required_epochs return def infer_positions(self, times, fixed_params_dict=None, fill_value=np.nan): diff --git a/flystar/tests/test_align.py b/flystar/tests/test_align.py index 5f24208..b914200 100644 --- a/flystar/tests/test_align.py +++ b/flystar/tests/test_align.py @@ -3,6 +3,7 @@ import numpy as np import matplotlib.pyplot as plt from astropy.table import Table +from flystar.plots import plot_stars from flystar import align, starlists, transforms, analysis, motion_model test_data_path = f'{flystar.__path__[0]}/tests/test_data' @@ -1438,28 +1439,29 @@ def make_fake_starlists_poly1_par(seed=-1): return (xy_trans, mag_trans) -# if __name__ == '__main__': -# import pickle -# import matplotlib.pyplot as plt -# with open(f'{test_data_path}/my_gaia.pkl', 'rb') as f: -# my_gaia = pickle.load(f) -# with open(f'{test_data_path}/list_of_starlists.pkl', 'rb') as f: -# list_of_starlists = pickle.load(f) -# ra_deg, dec_deg = 18.0, -30.0 -# my_gaia.remove_column('motion_model_used') -# msc = align.MosaicToRef(my_gaia, list_of_starlists, iters=3, -# dr_tol=[0.2, 0.1, 0.08], dm_tol=[5,5,5], -# outlier_tol=[None, None, 3], mag_lim=[6, 20], -# trans_class=transforms.PolyTransform, -# trans_args=[{'order': 1}, {'order': 1}, {'order': 1}], -# motion_models=['Linear','Parallax'], -# fixed_params_dict = {'ra':ra_deg, 'dec':dec_deg, 'pa':0.0, 'obsLocation':'earth'}, -# use_ref_new=True, -# update_ref_orig=False, -# mag_trans=True, -# trans_weighting='both,std', -# init_guess_mode='name', verbose=3) -# msc.fit() -# for i in range(msc.ref_table['x'].shape[1]): -# plt.scatter(msc.ref_table['x'][:, i], msc.ref_table['y'][:, i]) -# plt.show() \ No newline at end of file +if __name__ == '__main__': + import pickle + import matplotlib.pyplot as plt + with open(f'{test_data_path}/my_gaia.pkl', 'rb') as f: + my_gaia = pickle.load(f) + with open(f'{test_data_path}/list_of_starlists.pkl', 'rb') as f: + list_of_starlists = pickle.load(f) + ra_deg, dec_deg = 18.0, -30.0 + my_gaia.remove_column('motion_model_used') + msc = align.MosaicToRef(my_gaia, list_of_starlists, iters=3, + dr_tol=[0.2, 0.1, 0.08], dm_tol=[5,5,5], + outlier_tol=[None, None, 3], mag_lim=[6, 20], + trans_class=transforms.PolyTransform, + trans_args=[{'order': 1}, {'order': 1}, {'order': 1}], + motion_models=['Linear','Parallax'], + fixed_params_dict = {'ra':ra_deg, 'dec':dec_deg, 'pa':0.0, 'obsLocation':'earth'}, + use_ref_new=True, + update_ref_orig=False, + mag_trans=True, + trans_weighting='both,std', + init_guess_mode='name', verbose=3) + msc.fit() + for i in range(msc.ref_table['x'].shape[1]): + plt.scatter(msc.ref_table['x'][:, i], msc.ref_table['y'][:, i]) + plt.show() + plot_stars(msc.ref_table, msc.ref_table['name'][:3]) \ No newline at end of file From d494c232d7e67593acb47cee777985a3a3fd8a60 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Thu, 18 Jun 2026 14:16:41 -0700 Subject: [PATCH 214/254] Revert n_params name and add n_fit_params attribute --- flystar/align.py | 54 ++++++------------- flystar/motion_model.py | 111 ++++++++++++++++++++-------------------- flystar/plots.py | 10 ++-- flystar/startables.py | 28 +++++----- 4 files changed, 91 insertions(+), 112 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 53a5413..7986bd8 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -275,7 +275,7 @@ def = None. If not None, then this should contain an array or list of transform motion_models.append(all_mm_map['Fixed']) # Sort by increasing n_params - motion_models = sorted(motion_models, key=lambda mm: mm.required_epochs) + motion_models = sorted(motion_models, key=lambda mm: mm.n_params) self.motion_models = motion_models # if motion_model_for_new_star is None: @@ -1030,7 +1030,7 @@ def update_ref_table_aggregates(self, keep_orig=None, n_boot=0): if 'motion_model_used' in self.ref_table.keys(): motion_model_class_names += self.ref_table['motion_model_used'][keep_orig].tolist() vals_orig['motion_model_used'] = self.ref_table['motion_model_used'][keep_orig] - vals_orig['required_epochs'] = self.ref_table['required_epochs'][keep_orig] + vals_orig['n_params'] = self.ref_table['n_params'][keep_orig] # vals_orig['n_fit'] = self.ref_table['n_fit'][keep_orig] motion_model_col_names = motion_model.motion_model_param_names(motion_model_class_names, with_errors=True, with_fixed=True) for mm in motion_model_col_names: @@ -1074,33 +1074,13 @@ def update_ref_table_aggregates(self, keep_orig=None, n_boot=0): # if (keep_orig is not None) and (sum(keep_orig) > 0): # Determine motion_model_used for keep_orig stars # Filter possible motion models based on available columns - motion_model_used, required_epochs = determine_motion_model(self.ref_table, self.motion_models, self.fixed_params_dict) - - # motion_models_possible = [] - # for mm in self.motion_models: - # required_columns = mm.fit_param_names + mm.fixed_param_names - # if all(col in self.ref_table.colnames or (self.fixed_params_dict is not None and col in self.fixed_params_dict.keys()) for col in required_columns): - # motion_models_possible.append((mm, required_columns)) - - # # Check if values are finite for required columns in possible motion models - # motion_model_used = [] - # # for k in np.where(keep_orig)[0]: - # for k in range(len(self.ref_table)): - # for mm, req in motion_models_possible[::-1]: - # # if all(np.isfinite(self.ref_table[k][col]) for col in req if self.ref_table[col].dtype.kind in 'f'): - # req_col_in_table = [col for col in req if col in self.ref_table.colnames] - # req_col_in_dict = [col for col in req if (self.fixed_params_dict is not None) and (col in self.fixed_params_dict.keys())] - # # If requested column in table/fixed_params dict is numeric, check if values are finite. - # if all(np.isfinite(self.ref_table[col][k]) for col in req_col_in_table if np.issubdtype(self.ref_table[col].dtype, np.number)) \ - # and all(np.isfinite(self.fixed_params_dict[col]) for col in req_col_in_dict if np.issubdtype(np.array(self.fixed_params_dict[col]).dtype, np.number)): - # motion_model_used.append(mm.name) - # break + motion_model_used, n_params = determine_motion_model(self.ref_table, self.motion_models, self.fixed_params_dict) # Assign the determined motion models # self.ref_table['motion_model_used'][keep_orig] = motion_model_used self.ref_table['motion_model_used'] = Column(motion_model_used, name='motion_model_used', dtype='U20') - # self.ref_table['n_fit'] = Column(required_epochs, name='n_fit', dtype=int) - self.ref_table['required_epochs'] = Column(required_epochs, name='required_epochs', dtype=int) + # self.ref_table['n_fit'] = Column(n_params, name='n_fit', dtype=int) + self.ref_table['n_params'] = Column(n_params, name='n_params', dtype=int) # Replace the originals if we are supposed to keep them fixed. if (keep_orig is not None) and (sum(keep_orig) > 0): @@ -1216,10 +1196,10 @@ def get_ref_list_from_table(self, epoch): # Calculate x, y, xe, ye if 'motion_model_used' not in self.ref_table.colnames: - motion_model_used, required_epochs = determine_motion_model(self.ref_table) + motion_model_used, n_params = determine_motion_model(self.ref_table) self.ref_table['motion_model_used'] = Column(motion_model_used, name='motion_model_used', dtype='U20') - # self.ref_table['n_fit'] = Column(required_epochs, name='n_fit', dtype=int) - self.ref_table['required_epochs'] = Column(required_epochs, name='required_epochs', dtype=int) + # self.ref_table['n_fit'] = Column(n_params, name='n_fit', dtype=int) + self.ref_table['n_params'] = Column(n_params, name='n_params', dtype=int) x, y, xe, ye = self.ref_table.infer_positions(epoch, fixed_params_dict=self.fixed_params_dict) # else: @@ -1420,7 +1400,7 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot all_mm_map = motion_model.motion_model_map() motion_model_list = [all_mm_map[mm_name] for mm_name in motion_model_list] - motion_boot_min_epochs = np.max([mm.required_epochs for mm in motion_model_list]) + motion_boot_min_epochs = np.max([mm.n_params for mm in motion_model_list]) ### IF MEMORY PROBLEMS HERE: ### DEFINE MEAN, STD VARIABLES AND BUILD THEM RATHER THAN SAVING FULL ARRAY @@ -2258,7 +2238,7 @@ def infer_positions(t, startable, motion_models=None, fixed_params_dict=None, re # Otherwise, infer positions using the most complex motion model with the existing columns, until it reaches Fixed or Empty # Sort motion models inversely by mm.n_params - motion_models = sorted(motion_models, key=lambda mm: mm.required_epochs, reverse=True) + motion_models = sorted(motion_models, key=lambda mm: mm.n_params, reverse=True) for mm in motion_models: if mm.name == 'Empty': x = startable['x'] @@ -2313,8 +2293,8 @@ def determine_motion_model(startable, motion_models=None, fixed_params_dict=None ------- motion_model_used : list List of motion model used for each star - required_epochs : list - List of required epochs for each star + n_params : list + List of n parameters per direction for each star """ if motion_models is None: @@ -2336,7 +2316,7 @@ def determine_motion_model(startable, motion_models=None, fixed_params_dict=None # Check if values are finite for required columns in possible motion models motion_model_used = [] - required_epochs = [] + n_params = [] for k in range(len(startable)): for mm, req_col_in_table, req_col_in_dict in motion_models_possible[::-1]: @@ -2344,9 +2324,9 @@ def determine_motion_model(startable, motion_models=None, fixed_params_dict=None if all(np.isfinite(startable[col][k]) for col in req_col_in_table if np.issubdtype(startable[col].dtype, np.number)) \ and all(np.isfinite(fixed_params_dict[col]) for col in req_col_in_dict if np.issubdtype(np.array(fixed_params_dict[col]).dtype, np.number)): motion_model_used.append(mm.name) - required_epochs.append(mm.required_epochs) + n_params.append(mm.n_params) break - return motion_model_used, required_epochs + return motion_model_used, n_params def get_all_epochs(t): @@ -2545,8 +2525,8 @@ def add_rows_for_new_stars(ref_table, star_list, idx_list, motion_model_name='Fi if col_name in fixed_params_dict.keys(): new_col_empty = fixed_params_dict[col_name] - elif col_name=='required_epochs': - new_col_empty = mm.required_epochs + elif col_name=='n_params': + new_col_empty = mm.n_params elif col_name=='motion_model_input': new_col_empty = motion_model_name elif col_name=='motion_model_used': diff --git a/flystar/motion_model.py b/flystar/motion_model.py index f75528d..778255a 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -10,10 +10,9 @@ class MotionModel(ABC): # Fit paramters: Shared fit parameters fit_param_names = [] - + n_fit_params = len(fit_param_names) # Number of fit parameters/required observations in each direction - n_params = len(fit_param_names) - required_epochs = int((n_params + 1) / 2) + n_params = int((n_fit_params + 1) / 2) # Fixed parameters: These are parameters that are required for the model, but are not # fit quantities. For example, RA and Dec in a parallax model. @@ -42,9 +41,9 @@ def _check_param_dimensions(self, fit_params, fit_params_errs, fixed_params_dict Parameters ---------- fit_params: array-like - Fit parameters, shape (N_params,) or (N_stars, N_params) + Fit parameters, shape (N_fit_params,) or (n_stars, N_fit_params) fit_params_errs: array-like - Errors of fit parameters, shape (N_params,) or (N_stars, N_params) + Errors of fit parameters, shape (N_fit_params,) or (n_stars, N_fit_params) fixed_params_dict : dict Dictionary of fixed parameters """ @@ -83,8 +82,8 @@ def run_fit( ): # Run a single fit (used both for overall fit + bootstrap iterations) if return_chi2: - return np.full(self.n_params, fill_value), np.full(self.n_params, np.inf), np.nan, np.nan - return np.full(self.n_params, fill_value), np.full(self.n_params, np.inf) + return np.full(self.n_fit_params, fill_value), np.full(self.n_fit_params, np.inf), np.nan, np.nan + return np.full(self.n_fit_params, fill_value), np.full(self.n_fit_params, np.inf) def calc_weights(self, xe, ye, weighting='var'): if weighting=='std': @@ -174,18 +173,18 @@ def fit( # Bootstrap errors n_obs = len(t) - if (bootstrap > 0) and (n_obs > self.required_epochs): + if (bootstrap > 0) and (n_obs > self.n_params): rng = np.random.default_rng(seed) edx = np.arange(n_obs, dtype=int) # Precompute All Bootstrap Draws at Once # Ensure there are enough unique points in each bootstrap sample bdx_unique = np.stack([ - rng.choice(edx, size=self.required_epochs, replace=False) + rng.choice(edx, size=self.n_params, replace=False) for _ in range(bootstrap) ]) # Draw with replacement for the rest bdx_extra = np.stack([ - rng.choice(edx, size=n_obs - self.required_epochs, replace=True) + rng.choice(edx, size=n_obs - self.n_params, replace=True) for _ in range(bootstrap) ]) bdx_all = np.hstack((bdx_unique, bdx_extra)) @@ -235,10 +234,10 @@ def fit( # chi2_y = residual_y.T @ W_mat_y @ residual_y # if reduced: - # if len(dt) == self.required_epochs: + # if len(dt) == self.n_params: # return np.inf, np.inf # if not parallax: - # degree_of_freedom = len(x) - self.required_epochs + # degree_of_freedom = len(x) - self.n_params # else: # degree_of_freedom = 2*len(x) - len(self.fit_param_names) # chi2_x, chi2_y = chi2_x / degree_of_freedom, chi2_y / degree_of_freedom @@ -252,10 +251,10 @@ def calc_chi2(self, t, x, y, xe, ye, fit_params, fixed_params_dict=None, reduced chi2x = np.sum((x - x_pred)**2 / xe**2) chi2y = np.sum((y - y_pred)**2 / ye**2) if reduced: - if len(t) == self.required_epochs: + if len(t) == self.n_params: return np.inf, np.inf if not parallax: - degree_of_freedom = len(x) - self.required_epochs + degree_of_freedom = len(x) - self.n_params else: degree_of_freedom = 2*len(x) - len(self.fit_param_names) chi2x, chi2y = chi2x / degree_of_freedom, chi2y / degree_of_freedom @@ -268,9 +267,9 @@ class Empty(MotionModel): required_fixed_param_names = [] optional_fixed_params = {} + n_fit_params = len(fit_param_names) # Number of fit parameters/required observations in each direction - n_params = len(fit_param_names) - required_epochs = int((n_params + 1) / 2) + n_params = int((n_fit_params + 1) / 2) def __init__(self, **kwargs): """Empty motion model, returns nan for values and inf for uncertainties. @@ -289,7 +288,7 @@ def model(self, t, fit_params, fit_param_errs=None, fixed_params_dict=None): t : float or array-like Time array, shape (N_times,) fit_params : array-like - Fit parameters, shape (N_params,) or (N_stars, N_params) + Fit parameters, shape (N_fit_params,) or (N_stars, N_fit_params) fit_param_errs : array-like, optional Uncertainties for fit parameters, not applicable for Empty model, by default None fixed_params_dict : dict, optional @@ -303,7 +302,7 @@ def model(self, t, fit_params, fit_param_errs=None, fixed_params_dict=None): self._check_param_dimensions(fit_params, fit_param_errs, fixed_params_dict) t = np.atleast_1d(t) - fit_params = np.atleast_2d(fit_params) # (N_stars, N_params) + fit_params = np.atleast_2d(fit_params) # (N_stars, N_fit_params) N_stars = fit_params.shape[0] N_times = len(t) @@ -370,8 +369,8 @@ def run_fit( self.fixed_params_dict = fixed_params_dict if verbose: warnings.warn(f"Empty data cannot be fit. Setting parameters to {fill_value} and uncertainties to np.inf.", OptimizeWarning, stacklevel=2) - params = np.full(self.n_params, fill_value) - param_errors = np.full(self.n_params, np.inf) + params = np.full(self.n_fit_params, fill_value) + param_errors = np.full(self.n_fit_params, np.inf) if return_chi2: return params, param_errors, np.nan, np.nan else: @@ -388,9 +387,9 @@ class Fixed(MotionModel): required_fixed_param_names = [] optional_fixed_params = {} + n_fit_params = len(fit_param_names) # Number of fit parameters/required observations in each direction - n_params = len(fit_param_names) - required_epochs = int((n_params + 1) / 2) + n_params = int((n_fit_params + 1) / 2) def __init__(self, **kwargs): # Must call after setting parameters. @@ -423,9 +422,9 @@ def model(self, t, fit_params, fit_param_errs=None, fixed_params_dict=None): t : float or array-like Time array, shape (N_times,) fit_params : array-like - x0, y0 in shape (N_params,) or (N_stars, N_params) + x0, y0 in shape (N_fit_params,) or (N_stars, N_fit_params) fit_param_errs : array-like, optional - Uncertainties for x0, y0 in shape (N_params,) or (N_stars, N_params), by default None + Uncertainties for x0, y0 in shape (N_fit_params,) or (N_stars, N_fit_params), by default None fixed_params_dict : dict, optional Not applicable for Fixed, by default None @@ -437,7 +436,7 @@ def model(self, t, fit_params, fit_param_errs=None, fixed_params_dict=None): """ self.fixed_params_dict = fixed_params_dict t = np.atleast_1d(t) - fit_params = np.atleast_2d(fit_params) # (N_stars, N_params) + fit_params = np.atleast_2d(fit_params) # (N_stars, N_fit_params) self._check_param_dimensions(fit_params, fit_param_errs, fixed_params_dict) N_stars = fit_params.shape[0] @@ -465,7 +464,7 @@ def model(self, t, fit_params, fit_param_errs=None, fixed_params_dict=None): if fit_param_errs is None: return x, y - fit_param_errs = np.atleast_2d(fit_param_errs) # (N_stars, N_params) + fit_param_errs = np.atleast_2d(fit_param_errs) # (N_stars, N_fit_params) x0_err, y0_err = fit_param_errs.T # Return results in (N_stars, N_times) shape @@ -494,15 +493,15 @@ def run_fit( warnings.warn("Fixed model has no non-scipy fitter option. Running with scipy.") n_obs = len(t) - degree_of_freedom = n_obs - self.required_epochs + degree_of_freedom = n_obs - self.n_params # Not enough data points to fit model if degree_of_freedom < 0: warnings.warn( f'Not enough data points to fit model. Setting parameters to {fill_value} and uncertainties to np.inf.', OptimizeWarning, stacklevel=2 ) - params = np.full(self.n_params, fill_value) - param_errors = np.full(self.n_params, np.inf) + params = np.full(self.n_fit_params, fill_value) + param_errors = np.full(self.n_fit_params, np.inf) return params, param_errors, np.nan, np.nan # degree_of_freedom >= 0 @@ -552,9 +551,9 @@ class Linear(MotionModel): optional_fixed_params = {} fixed_param_names = required_fixed_param_names + list(optional_fixed_params.keys()) + n_fit_params = len(fit_param_names) # Number of fit parameters/required observations in each direction - n_params = len(fit_param_names) - required_epochs = int((n_params + 1) / 2) + n_params = int((n_fit_params + 1) / 2) def __init__(self, **kwargs): # Must call after setting parameters. @@ -589,9 +588,9 @@ def model(self, t, fit_params, fit_param_errs=None, fixed_params_dict=None): t : float or array-like Time(s) at which to evaluate the model fit_params : array-like - x0, vx, y0, vy in shape (N_params,) or (N_stars, N_params) + x0, vx, y0, vy in shape (N_fit_params,) or (N_stars, N_fit_params) fit_param_errs : array-like, optional - Uncertainties of fit parameters in shape (N_params,) or (N_stars, N_params), by default None + Uncertainties of fit parameters in shape (N_fit_params,) or (N_stars, N_fit_params), by default None fixed_params_dict : dict t0, shape (1,) or (N_stars,) @@ -606,7 +605,7 @@ def model(self, t, fit_params, fit_param_errs=None, fixed_params_dict=None): self._check_param_dimensions(fit_params, fit_param_errs, fixed_params_dict) t = np.atleast_1d(t) - fit_params = np.atleast_2d(fit_params) # (N_stars, N_params) + fit_params = np.atleast_2d(fit_params) # (N_stars, N_fit_params) N_stars = fit_params.shape[0] N_times = len(t) @@ -633,7 +632,7 @@ def model(self, t, fit_params, fit_param_errs=None, fixed_params_dict=None): if fit_param_errs is None: return x, y - fit_param_errs = np.atleast_2d(fit_param_errs) # (N_stars, N_params) + fit_param_errs = np.atleast_2d(fit_param_errs) # (N_stars, N_fit_params) x0_err, vx_err, y0_err, vy_err = fit_param_errs.T # Each shape (N_stars,) x_err = np.hypot(x0_err[:, np.newaxis], vx_err[:, np.newaxis] * dt) # Shape (N_stars, N_times) y_err = np.hypot(y0_err[:, np.newaxis], vy_err[:, np.newaxis] * dt) # Shape (N_stars, N_times) @@ -669,15 +668,15 @@ def run_fit( ye = np.atleast_1d(ye) n_obs = len(t) - degree_of_freedom = n_obs - self.required_epochs + degree_of_freedom = n_obs - self.n_params # Not enough data points to fit model if degree_of_freedom < 0: warnings.warn( f'Not enough data points to fit model. Setting parameters to {fill_value} and uncertainties to np.inf.', OptimizeWarning, stacklevel=2 ) - params = np.full(self.n_params, fill_value) - param_errors = np.full(self.n_params, np.inf) + params = np.full(self.n_fit_params, fill_value) + param_errors = np.full(self.n_fit_params, np.inf) if return_chi2: return params, param_errors, np.nan, np.nan else: @@ -791,9 +790,9 @@ class Acceleration(MotionModel): optional_fixed_params = {} fixed_param_names = required_fixed_param_names + list(optional_fixed_params.keys()) + n_fit_params = len(fit_param_names) # Number of required observations in each direction - n_params = len(fit_param_names) - required_epochs = int((n_params + 1) / 2) + n_params = int((n_fit_params + 1) / 2) def __init__(self): # Must call after setting parameters. @@ -830,9 +829,9 @@ def model(self, t, fit_params, fit_param_errs=None, fixed_params_dict=None): t : float or array-like Time(s) at which to evaluate the model fit_params : array-like - x0, vx, ax, y0, vy, ay in shape (N_params,) or (N_stars, N_params) + x0, vx, ax, y0, vy, ay in shape (N_fit_params,) or (N_stars, N_fit_params) fit_param_errs : array-like, optional - Fit parameter uncertainties with shape (N_stars, N_params) or (N_params,), by default None + Fit parameter uncertainties with shape (N_stars, N_fit_params) or (N_fit_params,), by default None fixed_params_dict : dict t0, shape (1,) or (N_stars,) @@ -847,7 +846,7 @@ def model(self, t, fit_params, fit_param_errs=None, fixed_params_dict=None): self._check_param_dimensions(fit_params, fit_param_errs, fixed_params_dict) t = np.atleast_1d(t) - fit_params = np.atleast_2d(fit_params) # (N_stars, N_params) + fit_params = np.atleast_2d(fit_params) # (N_stars, N_fit_params) N_stars = fit_params.shape[0] N_times = len(t) @@ -874,7 +873,7 @@ def model(self, t, fit_params, fit_param_errs=None, fixed_params_dict=None): if fit_param_errs is None: return x, y - fit_param_errs = np.atleast_2d(fit_param_errs) # (N_stars, N_params) + fit_param_errs = np.atleast_2d(fit_param_errs) # (N_stars, N_fit_params) x0_err, vx0_err, ax_err, y0_err, vy0_err, ay_err = fit_param_errs.T x_err = np.sqrt(x0_err[:, np.newaxis]**2 + (vx0_err[:, np.newaxis] * dt)**2 + (0.5 * ax_err[:, np.newaxis] * dt**2)**2) # Shape (N_stars, N_times) y_err = np.sqrt(y0_err[:, np.newaxis]**2 + (vy0_err[:, np.newaxis] * dt)**2 + (0.5 * ay_err[:, np.newaxis] * dt**2)**2) # Shape (N_stars, N_times) @@ -916,15 +915,15 @@ def run_fit( warnings.warn("Acceleration model has no non-scipy fitter option. Running with scipy.") n_obs = len(t) - degree_of_freedom = n_obs - self.required_epochs + degree_of_freedom = n_obs - self.n_params # Not enough data points to fit model if degree_of_freedom < 0: warnings.warn( f'Not enough data points to fit model. Setting parameters to {fill_value} and uncertainties to np.inf.', OptimizeWarning, stacklevel=2 ) - params = np.full(self.n_params, fill_value) - param_errors = np.full(self.n_params, np.inf) + params = np.full(self.n_fit_params, fill_value) + param_errors = np.full(self.n_fit_params, np.inf) if return_chi2: return params, param_errors, np.nan, np.nan else: @@ -971,9 +970,9 @@ class Parallax(MotionModel): fixed_param_names = required_fixed_param_names + list(optional_fixed_params.keys()) + n_fit_params = len(fit_param_names) # Number of required observations in each direction - n_params = len(fit_param_names) - required_epochs = int((n_params + 1) / 2) + n_params = int((n_fit_params + 1) / 2) def __init__(self): super().__init__() @@ -1066,7 +1065,7 @@ def model(self, t, fit_params, fit_param_errs=None, fixed_params_dict=None): t : float or array-like Times at which to evaluate the model fit_params : array-like - x0, vx, y0, vy, pi in shape (N_params,) or (N_stars, N_params) + x0, vx, y0, vy, pi in shape (N_fit_params,) or (N_stars, N_fit_params) fit_param_errs : array-like, optional Uncertainties in fit parameters, by default None fixed_params : dict @@ -1087,7 +1086,7 @@ def model(self, t, fit_params, fit_param_errs=None, fixed_params_dict=None): self._check_param_dimensions(fit_params, fit_param_errs, fixed_params_dict) t = np.atleast_1d(t) - fit_params = np.atleast_2d(fit_params) # (N_stars, N_params) + fit_params = np.atleast_2d(fit_params) # (N_stars, N_fit_params) N_stars = fit_params.shape[0] N_times = len(t) @@ -1125,7 +1124,7 @@ def model(self, t, fit_params, fit_param_errs=None, fixed_params_dict=None): if fit_param_errs is None: return x, y - fit_param_errs = np.atleast_2d(fit_param_errs) # (N_stars, N_params) + fit_param_errs = np.atleast_2d(fit_param_errs) # (N_stars, N_fit_params) x0_err, vx_err, y0_err, vy_err, pi_err = fit_param_errs.T x_err = np.sqrt(x0_err[:, np.newaxis]**2 + (vx_err[:, np.newaxis] * dt)**2 + (pi_err[:, np.newaxis] * self.pvec[:, 0, :])**2) # Shape (N_stars, N_times) y_err = np.sqrt(y0_err[:, np.newaxis]**2 + (vy_err[:, np.newaxis] * dt)**2 + (pi_err[:, np.newaxis] * self.pvec[:, 1, :])**2) # Shape (N_stars, N_times) @@ -1168,15 +1167,15 @@ def run_fit( obsLocation = fixed_params_dict['obsLocation'] n_fit = len(t) - degree_of_freedom = n_fit - self.required_epochs + degree_of_freedom = n_fit - self.n_params # Not enough data points to fit model if degree_of_freedom < 0: warnings.warn( f'Not enough data points to fit model. Setting parameters to {fill_value} and uncertainties to np.inf.', OptimizeWarning, stacklevel=2 ) - params = np.full(self.n_params, fill_value) - param_errors = np.full(self.n_params, np.inf) + params = np.full(self.n_fit_params, fill_value) + param_errors = np.full(self.n_fit_params, np.inf) if return_chi2: return params, param_errors, np.nan, np.nan else: @@ -1298,5 +1297,5 @@ def motion_model_map(): [(mm.__name__, mm) for mm in MotionModel.__subclasses__()] ) # Sort by required epochs - mm_map = dict(sorted(mm_map.items(), key=lambda item: item[1].required_epochs)) + mm_map = dict(sorted(mm_map.items(), key=lambda item: item[1].n_params)) return mm_map \ No newline at end of file diff --git a/flystar/plots.py b/flystar/plots.py index 083676d..7213174 100755 --- a/flystar/plots.py +++ b/flystar/plots.py @@ -2236,9 +2236,9 @@ def plot_chi2_dist(tab, Ndetect, xlim=40, n_bins=50, boot_err=False): idx = np.where(fnd == Ndetect)[0] # Fitting position and velocity... so subtract 2 to get Ndof - n_params = np.nanmean(tab['required_epochs'][idx]) + n_params = np.nanmean(tab['n_params'][idx]) Ndof = Ndetect - n_params - if len(np.unique(tab['required_epochs'][idx]))>1: + if len(np.unique(tab['n_params'][idx]))>1: print("** Warning: using average Ndof for multiple motion models. **") print("** Consider using plot_chi2_reduced_dist. **") print(f"Ndof={Ndof:.2f}, Ndetect={Ndetect}, Nparams={n_params:.2f}") @@ -2321,7 +2321,7 @@ def plot_chi2_reduced_dist(tab, Ndetect, xlim=8, n_bins=50, boot_err=False): fnd = np.array(fnd_list) idx = np.where(fnd == Ndetect)[0] - n_params = tab['required_epochs'] + n_params = tab['n_params'] Ndof = Ndetect - n_params print("Reduced chi2 for Ndetect="+str(Ndetect)) chi2_bins = np.linspace(0, xlim, n_bins) @@ -2405,7 +2405,7 @@ def plot_chi2_dist_per_filter(tab, Ndetect, xlim=40, n_bins=50, filter=None, boo idx = np.where(fnd == Ndetect)[0] # Fitting position and velocity... so subtract n_params to get Ndof - n_params = np.nanmean(tab['required_epochs'][idx]) + n_params = np.nanmean(tab['n_params'][idx]) Ndof = Ndetect - n_params print(f"Ndof={Ndof}, Ndetect={Ndetect}, Nparams={n_params}") chi2_xaxis = np.linspace(0, xlim, xlim*3) @@ -2871,7 +2871,7 @@ def rs(x): chi2_y = np.sum(sigY**2) chi2_m = np.sum(sigM**2) - dof = (len(tab['x'][ii])-tab['required_epochs'][ii]).astype(int) + dof = (len(tab['x'][ii])-tab['n_params'][ii]).astype(int) dofM = len(m) - 1 chi2_red_x = chi2_x / dof diff --git a/flystar/startables.py b/flystar/startables.py index d4377fa..7a245ca 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -567,12 +567,12 @@ def fit_motion_models( Empty and Fixed models are always added automatically for stars with n_fit = 0 or 1. The behavior is as follows: 1. If 'motion_model_input' column is NOT in table: - - Use the most complex model that has enough parameters to fit the data (n_fit >= required_epochs). + - Use the most complex model that has enough parameters to fit the data (n_fit >= n_params). - If multiple models are supplied, prioritize the model with the most parameters to fit. - If multiple models have the same number of parameters, raise AssertionError: not sure which to use. 2. If 'motion_model_input' column IS in table: - Use the model specified in the 'motion_model_input' column. - - If not enough data points to fit the specified model, use the most complex model in any 'motion_model_input' column that has enough parameters to fit the data (n_fit >= required_epochs) among the provided motion_models and 'motion_model_input'. + - If not enough data points to fit the specified model, use the most complex model in any 'motion_model_input' column that has enough parameters to fit the data (n_fit >= n_params) among the provided motion_models and 'motion_model_input'. The actual used motion model is stored in the 'motion_model_used' column. The default motion_models are [Empty, Fixed, Linear]. fixed_params_dict : dict, optional Dictionary of fixed parameters for motion models, e.g., {'t0': 0., 'ra': np.array([...]), 'dec': np.array([...])}. @@ -657,16 +657,16 @@ def fit_motion_models( motion_models.append(all_mm_map[mm_name]) # Sort motion models by required epochs - motion_models = sorted(motion_models, key=lambda mm: mm.required_epochs) + motion_models = sorted(motion_models, key=lambda mm: mm.n_params) input_mm_map = {mm.name: mm for mm in motion_models} - mm_required_epochs = np.sort([mm.required_epochs for mm in motion_models]) + mm_n_params = np.sort([mm.n_params for mm in motion_models]) if 'motion_model_input' not in self.colnames: - # If motion_model_input column is not provided, assert that motion model required_epochs are unique and sorted + # If motion_model_input column is not provided, assert that motion model n_params are unique and sorted # Otherwise the fitter does not know which motion model to use based on n_obs - assert len(mm_required_epochs) == len(set(mm_required_epochs)), \ - f"fit_motion_models: Provided motion model required_epochs are not unique! Motion Models are: {[_.name for _ in motion_models]}" + '\n' + "Cannot decide which motion model to use based on n_obs. Please provide unique motion_models or a 'motion_model_input' column." + assert len(mm_n_params) == len(set(mm_n_params)), \ + f"fit_motion_models: Provided motion model n_params are not unique! Motion Models are: {[_.name for _ in motion_models]}" + '\n' + "Cannot decide which motion model to use based on n_obs. Please provide unique motion_models or a 'motion_model_input' column." ########################### @@ -768,13 +768,13 @@ def fit_motion_models( n_fit = np.array(self['n_fit']) if 'motion_model_input' in self.colnames: # Determine which motion model to use based on motion_model_input column - # If n_fit < required_epochs for the input motion model, use the most complicated motion model with n_fit >= required_epochs - required_params = np.array([all_mm_map[mm_name].required_epochs for mm_name in self['motion_model_input']]) + # If n_fit < n_params for the input motion model, use the most complicated motion model with n_fit >= n_params + required_params = np.array([all_mm_map[mm_name].n_params for mm_name in self['motion_model_input']]) reassign_mm = n_fit < required_params mm_digitized = np.digitize( x=n_fit[reassign_mm], - bins=mm_required_epochs + bins=mm_n_params ) - 1 # Convert to 0-based index # Assign motion models to stars @@ -782,10 +782,10 @@ def fit_motion_models( self['motion_model_used'][reassign_mm] = np.array([motion_models[d].name for d in mm_digitized], dtype='U20') else: - # If motion_model_input column is not provided, use the most complicated model in motion_models with n_fit >= required_epochs. + # If motion_model_input column is not provided, use the most complicated model in motion_models with n_fit >= n_params. mm_digitized = np.digitize( x=n_fit, - bins=mm_required_epochs + bins=mm_n_params ) - 1 # Convert to 0-based index # Assign motion models to stars @@ -974,9 +974,9 @@ def fit_motion_models( self['chi2_y'][unique_index] = chi2_y_array self['t0'][unique_index] = t0[unique_index] - # Update required_epochs regardless of selections + # Update n_params regardless of selections for mm in motion_model_used: - self['required_epochs'][self['motion_model_used'] == mm.name] = mm.required_epochs + self['n_params'][self['motion_model_used'] == mm.name] = mm.n_params return def infer_positions(self, times, fixed_params_dict=None, fill_value=np.nan): From 3d70e5a86b34725f580dd7f477f7dc92f9f2e053 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Thu, 18 Jun 2026 17:51:06 -0700 Subject: [PATCH 215/254] Search for fixed params in the order of fixed_params_dict -> table columns -> meta data -> default value; Added random seed control in bootstrap functions. --- flystar/align.py | 64 ++++------------ flystar/motion_model.py | 9 ++- flystar/startables.py | 118 +++++++++++++++++++---------- flystar/tests/test_align.py | 20 +++-- flystar/tests/test_motion_model.py | 5 +- 5 files changed, 111 insertions(+), 105 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 7986bd8..0394b0b 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -1000,12 +1000,12 @@ def update_ref_table_from_list(self, star_list, star_list_T, ii, idx_ref, idx_li return - def update_ref_table_aggregates(self, keep_orig=None, n_boot=0): + def update_ref_table_aggregates(self, keep_orig=None, n_boot=0, seed=None): """ Average positions or fit velocities. Average magnitudes. Calculate bootstrap errors if desired. - Update the use_in_trans values as needed. TODO: ???? + Update the use_in_trans values as needed. TODO: ????. Updates aggregate columns in self.ref_table in place. @@ -1016,6 +1016,9 @@ def update_ref_table_aggregates(self, keep_orig=None, n_boot=0): Boolean array indicating which stars to keep original values for, by default None n_boot : int, optional Number of bootstrap iterations, by default 0 + seed : int, optional + Random seed for reproducible bootstrap results, by default None + """ # Keep track of the original reference values. # In certain cases, we will NOT update these. @@ -1062,6 +1065,7 @@ def update_ref_table_aggregates(self, keep_orig=None, n_boot=0): absolute_sigma=self.absolute_sigma, select_stars=fit_star_idxs, bootstrap=n_boot, + seed=seed, verbose=self.verbose ) # Combine (transformed) magnitudes @@ -1202,50 +1206,6 @@ def get_ref_list_from_table(self, epoch): self.ref_table['n_params'] = Column(n_params, name='n_params', dtype=int) x, y, xe, ye = self.ref_table.infer_positions(epoch, fixed_params_dict=self.fixed_params_dict) - # else: - # # Otherwise, infer positions using the most complex motion model with the existing columns, until it reaches Fixed or Empty - # for mm in self.motion_models[::-1]: - # required_columns = mm.fit_param_names + mm.fixed_param_names - # if all([(param in self.ref_table.colnames) or (self.fixed_params_dict is not None and param in self.fixed_params_dict.keys()) for param in required_columns]): - # # Check if the values are finite for non-string columns in the required columns for this motion model. If not, skip to the next motion model. - - # if any([param not in self.ref_table.colnames for param in required_columns]): - # # If any required column is missing, skip to the next motion model. - # continue - - # if not all([np.isfinite(self.ref_table[param]).all() for param in required_columns if self.ref_table[param].dtype.kind in 'if']): - # # If any required column has non-finite values, skip to the next motion model. - # continue - - # print(f"Inferring positions using motion model {mm.name}.") - # # If we have error columns for all fit parameters, then use them in the model inference. Otherwise, just use the fit parameters without errors. - # if all([f'{param}_err' in self.ref_table.colnames for param in mm.fit_param_names]) and all([np.isfinite(self.ref_table[f'{param}_err']).all() for param in mm.fit_param_names]): - # x, y, xe, ye = mm().model( - # t=epoch, - # fit_params=np.array([self.ref_table[param] for param in mm.fit_param_names]).T, - # fit_param_errs=np.array([self.ref_table[f'{param}_err'] for param in mm.fit_param_names]).T, - # fixed_params_dict={param: self.ref_table[param] for param in mm.fixed_param_names} - # ) - # else: - # x, y = mm().model( - # t=epoch, - # fit_params=np.array([self.ref_table[param] for param in mm.fit_param_names]).T, - # fixed_params_dict={param: self.ref_table[param] for param in mm.fixed_param_names} - # ) - # xe = None - # ye = None - # break - - # # No velocities... just used average positions. - # x = self.ref_table['x0'] - # y = self.ref_table['y0'] - - # if 'x0_err' in self.ref_table.colnames: - # xe = self.ref_table['x0_err'] - # ye = self.ref_table['y0_err'] - # else: - # xe = None - # ye = None m = self.ref_table['m0'] @@ -1292,7 +1252,7 @@ def reset_ref_values(self, exclude=None): return - def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_bootstrap=True, update_errors=False, verbose=True): + def calc_bootstrap_errors(self, n_boot=100, seed=None, boot_epochs_min=-1, calc_vel_in_bootstrap=True, update_errors=False, verbose=True): """ Function to calculate bootstrap errors for the transformations as well as the proper motions. For each iteration, this will: @@ -1321,6 +1281,9 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot PM bootstrap is only done for final proper motion calculation (e.g., not for each iteration of the starlist for matching) + seed: int, optional + Random seed for reproducible bootstrap results. + boot_epochs_min: int or -1 In order to be included in bootstrap analysis, non-reference stars must be detected in at least boot_epochs_min epochs. If boot_epochs_min = -1, then all stars will @@ -1406,6 +1369,7 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot ### DEFINE MEAN, STD VARIABLES AND BUILD THEM RATHER THAN SAVING FULL ARRAY ### DECREASE PRECISION ON ARRAYS (32 bit instead of 64: dtype=np.float32) ### AT SOME POINT, NEED TO CONVERT BACK (LOOK UP HOW TO DO THIS CAREFULLY) + rng = np.random.default_rng(seed) for ii in tqdm(range(n_boot), desc='Bootstrap iterations', disable=not verbose): # Recalculate transformations using bootstrap sample of # reference stars. Use a loop for each epoch here, so we @@ -1424,7 +1388,7 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot # Extract bootstrap sample of matched reference stars for this epoch #good = np.where(~np.isnan(ref_table['x_orig'][idx_ref][:,jj])) good = np.where( (ref_table['used_in_trans'][:,jj] == True) & (~np.isnan(ref_table['x_orig'][:,jj])) ) - samp_idx = np.random.choice(good[0], len(good[0]), replace=True) + samp_idx = rng.choice(good[0], len(good[0]), replace=True) # Get reference star positions in particular epoch from ref_list. t_epoch = t_arr[jj] @@ -1526,9 +1490,9 @@ def calc_bootstrap_errors(self, n_boot=100, boot_epochs_min=-1, calc_vel_in_boot # for each star, if desired. Draw a full-sample bootstrap over the epochs # for each star, and then run it through the startable fit_velocities machinery if calc_vel_in_bootstrap: - boot_idx = np.random.choice(np.arange(0, n_epochs, 1), size=n_epochs) + boot_idx = rng.choice(np.arange(0, n_epochs, 1), size=n_epochs) while len(np.unique(boot_idx)) < motion_boot_min_epochs: - boot_idx = np.random.choice(np.arange(0, n_epochs, 1), size=n_epochs) + boot_idx = rng.choice(np.arange(0, n_epochs, 1), size=n_epochs) t_boot = t_arr[boot_idx] star_table = StarTable(name=ref_table['name'], diff --git a/flystar/motion_model.py b/flystar/motion_model.py index 778255a..d4750bc 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -104,8 +104,8 @@ def fit( params_guess=None, return_chi2=False, bootstrap=0, - verbose=True, - seed=None + seed=None, + verbose=True ): """Fit stellar motion parameters @@ -137,10 +137,11 @@ def fit( Return chi^2 values along with parameters and uncertainties in params, param_errs, chi2_x, chi2_y, by default False bootstrap : int, optional Bootstrapping uncertainties, by default 0 - verbose : bool, optional - Print warning messages, by default True seed : int, optional Seed for the random number generator, by default None + verbose : bool, optional + Print warning messages, by default True + Returns ------- params, param_errs(, chi2_x, chi2_y) diff --git a/flystar/startables.py b/flystar/startables.py index 7a245ca..0062921 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -553,10 +553,11 @@ def fit_motion_models( absolute_sigma=True, select_stars=None, bootstrap=0, - verbose=True, + seed=None, mask_value=None, mask_lists=None, - fill_value=np.nan + fill_value=np.nan, + verbose=True ): """Fit velocity for star table @@ -589,15 +590,18 @@ def fit_motion_models( Indices of stars to fit, by default None (fit all stars) bootstrap : int, optional Number of bootstrap for uncertainty resampling, by default 0 - verbose : bool, optional - Print verbose messages or not, by default True + seed : int, optional + Random seed for bootstrap resampling, by default None mask_value : float, optional Values to mask in data, by default None mask_lists : list of int, optional Indices of lists to mask/exclude from fitting, by default None fill_value : float, optional Fill value when there is not enough data points to fit, by default np.nan + verbose : bool, optional + Print verbose messages or not, by default True + Raises ------ ValueError @@ -611,14 +615,14 @@ def fit_motion_models( ####### Check Params ###### ########################### if weighting not in ['var', 'std']: - raise ValueError(f"fit_motion_model: Weighting must either be 'var' or 'std', not {weighting}!") + raise ValueError(f"fit_motion_models: Weighting must either be 'var' or 'std', not {weighting}!") if ('t' not in self.colnames) and ('list_times' not in self.meta): - raise KeyError("fit_motion_model: Failed to access time values. No 't' column in table, no 'list_times' in meta.") + raise KeyError("fit_motion_models: Failed to access time values. No 't' column in table, no 'list_times' in meta.") # Check if we have the required columns if not all([_ in self.colnames for _ in ['x', 'y']]): - raise KeyError(f"fit_motion_model: Missing required columns in the table: {', '.join(['x', 'y'])}!") + raise KeyError(f"fit_motion_models: Missing required columns in the table: {', '.join(['x', 'y'])}!") # Make a copy of fixed_params_dict to avoid modifying the original one outside the function fixed_params_dict = copy.deepcopy(fixed_params_dict) @@ -626,7 +630,7 @@ def fit_motion_models( # Check fixed_params_dict is a dict if fixed_params_dict is not None: if not isinstance(fixed_params_dict, dict): - raise ValueError("fit_motion_model: fixed_params_dict must be a dictionary!") + raise ValueError("fit_motion_models: fixed_params_dict must be a dictionary!") # Convert motion_models to MotionModel objects if they are strings: if motion_models is None: @@ -651,7 +655,7 @@ def fit_motion_models( if 'motion_model_input' in self.colnames: input_mm_names = np.unique(self['motion_model_input']) assert all([name in all_mm_map.keys() for name in input_mm_names]), \ - f"fit_motion_model: Unknown motion model name(s) in 'motion_model_input' column. Available motion models are: {', '.join(all_mm_map.keys())}." + f"fit_motion_models: Unknown motion model name(s) in 'motion_model_input' column. Available motion models are: {', '.join(all_mm_map.keys())}." for mm_name in input_mm_names: if mm_name not in mm_names: motion_models.append(all_mm_map[mm_name]) @@ -721,11 +725,9 @@ def fit_motion_models( t_data = copy.deepcopy(np.array(self.meta['list_times']))[list_indices] t_data = np.broadcast_to(t_data, x_data.shape) + fixed_params_dict = {} if fixed_params_dict is None else fixed_params_dict # Add default t0 if not provided in fixed_params_dict - if fixed_params_dict is None: - weights = 1/np.hypot(xe_data, ye_data) if (xe_data is not None) and (ye_data is not None) else None - fixed_params_dict = {'t0': np.average(t_data, axis=1, weights=weights)} - elif 't0' not in fixed_params_dict: + if 't0' not in fixed_params_dict: weights = 1/np.hypot(xe_data, ye_data) if (xe_data is not None) and (ye_data is not None) else None fixed_params_dict['t0'] = np.average(t_data, axis=1, weights=weights) else: @@ -791,12 +793,6 @@ def fit_motion_models( # Assign motion models to stars self['motion_model_used'] = np.array([motion_models[d].name for d in mm_digitized], dtype='U20') - # Add default obsLocation if not provided in fixed_params_dict - mm_used = np.unique(self['motion_model_used'].name) - if 'Parallax' in mm_used and 'obsLocation' not in fixed_params_dict: - fixed_params_dict['obsLocation'] = 'earth' - - ############################ # Prepare Fixed Parameters # ############################ @@ -808,6 +804,7 @@ def fit_motion_models( # Check required fixed parameters for param in mm.required_fixed_param_names: if param not in fixed_params_dict: + # If not provided in fixed_params_dict, it must be in table columns if param in self.colnames: fixed_params_dict[param] = self[param].data else: @@ -818,13 +815,17 @@ def fit_motion_models( # Set to default value if not provided in fixed_params_dict or in self for param, value in mm.optional_fixed_params.items(): if param not in fixed_params_dict: + # If param is not provided in fixed_params_dict if param in self.colnames: + # Set to column value if column exists fixed_params_dict[param] = self[param].data else: + # Set to default value if neither in columns nor provided in fixed_params_dict fixed_params_dict[param] = value + self.meta[param] = value if raise_key_error: - raise KeyError(f"fit_motion_model: Missing required fixed parameter(s) for the motion models used: {', '.join(missing_params)}! Please provide them in fixed_params_dict or as columns in the table.") + raise KeyError(f"fit_motion_models: Missing required fixed parameter(s) for the motion models used: {', '.join(missing_params)}! Please provide them in fixed_params_dict or as columns in the table.") # Prepare fixed_params_dict for each star @@ -847,7 +848,7 @@ def fit_motion_models( ############################ # Fill table with all possible motion model parameter names as new columns. new_col_list = motion_model.motion_model_param_names(motion_model_used, with_errors=True, with_fixed=False) - new_col_list += ['chi2_x', 'chi2_y', 'required_epochs'] + new_col_list += ['chi2_x', 'chi2_y', 'n_params'] if 't0' not in new_col_list: new_col_list.append('t0') @@ -956,9 +957,10 @@ def fit_motion_models( weighting=weighting, use_scipy=use_scipy, absolute_sigma=absolute_sigma, - bootstrap=bootstrap, fill_value=fill_value, return_chi2=True, + bootstrap=bootstrap, + seed=seed, verbose=verbose ) params_array[idx] = params @@ -988,7 +990,7 @@ def infer_positions(self, times, fixed_params_dict=None, fill_value=np.nan): Times at which to predict positions. Scalar, or (N_times,) array, or (N_stars, N_times) array. fixed_params_dict : None or dict, optional Dictionary of fixed parameters to use for prediction. - If not provided, will try to look for fixed parameters in the table columns. + If not provided, will try to look for fixed parameters in the meta data then in table columns. If fixed params are found in both the table and the fixed_params_dict, the values in the table will be used and the fixed_params_dict values will be ignored, by default None fill_value : float, optional @@ -1000,7 +1002,7 @@ def infer_positions(self, times, fixed_params_dict=None, fill_value=np.nan): Arrays of predicted x, y positions and their uncertainties xe, ye, with shape (N_stars, N_times) or (N_stars,) if N_times=1, or (N_times,) if N_stars=1, or scalar. """ assert 'motion_model_used' in self.colnames, \ - "infer_positions: 'motion_model_used' column not found in the table. Please run fit_motion_model() first." + "infer_positions: 'motion_model_used' column not found in the table. Please run fit_motion_models() first." N_stars = len(self) times = np.atleast_1d(times) @@ -1014,11 +1016,12 @@ def infer_positions(self, times, fixed_params_dict=None, fill_value=np.nan): # Calculate the dictionary of {motion_model: indices of stars with this motion model} for faster access during prediction unique_motion_models, unique_inv_indices = np.unique(self['motion_model_used'], return_inverse=True) indices_by_motion_model = {key: np.flatnonzero(unique_inv_indices == k) for k, key in enumerate(unique_motion_models)} - + + mm_map = motion_model.motion_model_map() # Prepare fit_params, fixed_params, fit_param_errs for each star for unique_motion_model, unique_index in indices_by_motion_model.items(): # Create motion model instance - motion_model_instance = motion_model.motion_model_map()[unique_motion_model]() + motion_model_instance = mm_map[unique_motion_model]() # Prepare parameters for prediction fit_params = np.array([ self[param_name][unique_index] for param_name in motion_model_instance.fit_param_names @@ -1028,26 +1031,57 @@ def infer_positions(self, times, fixed_params_dict=None, fill_value=np.nan): self[param_name + '_err'][unique_index] for param_name in motion_model_instance.fit_param_names ]).T # shape (N_stars_this_model, N_params) - fixed_params = {} - for param_name in motion_model_instance.fixed_param_names: - col_name = copy.deepcopy(param_name) - # If column not in table, check if it's provided in fixed_params_dict. If not, raise error. If provided, use the value from fixed_params_dict for all stars. - if (col_name not in self.colnames) and (f'{col_name}_mm' not in self.colnames): - if col_name in fixed_params_dict: - fixed_params[param_name] = fixed_params_dict[col_name] - continue + # Construct fixed_params: Look for fixed_params_dict -> table columns -> meta data -> default value + fixed_params = fixed_params_dict.copy() if fixed_params_dict is not None else {} + for param in motion_model_instance.required_fixed_param_names: + if param not in fixed_params: + # If required fixed param not provided, find it in the table columns or meta data + if param in self.colnames: + fixed_params[param] = self[param][unique_index] + elif param in self.meta: + fixed_params[param] = self.meta[param] else: - raise KeyError(f"infer_positions: Fixed parameter '{param_name}' not found in table columns or fixed_params_dict. Please provide the value for this parameter in fixed_params_dict or add a column named '{param_name}' to the table.") + raise KeyError(f"infer_positions: Required fixed parameter '{param}' not found for motion model '{unique_motion_model}'. Please provide it in fixed_params_dict, or add it as a column in the table, or add it to the meta data.") + else: + fixed_params[param] = fixed_params_dict[param] - # If original table has column and fit_motion_model added the column with _mm suffix, use the _mm column for prediction. - if param_name + '_mm' in self.colnames: - col_name = param_name + '_mm' - fixed_params[param_name] = self[col_name][unique_index] + for param, default_value in motion_model_instance.optional_fixed_params.items(): + if param not in fixed_params: + # If optional fixed param not provided, find it in the table columns or meta data, otherwise use default value + if param in self.colnames: + fixed_params[param] = self[param][unique_index] + elif param in self.meta: + fixed_params[param] = self.meta[param] + else: + fixed_params[param] = default_value + else: + fixed_params[param] = fixed_params_dict[param] - if (param_name == 'obsLocation'): - assert np.unique(fixed_params[param_name]).size == 1, \ + # Special case for obsLocation: no vectorization implemented yet + if (param == 'obsLocation'): + assert np.unique(fixed_params[param]).size == 1, \ "infer_positions: obsLocation fixed parameter has different values for different stars. Vectorized handling not implemented yet." - fixed_params[param_name] = fixed_params[param_name][0] + fixed_params[param] = fixed_params[param][0] + + # for param_name in motion_model_instance.fixed_param_names: + # col_name = copy.deepcopy(param_name) + # # If column not in table, check if it's provided in fixed_params_dict. If not, raise error. If provided, use the value from fixed_params_dict for all stars. + # if (col_name not in self.colnames) and (f'{col_name}_mm' not in self.colnames): + # if col_name in fixed_params_dict: + # fixed_params[param_name] = fixed_params_dict[col_name] + # continue + # else: + # raise KeyError(f"infer_positions: Fixed parameter '{param_name}' not found in table columns or fixed_params_dict. Please provide the value for this parameter in fixed_params_dict or add a column named '{param_name}' to the table.") + + # # If original table has column and fit_motion_models added the column with _mm suffix, use the _mm column for prediction. + # if param_name + '_mm' in self.colnames: + # col_name = param_name + '_mm' + # fixed_params[param_name] = self[col_name][unique_index] + + # if (param_name == 'obsLocation'): + # assert np.unique(fixed_params[param_name]).size == 1, \ + # "infer_positions: obsLocation fixed parameter has different values for different stars. Vectorized handling not implemented yet." + # fixed_params[param_name] = fixed_params[param_name][0] # Predict positions # shape = (N_stars_this_model, N_times) or (N_stars_this_model,) if N_times=1 or (N_times,) if N_stars_this_model=1 or scalar diff --git a/flystar/tests/test_align.py b/flystar/tests/test_align.py index b914200..1e4ffdf 100644 --- a/flystar/tests/test_align.py +++ b/flystar/tests/test_align.py @@ -587,7 +587,7 @@ def test_bootstrap(): assert 'vye_boot' not in match1.ref_table.keys() # Run bootstrap: no boot_epochs_min - match1.calc_bootstrap_errors(n_boot=n_boot, boot_epochs_min=boot_epochs_min) + match1.calc_bootstrap_errors(n_boot=n_boot, boot_epochs_min=boot_epochs_min, seed=42) # Make sure columns exist, and none of them are nan values assert np.sum(np.isnan(match1.ref_table['xe_boot'])) == 0 assert np.sum(np.isnan(match1.ref_table['ye_boot'])) == 0 @@ -616,7 +616,7 @@ def test_bootstrap(): # Now run_calc_bootstrap_error, with boot_epochs_min engaged boot_epochs_min2 = 2 - match2.calc_bootstrap_errors(n_boot=n_boot, boot_epochs_min=boot_epochs_min2) + match2.calc_bootstrap_errors(n_boot=n_boot, boot_epochs_min=boot_epochs_min2, seed=42) # Make sure boot_epochs_min cut worked as intended out = match2.ref_table @@ -698,7 +698,7 @@ def test_calc_vel_in_bootstrap(): # Run calc_bootstrap_error function with calc_vel_in_bootstrap=True. # Make sure bootstrap velocity errors are calculated and valid n_boot = 50 - match_vel.calc_bootstrap_errors(n_boot=n_boot, calc_vel_in_bootstrap=True) + match_vel.calc_bootstrap_errors(n_boot=n_boot, calc_vel_in_bootstrap=True, seed=42) assert 'xe_boot' in match_vel.ref_table.keys() assert np.sum(np.isnan(match_vel.ref_table['xe_boot'])) == 0 @@ -706,7 +706,7 @@ def test_calc_vel_in_bootstrap(): assert np.sum(np.isnan(match_vel.ref_table['vx_err_boot'])) == 0 # Run without calc_vel_in_bootstrap, make sure velocities are NOT calculated - match.calc_bootstrap_errors(n_boot=n_boot, calc_vel_in_bootstrap=False) + match.calc_bootstrap_errors(n_boot=n_boot, calc_vel_in_bootstrap=False, seed=42) assert 'xe_boot' in match.ref_table.keys() assert np.sum(np.isnan(match.ref_table['xe_boot'])) == 0 @@ -758,7 +758,7 @@ def test_transform_xym(): verbose=False) match1.fit() - match1.calc_bootstrap_errors(n_boot=n_boot) + match1.calc_bootstrap_errors(n_boot=n_boot, seed=42) # Make sure all transformations have mag_offset = 0 trans_list = match1.trans_list @@ -793,7 +793,7 @@ def test_transform_xym(): verbose=False) match2.fit() - match2.calc_bootstrap_errors(n_boot=n_boot) + match2.calc_bootstrap_errors(n_boot=n_boot, seed=42) # Make sure all transformations have correct mag offset @@ -1026,6 +1026,14 @@ def make_fake_starlists_poly1(seed=-1): yde = ype mde = lis['m0_err'] + # fig, ax = plt.subplots() + # ax.scatter(x0, y0, s=2, label='Reference') + # ax.scatter(xd, yd, s=2, label='Starlist') + # ax.set_xlabel('X (pix)') + # ax.set_ylabel('Y (pix)') + # ax.legend() + # plt.show() + # Save the new list as a starlist. new_lis = starlists.StarList([lis['name'], md, mde, xd, xde, yd, yde, t], names=('name', 'm', 'me', 'x', 'xe', 'y', 'ye', 't')) diff --git a/flystar/tests/test_motion_model.py b/flystar/tests/test_motion_model.py index 5bd4ad2..de67d65 100644 --- a/flystar/tests/test_motion_model.py +++ b/flystar/tests/test_motion_model.py @@ -131,8 +131,7 @@ def test_Linear(): fit_param_errs=np.array([x0_err_batch, vx_err_batch, y0_err_batch, vy_err_batch]).T, fixed_params_dict={'t0': t0_batch} ) - - + np.testing.assert_allclose(x_t_batch, (x0_batch+(t_batch-t0_batch)*vx_batch), atol=1e-5) np.testing.assert_allclose(y_t_batch, (y0_batch+(t_batch-t0_batch)*vy_batch), atol=1e-5) np.testing.assert_allclose(x_err_t_batch, np.hypot(x0_err_batch, (t_batch-t0_batch)*vx_err_batch), atol=1e-5) @@ -219,7 +218,7 @@ def linear(t, x0, vx): x_sim = np.random.normal(x_true, x_true_err) y_sim = np.random.normal(y_true, y_true_err) # Run fit - params, param_errs = mod.fit(t, x_sim, y_sim, x_true_err, y_true_err, fixed_params_dict={'t0': true_params['t0']}, bootstrap=10) + params, param_errs = mod.fit(t, x_sim, y_sim, x_true_err, y_true_err, fixed_params_dict={'t0': true_params['t0']}, bootstrap=10, seed=42) # Confirm true value is within error bar of fit value assert np.all([within_error(true_params[param_list[i]], params[i], param_errs[i]) for i in range(len(params))]) From 4520ed986268364ef8119a76dff0adb4bc210d28 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Thu, 18 Jun 2026 18:24:49 -0700 Subject: [PATCH 216/254] Update adding fixed parameter into meta or column in fit_motion_models --- flystar/startables.py | 49 +++++++++++++++++++++++-------------------- 1 file changed, 26 insertions(+), 23 deletions(-) diff --git a/flystar/startables.py b/flystar/startables.py index 0062921..0c2ec41 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -869,7 +869,7 @@ def fit_motion_models( rename_duplicate=True ) - # Add fixed parameter columns if they do not exist + # Add fixed parameter meta if scalar, column if array. fixed_param_names = [] for mm in motion_model_used: for param in mm.fixed_param_names: @@ -879,26 +879,31 @@ def fit_motion_models( if 't0' in fixed_param_names: fixed_param_names.remove('t0') - # Add fixed parameter columns + for param in fixed_param_names: - coldata = np.array([fixed_params_stars[i][param] for i in range(N_stars)]) + coldata = np.array([fps[param] for fps in fixed_params_stars]) + if param in self.colnames: - if is_string_dtype(self[param]): - if np.array_equal(self[param], coldata): - # Same data, skip - continue - # If the column already exists, check if the data are the same - elif np.allclose(self[param], coldata, equal_nan=True): - # Same data, skip - continue - else: - # Different data, add with _mm suffix to avoid name conflict - colname = param + '_mm' - else: - colname = param + existing = self[param] + + # Skip if identical + same = ( + np.array_equal(existing, coldata) + if is_string_dtype(existing) + else np.allclose(existing, coldata, equal_nan=True) + ) - self.add_column(Column(data=coldata, name=colname), rename_duplicate=True) + if same: + continue + # Different (or column does not yet exist) + if len(np.unique(coldata)) == 1: + self.meta[param] = coldata[0] + else: + self.add_column( + Column(data=coldata, name=f"{param}_mm"), + rename_duplicate=True, + ) # Add a column to keep track of the number of points used in a fit and number of bootstrap used. self.meta['n_bootstrap'] = bootstrap @@ -1049,6 +1054,10 @@ def infer_positions(self, times, fixed_params_dict=None, fill_value=np.nan): if param not in fixed_params: # If optional fixed param not provided, find it in the table columns or meta data, otherwise use default value if param in self.colnames: + if param == 'obsLocation': + # Special case for obsLocation: no vectorization implemented yet, use the value from the first star + assert np.unique(self[param][unique_index]).size == 1, \ + f"infer_positions: obsLocation fixed parameter has different values ({np.unique(self[param][unique_index])}) for different stars. Vectorized handling not implemented yet." fixed_params[param] = self[param][unique_index] elif param in self.meta: fixed_params[param] = self.meta[param] @@ -1057,12 +1066,6 @@ def infer_positions(self, times, fixed_params_dict=None, fill_value=np.nan): else: fixed_params[param] = fixed_params_dict[param] - # Special case for obsLocation: no vectorization implemented yet - if (param == 'obsLocation'): - assert np.unique(fixed_params[param]).size == 1, \ - "infer_positions: obsLocation fixed parameter has different values for different stars. Vectorized handling not implemented yet." - fixed_params[param] = fixed_params[param][0] - # for param_name in motion_model_instance.fixed_param_names: # col_name = copy.deepcopy(param_name) # # If column not in table, check if it's provided in fixed_params_dict. If not, raise error. If provided, use the value from fixed_params_dict for all stars. From e6f258389c6e90f3ae10582ddf4031725772b4bd Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Mon, 22 Jun 2026 17:12:41 -0700 Subject: [PATCH 217/254] Remove extra import --- flystar/tests/test_align.py | 1 - 1 file changed, 1 deletion(-) diff --git a/flystar/tests/test_align.py b/flystar/tests/test_align.py index 1e4ffdf..5b9494b 100644 --- a/flystar/tests/test_align.py +++ b/flystar/tests/test_align.py @@ -1449,7 +1449,6 @@ def make_fake_starlists_poly1_par(seed=-1): if __name__ == '__main__': import pickle - import matplotlib.pyplot as plt with open(f'{test_data_path}/my_gaia.pkl', 'rb') as f: my_gaia = pickle.load(f) with open(f'{test_data_path}/list_of_starlists.pkl', 'rb') as f: From e76292cf63c0d46a4cd9910ecb3c900e87590b24 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Tue, 23 Jun 2026 00:10:17 -0700 Subject: [PATCH 218/254] Add missing parameters in determine_motion_model --- flystar/align.py | 10 +++++++--- flystar/match.py | 2 +- flystar/startables.py | 6 +++--- 3 files changed, 11 insertions(+), 7 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 0394b0b..a99d354 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -1200,7 +1200,7 @@ def get_ref_list_from_table(self, epoch): # Calculate x, y, xe, ye if 'motion_model_used' not in self.ref_table.colnames: - motion_model_used, n_params = determine_motion_model(self.ref_table) + motion_model_used, n_params = determine_motion_model(self.ref_table, self.motion_models, self.fixed_params_dict) self.ref_table['motion_model_used'] = Column(motion_model_used, name='motion_model_used', dtype='U20') # self.ref_table['n_fit'] = Column(n_params, name='n_fit', dtype=int) self.ref_table['n_params'] = Column(n_params, name='n_params', dtype=int) @@ -3448,6 +3448,8 @@ def trans_initial_guess( warnings.filterwarnings('ignore', category=AstropyUserWarning) if motion_models is None: motion_models = [] + + # Match by name if mode == 'name': # First trim the two lists down to only those that don't contain # the "ignore_contains" string. @@ -3468,8 +3470,8 @@ def trans_initial_guess( m2m = ref_list['m'][idx_r][ndx_r] N = len(x1m) - else: - # Default is miracle match. + # Default is miracle match. + elif mode == 'miracle': if briteN is None: briteN = min(50, len(star_list)) else: @@ -3495,6 +3497,8 @@ def trans_initial_guess( polygon_starlist, buffer=buffer ) + else: + raise ValueError(f'flystar.align.trans_initial_guess: Unknown mode: {mode}. Must be one of ["name", "miracle"].') if len(x1m) < n_req_match: fig, ax = plt.subplots() diff --git a/flystar/match.py b/flystar/match.py index b3a4271..241d334 100644 --- a/flystar/match.py +++ b/flystar/match.py @@ -41,7 +41,7 @@ def miracle_match_briteN(xin1, yin1, min1, xin2, yin2, min2, Nbrite, yin2 = np.array(yin2) min2 = np.array(min2) - if polygon1 is not None and polygon2 is not None: + if (polygon1 is not None) and (polygon2 is not None): import shapely points1 = shapely.points(xin1, yin1) points2 = shapely.points(xin2, yin2) diff --git a/flystar/startables.py b/flystar/startables.py index 0c2ec41..367436a 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -490,7 +490,7 @@ def combine_lists(self, col_name_in, weights_col=None, mask_val=None, # Calculate the weighted mean and uncertainty avg = np.ma.average(val_2d_clip, weights=wgt_2d, axis=1) - std = np.ma.sqrt(1 / np.ma.sum(wgt_2d, axis=1)) + std = np.ma.sqrt(1 / np.ma.sum(wgt_2d, axis=1)) # Error propagation for weighted mean if meta_add: self.meta[col_name_in + '0'] = 'weighted' @@ -498,7 +498,7 @@ def combine_lists(self, col_name_in, weights_col=None, mask_val=None, wgt_2d = None # Calculate the weighted mean and uncertainty avg = np.ma.mean(val_2d_clip, axis=1) - std = np.ma.std(val_2d_clip, axis=1) / np.sqrt(len(list_indices)) # Standard error of the mean + std = np.ma.std(val_2d_clip, axis=1) / np.sqrt(len(list_indices)) # Standard error of the mean if meta_add: self.meta[col_name_in + '0'] = 'not_weighted' @@ -515,7 +515,7 @@ def combine_lists(self, col_name_in, weights_col=None, mask_val=None, col_name_std = col_name_in + '0_err' if ismag: - std = 2.5 / np.log(10) * std / avg + std = 2.5 / np.log(10) * std / avg # Error propagation avg = -2.5 * np.ma.log10(avg) # Fill mask with nan or inf From d0ae608f553ebe5ffe2cf6ac89aac6c1fe93c0bb Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Thu, 2 Jul 2026 16:28:51 -0700 Subject: [PATCH 219/254] Switched back to standard deviation as uncertainties in startables.combine_lists; Added support for artificial star motion model fitting with x/y out (index 1). --- flystar/startables.py | 32 +++++++++++++++++++++++++------- 1 file changed, 25 insertions(+), 7 deletions(-) diff --git a/flystar/startables.py b/flystar/startables.py index 367436a..8ab9b00 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -490,7 +490,9 @@ def combine_lists(self, col_name_in, weights_col=None, mask_val=None, # Calculate the weighted mean and uncertainty avg = np.ma.average(val_2d_clip, weights=wgt_2d, axis=1) - std = np.ma.sqrt(1 / np.ma.sum(wgt_2d, axis=1)) # Error propagation for weighted mean + # std = np.ma.sqrt(1 / np.ma.sum(wgt_2d, axis=1)) # Error propagation for weighted mean + # Use standard deviation of the weighted residuals as the uncertainty + std = np.ma.sqrt(np.ma.average((val_2d_clip.T - avg).T**2, weights=wgt_2d, axis=1)) if meta_add: self.meta[col_name_in + '0'] = 'weighted' @@ -498,7 +500,9 @@ def combine_lists(self, col_name_in, weights_col=None, mask_val=None, wgt_2d = None # Calculate the weighted mean and uncertainty avg = np.ma.mean(val_2d_clip, axis=1) - std = np.ma.std(val_2d_clip, axis=1) / np.sqrt(len(list_indices)) # Standard error of the mean + # std = np.ma.std(val_2d_clip, axis=1) / np.sqrt(len(list_indices)) # Standard error of the mean + # Use standard deviation of the residuals as the uncertainty + std = np.ma.std(val_2d_clip, axis=1) if meta_add: self.meta[col_name_in + '0'] = 'not_weighted' @@ -557,6 +561,7 @@ def fit_motion_models( mask_value=None, mask_lists=None, fill_value=np.nan, + art_star=False, verbose=True ): """Fit velocity for star table @@ -598,6 +603,8 @@ def fit_motion_models( Indices of lists to mask/exclude from fitting, by default None fill_value : float, optional Fill value when there is not enough data points to fit, by default np.nan + art_star : bool, optional + Artifical star table or observed star table. If artificial stars, Use the output coordinates for fitting motion models (x[..., 1], y[..., 1]) verbose : bool, optional Print verbose messages or not, by default True @@ -678,15 +685,26 @@ def fit_motion_models( ########################### # Prepare data for fitting N_stars = len(self) - N_times = self['x'].data.shape[1] + if art_star: + x = self['x'].data[..., 1] + y = self['y'].data[..., 1] + else: + x = self['x'].data + y = self['y'].data + + xe = self['xe'].data if 'xe' in self.colnames else np.ones_like(x) + ye = self['ye'].data if 'ye' in self.colnames else np.ones_like(y) + + N_times = x.shape[1] if mask_lists is not None: list_indices = np.array([i for i in range(N_times) if i not in mask_lists]) else: list_indices = np.arange(N_times) - x_data = np.ma.masked_invalid(self['x'].data[:, list_indices], copy=True) - y_data = np.ma.masked_invalid(self['y'].data[:, list_indices], copy=True) - xe_data = np.ma.masked_invalid(self['xe'].data[:, list_indices], copy=True) if 'xe' in self.colnames else np.ones_like(x_data) - ye_data = np.ma.masked_invalid(self['ye'].data[:, list_indices], copy=True) if 'ye' in self.colnames else np.ones_like(y_data) + + x_data = np.ma.masked_invalid(x[:, list_indices], copy=True) + y_data = np.ma.masked_invalid(y[:, list_indices], copy=True) + xe_data = np.ma.masked_invalid(xe[:, list_indices], copy=True) + ye_data = np.ma.masked_invalid(ye[:, list_indices], copy=True) # Mask out close to 0 values to avoid infinite weights if xe_data is not None: From 90b2bb6bf3987338511f0a5f10c987921559593c Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Thu, 2 Jul 2026 22:05:47 -0700 Subject: [PATCH 220/254] Change default absolute_sigma=True in align, consistent with motion models --- flystar/align.py | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index a99d354..02ec393 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -46,7 +46,7 @@ def __init__( fixed_params_dict=None, vel_weighting='var', use_scipy=True, - absolute_sigma=False, + absolute_sigma=True, # Advanced options iter_callback=None, save_path=None, @@ -542,7 +542,15 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar print(" **********") star_list = self.star_lists[ii] + # FIXME: why [0]? ref_list = self.get_ref_list_from_table(star_list['t'][0]) + # if 't' in star_list.meta: + # ref_list = self.get_ref_list_from_table(star_list.meta['t']) + # elif 't' in star_list.colnames: + # ref_list = self.get_ref_list_from_table(star_list['t']) + # else: + # raise KeyError(f"StarList at index {ii} does not have a 't' column or meta['t'] entry. Cannot determine reference list for matching.") + trans = self.trans_list[ii] # Trim a COPY of the reference and star lists based on magnitude. @@ -1667,7 +1675,7 @@ def __init__( fixed_params_dict=None, vel_weighting='var', use_scipy=True, - absolute_sigma=False, + absolute_sigma=True, # Advanced options iter_callback=None, save_path=None, From 43e285e6605764daa3865a73ca4a6f4079a58a6a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Mon, 6 Jul 2026 22:59:49 -0700 Subject: [PATCH 221/254] Reverted back to trans_weights and vel_weights --- flystar/align.py | 40 +++++++++++++++++-------------------- flystar/tests/test_align.py | 20 +++++++++---------- 2 files changed, 28 insertions(+), 32 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index a99d354..c307f87 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -32,7 +32,7 @@ def __init__( trans_class=transforms.PolyTransform, trans_args=[{'order': 2}, {'order': 2}], trans_input=None, - trans_weighting=None, + trans_weights=None, init_order=1, init_guess_mode='miracle', briteN=None, @@ -44,7 +44,7 @@ def __init__( motion_models=['Empty', 'Fixed'], # motion_model_for_new_star=None, fixed_params_dict=None, - vel_weighting='var', + vel_weights='var', use_scipy=True, absolute_sigma=False, # Advanced options @@ -115,7 +115,7 @@ def __init__( def = None. If not None, then this should contain an array or list of transform objects that will be used as the initial guess in the alignment and matching. - trans_weighting : str + trans_weights : str Either None (def), 'both,var', 'list,var', or 'ref,var' depending on whether you want to weight by the positional uncertainties (variances) in the individual starlists, or also with the uncertainties in the reference frame itself. Note weighting only works when there @@ -165,7 +165,7 @@ def = None. If not None, then this should contain an array or list of transform fixed_params_dict : None or dict Dictionary of motion model fixed parameters, e.g., ra, dec, pa, obsLocation, t0, etc. See motion_model classes for details. - vel_weighting : str + vel_weights : str Either 'var' (def) or 'std', depending on whether you want to weight the motion model fits by the variance or standard deviation of the position data @@ -231,8 +231,8 @@ def = None. If not None, then this should contain an array or list of transform self.init_order = init_order self.mag_trans = mag_trans self.mag_lim = mag_lim - self.trans_weighting = trans_weighting - self.vel_weighting = vel_weighting + self.trans_weighting = trans_weights + self.vel_weighting = vel_weights self.trans_input = trans_input self.trans_class = trans_class self.calc_trans_inverse = calc_trans_inverse @@ -372,7 +372,7 @@ def fit(self): 'trans_class': self.trans_class, 'trans_args': self.trans_args, 'trans_input': self.trans_input, - 'trans_weighting': self.trans_weighting, + 'trans_weights': self.trans_weighting, 'init_order': self.init_order, 'init_guess_mode': self.init_guess_mode, 'calc_trans_inverse': self.calc_trans_inverse, @@ -380,7 +380,7 @@ def fit(self): 'mag_lim': self.mag_lim, 'motion_models': self.motion_models, 'fixed_params_dict': self.fixed_params_dict, - 'vel_weighting': self.vel_weighting, + 'vel_weights': self.vel_weighting, 'use_scipy': self.use_scipy, 'absolute_sigma': self.absolute_sigma, 'iter_callback': self.iter_callback, @@ -1034,7 +1034,6 @@ def update_ref_table_aggregates(self, keep_orig=None, n_boot=0, seed=None): motion_model_class_names += self.ref_table['motion_model_used'][keep_orig].tolist() vals_orig['motion_model_used'] = self.ref_table['motion_model_used'][keep_orig] vals_orig['n_params'] = self.ref_table['n_params'][keep_orig] - # vals_orig['n_fit'] = self.ref_table['n_fit'][keep_orig] motion_model_col_names = motion_model.motion_model_param_names(motion_model_class_names, with_errors=True, with_fixed=True) for mm in motion_model_col_names: if f'{mm}_mm' in self.ref_table.keys(): @@ -1081,9 +1080,7 @@ def update_ref_table_aggregates(self, keep_orig=None, n_boot=0, seed=None): motion_model_used, n_params = determine_motion_model(self.ref_table, self.motion_models, self.fixed_params_dict) # Assign the determined motion models - # self.ref_table['motion_model_used'][keep_orig] = motion_model_used self.ref_table['motion_model_used'] = Column(motion_model_used, name='motion_model_used', dtype='U20') - # self.ref_table['n_fit'] = Column(n_params, name='n_fit', dtype=int) self.ref_table['n_params'] = Column(n_params, name='n_params', dtype=int) # Replace the originals if we are supposed to keep them fixed. @@ -1202,7 +1199,6 @@ def get_ref_list_from_table(self, epoch): if 'motion_model_used' not in self.ref_table.colnames: motion_model_used, n_params = determine_motion_model(self.ref_table, self.motion_models, self.fixed_params_dict) self.ref_table['motion_model_used'] = Column(motion_model_used, name='motion_model_used', dtype='U20') - # self.ref_table['n_fit'] = Column(n_params, name='n_fit', dtype=int) self.ref_table['n_params'] = Column(n_params, name='n_params', dtype=int) x, y, xe, ye = self.ref_table.infer_positions(epoch, fixed_params_dict=self.fixed_params_dict) @@ -1652,7 +1648,7 @@ def __init__( trans_class=transforms.PolyTransform, trans_args=[{'order': 2}, {'order': 2}], trans_input=None, - trans_weighting=None, + trans_weights=None, init_order=1, init_guess_mode='miracle', briteN=None, @@ -1665,7 +1661,7 @@ def __init__( motion_models=['Empty', 'Fixed'], # motion_model_for_new_star=None, fixed_params_dict=None, - vel_weighting='var', + vel_weights='var', use_scipy=True, absolute_sigma=False, # Advanced options @@ -1756,7 +1752,7 @@ def __init__( def = None. If not None, then this should contain an array or list of transform objects that will be used as the initial guess in the alignment and matching. - trans_weighting : str + trans_weights : str Either None (def), 'both,var', 'list,var', or 'ref,var' depending on whether you want to weight by the positional uncertainties (variances) in the individual starlists, or also with the uncertainties in the reference frame itself. Note weighting only works when there @@ -1811,7 +1807,7 @@ def = None. If not None, then this should contain an array or list of transform fixed_params_dict : None or dict Dictionary of fixed parameters for motion models - vel_weighting : str + vel_weights : str Either 'var' (def) or 'std', depending on whether you want to weight the motion model fits by the variance or standard deviation of the position data @@ -1876,7 +1872,7 @@ def = None. If not None, then this should contain an array or list of transform trans_class=trans_class, trans_args=trans_args, trans_input=trans_input, - trans_weighting=trans_weighting, + trans_weights=trans_weights, init_order=init_order, init_guess_mode=init_guess_mode, briteN=briteN, @@ -1888,7 +1884,7 @@ def = None. If not None, then this should contain an array or list of transform motion_models=motion_models, # motion_model_for_new_star=motion_model_for_new_star, fixed_params_dict=fixed_params_dict, - vel_weighting=vel_weighting, + vel_weights=vel_weights, use_scipy=use_scipy, absolute_sigma=absolute_sigma, # Advanced options @@ -1979,7 +1975,7 @@ def fit(self): 'trans_class': self.trans_class, 'trans_args': self.trans_args, 'trans_input': self.trans_input, - 'trans_weighting': self.trans_weighting, + 'trans_weights': self.trans_weighting, 'init_order': self.init_order, 'init_guess_mode': self.init_guess_mode, 'calc_trans_inverse': self.calc_trans_inverse, @@ -1988,7 +1984,7 @@ def fit(self): 'ref_mag_lim': self.ref_mag_lim, 'motion_models': self.motion_models, 'fixed_params_dict': self.fixed_params_dict, - 'vel_weighting': self.vel_weighting, + 'vel_weights': self.vel_weighting, 'use_scipy': self.use_scipy, 'absolute_sigma': self.absolute_sigma, 'iter_callback': self.iter_callback, @@ -2015,8 +2011,8 @@ def fit(self): # logger(_log, ' mag_trans = ' + str(self.mag_trans), self.verbose) # logger(_log, ' mag_lim = ' + str(self.mag_lim), self.verbose) # logger(_log, ' ref_mag_lim = ' + str(self.ref_mag_lim), self.verbose) - # logger(_log, ' trans_weighting = ' + str(self.trans_weighting), self.verbose) - # logger(_log, ' vel_weighting = ' + str(self.vel_weighting), self.verbose) + # logger(_log, ' trans_weights = ' + str(self.trans_weighting), self.verbose) + # logger(_log, ' vel_weights = ' + str(self.vel_weighting), self.verbose) # logger(_log, ' trans_input = ' + str(self.trans_input), self.verbose) # logger(_log, ' trans_class = ' + str(self.trans_class), self.verbose) # logger(_log, ' calc_trans_inverse = ' + str(self.calc_trans_inverse), self.verbose) diff --git a/flystar/tests/test_align.py b/flystar/tests/test_align.py index 5b9494b..4717d76 100644 --- a/flystar/tests/test_align.py +++ b/flystar/tests/test_align.py @@ -524,7 +524,7 @@ def test_MosaicToRef_hst_me(): use_ref_new=False, update_ref_orig=False, mag_trans=False, - trans_weighting='both,std', + trans_weights='both,std', init_guess_mode='miracle', # save_path=f'{test_data_path}/mb10364_data/test_MosaicToRef_hst_me.pkl', verbose=False @@ -558,7 +558,7 @@ def test_bootstrap(): outlier_tol = None mag_lim = None ref_mag_lim = None - trans_weighting = 'both,var' + trans_weights = 'both,var' mag_trans = False n_boot = 15 @@ -572,7 +572,7 @@ def test_bootstrap(): mag_trans=mag_trans, mag_lim=mag_lim, ref_mag_lim=ref_mag_lim, - trans_weighting=trans_weighting, + trans_weights=trans_weights, motion_models=['Linear'], use_ref_new=False, update_ref_orig=False, @@ -606,7 +606,7 @@ def test_bootstrap(): mag_trans=mag_trans, mag_lim=mag_lim, ref_mag_lim=ref_mag_lim, - trans_weighting=trans_weighting, + trans_weights=trans_weights, motion_models=['Linear'], use_ref_new=False, update_ref_orig=False, @@ -669,7 +669,7 @@ def test_calc_vel_in_bootstrap(): outlier_tol = None mag_lim = None ref_mag_lim = None - trans_weighting = 'both,var' + trans_weights = 'both,var' mag_trans = False n_boot = 15 @@ -683,7 +683,7 @@ def test_calc_vel_in_bootstrap(): mag_trans=mag_trans, mag_lim=mag_lim, ref_mag_lim=ref_mag_lim, - trans_weighting=trans_weighting, + trans_weights=trans_weights, motion_models=['Linear'], use_ref_new=False, update_ref_orig=False, @@ -737,7 +737,7 @@ def test_transform_xym(): outlier_tol = None mag_lim = None ref_mag_lim = None - trans_weighting = 'both,var' + trans_weights = 'both,var' n_boot = 15 mag_trans = False @@ -750,7 +750,7 @@ def test_transform_xym(): mag_trans=mag_trans, mag_lim=mag_lim, ref_mag_lim=ref_mag_lim, - trans_weighting=trans_weighting, + trans_weights=trans_weights, motion_models=['Fixed'], use_ref_new=False, update_ref_orig=False, @@ -785,7 +785,7 @@ def test_transform_xym(): mag_trans=mag_trans, mag_lim=mag_lim, ref_mag_lim=ref_mag_lim, - trans_weighting=trans_weighting, + trans_weights=trans_weights, motion_models=['Fixed'], use_ref_new=False, update_ref_orig=False, @@ -1465,7 +1465,7 @@ def make_fake_starlists_poly1_par(seed=-1): use_ref_new=True, update_ref_orig=False, mag_trans=True, - trans_weighting='both,std', + trans_weights='both,std', init_guess_mode='name', verbose=3) msc.fit() for i in range(msc.ref_table['x'].shape[1]): From 22eefd47e456bcb9e6e0e40985c9d434f2fe8829 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Mon, 6 Jul 2026 23:00:19 -0700 Subject: [PATCH 222/254] Reverted back to snr --- flystar/starlists.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/flystar/starlists.py b/flystar/starlists.py index ef8a666..c6e92d0 100644 --- a/flystar/starlists.py +++ b/flystar/starlists.py @@ -641,18 +641,18 @@ def from_lis_file(cls, filename, error=True, fvu_file=None): if error==True: t_ref.rename_column(cols[5], 'xe') t_ref.rename_column(cols[6], 'ye') - t_ref.rename_column(cols[7], 'me') + t_ref.rename_column(cols[7], 'snr') t_ref.rename_column(cols[8], 'corr') t_ref.rename_column(cols[9], 'N_frames') t_ref.rename_column(cols[10], 'flux') else: - t_ref.rename_column(cols[5], 'me') + t_ref.rename_column(cols[5], 'snr') t_ref.rename_column(cols[6], 'corr') t_ref.rename_column(cols[7], 'N_frames') t_ref.rename_column(cols[8], 'flux') - # if ('me' not in cols) and ('snr' in cols) and (error == True): - # t_ref['me'] = 1.0 / t_ref['snr'] + if ('me' not in cols) and ('snr' in cols) and (error == True): + t_ref['me'] = 1.0 / t_ref['snr'] if fvu_file is not None: t_fvu = Table.read(fvu_file, format='ascii.no_header') From 7407bce406b4a8ccbce1302bca40289ad2d5c81b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Mon, 6 Jul 2026 23:00:55 -0700 Subject: [PATCH 223/254] Added checking metadata for optional fixed parameters checking --- flystar/startables.py | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/flystar/startables.py b/flystar/startables.py index 8ab9b00..7cbc36b 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -814,17 +814,21 @@ def fit_motion_models( ############################ # Prepare Fixed Parameters # ############################ - # If required fixed params in self but not provided in fixed_params_dict, add them to fixed_params_dict + # If required fixed params in self.meta or columns, but not provided in fixed_params_dict, add them to fixed_params_dict motion_model_used = [all_mm_map[name] for name in np.unique(self['motion_model_used'])] raise_key_error = False missing_params = [] for mm in motion_model_used: # Check required fixed parameters for param in mm.required_fixed_param_names: + # Check in the order of fixed_params_dict -> self.meta -> self columns if param not in fixed_params_dict: # If not provided in fixed_params_dict, it must be in table columns if param in self.colnames: fixed_params_dict[param] = self[param].data + elif param in self.meta: + # Check if the parameter is in self.meta + fixed_params_dict[param] = self.meta[param] else: raise_key_error = True missing_params.append(f"'{param}'") @@ -837,13 +841,16 @@ def fit_motion_models( if param in self.colnames: # Set to column value if column exists fixed_params_dict[param] = self[param].data + elif param in self.meta: + # Check if the parameter is in self.meta + fixed_params_dict[param] = self.meta[param] else: # Set to default value if neither in columns nor provided in fixed_params_dict fixed_params_dict[param] = value self.meta[param] = value if raise_key_error: - raise KeyError(f"fit_motion_models: Missing required fixed parameter(s) for the motion models used: {', '.join(missing_params)}! Please provide them in fixed_params_dict or as columns in the table.") + raise KeyError(f"fit_motion_models: Missing required fixed parameter(s) for the motion models used: {', '.join(missing_params)}! Please provide them in fixed_params_dict, or as columns in the table, or as table metadata.") # Prepare fixed_params_dict for each star From af80a05bbd09cd949bcb73bb5e70dbb2a61cbb8b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Tue, 7 Jul 2026 13:32:11 -0700 Subject: [PATCH 224/254] Updated shift_reference_frame --- flystar/motion_model.py | 2 +- flystar/startables.py | 32 ++++++++++++++++++++------------ 2 files changed, 21 insertions(+), 13 deletions(-) diff --git a/flystar/motion_model.py b/flystar/motion_model.py index d4750bc..5bf737e 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -1000,7 +1000,7 @@ def calc_parallax_vector(self, t_mjd, ra, dec, pa=0., obsLocation='earth'): Returns ------- pvec - Parallax vector of shape (N_stars, 2, N_times) + Parallax vector of shape (N_stars, 2, N_times), where 2 corresponds to (x, y) components. """ if self.pvec_cached is not None: t_mjd = np.atleast_1d(t_mjd) diff --git a/flystar/startables.py b/flystar/startables.py index 7cbc36b..6acc6a9 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -1196,8 +1196,7 @@ def get_star_positions_at_time(self, t, motion_model_dict, allow_alt_models=True - def shift_reference_frame(self, delta_vx=0.0, delta_vy=0.0, delta_pi=0.0, - motion_model_dict={}): + def shift_reference_frame(self, delta_vx=0.0, delta_vy=0.0, delta_pi=0.0, fixed_params_dict=None): """ After completing an alignment, shift from your relative reference frame to the absolute frame using either Gaia or a Galactic model. This modified the @@ -1212,8 +1211,9 @@ def shift_reference_frame(self, delta_vx=0.0, delta_vy=0.0, delta_pi=0.0, velocity shift in y-direction (as/yr) delta_pi : float, optional parallax shift (as) + fixed_params_dict : None or dict, optional + Dictionary of fixed parameters to use for prediction: ra, dec, obsLocation, specifically in this case """ - motion_model_dict = motion_model.validate_motion_model_dict(motion_model_dict, self, None) if delta_vx==0.0 and delta_vy==0.0 and delta_pi==0.0: print("No shifts input, reference frame unchanged.") print("Specify delta_vx, delta_vy, and/or delta_pi to perform a reference frame shift.") @@ -1223,16 +1223,20 @@ def shift_reference_frame(self, delta_vx=0.0, delta_vy=0.0, delta_pi=0.0, self['vy'] += delta_vy self['y'] += delta_vy*(self['t']-self['t0'][:, np.newaxis]) if delta_pi!=0.0: + from .motion_model import Parallax + ra = fixed_params_dict['ra'] + dec = fixed_params_dict['dec'] + pa = fixed_params_dict.get('pa', 0.0) + obsLocation = fixed_params_dict.get('obsLocation', 'earth') t_all = self['t'][np.where(~np.any(np.isnan(self['t']), axis=1))[0][0]] t_mjd = Time(t_all, format='decimalyear', scale='utc').mjd - pvec = motion_model_dict['Parallax'].get_parallax_vector(t_mjd) + pvec = Parallax().calc_parallax_vector(t_mjd, ra=ra, dec=dec, pa=pa, obsLocation=obsLocation) self['pi'] += delta_pi - self['x'] += delta_pi*pvec[0] - self['y'] += delta_pi*pvec[1] + self['x'] += delta_pi*pvec[:, 0, :] # Shape (N_stars, N_times) + self['y'] += delta_pi*pvec[:, 1, :] # Shape (N_stars, N_times) return -def shift_reference_frame(table, delta_vx=0.0, delta_vy=0.0, delta_pi=0.0, - motion_model_dict={}): +def shift_reference_frame(table, delta_vx=0.0, delta_vy=0.0, delta_pi=0.0, fixed_params_dict=None): """ After completing an alignment, shift from your relative reference frame to the absolute frame using either Gaia or a Galactic model. This modified the @@ -1248,7 +1252,6 @@ def shift_reference_frame(table, delta_vx=0.0, delta_vy=0.0, delta_pi=0.0, delta_pi : float, optional parallax shift (as) """ - motion_model_dict = motion_model.validate_motion_model_dict(motion_model_dict, table, None) if delta_vx==0.0 and delta_vy==0.0 and delta_pi==0.0: print("No shifts input, reference frame unchanged.") print("Specify delta_vx, delta_vy, and/or delta_pi to perform a reference frame shift.") @@ -1258,10 +1261,15 @@ def shift_reference_frame(table, delta_vx=0.0, delta_vy=0.0, delta_pi=0.0, table['vy'] += delta_vy table['y'] += delta_vy*(table['t']-table['t0'][:, np.newaxis]) if delta_pi!=0.0: + from .motion_model import Parallax + ra = fixed_params_dict['ra'] + dec = fixed_params_dict['dec'] + pa = fixed_params_dict.get('pa', 0.0) + obsLocation = fixed_params_dict.get('obsLocation', 'earth') t_all = table['t'][np.where(~np.any(np.isnan(table['t']), axis=1))[0][0]] t_mjd = Time(t_all, format='decimalyear', scale='utc').mjd - pvec = motion_model_dict['Parallax'].get_parallax_vector(t_mjd) + pvec = Parallax().calc_parallax_vector(t_mjd, ra=ra, dec=dec, pa=pa, obsLocation=obsLocation) table['pi'] += delta_pi - table['x'] += delta_pi*pvec[0] - table['y'] += delta_pi*pvec[1] + table['x'] += delta_pi*pvec[:, 0, :] # Shape (N_stars, N_times) + table['y'] += delta_pi*pvec[:, 1, :] # Shape (N_stars, N_times) return table From 34227dbc00aa3f325a6b1d85e4d365b599005201 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Tue, 7 Jul 2026 13:34:42 -0700 Subject: [PATCH 225/254] Added keyword check in shift_reference_frame --- flystar/startables.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/flystar/startables.py b/flystar/startables.py index 6acc6a9..93e7831 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -1223,6 +1223,9 @@ def shift_reference_frame(self, delta_vx=0.0, delta_vy=0.0, delta_pi=0.0, fixed_ self['vy'] += delta_vy self['y'] += delta_vy*(self['t']-self['t0'][:, np.newaxis]) if delta_pi!=0.0: + fixed_params_dict = {} if fixed_params_dict is None else fixed_params_dict + if 'ra' not in fixed_params_dict or 'dec' not in fixed_params_dict: + raise KeyError("shift_reference_frame: 'ra' and 'dec' must be provided in fixed_params_dict for parallax shift.") from .motion_model import Parallax ra = fixed_params_dict['ra'] dec = fixed_params_dict['dec'] @@ -1262,6 +1265,9 @@ def shift_reference_frame(table, delta_vx=0.0, delta_vy=0.0, delta_pi=0.0, fixed table['y'] += delta_vy*(table['t']-table['t0'][:, np.newaxis]) if delta_pi!=0.0: from .motion_model import Parallax + fixed_params_dict = {} if fixed_params_dict is None else fixed_params_dict + if 'ra' not in fixed_params_dict or 'dec' not in fixed_params_dict: + raise KeyError("shift_reference_frame: 'ra' and 'dec' must be provided in fixed_params_dict for parallax shift.") ra = fixed_params_dict['ra'] dec = fixed_params_dict['dec'] pa = fixed_params_dict.get('pa', 0.0) From 9d7c12bb4360cf2d21cbf2f7602c3b075364959e Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Wed, 8 Jul 2026 19:44:28 -0700 Subject: [PATCH 226/254] Added xlabel and ylabel control for plotly_stars --- flystar/plots.py | 20 +++++++++++++++----- 1 file changed, 15 insertions(+), 5 deletions(-) diff --git a/flystar/plots.py b/flystar/plots.py index 7213174..c82d504 100755 --- a/flystar/plots.py +++ b/flystar/plots.py @@ -3897,7 +3897,7 @@ def __call__(self, event): return -def plotly_stars(x, y, m=None, star_name=None, marker_size=3, color=None, alpha=0.7, symbol='circle', label='starlist', fig=None, figsize=(700, 700), show=True): +def plotly_stars(x, y, m=None, star_name=None, marker_size=3, color=None, alpha=0.7, symbol='circle', label=None, xlabel='x', ylabel='y', fig=None, figsize=(700, 700), show=None): """Plot stars with plotly in interactive html format Parameters @@ -3919,13 +3919,17 @@ def plotly_stars(x, y, m=None, star_name=None, marker_size=3, color=None, alpha= symbol : str, optional Marker symbol, by default 'circle' label : str, optional - Label for the star list, by default 'starlist' + Label for the star list, by default None + xlabel : str, optional + Label for the x-axis, by default 'x' + ylabel : str, optional + Label for the y-axis, by default 'y' fig : plotly.graph_objects.Figure object, optional Figure if the stars are to be added to an exisiting plot, by default None figsize : tuple, optional Figure size, by default (700, 700) show : bool, optional - Show figure or not, by default True + Show figure or not. By default: True if fig is None, False if fig is not None, by default None Returns ------- @@ -3942,6 +3946,10 @@ def plotly_stars(x, y, m=None, star_name=None, marker_size=3, color=None, alpha= color = f'rgba({color[0]*255}, {color[1]*255}, {color[2]*255}, {color[3]:.2f})' customdata = [] + + if label is not None: + hover_template = f'{label}
' + hover_template + if star_name is not None: hover_template = 'name: %{customdata[0]}
' + hover_template customdata.append(star_name) @@ -3972,12 +3980,14 @@ def plotly_stars(x, y, m=None, star_name=None, marker_size=3, color=None, alpha= if fig is None: fig = go.Figure(data=[fig_data]) + show = True if show is None else show else: fig.add_trace(fig_data) + show = False if show is None else show fig.update_layout( - xaxis_title='x', - yaxis_title='y', + xaxis_title=xlabel, + yaxis_title=ylabel, xaxis=dict(scaleanchor='y', scaleratio=1), # Ensure equal aspect ratio width=figsize[0], height=figsize[1] From dc943a0269e74367498a03f4c8a37cd837adc762 Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Wed, 8 Jul 2026 19:45:17 -0700 Subject: [PATCH 227/254] Add t0 if all motion_model_input==Fixed --- flystar/align.py | 43 +++++++++++++++++++++++++++++++++++++------ flystar/startables.py | 2 +- 2 files changed, 38 insertions(+), 7 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 02ec393..28b1679 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -402,6 +402,8 @@ def fit(self): # x_orig, y_orig, m_orig, (opt. errors) -- the transformed errors for the lists: 2D # w, w_orig (optiona) -- the input and output weights of stars in transform: 2D ########## + if 't0' in self.star_lists[self.ref_index].colnames: self.t0_provided = True + else: self.t0_provided = False self.ref_table = self.setup_ref_table_from_starlist(self.star_lists[self.ref_index]) # Save the reference index to the meta data on the reference list. self.ref_table.meta['ref_list'] = self.ref_index @@ -1053,7 +1055,7 @@ def update_ref_table_aggregates(self, keep_orig=None, n_boot=0, seed=None): else: fit_star_idxs = None - if ('motion_model_input' in self.ref_table.keys()) and np.all(self.ref_table['motion_model_input']=='Fixed'): + if np.all(self.ref_table['motion_model_input']=='Fixed'): # self.ref_table.fit_motion_models( # motion_models=['Fixed'], # weighting=self.vel_weighting, @@ -1064,6 +1066,32 @@ def update_ref_table_aggregates(self, keep_orig=None, n_boot=0, seed=None): weighted_xy = ('xe' in self.ref_table.colnames) and ('ye' in self.ref_table.colnames) weighted_m = ('me' in self.ref_table.colnames) self.ref_table.combine_lists_xym(weighted_xy=weighted_xy, weighted_m=weighted_m) + + # Update t0, adapted from startables.fit_motion_models + if not self.t0_provided: + print('t0 not provided, calculating t0 as weighted average of t') + if weighted_xy: + xe_data = np.ma.masked_invalid(self.ref_table['xe'].data, copy=True) + ye_data = np.ma.masked_invalid(self.ref_table['ye'].data, copy=True) + xe_data.mask[np.isclose(xe_data, 0.)] = True + ye_data.mask[np.isclose(ye_data, 0.)] = True + fill_with_one = np.all(xe_data.mask, axis=1) & np.all(ye_data.mask, axis=1) + xe_data[fill_with_one] = 1. + ye_data[fill_with_one] = 1. + if np.ndim(xe_data) == 1: + xe_data = xe_data[:, np.newaxis] + if np.ndim(ye_data) == 1: + ye_data = ye_data[:, np.newaxis] + + if 't' in self.ref_table.colnames: + t_data = copy.deepcopy(self.ref_table['t'].data) + else: + t_data = copy.deepcopy(np.array(self.ref_table.meta['list_times'])) + t_data = np.broadcast_to(t_data, xe_data.shape) + + weights = 1. / np.hypot(xe_data, ye_data) if weighted_xy else None + self.ref_table['t0'] = np.average(t_data, axis=1, weights=weights) + else: self.ref_table.fit_motion_models( motion_models=self.motion_models, @@ -1687,7 +1715,10 @@ def __init__( Required Parameters ------------------- ref_list : StarList object - Can optionally have velocities. All starlists will be aligned to this one. + All starlists will be aligned to this one. + Must have columns (x, y, m, xe, ye, me) or (x0, y0, m0, x0_err, y0_err, m0_err). + May have t or t0 columns. + May have motion model parameters list_of_starlists : array of StarList objects An array or list of flystar.starlists.StarList objects (which are Astropy Tables). @@ -2043,6 +2074,8 @@ def fit(self): # x_orig, y_orig, m_orig, (opt. errors) -- the transformed errors for the lists: 2D # w, w_orig (optiona) -- the input and output weights of stars in transform: 2D ########## + if 't0' in self.ref_list.colnames: self.t0_provided = True + else: self.t0_provided = False self.ref_table = self.setup_ref_table_from_starlist(self.ref_list) ########## @@ -3125,10 +3158,6 @@ def transform_from_object(starlist, transform): # calculate the transformed position and velocity x_new, y_new, xe_new, ye_new = position_transform_from_object(x, y, xe, ye, transform) - if vel: - x0_new, y0_new, x0e_new, y0e_new = position_transform_from_object(x0, y0, x0e, y0e, transform) - vx_new, vy_new, vxe_new, vye_new = velocity_transform_from_object(x0, y0, x0e, y0e, vx, vy, vxe, vye, transform) - # update transformed coords to copy of astropy table starlist_f['x'] = x_new starlist_f['y'] = y_new @@ -3136,6 +3165,8 @@ def transform_from_object(starlist, transform): starlist_f['ye'] = ye_new if vel: + x0_new, y0_new, x0e_new, y0e_new = position_transform_from_object(x0, y0, x0e, y0e, transform) + vx_new, vy_new, vxe_new, vye_new = velocity_transform_from_object(x0, y0, x0e, y0e, vx, vy, vxe, vye, transform) starlist_f['x0'] = x0_new starlist_f['y0'] = y0_new starlist_f['x0_err'] = x0e_new diff --git a/flystar/startables.py b/flystar/startables.py index 8ab9b00..e34e894 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -746,7 +746,7 @@ def fit_motion_models( fixed_params_dict = {} if fixed_params_dict is None else fixed_params_dict # Add default t0 if not provided in fixed_params_dict if 't0' not in fixed_params_dict: - weights = 1/np.hypot(xe_data, ye_data) if (xe_data is not None) and (ye_data is not None) else None + weights = 1. / np.hypot(xe_data, ye_data) if (xe_data is not None) and (ye_data is not None) else None fixed_params_dict['t0'] = np.average(t_data, axis=1, weights=weights) else: if np.ndim(fixed_params_dict['t0']) == 0: From 10280eb553f078e05dff6758ec5a966911782d09 Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Thu, 9 Jul 2026 17:02:07 -0700 Subject: [PATCH 228/254] Update adding t0 and n_fit at the end of fit --- flystar/align.py | 121 +++++++++++++++++++++++++++++++++++++---------- 1 file changed, 96 insertions(+), 25 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 183fea0..90017bb 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -506,6 +506,54 @@ def fit(self): self.ref_table['chi2_x'] = chi2_x self.ref_table['chi2_y'] = chi2_y + # Update t0 and n_fit when no fitting is run because all motion_model_input==Fixed + if ('t0' not in self.ref_table.colnames) or ('n_fit' not in self.ref_table.colnames): + x_data = np.ma.masked_invalid(self.ref_table['x'].data, copy=True) + y_data = np.ma.masked_invalid(self.ref_table['y'].data, copy=True) + xe = self.ref_table['xe'] if 'xe' in self.ref_table.colnames else None + ye = self.ref_table['ye'] if 'ye' in self.ref_table.colnames else None + weighted_xy = (xe is not None) and (ye is not None) + xe_data = np.ma.masked_invalid(self.ref_table['xe'].data, copy=True) if weighted_xy else None + ye_data = np.ma.masked_invalid(self.ref_table['ye'].data, copy=True) if weighted_xy else None + if (xe_data is not None) and (ye_data is not None): + xe_data.mask[np.isclose(xe_data, 0.)] = True + ye_data.mask[np.isclose(ye_data, 0.)] = True + fill_with_one = np.all(xe_data.mask, axis=1) & np.all(ye_data.mask, axis=1) + xe_data[fill_with_one] = 1. + ye_data[fill_with_one] = 1. + + if np.ndim(x_data) == 1: + x_data = x_data[:, np.newaxis] + if np.ndim(y_data) == 1: + y_data = y_data[:, np.newaxis] + if weighted_xy: + if np.ndim(xe_data) == 1: + xe_data = xe_data[:, np.newaxis] + if np.ndim(ye_data) == 1: + ye_data = ye_data[:, np.newaxis] + + if 't' in self.ref_table.colnames: + t_data = copy.deepcopy(self.ref_table['t'].data) + else: + t_data = copy.deepcopy(np.array(self.ref_table.meta['list_times'])) + t_data = np.broadcast_to(t_data, xe_data.shape) + + # Update t0, adapted from startables.fit_motion_models + if 't0' not in self.ref_table.colnames: + weights = 1. / np.hypot(xe_data, ye_data) if weighted_xy else None + self.ref_table['t0'] = np.average(t_data, axis=1, weights=weights) + + # Update n_fit: unique epochs with valid data + if 'n_fit' not in self.ref_table.colnames: + xy_mask = ~ (x_data.mask | y_data.mask) + if weighted_xy: + xy_mask &= ~ (xe_data.mask | ye_data.mask) + + self.ref_table['n_fit'] = np.array([ + len(set(t_data[i][xy_mask[i]])) + for i in range(len(self.ref_table)) + ]) + if self.save_path is not None: filename = f'{self.prefix_name}.pkl' with open(os.path.join(self.save_path, filename), 'wb') as file: @@ -1066,31 +1114,6 @@ def update_ref_table_aggregates(self, keep_orig=None, n_boot=0, seed=None): weighted_m = ('me' in self.ref_table.colnames) self.ref_table.combine_lists_xym(weighted_xy=weighted_xy, weighted_m=weighted_m) - # Update t0, adapted from startables.fit_motion_models - if not self.t0_provided: - print('t0 not provided, calculating t0 as weighted average of t') - if weighted_xy: - xe_data = np.ma.masked_invalid(self.ref_table['xe'].data, copy=True) - ye_data = np.ma.masked_invalid(self.ref_table['ye'].data, copy=True) - xe_data.mask[np.isclose(xe_data, 0.)] = True - ye_data.mask[np.isclose(ye_data, 0.)] = True - fill_with_one = np.all(xe_data.mask, axis=1) & np.all(ye_data.mask, axis=1) - xe_data[fill_with_one] = 1. - ye_data[fill_with_one] = 1. - if np.ndim(xe_data) == 1: - xe_data = xe_data[:, np.newaxis] - if np.ndim(ye_data) == 1: - ye_data = ye_data[:, np.newaxis] - - if 't' in self.ref_table.colnames: - t_data = copy.deepcopy(self.ref_table['t'].data) - else: - t_data = copy.deepcopy(np.array(self.ref_table.meta['list_times'])) - t_data = np.broadcast_to(t_data, xe_data.shape) - - weights = 1. / np.hypot(xe_data, ye_data) if weighted_xy else None - self.ref_table['t0'] = np.average(t_data, axis=1, weights=weights) - else: self.ref_table.fit_motion_models( motion_models=self.motion_models, @@ -2171,6 +2194,54 @@ def fit(self): self.ref_table['chi2_x'] = chi2_x self.ref_table['chi2_y'] = chi2_y + # Update t0 and n_fit when no fitting is run because all motion_model_input==Fixed + if ('t0' not in self.ref_table.colnames) or ('n_fit' not in self.ref_table.colnames): + x_data = np.ma.masked_invalid(self.ref_table['x'].data, copy=True) + y_data = np.ma.masked_invalid(self.ref_table['y'].data, copy=True) + xe = self.ref_table['xe'] if 'xe' in self.ref_table.colnames else None + ye = self.ref_table['ye'] if 'ye' in self.ref_table.colnames else None + weighted_xy = (xe is not None) and (ye is not None) + xe_data = np.ma.masked_invalid(self.ref_table['xe'].data, copy=True) if weighted_xy else None + ye_data = np.ma.masked_invalid(self.ref_table['ye'].data, copy=True) if weighted_xy else None + if (xe_data is not None) and (ye_data is not None): + xe_data.mask[np.isclose(xe_data, 0.)] = True + ye_data.mask[np.isclose(ye_data, 0.)] = True + fill_with_one = np.all(xe_data.mask, axis=1) & np.all(ye_data.mask, axis=1) + xe_data[fill_with_one] = 1. + ye_data[fill_with_one] = 1. + + if np.ndim(x_data) == 1: + x_data = x_data[:, np.newaxis] + if np.ndim(y_data) == 1: + y_data = y_data[:, np.newaxis] + if weighted_xy: + if np.ndim(xe_data) == 1: + xe_data = xe_data[:, np.newaxis] + if np.ndim(ye_data) == 1: + ye_data = ye_data[:, np.newaxis] + + if 't' in self.ref_table.colnames: + t_data = copy.deepcopy(self.ref_table['t'].data) + else: + t_data = copy.deepcopy(np.array(self.ref_table.meta['list_times'])) + t_data = np.broadcast_to(t_data, xe_data.shape) + + # Update t0, adapted from startables.fit_motion_models + if 't0' not in self.ref_table.colnames: + weights = 1. / np.hypot(xe_data, ye_data) if weighted_xy else None + self.ref_table['t0'] = np.average(t_data, axis=1, weights=weights) + + # Update n_fit: unique epochs with valid data + if 'n_fit' not in self.ref_table.colnames: + xy_mask = ~ (x_data.mask | y_data.mask) + if weighted_xy: + xy_mask &= ~ (xe_data.mask | ye_data.mask) + + self.ref_table['n_fit'] = np.array([ + len(set(t_data[i][xy_mask[i]])) + for i in range(len(self.ref_table)) + ]) + if self.save_path is not None: filename = f'{self.prefix_name}.pkl' with open(os.path.join(self.save_path, filename), 'wb') as file: From 78bd1a093406c0016854811ba749d24c9c513748 Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Thu, 9 Jul 2026 17:02:30 -0700 Subject: [PATCH 229/254] Update xe ye logic when not provided --- flystar/startables.py | 50 +++++++++++++++++++------------------------ 1 file changed, 22 insertions(+), 28 deletions(-) diff --git a/flystar/startables.py b/flystar/startables.py index 567d444..8978c00 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -692,8 +692,9 @@ def fit_motion_models( x = self['x'].data y = self['y'].data - xe = self['xe'].data if 'xe' in self.colnames else np.ones_like(x) - ye = self['ye'].data if 'ye' in self.colnames else np.ones_like(y) + xe = self['xe'].data if 'xe' in self.colnames else None + ye = self['ye'].data if 'ye' in self.colnames else None + with_xe_ye = (xe is not None) and (ye is not None) N_times = x.shape[1] if mask_lists is not None: @@ -703,18 +704,17 @@ def fit_motion_models( x_data = np.ma.masked_invalid(x[:, list_indices], copy=True) y_data = np.ma.masked_invalid(y[:, list_indices], copy=True) - xe_data = np.ma.masked_invalid(xe[:, list_indices], copy=True) - ye_data = np.ma.masked_invalid(ye[:, list_indices], copy=True) + xe_data = np.ma.masked_invalid(xe[:, list_indices], copy=True) if with_xe_ye else None + ye_data = np.ma.masked_invalid(ye[:, list_indices], copy=True) if with_xe_ye else None # Mask out close to 0 values to avoid infinite weights - if xe_data is not None: + if with_xe_ye: xe_data.mask[np.isclose(xe_data, 0)] = True - if ye_data is not None: ye_data.mask[np.isclose(ye_data, 0)] = True # If all of xe and ye is masked for a star, effectively no uncertainties provided, fill with 1. # Note that this automatically turn the mask to False for these stars - if (xe_data is not None) and (ye_data is not None): + if with_xe_ye: fill_with_one = np.all(xe_data.mask, axis=1) & np.all(ye_data.mask, axis=1) xe_data[fill_with_one] = 1. ye_data[fill_with_one] = 1. @@ -724,16 +724,11 @@ def fit_motion_models( x_data = x_data[:, np.newaxis] if np.ndim(y_data) == 1: y_data = y_data[:, np.newaxis] - if np.ndim(xe_data) == 1: - xe_data = xe_data[:, np.newaxis] - if np.ndim(ye_data) == 1: - ye_data = ye_data[:, np.newaxis] - - # if mask_lists is not None: - # x_data.mask[:, mask_lists] = True - # y_data.mask[:, mask_lists] = True - # xe_data.mask[:, mask_lists] = True - # ye_data.mask[:, mask_lists] = True + if with_xe_ye: + if np.ndim(xe_data) == 1: + xe_data = xe_data[:, np.newaxis] + if np.ndim(ye_data) == 1: + ye_data = ye_data[:, np.newaxis] # t_data: 2d array with shape (N_stars, N_epochs) # t0: 1d array with shape (N_stars,) @@ -746,7 +741,7 @@ def fit_motion_models( fixed_params_dict = {} if fixed_params_dict is None else fixed_params_dict # Add default t0 if not provided in fixed_params_dict if 't0' not in fixed_params_dict: - weights = 1. / np.hypot(xe_data, ye_data) if (xe_data is not None) and (ye_data is not None) else None + weights = 1. / np.hypot(xe_data, ye_data) if with_xe_ye else None fixed_params_dict['t0'] = np.average(t_data, axis=1, weights=weights) else: if np.ndim(fixed_params_dict['t0']) == 0: @@ -758,26 +753,25 @@ def fit_motion_models( if mask_value: x_data = np.ma.masked_values(x_data, mask_value) y_data = np.ma.masked_values(y_data, mask_value) - if xe_data is not None: + if with_xe_ye: xe_data = np.ma.masked_values(xe_data, mask_value) - if ye_data is not None: ye_data = np.ma.masked_values(ye_data, mask_value) # Calculate mask array - xy_mask = ~ (x_data.mask | y_data.mask) - if (xe_data is not None) and (ye_data is not None): - xy_mask = xy_mask & (~ (xe_data.mask | ye_data.mask)) + valid_xy = ~ (x_data.mask | y_data.mask) + if with_xe_ye: + valid_xy &= ~ (xe_data.mask | ye_data.mask) # Calculate n_fit: unmasked x y values # This will be used to determine which motion model to use for each star. # Note that we don't require unique times here # as scipy.curve_fit and Linear algebra can fit non-unique times. - # self['n_fit'] = np.sum(xy_mask, axis=1) + # self['n_fit'] = np.sum(valid_xy, axis=1) # Calculate n_fit: unique times & unmasked x y values self['n_fit'] = np.array([ - len(set(t_data[i][xy_mask[i]])) + len(set(t_data[i][valid_xy[i]])) for i in range(N_stars) ]) @@ -949,7 +943,7 @@ def fit_motion_models( indices_by_motion_model = {key: np.flatnonzero(unique_inv_indices == k) for k, key in enumerate(unique_motion_models)} # Unmasked indices for each star: - unmasked_idx = [np.flatnonzero(xy_mask[i]) for i in range(N_stars)] + unmasked_idx = [np.flatnonzero(valid_xy[i]) for i in range(N_stars)] # For each motion model for unique_motion_model, unique_index in indices_by_motion_model.items(): @@ -969,8 +963,8 @@ def fit_motion_models( t_stars = [np.array(t_data[i][unmasked_idx[i]]) for i in unique_index] x_stars = [np.array(x_data[i][unmasked_idx[i]]) for i in unique_index] y_stars = [np.array(y_data[i][unmasked_idx[i]]) for i in unique_index] - xe_stars = [np.array(xe_data[i][unmasked_idx[i]]) for i in unique_index] if xe_data is not None else [None]*n_stars_this_model - ye_stars = [np.array(ye_data[i][unmasked_idx[i]]) for i in unique_index] if ye_data is not None else [None]*n_stars_this_model + xe_stars = [np.array(xe_data[i][unmasked_idx[i]]) for i in unique_index] if with_xe_ye else [np.ones_like(x_star) for x_star in x_stars] + ye_stars = [np.array(ye_data[i][unmasked_idx[i]]) for i in unique_index] if with_xe_ye else [np.ones_like(y_star) for y_star in y_stars] # For each star # Expensive for loop! Prepare everything beforehand to speed up. From 06df7bfa096214ec1ae04a970e36c758539feff3 Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Thu, 9 Jul 2026 19:07:05 -0700 Subject: [PATCH 230/254] Adjusted hover label ordering; Updated docstring --- flystar/align.py | 14 +++++++------- flystar/plots.py | 6 +++--- 2 files changed, 10 insertions(+), 10 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 90017bb..ff66993 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -151,9 +151,9 @@ def = None. If not None, then this should contain an array or list of transform mag_lim : array If different from None, it indicates the minimum and maximum magnitude - on the catalogs for finding the transformations. Note, if you want specify the mag_lim - separately for each list and each iteration, you need to pass in a 2D array that - has shape (N_lists, 2). + on the starlists for finding the transformations BEFORE mag trans. + Note, if you want specify the mag_lim separately for each list and each iteration, + you need to pass in a 2D array that has shape (N_lists, N_iters). motion_models : list of MotionModel or str, optional Motion models or their names to use for new or unassigned stars @@ -1575,7 +1575,7 @@ def calc_bootstrap_errors(self, n_boot=100, seed=None, boot_epochs_min=-1, calc_ weighting=self.vel_weighting, use_scipy=self.use_scipy, absolute_sigma=self.absolute_sigma, - verbose=self.verbose + verbose=False ) # Save proper motion fit results to output arrays @@ -1851,9 +1851,9 @@ def = None. If not None, then this should contain an array or list of transform mag_lim : array If different from None, it indicates the minimum and maximum magnitude - on the catalogs for finding the transformations. Note, if you want specify the mag_lim - separately for each list and each iteration, you need to pass in a 2D array that - has shape (N_lists, N_iters). + on the starlists for finding the transformations BEFORE mag trans. + Note, if you want specify the mag_lim separately for each list and each iteration, + you need to pass in a 2D array that has shape (N_lists, N_iters). ref_mag_lim : array If different from None, it indicates the minimum and maximum magnitude diff --git a/flystar/plots.py b/flystar/plots.py index c82d504..86904b8 100755 --- a/flystar/plots.py +++ b/flystar/plots.py @@ -3946,14 +3946,14 @@ def plotly_stars(x, y, m=None, star_name=None, marker_size=3, color=None, alpha= color = f'rgba({color[0]*255}, {color[1]*255}, {color[2]*255}, {color[3]:.2f})' customdata = [] - - if label is not None: - hover_template = f'{label}
' + hover_template if star_name is not None: hover_template = 'name: %{customdata[0]}
' + hover_template customdata.append(star_name) + if label is not None: + hover_template = f'{label}
' + hover_template + if m is not None: m = np.asarray(m) m_idx = len(customdata) From 702674fc2e952f47ede62dbad90fd0546d64ae1d Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Tue, 28 Jul 2026 23:37:52 -0700 Subject: [PATCH 231/254] Moved use_in_trans selection BEFORE deriving transformation to avoid returning empty matches; Changed iters to a placeholder variable as it is redundant; Updated chi2 calculation when there is no weights; Changed !=None to is not None. --- flystar/align.py | 293 ++++++++++++++++++++++++++++++----------------- 1 file changed, 191 insertions(+), 102 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index ff66993..c2dda85 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -152,8 +152,8 @@ def = None. If not None, then this should contain an array or list of transform mag_lim : array If different from None, it indicates the minimum and maximum magnitude on the starlists for finding the transformations BEFORE mag trans. - Note, if you want specify the mag_lim separately for each list and each iteration, - you need to pass in a 2D array that has shape (N_lists, N_iters). + Note, if you want specify the mag_lim separately for each list, + you need to pass in a 2D array that has shape (N_lists, 2). motion_models : list of MotionModel or str, optional Motion models or their names to use for new or unassigned stars @@ -220,11 +220,15 @@ def = None. If not None, then this should contain an array or list of transform plt.errorbar(times, stars_table['x'][0, :], yerr=stars_table['xe'][0, :]) plt.axhline(stars_table['x0'][0] + stars_table['vx'][0]*(times - stars_table['t0'][0])) """ + self.iters = len(dr_tol) + if dm_tol is not None: + assert self.iters == len(dm_tol), f'dr_tol (len={self.iters}) and dm_tol (len={len(dm_tol)}) must all have the same length!' + if outlier_tol is not None: + assert self.iters == len(outlier_tol), f'dr_tol (len={self.iters}) and outlier_tol (len={len(outlier_tol)}) must all have the same length!' self.star_lists = list_of_starlists self.starlist_vertices = starlist_vertices self.ref_index = ref_index - self.iters = iters self.dr_tol = dr_tol self.dm_tol = dm_tol self.trans_args = trans_args @@ -300,7 +304,6 @@ def = None. If not None, then this should contain an array or list of transform # Error checking for parameters. ########## self.fix_iterable_conditions() # fix dr_tol, dm_tol, outlier_tol, mag_lim to be iterable. - check_iter_tolerances(self.iters, self.dr_tol, self.dm_tol, self.outlier_tol) check_trans_input(self.star_lists, self.trans_input, self.mag_trans) ########## @@ -444,7 +447,7 @@ def fit(self): print(' *** Getting rid of {0:d} out of {1:d} junk sources'.format(len(idx), len(self.ref_table))) self.ref_table.remove_rows(idx) - if self.iter_callback != None: + if self.iter_callback is not None: self.iter_callback(self.ref_table, nn) @@ -482,7 +485,7 @@ def fit(self): print(f' *** Getting rid of {len(idx):d} out of {len(self.ref_table):d} junk sources') self.ref_table.remove_rows(idx) - if self.iter_callback != None: + if self.iter_callback is not None: self.iter_callback(self.ref_table, nn) # Add times into ref_table meta data @@ -497,8 +500,13 @@ def fit(self): x_inferred = x_inferred[:, np.newaxis] if y_inferred.ndim == 1: y_inferred = y_inferred[:, np.newaxis] - chi2_x_2d = ((self.ref_table['x'] - x_inferred) / self.ref_table['xe'])**2 - chi2_y_2d = ((self.ref_table['y'] - y_inferred) / self.ref_table['ye'])**2 + weighted_xy = ('xe' in self.ref_table.colnames) and ('ye' in self.ref_table.colnames) + if weighted_xy: + chi2_x_2d = ((self.ref_table['x'] - x_inferred) / self.ref_table['xe'])**2 + chi2_y_2d = ((self.ref_table['y'] - y_inferred) / self.ref_table['ye'])**2 + else: + chi2_x_2d = (self.ref_table['x'] - x_inferred)**2 + chi2_y_2d = (self.ref_table['y'] - y_inferred)**2 chi2_x = np.nansum(chi2_x_2d, axis=1) chi2_y = np.nansum(chi2_y_2d, axis=1) chi2_x[~np.isfinite(chi2_x_2d).any(axis=1)] = np.nan @@ -510,11 +518,15 @@ def fit(self): if ('t0' not in self.ref_table.colnames) or ('n_fit' not in self.ref_table.colnames): x_data = np.ma.masked_invalid(self.ref_table['x'].data, copy=True) y_data = np.ma.masked_invalid(self.ref_table['y'].data, copy=True) - xe = self.ref_table['xe'] if 'xe' in self.ref_table.colnames else None - ye = self.ref_table['ye'] if 'ye' in self.ref_table.colnames else None - weighted_xy = (xe is not None) and (ye is not None) - xe_data = np.ma.masked_invalid(self.ref_table['xe'].data, copy=True) if weighted_xy else None - ye_data = np.ma.masked_invalid(self.ref_table['ye'].data, copy=True) if weighted_xy else None + if weighted_xy: + xe = self.ref_table['xe'] + ye = self.ref_table['ye'] + xe_data = np.ma.masked_invalid(xe.data, copy=True) + ye_data = np.ma.masked_invalid(ye.data, copy=True) + else: + xe_data = None + ye_data = None + if (xe_data is not None) and (ye_data is not None): xe_data.mask[np.isclose(xe_data, 0.)] = True ye_data.mask[np.isclose(ye_data, 0.)] = True @@ -578,28 +590,26 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar import shapely for ii in range(len(self.star_lists)): if self.verbose > 0: - msg = ' Matching catalog {0} / {1} with {2:d} stars' - msg2 = ' {0:8s} < {1:0.3f}' - print(" ") - print(" **********") + print() + print(" **********") if nn is not None: - print(f" Iteration {nn+1} / {self.iters}") - print(msg.format((ii + 1), len(self.star_lists), len(self.star_lists[ii]))) - print(msg2.format('dr', dr_tol)) - print(msg2.format('|dm|', dm_tol)) - print(' outlier tol: ', outlier_tol) - print(' mag_lim: ', self.mag_lim[ii]) - print(" **********") + print(f" Iteration {nn+1} / {self.iters}") + print(f' Matching catalog {ii + 1} / {len(self.star_lists)} with {len(self.star_lists[ii]):d} stars') + print(f' dr < {dr_tol}') + print(f' |dm| < {dm_tol}') + print(f' outlier tol: {outlier_tol}') + print(f' mag_lim: {self.mag_lim[ii]}') + print(" **********") star_list = self.star_lists[ii] - # FIXME: why [0]? - ref_list = self.get_ref_list_from_table(star_list['t'][0]) - # if 't' in star_list.meta: - # ref_list = self.get_ref_list_from_table(star_list.meta['t']) - # elif 't' in star_list.colnames: - # ref_list = self.get_ref_list_from_table(star_list['t']) - # else: - # raise KeyError(f"StarList at index {ii} does not have a 't' column or meta['t'] entry. Cannot determine reference list for matching.") + + if 't' in star_list.meta: + ref_list = self.get_ref_list_from_table(star_list.meta['t']) + elif 't' in star_list.colnames: + assert np.unique(star_list['t']).size == 1, f"StarList at index {ii} has multiple unique times in the 't' column. Cannot determine reference list for matching." + ref_list = self.get_ref_list_from_table(star_list['t'][0]) + else: + raise KeyError(f"StarList at index {ii} does not have a 't' column or meta['t'] entry. Cannot determine reference list for matching.") trans = self.trans_list[ii] @@ -609,7 +619,9 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar # star_list_T is trimmed and transformed self.apply_mag_lim_via_use_in_trans(ref_list, ref_mag_lim) star_list_orig_trim = apply_mag_lim(star_list, self.mag_lim[ii]) # trimmed, untransformed copy - star_list_T = apply_mag_lim(star_list, self.mag_lim[ii]) # trimmed, will be transformed copy + star_list_T = copy.deepcopy(star_list_orig_trim) # trimmed, will be transformed copy + + assert len(star_list_orig_trim) > 0, f"No stars remain after applying mag_lim={self.mag_lim[ii]} to star_list at index {ii}. Please check your mag_lim." ### Initial match and transform: 1st order (if we haven't already). if trans is None: @@ -639,15 +651,91 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar star_list_T.transform_xy(trans) # Match stars between the transformed, trimmed lists. - idx1, idx2, dr, dm = match.match(star_list_T['x'], star_list_T['y'], star_list_T['m'], - ref_list['x'], ref_list['y'], ref_list['m'], - dr_tol=dr_tol, dm_tol=dm_tol, verbose=self.verbose) + if 'use_in_trans' in ref_list.colnames: + # Only use stars specified by "use_in_trans" column. + use_in_trans = ref_list['use_in_trans'] + idx1, idx2, dr, dm = match.match( + star_list_T['x'], star_list_T['y'], star_list_T['m'], + ref_list['x'][use_in_trans], ref_list['y'][use_in_trans], ref_list['m'][use_in_trans], + dr_tol=dr_tol, dm_tol=dm_tol, verbose=self.verbose + ) + # Restore idx2 to the full reference list indices + idx2 = np.where(use_in_trans)[0][idx2] + + else: + idx1, idx2, dr, dm = match.match( + star_list_T['x'], star_list_T['y'], star_list_T['m'], + ref_list['x'], ref_list['y'], ref_list['m'], + dr_tol=dr_tol, dm_tol=dm_tol, verbose=self.verbose + ) + if self.verbose > 1: print( ' Match 1: Found ', len(idx1), ' matches out of ', len(star_list_T), '. If match count is low, check dr_tol, dm_tol.' ) + # # Plot matched stars + # unmatched_starlist = np.array([True if i not in idx1 else False for i in range(len(star_list_T))]) + # unmatched_ref = np.array([True if i not in idx2 else False for i in range(len(ref_list))]) + # use_in_trans = ref_list[idx2]['use_in_trans'] + + # fig = go.Figure() + # plotly_stars( + # x=star_list_T['x'][unmatched_starlist], + # y=star_list_T['y'][unmatched_starlist], + # m=star_list_T['m'][unmatched_starlist], + # star_name=star_list_T['name'][unmatched_starlist], + # label='Unmatched Star List', + # color='C9', + # fig=fig + # ) + # plotly_stars( + # x=star_list_T['x'][idx1], + # y=star_list_T['y'][idx1], + # m=star_list_T['m'][idx1], + # label='Matched Star List', + # color='C0', + # fig=fig + # ) + # plotly_stars( + # x=ref_list['x'][idx2][~use_in_trans], + # y=ref_list['y'][idx2][~use_in_trans], + # m=ref_list['m'][idx2][~use_in_trans], + # label='Matched ref (use_in_trans=False)', + # color='C4', + # fig=fig + # ) + # plotly_stars( + # x=ref_list['x'][unmatched_ref & ref_list['use_in_trans']], + # y=ref_list['y'][unmatched_ref & ref_list['use_in_trans']], + # m=ref_list['m'][unmatched_ref & ref_list['use_in_trans']], + # star_name=ref_list['name'][unmatched_ref & ref_list['use_in_trans']], + # label='Unmatched ref (use_in_trans=True)', + # symbol='star-open', + # marker_size=5, + # color='C3', + # fig=fig + # ) + # plotly_stars( + # x=ref_list['x'][idx2][use_in_trans], + # y=ref_list['y'][idx2][use_in_trans], + # m=ref_list['m'][idx2][use_in_trans], + # label='Matched ref (use_in_trans=True)', + # symbol='star', + # marker_size=5, + # color='C3', + # fig=fig + # ) + # fig.update_layout( + # title=f'Matched Stars - Catalog {ii + 1}', + # xaxis_title='X', + # yaxis_title='Y' + # ) + # fig.write_html(os.path.join(self.save_path, f'matched_stars_{ii + 1}.html')) + # fig.show() + + # Outlier rejection - if outlier_tol != None: + if outlier_tol is not None: keepers = self.outlier_rejection_indices(star_list_T[idx1], ref_list[idx2], outlier_tol, verbose=self.verbose) if self.verbose > 1: print( ' Rejected ', len(idx1) - sum(keepers), ' outliers.' ) @@ -655,15 +743,17 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar idx1 = idx1[keepers] idx2 = idx2[keepers] - # Only use stars specified by "use_in_trans" column. - if 'use_in_trans' in ref_list.colnames: - keepers = ref_list[idx2]['use_in_trans'] + # # Only use stars specified by "use_in_trans" column. + # if 'use_in_trans' in ref_list.colnames: + # keepers = ref_list[idx2]['use_in_trans'] + # if sum(keepers) == 0: + # raise ValueError(f"No stars are marked as 'use_in_trans' in the reference list for starlist index {ii}. Cannot derive transformation.") - if self.verbose > 1: - print( ' Rejected ', len(idx1) - sum(keepers), ' with use_in_trans=False.' ) + # if self.verbose > 1: + # print( f' Rejected {len(idx1) - sum(keepers)} stars out of {len(idx1)} with use_in_trans=False.' ) - idx1 = idx1[keepers] - idx2 = idx2[keepers] + # idx1 = idx1[keepers] + # idx2 = idx2[keepers] # Determine weights in the fit. weight = self.get_weights_for_lists(ref_list[idx2], star_list_T[idx1]) @@ -671,11 +761,13 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar # Derive the best-fit transformation parameters. if self.verbose > 1: print( ' Using ', len(idx1), ' stars in transformation.' ) - trans = self.trans_class.derive_transform(star_list_orig_trim['x'][idx1], star_list_orig_trim['y'][idx1], - ref_list['x'][idx2], ref_list['y'][idx2], - **trans_args, - m=star_list_orig_trim['m'][idx1], mref=ref_list['m'][idx2], - weights=weight, mag_trans=self.mag_trans) + trans = self.trans_class.derive_transform( + star_list_orig_trim['x'][idx1], star_list_orig_trim['y'][idx1], + ref_list['x'][idx2], ref_list['y'][idx2], + **trans_args, + m=star_list_orig_trim['m'][idx1], mref=ref_list['m'][idx2], + weights=weight, mag_trans=self.mag_trans + ) # Save the final transformation. self.trans_list[ii] = trans @@ -684,12 +776,15 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar # NOTE: We will not recalculate weights here if self.calc_trans_inverse: if self.verbose > 1: - print('Doing inverse') - trans_inv = self.trans_class.derive_transform(ref_list['x'][idx2], ref_list['y'][idx2], - star_list_orig_trim['x'][idx1], star_list_orig_trim['y'][idx1], - trans_args['order'], m=ref_list['m'][idx2], - mref=star_list_orig_trim['m'][idx1], weights=weight, - mag_trans=self.mag_trans) + print('Calculating inverse transformation...') + + trans_inv = self.trans_class.derive_transform( + ref_list['x'][idx2], ref_list['y'][idx2], + star_list_orig_trim['x'][idx1], star_list_orig_trim['y'][idx1], + trans_args['order'], m=ref_list['m'][idx2], + mref=star_list_orig_trim['m'][idx1], weights=weight, + mag_trans=self.mag_trans + ) self.trans_list_inverse[ii] = trans_inv # Apply the XY transformation to a new copy of the starlist and @@ -724,9 +819,11 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar dm=(star_t['m'] - star_r['m']), xo=star_s['x'], yo=star_s['y'], mo=star_s['m'])) - idx_lis, idx_ref, dr, dm = match.match(star_list_T['x'], star_list_T['y'], star_list_T['m'], - ref_list['x'], ref_list['y'], ref_list['m'], - dr_tol=dr_tol, dm_tol=dm_tol, verbose=self.verbose) + idx_lis, idx_ref, dr, dm = match.match( + star_list_T['x'], star_list_T['y'], star_list_T['m'], + ref_list['x'], ref_list['y'], ref_list['m'], + dr_tol=dr_tol, dm_tol=dm_tol, verbose=self.verbose + ) if self.verbose > 1: print( ' Match 2: After trans, found ', len(idx_lis), ' matches out of ', len(star_list_T), @@ -735,7 +832,7 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar ## Make plot, if desired if self.save_path: plots.trans_positions(ref_list, ref_list[idx_ref], star_list_T, star_list_T[idx_lis], - save_path=os.path.join(self.save_path, f"Transformed_Positions_{ii}_{star_list_T['t'][0]}.png"), + save_path=os.path.join(self.save_path, 'plots', f'iter{nn}', f"Transformed_Positions_Starlist_{ii}_t_{star_list_T['t'][0]}.png"), show_plot=False) ### Update the observed (but transformed) values in the reference table. self.update_ref_table_from_list(star_list, star_list_T, ii, idx_ref, idx_lis, idx2) @@ -743,11 +840,11 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar ### Update the "average" values to be used as the reference frame for the next list. keep_ref_orig = (self.update_ref_orig==False) or (self.update_ref_orig=='atend') or (self.update_ref_orig=='periter' and ii<(len(self.star_lists) - 1)) if keep_ref_orig and ii < (len(self.star_lists) - 1): - keep_orig = self.ref_table['ref_orig'] | np.isnan(self.ref_table['x'][:,ii]) + keep_orig = self.ref_table['ref_orig'] | (~np.isfinite(self.ref_table['x'][:,ii])) elif keep_ref_orig: keep_orig = self.ref_table['ref_orig'] elif ii < (len(self.star_lists) - 1): - keep_orig = np.isnan(self.ref_table['x'][:,ii]) + keep_orig = ~np.isfinite(self.ref_table['x'][:,ii]) else: keep_orig=None self.update_ref_table_aggregates(keep_orig=keep_orig) @@ -759,7 +856,7 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar # Print out some metrics if self.verbose > 0: msg1 = ' {0:2s} (mean and std) for {1:10s}: {2:8.5f} +/- {3:8.5f}' - print(' Residuals: ') + print(' Residuals: ') print(msg1.format('dr', 'all stars', dr.mean(), dr.std())) print(msg1.format('dm', 'all stars', dm.mean(), dm.std())) @@ -775,6 +872,13 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar print(' Used {0:d} trans ref stars.'.format(len(used))) print(' Dropped {0:d} matches after transform.'.format(len(used) - len(used_good))) gc.collect() # clean up memory after each iteration + + # Save ref_table after each iteration + # print(f"Saving self after iteration {ii=}") + # if self.save_path: + # with open(os.path.join(self.save_path, f"{self.prefix_name}_iter.pkl"), 'wb') as file: + # pickle.dump(self, file) + return def setup_trans_info(self): @@ -791,7 +895,7 @@ def setup_trans_info(self): iters = self.iters trans_list = [None for ii in range(N_lists)] - if trans_input != None: + if trans_input is not None: trans_list = [trans_input[ii] for ii in range(N_lists)] # Keep a list of trans_args, one for each starlist. If only @@ -933,7 +1037,7 @@ def apply_mag_lim_via_use_in_trans(self, ref_list, ref_mag_lim): This should really only be applied to reference star lists. """ - if ((ref_mag_lim is not None) and (ref_mag_lim[0] is not None)): + if ref_mag_lim is not None: # Support 'm0' (primary) or 'm' column name. if 'm0' in ref_list.colnames: mcol = 'm0' @@ -1164,7 +1268,7 @@ def get_weights_for_lists(self, ref_list, star_list): var_xlis = 0.0 var_ylis = 0.0 - if self.trans_weighting != None: + if self.trans_weighting is not None: if self.trans_weighting == 'both,var': weight = 1.0 / (var_xref + var_xlis + var_yref + var_ylis) if self.trans_weighting == 'both,std': @@ -1297,7 +1401,7 @@ def reset_ref_values(self, exclude=None): """ # All 2D columns should be reset. for col_name in self.ref_table.colnames: - if (exclude != None) and (col_name in exclude): + if (exclude is not None) and (col_name in exclude): continue if len(self.ref_table[col_name].data.shape) == 2: # Find the 2D columns @@ -1481,7 +1585,7 @@ def calc_bootstrap_errors(self, n_boot=100, seed=None, boot_epochs_min=-1, calc_ # Calculate weights based on weights keyword. If weights desired, will need to # make starlist objects for this - if self.trans_weighting != None: + if self.trans_weighting is not None: # In order for weights calculation to work, we need to apply a transformation # to the star_list_T so it is in the same units as ref_boot. So, we'll apply # the final transformation for the epoch to get close enough for the @@ -1852,8 +1956,8 @@ def = None. If not None, then this should contain an array or list of transform mag_lim : array If different from None, it indicates the minimum and maximum magnitude on the starlists for finding the transformations BEFORE mag trans. - Note, if you want specify the mag_lim separately for each list and each iteration, - you need to pass in a 2D array that has shape (N_lists, N_iters). + Note, if you want specify the mag_lim separately for each list, + you need to pass in a 2D array that has shape (N_lists, 2). ref_mag_lim : array If different from None, it indicates the minimum and maximum magnitude @@ -1926,7 +2030,6 @@ def = None. If not None, then this should contain an array or list of transform list_of_starlists, # Alignment parameters ref_index=-1, - iters=iters, dr_tol=dr_tol, dm_tol=dm_tol, outlier_tol=outlier_tol, @@ -2061,6 +2164,9 @@ def fit(self): for key, value in input_dict.items(): file.write(f'{key}:\t{value}\n') + if self.ref_mag_lim is not None: + self.ref_mag_lim[0] = self.ref_mag_lim[0] if self.ref_mag_lim[0] is not None else -np.inf + self.ref_mag_lim[1] = self.ref_mag_lim[1] if self.ref_mag_lim[1] is not None else np.inf # if self.save_path is not None: # with open(f'{os.path.dirname(self.save_path)}/MosaicToRef_input_params.log', 'w',) as _log: @@ -2129,10 +2235,10 @@ def fit(self): ### Drop all stars that have 0 detections. idx = np.where((self.ref_table['n_detect'] == 0))[0] # & (self.ref_table['ref_orig'] == False))[0] if self.verbose > 0: - print(' *** Getting rid of {0:d} out of {1:d} junk sources'.format(len(idx), len(self.ref_table))) + print(' *** Getting rid of {0:d} out of {1:d} junk sources'.format(len(idx), len(self.ref_table))) self.ref_table.remove_rows(idx) - if self.iter_callback != None: + if self.iter_callback is not None: self.iter_callback(self.ref_table, nn) ########## @@ -2161,17 +2267,17 @@ def fit(self): ########## # Find where stars are detected. if self.verbose > 0: - print(' Preparing the reference table...') + print(' Preparing the reference table...') self.ref_table.detections() ### Drop all stars that have 0 detections. idx = np.where((self.ref_table['n_detect'] == 0))[0] # & (self.ref_table['ref_orig'] == False))[0] if self.verbose: - print(' *** Getting rid of {0:d} out of {1:d} junk sources'.format(len(idx), len(self.ref_table))) + print(' *** Getting rid of {0:d} out of {1:d} junk sources'.format(len(idx), len(self.ref_table))) self.ref_table.remove_rows(idx) - if self.iter_callback != None: + if self.iter_callback is not None: self.iter_callback(self.ref_table, nn) # Add times into ref_table meta data @@ -2665,8 +2771,7 @@ def calc_mag_avg_all_stars(d): -def initial_align(table1, table2, briteN=100, - transformModel=transforms.PolyTransform, order=1, req_match=5): +def initial_align(table1, table2, briteN=100, transformModel=transforms.PolyTransform, order=1): """ Calculates an initial (unweighted) transformation from table1 starlist into table2 starlist (i.e., table2 is the reference starlist). Matching is done using @@ -2717,9 +2822,6 @@ def initial_align(table1, table2, briteN=100, -order: int Order of the transformation. Not relevant for 4 parameter or spline fit - -req_match: int - Number of required matches of the input catalog to the total reference - Output: ------ Transformation object @@ -2958,13 +3060,13 @@ def find_transform_new(table1_mat, table2_mat, y2 = table2_mat['y'] # Get the uncertainties (if needed) and calculate the weights. - if weights != None: + if weights is not None: x1e = table1_mat['xe'] y1e = table1_mat['ye'] x2e = table2_mat['xe'] y2e = table2_mat['ye'] - if transInit != None: + if transInit is not None: table1T_mat = table1_mat.copy() table1T_mat = transform_from_object(table1T_mat, transInit) @@ -3498,26 +3600,16 @@ def velocity_transform_from_object(x0, y0, x0e, y0e, vx, vy, vxe, vye, transform return vx_new, vy_new, vxe_new, vye_new - - -def check_iter_tolerances(iters, dr_tol, dm_tol, outlier_tol): - # iteration tolerances must match the number of iterations requested. - assert iters == len(dr_tol) - assert iters == len(dm_tol) - assert iters == len(outlier_tol) - - return - def check_trans_input(list_of_starlists, trans_input, mag_trans): # Check trans_input # If we are transforming magnitudes and their are input transformations, # then they need to have a mag_offset on them. - if trans_input != None: - assert len(trans_input) == len(list_of_starlists) + if trans_input is not None: + assert len(trans_input) == len(list_of_starlists), f'trans_input (len={len(trans_input)}) must have the same length as list_of_starlists (len={len(list_of_starlists)})!' if mag_trans: for ii in range(len(trans_input)): - if trans_input[ii] != None: + if trans_input[ii] is not None: try: trans_input[ii].mag_offset except NameError: @@ -3648,12 +3740,9 @@ def trans_initial_guess( def update_old_and_new_names(ref_table, list_index, idx_ref_new): # Make new ref_list names for the new stars. new_names = [] - new_name_len_max = 0 - for ss in idx_ref_new: - new_name = f"{list_index:3d}_{str(ref_table['name_in_list'][ss, list_index]):s}" - new_names.append(new_name) - new_name_len_max = max(new_name_len_max, len(new_name)) + new_names = [f"{list_index:3d}_{name}" for name in ref_table['name_in_list'][idx_ref_new, list_index]] + new_name_len_max = np.max([len(new_name) for new_name in new_names]) old_names = ref_table['name'] old_name_len = [len(old_name) for old_name in old_names] @@ -3758,7 +3847,7 @@ def setup_trans_info(trans_input, trans_args, N_lists, iters): iters : int """ trans_list = [None for ii in range(N_lists)] - if trans_input != None: + if trans_input is not None: trans_list = [trans_input[ii] for ii in range(N_lists)] # Keep a list of trans_args, one for each starlist. If only @@ -3816,7 +3905,7 @@ def get_weighting_scheme(weights, ref_list, star_list): var_xlis = 0.0 var_ylis = 0.0 - if weights != None: + if weights is not None: if weights == 'both,var': weight = 1.0 / (var_xref + var_xlis + var_yref + var_ylis) if weights == 'both,std': From 0fc17a15599cfd23b8e620b2dfd16cc879699825 Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Tue, 28 Jul 2026 23:39:43 -0700 Subject: [PATCH 232/254] Optimized calculation and added data checking in match.match --- flystar/match.py | 60 ++++++++++++++++++++++-------------------------- 1 file changed, 27 insertions(+), 33 deletions(-) diff --git a/flystar/match.py b/flystar/match.py index 241d334..c75b9e2 100644 --- a/flystar/match.py +++ b/flystar/match.py @@ -292,11 +292,12 @@ def match(x1, y1, m1, x2, y2, m2, dr_tol, dm_tol=None, verbose=True): For two stars to be matched, they must be within a specified radius (dr_tol) and delta-magnitude (dm_tol). For stars with more than 1 neighbor (within the tolerances), if one is found that is the best match in both brightness and positional offsets - (closest in both), then the match is made. Otherwise, - their is a conflict and no match is returned for the star. + (closest in both), then the match is made. + Otherwise, their is a conflict and no match is returned for the star. Parameters + ---------- x1 : array-like X coordinate in the first catalog y1 : array-like @@ -333,6 +334,11 @@ def match(x1, y1, m1, x2, y2, m2, dr_tol, dm_tol=None, verbose=True): dm : float array Delta-mag between the matches. (m1 - m2) + Raises + ------ + ValueError + If the input arrays do not have the same shape or if they do not contain any finite values. + Or when no match is found between the two catalogs. """ x1 = np.array(x1, copy=False) @@ -342,10 +348,15 @@ def match(x1, y1, m1, x2, y2, m2, dr_tol, dm_tol=None, verbose=True): y2 = np.array(y2, copy=False) m2 = np.array(m2, copy=False) - if x1.shape != y1.shape: - raise ValueError('x1 and y1 do not match!') - if x2.shape != y2.shape: - raise ValueError('x2 and y2 do not match!') + if not np.isfinite(x1).any(): raise ValueError('x1 does not contain any finite values!') + if not np.isfinite(y1).any(): raise ValueError('y1 does not contain any finite values!') + if not np.isfinite(m1).any(): raise ValueError('m1 does not contain any finite values!') + if not np.isfinite(x2).any(): raise ValueError('x2 does not contain any finite values!') + if not np.isfinite(y2).any(): raise ValueError('y2 does not contain any finite values!') + if not np.isfinite(m2).any(): raise ValueError('m2 does not contain any finite values!') + + assert x1.shape == y1.shape, 'x1 and y1 do not match!' + assert x2.shape == y2.shape, 'x2 and y2 do not match!' # Setup coords1 pairs and coords 2 pairs # this is equivalent to, but faster than just doing np.array([x1, y1]) @@ -403,18 +414,16 @@ def match(x1, y1, m1, x2, y2, m2, dr_tol, dm_tol=None, verbose=True): else: i2_tmp = np.array([i2_match[mm] for mm in i1_nn]) - # Repeat star list 1 positions and magnitudes - # for nn times (tile then transpose) - x1_nn = np.tile(x1[i1_nn], (nn, 1)).T - y1_nn = np.tile(y1[i1_nn], (nn, 1)).T - m1_nn = np.tile(m1[i1_nn], (nn, 1)).T + x1_nn = x1[i1_nn] + y1_nn = y1[i1_nn] + m1_nn = m1[i1_nn] # Get out star list 2 positions and magnitudes x2_nn = x2[i2_tmp] y2_nn = y2[i2_tmp] m2_nn = m2[i2_tmp] - dr = np.hypot(x1_nn - x2_nn, y1_nn - y2_nn) - dm = np.abs(m1_nn - m2_nn) + dr = np.hypot(x2_nn - x1_nn[:, np.newaxis], y2_nn - y1_nn[:, np.newaxis]) + dm = np.abs(m2_nn - m1_nn[:, np.newaxis]) if dm_tol is not None: # Don't even consider stars that exceed our @@ -462,23 +471,16 @@ def match(x1, y1, m1, x2, y2, m2, dr_tol, dm_tol=None, verbose=True): # Index into the idxs1, idxs2 array of this duplicate. dups = np.where(idxs2 == duplicates[dd])[0] - # Assume the duplicates are confused first... see if we - # can resolve the confusion below. - keep[dups] = False - - dm_dups = m1[idxs1[dups]] - m2[idxs2[dups]] - dr_dups = np.hypot(x1[idxs1[dups]] - x2[idxs2[dups]], y1[idxs1[dups]] - y2[idxs2[dups]]) - - dm_min = np.abs(dm_dups).argmin() - dr_min = np.abs(dr_dups).argmin() + dm_min = np.abs(dm[dups]).argmin() + dr_min = np.abs(dr[dups]).argmin() # If there is a clearly preferred match (closest in distance and brightness), then - # keep it and dump the other duplicates. + # keep it and dump the other duplicates. Otherwise, drop the match as confused. if dm_min == dr_min: - keep[dups[dm_min]] = True - else: if verbose > 3: print(' confused, dropping star at',x2[idxs2[dups]][0],y2[idxs2[dups]][0]) + else: + keep[dups[dm_min]] = False # Clean up the duplicates @@ -666,14 +668,6 @@ def generic_match(sl1, sl2, init_mode='triangle', for i_loop in range(len(order_dr)): # Transform and match the catalog to the reference frame -# sl2_idx, sl1_idx = align.transform_and_match(sl2_match, sl1_match, transf, -# dr_tol=order_dr[i_loop][1], -# verbose=verbose) - import matplotlib.pyplot as plt - plt.clf() - plt.plot(sl1_match['x'], sl1_match['y'], 'x', ms=10) - plt.plot(sl2_match['x'], sl2_match['y'], 'o') - sl2_idx, sl1_idx = align.transform_and_match(sl2_match, sl1_match, transf, dr_tol=order_dr[1], verbose=verbose) From b52b880dd7cd4c1cdebd2d1444176c177eb2ebd5 Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Tue, 28 Jul 2026 23:50:33 -0700 Subject: [PATCH 233/254] Added plt.show/close! Adjusted figure size --- flystar/motion_model.py | 6 +- flystar/plots.py | 123 +++++++++++++++++++++++----------------- 2 files changed, 74 insertions(+), 55 deletions(-) diff --git a/flystar/motion_model.py b/flystar/motion_model.py index 5bf737e..0081fba 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -254,10 +254,10 @@ def calc_chi2(self, t, x, y, xe, ye, fit_params, fixed_params_dict=None, reduced if reduced: if len(t) == self.n_params: return np.inf, np.inf - if not parallax: - degree_of_freedom = len(x) - self.n_params - else: + if parallax: degree_of_freedom = 2*len(x) - len(self.fit_param_names) + else: + degree_of_freedom = len(x) - self.n_params chi2x, chi2y = chi2x / degree_of_freedom, chi2y / degree_of_freedom return chi2x, chi2y diff --git a/flystar/plots.py b/flystar/plots.py index 86904b8..277e472 100755 --- a/flystar/plots.py +++ b/flystar/plots.py @@ -1,20 +1,18 @@ -import pdb +import os import math import astropy import matplotlib import numpy as np -import matplotlib.mlab as mlab import matplotlib.pyplot as plt import matplotlib.colors as mcolors from matplotlib import cm +from matplotlib.ticker import FormatStrFormatter from scipy.stats import chi2 from scipy.stats import norm from scipy.optimize import curve_fit -from astropy.io import ascii from astropy import units as u from astropy.table import Table from astropy.coordinates import SkyCoord -from . import motion_model, startables #################################################### # Code for making diagnostic plots for astrometry @@ -62,7 +60,7 @@ def trans_positions(ref, ref_mat, starlist, starlist_mat, xlim=None, ylim=None, If true, show the plot. Default is True """ - plt.figure(figsize=(10,10)) + plt.figure(figsize=(6, 6)) plt.clf() plt.plot(ref['x'], ref['y'], 'g+', ms=5, label='Reference') plt.plot(starlist['x'], starlist['y'], 'rx', ms=5, label='starlist') @@ -72,7 +70,7 @@ def trans_positions(ref, ref_mat, starlist, starlist_mat, xlim=None, ylim=None, linestyle='None', label='Matched starlist') plt.xlabel('X position (Reference Coords)') plt.ylabel('Y position (Reference Coords)') - plt.legend(numpoints=1) + plt.legend(numpoints=1, loc='lower right') plt.title('Label.dat Positions After Transformation') if xlim != None: plt.axis([xlim[0], xlim[1], ylim[0], ylim[1]]) @@ -80,6 +78,9 @@ def trans_positions(ref, ref_mat, starlist, starlist_mat, xlim=None, ylim=None, plt.axis('equal') if save_path: + if not os.path.exists(os.path.dirname(save_path)): + os.makedirs(os.path.dirname(save_path)) + plt.tight_layout() plt.savefig(save_path, dpi=300) if show_plot: plt.show() @@ -126,7 +127,7 @@ def pos_diff_hist(ref_mat, starlist_mat, nbins=25, bin_width=None, xlim=None, fi bins = np.arange(min_range, max_range+bin_width, bin_width) - plt.figure(figsize=(10,10)) + plt.figure(figsize=(6, 6)) plt.clf() plt.hist(diff_x, histtype='step', bins=bins, color='blue', label='X') plt.hist(diff_y, histtype='step', bins=bins, color='red', label='Y') @@ -136,6 +137,7 @@ def pos_diff_hist(ref_mat, starlist_mat, nbins=25, bin_width=None, xlim=None, fi if xlim != None: plt.xlim([xlim[0], xlim[1]]) plt.legend() + plt.tight_layout() if fileName != None: plt.savefig(root + fileName[3:8] + 'Positions_hist_' + '.png', dpi=300) else: @@ -254,7 +256,7 @@ def pos_diff_err_hist(ref_mat, starlist_mat, transform, nbins=25, bin_width=None bins = np.arange(min_range, max_range+bin_width, bin_width) - plt.figure(figsize=(10,10)) + plt.figure(figsize=(6, 6)) plt.clf() n_x, bins_x, p = plt.hist(ratio_x, histtype='step', bins=bins, color='blue', label='X', density=True, linewidth=2) @@ -293,6 +295,7 @@ def pos_diff_err_hist(ref_mat, starlist_mat, transform, nbins=25, bin_width=None if xlim != None: plt.xlim([xlim[0], xlim[1]]) plt.legend() + plt.tight_layout() if fileName != None: plt.savefig(root + fileName[3:8] + 'Positions_err_ratio_hist_' + '.png', dpi=300) else: @@ -325,12 +328,13 @@ def mag_diff_hist(ref_mat, starlist_mat, bins=25, fileName=None, root='./'): bad2 = np.where(bad == True) diff_m = np.delete(diff_m, bad2) - plt.figure(figsize=(10,10)) + plt.figure(figsize=(6, 6)) plt.clf() plt.hist(diff_m, bins=bins) plt.xlabel('Reference Mag - TransStarlist Mag') plt.ylabel('N stars') plt.title('Magnitude Difference for matched stars') + plt.tight_layout() if fileName != None: plt.savefig(root + fileName[3:8] + 'Magnitude_hist_' + '.png', dpi=300) else: @@ -417,7 +421,7 @@ def pos_diff_quiver(ref_mat, starlist_mat, qscale=10, keyLength=0.2, xlim=None, s = len(xpos) - plt.figure(figsize=(10,10)) + plt.figure(figsize=(6, 6)) plt.clf() q = plt.quiver(xpos, ypos, diff_x, diff_y, scale=qscale) fmt = '{0} ref units'.format(keyLength) @@ -432,19 +436,22 @@ def pos_diff_quiver(ref_mat, starlist_mat, qscale=10, keyLength=0.2, xlim=None, plt.axis([xlim[0], ylim[1], ylim[0], ylim[1]]) if sigma: if fileName != None: - plt.title('(Reference - Transformed Starlist positions) / sigma') - plt.savefig(root + fileName[3:8] + 'Positions_quiver_sigma_' + '.png', dpi=300) + title = '(Reference - Transformed Starlist positions) / sigma' + save_path = root + fileName[3:8] + 'Positions_quiver_sigma.png' else: - plt.title('(Reference - Transformed Starlist positions) / sigma') - plt.savefig(root + 'Positions_quiver_sigma.png', dpi=300) + title = '(Reference - Transformed Starlist positions) / sigma' + save_path = root + 'Positions_quiver_sigma.png' else: if fileName != None: - plt.title('Reference - Transformed Starlist positions') - plt.savefig(root + fileName[3:8] + 'Positions_quiver_' + '.png', dpi=300) + title = 'Reference - Transformed Starlist positions' + save_path = root + fileName[3:8] + 'Positions_quiver.png' else: - plt.title('Reference - Transformed Starlist positions') - plt.savefig(root + 'Positions_quiver.png', dpi=300) + title = 'Reference - Transformed Starlist positions' + save_path = root + 'Positions_quiver.png' + plt.title(title) + plt.tight_layout() + plt.savefig(save_path, dpi=300) plt.close() return @@ -478,7 +485,7 @@ def vpd(ref, starlist_trans, vxlim, vylim): trans_vx = starlist_trans['vx'] trans_vy = starlist_trans['vy'] - plt.figure(figsize=(10,10)) + plt.figure(figsize=(6, 6)) plt.clf() plt.plot(trans_vx, trans_vy, 'k.', ms=8, label='Transformed', alpha=0.4) plt.plot(ref_vx, ref_vy, 'r.', ms=8, label='Reference', alpha=0.4) @@ -488,7 +495,9 @@ def vpd(ref, starlist_trans, vxlim, vylim): plt.axis([vxlim[0], vylim[1], vylim[0], vylim[1]]) plt.title('Reference and Transformed Proper Motions') plt.legend() + plt.tight_layout() plt.savefig('Transformed_velocities.png', dpi=300) + plt.close() return @@ -544,7 +553,7 @@ def vel_diff_err_hist(ref_mat, starlist_mat, nbins=25, bin_width=None, vxlim=Non sigma = 1 x = np.arange(-6, 6, 0.1) - plt.figure(figsize=(20,10)) + plt.figure(figsize=(12, 6)) plt.subplot(121) plt.subplots_adjust(left=0.1) plt.hist(ratio_vx, bins=xbins, histtype='step', color='black', density=True, @@ -612,7 +621,7 @@ def residual_vpd(ref_mat, starlist_trans_mat, pscale=None): yerr = np.hypot(ref_mat['vy_err'], starlist_trans_mat['vy_err']) # Plotting - plt.figure(figsize=(10,10)) + plt.figure(figsize=(6, 6)) plt.clf() plt.errorbar(diff_x, diff_y, xerr=xerr, yerr=yerr, fmt='k.', ms=8, alpha=0.5) if pscale != None: @@ -622,13 +631,15 @@ def residual_vpd(ref_mat, starlist_trans_mat, pscale=None): plt.xlabel('Reference_vx - Transformed_vx (reference coords)') plt.ylabel('Reference_vy - Transformed_vy (reference coords)') plt.title('Proper Motion Residuals') + plt.tight_layout() plt.savefig('resid_vpd.png', dpi=300) + plt.close() return def plotStar(starNames, rootDir='./', align='align/align_d_rms_1000_abs_t', - poly='polyfit_d/fit', points='points_d/', radial=False, NcolMax=3, figsize=(15,15)): + poly='polyfit_d/fit', points='points_d/', radial=False, NcolMax=3, figsize=(6, 6)): print( 'Creating residuals plots for star(s):' ) print( starNames ) @@ -771,7 +782,6 @@ def plotStar(starNames, rootDir='./', align='align/align_d_rms_1000_abs_t', maxErr = np.array([xerr, yerr]).max() resTicRng = [-1.1*maxErr, 1.1*maxErr] - from matplotlib.ticker import FormatStrFormatter fmtX = FormatStrFormatter('%5i') fmtY = FormatStrFormatter('%6.2f') fontsize1 = 10 @@ -914,7 +924,6 @@ def plotStar(starNames, rootDir='./', align='align/align_d_rms_1000_abs_t', maxErr = np.array([rerr, terr]).max() resTicRng = [-3*maxErr, 3*maxErr] - from matplotlib.ticker import FormatStrFormatter fmtX = FormatStrFormatter('%5i') fmtY = FormatStrFormatter('%6.2f') @@ -1005,7 +1014,7 @@ def plotStar(starNames, rootDir='./', align='align/align_d_rms_1000_abs_t', ################################################## def plot_pm(tab): - plt.figure(figsize=(6,6)) + plt.figure(figsize=(6, 6)) plt.clf() plt.subplots_adjust(top=0.85) q = plt.quiver(tab['x0'].data, tab['y0'].data, @@ -1031,7 +1040,7 @@ def plot_gaia(gaia): pmra = gaia['pmra'] pmdec = gaia['pmdec'] - plt.figure(figsize=(6,6)) + plt.figure(figsize=(6, 6)) plt.clf() plt.subplots_adjust(top=0.85) q = plt.quiver(d_ra_tan.data, d_de_tan.data, @@ -1045,8 +1054,6 @@ def plot_gaia(gaia): fmt = r'[$\alpha$, $\delta$] = [{0:8.3f}$^\circ$, {1:8.3f}$^\circ$]' plt.title(fmt.format(ra_tan_mean, de_tan_mean)) plt.gca().invert_xaxis() - - return def plot_pm_error(tab, save_path=None): @@ -1430,8 +1437,7 @@ def plot_mag_scatter(m_t, m0, m0e, x_t, y_t, xe_t, ye_t, x_ref, y_ref, good_idx, xgood = np.cos(np.radians(agood)) * rgood xref = np.cos(np.radians(aref)) * rref - fig, ax = plt.subplots(7, 1, figsize=(6,18), sharex=True, num=103) -# plt.clf() + fig, ax = plt.subplots(7, 1, figsize=(6, 18), sharex=True, num=103) plt.subplots_adjust(hspace=0.01) ax[0].scatter(mgood, agood, color='black', alpha=0.3, s=2) ax[0].scatter(mref, aref, color='red', alpha=0.3, s=2) @@ -1481,8 +1487,10 @@ def plot_mag_scatter(m_t, m0, m0e, x_t, y_t, xe_t, ye_t, x_ref, y_ref, good_idx, ax[6].axhline(y=0) ax[0].set_title(title) + plt.tight_layout() plt.show() plt.pause(1) + return def plot_y_scatter(m_t, m0, m0e, x_t, y_t, xe_t, ye_t, x_ref, y_ref, good_idx, ref_idx, title, da=0, xorig=None, yorig=None, cte_fit=None, mlim=15): @@ -1527,7 +1535,7 @@ def plot_y_scatter(m_t, m0, m0e, x_t, y_t, xe_t, ye_t, x_ref, y_ref, good_idx, r xgood = np.cos(np.radians(agood)) * rgood xref = np.cos(np.radians(aref)) * rref - fig, ax = plt.subplots(7, 1, figsize=(6,18), sharex=True, num=103) + fig, ax = plt.subplots(7, 1, figsize=(6, 18), sharex=True, num=103) # plt.clf() plt.subplots_adjust(hspace=0.01) ax[0].scatter(yorig[good_idx], agood, color='black', alpha=0.3, s=2) @@ -1580,6 +1588,7 @@ def plot_y_scatter(m_t, m0, m0e, x_t, y_t, xe_t, ye_t, x_ref, y_ref, good_idx, r ax[6].axhline(y=0) ax[0].set_title(title) + plt.tight_layout() plt.show() plt.pause(1) @@ -1617,7 +1626,7 @@ def plot_y_scatter(m_t, m0, m0e, x_t, y_t, xe_t, ye_t, x_ref, y_ref, good_idx, r aref_new = angle_from_xy(xref, yref_new) % 360 rref_new = np.hypot(xref, yref_new) - fig, ax = plt.subplots(4, 2, figsize=(12,12), sharex=True, sharey='row', num=105) + fig, ax = plt.subplots(4, 2, figsize=(12, 12), sharex=True, sharey='row', num=105) plt.subplots_adjust(hspace=0.01, wspace=0.01) ax[0,0].scatter(mgood, ygood, color='black', alpha=0.3, s=2) ax[0,0].scatter(mref, yref, color='red', alpha=0.3, s=2) @@ -1670,6 +1679,9 @@ def plot_y_scatter(m_t, m0, m0e, x_t, y_t, xe_t, ye_t, x_ref, y_ref, good_idx, r ax[3,1].scatter(mref, aref_new, color='red', alpha=0.3, s=2) ax[3,1].set_xlabel('mag') + plt.tight_layout() + plt.show() + if cte_fit=='power_line': idx1 = np.where((mgood > 15) & (mgood < 18.5))[0] idx2 = np.where(mgood > 18.5)[0] @@ -1734,7 +1746,7 @@ def plot_y_scatter(m_t, m0, m0e, x_t, y_t, xe_t, ye_t, x_ref, y_ref, good_idx, r aref_new2 = angle_from_xy(xref2, yref_new2) % 360 rref_new2 = np.hypot(xref2, yref_new2) - fig, ax = plt.subplots(4, 2, figsize=(12,12), sharex=True, sharey='row', num=105) + fig, ax = plt.subplots(4, 2, figsize=(12, 12), sharex=True, sharey='row', num=105) plt.subplots_adjust(hspace=0.01, wspace=0.01) ax[0,0].scatter(mgood, ygood, color='black', alpha=0.3, s=2) ax[0,0].scatter(mref, yref, color='red', alpha=0.3, s=2) @@ -1996,7 +2008,7 @@ def plot_quiver_residuals_magcolor(x_t, y_t, x_ref, y_ref, mag, good_idx, ref_id plt.tight_layout() plt.show() - fig, ax = plt.subplots(1, 1, figsize=(6,6)) + fig, ax = plt.subplots(1, 1, figsize=(6, 6)) q = ax.quiver(x_ref[good_idx][ref_idx], y_ref[good_idx][ref_idx], dx[good_idx][ref_idx], dy[good_idx][ref_idx], color=cm(norm(mag[good_idx][ref_idx])), scale=quiv_scale, angles='xy', alpha=0.8) ax.quiverkey(q, 0.5, 0.85, quiv_label_val, quiv_label, @@ -2160,13 +2172,13 @@ def plot_quiver_residuals_orig_angle_xy(x_t, y_t, x_ref, y_ref, good_idx, ref_id agood = agood % 360 aref = aref % 360 - plt.figure(figsize=(14,6)) - plt.clf() - ax1 = plt.subplot(1, 2, 1) - ax2 = plt.subplot(1, 2, 2) - plt.subplots_adjust(wspace=0.3) + # plt.figure(figsize=(12,6)) + # plt.clf() + # ax1 = plt.subplot(1, 2, 1) + # ax2 = plt.subplot(1, 2, 2) + # plt.subplots_adjust(wspace=0.3) - plt.clf() + # plt.clf() fig, ax = plt.subplots(1, 2, figsize=(12,6), sharey=True) # plt.clf() plt.subplots_adjust(wspace=0.01) @@ -2183,6 +2195,7 @@ def plot_quiver_residuals_orig_angle_xy(x_t, y_t, x_ref, y_ref, good_idx, ref_id if plotlim is not None: plt.xlim(-1 * plotlim, plotlim) plt.ylim(-1 * plotlim, plotlim) + plt.tight_layout() plt.show() plt.pause(1) @@ -2247,7 +2260,7 @@ def plot_chi2_dist(tab, Ndetect, xlim=40, n_bins=50, boot_err=False): chi2_xaxis = np.linspace(0, xlim, xlim*3) chi2_bins = np.linspace(0, xlim, n_bins) - plt.figure(figsize=(6,4)) + plt.figure(figsize=(6, 4)) plt.clf() plt.hist(x[idx], bins=chi2_bins, histtype='step', label='X', density=True) plt.hist(y[idx], bins=chi2_bins, histtype='step', label='Y', density=True) @@ -2256,6 +2269,8 @@ def plot_chi2_dist(tab, Ndetect, xlim=40, n_bins=50, boot_err=False): plt.title('$N_{epoch} = $' + str(Ndetect) + ', $N_{dof} = $' + str(round(Ndof,2))) plt.xlim(0, xlim) plt.legend() + plt.tight_layout() + plt.show() chi2red_x = x / Ndof chi2red_y = y / Ndof @@ -2326,7 +2341,7 @@ def plot_chi2_reduced_dist(tab, Ndetect, xlim=8, n_bins=50, boot_err=False): print("Reduced chi2 for Ndetect="+str(Ndetect)) chi2_bins = np.linspace(0, xlim, n_bins) - plt.figure(figsize=(6,4)) + plt.figure(figsize=(6, 4)) plt.clf() plt.hist(x[idx]/Ndof[idx], bins=chi2_bins, histtype='step', label='X', density=True) plt.hist(y[idx]/Ndof[idx], bins=chi2_bins, histtype='step', label='Y', density=True) @@ -2335,6 +2350,8 @@ def plot_chi2_reduced_dist(tab, Ndetect, xlim=8, n_bins=50, boot_err=False): plt.title('Reduced chi2, $N_{epoch} = $' + str(Ndetect)) plt.xlim(0, xlim) plt.legend() + plt.tight_layout() + plt.show() chi2red_x = x / Ndof chi2red_y = y / Ndof @@ -2413,7 +2430,7 @@ def plot_chi2_dist_per_filter(tab, Ndetect, xlim=40, n_bins=50, filter=None, boo print(x[idx]) #pdb.set_trace() - plt.figure(figsize=(6,4)) + plt.figure(figsize=(6, 4)) plt.clf() plt.hist(x[idx], bins=chi2_bins, histtype='stepfilled', label='RA', density=True, color='skyblue', alpha=0.8, edgecolor='k') plt.hist(y[idx], bins=chi2_bins, histtype='stepfilled', label='DEC', density=True, color='orange', alpha=0.8, edgecolor='k') @@ -2424,10 +2441,10 @@ def plot_chi2_dist_per_filter(tab, Ndetect, xlim=40, n_bins=50, filter=None, boo plt.xlim(0, xlim) plt.ylabel(r'PDF', fontsize=28) plt.legend(fontsize=20) - plt.tick_params(labelsize=20, direction='in', right=True, top=True) - + plt.tight_layout() plt.savefig(str(filter)+'_chi2_dist.png', dpi=300) + plt.close() chi2red_x = x / Ndof chi2red_y = y / Ndof @@ -2702,7 +2719,7 @@ def plot_chi2_dist_mag(tab, Ndetect, xlim=40, n_bins=30, boot_err=False): chi2_maxis = np.linspace(0, xlim, xlim*3) chi2_bins = np.linspace(0, xlim, n_bins) - plt.figure(figsize=(6,4)) + plt.figure(figsize=(6, 4)) plt.clf() plt.hist(chi2_m[idx], bins=np.arange(xlim*10), histtype='step', density=True) plt.plot(chi2_maxis, chi2.pdf(chi2_maxis, Ndof), 'r-', alpha=0.6, @@ -2710,6 +2727,8 @@ def plot_chi2_dist_mag(tab, Ndetect, xlim=40, n_bins=30, boot_err=False): plt.title('$N_{epoch} = $' + str(Ndetect) + ', $N_{dof} = $' + str(Ndof)) plt.xlim(0, xlim) plt.legend() + plt.tight_layout() + plt.show() print('Mean reduced chi^2: (Ndetect = {0:d} of {1:d})'.format(len(idx), len(tab))) fmt = ' {0:s} = {1:.1f} for N_detect and {2:.1f} for all' @@ -2751,7 +2770,7 @@ def plot_chi2_dist_mag_per_filter(tab, Ndetect, mlim=40, n_bins=30, xlim=40, fil chi2_maxis = np.linspace(0, xlim, xlim*3) chi2_bins = np.linspace(0, xlim, n_bins) - plt.figure(figsize=(6,4)) + plt.figure(figsize=(6, 4)) plt.clf() plt.hist(chi2_m[idx], bins=np.arange(xlim*10), label='mag', histtype='stepfilled', density=True, color='green', alpha=0.7, edgecolor='k') plt.plot(chi2_maxis, chi2.pdf(chi2_maxis, Ndof), 'r-', alpha=0.6, @@ -2761,10 +2780,10 @@ def plot_chi2_dist_mag_per_filter(tab, Ndetect, mlim=40, n_bins=30, xlim=40, fil plt.xlabel(r'$\chi^{2}$', fontsize=28) plt.ylabel(r'PDF', fontsize=28) plt.legend(fontsize=20) - plt.tick_params(labelsize=20, direction='in', right=True, top=True) - + plt.tight_layout() plt.savefig(str(filter)+'_chi2_dist_mag.png', dpi=300) + plt.close() print('Mean reduced chi^2: (Ndetect = {0:d} of {1:d})'.format(len(idx), len(tab))) fmt = ' {0:s} = {1:.1f} for N_detect and {2:.1f} for all' @@ -2919,7 +2938,6 @@ def rs(x): resTicRng = [-1.1*maxErr, 1.1*maxErr] resTicRngM = [-1.1*maxErrM, 1.1*maxErrM] - from matplotlib.ticker import FormatStrFormatter fmtX = FormatStrFormatter('%5i') fmtY = FormatStrFormatter('%6.3f') fmtM = FormatStrFormatter('%5.2f') @@ -3346,7 +3364,6 @@ def rs(x): resTicRng = [-1.1*maxErr, 1.1*maxErr] resTicRngM = [-1.1*maxErrM, 1.1*maxErrM] - from matplotlib.ticker import FormatStrFormatter fmtX = FormatStrFormatter('%5i') fmtY = FormatStrFormatter('%6.3f') fmtM = FormatStrFormatter('%5.2f') @@ -3636,13 +3653,15 @@ def plot_errors_vs_r_m(star_tab, vmax_perr=0.75, vmax_pmerr=0.75): plt.scatter(star_tab['m0'], r, c=p_err, s=8, vmin=0, vmax=vmax_perr) plt.colorbar(label='Pos Err (mas)') plt.xlabel('Mag') - plt.ylabel('Radius (")') + plt.ylabel('Radius (")') plt.subplot(1, 2, 2) plt.scatter(star_tab['m0'], r, c=pm_err, s=8, vmin=0, vmax=vmax_pmerr) plt.colorbar(label='PM Err (mas/yr)') plt.xlabel('Mag') plt.ylabel('Radius (")') + plt.tight_layout() + plt.show() return From 1bc48ced70e5069f945fb7c719ae0c7c716eaa9e Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Tue, 28 Jul 2026 23:52:20 -0700 Subject: [PATCH 234/254] Changed uncertainty to error propagation (error on the mean) in combine_lists, instead of weighted residuals --- flystar/startables.py | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/flystar/startables.py b/flystar/startables.py index 8978c00..49ab6e9 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -490,9 +490,10 @@ def combine_lists(self, col_name_in, weights_col=None, mask_val=None, # Calculate the weighted mean and uncertainty avg = np.ma.average(val_2d_clip, weights=wgt_2d, axis=1) - # std = np.ma.sqrt(1 / np.ma.sum(wgt_2d, axis=1)) # Error propagation for weighted mean + std = np.ma.sqrt(1 / np.ma.sum(wgt_2d, axis=1)) # Error propagation for weighted mean + # Use standard deviation of the weighted residuals as the uncertainty - std = np.ma.sqrt(np.ma.average((val_2d_clip.T - avg).T**2, weights=wgt_2d, axis=1)) + # std = np.ma.sqrt(np.ma.average((val_2d_clip.T - avg).T**2, weights=wgt_2d, axis=1)) if meta_add: self.meta[col_name_in + '0'] = 'weighted' @@ -500,19 +501,18 @@ def combine_lists(self, col_name_in, weights_col=None, mask_val=None, wgt_2d = None # Calculate the weighted mean and uncertainty avg = np.ma.mean(val_2d_clip, axis=1) - # std = np.ma.std(val_2d_clip, axis=1) / np.sqrt(len(list_indices)) # Standard error of the mean + std = np.ma.std(val_2d_clip, axis=1) / np.sqrt(len(list_indices)) # Standard error of the mean # Use standard deviation of the residuals as the uncertainty - std = np.ma.std(val_2d_clip, axis=1) + # std = np.ma.std(val_2d_clip, axis=1) if meta_add: self.meta[col_name_in + '0'] = 'not_weighted' - # FIXME: What does this part do? - # To Do: bring the previous uncertainties of stars that are detected - # in only one input frame. - if (weights_col and weights_col in self.colnames) and (val_2d.shape[1] > 1): - mask_for_singles = ((np.isfinite(val_2d_clip)).sum(axis=1)==1) - std[mask_for_singles]=np.nanmean(err_2d[mask_for_singles], axis=1) + std = np.ma.masked_values(std, 0.) # Mask out any zero uncertainties (i.e., 1 or less valid points) + # # Mask out stars with only 1 valid measurement (i.e., std = 0). + # if (weights_col and weights_col in self.colnames) and (val_2d.shape[1] > 1): + # mask_for_singles = ((np.isfinite(val_2d_clip)).sum(axis=1)==1) + # std[mask_for_singles]=np.nanmean(err_2d[mask_for_singles], axis=1) # Save off our new AVG and STD into new columns with shape (N_stars). col_name_avg = col_name_in + '0' From 98854cd1caf9e63d832eebfb9a457e9cd131f470 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Wed, 29 Jul 2026 19:30:28 -0700 Subject: [PATCH 235/254] Fix testing errors --- flystar/align.py | 8 ++++---- flystar/match.py | 20 ++++++++++---------- 2 files changed, 14 insertions(+), 14 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index c2dda85..64698e8 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -220,6 +220,8 @@ def = None. If not None, then this should contain an array or list of transform plt.errorbar(times, stars_table['x'][0, :], yerr=stars_table['xe'][0, :]) plt.axhline(stars_table['x0'][0] + stars_table['vx'][0]*(times - stars_table['t0'][0])) """ + dr_tol = np.atleast_1d(dr_tol) + dm_tol = np.atleast_1d(dm_tol) self.iters = len(dr_tol) if dm_tol is not None: assert self.iters == len(dm_tol), f'dr_tol (len={self.iters}) and dm_tol (len={len(dm_tol)}) must all have the same length!' @@ -330,7 +332,7 @@ def fix_iterable_conditions(self): assert len(self.outlier_tol) == self.iters, f'len(outlier_tol)={len(self.outlier_tol)} != iters={self.iters}' if self.mag_lim is None: - self.mag_lim = np.repeat([[None, None]], len(self.star_lists), axis=0) + self.mag_lim = np.repeat([None], len(self.star_lists), axis=0) elif (len(self.mag_lim) == 2) and (np.ndim(self.mag_lim) == 1): self.mag_lim = np.repeat([self.mag_lim], len(self.star_lists), axis=0) assert len(self.mag_lim) == len(self.star_lists) @@ -947,9 +949,7 @@ def setup_ref_table_from_starlist(self, star_list): # of the old columns and will only include x, y, m, xe, ye, me. # The columns we have already created will hold transformed values. trans_col_names = ['x', 'y', 'm', 'xe', 'ye', 'me', 'w'] - for tt in range(len(trans_col_names)): - old_name = trans_col_names[tt] - + for old_name in trans_col_names: if old_name in ref_table.colnames: new_col = ref_table[old_name].copy() new_col.name = old_name + '_orig' diff --git a/flystar/match.py b/flystar/match.py index c75b9e2..182cb26 100644 --- a/flystar/match.py +++ b/flystar/match.py @@ -459,8 +459,8 @@ def match(x1, y1, m1, x2, y2, m2, dr_tol, dm_tol=None, verbose=True): idxs1 = idxs1[idxs1 >= 0] idxs2 = idxs2[idxs2 >= 0] - dr = np.hypot(x1[idxs1] - x2[idxs2], y1[idxs1] - y2[idxs2]) - dm = m1[idxs1] - m2[idxs2] + dr = np.hypot(x2[idxs2] - x1[idxs1], y2[idxs2] - y1[idxs1]) + dm = np.abs(m2[idxs2] - m1[idxs1]) # Deal with duplicates duplicates = [item for item, count in list(Counter(idxs2).items()) if count > 1] @@ -471,17 +471,17 @@ def match(x1, y1, m1, x2, y2, m2, dr_tol, dm_tol=None, verbose=True): # Index into the idxs1, idxs2 array of this duplicate. dups = np.where(idxs2 == duplicates[dd])[0] - dm_min = np.abs(dm[dups]).argmin() - dr_min = np.abs(dr[dups]).argmin() + # Assume the duplicates are confused first... see if we can resolve the confusion below. + keep[dups] = False + best_dm = np.abs(m2[idxs2[dups]] - m1[idxs1[dups]]).argmin() + best_dr = np.hypot(x2[idxs2[dups]] - x1[idxs1[dups]], y2[idxs2[dups]] - y1[idxs1[dups]]).argmin() # If there is a clearly preferred match (closest in distance and brightness), then # keep it and dump the other duplicates. Otherwise, drop the match as confused. - if dm_min == dr_min: - if verbose > 3: - print(' confused, dropping star at',x2[idxs2[dups]][0],y2[idxs2[dups]][0]) - else: - keep[dups[dm_min]] = False - + if best_dm == best_dr: + keep[dups[best_dm]] = True + elif verbose > 3: + print(' confused, dropping star at',x2[idxs2[dups]][0],y2[idxs2[dups]][0]) # Clean up the duplicates idxs1 = idxs1[keep] From 598029dcf53380af744ff72f65213b55e5aeb43b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Wed, 29 Jul 2026 19:31:54 -0700 Subject: [PATCH 236/254] Implemented multiprocesssing for fit_motion_models and added testing function --- flystar/motion_model.py | 12 ++- flystar/startables.py | 145 ++++++++++++++++++++++++-------- flystar/tests/test_startable.py | 64 +++++++++++++- 3 files changed, 182 insertions(+), 39 deletions(-) diff --git a/flystar/motion_model.py b/flystar/motion_model.py index 0081fba..4224a42 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -147,8 +147,16 @@ def fit( params, param_errs(, chi2_x, chi2_y) Parameters, uncertainties, and chi squares if return_chi2 is True. The corresponding parameter names are in self.fit_param_names. """ - assert np.ndim(t) == np.ndim(x) == np.ndim(y) == np.ndim(xe) == np.ndim(ye) == 1, "Input arrays must be 1D! Motion model can only fit individual stars" - assert len(t) == len(x) == len(y) == len(xe) == len(ye), "Input arrays must have the same length!" + assert np.ndim(t) == 1, f"Input time array must be 1D! Got shape {np.shape(t)}" + assert np.ndim(x) == 1, f"Input x array must be 1D! Got shape {np.shape(x)}" + assert np.ndim(y) == 1, f"Input y array must be 1D! Got shape {np.shape(y)}" + assert np.ndim(xe) == 1, f"Input xe array must be 1D! Got shape {np.shape(xe)}" + assert np.ndim(ye) == 1, f"Input ye array must be 1D! Got shape {np.shape(ye)}" + + assert len(t) == len(x), f'Input x must have the same length as t! Got len(t)={len(t)}, len(x)={len(x)}' + assert len(t) == len(y), f'Input y must have the same length as t! Got len(t)={len(t)}, len(y)={len(y)}' + assert len(t) == len(xe), f'Input xe must have the same length as t! Got len(t)={len(t)}, len(xe)={len(xe)}' + assert len(t) == len(ye), f'Input ye must have the same length as t! Got len(t)={len(t)}, len(ye)={len(ye)}' if not verbose: warnings.filterwarnings("ignore", category=OptimizeWarning) diff --git a/flystar/startables.py b/flystar/startables.py index 49ab6e9..b8bc54b 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -1,19 +1,22 @@ -from astropy.table import Table, Column, MaskedColumn, hstack -from astropy.stats import sigma_clip -from astropy.time import Time -from scipy.optimize import curve_fit -from tqdm import tqdm -import numpy as np -import warnings -import collections import pdb import time import copy -from flystar import motion_model +import warnings +import collections +import numpy as np import pandas as pd -from flystar.motion_model import Empty, Fixed, Linear + +from tqdm import tqdm +from multiprocessing import Pool +from astropy.time import Time +from astropy.stats import sigma_clip +from astropy.table import Table, Column, MaskedColumn, hstack +from scipy.optimize import curve_fit from pandas.api.types import is_string_dtype from collections.abc import Iterable +from . import motion_model +from .motion_model import Empty, Fixed, Linear + class StarTable(Table): def __init__(self, *args, ref_list=0, **kwargs): """ @@ -562,6 +565,8 @@ def fit_motion_models( mask_lists=None, fill_value=np.nan, art_star=False, + processes=1, + chunksize=None, verbose=True ): """Fit velocity for star table @@ -605,6 +610,10 @@ def fit_motion_models( Fill value when there is not enough data points to fit, by default np.nan art_star : bool, optional Artifical star table or observed star table. If artificial stars, Use the output coordinates for fitting motion models (x[..., 1], y[..., 1]) + processes : int, optional + Number of processes to use for parallel processing, maximum os.cpu_count(), by default 1 (no multiprocessing) + chunksize : int, optional + Chunk size for multiprocessing, by default None (auto) verbose : bool, optional Print verbose messages or not, by default True @@ -967,30 +976,68 @@ def fit_motion_models( ye_stars = [np.array(ye_data[i][unmasked_idx[i]]) for i in unique_index] if with_xe_ye else [np.ones_like(y_star) for y_star in y_stars] # For each star - # Expensive for loop! Prepare everything beforehand to speed up. if len(unique_index) > 0: - for idx, i_star in enumerate(tqdm(unique_index, disable=not verbose, desc=f"Fitting motion model {unique_motion_model}")): - # Fit the star - params, param_errs, chi2_x, chi2_y = motion_model_instance.fit( - t=t_stars[idx], - x=x_stars[idx], - y=y_stars[idx], - xe=xe_stars[idx], - ye=ye_stars[idx], - fixed_params_dict=fixed_params_stars[i_star], - weighting=weighting, - use_scipy=use_scipy, - absolute_sigma=absolute_sigma, - fill_value=fill_value, - return_chi2=True, - bootstrap=bootstrap, - seed=seed, - verbose=verbose - ) - params_array[idx] = params - param_errs_array[idx] = param_errs - chi2_x_array[idx] = chi2_x - chi2_y_array[idx] = chi2_y + if processes > 1: + # Use multiprocessing to fit stars in parallel + arguments = [( + motion_model_instance, + t_stars[idx], + x_stars[idx], + y_stars[idx], + xe_stars[idx], + ye_stars[idx], + fixed_params_stars[i_star], + weighting, + use_scipy, + absolute_sigma, + fill_value, + True, + bootstrap, + seed, + verbose + ) for idx, i_star in enumerate(unique_index)] + + with Pool(processes) as pool: + results = list(pool.starmap( + fit_motion_model, + tqdm( + arguments, + desc=f"Fitting motion model {unique_motion_model}", + disable=not verbose + ), + chunksize=chunksize + )) + + for idx, (params, param_errs, chi2_x, chi2_y) in enumerate(results): + params_array[idx] = params + param_errs_array[idx] = param_errs + chi2_x_array[idx] = chi2_x + chi2_y_array[idx] = chi2_y + + else: + # Expensive for loop! Prepare everything beforehand to speed up. + for idx, i_star in enumerate(tqdm(unique_index, disable=not verbose, desc=f"Fitting motion model {unique_motion_model}")): + # Fit the star + params, param_errs, chi2_x, chi2_y = motion_model_instance.fit( + t=t_stars[idx], + x=x_stars[idx], + y=y_stars[idx], + xe=xe_stars[idx], + ye=ye_stars[idx], + fixed_params_dict=fixed_params_stars[i_star], + weighting=weighting, + use_scipy=use_scipy, + absolute_sigma=absolute_sigma, + fill_value=fill_value, + return_chi2=True, + bootstrap=bootstrap, + seed=seed, + verbose=verbose + ) + params_array[idx] = params + param_errs_array[idx] = param_errs + chi2_x_array[idx] = chi2_x + chi2_y_array[idx] = chi2_y # Store results back to the table for j, param_name in enumerate(param_names): @@ -1273,3 +1320,35 @@ def shift_reference_frame(table, delta_vx=0.0, delta_vy=0.0, delta_pi=0.0, fixed table['x'] += delta_pi*pvec[:, 0, :] # Shape (N_stars, N_times) table['y'] += delta_pi*pvec[:, 1, :] # Shape (N_stars, N_times) return table + + +# Helper function to fit motion model for each star for multiprocessing +def fit_motion_model( + motion_model_instance, + t, x, y, xe, ye, + fixed_params_dict, + weighting, + use_scipy, + absolute_sigma, + fill_value, + return_chi2, + bootstrap, + seed, + verbose +): + return motion_model_instance.fit( + t=t, + x=x, + y=y, + xe=xe, + ye=ye, + fixed_params_dict=fixed_params_dict, + weighting=weighting, + use_scipy=use_scipy, + absolute_sigma=absolute_sigma, + fill_value=fill_value, + return_chi2=return_chi2, + bootstrap=bootstrap, + seed=seed, + verbose=verbose + ) \ No newline at end of file diff --git a/flystar/tests/test_startable.py b/flystar/tests/test_startable.py index f8e24de..6630a83 100644 --- a/flystar/tests/test_startable.py +++ b/flystar/tests/test_startable.py @@ -437,6 +437,66 @@ def test_fit_motion_model_2epoch(): return +def test_multiprocessing(): + rng = np.random.default_rng(42) + N = 10000 + x = rng.random((N, 5)) + y = rng.random((N, 5)) + m = rng.random((N, 5)) + xe = rng.random((N, 5)) + ye = rng.random((N, 5)) + t = np.arange(5) + 2026 + fixed_params_dict = [None for _ in range(N)] + weighting = 'var' + fill_value = np.nan + verbose = True + + st1 = StarTable( + name=np.arange(N), + x=x, + y=y, + m=m, + xe=xe, + ye=ye + ) + st1.meta['list_times'] = t + + st2 = StarTable( + name=np.arange(N), + x=x, + y=y, + m=m, + xe=xe, + ye=ye + ) + st2.meta['list_times'] = t + + st1.fit_motion_models( + motion_models=['Linear'], + weighting=weighting, + use_scipy=True, + absolute_sigma=True, + bootstrap=0, + fill_value=fill_value, + verbose=verbose + ) + + st2.fit_motion_models( + motion_models=['Linear'], + weighting=weighting, + use_scipy=True, + absolute_sigma=True, + bootstrap=0, + fill_value=fill_value, + processes=10, + verbose=verbose + ) + + for key in ['x0', 'x0_err', 'y0', 'y0_err', 'vx', 'vx_err', 'vy', 'vy_err', 'chi2_x', 'chi2_y', 'n_params', 't0']: + np.testing.assert_array_equal(st1[key], st2[key], err_msg=f"Mismatch in {key} between single and multi-processing runs.") + return + + def make_star_table(): # User input cat_file = f'{test_data_path}/test_catalog.fits' @@ -557,7 +617,3 @@ def make_tiny_star_table(): xe=xe_in, ye=ye_in, me=me_in) return startable - - -if __name__ == "__main__": - test_fit_motion_models() From 91a5c27bc1a2b549667e8c61e111347c74d1c5f3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Wed, 29 Jul 2026 21:17:57 -0700 Subject: [PATCH 237/254] Implemented multiprocessing in align --- flystar/align.py | 160 ++++++++++++++++++++++++++++++------------ flystar/startables.py | 5 +- 2 files changed, 117 insertions(+), 48 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 64698e8..7d599ac 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -9,6 +9,7 @@ import numpy as np import matplotlib.pyplot as plt from tqdm import tqdm +from multiprocessing import Pool from . import match, transforms, plots, motion_model from .starlists import StarList from .startables import StarTable @@ -340,7 +341,7 @@ def fix_iterable_conditions(self): return - def fit(self): + def fit(self, processes=1, chunksize=None): """ Using the current parameter settings, match and transform all the lists to a reference position. Note in the first pass, the reference position @@ -360,6 +361,12 @@ def fit(self): m0e additional motion_model columns + Parameters + ---------- + processes : int, optional + Number of processes to use for parallel processing, maximum os.cpu_count(), by default 1 (no multiprocessing) + chunksize : int, optional + Chunk size for multiprocessing, by default None (auto) """ # Setup save_path: if self.save_path: @@ -435,9 +442,16 @@ def fit(self): # ALL the action is in here. Match and transform the stack of starlists. # This updates trans objects and the ref_table. - self.match_and_transform(self.mag_lim[self.ref_index], - self.dr_tol[nn], self.dm_tol[nn], self.outlier_tol[nn], - self.trans_args[nn], nn) + self.match_and_transform( + self.mag_lim[self.ref_index], + self.dr_tol[nn], + self.dm_tol[nn], + self.outlier_tol[nn], + self.trans_args[nn], + nn, + processes=processes, + chunksize=chunksize + ) # Clean up the reference table # Find where stars are detected. @@ -468,7 +482,7 @@ def fit(self): self.match_lists(self.dr_tol[-1], self.dm_tol[-1]) # Hard-coded not to keep ref values for MosaicSelfRef - self.update_ref_table_aggregates() + self.update_ref_table_aggregates(processes=processes, chunksize=chunksize) ########## # Clean up output table. @@ -583,7 +597,7 @@ def fit(self): print('===================================') return - def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_args, nn=None): + def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_args, nn=None, processes=1, chunksize=None): """ Given some reference list of positions, loop through all the starlists transform and match them. @@ -606,10 +620,10 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar star_list = self.star_lists[ii] if 't' in star_list.meta: - ref_list = self.get_ref_list_from_table(star_list.meta['t']) + ref_list = self.get_ref_list_from_table(star_list.meta['t'], processes=processes, chunksize=chunksize) elif 't' in star_list.colnames: assert np.unique(star_list['t']).size == 1, f"StarList at index {ii} has multiple unique times in the 't' column. Cannot determine reference list for matching." - ref_list = self.get_ref_list_from_table(star_list['t'][0]) + ref_list = self.get_ref_list_from_table(star_list['t'][0], processes=processes, chunksize=chunksize) else: raise KeyError(f"StarList at index {ii} does not have a 't' column or meta['t'] entry. Cannot determine reference list for matching.") @@ -849,7 +863,7 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar keep_orig = ~np.isfinite(self.ref_table['x'][:,ii]) else: keep_orig=None - self.update_ref_table_aggregates(keep_orig=keep_orig) + self.update_ref_table_aggregates(keep_orig=keep_orig, processes=processes, chunksize=chunksize) # Update ref list polygon if self.starlist_vertices is not None: @@ -1162,7 +1176,7 @@ def update_ref_table_from_list(self, star_list, star_list_T, ii, idx_ref, idx_li return - def update_ref_table_aggregates(self, keep_orig=None, n_boot=0, seed=None): + def update_ref_table_aggregates(self, keep_orig=None, n_boot=0, seed=None, processes=1, chunksize=None): """ Average positions or fit velocities. Average magnitudes. Calculate bootstrap errors if desired. @@ -1228,6 +1242,8 @@ def update_ref_table_aggregates(self, keep_orig=None, n_boot=0, seed=None): select_stars=fit_star_idxs, bootstrap=n_boot, seed=seed, + processes=processes, + chunksize=chunksize, verbose=self.verbose ) # Combine (transformed) magnitudes @@ -1240,7 +1256,7 @@ def update_ref_table_aggregates(self, keep_orig=None, n_boot=0, seed=None): # if (keep_orig is not None) and (sum(keep_orig) > 0): # Determine motion_model_used for keep_orig stars # Filter possible motion models based on available columns - motion_model_used, n_params = determine_motion_model(self.ref_table, self.motion_models, self.fixed_params_dict) + motion_model_used, n_params = determine_motion_models(self.ref_table, self.motion_models, self.fixed_params_dict, processes, chunksize, self.verbose > 0) # Assign the determined motion models self.ref_table['motion_model_used'] = Column(motion_model_used, name='motion_model_used', dtype='U20') @@ -1340,7 +1356,7 @@ def match_lists(self, dr_tol, dm_tol): return - def get_ref_list_from_table(self, epoch): + def get_ref_list_from_table(self, epoch, processes=1, chunksize=None): """ Convert the averaged quantites in self.ref_table into a StarList object appropriate for the specified epoch. @@ -1360,7 +1376,7 @@ def get_ref_list_from_table(self, epoch): # Calculate x, y, xe, ye if 'motion_model_used' not in self.ref_table.colnames: - motion_model_used, n_params = determine_motion_model(self.ref_table, self.motion_models, self.fixed_params_dict) + motion_model_used, n_params = determine_motion_models(self.ref_table, self.motion_models, self.fixed_params_dict, processes, chunksize, self.verbose > 0) self.ref_table['motion_model_used'] = Column(motion_model_used, name='motion_model_used', dtype='U20') self.ref_table['n_params'] = Column(n_params, name='n_params', dtype=int) @@ -1411,7 +1427,7 @@ def reset_ref_values(self, exclude=None): return - def calc_bootstrap_errors(self, n_boot=100, seed=None, boot_epochs_min=-1, calc_vel_in_bootstrap=True, update_errors=False, verbose=True): + def calc_bootstrap_errors(self, n_boot=100, seed=None, boot_epochs_min=-1, calc_vel_in_bootstrap=True, update_errors=False, processes=1, chunksize=None, verbose=True): """ Function to calculate bootstrap errors for the transformations as well as the proper motions. For each iteration, this will: @@ -1432,33 +1448,39 @@ def calc_bootstrap_errors(self, n_boot=100, seed=None, boot_epochs_min=-1, calc_ Parameters: ---------- - mosaic_object: MosaicToRef object + mosaic_object : MosaicToRef object MosaicToRef object after the complete match_and_transform process - n_boot: int, must be greater than 0 + n_boot : int, optional Number of bootstrap iterations when calculating transformations and the proper motion. - PM bootstrap is only done for final proper motion - calculation (e.g., not for each iteration of the starlist for matching) + PM bootstrap is only done for final proper motion calculation + (e.g., not for each iteration of the starlist for matching), by default 100 - seed: int, optional + seed : int, optional Random seed for reproducible bootstrap results. - boot_epochs_min: int or -1 + boot_epochs_min : int, optional In order to be included in bootstrap analysis, non-reference stars must be detected in at least boot_epochs_min epochs. If boot_epochs_min = -1, then all stars will be included in the analysis, regardless of the number of epochs detected. - For stars that fail boot_epochs_min criteria, np.nan is used + For stars that fail boot_epochs_min criteria, np.nan is used, by default -1 - calc_vel_in_bootstrap: boolean + calc_vel_in_bootstrap : boolean, optional If true, do bootstrap sample w/ replacement over the epochs and calculate stellar proper motions, as well as the bootstrap over reference stars to calculate positional alignment errors. If false, only - calculate position alignment errors. + calculate position alignment errors, by default True - update_errors: boolean + update_errors : boolean If True, save the starlist errors as xe_list, bootstrap errors as xe_boot, and their quad sum as xe (and likewise for ye and me). If False (default), leave the starlist errors in place as xe and bootstrap errors as xe_boot. - verbose: boolean + processes : int, optional + Number of processes to use for parallel processing, maximum os.cpu_count(), by default 1 (no multiprocessing) + + chunksize : int, optional + Chunk size for multiprocessing, by default None (auto) + + verbose : boolean, optional Print verbose information or not, by default True Output: @@ -1551,7 +1573,7 @@ def calc_bootstrap_errors(self, n_boot=100, seed=None, boot_epochs_min=-1, calc_ # Get reference star positions in particular epoch from ref_list. t_epoch = t_arr[jj] - ref_orig = self.get_ref_list_from_table(t_epoch)[idx_good] + ref_orig = self.get_ref_list_from_table(t_epoch, processes=processes, chunksize=chunksize)[idx_good] ## Get idx of reference stars in bootstrap sample in the ref_orig. ## Then, use these to build reference starlist for the alignment @@ -1679,6 +1701,8 @@ def calc_bootstrap_errors(self, n_boot=100, seed=None, boot_epochs_min=-1, calc_ weighting=self.vel_weighting, use_scipy=self.use_scipy, absolute_sigma=self.absolute_sigma, + processes=processes, + chunksize=chunksize, verbose=False ) @@ -2101,7 +2125,7 @@ def = None. If not None, then this should contain an array or list of transform return - def fit(self): + def fit(self, processes=1, chunksize=None): """ Using the current parameter settings, match and transform all the lists to a reference position. Note in the first pass, the reference position @@ -2121,6 +2145,12 @@ def fit(self): m0e addl. motion_model parameters + Parameters + ---------- + processes : int, optional + Number of processes to use for parallel processing, maximum os.cpu_count(), by default 1 (no multiprocessing) + chunksize : int, optional + Chunk size for multiprocessing, by default None (auto) """ # Create a log file of the parameters used in the fit. # Setup save_path: @@ -2224,9 +2254,16 @@ def fit(self): # ALL the action is in here. Match and transform the stack of starlists. # This updates trans objects and the ref_table. - self.match_and_transform(self.ref_mag_lim, - self.dr_tol[nn], self.dm_tol[nn], self.outlier_tol[nn], - self.trans_args[nn], nn) + self.match_and_transform( + self.ref_mag_lim, + self.dr_tol[nn], + self.dm_tol[nn], + self.outlier_tol[nn], + self.trans_args[nn], + nn, + processes=processes, + chunksize=chunksize + ) # Clean up the reference table # Find where stars are detected. @@ -2259,7 +2296,7 @@ def fit(self): keep_orig=None else: keep_orig = self.ref_table['ref_orig'] - self.update_ref_table_aggregates(keep_orig=keep_orig) + self.update_ref_table_aggregates(keep_orig=keep_orig, processes=processes, chunksize=chunksize) ########## # Clean up output table. @@ -2454,7 +2491,17 @@ def infer_positions(t, startable, motion_models=None, fixed_params_dict=None, re # y = startable['y'] # return x, y -def determine_motion_model(startable, motion_models=None, fixed_params_dict=None): +def determine_motion_model(motion_models_possible, k, fixed_params_dict): + """Helper function for multiprocessing determine_motion_models for each star + """ + for mm, req_col_in_table, req_cols, req_col_in_dict in motion_models_possible[::-1]: + # If required column in table/fixed_params dict is numeric, check if all values are finite. + # If so, use mm as motion model and stop further searching + if all(np.isfinite(req_cols[col][k]) for col in req_col_in_table if np.issubdtype(req_cols[col].dtype, np.number)) \ + and all(np.isfinite(fixed_params_dict[col]) for col in req_col_in_dict if np.issubdtype(np.array(fixed_params_dict[col]).dtype, np.number)): + return mm.name, mm.n_params + +def determine_motion_models(startable, motion_models=None, fixed_params_dict=None, processes=1, chunksize=None, verbose=True): """Determine motion model used in star table based on the finite model parameter columns Parameters @@ -2466,6 +2513,8 @@ def determine_motion_model(startable, motion_models=None, fixed_params_dict=None If None, all available motion models will be considered, by default None fixed_params_dict : dict, optional Dictionary of fixed parameters, by default None + verbose : bool, optional + Show progress bar or not Returns ------- @@ -2489,21 +2538,40 @@ def determine_motion_model(startable, motion_models=None, fixed_params_dict=None required_columns = mm.fit_param_names + mm.fixed_param_names req_col_in_table = [col for col in required_columns if (col in startable.colnames)] req_col_in_dict = [col for col in required_columns if (col in fixed_params_dict.keys())] + req_cols = startable[req_col_in_table] if all((col in startable.colnames) or (col in fixed_params_dict.keys()) for col in required_columns): - motion_models_possible.append((mm, req_col_in_table, req_col_in_dict)) - - # Check if values are finite for required columns in possible motion models - motion_model_used = [] - n_params = [] - - for k in range(len(startable)): - for mm, req_col_in_table, req_col_in_dict in motion_models_possible[::-1]: - # If requested column in table/fixed_params dict is numeric, check if values are finite. - if all(np.isfinite(startable[col][k]) for col in req_col_in_table if np.issubdtype(startable[col].dtype, np.number)) \ - and all(np.isfinite(fixed_params_dict[col]) for col in req_col_in_dict if np.issubdtype(np.array(fixed_params_dict[col]).dtype, np.number)): - motion_model_used.append(mm.name) - n_params.append(mm.n_params) - break + motion_models_possible.append((mm, req_col_in_table, req_cols, req_col_in_dict)) + + if processes == 1: + motion_model_used = [] + n_params = [] + for k in tqdm(range(len(startable)), desc='Determining motion models', disable=not verbose): + for mm, req_col_in_table, req_cols, req_col_in_dict in motion_models_possible[::-1]: + # If required column in table/fixed_params dict is numeric, check if all values are finite. + # If so, use mm as motion model and stop further searching + if all(np.isfinite(req_cols[col][k]) for col in req_col_in_table if np.issubdtype(req_cols[col].dtype, np.number)) \ + and all(np.isfinite(fixed_params_dict[col]) for col in req_col_in_dict if np.issubdtype(np.array(fixed_params_dict[col]).dtype, np.number)): + motion_model_used.append(mm.name) + n_params.append(mm.n_params) + break + else: + arguments = [( + motion_models_possible, k, fixed_params_dict + ) for k in range(len(startable))] + + with Pool(processes) as pool: + results = list(pool.starmap( + determine_motion_model, + tqdm( + arguments, + desc=f'Determining motion models with {processes} processes', + disable=not verbose + ), + chunksize=chunksize + )) + motion_model_used = [result[0] for result in results] + n_params = [result[1] for result in results] + return motion_model_used, n_params diff --git a/flystar/startables.py b/flystar/startables.py index b8bc54b..f78cb38 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -926,6 +926,7 @@ def fit_motion_models( # Different (or column does not yet exist) if len(np.unique(coldata)) == 1: + # If param is the same for all stars, save it as meta self.meta[param] = coldata[0] else: self.add_column( @@ -1002,7 +1003,7 @@ def fit_motion_models( fit_motion_model, tqdm( arguments, - desc=f"Fitting motion model {unique_motion_model}", + desc=f"Fitting motion model {unique_motion_model} with {processes} processes", disable=not verbose ), chunksize=chunksize @@ -1073,7 +1074,7 @@ def infer_positions(self, times, fixed_params_dict=None, fill_value=np.nan): Arrays of predicted x, y positions and their uncertainties xe, ye, with shape (N_stars, N_times) or (N_stars,) if N_times=1, or (N_times,) if N_stars=1, or scalar. """ assert 'motion_model_used' in self.colnames, \ - "infer_positions: 'motion_model_used' column not found in the table. Please run fit_motion_models() first." + "infer_positions: 'motion_model_used' column not found in the table. Please run fit_motion_models first." N_stars = len(self) times = np.atleast_1d(times) From 3ecbf8572d43ab03c3e46bc8c10fd8e350e4b5e5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Thu, 30 Jul 2026 00:45:10 -0700 Subject: [PATCH 238/254] Added control of scipy curve_fit method in startable and align --- flystar/align.py | 13 +++++++++++-- flystar/motion_model.py | 22 +++++++++++++++++----- flystar/startables.py | 6 +++++- 3 files changed, 33 insertions(+), 8 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 7d599ac..7ec1f61 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -43,11 +43,11 @@ def __init__( mag_lim=None, # Motion model parameters motion_models=['Empty', 'Fixed'], - # motion_model_for_new_star=None, fixed_params_dict=None, vel_weights='var', use_scipy=True, absolute_sigma=True, + scipy_method=None, # Advanced options iter_callback=None, save_path=None, @@ -178,6 +178,9 @@ def = None. If not None, then this should contain an array or list of transform If True, the velocity fit will use absolute errors in the data. If False, relative errors will be used, by default False. + scipy_method : str, optional + Method of scipy.curve_fit, {'lm', 'trf', 'dogbox'}, by default None + iter_callback : None or function A function to call (that accepts a StarTable object and an iteration number) at the end of every iteration. This can be used for plotting or printing state. @@ -245,6 +248,7 @@ def = None. If not None, then this should contain an array or list of transform self.calc_trans_inverse = calc_trans_inverse self.use_scipy = use_scipy self.absolute_sigma = absolute_sigma + self.scipy_method = scipy_method self.fixed_params_dict = fixed_params_dict self.init_guess_mode = init_guess_mode self.briteN = briteN @@ -1239,6 +1243,7 @@ def update_ref_table_aggregates(self, keep_orig=None, n_boot=0, seed=None, proce weighting=self.vel_weighting, use_scipy=self.use_scipy, absolute_sigma=self.absolute_sigma, + method=self.scipy_method, select_stars=fit_star_idxs, bootstrap=n_boot, seed=seed, @@ -1701,6 +1706,7 @@ def calc_bootstrap_errors(self, n_boot=100, seed=None, boot_epochs_min=-1, calc_ weighting=self.vel_weighting, use_scipy=self.use_scipy, absolute_sigma=self.absolute_sigma, + method=self.scipy_method, processes=processes, chunksize=chunksize, verbose=False @@ -1846,11 +1852,11 @@ def __init__( ref_mag_lim=None, # Motion model parameters motion_models=['Empty', 'Fixed'], - # motion_model_for_new_star=None, fixed_params_dict=None, vel_weights='var', use_scipy=True, absolute_sigma=True, + scipy_method=None, # Advanced options iter_callback=None, save_path=None, @@ -2007,6 +2013,9 @@ def = None. If not None, then this should contain an array or list of transform absolute_sigma : bool, optional If True, the velocity fit will use absolute errors in the data. If False, relative errors will be used, by default False. + scipy_method : str, optional + Method of scipy.curve_fit, {'lm', 'trf', 'dogbox'}, by default None + iter_callback : None or function A function to call (that accepts a StarTable object and an iteration number) at the end of every iteration. This can be used for plotting or printing state. diff --git a/flystar/motion_model.py b/flystar/motion_model.py index 4224a42..d9a1b78 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -78,6 +78,7 @@ def run_fit( params_guess=None, fill_value=np.nan, return_chi2=False, + method=None, verbose=True ): # Run a single fit (used both for overall fit + bootstrap iterations) @@ -105,6 +106,7 @@ def fit( return_chi2=False, bootstrap=0, seed=None, + method=None, verbose=True ): """Fit stellar motion parameters @@ -139,6 +141,8 @@ def fit( Bootstrapping uncertainties, by default 0 seed : int, optional Seed for the random number generator, by default None + method : str, optional + Method of scipy.curve_fit, {'lm', 'trf', 'dogbox'}, by default None verbose : bool, optional Print warning messages, by default True @@ -210,6 +214,7 @@ def fit( params_guess=params, fill_value=fill_value, return_chi2=False, + method=method, verbose=verbose ) bb_params.append(params_bdx) @@ -337,6 +342,7 @@ def run_fit( params_guess=None, fill_value=np.nan, return_chi2=False, + method=None, verbose=True ): """Fit stellar motion parameters @@ -367,6 +373,8 @@ def run_fit( Initial guess for parameters, by default None return_chi2 : bool, optional Whether to return chi-squared value, by default False + method : str, optional + Method of scipy.curve_fit, {'lm', 'trf', 'dogbox'}, by default None verbose : bool, optional Whether to print verbose output, by default True @@ -496,6 +504,7 @@ def run_fit( params_guess=None, fill_value=np.nan, return_chi2=False, + method=None, verbose=True ): if verbose and (not use_scipy): @@ -661,6 +670,7 @@ def run_fit( params_guess=None, fill_value=np.nan, return_chi2=False, + method=None, verbose=True ): if fixed_params_dict is None: @@ -698,8 +708,8 @@ def run_fit( params_guess = [x.mean(), 0., y.mean(), 0.] if use_scipy: - x_opt, x_cov, x_info, x_msg, x_ier = curve_fit(self.model_fit, dt, x, p0=np.array(params_guess[:2]), sigma=1/x_wt**0.5, absolute_sigma=absolute_sigma, full_output=True) - y_opt, y_cov, y_info, y_msg, y_ier = curve_fit(self.model_fit, dt, y, p0=np.array(params_guess[2:]), sigma=1/y_wt**0.5, absolute_sigma=absolute_sigma, full_output=True) + x_opt, x_cov, x_info, x_msg, x_ier = curve_fit(self.model_fit, dt, x, p0=np.array(params_guess[:2]), sigma=1/x_wt**0.5, absolute_sigma=absolute_sigma, full_output=True, method=method) + y_opt, y_cov, y_info, y_msg, y_ier = curve_fit(self.model_fit, dt, y, p0=np.array(params_guess[2:]), sigma=1/y_wt**0.5, absolute_sigma=absolute_sigma, full_output=True, method=method) x0, vx = x_opt y0, vy = y_opt x0e, vxe = np.sqrt(x_cov.diagonal()) @@ -904,6 +914,7 @@ def run_fit( params_guess=None, fill_value=np.nan, return_chi2=False, + method=None, verbose=True ): if fixed_params_dict is None: @@ -947,8 +958,8 @@ def run_fit( t_span = t[idx_last] - t[idx_first] params_guess = [x.mean(), (x[idx_last] - x[idx_first]) / t_span, 0., y.mean(), (y[idx_last] - y[idx_first]) / t_span, 0.] - x_opt, x_cov, x_info, x_msg, x_ier = curve_fit(self.model_fit, dt, x, p0=np.array(params_guess[:3]), sigma=1/x_wt**0.5, absolute_sigma=absolute_sigma, full_output=True) - y_opt, y_cov, y_info, y_msg, y_ier = curve_fit(self.model_fit, dt, y, p0=np.array(params_guess[3:]), sigma=1/y_wt**0.5, absolute_sigma=absolute_sigma, full_output=True) + x_opt, x_cov, x_info, x_msg, x_ier = curve_fit(self.model_fit, dt, x, p0=np.array(params_guess[:3]), sigma=1/x_wt**0.5, absolute_sigma=absolute_sigma, full_output=True, method=method) + y_opt, y_cov, y_info, y_msg, y_ier = curve_fit(self.model_fit, dt, y, p0=np.array(params_guess[3:]), sigma=1/y_wt**0.5, absolute_sigma=absolute_sigma, full_output=True, method=method) x0, vx0, ax = x_opt y0, vy0, ay = y_opt x0e, vx0e, axe = np.sqrt(x_cov.diagonal()) @@ -1154,6 +1165,7 @@ def run_fit( params_guess=None, fill_value=np.nan, return_chi2=False, + method=None, verbose=True ): if not use_scipy: @@ -1219,7 +1231,7 @@ def run_fit( popt, pcov, infodict, mesg, ier = curve_fit( self._model_fit, t - t0, np.hstack([x, y]), p0=params_guess, sigma=np.hstack([sigma_x, sigma_y]), - absolute_sigma=absolute_sigma, full_output=True + absolute_sigma=absolute_sigma, full_output=True, method=method ) x0, vx, y0, vy, pi = popt x0_err, vx_err, y0_err, vy_err, pi_err = np.sqrt(pcov.diagonal()) diff --git a/flystar/startables.py b/flystar/startables.py index f78cb38..2bcd443 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -558,6 +558,7 @@ def fit_motion_models( weighting='var', use_scipy=True, absolute_sigma=True, + method=None, select_stars=None, bootstrap=0, seed=None, @@ -596,10 +597,12 @@ def fit_motion_models( Use scipy.optimize.curve_fit or algebraic solution (for Linear model only), by default False absolute_sigma : bool, optional Use absolute sigma or not, see scipy curve_fit for details, by default True + method : str, optional + Method of scipy.curve_fit, {'lm', 'trf', 'dogbox'}, by default None select_stars : list of int, optional Indices of stars to fit, by default None (fit all stars) bootstrap : int, optional - Number of bootstrap for uncertainty resampling, by default 0 + Number of bootstrap samples for uncertainty resampling, by default 0 seed : int, optional Random seed for bootstrap resampling, by default None mask_value : float, optional @@ -1029,6 +1032,7 @@ def fit_motion_models( weighting=weighting, use_scipy=use_scipy, absolute_sigma=absolute_sigma, + method=method, fill_value=fill_value, return_chi2=True, bootstrap=bootstrap, From cc05db4946545d22f7a66f513afb6041a7460ad0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Thu, 6 Aug 2026 02:05:01 -0700 Subject: [PATCH 239/254] Fixed bugs and match updated mm_rework results --- flystar/align.py | 22 ++------- flystar/match.py | 2 +- flystar/motion_model.py | 58 ++++++++++------------- flystar/startables.py | 76 +++++++++++++++---------------- flystar/tests/compare_branches.py | 45 ++++++++++++++++++ 5 files changed, 112 insertions(+), 91 deletions(-) create mode 100644 flystar/tests/compare_branches.py diff --git a/flystar/align.py b/flystar/align.py index 7ec1f61..2101663 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -763,18 +763,6 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar idx1 = idx1[keepers] idx2 = idx2[keepers] - # # Only use stars specified by "use_in_trans" column. - # if 'use_in_trans' in ref_list.colnames: - # keepers = ref_list[idx2]['use_in_trans'] - # if sum(keepers) == 0: - # raise ValueError(f"No stars are marked as 'use_in_trans' in the reference list for starlist index {ii}. Cannot derive transformation.") - - # if self.verbose > 1: - # print( f' Rejected {len(idx1) - sum(keepers)} stars out of {len(idx1)} with use_in_trans=False.' ) - - # idx1 = idx1[keepers] - # idx2 = idx2[keepers] - # Determine weights in the fit. weight = self.get_weights_for_lists(ref_list[idx2], star_list_T[idx1]) @@ -878,21 +866,21 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar msg1 = ' {0:2s} (mean and std) for {1:10s}: {2:8.5f} +/- {3:8.5f}' print(' Residuals: ') print(msg1.format('dr', 'all stars', dr.mean(), dr.std())) - print(msg1.format('dm', 'all stars', dm.mean(), dm.std())) + print(msg1.format('dm', 'all stars', dm.mean(), dm.std())) # ref_list - ref_table # Calculate the residuals just for those used in the transformation used = np.where(self.ref_table['used_in_trans'][:, ii] == True)[0] used_good = used[ np.where(np.isin(used, idx_ref) == True)[0] ] - dr_u = np.hypot(self.ref_table['x'][used_good, ii] - ref_list['x'][used_good], - self.ref_table['y'][used_good, ii] - ref_list['y'][used_good]) - dm_u = np.abs(self.ref_table['m'][used_good, ii] - ref_list['m'][used_good]) + dr_u = np.hypot(ref_list['x'][used_good] - self.ref_table['x'][used_good, ii], + ref_list['y'][used_good] - self.ref_table['y'][used_good, ii]) + dm_u = ref_list['m'][used_good] - self.ref_table['m'][used_good, ii] print(msg1.format('dr', 'trans stars', dr_u.mean(), dr_u.std())) print(msg1.format('dm', 'trans stars', dm_u.mean(), dm_u.std())) print(' Used {0:d} trans ref stars.'.format(len(used))) print(' Dropped {0:d} matches after transform.'.format(len(used) - len(used_good))) gc.collect() # clean up memory after each iteration - + # Save ref_table after each iteration # print(f"Saving self after iteration {ii=}") # if self.save_path: diff --git a/flystar/match.py b/flystar/match.py index 182cb26..66363ac 100644 --- a/flystar/match.py +++ b/flystar/match.py @@ -460,7 +460,7 @@ def match(x1, y1, m1, x2, y2, m2, dr_tol, dm_tol=None, verbose=True): idxs2 = idxs2[idxs2 >= 0] dr = np.hypot(x2[idxs2] - x1[idxs1], y2[idxs2] - y1[idxs1]) - dm = np.abs(m2[idxs2] - m1[idxs1]) + dm = m2[idxs2] - m1[idxs1] # Deal with duplicates duplicates = [item for item, count in list(Counter(idxs2).items()) if count > 1] diff --git a/flystar/motion_model.py b/flystar/motion_model.py index d9a1b78..efc574d 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -86,14 +86,14 @@ def run_fit( return np.full(self.n_fit_params, fill_value), np.full(self.n_fit_params, np.inf), np.nan, np.nan return np.full(self.n_fit_params, fill_value), np.full(self.n_fit_params, np.inf) - def calc_weights(self, xe, ye, weighting='var'): + def calc_sigma(self, xe, ye, weighting='var'): if weighting=='std': - return 1./xe, 1./ye + return np.sqrt(np.abs(xe)), np.sqrt(np.abs(ye)) elif weighting=='var': - return 1./xe**2, 1./ye**2 + return np.abs(xe), np.abs(ye) else: warnings.warn("Invalid weighting, using default weighting scheme var.", UserWarning) - return 1./xe**2, 1./ye**2 + return np.abs(xe), np.abs(ye) def fit( self, t, x, y, xe, ye, @@ -151,16 +151,10 @@ def fit( params, param_errs(, chi2_x, chi2_y) Parameters, uncertainties, and chi squares if return_chi2 is True. The corresponding parameter names are in self.fit_param_names. """ - assert np.ndim(t) == 1, f"Input time array must be 1D! Got shape {np.shape(t)}" - assert np.ndim(x) == 1, f"Input x array must be 1D! Got shape {np.shape(x)}" - assert np.ndim(y) == 1, f"Input y array must be 1D! Got shape {np.shape(y)}" - assert np.ndim(xe) == 1, f"Input xe array must be 1D! Got shape {np.shape(xe)}" - assert np.ndim(ye) == 1, f"Input ye array must be 1D! Got shape {np.shape(ye)}" - - assert len(t) == len(x), f'Input x must have the same length as t! Got len(t)={len(t)}, len(x)={len(x)}' - assert len(t) == len(y), f'Input y must have the same length as t! Got len(t)={len(t)}, len(y)={len(y)}' - assert len(t) == len(xe), f'Input xe must have the same length as t! Got len(t)={len(t)}, len(xe)={len(xe)}' - assert len(t) == len(ye), f'Input ye must have the same length as t! Got len(t)={len(t)}, len(ye)={len(ye)}' + for variable, name in zip([t, x, y, xe, ye], ['t', 'x', 'y', 'xe', 'ye']): + assert np.ndim(variable) == 1, f"Input {name} array must be 1D! Got shape {np.shape(variable)}" + if name != 't': + assert len(t) == len(variable), f'Input {name} must have the same length as t! Got len(t)={len(t)}, len({name})={len(variable)}' if not verbose: warnings.filterwarnings("ignore", category=OptimizeWarning) @@ -524,13 +518,14 @@ def run_fit( # degree_of_freedom >= 0 # Calculate weighted average position - x_wt, y_wt = self.calc_weights(xe, ye, weighting=weighting) - x_wt_norm = x_wt / np.sum(x_wt) - y_wt_norm = y_wt / np.sum(y_wt) + sigma_x, sigma_y = self.calc_sigma(xe, ye, weighting=weighting) + x_wt, y_wt = 1. / sigma_x**2, 1. / sigma_y**2 x0 = np.average(x, weights=x_wt) - x0e = (np.sum(x_wt_norm**2 * xe**2))**0.5 # Error propagation + # x0e = (np.sum(x_wt_norm**2 * xe**2))**0.5 # Error propagation + x0e = 1. / np.sum(x_wt)**0.5 # Error propagation y0 = np.average(y, weights=y_wt) - y0e = (np.sum(y_wt_norm**2 * ye**2))**0.5 # Error propagation + # y0e = (np.sum(y_wt_norm**2 * ye**2))**0.5 # Error propagation + y0e = 1. / np.sum(y_wt)**0.5 # Error propagation params = np.array([x0, y0]) param_errors = np.array([x0e, y0e]) @@ -703,13 +698,15 @@ def run_fit( # degree_of_freedom >= 0 dt = t - t0 - x_wt, y_wt = self.calc_weights(xe, ye, weighting=weighting) + sigma_x, sigma_y = self.calc_sigma(xe, ye, weighting=weighting) + x_wt, y_wt = 1. / sigma_x**2, 1. / sigma_y**2 + if params_guess is None: params_guess = [x.mean(), 0., y.mean(), 0.] if use_scipy: - x_opt, x_cov, x_info, x_msg, x_ier = curve_fit(self.model_fit, dt, x, p0=np.array(params_guess[:2]), sigma=1/x_wt**0.5, absolute_sigma=absolute_sigma, full_output=True, method=method) - y_opt, y_cov, y_info, y_msg, y_ier = curve_fit(self.model_fit, dt, y, p0=np.array(params_guess[2:]), sigma=1/y_wt**0.5, absolute_sigma=absolute_sigma, full_output=True, method=method) + x_opt, x_cov, x_info, x_msg, x_ier = curve_fit(self.model_fit, dt, x, p0=np.array(params_guess[:2]), sigma=sigma_x, absolute_sigma=absolute_sigma, full_output=True, method=method) + y_opt, y_cov, y_info, y_msg, y_ier = curve_fit(self.model_fit, dt, y, p0=np.array(params_guess[2:]), sigma=sigma_y, absolute_sigma=absolute_sigma, full_output=True, method=method) x0, vx = x_opt y0, vy = y_opt x0e, vxe = np.sqrt(x_cov.diagonal()) @@ -951,15 +948,15 @@ def run_fit( # degree_of_freedom >= 0 dt = t - t0 - x_wt, y_wt = self.calc_weights(xe,ye, weighting=weighting) + sigma_x, sigma_y = self.calc_sigma(xe, ye, weighting=weighting) if params_guess is None: # Initial guess for velocity: idx_first, idx_last = np.argmin(t), np.argmax(t) t_span = t[idx_last] - t[idx_first] params_guess = [x.mean(), (x[idx_last] - x[idx_first]) / t_span, 0., y.mean(), (y[idx_last] - y[idx_first]) / t_span, 0.] - x_opt, x_cov, x_info, x_msg, x_ier = curve_fit(self.model_fit, dt, x, p0=np.array(params_guess[:3]), sigma=1/x_wt**0.5, absolute_sigma=absolute_sigma, full_output=True, method=method) - y_opt, y_cov, y_info, y_msg, y_ier = curve_fit(self.model_fit, dt, y, p0=np.array(params_guess[3:]), sigma=1/y_wt**0.5, absolute_sigma=absolute_sigma, full_output=True, method=method) + x_opt, x_cov, x_info, x_msg, x_ier = curve_fit(self.model_fit, dt, x, p0=np.array(params_guess[:3]), sigma=sigma_x, absolute_sigma=absolute_sigma, full_output=True, method=method) + y_opt, y_cov, y_info, y_msg, y_ier = curve_fit(self.model_fit, dt, y, p0=np.array(params_guess[3:]), sigma=sigma_y, absolute_sigma=absolute_sigma, full_output=True, method=method) x0, vx0, ax = x_opt y0, vy0, ay = y_opt x0e, vx0e, axe = np.sqrt(x_cov.diagonal()) @@ -1205,7 +1202,6 @@ def run_fit( # degree_of_freedom >= 0 t_mjd = Time(t, format='decimalyear', scale='utc').mjd self.pvec = self.calc_parallax_vector(t_mjd, ra, dec, pa=pa, obsLocation=obsLocation) # Shape (2, N_times) - x_wt, y_wt = self.calc_weights(xe, ye, weighting=weighting) # Initial guesses, x0,y0 as x,y averages; # vx,vy as average velocity if first and last points are perfectly measured; @@ -1219,15 +1215,7 @@ def run_fit( 0.1 ]) - # Convert weights to 1-sigma uncertainties for curve_fit. - # calc_weights returns w = 1/sigma^2 for 'var' and w = 1/sigma for 'std'. - if weighting == 'std': - sigma_x = 1.0 / x_wt - sigma_y = 1.0 / y_wt - else: - sigma_x = 1.0 / np.sqrt(x_wt) - sigma_y = 1.0 / np.sqrt(y_wt) - + sigma_x, sigma_y = self.calc_sigma(xe, ye, weighting=weighting) popt, pcov, infodict, mesg, ier = curve_fit( self._model_fit, t - t0, np.hstack([x, y]), p0=params_guess, sigma=np.hstack([sigma_x, sigma_y]), diff --git a/flystar/startables.py b/flystar/startables.py index 2bcd443..9c8ac13 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -493,7 +493,7 @@ def combine_lists(self, col_name_in, weights_col=None, mask_val=None, # Calculate the weighted mean and uncertainty avg = np.ma.average(val_2d_clip, weights=wgt_2d, axis=1) - std = np.ma.sqrt(1 / np.ma.sum(wgt_2d, axis=1)) # Error propagation for weighted mean + std = np.ma.sqrt(1. / np.ma.sum(wgt_2d, axis=1)) # Error propagation for weighted mean # Use standard deviation of the weighted residuals as the uncertainty # std = np.ma.sqrt(np.ma.average((val_2d_clip.T - avg).T**2, weights=wgt_2d, axis=1)) @@ -512,10 +512,6 @@ def combine_lists(self, col_name_in, weights_col=None, mask_val=None, self.meta[col_name_in + '0'] = 'not_weighted' std = np.ma.masked_values(std, 0.) # Mask out any zero uncertainties (i.e., 1 or less valid points) - # # Mask out stars with only 1 valid measurement (i.e., std = 0). - # if (weights_col and weights_col in self.colnames) and (val_2d.shape[1] > 1): - # mask_for_singles = ((np.isfinite(val_2d_clip)).sum(axis=1)==1) - # std[mask_for_singles]=np.nanmean(err_2d[mask_for_singles], axis=1) # Save off our new AVG and STD into new columns with shape (N_stars). col_name_avg = col_name_in + '0' @@ -525,6 +521,7 @@ def combine_lists(self, col_name_in, weights_col=None, mask_val=None, std = 2.5 / np.log(10) * std / avg # Error propagation avg = -2.5 * np.ma.log10(avg) + # FIXME: why change? # Fill mask with nan or inf avg = avg.filled(np.nan) std = std.filled(np.inf) @@ -1086,8 +1083,17 @@ def infer_positions(self, times, fixed_params_dict=None, fill_value=np.nan): x_pred = np.full((N_stars, N_times), fill_value, dtype=float) y_pred = np.full((N_stars, N_times), fill_value, dtype=float) - xe_pred = np.full((N_stars, N_times), np.inf, dtype=float) - ye_pred = np.full((N_stars, N_times), np.inf, dtype=float) + + # Only calculate xe ye if columns exist in table, otherwise fill with np.inf + if 'x0_err' in self.colnames and 'y0_err' in self.colnames: + # 'x0_err' and 'y0_err' are the common uncertainty params for all motion models + with_xe_ye = True + xe_pred = np.full((N_stars, N_times), np.inf, dtype=float) + ye_pred = np.full((N_stars, N_times), np.inf, dtype=float) + else: + with_xe_ye = False + xe_pred = np.full((N_stars, N_times), np.inf, dtype=float) + ye_pred = np.full((N_stars, N_times), np.inf, dtype=float) # Calculate the dictionary of {motion_model: indices of stars with this motion model} for faster access during prediction unique_motion_models, unique_inv_indices = np.unique(self['motion_model_used'], return_inverse=True) @@ -1105,7 +1111,7 @@ def infer_positions(self, times, fixed_params_dict=None, fill_value=np.nan): fit_param_errs = np.array([ self[param_name + '_err'][unique_index] for param_name in motion_model_instance.fit_param_names - ]).T # shape (N_stars_this_model, N_params) + ]).T if with_xe_ye else None # shape (N_stars_this_model, N_params) # Construct fixed_params: Look for fixed_params_dict -> table columns -> meta data -> default value fixed_params = fixed_params_dict.copy() if fixed_params_dict is not None else {} @@ -1137,55 +1143,49 @@ def infer_positions(self, times, fixed_params_dict=None, fill_value=np.nan): else: fixed_params[param] = fixed_params_dict[param] - # for param_name in motion_model_instance.fixed_param_names: - # col_name = copy.deepcopy(param_name) - # # If column not in table, check if it's provided in fixed_params_dict. If not, raise error. If provided, use the value from fixed_params_dict for all stars. - # if (col_name not in self.colnames) and (f'{col_name}_mm' not in self.colnames): - # if col_name in fixed_params_dict: - # fixed_params[param_name] = fixed_params_dict[col_name] - # continue - # else: - # raise KeyError(f"infer_positions: Fixed parameter '{param_name}' not found in table columns or fixed_params_dict. Please provide the value for this parameter in fixed_params_dict or add a column named '{param_name}' to the table.") - - # # If original table has column and fit_motion_models added the column with _mm suffix, use the _mm column for prediction. - # if param_name + '_mm' in self.colnames: - # col_name = param_name + '_mm' - # fixed_params[param_name] = self[col_name][unique_index] - - # if (param_name == 'obsLocation'): - # assert np.unique(fixed_params[param_name]).size == 1, \ - # "infer_positions: obsLocation fixed parameter has different values for different stars. Vectorized handling not implemented yet." - # fixed_params[param_name] = fixed_params[param_name][0] # Predict positions # shape = (N_stars_this_model, N_times) or (N_stars_this_model,) if N_times=1 or (N_times,) if N_stars_this_model=1 or scalar - x, y, xe, ye = motion_model_instance.model( - times, fit_params, fit_param_errs, fixed_params - ) + if with_xe_ye: + x, y, xe, ye = motion_model_instance.model( + times, fit_params, fit_param_errs, fixed_params + ) + else: + x, y = motion_model_instance.model( + times, fit_params, fixed_params=fixed_params + ) + if N_stars==1 and N_times > 1: # Reshape (N_times,) to (1, N_times) x = x[np.newaxis, :] y = y[np.newaxis, :] - xe = xe[np.newaxis, :] - ye = ye[np.newaxis, :] + if with_xe_ye: + xe = xe[np.newaxis, :] + ye = ye[np.newaxis, :] elif N_times==1 and N_stars > 1: # Reshape (N_stars,) to (N_stars, 1) x = x[:, np.newaxis] y = y[:, np.newaxis] - xe = xe[:, np.newaxis] - ye = ye[:, np.newaxis] + if with_xe_ye: + xe = xe[:, np.newaxis] + ye = ye[:, np.newaxis] x_pred[unique_index] = x y_pred[unique_index] = y - xe_pred[unique_index] = xe - ye_pred[unique_index] = ye + if with_xe_ye: + xe_pred[unique_index] = xe + ye_pred[unique_index] = ye if N_stars==1 or N_times==1: # Reshape back to 1D array or scalar x_pred = x_pred.flatten() y_pred = y_pred.flatten() - xe_pred = xe_pred.flatten() - ye_pred = ye_pred.flatten() + if with_xe_ye: + xe_pred = xe_pred.flatten() + ye_pred = ye_pred.flatten() + + xe_pred = xe_pred if with_xe_ye else np.full_like(x_pred, np.inf) + ye_pred = ye_pred if with_xe_ye else np.full_like(y_pred, np.inf) return x_pred, y_pred, xe_pred, ye_pred diff --git a/flystar/tests/compare_branches.py b/flystar/tests/compare_branches.py new file mode 100644 index 0000000..6dd71b8 --- /dev/null +++ b/flystar/tests/compare_branches.py @@ -0,0 +1,45 @@ +import pickle +import flystar +import matplotlib.pyplot as plt +from flystar import align, transforms, motion_model +from flystar.plots import plot_stars + +branch = 'mm_rework_lingfeng' # 'mm_rework_lingfeng' or 'mm_rework' + +test_data_path = f'{flystar.__path__[0]}/tests/test_data' + +with open(f'{test_data_path}/my_gaia.pkl', 'rb') as f: + my_gaia = pickle.load(f) +with open(f'{test_data_path}/list_of_starlists.pkl', 'rb') as f: + list_of_starlists = pickle.load(f) +ra_deg, dec_deg = 18.0, -30.0 +my_gaia.remove_column('motion_model_used') +# my_gaia['motion_model_input'] = 'Fixed' +if branch == 'mm_rework_lingfeng': + msc = align.MosaicToRef(my_gaia, list_of_starlists, iters=1, + dr_tol=[0.2], dm_tol=[5], + outlier_tol=[None], mag_lim=[6, 20], + trans_class=transforms.PolyTransform, + trans_args=[{'order': 1}], + motion_models=['Fixed', 'Parallax'], + fixed_params_dict = {'ra':ra_deg, 'dec':dec_deg, 'pa':0.0, 'obsLocation':'earth'}, + use_ref_new=True, + update_ref_orig=False, + mag_trans=True, + trans_weights='both,std', + init_guess_mode='name', verbose=3) +elif branch == 'mm_rework': + msc = align.MosaicToRef(my_gaia, list_of_starlists, iters=1, + dr_tol=[0.2], dm_tol=[5], + outlier_tol=[None], mag_lim=[6, 20], + trans_class=transforms.PolyTransform, + trans_args=[{'order': 1}], + default_motion_model='Parallax', + motion_model_dict = {'Parallax': motion_model.Parallax(RA=ra_deg, Dec=dec_deg, PA=0.0, obsLocation='earth')}, + use_ref_new=True, + update_ref_orig=False, + mag_trans=True, + trans_weights='both,std', + init_guess_mode='name', verbose=3) + +msc.fit() \ No newline at end of file From a6991e923df99f9e38ae886fac45f82e7bff26b4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Thu, 6 Aug 2026 16:23:13 -0700 Subject: [PATCH 240/254] Cleaned up imports, avoided cyclic imports, and reverted back to absolute import paths --- flystar/align.py | 218 ++++++++++++++++++++++++++++++++++++-- flystar/analysis.py | 88 +-------------- flystar/examples.py | 2 +- flystar/match.py | 208 ------------------------------------ flystar/motion_model.py | 2 +- flystar/parallax.py | 14 ++- flystar/plots.py | 93 ++++++++++++++-- flystar/starlists.py | 5 +- flystar/startables.py | 17 +-- flystar/stitch_method2.py | 4 +- flystar/template.py | 11 +- flystar/transforms.py | 14 +-- 12 files changed, 326 insertions(+), 350 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 2101663..c898e3a 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -2,7 +2,6 @@ import gc import pdb import copy -import time import pickle import warnings import datetime @@ -10,10 +9,9 @@ import matplotlib.pyplot as plt from tqdm import tqdm from multiprocessing import Pool -from . import match, transforms, plots, motion_model -from .starlists import StarList -from .startables import StarTable -from .motion_model import Empty, Fixed +from flystar import match, transforms, plots, motion_model +from flystar.starlists import StarList +from flystar.startables import StarTable from astropy.table import Table, Column, vstack from astropy.utils.exceptions import AstropyUserWarning @@ -2434,7 +2432,7 @@ def infer_positions(t, startable, motion_models=None, fixed_params_dict=None, re if motion_models is None: # Setting the default to None to avoid mutable default argument issue # See https://stackoverflow.com/questions/15189245/assigning-class-variable-as-default-value-to-class-method-argument - motion_models = [Empty, Fixed] + motion_models = [motion_model.Empty, motion_model.Fixed] all_mm_map = motion_model.motion_model_map() if all(isinstance(mm, str) for mm in motion_models): mm_names = motion_models @@ -2444,9 +2442,9 @@ def infer_positions(t, startable, motion_models=None, fixed_params_dict=None, re # Always add Empty and Fixed in motion models if 'Fixed' not in mm_names: - motion_models.insert(0, Fixed) + motion_models.insert(0, motion_model.Fixed) if 'Empty' not in mm_names: - motion_models.insert(0, Empty) + motion_models.insert(0, motion_model.Empty) # Otherwise, infer positions using the most complex motion model with the existing columns, until it reaches Fixed or Empty # Sort motion models inversely by mm.n_params @@ -3998,4 +3996,206 @@ def logger(logfile, message, verbose = 9): if verbose > 4: print(message) logfile.write(message + '\n') - return \ No newline at end of file + return + + +def generic_match(sl1, sl2, init_mode='triangle', + model=transforms.PolyTransform, order_dr=(1, 1.0), + dr_final=1.0, + xy_match=(None, None, None, None, None, None, None, None), + m_match=(None, None, None, None), sigma_match=None, + n_bright=100, verbose=True, **kwargs): + """ + Finds the transformation between two starlists using the first one + as reference frame. Different matching methods can be used. If no + transformation is found, it returns an error message. + + + Parameters + sl1 : StarList + starlist used for reference frame + sl2 : StarList + starlist transformed + init_mode : str + Initial matching method. + If 'triangle', uses the blind triangle method. + If 'match_name', uses match by name + If 'load', uses the transformation from a loaded file + model : str + Transformation model to be used with the 'triangle' initial mode + poly_order : int + Order of the transformation model + order_dr : int, float [n, 2] + Combinations of polinomial order (first column) and search radius + (second column) to refine the transformation. Rows are executed in + orders + dr_final: float + Search radius used for the final matching + n_bright : int + Number of bright stars used in the initial blind triangles matching + xy_match : array + Area of the images to remove in the matching [reference catalog min x, + reference catalog max x, reference catalog min y, reference catalog max y, + transformed catalog min x, transformed catalog max x, + transformed catalog min y, transformed catalog max y]. Use None for values not used. + m_match : array + Magnitude limits of matching stars used to find transformations + [reference catalog min mag, reference catalog max mag, transformed + catalog min mag, transformed catalog max mag]. Use None for values not + used + sigma_match : array + Number of Deltap movement sigmas [0] used for sigma-cutting matched + stars for a number of times [1]. Use None for no sigma-cut. The last + polynomial order and search radius in 'order_dr' are used + transf_file : str + File name and path of the transformation file used with the 'load' + init_mode + verbose : bool, optional + Prints on screen information on the matching + + Returns + ------- + transf : Transform2D + Transformation of the second starlist respect to the first + st : StarTable + Startable of the two matched catalogs + + """ + from flystar import starlists, startables + # Check the input StarLists and transform them into astropy Tables + if not isinstance(sl1, starlists.StarList): + raise TypeError("The first catalog has to be a StarList") + if not isinstance(sl2, starlists.StarList): + raise TypeError("The second catalog has to be a StarList") + + # Find the initial transformation + if init_mode == 'triangle': # Blind triangles method + + # Prepare the reduced starlists for matching + sl1_cut = copy.deepcopy(sl1) + sl2_cut = copy.deepcopy(sl2) + sl1_cut.restrict_by_value(x_min=xy_match[0], x_max=xy_match[1], + y_min=xy_match[2], y_max=xy_match[3]) + sl2_cut.restrict_by_value(x_min=xy_match[4], x_max=xy_match[5], + y_min=xy_match[6], y_max=xy_match[7]) + sl1_cut.restrict_by_value(m_min=m_match[0], m_max=m_match[1]) + sl2_cut.restrict_by_value(m_min=m_match[2], m_max=m_match[3]) + + # Find the transformation + # TODO: test 'initial_align' with StarList input + transf = initial_align(sl1_cut, sl2_cut, briteN=n_bright, transformModel=model, order=order_dr[0]) #order_dr[i_loop][0] ? + + elif init_mode == 'match_name': # Name match + sl1_idx_init, sl2_idx_init, _ = starlists.restrict_by_name(sl1, sl2) + transf = model(sl2['x'][sl2_idx_init], sl2['y'][sl2_idx_init], + sl1['x'][sl1_idx_init], sl1['y'][sl1_idx_init], + order=int(order_dr[0][0])) + + elif init_mode == 'load': # Load a transformation file + transf = transforms.Transform2D.from_file(kwargs['transf_file']) + + else: # None of the above + raise TypeError("Unrecognized initial matching method") + + # Restrict the matching catalogs + sl1_match = copy.deepcopy(sl1) + sl2_match = copy.deepcopy(sl2) + sl1_match.restrict_by_value(m_min=m_match[0], m_max=m_match[1]) + sl2_match.restrict_by_value(m_min=m_match[2], m_max=m_match[3]) + + # Refine the transformation + if sigma_match: + order_dr_len = len(order_dr) + + for i_loop in range(sigma_match[1]): + order_dr = np.vstack((np.array(order_dr), np.array(order_dr[-1]))) + + for i_loop in range(len(order_dr)): + + # Transform and match the catalog to the reference frame + sl2_idx, sl1_idx = transform_and_match(sl2_match, sl1_match, transf, + dr_tol=order_dr[1], + verbose=verbose) + + # Transform the catalog to the reference frame + sl2_transf_match = transform_from_object(sl2_match, transf) + + # Sigma-rejection + if sigma_match and (i_loop >= order_dr_len): + resid = np.sqrt((sl1_match['x'][sl1_idx] - + sl2_transf_match['x'][sl2_idx])**2 + + (sl1_match['y'][sl1_idx] - + sl2_transf_match['y'][sl2_idx])**2) + sl1_idx = sl1_idx[resid <= (sigma_match[0] * np.std(resid))] + sl2_idx = sl2_idx[resid <= (sigma_match[0] * np.std(resid))] + + # Test section to observe the matching catalogs before refining the transformation + """ + from matplotlib import pyplot + + _, axarr = pyplot.subplots(nrows=1, ncols=1, figsize=(10,10)) + axarr.scatter(sl1_match['x'][sl1_idx], sl1_match['y'][sl1_idx]) + xlim = axarr.get_xlim() + ylim = axarr.get_ylim() + + _, axarr = pyplot.subplots(nrows=1, ncols=1, figsize=(10, 10)) + axarr.scatter(sl2_transf_match['x'][sl2_idx], sl2_transf_match['y'][sl2_idx]) + axarr.set_xlim(xlim) + axarr.set_ylim(ylim) + """ + + # Find a better transformation + transf, _ = find_transform( + sl2_match[sl2_idx], + sl2_transf_match[sl2_idx], + sl1_match[sl1_idx], transModel=model, + order=order_dr[0], verbose=verbose + ) + + # This section was used for testing transformations with normalized + # coordinates. Only several catalogs had reduced residuals when using + # high order polynomials (>3), some of them became unstable + """sl1_match_norm = sl1_match[sl1_idx] + sl2_match_norm = sl2_match[sl2_idx] + sl2_transf_match_norm = sl2_transf_match[sl2_idx] + mm = max(max(sl1_match_norm['x']), max(sl1_match_norm['y']), + max(sl2_transf_match_norm['x']), max(sl2_transf_match_norm['y'])) + sl1_match_norm['x'] = sl1_match_norm['x'] / mm + sl1_match_norm['y'] = sl1_match_norm['y'] / mm + sl2_match_norm['x'] = sl2_match_norm['x'] / mm + sl2_match_norm['y'] = sl2_match_norm['y'] / mm + sl2_transf_match_norm['x'] = sl2_transf_match_norm['x'] / mm + sl2_transf_match_norm['y'] = sl2_transf_match_norm['y'] / mm + transf, _ = align.find_transform(sl2_match_norm, sl2_transf_match_norm, + sl1_match_norm, transModel=model, + order=poly_order, verbose=verbose) + c_exp = np.zeros(len(transf.px._parameters)) + + for i_c in range(len(transf.px._parameters)): + c_exp[i_c] = int(transf.px._param_names[i_c][1:].split('_')[0]) +\ + int(transf.px._param_names[i_c][1:].split('_')[1]) + + c_corr = mm ** (1 - c_exp) + transf.px._parameters = transf.px._parameters * c_corr + transf.py._parameters = transf.py._parameters * c_corr""" + + # Do the final transformation and matching using + sl2_idx, sl1_idx = transform_and_match(sl2, sl1, transf, dr_tol=dr_final, verbose=verbose) + # StarTable output + sl2_transf = transform_from_object(sl2, transf) + unames = np.array(range(len(sl1_idx))) + st = startables.StarTable(name=unames, + x=np.column_stack((np.array(sl1['x'][sl1_idx]), np.array(sl2_transf['x'][sl2_idx]))), + y=np.column_stack((np.array(sl1['y'][sl1_idx]), np.array(sl2_transf['y'][sl2_idx]))), + m=np.column_stack((np.array(sl1['m'][sl1_idx]), np.array(sl2_transf['m'][sl2_idx]))), + ep_name=np.column_stack((np.array(sl1['name'][sl1_idx]), np.array(sl2_transf['name'][sl2_idx])))) +# ep_name=np.column_stack((np.array(sl1['name'][sl1_idx]), np.array(sl2_transf['name'][sl2_idx]))), +# list_times=[sl1.meta['list_time'], sl2.meta['list_time']], +# list_names=[sl1.meta['list_name'], sl2.meta['list_name']]) + + for col in sl1.colnames: + if col in sl2.colnames: + if col not in ['name', 'x', 'y', 'm']: + st.add_column(Column(np.column_stack((np.array(sl1[col][sl1_idx]),np.array(sl2_transf[col][sl2_idx]))), name=col)) + + return transf, st diff --git a/flystar/analysis.py b/flystar/analysis.py index 7deaa36..429eab7 100644 --- a/flystar/analysis.py +++ b/flystar/analysis.py @@ -1,14 +1,12 @@ +import copy import numpy as np import pylab as plt -from . import starlists, match +from scipy.stats import f from astropy import table from astropy.table import Table, Column from astropy.coordinates import SkyCoord from astropy import units as u -from astropy.wcs import WCS -import pdb, copy -import math -from scipy.stats import f +from flystar import starlists, match ################################################## # New codes for velocity support in FlyStar and using @@ -474,86 +472,6 @@ def startable_subset(tab, idx, mag_trans=True, mag_trans_orig=False): # Old codes. ################################################## -def calc_chi2(ref_mat, starlist_mat, transform, errs='both'): - """ - calculate the chi2 and reduced chi2 of the position - between two matched starlists. - Input: - ref_mat: astropy table - Reference starlist only containing matched stars that were used in the - transformation. Standard column headers are assumed. - - starlist_mat: astropy table - Transformed starlist only containing the matched stars used in - the transformation. Standard column headers are assumed. - - transform: transformation object - Transformation object of final transform. Used in chi-square - determination - - errs: string; 'both', 'reference', or 'starlist' - If both, add starlist errors in quadrature with reference errors. - - If reference, only consider reference errors. This should be used if the starlist - does not have valid errors - - If starlist, only consider starlist errors. This should be used if the reference - does not have valid errors - - Output: - chi_sq: float - chi2 = sum (diff_x**2 / xerr**2 + diff_y**2 /yerr**2) - chi_sq_red: float - reduced chi2 = chi2/ degree of freedom - deg_freedom: int - degree of freedom - - """ - diff_x = ref_mat['x'] - starlist_mat['x'] - diff_y = ref_mat['y'] - starlist_mat['y'] - - # Set errors as per user input - if errs == 'both': - xerr = np.hypot(ref_mat['xe'], starlist_mat['xe']) - yerr = np.hypot(ref_mat['ye'], starlist_mat['ye']) - elif errs == 'reference': - xerr = ref_mat['xe'] - yerr = ref_mat['ye'] - elif errs == 'starlist': - xerr = starlist_mat['xe'] - yerr = starlist_mat['ye'] - - - # For both X and Y, calculate chi-square. Combine arrays to get combined - # chi-square - chi_sq_x = diff_x**2. / xerr**2. - chi_sq_y = diff_y**2. / yerr**2. - - chi_sq = np.append(chi_sq_x, chi_sq_y) - - # Calculate degrees of freedom in transformation - num_mod_params = calc_nparam(transform) - deg_freedom = len(chi_sq) - num_mod_params - - # Calculate reduced chi-square - chi_sq = np.sum(chi_sq) - chi_sq_red = chi_sq / deg_freedom - - return chi_sq, chi_sq_red, deg_freedom - - -def calc_nparam(transformation): - """ - calculate the degree of freedom for a transformation - """ - # Read transformation: Extract X, Y coefficients from transform - if transformation.__class__.__name__ == 'four_paramNW': - nparam = 4 - elif transformation.__class__.__name__ == 'PolyTransform': - order = transformation.order - nparam = (order+1) * (order+2) - return nparam - def calc_F(red_chi2_1, red_chi2_2, v1, v2): """ compare two different models to get the proper polynomial fitting order diff --git a/flystar/examples.py b/flystar/examples.py index 0165cb3..d70a880 100644 --- a/flystar/examples.py +++ b/flystar/examples.py @@ -1,5 +1,5 @@ import numpy as np -from . import transforms, match, align, starlists, plots +from flystar import transforms, match, align, starlists, plots def align_example(labelFile, reference, transModel=transforms.four_paramNW, order=1, N_loop=2, diff --git a/flystar/match.py b/flystar/match.py index 66363ac..0b25612 100644 --- a/flystar/match.py +++ b/flystar/match.py @@ -1,10 +1,6 @@ -import copy import itertools import numpy as np -import matplotlib.pyplot as plt -from . import starlists, transforms, startables from collections import Counter -from astropy.table import Column from scipy.spatial import KDTree as KDT @@ -550,207 +546,3 @@ def add_votes(votes, match1, match2): votes.flat[unique_idx] += deltas return - - -def generic_match(sl1, sl2, init_mode='triangle', - model=transforms.PolyTransform, order_dr=(1, 1.0), - dr_final=1.0, - xy_match=(None, None, None, None, None, None, None, None), - m_match=(None, None, None, None), sigma_match=None, - n_bright=100, verbose=True, **kwargs): - """ - Finds the transformation between two starlists using the first one - as reference frame. Different matching methods can be used. If no - transformation is found, it returns an error message. - - - Parameters - sl1 : StarList - starlist used for reference frame - sl2 : StarList - starlist transformed - init_mode : str - Initial matching method. - If 'triangle', uses the blind triangle method. - If 'match_name', uses match by name - If 'load', uses the transformation from a loaded file - model : str - Transformation model to be used with the 'triangle' initial mode - poly_order : int - Order of the transformation model - order_dr : int, float [n, 2] - Combinations of polinomial order (first column) and search radius - (second column) to refine the transformation. Rows are executed in - orders - dr_final: float - Search radius used for the final matching - n_bright : int - Number of bright stars used in the initial blind triangles matching - xy_match : array - Area of the images to remove in the matching [reference catalog min x, - reference catalog max x, reference catalog min y, reference catalog max y, - transformed catalog min x, transformed catalog max x, - transformed catalog min y, transformed catalog max y]. Use None for values not used. - m_match : array - Magnitude limits of matching stars used to find transformations - [reference catalog min mag, reference catalog max mag, transformed - catalog min mag, transformed catalog max mag]. Use None for values not - used - sigma_match : array - Number of Deltap movement sigmas [0] used for sigma-cutting matched - stars for a number of times [1]. Use None for no sigma-cut. The last - polynomial order and search radius in 'order_dr' are used - transf_file : str - File name and path of the transformation file used with the 'load' - init_mode - verbose : bool, optional - Prints on screen information on the matching - - Returns - ------- - transf : Transform2D - Transformation of the second starlist respect to the first - st : StarTable - Startable of the two matched catalogs - - """ - from . import align - - # Check the input StarLists and transform them into astropy Tables - if not isinstance(sl1, starlists.StarList): - raise TypeError("The first catalog has to be a StarList") - if not isinstance(sl2, starlists.StarList): - raise TypeError("The second catalog has to be a StarList") - - # Find the initial transformation - if init_mode == 'triangle': # Blind triangles method - - # Prepare the reduced starlists for matching - sl1_cut = copy.deepcopy(sl1) - sl2_cut = copy.deepcopy(sl2) - sl1_cut.restrict_by_value(x_min=xy_match[0], x_max=xy_match[1], - y_min=xy_match[2], y_max=xy_match[3]) - sl2_cut.restrict_by_value(x_min=xy_match[4], x_max=xy_match[5], - y_min=xy_match[6], y_max=xy_match[7]) - sl1_cut.restrict_by_value(m_min=m_match[0], m_max=m_match[1]) - sl2_cut.restrict_by_value(m_min=m_match[2], m_max=m_match[3]) - - # Find the transformation - # TODO: test 'initial_align' with StarList input - transf = align.initial_align(sl1_cut, sl2_cut, briteN=n_bright, - transformModel=model, order=order_dr[0]) #order_dr[i_loop][0] ? - - elif init_mode == 'match_name': # Name match - sl1_idx_init, sl2_idx_init, _ = starlists.restrict_by_name(sl1, sl2) - transf = model(sl2['x'][sl2_idx_init], sl2['y'][sl2_idx_init], - sl1['x'][sl1_idx_init], sl1['y'][sl1_idx_init], - order=int(order_dr[0][0])) - - elif init_mode == 'load': # Load a transformation file - transf = transforms.Transform2D.from_file(kwargs['transf_file']) - - else: # None of the above - raise TypeError("Unrecognized initial matching method") - - # Restrict the matching catalogs - sl1_match = copy.deepcopy(sl1) - sl2_match = copy.deepcopy(sl2) - sl1_match.restrict_by_value(m_min=m_match[0], m_max=m_match[1]) - sl2_match.restrict_by_value(m_min=m_match[2], m_max=m_match[3]) - - # Refine the transformation - if sigma_match: - order_dr_len = len(order_dr) - - for i_loop in range(sigma_match[1]): - order_dr = np.vstack((np.array(order_dr), np.array(order_dr[-1]))) - - for i_loop in range(len(order_dr)): - - # Transform and match the catalog to the reference frame - sl2_idx, sl1_idx = align.transform_and_match(sl2_match, sl1_match, transf, - dr_tol=order_dr[1], - verbose=verbose) - - # Transform the catalog to the reference frame - sl2_transf_match = align.transform_from_object(sl2_match, transf) - - # Sigma-rejection - if sigma_match and (i_loop >= order_dr_len): - resid = np.sqrt((sl1_match['x'][sl1_idx] - - sl2_transf_match['x'][sl2_idx])**2 + - (sl1_match['y'][sl1_idx] - - sl2_transf_match['y'][sl2_idx])**2) - sl1_idx = sl1_idx[resid <= (sigma_match[0] * np.std(resid))] - sl2_idx = sl2_idx[resid <= (sigma_match[0] * np.std(resid))] - - # Test section to observe the matching catalogs before refining the transformation - """ - from matplotlib import pyplot - - _, axarr = pyplot.subplots(nrows=1, ncols=1, figsize=(10,10)) - axarr.scatter(sl1_match['x'][sl1_idx], sl1_match['y'][sl1_idx]) - xlim = axarr.get_xlim() - ylim = axarr.get_ylim() - - _, axarr = pyplot.subplots(nrows=1, ncols=1, figsize=(10, 10)) - axarr.scatter(sl2_transf_match['x'][sl2_idx], sl2_transf_match['y'][sl2_idx]) - axarr.set_xlim(xlim) - axarr.set_ylim(ylim) - """ - - # Find a better transformation - transf, _ = align.find_transform(sl2_match[sl2_idx], - sl2_transf_match[sl2_idx], - sl1_match[sl1_idx], transModel=model, - order=order_dr[0], verbose=verbose) -# order=int(order_dr[i_loop][0]), verbose=verbose) - - # This section was used for testing transformations with normalized - # coordinates. Only several catalogs had reduced residuals when using - # high order polynomials (>3), some of them became unstable - """sl1_match_norm = sl1_match[sl1_idx] - sl2_match_norm = sl2_match[sl2_idx] - sl2_transf_match_norm = sl2_transf_match[sl2_idx] - mm = max(max(sl1_match_norm['x']), max(sl1_match_norm['y']), - max(sl2_transf_match_norm['x']), max(sl2_transf_match_norm['y'])) - sl1_match_norm['x'] = sl1_match_norm['x'] / mm - sl1_match_norm['y'] = sl1_match_norm['y'] / mm - sl2_match_norm['x'] = sl2_match_norm['x'] / mm - sl2_match_norm['y'] = sl2_match_norm['y'] / mm - sl2_transf_match_norm['x'] = sl2_transf_match_norm['x'] / mm - sl2_transf_match_norm['y'] = sl2_transf_match_norm['y'] / mm - transf, _ = align.find_transform(sl2_match_norm, sl2_transf_match_norm, - sl1_match_norm, transModel=model, - order=poly_order, verbose=verbose) - c_exp = np.zeros(len(transf.px._parameters)) - - for i_c in range(len(transf.px._parameters)): - c_exp[i_c] = int(transf.px._param_names[i_c][1:].split('_')[0]) +\ - int(transf.px._param_names[i_c][1:].split('_')[1]) - - c_corr = mm ** (1 - c_exp) - transf.px._parameters = transf.px._parameters * c_corr - transf.py._parameters = transf.py._parameters * c_corr""" - - # Do the final transformation and matching using - sl2_idx, sl1_idx = align.transform_and_match(sl2, sl1, transf, dr_tol=dr_final, - verbose=verbose) - # StarTable output - sl2_transf = align.transform_from_object(sl2, transf) - unames = np.array(range(len(sl1_idx))) - st = startables.StarTable(name=unames, - x=np.column_stack((np.array(sl1['x'][sl1_idx]), np.array(sl2_transf['x'][sl2_idx]))), - y=np.column_stack((np.array(sl1['y'][sl1_idx]), np.array(sl2_transf['y'][sl2_idx]))), - m=np.column_stack((np.array(sl1['m'][sl1_idx]), np.array(sl2_transf['m'][sl2_idx]))), - ep_name=np.column_stack((np.array(sl1['name'][sl1_idx]), np.array(sl2_transf['name'][sl2_idx])))) -# ep_name=np.column_stack((np.array(sl1['name'][sl1_idx]), np.array(sl2_transf['name'][sl2_idx]))), -# list_times=[sl1.meta['list_time'], sl2.meta['list_time']], -# list_names=[sl1.meta['list_name'], sl2.meta['list_name']]) - - for col in sl1.colnames: - if col in sl2.colnames: - if col not in ['name', 'x', 'y', 'm']: - st.add_column(Column(np.column_stack((np.array(sl1[col][sl1_idx]),np.array(sl2_transf[col][sl2_idx]))), name=col)) - - return transf, st diff --git a/flystar/motion_model.py b/flystar/motion_model.py index efc574d..25410d1 100644 --- a/flystar/motion_model.py +++ b/flystar/motion_model.py @@ -1,9 +1,9 @@ +import warnings import numpy as np from abc import ABC from flystar import parallax from astropy.time import Time from scipy.optimize import curve_fit, OptimizeWarning -import warnings class MotionModel(ABC): name = "MotionModel" diff --git a/flystar/parallax.py b/flystar/parallax.py index 1605060..47da5aa 100755 --- a/flystar/parallax.py +++ b/flystar/parallax.py @@ -1,19 +1,15 @@ # Parallax calculation module for motion models involving parallax # Adapted from BAGLE's parallax.py -import math - +import os import numpy as np from joblib import Memory -import os +from astropy.time import Time from astropy import units, units as u from astropy.coordinates import SkyCoord, get_body_barycentric, get_body_barycentric_posvel, solar_system_ephemeris, \ CartesianRepresentation -from astropy.time import Time - -# Use the JPL ephemerides. -solar_system_ephemeris.set('jpl') +# FIXME: Do we still need this? # Setup a parallax cache try: cache_dir = os.environ['PARALLAX_CACHE_DIR'] @@ -110,7 +106,7 @@ def get_observer_barycentric(body, times, min_ephem_step=1, velocity=False): Minimum time step to query JPL in days. Must not be <1 and must be in integer days. - veloctiy : bool + velocity : bool If true, return both position and velocity vectors over time. Return @@ -119,6 +115,8 @@ def get_observer_barycentric(body, times, min_ephem_step=1, velocity=False): The xyz coordinates in the plane of the Solar System at the input times. """ + # Use the JPL ephemerides. + solar_system_ephemeris.set('jpl') if body in solar_system_ephemeris.bodies: if velocity: diff --git a/flystar/plots.py b/flystar/plots.py index 277e472..1068452 100755 --- a/flystar/plots.py +++ b/flystar/plots.py @@ -7,13 +7,95 @@ import matplotlib.colors as mcolors from matplotlib import cm from matplotlib.ticker import FormatStrFormatter -from scipy.stats import chi2 -from scipy.stats import norm +from scipy.stats import chi2, norm from scipy.optimize import curve_fit from astropy import units as u from astropy.table import Table from astropy.coordinates import SkyCoord + +# Moved from analysis old codes +def calc_chi2(ref_mat, starlist_mat, transform, errs='both'): + """ + calculate the chi2 and reduced chi2 of the position + between two matched starlists. + Input: + ref_mat: astropy table + Reference starlist only containing matched stars that were used in the + transformation. Standard column headers are assumed. + + starlist_mat: astropy table + Transformed starlist only containing the matched stars used in + the transformation. Standard column headers are assumed. + + transform: transformation object + Transformation object of final transform. Used in chi-square + determination + + errs: string; 'both', 'reference', or 'starlist' + If both, add starlist errors in quadrature with reference errors. + + If reference, only consider reference errors. This should be used if the starlist + does not have valid errors + + If starlist, only consider starlist errors. This should be used if the reference + does not have valid errors + + Output: + chi_sq: float + chi2 = sum (diff_x**2 / xerr**2 + diff_y**2 /yerr**2) + chi_sq_red: float + reduced chi2 = chi2/ degree of freedom + deg_freedom: int + degree of freedom + + """ + diff_x = ref_mat['x'] - starlist_mat['x'] + diff_y = ref_mat['y'] - starlist_mat['y'] + + # Set errors as per user input + if errs == 'both': + xerr = np.hypot(ref_mat['xe'], starlist_mat['xe']) + yerr = np.hypot(ref_mat['ye'], starlist_mat['ye']) + elif errs == 'reference': + xerr = ref_mat['xe'] + yerr = ref_mat['ye'] + elif errs == 'starlist': + xerr = starlist_mat['xe'] + yerr = starlist_mat['ye'] + + + # For both X and Y, calculate chi-square. Combine arrays to get combined + # chi-square + chi_sq_x = diff_x**2. / xerr**2. + chi_sq_y = diff_y**2. / yerr**2. + + chi_sq = np.append(chi_sq_x, chi_sq_y) + + # Calculate degrees of freedom in transformation + num_mod_params = calc_nparam(transform) + deg_freedom = len(chi_sq) - num_mod_params + + # Calculate reduced chi-square + chi_sq = np.sum(chi_sq) + chi_sq_red = chi_sq / deg_freedom + + return chi_sq, chi_sq_red, deg_freedom + + +def calc_nparam(transformation): + """ + calculate the degree of freedom for a transformation + """ + # Read transformation: Extract X, Y coefficients from transform + if transformation.__class__.__name__ == 'four_paramNW': + nparam = 4 + elif transformation.__class__.__name__ == 'PolyTransform': + order = transformation.order + nparam = (order+1) * (order+2) + return nparam + + #################################################### # Code for making diagnostic plots for astrometry # alignment @@ -195,7 +277,6 @@ def pos_diff_err_hist(ref_mat, starlist_mat, transform, nbins=25, bin_width=None an outlier. """ - from . import analysis diff_x = ref_mat['x'] - starlist_mat['x'] diff_y = ref_mat['y'] - starlist_mat['y'] @@ -234,15 +315,15 @@ def pos_diff_err_hist(ref_mat, starlist_mat, transform, nbins=25, bin_width=None chi_sq_red = np.sum(chi_sq) / deg_freedom """ # Chi-square analysis for all stars, including outliers - chi_sq, chi_sq_red, deg_freedom = analysis.calc_chi2(ref_mat, starlist_mat, + chi_sq, chi_sq_red, deg_freedom = calc_chi2(ref_mat, starlist_mat, transform, errs=errs) # Chi-square analysis for only non-outlier stars - chi_sq_good, chi_sq_red_good, deg_freedom_good = analysis.calc_chi2(ref_mat[good], + chi_sq_good, chi_sq_red_good, deg_freedom_good = calc_chi2(ref_mat[good], starlist_mat[good], transform, errs=errs) - num_mod_params = analysis.calc_nparam(transform) + num_mod_params = calc_nparam(transform) #-------------------------------------------# # Plotting diff --git a/flystar/starlists.py b/flystar/starlists.py index c6e92d0..ea9f621 100644 --- a/flystar/starlists.py +++ b/flystar/starlists.py @@ -1,8 +1,7 @@ +import warnings import numpy as np -from astropy.table import Table, Column, MaskedColumn import astropy.table -import warnings -import pdb +from astropy.table import Table, Column, MaskedColumn try: set diff --git a/flystar/startables.py b/flystar/startables.py index 9c8ac13..ab0155b 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -1,21 +1,14 @@ -import pdb -import time import copy import warnings -import collections import numpy as np -import pandas as pd - from tqdm import tqdm from multiprocessing import Pool from astropy.time import Time from astropy.stats import sigma_clip -from astropy.table import Table, Column, MaskedColumn, hstack -from scipy.optimize import curve_fit +from astropy.table import Table, Column from pandas.api.types import is_string_dtype from collections.abc import Iterable -from . import motion_model -from .motion_model import Empty, Fixed, Linear +from flystar import motion_model class StarTable(Table): def __init__(self, *args, ref_list=0, **kwargs): @@ -652,7 +645,7 @@ def fit_motion_models( if motion_models is None: # Setting the default to None to avoid mutable default argument issue # See https://stackoverflow.com/questions/15189245/assigning-class-variable-as-default-value-to-class-method-argument - motion_models = [Empty, Fixed, Linear] + motion_models = [motion_model.Empty, motion_model.Fixed, motion_model.Linear] all_mm_map = motion_model.motion_model_map() if all(isinstance(mm, str) for mm in motion_models): mm_names = motion_models @@ -662,9 +655,9 @@ def fit_motion_models( # Always add Empty and Fixed in motion models if 'Fixed' not in mm_names: - motion_models.insert(0, Fixed) + motion_models.insert(0, motion_model.Fixed) if 'Empty' not in mm_names: - motion_models.insert(0, Empty) + motion_models.insert(0, motion_model.Empty) mm_names = [mm.name for mm in motion_models] # Construct motion models if motion_model_input column exists diff --git a/flystar/stitch_method2.py b/flystar/stitch_method2.py index f9aa4e0..100fcc3 100644 --- a/flystar/stitch_method2.py +++ b/flystar/stitch_method2.py @@ -1,7 +1,7 @@ -from flystar import starlists,plots,match,align,analysis, transforms import numpy as np -from astropy.table import vstack, Table import pandas as pd +from astropy.table import Table +from flystar import starlists, match, align, transforms def align_starlists(starlist, ref, transModel=transforms.PolyTransform, order=2, N_loop=2, dr_tol=1.0, briteN=None, weights='both'): diff --git a/flystar/template.py b/flystar/template.py index 1373799..333b411 100644 --- a/flystar/template.py +++ b/flystar/template.py @@ -1,11 +1,6 @@ -from flystar import match -from flystar import align -from flystar import starlists -from flystar import plots -from flystar import transforms -from astropy.table import Table -import numpy as np import pdb +import numpy as np +from flystar import align, starlists, plots, transforms def align_template(labelFile, reference, transModel=transforms.PolyTransform, order=1, N_loop=2, @@ -101,7 +96,7 @@ def align_template(labelFile, reference, transModel=transforms.PolyTransform, or if restrict: label_match = label[idx_label] starlist_match = starlist[idx_starlist] - idx_label, idx_starlist = stalists.restrict_by_use(label_match, starlist_match, + idx_label, idx_starlist = starlists.restrict_by_use(label_match, starlist_match, idx_label, idx_starlist) # use the matched stars to calculate new transformation diff --git a/flystar/transforms.py b/flystar/transforms.py index 968ccfa..c9acf4b 100755 --- a/flystar/transforms.py +++ b/flystar/transforms.py @@ -1,13 +1,13 @@ -from astropy.modeling import models, fitting -import numpy as np -from scipy.interpolate import LSQBivariateSpline as spline -from scipy import stats -from astropy.table import Table -import collections import re import copy import datetime -from . import motion_model +import numpy as np +import collections +from flystar import motion_model +from astropy.table import Table +from astropy.modeling import models, fitting +from scipy import stats +from scipy.interpolate import LSQBivariateSpline as spline class Transform2D(object): ''' From 3209314cd75717071325f21fde4a909e0f890ce0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Thu, 6 Aug 2026 16:29:25 -0700 Subject: [PATCH 241/254] Added Parallax testing data --- flystar/tests/test_data/list_of_starlists.pkl | Bin 0 -> 70070 bytes flystar/tests/test_data/my_gaia.pkl | Bin 0 -> 8805 bytes 2 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 flystar/tests/test_data/list_of_starlists.pkl create mode 100644 flystar/tests/test_data/my_gaia.pkl diff --git a/flystar/tests/test_data/list_of_starlists.pkl b/flystar/tests/test_data/list_of_starlists.pkl new file mode 100644 index 0000000000000000000000000000000000000000..3662f0f65f88a8a944aaac52225bb815feff41d4 GIT binary patch literal 70070 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zIF^?8%4QeWeR2MSY+G2@sexmCZmoGd+@D^teBnq!Yt&Yq{%&)|!^REgXUW%;`NQL< z$HuMORX!;2PGe)%mFk&cxMTyVoiaCHfxkip`mI0u>W9{tAoe@4vJYIf|x1RYCOUqYP&M&qw8r^No7=pod3bD{Vy*cu(Yyo**ep~GySVg;LS5&P@+RHEaJp?68JiQU%##%tHNN}g$Fyd?DBS;R3s(Cf z(rSHu9zSD)e8qm<0MNNYL8k+Yi26jQ^i+vyIw!G0+6xZX#A&wSo&HT(K!5TVo}^z#0qU4 zu{h}h(a0l2b=4T6E^0Ng`gd;;Ylf8*t4@t3melVcYDc9J%Qo7Hm5MB)q3%aw#WTAI z-n55UTxTL`KN(6aymFOj{Bt(3Vx6C;B@?94zf}*koeyflIYW@`}|6i_J;;pbk3nIy!Y=0C_g> zZqugI!94KEO5tjtCzp%@!9sb0aO^G)9lJ{~QQUSRF)>Ycdq6DGDFZyHi}>vnF3VQQ zwK6Zo$b?!-w+fV~fmewJ9^6e2ki}tf%-q0<*;b(>0mTe=JI_!C-+ejmiL9Taj5uSzhKs3J9d9o^rmG5a3VOm7t1qZ7ShQKY2rEC=VtVxJp`hz2Jt#>V33{<&lhh!!7Odoh7V4EZ2I)Mnx`fk zrH{V4`EYPBH!?gP)tb>#osbhAH*MOzdi)Tju|eH2Jeti!z2y%s zzMvZy@8h<3NrrU6kC)f`LD&)b@$QmqeGZ>+@q+(B5U*d_kEE0MNv$+4wGzCiR>F^L YBk82^NH`d8Nmt|f#owEr<*o960VeOH%>V!Z literal 0 HcmV?d00001 From 46be4059acb007d7ca7f6c4920766f17cd9ffd81 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Thu, 6 Aug 2026 20:06:00 -0700 Subject: [PATCH 242/254] Moved generic_match to align to avoid cyclic imports, as it does not use any function in match.py, only in align.py --- flystar/tests/test_match.py | 9 ++------- 1 file changed, 2 insertions(+), 7 deletions(-) diff --git a/flystar/tests/test_match.py b/flystar/tests/test_match.py index 1022f6d..80ac59f 100644 --- a/flystar/tests/test_match.py +++ b/flystar/tests/test_match.py @@ -1,4 +1,4 @@ -from flystar import match, starlists, transforms +from flystar import align, match, starlists, transforms import numpy as np import pdb from astropy.table import Table @@ -173,14 +173,9 @@ def test_generic_match(): starlist1 = starlists.StarList.from_table(list1) starlist2 = starlists.StarList.from_table(list2) - out = match.generic_match(starlist1, starlist2, init_mode='triangle', + out = align.generic_match(starlist1, starlist2, init_mode='triangle', model=transforms.PolyTransform, order_dr=(1, 1.0), dr_final=1.0, xy_match=(None, None, None, None, None, None, None, None), m_match=(None, None, None, None), sigma_match=None, n_bright=8, verbose=True) - -if __name__ == '__main__': - # test_match_duplicates() - # test_miracle_match_briteN() - test_generic_match() \ No newline at end of file From 2e0a1a2dc0a7f7f5e1545020151392e3519d52c1 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Thu, 6 Aug 2026 20:08:37 -0700 Subject: [PATCH 243/254] Updated transformation save plot folder name --- flystar/align.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/flystar/align.py b/flystar/align.py index c898e3a..f61bb66 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -838,7 +838,7 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar ## Make plot, if desired if self.save_path: plots.trans_positions(ref_list, ref_list[idx_ref], star_list_T, star_list_T[idx_lis], - save_path=os.path.join(self.save_path, 'plots', f'iter{nn}', f"Transformed_Positions_Starlist_{ii}_t_{star_list_T['t'][0]}.png"), + save_path=os.path.join(self.save_path, 'transformation_plots', f'iter{nn}', f"Transformed_Positions_Starlist_{ii}_t_{star_list_T['t'][0]}.png"), show_plot=False) ### Update the observed (but transformed) values in the reference table. self.update_ref_table_from_list(star_list, star_list_T, ii, idx_ref, idx_lis, idx2) From 6ea68a5d0bceb2577e45a395e27203fbb3c53522 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CLingfeng?= Date: Thu, 6 Aug 2026 20:30:57 -0700 Subject: [PATCH 244/254] Added keep existing option for fit_motion_models --- flystar/startables.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/flystar/startables.py b/flystar/startables.py index ab0155b..a9abdb4 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -550,6 +550,7 @@ def fit_motion_models( absolute_sigma=True, method=None, select_stars=None, + keep_existing=True, bootstrap=0, seed=None, mask_value=None, @@ -591,6 +592,8 @@ def fit_motion_models( Method of scipy.curve_fit, {'lm', 'trf', 'dogbox'}, by default None select_stars : list of int, optional Indices of stars to fit, by default None (fit all stars) + keep_existing : bool, optional + Keep existing motion model results in the table, or set them to fill_value and Inf for stars not in select_stars, by default True bootstrap : int, optional Number of bootstrap samples for uncertainty resampling, by default 0 seed : int, optional @@ -878,7 +881,11 @@ def fit_motion_models( for col in new_col_list: if col in self.colnames: # Keep old data if the column already exists - continue + if keep_existing: + continue + else: + self.remove_column(col) + if col.endswith('_err'): self.add_column( Column(data=np.full(N_stars, np.inf, dtype=float), name=col), From 8f7a850547cc02700f371f3ba768cb8b58817a85 Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Fri, 7 Aug 2026 00:03:31 -0700 Subject: [PATCH 245/254] Update plot save path; Added indices mode in trans_initial_guess --- flystar/align.py | 17 ++++++++++++++++- flystar/match.py | 9 +++------ 2 files changed, 19 insertions(+), 7 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 7ec1f61..c3512de 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -851,8 +851,12 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar ## Make plot, if desired if self.save_path: + if 't' in star_list_T.meta: + plot_path = os.path.join(self.save_path, 'transformation_plots', f'iter{nn}', f"Transformed_Positions_Starlist_{ii}_t_{star_list_T.meta['t']}.png") + elif 't' in star_list_T.colnames: + plot_path = os.path.join(self.save_path, 'transformation_plots', f'iter{nn}', f"Transformed_Positions_Starlist_{ii}_t_{star_list_T['t'][0]}.png") plots.trans_positions(ref_list, ref_list[idx_ref], star_list_T, star_list_T[idx_lis], - save_path=os.path.join(self.save_path, 'plots', f'iter{nn}', f"Transformed_Positions_Starlist_{ii}_t_{star_list_T['t'][0]}.png"), + save_path=plot_path, show_plot=False) ### Update the observed (but transformed) values in the reference table. self.update_ref_table_from_list(star_list, star_list_T, ii, idx_ref, idx_lis, idx2) @@ -3701,6 +3705,7 @@ def trans_initial_guess( star_list, trans_args, mode='miracle', + indices=None, order=1, briteN=None, n_req_match=3, @@ -3772,6 +3777,16 @@ def trans_initial_guess( polygon_starlist, buffer=buffer ) + elif mode == 'indices': + idx_r, idx_s = indices + x1m = star_list['x'][idx_s] + y1m = star_list['y'][idx_s] + m1m = star_list['m'][idx_s] + x2m = ref_list['x'][idx_r] + y2m = ref_list['y'][idx_r] + m2m = ref_list['m'][idx_r] + N = len(indices) + else: raise ValueError(f'flystar.align.trans_initial_guess: Unknown mode: {mode}. Must be one of ["name", "miracle"].') diff --git a/flystar/match.py b/flystar/match.py index 182cb26..4fd4ea1 100644 --- a/flystar/match.py +++ b/flystar/match.py @@ -348,12 +348,9 @@ def match(x1, y1, m1, x2, y2, m2, dr_tol, dm_tol=None, verbose=True): y2 = np.array(y2, copy=False) m2 = np.array(m2, copy=False) - if not np.isfinite(x1).any(): raise ValueError('x1 does not contain any finite values!') - if not np.isfinite(y1).any(): raise ValueError('y1 does not contain any finite values!') - if not np.isfinite(m1).any(): raise ValueError('m1 does not contain any finite values!') - if not np.isfinite(x2).any(): raise ValueError('x2 does not contain any finite values!') - if not np.isfinite(y2).any(): raise ValueError('y2 does not contain any finite values!') - if not np.isfinite(m2).any(): raise ValueError('m2 does not contain any finite values!') + for val, name in zip([x1, y1, m1, x2, y2, m2], ['x1', 'y1', 'm1', 'x2', 'y2', 'm2']): + if not np.isfinite(val).any(): + raise ValueError(f'{name} does not contain any finite values!') assert x1.shape == y1.shape, 'x1 and y1 do not match!' assert x2.shape == y2.shape, 'x2 and y2 do not match!' From a0e4570ee81b6857f39e245d4c4a7c470a30f1d6 Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Fri, 7 Aug 2026 13:58:07 -0700 Subject: [PATCH 246/254] Removed unecessary if statement --- flystar/align.py | 28 +++++++++++----------------- 1 file changed, 11 insertions(+), 17 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 070748b..bcc129f 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -669,23 +669,15 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar star_list_T.transform_xy(trans) # Match stars between the transformed, trimmed lists. - if 'use_in_trans' in ref_list.colnames: - # Only use stars specified by "use_in_trans" column. - use_in_trans = ref_list['use_in_trans'] - idx1, idx2, dr, dm = match.match( - star_list_T['x'], star_list_T['y'], star_list_T['m'], - ref_list['x'][use_in_trans], ref_list['y'][use_in_trans], ref_list['m'][use_in_trans], - dr_tol=dr_tol, dm_tol=dm_tol, verbose=self.verbose - ) - # Restore idx2 to the full reference list indices - idx2 = np.where(use_in_trans)[0][idx2] - - else: - idx1, idx2, dr, dm = match.match( - star_list_T['x'], star_list_T['y'], star_list_T['m'], - ref_list['x'], ref_list['y'], ref_list['m'], - dr_tol=dr_tol, dm_tol=dm_tol, verbose=self.verbose - ) + # Only use stars specified by "use_in_trans" column. + use_in_trans = ref_list['use_in_trans'] + idx1, idx2, dr, dm = match.match( + star_list_T['x'], star_list_T['y'], star_list_T['m'], + ref_list['x'][use_in_trans], ref_list['y'][use_in_trans], ref_list['m'][use_in_trans], + dr_tol=dr_tol, dm_tol=dm_tol, verbose=self.verbose + ) + # Restore idx2 to the full reference list indices + idx2 = np.where(use_in_trans)[0][idx2] if self.verbose > 1: print( ' Match 1: Found ', len(idx1), ' matches out of ', len(star_list_T), @@ -774,6 +766,8 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar m=star_list_orig_trim['m'][idx1], mref=ref_list['m'][idx2], weights=weight, mag_trans=self.mag_trans ) + if np.isnan(trans.px.parameters).any() or np.isnan(trans.py.parameters).any(): + raise ValueError(f"Derived transformation contains NaN parameters! Check your input data and tolerances.") # Save the final transformation. self.trans_list[ii] = trans From 8834a49e0cebef9ee2c79d12112a7b1ff22bebc2 Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Fri, 7 Aug 2026 14:04:22 -0700 Subject: [PATCH 247/254] Fixed bugs --- flystar/align.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index cc5b7b7..08aff51 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -754,7 +754,7 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar idx2 = idx2[keepers] # Determine weights in the fit. - weight = self.get_weights_for_lists(ref_list[idx2], star_list_T[idx1]) + weight = self.get_weights_for_lists(ref_list[idx2], star_list_T[idx1]) # Derive the best-fit transformation parameters. if self.verbose > 1: @@ -842,7 +842,7 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar star_list_T.transform_xym(self.trans_list[ii]) else: star_list_T.transform_xy(self.trans_list[ii]) - + if self.verbose > 7: hdr = '{nr:20s} {n:s} {xl:9s} {xr:9s} {yl:9s} {yr:9s} {ml:6s} {mr:6s} ' hdr += '{dx:7s} {dy:7s} {dm:6s} {xo:9s} {yo:9s} {mo:6s}' @@ -913,7 +913,7 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar print(msg1.format('dm', 'all stars', dm.mean(), dm.std())) # ref_list - ref_table # Calculate the residuals just for those used in the transformation - used = self.ref_table['used_in_trans'][:, ii]) + used = np.where(self.ref_table['used_in_trans'][:, ii])[0] used_good = used[np.isin(used, idx_ref)] dr_u = np.hypot(ref_list['x'][used_good] - self.ref_table['x'][used_good, ii], @@ -1547,7 +1547,7 @@ def calc_bootstrap_errors(self, n_boot=100, seed=None, boot_epochs_min=-1, calc_ ref_table = ref_table[idx_good] t0_arr = t0_arr[idx_good] else: - idx_good = np.arange(0, len(ref_table), 1) + idx_good = np.ones(len(ref_table), dtype=bool) # Initialize sums for output x_boot_sum = np.zeros((len(ref_table['x']), n_epochs)) @@ -1601,8 +1601,8 @@ def calc_bootstrap_errors(self, n_boot=100, seed=None, boot_epochs_min=-1, calc_ for jj in range(n_epochs): # Extract bootstrap sample of matched reference stars for this epoch - good = (ref_table['used_in_trans'][:,jj] == True) & (~np.isnan(ref_table['x_orig'][:,jj])) - samp_idx = rng.choice(good, sum(good), replace=True) + good = np.where(ref_table['used_in_trans'][:,jj] == True) & (~np.isnan(ref_table['x_orig'][:,jj]))[0] + samp_idx = rng.choice(good, len(good), replace=True) # Get reference star positions in particular epoch from ref_list. t_epoch = t_arr[jj] From 4642becdd0bf06828015156d9c4b38d3664ffebe Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Fri, 7 Aug 2026 14:04:41 -0700 Subject: [PATCH 248/254] Removed white trailing spaces --- flystar/align.py | 36 ++++++++++++++++++------------------ 1 file changed, 18 insertions(+), 18 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 08aff51..a6b979a 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -551,7 +551,7 @@ def fit(self, processes=1, chunksize=None): fill_with_one = np.all(xe_data.mask, axis=1) & np.all(ye_data.mask, axis=1) xe_data[fill_with_one] = 1. ye_data[fill_with_one] = 1. - + if np.ndim(x_data) == 1: x_data = x_data[:, np.newaxis] if np.ndim(y_data) == 1: @@ -657,7 +657,7 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar buffer=dr_tol, motion_models=self.motion_models, fixed_params_dict=self.fixed_params_dict, - mag_trans=self.mag_trans, + mag_trans=self.mag_trans, verbose=self.verbose ) if np.isnan(trans.px.parameters).any() or np.isnan(trans.py.parameters).any(): @@ -687,7 +687,7 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar # unmatched_starlist = np.array([True if i not in idx1 else False for i in range(len(star_list_T))]) # unmatched_ref = np.array([True if i not in idx2 else False for i in range(len(ref_list))]) # use_in_trans = ref_list[idx2]['use_in_trans'] - + # fig = go.Figure() # plotly_stars( # x=star_list_T['x'][unmatched_starlist], @@ -781,7 +781,7 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar idx_lis, idx_ref, dr, dm = match.match(star_list_T['x'], star_list_T['y'], star_list_T['m'], ref_list['x'], ref_list['y'], ref_list['m'], dr_tol=dr_tol, dm_tol=dm_tol, verbose=self.verbose) - + # Let's look at just the ref stars used in the transformation, which are idx1 and idx2 keepers = self.outlier_rejection_indices(star_list_T[idx1], ref_list[idx2], outlier_tol) @@ -800,7 +800,7 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar for jj in outlier_names: print('{0}'.format(jj)) print('=========================') - + # Update set of ref stars (indices are idx1, idx2 here, to be compatible downstream) idx1 = idx1[keepers] idx2 = idx2[keepers] @@ -811,7 +811,7 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar # Redo transformation if self.verbose > 1: print( 'Recalculating trans after outlier reject. Using ', len(idx1), ' stars in transformation.' ) - trans = self.trans_class.derive_transform(star_list_orig_trim['x'][idx1], star_list_orig_trim['y'][idx1], + trans = self.trans_class.derive_transform(star_list_orig_trim['x'][idx1], star_list_orig_trim['y'][idx1], ref_list['x'][idx2], ref_list['y'][idx2], **trans_args, m=star_list_orig_trim['m'][idx1], mref=ref_list['m'][idx2], @@ -852,7 +852,7 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar ml='m_lis_T', mr='m_ref', dx='dx_mpix', dy='dy_mpix', dm='dm', xo='x_orig', yo='y_orig', mo='m_orig')) - + fmt = '{nr:20s} {n:s} {xl:9.5f} {xr:9.5f} {yl:9.5f} {yr:9.5f} {ml:6.2f} {mr:6.2f} ' fmt += '{dx:7.2f} {dy:7.2f} {dm:6.2f} {xo:9.5f} {yo:9.5f} {mo:6.2f}' for foo in range(len(idx1)): @@ -930,7 +930,7 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar # if self.save_path: # with open(os.path.join(self.save_path, f"{self.prefix_name}_iter.pkl"), 'wb') as file: # pickle.dump(self, file) - + return def setup_trans_info(self): @@ -2293,12 +2293,12 @@ def fit(self, processes=1, chunksize=None): # This updates trans objects and the ref_table. self.match_and_transform( self.ref_mag_lim, - self.dr_tol[nn], - self.dm_tol[nn], + self.dr_tol[nn], + self.dm_tol[nn], self.outlier_tol[nn], - self.trans_args[nn], - nn, - processes=processes, + self.trans_args[nn], + nn, + processes=processes, chunksize=chunksize ) @@ -2322,7 +2322,7 @@ def fit(self, processes=1, chunksize=None): # First reset the reference table 2D values. ########## self.reset_ref_values(exclude=['used_in_trans']) - + if self.verbose > 0: print("**********") print("Final Matching") @@ -2389,7 +2389,7 @@ def fit(self, processes=1, chunksize=None): fill_with_one = np.all(xe_data.mask, axis=1) & np.all(ye_data.mask, axis=1) xe_data[fill_with_one] = 1. ye_data[fill_with_one] = 1. - + if np.ndim(x_data) == 1: x_data = x_data[:, np.newaxis] if np.ndim(y_data) == 1: @@ -2608,7 +2608,7 @@ def determine_motion_models(startable, motion_models=None, fixed_params_dict=Non )) motion_model_used = [result[0] for result in results] n_params = [result[1] for result in results] - + return motion_model_used, n_params @@ -2805,7 +2805,7 @@ def add_rows_for_new_stars(ref_table, star_list, idx_list, motion_model_name='Fi for col_name in ref_table.colnames: new_col_name = col_name - + if col_name in fixed_params_dict.keys(): new_col_empty = fixed_params_dict[col_name] elif col_name=='n_params': @@ -2830,7 +2830,7 @@ def add_rows_for_new_stars(ref_table, star_list, idx_list, motion_model_name='Fi new_col_data = Column( data=np.tile(new_col_empty, new_col_shape), - name=col_name, + name=col_name, dtype=ref_table[col_name].dtype ) From 510674218eb125c058a9b21163ea43f4e0bb60fd Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Fri, 7 Aug 2026 14:54:01 -0700 Subject: [PATCH 249/254] Fixed testing errors and trimmed white spaces --- flystar/align.py | 2 +- flystar/match.py | 8 ++++---- flystar/plots.py | 8 ++++---- flystar/starlists.py | 17 +++++------------ flystar/startables.py | 12 ++++++------ flystar/tests/test_align.py | 12 ++++++------ 6 files changed, 26 insertions(+), 33 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index a6b979a..99996da 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -1601,7 +1601,7 @@ def calc_bootstrap_errors(self, n_boot=100, seed=None, boot_epochs_min=-1, calc_ for jj in range(n_epochs): # Extract bootstrap sample of matched reference stars for this epoch - good = np.where(ref_table['used_in_trans'][:,jj] == True) & (~np.isnan(ref_table['x_orig'][:,jj]))[0] + good = np.where((ref_table['used_in_trans'][:,jj] == True) & (~np.isnan(ref_table['x_orig'][:,jj])))[0] samp_idx = rng.choice(good, len(good), replace=True) # Get reference star positions in particular epoch from ref_list. diff --git a/flystar/match.py b/flystar/match.py index 3e3ba10..84e2b2e 100644 --- a/flystar/match.py +++ b/flystar/match.py @@ -4,13 +4,13 @@ from scipy.spatial import KDTree as KDT -def miracle_match_briteN(xin1, yin1, min1, xin2, yin2, min2, Nbrite, +def miracle_match_briteN(xin1, yin1, min1, xin2, yin2, min2, Nbrite, polygon1=None, polygon2=None, buffer=0, Nbins_vmax=200, Nbins_angle=360,verbose=False): """ Take two input starlists and select the brightest stars from each. Then perform a triangle matching algorithm along the lines of Groth 1986. - + For every possible triangle (combination of 3 stars) in a starlist, compute the ratio of two sides and the angle between those sides. These quantities are invariant under scale and rotation transformations. @@ -90,7 +90,7 @@ def miracle_match_briteN(xin1, yin1, min1, xin2, yin2, min2, Nbrite, # in_overlap2 = (xin2 >= x_overlap[0]) & (xin2 <= x_overlap[1]) & (yin2 >= y_overlap[0]) & (yin2 <= y_overlap[1]) # if sum(in_overlap1) < Nbrite or sum(in_overlap2) < Nbrite: # raise ValueError( - # 'Not enough stars in the overlapping region to find matches!\n' + + # 'Not enough stars in the overlapping region to find matches!\n' + # f'Stars in overlap for list 1: {sum(in_overlap1)}\n' + # f'Stars in overlap for list 2: {sum(in_overlap2)}\n' # ) @@ -288,7 +288,7 @@ def match(x1, y1, m1, x2, y2, m2, dr_tol, dm_tol=None, verbose=True): For two stars to be matched, they must be within a specified radius (dr_tol) and delta-magnitude (dm_tol). For stars with more than 1 neighbor (within the tolerances), if one is found that is the best match in both brightness and positional offsets - (closest in both), then the match is made. + (closest in both), then the match is made. Otherwise, their is a conflict and no match is returned for the star. diff --git a/flystar/plots.py b/flystar/plots.py index 1068452..60966f6 100755 --- a/flystar/plots.py +++ b/flystar/plots.py @@ -3734,7 +3734,7 @@ def plot_errors_vs_r_m(star_tab, vmax_perr=0.75, vmax_pmerr=0.75): plt.scatter(star_tab['m0'], r, c=p_err, s=8, vmin=0, vmax=vmax_perr) plt.colorbar(label='Pos Err (mas)') plt.xlabel('Mag') - plt.ylabel('Radius (")') + plt.ylabel('Radius (")') plt.subplot(1, 2, 2) plt.scatter(star_tab['m0'], r, c=pm_err, s=8, vmin=0, vmax=vmax_pmerr) @@ -4040,7 +4040,7 @@ def plotly_stars(x, y, m=None, star_name=None, marker_size=3, color=None, alpha= x = np.asarray(x) y = np.asarray(y) hover_template = 'x: %{x:.3f}
y: %{y:.3f}' - + if isinstance(color, str) and color.startswith('C') and color[1:].isdigit(): color = mcolors.to_rgba(color, alpha=alpha) color = f'rgba({color[0]*255}, {color[1]*255}, {color[2]*255}, {color[3]:.2f})' @@ -4077,14 +4077,14 @@ def plotly_stars(x, y, m=None, star_name=None, marker_size=3, color=None, alpha= hovertemplate=hover_template, name=label ) - + if fig is None: fig = go.Figure(data=[fig_data]) show = True if show is None else show else: fig.add_trace(fig_data) show = False if show is None else show - + fig.update_layout( xaxis_title=xlabel, yaxis_title=ylabel, diff --git a/flystar/starlists.py b/flystar/starlists.py index ea9f621..6714853 100644 --- a/flystar/starlists.py +++ b/flystar/starlists.py @@ -512,8 +512,7 @@ def __init__(self, *args, **kwargs): else: # If we have errors, we need them in both dimensions. if ('xe' in kwargs) ^ ('ye' in kwargs): - raise TypeError("The StarList class requires both 'xe' and" + - " 'ye' arguments") + raise TypeError("The StarList class requires both 'xe' and 'ye' arguments") # Figure out the shape n_stars = kwargs['x'].shape[0] @@ -523,9 +522,7 @@ def __init__(self, *args, **kwargs): kwargs['name'] = np.asarray(kwargs['name']) if (not isinstance(kwargs['name'], np.ndarray)) or ( len(kwargs['name']) != n_stars): - err_msg = "The '{0:s}' argument has to be a numpy array " - err_msg += "with length = {1:d}" - raise TypeError(err_msg.format('name', n_stars)) + raise TypeError(f"The 'name' argument has to be a numpy array with length {n_stars}, but has type {type(kwargs['name'])} and length {len(kwargs['name'])}") # Check all the arrays. arg_tab = ('x', 'y', 'm', 'xe', 'ye', 'me', 'corr') @@ -535,12 +532,10 @@ def __init__(self, *args, **kwargs): for arg_test in arg_tab: if arg_test in kwargs: if not isinstance(kwargs[arg_test], np.ndarray): - err_msg = "The '{0:s}' argument has to be a numpy array" - raise TypeError(err_msg.format(arg_test)) + raise TypeError(f"The '{arg_test:s}' argument has to be a numpy array") if kwargs[arg_test].shape != (n_stars,): - err_msg = "The '{0:s}' argument has to have shape = ({1:d},), but has shape = {2}" - raise TypeError(err_msg.format(arg_test, n_stars, kwargs[arg_test].shape)) + raise TypeError(f"The '{arg_test:s}' argument has to have shape ({n_stars:d},), but has shape {kwargs[arg_test].shape}") # We have to have special handling of meta-data meta_tab = ('list_time', 'list_name') @@ -552,9 +547,7 @@ def __init__(self, *args, **kwargs): if meta_test in kwargs: if not isinstance(kwargs[meta_test], meta_type_test): - err_msg = "The '{0:s}' argument has to be a {1:s}." - raise TypeError( - err_msg.format(meta_test, str(meta_type_test))) + raise TypeError(f"The '{meta_test:s}' argument has to be a {meta_type_test:s}, but has type {type(kwargs[meta_test])}") ##### # Create the starlist diff --git a/flystar/startables.py b/flystar/startables.py index a9abdb4..f886d06 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -613,7 +613,7 @@ def fit_motion_models( verbose : bool, optional Print verbose messages or not, by default True - + Raises ------ ValueError @@ -769,7 +769,7 @@ def fit_motion_models( valid_xy &= ~ (xe_data.mask | ye_data.mask) # Calculate n_fit: unmasked x y values - # This will be used to determine which motion model to use for each star. + # This will be used to determine which motion model to use for each star. # Note that we don't require unique times here # as scipy.curve_fit and Linear algebra can fit non-unique times. # self['n_fit'] = np.sum(valid_xy, axis=1) @@ -1005,7 +1005,7 @@ def fit_motion_models( arguments, desc=f"Fitting motion model {unique_motion_model} with {processes} processes", disable=not verbose - ), + ), chunksize=chunksize )) @@ -1098,7 +1098,7 @@ def infer_positions(self, times, fixed_params_dict=None, fill_value=np.nan): # Calculate the dictionary of {motion_model: indices of stars with this motion model} for faster access during prediction unique_motion_models, unique_inv_indices = np.unique(self['motion_model_used'], return_inverse=True) indices_by_motion_model = {key: np.flatnonzero(unique_inv_indices == k) for k, key in enumerate(unique_motion_models)} - + mm_map = motion_model.motion_model_map() # Prepare fit_params, fixed_params, fit_param_errs for each star for unique_motion_model, unique_index in indices_by_motion_model.items(): @@ -1330,8 +1330,8 @@ def shift_reference_frame(table, delta_vx=0.0, delta_vy=0.0, delta_pi=0.0, fixed # Helper function to fit motion model for each star for multiprocessing def fit_motion_model( motion_model_instance, - t, x, y, xe, ye, - fixed_params_dict, + t, x, y, xe, ye, + fixed_params_dict, weighting, use_scipy, absolute_sigma, diff --git a/flystar/tests/test_align.py b/flystar/tests/test_align.py index 4717d76..202040d 100644 --- a/flystar/tests/test_align.py +++ b/flystar/tests/test_align.py @@ -157,7 +157,7 @@ def test_MosaicSelfRef_vel(): # Test instantiation and basic fitting. ########## msc = align.MosaicSelfRef(lists, ref_index=0, iters=3, - dr_tol=[5, 3, 3], dm_tol=[1, 1, 0.5], outlier_tol=None, briteN=30, + dr_tol=[5, 3, 3], dm_tol=[1, 1, 0.5], outlier_tol=None, briteN=30, trans_class=transforms.PolyTransform, trans_args={'order': 2}, motion_models=['Empty', 'Fixed', 'Linear'], verbose=False) @@ -525,7 +525,7 @@ def test_MosaicToRef_hst_me(): update_ref_orig=False, mag_trans=False, trans_weights='both,std', - init_guess_mode='miracle', + init_guess_mode='miracle', # save_path=f'{test_data_path}/mb10364_data/test_MosaicToRef_hst_me.pkl', verbose=False ) @@ -869,8 +869,8 @@ def test_masked_cols(): } # Get gaia catalog stars. Note that this produces a masked column table - search_rad = 10.0 # arcsec - gaia = analysis.query_gaia(ra, dec, search_radius=search_rad) + search_radius = 10.0 # arcsec + gaia = analysis.query_gaia(ra, dec, search_radius=search_radius) my_gaia = analysis.prepare_gaia_for_flystar(gaia, ra, dec, targets_dict=targets_dict) assert isinstance(my_gaia, Table) @@ -1459,11 +1459,11 @@ def make_fake_starlists_poly1_par(seed=-1): dr_tol=[0.2, 0.1, 0.08], dm_tol=[5,5,5], outlier_tol=[None, None, 3], mag_lim=[6, 20], trans_class=transforms.PolyTransform, - trans_args=[{'order': 1}, {'order': 1}, {'order': 1}], + trans_args=[{'order': 1}, {'order': 1}, {'order': 1}], motion_models=['Linear','Parallax'], fixed_params_dict = {'ra':ra_deg, 'dec':dec_deg, 'pa':0.0, 'obsLocation':'earth'}, use_ref_new=True, - update_ref_orig=False, + update_ref_orig=False, mag_trans=True, trans_weights='both,std', init_guess_mode='name', verbose=3) From dcf9e2c7ba8b7f22ca630a924276a44e0495575a Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Tue, 11 Aug 2026 00:50:17 -0700 Subject: [PATCH 250/254] Added support for only setting list_time meta in starlists and optimized list_times calculation; Fixed deepcopy problem of astropy Table; Changed all meta to list as astropy does not support numpy array in meta data; Changed default dr_tol and dm_tol for MosaicSelfRef and MosaicToRef to one iteration --- flystar/align.py | 246 +++++++++++++++++++---------------------------- 1 file changed, 101 insertions(+), 145 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index 99996da..a362f96 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -23,13 +23,13 @@ def __init__( starlist_vertices=None, # Alignment parameters ref_index=0, - iters=2, - dr_tol=[1, 1], - dm_tol=[2, 1], + iters=1, + dr_tol=[1.], + dm_tol=[1.], outlier_tol=None, # Transformation parameters trans_class=transforms.PolyTransform, - trans_args=[{'order': 2}, {'order': 2}], + trans_args=[{'order': 1}], trans_input=None, trans_weights=None, init_order=1, @@ -261,10 +261,16 @@ def = None. If not None, then this should contain an array or list of transform else: self.reflist_polygon = None - # Check x and y are 1d for ii in range(len(self.star_lists)): + # Check x and y are 1d if self.star_lists[ii]['x'].ndim != 1 or self.star_lists[ii]['y'].ndim != 1: raise ValueError(f"StarList at index {ii} has x and y that are not 1D. x.ndim={self.star_lists[ii]['x'].ndim}, y.ndim={self.star_lists[ii]['y'].ndim}. Please flatten these columns to be 1D.") + # Add list_time to meta if not present + if 'list_time' not in self.star_lists[ii].meta: + assert 't' in self.star_lists[ii].colnames, f"StarList at index {ii} does not have 'list_time' in meta and does not have 't' column. Please add one of these." + unique_t = np.unique(self.star_lists[ii]['t']) + assert unique_t.size == 1, f"The time values of starlist at index {ii} are not unique." + self.star_lists[ii].meta['list_time'] = unique_t[0] if outlier_tol is None: self.outlier_tol = [None] * self.iters @@ -435,8 +441,7 @@ def fit(self, processes=1, chunksize=None): self.reset_ref_values() if self.verbose > 0: - print(" ") - print("**********") + print("\n**********") print("**********") print('Starting iter {0:d} with ref_table shape:'.format(nn), self.ref_table['x'].shape) print("**********") @@ -454,7 +459,6 @@ def fit(self, processes=1, chunksize=None): processes=processes, chunksize=chunksize ) - # Clean up the reference table # Find where stars are detected. self.ref_table.detections() @@ -507,9 +511,8 @@ def fit(self, processes=1, chunksize=None): self.iter_callback(self.ref_table, nn) # Add times into ref_table meta data - # complete_times = np.array([np.unique(col[~np.isnan(col)])[0] for col in self.ref_table['t'].T]) - all_epochs = get_all_epochs(self.ref_table) - self.ref_table.meta['list_times'] = list(all_epochs) + all_epochs = [s.meta['list_time'] for s in self.star_lists] + self.ref_table.meta['list_times'] = all_epochs # Update chi2 values in ref table, as motion_model_used may have changed x_inferred, y_inferred, _, _ = self.ref_table.infer_positions(all_epochs) @@ -537,20 +540,16 @@ def fit(self, processes=1, chunksize=None): x_data = np.ma.masked_invalid(self.ref_table['x'].data, copy=True) y_data = np.ma.masked_invalid(self.ref_table['y'].data, copy=True) if weighted_xy: - xe = self.ref_table['xe'] - ye = self.ref_table['ye'] - xe_data = np.ma.masked_invalid(xe.data, copy=True) - ye_data = np.ma.masked_invalid(ye.data, copy=True) - else: - xe_data = None - ye_data = None - - if (xe_data is not None) and (ye_data is not None): + xe_data = np.ma.masked_invalid(self.ref_table['xe'].data, copy=True) + ye_data = np.ma.masked_invalid(self.ref_table['ye'].data, copy=True) xe_data.mask[np.isclose(xe_data, 0.)] = True ye_data.mask[np.isclose(ye_data, 0.)] = True fill_with_one = np.all(xe_data.mask, axis=1) & np.all(ye_data.mask, axis=1) xe_data[fill_with_one] = 1. ye_data[fill_with_one] = 1. + else: + xe_data = None + ye_data = None if np.ndim(x_data) == 1: x_data = x_data[:, np.newaxis] @@ -565,7 +564,7 @@ def fit(self, processes=1, chunksize=None): if 't' in self.ref_table.colnames: t_data = copy.deepcopy(self.ref_table['t'].data) else: - t_data = copy.deepcopy(np.array(self.ref_table.meta['list_times'])) + t_data = np.array(self.ref_table.meta['list_times']) t_data = np.broadcast_to(t_data, xe_data.shape) # Update t0, adapted from startables.fit_motion_models @@ -621,13 +620,8 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar star_list = self.star_lists[ii] - if 't' in star_list.meta: - ref_list = self.get_ref_list_from_table(star_list.meta['t'], processes=processes, chunksize=chunksize) - elif 't' in star_list.colnames: - assert np.unique(star_list['t']).size == 1, f"StarList at index {ii} has multiple unique times in the 't' column. Cannot determine reference list for matching." - ref_list = self.get_ref_list_from_table(star_list['t'][0], processes=processes, chunksize=chunksize) - else: - raise KeyError(f"StarList at index {ii} does not have a 't' column or meta['t'] entry. Cannot determine reference list for matching.") + list_epoch = star_list.meta['list_time'] + ref_list = self.get_ref_list_from_table(list_epoch, processes=processes, chunksize=chunksize) trans = self.trans_list[ii] @@ -637,7 +631,7 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar # star_list_T is trimmed and transformed self.apply_mag_lim_via_use_in_trans(ref_list, ref_mag_lim) star_list_orig_trim = apply_mag_lim(star_list, self.mag_lim[ii]) # trimmed, untransformed copy - star_list_T = copy.deepcopy(star_list_orig_trim) # trimmed, will be transformed copy + star_list_T = StarList(star_list_orig_trim, copy=True) # trimmed, will be transformed copy assert len(star_list_orig_trim) > 0, f"No stars remain after applying mag_lim={self.mag_lim[ii]} to star_list at index {ii}. Please check your mag_lim." @@ -679,71 +673,21 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar # Restore idx2 to the full reference list indices idx2 = np.where(use_in_trans)[0][idx2] + if len(idx1) == 0 or len(idx2) == 0: + fig, ax = plt.subplots() + ax.scatter(star_list_T['x'], star_list_T['y'], s=1, c='C0', alpha=0.5, label='Transformed Star List') + ax.scatter(ref_list['x'][use_in_trans], ref_list['y'][use_in_trans], s=1, c='C3', alpha=0.5, label='Reference List (use_in_trans=True)') + ax.set_xlabel('X') + ax.set_ylabel('Y') + ax.set_title(f'Matching Results for Catalog {ii + 1}') + ax.legend() + plt.show() + raise ValueError(f"align.match_and_transform: No matches found between star_list at index {ii} and the reference list. Check your dr_tol={dr_tol} and dm_tol={dm_tol} values.") + if self.verbose > 1: print( ' Match 1: Found ', len(idx1), ' matches out of ', len(star_list_T), '. If match count is low, check dr_tol, dm_tol.' ) - # # Plot matched stars - # unmatched_starlist = np.array([True if i not in idx1 else False for i in range(len(star_list_T))]) - # unmatched_ref = np.array([True if i not in idx2 else False for i in range(len(ref_list))]) - # use_in_trans = ref_list[idx2]['use_in_trans'] - - # fig = go.Figure() - # plotly_stars( - # x=star_list_T['x'][unmatched_starlist], - # y=star_list_T['y'][unmatched_starlist], - # m=star_list_T['m'][unmatched_starlist], - # star_name=star_list_T['name'][unmatched_starlist], - # label='Unmatched Star List', - # color='C9', - # fig=fig - # ) - # plotly_stars( - # x=star_list_T['x'][idx1], - # y=star_list_T['y'][idx1], - # m=star_list_T['m'][idx1], - # label='Matched Star List', - # color='C0', - # fig=fig - # ) - # plotly_stars( - # x=ref_list['x'][idx2][~use_in_trans], - # y=ref_list['y'][idx2][~use_in_trans], - # m=ref_list['m'][idx2][~use_in_trans], - # label='Matched ref (use_in_trans=False)', - # color='C4', - # fig=fig - # ) - # plotly_stars( - # x=ref_list['x'][unmatched_ref & ref_list['use_in_trans']], - # y=ref_list['y'][unmatched_ref & ref_list['use_in_trans']], - # m=ref_list['m'][unmatched_ref & ref_list['use_in_trans']], - # star_name=ref_list['name'][unmatched_ref & ref_list['use_in_trans']], - # label='Unmatched ref (use_in_trans=True)', - # symbol='star-open', - # marker_size=5, - # color='C3', - # fig=fig - # ) - # plotly_stars( - # x=ref_list['x'][idx2][use_in_trans], - # y=ref_list['y'][idx2][use_in_trans], - # m=ref_list['m'][idx2][use_in_trans], - # label='Matched ref (use_in_trans=True)', - # symbol='star', - # marker_size=5, - # color='C3', - # fig=fig - # ) - # fig.update_layout( - # title=f'Matched Stars - Catalog {ii + 1}', - # xaxis_title='X', - # yaxis_title='Y' - # ) - # fig.write_html(os.path.join(self.save_path, f'matched_stars_{ii + 1}.html')) - # fig.show() - - # Outlier rejection if outlier_tol is not None: keepers = self.outlier_rejection_indices(star_list_T[idx1], ref_list[idx2], outlier_tol, verbose=self.verbose) @@ -766,13 +710,11 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar m=star_list_orig_trim['m'][idx1], mref=ref_list['m'][idx2], weights=weight, mag_trans=self.mag_trans ) - if np.isnan(trans.px.parameters).any() or np.isnan(trans.py.parameters).any(): - raise ValueError(f"Derived transformation contains NaN parameters! Check your input data and tolerances.") # Outlier rejection: ref stars in final transformation, if desired if outlier_tol != None: # Apply transformation to starlist, run match between starlist and ref_list - star_list_T = copy.deepcopy(star_list) + star_list_T = StarList(star_list, copy=True) if self.mag_trans: star_list_T.transform_xym(trans) else: @@ -837,7 +779,7 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar # Apply the XY transformation to a new copy of the starlist and # do one final match between the two (now transformed) lists. - star_list_T = copy.deepcopy(star_list) + star_list_T = StarList(star_list, copy=True) if self.mag_trans: star_list_T.transform_xym(self.trans_list[ii]) else: @@ -879,13 +821,9 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar ## Make plot, if desired if self.save_path: - if 't' in star_list_T.meta: - plot_path = os.path.join(self.save_path, 'transformation_plots', f'iter{nn}', f"Transformed_Positions_Starlist_{ii}_t_{star_list_T.meta['t']}.png") - elif 't' in star_list_T.colnames: - plot_path = os.path.join(self.save_path, 'transformation_plots', f'iter{nn}', f"Transformed_Positions_Starlist_{ii}_t_{star_list_T['t'][0]}.png") - plots.trans_positions(ref_list, ref_list[idx_ref], star_list_T, star_list_T[idx_lis], - save_path=plot_path, - show_plot=False) + plot_path = os.path.join(self.save_path, 'transformation_plots', f'iter{nn}', f"Transformed_Positions_Starlist_{ii}_t_{list_epoch}.png") + plots.trans_positions(ref_list, ref_list[idx_ref], star_list_T, star_list_T[idx_lis], save_path=plot_path, show_plot=False) + ### Update the observed (but transformed) values in the reference table. self.update_ref_table_from_list(star_list, star_list_T, ii, idx_ref, idx_lis, idx2) @@ -989,7 +927,7 @@ def setup_ref_table_from_starlist(self, star_list): if col_name in motion_model_col_names: col_arrays[new_col_name] = star_list[col_name].data else: - new_col_data = star_list[col_name].data[:, None] + new_col_data = star_list[col_name].data[:, np.newaxis] col_arrays[new_col_name] = new_col_data # Use the columns from the ref list to make the ref_table. @@ -1069,15 +1007,10 @@ def setup_ref_table_from_starlist(self, star_list): if 'motion_model_input' not in ref_table.colnames: ref_table.add_column(np.repeat(self.motion_models[-1].name, len(ref_table)), name='motion_model_input') - # FIXME: Why do we need to set motion_model_used here before fitting? - # if 'motion_model_used' not in ref_table.colnames: - # # Order self.motion_models by decreasing n_params - # sorted_mms = sorted(self.motion_models, key=lambda mm: mm.n_params, reverse=True) - # # Save the most complex motion model that can infer the positions with the existing columns. - # for mm in sorted_mms: - # if all([_ in ref_table.colnames for _ in mm.fit_param_names]) and all([_ in ref_table.colnames for _ in mm.fixed_param_names]): - # ref_table.add_column(np.repeat(mm.name, len(ref_table)), name='motion_model_used') - # break + + # Add time column if it doesn't exist + if 't' not in ref_table.colnames: + ref_table.add_column(np.full((len(ref_table), 1), np.nan), name='t') return ref_table @@ -1173,10 +1106,10 @@ def update_ref_table_from_list(self, star_list, star_list_T, ii, idx_ref, idx_li ### Update the reference table for matched stars. # Add the matched stars to the reference table. # For every epoch except the reference, we need to add a starlist. + if ((self.ref_table['x'].shape[1] != len(self.star_lists)) and (ii != self.ref_index) and (ii >= self.ref_table['x'].shape[1])): - self.ref_table.add_starlist() copy_over_values(self.ref_table, star_list, star_list_T, ii, idx_ref, idx_lis) @@ -1372,13 +1305,13 @@ def match_lists(self, dr_tol, dm_tol): for ii in range(self.N_lists): # Apply the XY transformation to a new copy of the starlist and # do one final match between the two (now transformed) lists. - star_list_T = copy.deepcopy(self.star_lists[ii]) + star_list_T = StarList(self.star_lists[ii], copy=True) if self.mag_trans: star_list_T.transform_xym(self.trans_list[ii]) else: star_list_T.transform_xy(self.trans_list[ii]) - xref, yref = infer_positions(star_list_T['t'][0], self.ref_table, self.motion_models, self.fixed_params_dict) + xref, yref = infer_positions(star_list_T.meta['list_time'], self.ref_table, self.motion_models, self.fixed_params_dict) mref = self.ref_table['m0'] idx_lis, idx_ref, dr, dm = match.match(star_list_T['x'], star_list_T['y'], star_list_T['m'], @@ -1535,9 +1468,9 @@ def calc_bootstrap_errors(self, n_boot=100, seed=None, boot_epochs_min=-1, calc_ # First, assert than n_boot > 0 assert n_boot > 0, f'{n_boot=} is not possive!' - ref_table = copy.deepcopy(self.ref_table) + ref_table = StarTable(self.ref_table, copy=True) n_epochs = len(ref_table['x'][0]) - t_arr = get_all_epochs(ref_table) + t_arr = np.array(ref_table.meta['list_times']) t0_arr = ref_table['t0'] # Identify reference stars. If desired, trim ref_table to only stars to only @@ -1645,7 +1578,7 @@ def calc_bootstrap_errors(self, n_boot=100, seed=None, boot_epochs_min=-1, calc_ # to the star_list_T so it is in the same units as ref_boot. So, we'll apply # the final transformation for the epoch to get close enough for the # purposes of the bootstrap calculation - starlist_boot_T = copy.deepcopy(starlist_boot) + starlist_boot_T = StarList(starlist_boot, copy=True) if self.mag_trans: starlist_boot_T.transform_xym(self.trans_list[jj]) else: @@ -1673,7 +1606,7 @@ def calc_bootstrap_errors(self, n_boot=100, seed=None, boot_epochs_min=-1, calc_ xe=ref_table['xe_orig'][:,jj], ye=ref_table['ye_orig'][:,jj], me=ref_table['me_orig'][:,jj]) - starlist_T = copy.deepcopy(starlist) + starlist_T = StarList(starlist, copy=True) if self.mag_trans: starlist_T.transform_xym(trans) else: @@ -1858,16 +1791,16 @@ def __init__( reflist_vertex=None, starlist_vertices=None, # Alignment parameters - iters=2, - dr_tol=[1, 1], - dm_tol=[2, 1], + iters=1, + dr_tol=[1.], + dm_tol=[1.], outlier_tol=None, # Reference behavior (MosiacToRef specific) use_ref_new=False, update_ref_orig=False, # Transformation parameters trans_class=transforms.PolyTransform, - trans_args=[{'order': 2}, {'order': 2}], + trans_args=[{'order': 1}], trans_input=None, trans_weights=None, init_order=1, @@ -2113,6 +2046,7 @@ def = None. If not None, then this should contain an array or list of transform vel_weights=vel_weights, use_scipy=use_scipy, absolute_sigma=absolute_sigma, + scipy_method=scipy_method, # Advanced options iter_callback=iter_callback, save_path=save_path, @@ -2121,7 +2055,7 @@ def = None. If not None, then this should contain an array or list of transform ) self.starlist_vertices = starlist_vertices - self.ref_list = copy.deepcopy(ref_list) + self.ref_list = StarList(ref_list, copy=True) self.ref_mag_lim = ref_mag_lim self.update_ref_orig = update_ref_orig self.use_ref_new = use_ref_new @@ -2355,7 +2289,7 @@ def fit(self, processes=1, chunksize=None): self.iter_callback(self.ref_table, nn) # Add times into ref_table meta data - all_epochs = get_all_epochs(self.ref_table) + all_epochs = [s.meta['list_time'] for s in self.star_lists] self.ref_table.meta['list_times'] = all_epochs # Update chi2 values in ref table, as motion_model_used may have changed @@ -2365,8 +2299,13 @@ def fit(self, processes=1, chunksize=None): x_inferred = x_inferred[:, np.newaxis] if y_inferred.ndim == 1: y_inferred = y_inferred[:, np.newaxis] - chi2_x_2d = ((self.ref_table['x'] - x_inferred) / self.ref_table['xe'])**2 - chi2_y_2d = ((self.ref_table['y'] - y_inferred) / self.ref_table['ye'])**2 + weighted_xy = ('xe' in self.ref_table.colnames) and ('ye' in self.ref_table.colnames) + if weighted_xy: + chi2_x_2d = ((self.ref_table['x'] - x_inferred) / self.ref_table['xe'])**2 + chi2_y_2d = ((self.ref_table['y'] - y_inferred) / self.ref_table['ye'])**2 + else: + chi2_x_2d = (self.ref_table['x'] - x_inferred)**2 + chi2_y_2d = (self.ref_table['y'] - y_inferred)**2 chi2_x = np.nansum(chi2_x_2d, axis=1) chi2_y = np.nansum(chi2_y_2d, axis=1) chi2_x[~np.isfinite(chi2_x_2d).any(axis=1)] = np.nan @@ -2378,17 +2317,17 @@ def fit(self, processes=1, chunksize=None): if ('t0' not in self.ref_table.colnames) or ('n_fit' not in self.ref_table.colnames): x_data = np.ma.masked_invalid(self.ref_table['x'].data, copy=True) y_data = np.ma.masked_invalid(self.ref_table['y'].data, copy=True) - xe = self.ref_table['xe'] if 'xe' in self.ref_table.colnames else None - ye = self.ref_table['ye'] if 'ye' in self.ref_table.colnames else None - weighted_xy = (xe is not None) and (ye is not None) - xe_data = np.ma.masked_invalid(self.ref_table['xe'].data, copy=True) if weighted_xy else None - ye_data = np.ma.masked_invalid(self.ref_table['ye'].data, copy=True) if weighted_xy else None - if (xe_data is not None) and (ye_data is not None): + if weighted_xy: + xe_data = np.ma.masked_invalid(self.ref_table['xe'].data, copy=True) + ye_data = np.ma.masked_invalid(self.ref_table['ye'].data, copy=True) xe_data.mask[np.isclose(xe_data, 0.)] = True ye_data.mask[np.isclose(ye_data, 0.)] = True fill_with_one = np.all(xe_data.mask, axis=1) & np.all(ye_data.mask, axis=1) xe_data[fill_with_one] = 1. ye_data[fill_with_one] = 1. + else: + xe_data = None + ye_data = None if np.ndim(x_data) == 1: x_data = x_data[:, np.newaxis] @@ -2401,9 +2340,9 @@ def fit(self, processes=1, chunksize=None): ye_data = ye_data[:, np.newaxis] if 't' in self.ref_table.colnames: - t_data = copy.deepcopy(self.ref_table['t'].data) + t_data = self.ref_table['t'].data else: - t_data = copy.deepcopy(np.array(self.ref_table.meta['list_times'])) + t_data = np.array(self.ref_table.meta['list_times']) t_data = np.broadcast_to(t_data, xe_data.shape) # Update t0, adapted from startables.fit_motion_models @@ -2728,16 +2667,26 @@ def copy_over_values(ref_table, star_list, star_list_T, idx_epoch, idx_ref, idx_ idx_lis : list or array The indices into the star_list or star_list_T where values are copied from. """ + idx_lis = np.array(idx_lis) for col_name in ref_table.colnames: if col_name in star_list_T.colnames: if col_name == 'name': - ref_table['name_in_list'][idx_ref, idx_epoch] = star_list_T[col_name][list(idx_lis)] + ref_table['name_in_list'][idx_ref, idx_epoch] = star_list_T[col_name][idx_lis] else: - ref_table[col_name][idx_ref, idx_epoch] = star_list_T[col_name][list(idx_lis)] + ref_table[col_name][idx_ref, idx_epoch] = star_list_T[col_name][idx_lis] orig_col_name = col_name + '_orig' if orig_col_name in ref_table.colnames: - ref_table[orig_col_name][idx_ref, idx_epoch] = star_list[col_name][list(idx_lis)] + ref_table[orig_col_name][idx_ref, idx_epoch] = star_list[col_name][idx_lis] + + # Special case for list_time + if 't' not in star_list.colnames: + ref_table['t'][idx_ref, idx_epoch] = star_list.meta['list_time'] + # Add list_times in meta + if 'list_times' not in ref_table.meta: + ref_table.meta['list_times'] = [star_list.meta['list_time']] + else: + ref_table.meta['list_times'].append(star_list.meta['list_time']) return @@ -3387,7 +3336,7 @@ def transform_from_object(starlist, transform): """ # Make a copy of starlist. This is what we will eventually modify with # the transformed coordinates - starlist_f = copy.deepcopy(starlist) + starlist_f = StarList(starlist, copy=True) keys = list(starlist.keys()) # Check to see if velocities or motion_model are present in starlist. @@ -3783,7 +3732,14 @@ def trans_initial_guess( # If there are velocities in the reference list, use them. # We assume velocities are in the same units as the positions. - xref, yref = infer_positions(star_list['t'][0], ref_list, motion_models, fixed_params_dict=fixed_params_dict) + if 't' in ref_list.colnames: + epoch = star_list['t'][0] + elif 'list_time' in star_list.meta: + epoch = star_list.meta['list_time'] + else: + raise ValueError('star_list must have either a "t" column or a "list_time" meta key to use miracle matching.') + + xref, yref = infer_positions(epoch, ref_list, motion_models, fixed_params_dict=fixed_params_dict) if 'm' in ref_list.colnames: mref = ref_list['m'] else: @@ -3900,7 +3856,7 @@ def copy_and_rename_for_ref(star_list): old_cols += ['w'] new_cols += ['w'] - ref_list = copy.deepcopy(star_list) + ref_list = StarList(star_list, copy=True) for ii in range(len(old_cols)): ref_list.rename_column(old_cols[ii], new_cols[ii]) @@ -3985,7 +3941,7 @@ def apply_mag_lim(star_list, mag_lim): no magnitude cut is applied. """ - star_list_T = copy.deepcopy(star_list) + star_list_T = StarList(star_list, copy=True) if (mag_lim is not None): # Support 'm0' (primary) or 'm' column name. @@ -4125,8 +4081,8 @@ def generic_match(sl1, sl2, init_mode='triangle', if init_mode == 'triangle': # Blind triangles method # Prepare the reduced starlists for matching - sl1_cut = copy.deepcopy(sl1) - sl2_cut = copy.deepcopy(sl2) + sl1_cut = StarList(sl1, copy=True) + sl2_cut = StarList(sl2, copy=True) sl1_cut.restrict_by_value(x_min=xy_match[0], x_max=xy_match[1], y_min=xy_match[2], y_max=xy_match[3]) sl2_cut.restrict_by_value(x_min=xy_match[4], x_max=xy_match[5], @@ -4151,8 +4107,8 @@ def generic_match(sl1, sl2, init_mode='triangle', raise TypeError("Unrecognized initial matching method") # Restrict the matching catalogs - sl1_match = copy.deepcopy(sl1) - sl2_match = copy.deepcopy(sl2) + sl1_match = StarList(sl1, copy=True) + sl2_match = StarList(sl2, copy=True) sl1_match.restrict_by_value(m_min=m_match[0], m_max=m_match[1]) sl2_match.restrict_by_value(m_min=m_match[2], m_max=m_match[3]) From e536b6a7b61c2eafb422c4418451bda24333e510 Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Tue, 11 Aug 2026 00:53:13 -0700 Subject: [PATCH 251/254] Updated initialization; Added support for list_time meta in add_starlist --- flystar/starlists.py | 33 +++++++++++------------------- flystar/startables.py | 47 +++++++++++++++++++++++++------------------ flystar/transforms.py | 1 + 3 files changed, 40 insertions(+), 41 deletions(-) diff --git a/flystar/starlists.py b/flystar/starlists.py index 6714853..6c826ae 100644 --- a/flystar/starlists.py +++ b/flystar/starlists.py @@ -503,23 +503,23 @@ def __init__(self, *args, **kwargs): found_all_required = False if not found_all_required: - if not ('copy' in kwargs) | ('names' in kwargs.keys()) | \ - ('masked' in kwargs.keys()): # If it's not making a copy of the - # StarList or replacing columns or selecting from slices + if not any(key in kwargs for key in ['copy', 'names', 'masked']): + # If it's not making a copy of the StarList or replacing columns or selecting from slices err_msg = "The StarList class requires a arguments" + str(arg_req) warnings.warn(err_msg, UserWarning) - Table.__init__(self, *args, **kwargs) + super().__init__(*args, **kwargs) else: # If we have errors, we need them in both dimensions. if ('xe' in kwargs) ^ ('ye' in kwargs): raise TypeError("The StarList class requires both 'xe' and 'ye' arguments") # Figure out the shape + kwargs['x'] = np.array(kwargs['x']) n_stars = kwargs['x'].shape[0] # Check if the type and size of the arguments are correct. # Name checking: type and shape - kwargs['name'] = np.asarray(kwargs['name']) + kwargs['name'] = np.array(kwargs['name']) if (not isinstance(kwargs['name'], np.ndarray)) or ( len(kwargs['name']) != n_stars): raise TypeError(f"The 'name' argument has to be a numpy array with length {n_stars}, but has type {type(kwargs['name'])} and length {len(kwargs['name'])}") @@ -531,28 +531,21 @@ def __init__(self, *args, **kwargs): for arg_test in arg_tab: if arg_test in kwargs: - if not isinstance(kwargs[arg_test], np.ndarray): - raise TypeError(f"The '{arg_test:s}' argument has to be a numpy array") - + kwargs[arg_test] = np.array(kwargs[arg_test]) if kwargs[arg_test].shape != (n_stars,): - raise TypeError(f"The '{arg_test:s}' argument has to have shape ({n_stars:d},), but has shape {kwargs[arg_test].shape}") + raise ValueError(f"The '{arg_test:s}' argument has to match the shape of x ({n_stars:d},), but has shape {kwargs[arg_test].shape}") # We have to have special handling of meta-data meta_tab = ('list_time', 'list_name') meta_type = ((float, int), str) - for mm in range(len(meta_tab)): - meta_test = meta_tab[mm] - meta_type_test = meta_type[mm] - - if meta_test in kwargs: - - if not isinstance(kwargs[meta_test], meta_type_test): - raise TypeError(f"The '{meta_test:s}' argument has to be a {meta_type_test:s}, but has type {type(kwargs[meta_test])}") + for mtab, mtype in zip(meta_tab, meta_type): + if (mtab in kwargs) and (not isinstance(kwargs[mtab], mtype)): + raise TypeError(f"The '{mtab:s}' argument has to be a {mtype:s}, but has type {type(kwargs[mtab])}") ##### # Create the starlist ##### - Table.__init__(self, + super().__init__( (kwargs['name'], kwargs['x'], kwargs['y'], kwargs['m']), names=('name', 'x', 'y', 'm')) self.meta = {'n_stars': n_stars} @@ -565,8 +558,7 @@ def __init__(self, *args, **kwargs): if arg in ['name', 'x', 'y', 'm']: continue if arg in kwargs: - # 2022-08-25: Need to explicitly add MaskedColumn if - # data is masked + # 2022-08-25: Need to explicitly add MaskedColumn if data is masked if isinstance(kwargs[arg], MaskedColumn): self.add_column(MaskedColumn(data=kwargs[arg], name=arg)) else: @@ -824,4 +816,3 @@ def write_starlist(list, outfile): return outfile - diff --git a/flystar/startables.py b/flystar/startables.py index f886d06..394331b 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -123,24 +123,19 @@ def __init__(self, *args, ref_list=0, **kwargs): # We have to have special handling of meta-data (i.e. info that has # dimensions of n_lists). meta_tab = ('list_times', 'list_names') - meta_tab = ('list_times', 'list_names') meta_type = ((float, int), str) - for mm in range(len(meta_tab)): - meta_test = meta_tab[mm] - meta_type_test = meta_type[mm] - - if meta_test in kwargs: - if len(kwargs[meta_test]) != n_lists: - err_msg = f"The '{meta_test}' argument has to have length = {n_lists}" - raise ValueError(err_msg) + for mtab, mtype in zip(meta_tab, meta_type): + if mtab in kwargs: + kwargs[mtab] = list(kwargs[mtab]) # Convert to list, as astropy.Table doesn't like numpy arrays in meta-data. + if len(kwargs[mtab]) != n_lists: + raise ValueError(f"The '{mtab}' argument has to have length = {n_lists}") + if not all(isinstance(tt, mtype) for tt in kwargs[mtab]): + raise TypeError(f"The '{mtab}' argument has to be a list of {str(mtype)}.") - if not all(isinstance(tt, meta_type_test) for tt in kwargs[meta_test]): - err_msg = f"The '{meta_test}' argument has to be a list of {str(meta_type_test)}." - raise TypeError(err_msg) ##### # Create the startable ##### - Table.__init__(self, (kwargs['name'], kwargs['x'], kwargs['y'], kwargs['m']), + super().__init__((kwargs['name'], kwargs['x'], kwargs['y'], kwargs['m']), names=('name', 'x', 'y', 'm')) self['name'] = self['name'].astype('U20') self.meta = {'n_stars': n_stars, 'n_lists': n_lists, 'ref_list': ref_list} @@ -218,6 +213,17 @@ def _add_list_data_from_starlist(self, starlist): else: # Add junk data it if wasn't input self._set_invalid_list_values(col_name, -1) + # Special case for list_times: Update 't' column in startable + if ('list_time' in starlist.meta): + if 't' not in self.colnames: + self.add_column(Column(data=np.full((len(self), 1), starlist.meta['list_time']), name='t')) + else: + old_data = self['t'].data + old_type = self['t'].info.dtype + new_data = np.empty((old_data.shape[0], old_data.shape[1] + 1), dtype=old_type) + new_data[:, :-1] = old_data + self['t'] = new_data + self['t'][:, -1] = starlist.meta['list_time'] ########## # Update the table meta-data. Remember that entries are lists not numpy arrays. @@ -234,13 +240,13 @@ def _add_list_data_from_starlist(self, starlist): # Meta table entries with a size that matches the n_lists size are the ones # that need a new value. We have to add something... whatever was passed in or None if isinstance(self.meta[tab_key], Iterable) and (len(self.meta[tab_key]) == self.meta['n_lists']) and (not isinstance(self.meta[tab_key], str)): - # If we find the key in the starlists' meta argument, then add the new values. # Otherwise, add "None". + self.meta[tab_key] = list(self.meta[tab_key]) # Convert to list, as astropy.Table doesn't like numpy arrays in meta-data. idx = lis_meta_keys_plural.index(tab_key) if tab_key in lis_meta_keys_plural else None if idx is not None: lis_key = lis_meta_keys[idx] - self.meta[tab_key] = np.append(self.meta[tab_key], [starlist.meta[lis_key]]) + self.meta[tab_key].append(starlist.meta[lis_key]) else: self._append_invalid_meta_values(tab_key) @@ -293,10 +299,11 @@ def _add_list_data_from_keywords(self, **kwargs): if isinstance(self.meta[key], Iterable) and (len(self.meta[key]) == self.meta['n_lists']) and (not isinstance(self.meta[key], str)): # If we find the key is the passed in meta argument, then add the new values. # Otherwise, add "None". + self.meta[key] = list(self.meta[key]) # Convert to list, as astropy.Table doesn't like numpy arrays in meta-data. if 'meta' in kwargs: new_meta_keys = kwargs['meta'].keys() if key in new_meta_keys: - self.meta[key] = np.append(self.meta[key], [kwargs['meta'][key]]) + self.meta[key].append(kwargs['meta'][key]) else: self._append_invalid_meta_values(key) else: @@ -341,13 +348,13 @@ def _append_invalid_meta_values(self, key): add an invalid value depending on the type. """ if issubclass(type(self.meta[key][0]), np.integer): - self.meta[key] = np.append(self.meta[key], [-1]) + self.meta[key].append(-1) elif issubclass(type(self.meta[key][0]), np.floating): - self.meta[key] = np.append(self.meta[key], [np.nan]) + self.meta[key].append(np.nan) elif issubclass(type(self.meta[key][0]), str): - self.meta[key] = np.append(self.meta[key], ['']) + self.meta[key].append('') else: - self.meta[key] = np.append(self.meta[key], [None]) + self.meta[key].append(None) # Print a warning message: err_msg = "StarTable.add_starlist(): Missing meta keyword: {0:s}".format(key) diff --git a/flystar/transforms.py b/flystar/transforms.py index c9acf4b..bdf8cb3 100755 --- a/flystar/transforms.py +++ b/flystar/transforms.py @@ -1,3 +1,4 @@ +import os import re import copy import datetime From 278fee38ff0520120cad7fa5c897c8ae211ba6c2 Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Tue, 11 Aug 2026 23:49:21 -0700 Subject: [PATCH 252/254] Added support for different mag limits in each iteration in align --- flystar/align.py | 29 ++++++++++++-------- flystar/starlists.py | 61 +++++++++++++++++++++++++++++++++++++++++++ flystar/startables.py | 23 +++++++++++----- 3 files changed, 96 insertions(+), 17 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index a362f96..dc5d939 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -223,9 +223,9 @@ def = None. If not None, then this should contain an array or list of transform plt.axhline(stars_table['x0'][0] + stars_table['vx'][0]*(times - stars_table['t0'][0])) """ dr_tol = np.atleast_1d(dr_tol) - dm_tol = np.atleast_1d(dm_tol) self.iters = len(dr_tol) if dm_tol is not None: + dm_tol = np.atleast_1d(dm_tol) assert self.iters == len(dm_tol), f'dr_tol (len={self.iters}) and dm_tol (len={len(dm_tol)}) must all have the same length!' if outlier_tol is not None: assert self.iters == len(outlier_tol), f'dr_tol (len={self.iters}) and outlier_tol (len={len(outlier_tol)}) must all have the same length!' @@ -238,7 +238,7 @@ def = None. If not None, then this should contain an array or list of transform self.trans_args = trans_args self.init_order = init_order self.mag_trans = mag_trans - self.mag_lim = mag_lim + self.mag_lim = np.array(mag_lim) self.trans_weighting = trans_weights self.vel_weighting = vel_weights self.trans_input = trans_input @@ -340,12 +340,19 @@ def fix_iterable_conditions(self): self.outlier_tol = np.repeat(self.outlier_tol, self.iters) assert len(self.outlier_tol) == self.iters, f'len(outlier_tol)={len(self.outlier_tol)} != iters={self.iters}' + # Format self.mag_lim to be (N_iters, N_lists, 2) array. If only a single mag_lim is passed in, replicate for all lists. if self.mag_lim is None: - self.mag_lim = np.repeat([None], len(self.star_lists), axis=0) - elif (len(self.mag_lim) == 2) and (np.ndim(self.mag_lim) == 1): - self.mag_lim = np.repeat([self.mag_lim], len(self.star_lists), axis=0) - assert len(self.mag_lim) == len(self.star_lists) - + self.mag_lim = np.array([[[None] * len(self.star_lists)] * self.iters]) + elif (np.ndim(self.mag_lim) == 1) and (len(self.mag_lim) == 2): + # 2-element array, replicate for all lists and iterations + self.mag_lim = np.array([[[self.mag_lim] * len(self.star_lists)] * self.iters]) + elif (np.ndim(self.mag_lim) == 2) and (len(self.mag_lim) == len(self.star_lists)) and (self.mag_lim.shape[1] == 2): + # (N_lists, 2) array, replicate for all iterations + self.mag_lim = np.array([self.mag_lim] * self.iters) + elif np.ndim(self.mag_lim) == 3: + assert np.shape(self.mag_lim) == (self.iters, len(self.star_lists), 2), f"mag_lim must have shape (iters, N_lists, 2) = ({self.iters}, {len(self.star_lists)}, 2), but has shape {np.shape(self.mag_lim)}" + else: + raise ValueError(f"mag_lim must be None, a 2-element array, a (N_lists, 2) array, or a (N_iters, N_lists, 2) array. Got shape {np.shape(self.mag_lim)}") return @@ -450,7 +457,7 @@ def fit(self, processes=1, chunksize=None): # ALL the action is in here. Match and transform the stack of starlists. # This updates trans objects and the ref_table. self.match_and_transform( - self.mag_lim[self.ref_index], + self.mag_lim[nn][self.ref_index], self.dr_tol[nn], self.dm_tol[nn], self.outlier_tol[nn], @@ -615,7 +622,7 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar print(f' dr < {dr_tol}') print(f' |dm| < {dm_tol}') print(f' outlier tol: {outlier_tol}') - print(f' mag_lim: {self.mag_lim[ii]}') + print(f' mag_lim: {self.mag_lim[nn][ii]}') print(" **********") star_list = self.star_lists[ii] @@ -630,10 +637,10 @@ def match_and_transform(self, ref_mag_lim, dr_tol, dm_tol, outlier_tol, trans_ar # star_list_orig_trim is actually trimmed but not yet transformed. # star_list_T is trimmed and transformed self.apply_mag_lim_via_use_in_trans(ref_list, ref_mag_lim) - star_list_orig_trim = apply_mag_lim(star_list, self.mag_lim[ii]) # trimmed, untransformed copy + star_list_orig_trim = apply_mag_lim(star_list, self.mag_lim[nn][ii]) # trimmed, untransformed copy star_list_T = StarList(star_list_orig_trim, copy=True) # trimmed, will be transformed copy - assert len(star_list_orig_trim) > 0, f"No stars remain after applying mag_lim={self.mag_lim[ii]} to star_list at index {ii}. Please check your mag_lim." + assert len(star_list_orig_trim) > 0, f"No stars remain after applying mag_lim={self.mag_lim[nn][ii]} to star_list at index {ii}. Please check your mag_lim." ### Initial match and transform: 1st order (if we haven't already). if trans is None: diff --git a/flystar/starlists.py b/flystar/starlists.py index 6c826ae..6eceed7 100644 --- a/flystar/starlists.py +++ b/flystar/starlists.py @@ -793,6 +793,67 @@ def transform_m(self, trans): return + +def write_region(x, y, save_path, frame='image', colors='magenta', shape='circle', shape_properties={'radius': 10}): + """ + Write a DS9 region file with the given x, y coordinates. + + Parameters: + ---------- + x: 1D numpy.array + X coordinates of the stars to write to the region file. + y: 1D numpy.array + Y coordinates of the stars to write to the region file. + frame: str + Frame of reference for the coordinates. Default is 'image'. Other options include 'fk5', 'icrs', 'galactic', 'wcs', etc. + See https://ds9.si.edu/doc/ref/region.html for more details. + save_path: str + Path to the file where the region file will be saved. + colors: str or list of str + Color(s) of the regions. If a single string is given, all regions will be that color. + If a list of strings is given, it must have the same length as x and y. + shape: str + Shape of the regions. Default is 'circle'. Other options include 'box', 'ellipse', etc. + shape_properties: dict + Dictionary of properties for the shape. For example, for circles, you can specify {'radius': 10}. + For boxes, you can specify {'width': 20, 'height': 10}. + + Output: + ------ + A DS9 region file will be created at the specified save_path. + """ + if isinstance(colors, str): + colors = [colors] * len(x) + + if shape == 'circle': + radius = shape_properties.get('radius', 1) + write_format = f'circle {{x}} {{y}} {radius} # color={{color}}\n' + elif shape == 'box': + width = shape_properties.get('width', 3) + height = shape_properties.get('height', 3) + angle = shape_properties.get('angle', 0) + write_format = f'box {{x}} {{y}} {width} {height} {angle} # color={{color}}\n' + elif shape == 'ellipse': + semimajor = shape_properties.get('semi-major', 6) + semiminor = shape_properties.get('semi-minor', 3) + angle = shape_properties.get('angle', 0) + write_format = f'ellipse {{x}} {{y}} {semimajor} {semiminor} {angle} # color={{color}}\n' + elif shape == 'point': + point = shape_properties.get('point', 'circle') + size = shape_properties.get('size', 3) + write_format = f'point {{x}} {{y}} # point={point} {size} color={{color}}\n' + else: + raise ValueError(f"Unsupported shape: {shape}") + + with open(save_path, 'w') as f: + f.write('# Region file format: DS9 version 4.1\n') + f.write('global color=green dashlist=8 3 width=1 font="helvetica 10 normal" select=1 highlite=1 dash=0 fixed=0 edit=1 move=1 delete=1 include=1 source=1\n') + f.write(f'{frame}\n') + for i in range(len(x)): + f.write(write_format.format(x=x[i], y=y[i], color=colors[i])) + + return + def write_starlist(list, outfile): formats = {'name': '%-13s', diff --git a/flystar/startables.py b/flystar/startables.py index 394331b..5c4fd5e 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -74,7 +74,6 @@ def __init__(self, *args, ref_list=0, **kwargs): # Check if the required arguments are present arg_req = ('name', 'x', 'y', 'm') - found_all_required = all(arg in kwargs for arg in arg_req) if not found_all_required: @@ -90,6 +89,15 @@ def __init__(self, *args, ref_list=0, **kwargs): if ('xe' in kwargs) ^ ('ye' in kwargs): raise TypeError("The StarTable class requires both 'xe' and" + " 'ye' arguments") + kwargs['name'] = np.array(kwargs['name']) + kwargs['x'] = np.array(kwargs['x']) + kwargs['y'] = np.array(kwargs['y']) + kwargs['m'] = np.array(kwargs['m']) + if ('xe' in kwargs) and ('ye' in kwargs): + kwargs['xe'] = np.array(kwargs['xe']) + kwargs['ye'] = np.array(kwargs['ye']) + if 'me' in kwargs: + kwargs['me'] = np.array(kwargs['me']) # Figure out the shape n_stars = kwargs['x'].shape[0] @@ -97,9 +105,8 @@ def __init__(self, *args, ref_list=0, **kwargs): # Check if the type and size of the arguments are correct. # Name checking: type and shape - if (not isinstance(kwargs['name'], np.ndarray)) or (len(kwargs['name']) != n_stars): - err_msg = f"The 'name' argument has to be a numpy array, not {type(kwargs['name'])};" - err_msg += f"Its length should be {n_stars}, not {len(kwargs['name'])}." + if len(kwargs['name']) != n_stars: + err_msg += f"The 'name' argument length should be {n_stars}, but got {len(kwargs['name'])}." raise TypeError(err_msg) # Check all the 2D arrays. @@ -112,7 +119,7 @@ def __init__(self, *args, ref_list=0, **kwargs): raise TypeError(err_msg) if kwargs[arg_test].shape != (n_stars, n_lists): - err_msg = f"The '{arg_test}' argument has to have shape = ({n_stars}, {n_lists})" + err_msg = f"The '{arg_test}' argument has to have shape = ({n_stars}, {n_lists}), but got {kwargs[arg_test].shape}" raise TypeError(err_msg) # Check that the reference list is specified. @@ -656,6 +663,11 @@ def fit_motion_models( # Setting the default to None to avoid mutable default argument issue # See https://stackoverflow.com/questions/15189245/assigning-class-variable-as-default-value-to-class-method-argument motion_models = [motion_model.Empty, motion_model.Fixed, motion_model.Linear] + elif isinstance(motion_models, (motion_model.MotionModel, str)): + motion_models = [motion_models] + elif not isinstance(motion_models, list): + raise ValueError("fit_motion_models: motion_models must be a list of MotionModel objects or strings!") + all_mm_map = motion_model.motion_model_map() if all(isinstance(mm, str) for mm in motion_models): mm_names = motion_models @@ -861,7 +873,6 @@ def fit_motion_models( # Prepare fixed_params_dict for each star # This avoids checking types and slicing inside the fitting loop - fixed_params_stars = [{} for _ in range(N_stars)] # Identify array parameters (length N_stars) and scalar parameters array_params = {k: v for k, v in fixed_params_dict.items() if np.ndim(v) > 0 and len(v) == N_stars} scalar_params = {k: v for k, v in fixed_params_dict.items() if k not in array_params} From 59f4d7c0b4988740b2b1eacbd9f2e3c3837c5d8e Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Wed, 12 Aug 2026 14:43:26 -0700 Subject: [PATCH 253/254] Save ref table in fits as well --- flystar/align.py | 1 + 1 file changed, 1 insertion(+) diff --git a/flystar/align.py b/flystar/align.py index dc5d939..c15121d 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -598,6 +598,7 @@ def fit(self, processes=1, chunksize=None): filename = f'{self.prefix_name}_ref_table.pkl' with open(os.path.join(self.save_path, filename), 'wb') as file: pickle.dump(self.ref_table, file) + self.ref_table.write(os.path.join(self.save_path, f'{self.prefix_name}_ref_table.fits'), overwrite=True) if self.verbose > 0: print('===================================') From cf9cd2429795317968eed9cd7bd0c69cc3863062 Mon Sep 17 00:00:00 2001 From: Lingfeng Wei Date: Thu, 13 Aug 2026 00:16:04 -0700 Subject: [PATCH 254/254] Fix mag_lim errors; Initialize uncertainties to be infinity --- flystar/align.py | 12 ++++++++---- flystar/startables.py | 1 + 2 files changed, 9 insertions(+), 4 deletions(-) diff --git a/flystar/align.py b/flystar/align.py index c15121d..0bab2ee 100755 --- a/flystar/align.py +++ b/flystar/align.py @@ -238,7 +238,7 @@ def = None. If not None, then this should contain an array or list of transform self.trans_args = trans_args self.init_order = init_order self.mag_trans = mag_trans - self.mag_lim = np.array(mag_lim) + self.mag_lim = mag_lim self.trans_weighting = trans_weights self.vel_weighting = vel_weights self.trans_input = trans_input @@ -342,10 +342,10 @@ def fix_iterable_conditions(self): # Format self.mag_lim to be (N_iters, N_lists, 2) array. If only a single mag_lim is passed in, replicate for all lists. if self.mag_lim is None: - self.mag_lim = np.array([[[None] * len(self.star_lists)] * self.iters]) + self.mag_lim = np.array([[None] * len(self.star_lists)] * self.iters) elif (np.ndim(self.mag_lim) == 1) and (len(self.mag_lim) == 2): # 2-element array, replicate for all lists and iterations - self.mag_lim = np.array([[[self.mag_lim] * len(self.star_lists)] * self.iters]) + self.mag_lim = np.array([[self.mag_lim] * len(self.star_lists)] * self.iters) elif (np.ndim(self.mag_lim) == 2) and (len(self.mag_lim) == len(self.star_lists)) and (self.mag_lim.shape[1] == 2): # (N_lists, 2) array, replicate for all iterations self.mag_lim = np.array([self.mag_lim] * self.iters) @@ -353,6 +353,7 @@ def fix_iterable_conditions(self): assert np.shape(self.mag_lim) == (self.iters, len(self.star_lists), 2), f"mag_lim must have shape (iters, N_lists, 2) = ({self.iters}, {len(self.star_lists)}, 2), but has shape {np.shape(self.mag_lim)}" else: raise ValueError(f"mag_lim must be None, a 2-element array, a (N_lists, 2) array, or a (N_iters, N_lists, 2) array. Got shape {np.shape(self.mag_lim)}") + return @@ -1011,7 +1012,10 @@ def setup_ref_table_from_starlist(self, star_list): # Note that these are all the 1D columsn. for col_name in ref_table.colnames: if len(ref_table[col_name].data.shape) == 2: # Find the 2D columns - ref_table._set_invalid_list_values(col_name, -1) + if col_name in ['xe', 'ye', 'me']: + ref_table[col_name][:, -1] = np.inf + else: + ref_table._set_invalid_list_values(col_name, -1) if 'motion_model_input' not in ref_table.colnames: ref_table.add_column(np.repeat(self.motion_models[-1].name, len(ref_table)), name='motion_model_input') diff --git a/flystar/startables.py b/flystar/startables.py index 5c4fd5e..13789b8 100644 --- a/flystar/startables.py +++ b/flystar/startables.py @@ -1,3 +1,4 @@ +import pdb import copy import warnings import numpy as np