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Remove spike detection #39

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42 changes: 17 additions & 25 deletions lts_array/classes/lts_classes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -61,11 +61,6 @@ def fast_LTS(nits, tau, time_delay_mad, xij_standardized, xij_mad, dimension_num
"""
for jj in range(nits):

# Check for data spike.
if (time_delay_mad[jj] == 0) or (np.count_nonzero(tau[:, jj, :]) < (co_array_num - 2)):
# We have a data spike, so do not process.
continue

# Standardize the y-values
y_var = tau[:, jj, :] / time_delay_mad[jj]
X_var = xij_standardized
Comment on lines 62 to 66
Expand Down Expand Up @@ -659,11 +654,6 @@ def post_process(dimension_number, co_array_num, alpha, h, nits, tau, xij, coeff

for jj in range(0, nits):

# Check for data spike:
if np.count_nonzero(tau[:, jj, :]) < (co_array_num - 2):
# We have a data spike, so do not process.
continue

# Now use original arrays
y_var = tau[:, jj, :]

Expand Down Expand Up @@ -722,10 +712,12 @@ def post_process(dimension_number, co_array_num, alpha, h, nits, tau, xij, coeff
# Calculate the sigma_tau value (Szuberla et al. 2006).
residuals = tau[weights, jj, :] - (xij[weights, :] @ z_final)
m_w, _ = np.shape(xij[weights, :])
with np.errstate(invalid='raise'):

with np.errstate(invalid="raise"):
try:
sigma_tau[jj] = np.sqrt(tau[weights, jj, :].T @ residuals / (
m_w - dimension_number))[0]
sigma_tau_value = tau[weights, jj, :].T @ residuals
sigma_tau_value = sigma_tau_value / (m_w - dimension_number)
sigma_tau[jj] = np.sqrt(float(np.asarray(sigma_tau_value).squeeze()))
except FloatingPointError:
pass

Expand Down Expand Up @@ -755,8 +747,8 @@ def post_process(dimension_number, co_array_num, alpha, h, nits, tau, xij, coeff
# Halving here s.t. +/- value expresses uncertainty bounds.
# Remove the 1/2's to get full values to express
# coverage ellipse area.
conf_int_baz[jj] = 0.5 * sig_theta
conf_int_vel[jj] = 0.5 * np.abs(np.diff(1 / eExtrm[:2]))
conf_int_baz[jj] = 0.5 * float(np.asarray(sig_theta).squeeze())
conf_int_vel[jj] = 0.5 * float(np.asarray(np.abs(np.diff(1 / eExtrm[:2]))).squeeze())

# Cast weights to int for output
element_weights[:, jj] = weights * 1
Expand Down Expand Up @@ -907,11 +899,6 @@ def solve(self, data):
# Loop through time
for jj in range(data.nits):

# Check for data spike.
if (self.time_delay_mad[jj] == 0) or (np.count_nonzero(self.tau[:, jj, :]) < (self.co_array_num - 2)):
# We have a data spike, so do not process.
continue

y_var = self.tau[:, jj, :] / self.time_delay_mad[jj]
qt = self.q_xij.conj().T @ y_var
Comment on lines 899 to 903
z_final = lstsq(self.r_xij, qt)[0]
Expand All @@ -932,8 +919,13 @@ def solve(self, data):

# Calculate the sigma_tau value (Szuberla et al. 2006).
residuals = self.tau[:, jj, :] - (self.xij @ z_final)
self.sigma_tau[jj] = np.sqrt(self.tau[:, jj, :].T @ residuals / (
self.co_array_num - self.dimension_number))[0]

sigma_tau_value = self.tau[:, jj, :].T @ residuals
sigma_tau_value = sigma_tau_value / (
self.co_array_num - self.dimension_number
)

self.sigma_tau[jj] = np.sqrt(float(np.asarray(sigma_tau_value).squeeze()))

# Calculate uncertainties from Szuberla & Olson, 2004
# Equation 16
Expand All @@ -960,8 +952,8 @@ def solve(self, data):
# Halving here s.t. +/- value expresses uncertainty bounds.
# Remove the 1/2's to get full values to express
# coverage ellipse area.
self.conf_int_baz[jj] = 0.5 * sig_theta
self.conf_int_vel[jj] = 0.5 * np.abs(np.diff(1 / eExtrm[:2]))
self.conf_int_baz[jj] = 0.5 * float(np.asarray(sig_theta).squeeze())
self.conf_int_vel[jj] = 0.5 * float(np.asarray(np.abs(np.diff(1 / eExtrm[:2]))).squeeze())

except ValueError:
self.conf_int_baz[jj] = np.nan
Expand Down Expand Up @@ -1033,4 +1025,4 @@ def solve(self, data):
tval = str(self.t[jj])
self.stdict[tval] = stns
if jj == (data.nits - 1) and data.alpha != 1.0:
self.stdict['size'] = data.nchans
self.stdict['size'] = data.nchans