v2.0.0.dev27

pyobs/modules/pointing/guidingstatistics/pixeloffset.py

@@ -36,8 +36,8 @@ def _build_header(self, data: list[tuple[float, float]]) -> dict[str, FitsHeader
         rms = self._calc_rms(data)
 
         if rms is not None:
-            header["GUIDING RMS1"] = FitsHeaderEntry(float(rms[0]), "RMS for guiding on axis 1")
-            header["GUIDING RMS2"] = FitsHeaderEntry(float(rms[1]), "RMS for guiding on axis 2")
+            header["HIERARCH GUIDING RMS1"] = FitsHeaderEntry(float(rms[0]), "RMS for guiding on axis 1")
+            header["HIERARCH GUIDING RMS2"] = FitsHeaderEntry(float(rms[1]), "RMS for guiding on axis 2")
 
         return header
 

pyobs/modules/pointing/guidingstatistics/skyoffsets.py

@@ -37,7 +37,7 @@ def _build_header(self, data: list[float]) -> dict[str, FitsHeaderEntry]:
         rms = self._calc_rms(data)
 
         if rms is not None:
-            header["GUIDING RMS"] = FitsHeaderEntry(float(rms), "RMS for guiding on sky")
+            header["HIERARCH GUIDING RMS"] = FitsHeaderEntry(float(rms), "RMS for guiding on sky")
 
         return header
 

pyobs/modules/pointing/guidingstatistics/uptime.py

@@ -44,7 +44,7 @@ def _build_header(self, data: list[tuple[bool | None, datetime]]) -> dict[str, F
         data.append((None, now))
 
         uptime_percentage = self._calc_uptime_percentage(data)
-        return {"GUIDING UPTIME": FitsHeaderEntry(uptime_percentage, "Time the guiding loop was closed [%]")}
+        return {"HIERARCH GUIDING UPTIME": FitsHeaderEntry(uptime_percentage, "Time the guiding loop was closed [%]")}
 
     def _get_session_data(self, input_data: bool) -> tuple[bool | None, datetime] | None:
         now = datetime.now()

pyobs/utils/curvefit.py

@@ -29,8 +29,20 @@ def fit_hyperbola(x_arr: list[float], y_arr: list[float], y_err: list[float]) ->
     p0 = [a, b, c]
 
     # fit
+    #
+    # absolute_sigma=True is required here: without it, curve_fit treats y_err as only
+    # relative weights and rescales the returned covariance matrix so that the fit's
+    # reduced chi-square equals 1. That makes the reported variance reflect how well the
+    # hyperbola shape matches this particular focus run rather than the actual per-point
+    # measurement uncertainty passed in via y_err, which silently miscalibrates every
+    # downstream error estimate.
     coeffs, cov = curve_fit(
-        lambda xx, aa, bb, cc: bb * np.sqrt((xx - cc) ** 2 / aa**2 + 1.0), x_arr, y_arr, sigma=y_err, p0=p0
+        lambda xx, aa, bb, cc: bb * np.sqrt((xx - cc) ** 2 / aa**2 + 1.0),
+        x_arr,
+        y_arr,
+        sigma=y_err,
+        p0=p0,
+        absolute_sigma=True,
     )
 
     # return result

pyobs/utils/focusseries/photometry.py

@@ -83,7 +83,12 @@ def fit_focus(self) -> tuple[float, float]:
 
         # fit focus
         try:
-            foc, err = fit_hyperbola(focus, r, rerr)
+            # fit_hyperbola returns (focus, variance), not (focus, error) -- take the
+            # square root, otherwise the reported error is actually the variance and is
+            # systematically too small (e.g. 0.01 mm true error would be reported as
+            # 0.0001).
+            foc, var = fit_hyperbola(focus, r, rerr)
+            err = np.sqrt(var)
         except (RuntimeError, RuntimeWarning):
             raise ValueError("Could not find best focus.")
 

pyobs/utils/focusseries/projection.py

@@ -126,14 +126,15 @@ def fit_focus(self) -> tuple[float, float]:
 
         # fit focus
         try:
-            xfoc, xerr = fit_hyperbola(focus, xfwhm, xsig)
-            yfoc, yerr = fit_hyperbola(focus, yfwhm, ysig)
-
-            # weighted mean
-            xerr = np.sqrt(xerr)
-            yerr = np.sqrt(yerr)
-            foc = (xfoc / xerr + yfoc / yerr) / (1.0 / xerr + 1.0 / yerr)
-            err = 2.0 / (1.0 / xerr + 1.0 / yerr)
+            # fit_hyperbola returns (focus, variance) for each axis
+            xfoc, xvar = fit_hyperbola(focus, xfwhm, xsig)
+            yfoc, yvar = fit_hyperbola(focus, yfwhm, ysig)
+
+            # inverse-variance weighted mean of the two independent axis estimates
+            xweight = 1.0 / xvar
+            yweight = 1.0 / yvar
+            foc = (xfoc * xweight + yfoc * yweight) / (xweight + yweight)
+            err = np.sqrt(1.0 / (xweight + yweight))
         except (RuntimeError, RuntimeWarning):
             raise ValueError("Could not find best focus.")
 

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "pyobs-core"
-version = "2.0.0.dev26"
+version = "2.0.0.dev27"
 description = "robotic telescope software"
 authors = [{ name = "Tim-Oliver Husser", email = "thusser@uni-goettingen.de" }]
 requires-python = ">=3.11"

tests/modules/pointing/test_guiding_statistic_uptime.py

@@ -13,7 +13,7 @@ def test_end_to_end() -> None:
 
     header = statistic.add_to_header(client, {})
 
-    assert header["GUIDING UPTIME"].value == 100.0
+    assert header["HIERARCH GUIDING UPTIME"].value == 100.0
 
 
 def test_calc_uptime_percentage() -> None:

tests/modules/pointing/test_guiding_statistics_pixel_offset.py

@@ -24,10 +24,10 @@ def test_end_to_end(mock_meta_image) -> None:
 
     header = statistic.add_to_header(client, {})
 
-    assert header["GUIDING RMS1"].value == 1.0
-    assert header["GUIDING RMS1"].comment == "RMS for guiding on axis 1"
-    assert header["GUIDING RMS2"].value == 1.0
-    assert header["GUIDING RMS2"].comment == "RMS for guiding on axis 2"
+    assert header["HIERARCH GUIDING RMS1"].value == 1.0
+    assert header["HIERARCH GUIDING RMS1"].comment == "RMS for guiding on axis 1"
+    assert header["HIERARCH GUIDING RMS2"].value == 1.0
+    assert header["HIERARCH GUIDING RMS2"].comment == "RMS for guiding on axis 2"
 
 
 def test_build_header_to_few_values() -> None:

tests/modules/pointing/test_guiding_statistics_sky_offset.py

@@ -30,8 +30,8 @@ def test_end_to_end(mock_meta_image) -> None:
 
     header = statistic.add_to_header(client, {})
 
-    np.testing.assert_almost_equal(header["GUIDING RMS"].value, 10.0)
-    assert header["GUIDING RMS"].comment == "RMS for guiding on sky"
+    np.testing.assert_almost_equal(header["HIERARCH GUIDING RMS"].value, 10.0)
+    assert header["HIERARCH GUIDING RMS"].comment == "RMS for guiding on sky"
 
 
 def test_build_header_to_few_values() -> None: