make astrom_precision and m2snr more backwards compatible

rubin_sim/maf/metrics/brown_dwarf_metric.py

@@ -126,9 +126,7 @@ def run(self, data_slice, slice_point=None):
                     self.mags[str(filt)][:, np.newaxis], data_slice[self.m5_col][good]
                 )
 
-        position_errors = np.sqrt(
-            mafUtils.astrom_precision(data_slice[self.seeing_col], snr) ** 2 + self.atm_err**2
-        )
+        position_errors = mafUtils.astrom_precision(data_slice[self.seeing_col], snr, self.atm_err)
         # uncertainty in the parallax in mas
         sigma = self._final_sigma(position_errors, data_slice["ra_pi_amp"], data_slice["dec_pi_amp"])
         fitted_parallax_snr = self.parallaxes / sigma

rubin_sim/maf/metrics/calibration_metrics.py

@@ -240,9 +240,8 @@ def run(self, data_slice, slice_point=None):
             else:
                 snr = mafUtils.m52snr(self.mags[f], data_slice[self.m5_col][observations])
                 precis[observations] = mafUtils.astrom_precision(
-                    data_slice[self.seeing_col][observations], snr
+                    data_slice[self.seeing_col][observations], snr, self.atm_err
                 )
-                precis[observations] = np.sqrt(precis[observations] ** 2 + self.atm_err**2)
         good = np.where(precis != self.badval)
         result = mafUtils.sigma_slope(data_slice[self.mjd_col][good], precis[good])
         result = result * 365.25 * 1e3  # Convert to mas/yr
@@ -364,9 +363,7 @@ def _theta_check(self, ra_pi_amp, dec_pi_amp, snr):
 
     def _compute_weights(self, data_slice, snr):
         # Compute centroid uncertainty in each visit
-        position_errors = np.sqrt(
-            mafUtils.astrom_precision(data_slice[self.seeing_col], snr) ** 2 + self.atm_err**2
-        )
+        position_errors = mafUtils.astrom_precision(data_slice[self.seeing_col], snr, self.atm_err)
         weights = 1.0 / position_errors**2
         return weights
 
@@ -493,9 +490,7 @@ def run(self, data_slice, slice_point=None):
         # Compute the centroiding uncertainties
         # Note that these centroiding uncertainties depend on the physical size of the PSF, thus
         # we are using seeingFwhmGeom for these metrics, not seeingFwhmEff.
-        position_errors = np.sqrt(
-            mafUtils.astrom_precision(data_slice[self.seeing_col], snr) ** 2 + self.atm_err**2
-        )
+        position_errors = mafUtils.astrom_precision(data_slice[self.seeing_col], snr, self.atm_err)
         # Construct the vectors of RA/Dec offsets. xdata is the "input data". ydata is the "output".
         xdata = np.empty((2, data_slice.size * 2), dtype=float)
         xdata[0, :] = np.concatenate((data_slice["ra_pi_amp"], data_slice["dec_pi_amp"]))

rubin_sim/maf/utils/astrometry_utils.py

@@ -32,7 +32,7 @@ def sigma_slope(x, sigma_y):
         return result
 
 
-def m52snr(m, m5):
+def m52snr(m, m5, gamma=0.039):
     """
     Calculate the SNR for a star of magnitude m in an
     observation with 5-sigma limiting magnitude depth m5.
@@ -52,14 +52,13 @@ def m52snr(m, m5):
     snr : `float` or `np.ndarray` (N,)
         The SNR
     """
-    # gamma varies per band, but is fairly close to this value
-    rgamma = 0.039
+    # gamma varies per band, but is fairly close to 0.039 or 0.04
     xval = np.power(10, 0.4 * (m - m5))
-    snr = 1 / np.sqrt((0.04 - rgamma) * xval + rgamma * xval * xval)
+    snr = 1 / np.sqrt((0.04 - gamma) * xval + gamma * xval * xval)
     return snr
 
 
-def astrom_precision(fwhm, snr, systematic_floor=0.01):
+def astrom_precision(fwhm, snr, systematic_floor=0.00):
     """
     Calculate the approximate precision of astrometric measurements,
     given a particular seeing and SNR value.
@@ -71,12 +70,14 @@ def astrom_precision(fwhm, snr, systematic_floor=0.01):
     snr : float` or `np.ndarray` (N,)
         The SNR of the object.
     systematic_floor : `float`
-        Systematic noise floor for astrometric error.
+        Systematic noise floor for astrometric error, in arcseconds.
+        Default here is 0, for backwards compatibility.
+        General Rubin use should be 0.01.
 
     Returns
     -------
-    astrp,+err " `float` or `numpy.ndarray` (N,)
-        The astrometric precision.
+    astrom_err : `float` or `numpy.ndarray` (N,)
+        The astrometric precision, in arcseconds.
     """
     astrom_err = np.sqrt((fwhm / snr) ** 2 + systematic_floor**2)
     return astrom_err