Merge pull request #979 from HERA-Team/verbose_frop_hotfix

Add verbose option

hera_cal/frf.py

@@ -2127,7 +2127,7 @@ def get_frop_for_noise(times, filter_cent_use, filter_half_wid_use, freqs,
     return frop
 
 def prep_var_for_frop(data, nsamples, weights, cross_antpairpol, freq_slice, 
-                      auto_ant=None, default_value=0.):
+                      auto_ant=None, default_value=0., verbose=False):
     """
     Wrapper around hera_cal.noise.predict_noise_variance_from_autos that preps
     the noise variance calculation for FRF + coherent average.
@@ -2153,6 +2153,8 @@ def prep_var_for_frop(data, nsamples, weights, cross_antpairpol, freq_slice,
         default_value: (float)
             The default variance to use in locations with 0 nsamples to avoid
             nans.
+        verbose: (bool)
+            Whether to print that it had to replace from nonfinite variances.
 
     Returns:
         var: array
@@ -2169,7 +2171,8 @@ def prep_var_for_frop(data, nsamples, weights, cross_antpairpol, freq_slice,
         if not all_nonfinite_zero:
             warnings.warn("Not all nonfinite variance locations are of zero weight!")
 
-        print(f"Replacing nonfinite variances with default value: {default_value}")
+        if verbose:
+            print(f"Replacing nonfinite variances with default value: {default_value}")
         var[var_isnotfinite] = default_value
 
     return var

hera_cal/tests/test_frf.py

@@ -1226,10 +1226,11 @@ def test_get_FRF_cov():
     times = data.times * 24 * 3600
     eval_cutoff = 1e-12
 
+    # Set verbose to true just to cover the line -- not super important that
+    # we test it actually went to some console/log/whatever
     var_for_frop = frf.prep_var_for_frop(data, nsamples, weights,
                                          cross_antpairpol, freq_slice,
-                                         auto_ant=53)
-
+                                         auto_ant=53, verbose=True)
     dt = times[1] - times[0]
     Navg = int(np.round(300. / dt))
     n_avg_int = int(np.ceil(len(data.lsts) / Navg))