Updating Gaia WCS solutions and sky subtraction

potpyri/instruments/MMIRS.py

@@ -83,7 +83,7 @@ def __init__(self):
         self.bad_keywords = ['MOSID']
         self.bad_values = ['closed']
 
-        self.detrend = True
+        self.detrend = False
         self.catalog_zp = '2MASS'
 
         self.out_size = 2500

potpyri/instruments/instrument.py

@@ -525,14 +525,16 @@ def create_sky(self, sky_list, fil, amp, binn, paths, log=None, **kwargs):
             if log: log.info(f'Importing {sky}')
             sky_full = self.import_sci_image(sky, log=log)
 
-            mean, med, stddev = sigma_clipped_stats(sky_full.data)
+            mean, med, stddev = sigma_clipped_stats(sky_full.data,
+                sigma_upper=2.5, sigma_lower=3.5)
 
             # Mask outliers
-            mask = sky_full.data > med + 5 * stddev
+            mask = sky_full.data > med + 2.5 * stddev
             sky_full.data[mask]=np.nan
 
             # Normalize by median sky background
-            mean, med, stddev = sigma_clipped_stats(sky_full.data)
+            mean, med, stddev = sigma_clipped_stats(sky_full.data,
+                sigma_upper=2.5, sigma_lower=3.5)
             norm = 1./med
             sky_full = sky_full.multiply(norm)
 

potpyri/primitives/image_procs.py

@@ -357,7 +357,10 @@ def image_proc(image_data, tel, paths, skip_skysub=False,
         sci_med = add_stack_mask(sci_med, aligned_data)
 
         if tel.detrend:
+            if log: log.info('Detrending stack')
             sci_med = detrend_stack(sci_med)
+        else:
+            if log: log.info('Skipping detrending')
 
     else:
         sci_med = fits.open(data_images[0])
@@ -426,12 +429,14 @@ def detrend_stack(stack):
     data = stack[0].data
     mask = stack[1].data.astype(bool)
 
-    mean, med, stddev = sigma_clipped_stats(data, mask=mask, axis=1)
+    mean, med, stddev = sigma_clipped_stats(data, mask=mask, axis=1,
+        sigma_upper=2.5)
     data = data - med[:,None]
 
     row_med = np.nanmedian(med)
 
-    mean, med, stddev = sigma_clipped_stats(data, mask=mask, axis=0)
+    mean, med, stddev = sigma_clipped_stats(data, mask=mask, axis=0,
+        sigma_upper=2.5)
     data = data - med[None,:]
 
     col_med = np.nanmedian(med)

potpyri/primitives/solve_wcs.py

@@ -335,7 +335,7 @@ def solve_astrometry(file, tel, binn, paths, radius=0.5, replace=True,
         return(False)
 
 def align_to_gaia(file, tel, radius=0.5, max_search_radius=5.0*u.arcsec,
-    save_centroids=False, min_gaia_match=5, log=None):
+    save_centroids=False, min_gaia_match=7, log=None):
 
     cat = get_gaia_catalog(file, log=log)