added output filename as argument of main()

hires_module.py

 import numpy as np
-# import xarray as xr
+import xarray as xr
 import netCDF4 as nc
 
 from glob import glob
@@ -60,7 +60,8 @@ def main(*,
          geos_constants: str = _geos_constants,
          ndvi_file: str = _ndvi_file,
          sst_file: str = _sst_file,
-         cloud_mask_file=_cld_mask) -> None:
+         cloud_mask_file: str = _cld_mask,
+         out_fname: str = 'temp.nc') -> None:
 
     f = nc.Dataset(cloud_mask_file)
 
@@ -165,17 +166,33 @@ def main(*,
                      (scene_flags['uniform'] == 1) & (confidence <= 0.95))
     confidence[idx] = restoral.sunglint(viirs_data, thresholds['Sun_Glint'], total_bit, temp_confidence)[idx]
 
-    total_confidence = np.sqrt(confidence * mvcm_conf.repeat(2, 0).repeat(2, 1))
+    # total_confidence = np.sqrt(confidence * mvcm_conf.repeat(2, 0).repeat(2, 1))
 
     date = mod02.split('.')[1]
     time = mod02.split('.')[2]
 
-    # np.savez(f'{data_path}/pyMVCM_{date}.{time}',
-    np.savez(f'{data_path}/outputs/pyMVCM_{date}.{time}',
-             confidence=total_confidence, py_conf=confidence, mvcm_conf=mvcm_conf,
-             lat=viirs_data.latitude.values, lon=viirs_data.longitude.values,
-             ocean_day_mask=viirs_data.Ocean_Day.values)
-    # c1=cmin_G1, c2=cmin_G2, c3=cmin_G3)
+    mvcm_confidence = mvcm_conf.repeat(2, 0).repeat(2, 1)
+    out_xr = xr.Dataset(
+                data_vars=dict(
+                    hires_confidence=(['number_of_lines', 'number_of_pixels'], confidence),
+                    mvcm_confidence=(['number_of_lines', 'number_of_pixels'], mvcm_confidence),
+                    ocean_day_mask=(['number_of_lines', 'number_of_pixels'], viirs_data.Ocean_Day.values),
+                    ),
+                coords=dict(
+                    latitude=(['number_of_lines', 'number_of_pixels'], viirs_data.latitude.values),
+                    longitude=(['number_of_lines', 'number_of_pixels'], viirs_data.longitude.values),
+                    )
+                )
+
+    # out_xr.to_netcdf(f'hires_MVCM.{date}.{time}.nc')
+    out_xr.to_netcdf(out_fname)
+
+#    # np.savez(f'{data_path}/pyMVCM_{date}.{time}',
+#    np.savez(f'{data_path}/outputs/pyMVCM_{date}.{time}',
+#             confidence=total_confidence, py_conf=confidence, mvcm_conf=mvcm_conf,
+#             lat=viirs_data.latitude.values, lon=viirs_data.longitude.values,
+#             ocean_day_mask=viirs_data.Ocean_Day.values)
+#    # c1=cmin_G1, c2=cmin_G2, c3=cmin_G3)
 
     # return confidence  # , conf, total_bit
 

spectral_tests.py

@@ -115,7 +115,7 @@ class CloudTests(object):
         cmin = np.fmin(cmin, confidence)
 
         # return cmin, np.abs(1-test_bit)*qa_bit
-        return sfcdif, test_bit
+        return cmin, test_bit
 
     @run_if_test_exists_for_scene
     def bt_diff_86_11um(self,