added csv pull

utils/eval_baselines.py

@@ -70,14 +70,23 @@ def main(dim):
 
     file_paths = find_matching_files()
     
-    df = pd.DataFrame(columns=columns, index=[str(p) for p in file_paths])
+    if os.path.isfile(save_path):
+        df = pd.read_csv(save_path)
+    else:
+        pass
+        #df = pd.DataFrame(columns=columns, index=[str(p) for p in file_paths])
     
     print(f"Number of files to be tested: {len(file_paths)}")
     
     ct = 0
+    
+    #print(df)
 
     for path in tqdm(file_paths):
         for hdu_idx in [1, 4]:
+            df_idx = path + f"_hdu{hdu_idx}"
+            if df_idx in df[df.columns[0]].values:
+                continue
             with fits.open(path) as hdul:
                 if dim == '2d':
                     arr = hdul[hdu_idx].data
@@ -86,7 +95,7 @@ def main(dim):
 
             ct += 1
             if ct % 10 == 0:
-                print(df.mean())
+                #print(df.mean())
                 df.to_csv(save_path)
 
             for algo in ALL_CODECS.keys():
@@ -98,12 +107,13 @@ def main(dim):
 
                     for column, value in test_results.items():
                         if column in df.columns:
-                            df.at[path + f"_hdu{hdu_idx}", column] = value
+                            df.at[df_idx, column] = value
 
                 except Exception as e:
                     print(f"Failed at {path} under exception {e}.")
             
-
+            df.to_csv(save_path)
+            
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description="Process some 2D or 3D data.")
     parser.add_argument(

utils/pull_hubble_csv.py

@@ -0,0 +1,80 @@
+from astroquery.mast.missions import MastMissions
+
+"""
+Used to pull the list of observations that fit our filter criteria from 
+online Hubble archives. See all filter details in the function call below.
+"""
+
+missions = MastMissions(mission='hst')
+
+# Use query_criteria method to use selected form search conditions for making missions_mast search API call
+results = missions.query_criteria(select_cols=[
+    'sci_aec',
+    'ang_sep',
+    'sci_aper_1234',
+    'sci_archive_class',
+    'sci_archive_date',
+    'sci_asn_id',
+    'sci_bandwidth',
+    'sci_broad_category',
+    'sci_central_wavelength',
+    'sci_costar_deploy',
+    'sci_data_set_name',
+    'sci_dec',
+    'sci_dec_v1',
+    'sci_dispersion',
+    'sci_ecliptic_latitude',
+    'sci_ecliptic_longitude',
+    'sci_expflag',
+    'sci_actual_duration',
+    'sci_fgslock',
+    'sci_spec_1234',
+    'sci_fov_config',
+    'sci_galactic_latitude',
+    'sci_galactic_longitude',
+    'sci_generation_date',
+    'sci_hapnum',
+    'sci_haspnum',
+    'sci_hlsp',
+    'sci_instrume',
+    'sci_instrument_config',
+    'sci_mtflag',
+    'sci_obs_type',
+    'sci_obset_id',
+    'sci_obsnum',
+    'sci_operating_mode',
+    'sci_pa_aper',
+    'sci_pi_last_name',
+    'sci_pixel_res',
+    'sci_preview_name',
+    'sci_program_id',
+    'sci_pep_id',
+    'sci_ra',
+    'sci_ra_v1',
+    'sci_refnum',
+    'sci_release_date',
+    'scp_scan_type',
+    'sci_spectral_res',
+    'sci_spectrum_end',
+    'sci_spectrum_start',
+    'sci_start_time',
+    'sci_status',
+    'sci_stop_time',
+    'sci_sun_alt',
+    'sci_target_descrip',
+    'sci_targname',
+    'sci_v3_pos_angle'],
+    sci_spec_1234='F606W,F606W;*,*;F606W,*;F606W;*',
+    sci_aec='S',
+    sci_instrume='acs',
+    sci_instrument_config='ACS/WFC',
+    sci_expflag='NORMAL',
+    sci_actual_duration='>300',
+    sci_aper_1234='WFC,WFC1,WFC2,WFCENTER',
+    limit=25000
+                                 
+                                 )
+
+df = results.to_pandas()
+print(len(df))
+df.to_csv('hst_FINAL.csv')