fixed bug in setting flags

clients/python/utils/bathy_runner.py

@@ -41,9 +41,10 @@
 
 # Build Output File Path (and credentials if necessary)
 if args.output_file == None:
-    output_filename         = "/tmp/" + args.granule.replace("ATL03", "ATL24").replace(".h5", args.output_format)
+    output_filename         = "/tmp/" + args.granule.replace("ATL03", "ATL24").replace(".h5", "." + args.output_format)
+    credentials             = None
 elif args.output_file == "stage":
-    output_filename         = "s3://sliderule/data/ATL24/" + args.granule.replace("ATL03", "ATL24").replace(".h5", args.output_format)
+    output_filename         = "s3://sliderule/data/ATL24/" + args.granule.replace("ATL03", "ATL24").replace(".h5", "." + args.output_format)
     home_directory          = os.path.expanduser('~')
     aws_credential_file     = os.path.join(home_directory, '.aws', 'credentials')
     config                  = configparser.RawConfigParser()

plugins/icesat2/containers/oceaneyes/atl24uncertainty/runner.py

@@ -215,7 +215,7 @@ def find_kd_range(kd_490):
 print("Calculating processing flags")
 data["max_sensor_depth"] = 1.8 / data["kd_490"]
 data["flags"] = 0
-data.loc[subaqueous & (data["kd_490"] > 0) & (data["depth"] > data["max_sensor_depth"])] = 1
+data.loc[subaqueous & (data["kd_490"] > 0) & (data["depth"] > data["max_sensor_depth"]), "flags"] = 1
 
 ################
 # WRITE OUTPUTS

plugins/icesat2/containers/oceaneyes/cshelph/runner.py

@@ -104,9 +104,12 @@
                                         min_buffer=min_buffer, max_buffer=max_buffer,
                                         sea_surface_label=41,
                                         bathymetry_label=40)
-point_cloud['class_ph'] = results['classification']
+
+df = pd.DataFrame()
+df["index_ph"] = results['index_ph']
+df["class_ph"] = results['classification']
 
 if output_csv != None:
-    point_cloud.to_csv(output_csv, index=False, columns=["index_ph", "class_ph"])
+    df.to_csv(output_csv, index=False, columns=["index_ph", "class_ph"])
 else:
-    print(point_cloud.to_string(index=False, header=True, columns=['index_ph', 'class_ph']))
+    print(df.to_string(index=False, header=True, columns=['index_ph', 'class_ph']))

plugins/icesat2/containers/oceaneyes/medianfilter/runner.py

@@ -104,9 +104,11 @@
                                                     segment_length=segment_length,
                                                     compress_heights=compress_heights,
                                                     compress_lats=compress_lats)
-point_cloud['class_ph'] = results['classification']
+df = pd.DataFrame()
+df["index_ph"] = results['index_ph']
+df["class_ph"] = results['classification']
 
 if output_csv != None:
-    point_cloud.to_csv(output_csv, index=False, columns=["index_ph", "class_ph"])
+    df.to_csv(output_csv, index=False, columns=["index_ph", "class_ph"])
 else:
-    print(point_cloud.to_string(index=False, header=True, columns=['index_ph', 'class_ph']))
+    print(df.to_string(index=False, header=True, columns=['index_ph', 'class_ph']))

plugins/icesat2/containers/oceaneyes/qtrees/combiner.py

@@ -43,19 +43,20 @@
 qtrees_csv_file = sys.argv[2]
 
 # read in data
+spot_df = pd.read_csv(spot_csv_file)
 qtrees_df = pd.read_csv(qtrees_csv_file)
 print("Read all into data frame")
 
+# merge qtrees predictions into spot dataframe
+trimmed_qtrees_df = pd.DataFrame()
+trimmed_qtrees_df["index_ph"] = qtrees_df["index_ph"]
+trimmed_qtrees_df["prediction"] = qtrees_df["prediction"]
+spot_df = pd.merge(spot_df, trimmed_qtrees_df, on="index_ph", how='left')
+del spot_df["prediction"]
+spot_df.rename(columns={"prediction": "class_ph"}, inplace=True)
+
 # write out new qtrees file
-new_qtrees_df = pd.DataFrame()
-new_qtrees_df["index_ph"] = qtrees_df["index_ph"]
-new_qtrees_df["class_ph"] = qtrees_df["prediction"]
-new_qtrees_df.to_csv(qtrees_csv_file, index=False)
+trimmed_qtrees_df.to_csv(qtrees_csv_file, index=False)
 
 # write out new spot file
-qtrees_df["class_ph"] = qtrees_df["prediction"]
-qtrees_df["surface_h"] = qtrees_df["sea_surface_h"]
-del qtrees_df["prediction"]
-del qtrees_df["sea_surface_h"]
-del qtrees_df["bathy_h"]
-qtrees_df.to_csv(spot_csv_file, index=False)
+spot_df.to_csv(spot_csv_file, index=False)