update the csv files to no save the index

SatelliteShorelines · Apr 19, 2024 · 0f4859d · 0f4859d
1 parent 3391239
commit 0f4859d
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Showing 4 changed files with 10 additions and 10 deletions.
diff --git a/scripts/apply_tidal_correction.py b/scripts/apply_tidal_correction.py
@@ -1166,7 +1166,7 @@ def main():
     # Reset index if you want 'dates' back as a column
     pivot_df.reset_index(inplace=True)    
 
-    pivot_df.to_csv(TIDE_PREDICTIONS_FILE_NAME)
+    pivot_df.to_csv(TIDE_PREDICTIONS_FILE_NAME,index=False)
 
     print(f"Applying tide corrections to {RAW_TIMESERIES_FILE_PATH}")
     tide_corrected_timeseries_df = tidally_correct_timeseries(
@@ -1188,12 +1188,12 @@ def main():
                                                                                                 only_keep_points_on_transects=DROP_INTERSECTIONS,extension='tidally_corrected')
     # optionally save to session location in ROI the tide_corrected_timeseries_df to csv
     tide_corrected_timeseries_df.to_csv(
-        os.path.join(os.getcwd(), "tidally_corrected_transect_time_series_merged.csv")
+        os.path.join(os.getcwd(), "tidally_corrected_transect_time_series_merged.csv"),index=False
     )
     # Tidally correct the raw time series
     print(f"Tidally corrected data saved to {os.path.abspath(TIDALLY_CORRECTED_FILE_NAME)}")
     # Save the Tidally corrected time series
-    tide_corrected_timeseries_df.to_csv(TIDALLY_CORRECTED_FILE_NAME)
+    tide_corrected_timeseries_df.to_csv(TIDALLY_CORRECTED_FILE_NAME,index=False)
 
     # save the time series as a matrix of date vs transect id with the cross_distance as the values
     pivot_df = tide_corrected_timeseries_df.pivot_table(index='dates', columns='transect_id', values='cross_distance', aggfunc='first')
@@ -1203,7 +1203,7 @@ def main():
     # Tidally correct the raw time series
     print(f"Tidally corrected data saved to {os.path.abspath(TIDALLY_CORRECTED_MATRIX_FILE_NAME)}")
     # Save the Tidally corrected time series
-    pivot_df.to_csv(TIDALLY_CORRECTED_MATRIX_FILE_NAME)
+    pivot_df.to_csv(TIDALLY_CORRECTED_MATRIX_FILE_NAME,index=False)
 
 if __name__ == "__main__":
     main()
diff --git a/scripts/gen_csv_per_transect_tide_corrected.py b/scripts/gen_csv_per_transect_tide_corrected.py
@@ -12,7 +12,7 @@ def save_csv_per_id(
     for uid in unique_ids:
         new_filename = f"{uid}_{filename}"
         new_df = df[df[id_column_name] == uid]
-        new_df.to_csv(os.path.join(save_location, new_filename))
+        new_df.to_csv(os.path.join(save_location, new_filename),index=False)
 
 # 1. Enter the path to the csv file that contains the tide corrected time series
 # - replace the path below with the path to the csv file

diff --git a/src/coastseg/common.py b/src/coastseg/common.py
@@ -1915,10 +1915,10 @@ def save_transects(
                               "raw")
     # save the raw transect time series which contains the columns ['dates', 'x', 'y', 'transect_id', 'cross_distance','shore_x','shore_y']  to file
     filepath = os.path.join(save_location, f"raw_transect_time_series_merged.csv")
-    merged_timeseries_df.to_csv(filepath, sep=",") 
+    merged_timeseries_df.to_csv(filepath, sep=",",index=False) 
 
     filepath = os.path.join(save_location, f"raw_transect_time_series.csv")
-    timeseries_df.to_csv(filepath, sep=",")
+    timeseries_df.to_csv(filepath, sep=",",index=False)
     # save transect settings to file
     transect_settings = get_transect_settings(settings)
     transect_settings_path = os.path.join(save_location, "transects_settings.json")

diff --git a/src/coastseg/tide_correction.py b/src/coastseg/tide_correction.py
@@ -217,7 +217,7 @@ def correct_tides(
         pivot_df = predicted_tides_df.pivot_table(index='dates', columns='transect_id', values='tide', aggfunc='first')
         # Reset index if you want 'dates' back as a column
         pivot_df.reset_index(inplace=True)
-        pivot_df.to_csv(os.path.join(session_path, "predicted_tides.csv"))
+        pivot_df.to_csv(os.path.join(session_path, "predicted_tides.csv"),index=False)
 
         tide_corrected_timeseries_df = tidally_correct_timeseries(
             raw_timeseries_df,
@@ -237,12 +237,12 @@ def correct_tides(
                                 'tidally_corrected')
 
         # Save the Tidally corrected time series
-        timeseries_df.to_csv(os.path.join(session_path, 'tidally_corrected_transect_time_series.csv'))
+        timeseries_df.to_csv(os.path.join(session_path, 'tidally_corrected_transect_time_series.csv'),index=False)
 
 
         # optionally save to session location in ROI the tide_corrected_timeseries_df to csv
         tide_corrected_timeseries_merged_df.to_csv(
-            os.path.join(session_path, "tidally_corrected_transect_time_series_merged.csv")
+            os.path.join(session_path, "tidally_corrected_transect_time_series_merged.csv"),index=False
         )
 
         update(f"{roi_id} was tidally corrected")