#224 Add scripts to generate csv per transect

scripts/gen_csv_per_transect_raw.py

@@ -0,0 +1,60 @@
+import os 
+import pandas as pd
+
+def create_csv_per_transect(
+    timeseries_df: pd.DataFrame,
+    save_path: str,
+    file_extension: str = "_timeseries_raw.csv",
+) -> None:
+    """
+    Create a CSV file per transect from a given timeseries DataFrame.
+
+    Args:
+        timeseries_df (pd.DataFrame): The timeseries DataFrame containing transect data.
+        save_path (str): The path to save the CSV files.
+        file_extension (str, optional): The file extension for the CSV files. Defaults to "_timeseries_raw.csv".
+
+    Returns:
+        None
+    """
+    for transect_id in timeseries_df.columns:
+        if transect_id == "dates":
+            continue
+        print(f"Processing {transect_id}")
+        # 3. Save the csv file per transect
+        df = pd.DataFrame(
+                    {
+                        "dates": timeseries_df["dates"],
+                        transect_id: timeseries_df[transect_id],
+
+                    },
+                )
+        # Save to csv file
+        fn = f"{transect_id}{file_extension}"
+        file_path = os.path.join(save_path, fn)
+        df.to_csv(
+            file_path, sep=",", index=False
+        )
+
+
+
+# 1. Enter the path to the csv file that contains the time series
+# - replace the path below with the path to the csv file
+input_file = r"C:\development\doodleverse\coastseg\CoastSeg\sessions\rym1_model_extract_shorelines\transect_time_series.csv"
+# 2. The output file will be saved in the same directory as the input file
+save_location = os.path.dirname(os.path.abspath(input_file))
+
+
+if not os.path.exists(input_file):
+    print(f"File not found: {input_file}")
+    exit()
+
+# read the csv file
+tide_corrected_timeseries_df = pd.read_csv(input_file)
+
+# save a csv for each transect that was tidally corrected
+create_csv_per_transect(
+    tide_corrected_timeseries_df,
+    save_location,
+)
+print(f"Saved the time series for each transect with intersections with the shoreline to {save_location}")

scripts/gen_csv_per_transect_tide_corrected.py

@@ -0,0 +1,39 @@
+import os 
+import pandas as pd
+
+def save_csv_per_id(
+    df: pd.DataFrame,
+    save_location: str,
+    filename: str = "timeseries_tidally_corrected.csv",
+    id_column_name: str = "transect_id",
+):
+    new_df = pd.DataFrame()
+    unique_ids = df[id_column_name].unique()
+    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))
+
+# 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
+input_file = r"C:\development\doodleverse\coastseg\CoastSeg\sessions\rym1_model_extract_shorelines\transect_time_series_tidally_corrected.csv"
+# 2. The output file will be saved in the same directory as the input file
+save_location = os.path.dirname(os.path.abspath(input_file))
+
+
+if not os.path.exists(input_file):
+    print(f"File not found: {input_file}")
+    exit()
+
+# read the csv file
+timeseries_df = pd.read_csv(input_file)
+if "Unnamed: 0" in timeseries_df.columns:
+    timeseries_df.drop(columns=["Unnamed: 0"], inplace=True)
+
+# save a csv for each transect that was tidally corrected
+save_csv_per_id(
+    timeseries_df,
+    save_location,
+    filename="timeseries_tidally_corrected.csv",
+)
+print(f"Saved the tidally corrected time series for each transect with intersections with the shoreline to {save_location} ")