chore: 🧹 Cleaning and organising

modules/generate_timeseries.py

@@ -0,0 +1,127 @@
+from __future__ import division, print_function
+import numpy as np
+import glob
+import pandas as pd
+from datetime import datetime
+
+# Configuration
+asc_path = "asc_files/"
+asc_wildcard_file = "*.asc"
+asc_mult_source = asc_path + asc_wildcard_file
+
+def read_ascii_header(ascii_raster_file: str) -> list:
+    """Reads header information from an ASCII DEM
+
+    Args:
+        ascii_raster_file (str): Path to the ASCII raster file
+
+    Returns:
+        list: Header data as a list of floats
+    """
+    with open(ascii_raster_file) as f:
+        header_data = [float(f.__next__().split()[1]) for x in range(6)]
+    return header_data
+
+
+def calculate_crop_coords(basin_header: list, radar_header: list) -> tuple:
+    """Calculate crop coordinates based on header data
+
+    Args:
+        basin_header (list): Basin header data
+        radar_header (list): Radar header data
+
+    Returns:
+        tuple: (start_col, start_row, end_col, end_row) as integers
+    """
+    y0_radar = radar_header[3]
+    x0_radar = radar_header[2]
+
+    y0_basin = basin_header[3]
+    x0_basin = basin_header[2]
+
+    nrows_radar = radar_header[1]
+
+    nrows_basin = 2  # hardcoded, we always expect 2 rows
+    ncols_basin = 2  # hardcoded, we always expect 2 columns
+
+    cellres_radar = radar_header[4]
+    cellres_basin = basin_header[4]
+
+    xp = x0_basin - x0_radar
+    yp = y0_basin - y0_radar
+
+    xpp = ncols_basin * cellres_basin
+    ypp = nrows_basin * cellres_basin
+
+    start_col = np.floor(xp / cellres_radar)
+    end_col = np.ceil((xpp + xp) / cellres_radar)
+
+    start_row = np.floor(nrows_radar - ((yp + ypp) / cellres_radar))
+    end_row = np.ceil(nrows_radar - (yp / cellres_radar))
+
+    #print(start_col, start_row, end_col, end_row)
+    return int(start_col), int(start_row), int(end_col), int(end_row)
+
+
+def extract_cropped_rain_data(location):
+    """Extract cropped rain data and create rainfall timeseries
+
+    Returns:
+        None
+    """
+    rainfile = []
+
+    # Create datetime list
+    datetime_list = []
+    print(location)
+    for f in glob.iglob(asc_mult_source):
+        # print(f)
+        radar_header = read_ascii_header(f)
+        start_col, start_row, end_col, end_row = calculate_crop_coords(
+            location, radar_header
+        )
+
+        start_col = int(round(start_col))
+        start_row = int(round(start_row))
+        end_col = int(round(end_col))
+        end_row = int(round(end_row))
+
+        cur_rawgrid = np.genfromtxt(
+            f, skip_header=6, filling_values=0.0, loose=True, invalid_raise=False
+        )
+
+        cur_croppedrain = cur_rawgrid[start_row:end_row, start_col:end_col]
+        # Flatten the cropped rain data into a 1D array
+        cur_rainrow = cur_croppedrain.flatten()
+        rainfile.append(cur_rainrow)
+
+        # Extract datetime from filename
+        filename = f.split("/")[-1]  # Get just the filename
+        # 20240929 0015
+        date_str = filename[:8]  # YYYYMMDD
+        time_str = filename[8:12]  # HHMM
+
+        # Parse datetime
+        parsed_date = datetime.strptime(f"{date_str}{time_str}", "%Y%m%d%H%M")
+        datetime_list.append(parsed_date)
+
+    rainfile_arr = np.vstack(rainfile)
+
+    # Create DataFrame with datetime index
+    df = pd.DataFrame(rainfile_arr, index=datetime_list)
+    # sort the dataframe into date order 
+    sorted_df = df.sort_index()
+    # add headers 
+    header_row = ['rainfall_1', 'rainfall_2', 'rainfall_3', 'rainfall_4']
+    file_name = f"csv_files/{location[0]}_timeseries_data.csv"
+    sorted_df.to_csv(file_name, sep=",", float_format="%1.4f", header=header_row, index_label='datetime')
+
+
+if __name__ == "__main__":
+    locations = [
+        # loc name, loc id, x loc,   y loc,  resolution
+        ["BRICSC", "TM0816", 608500, 216500, 1000],  
+        ["HEACSC", "TF6842", 568500, 342500, 1000], 
+    ]
+    for place in locations:
+        extract_cropped_rain_data(place)