chore: 🔧 More cleaning
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-1
@@ -1,2 +1,3 @@
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from .nimrod import Nimrod
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from .batch_nimrod import process_nimrod_files
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from .batch_nimrod import BatchNimrod
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from .generate_timeseries import GenerateTimeseries
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@@ -13,22 +13,22 @@ class BatchNimrod():
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Process all Nimrod files in the input directory, applying bounding box clipping
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and exporting to ASC format.
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This function reads all files from IN_TOP_FOLDER, applies the appropriate bounding
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This function reads all files from DAT_TOP_FOLDER, applies the appropriate bounding
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box for each area, and exports clipped raster data to OUT_TOP_FOLDER.
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"""
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# Read all file names in the folder
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files_to_process = [f for f in os.listdir(Path(self.config.IN_TOP_FOLDER))]
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files_to_process = [f for f in os.listdir(Path(self.config.DAT_TOP_FOLDER))]
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logging.info(f"Processing {len(files_to_process)} files...")
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for in_file in os.listdir(Path(self.config.IN_TOP_FOLDER)):
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in_file_full = Path(self.config.IN_TOP_FOLDER, in_file)
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for in_file in os.listdir(Path(self.config.DAT_TOP_FOLDER)):
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in_file_full = Path(self.config.DAT_TOP_FOLDER, in_file)
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try:
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image = Nimrod(open(in_file_full, "rb"))
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out_file_name = f"{image.get_validity_time()}.asc"
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out_file_path = Path(self.config.OUT_TOP_FOLDER, out_file_name)
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out_file_path = Path(self.config.ASC_TOP_FOLDER, out_file_name)
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with open(out_file_path, "w") as outfile:
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image.extract_asc(outfile)
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@@ -4,124 +4,113 @@ import glob
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import pandas as pd
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from datetime import datetime
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# Configuration
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asc_path = "asc_files/"
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asc_wildcard_file = "*.asc"
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asc_mult_source = asc_path + asc_wildcard_file
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def read_ascii_header(ascii_raster_file: str) -> list:
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"""Reads header information from an ASCII DEM
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class GenerateTimeseries:
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def __init__(self, config):
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self.config = config
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Args:
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ascii_raster_file (str): Path to the ASCII raster file
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def _read_ascii_header(self, ascii_raster_file: str) -> list:
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"""Reads header information from an ASCII DEM
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Returns:
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list: Header data as a list of floats
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"""
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with open(ascii_raster_file) as f:
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header_data = [float(f.__next__().split()[1]) for x in range(6)]
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return header_data
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Args:
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ascii_raster_file (str): Path to the ASCII raster file
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Returns:
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list: Header data as a list of floats
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"""
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with open(ascii_raster_file) as f:
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header_data = [float(f.__next__().split()[1]) for x in range(6)]
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return header_data
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def calculate_crop_coords(basin_header: list, radar_header: list) -> tuple:
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"""Calculate crop coordinates based on header data
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def _calculate_crop_coords(self, basin_header: list, radar_header: list) -> tuple:
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"""Calculate crop coordinates based on header data
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Args:
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basin_header (list): Basin header data
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radar_header (list): Radar header data
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Args:
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basin_header (list): Basin header data
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radar_header (list): Radar header data
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Returns:
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tuple: (start_col, start_row, end_col, end_row) as integers
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"""
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y0_radar = radar_header[3]
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x0_radar = radar_header[2]
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Returns:
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tuple: (start_col, start_row, end_col, end_row) as integers
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"""
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y0_radar = radar_header[3]
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x0_radar = radar_header[2]
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y0_basin = basin_header[3]
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x0_basin = basin_header[2]
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y0_basin = basin_header[3]
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x0_basin = basin_header[2]
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nrows_radar = radar_header[1]
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nrows_radar = radar_header[1]
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nrows_basin = 2 # hardcoded, we always expect 2 rows
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ncols_basin = 2 # hardcoded, we always expect 2 columns
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nrows_basin = 2 # hardcoded, likely to change?
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ncols_basin = 2 # hardcoded, likely to change?
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cellres_radar = radar_header[4]
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cellres_basin = basin_header[4]
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cellres_radar = radar_header[4]
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cellres_basin = basin_header[4]
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xp = x0_basin - x0_radar
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yp = y0_basin - y0_radar
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xp = x0_basin - x0_radar
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yp = y0_basin - y0_radar
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xpp = ncols_basin * cellres_basin
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ypp = nrows_basin * cellres_basin
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xpp = ncols_basin * cellres_basin
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ypp = nrows_basin * cellres_basin
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start_col = np.floor(xp / cellres_radar)
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end_col = np.ceil((xpp + xp) / cellres_radar)
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start_col = np.floor(xp / cellres_radar)
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end_col = np.ceil((xpp + xp) / cellres_radar)
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start_row = np.floor(nrows_radar - ((yp + ypp) / cellres_radar))
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end_row = np.ceil(nrows_radar - (yp / cellres_radar))
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start_row = np.floor(nrows_radar - ((yp + ypp) / cellres_radar))
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end_row = np.ceil(nrows_radar - (yp / cellres_radar))
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#print(start_col, start_row, end_col, end_row)
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return int(start_col), int(start_row), int(end_col), int(end_row)
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#print(start_col, start_row, end_col, end_row)
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return int(start_col), int(start_row), int(end_col), int(end_row)
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def extract_cropped_rain_data(location):
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"""Extract cropped rain data and create rainfall timeseries
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def extract_cropped_rain_data(self, location):
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"""Extract cropped rain data and create rainfall timeseries
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Returns:
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None
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"""
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rainfile = []
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Returns:
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None
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"""
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rainfile = []
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datetime_list = []
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# Create datetime list
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datetime_list = []
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print(location)
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for f in glob.iglob(asc_mult_source):
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# print(f)
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radar_header = read_ascii_header(f)
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start_col, start_row, end_col, end_row = calculate_crop_coords(
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location, radar_header
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)
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for f in glob.iglob(f'{self.config.ASC_TOP_FOLDER}/*.asc'):
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# print(f)
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radar_header = self._read_ascii_header(f)
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start_col, start_row, end_col, end_row = self._calculate_crop_coords(
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location, radar_header
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)
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start_col = int(round(start_col))
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start_row = int(round(start_row))
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end_col = int(round(end_col))
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end_row = int(round(end_row))
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start_col = int(round(start_col))
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start_row = int(round(start_row))
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end_col = int(round(end_col))
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end_row = int(round(end_row))
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cur_rawgrid = np.genfromtxt(
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f, skip_header=6, filling_values=0.0, loose=True, invalid_raise=False
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)
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cur_rawgrid = np.genfromtxt(
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f, skip_header=6, filling_values=0.0, loose=True, invalid_raise=False
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)
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cur_croppedrain = cur_rawgrid[start_row:end_row, start_col:end_col]
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# Flatten the cropped rain data into a 1D array
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cur_rainrow = cur_croppedrain.flatten()
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rainfile.append(cur_rainrow)
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cur_croppedrain = cur_rawgrid[start_row:end_row, start_col:end_col]
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# Flatten the cropped rain data into a 1D array
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cur_rainrow = cur_croppedrain.flatten()
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rainfile.append(cur_rainrow[2]/32)
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# Extract datetime from filename
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filename = f.split("/")[-1] # Get just the filename
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# 20240929 0015
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date_str = filename[:8] # YYYYMMDD
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time_str = filename[8:12] # HHMM
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# Extract datetime from filename
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filename = f.split("/")[-1] # Get just the filename
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date_str = filename[:8] # YYYYMMDD
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time_str = filename[8:12] # HHMM
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# Parse datetime
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parsed_date = datetime.strptime(f"{date_str}{time_str}", "%Y%m%d%H%M")
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datetime_list.append(parsed_date)
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# Parse datetime
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parsed_date = datetime.strptime(f"{date_str}{time_str}", "%Y%m%d%H%M")
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datetime_list.append(parsed_date)
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rainfile_arr = np.vstack(rainfile)
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rainfile_arr = np.vstack(rainfile)
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# Create DataFrame with datetime index
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df = pd.DataFrame(rainfile_arr, index=datetime_list)
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# sort the dataframe into date order
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sorted_df = df.sort_index()
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# add headers
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header_row = ['rainfall_1', 'rainfall_2', 'rainfall_3', 'rainfall_4']
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file_name = f"csv_files/{location[0]}_timeseries_data.csv"
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sorted_df.to_csv(file_name, sep=",", float_format="%1.4f", header=header_row, index_label='datetime')
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# Create DataFrame with datetime index
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df = pd.DataFrame(rainfile_arr, index=datetime_list)
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# sort the dataframe into date order
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sorted_df = df.sort_index()
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# add headers
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header_row = [location[1]]
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file_name = f"csv_files/{location[0]}_timeseries_data.csv"
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sorted_df.to_csv(file_name, sep=",", float_format="%1.4f", header=header_row, index_label='datetime')
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if __name__ == "__main__":
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locations = [
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# loc name, loc id, x loc, y loc, resolution
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["BRICSC", "TM0816", 608500, 216500, 1000],
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["HEACSC", "TF6842", 568500, 342500, 1000],
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]
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for place in locations:
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extract_cropped_rain_data(place)
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