From aa1879582ad89b27ca3ff93fd25dda25859729df Mon Sep 17 00:00:00 2001 From: Jake Pullen Date: Tue, 18 Nov 2025 22:09:20 +0000 Subject: [PATCH] multithreading --- .python-version | 2 +- modules/batch_nimrod.py | 92 ++++++++++++------- modules/generate_timeseries.py | 157 ++++++++++++++++++--------------- pyproject.toml | 2 +- uv.lock | 75 +--------------- 5 files changed, 150 insertions(+), 178 deletions(-) diff --git a/.python-version b/.python-version index e4fba21..a469d8f 100644 --- a/.python-version +++ b/.python-version @@ -1 +1 @@ -3.12 +3.14t diff --git a/modules/batch_nimrod.py b/modules/batch_nimrod.py index c8963fb..81d0e97 100644 --- a/modules/batch_nimrod.py +++ b/modules/batch_nimrod.py @@ -2,50 +2,78 @@ from modules.nimrod import Nimrod import os from pathlib import Path import logging +import concurrent.futures + class BatchNimrod: def __init__(self, config) -> None: self.config = config - def process_nimrod_files(self) -> None: + def _process_single_file(self, in_file): + """Process a single Nimrod DAT file. + + Args: + in_file (str): Filename of the DAT file. + + Returns: + bool: True if successful, False otherwise. """ - Process all Nimrod files in the input directory, applying bounding box clipping - and exporting to ASC format. - - This function reads all files from DAT_TOP_FOLDER, applies the appropriate bounding - box for each area, and exports clipped raster data to OUT_TOP_FOLDER. - """ - # Read all file names in the folder - files_to_process = len([f for f in os.listdir(Path(self.config.DAT_TOP_FOLDER))]) - - logging.info(f"Processing {files_to_process} files...") - file_counter = 0 - for in_file in os.listdir(Path(self.config.DAT_TOP_FOLDER)): - in_file_full = Path(self.config.DAT_TOP_FOLDER, in_file) - - try: - image = Nimrod(open(in_file_full, "rb")) - + in_file_full = Path(self.config.DAT_TOP_FOLDER, in_file) + + try: + # We need to open the file here, inside the thread + with open(in_file_full, "rb") as f: + image = Nimrod(f) + out_file_name = f"{image.get_validity_time()}.asc" out_file_path = Path(self.config.ASC_TOP_FOLDER, out_file_name) with open(out_file_path, "w") as outfile: image.extract_asc(outfile) - if self.config.delete_dat_after_processing: - os.remove(in_file_full) + if self.config.delete_dat_after_processing: + os.remove(in_file_full) - file_counter += 1 - logging.debug(f"Successfully processed: {in_file_full}") - if file_counter %10 == 0: - logging.info(f'processed {file_counter} out of {files_to_process} files') + logging.debug(f"Successfully processed: {in_file_full}") + return True + + except Nimrod.HeaderReadError as e: + logging.error(f"Failed to read file {in_file_full}, is it corrupt?") + logging.error(e) + return False + except Nimrod.PayloadReadError as e: + logging.error(f"Failed to load the raster data in {in_file_full}") + logging.error(e) + return False + except Exception as e: + logging.error(f"Unexpected error processing {in_file_full}: {e}") + return False + + def process_nimrod_files(self) -> None: + """ + Process all Nimrod files in the input directory concurrently, applying bounding box clipping + and exporting to ASC format. + + This function reads all files from DAT_TOP_FOLDER, applies the appropriate bounding + box for each area, and exports clipped raster data to OUT_TOP_FOLDER. + """ + # Read all file names in the folder + files_to_process = [f for f in os.listdir(Path(self.config.DAT_TOP_FOLDER)) if not f.startswith('.')] + total_files = len(files_to_process) + + logging.info(f"Processing {total_files} files concurrently...") + + with concurrent.futures.ThreadPoolExecutor() as executor: + # Submit all tasks + future_to_file = { + executor.submit(self._process_single_file, in_file): in_file + for in_file in files_to_process + } + + completed_count = 0 + for future in concurrent.futures.as_completed(future_to_file): + completed_count += 1 + if completed_count % 10 == 0: + logging.info(f'processed {completed_count} out of {total_files} files') - except Nimrod.HeaderReadError as e: - logging.error(f"Failed to read file {in_file_full}, is it corrupt?") - logging.error(e) - continue - except Nimrod.PayloadReadError as e: - logging.error(f"Failed to load the raster data in {in_file_full}") - logging.error(e) - continue diff --git a/modules/generate_timeseries.py b/modules/generate_timeseries.py index c85221a..9994c71 100644 --- a/modules/generate_timeseries.py +++ b/modules/generate_timeseries.py @@ -4,6 +4,8 @@ from pathlib import Path import polars as pd from datetime import datetime import os +import concurrent.futures + class GenerateTimeseries: @@ -61,8 +63,67 @@ class GenerateTimeseries: return int(start_col), int(start_row), int(end_col), int(end_row) + def _process_single_file(self, file_name, locations): + """Process a single ASC file and extract data for all locations. + + Args: + file_name (str): Name of the ASC file. + locations (list): List of locations. + + Returns: + list: A list of dictionaries containing extracted data for each location, + or None if processing fails. + Format: [{'zone_id': id, 'date': datetime, 'value': float}, ...] + """ + if not file_name.endswith('.asc'): + return None + + file_path = Path(self.config.ASC_TOP_FOLDER, file_name) + results = [] + + try: + radar_header = self._read_ascii_header(str(file_path)) + + # Read grid once + cur_rawgrid = np.loadtxt(file_path, skiprows=6, dtype=float, delimiter=None) + + # Parse datetime from filename once + filename = os.path.basename(file_path) + date_str = filename[:8] # YYYYMMDD + time_str = filename[8:12] # HHMM + parsed_date = datetime.strptime(f"{date_str}{time_str}", "%Y%m%d%H%M") + + # Extract data for each location + for location in locations: + zone_id = location[0] + + # Calculate crop coordinates + start_col, start_row, end_col, end_row = self._calculate_crop_coords( + location, radar_header + ) + + cur_croppedrain = cur_rawgrid[start_row:end_row, start_col:end_col] + + if cur_croppedrain.size > 2: + val = cur_croppedrain.flatten()[2] / 32 + else: + # Handle edge case + # print(f"Warning: Crop too small for {zone_id} in {file_name}") + val = 0.0 + + results.append({ + 'zone_id': zone_id, + 'date': parsed_date, + 'value': val + }) + return results + + except Exception as e: + print(f"Error processing file {file_name}: {e}") + return None + def extract_data_for_all_locations(self, locations): - """Extract cropped rain data for all locations by iterating over ASC files once. + """Extract cropped rain data for all locations by iterating over ASC files concurrently. Args: locations (list): List of location data [zone_id, easting, northing, zone] @@ -70,74 +131,33 @@ class GenerateTimeseries: # Initialize data structure to hold results: {zone_id: {'dates': [], 'values': []}} results = {loc[0]: {'dates': [], 'values': []} for loc in locations} - # Get list of ASC files and sort them to ensure chronological order if needed + # Get list of ASC files asc_files = sorted(os.listdir(Path(self.config.ASC_TOP_FOLDER))) - total_files = len(asc_files) - print(f"Processing {total_files} ASC files...") + print(f"Processing {total_files} ASC files concurrently...") - for i, file_name in enumerate(asc_files): - if not file_name.endswith('.asc'): - continue - - file_path = Path(self.config.ASC_TOP_FOLDER, file_name) - - try: - radar_header = self._read_ascii_header(str(file_path)) - - # Read grid once - cur_rawgrid = np.loadtxt(file_path, skiprows=6, dtype=float, delimiter=None) - - # Parse datetime from filename once - filename = os.path.basename(file_path) - date_str = filename[:8] # YYYYMMDD - time_str = filename[8:12] # HHMM - parsed_date = datetime.strptime(f"{date_str}{time_str}", "%Y%m%d%H%M") - - # Extract data for each location - for location in locations: - zone_id = location[0] - - # Calculate crop coordinates - start_col, start_row, end_col, end_row = self._calculate_crop_coords( - location, radar_header - ) - - # Extract value - # Note: The original code used cur_croppedrain.flatten()[2] / 32 - # We need to ensure the crop is valid and has enough elements. - # Assuming the crop size is fixed as per original code (2x2 basin -> 4 cells?) - # Original: - # nrows_basin = 2 - # ncols_basin = 2 - # cellres_basin = 1000 - # cellres_radar = radar_header[4] (usually 1000) - # So it's likely a small grid. - - cur_croppedrain = cur_rawgrid[start_row:end_row, start_col:end_col] - - # Original logic: rainfile.append(cur_croppedrain.flatten()[2] / 32) - # We replicate this exactly. - if cur_croppedrain.size > 2: - val = cur_croppedrain.flatten()[2] / 32 - else: - val = 0.0 # Handle edge case if crop is too small? Or maybe NaN? - # For now, let's assume it works as before, but maybe add a check? - # If the original code worked, this should work too provided indices are correct. - # If size is too small, it would raise IndexError in original code too. - if cur_croppedrain.size <= 2: - print(f"Warning: Crop too small for {zone_id} in {file_name}") - val = 0.0 # Default or error? - - results[zone_id]['dates'].append(parsed_date) - results[zone_id]['values'].append(val) + # Use ThreadPoolExecutor for concurrent processing + # Since we are using Python 3.14t (free-threaded), this should scale well even for CPU work + # mixed with I/O. + with concurrent.futures.ThreadPoolExecutor() as executor: + # Submit all tasks + future_to_file = { + executor.submit(self._process_single_file, file_name, locations): file_name + for file_name in asc_files + } - except Exception as e: - print(f"Error processing file {file_name}: {e}") - continue - - if (i + 1) % 100 == 0: - print(f"Processed {i + 1}/{total_files} files") + completed_count = 0 + for future in concurrent.futures.as_completed(future_to_file): + file_results = future.result() + if file_results: + for res in file_results: + zone_id = res['zone_id'] + results[zone_id]['dates'].append(res['date']) + results[zone_id]['values'].append(res['value']) + + completed_count += 1 + if completed_count % 100 == 0: + print(f"Processed {completed_count}/{total_files} files") # Write CSVs for each location print("Writing CSV files...") @@ -151,12 +171,9 @@ class GenerateTimeseries: df = pd.DataFrame({"datetime": data['dates'], zone_id: data['values']}) - # Sort and set index (Polars) + # Sort the dataframe into date order sorted_df = df.sort("datetime") - sorted_df = sorted_df.with_columns( - pd.Series(data['dates']).alias("datetime") - ).set_sorted("datetime") - + output_path = Path(self.config.CSV_TOP_FOLDER) / f"{zone_id}_timeseries_data.csv" sorted_df.write_csv( output_path, diff --git a/pyproject.toml b/pyproject.toml index 9d7c0f0..6de0cec 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -3,7 +3,7 @@ name = "met-office" version = "1.0.0" description = "Convert .dat nimrod files to .asc files" readme = "README.md" -requires-python = ">=3.12" +requires-python = ">=3.14" dependencies = [ "numpy>=2.3.4", "pandas>=2.3.3", diff --git a/uv.lock b/uv.lock index 35cd4fa..59360e5 100644 --- a/uv.lock +++ b/uv.lock @@ -1,6 +1,6 @@ version = 1 revision = 3 -requires-python = ">=3.12" +requires-python = ">=3.14" [[package]] name = "met-office" @@ -29,39 +29,6 @@ version = "2.3.4" source = { registry = "https://pypi.org/simple" } sdist = { url = "https://files.pythonhosted.org/packages/b5/f4/098d2270d52b41f1bd7db9fc288aaa0400cb48c2a3e2af6fa365d9720947/numpy-2.3.4.tar.gz", hash = "sha256:a7d018bfedb375a8d979ac758b120ba846a7fe764911a64465fd87b8729f4a6a", size = 20582187, upload-time = "2025-10-15T16:18:11.77Z" } wheels = [ - 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