feat: ✨ Extraction now part of the main workflow

.gitignore

@@ -9,6 +9,9 @@ wheels/
 # Virtual environments
 .venv
 
+dat_other/*
+tar_files/*
+gz_files/*
 dat_files/*
 asc_files/*
 csv_files/*
@@ -16,3 +19,5 @@ combined_files/*
 zone_inputs/*
 
 *.tar.gz
+
+generate_test_data.py

README.MD

@@ -1,35 +1,42 @@
 # UK Met Office Rain Radar NIMROD Data Processor
 
-This project provides tools for processing UK Met Office Rain Radar NIMROD image files. It allows extraction of raster data from NIMROD .dat format files and conversion to ESRI ASCII (.asc) format. It also allows the creation of timeseries data from the ASC files. 
+This project provides tools for processing UK Met Office Rain Radar NIMROD image files. It allows extraction of raster data from NIMROD .dat format files and conversion to ESRI ASCII (.asc) format. It also allows the creation of timeseries data from the ASC files, formatted for Infoworks ICM. 
 
 ## Overview
 
 The project consists of a main pipeline workflow that processes multiple modules in sequence:
+
 - `main.py`: Main pipeline orchestrator that calls on the modules as needed
 - `batch_nimrod.py`: Module for batch processing multiple NIMROD files with configurable bounding boxes
 - `generate_timeseries.py`: Module for extracting cropped rain data and creating rainfall timeseries
-- `combine_timeseries.py`: Module for combining grouped timeseries CSVs into consolidated datasets
+- `extract.py`: Module for extracting the dat files from the .gz.tar files that are downloaded from source
 
 ## Features
 
 ### main.py
+
 - Orchestrates the entire workflow pipeline
+- Uncompress the packed .gz.tar files to DAT files
 - Processes DAT files to ASC format
 - Generates timeseries data for specified locations 
-- Combines grouped CSV files into consolidated datasets
+- Combines grouped CSV files into consolidated datasets formatted for Infoworks ICM
+
+### extract.py
+
+- Converts all .gz.tar files first to 288 (1 day) of .gz files
+- Converts all .gz files to .dat files ready for processing.
 
 ### batch_nimrod.py
+
 - Process multiple NIMROD dat files
 - Automatically extract datetime from file data
 - Export clipped raster data to ASC format
 
 ### generate_timeseries.py
+
 - Extract cropped rain data based on specified locations
 - Create rainfall timeseries CSVs for each location
 - Parse datetime from filename and create proper datetime index
-
-### combine_timeseries.py
-- Combine multiple timeseries CSV files into grouped datasets
 - Group locations by specified output groups
 - Create consolidated CSV files for each group
 
@@ -44,34 +51,40 @@ It is recommended to use UV for environment and package handling.
 
 1. Ensure all required packages are installed `uv sync`
 1. Adjust the config.py file to match your needs.
-1. Ensure your .dat files are in the DAT_TOP_FOLDER (as per config location)
+1. Ensure your .gz.tar files are in the TAR_TOP_FOLDER (as per config location)
 1. Ensure your zone csv files are in the ZONE_FOLDER (as per config location)
-1. RunMain Pipeline `uv run main.py
+1. RunMain Pipeline `uv run main.py` Note that you will have to set your environment variable `PYTHON_GIL=0` first
 1. find the output in the COMBINED_FOLDER (as per config location)
 
-
 The main pipeline will:
-1. Process DAT files to ASC format if needed
-2. Generate timeseries data for specified locations
-3. Combine grouped CSV files into consolidated datasets
+
+1. Uncompress the .gz.tar files ready for processing
+1. Process DAT files to ASC format
+1. Generate timeseries data for specified locations
+1. Combine grouped locations into consolidated datasets
 
 ## Configuration
 
-The `config.py` file defines folder paths:
-- DAT_TOP_FOLDER: "./dat_files"
-- ASC_TOP_FOLDER: "./asc_files"
-- CSV_TOP_FOLDER: "./csv_files"
-- COMBINED_FOLDER: "./combined_files"
+The `config.py` file defines folder paths and file deletion options:
+
+- TAR_TOP_FOLDER = "./tar_files"
+- GZ_TOP_FOLDER = "./gz_files"
+- DAT_TOP_FOLDER = "./dat_files"
+- ASC_TOP_FOLDER = "./asc_files"
+- COMBINED_FOLDER = "./combined_files"
+- ZONE_FOLDER = "./zone_inputs"
 
 Example of how the zone csv files should look:
-```
-filler, zone_name, easting, northing, other_filler, last_filler, zone_number
-aa, TM0816, 608500, 216500, a, a, 1
-aa, TF6842, 568500, 342500, a, a, 1
+
+```csv
+1K Grid, easting, northing, zone_number
+TM0816, 608500, 216500, 1
+TF6842, 568500, 342500, 1
 ```
 
 ## Acknowledgments 
 
 Thank you to the following projects for their inspiration and code:  
-* [Richard Thomas - Original Nimrod dat to asc file conversion](https://github.com/richard-thomas/MetOffice_NIMROD)  
-* [Declan Valters - building the timeseries from the asc files](https://github.com/dvalters/NIMROD-toolbox)
+
+- [Richard Thomas - Original Nimrod dat to asc file conversion](https://github.com/richard-thomas/MetOffice_NIMROD)
+- [Declan Valters - building the timeseries from the asc files](https://github.com/dvalters/NIMROD-toolbox)

config.py

@@ -1,10 +1,13 @@
 class Config:
+    TAR_TOP_FOLDER = "./tar_files"
+    GZ_TOP_FOLDER = "./gz_files"
     DAT_TOP_FOLDER = "./dat_files"
     ASC_TOP_FOLDER = "./asc_files"
-    CSV_TOP_FOLDER = "./csv_files"
     COMBINED_FOLDER = "./combined_files"
+
     ZONE_FOLDER = "./zone_inputs"
 
-    delete_dat_after_processing = False
+    delete_tar_after_processing = False
+    delete_gz_after_processing = True
+    delete_dat_after_processing = True
     delete_asc_after_processing = True
-    delete_csv_after_combining = True

main.py

@@ -2,58 +2,118 @@ import logging
 import time
 import os
 import csv
+import concurrent.futures
 from pathlib import Path
 
 from config import Config
-from modules import BatchNimrod, GenerateTimeseries, CombineTimeseries
+from modules import BatchNimrod, GenerateTimeseries, Extract
 
 logging.basicConfig(
     level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
 )
 
+
+def process_pipeline(dat_file):
+    # 1. Process DAT to ASC
+    asc_file = batch._process_single_file(dat_file)
+    if not asc_file:
+        return None
+
+    # 2. Extract data from ASC
+    file_results = timeseries.process_asc_file(asc_file, locations)
+    return file_results
+
+
+def initialise_folders():
+    folder_list = [
+        Config.ASC_TOP_FOLDER,
+        Config.COMBINED_FOLDER,
+        Config.GZ_TOP_FOLDER,
+        Config.DAT_TOP_FOLDER,
+        Config.TAR_TOP_FOLDER,
+    ]
+    for path in folder_list:
+        Path(path).mkdir(exist_ok=True)
+
+
 if __name__ == "__main__":
-    os.makedirs(Path(Config.ASC_TOP_FOLDER), exist_ok=True)
-    os.makedirs(Path(Config.CSV_TOP_FOLDER), exist_ok=True)
-    os.makedirs(Path(Config.COMBINED_FOLDER), exist_ok=True)
+    initialise_folders()
 
     locations = []
+    zones = set()
     # load zone inputs here
     for file in os.listdir(Path(Config.ZONE_FOLDER)):
-        with open(Path(Config.ZONE_FOLDER,file), 'r') as csvfile:
+        with open(Path(Config.ZONE_FOLDER, file), "r") as csvfile:
             reader = csv.reader(csvfile)
             header = next(reader)  # Skip header row
             for row in reader:
-                # Extract the relevant fields: Ossheet (location ID), Easting, Northing, Zone
-                zone_id = row[1]  # Ossheet column
-                easting = int(row[2])  # Easting column
-                northing = int(row[3])  # Northing column
-                zone = int(row[6])  # ZoneID column
-                locations.append([zone_id, easting, northing, zone])
+                # Extract the relevant fields: 1K Grid, Easting, Northing, Zone
+                grid_name = row[0]  # 1k Grid name
+                easting = int(row[1])  # Easting column
+                northing = int(row[2])  # Northing column
+                zone = int(row[3])  # ZoneID column
+                locations.append([grid_name, easting, northing, zone])
+                zones.add(zone)
+    logging.info(f"Count of 1km Grids: {len(locations)}")
+    logging.info(f"Count of Zones: {len(zones)}")
 
+    extraction = Extract(Config)
     batch = BatchNimrod(Config)
-    timeseries = GenerateTimeseries(Config)
-    combiner = CombineTimeseries(Config, locations)
+    timeseries = GenerateTimeseries(Config, locations)
 
     start = time.time()
-    logging.info("Starting to process DAT to ASC")
+    logging.info(
+        "Starting interleaved processing of DAT files and Timeseries generation"
+    )
 
-    batch.process_nimrod_files()
-    batch_checkpoint = time.time()
-    elapsed_time = batch_checkpoint - start
-    logging.info(f"DAT to ASC completed in {elapsed_time:.2f} seconds")
+    # Initialize results structure
+    results = {loc[0]: {"dates": [], "values": []} for loc in locations}
 
-    logging.info("Starting generating timeseries data for all locations.")
-    place_start = time.time()
-    timeseries.extract_data_for_all_locations(locations)
-    place_end = time.time()
-    place_create_time = place_end - place_start
-    elapsed_time = place_end - start
-    logging.info(f"Timeseries generation completed in {place_create_time:.2f} seconds")
-    logging.info(f"Total time so far {elapsed_time:.2f} seconds")
+    logging.info("Extracting tar and gz files")
+    extraction.run_extraction()
 
-    logging.info("combining CSVs into groups")
-    combiner.combine_csv_files()
-    logging.info("CSVs combined!")
+    # Get list of DAT files
+    dat_files = [
+        f for f in os.listdir(Path(Config.DAT_TOP_FOLDER)) if not f.startswith(".")
+    ]
+    total_files = len(dat_files)
+
+    logging.info(f"Processing {total_files} files concurrently...")
+
+    with concurrent.futures.ThreadPoolExecutor() as executor:
+        future_to_file = {
+            executor.submit(process_pipeline, dat_file): dat_file
+            for dat_file in dat_files
+        }
+
+        completed_count = 0
+        try:
+            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:
+                    elapsed_time = time.time() - start
+                    files_per_minute = (completed_count / elapsed_time) * 60
+                    remaining_files = total_files - completed_count
+                    eta_minutes = remaining_files / (files_per_minute / 60) / 60
+                    logging.info(f"""Processed {completed_count} out of {total_files} files.
+    Speed: {files_per_minute:.2f} files/min. ETA: {eta_minutes:.2f} minutes""")
+        except KeyboardInterrupt:
+            logging.warning("KeyboardInterrupt received. Cancelling pending tasks...")
+            executor.shutdown(wait=False, cancel_futures=True)
+            raise
+
+    elapsed_time = time.time() - start
+    logging.info(f"Interleaved processing completed in {elapsed_time:.2f} seconds")
+
+    logging.info("Writing CSV files...")
+    timeseries.write_results_to_csv(results, locations)
     end = time.time()
     elapsed_time = end - start
 

modules/__init__.py

@@ -1,11 +1,6 @@
 from .nimrod import Nimrod
 from .batch_nimrod import BatchNimrod
 from .generate_timeseries import GenerateTimeseries
-from .combine_timeseries import CombineTimeseries
+from .extract import Extract
 
-__all__ = [
-    "Nimrod",
-    "BatchNimrod",
-    "GenerateTimeseries",
-    "CombineTimeseries"
-]
+__all__ = ["Nimrod", "BatchNimrod", "GenerateTimeseries", "Extract"]

modules/batch_nimrod.py

@@ -5,7 +5,6 @@ import logging
 import concurrent.futures
 
 
-
 class BatchNimrod:
     def __init__(self, config) -> None:
         self.config = config
@@ -36,7 +35,7 @@ class BatchNimrod:
                 os.remove(in_file_full)
 
             logging.debug(f"Successfully processed: {in_file_full}")
-            return True
+            return out_file_name
 
         except Nimrod.HeaderReadError as e:
             logging.error(f"Failed to read file {in_file_full}, is it corrupt?")
@@ -59,7 +58,11 @@ class BatchNimrod:
         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('.')]
+        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...")
@@ -72,8 +75,16 @@ class BatchNimrod:
             }
 
             completed_count = 0
+            try:
                 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')
-
+                        logging.info(
+                            f"processed {completed_count} out of {total_files} files"
+                        )
+            except KeyboardInterrupt:
+                logging.warning(
+                    "KeyboardInterrupt received. Cancelling pending tasks..."
+                )
+                executor.shutdown(wait=False, cancel_futures=True)
+                raise

modules/combine_timeseries.py

@@ -1,37 +0,0 @@
-import polars as pd
-import os
-
-
-class CombineTimeseries:
-    def __init__(self, config, locations):
-        self.config = config
-        self.locations = locations
-        self.grouped_locations = {}
-        self.build_location_groups()
-
-    def build_location_groups(self):
-        for location in self.locations:
-            group = location[3]  # zone number
-            if group not in self.grouped_locations:
-                self.grouped_locations[group] = []
-            self.grouped_locations[group].append(location)
-
-    def combine_csv_files(self):
-        for group, loc_list in self.grouped_locations.items():
-            combined_df = None
-            for loc in loc_list:
-                csv_to_load = f"./csv_files/{loc[0]}_timeseries_data.csv"
-                df = pd.read_csv(csv_to_load)
-                if combined_df is None:
-                    combined_df = df
-                else:
-                    combined_df = combined_df.join(df, on='datetime')
-
-                if self.config.delete_csv_after_combining:
-                    os.remove(csv_to_load)
-
-            output_file = (
-                f"{self.config.COMBINED_FOLDER}/zone_{group}_timeseries_data.csv"
-            )
-            sorted_df = combined_df.sort('datetime')
-            sorted_df.write_csv(output_file)

modules/extract.py

@@ -0,0 +1,62 @@
+import tarfile
+import gzip
+import shutil
+import os
+from pathlib import Path
+
+
+class Extract:
+    # Directory containing .tar files
+    def __init__(self, Config):
+        self.config = Config
+
+    def _extract_tar(self):
+        for tar_file in os.listdir(self.config.TAR_TOP_FOLDER):
+            # only handle .tar files
+            if not tar_file.endswith(".tar"):
+                pass
+
+            tar_path = Path(self.config.TAR_TOP_FOLDER, tar_file)
+
+            # Create a folder for extracted tar contents
+            extract_folder = Path(
+                self.config.GZ_TOP_FOLDER, tar_file.replace(".tar", "")
+            )
+            Path(extract_folder).mkdir(exist_ok=True)
+
+            # Extract .tar file
+            with tarfile.open(tar_path, "r") as tar:
+                tar.extractall(path=extract_folder)
+
+            if self.config.delete_tar_after_processing:
+                os.remove(tar_path)
+
+    def _extract_gz(self):
+        for root, _, files in os.walk(self.config.GZ_TOP_FOLDER):
+            for file in files:
+                # only handle .gz files
+                if not file.endswith(".dat.gz"):
+                    pass  # adjust if extension differs
+                gz_path = Path(root, file)
+                dat_path = Path(self.config.DAT_TOP_FOLDER, file.replace(".gz", ""))
+
+                # Unzip .gz file
+                with gzip.open(gz_path, "rb") as f_in:
+                    with open(dat_path, "wb") as f_out:
+                        shutil.copyfileobj(f_in, f_out)
+
+                if self.config.delete_gz_after_processing:
+                    os.remove(gz_path)
+
+        try:
+            shutil.rmtree(self.config.GZ_TOP_FOLDER)
+            print("processing complete and GZ files deleted")
+        except Exception as e:
+            print(str(e))
+            print(
+                f"processing complete but GZ folder delete failed. Please delete manually ({self.config.GZ_TOP_FOLDER})"
+            )
+
+    def run_extraction(self):
+        self._extract_tar()
+        self._extract_gz()

modules/generate_timeseries.py

@@ -5,12 +5,13 @@ import polars as pd
 from datetime import datetime
 import os
 import concurrent.futures
-
+import logging
 
 
 class GenerateTimeseries:
-    def __init__(self, config):
+    def __init__(self, config, locations):
         self.config = config
+        self.locations = locations
 
     def _read_ascii_header(self, ascii_raster_file: str) -> list:
         """Reads header information from an ASCII DEM
@@ -63,7 +64,7 @@ class GenerateTimeseries:
 
         return int(start_col), int(start_row), int(end_col), int(end_row)
 
-    def _process_single_file(self, file_name, locations):
+    def process_asc_file(self, file_name, locations):
         """Process a single ASC file and extract data for all locations.
 
         Args:
@@ -75,7 +76,7 @@ class GenerateTimeseries:
                   or None if processing fails.
                   Format: [{'zone_id': id, 'date': datetime, 'value': float}, ...]
         """
-        if not file_name.endswith('.asc'):
+        if not file_name.endswith(".asc"):
             return None
 
         file_path = Path(self.config.ASC_TOP_FOLDER, file_name)
@@ -111,18 +112,13 @@ class GenerateTimeseries:
                     # 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
-                })
+                results.append({"zone_id": zone_id, "date": parsed_date, "value": val})
 
             if self.config.delete_asc_after_processing:
                 os.remove(file_path)
 
             return results
 
-
         except Exception as e:
             print(f"Error processing file {file_name}: {e}")
             return None
@@ -134,7 +130,7 @@ class GenerateTimeseries:
             locations (list): List of location data [zone_id, easting, northing, zone]
         """
         # Initialize data structure to hold results: {zone_id: {'dates': [], 'values': []}}
-        results = {loc[0]: {'dates': [], 'values': []} for loc in locations}
+        results = {loc[0]: {"dates": [], "values": []} for loc in locations}
 
         # Get list of ASC files
         asc_files = sorted(os.listdir(Path(self.config.ASC_TOP_FOLDER)))
@@ -143,40 +139,87 @@ class GenerateTimeseries:
 
         # 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 
+                executor.submit(self.process_asc_file, file_name, locations): file_name
                 for file_name in asc_files
             }
 
             completed_count = 0
+            try:
                 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'])
+                            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")
+            except KeyboardInterrupt:
+                print("KeyboardInterrupt received. Cancelling pending tasks...")
+                executor.shutdown(wait=False, cancel_futures=True)
+                raise
 
-        # Write CSVs for each location
-        print("Writing CSV files...")
-        for location in locations:
-            zone_id = location[0]
-            data = results[zone_id]
+    def write_results_to_csv(self, results, locations):
+        """Write extracted data to CSV files for each zone.
 
-            if not data['dates']:
-                print(f"No data found for {zone_id}")
-                continue
+        Args:
+            results (dict): Aggregated results {zone_id: {'dates': [], 'values': []}}
+            locations (list): List of location data [zone_id, easting, northing, zone]
+        """
+        # Group results by zone and collect all unique dates
+        zone_data = {}
+        for loc in locations:
+            zone_id = loc[0]
+            zone_name = loc[3]
 
-            df = pd.DataFrame({"datetime": data['dates'], zone_id: data['values']})
+            if zone_name not in zone_data:
+                zone_data[zone_name] = {"dates": [], "values": {}}
 
-            # Sort the dataframe into date order
+            zone_data[zone_name]["values"][zone_id] = results[zone_id]["values"]
+            zone_data[zone_name]["dates"].extend(results[zone_id]["dates"])
+
+        # Get unique sorted dates across all zones
+        for zone_name, data in zone_data.items():
+            data["dates"] = sorted(set(data["dates"]))
+
+        # Now write one CSV per zone with aligned timestamps
+        for zone_name, data in zone_data.items():
+            dates = data["dates"]
+            values_dict = data["values"]
+
+            # Create aligned DataFrame
+            df_dict = {"datetime": dates}
+            for grid_square, values in values_dict.items():
+                # Align values to the common dates
+                aligned_values = []
+                value_iter = iter(values)
+                date_iter = iter(dates)
+
+                current_date = next(date_iter, None)
+                current_value = next(value_iter, None)
+
+                for expected_date in dates:
+                    if current_date == expected_date:
+                        aligned_values.append(current_value)
+                        try:
+                            current_date = next(date_iter)
+                            current_value = next(value_iter)
+                        except StopIteration:
+                            current_date = None
+                            current_value = None
+                    else:
+                        aligned_values.append(None)  # Missing value
+
+                df_dict[grid_square] = aligned_values
+
+            df = pd.DataFrame(df_dict)
+
+            # Sort by datetime (already sorted)
             sorted_df = df.sort("datetime")
 
             # Format datetime column
@@ -184,9 +227,9 @@ class GenerateTimeseries:
                 pd.col("datetime").dt.strftime("%Y-%m-%d %H:%M:%S")
             )
 
-            output_path = Path(self.config.CSV_TOP_FOLDER) / f"{zone_id}_timeseries_data.csv"
-            sorted_df.write_csv(
-                output_path,
-                float_precision=4
+            output_path = (
+                Path(self.config.COMBINED_FOLDER) / f"{zone_name}_timeseries_data.csv"
             )
-        print("All CSV files written.")
+            sorted_df.write_csv(output_path, float_precision=4)
+
+        logging.info("All CSV files written.")

modules/nimrod.py

@@ -258,7 +258,7 @@ class Nimrod:
             # Read data as big-endian 16-bit integers
             # numpy.frombuffer is efficient for reading from bytes
             data_bytes = infile.read(array_size * 2)
-            self.data = np.frombuffer(data_bytes, dtype='>h').astype(np.int16)
+            self.data = np.frombuffer(data_bytes, dtype=">h").astype(np.int16)
 
             # Reshape to (nrows, ncols) for easier 2D manipulation
             # Note: NIMROD data is row-major (C-style), starting from top-left
@@ -392,7 +392,9 @@ class Nimrod:
         # Use numpy slicing to extract the sub-array
         # Note: y indices correspond to rows, x indices to columns
         # Slicing is [start:end], so we need +1 for the end index
-        self.data = self.data[yMinPixelId : yMaxPixelId + 1, xMinPixelId : xMaxPixelId + 1]
+        self.data = self.data[
+            yMinPixelId : yMaxPixelId + 1, xMinPixelId : xMaxPixelId + 1
+        ]
 
         # Update object where necessary
         self.x_right = self.x_left + xMaxPixelId * self.x_pixel_size
@@ -435,7 +437,7 @@ class Nimrod:
 
         # Write raster data to output file using numpy.savetxt
         # This is significantly faster than iterating in Python
-        np.savetxt(outfile, self.data, fmt='%d', delimiter=' ')
+        np.savetxt(outfile, self.data, fmt="%d", delimiter=" ")
         outfile.close()
 
 

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "met-office"
-version = "1.0.0"
+version = "1.2.0"
 description = "Convert .dat nimrod files to .asc files"
 readme = "README.md"
 requires-python = ">=3.14"

uv.lock

@@ -4,7 +4,7 @@ requires-python = ">=3.14"
 
 [[package]]
 name = "met-office"
-version = "1.0.0"
+version = "1.2.0"
 source = { virtual = "." }
 dependencies = [
     { name = "numpy" },