feat: ✨ Added reading zone info from csv

.gitignore

@@ -13,4 +13,6 @@ dat_files/*
 asc_files/*
 csv_files/*
 combined_files/*
+zone_inputs/*
+
 *.tar.gz

README.md

@@ -1,90 +1,86 @@
 # 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 format files and conversion to ESRI ASCII (.asc) format with optional bounding box clipping.
+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 with optional bounding box clipping.
 
 ## Overview
 
-The project consists of two main Python modules:
+The project consists of a main pipeline workflow that processes multiple modules in sequence:
-- `nimrod.py`: Core library for parsing NIMROD files, extracting metadata, and converting to ASCII format
+- `main.py`: Main pipeline orchestrator that calls on the modules as needed
-- `batch_nimrod.py`: Script for batch processing multiple NIMROD files with configurable bounding boxes
+- `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
 
 ## Features
 
-### nimrod.py
+### main.py
-- Parse NIMROD format files (v1.7 and v2.6-4)
+- Orchestrates the entire workflow pipeline
-- Extract header information and metadata
+- Processes DAT files to ASC format
-- Convert raster data to ESRI ASCII (.asc) format
+- Generates timeseries data for specified locations
-- Apply bounding box clipping to extract specific regions
+- Combines grouped CSV files into consolidated datasets
-- Support for command-line usage or import as module
 
 ### batch_nimrod.py
-- Process multiple NIMROD files in batches
+- Process multiple NIMROD dat files
-- Apply configurable bounding boxes per area
+- Automatically extract datetime from file data
-- Automatically extract datetime from filenames
 - 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
+
 ## Usage
 
-### Command Line (nimrod_3.py)
-```bash
-python nimrod.py [-h] [-q] [-x] [-bbox XMIN XMAX YMIN YMAX] [infile] [outfile]
-```
-
-Options:
-- `-h, --help`: Show help message
-- `-q, --query`: Display metadata
-- `-x, --extract`: Extract raster file in ASC format
-- `-bbox XMIN XMAX YMIN YMAX`: Bounding box to clip raster data to
-
-### Python Example Module Usage (nimrod.py)
-```python
-from nimrod import Nimrod
-# Open the .dat or nimrod compliant file
-a = Nimrod(open('filename.dat'))
-# Show the information about the file
-a.query()
-# output the .asc file
-a.extract_asc(open('output.asc', 'w'))
-# shrink the file down to a box area
-a.apply_bbox(279906, 285444, 283130, 290440)
-# show the shrunken down information about the file 
-a.query()
-# output the shrunken down .asc file
-a.extract_asc(open('clipped_output.asc', 'w'))
-```
-
-### Batch Processing (batch_nimrod.py)
 It is recommended to use UV for environment and package handling.
 [Link to uv install](https://docs.astral.sh/uv/getting-started/installation/)
 
+
+### Main Pipeline (main.py)
 ```bash
-uv sync 
+uv run main.py
-uv run batch_nimrod.py
 ```
 
+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
+
 ## Configuration
 
-The `config.yaml` file defines bounding box information for different areas. Default configuration includes:
+The `config.py` file defines folder paths:
-- BRISCS: (607000, 608000, 217000, 218000)
+- DAT_TOP_FOLDER: "./dat_files"
-- WINTSC: (499000, 500000, 416000, 417000)
+- ASC_TOP_FOLDER: "./asc_files"
+- CSV_TOP_FOLDER: "./csv_files"
+- COMBINED_FOLDER: "./combined_files"
+
+The `main.py` script defines locations and their properties:
+- Location name (e.g., "BRICSC")
+- Location ID (e.g., "TM0816")
+- X coordinate (e.g., 608500)
+- Y coordinate (e.g., 216500)
+- Output group (e.g., 1)
 
 ## Directory Structure
 
-Inside the dat_files folder, each site short code should be its own folder with the .dat files inside of them.  
-The site short code folder name should match EXACTLY to the config site short code  
-Each dat file should have a datetime in its name in the format of yyyymmddhhmm (e.g: 202405260905 )  
-
 ```
 dat_files/
-├── BRISCS/
+└──*.dat files
-│   └── *.dat files
+
-└── WINTSC/
-    └── *.dat files
 asc_files/
-├── BRISCS/
+└──*.dat files
-│   └── YYYYMMDDHHMM_BRISCS.asc files
+
-└── WINTSC/
+csv_files/
-    └── YYYYMMDDHHMM_WINTSC.asc files
+├── TQ1234_timeseries_data.csv
+├── ...
+└── TQ5678_timeseries_data.csv
+combined_files/
+├── zone_1_timeseries_data.csv
+├── ...
+└── zone_50_timeseries_data.csv
 ```
 
 ## Requirements
@@ -92,16 +88,14 @@ asc_files/
 - Python 3.12+
 - [UV Installed](https://docs.astral.sh/uv/getting-started/installation/)
 
-## License
+## Acknowledgments 
 
-Copyright (c) 2015 [Richard Thomas](https://github.com/richard-thomas/MetOffice_NIMROD)
+[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)
 
-This program is free software: you can redistribute it and/or modify it under the terms of the Artistic License 2.0 as published by the Open Source Initiative.
+## Version update 2025
-
-This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
-
-## Version update 2025-10-31 👻
 
 Update by Jake Pullen, for the use of Anglian Water.
 Added the batch_nimrod module to convert large amounts of files
-Cleaned up the original code and added docstrings & typehints
+Cleaned up the original codes and added docstrings & typehints
+Added main pipeline workflow that calls on the modules as needed to take the dat files and create grouped timeseries data CSVs

config.py

@@ -3,4 +3,6 @@ class Config:
     ASC_TOP_FOLDER = "./asc_files"
     CSV_TOP_FOLDER = "./csv_files"
     COMBINED_FOLDER = "./combined_files"
+    ZONE_FOLDER = "./zone_inputs"
     AREAS_FILE = "areas.csv"
+    delete_dat_after_processing = False

main.py

@@ -1,6 +1,7 @@
 import logging
 import time
 import os
+import csv
 from pathlib import Path
 
 from config import Config
@@ -14,14 +15,25 @@ 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)
-    dat_file_count = [f for f in os.listdir(Path(Config.DAT_TOP_FOLDER))]
-    asc_file_count = [f for f in os.listdir(Path(Config.ASC_TOP_FOLDER))]
 
-    locations = [
+    locations = []
-        # loc name, loc id, x loc,   y loc, output group
+    #load zone inputs here
-        ["BRICSC", "TM0816", 608500, 216500, 1],
+    for file in os.listdir(Path(Config.ZONE_FOLDER)):
-        ["HEACSC", "TF6842", 568500, 342500, 1],
+        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])
+
+    # testing locations, can be removed.
+    locations.append(["TM0816", 608500, 216500, 1])
+    locations.append(["TF6842", 568500, 342500, 1])
 
     batch = BatchNimrod(Config)
     timeseries = GenerateTimeseries(Config)
@@ -29,24 +41,21 @@ if __name__ == "__main__":
 
     start = time.time()
     logging.info("Starting to process DAT to ASC")
-    if len(dat_file_count) != len(asc_file_count):
+
-        batch.process_nimrod_files()
+    batch.process_nimrod_files()
-        batch_checkpoint = time.time()
+    batch_checkpoint = time.time()
-        elapsed_time = batch_checkpoint - start
+    elapsed_time = batch_checkpoint - start
-        logging.info(f"DAT to ASC completed in {elapsed_time:.2f} seconds")
+    logging.info(f"DAT to ASC completed in {elapsed_time:.2f} seconds")
-    else:
-        logging.info("No need to process DAT files, skipping...")
-        batch_checkpoint = time.time()
-        time.sleep(1)
 
     for place in locations:
         logging.info(f"{place[0]} started generating timeseries data.")
+        place_start = time.time()
         timeseries.extract_cropped_rain_data(place)
-        place_checkpoint = time.time()
+        place_end = time.time()
-        since_asc_create = place_checkpoint - batch_checkpoint
+        place_create_time = place_end - place_start
-        elapsed_time = place_checkpoint - start
+        elapsed_time = place_end - start
-        logging.info(f"{place[0]} completed in {since_asc_create:.2f} seconds")
+        logging.info(f"{place[0]} completed in {place_create_time:.2f} seconds")
-        logging.info(f"total time so far {elapsed_time:.2f} seconds")
+        logging.info(f"Total time so far {elapsed_time:.2f} seconds")
 
     logging.info("combining CSVs into groups")
     combiner.combine_csv_files()

modules/batch_nimrod.py

@@ -17,10 +17,10 @@ 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))]
+        files_to_process = len([f for f in os.listdir(Path(self.config.DAT_TOP_FOLDER))])
-
-        logging.info(f"Processing {len(files_to_process)} files...")
 
+        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)
 
@@ -33,9 +33,13 @@ class BatchNimrod:
                 with open(out_file_path, "w") as outfile:
                     image.extract_asc(outfile)
 
-                # delete dat file here
+                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')
 
             except Nimrod.HeaderReadError as e:
                 logging.error(f"Failed to read file {in_file_full}, is it corrupt?")

modules/combine_timeseries.py

@@ -1,4 +1,4 @@
-import pandas as pd
+import polars as pd
 
 
 class CombineTimeseries:
@@ -10,7 +10,7 @@ class CombineTimeseries:
 
     def build_location_groups(self):
         for location in self.locations:
-            group = location[4]  # output group is at index 4
+            group = location[3]  # zone number
             if group not in self.grouped_locations:
                 self.grouped_locations[group] = []
             self.grouped_locations[group].append(location)
@@ -20,12 +20,12 @@ class CombineTimeseries:
             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, index_col=0)
+                df = pd.read_csv(csv_to_load)
                 if combined_df is None:
                     combined_df = df
                 else:
-                    combined_df = combined_df.join(df, how="inner")
+                    combined_df = combined_df.join(df, on='datetime')
             output_file = (
-                f"{self.config.COMBINED_FOLDER}/group_{group}_timeseries_data.csv"
+                f"{self.config.COMBINED_FOLDER}/zone_{group}_timeseries_data.csv"
             )
-            combined_df.to_csv(output_file)
+            combined_df.write_csv(output_file)

modules/generate_timeseries.py

@@ -1,7 +1,7 @@
 from __future__ import division, print_function
 import numpy as np
 from pathlib import Path
-import pandas as pd
+import polars as pd
 from datetime import datetime
 import os
 
@@ -36,8 +36,8 @@ class GenerateTimeseries:
         y0_radar = radar_header[3]
         x0_radar = radar_header[2]
 
-        y0_basin = basin_header[3]
+        y0_basin = basin_header[2]
-        x0_basin = basin_header[2]
+        x0_basin = basin_header[1]
 
         nrows_radar = radar_header[1]
 
@@ -96,15 +96,17 @@ class GenerateTimeseries:
             datetime_list.append(parsed_date)
 
         # Create DataFrame with datetime index
-        df = pd.DataFrame({"rainfall": rainfile}, index=datetime_list)
+        df = pd.DataFrame({"datetime": datetime_list, location[0]: rainfile})
 
         # Sort the dataframe into date order
-        sorted_df = df.sort_index()
+        sorted_df = df.sort("datetime")
 
-        sorted_df.to_csv(
+        # Set datetime as index
+        sorted_df = sorted_df.with_columns(
+            pd.Series(datetime_list).alias("datetime")
+        ).set_sorted("datetime")
+
+        sorted_df.write_csv(
             f"csv_files/{location[0]}_timeseries_data.csv",
-            sep=",",
+            float_precision=4
-            float_format="%1.4f",
-            header=[location[1]],
-            index_label="datetime",
         )

pyproject.toml

@@ -7,6 +7,7 @@ requires-python = ">=3.12"
 dependencies = [
     "numpy>=2.3.4",
     "pandas>=2.3.3",
+    "polars>=1.35.2",
     "pyyaml>=6.0.3",
     "ruff>=0.14.3",
 ]

uv.lock

@@ -9,6 +9,7 @@ source = { virtual = "." }
 dependencies = [
     { name = "numpy" },
     { name = "pandas" },
+    { name = "polars" },
     { name = "pyyaml" },
     { name = "ruff" },
 ]
@@ -17,6 +18,7 @@ dependencies = [
 requires-dist = [
     { name = "numpy", specifier = ">=2.3.4" },
     { name = "pandas", specifier = ">=2.3.3" },
+    { name = "polars", specifier = ">=1.35.2" },
     { name = "pyyaml", specifier = ">=6.0.3" },
     { name = "ruff", specifier = ">=0.14.3" },
 ]
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 ]
 
+[[package]]
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+version = "1.35.2"
+source = { registry = "https://pypi.org/simple" }
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