diff --git a/README.MD b/README.MD
index 910aa6c..164081f 100644
--- a/README.MD
+++ b/README.MD
@@ -5,6 +5,7 @@ This project provides tools for processing UK Met Office Rain Radar NIMROD image
 ## 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
@@ -13,22 +14,26 @@ The project consists of a main pipeline workflow that processes multiple modules
 ## Features
 
 ### main.py
+
 - Orchestrates the entire workflow pipeline
 - Processes DAT files to ASC format
 - Generates timeseries data for specified locations
 - Combines grouped CSV files into consolidated datasets
 
 ### 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
@@ -49,29 +54,32 @@ It is recommended to use UV for environment and package handling.
 1. RunMain Pipeline `uv run main.py
 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. Generate timeseries data for specified locations
+1. Combine grouped CSV files 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"
 
 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)
diff --git a/main.py b/main.py
index 617122c..1b0e509 100644
--- a/main.py
+++ b/main.py
@@ -24,11 +24,11 @@ if __name__ == "__main__":
             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
+                # Extract the relevant fields: 1K Grid, Easting, Northing, Zone
+                zone_id = row[0]  # Ossheet column
+                easting = int(row[1])  # Easting column
+                northing = int(row[2])  # Northing column
+                zone = int(row[3])  # ZoneID column
                 locations.append([zone_id, easting, northing, zone])
 
     batch = BatchNimrod(Config)