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docs: 📜 ReadMe Clarity tweaks

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Jake Pullen
2025-12-17 09:54:46 +00:00
parent 354f4c7fc6
commit 0e682aca35
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README.MD
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@@ -17,6 +17,7 @@ The project consists of a main pipeline workflow that processes multiple modules
- **Startup Safety Check**: Scans the `COMBINED_FOLDER` at startup and warns the user if existing files are found, Deleting existing files if continue is accepted. - **Startup Safety Check**: Scans the `COMBINED_FOLDER` at startup and warns the user if existing files are found, Deleting existing files if continue is accepted.
- **Batch Processing**: Processes input tar files in configurable batches to manage resource usage. - **Batch Processing**: Processes input tar files in configurable batches to manage resource usage.
- **Tidy by Default**: Default settings wil delete all mid step files and keep only the original Tar files. Can be changed in config.py
- **End-to-End Processing**: Extracts GZ files, processes DAT/ASC, and appends to CSV in a single thread per file. - **End-to-End Processing**: Extracts GZ files, processes DAT/ASC, and appends to CSV in a single thread per file.
- **Concurrency**: Uses multi-threading to process individual GZ files within a batch concurrently. - **Concurrency**: Uses multi-threading to process individual GZ files within a batch concurrently.
- **Cumulative Data**: Automatically appends new query results to the existing CSV files in `COMBINED_FOLDER` for each batch, ensuring no data is lost and columns are correctly aligned. - **Cumulative Data**: Automatically appends new query results to the existing CSV files in `COMBINED_FOLDER` for each batch, ensuring no data is lost and columns are correctly aligned.
@@ -31,7 +32,7 @@ The project consists of a main pipeline workflow that processes multiple modules
- Process multiple NIMROD dat files - Process multiple NIMROD dat files
- Automatically extract datetime from file data - Automatically extract datetime from file data
- Export clipped raster data to ASC format - Export raster data to ASC format
### generate_timeseries.py ### generate_timeseries.py