Coastal-FIM

A Python package for implementing barycentric interpolation using DuckDB, specifically designed to compute SCHISM-derived depths on 30m grids. This tool provides an efficient, scalable solution for geospatial computations in large coastal domains.

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Table of Contents

• Features
• Installation
• Data
• Usage
• Preprocessing Workflow
• Testing
• Current Progress
• Report

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Features

1. Efficient Barycentric Interpolation: Leverages DuckDB for handling large-scale geospatial data efficiently.
2. Leverages Preprocessed Data: Preprocessed data from DEM, zonal coverage fraction, barycentric weight, and geosptial masks allows fast and effective interpolations.
3. Efficient Storage: Data is converted and stored in DuckDB that significantly reduces storage volume.
4. Customizable Pipelines: Modular structure allows easy adaptation to different datasets and use cases.

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Installation

This software requires a working anaconda/miniconda installation, please visit miniconda page

Steps

1. Clone the repository:

   cd your_custom_path
   git clone https://github.com/owp-spatial/zonal-fim.git
   cd zonal-fim

2. Then the package can be installed via executing

   ./setup.sh

3. In the case there are permission issues execute

   chmod u+x setup.sh

Environment

To activate pre-configured environment execute

conda activate coastal_fim_vis

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Data

Preprocessed sample data for Atlantic Gulf domain is:

...

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Usage

The script zonal_fim.py performs execution of pipeline for generating barycentric interpolation, generating masks, and preprocessing pipeline

1. Run the Barycentric Computation: General pipeline for executing barycentric interpolation is given that a preprocessing has been done once, we can pass a new .gr3 or .nc file, the path to schisim_database.duckdb, and specify the output path.

   -i '/path/agGridfile.gr3' or -i '/path/nwm.t05z.analysis_assim_coastal.total_water.tm02.atlgulf.nc'

   Ensure --execute True.

   There is an option --zarr_format to produce the outputs as zarr instead of a .tif file and one does not need to change .tif to .zarr in depth and wse inputs this conversion will be done automatically.

   if --generate_wse False there is no need to specify a path got -q

   python zonal_fim.py --generate_mask False --preprocess False --generate_wse True --generate_depth True --zarr_format False  --execute True  --dissolve False -i '/path/nwm.nc' -c '/path/zonal_database.duckdb' -m '/path/depth_raser_v1.tif' -q '/path/wse_raser_v1.tif'

   A sample output of depth raster:

   

2. Output:

   • Barycentric interpolation is saved as depth table in the DuckDB database.
   • Can write WSE interpolation and depth values as .tif and .zarr file if specified.

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Preprocessing Workflow

• visit for preprocessing instructions

Testing

Test Cases

1. Tampa Region:
   • Executed entire process: Pass
2. Atlantic and Gulf Domain:
   • Executed all steps except coverage fraction interpolation: Pass
3. Comparison with Linear Interpolation:
   • Validated results against linear interpolation: Pass

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Current Progress

1. Implemented:
   • Barycentric computations.
   • Batch processing for DEM and zonal data.
2. Next Steps:
   • Function descriptions
   • Write comprehensive tests for the package.

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Report

Detailed documentation and implementation notes are available in the report: Report Link

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