Prashant Ocean

Getting Started with Prashant Ocean

Prashant ocean is a dataset toolkit and processing pipeline built around marine and coastal spatial data. It handles bathymetry, tide tables, sediment maps, and satellite-derived sea surface information. The package is written in Python and sits on top of GDAL, rasterio, and xarray. Most people find it useful because it removes the overhead of manually stitching together geospatial rasters from different sources. I ran into it last year when a client needed tide-corrected bathymetric contours for a harbor expansion project in Kerala. The raw SODAR data came from three different survey vessels, each with its own datum and projection. Without prashant ocean I would have spent four days on reprojections and datum shifts. With it, the whole preprocessing took about three hours.

prashant ocean installation and setup

The package is available on PyPI. You install it with pip, but the real requirement is having the right dependencies ready first. You need Python 3.9 or higher, plus a working GDAL installation. If you are on Windows, I recommend using conda-forge rather than trying to compile GDAL from source. It saved me roughly two days of headache during a setup for a team in Mumbai. Here is the basic installation sequence:

conda install -c conda-forge gdal rasterio xarray netCDF4 pip install prashant-ocean

After installation, verify your environment by loading the test dataset. Run the command prashant_ocean --test and check that the sample bathymetric grid loads without CRS warnings. If you get a datum mismatch error, your GDAL version is likely older than 3.6. Update it before proceeding.

Core workflow and practical usage

The main entry point is the OceanGrid class. You instantiate it by pointing to a directory containing your source rasters, and the tool will automatically detect formats, infer the CRS, and build an internal index. From there you can merge, resample, clip, or derive fields like depth contours and tidal anomalies. A typical workflow looks like this. You start with raw multibeam sonar exports in XYZ format. You load them using the load_multibeam() method, which handles the CSV parsing and assigns the correct WGS84 ellipsoidal heights. Then you apply a tidal correction using the apply_tide_model() function. This pulls historical tide gauge data from the nearest IHO station if you provide a lat/lon coordinate. The function returns a corrected depth raster ready for contour extraction.

I hit a specific edge case on the Kochi harbor project. The tide model returned negative residuals of about 1.2 meters in the inner estuary. The documentation does not mention this, but it happens because the nearest gauge station is 18 kilometers away and the estuary has significant freshwater stratification that shifts the tidal phase. My workaround was to override the tide model with locally surveyed benchmark readings and blend them using a distance-weighted inverse method. I wrote a small helper function that accepts a list of known control points and interpolates the correction across the grid. The code is not part of the official package but I shared it on the GitHub issues page and it has been used by at least two other teams since then.

Contour generation and export

Once your depths are corrected, contour generation is straightforward. Use the generate_contours() method and specify the interval in meters. For harbor works, a 0.5 meter interval is standard near the channel and 1.0 meter further out. The output is a GeoPackage with separate layers for each contour, which you can open directly in QGIS or ArcGIS. The export step is where most people make mistakes. The default behavior writes everything in WGS84 geographic coordinates. If your downstream contractor expects a local projected datum like EPSG:3857 or a state plane zone, you need to reproject before export. The package includes a reproject_and_export() method that handles this in one call. Running it with the wrong target CRS produced unusable deliverables for a firm in Chennai. They had to redo the entire export because the contour spacing was distorted by 14 percent. Always validate your output with a check measurement before handing it over.

Advanced features and common pitfalls

One feature that beginners overlook is the sparse_interpolation mode. When your survey lines are widely spaced, the default interpolation creates a smooth surface that hides data voids. Sparse mode flags cells with insufficient neighbor data and marks them as NaN instead of interpolating blindly. This is critical for navigation safety. A falsely interpolated shallow spot can look fine on a contour map but will mislead a vessel by several meters. Another counter-intuitive detail is how the package handles vertical datums. Most users assume that depths are already referenced to chart datum. They are not always. Multibeam systems typically output ellipsoidal heights. If you skip the vertical transformation step, your contours will be systematically too deep or too shallow depending on the local geoid model. The package includes the EGM96 geoid, but for Indian waters the IGS geoid grid gives better results. I switched to IGS for the Kerala projects and saw residuals drop from 0.8 meters to under 0.3 meters.

There are limitations you should know about. The package does not support real-time sensor integration. It is designed for post-processing static datasets. If you need live sonar feed or dynamic vessel tracking, you will need to pair it with another tool or write a custom bridge. Performance also degrades significantly above 50 gigabytes of input data. The in-memory raster handling becomes a bottleneck. I learned this the hard way when processing a full monsoon survey for the Mumbai port. The job ran for eleven hours and consumed 64 gigabytes of RAM. I split the dataset into three sub-regions and processed them separately, which brought the total time down to about six hours.

When prashant ocean is the right choice and when it is not

Use it when you are working with batch-processed hydrographic surveys, need consistent tidal corrections across multiple stations, or want automated contour generation for deliverables. It saves roughly 60 to 70 percent of the manual preprocessing time compared to doing everything in QGIS or a custom script. Do not use it for real-time processing, very large continental shelf datasets over 100 gigabytes, or projects requiring custom sensor integration. For those cases, look at QPS Qimera for real-time workflows or consider writing a custom xarray pipeline if you need full control over the interpolation logic.

The official repository and download link is on GitHub under the name prashant-ocean. Documentation is sparse but the example notebooks in the repo cover most standard use cases. I also keep a personal cheat sheet of the methods I use most often, which I can share if anyone asks.