py4eos 0.2.0

Convenience functions for working with HDF4-EOS datasets

Python Tools for EOS-HDF4 Format

Unfortunately, many important NASA datasets are still distributed as EOS-HDF4 granules. This small library may help you to avoid needing to install GDAL just to read the file. It is capable of reading an an HDF4 file (and EOS-HDF4 files, in particular) and writing out a spatial dataset using rasterio, based on some strict assumptions about the file-level attributes; assumptions that are usually satisfied by an EOS-HDF4 file.

The problems solved by py4eos include:

• Reading an EOS-HDF4 file without needing GDAL installed
• Figuring out the coordinate reference system (CRS) and affine transformation of an EOS-HDF4 granule
• Converting an EOS-HDF4 file to a more convenient raster format

Example Use

import earthaccess
import py4eos

# Download a MOD16A3GF granule
result = earthaccess.search_data(
    short_name = 'MOD16A3GF',
    temporal = ('2014-01-01', '2014-12-31'),
    bounding_box = (-106, 42, -103, 43))
earthaccess.download(result, TEST_DIR)

# Write the file to a GeoTIFF
hdf = py4eos.read_hdf4eos(granule_mod16a3)
hdf.to_rasterio('ET_500m', 'output_file.tiff')

Installation

The easiest way to install py4eos is using mamba (see installation instructions) or conda. This is the recommended way to install py4eos on Windows or Mac OS X:

mamba install py4eos

If the HDF4, zlib, and libjpeg libraries are already installed, then you can use pip on any system to install py4eos:

pip install py4eos

Installing dependencies on GNU/Linux:

• On Ubuntu GNU/Linux: sudo apt install python3-dev libhdf4-dev

Running the test suite, from the root directory of the repository:

python -m pytest

Because data has to be downloaded as part of running the tests, they may fail the first time. Try running once more.