nisar-pytools 0.1.1

Open source Python tools for working with NISAR datasets

nisar_pytools

Open source Python tools for working with NISAR datasets.

About

nisar_pytools provides utilities for searching, downloading, reading, and processing data products from NASA's NISAR (NASA-ISRO Synthetic Aperture Radar) mission.

Supported Products

• GSLC - Geocoded Single Look Complex
• GUNW - Geocoded Unwrapped Interferogram

Additional NISAR product types will be added over time.

Getting Started

Prerequisites

• Python 3.10+
• Miniforge (recommended)
• NASA Earthdata login (for downloading from ASF)

Installation

From PyPI:

pip install nisar-pytools

With optional extras:

pip install nisar-pytools[dem]       # DEM fetching (dem_stitcher)
pip install nisar-pytools[viz]       # Visualization (matplotlib)
pip install nisar-pytools[all]       # Everything

From source (for development):

git clone https://github.com/zmhoppinen/nisar_pytools.git
cd nisar_pytools
mamba env create -f environment.yml
conda activate nisar_pytools

Usage

Search and Download

from nisar_pytools import find_nisar, download_urls

# Search ASF for GSLC products over an area of interest
urls = find_nisar(
    aoi=[-115, 43, -114, 44],
    start_date="2025-06-01",
    end_date="2025-12-01",
    product_type="GSLC",
    path_number=77,
    direction="ASCENDING",
)

# Download in parallel with automatic validation
fps = download_urls(urls, "local/gslcs/")

Read a Single File

from nisar_pytools import open_nisar

dt = open_nisar("NISAR_L2_PR_GSLC_...h5")

# Access a frequency/polarization group
freq_a = dt["science/LSAR/GSLC/grids/frequencyA"].dataset
hh = freq_a["HH"]  # lazy dask-backed DataArray with CRS set

# Coordinates and CRS are assigned automatically
print(hh.rio.crs)  # e.g. EPSG:32611

Stack GSLCs into a Time Series

from nisar_pytools import stack_gslcs

# Stack multiple same-track GSLCs into a (time, y, x) DataArray
stack = stack_gslcs(
    ["gslc_date1.h5", "gslc_date2.h5", "gslc_date3.h5"],
    frequency="frequencyA",
    polarization="HH",
)
# Sorted by time, grid-validated, dask-backed, CRS assigned

SAR Processing

from nisar_pytools.processing import (
    interferogram, coherence, multilook, unwrap, calculate_phase
)

# Interferogram (validates matching grids)
ifg = interferogram(slc1, slc2)

# Multilooked interferogram
ml_ifg = multilook(ifg, looks_y=4, looks_x=4)

# Coherence estimation
coh = coherence(slc1, slc2, window_size=11)

# Phase unwrapping with SNAPHU
unw, conncomp = unwrap(ifg, coh, nlooks=20.0)

Phase Linking

from nisar_pytools.processing import phase_link

# EMI phase linking on a GSLC stack
linked, temporal_coh = phase_link(stack, window_size=11, confidence=0.95)

Polarimetric Decomposition

from nisar_pytools.processing import h_a_alpha

# H-A-alpha decomposition from quad-pol SLC channels
ds = h_a_alpha(hh, hv, vv)
# Returns Dataset with: entropy, anisotropy, alpha, mean_alpha

Local Incidence Angle

from nisar_pytools.utils.local_incidence_angle import local_incidence_angle

lia = local_incidence_angle(dem, los_x, los_y, los_z, heights, x_rg, y_rg, epsg=32611)

Roadmap

• Lazy HDF5 reader returning xarray DataTree with CRS
• GSLC and GUNW support
• ASF search and parallel download with validation
• GSLC time-series stacking
• Interferogram, coherence, multilooking, phase extraction
• Phase unwrapping (SNAPHU)
• Phase linking (EMI with SHP selection)
• Polarimetric decomposition (H-A-alpha)
• Local incidence angle computation
• Visualization helpers (amplitude, phase, interferogram, coherence)
• Support for additional NISAR product types

Contributing

Contributions are welcome! Please fork the repo and open a pull request, or open an issue to suggest improvements.

License

Distributed under the MIT License. See LICENSE.txt for more information.