Skip to main content

Lazy raster band processing

Project description


Small Python library for processing large raster images.

Currently a work in progress.

Example usage

source = AWSLandsat8Source("LC08_L1TP_139045_20170304_20170316_01_T1", band="8")

# Lazily create a band and build a description of processing steps
band = Band(source) \
    .and_then(Resize(extents)) \
    .and_then(HistogramAdjust()) \

# Forces computation
array = band.render_to_array()



Installing GDAL is the most challenging part. Installing directly from PyPI is historically unlikely to work.

First, install numpy: pip install numpy

Then, choose one of the following:

  • install with a system package manager
    • Ubuntu: sudo apt install libgdal-dev
    • MacOS: brew install gdal
  • install from conda-forge: conda install -c conda-forge gdal
  • compile manually

Afterwards, the correct Python bindings can be installed with

pip install GDAL==$(gdal-config --version) --global-option=build_ext --global-option="-I/usr/include/gdal" 

Finally, pip install picoraster

Running tests

python -m src.tests

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

picoraster-0.0.2.tar.gz (5.0 kB view hashes)

Uploaded source

Built Distribution

picoraster-0.0.2-py3-none-any.whl (7.7 kB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page