Lazy raster band processing
Project description
picoraster
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()) \
.and_then(Reproject(crs))
# Forces computation
array = band.render_to_array()
band.render_to_file("output.tif")
Installation
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
- Ubuntu:
- 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
Release history Release notifications | RSS feed
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)
Built Distribution
Close
Hashes for picoraster-0.0.2-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 3eac432fc2f1f46fa3d9b1485cb24e14cfed664d254f6f213c599b2768a20e9b |
|
MD5 | 7b5842ffb780c3c97551c65ebe67f55c |
|
BLAKE2b-256 | 9fac5a2745abe54bf50791b1425fdc7cce3402acda220b01352dac60e1c35971 |