Rastereasy Library
==================

Overview
---------

The rastereasy library provides functions for the easy manipulation (resampling, cropping, reprojection, tiling, ...)
and visualization (color composites, spectra) of geospatial images (`*.tif`, `*.jp2`, `*.shp`, ...)..  It simplifies geospatial workflows with efficiency.
A geospatial image is read and represented within the :class:`~rastereasy.Geoimage` class, which contains most of the
required functions for processing and visualization. Other classes are related to :class:`~rastereasy.InferenceTools` (some
functions related to clustering, domain adaptation, fusion) :class:`~rastereasy.shpfiles` or :class:`~rastereasy.rasters` (to deal with shapefiles
and rasters respectively)

The goal of `rastereasy` is to simplify geospatial workflows by offering tools for:

- Reading and processing raster and vector files.

- Resampling, cropping, reprojecting, stacking, ... raster images.

- Creating visualizations such as color composites or spectral analyses.

- Use (train / apply) some classical Machine Learning algorithms.

- Provide some tools for late fusion of classifications (Dempster-Shafer).

- Provide some tools for some ML algorithms, basic domain adaptation, ...

- ...


.. note::
   This module requires external dependencies such as `rasterio`, `numpy`, and `matplotlib`.




.. contents:: Table of Contents
   :depth: 2
   :local:






 Simple start example
 --------------------

 Here is a quick overview of what you can do with rastereasy:

 .. code-block:: python

    import rastereasy
    # Load a georeferenced image
    image = rastereasy.Geoimage("example.tif")

    # Get image information
    image.info()

    # Create a color composite
    image.colorcomp(['4', '3', '2'])

    # Resample and reproject
    image_resampling = image.resampling(2)
    image_reprojected = image.reproject("EPSG:4326")

    # Save the processed image
    image.save("processed_image.tif")


Using and citing the toolbox
-----------------------------

If you use this toolbox in your research, please cite it as:

Corpetti, T., Matelot, P., de la Brosse, A., & Lissak, C. (2025). *Rastereasy: A Python package for easy manipulation of remote sensing images*. Manuscript submitted for publication, Journal of Open Source Software.




Some interesting functions
---------------------------

Below are some of the primary functions provided by the module:


Resampling Function
~~~~~~~~~~~~~~~~~~~

.. autofunction:: rastereasy.Geoimage.resampling

Projection Function
~~~~~~~~~~~~~~~~~~~

.. autofunction:: rastereasy.Geoimage.reproject

Cropping Function
~~~~~~~~~~~~~~~~~

.. autofunction:: rastereasy.Geoimage.crop

Machine Learning
~~~~~~~~~~~~~~~~

.. autofunction:: rastereasy.Geoimage.kmeans
.. autofunction:: rastereasy.Geoimage.apply_ML_model

Band manipulation
~~~~~~~~~~~~~~~~~

.. autofunction:: rastereasy.Geoimage.stack
.. autofunction:: rastereasy.Geoimage.reorder_bands
.. autofunction:: rastereasy.Geoimage.remove_bands

Access to numpy
~~~~~~~~~~~~~~~

.. autofunction:: rastereasy.Geoimage.numpy_channel_first
.. autofunction:: rastereasy.Geoimage.numpy_channel_last
.. autofunction:: rastereasy.Geoimage.image_from_table


Processing on images
~~~~~~~~~~~~~~~~~~~~

.. autofunction:: rastereasy.Geoimage.adapt
.. autofunction:: rastereasy.Geoimage.fuse_dempster_shafer_2hypotheses
.. autofunction:: rastereasy.Geoimage.image_from_table



Additional Notes
----------------

Refer to the examples in the :doc:`examples gallery <my_html_gallery>` section for practical applications of the library.