Metadata-Version: 2.1
Name: geowombat
Version: 2.2.0.1
Summary: Utilities for geospatial data
Author: Jordan Graesser
Maintainer: Jordan Graesser
License: MIT License
         
         Copyright (c) 2019-- GeoWombat
         
         Permission is hereby granted, free of charge, to any person obtaining a copy
         of this software and associated documentation files (the "Software"), to deal
         in the Software without restriction, including without limitation the rights
         to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
         copies of the Software, and to permit persons to whom the Software is
         furnished to do so, subject to the following conditions:
         
         The above copyright notice and this permission notice shall be included in all
         copies or substantial portions of the Software.
         
         THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
         IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
         FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
         AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
         LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
         OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
         SOFTWARE.
         
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: GIS
Classifier: Programming Language :: Cython
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Project-URL: Homepage, https://github.com/jgrss/geowombat
Project-URL: Documentation, https://geowombat.readthedocs.io
Project-URL: Repository, https://github.com/jgrss/geowombat
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Description-Content-Type: text/markdown

![](data/logo.png)

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[GeoWombat on Anaconda](https://anaconda.org/conda-forge/geowombat)

[![Anaconda-Server Badge](https://anaconda.org/conda-forge/geowombat/badges/version.svg)](https://anaconda.org/conda-forge/geowombat)
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[![Anaconda-Server Badge](https://anaconda.org/conda-forge/geowombat/badges/latest_release_date.svg)](https://anaconda.org/conda-forge/geowombat)

### GeoWombat: Utilities for geospatial data

Like a wombat, GeoWombat has a simple interface (for raster I/O) with a strong backend (for data processing at scale).

## Common Remote Sensing Uses
* Simple read/write for a variety of sensors, including:
    * Sentinel 2
    * Landsat 5-8
    * PlanetScope
    * Others
* Image mosaicking
* On-the-fly image transformations (reprojection)
* Point / polygon raster sampling, extraction
* Time series analysis
* Band math (NDVI, Tasseled cap, EVI etc)
* Image classification and regression
* Radiometry (BRDF normalization)
* Distributed processing

## Basic usage - Sentinel & Landsat

```python
>>> import geowombat as gw
```

Use a context manager and Xarray plotting to analyze processing chains

```python
>>> # Define satellite sensors (here, Landsat 7)
>>> with gw.config.update(sensor='l7'):
>>>
>>>     # Open images as Xarray DataArrays
>>>     with gw.open('LT05_L1TP_227083_20110123_20161011_01_T1.tif') as src:
>>>
>>>         # Apply calculations using Xarray and Dask
>>>         results = src.sel(band=['blue', 'green', 'red']).mean(dim='band')
>>>
>>>         # Check results by computing the task and plotting
>>>         results.gw.imshow()
```

Use a context manager to pass sensor information to geowombat methods

```python
>>> # Set the sensor as Sentinel 2
>>> with gw.config.update(sensor='s2'):
>>>
>>>     # Open a Sentinel 2 image
>>>     with gw.open('L1C_T20HPH_A002352_20151204T141125_MTD.tif') as src:
>>>
>>>         # Use built-in normalization methods, such as the NDVI
>>>         ndvi = src.gw.ndvi(scale_factor=0.0001)
>>>
>>>         # Check results by computing the task and plotting
>>>         ndvi.gw.imshow()
```

Computation scales easily over large datasets with minimal changes to the code.

```python
>>> # Set a reference image to align to
>>> with gw.config.update(ref_image='ref_image.tif'):
>>>
>>>     # Open images as Xarray DataArrays
>>>     with gw.open('image_a.tif') as srca, gw.open('image_b.tif') as srcb:
>>>
>>>         # The size of srca, srcb, and results are determined by the configuration context
>>>         results = srca.sel(band=1) * srcb.sel(band=[1, 2, 3]).mean(dim='band')
>>>
>>>         # Initiate computation by writing the results to file.
>>>         # Compute the task in parallel using dask.
>>>         results.gw.save(
>>>             'output.tif',
>>>             num_workers=4,
>>>             compress='lzw'
>>>         )
```


## Documentation

For more details, see [https://geowombat.readthedocs.io](https://geowombat.readthedocs.io).

## Installation

### Conda Install
To allow easy installation and build of all dependencies we recommend installing via conda-forge:

Installing geowombat from the conda-forge channel can be achieved by adding conda-forge to your channels with:

```commandline
conda config --add channels conda-forge
conda config --set channel_priority strict
```
Once the conda-forge channel has been enabled, geowombat can be installed with conda:

```commandline
conda install geowombat
```

or faster with mamba:

```commandline
mamba install geowombat
```

### Pip Install
GeoWombat is not on PyPi, but it can be installed with `pip`. We provide detailed instructions in our [documentation](https://geowombat.readthedocs.io/en/latest/install.html).

### Universal Install Via Docker
If you are having trouble installing geowombat, the surest way to get it up and running is with Docker containers.
See the `Dockerfile`, or for more details instructions, see the guide on [pygis.io](https://mmann1123.github.io/pyGIS/docs/b_conda_started.html).

## Learning

If you are new to geospatial programming in Python please refer to [pygis.io](https://pygis.io)
