Metadata-Version: 2.1
Name: mixmasta
Version: 0.2.4
Summary: A library for common scientific model transforms
Home-page: https://github.com/jataware/mixmasta
Author: Brandon Rose
Author-email: brandon@jataware.com
License: MIT license
Description: # mixmasta
        
        A library for common scientific model transforms. This library enables fast and intuitive transforms including:
        
        * Converting a `geotiff` to a `csv`
        * Converting a `NetCDF` to a `csv`
        * Geocoding `csv` data that contains latitude and longitude
        
        
        ## Setup
        
        Ensure you have a working installation of [GDAL](https://trac.osgeo.org/gdal/wiki/FAQInstallationAndBuilding#FAQ-InstallationandBuilding)
        
        You also need to ensure that `numpy` is installed prior to `mixmasta` installation. This is an artifact of GDAL, which will build incorrectly if `numpy` is not already configured:
        
        ```
        pip install numpy==1.20.1
        pip install mixmasta
        ```
        
        You must install the GADM2 data with:
        
        ```
        mixmasta download
        ```
        
        ## Usage
        
        
        Examples can be found in the `examples` directory.
        
        Convert a geotiff to a dataframe with:
        
        ```
        from mixmasta import mixmasta as mix
        df = mix.raster2df('chirps-v2.0.2021.01.3.tif', feature_name='rainfall', band=1)
        ```
        
        Note that you should specify the data band of the geotiff to process if it is multi-band. You may also specify the name of the feature column to produce.
        
        Convert a NetCDF to a dataframe with:
        
        ```
        from mixmasta import mixmasta as mix
        df = mix.netcdf2df('tos_O1_2001-2002.nc')
        ```
        
        Geocode a dataframe:
        
        ```
        from mixmasta import mixmasta as mix
        
        # First, load in the geotiff as a dataframe
        df = mix.raster2df('chirps-v2.0.2021.01.3.tif', feature_name='rainfall', band=1)
        
        # next, we can geocode the dataframe by specifying the names of the x and y columns
        # in this case, they are 'longitude' and 'latitude'
        df_g = mix.geocode(df, x='longitude', y='latitude')
        ```
        
        ## Credits
        
        This package was created with [Cookiecutter](https://github.com/audreyr/cookiecutter) and the [audreyr/cookiecutter-pypackage](https://github.com/audreyr/cookiecutter-pypackage) project template.
        
        
        # History
        
        ## 0.1.0 (2021-02-24)
        
        -   First release on PyPI.
        
        
Keywords: mixmasta
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.5
Description-Content-Type: text/markdown
