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
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
Requires-Dist: pip (==19.2.3)
Requires-Dist: bump2version (==1.0.1)
Requires-Dist: wheel (==0.33.6)
Requires-Dist: watchdog (==0.9.0)
Requires-Dist: flake8 (==3.7.8)
Requires-Dist: tox (==3.14.0)
Requires-Dist: coverage (==4.5.4)
Requires-Dist: Sphinx (==1.8.5)
Requires-Dist: twine (==1.14.0)
Requires-Dist: Click (>=7.0)
Requires-Dist: pyproj (==2.6.1.post1)
Requires-Dist: numpy (>=1.20.1)
Requires-Dist: Shapely (==1.7.1)
Requires-Dist: geopandas (==0.8.1)
Requires-Dist: Rtree (==0.8.3)
Requires-Dist: GDAL (==3.2.1)
Requires-Dist: netCDF4 (==1.5.3)
Requires-Dist: rasterio (>=1.1.0)
Requires-Dist: xarray (==0.16.1)
Requires-Dist: tqdm (<5.0.0,>=4.41.1)

# 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.



