mixmasta 0.2.5

A library for common scientific model transforms

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

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

Note: if you had a prior installation of GDAL you may need to run pip install mixmasta --no-cache-dir in a clean environment.

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. You may optionally specify a date if the geotiff has an associated date. For example:

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 and the audreyr/cookiecutter-pypackage project template.

History

0.1.0 (2021-02-24)

• First release on PyPI.