medaprep 0.1.0

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porcupine #########

High-dimensional data can be prickly, use porcupine to prepare xarray Datasets for downstream tasks.

Usage

porcupine.skim.features

.. code-block:: python

>>> import numpy as np
>>> import pandas as pd
>>> import xarray as xr
>>> from porcupine import skim
>>> temp = 15 + 8 * np.random.randn(2, 2, 3)
>>> precip = 10 * np.random.rand(2, 2, 3)
>>> lon = [[-99.83, -99.32], [-99.79, -99.23]]
>>> lat = [[42.25, 42.21], [42.63, 42.59]]
>>> ds = xr.Dataset(
  {
      "temperature": (["x", "y", "time"], temp),
      "precipitation": (["x", "y", "time"], precip),
      },
  coords={
      "lon": (["x", "y"], lon),
      "lat": (["x", "y"], lat),
      "time": pd.date_range("2014-09-06", periods=3),
      "reference_time": pd.Timestamp("2014-09-05"),
      },
                 )
 >>> df = skim.features(ds)
 >>> df
     variables       data_types  NaNs    mean    std     maximums    minimums
 0   temperature     float64     False   14.3177 9.08339 30.3361     -7.76803
 1   precipitation   float64     False   4.62568 3.03081 9.89768     0.147005

For more details see Documentation_ and Example Notebooks_.

Installation ############

Using pip

.. code-block:: bash

pip install porcupine

Using Conda

.. code-block:: bash

conda install -c conda-forge porcupine

Developing ##########

pre-commit setup

This project uses pre-commit, isort, black, and flake8 to help enforce best practices. These libraries are all included in requirements-dev.txt and can be installed with pip by running:

.. code-block:: bash

pip install -r requirements-dev.txt

Once pre-commit is installed, install the hooks specified by the config file into .git:

.. code-block:: bash

pre-commit install

You can then test pre-commit by running:

.. code-block:: bash

pre-commit