.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/ver_deterministic.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_auto_examples_ver_deterministic.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_ver_deterministic.py:


Binary Forecast Verification
============================

This example demonstrates how to perform binary forecast verification.

If you are not familiar with the terms, you may wish to refer to the following
resources:
    
* `Verification of categorical predictands by Anna Ghelli of ECMWF <https://www.swpc.noaa.gov/sites/default/files/images/u30/Verification%20of%20Categorical%20Predictands.pdf>`_
* `Verification Measures by Weather Forecasting ... On-Line <https://www.wxonline.info/topics/verif2.html>`_

.. GENERATED FROM PYTHON SOURCE LINES 15-18

Definitions
--------------------------------------------------------


.. GENERATED FROM PYTHON SOURCE LINES 20-21

Import all required modules and methods:

.. GENERATED FROM PYTHON SOURCE LINES 21-95

.. code-block:: default


    # Python package to manage warning message
    import warnings
    # Python package for time calculations
    import pandas as pd
    # Python package for numerical calculations
    import numpy as np
    # Python package for xarrays to read and handle netcdf data
    import xarray as xr
    # swirlspy traditional binary verification package
    from swirlspy.ver.crosstab import contingency
    # swirlspy verification metric package
    import swirlspy.ver.metric as mt

    warnings.filterwarnings("ignore")

    # Generating sample forecast and observed data
    # Dimensions: x, y, time (3 x 3 x 4)
    forecast_data = [
        [[7, 4, 6, 5], [4, 7, 9, 2], [3, 1, 6, 5]],
        [[4, 6, 8, 1], [11, 0, 3, 6], [0, 3, 6, 8]],
        [[5, 3, 7, 5], [7, 7, 6, 2], [3, 8, 9, 2]]
    ]

    observed_data = [
        [[8, 7, 11, 4], [6, 7, 6, 0], [3, 3, 7, 3]],
        [[8, 8, 9, 5], [12, 1, 2, 8], [1, 4, 3, 12]],
        [[5, 6, 6, 2], [3, 6, 4, 7], [5, 7, 8, 2]]
    ]

    # Creating xarrays
    x = np.arange(3)
    y = np.arange(3)
    time = pd.date_range('1/1/2011', periods=4, freq='D')
    forecast = xr.DataArray(
        forecast_data,
        coords=[('x', x), ('y', y), ('time', time)])
    observed = xr.DataArray(
        observed_data,
        coords=[('x', x), ('y', y), ('time', time)])

    # Applying the contingency function
    cont = contingency(5, forecast, observed)

    # Generating skill metrics
    pod = mt.pod(cont)
    far = mt.far(cont)
    csi = mt.csi(cont)
    freq_bias = mt.freq_bias(cont)
    accuracy = mt.accuracy(cont)
    pofd = mt.pofd(cont)
    hss = mt.hss(cont)
    ets = mt.ets(cont)
    f1 = mt.f1Score(forecast, observed, average='weighted')

    # Displaying results as pandas series
    d = {
        'Hits': cont[0],
        'Misses': cont[1],
        'False Alarm': cont[2],
        'Correct Negative': cont[3],
        'Probability of Detection': pod,
        'False Alarm Ratio': far,
        'Critical Success Index': csi,
        'Frequency Bias': freq_bias,
        'Accuracy': accuracy,
        'Probability of False Detection': pofd,
        'Heidke Skill Score': hss,
        'Equitable Threat Score': ets,
        'f1 Score': f1
    }

    pd_series_d = pd.Series(d)
    print(pd_series_d)




.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Hits                              15.000000
    Misses                             7.000000
    False Alarm                        6.000000
    Correct Negative                   8.000000
    Probability of Detection           0.681818
    False Alarm Ratio                  0.285714
    Critical Success Index             0.535714
    Frequency Bias                     0.954545
    Accuracy                           0.638889
    Probability of False Detection     0.428571
    Heidke Skill Score                 0.250000
    Equitable Threat Score             0.142857
    f1 Score                           0.107143
    dtype: float64





.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  0.037 seconds)


.. _sphx_glr_download_auto_examples_ver_deterministic.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example


    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: ver_deterministic.py <ver_deterministic.py>`

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: ver_deterministic.ipynb <ver_deterministic.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_