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

    Click :ref:`here <sphx_glr_download_auto_examples_ver.py>` to download the full example code
.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_ver.py:


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

This example demonstrates how to perform traditional
binary forecast verification





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

 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
    dtype: float64




|


.. code-block:: python


    import numpy as np
    import pandas as pd
    from swirlspy.ver.crosstab import contingency
    import swirlspy.ver.metric as mt
    import xarray as xr


    # 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)

    # 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
        }

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

**Total running time of the script:** ( 0 minutes  2.246 seconds)


.. _sphx_glr_download_auto_examples_ver.py:


.. only :: html

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



  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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