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

This example demonstrates how to perform traditional
binary forecast verification
"""

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)