"""
Clipping Detection
===================

Identifying clipping periods using the PVAnalytics clipping module.
"""

# %%
# Identifying and removing clipping periods from AC power time series
# data aids in generating more accurate degradation analysis results,
# as using clipped data can lead to under-predicting degradation. In this
# example, we show how to use
# :py:func:`pvanalytics.features.clipping.geometric`
# to mask clipping periods in an AC power time series. We use a
# normalized time series example provided by the PV Fleets Initiative,
# where clipping periods are labeled as True, and non-clipping periods are
# labeled as False. This example is adapted from the DuraMAT DataHub
# clipping data set:
# https://datahub.duramat.org/dataset/inverter-clipping-ml-training-set-real-data

import pvanalytics
from pvanalytics.features.clipping import geometric
import matplotlib.pyplot as plt
import pandas as pd
import pathlib
import numpy as np

# %%
# First, read in the ac_power_inv_7539 example, and visualize a subset of the
# clipping periods via the "label" mask column.

pvanalytics_dir = pathlib.Path(pvanalytics.__file__).parent
ac_power_file_1 = pvanalytics_dir / 'data' / 'ac_power_inv_7539.csv'
data = pd.read_csv(ac_power_file_1, index_col=0, parse_dates=True)
data['label'] = data['label'].astype(bool)
# This is the known frequency of the time series. You may need to infer
# the frequency or set the frequency with your AC power time series.
freq = "15min"

data['value_normalized'].plot()
data.loc[data['label'], 'value_normalized'].plot(ls='', marker='o')
plt.legend(labels=["AC Power", "Labeled Clipping"],
           title="Clipped")
plt.xticks(rotation=20)
plt.xlabel("Date")
plt.ylabel("Normalized AC Power")
plt.tight_layout()
plt.show()

# %%
# Now, use :py:func:`pvanalytics.features.clipping.geometric` to identify
# clipping periods in the time series. Re-plot the data subset with this mask.
predicted_clipping_mask = geometric(ac_power=data['value_normalized'],
                                    freq=freq)
data['value_normalized'].plot()
data.loc[predicted_clipping_mask, 'value_normalized'].plot(ls='', marker='o')
plt.legend(labels=["AC Power", "Detected Clipping"],
           title="Clipped")
plt.xticks(rotation=20)
plt.xlabel("Date")
plt.ylabel("Normalized AC Power")
plt.tight_layout()
plt.show()


# %%
# Compare the filter results to the ground-truth labeled data side-by-side,
# and generate an accuracy metric.
acc = 100 * np.sum(np.equal(data.label,
                            predicted_clipping_mask))/len(data.label)
print("Overall model prediction accuracy: " + str(round(acc, 2)) + "%")