"""
Calculate Performance Ratio (NREL)
==================================

Calculate the NREL Performance Ratio for a system.
"""

# %%
# When evaluating PV system performance it is often desirable to distinguish
# uncontrollable effects like weather variation from controllable effects
# like soiling and hardware issues. The NREL Performance Ratio
# (or "Weather-Corrected Performance Ratio") is a unitless metric that
# normalizes system output for variation in irradiance and temperature,
# making it insensitive to uncontrollable weather variation and more
# reflective of system health. In this example, we
# show how to calculate the NREL PR at two different frequencies: for a
# complete time series, and at daily intervals. We use the
# :py:func:`pvanalytics.metrics.performance_ratio_nrel` function.

import pvanalytics
from pvanalytics.metrics import performance_ratio_nrel
import pandas as pd
import pathlib
import matplotlib.pyplot as plt

# %%
# First, we read in data from the NREL RSF II system. This data
# set contains 15-minute interval data for AC power, POA irradiance, ambient
# temperature, and wind speed, among others. The complete data set for the
# NREL RSF II installation is available in the PVDAQ database, under system
# ID 1283.

pvanalytics_dir = pathlib.Path(pvanalytics.__file__).parent
file = pvanalytics_dir / 'data' / 'nrel_RSF_II.csv'
data = pd.read_csv(file, index_col=0, parse_dates=True)

# %%
# Now we calculate the PR for the entire time series, using the
# POA, ambient temperature, wind speed, and AC power fields. We use this
# data as parameters in the
# :py:func:`pvanalytics.metrics.performance_ratio_nrel` function.
# In this example we are calculating PR for a single inverter connected
# to a 204.12 kW PV array.
pr_whole_series = performance_ratio_nrel(data['poa_irradiance__1055'],
                                         data['ambient_temp__1053'],
                                         data['wind_speed__1051'],
                                         data['inv2_ac_power_w__1047']/1000,
                                         204.12)

print("RSF II, PR for the whole time series:")
print(pr_whole_series)

# %%
# Next, we recalculate the PR on a daily basis. We separate the time
# series into daily intervals, and calculate the PR for each day.
# Note that this inverter was offline for the last day in this
# dataset, resulting in a PR value of zero for that day.
dates = list(pd.Series(data.index.date).drop_duplicates())

daily_pr_list = list()
for date in dates:
    data_subset = data[data.index.date == date]
    # Run the PR calculation for the specific day.
    pr = performance_ratio_nrel(data_subset['poa_irradiance__1055'],
                                data_subset['ambient_temp__1053'],
                                data_subset['wind_speed__1051'],
                                data_subset['inv2_ac_power_w__1047']/1000,
                                204.12)
    daily_pr_list.append({"date": date,
                          "PR": pr})

daily_pr_df = pd.DataFrame(daily_pr_list)

# Plot the PR time series to visualize it
daily_pr_df.set_index('date').plot()
plt.axhline(pr_whole_series, color='r', ls='--', label='PR, Entire Series')
plt.xticks(rotation=25)
plt.legend()
plt.ylabel('NREL PR')
plt.xlabel('Date')
plt.tight_layout()
plt.show()