"""
Infer Array Tilt/Azimuth - PVWatts Method
=========================================

Infer the azimuth and tilt of a system using PVWatts-based methods
"""

# %%
# Identifing and/or validating  the azimuth and tilt information for a
# system is important, as these values must be correct for degradation
# and system yield analysis. This example shows how to use
# :py:func:`pvanalytics.system.infer_orientation_fit_pvwatts` to estimate
# a fixed-tilt system's azimuth and tilt, using the system's known
# latitude-longitude coordinates and an associated AC power time series.

import pvanalytics
import matplotlib.pyplot as plt
from pvanalytics import system
import pandas as pd
import pathlib
import pvlib

# %%
# First, we import an AC power data stream from the SERF East site located at
# NREL. This data set is publicly available via the PVDAQ database in the
# DOE Open Energy Data Initiative (OEDI)
# (https://data.openei.org/submissions/4568), under system ID 50.
# This data is timezone-localized.

pvanalytics_dir = pathlib.Path(pvanalytics.__file__).parent
ac_power_file = pvanalytics_dir / 'data' / 'serf_east_15min_ac_power.csv'
data = pd.read_csv(ac_power_file, index_col=0, parse_dates=True)
data = data.sort_index()
time_series = data['ac_power']
time_series = time_series.asfreq('15min')

# Plot the first few days of the time series to visualize it
time_series[:pd.to_datetime("2016-07-06 00:00:00-07:00")].plot()
plt.show()

# Outline the ground truth metadata associated with the system
latitude = 39.742
longitude = -105.1727
actual_azimuth = 158
actual_tilt = 45

# %%
# Next, we import the PSM3 data generated via the
# :py:func:`pvlib.iotools.get_psm3` function, using
# site latitude-longitude coordinates. To generate the
# PSM3 data, you must first register for NREL's NSDRB API at the
# following link: https://developer.nrel.gov/signup/.
# PSM3 data can then be retrieved using :py:func:`pvlib.iotools.get_psm3`.
# The PSM3 data has been resampled to 15 minute intervals, to match the AC
# power data.

psm3_file = pvanalytics_dir / 'data' / 'serf_east_psm3_data.csv'
psm3 = pd.read_csv(psm3_file, index_col=0, parse_dates=True)

# %%
# Filter the PSM3 data to only include clearsky periods
is_clear = (psm3.ghi_clear == psm3.ghi)
is_daytime = (psm3.ghi > 0)
time_series_clearsky = time_series[is_clear & is_daytime]
time_series_clearsky = time_series_clearsky.dropna()
psm3_clearsky = psm3.loc[time_series_clearsky.index]

# Get solar azimuth and zenith from pvlib, based on
# lat-long coords
solpos_clearsky = pvlib.solarposition.get_solarposition(
    time_series_clearsky.index, latitude, longitude)

# %%
# Run the pvlib data and the sensor-based time series data through
# the :py:func:`pvanalytics.system.infer_orientation_fit_pvwatts` function.
best_tilt, best_azimuth, r2 = system.infer_orientation_fit_pvwatts(
    time_series_clearsky,
    psm3_clearsky.ghi_clear,
    psm3_clearsky.dhi_clear,
    psm3_clearsky.dni_clear,
    solpos_clearsky.zenith,
    solpos_clearsky.azimuth,
    temperature=psm3_clearsky.temp_air,
)

# Compare actual system azimuth and tilt to predicted azimuth and tilt
print("Actual Azimuth: " + str(actual_azimuth))
print("Predicted Azimuth: " + str(best_azimuth))
print("Actual Tilt: " + str(actual_tilt))
print("Predicted Tilt: " + str(best_tilt))