"""
GHI to POA Transposition
=========================

Example of generating clearsky GHI and POA irradiance.
"""

# %%
# This example shows how to use the
# :py:meth:`pvlib.location.Location.get_clearsky` method to generate clearsky
# GHI data as well as how to use the
# :py:meth:`pvlib.irradiance.get_total_irradiance` function to transpose
# GHI data to Plane of Array (POA) irradiance.

from pvlib import location
from pvlib import irradiance
import pandas as pd
from matplotlib import pyplot as plt

# For this example, we will be using Golden, Colorado
tz = 'MST'
lat, lon = 39.755, -105.221

# Create location object to store lat, lon, timezone
site = location.Location(lat, lon, tz=tz)


# Calculate clear-sky GHI and transpose to plane of array
# Define a function so that we can re-use the sequence of operations with
# different locations
def get_irradiance(site_location, date, tilt, surface_azimuth):
    # Creates one day's worth of 10 min intervals
    times = pd.date_range(date, freq='10min', periods=6*24,
                          tz=site_location.tz)
    # Generate clearsky data using the Ineichen model, which is the default
    # The get_clearsky method returns a dataframe with values for GHI, DNI,
    # and DHI
    clearsky = site_location.get_clearsky(times)
    # Get solar azimuth and zenith to pass to the transposition function
    solar_position = site_location.get_solarposition(times=times)
    # Use the get_total_irradiance function to transpose the GHI to POA
    POA_irradiance = irradiance.get_total_irradiance(
        surface_tilt=tilt,
        surface_azimuth=surface_azimuth,
        dni=clearsky['dni'],
        ghi=clearsky['ghi'],
        dhi=clearsky['dhi'],
        solar_zenith=solar_position['apparent_zenith'],
        solar_azimuth=solar_position['azimuth'])
    # Return DataFrame with only GHI and POA
    return pd.DataFrame({'GHI': clearsky['ghi'],
                         'POA': POA_irradiance['poa_global']})


# Get irradiance data for summer and winter solstice, assuming 25 degree tilt
# and a south facing array
summer_irradiance = get_irradiance(site, '06-20-2020', 25, 180)
winter_irradiance = get_irradiance(site, '12-21-2020', 25, 180)

# Convert Dataframe Indexes to Hour:Minute format to make plotting easier
summer_irradiance.index = summer_irradiance.index.strftime("%H:%M")
winter_irradiance.index = winter_irradiance.index.strftime("%H:%M")

# Plot GHI vs. POA for winter and summer
fig, (ax1, ax2) = plt.subplots(1, 2, sharey=True)
summer_irradiance['GHI'].plot(ax=ax1, label='GHI')
summer_irradiance['POA'].plot(ax=ax1, label='POA')
winter_irradiance['GHI'].plot(ax=ax2, label='GHI')
winter_irradiance['POA'].plot(ax=ax2, label='POA')
ax1.set_xlabel('Time of day (Summer)')
ax2.set_xlabel('Time of day (Winter)')
ax1.set_ylabel('Irradiance ($W/m^2$)')
ax1.legend()
ax2.legend()
plt.show()

# %%
# Note that in Summer, there is not much gain when comparing POA irradiance to
# GHI. In the winter, however, POA irradiance is significantly higher than
# GHI. This is because, in winter, the sun is much lower in the sky, so a
# tilted array will be at a more optimal angle compared to a flat array.
# In summer, the sun gets much higher in the sky, and there is very little
# gain for a tilted array compared to a flat array.