"""
Seasonal Tilt
=============

Example of a custom Mount class.
"""

# %%
# Some PV systems are built with the option to adjust the module
# tilt to follow seasonal changes in solar position.  For example,
# SAM calls this strategy "Seasonal Tilt".  This example shows how
# to use a custom Mount class to use the Seasonal Tilt strategy
# with :py:class:`~pvlib.modelchain.ModelChain`.

import pvlib
from pvlib import pvsystem, location, modelchain, iotools
from pvlib.temperature import TEMPERATURE_MODEL_PARAMETERS
import pandas as pd
import pathlib
import matplotlib.pyplot as plt
from dataclasses import dataclass


# %%
# New Mount classes should extend ``pvlib.pvsystem.AbstractMount``
# and must implement a ``get_orientation(solar_zenith, solar_azimuth)`` method:


@dataclass
class SeasonalTiltMount(pvsystem.AbstractMount):
    monthly_tilts: list  # length 12, one tilt per calendar month
    surface_azimuth: float = 180.0

    def get_orientation(self, solar_zenith, solar_azimuth):
        # note: determining tilt based on month may produce different
        # results depending on the time zone of the timestamps
        tilts = [self.monthly_tilts[m-1] for m in solar_zenith.index.month]
        return pd.DataFrame({
            'surface_tilt': tilts,
            'surface_azimuth': self.surface_azimuth,
        }, index=solar_zenith.index)


# %%
# First let's grab some weather data and make sure our mount produces tilts
# like we expect:

DATA_DIR = pathlib.Path(pvlib.__file__).parent / 'data'
tmy, metadata = iotools.read_tmy3(DATA_DIR / '723170TYA.CSV', coerce_year=1990,
                                  map_variables=True)
# shift from TMY3 right-labeled index to left-labeled index:
tmy.index = tmy.index - pd.Timedelta(hours=1)
weather = pd.DataFrame({
    'ghi': tmy['ghi'], 'dhi': tmy['dhi'], 'dni': tmy['dni'],
    'temp_air': tmy['temp_air'], 'wind_speed': tmy['wind_speed'],
})
loc = location.Location.from_tmy(metadata)
solpos = loc.get_solarposition(weather.index)
# same default monthly tilts as SAM:
tilts = [40, 40, 40, 20, 20, 20, 20, 20, 20, 40, 40, 40]
mount = SeasonalTiltMount(monthly_tilts=tilts)
orientation = mount.get_orientation(solpos.apparent_zenith, solpos.azimuth)
orientation['surface_tilt'].plot()
plt.ylabel('Surface Tilt [degrees]')
plt.show()

# %%
# With our custom tilt strategy defined, we can create the corresponding
# Array and PVSystem, and then run a ModelChain as usual:

module_parameters = {'pdc0': 1, 'gamma_pdc': -0.004, 'b': 0.05}
temp_params = TEMPERATURE_MODEL_PARAMETERS['sapm']['open_rack_glass_polymer']
array = pvsystem.Array(mount=mount, module_parameters=module_parameters,
                       temperature_model_parameters=temp_params)
system = pvsystem.PVSystem(arrays=[array], inverter_parameters={'pdc0': 1})
mc = modelchain.ModelChain(system, loc, spectral_model='no_loss')

_ = mc.run_model(weather)

# %%
# Now let's re-run the simulation assuming tilt=30 for the entire year:

array2 = pvsystem.Array(mount=pvsystem.FixedMount(30, 180),
                        module_parameters=module_parameters,
                        temperature_model_parameters=temp_params)
system2 = pvsystem.PVSystem(arrays=[array2], inverter_parameters={'pdc0': 1})
mc2 = modelchain.ModelChain(system2, loc, spectral_model='no_loss')
_ = mc2.run_model(weather)

# %%
# And finally, compare simulated monthly generation between the two tilt
# strategies:

# sphinx_gallery_thumbnail_number = 2
results = pd.DataFrame({
    'Seasonal 20/40 Production': mc.results.ac,
    'Fixed 30 Production': mc2.results.ac,
})
results.resample('m').sum().plot()
plt.ylabel('Monthly Production')
plt.show()