# -*- coding: utf-8 -*-

"""
Modeling Photocurrent Density for Tandem Solar Cells
====================================================
Using spectral on-demand data from NREL and simulated EQE data from GENPRO4.
"""

# sphinx_gallery_thumbnail_number = 3

# %%
# This example shows how to calculate the photocurrent density in the subcells
# of a tandem solar cell. It uses "spectral-on-demand" data from the NSRDB
# providded by NREL.
# For the absorptances of the subcells GENPRO4 simulated EQE curves are used
# originally createf for the following publication:
# Reference
# ----------
# .. [1] P. Tillmann, K. Jäger, A. Karsenti, L. Kreinin, C. Becker (2022)
#    “Model-Chain Validation for Estimating the Energy Yield of Bifacial
#    Perovskite/Silicon Tandem Solar Cells,” Solar RRL 2200079,
#    DOI: 10.1002/solr.202200079


import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from pv_tandem import utils

plt.rcParams["figure.dpi"] = 140

# %%
# First, the simulated EQE data for the perovskite on silicon 2 terminal tandem
# is loaded from the corresponding csv-file. In order to work with pv_tandem
# the EQE data needs to be stored as a pandas DataFrame with the column names
# specific to the subcell names and the wavelengthsin nm as index. The EQE data
# has to be between 0 and 1.

example_eqe = pd.read_csv("../data/eqe_tandem_2t.csv", index_col=0)
ax = (example_eqe * 100).plot()
ax.set_xlabel("Wavelength (nm)")
ax.set_ylabel("Absorptance (%)")
ax.legend(["Perovskite cell", "Silicon cell"], loc="lower right")
plt.show()

# %%
# Next, the spectral data is loaded. The spectral data has to be stored as a
# pandas DataFrame, with the column names corresponding to the wavelengths in
# nm and should have the unit W/m²/nm

spec_irrad_ts = pd.read_csv(
    "../data/spec_poa_dallas_2020.csv", index_col=0, parse_dates=True
)
spec_irrad_ts.columns = spec_irrad_ts.columns.astype(float)
# converting to W/m²/nm from W/m²/µm and clipping negative values to zero
spec_irrad_ts = spec_irrad_ts.clip(lower=0) / 1000


# %%
# In order to calculate the photocurrent from the EQE and spectral irradiance
# both need to be specified for the same wavelengths. pv_tandem provides a
# convinice function to transform either the spectral data at the same wavelength
# grid as the EQE or vice versa.

eqe = utils.interp_eqe_to_spec(example_eqe, spec_irrad_ts)
ax = (eqe * 100).plot()
ax.set_xlabel("Wavelength (nm)")
ax.set_ylabel("Absorptance (%)")
ax.legend(["Perovskite cell", "Silicon cell"], loc="lower right")
plt.show()

# %%
# Finally the photocurrent density is calculated. This has to be done seperatly
# for each subcell. In this example we aggrgate the generated charge density
# (time integrated curent) for each day of the year for the purpose of visulizing
# the small differences between the perovskite and silicon subcell.

j_ph = pd.concat(
    [
        utils.calc_current(spec_irrad_ts, eqe["pero"]),
        utils.calc_current(spec_irrad_ts, eqe["si"]),
    ],
    axis=1,
)

ax = (j_ph.groupby(j_ph.index.dayofyear).sum() * 3.6 / 1000).plot()
ax.set_xlabel("Day of year")
ax.set_ylabel("Daily generated Charge (MC/day)")
ax.legend(["Perovskite cell", "Silicon cell"], loc="upper right")
plt.show()