#!/usr/bin/env python
# coding: utf-8

# <h1> Refractive Index Information DB </h1>

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from solcore.absorption_calculator.nk_db import download_db, search_db
from solcore import material
from solcore import si
from solcore.solar_cell import SolarCell
from solcore.structure import Layer
from solcore.solar_cell_solver import solar_cell_solver, default_options

import numpy as np
import matplotlib.pyplot as plt


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wl = si(np.arange(100, 900, 10), 'nm')

opts = default_options
opts.optics_method = 'TMM'
opts.wavelength = wl


# Download the database from refractiveindex.info. This only needs to be done once.
# Can specify the source URL and number of interpolation points.

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# download_db()


# Can search the database to select an appropriate entry. Search by element/chemical formula.
# In this case, look for silver.

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# search_db('Ag', exact = True)


# This prints out, line by line, matching entries. This shows us entries with
# "pageid"s 0 to 14 correspond to silver.
# 
# Let's compare the optical behaviour of some of those sources:
# (The pageid values listed are for the 2021-07-18 version of the refractiveindex.info database)
# pageid = 0, Johnson
# pageid = 2, Jiang
# pageid = 4, McPeak
# pageid = 10, Hagemann
# pageid = 14, Rakic (BB)
# 
# Then, create instances of materials with the optical constants from the database.
# The name (when using Solcore's built-in materials, this would just be the
# name of the material or alloy, like 'GaAs') is the pageid, AS A STRING, while
# the flag nk_db must be set to True to tell Solcore to look in the previously
# downloaded database from refractiveindex.info

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# Ag_Joh = material(name='0', nk_db=True)()
# Ag_Jia = material(name='2', nk_db=True)()
# Ag_McP = material(name='4', nk_db=True)()
# Ag_Hag = material(name='10', nk_db=True)()
# Ag_Rak = material(name='14', nk_db=True)()
# Ag_Sol = material(name='Ag')() # Solcore built-in (from SOPRA)


# plot the n and k data. Note that not all the data covers the full wavelength range,
# so the n/k value stays flat.

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# names = ['Johnson', 'Jiang', 'McPeak', 'Hagemann', 'Rakic', 'Solcore built-in']
#
# plt.figure(figsize=(8,4))
# plt.subplot(121)
# plt.plot(wl*1e9, np.array([Ag_Joh.n(wl), Ag_Jia.n(wl), Ag_McP.n(wl),
#                            Ag_Hag.n(wl), Ag_Rak.n(wl), Ag_Sol.n(wl)]).T)
# plt.legend(labels=names)
# plt.xlabel("Wavelength (nm)")
# plt.title("(2) $n$ and $\kappa$ values for Ag from different literature sources")
# plt.ylabel("n")
#
# plt.subplot(122)
# plt.plot(wl*1e9, np.array([Ag_Joh.k(wl), Ag_Jia.k(wl), Ag_McP.k(wl),
#                            Ag_Hag.k(wl), Ag_Rak.k(wl), Ag_Sol.k(wl)]).T)
# plt.legend(labels=names)
# plt.xlabel("Wavelength (nm)")
# plt.ylabel("k")
# plt.show()


# Compare performance as a back mirror on a GaAs 'cell'
# 
# Solid line: absorption in GaAs
# Dashed line: absorption in Ag

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# GaAs = material('GaAs')()
#
# colors = ['k', 'r', 'g', 'y', 'b', 'm']
#
# plt.figure()
# for c, Ag_mat in enumerate([Ag_Joh, Ag_Jia, Ag_McP, Ag_Hag, Ag_Rak, Ag_Sol]):
#     my_solar_cell = OptiStack([Layer(width=si('50nm'), material = GaAs)], substrate=Ag_mat)
#     RAT = calculate_rat(my_solar_cell, wl*1e9, no_back_reflection=False)
#     GaAs_abs = RAT["A_per_layer"][1]
#     Ag_abs = RAT["T"]
#     plt.plot(wl*1e9, GaAs_abs, color=colors[c], linestyle='-', label=names[c])
#     plt.plot(wl*1e9, Ag_abs, color=colors[c], linestyle='--')
#
# plt.legend()
# plt.xlabel("Wavelength (nm)")
# plt.ylabel("Absorbed")
# plt.title("(3) Absorption in GaAs depending on silver optical constants")
# plt.show()