#    The ImpedanceFitter is a package to fit impedance spectra to
#    equivalent-circuit models using open-source software.
#
#    Copyright (C) 2018, 2019 Leonard Thiele, leonard.thiele[AT]uni-rostock.de
#    Copyright (C) 2018, 2019, 2020 Julius Zimmermann,
#                                   julius.zimmermann[AT]uni-rostock.de
#
#    This program is free software: you can redistribute it and/or modify
#    it under the terms of the GNU General Public License as published by
#    the Free Software Foundation, either version 3 of the License, or
#    (at your option) any later version.
#
#    This program is distributed in the hope that it will be useful,
#    but WITHOUT ANY WARRANTY; without even the implied warranty of
#    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
#    GNU General Public License for more details.
#
#    You should have received a copy of the GNU General Public License
#    along with this program.  If not, see <https://www.gnu.org/licenses/>.

import os
from collections import OrderedDict

import numpy as np
import pandas as pd
from impedancefitter import Fitter, PostProcess, get_equivalent_circuit_model
from matplotlib import rcParams

rcParams["figure.figsize"] = [20, 10]

# parameters
f = np.logspace(1, 8)
omega = 2.0 * np.pi * f
R = 1000.0
C = 1e-6

data = OrderedDict()
data["f"] = f

samples = 1000

model = "parallel(R, C)"
m = get_equivalent_circuit_model(model)
# generate random samples
for i in range(samples):
    Ri = 0.05 * R * np.random.randn() + R
    Ci = 0.05 * C * np.random.randn() + C

    Z = m.eval(omega=omega, R=Ri, C=Ci)
    # add some noise
    Z += np.random.randn(Z.size)

    data["real" + str(i)] = Z.real
    data["imag" + str(i)] = Z.imag

# save data to file
pd.DataFrame(data=data).to_csv("test.csv", index=False)

# initialize fitter
# LogLevel should be WARNING; otherwise there
# will be a lot of output


fitter = Fitter("CSV", LogLevel="WARNING")
os.remove("test.csv")
parameters = {"R": {"value": R}, "C": {"value": C}}
fitter.run(model, parameters=parameters)

postp = PostProcess(fitter.fit_data)
# show histograms
postp.plot_histograms()

# compare different algorithms to find matching model
print(
    "BIC best model for R:", postp.best_model_bic("R", ["Normal", "Beta", "Gamma"])[0]
)
print(
    "Chisquared best model for R:",
    postp.best_model_chisquared("R", ["Normal", "Beta", "Gamma"])[0],
)
print(
    "Kolmogorov best model for R:",
    postp.best_model_kolmogorov("R", ["Normal", "Beta", "Gamma"])[0],
)
print(
    "Lilliefors best model for R:",
    postp.best_model_lilliefors("R", ["Normal", "Beta", "Gamma"])[0],
)
print(f"Expected result:\nNormal(mu = {R}, sigma = {0.05 * R})")