# file to evaluate a measurement
import os.path, glob
from os.path import join
import json

import pandas as pd
from scipy.ndimage import gaussian_filter1d
from scipy.optimize import minimize, curve_fit
import scipy.constants as c


import numpy as np
import matplotlib.pyplot as pl
from helper import *
from style import *

path_data_repo = "../data_supplementary"

meas = {
    "path": "current_phase_relation.csv",
    "save_path": "Projects/Shapiro/Shapiro_steps/current_phase_relation"
}

# open data
data = pd.read_csv(join(path_data_repo, meas["path"]))

current = data["current"].to_numpy()
phase = data['phase'].to_numpy()
phase_err = data["phase_err"].to_numpy()


phi_0 = np.mean(phase[0:5])
phase = phase - phi_0


p0 = [0.8, 0.01]

#  function for the minimization fitting
phi_fit = np.arange(0, np.pi/2, 0.001)
def func_mini(x):
    f = x[0]* np.sin(phase * np.pi) + x[1] * np.sin(2 * phase * np.pi)
    f_phi = x[0]* np.sin(phi_fit) + x[1] * np.sin(2 * phi_fit)

    return np.sum(current - f)**2 + 1e2 * (1-np.max(f_phi))**2


res = minimize(func_mini, p0, method='Nelder-Mead', tol=1e-8)

print(res.x)




def current_theo(phi):
    return np.sin(phi * np.pi)


def func(phi, a, b):
    return ( a* np.sin(phi * np.pi) + b * np.sin(2 * phi * np.pi))


phi_fit = np.arange(0, 0.65, 0.001)

print(np.max(func(phi_fit, *res.x)))


fig = pl.figure(figsize=(3.5 * 1.61, 3.5))


pl.errorbar(func(phi_fit, *res.x), phi_fit, label='Fit', color=RPTU_COLORS["himbeere"],zorder = 2,alpha = 0.8)

pl.errorbar(current_theo(phi_fit), phi_fit, label=fr'$\sin(\varphi)$', color=RPTU_COLORS["mango"],zorder = 2,alpha = 0.95)



pl.errorbar(current, phase, yerr=phase_err, **get_style(RPTU_COLORS["nacht"]),zorder = 1)

pl.ylabel(r"Phase $\varphi [\pi]$")
pl.xlabel(r"Current $I/I_\mathrm{c}$")

pl.legend()
pl.tight_layout()
#pl.savefig(("current_phase_relation.pdf"),dpi=300)
pl.show()