import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit


datapath = '/Volumes/TOSHIBA EXT/23-07-19 - Test Pulses (22deg)/'
data_path_output = '/Users/marialauraperez/Desktop/SummerCERN/Results/TestPulses-22deg/'
first_charges = np.arange(500, 1600, 100)
second_charges = np.arange(2000, 12000, 1000)
charges = np.concatenate((first_charges, second_charges))
ToT_data = list()
mean_ToT = list()
energies = charges*3.62/1000

for i in range(len(charges)):
    matrix = np.genfromtxt(datapath+str(charges[i])+'e.txt_iToT.txt')
    ToT_data.append(matrix)
    mean_ToT.append(np.mean(matrix)/100)

# isotope_energies = [5.89, 22.16, 59.54]
# isotope_ToTs = [14.21, 48.76, 118.28]
theoretical_XRF = [4.512, 5.9, 6.405, 8.046, 15.775, 17.48, 25.271, 59.5]
ToT_XRF = [12.05, 15.82, 16.91, 20.98, 38.83, 42.64, 58.97, 118.54]


def surrogate_function(x, a, b, c, t):
    y = a*x + b - c/(x-t)
    y[np.where(x < t)] = 0
    y *= y > 0
    return y


poptmean, pcovn = curve_fit(surrogate_function, energies, mean_ToT)

x = np.linspace(1.5, 65, 100)

plt.figure()
plt.scatter(energies, mean_ToT, s=10, label='TP Data', color='navy')
plt.plot(x, surrogate_function(x, *poptmean), label='TP Fit: a = '+str(np.round(poptmean[0], 3))
                                                    + '; b = ' + str(np.round(poptmean[1], 3))
                                                    + '; c = '+str(np.round(poptmean[2], 3))
                                                    + '; t = '+str(np.round(poptmean[3], 3)), color='blue')
plt.scatter(theoretical_XRF, ToT_XRF, s=10, label='Source Data', color='red')
plt.xlabel('Energy [keV]')
plt.ylabel('ToT value')
plt.title('Source and test pulse calibrations (whole detector)', fontweight='bold')
plt.legend()
plt.savefig('/Users/marialauraperez/Desktop/total_per_source/Source+TestPulse_Calibs22deg.png')
plt.show()

# plt.figure()
# plt.scatter(energies, mean_ToT, s=10, label='Data', color='navy')
# plt.plot(x, surrogate_function(x, *poptmean), label='Fit: a = '+str(np.round(poptmean[0], 3))
#                                                     + '; b = ' + str(np.round(poptmean[1], 3))
#                                                     + '; c = '+str(np.round(poptmean[2], 3))
#                                                     + '; t = '+str(np.round(poptmean[3], 3)), color='blue')
# plt.xlabel('Test pulse height [keV]')
# plt.ylabel('Mean ToT value')
# plt.title('Calibration with test pulses', fontweight='bold')
# plt.legend()
# plt.savefig(data_path_output+'22degTestPulse_AvgCalib.png')


def get_calibration_matrices(energy_vals, ToT_matrices):
    nrows = len(ToT_matrices[0][:, 0])
    ncols = len(ToT_matrices[0][0, :])
    a = np.zeros(np.shape(ToT_matrices[0]))
    b = a.copy()
    c = a.copy()
    t = a.copy()
    test_vals_y = list()
    pixel_i = list()
    pixel_j = list()
    test_popt = list()
    for i in range(nrows):
        for j in range(ncols):
            entry = list()
            for k in range(len(ToT_matrices)):
                entry.append(ToT_matrices[k][i, j]/100)
            try:
                popt, pcov = curve_fit(surrogate_function, energy_vals, entry, p0=poptmean)
                a[i, j] = popt[0]
                b[i, j] = popt[1]
                c[i, j] = popt[2]
                t[i, j] = popt[3]
                if (i is 20 or i is 148 or i is 50) and (j is 130 or j is 210 or j is 90):
                    test_vals_y.append(entry)
                    pixel_i.append(i)
                    pixel_j.append(j)
                    test_popt.append(popt)
            except RuntimeError:
                print('Error: calibration not performed in pixel (', str(i)+', '+str(j)+' )')
                plt.figure()
                plt.scatter(energy_vals, entry)
                plt.show()

    return a, b, c, t, pixel_i, pixel_j, test_vals_y, test_popt


# a_matrix, b_matrix, c_matrix, t_matrix, pixels_x, pixels_y, test_vals, test_params = \
#     get_calibration_matrices(energies, ToT_data)
#
# plt.figure()
# for k in range(len(pixels_x)):
#     plt.scatter(energies, test_vals[k], s=10, label='('+str(pixels_x[k])+', '+str(pixels_y[k])+')')
#     plt.plot(x, surrogate_function(x, *test_params[k]), label='Fit')
# plt.xlabel('Test pulse height [keV]')
# plt.ylabel('ToT value')
# plt.title('Calibration per pixel with test pulses', fontweight='bold')
# plt.legend(fontsize='small', ncol=2)
# plt.savefig(data_path_output+'22degTestPulse_CalibsPixels.png')
#
# np.savetxt(data_path_output+'A_TestPulse22deg.txt', a_matrix, fmt='%.5f')
# np.savetxt(data_path_output+'B_TestPulse22deg.txt', b_matrix, fmt='%.5f')
# np.savetxt(data_path_output+'C_TestPulse22deg.txt', c_matrix, fmt='%.5f')
# np.savetxt(data_path_output+'T_TestPulse22deg.txt', t_matrix, fmt='%.5f')