import numpy as np
import matplotlib.pyplot as plt
import Cluster
import Fit

output_data_path = '/Users/marialauraperez/Desktop/'
input_data_path = '/Volumes/TOSHIBA EXT/Temperatures/'
sources = ['22C', '30C', '35C']
colors = ['purple', 'dodgerblue', 'yellowgreen']


def histos_and_peaks(minargs, maxargs, maximum, maxrows):
    numbins = maximum
    # clusters, may calibrate, gives histograms and returns info of histogram
    # maximum: in histogram
    # maxrows: number of events to import
    # calibration_type: string, non calibrated, test pulse or default
    # if calibrate is false, everything is done with ToT values
    mus = list()
    x1 = np.linspace(0, maximum, 500)
    plt.figure()
    plt.title('Am241 spectrum with TPX3', fontweight='bold')
    for i in range(len(sources)):
        minarg = minargs[i]
        maxarg = maxargs[i]
        source = sources[i]
        print('Processing '+sources[i]+' data...')
        data = np.genfromtxt(input_data_path+'am241_'+source+'.t3pa', skip_header=1, delimiter='\t', unpack=True,
                             max_rows=maxrows)
        indexes = np.ndarray.astype(data[1, :], int)
        ToA_vals = data[2, :]
        ToT_vals = data[3, :]
        x_vals, y_vals = np.unravel_index(indexes, [256, 256])
        energies = ToT_vals
        clusters = Cluster.db_cluster(x_vals, y_vals, ToA_vals, energies, eps=1.99, scale=[1.0, 1.0, 100.])
        x_cvals, y_cvals, t_cvals, e_cvals = Cluster.get_clustered_energies(clusters)
        del data
        del energies
        del clusters
        n, bins, patches = plt.hist(e_cvals, bins=numbins, range=[0, maximum], histtype='step', color=colors[i],
                                    label=sources[i] + ' data')
        bins2 = np.zeros(len(bins)-1)
        for w in range(len(bins)-1):
            bins2[w] = (bins[w+1] + bins[w])/2
        gauss = Fit.FitGauss().fit(x=bins2[minarg:maxarg], y=n[minarg:maxarg])
        y1 = Fit.gauss(x1, gauss[0])
        mu = gauss[0][1]
        mus.append(mu)
        plt.plot(x1, y1, label=r'Fit ($\mu$ = ' + str(np.round(mu, 3)) + ')', linestyle='--', color=colors[i])
    plt.xlim(80, maximum)
    plt.xlabel('ToT value')
    plt.ylabel('Total counts')
    plt.legend(fontsize='small')
    plt.savefig(output_data_path+'Am_drifting.png')
    return mus


def load_parameter_matrices(suffix, path_to_data):
    # suffix: of the data
    # path_to_data: where txt matrices are
    parameter_matrices = []
    names = ['A', 'B', 'C', 'T']
    for i in range(len(names)):
        if i is 0:
            parameter_matrices.append(Cluster.remove_dead_pixels(np.genfromtxt(
                path_to_data + names[i] + suffix)))
        else:
            parameter_matrices.append(np.genfromtxt(path_to_data + names[i] + suffix))
    return parameter_matrices


def histos_and_peaks2(matrices, minargs, maxargs, calibration_type, maximum, maxrows):
    # clusters, may calibrate, gives histograms and returns info of histogram
    # maximum: in histogram
    # maxrows: number of events to import
    # calibration_type: string, non calibrated, test pulse or default
    # if calibrate is false, everything is done with ToT values
    numbins = maximum
    mus = list()
    x1 = np.linspace(0, maximum, 500)
    plt.figure()
    plt.title('Am241 spectrum with TPX3 ('+calibration_type+')', fontweight='bold')
    for i in range(len(sources)):
        minarg = minargs[i]
        maxarg = maxargs[i]
        source = sources[i]
        print('Processing '+sources[i]+' data...')
        data = np.genfromtxt(input_data_path+'am241_'+source+'.t3pa', skip_header=1, delimiter='\t', unpack=True,
                             max_rows=maxrows)
        indexes = np.ndarray.astype(data[1, :], int)
        ToA_vals = data[2, :]
        ToT_vals = data[3, :]
        x_vals, y_vals = np.unravel_index(indexes, [256, 256])
        energies = Cluster.calibrate(matrices, x_vals, y_vals, ToT_vals)
        clusters = Cluster.db_cluster(x_vals, y_vals, ToA_vals, energies, eps=1.99, scale=[1.0, 1.0, 100.])
        x_cvals, y_cvals, t_cvals, e_cvals = Cluster.get_clustered_energies(clusters)
        del data
        del energies
        del clusters
        n, bins, patches = plt.hist(e_cvals, bins=numbins, range=[0, maximum], histtype='step', color=colors[i],
                                    label=sources[i] + ' data')
        bins2 = np.zeros(len(bins)-1)
        for w in range(len(bins)-1):
            bins2[w] = (bins[w+1] + bins[w])/2
        gauss = Fit.FitGauss().fit(x=bins2[minarg:maxarg], y=n[minarg:maxarg])
        y1 = Fit.gauss(x1, gauss[0])
        mu = gauss[0][1]
        mus.append(mu)
        plt.plot(x1, y1, label=r'Fit ($\mu$ = ' + str(np.round(mu, 3)) + ')', linestyle='--', color=colors[i])
    plt.xlim(35, maximum)
    plt.xlabel('Energy [keV]')
    plt.ylabel('Total counts')
    plt.legend(fontsize='small')
    plt.savefig(output_data_path+calibration_type+'_Am_drifting.png')
    return mus


matpath = '/Users/marialauraperez/Desktop/param_mats/'
param_mats_default = load_parameter_matrices('_SiTPX3.txt', matpath)
param_mats_stp = load_parameter_matrices('_SourceTestPulse.txt', matpath)

print('No calibration:')
histos_and_peaks([115, 90, 75], [200, 200, 200], 200, 100000)
print('Default:')
histos_and_peaks2(param_mats_default, [53, 50, 45], [70, 70, 70], 'Default', 70, 100000)
print('STP:')
histos_and_peaks2(param_mats_stp, [53, 50, 45], [70, 70, 70], 'STP', 70, 100000)