"""
Plotting  of the comparison of 2d and 3d-SPADE with respect to pattern duration

The script produces:
Top panel: raster plot of parallel spike trains (y−axis).
Background spikes are shown in black , and the injected patterns in red.
Only 15 out of 100 parallel spike trains are shown for a duration of 350 ms,
and only 3 out of 6 possible patterns. Middle and lower panels: 2d-
SPADE results on the left, and 3d-SPADE on the right.
Middle panel: histograms of the number of detected significant patterns (STPs)
when using different analysis window length. Green crosses show the total
number of injected patterns.
Bottom panel: p−value spectra for each method. The p−values are indicated by
color scale. Significant signatures are marked with a red dot.
The script reproduces Figure 3 of the publication:
<NAME>., <NAME>., <NAME>., & <NAME>. (2019). 3d-SPADE: Significance
Evaluation of Spatio-Temporal Patterns of Various Temporal Extents. Biosystems.
"""
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
from matplotlib import rc
import yaml
from yaml import Loader
import quantities as pq
from utils import mkdirp, split_path


def plot(data_file, spectra, binsize, winlens, label_size=6, text_size=8,
         tick_size=5):
    """
    Function creating figure representing the difference of 2d- to 3d-SPADE
    in terms of statistical performance in the case of a data set with patterns
    of multiple durations.
    Parameters
    ----------
    data_file : list of neo.SpikeTrains
        file with the generated artificial data set
    spectra : str
        flag indicating the type of spectra employed (thus the correspondent
        SPADE version)
        if spectra == '#':
            then 2d-SPADE is called
        elif spectra == '3d#'
            then 3d-SPADE is called
    binsize : quantity
        bin size necessary for the SPADE analysis
    winlens : lis
        list of window length used for each run of SPADE
    label_size: int
        label size of the plot. Default is 6
    tick_size: int
        tick size of the plot. Default is 5
    text_size: int
        tick size of the plot. Default is 8
    """
    # Plotting parameters
    inch2cm = 0.3937

    sts = data_file['data']
    stps = data_file['patt']

    figure = plt.figure(figsize=(12*inch2cm, 9*inch2cm))
    figure.subplots_adjust(
        left=.08,
        right=.91,
        wspace=.8,
        hspace=1,
        top=.95,
        bottom=.1)
    ax_raster = plt.subplot2grid((3, 2 * np.sum(winlens[:5])), (0, 0),
                                 rowspan=1,
                                 colspan=2 * np.sum(winlens[:5]))
    for s_idx, s in enumerate(sts[:len(stps)]):
        s_sliced = s.time_slice(s.t_start + .15*pq.s, 0.4 * pq.s)
        ax_raster.plot(s_sliced, [s_idx] * len(s_sliced), 'k.', markersize=2)
    for p_idx, p in enumerate(stps):
        p_sliced = p.time_slice(s.t_start + .15*pq.s, 0.4 * pq.s)
        if p_idx == len(stps) - 1:
            ax_raster.plot(p_sliced, [p_idx] * len(p_sliced), 'ro',
                           label='STPs',
                           fillstyle='full',
                           markeredgecolor='red',
                           markersize=1.5)
        else:
            ax_raster.plot(p_sliced, [p_idx] * len(p_sliced), 'ro',
                           fillstyle='full',
                           markeredgecolor='red',
                           markersize=1.5)
    ax_raster.set_xlabel('time (s)', size=label_size)
    ax_raster.set_ylabel('neuron id', size=label_size)
    ax_raster.set_ylim(-0.5, 15)
    ax_raster.tick_params(labelsize=tick_size)

    # Plotting SPADE results
    p_values_table = {}
    for spectrum_idx, spectrum in enumerate(spectra):
        p_values_table[str(spectrum)] = {}
        ax_count = plt.subplot2grid((3, 2 * np.sum(winlens[:5])),
                                    (1, spectrum_idx*np.sum(winlens[:5])),
                                    rowspan=1,
                                    colspan=np.sum(winlens[:5]))
        # Plotting count and pval spectrum of patterns for each window length
        for w_idx, w in enumerate(winlens):
            # Load filtered results
            patterns, pval_spectrum, ns_sgnt, params = np.load(
                '../results/{}/winlen{}/filtered_patterns.npy'.format(spectrum,
                                                                      w),
                 encoding='latin1')

            ax_count.bar(w_idx+1, len(patterns), color='k', width=0.05)
            if w_idx == len(winlens)-1:
                ax_count.plot(w_idx + 1, w_idx + 1, 'x', markersize=4,
                              label='Number of STPs', color='g')
            else:
                ax_count.plot(w_idx + 1, w_idx + 1, 'x', markersize=4,
                              color='g')
            ax_count.set_yticks(np.arange(0, len(stps)+1))
            for tick in ax_count.xaxis.get_major_ticks():
                tick.label.set_fontsize(tick_size)
            # Plotting pvalue spectra
            pval_matrix = np.zeros((4, w))
            for sgnt in pval_spectrum:
                if spectrum == '#':
                    for length in range(w):
                        pval_matrix[sgnt[1]-2, length] = sgnt[2]
                elif spectrum == '3d#':
                    pval_matrix[sgnt[1]-2, sgnt[2]] = sgnt[3]
            ax_pval_spectrum = plt.subplot2grid(
                (3, 2 * np.sum(winlens[:5])),
                (2, spectrum_idx*np.sum(winlens[:5]) + sum(winlens[:w_idx])),
                colspan=sum(winlens[w_idx:w_idx + 1]))
            pcol = ax_pval_spectrum.pcolor(
                pval_matrix,  norm=LogNorm(vmin=0.0001, vmax=1),
                cmap=plt.cm.YlGnBu)
            if spectrum == '#':
                for duration in range(pval_matrix.shape[1]):
                    for occ in range(2, pval_matrix.shape[0]+2):
                        if (3, occ) not in ns_sgnt:
                            ax_pval_spectrum.plot(duration+0.5, occ-2+0.5,
                                                  'ro',
                                                  markersize=1)
            elif spectrum == '3d#':
                for duration in range(pval_matrix.shape[1]):
                    for occ in range(2, pval_matrix.shape[0]+2):
                        if (3, occ, duration) not in ns_sgnt:
                            ax_pval_spectrum.plot(duration+0.5, occ-2+0.5,
                                                  'ro',
                                                  markersize=1)
            x_grid = np.arange(0, pval_matrix.shape[1]+1, 1)
            y_grid = np.arange(0, pval_matrix.shape[0]+1, 1)
            x_ticks = np.arange(0.5, pval_matrix.shape[1], 2)
            y_ticks = np.arange(0.5, pval_matrix.shape[0])
            x, y = np.meshgrid(x_grid, y_grid)
            c = np.ones_like(x)
            ax_pval_spectrum.pcolor(x, y, c, facecolor='none', edgecolor='k')
            ax_pval_spectrum.set_xticks(x_ticks)
            ax_pval_spectrum.set_xticklabels(range(0, pval_matrix.shape[1], 2),
                                             size=tick_size)
            if w_idx == len(winlens)//2+1:
                ax_pval_spectrum.set_xlabel('d (ms)', size=label_size)
            if w_idx == 0 and spectrum == '#':
                ax_pval_spectrum.set_yticks(y_ticks)
                ax_pval_spectrum.set_yticklabels(range(2, pval_matrix.shape[0] + 2), size=tick_size)
                ax_pval_spectrum.set_ylabel('c', size=label_size)
            else:
                ax_pval_spectrum.set_yticklabels(())
                ax_pval_spectrum.set_yticks(())
            if w_idx == len(winlens) - 1 and spectrum == '3d#':
                cbar = figure.colorbar(pcol, ticks=[0.0001, 0.001, 0.01, 0.1, 1], ax=ax_pval_spectrum)
                cbar.set_label('p-values', size=label_size)
                cbar.ax.tick_params(labelsize=tick_size)
                cbar.ax.minorticks_off()
            ax_pval_spectrum.set_xlim(0, pval_matrix.shape[1])
            ax_pval_spectrum.set_title(int(w*binsize.magnitude),
                                       size=tick_size)
            if w == 13:
                # print(pval_matrix[-2, :])
                pattern_length = [0, 2, 6, 8, 12]
                p_val_arr = np.take(pval_matrix[2, :], pattern_length)
                p_values_table[str(spectrum)][str(w)] = p_val_arr
                # print(p_val_arr)

        if spectrum == '#':
            ax_count.set_title('2d-SPADE', size=text_size)
            ax_count.set_ylabel('detected STPs', size=label_size)
        elif spectrum == '3d#':
            ax_count.set_title('3d-SPADE', size=text_size)
            ax_count.set_yticks(())
        ax_count.set_xticks(range(1, len(winlens)+1))
        ax_count.set_xticklabels(winlens*int(binsize.magnitude))
        ax_count.tick_params(labelsize=tick_size)
        ax_count.set_ylim([0, len(winlens)+1])
        ax_count.set_xlabel('w (ms)', size=label_size)
        if spectrum_idx == 0:
            ax_count.legend(loc='best', fontsize=label_size)
    figure_path = '../figures'
    path_temp = './'
    for folder in split_path(figure_path):
        path_temp = path_temp + '/' + folder
        mkdirp(path_temp)
    fig_formats = ['eps', 'png']
    for format in fig_formats:
        figure.savefig(figure_path + '/raster_patt_count_spectra.{}'.
                       format(format))
    # save p_values_table (only for window = 13 and n_occ = 4)
    #np.save('../figures/pvalues_table.npy', p_values_table)
    plt.show()


if __name__ == '__main__':
    # activate latex text rendering
    rc('text', usetex=True)

    # Load parameter from configfile
    with open("configfile.yaml", 'r') as stream:
        config = yaml.load(stream, Loader=Loader)
    spectra = config['spectra']
    # Load parameters dictionary
    param_dict = np.load('../data/art_data.npy', encoding='latin1').item()[
        'params']
    lengths = param_dict['lengths']
    binsize = param_dict['binsize']
    winlens = [int(l / binsize) + 1 for l in lengths]
    # Loading spiketrains and patterns
    data_file = np.load('../data/art_data.npy', encoding='latin1').item()
    # call of the plotting function
    plot(data_file=data_file,
         spectra=spectra,
         binsize=binsize,
         winlens=winlens)

"""
Plotting  of the comparison of 2d and 3d-SPADE with respect to pattern duration

The script produces:
Top panel: raster plot of parallel spike trains (y−axis).
Background spikes are shown in black , and the injected patterns in red.
Only 15 out of 100 parallel spike trains are shown for a duration of 350 ms,
and only 3 out of 6 possible patterns. Middle and lower panels: 2d-
SPADE results on the left, and 3d-SPADE on the right.
Middle panel: histograms of the number of detected significant patterns (STPs)
when using different analysis window length. Green crosses show the total
number of injected patterns.
Bottom panel: p−value spectra for each method. The p−values are indicated by
color scale. Significant signatures are marked with a red dot.
The script reproduces Figure 3 of the publication:
<NAME>., <NAME>., <NAME>., & <NAME>. (2019). 3d-SPADE: Significance
Evaluation of Spatio-Temporal Patterns of Various Temporal Extents. Biosystems.
"""
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
from matplotlib import rc
import yaml
from yaml import Loader
import quantities as pq
from utils import mkdirp, split_path


def plot(data_file, spectra, binsize, winlens, label_size=6, text_size=8,
         tick_size=5):
    """
    Function creating figure representing the difference of 2d- to 3d-SPADE
    in terms of statistical performance in the case of a data set with patterns
    of multiple durations.
    Parameters
    ----------
    data_file : list of neo.SpikeTrains
        file with the generated artificial data set
    spectra : str
        flag indicating the type of spectra employed (thus the correspondent
        SPADE version)
        if spectra == '#':
            then 2d-SPADE is called
        elif spectra == '3d#'
            then 3d-SPADE is called
    binsize : quantity
        bin size necessary for the SPADE analysis
    winlens : lis
        list of window length used for each run of SPADE
    label_size: int
        label size of the plot. Default is 6
    tick_size: int
        tick size of the plot. Default is 5
    text_size: int
        tick size of the plot. Default is 8
    """
    # Plotting parameters
    inch2cm = 0.3937

    sts = data_file['data']
    stps = data_file['patt']

    figure = plt.figure(figsize=(12*inch2cm, 9*inch2cm))
    figure.subplots_adjust(
        left=.08,
        right=.91,
        wspace=.8,
        hspace=1,
        top=.95,
        bottom=.1)
    ax_raster = plt.subplot2grid((3, 2 * np.sum(winlens[:5])), (0, 0),
                                 rowspan=1,
                                 colspan=2 * np.sum(winlens[:5]))
    for s_idx, s in enumerate(sts[:len(stps)]):
        s_sliced = s.time_slice(s.t_start + .15*pq.s, 0.4 * pq.s)
        ax_raster.plot(s_sliced, [s_idx] * len(s_sliced), 'k.', markersize=2)
    for p_idx, p in enumerate(stps):
        p_sliced = p.time_slice(s.t_start + .15*pq.s, 0.4 * pq.s)
        if p_idx == len(stps) - 1:
            ax_raster.plot(p_sliced, [p_idx] * len(p_sliced), 'ro',
                           label='STPs',
                           fillstyle='full',
                           markeredgecolor='red',
                           markersize=1.5)
        else:
            ax_raster.plot(p_sliced, [p_idx] * len(p_sliced), 'ro',
                           fillstyle='full',
                           markeredgecolor='red',
                           markersize=1.5)
    ax_raster.set_xlabel('time (s)', size=label_size)
    ax_raster.set_ylabel('neuron id', size=label_size)
    ax_raster.set_ylim(-0.5, 15)
    ax_raster.tick_params(labelsize=tick_size)

    # Plotting SPADE results
    p_values_table = {}
    for spectrum_idx, spectrum in enumerate(spectra):
        p_values_table[str(spectrum)] = {}
        ax_count = plt.subplot2grid((3, 2 * np.sum(winlens[:5])),
                                    (1, spectrum_idx*np.sum(winlens[:5])),
                                    rowspan=1,
                                    colspan=np.sum(winlens[:5]))
        # Plotting count and pval spectrum of patterns for each window length
        for w_idx, w in enumerate(winlens):
            # Load filtered results
            patterns, pval_spectrum, ns_sgnt, params = np.load(
                '../results/{}/winlen{}/filtered_patterns.npy'.format(spectrum,
                                                                      w),
                 encoding='latin1')

            ax_count.bar(w_idx+1, len(patterns), color='k', width=0.05)
            if w_idx == len(winlens)-1:
                ax_count.plot(w_idx + 1, w_idx + 1, 'x', markersize=4,
                              label='Number of STPs', color='g')
            else:
                ax_count.plot(w_idx + 1, w_idx + 1, 'x', markersize=4,
                              color='g')
            ax_count.set_yticks(np.arange(0, len(stps)+1))
            for tick in ax_count.xaxis.get_major_ticks():
                tick.label.set_fontsize(tick_size)
            # Plotting pvalue spectra
            pval_matrix = np.zeros((4, w))
            for sgnt in pval_spectrum:
                if spectrum == '#':
                    for length in range(w):
                        pval_matrix[sgnt[1]-2, length] = sgnt[2]
                elif spectrum == '3d#':
                    pval_matrix[sgnt[1]-2, sgnt[2]] = sgnt[3]
            ax_pval_spectrum = plt.subplot2grid(
                (3, 2 * np.sum(winlens[:5])),
                (2, spectrum_idx*np.sum(winlens[:5]) + sum(winlens[:w_idx])),
                colspan=sum(winlens[w_idx:w_idx + 1]))
            pcol = ax_pval_spectrum.pcolor(
                pval_matrix,  norm=LogNorm(vmin=0.0001, vmax=1),
                cmap=plt.cm.YlGnBu)
            if spectrum == '#':
                for duration in range(pval_matrix.shape[1]):
                    for occ in range(2, pval_matrix.shape[0]+2):
                        if (3, occ) not in ns_sgnt:
                            ax_pval_spectrum.plot(duration+0.5, occ-2+0.5,
                                                  'ro',
                                                  markersize=1)
            elif spectrum == '3d#':
                for duration in range(pval_matrix.shape[1]):
                    for occ in range(2, pval_matrix.shape[0]+2):
                        if (3, occ, duration) not in ns_sgnt:
                            ax_pval_spectrum.plot(duration+0.5, occ-2+0.5,
                                                  'ro',
                                                  markersize=1)
            x_grid = np.arange(0, pval_matrix.shape[1]+1, 1)
            y_grid = np.arange(0, pval_matrix.shape[0]+1, 1)
            x_ticks = np.arange(0.5, pval_matrix.shape[1], 2)
            y_ticks = np.arange(0.5, pval_matrix.shape[0])
            x, y = np.meshgrid(x_grid, y_grid)
            c = np.ones_like(x)
            ax_pval_spectrum.pcolor(x, y, c, facecolor='none', edgecolor='k')
            ax_pval_spectrum.set_xticks(x_ticks)
            ax_pval_spectrum.set_xticklabels(range(0, pval_matrix.shape[1], 2),
                                             size=tick_size)
            if w_idx == len(winlens)//2+1:
                ax_pval_spectrum.set_xlabel('d (ms)', size=label_size)
            if w_idx == 0 and spectrum == '#':
                ax_pval_spectrum.set_yticks(y_ticks)
                ax_pval_spectrum.set_yticklabels(range(2, pval_matrix.shape[0] + 2), size=tick_size)
                ax_pval_spectrum.set_ylabel('c', size=label_size)
            else:
                ax_pval_spectrum.set_yticklabels(())
                ax_pval_spectrum.set_yticks(())
            if w_idx == len(winlens) - 1 and spectrum == '3d#':
                cbar = figure.colorbar(pcol, ticks=[0.0001, 0.001, 0.01, 0.1, 1], ax=ax_pval_spectrum)
                cbar.set_label('p-values', size=label_size)
                cbar.ax.tick_params(labelsize=tick_size)
                cbar.ax.minorticks_off()
            ax_pval_spectrum.set_xlim(0, pval_matrix.shape[1])
            ax_pval_spectrum.set_title(int(w*binsize.magnitude),
                                       size=tick_size)
            if w == 13:
                # print(pval_matrix[-2, :])
                pattern_length = [0, 2, 6, 8, 12]
                p_val_arr = np.take(pval_matrix[2, :], pattern_length)
                p_values_table[str(spectrum)][str(w)] = p_val_arr
                # print(p_val_arr)

        if spectrum == '#':
            ax_count.set_title('2d-SPADE', size=text_size)
            ax_count.set_ylabel('detected STPs', size=label_size)
        elif spectrum == '3d#':
            ax_count.set_title('3d-SPADE', size=text_size)
            ax_count.set_yticks(())
        ax_count.set_xticks(range(1, len(winlens)+1))
        ax_count.set_xticklabels(winlens*int(binsize.magnitude))
        ax_count.tick_params(labelsize=tick_size)
        ax_count.set_ylim([0, len(winlens)+1])
        ax_count.set_xlabel('w (ms)', size=label_size)
        if spectrum_idx == 0:
            ax_count.legend(loc='best', fontsize=label_size)
    figure_path = '../figures'
    path_temp = './'
    for folder in split_path(figure_path):
        path_temp = path_temp + '/' + folder
        mkdirp(path_temp)
    fig_formats = ['eps', 'png']
    for format in fig_formats:
        figure.savefig(figure_path + '/raster_patt_count_spectra.{}'.
                       format(format))
    # save p_values_table (only for window = 13 and n_occ = 4)
    #np.save('../figures/pvalues_table.npy', p_values_table)
    plt.show()


if __name__ == '__main__':
    # activate latex text rendering
    rc('text', usetex=True)

    # Load parameter from configfile
    with open("configfile.yaml", 'r') as stream:
        config = yaml.load(stream, Loader=Loader)
    spectra = config['spectra']
    # Load parameters dictionary
    param_dict = np.load('../data/art_data.npy', encoding='latin1').item()[
        'params']
    lengths = param_dict['lengths']
    binsize = param_dict['binsize']
    winlens = [int(l / binsize) + 1 for l in lengths]
    # Loading spiketrains and patterns
    data_file = np.load('../data/art_data.npy', encoding='latin1').item()
    # call of the plotting function
    plot(data_file=data_file,
         spectra=spectra,
         binsize=binsize,
         winlens=winlens)

Plotting  of the comparison of 2d and 3d-SPADE with respect to pattern duration  The script produces: Top panel: raster plot of parallel spike trains (y−axis). Background spikes are shown in black , and the injected patterns in red. Only 15 out of 100 parallel spike trains are shown for a duration of 350 ms, and only 3 out of 6 possible patterns. Middle and lower panels: 2d- SPADE results on the left, and 3d-SPADE on the right. Middle panel: histograms of the number of detected significant patterns (STPs) when using different analysis window length. Green crosses show the total number of injected patterns. Bottom panel: p−value spectra for each method. The p−values are indicated by color scale. Significant signatures are marked with a red dot. The script reproduces Figure 3 of the publication: <NAME>., <NAME>., <NAME>., & <NAME>. (2019). 3d-SPADE: Significance Evaluation of Spatio-Temporal Patterns of Various Temporal Extents. Biosystems. Function creating figure representing the difference of 2d- to 3d-SPADE     in terms of statistical performance in the case of a data set with patterns     of multiple durations.     Parameters     ----------     data_file : list of neo.SpikeTrains         file with the generated artificial data set     spectra : str         flag indicating the type of spectra employed (thus the correspondent         SPADE version)         if spectra == '#':             then 2d-SPADE is called         elif spectra == '3d#'             then 3d-SPADE is called     binsize : quantity         bin size necessary for the SPADE analysis     winlens : lis         list of window length used for each run of SPADE     label_size: int         label size of the plot. Default is 6     tick_size: int         tick size of the plot. Default is 5     text_size: int         tick size of the plot. Default is 8 # Plotting parameters # Plotting SPADE results # Plotting count and pval spectrum of patterns for each window length # Load filtered results # Plotting pvalue spectra #': #': #': # print(pval_matrix[-2, :]) # print(p_val_arr) #': # save p_values_table (only for window = 13 and n_occ = 4) #np.save('../figures/pvalues_table.npy', p_values_table) # activate latex text rendering # Load parameter from configfile # Load parameters dictionary # Loading spiketrains and patterns # call of the plotting function