plotters.py

import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import rayleigh
from scipy.stats import norm

"""
Functions for helping with plotting of various figures and data
"""

def detection(x, y, t, v, fixations, saccades, pursuits, blinks, trials, trial, axs, hz):
    if trials > 1:
        axs1 = axs[trial - 1]
    else:
        axs1 = axs

    hz = round(hz)
    axs1.set_title('Trial {}, recorded at {}hz'.format(trial,hz))
    axs1.set_xlabel('Time [s]')

    axs1.plot(t, v, 'silver', label="Velocity")
    axs1.set_ylabel('Velocity [deg/s]')
    axs1.set_ylim([-1000, 1000])

    axs2 = axs1.twinx()
    axs2.plot(t, x, 'tab:orange', label="Horizontal")
    axs2.plot(t, y, 'tab:green', label="Vertical")
    axs2.set_ylabel('Angel [deg]')
    axs2.set_ylim([-22.5, 22.5])

    for i in range(len(fixations)):
        axs1.axvspan(fixations[i, 0], fixations[i, 1], color='g', alpha=.1, label =  "_"*i + "Fixations")
    for i in range(len(saccades)):
        axs1.axvspan(saccades[i, 0], saccades[i, 1], color='r', alpha=.1, label =  "_"*i + "Saccades")
    for i in range(len(pursuits)):
        axs1.axvspan(pursuits[i, 0], pursuits[i, 1], color='y', alpha=.1, label =  "_"*i + "Pursuits")
    for i in range(len(blinks)):
        axs1.axvspan(blinks[i, 0], blinks[i, 1], color='b', alpha=.1, label =  "_"*i + "Blinks")

    axs1.legend(loc='upper left')
    axs2.legend(loc='upper right')

def calculation(fixations, saccades, pursuits, blinks, trial, participant):
    plt.figure(trial + 1, figsize=[25.60, 14.40])
    plt.suptitle('Gaze event analysis for participant {}, trail {}'.format(participant, trial))
    if fixations.any():
        plt.subplot(4, 4, 1)
        histogramreighley(fixations[:, 2])
        plt.title("Fixation duration")
        plt.xlabel('Time [s]')

    if saccades.any():
        plt.subplot(4, 4, 5)
        histogramreighley(saccades[:, 2])
        plt.title("Saccade duration")
        plt.xlabel('Time [s]')
        plt.subplot(4, 4, 6)
        histogramreighley(saccades[:, 7])
        plt.title("Saccade amplitude")
        plt.xlabel('Amplitude [deg]')
        plt.subplot(4, 4, 7)
        histogramreighley(saccades[:, 8])
        plt.title("Saccade mean velocity")
        plt.xlabel('Velocity [deg/s]')
        plt.subplot(4, 4, 8)
        histogramreighley(saccades[:, 9])
        plt.title("Saccade max velocity")
        plt.xlabel('Velocity [deg/s]')

    if pursuits.any():
        plt.subplot(4, 4, 9)
        histogramreighley(pursuits[:, 2])
        plt.title("Pursuit duration")
        plt.xlabel('Time [s]')
        plt.subplot(4, 4, 10)
        histogramreighley(pursuits[:, 7])
        plt.title("Pursuit amplitude")
        plt.xlabel('Amplitude [deg]')
        plt.subplot(4, 4, 11)
        plt.xlabel('Amplitude [deg]')
        histogramreighley(pursuits[:, 8])
        plt.title("Pursuit mean velocity")
        plt.xlabel('Velocity [deg/s]')
        plt.subplot(4, 4, 12)
        histogramreighley(pursuits[:, 9])
        plt.title("Pursuit max velocity")
        plt.xlabel('Velocity [deg/s]')

    if blinks.any():
        plt.subplot(4, 4, 13)
        histogramreighley(blinks[:, 2])
        plt.title("Blink duration")
        plt.xlabel('Time [s]')

    plt.tight_layout()


def histogramreighley(data):
    N = len(data)
    scale = data.mean() / np.sqrt(np.pi / 2)
    V_norm_hist = scale * np.sqrt(-2 * np.log(np.random.uniform(0, 1, N)))

    num_bins = 30
    _binvalues, bins, _patches = plt.hist(V_norm_hist, bins=num_bins, density=False, rwidth=1, ec='white',
                                          label='Histogram data')
    x = np.linspace(bins[0], bins[-1], 100)
    binwidth = (bins[-1] - bins[0]) / num_bins

    scale = V_norm_hist.mean() / np.sqrt(np.pi / 2)

    plt.plot(x, rayleigh(loc=0, scale=scale).pdf(x) * len(V_norm_hist) * binwidth, lw=5, alpha=0.6,
             label=f'Rayleigh pdf (s={scale:.3f})')
    plt.axvline(data.mean(), color='red', lw=3, alpha=0.6, label='Mean = ' + str(data.mean()))
    plt.ylabel('samples [n]')
    plt.grid(True)
    plt.legend()


def histogramfit(data):
    # best fit of data
    (mu, sigma) = norm.fit(data)

    # the histogram of the data
    n, bins, patches = plt.hist(data, len(data), alpha=0.75)

    # add a 'best fit' line
    y = norm.pdf(bins, mu, sigma)
    l = plt.plot(bins, y, 'r--', linewidth=2)

    # plot
    plt.ylabel('samples [n]')
    plt.title(r'$\mathrm{Histogram\ of\ IQ:}\ \mu=%.3f,\ \sigma=%.3f$' % (mu, sigma))
    plt.grid(True)