DREAM/utils.py at main · fcomlop/DREAM · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import minimize
from scipy.special import erfi
from math import pi

def cdf(data, bins=10):
    data = data[data[:,1]==1]
    p=np.arange(100/bins, 100, 100/bins)
    cdf=np.percentile(data[:,0], p)
    return cdf

def delta(times_incongr, times_congr, bins=10):
    p = np.arange(100/bins, 100, 100/bins)
    diffs=np.percentile(times_incongr, p) - np.percentile(times_congr, p)
    means=(np.percentile(times_incongr, p)+np.percentile(times_congr, p))/2
    return means, diffs

def caf(data, bins=5):
    data = data[data[:,0].argsort()]
    trials_per_bin = int(np.floor(len(data[:,0]) / bins))
    caf = np.zeros(bins)
    for idx in range(bins):
        caf[idx] = np.mean(data[trials_per_bin*idx:trials_per_bin*(idx+1), 1])
    return caf

def plot_all_sim(caf_congr, caf_incongr, cdf_congr, cdf_incongr, save_name=None):
    fig, ax = plt.subplots(1,3, figsize=(16,4))

    #CAF
    percentiles = np.linspace(1/len(caf_congr), 1, len(caf_congr))
    ax[0].plot(percentiles, caf_congr, color='black', linewidth=1, label="Congruent")
    ax[0].plot(percentiles, caf_incongr, color='darkgray', linewidth=1, label="Incongruent")
    ax[0].set_ylabel('CAF')
    ax[0].set_xlabel('Time bin')
    ax[0].legend()
    #CDF
    percentiles = np.linspace(1/len(cdf_congr), 1, len(cdf_congr))
    ax[1].plot(cdf_congr, percentiles, color='black', linewidth=1, label="Congruent")
    ax[1].plot(cdf_incongr, percentiles, color='darkgray', linewidth=1, label="Incongruent")
    ax[1].set_ylabel('CDF')
    ax[1].set_xlabel('Time [ms]')
    #Delta
    exp_delta_x = (cdf_congr + cdf_incongr)/2
    exp_delta_y = cdf_incongr - cdf_congr
    ax[2].plot(exp_delta_x, exp_delta_y, linestyle='--', color='black', linewidth=1)
    ax[2].set_ylabel('Delta')
    ax[2].set_xlabel('Time [ms]')

    if save_name:
        fig.savefig('results/'+save_name+'.pdf', format='pdf', dpi=1200, bbox_inches='tight')

def plot_all_exp(caf_exp_congr, caf_exp_incongr, cdf_exp_congr, cdf_exp_incongr, save_name=None):
    fig, ax = plt.subplots(1,3, figsize=(16,4))

    #CAF
    percentiles = np.linspace(1/len(caf_exp_congr), 1, len(caf_exp_congr))
    ax[0].scatter(percentiles, caf_exp_congr, marker='o', s=50, facecolors='none', edgecolors='black', label='Congruent')
    ax[0].scatter(percentiles, caf_exp_incongr, marker='v', s=50, facecolors='none', edgecolors='black', label='Incongruent')
    ax[0].set_ylabel('CAF')
    ax[0].set_xlabel('Time bin')
    ax[0].legend()
    #CDF
    percentiles = np.linspace(1/len(cdf_exp_congr), 1, len(cdf_exp_congr))
    ax[1].scatter(cdf_exp_congr, percentiles, marker='o', s=50, facecolors='none', edgecolors='black', label='Congruent')
    ax[1].scatter(cdf_exp_incongr, percentiles, marker='v', s=50, facecolors='none', edgecolors='black', label='Incongruent')
    ax[1].set_ylabel('CDF')
    ax[1].set_xlabel('Time [ms]')
    #Delta
    exp_delta_x = (cdf_exp_congr + cdf_exp_incongr)/2
    exp_delta_y = cdf_exp_incongr - cdf_exp_congr
    ax[2].scatter(exp_delta_x, exp_delta_y, marker='D', s=50, facecolors='none', edgecolors='black')
    ax[2].set_ylabel('Delta')
    ax[2].set_xlabel('Time [ms]')

    if save_name:
        fig.savefig('results/'+save_name+'.pdf', format='pdf', dpi=1200, bbox_inches='tight')


def plot_all_fits(caf_exp_congr, caf_exp_incongr, caf_fit_congr, caf_fit_incongr,
              cdf_exp_congr, cdf_exp_incongr, cdf_fit_congr, cdf_fit_incongr,
              save_name=None):
    fig, ax = plt.subplots(1,3, figsize=(16,4))

    #CAF
    percentiles = np.linspace(1/len(caf_exp_congr), 1, len(caf_exp_congr))
    ax[0].scatter(percentiles, caf_exp_congr, marker='o', s=50, facecolors='none', edgecolors='black', label='Congruent, observed')
    ax[0].scatter(percentiles, caf_exp_incongr, marker='v', s=50, facecolors='none', edgecolors='black', label='Incongruent, observed')
    ax[0].plot(percentiles, caf_fit_congr, color='black', linewidth=1, label="Congruent, predicted")
    ax[0].plot(percentiles, caf_fit_incongr, color='darkgray', linewidth=1, label="Incongruent, predicted")
    ax[0].set_ylabel('CAF')
    ax[0].set_xlabel('Time bin')
    ax[0].legend()

    #CDF
    percentiles = np.linspace(1/len(cdf_exp_congr), 1, len(cdf_exp_congr))
    ax[1].scatter(cdf_exp_congr, percentiles, marker='o', s=50, facecolors='none', edgecolors='black', label='Congruent, observed')
    ax[1].scatter(cdf_exp_incongr, percentiles, marker='v', s=50, facecolors='none', edgecolors='black', label='Incongruent, observed')
    ax[1].plot(cdf_fit_congr, percentiles, color='black', linewidth=1, label="Congruent, predicted")
    ax[1].plot(cdf_fit_incongr, percentiles, color='darkgray', linewidth=1, label="Incongruent, predicted")
    ax[1].set_ylabel('CDF')
    ax[1].set_xlabel('Time [ms]')

    #Delta
    exp_delta_x = (cdf_exp_congr + cdf_exp_incongr)/2
    exp_delta_y = cdf_exp_incongr - cdf_exp_congr
    fit_delta_x = (cdf_fit_congr + cdf_fit_incongr)/2
    fit_delta_y = cdf_fit_incongr - cdf_fit_congr
    plt.scatter(exp_delta_x, exp_delta_y, marker='D', s=50, facecolors='none', edgecolors='black', label='Observed')
    ax[2].plot(fit_delta_x, fit_delta_y, linestyle='--', color='black', linewidth=1, label="Predicted")
    ax[2].set_ylabel('Delta')
    ax[2].set_xlabel('Time [ms]')
    ax[2].legend()

    if save_name:
        fig.savefig('results/'+save_name+'.pdf', format='pdf', dpi=1200, bbox_inches='tight')

def plot_activations(t, expected_X_c, expected_X_a_congr, expected_X_s_congr, expected_X_a_incongr, expected_X_s_incongr,
                     multi_X_s_congr, multi_X_s_incongr, save_name=None):
    fig, ax = plt.subplots(1,2, figsize=(16,4))

    # Expected activations
    ax[0].plot(t, expected_X_c, color='black', linestyle='dotted', label='Controlled')
    ax[0].plot(t, expected_X_a_congr, color='black', linestyle='--', label='Automatic, congruent')
    ax[0].plot(t, expected_X_a_incongr, color='darkgray', linestyle='--', label='Automatic, incongruent')
    ax[0].plot(t, expected_X_s_congr, color='black',  label='Superimposed, congruent')
    ax[0].plot(t, expected_X_s_incongr, color='darkgray',  label='Superimposed, incongruent')
    ax[0].set_xlabel('Time [ms]')
    ax[0].set_ylabel('Mean activations')
    ax[0].set_ylim(-np.max(expected_X_s_congr), np.max(expected_X_s_congr))
    ax[0].legend()

    # Examples
    for idx in range(multi_X_s_congr.shape[0]):
        ax[1].plot(t, multi_X_s_congr[idx,:], color='black', label='Congruent' if idx==0 else '_nolegend_')
        ax[1].plot(t, multi_X_s_incongr[idx,:], color='darkgray', label='Incongruent' if idx==0 else '_nolegend_')
    ax[1].set_xlabel('Time [ms]')
    ax[1].set_ylabel('Example activations')
    ax[1].set_ylim(-np.max(expected_X_s_congr), np.max(expected_X_s_congr))
    ax[1].legend()

    if save_name:
        fig.savefig('results/'+save_name+'.pdf', format='pdf', dpi=1200, bbox_inches='tight')

def plot_multi_delta(cdfs_congr, cdfs_incongr, labels, title, save_name=None):
    fig, ax = plt.subplots(figsize=(16/3,4))
    grays = ['#000000','#282828','#505050','#707070','#909090','#A9A9A9','#C0C0C0']

    for idx in range(7):
        fit_delta_x = (cdfs_congr[idx,:] + cdfs_incongr[idx,:])/2
        fit_delta_y = cdfs_incongr[idx,:] - cdfs_congr[idx,:]
        ax.plot(fit_delta_x, fit_delta_y, color=grays[idx], linewidth=1, label='%.1e' %labels[idx])
        ax.set_ylabel('Delta')
        ax.set_xlabel('Time [ms]')
        ax.set_title(title)
        ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))

    if save_name:
        fig.savefig('results/'+save_name+'.pdf', format='pdf', dpi=1200, bbox_inches='tight')