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aSPEM.py
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aSPEM.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
""" Using psychopy to perform an experiment on the role of a bias in the direction """
import sys
import os
import numpy as np
import pickle
def binomial_motion(N_trials, N_blocks, tau, seed, Jeffreys=True, N_layer=3):
"""
about Jeffrey's prior : see wikipedia
st_dict = dict( fontsize =14, fontstyle = 'italic’) % déjà j’ai pensé à ca :)
"""
from scipy.stats import beta
np.random.seed(seed)
trials = np.arange(N_trials)
p = np.random.rand(N_trials, N_blocks, N_layer)
for trial in trials:
p[trial, :, 2] = np.random.rand(1, N_blocks) < 1/tau # switch
if Jeffreys: p_random = beta.rvs(a=.5, b=.5, size=N_blocks)
else: p_random = np.random.rand(1, N_blocks)
p[trial, :, 1] = (1 - p[trial, :, 2])*p[trial-1, :, 1] + p[trial, :, 2] * p_random # probability
p[trial, :, 0] = p[trial, :, 1] > np.random.rand(1, N_blocks) # Bernouilli trial
return (trials, p)
class aSPEM(object):
""" docstring for the aSPEM class. """
def __init__(self, mode, timeStr, observer='test') :
self.mode = mode
self.observer = observer
self.timeStr = str(timeStr)
self.init()
def init(self) :
self.dry_run = True
self.dry_run = False
self.experiment = 'aSPEM'
self.instructions = """ TODO """
# ---------------------------------------------------
# setup values
# ---------------------------------------------------
cachedir = 'data_cache'
datadir = 'data'
import os
for dir_ in [datadir, cachedir] :
try: os.mkdir(dir_)
except: pass
file = self.mode + '_' + self.observer + '_' + self.timeStr + '.pkl'
if file in os.listdir(datadir) :
with open(os.path.join(datadir, file), 'rb') as fichier :
self.param_exp = pickle.load(fichier, encoding='latin1')
else :
expInfo = {"Sujet":'', "Age":''}
if not self.mode is 'model':
# Présente un dialogue pour changer les paramètres
Nom_exp = u'aSPEM'
try:
from psychopy import gui
dlg = gui.DlgFromDict(expInfo, title=Nom_exp)
PSYCHOPY = True
except:
PSYCHOPY = False
self.observer = expInfo["Sujet"]
self.age = expInfo["Age"]
# width and height of your screen
screen_width_px = 1280 #1920 #1280 for ordi enregistrement
screen_height_px = 1024 #1080 #1024 for ordi enregistrement
framerate = 60 #100.for ordi enregistrement
screen = 0 # 1 pour afficher sur l'écran 2 (ne marche pas pour eyeMvt (mac))
screen_width_cm = 37 #57. # (cm)
viewingDistance = 57. # (cm) TODO : what is the equivalent viewing distance?
screen_width_deg = 2. * np.arctan((screen_width_cm/2) / viewingDistance) * 180/np.pi
#px_per_deg = screen_height_px / screen_width_deg
px_per_deg = screen_width_px / screen_width_deg
# ---------------------------------------------------
# stimulus parameters
# ---------------------------------------------------
dot_size = 10 # (0.02*screen_height_px)
V_X_deg = 15 #20 # deg/s # 15 for 'eyeMvt'
V_X = px_per_deg * V_X_deg # pixel/s
RashBass = 100 # ms - pour reculer la cible à t=0 de sa vitesse * latency=RashBass
saccade_px = .618*screen_height_px
offset = 0 #.2*screen_height_px
# ---------------------------------------------------
# exploration parameters
# ---------------------------------------------------
N_blocks = 3 # 4 blocks avant
seed = 51 #119 #2017
N_trials = 200
tau = N_trials/5.
(trials, p) = binomial_motion(N_trials, N_blocks, tau=tau, seed=seed, N_layer=3)
stim_tau = .75 #1 #.35 # in seconds # 1.5 for 'eyeMvt'
gray_tau = .0 # in seconds
T = stim_tau + gray_tau
N_frame_stim = int(stim_tau*framerate)
# ---------------------------------------------------
self.param_exp = dict(N_blocks=N_blocks, seed=seed, N_trials=N_trials, p=p, tau=tau,
stim_tau =stim_tau,
N_frame_stim=N_frame_stim, T=T,
datadir=datadir, cachedir=cachedir,
framerate=framerate,
screen=screen,
screen_width_px=screen_width_px, screen_height_px=screen_height_px,
px_per_deg=px_per_deg, offset=offset,
dot_size=dot_size, V_X_deg=V_X_deg, V_X =V_X, RashBass=RashBass, saccade_px=saccade_px,
mode=self.mode, observer=self.observer, age=self.age, timeStr=self.timeStr)
def print_protocol(self):
if True: #try:
N_blocks = self.param_exp['N_blocks']
N_trials = self.param_exp['N_trials']
N_frame_stim = self.param_exp['N_frame_stim']
T = self.param_exp['T']
return "TODO"
# return """
# ##########################
# # PROTOCOL #
# ##########################
#
# except:
# return 'blurg'
def exp_name(self):
return os.path.join(self.param_exp['datadir'], self.mode + '_' + self.observer + '_' + self.timeStr + '.pkl')
def run_experiment(self, verb=True):
#if verb: print('launching experiment')
from psychopy import visual, core, event, logging, prefs
prefs.general['audioLib'] = [u'pygame']
from psychopy import sound
if self.mode=='eyeMvt' :
import EyeTracking as ET
ET = ET.EyeTracking(self.param_exp['screen_width_px'], self.param_exp['screen_height_px'], self.param_exp['dot_size'], self.param_exp['N_trials'], self.observer, self.param_exp['datadir'], self.timeStr)
#logging.console.setLevel(logging.WARNING)
#if verb: print('launching experiment')
#logging.console.setLevel(logging.WARNING)
#if verb: print('go!')
# ---------------------------------------------------
win = visual.Window([self.param_exp['screen_width_px'], self.param_exp['screen_height_px']], color=(0, 0, 0),
allowGUI=False, fullscr=True, screen=self.param_exp['screen'], units='pix') # enlever fullscr=True pour écran 2
win.setRecordFrameIntervals(True)
win._refreshThreshold = 1/self.param_exp['framerate'] + 0.004 # i've got 50Hz monitor and want to allow 4ms tolerance
# ---------------------------------------------------
if verb: print('FPS = ', win.getActualFrameRate() , 'framerate=', self.param_exp['framerate'])
# ---------------------------------------------------
target = visual.Circle(win, lineColor='white', size=self.param_exp['dot_size'], lineWidth=2)
fixation = visual.GratingStim(win, mask='circle', sf=0, color='white', size=self.param_exp['dot_size'])
ratingScale = visual.RatingScale(win, scale=None, low=-1, high=1, precision=100, size=.7, stretch=2.5,
labels=('Left', 'unsure', 'Right'), tickMarks=[-1, 0., 1], tickHeight=-1.0,
marker='triangle', markerColor='black', lineColor='White', showValue=False, singleClick=True,
showAccept=False, pos=(0, -self.param_exp['screen_height_px']/3)) #size=.4
#Bip_pos = sound.Sound('2000', secs=0.05)
#Bip_neg = sound.Sound('200', secs=0.5) # augmenter les fq
# ---------------------------------------------------
# fonction pause avec possibilité de quitter l'expérience
msg_pause = visual.TextStim(win, text=u"\n\n\nTaper sur une touche pour continuer\n\nESCAPE pour arrêter l'expérience",
font='calibri', height=25,
alignHoriz='center')#, alignVert='top')
text_score = visual.TextStim(win, font='calibri', height=30, pos=(0, self.param_exp['screen_height_px']/9))
def pause(mode) :
msg_pause.draw()
win.flip()
event.clearEvents()
allKeys=event.waitKeys()
for thisKey in allKeys:
if thisKey in ['escape', 'a', 'q']:
core.quit()
win.close()
if mode=='eyeMvt' :
ET.End_trial()
ET.End_exp()
if mode=='eyeMvt' :
win.winHandle.set_fullscreen(False)
win.winHandle.set_visible(False) # remis pour voir si ça enléve l'écran blanc juste après calibration
ET.drift_correction()
win.winHandle.set_visible(True) # remis pour voir si ça enléve l'écran blanc juste après calibration
win.winHandle.set_fullscreen(True)
def escape_possible(mode) :
if event.getKeys(keyList=['escape', 'a', 'q']):
win.close()
core.quit()
if mode=='eyeMvt' :
ET.End_trial()
ET.End_exp()
# ---------------------------------------------------
def presentStimulus_fixed(dir_bool):
dir_sign = dir_bool * 2 - 1
target.setPos((dir_sign * (self.param_exp['saccade_px']), self.param_exp['offset']))
target.draw()
win.flip()
core.wait(0.3)
clock = core.Clock()
myMouse = event.Mouse(win=win)
def presentStimulus_move(dir_bool):
clock.reset()
#myMouse.setVisible(0)
dir_sign = dir_bool * 2 - 1
while clock.getTime() < self.param_exp['stim_tau']:
escape_possible(self.mode)
# la cible à t=0 recule de sa vitesse * latency=RashBass (ici mis en s)
target.setPos(((dir_sign * self.param_exp['V_X']*clock.getTime())-(dir_sign * self.param_exp['V_X']*(self.param_exp['RashBass']/1000)), self.param_exp['offset']))
target.draw()
win.flip()
win.flip()
escape_possible(self.mode)
#win.flip()
# ---------------------------------------------------
# EXPERIMENT
# ---------------------------------------------------
if self.mode == 'bet' : results = np.zeros((self.param_exp['N_trials'], self.param_exp['N_blocks'] ))
if self.mode == 'eyeMvt':
ET.Start_exp()
# Effectuez la configuration du suivi au début de l'expérience.
win.winHandle.set_fullscreen(False)
win.winHandle.set_visible(False) # remis pour voir si ça enléve l'écran blanc juste après calibration
ET.calibration()
win.winHandle.set_visible(True) # remis pour voir si ça enléve l'écran blanc juste après calibration
win.winHandle.set_fullscreen(True)
if self.mode == 'bet' : score = 0
for block in range(self.param_exp['N_blocks']):
x = 0
if self.mode == 'bet' :
text_score.text = '%1.0f/100' %(score / 50 * 100)
text_score.draw()
score = 0
pause(self.mode)
for trial in range(self.param_exp['N_trials']):
# ---------------------------------------------------
# PAUSE tous les 50 essais
# ---------------------------------------------------
if x == 50 :
if self.mode == 'bet' :
text_score.text = '%1.0f/100' %(score / 50 * 100)
text_score.draw()
score = 0
pause(self.mode)
x = 0
x = x +1
# ---------------------------------------------------
# FIXATION
# ---------------------------------------------------
if self.mode == 'bet' :
event.clearEvents()
ratingScale.reset()
while ratingScale.noResponse :
fixation.draw()
ratingScale.draw()
escape_possible(self.mode)
win.flip()
ans = ratingScale.getRating()
results[trial, block] = ans
if self.mode == 'eyeMvt':
ET.check()
ET.Start_trial(trial)
fixation.draw()
tps_start_fix = time.time()
win.flip()
escape_possible(self.mode)
ET.StimulusON(tps_start_fix)
ET.Fixation(fixation, tps_start_fix, win, escape_possible)
# ---------------------------------------------------
# GAP
# ---------------------------------------------------
win.flip()
escape_possible(self.mode)
if self.mode == 'eyeMvt': ET.StimulusOFF()
core.wait(0.3)
# ---------------------------------------------------
# Mouvement cible
# ---------------------------------------------------
escape_possible(self.mode)
dir_bool = self.param_exp['p'][trial, block, 0]
if self.mode == 'eyeMvt': ET.TargetON()
presentStimulus_move(dir_bool)
escape_possible(self.mode)
win.flip()
if self.mode == 'bet' :
score_trial = ans * (dir_bool * 2 - 1)
# if score_trial > 0 :
# Bip_pos.setVolume(score_trial)
# Bip_pos.play()
# else :
# Bip_neg.setVolume(-score_trial)
# Bip_neg.play()
# core.wait(0.1)
score += score_trial
if self.mode == 'eyeMvt':
ET.TargetOFF()
ret_value = ET.fin_enregistrement()
ET.check_trial(ret_value)
if self.mode == 'bet' : self.param_exp['results'] = results
if self.mode == 'eyeMvt': ET.End_exp()
with open(self.exp_name(), 'wb') as fichier:
f = pickle.Pickler(fichier)
f.dump(self.param_exp)
win.update()
core.wait(0.5)
win.saveFrameIntervals(fileName=None, clear=True)
win.close()
core.quit()
#############################################################################
######################### ANALYSIS ##########################################
#############################################################################
def mutual_information(p, data, bin_p=20, bin_data=30):
"""
Script to calculate Mutual Information between two discrete random variables
Roberto maestre - rmaestre@gmail.com
Bojan Mihaljevic - boki.mihaljevic@gmail.com
Calculate and return Mutual information between two random variables
https://github.com/rmaestre/Mutual-Information/blob/master/it_tool.py
https://fr.wikipedia.org/wiki/Information_mutuelle
"""
#----------------------------------------------------------------------
#def mutual_information(hgram):
# """ Mutual information for joint histogram
# https://matthew-brett.github.io/teaching/mutual_information.html"""
# # Convert bins counts to probability values
# pxy = hgram / float(np.sum(hgram))
# px = np.sum(pxy, axis=1) # marginal for x over y
# py = np.sum(pxy, axis=0) # marginal for y over x
# px_py = px[:, None] * py[None, :] # Broadcast to multiply marginals
# # Now we can do the calculation using the pxy, px_py 2D arrays
# nzs = pxy > 0 # Only non-zero pxy values contribute to the sum
# return np.sum(pxy[nzs] * np.log(pxy[nzs] / px_py[nzs]))
#----------------------------------------------------------------------
#x = np.round(p, decimals=base)
##y = np.round(data, decimals=base)
#
#if min(data)<0 : y = np.round(data, decimals=base-1)
#else : y = np.round(data, decimals=base)
#
#support_x = set(x)
#support_y = set(y)
#summation = 0.00
#for value_x in support_x:
# for value_y in support_y:
# px = np.shape(np.where(x==value_x))[1] / len(x) #px=sum(x==value_x)/len(x) -> bcp plus long
# py = np.shape(np.where(y==value_y))[1] / len(x) #py=sum(y==value_y)/len(y) -> bcp plus long
# pxy = len(np.where(np.in1d(np.where(x==value_x)[0],
# np.where(y==value_y)[0])==True)[0]) / len(x)
# if pxy>0.00: summation += pxy * np.log2(pxy / (px*py))
#----------------------------------------------------------------------
x = p
y = data
support_x = np.linspace(min(x), max(x), bin_p)
support_y = np.linspace(min(y), max(y), int(len(data)/bin_data))
summation = 0.00
for a in range(len(support_x)-1):
for b in range(len(support_y)-1):
ind_x = np.where((x>=support_x[a]) & (x<=support_x[a+1]))[0]
ind_y = np.where((y>=support_y[b]) & (y<=support_y[b+1]))[0]
px = len(ind_x) / len(x)
py = len(ind_y) / len(x)
pxy = len(np.where(np.in1d(ind_x, ind_y)==True)[0]) / len(x)
if pxy>0.00: summation += pxy * np.log2(pxy / (px*py))
return summation
def regress(ax, p, data, y1=0, y2=1, t_label=10, color='k', x1=-0.032, x2=1.032, pos='right', line=True, text=True, return_r_mi=False, lw=2) :
from scipy import stats
slope, intercept, r_, p_value, std_err = stats.linregress(p, data)
x_test = np.linspace(np.min(p), np.max(p), 100)
fitLine = slope * x_test + intercept
#hist, x_edges, y_edges = np.histogram2d(p, data, bins=20)
#hist, x_edges, y_edges = np.histogram2d(p, data, bins=len(data)/30)
# mi =mutual_information(hist)
mi = mutual_information(p, data)
if line is True : ax.plot(x_test, fitLine, c=color, linewidth=lw)
if pos[-5:]=='right' : x_pos=x2-(x2-x1)/10 ; h_pos='right'
else: x_pos=x1+(x2-x1)/10 ; h_pos='left'
if pos[:5]=='upper' : y_pos_r=y2-(y2-y1)/10 ; y_pos_m=y2-2*(y2-y1)/10
else: y_pos_r=y1+(y2-y1)/10 ; y_pos_m=y1+2*(y2-y1)/10
if text is True :
ax.text(x_pos, y_pos_r, 'r = %0.3f'%(r_), color=color, fontsize=t_label/1.2, ha=h_pos)
ax.text(x_pos, y_pos_m, 'MI = %0.3f'%(mi), color=color, fontsize=t_label/1.2, ha=h_pos)
if return_r_mi is True : return ax, r_, mi
else : return ax
def results_sujet(self, ax, sujet, s, mode_bcp, tau, t_label, pause, color = [['k', 'k'], ['r', 'r'], ['k','w']], alpha = [[.35,.15],[.35,.15],[1,0]], color_bcp='darkred'):
import bayesianchangepoint as bcp
from scipy import stats
lw = 1.3
ec = 0.2 # pour l'écart entre les différents blocks
suj = sujet[s]
print('Subject', suj, '=', self.subjects[suj])
N_trials = self.BET[self.subjects[suj]]['N_trials']
N_blocks = self.BET[self.subjects[suj]]['N_blocks']
p = self.BET[self.subjects[suj]]['p']
# tau = N_trials/5.
h = 1./tau
results = (self.BET[self.subjects[suj]]['results']+1)/2 # results est sur [-1,1] on le ramene sur [0,1]
a_anti = self.EYEMVT[self.subjects[suj]]['a_anti']
for block in range(N_blocks) :
#----------------------------------------------------------------------------------
ax.step(range(N_trials), block+p[:, block, 1]+ec*block, lw=1, alpha=alpha[1][0], c=color[1][0])
ax.fill_between(range(N_trials), block+np.zeros_like(p[:, block, 1])+ec*block, block+p[:, block, 1]+ec*block,
lw=0, alpha=alpha[1][0], facecolor=color[1][0], step='pre')
ax.fill_between(range(N_trials), block+np.ones_like(p[:, block, 1])+ec*block, block+p[:, block, 1]+ec*block,
lw=0, alpha=alpha[1][1], facecolor=color[1][1], step='pre')
ax.step(range(N_trials), block+((np.array(a_anti[block])-np.nanmin(a_anti))/(np.nanmax(a_anti)-np.nanmin(a_anti)))+ec*block,
color='k', lw=1.2, label='Eye movements' if block==0 else '')
ax.step(range(N_trials), block+results[:, block]+ec*block, color='r', lw=1.2, label='Individual guess' if block==0 else '')
#----------------------------------------------------------------------------------
if mode_bcp is not None :
if pause is True : liste = [0,50,100,150,200]
else : liste = [0, 200]
for a in range(len(liste)-1) :
p_bar, r, beliefs = bcp.inference(p[liste[a]:liste[a+1], block, 0], h=h, p0=.5)
p_hat, r_hat = bcp.readout(p_bar, r, beliefs,mode=mode_bcp)
p_low, p_sup = np.zeros_like(p_hat), np.zeros_like(p_hat)
for i_trial in range(liste[a+1]-liste[a]):
p_low[i_trial], p_sup[i_trial] = stats.beta.ppf([.05, .95], a=p_hat[i_trial]*r_hat[i_trial], b=(1-p_hat[i_trial])*r_hat[i_trial])
ax.plot(np.arange(liste[a], liste[a+1]), block+p_hat+ec*block, c=color_bcp, alpha=.9, lw=1.5,
label='$\hat{x}_1$' if block==0 and a==0 else '')
ax.plot(np.arange(liste[a], liste[a+1]), block+p_sup+ec*block, c=color_bcp, ls='--', lw=1.2, label='CI' if block==0 and a==0 else '')
ax.plot(np.arange(liste[a], liste[a+1]), block+p_low+ec*block, c=color_bcp, ls= '--', lw=1.2)
ax.fill_between(np.arange(liste[a], liste[a+1]), block+p_sup+ec*block, block+p_low+ec*block, lw=.5, alpha=.11, facecolor=color_bcp)
#------------------------------------------------
# affiche les numéro des block sur le côté gauche
#------------------------------------------------
ax_block = ax.twinx()
if s == 0 : ax_block.set_ylabel('Block', fontsize=t_label/1.5, rotation='horizontal', ha='left', va='bottom')
ax_block.yaxis.set_label_coords(1.01, 1.08)
ax_block.set_ylim(0, N_blocks)
ax_block.set_yticks(np.arange(N_blocks)+0.5)
ax_block.set_yticklabels(np.arange(N_blocks)+1, fontsize=t_label/1.5)
ax_block.yaxis.set_tick_params(width=0, pad=(t_label/1.5)+10)
#------------------------------------------------
ax.set_yticks([0, 1, 1+ec, 2+ec, 2+ec*2, 3+ec*2])
ax.set_yticklabels(['0','1','0','1','0','1'],fontsize=t_label/1.8)
ax.yaxis.set_label_coords(-0.02, 0.5)
ax.set_ylabel('Subject %s'%(suj), fontsize=t_label)
ax.set_ylim(-(ec/2), N_blocks +ec*3-(ec/2))
ax.set_xlim(0, N_trials)
#------------------------------------------------
#------------------------------------------------
# Barre Pause
#------------------------------------------------
ax.bar(50, 3+ec*3, bottom=-ec/2, color='k', width=.2, linewidth=0)
ax.bar(100, 3+ec*3, bottom=-ec/2, color='k', width=.2, linewidth=0)
ax.bar(150, 3+ec*3, bottom=-ec/2, color='k', width=.2, linewidth=0)
return ax
class Analysis(object):
""" docstring for the aSPEM class. """
def __init__(self, observer=None, mode=None, name_file_fit='fct_velocity_sigmo') :
self.subjects = ['s01','s02','s03','s04','s05','s06','s07','s08','s09','s10','s11','s12']
self.name_file_fit = name_file_fit
self.mode = mode
self.observer = observer
self.init()
def init(self) :
self.dry_run = True
self.dry_run = False
# ---------------------------------------------------
# setup values
# ---------------------------------------------------
cachedir = 'data_cache'
datadir = 'data'
import os
for dir_ in [datadir, cachedir] :
try: os.mkdir(dir_)
except: pass
# ---------------------------------------------------
# récuperation de toutes les données
# ---------------------------------------------------
import glob
liste = {'bet':{}, 'eyeMvt':{}}
for fname in glob.glob('data/*pkl'):
a = fname.split('/')[1].split('.')[0].split('_')
liste[a[0]][a[1]] = a[2]+'_'+a[3]
self.BET = {}
for s in liste['bet'].keys() :
if s in self.subjects :
a = 'data/bet_%s_%s.pkl'%(s, liste['bet'][s])
with open(a, 'rb') as fichier :
b = pickle.load(fichier, encoding='latin1')
self.BET[s] = b
a = 'parametre/Delete/Delete_list_BadTrials_Full_%s.pkl'%(self.name_file_fit)
try :
with open(a, 'rb') as fichier :
b = pickle.load(fichier, encoding='latin1')
self.list_delete = b
except :
self.list_delete = None
print('/!\ Le fichier Delete n\'existe pas pour %s !'%(self.name_file_fit))
self.EYEMVT = {}
for s in liste['eyeMvt'].keys() :
if s in self.subjects:
a = 'parametre/%s/param_Fit_%s_%s.pkl'%(self.name_file_fit, s, self.name_file_fit)
try :
with open(a, 'rb') as fichier :
b = pickle.load(fichier, encoding='latin1')
self.EYEMVT[s] = b
except :
print('/!\ Le fichier param Fit n\'existe pas pour %s !'%(s))
# ---------------------------------------------------
if self.observer is None : self.observer = 's08'
if self.mode is None : self.mode = 'eyeMvt'
self.timeStr = liste[self.mode][self.observer]
self.param_exp = self.BET[self.observer]
if self.mode == 'eyeMvt' : self.param = self.EYEMVT[self.observer]
def Full_list(self, modes_bcp=['expectation', 'max', 'mean', 'fixed', 'leaky', 'hindsight'], pause=True):
import pandas as pd
pd.set_option('mode.chained_assignment', None)
N_trials = self.param_exp['N_trials']
N_blocks = self.param_exp['N_blocks']
p = self.param_exp['p']
full = pd.DataFrame(index=np.arange(len(self.subjects)*N_trials*N_blocks), columns=('sujet', 'proba','bino','results','aa','va'))
if modes_bcp is not None :
if type(modes_bcp) is not list : modes_bcp = [modes_bcp]
import bayesianchangepoint as bcp
for m in modes_bcp : full['p_hat_%s'%m] = np.arange(len(self.subjects)*N_trials*N_blocks)*np.nan
for i, suj in enumerate(self.subjects) :
results = (self.BET[suj]['results']+1)/2
a_anti = self.EYEMVT[suj]['a_anti']
start_anti = self.EYEMVT[suj]['start_anti']
latency = self.EYEMVT[suj]['latency']
for block in range(N_blocks):
nb = i*N_trials*N_blocks
a = nb + N_trials*block
b = (nb + N_trials*(block+1))
full['sujet'][a:b] = suj
full['proba'][a:b] = p[:, block, 1]
full['bino'][a:b] = p[:, block, 0]
full['results'][a:b] = results[:, block]
full['aa'][a:b] = a_anti[block]
full['va'][a:b] = (np.array(a_anti[block])*((np.array(latency[block])-np.array(start_anti[block]))/1000))
for t in self.list_delete[suj][block] :
full['aa'][a+t] = np.nan
full['va'][a+t] = np.nan
if modes_bcp is not None :
tau = N_trials/5.
h = 1./tau
p_hat_block = {}
for m in modes_bcp : p_hat_block[m] = []
if pause is True :
liste = [0,50,100,150,200]
for s in range(len(liste)-1) :
p_bar, r_bar, beliefs = bcp.inference(p[liste[s]:liste[s+1], block, 0], h=h, p0=.5)
for m in modes_bcp :
p_hat, r_hat = bcp.readout(p_bar, r_bar, beliefs, mode=m)
p_hat_block[m].extend(p_hat)
else :
p_bar, r_bar, beliefs = bcp.inference(p[:, block, 0], h=h, p0=.5)
for m in modes_bcp :
p_hat, r_hat = bcp.readout(p_bar, r_bar, beliefs, mode=m)
p_hat_block[m] = p_hat
for m in modes_bcp : full['p_hat_%s'%m][a:b] = p_hat_block[m]
return full
def Data_Scalling(self):
Full = Analysis.Full_list(self, modes_bcp=None)
scal_va_sujet, scal_va_full = {}, []
scal_bet_sujet, scal_bet_full = {}, []
va_full, bet_full = [], []
for x in self.subjects :
bet = list(Full['results'][Full.sujet==x])
va = list(Full['va'][Full.sujet==x])
bet_full.extend(bet)
va_full.extend(va)
scal_bet_sujet[x] = np.sort(bet)
scal_va_sujet[x] = np.sort(va)
scal_va_full = np.sort(va_full)
scal_bet_full = np.sort(bet_full)
new_proba_full = np.linspace(0,1,len(scal_va_full))
new_va_sujet, new_va_full = {}, {}
new_bet_sujet, new_bet_full = {}, {}
for x in self.subjects :
N_trials = self.param_exp['N_trials']
N_blocks = self.param_exp['N_blocks']
new_proba_sujet = np.linspace(0, 1, len(scal_va_sujet[x]))
va_sujet = list(Full['va'][Full.sujet==x])
bet_sujet = list(Full['results'][Full.sujet==x])
nb_trial = 0
new_va_sujet[x], new_va_full[x] = [], []
new_bet_sujet[x], new_bet_full[x] = [], []
for block in range(N_blocks) :
new_va_sujet[x].append([])
new_va_full[x].append([])
new_bet_sujet[x].append([])
new_bet_full[x].append([])
va = va_sujet[nb_trial : N_trials + nb_trial*block]
bet = bet_sujet[nb_trial : N_trials + nb_trial*block]
for trial in range(N_trials) :
new_va_sujet[x][block].append(np.mean([new_proba_sujet[t] for t in range(len(scal_va_sujet[x]))
if scal_va_sujet[x][t]==va[trial]]))
new_va_full[x][block].append(np.mean([new_proba_full[t] for t in range(len(scal_va_full))
if scal_va_full[t]==va[trial]]))
new_bet_sujet[x][block].append(np.mean([new_proba_sujet[t] for t in range(len(scal_bet_sujet[x]))
if scal_bet_sujet[x][t]==bet[trial]]))
new_bet_full[x][block].append(np.mean([new_proba_full[t] for t in range(len(scal_bet_full))
if scal_bet_full[t]==bet[trial]]))
nb_trial = nb_trial + N_trials
new_data = {}
new_data['new_bet_full'] = new_bet_full
new_data['new_bet_sujet'] = new_bet_sujet
new_data['new_va_full'] = new_va_full
new_data['new_va_sujet'] = new_va_sujet
return new_data
def Find_h(self, new_bet, new_va, modes_bcp='mean') :
from lmfit import Model, Parameters
from lmfit import minimize
import bayesianchangepoint as bcp
def fct_BCP(x, tau, fixed_window, sujet, block, cent) :
# TODO merge des blocks puis range(0, 600, 50) pour les pauses plus param = taille de window
h = 1/tau
p_hat = np.zeros(len(x))
if sujet==True :
for b in range(self.param_exp['N_blocks']):
nb = self.param_exp['N_trials']*b
liste = [0, 50, 100, 150, 200]
for a in range(len(liste)-1) :
p_bar, r_bar, beliefs = bcp.inference(x[nb+liste[a]:nb+liste[a+1]], h=h, p0=.5, r0=1.)
p_hat_p, r_hat = bcp.readout(p_bar, r_bar, beliefs, mode=modes_bcp, p0=.5, fixed_window_size=fixed_window)
p_hat[nb+liste[a]:nb+liste[a+1]] = p_hat_p
elif block==True :
liste = [0, 50, 100, 150, 200]
for a in range(len(liste)-1) :
p_bar, r_bar, beliefs = bcp.inference(x[liste[a]:liste[a+1]], h=h, p0=.5, r0=1.)
p_hat_p, r_hat = bcp.readout(p_bar, r_bar, beliefs, mode=modes_bcp, p0=.5, fixed_window_size=fixed_window)
p_hat[liste[a]:liste[a+1]] = p_hat_p
elif cent==True :
liste = [0, 50, 100]
for a in range(len(liste)-1) :
p_bar, r_bar, beliefs = bcp.inference(x[liste[a]:liste[a+1]], h=h, p0=.5, r0=1.)
p_hat_p, r_hat = bcp.readout(p_bar, r_bar, beliefs, mode=modes_bcp, p0=.5, fixed_window_size=fixed_window)
p_hat[liste[a]:liste[a+1]] = p_hat_p
else :
p_bar, r_bar, beliefs = bcp.inference(x, h=h, p0=.5, r0=1.)
p_hat, r_hat = bcp.readout(p_bar, r_bar, beliefs, mode=modes_bcp, p0=.5, fixed_window_size=fixed_window)
return p_hat
def KL_distance(p_data, p_hat):
distance = p_hat * np.log2(p_hat) - p_hat * np.log2(p_data + 1.*(p_data==0.))
distance += (1-p_hat) * np.log2(1-p_hat) - (1-p_hat) * np.log2(1-p_data + 1.*(p_data==1.))
return distance
def residual(params, x, data):
model = fct_BCP(x, params['tau'], params['fixed_window'], params['sujet'], params['block'], params['cent'])
return KL_distance(data, model)
def fit(h, x, bet, va, sujet=False, block=False, cent=False):
tau = 1/h
x, bet, va = np.array(x), np.array(bet), np.array(va)
params = Parameters()
if modes_bcp in ['leaky', 'fixed'] :
params.add('tau', value=tau, vary=False)
params.add('fixed_window', value=tau, min=1)
else :
params.add('tau', value=tau, min=1)
params.add('fixed_window', value=tau, vary=False)
params.add('sujet', value=sujet, vary=False)
params.add('block', value=block, vary=False)
params.add('cent', value=cent, vary=False)
result_res = minimize(residual, params, args=(x, bet), nan_policy='omit')
result_v_ant = minimize(residual, params, args=(x, va), nan_policy='omit')
if modes_bcp in ['leaky', 'fixed'] :
h_bet = 1/result_res.params['fixed_window'].value
h_va = 1/result_v_ant.params['fixed_window'].value
else :
h_bet = 1/result_res.params['tau'].value
h_va = 1/result_v_ant.params['tau'].value
return h_bet, h_va
h_bet, h_va = {}, {}
for l in ['pause', 'block', 'sujet', '100'] : h_bet[l], h_va[l] = {}, {}
for x, sujet in enumerate(self.subjects) :
print(sujet, end=' ')
prob_sujet, bet_sujet, a_anti_sujet = [], [], []
p = p = self.param_exp['p']
tau = self.param_exp['N_trials']/5.
h = 1./tau
for l in ['pause', 'block', 'sujet', '100'] : h_bet[l][sujet], h_va[l][sujet] = [], []
#----------------------------------------------------
# BLOCK
#----------------------------------------------------
for block in range(self.param_exp['N_blocks']):
bet = new_bet[sujet][block]
va = new_va[sujet][block]
prob_block = p[:, block, 0]
h_b, h_v = fit(h, prob_block, bet, va, block=True)
h_bet['block'][sujet].append(h_b)
h_va['block'][sujet].append(h_v)
prob_sujet.extend(p[:, block, 0])
bet_sujet.extend(bet)
a_anti_sujet.extend(va)
#----------------------------------------------------
# PAUSE
#----------------------------------------------------
liste = [0,50,100,150,200]
for a in range(len(liste)-1) :
va_p = va[liste[a]:liste[a+1]]
bet_p = bet[liste[a]:liste[a+1]]
prob_pause = p[liste[a]:liste[a+1], block, 0]
h_b, h_v = fit(h, prob_pause, bet_p, va_p)
h_bet['pause'][sujet].append(h_b)
h_va['pause'][sujet].append(h_v)
#----------------------------------------------------
# SUJET
#----------------------------------------------------
h_b, h_v = fit(h, prob_sujet, bet_sujet, a_anti_sujet, sujet=True)
h_bet['sujet'][sujet].append(h_b)
h_va['sujet'][sujet].append(h_v)
#----------------------------------------------------
# 100 Trials
#----------------------------------------------------
for a in range(0, self.param_exp['N_blocks']*self.param_exp['N_trials']-50, 50) :
h_b, h_v = fit(h, prob_sujet[a:a+100], bet_sujet[a:a+100], a_anti_sujet[a:a+100], cent=True)
h_bet['100'][sujet].append(h_b)
h_va['100'][sujet].append(h_v)
return h_bet, h_va
#------------------------------------------------------------------------------------------------------------------------
def plot_equation(self, equation='fct_velocity', fig_width=15, t_titre=35, t_label=20) :
'''
Returns figure of the equation used for the fit with the parameters of the fit
Parameters
----------
equation : str or function
if 'fct_velocity' displays the fct_velocity equation
if 'fct_position' displays the fct_position equation
if 'fct_saccades' displays the fct_saccades equation
if function displays the function equation
fig_width : int
figure size
t_titre : int
size of the title of the figure
t_label : int
size x and y label
Returns
-------
fig : matplotlib.figure.Figure
figure
ax : AxesSubplot
figure
'''
# from pygazeanalyser.edfreader import read_edf
from ANEMO import read_edf
#from edfreader import read_edf
from ANEMO import ANEMO
Plot = ANEMO.Plot(self.param_exp)
resultats = os.path.join('data', self.mode + '_' + self.observer + '_' + self.timeStr + '.asc')
data = read_edf(resultats, 'TRIALID')
fig, axs = Plot.plot_equation(equation=equation, fig_width=fig_width, t_titre=t_titre, t_label=t_label)
return fig, axs
def plot_data(self, show='velocity', trials=0, block=0,
N_trials=None,
fig_width=15, t_titre=35, t_label=20,
stop_search_misac=None, name_trial_show=False, before_sacc=5, after_sacc=15) :
'''
Returns the data figure
Parameters
----------
show : str
if 'velocity' show the velocity of the eye
if 'position' show the position of the eye
if 'saccades' shows the saccades of the eye
trials : int or list
number or list of trials to display
block : int
number of the block in which it finds the trials to display
N_trials : int
number of trials per block
if None went searched in param_exp
before_sacc: int
time to remove before saccades
it is advisable to put :
5 for 'fct_velocity' and 'fct_position'
0 for 'fct_saccade'
after_sacc: int
time to delete after saccades
it is advisable to put : 15
stop_search_misac : int
stop search of micro_saccade
if None: stops searching at the end of fixation + 100ms
name_trial_show : bool
if True the num is written of the trial in y_label
fig_width : int
figure size