def get_unconditional_results(df, action, monitor_num, dep_var):
data = utils.get_data(df, action, monitor_num)
results = utils.group_bootstrap(lambda x: utils.diff_in_means(
x, dep_var, 'condition', 'control', action),
data,
'coin_name',
samples=1000)
return results
def empirical_state_actions(version):
import sys
sys.path.append('../experiment/lib')
from analysis_utils import get_data
data = get_data(version, '../experiment/data')
tdf = data['mouselab-mdp'].query('block == "test"')
def extract(q):
return list(map(int, q['click']['state']['target']))
return zip(tdf.trial_id, tdf.queries.apply(extract))
def main(file_name=None):
""" Run the analysis on data in a file"""
# Define these two within the scope of main:
def func(pars):
a,b,c = pars
return a*(x**2) + b*x + c
def errfunc(pars):
return y-func(pars)
if file_name is None:
#path_to_files = '/Volumes/Plata1/Shared/Ariel/texture_data/'
file_name = fileOpenDlg()[0]
p,l,data_rec = utils.get_data(file_name)
neutral = data_rec[np.where(data_rec['neutral'])]
peripheral = data_rec[np.where(data_rec['neutral']==0)]
cond_str = ['Neutral', 'Cued']
colors = ['b','r']
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
print("SOA used was: %s msec"%(1000*p[' texture_dur']))
print("% correct: ")
for cond_idx,cond_rec in enumerate([neutral,peripheral]):
correct = cond_rec['correct']
ecc = cond_rec['target_ecc']
#Bin the eccentricities:
a = np.floor(ecc)
eccs_used = np.unique(a)
#Initialize counters:
b = np.zeros(len(eccs_used))
c = np.zeros(len(eccs_used))
#Loop over the trials and add the correct to one counter and the number of
#trials to the other:
for i in xrange(len(correct)):
idx = np.where(eccs_used==np.floor(ecc[i]))
b[idx]+=correct[i]
c[idx]+=1.0
p_correct = b/c
print("%s: %s "%(cond_str[cond_idx], np.mean(p_correct)*100))
for i,p in enumerate(p_correct):
ax.plot(eccs_used[i],p,'o',color=colors[cond_idx],markersize=c[i])
x = []
y = []
for i,this_ecc in enumerate(eccs_used):
x = np.hstack([x,c[i]*[this_ecc]])
y = np.hstack([y,c[i]*[p_correct[i]]])
guess = 1,1,1
fit, mesg = leastsq(errfunc,guess)
x = np.arange(0,np.max(x),0.01)
ax.plot(x,func(fit),'--',color=colors[cond_idx],
label=cond_str[cond_idx])
ax.legend()
ax.set_xlim([-1,13])
ax.set_ylim([0,1.1])
ax.set_xlabel('Eccentricity (degrees)')
ax.set_ylabel('Proportion correct responses')
fig_name = 'figures/' + file_name.split('.')[0].split('/')[-1] + '.png'
fig.savefig(fig_name)
os.system('open %s'%fig_name)
import sys
from matplotlib.mlab import csv2rec
import matplotlib.pyplot as plt
import numpy as np
import analysis_utils as a
if __name__ == "__main__":
path_to_files = '/Volumes/Plata1/Shared/Ariel/fiorentini_data/'
file_name = sys.argv[1]
p, l, data_rec = a.get_data('%s/%s' % (path_to_files, file_name))
fix_idx = np.where(data_rec['task'] == 'fixation')
per_idx = np.where(data_rec['task'] == 'periphery')
fix_idx = np.where(data_rec['task'] == 'fixation')
c = data_rec['correct']
amp = data_rec['contrast']
th_per = a.analyze(amp[per_idx], c[per_idx])
th_fix = a.analyze(amp[fix_idx], c[fix_idx])
x, y = th_per[4], th_per[5]
fig = plt.figure()
ax = fig.add_subplot(1, 2, 1)
ax.plot(x, y, 'o')
x_for_plot = np.linspace(np.min(x), np.max(x), 100)
ax.plot(x_for_plot, a.weibull(x_for_plot, th_per[0], th_per[3]))
def main(file_name=None):
""" Run the analysis on data in a file"""
# Define these two within the scope of main:
def func(pars):
a, b, c = pars
return a * (x ** 2) + b * x + c
def errfunc(pars):
return y - func(pars)
if file_name is None:
# path_to_files = '/Volumes/Plata1/Shared/Ariel/texture_data/'
file_name = fileOpenDlg()[0]
p, l, data_rec = utils.get_data(file_name)
# For backwards compatibility, check if this variable exists:
if "eye_moved" in l:
data_rec = data_rec[np.where(data_rec["eye_moved"] == 0)]
neutral = data_rec[np.where(data_rec["neutral"])]
peripheral = data_rec[np.where(data_rec["neutral"] == 0)]
cond_str = ["Neutral", "Cued"]
colors = ["b", "r"]
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
print("SOA used was: %s msec" % (1000 * p[" texture_dur"]))
print("% correct: ")
for cond_idx, cond_rec in enumerate([neutral, peripheral]):
correct = cond_rec["correct"]
ecc = cond_rec["target_ecc"]
# Bin the eccentricities:
a = np.floor(ecc)
eccs_used = np.unique(a)
# Initialize counters:
b = np.zeros(len(eccs_used))
c = np.zeros(len(eccs_used))
# Loop over the trials and add the correct to one counter and the number of
# trials to the other:
for i in xrange(len(correct)):
idx = np.where(eccs_used == np.floor(ecc[i]))
b[idx] += correct[i]
c[idx] += 1.0
p_correct = b / c
print("%s: %s " % (cond_str[cond_idx], np.mean(p_correct) * 100))
for i, p in enumerate(p_correct):
ax.plot(eccs_used[i], p, "o", color=colors[cond_idx], markersize=c[i])
x = []
y = []
for i, this_ecc in enumerate(eccs_used):
x = np.hstack([x, c[i] * [this_ecc]])
y = np.hstack([y, c[i] * [p_correct[i]]])
guess = 1, 1, 1
fit, mesg = leastsq(errfunc, guess)
x = np.arange(0, np.max(x), 0.01)
ax.plot(x, func(fit), "--", color=colors[cond_idx], label=cond_str[cond_idx])
ax.legend()
ax.set_xlim([-1, 13])
ax.set_ylim([0, 1.1])
ax.set_xlabel("Eccentricity (degrees)")
ax.set_ylabel("Proportion correct responses")
fig_name = "figures/" + file_name.split(".")[0].split("/")[-1] + ".png"
fig.savefig(fig_name)
os.system("open %s" % fig_name)
bootstrap_n = 1000
#Weibull params set in analysis_utils: The guessing rate is 0.25 for 4afc
guess = 0.5
flake = 0.01
slope = 3.5
file_names = gui.fileOpenDlg(tryFilePath='./data')
for file_idx, file_name in enumerate(file_names):
print file_idx
if file_idx == 0:
file_stem = file_name.split('/')[-1].split('.')[0]
else:
file_stem = file_stem + file_name[-8]
p, l, data_rec = ana.get_data(str(file_name))
trials_per_condition = float(p[' trials_per_block'])*(float(p[' num_blocks'])/2.0)
print trials_per_condition
contrast = np.ones([len(file_names)*trials_per_condition,1])
correct = np.ones([len(file_names)*trials_per_condition,1])
data_rec = csv2rec(file_name)
contrast_this_run = data_rec['annulus_target_contrast']
correct_this_run = data_rec['correct']
block_type = data_rec['block_type']
print p[' trials_per_dummy']
for n in range(trials_per_condition):
if n >= p[' trials_per_dummy']:
contrast[n+(trials_per_condition*file_idx)] *= contrast_this_run[n]
correct[n+(trials_per_condition*file_idx)] *= correct_this_run[n]
if not os.path.exists('data/analyzed_data'):
os.mkdir('data/analyzed_data')
import sys
from matplotlib.mlab import csv2rec
import matplotlib.pyplot as plt
import numpy as np
import analysis_utils as a
if __name__ == "__main__":
path_to_files = "/Volumes/Plata1/Shared/Ariel/fiorentini_data/"
file_name = sys.argv[1]
p, l, data_rec = a.get_data("%s/%s" % (path_to_files, file_name))
fix_idx = np.where(data_rec["task"] == "fixation")
per_idx = np.where(data_rec["task"] == "periphery")
fix_idx = np.where(data_rec["task"] == "fixation")
c = data_rec["correct"]
amp = data_rec["contrast"]
th_per = a.analyze(amp[per_idx], c[per_idx])
th_fix = a.analyze(amp[fix_idx], c[fix_idx])
x, y = th_per[4], th_per[5]
fig = plt.figure()
ax = fig.add_subplot(1, 2, 1)
ax.plot(x, y, "o")
x_for_plot = np.linspace(np.min(x), np.max(x), 100)
ax.plot(x_for_plot, a.weibull(x_for_plot, th_per[0], th_per[3]))
