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generate_design_matrix_original.py
407 lines (347 loc) · 15.8 KB
/
generate_design_matrix_original.py
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# -*- coding: utf-8 -*-
"""
function for the
A script that extracts the conditions from eprime csv file,
and saves them into an SPM multiconditions file (.mat).
It saves the corresponding movement regressors from movement_files/
into a (.mat) file in movement_files
The conditions are :
*
- Anticip_hit_largewin
- Anticip_hit_smallwin
- Anticip_hit_nowin
- Anticip_missed_largewin
- Anticip_missed_smallwin
- Anticip_missed_nowin
- Anticip_noresp
*
- Feedback_hit_largewin
- Feedback_hit_smallwin
- Feedback_hit_nowin
- Feedback_missed_largewin
- Feedback_missed_smallwin
- Feedback_missed_nowin
- Feedback_noresp
*
- Press_left
- Press_right
"""
import os, glob
from collections import OrderedDict
import numpy as np
from scipy import io
import matplotlib.pyplot as plt
import pandas as pd
import xlsxwriter
from nipy.modalities.fmri import design_matrix
from nipy.modalities.fmri.experimental_paradigm import BlockParadigm
BASE_DIR = os.path.join('eprime_files', 'csv')
DST_BASE_DIR = os.path.join('eprime_files', 'mat')
N_SCANS = 289
TR = 2400.
START_DELAY = 6000.
TASK_DURATION = {'anticip': 4., 'feedback': 1.45}
def check_subject_eprime(eprime_file, mapping):
"""A temporary function that checks if the eprime id
has an existing corresponding subject
"""
eprime_nb = eprime_file.split('/')[-1].split('.')[0].rsplit('-')[-2]
res = mapping[mapping['eprime'] == int(eprime_nb)]['subject'].values
return res, eprime_nb
def generate_multiconditions_excel(output_file, conditions, onset, condition):
"""Generate a *.xlsx file which contains the conditions
"""
work = xlsxwriter.Workbook(output_file + '.xlsx')
wsheet = work.add_worksheet('Conditions')
col = 0
for key in conditions.keys():
wsheet.write(0, col, key)
row = 1
for item in conditions[key]:
wsheet.write(row, col, item)
row += 1
col += 1
wsheet2 = work.add_worksheet('Timeline')
order = onset.argsort()
wsheet2.write(0, 0, 'onset')
wsheet2.write(0, 1, 'condition')
row = 1
for i in order:
wsheet2.write(row, 0, onset[i])
wsheet2.write(row, 1, condition[i])
row += 1
work.close()
def generate_multiregressors_mat(output_file, regressors):
"""Generate a *.mat file that contains the regressors
"""
io.savemat(output_file, {'R': regressors})
def generate_multiconditions_mat(output_file, conditions, ddurations):
"""Generate a *.mat file that contains the names, the onsets
and the durations, according to SPM's mutiple coditions file
"""
names = np.zeros((len(conditions),), dtype=np.object)
onsets = np.zeros((len(conditions),), dtype=np.object)
durations = np.zeros((len(conditions),), dtype=np.object)
for i in np.arange(0, len(conditions)):
names[i] = conditions.keys()[i]
onsets[i] = conditions[names[i]]
durations[i] = ddurations[names[i]]
if len(onsets[i])==0:
durations[i] = 0.
onsets[i] = [3600.]
print output_file
io.savemat(output_file, {'names' : names,
'onsets' : onsets,
'durations' : durations})
return names, onsets, durations
def compute_mid_conditions(filename):
df = pd.read_csv(filename)
# Extract hits, misses and noresps
# hits
hit = np.zeros(len(df))
h_idx = df[df['PictureTarget.RESP'].notnull()]['TrialList']
hit[h_idx.values - 1] = 1
# noresps
noresp = np.zeros(len(df))
n_idx = df[df['PictureTarget.RESP'].isnull()]['TrialList']
noresp[n_idx.values - 1] = 1
# misses
miss = np.zeros(len(df))
m_idx = df[df['PictureTarget.RESP'].isnull()]['TrialList']
miss[m_idx.values - 1] = 1
# Extract bigwins, smallwins and nowins
# big wins
largewin = np.zeros(len(df))
lw_idx = df[df['prize']==10]['TrialList']
largewin[lw_idx.values - 1] = 1
# small wins
smallwin = np.zeros(len(df))
sw_idx = df[df['prize']==2]['TrialList']
smallwin[sw_idx.values - 1] = 1
# no wins
nowin = np.zeros(len(df))
nw_idx = df[df['prize']==0]['TrialList']
nowin[nw_idx.values - 1] = 1
# Extract press left (5), press right (4)
# press left
pleft = np.zeros(len(df))
pl_idx = df[df['PictureTarget.RESP'] == 5]['TrialList']
pleft[pl_idx.values - 1] = 1
# press right
pright = np.zeros(len(df))
pr_idx = df[df['PictureTarget.RESP'] == 4]['TrialList']
pright[pr_idx.values - 1] = 1
# Extract times
first_onset = df['PicturePrime.OnsetTime'][0]
anticip_start_time = (df['PicturePrime.OnsetTime'] - first_onset + START_DELAY - 2 * TR)/1000.
response_time = (df['PictureTarget.RTTime'] - first_onset + START_DELAY - 2 * TR)/1000.
feedback_start_time = (df['PictureTarget.OnsetTime'] + df['Target_time'] - first_onset + START_DELAY - 2 * TR)/1000.
# Compute conditions
cond = pd.DataFrame({'response_time': response_time,
'anticip_start_time': anticip_start_time,
'feedback_start_time': feedback_start_time})
# Anticipation
anticip_hit_largewin = cond[(hit==1) & (largewin==1)]['anticip_start_time'].values
anticip_hit_smallwin = cond[(hit==1) & (smallwin==1)]['anticip_start_time'].values
anticip_hit_nowin = cond[(hit==1) & (nowin==1)]['anticip_start_time'].values
anticip_hit = np.hstack((anticip_hit_largewin,
anticip_hit_smallwin, anticip_hit_nowin))
anticip_hit_modgain = np.hstack([[3.]*len(anticip_hit_largewin),
[2.]*len(anticip_hit_smallwin),
[1.]*len(anticip_hit_nowin)])
anticip_missed_largewin = cond[(miss==1) & (largewin==1)]['anticip_start_time'].values
anticip_missed_smallwin = cond[(miss==1) & (smallwin==1)]['anticip_start_time'].values
anticip_missed_nowin = cond[(miss==1) & (nowin==1)]['anticip_start_time'].values
anticip_missed = np.hstack((anticip_missed_largewin,
anticip_missed_smallwin, anticip_missed_nowin))
anticip_missed_modgain = np.hstack([[3.]*len(anticip_missed_largewin),
[2.]*len(anticip_missed_smallwin),
[1.]*len(anticip_missed_nowin)])
anticip_noresp = cond[(noresp==1)]['anticip_start_time'].values
# Feedback
feedback_hit_largewin = cond[(hit==1) & (largewin==1)]['feedback_start_time'].values
feedback_hit_smallwin = cond[(hit==1) & (smallwin==1)]['feedback_start_time'].values
feedback_hit_nowin = cond[(hit==1) & (nowin==1)]['feedback_start_time'].values
feedback_hit = np.hstack((feedback_hit_largewin,
feedback_hit_smallwin, feedback_hit_nowin))
feedback_hit_modgain = np.hstack([[3.]*len(feedback_hit_largewin),
[2.]*len(feedback_hit_smallwin),
[1.]*len(feedback_hit_nowin)])
feedback_missed_largewin = cond[(miss==1) & (largewin==1)]['feedback_start_time'].values
feedback_missed_smallwin = cond[(miss==1) & (smallwin==1)]['feedback_start_time'].values
feedback_missed_nowin = cond[(miss==1) & (nowin==1)]['feedback_start_time'].values
feedback_missed = np.hstack((feedback_missed_largewin,
feedback_missed_smallwin, feedback_missed_nowin))
feedback_missed_modgain = np.hstack([[3.]*len(feedback_missed_largewin),
[2.]*len(feedback_missed_smallwin),
[1.]*len(feedback_missed_nowin)])
feedback_noresp = cond[(noresp==1)]['feedback_start_time'].values
# Response
press_left = cond[(pleft==1)]['response_time'].values
press_right = cond[(pright==1)]['response_time'].values
# namelist
namelist = ['anticip_hit', 'anticip_missed', 'anticip_noresp',
'feedback_hit', 'feedback_missed', 'feedback_noresp',
'press_left', 'press_right']
modulationnamelist = ['anticip_hit_modgain', 'anticip_missed_modgain',
'feedback_hit_modgain', 'feedback_missed_modgain']
conditions = OrderedDict()
"""
#XXX As the missed case is often missing, we won't use it at this time
#XXX missed case has been corrected
conditions = {'anticip_hit_largewin' : anticip_hit_largewin,
'anticip_hit_smallwin' : anticip_hit_smallwin,
'anticip_hit_nowin' : anticip_hit_nowin,
'anticip_missed_largewin' : anticip_missed_largewin,
'anticip_missed_smallwin' : anticip_missed_smallwin,
'anticip_missed_nowin' : anticip_missed_nowin,
'anticip_noresp' : anticip_noresp,
'feedback_hit_largewin' : feedback_hit_largewin,
'feedback_hit_smallwin' : feedback_hit_smallwin,
'feedback_hit_nowin' : feedback_hit_nowin,
'feedback_missed_largewin' : feedback_missed_largewin,
'feedback_missed_smallwin' : feedback_missed_smallwin,
'feedback_missed_nowin' : feedback_missed_nowin,
'feedback_noresp' : feedback_noresp,
'press_left' : press_left,
'press_right' : press_right}
"""
conditions['anticip_hit_largewin'] = anticip_hit_largewin
conditions['anticip_hit_smallwin'] = anticip_hit_smallwin
conditions['anticip_hit_nowin'] = anticip_hit_nowin
conditions['anticip_missed_largewin'] = anticip_missed_largewin
conditions['anticip_missed_smallwin'] = anticip_missed_smallwin
conditions['anticip_missed_nowin'] = anticip_missed_nowin
#conditions['anticip_noresp'] = anticip_noresp
conditions['feedback_hit_largewin'] = feedback_hit_largewin
conditions['feedback_hit_smallwin'] = feedback_hit_smallwin
conditions['feedback_hit_nowin'] = feedback_hit_nowin
conditions['feedback_missed_largewin'] = feedback_missed_largewin
conditions['feedback_missed_smallwin'] = feedback_missed_smallwin
conditions['feedback_missed_nowin'] = feedback_missed_nowin
#conditions['feedback_noresp'] = feedback_noresp
conditions['press_left'] = press_left
conditions['press_right'] = press_right
durations = OrderedDict()
for k in conditions.keys():
if 'feedback' in k:
durations[k] = TASK_DURATION['feedback']
elif 'anticip' in k:
durations[k] = TASK_DURATION['anticip']
else:
durations[k] = 0.
"""
durations = {'anticip_hit_largewin' : 4.,
'anticip_hit_smallwin' : 4.,
'anticip_hit_nowin' : 4.,
'anticip_missed_largewin' : 4.,
'anticip_missed_smallwin' : 4.,
'anticip_missed_nowin' : 4.,
'anticip_noresp' : 4.,
'feedback_hit_largewin' : 1.45,
'feedback_hit_smallwin' : 1.45,
'feedback_hit_nowin' : 1.45,
'feedback_missed_largewin' : 1.45,
'feedback_missed_smallwin' : 1.45,
'feedback_missed_nowin' : 1.45,
'feedback_noresp' : 1.45,
'press_left' : 0.,
'press_right' : 0.}
"""
return conditions, durations
##############################################################################
##############################################################################
##############################################################################
##############################################################################
##############################################################################
# Load eprime csv file
file_list = sorted(glob.glob(os.path.join(BASE_DIR, 'c_*.csv')))
for f in file_list:
# Compute conditions
conditions, durations = compute_mid_conditions(f)
# Load regressors if they exist
mapping = pd.read_csv(os.path.join('eprime_files', 'mapping.csv'),
names=['eprime','subject'])
subject_id, eprime_id = check_subject_eprime(f, mapping)
if len(subject_id)>0:
print subject_id[0]
filepath = os.path.join('movement_files',
''.join(['S',str(subject_id[0]), '_reg.csv']))
if os.path.isfile(filepath):
reg = pd.read_csv(filepath)
regressors = reg.values[:,1:]
# Create paradigms
empty_conditions = []
pseudo_empty_conditions = []
condition = []
onset = []
duration = []
for c in conditions:
if len(conditions[c]) < 1:
empty_conditions.append(c)
elif len(conditions[c]) < 2:
pseudo_empty_conditions.append(c)
condition += [c]*len(conditions[c])
onset = np.hstack([onset, conditions[c]])
duration += [durations[c]]*len(conditions[c])
paradigm = BlockParadigm(con_id=condition,
onset=onset,
duration=duration)
frametimes = np.linspace(0, (N_SCANS-1)*TR/1000., num=N_SCANS)
design_mat = design_matrix.make_dmtx(frametimes, paradigm,
hrf_model='Canonical with derivative',
add_regs=regressors,
drift_model='Cosine',
hfcut=128)
# Fill empty conditions
for c in empty_conditions:
if c == 'anticip_missed_largewin':
ind = 6
elif c == 'feedback_missed_largewin':
ind = 18
elif c == 'anticip_missed_smallwin':
ind = 10
elif c == 'feedback_missed_smallwin':
ind = 20
elif c == 'anticip_missed_nowin':
ind = 8
elif c == 'feedback_missed_nowin':
ind = 22
design_mat.matrix = np.insert(design_mat.matrix,
ind, 1, axis=1)
design_mat.matrix = np.insert(design_mat.matrix,
ind+1, 1, axis=1)
design_mat.names.insert(ind, c)
design_mat.names.insert(ind+1, c + '_derivative')
# Fill pseudo empty conditions
print empty_conditions
print pseudo_empty_conditions
for c in pseudo_empty_conditions:
if c == 'anticip_missed_largewin':
ind = 6
elif c == 'feedback_missed_largewin':
ind = 18
elif c == 'anticip_missed_smallwin':
ind = 10
elif c == 'feedback_missed_smallwin':
ind = 20
elif c == 'anticip_missed_nowin':
ind = 8
elif c == 'feedback_missed_nowin':
ind = 22
design_mat.matrix[:, ind] = np.ones(N_SCANS)
design_mat.matrix[:, ind+1] = np.ones(N_SCANS)
# Reorder conditions
for i in np.arange(2,21,6):
for j in range(2):
tmp = design_mat.matrix[:,i+j].copy()
design_mat.matrix[:,i+j] = design_mat.matrix[:,i+j+2]
design_mat.matrix[:,i+j+2] = tmp
design_mat.names[i+j], design_mat.names[i+j+2] = design_mat.names[i+j+2], design_mat.names[i+j]
design_mat.write_csv(os.path.join('design_matrices',
str(subject_id[0]) + '.csv'))
fig_title = 'S'+ str(subject_id[0])
design_mat.show()
plt.title(fig_title)
plt.set_cmap('gray')
plt.savefig(os.path.join('figures', fig_title+'.png'))