def correlation_map(data, cond_filename):
tr_times = np.arange(0, 30, TR)
convolved = conv_main(data.shape[-1], cond_filename, TR)
corrs = np.zeros((data.shape[:-1]))
for i in general_utils.vol_index_iter(data.shape[:-1]):
corrs[i] = np.corrcoef(data[i], convolved)[1, 0]
return corrs
def correlation_map(data, cond_filename):
tr_times = np.arange(0, 30, TR)
convolved = conv_main(data.shape[-1], cond_filename, TR)
corrs = np.zeros((data.shape[:-1]))
for i in general_utils.vol_index_iter(data.shape[:-1]):
corrs[i] = np.corrcoef(data[i], convolved)[1,0]
return corrs
def correlation_map(data, cond_filename):
"""
Generate a cross-correlation per voxel between BOLD signals and a convolved
gamma baseline function. Assume that the first 5 images are already dropped.
"""
convolved = conv_main(data.shape[-1] + 5, cond_filename, TR)[5:]
corrs = np.zeros((data.shape[:-1]))
for i in gu.vol_index_iter(data.shape[:-1]):
r = np.corrcoef(data[i], convolved)[1,0]
if np.isnan(r):
r = 0
corrs[i] = r
return corrs
def correlation_map(data, cond_filename):
"""
Generate a cross-correlation per voxel between BOLD signals and a convolved
gamma baseline function. Assume that the first 5 images are already dropped.
"""
convolved = conv_main(data.shape[-1] + 5, cond_filename, TR)[5:]
corrs = np.zeros((data.shape[:-1]))
for i in gu.vol_index_iter(data.shape[:-1]):
r = np.corrcoef(data[i], convolved)[1, 0]
if np.isnan(r):
r = 0
corrs[i] = r
return corrs
def correlation_map_linear(data, cond_filename):
"""
This is different from correlation_map in that it accepts a 2d data
(n_samples, n_time_slices) so that it is suitable for working with
brain masks.
"""
convolved = conv_main(data.shape[-1] + 5, cond_filename, TR)[5:]
corrs = np.zeros((data.shape[:-1]))
for i in range(data.shape[0]):
r = np.corrcoef(data[i], convolved)[1,0]
if np.isnan(r):
r = 0
corrs[i] = r
return corrs
def correlation_map_linear(data, cond_filename):
"""
This is different from correlation_map in that it accepts a 2d data
(n_samples, n_time_slices) so that it is suitable for working with
brain masks.
"""
convolved = conv_main(data.shape[-1] + 5, cond_filename, TR)[5:]
corrs = np.zeros((data.shape[:-1]))
for i in range(data.shape[0]):
r = np.corrcoef(data[i], convolved)[1, 0]
if np.isnan(r):
r = 0
corrs[i] = r
return corrs
def correlation_map_linear(data, cond_filename):
"""
This function computes the correlation matrix based on the baseline method.
Input:
data: brain image data
cond_filename: condition file which contains the info about time time_course
Output:
correlation matrix
"""
convolved = conv_main(data.shape[-1] + 5, cond_filename, TR)[5:]
corrs = np.zeros((data.shape[:-1]))
for i in range(data.shape[0]):
r = np.corrcoef(data[i], convolved)[1, 0]
if np.isnan(r):
r = 0
corrs[i] = r
return corrs
def correlation_map_linear(data, cond_filename):
"""
This function computes the correlation matrix based on the baseline method.
Input:
data: brain image data
cond_filename: condition file which contains the info about time time_course
Output:
correlation matrix
"""
convolved = conv_main(data.shape[-1] + 5, cond_filename, TR)[5:]
corrs = np.zeros((data.shape[:-1]))
for i in range(data.shape[0]):
r = np.corrcoef(data[i], convolved)[1,0]
if np.isnan(r):
r = 0
corrs[i] = r
return corrs
import project_config
import numpy as np
import matplotlib.pyplot as plt
import scipy.stats
from scipy.stats import gamma
from stimuli_revised import events2neural
from conv import conv_main
"""
Replace these variables before running the script
"""
cond_filename = "../../../ds115_sub010-014/sub013/model/model001/onsets/task001_run001/cond002.txt"
n_trs = 132
TR = project_config.TR
convolved = conv_main(n_trs, cond_filename, TR)
#save convolved data in txt file:
np.savetxt('results/conv_data.txt', convolved)
# tr_times = np.arange(0, time_length, time_unit)
# hrf_at_trs = hrf(tr_times)
# len(hrf_at_trs)
# plt.plot(tr_times, hrf_at_trs)
# plt.xlabel('time')
# plt.ylabel('HRF sampled every 2.5 seconds')
# n_vols = 132
# neural_prediction = events2neural('cond_nb_tar.txt',
# 0.1, n_vols)
# all_tr_times = np.arange(n_vols*TR/time_unit)*.1
# plt.plot(all_tr_times, neural_prediction)
import numpy as np
import matplotlib.pyplot as plt
import scipy.stats
from scipy.stats import gamma
from stimuli_revised import events2neural
from conv import conv_main
"""
Replace these variables before running the script
"""
cond_filename = "../../../ds115_sub010-014/sub013/model/model001/onsets/task001_run001/cond002.txt"
n_trs = 132
TR = project_config.TR
convolved = conv_main(n_trs, cond_filename, TR)
#save convolved data in txt file:
np.savetxt('results/conv_data.txt', convolved)
# tr_times = np.arange(0, time_length, time_unit)
# hrf_at_trs = hrf(tr_times)
# len(hrf_at_trs)
# plt.plot(tr_times, hrf_at_trs)
# plt.xlabel('time')
# plt.ylabel('HRF sampled every 2.5 seconds')
# n_vols = 132
# neural_prediction = events2neural('cond_nb_tar.txt',
