def _csv2ts(self):
""" Read data from the in_file and generate a nitime TimeSeries object"""
data, roi_names = self._read_csv()
TS = TimeSeries(data=data,
sampling_interval=self.inputs.TR,
time_unit='s')
TS.metadata = dict(ROIs=roi_names)
return TS
def get_bold_signals (image, mask, TR,
normalize=True,
ts_extraction='mean',
filter_par=None,
roi_values=None):
'''
Image and mask must be in nibabel format
'''
mask_data = np.int_(mask.get_data())
if roi_values == None:
labels = np.unique(mask_data)[1:]
else:
labels = np.int_(roi_values)
final_data = []
#print labels
for v in labels[:]:
#print str(v)
data = image.get_data()[mask_data == v]
if normalize == True:
data = zscore(data, axis = 1)
data[np.isnan(data)] = 0
if ts_extraction=='mean':
#assert np.mean(data, axis=0) == data.mean(axis=0)
data = data.mean(axis=0)
elif ts_extraction=='pca':
if data.shape[0] > 0:
data = PCA(n_components=1).fit_transform(data.T)
data = np.squeeze(data)
else:
data = data.mean(axis=0)
ts = TimeSeries(data, sampling_interval=float(TR))
if filter_par != None:
upperf = filter_par['ub']
lowerf = filter_par['lb']
F = FilterAnalyzer(ts, ub=upperf, lb=lowerf)
ts = TimeSeries(F.fir.data, sampling_interval=float(TR))
del F
final_data.append(ts.data)
del data
del mask_data
del ts
return TimeSeries(np.vstack(final_data), sampling_interval=float(TR))
def _csv2ts(self):
""" Read data from the in_file and generate a nitime TimeSeries object"""
data,roi_names = self._read_csv()
TS = TimeSeries(data=data,
sampling_interval=self.inputs.TR,
time_unit='s')
TS.metadata = dict(ROIs=roi_names)
return TS
def estimate_corr_coh(tc_array, tr):
print 'estimating correlation and coherence matrices...'
TR = tr
f_lb = 0.01
f_ub = 0.15
T = TimeSeries(tc_array.T, sampling_interval=TR)
T.metadata['roi'] = tc_array.columns
Corr = CorrelationAnalyzer(T)
Coh = CoherenceAnalyzer(T)
freq_idx = np.where((Coh.frequencies > f_lb) * \
(Coh.frequencies < f_ub))[0]
return Corr.corrcoef, np.mean(Coh.coherence[:, :, freq_idx], -1)
def measure(self):
vars_ = self.time_serie.data.shape[0]
result = np.zeros((vars_, vars_))
for i in range(vars_):
ts_seed = TimeSeries(self.time_serie.data[i], sampling_interval=1.)
ts_target = TimeSeries(self.time_serie.data[i + 1:],
sampling_interval=1.)
S = SeedAnalyzer(ts_seed, ts_target, self._measure)
result[i, i + 1:] = S.measure
return result
def remove_bold_effect(bold_ts, distance_ts, ts_param, **kwargs):
for arg in kwargs:
if arg == 'runs':
n_runs = np.int(kwargs[arg])
if arg == 'tr':
TR = np.float(kwargs[arg])
n_roi = bold_ts.data.shape[0]
rl = bold_ts.data.shape[1]/n_runs - 1 #Run Length (minus one is deconvolution effect)
dl = distance_ts.data.shape[1]/n_runs #Distance Length
diff = rl - dl
print(distance_ts.data.shape)
deconv_ts = []
for i in range(n_roi):
deconv_distance = []
for j in range(n_runs):
full_data = np.ones(rl)
full_data[diff:] = distance_ts.data[i][j*dl:(j+1)*dl]
n_data = full_data/(bold_ts.data[i][j*rl:(j+1)*rl])
deconv_distance.append(n_data[diff:])
assert np.hstack(deconv_distance).shape[0] == dl*n_runs
deconv_ts.append(np.hstack(deconv_distance))
ts_deconv = TimeSeries(np.vstack(deconv_ts), sampling_interval=TR)
return ts_deconv
def compute_coherence(time_windows, TR, f_lb, f_ub, roi_names):
n_timewindows = time_windows.shape[0]
n_samples = time_windows.shape[1]
n_rois = time_windows.shape[2]
coherence_3Darray = np.zeros((n_timewindows, n_rois, n_rois))
if n_rois == len(roi_names):
for time_index in range(n_timewindows):
ts = time_windows[time_index, :, :]
data = np.zeros((n_rois, n_samples))
for n_idx, roi in enumerate(roi_names):
data[n_idx] = ts[:, n_idx]
data = percent_change(data)
T = TimeSeries(data, sampling_interval=TR)
C = CoherenceAnalyzer(T)
freq_idx = np.where(
(C.frequencies > f_lb) * (C.frequencies < f_ub))[0]
coh = np.mean(C.coherence[:, :, freq_idx], -1)
coherence_3Darray[time_index] = coh
else:
raise Exception(
"Number of ROIs in 3D Array do not match number of ROI names provided."
)
return coherence_3Darray
def get_state_frequencies(state_dynamics, method='spectrum_fourier'):
"""
Returns the spectrum of the state occurence for each subject.
Parameters
----------
state_dynamics : n_states x n_subjects x n_timepoints array
The state dynamics output from fit_states
function.
method : a string, check nitime.spectral.SpectralAnalyzer for
allowed methods.
Returns
-------
results : n_subjects list of tuple,
first element is the array of frequencies,
second element is the array n_states x frequencies
of the spectrum.
"""
results = []
for s in state_dynamics:
ts = TimeSeries(s, sampling_interval=1.)
S = SpectralAnalyzer(ts)
try:
result = getattr(S, method)
except AttributeError as _:
result = S.spectrum_fourier
results.append(result)
return results
def hurstexp_welchper(data, samp=1.05, f_max=0, consider_fBm=False):
"""
These functions compute the Hurst exponent of a signal using the
Welch periodogram
data : your signal
samp : sampling rate in Hz 1 for an fMRI series
f_max: the higher frequency you want to take into account
"""
#data could be two dimensional(but no more...) in that cas time should
#be on second position
time_series = TimeSeries(data=data, sampling_rate=samp)
spectral_analysis = SpectralAnalyzer(time_series)
frq, pwr = spectral_analysis.psd
#We need to take only the small frequency, but the exact choice is a
#bit arbitrary we need to have alpha between 0 and 1
if f_max == 0:
masker = frq > 0
else:
masker = np.all([(frq > 0), (frq < f_max)], axis=0)
log2frq = np.log2(frq[masker])
log2pwr = np.log2(pwr.T[masker])
tmp = np.polyfit(log2frq, log2pwr, deg=1)
if consider_fBm:
return (1 - tmp[0]) / 4, {
'aest': tmp[1],
'log2frq': log2frq,
'log2pwr': log2pwr
}
return (1 - tmp[0]) / 2, {
'aest': tmp[1],
'log2frq': log2frq,
'log2pwr': log2pwr
}
def process_enc_timestamps(masked_data, timestamp_run, TR=2.335):
n_voxels = np.shape(masked_data)[1]
#### 2. Apply a filter for each voxel
data_filtered = np.zeros(np.shape(masked_data))
for voxel in range(0, n_voxels):
data_to_filter = masked_data[:,
voxel] #data of the voxel along the session
#apply the filter
data_to_filter = TimeSeries(data_to_filter, sampling_interval=TR)
F = FilterAnalyzer(data_to_filter, ub=0.15,
lb=0.02) ##upper and lower boundaries
data_filt = F.filtered_boxcar.data
data_filtered[:, voxel] = data_filt
#### 3. Subset of data corresponding to the delay times (all voxels)
encoding_delay_activity = np.zeros(
(len(timestamp_run), n_voxels)) ## emply matrix (n_trials, n_voxels)
for idx, t in enumerate(timestamp_run): #in each trial
delay_TRs = data_filtered[
t:t + 2, :] #take the first scan of the delay and the next
delay_TRs_mean = np.mean(delay_TRs,
axis=0) #make the mean in each voxel of 2TR
encoding_delay_activity[
idx, :] = delay_TRs_mean #index the line in the matrix
### 4. zscore in each voxel in the temporal dimension (with the other 2TR of the same session)
for voxel in range(0, n_voxels): # by voxel
vx_act = encoding_delay_activity[:, voxel]
vx_act_zs = np.array(stats.zscore(vx_act))
## zscore
encoding_delay_activity[:,
voxel] = vx_act_zs ## replace previos activity
return encoding_delay_activity
def transform(self, target_ds):
seed_ds = self.seed_ds
ts_seed = TimeSeries(seed_ds, sampling_interval=1.)
ts_target = TimeSeries(target_ds, sampling_interval=1.)
kwargs = self.kwargs
seed_analyzer = self.seed_analyzer(ts_seed, ts_target, **kwargs)
#print ts_seed.shape, ts_target.shape
self._measure = seed_analyzer.measure
return self._measure
def get_similarity_timeserie(path, name, condition, time, **kwargs):
TR = 1.
for arg in kwargs:
if arg == 'TR':
TR = np.float(kwargs[arg])
file_list = os.listdir(path)
file_list = [f for f in file_list if f.find(name) != -1
and f.find('_'+condition) != -1
and f.find(time) != -1
]
total_data = []
for f in file_list:
print(os.path.join(path, f))
data = np.loadtxt(os.path.join(path, f), delimiter=',')
data = np.sqrt(data.T)
data_z = zscore(data, axis=1)
total_data.append(data_z)
ts = TimeSeries(np.vstack(total_data), sampling_interval=TR)
return ts
def euclidean_measure(seed_ds, target_ds):
roi_correlation = []
for roi in np.unique(seed_ds.fa.roi_labels)[1:]:
mask_roi = seed_ds.fa.roi_labels == roi
seed_ts = TimeSeries(seed_ds[:, mask_roi], sampling_interval=1.)
target_ts = TimeSeries(target_ds[:, mask_roi], sampling_interval=1.)
S = SeedAnalyzer(seed_ts, target_ts)
roi_correlation.append(S.measure)
return roi_correlation
def mask_fmri_process(fmri_path, masks, sys_use='unix'):
### 1. Load and mask the data
fmri_path = ub_wind_path(fmri_path, system=sys_use) #change the path format wind-unix
mask_img_rh= masks[0] #right hemisphere mask
mask_img_rh = ub_wind_path(mask_img_rh, system=sys_use)
mask_img_lh= masks[1] #left hemisphere mask
mask_img_lh = ub_wind_path(mask_img_lh, system=sys_use)
#Apply the masks and concatenate
masked_data_rh = apply_mask(fmri_path, mask_img_rh)
masked_data_lh = apply_mask(fmri_path, mask_img_lh)
masked_data=np.hstack([masked_data_rh, masked_data_lh])
### 2. Filter ####and zscore
n_voxels = np.shape(masked_data)[1]
for voxel in range(0, n_voxels):
data_to_filter = masked_data[:,voxel]
#apply the filter
data_to_filter = TimeSeries(data_to_filter, sampling_interval=2.335)
F = FilterAnalyzer(data_to_filter, ub=0.15, lb=0.02)
data_filtered=F.filtered_boxcar.data
masked_data[:,voxel] = data_filtered
#Z score
masked_data[:,voxel] = np.array( stats.zscore( masked_data[:,voxel] ) ) ; ## zscore + 5 just to get + values
#append it and save the data
return masked_data
def execute(self,
gsr=True,
filter_params={
'ub': 0.08,
'lb': 0.009
},
tr=4.):
# Get timeseries
if not self.loadedSignals:
self._load_signals(tr, gsr, filter_params=filter_params)
elif self.loadedSignals['gsr'] != gsr or self.loadedSignals[
'filter_params'] != filter_params:
self._load_signals(tr, gsr, filter_params=filter_params)
beta = glm(self.fmri_ts.data.T, self.regressors.T)
residuals = self.fmri_ts.data.T - np.dot(self.regressors.T, beta)
ts_residual = TimeSeries(residuals.T, sampling_interval=tr)
'''
ub = filter_params['ub']
lb = filter_params['lb']
F = FilterAnalyzer(ts_residual, ub=ub, lb=lb)
'''
residual_4d = np.zeros_like(self.bold.get_data())
residual_4d[self.brain_mask.get_data() > 0] = ts_residual.data
residual_4d[np.isnan(residual_4d)] = 0
self._save(residual_4d, gsr=gsr)
def __init__(self,spike_data,**kwargs):
sampling_rate = kwargs.pop('sampling_rate',1000)
starttime = float(kwargs.setdefault('starttime',0))
compute = kwargs.pop('compute',True)
# Parse multi-trial spike time data as input
if isinstance(spike_data,Timestamps):
multi = spike_data
data = multi.to_rate(sampling_rate,**kwargs)
# Attempt to convert input to array
else:
multi = None
data = asarray(spike_data)
TimeSeries.__init__(self,data,sampling_rate=sampling_rate,t0=starttime)
self.multi = multi
def correlation_measure(seed_ds, target_ds):
roi_correlation = []
rois = [k for k in seed_ds.fa.keys() if k != 'voxel_indices']
roi_values = seed_ds.fa[rois[0]].value
for roi in np.unique(roi_values)[1:]:
mask_roi = roi_values == roi
seed_ts = TimeSeries(seed_ds[:, mask_roi], sampling_interval=1.)
target_ts = TimeSeries(target_ds[:, mask_roi], sampling_interval=1.)
S = SeedCorrelationAnalyzer(seed_ts, target_ts)
roi_correlation.append(S.corrcoef)
return roi_correlation
def analyze_connectivity(imagelist, path_roi, roi_names, ts_param, **kwargs):
TR = 1.
for arg in kwargs:
if arg == 'TR':
TR = np.float(kwargs[arg])
roi_list = os.listdir(path_roi)
roi_list = [r for r in roi_list if r.find('.hdr') != -1 \
or r.find('nii.gz') != -1]
'''
roi_list = ['lower_left_new_.nii.gz',
'lower_right_new_.nii.gz',
'upper_left_new_.nii.gz',
'upper_right_new_.nii.gz',
'roi_attention_all.4dfp.hdr',
'roi_default_all.4dfp.hdr',
'searchlight_separated.nii.gz'
]
'''
#roi_names = np.loadtxt('/media/DATA/fmri/learning/roi_labels', dtype=np.str)
print('Length of image list is '+str(len(imagelist)))
volume_shape = imagelist[0].get_data().shape[:-1]
n_shape = list(volume_shape)
n_shape.append(-1)
coords = list(np.ndindex(volume_shape))
coords = np.array(coords).reshape(n_shape)
data_arr = []
for roi in roi_list:
r_mask = ni.load(os.path.join(path_roi, roi))
mask = r_mask.get_data().squeeze()
roi_filt = roi_names[roi_names.T[1] == roi]
for label in np.unique(mask)[1:]:
roi_m = np.int_(roi_filt.T[2]) == label
if roi_m.any():
print('Loading voxels from '+roi_filt[roi_m].T[0][0])
time_serie = io.time_series_from_file([f.get_filename() for f in imagelist], \
coords[mask==label].T, \
TR=float(TR), \
normalize=ts_param['normalize'], \
average=ts_param['average'], \
filter=ts_param['filter'])
data_arr.append(time_serie)
del r_mask
data = np.array(data_arr)
ts = TimeSeries(data, sampling_interval=float(TR))
del imagelist, time_serie
C = nta.CorrelationAnalyzer(ts)
return C, ts
def connectivity_analysis(ts_condition, sampling_interval=1.):
matrices = dict()
for c in ts_condition:
matrices[c] = []
for i in range(len(ts_condition[c])):
ts = TimeSeries(ts_condition[c][i], sampling_interval=sampling_interval)
C = CorrelationAnalyzer(ts)
matrices[c].append(C.corrcoef)
return matrices
def roc(brain_ts, **kwargs):
conditions = kwargs['conditions']
#label_data = LabelEncoder().fit_transform(conditions_)
label_data = np.zeros_like(conditions, dtype=np.int)
label_data[conditions == "C"] = 1
seed_ts = TimeSeries(label_data[np.newaxis,:], sampling_interval=1.)
S = SeedAnalyzer(seed_ts, brain_ts, roc_auc_score)
measure = S.measure
return measure
def bold_convolution(bold_timeseries, duration, win_func=boxcar):
window=win_func(duration)
n_roi = bold_timeseries.data.shape[0]
convolved_bold = []
for i in range(n_roi):
convolution = convolve(np.abs(bold_timeseries.data[i]), window)
convolved_bold.append(convolution)
ts_convolved = TimeSeries(np.vstack(convolved_bold),
sampling_interval=np.float(bold_timeseries.sampling_interval))
return ts_convolved
def fit_states(X, centroids, distance=euclidean):
"""
Returns the similarity of the dataset to each centroid,
using a dissimilarity distance function.
Parameters
----------
X : n_samples x n_features array
The full dataset used for clustering
centroids : n_cluster x n_features array
The cluster centroids.
distance : a scipy.spatial.distance function | default: euclidean
This is the dissimilarity measure, this should be a python
function, see scipy.spatial.distance.
Returns
-------
results : n_samples x n_centroids array
The result of the analysis,
"""
ts_seed = TimeSeries(centroids, sampling_interval=1.)
results_ = []
for subj in X:
ts_target = TimeSeries(subj, sampling_interval=1.)
S = SeedAnalyzer(ts_seed, ts_target, distance)
results_.append(S.measure)
return results_
def beta_series(brain_ts, **kwargs):
seed_fname = kwargs['seed_fname'][0]
img_data = kwargs['img_data']
time_mask = kwargs['time_mask']
seed = ni.load(seed_fname).get_data().squeeze()
seed_data = img_data[np.nonzero(seed)][:,time_mask]
seed_data = seed_data.mean(0)[np.newaxis,:]
seed_ts = TimeSeries(seed_data, sampling_interval=1.)
S = SeedCorrelationAnalyzer(seed_ts, brain_ts)
measure = S.corrcoef
return measure
def getCorrelation(group_id, subject_id):
# obtains the correlation between empirical functional connectivity and simulated functional connectivity
input_path_sim = "fMRI.txt"
input_path_em = get_path(group_id, subject_id, "T1") + "FC.mat"
em_mat = scipy.io.loadmat(input_path_em)
em_fc_matrix = em_mat["FC_cc_DK68"]
sampling_interval = em_mat["TR"][0][0]
uidx = np.triu_indices(68, 1)
em_fc_z = np.arctanh(em_fc_matrix)
em_fc = em_fc_z[uidx]
tsr = np.loadtxt(input_path_sim)
T = TimeSeries(tsr, sampling_interval=sampling_interval)
C = CorrelationAnalyzer(T)
sim_fc = np.arctanh(C.corrcoef)[uidx]
sim_fc = np.nan_to_num(sim_fc)
pearson_corr, _ = stat.pearsonr(sim_fc, em_fc)
return pearson_corr
def getCorrelation(run, encoding, subject_id):
# obtains the correlation between empirical functional connectivity and simulated functional connectivity
key = str(run) + "_" + str(encoding) + "_" + str(subject_id) + "_rsHRF"
input_path_sim = "weights" + key + "fMRI.txt"
em_fc_matrix = get_FC(run, encoding, subject_id)
sampling_interval = 0.72
uidx = np.triu_indices(379, 1)
em_fc_z = np.arctanh(em_fc_matrix)
em_fc = em_fc_z[uidx]
tsr = np.loadtxt(input_path_sim)
T = TimeSeries(tsr, sampling_interval=sampling_interval)
C = CorrelationAnalyzer(T)
sim_fc = np.arctanh(C.corrcoef)[uidx]
sim_fc = np.nan_to_num(sim_fc)
pearson_corr, _ = stat.pearsonr(sim_fc, em_fc)
os.remove(input_path_sim)
return pearson_corr
def plot_spectrum(Timeseries, tr):
from nitime.timeseries import TimeSeries
from nitime.analysis import SpectralAnalyzer
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import os
import numpy as np
figure= []
for i, timeseries in enumerate(Timeseries):
#T = io.time_series_from_file(in_file,TR=tr)
title = os.path.abspath('spectra')
timeseries = np.asarray(timeseries[1:])
timeseries = timeseries-np.mean(timeseries)*np.ones(timeseries.shape)
T = TimeSeries(timeseries,sampling_interval=tr)
S_original = SpectralAnalyzer(T)
# Initialize a figure to put the results into:
fig01 = plt.figure(figsize = (8,3))
ax01 = fig01.add_subplot(1, 1, 1)
ax01.plot(S_original.psd[0],
S_original.psd[1],
label='Welch PSD')
ax01.plot(S_original.spectrum_fourier[0],
S_original.spectrum_fourier[1],
label='FFT')
ax01.plot(S_original.periodogram[0],
S_original.periodogram[1],
label='Periodogram')
ax01.plot(S_original.spectrum_multi_taper[0],
S_original.spectrum_multi_taper[1],
label='Multi-taper')
ax01.set_xlabel('Frequency (Hz)')
ax01.set_ylabel('Power')
ax01.legend()
Figure = title+'%02d.png'%i
plt.savefig(Figure, bbox_inches='tight')
figure.append(Figure)
plt.close()
return figure
def difference(brain_ts, **kwargs):
seed_list = kwargs['seed_fname']
img_data = kwargs['img_data']
time_mask = kwargs['time_mask']
multiseed_data = []
for s in seed_list:
seed = ni.load(s).get_data().squeeze()
seed_data = img_data[np.nonzero(seed)][:,time_mask]
seed_data = seed_data.mean(0)[np.newaxis,:]
multiseed_data.append(seed_data)
multiseed_data = np.vstack(multiseed_data)
diff_ = np.abs(np.diff(multiseed_data, axis=0)[0])
seed_ts = TimeSeries(diff_, sampling_interval=1.)
S = SeedCorrelationAnalyzer(seed_ts, brain_ts)
measure = S.corrcoef
return measure
Smoke testing of the viz module.
"""
import numpy as np
from nitime.timeseries import TimeSeries
from nitime.analysis import CorrelationAnalyzer
from nitime.viz import drawmatrix_channels, drawgraph_channels, plot_xcorr
roi_names = ['a','b','c','d','e','f','g','h','i','j']
data = np.random.rand(10,1024)
T = TimeSeries(data, sampling_interval=np.pi)
T.metadata['roi'] = roi_names
#Initialize the correlation analyzer
C = CorrelationAnalyzer(T)
def test_drawmatrix_channels():
fig01 = drawmatrix_channels(C.corrcoef, roi_names, size=[10., 10.], color_anchor=0)
def test_plot_xcorr():
xc = C.xcorr_norm
fig02 = plot_xcorr(xc,
((0, 1),
(2, 3)),
line_labels=['a', 'b'])
color_anchor=0,
title='MTM Coherence')
"""
.. image:: fig/multi_taper_coh_01.png
Next we perform the same analysis, using the nitime object oriented interface.
We start by initializing a TimeSeries object with this data and with the
sampling_interval provided above. We set the metadata 'roi' field with the ROI
names.
"""
T = TimeSeries(pdata, sampling_interval=TR)
T.metadata['roi'] = roi_names
"""
We initialize an MTCoherenceAnalyzer object with the TimeSeries object
"""
C2 = MTCoherenceAnalyzer(T)
"""
The relevant indices in the Analyzer object are derived:
"""
freq_idx = np.where((C2.frequencies > 0.02) * (C2.frequencies < 0.15))[0]
def granger_scores(timeseries, order):
timeseries = TimeSeries(timeseries, sampling_interval=1)
g = nta.GrangerAnalyzer(timeseries, order=order)
g_xy_mat = np.mean(g.causality_xy, axis=-1)
g_yx_mat = np.mean(g.causality_yx, axis=-1)
return np.concatenate([g_xy_mat[np.tril_indices(3,-1)], g_yx_mat.T[np.triu_indices(3,1)]])
X = np.expand_dims(regressor, axis=1)
glm_dist = GeneralLinearModel(X)
glm_dist.fit(Y)
beta_dist = glm_dist.get_beta()
r_signal = np.dot(X, beta_dist)
regressed_s = Y - r_signal
save_fn = os.path.join(
path_dist, 'similarity_regressed_' + name + '_' + condition +
'_' + rest + '_.txt')
#np.savetxt(save_fn, regressed_s, fmt='%.4f', delimiter=',')
r_ts = TimeSeries(regressed_s.T,
sampling_interval=dist_ts.sampling_interval)
C = CorrelationAnalyzer(r_ts)
s_res.append(np.arctanh(C.corrcoef))
res.append(s_res)
######################## Cross Correlation ###################################
correlation_list = []
connectivity_list = []
coher_dist = []
coher_bold = []
for name in subjects:
def connectivity(x):
data = np.vstack(x)
ts = TimeSeries(data, sampling_interval=1.)
return CorrelationAnalyzer(ts).corrcoef
def analysis(subjects, **kwargs):
default_config = {
"path":'/root/robbis/fmri/carlo_ofp/',
"file_dir":"analysis_SEP/DE_ASS_noHP/SINGLE_TRIAL_MAGS_voxelwise/",
"fields": ["encoding", "level", "response"],
"conditions":{
"encoding": ["F", "L"],
"response": ["C"],
"level": ["1","2","3"]
},
"condition_dir_dict":{
"encoding":0,
"level":1,
"response":2
},
"cond_file":"%s_condition_list.txt",
"filename":"residuals.nii.gz",
"brain_mask":"glm_atlas_mask_333.nii",
"mask_dir":"1_single_ROIs",
"seed_mask": ["L_FFA.nii", "L_PPA.nii"],
"analysis":"beta"
}
default_config.update(kwargs)
mapper = {
"beta": beta_series,
"roc": roc,
"abs_difference": difference,
}
method = mapper[default_config["analysis"]]
method_name = default_config["analysis"]
path = default_config['path']
filename = default_config['filename']
cond_file = default_config['cond_file']
brain_mask = default_config['brain_mask']
seed_mask = default_config['seed_mask']
mask_dir = default_config['mask_dir']
file_dir = default_config["file_dir"]
fields = default_config["fields"]
conditions = default_config["conditions"]
conditions_dict = default_config["condition_dir_dict"]
mask_fname = os.path.join(path, mask_dir, brain_mask)
seed_fname = [os.path.join(path, mask_dir, s) for s in seed_mask]
default_config['seed_fname'] = seed_fname
mask = ni.load(mask_fname)
mask_data = mask.get_data().squeeze()
total_results = []
for s in subjects:
condition_list = np.genfromtxt(os.path.join(path, s, file_dir, cond_file %(s)),
dtype=np.str,
delimiter=',')
condition_mask = get_condition_mask(condition_list,
fields,
conditions,
conditions_dict)
conditions_ = condition_list[condition_mask].T[-1]
img_fname = os.path.join(path, s, file_dir, filename)
time_mask = condition_mask
default_config['time_mask'] = time_mask
# Load brain
img = ni.load(img_fname)
img_data = img.get_data()
default_config['img_data'] = img_data
brain_img = img_data[np.nonzero(mask_data)][:,time_mask]
brain_ts = TimeSeries(brain_img, sampling_interval=1.)
measure = method(brain_ts, **default_config)
### Save file
result = np.zeros(mask.shape)
result[...,0][np.nonzero(mask_data)] = measure
condition_unique = np.unique([''.join(c) for c in condition_list[condition_mask]])
fname = "%s_%s_%s_%s.nii.gz" % (method_name, '-'.join(seed_mask), '_'.join(condition_unique), s)
ni.save(ni.Nifti1Image(result, mask.affine), os.path.join(path, "0_results", fname))
total_results.append(result)
total_results = np.concatenate(total_results, axis=3)
fname = "0_%s_%s_%s_%s.nii.gz" % (method_name, '-'.join(seed_mask), '_'.join(condition_unique), "total")
ni.save(ni.Nifti1Image(total_results, mask.affine), os.path.join(path, "0_results", fname))
fname = "0_%s_%s_%s_%s.nii.gz" % (method_name, '-'.join(seed_mask), '_'.join(condition_unique), "total_avg")
ni.save(ni.Nifti1Image(total_results.mean(3), mask.affine), os.path.join(path, "0_results", fname))
In this case, we generate an order 2 AR process, with the following coefficients: """ coefs = np.array([0.9, -0.5]) """ This generates the AR(2) time series: """ X, noise, _ = utils.ar_generator(npts, sigma, coefs, drop_transients) ts_x = TimeSeries(X, sampling_rate=Fs, time_unit='s') ts_noise = TimeSeries(noise, sampling_rate=1000, time_unit='s') """ We use the plot_tseries function in order to visualize the process: """ fig01 = plot_tseries(ts_x, label='AR signal') fig01 = plot_tseries(ts_noise, fig=fig01, label='Noise') fig01.axes[0].legend() """ .. image:: fig/ar_est_1var_01.png
import nitime.viz
reload(nitime.viz)
from nitime.viz import drawmatrix_channels
# This time Import the coherence analyzer
from nitime.analysis import CoherenceAnalyzer
# This part is the same as before
TR = 1.89
data_rec = csv2rec("data/fmri_timeseries.csv")
roi_names = np.array(data_rec.dtype.names)
n_samples = data_rec.shape[0]
data = np.zeros((len(roi_names), n_samples))
for n_idx, roi in enumerate(roi_names):
data[n_idx] = data_rec[roi]
data = percent_change(data)
T = TimeSeries(data, sampling_interval=TR)
T.metadata["roi"] = roi_names
C = CoherenceAnalyzer(T)
# We look only at frequencies between 0.02 and 0.15 (the physiologically
# relevant band, see http://imaging.mrc-cbu.cam.ac.uk/imaging/DesignEfficiency:
freq_idx = np.where((C.frequencies > 0.02) * (C.frequencies < 0.15))[0]
# Extract the coherence and average across these frequency bands:
coh = np.mean(C.coherence[:, :, freq_idx], -1) # Averaging on the last dimension
drawmatrix_channels(coh, roi_names, size=[10.0, 10.0], color_anchor=0)