def loading_data(img_pattern):
loader = DataLoader(configuration_file=conf_file,
loader='mat',
task='fcmri',
event_file=img_pattern[:-4] + ".txt",
img_pattern=img_pattern,
atlas='findlab')
prepro = PreprocessingPipeline(nodes=[
Transformer(),
#Detrender(),
#SampleZNormalizer(),
#FeatureZNormalizer()
])
#prepro = PreprocessingPipeline()
ds = loader.fetch(prepro=prepro)
return ds
from pyitab.preprocessing.pipelines import PreprocessingPipeline
from pyitab.io.connectivity import load_mat_ds
from pyitab.simulation.loader import load_simulations
from pyitab.preprocessing.math import AbsoluteValueTransformer, SignTransformer
from pyitab.preprocessing.base import Transformer
from pyitab.analysis.states.base import Clustering
from sklearn import cluster, mixture
from joblib import Parallel, delayed
conf_file = "/media/robbis/DATA/fmri/working_memory/working_memory.conf"
conf_file = '/m/home/home9/97/guidotr1/unix/data/simulations/meg/simulations.conf'
loader = DataLoader(configuration_file=conf_file,
loader='simulations',
task='simulations')
ds = loader.fetch(prepro=Transformer())
_default_options = {
'estimator': [
[[('clf1', cluster.MiniBatchKMeans())]],
[[('clf1', cluster.KMeans())]],
[[('clf1', cluster.SpectralClustering())]],
[[('clf1', cluster.AgglomerativeClustering())]],
[[('clf5', mixture.GaussianMixture())]],
],
'sample_slicer__subject': [[trial] for trial in np.unique(ds.sa.subject)],
'estimator__clf1__n_clusters':
range(2, 10),
from pyitab.analysis.states.gsbs import GSBS
from pyitab.io.loader import DataLoader
from pyitab.analysis.configurator import AnalysisConfigurator
from pyitab.preprocessing import SampleSlicer, FeatureSlicer
from pyitab.analysis.roi import RoiAnalyzer
import os
import numpy as np
conf_file = "/home/robbis/mount/permut1/sherlock/bids/bids.conf"
loader = DataLoader(configuration_file=conf_file,
loader='bids',
task='preproc',
bids_task=['day1'])
subjects = ['marcer', 'matsim', 'simpas']
for s in subjects:
ds = loader.fetch(subject_names=[s],
prepro=[SampleSlicer(trial_type=np.arange(1, 32))])
roi_analyzer = RoiAnalyzer(analysis=GSBS())
roi_analyzer.fit(ds, roi=['aal'], kmax=50)
roi_analyzer.save()
################## Resting state ##########################
conf_file = path = "/home/robbis/mount/permut1/sherlock/bids/bids.conf"
loader = DataLoader(configuration_file=conf_file,
data_path="/home/robbis/mount/permut1/sherlock/bids/",
subjects='participants.tsv',
from imblearn.over_sampling import SMOTE
import numpy as np
from imblearn.under_sampling import *
from imblearn.over_sampling import *
conf_file = "/home/carlos/mount/megmri03/fmri/carlo_ofp/ofp.conf"
conf_file = "/media/robbis/DATA/fmri/carlo_ofp/ofp.conf"
#conf_file = "/home/carlos/fmri/carlo_ofp/ofp_new.conf"
if conf_file[1] == 'h':
from mvpa_itab.utils import enable_logging
root = enable_logging()
loader = DataLoader(configuration_file=conf_file, task='OFP')
ds = loader.fetch()
return_ = True
ratio = 'auto'
_default_options = {
'sample_slicer__evidence': [[1]],
'sample_slicer__subject': [[s] for s in np.unique(ds.sa.subject)],
'balancer__balancer': [
AllKNN(return_indices=return_, ratio=ratio),
CondensedNearestNeighbour(return_indices=return_, ratio=ratio),
EditedNearestNeighbours(return_indices=return_, ratio=ratio),
InstanceHardnessThreshold(return_indices=return_, ratio=ratio),
NearMiss(return_indices=return_, ratio=ratio),
OneSidedSelection(return_indices=return_, ratio=ratio),
from pyitab.analysis.iterator import AnalysisIterator
from pyitab.analysis.configurator import AnalysisConfigurator
from pyitab.analysis.pipeline import AnalysisPipeline
from pyitab.analysis.decoding.roi_decoding import RoiDecoding
from pyitab.preprocessing.pipelines import PreprocessingPipeline
from pyitab.preprocessing.functions import Detrender, SampleSlicer, \
TargetTransformer, Transformer
from pyitab.preprocessing.normalizers import SampleZNormalizer
import warnings
warnings.filterwarnings("ignore")
conf_file = "/media/robbis/DATA/meg/reftep/bids.conf"
loader = DataLoader(configuration_file=conf_file,
task='reftep',
load_fx='reftep-conn',
loader='bids-meg',
bids_pipeline='connectivity+lv')
ds = loader.fetch(n_subjects=9)
_default_options = {
'prepro': [
['sample_slicer', 'target_transformer'],
['sample_slicer', 'feature_znormalizer', 'target_transformer'],
['sample_slicer', 'sample_znormalizer', 'target_transformer'],
],
'sample_slicer__subject': [[s] for s in np.unique(ds.sa.subject)],
'estimator__fsel__k': [50, 100, 150],
'estimator__clf': [
LogisticRegression(penalty='l1', solver='liblinear'),
from pyitab.analysis.decoding.roi_decoding import RoiDecoding
from pyitab.io.loader import DataLoader
from pyitab.preprocessing.pipelines import PreprocessingPipeline
from pyitab.analysis.iterator import AnalysisIterator
from pyitab.analysis.configurator import AnalysisConfigurator
from pyitab.analysis.pipeline import AnalysisPipeline
from sklearn.feature_selection.univariate_selection import SelectKBest
from sklearn.model_selection import *
from sklearn.svm.classes import SVC
import _pickle as pickle
loader = DataLoader(
configuration_file="/home/carlos/fmri/carlo_ofp/ofp_new.conf",
task='OFP_NORES')
ds = loader.fetch()
decoding = RoiDecoding(n_jobs=20, scoring=['accuracy'])
results = dict()
for subject in np.unique(ds.sa.subject):
results[subject] = []
for evidence in [1, 2, 3]:
pipeline = PreprocessingPipeline(nodes=[
TargetTransformer('decision'),
SampleSlicer(**{
'subject': [subject],
'evidence': [evidence]
return np.mean(error)
conf_file = "/media/robbis/DATA/fmri/monks/meditation.conf"
matrix_list = glob.glob("/media/robbis/DATA/fmri/monks/061102chrwoo/fcmri/*.mat")
matrix_list = [m.split("/")[-1] for m in matrix_list]
for m in matrix_list:
m = '20151103_132009_connectivity_filtered_first_filtered_after_each_run_no_gsr_findlab_fmri.mat'
loader = DataLoader(configuration_file=conf_file,
loader='mat',
task='fcmri',
atlas='findlab',
event_file=m[:-4]+".txt",
img_pattern=m)
prepro = PreprocessingPipeline(nodes=[
#Transformer(),
#Detrender(),
SampleZNormalizer(),
#FeatureZNormalizer()
])
#prepro = PreprocessingPipeline()
ds = loader.fetch(prepro=prepro)
_default_options = [
from pyitab.io.loader import DataLoader
conf_file = "/media/robbis/DATA/meg/reftep/bids.conf"
loader = DataLoader(configuration_file=conf_file,
task='reftep',
loader='bids-meg',
load_fx='reftep-iplv',
bids_pipeline='sensor+connectivity')
ds = loader.fetch()
from pyitab.preprocessing import Node
from pyitab.analysis.decoding.roi_decoding import Decoding
from pyitab.io.connectivity import load_mat_ds
from pyitab.preprocessing.math import AbsoluteValueTransformer
import warnings
warnings.filterwarnings("ignore")
conf_file = "/media/robbis/DATA/fmri/carlo_mdm/memory.conf"
loader = DataLoader(configuration_file=conf_file,
#loader=load_mat_ds,
task='BETA_MVPA',
roi_labels={'conjunction':"/media/robbis/DATA/fmri/carlo_mdm/1_single_ROIs/conjunction_map_mask.nii.gz"})
prepro = PreprocessingPipeline(nodes=[
#Transformer(),
Detrender(),
SampleZNormalizer(),
FeatureZNormalizer()
])
#prepro = PreprocessingPipeline()
ds = loader.fetch(prepro=prepro)
_default_options = {
'kwargs__use_partialcorr': [True, False],
#conf_file = "/media/robbis/DATA/fmri/carlo_mdm/memory.conf"
conf_file = "/home/carlos/fmri/carlo_mdm/memory.conf"
roi_labels_fname = glob.glob(
'/home/carlos/fmri/carlo_mdm/1_single_ROIs/*mask.nii.gz')
#roi_labels_fname = glob.glob('/home/robbis/mount/permut1/fmri/carlo_mdm/1_single_ROIs/*mask.nii.gz')
roi_labels_fname = glob.glob(
'/media/robbis/DATA/fmri/carlo_mdm/1_single_ROIs/*mask.nii.gz')
roi_labels = {
os.path.basename(fname).split('_')[0]: fname
for fname in roi_labels_fname
}
loader = DataLoader(configuration_file=conf_file,
event_file='residuals_attributes_full',
roi_labels=roi_labels,
task='RESIDUALS_MVPA')
prepro = PreprocessingPipeline(nodes=[
#Transformer(),
#Detrender(attr='file'),
Detrender(attr='chunks'),
SampleZNormalizer(),
FeatureZNormalizer(),
SampleSlicer(frame=[1, 2, 3, 4, 5, 6, 7]),
#TargetTransformer(attr='decision'),
MemoryReducer(dtype=np.float16),
#Balancer(attr='frame'),
])
ds = loader.fetch(prepro=prepro, n_subjects=8)
from pyitab.analysis.states.gsbs import GSBS
from pyitab.io.loader import DataLoader
from pyitab.analysis.configurator import AnalysisConfigurator
from pyitab.preprocessing import SampleSlicer, FeatureSlicer
from pyitab.preprocessing.connectivity import SpeedEstimator
import os
import numpy as np
conf_file = path = "/home/robbis/mount/permut1/sherlock/bids/bids.conf"
loader = DataLoader(configuration_file=conf_file,
loader='bids',
task='preproc',
bids_task=['day1'],
bids_run=['01', '02', '03'])
ds = loader.fetch(
subject_names=['matsim'],
prepro=[SampleSlicer(trial_type=np.arange(1, 32)),
FeatureSlicer(aal=[1])])
X = ds.samples
speed = SpeedEstimator().transform(ds)
peaks = speed > np.mean(speed) + 2 * np.std(speed)
peaks_idx = np.nonzero(peaks.flatten())[0]
X_ = np.split(X, peaks_idx, axis=0)
cluster = [i * np.ones(x.shape[0]) for i, x in X_]
SampleSlicer(selection_dictionary={'events_number': range(1, 13)})
]
PreprocessingPipeline.__init__(self, nodes=self.nodes)
conf_file = "/media/robbis/DATA/fmri/monks/meditation.conf"
matrix_list = glob.glob(
"/media/robbis/DATA/fmri/monks/061102chrwoo/fcmri/*.mat")
matrix_list = [m.split("/")[-1] for m in matrix_list]
for m in matrix_list:
loader = DataLoader(configuration_file=conf_file,
loader=load_mat_ds,
task='fcmri',
event_file=m[:-4] + ".txt",
img_pattern=m)
prepro = PreprocessingPipeline(nodes=[
Transformer(),
#Detrender(),
SampleZNormalizer(),
#FeatureZNormalizer()
])
#prepro = PreprocessingPipeline()
ds = loader.fetch(prepro=prepro)
_default_options = {
'sample_slicer__targets': [['Vipassana'], ['Samatha']],
SampleZNormalizer, SampleZNormalizer, SampleSigmaNormalizer, \
FeatureSigmaNormalizer
from pyitab.analysis.decoding.temporal_decoding import TemporalDecoding
from mne.decoding import (SlidingEstimator, GeneralizingEstimator, Scaler,
cross_val_multiscore, LinearModel, get_coef,
Vectorizer, CSP)
from sklearn.linear_model import LogisticRegression
import warnings
warnings.filterwarnings("ignore")
conf_file = "/media/robbis/DATA/meg/c2b/meeting-december-data/bids.conf"
loader = DataLoader(configuration_file=conf_file,
loader='bids-meg',
bids_window='300',
bids_ses='01',
task='power')
ds = loader.fetch(subject_names=['sub-109123'], prepro=[Transformer()])
_default_options = {
'loader__bids_ses': ['01', '02'],
'sample_slicer__targets' : [
['LH', 'RH'],
['LF', 'RF'],
#['LH', 'RH', 'LF', 'RF']
],
from pyitab.io.loader import DataLoader
from pyitab.preprocessing.pipelines import PreprocessingPipeline
from sklearn.model_selection import *
from pyitab.analysis.searchlight import SearchLight
from pyitab.analysis.rsa import RSA
from sklearn.pipeline import Pipeline
from sklearn.svm import SVC
from pyitab.analysis.iterator import AnalysisIterator
from pyitab.analysis.pipeline import AnalysisPipeline
from pyitab.analysis.configurator import AnalysisConfigurator
from pyitab.preprocessing import SampleSlicer
import os
conf_file = path = "/home/robbis/mount/permut1/sherlock/bids/bids.conf"
loader = DataLoader(configuration_file=conf_file,
loader='bids',
task='preproc',
bids_task=['day1'])
subjects = ['marcer', 'matsim', 'simpas']
for s in subjects:
ds = loader.fetch(subject_names=[s],
prepro=[SampleSlicer(trial_type=np.arange(1, 32))])
rsa = RSA(distance='correlation')
rsa.fit(ds, roi=['aal'])
rsa.save()
del ds, rsa
from pyitab.preprocessing.functions import Detrender, Transformer
from pyitab.preprocessing.normalizers import FeatureZNormalizer, \
SampleZNormalizer, SampleZNormalizer, SampleSigmaNormalizer, \
FeatureSigmaNormalizer, DatasetFxNormalizer
from pyitab.analysis.decoding.roi_decoding import RoiDecoding
from joblib import Parallel, delayed
path = "/media/robbis/Seagate_Pt1/data/working_memory/"
conf_file = "%s/data/working_memory.conf" % (path)
task = 'PSI'
task = 'PSICORR'
loader = DataLoader(configuration_file=conf_file,
loader='mat',
task=task,
data_path="%s/data/" % (path),
subjects="%s/data/participants.csv" % (path))
prepro = PreprocessingPipeline(nodes=[
Transformer(),
#SampleZNormalizer()
])
ds = loader.fetch(prepro=prepro)
_default_options = {
'sample_slicer__targets': [['0back', '2back']],
'sample_slicer__band': [[c] for c in np.unique(ds.sa.band)],
'estimator__fsel__k': np.arange(1, 1200, 50),
}
#####################################
roi_labels_fname = glob.glob(
'/home/carlos/fmri/carlo_mdm/1_single_ROIs/*mask.nii.gz')
roi_labels = {
os.path.basename(fname).split('_')[0]: fname
for fname in roi_labels_fname
}
configuration_file = "/home/carlos/fmri/carlo_mdm/memory.conf"
#configuration_file = "/media/robbis/DATA/fmri/carlo_mdm/memory.conf"
loader = DataLoader(
configuration_file=configuration_file,
#data_path="/home/carlos/mount/meg_workstation/Carlo_MDM/",
task='BETA_MVPA',
roi_labels=roi_labels,
event_file="beta_attributes_full",
brain_mask="mask_intersection")
prepro = PreprocessingPipeline(nodes=[
#Transformer(),
Detrender(),
SampleZNormalizer(),
FeatureZNormalizer(),
])
#prepro = PreprocessingPipeline()
ds = loader.fetch(prepro=prepro)
ds = MemoryReducer(dtype=np.float16).transform(ds)
import warnings
from pyitab.preprocessing.math import AbsoluteValueTransformer
warnings.filterwarnings("ignore")
######################################
# Only when running on permut1
from mvpa_itab.utils import enable_logging
root = enable_logging()
#####################################
conf_file = "/media/robbis/DATA/fmri/carlo_mdm/memory.conf"
loader = DataLoader(
configuration_file=conf_file,
#loader=load_mat_ds,
task='BETA_MVPA')
prepro = PreprocessingPipeline(nodes=[
#Transformer(),
Detrender(),
SampleZNormalizer(),
FeatureZNormalizer()
])
#prepro = PreprocessingPipeline()
ds = loader.fetch(prepro=prepro)
_default_options = {
#'target_trans__target': ["decision"],
'sample_slicer__accuracy': [[1], [0]],
from pyitab.preprocessing.math import AbsoluteValueTransformer, SignTransformer
import warnings
warnings.filterwarnings("ignore")
######################################
# Only when running on permut1
from pyitab.utils import enable_logging
root = enable_logging()
#####################################
conf_file = ""
conf_file = '/media/guidotr1/Seagate_Pt1/data/Viviana2018/meg/movie.conf'
loader = DataLoader(configuration_file=conf_file,
loader='mat',
task='conn')
prepro = PreprocessingPipeline(nodes=[
#SampleZNormalizer(),
#FeatureZNormalizer(),
Resampler(down=5)
])
#prepro = PreprocessingPipeline()
ds = loader.fetch(prepro=prepro)
_default_options = {
'sample_slicer__targets' : [['movie', 'rest'],
import warnings
warnings.filterwarnings("ignore")
######################################
# Only when running on permut1
from pyitab.utils import enable_logging
root = enable_logging()
#####################################
#conf_file = "/home/carlos/mount/megmri03/working_memory/working_memory_remote.conf"
conf_file = "/media/robbis/DATA/fmri/working_memory/working_memory.conf"
conf_file = "/media/robbis/Seagate_Pt1/data/working_memory/working_memory.conf"
loader = DataLoader(configuration_file=conf_file,
loader=load_mat_ds,
task='CONN')
prepro = PreprocessingPipeline(nodes=[
Transformer(),
#SignTransformer(),
Detrender(),
#AbsoluteValueTransformer(),
SignTransformer(),
#SampleSigmaNormalizer(),
#FeatureSigmaNormalizer(),
])
#prepro = PreprocessingPipeline()
ds = loader.fetch(prepro=prepro)
from sklearn.model_selection import *
from pyitab.analysis.searchlight import SearchLight
from sklearn.pipeline import Pipeline
from sklearn.svm.classes import SVC
from pyitab.analysis.iterator import AnalysisIterator
from pyitab.analysis.pipeline import AnalysisPipeline
from pyitab.analysis.configurator import AnalysisConfigurator
import os
from pyitab.utils import enable_logging
root = enable_logging()
conf_file = "/home/carlos/fmri/carlo_ofp/ofp_new.conf"
#conf_file = "/media/robbis/DATA/fmri/carlo_ofp/ofp.conf"
loader = DataLoader(configuration_file=conf_file, task='OFP_NORES')
ds = loader.fetch()
import numpy as np
######################## Across Memory ##################################
_default_options = {
'target_trans__target': ["memory_status"],
}
_default_config = {
'prepro': ['sample_slicer', 'target_trans'],
'sample_slicer__memory_status': ['L', 'F'],
'sample_slicer__evidence': [1],
'target_trans__target': "memory_status",
from pyitab.preprocessing.math import AbsoluteValueTransformer
warnings.filterwarnings("ignore")
######################################
# Only when running on permut1
from pyitab.utils import enable_logging
root = enable_logging()
#####################################
configuration_file = "/home/carlos/fmri/carlo_mdm/memory.conf"
#configuration_file = "/media/robbis/DATA/fmri/carlo_mdm/memory.conf"
loader = DataLoader(configuration_file=configuration_file,
data_path="/home/carlos/mount/meg_workstation/Carlo_MDM/",
task='BETA_MVPA',
event_file="full",
brain_mask="mask_intersection")
prepro = PreprocessingPipeline(nodes=[
#Transformer(),
Detrender(),
SampleZNormalizer(),
FeatureZNormalizer(),
])
#prepro = PreprocessingPipeline()
ds = loader.fetch(prepro=prepro)
import warnings
warnings.filterwarnings("ignore")
from pyitab.utils import enable_logging
root = enable_logging()
data_path = '/media/robbis/DATA/meg/viviana-hcp/'
conf_file = "/media/robbis/DATA/meg/viviana-hcp/bids.conf"
loader = DataLoader(
configuration_file=conf_file,
data_path=data_path,
subjects="/media/robbis/DATA/meg/viviana-hcp/participants.tsv",
loader='bids-meg',
task='blp',
bids_atlas="complete",
bids_correction="corr",
bids_derivatives='True',
load_fx='hcp-blp')
ds = loader.fetch()
nodes = ds.fa.nodes_1
matrix = np.zeros_like(ds.samples[0])
nanmask = np.logical_not(np.isnan(ds.samples).sum(0))
ds = ds[:, nanmask]
networks = ['AN', 'CON', 'DAN', 'DMN', 'FPN', 'LN', 'MN', 'VAN', 'VFN', 'VPN']
networks = [[n] for n in networks]
kwargs_list = [{'nodes_1': v, 'nodes_2': v} for v in networks]
from sklearn.feature_selection import f_regression
import numpy as np
from pyitab.io.loader import DataLoader
import os
from sklearn.pipeline import Pipeline
from sklearn.feature_selection.univariate_selection import SelectKBest
from pyitab.analysis.iterator import AnalysisIterator
from pyitab.analysis.configurator import AnalysisConfigurator
from pyitab.analysis.pipeline import AnalysisPipeline
from pyitab.analysis.decoding.regression import RoiRegression
from pyitab.preprocessing.pipelines import PreprocessingPipeline
from pyitab.preprocessing.functions import Detrender, SampleSlicer, \
TargetTransformer, Transformer
from pyitab.preprocessing.normalizers import FeatureZNormalizer, \
SampleZNormalizer, SampleZNormalizer, SampleSigmaNormalizer, \
FeatureSigmaNormalizer
import warnings
warnings.filterwarnings("ignore")
conf_file = "/media/robbis/DATA/meg/reftep/bids.conf"
loader = DataLoader(configuration_file=conf_file,
task='reftep',
load_fx='reftep-sensor',
loader='bids-meg',
bids_space='sensor')
ds = loader.fetch(n_subjects=1)