def test_fmri_example():
# Run FMRI analysis using example data
from nipy.utils import example_data
fmri_files = [example_data.get_filename("fiac", "fiac0", run) for run in ["run1.nii.gz", "run2.nii.gz"]]
design_files = [example_data.get_filename("fiac", "fiac0", run) for run in ["run1_design.npz", "run2_design.npz"]]
mask = example_data.get_filename("fiac", "fiac0", "mask.nii.gz")
multi_session_model = FMRILinearModel(fmri_files, design_files, mask)
multi_session_model.fit()
z_image, = multi_session_model.contrast([np.eye(13)[1]] * 2)
# Check number of voxels with p < 0.001
assert_equal(np.sum(z_image.get_data() > 3.09), 671)
def make_matcher():
I = Image(load(example_data.get_filename('neurospin',
'sulcal2000',
'nobias_ammon.nii.gz')))
J = Image(load(example_data.get_filename('neurospin',
'sulcal2000',
'nobias_anubis.nii.gz')))
# Create a registration instance
R = IconicRegistration(I, J)
R.set_source_fov(fixed_npoints=64**3)
R.similarity = 'llr_cc'
return R
def test_fmri_example():
# Run FMRI analysis using example data
from nipy.utils import example_data
fmri_files = [
example_data.get_filename('fiac', 'fiac0', run)
for run in ['run1.nii.gz', 'run2.nii.gz']
]
design_files = [
example_data.get_filename('fiac', 'fiac0', run)
for run in ['run1_design.npz', 'run2_design.npz']
]
mask = example_data.get_filename('fiac', 'fiac0', 'mask.nii.gz')
multi_session_model = FMRILinearModel(fmri_files, design_files, mask)
multi_session_model.fit()
z_image, = multi_session_model.contrast([np.eye(13)[1]] * 2)
# Check number of voxels with p < 0.001
assert_equal(np.sum(z_image.get_data() > 3.09), 671)
def remake_images():
# Get group data
group_data = example_data.get_filename("neurospin", "language_babies", "offset_002.npz")
f = np.load(group_data)
data, vardata, xyz = f["mat"], f["var"], f["xyz"]
dX = xyz[0, :].max() + 1
dY = xyz[1, :].max() + 1
dZ = xyz[2, :].max() + 1
aux = np.zeros([dX, dY, dZ])
data_images = []
vardata_images = []
mask_images = []
for i in range(data.shape[0]):
aux[list(xyz)] = data[i, :]
data_images.append(Image(aux.copy(), np.eye(4)))
aux[list(xyz)] = vardata[i, :]
vardata_images.append(Image(aux.copy(), np.eye(4)))
aux[list(xyz)] = 1
mask_images.append(Image(aux.copy(), np.eye(4)))
return data_images, vardata_images, mask_images
def remake_images():
# Get group data
group_data = example_data.get_filename(
'neurospin', 'language_babies', 'offset_002.npz')
f = np.load(group_data)
data, vardata, xyz = f['mat'], f['var'], f['xyz']
dX = xyz[0].max() + 1
dY = xyz[1].max() + 1
dZ = xyz[2].max() + 1
aux = np.zeros([dX, dY, dZ])
data_images = []
vardata_images = []
mask_images = []
for i in range(data.shape[0]):
aux[list(xyz)] = data[i]
data_images.append(Image(aux.copy(), np.eye(4)))
aux[list(xyz)] = vardata[i]
vardata_images.append(Image(aux.copy(), np.eye(4)))
aux[list(xyz)] = 1
mask_images.append(aux)
return data_images, vardata_images, mask_images
def remake_images():
# Get group data
group_data = example_data.get_filename(
'neurospin', 'language_babies', 'offset_002.npz')
f = np.load(group_data)
data, vardata, xyz = f['mat'], f['var'], f['xyz']
dX = xyz[0].max() + 1
dY = xyz[1].max() + 1
dZ = xyz[2].max() + 1
aux = np.zeros([dX, dY, dZ])
data_images = []
vardata_images = []
mask_images = []
for i in range(data.shape[0]):
aux[list(xyz)] = data[i]
data_images.append(Image(aux.copy(), np.eye(4)))
aux[list(xyz)] = vardata[i]
vardata_images.append(Image(aux.copy(), np.eye(4)))
aux[list(xyz)] = 1
mask_images.append(aux)
return data_images, vardata_images, mask_images
# get the data
data_dir = get_second_level_dataset()
# First example, with a anatomical template
img = load(os.path.join(data_dir, 'spmT_0029.nii.gz'))
data = img.get_data()
affine = img.get_affine()
viz.plot_map(data, affine, cut_coords=(-52, 10, 22),
threshold=2.0, cmap=viz.cm.cold_hot)
plt.savefig('ortho_view.png')
# Second example, with a given anatomical image slicing in the Z direction
try:
anat_img = load(example_data.get_filename('neurospin', 'sulcal2000',
'nobias_anubis.nii.gz'))
anat = anat_img.get_data()
anat_affine = anat_img.get_affine()
except OSError as e:
# File does not exist: the data package is not installed
print(e)
anat = None
anat_affine = None
viz.plot_map(data, affine, anat=anat, anat_affine=anat_affine,
slicer='z', threshold=2, cmap=viz.cm.cold_hot, black_bg=True)
plt.savefig('z_view.png')
viz.plot_map(data, affine, anat=anat, anat_affine=anat_affine,
slicer='x', threshold=2, cmap=viz.cm.cold_hot, black_bg=True)
plt.savefig('x_view.png')
# First example, with a anatomical template
img = load(os.path.join(data_dir, 'spmT_0029.nii.gz'))
data = img.get_data()
affine = img.get_affine()
viz.plot_map(data,
affine,
cut_coords=(-52, 10, 22),
threshold=2.0,
cmap=viz.cm.cold_hot)
plt.savefig('ortho_view.png')
# Second example, with a given anatomical image slicing in the Z direction
try:
anat_img = load(
example_data.get_filename('neurospin', 'sulcal2000',
'nobias_anubis.nii.gz'))
anat = anat_img.get_data()
anat_affine = anat_img.get_affine()
except OSError, e:
# File does not exist: the data package is not installed
print e
anat = None
anat_affine = None
viz.plot_map(data,
affine,
anat=anat,
anat_affine=anat_affine,
slicer='z',
threshold=2,
cmap=viz.cm.cold_hot)
rarun2.nii
Author: Alexis Roche, 2009.
"""
from nipy.algorithms.registration import FmriRealign4d
from nipy import load_image, save_image
from nipy.utils import example_data
from os.path import join, split
import sys
import tempfile
# Input images are provided with the nipy-data package
runnames = [example_data.get_filename('fiac','fiac0',run+'.nii.gz') \
for run in ('run1','run2')]
runs = [load_image(run) for run in runnames]
# Spatio-temporal realigner
R = FmriRealign4d(runs, tr=2.5, slice_order='ascending', interleaved=True)
# Estimate motion within- and between-sessions
R.estimate()
# Resample data on a regular space+time lattice using 4d interpolation
corr_runs = R.resample()
# Save images
savedir = tempfile.mkdtemp()
for i in range(len(runs)):
import numpy as np
import sys
# Optional argument
cvect = [1,0,0,0]
if len(sys.argv)>1:
tmp = list(sys.argv[1])
tmp.remove('[')
tmp.remove(']')
for i in range(tmp.count(',')):
tmp.remove(',')
cvect = map(float, tmp)
# Input files
fmri_files = [example_data.get_filename('fiac','fiac0',run) for run in ['run1.nii.gz','run2.nii.gz']]
design_files = [example_data.get_filename('fiac','fiac0',run) for run in ['run1_design.npz','run2_design.npz']]
mask_file = example_data.get_filename('fiac','fiac0','mask.nii.gz')
# Get design matrix as numpy array
print('Loading design matrices...')
X = [np.load(f)['X'] for f in design_files]
# Get multi-session fMRI data
print('Loading fmri data...')
Y = [load_image(f) for f in fmri_files]
# Get mask image
print('Loading mask...')
mask = load_image(mask_file)
else:
if nargs == 2: # contrast as one string
args = sys.argv[1].split(',')
elif nargs == 5: # contrast as sequence of strings
args = [arg.replace(',', '') for arg in sys.argv[1:]]
if len(args) != 4:
print USAGE
exit(1)
try:
cvect = [float(arg) for arg in args]
except ValueError:
print USAGE
exit(1)
# Input files
fmri_files = [example_data.get_filename('fiac', 'fiac0', run)
for run in ['run1.nii.gz', 'run2.nii.gz']]
design_files = [example_data.get_filename('fiac', 'fiac0', run)
for run in ['run1_design.npz', 'run2_design.npz']]
mask_file = example_data.get_filename('fiac', 'fiac0', 'mask.nii.gz')
# Get design matrix as numpy array
print('Loading design matrices...')
X = [np.load(f)['X'] for f in design_files]
# Get multi-session fMRI data
print('Loading fmri data...')
Y = [load_image(f) for f in fmri_files]
# Get mask image
print('Loading mask...')
from optparse import OptionParser
import time
import numpy as np
from nipy.algorithms.registration import HistogramRegistration, resample
from nipy.utils import example_data
from nipy import load_image, save_image
print("Scanning data directory...")
# Input images are provided with the nipy-data package
source = "ammon"
target = "anubis"
source_file = example_data.get_filename("neurospin", "sulcal2000", "nobias_" + source + ".nii.gz")
target_file = example_data.get_filename("neurospin", "sulcal2000", "nobias_" + target + ".nii.gz")
# Parse arguments
parser = OptionParser(description=__doc__)
doc_similarity = "similarity measure: cc (correlation coefficient), \
cr (correlation ratio), crl1 (correlation ratio in L1 norm), \
mi (mutual information), nmi (normalized mutual information), \
pmi (Parzen mutual information), dpmi (discrete Parzen mutual \
information). Default is crl1."
doc_interp = "interpolation method: tri (trilinear), pv (partial volume), \
rand (random). Default is pv."
doc_optimizer = "optimization method: simplex, powell, steepest, cg, bfgs. \
pylab.pink()
array = im.data
if threshold==None:
pylab.imshow(array[:,slice,:])
else:
pylab.imshow(array[:,slice,:]>threshold)
"""
Main
"""
# Load images
I = load_image(example_data.get_filename('neurospin',
'sulcal2000',
'nobias_ammon.nii.gz'))
J = load_image(example_data.get_filename('neurospin',
'sulcal2000',
'nobias_anubis.nii.gz'))
# Create a registration instance
R = IconicRegistration(I, J)
R.set_source_fov(fixed_npoints=64**3)
R.similarity = 'llr_mi'
T = np.eye(4)
#T[0:3,3] = [4,5,6]
print R.eval(T)
if nargs == 2: # contrast as one string
args = sys.argv[1].split(',')
elif nargs == 5: # contrast as sequence of strings
args = [arg.replace(',', '') for arg in sys.argv[1:]]
if len(args) != 4:
print USAGE
exit(1)
try:
cvect = [float(arg) for arg in args]
except ValueError:
print USAGE
exit(1)
# Input files
fmri_files = [
example_data.get_filename('fiac', 'fiac0', run)
for run in ['run1.nii.gz', 'run2.nii.gz']
]
design_files = [
example_data.get_filename('fiac', 'fiac0', run)
for run in ['run1_design.npz', 'run2_design.npz']
]
mask_file = example_data.get_filename('fiac', 'fiac0', 'mask.nii.gz')
# Get design matrix as numpy array
print('Loading design matrices...')
X = [np.load(f)['X'] for f in design_files]
# Get multi-session fMRI data
print('Loading fmri data...')
Y = [load_image(f) for f in fmri_files]
#!/usr/bin/env python
# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
""" Example of onesample permutation test
Needs *example data* package
"""
import numpy as np
from nipy.labs.group.permutation_test import permutation_test_onesample
from nipy.utils import example_data
# Get group data
group_data = example_data.get_filename('neurospin', 'language_babies',
'offset_002.npz')
f = np.load(group_data)
data, vardata, xyz = f['mat'], f['var'], f['xyz']
# Create one-sample permutation test instance
ptest = permutation_test_onesample(data, xyz, stat_id='wilcoxon')
# Cluster definition: (threshold, diameter)
# Note that a list of definitions can be passed to ptest.calibrate
cluster_def = (ptest.height_threshold(0.01), None)
print cluster_def
# Multiple calibration
# To get accurate pvalues, don't pass nperms (default is 1e4)
# Yet it will take longer to run
Author: Alexis Roche, 2009.
"""
from nipy.neurospin import register, transform
from nipy.utils import example_data
from nipy.io.imageformats import load as load_image, save as save_image
from os.path import join
import sys
import time
print('Scanning data directory...')
# Input images are provided with the nipy-data package
source = 'ammon'
target = 'anubis'
source_file = example_data.get_filename('neurospin','sulcal2000','nobias_'+source+'.nii.gz')
target_file = example_data.get_filename('neurospin','sulcal2000','nobias_'+target+'.nii.gz')
# Optional arguments
similarity = 'cr'
interp = 'pv'
optimizer = 'powell'
if len(sys.argv)>1:
similarity = sys.argv[1]
if len(sys.argv)>2:
interp = sys.argv[2]
if len(sys.argv)>3:
optimizer = sys.argv[3]
# Print messages
print ('Source brain: %s' % source)
try:
import matplotlib.pyplot as plt
except ImportError:
raise RuntimeError("This script needs the matplotlib library")
from nibabel import load, save, Nifti1Image
from nipy.modalities.fmri.glm import GeneralLinearModel, data_scaling
from nipy.utils import example_data
from nipy.labs.viz import plot_map, cm
# -----------------------------------------------------------
# --------- Get the data -----------------------------------
# -----------------------------------------------------------
fmri_files = [example_data.get_filename("fiac", "fiac0", run) for run in ["run1.nii.gz", "run2.nii.gz"]]
design_files = [example_data.get_filename("fiac", "fiac0", run) for run in ["run1_design.npz", "run2_design.npz"]]
mask_file = example_data.get_filename("fiac", "fiac0", "mask.nii.gz")
affine = load(mask_file).get_affine()
# Get design matrix as numpy array
print ("Loading design matrices...")
X = [np.load(f)["X"] for f in design_files]
# Get multi-run fMRI data
print ("Loading fmri data...")
Y = [load(f) for f in fmri_files]
# Get mask image
print ("Loading mask...")
mask = load(mask_file)
from os.path import join
from nipy.io.imageformats import Nifti1Image, load as load_image, save as save_image
from nipy.utils import example_data
from nipy.neurospin.registration import FmriRealign4d
from pylab import *
# Create Nifti1Image instances from both input files
rootpath = 'D:\\home\\Alexis\\data\\eccv'
mask = load_image(join(rootpath, 'mask.nii'))
xyz = np.where(mask.get_data()>0)
run = 'run1'
im_raw = load_image(example_data.get_filename('fiac','fiac0',run+'.nii.gz'))
im_npy = load_image(join(rootpath, 'npy_ra'+run+'.nii.gz'))
im_spm = load_image(join(rootpath, 'ra'+run+'.nii'))
raw = im_raw.get_data()
npy = im_npy.get_data()
spm = im_spm.get_data()
TR = 2.5
def compare(x, y, z):
t = TR*np.arange(raw.shape[3])
plot(t, raw[x,y,z,:], 'r')
plot(t, spm[x,y,z,:], 'g')
plot(t, npy[x,y,z,:], 'b')
xlabel('time (sec)')
Author: Alexis Roche, 2009.
"""
from __future__ import print_function # Python 2/3 compatibility
import os
from os.path import split as psplit, abspath
from nipy.algorithms.registration import FmriRealign4d
from nipy import load_image, save_image
from nipy.utils import example_data
# Input images are provided with the nipy-data package
runnames = [
example_data.get_filename('fiac', 'fiac0', run + '.nii.gz')
for run in ('run1', 'run2')
]
runs = [load_image(run) for run in runnames]
# Declare interleaved ascending slice order
nslices = runs[0].shape[2]
slice_order = list(range(0, nslices, 2)) + list(range(1, nslices, 2))
print('Slice order: %s' % slice_order)
# Spatio-temporal realigner
R = FmriRealign4d(runs, tr=2.5, slice_order=slice_order)
# Estimate motion within- and between-sessions
R.estimate(refscan=None)
from optparse import OptionParser
import time
import numpy as np
from nipy.algorithms.registration import HistogramRegistration, resample
from nipy.utils import example_data
from nipy import load_image, save_image
print('Scanning data directory...')
# Input images are provided with the nipy-data package
source = 'ammon'
target = 'anubis'
source_file = example_data.get_filename('neurospin', 'sulcal2000',
'nobias_' + source + '.nii.gz')
target_file = example_data.get_filename('neurospin', 'sulcal2000',
'nobias_' + target + '.nii.gz')
# Parse arguments
parser = OptionParser(description=__doc__)
doc_similarity = 'similarity measure: cc (correlation coefficient), \
cr (correlation ratio), crl1 (correlation ratio in L1 norm), \
mi (mutual information), nmi (normalized mutual information), \
pmi (Parzen mutual information), dpmi (discrete Parzen mutual \
information). Default is crl1.'
doc_interp = 'interpolation method: tri (trilinear), pv (partial volume), \
rand (random). Default is pv.'
try:
import matplotlib.pyplot as plt
except ImportError:
raise RuntimeError("This script needs the matplotlib library")
from nibabel import load, save, Nifti1Image
from nipy.modalities.fmri.glm import GeneralLinearModel, data_scaling
from nipy.utils import example_data
from nipy.labs.viz import plot_map, cm
# -----------------------------------------------------------
# --------- Get the data -----------------------------------
#-----------------------------------------------------------
fmri_files = [example_data.get_filename('fiac', 'fiac0', run)
for run in ['run1.nii.gz', 'run2.nii.gz']]
design_files = [example_data.get_filename('fiac', 'fiac0', run)
for run in ['run1_design.npz', 'run2_design.npz']]
mask_file = example_data.get_filename('fiac', 'fiac0', 'mask.nii.gz')
affine = load(mask_file).get_affine()
# Get design matrix as numpy array
print('Loading design matrices...')
X = [np.load(f)['X'] for f in design_files]
# Get multi-run fMRI data
print('Loading fmri data...')
Y = [load(f) for f in fmri_files]
# Get mask image
