def test_iterate_over_image():
# Fit a model, iterating over the slices of an array
# associated to an FmriImage.
c = np.zeros(FDATA.shape[1:]) + 0.5
res_gen = result_generator(flatten_generator(axis0_generator(FDATA)))
write_data(c, unflatten_generator(contrast_generator(res_gen)))
# Fit a model, iterating over the array associated to an
# FmriImage, iterating over a list of ROIs defined by binary
# regions of the same shape as a frame of FmriImage
# this might really be an anatomical image or AR(1) coefficients
a = np.asarray(FDATA[0])
p = np.greater(a, a.mean())
d = np.ones(FDATA.shape[1:]) * 2.0
flat_gen = flatten_generator(axis0_generator(FDATA, parcels(p)))
write_data(d, contrast_generator(result_generator(flat_gen)))
assert_array_almost_equal(d, c)
e = np.zeros(FDATA.shape[1:]) + 3.0
flat_gen2 = flatten_generator(axis0_generator(FDATA, parcels(p)))
write_data(e, f_generator(contrast, result_generator(flat_gen2)))
assert_array_almost_equal(d, e)
def test_iterate_over_image():
# Fit a model, iterating over the slices of an array
# associated to an FmriImage.
c = np.zeros(FDATA.shape[1:]) + 0.5
res_gen = result_generator(flatten_generator(axis0_generator(FDATA)))
write_data(c, unflatten_generator(contrast_generator(res_gen)))
# Fit a model, iterating over the array associated to an
# FmriImage, iterating over a list of ROIs defined by binary
# regions of the same shape as a frame of FmriImage
# this might really be an anatomical image or AR(1) coefficients
a = np.asarray(FDATA[0])
p = np.greater(a, a.mean())
d = np.ones(FDATA.shape[1:]) * 2.0
flat_gen = flatten_generator(axis0_generator(FDATA, parcels(p)))
write_data(d, contrast_generator(result_generator(flat_gen)))
assert_array_almost_equal(d, c)
e = np.zeros(FDATA.shape[1:]) + 3.0
flat_gen2 = flatten_generator(axis0_generator(FDATA, parcels(p)))
write_data(e, f_generator(contrast, result_generator(flat_gen2)))
assert_array_almost_equal(d, e)
from os.path import dirname, join as pjoin
import nipy
from nipy.core.utils.generators import parcels
OUR_PATH = dirname(__file__)
DATA_PATH = pjoin(OUR_PATH, '..', 'data')
BG_IMAGE_FNAME = pjoin(DATA_PATH, 'mni_basal_ganglia.nii.gz')
bg_img = nipy.load_image(BG_IMAGE_FNAME)
bg_data = bg_img.get_data()
"""
I happen to know that the image has these codes:
14 - Left striatum
16 - Right striatum
39 - Left caudate
53 - Right caudate
All the other voxels are zero, I don't want those.
"""
print("Number of voxels for L, R striatum; L, R caudate")
for mask in parcels(bg_data, exclude=(0, )):
print(mask.sum())
""" Given we know the codes we can also give them directly """
print("Again with the number of voxels for L, R striatum; L, R caudate")
for mask in parcels(bg_data, labels=(14, 16, 39, 53)):
print(mask.sum())
import nipy
from nipy.core.utils.generators import parcels
OUR_PATH = dirname(__file__)
DATA_PATH = pjoin(OUR_PATH, '..', 'data')
BG_IMAGE_FNAME = pjoin(DATA_PATH, 'mni_basal_ganglia.nii.gz')
bg_img = nipy.load_image(BG_IMAGE_FNAME)
bg_data = bg_img.get_data()
"""
I happen to know that the image has these codes:
14 - Left striatum
16 - Right striatum
39 - Left caudate
53 - Right caudate
All the other voxels are zero, I don't want those.
"""
print("Number of voxels for L, R striatum; L, R caudate")
for mask in parcels(bg_data, exclude=(0,)):
print(mask.sum())
""" Given we know the codes we can also give them directly """
print("Again with the number of voxels for L, R striatum; L, R caudate")
for mask in parcels(bg_data, labels=(14, 16, 39, 53)):
print(mask.sum())