def crop_data_file(data_fn, mask_fn, output_data_fn):
data,h = read_volume(data_fn)
mask,mh = read_volume(mask_fn)
if data.ndim > mask.ndim:
mask = mask[:,:,:,np.newaxis] * np.ones(data.shape, dtype=int)
elif mask.ndim > data.ndim:
raise Exception('Mask has more dims (%d) than data (%d).' \
'Maybe data and mask were switched?' \
%(mask.ndim, data.ndim))
cropped_data = crop_array(data, mask)
if 1:
print 'cropped_data:', cropped_data.shape
print 'input_data_fn:', data_fn
print 'output_data_fn:', output_data_fn
if len(h[1].extensions) > 0:
#remove cuboid nifti extension
h[1].extensions.pop()
write_volume(cropped_data, output_data_fn, h)
def crop_data_file(data_fn, mask_fn, output_data_fn):
data, h = read_volume(data_fn)
mask, mh = read_volume(mask_fn)
if data.ndim > mask.ndim:
mask = mask[:, :, :, np.newaxis] * np.ones(data.shape, dtype=int)
elif mask.ndim > data.ndim:
raise Exception('Mask has more dims (%d) than data (%d).'
'Maybe data and mask were switched?'
% (mask.ndim, data.ndim))
cropped_data = crop_array(data, mask)
if 1:
print 'cropped_data:', cropped_data.shape
print 'input_data_fn:', data_fn
print 'output_data_fn:', output_data_fn
if len(h[1].extensions) > 0:
# remove cuboid nifti extension
h[1].extensions.pop()
write_volume(cropped_data, output_data_fn, h)
def plot_func_slice(func_slice_data,
anatomy=None,
parcellation=None,
parcel_col='white',
parcels_line_width=2.5,
func_cmap=None,
func_norm=None,
anat_norm=None,
func_mask=None,
highlighted_parcels_col=None,
highlighted_parcels_line_width=2.5,
resolution=None,
crop_extension=None,
blend=.5):
import matplotlib.pyplot as plt
if highlighted_parcels_col is None:
highlighted_parcels_col = {}
if func_cmap is None:
func_cmap = plt.get_cmap('jet')
if anatomy is not None:
if func_slice_data.shape != anatomy.shape:
func_slice_data = rebin(func_slice_data, anatomy.shape)
if parcellation is not None:
parcellation = parcellation.astype(int)
if anatomy is not None:
if parcellation.shape != anatomy.shape:
parcellation = rebin(parcellation, anatomy.shape)
fmask = (parcellation > 0).astype(bool)
else:
fmask = np.ones_like(func_slice_data).astype(bool)
if func_mask is not None:
if anatomy is not None:
func_mask = rebin(func_mask.astype(bool), anatomy.shape)
#print 'func_mask:', func_mask.sum()
fmask = np.bitwise_and(fmask, func_mask)
if crop_extension is not None:
from pyhrf.tools import crop_array
func_slice_data = crop_array(func_slice_data, fmask, crop_extension)
if anatomy is not None:
m_tmp = np.where(fmask != 0)
mask_anat_tmp = np.zeros_like(anatomy)
e = crop_extension
start_i = m_tmp[0].min() - e - 1
start_j = m_tmp[1].min() - e - 1
if start_i < 0 or start_j < 0:
raise Exception('Anatomy is not large enough to extend view.')
mask_anat_tmp[start_i:m_tmp[0].max() + e,
start_j:m_tmp[1].max() + e] = 1
anatomy = crop_array(anatomy, mask_anat_tmp)
# print 'func_slice_data:', func_slice_data.shape
# print 'anatomy:', anatomy.shape
fmask = crop_array(fmask, fmask, crop_extension)
func_masked = np.ma.masked_where(fmask == 0, func_slice_data)
anat_cmap = plt.get_cmap('gray')
plt.figure()
# plt.hold(True) Deprecated function, no longer necessary to call it.
if anatomy is not None:
ax = plt.imshow(mix_cmap(func_masked,
func_cmap,
anatomy,
anat_cmap,
norm1=func_norm,
norm2=anat_norm,
blend_r=blend),
interpolation='nearest')
else:
ax = plt.imshow(func_masked,
cmap=func_cmap,
norm=func_norm,
interpolation='nearest')
ax.axes.set_axis_off()
if resolution is not None:
ratio = resolution[0] / resolution[1]
print 'Set aspect ratio to:', ratio
ax.axes.set_aspect(ratio)
if parcellation is not None:
labs = np.unique(parcellation)
# nr, nc = parcel_rebin.shape
# extent = [-0.5, nc-0.5, -0.5, nr-0.5]
for lab in labs: #[484,427,540]: #
if lab != 0:
col = highlighted_parcels_col.get(lab, parcel_col)
if lab in highlighted_parcels_col:
lw = highlighted_parcels_line_width
else:
lw = parcels_line_width
plt.contour(
(parcellation == lab).astype(int),
1,
colors=[col],
linewidths=lw,
alpha=.6,
)
def plot_func_slice(func_slice_data, anatomy=None, parcellation=None,
parcel_col='white',
parcels_line_width=2.5, func_cmap=None,
func_norm=None, anat_norm=None, func_mask=None,
highlighted_parcels_col=None,
highlighted_parcels_line_width=2.5,
resolution=None, crop_extension=None, blend=.5):
if highlighted_parcels_col is None:
highlighted_parcels_col = {}
if func_cmap is None:
func_cmap = plt.get_cmap('jet')
if anatomy is not None:
if func_slice_data.shape != anatomy.shape:
func_slice_data = rebin(func_slice_data, anatomy.shape)
if parcellation is not None:
parcellation = parcellation.astype(int)
if anatomy is not None:
if parcellation.shape != anatomy.shape:
parcellation = rebin(parcellation, anatomy.shape)
fmask = (parcellation>0).astype(bool)
else:
fmask = np.ones_like(func_slice_data).astype(bool)
if func_mask is not None:
if anatomy is not None:
func_mask = rebin(func_mask.astype(bool), anatomy.shape)
#print 'func_mask:', func_mask.sum()
fmask = np.bitwise_and(fmask, func_mask)
if crop_extension is not None:
from pyhrf.tools import crop_array
func_slice_data = crop_array(func_slice_data, fmask, crop_extension)
if anatomy is not None:
m_tmp = np.where(fmask!=0)
mask_anat_tmp = np.zeros_like(anatomy)
e = crop_extension
start_i = m_tmp[0].min()-e-1
start_j = m_tmp[1].min()-e-1
if start_i < 0 or start_j < 0:
raise Exception('Anatomy is not large enough to extend view.')
mask_anat_tmp[start_i:m_tmp[0].max()+e,start_j:m_tmp[1].max()+e] = 1
anatomy = crop_array(anatomy, mask_anat_tmp)
# print 'func_slice_data:', func_slice_data.shape
# print 'anatomy:', anatomy.shape
fmask = crop_array(fmask, fmask, crop_extension)
func_masked = np.ma.masked_where(fmask==0, func_slice_data)
anat_cmap = plt.get_cmap('gray')
plt.figure()
plt.hold(True)
if anatomy is not None:
ax = plt.imshow(mix_cmap(func_masked, func_cmap,
anatomy, anat_cmap,
norm1=func_norm,
norm2=anat_norm, blend_r=blend),
interpolation='nearest')
else:
ax = plt.imshow(func_masked, cmap=func_cmap,
norm=func_norm,
interpolation='nearest')
ax.get_axes().set_axis_off()
if resolution is not None:
ratio = resolution[0] / resolution[1]
print 'Set aspect ratio to:', ratio
ax.get_axes().set_aspect(ratio)
if parcellation is not None:
labs = np.unique(parcellation)
# nr, nc = parcel_rebin.shape
# extent = [-0.5, nc-0.5, -0.5, nr-0.5]
for lab in labs: #[484,427,540]: #
if lab != 0:
col = highlighted_parcels_col.get(lab, parcel_col)
if lab in highlighted_parcels_col:
lw = highlighted_parcels_line_width
else:
lw = parcels_line_width
plt.contour((parcellation==lab).astype(int), 1,
colors=[col], linewidths=lw, alpha=.6,)
