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,)
def plot_anat_parcel_func_fusion(anat,
func,
parcel,
parcel_col='white',
parcels_line_width=.5,
func_cmap=None,
func_norm=None,
anat_norm=None,
func_mask=None,
highlighted_parcels_col=None,
highlighted_parcels_line_width=1.5):
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')
parcel = parcel.astype(int)
parcel_rebin = rebin(parcel, anat.shape)
#parcel_rebin_ma = np.ma.masked_where(parcel_rebin==0, parcel_rebin)
func_rebin = rebin(func, anat.shape)
fmask = (parcel_rebin > 0).astype(bool)
if func_mask is not None:
func_mask_rebin = rebin(func_mask.astype(bool), anat.shape)
#print 'func_mask:', func_mask_rebin.sum()
fmask = np.bitwise_and(fmask, func_mask_rebin)
func_rebin_ma = np.ma.masked_where(fmask == 0, func_rebin)
anat_cmap = plt.get_cmap('gray')
plt.figure()
plt.hold(True)
if 0:
#TODO: better color mixing
ax = plt.imshow(anat,
cmap=anat_cmap,
norm=anat_norm,
interpolation='nearest')
plt.imshow(func_rebin_ma,
interpolation='nearest',
alpha=.5,
cmap=func_cmap,
norm=func_norm,
axes=ax)
if 1:
ax = plt.imshow(mix_cmap(func_rebin_ma,
func_cmap,
anat,
anat_cmap,
norm1=func_norm,
norm2=anat_norm,
blend_r=.5),
interpolation='nearest')
ax.axes.set_axis_off()
if 0:
plt.imshow(func_rebin_ma * anat,
interpolation='nearest',
cmap=func_cmap,
norm=func_norm)
if 0:
ax = plt.imshow(func_rebin_ma,
interpolation='nearest',
cmap=func_cmap,
norm=func_norm)
plt.colorbar()
plt.imshow(anat,
cmap=plt.get_cmap('gray'),
alpha=.65,
interpolation='nearest',
axes=ax)
if 0:
plt.imshow(mix_cmap(func_rebin_ma, func_cmap, anat, anat_cmap),
interpolation='nearest')
labs = np.unique(parcel)
# 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
alpha = .5
else:
lw = parcels_line_width
alpha = .2
plt.contour(
(parcel_rebin == lab).astype(int),
1,
colors=[col],
linewidths=lw,
alpha=alpha,
)
def plot_anat_parcel_func_fusion(anat, func, parcel, parcel_col='white',
parcels_line_width=.5, func_cmap=None,
func_norm=None, anat_norm=None, func_mask=None,
highlighted_parcels_col=None,
highlighted_parcels_line_width=1.5):
if highlighted_parcels_col is None:
highlighted_parcels_col = {}
if func_cmap is None:
func_cmap = plt.get_cmap('jet')
parcel = parcel.astype(int)
parcel_rebin = rebin(parcel, anat.shape)
#parcel_rebin_ma = np.ma.masked_where(parcel_rebin==0, parcel_rebin)
func_rebin = rebin(func, anat.shape)
fmask = (parcel_rebin>0).astype(bool)
if func_mask is not None:
func_mask_rebin = rebin(func_mask.astype(bool), anat.shape)
#print 'func_mask:', func_mask_rebin.sum()
fmask = np.bitwise_and(fmask, func_mask_rebin)
func_rebin_ma = np.ma.masked_where(fmask==0, func_rebin)
anat_cmap = plt.get_cmap('gray')
plt.figure()
plt.hold(True)
if 0:
#TODO: better color mixing
ax = plt.imshow(anat, cmap=anat_cmap, norm=anat_norm,
interpolation='nearest')
plt.imshow(func_rebin_ma, interpolation='nearest',
alpha=.5, cmap=func_cmap, norm=func_norm, axes=ax)
if 1:
ax = plt.imshow(mix_cmap(func_rebin_ma, func_cmap,
anat, anat_cmap,
norm1=func_norm,
norm2=anat_norm, blend_r=.5),
interpolation='nearest')
ax.get_axes().set_axis_off()
if 0:
plt.imshow(func_rebin_ma*anat, interpolation='nearest',
cmap=func_cmap, norm=func_norm)
if 0:
ax = plt.imshow(func_rebin_ma, interpolation='nearest',
cmap=func_cmap, norm=func_norm)
plt.colorbar()
plt.imshow(anat, cmap=plt.get_cmap('gray'), alpha=.65,
interpolation='nearest', axes=ax)
if 0:
plt.imshow(mix_cmap(func_rebin_ma, func_cmap, anat, anat_cmap),
interpolation='nearest')
labs = np.unique(parcel)
# 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
alpha = .5
else:
lw = parcels_line_width
alpha = .2
plt.contour((parcel_rebin==lab).astype(int), 1,
colors=[col], linewidths=lw, alpha=alpha,)
