def disp_slopes(mask, ls_fit, prop):
"""
Prepares and displays the slopes in a mosaic.
Parameters
----------
mask: 3 dimensional array
Brain mask of the data
ls_fit: 1 dimensional array
An array with the results from the least squares fit
Returns
-------
slopeProp_all: 3 dimensional array
Slope of the desired property across b values at each voxel
"""
prop_dict = {'FA':'FA', 'MD':'MD', 'mean diffusivity':'MD',
'fractional anisotropy':'FA', 'dispersion index':'DI',
'DI':'DI','SNR':'SNR', 'signal-to-noise ratio':'SNR',
'signal to noise ratio':'SNR'}
slopeProp_all = np.zeros_like(mask)
slopeProp_all[np.where(mask)] = np.squeeze(np.array(ls_fit[0,:][np.isfinite(ls_fit[0,:])]))
if prop_dict[prop] is 'SNR':
fig = mpl.mosaic(slopeProp_all, cmap=matplotlib.cm.bone)
fig.set_size_inches([20,10])
else:
fig = mpl.mosaic(slopeProp_all, cmap=matplotlib.cm.PuOr_r, vmin = -0.75, vmax = 0.75)
fig.set_size_inches([20,10])
return slopeProp_all
def display(data):
"""
Displays the snr across the brain as a mosaic
Saves the data as a nifti file
Parameters
----------
snr_data: 3 dimensional array
SNR at each voxel
"""
fig = mpl.mosaic(data, cmap=matplotlib.cm.bone)
fig.set_size_inches([20, 10])
return mean_snr
def display(data):
"""
Displays the snr across the brain as a mosaic
Saves the data as a nifti file
Parameters
----------
snr_data: 3 dimensional array
SNR at each voxel
"""
fig = mpl.mosaic(data, cmap=matplotlib.cm.bone)
fig.set_size_inches([20,10])
return mean_snr
def disp_sqrd_err(sum_sqrd_err, mask):
"""
Prepares and displays the squared error in a mosaic.
Parameters
----------
sum_sqrd_err: 1 dimensional array
An array with the sum squared error at each voxel
mask: 3 dimensional array
Brain mask of the data
Returns
-------
sqrd_err_all: 3 dimensional array
Squared error from model fit at each voxel
"""
sqrd_err_all = np.zeros_like(mask)
sqrd_err_all[np.where(mask)] = sum_sqrd_err
fig = mpl.mosaic(sqrd_err_all, cmap=matplotlib.cm.bone, vmax = 1)
fig.set_size_inches([20,10])
return sqrd_err_all
def plot_slopes(mask, ls_fit):
"""
Prepares and plots arrays in a mosaic.
Parameters
----------
mask: 3 dimensional array
Brain mask of the data
ls_fit: 1 dimensional array
An array with the results from the least squares fit
Returns
-------
slopeProp_all: 3 dimensional array
Slope of the desired property across b values at each voxel
"""
idx_mask = np.where(mask)
slopeProp_all = np.zeros_like(mask)
slopeProp_all[idx_mask] = np.squeeze(np.array(ls_fit[0, :][np.isfinite(ls_fit[0, :])]))
fig = mpl.mosaic(slopeProp_all, cmap=matplotlib.cm.bone)
fig.set_size_inches([20, 10])
return slopeProp_all
