global size

if size != obj.GetSize():

size_old = size

size = obj.GetSize()

size_change = [size[0] - size_old[0], 0]

""" Sets up figure that shows differences between two data arrays

with assumption that one is standard and other is after denoising

Parameters

data: arr

Initial data

denoised_arr: arr

denoised data array

type: str

Type of denoising

savefigpath: str

path of saved output of figure, if none no figure is saved

"""

sli = data.shape[2] // 2

gra = data.shape[3] - 1

orig = data[:, :, sli, gra]

den = denoised_arr[:, :, sli, gra]

rms_diff = np.sqrt((orig - den) ** 2)

if show_orig_denoised:

fig1, ax = plt.subplots(1, 3, figsize=(12, 6),

subplot_kw={'xticks': [], 'yticks': []})

fig1.subplots_adjust(hspace=0.3, wspace=0.05)

ax.flat[0].imshow(orig.T, cmap='gray', interpolation='none',

origin='lower')

ax.flat[0].set_title('Original')

ax.flat[1].imshow(den.T, cmap='gray', interpolation='none',

origin='lower')

ax.flat[1].set_title('Denoised Output')

if type == 'macenko':

ax.flat[2].imshow(rms_diff.T, cmap='gray', interpolation='none',

origin='lower')

ax.flat[2].set_title('Residuals')

if type == 'gibbs':

ax.flat[2].imshow(data_corrected[:, :, 0, 0].T - data_slices[:, :, 0, 0].T,

cmap='gray', origin='lower', vmin=-500, vmax=500)

ax.flat[2].set_title('Gibbs residuals')

if savefigpath.lower=='none':

pass

else:

scene = window.Scene()

scene.SetBackground(1, 1, 1)

#colors = ['white', 'cadmium_red_deep', 'misty_rose', 'slate_grey_dark', 'ivory_black', 'chartreuse']

colors = [window.colors.white, window.colors.cadmium_red_deep, window.colors.misty_rose, window.colors.slate_grey_dark, window.colors.ivory_black, window.colors.chartreuse]

streamline_cut = []

i = 0

if ratio != 1:

for streamline in streamlines_full:

if i % ratio == 0:

streamline_cut.append(streamline)

i += 1

else:

streamline_cut = streamlines_full

qb = QuickBundles(threshold=threshold)

clusters = qb.cluster(streamline_cut)

if verbose:

print("Nb. clusters:", len(clusters))

print("Cluster sizes:", map(len, clusters))

print("Small clusters:", clusters < 10)

print("Streamlines indices of the first cluster:\n", clusters[0].indices)

print("Centroid of the last clustker:\n", clusters[-1].centroid)

j=0

scene = window.Scene()

scene.add(actor.streamtube(streamline_cut, colors[j]))

slicer_opacity = 0.6

j += 1

if isinstance(anat_path, str) and os.path.exists(anat_path):

anat_nifti = load_nifti(anat_path)

try:

data = anat_nifti.data

except AttributeError:

data = anat_nifti[0]

if affine is None:

try:

affine = anat_nifti.affine

except AttributeError:

affine = anat_nifti[1]

else:

data = anat_path

shape = np.shape(data)

if np.size(shape)==4:

data=data[:,:,:,0]

image_actor_z = actor.slicer(data, affine)

image_actor_z.opacity(slicer_opacity)

image_actor_x = image_actor_z.copy()

x_midpoint = int(np.round(shape[0] / 2))

image_actor_x.display_extent(x_midpoint,

x_midpoint, 0,

shape[1] - 1,

0,

shape[2] - 1)

image_actor_y = image_actor_z.copy()

y_midpoint = int(np.round(shape[1] / 2))

image_actor_y.display_extent(0, shape[0] - 1,

y_midpoint,

y_midpoint,

0,

shape[2] - 1)

scene.add(image_actor_z)

scene.add(image_actor_x)

scene.add(image_actor_y)

global size

size = scene.GetSize()

show_m = window.ShowManager(scene, size=(1200, 900))

show_m.initialize()

interactive = True

interactive = False

if interactive:

show_m.add_window_callback(win_callback)

show_m.render()

show_m.start()

else:

window.record(scene, out_path='bundles_and_3_slices.png', size=(1200, 900),

#streamlines = Streamlines(res['af.left'])

#streamlines.extend(res['cst.right'])

#streamlines.extend(res['cc_1'])

world_coords = True

# Cluster sizes: [64, 191, 47, 1]

# Small clusters: array([False, False, False, True], dtype=bool)

scene = window.Scene()

scene.SetBackground(1, 1, 1)

colors = ['white', 'cadmium_red_deep', 'misty_rose', 'slate_grey_dark', 'ivory_black', 'chartreuse']

colors = [window.colors.white, window.colors.cadmium_red_deep, window.colors.misty_rose, window.colors.slate_grey_dark, window.colors.ivory_black, window.colors.chartreuse]

i = 0

for ROI in ROI_streamlines:

ROI_streamline = allstreamlines[ROI]

qb = QuickBundles(threshold=threshold)

clusters = qb.cluster(ROI_streamline)

if verbose:

print("Nb. clusters:", len(clusters))

print("Cluster sizes:", map(len, clusters))

print("Small clusters:", clusters < 10)

print("Streamlines indices of the first cluster:\n", clusters[0].indices)

print("Centroid of the last cluster:\n", clusters[-1].centroid)

#if not world_coords:

# from dipy.tracking.streamline import transform_streamlines

# streamlines = transform_streamlines(ROI_streamline, np.linalg.inv(affine))

scene = window.Scene()

#stream_actor = actor.line(ROI_streamline)

#scene.add(actor.streamtube(ROI_streamline, window.colors.misty_rose))

scene.add(actor.streamtube(ROI_streamline, colors[i]))

#if not world_coords:

# image_actor_z = actor.slicer(data, affine=np.eye(4))

#else:

# image_actor_z = actor.slicer(data, affine)

slicer_opacity = 0.6

i = i + 1

anat_nifti = load_nifti(anat_path)

try:

data = anat_nifti.data

except AttributeError:

data = anat_nifti[0]

try:

affine = anat_nifti.affine

except AttributeError:

affine = anat_nifti[1]

shape = np.shape(data)

image_actor_z = actor.slicer(data[:,:,:,0], affine)

image_actor_z.opacity(slicer_opacity)

image_actor_x = image_actor_z.copy()

x_midpoint = int(np.round(shape[0] / 2))

image_actor_x.display_extent(x_midpoint,

x_midpoint, 0,

shape[1] - 1,

0,

shape[2] - 1)

image_actor_y = image_actor_z.copy()

y_midpoint = int(np.round(shape[1] / 2))

image_actor_y.display_extent(0, shape[0] - 1,

y_midpoint,

y_midpoint,

0,

shape[2] - 1)

scene.add(image_actor_z)

scene.add(image_actor_x)

scene.add(image_actor_y)

global size

size = scene.GetSize()

show_m = window.ShowManager(scene, size=(1200, 900))

show_m.initialize()

interactive = True

interactive = False

if interactive:

show_m.add_window_callback(win_callback)

show_m.render()

show_m.start()

else:

window.record(scene, out_path='bundles_and_3_slices.png', size=(1200, 900),

#bvecs[1:] = (bvecs[1:] /

# np.sqrt(np.sum(bvecs[1:] * bvecs[1:], axis=1))[:, None])

#gtab = gradient_table(bvals, bvecs)

if verbose:

print('data.shape (%d, %d, %d, %d)' % data.shape)

asm = ShoreModel(gtab)

#Let’s just use only one slice only from the data.

dataslice = data[30:70, 20:80, data.shape[2] // 2]

#Fit the signal with the model and calculate the SHORE coefficients.

asmfit = asm.fit(dataslice)

#Calculate the analytical RTOP on the signal that corresponds to the integral of the signal.

if verbose:

print('Calculating... rtop_signal')

rtop_signal = asmfit.rtop_signal()

#Now we calculate the analytical RTOP on the propagator, that corresponds to its central value.

if verbose:

print('Calculating... rtop_pdf')

rtop_pdf = asmfit.rtop_pdf()

#In theory, these two measures must be equal, to show that we calculate the mean square error on this two measures.

mse = np.sum((rtop_signal - rtop_pdf) ** 2) / rtop_signal.size

if verbose:

print("MSE = %f" % mse)

MSE = 0.000000

#Let’s calculate the analytical mean square displacement on the propagator.

if verbose:

print('Calculating... msd')

msd = asmfit.msd()

#Show the maps and save them to a file.

fig = plt.figure(figsize=(6, 6))

ax1 = fig.add_subplot(2, 2, 1, title='rtop_signal')

ax1.set_axis_off()

ind = ax1.imshow(rtop_signal.T, interpolation='nearest', origin='lower')

plt.colorbar(ind)

ax2 = fig.add_subplot(2, 2, 2, title='rtop_pdf')

ax2.set_axis_off()

ind = ax2.imshow(rtop_pdf.T, interpolation='nearest', origin='lower')

plt.colorbar(ind)

ax3 = fig.add_subplot(2, 2, 3, title='msd')

ax3.set_axis_off()

ind = ax3.imshow(msd.T, interpolation='nearest', origin='lower', vmin=0)

plt.colorbar(ind)

print("about to save")

plt.savefig(outpath_fig)

""" Displays bundles

Parameters

---------

bundles: bundles object

"""

ren = window.Renderer()

ren.SetBackground(1., 1, 1)

if str_tube:

bundle_actor = actor.streamtube(bundles, colors, linewidth=0.5)

ren.add(bundle_actor)

else:

for (i, bundle) in enumerate(bundles):

color = colors[i]

# lines_actor = actor.streamtube(bundle, color, linewidth=0.05

lines_actor = actor.line(bundle, color,linewidth=2.5)

#lines_actor.RotateX(-90)

#lines_actor.RotateZ(90)

ren.add(lines_actor)

if fa:

fa, affine_fa= load_nifti('/Users/alex/code/Wenlin/data/wenlin_results/bmfaN54900.nii.gz')

fa_actor = actor.slicer(fa, affine_fa)

ren.add(fa_actor)

if show:

window.show(ren)

if fname is not None:

sleep(1)

"""

:param bundles:

:param mask_roi:

:param anat:

:param interactive:

:param outpath:

:return:

"""

candidate_streamlines_actor = actor.streamtube(bundles,

cmap.line_colors(candidate_sl))

ROI_actor = actor.contour_from_roi(mask_roi, color=(1., 1., 0.),

opacity=0.5)

ren = window.Renderer()

if anat:

vol_actor = actor.slicer(anat)

vol_actor.display(x=40)

vol_actor2 = vol_actor.copy()

vol_actor2.display(z=35)

# Add display objects to canvas

ren.add(candidate_streamlines_actor)

ren.add(ROI_actor)

ren.add(vol_actor)

ren.add(vol_actor2)

if outpath is not None:

window.record(ren, n_frames=1,

out_path=outpath,

size=(800, 800))

if interactive:

#fiber_fit_beta_path = glob.glob(pickles_folder + '/*beta.p')[0]

#mean_rmse_path = glob.glob(pickles_folder + '/*mean_rmse.p')[0]

#model_rmse_path = glob.glob(pickles_folder + '/*model_rmse.p')[0]

#fiber_fit_beta = pickle.load(open(fiber_fit_beta_path, "rb"))

#mean_rmse = pickle.load(open(mean_rmse_path, "rb"))

#model_rmse = pickle.load(open(model_rmse_path, "rb"))

fig, ax = plt.subplots(1)

ax.hist(fiber_fit_beta, bins=100, histtype='step')

ax.set_xlabel('Fiber weights')

ax.set_ylabel('# fibers')

#ROI_actor = actor.contour_from_roi(roimask, color=(1., 1., 0.),

# opacity=0.5)

#sizebeta=getsize(fiber_fit_beta)

if interactive:

plt.show()

if outpathfig is not None:

histofig_path = (outpathfig + subject + strproperty + "_beta_histogram.png")

fig.savefig(histofig_path)

if verbose:

txt="file saved at "+histofig_path

print(txt)

send_mail(txt,subject="LifE save msg ")

"""

vol_actor = actor.slicer(t1_data)

vol_actor.display(x=40)

vol_actor2 = vol_actor.copy()

vol_actor2.display(z=35)

"""

fig, ax = plt.subplots(1)

ax.hist(mean_rmse - model_rmse, bins=100, histtype='step')

ax.text(0.2, 0.9, 'Median RMSE, mean model: %.2f' % np.median(mean_rmse),

horizontalalignment='left',

verticalalignment='center',

transform=ax.transAxes)

ax.text(0.2, 0.8, 'Median RMSE, LiFE: %.2f' % np.median(model_rmse),

horizontalalignment='left',

verticalalignment='center',

transform=ax.transAxes)

ax.set_xlabel('RMS Error')

ax.set_ylabel('# voxels')

if interactive:

plt.show()

if outpathfig is not None:

errorhistofig_path=(outpathfig + subject + strproperty + "_error_histograms.png")

fig.savefig(errorhistofig_path)

if verbose:

txt="file saved at "+errorhistofig_path

print(txt)

send_mail(txt,subject="LifE save msg ")

runspatialerrors=True

try:

dwidata.shape[:3]

except AttributeError:

runspatialerrors=False

if runspatialerrors:

vol_model = np.ones(dwidata.shape[:3]) * np.nan

vol_model[vox_coords[:, 0],

vox_coords[:, 1],

vox_coords[:, 2]] = model_rmse

vol_mean = np.ones(dwidata.shape[:3]) * np.nan

vol_mean[vox_coords[:, 0],

vox_coords[:, 1],

vox_coords[:, 2]] = mean_rmse

vol_improve = np.ones(dwidata.shape[:3]) * np.nan

vol_improve[vox_coords[:, 0],

vox_coords[:, 1],

vox_coords[:, 2]] = mean_rmse - model_rmse

sl_idx = 49

fig = plt.figure()

fig.subplots_adjust(left=0.05, right=0.95)

ax = AxesGrid(fig, 111,

nrows_ncols=(1, 3),

label_mode="1",

share_all=True,

cbar_location="top",

cbar_mode="each",

cbar_size="10%",

cbar_pad="5%")

ax[0].matshow(np.rot90(t1_data[sl_idx, :, :]), cmap=matplotlib.cm.bone)

im = ax[0].matshow(np.rot90(vol_model[sl_idx, :, :]), cmap=matplotlib.cm.hot)

ax.cbar_axes[0].colorbar(im)

ax[1].matshow(np.rot90(t1_data[sl_idx, :, :]), cmap=matplotlib.cm.bone)

im = ax[1].matshow(np.rot90(vol_mean[sl_idx, :, :]), cmap=matplotlib.cm.hot)

ax.cbar_axes[1].colorbar(im)

ax[2].matshow(np.rot90(t1_data[sl_idx, :, :]), cmap=matplotlib.cm.bone)

im = ax[2].matshow(np.rot90(vol_improve[sl_idx, :, :]),

cmap=matplotlib.cm.RdBu)

ax.cbar_axes[2].colorbar(im)

for lax in ax:

lax.set_xticks([])

lax.set_yticks([])

if outpathfig is not None:

histofig_path=(outpathfig+ subject+ strproperty + "_spatial_errors.png")

fig.savefig(histofig_path)

if verbose:

txt="spatial errors figure saved at " + histofig_path

print(txt)