if cfg.export_gramag:
export_gradient_magnitude_image(gra, nii.get_filename(), nii.affine)
# Reshape ima (more intuitive for voxel-wise operations)
ima = np.ndarray.flatten(orig)
gra = np.ndarray.flatten(gra)
#
"""Plots"""
print("Preparing GUI...")
# Plot 2D histogram
fig = plt.figure(facecolor='0.775')
ax = fig.add_subplot(121)
counts, volHistH, d_min, d_max, nr_bins, bin_edges \
= prep_2D_hist(ima, gra, discard_zeros=cfg.discard_zeros)
ax.set_xlim(d_min, d_max)
ax.set_ylim(d_min, d_max)
ax.set_xlabel("Intensity f(x)")
ax.set_ylabel("Gradient Magnitude f'(x)")
ax.set_title("2D Histogram")
# Plot map for poltical borders
pltMap = np.zeros((nr_bins, nr_bins, 1)).repeat(4, 2)
cmapPltMap = ListedColormap([[1, 1, 1, 0], # transparent zeros
[0, 0, 0, 0.75], # political borders
[1, 0, 0, 0.5], # other colors for future use
[0, 0, 1, 0.5]])
boundsPltMap = [0, 1, 2, 3, 4]
normPltMap = BoundaryNorm(boundsPltMap, cmapPltMap.N)
if cfg.export_gramag:
export_gradient_magnitude_image(gra, nii.get_filename(), cfg.gramag,
nii.affine)
# Reshape for voxel-wise operations
ima = np.copy(orig.flatten())
gra = gra.flatten()
#
"""Plots"""
print("Preparing GUI...")
# Plot 2D histogram
fig = plt.figure(facecolor='0.775')
ax = fig.add_subplot(121)
counts, volHistH, d_min, d_max, nr_bins, bin_edges \
= prep_2D_hist(ima, gra, discard_zeros=cfg.discard_zeros)
# Set x-y axis range to the same (x-axis range)
ax.set_xlim(d_min, d_max)
ax.set_ylim(d_min, d_max)
ax.set_xlabel("Intensity f(x)")
ax.set_ylabel("Gradient Magnitude f'(x)")
ax.set_title("2D Histogram")
# Plot colorbar for 2D hist
volHistH.set_norm(LogNorm(vmax=np.power(10, cfg.cbar_init)))
fig.colorbar(volHistH, fraction=0.046, pad=0.04) # magical scaling
# Plot 3D ima by default
ax2 = fig.add_subplot(122)
sliceNr = int(0.5 * dims[2])