def test_scale_range():
"""Test range scaling."""
# Given
data = np.random.random(100) - 0.5
data.ravel()[np.random.choice(data.size, 10, replace=False)] = 0.
data.ravel()[np.random.choice(data.size, 5, replace=False)] = np.nan
s = 42. # scaling factor
expected = [0., s] # min and max
# When
output = scale_range(data, scale_factor=s, delta=0.01, discard_zeros=False)
# Then
assert all([np.nanmin(output) >= expected[0],
np.nanmax(output) < expected[1]])
def test_scale_range():
"""Test range scaling."""
# Given
data = np.random.random(100) - 0.5
data.ravel()[np.random.choice(data.size, 10, replace=False)] = 0.
data.ravel()[np.random.choice(data.size, 5, replace=False)] = np.nan
s = 42. # scaling factor
expected = [0., s] # min and max
# When
output = scale_range(data, scale_factor=s, delta=0.01, discard_zeros=False)
# Then
assert all(
[np.nanmin(output) >= expected[0],
np.nanmax(output) < expected[1]])
counter = counter + nrUniqueVals
ncut_labels[:, :, ind] = tmp2
lMax = np.max(ncut_labels)
orig_ncut_labels = ncut_labels.copy()
ima_ncut_labels = ncut_labels.copy()
#
"""Data Processing"""
orig, dims = check_data(nii.get_data(), cfg.force_original_precision)
# Save min and max truncation thresholds to be used in axis labels
orig, pMin, pMax = truncate_range(orig, percMin=cfg.perc_min,
percMax=cfg.perc_max)
# Continue with scaling the original truncated image and recomputing gradient
orig = scale_range(orig, scale_factor=cfg.scale, delta=0.0001)
gra = set_gradient_magnitude(orig, cfg.gramag)
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)
#
"""Data Processing"""
nii = load(cfg.filename)
orig, dims = check_data(nii.get_data(), cfg.force_original_precision)
# Save min and max truncation thresholds to be used in axis labels
if np.isnan(cfg.valmin) or np.isnan(cfg.valmax):
orig, pMin, pMax = truncate_range(orig,
percMin=cfg.perc_min,
percMax=cfg.perc_max)
else: # TODO: integrate this into truncate range function
orig[orig < cfg.valmin] = cfg.valmin
orig[orig > cfg.valmax] = cfg.valmax
pMin, pMax = cfg.valmin, cfg.valmax
# Continue with scaling the original truncated image and recomputing gradient
orig = scale_range(orig, scale_factor=cfg.scale, delta=0.0001)
gra = set_gradient_magnitude(orig, cfg.gramag)
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)
