def test_truncate_range():
"""Test range truncation."""
# Given
data = np.random.random(100)
data.ravel()[np.random.choice(data.size, 10, replace=False)] = 0
data.ravel()[np.random.choice(data.size, 5, replace=False)] = np.nan
p_min, p_max = 2.5, 97.5
expected = np.nanpercentile(data, [p_min, p_max])
# When
output = truncate_range(data,
percMin=p_min,
percMax=p_max,
discard_zeros=False)
# Then
assert all(np.nanpercentile(output, [0, 100]) == expected)
# Centering
center = coda.sample_center(p_comp)
temp = np.ones(p_comp.shape) * center
p_comp = coda.perturb(p_comp, temp**-1.)
# Standardize
totvar = coda.sample_total_variance(p_comp, center)
p_comp = coda.power(p_comp, np.power(totvar, -1. / 2.))
# Use Aitchison norm and powerinf for truncation of extreme compositions
anorm_thr = 3
anorm = coda.aitchison_norm(p_comp)
idx_trunc = anorm > anorm_thr
truncation_power = anorm[idx_trunc] / anorm_thr
correction = np.ones(anorm.shape)
correction[idx_trunc] = truncation_power
comp_bal = coda.power(p_comp, correction[:, None])
# go to hexcone lattice for exports
comp[p_mask > 0] = comp_bal
# lightness balance
light = truncate_range(light, percMin=1, percMax=99)
hexc = comp * light[:, None]
hexc = hexc.reshape(dims[0], dims[1], dims[2], dims[3])
for i in range(hexc.shape[3]):
img = Nifti1Image(hexc[..., i], affine=nii1.affine)
save(img,
os.path.join(dirname, 'input' + str(i + 1) + '_simplex_cbal.nii.gz'))
from compoda.utils import truncate_range
from nibabel import load, save, Nifti1Image
# Load data
nii1 = load('/path/to/file1.nii.gz')
nii2 = load('/path/to/file2.nii.gz')
nii3 = load('/path/to/file3.nii.gz')
mask = load("/path/to/mask.nii.gz").get_data()
mask[mask > 0] = 1. # binarize
basename = nii1.get_filename().split(os.extsep, 1)[0]
dirname = os.path.dirname(nii1.get_filename())
vol1 = nii1.get_data()
vol2 = nii2.get_data()
vol3 = nii3.get_data()
dims = vol1.shape + (3,)
orig = np.zeros(dims)
orig[..., 0] = vol1 * mask
orig[..., 1] = vol2 * mask
orig[..., 2] = vol3 * mask
# simplest color balance
for i in range(orig.shape[-1]):
img = orig[..., i]
img = truncate_range(img, percMin=1, percMax=99, discard_zeros=True)
out = Nifti1Image(img, affine=nii1.affine)
save(out, os.path.join(dirname, 'input'+str(i+1)+'_simplest_cbal.nii.gz'))
vol1 = nii1.get_data()
vol2 = nii2.get_data()
vol3 = nii3.get_data()
dims = vol1.shape + (3, )
comp = np.zeros(dims)
comp[..., 0] = vol1 * mask
comp[..., 1] = vol2 * mask
comp[..., 2] = vol3 * mask
comp = comp.reshape(dims[0] * dims[1] * dims[2], dims[3])
# (optional) truncate and rescale
for i in range(comp.shape[1]):
temp = comp[:, i]
temp = truncate_range(temp)
temp = scale_range(temp, scale_factor=1000)
comp[:, i] = temp
# Impute
comp[comp == 0] = 1.
# Closure
comp = tet.closure(comp)
# Plot related operations
p_mask = mask.reshape(dims[0] * dims[1] * dims[2])
p_comp = comp[p_mask > 0]
# Isometric logratio transformation before any centering
ilr_orig = tet.ilr_transformation(np.copy(p_comp))
ref[:, 2] = ref[:, 2] * 0.1
# compute aitchion angular difference
ainp = coda.aitchison_inner_product(bary, ref)
ref_norm = coda.aitchison_norm(ref)
ang_dif = np.zeros(anorm.shape)
# deal with zero norms
idx = anorm != 0
# (choose one) wrapped angle range
ang_dif[idx] = np.arccos(ainp[idx]/(anorm[idx] * ref_norm[idx]))
ang_dif[np.isnan(ang_dif)] = 0 # fix nans assigned to bright
# (choose one) full angle range
idx_s = bary[:, 1] > bary[:, 2]
# ang_dif[idx_s] = np.arccos(ainp[idx_s]/(anorm[idx_s] * ref_norm[idx_s]))
# ang_dif[~idx_s] = 2*np.pi - np.arccos(ainp[~idx_s]/(anorm[~idx_s] * ref_norm[~idx_s]))
# truncate anorm
anorm = truncate_range(anorm, percMin=0, percMax=99)
# reassign
angdif_norm_int[..., 0] = ang_dif.reshape(dims[:-1])
angdif_norm_int[..., 1] = anorm.reshape(dims[:-1])
angdif_norm_int[..., 2] = np.sum(img, axis=-1) / 3.
# Exports
print('Exporting images...')
imsave('00_original.png', orig)
out = hsv[..., 0]
imsave('01_hue.png', out/out.max())
out = hsv[..., 1]
imsave('02_saturation.png', out/out.max())
out = hsv[..., 2]
imsave('03_value.png', out/out.max())