def test_sample_center():
"""Test compositional sample center."""
# Given
data = np.array([[1/5, 2/5, 2/5],
[2/5, 1/5, 2/5],
[2/5, 2/5, 1/5]])
# When
output = coda.sample_center(data)
# Then
assert output[0] == pytest.approx(np.array([1/3, 1/3, 1/3]))
comp[..., 2] = vol3 * mask
comp = comp.reshape(dims[0] * dims[1] * dims[2], dims[3])
# 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]
# Centering
center = tet.sample_center(p_comp)
# center = np.array([[ 0.06521165, 0.66942364, 0.26536471]]) # 0 noise center
print "Sample center: " + str(center)
c_temp = np.ones(p_comp.shape) * center
p_comp = tet.perturb(p_comp, c_temp**-1)
# Standardize
totvar = tet.sample_total_variance(p_comp, center)
p_comp = tet.power(p_comp, np.power(totvar, -1. / 2.))
# Isometric logratio transformation for plotting
ilr = tet.ilr_transformation(p_comp)
# Plot 2D histogram of ilr transformed data
plt.hist2d(
ilr[:, 0],
ilr[:, 1],
def simplex_color_balance(bary, center=True, standardize=False,
trunc_max=False):
"""Compositional data based method for color balance.
Highly experimental!
Parameters
----------
bary: numpy.ndarray
Barycentric coordinates. Sum of all channels should add up to 1
(closed).
center: bool
Center barycentric coordinates (similar to de-meaning). Single-hue
dominated images will be balanced to cover all hues.
standardize: bool
Standardize barycentric coordinates. Standardized compositions make
better use of the simplex space dynamic range.
trunc_max: bool
Truncate maximum barycentric coordinates to eliminate extreme hues that
are not prevalent in the image.
Returns
-------
bary: numpy.ndarray
Processed composition.
"""
dims = bary.shape
bary = bary.reshape([np.prod(dims[:-1]), dims[-1]])
bary = np.nan_to_num(bary)
# Do not consider values <= 0
mask = np.prod(bary, axis=-1)
mask = mask > 0
temp = bary[mask]
# Interpretation of centering: Compositions cover the simplex space more
# balanced across components. Similar to de-mean data.
if center:
sample_center = coda.sample_center(temp)
temp2 = np.full(temp.shape, sample_center)
temp = coda.perturb(temp, temp2**-1.)
temp2 = None
# Interpretation of standardization: Centered compositions cover the
# dynamic range of simplex space more.
if standardize: # Standardize
totvar = coda.sample_total_variance(temp, sample_center)
temp = coda.power(temp, np.power(totvar, -1./2.))
# Interpretation of max truncation: Pull the exterme compositions to
# threshold distance by using scaling. Scaling factor is determined for
# each outlier composition to pull more extreme compositions more strongly.
if trunc_max:
# Use Aitchison norm and powering to truncate extreme compositions
anorm = coda.aitchison_norm(temp)
anorm_thr_min, anorm_thr_max = np.percentile(anorm, [1., 99.])
# Max truncate
idx_trunc = anorm > anorm_thr_max
truncation_power = anorm[idx_trunc] / anorm_thr_max
correction = np.ones(anorm.shape)
correction[idx_trunc] = truncation_power
temp = coda.power(temp, correction[:, None])
# min truncate
idx_trunc = anorm < anorm_thr_min
truncation_power = anorm[idx_trunc] / anorm_thr_min
correction = np.ones(anorm.shape)
correction[idx_trunc] = truncation_power
temp = coda.power(temp, correction[:, None])
# TODO: Implement this similar to truncate and scpe function but for
# simplex space. Proportion of dynamic range to the distance of between
# aitchison norm percentiles gives the global scaling factor. This should
# be done after truncation though.
# Put back processed composition
bary[mask] = temp
return bary.reshape(dims)
# temp = comp[:, i]
# temp = truncate_range(temp)
# temp = scale_range(temp, scale_factor=1000)
# comp[:, i] = temp
# Impute
comp[comp == 0] = 1.
# Closure
comp = coda.closure(comp)
# Isometric logratio transformation before any centering
ilr_orig = coda.ilr_transformation(np.copy(comp))
# Centering
center = coda.sample_center(comp)
print("Sample center: " + str(center))
c_temp = np.ones(comp.shape) * center
p_comp = coda.perturb(comp, c_temp**-1)
# Standardize
totvar = coda.sample_total_variance(comp, center)
comp = coda.power(comp, np.power(totvar, -1. / 2.))
# Isometric logratio transformation for plotting
ilr = coda.ilr_transformation(comp)
# Plots
fig = plt.figure()
limits = [-2.5, 2.5]
ax_1 = plt.subplot(121)
# Plot 2D histogram of ilr transformed data
# Impute orig[orig <= 0] = 1 comp = np.copy(orig) # Lightness light = (np.max(comp, axis=1) + np.min(comp, axis=1)) / 2. # Closure comp = coda.closure(comp) # Do not consider masked values p_mask = mask.reshape(dims[0] * dims[1] * dims[2]) p_comp = comp[p_mask > 0] # 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])