def test_update_histogram_fix_bins():
""" Test that the function properly update the value of the histogram
and fixing the number of bins. """
# Load the data and then call the function independently
currdir = os.path.dirname(os.path.abspath(__file__))
path_data = os.path.join(currdir, 'data', 'dce')
# Create an object to handle the data
dce_mod = DCEModality()
dce_mod.read_data_from_path(path_data)
# Change something in the data to check that the computation
# is working
dce_mod.data_[0, 20:40, :, :] = 1000.
nb_bins = [100, 100]
dce_mod.update_histogram(nb_bins=nb_bins)
# We need to check that the minimum and maximum were proprely computed
assert_equal(dce_mod.min_series_, 0.)
assert_equal(dce_mod.max_series_, 1000.)
# Check that bin is what we expect
data = np.load(os.path.join(currdir, 'data',
'bin_dce_data_update_100_bins.npy'))
# Check that each array are the same
for exp, gt in zip(dce_mod.bin_series_, data):
assert_array_equal(exp, gt)
# Check that pdf is what we expect
data = np.load(os.path.join(currdir, 'data',
'pdf_dce_data_update_100_bins.npy'))
# Check that each array are the same
for exp, gt in zip(dce_mod.pdf_series_, data):
assert_array_equal(exp, gt)
def test_build_heatmap_roi():
""" Test if the heatmap is built properly when providing a ROI. """
# Load the data with only a single serie
currdir = os.path.dirname(os.path.abspath(__file__))
path_data = os.path.join(currdir, 'data', 'dce')
# Create an object to handle the data
dce_mod = DCEModality()
# Read the data
dce_mod.read_data_from_path(path_data)
dce_mod.data_ /= 2.
dce_mod.update_histogram()
# Create some ground-truth
pos = np.ones((368, 448), dtype=bool)
neg = np.zeros((368, 448), dtype=bool)
gt_index = np.rollaxis(np.array([neg, pos, pos, pos, neg]), 0, 3)
# Build the heatmap
heatmap, bins_heatmap = dce_mod.build_heatmap(roi_data=(gt_index))
# Check that heatmap is what we expect
data = np.load(os.path.join(currdir, 'data', 'heatmap_roi_mod.npy'))
assert_array_equal(heatmap, data)
data = np.load(os.path.join(currdir, 'data', 'bins_heatmap_roi_mod.npy'))
assert_array_equal(bins_heatmap, data)
# Read the GT
print 'Read GT images'
gt_mod = GTModality()
gt_mod.read_data_from_path(label_gt, p_gt)
# Load the approproate normalization object
filename_norm = (pat.lower().replace(' ', '_') +
'_norm.p')
dce_norm = StandardTimeNormalization.load_from_pickles(
os.path.join(path_norm, filename_norm))
dce_mod = dce_norm.normalize(dce_mod)
for idx in range(dce_mod.data_.shape[0]):
dce_mod.data_[idx, :] += shift[idx]
dce_mod.update_histogram()
# Fit the parameters for Brix
print 'Extract Brix'
brix_ext.fit(dce_mod, ground_truth=gt_mod, cat=label_gt[0])
# Extract the matrix
print 'Extract the feature matrix'
data = brix_ext.transform(dce_mod, ground_truth=gt_mod, cat=label_gt[0])
pat_chg = pat.lower().replace(' ', '_') + '_brix.npy'
filename = os.path.join(path_store, pat_chg)
np.save(filename, data)
dce_mod.read_data_from_path(p_dce)
# Read the GT
print 'Read GT images'
gt_mod = GTModality()
gt_mod.read_data_from_path(label_gt, p_gt)
# Load the approproate normalization object
filename_norm = (pat.lower().replace(' ', '_') + '_norm.p')
dce_norm = StandardTimeNormalization.load_from_pickles(
os.path.join(path_norm, filename_norm))
dce_mod = dce_norm.normalize(dce_mod)
for idx in range(dce_mod.data_.shape[0]):
dce_mod.data_[idx, :] += shift[idx]
dce_mod.update_histogram()
# Fit the parameters for Brix
print 'Extract Weibull'
pun_ext.fit(dce_mod, ground_truth=gt_mod, cat=label_gt[0])
# Extract the matrix
print 'Extract the feature matrix'
data = pun_ext.transform(dce_mod, ground_truth=gt_mod, cat=label_gt[0])
pat_chg = pat.lower().replace(' ', '_') + '_pun.npy'
filename = os.path.join(path_store, pat_chg)
np.save(filename, data)
