def test_cluster_map_get_small_and_large_clusters():
rng = np.random.RandomState(42)
nb_clusters = 11
cluster_map = ClusterMap()
# Randomly generate small clusters
indices = [rng.randint(0, 10, size=i) for i in range(1, nb_clusters + 1)]
small_clusters = [Cluster(indices=indices[i]) for i in range(nb_clusters)]
cluster_map.add_cluster(*small_clusters)
# Randomly generate small clusters
indices = [
rng.randint(0, 10, size=i)
for i in range(nb_clusters + 1, 2 * nb_clusters + 1)
]
large_clusters = [Cluster(indices=indices[i]) for i in range(nb_clusters)]
cluster_map.add_cluster(*large_clusters)
assert_equal(len(cluster_map), 2 * nb_clusters)
assert_equal(len(cluster_map.get_small_clusters(nb_clusters)),
len(small_clusters))
assert_arrays_equal(cluster_map.get_small_clusters(nb_clusters),
small_clusters)
assert_equal(len(cluster_map.get_large_clusters(nb_clusters + 1)),
len(large_clusters))
assert_arrays_equal(cluster_map.get_large_clusters(nb_clusters + 1),
large_clusters)
def test_cluster_map_centroid_iter():
rng = np.random.RandomState(42)
nb_clusters = 11
cluster_map = ClusterMapCentroid()
clusters = []
for i in range(nb_clusters):
new_centroid = np.zeros_like(features)
new_cluster = ClusterCentroid(new_centroid,
indices=rng.randint(0,
len(data),
size=10))
cluster_map.add_cluster(new_cluster)
clusters.append(new_cluster)
assert_true(
all([c1 is c2 for c1, c2 in zip(cluster_map.clusters, clusters)]))
assert_array_equal(cluster_map, clusters)
assert_array_equal(cluster_map.clusters, clusters)
assert_array_equal(cluster_map, [cluster.indices for cluster in clusters])
# Set refdata
cluster_map.refdata = data
for c1, c2 in zip(cluster_map, clusters):
assert_arrays_equal(c1, [data[i] for i in c2.indices])
def test_cluster_map_get_small_and_large_clusters():
rng = np.random.RandomState(42)
nb_clusters = 11
cluster_map = ClusterMap()
# Randomly generate small clusters
indices = [rng.randint(0, 10, size=i) for i in range(1, nb_clusters+1)]
small_clusters = [Cluster(indices=indices[i]) for i in range(nb_clusters)]
cluster_map.add_cluster(*small_clusters)
# Randomly generate small clusters
indices = [rng.randint(0, 10, size=i)
for i in range(nb_clusters+1, 2*nb_clusters+1)]
large_clusters = [Cluster(indices=indices[i]) for i in range(nb_clusters)]
cluster_map.add_cluster(*large_clusters)
assert_equal(len(cluster_map), 2*nb_clusters)
assert_equal(len(cluster_map.get_small_clusters(nb_clusters)),
len(small_clusters))
assert_arrays_equal(cluster_map.get_small_clusters(nb_clusters),
small_clusters)
assert_equal(len(cluster_map.get_large_clusters(nb_clusters+1)),
len(large_clusters))
assert_arrays_equal(cluster_map.get_large_clusters(nb_clusters+1),
large_clusters)
def test_cluster_map_iter():
rng = np.random.RandomState(42)
nb_clusters = 11
# Test without specifying refdata in ClusterMap
cluster_map = ClusterMap()
clusters = []
for i in range(nb_clusters):
new_cluster = Cluster(indices=rng.randint(0, len(data), size=10))
cluster_map.add_cluster(new_cluster)
clusters.append(new_cluster)
assert_true(all([c1 is c2 for c1, c2 in zip(cluster_map.clusters,
clusters)]))
assert_array_equal(cluster_map, clusters)
assert_array_equal(cluster_map.clusters, clusters)
assert_array_equal(cluster_map, [cluster.indices for cluster in clusters])
# Set refdata
cluster_map.refdata = data
for c1, c2 in zip(cluster_map, clusters):
assert_arrays_equal(c1, [data[i] for i in c2.indices])
# Remove refdata, i.e. back to indices
cluster_map.refdata = None
assert_array_equal(cluster_map, [cluster.indices for cluster in clusters])
def test_cluster_map_iter():
rng = np.random.RandomState(42)
nb_clusters = 11
# Test without specifying refdata in ClusterMap
cluster_map = ClusterMap()
clusters = []
for i in range(nb_clusters):
new_cluster = Cluster(indices=rng.randint(0, len(data), size=10))
cluster_map.add_cluster(new_cluster)
clusters.append(new_cluster)
assert_true(
all([c1 is c2 for c1, c2 in zip(cluster_map.clusters, clusters)]))
assert_array_equal(cluster_map, clusters)
assert_array_equal(cluster_map.clusters, clusters)
assert_array_equal(cluster_map, [cluster.indices for cluster in clusters])
# Set refdata
cluster_map.refdata = data
for c1, c2 in zip(cluster_map, clusters):
assert_arrays_equal(c1, [data[i] for i in c2.indices])
# Remove refdata, i.e. back to indices
cluster_map.refdata = None
assert_array_equal(cluster_map, [cluster.indices for cluster in clusters])
def bench_quickbundles():
dtype = "float32"
repeat = 10
nb_points = 18
streams, hdr = nib.trackvis.read(get_data('fornix'))
fornix = [s[0].astype(dtype) for s in streams]
fornix = streamline_utils.set_number_of_points(fornix, nb_points)
# Create eight copies of the fornix to be clustered (one in each octant).
streamlines = []
streamlines += [s + np.array([100, 100, 100], dtype) for s in fornix]
streamlines += [s + np.array([100, -100, 100], dtype) for s in fornix]
streamlines += [s + np.array([100, 100, -100], dtype) for s in fornix]
streamlines += [s + np.array([100, -100, -100], dtype) for s in fornix]
streamlines += [s + np.array([-100, 100, 100], dtype) for s in fornix]
streamlines += [s + np.array([-100, -100, 100], dtype) for s in fornix]
streamlines += [s + np.array([-100, 100, -100], dtype) for s in fornix]
streamlines += [s + np.array([-100, -100, -100], dtype) for s in fornix]
# The expected number of clusters of the fornix using threshold=10 is 4.
threshold = 10.
expected_nb_clusters = 4 * 8
print("Timing QuickBundles 1.0 vs. 2.0")
qb = QB_Old(streamlines, threshold, pts=None)
qb1_time = measure("QB_Old(streamlines, threshold, nb_points)", repeat)
print("QuickBundles time: {0:.4}sec".format(qb1_time))
assert_equal(qb.total_clusters, expected_nb_clusters)
sizes1 = [qb.partitions()[i]['N'] for i in range(qb.total_clusters)]
indices1 = [
qb.partitions()[i]['indices'] for i in range(qb.total_clusters)
]
qb2 = QB_New(threshold)
qb2_time = measure("clusters = qb2.cluster(streamlines)", repeat)
print("QuickBundles2 time: {0:.4}sec".format(qb2_time))
print("Speed up of {0}x".format(qb1_time / qb2_time))
clusters = qb2.cluster(streamlines)
sizes2 = map(len, clusters)
indices2 = map(lambda c: c.indices, clusters)
assert_equal(len(clusters), expected_nb_clusters)
assert_array_equal(sizes2, sizes1)
assert_arrays_equal(indices2, indices1)
qb = QB_New(threshold, metric=MDFpy())
qb3_time = measure("clusters = qb.cluster(streamlines)", repeat)
print("QuickBundles2_python time: {0:.4}sec".format(qb3_time))
print("Speed up of {0}x".format(qb1_time / qb3_time))
clusters = qb.cluster(streamlines)
sizes3 = map(len, clusters)
indices3 = map(lambda c: c.indices, clusters)
assert_equal(len(clusters), expected_nb_clusters)
assert_array_equal(sizes3, sizes1)
assert_arrays_equal(indices3, indices1)
def bench_quickbundles():
dtype = "float32"
repeat = 10
nb_points = 12
streams, hdr = nib.trackvis.read(get_fnames('fornix'))
fornix = [s[0].astype(dtype) for s in streams]
fornix = streamline_utils.set_number_of_points(fornix, nb_points)
# Create eight copies of the fornix to be clustered (one in each octant).
streamlines = []
streamlines += [s + np.array([100, 100, 100], dtype) for s in fornix]
streamlines += [s + np.array([100, -100, 100], dtype) for s in fornix]
streamlines += [s + np.array([100, 100, -100], dtype) for s in fornix]
streamlines += [s + np.array([100, -100, -100], dtype) for s in fornix]
streamlines += [s + np.array([-100, 100, 100], dtype) for s in fornix]
streamlines += [s + np.array([-100, -100, 100], dtype) for s in fornix]
streamlines += [s + np.array([-100, 100, -100], dtype) for s in fornix]
streamlines += [s + np.array([-100, -100, -100], dtype) for s in fornix]
# The expected number of clusters of the fornix using threshold=10 is 4.
threshold = 10.
expected_nb_clusters = 4 * 8
print("Timing QuickBundles 1.0 vs. 2.0")
qb = QB_Old(streamlines, threshold, pts=None)
qb1_time = measure("QB_Old(streamlines, threshold, nb_points)", repeat)
print("QuickBundles time: {0:.4}sec".format(qb1_time))
assert_equal(qb.total_clusters, expected_nb_clusters)
sizes1 = [qb.partitions()[i]['N'] for i in range(qb.total_clusters)]
indices1 = [qb.partitions()[i]['indices']
for i in range(qb.total_clusters)]
qb2 = QB_New(threshold)
qb2_time = measure("clusters = qb2.cluster(streamlines)", repeat)
print("QuickBundles2 time: {0:.4}sec".format(qb2_time))
print("Speed up of {0}x".format(qb1_time / qb2_time))
clusters = qb2.cluster(streamlines)
sizes2 = map(len, clusters)
indices2 = map(lambda c: c.indices, clusters)
assert_equal(len(clusters), expected_nb_clusters)
assert_array_equal(list(sizes2), sizes1)
assert_arrays_equal(indices2, indices1)
qb = QB_New(threshold, metric=MDFpy())
qb3_time = measure("clusters = qb.cluster(streamlines)", repeat)
print("QuickBundles2_python time: {0:.4}sec".format(qb3_time))
print("Speed up of {0}x".format(qb1_time / qb3_time))
clusters = qb.cluster(streamlines)
sizes3 = map(len, clusters)
indices3 = map(lambda c: c.indices, clusters)
assert_equal(len(clusters), expected_nb_clusters)
assert_array_equal(list(sizes3), sizes1)
assert_arrays_equal(indices3, indices1)
def test_cluster_iter():
indices = list(range(len(data)))
np.random.shuffle(indices) # None trivial ordering
# Test without specifying refdata
cluster = Cluster()
cluster.assign(*indices)
assert_array_equal(cluster.indices, indices)
assert_array_equal(list(cluster), indices)
# Test with specifying refdata in ClusterMap
cluster.refdata = data
assert_arrays_equal(list(cluster), [data[i] for i in indices])
def test_cluster_iter():
indices = list(range(len(data)))
np.random.shuffle(indices) # None trivial ordering
# Test without specifying refdata
cluster = Cluster()
cluster.assign(*indices)
assert_array_equal(cluster.indices, indices)
assert_array_equal(list(cluster), indices)
# Test with specifying refdata in ClusterMap
cluster.refdata = data
assert_arrays_equal(list(cluster), [data[i] for i in indices])
def test_cluster_map_getitem():
nb_clusters = 11
indices = list(range(nb_clusters))
np.random.shuffle(indices) # None trivial ordering
advanced_indices = indices + [0, 1, 2, -1, -2, -3]
cluster_map = ClusterMap()
clusters = []
for i in range(nb_clusters):
new_cluster = Cluster(indices=range(i))
cluster_map.add_cluster(new_cluster)
clusters.append(new_cluster)
# Test indexing
for i in advanced_indices:
assert_true(cluster_map[i] == clusters[i])
# Test advanced indexing
assert_arrays_equal(cluster_map[advanced_indices],
[clusters[i] for i in advanced_indices])
# Test index out of bounds
assert_raises(IndexError, cluster_map.__getitem__, len(clusters))
assert_raises(IndexError, cluster_map.__getitem__, -len(clusters) - 1)
# Test slicing and negative indexing
assert_equal(cluster_map[-1], clusters[-1])
assert_array_equal(np.array(cluster_map[::2], dtype=object),
np.array(clusters[::2], dtype=object))
assert_arrays_equal(cluster_map[::-1], clusters[::-1])
assert_arrays_equal(cluster_map[:-1], clusters[:-1])
assert_arrays_equal(cluster_map[1:], clusters[1:])
def test_cluster_map_centroid_getitem():
nb_clusters = 11
indices = list(range(len(data)))
np.random.shuffle(indices) # None trivial ordering
advanced_indices = indices + [0, 1, 2, -1, -2, -3]
cluster_map = ClusterMapCentroid()
clusters = []
for i in range(nb_clusters):
centroid = np.zeros_like(features)
cluster = ClusterCentroid(centroid)
cluster.id = cluster_map.add_cluster(cluster)
clusters.append(cluster)
# Test indexing
for i in advanced_indices:
assert_equal(cluster_map[i], clusters[i])
# Test advanced indexing
assert_arrays_equal(cluster_map[advanced_indices],
[clusters[i] for i in advanced_indices])
# Test index out of bounds
assert_raises(IndexError, cluster_map.__getitem__, len(clusters))
assert_raises(IndexError, cluster_map.__getitem__, -len(clusters)-1)
# Test slicing and negative indexing
assert_equal(cluster_map[-1], clusters[-1])
assert_array_equal(cluster_map[::2], clusters[::2])
assert_arrays_equal(cluster_map[::-1], clusters[::-1])
assert_arrays_equal(cluster_map[:-1], clusters[:-1])
assert_arrays_equal(cluster_map[1:], clusters[1:])
def test_cluster_map_centroid_getitem():
nb_clusters = 11
indices = list(range(len(data)))
np.random.shuffle(indices) # None trivial ordering
advanced_indices = indices + [0, 1, 2, -1, -2, -3]
cluster_map = ClusterMapCentroid()
clusters = []
for i in range(nb_clusters):
centroid = np.zeros_like(features)
cluster = ClusterCentroid(centroid)
cluster.id = cluster_map.add_cluster(cluster)
clusters.append(cluster)
# Test indexing
for i in advanced_indices:
assert_equal(cluster_map[i], clusters[i])
# Test advanced indexing
assert_arrays_equal(cluster_map[advanced_indices],
[clusters[i] for i in advanced_indices])
# Test index out of bounds
assert_raises(IndexError, cluster_map.__getitem__, len(clusters))
assert_raises(IndexError, cluster_map.__getitem__, -len(clusters) - 1)
# Test slicing and negative indexing
assert_equal(cluster_map[-1], clusters[-1])
assert_array_equal(cluster_map[::2], clusters[::2])
assert_arrays_equal(cluster_map[::-1], clusters[::-1])
assert_arrays_equal(cluster_map[:-1], clusters[:-1])
assert_arrays_equal(cluster_map[1:], clusters[1:])
def test_cluster_centroid_iter():
indices = list(range(len(data)))
np.random.shuffle(indices) # None trivial ordering
# Test without specifying refdata in ClusterCentroid
centroid = np.zeros(features_shape)
cluster = ClusterCentroid(centroid)
for idx in indices:
cluster.assign(idx, (idx + 1) * features)
assert_array_equal(cluster.indices, indices)
assert_array_equal(list(cluster), indices)
# Test with specifying refdata in ClusterCentroid
cluster.refdata = data
assert_arrays_equal(list(cluster), [data[i] for i in indices])
def test_cluster_centroid_iter():
indices = list(range(len(data)))
np.random.shuffle(indices) # None trivial ordering
# Test without specifying refdata in ClusterCentroid
centroid = np.zeros(features_shape)
cluster = ClusterCentroid(centroid)
for idx in indices:
cluster.assign(idx, (idx+1)*features)
assert_array_equal(cluster.indices, indices)
assert_array_equal(list(cluster), indices)
# Test with specifying refdata in ClusterCentroid
cluster.refdata = data
assert_arrays_equal(list(cluster), [data[i] for i in indices])
def test_quickbundles_shape_uncompatibility():
# QuickBundles' old default metric (AveragePointwiseEuclideanMetric,
# aka MDF) requires that all streamlines have the same number of points.
metric = dipymetric.AveragePointwiseEuclideanMetric()
qb = QuickBundles(threshold=20., metric=metric)
assert_raises(ValueError, qb.cluster, data)
# QuickBundles' new default metric (AveragePointwiseEuclideanMetric,
# aka MDF combined with ResampleFeature) will automatically resample
# streamlines so they all have 18 points.
qb = QuickBundles(threshold=20.)
clusters1 = qb.cluster(data)
feature = dipymetric.ResampleFeature(nb_points=18)
metric = dipymetric.AveragePointwiseEuclideanMetric(feature)
qb = QuickBundles(threshold=20., metric=metric)
clusters2 = qb.cluster(data)
assert_arrays_equal(list(itertools.chain(*clusters1)),
list(itertools.chain(*clusters2)))
def test_quickbundles_shape_uncompatibility():
# QuickBundles' old default metric (AveragePointwiseEuclideanMetric,
# aka MDF) requires that all streamlines have the same number of points.
metric = dipymetric.AveragePointwiseEuclideanMetric()
qb = QuickBundles(threshold=20., metric=metric)
assert_raises(ValueError, qb.cluster, data)
# QuickBundles' new default metric (AveragePointwiseEuclideanMetric,
# aka MDF combined with ResampleFeature) will automatically resample
# streamlines so they all have 18 points.
qb = QuickBundles(threshold=20.)
clusters1 = qb.cluster(data)
feature = dipymetric.ResampleFeature(nb_points=18)
metric = dipymetric.AveragePointwiseEuclideanMetric(feature)
qb = QuickBundles(threshold=20., metric=metric)
clusters2 = qb.cluster(data)
assert_arrays_equal(list(itertools.chain(*clusters1)),
list(itertools.chain(*clusters2)))
def test_cluster_map_centroid_iter():
rng = np.random.RandomState(42)
nb_clusters = 11
cluster_map = ClusterMapCentroid()
clusters = []
for i in range(nb_clusters):
new_centroid = np.zeros_like(features)
new_cluster = ClusterCentroid(new_centroid,
indices=rng.randint(0, len(data),
size=10))
cluster_map.add_cluster(new_cluster)
clusters.append(new_cluster)
assert_true(all([c1 is c2 for c1, c2 in
zip(cluster_map.clusters, clusters)]))
assert_array_equal(cluster_map, clusters)
assert_array_equal(cluster_map.clusters, clusters)
assert_array_equal(cluster_map, [cluster.indices for cluster in clusters])
# Set refdata
cluster_map.refdata = data
for c1, c2 in zip(cluster_map, clusters):
assert_arrays_equal(c1, [data[i] for i in c2.indices])
def test_ui_file_menu_2d(interactive=False):
filename = "test_ui_file_menu_2d"
recording_filename = pjoin(DATA_DIR, filename + ".log.gz")
expected_events_counts_filename = pjoin(DATA_DIR, filename + ".pkl")
# Create temporary directory and files
os.mkdir(os.path.join(os.getcwd(), "testdir"))
os.chdir("testdir")
os.mkdir(os.path.join(os.getcwd(), "tempdir"))
for i in range(10):
open(os.path.join(os.getcwd(), "tempdir", "test" + str(i) + ".txt"),
'wt').close()
open("testfile.txt", 'wt').close()
filemenu = ui.FileMenu2D(size=(500, 500), extensions=["txt"],
directory_path=os.getcwd())
# We will collect the sequence of files that have been selected.
selected_files = []
def _on_change():
selected_files.append(list(filemenu.listbox.selected))
# Set up a callback when selection changes.
filemenu.listbox.on_change = _on_change
# Assign the counter callback to every possible event.
event_counter = EventCounter()
event_counter.monitor(filemenu)
# Create a show manager and record/play events.
show_manager = window.ShowManager(size=(600, 600),
title="DIPY FileMenu")
show_manager.ren.add(filemenu)
# Recorded events:
# 1. Click on 'testfile.txt'
# 2. Click on 'tempdir/'
# 3. Click on 'test0.txt'.
# 4. Shift + Click on 'test6.txt'.
# 5. Click on '../'.
# 2. Click on 'testfile.txt'.
show_manager.play_events_from_file(recording_filename)
expected = EventCounter.load(expected_events_counts_filename)
event_counter.check_counts(expected)
# Check if the right files were selected.
expected = [["testfile.txt"], ["tempdir"], ["test0.txt"],
["test0.txt", "test1.txt", "test2.txt", "test3.txt",
"test4.txt", "test5.txt", "test6.txt"],
["../"], ["testfile.txt"]]
assert len(selected_files) == len(expected)
assert_arrays_equal(selected_files, expected)
# Remove temporary directory and files
os.remove("testfile.txt")
for i in range(10):
os.remove(os.path.join(os.getcwd(), "tempdir",
"test" + str(i) + ".txt"))
os.rmdir(os.path.join(os.getcwd(), "tempdir"))
os.chdir("..")
os.rmdir("testdir")
if interactive:
filemenu = ui.FileMenu2D(size=(500, 500), directory_path=os.getcwd())
show_manager = window.ShowManager(size=(600, 600),
title="DIPY FileMenu")
show_manager.ren.add(filemenu)
show_manager.start()
def test_select_by_rois():
streamlines = [np.array([[0, 0., 0.9],
[1.9, 0., 0.]]),
np.array([[0.1, 0., 0],
[0, 1., 1.],
[0, 2., 2.]]),
np.array([[2, 2, 2],
[3, 3, 3]])]
# Make two ROIs:
mask1 = np.zeros((4, 4, 4), dtype=bool)
mask2 = np.zeros_like(mask1)
mask1[0, 0, 0] = True
mask2[1, 0, 0] = True
selection = select_by_rois(streamlines, [mask1], [True],
tol=1)
assert_arrays_equal(list(selection), [streamlines[0],
streamlines[1]])
selection = select_by_rois(streamlines, [mask1, mask2], [True, True],
tol=1)
assert_arrays_equal(list(selection), [streamlines[0],
streamlines[1]])
selection = select_by_rois(streamlines, [mask1, mask2], [True, False])
assert_arrays_equal(list(selection), [streamlines[1]])
# Setting tolerance too low gets overridden:
selection = select_by_rois(streamlines, [mask1, mask2], [True, False],
tol=0.1)
assert_arrays_equal(list(selection), [streamlines[1]])
selection = select_by_rois(streamlines, [mask1, mask2], [True, True],
tol=0.87)
assert_arrays_equal(list(selection), [streamlines[1]])
mask3 = np.zeros_like(mask1)
mask3[0, 2, 2] = 1
selection = select_by_rois(streamlines, [mask1, mask2, mask3],
[True, True, False], tol=1.0)
assert_arrays_equal(list(selection), [streamlines[0]])
# Select using only one ROI
selection = select_by_rois(streamlines, [mask1], [True], tol=0.87)
assert_arrays_equal(list(selection), [streamlines[1]])
selection = select_by_rois(streamlines, [mask1], [True], tol=1.0)
assert_arrays_equal(list(selection), [streamlines[0],
streamlines[1]])
# Use different modes:
selection = select_by_rois(streamlines, [mask1, mask2, mask3],
[True, True, False],
mode="all",
tol=1.0)
assert_arrays_equal(list(selection), [streamlines[0]])
selection = select_by_rois(streamlines, [mask1, mask2, mask3],
[True, True, False],
mode="either_end",
tol=1.0)
assert_arrays_equal(list(selection), [streamlines[0]])
selection = select_by_rois(streamlines, [mask1, mask2, mask3],
[True, True, False],
mode="both_end",
tol=1.0)
assert_arrays_equal(list(selection), [streamlines[0]])
mask2[0, 2, 2] = True
selection = select_by_rois(streamlines, [mask1, mask2, mask3],
[True, True, False],
mode="both_end",
tol=1.0)
assert_arrays_equal(list(selection), [streamlines[0],
streamlines[1]])
# Test with generator input:
selection = select_by_rois(generate_sl(streamlines), [mask1], [True],
tol=1.0)
assert_arrays_equal(list(selection), [streamlines[0],
streamlines[1]])
def test_cluster_centroid_getitem():
indices = list(range(len(data)))
np.random.shuffle(indices) # None trivial ordering
advanced_indices = indices + [0, 1, 2, -1, -2, -3]
# Test without specifying refdata in ClusterCentroid
centroid = np.zeros(features_shape)
cluster = ClusterCentroid(centroid)
for idx in indices:
cluster.assign(idx, (idx + 1) * features)
# Test indexing
for i in advanced_indices:
assert_equal(cluster[i], indices[i])
# Test advanced indexing
assert_array_equal(cluster[advanced_indices],
[indices[i] for i in advanced_indices])
# Test index out of bounds
assert_raises(IndexError, cluster.__getitem__, len(cluster))
assert_raises(IndexError, cluster.__getitem__, -len(cluster) - 1)
# Test slicing and negative indexing
assert_equal(cluster[-1], indices[-1])
assert_array_equal(cluster[::2], indices[::2])
assert_arrays_equal(cluster[::-1], indices[::-1])
assert_arrays_equal(cluster[:-1], indices[:-1])
assert_arrays_equal(cluster[1:], indices[1:])
# Test with specifying refdata in ClusterCentroid
cluster.refdata = data
# Test indexing
for i in advanced_indices:
assert_array_equal(cluster[i], data[indices[i]])
# Test advanced indexing
assert_arrays_equal(cluster[advanced_indices],
[data[indices[i]] for i in advanced_indices])
# Test index out of bounds
assert_raises(IndexError, cluster.__getitem__, len(cluster))
assert_raises(IndexError, cluster.__getitem__, -len(cluster) - 1)
# Test slicing and negative indexing
assert_array_equal(cluster[-1], data[indices[-1]])
assert_arrays_equal(cluster[::2], [data[i] for i in indices[::2]])
assert_arrays_equal(cluster[::-1], [data[i] for i in indices[::-1]])
assert_arrays_equal(cluster[:-1], [data[i] for i in indices[:-1]])
assert_arrays_equal(cluster[1:], [data[i] for i in indices[1:]])
def test_select_by_rois():
streamlines = [
np.array([[0, 0., 0.9], [1.9, 0., 0.]]),
np.array([[0.1, 0., 0], [0, 1., 1.], [0, 2., 2.]]),
np.array([[2, 2, 2], [3, 3, 3]])
]
# Make two ROIs:
mask1 = np.zeros((4, 4, 4), dtype=bool)
mask2 = np.zeros_like(mask1)
mask1[0, 0, 0] = True
mask2[1, 0, 0] = True
selection = select_by_rois(streamlines, [mask1], [True], tol=1)
assert_arrays_equal(list(selection), [streamlines[0], streamlines[1]])
selection = select_by_rois(streamlines, [mask1, mask2], [True, True],
tol=1)
assert_arrays_equal(list(selection), [streamlines[0], streamlines[1]])
selection = select_by_rois(streamlines, [mask1, mask2], [True, False])
assert_arrays_equal(list(selection), [streamlines[1]])
# Setting tolerance too low gets overridden:
selection = select_by_rois(streamlines, [mask1, mask2], [True, False],
tol=0.1)
assert_arrays_equal(list(selection), [streamlines[1]])
selection = select_by_rois(streamlines, [mask1, mask2], [True, True],
tol=0.87)
assert_arrays_equal(list(selection), [streamlines[1]])
mask3 = np.zeros_like(mask1)
mask3[0, 2, 2] = 1
selection = select_by_rois(streamlines, [mask1, mask2, mask3],
[True, True, False],
tol=1.0)
assert_arrays_equal(list(selection), [streamlines[0]])
# Select using only one ROI
selection = select_by_rois(streamlines, [mask1], [True], tol=0.87)
assert_arrays_equal(list(selection), [streamlines[1]])
selection = select_by_rois(streamlines, [mask1], [True], tol=1.0)
assert_arrays_equal(list(selection), [streamlines[0], streamlines[1]])
# Use different modes:
selection = select_by_rois(streamlines, [mask1, mask2, mask3],
[True, True, False],
mode="all",
tol=1.0)
assert_arrays_equal(list(selection), [streamlines[0]])
selection = select_by_rois(streamlines, [mask1, mask2, mask3],
[True, True, False],
mode="either_end",
tol=1.0)
assert_arrays_equal(list(selection), [streamlines[0]])
selection = select_by_rois(streamlines, [mask1, mask2, mask3],
[True, True, False],
mode="both_end",
tol=1.0)
assert_arrays_equal(list(selection), [streamlines[0]])
mask2[0, 2, 2] = True
selection = select_by_rois(streamlines, [mask1, mask2, mask3],
[True, True, False],
mode="both_end",
tol=1.0)
assert_arrays_equal(list(selection), [streamlines[0], streamlines[1]])
# Test with generator input:
selection = select_by_rois(generate_sl(streamlines), [mask1], [True],
tol=1.0)
assert_arrays_equal(list(selection), [streamlines[0], streamlines[1]])
def bench_quickbundles():
dtype = "float32"
repeat = 10
nb_points = 12
fname = get_fnames('fornix')
fornix = load_tractogram(fname, 'same', bbox_valid_check=False).streamlines
fornix_streamlines = Streamlines(fornix)
fornix_streamlines = set_number_of_points(fornix_streamlines, nb_points)
# Create eight copies of the fornix to be clustered (one in each octant).
streamlines = []
streamlines += [
s + np.array([100, 100, 100], dtype) for s in fornix_streamlines
]
streamlines += [
s + np.array([100, -100, 100], dtype) for s in fornix_streamlines
]
streamlines += [
s + np.array([100, 100, -100], dtype) for s in fornix_streamlines
]
streamlines += [
s + np.array([100, -100, -100], dtype) for s in fornix_streamlines
]
streamlines += [
s + np.array([-100, 100, 100], dtype) for s in fornix_streamlines
]
streamlines += [
s + np.array([-100, -100, 100], dtype) for s in fornix_streamlines
]
streamlines += [
s + np.array([-100, 100, -100], dtype) for s in fornix_streamlines
]
streamlines += [
s + np.array([-100, -100, -100], dtype) for s in fornix_streamlines
]
# The expected number of clusters of the fornix using threshold=10 is 4.
threshold = 10.
expected_nb_clusters = 4 * 8
print("Timing QuickBundles 1.0 vs. 2.0")
qb2 = QB_New(threshold)
qb2_time = measure("clusters = qb2.cluster(streamlines)", repeat)
print("QuickBundles2 time: {0:.4}sec".format(qb2_time))
print("Speed up of {0}x".format(qb1_time / qb2_time))
clusters = qb2.cluster(streamlines)
sizes2 = map(len, clusters)
indices2 = map(lambda c: c.indices, clusters)
assert_equal(len(clusters), expected_nb_clusters)
assert_array_equal(list(sizes2), sizes1)
assert_arrays_equal(indices2, indices1)
qb = QB_New(threshold, metric=MDFpy())
qb3_time = measure("clusters = qb.cluster(streamlines)", repeat)
print("QuickBundles2_python time: {0:.4}sec".format(qb3_time))
print("Speed up of {0}x".format(qb1_time / qb3_time))
clusters = qb.cluster(streamlines)
sizes3 = map(len, clusters)
indices3 = map(lambda c: c.indices, clusters)
assert_equal(len(clusters), expected_nb_clusters)
assert_array_equal(list(sizes3), sizes1)
assert_arrays_equal(indices3, indices1)
def test_cluster_getitem():
indices = list(range(len(data)))
np.random.shuffle(indices) # None trivial ordering
advanced_indices = indices + [0, 1, 2, -1, -2, -3]
# Test without specifying refdata in ClusterMap
cluster = Cluster()
cluster.assign(*indices)
# Test indexing
for i in advanced_indices:
assert_equal(cluster[i], indices[i])
# Test advanced indexing
assert_array_equal(cluster[advanced_indices], [indices[i] for i in advanced_indices])
# Test index out of bounds
assert_raises(IndexError, cluster.__getitem__, len(cluster))
assert_raises(IndexError, cluster.__getitem__, -len(cluster)-1)
# Test slicing and negative indexing
assert_equal(cluster[-1], indices[-1])
assert_array_equal(cluster[::2], indices[::2])
assert_arrays_equal(cluster[::-1], indices[::-1])
assert_arrays_equal(cluster[:-1], indices[:-1])
assert_arrays_equal(cluster[1:], indices[1:])
# Test with specifying refdata in ClusterMap
cluster.refdata = data
# Test indexing
for i in advanced_indices:
assert_array_equal(cluster[i], data[indices[i]])
# Test advanced indexing
assert_array_equal(cluster[advanced_indices], [data[indices[i]] for i in advanced_indices])
# Test index out of bounds
assert_raises(IndexError, cluster.__getitem__, len(cluster))
assert_raises(IndexError, cluster.__getitem__, -len(cluster)-1)
# Test slicing and negative indexing
assert_array_equal(cluster[-1], data[indices[-1]])
assert_arrays_equal(cluster[::2], [data[i] for i in indices[::2]])
assert_arrays_equal(cluster[::-1], [data[i] for i in indices[::-1]])
assert_arrays_equal(cluster[:-1], [data[i] for i in indices[:-1]])
assert_arrays_equal(cluster[1:], [data[i] for i in indices[1:]])
def test_ui_listbox_2d(interactive=False):
filename = "test_ui_listbox_2d"
recording_filename = pjoin(DATA_DIR, filename + ".log.gz")
expected_events_counts_filename = pjoin(DATA_DIR, filename + ".pkl")
# Values that will be displayed by the listbox.
values = list(range(1, 42 + 1))
if interactive:
listbox = ui.ListBox2D(values=values,
size=(500, 500),
multiselection=True,
reverse_scrolling=False)
listbox.center = (300, 300)
show_manager = window.ShowManager(size=(600, 600),
title="DIPY ListBox")
show_manager.ren.add(listbox)
show_manager.start()
# Recorded events:
# 1. Click on 1
# 2. Ctrl + click on 2,
# 3. Ctrl + click on 2.
# 4. Use scroll bar to scroll to the bottom.
# 5. Click on 42.
# 6. Use scroll bar to scroll to the top.
# 7. Click on 1
# 8. Use mouse wheel to scroll down.
# 9. Shift + click on 42.
# 10. Use mouse wheel to scroll back up.
listbox = ui.ListBox2D(values=values,
size=(500, 500),
multiselection=True,
reverse_scrolling=False)
listbox.center = (300, 300)
# We will collect the sequence of values that have been selected.
selected_values = []
def _on_change():
selected_values.append(list(listbox.selected))
# Set up a callback when selection changes.
listbox.on_change = _on_change
# Assign the counter callback to every possible event.
event_counter = EventCounter()
event_counter.monitor(listbox)
show_manager = window.ShowManager(size=(600, 600),
title="DIPY ListBox")
show_manager.ren.add(listbox)
show_manager.play_events_from_file(recording_filename)
expected = EventCounter.load(expected_events_counts_filename)
event_counter.check_counts(expected)
# Check if the right values were selected.
expected = [[1], [1, 2], [1], [42], [1], values]
assert len(selected_values) == len(expected)
assert_arrays_equal(selected_values, expected)
# Test without multiselection enabled.
listbox.multiselection = False
del selected_values[:] # Clear the list.
show_manager.play_events_from_file(recording_filename)
# Check if the right values were selected.
expected = [[1], [2], [2], [42], [1], [42]]
assert len(selected_values) == len(expected)
assert_arrays_equal(selected_values, expected)
def test_ui_listbox_2d(recording=False):
filename = "test_ui_listbox_2d"
recording_filename = pjoin(DATA_DIR, filename + ".log.gz")
expected_events_counts_filename = pjoin(DATA_DIR, filename + ".pkl")
# Values that will be displayed by the listbox.
values = list(range(1, 42 + 1))
listbox = ui.ListBox2D(values=values,
size=(500, 500),
multiselection=True,
reverse_scrolling=False)
listbox.center = (300, 300)
# We will collect the sequence of values that have been selected.
selected_values = []
def _on_change():
selected_values.append(list(listbox.selected))
# Set up a callback when selection changes.
listbox.on_change = _on_change
# Assign the counter callback to every possible event.
event_counter = EventCounter()
event_counter.monitor(listbox)
# Create a show manager and record/play events.
show_manager = window.ShowManager(size=(600, 600), title="DIPY ListBox")
show_manager.ren.add(listbox)
if recording:
# Record the following events:
# 1. Click on 1
# 2. Ctrl + click on 2,
# 3. Ctrl + click on 2.
# 4. Click on down arrow (4 times).
# 5. Click on 21.
# 6. Click on up arrow (5 times).
# 7. Click on 1
# 8. Use mouse wheel to scroll down.
# 9. Shift + click on 42.
# 10. Use mouse wheel to scroll back up.
show_manager.record_events_to_file(recording_filename)
print(list(event_counter.events_counts.items()))
event_counter.save(expected_events_counts_filename)
else:
show_manager.play_events_from_file(recording_filename)
expected = EventCounter.load(expected_events_counts_filename)
event_counter.check_counts(expected)
# Check if the right values were selected.
expected = [[1], [1, 2], [1], [21], [1], values]
assert len(selected_values) == len(expected)
assert_arrays_equal(selected_values, expected)
# Test without multiselection enabled.
listbox.multiselection = False
del selected_values[:] # Clear the list.
show_manager.play_events_from_file(recording_filename)
# Check if the right values were selected.
expected = [[1], [2], [2], [21], [1], [42]]
assert len(selected_values) == len(expected)
assert_arrays_equal(selected_values, expected)
def test_ui_radio_button(interactive=False):
filename = "test_ui_radio_button"
recording_filename = pjoin(DATA_DIR, filename + ".log.gz")
expected_events_counts_filename = pjoin(DATA_DIR, filename + ".pkl")
radio_button_test = ui.RadioButton(
labels=["option 1", "option 2\nOption 2", "option 3", "option 4"],
position=(10, 10))
old_positions = []
for option in radio_button_test.options:
old_positions.append(option.position)
old_positions = np.asarray(old_positions)
radio_button_test.position = (100, 100)
new_positions = []
for option in radio_button_test.options:
new_positions.append(option.position)
new_positions = np.asarray(new_positions)
npt.assert_allclose(new_positions - old_positions, 90 * np.ones((4, 2)))
selected_option = []
def _on_change(radio_button):
selected_option.append(radio_button.checked)
# Set up a callback when selection changes
radio_button_test.on_change = _on_change
event_counter = EventCounter()
event_counter.monitor(radio_button_test)
# Create a show manager and record/play events.
show_manager = window.ShowManager(size=(600, 600), title="DIPY Checkbox")
show_manager.ren.add(radio_button_test)
# Recorded events:
# 1. Click on button of option 1.
# 2. Click on button of option 2.
# 3. Click on button of option 2.
# 4. Click on text of option 2.
# 5. Click on button of option 1.
# 6. Click on text of option 3.
# 7. Click on button of option 4.
# 8. Click on text of option 4.
show_manager.play_events_from_file(recording_filename)
expected = EventCounter.load(expected_events_counts_filename)
event_counter.check_counts(expected)
# Check if the right options were selected.
expected = [['option 1'], ['option 2\nOption 2'], ['option 2\nOption 2'],
['option 2\nOption 2'], ['option 1'], ['option 3'],
['option 4'], ['option 4']]
assert len(selected_option) == len(expected)
assert_arrays_equal(selected_option, expected)
del show_manager
if interactive:
radio_button_test = ui.RadioButton(
labels=["option 1", "option 2\nOption 2", "option 3", "option 4"],
position=(100, 100))
showm = window.ShowManager(size=(600, 600))
showm.ren.add(radio_button_test)
showm.start()
def test_ui_file_menu_2d(interactive=False):
filename = "test_ui_file_menu_2d"
recording_filename = pjoin(DATA_DIR, filename + ".log.gz")
expected_events_counts_filename = pjoin(DATA_DIR, filename + ".pkl")
# Create temporary directory and files
os.mkdir(os.path.join(os.getcwd(), "testdir"))
os.chdir("testdir")
os.mkdir(os.path.join(os.getcwd(), "tempdir"))
for i in range(10):
open(os.path.join(os.getcwd(), "tempdir", "test" + str(i) + ".txt"),
'wt').close()
open("testfile.txt", 'wt').close()
filemenu = ui.FileMenu2D(size=(500, 500),
extensions=["txt"],
directory_path=os.getcwd())
# We will collect the sequence of files that have been selected.
selected_files = []
def _on_change():
selected_files.append(list(filemenu.listbox.selected))
# Set up a callback when selection changes.
filemenu.listbox.on_change = _on_change
# Assign the counter callback to every possible event.
event_counter = EventCounter()
event_counter.monitor(filemenu)
# Create a show manager and record/play events.
show_manager = window.ShowManager(size=(600, 600), title="DIPY FileMenu")
show_manager.ren.add(filemenu)
# Recorded events:
# 1. Click on 'testfile.txt'
# 2. Click on 'tempdir/'
# 3. Click on 'test0.txt'.
# 4. Shift + Click on 'test6.txt'.
# 5. Click on '../'.
# 2. Click on 'testfile.txt'.
show_manager.play_events_from_file(recording_filename)
expected = EventCounter.load(expected_events_counts_filename)
event_counter.check_counts(expected)
# Check if the right files were selected.
expected = [["testfile.txt"], ["tempdir"], ["test0.txt"],
[
"test0.txt", "test1.txt", "test2.txt", "test3.txt",
"test4.txt", "test5.txt", "test6.txt"
], ["../"], ["testfile.txt"]]
assert len(selected_files) == len(expected)
assert_arrays_equal(selected_files, expected)
# Remove temporary directory and files
os.remove("testfile.txt")
for i in range(10):
os.remove(
os.path.join(os.getcwd(), "tempdir", "test" + str(i) + ".txt"))
os.rmdir(os.path.join(os.getcwd(), "tempdir"))
os.chdir("..")
os.rmdir("testdir")
if interactive:
filemenu = ui.FileMenu2D(size=(500, 500), directory_path=os.getcwd())
show_manager = window.ShowManager(size=(600, 600),
title="DIPY FileMenu")
show_manager.ren.add(filemenu)
show_manager.start()
def test_cluster_getitem():
indices = list(range(len(data)))
np.random.shuffle(indices) # None trivial ordering
advanced_indices = indices + [0, 1, 2, -1, -2, -3]
# Test without specifying refdata in ClusterMap
cluster = Cluster()
cluster.assign(*indices)
# Test indexing
for i in advanced_indices:
assert_equal(cluster[i], indices[i])
# Test advanced indexing
assert_array_equal(cluster[advanced_indices],
[indices[i] for i in advanced_indices])
# Test index out of bounds
assert_raises(IndexError, cluster.__getitem__, len(cluster))
assert_raises(IndexError, cluster.__getitem__, -len(cluster) - 1)
# Test slicing and negative indexing
assert_equal(cluster[-1], indices[-1])
assert_array_equal(cluster[::2], indices[::2])
assert_arrays_equal(cluster[::-1], indices[::-1])
assert_arrays_equal(cluster[:-1], indices[:-1])
assert_arrays_equal(cluster[1:], indices[1:])
# Test with wrong indexing object
assert_raises(TypeError, cluster.__getitem__, "wrong")
# Test with specifying refdata in ClusterMap
cluster.refdata = data
# Test indexing
for i in advanced_indices:
assert_array_equal(cluster[i], data[indices[i]])
# Test advanced indexing
assert_arrays_equal(cluster[advanced_indices],
[data[indices[i]] for i in advanced_indices])
# Test index out of bounds
assert_raises(IndexError, cluster.__getitem__, len(cluster))
assert_raises(IndexError, cluster.__getitem__, -len(cluster) - 1)
# Test slicing and negative indexing
assert_array_equal(cluster[-1], data[indices[-1]])
assert_arrays_equal(cluster[::2], [data[i] for i in indices[::2]])
assert_arrays_equal(cluster[::-1], [data[i] for i in indices[::-1]])
assert_arrays_equal(cluster[:-1], [data[i] for i in indices[:-1]])
assert_arrays_equal(cluster[1:], [data[i] for i in indices[1:]])
# Test with wrong indexing object
assert_raises(TypeError, cluster.__getitem__, "wrong")
def test_ui_radio_button(interactive=False):
filename = "test_ui_radio_button"
recording_filename = pjoin(DATA_DIR, filename + ".log.gz")
expected_events_counts_filename = pjoin(DATA_DIR, filename + ".pkl")
radio_button_test = ui.RadioButton(
labels=["option 1", "option 2\nOption 2", "option 3", "option 4"],
position=(10, 10))
old_positions = []
for option in radio_button_test.options:
old_positions.append(option.position)
old_positions = np.asarray(old_positions)
radio_button_test.position = (100, 100)
new_positions = []
for option in radio_button_test.options:
new_positions.append(option.position)
new_positions = np.asarray(new_positions)
npt.assert_allclose(new_positions - old_positions,
90 * np.ones((4, 2)))
selected_option = []
def _on_change(radio_button):
selected_option.append(radio_button.checked)
# Set up a callback when selection changes
radio_button_test.on_change = _on_change
event_counter = EventCounter()
event_counter.monitor(radio_button_test)
# Create a show manager and record/play events.
show_manager = window.ShowManager(size=(600, 600),
title="DIPY Checkbox")
show_manager.ren.add(radio_button_test)
# Recorded events:
# 1. Click on button of option 1.
# 2. Click on button of option 2.
# 3. Click on button of option 2.
# 4. Click on text of option 2.
# 5. Click on button of option 1.
# 6. Click on text of option 3.
# 7. Click on button of option 4.
# 8. Click on text of option 4.
show_manager.play_events_from_file(recording_filename)
expected = EventCounter.load(expected_events_counts_filename)
event_counter.check_counts(expected)
# Check if the right options were selected.
expected = [['option 1'], ['option 2\nOption 2'], ['option 2\nOption 2'],
['option 2\nOption 2'], ['option 1'], ['option 3'],
['option 4'], ['option 4']]
assert len(selected_option) == len(expected)
assert_arrays_equal(selected_option, expected)
del show_manager
if interactive:
radio_button_test = ui.RadioButton(
labels=["option 1", "option 2\nOption 2", "option 3", "option 4"],
position=(100, 100))
showm = window.ShowManager(size=(600, 600))
showm.ren.add(radio_button_test)
showm.start()
