def test_remote_bifurcation_angles():
ref_remote_bifangles = list(nf.remote_bifurcation_angles(NEURON))
remote_bifangles = fst_get('remote_bifurcation_angles', NEURON)
nt.eq_(len(remote_bifangles), 40)
assert_allclose(remote_bifangles, ref_remote_bifangles)
remote_bifangles = fst_get('remote_bifurcation_angles',
NEURON,
neurite_type=NeuriteType.all)
nt.eq_(len(remote_bifangles), 40)
assert_allclose(remote_bifangles, ref_remote_bifangles)
s = fst_get('remote_bifurcation_angles',
NEURON,
neurite_type=NeuriteType.axon)
nt.eq_(len(s), 10)
s = fst_get('remote_bifurcation_angles',
NEURON,
neurite_type=NeuriteType.basal_dendrite)
nt.eq_(len(s), 20)
s = fst_get('remote_bifurcation_angles',
NEURON,
neurite_type=NeuriteType.apical_dendrite)
nt.eq_(len(s), 10)
def assert_features_for_neurite(feat, neurons, expected, exact=True):
for neurite_type, expected_values in expected.items():
# print('neurite_type: %s' % neurite_type)
if neurite_type is None:
res_pop = fst_get(feat, neurons)
res = fst_get(feat, neurons[0])
else:
res_pop = fst_get(feat, neurons, neurite_type=neurite_type)
res = fst_get(feat, neurons[0], neurite_type=neurite_type)
if exact:
assert_items_equal(res_pop, expected_values)
else:
assert_allclose(res_pop, expected_values)
#test for single neuron
if isinstance(res, np.ndarray):
# some features, ex: total_length return arrays w/ one element when
# called on a single neuron
nt.eq_(len(res), 1)
res = res[0]
if exact:
nt.eq_(res, expected_values[0])
else:
assert_allclose(res, expected_values[0])
def test_neurite_density_pop():
feat = 'neurite_volume_density'
nt.ok_(
np.allclose(_stats(fst_get(feat, POP)),
(6.1847539631150784e-06, 0.52464681266899216,
1.9767794901940539, 0.19767794901940539)))
nt.ok_(
np.allclose(_stats(fst_get(feat, POP, neurite_type=NeuriteType.all)),
(6.1847539631150784e-06, 0.52464681266899216,
1.9767794901940539, 0.19767794901940539)))
nt.ok_(
np.allclose(_stats(fst_get(feat, POP, neurite_type=NeuriteType.axon)),
(6.1847539631150784e-06, 0.26465213325053372,
0.5275513670655404, 0.17585045568851346)))
nt.ok_(
np.allclose(
_stats(fst_get(feat, POP,
neurite_type=NeuriteType.basal_dendrite)),
(0.00034968816544949771, 0.52464681266899216, 1.0116620531455183,
0.16861034219091972)))
nt.ok_(
np.allclose(
_stats(fst_get(feat, POP,
neurite_type=NeuriteType.apical_dendrite)),
(0.43756606998299519, 0.43756606998299519, 0.43756606998299519,
0.43756606998299519)))
def test_neurite_density_nrn():
feat = 'neurite_volume_density'
nt.ok_(
np.allclose(_stats(fst_get(feat, NRN)),
(0.24068543213643726, 0.52464681266899216,
1.4657913638494682, 0.36644784096236704)))
nt.ok_(
np.allclose(_stats(fst_get(feat, NRN, neurite_type=NeuriteType.all)),
(0.24068543213643726, 0.52464681266899216,
1.4657913638494682, 0.36644784096236704)))
nt.ok_(
np.allclose(_stats(fst_get(feat, NRN, neurite_type=NeuriteType.axon)),
(0.26289304906104355, 0.26289304906104355,
0.26289304906104355, 0.26289304906104355)))
nt.ok_(
np.allclose(
_stats(fst_get(feat, NRN,
neurite_type=NeuriteType.basal_dendrite)),
(0.24068543213643726, 0.52464681266899216, 0.76533224480542938,
0.38266612240271469)))
nt.ok_(
np.allclose(
_stats(fst_get(feat, NRN,
neurite_type=NeuriteType.apical_dendrite)),
(0.43756606998299519, 0.43756606998299519, 0.43756606998299519,
0.43756606998299519)))
def test_segment_radial_distances_origin():
origin = (-100, -200, -300)
ref_segs = nf.segment_radial_distances(NEURON)
ref_segs_origin = nf.segment_radial_distances(NEURON, origin=origin)
rad_dists = fst_get('segment_radial_distances', NEURON)
rad_dists_origin = fst_get('segment_radial_distances',
NEURON,
origin=origin)
nt.ok_(np.all(rad_dists == ref_segs))
nt.ok_(np.all(rad_dists_origin == ref_segs_origin))
nt.ok_(np.all(rad_dists_origin != ref_segs))
nrns = [
nm.load_neuron(os.path.join(SWC_PATH, f))
for f in ('point_soma_single_neurite.swc',
'point_soma_single_neurite2.swc')
]
pop = Population(nrns)
rad_dist_nrns = []
for nrn in nrns:
rad_dist_nrns.extend(nm.get('segment_radial_distances', nrn))
rad_dist_nrns = np.array(rad_dist_nrns)
rad_dist_pop = nm.get('segment_radial_distances', pop)
assert_allclose(rad_dist_nrns, rad_dist_pop)
def test_section_tortuosity_pop():
feat = 'section_tortuosity'
assert_allclose(_stats(fst_get(feat, POP)),
(1.0,
4.6571118550276704,
440.40884450374455,
1.3427098917797089))
assert_allclose(_stats(fst_get(feat, POP, neurite_type=NeuriteType.all)),
(1.0,
4.6571118550276704,
440.40884450374455,
1.3427098917797089))
assert_allclose(_stats(fst_get(feat, POP, neurite_type=NeuriteType.apical_dendrite)),
(1.0702760052031615,
1.5732825321954913,
26.919574286670883,
1.2818844898414707))
assert_allclose(_stats(fst_get(feat, POP, neurite_type=NeuriteType.basal_dendrite)),
(1.042614577410971,
1.6742599832295344,
106.5960839640893,
1.239489348419643))
def test_neurite_volumes_nrn():
feat = 'neurite_volumes'
nt.ok_(
np.allclose(_stats(fst_get(feat, NRN)),
(271.94122143951864, 281.24754646913954,
1104.9077698137021, 276.22694245342552)))
nt.ok_(
np.allclose(_stats(fst_get(feat, NRN, neurite_type=NeuriteType.all)),
(271.94122143951864, 281.24754646913954,
1104.9077698137021, 276.22694245342552)))
nt.ok_(
np.allclose(_stats(fst_get(feat, NRN, neurite_type=NeuriteType.axon)),
(276.73860261723024, 276.73860261723024,
276.73860261723024, 276.73860261723024)))
nt.ok_(
np.allclose(
_stats(fst_get(feat, NRN,
neurite_type=NeuriteType.basal_dendrite)),
(274.98039928781355, 281.24754646913954, 556.22794575695309,
278.11397287847655)))
nt.ok_(
np.allclose(
_stats(fst_get(feat, NRN,
neurite_type=NeuriteType.apical_dendrite)),
(271.94122143951864, 271.94122143951864, 271.94122143951864,
271.94122143951864)))
def test_section_tortuosity_nrn():
feat = 'section_tortuosity'
nt.ok_(
np.allclose(_stats(fst_get(feat, NRN)),
(1.0702760052031612, 1.5732825321954911,
106.42449427885093, 1.2669582652244158)))
nt.ok_(
np.allclose(_stats(fst_get(feat, NRN, neurite_type=NeuriteType.all)),
(1.0702760052031612, 1.5732825321954911,
106.42449427885093, 1.2669582652244158)))
nt.ok_(
np.allclose(
_stats(fst_get(feat, NRN,
neurite_type=NeuriteType.apical_dendrite)),
(1.0702760052031612, 1.5732825321954911, 26.919574286670883,
1.2818844898414707)))
nt.ok_(
np.allclose(
_stats(fst_get(feat, NRN,
neurite_type=NeuriteType.basal_dendrite)),
(1.0788578286296124, 1.5504287518256337, 51.540901640170489,
1.227164324765964)))
def test_section_tortuosity_nrn():
feat = 'section_tortuosity'
nt.ok_(np.allclose(_stats(fst_get(feat, NRN)),
(1.0702760052031612,
1.5732825321954911,
106.42449427885093,
1.2669582652244158)))
nt.ok_(np.allclose(_stats(fst_get(feat, NRN, neurite_type=NeuriteType.all)),
(1.0702760052031612,
1.5732825321954911,
106.42449427885093,
1.2669582652244158)))
nt.ok_(np.allclose(_stats(fst_get(feat, NRN, neurite_type=NeuriteType.apical_dendrite)),
(1.0702760052031612,
1.5732825321954911,
26.919574286670883,
1.2818844898414707)))
nt.ok_(np.allclose(_stats(fst_get(feat, NRN, neurite_type=NeuriteType.basal_dendrite)),
(1.0788578286296124,
1.5504287518256337,
51.540901640170489,
1.227164324765964)))
def test_segment_radii_pop():
feat = 'segment_radii'
nt.ok_(np.allclose(_stats(fst_get(feat, POP)),
(0.079999998211860657,
1.2150000333786011,
1301.9191725363567,
0.20222416473071708)))
nt.ok_(np.allclose(_stats(fst_get(feat, POP, neurite_type=NeuriteType.all)),
(0.079999998211860657,
1.2150000333786011,
1301.9191725363567,
0.20222416473071708)))
nt.ok_(np.allclose(_stats(fst_get(feat, POP, neurite_type=NeuriteType.apical_dendrite)),
(0.13142434507608414,
1.0343990325927734,
123.41135908663273,
0.58767313850777492)))
nt.ok_(np.allclose(_stats(fst_get(feat, POP, neurite_type=NeuriteType.basal_dendrite)),
(0.079999998211860657,
1.2150000333786011,
547.43900821779164,
0.42078324997524336)))
def test_segment_meander_angles_nrn():
feat = 'segment_meander_angles'
nt.ok_(
np.allclose(_stats(fst_get(feat, NRN)),
(0.326101999292573, 3.129961675751181, 1842.351779156608,
2.4369732528526562)))
nt.ok_(
np.allclose(_stats(fst_get(feat, NRN, neurite_type=NeuriteType.all)),
(0.326101999292573, 3.129961675751181, 1842.351779156608,
2.4369732528526562)))
nt.ok_(
np.allclose(
_stats(fst_get(feat, NRN,
neurite_type=NeuriteType.apical_dendrite)),
(0.326101999292573, 3.0939261437163492, 461.98168732359414,
2.4443475519766884)))
nt.ok_(
np.allclose(
_stats(fst_get(feat, NRN,
neurite_type=NeuriteType.basal_dendrite)),
(0.47318725279312024, 3.129961675751181, 926.33847274926438,
2.4506308802890593)))
def test_neurite_volumes_pop():
feat = 'neurite_volumes'
nt.ok_(
np.allclose(_stats(fst_get(feat, POP)),
(28.356406629821159, 281.24754646913954,
2249.4613918388391, 224.9461391838839)))
nt.ok_(
np.allclose(_stats(fst_get(feat, POP, neurite_type=NeuriteType.all)),
(28.356406629821159, 281.24754646913954,
2249.4613918388391, 224.9461391838839)))
nt.ok_(
np.allclose(_stats(fst_get(feat, POP, neurite_type=NeuriteType.axon)),
(276.58135508666612, 277.5357232437392, 830.85568094763551,
276.95189364921185)))
nt.ok_(
np.allclose(
_stats(fst_get(feat, POP,
neurite_type=NeuriteType.basal_dendrite)),
(28.356406629821159, 281.24754646913954, 1146.6644894516851,
191.1107482419475)))
nt.ok_(
np.allclose(
_stats(fst_get(feat, POP,
neurite_type=NeuriteType.apical_dendrite)),
(271.94122143951864, 271.94122143951864, 271.94122143951864,
271.94122143951864)))
def test_segment_meander_angles_pop():
feat = 'segment_meander_angles'
nt.ok_(
np.allclose(
_stats(fst_get(feat, POP)),
(0.0, 3.1415926535897931, 14637.977670710961, 2.3957410263029395)))
nt.ok_(
np.allclose(
_stats(fst_get(feat, POP, neurite_type=NeuriteType.all)),
(0.0, 3.1415926535897931, 14637.977670710961, 2.3957410263029395)))
nt.ok_(
np.allclose(
_stats(fst_get(feat, POP,
neurite_type=NeuriteType.apical_dendrite)),
(0.326101999292573, 3.0939261437163492, 461.98168732359414,
2.4443475519766884)))
nt.ok_(
np.allclose(
_stats(fst_get(feat, POP,
neurite_type=NeuriteType.basal_dendrite)),
(0.0, 3.1415926535897931, 2926.2411975307768, 2.4084289691611334)))
def test_segment_radii_nrn():
feat = 'segment_radii'
nt.ok_(
np.allclose(_stats(fst_get(feat, NRN)),
(0.12087134271860123, 1.0343990325927734,
507.01994501426816, 0.60359517263603357)))
nt.ok_(
np.allclose(_stats(fst_get(feat, NRN, neurite_type=NeuriteType.all)),
(0.12087134271860123, 1.0343990325927734,
507.01994501426816, 0.60359517263603357)))
nt.ok_(
np.allclose(
_stats(fst_get(feat, NRN,
neurite_type=NeuriteType.apical_dendrite)),
(0.13142434507608414, 1.0343990325927734, 123.41135908663273,
0.58767313850777492)))
nt.ok_(
np.allclose(
_stats(fst_get(feat, NRN,
neurite_type=NeuriteType.basal_dendrite)),
(0.14712842553853989, 1.0215770602226257, 256.71241207793355,
0.61122002875698467)))
def test_segment_radii_pop():
feat = 'segment_radii'
nt.ok_(
np.allclose(_stats(fst_get(feat, POP)),
(0.079999998211860657, 1.2150000333786011,
1301.9191725363567, 0.20222416473071708)))
nt.ok_(
np.allclose(_stats(fst_get(feat, POP, neurite_type=NeuriteType.all)),
(0.079999998211860657, 1.2150000333786011,
1301.9191725363567, 0.20222416473071708)))
nt.ok_(
np.allclose(
_stats(fst_get(feat, POP,
neurite_type=NeuriteType.apical_dendrite)),
(0.13142434507608414, 1.0343990325927734, 123.41135908663273,
0.58767313850777492)))
nt.ok_(
np.allclose(
_stats(fst_get(feat, POP,
neurite_type=NeuriteType.basal_dendrite)),
(0.079999998211860657, 1.2150000333786011, 547.43900821779164,
0.42078324997524336)))
def assert_features_for_neurite(feat, neurons, expected, exact=True):
for neurite_type, expected_values in expected.items():
print('neurite_type: %s' % neurite_type)
if neurite_type is None:
res_pop = fst_get(feat, neurons)
res = fst_get(feat, neurons[0])
else:
res_pop = fst_get(feat, neurons, neurite_type=neurite_type)
res = fst_get(feat, neurons[0], neurite_type=neurite_type)
if exact:
assert_items_equal(res_pop, expected_values)
else:
assert_allclose(res_pop, expected_values)
#test for single neuron
if isinstance(res, np.ndarray):
# some features, ex: total_length return arrays w/ one element when
# called on a single neuron
nt.eq_(len(res), 1)
res = res[0]
if exact:
nt.eq_(res, expected_values[0])
else:
assert_allclose(res, expected_values[0])
def test_segment_radii_nrn():
feat = 'segment_radii'
nt.ok_(np.allclose(_stats(fst_get(feat, NRN)),
(0.12087134271860123,
1.0343990325927734,
507.01994501426816,
0.60359517263603357)))
nt.ok_(np.allclose(_stats(fst_get(feat, NRN, neurite_type=NeuriteType.all)),
(0.12087134271860123,
1.0343990325927734,
507.01994501426816,
0.60359517263603357)))
nt.ok_(np.allclose(_stats(fst_get(feat, NRN, neurite_type=NeuriteType.apical_dendrite)),
(0.13142434507608414,
1.0343990325927734,
123.41135908663273,
0.58767313850777492)))
nt.ok_(np.allclose(_stats(fst_get(feat, NRN, neurite_type=NeuriteType.basal_dendrite)),
(0.14712842553853989,
1.0215770602226257,
256.71241207793355,
0.61122002875698467)))
def test_section_number_invalid():
nt.eq_(
fst_get('number_of_sections', NEURON,
neurite_type=NeuriteType.soma)[0], 0)
nt.eq_(
fst_get('number_of_sections',
NEURON,
neurite_type=NeuriteType.undefined)[0], 0)
def test_segment_lengths():
ref_seglen = nf.segment_lengths(NEURON)
seglen = fst_get('segment_lengths', NEURON)
nt.eq_(len(seglen), 840)
assert_allclose(seglen, ref_seglen)
seglen = fst_get('segment_lengths', NEURON, neurite_type=NeuriteType.all)
nt.eq_(len(seglen), 840)
assert_allclose(seglen, ref_seglen)
def test_remote_bifurcation_angles_invalid():
s = fst_get('remote_bifurcation_angles',
NEURON,
neurite_type=NeuriteType.soma)
nt.eq_(len(s), 0)
s = fst_get('remote_bifurcation_angles',
NEURON,
neurite_type=NeuriteType.undefined)
nt.eq_(len(s), 0)
def test_segment_lengths():
ref_seglen = nf.segment_lengths(NEURON)
seglen = fst_get('segment_lengths', NEURON)
nt.eq_(len(seglen), 840)
assert_allclose(seglen, ref_seglen)
seglen = fst_get('segment_lengths', NEURON, neurite_type=NeuriteType.all)
nt.eq_(len(seglen), 840)
assert_allclose(seglen, ref_seglen)
def test_get_trunk_section_lengths():
assert_allclose(fst_get('trunk_section_lengths', NEURON),
[9.579117366740002,
7.972322416776259,
8.2245287740603779,
9.212707985134525])
assert_allclose(fst_get('trunk_section_lengths', NEURON, neurite_type=NeuriteType.apical_dendrite),
[9.212707985134525])
assert_allclose(fst_get('trunk_section_lengths', NEURON, neurite_type=NeuriteType.basal_dendrite),
[7.972322416776259, 8.2245287740603779])
assert_allclose(fst_get('trunk_section_lengths', NEURON, neurite_type=NeuriteType.axon),
[9.579117366740002])
def test_get_trunk_section_lengths():
assert_allclose(fst_get('trunk_section_lengths', NEURON),
[9.579117366740002,
7.972322416776259,
8.2245287740603779,
9.212707985134525])
assert_allclose(fst_get('trunk_section_lengths', NEURON, neurite_type=NeuriteType.apical_dendrite),
[9.212707985134525])
assert_allclose(fst_get('trunk_section_lengths', NEURON, neurite_type=NeuriteType.basal_dendrite),
[7.972322416776259, 8.2245287740603779])
assert_allclose(fst_get('trunk_section_lengths', NEURON, neurite_type=NeuriteType.axon),
[9.579117366740002])
def test_trunk_origin_radii():
assert_allclose(fst_get('trunk_origin_radii', NEURON),
[0.85351288499400002,
0.18391483031299999,
0.66943255462899998,
0.14656092843999999])
assert_allclose(fst_get('trunk_origin_radii', NEURON, neurite_type=NeuriteType.apical_dendrite),
[0.14656092843999999])
assert_allclose(fst_get('trunk_origin_radii', NEURON, neurite_type=NeuriteType.basal_dendrite),
[0.18391483031299999,
0.66943255462899998])
assert_allclose(fst_get('trunk_origin_radii', NEURON, neurite_type=NeuriteType.axon),
[0.85351288499400002])
def test_trunk_origin_radii():
assert_allclose(fst_get('trunk_origin_radii', NEURON),
[0.85351288499400002,
0.18391483031299999,
0.66943255462899998,
0.14656092843999999])
assert_allclose(fst_get('trunk_origin_radii', NEURON, neurite_type=NeuriteType.apical_dendrite),
[0.14656092843999999])
assert_allclose(fst_get('trunk_origin_radii', NEURON, neurite_type=NeuriteType.basal_dendrite),
[0.18391483031299999,
0.66943255462899998])
assert_allclose(fst_get('trunk_origin_radii', NEURON, neurite_type=NeuriteType.axon),
[0.85351288499400002])
def test_sholl_frequency():
assert_allclose(fst_get('sholl_frequency', NEURON),
[4, 8, 8, 14, 9, 8, 7, 7])
assert_allclose(fst_get('sholl_frequency', NEURON, neurite_type=NeuriteType.all),
[4, 8, 8, 14, 9, 8, 7, 7])
assert_allclose(fst_get('sholl_frequency', NEURON, neurite_type=NeuriteType.apical_dendrite),
[1, 2, 2, 2, 2, 2, 1, 1])
assert_allclose(fst_get('sholl_frequency', NEURON, neurite_type=NeuriteType.basal_dendrite),
[2, 4, 4, 6, 5, 4, 4, 4])
assert_allclose(fst_get('sholl_frequency', NEURON, neurite_type=NeuriteType.axon),
[1, 2, 2, 6, 2, 2, 2, 2])
def test_segment_meander_angles_nrn():
feat = 'segment_meander_angles'
nt.ok_(np.allclose(_stats(fst_get(feat, NRN)),
(0.326101999292573, 3.129961675751181, 1842.351779156608, 2.4369732528526562)))
nt.ok_(np.allclose(_stats(fst_get(feat, NRN, neurite_type=NeuriteType.all)),
(0.326101999292573, 3.129961675751181, 1842.351779156608, 2.4369732528526562)))
nt.ok_(np.allclose(_stats(fst_get(feat, NRN, neurite_type=NeuriteType.apical_dendrite)),
(0.326101999292573, 3.0939261437163492, 461.98168732359414, 2.4443475519766884)))
nt.ok_(np.allclose(_stats(fst_get(feat, NRN, neurite_type=NeuriteType.basal_dendrite)),
(0.47318725279312024, 3.129961675751181, 926.33847274926438, 2.4506308802890593)))
def test_sholl_frequency():
assert_allclose(fst_get('sholl_frequency', NEURON),
[4, 8, 8, 14, 9, 8, 7, 7])
assert_allclose(fst_get('sholl_frequency', NEURON, neurite_type=NeuriteType.all),
[4, 8, 8, 14, 9, 8, 7, 7])
assert_allclose(fst_get('sholl_frequency', NEURON, neurite_type=NeuriteType.apical_dendrite),
[1, 2, 2, 2, 2, 2, 1, 1])
assert_allclose(fst_get('sholl_frequency', NEURON, neurite_type=NeuriteType.basal_dendrite),
[2, 4, 4, 6, 5, 4, 4, 4])
assert_allclose(fst_get('sholl_frequency', NEURON, neurite_type=NeuriteType.axon),
[1, 2, 2, 6, 2, 2, 2, 2])
def test_segment_meander_angles_pop():
feat = 'segment_meander_angles'
nt.ok_(np.allclose(_stats(fst_get(feat, POP)),
(0.0, 3.1415926535897931, 14637.977670710961, 2.3957410263029395)))
nt.ok_(np.allclose(_stats(fst_get(feat, POP, neurite_type=NeuriteType.all)),
(0.0, 3.1415926535897931, 14637.977670710961, 2.3957410263029395)))
nt.ok_(np.allclose(_stats(fst_get(feat, POP, neurite_type=NeuriteType.apical_dendrite)),
(0.326101999292573, 3.0939261437163492, 461.98168732359414, 2.4443475519766884)))
nt.ok_(np.allclose(_stats(fst_get(feat, POP, neurite_type=NeuriteType.basal_dendrite)),
(0.0, 3.1415926535897931, 2926.2411975307768, 2.4084289691611334)))
def test_section_radial_distances_origin():
origin = (-100, -200, -300)
ref_sec_rad_dist_origin = nf.section_radial_distances(NEURON,
origin=origin)
rad_dists = fst_get('section_radial_distances', NEURON, origin=origin)
nt.eq_(len(rad_dists), 84)
nt.ok_(np.all(rad_dists == ref_sec_rad_dist_origin))
def test_number_of_sections_pop():
feat = 'number_of_sections'
nt.assert_items_equal(fst_get(feat, POP), [84, 42, 202])
nt.assert_items_equal(fst_get(feat, POP, neurite_type=NeuriteType.all),
[84, 42, 202])
nt.assert_items_equal(fst_get(feat, POP, neurite_type=NeuriteType.axon),
[21, 21, 179])
nt.assert_items_equal(fst_get(feat, POP, neurite_type=NeuriteType.apical_dendrite),
[21, 0, 0])
nt.assert_items_equal(fst_get(feat, POP, neurite_type=NeuriteType.basal_dendrite),
[42, 21, 23])
def test_number_of_sections_nrn():
feat = 'number_of_sections'
nt.assert_items_equal(fst_get(feat, NRN), [84])
nt.assert_items_equal(fst_get(feat, NRN, neurite_type=NeuriteType.all),
[84])
nt.assert_items_equal(fst_get(feat, NRN, neurite_type=NeuriteType.axon),
[21])
nt.assert_items_equal(fst_get(feat, NRN, neurite_type=NeuriteType.apical_dendrite),
[21])
nt.assert_items_equal(fst_get(feat, NRN, neurite_type=NeuriteType.basal_dendrite),
[42])
def test_partition_asymmetry():
nt.ok_(
np.allclose(
fst_get('partition_asymmetry', NRNS)[:10],
np.array([
0.9, 0.88888889, 0.875, 0.85714286, 0.83333333, 0.8, 0.75,
0.66666667, 0.5, 0.
])))
def test_section_path_distances_endpoint():
ref_sec_path_len_start = list(iter_neurites(NEURON, sec.start_point_path_length))
ref_sec_path_len = list(iter_neurites(NEURON, sec.end_point_path_length))
path_lengths = fst_get('section_path_distances', NEURON)
nt.ok_(ref_sec_path_len != ref_sec_path_len_start)
nt.eq_(len(path_lengths), 84)
nt.ok_(np.all(path_lengths == ref_sec_path_len))
def test_section_path_distances_endpoint():
ref_sec_path_len_start = list(iter_neurites(NEURON, sec.start_point_path_length))
ref_sec_path_len = list(iter_neurites(NEURON, sec.end_point_path_length))
path_lengths = fst_get('section_path_distances', NEURON)
nt.ok_(ref_sec_path_len != ref_sec_path_len_start)
nt.eq_(len(path_lengths), 84)
nt.ok_(np.all(path_lengths == ref_sec_path_len))
def test_number_of_segments_pop():
feat = 'number_of_segments'
nt.assert_items_equal(fst_get(feat, POP), [840, 419, 5179])
nt.assert_items_equal(fst_get(feat, POP, neurite_type=NeuriteType.all),
[840, 419, 5179])
nt.assert_items_equal(fst_get(feat, POP, neurite_type=NeuriteType.axon),
[210, 209, 4508])
nt.assert_items_equal(fst_get(feat, POP, neurite_type=NeuriteType.apical_dendrite),
[210, 0, 0])
nt.assert_items_equal(fst_get(feat, POP, neurite_type=NeuriteType.basal_dendrite),
[420, 210, 671])
def test_number_of_forking_points_pop():
feat = 'number_of_forking_points'
nt.assert_items_equal(fst_get(feat, POP), [40, 20, 98])
nt.assert_items_equal(fst_get(feat, POP, neurite_type=NeuriteType.all),
[40, 20, 98])
nt.assert_items_equal(fst_get(feat, POP, neurite_type=NeuriteType.axon),
[10, 10, 88])
nt.assert_items_equal(fst_get(feat, POP, neurite_type=NeuriteType.apical_dendrite),
[10, 0, 0])
nt.assert_items_equal(fst_get(feat, POP, neurite_type=NeuriteType.basal_dendrite),
[20, 10, 10])
def test_number_of_forking_points_nrn():
feat = 'number_of_forking_points'
nt.assert_items_equal(fst_get(feat, NRN), [40])
nt.assert_items_equal(fst_get(feat, NRN, neurite_type=NeuriteType.all),
[40])
nt.assert_items_equal(fst_get(feat, NRN, neurite_type=NeuriteType.axon),
[10])
nt.assert_items_equal(fst_get(feat, NRN, neurite_type=NeuriteType.apical_dendrite),
[10])
nt.assert_items_equal(fst_get(feat, NRN, neurite_type=NeuriteType.basal_dendrite),
[20])
def test_number_of_terminations_pop():
feat = 'number_of_terminations'
nt.assert_items_equal(fst_get(feat, POP), [44, 22, 103])
nt.assert_items_equal(fst_get(feat, POP, neurite_type=NeuriteType.all),
[44, 22, 103])
nt.assert_items_equal(fst_get(feat, POP, neurite_type=NeuriteType.axon),
[11, 11, 90])
nt.assert_items_equal(fst_get(feat, POP, neurite_type=NeuriteType.apical_dendrite),
[11, 0, 0])
nt.assert_items_equal(fst_get(feat, POP, neurite_type=NeuriteType.basal_dendrite),
[22, 11, 13])
def test_neurite_features_accept_single_tree():
features = NEURITEFEATURES.keys()
for f in features:
ret = fst_get(f, NRN.neurites[0])
nt.ok_(ret.dtype.kind in ('i', 'f'))
nt.ok_(len(ret) or len(ret) == 0) # make sure that len() resolves
def test_remote_bifurcation_angles():
ref_remote_bifangles = list(nf.remote_bifurcation_angles(NEURON))
remote_bifangles = fst_get('remote_bifurcation_angles', NEURON)
nt.eq_(len(remote_bifangles), 40)
assert_allclose(remote_bifangles, ref_remote_bifangles)
remote_bifangles = fst_get('remote_bifurcation_angles', NEURON, neurite_type=NeuriteType.all)
nt.eq_(len(remote_bifangles), 40)
assert_allclose(remote_bifangles, ref_remote_bifangles)
s = fst_get('remote_bifurcation_angles', NEURON, neurite_type=NeuriteType.axon)
nt.eq_(len(s), 10)
s = fst_get('remote_bifurcation_angles', NEURON, neurite_type=NeuriteType.basal_dendrite)
nt.eq_(len(s), 20)
s = fst_get('remote_bifurcation_angles', NEURON, neurite_type=NeuriteType.apical_dendrite)
nt.eq_(len(s), 10)
def test_number_of_bifurcations_pop():
feat = 'number_of_bifurcations'
nt.assert_items_equal(fst_get(feat, POP), [40, 20, 97])
nt.assert_items_equal(fst_get(feat, POP, neurite_type=NeuriteType.all),
[40, 20, 97])
nt.assert_items_equal(fst_get(feat, POP, neurite_type=NeuriteType.axon),
[10, 10, 87])
nt.assert_items_equal(fst_get(feat, POP, neurite_type=NeuriteType.apical_dendrite),
[10, 0, 0])
nt.assert_items_equal(fst_get(feat, POP, neurite_type=NeuriteType.basal_dendrite),
[20, 10, 10])
def test_neurite_features_accept_single_tree():
features = NEURITEFEATURES.keys()
for f in features:
ret = fst_get(f, NRN.neurites[0])
nt.ok_(ret.dtype.kind in ('i', 'f'))
nt.ok_(len(ret) or len(ret) == 0) # make sure that len() resolves
def test_number_of_neurites_pop():
feat = 'number_of_neurites'
nt.assert_items_equal(fst_get(feat, POP), [4, 2, 4])
nt.assert_items_equal(fst_get(feat, POP, neurite_type=NeuriteType.all),
[4, 2, 4])
nt.assert_items_equal(fst_get(feat, POP, neurite_type=NeuriteType.axon),
[1, 1, 1])
nt.assert_items_equal(fst_get(feat, POP, neurite_type=NeuriteType.apical_dendrite),
[1, 0, 0])
nt.assert_items_equal(fst_get(feat, POP, neurite_type=NeuriteType.basal_dendrite),
[2, 1, 3])
def test_section_path_distances_start_point():
ref_sec_path_len_start = list(
iter_neurites(NEURON, sec.start_point_path_length))
path_lengths = fst_get('section_path_distances',
NEURON,
use_start_point=True)
nt.eq_(len(path_lengths), 84)
nt.ok_(np.all(path_lengths == ref_sec_path_len_start))
def test_neurite_density_pop():
feat = 'neurite_volume_density'
nt.ok_(np.allclose(_stats(fst_get(feat, POP)),
(6.1847539631150784e-06, 0.52464681266899216, 1.9767794901940539, 0.19767794901940539)))
nt.ok_(np.allclose(_stats(fst_get(feat, POP, neurite_type=NeuriteType.all)),
(6.1847539631150784e-06, 0.52464681266899216, 1.9767794901940539, 0.19767794901940539)))
nt.ok_(np.allclose(_stats(fst_get(feat, POP, neurite_type=NeuriteType.axon)),
(6.1847539631150784e-06, 0.26465213325053372, 0.5275513670655404, 0.17585045568851346)))
nt.ok_(np.allclose(_stats(fst_get(feat, POP, neurite_type=NeuriteType.basal_dendrite)),
(0.00034968816544949771, 0.52464681266899216, 1.0116620531455183, 0.16861034219091972)))
nt.ok_(np.allclose(_stats(fst_get(feat, POP, neurite_type=NeuriteType.apical_dendrite)),
(0.43756606998299519, 0.43756606998299519, 0.43756606998299519, 0.43756606998299519)))
def test_neurite_density_nrn():
feat = 'neurite_volume_density'
nt.ok_(np.allclose(_stats(fst_get(feat, NRN)),
(0.24068543213643726, 0.52464681266899216, 1.4657913638494682, 0.36644784096236704)))
nt.ok_(np.allclose(_stats(fst_get(feat, NRN, neurite_type=NeuriteType.all)),
(0.24068543213643726, 0.52464681266899216, 1.4657913638494682, 0.36644784096236704)))
nt.ok_(np.allclose(_stats(fst_get(feat, NRN, neurite_type=NeuriteType.axon)),
(0.26289304906104355, 0.26289304906104355, 0.26289304906104355, 0.26289304906104355)))
nt.ok_(np.allclose(_stats(fst_get(feat, NRN, neurite_type=NeuriteType.basal_dendrite)),
(0.24068543213643726, 0.52464681266899216, 0.76533224480542938, 0.38266612240271469)))
nt.ok_(np.allclose(_stats(fst_get(feat, NRN, neurite_type=NeuriteType.apical_dendrite)),
(0.43756606998299519, 0.43756606998299519, 0.43756606998299519, 0.43756606998299519)))
def test_partition_asymmetry():
nt.ok_(np.allclose(fst_get('partition_asymmetry', NRNS)[:10], np.array([0.9, 0.88888889, 0.875,
0.85714286, 0.83333333,
<<<<<<< HEAD
=======
>>>>>>> upstream/master
0.8, 0.75, 0.66666667,
0.5, 0.])))
def test_total_length_nrn():
feat = 'total_length'
nt.ok_(np.allclose(fst_get(feat, NRN),
[840.68522362]))
nt.ok_(np.allclose(fst_get(feat, NRN, neurite_type=NeuriteType.all),
[840.68522362]))
nt.ok_(np.allclose(fst_get(feat, NRN, neurite_type=NeuriteType.axon),
[207.8797736]))
nt.ok_(np.allclose(fst_get(feat, NRN, neurite_type=NeuriteType.apical_dendrite),
[214.37302709]))
nt.ok_(np.allclose(fst_get(feat, NRN, neurite_type=NeuriteType.basal_dendrite),
[418.43242293]))
def test_total_length_pop():
feat = 'total_length'
nt.ok_(np.allclose(fst_get(feat, POP),
[840.68522362, 418.83424432, 13250.82577394]))
nt.ok_(np.allclose(fst_get(feat, POP, neurite_type=NeuriteType.all),
[840.68522362, 418.83424432, 13250.82577394]))
nt.ok_(np.allclose(fst_get(feat, POP, neurite_type=NeuriteType.axon),
[207.8797736, 207.81088342, 11767.15611522]))
nt.ok_(np.allclose(fst_get(feat, POP, neurite_type=NeuriteType.apical_dendrite),
[214.37302709, 0, 0]))
nt.ok_(np.allclose(fst_get(feat, POP, neurite_type=NeuriteType.basal_dendrite),
[418.43242293, 211.0233609, 1483.66965872]))
def test_neurite_number():
nt.eq_(fst_get('number_of_neurites', NEURON)[0], 4)
nt.eq_(fst_get('number_of_neurites', NEURON, neurite_type=NeuriteType.all)[0], 4)
nt.eq_(fst_get('number_of_neurites', NEURON, neurite_type=NeuriteType.axon)[0], 1)
nt.eq_(fst_get('number_of_neurites', NEURON, neurite_type=NeuriteType.basal_dendrite)[0], 2)
nt.eq_(fst_get('number_of_neurites', NEURON, neurite_type=NeuriteType.apical_dendrite)[0], 1)
nt.eq_(fst_get('number_of_neurites', NEURON, neurite_type=NeuriteType.soma)[0], 0)
nt.eq_(fst_get('number_of_neurites', NEURON, neurite_type=NeuriteType.undefined)[0], 0)
def test_neurite_number():
nt.eq_(fst_get('number_of_neurites', NEURON)[0], 4)
nt.eq_(fst_get('number_of_neurites', NEURON, neurite_type=NeuriteType.all)[0], 4)
nt.eq_(fst_get('number_of_neurites', NEURON, neurite_type=NeuriteType.axon)[0], 1)
nt.eq_(fst_get('number_of_neurites', NEURON, neurite_type=NeuriteType.basal_dendrite)[0], 2)
nt.eq_(fst_get('number_of_neurites', NEURON, neurite_type=NeuriteType.apical_dendrite)[0], 1)
nt.eq_(fst_get('number_of_neurites', NEURON, neurite_type=NeuriteType.soma)[0], 0)
nt.eq_(fst_get('number_of_neurites', NEURON, neurite_type=NeuriteType.undefined)[0], 0)
def test_segment_meander_angles_single_section():
class Mock(object):
pass
feat = 'segment_meander_angles'
sec = core.Section(
np.array([[0, 0, 0], [1, 0, 0], [1, 1, 0], [2, 1, 0], [2, 2, 0]]))
nrt = core.Neurite(sec)
nrn = Mock()
nrn.neurites = [nrt]
nrn.soma = None
pop = core.Population([nrn])
ref = [math.pi / 2, math.pi / 2, math.pi / 2]
nt.eq_(ref, fst_get(feat, nrt).tolist())
nt.eq_(ref, fst_get(feat, nrn).tolist())
nt.eq_(ref, fst_get(feat, pop).tolist())
def test_neurite_volumes_nrn():
feat = 'neurite_volumes'
nt.ok_(np.allclose(_stats(fst_get(feat, NRN)),
(271.94122143951864, 281.24754646913954, 1104.9077698137021, 276.22694245342552)))
nt.ok_(np.allclose(_stats(fst_get(feat, NRN, neurite_type=NeuriteType.all)),
(271.94122143951864, 281.24754646913954, 1104.9077698137021, 276.22694245342552)))
nt.ok_(np.allclose(_stats(fst_get(feat, NRN, neurite_type=NeuriteType.axon)),
(276.73860261723024, 276.73860261723024, 276.73860261723024, 276.73860261723024)))
nt.ok_(np.allclose(_stats(fst_get(feat, NRN, neurite_type=NeuriteType.basal_dendrite)),
(274.98039928781355, 281.24754646913954, 556.22794575695309, 278.11397287847655)))
nt.ok_(np.allclose(_stats(fst_get(feat, NRN, neurite_type=NeuriteType.apical_dendrite)),
(271.94122143951864, 271.94122143951864, 271.94122143951864, 271.94122143951864)))
def test_neurite_volumes_pop():
feat = 'neurite_volumes'
nt.ok_(np.allclose(_stats(fst_get(feat, POP)),
(28.356406629821159, 281.24754646913954, 2249.4613918388391, 224.9461391838839)))
nt.ok_(np.allclose(_stats(fst_get(feat, POP, neurite_type=NeuriteType.all)),
(28.356406629821159, 281.24754646913954, 2249.4613918388391, 224.9461391838839)))
nt.ok_(np.allclose(_stats(fst_get(feat, POP, neurite_type=NeuriteType.axon)),
(276.58135508666612, 277.5357232437392, 830.85568094763551, 276.95189364921185)))
nt.ok_(np.allclose(_stats(fst_get(feat, POP, neurite_type=NeuriteType.basal_dendrite)),
(28.356406629821159, 281.24754646913954, 1146.6644894516851, 191.1107482419475)))
nt.ok_(np.allclose(_stats(fst_get(feat, POP, neurite_type=NeuriteType.apical_dendrite)),
(271.94122143951864, 271.94122143951864, 271.94122143951864, 271.94122143951864)))
def test_section_tortuosity_pop():
feat = 'section_tortuosity'
assert_allclose(
_stats(fst_get(feat, POP)),
(1.0, 4.6571118550276704, 440.40884450374455, 1.3427098917797089))
assert_allclose(
_stats(fst_get(feat, POP, neurite_type=NeuriteType.all)),
(1.0, 4.6571118550276704, 440.40884450374455, 1.3427098917797089))
assert_allclose(
_stats(fst_get(feat, POP, neurite_type=NeuriteType.apical_dendrite)),
(1.0702760052031615, 1.5732825321954913, 26.919574286670883,
1.2818844898414707))
assert_allclose(
_stats(fst_get(feat, POP, neurite_type=NeuriteType.basal_dendrite)),
(1.042614577410971, 1.6742599832295344, 106.5960839640893,
1.239489348419643))
def test_segment_radial_distances_origin():
origin = (-100, -200, -300)
ref_segs = nf.segment_radial_distances(NEURON)
ref_segs_origin = nf.segment_radial_distances(NEURON, origin=origin)
rad_dists = fst_get('segment_radial_distances', NEURON)
rad_dists_origin = fst_get('segment_radial_distances', NEURON, origin=origin)
nt.ok_(np.all(rad_dists == ref_segs))
nt.ok_(np.all(rad_dists_origin == ref_segs_origin))
nt.ok_(np.all(rad_dists_origin != ref_segs))
nrns = [nm.load_neuron(os.path.join(SWC_PATH, f)) for
f in ('point_soma_single_neurite.swc', 'point_soma_single_neurite2.swc')]
pop = Population(nrns)
rad_dist_nrns = []
for nrn in nrns:
rad_dist_nrns.extend(nm.get('segment_radial_distances', nrn))
rad_dist_nrns = np.array(rad_dist_nrns)
rad_dist_pop = nm.get('segment_radial_distances', pop)
assert_allclose(rad_dist_nrns, rad_dist_pop)
def test_section_radial_distances_endpoint():
ref_sec_rad_dist = nf.section_radial_distances(NEURON)
rad_dists = fst_get('section_radial_distances', NEURON)
nt.eq_(len(rad_dists), 84)
nt.ok_(np.all(rad_dists == ref_sec_rad_dist))
nrns = [nm.load_neuron(os.path.join(SWC_PATH, f)) for
f in ('point_soma_single_neurite.swc', 'point_soma_single_neurite2.swc')]
pop = Population(nrns)
rad_dist_nrns = [nm.get('section_radial_distances', nrn) for nrn in nrns]
rad_dist_pop = nm.get('section_radial_distances', pop)
assert_items_equal(rad_dist_pop, rad_dist_nrns)
def test_partition():
nt.ok_(np.all(fst_get('partition', NRNS)[:10] == np.array([ 19., 17., 15., 13., 11., 9., 7., 5., 3., 1.])))
def test_soma_surface_areas():
area = 4. * math.pi * fst_get('soma_radii', NEURON)[0] ** 2
nt.eq_(fst_get('soma_surface_areas', NEURON), area)
