def test_extract_stats_single_neuron():
nrn = nm.load_neuron(SWC_PATH / 'Neuron.swc')
res = ms.extract_stats(nrn, REF_CONFIG)
assert set(res.keys()) == set(REF_OUT.keys())
for k in ('neuron', 'all', 'axon', 'basal_dendrite', 'apical_dendrite'):
assert set(res[k].keys()) == set(REF_OUT[k].keys())
for kk in res[k].keys():
assert_almost_equal(res[k][kk], REF_OUT[k][kk], decimal=4)
def test_extract_stats_single_neuron():
nrn = nm.load_neuron(os.path.join(DATA_PATH, 'Neuron.swc'))
res = ms.extract_stats(nrn, REF_CONFIG)
nt.eq_(res.keys(), REF_OUT.keys())
nt.assert_almost_equal(res['mean_soma_radius'], REF_OUT['mean_soma_radius'])
for k in ('all', 'axon', 'basal_dendrite', 'apical_dendrite'):
nt.eq_(res[k].keys(), REF_OUT[k].keys())
for kk in res[k].keys():
nt.assert_almost_equal(res[k][kk], REF_OUT[k][kk])
def test_extract_stats_single_neuron():
nrn = nm.load_neuron(os.path.join(DATA_PATH, 'Neuron.swc'))
res = ms.extract_stats(nrn, REF_CONFIG)
nt.eq_(res.keys(), REF_OUT.keys())
nt.assert_almost_equal(res['mean_soma_radius'],
REF_OUT['mean_soma_radius'])
for k in ('all', 'axon', 'basal_dendrite', 'apical_dendrite'):
nt.eq_(res[k].keys(), REF_OUT[k].keys())
for kk in res[k].keys():
nt.assert_almost_equal(res[k][kk], REF_OUT[k][kk])
def test_extract_stats_single_neuron():
nrn = nm.load_neuron(os.path.join(DATA_PATH, 'Neuron.swc'))
res = ms.extract_stats(nrn, REF_CONFIG)
nt.eq_(res.keys(), REF_OUT.keys())
#Note: soma radius is calculated from the sphere that gives the area
# of the cylinders described in Neuron.swc
nt.assert_almost_equal(res['mean_soma_radius'], REF_OUT['mean_soma_radius'])
for k in ('all', 'axon', 'basal_dendrite', 'apical_dendrite'):
nt.eq_(res[k].keys(), REF_OUT[k].keys())
for kk in res[k].keys():
nt.assert_almost_equal(res[k][kk], REF_OUT[k][kk])
def test_extract_stats_single_neuron():
nrn = nm.load_neuron(Path(SWC_PATH, 'Neuron.swc'))
res = ms.extract_stats(nrn, REF_CONFIG)
assert_equal(set(res.keys()), set(REF_OUT.keys()))
# Note: soma radius is calculated from the sphere that gives the area
# of the cylinders described in Neuron.swc
assert_almost_equal(res['neuron']['mean_soma_radius'], REF_OUT['neuron']['mean_soma_radius'])
for k in ('all', 'axon', 'basal_dendrite', 'apical_dendrite'):
assert_equal(set(res[k].keys()), set(REF_OUT[k].keys()))
for kk in res[k].keys():
assert_almost_equal(res[k][kk], REF_OUT[k][kk], places=3)
def test_extract_stats_single_neuron():
nrn = nm.load_neuron(os.path.join(DATA_PATH, 'Neuron.swc'))
res = ms.extract_stats(nrn, REF_CONFIG)
nt.eq_(set(res.keys()), set(REF_OUT.keys()))
#Note: soma radius is calculated from the sphere that gives the area
# of the cylinders described in Neuron.swc
nt.assert_almost_equal(res['mean_soma_radius'], REF_OUT['mean_soma_radius'])
for k in ('all', 'axon', 'basal_dendrite', 'apical_dendrite'):
nt.eq_(set(res[k].keys()), set(REF_OUT[k].keys()))
for kk in res[k].keys():
nt.assert_almost_equal(res[k][kk], REF_OUT[k][kk])
def test_extract_stats_scalar_feature():
nrn = nm.load_neuron(DATA_PATH / 'neurolucida' / 'bio_neuron-000.asc')
config = {
'neurite_type': ['ALL'],
'neurite': {
'n_forking_points': ['max'],
},
'neuron': {
'soma_volume': ['total'],
}
}
res = ms.extract_stats(nrn, config)
assert res == {
'all': {
'max_n_forking_point': 277
},
'neuron': {
'total_soma_volume': 1424.4383771584492
}
}
