def test_trunk_origin_azimuth():
t = Tree((0, 0, 0, 2))
s = make_soma([[0, 0, 1, 4]])
nt.assert_equal(mtr.trunk_origin_azimuth(t, s), -np.pi/2)
s = make_soma([[0, 0, -1, 4]])
nt.assert_equal(mtr.trunk_origin_azimuth(t, s), np.pi/2)
s = make_soma([[0, 0, 0, 4]])
nt.assert_equal(mtr.trunk_origin_azimuth(t, s), 0.0)
s = make_soma([[-1, 0, -1, 4]])
nt.assert_equal(mtr.trunk_origin_azimuth(t, s), np.pi/4)
s = make_soma([[-1, 0, 0, 4]])
nt.assert_equal(mtr.trunk_origin_azimuth(t, s), 0.0)
s = make_soma([[1, 0, 0, 4]])
nt.assert_equal(mtr.trunk_origin_azimuth(t, s), np.pi)
_min = min(min(data), dmin)
_max = max(max(data), dmax)
padding = padding * (_max - _min)
return _min - padding, _max + padding
# Neurite types of interest
NEURITES_ = (ezy.TreeType.axon,
ezy.TreeType.apical_dendrite,
ezy.TreeType.basal_dendrite,)
# map feature names to functors that get us arrays of that
# feature, for a given tree type
GET_NEURITE_FEATURE = {
'trunk_origin_azimuth': lambda nrn, typ: [mt.trunk_origin_azimuth(n, nrn.soma)
for n in nrn.neurites if n.type == typ],
'trunk_origin_elevation': lambda nrn, typ: [mt.trunk_origin_elevation(n, nrn.soma)
for n in nrn.neurites if n.type == typ],
'segment_length': lambda n, typ: n.get_segment_lengths(typ),
'section_length': lambda n, typ: n.get_section_lengths(typ),
}
# For now we use all the features in the map
FEATURES = GET_NEURITE_FEATURE.keys()
def load_neurite_features(filepath):
'''Unpack relevant data into megadict'''
stuff = defaultdict(lambda: defaultdict(list))
nrns = ezy.load_neurons(filepath)
