def test_load_trees_postaction():
def post_action(t):
t.foo = 'bar'
filepath = os.path.join(SWC_PATH, 'Neuron.swc')
trees = utils.load_trees(filepath, tree_action=post_action)
nt.eq_(len(trees), 4) # sanity check
for t in trees:
nt.ok_(hasattr(t, 'foo') and t.foo == 'bar')
def test_load_neuron_disconnected_components():
filepath = DISCONNECTED_POINTS_FILE
trees = utils.load_trees(filepath)
nt.eq_(len(trees), 8)
# tree ID - number of points map
ref_ids_pts = {4: 1, 215: 1, 426: 1, 637: 1, 6: 209, 217: 209, 428: 209, 639: 209}
ids_pts = {}
for t in trees:
ids_pts[t.value[COLS.ID]] = pts.count(t)
nt.eq_(ref_ids_pts, ids_pts)
def test_load_neuron_disconnected_components():
filepath = os.path.join(SWC_PATH, 'Neuron_disconnected_components.swc')
trees = utils.load_trees(filepath)
nt.eq_(len(trees), 8)
# tree ID - number of points map
ref_ids_pts = {4: 1, 215: 1, 426: 1, 637: 1, 6: 209, 217: 209, 428: 209, 639: 209}
ids_pts = {}
for t in trees:
ids_pts[t.value[COLS.ID]] = pts.count(t)
nt.eq_(ref_ids_pts, ids_pts)
def test_load_trees_good_neuron():
'''Check trees in good neuron are the same as trees from loaded neuron'''
filepath = os.path.join(SWC_PATH, 'Neuron.swc')
nrn = utils.load_neuron(filepath)
trees = utils.load_trees(filepath)
nt.eq_(len(nrn.neurites), 4)
nt.eq_(len(nrn.neurites), len(trees))
nrn2 = MockNeuron(trees)
@pts.point_function(as_tree=False)
def elem(point):
return point
# Check data are the same in tree collection and neuron's neurites
for a, b in izip(iter_neurites(nrn, elem), iter_neurites(nrn2, elem)):
nt.ok_(np.all(a == b))
def test_load_trees_invalid_id_sequence_raises():
utils.load_trees(INVALID_ID_SEQUENCE_FILE);
def test_load_trees_no_soma():
trees = utils.load_trees(NO_SOMA_FILE)
nt.eq_(len(trees), 1)
pass
stylize(ax, cell.name, feature)
return fig
if __name__ == "__main__":
import numpy as np
import os
args = parse_args()
nrns = []
for j, neuronFile in enumerate(get_morph_files(args.datapath)):
_name = os.path.splitext(os.path.split(neuronFile)[-1])[0]
nrn = None
try:
soma = make_soma([np.array([11, 22, 33, 44, 1, 1, -1])])
trees = load_trees(neuronFile)
cn = coreNeuron(soma, trees, name=_name)
nrn = ezyNeuron(cn)
except IDSequenceError:
pass
for i, _feature in enumerate(args.features):
f = plot_feature(_feature, nrn)
figname = "{0}_{1}_{2}.eps".format(i, j, _name)
f.savefig(os.path.join(args.odir, figname))
pl.close(f)