def test_get_connectors(self):
cn = pymaid.get_connectors(config_test.test_skids,
remote_instance=self.rm)
self.assertIsInstance(cn, pd.DataFrame)
self.assertIsInstance(
pymaid.get_connector_details(cn.connector_id.tolist(),
remote_instance=self.rm),
pd.DataFrame)
def get_pre_synaptic_positions(self, skid):
pymaid.clear_cache()
connectors = pymaid.get_connectors(skid,
relation_type='presynaptic_to')
x = connectors["x"].to_numpy()
y = connectors["y"].to_numpy()
z = connectors["z"].to_numpy()
pos_array, ids = np.vstack(
[z, y, x]).T, connectors["connector_id"].to_numpy()
return [tuple(np.round(p).astype(np.uint64)) for p in pos_array], ids
def sum_Conns_on_Branch(path,neuron,confidence = 5):
""" Input: list of leafnode ids
CatmaidNeuron object
Confidence value
Output:
"""
conn_dets = pymaid.get_connectors(neuron)
neuron_conns = neuron.connectors[neuron.connectors.connector_id.isin(conn_dets.connector_id[conn_dets.confidence == confidence])]
return sum(neuron_conns.node_id.isin(path))
def find_unlinked_connectors(remote_instance=None, verbose=True):
if remote_instance in [None, 'source']:
remote_instance = source_project
if verbose:
print('Searching for unlinked connectors in source project.')
elif remote_instance == 'target':
remote_instance = target_project
if verbose:
print('Searching for unlinked connectors in target project.')
all_connectors = pymaid.get_connectors(None,
remote_instance=remote_instance)
# A connector's type being null seems to indicate it is unlinked.
# I'm not confident this will always be true in future versions of pymaid
# and catmaid. A more robust but slower approach would actually go check
# there are no links.
unlinked_connectors = all_connectors.connector_id[
all_connectors.type.isnull()]
return unlinked_connectors.to_list()
def get_connectors(nl):
connectors = pymaid.get_connectors(nl)
connectors.set_index("connector_id", inplace=True)
connectors.drop(
[
"confidence",
"creation_time",
"edition_time",
"tags",
"creator",
"editor",
"type",
],
inplace=True,
axis=1,
)
details = pymaid.get_connector_details(connectors.index.values)
details.set_index("connector_id", inplace=True)
connectors = pd.concat((connectors, details), ignore_index=False, axis=1)
connectors.reset_index(inplace=True)
return connectors
def find_overlapping_connectors(remote_instance=None,
verbose=True,
tolerance=1):
"""Find pairs of connectors that are at exactly the same location"""
if remote_instance in [None, 'source']:
remote_instance = source_project
if verbose:
print('Searching for overlapping connectors in source project.')
elif remote_instance == 'target':
remote_instance = target_project
if verbose:
print('Searching for overlapping connectors in target project.')
all_connectors = pymaid.get_connectors(None,
remote_instance=remote_instance)
all_connectors.sort_values(by=['x', 'y', 'z'],
inplace=True,
ignore_index=True)
n_connectors = len(all_connectors)
hits = []
#d = lambda pt1, pt2: ((pt2 - pt1)**2).sum()**0.5 # euclidian distance
#d = lambda pt1, pt2: np.abs(pt2 - pt1).max() # Chebyshev distance
d = lambda row1, row2: np.abs(all_connectors.loc[
row2, ['x', 'y', 'z']].values - all_connectors.loc[
row1, ['x', 'y', 'z']].values).max() # Chebyshev distance
for row1 in tqdm(range(n_connectors)):
row2 = row1 + 1
while (row2 < n_connectors
and np.abs(all_connectors.at[row2, 'x'] -
all_connectors.at[row1, 'x']) <= tolerance):
if d(row2, row1) <= tolerance:
hits.append([
all_connectors.at[row1, 'connector_id'],
all_connectors.at[row2, 'connector_id']
])
row2 += 1
print(f'Found {len(hits)} pairs of overlapping connectors')
return hits
# %% [markdown]
# #
mg = mg.sort_values("Pair ID")
nl = pymaid.get_neurons(mg.meta[mg.meta["Merge Class"] == "sens-ORN"].index.values)
fig, ax = nl.plot2d()
# %%
nl = pymaid.get_neurons(mg.meta.index.values)
print(len(nl))
# %% [markdown]
# #
import pandas as pd
import seaborn as sns
connectors = pymaid.get_connectors(nl)
connectors.set_index("connector_id", inplace=True)
connectors.drop(
[
"confidence",
"creation_time",
"edition_time",
"tags",
"creator",
"editor",
"type",
],
inplace=True,
axis=1,
)
details = pymaid.get_connector_details(connectors.index.values)