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 merge_overlapping_connectors(remote_instance=None,
verbose=True,
tolerance=1):
"""Merge pairs of connectors that are at exactly the same coordinate"""
if remote_instance in [None, 'source']:
remote_instance = source_project
if verbose: print('Merging overlapping connectors in source project.')
elif remote_instance == 'target':
remote_instance = target_project
if verbose: print('Merging overlapping connectors in target project.')
overlapping_pairs_ids = find_overlapping_connectors(
remote_instance=remote_instance, verbose=False, tolerance=tolerance)
user_input = input('Continue with merge? [Y/n] ')
if user_input not in ('y', 'Y'):
return
resp = []
for ids in overlapping_pairs_ids:
print('ids', ids)
details = pymaid.get_connector_details(ids,
remote_instance=remote_instance)
# Put the connector with presynaptic links first, if there is one
details.sort_values(by='presynaptic_to',
inplace=True,
ignore_index=True)
# Make sure it's not the case that both connectors have presynaptic links
assert details.at[
1,
'presynaptic_to'] is None, "Can't merge two presynaptic connectors"
#Add postsynaptic connections between the winner and the loser's postsynaptic nodes
print('linking')
link_resp = pymaid.link_connector([
(postsyn_node_id, details.at[0, 'connector_id'], 'postsynaptic_to')
for postsyn_node_id in details.at[1, 'postsynaptic_to_node']
],
remote_instance=remote_instance)
#Delete the loser
print('deleting')
del_resp = pymaid.delete_nodes(details.at[1, 'connector_id'],
'CONNECTOR',
no_prompt=True,
remote_instance=remote_instance)
resp.append((link_resp, del_resp))
print('done')
return resp
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
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)
# %% [markdown]
# #
pg = sns.PairGrid(data=connectors, x_vars=["x", "y", "z"], y_vars=["x", "y", "z"])
pg.map(sns.scatterplot, alpha=0.02, linewidth=0, s=1)
# %% [markdown]
# #
import matplotlib.pyplot as plt
import numpy as np
import matplotlib as mpl