Source code for bmtk.builder.auxi.edge_connectors

# Copyright 2017. Allen Institute. All rights reserved
#
# Redistribution and use in source and binary forms, with or without modification, are permitted provided that the
# following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following
# disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following
# disclaimer in the documentation and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote
# products derived from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES,
# INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
import numpy as np
import random


[docs]def distance_connector(source, target, d_weight_min, d_weight_max, d_max, nsyn_min, nsyn_max): # Avoid self-connections. sid = source.node_id tid = target.node_id if sid == tid: return None # first create weights by euclidean distance between cells r = np.linalg.norm(np.array(source['positions']) - np.array(target['positions'])) if r > d_max: dw = 0.0 else: t = r / d_max dw = d_weight_max * (1.0 - t) + d_weight_min * t # drop the connection if the weight is too low if dw <= 0: return None # filter out nodes by treating the weight as a probability of connection if random.random() > dw: return None # Add the number of synapses for every connection. tmp_nsyn = random.randint(nsyn_min, nsyn_max) return tmp_nsyn
[docs]def connect_random(source, target, nsyn_min=0, nsyn_max=10, distribution=None): return np.random.randint(nsyn_min, nsyn_max)