
Security News
Deno 2.4 Brings Back deno bundle, Improves Dependency Management and Observability
Deno 2.4 brings back bundling, improves dependency updates and telemetry, and makes the runtime more practical for real-world JavaScript projects.
Tools for screening significant loop structures in a graph, typically a brain structure graph with physiological or anatomical edge data.
$ pip install brain-loop-search
Packing vertices:
import brain_loop_search as bls
vertices = [322, 329, 981, 337, 453, 8, 1070] # ccf brain id
# ccf ontology
ontology = bls.brain_utils.CCFv3Ontology()
vp = bls.packing.VertexPacker(vertices, ontology)
# filtering by level
vp.filter_by_level(fro=1, to=2)
Packing a graph:
import brain_loop_search as bls
import pandas as pd
import numpy as np
vertices = [322, 329, 981, 337, 453, 1070, 345, 353, 361] # ccf brain id
# adjacent matrix
adj_mat = pd.DataFrame(np.array([
[0, 1, 1, 0, 0, 0, 1, 1, 0],
[0, 0, 1, 1, 1, 1, 0, 1, 0],
[0, 0, 0, 0, 1, 1, 1, 1, 1],
[0, 0, 1, 0, 0, 1, 0, 1, 0],
[1, 0, 1, 0, 0, 1, 0, 1, 0],
[0, 1, 1, 1, 1, 1, 0, 1, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 1],
[1, 0, 0, 0, 0, 1, 1, 1, 0],
[1, 1, 1, 0, 0, 1, 0, 1, 1]
]), index=vertices, columns=vertices)
# ccf ontology
ontology = bls.brain_utils.CCFv3Ontology()
# packing
new_rows = [322]
new_cols = [337, 329, 353]
graph_packer = bls.packing.GraphPacker(adj_mat, ontology)
new_mat = graph_packer.pack(new_rows, new_cols, def_val=0, superior_as_complement=True, aggr_func=np.sum)
Screen simple loops from the graph.
import brain_loop_search as bls
import pandas as pd
edges = pd.DataFrame({
"a": [1, 2, 3, 4, 5],
"b": [4, -1, -1, 1, -1],
"c": [5, 6, 7, 8, 9],
"d": [-1, 2, 3, 1, 2],
"e": [-1, -1, -1, -1, -1]
}, index=["a", "b", "c", "d", "e"])
g = bls.search.ShortestPathLoopSearch()
g.add_subgraph(edges)
# search by single shortest path with a reverse edge
loops = g.pair_complement(axis_pool=['a', 'b', 'c'])
# search by chaining 3 of the shortest paths found
loops, sssp = g.chain_screen(n_axis=3)
Generate a new graph of potentially integrated loops.
import brain_loop_search as bls
import pandas as pd
edges = pd.DataFrame({
"a": [1, 2, 3, 4, 5],
"b": [4, -1, -1, 1, -1],
"c": [5, 6, 7, 8, 9],
"d": [-1, 2, 3, 1, 2],
"e": [-1, -1, -1, -1, -1]
}, index=["a", "b", "c", "d", "e"])
g = bls.search.ShortestPathLoopSearch()
g.add_subgraph(edges)
# find a single max flow with a reverse edge (like a magnet field)
new_g = g.magnet_flow(s='b', t='a')
# find cycled max flows and merge them into a new graph
new_g = g.merged_cycle_flow(axes=['b', 'c', 'a'])
Visualize a single loop
import brain_loop_search as bls
# a loop is a list of list, with the head and tail of the sublist as axes
# here are some random picked brain regions
loop = [[950, 974, 417], [417, 993], [993, 234, 289, 950]]
bls.brain_utils.draw_single_loop(loop, 'test.png')
Figure:
Visualize a graph
import numpy as np
import pandas as pd
import brain_loop_search as bls
vertices = [322, 329, 981, 337, 453, 1070, 345, 353, 361]
adj_mat = pd.DataFrame(np.array([
[0, 2, 1, 0, 0, 0, 1, 8, 0],
[0, 0, 3, 1, 5, 1, 0, 5, 0],
[0, 0, 0, 0, 1, 3, 2, 1, 2],
[0, 0, 6, 0, 0, 1, 0, 4, 0],
[1, 0, 1, 0, 0, 1, 0, 4, 0],
[0, 1, 7, 1, 4, 1, 0, 1, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 1],
[1, 0, 0, 0, 0, 2, 2, 1, 0],
[1, 2, 2, 0, 0, 1, 0, 1, 1]
]), index=vertices, columns=vertices)
g = bls.search.GraphMaintainer()
g.add_subgraph(adj_mat)
bls.brain_utils.draw_brain_graph(g.graph, 'test2.png', thr=3)
Figure:
Github project: https://github.com/SEU-ALLEN-codebase/brain-loop-search
Documentation: https://SEU-ALLEN-codebase.github.io/brain-loop-search
FAQs
Screen loops among brain structures(or any entities comprising a graph).
We found that brain-loop-search demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Security News
Deno 2.4 brings back bundling, improves dependency updates and telemetry, and makes the runtime more practical for real-world JavaScript projects.
Security News
CVEForecast.org uses machine learning to project a record-breaking surge in vulnerability disclosures in 2025.
Security News
Browserslist-rs now uses static data to reduce binary size by over 1MB, improving memory use and performance for Rust-based frontend tools.