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Efficient simulation of 3D reaction-diffusion in models of neurons (McDougal et al, 2022)

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Accession:267018
Validation, visualization, and analysis scripts for NEURON's 3D reaction-diffusion support.
Reference:
1 . McDougal RA, Conte C, Eggleston L, Newton AJH, Galijasevic H (2022) Efficient Simulation of 3D Reaction-Diffusion in Models of Neurons and Networks Front. Neuroinform.
Model Information (Click on a link to find other models with that property)
Model Type:
Brain Region(s)/Organism:
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; Python;
Model Concept(s): Methods; Reaction-diffusion;
Implementer(s):
import os
import sys
import pandas as pd
import sqlite3
base_dir = "swc"

dx = float(sys.argv[1])
for filename in os.listdir(base_dir):
    # TODO: skip if already in the db
    # skip hidden files
    with sqlite3.connect("discretization.db") as conn:
        old_data = pd.read_sql("SELECT * FROM morphology", conn)

    if any((old_data["dx"] == dx) & (old_data["morphology"] == os.path.join(base_dir, filename))):
        print(f"skipping: dx: {dx}, morph: {filename}")
        continue

    if not filename.startswith("."):
        true_filename = os.path.join(base_dir, filename)
        os.system(f"python time_discretization.py {true_filename} {dx}")

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