Process Cytosim no compression simulations

Notebook contains steps for post processing of Cytosim simulations for a baseline single actin fiber with no compression.

This notebook provides an example of processing a simulation series for a single condition with multiple replicates. For an example of processing a simulation series with multiple conditions, each of which have multiple replicates, see process_cytosim_compression_simulations.py.

if __name__ != "__main__":
    raise ImportError("This module is a notebook and is not meant to be imported")

from subcell_pipeline.simulation.cytosim.post_processing import (
    parse_cytosim_simulation_data,
)
from subcell_pipeline.simulation.post_processing import sample_simulation_data

Define simulation conditions

Defines the ACTIN_NO_COMPRESSION simulation series, which simulates a single actin fiber with a free barbed end across five replicates (random seeds 1, 2, 3, 4, and 5).

# Name of the simulation series
series_name: str = "ACTIN_NO_COMPRESSION"

# S3 bucket for input and output files
bucket: str = "s3://cytosim-working-bucket"

# Random seeds for simulations
random_seeds: list[int] = [1, 2, 3, 4, 5]

Parse simulation data

Iterate through all condition keys and random seeds to load simulation output files and parse them into a tidy data format. If the parsed file for a given condition key and random seed already exists, parsing is skipped.

  • Input: (series_name)/outputs/(series_name)_(index)/

  • Output: (series_name)/data/(series_name)_(seed).csv

parse_cytosim_simulation_data(bucket, series_name, [""], random_seeds)

Define sampling settings

Defines the settings used for sub sampling data timepoints and monomer points.

# Number of timepoints
n_timepoints = 200

# Number of monomer points per fiber
n_monomer_points = 200

Sample simulation data

Iterate through all condition keys and random seeds to load the parsed data and sample the timepoints and monomer points. If the sampled file for a given condition key and random seed already exists, sampling is skipped.

  • Input: (series_name)/data/(series_name)_(condition_key)_(seed).csv

  • Output: (series_name)/samples/(series_name)_(condition_key)_(seed).csv

sample_simulation_data(
    bucket, series_name, [""], random_seeds, n_timepoints, n_monomer_points
)