Process ReaDDy compression simulations

Notebook contains steps for post processing of ReaDDy simulations in which a single actin fiber is compressed at different compression velocities.

This notebook provides an example of processing a simulation series in which there are multiple conditions, each of which were run with multiple replicates. For an example of processing a simulation series with a single condition with multiple replicates, see process_readdy_no_compression_simulations.py.

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

from pathlib import Path

from subcell_pipeline.simulation.readdy.parser import parse_readdy_simulation_data

Define simulation conditions

Defines the ACTIN_COMPRESSION_VELOCITY simulation series, which compresses a single 500 nm actin fiber at four different velocities (4.7, 15, 47, and 150 μm/s) with five replicates each.

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

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

# Number of simulation replicates
n_replicates: int = 5

# List of condition file keys for each velocity
condition_keys: list[str] = ["0047", "0150", "0470", "1500"]

# Number of timepoints
n_timepoints = 200

# Number of monomer points per fiber
n_monomer_points = 200

# Temporary path to save downloaded trajectories
temp_path: Path = Path(__file__).parents[3] / "aws_downloads"
temp_path.mkdir(parents=True, exist_ok=True)

Parse simulation data

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

  • Input: (series_name)/outputs/(series_name)_(condition_key)_(index + 1).h5

  • Input: (series_name)/data/(series_name)_(condition_key)_(index + 1).pkl

  • Output: (series_name)/samples/(series_name)_(condition_key)_(index + 1).csv

parse_readdy_simulation_data(
    bucket,
    series_name,
    condition_keys,
    n_replicates,
    n_timepoints,
    n_monomer_points,
    compression=True,
    temp_path=str(temp_path),
)