Compare compression metrics between simulators¶

Notebook contains steps to compare metrics of fiber compression across different simulators. Currently supports comparison of Cytosim and ReaDDy simulations.

Define simulation conditions
Select metrics to analyze
Calculate metrics for Cytosim data
Calculate metrics for ReaDDy data
Combine metrics from both simulators
Save combined compression metrics
Plot metrics vs time
Plot metrics histograms

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

import pandas as pd

from subcell_pipeline.analysis.compression_metrics.compression_analysis import (
    get_compression_metric_data,
    plot_metric_distribution,
    plot_metrics_vs_time,
    save_compression_metrics,
)
from subcell_pipeline.analysis.compression_metrics.compression_metric import (
    CompressionMetric,
)

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 and the baseline ACTIN_NO_COMPRESSION simulation series, which simulates a single actin fiber with a free barbed end across five replicates.

# Name of the simulation series
compression_series_name: str = "ACTIN_COMPRESSION_VELOCITY"
no_compression_series_name: str = "ACTIN_NO_COMPRESSION"

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

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

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

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

# Location to save analysis results (S3 bucket or local path)
save_location: str = "s3://subcell-working-bucket"

# Specify whether the metrics should be recalculated. Set this to true if you
# make changes to any metric calculation functions.
recalculate: bool = False

Select metrics to analyze¶

Available metrics are defined in the CompressionMetric enum.

metrics = [
    CompressionMetric.NON_COPLANARITY,
    CompressionMetric.PEAK_ASYMMETRY,
    CompressionMetric.AVERAGE_PERP_DISTANCE,
    CompressionMetric.CALC_BENDING_ENERGY,
    CompressionMetric.CONTOUR_LENGTH,
    CompressionMetric.COMPRESSION_RATIO,
    CompressionMetric.TOTAL_FIBER_TWIST,
    CompressionMetric.TWIST_ANGLE,
]

Calculate metrics for Cytosim data¶

cytosim_metrics_compression = get_compression_metric_data(
    bucket=cytosim_bucket,
    series_name=compression_series_name,
    condition_keys=condition_keys,
    random_seeds=random_seeds,
    metrics=metrics,
    recalculate=recalculate,
)
cytosim_metrics_compression["simulator"] = "cytosim"

cytosim_metrics_no_compression = get_compression_metric_data(
    bucket=cytosim_bucket,
    series_name=no_compression_series_name,
    condition_keys=[""],
    random_seeds=random_seeds,
    metrics=metrics,
    recalculate=recalculate,
)
cytosim_metrics_no_compression["simulator"] = "cytosim"

Calculate metrics for ReaDDy data¶

readdy_metrics_compression = get_compression_metric_data(
    bucket=readdy_bucket,
    series_name=compression_series_name,
    condition_keys=condition_keys,
    random_seeds=random_seeds,
    metrics=metrics,
    recalculate=recalculate,
)
readdy_metrics_compression["simulator"] = "readdy"

readdy_metrics_no_compression = get_compression_metric_data(
    bucket=readdy_bucket,
    series_name=no_compression_series_name,
    condition_keys=[""],
    random_seeds=random_seeds,
    metrics=metrics,
    recalculate=recalculate,
)
readdy_metrics_no_compression["simulator"] = "readdy"

Combine metrics from both simulators¶

combined_metrics = pd.concat([cytosim_metrics_compression, readdy_metrics_compression])
combined_metrics["repeat"] = combined_metrics["seed"] - 1
combined_metrics["velocity"] = combined_metrics["key"].astype("int") / 10

Save combined compression metrics¶

save_compression_metrics(
    combined_metrics,
    save_location,
    "compression_metrics/actin_compression_combined_metrics.csv",
)

Plot metrics vs time¶

plot_metrics_vs_time(
    df=combined_metrics,
    metrics=metrics,
    save_location=save_location,
    save_key_template="compression_metrics/actin_compression_metrics_over_time_subsampled_%s.png",
)

Plot metrics histograms¶

plot_metric_distribution(
    df=combined_metrics,
    metrics=metrics,
    save_location=save_location,
    save_key_template="compression_metrics/actin_compression_metrics_histograms_subsampled_%s.png",
)