postProcess/plot_vcm_vs_time-2.py

plot_vcm_vs_time-2.py

Robust log parser for center-of-mass velocity time series.

Unlike the minimal parser, this script skips descriptive header lines before plotting vcm against time.

Dependencies

• numpy: array conversion for parsed columns.
• matplotlib: time-series visualization.

Example

python3 postProcess/plot_vcm_vs_time-2.py

import matplotlib.pyplot as plt
import numpy as np


def read_log(path: str = "log") -> tuple[np.ndarray, np.ndarray]:

Parse a simulation log file and return (t, vcm) arrays.

Args

• path: Log file path (default: log).

Returns

• tuple[np.ndarray, np.ndarray]: Time and center-of-mass velocity arrays.

Raises

• OSError: The log file cannot be opened.
• ValueError: A parsed row contains non-numeric values.

    t_values = []
    vcm_values = []

    with open(path, "r", encoding="utf-8") as handle:
        for line in handle:
            line = line.strip()
            if not line:
                continue
            if line.startswith("Level") or line.startswith("i"):
                continue

            parts = line.split()
            if len(parts) < 5:
                continue

            t_values.append(float(parts[2]))
            vcm_values.append(float(parts[4]))

    return np.array(t_values), np.array(vcm_values)


def main() -> None:

Load data with header filtering and plot droplet velocity vs time.

Raises

• OSError: The input log file cannot be opened.
• ValueError: Parsed data is malformed.

    t, vcm = read_log("log")

    plt.figure()
    plt.plot(t, vcm, marker="o")
    plt.xlabel("Time")
    plt.ylabel(r"$v_{\mathrm{cm}}$")
    plt.title("Center-of-mass velocity vs time")
    plt.grid(True)
    plt.tight_layout()
    plt.show()


if __name__ == "__main__":
    main()