psychopy_msit/MSIT_SummaryStatisticsPlot.py at master · tne-lab/psychopy_msit · GitHub

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import os
import pandas as pd
import matplotlib.pyplot as plt


INPUT_CSV = "msit_stats.csv"
OUTPUT_PNG = "msit_stats_plots.png"


def main():
    if not os.path.exists(INPUT_CSV):
        raise FileNotFoundError(
            f"Could not find {INPUT_CSV}. Run parse_msit.py first."
        )

    df = pd.read_csv(INPUT_CSV)

    if "Unnamed: 0" in df.columns:
        df = df.drop(columns=["Unnamed: 0"])

    required_columns = [
        "id",
        "congruent_correct",
        "congruent_rt_mean",
        "congruent_rt_var",
        "incongruent_correct",
        "incongruent_rt_mean",
        "incongruent_rt_var",
    ]

    missing = [col for col in required_columns if col not in df.columns]
    if missing:
        raise ValueError(f"Missing required columns in {INPUT_CSV}: {missing}")

    df = df.sort_values("id").reset_index(drop=True)
    x = range(len(df))
    labels = df["id"]

    fig, axes = plt.subplots(2, 2, figsize=(14, 10))
    fig.suptitle("MSIT Summary Statistics", fontsize=16)

    # Accuracy
    axes[0, 0].bar(
        [i - 0.2 for i in x],
        df["congruent_correct"],
        width=0.4,
        label="Congruent",
        color="steelblue",
    )
    axes[0, 0].bar(
        [i + 0.2 for i in x],
        df["incongruent_correct"],
        width=0.4,
        label="Incongruent",
        color="indianred",
    )
    axes[0, 0].set_title("Accuracy")
    axes[0, 0].set_ylabel("Proportion Correct")
    axes[0, 0].set_xticks(list(x))
    axes[0, 0].set_xticklabels(labels, rotation=45, ha="right")
    axes[0, 0].set_ylim(0, 1.05)
    axes[0, 0].legend()
    axes[0, 0].grid(axis="y", alpha=0.3)

    # Mean RT
    axes[0, 1].bar(
        [i - 0.2 for i in x],
        df["congruent_rt_mean"],
        width=0.4,
        label="Congruent",
        color="steelblue",
    )
    axes[0, 1].bar(
        [i + 0.2 for i in x],
        df["incongruent_rt_mean"],
        width=0.4,
        label="Incongruent",
        color="indianred",
    )
    axes[0, 1].set_title("Mean Reaction Time")
    axes[0, 1].set_ylabel("Seconds")
    axes[0, 1].set_xticks(list(x))
    axes[0, 1].set_xticklabels(labels, rotation=45, ha="right")
    axes[0, 1].legend()
    axes[0, 1].grid(axis="y", alpha=0.3)

    # RT Variance
    axes[1, 0].bar(
        [i - 0.2 for i in x],
        df["congruent_rt_var"],
        width=0.4,
        label="Congruent",
        color="steelblue",
    )
    axes[1, 0].bar(
        [i + 0.2 for i in x],
        df["incongruent_rt_var"],
        width=0.4,
        label="Incongruent",
        color="indianred",
    )
    axes[1, 0].set_title("Reaction Time Variance")
    axes[1, 0].set_ylabel("Variance (s²)")
    axes[1, 0].set_xticks(list(x))
    axes[1, 0].set_xticklabels(labels, rotation=45, ha="right")
    axes[1, 0].legend()
    axes[1, 0].grid(axis="y", alpha=0.3)

    # Interference effect: incongruent - congruent mean RT
    rt_diff = df["incongruent_rt_mean"] - df["congruent_rt_mean"]
    axes[1, 1].bar(x, rt_diff, color="darkslategray")
    axes[1, 1].set_title("Interference Effect")
    axes[1, 1].set_ylabel("Incongruent RT - Congruent RT (s)")
    axes[1, 1].set_xticks(list(x))
    axes[1, 1].set_xticklabels(labels, rotation=45, ha="right")
    axes[1, 1].axhline(0, color="black", linewidth=1)
    axes[1, 1].grid(axis="y", alpha=0.3)

    plt.tight_layout(rect=[0, 0, 1, 0.96])
    plt.savefig(OUTPUT_PNG, dpi=300, bbox_inches="tight")
    plt.show()

    print(f"Saved plot to {OUTPUT_PNG}")


if __name__ == "__main__":
    main()