def plot_binary_heatmap(csv_file="./data/down/combined_binary.csv", title="Binary Heatmap of Signals"):
    """
    Reads data from a combined binary CSV, processes filenames, and plots a heatmap.
    The x-axis represents time in seconds, and the y-axis represents file numbers in ascending order.
    """
    df = pd.read_csv(csv_file)

    # 1. Extract the time column (keep as float)
    if 'Time' in df.columns:
        time = df['Time'].astype(float)
        df = df.drop(columns=['Time'])
    elif 'time' in df.columns:
        time = df['time'].astype(float)
        df = df.drop(columns=['time'])
    else:
        # If no time column exists, create an index
        time = pd.Series(range(df.shape[0]))

    # 2. Transpose: each row is a file, each column is a time point
    df_t = df.T
    df_t.index.name = "File"

    # Update index, keep only the part before "_"
    df_t.index = df_t.index.to_series().str.split('_').str[0]

    # 3. Set time as column names (keep as float)
    df_t.columns = time

    z = df_t.to_numpy()
    y_labels = df_t.index.tolist()
    x_values = df_t.columns.to_numpy()

    # 4. Create the heatmap
    fig = px.imshow(
        z,
        labels=dict(x="Time (s)", y="Channel", color="Binary"),
        x=x_values,
        y=y_labels,
        color_continuous_scale=["#ffffff", "#000000"],
        aspect="auto",
        title=title
    )

    # 5. Update chart layout
    fig.update_layout(
        xaxis_title="Time (s)",
        yaxis_title="Channel",
        height=600,
        width=1000,
    )

    fig.write_html(f"{title}.html")

plot_binary_heatmap(csv_file="BAO_spikes_sorted_output.csv", title="BAO Spike Heatmap")