[BUG] Data in `px.imshow` disappears at default zoom, reappears when zooming in with dense data

Environment
Plotly Version: 6.1.2
Operating System: Windows 10 22H2 19045.5854 Windows Feature Experience Pack 1000.19061.1000.0
Browser: Chrome 137.0.7151.41

When trying to visualize dense binary time-series data (e.g., 10 channels over 4,000+ time points), both px.imshow and px.timeline fail to render the data correctly in the default, zoomed-out view. The areas that should contain data appear blank.

However, if you manually zoom into any region of the plot, the data correctly appears. Resetting the zoom causes the data to disappear again. This suggests the issue is not with the data itself, but with the rendering engine's handling of very thin graphical elements (heatmap cells or gantt bars) at a low zoom level.

Here is my origin data.
BAO_spikes_sorted_output.csv

Here is my html.(Please change the suffix to html)

BAO Spike Heatmap.txt

Here is my code.

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")

首先使用附带的 BAO_spikes_sorted_output.csv、提供的 Python px.imshow 示例和 Plotly 6.1.2 重现报告，并比较默认视图与缩放后的视图。完成的标准是：密集的二进制热图数据无需手动缩放即可保持可见，并且已根据提供的案例验证该行为。