def visualize_distribution(
    topic_model,
    probabilities: np.ndarray,
    min_probability: float = 0.015,
    custom_labels: Union[bool, str] = False,
    title: str = "<b>Topic Probability Distribution</b>",
    width: int = 800,
    height: int = 600,
) -> go.Figure:
    """Visualize the distribution of topic probabilities.

    Arguments:
        topic_model: A fitted BERTopic instance.
        probabilities: An array of probability scores
        min_probability: The minimum probability score to visualize.
                         All others are ignored.
        custom_labels: If bool, whether to use custom topic labels that were defined using
                       `topic_model.set_topic_labels`.
                       If `str`, it uses labels from other aspects, e.g., "Aspect1".
        title: Title of the plot.
        width: The width of the figure.
        height: The height of the figure.

    Examples:
    Make sure to fit the model before and only input the
    probabilities of a single document:

    ```python
    topic_model.visualize_distribution(probabilities[0])
    ```

    Or if you want to save the resulting figure:

    ```python
    fig = topic_model.visualize_distribution(probabilities[0])
    fig.write_html("path/to/file.html")
    ```
    <iframe src="../../getting_started/visualization/probabilities.html"
    style="width:1000px; height: 500px; border: 0px;""></iframe>
    """
    if len(probabilities.shape) != 1:
        raise ValueError(
            "This visualization cannot be used if you have set `calculate_probabilities` to False "
            "as it uses the topic probabilities of all topics. "
        )
    if len(probabilities[probabilities > min_probability]) == 0:
        raise ValueError(
            "There are no values where `min_probability` is higher than the "
            "probabilities that were supplied. Lower `min_probability` to prevent this error."
        )

    # Get values and indices equal or exceed the minimum probability
    labels_idx = np.argwhere(probabilities >= min_probability).flatten()
    vals = probabilities[labels_idx].tolist()

    # Create labels
    if isinstance(custom_labels, str):
        labels = [[[str(topic), None]] + topic_model.topic_aspects_[custom_labels][topic] for topic in labels_idx]
        labels = ["_".join([label[0] for label in l[:4]]) for l in labels]  # noqa: E741
        labels = [label if len(label) < 30 else label[:27] + "..." for label in labels]
    elif topic_model.custom_labels_ is not None and custom_labels:
        labels = [topic_model.custom_labels_[idx + topic_model._outliers] for idx in labels_idx]
    else:
        labels = []
        for idx in labels_idx:
            words = topic_model.get_topic(idx)
            if words:
                label = [word[0] for word in words[:5]]
                label = f"<b>Topic {idx}</b>: {'_'.join(label)}"
                label = label[:40] + "..." if len(label) > 40 else label
                labels.append(label)
            else:
                vals.remove(probabilities[idx])

    # Create Figure
    fig = go.Figure(
        go.Bar(
            x=vals,
            y=labels,
            marker=dict(
                color="#C8D2D7",
                line=dict(color="#6E8484", width=1),
            ),
            orientation="h",
        )
    )

    fig.update_layout(
        xaxis_title="Probability",
        title={
            "text": f"{title}",
            "y": 0.95,
            "x": 0.5,
            "xanchor": "center",
            "yanchor": "top",
            "font": dict(size=22, color="Black"),
        },
        template="simple_white",
        width=width,
        height=height,
        hoverlabel=dict(bgcolor="white", font_size=16, font_family="Rockwell"),
    )

    return fig
Visualize the distribution of topic probabilities.

    Arguments:
        topic_model: A fitted BERTopic instance.
        probabilities: An array of probability scores
        min_probability: The minimum probability score to visualize.
                         All others are ignored.
        custom_labels: If bool, whether to use custom topic labels that were defined using
                       `topic_model.set_topic_labels`.
                       If `str`, it uses labels from other aspects, e.g., "Aspect1".
        title: Title of the plot.
        width: The width of the figure.
        height: The height of the figure.

    Examples:
    Make sure to fit the model before and only input the
    probabilities of a single document:

    ```python
    topic_model.visualize_distribution(probabilities[0])
    ```

    Or if you want to save the resulting figure:

    ```python
    fig = topic_model.visualize_distribution(probabilities[0])
    fig.write_html("path/to/file.html")
    ```
    <iframe src="../../getting_started/visualization/probabilities.html"
    style="width:1000px; height: 500px; border: 0px;""></iframe>
    
bertopic/plotting/_distribution.py