Skip to content

plots

plot_empty_heat_map(low=1, high=5)

Creates an empty heat map with customizable axis range and color scale.

Parameters:

low : int, optional The lower bound of the axis range, by default 1. high : int, optional The upper bound of the axis range, by default 5.

Returns:

go.Figure A Plotly figure object representing the empty heat map.

Source code in darpi/qualitative/plots.py 
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
def plot_empty_heat_map(low: int = 1, high: int = 5) -> go.Figure:
    """
    Creates an empty heat map with customizable axis range and color scale.

    Parameters:
    -----------
    low : int, optional
        The lower bound of the axis range, by default 1.
    high : int, optional
        The upper bound of the axis range, by default 5.

    Returns:
    --------
    go.Figure
        A Plotly figure object representing the empty heat map.
    """
    axis_range = [low - 0.5, high + 0.5]
    array = np.linspace(axis_range[0], axis_range[1])

    custom_color_scale = [
        [0.0, "green"],
        [0.1, "yellow"],
        [1.0, "red"],
    ]

    res = np.outer(array, array)

    fig = go.Figure(
        data=go.Contour(
            z=res,
            x=array,
            y=array,
            contours=dict(coloring="heatmap"),
            colorscale=custom_color_scale,
            line_width=0,
            showscale=False,
        )
    ).update(
        layout=dict(
            height=600,
            width=600,
            xaxis=dict(
                title=dict(text="Probability of Occurrence", font=dict(size=18, color='black')),
                range=axis_range,
                mirror=True,
                constrain="domain",
                showgrid=False,
                tickfont=dict(size=16, color='black'),
            ),
            yaxis=dict(
                title=dict(text="Impact", font=dict(size=18, color='black')),
                range=axis_range,
                mirror=True,
                constrain="domain",
                scaleanchor="x",
                scaleratio=1,
                showgrid=False,
                tickfont=dict(size=16, color='black'),
            ),
        )
    )

    grid_lines = np.arange(axis_range[0], axis_range[1])[1:]
    for i in grid_lines:
        fig.add_vline(x=i, line=dict(color="black", width=2))
        fig.add_hline(y=i, line=dict(color="black", width=2))

    return fig

add_scatter_to_heat_map(risk_score_counts, title)

Adds a scatter plot to the heat map, representing risks with their impact and probability.

Parameters:

risks : List[Dict[str, Any]] A list of dictionaries where each dictionary contains 'impact', 'probability', and 'counts' keys, representing the risk's impact, probability, and occurrence count. title : str The title of the resulting heat map with scatter plot.

Returns:

go.Figure A Plotly figure object representing the heat map with the added scatter plot.

Source code in darpi/qualitative/plots.py 
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
def add_scatter_to_heat_map(
    risk_score_counts: list[dict[str, float]], title: str
) -> go.Figure:
    """
    Adds a scatter plot to the heat map, representing risks with their impact and probability.

    Parameters:
    -----------
    risks : List[Dict[str, Any]]
        A list of dictionaries where each dictionary contains 'impact', 'probability',
        and 'counts' keys, representing the risk's impact, probability, and occurrence count.
    title : str
        The title of the resulting heat map with scatter plot.

    Returns:
    --------
    go.Figure
        A Plotly figure object representing the heat map with the added scatter plot.
    """
    impacts = np.array([risk["impact"] for risk in risk_score_counts])
    probabilities = np.array([risk["probability"] for risk in risk_score_counts])
    counts = np.array([risk["counts"] for risk in risk_score_counts])

    gmean_prob = (probabilities * counts).sum() / counts.sum()
    gmean_impact = (impacts * counts).sum() / counts.sum()
    max_count = max(counts)

    marker_size = 12
    sizeref = (max_count / (marker_size**2)) / 12
    mean_label_offset = 0.2

    scatter = go.Scatter(
        x=probabilities,
        y=impacts,
        text=counts,
        textfont=dict(size=14, color="black"),
        mode="markers+text",
        hoverinfo="none",
        showlegend=False,
        marker=dict(
            size=counts,
            sizemode="area",
            sizeref=sizeref,
            color="#63967D",
            opacity=1,
        ),
    )

    fig = plot_empty_heat_map()
    fig.add_trace(scatter).add_trace(
        go.Scatter(
            x=[gmean_prob],
            y=[gmean_impact],
            marker=dict(
                symbol="cross",
                size=16,
                color="red",
            ),
            showlegend=False,
        )
    ).add_annotation(
        text=f"<b>P:{gmean_prob:.2f}, I:{gmean_impact:.2f}</b>",
        font=dict(size=14, color="black"),
        x=gmean_prob,
        y=gmean_impact - mean_label_offset,
        showarrow=False,
    ).update(
        layout=dict(title=dict(text=title, font=dict(size=18)))
    )

    return fig