isaricanalytics.visualisation

isaricanalytics.visualisation.fig_bar_chart(data: DataFrame, title: str = 'Bar Chart', xlabel: str = '', ylabel: str = '', index_column: str = 'index', barmode: str = 'stack', xaxis_tickformat: str = '%m-%Y', base_color_map: dict[str, str] | None = None, height: int = 340) → Figure

plotly.graph_objs.Figure : Returns a bar chart.

Parameters:

datapandas.DataFrame

Incoming data.

titlestr, default=”Bar Chart”

Figure title.

xlabelstr, default=””

Figure x-axis label.

ylabelstr, default=””

Figure y-axis label.

index_columnstr, default=”index”

Index column.

barmodestr, default=”stack”

How bars with the same location coordinate are displayed: possible values are “stack”, “relative”, “group”, “overlay”`. For reference see the Plotly documentation.

xaxis_tickformatstr, default=”%m-%Y”

x-axis tick format.

base_color_mapdict, default=None

Map of bar values and colours.

heightint, default=340

Figure height.

Returns:

plotly.graph_objs.Figure

The Plotly figure.

isaricanalytics.visualisation.fig_bar_line_chart(data: DataFrame, title: str = 'Combined bar line chart', xlabel: str = '', ylabel_left: str = '', ylabel_right: str = '', bar_column: str = '', line_column: str = '', index_column: str = 'index', lower_column: str | None = None, upper_column: str | None = None, bar_color: str | None = None, line_color: str | None = None, height: int = 500) → Figure

plotly.graph_objs.Figure : Returns a bar-line chart.

Parameters:

datapandas.DataFrame

Incoming data.

titlestr, default=”Combined bar line chart”

Figure title.

xlabelstr, default=””

Figure x-axis label.

ylabelstr, default=””

Figure y-axis label.

bar_columnstr, default=””

Bar column.

line_columnstr, default=””

Line column.

index_columnstr, default=”index”

Index column.

lower_columnstr, default=None

Lower column.

upper_columnstr, default=None

Upper column.

bar_colorstr, default=None

Bar colour.

line_colorstr, default=None

Line colour.

heightint, default=500

Figure height.

Returns:

plotly.graph_objs.Figure

The Plotly figure.

isaricanalytics.visualisation.fig_count_chart(data: DataFrame, title: str = 'Count Chart', xlabel: str = 'Count', ylabel: str = 'Variable', base_color_map: dict[str, str] | None = None, height: int = 350) → Figure

plotly.graph_objs.Figure : Returns a count chart.

Parameters:

datapandas.DataFrame

Incoming data.

titlestr, default=”Count Chart”

Figure title.

xlabelstr, default=”Count”

Figure x-axis label.

ylabelstr, default=”Variable”

Figure y-axis label.

base_color_mapdict, default=None

Map of bar values and colours.

heightint, default=350

Figure height.

Returns:

plotly.graph_objs.Figure

The Plotly figure.

isaricanalytics.visualisation.fig_dual_stack_pyramid(data: DataFrame, title: str = 'Dual-Sided Stacked Pyramid Chart', xlabel: str = 'Count', ylabel: str = 'Category', base_color_map: dict[str, str] | None = None, height: int = 430) → Figure

plotly.graph_objs.Figure : Returns a dual-sided stacked pyramid chart.

Parameters:

datapandas.DataFrame

Incoming data.

titlestr, default=”Dual-Sided Stacked Pyramid Chart”

Figure title.

xlabelstr, default=”Count”

Figure x-axis label.

ylabelstr, default=”Category”

Figure y-axis label.

base_color_mapdict, default=None

Map of bar values and colours.

heightint, default=430

Figure height.

Returns:

plotly.graph_objs.Figure

The Plotly figure.

isaricanalytics.visualisation.fig_flowchart(data: DataFrame, height: int = 430) → Figure

plotly.graph_objs.Figure : Returns a flowchart.

Parameters:

datapandas.DataFrame

Incoming data.

heightint, default=430

Figure height.

Returns:

plotly.graph_objs.Figure

The Plotly figure.

isaricanalytics.visualisation.fig_forest_plot(data: DataFrame, title: str = 'Forest Plot', xlabel: str = 'Odds Ratio (95% CI)', ylabel: str = '', reorder: bool = True, labels: Iterable[str] = ['Variable', 'OddsRatio', 'LowerCI', 'UpperCI'], marker: dict[str, Any] | None = None, noeffect_line: bool = True, height: int = 600) → Figure

plotly.graph_objs.Figure : Returns a forest plot.

Parameters:

datapandas.DataFrame

Incoming data.

titlestr, default=”Forest Plot”

Figure title.

xlabelstr, default=”Odds Ratio (95% CI)”

Figure x-axis label.

ylabelstr, default=””

Figure y-axis label.

reorderbool, default=True

Sort values.

labelstyping.Iterable, default=[“Variable”, “OddsRatio”, “LowerCI”, “UpperCI”]

Column of labels.

markerdict, default=None

Marker properties dict.

no_effect_linebool, default=True

Add no effect line.

heightint, default=600

Figure height.

Returns:

plotly.graph_objs.Figure

The Plotly figure.

isaricanalytics.visualisation.fig_frequency_chart(data: DataFrame, title: str = 'Frequency Chart', xlabel: str = 'Proportion', ylabel: str = 'Variable', base_color_map: dict[str, str] | None = None, height: int = 350) → Figure

plotly.graph_objs.Figure : Returns a frequency chart.

Parameters:

datapandas.DataFrame

Incoming data.

titlestr, default=”Frequency Chart”

Figure title.

xlabelstr, default=”Proportion”

Figure x-axis label.

ylabelstr, default=”Variable”

Figure y-axis label.

base_color_mapdict

Map of bar values and colours.

heightint, default=350

Figure height.

Returns:

plotly.graph_objs.Figure

The Plotly figure.

isaricanalytics.visualisation.fig_heatmaps(data: DataFrame, title: str = '', subplot_titles: list[str] | None = None, ylabel: str = '', xlabel: str = '', colorbar_label: str = '', index_column: str = 'index', zmin: float | None = None, zmax: float | None = None, include_annotations: bool = False, base_color_map: dict[str, str] | None = None, height: int = 750) → Figure

plotly.graph_objs.Figure : Returns a heatmaps chart.

Parameters:

datapandas.DataFrame

Incoming data.

titlestr, default=””

Figure title.

subplot_titleslist, default=None

Subplot titles.

xlabelstr, default=””

Figure x-axis label.

ylabelstr, default=””

Figure y-axis label.

colorbar_labelstr, default=””

Colour bar label.

index_columnstr, default=”index”

Index column.

zminfloat, default=None

zmin.

zmaxfloat, default=None

zmax.

include_annotationsbool, default=False

Include annotations.

base_color_mapdict, default=None

Colour map.

heightint, default=750

Figure height.

Returns:

plotly.graph_objs.Figure

The Plotly figure.

isaricanalytics.visualisation.fig_kaplan_meier(data: tuple[DataFrame], title: str = 'Kaplan-Meier Plot', xlabel: str = 'Time (days)', ylabel: str = 'Survival Probability', groups: Iterable[str] | None = None, index_column: str = 'index', base_color_map: dict[str, str] | None = None, xlim: Iterable[float | int] | None = None, p_value: float | None = None, height: int = 480) → Figure

plotly.graph_objs.Figure : Returns a Kaplan-Meier plot.

Parameters:

datatuple

Incoming data as two Pandas dataframes, the first for the plot, and the second for the risk table.

titlestr, default=”Kaplan-Meier Plot”

Figure title.

xlabelstr, default=”Time (days)”

Figure x-axis label.

ylabelstr, default=”Survival Probability”

Figure y-axis label.

groupstyping.Iterable, default=None

Groups.

index_columnstr, default=”index”

Index column.

base_color_mapdict, default=None

Colour map.

xlimtyping.Iterable, default=None

xlim.

p_valuefloat, default=None

p-value.

heightint, default=480

Figure height.

Returns:

plotly.graph_objs.Figure

The Plotly figure.

isaricanalytics.visualisation.fig_line_chart(data: DataFrame, title: str = 'Line chart', xlabel: str = '', ylabel: str = '', height: int = 480, line_column: str = '', index_column: str = 'index', lower_column: str | None = None, upper_column: str | None = None, line_color: str | None = None) → Figure

plotly.graph_objs.Figure : Returns a line chart.

Parameters:

datapandas.DataFrame

Incoming data.

titlestr, default=”Line chart”

Figure title.

xlabelstr, default=””

Figure x-axis label.

ylabelstr, default=””

Figure y-axis label.

heightint, default=480

Figure height.

line_columnstr, default=””

Line column.

index_columnstr, default=”index”

Index column.

lower_columnstr, default=None

Lower column.

upper_columnstr, default=None

Upper column.

line_colorstr, default=None

Line colour.

Returns:

plotly.graph_objs.Figure

The Plotly figure.

isaricanalytics.visualisation.fig_pie(data: DataFrame, title: str = 'Pie chart', xlabel: str = '', ylabel: str = '', base_color_map: dict[str, str] | None = None, names: str | int | Series | Iterable = '', values: str | int | Series | Iterable = '', height: int = 450)

plotly.graph_objs.Figure : Returns a pie chart figure.

Parameters:

datapandas.DataFrame

Incoming data.

titlestr, default=”Placeholder scatter plot”

Figure title.

xlabelstr, default=””

Figure x-axis label.

ylabelstr, default=””

Figure y-axis label.

base_color_mapdict

Map of sector values and colours.

namesstr, int, pd.Series, typing.Iterable, default=””

Sector name(s)/label(s).

valuesstr, int, pd.Series, typing.Iterable, default=””

Sector values.

heightint, default=450

Figure height.

Returns:

plotly.graph_objs.Figure

The Plotly figure.

isaricanalytics.visualisation.fig_placeholder(data: DataFrame, title: str = 'Placeholder scatter plot', xlabel: str = '', ylabel: str = '', height: int = 450) → Figure

plotly.graph_objs.Figure : Returns a placeholder scatter plot.

Parameters:

datapandas.DataFrame

Incoming data.

titlestr, default=”Placeholder scatter plot”

Figure title.

xlabelstr, default=””

Figure x-axis label.

ylabelstr, default=””

Figure y-axis label.

heightint, default=450

Figure height.

Returns:

plotly.graph_objs.Figure

The Plotly figure.

isaricanalytics.visualisation.fig_sankey(data: DataFrame, height: int = 500) → Figure

plotly.graph_objs.Figure : Returns a Sankey plot.

Parameters:

datapandas.DataFrame

Incoming data.

heightint, default=500

Figure height.

Returns:

plotly.graph_objs.Figure

The Plotly figure.

isaricanalytics.visualisation.fig_sunburst(data: DataFrame, title: str = 'Sunburst Chart', path: list[str | int] | Series | Iterable | None = ['level0', 'level1'], values: str | int | Series | Iterable = 'values', base_color_map: dict[str, str] | None = None, height: int = 430) → Figure

plotly.graph_objs.Figure : Returns a sunburst plot.

Parameters:

datapandas.DataFrame

Incoming data.

titlestr, default=”Sunburst Chart”

Figure title.

pathstr, int, pd.Series, typing.Iterable, None, default=[“level0”, “level1”]

Column names defining a hierarhy of sectors, from root to leaves.

valuesstr, int, pd.Series, typing.Iterable, default=”values”

A column name in the data defining sector values, or a Pandas Series or an iterable containing sector values.

base_color_mapdict, default=None

Map of sector values/marks and colours.

heightint, default=430

Figure height.

Returns:

plotly.graph_objs.Figure

The Plotly figure.

isaricanalytics.visualisation.fig_table(data: DataFrame, table_key: str = '', columnwidth: Iterable[float | int] | None = None, height: int = 500) → Figure

plotly.graph_objs.Figure : Returns a table figure.

Parameters:

datapandas.DataFrame

Incoming data.

table_keystr, default=””

Table key.

columnwidthtyping.Iterable, default=None

An iterable of column widths.

heightint, default=500

Figure height.

Returns:

plotly.graph_objs.Figure

The Plotly figure.

isaricanalytics.visualisation.fig_text(data: DataFrame, height: int = 430) → Figure

plotly.graph_objs.Figure : Returns a figure with an annotation.

Parameters:

datapandas.DataFrame

Incoming data.

heightint, default=430

Figure height.

Returns:

plotly.graph_objs.Figure

The Plotly figure.

isaricanalytics.visualisation.fig_timelines(data: DataFrame, title: str = 'Timeline', label_col: str = '', group_col: str = '', start_date: str = 'start_date', end_date: str = 'end_date', size_col: str | None = None, min_width: int = 2, max_width: int = 10, height: int = 500) → Figure

plotly.graph_objs.Figure : Returns a timeline figure.

Parameters:

datapandas.DataFrame

Incoming data.

titlestr, default=”Timeline”

Figure title.

label_colstr, default=””

Label column.

group_colstr, default=””

Group column.

start_datestr, default=”start_date”

Start date column.

end_datestr, default=”end_date”

End date column.

size_colstr, None, default=None

Size column.

min_widthint, default=2

Figure minimum width.

max_widthint, default=10

Figure maximum width.

heightint, default=500

Figure height.

Returns:

plotly.graph_objs.Figure

The Plotly figure.

isaricanalytics.visualisation.fig_upset(data: tuple[DataFrame], title: str = 'Upset Plot', height: int = 480) → Figure

plotly.graph_objs.Figure : Returns an upset plot.

Parameters:

datatuple

Incoming data as two Pandas dataframes, the first for counts, and the second for intersections.

titlestr, default=”Upset Plot”

Figure title.

heightint, default=480

Figure height.

Returns:

plotly.graph_objs.Figure

The Plotly figure.

isaricanalytics.visualisation.hex_to_rgb(hex_color: str) → tuple[int]

tuple : Converts a hex colour to an RGB colour tuple.

Parameters:

hex_colorstr

Hex colour string.

Returns:

tuple

RGB colour tuple.

isaricanalytics.visualisation.hex_to_rgba(hex_color: str, opacity: float) → str

str : Converts a hex colour to an RGBA (red-green-blue-alpha) colour string.

Parameters:

hex_colorstr

Hex colour string.

opacityfloat

Opacity/transparency, a value between 0.0 (fully transparent) and 1.0 (fully opaque).

Returns:

str

An RGBA colour string (RGB + opacity/transparency).

isaricanalytics.visualisation.rgb_to_rgba(rgb_color: tuple[int], alpha: float) → str

str : Converts an RGB colour tuple and alpha value to an RGBA colour string.

Parameters:

rgb_colortuple

RGB color tuple.

alphafloat

Opacity/transparency value between 0.0 (fully transparent) and 1.0 (fully opaque).

Returns:

str

RGBA colour string.