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_value: str, alpha: float) → str

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

The RGB colour string is specified with an "rgb" prefix, e.g. "rgb(0, 0, 0)", and any spaces are stripped.

Parameters:

rgb_valuestr

RGB color string with a "rgb" prefix, e.g. "rgb(0, 0, 0)".

alphafloat

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

Returns:

str

RGBA colour string, e.g. "rgba(0, 0, 0, 1)".

Examples

>>> from isaricanalytics.visualisation import rgb_to_rgba
>>> rgb_to_rgba("rgb(0, 0, 0)", 1)
'rgba(0,0,0,1)'