ruins.plotting#

Submodules#

Package Contents#

Functions#

plt_map(→ plotly.graph_objs.Figure)

kde

monthlyx(→ matplotlib.pyplot.Figure)

yrplot_hm(sr[, ref, ag, qa, cbar_title, cmx, cmxeq, ...])

Yearly stripes plot.

climate_projection_parcoords(data[, fig, align_range, ...])

Parallel coordinates plot for climate projections.

distribution_plot(→ plotly.graph_objects.Figure)

Concept plot for PDFs of different events.

plot_climate_indices(, fig)

Generate a climate indices plot.

sunburst(→ plotly.graph_objects.Figure)

Create a sunburst plot of all climate models included in the current

plot_extreme_pdf(, x_res, y_res[, label_margin_scale])

Temperature PDF shift plot.

pdsi_plot(→ plotly.graph_objects.Figure)

Heatmap plot for Palmer drought severity index

tree_plot(→ plotly.graph_objects.Figure)

variable_plot(→ plotly.graph_objects.Figure)

Plot one of the climate model predicted variables, grouped by RCP scenario.

windpower_distplot(→ plotly.graph_objects.Figure)

Plot the actions projected to climate models

ternary_provision_plot(→ plotly.graph_objects.Figure)

Make a ternary plot of the three turbines shares on the axes and the provisioned Windpower as contours

management_scatter_plot(→ plotly.graph_objects.Figure)

ruins.plotting.plt_map(dataManager: ruins.core.DataManager, sel='all', cm='none') → plotly.graph_objs.Figure#
ruins.plotting.kde(data, cmdata='none', split_ts=1, cplot=True, eq_period=True)#
ruins.plotting.monthlyx(dy, dyx=1, ylab='T (°C)', clab1='Monthly Mean in Year', clab2='Monthly Max in Year', pls='cividis_r') → matplotlib.pyplot.Figure#
ruins.plotting.yrplot_hm(sr, ref=[1980, 2000], ag='sum', qa=0.95, cbar_title='Temperature anomaly (K)', cmx='coolwarm', cmxeq=True, li=False, lang='en', backend='plotly')#

Yearly stripes plot. Creates a monthly resloved stripes plot for each year in the data. The data is plotted as a heatmap showing the Temperature anomaly to the passed reference period based on the adjustable quartile value.

Parameters:

  • sr (pandas.DataFrame) – Input data originating from one ground station

  • ref (Tuple[int, int]) – Reference period to calculate the anomaly.

  • ag (str) – Reduction function to use. Can be used to calculate the temperature anomaly in ‘sum’, ‘min’, ‘max’ or ‘mean’

  • qa (float) – Quartile to use. Has to be between 0 and 1.

  • cbar_title (str) – Optional title for the colorbar

  • cmx (str) – Colorbar scale. Can be any string accepted by the plotting backend.

  • cmxeq (bool) – If True (default), the colorbar range is calculated for the full dataset.

  • lang (str) – Can be either 'en' or ``’de’`. The language used for axis labels.

  • backend (str) – Can be either matplotlib or plotly. The plotting backend to use.

ruins.plotting.climate_projection_parcoords(data: pandas.DataFrame, fig: plotly.graph_objects.Figure = None, align_range: bool = True, colorscale='electric', row: int = 1, col: int = 1, lang='en')#

Parallel coordinates plot for climate projections. This plot uses each month in the year as a coordinate dimension. By sorting the dimensions into the correct order, the cycle of annual temperature aggregates is preserved, while the full dataset can easily be compared.

Parameters:

  • data (pandas.DataFrame) – Dataframe with the data to plot. The DataFrame has to be indexed by a Datetime Index and does accept more than one column (ground station, RCP scenario or grid cell).

  • fig (plotly.graph_objects.Figure) – If not None, the given figure will be used to plot the data. Note, that subfigures need to use the 'domain' type.

  • align_range (bool) – If True (default) each dimension (aka month) will use the same value range, to focus the differences between the months. If False, the range will be adapted to span from min to max for each dimension, putting more focus on the differences between the years (decades).

  • colorscale (str) – Name identifier of teh colorscale. See plotly to learn about available options.

  • row (int) – If figure is not None, row and column can be used to plot into the correct subplot.

  • col (int) – If figure is not None, row and column can be used to plot into the correct subplot.

  • lang (str) – Can either be 'en' or 'de'. As of now, the language does not have any effect.

ruins.plotting.distribution_plot(*events: List[dict], fig: plotly.graph_objects.Figure = None, **kwargs) → plotly.graph_objects.Figure#

Concept plot for PDFs of different events. This plot illustrates the interdependece of Kngihtian uncertainty and risk for a set of events. At least one event has to be passed.

Parameters:

events (List) –

Each event is represented by a dict, that has to contain the following keys: outcomes: List[Tuple[float, float]]

The mu and std of each outcome for this event

Additionally, the following keys are optional: dist : str

The distribution of the event. Default is ‘norm’.

coloscalestr

The colorscale used to color the events. Default is a cycle through [‘Greens’, ‘Reds’, ‘Blues’]

namestr

Name for this event

Returns:

fig – Result plot

Return type:

go.Figure

ruins.plotting.plot_climate_indices(data: pandas.DataFrame, rcps=('rcp26', 'rcp45', 'rcp85'), fig: plotly.graph_objects.Figure = None) → plotly.graph_objects.Figure#

Generate a climate indices plot. Refer to ruins.processing.calculate_climate_indices() to learn about the structure needed for the DataFrame.

ruins.plotting.sunburst(df: pandas.DataFrame, maxdepth: int = 4, fig: plotly.graph_objects.Figure = None, width: int = 700, height: int = 700) → plotly.graph_objects.Figure#

Create a sunburst plot of all climate models included in the current dataset used in RUINS. It will group them by GCM -> RCM -> RCP. The size of the sun can be restricted by the maxdepth argument.

ruins.plotting.plot_extreme_pdf(mus: float, stds: float, fig: plotly.graph_objects.Figure = None, border_colors=BORDERS_COLORS, border_names=BORDER_NAMES, x_range=(0, 25), x_res: int = 100, y_res: int = 25, label_margin_scale=1.1) → plotly.graph_objects.Figure#

Temperature PDF shift plot. This function returns a plotly figure containing a heavily styled plotly PDF plot. It is controlled by passing the PDF moment(s) to the plot. If called with only one location and scale, the styled PDF is returned. If two locations and scales are passed, but both scales are the same, the PDF is only shifted by location and the original PDF is sketched. In case the scales differ, the new PDF will be correctly annotated.

ruins.plotting.pdsi_plot(data: pandas.DataFrame, colorscale: str = 'RdBu', fig: plotly.graph_objects.Figure = None, row: int = 1, col: int = 1, **kwargs) → plotly.graph_objects.Figure#

Heatmap plot for Palmer drought severity index

ruins.plotting.tree_plot(data: pandas.DataFrame, heights=[1, 0.3, 0], fig: plotly.graph_objects.Figure = None, row: int = 1, col: int = 1) → plotly.graph_objects.Figure#
ruins.plotting.variable_plot(climate: xarray.Dataset, variable: str, rcp: str = 'rcp85', color='green', bgcolor='lightgreen', fig: plotly.graph_objects.Figure = None, col: int = 1, row: int = 1) → plotly.graph_objects.Figure#

Plot one of the climate model predicted variables, grouped by RCP scenario. If rcp is None, the grouping will not be applied.

ruins.plotting.windpower_distplot(actions: List[pandas.DataFrame], names: List[str] = None, fig: plotly.graph_objects.Figure = None, fill: str = None, showlegend: Union[bool, List[bool]] = False, colors: Union[str, List[str]] = None, col: int = 1, row: int = 1) → plotly.graph_objects.Figure#

Plot the actions projected to climate models

ruins.plotting.ternary_provision_plot(dataManager: ruins.core.DataManager, filter_: dict = {}, turbines: List[str] = ['e53', 'e115', 'e126'], colorscale: str = 'Cividis', showscale: bool = True) → plotly.graph_objects.Figure#

Make a ternary plot of the three turbines shares on the axes and the provisioned Windpower as contours

ruins.plotting.management_scatter_plot(data: pandas.DataFrame = None, scenarios: List[Tuple[float, float, float]] = None, x: str = 'ce', y: str = 'up', fig: plotly.graph_objects.Figure = None) → plotly.graph_objects.Figure#