uadapy.plotting.plots_timeseries module

uadapy.plotting.plots_timeseries.plot_corr_length(timeseries, fig=None, axs=None, show_plot=False)

Plot correlation length for single uncertain timeseries data.

Parameters:

  • timeseries (Timeseries object) – An instance of the TimeSeries class, which represents a univariate time series.

  • fig (matplotlib.figure.Figure or None, optional) – Figure object to use for plotting. If None, a new figure will be created.

  • axs (matplotlib.axes.Axes or None, optional) – Axes object to use for plotting. If None, new axes will be created.

  • show_plot (bool, optional) – If True, display the plot. Default is False.

Returns:

  • matplotlib.figure.Figure – The figure object containing the plot.

  • list – List of Axes objects used for plotting.

uadapy.plotting.plots_timeseries.plot_correlated_corr_length(corr_timeseries, fig=None, axs=None, show_plot=False)

Plot correlation length for correlated uncertain timeseries data.

Parameters:

  • corr_timeseries (CorrelatedDistributions object) – An instance of the CorrelatedDistributions class.

  • fig (matplotlib.figure.Figure or None, optional) – Figure object to use for plotting. If None, a new figure will be created.

  • axs (Array of matplotlib.axes.Axes or None, optional) – Axes objects to use for plotting. If None, new axes will be created.

  • show_plot (bool, optional) – If True, display the plot. Default is False.

Returns:

  • matplotlib.figure.Figure – The figure object containing the plot.

  • list – List of Axes objects used for plotting.

uadapy.plotting.plots_timeseries.plot_correlated_timeseries(corr_timeseries: CorrelatedDistributions, n_samples, co_point, seed=55, fig=None, axs=None, colorblind_safe=False, show_plot=False)

Plot correlated uncertain timeseries data.

Parameters:

  • corr_timeseries (CorrelatedDistributions object) – An instance of the CorrelatedDistributions class.

  • n_samples (int) – The number of samples to draw from the given timeseries distribution.

  • co_point (int) – Interactive point for correlation exploration.

  • seed (int) – Seed for the random number generator for reproducibility. It defaults to 55 if not provided.

  • fig (matplotlib.figure.Figure or None, optional) – Figure object to use for plotting. If None, a new figure will be created.

  • axs (Array of matplotlib.axes.Axes or None, optional) – Axes objects to use for plotting. If None, new axes will be created.

  • colorblind_safe (bool, optional) – If True, the plot will use colors suitable for colorblind individuals. Default is False.

  • show_plot (bool, optional) – If True, display the plot. Default is False.

Returns:

  • matplotlib.figure.Figure – The figure object containing the plot.

  • list – List of Axes objects used for plotting.

uadapy.plotting.plots_timeseries.plot_correlation_matrix(corr_timeseries: CorrelatedDistributions, fig=None, axs=None, discretize=True, show_plot=False)

Plot correlation matrix for the timeseries data.

Parameters:

  • corr_timeseries (CorrelatedDistributions object) – An instance of the CorrelatedDistributions class.

  • fig (matplotlib.figure.Figure or None, optional) – Figure object to use for plotting. If None, a new figure will be created.

  • axs (matplotlib.axes.Axes or None, optional) – Axes object to use for plotting. If None, new axes will be created.

  • discretize (bool, optional) – If True, discretize the colormap. Default is True.

  • show_plot (bool, optional) – If True, display the plot. Default is False.

Returns:

  • matplotlib.figure.Figure – The figure object containing the plot.

  • list – List of Axes objects used for plotting.

uadapy.plotting.plots_timeseries.plot_timeseries(timeseries, n_samples, percentiles=False, seed=55, fig=None, axs=None, colorblind_safe=False, show_plot=False, x_label='timesteps', y_label='value')

Plot single uncertain timeseries data.

Parameters:

  • timeseries (Timeseries object) – An instance of the TimeSeries class, which represents a univariate time series.

  • n_samples (int) – The number of samples to draw from the given timeseries distribution.

  • percentiles (bool, optional) – If True, plot the 50th, and 95th percentiles of the distribution.

  • seed (int) – Seed for the random number generator for reproducibility. It defaults to 55 if not provided.

  • fig (matplotlib.figure.Figure or None, optional) – Figure object to use for plotting. If None, a new figure will be created.

  • axs (matplotlib.axes.Axes or None, optional) – Axes object to use for plotting. If None, new axes will be created.

  • colorblind_safe (bool, optional) – If True, the plot will use colors suitable for colorblind individuals. Default is False.

  • show_plot (bool, optional) – If True, display the plot. Default is False.

  • x_label (str, optional) – Label for the x-axis. Default is ‘timesteps’.

  • y_label (str, optional) – Label for the y-axis. Default is ‘value’.

Returns:

  • matplotlib.figure.Figure – The figure object containing the plot.

  • list – List of Axes objects used for plotting.