uadapy.plotting package

Submodules

uadapy.plotting.distribution_plot module

class uadapy.plotting.distribution_plot.InteractiveNormal(mean: ndarray, cov: ndarray, ax: Axes, n_std: float = 1.0, extends: float = 10, epsilon: float = 10, x_label: str = '', y_label: str = '')

Bases: object

get_ind_under_point(event)

get the index of the vertex under point if within epsilon tolerance

init_points()
update(plot_int: bool = False)
uadapy.plotting.distribution_plot.confidence_ellipse(mean: ndarray, cov: ndarray, ax: Axes, n_std: float = 3.0, facecolor='blue', **kwargs)

Create a plot of the covariance confidence ellipse of x and y.

Parameters:

  • ax (matplotlib.axes.Axes) – The axes object to draw the ellipse into.

  • n_std (float) – The number of standard deviations to determine the ellipse’s radiuses.

  • **kwargs – Forwarded to ~matplotlib.patches.Ellipse

Return type:

matplotlib.patches.Ellipse

uadapy.plotting.interactive_splom module

uadapy.plotting.plots2D module

uadapy.plotting.plots2D.generate_random_colors(length)
uadapy.plotting.plots2D.generate_spectrum_colors(length)
uadapy.plotting.plots2D.plot_contour(distributions, resolution=128, ranges=None, quantiles: list | None = None, seed=55, **kwargs)

Plot contour plots for samples drawn from given distributions.

Parameters:

  • distributions (list) – List of distributions to plot.

  • resolution (int, optional) – The resolution of the plot. Default is 128.

  • ranges (list or None, optional) – The ranges for the x and y axes. If None, the ranges are calculated based on the distributions.

  • quantiles (list or None, optional) – List of quantiles to use for determining isovalues. If None, the 99.7%, 95%, and 68% quantiles are used.

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

  • **kwargs (additional keyword arguments) –

    Additional optional plotting arguments. - 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.

Raises:

ValueError – If a quantile is not between 0 and 100 (exclusive), or if a quantile results in an index that is out of bounds.

uadapy.plotting.plots2D.plot_contour_bands(distributions, n_samples, resolution=128, ranges=None, quantiles: list | None = None, seed=55, **kwargs)

Plot contour bands for samples drawn from given distributions.

Parameters:

  • distributions (list) – List of distributions to plot.

  • n_samples (int) – Number of samples per distribution.

  • resolution (int, optional) – The resolution of the plot. Default is 128.

  • ranges (list or None, optional) – The ranges for the x and y axes. If None, the ranges are calculated based on the distributions.

  • quantiles (list or None, optional) – List of quantiles to use for determining isovalues. If None, the 99.7%, 95%, and 68% quantiles are used.

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

  • **kwargs (additional keyword arguments) –

    Additional optional plotting arguments. - 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.

Raises:

ValueError – If a quantile is not between 0 and 100 (exclusive), or if a quantile results in an index that is out of bounds.

uadapy.plotting.plots2D.plot_samples(distributions, n_samples, seed=55, **kwargs)

Plot samples from the given distribution. If several distributions should be plotted together, an array can be passed to this function.

Parameters:

  • distributions (list) – List of distributions to plot.

  • n_samples (int) – Number of samples per distribution.

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

  • **kwargs (additional keyword arguments) –

    Additional optional plotting arguments. - xlabel : string, optional

    label for x-axis.

    • ylabelstring, optional

      label for y-axis.

    • show_plotbool, 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.plotsND module

uadapy.plotting.plotsND.plot_contour(distributions, n_samples, resolution=128, ranges=None, quantiles: list | None = None, seed=55, **kwargs)

Visualizes a multidimensional distribution in a matrix of contour plots.

Parameters:

  • distributions (list) – List of distributions to plot.

  • n_samples (int) – Number of samples per distribution.

  • resolution (int, optional) – The resolution for the pdf. Default is 128.

  • ranges (list or None, optional) – Array of ranges for all dimensions. If None, the ranges are calculated based on the distributions.

  • quantiles (list or None, optional) – List of quantiles to use for determining isovalues. If None, the 99.7%, 95%, and 68% quantiles are used.

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

  • **kwargs (additional keyword arguments) –

    Additional optional plotting arguments. - 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.

Raises:

  • ValueError – If a quantile is not between 0 and 100 (exclusive), or if a quantile results in an index that is out of bounds.

  • Exception – If the dimension of the distribution is less than 2.

uadapy.plotting.plotsND.plot_contour_samples(distributions, n_samples, resolution=128, ranges=None, quantiles: list | None = None, seed=55, **kwargs)

Visualizes a multidimensional distribution in a matrix visualization where the upper diagonal contains contour plots and the lower diagonal contains scatterplots.

Parameters:

  • distributions (list) – List of distributions to plot.

  • n_samples (int) – Number of samples for the scatterplot.

  • resolution (int, optional) – The resolution for the pdf. Default is 128.

  • ranges (list or None, optional) – Array of ranges for all dimensions. If None, the ranges are calculated based on the distributions.

  • quantiles (list or None, optional) – List of quantiles to use for determining isovalues. If None, the 99.7%, 95%, and 68% quantiles are used.

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

  • **kwargs (additional keyword arguments) –

    Additional optional plotting arguments. - 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.

Raises:

  • ValueError – If a quantile is not between 0 and 100 (exclusive), or if a quantile results in an index that is out of bounds.

  • Exception – If the dimension of the distribution is less than 2.

uadapy.plotting.plotsND.plot_samples(distributions, n_samples, seed=55, **kwargs)

Plot samples from the multivariate distribution as a SLOM.

Parameters:

  • distributions (list) – List of distributions to plot.

  • n_samples (int) – Number of samples per distribution.

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

  • **kwargs (additional keyword arguments) –

    Additional optional plotting arguments. - 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.utils module

uadapy.plotting.utils.generate_random_colors(n)
uadapy.plotting.utils.generate_spectrum_colors(n)

Module contents