wildboar.utils.plot#

Module Contents#

Classes#

MidpointNormalize

Normalise the colorbar.

Functions#

plot_frequency_domain(x[, y, ax, n_samples, jitter, ...])

Plot the samples in the freqency domain

plot_time_domain(x[, y, n_samples, ax, alpha, ...])

Plot the samples in the time domain

Attributes#

matplotlib_missing

class wildboar.utils.plot.MidpointNormalize(vmin=None, vmax=None, midpoint=None, clip=False)[source]#

Bases: matplotlib.colors.Normalize

Normalise the colorbar.

Parameters:

  • vmin (float or None) – If vmin and/or vmax is not given, they are initialized from the minimum and maximum value, respectively, of the first input processed; i.e., __call__(A) calls autoscale_None(A).

  • vmax (float or None) – If vmin and/or vmax is not given, they are initialized from the minimum and maximum value, respectively, of the first input processed; i.e., __call__(A) calls autoscale_None(A).

  • clip (bool, default: False) –

    Determines the behavior for mapping values outside the range [vmin, vmax].

    If clipping is off, values outside the range [vmin, vmax] are also transformed linearly, resulting in values outside [0, 1]. For a standard use with colormaps, this behavior is desired because colormaps mark these outside values with specific colors for over or under.

    If True values falling outside the range [vmin, vmax], are mapped to 0 or 1, whichever is closer. This makes these values indistinguishable from regular boundary values and can lead to misinterpretation of the data.

Notes

Returns 0 if vmin == vmax.

__call__(value, clip=None)[source]#

Normalize value data in the [vmin, vmax] interval into the [0.0, 1.0] interval and return it.

Parameters:

  • value – Data to normalize.

  • clip (bool, optional) –

    See the description of the parameter clip in .Normalize.

    If None, defaults to self.clip (which defaults to False).

Notes

If not already initialized, self.vmin and self.vmax are initialized using self.autoscale_None(value).

wildboar.utils.plot.plot_frequency_domain(x, y=None, *, ax=None, n_samples=100, jitter=False, sample_spacing=1, frequency=False, cmap='Dark2')[source]#

Plot the samples in the freqency domain

Parameters:

  • x (array-like of shape (n_sample, n_timestep)) – The samples

  • y (array-like of shape (n_samples, ), optional) – The labels, by default None

  • ax (Axes, optional) – The matplotlib Axes-object, by default None

  • jitter (bool, optional) – Add jitter to the amplitude lines, by default False

  • sample_spacing (int, optional) – The frequency domain sample spacing, by default 1

  • frequency (bool, optional) – Show the frequency bins, by default False

  • cmap (str, optional) – The colormap, by default “Dark2”

wildboar.utils.plot.plot_time_domain(x, y=None, *, n_samples=100, ax=None, alpha=0.5, linewidth=0.5, zorder=-1, cmap='Dark2', show_legend=8)[source]#

Plot the samples in the time domain

Parameters:

  • x (array-like of shape (n_sample, n_timestep)) – The samples

  • y (array-like of shape (n_samples, ), optional) – The labels

  • n_samples (int, optional) – The maximum number of samples to plot. If n_samples is larger than the number of samples in x or None, all samples are plotted.

  • ax (Axes, optional) – The matplotlib Axes-object

  • alpha (float, optional) – The opacity of the samples.

  • linewidth (float, optional) – The width of the sample lines.

  • zorder (int, optional) – The order where the samples are plotted. By default we plot the samples at -1.

  • cmap (str, optional) – The colormap used to colorize samples according to its label.

  • show_legend (bool or int, optional) –

    Whether the legend of labels are show.

    • if bool, show the legend if y is not None

    • if int, show the legend if the number of labels are lower than the show_legend parameter value

Returns:

ax – The axes object that has been plotted.

Return type:

Axes

wildboar.utils.plot.matplotlib_missing[source]#