Histogram.plot (function)


def plot(self, size=100000.0, unit=None, wrap_at=None, seed=None, samples=None, plot_sample=True, plot_sample_kwargs={'color': 'gray'}, plot_pdf=True, plot_pdf_kwargs={'color': 'red'}, plot_cdf=False, plot_cdf_kwargs={'color': 'green'}, plot_gaussian=False, plot_gaussian_kwargs={'color': 'blue'}, plot_uncertainties=True, plot_uncertainties_kwargs={'color': 'black', 'linestyle': 'dashed'}, label=None, xlabel=None, show=False, **kwargs)

Plot both the analytic distribution function as well as a sampled histogram from the distribution. Requires matplotlib to be installed.

See also:

Arguments

  • size (int, optional, default=1e5): number of points to sample for the histogram. See also Histogram.sample. Will be ignored if samples is provided.
  • unit (astropy.unit, optional, default=None): units to use along the x-axis. Astropy must be installed. If samples is provided, the passed values will be assumed to be in the correct units.
  • wrap_at (float, None, or False, optional, default=None): value to use for wrapping. See Histogram.wrap. If not provided or None, will use the value from Histogram.wrap_at. Note: wrapping is computed before changing units, so wrap_at must be provided according to Histogram.unit not unit. Will be ignored if samples is provided.
  • seed (int, optional): seed to use when sampling. See also Histogram.sample. Will be ignored if samples is provided.
  • samples (array, optional, default=None): plot specific sampled values instead of calling Histogram.sample internally. Will override size.
  • plot_sample (bool, optional, default=True): whether to plot the histogram from sampling. See also Histogram.plot_sample.
  • plot_sample_kwargs (dict, optional, default={'color': 'gray'}): keyword arguments to send to Histogram.plot_sample.
  • plot_pdf (bool, optional, default=True): whether to plot the analytic form of the underlying distribution, if applicable. See also Histogram.plot_pdf.
  • plot_pdf_kwargs (dict, optional, default={'color': 'red'}): keyword arguments to send to Histogram.plot_pdf.
  • plot_cdf (bool, optional, default=True): whether to plot the analytic form of the cdf, if applicable. See also Histogram.plot_cdf.
  • plot_cdf_kwargs (dict, optional, default={'color': 'green'}): keyword arguments to send to Histogram.plot_cdf.
  • plot_gaussian (bool, optional, default=False): whether to plot a guassian distribution fit to the sample. Only supported for distributions that have Histogram.to_gaussian methods.
  • plot_gaussian_kwargs (dict, optional, default={'color': 'blue'}): keyword arguments to send to Histogram.plot_gaussian.
  • plot_uncertainties (bool or int, optional, default=True): whether to plot uncertainties (as vertical lines) and include the representation of the uncertainties in the plot title. If an integer, will plot at that sigma. If True, will default to sigma=1. See Histogram.uncertainties.
  • plot_uncertainties_kwargs (dict, optional, default={'color': 'black', 'linestyle': 'dashed'}): keyword arguments to send to Histogram.plot_uncertainties.
  • label (string, optional, default=None): override the label on the x-axis. If not provided or None, will use Histogram.label. Will only be used if show=True. Unit will automatically be appended. Will be ignored if xlabel is provided.
  • xlabel (string, optional, default=None): override the label on the x-axis without appending the unit. Will override label.
  • show (bool, optional, default=True): whether to show the resulting matplotlib figure.
  • **kwargs: all keyword arguments (except for bins) will be passed on to Histogram.plot_pdf and Histogram.plot_gaussian and all keyword arguments will be passed on to Histogram.plot_sample. Keyword arguments defined in plot_sample_kwargs, plot_pdf_kwargs, and plot_gaussian_kwargs will override the values sent in kwargs.

Returns

Raises

  • ImportError: if matplotlib dependency is not met.