scipy.stats.Uniform.

plot#

Uniform.plot(x='x', y='pdf', *, t=('cdf', 0.0005, 0.9995), ax=None)[source]#

Plot a function of the distribution.

Convenience function for quick visualization of the distribution underlying the random variable.

Parameters:

x, ystr, optional: String indicating the quantities to be used as the abscissa and ordinate (horizontal and vertical coordinates), respectively. Defaults are 'x' (the domain of the random variable) and 'pdf' (the probability density function). Valid values are: ‘x’, ‘pdf’, ‘cdf’, ‘ccdf’, ‘icdf’, ‘iccdf’, ‘logpdf’, ‘logcdf’, ‘logccdf’, ‘ilogcdf’, ‘ilogccdf’.
t3-tuple of (str, float, float), optional: Tuple indicating the limits within which the quantities are plotted. Default is ('cdf', 0.001, 0.999) indicating that the central 99.9% of the distribution is to be shown. Valid values are: ‘x’, ‘cdf’, ‘ccdf’, ‘icdf’, ‘iccdf’, ‘logcdf’, ‘logccdf’, ‘ilogcdf’, ‘ilogccdf’.
axmatplotlib.axes, optional: Axes on which to generate the plot. If not provided, use the current axes.

Returns:

axmatplotlib.axes: Axes on which the plot was generated. The plot can be customized by manipulating this object.

Examples

Instantiate a distribution with the desired parameters:

>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> from scipy import stats
>>> X = stats.Normal(mu=1., sigma=2.)

Plot the PDF over the central 99.9% of the distribution. Compare against a histogram of a random sample.

>>> ax = X.plot()
>>> sample = X.sample(10000)
>>> ax.hist(sample, density=True, bins=50, alpha=0.5)
>>> plt.show()

Plot logpdf(x) as a function of x in the left tail, where the log of the CDF is between -10 and np.log(0.5).

>>> X.plot('x', 'logpdf', t=('logcdf', -10, np.log(0.5)))
>>> plt.show()

Plot the PDF of the normal distribution as a function of the CDF for various values of the scale parameter.

>>> X = stats.Normal(mu=0., sigma=[0.5, 1., 2])
>>> X.plot('cdf', 'pdf')
>>> plt.show()