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bundles / scipy 2.0.0.dev0+git20260907.41eeb59 / scipy / stats / _continuous_distns / levy_gen

class

scipy.stats._continuous_distns:levy_gen

source: build-install/usr/lib/python3.14/site-packages/scipy/stats/_continuous_distns.py:6313

Signature

class levy_gen(momtype = 1, a = None, b = None, xtol = 1e-14, badvalue = None, name = None, longname = None, shapes = None, seed = None)

Members

Summary

A Levy continuous random variable.

Extended Summary

%(before_notes)s

Notes

The probability density function for levy is:

for .

This is the same as the Levy-stable distribution with and .

%(after_notes)s

Examples

import numpy as np
from scipy.stats import levy
import matplotlib.pyplot as plt
fig, ax = plt.subplots(1, 1)
✓
Calculate the first four moments:
mean, var, skew, kurt = levy.stats(moments='mvsk')
✓
Display the probability density function (``pdf``):
a, b = levy.ppf(0), levy.ppf(0.6)
x = np.linspace(a, b, 100)
✓
ax.plot(x, levy.pdf(x),
       'r-', lw=5, alpha=0.6, label='levy pdf')
✗
[<matplotlib.lines.Line2D object at 0x7f3f601c2e40>]
Alternatively, the distribution object can be called (as a function) to fix the shape, location and scale parameters. This returns a "frozen" RV object holding the given parameters fixed. Freeze the distribution and display the frozen ``pdf``:
rv = levy()
✓
ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')
✗
[<matplotlib.lines.Line2D object at 0x7f3f601c3380>]
Check accuracy of ``cdf`` and ``ppf``:
vals = levy.ppf([0.001, 0.5, 0.999])
np.allclose([0.001, 0.5, 0.999], levy.cdf(vals))
✓
True
Generate random numbers:
r = levy.rvs(size=1000)
✓
And compare the histogram:
bins = np.concatenate((np.linspace(a, b, 20), [np.max(r)]))
✓
ax.hist(r, bins=bins, density=True, histtype='stepfilled', alpha=0.2)
ax.set_xlim([x[0], x[-1]])
ax.legend(loc='best', frameon=False)
✗
<matplotlib.legend.Legend object at 0x7f3f601c1160>
plt.show()
✓
fig-ce716c2a329d13eb.png

See also

levy_l
levy_stable

Aliases

  • scipy.stats._continuous_distns.levy_gen