def getPDF(self, points=None):
"""
A Chebyshev probability density function.
:param Chebyshev self:
An instance of the Chebyshev (arcsine) class.
:param points:
Matrix of points for defining the probability density function.
:return:
An array of N the support of the Chebyshev (arcsine) distribution.
:return:
Probability density values along the support of the Chebyshev (arcsine) distribution.
"""
if points is not None:
return arcsine.pdf(points)
else:
raise (ValueError, 'Please digit an input for getPDF method')
def getPDF(self, points=None):
"""
A Chebyshev probability density function.
:param Chebyshev self:
An instance of the Chebyshev (arcsine) class.
:param points:
Matrix of points for defining the probability density function.
:return:
An array of N the support of the Chebyshev (arcsine) distribution.
:return:
Probability density values along the support of the Chebyshev (arcsine) distribution.
"""
if points is not None:
return arcsine.pdf(points)
else:
raise(ValueError, 'Please digit an input for getPDF method')
from scipy.stats import arcsine
import matplotlib.pyplot as plt
fig, ax = plt.subplots(1, 1)
# Calculate a few first moments:
mean, var, skew, kurt = arcsine.stats(moments='mvsk')
# Display the probability density function (``pdf``):
x = np.linspace(arcsine.ppf(0.01),
arcsine.ppf(0.99), 100)
ax.plot(x, arcsine.pdf(x),
'r-', lw=5, alpha=0.6, label='arcsine pdf')
# 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 = arcsine()
ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')
# Check accuracy of ``cdf`` and ``ppf``:
vals = arcsine.ppf([0.001, 0.5, 0.999])
np.allclose([0.001, 0.5, 0.999], arcsine.cdf(vals))
# True
# Generate random numbers:
r = anglit.rvs(size=1000) #And compare the histogram: ax.hist(r, density=True, histtype='stepfilled', alpha=0.2) ax.legend(loc='best', frameon=False) plt.show() #arcsine Continuous distributions¶ from scipy.stats import arcsine import matplotlib.pyplot as plt fig, ax = plt.subplots(1, 1) #Calculate a few first moments: mean, var, skew, kurt = arcsine.stats(moments='mvsk') #Display the probability density function (pdf): x = np.linspace(arcsine.ppf(0.01), arcsine.ppf(0.99), 100) ax.plot(x, arcsine.pdf(x), 'r-', lw=5, alpha=0.6, label='arcsine pdf') #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 = arcsine() ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf') #Check accuracy of cdf and ppf: vals = arcsine.ppf([0.001, 0.5, 0.999]) np.allclose([0.001, 0.5, 0.999], arcsine.cdf(vals)) True #Generate random numbers: r = arcsine.rvs(size=1000) #And compare the histogram: ax.hist(r, density=True, histtype='stepfilled', alpha=0.2) ax.legend(loc='best', frameon=False) plt.show()