def test_vaneylen19_single(self):
with self._model():
vaneylen19("ecc", fixed=True, multi=False, shape=2)
trace = self._sample()
ecc = trace["ecc"].flatten()
assert np.all((0 <= ecc) & (ecc <= 1))
f = 0.76
cdf = lambda x: ( # NOQA
(1 - f) * halfnorm.cdf(x, scale=0.049) + f * rayleigh.cdf(
x, scale=0.26))
s, p = kstest(ecc, cdf)
assert s < 0.05
x = np.linspace(halfnorm.ppf(0.01),
halfnorm.ppf(0.99), 100)
ax.plot(x, halfnorm.pdf(x),
'r-', lw=5, alpha=0.6, label='halfnorm 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 = halfnorm()
ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')
# Check accuracy of ``cdf`` and ``ppf``:
vals = halfnorm.ppf([0.001, 0.5, 0.999])
np.allclose([0.001, 0.5, 0.999], halfnorm.cdf(vals))
# True
# Generate random numbers:
r = halfnorm.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()