#! /usr/bin/env python
from __future__ import print_function
import openturns as ot
ot.TESTPREAMBLE()
ot.PlatformInfo.SetNumericalPrecision(5)
size = 10000
for distribution in [ot.GeneralizedExtremeValue(2.0, 1.5, -0.15),
ot.GeneralizedExtremeValue(2.0, 1.5, 0.0)]:
# ot.GeneralizedExtremeValue(2.0, 1.5, 0.15)]:
sample = distribution.getSample(size)
factory = ot.GeneralizedExtremeValueFactory()
estimatedDistribution = factory.build(sample)
print("distribution=", repr(distribution))
print("Estimated distribution=", repr(estimatedDistribution))
estimatedDistribution = factory.build()
print("Default distribution=", estimatedDistribution)
estimatedDistribution = factory.build(distribution.getParameter())
print("Distribution from parameters=", estimatedDistribution)
estimatedGeneralizedExtremeValue = factory.buildAsGeneralizedExtremeValue(
sample)
print("GeneralizedExtremeValue =", distribution)
print("Estimated GeneralizedExtremeValue=",
estimatedGeneralizedExtremeValue)
estimatedGeneralizedExtremeValue = factory.buildAsGeneralizedExtremeValue()
import openturns as ot
from matplotlib import pyplot as plt
from openturns.viewer import View
pdf_graph = ot.Graph('PDF graph', 'x', 'PDF', True, 'topleft')
cdf_graph = ot.Graph('CDF graph', 'x', 'CDF', True, 'topleft')
palette = ot.Drawable.BuildDefaultPalette(10)
for i, p in enumerate([-1.0, 0.0, 1.0]):
distribution = ot.GeneralizedExtremeValue(0.0, 1.0, p)
pdf_curve = distribution.drawPDF().getDrawable(0)
cdf_curve = distribution.drawCDF().getDrawable(0)
pdf_curve.setColor(palette[i])
cdf_curve.setColor(palette[i])
pdf_curve.setLegend('xi={}'.format(p))
cdf_curve.setLegend('xi={}'.format(p))
pdf_graph.add(pdf_curve)
cdf_graph.add(cdf_curve)
fig = plt.figure(figsize=(10, 4))
pdf_axis = fig.add_subplot(121)
cdf_axis = fig.add_subplot(122)
View(pdf_graph, figure=fig, axes=[pdf_axis], add_legend=True)
View(cdf_graph, figure=fig, axes=[cdf_axis], add_legend=True)
fig.suptitle('GeneralizedExtremeValue(0,1,xi)')
import openturns as ot
from matplotlib import pyplot as plt
from openturns.viewer import View
if ot.GeneralizedExtremeValue().__class__.__name__ == 'ComposedDistribution':
correlation = ot.CorrelationMatrix(2)
correlation[1, 0] = 0.25
aCopula = ot.NormalCopula(correlation)
marginals = [ot.Normal(1.0, 2.0), ot.Normal(2.0, 3.0)]
distribution = ot.ComposedDistribution(marginals, aCopula)
elif ot.GeneralizedExtremeValue(
).__class__.__name__ == 'CumulativeDistributionNetwork':
distribution = ot.CumulativeDistributionNetwork(
[ot.Normal(2), ot.Dirichlet([0.5, 1.0, 1.5])],
ot.BipartiteGraph([[0, 1], [0, 1]]))
elif ot.GeneralizedExtremeValue().__class__.__name__ == 'Histogram':
distribution = ot.Histogram([-1.0, 0.5, 1.0, 2.0], [0.45, 0.4, 0.15])
else:
distribution = ot.GeneralizedExtremeValue()
dimension = distribution.getDimension()
if dimension == 1:
distribution.setDescription(['$x$'])
pdf_graph = distribution.drawPDF()
cdf_graph = distribution.drawCDF()
fig = plt.figure(figsize=(10, 4))
plt.suptitle(str(distribution))
pdf_axis = fig.add_subplot(121)
cdf_axis = fig.add_subplot(122)
View(pdf_graph, figure=fig, axes=[pdf_axis], add_legend=False)
View(cdf_graph, figure=fig, axes=[cdf_axis], add_legend=False)
elif dimension == 2:
distribution.setDescription(['$x_1$', '$x_2$'])
import openturns as ot
from matplotlib import pyplot as plt
from openturns.viewer import View
if (ot.GeneralizedExtremeValue().__class__.__name__ == 'ComposedDistribution'):
correlation = ot.CorrelationMatrix(2)
correlation[1, 0] = 0.25
aCopula = ot.NormalCopula(correlation)
marginals = [ot.Normal(1.0, 2.0), ot.Normal(2.0, 3.0)]
distribution = ot.ComposedDistribution(marginals, aCopula)
elif (ot.GeneralizedExtremeValue().__class__.__name__ ==
'CumulativeDistributionNetwork'):
distribution = ot.CumulativeDistributionNetwork(
[ot.Normal(2), ot.Dirichlet([0.5, 1.0, 1.5])],
ot.BipartiteGraph([[0, 1], [0, 1]]))
else:
distribution = ot.GeneralizedExtremeValue()
dimension = distribution.getDimension()
if dimension <= 2:
if distribution.getDimension() == 1:
distribution.setDescription(['$x$'])
pdf_graph = distribution.drawPDF()
cdf_graph = distribution.drawCDF()
fig = plt.figure(figsize=(10, 4))
plt.suptitle(str(distribution))
pdf_axis = fig.add_subplot(121)
cdf_axis = fig.add_subplot(122)
View(pdf_graph, figure=fig, axes=[pdf_axis], add_legend=False)
View(cdf_graph, figure=fig, axes=[cdf_axis], add_legend=False)
else:
distribution.setDescription(['$x_1$', '$x_2$'])
pdf_graph = distribution.drawPDF()
#! /usr/bin/env python
from __future__ import print_function
import openturns as ot
ot.TESTPREAMBLE()
ot.PlatformInfo.SetNumericalPrecision(5)
size = 10000
for distribution in [
ot.GeneralizedExtremeValue(2.0, 1.5, -0.15),
ot.GeneralizedExtremeValue(2.0, 1.5, 0.0)
]:
# ot.GeneralizedExtremeValue(2.0, 1.5, 0.15)]:
sample = distribution.getSample(size)
factory = ot.GeneralizedExtremeValueFactory()
estimatedDistribution = factory.build(sample)
print("distribution=", repr(distribution))
print("Estimated distribution=", repr(estimatedDistribution))
estimatedDistribution = factory.build()
print("Default distribution=", estimatedDistribution)
estimatedDistribution = factory.build(distribution.getParameter())
print("Distribution from parameters=", estimatedDistribution)
estimatedGeneralizedExtremeValue = factory.buildAsGeneralizedExtremeValue(
sample)
print("GeneralizedExtremeValue =", distribution)
print("Estimated GeneralizedExtremeValue=",
estimatedGeneralizedExtremeValue)
