#! /usr/bin/env python
from __future__ import print_function
import openturns as ot
distribution = ot.Gumbel(2.0, 2.5)
size = 10000
sample = distribution.getSample(size)
factory = ot.GumbelFactory()
print('Distribution =', repr(distribution))
result = factory.buildEstimator(sample)
estimatedDistribution = result.getDistribution()
print('Estimated distribution =', repr(estimatedDistribution))
parameterDistribution = result.getParameterDistribution()
print('Parameter distribution =', parameterDistribution)
defaultDistribution = factory.build()
print('Default distribution =', defaultDistribution)
fromParameterDistribution = factory.build(distribution.getParameter())
print('Distribution from parameters =', fromParameterDistribution)
typedEstimatedDistribution = factory.buildAsGumbel(sample)
print('Typed estimated distribution =', typedEstimatedDistribution)
defaultTypedDistribution = factory.buildAsGumbel()
print('Default typed distribution =', defaultTypedDistribution)
typedFromParameterDistribution = factory.buildAsGumbel(
distribution.getParameter())
print('Typed distribution from parameters=', typedFromParameterDistribution)
result = factory.buildEstimator(sample, ot.GumbelAB())
estimatedDistribution = result.getDistribution()
print('Estimated distribution (AB) =', repr(estimatedDistribution))
parameterDistribution = result.getParameterDistribution()
print('Parameter distribution (AB) =', parameterDistribution)
ot.RandomGenerator.SetSeed(0)
sample = ot.Normal(3).getSample(300)
sample.stack(ot.Gumbel().getSample(300))
sample.setDescription(['X0', 'X1', 'X2', 'X3'])
sample.exportToCSVFile(filename, ',')
columns = [0, 2, 3]
model = persalys.DataModel('myDataModel', "data1.csv", columns)
myStudy.add(model)
print(model)
# Inference analysis ##
analysis = persalys.InferenceAnalysis('analysis', model)
variables = ["X0", "X3"]
analysis.setInterestVariables(variables)
factories = [ot.NormalFactory(), ot.GumbelFactory()]
analysis.setDistributionsFactories("X3", factories)
analysis.setLevel(0.1)
myStudy.add(analysis)
print(analysis)
analysis.run()
result = analysis.getResult()
print("result=", result)
print(result.getFittingTestResultForVariable('X3'))
# script
script = myStudy.getPythonScript()
print(script)
exec(script)
# %%
# We compare different fitting strategies for this sample:
#
# - use the histogram from the data (red)
# - the GEV fitted distribution (black)
# - the pure Frechet fitted distribution (green)
# - the pure Gumbel fitted distribution (blue)
# - the pure WeibullMax fitted distribution (dashed orange)
#
graph = myDistribution.drawPDF()
graph.add(ot.HistogramFactory().build(sample).drawPDF())
distFrechet = ot.FrechetFactory().buildAsFrechet(sample)
graph.add(distFrechet.drawPDF())
distGumbel = ot.GumbelFactory().buildAsGumbel(sample)
graph.add(distGumbel.drawPDF())
# We change the line style of the WeibullMax.
distWeibullMax = ot.WeibullMaxFactory().buildAsWeibullMax(sample)
curveWeibullMax = distWeibullMax.drawPDF().getDrawable(0)
curveWeibullMax.setLineStyle("dashed")
graph.add(curveWeibullMax)
graph.setColors(["black", "red", "green", "blue", "orange"])
graph.setLegends([
"GEV fitting",
"histogram",
"Frechet fitting",
"Gumbel fitting",
"WeibullMax fitting",
# In this example we are going to perform a visual goodness-of-fit test for an 1-d distribution with the QQ plot. # %% from __future__ import print_function import openturns as ot import openturns.viewer as viewer from matplotlib import pylab as plt ot.Log.Show(ot.Log.NONE) # %% # Create data ot.RandomGenerator.SetSeed(0) distribution = ot.Gumbel(0.2, 0.5) sample = distribution.getSample(100) sample.setDescription(['Sample']) # %% # Fit a distribution distribution = ot.GumbelFactory().build(sample) # %% # Draw QQ plot graph = ot.VisualTest.DrawQQplot(sample, distribution) view = viewer.View(graph) # %% # Incorrect proposition graph = ot.VisualTest.DrawQQplot(sample, ot.WeibullMin()) view = viewer.View(graph) plt.show()