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#! /usr/bin/env python
import openturns as ot
import openturns.testing
import persalys
import os
myStudy = persalys.Study("myStudy")
# Model 1
filename = "data.csv"
ot.RandomGenerator.SetSeed(0)
ot.Normal(3).getSample(10).exportToCSVFile(filename)
inColumns = [0, 2]
model = persalys.DataModel("myDataModel", filename, inColumns)
myStudy.add(model)
print(model)
print("inputNames=", model.getInputNames())
print("outputNames=", model.getOutputNames())
# Data analysis ##
analysis = persalys.DataAnalysis("aDataAnalysis", model)
myStudy.add(analysis)
analysis.run()
result = analysis.getResult()
print("result=", result)
print("PDF=", result.getPDF())
print("CDF=", result.getCDF())
print("SurvivalFunction=", result.getSurvivalFunction())
print("outliers=", result.getOutliers())
# Comparaison
openturns.testing.assert_almost_equal(0.2012538261144671, result.getMean()[0][0], 1e-13)
openturns.testing.assert_almost_equal(
-0.14315074899830527, result.getMean()[1][0], 1e-13
)
# Model 2
outColumns = [1]
model2 = persalys.DataModel(
"myDataModel2", filename, inColumns, outColumns, ["var1", "var2"], ["var3"]
)
myStudy.add(model2)
print(model2)
print("inputNames=", model2.getInputNames())
print("outputNames=", model2.getOutputNames())
print("min=", model2.getListXMin())
print("max=", model2.getListXMax())
# Model 3
model3 = persalys.DataModel("myDataModel3", filename, inColumns, outColumns)
myStudy.add(model3)
print(model3)
print("inputNames=", model3.getInputNames())
print("outputNames=", model3.getOutputNames())
print("inputSample=", model3.getInputSample())
print("outputSample=", model3.getOutputSample())
print("min=", model3.getListXMin())
print("max=", model3.getListXMax())
# Quantile analysis
# Model 4
filename = "data_500.csv"
ot.RandomGenerator.SetSeed(0)
ot.Normal(3).getSample(500).exportToCSVFile(filename)
inColumns = [0, 1, 2]
model4 = persalys.DataModel("myDataModel4", filename, inColumns)
myStudy.add(model4)
print(model4)
print("inputNames=", model4.getInputNames())
print("outputNames=", model4.getOutputNames())
# Monte Carlo
analysis2 = persalys.QuantileAnalysis("aQuantileAnalysis", model4)
analysis2.setTargetProbabilities([[1e-2, 1e-3], [6e-3], [1e-1, 1e-2, 1e-3]])
analysis2.setTailTypes(
[persalys.QuantileAnalysisResult.Upper | persalys.QuantileAnalysisResult.Lower | persalys.QuantileAnalysisResult.Bilateral,
persalys.QuantileAnalysisResult.Lower,
persalys.QuantileAnalysisResult.Bilateral])
analysis2.setType(persalys.QuantileAnalysisResult.MonteCarlo)
myStudy.add(analysis2)
analysis2.run()
result = analysis2.getResult()
x0ref = [[[-2.46389, -2.23369, -2.0035],
[-2.51804, -2.4067, -2.29536]],
[[1.92083, 2.30459, 2.68835],
[2.85639, 3.15958, 3.46277]],
[[-2.47455, -2.29006, -2.10558],
[-2.48545, -2.4067, -2.32795]],
[[2.41487, 3.02799, 3.64111],
[2.94514, 3.15958, 3.37402]]]
x1ref = [[[-3.36709, -2.84159, -2.31609]]]
x2ref = [[[-1.73368, -1.5705, -1.40732],
[-3.6264, -2.98535, -2.3443],
[-3.32293, -3.09834, -2.87375]],
[[1.51902, 1.69843, 1.87784],
[2.29461, 2.5038, 2.71299],
[2.53902, 2.63821, 2.7374]]]
for i, qx in enumerate(result.getQuantiles('X0')):
for j, qxi in enumerate(qx):
openturns.testing.assert_almost_equal(qxi, x0ref[i][j])
for i, qx in enumerate(result.getQuantiles('X1')):
for j, qxi in enumerate(qx):
openturns.testing.assert_almost_equal(qxi, x1ref[i][j])
for i, qx in enumerate(result.getQuantiles('X2')):
for j, qxi in enumerate(qx):
openturns.testing.assert_almost_equal(qxi, x2ref[i][j])
print(result.getSampleSizeValidity('X0', persalys.QuantileAnalysisResult.Upper))
print(result.getSampleSizeValidity('X0', persalys.QuantileAnalysisResult.Lower))
print(result.getSampleSizeValidity('X0', persalys.QuantileAnalysisResult.Bilateral))
print(result.getSampleSizeValidity('X1', persalys.QuantileAnalysisResult.Lower))
print(result.getSampleSizeValidity('X2', persalys.QuantileAnalysisResult.Bilateral))
# Generalized Pareto
# test wrong threshold
analysis2.setType(persalys.QuantileAnalysisResult.GeneralizedPareto)
try:
analysis2.run()
except Exception as e:
print(e)
analysis2.setTargetProbabilities([[1e-2, 1e-3], [6e-3], [2e-2, 1e-2, 1e-3]])
analysis2.run()
# correct threshold
ot.RandomGenerator.SetSeed(0)
analysis2.setThreshold(ot.Sample([[-1.2] * 3, [1.3] * 3]))
print(analysis2.getCDFThreshold())
analysis2.run()
lower = persalys.QuantileAnalysisResult.Lower
upper = persalys.QuantileAnalysisResult.Upper
result = analysis2.getResult()
x0ref = [[[-2.44824, -2.00098, -1.77584],
[-3.41554, -2.30532, -1.81387]],
[[1.963, 2.39031, 2.80089],
[2.00437, 3.32964, 4.43636]],
[[-2.792, -2.12135, -1.85867],
[-3.78112, -2.3562, -1.89605]],
[[2.1527, 2.70119, 3.29286],
[2.21385, 3.56467, 5.04221]]]
x1ref = [[[-3.41533, -2.61337, -2.14595]]]
x2ref = [[[-2.69465, -2.37834, -1.91663],
[-3.03319, -2.68309, -1.95693],
[-4.48605, -3.59798, -1.98668]],
[[1.85113, 2.16798, 2.66877],
[1.88352, 2.30282, 2.93784],
[1.9117, 2.56313, 3.89188]]]
for i, qx in enumerate(result.getQuantiles('X0')):
for j, qxi in enumerate(qx):
openturns.testing.assert_almost_equal(qxi, x0ref[i][j])
for i, qx in enumerate(result.getQuantiles('X1')):
for j, qxi in enumerate(qx):
openturns.testing.assert_almost_equal(qxi, x1ref[i][j])
for i, qx in enumerate(result.getQuantiles('X2')):
for j, qxi in enumerate(qx):
openturns.testing.assert_almost_equal(qxi, x2ref[i][j])
openturns.testing.assert_almost_equal(result.getPValue('X0', upper), 0.9378335994, 1e-3)
openturns.testing.assert_almost_equal(result.getPValue('X0', lower), 0.5392217742, 1e-3)
openturns.testing.assert_almost_equal(result.getPValue('X1', lower), 0.9037923523, 1e-3)
openturns.testing.assert_almost_equal(result.getPValue('X2', upper), 0.0674410269, 1e-3)
openturns.testing.assert_almost_equal(result.getPValue('X2', lower), 0.4360724055, 1e-3)
# test interest variables
analysis2.setInterestVariables(["X0", "X2"])
analysis2.setTailTypes(
[persalys.QuantileAnalysisResult.Upper | persalys.QuantileAnalysisResult.Lower | persalys.QuantileAnalysisResult.Bilateral,
persalys.QuantileAnalysisResult.Bilateral])
analysis2.setTargetProbabilities([[1e-2, 1e-3], [2e-2, 1e-2, 1e-3]])
analysis2.setThreshold(ot.Sample([[-1.2] * 2, [1.3] * 2]))
print(analysis2.getCDFThreshold())
analysis2.run()
# script
script = myStudy.getPythonScript()
print(script)
exec(script)
# check ambiguous import
sample = ot.Normal(2).getSample(10)
sample[0] = [1, 2]
inColumns = [0, 1]
for col_sep in [";", ",", " "]:
for num_sep in [".", ","]:
if col_sep == num_sep:
continue
with open(filename, "w") as csv:
csv.write('"x"' + col_sep + '"y"\n')
for p in sample:
for j in range(len(p)):
csv.write(str(p[j]).replace(".", num_sep))
if j < len(p) - 1:
csv.write(col_sep)
csv.write("\n")
model = persalys.DataModel("myDataModel2", filename, inColumns)
assert model.getSampleFromFile().getDimension() == 2, (
"wrong dimension sep=" + col_sep
)
assert model.getSampleFromFile().getSize() == 10, "wrong size"
os.remove(filename)
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