def __init__(self, studyName = "resultKL.xml"):
self.resultKL = ot.KarhunenLoeveResult()
study = ot.Study(studyName)
study.load()
study.fillObject("resultKL", self.resultKL)
mean = ot.Point()
study.fillObject("mean", mean)
self.meanMax = mean[-1]
super(StationaryPressure, self).__init__(self.resultKL.getEigenValues().getSize(), 1)
def _export_xml(self):
"""
Export the OpenTurns function as xml.
Parameters
----------
"""
study = ot.Study()
study.setStorageManager(ot.XMLStorageManager(self._xml_path))
study.add('function', self.function_)
study.save()
#! /usr/bin/env python
from __future__ import print_function
import openturns as ot
import openturns.testing
import os
import sys
import math as m
ot.TESTPREAMBLE()
try:
fileName = 'myStudy.xml'
# Create a Study Object by name
myStudy = ot.Study(fileName)
point = ot.Point(2, 1.0)
myStudy.add("point", point)
myStudy.save()
myStudy2 = ot.Study(fileName)
myStudy2.load()
point2 = ot.Point()
myStudy2.fillObject("point", point2)
# cleanup
os.remove(fileName)
# Create a Study Object with compression
myStudy = ot.Study()
compressionLevel = 5
myStudy.setStorageManager(
ot.XMLStorageManager(fileName + ".gz", compressionLevel))
#! /usr/bin/env python
from __future__ import print_function
import openturns as ot
import os
ot.TESTPREAMBLE()
f = ot.Function()
# load
study = ot.Study()
study.setStorageManager(ot.XMLStorageManager('pyf.xml'))
study.load()
study.fillObject('f', f)
x = [4, 5]
print(f(x))
os.remove('pyf.xml')
#! /usr/bin/env python
from __future__ import print_function
import openturns as ot
import openturns.testing
import os
ot.TESTPREAMBLE()
try:
fileName = 'myStudy.xml'
# Create a Study Object by name
myStudy = ot.Study(fileName)
point = ot.Point(2, 1.0)
myStudy.add("point", point)
myStudy.save()
myStudy2 = ot.Study(fileName)
myStudy2.load()
point2 = ot.Point()
myStudy2.fillObject("point", point2)
# cleanup
os.remove(fileName)
# Create a Study Object with compression
myStudy = ot.Study()
compressionLevel = 5
myStudy.setStorageManager(ot.XMLStorageManager(fileName, compressionLevel))
point = ot.Point(2, 1.0)
myStudy.add("point", point)
myStudy.save()
def getParameterDescription(self):
paramDesc = ['a_' + str(i) for i in range(len(self.a))]
paramDesc.extend(['b_' + str(i) for i in range(len(self.a))])
return paramDesc
def setParameter(self, parameter):
dim = len(self.a)
for i in range(dim):
self.a[i] = parameter[i]
self.b[i] = parameter[dim + i]
myDist = ot.Distribution(UniformNdPy([0.0] * 2, [2.0] * 2))
st = ot.Study()
fileName = 'PyDIST.xml'
st.setStorageManager(ot.XMLStorageManager(fileName))
st.add("myDist", myDist)
st.save()
print('saved dist=', myDist)
dist = ot.Distribution()
st = ot.Study()
st.setStorageManager(ot.XMLStorageManager(fileName))
st.load()
# **With OpenTURNS' Study**
#
# In order to be able to manipulate the objects contained in a Study, it is necessary to:
#
# - create the same empty structure in the new study,
# - fill this new empty structure with the content of the loaded structure, identified with its name or its id.
#
# Each object is identified whether with:
#
# - its name: it is useful to give names to the objects we want to save. If no name has been given by the user, we can use the default name. The name of each saved object can be checked in the output XML file or with the python `print` command (applied to the `Study` object).
# - its id number: this id number is unique to each object. It distinguishes objects with identical type and name (like the default name "Unnamed"). This id number may be checked by printing the study **after** it has been loaded in the python interface (with the `print` command). It can differ from the id number indicated in the XML file the study was loaded from.
# - for HDF5 storage (see below): the id serves both as xml id and hdf5 dataset name. Id uniqueness forbids any misleading in reading/writing hdf5 datasets.
# %%
# Create a Study Object
study = ot.Study()
# %%
# Associate it to an XML file
fileName = 'study.xml'
study.setStorageManager(ot.XMLStorageManager(fileName))
# %%
# Alternatively, large amounts of data can be stored in binary HDF5 file. An XML file (`study_h5.xml`) serves as header for binary data, which are stored in the automatically created `study_h5.h5` file.
study_h5 = ot.Study()
fileName_h5 = 'study_h5.xml'
study_h5.setStorageManager(ot.XMLH5StorageManager(fileName_h5))
# %%
# Add an object to the study; at this point it is not written to disk yet
study.add('distribution', distribution)
#! /usr/bin/env python
from __future__ import print_function
import openturns as ot
import os
ot.TESTPREAMBLE()
fileName = 'myStudy.xml'
# Create a Study Object
myStudy = ot.Study()
myStudy.setStorageManager(ot.XMLStorageManager(fileName))
# Add a PersistentObject to the Study (here a NumericalPoint)
p1 = ot.NumericalPoint(3, 0.)
p1.setName("Good")
p1[0] = 10.
p1[1] = 11.
p1[2] = 12.
myStudy.add(p1)
# Add another PersistentObject to the Study (here a NumericalSample)
s1 = ot.NumericalSample(3, 2)
s1.setName("mySample")
p2 = ot.NumericalPoint(2, 0.)
p2.setName("One")
p2[0] = 100.
p2[1] = 200.
s1[0] = p2
p3 = ot.NumericalPoint(2, 0.)
#! /usr/bin/env python
import openturns as ot
import openturns.testing
import os
import sys
import math as m
ot.TESTPREAMBLE()
fileName = 'pyxmlh5.xml.gz'
study = ot.Study()
study.setStorageManager(ot.XMLH5StorageManager(fileName, 5))
point = ot.Point([123.456, 125.43, 3975.4567])
point2 = ot.Point(3, 789.123)
point3 = ot.Point(3, 1673.456)
point4 = ot.Point(3, 789.654123)
sample = ot.Sample(1, point)
sample.add(point2)
sample.add(point3)
sample.add(point4)
sample.add(point2)
sample.add(point4)
sample.add(point3)
print(sample)
study.add('sample', sample)
mesh = ot.IntervalMesher([50] * 3).build(ot.Interval(3))
study.add('mesh', mesh)
