ot.Log.Show(ot.Log.NONE)
# prepare some data
formulas = ['cos(x1 + x2)', '(x2 + 1) * exp(x1 - 2 * x2)']
model = ot.SymbolicFunction(['x1', 'x2'], formulas)
# center of the approximation
x0 = [-0.4, -0.4]
# drawing bounds
a=-0.4
b=0.0
# %%
# create a linear (first order) Taylor approximation
algo = ot.LinearTaylor(x0, model)
algo.run()
responseSurface = algo.getMetaModel()
# %%
# plot 2nd output of our model with x1=x0_1
graph = ot.ParametricFunction(responseSurface, [0], [x0[1]]).getMarginal(1).draw(a, b)
graph.setLegends(['taylor'])
curve = ot.ParametricFunction(model, [0], [x0[1]]).getMarginal(1).draw(a, b).getDrawable(0)
curve.setColor('red')
curve.setLegend('model')
graph.add(curve)
graph.setLegendPosition('topright')
view = viewer.View(graph)
# %%
#! /usr/bin/env python
from __future__ import print_function
import openturns as ot
eps = 0.2
# Instance creation
myFunc = ot.SymbolicFunction(
['x1', 'x2'], ['x1*sin(x2)', 'cos(x1+x2)', '(x2+1)*exp(x1-2*x2)'])
center = ot.Point(myFunc.getInputDimension())
for i in range(center.getDimension()):
center[i] = 1.0 + i
myTaylor = ot.LinearTaylor(center, myFunc)
myTaylor.run()
responseSurface = ot.Function(myTaylor.getResponseSurface())
print("myTaylor=", repr(myTaylor))
print("responseSurface=", repr(responseSurface))
print("myFunc(", repr(center), ")=", repr(myFunc(center)))
print("responseSurface(", repr(center), ")=", repr(responseSurface(center)))
inPoint = ot.Point(center)
inPoint[0] += eps
inPoint[1] -= eps / 2
print("myFunc(", repr(inPoint), ")=", repr(myFunc(inPoint)))
print("responseSurface(", repr(inPoint), ")=", repr(responseSurface(inPoint)))