/usr/include/openturns/swig/ValueFunction_doc.i is in libopenturns-dev 1.9-5.
This file is owned by root:root, with mode 0o644.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 | %feature("docstring") OT::ValueFunction
"Spatial function.
Available constructors:
ValueFunction(*meshDimension=1*)
ValueFunction(*g, meshDimension=1*)
Parameters
----------
g : :class:`~openturns.Function`
Function :math:`g: \\\\Rset^d \\\\mapsto \\\\Rset^q`.
meshDimension : int, :math:`n \\\\geq 0`
Dimension of the vertices of the mesh :math:`\\\\cM`. This data is required
for tests on the compatibility of dimension when a composite process is
created using the spatial function.
Notes
-----
A spatial function
:math:`f_{spat}: \\\\cD \\\\times \\\\Rset^d \\\\mapsto \\\\cD \\\\times \\\\Rset^q`, with
:math:`\\\\cD \\\\in \\\\Rset^n`, is a particular
:class:`field function <openturns.FieldFunction>` that lets invariant
the mesh of a field and defined by a function
:math:`g : \\\\Rset^d \\\\mapsto \\\\Rset^q` such that:
.. math::
f_{spat}(\\\\vect{t}, \\\\vect{x})=(\\\\vect{t}, g(\\\\vect{x}))
Let's note that the input dimension of :math:`f_{spat}` still designs the
dimension of :math:`\\\\vect{x}`: :math:`d`. Its output dimension is equal to
:math:`q`.
See also
--------
VertexValueFunction
Examples
--------
>>> import openturns as ot
Create a function :math:`g : \\\\Rset^d \\\\mapsto \\\\Rset^q` such as:
.. math::
g: \\\\left|\\\\begin{array}{rcl}
\\\\Rset & \\\\rightarrow & \\\\Rset \\\\\\\\
x & \\\\mapsto & x^2
\\\\end{array}\\\\right.
>>> g = ot.SymbolicFunction(['x'], ['x^2'])
Convert :math:`g` into a spatial function with :math:`n` the dimension of the
mesh of the field on which :math:`g` will be applied:
>>> n = 1
>>> myValueFunction = ot.ValueFunction(g, n)
>>> # Create a TimeSeries
>>> tg = ot.RegularGrid(0.0, 0.2, 6)
>>> data = ot.Sample(tg.getN(), g.getInputDimension())
>>> for i in range(data.getSize()):
... for j in range(data.getDimension()):
... data[i, j] = i * data.getDimension() + j
>>> ts = ot.TimeSeries(tg, data)
>>> print(ts)
[ t v0 ]
0 : [ 0 0 ]
1 : [ 0.2 1 ]
2 : [ 0.4 2 ]
3 : [ 0.6 3 ]
4 : [ 0.8 4 ]
5 : [ 1 5 ]
>>> print(myValueFunction(ts))
[ t y0 ]
0 : [ 0 0 ]
1 : [ 0.2 1 ]
2 : [ 0.4 4 ]
3 : [ 0.6 9 ]
4 : [ 0.8 16 ]
5 : [ 1 25 ]"
// ---------------------------------------------------------------------
%feature("docstring") OT::ValueFunction::getEvaluation
"Get the evaluation function of :math:`g`.
Returns
-------
g : :class:`~openturns.EvaluationImplementation`
Evaluation function of :math:`g: \\\\Rset^d \\\\mapsto \\\\Rset^q`.
Examples
--------
>>> import openturns as ot
>>> g = ot.SymbolicFunction(['x'], ['x^2'])
>>> n = 1
>>> myValueFunction = ot.ValueFunction(g, n)
>>> print(myValueFunction.getEvaluation())
[x]->[x^2]"
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