Note

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# Create a Python function¶

In this example we are going to create a Function object (ie usable in the OpenTURNS context) from a pure Python function.

The pure Python function to wrap must accept a sequence of floats and return a sequence of float.

```
import numpy as np
import openturns as ot
import math as m
ot.Log.Show(ot.Log.NONE)
```

define a pure Python function from R^3 to R^2

```
def regularFunc(X):
x0, x1, x2 = X
y0 = x0 + x1 + x2
y1 = (x1 - 1.0) * m.exp(x0) * x2
return [y0, y1]
```

create a Function object from a regular Python function

```
function = ot.PythonFunction(3, 2, regularFunc)
```

evaluate the function on a Point

```
x = [1.0, 2.0, 3.0]
print("x=", x, "f(x)=", function(x))
```

```
x= [1.0, 2.0, 3.0] f(x)= [6,8.15485]
```

evaluate the function on a Sample

```
xs = [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]
print("xs=", xs, "\nf(xs)=", function(xs))
```

```
xs= [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]
f(xs)= [ y0 y1 ]
0 : [ 6 8.15485 ]
1 : [ 15 1310.36 ]
```

now we can use the Function object services such as the gradient

```
function.gradient(x)
```

## Performance issues¶

When this function is used internally to evaluate a Sample, it loops over its points. This requires many memory allocations; moreover this loop is done in Python, it may thus be slow if Sample is large. We can define a function to operate on a Sample, and return a Sample.

For maximum performance, argument is in fact not a Sample, but a wrapper object which contains a pointer to data. When using Numpy arrays without copies and loops, performance is similar to C code, but Python definition is somewhat convoluted; please refer to Numpy documentation to learn how to efficiently define such functions.

define the same function on a Sample

```
def regularFuncSample(X):
# Create a numpy array with the contents of X without copy
xarray = np.array(X, copy=False)
# Get columns as vectors, there is also no copy
x0, x1, x2 = xarray.T
# Allocate a numpy array to store result
y = np.zeros((len(X), 2))
y[:, 0] = x0 + x1 + x2
y[:, 1] = (x1 - 1.0) * np.exp(x0) * x2
return y
```

create a Function object from a regular Python function

```
functionSample = ot.PythonFunction(3, 2, func_sample=regularFuncSample)
```

evaluate the function on a Sample

```
print("xs=", xs, "\nf(xs)=", functionSample(xs))
```

```
xs= [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]
f(xs)= [ y0 y1 ]
0 : [ 6 8.15485 ]
1 : [ 15 1310.36 ]
```

evaluate the function on a Point

```
print("x=", x, "f(x)=", functionSample(x))
```

```
x= [1.0, 2.0, 3.0] f(x)= [6,8.15485]
```

The most efficient solution is to provide evaluations both on Point and Sample. This requires two Python function definitions, but if your code takes a lot of time, you should consider this option.

```
functionFast = ot.PythonFunction(3, 2, func=regularFunc, func_sample=regularFuncSample)
```