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# A quick start guide to the Point and Sample classes¶

## Abstract¶

In this example, we present the `Point`

and `Sample`

classes, two fundamental objects in the library.
We present the principles behind these classes and the way to create and use these objects.
We show how to extract a row or a column with the slicing operator.
We show how these objects interacts with Python variables and with the numpy module.

## Introduction¶

Two fundamental objects in the library are:

Point: a multidimensional point in dimensions () ;

Sample: a multivariate sample made of points in dimensions.

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

## The Point class¶

In this section, we see how to:

create a point in ,

access its components,

update its components.

By default, points are filled with zeros.

```
p = ot.Point(3)
p
```

The following statement returns the value of the second component (with index 1). Python beginners should remember that Python indices start at zero.

```
p[1]
```

```
0.0
```

The following statements sets the second component.

```
p[1] = 2
p
```

```
p.getDimension()
```

```
3
```

## The Sample class¶

The Sample class represents a multivariate sample made of points in .

is the

*dimension*of the sample,is the

*size*of the sample.

A Sample can be seen as an array of with rows and columns.

*Remark.* The `ProcessSample`

class can be used to manage a sample of stochastic processes.

The script below creates a Sample with size and dimension .

```
data = ot.Sample(5, 3)
data
```

```
data.getSize()
```

```
5
```

```
data.getDimension()
```

```
3
```

The following statement sets the third component (with index 2) of the fourth point (with index 3) in the Sample.

```
data[3, 2] = 32
data
```

Notice that the rendering is different when we use the print statement.

```
print(data)
```

```
0 : [ 0 0 0 ]
1 : [ 0 0 0 ]
2 : [ 0 0 0 ]
3 : [ 0 0 32 ]
4 : [ 0 0 0 ]
```

We can customize the format used to print the floating point numbers
with the Sample-PrintFormat key of the `ResourceMap`

.

## Get a row or a column of a Sample¶

As with numpy arrays, we can extract a row or a column with the : slicing operator.
As a reminder for Python beginners, *slicing* is the fact of extracting a part of an array with one single statement; this avoids for loops and improves performance and readability.

```
row = data[3, :]
row
```

```
print(type(row))
```

```
<class 'openturns.typ.Point'>
```

```
column = data[:, 2]
column
```

```
print(type(column))
```

```
<class 'openturns.typ.Sample'>
```

We see that:

the row is a Point,

the column is a Sample.

This is consistent with the fact that, in a dimension Sample, a row is a -dimensional Point.

The following statement extracts several columns (with indices 0 and 2) and creates a new Sample.

```
data.getMarginal([0, 2])
```

Slicing can also be used to set a Sample row or column.

```
sample = ot.Sample([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])
p = [8.0, 10.0]
sample[2, :] = p
sample
```

```
sample = ot.Sample([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])
s = ot.Sample([[3.0], [5.0], [7.0]])
sample[:, 1] = s
sample
```

## Create a Point or a Sample from a Python list¶

The following statement creates a Point from a Python list.

```
p1 = ot.Point([2, 3])
p1
```

```
p2 = ot.Point(range(2))
p2
```

The first useful *Pythonism* that we will review is the *list comprehension*. This creates a list from a for loop.
This kind of statements is often used in the examples, so that they can be as short as possible.
In the following statement, we create a point by iterating over the components of a Point.

```
p3 = ot.Point([i * i for i in p1])
p3
```

The second useful *Pythonism* is the repetition with the * operator.

The following statements creates a list with three 5s.

```
p4 = [5] * 3
p4
```

```
[5, 5, 5]
```

We can also create a Sample from a list of Point.

```
sample = ot.Sample([p1, p2, p3])
sample
```

We can loop over the points in a sample, using a list comprehension. In the following example, we compute the Euclidian norm of the points in the previous sample.

```
[point.norm() for point in sample]
```

```
[3.605551275463989, 1.0, 9.848857801796104]
```

We can also create a Sample based on a Point, repeated three times.

```
sample = ot.Sample([p4] * 3)
sample
```

A nested list of floats is the easiest way to create a non-trivial Sample.

```
sample = ot.Sample([[0, 1], [2, 3], [4, 5]])
sample
```

## Interactions with Numpy¶

The Python classes defined in Python modules are unknown to OpenTURNS and hence cannot be used by the library. This is why it is useful to know how to convert to and from more basic Python variable types, especially Numpy arrays.

The following statement creates a Sample and converts it into a bidimensional Numpy array.

```
sample = ot.Sample(5, 3)
array = np.array(sample)
array
```

```
array([[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.]])
```

```
type(array)
```

Conversely, the following script creates a Numpy array, then converts it into a Sample.

```
array = 3.14 * np.ones((5, 3))
sample = ot.Sample(array)
sample
```

```
sample.getSize()
```

```
5
```

```
sample.getDimension()
```

```
3
```

There is an ambiguous situation: a Sample based on several scalar values.

For example, is a Sample based on 5 values:

a Sample with size 5 in 1 dimension or

a Sample with size 1 in 5 dimensions?

In order to solve the case, we can use the second input argument of the Sample constructor, which specifies the dimension.

The following statement creates an array containing 5 values from 0 to 1.

```
u = np.linspace(0, 1, 5)
u
```

```
array([0. , 0.25, 0.5 , 0.75, 1. ])
```

Choice A: we create a Sample with size 5 in 1 dimension.

```
sample = ot.Sample([[ui] for ui in u])
sample
```

Choice B: we create a Sample with size 1 in 5 dimensions.

```
sample = ot.Sample([u[i: i + 5] for i in range(len(u) // 5)])
sample
```

If we do not set the optional size parameter, the library cannot solve the case and an InvalidArgumentException is generated. More precisely, the code:

```
sample = ot.Sample(u)
```

produces the exception:

```
TypeError: InvalidArgumentException : Invalid array dimension: 1
```