# NumPy and SciPy¶

As a lightweight computer algebra system, PICOS sits one level above numerics libraries such as NumPy and SciPy and acts in concert with them. Let’s define a variable and some data:

```
>>> import picos, numpy, scipy.sparse
>>> x = picos.RealVariable("x", 4)
>>> N = numpy.reshape(range(16), (4, 4))
>>> type(N)
<class 'numpy.ndarray'>
>>> S = scipy.sparse.spdiags(range(4), 0, 4, 4)
>>> type(S)
<class 'scipy.sparse._dia.dia_matrix'>
```

Taking input from NumPy or SciPy

PICOS also allows loading of NumPy and SciPy data on the fly, with one caveat to watch out for:

```
>>> x.T*N
<1×4 Real Linear Expression: xᵀ·[4×4]>
>>> N*x
<4×1 Real Linear Expression: [4×4]·x>
>>> x.T*S
<1×4 Real Linear Expression: xᵀ·[4×4]>
>>> S*x
Traceback (most recent call last):
[...]
picos.valuable.NotValued: Mutable x is not valued.
```

The last command fails as SciPy sparse matrices do not currently respect the
__array_priority__ attribute, so
that SciPy tries to load `x`

as an array as opposed to conceding the operation
to PICOS like NumPy does. You can fix this behavior as follows:

```
>>> picos.patch_scipy_array_priority()
>>> S*x
<4×1 Real Linear Expression: [4×4]·x>
```

Note that this monkey-patches
SciPy, so that applications importing your code calling
`patch_scipy_array_priority`

will also see a patched
version of SciPy.

Returning NumPy or SciPy data as output

PICOS uses CVXOPT as a numerics backend and thus outputs numeric values as CVXOPT (sparse) matrices or Python scalar types by default:

```
>>> x.value = range(4)
>>> x.value
<4x1 matrix, tc='d'>
>>> type(x.value)
<class 'cvxopt.base.matrix'>
```

However, all objects that can be valued, in particular expressions and problem
instances, also offer properties to query that value as a NumPy type, namely
`np`

and `np2d`

:

```
>>> x.np # Returns a NumPy scalar, 1D, or 2D array.
array([0., 1., 2., 3.])
>>> type(x.np)
<class 'numpy.ndarray'>
>>> x.np.shape
(4,)
>>> x.np2d # Always returns a 2D array.
array([[0.],
[1.],
[2.],
[3.]])
>>> x.np2d.shape
(4, 1)
```

For SciPy, the `sp`

property returns a sparse
matrix whenever the data stored by PICOS internally is sparse and a NumPy 2D
array otherwise:

```
>>> I = picos.I(3)
>>> print(I)
[ 1.00e+00 0 0 ]
[ 0 1.00e+00 0 ]
[ 0 0 1.00e+00]
>>> type(I.sp)
<class 'scipy.sparse._csc.csc_matrix'>
>>> J = picos.J(3, 3)
>>> print(J)
[ 1.00e+00 1.00e+00 1.00e+00]
[ 1.00e+00 1.00e+00 1.00e+00]
[ 1.00e+00 1.00e+00 1.00e+00]
>>> type(J.sp)
<class 'numpy.ndarray'>
```

A full list of methods for returning values in different formats can be found in
the documentation of the `Valuable`

base class.