Operators and solvers for high-performance computing.

## Project description

The PyOperators package defines operators and solvers for high-performance computing. These operators are multi-dimensional functions with optimised and controlled memory management. If linear, they behave like matrices with a sparse storage footprint.

More documentation can be found here: http://pchanial.github.io/pyoperators.

## Getting started

To define an operator, one needs to define a direct function which will replace the usual matrix-vector operation:

>>> def f(x, out): ... out[...] = 2 * x

Then, you can instantiate an `Operator`:

>>> A = pyoperators.Operator(direct=f, flags='symmetric')

An alternative way to define an operator is to define a subclass:

>>> from pyoperators import flags, Operator ... @flags.symmetric ... class MyOperator(Operator): ... def direct(x, out): ... out[...] = 2 * x ... ... A = MyOperator()

This operator does not have an explicit shape, it can handle inputs of any shape:

>>> A(np.ones(5)) array([ 2., 2., 2., 2., 2.]) >>> A(np.ones((2,3))) array([[ 2., 2., 2.], [ 2., 2., 2.]])

By setting the `symmetric` flag, we ensure that A’s transpose is A:

>>> A.T is A True

For non-explicit shape operators, we get the corresponding dense matrix by specifying the input shape:

>>> A.todense(shapein=2) array([[2, 0], [0, 2]])

Operators do not have to be linear. Many operators are already predefined, such as the `IdentityOperator`, the `DiagonalOperator` or the nonlinear `ClipOperator`.

The previous `A` matrix could be defined more easily like this:

>>> from pyoperators import I >>> A = 2 * I

where `I` is the identity operator with no explicit shape.

Operators can be combined together by addition, element-wise multiplication or composition. Note that the operator `*` stands for matrix multiplication if the two operators are linear, or for element-wise multiplication otherwise:

>>> from pyoperators import I, DiagonalOperator >>> B = 2 * I + DiagonalOperator(range(3)) >>> B.todense() array([[2, 0, 0], [0, 3, 0], [0, 0, 4]])

Algebraic rules can easily be attached to operators. They are used to simplify expressions to speed up their execution. The `B` Operator has been reduced to:

>>> B DiagonalOperator(array([2, ..., 4], dtype=int64), broadcast='disabled', dtype=int64, shapein=3, shapeout=3)

Many simplifications are available. For instance:

>>> from pyoperators import Operator >>> C = Operator(flags='idempotent,linear') >>> C * C is C True >>> D = Operator(flags='involutary') >>> D(D) IdentityOperator()

## Requirements

List of requirements:

- python 2.6
- numpy >= 1.6
- scipy >= 0.9

Optional requirements:

- numexpr (>= 2.0 is better)
- PyWavelets : wavelet transforms

## Project details

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