RoughPy 0.0.1

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RoughPy

RoughPy is a package for working with streaming data as rough paths, and working with algebraic objects such as free tensors, shuffle tensors, and elements of the free Lie algebra.

This library is currently in an alpha stage, and as such many features are still incomplete or not fully implemented. Please bear this in mind when looking at the source code.

Installation

Currently, RoughPy is only available as a source distribution. It should still be installable via pip, provided you have all the dependencies set up. Use

pip install https://github.com/datasig-ac-uk/roughpy

The compilation process is quite long - there is a lot to build. For the full release, we will add prebuilt binaries for major platforms. The following packages are required to build RoughPy:

• Boost (at least version 1.81, components system threads filesystem serialization)
• libsndfile
• Eigen3
• (More to be added)

Microsoft vcpkg can be used to install these dependencies - see the vcpkg.json file for the requirements.

Usage

Following the NumPy (and related) convention, we import RoughPy under the alias rp as follows:

import roughpy as rp

The main object(s) that you will interact with are Stream objects or the family of factory classes such as LieIncrementStream. For example, we can create a LieIncrementStream using the following commands:

stream = rp.LieIncrementStream.from_increments([[0., 1., 2.], [3., 4., 5.]], depth=2)

This will create a stream whose (hidden) underlying data are the two increments [0., 1., 2.] and [3., 4., 5.], and whose algebra elements are truncated at maximum depth 2. To compute the log signature over an interval we use the log_signature method on the stream, for example

interval = rp.RealInterval(0., 1.)
lsig = stream.log_signature(interval)

Printing this new object lsig should give the following result

{ 1(2) 2(3) }

which is the first increment from the underlying data. (By default, the increments are assumed to occur at parameter values equal to their row index in the provided data.)

Similarly, the signature can be computed using the signature method on the stream object:

sig = stream.signature(interval)

Notice that the lsig and sig objects have types Lie and FreeTensor, respectively. They behave exactly as you would expect elements of these algebras to behave. Moreover, they will (usually) be convertible directly to a NumPy array (or TensorFlow, PyTorch, JAX tensor type in the future) of the underlying data, so you can interact with them as if they were simple arrays.

Support

If you have a specific problem, the best way to record this is to open an issue on GitHub. We welcome any feedback or bug reports.

Contributing

In the future, we will welcome pull requests to implement new features, fix bugs, add documentation or examples, or add tests to the project. However, at present, we do not have robust CI pipelines set up to rigorously test incoming changes, and therefor will not be accepting pull requests made from outside the current team.

Contributors

The full list of contributors is listed in THANKS alongside this readme. The people mentioned in this document constitute The RoughPy Developers.

License

RoughPy is licensed under a BSD-3-Clause license. This was chosen specifically to match the license of NumPy.

Changelog

version 0.0.1: First alpha release