SeqTools 1.4.1

Example

>>> def count_lines(filename):
...     with open(filename) as f:
...         return len(f.readlines())
>>>
>>> def count_words(filename):
...     with open(filename) as f:
...         return len(f.read().split())
>>>
>>> filenames = ["a.txt", "b.txt", "c.txt", "d.txt"]
>>> lc = seqtools.smap(count_lines, filenames)
>>> wc = seqtools.smap(count_words, filenames)
>>> counts = seqtools.collate([lc, wc])
>>> # no computations so far!
>>> lc[2]  # only evaluates on index 2
3
>>> counts[1]  # same for index 1
(1, 2)

Batteries included!

The library comes with a set of functions to manipulate sequences:

and others (suggestions are also welcome).

Installation

pip install seqtools

Documentation

The documentation is hosted at https://seqtools-doc.readthedocs.io.

Contributing and Support

Use the issue tracker to request features, propose improvements or report issues. For questions regarding usage, please send an email.

Related libraries

Joblib, proposes low-level functions with many optimization settings to optimize pipelined transformations. This library notably provides advanced caching mechanisms which are not the primary concern of SeqTool. SeqTool uses a simpler container-oriented interface with multiple utility functions in order to assist fast prototyping. On-demand evaluation is its default behaviour and applies at all layers of a transformation pipeline. Eager evaluation of elements in SeqTools does not break the list-like interface and can be used in the middle of a transformation pipeline.

SeqTools is conceived to connect nicely to the data loading pipeline of Machine Learning libraries such as PyTorch’s torch.utils.data and torchvision.transforms or Tensorflow’s tf.data. The interface of these libraries focuses on iterators to access transformed elements, contrary to SeqTools which also provides arbitrary reads via indexing.