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kwiklib2

This is a rewrite of the Python library providing I/O access to files in the kwik format and in the previous Buzsaki format. Compared to the previous kwiklib implementation, this new version should provide:

  • Python 3 support with six
  • An implementation based exclusively on h5py instead of PyTables
  • A saner API and implementation

Interfaces

There will be two interfaces for data access.

High-level API

High-level API with user-friendly functions for common use-cases: accessing spike times and spike clusters for a given shank, get the features and the waveforms, etc.

Low-level API

A very simple API that basically provides a single function to access any part of the HDF5 file, given the full HDF5 path.

Data conversion

There will also be simple facilities to convert from/to the old Buzsaki format.

Mock datasets

Early on, we should create a complete suite of utilities to generate random data (but not "stupid" random: waveforms should look like waveforms, etc.). The random data could be generated in memory or in files (Buzsaki or Kwik format). This will be crucial for unit testing.

API Specification

The very first thing to do is to work out the complete specification of the APIs: list of classes, methods, and functions with their docstrings and code examples.

We could start writing unit tests and code examples before working on the implementation (test-driven development).

Best practices

  • Pure Python (no Cython, no C, etc.)
  • Dependencies: NumPy and h5py only
  • Unit testing framework with nose
  • Test coverage with coverage
  • Python 2.7/3.3+ support with six
  • Continuous integration with Travis CI, that runs unit tests, code coverage, on both Python 2 and Python 3
  • Contributions by pull requests exclusively as early as possible
  • Local development on Python 3 (Anaconda 64-bit for Python 3)

Anaconda is the only Python distribution we'll officially support (much simpler in terms of maintenance/support). This distribution is free and works on all operating systems.

Misc notes

  • We'll need a special data structure that derives from ndarray, with special index labels (absolute/relative indexing)
  • We should also think about a good data structure for time-dependent data (continuous signals, spike times, event, epochs, etc.). See Neo for some inspiration.
  • Decide early on common conventions, data structures, ontology for the objects (like in sklearn and skimage).
    • Data type/units for spike times: int64 exclusively, in samples, for perfect precision.
    • No floating point data type for spike times, and convert at the last moment only.
    • Data structure for a single spike train: int64 (N,)
    • Data structure for spike trains: int64 (N, 2) (spike time, cluster index)
  • Use native data structs as much as possible (see native collections module for example)
  • Prefer pure functions over classes as much as possible