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# PySpike
PySpike is a Python library for the numerical analysis of spike train similarity.
Its core functionality is the implementation of the bivariate [ISI and SPIKE distance](http://www.scholarpedia.org/article/Measures_of_spike_train_synchrony).
Additionally, it provides functions to compute multi-variate SPIKE and ISI distances, as well as averaging and general spike train processing.
All computation intensive parts are implemented in C via [cython](http://www.cython.org) to reach a competitive performance (factor 100-200 over plain Python).
All source codes are published under the [BSD License](http://opensource.org/licenses/BSD-2-Clause).
## Requirements and Installation
To use PySpike you need Python installed with the following additional packages:
- numpy
- scipy
- matplotlib
- cython
- nosetests (for running the tests)
In particular, make sure that [cython](http://www.cython.org) is configured properly and able to locate a C compiler.
To install PySpike, simply download the source, e.g. from Github, and run the `setup.py` script:
git clone https://github.com/mariomulansky/PySpike.git
cd PySpike
python setup.py build_ext --inplace
Then you can run the tests using the `nosetests` test framework:
cd test
nosetests
Finally, you should make PySpike's installation folder known to Python to be able to import pyspike in your own projects.
Therefore, add your `/path/to/PySpike` to the `$PYTHONPATH` environment variable.
## Spike trains
In PySpike, spike trains are represented by one-dimensional numpy arrays containing the sequence of spike times as double values.
The following code creates such a spike train with some arbitrary spike times:
import numpy as np
spike_train = np.array([0.1, 0.3, 0.45, 0.6, 0.9])
### Loading from text files
Typically, spike train data is loaded into PySpike from data files.
The most straight-forward data files are text files where each line represents one spike train given as an sequence of spike times.
An exemplary file with several spike trains is [PySpike_testdata.txt](https://github.com/mariomulansky/PySpike/blob/master/examples/PySpike_testdata.txt).
To quickly obtain spike trains from such files, PySpike provides the function `load_spike_trains_from_txt`.
import numpy as np
import pyspike as spk
spike_trains = spk.load_spike_trains_from_txt("SPIKY_testdata.txt",
time_interval=(0,4000))
This function expects the name of the data file as first parameter, and additionally the time intervall of the spike train measurement can be provided as a pair of start- and end-time values.
If the time interval is provided (`time_interval is not None`), auxiliary spikes at the start- and end-time of the interval are added to the spike trains.
Furthermore, the spike trains are ordered via `np.sort` (disable this feature by providing `sort=False` as a parameter to the load function).
As result, `load_spike_trains_from_txt` returns a *list of arrays* containing the spike trains in the text file.
## Computing bi-variate distances
----------------------
**Important note:**
>Spike trains are expected to be *ordered sequences*!
>For performance reasons, the PySpike distance functions do not check if the spike trains provided are indeed ordered.
>Make sure that all your spike trains are ordered.
>If in doubt, use `spike_train = np.sort(spike_train)` to obtain a correctly ordered spike train.
----------------------
## Computing multi-variate distances
## Plotting
## Averaging
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