Wednesday, April 8, 2015

Elephant 0.1.0 released

We are pleased to announce the first release of the Elephant toolbox for the analysis of neurophysiology data.

Elephant builds on the Python scientific stack (NumPy, SciPy) to provide a set of well-tested analysis functions for spike train data and time series recordings from electrodes, such as spike train statistics, power spectrum analysis, filtering, cross-correlation and spike-triggered averaging. The toolbox also provides tools to generate artificial spike trains, either from stochastic processes or by controlled perturbations of real spike trains. Elephant is built on the Neo data model, and takes advantage of the broad file-format support provided by the Neo library. A bridge to the Pandas data analysis library is also provided.

Elephant is a community-based effort, aimed at providing a common platform to test and distribute code from different laboratories, with the goal of improving the reproducibility of modern neuroscience research. If you are a neuroscience researcher or student using Python for data analysis, please consider joining us, either to contribute your own code or to help with code review and testing.

Elephant is the direct successor to NeuroTools and maintains ties to complementary projects such as OpenElectrophy and SpykeViewer. It is also the default tool for electrophysiology data analysis in the Human Brain Project.

As a simple example, let's generate some artificial spike train data using a homogeneous Poisson process:

from elephant.spike_train_generation import homogeneous_poisson_process
from quantities import Hz, s, ms
spiketrains = [
    homogeneous_poisson_process(rate=10.0*Hz, t_start=0.0*s, t_stop=100.0*s)
    for i in range(100)]

and visualize it in Matplotlib:

import matplotlib.pyplot as plt
import numpy as np
for i, spiketrain in enumerate(spiketrains):
    t = spiketrain.rescale(ms)
    plt.plot(t, i * np.ones_like(t), 'k.', markersize=2)
plt.xlim(0, 1000)
plt.xlabel('Time (ms)', fontsize=16)
plt.ylabel('Spike Train Index', fontsize=16)
plt.gca().tick_params(axis='both', which='major', labelsize=14)
Now we calculate the coefficient of variation of the inter-spike interval for each of the 100 spike trains.

from elephant.statistics import isi, cv
cv_list = [cv(isi(spiketrain)) for spiketrain in spiketrains]

As expected for a Poisson process, the values cluster around 1:

plt.xlabel('CV', fontsize=16)
plt.ylabel('count', fontsize=16)
plt.gca().tick_params(axis='both', which='major', labelsize=14)


Wednesday, April 1, 2015

ANN: HoloViews 1.0 data visualization and ImaGen 2.0 pattern generation in Python

We are pleased to announce the first public release of HoloViews, a free Python package for scientific and engineering data visualization:

and version 2.0 of ImaGen, a free Python package for generating two-dimensional patterns useful for vision research and computational modeling:

HoloViews provides composable, sliceable, declarative data structures for building even complex visualizations of any scientific data very easily.  With HoloViews, you can see your data as publication-quality figures almost instantly, so that you can focus on the data itself, rather than on laboriously putting together your figures.  Even complex multi-subfigure layouts and animations are very easily built using HoloViews.

ImaGen provides highly configurable, resolution-independent input patterns, directly visualizable using HoloViews but also available without any plotting package so that they can easily be incorporated directly into your computational modeling or visual stimulus generation code.  With ImaGen, any software with a Python interface can immediately support configurable streams of 0D, 1D, or 2D patterns, without any extra coding.

HoloViews and ImaGen are very general tools, but they were designed to solve common problems faced by vision scientists and computational modelers.  HoloViews makes it very easy to visualize data from vision research, whether it is visual patterns, neural activity patterns, or more abstract measurements or analyses.  Essentially, HoloViews provides a set of general, compositional, multidimensional data structures suitable for both discrete and continuous real-world data, and pairs them with separate customizable plotting classes to visualize them without extensive coding.

ImaGen 2.0 uses the continuous coordinate systems provided by HoloViews to implement flexible resolution-independent generation of streams of patterns, with parameters controlled by the user and allowing randomness or other arbitrary changes over time.  These patterns can be used for visual stimulus generation, testing or training computational models, initializing connectivity in models, or any other application where random or dynamic but precisely controlled streams of patterns are needed.


- Freely available under a BSD license
- Python 2 and 3 compatible
- Minimal external dependencies -- easy to integrate into your workflow
- Declarative approach provides powerful compositionality with minimal coding
- Include extensive, continuously tested IPython Notebook tutorials
- Easily reconfigurable using documented and validated parameters
- Animations are supported natively, with no extra work
- Supports reproducible research -- simple specification, archived in an IPython Notebook, providing a recipe for regenerating your results
- HoloViews is one of three winners of the 2015 UK Open Source Awards

To get started, check out and!

Jean-Luc Stevens
Philipp Rudiger
Christopher Ball
James A. Bednar