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https://github.com/sri-csl/ndk

Neurogram Deconvolution Kit
https://github.com/sri-csl/ndk

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Neurogram Deconvolution Kit

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README 

        
The Neurogram Deconvolution Kit:



    A mostly-python library for analysis and visualization of local

    field potential recordings of peripheral nerves.  Although the

    primary focus is peripheral nerve activity, this library will also

    be exercised on Cardiac, EEG and possibly other biological

    time-series data.



Coauthors and contributors:



	  Chris Connolly

	  Maneesh Yadav

	  Andy Poggio

	  Pat Lincoln



The high-level view: Dense, raw electrical recordings are stored in

Apache Cassandra, or any format supported by the 'neo' python package.

While Cassandra was an initial data store substrate, we also use an

internal binary format called 'nbf'.  The 'nbf' format consists of

memory-mappable raw data files, along with metadata stored in pickled

python form.  Look at nbf.py for details. 



The ndk package extracts sparser discrete features (action potential

spikes, for example) from dense time series data and stores them in a

separate data store that allows such features to be classified and

visualized.  These data stores are currently restricted to sqlite3 .db

files.  These .db files always point to the raw data source from which

they're derived.



In the interests of standardization of view and efficiency of data

access, NDK also supports a memory-mapped raw data format called

'nbf'.



NDK works using Python 2.7 and Python 3.5 on Ubuntu Linux, Mac OSX,

and Windows.  See below for Windows installation instructions.  Note

that the Windows build has only been tested under Windows 7.



INSTALLATION ON LINUX AND MACOSX

================================



NDK was originally developed in Python 2.7 under Ubuntu Linux and Mac

OS X.  Current and future development will rely on Python 3, but will

be backward compatible with Python 2.7.



The ndk can be installed by cd'ing into the ndk top-level directory

and doing the following:



    pip install .



NDK's setup.py file includes all of the needed dependencies, so they

should be autoloaded when you use pip.



here are the module dependencies:



	neo

	numpy

	scipy

	sqlite3

	sklearn

	pyOpenGL

	quantities

	matplotlib



	Theano (optional)



The 'mkspikes2' script uses Theano for fast convolution.  If you don't

have theano installed, 'mkspikes' will do the job somewhat more slowly.



The ndk module has two submodules, ds, and es, that provide access to

raw data stores and event stores respectively (hence the names).



Matlab notes: with version R2014b and higher, python functionality is

easily accessed within Matlab.  The ndk subdirectory 'matlab' contains

.m files that define the NDK Matlab API.



NOTES FOR INSTALLING ON WINDOWS:

================================



You can do a pip install of NDK on Windows, but there's a chance that

the 'vscatter' and 'vrasters' utilities won't work.  This is because

these utilities require OpenGL.  This can be a little tricky,

primarily because PyOpenGL.GLUT depends on the correct version and

placement of the GLUT DLL.  The following recipe has been tested on a

bare Windows 7 VM using:



   o 32-bit Python 2.7 (Anaconda)

     32-bit PyOpenGL‑3.1.1‑cp27‑cp27m‑win32.whl

     32-bit PyOpenGL_accelerate‑3.1.1‑cp27‑cp27m‑win32.whl



1) Install the 32-bit Anaconda Python Distribution for Python version

   2.7 from https://www.continuum.io/downloads#windows



[Note, the following two steps are thanks to stackoverflow answers. See the thread in

  http://stackoverflow.com/questions/26700719/pyopengl-glutinit-nullfunctionerror  ]



2) Go to http://www.lfd.uci.edu/~gohlke/pythonlibs/#pyopengl and 

download these two files:

         PyOpenGL‑3.1.1‑cp27‑cp27m‑win32.whl

	 PyOpenGL_accelerate‑3.1.1‑cp27‑cp27m‑win32.whl



3) Open the Anaconda Command Shell (preferably as Administrator) and use pip to install them:

         pip install PyOpenGL‑3.1.1‑cp27‑cp27m‑win32.whl

	 pip install PyOpenGL_accelerate‑3.1.1‑cp27‑cp27m‑win32.whl



4) Install git (windows version) https://git-scm.com/downloads



5) Clone the NDK repo: "git clone https://github.com/SRI-CSL/ndk.git"



6) cd to the 'ndk' directory and install:

     pip install .



More to come!