Clustering for high dimensional neural data
NOTE: please follow the instructions for the klusta package https://github.com/kwikteam/klusta : the instructions below should not be used.
Install Python using the Anaconda distribution. You will need to install the packages numpy, scipy, cython and nose. For Windows, Python 2.7 might be a better option than 3.x.
On all platforms, KlustaKwik can be installed using pip install klustakwik2 (from source) or conda install -c kwikteam klustakwik2 (precompiled binary). The default installation options are as follows:
- Linux: Multithreading on by default.
- Windows: Multithreading on by default if MSVC installed, otherwise off. Note that under the Anaconda distribution, multithreading will be off by default, but this can be easily resolved if you have MSVC installed, see below.
- Mac: Multithreading off by default.
To override these options, install from source (see below).
Multithreading with Anaconda on Windows
The Anaconda distribution installs its own compiler that doesn’t support OpenMP for multithreading and uses this by default instead of the MSVC compiler, which does support OpenMP. To disable the Anaconda compiler, simply run the following command before installing KlustaKwik.
conda remove libpython
Installing from source
Download the source, either from one of the source distributions on PyPI or get the latest version from GitHub. Run one of the following commands from a command prompt in this directory. For default options:
python setup.py install
To force multithreading to be on:
python setup.py install –with-openmp
To force multithreading to be off:
python setup.py install –no-openmp
- Install a precompiled Windows binary with Anaconda in an anaconda package:
- conda install -c kwikteam klustakwik2
If you wish to compile from source, the instructions are a bit more complicated:
Using Python 2.7, you will need a copy of MS Visual Studio Express 2008 for Python, available for free download here. Python 3.x might require a different version of Visual Studio, we haven’t tested this.
Download the source as above. Open a command prompt in the directory where you downloaded and extracted the files. If you installed Python for all users, then you will need admin rights on this command prompt. To get this in Windows, press the Windows key, type “cmd”, right click on “cmd.exe” and click “Run as administrator”.
Now run the commands as in the section on installing from source above.
It is possible to install a version of gcc that allows for multithreading. TODO: details.
To cluster a pair of files name.fet.n, name.fmask.n run the command:
kk2_legacy name n
This will generate a name.klg.n and name.clu.n file. Note that the first time you run it, it will generate a whole lot of warnings and compiler output: ignore this, it is normal.
You can specify additional options to this script. The major ones are explained below:
- max_iterations=1000: the maximum number of full iterations to carry out before giving up.
- max_possible_clusters=1000: the maximum number of clusters to output at the end. At the moment, this option limits RAM usage but in a future version there will be no such limit.
- drop_last_n_features=0: don’t use the last N features of the fet file for clustering.
- save_clu_every=None: how often (in minutes) to save a temporary .clu file.
- num_starting_clusters=500: how many initial clusters to use (should probably be higher than the number of final clusters you expect to find).
- start_from_clu=None: if specified, start the clustering from a previously saved .clu file.
- use_mua_cluster=True: whether or not to use “black hole” MUA cluster variant of the algorithm. This is designed to improve clustering on noisier data sets, but you can turn it off if you are having problems with it. Note that if this is on, cluster 2 will be the MUA cluster, and the first ‘normal’ cluster will be cluster 3.
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