Metadata-Version: 1.1
Name: thorns
Version: 0.4
Summary: Spike analysis software
Home-page: https://github.com/mrkrd/thorns
Author: Marek Rudnicki
Author-email: marek.rudnicki@tum.de
License: GPLv3+
Download-URL: https://github.com/mrkrd/thorns/tarball/master
Description: thorns and waves
        ================
        
        *Spike analysis software*
        
        ::
        
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        :Name: thorns
        :Author: Marek Rudnicki
        :Email: marek.rudnicki@tum.de
        :URL: https://github.com/mrkrd/thorns
        :Documentation: https://pythonhosted.org/thorns/
        :License: GNU General Public License v3 or later (GPLv3+)
        
        
        
        Description
        -----------
        
        
        With *thorns* you can analyze and display `spike trains`_ generated by
        neurons.  It can be useful for both experimental and simulation data
        using Python.  For example, you can easily calculate peristimulus time
        histogram (PSTH), interspike time histogram (ISIH), vector strength
        (VS), entrainment and visualize action potentials with raster plot.
        
        *waves* is a submodule with some useful signal processing and
        generation functions, e.g. generate ramped tone, amplitude modulation
        tone, FFT filter, set level (dB_SPL).
        
        I developed the package during my PhD in the group of `Werner
        Hemmert`_ at the TUM.  It is oriented towards auditory research, but
        it could be easily extended.
        
        
        .. _`spike trains`: https://en.wikipedia.org/wiki/Spike_train
        .. _`Werner Hemmert`: http://www.imetum.tum.de/research/bai/home/?L=1
        
        
        Usage
        -----
        
        Please, check our IPython Notebook DEMO_.
        
        Initialize and load spike trains::
        
          import thorns as th
          from thorns.datasets import load_anf_zilany2014
        
          spike_trains = load_anf_zilany2014()
        
        
        
        Calculate vector strength::
        
          th.vector_strength(spike_trains, freq=1000)
        
        
        
        Raster plot::
        
          th.plot_raster(spike_trains)
          th.show()
        
        
        
        Generate and plot AM tone::
        
          import thorns.waves as wv
        
          sound = wv.amplitude_modulated_tone(
              fs=48e3,
              fm=100,
              fc=1e3,
              m=0.7,
              duration=0.1,
          )
        
          wv.plot_signal(sound, fs=48e3)
        
          wv.show()
        
        
        
        .. _DEMO: http://nbviewer.ipython.org/github/mrkrd/thorns/blob/master/examples/thorns_demo.ipynb
        
        
        Requirements
        ------------
        
        - Python (2.7)
        - Numpy
        - Scipy
        - Pandas
        - Matplotlib
        
        
        Installation
        ------------
        
        Quick install::
        
           pip install thorns
        
        
        
        Contributors
        ------------
        
        - Jörg Encke
        
Platform: Linux
Platform: Windows
Platform: FreeBSD
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Intended Audience :: End Users/Desktop
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
Classifier: Operating System :: POSIX
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2.7
