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diff --git a/Readme.md b/Readme.md deleted file mode 100644 index be42c4a..0000000 --- a/Readme.md +++ /dev/null @@ -1,131 +0,0 @@ -# PySpike
-
-[](https://travis-ci.org/mariomulansky/PySpike)
-
-PySpike is a Python library for the numerical analysis of spike train similarity.
-Its core functionality is the implementation of the bivariate [ISI](http://www.scholarpedia.org/article/Measures_of_spike_train_synchrony#ISI-distance) [1] and [SPIKE](http://www.scholarpedia.org/article/SPIKE-distance) [2] distance.
-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).
-
->[1] Kreuz T, Haas JS, Morelli A, Abarbanel HDI, Politi A, *Measuring spike train synchrony.* J Neurosci Methods 165, 151 (2007)
-
->[2] Kreuz T, Chicharro D, Houghton C, Andrzejak RG, Mormann F, *Monitoring spike train synchrony.* J Neurophysiol 109, 1457 (2013)
-
-## 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:
-
- 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.
-
-If you load spike trains yourself, i.e. from data files with different structure, you can use the helper function `add_auxiliary_spikes` to add the auxiliary spikes at the beginning and end of the observation interval.
-Both the ISI and the SPIKE distance computation require the presence of auxiliary spikes, so make sure you have those in your spike trains:
-
- spike_train = spk.add_auxiliary_spikes(spike_train, (T_start, T_end))
- # if you provide only a single value, it is interpreted as T_end, while T_start=0
- spike_train = spk.add_auxiliary_spikes(spike_train, T_end)
-
-## 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.
->
->Furthermore, the spike trains should have auxiliary spikes at the beginning and end of the observation interval.
->You can ensure this by providing the `time_interval` in the `load_spike_trains_from_txt` function, or calling `add_auxiliary_spikes` for your spike trains.
->The spike trains must have *the same* observation interval!
-
-----------------------
-
-### ISI-distance
-
-The following code loads some exemplary spike trains, computes the dissimilarity profile of the ISI-distance of the first two spike trains, and plots it with matplotlib:
-
- import matplotlib.pyplot as plt
- import pyspike as spk
-
- spike_trains = spk.load_spike_trains_from_txt("PySpike_testdata.txt",
- time_interval=(0, 4000))
- isi_profile = spk.isi_profile(spike_trains[0], spike_trains[1])
- x, y = isi_profile.get_plottable_data()
- plt.plot(x, y, '--k')
- print("ISI distance: %.8f" % isi_profil.avrg())
- plt.show()
-
-The ISI-profile is a piece-wise constant function, there the function `isi_profile` returns an instance of the `PieceWiseConstFunc` class.
-As shown above, this class allows you to obtain arrays that can be used to plot the function with `plt.plt`, but also to compute the absolute average, which amounts to the final scalar ISI-distance.
-If you are only interested in the scalar ISI-distance and not the profile, you can simly use:
-
- isi_dist = spk.isi_distance(spike_trains[0], spike_trains[1])
-
-Furthermore, PySpike provides the `average_profile` function that can be used to compute the average profile of a list of given `PieceWiseConstFunc` instances.
-
- avrg_profile = spk.average_profile([spike_train1, spike_train2])
- x, y = avrg_profile.get_plottable_data()
- plt.plot(x, y, label="Average profile")
-
-Note the difference between the `average_profile` function, which returns a `PieceWiseConstFunc` (or `PieceWiseLinFunc`, see below), and the `avrg` member function above, that computes the integral over the time profile.
-So to obtain overall average ISI-distance of a list of ISI profiles you can first compute the average profile using `average_profile` and the use
-
- avrg_isi = avrg_profile.avrg()
-
-to obtain the final, scalar average ISI distance of the whole set (see also "Computing multi-variate distance" below).
-
-## Computing multi-variate distances
-
-
-## Plotting
-
-
-## Averaging
|