diff options
| author | Mario Mulansky <mario.mulansky@gmx.net> | 2014-10-12 18:29:43 +0200 |
|---|---|---|
| committer | Mario Mulansky <mario.mulansky@gmx.net> | 2014-10-12 18:29:43 +0200 |
| commit | 94408401e31830e7db7902afdeef714cb981bce2 (patch) | |
| tree | c07d5e059c283963a6c4b2a827551fd4650c863b | |
| parent | 9622ab19172f5fc2cfb9dab62b363182ba4302f2 (diff) | |
+ load section
| -rw-r--r-- | Readme.md | 42 |
1 files changed, 39 insertions, 3 deletions
@@ -18,9 +18,9 @@ To use PySpike you need Python installed with the following additional packages: 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, i.e. via git, and run the setup.py script:
+To install PySpike, simply download the source, i.e. via git clone, and run the setup.py script:
- git clone ...
+ git clone https://github.com/mariomulansky/PySpike.git
cd PySpike
python setup.py build_ext --inplace
@@ -29,11 +29,47 @@ Then you can run the tests using the `nosetests` test framework: cd test
nosetests
-## Loading spike trains
+Finally, you should make the 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])
+
+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 datafile 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`.
+As result, `load_spike_trains_from_txt` returns a *list of arrays* containing the spike trains in the text file.
## Computing bi-variate distances
+----------------------
+**Important note:**
+
+>Spike trains are expected to be *ordered sequences*!
+>For performance reasons, the PySpike distance function 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.
+
+----------------------
+
## Computing multi-variate distances
|