File formats

PyNN supports writing datafiles in both text and binary formats. PyNN comes with several built-in formats, but it is very easy to define your own.

The default format is text-based. If we assume that you have run a simulation, and have recorded spikes, membrane potential and/or synaptic conductances for the neurons in a Population p, then you can write the recorded data to a file in text format simply by specifying the filename:

>>> p.printSpikes("my_spike_data.dat")
>>> p.print_v("my_Vm_data.dat")

(the file extension can be anything you like).

If you would like to write the data in a binary format, you must first create a PyNN File object`:

>>> from pyNN.recording.files import NumpyBinaryFile, HDF5ArrayFile
>>> spike_file = NumpyBinaryFile("my_spike_data.npz", "w")
>>> p.printSpikes(spike_file)
>>> spike_file.close()
>>> vm_file = HDF5ArrayFile("my_Vm_data.h5", "w")
>>> p.print_v(vm_file)
>>> vm_file.close()

Note that we do not currently take advantage of the ability of HDF5 or NumPy binary files to contain multiple data sets. In addition to NumpyBinaryFile and HDF5ArrayFile (which requires PyTables to be installed), the recording.files module also contains PickleFile and StandardTextFile.

The file contents can then be accessed using NumPy, PyTables or the standard pickle/cPickle module, or by creating a PyNN File object in read mode:

>>> spike_file = NumpyBinaryFile("my_spike_data.npz", "r")
>>> metadata = spike_file.get_metadata()
>>> spikes =

Defining your own file formats

If you wish to define your own file format, it is straightforward to create new PyNN-compatible File class by subclassing recording.files.BaseFile: the only requirement is that the class should implement a method write(data, metadata), where data will be a NumPy array and metadata will be a dictionary.