This is a brief introduction to the nixpy library. We will demonstrate how electrophsiology data can be stored in a NIX file and how it can be retrieved and plotted. We will use a rather simple example with just a single time-series and a series of neural events.
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import datetime
from nixio import *
from utils.plotting import Plotter
from utils.notebook import print_stats, print_metadata_table
%matplotlib inline
First we open a new file. The open method gets two parameters: the name of the file to open and and a file mode.
The file mode determines whether the file should be opened for reading or writing.
Choosing the file mode Overwrite opens the file for reading and writing and overwrites any existing content.
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f = File.open("first_file.demo.h5", FileMode.Overwrite)
print "Format: %s %s" % (f.format, f.version)
All other data objects of the NIX data model reside inside a Block element. A block is used to group data that belongs together. Each data element in NIX has a property type which is used to provide additional semantic meaning. We will now create a single block and assign a type which indicates that the block represents a recording session.
Further we print out the block's id. The id is unique and automatically generated.
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block = f.create_block("Test Session", "nix.session")
print "Name: %s" % block.name
print "Id: %s" % block.id
print_stats(f.blocks)
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from utils.lif import lif
model = lif()
times, voltage, spike_times = model.run_const_stim(10000, 0.005)
sampling_interval = times[1] - times[0]
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# create a data array for the membrane voltage
signal = block.create_data_array("Membrane Voltage", "nix.regular_sampled.time_series", data=voltage)
signal.label = "voltage"
signal.unit = "V"
# describe the x-axis of the array
xaxis = signal.append_sampled_dimension(sampling_interval)
xaxis.label = "time"
xaxis.unit = "s"
# create a data array for the spiketimes
spikes = block.create_data_array("Spike Times", "nix.events.spike_times", data=spike_times)
spikes.label = "time"
spikes.unit = "s"
spikes.append_set_dimension()
print_stats(block.data_arrays)
The signal and the spike train are stored such that all important information like units, labels and sampling interval etc. can be interpreted correctly.
However, one information is still missing: we would like to indicate that the spike train was derived from the time series and that the spike times correspond to the time-serie's x-axis. In NIX this can be achieved using a MultiTag. This tag can be used to define points of interest (e.g. spike times) inside other data (e.g. time-series).
In the next step we create a tag in order to indicate that the times in spikes occured in the recorded signal
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spike_tag = block.create_multi_tag("Spike Times", "nix.events.spike_times", spikes)
spike_tag.references.append(signal)
print_stats(block.multi_tags)
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source = block.create_source("Electrode 01", "nix.electrode")
signal.sources.append(source)
print_stats(signal.sources)
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plotter = Plotter(width=1000, height=500)
plotter.add(spikes, color="red")
plotter.add(signal)
plotter.plot()
print_stats(f.blocks)
print_stats(f.blocks[0].data_arrays)
print_stats(f.blocks[0].multi_tags)
print_stats(f.blocks[0].sources)
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session = f.create_section("Test Session", "nix.session")
session["experimenter"] = "Adrian Stoewer"
session["date"] = "2015-06-14"
electrode = session.create_section("Electrode 01", "nix.electrode")
electrode["No"] = 1
electrode["Manufacturer"] = "Multichannel Systems"
t = print_metadata_table(session)
t = print_metadata_table(electrode)
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block.metadata = session
source.metadata = electrode
print(block.metadata["experimenter"])
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f.close()
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