Neo is a Python package for working with electrophysiology data in Python, together with support for reading a wide range of neurophysiology file formats, including Spike2, NeuroExplorer, AlphaOmega, Axon, Blackrock, Plexon, Tdt, Igor Pro, and support for writing to a subset of these formats plus non-proprietary formats including Klustakwik and HDF5.

The goal of Neo is to improve interoperability between Python tools for analyzing, visualizing and generating electrophysiology data, by providing a common, shared object model. In order to be as lightweight a dependency as possible, Neo is deliberately limited to represention of data, with no functions for data analysis or visualization.

Neo is used by a number of other software tools, including OpenElectrophy and SpykeViewer (data analysis and visualization), Elephant (data analysis), the G-node suite (databasing) and PyNN (simulations).

Neo implements a hierarchical data model well adapted to intracellular and extracellular electrophysiology and EEG data with support for multi-electrodes (for example tetrodes). Neo’s data objects build on the quantities package, which in turn builds on NumPy by adding support for physical dimensions. Thus Neo objects behave just like normal NumPy arrays, but with additional metadata, checks for dimensional consistency and automatic unit conversion.

A project with similar aims but for neuroimaging file formats is NiBabel.

Documentation

Installation

Neo is a pure Python package, so it should be easy to get it running on any system.

Dependencies

• Python >= 2.7
• numpy >= 1.7.1
• quantities >= 0.9.0

For Debian/Ubuntu, you can install these using:

$ apt-get install python-numpy python-pip
$ pip install quantities

You may need to run these as root. For other operating systems, you can download installers from the links above, or use a scientific Python distribution such as Anaconda.

Certain IO modules have additional dependencies. If these are not satisfied, Neo will still install but the IO module that uses them will fail on loading:

• scipy >= 0.12.0 for NeoMatlabIO
• h5py >= 2.5 for Hdf5IO, KwikIO
• klusta for KwikIO
• igor >= 0.2 for IgorIO
• nixio >= 1.2 for NixIO
• stfio for StimfitIO

Installing from the Python Package Index

Warning

alpha and beta releases cannot be installed from PyPI.

If you have pip installed:

$ pip install neo

This will automatically download and install the latest release (again you may need to have administrator privileges on the machine you are installing on).

To download and install manually, download:

https://github.com/NeuralEnsemble/python-neo/archive/neo-0.5.2.zip

Then:

$ unzip neo-0.5.2.zip
$ cd neo-0.5.2
$ python setup.py install

or:

$ python3 setup.py install

depending on which version of Python you are using.

Installing from source

To install the latest version of Neo from the Git repository:

$ git clone git://github.com/NeuralEnsemble/python-neo.git
$ cd python-neo
$ python setup.py install

Neo core

This figure shows the main data types in Neo:

Neo objects fall into three categories: data objects, container objects and grouping objects.

Data objects

These objects directly represent data as arrays of numerical values with associated metadata (units, sampling frequency, etc.).

• AnalogSignal: A regular sampling of a single- or multi-channel continuous analog signal.
• IrregularlySampledSignal: A non-regular sampling of a single- or multi-channel continuous analog signal.
• SpikeTrain: A set of action potentials (spikes) emitted by the same unit in a period of time (with optional waveforms).
• Event: An array of time points representing one or more events in the data.
• Epoch: An array of time intervals representing one or more periods of time in the data.

Container objects

There is a simple hierarchy of containers:

• Segment: A container for heterogeneous discrete or continous data sharing a common clock (time basis) but not necessarily the same sampling rate, start time or end time. A Segment can be considered as equivalent to a “trial”, “episode”, “run”, “recording”, etc., depending on the experimental context. May contain any of the data objects.
• Block: The top-level container gathering all of the data, discrete and continuous, for a given recording session. Contains Segment, Unit and ChannelIndex objects.

Grouping objects

These objects express the relationships between data items, such as which signals were recorded on which electrodes, which spike trains were obtained from which membrane potential signals, etc. They contain references to data objects that cut across the simple container hierarchy.

• ChannelIndex: A set of indices into AnalogSignal objects, representing logical and/or physical recording channels. This has two uses:

  1. for linking AnalogSignal objects recorded from the same (multi)electrode across several Segments.
  2. for spike sorting of extracellular signals, where spikes may be recorded on more than one recording channel, and the ChannelIndex can be used to associate each Unit with the group of recording channels from which it was obtained.

• Unit: links the SpikeTrain objects within a Block, possibly across multiple Segments, that were emitted by the same cell. A Unit is linked to the ChannelIndex object from which the spikes were detected.

NumPy compatibility

Neo data objects inherit from Quantity, which in turn inherits from NumPy ndarray. This means that a Neo AnalogSignal is also a Quantity and an array, giving you access to all of the methods available for those objects.

For example, you can pass a SpikeTrain directly to the numpy.histogram() function, or an AnalogSignal directly to the numpy.std() function.

Relationships between objects

Container objects like Block or Segment are gateways to access other objects. For example, a Block can access a Segment with:

>>> bl = Block()
>>> bl.segments
# gives a list of segments

A Segment can access the AnalogSignal objects that it contains with:

>>> seg = Segment()
>>> seg.analogsignals
# gives a list of AnalogSignals

In the Neo diagram below, these one to many relationships are represented by cyan arrows. In general, an object can access its children with an attribute childname+s in lower case, e.g.

• Block.segments
• Segments.analogsignals
• Segments.spiketrains
• Block.channel_indexes

These relationships are bi-directional, i.e. a child object can access its parent:

• Segment.block
• AnalogSignal.segment
• SpikeTrain.segment
• ChannelIndex.block

Here is an example showing these relationships in use:

from neo.io import AxonIO
import urllib
url = "https://portal.g-node.org/neo/axon/File_axon_3.abf"
filename = './test.abf'
urllib.urlretrieve(url, filename)

r = AxonIO(filename=filename)
bl = r.read() # read the entire file > a Block
print(bl)
print(bl.segments) # child access
for seg in bl.segments:
    print(seg)
    print(seg.block) # parent access

In some cases, a one-to-many relationship is sufficient. Here is a simple example with tetrodes, in which each tetrode has its own group.:

from neo import Block, ChannelIndex
bl = Block()

# the four tetrodes
for i in range(4):
    chx = ChannelIndex(name='Tetrode %d' % i,
                       index=[0, 1, 2, 3])
    bl.channelindexes.append(chx)

# now we load the data and associate it with the created channels
# ...

Now consider a more complex example: a 1x4 silicon probe, with a neuron on channels 0,1,2 and another neuron on channels 1,2,3. We create a group for each neuron to hold the Unit object associated with this spike sorting group. Each group also contains the channels on which that neuron spiked. The relationship is many-to-many because channels 1 and 2 occur in multiple groups.:

bl = Block(name='probe data')

# one group for each neuron
chx0 = ChannelIndex(name='Group 0',
                    index=[0, 1, 2])
bl.channelindexes.append(chx0)

chx1 = ChannelIndex(name='Group 1',
                    index=[1, 2, 3])
bl.channelindexes.append(chx1)

# now we add the spiketrain from Unit 0 to chx0
# and add the spiketrain from Unit 1 to chx1
# ...

Note that because neurons are sorted from groups of channels in this situation, it is natural that the ChannelIndex contains a reference to the Unit object. That unit then contains references to its spiketrains. Also note that recording channels can be identified by names/labels as well as, or instead of, integer indices.

See Typical use cases for more examples of how the different objects may be used.

Neo diagram

Object:

• With a star = inherits from Quantity

Attributes:

• In red = required
• In white = recommended

Relationship:

• In cyan = one to many
• In yellow = properties (deduced from other relationships)

Click here for a better quality SVG diagram

For more details, see the API Reference.

Initialization

Neo objects are initialized with “required”, “recommended”, and “additional” arguments.

• Required arguments MUST be provided at the time of initialization. They are used in the construction of the object.
• Recommended arguments may be provided at the time of initialization. They are accessible as Python attributes. They can also be set or modified after initialization.
• Additional arguments are defined by the user and are not part of the Neo object model. A primary goal of the Neo project is extensibility. These additional arguments are entries in an attribute of the object: a Python dict called annotations.

Example: SpikeTrain

SpikeTrain is a Quantity, which is a NumPy array containing values with physical dimensions. The spike times are a required attribute, because the dimensionality of the spike times determines the way in which the Quantity is constructed.

Here is how you initialize a SpikeTrain with required arguments:

>>> import neo
>>> st = neo.SpikeTrain([3, 4, 5], units='sec', t_stop=10.0)
>>> print(st)
[ 3.  4.  5.] s

You will see the spike times printed in a nice format including the units. Because st “is a” Quantity array with units of seconds, it absolutely must have this information at the time of initialization. You can specify the spike times with a keyword argument too:

>>> st = neo.SpikeTrain(times=[3, 4, 5], units='sec', t_stop=10.0)

The spike times could also be in a NumPy array.

If it is not specified, t_start is assumed to be zero, but another value can easily be specified:

>>> st = neo.SpikeTrain(times=[3, 4, 5], units='sec', t_start=1.0, t_stop=10.0)
>>> st.t_start
array(1.0) * s

Recommended attributes must be specified as keyword arguments, not positional arguments.

Finally, let’s consider “additional arguments”. These are the ones you define for your experiment:

>>> st = neo.SpikeTrain(times=[3, 4, 5], units='sec', t_stop=10.0, rat_name='Fred')
>>> print(st.annotations)
{'rat_name': 'Fred'}

Because rat_name is not part of the Neo object model, it is placed in the dict annotations. This dict can be modified as necessary by your code.

Annotations

As well as adding annotations as “additional” arguments when an object is constructed, objects may be annotated using the annotate() method possessed by all Neo core objects, e.g.:

>>> seg = Segment()
>>> seg.annotate(stimulus="step pulse", amplitude=10*nA)
>>> print(seg.annotations)
{'amplitude': array(10.0) * nA, 'stimulus': 'step pulse'}

Since annotations may be written to a file or database, there are some limitations on the data types of annotations: they must be “simple” types or containers (lists, dicts, NumPy arrays) of simple types, where the simple types are integer, float, complex, Quantity, string, date, time and datetime.

See specific_annotations

Typical use cases

Recording multiple trials from multiple channels

In this example we suppose that we have recorded from an 8-channel probe, and that we have recorded three trials/episodes. We therefore have a total of 8 x 3 = 24 signals, grouped into three AnalogSignal objects, one per trial.

Our entire dataset is contained in a Block, which in turn contains:

• 3 Segment objects, each representing data from a single trial,
• 1 ChannelIndex.

Segment and ChannelIndex objects provide two different ways to access the data, corresponding respectively, in this scenario, to access by time and by space.

Note

Segments do not always represent trials, they can be used for many purposes: segments could represent parallel recordings for different subjects, or different steps in a current clamp protocol.

Temporal (by segment)

In this case you want to go through your data in order, perhaps because you want to correlate the neural response with the stimulus that was delivered in each segment. In this example, we’re averaging over the channels.

import numpy as np
from matplotlib import pyplot as plt

for seg in block.segments:
    print("Analyzing segment %d" % seg.index)

    avg = np.mean(seg.analogsignals[0], axis=1)

    plt.figure()
    plt.plot(avg)
    plt.title("Peak response in segment %d: %f" % (seg.index, avg.max()))

Spatial (by channel)

In this case you want to go through your data by channel location and average over time. Perhaps you want to see which physical location produces the strongest response, and every stimulus was the same:

# We assume that our block has only 1 ChannelIndex
chx = block.channelindexes[0]:
siglist = [sig[:, chx.index] for sig in chx.analogsignals]
avg = np.mean(siglist, axis=0)

plt.figure()
for index, name in zip(chx.index, chx.channel_names):
    plt.plot(avg[:, index])
    plt.title("Average response on channels %s: %s' % (index, name)

Mixed example

Combining simultaneously the two approaches of descending the hierarchy temporally and spatially can be tricky. Here’s an example. Let’s say you saw something interesting on the 6th channel (index 5) on even numbered trials during the experiment and you want to follow up. What was the average response?

index = chx.index[5]
avg = np.mean([seg.analogsignals[0][:, index] for seg in block.segments[::2]], axis=1)
plt.plot(avg)

Recording spikes from multiple tetrodes

Here is a similar example in which we have recorded with two tetrodes and extracted spikes from the extra-cellular signals. The spike times are contained in SpikeTrain objects.

Again, our data set is contained in a Block, which contains:

• 3 Segments (one per trial).
• 2 ChannelIndexes (one per tetrode), which contain:
  • 2 Unit objects (= 2 neurons) for the first ChannelIndex
  • 5 Units for the second ChannelIndex.

In total we have 3 x 7 = 21 SpikeTrains in this Block.

There are three ways to access the SpikeTrain data:

• by Segment
• by RecordingChannel
• by Unit

By Segment

In this example, each Segment represents data from one trial, and we want a PSTH for each trial from all units combined:

for seg in block.segments:
    print("Analyzing segment %d" % seg.index)
    stlist = [st - st.t_start for st in seg.spiketrains]
    plt.figure()
    count, bins = np.histogram(stlist)
    plt.bar(bins[:-1], count, width=bins[1] - bins[0])
    plt.title("PSTH in segment %d" % seg.index)

By Unit

Now we can calculate the PSTH averaged over trials for each unit, using the block.list_units property:

for unit in block.list_units:
    stlist = [st - st.t_start for st in unit.spiketrains]
    plt.figure()
    count, bins = np.histogram(stlist)
    plt.bar(bins[:-1], count, width=bins[1] - bins[0])
    plt.title("PSTH of unit %s" % unit.name)

By ChannelIndex

Here we calculate a PSTH averaged over trials by channel location, blending all units:

for chx in block.channelindexes:
    stlist = []
    for unit in chx.units:
        stlist.extend([st - st.t_start for st in unit.spiketrains])
    plt.figure()
    count, bins = np.histogram(stlist)
    plt.bar(bins[:-1], count, width=bins[1] - bins[0])
    plt.title("PSTH blend of tetrode  %s" % chx.name)

Spike sorting

Spike sorting is the process of detecting and classifying high-frequency deflections (“spikes”) on a group of physically nearby recording channels.

For example, let’s say you have defined a ChannelIndex for a tetrode containing 4 separate channels. Here is an example showing (with fake data) how you could iterate over the contained signals and extract spike times. (Of course in reality you would use a more sophisticated algorithm.)

# generate some fake data
seg = Segment()
seg.analogsignals.append(
    AnalogSignal([[0.1, 0.1, 0.1, 0.1],
                  [-2.0, -2.0, -2.0, -2.0],
                  [0.1, 0.1, 0.1, 0.1],
                  [-0.1, -0.1, -0.1, -0.1],
                  [-0.1, -0.1, -0.1, -0.1],
                  [-3.0, -3.0, -3.0, -3.0],
                  [0.1, 0.1, 0.1, 0.1],
                  [0.1, 0.1, 0.1, 0.1]],
                 sampling_rate=1000*Hz, units='V'))
chx = ChannelIndex(channel_indexes=[0, 1, 2, 3])
chx.analogsignals.append(seg.analogsignals[0])


# extract spike trains from each channel
st_list = []
for signal in chx.analogsignals:
    # use a simple threshhold detector
    spike_mask = np.where(np.min(signal.magnitude, axis=1) < -1.0)[0]

    # create a spike train
    spike_times = signal.times[spike_mask]
    st = neo.SpikeTrain(spike_times, t_start=signal.t_start, t_stop=signal.t_stop)

    # remember the spike waveforms
    wf_list = []
    for spike_idx in np.nonzero(spike_mask)[0]:
        wf_list.append(signal[spike_idx-1:spike_idx+2, :])
    st.waveforms = np.array(wf_list)

    st_list.append(st)

At this point, we have a list of spiketrain objects. We could simply create a single Unit object, assign all spike trains to it, and then assign the Unit to the group on which we detected it.

u = Unit()
u.spiketrains = st_list
chx.units.append(u)

Now the recording channel group (tetrode) contains a list of analogsignals, and a single Unit object containing all of the detected spiketrains from those signals.

Further processing could assign each of the detected spikes to an independent source, a putative single neuron. (This processing is outside the scope of Neo. There are many open-source toolboxes to do it, for instance our sister project OpenElectrophy.)

In that case we would create a separate Unit for each cluster, assign its spiketrains to it, and then store all the units in the original recording channel group.

Neo IO

Preamble

The Neo io module aims to provide an exhaustive way of loading and saving several widely used data formats in electrophysiology. The more these heterogeneous formats are supported, the easier it will be to manipulate them as Neo objects in a similar way. Therefore the IO set of classes propose a simple and flexible IO API that fits many format specifications. It is not only file-oriented, it can also read/write objects from a database.

At the moment, there are 3 families of IO modules:

1. for reading closed manufacturers’ formats (Spike2, Plexon, AlphaOmega, BlackRock, Axon, ...)
2. for reading(/writing) formats from open source tools (KlustaKwik, Elan, WinEdr, WinWcp, PyNN, ...)
3. for reading/writing Neo structure in neutral formats (HDF5, .mat, ...) but with Neo structure inside (NeoHDF5, NeoMatlab, ...)

Combining 1 for reading and 3 for writing is a good example of use: converting your datasets to a more standard format when you want to share/collaborate.

Introduction

There is an intrinsic structure in the different Neo objects, that could be seen as a hierachy with cross-links. See Neo core. The highest level object is the Block object, which is the high level container able to encapsulate all the others.

A Block has therefore a list of Segment objects, that can, in some file formats, be accessed individually. Depending on the file format, i.e. if it is streamable or not, the whole Block may need to be loaded, but sometimes particular Segment objects can be accessed individually. Within a Segment, the same hierarchical organisation applies. A Segment embeds several objects, such as SpikeTrain, AnalogSignal, AnaloSignalArray, EpochArray, EventArray (basically, all the different Neo objects).

Depending on the file format, these objects can sometimes be loaded separately, without the need to load the whole file. If possible, a file IO therefore provides distinct methods allowing to load only particular objects that may be present in the file. The basic idea of each IO file format is to have, as much as possible, read/write methods for the individual encapsulated objects, and otherwise to provide a read/write method that will return the object at the highest level of hierarchy (by default, a Block or a Segment).

The neo.io API is a balance between full flexibility for the user (all read_XXX() methods are enabled) and simple, clean and understandable code for the developer (few read_XXX() methods are enabled). This means that not all IOs offer the full flexibility for partial reading of data files.

One format = one class

The basic syntax is as follows. If you want to load a file format that is implemented in a generic MyFormatIO class:

>>> from neo.io import MyFormatIO
>>> reader = MyFormatIO(filename="myfile.dat")

you can replace MyFormatIO by any implemented class, see List of implemented formats

Modes

IO can be based on a single file, a directory containing files, or a database. This is described in the mode attribute of the IO class.

>>> from neo.io import MyFormatIO
>>> print MyFormatIO.mode
'file'

For file mode the filename keyword argument is necessary. For directory mode the dirname keyword argument is necessary.

Ex:

>>> reader = io.PlexonIO(filename='File_plexon_1.plx')
>>> reader = io.TdtIO(dirname='aep_05')

Supported objects/readable objects

To know what types of object are supported by a given IO interface:

>>> MyFormatIO.supported_objects
[Segment , AnalogSignal , SpikeTrain, Event, Spike]

Supported objects does not mean objects that you can read directly. For instance, many formats support AnalogSignal but don’t allow them to be loaded directly, rather to access the AnalogSignal objects, you must read a Segment:

>>> seg = reader.read_segment()
>>> print(seg.analogsignals)
>>> print(seg.analogsignals[0])

To get a list of directly readable objects

>>> MyFormatIO.readable_objects
[Segment]

The first element of the previous list is the highest level for reading the file. This mean that the IO has a read_segment() method:

>>> seg = reader.read_segment()
>>> type(seg)
neo.core.Segment

All IOs have a read() method that returns a list of Block objects (representing the whole content of the file):

>>> bl = reader.read()
>>> print bl[0].segments[0]
neo.core.Segment

Lazy and cascade options

In some cases you may not want to load everything in memory because it could be too big. For this scenario, two options are available:

• lazy=True/False. With lazy=True all arrays will have a size of zero, but all the metadata will be loaded. lazy_shape attribute is added to all object that inheritate Quantitities or numpy.ndarray (AnalogSignal, AnalogSignalArray, SpikeTrain) and to object that have array like attributes (EpochArray, EventArray) In that cases, lazy_shape is a tuple that have the same shape with lazy=False.
• cascade=True/False. With cascade=False only one object is read (and one_to_many and many_to_many relationship are not read).

By default (if they are not specified), lazy=False and cascade=True, i.e. all data is loaded.

Example cascade:

>>> seg = reader.read_segment( cascade=True)
>>> print(len(seg.analogsignals))  # this is N
>>> seg = reader.read_segment(cascade=False)
>>> print(len(seg.analogsignals))  # this is zero

Example lazy:

>>> seg = reader.read_segment(lazy=False)
>>> print(seg.analogsignals[0].shape)  # this is N
>>> seg = reader.read_segment(lazy=True)
>>> print(seg.analogsignals[0].shape)  # this is zero, the AnalogSignal is empty
>>> print(seg.analogsignals[0].lazy_shape)  # this is N

Some IOs support advanced forms of lazy loading, cascading or both (these features are currently limited to the HDF5 IO, which supports both forms).

• For lazy loading, these IOs have a load_lazy_object() method that takes a single parameter: a data object previously loaded by the same IO in lazy mode. It returns the fully loaded object, without links to container objects (Segment etc.). Continuing the lazy example above:

  >>> lazy_sig = seg.analogsignals[0]  # Empty signal
  >>> full_sig = reader.load_lazy_object(lazy_sig)
  >>> print(lazy_sig.lazy_shape, full_sig.shape)  # Identical
  >>> print(lazy_sig.segment)  # Has the link to the object "seg"
  >>> print(full_sig.segment)  # Does not have the link: None
  

• For lazy cascading, IOs have a load_lazy_cascade() method. This method is not called directly when interacting with the IO, but its presence can be used to check if an IO supports lazy cascading. To use lazy cascading, the cascade parameter is set to 'lazy':

  >>> block = reader.read(cascade='lazy')
  

  You do not have to do anything else, lazy cascading is now active for the object you just loaded. You can interact with the object in the same way as if it was loaded with cascade=True. However, only the objects that are actually accessed are loaded as soon as they are needed:

  >>> print(block.channelindexes[0].name)  # The first ChannelIndex is loaded
  >>> print(block.segments[0].analogsignals[1])  # The first Segment and its second AnalogSignal are loaded
  

  Once an object has been loaded with lazy cascading, it stays in memory:

  >>> print(block.segments[0].analogsignals[0])  # The first Segment is already in memory, its first AnalogSignal is loaded
  

Details of API

The neo.io API is designed to be simple and intuitive:

• each file format has an IO class (for example for Spike2 files you have a Spike2IO class).
• each IO class inherits from the BaseIO class.
• each IO class can read or write directly one or several Neo objects (for example Segment, Block, ...): see the readable_objects and writable_objects attributes of the IO class.
• each IO class supports part of the neo.core hierachy, though not necessarily all of it (see supported_objects).
• each IO class has a read() method that returns a list of Block objects. If the IO only supports Segment reading, the list will contain one block with all segments from the file.
• each IO class that supports writing has a write() method that takes as a parameter a list of blocks, a single block or a single segment, depending on the IO’s writable_objects.
• each IO is able to do a lazy load: all metadata (e.g. sampling_rate) are read, but not the actual numerical data. lazy_shape attribute is added to provide information on real size.
• each IO is able to do a cascade load: if True (default) all child objects are loaded, otherwise only the top level object is loaded.
• each IO is able to save and load all required attributes (metadata) of the objects it supports.
• each IO can freely add user-defined or manufacturer-defined metadata to the annotations attribute of an object.

If you want to develop your own IO

See IO developers’ guide for information on how to implement a new IO.

List of implemented formats

neo.io provides classes for reading and/or writing electrophysiological data files.

Note that if the package dependency is not satisfied for one io, it does not raise an error but a warning.

neo.io.iolist provides a list of succesfully imported io classes.

Classes:

class neo.io.AlphaOmegaIO(filename=None)

Class for reading data from Alpha Omega .map files (experimental)

This class is an experimental reader with important limitations. See the source code for details of the limitations. The code of this reader is of alpha quality and received very limited testing.

Usage:

>>> from neo import io
>>> r = io.AlphaOmegaIO( filename = 'File_AlphaOmega_1.map')
>>> blck = r.read_block(lazy = False, cascade = True)
>>> print blck.segments[0].analogsignals

class neo.io.AsciiSignalIO(filename=None)

Class for reading signal in generic ascii format. Columns respresents signals. They all share the same sampling rate. The sampling rate is externally known or the first columns could hold the time vector.

Usage:

>>> from neo import io
>>> r = io.AsciiSignalIO(filename='File_asciisignal_2.txt')
>>> seg = r.read_segment(lazy=False, cascade=True)
>>> print seg.analogsignals
[<AnalogSignal(array([ 39.0625    ,   0.        ,   0.        , ..., -26.85546875 ...

class neo.io.AsciiSpikeTrainIO(filename=None)

Classe for reading/writing SpikeTrains in a text file. Each Spiketrain is a line.

Usage:

>>> from neo import io
>>> r = io.AsciiSpikeTrainIO( filename = 'File_ascii_spiketrain_1.txt')
>>> seg = r.read_segment(lazy = False, cascade = True,)
>>> print seg.spiketrains     
[<SpikeTrain(array([ 3.89981604,  4.73258781,  0.608428  ,  4.60246277,  1.23805797,
...

class neo.io.AxonIO(filename=None)

Class for reading data from pCLAMP and AxoScope files (.abf version 1 and 2), developed by Molecular Device/Axon Technologies.

Usage:

>>> from neo import io
>>> r = io.AxonIO(filename='File_axon_1.abf')
>>> bl = r.read_block(lazy=False, cascade=True)
>>> print bl.segments
[<neo.core.segment.Segment object at 0x105516fd0>]
>>> print bl.segments[0].analogsignals
[<AnalogSignal(array([ 2.18811035,  2.19726562,  2.21252441, ...,
    1.33056641,  1.3458252 ,  1.3671875 ], dtype=float32) * pA,
    [0.0 s, 191.2832 s], sampling rate: 10000.0 Hz)>]
>>> print bl.segments[0].events
[]

class neo.io.BlackrockIO(filename, nsx_override=None, nev_override=None, sif_override=None, ccf_override=None, verbose=False)

Class for reading data in from a file set recorded by the Blackrock (Cerebus) recording system.

Upon initialization, the class is linked to the available set of Blackrock files. Data can be read as a neo Block or neo Segment object using the read_block or read_segment function, respectively.

Note: This routine will handle files according to specification 2.1, 2.2, and 2.3. Recording pauses that may occur in file specifications 2.2 and 2.3 are automatically extracted and the data set is split into different segments.

Inherits from:

neo.io.BaseIO

The Blackrock data format consists not of a single file, but a set of different files. This constructor associates itself with a set of files that constitute a common data set. By default, all files belonging to the file set have the same base name, but different extensions. However, by using the override parameters, individual filenames can be set.

Args:

filename (string):

File name (without extension) of the set of Blackrock files to associate with. Any .nsX or .nev, .sif, or .ccf extensions are ignored when parsing this parameter.

nsx_override (string):

File name of the .nsX files (without extension). If None, filename is used. Default: None.

nev_override (string):

File name of the .nev file (without extension). If None, filename is used. Default: None.

sif_override (string):

File name of the .sif file (without extension). If None, filename is used. Default: None.

ccf_override (string):

File name of the .ccf file (without extension). If None, filename is used. Default: None.

verbose (boolean):

If True, the class will output additional diagnostic information on stdout. Default: False

Returns:

Examples:

>>> a = BlackrockIO('myfile')

Loads a set of file consisting of files myfile.ns1, ..., myfile.ns6, and myfile.nev

>>> b = BlackrockIO('myfile', nev_override='sorted')

Loads the analog data from the set of files myfile.ns1, ..., myfile.ns6, but reads spike/event data from sorted.nev

class neo.io.BrainVisionIO(filename=None)

Class for reading/writing data from BrainVision products (brainAmp, brain analyser...)

Usage:

>>> from neo import io
>>> r = io.BrainVisionIO( filename = 'File_brainvision_1.eeg')
>>> seg = r.read_segment(lazy = False, cascade = True,)

class neo.io.BrainwareDamIO(filename=None)

Class for reading Brainware raw data files with the extension ‘.dam’.

The read_block method returns the first Block of the file. It will automatically close the file after reading. The read method is the same as read_block.

Note:

The file format does not contain a sampling rate. The sampling rate is set to 1 Hz, but this is arbitrary. If you have a corresponding .src or .f32 file, you can get the sampling rate from that. It may also be possible to infer it from the attributes, such as “sweep length”, if present.

Usage:

>>> from neo.io.brainwaredamio import BrainwareDamIO
>>> damfile = BrainwareDamIO(filename='multi_500ms_mulitrep_ch1.dam')
>>> blk1 = damfile.read()
>>> blk2 = damfile.read_block()
>>> print blk1.segments
>>> print blk1.segments[0].analogsignals
>>> print blk1.units
>>> print blk1.units[0].name
>>> print blk2
>>> print blk2[0].segments

class neo.io.BrainwareF32IO(filename=None)

Class for reading Brainware Spike ReCord files with the extension ‘.f32’

The read_block method returns the first Block of the file. It will automatically close the file after reading. The read method is the same as read_block.

The read_all_blocks method automatically reads all Blocks. It will automatically close the file after reading.

The read_next_block method will return one Block each time it is called. It will automatically close the file and reset to the first Block after reading the last block. Call the close method to close the file and reset this method back to the first Block.

The isopen property tells whether the file is currently open and reading or closed.

Note 1:

There is always only one ChannelIndex. BrainWare stores the equivalent of ChannelIndexes in separate files.

Usage:

>>> from neo.io.brainwaref32io import BrainwareF32IO
>>> f32file = BrainwareF32IO(filename='multi_500ms_mulitrep_ch1.f32')
>>> blk1 = f32file.read()
>>> blk2 = f32file.read_block()
>>> print blk1.segments
>>> print blk1.segments[0].spiketrains
>>> print blk1.units
>>> print blk1.units[0].name
>>> print blk2
>>> print blk2[0].segments

class neo.io.BrainwareSrcIO(filename=None)

Class for reading Brainware Spike ReCord files with the extension ‘.src’

The read_block method returns the first Block of the file. It will automatically close the file after reading. The read method is the same as read_block.

The read_all_blocks method automatically reads all Blocks. It will automatically close the file after reading.

The read_next_block method will return one Block each time it is called. It will automatically close the file and reset to the first Block after reading the last block. Call the close method to close the file and reset this method back to the first Block.

The _isopen property tells whether the file is currently open and reading or closed.

Note 1:

The first Unit in each ChannelIndex is always UnassignedSpikes, which has a SpikeTrain for each Segment containing all the spikes not assigned to any Unit in that Segment.

Note 2:

The first Segment in each Block is always Comments, which stores all comments as an Event object.

Note 3:

The parameters from the BrainWare table for each condition are stored in the Segment annotations. If there are multiple repetitions of a condition, each repetition is stored as a separate Segment.

Note 4:

There is always only one ChannelIndex. BrainWare stores the equivalent of ChannelIndexes in separate files.

Usage:

>>> from neo.io.brainwaresrcio import BrainwareSrcIO
>>> srcfile = BrainwareSrcIO(filename='multi_500ms_mulitrep_ch1.src')
>>> blk1 = srcfile.read()
>>> blk2 = srcfile.read_block()
>>> blks = srcfile.read_all_blocks()
>>> print blk1.segments
>>> print blk1.segments[0].spiketrains
>>> print blk1.units
>>> print blk1.units[0].name
>>> print blk2
>>> print blk2[0].segments
>>> print blks
>>> print blks[0].segments

class neo.io.ElanIO(filename=None)

Classe for reading/writing data from Elan.

Usage:

>>> from neo import io
>>> r = io.ElanIO(filename='File_elan_1.eeg')
>>> seg = r.read_segment(lazy = False, cascade = True,)
>>> print seg.analogsignals 
[<AnalogSignal(array([ 89.21203613,  88.83666992,  87.21008301, ...,
    64.56298828, 67.94128418,  68.44177246], dtype=float32) * pA,
    [0.0 s, 101.5808 s], sampling rate: 10000.0 Hz)>]
>>> print seg.spiketrains   
[]
>>> print seg.events   
[]

class neo.io.IgorIO(filename=None, parse_notes=None)

Class for reading Igor Binary Waves (.ibw) written by WaveMetrics’ IGOR Pro software.

Support for Packed Experiment (.pxp) files is planned.

It requires the igor Python package by W. Trevor King.

Usage:

>>> from neo import io
>>> r = io.IgorIO(filename='...ibw')

class neo.io.KlustaKwikIO(filename, sampling_rate=30000.0)

Reading and writing from KlustaKwik-format files.

class neo.io.KwikIO(filename)

Class for “reading” experimental data from a .kwik file.

Generates a Segment with a AnalogSignal

class neo.io.MicromedIO(filename=None)

Class for reading data from micromed (.trc).

Usage:

>>> from neo import io
>>> r = io.MicromedIO(filename='File_micromed_1.TRC')
>>> seg = r.read_segment(lazy=False, cascade=True)
>>> print seg.analogsignals 
[<AnalogSignal(array([ -1.77246094e+02,  -2.24707031e+02,
    -2.66015625e+02, ...

class neo.io.NeoHdf5IO(filename)

Class for reading HDF5 format files created by Neo version 0.4 or earlier.

Writing to HDF5 is not supported by this IO; we recommend using NixIO for this.

class neo.io.NeoMatlabIO(filename=None)

Class for reading/writing Neo objects in MATLAB format (.mat) versions 5 to 7.2.

This module is a bridge for MATLAB users who want to adopt the Neo object representation. The nomenclature is the same but using Matlab structs and cell arrays. With this module MATLAB users can use neo.io to read a format and convert it to .mat.

Rules of conversion:

• Neo classes are converted to MATLAB structs. e.g., a Block is a struct with attributes “name”, “file_datetime”, ...
• Neo one_to_many relationships are cellarrays in MATLAB. e.g., seg.analogsignals[2] in Python Neo will be seg.analogsignals{3} in MATLAB.
• Quantity attributes are represented by 2 fields in MATLAB. e.g., anasig.t_start = 1.5 * s in Python will be anasig.t_start = 1.5 and anasig.t_start_unit = 's' in MATLAB.
• classes that inherit from Quantity (AnalogSignal, SpikeTrain, ...) in Python will have 2 fields (array and units) in the MATLAB struct. e.g.: AnalogSignal( [1., 2., 3.], 'V') in Python will be anasig.array = [1. 2. 3] and anasig.units = 'V' in MATLAB.

1 - Scenario 1: create data in MATLAB and read them in Python

This MATLAB code generates a block:

block = struct();
block.segments = { };
block.name = 'my block with matlab';
for s = 1:3
    seg = struct();
    seg.name = strcat('segment ',num2str(s));

    seg.analogsignals = { };
    for a = 1:5
        anasig = struct();
        anasig.signal = rand(100,1);
        anasig.signal_units = 'mV';
        anasig.t_start = 0;
        anasig.t_start_units = 's';
        anasig.sampling_rate = 100;
        anasig.sampling_rate_units = 'Hz';
        seg.analogsignals{a} = anasig;
    end

    seg.spiketrains = { };
    for t = 1:7
        sptr = struct();
        sptr.times = rand(30,1)*10;
        sptr.times_units = 'ms';
        sptr.t_start = 0;
        sptr.t_start_units = 'ms';
        sptr.t_stop = 10;
        sptr.t_stop_units = 'ms';
        seg.spiketrains{t} = sptr;
    end

    event = struct();
    event.times = [0, 10, 30];
    event.times_units = 'ms';
    event.labels = ['trig0'; 'trig1'; 'trig2'];
    seg.events{1} = event;

    epoch = struct();
    epoch.times = [10, 20];
    epoch.times_units = 'ms';
    epoch.durations = [4, 10];
    epoch.durations_units = 'ms';
    epoch.labels = ['a0'; 'a1'];
    seg.epochs{1} = epoch;

    block.segments{s} = seg;
    
end

save 'myblock.mat' block -V7

This code reads it in Python:

import neo
r = neo.io.NeoMatlabIO(filename='myblock.mat')
bl = r.read_block()
print bl.segments[1].analogsignals[2]
print bl.segments[1].spiketrains[4]

2 - Scenario 2: create data in Python and read them in MATLAB

This Python code generates the same block as in the previous scenario:

import neo
import quantities as pq
from scipy import rand, array

bl = neo.Block(name='my block with neo')
for s in range(3):
    seg = neo.Segment(name='segment' + str(s))
    bl.segments.append(seg)
    for a in range(5):
        anasig = neo.AnalogSignal(rand(100)*pq.mV, t_start=0*pq.s, sampling_rate=100*pq.Hz)
        seg.analogsignals.append(anasig)
    for t in range(7):
        sptr = neo.SpikeTrain(rand(40)*pq.ms, t_start=0*pq.ms, t_stop=10*pq.ms)
        seg.spiketrains.append(sptr)
    ev = neo.Event([0, 10, 30]*pq.ms, labels=array(['trig0', 'trig1', 'trig2']))
    ep = neo.Epoch([10, 20]*pq.ms, durations=[4, 10]*pq.ms, labels=array(['a0', 'a1']))
    seg.events.append(ev)
    seg.epochs.append(ep)

from neo.io.neomatlabio import NeoMatlabIO
w = NeoMatlabIO(filename='myblock.mat')
w.write_block(bl)

This MATLAB code reads it:

load 'myblock.mat'
block.name
block.segments{2}.analogsignals{3}.signal
block.segments{2}.analogsignals{3}.signal_units
block.segments{2}.analogsignals{3}.t_start
block.segments{2}.analogsignals{3}.t_start_units

3 - Scenario 3: conversion

This Python code converts a Spike2 file to MATLAB:

from neo import Block
from neo.io import Spike2IO, NeoMatlabIO

r = Spike2IO(filename='spike2.smr')
w = NeoMatlabIO(filename='convertedfile.mat')
blocks = r.read()
w.write(blocks[0])

class neo.io.NestIO(filenames=None)

Class for reading NEST output files. GDF files for the spike data and DAT files for analog signals are possible.

Usage:

>>> from neo.io.nestio import NestIO

>>> files = ['membrane_voltages-1261-0.dat',
         'spikes-1258-0.gdf']
>>> r = NestIO(filenames=files)
>>> seg = r.read_segment(gid_list=[], t_start=400 * pq.ms,
                     t_stop=600 * pq.ms,
                     id_column_gdf=0, time_column_gdf=1,
                     id_column_dat=0, time_column_dat=1,
                     value_columns_dat=2)

class neo.io.NeuralynxIO(sessiondir=None, cachedir=None, use_cache='hash', print_diagnostic=False, filename=None)

Class for reading Neuralynx files.

It enables reading: - :class:’Block’ - :class:’Segment’ - :class:’AnalogSignal’ - :class:’SpikeTrain’

Usage:

from neo import io import quantities as pq import matplotlib.pyplot as plt

session_folder = ‘../Data/2014-07-24_10-31-02’ NIO = io.NeuralynxIO(session_folder,print_diagnostic = True) block = NIO.read_block(t_starts = 0.1*pq.s, t_stops = 0.2*pq.s, events=True) seg = block.segments[0] analogsignal = seg.analogsignals[0] plt.plot(analogsignal.times.rescale(pq.ms), analogsignal.magnitude) plt.show()

class neo.io.NeuroExplorerIO(filename=None)

Class for reading nex files.

Usage:

>>> from neo import io
>>> r = io.NeuroExplorerIO(filename='File_neuroexplorer_1.nex')
>>> seg = r.read_segment(lazy=False, cascade=True)
>>> print seg.analogsignals 
[<AnalogSignal(array([ 39.0625    ,   0.        ,   0.        , ...,
>>> print seg.spiketrains   
[<SpikeTrain(array([  2.29499992e-02,   6.79249987e-02, ...
>>> print seg.events   
[<Event: @21.1967754364 s, @21.2993755341 s, @21.350725174 s, ...
>>> print seg.epochs   
[<neo.core.epoch.Epoch object at 0x10561ba90>,
 <neo.core.epoch.Epoch object at 0x10561bad0>]

class neo.io.NeuroScopeIO(filename=None)

neo.io.NeuroshareIO

alias of NeurosharectypesIO

class neo.io.NixIO(filename, mode='rw')

Class for reading and writing NIX files.

class neo.io.NSDFIO(filename=None)

Class for reading and writing files in NSDF Format.

It supports reading and writing: Block, Segment, AnalogSignal, ChannelIndex, with all relationships and metadata.

class neo.io.PickleIO(filename=None, **kargs)

A class for reading and writing Neo data from/to the Python “pickle” format.

Note that files in this format may not be readable if using a different version of Neo to that used to create the file. It should therefore not be used for long-term storage, but rather for intermediate results in a pipeline.

class neo.io.PlexonIO(filename=None)

Class for reading data from Plexon acquisition systems (.plx)

Compatible with versions 100 to 106. Other versions have not been tested.

Usage:

>>> from neo import io
>>> r = io.PlexonIO(filename='File_plexon_1.plx')
>>> seg = r.read_segment(lazy=False, cascade=True)
>>> print seg.analogsignals
[]
>>> print seg.spiketrains  
[<SpikeTrain(array([  2.75000000e-02,   5.68250000e-02, ...,
...
>>> print seg.events
[]

class neo.io.PyNNNumpyIO(filename=None, **kargs)

Reads/writes data from/to PyNN NumpyBinaryFile format

class neo.io.PyNNTextIO(filename=None, **kargs)

Reads/writes data from/to PyNN StandardTextFile format

class neo.io.RawBinarySignalIO(filename=None)

Class for reading/writing data in a raw binary interleaved compact file.

Usage:

>>> from neo import io
>>> r = io.RawBinarySignalIO( filename = 'File_ascii_signal_2.txt')
>>> seg = r.read_segment(lazy = False, cascade = True,)
>>> print seg.analogsignals  
...

class neo.io.StimfitIO(filename=None)

Class for converting a stfio Recording to a Neo object. Provides a standardized representation of the data as defined by the neo project; this is useful to explore the data with an increasing number of electrophysiology software tools that rely on the Neo standard.

stfio is a standalone file i/o Python module that ships with the Stimfit program (http://www.stimfit.org). It is a Python wrapper around Stimfit’s file i/o library (libstfio) that natively provides support for the following file types:

• ABF (Axon binary file format; pClamp 6–9)
• ABF2 (Axon binary file format 2; pClamp 10+)
• ATF (Axon text file format)
• AXGX/AXGD (Axograph X file format)
• CFS (Cambridge electronic devices filing system)
• HEKA (HEKA binary file format)
• HDF5 (Hierarchical data format 5; only hdf5 files written by Stimfit or stfio are supported)

In addition, libstfio can use the biosig file i/o library as an additional file handling backend (http://biosig.sourceforge.net/), extending support to more than 30 additional file formats (http://pub.ist.ac.at/~schloegl/biosig/TESTED).

Example usage:

>>> import neo
>>> neo_obj = neo.io.StimfitIO("file.abf")
or
>>> import stfio
>>> stfio_obj = stfio.read("file.abf")
>>> neo_obj = neo.io.StimfitIO(stfio_obj)

class neo.io.TdtIO(dirname=None)

Class for reading data from from Tucker Davis TTank format.

Usage:

>>> from neo import io
>>> r = io.TdtIO(dirname='aep_05')
>>> bl = r.read_block(lazy=False, cascade=True)
>>> print bl.segments
[<neo.core.segment.Segment object at 0x1060a4d10>]
>>> print bl.segments[0].analogsignals
[<AnalogSignal(array([ 2.18811035,  2.19726562,  2.21252441, ...,
    1.33056641, 1.3458252 ,  1.3671875 ], dtype=float32) * pA,
    [0.0 s, 191.2832 s], sampling rate: 10000.0 Hz)>]
>>> print bl.segments[0].events
[]

class neo.io.WinEdrIO(filename=None)

Class for reading data from WinEDR.

Usage:

>>> from neo import io
>>> r = io.WinEdrIO(filename='File_WinEDR_1.EDR')
>>> seg = r.read_segment(lazy=False, cascade=True,)
>>> print seg.analogsignals
[<AnalogSignal(array([ 89.21203613,  88.83666992,  87.21008301, ...,  64.56298828,
        67.94128418,  68.44177246], dtype=float32) * pA, [0.0 s, 101.5808 s], sampling rate: 10000.0 Hz)>]

class neo.io.WinWcpIO(filename=None)

Class for reading from a WinWCP file.

Usage:

>>> from neo import io
>>> r = io.WinWcpIO( filename = 'File_winwcp_1.wcp')
>>> bl = r.read_block(lazy = False, cascade = True,)
>>> print bl.segments   
[<neo.core.segment.Segment object at 0x1057bd350>, <neo.core.segment.Segment object at 0x1057bd2d0>,
...
>>> print bl.segments[0].analogsignals
[<AnalogSignal(array([-2438.73388672, -2428.96801758, -2425.61083984, ..., -2695.39453125,
...

Logging

neo uses the standard Python logging module for logging. All neo.io classes have logging set up by default, although not all classes produce log messages. The logger name is the same as the full qualified class name, e.g. neo.io.hdf5io.NeoHdf5IO. By default, only log messages that are critically important for users are displayed, so users should not disable log messages unless they are sure they know what they are doing. However, if you wish to disable the messages, you can do so:

>>> import logging
>>>
>>> logger = logging.getLogger('neo')
>>> logger.setLevel(100)

Some io classes provide additional information that might be interesting to advanced users. To enable these messages, do the following:

>>> import logging
>>>
>>> logger = logging.getLogger('neo')
>>> logger.setLevel(logging.INFO)

It is also possible to log to a file in addition to the terminal:

>>> import logging
>>>
>>> logger = logging.getLogger('neo')
>>> handler = logging.FileHandler('filename.log')
>>> logger.addHandler(handler)

To only log to the terminal:

>>> import logging
>>> from neo import logging_handler
>>>
>>> logger = logging.getLogger('neo')
>>> handler = logging.FileHandler('filename.log')
>>> logger.addHandler(handler)
>>>
>>> logging_handler.setLevel(100)

This can also be done for individual IO classes:

>>> import logging
>>>
>>> logger = logging.getLogger('neo.io.hdf5io.NeoHdf5IO')
>>> handler = logging.FileHandler('filename.log')
>>> logger.addHandler(handler)

Individual IO classes can have their loggers disabled as well:

>>> import logging
>>>
>>> logger = logging.getLogger('neo.io.hdf5io.NeoHdf5IO')
>>> logger.setLevel(100)

And more detailed logging messages can be enabled for individual IO classes:

>>> import logging
>>>
>>> logger = logging.getLogger('neo.io.hdf5io.NeoHdf5IO')
>>> logger.setLevel(logging.INFO)

The default handler, which is used to print logs to the command line, is stored in neo.logging_handler. This example changes how the log text is displayed:

>>> import logging
>>> from neo import logging_handler
>>>
>>> formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
>>> logging_handler.setFormatter(formatter)

For more complex logging, please see the documentation for the logging module.

Note

If you wish to implement more advanced logging as describe in the documentation for the logging module or elsewhere on the internet, please do so before calling any neo functions or initializing any neo classes. This is because the default handler is created when neo is imported, but it is not attached to the neo logger until a class that uses logging is initialized or a function that uses logging is called. Further, the handler is only attached if there are no handlers already attached to the root logger or the neo logger, so adding your own logger will override the default one. Additional functions and/or classes may get logging during bugfix releases, so code relying on particular modules not having logging may break at any time without warning.

Examples

Introduction

A set of examples in neo/examples/ illustrates the use of Neo classes.

# -*- coding: utf-8 -*-
"""
This is an example for reading files with neo.io
"""

import urllib

import neo


# Plexon files
distantfile = 'https://portal.g-node.org/neo/plexon/File_plexon_3.plx'
localfile = './File_plexon_3.plx'
urllib.request.urlretrieve(distantfile, localfile)

# create a reader
reader = neo.io.PlexonIO(filename='File_plexon_3.plx')
# read the blocks
blks = reader.read(cascade=True, lazy=False)
print (blks)
# access to segments
for blk in blks:
    for seg in blk.segments:
        print (seg)
        for asig in seg.analogsignals:
            print (asig)
        for st in seg.spiketrains:
            print (st)


# CED Spike2 files
distantfile = 'https://portal.g-node.org/neo/spike2/File_spike2_1.smr'
localfile = './File_spike2_1.smr'
urllib.request.urlretrieve(distantfile, localfile)

# create a reader
reader = neo.io.Spike2IO(filename='File_spike2_1.smr')
# read the block
bl = reader.read(cascade=True, lazy=False)[0]
print (bl)
# access to segments
for seg in bl.segments:
    print (seg)
    for asig in seg.analogsignals:
        print (asig)
    for st in seg.spiketrains:
        print (st)

# -*- coding: utf-8 -*-
"""
This is an example for plotting a Neo object with matplotlib.
"""

import urllib

import numpy as np
import quantities as pq
from matplotlib import pyplot

import neo

url = 'https://portal.g-node.org/neo/'
# distantfile = url + 'neuroexplorer/File_neuroexplorer_2.nex'
# localfile = 'File_neuroexplorer_2.nex'

distantfile = 'https://portal.g-node.org/neo/plexon/File_plexon_3.plx'
localfile = './File_plexon_3.plx'


urllib.request.urlretrieve(distantfile, localfile)


# reader = neo.io.NeuroExplorerIO(filename='File_neuroexplorer_2.nex')
reader = neo.io.PlexonIO(filename='File_plexon_3.plx')


bl = reader.read(cascade=True, lazy=False)[0]
for seg in bl.segments:
    print ("SEG: "+str(seg.file_origin))
    fig = pyplot.figure()
    ax1 = fig.add_subplot(2, 1, 1)
    ax2 = fig.add_subplot(2, 1, 2)
    ax1.set_title(seg.file_origin)
    mint = 0 * pq.s
    maxt = np.inf * pq.s
    for i, asig in enumerate(seg.analogsignals):
        times = asig.times.rescale('s').magnitude        
        asig = asig.rescale('mV').magnitude
        ax1.plot(times, asig)

    trains = [st.rescale('s').magnitude for st in seg.spiketrains]
    colors = pyplot.cm.jet(np.linspace(0, 1, len(seg.spiketrains)))
    ax2.eventplot(trains, colors=colors)

pyplot.show()

API Reference

neo.core provides classes for storing common electrophysiological data types. Some of these classes contain raw data, such as spike trains or analog signals, while others are containers to organize other classes (including both data classes and other container classes).

Classes from neo.io return nested data structures containing one or more class from this module.

Classes:

class neo.core.Block(name=None, description=None, file_origin=None, file_datetime=None, rec_datetime=None, index=None, **annotations)

Main container gathering all the data, whether discrete or continous, for a given recording session.

A block is not necessarily temporally homogeneous, in contrast to Segment.

Usage:

>>> from neo.core import (Block, Segment, ChannelIndex,
...                       AnalogSignal)
>>> from quantities import nA, kHz
>>> import numpy as np
>>>
>>> # create a Block with 3 Segment and 2 ChannelIndex objects
,,, blk = Block()
>>> for ind in range(3):
...     seg = Segment(name='segment %d' % ind, index=ind)
...     blk.segments.append(seg)
...
>>> for ind in range(2):
...     chx = ChannelIndex(name='Array probe %d' % ind,
...                        index=np.arange(64))
...     blk.channel_indexes.append(chx)
...
>>> # Populate the Block with AnalogSignal objects
... for seg in blk.segments:
...     for chx in blk.channel_indexes:
...         a = AnalogSignal(np.random.randn(10000, 64)*nA,
...                          sampling_rate=10*kHz)
...         chx.analogsignals.append(a)
...         seg.analogsignals.append(a)

Required attributes/properties:

None

Recommended attributes/properties:

name:(str) A label for the dataset. description:(str) Text description. file_origin:(str) Filesystem path or URL of the original data file. file_datetime:(datetime) The creation date and time of the original data file. rec_datetime:(datetime) The date and time of the original recording.

Properties available on this object:

list_units:descends through hierarchy and returns a list of Unit objects existing in the block. This shortcut exists because a common analysis case is analyzing all neurons that you recorded in a session.

Note: Any other additional arguments are assumed to be user-specific metadata and stored in annotations.

Container of:

Segment ChannelIndex

class neo.core.Segment(name=None, description=None, file_origin=None, file_datetime=None, rec_datetime=None, index=None, **annotations)

A container for data sharing a common time basis.

A Segment is a heterogeneous container for discrete or continous data sharing a common clock (time basis) but not necessary the same sampling rate, start or end time.

Usage::

>>> from neo.core import Segment, SpikeTrain, AnalogSignal
>>> from quantities import Hz, s
>>>
>>> seg = Segment(index=5)
>>>
>>> train0 = SpikeTrain(times=[.01, 3.3, 9.3], units='sec', t_stop=10)
>>> seg.spiketrains.append(train0)
>>>
>>> train1 = SpikeTrain(times=[100.01, 103.3, 109.3], units='sec',
...                     t_stop=110)
>>> seg.spiketrains.append(train1)
>>>
>>> sig0 = AnalogSignal(signal=[.01, 3.3, 9.3], units='uV',
...                     sampling_rate=1*Hz)
>>> seg.analogsignals.append(sig0)
>>>
>>> sig1 = AnalogSignal(signal=[100.01, 103.3, 109.3], units='nA',
...                     sampling_period=.1*s)
>>> seg.analogsignals.append(sig1)

Required attributes/properties:

None

Recommended attributes/properties:

name:(str) A label for the dataset. description:(str) Text description. file_origin:(str) Filesystem path or URL of the original data file. file_datetime:(datetime) The creation date and time of the original data file. rec_datetime:(datetime) The date and time of the original recording index:(int) You can use this to define a temporal ordering of your Segment. For instance you could use this for trial numbers.

Note: Any other additional arguments are assumed to be user-specific metadata and stored in annotations.

Properties available on this object:

all_data:(list) A list of all child objects in the Segment.

Container of:

Epoch Event AnalogSignal IrregularlySampledSignal SpikeTrain

class neo.core.ChannelIndex(index, channel_names=None, channel_ids=None, name=None, description=None, file_origin=None, coordinates=None, **annotations)

A container for indexing/grouping data channels.

This container has several purposes:

• Grouping all AnalogSignals and IrregularlySampledSignals inside a Block across Segments;
• Indexing a subset of the channels within an AnalogSignal and IrregularlySampledSignals;
• Container of Units. Discharges of multiple neurons (Unit‘s) can be seen on the same channel.

Usage 1 providing channel IDs across multiple Segment::

• Recording with 2 electrode arrays across 3 segments
• Each array has 64 channels and is data is represented in a single AnalogSignal object per electrode array
• channel ids range from 0 to 127 with the first half covering electrode 0 and second half covering electrode 1

>>> from neo.core import (Block, Segment, ChannelIndex,
...                       AnalogSignal)
>>> from quantities import nA, kHz
>>> import numpy as np
...
>>> # create a Block with 3 Segment and 2 ChannelIndex objects
>>> blk = Block()
>>> for ind in range(3):
...     seg = Segment(name='segment %d' % ind, index=ind)
...     blk.segments.append(seg)
...
>>> for ind in range(2):
...     channel_ids=np.arange(64)+ind
...     chx = ChannelIndex(name='Array probe %d' % ind,
...                        index=np.arange(64),
...                        channel_ids=channel_ids,
...                        channel_names=['Channel %i' % chid
...                                       for chid in channel_ids])
...     blk.channel_indexes.append(chx)
...
>>> # Populate the Block with AnalogSignal objects
>>> for seg in blk.segments:
...     for chx in blk.channel_indexes:
...         a = AnalogSignal(np.random.randn(10000, 64)*nA,
...                          sampling_rate=10*kHz)
...         # link AnalogSignal and ID providing channel_index
...         a.channel_index = chx
...         chx.analogsignals.append(a)
...         seg.analogsignals.append(a)

Usage 2 grouping channels::

• Recording with a single probe with 8 channels, 4 of which belong to a Tetrode
• Global channel IDs range from 0 to 8
• An additional ChannelIndex is used to group subset of Tetrode channels

>>> from neo.core import Block, ChannelIndex
>>> import numpy as np
>>> from quantities import mV, kHz
...
>>> # Create a Block
>>> blk = Block()
>>> blk.segments.append(Segment())
...
>>> # Create a signal with 8 channels and a ChannelIndex handling the
>>> # channel IDs (see usage case 1)
>>> sig = AnalogSignal(np.random.randn(1000, 8)*mV, sampling_rate=10*kHz)
>>> chx = ChannelIndex(name='Probe 0', index=range(8),
...                    channel_ids=range(8),
...                    channel_names=['Channel %i' % chid
...                                   for chid in range(8)])
>>> chx.analogsignals.append(sig)
>>> sig.channel_index=chx
>>> blk.segments[0].analogsignals.append(sig)
...
>>> # Create a new ChannelIndex which groups four channels from the
>>> # analogsignal and provides a second ID scheme
>>> chx = ChannelIndex(name='Tetrode 0',
...                    channel_names=np.array(['Tetrode ch1',
...                                            'Tetrode ch4',
...                                            'Tetrode ch6',
...                                            'Tetrode ch7']),
...                    index=np.array([0, 3, 5, 6]))
>>> # Attach the ChannelIndex to the the Block,
>>> # but not the to the AnalogSignal, since sig.channel_index is
>>> # already linked to the global ChannelIndex of Probe 0 created above
>>> chx.analogsignals.append(sig)
>>> blk.channel_indexes.append(chx)

Usage 3 dealing with Unit objects::

• Group 5 unit objects in a single ChannelIndex object

>>> from neo.core import Block, ChannelIndex, Unit
...
>>> # Create a Block
>>> blk = Block()
...
>>> # Create a new ChannelIndex and add it to the Block
>>> chx = ChannelIndex(index=None, name='octotrode A')
>>> blk.channel_indexes.append(chx)
...
>>> # create several Unit objects and add them to the
>>> # ChannelIndex
>>> for ind in range(5):
...     unit = Unit(name = 'unit %d' % ind,
...                 description='after a long and hard spike sorting')
...     chx.units.append(unit)

Required attributes/properties:

index:(numpy.array 1D dtype=’i’) Index of each channel in the attached signals (AnalogSignals and IrregularlySampledSignals). The order of the channel IDs needs to be consistent across attached signals.

Recommended attributes/properties:

name:(str) A label for the dataset. description:(str) Text description. file_origin:(str) Filesystem path or URL of the original data file. channel_names:(numpy.array 1D dtype=’S’) Names for each recording channel. channel_ids:(numpy.array 1D dtype=’int’) IDs of the corresponding channels referenced by ‘index’. coordinates:(quantity array 2D (x, y, z)) Physical or logical coordinates of all channels.

Note: Any other additional arguments are assumed to be user-specific metadata and stored in annotations.

Container of:

AnalogSignal IrregularlySampledSignal Unit

class neo.core.Unit(name=None, description=None, file_origin=None, **annotations)

A container of SpikeTrain objects from a unit.

A Unit regroups all the SpikeTrain objects that were emitted by a single spike source during a Block. A spike source is often a single neuron but doesn’t have to be. The spikes may come from different Segment objects within the Block, so this object is not contained in the usual Block/ Segment/SpikeTrain hierarchy.

A Unit is linked to ChannelIndex objects from which it was detected. With tetrodes, for instance, multiple channels may record the same Unit.

Usage:

>>> from neo.core import Unit, SpikeTrain
>>>
>>> unit = Unit(name='pyramidal neuron')
>>>
>>> train0 = SpikeTrain(times=[.01, 3.3, 9.3], units='sec', t_stop=10)
>>> unit.spiketrains.append(train0)
>>>
>>> train1 = SpikeTrain(times=[100.01, 103.3, 109.3], units='sec',
...                  t_stop=110)
>>> unit.spiketrains.append(train1)

Required attributes/properties:

None

Recommended attributes/properties:

name:(str) A label for the dataset. description:(str) Text description. file_origin:(str) Filesystem path or URL of the original data file.

Note: Any other additional arguments are assumed to be user-specific

metadata and stored in annotations.

Container of:

SpikeTrain

class neo.core.AnalogSignal(signal, units=None, dtype=None, copy=True, t_start=array(0.0) * s, sampling_rate=None, sampling_period=None, name=None, file_origin=None, description=None, **annotations)

Array of one or more continuous analog signals.

A representation of several continuous, analog signals that have the same duration, sampling rate and start time. Basically, it is a 2D array: dim 0 is time, dim 1 is channel index

Inherits from quantities.Quantity, which in turn inherits from numpy.ndarray.

Usage:

>>> from neo.core import AnalogSignal
>>> import quantities as pq
>>>
>>> sigarr = AnalogSignal([[1, 2, 3], [4, 5, 6]], units='V',
...                            sampling_rate=1*pq.Hz)
>>>
>>> sigarr
<AnalogSignal(array([[1, 2, 3],
      [4, 5, 6]]) * mV, [0.0 s, 2.0 s], sampling rate: 1.0 Hz)>
>>> sigarr[:,1]
<AnalogSignal(array([2, 5]) * V, [0.0 s, 2.0 s],
    sampling rate: 1.0 Hz)>
>>> sigarr[1, 1]
array(5) * V

Required attributes/properties:

signal:(quantity array 2D, numpy array 2D, or list (data, channel)) The data itself. units:(quantity units) Required if the signal is a list or NumPy array, not if it is a Quantity t_start:(quantity scalar) Time when signal begins sampling_rate:or sampling_period (quantity scalar) Number of samples per unit time or interval between two samples. If both are specified, they are checked for consistency.

Recommended attributes/properties:

name:(str) A label for the dataset. description:(str) Text description. file_origin:(str) Filesystem path or URL of the original data file.

Optional attributes/properties:

dtype:(numpy dtype or str) Override the dtype of the signal array. copy:(bool) True by default.

Note: Any other additional arguments are assumed to be user-specific metadata and stored in annotations.

Properties available on this object:

sampling_rate:(quantity scalar) Number of samples per unit time. (1/sampling_period) sampling_period:  (quantity scalar) Interval between two samples. (1/quantity scalar) duration:(Quantity) Signal duration, read-only. (size * sampling_period) t_stop:(quantity scalar) Time when signal ends, read-only. (t_start + duration) times:(quantity 1D) The time points of each sample of the signal, read-only. (t_start + arange(shape`[0])/:attr:`sampling_rate) channel_index:access to the channel_index attribute of the principal ChannelIndex associated with this signal.

Slicing:

AnalogSignal objects can be sliced. When taking a single column (dimension 0, e.g. [0, :]) or a single element, a Quantity is returned. Otherwise an AnalogSignal (actually a view) is returned, with the same metadata, except that t_start is changed if the start index along dimension 1 is greater than 1.

Operations available on this object:

== != + * /

class neo.core.IrregularlySampledSignal(times, signal, units=None, time_units=None, dtype=None, copy=True, name=None, file_origin=None, description=None, **annotations)

An array of one or more analog signals with samples taken at arbitrary time points.

A representation of one or more continuous, analog signals acquired at time t_start with a varying sampling interval. Each channel is sampled at the same time points.

Usage:

>>> from neo.core import IrregularlySampledSignal
>>> from quantities import s, nA
>>>
>>> irsig0 = IrregularlySampledSignal([0.0, 1.23, 6.78], [1, 2, 3],
...                                   units='mV', time_units='ms')
>>> irsig1 = IrregularlySampledSignal([0.01, 0.03, 0.12]*s,
...                                   [[4, 5], [5, 4], [6, 3]]*nA)

Required attributes/properties:

times:(quantity array 1D, numpy array 1D, or list) The time of each data point. Must have the same size as signal. signal:(quantity array 2D, numpy array 2D, or list (data, channel)) The data itself. units:(quantity units) Required if the signal is a list or NumPy array, not if it is a Quantity. time_units:(quantity units) Required if times is a list or NumPy array, not if it is a Quantity.

Recommended attributes/properties:.

name:(str) A label for the dataset description:(str) Text description. file_origin:(str) Filesystem path or URL of the original data file.

Optional attributes/properties:

dtype:(numpy dtype or str) Override the dtype of the signal array. (times are always floats). copy:(bool) True by default.

Note: Any other additional arguments are assumed to be user-specific metadata and stored in annotations.

Properties available on this object:

sampling_intervals:  (quantity array 1D) Interval between each adjacent pair of samples. (times[1:] - times[:-1]) duration:(quantity scalar) Signal duration, read-only. (times[-1] - times[0]) t_start:(quantity scalar) Time when signal begins, read-only. (times[0]) t_stop:(quantity scalar) Time when signal ends, read-only. (times[-1])

Slicing:

IrregularlySampledSignal objects can be sliced. When this occurs, a new IrregularlySampledSignal (actually a view) is returned, with the same metadata, except that times is also sliced in the same way.

Operations available on this object:

== != + * /

class neo.core.Event(times=None, labels=None, units=None, name=None, description=None, file_origin=None, **annotations)

Array of events.

Usage:

>>> from neo.core import Event
>>> from quantities import s
>>> import numpy as np
>>>
>>> evt = Event(np.arange(0, 30, 10)*s,
...             labels=np.array(['trig0', 'trig1', 'trig2'],
...                             dtype='S'))
>>>
>>> evt.times
array([  0.,  10.,  20.]) * s
>>> evt.labels
array(['trig0', 'trig1', 'trig2'],
      dtype='|S5')

Required attributes/properties:

times:(quantity array 1D) The time of the events. labels:(numpy.array 1D dtype=’S’) Names or labels for the events.

Recommended attributes/properties:

name:(str) A label for the dataset. description:(str) Text description. file_origin:(str) Filesystem path or URL of the original data file.

Note: Any other additional arguments are assumed to be user-specific metadata and stored in annotations.

class neo.core.Epoch(times=None, durations=None, labels=None, units=None, name=None, description=None, file_origin=None, **annotations)

Array of epochs.

Usage:

>>> from neo.core import Epoch
>>> from quantities import s, ms
>>> import numpy as np
>>>
>>> epc = Epoch(times=np.arange(0, 30, 10)*s,
...             durations=[10, 5, 7]*ms,
...             labels=np.array(['btn0', 'btn1', 'btn2'], dtype='S'))
>>>
>>> epc.times
array([  0.,  10.,  20.]) * s
>>> epc.durations
array([ 10.,   5.,   7.]) * ms
>>> epc.labels
array(['btn0', 'btn1', 'btn2'],
      dtype='|S4')

Required attributes/properties:

times:(quantity array 1D) The starts of the time periods. durations:(quantity array 1D) The length of the time period. labels:(numpy.array 1D dtype=’S’) Names or labels for the time periods.

Recommended attributes/properties:

name:(str) A label for the dataset, description:(str) Text description, file_origin:(str) Filesystem path or URL of the original data file.

Note: Any other additional arguments are assumed to be user-specific metadata and stored in annotations,

class neo.core.SpikeTrain(times, t_stop, units=None, dtype=<type 'float'>, copy=True, sampling_rate=array(1.0) * Hz, t_start=array(0.0) * s, waveforms=None, left_sweep=None, name=None, file_origin=None, description=None, **annotations)

SpikeTrain is a Quantity array of spike times.

It is an ensemble of action potentials (spikes) emitted by the same unit in a period of time.

Usage:

>>> from neo.core import SpikeTrain
>>> from quantities import s
>>>
>>> train = SpikeTrain([3, 4, 5]*s, t_stop=10.0)
>>> train2 = train[1:3]
>>>
>>> train.t_start
array(0.0) * s
>>> train.t_stop
array(10.0) * s
>>> train
<SpikeTrain(array([ 3.,  4.,  5.]) * s, [0.0 s, 10.0 s])>
>>> train2
<SpikeTrain(array([ 4.,  5.]) * s, [0.0 s, 10.0 s])>

Required attributes/properties:

times:(quantity array 1D, numpy array 1D, or list) The times of each spike. units:(quantity units) Required if times is a list or ndarray, not if it is a Quantity. t_stop:(quantity scalar, numpy scalar, or float) Time at which SpikeTrain ended. This will be converted to the same units as times. This argument is required because it specifies the period of time over which spikes could have occurred. Note that t_start is highly recommended for the same reason.

Note: If times contains values outside of the range [t_start, t_stop], an Exception is raised.

Recommended attributes/properties:

name:(str) A label for the dataset. description:(str) Text description. file_origin:(str) Filesystem path or URL of the original data file. t_start:(quantity scalar, numpy scalar, or float) Time at which SpikeTrain began. This will be converted to the same units as times. Default: 0.0 seconds. waveforms:(quantity array 3D (spike, channel_index, time)) The waveforms of each spike. sampling_rate:(quantity scalar) Number of samples per unit time for the waveforms. left_sweep:(quantity array 1D) Time from the beginning of the waveform to the trigger time of the spike. sort:(bool) If True, the spike train will be sorted by time.

Optional attributes/properties:

dtype:(numpy dtype or str) Override the dtype of the signal array. copy:(bool) Whether to copy the times array. True by default. Must be True when you request a change of units or dtype.

Note: Any other additional arguments are assumed to be user-specific metadata and stored in annotations.

Properties available on this object:

sampling_period:  (quantity scalar) Interval between two samples. (1/sampling_rate) duration:(quantity scalar) Duration over which spikes can occur, read-only. (t_stop - t_start) spike_duration:(quantity scalar) Duration of a waveform, read-only. (waveform.shape[2] * sampling_period) right_sweep:(quantity scalar) Time from the trigger times of the spikes to the end of the waveforms, read-only. (left_sweep + spike_duration) times:(SpikeTrain) Returns the SpikeTrain without modification or copying.

Slicing:

SpikeTrain objects can be sliced. When this occurs, a new SpikeTrain (actually a view) is returned, with the same metadata, except that waveforms is also sliced in the same way (along dimension 0). Note that t_start and t_stop are not changed automatically, although you can still manually change them.

Release notes

Neo 0.5.0 release notes

22nd March 2017

For Neo 0.5, we have taken the opportunity to simplify the Neo object model.

Although this will require an initial time investment for anyone who has written code with an earlier version of Neo, the benefits will be greater simplicity, both in your own code and within the Neo code base, which should allow us to move more quickly in fixing bugs, improving performance and adding new features.

More detail on these changes follows:

Merging of “single-value” and “array” versions of data classes

In previous versions of Neo, we had AnalogSignal for one-dimensional (single channel) signals, and AnalogSignalArray for two-dimensional (multi-channel) signals. In Neo 0.5.0, these have been merged under the name AnalogSignal. AnalogSignal has the same behaviour as the old AnalogSignalArray.

It is still possible to create an AnalogSignal from a one-dimensional array, but this will be converted to an array with shape (n, 1), e.g.:

>>> signal = neo.AnalogSignal([0.0, 0.1, 0.2, 0.5, 0.6, 0.5, 0.4, 0.3, 0.0],
...                           sampling_rate=10*kHz,
...                           units=nA)
>>> signal.shape
(9, 1)

Multi-channel arrays are created as before, but using AnalogSignal instead of AnalogSignalArray:

>>> signal = neo.AnalogSignal([[0.0, 0.1, 0.2, 0.5, 0.6, 0.5, 0.4, 0.3, 0.0],
...                            [0.0, 0.2, 0.4, 0.7, 0.9, 0.8, 0.7, 0.6, 0.3]],
...                           sampling_rate=10*kHz,
...                           units=nA)
>>> signal.shape
(9, 2)

Similarly, the Epoch and EpochArray classes have been merged into an array-valued class Epoch, ditto for Event and EventArray, and the Spike class, whose main function was to contain the waveform data for an individual spike, has been suppressed; waveform data are now available as the waveforms attribute of the SpikeTrain class.

Recording channels

As a consequence of the removal of “single-value” data classes, information on recording channels and the relationship between analog signals and spike trains is also stored differently.

In Neo 0.5, we have introduced a new class, ChannelIndex, which replaces both RecordingChannel and RecordingChannelGroup.

In older versions of Neo, a RecordingChannel object held metadata about a logical recording channel (a name and/or integer index) together with references to one or more AnalogSignals recorded on that channel at different points in time (different Segments); redundantly, the AnalogSignal also had a channel_index attribute, which could be used in addition to or instead of creating a RecordingChannel.

Metadata about AnalogSignalArrays could be contained in a RecordingChannelGroup in a similar way, i.e. RecordingChannelGroup functioned as an array-valued version of RecordingChannel, but RecordingChannelGroup could also be used to group together individual RecordingChannel objects.

With Neo 0.5, information about the channel names and ids of an AnalogSignal is contained in a ChannelIndex, e.g.:

>>> signal = neo.AnalogSignal([[0.0, 0.1, 0.2, 0.5, 0.6, 0.5, 0.4, 0.3, 0.0],
...                            [0.0, 0.2, 0.4, 0.7, 0.9, 0.8, 0.7, 0.6, 0.3]],
...                            [0.0, 0.1, 0.3, 0.6, 0.8, 0.7, 0.6, 0.5, 0.3]],
...                           sampling_rate=10*kHz,
...                           units=nA)
>>> channels = neo.ChannelIndex(index=[0, 1, 2],
...                             channel_names=["chan1", "chan2", "chan3"])
>>> signal.channel_index = channels

In this use, it replaces RecordingChannel.

ChannelIndex may also be used to group together a subset of the channels of a multi-channel signal, for example:

>>> channel_group = neo.ChannelIndex(index=[0, 2])
>>> channel_group.analogsignals.append(signal)
>>> unit = neo.Unit()  # will contain the spike train recorded from channels 0 and 2.
>>> unit.channel_index = channel_group

Checklist for updating code from 0.3/0.4 to 0.5

To update your code from Neo 0.3/0.4 to 0.5, run through the following checklist:

1. Change all usages of AnalogSignalArray to AnalogSignal.
2. Change all usages of EpochArray to Epoch.
3. Change all usages of EventArray to Event.
4. Where you have a list of (single channel) AnalogSignals all of the same length, consider converting them to a single, multi-channel AnalogSignal.
5. Replace RecordingChannel and RecordingChannelGroup with ChannelIndex.

Note

in points 1-3, the data structure is still an array, it just has a shorter name.

Other changes

• added NixIO (about the NIX format)
• added IgorIO
• added NestIO (for data files produced by the NEST simulator)
• NeoHdf5IO is now read-only. It will read data files produced by earlier versions of Neo, but another HDF5-based IO, e.g. NixIO, should be used for writing data.
• many fixes/improvements to existing IO modules. All IO modules should now work with Python 3.

Neo 0.5.1 release notes

4th May 2017

• Fixes to AxonIO (thanks to @erikli and @cjfraz) and NeuroExplorerIO (thanks to Mark Hollenbeck)
• Fixes to pickling of Epoch and Event objects (thanks to Hélissande Fragnaud)
• Added methods as_array() and as_quantity() to Neo data objects to simplify the common tasks of turning a Neo data object back into a plain Numpy array
• Added NeuralynxIO, which reads standard Neuralynx output files in ncs, nev, nse and ntt format (thanks to Julia Sprenger and Carlos Canova).
• Added the extras_require field to setup.py, to clearly document the requirements for different io modules. For example, this allows you to run pip install neo[neomatlabio] and have the extra dependency needed for the neomatlabio module (scipy in this case) be automatically installed.
• Fixed a bug where slicing an AnalogSignal did not modify the linked ChannelIndex.

(Full list of closed issues)

Neo 0.5.2 release notes

27th September 2017

• Removed support for Python 2.6
• Pickling AnalogSignal and SpikeTrain now preserves parent objects
• Added NSDFIO, which reads and writes NSDF files
• Fixes and improvements to PlexonIO, NixIO, BlackrockIO, NeuralynxIO, IgorIO, ElanIO, MicromedIO, TdtIO and others.

Thanks to Michael Denker, Achilleas Koutsou, Mieszko Grodzicki, Samuel Garcia, Julia Sprenger, Andrew Davison, Rohan Shah, Richard C Gerkin, Mieszko Grodzicki, Mikkel Elle Lepperød, Joffrey Gonin, Hélissande Fragnaud, Elodie Legouée and Matthieu Sénoville for their contributions to this release.

(Full list of closed issues)

Version 0.4.0

• added StimfitIO
• added KwikIO
• significant improvements to AxonIO, BlackrockIO, BrainwareSrcIO, NeuroshareIO, PlexonIO, Spike2IO, TdtIO,
• many test suite improvements
• Container base class

Version 0.3.3

• fix a bug in PlexonIO where some EventArrays only load 1 element.
• fix a bug in BrainwareSrcIo for segments with no spikes.

Version 0.3.2

• cleanup of io test code, with additional helper functions and methods
• added BrainwareDamIo
• added BrainwareF32Io
• added BrainwareSrcIo

Version 0.3.1

• lazy/cascading improvement
• load_lazy_olbject() in neo.io added
• added NeuroscopeIO

Version 0.3.0

• various bug fixes in neo.io
• added ElphyIO
• SpikeTrain performence improved
• An IO class now can return a list of Block (see read_all_blocks in IOs)
• python3 compatibility improved

Version 0.2.1

• assorted bug fixes
• added time_slice() method to the SpikeTrain and AnalogSignalArray classes.
• improvements to annotation data type handling
• added PickleIO, allowing saving Neo objects in the Python pickle format.
• added ElphyIO (see http://www.unic.cnrs-gif.fr/software.html)
• added BrainVisionIO (see http://www.brainvision.com/)
• improvements to PlexonIO
• added merge() method to the Block and Segment classes
• development was mostly moved to GitHub, although the issue tracker is still at neuralensemble.org/neo

Version 0.2.0

New features compared to neo 0.1:

• new schema more consistent.
• new objects: RecordingChannelGroup, EventArray, AnalogSignalArray, EpochArray
• Neuron is now Unit
• use the quantities module for everything that can have units.
• Some objects directly inherit from Quantity: SpikeTrain, AnalogSignal, AnalogSignalArray, instead of having an attribute for data.
• Attributes are classifyed in 3 categories: necessary, recommended, free.
• lazy and cascade keywords are added to all IOs
• Python 3 support
• better tests

Developers’ guide

These instructions are for developing on a Unix-like platform, e.g. Linux or Mac OS X, with the bash shell. If you develop on Windows, please get in touch.

Mailing lists

General discussion of Neo development takes place in the NeuralEnsemble Google group.

Discussion of issues specific to a particular ticket in the issue tracker should take place on the tracker.

Using the issue tracker

If you find a bug in Neo, please create a new ticket on the issue tracker, setting the type to “defect”. Choose a name that is as specific as possible to the problem you’ve found, and in the description give as much information as you think is necessary to recreate the problem. The best way to do this is to create the shortest possible Python script that demonstrates the problem, and attach the file to the ticket.

If you have an idea for an improvement to Neo, create a ticket with type “enhancement”. If you already have an implementation of the idea, create a patch (see below) and attach it to the ticket.

To keep track of changes to the code and to tickets, you can register for a GitHub account and then set to watch the repository at GitHub Repository (see https://help.github.com/articles/watching-repositories/).

Requirements

• Python 2.6, 2.7, 3.3-3.5
• numpy >= 1.7.1
• quantities >= 0.9.0
• if using Python 2.6, unittest2 >= 0.5.1
• Setuptools >= 0.7
• nose >= 0.11.1 (for running tests)
• Sphinx >= 0.6.4 (for building documentation)
• (optional) tox >= 0.9 (makes it easier to test with multiple Python versions)
• (optional) coverage >= 2.85 (for measuring test coverage)
• (optional) scipy >= 0.12 (for MatlabIO)
• (optional) h5py >= 2.5 (for KwikIO, NeoHdf5IO)

We strongly recommend you develop within a virtual environment (from virtualenv, venv or conda). It is best to have at least one virtual environment with Python 2.7 and one with Python 3.x.

Getting the source code

We use the Git version control system. The best way to contribute is through GitHub. You will first need a GitHub account, and you should then fork the repository at GitHub Repository (see http://help.github.com/fork-a-repo/).

To get a local copy of the repository:

$ cd /some/directory
$ git clone git@github.com:<username>/python-neo.git

Now you need to make sure that the neo package is on your PYTHONPATH. You can do this either by installing Neo:

$ cd python-neo
$ python setup.py install
$ python3 setup.py install

(if you do this, you will have to re-run setup.py install any time you make changes to the code) or by creating symbolic links from somewhere on your PYTHONPATH, for example:

$ ln -s python-neo/neo
$ export PYTHONPATH=/some/directory:${PYTHONPATH}

An alternate solution is to install Neo with the develop option, this avoids reinstalling when there are changes in the code:

$ sudo python setup.py develop

or using the “-e” option to pip:

$ pip install -e python-neo

To update to the latest version from the repository:

$ git pull

Running the test suite

Before you make any changes, run the test suite to make sure all the tests pass on your system:

$ cd neo/test

With Python 2.7 or 3.3:

$ python -m unittest discover
$ python3 -m unittest discover

If you have nose installed:

$ nosetests

At the end, if you see “OK”, then all the tests passed (or were skipped because certain dependencies are not installed), otherwise it will report on tests that failed or produced errors.

To run tests from an individual file:

$ python test_analogsignal.py
$ python3 test_analogsignal.py

Writing tests

You should try to write automated tests for any new code that you add. If you have found a bug and want to fix it, first write a test that isolates the bug (and that therefore fails with the existing codebase). Then apply your fix and check that the test now passes.

To see how well the tests cover the code base, run:

$ nosetests --with-coverage --cover-package=neo --cover-erase

Working on the documentation

All modules, classes, functions, and methods (including private and subclassed builtin methods) should have docstrings. Please see PEP257 for a description of docstring conventions.

Module docstrings should explain briefly what functions or classes are present. Detailed descriptions can be left for the docstrings of the respective functions or classes. Private functions do not need to be explained here.

Class docstrings should include an explanation of the purpose of the class and, when applicable, how it relates to standard neuroscientific data. They should also include at least one example, which should be written so it can be run as-is from a clean newly-started Python interactive session (that means all imports should be included). Finally, they should include a list of all arguments, attributes, and properties, with explanations. Properties that return data calculated from other data should explain what calculation is done. A list of methods is not needed, since documentation will be generated from the method docstrings.

Method and function docstrings should include an explanation for what the method or function does. If this may not be clear, one or more examples may be included. Examples that are only a few lines do not need to include imports or setup, but more complicated examples should have them.

Examples can be tested easily using the iPython %doctest_mode magic. This will strip >>> and ... from the beginning of each line of the example, so the example can be copied and pasted as-is.

The documentation is written in reStructuredText, using the Sphinx documentation system. Any mention of another Neo module, class, attribute, method, or function should be properly marked up so automatic links can be generated. The same goes for quantities or numpy.

To build the documentation:

$ cd python-neo/doc
$ make html

Then open some/directory/python-neo/doc/build/html/index.html in your browser.

Committing your changes

Once you are happy with your changes, run the test suite again to check that you have not introduced any new bugs. It is also recommended to check your code with a code checking program, such as pyflakes or flake8. Then you can commit them to your local repository:

$ git commit -m 'informative commit message'

If this is your first commit to the project, please add your name and affiliation/employer to doc/source/authors.rst

You can then push your changes to your online repository on GitHub:

$ git push

Once you think your changes are ready to be included in the main Neo repository, open a pull request on GitHub (see https://help.github.com/articles/using-pull-requests).

Python 3

Neo core should work with both recent versions of Python 2 (versions 2.6 and 2.7) and Python 3 (version 3.3 or newer). Neo IO modules should ideally work with both Python 2 and 3, but certain modules may only work with one or the other (see Installation).

So far, we have managed to write code that works with both Python 2 and 3. Mainly this involves avoiding the print statement (use logging.info instead), and putting from __future__ import division at the beginning of any file that uses division.

If in doubt, Porting to Python 3 by Lennart Regebro is an excellent resource.

The most important thing to remember is to run tests with at least one version of Python 2 and at least one version of Python 3. There is generally no problem in having multiple versions of Python installed on your computer at once: e.g., on Ubuntu Python 2 is available as python and Python 3 as python3, while on Arch Linux Python 2 is python2 and Python 3 python. See PEP394 for more on this. Using virtual environments makes this very straightforward.

Coding standards and style

All code should conform as much as possible to PEP 8, and should run with Python 2.6, 2.7, and 3.3 or newer.

You can use the pep8 program to check the code for PEP 8 conformity. You can also use flake8, which combines pep8 and pyflakes.

However, the pep8 and flake8 programs do not check for all PEP 8 issues. In particular, they do not check that the import statements are in the correct order.

Also, please do not use from xyz import *. This is slow, can lead to conflicts, and makes it difficult for code analysis software.

Making a release

Add a section in /doc/source/whatisnew.rst for the release.

First check that the version string (in neo/version.py, setup.py, doc/conf.py and doc/install.rst) is correct.

To build a source package:

$ python setup.py sdist

To upload the package to PyPI (currently Samuel Garcia and Andrew Davison have the necessary permissions to do this):

$ python setup.py sdist upload
$ python setup.py upload_docs --upload-dir=doc/build/html

Finally, tag the release in the Git repository and push it:

$ git tag <version>
$ git push --tags origin

If you want to develop your own IO module

See IO developers’ guide for implementation of a new IO.

IO developers’ guide

Guidelines for IO implementation

Recipe to develop an IO module for a new data format:

1. Fully understand the object model. See Neo core. If in doubt ask the mailing list.
2. Fully understand neo.io.exampleio, It is a fake IO to explain the API. If in doubt ask the list.
3. Copy/paste exampleio.py and choose clear file and class names for your IO.
4. Decide which supported objects and readable objects your IO will deal with. This is the crucial point.
5. Implement all methods read_XXX() related to readable objects.
6. Optional: If your IO supports reading multiple blocks from one file, implement a read_all_blocks() method.
7. Do not forget all lazy and cascade combinations.
8. Optional: Support loading lazy objects by implementing a load_lazy_object() method and / or lazy cascading by implementing a load_lazy_cascade() method.
9. Write good docstrings. List dependencies, including minimum version numbers.
10. Add your class to neo.io.__init__. Keep the import inside try/except for dependency reasons.
11. Contact the Neo maintainers to put sample files for testing on the G-Node server (write access is not public).
12. Write tests in neo/test/io/test_xxxxxio.py. You must at least pass the standard tests (inherited from BaseTestIO).
13. Commit or send a patch only if all tests pass.

Miscellaneous

• If your IO supports several version of a format (like ABF1, ABF2), upload to G-node test file repository all file version possible. (for utest coverage).
• neo.core.Block.create_many_to_one_relationship() offers a utility to complete the hierachy when all one-to-many relationships have been created.
• neo.io.tools.populate_RecordingChannel() offers a utility to create inside a Block all RecordingChannel objects and links to AnalogSignal, SpikeTrain, ...
• In the docstring, explain where you obtained the file format specification if it is a closed one.
• If your IO is based on a database mapper, keep in mind that the returned object MUST be detached, because this object can be written to another url for copying.

Advanced lazy loading

If your IO supports a format that might take a long time to load or require lots of memory, consider implementing one or both of the following methods to enable advanced lazy loading:

• load_lazy_object(self, obj): This method takes a lazily loaded object and returns the corresponding fully loaded object. It does not set any links of the newly loaded object (e.g. the segment attribute of a SpikeTrain). The information needed to fully load the lazy object should usually be stored in the IO object (e.g. in a dictionary with lazily loaded objects as keys and the address in the file as values).

• load_lazy_cascade(self, address, lazy): This method takes two parameters: The information required by your IO to load an object and a boolean that indicates if data objects should be lazy loaded (in the same way as with regular read_XXX() methods). The method should return a loaded objects, including all the links for one-to-many and many-to-many relationships (lists of links should be replaced by LazyList objects, see below).

  To implement lazy cascading, your read methods need to react when a user calls them with the cascade parameter set to lazy. In this case, you have to replace all the link lists of your loaded objects with instances of neo.io.tools.LazyList. Instead of the actual objects that your IO would load at this point, fill the list with items that load_lazy_cascade needs to load the object.

  Because the links of objects can point to previously loaded objects, you need to cache all loaded objects in the IO. If load_lazy_cascade() is called with the address of a previously loaded object, return the object instead of loading it again. Also, a call to load_lazy_cascade() might require you to load additional objects further up in the hierarchy. For example, if a SpikeTrain is accessed through a Segment, its Unit and the ChannelIndex of the Unit might have to be loaded at that point as well if they have not been accessed before.

  Note that you are free to restrict lazy cascading to certain objects. For example, you could use the LazyList only for the analogsignals property of Segment and RecordingChannel objects and load the rest of file immediately.

Tests

neo.test.io.commun_io_test.BaseTestIO provide standard tests. To use these you need to upload some sample data files at the G-Node portal. They will be publicly accessible for testing Neo. These tests:

• check the compliance with the schema: hierachy, attribute types, ...
• check if the IO respects the lazy and cascade keywords.
• For IO able to both write and read data, it compares a generated dataset with the same data after a write/read cycle.

The test scripts download all files from the G-Node portal and store them locally in neo/test/io/files_for_tests/. Subsequent test runs use the previously downloaded files, rather than trying to download them each time.

Here is an example test script taken from the distribution: test_axonio.py:

# -*- coding: utf-8 -*-
"""
Tests of neo.io.axonio
"""

# needed for python 3 compatibility
from __future__ import absolute_import

import sys

import unittest

from neo.io import AxonIO
from neo.test.iotest.common_io_test import BaseTestIO


class TestAxonIO(BaseTestIO, unittest.TestCase):
    files_to_test = ['File_axon_1.abf',
                     'File_axon_2.abf',
                     'File_axon_3.abf',
                     'File_axon_4.abf',
                     'File_axon_5.abf',
                     'File_axon_6.abf',
                     'File_axon_7.abf',
                     
                     ]
    files_to_download = files_to_test
    ioclass = AxonIO
    
    def test_read_protocol(self):
        for f in self.files_to_test:
            filename = self.get_filename_path(f)
            reader = AxonIO(filename=filename)
            bl = reader.read_block(lazy=True)
            if bl.annotations['abf_version'].startswith('2'):
                reader.read_protocol()
        

if __name__ == "__main__":
    unittest.main()

Logging

All IO classes by default have logging using the standard logging module: already set up. The logger name is the same as the full qualified class name, e.g. neo.io.hdf5io.NeoHdf5IO. The class.logger attribute holds the logger for easy access.

There are generally 3 types of situations in which an IO class should use a logger

• Recoverable errors with the file that the users need to be notified about. In this case, please use logger.warning() or logger.error(). If there is an exception associated with the issue, you can use logger.exception() in the exception handler to automatically include a backtrace with the log. By default, all users will see messages at this level, so please restrict it only to problems the user absolutely needs to know about.
• Informational messages that advanced users might want to see in order to get some insight into the file. In this case, please use logger.info().
• Messages useful to developers to fix problems with the io class. In this case, please use logger.debug().

A log handler is automatically added to neo, so please do not user your own handler. Please use the class.logger attribute for accessing the logger inside the class rather than logging.getLogger(). Please do not log directly to the root logger (e.g. logging.warning()), use the class’s logger instead (class.logger.warning()). In the tests for the io class, if you intentionally test broken files, please disable logs by setting the logging level to 100.

ExampleIO

class neo.io.ExampleIO(filename=None)

Class for “reading” fake data from an imaginary file.

For the user, it generates a Segment or a Block with a sinusoidal AnalogSignal, a SpikeTrain and an Event.

For a developer, it is just an example showing guidelines for someone who wants to develop a new IO module.

Two rules for developers:

• Respect the Neo IO API (Details of API)
• Follow Guidelines for IO implementation

Usage:

>>> from neo import io
>>> r = io.ExampleIO(filename='itisafake.nof')
>>> seg = r.read_segment(lazy=False, cascade=True)
>>> print(seg.analogsignals)  
[<AnalogSignal(array([ 0.19151945,  0.62399373,  0.44149764, ...,  0.96678374,
...
>>> print(seg.spiketrains)    
 [<SpikeTrain(array([ -0.83799524,   6.24017951,   7.76366686,   4.45573701,
    12.60644415,  10.68328994,   8.07765735,   4.89967804,
...
>>> print(seg.events)    
[<Event: TriggerB@9.6976 s, TriggerA@10.2612 s, TriggerB@2.2777 s, TriggerA@6.8607 s, ...
>>> anasig = r.read_analogsignal(lazy=True, cascade=False)
>>> print(anasig._data_description)
{'shape': (150000,)}
>>> anasig = r.read_analogsignal(lazy=False, cascade=False)

Here is the entire file:

# -*- coding: utf-8 -*-
"""
Class for "reading" fake data from an imaginary file.

For the user, it generates a :class:`Segment` or a :class:`Block` with a
sinusoidal :class:`AnalogSignal`, a :class:`SpikeTrain` and an
:class:`Event`.

For a developer, it is just an example showing guidelines for someone who wants
to develop a new IO module.

Depends on: scipy

Supported: Read

Author: sgarcia

"""

# needed for python 3 compatibility
from __future__ import absolute_import

# note neo.core needs only numpy and quantities
import numpy as np
import quantities as pq

# but my specific IO can depend on many other packages
try:
    from scipy import stats
except ImportError as err:
    HAVE_SCIPY = False
    SCIPY_ERR = err
else:
    HAVE_SCIPY = True
    SCIPY_ERR = None

# I need to subclass BaseIO
from neo.io.baseio import BaseIO

# to import from core
from neo.core import Segment, AnalogSignal, SpikeTrain, Event


# I need to subclass BaseIO
class ExampleIO(BaseIO):
    """
    Class for "reading" fake data from an imaginary file.

    For the user, it generates a :class:`Segment` or a :class:`Block` with a
    sinusoidal :class:`AnalogSignal`, a :class:`SpikeTrain` and an
    :class:`Event`.

    For a developer, it is just an example showing guidelines for someone who wants
    to develop a new IO module.

    Two rules for developers:
      * Respect the Neo IO API (:ref:`neo_io_API`)
      * Follow :ref:`io_guiline`

    Usage:
        >>> from neo import io
        >>> r = io.ExampleIO(filename='itisafake.nof')
        >>> seg = r.read_segment(lazy=False, cascade=True)
        >>> print(seg.analogsignals)  # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
        [<AnalogSignal(array([ 0.19151945,  0.62399373,  0.44149764, ...,  0.96678374,
        ...
        >>> print(seg.spiketrains)    # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
         [<SpikeTrain(array([ -0.83799524,   6.24017951,   7.76366686,   4.45573701,
            12.60644415,  10.68328994,   8.07765735,   4.89967804,
        ...
        >>> print(seg.events)    # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
        [<Event: TriggerB@9.6976 s, TriggerA@10.2612 s, TriggerB@2.2777 s, TriggerA@6.8607 s, ...
        >>> anasig = r.read_analogsignal(lazy=True, cascade=False)
        >>> print(anasig._data_description)
        {'shape': (150000,)}
        >>> anasig = r.read_analogsignal(lazy=False, cascade=False)

    """

    is_readable = True  # This class can only read data
    is_writable = False  # write is not supported

    # This class is able to directly or indirectly handle the following objects
    # You can notice that this greatly simplifies the full Neo object hierarchy
    supported_objects  = [ Segment , AnalogSignal, SpikeTrain, Event ]

    # This class can return either a Block or a Segment
    # The first one is the default ( self.read )
    # These lists should go from highest object to lowest object because
    # common_io_test assumes it.
    readable_objects    = [ Segment , AnalogSignal, SpikeTrain ]
    # This class is not able to write objects
    writeable_objects   = [ ]

    has_header         = False
    is_streameable     = False

    # This is for GUI stuff : a definition for parameters when reading.
    # This dict should be keyed by object (`Block`). Each entry is a list
    # of tuple. The first entry in each tuple is the parameter name. The
    # second entry is a dict with keys 'value' (for default value),
    # and 'label' (for a descriptive name).
    # Note that if the highest-level object requires parameters,
    # common_io_test will be skipped.
    read_params = {
        Segment : [
            ('segment_duration',
                {'value' : 15., 'label' : 'Segment size (s.)'}),
            ('num_analogsignal',
                {'value' : 8, 'label' : 'Number of recording points'}),
            ('num_spiketrain_by_channel',
                {'value' : 3, 'label' : 'Num of spiketrains'}),
            ],
        }

    # do not supported write so no GUI stuff
    write_params       = None

    name               = 'example'

    extensions          = [ 'nof' ]

    # mode can be 'file' or 'dir' or 'fake' or 'database'
    # the main case is 'file' but some reader are base on a directory or a database
    # this info is for GUI stuff also
    mode = 'fake'



    def __init__(self , filename = None) :
        """


        Arguments:
            filename : the filename

        Note:
            - filename is here just for exampe because it will not be take in account
            - if mode=='dir' the argument should be dirname (See TdtIO)

        """
        BaseIO.__init__(self)
        self.filename = filename
        # Seed so all instances can return the same values
        np.random.seed(1234)


    # Segment reading is supported so I define this :
    def read_segment(self,
                     # the 2 first keyword arguments are imposed by neo.io API
                     lazy = False,
                     cascade = True,
                     # all following arguments are decied by this IO and are free
                     segment_duration = 15.,
                     num_analogsignal = 4,
                     num_spiketrain_by_channel = 3,
                    ):
        """
        Return a fake Segment.

        The self.filename does not matter.

        In this IO read by default a Segment.

        This is just a example to be adapted to each ClassIO.
        In this case these 3 paramters are  taken in account because this function
        return a generated segment with fake AnalogSignal and fake SpikeTrain.

        Parameters:
            segment_duration :is the size in secend of the segment.
            num_analogsignal : number of AnalogSignal in this segment
            num_spiketrain : number of SpikeTrain in this segment

        """

        sampling_rate = 10000. #Hz
        t_start = -1.


        #time vector for generated signal
        timevect = np.arange(t_start, t_start+ segment_duration , 1./sampling_rate)

        # create an empty segment
        seg = Segment( name = 'it is a seg from exampleio')

        if cascade:
            # read nested analosignal
            for i in range(num_analogsignal):
                ana = self.read_analogsignal( lazy = lazy , cascade = cascade ,
                                            channel_index = i ,segment_duration = segment_duration, t_start = t_start)
                seg.analogsignals += [ ana ]

            # read nested spiketrain
            for i in range(num_analogsignal):
                for _ in range(num_spiketrain_by_channel):
                    sptr = self.read_spiketrain(lazy = lazy , cascade = cascade ,
                                                            segment_duration = segment_duration, t_start = t_start , channel_index = i)
                    seg.spiketrains += [ sptr ]


            # create an Event that mimic triggers.
            # note that ExampleIO  do not allow to acess directly to Event
            # for that you need read_segment(cascade = True)

            if lazy:
                # in lazy case no data are readed
                # eva is empty
                eva = Event()
            else:
                # otherwise it really contain data
                n = 1000

                # neo.io support quantities my vector use second for unit
                eva = Event(timevect[(np.random.rand(n)*timevect.size).astype('i')]* pq.s)
                # all duration are the same
                eva.durations = np.ones(n)*500*pq.ms  # Event doesn't have durations. Is Epoch intended here?
                # label
                l = [ ]
                for i in range(n):
                    if np.random.rand()>.6: l.append( 'TriggerA' )
                    else : l.append( 'TriggerB' )
                eva.labels = np.array( l )

            seg.events += [ eva ]

        seg.create_many_to_one_relationship()
        return seg


    def read_analogsignal(self ,
                          # the 2 first key arguments are imposed by neo.io API
                          lazy = False,
                          cascade = True,
                          channel_index = 0,
                          segment_duration = 15.,
                          t_start = -1,
                          ):
        """
        With this IO AnalogSignal can e acces directly with its channel number

        """
        sr = 10000.
        sinus_freq = 3. # Hz
        #time vector for generated signal:
        tvect = np.arange(t_start, t_start+ segment_duration , 1./sr)


        if lazy:
            anasig = AnalogSignal([], units='V', sampling_rate=sr * pq.Hz,
                                  t_start=t_start * pq.s,
                                  channel_index=channel_index)
            # we add the attribute lazy_shape with the size if loaded
            anasig.lazy_shape = tvect.shape
        else:
            # create analogsignal (sinus of 3 Hz)
            sig = np.sin(2*np.pi*tvect*sinus_freq + channel_index/5.*2*np.pi)+np.random.rand(tvect.size)
            anasig = AnalogSignal(sig, units= 'V', sampling_rate=sr * pq.Hz,
                                  t_start=t_start * pq.s,
                                  channel_index=channel_index)

        # for attributes out of neo you can annotate
        anasig.annotate(info = 'it is a sinus of %f Hz' %sinus_freq )

        return anasig





    def read_spiketrain(self ,
                                            # the 2 first key arguments are imposed by neo.io API
                                            lazy = False,
                                            cascade = True,

                                                segment_duration = 15.,
                                                t_start = -1,
                                                channel_index = 0,
                                                ):
        """
        With this IO SpikeTrain can e acces directly with its channel number
        """
        # There are 2 possibles behaviour for a SpikeTrain
        # holding many Spike instance or directly holding spike times
        # we choose here the first :
        if not HAVE_SCIPY:
            raise SCIPY_ERR

        num_spike_by_spiketrain = 40
        sr = 10000.

        if lazy:
            times = [ ]
        else:
            times = (np.random.rand(num_spike_by_spiketrain)*segment_duration +
                     t_start)

        # create a spiketrain
        spiketr = SpikeTrain(times, t_start = t_start*pq.s, t_stop = (t_start+segment_duration)*pq.s ,
                                            units = pq.s,
                                            name = 'it is a spiketrain from exampleio',
                                            )

        if lazy:
            # we add the attribute lazy_shape with the size if loaded
            spiketr.lazy_shape = (num_spike_by_spiketrain,)

        # ours spiketrains also hold the waveforms:

        # 1 generate a fake spike shape (2d array if trodness >1)
        w1 = -stats.nct.pdf(np.arange(11,60,4), 5,20)[::-1]/3.
        w2 = stats.nct.pdf(np.arange(11,60,2), 5,20)
        w = np.r_[ w1 , w2 ]
        w = -w/max(w)

        if not lazy:
            # in the neo API the waveforms attr is 3 D in case tetrode
            # in our case it is mono electrode so dim 1 is size 1
            waveforms  = np.tile( w[np.newaxis,np.newaxis,:], ( num_spike_by_spiketrain ,1, 1) )
            waveforms *=  np.random.randn(*waveforms.shape)/6+1
            spiketr.waveforms = waveforms*pq.mV
            spiketr.sampling_rate = sr * pq.Hz
            spiketr.left_sweep = 1.5* pq.s

        # for attributes out of neo you can annotate
        spiketr.annotate(channel_index = channel_index)

        return spiketr

Authors and contributors

The following people have contributed code and/or ideas to the current version of Neo. The institutional affiliations are those at the time of the contribution, and may not be the current affiliation of a contributor.

• Samuel Garcia [1]
• Andrew Davison [2]
• Chris Rodgers [3]
• Pierre Yger [2]
• Yann Mahnoun [4]
• Luc Estabanez [2]
• Andrey Sobolev [5]
• Thierry Brizzi [2]
• Florent Jaillet [6]
• Philipp Rautenberg [5]
• Thomas Wachtler [5]
• Cyril Dejean [7]
• Robert Pröpper [8]
• Domenico Guarino [2]
• Achilleas Koutsou [5]
• Erik Li [9]
• Georg Raiser [10]
• Joffrey Gonin [2]
• Kyler Brown [?]
• Mikkel Elle Lepperød [11]
• C Daniel Meliza [12]
• Julia Sprenger [13]
• Maximilian Schmidt [13]
• Johanna Senk [13]
• Carlos Canova [13]
• Hélissande Fragnaud [2]
• Mark Hollenbeck [14]
• Mieszko Grodzicki
• Rick Gerkin [15]
• Matthieu Sénoville [2]

1. Centre de Recherche en Neuroscience de Lyon, CNRS UMR5292 - INSERM U1028 - Universite Claude Bernard Lyon 1
2. Unité de Neuroscience, Information et Complexité, CNRS UPR 3293, Gif-sur-Yvette, France
3. University of California, Berkeley
4. Laboratoire de Neurosciences Intégratives et Adaptatives, CNRS UMR 6149 - Université de Provence, Marseille, France
5. G-Node, Ludwig-Maximilians-Universität, Munich, Germany
6. Institut de Neurosciences de la Timone, CNRS UMR 7289 - Université d’Aix-Marseille, Marseille, France
7. Centre de Neurosciences Integratives et Cignitives, UMR 5228 - CNRS - Université Bordeaux I - Université Bordeaux II
8. Neural Information Processing Group, TU Berlin, Germany
9. Department of Neurobiology & Anatomy, Drexel University College of Medicine, Philadelphia, PA, USA
10. University of Konstanz, Konstanz, Germany
11. Centre for Integrative Neuroplasticity (CINPLA), University of Oslo, Norway
12. University of Virginia
13. INM-6, Forschungszentrum Jülich, Germany
14. University of Texas at Austin
15. Arizona State University

If we’ve somehow missed you off the list we’re very sorry - please let us know.

License

Neo is free software, distributed under a 3-clause Revised BSD licence (BSD-3-Clause).

Support

If you have problems installing the software or questions about usage, documentation or anything else related to Neo, you can post to the NeuralEnsemble mailing list. If you find a bug, please create a ticket in our issue tracker.

Contributing

Any feedback is gladly received and highly appreciated! Neo is a community project, and all contributions are welcomed - see the Developers’ guide for more information. Source code is on GitHub.