Neo core API Reference#

Relationships between Neo objects#

Object:

With a star = inherits from Quantity

Attributes:

In red = required
In white = recommended

Relationships:

In cyan = one to many

Click here for a better quality SVG diagram

Note

This figure does not include ChannelView and RegionOfInterest.

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 classes 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 continuous, for a given recording session.

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

Usage:

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

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

Container of:: Segment Group

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 continuous 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.

Container of:: Epoch Event AnalogSignal IrregularlySampledSignal SpikeTrain

class neo.core.Group(objects=None, name=None, description=None, file_origin=None, allowed_types=None, **annotations)#

Can contain any of the data objects, views, or other groups, outside the hierarchy of the segment and block containers. A common use is to link the SpikeTrain objects within a Block, possibly across multiple Segments, that were emitted by the same neuron.

Required attributes/properties:

None

Recommended attributes/properties:

objects: (Neo object) Objects with which to pre-populate the Group
name: (str) A label for the group.
description: (str) Text description.
file_origin: (str) Filesystem path or URL of the original data file.

Optional arguments:

allowed_types: (list or tuple) Types of Neo object that are allowed to be added to the Group. If not specified, any Neo object can be added.

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

metadata and stored in annotations.

Container of:

AnalogSignal, IrregularlySampledSignal, SpikeTrain, Event, Epoch, ChannelView, Group

class neo.core.AnalogSignal(signal, units=None, dtype=None, copy=True, t_start=array(0.) * s, sampling_rate=None, sampling_period=None, name=None, file_origin=None, description=None, array_annotations=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.
array_annotations: (dict) Dict mapping strings to numpy arrays containing annotations for all data points

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)

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. Note that slicing an AnalogSignal may give a different result to slicing the underlying NumPy array since signals are always two-dimensional.

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, array_annotations=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.

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

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.
array_annotations: (dict) Dict mapping strings to numpy arrays containing annotations for all data points

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.ChannelView(obj, index, name=None, description=None, file_origin=None, array_annotations=None, **annotations)#

A tool for indexing a subset of the channels within an AnalogSignal or IrregularlySampledSignal;

Required attributes/properties:

obj: (AnalogSignal or IrregularlySampledSignal) The signal being indexed.
index: (list/1D-array) boolean or integer mask to select the channels of interest.

Recommended attributes/properties:

name: (str) A label for the view.
description: (str) Text description.
file_origin: (str) Filesystem path or URL of the original data file.
array_annotations: (dict) Dict mapping strings to numpy arrays containing annotations for all data points

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

metadata and stored in annotations.

class neo.core.Event(times=None, labels=None, units=None, name=None, description=None, file_origin=None, array_annotations=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='U'))
>>>
>>> evt.times
array([  0.,  10.,  20.]) * s
>>> evt.labels
array(['trig0', 'trig1', 'trig2'],
      dtype='<U5')

Required attributes/properties:

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

Optional attributes/properties:

array_annotations: (dict) Dict mapping strings to numpy arrays containing annotations for all data points

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, array_annotations=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='U'))
>>>
>>> epc.times
array([  0.,  10.,  20.]) * s
>>> epc.durations
array([ 10.,   5.,   7.]) * ms
>>> epc.labels
array(['btn0', 'btn1', 'btn2'],
      dtype='<U4')

Required attributes/properties:

times: (quantity array 1D, numpy array 1D or list) The start times of each time period.
durations: (quantity array 1D, numpy array 1D, list, quantity scalar or float) The length(s) of each time period. If a scalar/float, the same value is used for all time periods.
labels: (numpy.array 1D dtype=’U’ or list) Names or labels for the time periods.
units: (quantity units or str) Required if the 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:

array_annotations: (dict) Dict mapping strings to numpy arrays containing annotations for all data points

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=None, copy=True, sampling_rate=array(1.) * Hz, t_start=array(0.) * s, waveforms=None, left_sweep=None, name=None, file_origin=None, description=None, array_annotations=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, 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.
array_annotations: (dict) Dict mapping strings to numpy arrays containing annotations for all data points

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: (quantity array 1D) Returns the SpikeTrain as a quantity array.

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.

class neo.core.ImageSequence(image_data, units=None, dtype=None, copy=True, t_start=array(0.) * s, spatial_scale=None, frame_duration=None, sampling_rate=None, name=None, description=None, file_origin=None, **annotations)#

Representation of a sequence of images, as an array of three dimensions organized as [frame][row][column].

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

usage:

>>> from neo.core import ImageSequence
>>> import quantities as pq
>>>
>>> img_sequence_array = [[[column for column in range(20)]for row in range(20)]
...                         for frame in range(10)]
>>> image_sequence = ImageSequence(img_sequence_array, units='V',
...                                sampling_rate=1 * pq.Hz,
...                                spatial_scale=1 * pq.micrometer)
>>> image_sequence
ImageSequence 10 frames with width 20 px and height 20 px; units V; datatype int64
sampling rate: 1.0
spatial_scale: 1.0
>>> image_sequence.spatial_scale
array(1.) * um

Required attributes/properties:

image_data: (3D NumPy array, or a list of 2D arrays) The data itself
units: (quantity units)
sampling_rate: or frame_duration (quantity scalar) Number of samples per unit time or duration of a single image frame. If both are specified, they are checked for consistency.
spatial_scale: (quantity scalar) size for a pixel.
t_start: (quantity scalar) Time when sequence begins. Default 0.

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/frame_duration)
frame_duration: (quantity scalar) Duration of each image frame. (1/sampling_rate)
spatial_scale: Size of a pixel
duration: (Quantity) Sequence duration, read-only. (size * frame_duration)
t_stop: (quantity scalar) Time when sequence ends, read-only. (t_start + duration)

class neo.core.RectangularRegionOfInterest(x, y, width, height)#

Representation of a rectangular ROI

Usage:

>>> roi = RectangularRegionOfInterest(20.0, 20.0, width=5.0, height=5.0)
>>> signal = image_sequence.signal_from_region(roi)

Required attributes/properties:

x, y: (integers or floats) Pixel coordinates of the centre of the ROI
width: (integer or float) Width (x-direction) of the ROI in pixels
height: (integer or float) Height (y-direction) of the ROI in pixels

class neo.core.CircularRegionOfInterest(x, y, radius)#

Representation of a circular ROI

Usage:

>>> roi = CircularRegionOfInterest(20.0, 20.0, radius=5.0)
>>> signal = image_sequence.signal_from_region(roi)

Required attributes/properties:

x, y: (integers or floats) Pixel coordinates of the centre of the ROI
radius: (integer or float) Radius of the ROI in pixels

class neo.core.PolygonRegionOfInterest(*vertices)#

Representation of a polygonal ROI

Usage:

>>> roi = PolygonRegionOfInterest(
...     (20.0, 20.0),
...     (30.0, 20.0),
...     (25.0, 25.0)
... )
>>> signal = image_sequence.signal_from_region(roi)

Required attributes/properties:

vertices: tuples containing the (x, y) coordinates, as integers or floats, of the vertices of the polygon