neurochat.nc_event module¶

This module implements the NEvent Class for NeuroChaT software.

@author: Md Nurul Islam; islammn at tcd dot ie

class neurochat.nc_event.NEvent(event_names=[], timestamps=[], event_train=[], **kwargs)[source]¶

Bases: neurochat.nc_base.NBase

Store external events or stimuli and relate them to neural data.

This data class is the placeholder for the dataset that contains information about external events or stimuli. Events are stored as a name (str), tag (int) and a timestamp (float). Each tag is a unique integer number representing a particular event.

Parameters:	event_names (list of str, or np.ndarray) – The name of each timestamped event. timestamps (list of float, or np.ndarray) – The time of each event. event_train (list of int, or np.ndarry) – The unique integer tag of each timestamped event. **kwargs – Keyword arguments passed to NBase init.

__repr__()[source]¶: Return a REPL string, eval(repr(self)) = self.

__str__()[source]¶

Return a user friendly (time, name, tag) string.

Only returns the first and last 10 events if more than 20 events.

add_lfp(lfp=None, **kwargs)[source]¶

Add a new LFP node to current NEvent() object.

Parameters:	lfp (NLfp) – NLfp object. If None, new object is created
Returns:	` (obj:Nlfp`) – A new NLfp() object

add_spike(spike=None, **kwargs)[source]¶

Add new spike node to current NEvent() object.

Parameters:	spike (NSpike) – NSPike object. If None, new object is created
Returns:	` (obj:NSpike`) – A new NSpike() object

get_bytes_per_timestamp()[source]¶

Return the number of bytes per timestamp.

Parameters:	None –
Returns:	int – The number of bytes per timestamp.

get_event_name(event_tag=None)[source]¶

Return name of the event from its tag.

Parameters:	event_tag (int) –
Returns:	event_name (str) – Name of the event

get_event_stamp(event=None)[source]¶

Return timestamps for a particular event.

Parameters:	event (str or int) – If str, represent the name of the event. If int, represents event tag.
Returns:	timestamp (ndarray) – Timestamps of the event

get_event_train()[source]¶

Return tags for all events in temporal order.

Parameters:	None –
Returns:	ndarray – Train of events as train of tags

get_tag(event_name=None)[source]¶

Return tag of the event from its name.

Parameters:	event_name (str) –
Returns:	event_tag (int) – Tag of the event

get_timebase()[source]¶

Get timebase.

Returns:	float

get_timestamp()[source]¶

Return timestamps for all events.

Parameters:	None –
Returns:	timestamp (ndarray) – Timestamps of all the events

get_total_samples()[source]¶

Return the total number of samples.

Parameters:	None –
Returns:	int – The number of samples.

load(filename=None, system=None)[source]¶

Read event file from the recording formats.

This is currently only implemented for the Axona .stm format.

Parameters:	filename (str) – Full filepath of the event data. system (str) – Data format or the recording system. Currently, only “Axona” is supported.
Returns:	None

load_lfp(names=None)[source]¶

Load datasets of the LFP nodes.

The name of each node is used for obtaining the filenames.

Parameters:	names (list of str) – Names of the nodes to load. If all, all LFP nodes are loaded
Returns:	None

load_spike(names='all')[source]¶

Load datasets of the spike nodes.

The name of each node is used for obtaining the filenames.

Parameters:	names (list of str) – Names of the nodes to load. If ‘all’, all the spike nodes are loaded.
Returns:	None

phase_dist(lfp=None, **kwargs)[source]¶

Analysis of event to LFP phase distribution.

Delegates to NLfp().phase_dist()

Parameters:	lfp (NLfp) – LFP object which contains the LFP data. **kwargs – Keyword arguments
Returns:	dict – Graphical data of the analysis