ivneuro.nex

Submodules

Attributes

__doc__

Classes

NexData

Extract variables from a .Nex5 file.

Functions

pull_fp(fileData[, FP_of_interest])

Extract field potential variables from a Nex5 file data, previously created using reader.ReadNex5File(<filename>).

pull_events(fileData[, events_of_interest])

Extract events from a Nex5 file data, previously created using reader.ReadNex5File(<filename>).

pull_continuous(fileData[, continuous_of_interest])

Extract all continuous variables, except for field potential variables, from a Nex5 file data previously created using reader.ReadNex5File(<filename>).

pull_neurons(fileData[, neurons_of_interest])

Extract neurons from a Nex5 file data, previously created using reader.ReadNex5File(<filename>).

pull_markers(fileData[, markers_of_interest])

Extract markers from a Nex5 file data, previously created using reader.ReadNex5File(<filename>).

pull_centroids(fileData[, centroids_of_interest])

Extract field potential variables from a Nex5 file data, previously created using reader.ReadNex5File(<filename>).

Package Contents

ivneuro.nex.__doc__ = Multiline-String
Show Value
"""
ivneuro.nex
=================
A subpackage for extracting data from nex5 files.
It can also extract centroids and local field potentials, which do not have their own types in
NeuroExplorer, as a different type from continuous.
The NexData class puts together all the functions of this subpackage and creates a NexData object,
wich contains the extrated variables as attributes.
"""
ivneuro.nex.pull_fp(fileData, FP_of_interest=[])

Extract field potential variables from a Nex5 file data, previously created using reader.ReadNex5File(<filename>).

It assumes that “FP” will be in the name of every field potential variable, which is true for Nex files whose original variables names have not been modified. Therefore, restults of this function might be incorrect for files whose variable names have been modified. It also assumes all field potential variables have the same timestamps and sample rate.

Parameters:

  • fileData (dict) – Data extracted from a nex5 file using reader.ReadNex5File(<filename>).

  • FP_of_interest (list, optional) – List of names of field potentials to extract. If None, the function returns None; if an empty list, all the field potentials are extracted. The default is [].

  • Returns

  • index. (Dataframe with field potentials (in mV) in each column and timestaps as)

  • (float). (Sampling rate of local field potentials)

ivneuro.nex.pull_events(fileData, events_of_interest=[])

Extract events from a Nex5 file data, previously created using reader.ReadNex5File(<filename>).

Parameters:

  • fileData (dict) – Data extracted from a nex5 file using reader.ReadNex5File(<filename>).

  • events_of_interest (list, optional) – List of names of events to extract. If None, the function returns None; if an empty list, all the events are extracted. The default is [].

Return type:

Dictionary with event names as keys and list of timestamps as values.

ivneuro.nex.pull_continuous(fileData, continuous_of_interest=[])

Extract all continuous variables, except for field potential variables, from a Nex5 file data previously created using reader.ReadNex5File(<filename>).

It assumes that “FP” will be in the name of every field potential variable, which is true for Nex files whose original variables names have not been modified. Therefore, restults of this function might be incorrect for files whose variable names have been modified.

Parameters:

  • fileData (dict) – Data extracted from a nex5 file using reader.ReadNex5File(<filename>).

  • continuous_of_interest (list, optional) – List of names of continuous to extract. If None, the function returns None; if an empty list, all non-field potential continuous variables are extracted. The default is [].

Return type:

List of pandas DataFrame, each corresponding to a continuous variable values and timestaps as index.

ivneuro.nex.pull_neurons(fileData, neurons_of_interest=[])

Extract neurons from a Nex5 file data, previously created using reader.ReadNex5File(<filename>).

Parameters:

  • fileData (dict) – Data extracted from a nex5 file using reader.ReadNex5File(<filename>).

  • neurons_of_interest (list, optional) – List of names of events to extract. If None, the function returns None; if an empty list, all the neurons are extracted. The default is [].

Return type:

Dictionary with neuron names as keys and list of timestamps as values.

ivneuro.nex.pull_markers(fileData, markers_of_interest=[])

Extract markers from a Nex5 file data, previously created using reader.ReadNex5File(<filename>).

Parameters:

  • fileData (dict) – Data extracted from a nex5 file using reader.ReadNex5File(<filename>).

  • markers_of_interest (list, optional) – List of names of markers to extract. If None, the function returns None; if an empty list, all the markers are extracted. The default is [].

Return type:

Dictionary with markers names as keys and list of timestamps as values.

ivneuro.nex.pull_centroids(fileData, centroids_of_interest=[])

Extract field potential variables from a Nex5 file data, previously created using reader.ReadNex5File(<filename>).

It assumes that “centroid” (case insensitive) will be in the name of every centroid variable, which is true for Nex files whose original variables names have not been modified. It also exclude field potential variables that contain the string “FP” in their names. Therefore, restults of this function might be incorrect for files whose variable names have been modified. It also assumes that all centroids have the same timestamps.

Parameters:

  • fileData (dict) – Data extracted from a nex5 file using reader.ReadNex5File(<filename>).

  • centroids_of_interest (list, optional) – List of names of centroids to extract. If None, the function returns None; if an empty list, all centroid variables are extracted. The default is [].

Return type:

Dataframe with centroids in each column and timestaps as index.

class ivneuro.nex.NexData(file, FP_of_interest=[], events_of_interest=[], continuous_of_interest=[], neurons_of_interest=[], markers_of_interest=[], centroids_of_interest=[], clear_Nex_data=True)

Extract variables from a .Nex5 file.

Parameters:

  • file (str) – Complete path of the .Nex5 file to extract data from.

  • FP_of_interest (list, optional) – List of names of field potentials to extract. If None, nothing is extracted; if an empty list, all the field potentials are extracted. The default is [].

  • events_of_interest (list, optional) – List of names of events to extract. If None, nothing is extracted; if an empty list, all the events are extracted. The default is [].

  • continuous_of_interest (list, optional) – List of names of continuous to extract. If None, nothing is extracted; if an empty list, all non-field potential continuous variables are extracted. The default is [].

  • neurons_of_interest (list, optional) – List of names of events to extract. If None, nothing is extracted; if an empty list, all the neurons are extracted. The default is [].

  • markers_of_interest (list, optional) – List of names of markers to extract. If None, nothing is extracted; if an empty list, all the markers are extracted. The default is [].

  • centroids_of_interest (list, optional) – List of names of centroids to extract. If None, nothing is extracted; if an empty list, all centroid variables are extracted. The default is [].

  • clear_Nex_data (bool, optional) – If True, data atribute is set to None. Otherwise, result of reader.ReadNex5File(file) is stored in data atribute. The default is True.

file_path

File path of the .Nex5 file the data was extracted from.

Type:

str

FP

Field potentials (in mV) in each column and timestaps as index.

Type:

pandas.DataFrame

FP_sampling_rate

Sampling rate of local field potentials.

Type:

float

events

Event names as keys and list of timestamps as values.

Type:

dict

continuous

Each DataFrame of the list contains a column with the continuous values and the timestamps as index,for every continuous variable (excluding field potentials).

Type:

list of pandas.DataFrames

neurons

Neuron names as keys and list of timestamps as values.

Type:

dict

markers

Markers names as keys and list of timestamps as values.

Type:

dict

centroids

Centroids in each column and timestaps as index.

Type:

pandas.DataFrame

clear_fileData()

Set data atribute to None.

file_path
_FP_of_interest = []
_events_of_interest = []
_continuous_of_interest = []
_neurons_of_interest = []
_markers_of_interest = []
_centroids_of_interest = []
_clear_Nex_data = True
data
events = None
continuous = None
neurons = None
markers = None
centroids
__str__()
__repr__()
_pull_fp(FP_of_interest=[])
_pull_events(events_of_interest=[])
_pull_continuous(continuous_of_interest=[])
_pull_neurons(neurons_of_interest=[])
_pull_markers(markers_of_interest=[])
_pull_centroids(centroids_of_interest=[])
clear_fileData()