ivneuro.nex.NexData
A module with NexData class, wich puts together all the functions of pull_data subpackage and creates a NexData object, wich contains the extrated variables as attributes.
Classes
Extract variables from a .Nex5 file. |
Module Contents
- class ivneuro.nex.NexData.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()