# -*- coding: utf-8 -*-
import pandas as pd
from .eda_eventrelated import eda_eventrelated
from .eda_intervalrelated import eda_intervalrelated
[docs]def eda_analyze(data, sampling_rate=1000, method="auto"):
"""Performs EDA analysis on either epochs (event-related analysis) or on longer periods of data such as resting-
state data.
Parameters
----------
data : Union[dict, pd.DataFrame]
A dictionary of epochs, containing one DataFrame per epoch, usually obtained via `epochs_create()`,
or a DataFrame containing all epochs, usually obtained via `epochs_to_df()`.
Can also take a DataFrame of processed signals from a longer period of data, typically generated
by `eda_process()` or `bio_process()`. Can also take a dict containing sets of separate periods
of data.
sampling_rate : int
The sampling frequency of the signal (in Hz, i.e., samples/second).
Defaults to 1000Hz.
method : str
Can be one of 'event-related' for event-related analysis on epochs, or 'interval-related'
for analysis on longer periods of data. Defaults to 'auto' where the right method will be
chosen based on the mean duration of the data ('event-related' for duration under 10s).
Returns
-------
DataFrame
A dataframe containing the analyzed EDA features. If event-related analysis is conducted,
each epoch is indicated by the `Label` column. See `eda_eventrelated()` and
`eda_intervalrelated()` docstrings for details.
See Also
--------
bio_process, eda_process, epochs_create, eda_eventrelated, eda_intervalrelated
Examples
----------
>>> import neurokit2 as nk
>>> # Example 1: Download the data for event-related analysis
>>> data = nk.data("bio_eventrelated_100hz")
>>>
>>> # Process the data for event-related analysis
>>> df, info = nk.bio_process(eda=data["EDA"], sampling_rate=100)
>>> events = nk.events_find(data["Photosensor"], threshold_keep='below',
... event_conditions=["Negative", "Neutral", "Neutral", "Negative"])
>>> epochs = nk.epochs_create(df, events, sampling_rate=100, epochs_start=-0.1, epochs_end=1.9)
>>>
>>> # Analyze
>>> nk.eda_analyze(epochs, sampling_rate=100) #doctest: +SKIP
>>>
>>> # Example 2: Download the resting-state data
>>> data = nk.data("bio_resting_8min_100hz")
>>>
>>> # Process the data
>>> df, info = nk.eda_process(data["EDA"], sampling_rate=100)
>>>
>>> # Analyze
>>> nk.eda_analyze(df, sampling_rate=100) #doctest: +SKIP
"""
method = method.lower()
# Event-related analysis
if method in ["event-related", "event", "epoch"]:
# Sanity checks
if isinstance(data, dict):
for i in data:
colnames = data[i].columns.values
elif isinstance(data, pd.DataFrame):
colnames = data.columns.values
if len([i for i in colnames if "Label" in i]) == 0:
raise ValueError(
"NeuroKit error: eda_analyze(): Wrong input or method, we couldn't extract epochs features."
)
else:
features = eda_eventrelated(data)
# Interval-related analysis
elif method in ["interval-related", "interval", "resting-state"]:
features = eda_intervalrelated(data)
# Auto
elif method in ["auto"]:
if isinstance(data, dict):
for i in data:
duration = len(data[i]) / sampling_rate
if duration >= 10:
features = eda_intervalrelated(data)
else:
features = eda_eventrelated(data)
if isinstance(data, pd.DataFrame):
if "Label" in data.columns:
epoch_len = data["Label"].value_counts()[0]
duration = epoch_len / sampling_rate
else:
duration = len(data) / sampling_rate
if duration >= 10:
features = eda_intervalrelated(data)
else:
features = eda_eventrelated(data)
return features