# -*- coding: utf-8 -*-
import pandas as pd
from ..misc.report import create_report
from ..signal import signal_sanitize
from .eda_clean import eda_clean
from .eda_methods import eda_methods
from .eda_peaks import eda_peaks
from .eda_phasic import eda_phasic
from .eda_plot import eda_plot
[docs]
def eda_process(
eda_signal, sampling_rate=1000, method="neurokit", report=None, **kwargs
):
"""**Process Electrodermal Activity (EDA)**
Convenience function that automatically processes electrodermal activity (EDA) signal.
Parameters
----------
eda_signal : Union[list, np.array, pd.Series]
The raw EDA signal.
sampling_rate : int
The sampling frequency of ``"eda_signal"`` (in Hz, i.e., samples/second).
method : str
The processing pipeline to apply. Can be one of ``"biosppy"`` or ``"neurokit"`` (default).
report : str
The filename of a report containing description and figures of processing
(e.g. ``"myreport.html"``). Needs to be supplied if a report file
should be generated. Defaults to ``None``. Can also be ``"text"`` to
just print the text in the console without saving anything.
**kwargs
Other arguments to be passed to specific methods. For more information,
see :func:`.rsp_methods`.
Returns
-------
signals : DataFrame
A DataFrame of same length as ``"eda_signal"`` containing the following
columns:
.. codebookadd::
EDA_Raw|The raw signal.
EDA_Clean|The cleaned signal.
EDA_Tonic|The tonic component of the signal, or the Tonic Skin Conductance Level (SCL).
EDA_Phasic|The phasic component of the signal, or the Phasic Skin Conductance Response (SCR).
SCR_Onsets|The samples at which the onsets of the peaks occur, marked as "1" in a list of zeros.
SCR_Peaks|The samples at which the peaks occur, marked as "1" in a list of zeros.
SCR_Height|The SCR amplitude of the signal including the Tonic component. Note that cumulative \
effects of close-occurring SCRs might lead to an underestimation of the amplitude.
SCR_Amplitude|The SCR amplitude of the signal excluding the Tonic component.
SCR_RiseTime|The time taken for SCR onset to reach peak amplitude within the SCR.
SCR_Recovery|The samples at which SCR peaks recover (decline) to half amplitude, marked as "1" \
in a list of zeros.
info : dict
A dictionary containing the information of each SCR peak (see :func:`eda_findpeaks`),
as well as the signals' sampling rate.
See Also
--------
eda_simulate, eda_clean, eda_phasic, eda_findpeaks, eda_plot
Examples
--------
.. ipython:: python
import neurokit2 as nk
eda_signal = nk.eda_simulate(duration=30, scr_number=5, drift=0.1, noise=0)
signals, info = nk.eda_process(eda_signal, sampling_rate=1000)
@savefig p_eda_process.png scale=100%
nk.eda_plot(signals, info)
@suppress
plt.close()
"""
# Sanitize input
eda_signal = signal_sanitize(eda_signal)
methods = eda_methods(sampling_rate=sampling_rate, method=method, **kwargs)
# Preprocess
# Clean signal
eda_cleaned = eda_clean(
eda_signal,
sampling_rate=sampling_rate,
method=methods["method_cleaning"],
**methods["kwargs_cleaning"],
)
if methods["method_phasic"] is None or methods["method_phasic"].lower() == "none":
eda_decomposed = pd.DataFrame({"EDA_Phasic": eda_cleaned})
else:
eda_decomposed = eda_phasic(
eda_cleaned,
sampling_rate=sampling_rate,
method=methods["method_phasic"],
**methods["kwargs_phasic"],
)
# Find peaks
peak_signal, info = eda_peaks(
eda_decomposed["EDA_Phasic"].values,
sampling_rate=sampling_rate,
method=methods["method_peaks"],
amplitude_min=0.1,
**methods["kwargs_peaks"],
)
info["sampling_rate"] = sampling_rate # Add sampling rate in dict info
# Store
signals = pd.DataFrame({"EDA_Raw": eda_signal, "EDA_Clean": eda_cleaned})
signals = pd.concat([signals, eda_decomposed, peak_signal], axis=1)
if report is not None:
# Generate report containing description and figures of processing
if ".html" in str(report):
fig = eda_plot(signals, info, static=False)
else:
fig = None
create_report(file=report, signals=signals, info=methods, fig=fig)
return signals, info
