def test_read_acqknowledge(self):
data_path = os.getcwd() + r"/tests/test_bio_data.acq" # If running from travis
# data_path = "test_bio_data.acq" # If running in local
# Read data
df = nk.read_acqknowledge(data_path)
# Resample to 100Hz
df = df.resample("10L").mean()
df.columns = ['ECG', 'EDA', 'PPG', 'Photosensor', 'RSP']
# Check length
self.assertEqual(len(df), 35645)
return(df)
def test_read_acqknowledge():
if run_tests_in_local is False:
data_path = os.getcwd() + r"/tests/data/test_bio_data.acq" # If running from travis
else:
data_path = "data/test_bio_data.acq" # If running in local
# Read data
df, sampling_rate = nk.read_acqknowledge(data_path, return_sampling_rate=True)
# Resample to 100Hz
df = df.resample("10L").mean()
df.columns = ['ECG', 'EDA', 'PPG', 'Photosensor', 'RSP']
# Check length
assert len(df) == 35645
return(df)
def analyzeSCR(event_file, acq_file):
import neurokit as nk
import pandas as pd
import numpy as np
import seaborn as sns
## loading acq file
df, sampling_rate = nk.read_acqknowledge(acq_file,
return_sampling_rate=True)
bio = nk.bio_process(eda=df["GSR100C"],
add=df["Script"],
sampling_rate=sampling_rate)
# adding conditions
events = pd.read_csv(event_file, sep=r'\s+')
condition_list = events['trial_type']
events = {
'onset': np.array(events["onset"] * 1000),
'duration': np.array(events["duration"] * 1000)
}
epochs = nk.create_epochs(
bio["df"], events["onset"], duration=120000,
onset=0) # create epoch file with 120sec duration and -1sec begins
data = {} # Initialize an empty dict
for epoch_index in epochs:
data[epoch_index] = {
} # Initialize an empty dict for the current epoch
epoch = epochs[epoch_index]
# ECG
# baseline = epoch["ECG_RR_Interval"].ix[-100:0].mean() # Baseline
# rr_max = epoch["ECG_RR_Interval"].ix[0:400].max() # Maximum RR interval
# data[epoch_index]["HRV_MaxRR"] = rr_max - baseline # Corrected for baseline
# EDA - SCR
scr_max = epoch["SCR_Peaks"].ix[100:15000].max(
) # Maximum SCR peak - now its 30sec after initiation of script
if np.isnan(scr_max):
scr_max = 0 # If no SCR, consider the magnitude, i.e. that the value is 0
data[epoch_index]["SCR_Magnitude"] = scr_max
data = pd.DataFrame.from_dict(data,
orient="index") # Convert to a dataframe
data["Condition"] = condition_list # Add the conditions
data # Print
return data
__author__ = "Julian Tejada"
import matplotlib.pyplot as plt
import neurokit as nk
import pandas as pd
import numpy as np
import math
import sys
# import seaborn as sns
import biosppy
from biosppy.signals import eda
from biosppy import plotting, utils
###############################
# import data from acq
df, sampling_rate = nk.read_acqknowledge(str(sys.argv[1]),
return_sampling_rate=True)
# first filter
bio = biosppy.signals.eda.eda(df["EDA100C"],
sampling_rate=sampling_rate,
show=False,
min_amplitude=0.01)
# Create a ts vector with the time
length = len(df["EDA100C"])
T = (length - 1) / sampling_rate
ts = np.linspace(0, T, length, endpoint=False)
# Extract SCRs using Gamboa method
SCRs = biosppy.signals.eda.basic_scr(bio["filtered"], sampling_rate=2000)
#bio = biosppy.signals.eda.eda(df["EDA100C"], sampling_rate=2000)
# choose scripts channel
b = a.named_channels["GSR100C"].raw_data
import matplotlib.pyplot as plt
plt.plot(b)
#%% analyze data using NeuroKit
import neurokit as nk
import pandas as pd
import numpy as np
import seaborn as sns
# loading BioPack file
df, sampling_rate = nk.read_acqknowledge(
'/media/Data/PTSD_KPE/physio_data/raw/kpe1223/scan_2/kpe1223.2_scripts_2017-02-06T08_39_32.acq',
return_sampling_rate=True)
df[['GSR100C', 'Script']].plot()
# Process the signals
bio = nk.bio_process(eda=df["GSR100C"], add=df["Script"], sampling_rate=1000.)
# Plot the processed dataframe, normalizing all variables for viewing purpose
#%%
condition_list = [
"Trauma", "Relax", "Sad", "Relax", "Trauma", "Sad", "Relax", "Trauma",
"Sad"
]
#%%
def analyzeSCR(event_file, acq_file):
import neurokit as nk
import neurokit as nk
df = nk.read_acqknowledge("abnormal_ECG.acq")
events = nk.find_events(df["Photosensor"], cut="lower")
df = nk.create_epochs(df, events["onsets"], duration=events["durations"])[0]
#df["Photosensor"].plot()
df = nk.ecg_process(df['ECG, X, RSPEC-R'])
ecg = df["ECG"]
df = df["df"]
#rpeaks = nk.ecg_find_peaks(df['ECG_Filtered'])
nk.plot_events_in_signal(df['ECG_Filtered'], ecg["R_Peaks"])
#df['ECG, X, RSPEC-R'].iloc[104000:140000].plot()
import neurokit as nk
import pandas as pd
import pywt
"""
https://github.com/MITMediaLabAffectiveComputing/eda-explorer
http://eda-explorer.media.mit.edu/info/
"""
df, sampling_rate = nk.read_acqknowledge("bio_data.acq", return_sampling_rate=True)
eda = df["EDA, X, PPGED-R"]
eda_processed = nk.eda_process(eda, sampling_rate=sampling_rate)
eda = eda_processed["df"]["EDA_Phasic"]
def getWaveletData(eda):
'''
This function computes the wavelet coefficients
INPUT:
data: DataFrame, index is a list of timestamps at 8Hz, columns include EDA, filtered_eda
OUTPUT:
wave1Second: DateFrame, index is a list of timestamps at 1Hz, columns include OneSecond_feature1, OneSecond_feature2, OneSecond_feature3
waveHalfSecond: DateFrame, index is a list of timestamps at 2Hz, columns include HalfSecond_feature1, HalfSecond_feature2
'''