def test_create_epochs(self):
signal = [0] * 30 + [1] * 5 + [0] * 30 + [1] * 5 + [0] * 30
events = nk.find_events(np.array(signal))
data = nk.create_epochs(pd.DataFrame({"Signal": signal}),
events_onsets=events["onsets"],
duration=5)
self.assertEqual(len(data), 2)
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
def test_create_epochs(df):
df = pd.DataFrame({
"Trigger":
pd.Series(
scipy.signal.square(1 * np.pi * 5 *
np.linspace(0, 1, 2000, endpoint=False))),
"Signal":
pd.Series(np.sin(20 * np.pi * np.linspace(0, 1, 2000, endpoint=False)))
* np.random.normal(0, 1, 2000),
"Signal2":
pd.Series(np.sin(60 * np.pi * np.linspace(0, 1, 2000, endpoint=False)))
* np.random.normal(0, 2, 2000)
})
df.plot()
events = nk.find_events(df["Trigger"], cut="lower")
assert len(events) == 2
fig = nk.plot_events_in_signal(df, events["onsets"])
epochs = nk.create_epochs(df, events["onsets"], duration=0.1)
assert len(epochs) == 2
nk.z_score(bio["df"][["ECG_Filtered", "RSP_Filtered",
"RSA"]])[1000:2500].plot()
plt.title('RSA algorithm')
plt.savefig('graphs/rsaalg')
#define condition list
condition_list = ["Negative", "Neutral", "Neutral", "Negative"]
#dict containing onsets and durations of each event --> should be 1
events = nk.find_events(df["Photosensor"], cut="lower")
print("event finding")
print(events)
#create_epoch --> epochs of data corresponding to each event (since is 1 event, is the epoch[0])
epochs = nk.create_epochs(bio["df"],
events["onsets"],
duration=700,
onset=-100)
nk.z_score(epochs[0][["ECG_Filtered", "EDA_Filtered", "Photosensor"]]).plot()
plt.title('Epoch')
plt.savefig('graphs/epoch')
# itereate through the epochs and store the interesting results in a new dict that will be, at the end, converted to a dataframe
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"].loc[-100:0].mean() # Baseline
rr_max = epoch["ECG_RR_Interval"].loc[0:400].max() # Maximum RR interval
data[epoch_index][
import neurokit as nk
import pandas as pd
import numpy as np
import scipy
df = pd.read_csv("EDA_RSP_Artifacts.csv") # Sampled at 100Hz
conditions = pd.Series.from_csv("events.csv")
# Preprocessing
df = nk.bio_process(eda=df["EDA"], rsp=df["RSP"], add=df["Photosensor"], sampling_rate=100, scr_min_amplitude=0.02)
df = df["df"]
events = nk.find_events(df["Photosensor"], cut="lower")
epochs = nk.create_epochs(df, events["onsets"], duration=events["durations"]+800, onset=-400)
#==============================================================================
#
#==============================================================================
def eda_ERP(epoch, event_length, sampling_rate=1000, window_post=4):
"""
Extract event-related EDA and Skin Conductance Response (SCR).
Parameters
----------
epoch : pandas.DataFrame
An epoch contains in the epochs dict returned by :function:`nk.create_epochs()` on dataframe returned by :function:`nk.bio_process()`. Index must range from -4s to +4s (relatively to event onset and end).
event_length : int
In seconds.
sampling_rate : int
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()