import glob
from Conf.Settings import FS_ECG, FS_GSR, FS_RESP, DATASET_PATH
from ECG.ECGFeatures import ECGFeatures
from GSR.GSRFeatures import PPGFeatures, EDAFeatures
from Resp.RespFeatures import RespFeatures
import pandas as pd
data_path = "D:\\usr\\pras\\data\\YAMAHA\\Yamaha-Experiment (2020-10-26 - 2020-11-06)\\data\\*"
ecg_file = "\\ECG\\"
gsr_file = "\\GSR\\"
resp_file = "\\Resp\\"
ecg_features = ECGFeatures(fs=FS_ECG)
ppg_features = PPGFeatures(fs=FS_GSR)
eda_features = EDAFeatures(fs=FS_GSR)
resp_features = RespFeatures(fs=FS_RESP)
for folder in glob.glob(DATASET_PATH + "*"):
for subject in glob.glob(folder + "\\*-2020-*"):
print(subject)
try:
# filter ecg iPhone
# ecg = pd.read_csv(glob.glob(subject+ecg_file+"*.csv")[0])
# ecg_filtered = ecg_features.filterECG(ecg["ecg"].values)
# ecg["ecg"] = ecg_filtered
# ecg.to_csv(subject+ecg_file+"filtered_ecg.csv", index=False)
#
# # filter PPG and EDA
# gsr = pd.read_csv(glob.glob(subject + gsr_file + "*.csv")[0], header=[0,1])
# time = gsr.iloc[:, 0].values
# eda = gsr.iloc[:, 5].values
path_results = path + "results\\GSR\\"
experiment_results = pd.read_csv(path +
"Komiya_M_2020_7_9_15_22_44_gameResults.csv")
gsr_data = pd.read_csv(path + "Komiya_GSR.csv", header=[0, 1])
gsr_data["GSR_Timestamp_Unix_CAL"] = gsr_data["GSR_Timestamp_Unix_CAL"].apply(
utcToTimeStamp, axis=1)
experiment_results["Time_Start"] = experiment_results["Time_Start"].apply(
timeToInt)
experiment_results["Time_End"] = experiment_results["Time_End"].apply(
timeToInt)
format = '%H:%M:%S'
split_time = 45
eda_features_exct = EDAFeatures(FS_GSR)
ppg_features_exct = PPGFeatures(FS_GSR)
min_len = FS_GSR * (split_time + 1)
gsr_features = pd.DataFrame(
columns=["Idx", "Start", "End", "Valence", "Arousal", "Emotion", "Status"])
idx = 0
for i in range(len(experiment_results)):
tdelta = experiment_results.iloc[i]["Time_End"] - experiment_results.iloc[
i]["Time_Start"]
time_end = experiment_results.iloc[i]["Time_End"]
valence = experiment_results.iloc[i]["Valence"]
arousal = experiment_results.iloc[i]["Arousal"]
emotion = experiment_results.iloc[i]["Emotion"]
for j in np.arange(0, (tdelta // split_time), 0.4):
end = time_end - (j * split_time)
from GSR.GSRFeatures import PPGFeatures, EDAFeatures
from Conf import Settings as set
import pandas as pd
data = pd.read_csv("..\\Data\\Dummy\\GSR_sample.csv", header=[0, 1])
#PPG
ppg = data["GSR_PPG_A13_CAL"].iloc[0:900].values
ppgExtract = PPGFeatures(set.FS_GSR)
print(ppgExtract.extractTimeDomain(ppg.flatten()))
print(ppgExtract.extractFrequencyDomain(ppg.flatten()))
print(ppgExtract.extractNonLinear(ppg.flatten()))
#EDA
eda = data["GSR_GSR_Skin_Conductance_CAL"].iloc[0:900].values
edaExtract = EDAFeatures(set.FS_GSR)
print(edaExtract.extractMFCCFeatures(ppg.flatten()))
print(edaExtract.extractSCRFeatures(ppg.flatten()))