def time_series_variance(x):
"""
:param x: the time series to calculate the feature of
:type x: pandas.Series
:return: the value of this feature
:return type: float
"""
return ts_feature_calculators.variance(x)
def TS_features12(signal):
stand_deviation = ts.standard_deviation(signal)
sum_reoccurring = ts.sum_of_reoccurring_data_points(signal)
sum_r_value = ts.sum_of_reoccurring_values(signal)
sum_v = ts.sum_values(signal)
variance = ts.variance(signal)
variance_larger_than_sd = ts.variance_larger_than_standard_deviation(
signal)
return stand_deviation, sum_reoccurring, sum_r_value, sum_v, variance, variance_larger_than_sd
def scalar_feature_extraction(column):
retval = np.zeros([1, 10], dtype=float)
retval[0][0] = tffe.count_above_mean(column.values)
retval[0][1] = tffe.mean(column.values)
retval[0][2] = tffe.maximum(column.values)
retval[0][3] = tffe.median(column.values)
retval[0][4] = tffe.minimum(column.values)
retval[0][5] = tffe.sample_entropy(column.values)
if (isNaN(retval[0][5])):
retval[0][5] = 0
retval[0][6] = tffe.skewness(column.values)
retval[0][7] = tffe.variance(column.values)
retval[0][8] = tffe.longest_strike_above_mean(column.values)
retval[0][9] = tffe.longest_strike_below_mean(column.values)
return retval
def extract_heartrate_tsfresh(transformed: np.ndarray) -> np.ndarray:
"""Extract all tsfresh features from heart rate."""
ecg_features = []
print("Extracting TSFRESH statistics from heart rate signals...")
for x in tqdm(transformed):
vchange_quantiles_abs = change_quantiles(x[:, -1], 0, 0.8, True, "var")
vfft_aggregated_k = list(
fft_aggregated(x[:, -1], [{
"aggtype": "kurtosis"
}]))[0][1]
vmean_abs_change = mean_abs_change(x[:, -1])
vabsolute_sum_of_changes = absolute_sum_of_changes(x[:, -1])
vfft_aggregated_s = list(
fft_aggregated(x[:, -1], [{
"aggtype": "skew"
}]))[0][1]
vfft_aggregated_c = list(
fft_aggregated(x[:, -1], [{
"aggtype": "centroid"
}]))[0][1]
vvariance = variance(x[:, -1])
vvariation_coefficient = variation_coefficient(x[:, -1])
new_tsfresh = np.array([
vchange_quantiles_abs,
vfft_aggregated_k,
vmean_abs_change,
vabsolute_sum_of_changes,
vfft_aggregated_s,
vfft_aggregated_c,
vvariance,
vvariation_coefficient,
])
ecg_features.append(np.concatenate(new_tsfresh, axis=0))
return np.array(ecg_features)
def log_variance(x):
return np.log(fc.variance(x))
def variance(current_observation: pd.DataFrame, raw_key: str):
return tsf.variance(current_observation[raw_key])
def main():
dirname = os.path.realpath('.')
excelF = dirname + '\\Summary.xlsx'
myworkbook = openpyxl.load_workbook(excelF)
worksheet = myworkbook['SummaryPatients']
file = 1
for filename in glob.glob(dirname + "\*.txt"):
data = open(filename, 'r')
totalData = {}
time = []
totalForceL = []
totalForceR = []
id = []
for line in data:
tempForce = line.split()
id.append(1)
time.append(float(tempForce[0]))
totalForceL.append(float(tempForce[17]))
totalForceR.append(float(tempForce[18]))
totalData["id"] = id
totalData["time"] = time
totalData["totalForceL"] = totalForceL
totalData["totalForceR"] = totalForceR
dataPandas = pd.DataFrame.from_dict(totalData)
extracted_features = {}
#extract_featuresL = extract_features(dataPandas, column_id="id", column_kind=None, column_value=None)
worksheet['A' + str(file + 1)] = file
if 'Pt' in filename:
worksheet['B' + str(file + 1)] = 1
else:
worksheet['B' + str(file + 1)] = 0
worksheet['C' + str(file + 1)] = tf.abs_energy(
totalData["totalForceL"])
worksheet['D' + str(file + 1)] = tf.abs_energy(
totalData["totalForceR"])
worksheet['E' + str(file + 1)] = tf.kurtosis(totalData["totalForceL"])
worksheet['F' + str(file + 1)] = tf.kurtosis(totalData["totalForceR"])
worksheet['G' + str(file + 1)] = tf.skewness(totalData["totalForceL"])
worksheet['H' + str(file + 1)] = tf.skewness(totalData["totalForceR"])
worksheet['I' + str(file + 1)] = tf.median(totalData["totalForceL"])
worksheet['J' + str(file + 1)] = tf.median(totalData["totalForceR"])
worksheet['K' + str(file + 1)] = tf.mean(totalData["totalForceL"])
worksheet['L' + str(file + 1)] = tf.mean(totalData["totalForceR"])
worksheet['M' + str(file + 1)] = tf.variance(totalData["totalForceL"])
worksheet['N' + str(file + 1)] = tf.variance(totalData["totalForceR"])
temp = tf.fft_aggregated(totalData["totalForceL"],
[{
"aggtype": "centroid"
}, {
"aggtype": "variance"
}, {
"aggtype": "skew"
}, {
"aggtype": "kurtosis"
}])
int = 0
for list in temp:
if int == 0:
worksheet['O' + str(file + 1)] = list[1]
if int == 1:
worksheet['P' + str(file + 1)] = list[1]
if int == 2:
worksheet['Q' + str(file + 1)] = list[1]
if int == 3:
worksheet['R' + str(file + 1)] = list[1]
int += 1
temp2 = tf.fft_aggregated(totalData["totalForceR"],
[{
"aggtype": "centroid"
}, {
"aggtype": "variance"
}, {
"aggtype": "skew"
}, {
"aggtype": "kurtosis"
}])
int = 0
for list in temp2:
if int == 0:
worksheet['S' + str(file + 1)] = list[1]
if int == 1:
worksheet['T' + str(file + 1)] = list[1]
if int == 2:
worksheet['U' + str(file + 1)] = list[1]
if int == 3:
worksheet['V' + str(file + 1)] = list[1]
int += 1
file += 1
myworkbook.save(excelF)
def get_variance(arr):
res = np.array([variance(arr)])
res = np.nan_to_num(res)
return res
def main():
dirname = os.path.realpath('.')
filename = dirname + '\\GaPt07_01.txt'
data = open(filename, 'r')
totalData = {}
time = []
totalForceL = []
totalForceR = []
id = []
for line in data:
tempForce = line.split()
id.append(1)
time.append(float(tempForce[0]))
totalForceL.append(float(tempForce[17]))
totalForceR.append(float(tempForce[18]))
totalData["id"] = id
totalData["time"] = time
totalData["totalForceL"] = totalForceL
totalData["totalForceR"] = totalForceR
dataPandas = pd.DataFrame.from_dict(totalData)
extracted_features = {}
#extract_featuresL = extract_features(dataPandas, column_id="id", column_kind=None, column_value=None)
extracted_features["absEnergyL"] = tf.abs_energy(totalData["totalForceL"])
extracted_features["absEnergyR"] = tf.abs_energy(totalData["totalForceR"])
extracted_features["kurtosisL"] = tf.kurtosis(totalData["totalForceL"])
extracted_features["kurtosisR"] = tf.kurtosis(totalData["totalForceR"])
extracted_features["skewnessL"] = tf.skewness(totalData["totalForceL"])
extracted_features["skewnessR"] = tf.skewness(totalData["totalForceR"])
extracted_features["medianL"] = tf.median(totalData["totalForceL"])
extracted_features["medianR"] = tf.median(totalData["totalForceR"])
extracted_features["meanL"] = tf.mean(totalData["totalForceL"])
extracted_features["meanR"] = tf.mean(totalData["totalForceR"])
extracted_features["varianceL"] = tf.variance(totalData["totalForceL"])
extracted_features["varianceR"] = tf.variance(totalData["totalForceR"])
temp = tf.fft_aggregated(totalData["totalForceL"], [{
"aggtype": "centroid"
}, {
"aggtype": "variance"
}, {
"aggtype": "skew"
}, {
"aggtype": "kurtosis"
}])
int = 0
for list in temp:
if int == 0:
extracted_features["fftCentroidL"] = list
if int == 1:
extracted_features["fftVarianceL"] = list
if int == 2:
extracted_features["fftSkewL"] = list
if int == 3:
extracted_features["fftKurtosisL"] = list
int += 1
temp2 = tf.fft_aggregated(totalData["totalForceR"], [{
"aggtype": "centroid"
}, {
"aggtype": "variance"
}, {
"aggtype": "skew"
}, {
"aggtype": "kurtosis"
}])
int = 0
for list in temp2:
if int == 0:
extracted_features["fftCentroidR"] = list
if int == 1:
extracted_features["fftVarianceR"] = list
if int == 2:
extracted_features["fftSkewR"] = list
if int == 3:
extracted_features["fftKurtosisR"] = list
int += 1
