def test_util():
df = load_watch()
data = util.make_ts_data(df['X'], df['side'])
util.get_ts_data_parts(data)
util.check_ts_data(data, df['y'])
util.check_ts_data(df['X'], df['y'])
util.ts_stats(df['X'], df['y'], fs=1., class_labels=df['y_labels'])
def test_util():
df = load_watch()
data = TS_Data(df['X'], df['side'])
Xt, Xc = util.get_ts_data_parts(data)
assert np.array_equal(Xc, df['side'])
assert np.all([np.array_equal(Xt[i], df['X'][i]) for i in range(len(df['X']))])
util.check_ts_data(data, df['y'])
util.check_ts_data(df['X'], df['y'])
util.ts_stats(df['X'], df['y'], fs=1., class_labels=df['y_labels'])
from seglearn.util import check_ts_data, ts_stats
from seglearn.base import TS_Data
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
data = load_watch()
y = data['y']
Xt = data['X']
fs = 50 # sampling frequency
# create time series data object with no contextual variables
check_ts_data(Xt)
# create time series data object with 2 contextual variables
Xs = np.column_stack([data['side'], data['subject']])
X = TS_Data(Xt, Xs)
check_ts_data(X)
# recover time series and contextual variables
Xt = X.ts_data
Xs = X.context_data
# generate some statistics from the time series data
results = ts_stats(X, y, fs=fs, class_labels=data['y_labels'])
print("DATA STATS - AGGREGATED")
print(results['total'])
print("")