from sklearn.naive_bayes import GaussianNB
from sklearn.model_selection import GroupShuffleSplit, GridSearchCV
from sklearn.pipeline import Pipeline
import matplotlib.pyplot as plt
##############################################################################
# First, we load the dataset and plot an example of one subject performing
# the 'Walking' activity.
#
# .. tip::
#
# In the jupyter notebook version, change the first cell to ``%matplotlib
# notebook`` in order to get an interactive plot that you can zoom and pan.
X = load_wisdm_dataarray()
X_plot = X[np.logical_and(X.activity == 'Walking', X.subject == 1)]
X_plot = X_plot[:500] / 9.81
X_plot['sample'] = (X_plot.sample - X_plot.sample[0]) / np.timedelta64(1, 's')
f, axarr = plt.subplots(3, 1, sharex=True)
axarr[0].plot(X_plot.sample, X_plot.sel(axis='x'), color='#1f77b4')
axarr[0].set_title('Acceleration along x-axis')
axarr[1].plot(X_plot.sample, X_plot.sel(axis='y'), color='#ff7f0e')
axarr[1].set_ylabel('Acceleration [g]')
axarr[1].set_title('Acceleration along y-axis')
axarr[2].plot(X_plot.sample, X_plot.sel(axis='z'), color='#2ca02c')
def test_load_wisdm_dataarray():
load_wisdm_dataarray(folder=os.path.join(ROOT_DIR, "../data"))