if_runs.py

# plot histograms for multiple subjects
from numpy import array
from numpy import unique
from numpy import dstack
from pandas import read_csv
from matplotlib import pyplot

# load a single file as a numpy array
def load_file(filepath):
	dataframe = read_csv(filepath, header=None, delim_whitespace=True)
	return dataframe.values

# load a list of files, such as x, y, z data for a given variable
def load_group(filenames, prefix=''):
	loaded = list()
	for name in filenames:
		data = load_file(prefix + name)
		loaded.append(data)
	# stack group so that features are the 3rd dimension
	loaded = dstack(loaded)
	return loaded

# load a dataset group, such as train or test
def load_dataset(group, prefix=''):
	filepath = prefix + group + '/Inertial Signals/'
	# load all 9 files as a single array
	filenames = list()
	# total acceleration
	filenames += ['total_acc_x_'+group+'.txt', 'total_acc_y_'+group+'.txt', 'total_acc_z_'+group+'.txt']
	# body acceleration
	filenames += ['body_acc_x_'+group+'.txt', 'body_acc_y_'+group+'.txt', 'body_acc_z_'+group+'.txt']
	# body gyroscope
	filenames += ['body_gyro_x_'+group+'.txt', 'body_gyro_y_'+group+'.txt', 'body_gyro_z_'+group+'.txt']
	# load input data
	X = load_group(filenames, filepath)
	# load class output
	y = load_file(prefix + group + '/y_'+group+'.txt')
	return X, y

# get all data for one subject
def data_for_subject(X, y, sub_map, sub_id):
	# get row indexes for the subject id
	ix = [i for i in range(len(sub_map)) if sub_map[i]==sub_id]
	# return the selected samples
	return X[ix, :, :], y[ix]

# convert a series of windows to a 1D list
def to_series(windows):
	series = list()
	for window in windows:
		# remove the overlap from the window
		half = int(len(window) / 2) - 1
		for value in window[-half:]:
			series.append(value)
	return series

# plot histograms for multiple subjects
def plot_subject_histograms(X, y, sub_map, n=10):
	pyplot.figure()
	# get unique subjects
	subject_ids = unique(sub_map[:,0])
	# enumerate subjects
	xaxis = None
	for k in range(n):
		sub_id = subject_ids[k]
		# get data for one subject
		subX, _ = data_for_subject(X, y, sub_map, sub_id)
		# total acc
		for i in range(3):
			ax = pyplot.subplot(n, 1, k+1, sharex=xaxis)
			ax.set_xlim(-1,1)
			if k == 0:
				xaxis = ax
			pyplot.hist(to_series(subX[:,:,i]), bins=100)
	pyplot.show()

# load training dataset
X, y = load_dataset('train', 'HARDataset/')
# load mapping of rows to subjects
sub_map = load_file('HARDataset/train/subject_train.txt')
# plot histograms for subjects
plot_subject_histograms(X, y, sub_map)