def plot_singlewave(file):
sensor = import_sensorfile(file)
sensor_processed = process_input(sensor)
timestamps = []
[timestamps.append(str(item[0])) for item in sensor]
sensor_processed_calibrated = mf.calibrate_median(sensor_processed)
sensor_filtered = mf.medfilt(sensor_processed_calibrated, 3)
plt.plot(sensor_filtered, linewidth='0.8')
plt.xlim([0, 12000])
plt.ylim([-5, 5])
plt.ylabel('Acceleration (g)')
plt.xlabel('Time (ms)')
# plt.xticks(sensor_filtered, timestamps, rotation='vertical')
plt.show()
def process_triaxial(file_path, window_start_time, window_end_time):
with open(file_path) as csv_sensorfile:
# dictionary to hold features
features = {}
sensorfile = csv.reader(csv_sensorfile, delimiter=',', quotechar='|')
sensor_rows = []
for row in sensorfile:
# the correct format has 4 elements (avoids header and footer rows)
if len(row) == 4:
try:
timestamp = int(row[0])
x = float(row[1])
y = float(row[2])
z = float(row[3])
sensor_rows.append([timestamp, x, y, z])
except ValueError:
continue
# if no data exists generate empty stats
# otherwise calculate stats
if not sensor_rows:
features = produce_empty_triaxial_sensor_dict(features)
else:
# convert basic python lists to numPy arrays using slices
sensor_rows = np.array(sensor_rows)
# convert to list for sorting algorithm
timestamps = sensor_rows[:, 0].tolist()
# calculate window indexes
window_indexes = calculate_window_indexes(timestamps, window_start_time, window_end_time)
window_start_index = window_indexes[0]
window_end_index = window_indexes[1]
# validate window indexes
window_indexes_valid = window_index_conditions_valid(window_start_index, window_end_index)
# if valid, window the data, and calculate stats
if window_indexes_valid:
# make slices of data using indexes
x_win = sensor_rows[window_start_index:window_end_index, 1]
y_win = sensor_rows[window_start_index:window_end_index, 2]
z_win = sensor_rows[window_start_index:window_end_index, 3]
svm_win = get_magnitude(x_win, y_win, z_win)
# Pass through a median filter first using kernel of 21
kernel = 15 # must be an odd number
x_median_filter = mf.medfilt(x_win, kernel)
y_median_filter = mf.medfilt(y_win, kernel)
z_median_filter = mf.medfilt(z_win, kernel)
svm_median_filter = mf.medfilt(svm_win, kernel)
# Calibrate filtered SVM using min & median values
svm_median_filter_calibrated_min = mf.calibrate_minimum(svm_median_filter)
svm_median_filter_calibrated_med = mf.calibrate_median(svm_median_filter)
# calculate stats
stats_x = calc_stats_for_data_stream_as_dictionary(x_win)
stats_y = calc_stats_for_data_stream_as_dictionary(y_win)
stats_z = calc_stats_for_data_stream_as_dictionary(z_win)
stats_svm = calc_stats_for_data_stream_as_dictionary(svm_win)
stats_x_median_filter = calc_stats_for_data_stream_as_dictionary(x_median_filter)
stats_y_median_filter = calc_stats_for_data_stream_as_dictionary(y_median_filter)
stats_z_median_filter = calc_stats_for_data_stream_as_dictionary(z_median_filter)
stats_svm_median_filter = calc_stats_for_data_stream_as_dictionary(svm_median_filter)
stats_svm_median_filter_calibrated_min = calc_stats_for_data_stream_as_dictionary(
svm_median_filter_calibrated_min)
stats_svm_median_filter_calibrated_med = calc_stats_for_data_stream_as_dictionary(
svm_median_filter_calibrated_med)
features.update({'x': stats_x,
'y': stats_y,
'z': stats_z,
'svm': stats_svm,
'x_median_filter': stats_x_median_filter,
'y_median_filter': stats_y_median_filter,
'z_median_filter': stats_z_median_filter,
'svm_median_filter': stats_svm_median_filter,
'svm_median_filter_calibrated_min': stats_svm_median_filter_calibrated_min,
'svm_median_filter_calibrated_med': stats_svm_median_filter_calibrated_med
})
else:
features = produce_empty_triaxial_sensor_dict(features)
return features
def process_triaxial(file_path, window_start_time, window_end_time):
with open(file_path) as csv_sensorfile:
# dictionary to hold features
features = {}
sensorfile = csv.reader(csv_sensorfile, delimiter=',', quotechar='|')
sensor_rows = []
for row in sensorfile:
# the correct format has 4 elements (avoids header and footer rows)
if len(row) == 4:
try:
timestamp = int(row[0])
x = float(row[1])
y = float(row[2])
z = float(row[3])
sensor_rows.append([timestamp, x, y, z])
except ValueError:
continue
# if no data exists generate empty stats
# otherwise calculate stats
if not sensor_rows:
features = produce_empty_triaxial_sensor_dict(features)
else:
# convert basic python lists to numPy arrays using slices
sensor_rows = np.array(sensor_rows)
# convert to list for sorting algorithm
timestamps = sensor_rows[:, 0].tolist()
# calculate window indexes
window_indexes = calculate_window_indexes(timestamps,
window_start_time,
window_end_time)
window_start_index = window_indexes[0]
window_end_index = window_indexes[1]
# validate window indexes
window_indexes_valid = window_index_conditions_valid(
window_start_index, window_end_index)
# if valid, window the data, and calculate stats
if window_indexes_valid:
# make slices of data using indexes
x_win = sensor_rows[window_start_index:window_end_index, 1]
y_win = sensor_rows[window_start_index:window_end_index, 2]
z_win = sensor_rows[window_start_index:window_end_index, 3]
svm_win = get_magnitude(x_win, y_win, z_win)
# Pass through a median filter first using kernel of 21
kernel = 15 # must be an odd number
x_median_filter = mf.medfilt(x_win, kernel)
y_median_filter = mf.medfilt(y_win, kernel)
z_median_filter = mf.medfilt(z_win, kernel)
svm_median_filter = mf.medfilt(svm_win, kernel)
# Calibrate filtered SVM using min & median values
svm_median_filter_calibrated_min = mf.calibrate_minimum(
svm_median_filter)
svm_median_filter_calibrated_med = mf.calibrate_median(
svm_median_filter)
# calculate stats
stats_x = calc_stats_for_data_stream_as_dictionary(x_win)
stats_y = calc_stats_for_data_stream_as_dictionary(y_win)
stats_z = calc_stats_for_data_stream_as_dictionary(z_win)
stats_svm = calc_stats_for_data_stream_as_dictionary(svm_win)
stats_x_median_filter = calc_stats_for_data_stream_as_dictionary(
x_median_filter)
stats_y_median_filter = calc_stats_for_data_stream_as_dictionary(
y_median_filter)
stats_z_median_filter = calc_stats_for_data_stream_as_dictionary(
z_median_filter)
stats_svm_median_filter = calc_stats_for_data_stream_as_dictionary(
svm_median_filter)
stats_svm_median_filter_calibrated_min = calc_stats_for_data_stream_as_dictionary(
svm_median_filter_calibrated_min)
stats_svm_median_filter_calibrated_med = calc_stats_for_data_stream_as_dictionary(
svm_median_filter_calibrated_med)
features.update({
'x':
stats_x,
'y':
stats_y,
'z':
stats_z,
'svm':
stats_svm,
'x_median_filter':
stats_x_median_filter,
'y_median_filter':
stats_y_median_filter,
'z_median_filter':
stats_z_median_filter,
'svm_median_filter':
stats_svm_median_filter,
'svm_median_filter_calibrated_min':
stats_svm_median_filter_calibrated_min,
'svm_median_filter_calibrated_med':
stats_svm_median_filter_calibrated_med
})
else:
features = produce_empty_triaxial_sensor_dict(features)
return features
