def plot_new_values(files, activity_labels, remove_gravity=True):
mvs = []
stats = []
i = 0
for file in files:
print('reading {}'.format(file))
samples = read_csv_acc_samples_file(file)
if remove_gravity:
filter_gravity(samples)
samples = samples[new_start:]
# samples = samples[2500:7500]
# mv = calculate_magnitude_vector(samples)
# mvs.append(mv)
stat = get_statistics_per_window(samples,
mean_to_add=means[activity_labels[i]])
stats.append(stat)
i += 1
# plot_magnitude_vectors(mvs, activity_labels)
# statistics_ids = ['mean_with_mean', 'avg_max_range']
# plot_statistics(stats, statistics_ids, activity_labels)
#
# statistics_ids = ['mean_with_mean', 'std_dev']
# plot_statistics(stats, statistics_ids, activity_labels)
statistics_ids = ['std_dev_with_mean', 'mean_with_mean']
plot_statistics(stats, statistics_ids, [
'Standard deviation (+transportation mode mean)',
'Mean (+transportation mode mean)'
], activity_labels, 'c:\\users\\rafael\\desktop\\new-plot.pdf')
def show_normal_vs_ui(normal_file_path, ui_file_path):
samples_static_normal = read_csv_acc_samples_file(normal_file_path)
samples_static_ui = read_csv_acc_samples_file(ui_file_path)
# ts_top_threshold = 150
# samples_static_ui = list(filter(lambda x: x.timestamp < ts_top_threshold, samples_static_ui))
# samples_static_normal = list(filter(lambda x: x.timestamp < ts_top_threshold, samples_static_normal))
normalize_time(samples_static_normal)
normalize_time(samples_static_ui)
# samples_static_normal = samples_static_normal[10:]
# samples_static_ui = samples_static_ui[10:]
normalize_time(samples_static_normal, factor=1.0)
normalize_time(samples_static_ui, factor=1.0)
filter_gravity(samples_static_normal)
filter_gravity(samples_static_ui)
# normalize_time(samples_static_normal)
# normalize_time(samples_static_ui)
plot_activity_data(samples_static_normal, 'Static normal')
plot_activity_data(samples_static_ui, 'Static UI')
def calculate_global_statistics(files, remove_gravity=True):
for f in files:
samples = read_csv_acc_samples_file(f)
if remove_gravity:
filter_gravity(samples)
samples = samples[new_start:]
mv = calculate_magnitude_vector(samples)
mean_value = np.mean(mv)
print('For {} mean is {}'.format(f, mean_value))
def show_magnitude_vectors_of_activities(files, labels, remove_gravity=True):
mvs = []
for file in files:
print('reading {}'.format(file))
samples = read_csv_acc_samples_file(file)
if remove_gravity:
filter_gravity(samples)
samples = samples[20:]
# samples = samples[2500:7500]
mv = calculate_magnitude_vector(samples)
mvs.append(mv)
plot_magnitude_vectors(mvs, labels)
def show_magnitude_vector_per_alpha(alpha_values, file_path):
vectors = []
samples = read_csv_acc_samples_file(file_path)
normalize_time(samples)
timestamps = list(map(lambda x: x.timestamp, samples))
for alpha in alpha_values:
samples = read_csv_acc_samples_file(file_path)
normalize_time(samples)
filter_gravity(samples, alpha=alpha)
mv = calculate_magnitude_vector(samples)
vectors.append(mv)
plot_magnitude_vectors_per_alpha(vectors,
timestamps,
alpha_values,
single=True)
def analyze_three_dimensions(files, activity_labels, remove_gravity):
statistics = []
i = 0
for f in files:
samples = read_csv_acc_samples_file(f)
if remove_gravity:
filter_gravity(samples)
samples = samples[new_start:]
normalize_time(samples)
stats = get_statistics_per_window(
samples, mean_to_add=means[activity_labels[i]])
statistics.append(stats)
i += 1
attributes_to_plot = ['mean_with_mean', 'std_dev', 'avg_max_range']
plot_three_dimensions(statistics, activity_labels, attributes_to_plot)
def plot_portion_of_data(files, activity_labels, remove_gravity=True):
samples_list = []
new_start = 20
id_window_to_select = 90
for f in files:
samples = read_csv_acc_samples_file(f)
if remove_gravity:
filter_gravity(samples)
samples = samples[new_start:]
samples = list(
filter(lambda x: x.id_window < id_window_to_select, samples))
normalize_time(samples)
samples_list.append(samples)
plot_accelerations_and_mag_vectors(samples_list, activity_labels)
def show_different_filter_configurations(alpha=0.8):
samples_vehicle_1 = read_csv_acc_samples_file(
'c:\\users/rafael/desktop/vehicle/focus-guindo/vehicle-samples-2017-09-19-192509.csv'
)
samples_power_on = read_csv_acc_samples_file(
'c:\\users/rafael/desktop/static-power-on.csv')
# samples_power_off = read_csv_acc_samples_file('c:\\users/rafael/desktop/static-power-off.csv')
samples_power_on = samples_power_on[new_start:]
samples_vehicle_1 = samples_vehicle_1[new_start:]
# samples_power_off = samples_power_off[new_start:]
filter_gravity(samples_power_on, alpha=alpha)
filter_gravity(samples_vehicle_1, alpha=alpha)
# filter_gravity(samples_power_off, alpha=alpha)
normalize_time(samples_power_on)
normalize_time(samples_vehicle_1)
# normalize_time(samples_power_off)
ts_top_threshold = 150
samples_power_on = list(
filter(lambda x: x.timestamp < ts_top_threshold, samples_power_on))
samples_vehicle_1 = list(
filter(lambda x: x.timestamp < ts_top_threshold, samples_vehicle_1))
# samples_power_off = list(filter(lambda x: x.timestamp < ts_top_threshold, samples_power_off))
# samples_power_on = samples_power_on[:ts_top_threshold]
# samples_power_off = samples_power_off[:ts_top_threshold]
# plot_activity_data(samples_power_on, 'Powered on, alpha={}'.format(alpha))
# plot_activity_data(samples_vehicle_1, 'V1, alpha={}'.format(alpha))
# plot_activity_data(samples_power_off, 'Powered off, alpha={}'.format(alpha))
# plot the magnitude vectors
mv_static_1 = calculate_magnitude_vector(samples_power_on)
mv_vehicle_1 = calculate_magnitude_vector(samples_vehicle_1)
vectors = [mv_static_1, mv_vehicle_1]
activities = ['Static', 'V1']
vectors = [mv_static_1, mv_vehicle_1]
activities = ['Static', 'V1']
plot_magnitude_vectors(vectors, activities)
def do_work():
home = str(Path.home()) + '/'
desktop = home + 'desktop/'
samples_vehicle_1 = read_csv_acc_samples_file(
'c:\\users/rafael/desktop/vehicle/focus-guindo/vehicle-samples-2017-09-19-192509.csv'
)
samples_vehicle_2 = read_csv_acc_samples_file(
'c:\\users/rafael/desktop/vehicle/focus-guindo/vehicle-samples-2017-09-20-081651.csv'
)
samples_vehicle_3 = read_csv_acc_samples_file(
'c:\\users/rafael/desktop/vehicle/focus-guindo/vehicle-samples-2017-09-20-092617.csv'
)
samples_vehicle_4 = read_csv_acc_samples_file(
'c:\\users/rafael/desktop/vehicle/focus-arena/vehicle-samples-2017-09-20-131224.csv'
)
samples_vehicle_5 = read_csv_acc_samples_file(
'c:\\users/rafael/desktop/vehicle/focus-arena/vehicle-samples-2017-09-20-140314.csv'
)
samples_static_1 = read_csv_acc_samples_file(
'c:\\users/rafael/desktop/static/static-samples-2017-09-21-183652-ui.csv'
)
# filter_gravity_per_window(samples_vehicle_1)
# filter_gravity_per_window(samples_vehicle_2)
# filter_gravity_per_window(samples_vehicle_3)
# filter_gravity_per_window(samples_vehicle_4)
# filter_gravity_per_window(samples_vehicle_5)
# filter_gravity_per_window(samples_static_1)
filter_gravity(samples_vehicle_1)
filter_gravity(samples_vehicle_2)
filter_gravity(samples_vehicle_3)
filter_gravity(samples_vehicle_4)
filter_gravity(samples_vehicle_5)
filter_gravity(samples_static_1)
samples_vehicle_1 = samples_vehicle_1[new_start:]
samples_vehicle_2 = samples_vehicle_2[new_start:]
samples_vehicle_3 = samples_vehicle_3[new_start:]
samples_vehicle_4 = samples_vehicle_4[new_start:]
samples_vehicle_5 = samples_vehicle_5[new_start:]
samples_static_1 = samples_static_1[new_start:]
normalize_time(samples_vehicle_1)
normalize_time(samples_vehicle_2)
normalize_time(samples_vehicle_3)
normalize_time(samples_vehicle_4)
normalize_time(samples_vehicle_5)
normalize_time(samples_static_1)
plot_activity_data(samples_vehicle_1, 'Vehicle one')
plot_activity_data(samples_vehicle_2, 'Vehicle two')
plot_activity_data(samples_vehicle_3, 'Vehicle three')
plot_activity_data(samples_vehicle_4, 'Vehicle four')
plot_activity_data(samples_vehicle_5, 'Vehicle five')
plot_activity_data(samples_static_1, 'Static one')
# Plot of magnitude vectors
top_threshold = 500
static_1_portion = list(
filter(lambda x: x.timestamp < top_threshold, samples_static_1))
vehicle_1_portion = list(
filter(lambda x: x.timestamp < top_threshold, samples_vehicle_1))
vehicle_2_portion = list(
filter(lambda x: x.timestamp < top_threshold, samples_vehicle_2))
vehicle_3_portion = list(
filter(lambda x: x.timestamp < top_threshold, samples_vehicle_3))
vehicle_4_portion = list(
filter(lambda x: x.timestamp < top_threshold, samples_vehicle_4))
vehicle_5_portion = list(
filter(lambda x: x.timestamp < top_threshold, samples_vehicle_5))
mv_static_1 = calculate_magnitude_vector(static_1_portion)
mv_vehicle_1 = calculate_magnitude_vector(vehicle_1_portion)
mv_vehicle_2 = calculate_magnitude_vector(vehicle_2_portion)
mv_vehicle_3 = calculate_magnitude_vector(vehicle_3_portion)
mv_vehicle_4 = calculate_magnitude_vector(vehicle_4_portion)
mv_vehicle_5 = calculate_magnitude_vector(vehicle_5_portion)
vectors = [
mv_static_1, mv_vehicle_1, mv_vehicle_2, mv_vehicle_3, mv_vehicle_4,
mv_vehicle_5
]
activities = ['Static', 'V1', 'V2', 'V3', 'V4', 'V5']
vectors = [mv_static_1, mv_vehicle_5]
activities = ['Static', 'V5']
# plot_magnitude_vectors(vectors, activities)
# Plot of statistics
statistics_static_1 = get_statistics_per_window(samples_static_1)
statistics_vehicle_1 = get_statistics_per_window(samples_vehicle_1)
statistics_vehicle_2 = get_statistics_per_window(samples_vehicle_2)
statistics_vehicle_3 = get_statistics_per_window(samples_vehicle_3)
statistics_vehicle_4 = get_statistics_per_window(samples_vehicle_4)
statistics_vehicle_5 = get_statistics_per_window(samples_vehicle_5)
statistics_ids = ['std_dev', 'mean']
plot_statistics(statistics_static_1, statistics_vehicle_1,
statistics_vehicle_2, statistics_vehicle_3, statistics_ids,
['Static', 'Vehicle 1', 'Vehicle 2', 'Vehicle 3'])
# plot_statistics(statistics_static, statistics_walking, statistics_running, statistics_vehicle, statistics_ids)
plot_statistics(statistics_static_1, [], [], statistics_vehicle_1,
statistics_ids,
['Static', 'Vehicle 1', 'Vehicle 2', 'Vehicle 3'])
plt.show()