def main():
# Set a granularity (the discrete step size of our time series data) and choose if all resulting datasets should
# be saved. A course-grained granularity of one instance per minute, and a fine-grained one with four instances
# per second are used.
GRANULARITIES = [60000, 250]
SAVE_VERSIONS = False
# We can call Path.mkdir(exist_ok=True) to make any required directories if they don't already exist.
[path.mkdir(exist_ok=True, parents=True) for path in [DATASET_PATH, RESULT_PATH]]
# Create object to visualize the data and save figures
DataViz = VisualizeDataset(module_path=__file__)
datasets = []
for milliseconds_per_instance in GRANULARITIES:
print(
f'Creating numerical datasets from files in {DATASET_PATH} using granularity {milliseconds_per_instance}.')
# Create an initial dataset object with the base directory for our data and a granularity and add selected
# measurements to it
data_engineer = CreateDataset(base_dir=DATASET_PATH, granularity=milliseconds_per_instance)
# Add the accelerometer data (continuous numerical measurements) of the phone and the smartwatch
# and aggregate the values per timestep by averaging the values
data_engineer.add_numerical_dataset(file='accelerometer_phone.csv', timestamp_col='timestamps',
value_cols=['x', 'y', 'z'], aggregation='avg', prefix='acc_phone_')
data_engineer.add_numerical_dataset(file='accelerometer_smartwatch.csv', timestamp_col='timestamps',
value_cols=['x', 'y', 'z'], aggregation='avg', prefix='acc_watch_')
# Add the gyroscope data (continuous numerical measurements) of the phone and the smartwatch
# and aggregate the values per timestep by averaging the values
data_engineer.add_numerical_dataset(file='gyroscope_phone.csv', timestamp_col='timestamps',
value_cols=['x', 'y', 'z'], aggregation='avg', prefix='gyr_phone_')
data_engineer.add_numerical_dataset(file='gyroscope_smartwatch.csv', timestamp_col='timestamps',
value_cols=['x', 'y', 'z'], aggregation='avg', prefix='gyr_watch_')
# Add the heart rate (continuous numerical measurements) and aggregate by averaging the values
data_engineer.add_numerical_dataset(file='heart_rate_smartwatch.csv', timestamp_col='timestamps',
value_cols=['rate'], aggregation='avg', prefix='hr_watch_')
# Add the labels provided by the users as binary attributes (i.e. add a one to the attribute representing the
# specific value for a label if it occurs within an interval). These are categorical events that might overlap.
data_engineer.add_event_dataset(file='labels.csv', start_timestamp_col='label_start',
end_timestamp_col='label_end',
value_col='label', aggregation='binary')
# Add the amount of light sensed by the phone (continuous numerical measurements) and aggregate by averaging
data_engineer.add_numerical_dataset(file='light_phone.csv', timestamp_col='timestamps', value_cols=['lux'],
aggregation='avg', prefix='light_phone_')
# Add the magnetometer data (continuous numerical measurements) of the phone and the smartwatch
# and aggregate the values per timestep by averaging the values
data_engineer.add_numerical_dataset(file='magnetometer_phone.csv', timestamp_col='timestamps',
value_cols=['x', 'y', 'z'], aggregation='avg', prefix='mag_phone_')
data_engineer.add_numerical_dataset(file='magnetometer_smartwatch.csv', timestamp_col='timestamps',
value_cols=['x', 'y', 'z'], aggregation='avg', prefix='mag_watch_')
# Add the pressure sensed by the phone (continuous numerical measurements) and aggregate by averaging again
data_engineer.add_numerical_dataset(file='pressure_phone.csv', timestamp_col='timestamps',
value_cols=['pressure'],
aggregation='avg', prefix='press_phone_')
# Get the resulting pandas data table
dataset = data_engineer.data_table
# Create boxplots
DataViz.plot_dataset_boxplot(dataset=dataset, cols=['acc_phone_x', 'acc_phone_y', 'acc_phone_z', 'acc_watch_x',
'acc_watch_y', 'acc_watch_z'])
# Plot all data
DataViz.plot_dataset(data_table=dataset,
columns=['acc_', 'gyr_', 'hr_watch_rate', 'light_phone_lux', 'mag_', 'press_phone_',
'label'],
match=['like', 'like', 'like', 'like', 'like', 'like', 'like', 'like'],
display=['line', 'line', 'line', 'line', 'line', 'line', 'points', 'points'])
# Print a summary of the dataset
util.print_statistics(dataset=dataset)
datasets.append(copy.deepcopy(dataset))
# Save the various versions of the created datasets with logical filenames if needed
if SAVE_VERSIONS:
dataset.to_csv(RESULT_PATH / f'chapter2_result_{milliseconds_per_instance}')
# Make a table like the one shown in the book, comparing the two datasets produced
util.print_latex_table_statistics_two_datasets(dataset1=datasets[0], dataset2=datasets[1])
# Finally, store the last dataset we generated (250 ms)
dataset.to_csv(RESULT_PATH / RESULT_FNAME)
'labelSittingDown', 'labelSitting', 'labelStandingFromLying', 'labelOnAllFours',
'labelSittingOnTheGround', 'labelStandingFromSitting',
'labelStandingFromSittingOnTheGround'], 'max', '')
# Get the resulting pandas data table
dataset = DataSet.data_table
# Plot the data
DataViz = VisualizeDataset()
# Boxplot
DataViz.plot_dataset_boxplot(dataset, ['ankle_l_x', 'ankle_l_y', 'ankle_l_z', 'ankle_r_x', 'ankle_r_y', 'ankle_r_z',
'belt_x', 'belt_y', 'belt_z', 'chest_x', 'chest_y', 'chest_z'])
# Plot all data
DataViz.plot_dataset(dataset, ['ankle_l_', 'ankle_r_', 'belt_', 'chest_', 'label'], ['like', 'like', 'like', 'like', 'like'], ['line', 'line', 'line', 'line', 'points'])
# And print a summary of the dataset
util.print_statistics(dataset)
datasets.append(copy.deepcopy(dataset))
# And print the table that has been included in the book
util.print_latex_table_statistics_two_datasets(datasets[0], datasets[1])
# Finally, store the last dataset we have generated (250 ms).
#dataset.to_csv(result_dataset_path + 'chapter2_result.csv')
# We add the pressure sensed by the phone (continuous numerical measurements) and aggregate by averaging again
# Get the resulting pandas data table
dataset = DataSet.data_table
print(dataset)
# Plot the data
DataViz = VisualizeDataset()
# Boxplot
#DataViz.plot_dataset_boxplot(dataset, ['acc_phone_accelerometer-kx023.csv_0', 'acc_phone_accelerometer-kx023.csv_1','acc_phone_accelerometer-kx023.csv_2'])
# Plot all data
#DataViz.plot_dataset(dataset, ['acc_phone_','name', 'mag_phone_','light_phone_','gyr_phone_'], ['like','like','like','like','like'], ['line','points','line','line','line'])
# And print a summary of the dataset
util.print_statistics(dataset)
datasets.append(copy.deepcopy(dataset))
# And print the table that has been included in the book
#util.print_latex_table_statistics_two_datasets(datasets[0],datasets[1])
# Finally, store the last dataset we have generated (500 ms).
dataset.to_csv(result_dataset_path + 'chapter2_result-own.csv')
their = dataset.from_csv('./intermediate_datafiles/' + 'chapter2_result.csv')
util.print_latex_table_statistics_two_datasets(dataset, their)
