def process_commandline():
parser = argparse.ArgumentParser()
parser.add_argument('-ap', '--all-positions-image', action='store_true',
help='create image of all vehicle positions in the dataset')
parser.add_argument('-atl', '--all-trips-lines-image', action='store_true',
help='create image of all trips in the dataset')
parser.add_argument('-atp', '--all-trips-points-image', action='store_true',
help='create image of all trips in the dataset')
parser.add_argument('--symbol', type=str, default='.',
help='matplotlib symbol to indicate vehicles on the images'
' (default \'.\', larger \'o\')')
args = parser.parse_args()
result_dict = cmdline.read_json()
if args.all_positions_image:
output_file = output_file_name('all_positions', 'png')
graph.make_positions_graph(result_dict, output_file, args.symbol)
print(output_file)
if args.all_trips_lines_image:
output_file = output_file_name('all_trips', 'png')
graph.make_trips_graph(result_dict, output_file)
print(output_file)
if args.all_trips_points_image:
output_file = output_file_name('all_trips_points', 'png')
graph.make_trip_origin_destination_graph(result_dict, output_file,
args.symbol)
print(output_file)
def process_commandline():
parser = argparse.ArgumentParser()
parser.add_argument(
'-ap',
'--all-positions-image',
action='store_true',
help='create image of all vehicle positions in the dataset')
parser.add_argument('-atl',
'--all-trips-lines-image',
action='store_true',
help='create image of all trips in the dataset')
parser.add_argument('-atp',
'--all-trips-points-image',
action='store_true',
help='create image of all trips in the dataset')
parser.add_argument(
'--symbol',
type=str,
default='.',
help='matplotlib symbol to indicate vehicles on the images'
' (default \'.\', larger \'o\')')
args = parser.parse_args()
result_dict = cmdline.read_json()
if args.all_positions_image:
output_file = output_file_name('all_positions', 'png')
graph.make_positions_graph(result_dict, output_file, args.symbol)
print(output_file)
if args.all_trips_lines_image:
output_file = output_file_name('all_trips', 'png')
graph.make_trips_graph(result_dict, output_file)
print(output_file)
if args.all_trips_points_image:
output_file = output_file_name('all_trips_points', 'png')
graph.make_trip_origin_destination_graph(result_dict, output_file,
args.symbol)
print(output_file)
def process_commandline():
parser = argparse.ArgumentParser()
parser.add_argument('-tz',
'--tz-offset',
type=float,
default=0,
help='offset times by TZ_OFFSET hours')
parser.add_argument(
'-d',
'--distance',
type=float,
default=False,
help='highlight DISTANCE meters around each car on map')
parser.add_argument('--trips',
action='store_true',
help='show lines indicating vehicles\' trips')
parser.add_argument('--speeds',
action='store_true',
help='show vehicles\' speeds in addition to locations')
parser.add_argument(
'--symbol',
type=str,
default='.',
help='matplotlib symbol to indicate vehicles on the images' +
' (default \'.\', larger \'o\')')
args = parser.parse_args()
result_dict = cmdline.read_json()
metadata = result_dict['metadata']
output_filename_prefix = output_file_name(metadata['city'])
images_generator = video.make_video_frames(result_dict,
output_filename_prefix,
args.distance, args.trips,
args.speeds, args.symbol,
args.tz_offset)
# evaluate the generator to actually generate the images;
# use tqdm to display a progress bar
exp_timespan = metadata['ending_time'] - metadata['starting_time']
exp_frames = exp_timespan.total_seconds() / metadata['time_step']
generated_images = list(
tqdm(images_generator, total=exp_frames, leave=False))
# print animation information
animate_command_text = video.make_animate_command(result_dict,
output_filename_prefix,
len(generated_images))
print('\nto animate:')
print(animate_command_text)
def process_commandline():
parser = argparse.ArgumentParser()
parser.add_argument('-tz', '--tz-offset', type=float, default=0,
help='offset times when days are split by TZ_OFFSET hours')
args = parser.parse_args()
result_dict = cmdline.read_json()
output_file = output_file_name('stats', 'csv')
stats.stats(result_dict, output_file, args.tz_offset)
print(output_file) # provide output name for easier reuse
def process_commandline():
parser = argparse.ArgumentParser()
parser.add_argument('-tz', '--tz-offset', type=float, default=0,
help='offset times by TZ_OFFSET hours')
parser.add_argument('-d', '--distance', type=float, default=False,
help='highlight DISTANCE meters around each car on map')
parser.add_argument('--trips', action='store_true',
help='show lines indicating vehicles\' trips')
parser.add_argument('--speeds', action='store_true',
help='show vehicles\' speeds in addition to locations')
parser.add_argument('--symbol', type=str, default='.',
help='matplotlib symbol to indicate vehicles on the images' +
' (default \'.\', larger \'o\')')
args = parser.parse_args()
result_dict = cmdline.read_json()
metadata = result_dict['metadata']
output_filename_prefix = output_file_name(metadata['city'])
images_generator = video.make_video_frames(
result_dict, output_filename_prefix,
args.distance, args.trips, args.speeds,
args.symbol, args.tz_offset)
# evaluate the generator to actually generate the images;
# use tqdm to display a progress bar
exp_timespan = metadata['ending_time'] - metadata['starting_time']
exp_frames = exp_timespan.total_seconds() / metadata['time_step']
generated_images = list(tqdm(images_generator,
total=exp_frames, leave=False))
# print animation information
animate_command_text = video.make_animate_command(
result_dict, output_filename_prefix, len(generated_images))
print('\nto animate:')
print(animate_command_text)
