def main():
"""
Scenario is generated based on random flight arrangements. Flights are
appeared every `interval` seconds where `interval` is random variable given
a predefined mean and deviation.
"""
# Creates the output folder
create_output_folder(OUTPUT_FOLDER)
current_time = 0
# In this scenario, we only have departure flights to simply the problem
departures = []
while current_time < END_TIME:
flight = generate_flight_at(current_time)
departures.append(flight)
interval = get_random_time_interval()
current_time += interval
scenario = {"arrivals": [], "departures": departures}
# Saves to file
output_filename = OUTPUT_FOLDER + "scenario.json"
export_to_json(output_filename, scenario)
logger.debug("Done")
def process(self):
"""This function is used to save processed files"""
data_existence = check_if_data()
if data_existence:
log.info("Create output folder if not exists")
create_output_folder()
log.info("Process Movies")
movies = self.handle_movies_df()
self.store_df(movies, 'output/movies.csv')
log.info("Processed Movies stored")
log.info("Process Movie Genres")
transformed_genres = self.transform_genres()
self.store_df(transformed_genres, 'output/genres.csv')
log.info("Process Movies Cast")
transformed_cast = self.handle_actors_df()
self.store_df(transformed_cast, 'output/cast.csv')
log.info('Processed cast stored')
log.info('Process Movie Crew')
transformed_crew = self.handle_crew_df()
self.store_df(transformed_crew, 'output/crew.csv')
log.info('Processed Crew stored')
log.info('Process Movie keywords')
transformed_kws = self.handle_keywords()
self.store_df(transformed_kws, 'output/keywords.csv')
log.info("Processed Movie keywords stored")
else:
log.error("Place your dataset inside data folder")
def main():
# Creates the output folder
create_output_folder(OUTPUT_FOLDER)
# Gets KML document from file
kml_doc = get_kml_document()
# Generates airport data
logger.debug("Generating airport metadata")
generate_airport_data(kml_doc)
logger.debug("Airport metadata generated")
# Generates gate data
logger.debug("Generating gate data")
generate_node_data(kml_doc, LayerType.gate, "gates.json")
logger.debug("Gate data generated")
# Generates spot position
logger.debug("Generating spot position data")
generate_node_data(kml_doc, LayerType.spot, "spots.json")
logger.debug("Spot position data generated")
# Generates inter position
logger.debug("Generating inter position data")
generate_node_data(kml_doc, LayerType.inter, "inters.json")
logger.debug("Inter position data generated")
# Generates inter position for sijia
logger.debug("Generating debug position data")
generate_node_data(kml_doc, LayerType.debug, "debug.json")
logger.debug("Debug position data generated")
# Generates runway data
logger.debug("Genenrating runway data")
# generate_link_data(kml_doc, LayerType.runway_and_taxiway, "runways.json",
# "runway", True)
generate_link_data(kml_doc, LayerType.runway, "runways.json")
logger.debug("Runway data generated")
# Generates taxiway data
logger.debug("Genenrating taxiway data")
# generate_link_data(kml_doc, LayerType.runway_and_taxiway, "taxiways.json",
# "taxiway", True)
generate_link_data(kml_doc, LayerType.taxiway, "taxiways.json")
logger.debug("Taxiway data generated")
# Generates pushback way data
logger.debug("Genenrating pushback way data")
generate_link_data(kml_doc, LayerType.pushback_way, "pushback_ways.json")
logger.debug("Pushback way data generated")
# Warning: Generates scenario
logger.debug("Generate scenario using generate_scenario.py")
def main():
departures = get_departure_from_csv()
arrivals = get_arrival_from_csv()
print(len(departures))
print(len(arrivals))
scenario = {"arrivals": arrivals, "departures": departures}
print(scenario)
create_output_folder(OUTPUT_FOLDER)
output_filename = OUTPUT_FOLDER + "scenario.json"
export_to_json(output_filename, scenario)
logger.debug("Generating gate spots data")
gate_spots_filename = OUTPUT_FOLDER + "gates_spots.json"
export_to_json(gate_spots_filename, spots_to_gates)
logger.debug("Done")
def main():
config = load_config()
working_dir, output_path = create_output_folder()
img_file_list = list(working_dir.glob('**/*.bmp'))
for img_file in tqdm(img_file_list):
src = read_image(img_file, config)
dst = DistortionRemover(src, config).image_undist()
out_file = str(output_path / (img_file.stem + "_undist.bmp"))
write_image(dst, out_file)
return True
def main():
config = load_config()
working_dir, output_path = create_output_folder()
img_file_list = list(working_dir.glob('**/*.bmp'))
img_num = len(img_file_list) # For progress bar
for i, img_file in enumerate(img_file_list):
src = read_image(img_file, config)
dst = DistortionRemover(src, config).image_undist()
out_file = str(output_path / (img_file.stem + "_undist.bmp"))
write_image(dst, out_file)
print("Processed:" + str(i+1) + "/" + str(img_num))
return True
w2ply = True
# Try to run on GPU
ti.init(arch=ti.cuda,
kernel_profiler=True,
use_unified_memory=False,
device_memory_fraction=0.7)
max_num_particles = 4000000
if with_gui:
gui = ti.GUI("MLS-MPM", res=512, background_color=0x112F41)
if write_to_disk or w2ply:
output_dir = create_output_folder('./sim')
def load_mesh(fn, scale, offset):
print(f'loading {fn}')
plydata = PlyData.read(fn)
x = plydata['vertex']['x']
y = plydata['vertex']['y']
z = plydata['vertex']['z']
elements = plydata['face']
num_tris = len(elements['vertex_indices'])
triangles = np.zeros((num_tris, 9), dtype=np.float32)
for i, face in enumerate(elements['vertex_indices']):
assert len(face) == 3
for d in range(3):
