os.makedirs(temporal_folder)
except OSError:
None
##
# STEP-1:
# Initializing of the common and static context of each simulation
##
# 1.1 Mobile entities trough GPX traces
# The track normalization is an expensive computational task. A cached file is generated in each temporal path
if os.path.isfile(temporal_folder + "normalized_trajectories.csv"):
input_directory = temporal_folder + "normalized_trajectories.csv" #
logging.info("Loading trajectories from (cached file): %s" %
input_directory)
tracks = trackanimation.read_track(input_directory)
else:
input_directory = trajectories_path # can load csv files
logging.info("Loading trajectories from (raw files): %s" %
input_directory)
tracks = trackanimation.read_track(input_directory)
tracks = tracks.time_video_normalize(
time=number_simulation_steps, framerate=1) # framerate must be one
tracks.export(temporal_folder + "normalized_trajectories")
# 1.2 Network infrastructure
# Endpoint entities must have three attributes: level(=0) and lat/lng coordinates
t = Topology()
dataNetwork = json.load(open(experiment_path + 'networkDefinition.json'))
t.load_all_node_attr(dataNetwork)
import trackanimation
from trackanimation.animation import AnimationTrack
# Simple example
input_directory = "d:/robi/Turystyka/2018_Tajlandia/trk/"
tha_trk = trackanimation.read_track(input_directory)
tha_trk = tha_trk.time_video_normalize(time=10, framerate=10)
fig = AnimationTrack(df_points=tha_trk,
dpi=300,
bg_map=True,
map_transparency=0.9)
# fig = AnimationTrack(df_points=tha_trk, dpi=300, bg_map=True, map_transparency=0.5)
# print(type(fig))
fig.make_video(output_file='thailand4', framerate=10, linewidth=2.0)