def initPredictor():
global predictor, predictor_thread, chasemapper_config, pred_settings
try:
from cusfpredict.predict import Predictor
from cusfpredict.utils import gfs_model_age, available_gfs
# Check if we have any GFS data
_model_age = gfs_model_age(pred_settings['gfs_path'])
if _model_age == "Unknown":
logging.error("No GFS data in directory.")
chasemapper_config['pred_model'] = "No GFS Data."
flask_emit_event('predictor_model_update',
{'model': "No GFS data."})
chasemapper_config['pred_enabled'] = False
else:
# Check model contains data to at least 4 hours into the future.
(_model_start,
_model_end) = available_gfs(pred_settings['gfs_path'])
_model_now = datetime.utcnow() + timedelta(0, 60 * 60 * 4)
if (_model_now < _model_start) or (_model_now > _model_end):
# No suitable GFS data!
logging.error("GFS Data in directory does not cover now!")
chasemapper_config['pred_model'] = "Old GFS Data."
flask_emit_event('predictor_model_update',
{'model': "Old GFS data."})
chasemapper_config['pred_enabled'] = False
else:
chasemapper_config['pred_model'] = _model_age
flask_emit_event('predictor_model_update',
{'model': _model_age})
predictor = Predictor(bin_path=pred_settings['pred_binary'],
gfs_path=pred_settings['gfs_path'])
# Start up the predictor thread if it is not running.
if predictor_thread == None:
predictor_thread = Thread(target=predictorThread)
predictor_thread.start()
# Set the predictor to enabled, and update the clients.
chasemapper_config['pred_enabled'] = True
flask_emit_event('server_settings_update', chasemapper_config)
except Exception as e:
traceback.print_exc()
logging.error("Loading predictor failed: " + str(e))
flask_emit_event('predictor_model_update',
{'model': "Failed - Check Log."})
chasemapper_config['pred_model'] = "Failed - Check Log."
print("Loading Predictor failed.")
predictor = None
LAUNCH_TIME = datetime.datetime.utcnow(
) # Note that this time is interpreted as a UTC time
LAUNCH_LAT = -34.9499
LAUNCH_LON = 138.5194
LAUNCH_ALT = 0.0
ASCENT_RATE = 5.0
DESCENT_RATE = 5.0
BURST_ALT = 30000.0
# Output file
OUTPUT_KML = "prediction.kml"
# Create the predictor object.
pred = Predictor(bin_path=PRED_BINARY, gfs_path=GFS_PATH)
# Run the prediction
flight_path = pred.predict(launch_lat=LAUNCH_LAT,
launch_lon=LAUNCH_LON,
launch_alt=LAUNCH_ALT,
ascent_rate=ASCENT_RATE,
descent_rate=DESCENT_RATE,
burst_alt=BURST_ALT,
launch_time=LAUNCH_TIME)
# Check the output makes sense
if len(flight_path) == 1:
print("No Wind Data available for this prediction scenario!")
sys.exit(1)
def initPredictor():
global predictor, predictor_thread, chasemapper_config, pred_settings
if chasemapper_config["offline_predictions"]:
# Attempt to initialize an Offline Predictor instance
try:
from cusfpredict.predict import Predictor
from cusfpredict.utils import gfs_model_age, available_gfs
# Check if we have any GFS data
_model_age = gfs_model_age(pred_settings["gfs_path"])
if _model_age == "Unknown":
logging.error("No GFS data in directory.")
chasemapper_config["pred_model"] = "No GFS Data."
flask_emit_event("predictor_model_update",
{"model": "No GFS data."})
chasemapper_config["offline_predictions"] = False
else:
# Check model contains data to at least 4 hours into the future.
(_model_start,
_model_end) = available_gfs(pred_settings["gfs_path"])
_model_now = datetime.utcnow() + timedelta(0, 60 * 60 * 4)
if (_model_now < _model_start) or (_model_now > _model_end):
# No suitable GFS data!
logging.error("GFS Data in directory does not cover now!")
chasemapper_config["pred_model"] = "Old GFS Data."
flask_emit_event("predictor_model_update",
{"model": "Old GFS data."})
chasemapper_config["offline_predictions"] = False
else:
chasemapper_config[
"pred_model"] = _model_age + " (Offline)"
flask_emit_event("predictor_model_update",
{"model": _model_age + " (Offline)"})
predictor = Predictor(
bin_path=pred_settings["pred_binary"],
gfs_path=pred_settings["gfs_path"],
)
# Start up the predictor thread if it is not running.
if predictor_thread == None:
predictor_thread = Thread(target=predictorThread)
predictor_thread.start()
# Set the predictor to enabled, and update the clients.
chasemapper_config["offline_predictions"] = True
except Exception as e:
traceback.print_exc()
logging.error("Loading predictor failed: " + str(e))
flask_emit_event("predictor_model_update",
{"model": "Failed - Check Log."})
chasemapper_config["pred_model"] = "Failed - Check Log."
print("Loading Predictor failed.")
predictor = None
else:
# No initialization required for the online predictor
predictor = "Tawhiri"
flask_emit_event("predictor_model_update", {"model": "Tawhiri"})
# Start up the predictor thread if it is not running.
if predictor_thread == None:
predictor_thread = Thread(target=predictorThread)
predictor_thread.start()
flask_emit_event("server_settings_update", chasemapper_config)