def download_scenarios(mydate,
latBound=[43, 50],
lonBound=[-10 + 360, 360],
website='http://nomads.ncep.noaa.gov:9090/dods/',
scenario_ids=range(0, 21)):
"""
To download the weathers scenarios for a MCTS.
Scenarios are saved as '../data/' + mydate + '_' + s_id + '00z.obj' where s_id is the scenario id.
id = 0 corresponds to the mean scenario. They are stored as :class:`weatherTLKT.Weather` objects.
**Warning:** *you might need to launch it from a terminal and not your IDE*
:param string mydate: 'yyyymmdd' date of the day starting the weather forecast in US format
:param list(float,float) latBound: [latmin, latmax], latitude boundaries of the domain on which the forecast is desired
:param list(float,float) lonBound: [lonmin, lonmax], longitude boundaries of the domain on which the forecast is desired
:param string website: url of the server hosting the forecasts
:param range scenario_ids: range covering the forecasts scenarios we want to download
"""
pathToSaveObj = []
for ii in scenario_ids:
if ii < 10:
s_id = '0' + str(ii)
else:
s_id = str(ii)
if ii == 0:
url = (website + 'gens/gens' + mydate + '/gec' + s_id + '_00z')
else:
url = (website + 'gens/gens' + mydate + '/gep' + s_id + '_00z')
print("Downloading from : " + url)
pathToSaveObj.append(('../data/' + mydate + '_' + s_id + '00z.obj'))
Weather.download(url,
pathToSaveObj[ii],
latBound=latBound,
lonBound=lonBound,
timeSteps=[0, 64],
ens=True)
print("Saved into : " + pathToSaveObj[-1])
def doStep(self, action):
"""
Does one iteration of the simulation (one time step) following a provided action.\
Updates and returns the new boat state. Supposes constant wind during a time step. Also\
constant boat speed. Assumes that the boat moves following orthodomes trajectories. This \
is correct if the distances covered during the time step is relatively small (and we\
are not close to the poles): the orthodrome's headings do not vary much.
:param float action: Boat's heading in degree.
:return: Reference toward the updated simulator's state.
:rtype: self.state
"""
# we copy the current state into the previous one
self.prevState = list(self.state)
# we get the wind at current state
uWind, vWind = self.getWind()
windMag, windAng = Weather.returnPolarVel(uWind, vWind)
# We get speed from wind on sail
pOfSail = abs((windAng + 180) % 360 - action)
boatSpeedDet = Boat.getDeterDyn(pOfSail, windMag, Boat.FIT_VELOCITY)
boatSpeed = Boat.addUncertainty(boatSpeedDet)
# We integrate it
Dt = (self.times[self.state[0] + 1] -
self.times[self.state[0]]) * DAY_TO_SEC
DL = boatSpeed * Dt # distance travelled
# new position, correct if the boat follows an orthodrome
newLat, newLon = self.getDestination(DL, action,
[self.state[1], self.state[2]])
self.state[0], self.state[1], self.state[
2] = self.state[0] + 1, newLat, newLon
return self.state
def load_scenarios(mydate,
scenario_ids=range(0, 21),
latBound=[-90, 90],
lonBound=[0, 360],
timeSteps=[0, 64]):
"""
Loads previously downloaded scenarios and returns the corresponding list of :class:`weatherTLKT.Weather` objects.
:param string mydate: 'yyyymmdd' date of the day starting the weather forecast in US format
:param scenario_ids: range covering the forecasts scenarios we want to download
:param list(float,float) latBound: [latmin, latmax], latitude boundaries of the domain on which the forecast is desired.
If smaller than domain of stored object, returns a cropped object.
Returns stored object otherwise
:param list(float,float) lonBound: [lonmin, lonmax], longitude boundaries of the domain on which the forecast is desired.
If smaller than domain of stored object, returns a cropped object.
Returns stored object otherwise
:param list(int,int) timeSteps: [min_t_index, max_t_index], time span of the desired forecast.
If smaller than domain of stored object, returns a cropped object.
Returns stored object otherwise
:return: List of of :class:`weatherTLKT.Weather`
:rtype: list()
"""
pathToSaveObj = []
weather_scen = []
for ii in scenario_ids:
if ii < 10:
s_id = '0' + str(ii)
else:
s_id = str(ii)
pathToSaveObj.append(('../data/' + mydate + '_' + s_id + '00z.obj'))
weather_scen.append(
Weather.load(pathToSaveObj[ii], latBound, lonBound, timeSteps))
print("Loaded : " + pathToSaveObj[-1])
return weather_scen
typ = 'ens'
for ss in range(1, 9):
if typ == 'solo':
mydate = '20171127'
website = 'http://nomads.ncep.noaa.gov:9090/dods'
model = 'gfs'
resolution = '0p25'
url = website + '/' + model + '_' + resolution + '/' + model + mydate + '/' + model + '_' + resolution + '_00z'
pathToSaveObj = '../data/' + model + mydate + '_' + resolution
else:
mydate = '20171127'
website = 'http://nomads.ncep.noaa.gov:9090/dods'
model = 'gens'
resolution = '0p25'
num_scenario = '0' + str(ss)
url = website + '/' + model + '/' + model + mydate + '/' + 'gep' + num_scenario + '_00z'
pathToSaveObj = '../data/' + model + mydate + '_' + num_scenario
latBound = [43, 50]
lonBound = [-10 + 360, 360]
Weather.download(url,
pathToSaveObj,
latBound=latBound,
lonBound=lonBound,
timeSteps=[0, 85],
ens=True)
import copy
import pickle
import sys
sys.path.append("../solver")
#from MyTree import Tree
from worker import Tree
# %% We load the forecast files
#ATTENTION il faudra prendre le fichier de vent moyen à terme!!!
mydate = '20180108'
modelcycle = '0100z'
pathToSaveObj = '../data/' + mydate + '_gep_' + modelcycle + '.obj'
Wavg = Weather.load(pathToSaveObj)
Wavg=Wavg.crop(latBound=[40, 50], lonBound=[360 - 15, 360])
#mydate = '20170519'
#modelcycle = '00'
#pathToSaveObj = '../data/' + mydate + '_' + modelcycle + '.obj'
#Wavg = Weather.load(pathToSaveObj)
# %% We shift the times so that all times are in the correct bounds for interpolations
Tini = Wavg.time[0]
Wavg.time = Wavg.time - Tini
# %% We set up the parameters of the simulation
# times=np.arange(0,min([Wavg.time[-1],Wspr.time[-1]]),1*HOURS_TO_DAY)
# Tf=len(times)
lonBound = [-10 + 360, 360]
url = []
pathToSaveObj = []
urlold = 'http://nomads.ncep.noaa.gov:9090/dods/gfs_0p25/gfs20171014/gfs_0p25_00z'
for i in range(2):
url.append(website + 'gfs_' + resolution[i] + '/gfs' + mydate + '/gfs_' +
resolution[i] + '_' + modelcycle + 'z')
pathToSaveObj.append('../data/' + mydate + '_gfs_' + resolution[i] +
'.obj')
# launch in real python console
# Weather.download(url[0],pathToSaveObj[0],latBound=latBound,lonBound=lonBound,timeSteps=[0,81])
# Weather.download(url[1],pathToSaveObj[1],latBound=latBound,lonBound=lonBound,timeSteps=[0,81])
# %%
weather025 = Weather.load(pathToSaveObj[0])
weather1 = Weather.load(pathToSaveObj[1])
instant = 40
#weather025.plotMultipleQuiver(otherWeather = weather1)
time = weather025.time[instant]
weather1.Interpolators()
error = np.zeros((len(weather025.lat), len(weather025.lon)))
interp_u = np.zeros((len(weather025.lat), len(weather025.lon)))
interp_v = np.zeros((len(weather025.lat), len(weather025.lon)))
for i, lat in enumerate(weather025.lat):
for j, lon in enumerate(weather025.lon):
if lat >= min(weather1.lat) and lat <= max(
weather1.lat) and lon >= min(weather1.lon) and lon <= max(
weather1.lon):