main.py
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import numpy as np
import matplotlib.pyplot as plt
import scipy
from netCDF4 import Dataset as NetCDFFile
from mpl_toolkits.basemap import Basemap
import skfuzzy as fuzz
from skfuzzy import control as ctrl
from geopy import distance
# FAIRE GAFFE, LES DONNES NETCDF SONT : [ORDONNEE;ABSCISSE]
# mettre les notations de kauffman sur le rapport / dire que la T norm c'est min / Bibliographie,... TOUT METTRE
# dire ce qu'on aurait pu faire si on avait plus de temps
# longitude / latitude / time / d = divergence / r = humidity / t = temperature / u=uwind / v=vwind / vo=vortcity
def plotNC(path, var, unite, titre):
nc = NetCDFFile(path)
lat = nc.variables['latitude'][:]
lon = nc.variables['longitude'][:]
time = nc.variables['time'][:]
var = nc.variables[var][:]
nc.close()
print(var)
map = Basemap(width=5000000, height=3500000,
resolution='l', projection='cyl',
llcrnrlon=lon.min(), llcrnrlat=lat.min(), urcrnrlon=lon.max(), urcrnrlat=lat.max(), lat_0=lat.mean(),
lon_0=lon.mean())
lons, lats = np.meshgrid(lon, lat)
xi, yi = map(lons, lats)
map.drawmapboundary(fill_color='aqua')
map.fillcontinents(color='coral', lake_color='aqua')
map.drawcoastlines()
parallels = np.arange(lat.min(), lat.max(), 5.) # make latitude lines every 5 degrees from xx to xx
meridians = np.arange(lon.min(), lat.min(), 5.) # make longitude lines every 5 degrees from xx to xx
map.drawparallels(parallels, labels=[1, 0, 0, 0], fontsize=10)
map.drawmeridians(meridians, labels=[0, 0, 0, 1], fontsize=10)
jour = 26
heure = 10
date_exact = jour * 24 + heure
cs = map.pcolor(xi, yi, np.squeeze(var[date_exact, :, :]))
cbar = map.colorbar(cs, location='bottom', pad="10%")
cbar.set_label(unite)
plt.title(titre)
def plotTC(path):
plt.subplot(3, 3, 1)
plotNC(path, 'r', '%', 'Humidité relative')
plt.plot(-82, 24, ms=10, marker="o", markeredgecolor="red")
plt.subplot(3, 3, 2)
plotNC(path, 'd', 'jsp', 'Divergence')
plt.subplot(3, 3, 3)
plotNC(path, 'vo', 'jsp', 'Vorticité')
# plt.subplot(3, 3, 4)
# plotNC(path, 't', 'Kelvin', 'Température')
plt.subplot(3, 3, 5)
plotNC(path, 'u', 'm/s', 'Uwind') # U wind = composante horizontontale
plt.subplot(3, 3, 6)
plotNC(path, 'v', 'm/s', 'Vwind') # V wind = composante verticale
def plot_array(array, x, y):
lon = met_to_deg(x)
lat = met_to_deg(y)
map = Basemap(width=5000000, height=3500000,
resolution='l', projection='cyl',
llcrnrlon=lon.min(), llcrnrlat=lat.min(), urcrnrlon=lon.max(), urcrnrlat=lat.max(), lat_0=lat.mean(),
lon_0=lon.mean())
lons, lats = np.meshgrid(lon, lat)
xi, yi = map(lons, lats)
map.drawmapboundary(fill_color='aqua')
map.fillcontinents(color='coral', lake_color='aqua')
map.drawcoastlines()
cs = map.pcolor(xi, yi, array)
cbar = map.colorbar(cs, location='bottom', pad="10%")
def input_fuzz(variable, input_to_fuzz):
fuzz_input = []
for i in variable.terms.keys():
fuzz_input.append(fuzz.interp_membership(variable.universe, variable.terms[i].mf, input_to_fuzz))
return fuzz_input
def IRR_2var(SF_rules, input_fuzz1, input_fuzz2):
IRR = np.empty([len(SF_rules), 2])
k = -1
for j in range(0, len(SF_rules)):
if (j % len(input_fuzz2)) == 0:
k += 1
IRR[j, 0] = input_fuzz1[k]
IRR[j, 1] = input_fuzz2[j % len(input_fuzz2)]
return IRR
def IRR_3var(SF_rules, input_fuzz1, input_fuzz2, input_fuzz3):
IRR = np.empty([len(SF_rules), 3])
k = -1
m = -1
for j in range(0, len(SF_rules)):
if (j % len(input_fuzz3)) == 0:
k += 1
if (k % len(input_fuzz2)) == 0:
m += 1
IRR[j, 0] = input_fuzz1[m]
IRR[j, 1] = input_fuzz2[j % len(input_fuzz2)]
IRR[j, 2] = input_fuzz3[j % len(input_fuzz3)]
return IRR
def SF1_compute(input_temperature, input_d_speed, input_time):
pas = 0.01
temperature = ctrl.Antecedent(np.arange(263, 323, pas), 'Temperature (K)')
d_speed = ctrl.Antecedent(np.arange(-100, 100, pas), 'Variation of wind speed (m/s)')
time = ctrl.Antecedent(np.arange(0, 15, pas), 'Time since TC formation (days)')
lifespan = ctrl.Consequent(np.arange(0, 15, pas), 'Lifespan')
temperature['Low'] = fuzz.trapmf(temperature.universe, [263, 263, 293, 303])
temperature['Ok'] = fuzz.trapmf(temperature.universe, [293, 303, 323, 323])
d_speed['--'] = fuzz.trapmf(d_speed.universe, [-100, -100, -60, -20])
d_speed['-'] = fuzz.trimf(d_speed.universe, [-60, -30, 0])
d_speed['St'] = fuzz.trimf(d_speed.universe, [-30, 0, 20])
d_speed['+'] = fuzz.trimf(d_speed.universe, [0, 30, 60])
d_speed['++'] = fuzz.trapmf(d_speed.universe, [20, 60, 100, 100])
time['Young'] = fuzz.zmf(time.universe, 3, 6)
time['Middle'] = fuzz.gaussmf(time.universe, 6, 1)
time['Old'] = fuzz.smf(time.universe, 6, 9)
lifespan['Dying+'] = fuzz.trapmf(lifespan.universe, [0, 0, 3, 5])
lifespan['Dying'] = fuzz.trimf(lifespan.universe, [3, 5, 7])
lifespan['St'] = fuzz.trimf(lifespan.universe, [5, 7, 10])
lifespan['Living'] = fuzz.trimf(lifespan.universe, [7, 10, 13])
lifespan['Living+'] = fuzz.trapmf(lifespan.universe, [10, 13, 15, 15])
lifespan.view()
temperature.view()
d_speed.view()
time.view()
SF1_rules = [ctrl.Rule(temperature['Low'] & d_speed['--'] & time['Young'], lifespan['Dying+']),
ctrl.Rule(temperature['Low'] & d_speed['--'] & time['Middle'], lifespan['Dying+']),
ctrl.Rule(temperature['Low'] & d_speed['--'] & time['Old'], lifespan['Dying+']),
ctrl.Rule(temperature['Low'] & d_speed['-'] & time['Young'], lifespan['Dying']),
ctrl.Rule(temperature['Low'] & d_speed['-'] & time['Middle'], lifespan['Dying+']),
ctrl.Rule(temperature['Low'] & d_speed['-'] & time['Old'], lifespan['Dying+']),
ctrl.Rule(temperature['Low'] & d_speed['St'] & time['Young'], lifespan['Dying']),
ctrl.Rule(temperature['Low'] & d_speed['St'] & time['Middle'], lifespan['Dying']),
ctrl.Rule(temperature['Low'] & d_speed['St'] & time['Old'], lifespan['Dying']),
ctrl.Rule(temperature['Low'] & d_speed['+'] & time['Young'], lifespan['St']),
ctrl.Rule(temperature['Low'] & d_speed['+'] & time['Middle'], lifespan['St']),
ctrl.Rule(temperature['Low'] & d_speed['+'] & time['Old'], lifespan['St']),
ctrl.Rule(temperature['Low'] & d_speed['++'] & time['Young'], lifespan['Living']),
ctrl.Rule(temperature['Low'] & d_speed['++'] & time['Middle'], lifespan['Living']),
ctrl.Rule(temperature['Low'] & d_speed['++'] & time['Old'], lifespan['Living']),
ctrl.Rule(temperature['Ok'] & d_speed['--'] & time['Young'], lifespan['Dying']),
ctrl.Rule(temperature['Ok'] & d_speed['--'] & time['Middle'], lifespan['Dying']),
ctrl.Rule(temperature['Ok'] & d_speed['--'] & time['Old'], lifespan['Dying']),
ctrl.Rule(temperature['Ok'] & d_speed['-'] & time['Young'], lifespan['Dying']),
ctrl.Rule(temperature['Ok'] & d_speed['-'] & time['Middle'], lifespan['Dying']),
ctrl.Rule(temperature['Ok'] & d_speed['-'] & time['Old'], lifespan['Dying']),
ctrl.Rule(temperature['Ok'] & d_speed['St'] & time['Young'], lifespan['Living']),
ctrl.Rule(temperature['Ok'] & d_speed['St'] & time['Middle'], lifespan['St']),
ctrl.Rule(temperature['Ok'] & d_speed['St'] & time['Old'], lifespan['Dying']),
ctrl.Rule(temperature['Ok'] & d_speed['+'] & time['Young'], lifespan['Living']),
ctrl.Rule(temperature['Ok'] & d_speed['+'] & time['Middle'], lifespan['Living']),
ctrl.Rule(temperature['Ok'] & d_speed['+'] & time['Old'], lifespan['Living']),
ctrl.Rule(temperature['Ok'] & d_speed['++'] & time['Young'], lifespan['Living+']),
ctrl.Rule(temperature['Ok'] & d_speed['++'] & time['Middle'], lifespan['Living+']),
ctrl.Rule(temperature['Ok'] & d_speed['++'] & time['Old'], lifespan['Living+']),
]
SF1_ctrl = ctrl.ControlSystem(SF1_rules)
SF1 = ctrl.ControlSystemSimulation(SF1_ctrl)
input_temperature_fuzz = input_fuzz(temperature, input_temperature)
input_d_speed_fuzz = input_fuzz(d_speed, input_d_speed)
input_time_fuzz = input_fuzz(time, input_time)
print(input_temperature_fuzz)
print(input_d_speed_fuzz)
print(input_time_fuzz)
IRR = IRR_3var(SF1_rules, input_temperature_fuzz, input_d_speed_fuzz, input_time_fuzz)
declenchement = IRR.min(axis=1)
print(IRR)
print(declenchement)
csq_SF1 = {} # variable de type dictionnaire
for i in range(0, len(SF1_rules)): # on initialise à 0 pour chaque règle
csq_SF1[str(SF1_rules[i].consequent)] = 0
for i in range(0, len(SF1_rules)):
csq_i = str(SF1_rules[i].consequent)
csq_SF1[csq_i] = max(csq_SF1[csq_i], declenchement[i]) # Comme dans le TP6
print("Conséquences SF1 :", csq_SF1)
# return SF1
def SF2_compute(input_humidity, input_TC_size):
pas = 0.01
humidity = ctrl.Antecedent(np.arange(0, 100, pas), 'humidity') # Humidity (%)'
TC_size = ctrl.Antecedent(np.arange(0, 350, pas), 'TC_size') # TC size (km)
TC_size_variation = ctrl.Consequent(np.arange(0, 60, pas), 'TC_var') # Variation of TC size for next day (km)
humidity['--'] = fuzz.trapmf(humidity.universe, [0, 0, 20, 40]) # 20%
humidity['-'] = fuzz.trimf(humidity.universe, [20, 40, 60]) # 40%
humidity['+'] = fuzz.trimf(humidity.universe, [40, 60, 80]) # 60%
humidity['++'] = fuzz.trapmf(humidity.universe, [60, 80, 100, 100]) # 80%
TC_size['Little+'] = fuzz.trapmf(TC_size.universe, [0, 0, 75, 120])
TC_size['Little'] = fuzz.trimf(TC_size.universe, [75, 150, 200])
# TC_size['Normal'] = fuzz.trimf(TC_size.universe, [120, 150, 200])
TC_size['Huge'] = fuzz.trimf(TC_size.universe, [150, 200, 250])
TC_size['Huge+'] = fuzz.trapmf(TC_size.universe, [200, 250, 350, 350])
# 10 13 15 20 25 50
TC_size_variation['Very Low'] = fuzz.trapmf(TC_size_variation.universe, [0, 0, 10, 13]) # 10
TC_size_variation['Low'] = fuzz.trimf(TC_size_variation.universe, [10, 13, 15]) # 13
TC_size_variation['Quite Low'] = fuzz.trimf(TC_size_variation.universe, [13, 15, 20]) # 15
TC_size_variation['Quite High'] = fuzz.trimf(TC_size_variation.universe, [15, 20, 25]) # 20
TC_size_variation['High'] = fuzz.trimf(TC_size_variation.universe, [20, 25, 50]) # 25
TC_size_variation['Very High'] = fuzz.trapmf(TC_size_variation.universe, [25, 50, 50, 50]) # 50
humidity.view()
TC_size.view()
TC_size_variation.view()
SF2_rules = [ctrl.Rule(humidity['--'] & TC_size['Little+'], TC_size_variation['Very Low']),
ctrl.Rule(humidity['--'] & TC_size['Little'], TC_size_variation['Quite Low']),
ctrl.Rule(humidity['--'] & TC_size['Huge'], TC_size_variation['Quite Low']),
ctrl.Rule(humidity['--'] & TC_size['Huge+'], TC_size_variation['Quite Low']),
ctrl.Rule(humidity['-'] & TC_size['Little+'], TC_size_variation['Low']),
ctrl.Rule(humidity['-'] & TC_size['Little'], TC_size_variation['Quite Low']),
ctrl.Rule(humidity['-'] & TC_size['Huge'], TC_size_variation['Quite Low']),
ctrl.Rule(humidity['-'] & TC_size['Huge+'], TC_size_variation['Quite Low']),
ctrl.Rule(humidity['+'] & TC_size['Little+'], TC_size_variation['Quite High']),
ctrl.Rule(humidity['+'] & TC_size['Little'], TC_size_variation['Quite High']),
ctrl.Rule(humidity['+'] & TC_size['Huge'], TC_size_variation['Quite High']),
ctrl.Rule(humidity['+'] & TC_size['Huge+'], TC_size_variation['Quite High']),
ctrl.Rule(humidity['++'] & TC_size['Little+'], TC_size_variation['High']),
ctrl.Rule(humidity['++'] & TC_size['Little'], TC_size_variation['Very High']),
ctrl.Rule(humidity['++'] & TC_size['Huge'], TC_size_variation['Very High']),
ctrl.Rule(humidity['++'] & TC_size['Huge+'], TC_size_variation['Very Low'])]
SF2_ctrl = ctrl.ControlSystem(SF2_rules)
SF2 = ctrl.ControlSystemSimulation(SF2_ctrl)
input_TC_size_fuzz = input_fuzz(TC_size, input_TC_size)
input_humidity_fuzz = input_fuzz(humidity, input_humidity)
IRR = IRR_2var(SF2_rules, input_humidity_fuzz, input_TC_size_fuzz)
print(IRR)
declenchement = IRR.min(axis=1)
print(declenchement)
csq_SF2 = {} # variable de type dictionnaire
for i in range(0, len(SF2_rules)): # on initialise à 0 pour chaque règle
csq_SF2[str(SF2_rules[i].consequent)] = 0
for i in range(0, len(SF2_rules)):
csq_i = str(SF2_rules[i].consequent)
csq_SF2[csq_i] = max(csq_SF2[csq_i], declenchement[i]) # Comme dans le TP6
print("Conséquences SF2 :", csq_SF2)
# SF2.input['humidity'] = input_humidity
# SF2.input['TC_size'] = input_TC_size
# print(SF2.output['TC_var'])
# TC_size_variation.view(sim=SF2)
# https://stackoverflow.com/questions/11352047/finding-moving-average-from-data-points-in-python
def movingaverage(interval, window_size):
window = np.ones(int(window_size)) / float(window_size)
return np.convolve(interval, window, 'same')
def smooth_array(array, axis_array, smooth_parameter, axis):
if axis == 0: # latitude
for i in range(0, len(axis_array)):
array[i, :] = movingaverage(array[i, :], smooth_parameter)
elif axis == 1: # longitude
for i in range(0, len(axis_array)):
array[:, i] = movingaverage(array[:, i], smooth_parameter)
return array
def psi_compute(uwind, vwind, t):
uwind = np.squeeze(uwind[t, :, :])
vwind = np.squeeze(vwind[t, :, :]) # var[temps,latitude,longitude]
smooth = 5
intx = scipy.integrate.cumtrapz(vwind, lon, axis=1, initial=0)[0]
inty = scipy.integrate.cumtrapz(uwind, lat, axis=0, initial=0)
intx_av = np.zeros(np.shape(intx))
inty_av = np.zeros(np.shape(inty))
for i in range(0, len(lat)):
inty_av[i, :] = movingaverage(inty[i, :], smooth)
psi1 = intx - inty_av
intx = scipy.integrate.cumtrapz(vwind, lon, axis=1, initial=0)
inty = scipy.integrate.cumtrapz(uwind, lat, axis=0, initial=0)[:, 0][:, None]
intx_av = np.zeros(np.shape(intx))
for i in range(0, len(lon)):
intx_av[:, i] = movingaverage(intx[:, i], smooth)
psi2 = intx_av - inty
psi = 0.5 * (psi1 + psi2)
plot_array(psi,lon,lat)
return psi
def cart_to_polar(x0, y0, x1, y1): # fonctionne avec des matrices
x0 = met_to_deg(x0)
y0 = met_to_deg(y0)
x1 = met_to_deg(x1)
y1 = met_to_deg(y1)
x_ref = x1 - x0
y_ref = y1 - y0
x_ref, y_ref = np.meshgrid(x_ref, y_ref)
r = np.sqrt(deg_to_met(x_ref) ** 2 + deg_to_met(y_ref) ** 2)
angle = np.arctan2(x_ref, y_ref) - np.radians(90)
return r, angle
def psi_r1_compute(r, r_map, angle_map, psi):
study = np.isclose(r_map, r, atol=20000) # On prend tous les points à une distance r du TC ; tol en m
# Revoir la tolérance
angle_psi = np.where(study, angle_map,
0) # On prend les valeurs d'angle à une distance r du TC, on met 0 aux autres
psi_r = np.where(study, psi,
np.nan) # On va stocker suivant theta = [0,2pi] les différentes valeurs de psi sur le rayon r
# plot_array(psi_r,lon,lat)
return psi_r
def psi_TC_compute(x0, y0, study_univers):
r_map, angle_map = cart_to_polar(x0, y0, lon, lat)
psi_TC_r = [] # Liste : psi(r)
psi_TC_map = np.zeros(np.shape(angle_map)) # Map : psi(x;y)
k = 0
psi_inf = 0
i = 0 # Compteur
stable = False
# On se place ds le centre du TC, et on balaie selon x pour construire psi(r)
for x1 in study_univers:
y1 = y0
r1, angle1 = cart_to_polar(x0, y0, x1, y1)
psi_r1 = psi_r1_compute(r1, r_map, angle_map, psi)
to_add = np.nanmean(psi_r1) + psi_inf
if not stable:
to_add = np.nanmean(psi_r1) + psi_inf
else:
to_add = psi_TC_r[k]
psi_TC_r.append(to_add)
# là où il n'y a pas de NaN dans psi_r1 on met mean(psi_r1)+psi_inf, sinon on met psi_TC_map (= on change rien)
mask = np.invert(np.isnan(psi_r1))
psi_TC_map = np.where(mask, to_add, psi_TC_map)
# Test de Stabilité
if i > 30:
val_past = psi_TC_r[-30] / psi_TC_r[0]
val_now = to_add / psi_TC_r[0]
if abs(val_past - val_now) < 0.015:
stable = True
k = i
# Si Stable, alors on fixe la valeur à 0
i += 1
return psi_TC_r, psi_TC_map
def xy_to_indx_lonlat(x, y, lon, lat):
idx_lon = np.where(np.isclose(lon, x, atol=25000))[-1] # sachant que la résolution est de env 25000 km
idx_lat = np.where(np.isclose(lat, y, atol=25000))[-1] # au pire on a deux éléments
if len(idx_lon) > 1:
idx_lon = np.squeeze(idx_lon)[-1]
if len(idx_lat) > 1:
idx_lat = np.squeeze(idx_lat)[-1]
return idx_lon, idx_lat
def met_to_deg(x): # à préciser
return x / 111000
def deg_to_met(x): # à préciser
return x * 111000
def position_TC(jour, heure, path):
nc = NetCDFFile(path)
date_exact = jour * 24 + heure
x = scipy.integrate.cumtrapz(vwind[date_exact, :, :], lon, axis=1, initial=0)
y = scipy.integrate.cumtrapz(uwind[date_exact, :, :], lat, axis=0, initial=0)
psi_faux = y - x
latitude = np.array(nc.variables['latitude'][:])
longitude = np.array(nc.variables['longitude'][:])
# On trouve la vorticité max à cette date
a = np.unravel_index(np.argmax(psi_faux), psi_faux.shape)
# Puis on envoie les coordonnées
print(f"(Lon,Lat) = {latitude[a[0]], longitude[a[1]]}")
return latitude[a[0]], longitude[a[1]]
def taille_TC(jour, heure, seuil, path):
nc = NetCDFFile(path)
date_exact = jour * 24 + heure
# np.shape(vorticity) = (744,93,117)
# np.shape(latitude) = (93,)
# np.shape(longitude) = (117,)
vorticity = np.array(nc.variables['vo'][:])
latitude = np.array(nc.variables['latitude'][:])
longitude = np.array(nc.variables['longitude'][:])
# print(longitude[80]) gives -74.0
# Limit longitude study up to -74.0 to the right to ignore high vorticity where there is no cyclone; can be observed on the .gif
vo_max = np.unravel_index(np.argmax(vorticity[date_exact, :, 0:74]), vorticity[date_exact, :, 0:74].shape)
lon_TC = vo_max[1]
lat_TC = vo_max[0]
s = seuil
vorticity[vorticity < s] = 0
# Calculating min and max lon and lat
# Hovering the latitude, fixing longitude of current position
lat_vo_min = 0 # Initialising values
lat_vo_max = 0 # Initialising values
for i in range(93):
if vorticity[
date_exact, i, lon_TC] > 0: # Taking the first non zero value index as the start point of the TC latitude
lat_vo_min = i
break
if lat_vo_min == 0: # If lat_vo_min==0 it means that all values were below s
lat_vo_max = 0
else:
for i in range(lat_vo_min, 93):
if vorticity[
date_exact, i, lon_TC] == 0: # Taking the last non zero value index as the end point of the TC latitude
lat_vo_max = i - 1
break
# Hovering the longitude, fixing latitude of current position
lon_vo_min = 0 # Initialising values
lon_vo_max = 0 # Initialising values
for i in range(117):
if vorticity[
date_exact, lat_TC, i] > 0: # Taking the first non zero valueindex as the start point of the TC longitude
lon_vo_min = i
break
if lon_vo_min == 0:
lon_vo_max = 0
else:
for i in range(lon_vo_min, 93):
if vorticity[
date_exact, lat_TC, i] == 0: # Taking the last non zero value index as the end point of the TC longitude
lon_vo_max = i - 1
break
# print(f"LAT : Min:{lat_vo_min},Max:{lat_vo_max}")
# print(f"LON : Min{lon_vo_min},Max:{lon_vo_max}")
# ---------------------------------------------------------------------------------
lat1, lon1, lat2, lon2, R = lat_vo_min, lon_vo_min, lat_vo_max, lon_vo_max, 6373.0
# Distance between lon_vo_min and lon_vo_max, lat_TC constant
coordinates_from = [lat_TC, lon1]
coordinates_to = [lat_TC, lon2]
distance_geopy = distance.distance(coordinates_from, coordinates_to).km
distance_geopy_great_circle = distance.great_circle(coordinates_from, coordinates_to).km
longueur_longitudinale = (distance_geopy + distance_geopy_great_circle) / 2
# print('Longueur longitudinale', longueur_longitudinale)
# Distance between lat_vo_min and lat_vo_max, lon_TC constant
coordinates_from = [lat1, lon_TC]
coordinates_to = [lat2, lon_TC]
distance_geopy = distance.distance(coordinates_from, coordinates_to).km
distance_geopy_great_circle = distance.great_circle(coordinates_from, coordinates_to).km
longueur_latitudinale = (distance_geopy + distance_geopy_great_circle) / 2
# print('Longueur latitudinale', longueur_latitudinale)
# print('AIRE', longueur_latitudinale*longueur_longitudinale,'M2')
return longueur_latitudinale * longueur_longitudinale
def parametre_coriolis(latitude):
latitude = np.radians(met_to_deg(latitude))
return 2 * 0.72921 * (10 ** (-4)) * np.sin(latitude)
def get_fut_pos(x_TC, y_TC, psi):
# On dépasse de +20, nécessaire car on balaie selon le rayon, faut sortir du carré
univers = np.arange(x_TC, max(lon) + 2000000, 5000)
psi_TC_r, psi_TC_xy = psi_TC_compute(x_TC, y_TC, univers) # psi selon r, et psi selon x;y
psi_TC_on_psi0 = [-x / psi_TC_r[0] for x in psi_TC_r]
# TROUVER K
univers = (univers - min(univers))
study_r = int(np.round(0.05*int(np.where(np.isclose(max(psi_TC_on_psi0),psi_TC_on_psi0))[-1][-1])))
mymodel = np.poly1d(np.polyfit(univers[0:study_r], np.log(psi_TC_on_psi0[0:study_r]-min(psi_TC_on_psi0)+0.01), 1)) #faire avec exp
myline = np.linspace(0, 700000, 500)
L = myline[np.where(np.isclose(mymodel(myline), psi_TC_on_psi0[-1], atol=0.2))[-1][-1]]
K = (L ** 2) / 4
myline = np.linspace(0, L, 500)
psi_large = psi - psi_TC_xy
f = np.expand_dims(lat, axis=1) * np.ones(np.shape(psi_large)) # paramètre de Coriolis
f = parametre_coriolis(f)
delta = lat[0] - lat[1] # le même mais négatif en longitude
n = scipy.ndimage.laplace(psi_large) / (delta ** 2) + f
# axis 0 = dérivée selon les lignes ; axis 1 = dérivée selon les colonnes
# d'après leur définitino le - n'est pas là, mais jsp pk il le faut pour qu'on est le bon sesn du vent
v_large = np.array(np.gradient(psi_large, lon, axis=1))
u_large = np.array(-1) * np.array(np.gradient(psi_large, lat, axis=0))
n_grad_y = np.array(np.gradient(n, lat, axis=0))
n_grad_x = np.array(np.gradient(n, lon, axis=1))
smooth = 5
u_large = smooth_array(u_large, lon, smooth, axis=1)
v_large = smooth_array(v_large, lat, smooth, axis=0)
smooth = 5
n_grad_y = smooth_array(n_grad_y, lon, smooth, axis=1)
n_grad_x = smooth_array(n_grad_x, lat, smooth, axis=0)
a = 1
# Pour dans x heures :
t_1 = 12 * 60 * 60
dt = 1 * 60 * 60
C = 0.7156
nm = 1.2830
x_TC_fut = x_TC
y_TC_fut = y_TC
# A Faire : un Mask avec le TC, et faire une moyenne de u/v
for i in range(0, t_1, int(np.round(dt))):
TC_lat = np.radians(met_to_deg(y_TC_fut))
m = (C * (np.cos(TC_lat)) ** (-1)) * (np.tan(0.25 * (np.pi - 2 * TC_lat))) ** nm
idx_x, idx_y = xy_to_indx_lonlat(x_TC_fut, y_TC_fut, lon, lat)
Cx0 = a * u_large[idx_y, idx_x] - K * n_grad_y[idx_y, idx_x] # en m/s, normalement
Cy0 = a * v_large[idx_y, idx_x] + K * n_grad_x[idx_y, idx_x] # en m/s, normalement
x_TC_fut = x_TC_fut + Cx0 * dt#* m
y_TC_fut = y_TC_fut + Cy0 * dt#* m
print("dx", Cx0 * dt, a * u_large[idx_y, idx_x], - K * n_grad_y[idx_y, idx_x])
print("dy", Cy0 * dt, a * v_large[idx_y, idx_x], K * n_grad_x[idx_y, idx_x],m)
#plt.subplot(2, 2, 1)
#plot_array(psi, lon, lat)
# plt.plot(met_to_deg(x_TC), met_to_deg(y_TC), ms=4, marker='o', markeredgecolor="green")
#plt.subplot(2, 2, 2)
#plt.plot(myline, np.exp(mymodel(myline)))
#plt.plot(univers,psi_TC_on_psi0-min(psi_TC_on_psi0)+0.01)
#plt.plot(univers,np.grandient(psi_TC_on_psi0,univers))
#plt.plot(univers, smooth_array(np.expand_dims(np.gradient(psi_TC_on_psi0),1),[1],2,axis=1))
# plot_array(psi_TC_xy, lon, lat)
#plt.subplot(2, 2, 3)
#plot_array(vorticity[t, :, :], lon, lat)
# plt.plot(met_to_deg(x_TC), met_to_deg(y_TC), ms=10, marker='o', markeredgecolor="green")
# plt.streamplot(met_to_deg(lon),np.flip(met_to_deg(lat)),u_large,v_large )
#plt.subplot(2, 2, 4)
#plot_array(n_grad_y, lon, lat)
#plt.plot(met_to_deg(x_TC), met_to_deg(y_TC), ms=10, marker='o', markeredgecolor="green")
#plt.plot(met_to_deg(x_TC_fut), met_to_deg(y_TC_fut), ms=10, marker='o', markeredgecolor="red")
#y_v, x_v = position_TC(jour, heure + 12, data_path)
#plt.plot(x_v, y_v, ms=10, marker='x', markeredgecolor="yellow")
plt.figure() #Le Cyclone circule le long des lignes
plt.streamplot(met_to_deg(lon), met_to_deg(np.flip(lat)), np.flip(u_large, 0), np.flip(v_large, 0),
density=5,linewidth=0.3,color=np.flip(psi_large,axis=0))
# plt.streamplot(met_to_deg(lon), met_to_deg(np.flip(lat)), np.flip(n_grad_x, 0), np.flip(n_grad_y, 0),
# density=4, linewidth=0.3, color=np.flip(n, axis=0))
return x_TC_fut, y_TC_fut
jour = 28 # Commence au jour zéro
heure = 2
t = jour * 24 + heure
data_path = 'D:/TT/UV/SY10/KATRINA.nc'
nc = NetCDFFile(data_path)
lat = nc.variables['latitude'][:]
lon = nc.variables['longitude'][:]
time = nc.variables['time'][:]
uwind = nc.variables['u'][:]
vwind = nc.variables['v'][:]
vorticity = nc.variables['vo'][:]
lon = deg_to_met(lon)
lat = deg_to_met(lat)
lons, lats = np.meshgrid(lon, lat)
nc.close()
psi = psi_compute(uwind, vwind, t) # psi c'est la stream function
y_TC, x_TC = position_TC(jour, heure, data_path)
x_TC = deg_to_met(x_TC)
y_TC = deg_to_met(y_TC)
# plot_array(psi, lon, lat)
# plot_array(psi, lon, lat)
# plt.streamplot(met_to_deg(lon), met_to_deg(np.flip(lat)), np.flip(uwind[t,:,:], 0), np.flip(vwind[t,:,:], 0),
# density=5,linewidth=0.3,color=np.flip(psi, axis=0))
x_TC_fut, y_TC_fut = get_fut_pos(x_TC, y_TC, psi) # prévision de la position future du cyclone
# x_TC_fut2, y_TC_fut2 = get_fut_pos(x_TC_fut, y_TC_fut, K, psi)
# plt.plot(x_TC_fut2, y_TC_fut2, ms=10, marker='o', markeredgecolor="red")
# plt.plot(univers+np.array(-x_TC), fit_k)
# SF1_compute(298, -36.5, 8.1) # temperature ; d_speed ; âge
# SF2_compute(54, 224) # humidité ; taille du cyclone
plt.show()
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