def datos():
year, month, day, doy = fechas()
ti, tf = tiempos()
# path = f'../../../datos/clweb/{year}-{month}-{day}/' #path a los datos desde la laptop
path = f"../../../../../media/gabybosc/datos/clweb/{year}-{month}-{day}/"
mag = np.loadtxt(path + "MAG.asc")
M = len(mag[:, 0]) # el numero de datos
hh = mag[:, 3]
mm = mag[:, 4]
ss = mag[:, 5]
t = hh + mm / 60 + ss / 3600 # hdec
t_utc = [f"{int(hh[j])}:{int(mm[j])}" for j in range(len(t))]
posicion = np.zeros((M, 3))
for i in range(9, 12):
posicion[:, i - 9] = mag[:, i] / 3390
inicio = np.where(t == find_nearest_inicial(t, ti))[0][0]
fin = np.where(t == find_nearest_final(t, tf))[0][0]
posicion_cut = posicion[inicio:fin, :]
t_cut = t[inicio:fin]
return (t_cut, posicion_cut, year, month, day)
for i in range(5, 8):
MD[:, i] = posicion[:, i - 5] / 3390 #en radios marcianos
MD[:, 8] = np.linalg.norm(posicion, axis=1) - 3390 #altitud en km
#si quiero elegir entre ciertas horas:
# t1 = float(input("t1 = "))
# t2 = float(input("t2 = "))
# t3 = float(input("t3 = "))
# t4 = float(input("t4 = "))
t1 = 18.2167
t2 = 18.2204
t3 = 18.235
t4 = 18.2476
inicio = np.where(t == find_nearest_inicial(t, t2))[0][0]
fin = np.where(t == find_nearest_final(t, t3))[0][0]
t_1 = np.where(t == find_nearest(t, t1))[0][0]
t_2 = np.where(t == find_nearest(t, t2))[0][0]
t_3 = np.where(t == find_nearest(t, t3))[0][0]
t_4 = np.where(t == find_nearest(t, t4))[0][0]
#################
#ahora empieza el MVA con los datos que elegí
MD_cut = MD[inicio:fin + 1, :]
M_cut = np.size(MD_cut[:, 0])
B_cut = B[inicio:fin + 1, :]
M_ij = Mij(B_cut)
#la posición(x,y,z)
posicion = np.zeros((M, 3))
for i in range(11, 14):
posicion[:, i - 11] = mag[:, i]
#la matriz diaria:
MD = np.zeros((M, 9))
MD[:, 0] = t
for i in range(1, 4):
MD[:, i] = B[:, i - 1]
MD[:, 4] = np.linalg.norm(B, axis=1) #la norma de B
for i in range(5, 8):
MD[:, i] = posicion[:, i - 5] / 3390 #en radios marcianos
MD[:, 8] = np.linalg.norm(posicion, axis=1) - 3390 #altitud en km
inicio = np.where(t == find_nearest_inicial(t, t2))[0][0]
fin = np.where(t == find_nearest_final(t, t3))[0][0]
t_1 = np.where(t == find_nearest(t, t1))[0][0]
t_2 = np.where(t == find_nearest(t, t2))[0][0]
t_3 = np.where(t == find_nearest(t, t3))[0][0]
t_4 = np.where(t == find_nearest(t, t4))[0][0]
#################
#Filtramos los cantidad_datos
b, a = signal.butter(3, 0.01, btype='lowpass')
Bx_filtrado = signal.filtfilt(b, a, B[:, 0])
By_filtrado = signal.filtfilt(b, a, B[:, 1])
Bz_filtrado = signal.filtfilt(b, a, B[:, 2])
B_filtrado = np.linalg.norm(np.array([Bx_filtrado, By_filtrado, Bz_filtrado]),
axis=0) #es el axis 0 porque no está traspuesta
angulo_B_fit = angulo(normal_ajuste, B_medio_vectorial)
angulo_B_mva = angulo(x3, B_medio_vectorial)
angulo_B_boot = angulo(normal_boot, B_medio_vectorial)
######
# Espesor de la MPB proyectado sobre las distintas normales
x_14_fit, x_23_fit = ancho_mpb(t1, t2, t3, t4, normal_ajuste, v_media)
x_14_MVA, x_23_MVA = ancho_mpb(t1, t2, t3, t4, x3, v_media)
x_14_boot, x_23_boot = ancho_mpb(t1, t2, t3, t4, normal_boot, v_media)
#########
# Análisis de corrientes
inicio_up = np.where(t == find_nearest_inicial(t, t1 - 0.015))[0][0]
fin_up = np.where(t == find_nearest_final(t, t1))[0][0]
B_upstream = np.mean(B[inicio_up:fin_up, :], axis=0) # nT
inicio_down = np.where(t == find_nearest_inicial(t, t4))[0][0]
fin_down = np.where(t == find_nearest_final(t, t4 + 0.015))[0][0]
B_downstream = np.mean(B[inicio_down:fin_down, :], axis=0) # nT
omega = angulo(B_upstream, B_downstream)
J_s_MVA, J_v_MVA = corrientes(x3, B_upstream, B_downstream, x_23_MVA)
J_s_fit, J_v_fit = corrientes(normal_ajuste, B_upstream, B_downstream,
x_23_fit)
J_s_boot, J_v_boot = corrientes(normal_boot, B_upstream, B_downstream,
x_23_boot)
M = np.size(t) #el numero de datos
#el campo
B = np.zeros((M, 3))
for i in [1, 2, 3]:
B[:, i - 1] = datos[:, i]
MD = np.zeros((M, 5))
MD[:, 0] = t
for i in range(1, 4):
MD[:, i] = B[:, i - 1]
MD[:, 4] = datos[:, -1] #la norma de B
#si quiero elegir entre ciertas horas:
t1 = find_nearest_inicial(t, ti)
t2 = find_nearest_final(t, tf)
inicio = np.where(t == t1)[0][0]
fin = np.where(t == t2)[0][0]
t_1 = np.where(t == find_nearest(t, 18.2167))[0][0]
t_2 = np.where(t == find_nearest(t, 18.2204))[0][0]
t_3 = np.where(t == find_nearest(t, 18.235))[0][0]
t_4 = np.where(t == find_nearest(t, 18.2476))[0][0]
#################
#ahora empieza el MVA con los datos que elegí
MD_cut = MD[inicio:fin + 1, :]
M_cut = np.size(MD_cut[:, 0])
B_cut = B[inicio:fin + 1, :]
[1, 0, 0]),
-1.0,
1.0,
)) * 180 / np.pi)
B_norm_medio = np.linalg.norm(B_medio_vectorial)
# hodograma(B1, B2, B3, 'nT', 'MAVEN MAG MVA ')
# el error
phi, delta_B3 = error(lamb, B_cut, len(B_cut), x)
###############
####fit
orbita = (
posicion[np.where(t == find_nearest_inicial(t, t1 - 1))[0][0]:np.where(
t == find_nearest_final(t, t4 + 1))[0][0], :, ] / 3390
) # radios marcianos
t_nave = find_nearest(t, (t2 + t3) /
2) # el tiempo en el medio de la hoja de corriente
index = np.where(t == t_nave)[0][0]
x0 = 0.78
e = 0.9
normal_fit, X1, Y1, Z1, R, L0 = ajuste_conico(posicion, index, orbita, x3)
B3_fit = np.dot(B_cut, normal_fit)
#############
##Bootstrap
N_boot = 1000