# fin_swea = np.where(t_swea == find_nearest(t_swea, t4 + 0.075))[0][0]
# ####### densidad SWIA
# swia, t_swia, density = importar_swia(year, month, day, ti, tf)
t_swica, t_swifa, density_swica, density_swifa = importar_swicfa(
year, month, day, ti, tf)
# ###### densidad electrones
lpw, t_lpw, e_density = importar_lpw(year, month, day, ti, tf)
# ############ tiempos UTC
year = int(year)
month = int(month)
day = int(day)
tiempo_mag = np.array([np.datetime64(datenum(year, month, day, x))
for x in t]) # datenum es una función mía
tiempo_swea = np.array(
[np.datetime64(datenum(year, month, day, x)) for x in t_swea])
tiempo_swica = np.array(
[np.datetime64(datenum(year, month, day, x)) for x in t_swica])
tiempo_swifa = np.array(
[np.datetime64(datenum(year, month, day, x)) for x in t_swifa])
tiempo_lpw = np.array(
[np.datetime64(datenum(year, month, day, x)) for x in t_lpw])
tiempo_low = np.array(
[np.datetime64(datenum(year, month, day, x)) for x in t_plot])
tm1 = donde(t, t1)
tm2 = donde(t, t2)
tm3 = donde(t, t3)
####### densidad electrones
t_unix_lpw = lpw.varget("time_unix")
e_density = lpw.varget("density")
t_lpw = unix_to_decimal(t_unix_lpw)
ti_lpw = np.where(t_lpw == find_nearest(t_lpw, 17.85))[0][0]
tf_lpw = np.where(t_lpw == find_nearest(t_lpw, 18.4))[0][0]
tiempo_lpw = np.array([
np.datetime64(datetime(2016, 3, 16, int(x[0]), int(x[1]), int(x[2])))
for x in lpw[ti_lpw:tf_lpw, :]
]) # un array de datetimes con las horas entre 17.85 y 18.4
############# MAG
tiempo_mag = np.array([
np.datetime64(datenum(2016, 3, 16, x)) for x in t[j_inicial:j_final]
]) # datenum es una función mía
###########el plot
t1 = datetime(2016, 3, 16, 18, 13, 00)
t2 = np.where(t[j_inicial:j_final] == find_nearest(t, 18.2201))[0][0]
t3 = np.where(t[j_inicial:j_final] == find_nearest(t, 18.235))[0][0]
t4 = np.where(t[j_inicial:j_final] == find_nearest(t, 18.2476))[0][0]
t_bs = np.where(t[j_inicial:j_final] == find_nearest(t, 18.05))[0][0]
dstart = datetime(2016, 3, 16, 17, 50)
dend = datetime(2016, 3, 16, 18, 25)
plt.clf() # clear figure
fig = plt.figure(
inicio_swea = np.where(t_swea == find_nearest(t_swea, ti))[0][0]
fin_swea = np.where(t_swea == find_nearest(t_swea, tf))[0][0]
# ##############################################################################################SWIA
swia, t_swia, density = importar_swia(year, month, day, ti, tf)
# ############################################################################################## LPW
lpw, t_lpw, e_density = importar_lpw(year, month, day, ti, tf)
year = int(year)
month = int(month)
day = int(day)
tiempo_mag = np.array(
[np.datetime64(datenum(year, month, day, x)) for x in t]
) # datenum es una función mía
tiempo_swea = np.array([np.datetime64(datenum(year, month, day, x)) for x in t_swea])
tiempo_swia = np.array([np.datetime64(datenum(year, month, day, x)) for x in t_swia])
tiempo_lpw = np.array([np.datetime64(datenum(year, month, day, x)) for x in t_lpw])
tiempo_low = np.array(
[np.datetime64(datenum(year, month, day, x)) for x in t_plot]
) # datenum es una función mía
tm1 = np.where(t == find_nearest(t, t1))
tm2 = np.where(t == find_nearest(t, t2))
tm3 = np.where(t == find_nearest(t, t3))
tm4 = np.where(t == find_nearest(t, t4))
tiempo_lim = [tiempo_mag[tm1], tiempo_mag[tm2], tiempo_mag[tm3], tiempo_mag[tm4]]
swia, t_swia, proton_density, sw_vel = importar_swica(2016, "03", 16, t_cut[0],
t_cut[-1])
# los valores estos los elegi mirando los gráficos de la función ancho
ti_simu = t_simu[donde(x_tray, 1.15)]
tf_simu = t_simu[donde(x_tray, 1.01)]
ii = donde(t_simu, ti_simu)
jj = donde(t_simu, tf_simu)
year = 2016
month = 3
day = 16
tiempo_mag = np.array(
[np.datetime64(datenum(year, month, day, x)) for x in t_cut])
tiempo_simu = np.array(
[np.datetime64(datenum(year, month, day, x)) for x in t_simu])
tiempo_swia = np.array(
[np.datetime64(datenum(year, month, day, x)) for x in t_swia])
tiempo_lpw = np.array(
[np.datetime64(datenum(year, month, day, x)) for x in t_lpw])
# idx_flag = [i for i in range(len(flag)) if flag[i] > 50]
normal = np.array([0.920, -0.302, 0.251])
ancho_mpb = np.dot(r_simu[jj] - r_simu[ii], normal)
tt = donde(t_cut, t1)
ff = donde(t_cut, t4)
# ancho = np.dot(posicion_cut[tt] - posicion_cut[ff], normal)
inicio_swea = donde(t_swea, ti) # debería ser 0
fin_swea = donde(t_swea, tf)
# ######################################################################## SWIA
swia, t_swia, density = importar_swia(year, month, day, ti, tf)
# ########################## STATIC
static, t_static, mass, counts = importar_static(year, month, day, ti, tf)
# ############ tiempos UTC
year = int(year)
month = int(month)
day = int(day)
tiempo_mag = np.array([np.datetime64(datenum(year, month, day, x))
for x in t]) # datenum es una función mía
tiempo_swea = np.array(
[np.datetime64(datenum(year, month, day, x)) for x in t_swea])
tiempo_swia = np.array(
[np.datetime64(datenum(year, month, day, x)) for x in t_swia])
tiempo_static = np.array(
[np.datetime64(datenum(year, month, day, x)) for x in t_static])
tm1 = np.where(t == find_nearest(t, t1))
tm2 = np.where(t == find_nearest(t, t2))
tm3 = np.where(t == find_nearest(t, t3))
tm4 = np.where(t == find_nearest(t, t4))
# tm_up = np.where(t == find_nearest(t, t_up))
# tm_down = np.where(t == find_nearest(t, t_down))
# tmva = np.where(t == find_nearest(t, np.mean([t1, t2, t3, t4])))
tlow = (tlow - int(doy)) * 24 # para que me de sobre la cantidad de horas
Mlow = np.size(tlow) # el numero de datos
# el campo
Blow = np.zeros((Mlow, 3))
for i in range(7, 10):
Blow[:, i - 7] = mag_low[:, i]
B_para, B_perp_norm, t_plot = Bpara_Bperp(Blow, tlow, t[0], t[-1])
# ############ tiempos UTC
year = int(year)
month = int(month)
day = int(day)
tiempo_mag = np.array([np.datetime64(datenum(year, month, day, x))
for x in t]) # datenum es una función mía
tiempo_low = np.array(
[np.datetime64(datenum(year, month, day, x)) for x in t_plot])
tm1 = donde(t, t1)
tm2 = donde(t, t2)
tm3 = donde(t, t3)
tm4 = donde(t, t4)
tm_up = donde(t, t_up)
tm_down = donde(t, t_down)
tmva = donde(t, UTC_to_hdec("18:13:33"))
tbs = donde(t, UTC_to_hdec("18:02:00"))
tmpr = donde(t, UTC_to_hdec("18:19:00"))
tiempo_lim = [
datos_t = np.loadtxt("../outputs/t1t2t3t4.txt")
mag, t, B, posicion = importar_mag(year, month, day, ti, tf)
t1, t2, t3, t4 = importar_t1t2t3t4(year, doy, int(ti))
t_up = t1 - 0.015
t_down = t4 + 0.015
Bnorm = np.linalg.norm(B, axis=1)
# ############ tiempos UTC
year = int(year)
month = int(month)
day = int(day)
tiempo_mag = np.array([np.datetime64(datenum(year, month, day, x))
for x in t]) # datenum es una función mía
tm1 = donde(t, t1)
tm2 = donde(t, t2)
tm3 = donde(t, t3)
tm4 = donde(t, t4)
tm_up = donde(t, t_up)
tm_down = donde(t, t_down)
tiempo_lim = [
tiempo_mag[tm1], tiempo_mag[tm2], tiempo_mag[tm3], tiempo_mag[tm4]
]
B1, B2, B3 = proyecciones(B)
ti_simu = t_simu[donde(x, 1.15)]
tf_simu = t_simu[donde(x, 1.01)]
ii = donde(t_simu, ti_simu)
jj = donde(t_simu, tf_simu)
lpw, t_lpw, e_density, flag = importar_lpw(2016, "03", 16, t_cut[0], t_cut[-1])
swia, t_swia, proton_density, sw_vel = importar_swica(
2016, "03", 16, t_cut[0], t_cut[-1]
)
year = 2016
month = 3
day = 16
tiempo_mag = np.array([np.datetime64(datenum(year, month, day, x)) for x in t_cut])
tiempo_simu = np.array([np.datetime64(datenum(year, month, day, x)) for x in t_simu])
tiempo_swia = np.array([np.datetime64(datenum(year, month, day, x)) for x in t_swia])
tiempo_lpw = np.array([np.datetime64(datenum(year, month, day, x)) for x in t_lpw])
tiempo_static = np.array(
[np.datetime64(datenum(year, month, day, x)) for x in t_static]
)
idx_flag = [i for i in range(len(flag)) if flag[i] > 50]
normal = np.array([0.920, -0.302, 0.251])
ancho_mpb = np.dot(r_simu[jj] - r_simu[ii], normal)
posicion_cut = posicion[zi:zf]
t_cut = t[zi:zf]