def importar_swia(year, month, day, ti, tf):
date_orbit = dt.date(int(year), int(month), int(day))
year = date_orbit.strftime("%Y")
month = date_orbit.strftime("%m")
day = date_orbit.strftime("%d")
# if gethostname() == "magneto2":
# path = f"../../../../media/gabybosc/datos/SWIA/"
# elif gethostname() == "gabybosc":
# path = "../../datos/SWIA/"
# else:
path = f"../../../datos/SWIA/"
swia = cdf.CDF(path +
f"mvn_swi_l2_onboardsvymom_{year}{month}{day}_v01_r01.cdf")
t_unix = swia.varget("time_unix")
density = swia.varget("density") # cm⁻³
temperature = swia.varget("temperature_mso") # eV
vel_mso_xyz = swia.varget("velocity_mso") # km/s
t_swia = unix_to_decimal(t_unix)
inicio = donde(t_swia, ti)
fin = donde(t_swia, tf)
t_cut = t_swia[inicio:fin]
density_cut = density[inicio:fin]
temperature_cut = temperature[inicio:fin]
vel_mso_cut = vel_mso_xyz[inicio:fin] # km/s
return swia, t_cut, density_cut, temperature_cut, vel_mso_cut
def importar_swea(year, month, day, ti, tf):
date_orbit = dt.date(int(year), int(month), int(day))
year = date_orbit.strftime("%Y")
month = date_orbit.strftime("%m")
day = date_orbit.strftime("%d")
if gethostname() == "magneto2":
path = f"../../../../media/gabybosc/datos/SWEA/"
elif gethostname() == "gabybosc":
path = "../../datos/SWEA/"
else:
path = f"../../../datos/SWEA/"
swea = cdf.CDF(path +
f"/mvn_swe_l2_svyspec_{year}{month}{day}_v04_r01.cdf")
flux_all = swea.varget("diff_en_fluxes")
energia = swea.varget("energy")
t_unix = swea.varget("time_unix")
t = unix_to_decimal(t_unix)
inicio = donde(t, ti)
fin = donde(t, tf)
t_cut = t[inicio:fin]
flux = flux_all[inicio:fin]
flux_plot = np.transpose(flux)[::-1]
return swea, t_cut, energia, flux_plot
def importar_static(year, month, day, ti, tf):
date_orbit = dt.date(int(year), int(month), int(day))
year = date_orbit.strftime("%Y")
month = date_orbit.strftime("%m")
day = date_orbit.strftime("%d")
if gethostname() == "magneto2":
path = f"../../../../media/gabybosc/datos/STATIC/"
elif gethostname() == "gabybosc":
path = "../../datos/STATIC/"
else:
path = f"../../../datos/STATIC/"
static = cdf.CDF(path +
f"mvn_sta_l2_c6-32e64m_{year}{month}{day}_v02_r01.cdf")
t_unix = static.varget("time_unix")
mass = static.varget("mass_arr")
energy = static.varget("energy")
t = unix_to_decimal(t_unix)
inicio = donde(t, ti)
fin = donde(t, tf)
t_cut = t[inicio:fin]
mass_cut = mass[inicio:fin]
energy_cut = energy[inicio:fin]
return static, t_cut, mass_cut, energy_cut
def importar_lpw(year, month, day, ti, tf):
date_orbit = dt.date(int(year), int(month), int(day))
year = date_orbit.strftime("%Y")
month = date_orbit.strftime("%m")
day = date_orbit.strftime("%d")
if gethostname() == "magneto2":
path = f"../../../../media/gabybosc/datos/LPW/"
elif gethostname() == "gabybosc":
path = "../../datos/LPW/"
else:
path = f"../../../datos/LPW/"
lpw = cdf.CDF(path + f"mvn_lpw_l2_lpnt_{year}{month}{day}_v03_r02.cdf")
t_unix = lpw.varget("time_unix")
e_density = lpw.varget("data")[:, 3]
t = unix_to_decimal(t_unix)
inicio = donde(t, ti)
fin = donde(t, tf)
t_cut = t[inicio:fin]
e_density_cut = e_density[inicio:fin]
return lpw, t_cut, e_density_cut
def importar_swia(year, month, day, ti, tf):
date_orbit = dt.date(int(year), int(month), int(day))
year = date_orbit.strftime("%Y")
month = date_orbit.strftime("%m")
day = date_orbit.strftime("%d")
if gethostname() == "magneto2":
path = "../../../../media/gabybosc/datos/SWIA/"
elif gethostname() == "gabybosc":
path = "../../datos/SWIA/"
else:
path = "../../../datos/SWIA/"
if os.path.isfile(
path + f"mvn_swi_l2_coarsearc3d_{year}{month}{day}_v01_r01.cdf"):
# si no existe SWICA, usa los onboard
swia = cdf.CDF(
path +
f"mvn_swi_l2_coarsearc3d_{year}{month}{day}_v01_r01_orig.cdf")
else:
swia = cdf.CDF(
path + f"mvn_swi_l2_onboardsvymom_{year}{month}{day}_v01_r01.cdf")
t_unix = swia.varget("time_unix")
density = swia.varget("density") # cm⁻³
# creo que en los datos de PDS, SWICA no tiene estos ya calculados (son justamente los moments)
temperature = swia.varget("temperature_mso") # eV
vel_mso_xyz = swia.varget("velocity_mso") # km/s
t_swia = unix_to_decimal(t_unix)
inicio = donde(t_swia, ti)
fin = donde(t_swia, tf)
t_cut = t_swia[inicio:fin]
density_cut = density[inicio:fin]
temperature_cut = temperature[inicio:fin]
vel_mso_cut = vel_mso_xyz[inicio:fin] # km/s
return swia, t_cut, density_cut, temperature_cut, vel_mso_cut
'La corriente en volumen con la normal del ajuste es Jv = {} nA/m², |Jv| = {} nA/m²'
.format(J_v_ajuste, np.linalg.norm(J_v_ajuste)))
# fuerza_mva = np.cross(J_v * 1E-9, B[inicio_up:fin_down, :] * 1E-9) #A/m² * T = N/m³
# fuerza_ajuste = np.cross(J_v_ajuste * 1E-9, B[inicio_up:fin_down, :] * 1E-9) #A/m² * T = N/m³
fuerza_mva = np.cross(J_v * 1E-9, B[inicio_down, :] * 1E-9) #N/m^3 #en t4
fuerza_ajuste = np.cross(J_v_ajuste * 1E-9,
B[inicio_down, :] * 1E-9) #N/m^3 #en t4
print('La fuerza de lorentz del MVA es {} V/m, su magnitud es {}'.format(
fuerza_mva, np.linalg.norm(fuerza_mva)))
print('La fuerza de lorentz del ajuste es {} V/m, su magnitud es {}'.format(
fuerza_ajuste, np.linalg.norm(fuerza_ajuste)))
e_density = lpw.varget('data')[:, 3]
t_unix = lpw.varget('time_unix')
t_lpw = unix_to_decimal(t_unix)
ti_lpw = np.where(t_lpw == find_nearest(t_lpw, t2))[0][0]
tf_lpw = np.where(t_lpw == find_nearest(t_lpw, t3))[0][0]
n_e = np.nanmean(
e_density[ti_lpw:tf_lpw]) #hace el mean ignorando los nans #cm⁻³
n_e = n_e * 1E6 #m⁻³
# n_e = 1E7
q_e = 1.6E-19 #carga electron #C
E_Hall = np.cross(J_v * 1E-9, B[inicio_down, :] * 1E-9) / (q_e * n_e) #V/m
print('El campo de Hall del MVA es {} mV/m, su magnitud es {}'.format(
E_Hall * 1E3,
np.linalg.norm(E_Hall) * 1E3))
E_Hall_ajuste = np.cross(J_v_ajuste * 1E-9, B[inicio_down, :] * 1E-9) / (
q_e * n_e) #V/m
def flujo_energia(t1, t2, cdf_file):
# np.set_printoptions(precision=4)
path = '../../datos/SWEA/' #path a los datos
cdf_file = cdf.CDF(path + 'mvn_swe_l2_svyspec_20160402_v04_r01.cdf')
flux = cdf_file.varget('diff_en_fluxes')
energia = cdf_file.varget('energy')
t_unix = cdf_file.varget('time_unix')
tu = unix_to_decimal(t_unix)
ti = np.where(tu == find_nearest(tu, t1))[0][0]
tf = np.where(tu == find_nearest(tu, t2))[0][0]
t = tu[ti:tf]
flux = flux[ti:tf]
#elijo la energia mas baja
Ei = find_nearest(energia, 20)
inicio = np.where(energia == Ei)[0][0]
#empiezo un array al que le voy a meter las energias
E = Ei
i = inicio
E = np.append(E, find_nearest(energia, E + 20))
i = np.append(i, np.where(energia == E[1])[0][0])
for j in range(len(energia)):
if E[j + 1] > 100:
break
else:
E = np.append(E, find_nearest(energia, E[j + 1] + 20))
i = np.append(i, np.where(energia == E[j + 2])[0][0])
E = np.append(E, find_nearest(energia, 220))
E = np.append(E, find_nearest(energia, 300))
i = np.append(i, np.where(energia == E[-2])[0][0])
i = np.append(i, np.where(energia == E[-1])[0][0])
flux_cut = np.zeros((len(flux), len(i)))
for j in range(len(i)):
flux_cut[:, j] = flux[:, int(i[j])]
#borramos el punto donde el log(0) = inf
for j in range(len(flux_cut[:, 0])):
for i in range(len(flux_cut[0, :])):
if np.log(flux_cut[j, i]) < 1:
flux_cut[j, i] = None
t1 = t_unix[np.where(tu == find_nearest(tu, 18.2193))[0][0]]
t2 = t_unix[np.where(tu == find_nearest(tu, 18.2272))[0][0]]
t3 = t_unix[np.where(tu == find_nearest(tu, 18.2331))[0][0]]
t4 = t_unix[np.where(tu == find_nearest(tu, 18.2476))[0][0]]
n = len(flux_cut)
t_inicial = t_unix[ti]
t_final = t_unix[tf]
timestamps = np.linspace(t_inicial, t_final, n)
dates = [dt.datetime.utcfromtimestamp(ts)
for ts in timestamps] #me lo da en UTC
datenums = md.date2num(dates)
# flux = cdf_file.varget('diff_en_fluxes')
# energia = cdf_file.varget('energy')
# t_unix = cdf_file.varget('time_unix')
#
# tu = unix_to_decimal(t_unix)
# ti = np.where(tu == find_nearest(tu, t1))[0][0]
# tf = np.where(tu == find_nearest(tu, t2))[0][0]
# t = tu[ti:tf]
# flux = flux[ti:tf]
#
#
# #elijo la energia mas baja
# Ei = find_nearest(energia, 20)
# inicio = np.where(energia == Ei)[0][0]
#
# #empiezo un array al que le voy a meter las energias
# E = Ei
# i = inicio
# E = np.append(E, find_nearest(energia, E+20))
# i = np.append(i, np.where(energia == E[1])[0][0])
#
# for j in range(len(energia)):
# if E[j+1] > 100:
# break
# else:
# E = np.append(E, find_nearest(energia, E[j+1]+20))
# i = np.append(i, np.where(energia == E[j+2])[0][0])
#
# E = np.append(E, find_nearest(energia, 220))
# E = np.append(E,find_nearest(energia, 300))
# i = np.append(i, np.where(energia == E[-2])[0][0])
# i = np.append(i, np.where(energia == E[-1])[0][0])
#
# flux_cut = np.zeros((len(flux), len(i)))
#
# for j in range(len(i)):
# flux_cut[:, j] = flux[:, int(i[j])]
#
# #borramos el punto donde el log(0) = inf
# for j in range(len(flux_cut[:,0])):
# for i in range(len(flux_cut[0,:])):
# if np.log(flux_cut[j,i]) < 1:
# flux_cut[j, i] = None
return (t, flux_cut, E)
tf = t4 + 0.15
t_plot, B_para, B_perp_norm = Bpara_Bperp(B, t, ti, tf)
###############################################################################################SWEA
file_size_swea = os.path.getsize(
path + f'SWEA/mvn_swe_l2_svyspec_{year}{month}{day}_v04_r01.cdf')
if file_size_swea > 10000000:
swea = cdf.CDF(
path + f'SWEA/mvn_swe_l2_svyspec_{year}{month}{day}_v04_r01.cdf')
flux_all = swea.varget('diff_en_fluxes')
energia = swea.varget('energy')
t_unix = swea.varget('time_unix')
tu = unix_to_decimal(t_unix)
ti_swea = np.where(tu == find_nearest(tu, ti))[0][0]
tf_swea = np.where(tu == find_nearest(tu, tf))[0][0]
t_swea = tu[ti_swea:tf_swea]
flux = flux_all[ti_swea:tf_swea]
flux_plot = np.transpose(flux)[::-1]
else:
print('no hay datos de SWEA')
###############################################################################################SWIA
file_size_swia = os.path.getsize(
path + f'SWIA/mvn_swi_l2_onboardsvymom_{year}{month}{day}_v01_r01.cdf')
if file_size_swia > 2300000:
swia = cdf.CDF(
path +
from funciones import find_nearest, unix_to_decimal, unix_to_timestamp
from funciones_plot import plot_select
from matplotlib.colors import LogNorm
import datetime as dt
import matplotlib.dates as md
plt.ion()
# np.set_printoptions(precision=4)
path = '../../datos/SWEA/' #path a los datos
cdf_file = cdf.CDF(path + 'mvn_swe_l2_svyspec_20160316_v04_r01.cdf')
flux_all = cdf_file.varget('diff_en_fluxes')
energia = cdf_file.varget('energy')
t_unix = cdf_file.varget('time_unix')
tu = unix_to_decimal(t_unix)
ti = np.where(tu == find_nearest(tu, 17.85))[0][0]
tf = np.where(tu == find_nearest(tu, 18.5))[0][0]
t = tu[ti:tf]
flux = flux_all[ti:tf]
datenums = unix_to_timestamp(t_unix[ti:tf])
log_flux = np.flip(np.log(flux), axis=1)
log_flux[log_flux < -1000] = None # np.min(log_flux[log_flux>-1000])
flux_plot = np.transpose(flux)[::-1]
plt.figure()
plt.subplots_adjust(bottom=0.2)
plt.xticks(rotation=25)