IRENE (AP9 / AE9) including plasma energies

Model API name: irene

The International Radiation Environment Near Earth (IRENE) model.

Version: v1.57

Model developer:

Air Force Research Laboratory

MIT Lincoln Laboratory

Aerospace Corporation

Boston College Institute for Scientific Research

Los Alamos National Laboratory

Atmospheric and Environmental Research, Incorporated

Model provision:

SPENVIS

Category: model/environment/radiation/trapped

Keywords: radiation, trapped, irene, Ax9

Model flow

External Input: trajectory

Trajectory data.

Input	Value
Model name	sapre sapre_upload \| trajectory
Model result name	trajectory
Quantity	trajectory

Input group: irene / multiplicity: one

Input	Description	Valid values	Default	Quantity
includePlasmaEnergies	Include plasma energies	false=False true=True	false	number

IRENE

Input	Description	Valid values	Default	Quantity
numberOfRuns	Number of runs (for perturbed and Monte Carlo modes) Used if mode==perturbed	note: Positive integer min: 1 max: 999	20	number
protonEnergies	Proton energies array (length: 'numberProtonEnergies') in MeV.	note: Array of doubles	[0.1, 0.15, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 1.0, 1.5, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 10.0, 15.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 100.0, 150.0, 200.0, 300.0, 400.0, 500.0]	energy
electronEnergies	Electron energies. Number of energies must equal numberElectronEnergies (NENERE).	note: Array of doubles	[0.04, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0, 3.25, 3.5, 3.75, 4.0, 4.25, 4.5, 4.75, 5.0, 5.5, 6.0, 6.5, 7.0]	energy
protonPlasmaEnergies	Proton energies array (length: 'numberProtonEnergies') in MeV.	note: Array of doubles	[0.0015, 0.0021, 0.0037, 0.0065, 0.01155, 0.0204, 0.036, 0.06375, 0.085]	energy
electronPlasmaEnergies	Electron energies. Number of energies must equal numberElectronEnergies (NENERE).	note: Array of doubles	[0.001, 0.0013, 0.0017, 0.0021, 0.0028, 0.0036, 0.0046, 0.0059, 0.0077, 0.01, 0.013, 0.016, 0.021, 0.027, 0.035]	energy
aggregation	Aggregation method	mean=Mean percentile=Percentile	mean	text
energies	Energies	0=Default 1=Default AE9/Ap9 energies 2=user-defined	0	text
percentiles	Percentile(s)	note: Between 1 and 99 min: 1 max: 99	[]	number
mode	Model mode	mean_percentiles=Mean (and percentiles) perturbed=Perturbed montecarlo=Monte carlo	mean_percentile	text

Name

Quantity

Variables

orbit_averaged_proton_spectrum
Orbit averaged proton spectrum

energy_flux_spectrum

Qualifiers:
species: proton
aggregation: average

Name	Units	Quantity	Variable qualifiers
energy	(MeV)	energy
percentile	(1)	number
integral_flux Integral proton flux axis 1: energy axis 2: percentile	(cm^-2 s^-1)	number_flux	direction: omni-directional form: integral species: proton
differential_flux Differential proton flux axis 1: energy axis 2: percentile	(cm^-2 s^-1 (MeV/n)^-1)	number_flux	direction: omni-directional form: differential species: proton

orbit_averaged_electron_spectrum
Orbit averaged electron spectrum

energy_flux_spectrum

Qualifiers:
species: electron
aggregation: average

Name	Units	Quantity	Variable qualifiers
energy	(MeV)	energy
percentile	(1)	number
integral_flux Integral electron flux axis 1: energy axis 2: percentile	(cm^-2 s^-1)	number_flux	direction: omni-directional form: integral species: electron
differential_flux Differential proton flux axis 1: energy axis 2: percentile	(cm^-2 s^-1 (MeV/n)^-1)	number_flux	direction: omni-directional form: differential species: electron

proton_spectrum_over_trajectory
Proton spectrum over trajectory

energy_flux_spectrum

Qualifiers:
species: proton
aggregation: instantaneous
cadence: from_trajectory

Name	Units	Quantity	Variable qualifiers
MJD	(days)	datetime	format: cmjd
energy	(MeV)	energy
percentile	(1)	number
integral_flux Integral proton flux axis 1: energy axis 2: MJD axis 3: percentile	(cm^-2 s^-1)	number_flux	direction: omni-directional form: integral species: proton
differential_flux Differential proton flux axis 1: energy axis 2: MJD axis 3: percentile	(cm^-2 s^-1 (MeV/n)^-1)	number_flux	direction: omni-directional form: differential species: proton

electron_spectrum_over_trajectory
Electron spectrum over trajectory

energy_flux_spectrum

Qualifiers:
species: electron
aggregation: instantaneous
cadence: from_trajectory

Name	Units	Quantity	Variable qualifiers
MJD	(days)	datetime	format: cmjd
energy	(MeV)	energy
percentile	(1)	number
integral_flux Integral electron flux axis 1: energy axis 2: MJD axis 3: percentile	(cm^-2 s^-1)	number_flux	direction: omni-directional form: integral species: electron
differential_flux Differential proton flux axis 1: energy axis 2: MJD axis 3: percentile	(cm^-2 s^-1 (MeV/n)^-1)	number_flux	direction: omni-directional form: differential species: electron

None


import sys

import matplotlib.pyplot as plt

from nom_client.nom_client import NoMClient

plt.rcParams['figure.figsize'] = [15, 10]
plt.rc('font', size=18)


def plot(x_data, y_data, plot_types, line_types, xlabel, ylabel, labels, title):
    if not isinstance(plot_types, list):
        plot_types = [plot_types] * len(x_data)

    if not isinstance(line_types, list):
        line_types = [line_types] * len(x_data)

    if not isinstance(labels, list):
        labels = [labels] * len(x_data)

    for data_index in range(len(x_data)):
        if plot_types[data_index] == 'loglog':
            plt.loglog(x_data[data_index], y_data[data_index], linestyle=line_types[data_index], linewidth=2.0,
                       markersize=12.0,
                       label=labels[data_index])
        elif plot_types[data_index] == 'semilogy':
            plt.semilogy(x_data[data_index], y_data[data_index], linestyle=line_types[data_index], linewidth=2.0,
                         markersize=12.0,
                         label=labels[data_index])
        elif plot_types[data_index] == 'semilogx':
            plt.semilogx(x_data[data_index], y_data[data_index], linestyle=line_types[data_index], linewidth=2.0,
                         markersize=12.0,
                         label=labels[data_index])
        else:
            plt.plot(x_data[data_index], y_data[data_index], linestyle=line_types[data_index], linewidth=2.0,
                     markersize=12.0,
                     label=labels[data_index])

    plt.title(title, fontsize=18)
    plt.grid(True)
    plt.legend(bbox_to_anchor=(0.95, 0.95), loc=1, borderaxespad=0.,
               fancybox=True, fontsize=18, shadow=True)

    plt.xlabel(xlabel)
    plt.ylabel(ylabel)
    plt.show()


nom_client = NoMClient("irene SPM example", default_server_id="local_server")

sapre_model = nom_client.get_model('sapre')
sapre_model.set_params(orbitType="GEN", apogeeAltitude=20000, perigeeAltitude=600,
                       orbitStartYear=2022, orbitStartMonth=1, orbitStartDay=1, missionDuration=10)
sapre_result = nom_client.run_model(sapre_model)
print(sapre_result)

trajectory_model = nom_client.get_model('trajectory')
trajectory_model.set_params(epoch="2022-01-01", orbitSpecificationCode=205, naifCode=399, apogeeAltitude=20000, perigeeAltitude=600)
trajectory_result = nom_client.run_model(trajectory_model)
print(trajectory_result)

traj_res = [sapre_result, trajectory_result]

for tr in traj_res:

    print("Running Ax8 model")
    ap8ae8_model = nom_client.get_model('ae8ap8')
    ap8ae8_model.set_external_input(external_input_name="trajectory", external_input=tr)
    ap8ae8_results = nom_client.run_model(ap8ae8_model)
    print(ap8ae8_results)

    print("Running IRENE model")
    irene_model = nom_client.get_model('irene-spm')
    irene_model.set_params(mode="mean_percentiles", percentiles=[90], includePlasmaEnergies="true")

    irene_model.set_external_input(external_input_name="trajectory", external_input=tr)
    irene_results = nom_client.run_model(irene_model)
    print(irene_results)

    energy_ax8 = ap8ae8_results['orbit_averaged_proton_spectrum'].get_variable_data(variable_name="energy")
    int_flux_ax8 = ap8ae8_results['orbit_averaged_proton_spectrum'].get_variable_data(variable_name="integral_flux")
    diff_flux_ax8 = ap8ae8_results['orbit_averaged_proton_spectrum'].get_variable_data(variable_name="differential_flux")

    energy_ax9 = irene_results['orbit_averaged_proton_spectrum'].get_variable_data(variable_name="energy")
    int_flux_ax9 = irene_results['orbit_averaged_proton_spectrum'].get_variable_data(variable_name="integral_flux",
                                                                                     percentile=90)
    diff_flux_ax9 = irene_results['orbit_averaged_proton_spectrum'].get_variable_data(variable_name="differential_flux",
                                                                                      percentile=90)

    print(int_flux_ax9)
    # int_flux_ax9 = irene_results['orbit_averaged_proton_spectrum'].get_variable_data(variable_name="integral_flux")
    # diff_flux_ax9 = irene_results['orbit_averaged_proton_spectrum'].get_variable_data(variable_name="differential_flux")
    # plt.loglog(energy_ax9.T, int_flux_ax9.T, linewidth=2.0, markersize=12.0, label=['75%', '90%'])

    plot(x_data=[energy_ax8.T, energy_ax9.T],
         y_data=[diff_flux_ax8.T, diff_flux_ax9.T],
         plot_types="loglog",line_types="solid",xlabel="Energy (MeV)",
         ylabel=f"Differential flux ()",
         labels=['AP8', 'IRENE 50%'], title="AP8 vs. IRENE")



import datetime as dt

import matplotlib.pyplot as plt

from nom_client.nom_client import NoMClient

plt.rcParams['figure.figsize'] = [15, 10]
plt.rc('font', size=18)


def mjd50_to_datetime(mjd):
    dt1950 = dt.datetime(1950, 1, 1, 0, 0, 0, 0)

    if hasattr(mjd, "__len__"):
        rval = []
        for m in mjd:
            t = dt1950 + dt.timedelta(m)
            rval.append(t)
    else:
        rval = dt1950 + dt.timedelta(mjd)
    return rval


def plot(x_data, y_data, plot_types, line_types, xlabel, ylabel, labels, title):
    if not isinstance(plot_types, list):
        plot_types = [plot_types] * len(x_data)

    if not isinstance(line_types, list):
        line_types = [line_types] * len(x_data)

    if not isinstance(labels, list):
        labels = [labels] * len(x_data)

    for data_index in range(len(x_data)):
        if plot_types[data_index] == 'loglog':
            plt.loglog(x_data[data_index], y_data[data_index], linestyle=line_types[data_index], linewidth=2.0,
                       markersize=12.0,
                       label=labels[data_index])
        elif plot_types[data_index] == 'semilogy':
            plt.semilogy(x_data[data_index], y_data[data_index], linestyle=line_types[data_index], linewidth=2.0,
                         markersize=12.0,
                         label=labels[data_index])
        elif plot_types[data_index] == 'semilogx':
            plt.semilogx(x_data[data_index], y_data[data_index], linestyle=line_types[data_index], linewidth=2.0,
                         markersize=12.0,
                         label=labels[data_index])
        else:
            plt.plot(x_data[data_index], y_data[data_index], linestyle=line_types[data_index], linewidth=2.0,
                     markersize=12.0,
                     label=labels[data_index])

    plt.title(title, fontsize=18)
    plt.grid(True)
    plt.legend(bbox_to_anchor=(0.95, 0.95), loc=1, borderaxespad=0.,
               fancybox=True, fontsize=18, shadow=True)

    plt.xlabel(xlabel)
    plt.ylabel(ylabel)
    plt.show()


nom_client = NoMClient("IRENE example", default_server_id="local_server")

trajectory_model = nom_client.get_model('trajectory')
trajectory_model.set_params(orbitSpecificationCode=205,
                            naifCode=399, apogeeAltitude=20000, perigeeAltitude=600,
                            missionDuration=0.25)
trajectory_result = nom_client.run_model(trajectory_model)
print(trajectory_result)

mjd = trajectory_result['trajectory'].get_variable_data(variable_name="MJD")
alt = trajectory_result['trajectory'].get_variable_data(variable_name="altitude")

plot(x_data=[mjd.T],
     y_data=[alt.T],
     plot_types="plot",line_types="solid",xlabel="MJD",
     ylabel=f"Altitude (m)",
     labels=[''], title="IRENE by percentile")


print("Running Ax8 model")
ap8ae8_model = nom_client.get_model('ae8ap8')
ap8ae8_model.set_external_input(external_input_name="trajectory", external_input=trajectory_result)
ap8ae8_results = nom_client.run_model(ap8ae8_model)
print(ap8ae8_results)

print("Running IRENE model")
irene_model = nom_client.get_model('irene')
irene_model.set_params(mode="mean_percentiles", percentiles=[75, 90], includePlasmaEnergies="true")

irene_model.set_external_input(external_input_name="trajectory", external_input=trajectory_result)
irene_results = nom_client.run_model(irene_model)
print(irene_results)

# Get Ax8 results
energy_ax8 = ap8ae8_results['orbit_averaged_proton_spectrum'].get_variable_data(variable_name="energy")
int_flux_ax8 = ap8ae8_results['orbit_averaged_proton_spectrum'].get_variable_data(variable_name="integral_flux")
diff_flux_ax8 = ap8ae8_results['orbit_averaged_proton_spectrum'].get_variable_data(variable_name="differential_flux")

# Get Ax9 results
energy_ax9 = irene_results['orbit_averaged_proton_spectrum'].get_variable_data(variable_name="energy")
energy_units = irene_results['orbit_averaged_proton_spectrum'].get_variable_metadata(
    variable_name="energy")['units']

diff_flux_ax9 = irene_results['orbit_averaged_proton_spectrum'].get_variable_data(variable_name="differential_flux",
                                                                                  percentile=50).squeeze()
diff_units = irene_results['orbit_averaged_proton_spectrum'].get_variable_metadata(
    variable_name="differential_flux")['units']

plot(x_data=[energy_ax8.T, energy_ax9.T],
     y_data=[diff_flux_ax8.T, diff_flux_ax9.T],
     plot_types="loglog",line_types="solid",xlabel=f"Energy ({energy_units})",
     ylabel=f"Differential flux ({diff_units})",
     labels=['AP8', 'IRENE 50%'], title="AP8 vs. IRENE")

# Plot all percentiles

diff_flux_ax9_all_pc = irene_results['orbit_averaged_proton_spectrum'].get_variable_data(variable_name="differential_flux")
int_flux_ax9_all_pc = irene_results['orbit_averaged_proton_spectrum'].get_variable_data(variable_name="integral_flux")


plot(x_data=[energy_ax9.T],
     y_data=[diff_flux_ax9_all_pc.T],
     plot_types="loglog",line_types="solid", xlabel=f"Energy ({energy_units})",
     ylabel=f"Differential flux ({diff_units})",
     labels=['50%, 75%, 90%'], title="IRENE by percentile")



plot(x_data=[energy_ax9.T],
     y_data=[int_flux_ax9_all_pc.T],
     plot_types="loglog",line_types="solid", xlabel=f"Energy ({energy_units})",
     ylabel=f"Integral flux ({diff_units})",
     labels=['50%, 75%, 90%'], title="IRENE by percentile")


#
#
# mjd_over_traj_ax9 = irene_results['proton_spectrum_over_trajectory'].get_variable_data(variable_name="mjd").squeeze()
# irene_diff_flux_timeseries = irene_results['proton_spectrum_over_trajectory']
#
# diff_flux_over_traj_ax9 = irene_diff_flux_timeseries.get_variable_data(variable_name="differential_flux")
#
#
# datetimes = mjd50_to_datetime(mjd.squeeze()[1::40])
#
# diff_flux_over_traj_ax9_50pc = diff_flux_over_traj_ax9[0]
#
# plot(x_data=[mjd_over_traj_ax9.T],
#      y_data=[diff_flux_over_traj_ax9_50pc],
#      plot_types="semilogy",line_types="solid",xlabel="MJD (hr)",
#      ylabel=f"Differential flux ({diff_units})",
#      labels=energy_ax9.squeeze().T, title="IRENE")
#
# plot(x_data=[energy_ax9.T],
#      y_data=[diff_flux_over_traj_ax9_50pc[1::40].T],
#      plot_types="loglog",line_types="solid",xlabel=f"Energy ({energy_units})",
#      ylabel=f"Differential flux ({diff_units})",
#      labels=[datetimes], title="IRENE")

No references