Struct nyx_space::mc::MonteCarlo

source · 
pub struct MonteCarlo<S: Interpolatable, Distr: Distribution<DispersedState<S>>>
where
    DefaultAllocator: Allocator<S::Size> + Allocator<S::Size, S::Size> + Allocator<S::VecLength>,
{
    pub seed: Option<u128>,
    pub random_state: Distr,
    pub scenario: String,
    pub nominal_state: S,
}
Expand description

A Monte Carlo framework, automatically running on all threads via a thread pool. This framework is targeted toward analysis of time-continuous variables. One caveat of the design is that the trajectory is used for post processing, not each individual state. This may prevent some event switching from being shown in GNC simulations.

Fields§

§seed: Option<u128>

Seed of the 64bit PCG random number generator

§random_state: Distr

Generator of states for the Monte Carlo run

§scenario: String

Name of this run, will be reflected in the progress bar and in the output structure

§nominal_state: S

Implementations§

source§

impl<S: Interpolatable, Distr: Distribution<DispersedState<S>>> MonteCarlo<S, Distr>
where DefaultAllocator: Allocator<S::Size> + Allocator<S::Size, S::Size> + Allocator<S::VecLength>,

source

pub fn new( nominal_state: S, random_variable: Distr, scenario: String, seed: Option<u128>, ) -> Self

Examples found in repository?
examples/03_geo_analysis/stationkeeping.rs (lines 99-104)
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132

fn main() -> Result<(), Box<dyn Error>> {
    pel::init();
    // Set up the dynamics like in the orbit raise.
    let almanac = Arc::new(MetaAlmanac::latest().map_err(Box::new)?);
    let epoch = Epoch::from_gregorian_utc_hms(2024, 2, 29, 12, 13, 14);

    // Define the GEO orbit, and we're just going to maintain it very tightly.
    let earth_j2000 = almanac.frame_from_uid(EARTH_J2000)?;
    let orbit = Orbit::try_keplerian(42164.0, 1e-5, 0., 163.0, 75.0, 0.0, epoch, earth_j2000)?;
    println!("{orbit:x}");

    let sc = Spacecraft::builder()
        .orbit(orbit)
        .dry_mass_kg(1000.0) // 1000 kg of dry mass
        .fuel_mass_kg(1000.0) // 1000 kg of fuel, totalling 2.0 tons
        .srp(SrpConfig::from_area(3.0 * 6.0)) // Assuming 1 kW/m^2 or 18 kW, giving a margin of 4.35 kW for on-propulsion consumption
        .thruster(Thruster {
            // "NEXT-STEP" row in Table 2
            isp_s: 4435.0,
            thrust_N: 0.472,
        })
        .mode(GuidanceMode::Thrust) // Start thrusting immediately.
        .build();

    // Set up the spacecraft dynamics like in the orbit raise example.

    let prop_time = 30.0 * Unit::Day;

    // Define the guidance law -- we're just using a Ruggiero controller as demonstrated in AAS-2004-5089.
    let objectives = &[
        Objective::within_tolerance(StateParameter::SMA, 42_164.0, 5.0), // 5 km
        Objective::within_tolerance(StateParameter::Eccentricity, 0.001, 5e-5),
        Objective::within_tolerance(StateParameter::Inclination, 0.05, 1e-2),
    ];

    let ruggiero_ctrl = Ruggiero::from_max_eclipse(objectives, sc, EclipseState::Penumbra(0.2))?;
    println!("{ruggiero_ctrl}");

    let mut orbital_dyn = OrbitalDynamics::point_masses(vec![MOON, SUN]);

    let mut jgm3_meta = MetaFile {
        uri: "http://public-data.nyxspace.com/nyx/models/JGM3.cof.gz".to_string(),
        crc32: Some(0xF446F027), // Specifying the CRC32 avoids redownloading it if it's cached.
    };
    jgm3_meta.process()?;

    let harmonics = Harmonics::from_stor(
        almanac.frame_from_uid(IAU_EARTH_FRAME)?,
        HarmonicsMem::from_cof(&jgm3_meta.uri, 8, 8, true)?,
    );
    orbital_dyn.accel_models.push(harmonics);

    let srp_dyn = SolarPressure::default(EARTH_J2000, almanac.clone())?;
    let sc_dynamics = SpacecraftDynamics::from_model(orbital_dyn, srp_dyn)
        .with_guidance_law(ruggiero_ctrl.clone());

    println!("{sc_dynamics}");

    // Finally, let's use the Monte Carlo framework built into Nyx to propagate spacecraft.

    // Let's start by defining the dispersion.
    // The MultivariateNormal structure allows us to define the dispersions in any of the orbital parameters, but these are applied directly in the Cartesian state space.
    // Note that additional validation on the MVN is in progress -- https://github.com/nyx-space/nyx/issues/339.
    let mc_rv = MultivariateNormal::new(
        sc,
        vec![StateDispersion::zero_mean(StateParameter::SMA, 3.0)],
    )?;

    let my_mc = MonteCarlo::new(
        sc, // Nominal state
        mc_rv,
        "03_geo_sk".to_string(), // Scenario name
        None, // No specific seed specified, so one will be drawn from the computer's entropy.
    );

    // Build the propagator setup.
    let setup = Propagator::rk89(
        sc_dynamics.clone(),
        PropOpts::builder()
            .min_step(10.0_f64.seconds())
            .error_ctrl(RSSCartesianStep {})
            .build(),
    );

    let num_runs = 25;
    let rslts = my_mc.run_until_epoch(setup, almanac.clone(), sc.epoch() + prop_time, num_runs);

    assert_eq!(rslts.runs.len(), num_runs);

    // For all of the resulting trajectories, we'll want to compute the percentage of penumbra and umbra.

    rslts.to_parquet(
        "03_geo_sk.parquet",
        Some(vec![
            &EclipseLocator::cislunar(almanac.clone()).to_penumbra_event()
        ]),
        ExportCfg::default(),
        almanac,
    )?;

    Ok(())
}
More examples
Hide additional examples
examples/02_jwst_covar_monte_carlo/main.rs (lines 130-135)
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161

fn main() -> Result<(), Box<dyn Error>> {
    pel::init();
    // Dynamics models require planetary constants and ephemerides to be defined.
    // Let's start by grabbing those by using ANISE's latest MetaAlmanac.
    // For details, refer to https://github.com/nyx-space/anise/blob/master/data/latest.dhall.

    // Download the regularly update of the James Webb Space Telescope reconstucted (or definitive) ephemeris.
    // Refer to https://naif.jpl.nasa.gov/pub/naif/JWST/kernels/spk/aareadme.txt for details.
    let mut latest_jwst_ephem = MetaFile {
        uri: "https://naif.jpl.nasa.gov/pub/naif/JWST/kernels/spk/jwst_rec.bsp".to_string(),
        crc32: None,
    };
    latest_jwst_ephem.process()?;

    // Load this ephem in the general Almanac we're using for this analysis.
    let almanac = Arc::new(
        MetaAlmanac::latest()
            .map_err(Box::new)?
            .load_from_metafile(latest_jwst_ephem)?,
    );

    // By loading this ephemeris file in the ANISE GUI or ANISE CLI, we can find the NAIF ID of the JWST
    // in the BSP. We need this ID in order to query the ephemeris.
    const JWST_NAIF_ID: i32 = -170;
    // Let's build a frame in the J2000 orientation centered on the JWST.
    const JWST_J2000: Frame = Frame::from_ephem_j2000(JWST_NAIF_ID);

    // Since the ephemeris file is updated regularly, we'll just grab the latest state in the ephem.
    let (earliest_epoch, latest_epoch) = almanac.spk_domain(JWST_NAIF_ID)?;
    println!("JWST defined from {earliest_epoch} to {latest_epoch}");
    // Fetch the state, printing it in the Earth J2000 frame.
    let jwst_orbit = almanac.transform(JWST_J2000, EARTH_J2000, latest_epoch, None)?;
    println!("{jwst_orbit:x}");

    // Build the spacecraft
    // SRP area assumed to be the full sunshield and mass if 6200.0 kg, c.f. https://webb.nasa.gov/content/about/faqs/facts.html
    // SRP Coefficient of reflectivity assumed to be that of Kapton, i.e. 2 - 0.44 = 1.56, table 1 from https://amostech.com/TechnicalPapers/2018/Poster/Bengtson.pdf
    let jwst = Spacecraft::builder()
        .orbit(jwst_orbit)
        .srp(SrpConfig {
            area_m2: 21.197 * 14.162,
            cr: 1.56,
        })
        .dry_mass_kg(6200.0)
        .build();

    // Build up the spacecraft uncertainty builder.
    // We can use the spacecraft uncertainty structure to build this up.
    // We start by specifying the nominal state (as defined above), then the uncertainty in position and velocity
    // in the RIC frame. We could also specify the Cr, Cd, and mass uncertainties, but these aren't accounted for until
    // Nyx can also estimate the deviation of the spacecraft parameters.
    let jwst_uncertainty = SpacecraftUncertainty::builder()
        .nominal(jwst)
        .frame(LocalFrame::RIC)
        .x_km(0.5)
        .y_km(0.3)
        .z_km(1.5)
        .vx_km_s(1e-4)
        .vy_km_s(0.6e-3)
        .vz_km_s(3e-3)
        .build();

    println!("{jwst_uncertainty}");

    // Build the Kalman filter estimate.
    // Note that we could have used the KfEstimate structure directly (as seen throughout the OD integration tests)
    // but this approach requires quite a bit more boilerplate code.
    let jwst_estimate = jwst_uncertainty.to_estimate()?;

    // Set up the spacecraft dynamics.
    // We'll use the point masses of the Earth, Sun, Jupiter (barycenter, because it's in the DE440), and the Moon.
    // We'll also enable solar radiation pressure since the James Webb has a huge and highly reflective sun shield.

    let orbital_dyn = OrbitalDynamics::point_masses(vec![MOON, SUN, JUPITER_BARYCENTER]);
    let srp_dyn = SolarPressure::new(vec![EARTH_J2000, MOON_J2000], almanac.clone())?;

    // Finalize setting up the dynamics.
    let dynamics = SpacecraftDynamics::from_model(orbital_dyn, srp_dyn);

    // Build the propagator set up to use for the whole analysis.
    let setup = Propagator::default(dynamics);

    // All of the analysis will use this duration.
    let prediction_duration = 6.5 * Unit::Day;

    // === Covariance mapping ===
    // For the covariance mapping / prediction, we'll use the common orbit determination approach.
    // This is done by setting up a spacecraft OD process, and predicting for the analysis duration.

    let ckf = KF::no_snc(jwst_estimate);

    // Build the propagation instance for the OD process.
    let prop = setup.with(jwst.with_stm(), almanac.clone());
    let mut odp = SpacecraftODProcess::ckf(prop, ckf, None, almanac.clone());

    // Define the prediction step, i.e. how often we want to know the covariance.
    let step = 1_i64.minutes();
    // Finally, predict, and export the trajectory with covariance to a parquet file.
    odp.predict_for(step, prediction_duration)?;
    odp.to_parquet("./02_jwst_covar_map.parquet", ExportCfg::default())?;

    // === Monte Carlo framework ===
    // Nyx comes with a complete multi-threaded Monte Carlo frame. It's blazing fast.

    let my_mc = MonteCarlo::new(
        jwst, // Nominal state
        jwst_estimate.to_random_variable()?,
        "02_jwst".to_string(), // Scenario name
        None, // No specific seed specified, so one will be drawn from the computer's entropy.
    );

    let num_runs = 5_000;
    let rslts = my_mc.run_until_epoch(
        setup,
        almanac.clone(),
        jwst.epoch() + prediction_duration,
        num_runs,
    );

    assert_eq!(rslts.runs.len(), num_runs);
    // Finally, export these results, computing the eclipse percentage for all of these results.

    // For all of the resulting trajectories, we'll want to compute the percentage of penumbra and umbra.
    let eclipse_loc = EclipseLocator::cislunar(almanac.clone());
    let umbra_event = eclipse_loc.to_umbra_event();
    let penumbra_event = eclipse_loc.to_penumbra_event();

    rslts.to_parquet(
        "02_jwst_monte_carlo.parquet",
        Some(vec![&umbra_event, &penumbra_event]),
        ExportCfg::default(),
        almanac,
    )?;

    Ok(())
}
source

pub fn run_until_nth_event<'a, D, E, F>( self, prop: Propagator<'a, D, E>, almanac: Arc<Almanac>, max_duration: Duration, event: &F, trigger: usize, num_runs: usize, ) -> Results<S, PropResult<S>>
where D: Dynamics<StateType = S>, E: ErrorCtrl, F: EventEvaluator<S>, DefaultAllocator: Allocator<<D::StateType as State>::Size> + Allocator<<D::StateType as State>::Size, <D::StateType as State>::Size> + Allocator<<D::StateType as State>::VecLength>, <DefaultAllocator as Allocator<<D::StateType as State>::VecLength>>::Buffer<f64>: Send,

Generate states and propagate each independently until a specific event is found trigger times.

source

pub fn resume_run_until_nth_event<'a, D, E, F>( &self, prop: Propagator<'a, D, E>, almanac: Arc<Almanac>, skip: usize, max_duration: Duration, event: &F, trigger: usize, num_runs: usize, ) -> Results<S, PropResult<S>>
where D: Dynamics<StateType = S>, E: ErrorCtrl, F: EventEvaluator<S>, DefaultAllocator: Allocator<<D::StateType as State>::Size> + Allocator<<D::StateType as State>::Size, <D::StateType as State>::Size> + Allocator<<D::StateType as State>::VecLength>, <DefaultAllocator as Allocator<<D::StateType as State>::VecLength>>::Buffer<f64>: Send,

Generate states and propagate each independently until a specific event is found trigger times.

source

pub fn run_until_epoch<'a, D, E>( self, prop: Propagator<'a, D, E>, almanac: Arc<Almanac>, end_epoch: Epoch, num_runs: usize, ) -> Results<S, PropResult<S>>
where D: Dynamics<StateType = S>, E: ErrorCtrl, DefaultAllocator: Allocator<<D::StateType as State>::Size> + Allocator<<D::StateType as State>::Size, <D::StateType as State>::Size> + Allocator<<D::StateType as State>::VecLength>, <DefaultAllocator as Allocator<<D::StateType as State>::VecLength>>::Buffer<f64>: Send,

Generate states and propagate each independently until a specific event is found trigger times.

Examples found in repository?
examples/03_geo_analysis/stationkeeping.rs (line 116)
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132

fn main() -> Result<(), Box<dyn Error>> {
    pel::init();
    // Set up the dynamics like in the orbit raise.
    let almanac = Arc::new(MetaAlmanac::latest().map_err(Box::new)?);
    let epoch = Epoch::from_gregorian_utc_hms(2024, 2, 29, 12, 13, 14);

    // Define the GEO orbit, and we're just going to maintain it very tightly.
    let earth_j2000 = almanac.frame_from_uid(EARTH_J2000)?;
    let orbit = Orbit::try_keplerian(42164.0, 1e-5, 0., 163.0, 75.0, 0.0, epoch, earth_j2000)?;
    println!("{orbit:x}");

    let sc = Spacecraft::builder()
        .orbit(orbit)
        .dry_mass_kg(1000.0) // 1000 kg of dry mass
        .fuel_mass_kg(1000.0) // 1000 kg of fuel, totalling 2.0 tons
        .srp(SrpConfig::from_area(3.0 * 6.0)) // Assuming 1 kW/m^2 or 18 kW, giving a margin of 4.35 kW for on-propulsion consumption
        .thruster(Thruster {
            // "NEXT-STEP" row in Table 2
            isp_s: 4435.0,
            thrust_N: 0.472,
        })
        .mode(GuidanceMode::Thrust) // Start thrusting immediately.
        .build();

    // Set up the spacecraft dynamics like in the orbit raise example.

    let prop_time = 30.0 * Unit::Day;

    // Define the guidance law -- we're just using a Ruggiero controller as demonstrated in AAS-2004-5089.
    let objectives = &[
        Objective::within_tolerance(StateParameter::SMA, 42_164.0, 5.0), // 5 km
        Objective::within_tolerance(StateParameter::Eccentricity, 0.001, 5e-5),
        Objective::within_tolerance(StateParameter::Inclination, 0.05, 1e-2),
    ];

    let ruggiero_ctrl = Ruggiero::from_max_eclipse(objectives, sc, EclipseState::Penumbra(0.2))?;
    println!("{ruggiero_ctrl}");

    let mut orbital_dyn = OrbitalDynamics::point_masses(vec![MOON, SUN]);

    let mut jgm3_meta = MetaFile {
        uri: "http://public-data.nyxspace.com/nyx/models/JGM3.cof.gz".to_string(),
        crc32: Some(0xF446F027), // Specifying the CRC32 avoids redownloading it if it's cached.
    };
    jgm3_meta.process()?;

    let harmonics = Harmonics::from_stor(
        almanac.frame_from_uid(IAU_EARTH_FRAME)?,
        HarmonicsMem::from_cof(&jgm3_meta.uri, 8, 8, true)?,
    );
    orbital_dyn.accel_models.push(harmonics);

    let srp_dyn = SolarPressure::default(EARTH_J2000, almanac.clone())?;
    let sc_dynamics = SpacecraftDynamics::from_model(orbital_dyn, srp_dyn)
        .with_guidance_law(ruggiero_ctrl.clone());

    println!("{sc_dynamics}");

    // Finally, let's use the Monte Carlo framework built into Nyx to propagate spacecraft.

    // Let's start by defining the dispersion.
    // The MultivariateNormal structure allows us to define the dispersions in any of the orbital parameters, but these are applied directly in the Cartesian state space.
    // Note that additional validation on the MVN is in progress -- https://github.com/nyx-space/nyx/issues/339.
    let mc_rv = MultivariateNormal::new(
        sc,
        vec![StateDispersion::zero_mean(StateParameter::SMA, 3.0)],
    )?;

    let my_mc = MonteCarlo::new(
        sc, // Nominal state
        mc_rv,
        "03_geo_sk".to_string(), // Scenario name
        None, // No specific seed specified, so one will be drawn from the computer's entropy.
    );

    // Build the propagator setup.
    let setup = Propagator::rk89(
        sc_dynamics.clone(),
        PropOpts::builder()
            .min_step(10.0_f64.seconds())
            .error_ctrl(RSSCartesianStep {})
            .build(),
    );

    let num_runs = 25;
    let rslts = my_mc.run_until_epoch(setup, almanac.clone(), sc.epoch() + prop_time, num_runs);

    assert_eq!(rslts.runs.len(), num_runs);

    // For all of the resulting trajectories, we'll want to compute the percentage of penumbra and umbra.

    rslts.to_parquet(
        "03_geo_sk.parquet",
        Some(vec![
            &EclipseLocator::cislunar(almanac.clone()).to_penumbra_event()
        ]),
        ExportCfg::default(),
        almanac,
    )?;

    Ok(())
}
More examples
Hide additional examples
examples/02_jwst_covar_monte_carlo/main.rs (lines 138-143)
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161

fn main() -> Result<(), Box<dyn Error>> {
    pel::init();
    // Dynamics models require planetary constants and ephemerides to be defined.
    // Let's start by grabbing those by using ANISE's latest MetaAlmanac.
    // For details, refer to https://github.com/nyx-space/anise/blob/master/data/latest.dhall.

    // Download the regularly update of the James Webb Space Telescope reconstucted (or definitive) ephemeris.
    // Refer to https://naif.jpl.nasa.gov/pub/naif/JWST/kernels/spk/aareadme.txt for details.
    let mut latest_jwst_ephem = MetaFile {
        uri: "https://naif.jpl.nasa.gov/pub/naif/JWST/kernels/spk/jwst_rec.bsp".to_string(),
        crc32: None,
    };
    latest_jwst_ephem.process()?;

    // Load this ephem in the general Almanac we're using for this analysis.
    let almanac = Arc::new(
        MetaAlmanac::latest()
            .map_err(Box::new)?
            .load_from_metafile(latest_jwst_ephem)?,
    );

    // By loading this ephemeris file in the ANISE GUI or ANISE CLI, we can find the NAIF ID of the JWST
    // in the BSP. We need this ID in order to query the ephemeris.
    const JWST_NAIF_ID: i32 = -170;
    // Let's build a frame in the J2000 orientation centered on the JWST.
    const JWST_J2000: Frame = Frame::from_ephem_j2000(JWST_NAIF_ID);

    // Since the ephemeris file is updated regularly, we'll just grab the latest state in the ephem.
    let (earliest_epoch, latest_epoch) = almanac.spk_domain(JWST_NAIF_ID)?;
    println!("JWST defined from {earliest_epoch} to {latest_epoch}");
    // Fetch the state, printing it in the Earth J2000 frame.
    let jwst_orbit = almanac.transform(JWST_J2000, EARTH_J2000, latest_epoch, None)?;
    println!("{jwst_orbit:x}");

    // Build the spacecraft
    // SRP area assumed to be the full sunshield and mass if 6200.0 kg, c.f. https://webb.nasa.gov/content/about/faqs/facts.html
    // SRP Coefficient of reflectivity assumed to be that of Kapton, i.e. 2 - 0.44 = 1.56, table 1 from https://amostech.com/TechnicalPapers/2018/Poster/Bengtson.pdf
    let jwst = Spacecraft::builder()
        .orbit(jwst_orbit)
        .srp(SrpConfig {
            area_m2: 21.197 * 14.162,
            cr: 1.56,
        })
        .dry_mass_kg(6200.0)
        .build();

    // Build up the spacecraft uncertainty builder.
    // We can use the spacecraft uncertainty structure to build this up.
    // We start by specifying the nominal state (as defined above), then the uncertainty in position and velocity
    // in the RIC frame. We could also specify the Cr, Cd, and mass uncertainties, but these aren't accounted for until
    // Nyx can also estimate the deviation of the spacecraft parameters.
    let jwst_uncertainty = SpacecraftUncertainty::builder()
        .nominal(jwst)
        .frame(LocalFrame::RIC)
        .x_km(0.5)
        .y_km(0.3)
        .z_km(1.5)
        .vx_km_s(1e-4)
        .vy_km_s(0.6e-3)
        .vz_km_s(3e-3)
        .build();

    println!("{jwst_uncertainty}");

    // Build the Kalman filter estimate.
    // Note that we could have used the KfEstimate structure directly (as seen throughout the OD integration tests)
    // but this approach requires quite a bit more boilerplate code.
    let jwst_estimate = jwst_uncertainty.to_estimate()?;

    // Set up the spacecraft dynamics.
    // We'll use the point masses of the Earth, Sun, Jupiter (barycenter, because it's in the DE440), and the Moon.
    // We'll also enable solar radiation pressure since the James Webb has a huge and highly reflective sun shield.

    let orbital_dyn = OrbitalDynamics::point_masses(vec![MOON, SUN, JUPITER_BARYCENTER]);
    let srp_dyn = SolarPressure::new(vec![EARTH_J2000, MOON_J2000], almanac.clone())?;

    // Finalize setting up the dynamics.
    let dynamics = SpacecraftDynamics::from_model(orbital_dyn, srp_dyn);

    // Build the propagator set up to use for the whole analysis.
    let setup = Propagator::default(dynamics);

    // All of the analysis will use this duration.
    let prediction_duration = 6.5 * Unit::Day;

    // === Covariance mapping ===
    // For the covariance mapping / prediction, we'll use the common orbit determination approach.
    // This is done by setting up a spacecraft OD process, and predicting for the analysis duration.

    let ckf = KF::no_snc(jwst_estimate);

    // Build the propagation instance for the OD process.
    let prop = setup.with(jwst.with_stm(), almanac.clone());
    let mut odp = SpacecraftODProcess::ckf(prop, ckf, None, almanac.clone());

    // Define the prediction step, i.e. how often we want to know the covariance.
    let step = 1_i64.minutes();
    // Finally, predict, and export the trajectory with covariance to a parquet file.
    odp.predict_for(step, prediction_duration)?;
    odp.to_parquet("./02_jwst_covar_map.parquet", ExportCfg::default())?;

    // === Monte Carlo framework ===
    // Nyx comes with a complete multi-threaded Monte Carlo frame. It's blazing fast.

    let my_mc = MonteCarlo::new(
        jwst, // Nominal state
        jwst_estimate.to_random_variable()?,
        "02_jwst".to_string(), // Scenario name
        None, // No specific seed specified, so one will be drawn from the computer's entropy.
    );

    let num_runs = 5_000;
    let rslts = my_mc.run_until_epoch(
        setup,
        almanac.clone(),
        jwst.epoch() + prediction_duration,
        num_runs,
    );

    assert_eq!(rslts.runs.len(), num_runs);
    // Finally, export these results, computing the eclipse percentage for all of these results.

    // For all of the resulting trajectories, we'll want to compute the percentage of penumbra and umbra.
    let eclipse_loc = EclipseLocator::cislunar(almanac.clone());
    let umbra_event = eclipse_loc.to_umbra_event();
    let penumbra_event = eclipse_loc.to_penumbra_event();

    rslts.to_parquet(
        "02_jwst_monte_carlo.parquet",
        Some(vec![&umbra_event, &penumbra_event]),
        ExportCfg::default(),
        almanac,
    )?;

    Ok(())
}
source

pub fn resume_run_until_epoch<'a, D, E>( &self, prop: Propagator<'a, D, E>, almanac: Arc<Almanac>, skip: usize, end_epoch: Epoch, num_runs: usize, ) -> Results<S, PropResult<S>>
where D: Dynamics<StateType = S>, E: ErrorCtrl, DefaultAllocator: Allocator<<D::StateType as State>::Size> + Allocator<<D::StateType as State>::Size, <D::StateType as State>::Size> + Allocator<<D::StateType as State>::VecLength>, <DefaultAllocator as Allocator<<D::StateType as State>::VecLength>>::Buffer<f64>: Send,

Resumes a Monte Carlo run by skipping the first skip items, generating states only after that, and propagate each independently until the specified epoch.

source

pub fn generate_states( &self, skip: usize, num_runs: usize, seed: Option<u128>, ) -> Vec<(usize, DispersedState<S>)>

Set up the seed and generate the states. This is useful for checking the generated states before running a large scale Monte Carlo.

Trait Implementations§

source§

impl<S: Interpolatable, Distr: Distribution<DispersedState<S>>> Display for MonteCarlo<S, Distr>
where DefaultAllocator: Allocator<S::Size> + Allocator<S::Size, S::Size> + Allocator<S::VecLength>,

source§

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter. Read more
source§

impl<S: Interpolatable, Distr: Distribution<DispersedState<S>>> LowerHex for MonteCarlo<S, Distr>
where DefaultAllocator: Allocator<S::Size> + Allocator<S::Size, S::Size> + Allocator<S::VecLength>,

source§

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Returns a filename friendly name

Auto Trait Implementations§

§

impl<S, Distr> Freeze for MonteCarlo<S, Distr>
where DefaultAllocator: Sized, Distr: Freeze, S: Freeze,

§

impl<S, Distr> RefUnwindSafe for MonteCarlo<S, Distr>
where DefaultAllocator: Sized, Distr: RefUnwindSafe, S: RefUnwindSafe,

§

impl<S, Distr> Send for MonteCarlo<S, Distr>
where DefaultAllocator: Sized, Distr: Send,

§

impl<S, Distr> Sync for MonteCarlo<S, Distr>
where DefaultAllocator: Sized, Distr: Sync,

§

impl<S, Distr> Unpin for MonteCarlo<S, Distr>
where DefaultAllocator: Sized, Distr: Unpin, S: Unpin,

§

impl<S, Distr> UnwindSafe for MonteCarlo<S, Distr>
where DefaultAllocator: Sized, Distr: UnwindSafe, S: UnwindSafe,

Blanket Implementations§

source§

impl<T> Any for T
where T: 'static + ?Sized,

source§

fn type_id(&self) -> TypeId

Gets the TypeId of self. Read more
source§

impl<T> Borrow<T> for T
where T: ?Sized,

source§

fn borrow(&self) -> &T

Immutably borrows from an owned value. Read more
source§

impl<T> BorrowMut<T> for T
where T: ?Sized,

source§

fn borrow_mut(&mut self) -> &mut T

Mutably borrows from an owned value. Read more
source§

impl<T> From<T> for T

source§

fn from(t: T) -> T

Returns the argument unchanged.

§

impl<T> Instrument for T

§

fn instrument(self, span: Span) -> Instrumented<Self>

Instruments this type with the provided [Span], returning an Instrumented wrapper. Read more
§

fn in_current_span(self) -> Instrumented<Self>

Instruments this type with the current Span, returning an Instrumented wrapper. Read more
source§

impl<T, U> Into<U> for T
where U: From<T>,

source§

fn into(self) -> U

Calls U::from(self).

That is, this conversion is whatever the implementation of From<T> for U chooses to do.

source§

impl<T> IntoEither for T

source§

fn into_either(self, into_left: bool) -> Either<Self, Self>

Converts self into a Left variant of Either<Self, Self> if into_left is true. Converts self into a Right variant of Either<Self, Self> otherwise. Read more
source§

fn into_either_with<F>(self, into_left: F) -> Either<Self, Self>
where F: FnOnce(&Self) -> bool,

Converts self into a Left variant of Either<Self, Self> if into_left(&self) returns true. Converts self into a Right variant of Either<Self, Self> otherwise. Read more
§

impl<T> Pointable for T

§

const ALIGN: usize = _

The alignment of pointer.
§

type Init = T

The type for initializers.
§

unsafe fn init(init: <T as Pointable>::Init) -> usize

Initializes a with the given initializer. Read more
§

unsafe fn deref<'a>(ptr: usize) -> &'a T

Dereferences the given pointer. Read more
§

unsafe fn deref_mut<'a>(ptr: usize) -> &'a mut T

Mutably dereferences the given pointer. Read more
§

unsafe fn drop(ptr: usize)

Drops the object pointed to by the given pointer. Read more
source§

impl<T> Same for T

§

type Output = T

Should always be Self
§

impl<SS, SP> SupersetOf<SS> for SP
where SS: SubsetOf<SP>,

§

fn to_subset(&self) -> Option<SS>

The inverse inclusion map: attempts to construct self from the equivalent element of its superset. Read more
§

fn is_in_subset(&self) -> bool

Checks if self is actually part of its subset T (and can be converted to it).
§

fn to_subset_unchecked(&self) -> SS

Use with care! Same as self.to_subset but without any property checks. Always succeeds.
§

fn from_subset(element: &SS) -> SP

The inclusion map: converts self to the equivalent element of its superset.
source§

impl<T> ToString for T
where T: Display + ?Sized,

source§

default fn to_string(&self) -> String

Converts the given value to a String. Read more
source§

impl<T, U> TryFrom<U> for T
where U: Into<T>,

§

type Error = Infallible

The type returned in the event of a conversion error.
source§

fn try_from(value: U) -> Result<T, <T as TryFrom<U>>::Error>

Performs the conversion.
source§

impl<T, U> TryInto<U> for T
where U: TryFrom<T>,

§

type Error = <U as TryFrom<T>>::Error

The type returned in the event of a conversion error.
source§

fn try_into(self) -> Result<U, <U as TryFrom<T>>::Error>

Performs the conversion.
§

impl<V, T> VZip<V> for T
where V: MultiLane<T>,

§

fn vzip(self) -> V

§

impl<T> WithSubscriber for T

§

fn with_subscriber<S>(self, subscriber: S) -> WithDispatch<Self>
where S: Into<Dispatch>,

Attaches the provided Subscriber to this type, returning a [WithDispatch] wrapper. Read more
§

fn with_current_subscriber(self) -> WithDispatch<Self>

Attaches the current default Subscriber to this type, returning a [WithDispatch] wrapper. Read more
§

impl<T> Allocation for T
where T: RefUnwindSafe + Send + Sync,