Struct Results 

pub struct Results<S: Interpolatable, R>
where
    DefaultAllocator: Allocator<S::Size> + Allocator<S::Size, S::Size> + Allocator<S::VecLength>,
    <DefaultAllocator as Allocator<S::VecLength>>::Buffer<f64>: Send,
{
    pub runs: Vec<Run<S, R>>,
    pub scenario: String,
}

A structure of Monte Carlo results

Fields

runs: Vec<Run<S, R>>

Raw data from each run, sorted by run index for O(1) access to each run

scenario: String

Name of this scenario

Implementations

impl<S: Interpolatable> Results<S, PropResult<S>>

where DefaultAllocator: Allocator<S::Size> + Allocator<S::Size, S::Size> + Allocator<S::VecLength>, <DefaultAllocator as Allocator<S::VecLength>>::Buffer<f64>: Send,

pub fn every_value_of_between( &self, param: StateParameter, step: Duration, start: Epoch, end: Epoch, value_if_run_failed: Option<f64>, ) -> Vec<f64>

Returns the value of the requested state parameter for all trajectories from start to end every step and using the value of value_if_run_failed if set and skipping that run if the run failed

pub fn every_value_of( &self, param: StateParameter, step: Duration, value_if_run_failed: Option<f64>, ) -> Vec<f64>

Returns the value of the requested state parameter for all trajectories from the start to the end of each trajectory and using the value of value_if_run_failed if set and skipping that run if the run failed

pub fn first_values_of( &self, param: StateParameter, value_if_run_failed: Option<f64>, ) -> Vec<f64>

Returns the value of the requested state parameter for the first state and using the value of value_if_run_failed if set and skipping that run if the run failed

pub fn last_values_of( &self, param: StateParameter, value_if_run_failed: Option<f64>, ) -> Vec<f64>

Returns the value of the requested state parameter for the first state and using the value of value_if_run_failed if set and skipping that run if the run failed

pub fn dispersion_values_of( &self, param: StateParameter, ) -> Result<Vec<f64>, MonteCarloError>

Returns the dispersion values of the requested state parameter

pub fn to_parquet<P: AsRef<Path>>( &self, path: P, cfg: ExportCfg, ) -> Result<PathBuf, Box<dyn Error>>

Examples found in repository

examples/03_geo_analysis/stationkeeping.rs (line 131)

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_info(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)
        .mass(Mass::from_dry_and_prop_masses(1000.0, 1000.0)) // 1000 kg of dry mass and prop, totalling 2.0 tons
        .srp(SRPData::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::Element(OrbitalElement::SemiMajorAxis),
            42_165.0,
            20.0,
        ),
        Objective::within_tolerance(
            StateParameter::Element(OrbitalElement::Eccentricity),
            0.001,
            5e-5,
        ),
        Objective::within_tolerance(
            StateParameter::Element(OrbitalElement::Inclination),
            0.05,
            1e-2,
        ),
    ];

    let ruggiero_ctrl = Ruggiero::from_max_eclipse(objectives, sc, 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(true)?;

    let harmonics = Harmonics::from_stor(
        almanac.frame_info(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 = MvnSpacecraft::new(
        sc,
        vec![StateDispersion::zero_mean(
            StateParameter::Element(OrbitalElement::SemiMajorAxis),
            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(),
        IntegratorOptions::builder()
            .min_step(10.0_f64.seconds())
            .error_ctrl(ErrorControl::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);

    rslts.to_parquet("03_geo_sk.parquet", ExportCfg::default())?;

    Ok(())
}

examples/02_jwst_covar_monte_carlo/main.rs (line 151)

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(true)?;

    // 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, true)?,
    );

    // 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(SRPData {
            area_m2: 21.197 * 14.162,
            coeff_reflectivity: 1.56,
        })
        .mass(Mass::from_dry_mass(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 Kalman filter OD process, and predicting for the analysis duration.

    // Build the propagation instance for the OD process.
    let odp = SpacecraftKalmanOD::new(
        setup.clone(),
        KalmanVariant::DeviationTracking,
        None,
        BTreeMap::new(),
        almanac.clone(),
    );

    // The prediction step is 1 minute by default, configured in the OD process, i.e. how often we want to know the covariance.
    assert_eq!(odp.max_step, 1_i64.minutes());
    // Finally, predict, and export the trajectory with covariance to a parquet file.
    let od_sol = odp.predict_for(jwst_estimate, prediction_duration)?;
    od_sol.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.

    rslts.to_parquet("02_jwst_monte_carlo.parquet", ExportCfg::default())?;

    Ok(())
}