pub struct MultivariateNormal {
pub template: Spacecraft,
pub dispersions: Vec<StateDispersion>,
pub mean: SVector<f64, 9>,
pub sqrt_s_v: SMatrix<f64, 9, 9>,
pub std_norm_distr: Normal<f64>,
}
Expand description
A multivariate state generator for Monte Carlo analyses. Ensures that the covariance is properly applied on all provided state variables.
Fields§
§template: Spacecraft
The template state
dispersions: Vec<StateDispersion>
§mean: SVector<f64, 9>
The mean of the multivariate normal distribution
sqrt_s_v: SMatrix<f64, 9, 9>
The dot product \sqrt{\vec s} \cdot \vec v, where S is the singular values and V the V matrix from the SVD decomp of the covariance of multivariate normal distribution
std_norm_distr: Normal<f64>
The standard normal distribution used to seed the multivariate normal distribution
Implementations§
source§impl MultivariateNormal
impl MultivariateNormal
sourcepub fn new(
template: Spacecraft,
dispersions: Vec<StateDispersion>,
) -> Result<Self, Box<dyn Error>>
pub fn new( template: Spacecraft, dispersions: Vec<StateDispersion>, ) -> Result<Self, Box<dyn Error>>
Creates a new mulivariate state generator from a mean and covariance on the set of state parameters. The covariance must be positive semi definite.
§Algorithm
This function will build the rotation matrix to rotate the requested dispersions into the Spacecraft state space using OrbitDual. If there are any dispersions on the Cr and Cd, then these are dispersed independently (because they are iid).
Examples found in repository?
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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, 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_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(),
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);
// 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(())
}
sourcepub fn zero_mean(
template: Spacecraft,
dispersions: Vec<StateDispersion>,
) -> Result<Self, Box<dyn Error>>
pub fn zero_mean( template: Spacecraft, dispersions: Vec<StateDispersion>, ) -> Result<Self, Box<dyn Error>>
Same as new
but with a zero mean
sourcepub fn from_spacecraft_cov(
template: Spacecraft,
cov: SMatrix<f64, 9, 9>,
mean: SVector<f64, 9>,
) -> Result<Self, Box<dyn Error>>
pub fn from_spacecraft_cov( template: Spacecraft, cov: SMatrix<f64, 9, 9>, mean: SVector<f64, 9>, ) -> Result<Self, Box<dyn Error>>
Initializes a new multivariate distribution using the state data in the spacecraft state space.
Trait Implementations§
source§impl Distribution<DispersedState<Spacecraft>> for MultivariateNormal
impl Distribution<DispersedState<Spacecraft>> for MultivariateNormal
source§fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> DispersedState<Spacecraft>
fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> DispersedState<Spacecraft>
T
, using rng
as the source of randomness.source§fn sample_iter<R>(self, rng: R) -> DistIter<Self, R, T>
fn sample_iter<R>(self, rng: R) -> DistIter<Self, R, T>
T
, using rng
as
the source of randomness. Read moreAuto Trait Implementations§
impl Freeze for MultivariateNormal
impl RefUnwindSafe for MultivariateNormal
impl Send for MultivariateNormal
impl Sync for MultivariateNormal
impl Unpin for MultivariateNormal
impl UnwindSafe for MultivariateNormal
Blanket Implementations§
source§impl<T> BorrowMut<T> for Twhere
T: ?Sized,
impl<T> BorrowMut<T> for Twhere
T: ?Sized,
source§fn borrow_mut(&mut self) -> &mut T
fn borrow_mut(&mut self) -> &mut T
§impl<T> Instrument for T
impl<T> Instrument for T
§fn instrument(self, span: Span) -> Instrumented<Self>
fn instrument(self, span: Span) -> Instrumented<Self>
§fn in_current_span(self) -> Instrumented<Self>
fn in_current_span(self) -> Instrumented<Self>
source§impl<T> IntoEither for T
impl<T> IntoEither for T
source§fn into_either(self, into_left: bool) -> Either<Self, Self>
fn into_either(self, into_left: bool) -> Either<Self, Self>
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 moresource§fn into_either_with<F>(self, into_left: F) -> Either<Self, Self>
fn into_either_with<F>(self, into_left: F) -> Either<Self, Self>
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
impl<T> Pointable for T
§impl<SS, SP> SupersetOf<SS> for SPwhere
SS: SubsetOf<SP>,
impl<SS, SP> SupersetOf<SS> for SPwhere
SS: SubsetOf<SP>,
§fn to_subset(&self) -> Option<SS>
fn to_subset(&self) -> Option<SS>
self
from the equivalent element of its
superset. Read more§fn is_in_subset(&self) -> bool
fn is_in_subset(&self) -> bool
self
is actually part of its subset T
(and can be converted to it).§fn to_subset_unchecked(&self) -> SS
fn to_subset_unchecked(&self) -> SS
self.to_subset
but without any property checks. Always succeeds.§fn from_subset(element: &SS) -> SP
fn from_subset(element: &SS) -> SP
self
to the equivalent element of its superset.