Struct StochasticNoise 

pub struct StochasticNoise {
    pub white_noise: Option<WhiteNoise>,
    pub bias: Option<GaussMarkov>,
}

Stochastic noise modeling used primarily for synthetic orbit determination measurements.

This implementation distinguishes between the white noise model and the bias model. It also includes a constant offset.

Fields

white_noise: Option<WhiteNoise>

bias: Option<GaussMarkov>

Implementations

impl StochasticNoise

pub fn from_hardware_range_km( allan_deviation: f64, integration_time: Duration, chip_rate: ChipRate, s_n0: SN0, ) -> Self

Constructs a high precision zero-mean range noise model (accounting for clock error and thermal error) from the Allan deviation of the clock, integration time, chip rate (depends on the ranging code), and signal-power-to-noise-density ratio (S/N₀).

NOTE: The Allan Deviation should be provided given the integration time. For example, if the integration time is one second, the Allan Deviation should be the deviation over one second.

IMPORTANT: These do NOT include atmospheric noises, which add up to ~10 cm one-sigma.

Examples found in repository

nyx-core/examples/05_cislunar_spacecraft_link_od/main.rs (lines 129-134)

fn main() -> Result<(), Box<dyn Error>> {
    pel::init();

    // ====================== //
    // === ALMANAC SET UP === //
    // ====================== //

    let manifest_dir = PathBuf::from(env!("CARGO_MANIFEST_DIR"));

    let out = manifest_dir.join("data/04_output/");

    let almanac = Arc::new(
        Almanac::new(
            &manifest_dir
                .join("data/01_planetary/pck08.pca")
                .to_string_lossy(),
        )
        .unwrap()
        .load(
            &manifest_dir
                .join("data/01_planetary/de440s.bsp")
                .to_string_lossy(),
        )
        .unwrap(),
    );

    let eme2k = almanac.frame_info(EARTH_J2000).unwrap();
    let moon_iau = almanac.frame_info(IAU_MOON_FRAME).unwrap();

    let epoch = Epoch::from_gregorian_tai(2021, 5, 29, 19, 51, 16, 852_000);
    let nrho = Orbit::cartesian(
        166_473.631_302_239_7,
        -274_715.487_253_382_7,
        -211_233.210_176_686_7,
        0.933_451_604_520_018_4,
        0.436_775_046_841_900_9,
        -0.082_211_021_250_348_95,
        epoch,
        eme2k,
    );

    let tx_nrho_sc = Spacecraft::from(nrho);

    let state_luna = almanac.transform_to(nrho, MOON_J2000, None).unwrap();
    println!("Start state (dynamics: Earth, Moon, Sun gravity):\n{state_luna}");

    let bodies = vec![EARTH, SUN];
    let dynamics = SpacecraftDynamics::new(OrbitalDynamics::point_masses(bodies));

    let setup = Propagator::rk89(
        dynamics,
        IntegratorOptions::builder().max_step(0.5.minutes()).build(),
    );

    /* == Propagate the NRHO vehicle == */
    let prop_time = 1.1 * state_luna.period().unwrap();

    let (nrho_final, mut tx_traj) = setup
        .with(tx_nrho_sc, almanac.clone())
        .for_duration_with_traj(prop_time)
        .unwrap();

    tx_traj.name = Some("NRHO Tx SC".to_string());

    println!("{tx_traj}");

    /* == Propagate an LLO vehicle == */
    let llo_orbit =
        Orbit::try_keplerian_altitude(110.0, 1e-4, 90.0, 0.0, 0.0, 0.0, epoch, moon_iau).unwrap();

    let llo_sc = Spacecraft::builder().orbit(llo_orbit).build();

    let (_, llo_traj) = setup
        .with(llo_sc, almanac.clone())
        .until_epoch_with_traj(nrho_final.epoch())
        .unwrap();

    // Export the subset of the first two hours.
    llo_traj
        .clone()
        .filter_by_offset(..2.hours())
        .to_parquet_simple(out.join("05_caps_llo_truth.pq"))?;

    /* == Setup the interlink == */

    let mut measurement_types = IndexSet::new();
    measurement_types.insert(MeasurementType::Range);
    measurement_types.insert(MeasurementType::Doppler);

    let mut stochastics = IndexMap::new();

    let sa45_csac_allan_dev = 1e-11;

    stochastics.insert(
        MeasurementType::Range,
        StochasticNoise::from_hardware_range_km(
            sa45_csac_allan_dev,
            10.0.seconds(),
            link_specific::ChipRate::StandardT4B(),
            link_specific::SN0::Average(),
        ),
    );

    stochastics.insert(
        MeasurementType::Doppler,
        StochasticNoise::from_hardware_doppler_km_s(
            sa45_csac_allan_dev,
            10.0.seconds(),
            link_specific::CarrierFreq::SBand(),
            link_specific::CN0::Average(),
        ),
    );

    let interlink = InterlinkTxSpacecraft {
        traj: tx_traj,
        measurement_types,
        integration_time: None,
        timestamp_noise_s: None,
        ab_corr: Aberration::LT,
        stochastic_noises: Some(stochastics),
    };

    // Devices are the transmitter, which is our NRHO vehicle.
    let mut devices = BTreeMap::new();
    devices.insert("NRHO Tx SC".to_string(), interlink);

    let mut configs = BTreeMap::new();
    configs.insert(
        "NRHO Tx SC".to_string(),
        TrkConfig::builder()
            .strands(vec![Strand {
                start: epoch,
                end: nrho_final.epoch(),
            }])
            .build(),
    );

    let mut trk_sim =
        TrackingArcSim::with_seed(devices.clone(), llo_traj.clone(), configs, 0).unwrap();
    println!("{trk_sim}");

    let trk_data = trk_sim.generate_measurements(&almanac).unwrap();
    println!("{trk_data}");

    trk_data
        .to_parquet_simple(out.clone().join("nrho_interlink_msr.pq"))
        .unwrap();

    // Run a truth OD where we estimate the LLO position
    let llo_uncertainty = SpacecraftUncertainty::builder()
        .nominal(llo_sc)
        .x_km(1.0)
        .y_km(1.0)
        .z_km(1.0)
        .vx_km_s(1e-3)
        .vy_km_s(1e-3)
        .vz_km_s(1e-3)
        .build();

    let mut proc_devices = devices.clone();

    // Define the initial estimate, randomized, seed for reproducibility
    let mut initial_estimate = llo_uncertainty.to_estimate_randomized(Some(0)).unwrap();
    // Inflate the covariance -- https://github.com/nyx-space/nyx/issues/339
    initial_estimate.covar *= 2.5;

    // Increase the noise in the devices to accept more measurements.

    for link in proc_devices.values_mut() {
        for noise in &mut link.stochastic_noises.as_mut().unwrap().values_mut() {
            *noise.white_noise.as_mut().unwrap() *= 3.0;
        }
    }

    let init_err = initial_estimate
        .orbital_state()
        .ric_difference(&llo_orbit)
        .unwrap();

    println!("initial estimate:\n{initial_estimate}");
    println!("RIC errors = {init_err}",);

    let odp = InterlinkKalmanOD::new(
        setup.clone(),
        KalmanVariant::ReferenceUpdate,
        Some(SigmaRejection::default()),
        proc_devices,
        almanac.clone(),
    );

    // Shrink the data to process.
    let arc = trk_data.filter_by_offset(..2.hours());

    let od_sol = odp.process_arc(initial_estimate, &arc).unwrap();

    println!("{od_sol}");

    od_sol
        .to_parquet(
            out.join("05_caps_interlink_od_sol.pq"),
            ExportCfg::default(),
        )
        .unwrap();

    let od_traj = od_sol.to_traj().unwrap();

    od_traj
        .ric_diff_to_parquet(
            &llo_traj,
            out.join("05_caps_interlink_llo_est_error.pq"),
            ExportCfg::default(),
        )
        .unwrap();

    let final_est = od_sol.estimates.last().unwrap();
    assert!(final_est.within_3sigma(), "should be within 3 sigma");

    println!("ESTIMATE\n{final_est:x}\n");
    let truth = llo_traj.at(final_est.epoch()).unwrap();
    println!("TRUTH\n{truth:x}");

    let final_err = truth
        .orbit
        .ric_difference(&final_est.orbital_state())
        .unwrap();
    println!("ERROR {final_err}");

    // Build the residuals versus reference plot.
    let rvr_sol = odp
        .process_arc(initial_estimate, &arc.resid_vs_ref_check())
        .unwrap();

    rvr_sol
        .to_parquet(
            out.join("05_caps_interlink_resid_v_ref.pq"),
            ExportCfg::default(),
        )
        .unwrap();

    let final_rvr = rvr_sol.estimates.last().unwrap();

    println!("RMAG error {:.3} m", final_err.rmag_km() * 1e3);
    println!(
        "Pure prop error {:.3} m",
        final_rvr
            .orbital_state()
            .ric_difference(&final_est.orbital_state())
            .unwrap()
            .rmag_km()
            * 1e3
    );

    Ok(())
}

pub fn from_hardware_doppler_km_s( allan_deviation: f64, integration_time: Duration, carrier: CarrierFreq, c_n0: CN0, ) -> Self

Examples found in repository

nyx-core/examples/05_cislunar_spacecraft_link_od/main.rs (lines 139-144)

fn main() -> Result<(), Box<dyn Error>> {
    pel::init();

    // ====================== //
    // === ALMANAC SET UP === //
    // ====================== //

    let manifest_dir = PathBuf::from(env!("CARGO_MANIFEST_DIR"));

    let out = manifest_dir.join("data/04_output/");

    let almanac = Arc::new(
        Almanac::new(
            &manifest_dir
                .join("data/01_planetary/pck08.pca")
                .to_string_lossy(),
        )
        .unwrap()
        .load(
            &manifest_dir
                .join("data/01_planetary/de440s.bsp")
                .to_string_lossy(),
        )
        .unwrap(),
    );

    let eme2k = almanac.frame_info(EARTH_J2000).unwrap();
    let moon_iau = almanac.frame_info(IAU_MOON_FRAME).unwrap();

    let epoch = Epoch::from_gregorian_tai(2021, 5, 29, 19, 51, 16, 852_000);
    let nrho = Orbit::cartesian(
        166_473.631_302_239_7,
        -274_715.487_253_382_7,
        -211_233.210_176_686_7,
        0.933_451_604_520_018_4,
        0.436_775_046_841_900_9,
        -0.082_211_021_250_348_95,
        epoch,
        eme2k,
    );

    let tx_nrho_sc = Spacecraft::from(nrho);

    let state_luna = almanac.transform_to(nrho, MOON_J2000, None).unwrap();
    println!("Start state (dynamics: Earth, Moon, Sun gravity):\n{state_luna}");

    let bodies = vec![EARTH, SUN];
    let dynamics = SpacecraftDynamics::new(OrbitalDynamics::point_masses(bodies));

    let setup = Propagator::rk89(
        dynamics,
        IntegratorOptions::builder().max_step(0.5.minutes()).build(),
    );

    /* == Propagate the NRHO vehicle == */
    let prop_time = 1.1 * state_luna.period().unwrap();

    let (nrho_final, mut tx_traj) = setup
        .with(tx_nrho_sc, almanac.clone())
        .for_duration_with_traj(prop_time)
        .unwrap();

    tx_traj.name = Some("NRHO Tx SC".to_string());

    println!("{tx_traj}");

    /* == Propagate an LLO vehicle == */
    let llo_orbit =
        Orbit::try_keplerian_altitude(110.0, 1e-4, 90.0, 0.0, 0.0, 0.0, epoch, moon_iau).unwrap();

    let llo_sc = Spacecraft::builder().orbit(llo_orbit).build();

    let (_, llo_traj) = setup
        .with(llo_sc, almanac.clone())
        .until_epoch_with_traj(nrho_final.epoch())
        .unwrap();

    // Export the subset of the first two hours.
    llo_traj
        .clone()
        .filter_by_offset(..2.hours())
        .to_parquet_simple(out.join("05_caps_llo_truth.pq"))?;

    /* == Setup the interlink == */

    let mut measurement_types = IndexSet::new();
    measurement_types.insert(MeasurementType::Range);
    measurement_types.insert(MeasurementType::Doppler);

    let mut stochastics = IndexMap::new();

    let sa45_csac_allan_dev = 1e-11;

    stochastics.insert(
        MeasurementType::Range,
        StochasticNoise::from_hardware_range_km(
            sa45_csac_allan_dev,
            10.0.seconds(),
            link_specific::ChipRate::StandardT4B(),
            link_specific::SN0::Average(),
        ),
    );

    stochastics.insert(
        MeasurementType::Doppler,
        StochasticNoise::from_hardware_doppler_km_s(
            sa45_csac_allan_dev,
            10.0.seconds(),
            link_specific::CarrierFreq::SBand(),
            link_specific::CN0::Average(),
        ),
    );

    let interlink = InterlinkTxSpacecraft {
        traj: tx_traj,
        measurement_types,
        integration_time: None,
        timestamp_noise_s: None,
        ab_corr: Aberration::LT,
        stochastic_noises: Some(stochastics),
    };

    // Devices are the transmitter, which is our NRHO vehicle.
    let mut devices = BTreeMap::new();
    devices.insert("NRHO Tx SC".to_string(), interlink);

    let mut configs = BTreeMap::new();
    configs.insert(
        "NRHO Tx SC".to_string(),
        TrkConfig::builder()
            .strands(vec![Strand {
                start: epoch,
                end: nrho_final.epoch(),
            }])
            .build(),
    );

    let mut trk_sim =
        TrackingArcSim::with_seed(devices.clone(), llo_traj.clone(), configs, 0).unwrap();
    println!("{trk_sim}");

    let trk_data = trk_sim.generate_measurements(&almanac).unwrap();
    println!("{trk_data}");

    trk_data
        .to_parquet_simple(out.clone().join("nrho_interlink_msr.pq"))
        .unwrap();

    // Run a truth OD where we estimate the LLO position
    let llo_uncertainty = SpacecraftUncertainty::builder()
        .nominal(llo_sc)
        .x_km(1.0)
        .y_km(1.0)
        .z_km(1.0)
        .vx_km_s(1e-3)
        .vy_km_s(1e-3)
        .vz_km_s(1e-3)
        .build();

    let mut proc_devices = devices.clone();

    // Define the initial estimate, randomized, seed for reproducibility
    let mut initial_estimate = llo_uncertainty.to_estimate_randomized(Some(0)).unwrap();
    // Inflate the covariance -- https://github.com/nyx-space/nyx/issues/339
    initial_estimate.covar *= 2.5;

    // Increase the noise in the devices to accept more measurements.

    for link in proc_devices.values_mut() {
        for noise in &mut link.stochastic_noises.as_mut().unwrap().values_mut() {
            *noise.white_noise.as_mut().unwrap() *= 3.0;
        }
    }

    let init_err = initial_estimate
        .orbital_state()
        .ric_difference(&llo_orbit)
        .unwrap();

    println!("initial estimate:\n{initial_estimate}");
    println!("RIC errors = {init_err}",);

    let odp = InterlinkKalmanOD::new(
        setup.clone(),
        KalmanVariant::ReferenceUpdate,
        Some(SigmaRejection::default()),
        proc_devices,
        almanac.clone(),
    );

    // Shrink the data to process.
    let arc = trk_data.filter_by_offset(..2.hours());

    let od_sol = odp.process_arc(initial_estimate, &arc).unwrap();

    println!("{od_sol}");

    od_sol
        .to_parquet(
            out.join("05_caps_interlink_od_sol.pq"),
            ExportCfg::default(),
        )
        .unwrap();

    let od_traj = od_sol.to_traj().unwrap();

    od_traj
        .ric_diff_to_parquet(
            &llo_traj,
            out.join("05_caps_interlink_llo_est_error.pq"),
            ExportCfg::default(),
        )
        .unwrap();

    let final_est = od_sol.estimates.last().unwrap();
    assert!(final_est.within_3sigma(), "should be within 3 sigma");

    println!("ESTIMATE\n{final_est:x}\n");
    let truth = llo_traj.at(final_est.epoch()).unwrap();
    println!("TRUTH\n{truth:x}");

    let final_err = truth
        .orbit
        .ric_difference(&final_est.orbital_state())
        .unwrap();
    println!("ERROR {final_err}");

    // Build the residuals versus reference plot.
    let rvr_sol = odp
        .process_arc(initial_estimate, &arc.resid_vs_ref_check())
        .unwrap();

    rvr_sol
        .to_parquet(
            out.join("05_caps_interlink_resid_v_ref.pq"),
            ExportCfg::default(),
        )
        .unwrap();

    let final_rvr = rvr_sol.estimates.last().unwrap();

    println!("RMAG error {:.3} m", final_err.rmag_km() * 1e3);
    println!(
        "Pure prop error {:.3} m",
        final_rvr
            .orbital_state()
            .ric_difference(&final_est.orbital_state())
            .unwrap()
            .rmag_km()
            * 1e3
    );

    Ok(())
}

impl StochasticNoise

pub const ZERO: Self

Zero noise stochastic process.

pub const MIN: Self

The minimum stochastic noise process with a zero mean white noise of 1e-6.

pub fn default_range_km() -> Self

Default stochastic process of the Deep Space Network, as per DESCANSO Chapter 3, Table 3-3. Using the instrument bias as the white noise value, zero constant bias.

pub fn default_doppler_km_s() -> Self

Default stochastic process of the Deep Space Network, using as per DESCANSO Chapter 3, Table 3-3 for the GM process.

pub fn default_angle_deg() -> Self

Default stochastic process for an angle measurement (azimuth or elevation) Using the instrument bias as the white noise value, zero constant bias.

pub fn sample<R: Rng>(&mut self, epoch: Epoch, rng: &mut R) -> f64

Sample these stochastics

pub fn simulate<P: AsRef<Path>>( self, path: P, runs: Option<u32>, unit: Option<String>, ) -> Result<Vec<StochasticState>, Box<dyn Error>>

Executes a hardcoded 24-hour Monte Carlo simulation of the stochastic model, exporting the time history to a Parquet file.

Warning: Hardcoded Time Series & Diagnostic Data Gaps

This method does not accept a user-defined tracking schedule or time series. It inherently evaluates the stochastic process over a strict 24-hour period, beginning at the exact system clock moment of method execution, utilizing a 1-minute step size.

Furthermore, users will observe exactly 1,082 samples per simulation run, rather than the 1,441 samples expected from a continuous 24-hour 1-minute cadence. The simulation intentionally drops all epochs strictly greater than +6 hours and strictly less than +12 hours from the start time. This hardcoded artifact is designed to demonstrate variance bounds expansion in the absence of measurements (e.g., simulating a tracking dropout for a Gauss-Markov bias).

Algorithm

1. Establish start as the system clock time at invocation.
2. Construct an inclusive time series from start to start + 24 hours at 1-minute intervals.
3. For each configured run, seed a PRNG (Pcg64Mcg) using system entropy.
4. Evaluate the process covariance and sample the stochastic noise at each epoch.
5. Discard all epochs inside the (start + 6h, start + 12h) open interval.
6. Export the remaining 1,082 samples per run to an Apache Arrow RecordBatch and write to disk via Parquet.

impl StochasticNoise

pub fn covariance(&self, epoch: Epoch) -> f64

Return the covariance of these stochastics at a given time.

Trait Implementations

impl Clone for StochasticNoise

fn clone(&self) -> StochasticNoise

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Copy for StochasticNoise

impl Debug for StochasticNoise

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

Formats the value using the given formatter.

impl<'a> Decode<'a> for StochasticNoise

fn decode<R: Reader<'a>>(decoder: &mut R) -> Result<Self>

Attempt to decode this message using the provided decoder.

fn from_der(bytes: &'a [u8]) -> Result<Self, Error>

Parse Self from the provided DER-encoded byte slice.

impl Default for StochasticNoise

fn default() -> StochasticNoise

Returns the “default value” for a type.

impl DerefToPyAny for StochasticNoise

impl<'de> Deserialize<'de> for StochasticNoise

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Display for StochasticNoise

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

Formats the value using the given formatter.

impl Encode for StochasticNoise

fn encoded_len(&self) -> Result<Length>

Compute the length of this value in bytes when encoded as ASN.1 DER.

fn encode(&self, encoder: &mut impl Writer) -> Result<()>

Encode this value as ASN.1 DER using the provided [Writer].

fn encode_to_slice<'a>(&self, buf: &'a mut [u8]) -> Result<&'a [u8], Error>

Encode this value to the provided byte slice, returning a sub-slice containing the encoded message.

fn encode_to_vec(&self, buf: &mut Vec<u8>) -> Result<Length, Error>

Encode this message as ASN.1 DER, appending it to the provided byte vector.

fn to_der(&self) -> Result<Vec<u8>, Error>

Encode this type as DER, returning a byte vector.

impl<'a, 'py> FromPyObject<'a, 'py> for StochasticNoise

where Self: Clone,

type Error = PyClassGuardError<'a, 'py>

The type returned in the event of a conversion error.

fn extract( obj: Borrowed<'a, 'py, PyAny>, ) -> Result<Self, <Self as FromPyObject<'a, 'py>>::Error>

Extracts Self from the bound smart pointer obj.

impl<'py> IntoPyObject<'py> for StochasticNoise

type Target = StochasticNoise

The Python output type

type Output = Bound<'py, <StochasticNoise as IntoPyObject<'py>>::Target>

The smart pointer type to use.

type Error = PyErr

The type returned in the event of a conversion error.

fn into_pyobject( self, py: Python<'py>, ) -> Result<<Self as IntoPyObject<'_>>::Output, <Self as IntoPyObject<'_>>::Error>

Performs the conversion.

impl Mul<f64> for StochasticNoise

type Output = StochasticNoise

The resulting type after applying the * operator.

fn mul(self, rhs: f64) -> Self::Output

Performs the * operation.

impl MulAssign<f64> for StochasticNoise

fn mul_assign(&mut self, rhs: f64)

Performs the *= operation.

impl PartialEq for StochasticNoise

fn eq(&self, other: &StochasticNoise) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl PyClass for StochasticNoise

const NAME: &str = "StochasticNoise"

Name of the class.

type Frozen = False

Whether the pyclass is frozen.

impl PyClassImpl for StochasticNoise

const MODULE: Option<&str> = ::core::option::Option::None

Module which the class will be associated with.

const IS_BASETYPE: bool = false

#[pyclass(subclass)]

const IS_SUBCLASS: bool = false

#[pyclass(extends=…)]

const IS_MAPPING: bool = false

#[pyclass(mapping)]

const IS_SEQUENCE: bool = false

#[pyclass(sequence)]

const IS_IMMUTABLE_TYPE: bool = false

#[pyclass(immutable_type)]

const RAW_DOC: &'static CStr = /// Stochastic noise modeling used primarily for synthetic orbit determination measurements. /// /// This implementation distinguishes between the white noise model and the bias model. It also includes a constant offset.

Docstring for the class provided on the struct or enum.

const DOC: &'static CStr

Fully rendered class doc, including the text_signature if a constructor is defined.

type Layout = <<StochasticNoise as PyClassImpl>::BaseNativeType as PyClassBaseType>::Layout<StochasticNoise>

Description of how this class is laid out in memory

type BaseType = PyAny

Base class

type ThreadChecker = NoopThreadChecker

This handles following two situations:

type Inventory = Pyo3MethodsInventoryForStochasticNoise

type PyClassMutability = <<PyAny as PyClassBaseType>::PyClassMutability as PyClassMutability>::MutableChild

Immutable or mutable

type Dict = PyClassDummySlot

Specify this class has #[pyclass(dict)] or not.

type WeakRef = PyClassDummySlot

Specify this class has #[pyclass(weakref)] or not.

type BaseNativeType = PyAny

The closest native ancestor. This is PyAny by default, and when you declare #[pyclass(extends=PyDict)], it’s PyDict.

fn items_iter() -> PyClassItemsIter

fn lazy_type_object() -> &'static LazyTypeObject<Self>

fn dict_offset() -> Option<PyObjectOffset>

Used to provide the dictoffset slot (equivalent to tp_dictoffset)

fn weaklist_offset() -> Option<PyObjectOffset>

Used to provide the weaklistoffset slot (equivalent to tp_weaklistoffset

impl PyClassNewTextSignature for StochasticNoise

const TEXT_SIGNATURE: &'static str = "(white_noise=None, bias=None, name=None)"

impl PyTypeInfo for StochasticNoise

const NAME: &str = <Self as ::pyo3::PyClass>::NAME

👎Deprecated since 0.28.0:

prefer using ::type_object(py).name() to get the correct runtime value

Class name.

const MODULE: Option<&str> = <Self as ::pyo3::impl_::pyclass::PyClassImpl>::MODULE

👎Deprecated since 0.28.0:

prefer using ::type_object(py).module() to get the correct runtime value

Module name, if any.

fn type_object_raw(py: Python<'_>) -> *mut PyTypeObject

Returns the PyTypeObject instance for this type.

fn type_object(py: Python<'_>) -> Bound<'_, PyType>

Returns the safe abstraction over the type object.

fn is_type_of(object: &Bound<'_, PyAny>) -> bool

Checks if object is an instance of this type or a subclass of this type.

fn is_exact_type_of(object: &Bound<'_, PyAny>) -> bool

Checks if object is an instance of this type.

impl Serialize for StochasticNoise

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl StructuralPartialEq for StochasticNoise