Struct GaussMarkov 

pub struct GaussMarkov {
    pub tau: Duration,
    pub process_noise: f64,
    pub constant: Option<f64>,
    pub prev_epoch: Option<Epoch>,
    pub init_sample: Option<f64>,
}

A first order Gauss-Markov process for modeling biases as described in section 5.2.4 of the NASA Best Practices for Navigation Filters (D’Souza et al.).

The process is defined by the following stochastic differential equation:

\dot{b(t)} = -1/τ * b(t) + w(t)

Programmatically, it’s calculated by sampling from b(t) ~ 𝓝(0, p_b(t)), where

p_b(t) = exp((-2 / τ) * (t - t_0)) * p_b(t_0) + s(t - t_0)

s(t - t_0) = ((q * τ) / 2) * (1 - exp((-2 / τ) * (t - t_0)))

Fields

tau: Duration

The time constant, tau gives the correlation time, or the time over which the intensity of the time correlation will fade to 1/e of its prior value. (This is sometimes incorrectly referred to as the “half-life” of the process.)

process_noise: f64

constant: Option<f64>

An optional constant offset on top of the noise, defaults to zero.

prev_epoch: Option<Epoch>

Epoch of the previous realization, used to compute the time delta for the process noise.

init_sample: Option<f64>

Sample of previous realization

Implementations

impl GaussMarkov

pub const ZERO: Self

Zero noise Gauss-Markov process.

pub fn new(tau: Duration, process_noise: f64) -> Result<Self, ConfigError>

Create a new first order Gauss-Markov process.

Arguments

• tau - The time constant, tau gives the correlation time, or the time over which the intensity of the time correlation will fade to 1/e of its prior value.
• process_noise - process noise of the system.

pub fn default_range_km() -> Self

Default Gauss Markov noise of the Deep Space Network, as per DESCANSO Chapter 3, Table 3-3. Used the range value of 60 cm over a 60 second average.

pub fn default_doppler_km_s() -> Self

Default Gauss Markov noise of the Deep Space Network, as per DESCANSO Chapter 3, Table 3-3. Used the Doppler value of 0.03 mm/s over a 60 second average.

Trait Implementations

impl Clone for GaussMarkov

fn clone(&self) -> GaussMarkov

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl ConfigRepr for GaussMarkov

fn load<P>(path: P) -> Result<Self, ConfigError>

where P: AsRef<Path>,

Builds the configuration representation from the path to a yaml

fn load_many<P>(path: P) -> Result<Vec<Self>, ConfigError>

where P: AsRef<Path>,

Builds a sequence of “Selves” from the provided path to a yaml

fn load_named<P>(path: P) -> Result<BTreeMap<String, Self>, ConfigError>

where P: AsRef<Path>,

Builds a map of names to “selves” from the provided path to a yaml

fn loads_many(data: &str) -> Result<Vec<Self>, ConfigError>

Builds a sequence of “Selves” from the provided string of a yaml

fn loads_named(data: &str) -> Result<BTreeMap<String, Self>, ConfigError>

Builds a sequence of “Selves” from the provided string of a yaml

impl Debug for GaussMarkov

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

Formats the value using the given formatter.

impl<'a> Decode<'a> for GaussMarkov

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<'de> Deserialize<'de> for GaussMarkov

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

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Display for GaussMarkov

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

Formats the value using the given formatter.

impl Encode for GaussMarkov

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 GaussMarkov

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 GaussMarkov

type Target = GaussMarkov

The Python output type

type Output = Bound<'py, <GaussMarkov 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 GaussMarkov

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

Scale the Gauss Markov process by a constant, maintaining the same time constant.

type Output = GaussMarkov

The resulting type after applying the * operator.

impl MulAssign<f64> for GaussMarkov

fn mul_assign(&mut self, rhs: f64)

Performs the *= operation.

impl PartialEq for GaussMarkov

fn eq(&self, other: &GaussMarkov) -> 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 GaussMarkov

const NAME: &str = "GaussMarkov"

Name of the class.

type Frozen = False

Whether the pyclass is frozen.

impl PyClassImpl for GaussMarkov

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 = /// A first order Gauss-Markov process for modeling biases as described in section 5.2.4 of the NASA Best Practices for Navigation Filters (D'Souza et al.). /// /// The process is defined by the following stochastic differential equation: /// /// \dot{b(t)} = -1/τ * b(t) + w(t) /// /// Programmatically, it's calculated by sampling from b(t) ~ 𝓝(0, p_b(t)), where /// /// p_b(t) = exp((-2 / τ) * (t - t_0)) * p_b(t_0) + s(t - t_0) /// /// s(t - t_0) = ((q * τ) / 2) * (1 - exp((-2 / τ) * (t - t_0)))

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 = <<GaussMarkov as PyClassImpl>::BaseNativeType as PyClassBaseType>::Layout<GaussMarkov>

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 = Pyo3MethodsInventoryForGaussMarkov

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 PyTypeInfo for GaussMarkov

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 GaussMarkov

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

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl Stochastics for GaussMarkov

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

Return the next bias sample.

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

Return the variance of this stochastic noise model at a given time.

impl Copy for GaussMarkov

impl DerefToPyAny for GaussMarkov

impl StructuralPartialEq for GaussMarkov