Gravity Models¶

Gravity acceleration functions including point-mass and spherical harmonic models.

Note

For conceptual explanations and examples, see Gravity Models in the Learn section.

Point-Mass Gravity¶

accel_point_mass_gravity `builtin` ¶

accel_point_mass_gravity(r_object: ndarray, r_central_body: ndarray, gm: float) -> ndarray

Compute acceleration due to point-mass gravity.

Accepts either a 3D position vector or a 6D state vector for r_object.

Parameters:

Name	Type	Description	Default
`r_object`	`ndarray`	Position (length 3) or state (length 6) of the object. Units: (m)	required
`r_central_body`	`ndarray`	Position vector of the central body. Units: (m)	required
`gm`	`float`	Gravitational parameter. Units: (m³/s²)	required

Returns:

Type	Description
`ndarray`	np.ndarray: Acceleration due to gravity. Units: (m/s²)

Example

import brahe as bh
import numpy as np

r_object = np.array([bh.R_EARTH, 0.0, 0.0])
r_central_body = np.array([0.0, 0.0, 0.0])
a_grav = bh.accel_point_mass_gravity(r_object, r_central_body, bh.GM_EARTH)

Spherical Harmonic Gravity¶

accel_gravity_spherical_harmonics dispatches to whichever kernel the GravityModel's GravityModelCoefficients has precomputed (Clenshaw by default). accel_gravity_spherical_harmonics_clenshaw and accel_gravity_spherical_harmonics_cunningham force evaluation through a specific kernel instead of dispatching automatically.

accel_gravity_spherical_harmonics `builtin` ¶

accel_gravity_spherical_harmonics(r_eci: ndarray, R_i2b: ndarray, gravity_model: GravityModel, n_max: int, m_max: int) -> ndarray

Compute acceleration due to spherical harmonic gravity model.

This function computes the gravitational acceleration on an object using a spherical harmonic expansion of Earth's gravity field. It transforms the position to body-fixed coordinates, evaluates the spherical harmonics, and transforms the acceleration back to the inertial frame.

Accepts either a 3D position vector or a 6D state vector for r_eci.

Parameters:

Name	Type	Description	Default
`r_eci`	`ndarray`	Position (length 3) or state (length 6) in ECI frame. Units: (m)	required
`R_i2b`	`ndarray`	Rotation matrix from ECI to body-fixed frame (3x3)	required
`gravity_model`	`GravityModel`	Gravity model to use	required
`n_max`	`int`	Maximum degree to evaluate (n_max <= model.n_max)	required
`m_max`	`int`	Maximum order to evaluate (m_max <= min(n_max, model.m_max))	required

Returns:

Type	Description
`ndarray`	np.ndarray: Acceleration in ECI frame. Units: (m/s²)

Raises:

Type	Description
`ValueError`	If n_max or m_max exceed model limits or if m_max > n_max
`ValueError`	If the model has no precomputed gravity coefficients (neither Clenshaw nor Cunningham)
`ValueError`	If the Cunningham kernel is used (no Clenshaw coefficients present on the model) and its denormalized recursion overflows to a non-finite result (can occur above ~degree 150 at low altitude)

Example

import brahe as bh
import numpy as np

# Initialize EOP for frame transformations
bh.initialize_eop()

# Load gravity model
model = bh.GravityModel.from_model_type(bh.GravityModelType.JGM3)

# Create test position
r_eci = np.array([6525.919e3, 1710.416e3, 2508.886e3])

# Get rotation matrix (or use identity for simplified case)
R = np.eye(3)

# Compute acceleration
a_grav = bh.accel_gravity_spherical_harmonics(r_eci, R, model, 20, 20)
print(f"Acceleration: {a_grav} m/s²")

accel_gravity_spherical_harmonics_clenshaw `builtin` ¶

accel_gravity_spherical_harmonics_clenshaw(r_eci: ndarray, R_i2b: ndarray, gravity_model: GravityModel, n_max: int, m_max: int) -> ndarray

Compute acceleration due to spherical harmonic gravity model using the Clenshaw kernel.

Same behavior as accel_gravity_spherical_harmonics, but forces evaluation through the Clenshaw summation kernel rather than dispatching automatically.

Accepts either a 3D position vector or a 6D state vector for r_eci.

Parameters:

Name	Type	Description	Default
`r_eci`	`ndarray`	Position (length 3) or state (length 6) in ECI frame. Units: (m)	required
`R_i2b`	`ndarray`	Rotation matrix from ECI to body-fixed frame (3x3)	required
`gravity_model`	`GravityModel`	Gravity model to use	required
`n_max`	`int`	Maximum degree to evaluate (n_max <= model.n_max)	required
`m_max`	`int`	Maximum order to evaluate (m_max <= min(n_max, model.m_max))	required

Returns:

Type	Description
`ndarray`	np.ndarray: Acceleration in ECI frame. Units: (m/s²)

Raises:

Type	Description
`ValueError`	If Clenshaw coefficients are not precomputed for the model, or if n_max or m_max exceed model limits

Example

import brahe as bh
import numpy as np

bh.initialize_eop()
model = bh.GravityModel.from_model_type(bh.GravityModelType.JGM3)
r_eci = np.array([6525.919e3, 1710.416e3, 2508.886e3])
a_grav = bh.accel_gravity_spherical_harmonics_clenshaw(r_eci, np.eye(3), model, 20, 20)

accel_gravity_spherical_harmonics_cunningham `builtin` ¶

accel_gravity_spherical_harmonics_cunningham(r_eci: ndarray, R_i2b: ndarray, gravity_model: GravityModel, n_max: int, m_max: int) -> ndarray

Compute acceleration due to spherical harmonic gravity model using the Cunningham kernel.

Same behavior as accel_gravity_spherical_harmonics, but forces evaluation through the Cunningham V/W recursion kernel rather than dispatching automatically.

Accepts either a 3D position vector or a 6D state vector for r_eci.

Parameters:

Name	Type	Description	Default
`r_eci`	`ndarray`	Position (length 3) or state (length 6) in ECI frame. Units: (m)	required
`R_i2b`	`ndarray`	Rotation matrix from ECI to body-fixed frame (3x3)	required
`gravity_model`	`GravityModel`	Gravity model to use	required
`n_max`	`int`	Maximum degree to evaluate (n_max <= model.n_max)	required
`m_max`	`int`	Maximum order to evaluate (m_max <= min(n_max, model.m_max))	required

Returns:

Type	Description
`ndarray`	np.ndarray: Acceleration in ECI frame. Units: (m/s²)

Raises:

Type	Description
`ValueError`	If Cunningham coefficients are not precomputed for the model, if n_max or m_max exceed model limits, or if the recursion overflows to a non-finite result (can occur above ~degree 150 at low altitude)

Example

import brahe as bh
import numpy as np

bh.initialize_eop()
model = bh.GravityModel.from_model_type_with_coefficients(
    bh.GravityModelType.JGM3, bh.GravityModelCoefficients.Cunningham)
r_eci = np.array([6525.919e3, 1710.416e3, 2508.886e3])
a_grav = bh.accel_gravity_spherical_harmonics_cunningham(r_eci, np.eye(3), model, 20, 20)

Gravity Model Class¶

GravityModel ¶

GravityModel()

Spherical harmonic gravity model for high-fidelity gravitational acceleration computation.

This class represents a spherical harmonic expansion of Earth's gravitational potential, allowing for accurate modeling of Earth's non-uniform gravity field.

Initialize instance.

gm `property` ¶

gm: Any

Gravitational parameter. Units: (m³/s²)

m_max `property` ¶

m_max: Any

Maximum order of spherical harmonic expansion.

model_errors `property` ¶

model_errors: Any

Error estimation type.

model_name `property` ¶

model_name: Any

Name of the gravity model.

n_max `property` ¶

n_max: Any

Maximum degree of spherical harmonic expansion.

normalization `property` ¶

normalization: Any

Coefficient normalization convention.

radius `property` ¶

radius: Any

Reference radius. Units: (m)

tide_system `property` ¶

tide_system: Any

Tide system convention.

compute_spherical_harmonics `method descriptor` ¶

compute_spherical_harmonics(r_body: ndarray, n_max: int, m_max: int) -> ndarray

Compute gravitational acceleration in body-fixed frame using spherical harmonics.

Dispatches to whichever kernel this model has coefficients for. Clenshaw-first: if the model has precomputed Clenshaw coefficients (the default for every loader), evaluates with the Clenshaw summation kernel. Otherwise falls back to the Cunningham V/W recursion if Cunningham coefficients are present.

Parameters:

Name	Type	Description	Default
`r_body`	`ndarray`	Position vector in body-fixed frame. Units: (m)	required
`n_max`	`int`	Maximum degree to evaluate (n_max <= model.n_max)	required
`m_max`	`int`	Maximum order to evaluate (m_max <= min(n_max, model.m_max))	required

Returns:

Type	Description
`ndarray`	np.ndarray: Acceleration in body-fixed frame. Units: (m/s²)

Raises:

Type	Description
`ValueError`	If n_max or m_max exceed model limits or if m_max > n_max
`ValueError`	If the model has neither Clenshaw nor Cunningham coefficients
`ValueError`	If the Cunningham fallback is used and its denormalized recursion overflows to a non-finite result (can occur above ~degree 150 at low altitude)

Example

import brahe as bh
import numpy as np

model = bh.GravityModel.from_model_type(bh.GravityModelType.JGM3)
r_body = np.array([6525.919e3, 1710.416e3, 2508.886e3])

# Compute using 20x20 expansion
a_body = model.compute_spherical_harmonics(r_body, 20, 20)

compute_spherical_harmonics_clenshaw `method descriptor` ¶

compute_spherical_harmonics_clenshaw(r_body: ndarray, n_max: int, m_max: int) -> ndarray

Compute body-fixed gravitational acceleration with the Clenshaw kernel.

Parameters:

Name	Type	Description	Default
`r_body`	`ndarray`	Position in body-fixed frame (length 3). Units: (m)	required
`n_max`	`int`	Maximum degree to evaluate (n_max <= model.n_max)	required
`m_max`	`int`	Maximum order to evaluate (m_max <= min(n_max, model.m_max))	required

Returns:

Type	Description
`ndarray`	np.ndarray: Acceleration in body-fixed frame. Units: (m/s²)

Raises:

Type	Description
`ValueError`	If Clenshaw coefficients are not precomputed for this model, or if n_max or m_max exceed model limits

Example

import brahe as bh
import numpy as np

model = bh.GravityModel.from_model_type(bh.GravityModelType.JGM3)
a = model.compute_spherical_harmonics_clenshaw(
    np.array([bh.R_EARTH + 500e3, 0.0, 0.0]), 20, 20)

compute_spherical_harmonics_cunningham `method descriptor` ¶

compute_spherical_harmonics_cunningham(r_body: ndarray, n_max: int, m_max: int) -> ndarray

Compute body-fixed gravitational acceleration with the Cunningham kernel.

Parameters:

Name	Type	Description	Default
`r_body`	`ndarray`	Position in body-fixed frame (length 3). Units: (m)	required
`n_max`	`int`	Maximum degree to evaluate (n_max <= model.n_max)	required
`m_max`	`int`	Maximum order to evaluate (m_max <= min(n_max, model.m_max))	required

Returns:

Type	Description
`ndarray`	np.ndarray: Acceleration in body-fixed frame. Units: (m/s²)

Raises:

Type	Description
`ValueError`	If Cunningham coefficients are not precomputed for this model, if n_max or m_max exceed model limits, or if the recursion overflows to a non-finite result (can occur above ~degree 150 at low altitude)

Example

import brahe as bh
import numpy as np

model = bh.GravityModel.from_model_type_with_coefficients(
    bh.GravityModelType.JGM3, bh.GravityModelCoefficients.Cunningham)
a = model.compute_spherical_harmonics_cunningham(
    np.array([bh.R_EARTH + 500e3, 0.0, 0.0]), 20, 20)

convert_tide_system `method descriptor` ¶

convert_tide_system(from_system: GravityModelTideSystem, to_system: GravityModelTideSystem) -> Any

Convert the gravity model's tide system from one convention to another.

Adjusts the C̄₂₀ coefficient in-place so the model represents gravity in the target tide system. The conversion is exact for the permanent-tide component (IERS Conventions 2010, §6.2.2).

Parameters:

Name	Type	Description	Default
`from_system`	`GravityModelTideSystem`	The tide system the model is currently in.	required
`to_system`	`GravityModelTideSystem`	The tide system to convert to.	required

Raises:

Type	Description
`ValueError`	If `from_system` is `Unknown` (offset is undefined).

Example

import brahe as bh

model = bh.GravityModel.from_model_type(bh.GravityModelType.JGM3)
# Convert from tide-free to zero-tide convention
model.convert_tide_system(
    bh.GravityModelTideSystem.TideFree,
    bh.GravityModelTideSystem.ZeroTide,
)

drop_clenshaw_coefficients `method descriptor` ¶

drop_clenshaw_coefficients() -> Any

Drop the Clenshaw kernel coefficients, freeing their memory.

The Clenshaw kernel errors until precompute_clenshaw_coefficients() is called again.

Example

import brahe as bh

model = bh.GravityModel.from_model_type(bh.GravityModelType.JGM3)
model.drop_clenshaw_coefficients()

drop_cunningham_coefficients `method descriptor` ¶

drop_cunningham_coefficients() -> Any

Drop the Cunningham kernel coefficients, freeing their memory.

The Cunningham kernel errors until precompute_cunningham_coefficients() is called again.

Example

import brahe as bh

model = bh.GravityModel.from_model_type_with_coefficients(
    bh.GravityModelType.JGM3, bh.GravityModelCoefficients.Both)
model.drop_cunningham_coefficients()

from_file `builtin` ¶

from_file(filepath: str) -> GravityModel

Load gravity model from a .gfc file.

Parameters:

Name	Type	Description	Default
`filepath`	`str`	Path to the .gfc gravity model file	required

Returns:

Name	Type	Description
`GravityModel`	`GravityModel`	Loaded gravity model

Raises:

Type	Description
`Exception`	If file cannot be loaded or parsed

Example

import brahe as bh

# Load custom gravity model from file
model = bh.GravityModel.from_file("path/to/model.gfc")
print(f"Model: {model.model_name}, {model.n_max}x{model.m_max}")

from_file_with_coefficients `builtin` ¶

from_file_with_coefficients(filepath: str, coefficients: GravityModelCoefficients) -> GravityModel

Load a gravity model from a .gfc file with an explicit coefficient configuration.

Parameters:

Name	Type	Description	Default
`filepath`	`str`	Path to the .gfc gravity model file	required
`coefficients`	`GravityModelCoefficients`	Which precomputed evaluation coefficient set(s) to build	required

Returns:

Name	Type	Description
`GravityModel`	`GravityModel`	Loaded gravity model

Raises:

Type	Description
`Exception`	If file cannot be loaded or parsed

Example

import brahe as bh

model = bh.GravityModel.from_file_with_coefficients(
    "path/to/model.gfc", bh.GravityModelCoefficients.Both)

from_model_type `builtin` ¶

from_model_type(model_type: GravityModelType) -> GravityModel

Load a gravity model from a GravityModelType.

Parameters:

Name	Type	Description	Default
`model_type`	`GravityModelType`	Which model to load (packaged or from file)	required

Returns:

Name	Type	Description
`GravityModel`	`GravityModel`	Loaded gravity model

Raises:

Type	Description
`Exception`	If file loading fails (for FromFile variant)

Example

import brahe as bh

# Load JGM3 70x70 model
model = bh.GravityModel.from_model_type(bh.GravityModelType.JGM3)
print(f"Loaded {model.model_name}")

# Load EGM2008 120x120 model
model_hifi = bh.GravityModel.from_model_type(bh.GravityModelType.EGM2008_120)

# Load from custom file
custom_type = bh.GravityModelType.from_file("/path/to/model.gfc")
model_custom = bh.GravityModel.from_model_type(custom_type)

from_model_type_with_coefficients `builtin` ¶

from_model_type_with_coefficients(model_type: GravityModelType, coefficients: GravityModelCoefficients) -> GravityModel

Load a gravity model from a GravityModelType with an explicit coefficient configuration.

Parameters:

Name	Type	Description	Default
`model_type`	`GravityModelType`	Which model to load (packaged or from file)	required
`coefficients`	`GravityModelCoefficients`	Which precomputed evaluation coefficient set(s) to build	required

Returns:

Name	Type	Description
`GravityModel`	`GravityModel`	Loaded gravity model

Raises:

Type	Description
`Exception`	If file loading fails (for FromFile variant)

Example

import brahe as bh

model = bh.GravityModel.from_model_type_with_coefficients(
    bh.GravityModelType.JGM3, bh.GravityModelCoefficients.Both)
print(model.has_clenshaw_coefficients(), model.has_cunningham_coefficients())

get `method descriptor` ¶

get(n: int, m: int) -> Tuple

Get spherical harmonic coefficients for a specific degree and order.

Parameters:

Name	Type	Description	Default
`n`	`int`	Degree (0 <= n <= n_max)	required
`m`	`int`	Order (0 <= m <= min(n, m_max))	required

Returns:

Name	Type	Description
`tuple`	`Tuple`	(C_nm, S_nm) coefficients

Raises:

Type	Description
`Exception`	If n or m are out of bounds

Example

import brahe as bh

model = bh.GravityModel.from_model_type(bh.GravityModelType.JGM3)

# Get J2 coefficient (C20)
c20, s20 = model.get(2, 0)
print(f"J2 = {-c20}")

get_c `method descriptor` ¶

get_c(n: int, m: int) -> float

Get the cosine coefficient C_nm for a specific degree and order.

Parameters:

Name	Type	Description	Default
`n`	`int`	Degree (0 <= n <= n_max)	required
`m`	`int`	Order (0 <= m <= min(n, m_max))	required

Returns:

Name	Type	Description
`float`	`float`	The C_nm coefficient.

Raises:

Type	Description
`ValueError`	If n or m are out of bounds

Example

import brahe as bh

model = bh.GravityModel.from_model_type(bh.GravityModelType.JGM3)

# Get J2 coefficient (C20)
c20 = model.get_c(2, 0)
print(f"J2 = {-c20}")

get_s `method descriptor` ¶

get_s(n: int, m: int) -> float

Get the sine coefficient S_nm for a specific degree and order.

Parameters:

Name	Type	Description	Default
`n`	`int`	Degree (0 <= n <= n_max)	required
`m`	`int`	Order (0 <= m <= min(n, m_max))	required

Returns:

Name	Type	Description
`float`	`float`	The S_nm coefficient (0.0 for m == 0).

Raises:

Type	Description
`ValueError`	If n or m are out of bounds

Example

import brahe as bh

model = bh.GravityModel.from_model_type(bh.GravityModelType.JGM3)

s22 = model.get_s(2, 2)
print(f"S22 = {s22}")

has_clenshaw_coefficients `method descriptor` ¶

has_clenshaw_coefficients() -> bool

Check whether the Clenshaw kernel coefficients are precomputed for this model.

Returns:

Name	Type	Description
`bool`	`bool`	True when the coefficient set is present.

Example

import brahe as bh

model = bh.GravityModel.from_model_type(bh.GravityModelType.JGM3)
assert model.has_clenshaw_coefficients()

has_cunningham_coefficients `method descriptor` ¶

has_cunningham_coefficients() -> bool

Check whether the Cunningham kernel coefficients are precomputed for this model.

Returns:

Name	Type	Description
`bool`	`bool`	True when the coefficient set is present.

Example

import brahe as bh

model = bh.GravityModel.from_model_type(bh.GravityModelType.JGM3)
assert not model.has_cunningham_coefficients()

load_uncached `builtin` ¶

load_uncached(model_type: GravityModelType) -> GravityModel

Parse a GravityModelType directly from its source, bypassing the process-wide cache.

Most callers should prefer :meth:from_model_type, which is cache-backed and avoids re-parsing the model on repeat calls. Reach for load_uncached when you need deterministic memory (each call allocates its own coefficients), to profile cold-load performance, or to re-read a FromFile source whose contents changed on disk (the cache would otherwise return the stale model).

Builds Clenshaw coefficients only. Call :meth:precompute_cunningham_coefficients on the returned model for a different coefficient configuration.

Parameters:

Name	Type	Description	Default
`model_type`	`GravityModelType`	Which model to load (packaged or from file)	required

Returns:

Name	Type	Description
`GravityModel`	`GravityModel`	Freshly parsed gravity model

Raises:

Type	Description
`Exception`	If file loading fails (for FromFile variant)

Example

import brahe as bh

model = bh.GravityModel.load_uncached(bh.GravityModelType.JGM3)
print(f"Loaded {model.model_name}")

precompute_clenshaw_coefficients `method descriptor` ¶

precompute_clenshaw_coefficients() -> Any

Precompute the Clenshaw kernel coefficients, if not already present.

Example

import brahe as bh

model = bh.GravityModel.from_model_type_with_coefficients(
    bh.GravityModelType.JGM3, bh.GravityModelCoefficients.Cunningham)
model.precompute_clenshaw_coefficients()

precompute_cunningham_coefficients `method descriptor` ¶

precompute_cunningham_coefficients() -> Any

Precompute the Cunningham kernel coefficients, if not already present.

Example

import brahe as bh

model = bh.GravityModel.from_model_type(bh.GravityModelType.JGM3)
model.precompute_cunningham_coefficients()

set_max_degree_order `method descriptor` ¶

set_max_degree_order(n: int, m: int) -> Any

Truncate the gravity model to a smaller degree and order to save memory.

This method resizes the internal coefficient matrix in-place, discarding coefficients beyond the specified limits. This is useful when loading a high-fidelity model but only needing a lower-degree expansion.

Parameters:

Name	Type	Description	Default
`n`	`int`	New maximum degree (must be <= current n_max)	required
`m`	`int`	New maximum order (must be <= min(n, current m_max))	required

Raises:

Type	Description
`ValueError`	If m > n or if n/m exceed current model limits

Example

import brahe as bh

# Load full EGM2008 120x120 model
model = bh.GravityModel.from_model_type(bh.GravityModelType.EGM2008_120)
print(f"Before: {model.n_max}x{model.m_max}")  # 120x120

# Truncate to 70x70 to save memory
model.set_max_degree_order(70, 70)
print(f"After: {model.n_max}x{model.m_max}")  # 70x70

Gravity Model Coefficients¶

GravityModelCoefficients selects which kernel's precomputed coefficient set(s) a GravityModel builds when it is loaded (GravityModel.from_model_type / from_file default to GravityModelCoefficients.Clenshaw; from_model_type_with_coefficients / from_file_with_coefficients accept an explicit value): GravityModelCoefficients.Clenshaw (default), .Cunningham, or .Both. A model can only evaluate through a kernel whose coefficient set is present; GravityModel.precompute_clenshaw_coefficients() / precompute_cunningham_coefficients() add a coefficient set to an already-loaded model, and drop_clenshaw_coefficients() / drop_cunningham_coefficients() free one to reduce memory use.

GravityModelCoefficients ¶

GravityModelCoefficients()

Selects which precomputed coefficient set(s) a gravity model builds at load.

The Clenshaw and Cunningham kernels require different precomputed values, so each kernel can only run when its coefficient set is present. Default: Clenshaw.

Initialize instance.

Both `class-attribute` ¶

Both: Any = GravityModelCoefficients.Both

Selects which precomputed coefficient set(s) a gravity model builds at load.

The Clenshaw and Cunningham kernels require different precomputed values, so each kernel can only run when its coefficient set is present. Default: Clenshaw.

Clenshaw `class-attribute` ¶

Clenshaw: Any = GravityModelCoefficients.Clenshaw

Selects which precomputed coefficient set(s) a gravity model builds at load.

The Clenshaw and Cunningham kernels require different precomputed values, so each kernel can only run when its coefficient set is present. Default: Clenshaw.

Cunningham `class-attribute` ¶

Cunningham: Any = GravityModelCoefficients.Cunningham

Selects which precomputed coefficient set(s) a gravity model builds at load.

The Clenshaw and Cunningham kernels require different precomputed values, so each kernel can only run when its coefficient set is present. Default: Clenshaw.

Gravity Model Type¶

GravityModelType selects which spherical harmonic field is loaded into a GravityModel (or wired into a GravityConfiguration). In addition to the packaged constants EGM2008_120, GGM05S, and JGM3, two constructors load external models:

GravityModelType.from_file(path) — load any .gfc file from disk.
GravityModelType.icgem(body, name) — reference any model from the ICGEM catalog; the matching .gfc file is downloaded into $BRAHE_CACHE/icgem/models/<body>/ on first use and cached permanently.

The same GravityModelType can be passed to GravityConfiguration.spherical_harmonic(model_type=...) to use the model as the central-body field in a NumericalOrbitPropagator. For end-to-end usage examples see the ICGEM dataset guide and the Gravity Models user guide.

GravityModelType ¶

GravityModelType()

Default gravity models packaged with Brahe.

These models provide varying levels of fidelity for Earth's gravitational field. Models can either be packaged with Brahe or loaded from external files.

Initialize instance.

EGM2008_120 `class-attribute` ¶

EGM2008_120: Any = GravityModelType.EGM2008_120

Default gravity models packaged with Brahe.

These models provide varying levels of fidelity for Earth's gravitational field. Models can either be packaged with Brahe or loaded from external files.

GGM05S `class-attribute` ¶

GGM05S: Any = GravityModelType.GGM05S

Default gravity models packaged with Brahe.

These models provide varying levels of fidelity for Earth's gravitational field. Models can either be packaged with Brahe or loaded from external files.

JGM3 `class-attribute` ¶

JGM3: Any = GravityModelType.JGM3

Default gravity models packaged with Brahe.

These models provide varying levels of fidelity for Earth's gravitational field. Models can either be packaged with Brahe or loaded from external files.

from_file `staticmethod` ¶

from_file(filepath: str) -> GravityModelType

Load a custom gravity model from a file.

Validates that the file exists before returning the model type.

Parameters:

Name	Type	Description	Default
`filepath`	`str`	Path to the gravity model file in GFC format.	required

Returns:

Name	Type	Description
`GravityModelType`	`GravityModelType`	Gravity model type for loading from file.

Raises:

Type	Description
`FileNotFoundError`	If the file does not exist.
`IsADirectoryError`	If the path is a directory, not a file.

Example

import brahe as bh

model_type = bh.GravityModelType.from_file("path/to/custom_model.gfc")
model = bh.GravityModel.from_model_type(model_type)

icgem `staticmethod` ¶

icgem(body: str, name: str) -> GravityModelType

Build a GravityModelType referencing an ICGEM model.

Parameters:

Name	Type	Description	Default
`body`	`str`	Body name ("earth", "moon", "mars", "venus", "ceres", or other).	required
`name`	`str`	Model name, optionally with `-DEGREE` suffix.	required

Returns:

Name	Type	Description
`GravityModelType`	`GravityModelType`	A new GravityModelType referencing the ICGEM model.

Example

import brahe as bh

model_type = bh.GravityModelType.icgem("earth", "JGM3")
model = bh.GravityModel.from_model_type(model_type)

Gravity Models¶

Point-Mass Gravity¶

accel_point_mass_gravity builtin ¶

Spherical Harmonic Gravity¶

accel_gravity_spherical_harmonics builtin ¶

accel_gravity_spherical_harmonics_clenshaw builtin ¶

accel_gravity_spherical_harmonics_cunningham builtin ¶

Gravity Model Class¶

GravityModel ¶

gm property ¶

m_max property ¶

model_errors property ¶

model_name property ¶

n_max property ¶

normalization property ¶

radius property ¶

tide_system property ¶

compute_spherical_harmonics method descriptor ¶

compute_spherical_harmonics_clenshaw method descriptor ¶

compute_spherical_harmonics_cunningham method descriptor ¶

convert_tide_system method descriptor ¶

drop_clenshaw_coefficients method descriptor ¶

drop_cunningham_coefficients method descriptor ¶

from_file builtin ¶

from_file_with_coefficients builtin ¶

from_model_type builtin ¶

from_model_type_with_coefficients builtin ¶

get method descriptor ¶

get_c method descriptor ¶

get_s method descriptor ¶

has_clenshaw_coefficients method descriptor ¶

has_cunningham_coefficients method descriptor ¶

load_uncached builtin ¶

precompute_clenshaw_coefficients method descriptor ¶

precompute_cunningham_coefficients method descriptor ¶

set_max_degree_order method descriptor ¶

Gravity Model Coefficients¶

GravityModelCoefficients ¶

Both class-attribute ¶

Clenshaw class-attribute ¶

Cunningham class-attribute ¶

Gravity Model Type¶

GravityModelType ¶

EGM2008_120 class-attribute ¶

GGM05S class-attribute ¶

JGM3 class-attribute ¶

from_file staticmethod ¶

icgem staticmethod ¶

See Also¶

accel_point_mass_gravity `builtin` ¶

accel_gravity_spherical_harmonics `builtin` ¶

accel_gravity_spherical_harmonics_clenshaw `builtin` ¶

accel_gravity_spherical_harmonics_cunningham `builtin` ¶

gm `property` ¶

m_max `property` ¶

model_errors `property` ¶

model_name `property` ¶

n_max `property` ¶

normalization `property` ¶

radius `property` ¶

tide_system `property` ¶

compute_spherical_harmonics `method descriptor` ¶

compute_spherical_harmonics_clenshaw `method descriptor` ¶

compute_spherical_harmonics_cunningham `method descriptor` ¶

convert_tide_system `method descriptor` ¶

drop_clenshaw_coefficients `method descriptor` ¶

drop_cunningham_coefficients `method descriptor` ¶

from_file `builtin` ¶

from_file_with_coefficients `builtin` ¶

from_model_type `builtin` ¶

from_model_type_with_coefficients `builtin` ¶

get `method descriptor` ¶

get_c `method descriptor` ¶

get_s `method descriptor` ¶

has_clenshaw_coefficients `method descriptor` ¶

has_cunningham_coefficients `method descriptor` ¶

load_uncached `builtin` ¶

precompute_clenshaw_coefficients `method descriptor` ¶

precompute_cunningham_coefficients `method descriptor` ¶

set_max_degree_order `method descriptor` ¶

Both `class-attribute` ¶

Clenshaw `class-attribute` ¶

Cunningham `class-attribute` ¶

EGM2008_120 `class-attribute` ¶

GGM05S `class-attribute` ¶

JGM3 `class-attribute` ¶

from_file `staticmethod` ¶

icgem `staticmethod` ¶