Mondaic DocumentationRequired
model_a| Type: | UnstructuredModel |
| Description: | First model. |
Version:
2026.5.0
Salvus documentation version
2026.5.0
2025.1.3
2024.1.3
0.12.16
No results found.
salvus.opt.models.unstructured_model
salvus.opt.models.unstructured_model salvus opt models unstructured_modelAn unstructured mesh model.
Functions
retrieve_valence_and_mass_matrix()
Get the valence and mass matrix from the model that has them.
This method will check both models for presence of the valence and mass
matrix as fields, and fall back to computing them if neither model has
them.
If the model should deal with a region_of_interest, it should be
accounted for in the model fields and on-mesh fields already, and the
on-the-fly computation will not return appropriate values.
SIGNATURE
def retrieve_valence_and_mass_matrix(
model_a: UnstructuredModel, model_b: UnstructuredModel
) -> tuple[npt.NDArray, npt.NDArray]: ...ARGUMENTS
Required
model_b| Type: | UnstructuredModel |
| Description: | Second model. |
RETURNS
Return type:
tuple[npt.NDArray, npt.NDArray]A tuple containing the valence and mass matrix (FemMassMatrix as defined by SalvusCompute) from the model that has them, otherwise computed on the fly.
Classes
UnstructuredModel
Unstructured mesh inversion model.
SIGNATURE
class UnstructuredModel(salvus.opt.models.base_model.BaseModel):
def __init__(
self,
name: str,
model: str | pathlib.Path | UnstructuredMesh,
fields: list[str],
): ...ARGUMENTS
Required
name| Type: | str |
| Description: | Name of the model. |
Required
model| Type: | str | pathlib.Path | UnstructuredMesh |
| Description: | The unstructured mesh. |
Required
fields| Type: | list[str] |
| Description: | Names of the elemental fields to use. |
Properties
- FemMassMatrix(
npt.NDArray[np.float64])-The mass matrix as SalvusCompute would write it. - mass_matrix(
npt.NDArray[np.float64])-The mass matrix as defined on the mesh, without valence scaling. - model(
UnstructuredMesh)-The actual dataset representing the model. - name(
str)-Model name. - status(
ModelStatus)-Model status to keep track of whether the model has been modified. - valence(
npt.NDArray[np.float64])-The valence as defined on the mesh. - vector(
npt.NDArray[np.float64])-The unstructured mesh model as a vector.
Class Methods
UnstructuredModel.from_disk()
Load a model from disk. This will use the dictionary and path provided.
SIGNATURE
def from_disk(parent_folder: pathlib.Path, d: dict) -> Self: ...ARGUMENTS
Required
parent_folder| Type: | pathlib.Path |
| Description: | Path to store the model. |
Required
d| Type: | dict |
| Description: | Dictionary containing meta information of the model. |
UnstructuredModel.from_json()
Create a model from a dictionary.
SIGNATURE
def from_json(d: dict) -> Self: ...ARGUMENTS
Required
d| Type: | dict |
| Description: | Dictionary containing meta information of the model. |
Methods
UnstructuredModel.compute_inf_norm()
Infinity norm of the model.
SIGNATURE
def compute_inf_norm(self) -> float: ...UnstructuredModel.compute_norm()
Return the model norm.
SIGNATURE
def compute_norm(self) -> float: ...UnstructuredModel.copy()
Copy the model.
Will load the model from disk if necessary, and leave it in memory.
SIGNATURE
def copy(self, name: str) -> Self: ...ARGUMENTS
Required
name| Type: | str |
| Description: | Name of the new model. |
UnstructuredModel.dot()
Dot product of the model with another model.
SIGNATURE
def dot(self, other: Self) -> float: ...ARGUMENTS
Required
other| Type: | Self |
| Description: | The other model. |
UnstructuredModel.max()
In-place point-wise maximum.
SIGNATURE
def max(self, other: Self) -> Self: ...ARGUMENTS
Required
other| Type: | Self |
| Description: | Model to compare with. |
UnstructuredModel.min()
In-place point-wise minimum.
SIGNATURE
def min(self, other: Self) -> Self: ...ARGUMENTS
Required
other| Type: | Self |
| Description: | Model to compare with. |
UnstructuredModel.set_vector()
Set the model from a vector.
SIGNATURE
def set_vector(self, vector: npt.NDArray[np.float64]) -> None: ...ARGUMENTS
Required
vector| Type: | npt.NDArray[np.float64] |
| Description: | The vector to set the model from. |
UnstructuredModel.to_disk()
Serialize the model to JSON (and an associated npy file).
SIGNATURE
def to_disk(self, parent_folder: pathlib.Path | str) -> dict: ...ARGUMENTS
Required
parent_folder| Type: | pathlib.Path | str |
| Description: | Parent folder where the JSON + h5 files should be stored. |
UnstructuredModel.to_json()
Serialize the object to a dictionary that can be written to JSON.
SIGNATURE
def to_json(
self,
external_file_hash: types = None,
timer: types = None,
log_to_logger: bool = False,
comm: types = None,
) -> builtins.dict: ...ARGUMENTS
Optional
external_file_hash| Type: | types |
| Default value: | None |
| Description: | Hash of any external files associated with this object. Can be passed here in which case it will be stored in a centralized location in the JSON file. |
Optional
timer| Type: | types |
| Default value: | None |
| Description: | Execution timer. |
Optional
log_to_logger| Type: | bool |
| Default value: | False |
| Description: | Log timings to the logger. |
Optional
comm| Type: | types |
| Default value: | None |
| Description: | MPI communicator, if any. |
Used in tutorials
Functionality from this module is used in the following tutorials.
We use cookies to analyze our traffic, but won't track you until explicit consent is given. Using our contact form will create relevant cookies and transmit data to third party services to keep track of your request. Read more about how we use personal information in our Privacy Policy.
PAGE CONTENTS