Machines#

Base classes#

class Machine(**data)[source]#

Bases: PyRadPlanBaseModel

Base class for Machine objects.

Defines minimum meta-data a machine must hold:

radiation_mode#

The radiation mode of the machine.

Type:: str

description#

The description of the machine.

Type:: str

machine#

The name of the machine.

Type:: str

Attributes:

model_extra: Get extra fields set during validation.
model_fields_set: Returns the set of fields that have been explicitly set on this model instance.

Methods

`copy`(*[, include, exclude, update, deep])	Returns a copy of the model.
`model_construct`([_fields_set])	Creates a new instance of the Model class with validated data.
`model_copy`(*[, update, deep])	!!! abstract "Usage Documentation"
`model_dump`(*[, mode, include, exclude, ...])	!!! abstract "Usage Documentation"
`model_dump_json`(*[, indent, ensure_ascii, ...])	!!! abstract "Usage Documentation"
`model_json_schema`([by_alias, ref_template, ...])	Generates a JSON schema for a model class.
`model_parametrized_name`(params)	Compute the class name for parametrizations of generic classes.
`model_post_init`(context, /)	This function is meant to behave like a BaseModel method to initialize private attributes.
`model_rebuild`(*[, force, raise_errors, ...])	Try to rebuild the pydantic-core schema for the model.
`model_validate`(obj, *[, strict, extra, ...])	Validate a pydantic model instance.
`model_validate_json`(json_data, *[, strict, ...])	!!! abstract "Usage Documentation"
`model_validate_strings`(obj, *[, strict, ...])	Validate the given object with string data against the Pydantic model.
`to_matrad`([context])	Perform matRad compatible serialization.

construct
dict
from_orm
json
parse_file
parse_obj
parse_raw
schema
schema_json
update_forward_refs
validate
validate_datetime_variants

created_by: str | None#

created_on: datetime | None#

data_type: str | None#

description: str#

last_modified: datetime | None#

last_modified_by: str | None#

model_config: ClassVar[ConfigDict] = {'alias_generator': AliasGenerator(alias=<function to_camel>, validation_alias=None, serialization_alias=None), 'arbitrary_types_allowed': True, 'from_attributes': True, 'populate_by_name': True, 'validate_assignment': True, 'validate_by_alias': True, 'validate_by_name': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_post_init(context, /)#

This function is meant to behave like a BaseModel method to initialize private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters:

self (BaseModel) – The BaseModel instance.
context (Any) – The context.

Return type:

None

name: Annotated[str, StringConstraints(strip_whitespace=None, to_upper=None, to_lower=None, strict=None, min_length=1, max_length=None, pattern=None, ascii_only=None)]#

radiation_mode: str#

classmethod validate_datetime_variants(v)[source]#

version: int#

class ExternalBeamMachine(**data)[source]#

Bases: Machine

Base class for Machine used for external irradiation.

sad#

The source-to-axis distance of the machine

Type:: float

energies#

List of available photon energies

Type:: list[float]

Attributes:

model_extra: Get extra fields set during validation.
model_fields_set: Returns the set of fields that have been explicitly set on this model instance.

Methods

`copy`(*[, include, exclude, update, deep])	Returns a copy of the model.
`get_closest_energy_index`(energy)	Given an energy value, return the closest matching index.
`get_energy_index`(energy[, round_decimals])	Given an energy value, return the index of the exact matching.
`model_construct`([_fields_set])	Creates a new instance of the Model class with validated data.
`model_copy`(*[, update, deep])	!!! abstract "Usage Documentation"
`model_dump`(*[, mode, include, exclude, ...])	!!! abstract "Usage Documentation"
`model_dump_json`(*[, indent, ensure_ascii, ...])	!!! abstract "Usage Documentation"
`model_json_schema`([by_alias, ref_template, ...])	Generates a JSON schema for a model class.
`model_parametrized_name`(params)	Compute the class name for parametrizations of generic classes.
`model_post_init`(context, /)	This function is meant to behave like a BaseModel method to initialize private attributes.
`model_rebuild`(*[, force, raise_errors, ...])	Try to rebuild the pydantic-core schema for the model.
`model_validate`(obj, *[, strict, extra, ...])	Validate a pydantic model instance.
`model_validate_json`(json_data, *[, strict, ...])	!!! abstract "Usage Documentation"
`model_validate_strings`(obj, *[, strict, ...])	Validate the given object with string data against the Pydantic model.
`to_matrad`([context])	Perform matRad compatible serialization.
`validate_machine_input`(data)	Validate the input data for the machine model before passing to pydantic.

construct
dict
from_orm
json
parse_file
parse_obj
parse_raw
schema
schema_json
update_forward_refs
validate
validate_datetime_variants

energies: float64]#

get_closest_energy_index(energy)[source]#

Given an energy value, return the closest matching index.

Parameters:: energy (float) – Energy value to search for
Returns:: Index of the energy level
Return type:: int

get_energy_index(energy, round_decimals=4)[source]#

Given an energy value, return the index of the exact matching.

Parameters:

energy (float) – Energy value to search for
round_decimals (int, optional) – Number of decimals to round the energy values to, by default 4

Returns:

Index of the energy level

Return type:

Optional[int]

model_config: ClassVar[ConfigDict] = {'alias_generator': AliasGenerator(alias=<function to_camel>, validation_alias=None, serialization_alias=None), 'arbitrary_types_allowed': True, 'from_attributes': True, 'populate_by_name': True, 'validate_assignment': True, 'validate_by_alias': True, 'validate_by_name': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_post_init(context, /)#

This function is meant to behave like a BaseModel method to initialize private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters:

self (BaseModel) – The BaseModel instance.
context (Any) – The context.

Return type:

None

sad: float#

classmethod validate_machine_input(data)[source]#

Validate the input data for the machine model before passing to pydantic.

Return type:: Any

Photons#

class PhotonLINAC(**data)[source]#

Bases: ExternalBeamMachine

Base Class for Photon LINAC-like Machines.

Defines minimum meta-data a photon LINAC machine must hold Provides multiple data storage formats, currently supported - SVD Kernel Data (Bortfeld 1993, 10.1118/1.597070).

scd#

The source-to-collimator distance of the machine

Type:: float

pb_kernels#

Pencil-beam kernels for the machine

Type:: dict[float, PhotonSVDKernel]

of#

Output factor data

Type:: np.ndarray

tpr#

Tissue-to-phantom ratio data

Type:: np.ndarray

Attributes:

model_extra: Get extra fields set during validation.
model_fields_set: Returns the set of fields that have been explicitly set on this model instance.

Methods

`copy`(*[, include, exclude, update, deep])	Returns a copy of the model.
`get_closest_energy_index`(energy)	Given an energy value, return the closest matching index.
`get_energy_index`(energy[, round_decimals])	Given an energy value, return the index of the exact matching.
`get_kernel_by_energy`(energy)	Get the pencil beam kernel for a specific energy value.
`get_kernel_by_index`(ix_energy)	Get the pencil beam kernel for a specific energy index.
`model_construct`([_fields_set])	Creates a new instance of the Model class with validated data.
`model_copy`(*[, update, deep])	!!! abstract "Usage Documentation"
`model_dump`(*[, mode, include, exclude, ...])	!!! abstract "Usage Documentation"
`model_dump_json`(*[, indent, ensure_ascii, ...])	!!! abstract "Usage Documentation"
`model_json_schema`([by_alias, ref_template, ...])	Generates a JSON schema for a model class.
`model_parametrized_name`(params)	Compute the class name for parametrizations of generic classes.
`model_post_init`(context, /)	This function is meant to behave like a BaseModel method to initialize private attributes.
`model_rebuild`(*[, force, raise_errors, ...])	Try to rebuild the pydantic-core schema for the model.
`model_validate`(obj, *[, strict, extra, ...])	Validate a pydantic model instance.
`model_validate_json`(json_data, *[, strict, ...])	!!! abstract "Usage Documentation"
`model_validate_strings`(obj, *[, strict, ...])	Validate the given object with string data against the Pydantic model.
`to_matrad`([context])	Perform matRad compatible serialization.
`validate_machine_input`(data)	Validate the input data for the machine model before passing to pydantic.

construct
dict
from_orm
json
parse_file
parse_obj
parse_raw
schema
schema_json
update_forward_refs
validate
validate_datetime_variants
validate_pb_kernels

get_kernel_by_energy(energy)[source]#

Get the pencil beam kernel for a specific energy value.

Parameters:: energy (float) – Energy value to search for
Returns:: Pencil beam kernel data
Return type:: PhotonSVDKernel

get_kernel_by_index(ix_energy)[source]#

Get the pencil beam kernel for a specific energy index.

Parameters:: ix_energy (int) – Index of the energy level in the stored data
Returns:: Pencil beam kernel data
Return type:: PhotonSVDKernel

key: str#

model_config: ClassVar[ConfigDict] = {'alias_generator': AliasGenerator(alias=<function to_camel>, validation_alias=None, serialization_alias=None), 'arbitrary_types_allowed': True, 'from_attributes': True, 'populate_by_name': True, 'validate_assignment': True, 'validate_by_alias': True, 'validate_by_name': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_post_init(context, /)#

This function is meant to behave like a BaseModel method to initialize private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters:

self (BaseModel) – The BaseModel instance.
context (Any) – The context.

Return type:

None

name: str#

of: ndarray[tuple[Any, ...], dtype[_ScalarT]] | None#

pb_kernels: dict[float, PhotonSVDKernel] | None#

radiation_mode: str#

scd: float#

tpr: ndarray[tuple[Any, ...], dtype[_ScalarT]] | None#

classmethod validate_pb_kernels(v, handler, info)[source]#

Return type:: PhotonSVDKernel

class PhotonSVDKernel(**data)[source]#

Bases: PyRadPlanBaseModel

Kernel data for photon beams.

energy#

The maximum energy of the photon beam in MeV (corresponds to the LINAC voltage in MV).

Type:: float

Attributes:

model_extra: Get extra fields set during validation.
model_fields_set: Returns the set of fields that have been explicitly set on this model instance.
num_kernel_components

Methods

`copy`(*[, include, exclude, update, deep])	Returns a copy of the model.
`get_kernels_at_ssd`(ssd)	Get the kernel components at a specific SSD value.
`model_construct`([_fields_set])	Creates a new instance of the Model class with validated data.
`model_copy`(*[, update, deep])	!!! abstract "Usage Documentation"
`model_dump`(*[, mode, include, exclude, ...])	!!! abstract "Usage Documentation"
`model_dump_json`(*[, indent, ensure_ascii, ...])	!!! abstract "Usage Documentation"
`model_json_schema`([by_alias, ref_template, ...])	Generates a JSON schema for a model class.
`model_parametrized_name`(params)	Compute the class name for parametrizations of generic classes.
`model_post_init`(context, /)	Override this method to perform additional initialization after __init__ and model_construct.
`model_rebuild`(*[, force, raise_errors, ...])	Try to rebuild the pydantic-core schema for the model.
`model_validate`(obj, *[, strict, extra, ...])	Validate a pydantic model instance.
`model_validate_json`(json_data, *[, strict, ...])	!!! abstract "Usage Documentation"
`model_validate_strings`(obj, *[, strict, ...])	Validate the given object with string data against the Pydantic model.
`to_matrad`([context])	Perform matRad compatible serialization.

construct
dict
from_orm
json
kernel_interpolator
parse_file
parse_obj
parse_raw
schema
schema_json
update_forward_refs
validate
validate_kernel_data
validate_kernel_data_shapes

energy: float#

get_kernels_at_ssd(ssd)[source]#

Get the kernel components at a specific SSD value.

Parameters:: ssd (float) – The SSD value to search for.
Returns:: The kernels at the specified SSD value.
Return type:: NDArray[Shape[1-, 1-], float64]

kernel_betas: float64]#

kernel_data: float64]#

kernel_interpolator()[source]#

kernel_pos: float64]#

kernel_ssds: float64]#

m: float#

model_config: ClassVar[ConfigDict] = {'alias_generator': AliasGenerator(alias=<function to_camel>, validation_alias=None, serialization_alias=None), 'arbitrary_types_allowed': True, 'from_attributes': True, 'populate_by_name': True, 'validate_assignment': True, 'validate_by_alias': True, 'validate_by_name': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

property num_kernel_components#

penumbra: float#

primary_fluence: float64]#

validate_kernel_data()[source]#

Return type:: Self

classmethod validate_kernel_data_shapes(v, handler, info)[source]#

Return type:: NDArray

Ions#

class IonAccelerator(**data)[source]#

Bases: ParticleAccelerator

Machine Model for Ion Accelerators.

Defines minimum meta-data an ion machine must hold Provides multiple data storage formats, currently supported - Pencil-Beam Kernels - Monte Carlo Beam Models.

sad#

The source-to-axis (-isocenter) distance of the machine

Type:: float

Attributes:

has_alpha_beta_kernels: Check if it has alpha-beta kernels.
has_double_gaussian_kernel: Check if it has double gaussian pencil-beamkernels.
has_focused_gaussian_kernel: Check if it has focused gaussian pencil-beamkernels.
has_let_kernel: Check if it has LET values in the pencil-beam kernels.
has_multi_gaussian_kernel: Check if it has multi gaussian pencil-beamkernels.
has_pb_kernels: Test for existence of valid pencil-beam kernels.
has_single_gaussian_kernel: Check if it has single gaussian pencil-beamkernels.
model_extra: Get extra fields set during validation.
model_fields_set: Returns the set of fields that have been explicitly set on this model instance.
pb_kernels_have_let: Check if the kernel data contains LET values.

Methods

`copy`(*[, include, exclude, update, deep])	Returns a copy of the model.
`get_closest_energy_index`(energy)	Given an energy value, return the closest matching index.
`get_energy_index`(energy[, round_decimals])	Given an energy value, return the index of the exact matching.
`get_foci_by_index`(ix_energy)	Get the focus data of the kernel for a specific energy index.
`get_focus`(ix_energy)	Get the focus data of the kernel for a specific energy index.
`get_kernel_by_energy`(energy)	Get the pencil beam kernel for a specific energy value.
`get_kernel_by_index`(ix_energy)	Get the pencil beam kernel for a specific energy index.
`model_construct`([_fields_set])	Creates a new instance of the Model class with validated data.
`model_copy`(*[, update, deep])	!!! abstract "Usage Documentation"
`model_dump`(*[, mode, include, exclude, ...])	!!! abstract "Usage Documentation"
`model_dump_json`(*[, indent, ensure_ascii, ...])	!!! abstract "Usage Documentation"
`model_json_schema`([by_alias, ref_template, ...])	Generates a JSON schema for a model class.
`model_parametrized_name`(params)	Compute the class name for parametrizations of generic classes.
`model_post_init`(context, /)	This function is meant to behave like a BaseModel method to initialize private attributes.
`model_rebuild`(*[, force, raise_errors, ...])	Try to rebuild the pydantic-core schema for the model.
`model_validate`(obj, *[, strict, extra, ...])	Validate a pydantic model instance.
`model_validate_json`(json_data, *[, strict, ...])	!!! abstract "Usage Documentation"
`model_validate_strings`(obj, *[, strict, ...])	Validate the given object with string data against the Pydantic model.
`to_matrad`([context])	Perform matRad compatible serialization.
`update_kernel_at_index`(ix_energy, kernel)	Update the pencil beam kernel for a specific energy index.
`update_kernel_for_energy`(energy, kernel)	Update a pencil-beam kernel for a specific energy.
`validate_machine_input`(data)	Validate the input data for the machine model before passing to pydantic.
`validate_possible_cast_array`(v)	Validate if the input can be cast to a float64 array.

construct
dict
from_orm
json
parse_file
parse_obj
parse_raw
schema
schema_json
update_forward_refs
validate
validate_datetime_variants

model_config: ClassVar[ConfigDict] = {'alias_generator': AliasGenerator(alias=<function to_camel>, validation_alias=None, serialization_alias=None), 'arbitrary_types_allowed': True, 'from_attributes': True, 'populate_by_name': True, 'validate_assignment': True, 'validate_by_alias': True, 'validate_by_name': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_post_init(context, /)#

This function is meant to behave like a BaseModel method to initialize private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters:

self (BaseModel) – The BaseModel instance.
context (Any) – The context.

Return type:

None

pb_kernels: dict[float, IonPencilBeamKernel] | None#

peak_positions: float64]#

radiation_mode: str#

class LateralCutOff(**data)[source]#

Bases: PyRadPlanBaseModel

Lateral cut-off radius and compensation for ion pencil beam kernels.

Attributes:

model_extra: Get extra fields set during validation.
model_fields_set: Returns the set of fields that have been explicitly set on this model instance.

Methods

`copy`(*[, include, exclude, update, deep])	Returns a copy of the model.
`model_construct`([_fields_set])	Creates a new instance of the Model class with validated data.
`model_copy`(*[, update, deep])	!!! abstract "Usage Documentation"
`model_dump`(*[, mode, include, exclude, ...])	!!! abstract "Usage Documentation"
`model_dump_json`(*[, indent, ensure_ascii, ...])	!!! abstract "Usage Documentation"
`model_json_schema`([by_alias, ref_template, ...])	Generates a JSON schema for a model class.
`model_parametrized_name`(params)	Compute the class name for parametrizations of generic classes.
`model_post_init`(context, /)	Override this method to perform additional initialization after __init__ and model_construct.
`model_rebuild`(*[, force, raise_errors, ...])	Try to rebuild the pydantic-core schema for the model.
`model_validate`(obj, *[, strict, extra, ...])	Validate a pydantic model instance.
`model_validate_json`(json_data, *[, strict, ...])	!!! abstract "Usage Documentation"
`model_validate_strings`(obj, *[, strict, ...])	Validate the given object with string data against the Pydantic model.
`to_matrad`([context])	Perform matRad compatible serialization.
`validate_arrays`(v)	Validate the lateral cut-off radial distance and depths.
`validate_comp_fac`(v)	Validate the lateral cut-off compensation factor.

construct
dict
from_orm
json
parse_file
parse_obj
parse_raw
schema
schema_json
update_forward_refs
validate

comp_fac: float64#

cut_off: float64]#

depths: float64]#

model_config: ClassVar[ConfigDict] = {'alias_generator': AliasGenerator(alias=<function to_camel>, validation_alias=None, serialization_alias=None), 'arbitrary_types_allowed': True, 'from_attributes': True, 'populate_by_name': True, 'validate_assignment': True, 'validate_by_alias': True, 'validate_by_name': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod validate_arrays(v)[source]#

Validate the lateral cut-off radial distance and depths.

Return type:: Any

classmethod validate_comp_fac(v)[source]#

Validate the lateral cut-off compensation factor.

Return type:: Any

Loading and validating#

load_machine(filename)[source]#

Return type:: Machine

load_machine_from_mat(filename)[source]#

Return type:: dict

load_from_name(radiation_mode, machine_name)[source]#

Return type:: Machine

validate_machine(data=None, **kwargs)[source]#

Create and validate a Machine instance.

Parameters:

data (Union[dict[str, Any], Machine, None]) – A pre-existing Machine instance or a dictionary payload.
**kwargs (Any) – Fallback keyword arguments (used only when data is not a dict) and to supply radiation_mode when no dictionary is provided.

Return type:

Machine