pyRadPlan.scenarios#

Base class#

class ScenarioModel(ct=None, **kwargs)[source]#

Bases: PyRadPlanBaseModel

Abstract base class for scenario models.

range_rel_sd#

The relative standard deviation for range.

Type:: float

range_abs_sd#

The absolute standard deviation for range.

Type:: float

shift_sd#

The standard deviation for shift in x, y, and z directions.

Type:: Tuple[float]

wc_sigma#

The sigma value for the wc_factor.

Type:: float

ct_scen_prob#

The probability of each CT scenario.

Type:: list[Tuple[int,float]]

update_scenarios() → np.ndarray[float][source]#: Abstract method to update scenarios.

extract_single_scenario(scen_num: int) → ScenarioModel[source]#: Extracts a single scenario based on its number.

list_all_scenarios()[source]#: Lists all scenarios.

sub2scenIx(ctScen: int, shift_scen: int, range_shift_scen: int) → int#: Converts CT scenario, shift scenario, and range shift scenario to scenario index.

scenNum(fullScenIx: int) → int#: Converts full scenario index to scenario number.

Attributes:

abs_range_shift: Absolute range shift value.
ct_scen_ix: CT scenario indices of the used CT scenarios.
iso_shift: Isocenter shift values.
linear_mask: Linear mask matrix for scenario selection.
max_abs_range_shift: Maximum absolute range shift value.
max_rel_range_shift: Maximum relative range shift value.
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_of_available_ct_scen: Number of totally available CT scenarios in the CT.
num_of_ct_scen: Number of CT scenarios in the model.
rel_range_shift: Relative range shift value.
scen_for_prob: Scenarios organized for probability calculation.
scen_mask: Scenarios mask matrix describing how scenarios should be stored.
scen_prob: Scenarios probability matrix.
scen_weight: Scenarios weight matrix.
tot_num_range_scen: Total number of range shift scenarios.
tot_num_scen: Total number of scenarios.
tot_num_shift_scen: Total number of shift scenarios.

Methods

`copy`(*[, include, exclude, update, deep])	Returns a copy of the model.
`extract_single_scenario`(scen_num)	Extract a single scenario based on its number.
`list_all_scenarios`()	Print a list of all scenarios.
`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.
`scen_num`(full_scen_ix)	Return the scenario number given the scenario ray index.
`sub2scen_ix`(ct_scen, shift_scen, ...)	Convert a subscript index to a linear scenario index.
`to_matrad`([context])	Perform matRad compatible serialization.
`update_scenarios`()	Update scenario data from uncertainty model settings.

construct
dict
from_orm
json
parse_file
parse_obj
parse_raw
schema
schema_json
update_forward_refs
validate

property abs_range_shift: float#: Absolute range shift value.

property ct_scen_ix: uint16'>)]#: CT scenario indices of the used CT scenarios.

ct_scen_prob: list[Tuple[int, float]]#

abstractmethod extract_single_scenario(scen_num)[source]#

Extract a single scenario based on its number.

Return type:: ScenarioModel

property iso_shift: float64'>)]#: Isocenter shift values.

property linear_mask: uint16'>)]#: Linear mask matrix for scenario selection.

list_all_scenarios()[source]#

Print a list of all scenarios.

This method prints the details of all scenarios in a formatted table. The table includes the following columns: - xShift: The x-axis shift value - yShift: The y-axis shift value - zShift: The z-axis shift value - absRng: The absolute range value - relRng: The relative range value - prob: The probability value

Example output: # xShift yShift zShift absRng relRng prob. 1 0.000 0.000 0.000 0.000 0.000 0.000 2 0.100 0.200 0.300 0.400 0.500 0.600 3 0.700 0.800 0.900 1.000 1.100 1.200

property max_abs_range_shift: float#: Maximum absolute range shift value.

property max_rel_range_shift: float#: Maximum relative range shift value.

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: ClassVar[str]#

property num_of_available_ct_scen: int#: Number of totally available CT scenarios in the CT.

property num_of_ct_scen: int#: Number of CT scenarios in the model.

range_abs_sd: float#

range_rel_sd: float#

property rel_range_shift: float#: Relative range shift value.

property scen_for_prob: float64'>)]#: Scenarios organized for probability calculation.

property scen_mask: bool]#: Scenarios mask matrix describing how scenarios should be stored.

scen_num(full_scen_ix)[source]#

Return the scenario number given the scenario ray index.

This corresponds to the first occurrence of full_scen_ix in self.scen_mask.

Parameters:: full_scen_ix (int) – The value to search for in self.scen_mask.
Returns:: The index of the first occurrence of full_scen_ix in self.scen_mask.
Return type:: int

property scen_prob: float64'>)]#: Scenarios probability matrix.

property scen_weight: float64'>)]#: Scenarios weight matrix.

shift_sd: Tuple[float, float, float]#

short_name: ClassVar[str]#

sub2scen_ix(ct_scen, shift_scen, range_shift_scen)[source]#

Convert a subscript index to a linear scenario index.

Parameters:

(int) (range_shift_scen)
(int)
(int)

Return type:

int

Returns:

int: The corresponding scenario index.

Raises:

None –

Examples

# Example usage sub2scenIx(1, 2, 3) # Returns: 1

property tot_num_range_scen: int#: Total number of range shift scenarios.

property tot_num_scen: int#: Total number of scenarios.

property tot_num_shift_scen: int#: Total number of shift scenarios.

abstractmethod update_scenarios()[source]#

Update scenario data from uncertainty model settings.

Return type:: ndarray[float]

wc_sigma: float#

class NominalScenario(ct=None, **kwargs)[source]#

Bases: ScenarioModel

NominalScenario class represents an example scenario.

name#

The name of the scenario.

Type:: str

short_name#

The short name of the scenario.

Type:: str

update_scenarios()[source]#: Updates the scenarios.

extract_single_scenario(scen_num: int) → ScenarioModel[source]#: Extracts a single scenario.

Attributes:

abs_range_shift: Absolute range shift value.
ct_scen_ix: CT scenario indices of the used CT scenarios.
iso_shift: Isocenter shift values.
linear_mask: Linear mask matrix for scenario selection.
max_abs_range_shift: Maximum absolute range shift value.
max_rel_range_shift: Maximum relative range shift value.
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.
ndim: Dimensionality of the scenario model.
num_of_available_ct_scen: Number of totally available CT scenarios in the CT.
num_of_ct_scen: Number of CT scenarios in the model.
rel_range_shift: Relative range shift value.
scen_for_prob: Scenarios organized for probability calculation.
scen_mask: Scenarios mask matrix describing how scenarios should be stored.
scen_prob: Scenarios probability matrix.
scen_weight: Scenarios weight matrix.
tot_num_range_scen: Total number of range shift scenarios.
tot_num_scen: Total number of scenarios.
tot_num_shift_scen: Total number of shift scenarios.

Methods

`copy`(*[, include, exclude, update, deep])	Returns a copy of the model.
`extract_single_scenario`(scen_num)	Extract a single scenario based on its number.
`list_all_scenarios`()	Print a list of all scenarios.
`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.
`scen_num`(full_scen_ix)	Return the scenario number given the scenario ray index.
`sub2scen_ix`(ct_scen, shift_scen, ...)	Convert a subscript index to a linear scenario index.
`to_matrad`([context])	Perform matRad compatible serialization.
`update_scenarios`()	Update scenario data from uncertainty model settings.

construct
dict
from_orm
json
parse_file
parse_obj
parse_raw
schema
schema_json
update_forward_refs
validate

extract_single_scenario(scen_num)[source]#

Extract a single scenario based on its number.

Return type:: ScenarioModel

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: ClassVar[str] = 'Nominal Scenario'#

property ndim: int#: Dimensionality of the scenario model.

short_name: ClassVar[str] = 'nomScen'#

update_scenarios()[source]#

Update scenario data from uncertainty model settings.

Return type:: ndarray[float]

Helper functions

available_scenario_models()[source]#

Return a list of available scenario models.

Return type:: list[str]

create_scenario_model(model_def='nomScen', ct=None)[source]#

Return a scenario model object.

Parameters:: model_name (str) – The name of the scenario model.
Returns:: The scenario model object.
Return type:: ScenarioModel

validate_scenario_model(model_def, ct=None)[source]#

Validate a scenario model input and returns a scenario model object.

Parameters:: model_def (Union[ScenarioModel, str, dict]) – The scenario model object.
Returns:: The scenario model object.
Return type:: ScenarioModel