opentps.core.processing.planOptimization.objectives package

Submodules

opentps.core.processing.planOptimization.objectives.baseFunction module

class BaseFunc(**kwargs)

Bases: object

Function object interface The instanced objects are meant to be passed to the :func: “solver.solve” solving function

cap(x)

Test the capabilities of the function object

Parameters:

x (array_like) – Point at which the capabilities are tested

Returns:

cap – List of capabilities

Return type:

list

eval(x)

Function evaluation

Parameters:

x (array_like) – Point at which the function is evaluated

Returns:

sol – Function value at x

Return type:

float

grad(x)

Function gradient

Parameters:

x (array_like) – Point at which the gradient is evaluated

Returns:

sol – Gradient value at x

Return type:

array_like

prox(x, T)

Function proximal operator

Parameters:

  • x (array_like) – Point at which the proximal operator is evaluated

  • T (float) – Proximal operator parameter

Returns:

sol – Proximal operator value at x

Return type:

array_like

class Dummy(**kwargs)

Bases: BaseFunc

Dummy function which returns 0 (eval, prox, grad)

opentps.core.processing.planOptimization.objectives.doseFidelity module

class DoseFidelity(plan, xSquare=True, GPU_acceleration=False)

Bases: BaseFunc

Dose fidelity objective class. Inherits from BaseFunc.

Variables:

  • list (list) – List of objectives

  • xSquare (bool) – If true, the weights are squared. If false, the weights are not squared.

  • beamlets (sparse matrix) – Beamlet matrix

  • scenariosBL (list) – List of scenarios

  • GPU_acceleration (bool (default: False)) – If true, the GPU is used for the computation of the fidelity function and gradient.

computeFidelityFunction(x, returnWorstCase=False)

Computes the fidelity function.

Parameters:

  • x (array) – Weights

  • returnWorstCase (bool) – If true, the worst case scenario is returned. If false, the nominal scenario is returned.

Returns:

  • fTot (float) – Fidelity function value

  • worstCase (int (only if robust objectives are present)) – Worst case scenario index (-1 for nominal)

computeFidelityGradient(x)

Computes the fidelity gradient.

Parameters:

x (array) – Weights

Returns:

dfTot – Fidelity gradient

Return type:

array

Raises:

Exception – If the objective metric is not supported.

unload_blGPU()

opentps.core.processing.planOptimization.objectives.energySequencing module

class EnergySeq(plan, gamma, factor=0.001, **kwargs)

Bases: BaseFunc

Energy sequencing function (eval, grad): regularization used to sequence the energy layers by descending order (favor high-to-low energy sequence) gamma is the regularization parameter. Inherited from BaseFunc.

Variables:

  • plan (Plan) – Plan of which the energy sequencing function is calculated.

  • gamma (float) – Regularization parameter.

  • factor (float) – Factor used to scale the energy sequencing function.

opentps.core.processing.planOptimization.objectives.logBarrier module

class LogBarrier(plan, beta, **kwargs)

Bases: BaseFunc

Log barrier function (eval, grad): regularization function used to distribute the selected layers to the whole gantry rotating range. The term sums up the intensity of each beam and penalizes the zero intensities, therefore forcing each beam to keep at least one layer selected. beta is the regularization parameter for the log barrier function. Inherits from BaseFunc.

Variables:

  • plan (Plan) – The plan to be optimized.

  • beta (float) – Regularization parameter for the log barrier function.

  • struct (WeightStructure) – Weight structure of the plan.

dlogCols(x)

Calculates derivative log of each layer and sum in each beam

Parameters:

x (list) – List of beams and layers.

Returns:

res – List of derivative log of each layer and sum in each beam.

Return type:

list

logCols(x)

Calculates log of each beam

Parameters:

x (list) – List of beams and layers.

Returns:

res – List of log of each beam.

Return type:

list

opentps.core.processing.planOptimization.objectives.norms module

class Norm(lambda_=1, **kwargs)

Bases: BaseFunc

Base class which defines the attributes of the norm objects. Inherit from BaseFunc. Code from EPFL LTS2 toolbox.

Variables:

lambda (float (default: 1)) – Regularization parameter.

class NormL1(**kwargs)

Bases: Norm

L1-norm (eval, prox). Inherits from Norm. Code from EPFL LTS2 toolbox.

class NormL2(**kwargs)

Bases: Norm

L2-norm (eval, prox, grad). Inherits from Norm. Code from EPFL LTS2 toolbox.

class NormL21(plan=None, scaleReg='group_size', oldRegularisation=False, **kwargs)

Bases: Norm

L2,1-norm (eval, prox) for matrix (list of lists in our case): Sum of the Euclidean norms of the columns (items) of the matrix (list) The proximal operator for reg*||w||_2 (not squared). source lasso. Inherit from Norm. Code from EPFL LTS2 toolbox.

sumMuInlayer(X, index)

opentps.core.processing.planOptimization.objectives.projections module

class BaseProj(epsilon=1, method='FISTA', **kwargs)

Bases: BaseFunc

Base class which defines the attributes of the proj objects. Code from EPFL LTS2 toolbox.

Variables:

  • epsilon (float (default: 1)) – Regularization parameter.

  • method (str (default: 'FISTA')) – Method used to solve the proximal problem.

class Indicator(plan, smooth_fun, eps, **kwargs)

Bases: BaseProj

Projection on ? (eval, prox) Regularization function used to distribute the selected layers to the whole gantry rotating range. This function is the indicator function \(i_S(z)\) of the set S which is zero if z is in the set and infinite otherwise. The set S is defined by ? ?: Indicator activated if beam MU <= epsilon. If yes, MU redistributed on activated layers for the sum to reach epsilon (esp= regularization parameter). Inherits from BaseProj. Code from EPFL LTS2 toolbox.

Variables:

  • plan (Plan) – Plan object.

  • smooth_fun (SmoothFunction) – Smooth function object.

  • eps (float) – Regularization parameter.

  • struct (WeightStructure) – Weight structure object.

class PositiveProj(**kwargs)

Bases: BaseProj

Projection on nonnegative orthant (eval, prox) This function is the indicator function \(i_S(z)\) of the set S which is zero if z is in the set and infinite otherwise. The set S is defined by mathbb{R}^N_+ . Inherits from BaseProj. Code from EPFL LTS2 toolbox.

Module contents