xopto.util.sampler module¶

class ConstantSampler(value: float)[source]¶

Bases: xopto.util.sampler.UniformSampler

A sampler that returns a constant value.

Parameters: value (float) – Constant value that will be returned by the sampler.

todict() → dict[source]¶

Export object to a dict.

Returns: data – Instance data exported to a dict.
Return type: dict

property value: float¶: Value of the sampler.

class IncidenceTiltSampler(incidence: xopto.util.sampler.Sampler, tilt: xopto.util.sampler.Sampler, design_angle: float)[source]¶

Bases: xopto.util.sampler.Sampler

Samples the incidence angle of a collimated source and tilt of a surface detector from the given distribution. The angle between the collimated incident beam and the reference direction of the detector (design angle) is kept constant.

Parameters

incidence (Sampler) – Sampler of the angle of incidence (rad). The angle is measured in the incidence plane x-z from the z axis. The sampled direction vector of the collimated beam lies in the x-z plane.
tilt (Sampler) – Sampler of the detector tilt angle (rad). The detector direction lies in the x-z plane that is tilted/rotated around the x axis. The angle of tilt is defined as the angle between the tilted plane and the x-z plane.
design_angle (float) – Angle between the source and detector (rad) that is kept constant.

property design_angle: float¶: Design angle (rad).

classmethod fromdict(data: dict) → xopto.util.sampler.IncidenceTiltSampler [source]¶

Create a new instance of IncidenceTiltSampler from a dict.

Parameters: data (dict) – Data that were exported by the IncidenceTiltSampler.todict() method.
Returns: sampler – A new sampler instance.
Return type: IncidenceTiltSampler

reset()[source]¶: Reset the states of all the enclosed samplers.

todict() → dict[source]¶

Export object to a dict.

Returns: data – Instance data exported to a dict.
Return type: dict

update(src: xopto.mcml.mcsource.line.Line, detector: xopto.mcml.mcdetector.symmetric.SymmetricX, sample: Optional[dict] = None)[source]¶

Update the Monte Carlo source and detector with the sampled propagation directions.

Parameters

src (xopto.mcml.mc.mcsource.Line) – Source that will be updated with a new direction.
detector (xopto.mcml.mc.mcdetector.Detector) – Detector that will be updated with the new reference direction.

Returns

src (xopto.mcml.mc.mcsource.Line) – The updated source.
detector (xopto.mcml.mc.mcdetector.SymmetricX) – The updated detector.

class LayerSampler(mua: xopto.util.sampler.Sampler, musr: xopto.util.sampler.Sampler, d: xopto.util.sampler.Sampler, n: xopto.util.sampler.Sampler, pf: xopto.util.sampler.PfSampler)[source]¶

Bases: xopto.util.sampler.Sampler

Samples layer parameters.

Parameters

mua (Sampler) – Sampler of the absorption coefficient (1/m).
musr (Sampler) – Sampler of the reduced scattering coefficient (1/m).
d (Sampler) – Sampler of the layer thickness.
n (Sampler) – Sampler of the refractive index.
pf (PfSampler) – Sampler Of the scattering phase function.

property d: xopto.util.sampler.Sampler¶: Layer thickness sampler.

classmethod fromdict(data: dict) → xopto.util.sampler.LayerSampler [source]¶

Create a new instance of LayerSampler from a dict.

Parameters: data (dict) – Data that were exported by the LayerSampler.todict() method.
Returns: sampler – A new sampler instance.
Return type: LayerSampler

property mua: xopto.util.sampler.Sampler¶: Absorption coefficient sampler.

property musr: xopto.util.sampler.Sampler¶: Reduced scattering coefficient sampler.

property n: xopto.util.sampler.Sampler¶: Refractive index sampler.

property pf: xopto.util.sampler.Sampler¶: Scattering phase function sampler.

reset()[source]¶: Reset the states of all the enclosed samplers.

todict() → dict[source]¶

Export object to a dict.

Returns: data – Instance data exported to a dict.
Return type: dict

update(layer: xopto.mcml.mclayer.layer.Layer, sample: Optional[dict] = None) → xopto.mcml.mclayer.layer.Layer [source]¶

Update the layer with a new sample.

Parameters

layer (xopto.mcml.mc.mclayer.Layer) – Layer to update.
sample (dict) – Optional sample. If None, a new sample is generated.

Returns

layer – Updated input layer.

Return type

xopto.mcml.mc.mclayer.Layer

class MaterialSampler(mua: xopto.util.sampler.Sampler, musr: xopto.util.sampler.Sampler, n: xopto.util.sampler.Sampler, pf: xopto.util.sampler.PfSampler)[source]¶

Bases: xopto.util.sampler.Sampler

Samples a material.

Parameters

mua (Sampler) – Sampler of the absorption coefficient (1/m).
musr (Sampler) – Sampler of the reduced scattering coefficient (1/m).
n (Sampler) – Sampler of the refractive index.
pf (PfSampler) – Sampler Of the scattering phase function.

classmethod fromdict(data: dict) → xopto.util.sampler.MaterialSampler [source]¶

Create a new instance of MaterialSampler from a dict.

Parameters: data (dict) – Data that were exported by the MaterialSampler.todict() method.
Returns: sampler – A new sampler instance.
Return type: LayerSampler

property mua: xopto.util.sampler.Sampler¶: Absorption coefficient sampler.

property musr: xopto.util.sampler.Sampler¶: Reduced scattering coefficient sampler.

property n: xopto.util.sampler.Sampler¶: Refractive index sampler.

property pf: xopto.util.sampler.Sampler¶: Scattering phase function sampler.

reset()[source]¶: Reset the states of all the enclosed samplers.

todict() → dict[source]¶

Export object to a dict.

Returns: data – Instance data exported to a dict.
Return type: dict

update(layer: xopto.mcbase.mcmaterial.Material, sample: Optional[dict] = None) → xopto.mcbase.mcmaterial.Material [source]¶

Update the material with a new sample.

Parameters

layer (mcmaterial.Material) – Material to update.
sample (dict) – Optional sample. If None, a new sample is generated.

Returns

layer – Updated material.

Return type

mcmaterial.Material

class MultilayerSampler(samplers=typing.Tuple[xopto.util.sampler.LayerSampler])[source]¶

Bases: xopto.util.sampler.Sampler

classmethod fromdict(data: dict) → xopto.util.sampler.MultilayerSampler [source]¶

Create a new instance of MultilayerSampler from a dict.

Parameters: data (dict) – Data that were exported by the MultilayerSampler.todict() method.
Returns: sampler – A new sampler instance.
Return type: MultilayerSampler

reset()[source]¶: Reset the states of all the layer samplers.

todict() → dict[source]¶

Export object to a dict.

Returns: data – Instance data exported to a dict.
Return type: dict

update(layers: xopto.mcml.mclayer.layer.Layers, sample: Optional[dict] = None) → xopto.mcml.mclayer.layer.Layers [source]¶

Update the layer stack with a new sample.

Parameters

layers (xopto.mcml.mc.mclayer.Layers) – Layer stack to update.
sample (List[dict]) – Optional sample. If None, a new sample is generated.

Returns

layers – Updated layer stack.

Return type

xopto.mcml.mc.mclayer.Layers

class MultilayerSfdi(layers: xopto.util.sampler.MultilayerSampler, source_detector: xopto.util.sampler.IncidenceTiltSampler, sample_count: int = 0)[source]¶

Bases: object

A sampler for multilayer SFDI Monte Carlo simulations.

Parameters

layers (MultilayerSampler) – Sampler for the parameters of the layer stack.
source_detector (SfdiSourceDetectorSampler) – Sampler for the parameters of the photon packet source and top surface detector.
sample_count (int) – Zero-based index of the first sample.

mc_obj(acceptance: float = 1.5707963267948966, **kwargs) → xopto.mcml.mc.Mc [source]¶

Create and initialize a new Monte Carlo simulation model.

Parameters

acceptance (float) – Acceptance angle of the detector.
kwargs (dict) – Optional keyword arguments passed to the __init__() constructor.

Returns

mcobj – A new Monte Carlo simulator instance.

Return type

xopto.mcml.mc.Mc

reset()[source]¶: Reset the states of all the enclosed samplers and set the sample number to 0.

rmax(mcobj: xopto.mcml.mc.Mc) → numpy.ndarray[source]¶

Estimate simulation radius for the given simulator instance.

Parameters: mcobj (xopto.mcml.mc.Mc) – Monte Carlo simulator instance.
Returns: rmax – A numpy vector of simulation radius for all the sample layers (excluding the topmost and bottommost layers).
Return type: np.ndarray

property sample_index: int¶: Number of the current sample (1 for the first sample)

update(mcobj: xopto.mcml.mc.Mc) → dict[source]¶

Update the Monte Carlo simulation parameters with a new sample. This call will update the sample index by 1.

Parameters: mcobj (xopto.mcml.mc.Mc) – Monte Carlo simulator instance.
Returns: sample – The sample that was used to update the simulator as a dict with keys “layers” (a list of samples that were used to update the sample layers, “source_detector” (sample that was used to update the photon packet source and top surface detector).
Return type: dict

class NormalSampler(mean: float, sigma: float, clip: float = 5)[source]¶

Bases: xopto.util.sampler.Sampler

Sampler that follows a Normal distribution with the given mean and standard deviation.

Parameters

mean (float) – Mean value of the distribution.
sigma (float) – Standard deviation of the distribution.
clip (float) – Clip the values to $mean ± clip \cdot sigma$ .

property clip: float¶: Clip value range to mean ± clip*sigma.

property mean: float¶: Mean value of the distribution.

property sigma: float¶: Standard deviation of the distribution.

todict() → dict[source]¶

Export object to a dict.

Returns: data – Instance data exported to a dict.
Return type: dict

class PfSampler(pf_type: str, pf_args: Tuple[xopto.util.sampler.Sampler])[source]¶

Bases: xopto.util.sampler.Sampler

Monte Carlo simulator scattering phase function sampler.

Parameters

pf_type (mcpf.PfBase) – Scattering phase function type.
pf_args (Tuple[Sampler]) – Samplers of the scattering phase function arguments.

classmethod fromdict(data: dict) → xopto.util.sampler.PfSampler [source]¶

Create a new instance from a dict.

Parameters: data (dict) – Instance data that were exported to a dict.

property pf_args: Tuple[xopto.util.sampler.Sampler]¶: Samplers of the scattering phase function arguments.

property pf_type: xopto.mcbase.mcpf.pfbase.PfBase¶: Scattering phase function type used by the sampler.

reset()[source]¶: Reset the states of samplers that sample the arguments of the scattering phase function.

todict() → dict[source]¶

Export instance to a dict.

Returns: data – Instance data exported to a dict.
Return type: dict

class Sampler[source]¶

Bases: object

classmethod fromdict(data: dict) → xopto.util.sampler.Sampler [source]¶

Create a new instance from the data in the dict.

Parameters: data (dict) – A sampler instance exported to dict.
Returns: sampler – A new sampler instance.
Return type: Sampler

reset()[source]¶: Reset the state of the sampler. Reimplement this method for custom handling of the sampler state.

class SequenceSampler(sequence: numpy.ndarray, start: int = 0)[source]¶

Bases: xopto.util.sampler.Sampler

A sequence sampler.

Parameters

sequence (np.ndarray) – A sequence of vales to sample. The array is sampled from a flat view.
start (int) – Zero-based index of the first sample.

Note

After reaching the end of sequence, the sampling will continue with the first element in the sequence.

property pos: int¶: Zero-based index of the next sample.

reset()[source]¶: Reset the sequence sampler to start from the first item as specified by the start parameter of the constructor.

property sequence: int¶: The sampled data sequence.

class UniformSampler(start: float, stop: float, logscale: bool = False)[source]¶

Bases: xopto.util.sampler.Sampler

Randomly samples values from interval [start, stop]. Note that the values of start and stop can be provided in arbitrary order.

Parameters

start (float) – Start of the sampling interval.
stop (float) – Stop/end of the sampling interval.
logscale (bool) – Use logarithmic sampling on the start-stop interval.

property interval: Tuple[float, float]¶: Sampling interval as a tuple (start, stop)

property logscale: bool¶: Sampling in logarithmic scale.

property max: float¶: Upper bound of the sampling interval.

property min: float¶: Lower bound of the sampling interval.

property start: float¶: Start of the sampling interval.

property stop: float¶: Stop of the sampling interval.

todict() → dict[source]¶

Export object to a dict.

Returns: data – Instance data exported to a dict.
Return type: dict

detector_direction(incidence: float, tilt: float, design_angle: float, verbose: bool = False) → Tuple[float, float, float][source]¶

Determines the direction vector of the detector by solving a quadratic equation that arises from the following system of equations:

$x\sin(\Theta_i) + z\cos(\Theta_i) = -\cos(\Theta_0) y\cos(\Theta_t) - z\sin(\Theta_t) = 0 x^2 + y^2 + z^2 = 1$

In the above equations $\Theta_i$ is the angle of incidence, $\Theta_t$ the detector tilt angle and $\Theta_0$ the design angle, i.e. the angle between the source and detector.

The first equation is the dot product of the detector direction vector and the direction of the incident beam. The angle between the two is the design angle $180 - \Theta_0$ . The second equation is the dot product of the detector direction vector and the normal of the tilted plane that contains the detector direction vector. Note that the tilted plane is the x-z plane rotated around the x axis.

The solution with negative z component that has a polar angle in the x-z plane smaller than the polar angle of the incident source is taken (detector always rotates from source towards the x axis).

Parameters

incidence (float) – Source incidence angle (radians) in the x-z plane, measured from the z axis.
tilt (float) – Detector tilt angle (radians) measured as the angle between the tilted plane (x-z plane rotated around the x axis) and the z axis.
design_angle (float) – Design angle between the source and detector (radians).
verbose (bool) – Enables verbose report.

Returns

dir – Direction vector of the detector.

Return type

np.ndarray