MassiveMarchingAutomata

Inheritance diagram

Inheritance diagram of ORSModel.ors.MassiveMarchingAutomata, ORSModel.ors.Unmanaged, ORSModel.ors.ORSBaseClass

Classes

MassiveMarchingAutomata

class ORSModel.ors.MassiveMarchingAutomata(self)

Bases: Unmanaged

cleanSpeedMapChannel(self, outputChannel: ORSModel.ors.Channel)

Removes boundaries or non reached value from a Speed map channel.

Parameters:

outputChannel (ORSModel.ors.Channel) – a distance map channel (an Channel)

createDistanceMap(self, lOutputChannelDistanceMap: ORSModel.ors.Channel, lOutputChannelTraceBack: ORSModel.ors.Channel, lOutputChannelLabel: ORSModel.ors.Channel, nbIteration: int)
Parameters:
getClassNameStatic() → str

getClassNameStatic

Returns:

output (str) –

getEuclideanBias(self) → float

Gets the Euclidean bias that will be the minimumDijkstra distance between voxels.

Note

Neighbors of distance 1 will have a bias of spacialTerm

Note

Neighbors of distance sqrt(2) will have a bias of sqrt(2)*spacialTerm

Note

Neighbors of distance sqrt(3) will have a bias of sqrt(3)*spacialTerm

Returns:

output (float) – the minimum distance between voxels (a double)

getNeighborCount(self) → int
Returns:

output (int) –

getROICount(self) → int

Returns the number of ROIs that have been set as sources.

Note

A maximum of 10 ROI can be provided.

Returns:

output (int) – the number of ROIs that have been provided (an unsigned char)

getVolumeROI(self, index: int) → ORSModel.ors.ROI

Note

A maximum of 10 ROIs can be provided. The ROIs provided must be of the same shape as the input channel.

Parameters:

index (int) –

Returns:

output (ORSModel.ors.ROI) –

none() → MassiveMarchingAutomata
Returns:

output (MassiveMarchingAutomata) –

resetVolumeROIs(self)

Empties all the sourceROI slots.

setEuclideanBias(self, EuclideanBias: float)

Provides an Euclidean bias that will be the minimumDijkstra distance between voxels.

Note

Neighbors of distance 1 will have a bias of spacialTerm.

Note

Neighbors of distance sqrt(2) will have a bias of sqrt(2)*spacialTerm.

Note

Neighbor of distance sqrt(3) will have a bias of sqrt(3)*spacialTerm.

Parameters:

EuclideanBias (float) – the minimum distance between voxels (a double)

setInputChannelAndWorkingArea(self, inputChannel: ORSModel.ors.Channel, minX: int, minY: int, minZ: int, maxX: int, maxY: int, maxZ: int, currentT: int)

Note

The min and max boundaries must not describe a space bigger than the input channel.

Parameters:

  • inputChannel (ORSModel.ors.Channel) – the input channel (an Channel) *

  • minX (int) – the minimum X index in the input channel (a uint32_t) *

  • minY (int) – the minimum Y index in the input channel (a uint32_t)

  • minZ (int) – the minimum Z index in the input channel (a uint32_t)

  • maxX (int) – the maximum X index in the input channel (a uint32_t)

  • maxY (int) – the maximum Y index in the input channel (a uint32_t)

  • maxZ (int) – the maximum Z index in the input channel (a uint32_t)

  • currentT (int) – the T index (a uint32_t)

setNeighborCountTo18(self)
setNeighborCountTo26(self)
setNeighborCountTo6(self)
setVolumeROI(self, index: int, aVolROI: ORSModel.ors.ROI)

Note

A maximum of 10 ROIs can be provided. The ROIs provided must be of the same shape as the input channel.

Parameters:

Unmanaged

class ORSModel.ors.Unmanaged

Bases: ORSBaseClass

Abstract class for objects that are not managed by the core library. Unmanaged objects are transient objects.

atomicLoad(sFilename: str) → Unmanaged

Creates an object from a file where an object was saved.

Parameters:

sFilename (str) – path of the file to load

Returns:

output (Unmanaged) – an unmanaged object, or none() if the load fails

atomicSave(self, aFilename: str) → int

Saves the object to a file.

Parameters:

aFilename (str) – path of the file to save

Returns:

output (int) – 0 if successful, otherwise an error code

createFromPythonRepresentation(aPythonRepresentation: str) → ORSModel.ors.Unmanaged

Create aUnmanaged Object from a python representation a static method.

Parameters:

aPythonRepresentation (str) –

Returns:

output (ORSModel.ors.Unmanaged) –

fromPythonRepresentation(self, aPythonRepresentation: str) → bool

Create aUnmanaged object from a Python string representation.

Parameters:

aPythonRepresentation (str) – a Python evaluable string representation (a string)

Returns:

output (bool) – true if parsing worked, false otherwise (a bool)

getClassName(self) → str

Retrieves the class name of the core object wrapped by this Interface object.

Returns:

output (str) –

getClassNameStatic() → str

getClassNameStatic

Returns:

output (str) –

getDataChecksum(self) → str
Returns:

output (str) –

getIsInstanceOf(self, pProgId: str) → bool

Queries the object to know if it is an instance of a certain class.

Parameters:

pProgId (str) –

Returns:

output (bool) –

getPythonRepresentation(self) → str

Gets a Python evaluable string representation.

Returns:

output (str) –

isNone(self) → bool

Checks if the receiver is none.

Returns:

output (bool) –

isNotNone(self) → bool

Checks if the receiver is not none.

Returns:

output (bool) –

none() → Unmanaged
Returns:

output (Unmanaged) –

ORSBaseClass

class ORSModel.ors.ORSBaseClass

An abstract class from which all objects issued from the ORS Core Library inherit.

getPythonTraceBack() → List[str]

Set the python traceback for a call from python.

Returns:

output (List[str]) –

isManaged(self) → bool
Returns:

output (bool) –

isNone(self) → bool
Returns:

output (bool) –

setPythonTraceBack(tb: List[str])

Set the python traceback for a call from python.

Parameters:

tb (List[str]) –