PearsonsCorrelation (Fiji Javadocs 0-SNAPSHOT API)

java.lang.Object
- sc.fiji.coloc.algorithms.Algorithm<T>
- - sc.fiji.coloc.algorithms.PearsonsCorrelation<T>

Type Parameters:

T -
```
public class PearsonsCorrelation<T extends RealType<T>>
extends Algorithm<T>
```
A class that represents the mean calculation of the two source images in the data container.

Nested Class Summary

Nested Classes
Modifier and Type Class and Description

static class PearsonsCorrelation.Implementation

Nested Classes
Modifier and Type	Class and Description
`static class`	`PearsonsCorrelation.Implementation`

Field Summary
- Fields inherited from class sc.fiji.coloc.algorithms.Algorithm
  name

Constructor Summary

Constructors
Constructor and Description
`PearsonsCorrelation()` Creates a new Pearson's Correlation with default (fast) implementation parameter.
`PearsonsCorrelation(PearsonsCorrelation.Implementation implementation)` Creates a new Pearson's Correlation and allows us to define which implementation of the calculation to use.

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`<S extends RealType<S>> double`	`calculatePearsons(RandomAccessibleInterval<S> img1, RandomAccessibleInterval<S> img2)` Calculates Pearson's R value without any constraint in values, thus it uses no thresholds.
`<S extends RealType<S>> double`	`calculatePearsons(RandomAccessibleInterval<S> img1, RandomAccessibleInterval<S> img2, RandomAccessibleInterval<BitType> mask)` Calculates Pearson's R value without any constraint in values, thus it uses no thresholds.
`<S extends RealType<S>> double`	`calculatePearsons(TwinCursor<S> cursor, double mean1, double mean2, S thresholdCh1, S thresholdCh2, ThresholdMode tMode)` Calculates Pearson's R value with the possibility to constraint in values.
`static <T extends RealType<T>> double`	`classicPearsons(TwinCursor<T> cursor, double meanCh1, double meanCh2)` Calculates Person's R value by using a Classic implementation of the algorithm.
`static <T extends RealType<T>> double`	`classicPearsons(TwinCursor<T> cursor, double meanCh1, double meanCh2, T thresholdCh1, T thresholdCh2, ThresholdMode tMode)`
`void`	`execute(DataContainer<T> container)` Executes the previously initialized `Algorithm`.
`static <T extends RealType<T>> double`	`fastPearsons(TwinCursor<T> cursor)` Calculates Person's R value by using a fast implementation of the algorithm.
`static <T extends RealType<T>> double`	`fastPearsons(TwinCursor<T> cursor, T thresholdCh1, T thresholdCh2, ThresholdMode tMode)`
`double`	`getPearsonsCorrelationAboveThreshold()`
`double`	`getPearsonsCorrelationBelowThreshold()`
`double`	`getPearsonsCorrelationValue()`
`void`	`processResults(ResultHandler<T> handler)` A method to give the algorithm the opportunity to let its results being processed by the passed handler.

Methods inherited from class sc.fiji.coloc.algorithms.Algorithm
addWarning, getName, getWarnings

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail
- PearsonsCorrelation
```
public PearsonsCorrelation(PearsonsCorrelation.Implementation implementation)
```
  Creates a new Pearson's Correlation and allows us to define which implementation of the calculation to use.
  
  Parameters:
  
  implementation - The implementation of Pearson's Coefficient calculation to use.
- PearsonsCorrelation
```
public PearsonsCorrelation()
```
  Creates a new Pearson's Correlation with default (fast) implementation parameter.

Method Detail

execute
```
public void execute(DataContainer<T> container)
             throws MissingPreconditionException
```
Description copied from class: Algorithm

Executes the previously initialized Algorithm.

Specified by:

execute in class Algorithm<T extends RealType<T>>

Throws:

MissingPreconditionException

calculatePearsons
```
public <S extends RealType<S>> double calculatePearsons(RandomAccessibleInterval<S> img1,
                                                        RandomAccessibleInterval<S> img2)
                                                 throws MissingPreconditionException
```
Calculates Pearson's R value without any constraint in values, thus it uses no thresholds. If additional data like the images mean is needed, it is calculated.

Type Parameters:

S - The images base type.

Parameters:

img1 - The first image to walk over.

img2 - The second image to walk over.

Returns:

Pearson's R value.

Throws:

MissingPreconditionException

calculatePearsons

public <S extends RealType<S>> double calculatePearsons(RandomAccessibleInterval<S> img1,
                                                        RandomAccessibleInterval<S> img2,
                                                        RandomAccessibleInterval<BitType> mask)
                                                 throws MissingPreconditionException

Calculates Pearson's R value without any constraint in values, thus it uses no thresholds. A mask is required to mark which data points should be visited. If additional data like the images mean is needed, it is calculated.

Type Parameters:: S - The images base type.
Parameters:: img1 - The first image to walk over.; img2 - The second image to walk over.; mask - A mask for the images.
Returns:: Pearson's R value.
Throws:: MissingPreconditionException

calculatePearsons

public <S extends RealType<S>> double calculatePearsons(TwinCursor<S> cursor,
                                                        double mean1,
                                                        double mean2,
                                                        S thresholdCh1,
                                                        S thresholdCh2,
                                                        ThresholdMode tMode)
                                                 throws MissingPreconditionException

Calculates Pearson's R value with the possibility to constraint in values. This could be useful of one wants to apply thresholds. You need to provide the images means, albeit not used by all implementations.

Type Parameters:: S - The images base type.
Parameters:: cursor - The cursor to walk over both images.
Returns:: Pearson's R value.
Throws:: MissingPreconditionException

classicPearsons
```
public static <T extends RealType<T>> double classicPearsons(TwinCursor<T> cursor,
                                                             double meanCh1,
                                                             double meanCh2)
                                                      throws MissingPreconditionException
```
Calculates Person's R value by using a Classic implementation of the algorithm. This method allows the specification of a TwinValueRangeCursor. With such a cursor one for instance can combine different thresholding conditions for each channel. The cursor is not closed in here.

Type Parameters:

T - The image base type

Parameters:

cursor - The cursor that defines the walk over both images.

meanCh1 - Mean of channel 1.

meanCh2 - Mean of channel 2.

Returns:

Person's R value

Throws:

MissingPreconditionException

classicPearsons

public static <T extends RealType<T>> double classicPearsons(TwinCursor<T> cursor,
                                                             double meanCh1,
                                                             double meanCh2,
                                                             T thresholdCh1,
                                                             T thresholdCh2,
                                                             ThresholdMode tMode)
                                                      throws MissingPreconditionException

Throws:: MissingPreconditionException

fastPearsons
```
public static <T extends RealType<T>> double fastPearsons(TwinCursor<T> cursor)
                                                   throws MissingPreconditionException
```
Calculates Person's R value by using a fast implementation of the algorithm. This method allows the specification of a TwinValueRangeCursor. With such a cursor one for instance can combine different thresholding conditions for each channel. The cursor is not closed in here.

Type Parameters:

T - The image base type

Parameters:

cursor - The cursor that defines the walk over both images.

Returns:

Person's R value

Throws:

MissingPreconditionException

fastPearsons

public static <T extends RealType<T>> double fastPearsons(TwinCursor<T> cursor,
                                                          T thresholdCh1,
                                                          T thresholdCh2,
                                                          ThresholdMode tMode)
                                                   throws MissingPreconditionException

Throws:: MissingPreconditionException

processResults
```
public void processResults(ResultHandler<T> handler)
```
Description copied from class: Algorithm

A method to give the algorithm the opportunity to let its results being processed by the passed handler. By default this methods passes the collected warnings to the handler and sub-classes should make use of this by adding custom behavior and call the super class.

Overrides:

processResults in class Algorithm<T extends RealType<T>>

Parameters:

handler - The ResultHandler to process the results.

getPearsonsCorrelationValue

public double getPearsonsCorrelationValue()

getPearsonsCorrelationBelowThreshold

public double getPearsonsCorrelationBelowThreshold()

getPearsonsCorrelationAboveThreshold

public double getPearsonsCorrelationAboveThreshold()

Class PearsonsCorrelation<T extends RealType<T>>

Nested Class Summary

Field Summary

Fields inherited from class sc.fiji.coloc.algorithms.Algorithm

Constructor Summary

Method Summary

Methods inherited from class sc.fiji.coloc.algorithms.Algorithm

Methods inherited from class java.lang.Object

Constructor Detail

PearsonsCorrelation

PearsonsCorrelation

Method Detail

execute

calculatePearsons

calculatePearsons

calculatePearsons

classicPearsons

classicPearsons

fastPearsons

fastPearsons

processResults

getPearsonsCorrelationValue

getPearsonsCorrelationBelowThreshold

getPearsonsCorrelationAboveThreshold