org.gridfour.compress

Class PredictorModelDifferencingWithNulls

java.lang.Object

org.gridfour.compress.PredictorModelDifferencingWithNulls

All Implemented Interfaces:

IPredictorModel

public class PredictorModelDifferencingWithNulls
extends Object
implements IPredictorModel

Applies the differencing model to the data with special treatment for non-continuous values. The differencing model always predicts that the current value in a series will have the same value as the prior value.

Constructor Summary

Constructors

Constructor and Description
PredictorModelDifferencingWithNulls()

Method Summary

Modifier and Type	Method and Description
void	decode(int seed, int nRows, int nColumns, byte[] encoding, int offset, int length, int[] output) Decodes the specified data using the predictor model to compute output values from the specified input.
int	encode(int nRows, int nColumns, int[] values, byte[] output) Encodes the specified data using the predictor model to develop computed adjustments for the data grid.
PredictorModelType	getPredictorType() Gets the predictor type.
int	getSeed() Gets the seed value determined by the most recent call to the encoding method.
boolean	isNullDataSupported() Indicates that the predictor model is intended to handle null values.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

PredictorModelDifferencingWithNulls

public PredictorModelDifferencingWithNulls()

Method Detail

getSeed

public int getSeed()

Description copied from interface: IPredictorModel

Gets the seed value determined by the most recent call to the encoding method. This value is defined only if the encoding was successful.

Specified by:

getSeed in interface IPredictorModel

Returns:

an arbitrary integer value

encode

public int encode(int nRows,
                  int nColumns,
                  int[] values,
                  byte[] output)

Description copied from interface: IPredictorModel

Encodes the specified data using the predictor model to develop computed adjustments for the data grid.

This routine returns the seed value detected in the data. This value is usually the first entry in the input value. In cases where the data contains nulls, the seed is sometimes the mean value of the non-null entries.

The length of the encoded output will be a maximum of 5 times the total number of entries in the input (it is usually much shorter than that). The encoding array should be dimensioned large enough to accept the maximum sized output.

Specified by:

encode in interface IPredictorModel

Parameters:

nRows - the number of rows in the raster data grid

nColumns - the number of columns in the data data grid

values - the input values to be encoded

output - the output encoding

Returns:

if successful, the number of bytes in the output encoding; otherwise, a value -1 to indicate an encoding failure.

decode

public void decode(int seed,
                   int nRows,
                   int nColumns,
                   byte[] encoding,
                   int offset,
                   int length,
                   int[] output)

Description copied from interface: IPredictorModel

Decodes the specified data using the predictor model to compute output values from the specified input.

Specified by:

decode in interface IPredictorModel

Parameters:

seed - a seed value to provide the initial value for the encoding

nRows - the number of rows in the raster data grid

nColumns - the number of columns in the raster data grid

encoding - the input values for the encoding, usually dimensions to nRows*nColumns

offset - the starting offset in the encoding array from which data is to be taken.

length - the length of the valid section of the encoding array from which data is to be taken.

output - array to accept the output data values, dimensions to at least nRows*nColumns

isNullDataSupported

public boolean isNullDataSupported()

Description copied from interface: IPredictorModel

Indicates that the predictor model is intended to handle null values. In many real-world raster data products, the transition from regions of valid data to null data is frequently discontinuous. While most predictor models can handle the transition, the results often compress poorly. This method indicates if a predictor is intended to support cells with null data.

Specified by:

isNullDataSupported in interface IPredictorModel

Returns:

true if null values are permitted; otherwise false.

getPredictorType

public PredictorModelType getPredictorType()

Description copied from interface: IPredictorModel

Gets the predictor type.

Specified by:

getPredictorType in interface IPredictorModel

Returns:

a valid enumeration corresponding the the type of predictor