

PREV CLASS NEXT CLASS  FRAMES NO FRAMES  
SUMMARY: NESTED  FIELD  CONSTR  METHOD  DETAIL: FIELD  CONSTR  METHOD 
java.lang.Object uk.ac.leeds.ccg.andyt.grids.process.Grid2DSquareCellProcessor uk.ac.leeds.ccg.andyt.grids.process.Grid2DSquareCellProcessorDEM
public class Grid2DSquareCellProcessorDEM
A class of methods relevant to the processing of Digital Elevation Model Data.
Field Summary 

Constructor Summary  

Grid2DSquareCellProcessorDEM()
Creates a new Grid2DSquareCellProcessorDEM 

Grid2DSquareCellProcessorDEM(java.io.File _Directory)
Creates a new instance of Grid2DSquareCellProcessorDEM. 

Grid2DSquareCellProcessorDEM(java.io.File _Directory,
boolean appendToLogFile)
Creates a new instance of Grid2DSquareCellProcessorDEM. 
Method Summary  

Grid2DSquareCellDouble 
getHollowFilledDEM(AbstractGrid2DSquareCell _Grid2DSquareCell,
Grid2DSquareCellDoubleFactory _Grid2DSquareCellDoubleFactory,
double outflowHeight,
int maxIterations,
java.util.HashSet outflowCellIDsSet,
boolean _TreatNoDataValueAsOutflow,
boolean handleOutOfMemoryError)

java.util.HashSet 
getInitialPeaksHashSetAndSetTheirValue(Grid2DSquareCellDouble grid,
Grid2DSquareCellDouble upSlopeAreaMetrics,
boolean handleOutOfMemoryError)
Returns a HashSet containing _CellIDs which identifies cells for which neighbouring cells in the immediate 8 cell neighbourhood that are either the same value, lower or noDataValues 
Grid2DSquareCellDouble 
getMaxFlowDirection(Grid2DSquareCellDouble grid,
Grid2DSquareCellDoubleFactory gridFactory,
boolean handleOutOfMemoryError)
Returns an Grid2DSquareCellDouble result containing values which indicate the direction of the maximum down slope for the immediate 8 cell neighbourhood. 
AbstractGrid2DSquareCell[] 
getMetrics1(AbstractGrid2DSquareCell[] metrics1,
AbstractGrid2DSquareCell _Grid2DSquareCell,
java.math.BigDecimal[] dimensions,
double distance,
double weightIntersect,
double weightFactor,
boolean handleOutOfMemoryError)
Returns an Grid2DSquareCellDouble[] metrics1 where: \n metrics1[0] = no data count; \n metrics1[1] = flatness; \n metrics1[2] = roughness; \n metrics1[3] = slopyness; \n metrics1[4] = levelness; \n metrics1[5] = totalDownness; \n metrics1[6] = averageDownness; \n metrics1[7] = totalUpness; \n metrics1[8] = averageUpness; \n metrics1[9] = maxd_hhhh [ sum of distance weighted maximum height differences ]; \n metrics1[10] = mind_hhhh [ sum of distance weighted minimum height differences ]; \n metrics1[11] = sumd_hhhh [ sum of distance weighted height differences ]; \n metrics1[12] = aved_hhhh [ sum of distance weighted average height difference ]; \n metrics1[13] = count_hhhh [ count ]; \n metrics1[14] = w_hhhh [ sum of distance weights ]; \n metrics1[15] = mind_hxhx_ai_hhhl [ sum of distance weighted ( minimum difference of cells adjacent to lower cell ) ]; \n metrics1[16] = maxd_hxhx_ai_hhhl [ sum of distance weighted ( maximum difference of cells adjacent to lower cell ) ]; \n metrics1[17] = sumd_hxhx_ai_hhhl [ sum of distance weighted ( sum of differences of cells adjacent to lower cell ) ]; \n metrics1[18] = d_xhxx_ai_hhhl [ sum of distance weighted ( difference of cell opposite lower cell ) ]; \n metrics1[19] = d_xxxl_ai_hhhl [ sum of distance weighted ( difference of lower cell ) ]; \n metrics1[20] = sumd_xhxl_ai_hhhl [ sum of distance weighted ( sum of differences of lower cell and cell opposite ) ]; \n metrics1[21] = mind_abs_xhxl_ai_hhhl [ sum of distance weighted ( minimum difference magnitude of lower cell and cell opposite ) ]; \n metrics1[22] = maxd_abs_xhxl_ai_hhhl [ sum of distance weighted ( maximum difference magnitude of lower cell and cell opposite ) ]; \n metrics1[23] = sumd_abs_xhxl_ai_hhhl [ sum of distance weighted ( sum of difference magnitudes of lower cell and cell opposite ) ]; \n metrics1[24] = count_hhhl [ count ]; \n metrics1[25] = w_hhhl [ sum of distance weights ]; \n metrics1[26] = mind_hxhx_ai_hlhl [ sum of distance weighted ( minimum difference of higher cells ) ]; \n metrics1[27] = maxd_hxhx_ai_hlhl [ sum of distance weighted ( maximum difference of higher cells ) ]; \n metrics1[28] = sumd_hxhx_ai_hlhl [ sum of distance weighted ( sum differences of higher cells ) ]; \n metrics1[29] = mind_xlxl_ai_hlhl [ sum of distance weighted ( minimum difference of lower cells ) ]; \n metrics1[30] = maxd_xlxl_ai_hlhl [ sum of distance weighted ( maximum difference of lower cells ) ]; \n metrics1[31] = sumd_xlxl_ai_hlhl [ sum of distance weighted ( sum of differences of lower cells ) ]; \n metrics1[32] = mind_abs_hlhl [ sum of distance weighted ( minimum difference magnitude of cells ) ]; \n metrics1[33] = maxd_abs_hlhl [ sum of distance weighted ( maximum difference magnitude of cells ) ]; \n metrics1[34] = sumd_abs_hlhl [ sum of distance weighted ( sum of difference magnitudes of cells ) ]; \n metrics1[35] = count_hlhl [ count ]; \n metrics1[36] = w_hlhl [ sum of distance weights ]; \n metrics1[37] = mind_hhxx_ai_hhll [ sum of distance weighted ( minimum difference of higher cells ) ]; \n metrics1[38] = maxd_hhxx_ai_hhll [ sum of distance weighted ( maximum difference of higher cells ) ]; \n metrics1[39] = sumd_hhxx_ai_hhll [ sum of distance weighted ( sum of differences of higher cells ) ]; \n metrics1[40] = mind_xxll_ai_hhll [ sum of distance weighted ( minimum difference of lower cells ) ]; \n metrics1[41] = maxd_xxll_ai_hhll [ sum of distance weighted ( maximum difference of lower cells ) ]; \n metrics1[42] = sumd_xxll_ai_hhll [ sum of distance weighted ( sum of differences of lower cells ) ]; \n metrics1[43] = mind_abs_hhll [ sum of distance weighted ( minimum difference magnitude of cells ) ]; \n metrics1[44] = maxd_abs_hhll [ sum of distance weighted ( maximum difference magnitude of cells ) ]; \n metrics1[45] = sumd_abs_hhll [ sum of distance weighted ( sum of difference magnitudes of cells ) ]; \n metrics1[46] = count_hhll [ count ]; \n metrics1[47] = w_hhll [ sum of distance weights ]; \n metrics1[48] = mind_lxlx_ai_lllh [ sum of distance weighted ( minimum difference of cells adjacent to higher cell ) ]; \n metrics1[49] = maxd_lxlx_ai_lllh [ sum of distance weighted ( maximum difference of cells adjacent to higher cell ) ]; \n metrics1[50] = sumd_lxlx_ai_lllh [ sum of distance weighted ( sum of differences of cells adjacent to higher cell ) ]; \n metrics1[51] = d_xlxx_ai_lllh [ sum of distance weighted ( difference of cell opposite higher cell ) ]; \n metrics1[52] = d_xxxh_ai_lllh [ sum of distance weighted ( difference of higher cell ) ]; \n metrics1[53] = sumd_xlxh_ai_lllh [ sum of distance weighted ( sum of differences of higher cell and cell opposite ) ]; \n metrics1[54] = mind_abs_xlxh_ai_lllh [ sum of distance weighted ( minimum difference magnitude of higher cell and cell opposite ) ]; \n metrics1[55] = maxd_abs_xlxh_ai_lllh [ sum of distance weighted ( maximum difference magnitude of higher cell and cell opposite ) ]; \n metrics1[56] = sumd_abs_xlxh_ai_lllh [ sum of distance weighted ( sum of difference magnitudes of higher cell and cell opposite ) ]; \n metrics1[57] = count_lllh [ count ]; \n metrics1[58] = w_lllh [ sum of distance weights ]; \n metrics1[59] = maxd_llll [ sum of distance weighted maximum height differences ]; \n metrics1[60] = mind_llll [ sum of distance weighted minimum height differences ]; \n metrics1[61] = sumd_llll [ sum of distance weighted height differences ]; \n metrics1[62] = aved_llll [ sum of distance weighted average height difference ]; \n metrics1[63] = count_llll [ count ]; \n metrics1[64] = w_llll [ sum of distance weights ]; \n 
AbstractGrid2DSquareCell[] 
getMetrics1(AbstractGrid2DSquareCell _Grid2DSquareCell,
double distance,
double weightIntersect,
double weightFactor,
Grid2DSquareCellDoubleFactory _Grid2DSquareCellDoubleFactory,
Grid2DSquareCellIntFactory _Grid2DSquareCellIntFactory,
boolean handleOutOfMemoryError)
Returns an Grid2DSquareCellDouble[] metrics1 where: metrics1[0] = no data count; metrics1[1] = flatness; metrics1[2] = roughness; metrics1[3] = slopyness; metrics1[4] = levelness; metrics1[5] = totalDownness; metrics1[6] = averageDownness; metrics1[7] = totalUpness; metrics1[8] = averageUpness; metrics1[9] = maxd_hhhh [ sum of distance weighted maximum height differences ]; metrics1[10] = mind_hhhh [ sum of distance weighted minimum height differences ]; metrics1[11] = sumd_hhhh [ sum of distance weighted height differences ]; metrics1[12] = aved_hhhh [ sum of distance weighted average height difference ]; metrics1[13] = count_hhhh [ count ]; metrics1[14] = w_hhhh [ sum of distance weights ]; metrics1[15] = mind_hxhx_ai_hhhl [ sum of distance weighted ( minimum difference of cells adjacent to lower cell ) ]; metrics1[16] = maxd_hxhx_ai_hhhl [ sum of distance weighted ( maximum difference of cells adjacent to lower cell ) ]; metrics1[17] = sumd_hxhx_ai_hhhl [ sum of distance weighted ( sum of differences of cells adjacent to lower cell ) ]; metrics1[18] = d_xhxx_ai_hhhl [ sum of distance weighted ( difference of cell opposite lower cell ) ]; metrics1[19] = d_xxxl_ai_hhhl [ sum of distance weighted ( difference of lower cell ) ]; metrics1[20] = sumd_xhxl_ai_hhhl [ sum of distance weighted ( sum of differences of lower cell and cell opposite ) ]; metrics1[21] = mind_abs_xhxl_ai_hhhl [ sum of distance weighted ( minimum difference magnitude of lower cell and cell opposite ) ]; metrics1[22] = maxd_abs_xhxl_ai_hhhl [ sum of distance weighted ( maximum difference magnitude of lower cell and cell opposite ) ]; metrics1[23] = sumd_abs_xhxl_ai_hhhl [ sum of distance weighted ( sum of difference magnitudes of lower cell and cell opposite ) ]; metrics1[24] = count_hhhl [ count ]; metrics1[25] = w_hhhl [ sum of distance weights ]; metrics1[26] = mind_hxhx_ai_hlhl [ sum of distance weighted ( minimum difference of higher cells ) ]; metrics1[27] = maxd_hxhx_ai_hlhl [ sum of distance weighted ( maximum difference of higher cells ) ]; metrics1[28] = sumd_hxhx_ai_hlhl [ sum of distance weighted ( sum differences of higher cells ) ]; metrics1[29] = mind_xlxl_ai_hlhl [ sum of distance weighted ( minimum difference of lower cells ) ]; metrics1[30] = maxd_xlxl_ai_hlhl [ sum of distance weighted ( maximum difference of lower cells ) ]; metrics1[31] = sumd_xlxl_ai_hlhl [ sum of distance weighted ( sum of differences of lower cells ) ]; metrics1[32] = mind_abs_hlhl [ sum of distance weighted ( minimum difference magnitude of cells ) ]; metrics1[33] = maxd_abs_hlhl [ sum of distance weighted ( maximum difference magnitude of cells ) ]; metrics1[34] = sumd_abs_hlhl [ sum of distance weighted ( sum of difference magnitudes of cells ) ]; metrics1[35] = count_hlhl [ count ]; metrics1[36] = w_hlhl [ sum of distance weights ]; metrics1[37] = mind_hhxx_ai_hhll [ sum of distance weighted ( minimum difference of higher cells ) ]; metrics1[38] = maxd_hhxx_ai_hhll [ sum of distance weighted ( maximum difference of higher cells ) ]; metrics1[39] = sumd_hhxx_ai_hhll [ sum of distance weighted ( sum of differences of higher cells ) ]; metrics1[40] = mind_xxll_ai_hhll [ sum of distance weighted ( minimum difference of lower cells ) ]; metrics1[41] = maxd_xxll_ai_hhll [ sum of distance weighted ( maximum difference of lower cells ) ]; metrics1[42] = sumd_xxll_ai_hhll [ sum of distance weighted ( sum of differences of lower cells ) ]; metrics1[43] = mind_abs_hhll [ sum of distance weighted ( minimum difference magnitude of cells ) ]; metrics1[44] = maxd_abs_hhll [ sum of distance weighted ( maximum difference magnitude of cells ) ]; metrics1[45] = sumd_abs_hhll [ sum of distance weighted ( sum of difference magnitudes of cells ) ]; metrics1[46] = count_hhll [ count ]; metrics1[47] = w_hhll [ sum of distance weights ]; metrics1[48] = mind_lxlx_ai_lllh [ sum of distance weighted ( minimum difference of cells adjacent to higher cell ) ]; metrics1[49] = maxd_lxlx_ai_lllh [ sum of distance weighted ( maximum difference of cells adjacent to higher cell ) ]; metrics1[50] = sumd_lxlx_ai_lllh [ sum of distance weighted ( sum of differences of cells adjacent to higher cell ) ]; metrics1[51] = d_xlxx_ai_lllh [ sum of distance weighted ( difference of cell opposite higher cell ) ]; metrics1[52] = d_xxxh_ai_lllh [ sum of distance weighted ( difference of higher cell ) ]; metrics1[53] = sumd_xlxh_ai_lllh [ sum of distance weighted ( sum of differences of higher cell and cell opposite ) ]; metrics1[54] = mind_abs_xlxh_ai_lllh [ sum of distance weighted ( minimum difference magnitude of higher cell and cell opposite ) ]; metrics1[55] = maxd_abs_xlxh_ai_lllh [ sum of distance weighted ( maximum difference magnitude of higher cell and cell opposite ) ]; metrics1[56] = sumd_abs_xlxh_ai_lllh [ sum of distance weighted ( sum of difference magnitudes of higher cell and cell opposite ) ]; metrics1[57] = count_lllh [ count ]; metrics1[58] = w_lllh [ sum of distance weights ]; metrics1[59] = maxd_llll [ sum of distance weighted maximum height differences ]; metrics1[60] = mind_llll [ sum of distance weighted minimum height differences ]; metrics1[61] = sumd_llll [ sum of distance weighted height differences ]; metrics1[62] = aved_llll [ sum of distance weighted average height difference ]; metrics1[63] = count_llll [ count ]; metrics1[64] = w_llll [ sum of distance weights ]; 
protected java.lang.String[] 
getMetrics1Names()
TODO 
Grid2DSquareCellDouble[] 
getMetrics2(Grid2DSquareCellDouble grid,
double distance,
double weightIntersect,
double weightFactor,
int samplingDensity,
Grid2DSquareCellDoubleFactory gridFactory,
boolean handleOutOfMemoryError)
Returns an Grid2DSquareCellDouble[] metrics2 where: TODO: metrics2 is a mess. 
double[][] 
getNormalDistributionKernelWeights(AbstractGrid2DSquareCell a_Grid2DSquareCell,
double distance,
boolean handleOutOfMemoryError)

protected Grid2DSquareCellDouble[] 
getSlopeAspect(AbstractGrid2DSquareCell _Grid2DSquareCell)
Calculates and returns measures of the slope and aspect for the AbstractGrid2DSquareCell _Grid2DSquareCell passed in. 
Grid2DSquareCellDouble[] 
getSlopeAspect(AbstractGrid2DSquareCell _Grid2DSquareCell,
boolean handleOutOfMemoryError)
Calculates and returns measures of the slope and aspect for the AbstractGrid2DSquareCell _Grid2DSquareCell passed in. 
Grid2DSquareCellDouble[] 
getSlopeAspect(AbstractGrid2DSquareCell _Grid2DSquareCell,
double distance,
double weightIntersect,
double weightFactor,
boolean handleOutOfMemoryError)

protected double[] 
getSlopeAspect(AbstractGrid2DSquareCell _Grid2DSquareCell,
double x,
double y,
double distance,
double weightIntersect,
double weightFactor,
boolean handleOutOfMemoryError)
Returns a double[] _SlopeAndAspect where: _SlopeAndAspect[0] is the aggregate slope over the region weighted by distance, weightIntersect and weightFactor; _SlopeAndAspect[1] is the aggregate aspect over the region weighted by distance, weightIntersect and weightFactor. 
protected double[] 
getSlopeAspect(AbstractGrid2DSquareCell _Grid2DSquareCell,
long rowIndex,
long colIndex,
double distance,
double weightIntersect,
double weightFactor,
boolean handleOutOfMemoryError)
Returns a double[] _SlopeAndAspect where: _SlopeAndAspect[0] is the aggregate slope over the region weighted by distance, weightIntersect and weightFactor; _SlopeAndAspect[1] is the aggregate aspect over the region weighted by distance, weightIntersect and weightFactor. 
protected double[] 
getSlopeAspect(AbstractGrid2DSquareCell _Grid2DSquareCell,
long rowIndex,
long colIndex,
double x,
double y,
double distance,
double weightIntersect,
double weightFactor,
boolean handleOutOfMemoryError)
Returns a double[] _SlopeAndAspect where: _SlopeAndAspect[0] is the aggregate slope over the region weighted by distance, weightIntersect and weightFactor; _SlopeAndAspect[1] is the aggregate aspect over the region weighted by distance, weightIntersect and weightFactor. 
Grid2DSquareCellDouble 
getUpSlopeAreaMetrics(Grid2DSquareCellDouble grid,
double distance,
double weightFactor,
double weightIntersect,
Grid2DSquareCellDoubleFactory gridFactory,
boolean handleOutOfMemoryError)
Returns an Grid2DSquareCellDouble[] each element of which corresponds to a metrics of up slope cells of grid  a DEM The steeper the slope the higher the runoff? 
Methods inherited from class uk.ac.leeds.ccg.andyt.grids.process.Grid2DSquareCellProcessor 

_Output, _OutputESRIAsciiGrid, _OutputImage, _Rescale, addToGrid, addToGrid, addToGrid, addToGrid, addToGrid, addToGrid, addToGrid, addToGrid, aggregate, aggregate, angle, angle, angle, copyAndSetUpNewLog, distance, distance, distance, get_Directory, getGrid2DSquareCell, getGrid2DSquareCell, getRowProcessData, getRowProcessInitialData, getTime0, getTime0, log, log, log, mask, mask, mask, mask, set_Directory, set_Directory, set_Directory, setValueALittleBitLarger, setValueALittleBitSmaller 
Methods inherited from class java.lang.Object 

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

public Grid2DSquareCellProcessorDEM()
public Grid2DSquareCellProcessorDEM(java.io.File _Directory)
_Directory
 public Grid2DSquareCellProcessorDEM(java.io.File _Directory, boolean appendToLogFile)
_Directory
 appendToLogFile
 Method Detail 

public Grid2DSquareCellDouble[] getSlopeAspect(AbstractGrid2DSquareCell _Grid2DSquareCell, boolean handleOutOfMemoryError) throws java.io.IOException
_Grid2DSquareCell
 The AbstractGrid2DSquareCell to be processed.handleOutOfMemoryError
 If true then OutOfMemoryErrors are caught
in this method then caching operations are initiated prior to retrying.
If false then OutOfMemoryErrors are caught and thrown.
Defaults:
kernel to have
distance = ( _Grid2DSquareCell.get_Dimensions( handleOutOfMemoryError )[ 0 ].doubleValue() ) * ( 3.0d / 2.0d );
weightIntersect = 1.0d;
weightFactor = 0.0d;
java.io.IOException
protected Grid2DSquareCellDouble[] getSlopeAspect(AbstractGrid2DSquareCell _Grid2DSquareCell) throws java.io.IOException
_Grid2DSquareCell
 The AbstractGrid2DSquareCell to be processed.
Defaults:
kernel to have
distance = ( _Grid2DSquareCell.get_Dimensions( handleOutOfMemoryError )[ 0 ].doubleValue() ) * ( 3.0d / 2.0d );
weightIntersect = 1.0d;
weightFactor = 0.0d;
java.io.IOException
public double[][] getNormalDistributionKernelWeights(AbstractGrid2DSquareCell a_Grid2DSquareCell, double distance, boolean handleOutOfMemoryError)
public Grid2DSquareCellDouble[] getSlopeAspect(AbstractGrid2DSquareCell _Grid2DSquareCell, double distance, double weightIntersect, double weightFactor, boolean handleOutOfMemoryError) throws java.io.IOException
_Grid2DSquareCell
 The AbstractGrid2DSquareCell to be processed.distance
 the distance which defines the aggregate region.weightIntersect
 The kernel weighting weight at centre.weightFactor
 The kernel weighting distance decay.handleOutOfMemoryError
 If true then OutOfMemoryErrors are caught
in this method then caching operations are initiated prior to retrying.
If false then OutOfMemoryErrors are caught and thrown.
(NB. There are various strategies to reduce bias caused by noDataValues.
Here:
If the cell in grid for which _SlopeAndAspect is being calculated is a
noDataValue then the cells in _SlopeAndAspect are assigned their
noDataValue.
If one of the cells in the calculation of slope and aspect is a
noDataValue then its height is taken as the nearest cell value.
(Formerly the difference in its height was taken as the average
difference in height for those cells with values.)
)
java.io.IOException
protected double[] getSlopeAspect(AbstractGrid2DSquareCell _Grid2DSquareCell, long rowIndex, long colIndex, double distance, double weightIntersect, double weightFactor, boolean handleOutOfMemoryError)
_Grid2DSquareCell
 the Grid2DSquareCellDouble to be processed.rowIndex
 the rowIndex where _SlopeAndAspect is calculated.colIndex
 the colIndex where _SlopeAndAspect is calculated.distance
 the distance which defines the aggregate region.weightIntersect
 the kernel weighting weight at centre.weightFactor
 the kernel weighting distance decay.protected double[] getSlopeAspect(AbstractGrid2DSquareCell _Grid2DSquareCell, double x, double y, double distance, double weightIntersect, double weightFactor, boolean handleOutOfMemoryError)
_Grid2DSquareCell
 the Grid2DSquareCellDouble to be processed.x
 the x coordinate from where the aspect is calculatedy
 the y coordinate from where the aspect is calculateddistance
 the distance which defines the aggregate region.weightIntersect
 the kernel weighting weight at centre.weightFactor
 the kernel weighting distance decay.protected double[] getSlopeAspect(AbstractGrid2DSquareCell _Grid2DSquareCell, long rowIndex, long colIndex, double x, double y, double distance, double weightIntersect, double weightFactor, boolean handleOutOfMemoryError)
_Grid2DSquareCell
 The Grid2DSquareCellDouble to be processedrowIndex
 the rowIndex where the result is calculatedcolIndex
 the colIndex where the result is calculatedx
 the x coordinate from where the aspect is calculatedy
 the y coordinate from where the aspect is calculateddistance
 the distance which defines the regionweightIntersect
 weightFactor
 NB. If grid.getCell( x, y ) == grid.get_NoDataValue() then;
result[ 0 ] = grid.get_NoDataValue()
result[ 1 ] = grid.get_NoDataValue()
TODO:
x and y can be offset from a cell centroid so consider interpolationpublic Grid2DSquareCellDouble getHollowFilledDEM(AbstractGrid2DSquareCell _Grid2DSquareCell, Grid2DSquareCellDoubleFactory _Grid2DSquareCellDoubleFactory, double outflowHeight, int maxIterations, java.util.HashSet outflowCellIDsSet, boolean _TreatNoDataValueAsOutflow, boolean handleOutOfMemoryError)
_Grid2DSquareCell
 AbstractGrid2DSquareCell to be processed.
public AbstractGrid2DSquareCell[] getMetrics1(AbstractGrid2DSquareCell _Grid2DSquareCell, double distance, double weightIntersect, double weightFactor, Grid2DSquareCellDoubleFactory _Grid2DSquareCellDoubleFactory, Grid2DSquareCellIntFactory _Grid2DSquareCellIntFactory, boolean handleOutOfMemoryError) throws java.io.IOException
_Grid2DSquareCell
 the Grid2DSquareCellDouble to be processeddistance
 the distance within which metrics will be calculatedweightIntersect
 kernel parameter ( weight at the centre )weightFactor
 kernel parameter ( distance decay )_Grid2DSquareCellDoubleFactory
 The Grid2DSquareCellDoubleFactory for creating grids
java.io.IOException
protected java.lang.String[] getMetrics1Names()
public AbstractGrid2DSquareCell[] getMetrics1(AbstractGrid2DSquareCell[] metrics1, AbstractGrid2DSquareCell _Grid2DSquareCell, java.math.BigDecimal[] dimensions, double distance, double weightIntersect, double weightFactor, boolean handleOutOfMemoryError)
metrics1
 an Grid2DSquareCellDouble[] for storing result \n_Grid2DSquareCell
 the Grid2DSquareCellDouble to be processed \ndistance
 the distance within which metrics will be calculated \nweightIntersect
 kernel parameter ( weight at the centre ) \nweightFactor
 kernel parameter ( distance decay ) \n
Going directly to this method is useful if the initialisation of the
metrics1 is slow and has already been done.public Grid2DSquareCellDouble[] getMetrics2(Grid2DSquareCellDouble grid, double distance, double weightIntersect, double weightFactor, int samplingDensity, Grid2DSquareCellDoubleFactory gridFactory, boolean handleOutOfMemoryError)
public Grid2DSquareCellDouble getMaxFlowDirection(Grid2DSquareCellDouble grid, Grid2DSquareCellDoubleFactory gridFactory, boolean handleOutOfMemoryError)
grid
 the Grid2DSquareCellDouble to be processedgridFactory
 the Grid2DSquareCellDoubleFactory used to create resultpublic Grid2DSquareCellDouble getUpSlopeAreaMetrics(Grid2DSquareCellDouble grid, double distance, double weightFactor, double weightIntersect, Grid2DSquareCellDoubleFactory gridFactory, boolean handleOutOfMemoryError)
public java.util.HashSet getInitialPeaksHashSetAndSetTheirValue(Grid2DSquareCellDouble grid, Grid2DSquareCellDouble upSlopeAreaMetrics, boolean handleOutOfMemoryError)
grid
  the Grid2DSquareCellDouble to be processed


PREV CLASS NEXT CLASS  FRAMES NO FRAMES  
SUMMARY: NESTED  FIELD  CONSTR  METHOD  DETAIL: FIELD  CONSTR  METHOD 