Uses of Class
uk.ac.leeds.ccg.andyt.grids.core.Grid2DSquareCellDoubleFactory

Packages that use Grid2DSquareCellDoubleFactory

uk.ac.leeds.ccg.andyt.grids.process
uk.ac.leeds.ccg.andyt.grids.utilities

Uses of Grid2DSquareCellDoubleFactory in uk.ac.leeds.ccg.andyt.grids.process

Methods in uk.ac.leeds.ccg.andyt.grids.process with parameters of type Grid2DSquareCellDoubleFactory

Grid2DSquareCellDouble	Grid2DSquareCellProcessor.add(Grid2DSquareCellDouble grid0, Grid2DSquareCellDouble grid1, boolean data, Grid2DSquareCellDoubleFactory gridFactory, boolean handleOutOfMemoryError) TODO: 1.
Grid2DSquareCellDouble	Grid2DSquareCellProcessor.addToGrid(Grid2DSquareCellDouble grid, double value, Grid2DSquareCellDoubleFactory gridFactory, boolean handleOutOfMemoryError) Returns a copy of grid with value added to every cell via AbstractGrid2DSquareCellDouble.addToCell(value)
Grid2DSquareCellDouble	Grid2DSquareCellProcessor.addToGrid(Grid2DSquareCellDouble grid, java.util.HashSet cellIDs, double value, Grid2DSquareCellDoubleFactory gridFactory, boolean handleOutOfMemoryError) Returns a copy of grid with value added to all cells with IDs as Integer keys in cellIDKeys
Grid2DSquareCellDouble	Grid2DSquareCellProcessor.aggregate(Grid2DSquareCellDouble grid, int cellFactor, java.lang.String statistic, int rowOffset, int colOffset, Grid2DSquareCellDoubleFactory gridFactory, boolean handleOutOfMemoryError) Returns an Grid2DSquareCellDouble at a lower level of resolution than grid.
Grid2DSquareCellDouble	Grid2DSquareCellProcessor.aggregate(Grid2DSquareCellDouble grid, java.lang.String statistic, java.math.BigDecimal[] resultDimensions, Grid2DSquareCellDoubleFactory gridFactory, boolean handleOutOfMemoryError) Returns an Grid2DSquareCellDouble at a lower level of resolution than grid.
Grid2DSquareCellDouble	Grid2DSquareCellProcessor.distanceToDataValue(Grid2DSquareCellDouble grid0, Grid2DSquareCellDoubleFactory gridFactory, boolean handleOutOfMemoryError) Returns a new Grid2DSquareCellDouble the values of which are the distance to the nearest data value TODO: Optimise as it is currently very slow and inefficient!!!
Grid2DSquareCellDouble	Grid2DSquareCellProcessor.divide(Grid2DSquareCellDouble grid0, Grid2DSquareCellDouble grid1, Grid2DSquareCellDoubleFactory gridFactory, double defaultValue, boolean handleOutOfMemoryError)
Grid2DSquareCellDouble	Grid2DSquareCellProcessorDEM.doFlowAccumulation(Grid2DSquareCellDouble flowAccumulation, Grid2DSquareCellDouble grid, double precipitation, java.util.HashSet outflowCellIDs, Grid2DSquareCellDoubleFactory gridFactory) TODO: docs frictionFactor = 75.0d; constant = 8.0d * 9.81d / frictionFactor; velocity = Math.sqrt( constant * waterDepth * changeInDepth / ChangeInLength ); discharge = velocity * waterDepth
Grid2DSquareCellDouble[]	Grid2DSquareCellProcessorGWS.geometricDensity(Grid2DSquareCellDouble grid, double distance, Grid2DSquareCellDoubleFactory gridFactory) Returns an Grid2DSquareCellDouble[] containing geometric density surfaces at a range of scales: result[ 0 ] - is the result at the first scale ( double the cellsize of grid ) result[ 1 ] - if it exists is the result at the second scale ( double the cellsize of result[ 0 ] ) result[ n ] - if it exists is the result at the ( n + 1 )th scale ( double the cellsize of result[ n - 1 ] ) The algorithm used for generating a geometric density surface is described in: Turner A (2000) Density Data Generation for Spatial Data Mining Applications.
Grid2DSquareCellDouble	Grid2DSquareCellProcessorDEM.getFlowAccumulation(Grid2DSquareCellDouble grid, int iterations, double precipitation, java.util.HashSet outflowCellIDs, Grid2DSquareCellDoubleFactory gridFactory) There are many estimates of flow that can be generated and many models developed in hydrology.
Grid2DSquareCellDouble	Grid2DSquareCellProcessorDEM.getHollowFilledDEM(Grid2DSquareCellDouble grid, double outflowHeight, int maxIterations, java.util.HashSet outflowCellIDsSet, Grid2DSquareCellDoubleFactory gridFactory) Returns an Grid2DSquareCellDouble hollowFilledDEM which has cell values as in grid except with any hollows raised so that they are not hollows.
Grid2DSquareCellDouble	Grid2DSquareCellProcessorDEM.getInitialFlowAccumulation(Grid2DSquareCellDouble grid, double precipitation, java.util.HashSet outflowCellIDs, Grid2DSquareCellDoubleFactory gridFactory) TODO: docs frictionFactor = 75.0d; constant = 8.0d * 9.81d / frictionFactor; velocity = Math.sqrt( constant * waterDepth * changeInDepth / ChangeInLength ); discharge = velocity * waterDepth
Grid2DSquareCellDouble	Grid2DSquareCellProcessorDEM.getMaxFlowDirection(Grid2DSquareCellDouble grid, Grid2DSquareCellDoubleFactory gridFactory) Returns an Grid2DSquareCellDouble result containing values which indicate the direction of the maximum down slope for the immediate 8 cell neighbourhood.
Grid2DSquareCellDouble[]	Grid2DSquareCellProcessorDEM.getMetrics1(Grid2DSquareCellDouble grid, double distance, double weightIntersect, double weightFactor, Grid2DSquareCellDoubleFactory gridFactory) 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 ];
Grid2DSquareCellDouble[]	Grid2DSquareCellProcessorDEM.getMetrics2(Grid2DSquareCellDouble grid, double distance, double weightIntersect, double weightFactor, int samplingDensity, Grid2DSquareCellDoubleFactory gridFactory) Returns an Grid2DSquareCellDouble[] metrics2 where: TODO: metrics2 is a mess.
Grid2DSquareCellDouble[]	Grid2DSquareCellProcessorDEM.getSlopeAndAspect(Grid2DSquareCellDouble grid, double distance, double weightIntersect, double weightFactor, Grid2DSquareCellDouble slopeAndAspectDimensions, Grid2DSquareCellDoubleFactory gridFactory) Returns an Grid2DSquareCellDouble[] 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[]	Grid2DSquareCellProcessorDEM.getSlopeAndAspect(Grid2DSquareCellDouble grid, Grid2DSquareCellDoubleFactory gridFactory) Returns an Grid2DSquareCellDouble[] 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	Grid2DSquareCellProcessorDEM.getUpSlopeAreaMetrics(Grid2DSquareCellDouble grid, double distance, double weightFactor, double weightIntersect, Grid2DSquareCellDoubleFactory gridFactory) 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?
Grid2DSquareCellDouble	Grid2DSquareCellProcessor.linearRescale(Grid2DSquareCellDouble grid, double min, double max, Grid2DSquareCellDoubleFactory gridFactory, boolean handleOutOfMemoryError) Returns a new Grid2DSquareCellDouble that is a copy of grid, but with values linearly rescaled into the range [min,max].
Grid2DSquareCellDouble	Grid2DSquareCellProcessor.logRescale(Grid2DSquareCellDouble grid, double min, double max, Grid2DSquareCellDoubleFactory gridFactory, boolean handleOutOfMemoryError) Returns a new Grid2DSquareCellDouble that is a copy of grid, but with values linearly scaled to range [1.0d,1000000.0d] then logged using Math.log(double) and then linearly scaled into the range [min,max]
Grid2DSquareCellDouble	Grid2DSquareCellProcessor.mask(Grid2DSquareCellDouble grid, Grid2DSquareCellDouble mask, Grid2DSquareCellDoubleFactory gridFactory, boolean handleOutOfMemoryError) Returns a new Grid2DSquareCellDouble that is a copy of grid, but masked using mask.
Grid2DSquareCellDouble	Grid2DSquareCellProcessor.mask(Grid2DSquareCellDouble grid, long startRowIndex, long startColIndex, long endRowIndex, long endColIndex, Grid2DSquareCellDoubleFactory gridFactory, boolean handleOutOfMemoryError) Returns a new Grid2DSquareCellDouble that is a copy of grid, but with the value of cells in grid that intersect rectangle given by (startRowIndex,startColIndex,endRowIndex,endColIndex) set to its noDataValue.
Grid2DSquareCellDouble	Grid2DSquareCellProcessor.minus(Grid2DSquareCellDouble grid0, Grid2DSquareCellDouble grid1, boolean data, Grid2DSquareCellDoubleFactory gridFactory, boolean handleOutOfMemoryError) TODO: 1.
Grid2DSquareCellDouble	Grid2DSquareCellProcessor.multiply(Grid2DSquareCellDouble grid0, Grid2DSquareCellDouble grid1, Grid2DSquareCellDoubleFactory gridFactory, boolean handleOutOfMemoryError)
Grid2DSquareCellDouble[]	Grid2DSquareCellProcessorGWS.regionBivariateStatistics(Grid2DSquareCellDouble grid0, Grid2DSquareCellDouble grid1, java.util.Vector statistics, double distance, double weightIntersect, double weightFactor, Grid2DSquareCellDoubleFactory gridFactory) Returns an Grid2DSquareCellDouble[] result with elements based on statistics and values based on bivariate comparison of grid0 and grid1, distance, weightIntersect and weightFactor.
java.util.Vector	Grid2DSquareCellProcessorGWS.regionUnivariateStatistics(Grid2DSquareCellDouble grid, java.util.Vector statistics, double distance, double weightIntersect, double weightFactor, Grid2DSquareCellDoubleFactory gridFactory) Returns a Vector containing Grid2DSquareCellDoubles.
java.util.Vector	Grid2DSquareCellProcessorGWS.regionUnivariateStatisticsCrossScale(Grid2DSquareCellDouble grid, java.util.Vector statistics, double distance, double weightIntersept, double weightFactor, double scaleIntersept, double scaleFactor, Grid2DSquareCellDoubleFactory gridFactory) TODO
java.util.Vector	Grid2DSquareCellProcessorGWS.regionUnivariateStatisticsSlow(Grid2DSquareCellDouble grid, java.util.Vector statistics, double distance, double weightIntersect, double weightFactor, Grid2DSquareCellDoubleFactory gridFactory) Returns a Vector containing Grid2DSquareCellDoubles
Grid2DSquareCellDouble	Grid2DSquareCellProcessor.setValueALittleBitLarger(Grid2DSquareCellDouble grid, java.util.HashSet cellIDs, Grid2DSquareCellDoubleFactory gridFactory, boolean handleOutOfMemoryError) Returns a new Grid2DSquareCellDouble that is a copy of grid, but with value of cells with keys in cellKeys set to a value a little bit larger.
Grid2DSquareCellDouble	Grid2DSquareCellProcessor.setValueALittleBitSmaller(Grid2DSquareCellDouble grid, java.util.HashSet cellIDs, Grid2DSquareCellDoubleFactory gridFactory, boolean handleOutOfMemoryError) Returns a new Grid2DSquareCellDouble that is a copy of grid, but with value of cells with keys in cellKeys set to a value a little bit smaller.

Uses of Grid2DSquareCellDoubleFactory in uk.ac.leeds.ccg.andyt.grids.utilities

Methods in uk.ac.leeds.ccg.andyt.grids.utilities with parameters of type Grid2DSquareCellDoubleFactory

static java.lang.Object[]

Utilities.densityPlot(Grid2DSquareCellAbstract xGrid, Grid2DSquareCellAbstract yGrid, int divisions, Grid2DSquareCellDoubleFactory gridFactory) Returns a density plot of xGrid values against yGrid values.