fix_matrix_ratio: Fix matrix dimension ratio

In jmw86069/jamba: Jam Base Methods

Fix matrix dimension ratio

Description

Fix matrix dimension ratio

Usage

fix_matrix_ratio(
  x,
  maxRatioFix = 10,
  minRasterMultiple = NULL,
  rasterTarget = 200,
  ratioThreshold = 3,
  verbose = FALSE,
  ...
)

Arguments

x	matrix input
maxRatioFix	integer value indicating the maximum multiple used to duplicate columns or rows. This value is used to prevent replicating a matrix with 1 million rows and 10 columns into a 10 million by 10 million matrix. For example maxRatioFix=100 will not replicate columns or rows more than 100 times.
minRasterMultiple	integer vector of 1 or 2 values, referring to the minimum number of times each row or column is replicated, respectively. For example minRasterMultiple=c(2,5) will at minimum replicate each row 2 times, and each column 5 times.
rasterTarget	integer number reflecting the target minimum number of rows and columns. This value is used to protect from interpolating a 5x5 matrix, which yields a blurry result. When rasterTarget=200, a 5x5 matrix will be expanded to 200x200, and the 200x200 matrix will be interpolated to yield a sharp image.
ratioThreshold	numeric value indicating the ratio of nrow:ncol above which this function will adjust the dimensions of the output matrix. For example when ratioThreshold=3 there must be 3 times more rows than columns, or 3 times more columns than rows.
verbose	logical indicating whether to print verbose output.
...	additional arguments are ignored.

Details

This function is experimental, replicating the logic used inside imageDefault() to ensure a numeric matrix is roughly 1:1 ratio of nrow:ncol. It currently duplicates columns or rows n times in an effort to make the resulting matrix less than a 2:1 ratio. The purpose is to allow rasterImage() or grid.raster() with argument interpolate=TRUE to produce an output raster image that has interpolated the image with reasonably square pixels. Without this adjustment, a matrix with 2,000 rows and 10 columns would be interpolated much more on the x-axis than the y-axis, blurring the data along the x-axis.

The main goal is to enable arguments useRaster=TRUE and interpolate=TRUE which allows an output image to contain more rows than pixels, and still have the pixels represent properly smoothed content.

See the examples for visual examples of the effect, showing image.default(), jamba::imageDefault(), graphics::rasterImage(), and grid::grid.raster().

See Also

Other jam numeric functions: deg2rad(), noiseFloor(), normScale(), rad2deg(), rowGroupMeans(), rowRmMadOutliers(), warpAroundZero()

Examples

m <- matrix(rainbow(9), ncol=3);
m2 <- fix_matrix_ratio(m);
par("mfrow"=c(1,3));
imageByColors(m, useRaster=FALSE,
   main="m\nuseRaster=FALSE");
imageByColors(m, useRaster=TRUE, fixRasterRatio=FALSE,
   main="m\nuseRaster=FALSE\nfixRasterRatio=FALSE");
imageByColors(m2, useRaster=TRUE, fixRasterRatio=FALSE,
   main="m2\nuseRaster=FALSE\nfixRasterRatio=FALSE");

m <- matrix(colors()[1:90], ncol=3)
dim(m)
m2 <- fix_matrix_ratio(m);
dim(m2);
par("mfrow"=c(1,4));
imageByColors(m, useRaster=FALSE,
   main="m\nuseRaster=FALSE");
imageByColors(m, useRaster=TRUE, interpolate=FALSE,
   main="m\nuseRaster=TRUE\ninterpolate=FALSE");
imageByColors(m, useRaster=TRUE, interpolate=TRUE, fixRasterRatio=FALSE,
   main="m\nuseRaster=TRUE\ninterpolate=TRUE");
imageByColors(m2, useRaster=TRUE, fixRasterRatio=FALSE,
   main="fix_matrix_ratio(m)\nuseRaster=TRUE\ninterpolate=TRUE");
par("mfrow"=c(1,1));

## Complicated example showing the effect of interpolate=TRUE
testHeatdata <- matrix(rnorm(90000), ncol=9)[,1:9];
testHeatdata <- testHeatdata[order(testHeatdata[,5]),];
g1 <- seq(from=10, to=10000, by=1000);
testHeatdata[g1+rep(1:3, each=length(g1)),] <- 9;
for (i in seq(from=125, to=235, by=3)) {
   ix <- round(sin(deg2rad(i))*5+5);
   iy <- round(-cos(deg2rad(i))*5500 + 3500);
   testHeatdata[iy:(iy+4), ix] <- 10;
}
g2 <- 3011+c(1:12*90);
testHeatdata[g2+rep(1:3, each=length(g2)), c(3,7)] <- 10;
testHeatdata <- testHeatdata[10000:1,];
col <- getColorRamp("RdBu_r", n=15, lens=1, trimRamp=c(4,1));
par("mfrow"=c(1,2));
image.default(z=t(testHeatdata), col=col, useRaster=TRUE,
   main="image.default(..., useRaster=TRUE,\ninterpolate=FALSE)");
imageDefault(z=t(testHeatdata), col=col, useRaster=TRUE,
   main="imageDefault(..., useRaster=TRUE,\ninterpolate=TRUE)");
par("mfrow"=c(1,1));

m2r <- as.raster(m2);
nullPlot(xaxs="i", yaxs="i",
   main="using rasterImage()");
rasterImage(m2r, xleft=1, xright=2, ybottom=1, ytop=2);

if (require(grid)) {
   testHeatdata2 <- testHeatdata[10000:1,,drop=FALSE];
   testHeatdata2[] <- circlize::colorRamp2(breaks=seq(from=-10, to=10, length.out=25),
      colors=getColorRamp("RdBu_r", n=25))(testHeatdata2);
   testHeatdata2 <- fix_matrix_ratio(testHeatdata2);
   m2r <- as.raster(testHeatdata2);
   par("mfrow"=c(1,1));
   nullPlot(xaxs="i", yaxs="i",
      doBoxes=FALSE);
   grid::grid.raster(m2r,
      x=grid::unit(0.5, "npc"),
      y=grid::unit(0.5, "npc"),
      height=grid::unit(1, "npc"),
      width=grid::unit(1, "npc"),
      interpolate=FALSE);
   title(main="using grid.raster(..., interpolate=FALSE)")
   nullPlot(xaxs="i", yaxs="i",
      doBoxes=FALSE);
   grid::grid.raster(m2r,
      x=grid::unit(0.5, "npc"),
      y=grid::unit(0.5, "npc"),
      height=grid::unit(1, "npc"),
      width=grid::unit(1, "npc"),
      interpolate=TRUE);
   title(main="using grid.raster(..., interpolate=TRUE)")
}

## Example showing usr coordinates for grid.raster()
if (require(gridBase)) {
   nullPlot(xaxs="i", yaxs="i",
      main="gridBase grid.raster(..., interpolate=TRUE)");
   vps <- gridBase::baseViewports();
   grid::pushViewport(vps$inner, vps$figure, vps$plot);
   grid::grid.raster(m2r,
      x=grid::unit(1.5, "native"),
      y=grid::unit(1.5, "native"),
      height=grid::unit(1, "native"),
      width=grid::unit(1, "native"),
      interpolate=TRUE);
   grid::popViewport(3);
}

jmw86069/jamba documentation built on March 26, 2024, 5:26 a.m.