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#' Get rectangular image dimensions
#'
#' Given regional bounds image width and height are computed in order to display a region without distortion.
#'
#'@param visualizeVariables A dataframe containing $lon and $lat values which will be bounded by lon/lat_bounds (data.frame).
#'@param lon_bounds Array containing two values for longitude min and max (numeric).
#'@param lat_bounds Array containing two values for latitude min and max (numeric).
#'@param image_def Minimal image default size for width and height (numeric).
#'@param ihsf Image height rescaling factor (numeric).
#'
#' @export
recalculateImageDimensions <- function(visualizeVariables,
lon_bounds,
lat_bounds,
image_def,
ihsf){
lon <- visualizeVariables$lon[visualizeVariables$lon <= lon_bounds[2]]
lon <- lon[lon_bounds[1] <= lon]
lat <- visualizeVariables$lat[visualizeVariables$lat <= lat_bounds[2]]
lat <- lat[lat_bounds[1] <= lat]
# Update this value if you want to change min width/height of plot.
minSize <- 200
tmpWidth <- max(minSize, image_def)
tmpHeight <- max(minSize, image_def)
# Update width and height according to visualizeVariables lat and lon vectors
if (length(lon) >= length(lat)) {
# Shrink height
tmpHeight <- round(tmpWidth * length(lat) / length(lon))
if (tmpHeight < minSize) {
tmpWidth <- minSize / tmpHeight * tmpWidth
tmpHeight <- minSize
}
# Why are we doing this? (And why not in the else block?)
imageheight <- tmpHeight + (round(ihsf * tmpHeight))
imagewidth <- tmpWidth
} else {
# No need to check against minSize since we will multiply with a value > 1.
tmpWidth <- round(tmpHeight * length(lat) / length(lon))
imagewidth <- tmpWidth
imageheight <- tmpHeight
}
return(list(imagewidth = imagewidth, imageheight = imageheight))
}
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