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;
; PURPOSE:
; The purpose of this function is to warp an image into a map projection, given
; latitude and longitude values for each data point. It is similar to MAP_PATCH in
; IDL.
;
;******************************************************************************************;
; ;
; Copyright (c) 2012, by Fanning Software Consulting, Inc. All rights reserved. ;
; ;
; Redistribution and use in source and binary forms, with or without ;
; modification, are permitted provided that the following conditions are met: ;
; ;
; * Redistributions of source code must retain the above copyright ;
; notice, this list of conditions and the following disclaimer. ;
; * Redistributions in binary form must reproduce the above copyright ;
; notice, this list of conditions and the following disclaimer in the ;
; documentation and/or other materials provided with the distribution. ;
; * Neither the name of Fanning Software Consulting, Inc. nor the names of its ;
; contributors may be used to endorse or promote products derived from this ;
; software without specific prior written permission. ;
; ;
; THIS SOFTWARE IS PROVIDED BY FANNING SOFTWARE CONSULTING, INC. ''AS IS'' AND ANY ;
; EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES ;
; OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT ;
; SHALL FANNING SOFTWARE CONSULTING, INC. BE LIABLE FOR ANY DIRECT, INDIRECT, ;
; INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED ;
; TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; ;
; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ;
; ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT ;
; (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS ;
; SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ;
;******************************************************************************************;
;
;+
; The purpose of this function is to warp an image into a map projection, given
; latitude and longitude values for each data point. It is the Coyote Graphics
; equivalent of MAP_PATCH in IDL.
;
; Please note: I have only used and tested this function with the well-behaved
; data I am using in my own research and for which I needed this functionality.
; I have no doubt there might be problems with less well-behaved data sets.
; If you discover a problem with your own data, please let me know and I'll take
; another look at this function. DWF.
;
; :Categories:
; Graphics, Map Projections
;
; :Returns:
; An output 2D grid in which the data points have been warped into the
; particular map projection at the requested pixel resolution.
;
; :Params:
; data: in, required, type=numerical
; A vector or 2D data set. Longitude and latitude values must be present
; (or easily calculated) for each element in the data array.
; lons: in, required, type=float
; If data is a vector, a vector of longitude values corresponding to each
; data value. Values must be in the range -180 to 360. If data is 2D, either
; a 1D vector or a 2D array of corresponding longitude values. If data is 2D,
; and the LONS parameter is missing, a vector of appropriate length scaled into
; the range -180.0 to 180.0 will be created.
; lats: in, required, type=float
; If data is a vector, a vector of latitude values corresponding to each
; data value. Values must be in the range -90 to 90. If data is 2D, either
; a 1D vector or a 2D array of corresponding latitude values. If data is 2D,
; and the LONS parameter is missing, a vector of appropriate length scaled into
; the range -90.0 to 90.0 will be created.
;
; :Keywords:
; cubic: in, optional, type=boolean, default=0
; If this keyword is set, and the data is a two-dimensional grid, then cubic
; interpolation will be used to create the output image. It is ignored in the
; case of non-gridded input data.
; griddata: in, optional, type=boolean, default=1
; If the input data is non-gridded, setting this keyword will choose the GRIDDATA
; function to grid the data into a 2D output array. If not set, the data will be
; gridded using the TRIGRID function.
; map: in, optional, type=object
; An input map projection object (cgMap). If provided, the data will be gridded into
; this map projection. If not provided, a map object using a equirectangular map projection
; with a spherical datum will be used. The XRANGE and YRANGE properties of the map object
; will be set by the program in the course of doing the gridding if the `SetRange` keyword is
; set.
; missing: in, optional, type=varies
; Missing data in the gridding process will be set to this value.
; nearest_neighbor: in, optional, type=boolean, default=0
; If this keyword is set, the nearest neighbor algorithm will be used to create the output
; grid. Otherwise, bilinear (gridded input data) or natural neighbor (non-gridded input data)
; interpolation is used as the default algorithm. The keyword is ignored if non-grided input
; data is being used and the GRIDDATA keyword is not set, or if gridded input data is being
; used and the CUBIC keyword is set.
; resolution: in, optional, type=integer
; A two-element array giving the pixel resolution of the output array in X and Y.
; The default is a 400x400 array.
; setrange: in, optional, type=boolean, default=1
; If this keyword is set, the XRANGE and YRANGE parameters of the cgMap object will
; be set to the output X and Y ranges.
; xrange: out, optional, type=float
; The output X range in projected meter space (usually associated with the longitude).
; yrange: out, optional, type=float
; The output Y range in projected meter space (usually associated with the latitude).
;
; :Examples:
; To display a GOES image with map annotations::
; fileURL = 'http://www.idlcoyote.com/misc/goes_example_data.sav'
; filename = "goes_example_data.sav"
; netObject = Obj_New('IDLnetURL')
; void = netObject -> Get(URL=fileURL, FILENAME=filename)
; Obj_Destroy, netObject
; Restore, filename
; peru_lat = Temporary(peru_lat) / 10000.
; peru_lon = Temporary(peru_lon) / 10000.
; s = Size(peruimage, /DIMENSIONS)
; centerLat = peru_lat[s[0]/2, s[1]/2]
; centerLon = peru_lon[s[0]/2, s[1]/2]
; map = Obj_New('cgMap', 'Albers Equal Area', Ellipsoid='sphere', /OnImage, $
; STANDARD_PAR1=-19, STANDARD_PAR2=20, CENTER_LAT=centerLat, CENTER_LON=centerLon)
; warped = cgWarpToMap(peruImage, peru_lon, peru_lat, MAP=map, MISSING=0, $
; Resolution=[400, 300], /SetRange)
; cgDisplay, /Free, Title='Warped Image with cgWarpToMap'
; cgImage, warped, Stretch=2, Position=[0,0,1,1]
; map -> Draw
; cgMap_Grid, Map=map, /Label, Color='goldenrod'
; cgMap_Continents, MAP=map, Color='goldenrod'
; cgMap_Continents, MAP=map, Color='goldenrod', /Countries
;
; Additional examples can be found here: http://www.idlcoyote.com/map_tips/warptomap.php.
;
; :Author:
; FANNING SOFTWARE CONSULTING::
; David W. Fanning
; 1645 Sheely Drive
; Fort Collins, CO 80526 USA
; Phone: 970-221-0438
; E-mail: david@idlcoyote.com
; Coyote's Guide to IDL Programming: http://www.idlcoyote.com/
;
; :History:
; Modification History::
; Written by David W. Fanning, 12 Sept 2012.
; Modifications to accommodate lat/lon arrays that are one-dimensional to go along
; with 2D data. 13 Sept 2012. DWF.
; Fixed a problems in which projected meter X values were sometimes reversed in order. 31 Oct 2014. DWF.
;
; :Copyright:
; Copyright (c) 2012, Fanning Software Consulting, Inc.
;-
FUNCTION cgWarpToMap, data, lons, lats, $
CUBIC=cubic, $
GRIDDATA=griddata, $
MAP=map, $
MISSING=missing, $
NEAREST_NEIGHBOR=nearest_neighbor, $
RESOLUTION=resolution, $
SETRANGE=setrange, $
XRANGE=xrange, $
YRANGE=yrange
Compile_Opt idl2
Catch, theError
IF theError NE 0 THEN BEGIN
Catch, /CANCEL
void = cgErrorMsg()
RETURN, data
ENDIF
IF N_Params() EQ 0 THEN BEGIN
Print, 'Calling Syntax: warpedImage = cgWarpImage(image, lons, lats, MAP=map, RESOLUTION=resolution)'
RETURN, 0
ENDIF
; Handle keywords
IF N_Elements(missing) EQ 0 THEN missing = 0B
IF Keyword_Set(nearest_neighbor) THEN method = "Nearest Neighbor" ELSE method = "Natural Neighbor"
; Assume we are working with a 2D grid.
grid = 1
; Assume the latitude and longitude arrays are 2D.
latlon2d = 1
; If no map object, use a Equirectangular grid with a spherical datum.
IF N_Elements(map) EQ 0 THEN map = Obj_New('cgMap', 'Equirectangular', ELLIPSOID='Sphere')
; Is the data 1D or 2D. If 1D, lons and lats are required.
ndim = Size(data, /N_DIMENSIONS)
CASE ndim OF
1: BEGIN
IF (N_Elements(lons) EQ 0) || (N_Elements(lats) EQ 0) THEN BEGIN
Message, 'Must supply longitudes and latitudes with 1D input data.'
ENDIF
grid = 0
latlon2d = 0
END
2: BEGIN
s = Size(data, /DIMENSIONS)
IF N_Elements(lons) EQ 0 THEN lons = cgScaleVector(Findgen(s[0]), -180, 180)
IF N_Elements(lats) EQ 0 THEN lats = cgScaleVector(Findgen(s[1]), -90, 90)
IF Size(lons, /N_DIMENSIONS) EQ 1 THEN latlon2d = 0
IF Size(lats, /N_DIMENSIONS) EQ 1 THEN latlon2d = 0
END
ELSE: Message, 'Input data must be either 1D or 2D.'
ENDCASE
; Make sure the longitudes are in the range -180 to 180.
lons = ((lons + 180) MOD 360) - 180
; Convert to XY projected meter space.
IF N_Elements(lons) NE N_Elements(lats) THEN BEGIN
s = Size(data, /DIMENSIONS)
lattemp = Rebin(Reform(lats, 1, s[1]), s[0], s[1])
lontemp = Rebin(lons, s[0], s[1])
xy = map -> Forward(lontemp, lattemp)
ENDIF ELSE BEGIN
xy = map -> Forward(lons, lats)
ENDELSE
x = Reform(xy[0,*])
y = Reform(xy[1,*])
xmin = Min(x, MAX=xmax)
ymin = Min(y, MAX=ymax)
; Set the output resolution of the grid.
IF N_Elements(resolution) EQ 0 THEN BEGIN
delta_x = (xmax - xmin) / 399
delta_y = (ymax - ymin) / 399
resolution = [400,400]
ENDIF ELSE BEGIN
delta_x = (xmax - xmin) / (resolution[0]-1)
delta_y = (ymax - ymin) / (resolution[1]-1)
ENDELSE
; Find the sides of the image and make a boundary rectangle.
IF (grid EQ 0) || (latlon2d EQ 0) THEN BEGIN
xmin = Min(x, MAX=xmax)
ymin = Min(y, MAX=ymax)
ENDIF ELSE BEGIN
dims = Size(data, /DIMENSIONS)
x = Reform(x, dims[0], dims[1])
y = Reform(y, dims[0], dims[1])
xmin = x[0,Round(s[1]/2.)]
xmax = x[s[0]-1, Round(s[1]/2.)]
ymin = y[Round(s[0]/2.), 0]
ymax = y[Round(s[0]/2.), s[1]-1]
IF ymin GT ymax THEN BEGIN
temp = ymin
ymin = ymax
ymax = temp
ENDIF
IF xmin GT xmax THEN BEGIN
temp = xmin
xmin = xmax
xmax = temp
ENDIF
ENDELSE
rect = [xmin-(delta_x/2.), ymin-(delta_y/2.), xmax+(delta_x/2.), ymax+(delta_y/2.)]
xrange = rect[[0,2]]
yrange = rect[[1,3]]
IF Keyword_Set(setrange) THEN map -> SetProperty, XRANGE=xrange, YRANGE=yrange
; If you don't have a grid, then you have to do the gridding the slow way.
IF grid EQ 0 THEN BEGIN
; We need a set of Delaunay triangles.
QHull, x, y, triangles, /Delaunay
; A choice of GridData or TriGrid for the actual gridding.
IF Keyword_Set(griddata) THEN BEGIN
warpedImage = GridData(x, y, data, TRIANGLES=triangles, DELTA=[delta_x, delta_y], $
DIMENSION=resolution, START=[Min(x), Min(y)], MISSING=missing, METHOD=method)
ENDIF ELSE BEGIN
warpedImage = TriGrid(x, y, data, triangles, [delta_x, delta_y], rect, MISSING=missing)
ENDELSE
RETURN, warpedImage
ENDIF
; If you have a grid, you can do the gridding the fast way by interpolating the output
; grid from the input grid. First, create an output grid.
xvec = cgScaleVector(Findgen(resolution[0]), xmin-(delta_x/2.), xmax+(delta_x/2.))
yvec = cgScaleVector(Findgen(resolution[1]), ymin-(delta_y/2.), ymax+(delta_x/2.))
x_out = Rebin(xvec, resolution[0], resolution[1])
y_out = Rebin(Reform(yvec, 1, resolution[1]), resolution[0], resolution[1])
; Get the fractional indices of the output grid on the input grid.
dims = Size(data, /DIMENSIONS)
xindex = cgScaleVector(x_out, 0, dims[0], Min=xmin, Max=xmax)
yindex = cgScaleVector(y_out, 0, dims[1], MIN=ymin, MAX=ymax)
; Interpolate the data. Nearest neighbor, bilinear, or cubic interpolation is possible.
IF Keyword_Set(cubic) THEN BEGIN
warpedImage = Interpolate(data, xindex, yindex, CUBIC=-0.5, MISSING=missing)
ENDIF ELSE BEGIN
IF Keyword_Set(nearest_neighbor) THEN BEGIN
warpedImage = data[Round(xindex), Round(yindex)]
ENDIF ELSE BEGIN
warpedImage = Interpolate(data, xindex, yindex, MISSING=missing)
ENDELSE
ENDELSE
RETURN, warpedImage
END
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