mkdir $LAYERS/experimental/cruft

rm $ORGANISMS/DEM/asterGdem2/*.tif.aux.xml

rm $ORGANISMS/DEM/asterGdem2/90m_NoPixelOffset/*.tif.aux.xml

rm $ORGANISMS/DEM/asterGdem2/90m_NoPixelOffset/Mosaiced/N59to60/*.tif.aux.xml

rm $ORGANISMS/DEM/GlobalProduct/*.tif.aux.xml

rm $ORGANISMS/DEM/usgsGTOPO30/e020n90/*.DEM.aux.xml

rm $ORGANISMS/GMTED2010/*.tif.aux.xml

# regetz notes about nunokawa boundary datasets

# aster2/aster2_*_82N.tif             # Int16 59N-82N GDEM2 elevation

# aster2/aster2_*_below.tif           # Int16 59N-60N GDEM2 elevation

# aster2/aster2_*_above.tif           # Int16 60N-61N GDEM2 elevation

# aster2/aster2_*_straddle.tif        # Int16 59N-61N GDEM2 elevation (GDEM2 above, GDEM2 below)

# aster2/aster2_*_straddle_a.tif      # Flt32 59N-61N GDEM2-based aspect

# aster2/aster2_*_straddle_s.tif      # Flt32 59N-61N GDEM2-based slope

# aster2/aster2_*_below_blendgau.tif  # Flt32 59N-60N GDEM2/SRTM blended elevation

# aster2/fused_*_straddle.tif         # Int16 59N-61N GDEM2/SRTM unblended elevation (GDEM2 above, SRTM below)

# aster2/fused_*_straddle_a.tif       # Flt32 59N-61N unblended-based aspect

# aster2/fused_*_straddle_s.tif       # Flt32 59N-61N unblended-based aspect

# aster2/fused_*_blendgau.tif         # Int16 59N-61N GDEM2/SRTM blended elevation (GDEM2 above, blended below)

# aster2/fused_*_blendgau_a.tif       # Flt32 59N-61N blend-based aspect

# aster2/fused_*_blendgau_s.tif       # Flt32 59N-61N blend-based aspect

# srtm/srtm_*_below.tif               # Int16 59N-60N SRTM elevation

# srtm/srtm_*_below_a.tif             # Flt32 59N-60N SRTM-based aspect

# srtm/srtm_*_below_below_s.tif       # Flt32 59N-60N SRTM-based slope

# aster2/aster.vrt                    # VRT - GDEM2 from ~59N-82N (with 1/2-pixel offset)

# srtm/srtm.vrt                       # VRT - SRTM from ~55N-60N (with 1/2-pixel offset)

# migrate scripts

# migrate remaining Data contents to 60N boundary dir

mv -i $ORGANISMS/DEM/Yuni/Data \

      $LAYERS/experimental/terrain/north-60

# remove now-empty directories

rmdir $ORGANISMS/DEM/Yuni

rmdir $ORGANISMS/DEM

mv $ORGANISMS/DEM/checkBadAsterGDEMFiles.sh $LAYERS/experimental/cruft/

mv $ORGANISMS/DEM/CheckGDEMLog.txt $LAYERS/experimental/cruft/

      $LAYERS/experimental/cruft/clim-MOD11A1.004-OR-orig-hdf.txt

      $LAYERS/experimental/cruft/clim-MOD11A1.004-OR-orig-SDS_PctFills.txt

mv -i $ORGANISMS/file-extension-summary.txt $LAYERS/experimental/cruft/

mv $ORGANISMS/Desktop/MRT_download_Linux $LAYERS/experimental/cruft/

mv $ORGANISMS/pyhdf $LAYERS/experimental/cruft/

# for now just migrate to experimental area

mv $ORGANISMS/Oregon $LAYERS/experimental/oregon

export REPO="."

mv -i code/terrain/nunokawa-scripts/toProduceData/clipUSGS.r \

      $REPO/terrain/research/gtopo30/

mv -i code/terrain/nunokawa-scripts/toAnalyzeData/check.r \

      $REPO/terrain/tests/

mv -i code/terrain/nunokawa-scripts/toAnalyzeData/meanElv_OnlyN59.r

      $REPO/terrain/research/north-60/

mv -i code/terrain/nunokawa-scripts/toAnalyzeData/Deltas.r \

      $REPO/terrain/research/north-60/

mv -i code/terrain/nunokawa-scripts/toAnalyzeData/rmse_cor.r \

      $REPO/terrain/research/north-60/

mv -i code/terrain/nunokawa-scripts/toAnalyzeData/{aspect,slope}.r

      $REPO/terrain/research/north-60/

mv -i code/terrain/nunokawa-scripts/toAnalyzeData/negativeTable.r \

      $REPO/terrain/research/north-60/

mv -i code/terrain/nunokawa-scripts/toProduceData/*.{r,r~} \

      $REPO/terrain/research/north-60/

mv -i code/terrain/DEM_ProcessingScripts/Aster_CheckMosaicedTilesExtents.py \

      $REPO/terrain/tests/

mv -i code/terrain/DEM_ProcessingScripts/CheckPixelValuesAtOverlapZones.txt \

      $REPO/terrain/tests/

mv -i code/terrain/DEM_ProcessingScripts/Aster\&SRTM_* \

      $REPO/terrain/tests/

mv -i code/terrain/DEM_ProcessingScripts/Gaussian_Blend.r \

      $REPO/terrain/procedures/

mv -i terrain/tests/Aster\&SRTM_* \

      $REPO/terrain/procedures/

mv -i code/terrain/DEM_ProcessingScripts/SRTM_ClipToN59to60.txt \

      $REPO/terrain/procedures/

mv -i code/terrain/DEM_ProcessingScripts/Mosaicing_AllTiles_East\&WestHemispheres.txt \

      $REPO/terrain/procedures/

mv -i code/terrain/DEM_ProcessingScripts/AsterMosaicingScripts \

      $REPO/terrain/procedures/

mv -i code/land-cover/CheckForNoDataValues_LandCoverFiles.txt \

      $REPO/land-cover/tests/

mv -i code/climate/modis-lst-oregon/*.{r,R,py} \

      $REPO/climate/research/oregon/modis-lst/

mv -i code/climate/cru_3.0_data_extract.r \

      $REPO/climate/procedures/

mv -i code/climate/gdd-worldclim-tmean-map-algebra.txt \

      $REPO/climate/extra/

mv -i $LAYERS/code/terrain/R_files/AsterCheck_demAndnum.r \

      $REPO/terrain/test/

mv -i $LAYERS/code/terrain/R_files/PctNoLand.r \

      $REPO/terrain/research/gtopo30/

# set all file permissions to 644 before committing

find $REPO -type f -exec ^Cmod 644 {} \;

library(raster)

#Get list of tile names and add full pathname to beginning

Tiles <- list.files("DEM/asterGdem/N59to81_W20toE19", pattern="^ASTGTM.*_dem.tif$")

Tiles<- paste("DEM/asterGdem/N59to81_W20toE19/", Tiles, sep="")

# datavalues: 0= water, -9999= nodata

#Create new vectors for name of tile and accompanying % of cells= ocean and non-landmass

#   IF % OCEAN AND % NON-LAND ARE VERY DIFFERENT USE % NON-LAND FOR QC CHECK, IF THEY

#   ARE THE SAME, % OCEAN IS FASTER TO CALCULATE SO USE THIS

l<- length (Tiles)

Names<- c(1:l)*NA

PctNotLand<- c(1:l) * NA

PctOcean<- c(1:l)*NA

#Fill vectors with % of each tiles where pixel value  = 0 (water) OR 0 & -9999 (water or nodata)

for (i in 1:l){

    Names[i]<-sapply(strsplit(Tiles[i], '[/]'), '[[', 4)

    PctOcean[i]<- count(raster(Tiles[i]),0)/ncell(raster(Tiles[i]))

    PctNotLand[i]<- (count(raster(Tiles[i]),0)+count(raster(Tiles[i]),-9999))/ncell(raster(Tiles[i]))

}

data.frame (Names, PctOcean,PctNotLand)

#Use the above calculation to calculate % landmass for each tile and compare total over all tiles

#  in folder to % landmass of USGS coverage for same area.

# Requires aggregating aster tiles to match pixel size of USGS coverage:

LandAgg<- c(1:l) * NA

Counts_Agg<- c(1:l)*NA

for (i in 1:l){

  Counts_Agg[i]<- count(aggregate(raster(Tiles[i]),30),0)+count(aggregate(raster(Tiles[i]),30),-9999)

  LandAgg[i]<-  ncell(aggregate(raster(Tiles[i]),30))-Counts_Agg[i]

}

a<-sum(LandAgg)

#Calculate % landmass in USGS coverage and compare:

AstRast<- raster("DEM/asterGdem/N59to81_W20toE19/w020n90/W020N90_clipped.dem")

b<-ncell(AstRast)-cellStats(AstRast, "countNA") #Total # cells- cells with no value

1-(b/a)  #Difference= 4.49%

#------------------------------------------------------------------

#after first try, anytime PctNotLand was >= 99%, so too was PctOcean and visa versa

#    All subsequent runs will only calc. PctOcean to save time

Tiles <- list.files("DEM/asterGdem/N59to81_E20to59", pattern="^ASTGTM.*_dem.tif$")

Tiles<- paste("DEM/asterGdem/N59to81_E20to59/", Tiles, sep="")

l<- length (Tiles)

Names<- c(1:l)*NA

PctNotLand<- c(1:l) * NA

PctOcean<- c(1:l)*NA

for (i in 1:l){

    Names[i]<-sapply(strsplit(Tiles[i], '[/]'), '[[', 4)

    PctOcean[i]<- count(raster(Tiles[i]),0)/ncell(raster(Tiles[i]))

}

data.frame (Names, PctOcean)

LandAgg<- c(1:l) * NA

Counts_Agg<- c(1:l)*NA

for (i in 1:l){

  Counts_Agg[i]<- count(aggregate(raster(Tiles[i]),30),0)+count(aggregate(raster(Tiles[i]),30),-9999)

  LandAgg[i]<-  ncell(aggregate(raster(Tiles[i]),30))-Counts_Agg[i]

    print( paste(round(100*i/l),"%",sep=""), quote=FALSE )

}

a<-sum(LandAgg)

AstRast<- raster("DEM/asterGdem/N59to81_E20to59/e020n90/E020N90_clipped.dem")

b<-ncell(AstRast)-cellStats(AstRast, "countNA") #Total # cells- cells with no value

1-(b/a) #Difference= 2.21%

#-----------------------------------------------------------------------

Tiles <- list.files("DEM/asterGdem/N59to81_E60to99", pattern="^ASTGTM.*_dem.tif$")

Tiles<- paste("DEM/asterGdem/N59to81_E60to99/", Tiles, sep="")

l<- length (Tiles)

Names<- c(1:l)*NA

PctNotLand<- c(1:l) * NA

PctOcean<- c(1:l)*NA

for (i in 1:l){

    Names[i]<-sapply(strsplit(Tiles[i], '[/]'), '[[', 4)

    PctOcean[i]<- count(raster(Tiles[i]),0)/ncell(raster(Tiles[i]))

}

data.frame (Names, PctOcean)

LandAgg<- c(1:l) * NA

Counts_Agg<- c(1:l)*NA

for (i in 1:l){

  Counts_Agg[i]<- count(aggregate(raster(Tiles[i]),30),0)+count(aggregate(raster(Tiles[i]),30),-9999)

  LandAgg[i]<-  ncell(aggregate(raster(Tiles[i]),30))-Counts_Agg[i]

}

a<-sum(LandAgg)

AstRast<- raster("DEM/asterGdem/N59to81_E60to99/USGS_ErosDEM_N59to81E60to99/E060N90_clipped.dem")

b<-ncell(AstRast)-cellStats(AstRast, "countNA") #Total # cells- cells with no value

1-(b/a)  # Datasets differ by 2.27%

cellStats(AstRast,max)

cellStats(AstRast,min)

count(AstRast, NA)

#-----------------------------------------------------------------------

Tiles <- list.files("DEM/asterGdem/N59to81_E100to139", pattern="^ASTGTM.*_dem.tif$")

Tiles<- paste("DEM/asterGdem/N59to81_E100to139/", Tiles, sep="")

l<- length (Tiles)

Names<- c(1:l)*NA

PctNotLand<- c(1:l) * NA

PctOcean<- c(1:l)*NA

for (i in 1:l){

    Names[i]<-sapply(strsplit(Tiles[i], '[/]'), '[[', 4)

    PctOcean[i]<- count(raster(Tiles[i]),0)/ncell(raster(Tiles[i]))

}

data.frame (Names, PctOcean)

LandAgg<- c(1:l) * NA

Counts_Agg<- c(1:l)*NA

for (i in 1:l){

  Counts_Agg[i]<- count(aggregate(raster(Tiles[i]),30),0)+count(aggregate(raster(Tiles[i]),30),-9999)

  LandAgg[i]<-  ncell(aggregate(raster(Tiles[i]),30))-Counts_Agg[i]

    print( paste(round(100*i/l),"%",sep=""), quote=FALSE )

}

a<-sum(LandAgg)

AstRast<- raster("DEM/asterGdem/N59to81_E100to139/e100n90/E100N90_clipped.dem")

b<-ncell(AstRast)-cellStats(AstRast, "countNA") #Total # cells- cells with no value

1-(b/a)  #Difference= 3.1%

#-----------------------------------------------------------------------

Tiles <- list.files("DEM/asterGdem/N59to81_E140to179", pattern="^ASTGTM.*_dem.tif$")

Tiles<- paste("DEM/asterGdem/N59to81_E140to179/", Tiles, sep="")

l<- length (Tiles)

Names<- c(1:l)*NA

PctNotLand<- c(1:l) * NA

PctOcean<- c(1:l)*NA

for (i in 1:l){

    Names[i]<-sapply(strsplit(Tiles[i], '[/]'), '[[', 4)

    PctOcean[i]<- count(raster(Tiles[i]),0)/ncell(raster(Tiles[i]))

}

data.frame (Names, PctOcean)

LandAgg<- c(1:l) * NA

Counts_Agg<- c(1:l)*NA

for (i in 1:l){

  Counts_Agg[i]<- count(aggregate(raster(Tiles[i]),30),0)+count(aggregate(raster(Tiles[i]),30),-9999)

  LandAgg[i]<-  ncell(aggregate(raster(Tiles[i]),30))-Counts_Agg[i]

    print( paste(round(100*i/l),"%",sep=""), quote=FALSE )

}

a<-sum(LandAgg)

AstRast<- raster("DEM/asterGdem/N59to81_E140to179/e140n90/E140N90_clipped.dem")

b<-ncell(AstRast)-cellStats(AstRast, "countNA") #Total # cells- cells with no value

1-(b/a)   #Difference= 3.4%

#-----------------------------------------------------------------------

Tiles <- list.files("DEM/asterGdem/N59to81_W60to21", pattern="^ASTGTM.*_dem.tif$")

Tiles<- paste("DEM/asterGdem/N59to81_W60to21/", Tiles, sep="")

l<- length (Tiles)

Names<- c(1:l)*NA

PctNotLand<- c(1:l) * NA

PctOcean<- c(1:l)*NA

for (i in 1:l){

    Names[i]<-sapply(strsplit(Tiles[i], '[/]'), '[[', 4)

    PctOcean[i]<- count(raster(Tiles[i]),0)/ncell(raster(Tiles[i]))

}

data.frame (Names, PctOcean)

LandAgg<- c(1:l) * NA

Counts_Agg<- c(1:l)*NA

for (i in 1:l){

  Counts_Agg[i]<- count(aggregate(raster(Tiles[i]),30),0)+count(aggregate(raster(Tiles[i]),30),-9999)

  LandAgg[i]<-  ncell(aggregate(raster(Tiles[i]),30))-Counts_Agg[i]

    print( paste(round(100*i/l),"%",sep=""), quote=FALSE )

}

a<-sum(LandAgg)

AstRast<- raster("DEM/asterGdem/N59to81_W60to21/w060n90/W060N90_clipped.dem")

b<-ncell(AstRast)-cellStats(AstRast, "countNA") #Total # cells- cells with no value

1-(b/a)  #Difference= 3.03%

#-----------------------------------------------------------------------

Tiles <- list.files("DEM/asterGdem/N59to81_W180to141", pattern="^ASTGTM.*_dem.tif$")

Tiles<- paste("DEM/asterGdem/N59to81_W180to141/", Tiles, sep="")

l<- length (Tiles)

Names<- c(1:l)*NA

PctNotLand<- c(1:l) * NA

PctOcean<- c(1:l)*NA

for (i in 1:l){

    Names[i]<-sapply(strsplit(Tiles[i], '[/]'), '[[', 4)

    PctOcean[i]<- count(raster(Tiles[i]),0)/ncell(raster(Tiles[i]))

}

data.frame (Names, PctOcean)

LandAgg<- c(1:l) * NA

Counts_Agg<- c(1:l)*NA

for (i in 1:l){

  Counts_Agg[i]<- count(aggregate(raster(Tiles[i]),30),0)+count(aggregate(raster(Tiles[i]),30),-9999)

  LandAgg[i]<-  ncell(aggregate(raster(Tiles[i]),30))-Counts_Agg[i]

     print( paste(round(100*i/l),"%",sep=""), quote=FALSE )

}

a<-sum(LandAgg)

AstRast<- raster("DEM/asterGdem/N59to81_W180to141/w180n90/W180N90_clipped.dem")

b<-ncell(AstRast)-cellStats(AstRast, "countNA") #Total # cells- cells with no value

1-(b/a) #Difference= 2.87%

#-----------------------------------------------------------------------

Tiles <- list.files("DEM/asterGdem/N59to81_W140to101", pattern="^ASTGTM.*_dem.tif$")

Tiles<- paste("DEM/asterGdem/N59to81_W140to101/", Tiles, sep="")

l<- length (Tiles)

Names<- c(1:l)*NA

PctNotLand<- c(1:l) * NA

PctOcean<- c(1:l)*NA

for (i in 1:l){

    Names[i]<-sapply(strsplit(Tiles[i], '[/]'), '[[', 4)

    PctOcean[i]<- count(raster(Tiles[i]),0)/ncell(raster(Tiles[i]))

}

data.frame (Names, PctOcean)

LandAgg<- c(1:l) * NA

Counts_Agg<- c(1:l)*NA

for (i in 1:l){

  Counts_Agg[i]<- count(aggregate(raster(Tiles[i]),30),0)+count(aggregate(raster(Tiles[i]),30),-9999)

  LandAgg[i]<-  ncell(aggregate(raster(Tiles[i]),30))-Counts_Agg[i]

    print( paste(round(100*i/l),"%",sep=""), quote=FALSE )

}

a<-sum(LandAgg)

AstRast<- raster("DEM/asterGdem/N59to81_W140to101/w140n90/W140N90_clipped.dem")

b<-ncell(AstRast)-cellStats(AstRast, "countNA") #Total # cells- cells with no value

1-(b/a) #Difference= 3.84%

#-----------------------------------------------------------------------

Tiles <- list.files("DEM/asterGdem/N59to81_W100to61", pattern="^ASTGTM.*_dem.tif$")

Tiles<- paste("DEM/asterGdem/N59to81_W100to61/", Tiles, sep="")

l<- length (Tiles)

Names<- c(1:l)*NA

PctNotLand<- c(1:l) * NA

PctOcean<- c(1:l)*NA

for (i in 1:l){

    Names[i]<-sapply(strsplit(Tiles[i], '[/]'), '[[', 4)

    PctOcean[i]<- count(raster(Tiles[i]),0)/ncell(raster(Tiles[i]))

}

data.frame (Names, PctOcean)

LandAgg<- c(1:l) * NA

Counts_Agg<- c(1:l)*NA

for (i in 1:l){

  Counts_Agg[i]<- count(aggregate(raster(Tiles[i]),30),0)+count(aggregate(raster(Tiles[i]),30),-9999)

  LandAgg[i]<-  ncell(aggregate(raster(Tiles[i]),30))-Counts_Agg[i]

    print( paste(round(100*i/l),"%",sep=""), quote=FALSE )

}

a<-sum(LandAgg)

AstRast<- raster("DEM/asterGdem/N59to81_W100to61/w100n90/W100N90_clipped.dem")

b<-ncell(AstRast)-cellStats(AstRast, "countNA") #Total # cells- cells with no value

1-(b/a) #Difference= 7.21% (lot's of coastline in this set of tiles!)

#-------------------------------------------------------------------

c<- raster("DEM/asterGdem/N59to81_E60to99/USGS_ErosDEM_N59to81E60to99/E060N90.DEM")

a<- raster("DEM/asterGdem/N59to81_E60to99/USGS_ErosDEM_N59to81E60to99/E060N90_clipped.dem")

b<- raster(nrow=82800, ncol=144000)

d<- raster("DEM/asterGdem/N59to81_E60to99/ASTGTM_N59E060_dem.tif")

s<- resample(a,b,method='ngb')

disaggregate(a,30)

e<-aggregate(d,30)

count(e, -9999)

count (e,0)

library(rgdal)

aster.dir <- "DEM/asterGdem"

#Check that path=correct:

a<- GDALinfo("DEM/asterGdem/ASTGTM_N59E069_dem.tif", 

    silent=TRUE,returnRAT=TRUE)

# for a given file, return TRUE if file name matches the internally

# specified lower left corner, otherwise return string with those

# coordinates

check <- function(tilename, path=".", silent=TRUE) {

    # build expected filename

    origin <- round(GDALinfo(file.path(path, tilename),

        silent=silent)[c("ll.x", "ll.y")])

    ly <- origin["ll.y"]

    y <- sprintf("%s%02d", if (ly>=0) "N" else "S", abs(ly))

    lx <- origin["ll.x"]

    x <- sprintf("%s%03d", if (lx>=0) "E" else "W", abs(lx))

    expected.name <- paste("ASTGTM_", y, x, "_dem.tif", sep="")

    # compare to actual filename

    if (tilename==expected.name) {

        TRUE

    } else {

        paste(y, x)

    }

}

aster.tiles <- list.files(aster.dir, pattern="^ASTGTM.*_dem.tif$") 

titlecheck <- sapply(aster.tiles, check, path=aster.dir)

BadAster_df<- data.frame(expected=titlecheck[titlecheck!="TRUE"])

# ASTGTM_N59E069_dem.tif  N63 E109

#ASTGTM_N63E113_dem.tif  N69 E107

# ASTGTM_N63E117_dem.tif	N69 E113

#ASTGTM_N64E098_dem.tif	N70 E117

#ASTGTM_N65E104_dem.tif	N73 E084

#	ASTGTM_N65E111_dem.tif	N73 E098

#	ASTGTM_N65E117_dem.tif	N66 E130

KnownBadcheck <- function(badtilename, path="DEM/asterGdem/incorrectTilesJuly2011", silent=TRUE) {

    # build expected filename

    origin <- round(GDALinfo(file.path(path, badtilename),

        silent=silent)[c("ll.x", "ll.y")])

    ly <- origin["ll.y"]

    y <- sprintf("%s%02d", if (ly>=0) "N" else "S", abs(ly))

    lx <- origin["ll.x"]

    x <- sprintf("%s%03d", if (lx>=0) "E" else "W", abs(lx))

    Badexpected.name <- paste("ASTGTM_", y, x, "_dem.tif", sep="")

    # compare to actual filename

    if (badtilename==Badexpected.name) {

        TRUE

    } else {

        paste(y, x)

    }

}

bad.tiles <- list.files("DEM/asterGdem/incorrectTilesJuly2011", pattern="^ASTGTM.*_dem.tif$") 

badtitlecheck <- sapply(bad.tiles, KnownBadcheck, path="DEM/asterGdem/incorrectTilesJuly2011")

BadTitle_df<- data.frame(expected=badtitlecheck[badtitlecheck!="TRUE"])

#----------------------------------------------------------------------------------------------------------------------

#Check num.tif files

b<- GDALinfo("DEM/asterGdem/ASTGTM_N49E000_num.tif", 

silent=TRUE,returnRAT=TRUE)

b

Numcheck <- function(Numtilename, path=".", silent=TRUE) {

    # build expected filename

    origin <- round(GDALinfo(file.path(path, Numtilename),

        silent=silent)[c("ll.x", "ll.y")])

    ly <- origin["ll.y"]

    y <- sprintf("%s%02d", if (ly>=0) "N" else "S", abs(ly))

    lx <- origin["ll.x"]

    x <- sprintf("%s%03d", if (lx>=0) "E" else "W", abs(lx))

    Numexpected.name <- paste("ASTGTM_", y, x, "_num.tif", sep="")

    # compare to actual filename

    if (Numtilename==Numexpected.name) {

        TRUE

    } else {

        paste(y, x)

    }

}

Num.tiles <- list.files("DEM/asterGdem/NewTiles", pattern="^ASTGTM.*_num.tif$") 

Numtitlecheck <- sapply(Num.tiles, Numcheck, path="DEM/asterGdem/NewTiles")

NumTitle_df<- data.frame(expected=Numtitlecheck[Numtitlecheck!="TRUE"])

#Located one bad num.tif file

N49E007_NumRead<- GDALinfo("DEM/asterGdem/ASTGTM_N49E007_num.tif", 

silent=TRUE,returnRAT=TRUE)

GDALinfo("DEM/asterGdem/ASTGTM_N59E069_dem.tif", 

silent=TRUE,returnRAT=TRUE)

#--------------------------------------------------------------------------------

#Check new tiles uploaded to replace faulty tiles

Newcheck <- function(Newtilename, path=".", silent=TRUE) {

    # build expected filename

    origin <- round(GDALinfo(file.path(path, Newtilename),

        silent=silent)[c("ll.x", "ll.y")])

    ly <- origin["ll.y"]

    y <- sprintf("%s%02d", if (ly>=0) "N" else "S", abs(ly))

    lx <- origin["ll.x"]

    x <- sprintf("%s%03d", if (lx>=0) "E" else "W", abs(lx))

    Newexpected.name <- paste("ASTGTM_", y, x, "_dem.tif", sep="")

    # compare to actual filename

    if (Newtilename==Newexpected.name) {

        TRUE

    } else {

        paste(y, x)

    }

}

New.tiles <- list.files("DEM/asterGdem/NewTiles", pattern="^ASTGTM.*_dem.tif$") 

Newtitlecheck <- sapply(New.tiles, Newcheck, path="DEM/asterGdem/NewTiles")

NewAster_df<- data.frame(Newexpected=Newtitlecheck[Newtitlecheck!="TRUE"])

#Yeah, no mistakes in first 4!

g<- list.files("DEM/asterGdem/N59to81_E60to99", pattern="^ASTGTM.*_dem.tif$")

h<- list.files("DEM/asterGdem/N59to81_E60to99/Non_DEM's", pattern="^ASTGTM.*_num.tif$")

GDALinfo("DEM/asterGdem/N59to81_E60to99/ASTGTM_N64E098_dem.tif", 

silent=TRUE,returnRAT=TRUE)

#--------------------------------------------------------------------------

#new folder check

nfcheck <- function(nftilename, path=".", silent=TRUE) {

    # build expected filename

    origin <- round(GDALinfo(file.path(path, nftilename),

        silent=silent)[c("ll.x", "ll.y")])

    ly <- origin["ll.y"]

    y <- sprintf("%s%02d", if (ly>=0) "N" else "S", abs(ly))

    lx <- origin["ll.x"]

    x <- sprintf("%s%03d", if (lx>=0) "E" else "W", abs(lx))

    nfexpected.name <- paste("ASTGTM_", y, x, "_dem.tif", sep="")

    # compare to actual filename

    if (nftilename==nfexpected.name) {

        TRUE

    } else {

        paste(y, x)

    }

}

nf.tiles <- list.files("DEM/asterGdem/N59to81_E60to99", pattern="^ASTGTM.*_dem.tif$") 

nftitlecheck <- sapply(nf.tiles, nfcheck, path="DEM/asterGdem/N59to81_E60to99")

nfAster_df<- data.frame(nfexpected=nftitlecheck[nftitlecheck!="TRUE"])

Numcheck <- function(Numtilename, path=".", silent=TRUE) {

    # build expected filename

    origin <- round(GDALinfo(file.path(path, Numtilename),

        silent=silent)[c("ll.x", "ll.y")])

    ly <- origin["ll.y"]

    y <- sprintf("%s%02d", if (ly>=0) "N" else "S", abs(ly))

    lx <- origin["ll.x"]

    x <- sprintf("%s%03d", if (lx>=0) "E" else "W", abs(lx))

    Numexpected.name <- paste("ASTGTM_", y, x, "_num.tif", sep="")

    # compare to actual filename

    if (Numtilename==Numexpected.name) {

        TRUE

    } else {

        paste(y, x)

    }

}