# Test function for checking roxygen2, roxygenize package documentation building
is.pseudoprime(13, 4)
# Some MODIS files are stored in this package's "extdata/" directory
# Here are some example MODIS files in modiscloud/extdata/
# Code excluded from CRAN check because it depends on modiscdata
## Not run:
# library(devtools)
# # The modiscdata (MODIS c=cloud data=data) package is too big for CRAN (60 MB); so it is available on github:
# # https://github.com/nmatzke/modiscdata
# # If we can't get install_github() to work, try install_url():
# # install_github(repo="modiscdata", username="nnmatzke")
# install_url(url="https://github.com/nmatzke/modiscdata/archive/master.zip")
# library(modiscdata)
# moddir = system.file("extdata/2002raw/", package="modiscdata")
#
# # This directory actually has MYD files (from the MODIS-AQUA platform)
# # (*will* work with the default files stored in modiscloud/extdata/2002raw/)
# list.files(path=moddir, pattern="MYD")
#
# # Check for matches (for MODIS-AQUA platform)
# # (*will* work with the default files stored in modiscloud/extdata/2002raw/)
# fns_df = check_for_matching_geolocation_files(moddir=moddir, modtxt="MYD35_L2", geoloctxt="MYD03", return_geoloc=FALSE, return_product=FALSE)
#
# ## End(Not run)
#######################################################
# Run MRTSwath tool "swath2grid"
#######################################################
# Source MODIS files (both data and geolocation)
# Code excluded from CRAN check because it depends on modiscdata
## Not run:
# library(devtools)
# # The modiscdata (MODIS c=cloud data=data) package is too big for CRAN (60 MB); so it is available on github:
# # https://github.com/nmatzke/modiscdata
# # If we can't get install_github() to work, try install_url():
# # install_github(repo="modiscdata", username="nnmatzke")
# # install_url(url="https://github.com/nmatzke/modiscdata/archive/master.zip")
# library(modiscdata)
# moddir = system.file("extdata/2002raw/", package="modiscdata")
#
# # Get the matching data/geolocation file pairs
# fns_df = check_for_matching_geolocation_files(moddir, modtxt="MYD35_L2", geoloctxt="MYD03")
# fns_df
#
# # Resulting TIF files go in this directory
# tifsdir = getwd()
#
#
# # Box to subset
# ul_lat = 13
# ul_lon = -87
# lr_lat = 8
# lr_lon = -82
#
# for (i in 1:nrow(fns_df))
# {
#
# prmfn = write_MRTSwath_param_file(prmfn="tmpMRTparams.prm", tifsdir=tifsdir, modfn=fns_df$mod35_L2_fns[i], geoloc_fn=fns_df$mod03_fns[i], ul_lon=ul_lon, ul_lat=ul_lat, lr_lon=lr_lon, lr_lat=lr_lat)
# print(scan(file=prmfn, what="character", sep="\n"))
#
# run_swath2grid(mrtpath="swath2grid", prmfn="tmpMRTparams.prm", tifsdir=tifsdir, modfn=fns_df$mod35_L2_fns[i], geoloc_fn=fns_df$mod03_fns[i], ul_lon=ul_lon, ul_lat=ul_lat, lr_lon=lr_lon, lr_lat=lr_lat)
#
# }
#
# tiffns = list.files(tifsdir, pattern=".tif", full.names=TRUE)
# tiffns
#
#
# # For some unit testing etc., swath2grid may not be available. If so, use the default TIFs:
# if (length(tiffns) == 0)
# {
# library(modiscdata)
# tifsdir = system.file("extdata/2002tif/", package="modiscdata")
# tiffns = list.files(tifsdir, pattern=".tif", full.names=TRUE)
# }
#
# #######################################################
# # Load a TIF
# #######################################################
# library(rgdal) # for readGDAL
#
# # numpixels in subset
# xdim = 538
# ydim = 538
#
#
# # Read the grid and the grid metadata
# coarsen_amount = 1
# xdim_new = xdim / floor(coarsen_amount)
# ydim_new = ydim / floor(coarsen_amount)
#
# fn = tiffns[1]
# grd = readGDAL(fn, output.dim=c(ydim_new, xdim_new))
#
# grdproj = CRS(proj4string(grd))
# grdproj
# grdbbox = attr(grd, "bbox")
# grdbbox
#
#
#
#
#
# ###########################
# # Extract values from a particular pixel
# ###########################
# # Greg's field site
# greglat = 10.2971
# greglon = -84.79282
#
# grdr = raster(grd)
#
# # Input the points x (longitude), then y (latitude)
# point_to_sample = c(greglon, greglat)
# xycoords = adf(matrix(data=point_to_sample, nrow=1, ncol=2))
# names(xycoords) = c("x", "y")
#
# xy = SpatialPoints(coords=xycoords, proj4string=grdproj)
# #xy = spsample(x=grd, n=10, type="random")
# pixelval = extract(grdr, xy)
#
# # Have to convert to 8-bit binary string, and reverse to get the count correct
# # (also reverse the 2-bit strings in the MODIS Cloud Mask table)
# pixelval = rev(t(digitsBase(pixelval, base= 2, 8)))
# print(pixelval)
#
# ## End(Not run)
#######################################################
# Load a TIF
#######################################################
# Code excluded from CRAN check because it depends on modiscdata
## Not run:
# library(devtools)
# # The modiscdata (MODIS c=cloud data=data) package is too big for CRAN (60 MB); so it is available on github:
# # https://github.com/nmatzke/modiscdata
# # If we can't get install_github() to work, try install_url():
# # install_github(repo="modiscdata", username="nnmatzke")
# # install_url(url="https://github.com/nmatzke/modiscdata/archive/master.zip")
# library(modiscdata)
# tifsdir = system.file("extdata/2002tif/", package="modiscdata")
# tiffns = list.files(tifsdir, pattern=".tif", full.names=TRUE)
# tiffns
#
# library(rgdal) # for readGDAL
#
# # numpixels in subset
# xdim = 538
# ydim = 538
#
#
# # Read the grid and the grid metadata
# coarsen_amount = 1
# xdim_new = xdim / floor(coarsen_amount)
# ydim_new = ydim / floor(coarsen_amount)
#
# fn = tiffns[1]
# grd = readGDAL(fn, output.dim=c(ydim_new, xdim_new))
#
# grdproj = CRS(proj4string(grd))
# grdproj
# grdbbox = attr(grd, "bbox")
# grdbbox
#
# #######################################################
# # Extract a particular bit for all the pixels in the grid
# #######################################################
# bitnum = 2
# grdr_vals_bits = get_bitgrid(grd, bitnum)
# length(grdr_vals_bits)
# grdr_vals_bits[1:50]
#
# #######################################################
# # Extract a particular pair of bits for all the pixels in the grid
# #######################################################
# bitnum = 2
# grdr_vals_bitstrings = get_bitgrid_2bits(grd, bitnum)
# length(grdr_vals_bitstrings)
# grdr_vals_bitstrings[1:50]
#
# ## End(Not run)
#######################################################
# Load some bit TIFs (TIFs with just the cloud indicators extracted)
# and add up the number of cloudy days, out of the total
# number of observation attempts
#######################################################
# Code excluded from CRAN check because it depends on modiscdata
## Not run:
# library(devtools)
# # The modiscdata (MODIS c=cloud data=data) package is too big for CRAN (60 MB); so it is available on github:
# # https://github.com/nmatzke/modiscdata
# # If we can't get install_github() to work, try install_url():
# # install_github(repo="modiscdata", username="nnmatzke")
# # install_url(url="https://github.com/nmatzke/modiscdata/archive/master.zip")
# library(modiscdata)
# tifsdir = system.file("extdata/2002bit/", package="modiscdata")
# tiffns = list.files(tifsdir, pattern=".tif", full.names=TRUE)
# tiffns
#
# library(rgdal) # for readGDAL
#
# # numpixels in subset
# xdim = 538
# ydim = 538
#
# # Read the grid and the grid metadata
# coarsen_amount = 1
# xdim_new = xdim / floor(coarsen_amount)
# ydim_new = ydim / floor(coarsen_amount)
#
#
# sum_nums = NULL
# for (j in 1:length(tiffns))
# {
# fn = tiffns[j]
#
# grd = readGDAL(fn, output.dim=c(ydim_new, xdim_new))
#
# grdr = raster(grd)
# pointscount_on_SGDF_points = coordinates(grd)
# grdr_vals = extract(grdr, pointscount_on_SGDF_points)
#
# # Convert to 1/0 cloudy/not
# data_grdr = grdr_vals
# data_grdr[grdr_vals > 0] = 1
#
# grdr_cloudy = grdr_vals
# grdr_cloudy[grdr_vals < 4] = 0
# grdr_cloudy[grdr_vals == 4] = 1
#
# # Note: Don't run the double-commented lines unless you want to collapse different bit values.
# # grdr_clear = grdr_vals
# # grdr_clear[grdr_vals == 4] = 0
# # grdr_clear[grdr_vals == 3] = 1
# # grdr_clear[grdr_vals == 2] = 1
# # grdr_clear[grdr_vals == 1] = 1
# # grdr_clear[grdr_vals == 0] = 0
# #
#
# if (j == 1)
# {
# sum_cloudy = grdr_cloudy
# #sum_not_cloudy = grdr_clear
# sum_data = data_grdr
# } else {
# sum_cloudy = sum_cloudy + grdr_cloudy
# sum_data = sum_data + data_grdr
# }
#
# }
#
#
# # Calculate percentage cloudy
# sum_nums = sum_cloudy / sum_data
#
# grd_final = numslist_to_grd(numslist=sum_nums, grd=grd, ydim_new=ydim_new, xdim_new=xdim_new)
#
# # Display the image (this is just the sum of a few images)
# image(grd_final)
#
# ## End(Not run)
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