rplzzz

savepkgs <- function(saveloc)
{
    instpkg <- installed.packages()
    cranpkg <- available.packages()
    iscran <- instpkg[,'Package'] %in% cranpkg[,'Package']
    pkgdata <- data.frame(package=instpkg[,'Package'], iscran=iscran, instvers=instpkg[,'Version'])
    write.csv(pkgdata, saveloc)
}

restore_pkgs <- function(pkglistfile)

rplzzz

## a batch of 3 4x4x2 images as input.  We will upsample these to 8x8
input_data = np.ones([3, 4, 4, 2])

filter_shape = [3,3,4,2]  # width, height, channels-out, channels-in
tconv_filter = np.ones(filter_shape)  # make the filter all ones so that we can manually calculate the output
                                      # obviously, having all the filter channels have the same coefficients defeats
                                      # the purpose of having multiple channels, but this is just an example
output_shape = [8, 8, 4] # width, height, channels-out  (notice we don't have the batch size dimension -- more on that later)

## set up the slots for the data

rplzzz

library(sp)
library(rgdal)
library(raster)
library(ncdf)

##aez map
## extract data from shape file

GIS_DIR <- "./cropdata"
GIS_FILE <- "GCAM_region_AEZ"

mages

require(igraph)
require(googleVis)
g <- graph.tree(24, children = 4)
set.seed(123)
E(g)$weight = rpois(23, 4) + 1
edgelist <- get.data.frame(g) 
colnames(edgelist) <- c("source","target","value")
edgelist$source <- LETTERS[edgelist$source]
edgelist$target <- LETTERS[edgelist$target]

sjl

var empty_list = function(selector) {
    return selector(undefined, undefined, true);
};

var prepend = function(el, list) {
    return function(selector) {
        return selector(el, list, false);
    };
};
var head = function(list) {

kogakure

*.acn
*.acr
*.alg
*.aux
*.bak
*.bbl
*.bcf
*.blg
*.brf
*.bst