R/hp_CHN.R
In Menglinucas/HousePC:
Defines functions
hp_CHN
Documented in
hp_CHN
#' @title Batches used for House Price calculation of China
#' @description The main fuction of this package.
#' @description Combine all city results of each month, and crop to 16 tiles.
#' @param startmon The firt month in the form such as "200606", character
#' @param endmon The last month in the form as param startmon, character
#' @param resol Mesh resolution, unit: meter, numeric
#' @param configfile The configuration file, with the path, character
#' @param outpath The path storing outfiles, character
#' @param sys The system type, Linux or Wins ?
#' @details THe outputs mainly contains Magnitude, Link and Year-over-year distibution of the price.
#' @details Contains six dirs:
#' @details (1) ras_11_newcalprice --- altitude
#' @details (2) ras_11_newlike --- year-over-year
#' @details (3) ras_11_newlink --- link
#' @details (4) level --- price level
#' @details (5) minmaxp --- min and max price each month and city
#' @details (6) temp --- temperary dir in which files are not necessary, you can neglect it
#' @return Generate amounts of "tif" files to outpath
#' @export
hp_CHN <- function(startmon,endmon,resol,configfile,outpath,sys){
# set local GDAL installation options
gdal_setInstallation()
valid_install <- !is.null(getOption("gdalUtils_gdalPath"))
if (valid_install == FALSE) {return("Please install gdalUtils!")}
# delete the warning reminder
options(warn = -1)
######################################################
######## the path and dir saving output files ########
######################################################
outpath <- outpath
###########################################################################################
############## calculate the months, and create the month,level,minmax dir ################
###########################################################################################
if (!file.exists(outpath)) {dir.create(outpath)}
nmonth<-(as.numeric(substr(endmon,1,4))-as.numeric(substr(startmon,1,4)))*12+
as.numeric(substr(endmon,5,6))-as.numeric(substr(startmon,5,6))+1
months<-c()
months[1]<-as.numeric(startmon)
if (nmonth>1){
for (i in 2:nmonth)
{
if (as.numeric(substr(months[i-1],5,6))<12)
{
months[i]<-months[i-1]+1
}else{
months[i]<-months[i-1]+89
}
}
}
if (!file.exists(paste0(outpath,"/ras_11_newcalprice"))) {dir.create(paste0(outpath,"/ras_11_newcalprice"))}
if (!file.exists(paste0(outpath,"/ras_11_newlike"))) {dir.create(paste0(outpath,"/ras_11_newlike"))}
if (!file.exists(paste0(outpath,"/ras_11_newlink"))) {dir.create(paste0(outpath,"/ras_11_newlink"))}
if (!file.exists(paste0(outpath,"/level"))) {dir.create(paste0(outpath,"/level"))}
if (!file.exists(paste0(outpath,"/minmaxp"))) {dir.create(paste0(outpath,"/minmaxp"))}
if (!file.exists(paste0(outpath,"/temp"))) {dir.create(paste0(outpath,"/temp"))}
###############################################################################################
# 计算除禧泰数据库(city_info中的367个城市)中可克达拉和昆玉两个地区以外的365个城市的价格分布、 #
# 环比和同比涨跌幅分布 ########################################################################
###############################################################################################
cat("1. calculate the data of each city:\n")
if (!file.exists("city_info.txt")){
cat("There's no configure file with database information!!!")
return(0)
}else{
cityinfo <- read.table(configfile,header = TRUE, stringsAsFactors = FALSE, fileEncoding = 'UTF-8')
}
for (i in 1:nrow(cityinfo))
{
######################################################
################ server configuration ################
######################################################
# the city's name, pinyinabb ########################
district <- cityinfo$pinyinabb[i] ####################
# the city's server host ############################
host <- cityinfo$host[i] ############################
# the city's server port ############################
port <- cityinfo$port[i] ############################
# the city's server user ############################
user <- cityinfo$user[i] ############################
# the city's server password ########################
password <- cityinfo$password[i] #####################
# the city's database name ##########################
dbname <- cityinfo$dbname[i] ########################
######################################################
######################################################
######################################################
# calculate each city
cat(cityinfo$chinese[i],"(",i,")\t")
# the server exist? if not, stop!
if (is.na(district) | is.na(host) | is.na(port) | is.na(user) | is.na(password) | is.na(dbname)){
cat("There's no server for this city!\n")
next
}
hp_city(district,host,port,user,password,dbname,startmon,endmon,resol,outpath,sys)
cat(as.character(Sys.time()),"\tsucced\n")
}
# stop, if no any data
if (length(dir(paste0(outpath,"/ras_11_newcalprice"))) == 0) {return(0)}
###############################################################################################
########################### 将各城市数据拼合，并切割为16个瓦片 ################################
###############################################################################################
##################### tile file ######################
##### id st_xmin st_xmax st_ymin st_ymax #####
######################################################
# 1 73.502355 85.821018 43.6183054 53.563624 #
# 2 85.821018 98.139681 43.6183054 53.563624 #
# 3 98.139681 110.458344 43.6183054 53.563624 #
# 4 110.458344 122.777007 43.6183054 53.563624 #
# 5 122.777007 135.09567 43.6183054 53.563624 #
# 6 73.502355 85.821018 33.6729868 43.6183054 #
# 7 85.821018 98.139681 33.6729868 43.6183054 #
# 8 98.139681 110.458344 33.6729868 43.6183054 #
# 9 110.458344 122.777007 33.6729868 43.6183054 #
# 10 122.777007 135.09567 33.6729868 43.6183054 #
# 11 73.502355 85.821018 23.7276682 33.6729868 #
# 12 85.821018 98.139681 23.7276682 33.6729868 #
# 13 98.139681 110.458344 23.7276682 33.6729868 #
# 14 110.458344 122.777007 23.7276682 33.6729868 #
# 15 98.139681 110.458344 13.7823496 23.7276682 #
# 16 110.458344 122.777007 13.7823496 23.7276682 #
######################################################
# read the tile files
cat("\n2. combine all cities, and crop to tiles:\n")
#tiles <- read.table("data/tiles.txt",header = TRUE,stringsAsFactors = FALSE)
# set the backgroud raster
china_tiles <- raster(xmn=min(tiles$st_xmin),xmx=max(tiles$st_xmax),
ymn=min(tiles$st_ymin),ymx=max(tiles$st_ymax),ncols=5,nrows=4,
crs="+init=epsg:3857",res = c(resol,resol))
if (!file.exists(paste0(outpath,"/temp/china_tiles.tif"))) {
writeRaster(china_tiles,paste0(outpath,"/temp/china_tiles.tif"),format='GTiff', datatype="FLT8S")
}
# merge all districts in one raster map, every month [, every vars]
vars <- c("newcalprice", "newlink", "newlike") #价格、环比、同比
for (k in 1:length(vars))
{
cat("\n",vars[k],":\n")
for (j in 1:nmonth)
{
rs <- c()
ii <- 1
for (i in 1:nrow(cityinfo))
{
fname <- paste0(outpath,"/ras_11_",vars[k],"/ras_11_",cityinfo$pinyinabb[i],"_",vars[k],"_",months[j],".tif")
if (file.exists(fname)){
rs[ii] <- fname
ii <- ii+1
}
}
# combine, using do.call method
mosaic_rasters(c(rs,paste0(outpath,"/temp/china_tiles.tif")),paste0(outpath,"/temp/tmp.tif"),overwrite=TRUE)
# crop to tiles(method1=raster; method2=gdalUtils)
# method1
r0 <- raster(paste0(outpath,"/temp/tmp.tif"))
for (tile_i in 1:nrow(tiles))
{
crop(r0,extent(tiles$st_xmin[tile_i],tiles$st_xmax[tile_i],
tiles$st_ymin[tile_i],tiles$st_ymax[tile_i]),
filename=paste0(outpath,"/ras_11_",vars[k],"/ras_11_",tile_i,"_",vars[k],"_",months[j],"01.tif"),
overwrite=TRUE, datatype="FLT8S")
}
# method2
# src_dataset <- paste0(outpath,"/temp/tmp.tif")
# for (tile_i in 1:nrow(tiles))
# {
# dstfile <- paste0(outpath,"/ras_11_newcalprice","/ras_11_tile",tile_i,"_",vars[k],"_",months[j],"01.tif")
# gdalwarp(src_dataset,dstfile,te=c(tiles$st_xmin[tile_i], tiles$st_ymin[tile_i],
# tiles$st_xmax[tile_i], tiles$st_ymax[tile_i]),overwrite = TRUE)
# }
cat(months[j],"\t")
}
}
####### delete the temp files ########
unlink(paste0(outpath,"/temp"),recursive = TRUE)
####### THE END! #####################
cat("\nTHE END!")
return(0)
}
Menglinucas/HousePC documentation built on May 28, 2019, 1:52 p.m.
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions hp_CHN
Documented in hp_CHN
#' @title Batches used for House Price calculation of China
#' @description The main fuction of this package.
#' @description Combine all city results of each month, and crop to 16 tiles.
#' @param startmon The firt month in the form such as "200606", character
#' @param endmon The last month in the form as param startmon, character
#' @param resol Mesh resolution, unit: meter, numeric
#' @param configfile The configuration file, with the path, character
#' @param outpath The path storing outfiles, character
#' @param sys The system type, Linux or Wins ?
#' @details THe outputs mainly contains Magnitude, Link and Year-over-year distibution of the price.
#' @details Contains six dirs:
#' @details (1) ras_11_newcalprice --- altitude
#' @details (2) ras_11_newlike --- year-over-year
#' @details (3) ras_11_newlink --- link
#' @details (4) level --- price level
#' @details (5) minmaxp --- min and max price each month and city
#' @details (6) temp --- temperary dir in which files are not necessary, you can neglect it
#' @return Generate amounts of "tif" files to outpath
#' @export
hp_CHN <- function(startmon,endmon,resol,configfile,outpath,sys){
# set local GDAL installation options
gdal_setInstallation()
valid_install <- !is.null(getOption("gdalUtils_gdalPath"))
if (valid_install == FALSE) {return("Please install gdalUtils!")}
# delete the warning reminder
options(warn = -1)
######################################################
######## the path and dir saving output files ########
######################################################
outpath <- outpath
###########################################################################################
############## calculate the months, and create the month,level,minmax dir ################
###########################################################################################
if (!file.exists(outpath)) {dir.create(outpath)}
nmonth<-(as.numeric(substr(endmon,1,4))-as.numeric(substr(startmon,1,4)))*12+
as.numeric(substr(endmon,5,6))-as.numeric(substr(startmon,5,6))+1
months<-c()
months[1]<-as.numeric(startmon)
if (nmonth>1){
for (i in 2:nmonth)
{
if (as.numeric(substr(months[i-1],5,6))<12)
{
months[i]<-months[i-1]+1
}else{
months[i]<-months[i-1]+89
}
}
}
if (!file.exists(paste0(outpath,"/ras_11_newcalprice"))) {dir.create(paste0(outpath,"/ras_11_newcalprice"))}
if (!file.exists(paste0(outpath,"/ras_11_newlike"))) {dir.create(paste0(outpath,"/ras_11_newlike"))}
if (!file.exists(paste0(outpath,"/ras_11_newlink"))) {dir.create(paste0(outpath,"/ras_11_newlink"))}
if (!file.exists(paste0(outpath,"/level"))) {dir.create(paste0(outpath,"/level"))}
if (!file.exists(paste0(outpath,"/minmaxp"))) {dir.create(paste0(outpath,"/minmaxp"))}
if (!file.exists(paste0(outpath,"/temp"))) {dir.create(paste0(outpath,"/temp"))}
###############################################################################################
# 计算除禧泰数据库(city_info中的367个城市)中可克达拉和昆玉两个地区以外的365个城市的价格分布、 #
# 环比和同比涨跌幅分布 ########################################################################
###############################################################################################
cat("1. calculate the data of each city:\n")
if (!file.exists("city_info.txt")){
cat("There's no configure file with database information!!!")
return(0)
}else{
cityinfo <- read.table(configfile,header = TRUE, stringsAsFactors = FALSE, fileEncoding = 'UTF-8')
}
for (i in 1:nrow(cityinfo))
{
######################################################
################ server configuration ################
######################################################
# the city's name, pinyinabb ########################
district <- cityinfo$pinyinabb[i] ####################
# the city's server host ############################
host <- cityinfo$host[i] ############################
# the city's server port ############################
port <- cityinfo$port[i] ############################
# the city's server user ############################
user <- cityinfo$user[i] ############################
# the city's server password ########################
password <- cityinfo$password[i] #####################
# the city's database name ##########################
dbname <- cityinfo$dbname[i] ########################
######################################################
######################################################
######################################################
# calculate each city
cat(cityinfo$chinese[i],"(",i,")\t")
# the server exist? if not, stop!
if (is.na(district) | is.na(host) | is.na(port) | is.na(user) | is.na(password) | is.na(dbname)){
cat("There's no server for this city!\n")
next
}
hp_city(district,host,port,user,password,dbname,startmon,endmon,resol,outpath,sys)
cat(as.character(Sys.time()),"\tsucced\n")
}
# stop, if no any data
if (length(dir(paste0(outpath,"/ras_11_newcalprice"))) == 0) {return(0)}
###############################################################################################
########################### 将各城市数据拼合，并切割为16个瓦片 ################################
###############################################################################################
##################### tile file ######################
##### id st_xmin st_xmax st_ymin st_ymax #####
######################################################
# 1 73.502355 85.821018 43.6183054 53.563624 #
# 2 85.821018 98.139681 43.6183054 53.563624 #
# 3 98.139681 110.458344 43.6183054 53.563624 #
# 4 110.458344 122.777007 43.6183054 53.563624 #
# 5 122.777007 135.09567 43.6183054 53.563624 #
# 6 73.502355 85.821018 33.6729868 43.6183054 #
# 7 85.821018 98.139681 33.6729868 43.6183054 #
# 8 98.139681 110.458344 33.6729868 43.6183054 #
# 9 110.458344 122.777007 33.6729868 43.6183054 #
# 10 122.777007 135.09567 33.6729868 43.6183054 #
# 11 73.502355 85.821018 23.7276682 33.6729868 #
# 12 85.821018 98.139681 23.7276682 33.6729868 #
# 13 98.139681 110.458344 23.7276682 33.6729868 #
# 14 110.458344 122.777007 23.7276682 33.6729868 #
# 15 98.139681 110.458344 13.7823496 23.7276682 #
# 16 110.458344 122.777007 13.7823496 23.7276682 #
######################################################
# read the tile files
cat("\n2. combine all cities, and crop to tiles:\n")
#tiles <- read.table("data/tiles.txt",header = TRUE,stringsAsFactors = FALSE)
# set the backgroud raster
china_tiles <- raster(xmn=min(tiles$st_xmin),xmx=max(tiles$st_xmax),
ymn=min(tiles$st_ymin),ymx=max(tiles$st_ymax),ncols=5,nrows=4,
crs="+init=epsg:3857",res = c(resol,resol))
if (!file.exists(paste0(outpath,"/temp/china_tiles.tif"))) {
writeRaster(china_tiles,paste0(outpath,"/temp/china_tiles.tif"),format='GTiff', datatype="FLT8S")
}
# merge all districts in one raster map, every month [, every vars]
vars <- c("newcalprice", "newlink", "newlike") #价格、环比、同比
for (k in 1:length(vars))
{
cat("\n",vars[k],":\n")
for (j in 1:nmonth)
{
rs <- c()
ii <- 1
for (i in 1:nrow(cityinfo))
{
fname <- paste0(outpath,"/ras_11_",vars[k],"/ras_11_",cityinfo$pinyinabb[i],"_",vars[k],"_",months[j],".tif")
if (file.exists(fname)){
rs[ii] <- fname
ii <- ii+1
}
}
# combine, using do.call method
mosaic_rasters(c(rs,paste0(outpath,"/temp/china_tiles.tif")),paste0(outpath,"/temp/tmp.tif"),overwrite=TRUE)
# crop to tiles(method1=raster; method2=gdalUtils)
# method1
r0 <- raster(paste0(outpath,"/temp/tmp.tif"))
for (tile_i in 1:nrow(tiles))
{
crop(r0,extent(tiles$st_xmin[tile_i],tiles$st_xmax[tile_i],
tiles$st_ymin[tile_i],tiles$st_ymax[tile_i]),
filename=paste0(outpath,"/ras_11_",vars[k],"/ras_11_",tile_i,"_",vars[k],"_",months[j],"01.tif"),
overwrite=TRUE, datatype="FLT8S")
}
# method2
# src_dataset <- paste0(outpath,"/temp/tmp.tif")
# for (tile_i in 1:nrow(tiles))
# {
# dstfile <- paste0(outpath,"/ras_11_newcalprice","/ras_11_tile",tile_i,"_",vars[k],"_",months[j],"01.tif")
# gdalwarp(src_dataset,dstfile,te=c(tiles$st_xmin[tile_i], tiles$st_ymin[tile_i],
# tiles$st_xmax[tile_i], tiles$st_ymax[tile_i]),overwrite = TRUE)
# }
cat(months[j],"\t")
}
}
####### delete the temp files ########
unlink(paste0(outpath,"/temp"),recursive = TRUE)
####### THE END! #####################
cat("\nTHE END!")
return(0)
}
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