-
Notifications
You must be signed in to change notification settings - Fork 0
/
winter_olympic_forecast_3D_small_crontab.R
417 lines (353 loc) · 17.2 KB
/
winter_olympic_forecast_3D_small_crontab.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
#==========================================================
#
# 本程序用于批量制作冬奥1km/3km分辨率模式预报的三维图形产品.
#
# * 从Cassandra服务器上读取CMA_MESO_1km数据气温和10米U/V风速.
# * 采用rayshader将温度场和风流线场叠加到三维场上.
# * 在命令窗口中运行:
# * R --vanilla --verbose CMD BATCH /media/kan-dai/workspace/workcode/personal/dk_winter_olympic_3D/winter_olympic_forecast_3D_crontab.R
# * 如果要在crontab中进行定时作业, 请使用 winter_olympic_forecast_3D_crontab.sh 文件
# > crontab -e
# 50 * * * * (flock winter_olympic_forecast_3D_crontab.lock "/media/kan-dai/workspace/workcode/personal/dk_winter_olympic_3D/winter_olympic_forecast_3D_crontab.sh")
# 注意一定要加 "export DISPLAY=:1" (这里1为显示器序号, 每台机器不同, 网上说的0就不行)
# 如果使用render_snapshot(webshot=TRUE), 会运行很慢, 且字体不能正常显示.
#==========================================================
#==========================================================
# 载入所有用到的软件库
library(ggplot2)
library(stringr)
library(raster)
library(png)
library(rayshader)
library(leaflet)
library(metR)
library(geosphere)
library(osmdata)
library(sf)
library(dplyr)
library(data.table)
library(magick)
# 从Cassandra服务器上读取模式数据
# 安装命令, devtools::install_github("nmcdev/nmcMetIO")
library(nmcMetIO)
#==========================================================
# 设置本程序用到的文件及相关参数
#
# 以下参数需要手工配置
# 设置工作目录(请使用绝对路径, 其后的产品都在该目录下运行)
work_dir <- "/media/kan-dai/workspace/workcode/personal/dk_winter_olympic_3D"
setwd(work_dir)
# 设置图片产品的输出目录
# sudo mount -t cifs -o username=share,password=nmcwft,dir_mode=0755,file_mode=0755,uid=1000,gid=1000 //10.28.16.160/public /media/winter_olympic_160
# output_dir <- file.path(work_dir, 'data', 'winter_olympic_images')
output_dir <- "/media/winter_olympic_160/城区"
# output_dir <- "/media/kan-dai/workspace/workcode/personal/dk_winter_olympic_3D/products"
# 设置GMOSRR数据目录
# \\10.20.90.104\winter_olympic\GRD\NMCGRID-GMOSRR
gmosrr_dir <- "/media/winter_olympic_gmosrr"
# 地形数据文件,数据提前从下面网站下载, 输出为geotiff格式, 分辨率选择最大
# https://www.gmrt.org/services/gridserverinfo.php#!/services/getGMRTGrid
# 在工作目录下建立 /data/winter_olympic的目录, 把地形文件拷贝到该目录即可
elev_file <- file.path(work_dir, 'data',"stadium_topo_small.tif")
#
# 以下参数不用手工配置
# 地图图像文件,如果该文件不存在,则会采用nmcMetIO::get_arcgis_map_image从
# Arcgis网站上自动下载.
map_file <- file.path(work_dir, 'data', "stadium_map_small.png")
# 地图图像文件,如果该文件不存在, 则会自动用osmdata下载数据生成
overlay_file_lines <- file.path(work_dir, 'data', "stadium_highway_small.png")
# 地形阴影数据文件, 如果文件不存在, 程序会自动生成.
shademat_file <- file.path(work_dir, 'data', "shademat_small.rds")
ambmat_file <- file.path(work_dir, 'data', "ambmat_small.rds")
raymat_file <- file.path(work_dir, 'data', "raymat_small.rds")
#==========================================================
# 读入和处理地形数据
# load topography data
elev_img <- raster(elev_file)
# set coordinates
crs(elev_img) <- CRS('+init=EPSG:4326')
# convert it to a matrix which rayshader can handle.
elev_matrix <- raster_to_matrix(elev_img)
# fill NA values
elev_matrix[!is.finite(elev_matrix)] <- min(elev_matrix, na.rm=TRUE)
#============================
# 准备地图图像文件
# get map extent
map_bbox = extent(elev_img)
map_bbox = list(p1 = list(long = map_bbox[1], lat = map_bbox[3]),
p2 = list(long = map_bbox[2], lat = map_bbox[4]))
# get map image size
image_size <- define_image_size(map_bbox, major_dim = 2000)
# 下载和读入地图图形文件
if(!file.exists(map_file)){
get_arcgis_map_image(map_bbox, map_type = "World_Topo_Map", file = map_file,
width = image_size$width, height = image_size$height,
sr_bbox=4326)}
# 读入图形文件
overlay_img_map <- readPNG(map_file)
#============================
# 准备街道地图图像文件
if (!file.exists(overlay_file_lines)) {
osm_bbox = c(extent(elev_img)[1],extent(elev_img)[3],
extent(elev_img)[2],extent(elev_img)[4])
stadium_highway = opq(osm_bbox) %>%
add_osm_feature(key="highway",value=c("motorway","primary")) %>%
osmdata_sf()
stadium_lines = st_transform(stadium_highway$osm_lines, crs=crs(elev_img))
p_lines <- ggplot(stadium_lines,aes()) +
geom_sf(color="cyan", size=2) +
coord_sf(expand=FALSE, crs=crs(elev_img),
xlim=c(extent(elev_img)[1], extent(elev_img)[2]),
ylim=c(extent(elev_img)[3], extent(elev_img)[4]))+
theme(panel.spacing=grid::unit(0, "mm"),
plot.margin=grid::unit(rep(-1.25,4),"lines"),
panel.background = element_rect(fill = "transparent"), # bg of the panel
plot.background = element_rect(fill = "transparent", color = NA), # bg of the plot
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.text=element_blank(),
axis.ticks=element_blank(),
axis.title=element_blank())
# export png file
# 注意这里设置的aspect要让输出的png图像四周边缘无空白
width <- 2000
aspect <- 1.06 * (extent(elev_img)[2]-extent(elev_img)[1])/(extent(elev_img)[4]-extent(elev_img)[3])
png(overlay_file_lines, width=width, height=width*aspect, units="px", bg="transparent")
p_lines
dev.off()
}
# 读入图形文件
overlay_img_lines <- readPNG(overlay_file_lines)
#============================
# 准备地形阴影数据
if(!file.exists(shademat_file)){
shademat <- sphere_shade(elev_matrix, texture = "imhof1")
saveRDS(shademat, file=shademat_file)}
shademat <- readRDS(shademat_file)
if(!file.exists(ambmat_file)){
ambmat <- ambient_shade(elev_matrix, zscale=15)
saveRDS(ambmat, file=ambmat_file)}
ambmat <- readRDS(ambmat_file)
if(!file.exists(raymat_file)){
raymat <- ray_shade(elev_matrix, zscale=15, lambert = TRUE)
saveRDS(raymat, file=raymat_file)}
raymat <- readRDS(raymat_file)
#==========================================================
# 循环每个预报场, 生成相应的图片产品
# 设置模式名称
model_names <- c('GMOSRR', 'GMOSRR(EC)', 'CMA_MESO(1km)', 'CMA_MESO(3km)')
for (model in model_names){
# 获得模式的最新起报时间
if (model == 'CMA_MESO(1km)'){
filename <- gds_get_latest_filename("GRAPES_1KM/TMP/2M_ABOVE_GROUND/",
filter="*.023")
basefile <- sub('\\..[^\\.]*$', '', filename)
fhours <- seq(1, 24)
} else if (model == 'GMOSRR') {
filename <- gds_get_latest_filename("GMOSRR/ROLLING_UPDATE_1KM/TMP/2M_ABOVE_GROUND/",
filter="*.023")
basefile <- sub('\\..[^\\.]*$', '', filename)
fhours <- seq(0, 24)
} else if (model == 'GMOSRR(EC)') {
sub_dirs <- dir(file.path(gmosrr_dir, 'Temperature_Hour_Inst','Grid','Hourly_oly'), '20????????')
sub_dirs <- sort(sub_dirs, decreasing=TRUE)
basefile <- substr(sub_dirs[1],3,nchar(sub_dirs[1]))
fhours <- seq(0, 24)
} else {
filename <- gds_get_latest_filename("GRAPES_3KM/TMP/2M_ABOVE_GROUND/",
filter="*.035")
basefile <- sub('\\..[^\\.]*$', '', filename)
fhours <- seq(0, 36)
}
# 构建输出文件夹
output_subdir <- file.path(output_dir, model, basefile)
if (!dir.exists(output_subdir)){
dir.create(output_subdir, recursive=TRUE)}
# 循环每个预报时效
for (fhour in fhours){
# 构建数据文件名和输出图形文件名
if (model == 'GMOSRR(EC)') {
filename <- paste0(sub_dirs[1], '.', str_pad(fhour, 3, pad="0"))
outfilename <- substr(filename,3,nchar(filename))
outfilename <- file.path(output_subdir, paste0(outfilename,'.png'))
if (file.exists(outfilename)){
next}
} else {
filename <- paste0(basefile, '.', str_pad(fhour, 3, pad="0"))
outfilename <- file.path(output_subdir, paste0(filename,'.png'))
if (file.exists(outfilename)){
next}
}
#============================
# 制作温度场填充等值线图
if (model == 'CMA_MESO(1km)'){
dataT <- retrieve_micaps_model_grid("GRAPES_1KM/TMP/2M_ABOVE_GROUND/", filename=filename)
} else if (model == 'GMOSRR') {
dataT <- retrieve_micaps_model_grid("GMOSRR/ROLLING_UPDATE_1KM/TMP/2M_ABOVE_GROUND/", filename=filename)
} else if (model == 'GMOSRR(EC)') {
datafile <- file.path(gmosrr_dir, 'Temperature_Hour_Inst','Grid','Hourly_oly',sub_dirs[1],filename)
if (file.exists(datafile)) {
dataT <- read_micaps_4(datafile, outList=FALSE)
}
} else {
dataT <- retrieve_micaps_model_grid("GRAPES_3KM/TMP/2M_ABOVE_GROUND/", filename=filename)
}
if (is.null(dataT)) next
# extract time information
init_time_str <- format(dataT$initTime[1], '%Y/%m/%dT%H')
valid_time_str <- format(dataT$time[1], '%Y/%m/%dT%H')
fhour_str <- str_pad(fhour,3,pad="0")
# extract region
dataT <- dataT[lon >= map_bbox$p1$long & lon <= map_bbox$p2$long &
lat >= map_bbox$p1$lat & lat <= map_bbox$p2$lat]
# smooth the data
out <- smooth2d(dataT$lon, dataT$lat, dataT$var1, theta=0.02, nx=128, ny=128)
# plot contour image
colors <- c("#3D0239","#FA00FC","#090079","#5E9DF8","#2E5E7F",
"#06F9FB","#0BF40B","#006103","#FAFB07","#D50404","#5A0303")
colors <- c("#762a83", "#5db8ff", '#feffff', '#a33ab2', "#7ebd0d")
breaks <- round(seq(-10, 12, by=0.4), digits=2)
p_temp <- ggplot(out, aes(x, y, z=z)) +
geom_contour_fill(breaks=breaks) +
geom_contour(breaks=breaks, color="black", size=2.0) +
geom_text_contour(breaks=breaks, stroke = 0.3, size=16, min.size=3) +
scale_fill_gradientn(name="Temp", colours=colors, limits=c(-12, 12)) +
scale_x_continuous(expand=c(0,0)) +
scale_y_continuous(expand=c(0,0)) +
guides(fill="none")+
theme(panel.spacing=grid::unit(0, "mm"),
plot.margin=grid::unit(rep(-1.25,4),"lines"),
axis.text=element_blank(),
axis.ticks=element_blank(),
axis.title=element_blank(),
legend.key.size = unit(5, "cm"),
legend.key.height = unit(2.5, 'cm'),
legend.key.width = unit(6, 'cm'),
legend.position = c(0.25, 0.06),
legend.direction="horizontal",
legend.text = element_text(size=32),
legend.title = element_text(size=32),
legend.background = element_rect(fill="lightblue",
size=0.5, linetype="solid"))
# export png file
overlay_file_temp <- file.path(output_dir,paste0(model,"_temperature_image_small.png"))
png(overlay_file_temp, width=image_size$width, height=image_size$height,units="px", bg="transparent")
print(p_temp)
dev.off()
overlay_img_temp <- png::readPNG(overlay_file_temp)
#============================
# 制作风场流线图
# load fine gridded forecast
if (model == 'CMA_MESO(1km)'){
dataU <- retrieve_micaps_model_grid("GRAPES_1KM/UGRD/10M_ABOVE_GROUND/", filename=filename)
if (is.null(dataU)) next
dataV <- retrieve_micaps_model_grid("GRAPES_1KM/VGRD/10M_ABOVE_GROUND/", filename=filename)
if (is.null(dataV)) next
skip <- 1
uv_scale <- 0.5
} else if (model == 'GMOSRR') {
dataU <- retrieve_micaps_model_grid("GMOSRR/ROLLING_UPDATE_1KM/UGRD/10M_ABOVE_GROUND//", filename=filename)
if (is.null(dataU)) next
dataV <- retrieve_micaps_model_grid("GMOSRR/ROLLING_UPDATE_1KM/VGRD/10M_ABOVE_GROUND//", filename=filename)
if (is.null(dataV)) next
skip <- 1
uv_scale <- 0.5
} else if (model == 'GMOSRR(EC)') {
datafile <- file.path(gmosrr_dir, 'Wind_Hour_Inst','Grid','Hourly_oly',sub_dirs[1],filename)
if (file.exists(datafile)) {
dataUV <- read_micaps_11(datafile, outList=FALSE)
} else {
next}
if (is.null(dataUV)) next
dataU <- subset(dataUV, select=-var2)
dataV <- subset(dataUV, select=-var1)
setnames(dataV, 'var2', 'var1')
skip <- 1
uv_scale <- 0.5
} else {
dataU <- retrieve_micaps_model_grid("GRAPES_3KM/UGRD/10M_ABOVE_GROUND/", filename=filename)
if (is.null(dataU)) next
dataV <- retrieve_micaps_model_grid("GRAPES_3KM/VGRD/10M_ABOVE_GROUND/", filename=filename)
if (is.null(dataV)) next
skip <- 0
uv_scale <- 0.7
}
# compute wind speed at points
point = list(lon=116.39200, lat=39.99150)
pointU = dataU[, Interpolate(var1 ~ lon + lat, point$lon, point$lat, grid=FALSE)]
pointV = dataV[, Interpolate(var1 ~ lon + lat, point$lon, point$lat, grid=FALSE)]
pointWS = sqrt(pointU$var1 * pointU$var1 + pointV$var1 * pointV$var1)
# extract region
dataU <- dataU[lon >= map_bbox$p1$long & lon <= map_bbox$p2$long & lat >= map_bbox$p1$lat & lat <= map_bbox$p2$lat]
dataV <- dataV[lon >= map_bbox$p1$long & lon <= map_bbox$p2$long & lat >= map_bbox$p1$lat & lat <= map_bbox$p2$lat]
dataUV <- merge(dataU, dataV, by=c("lon", "lat", "lev", "time", "initTime", "fhour"))
# plot wind streamlines
p_wind <- ggplot(dataUV, aes(lon, lat)) +
#geom_streamline(aes(dx=dlon(dataUV$var1.x,lat), dy=dlat(dataUV$var1.y), size=..step..,
# alpha=..step.., color=sqrt(..dx..^2 + ..dy..^2)),
# L=0.03, res=2, n=10, S=5, lineend="butt", size=4, arrow=NULL) +
geom_arrow(aes(dx = dataUV$var1.x*uv_scale, dy = dataUV$var1.y*uv_scale),
skip=skip, color = "brown", arrow.length=2.5, size=1.5) +
viridis::scale_color_viridis(guide="none") +
scale_size(range=c(0,3), guide="none") +
scale_alpha(guide="none") +
scale_x_continuous(expand=c(0,0)) +
scale_y_continuous(expand=c(0,0)) +
theme(panel.spacing=grid::unit(0, "mm"),
plot.margin=grid::unit(rep(-1.25,4),"lines"),
panel.background = element_rect(fill = "transparent"), # bg of the panel
plot.background = element_rect(fill = "transparent", color = NA), # bg of the plot
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.text=element_blank(),
axis.ticks=element_blank(),
axis.title=element_blank())
# export png file
overlay_file_wind <- file.path(output_dir,paste0(model,"_wind_streamline_image_small.png"))
png(overlay_file_wind, width=image_size$width, height=image_size$height, units="px", bg="transparent")
print(p_wind)
dev.off()
overlay_img_wind <- png::readPNG(overlay_file_wind)
#============================
# 制作三维地图
# 设置垂直尺度
zscale <- 15
# 设置标题
title <- paste0("2022北京冬残奥",model," 风场及温度预报(",valid_time_str,")\n",
"起报: ",init_time_str, "; 时效: ",fhour_str)
rgl::clear3d()
shademat %>%
add_overlay(overlay_img_map, alphalayer = 0.95) %>%
add_overlay(overlay_img_temp, alphalayer = 0.75) %>%
add_overlay(overlay_img_wind, alphalayer = 0.95) %>%
add_overlay(overlay_img_lines, alphalayer = 0.95) %>%
add_shadow(raymat, max_darken = 0.6) %>%
add_shadow(ambmat, max_darken = 0.6) %>%
plot_3d(elev_matrix, zscale = zscale, windowsize = c(1000, 1000), background = "white",
water = FALSE, soliddepth = -50, wateralpha = 0)
label <- list(text=paste0("Olympic Stadium: ", as.character(round(pointWS,digits=2)), "m/s"))
label$pos <- find_image_coordinates(long=point$lon, lat=point$lat, bbox=map_bbox,
image_width=dim(elev_matrix)[1], image_height=dim(elev_matrix)[2])
render_label(elev_matrix, x = label$pos$x, y = label$pos$y, z = 7800,
zscale = zscale, text = label$text, textsize=1.2, linewidth=5, freetype=TRUE)
dist = distm(c(extent(elev_img)[1], extent(elev_img)[3]),
c(extent(elev_img)[1], extent(elev_img)[4]), fun = distHaversine)[,1]
# render_scalebar(limits=round(dist/1000.,1),label_unit = 'km')
render_compass(position = "N",compass_radius=50, scale_distance = 1.2)
rgl::par3d(windowRect=c(-450, -100, 1000, 1100))
rgl::view3d(theta=12, phi=30, fov=40, zoom=0.6)
render_snapshot(outfilename, title_text = title, instant_capture=FALSE, bring_to_front=TRUE,
title_size = 45, title_color = "black", title_font = "SimHei")
# add logo and colorbar image
if (file.exists(outfilename)){
img <- magick::image_read(outfilename)
img_nmclogo <- magick::image_read(file.path(work_dir, 'data', "nmc_large.png"))
img_colorbar <- magick::image_read(file.path(work_dir, 'data', "stadium_temp_colorbar.png"))
img <- magick::image_composite(img, img_nmclogo, offset = "+1200+100")
img <- magick::image_composite(img, image_resize(img_colorbar, "x70"), offset = "+180+170")
image_write(img, path=outfilename, format="png")
}
}
}
# close rgl window
rgl::rgl.close()