Mercurial > repos > greg > bmsb
comparison bmsb.R @ 19:d965e188feab draft
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author | greg |
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date | Tue, 16 Aug 2016 13:05:09 -0400 |
parents | c6668285a216 |
children | ce78cd25b873 |
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18:c6668285a216 | 19:d965e188feab |
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1 #!/usr/bin/env Rscript | 1 #!/usr/bin/env Rscript |
2 | 2 |
3 #suppressPackageStartupMessages(library("optparse")) | |
4 | |
3 options_list <- list( | 5 options_list <- list( |
4 make_option(c("-o", "--output"), action="store", help="Output dataset") | 6 make_option(c("-s", "--save_log"), action="store_true", default=FALSE, help="Save R logs"), |
7 make_option(c("-m", "--output_r_log"), help="Output dataset for R logs"), | |
8 make_option(c("-o", "--output"), help="Output dataset") | |
5 ) | 9 ) |
6 | 10 |
7 parser <- OptionParser(usage="%prog [options] file", options_list) | 11 parser <- OptionParser(usage="%prog [options] file", options_list) |
8 args <- parse_args(parser, positional_arguments=TRUE) | 12 args <- parse_args(parser, positional_arguments=TRUE) |
9 opt <- args$options | 13 opt <- args$options |
10 | 14 |
11 | 15 |
12 fileConn<-file(opt$output) | 16 daylength = function(L){ |
13 writeLines(c("Hello World!"), fileConn) | 17 # from Forsythe 1995 |
14 close(fileConn) | 18 p = 0.8333 |
19 dl <- NULL | |
20 for (i in 1:365) { | |
21 theta <- 0.2163108 + 2 * atan(0.9671396 * tan(0.00860 * (i - 186))) | |
22 phi <- asin(0.39795 * cos(theta)) | |
23 dl[i] <- 24 - 24/pi * acos((sin(p * pi/180) + sin(L * pi/180) * sin(phi))/(cos(L * pi/180) * cos(phi))) | |
24 } | |
25 # return a vector of daylength in 365 days | |
26 dl | |
27 } | |
28 | |
29 | |
30 # source("daylength.R") | |
31 hourtemp = function(L,date){ | |
32 # L = 37.5 specify this in main program | |
33 # base development threshold for BMSB | |
34 threshold <- 12.7 | |
35 # threshold2 <- threshold/24 degree hour accumulation | |
36 #expdata <- tempdata[1:365,11:13] # Use daily max, min, mean | |
37 # daily minimum | |
38 dnp <- expdata[date,2] | |
39 # daily maximum | |
40 dxp <- expdata[date,3] | |
41 dmean <- 0.5 * (dnp + dxp) | |
42 #if (dmean>0) { | |
43 #dnp <- dnp - k1 * dmean | |
44 #dxp <- dxp + k2 * dmean | |
45 #} else { | |
46 #dnp <- dnp + k1 * dmean | |
47 #dxp <- dxp - k2 * dmean | |
48 #} | |
49 dd <- 0 # initialize degree day accumulation | |
50 | |
51 if (dxp<threshold) { | |
52 dd <- 0 | |
53 } else { | |
54 # extract daylength data for entire year | |
55 dlprofile <- daylength(L) | |
56 # initialize hourly temperature | |
57 T <- NULL | |
58 #initialize degree hour vector | |
59 dh <- NULL | |
60 # calculate daylength in given date | |
61 # date <- 200 | |
62 y <- dlprofile[date] | |
63 # night length | |
64 z <- 24 - y | |
65 # lag coefficient | |
66 a <- 1.86 | |
67 # night coefficient | |
68 b <- 2.20 | |
69 #import raw data set | |
70 #tempdata <- read.csv("tempdata.csv") | |
71 # Should be outside function otherwise its redundant | |
72 # sunrise time | |
73 risetime <- 12 - y/2 | |
74 # sunset time | |
75 settime <- 12 + y/2 | |
76 ts <- (dxp - dnp) * sin(pi * (settime-5)/(y + 2 * a)) + dnp | |
77 for (i in 1:24) { | |
78 if (i > risetime && i < settime) { | |
79 # number of hours after Tmin until sunset | |
80 m <- i - 5 | |
81 T[i] = (dxp - dnp) * sin(pi * m/(y + 2 * a)) + dnp | |
82 if (T[i]<8.4) { | |
83 dh[i] <- 0 | |
84 } else { | |
85 dh[i] <- T[i] - 8.4 | |
86 } | |
87 } else if (i>settime) { | |
88 n <- i - settime | |
89 T[i] = dnp + (ts - dnp) * exp(-b * n/z) | |
90 if (T[i]<8.4) { | |
91 dh[i] <- 0 | |
92 } else { | |
93 dh[i] <- T[i] - 8.4 | |
94 } | |
95 } else { | |
96 n <- i + 24 - settime | |
97 T[i] = dnp + (ts - dnp) * exp(-b * n / z) | |
98 if (T[i]<8.4) { | |
99 dh[i] <- 0 | |
100 } else { | |
101 dh[i] <- T[i] - 8.4 | |
102 } | |
103 } | |
104 } | |
105 dd <- sum(dh) / 24 | |
106 } | |
107 return = c(dmean, dd) | |
108 return | |
109 } | |
110 | |
111 | |
112 mortality.egg = function(temperature) { | |
113 if (temperature < 12.7) { | |
114 mort.prob = 1 | |
115 } else { | |
116 # 100% mortality if <12.7 | |
117 mort.prob = 0.8 - temperature / 40 | |
118 if (mort.prob<0) { | |
119 mort.prob = 0.01 | |
120 } | |
121 } | |
122 return = mort.prob | |
123 return | |
124 } | |
125 | |
126 | |
127 mortality.nymph = function(temperature) { | |
128 if (temperature<12.7) { | |
129 mort.prob = 0.03 | |
130 } else { | |
131 # at low temperature | |
132 mort.prob = -temperature * 0.0008 + 0.03 | |
133 } | |
134 return = mort.prob | |
135 return | |
136 } | |
137 | |
138 | |
139 mortality.adult = function(temperature) { | |
140 if (temperature < 12.7) { | |
141 mort.prob = 0.002 | |
142 } else { | |
143 mort.prob = -temperature * 0.0005 + 0.02 | |
144 } | |
145 return = mort.prob | |
146 return | |
147 } | |
148 | |
149 | |
150 # model initialization | |
151 # TODO: add tool params for the following options. | |
152 # start with 1000 individuals | |
153 n <- 1000 | |
154 # Generation, Stage, DD, T, Diapause | |
155 vec.ini <- c(0,3,0,0,0) | |
156 # overwintering, previttelogenic, DD = 0, T = 0, no-diapause | |
157 vec.mat <- rep(vec.ini,n) | |
158 # complete matrix for the population | |
159 vec.mat <- t(matrix(vec.mat, nrow = 5)) | |
160 # latitude for Asheville NC | |
161 L <- 35.58 | |
162 # complete photoperiod profile in a year, requires daylength function | |
163 ph.p <- daylength(L) | |
164 | |
165 # load temperature data@location/year | |
166 load("asheville2014.Rdat") | |
167 | |
168 # time series of population size | |
169 tot.pop <- NULL | |
170 | |
171 # gen.0 pop size | |
172 gen0.pop <- rep(0, 365) | |
173 gen1.pop <- rep(0, 365) | |
174 gen2.pop <- rep(0, 365) | |
175 | |
176 # aggregate | |
177 S0 <- S1 <- S2 <- S3 <- S4 <- S5 <- rep(0, 365) | |
178 g0.adult <- g1.adult <- g2.adult <- rep(0, 365) | |
179 | |
180 # birth death adults | |
181 N.newborn <- N.death <- N.adult <- rep(0, 365) | |
182 | |
183 # degree-day accumulation | |
184 dd.day <- rep(0, 365) | |
185 | |
186 # start tick | |
187 ptm <- proc.time() | |
188 | |
189 for (n.sim in 1:1000) { | |
190 # loop through 1000 simulations | |
191 for (day in 1:365) { | |
192 # loop through 365 day/yr | |
193 photoperiod <- ph.p[day] | |
194 # photoperiod in the day | |
195 temp.profile <- hourtemp(L,day) | |
196 # temperature profile | |
197 mean.temp <- temp.profile[1] | |
198 # mean temp | |
199 dd.temp <- temp.profile[2] | |
200 # degree-day | |
201 dd.day[day] <- dd.temp | |
202 death.vec <- NULL | |
203 # trash bin for death | |
204 birth.vec <- NULL | |
205 # record new born | |
206 for (i in 1:n) { | |
207 # loop through all individual | |
208 vec.ind <- vec.mat[i,] | |
209 # find individual record | |
210 # first of all, still alive? | |
211 if (vec.ind[2] == 0) { | |
212 # egg | |
213 death.prob = mortality.egg(mean.temp) | |
214 } else if (vec.ind[2] == 1 | vec.ind[2] == 2) { | |
215 # nymph | |
216 death.prob = mortality.nymph(mean.temp) | |
217 } else if (vec.ind[2] == 3 | vec.ind[2] == 4 | vec.ind[2] == 5) { | |
218 # for adult | |
219 death.prob = mortality.adult(mean.temp) | |
220 } | |
221 u.d <- runif(1) | |
222 if (u.d<death.prob) { | |
223 death.vec <- c(death.vec,i) | |
224 } else { | |
225 # aggregrate index of dead bug | |
226 # event 1 end of diapause | |
227 if (vec.ind[1] == 0 && vec.ind[2] == 3) { | |
228 # overwintering adult (previttelogenic) | |
229 if (photoperiod>13.5 && vec.ind[3] > 68 && day < 180) { | |
230 # add 68C to become fully reproductively matured | |
231 # transfer to vittelogenic | |
232 vec.ind <- c(0,4,0,0,0) | |
233 vec.mat[i,] <- vec.ind | |
234 } else { | |
235 # add to DD | |
236 vec.ind[3] <- vec.ind[3] + dd.temp | |
237 vec.ind[4] <- vec.ind[4] + 1 # add 1 day in current stage | |
238 vec.mat[i,] <- vec.ind | |
239 } | |
240 } | |
241 if (vec.ind[1]!=0 && vec.ind[2] == 3) { | |
242 # NOT overwintering adult (previttelogenic) | |
243 current.gen <- vec.ind[1] | |
244 if (vec.ind[3]>68) { | |
245 # add 68C to become fully reproductively matured | |
246 # transfer to vittelogenic | |
247 vec.ind <- c(current.gen,4,0,0,0) | |
248 vec.mat[i,] <- vec.ind | |
249 } else { | |
250 # add to DD | |
251 vec.ind[3] <- vec.ind[3] + dd.temp | |
252 # add 1 day in current stage | |
253 vec.ind[4] <- vec.ind[4] + 1 | |
254 vec.mat[i,] <- vec.ind | |
255 } | |
256 } | |
257 # event 2 oviposition -- where population dynamics comes from | |
258 # vittelogenic stage, overwintering generation | |
259 if (vec.ind[2] == 4 && vec.ind[1] == 0 && mean.temp>10) { | |
260 if (vec.ind[4] == 0) { | |
261 # just turned in vittelogenic stage | |
262 n.birth = round(runif(1,10,20)) | |
263 } else { | |
264 p.birth = 1/4/75 | |
265 # prob of birth | |
266 u1 <- runif(1) | |
267 if (u1<p.birth) { | |
268 n.birth = n.birth | |
269 } | |
270 } | |
271 # add to DD | |
272 vec.ind[3] <- vec.ind[3] + dd.temp | |
273 # add 1 day in current stage | |
274 vec.ind[4] <- vec.ind[4] + 1 | |
275 vec.mat[i,] <- vec.ind | |
276 if (n.birth>0) { | |
277 # add new birth -- might be in different generations | |
278 # generation + 1 | |
279 new.gen <- vec.ind[1] + 1 | |
280 # egg profile | |
281 new.ind <- c(new.gen,0,0,0,0) | |
282 new.vec <- rep(new.ind,n.birth) | |
283 # update batch of egg profile | |
284 new.vec <- t(matrix(new.vec,nrow = 5)) | |
285 # group with total eggs laid in that day | |
286 birth.vec <- rbind(birth.vec,new.vec) | |
287 } | |
288 } | |
289 # event 2 oviposition -- for gen 1. | |
290 if (vec.ind[2] == 4 && vec.ind[1] == 1 && mean.temp>12.5 && day<222) { | |
291 # vittelogenic stage, 1st generation | |
292 if (vec.ind[4] == 0) { | |
293 # just turned in vittelogenic stage | |
294 n.birth = round(runif(1,10,20)) | |
295 } else { | |
296 p.birth = 1/4/75 | |
297 u1 <- runif(1) | |
298 if (u1<p.birth) { | |
299 n.birth = n.birth | |
300 } | |
301 } | |
302 # add to DD | |
303 vec.ind[3] <- vec.ind[3] + dd.temp | |
304 # add 1 day in current stage | |
305 vec.ind[4] <- vec.ind[4] + 1 | |
306 vec.mat[i,] <- vec.ind | |
307 if (n.birth>0) { | |
308 # add new birth -- might be in different generations | |
309 # generation + 1 | |
310 new.gen <- vec.ind[1] + 1 | |
311 # egg profile | |
312 new.ind <- c(new.gen,0,0,0,0) | |
313 new.vec <- rep(new.ind,n.birth) | |
314 # update batch of egg profile | |
315 new.vec <- t(matrix(new.vec,nrow = 5)) | |
316 # group with total eggs laid in that day | |
317 birth.vec <- rbind(birth.vec,new.vec) | |
318 } | |
319 } | |
320 # event 3 development (with diapause determination) | |
321 # event 3.1 egg development to young nymph (vec.ind[2] = 0 -> egg) | |
322 # egg stage | |
323 if (vec.ind[2] == 0) { | |
324 # add to DD | |
325 vec.ind[3] <- vec.ind[3] + dd.temp | |
326 # from egg to young nymph | |
327 if (vec.ind[3] >= 53.30 && -0.9843 * dd.temp + 33.438>0) { | |
328 current.gen <- vec.ind[1] | |
329 # transfer to young nym stage | |
330 vec.ind <- c(current.gen,1,0,0,0) | |
331 } else { | |
332 # add 1 day in current stage | |
333 vec.ind[4] <- vec.ind[4] + 1 | |
334 } | |
335 vec.mat[i,] <- vec.ind | |
336 } | |
337 # event 3.2 young nymph to old nymph (vec.ind[2] = 1 -> young nymph: determines diapause) | |
338 # young nymph stage | |
339 if (vec.ind[2] == 1) { | |
340 # add to DD | |
341 vec.ind[3] <- vec.ind[3] + dd.temp | |
342 # from young to old nymph | |
343 if (vec.ind[3] >= 537/2 && -0.45 * dd.temp + 18>0) { | |
344 current.gen <- vec.ind[1] | |
345 # transfer to old nym stage | |
346 vec.ind <- c(current.gen,2,0,0,0) | |
347 # prepare for diapausing | |
348 if (photoperiod<13.5 && day > 180) { | |
349 vec.ind[5] <- 1 | |
350 } | |
351 } else { | |
352 # add 1 day in current stage | |
353 vec.ind[4] <- vec.ind[4] + 1 | |
354 } | |
355 vec.mat[i,] <- vec.ind | |
356 } | |
357 # event 3.3 old nymph to adult: previttelogenic or diapausing? | |
358 # old nymph stage | |
359 if (vec.ind[2] == 2) { | |
360 # add to DD | |
361 vec.ind[3] <- vec.ind[3] + dd.temp | |
362 # from old to adult | |
363 if (vec.ind[3] >= 537/2 && -0.50 * dd.temp + 22>0) { | |
364 current.gen <- vec.ind[1] | |
365 # non-diapausing adult -- previttelogenic | |
366 if (vec.ind[5] == 0) { | |
367 vec.ind <- c(current.gen,3,0,0,0) | |
368 # diapausing | |
369 } else { | |
370 vec.ind <- c(current.gen,5,0,0,1) | |
371 } | |
372 } else { | |
373 # add 1 day in current stage | |
374 vec.ind[4] <- vec.ind[4] + 1 | |
375 } | |
376 vec.mat[i,] <- vec.ind | |
377 } | |
378 # event 4 growing of diapausing adult (unimportant, but still necessary)## | |
379 if (vec.ind[2] == 5) { | |
380 vec.ind[3] <- vec.ind[3] + dd.temp | |
381 vec.ind[4] <- vec.ind[4] + 1 | |
382 vec.mat[i,] <- vec.ind | |
383 } | |
384 } # else if it is still alive | |
385 } # end of the individual bug loop | |
386 # find how many died | |
387 n.death <- length(death.vec) | |
388 if (n.death>0) { | |
389 vec.mat <- vec.mat[-death.vec, ] | |
390 } | |
391 # remove record of dead | |
392 # find how many new born | |
393 n.newborn <- length(birth.vec[,1]) | |
394 vec.mat <- rbind(vec.mat,birth.vec) | |
395 # update population size for the next day | |
396 n <- n-n.death + n.newborn | |
397 | |
398 # aggregate results by day | |
399 tot.pop <- c(tot.pop,n) | |
400 # egg | |
401 s0 <- sum(vec.mat[,2] == 0) | |
402 # young nymph | |
403 s1 <- sum(vec.mat[,2] == 1) | |
404 # old nymph | |
405 s2 <- sum(vec.mat[,2] == 2) | |
406 # previtellogenic | |
407 s3 <- sum(vec.mat[,2] == 3) | |
408 # vitellogenic | |
409 s4 <- sum(vec.mat[,2] == 4) | |
410 # diapausing | |
411 s5 <- sum(vec.mat[,2] == 5) | |
412 # overwintering adult | |
413 gen0 <- sum(vec.mat[,1] == 0) | |
414 # first generation | |
415 gen1 <- sum(vec.mat[,1] == 1) | |
416 # second generation | |
417 gen2 <- sum(vec.mat[,1] == 2) | |
418 # sum of all adults | |
419 n.adult <- sum(vec.mat[,2] == 3) + sum(vec.mat[,2] == 4) + sum(vec.mat[,2] == 5) | |
420 # gen.0 pop size | |
421 gen0.pop[day] <- gen0 | |
422 gen1.pop[day] <- gen1 | |
423 gen2.pop[day] <- gen2 | |
424 S0[day] <- s0 | |
425 S1[day] <- s1 | |
426 S2[day] <- s2 | |
427 S3[day] <- s3 | |
428 S4[day] <- s4 | |
429 S5[day] <- s5 | |
430 g0.adult[day] <- sum(vec.mat[,1] == 0) | |
431 g1.adult[day] <- sum((vec.mat[,1] == 1 & vec.mat[,2] == 3) | (vec.mat[,1] == 1 & vec.mat[,2] == 4) | (vec.mat[,1] == 1 & vec.mat[,2] == 5)) | |
432 g2.adult[day] <- sum((vec.mat[,1] == 2 & vec.mat[,2] == 3) | (vec.mat[,1] == 2 & vec.mat[,2] == 4) | (vec.mat[,1] == 2 & vec.mat[,2] == 5)) | |
433 N.newborn[day] <- n.newborn | |
434 N.death[day] <- n.death | |
435 N.adult[day] <- n.adult | |
436 } | |
437 #print(n.sim) | |
438 } | |
439 | |
440 proc.time() - ptm | |
441 dd.cum <- cumsum(dd.day) | |
442 save(dd.day, dd.cum, S0, S1, S2, S3, S4, S5, N.newborn, N.death, N.adult, tot.pop, gen0.pop, gen1.pop, gen2.pop, g0.adult, g1.adult, g2.adult, file=opt$output) |