Running 'cte.R' [0s]
  Comparing 'cte.Rout' to 'cte.Rout.save' ... OK
  Running 'data.R' [0s]
  Comparing 'data.Rout' to 'data.Rout.save' ... OK
  Running 'disppollen.R' [0s]
  Comparing 'disppollen.Rout' to 'disppollen.Rout.save' ... OK
  Running 'dispseed.R' [164s]
  Running 'fdispersions.R' [169s]
  Running 'paysage.R' [169s]
  Running 'petit.R' [161s]
Running the tests in 'tests/dispseed.R' failed.
Complete output:
  > dispseed  <- function(X) {
  +   return(fseed(X[1]))
  + } # fin dispseed 
  > 
  > proc.time()
     user  system elapsed 
     0.20    0.03    0.21 
Running the tests in 'tests/fdispersions.R' failed.
Complete output:
  > #////////////////////////////
  > #// fonction de dispersion des graines (Colbach et al. 2000b)
  > #////////////////////////////
  > # Exemple:
  > # distance = seq(1,1.5, by=0.05)
  > # plot(x=distance, y = fseed(distance))
  > 
  > fseed=function  ( distance) 
  + {
  +  grainesB =  1.38930;
  +  grainesC =  2.08686;
  + 
  +    fd = (grainesB * grainesC) * distance **(grainesC-2) *
  +          exp(-grainesB * distance ** (grainesC)) / (2.0*pi);
  +  
  +   return(fd);
  + }
  > 
  > #////////////////////////////
  > #// f1: la fonction de dispersion du pollen de Klein
  > #////////////////////////////
  > # Exemple:
  > #a=matrix(distance, ncol=1)
  > #b= apply(a,1,fpollen)
  > # plot(x=distance, y =b)
  > # lines(x=distance, y = fseed(distance))
  > 
  > fpollen = function ( distance)
  + {
  +  r = distance
  + 
  +   if ( r<=1.5 ) {
  +     rt = 0.340-0.405*r+0.128*r**(2.0);
  +   }  else {
  +     if (r <= 50) {
  +       rt = 0.03985/(1+r**(3.12)/3.80);
  +     }
  +     else {
  +       rr = 50
  +     gamma = -2.29
  + #    //valeur la plus lourde -2.14, + l<e9>g<e8>re - 2.56
  +     K = (0.03985 / (1.0+rr**(3.12)/3.80)) / (1.0+ rr)**gamma
  +     rt = K * (1.0 + r)**gamma
  + }      
  +   }
  + 
  +   return (rt);
  + } 
  > 
  > proc.time()
     user  system elapsed 
     0.17    0.07    0.20 
Running the tests in 'tests/paysage.R' failed.
Complete output:
  > # source("moninit")
  >  library("RCALI")    
  > file <- "paysage.data"
  > dispf <-  4
  > param <-
  + list(input=2,output=3,warn.conv=FALSE, send.and.receive=TRUE,
  +      poly=list(c(1,2), c(3,4), c(11,13)))
  > 
  > #calcul avec RCALI cub
  > # -----------------------
  > param$method <- "cub"
  > resfile <- "paysage.cubres"
  > 
  > start.run <- Sys.time()
  > califlopp(file=file, dispf=dispf, param = param, resfile = resfile)
  
  Number of polygons: 279
  -------------------
  
  Parameters:
  -----------
  verbose: 0
  output: 3
  scale: 10
  maximal dispersion distances for each function: 1000
  minimal dispersion distances for each function: 500
  (the dispersion is calculated between centroids,
   for distances beyond these values)
  method:cubature
  function 4: relative precision = 0.001, absolute precision = 0.001
              maximal number of evaluations points fixed to 100000 
  mode of triangulation: 0
  
  Polygons  1 (1), 2 (2)
  -------------------
  
  Integrated flow for function 4:
   mean: 1.84933 mean/area1: 0.0300216 mean/area2: 0.00770876
  
  
  Polygons  2 (2), 1 (1)
  -------------------
  
  Integrated flow for function 4:
   mean: 4.5137 mean/area1: 0.0188149 mean/area2: 0.0732743
  
  
  Polygons  3 (3), 4 (4)
  -------------------
  
  Integrated flow for function 4:
   mean: 4.46959 mean/area1: 0.0886824 mean/area2: 0.00119288
  
  
  Polygons  4 (4), 3 (3)
  -------------------
  
  Integrated flow for function 4:
   mean: 6.20148 mean/area1: 0.0016551 mean/area2: 0.123045
  
  
  Polygons  11 (11), 13 (13)
  -------------------
  
  Integrated flow for function 4:
   mean: 57.1617 mean/area1: 0.0101757 mean/area2: 0.0105585
  
  
  Polygons  13 (13), 11 (11)
  -------------------
  
  Integrated flow for function 4:
   mean: 132.001 mean/area1: 0.0243823 mean/area2: 0.0234982
  
  
  Created file: paysage.cubres
  > end.run <- Sys.time()
  > duration <- end.run - start.run
  > # print(duration)
  > 
  > 
  > #calcul avec RCALI grid sur un couple
  > # -----------------------
  > param$method <- "grid"
  > param$poly <- c(1,1)
  > param$send.and.receive=FALSE
  > resfile <- "paysage.grid1.1res"
  > 
  > 
  > start.run <- Sys.time()
  > califlopp(file=file, dispf=dispf, param = param, resfile = resfile)
  
  Number of polygons: 279
  -------------------
  
  Parameters:
  -----------
  verbose: 0
  output: 3
  scale: 10
  maximal dispersion distances for each function: 1000
  minimal dispersion distances for each function: 500
  (the dispersion is calculated between centroids,
   for distances beyond these values)
  method:grid
  seed: 1
  x-axis step: 1 m. 
  y-axis step: 1 m.
  number of estimations: 10
  
  Polygons  1 (1), 1 (1)
  -------------------
  
  Integrated flow for function 4:
   mean: 15.2493 mean/area1: 0.247553 mean/area2: 0.247553
  
  
  Created file: paysage.grid1.1res
  > end.run <- Sys.time()
  > duration <- end.run - start.run
  > # print(duration)
  > 
  > 
  > # Charger le fichier de r<e9>sultats cub
  > # ---------------------------------
  > #avec la fonction getRes de Rcali
  > cat("avec la fonction getRes paysage.cubres de Rcali\n")
  avec la fonction getRes paysage.cubres de Rcali
  > start.run <- Sys.time()
  > dispcub.res <- getRes("paysage.cubres")
  Read 60 items
  > end.run <- Sys.time()
  > duration <- end.run - start.run
  > # print(duration)
  > 
  > #avec un read.table
  > cat("avec un read.table\n")
  avec un read.table
  > start.run <- Sys.time()
  > dispcubtab.res <- read.table("paysage.cubres", skip=1)
  > end.run <- Sys.time()
  > duration <- end.run - start.run
  > # print(duration)
  > 
  > proc.time()
     user  system elapsed 
     0.59    0.12    0.70 
Running the tests in 'tests/petit.R' failed.
Complete output:
  > #source("moninit")
  > library("RCALI")
  > 
  > # Cubature:
  > param <- list( output=1, input=2, dz=c(0,21), dp=c(100,0), tz=c(0,1))
  > califlopp("data2",c(fpollen,fseed),  param=param)
  
  Number of polygons: 2
  -------------------
  
  Parameters:
  -----------
  verbose: 0
  output: 1
  scale: 10
  maximal dispersion distances for each function: 0 21
  minimal dispersion distances for each function: 100 0
  (the dispersion is calculated between centroids,
   for distances beyond these values)
  method:cubature
  function 1: relative precision = 0.001, absolute precision = 0.001
              maximal number of evaluations points fixed to 100000 
  function 2: relative precision = 0.001, absolute precision = 0.001
              maximal number of evaluations points fixed to 100000 
  mode of triangulation: 0 1
  all pairs of polygons
  
  Polygons  1 (p1), 1 (p1)
  -------------------
  
  Integrated flow for function 1:
   mean: 1.00319e+06 mean/area1: 1.00319 mean/area2: 1.00319
   absolute error: 989.725 relative error: 0.000986578
   confidence interval: [1.0022e+06, 1.00418e+06]
   nb. evaluations: 12062
  
  Integrated flow for function 2:
   mean: 999051 mean/area1: 0.999051 mean/area2: 0.999051
   absolute error: 899.881 relative error: 0.000900736
   confidence interval: [998151, 999951]
   nb. evaluations: 5180
  
  area1: 1e+06 area2: 1e+06 
  
  Polygons  2 (p2), 1 (p1)
  -------------------
  
  Integrated flow for function 1:
   mean: 501713 mean/area1: 1.00343 mean/area2: 0.501713
   absolute error: 490.051 relative error: 0.000976755
   confidence interval: [501223, 502203]
   nb. evaluations: 11137
  
  Integrated flow for function 2:
   mean: 499526 mean/area1: 0.999051 mean/area2: 0.499526
   absolute error: 449.771 relative error: 0.000900396
   confidence interval: [499076, 499975]
   nb. evaluations: 5180
  
  area1: 500000 area2: 1e+06 
  
  Polygons  2 (p2), 2 (p2)
  -------------------
  
  Integrated flow for function 1:
   mean: 498957 mean/area1: 0.997915 mean/area2: 0.997915
   absolute error: 487.731 relative error: 0.000977501
   confidence interval: [498470, 499445]
   nb. evaluations: 14208
  
  Integrated flow for function 2:
   mean: 499185 mean/area1: 0.998371 mean/area2: 0.998371
   absolute error: 455.056 relative error: 0.000911598
   confidence interval: [498730, 499640]
   nb. evaluations: 5180
  
  area1: 500000 area2: 500000 
  > # Grid:
  > param <- list(method="grid",  grid=list(step=c(50,50)))
  >  califlopp("data2",dispf=c(3,1), param=param)
  
  Number of polygons: 2
  -------------------
  
  Parameters:
  -----------
  verbose: 0
  output: 1
  scale: 10
  maximal dispersion distances for each function: 0 0
  minimal dispersion distances for each function: 0 100
  (the dispersion is calculated between centroids,
   for distances beyond these values)
  method:grid
  seed: 1
  x-axis step: 50 m. 
  y-axis step: 50 m.
  number of estimations: 10
  all pairs of polygons
  
  Polygons  1 (p1), 1 (p1)
  -------------------
  
  Integrated flows at each replication:
  1. Function 3: 1e+12 
  1. Function 1: 57580 
  2. Function 3: 1e+12 
  2. Function 1: 115247 
  3. Function 3: 1e+12 
  3. Function 1: 1.15439e+06 
  4. Function 3: 1e+12 
  4. Function 1: 102898 
  5. Function 3: 1e+12 
  5. Function 1: 124834 
  6. Function 3: 1e+12 
  6. Function 1: 58439.8 
  7. Function 3: 1e+12 
  7. Function 1: 52883.7 
  8. Function 3: 1e+12 
  8. Function 1: 45182.5 
  9. Function 3: 1e+12 
  9. Function 1: 59840.1 
  10. Function 3: 1e+12 
  10. Function 1: 48219.5 
  Nb. evaluations: 16000
  
  Integrated flow for function 3:
   mean: 1e+12 mean/area1: 1e+06 mean/area2: 1e+06
   standard deviation: 0.000565285 
   coefficient of variation (std/mean): 5.65285e-16
  
  Integrated flow for function 1:
   mean: 181951 mean/area1: 0.181951 mean/area2: 0.181951
   standard deviation: 342942 
   coefficient of variation (std/mean): 1.8848
  
  area1: 1e+06 area2: 1e+06 
  
  Polygons  2 (p2), 1 (p1)
  -------------------
  
  Integrated flows at each replication:
  1. Function 3: 5e+11 
  1. Function 1: 28628.7 
  2. Function 3: 5e+11 
  2. Function 1: 58042.9 
  3. Function 3: 5e+11 
  3. Function 1: 580790 
  4. Function 3: 5e+11 
  4. Function 1: 50938.8 
  5. Function 3: 5e+11 
  5. Function 1: 62615 
  6. Function 3: 5e+11 
  6. Function 1: 29333 
  7. Function 3: 5e+11 
  7. Function 1: 26480.4 
  8. Function 3: 5e+11 
  8. Function 1: 22384 
  9. Function 3: 5e+11 
  9. Function 1: 29674.4 
  10. Function 3: 5e+11 
  10. Function 1: 24088.2 
  Nb. evaluations: 16000
  
  Integrated flow for function 3:
   mean: 5e+11 mean/area1: 1e+06 mean/area2: 500000
   standard deviation: 0.00383665 
   coefficient of variation (std/mean): 7.67329e-15
  
  Integrated flow for function 1:
   mean: 91297.6 mean/area1: 0.182595 mean/area2: 0.0912976
   standard deviation: 172624 
   coefficient of variation (std/mean): 1.89079
  
  area1: 500000 area2: 1e+06 
  
  Polygons  2 (p2), 2 (p2)
  -------------------
  
  Integrated flows at each replication:
  1. Function 3: 2.50013e+11 
  1. Function 1: 26762.7 
  2. Function 3: 2.50086e+11 
  2. Function 1: 55456 
  3. Function 3: 2.50346e+11 
  3. Function 1: 571926 
  4. Function 3: 2.50096e+11 
  4. Function 1: 49174.2 
  5. Function 3: 2.50141e+11 
  5. Function 1: 60509.5 
  6. Function 3: 2.49957e+11 
  6. Function 1: 27252.6 
  7. Function 3: 2.49938e+11 
  7. Function 1: 24572.7 
  8. Function 3: 2.49792e+11 
  8. Function 1: 20629.3 
  9. Function 3: 2.4994e+11 
  9. Function 1: 27848 
  10. Function 3: 2.49886e+11 
  10. Function 1: 22192.3 
  Nb. evaluations: 16000
  
  Integrated flow for function 3:
   mean: 2.50019e+11 mean/area1: 500039 mean/area2: 500039
   standard deviation: 1.55721e+08 
   coefficient of variation (std/mean): 0.000622835
  
  Integrated flow for function 1:
   mean: 88632.3 mean/area1: 0.177265 mean/area2: 0.177265
   standard deviation: 170442 
   coefficient of variation (std/mean): 1.92302
  
  area1: 500000 area2: 500000 
  > 
  > proc.time()
     user  system elapsed 
     0.70    0.12    0.79