Odd behavior of dismo's extract function
Just realized I pasted in the results backwards. It should have been system.time(extract.test(env, 250)) user system elapsed 124.562 0.516 125.061 system.time(extract.test(env, 251)) user system elapsed 2.807 0.084 2.891 Dan Warren, Ph.D. Department of Biology Macquarie University Email: dan.warren at mq.edu.au <dan.warren at anu.edu.au> Phone (US): 530-848-3809 Phone (Australia): 0468 696 897 Phone (Work): 02 9850 8587 Skype: dan.l.warren Google Scholar <https://scholar.google.com/citations?user=NTzu9c8AAAAJ&hl=en> Orcid <http://orcid.org/0000-0002-8747-2451> ResearcherID <http://www.researcherid.com/rid/B-3821-2010> Scopus <http://www.scopus.com/authid/detail.url?authorId=7202133982>
On Mon, Jul 25, 2016 at 10:34 AM, Dan Warren <dan.l.warren at gmail.com> wrote:
This is not an error per se so much as just something very weird that I have noticed with a project I've been working on recently. I'm wondering if anyone here has any insight as to what may be causing this behavior. I haven't yet been able to duplicate it with simulated rasters (more info on that below), but it appears very reliably with real environmental data including the PC rasters for Cuba I have hosted here: https://github.com/danlwarren/ENMTools/tree/master/test/testdata What's happening is this: if I go to extract data from those rasters using occurrence points, the amount of time it takes increases very rapidly up to exactly 250 points, and falls dramatically after that. So dramatically that it takes over two minutes to extract data for 250 points but just under three seconds for 251. I've established that it's not a question of the points themselves being wonky, because it happens with random points as well. extract.test <- function(env, N){ extract(env, randomPoints(env, N)) } env.files <- list.files(path = "testdata/", pattern = "pc", full.names = TRUE) env <- stack(env.files) system.time(extract.test(env, 250)) user system elapsed 2.807 0.084 2.891 system.time(extract.test(env, 251)) user system elapsed 124.562 0.516 125.061 numpoints,time 1,1.54 5,3.93 10,6.764 50,29.939 100,61.431 150,79.295 200,110.283 250,120.118 251,2.748 252,2.756 254,2.767 500,2.876 1000,3.153 The data being extracted looks perfectly reasonable in all cases. It's not just these layers, either. Although (as I mentioned above) I have yet to come up with simulated rasters that show this behavior, I see this behavior for both of the sets of rasters for real environmental data that I've tried. The results above are from a PCA on Worldclim data for Cuba, but I just tried them on some Climond data I've got for Australia and I get the same behavior. Those rasters are much larger, though, and a result the times are longer; 251 points took about 43 seconds, whereas I just had to give up and stop the 250 point extraction after about 30 minutes. As for those simulated rasters, I've tried the following: Plain grids of sequential numbers As above, but with a bunch of NAs added Filling the Cuban rasters with sequential numbers Filling the Cuban rasters with random numbers from a uniform (0,1) distribution None of those show this issue. Anyone have any thoughts about what might be going on here?