Comments (4)
Yes I think so =)
Generally I am looking for data manipulation/arithmetic/subsetting as lazy as possible, i.e. applying as many methods
as possible without using the memory. Ideally, this would include subsetting, but maybe this is technically nonsense ^_^:
methods(class="FileArray")
[1] $ $<- [ [<- [[ apply
[7] as.array coerce dim dimnames dimnames<- fwhich
[13] initialize length mapreduce max min range
[19] show subset sum typeof
Btw, would it be possible to lazy-load a netcdf file as FileArray
? This seems possible with the stars
package (called "proxy" there):
library(stars)
proxy <- stars::read_stars(system.file("nc/reduced.nc", package="stars"), proxy=T) # lazy-load nc file
message("proxy obj needs ", format(utils::object.size(proxy), units="auto"))
#proxy obj needs 12.3 Kb
stars <- stars::st_as_stars(proxy) # convert to accessible data = use memory
message("stars obj needs ", format(utils::object.size(stars), units="auto"))
#stars obj needs 519.3 Kb
methods(class="stars_proxy")
[1] Math Ops [ [<-
[5] [[<- adrop aggregate aperm
[9] as.data.frame c coerce dim
[13] droplevels filter hist initialize
[17] is.na merge plot predict
[21] print show slotsFromS3 split
[25] st_apply st_as_sf st_as_stars st_crop
[29] st_dimensions<- st_downsample st_mosaic st_redimension
[33] st_sample st_set_bbox write_stars
Thanks a lot for your great work!
from filearray.
Generally I am looking for data manipulation/arithmetic/subsetting as lazy as possible, i.e. applying as many methods as possible without using the memory. Ideally, this would include subsetting
That sounds like a good idea. There will be some limitations to the types of methods available. point-wise methods such as +-*/><
should be easiest. Indexing could be a little bit tricky (arr[arr>0.5]
) but doable. Other methods (such as tensor decomposition or matrix multiplication) will not be implemented at this time.
but maybe this is technically nonsense ^_^:
No you are good. Glad you brought up this feature request.
Btw, would it be possible to lazy-load a netcdf file as FileArray?
Not natively. I think you can convert the arrays though. I'm not very familiar with the low-level implementation of netCDF
... From what I have read, it seems netCDF
or hdf5
are often chunked for random access.
FileArray
has its own format. The file IO of filearray is written from scratch to make sure sequential IO is as fast as possible on NVMe SSD (2-4GB/s on Mac M1/M2, or 1GB/s on average windows).
The performance comes with costs. For example, random access is relatively slow. filearray does not use universal file formats that can be read from other programs. The data array is only expandable along the last margin... If you are OK with these disadvantages, or have alternative methods to get around, filearray
should be a great tool for out-of-memory analyses (my project often needs to analyze 200x200x300x300+ data arrays within seconds.)
from filearray.
Hi @chrisdane I have added this experimental feature to branch https://github.com/dipterix/filearray/tree/lazyeval
Would you mind helping me check this branch to see if there is method that you want to support? Also please let me know if you find any bugs :)
You can install and compile this dev branch via
remotes::install_github("dipterix/filearray@lazyeval")
If you run on Windows, rtools
is needed to compile. For osx, please run xcode-select --install
in terminal to install building tools.
Here's a sanity test:
> x <- as_filearray(1:24, dimension = c(4,6))
> y <- (2^(x - 1) + log(x)) > 10000 | x <= 2
> print(y)
Reference class object of class "FileArrayProxy"
Mode: readwrite
UUID: 0005-640eaaf8-c6e7-4f55-aa6e-2956a872155c (depth=5)
Dimension: 4x6
Partition count: 6
Partition size: 1
Data type: logical
Internal type: integer
Location: $TEMPDIR/tmpfilearray11ef51b065fe9.farr
> x[y]
[1] 1 2 15 16 17 18 19 20 21 22 23 24
> # Sanity check
> x[][(2^(x[] - 1) + log(x[])) > 10000 | x[] <= 2]
[1] 1 2 15 16 17 18 19 20 21 22 23 24
from filearray.
Added as of 0.1.6
from filearray.
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from filearray.