vjcitn

library(DelayedArray)

setClass("plBedSeed",
  contains="Array",
  slots = c(
   dim = "integer",
   bedreaderRef = "ANY",
   dimnames = "list"
   )
)

vjcitn

make1zip = function(pname, target="zips322") {
 if (!dir.exists(target)) dir.create(target)
 target = file.path(getwd(), target)
 ins = try(BiocManager::install(pname, ask=FALSE, update=FALSE, type="binary", 
                                dependencies=TRUE, force=TRUE)) # acquire dependencies as needed
 if (inherits(ins, "try-error")) stop(sprintf("can't install %s with BiocManager", pname))
 #real_inst = try(install.packages(pname, type="source", repos=NULL))  # this uses native windows build tools from Rtools
 bpath = pkgbuild::build(pname)
 if (inherits(bpath, "try-error")) stop(sprintf("can't build %s from source", pname))
 install.packages(bpath, type="source", repos=NULL)

vjcitn

library(BiocPkgTools)
all322 = biocPkgList(version="3.22", repo="BioCsoft")$Package
library(BiocBuildTools) # from github vjcitn
set322=PackageSet(all322, biocversion="3.22", branchname="RELEASE_3_22")
dir.create("srcs_322")
populate_local_gits(set322, gitspath="srcs_322")

vjcitn

from owlready2 import get_ontology, onto_path, sync_reasoner

def find_terms_by_keyword(ontology, keyword):
    # Simple case: search class labels and names
    results = []
    for cls in ontology.classes():
        label = cls.label.first() if hasattr(cls, "label") and cls.label else cls.name
        if keyword.lower() in label.lower():
            results.append(cls)
    return results

vjcitn

from owlready2 import get_ontology, onto_path
import numpy as np

def term_to_class(user_term, onto):
    all_terms = list(onto.classes())
    excluded_terms = get_excluded_terms(onto)
    # Try to find by label first, then by name
    found = None
    for cls in all_terms:
        if cls in excluded_terms:

vjcitn

library(SummarizedExperiment)
library(irlba)
ibiplot = function(se, choices=1:2, nfeat=5, ircomp=6, ...) {
  dat = t(assay(se))
  pcs = prcomp_irlba(dat, ircomp)
  rot = pcs$rotation
  ss = rowSums(abs(rot[,choices])^2)
  kp = order(ss, decreasing=TRUE)[seq_len(nfeat)]
  labs = rep(" ", nrow(rot))
  labs[kp] = rownames(se)[kp]

vjcitn

library(S4Vectors)
library(SummarizedExperiment)
library(GDSArray)
library(bigmelon)
data(melon)
tf = tempfile()
es2gds(melon, tf)

bm2SE = function(gdsf, elem="betas",
    fdkeep=c("ProbeID_A", "ProbeID_B", "ILMNID", "NAME",

vjcitn

# based on https://alisterburt.com/napari-workshops/notebooks/viewer_intro.html
library(reticulate)
py_require("napari[all]")
py_require("scikit-image")
nap = import("napari")
v = nap$Viewer()
si = import("skimage")
#id = si$data$cells3d()
if (!file.exists("nuclei.tif")) {
system("wget --no-check-certificate https://raw.githubusercontent.com/alisterburt/napari-workshops/main/napari-workshops/notebooks/data/nuclei.tif")

vjcitn

library(cropcircles)
library(magick)
y = image_read("owl.jpg") # https://commons.wikimedia.org/wiki/File:Great_Horned_Owl_(30706490070).jpg
library(cropcircles)
img_cropped_path <- hex_crop(
  images = "owl.jpg",
  border_colour = "#107e54",
  border_size = 24
)

vjcitn

# reference for managing BLAS usage -- https://cran.r-project.org/bin/macosx/RMacOSX-FAQ.html#Which-BLAS-is-used-and-how-can-it-be-changed_003f-1

# script

print(extSoftVersion()["BLAS"])
# global matrix value
set.seed(1234)
x = rnorm(500)
y = x %*% t(x)
# compute svd