add read_zarr and write_zarr (#33)

* add read_zarr and write_zarr

* update pkgdown

* update news

NAMESPACE

@@ -53,9 +53,11 @@ export(read_loom)
 export(read_mtx)
 export(read_text)
 export(read_umi_tools)
+export(read_zarr)
 export(write_csvs)
 export(write_h5ad)
 export(write_loom)
+export(write_zarr)
 importFrom(Matrix,sparseMatrix)
 importFrom(R6,R6Class)
 importFrom(assertthat,assert_that)

NEWS.md

@@ -1,4 +1,6 @@
-# anndata 0.7.5.6
+# anndata 0.8.0
+
+* FUNCTIONALITY: Added support for `read_zarr()` and `write_zarr()` (PR #33).
 
 * BUG FIX: Use the right interface for the `all.equal()` function (PR #32).
 

R/class_anndata.R

@@ -552,19 +552,18 @@ AnnDataR6 <- R6::R6Class(
     #' }
     write_csvs = function(dirname, skip_data = TRUE, sep = ",") {
       dirname <- normalizePath(dirname, mustWork = FALSE)
-      private$.anndata$write_csvs(
+      invisible(py_to_r_ifneedbe(private$.anndata$write_csvs(
         dirname = dirname,
         skip_data = skip_data,
         sep = sep
-      )
+      )))
     },
 
     #' @description Write .h5ad-formatted hdf5 file.
     #'
     #' Generally, if you have sparse data that are stored as a dense matrix, you can
     #' dramatically improve performance and reduce disk space by converting to a csr_matrix:
     #'
-    #' @param anndata An [AnnData()] object
     #' @param filename Filename of data file. Defaults to backing file.
     #' @param compression See the h5py [filter pipeline](http://docs.h5py.org/en/latest/high/dataset.html#dataset-compression).
     #'   Options are `"gzip"`, `"lzf"` or `NULL`.
@@ -591,17 +590,16 @@ AnnDataR6 <- R6::R6Class(
     #' }
     write_h5ad = function(filename, compression = NULL, compression_opts = NULL, as_dense = list()) {
       filename <- normalizePath(filename, mustWork = FALSE)
-      private$.anndata$write_h5ad(
+      invisible(py_to_r_ifneedbe(private$.anndata$write_h5ad(
         filename = filename,
         compression = compression,
         compression_opts = compression_opts,
         as_dense = as_dense
-      )
+      )))
     },
 
     #' @description Write .loom-formatted hdf5 file.
     #'
-    #' @param anndata An [AnnData()] object
     #' @param filename The filename.
     #' @param write_obsm_varm Whether or not to also write the varm and obsm.
     #'
@@ -625,10 +623,23 @@ AnnDataR6 <- R6::R6Class(
     #' }
     write_loom = function(filename, write_obsm_varm = FALSE) {
       filename <- normalizePath(filename, mustWork = FALSE)
-      private$.anndata$write_loom(
+      invisible(py_to_r_ifneedbe(private$.anndata$write_loom(
         filename = filename,
         write_obsm_varm = write_obsm_varm
-      )
+      )))
+    },
+
+    #' @description Write a hierarchical Zarr array store.
+    #' @param store The filename, a MutableMapping, or a Zarr storage class.
+    #' @param chunks Chunk size.
+    write_zarr = function(store, chunks = NULL) {
+      if (is.character(store)) {
+        store <- normalizePath(store, mustWork = FALSE)
+      }
+      invisible(py_to_r_ifneedbe(private$.anndata$write_zarr(
+        store = store,
+        chunks = chunks
+      )))
     },
 
     #' @description Print AnnData object

R/read_zarr.R

@@ -1,15 +1,15 @@
-# #' Read from a hierarchical Zarr array store.
-# #'
-# #' @param store The filename, a MutableMapping, or a Zarr storage class.
-# #'
-# #' @export
-# #'
-# #' @examples
-# #' \dontrun{
-# #' ad <- read_umi_tools("...")
-# #' }
-# read_zarr <- function(store) {
-#   read_zarr(
-#     store = store
-#   )
-# }
+#' Read from a hierarchical Zarr array store.
+#'
+#' @param store The filename, a MutableMapping, or a Zarr storage class.
+#'
+#' @export
+#'
+#' @examples
+#' \dontrun{
+#' ad <- read_zarr("...")
+#' }
+read_zarr <- function(store) {
+  read_zarr(
+    store = store
+  )
+}

R/write_zarr.R

@@ -1,28 +1,33 @@
-# #' Write a hierarchical Zarr array store.
-# #'
-# #' @param anndata An [AnnData()] object
-# #' @param store The filename, a MutableMapping, or a Zarr storage class.
-# #' @param chunks Chunk shape.
-# #'
-# #' @export
-# #'
-# #' @examples
-# #' ad <- AnnData(
-# #'   X = matrix(c(0, 1, 2, 3), nrow = 2, byrow = TRUE),
-# #'   obs = data.frame(group = c("a", "b"), row.names = c("s1", "s2")),
-# #'   var = data.frame(type = c(1L, 2L), row.names = c("var1", "var2")),
-# #'   varm = list(
-# #'     ones = matrix(rep(1L, 10), nrow = 2),
-# #'     rand = matrix(rnorm(6), nrow = 2),
-# #'     zeros = matrix(rep(0L, 10), nrow = 2)
-# #'   ),
-# #'   uns = list(a = 1, b = 2, c = list(c.a = 3, c.b = 4))
-# #' )
-# #'
-# #' write_zarr(ad, "output.zarr")
-# write_zarr <- function(anndata, store, chunks = NULL) {
-#   invisible(py_to_r_ifneedbe(anndata$write_zarr(
-#     store = store,
-#     chunks = chunks
-#   )))
-# }
+#' Write a hierarchical Zarr array store.
+#'
+#' @param anndata An [AnnData()] object
+#' @param store The filename, a MutableMapping, or a Zarr storage class.
+#' @param chunks Chunk shape.
+#'
+#' @export
+#'
+#' @examples
+#' \dontrun{
+#' ad <- AnnData(
+#'   X = matrix(c(0, 1, 2, 3), nrow = 2, byrow = TRUE),
+#'   obs = data.frame(group = c("a", "b"), row.names = c("s1", "s2")),
+#'   var = data.frame(type = c(1L, 2L), row.names = c("var1", "var2")),
+#'   varm = list(
+#'     ones = matrix(rep(1L, 10), nrow = 2),
+#'     rand = matrix(rnorm(6), nrow = 2),
+#'     zeros = matrix(rep(0L, 10), nrow = 2)
+#'   ),
+#'   uns = list(a = 1, b = 2, c = list(c.a = 3, c.b = 4))
+#' )
+#'
+#' ad$write_zarr("output.zarr")
+#' write_zarr(ad, "output.zarr")
+#'
+#' unlink("output.zarr", recursive = TRUE)
+#' }
+write_zarr <- function(anndata, store, chunks = NULL) {
+  invisible(py_to_r_ifneedbe(anndata$write_zarr(
+    store = store,
+    chunks = chunks
+  )))
+}

_pkgdown.yml

@@ -32,11 +32,13 @@ reference:
    - read_mtx
    - read_text
    - read_umi_tools
+   - read_zarr
  - title: Writing an AnnData object to a file
  - contents:
    - write_csvs
    - write_h5ad
    - write_loom
+   - write_zarr
  - title: Installer
    desc: Helper function for installing the anndata Python package
  - contents: