Repository: single-cell-data/TileDB-SOMA (language: python)

Issue:
[python/r/c++] Revisit `shape` for component arrays
## PRs

**Merged PRs:**

<details>

* #2909 `kerl/schevo-timestamp-methodize`
* #2913 `kerl/name-neaten`
* #2908 `kerl/ut-soma-exc-simplify`
* #2910 `kerl/test-common-parameterize`
* #2918 `kerl/cpp-test-deadstrip`
* #2919 `kerl/minor-unit-test-helper-mod`
* #2936 `kerl/cpp-ut-helper-neaten`
* #2938 `kerl/more-cur-dom-parameterize`
* #2915 `kerl/cpp-strict-int64-shape`
* #2911 `kerl/arrow-util-current-domain-optional`
* #2939 `kerl/step-two-temp`
* #2947 `kerl/cpp-ndarray-resize-testing`
* #2945 `kerl/dataframe-test-fixture`
* #2944 `kerl/cpp-variant-indexed-dataframes`
* #2916 `kerl/sdf-shape`
* #2917 `kerl/cpp-resizes`
* #2948 `kerl/upgrade-shape-int64`
* #2951 `kerl/sdf-test-accessors`
* #2953 `kerl/py-r-accessor-plumbing`
* #2957 `kerl/sdf-domain-accessors`
* #2960 `kerl/dense-link`
* #2963 `kerl/nightly-fix`
* #2970 `kerl/dense-writeable-after-create`
* #2968 `kerl/minor-trim`
* #2969 `kerl/more-py-domain-name-neaten`
* #2972 `kerl/libtiledbsoma-env-logging-level`
* #2962 `kerl/py-r-creation-paths`
* #2950 `kerl/py-r-test-2`
* #2994 `kerl/nanoarrow-helpers`
* #3011 `kerl/polydom3`
* #3017 `kerl/polydom5`
* #3018 `kerl/polydom6`
* #2990 `kerl/variant-nnz-bug`
* #3019 `kerl/index-swap`
* #3020 `kerl/ut-max-shape`
* #3012 `kerl/polydom4`
* #3025 `kerl/fix-3020-merge`
* #3026 `kerl/one-more-rename`
* #3028 `kerl/ff-not`
* #3029 `kerl/ut-vg`
* #3030 `kerl/table-utils-memory`
* #3067 `kerl/improve-sdf-test-field-names`
* #3068 `kerl/ut-generate`
* #3069 `kerl/cpp-sdf-domain-at-create`
* #3027 `kerl/hll-domainish`
* #3088 `kerl/max-domain-int64`
* #3090 `kerl/maybe-resize-soma-joinid-cpp-tweak`
* #3032 `kerl/sdf-domain-at-create` -- fixes #2967
* #3091 `kerl/maybe-resize-soma-joinid-py-r`
* #3095 `kerl/cpp-exp-resize-prep`
* #3089 `kerl/r-dataframe-shapeable`
* https://github.com/single-cell-data/SOMA/pull/233
* #3125 `kerl/cpp-ut-name-shortens`
* #3127 `kerl/helper-rename`
* #3130 `kerl/cpp-can-resizers-names`
* #3132 `kerl/cpp-dataframe-sizing-helpers`
* #3139 `kerl/cpp-dataframe-upgrade-test`
* #3140 `kerl/py-resizer-connects`
* #3151 `kerl/py-can-upgrade-shape`
* #3152 `kerl/registration-shape-acceessors`
* #3156 `kerl/py-exp-shaping`
* #3157 `kerl/py-exp-shaping2`
* #3148 `kerl/py-exp-resize`
* #3191 `kerl/py-domain-at-create-ut-1`
* #3190 `kerl/py-domain-at-create-ut-2`
* #3192 `kerl/py-domain-at-create-ut-3`
* #3193 `kerl/py-domain-at-create-ut-4`
* #3194 `kerl/py-domain-at-create-ut-5`
* #3203 `kerl/min-size-2`
* #3208 `kerl/r-min-sizing`
* #3211 `kerl/cpp-ugr-dom`
* #3232 `kerl/ff-interop`
* #3230 `kerl/ffon`
* #3234 `kerl/docstring-prune`
* #3236 `kerl/prefixing`
* #3241 `kerl/fix-bad-merge`
* #3235 `kerl/py-r-ugr-dom`
* #3237 `kerl/py-r-ugr-dom-2`
* #3238 `kerl/py-r-ugr-dom-3`
* #3253 `kerl/set-coords-rename`
* #3261 `kerl/pybind11-nda-sizing`
* #3265 `kerl/dense-227-a`
* #3263 `kerl/dense-range-trim`
* #3268 `kerl/dim-explosion`
* #3269 `kerl/python-227-dense-ned-read`
* #3270 `kerl/r-227-dense-fixes`
* #3280 `kerl/r-dense-227-more`
* #3286 `kerl/more-fn4m`
* #3283 `kerl/readthedocs-pre-1.15`
* #3288 `kerl/dense-ugrsh`
* #3289 `kerl/notebook-shape-upgrade`
* #3285 `kerl/new-shape-doc-updates`
* #3295 `kerl/notebook-data-refresh`
* #3290 `kerl/notebook-new-shape-refresh`
* #3301 `kerl/ffena`
* #3303 `kerl/r-data-refresh`
* #3300 `kerl/sdf-sjid-lower-zero`
* #3296 `kerl/dense-example-data-refresh`
* #3294 `kerl/new-shape-notebook-and-vignette`
* #3308 `kerl/upgrade-experiment-resources`
* #3309 `kerl/fix-notebook-merge`
* #3321 `kerl/more-use-shape`
* #3358 `kerl/revert-3300`
* #3368 `kerl/227a`
* #3369 `kerl/ucd1`
* #3370 `kerl/ucd2`
* #3371 `kerl/ucd3`
* #3372 `kerl/ucd4`
* #3396 `kerl/domain-at-create-docstrings`
* #3302 `kerl/new-shape-vignette`
* https://github.com/single-cell-data/SOMA/pull/250
* #3399 `kerl/new-shape-more-docstrings`
* #3400 `kerl/check-only-r`
* https://github.com/TileDB-Inc/somacore-feedstock/pull/27

</details>

**Closed/abandoned PRs:**

<details>

* #2785 -- This was only dogfooding for the core 2.25 release -- not to be merged
* #2952 `kerl/feature-flag-temp` -- folded into 2962
* #2995 `kerl/polydom`
* #2964 `kerl/tiledbsoma-io-test`
* #3189 `kerl/min-size`
* #3220 `kerl/cpp-ugr-dom-2`
* #3244 `kerl/dense-227-fixes`
* #3276 `kerl/dataframe-shape`

</details>

**Issues which are related but non-blocking:**

* https://github.com/single-cell-data/SOMA/pull/216
* https://github.com/TileDB-Inc/TileDB/pull/5303
* #2966
* #3081
* Note: R append mode does not exist yet -- see #1630 -- so an experiment-level resizer is not a priority in R
* #3271 
* #3272
* #3273

**See also:** [[sc-51048]](https://app.shortcut.com/tiledb-inc/story/51048).

## Problem to be solved

Users want to know the `shape` of an array, in the SciPy sense:

* Reads and writes are bounds-checked against the shape
* This retains its value regardless of which values of a sparse array are or are not actually occupied
* Users can `resize`.
  * Some users need the ability to grow their datasets later, using either `tiledbsoma.io`'s append mode, or subsequent writes using the `tiledbsoma` API.
  * Note that the cellxgene census doesn't need this: eact week's published census has fixed shape, and any updates will happen in new storage, on a new week.

Using TileDB-SOMA up until the present:

* The TIleDB `domain` is immutable after array creation
  * This does bounds-checking for reads and writes, which is good
  * To leverage this to function as a `shape`, users would need to set the `domain` at array-creation time. However, users lose the ability to grow their datasets later.
* There is a `non_empty_domain` accessor
  * This only indicates min/max coordinates _at which data exists_. Consider an `X` array for 100 cells and 200 genes. If non-zero expression counts exist only for cell join IDs 2-17, then the `non_empty_domain` will indicate `(2,17)` along `soma_dim_0`.
  * Consider an `obms["X_pca"]` within the same experiment. This may be 100 cells by 50 PCA components: we need a placd to store the number 50.
  * Therefore users cannot leverage this to function as a `shape` accessor.
* We have offered a `used_shape` accessor since TileDB-SOMA 1.5.
  * This functions as a `shape` accessor, in the SciPy sense, but it is not multi-writer safe.

New feature for TileDB-SOMA 1.15:

* Arrays will have a  `shape`
* Reads and writes are bounds-checked against the shape
* This retains its value regardless of which values of a sparse array are or are not actually occupied
* Users can `resize`
* The `used_shape` accessor will be deprecated in TileDB-SOMA 1.13, and slated for removal in TileDB-SOMA 1.14.

Compatiblity:

This will now require users to do an explicit `resize` before appending/growing TileDB-SOMA Experiments. Guidance in the form of example notebooks will be provided.

##  Tracking

See also: [[sc-41074]](https://app.shortcut.com/tiledb-inc/story/41074) and [[sc-51048]](https://app.shortcut.com/tiledb-inc/story/51048).

## Scheduling

Support arrives in TileDB Core 2.25. Deprecations for TileDB-SOMA will be released with 1.13. Full support within TileDB-SOMA will be release in 1.14.

## Details

SOMA API mods as we've discussed in a Google doc are as follows.

### `SOMADataFrame`

* `create`: Retain the `domain` argument
  * Issue:
    * Core has a `(lo, hi)` tuple per dim, e.g. `(0,99)` or `(10,19)`
    * SOMA has count per dim, with 0 implicit: e.g. 100 or 20
    * For `SparseNDArray` and `DenseNDArray` core can have `(lo, hi)` and SOMA can have `count`
    * For `DataFrame` there can be multiple dims --- default is a single `soma_joinid`
    * That could be treated either in `(lo, hi)` fashion or `count` fashion
    * However additional dims (e.g. `cell_type`) can be on any type, including strings, floats, etc. where there is no implicit lo=0
    * Therefore we need to keep the current SOMA API wherein `DataFrame` takes a `domain` argument (in `(lo, hi)` fashion) and not a `shape` argument (in `count` fashion)

### `SparseNDArray and DenseNDArray`

* `create`
  * Have an optional shape argument which is of type `Tuple[Int,...]` where each element is the cell count of the corresponding dimension
    * If unsupplied, or if supplied but None in any slot: use the minimum 0 in each slot – nothing larger makes sense since we will not support downsize
  * User guidance should make clear that it will not be possible to create an ‘old’ style array with the ‘new style’ API. (See also the upgrade logic below.)

### All three of `SOMADataFrame`, `SparseNDArray`, `DenseNDArray`

* `write`
  * For new arrays, created with the new shape feature:
    * Core will bounds-check that coordinates provided at `write` time are within the current shape
    * Core will raise `tiledb.cc.TileDBError` to TileDB-SOMA, which will catch and raise `IndexError`, and R-standard behavior on the R side
  * For old arrays created before this feature:
    * Core will not bounds-check that coordinates provided at write time are within the current shape
* Existing `used_shape` accessor
  * TileDB-SOMA will deprecate this over a release cycle.
  * For new arrays: raise `NotImplementedError`
  * For old arrays: return what’s currently returned, with a deprecation warning.
  * Mechanism for determining old vs. new: `array.schema.version` (the core storage version).
* Existing `shape` accessor
  * For new arrays:
    * Have this return the new shape as proposed by core, no longer returning the TileDB domain.
  * For old arrays created before this feature:
    * Return the TileDB domain as now.
* Existing `non_empty_domain` accessor
  * Same behavior for old and new arrays (unaffected by this proposal).
  * Keep this accessor supported, but, with user notes that it’s generally non-useful
  * This should return None (or R equivalent) when there is a schema but no data have been written.
* New `maxshape` accessor
  * Maps the core-level `(lo, hi)` accessor for domain to count-style accessor hi+1. E.g. if the core domain is either `(0,99)` or `(50,99)` then TileDB-SOMA `maxshape` will say 100.
  * Same behavior for old and new arrays.
  * Let users query for what the TileDB domain is, with user notes that it’s the maximum that users can reshape to.
  * Issac suggests: maybe `domain` or `maxshape` (see h5py).
* New `resize` mutator
  * Note: `reshape` means something else in the community (numpy, zarr, h5py), e.g. a 5x20 (total 100 cells) being reinterpreted as 4x25 (still 100 cells). The standard name for changing cell-count is `resize`.
  * For old arrays created before this feature: raise `NotImplementedError`.
  * For new arrays:
    * Will raise `ValueError` if the new shape is smaller on any dim than currently in storage
    * Regardless of whether any data have been written whatsoever
    * Will raise `ValueError` if the new shape exceeds the TileDB domain from create time, which will serve TileDB-SOMA in a role of “max possible shape the user can reshape to”
    * Otherwise, any calls to write from this point will bounds-check writes within this new shape
    * **We don’t expect resize to be multi-writer safe with regard to write ; user notes must be clear on this point**
* New `tiledbsoma_upgrade_shape` method for SparseNDArray and DenseNDArray
  * This will leverage `array.schema.version` to see if an upgrade is needed
  * Leverage core support for storage-version updates
  * This will take a shape argument as in `create`
  * For arrays created with “just-right” size: this will succeed
  * For arrays created with “room-for-growth” / “two billion-ish” size: this will succeed
  * If the user passes a shape which exceeds the current TileDB domain: this will fail
* New `tiledbsoma_upgrade_domain method` for `DataFrame`
  * Same as for `SparseNDArray`/`DenseNDArray` except it will take a domain at the SOMA-API level just as `DataFrame`'s create method

### `tiledbsoma.io`

* The user-facing API has no shape arguments and thus won’t need changing.
* Internally to `tiledbsoma.io`, we’ll still ask the tiledbsoma API for the “big domain” (2 billionish)
* Append mode:
  * Will need a new `resize` method at the `Experiment` level
  * Users will need to:
    * Register as now
    * Call the experiment-level `resize`
      * Could be `exp.resize(...)`, or (better) this could be `tiledbsoma.io.reshape_experiment`
  * In either case: this method will take the new `obs` and `var` counts as inputs:
    * `exp.obs.reshape` to new `obs` count
    * `exp.ms[name].var.reshape` to new `var` count
    * `exp.ms[name].X[name].reshape` to new `obs` count x `var` count
    * `exp.ms[name].obsm[name].reshape` to new `obs` count x same width
    * `exp.ms[name].obsp[name].reshape` to new `obs` count x `obs` count
    * `exp.ms[name].varm[name].reshape` to new `var` count x same width
    * `exp.ms[name].varp[name].reshape` to new `var` count x `var` count
  * Do the individual append-mode writes as now

[c++] Fix upgrade-shape for dataframes with non-standard dimensions
As detailed on #3416.


Discussion / hints:
#2785 is a quick-and-dirty concept-prover -- its sole function is to flush out any API misunderstandings we might have, in prep for 2.25.0 core release.


Fix patch:
diff --git a/libtiledbsoma/src/soma/soma_array.cc b/libtiledbsoma/src/soma/soma_array.cc
index e25958b460..337b1c86da 100644
--- a/libtiledbsoma/src/soma/soma_array.cc
+++ b/libtiledbsoma/src/soma/soma_array.cc
@@ -1038,8 +1038,7 @@ void SOMAArray::_set_soma_joinid_shape_helper(
                         tiledb::impl::type_to_str(dim.type())));
                 }
                 ndrect.set_range<int64_t>(dim_name, 0, newshape - 1);
-                continue;
-
+            } else {
                 switch (dim.type()) {
                     case TILEDB_STRING_ASCII:
                     case TILEDB_STRING_UTF8:


Test patch:
diff --git a/apis/python/testdata/nonstandard-dataframe-without-shapes.tgz b/apis/python/testdata/nonstandard-dataframe-without-shapes.tgz
new file mode 100644
index 0000000000..486c8ac344
Binary files /dev/null and b/apis/python/testdata/nonstandard-dataframe-without-shapes.tgz differ
diff --git a/apis/python/tests/test_shape.py b/apis/python/tests/test_shape.py
index 7b0437c0c2..0894003a02 100644
--- a/apis/python/tests/test_shape.py
+++ b/apis/python/tests/test_shape.py
@@ -671,3 +671,62 @@ def _check_ndarray(ndarray, has_shapes, expected_shape):
         _check_ndarray(exp.ms["RNA"].obsm["X_pca"], True, (2639, 50))
         _check_ndarray(exp.ms["RNA"].obsp["connectivities"], True, (2639, 2639))
         _check_ndarray(exp.ms["RNA"].varm["PCs"], True, (1839, 50))
+
+
+def test_canned_nonstandard_dataframe_upgrade(tmp_path):
+    uri = tmp_path.as_posix()
+
+    tgz = TESTDATA / "nonstandard-dataframe-without-shapes.tgz"
+
+    with tarfile.open(tgz) as handle:
+        handle.extractall(uri)
+
+    # ----------------------------------------------------------------
+    # As of tiledbsoma 1.15 we no longer write dataframes/arrays without
+    # core current domain (soma domain/shape) so it is crucial that in order
+    # to test upgrade-shape, we test saved-off data written before 1.15.0.
+    #
+    # Here is a dataframe created with tiledbsoma 1.14.5:
+    # ----------------------------------------------------------------
+    # import tiledbsoma
+    # import pyarrow as pa
+    #
+    # schema = pa.schema([
+    #     ("soma_joinid", pa.int64()),
+    #     ("mystring", pa.string()),
+    #     ("myint", pa.int32()),
+    #     ("myfloat", pa.float32()),
+    # ])
+    #
+    # data = pa.Table.from_pydict({
+    #     "soma_joinid": [10, 20],
+    #     "mystring": ["hello", "world"],
+    #     "myint": [1330, 1440],
+    #     "myfloat": [4.5, 5.5],
+    # })
+    #
+    # with tiledbsoma.DataFrame.create(
+    #     uri="data-sdf-dom-multi-py",
+    #     schema=schema,
+    #     index_column_names=["soma_joinid", "myint", "mystring"],
+    #     domain=None,
+    # ) as sdf:
+    #     sdf.write(data)
+    # ----------------------------------------------------------------
+
+    with tiledbsoma.DataFrame.open(uri) as sdf:
+        assert not sdf.tiledbsoma_has_upgraded_domain
+        assert sdf.non_empty_domain() == ((10, 20), (1330, 1440), ("hello", "world"))
+        assert sdf.domain == ((0, 2147483646), (-2147483648, 2147481598), ("", ""))
+        assert sdf.maxdomain == ((0, 2147483646), (-2147483648, 2147481598), ("", ""))
+
+    with tiledbsoma.DataFrame.open(uri, "w") as sdf:
+        ok, msg = sdf.tiledbsoma_upgrade_soma_joinid_shape(1, check_only=True)
+
+        sdf.tiledbsoma_upgrade_soma_joinid_shape(100)
+
+    with tiledbsoma.DataFrame.open(uri) as sdf:
+        assert sdf.tiledbsoma_has_upgraded_domain
+        assert sdf.non_empty_domain() == ((10, 20), (1330, 1440), ("hello", "world"))
+        assert sdf.domain == ((0, 99), (-2147483648, 2147481598), ("", ""))
+        assert sdf.maxdomain == ((0, 2147483646), (-2147483648, 2147481598), ("", ""))