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(feat): read_lazy
for whole AnnData
lazy-loading + xarray
reading + read_elem_as_dask
-> read_elem_lazy
#1247
base: main
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Original file line number | Diff line number | Diff line change |
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@@ -7,6 +7,7 @@ __pycache__/ | |
/*cache/ | ||
/node_modules/ | ||
/data/ | ||
/venv/ | ||
|
||
# Distribution / packaging | ||
/dist/ | ||
|
Original file line number | Diff line number | Diff line change |
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@@ -0,0 +1 @@ | ||
Add {func}`~anndata.experimental.read_elem_lazy` (in place of `read_elem_as_dask`) to handle backed dataframes, sparse arrays, and dense arrays, as well as a {func}`~anndata.experimental.read_lazy` to handle reading in as much of the on-disk data as possible to produce a {class}`~anndata.AnnData` object {user}`ilan-gold` |
Original file line number | Diff line number | Diff line change |
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|
@@ -80,9 +80,11 @@ doc = [ | |
"sphinx_design>=0.5.0", | ||
"readthedocs-sphinx-search", | ||
# for unreleased changes | ||
"anndata[dev-doc]", | ||
"anndata[dev-doc,dask]", | ||
"awkward>=2.3" | ||
] | ||
dev-doc = ["towncrier>=24.8.0"] # release notes tool | ||
test-full = ["anndata[test,lazy]"] | ||
test = [ | ||
"loompy>=3.0.5", | ||
"pytest>=8.2", | ||
|
@@ -108,6 +110,7 @@ cu12 = ["cupy-cuda12x"] | |
cu11 = ["cupy-cuda11x"] | ||
# https://github.com/dask/dask/issues/11290 | ||
dask = ["dask[array]>=2022.09.2,<2024.8.0"] | ||
lazy = ["xarray>=2024.06.0", "aiohttp", "requests", "zarr<3.0.0a0", "anndata[dask]"] | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Not sure what to do about optional deps here....I think
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[tool.hatch.version] | ||
source = "vcs" | ||
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Original file line number | Diff line number | Diff line change |
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@@ -50,10 +50,13 @@ def _normalize_index( | |
| pd.Index, | ||
index: pd.Index, | ||
) -> slice | int | np.ndarray: # ndarray of int or bool | ||
if not isinstance(index, pd.RangeIndex): | ||
msg = "Don’t call _normalize_index with non-categorical/string names" | ||
assert index.dtype != float, msg | ||
assert index.dtype != int, msg | ||
from ..experimental.backed._compat import DataArray | ||
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# TODO: why is this here? All tests pass without it and it seems at the minimum not strict enough. | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Note this comment. This line was causing problems with the There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more.
Ruff has a check to disallow There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. But it would be good to understand what we even going on here @ivirshup ? There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. I mean if the tests pass, we're good? And if we're not good, we should add a test? There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more.
Hmm, so it’s not possible that If that’s impossible, feel free to remove. Otherwise we should probably update this check to a TypeError or so. |
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# if not isinstance(index, pd.RangeIndex): | ||
# msg = "Don’t call _normalize_index with non-categorical/string names and non-range index" | ||
# assert index.dtype != float, msg | ||
# assert index.dtype != int, msg | ||
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# the following is insanely slow for sequences, | ||
# we replaced it using pandas below | ||
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@@ -107,6 +110,10 @@ def name_idx(i): | |
"are not valid obs/ var names or indices." | ||
) | ||
return positions # np.ndarray[int] | ||
elif isinstance(indexer, DataArray): | ||
if isinstance(indexer.data, DaskArray): | ||
return indexer.data.compute() | ||
return indexer.data | ||
else: | ||
raise IndexError(f"Unknown indexer {indexer!r} of type {type(indexer)}") | ||
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the other lines in this listing are type aliases, protocols, and so on. Are these here classes?
It’s OK to have whatever in experimental, but we need to be very careful about how big our API surface gets when we move these things out of experimental.
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Hmmm, that's an interesting point. I thought that by exporting
read_lazy
/making it general, I might appease @ivirshup pre-emptively by not creating only a monolith function and making something more composable a laread_elem
. But in fact, by doing that, we create more API surface, yes.Dataset2D
is something that will be visible on theAnnData
object viaadata.obs
but the others would not be if we didn't makeread_lazy
so general (i.e., because we added functionality to it from its previous state asread_elem_as_dask
). Tough question.There was a problem hiding this comment.
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As said in #1616, I think it’s fine to not export the full API surface of the classes we have and just commit to a carefully designed protocol instead.
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@flying-sheep I have been thinking about this a bit. Would it be acceptable to simply say "we subclass these things, but make no guarantees about the stability of our added APIs - please see the super class' documentation for supported methods." ?
I think this is perfectly fair.
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If we do this, we don’t need to write anything, people will only see the superclass, i.e. the stable API:
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Back to our sphinx-autodoc-singledispatch issue, I don't think we can do this because we don't install the package unless this can work without the package installed or mocked
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hmm, right the intersphinx mapping is independent of installed packages!
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Ok @flying-sheep I am not sure
qualname_overrides
works this way - I tried adding it in but could not get the links to redirect to the xarray docsThere was a problem hiding this comment.
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Yeah, I think they only work for param docs. I should get around to check if I can extend it to work for pure Sphinx as well.