diff --git a/.azure-pipelines.yml b/.azure-pipelines.yml
index 4642fe4435..cbe87aadb9 100644
--- a/.azure-pipelines.yml
+++ b/.azure-pipelines.yml
@@ -6,10 +6,9 @@ variables:
python.version: '3.11'
PIP_CACHE_DIR: $(Pipeline.Workspace)/.pip
PYTEST_ADDOPTS: '-v --color=yes --durations=0 --nunit-xml=test-data/test-results.xml'
- ANNDATA_DEV: no
- RUN_COVERAGE: no
TEST_EXTRA: 'test-full'
- PRERELEASE_DEPENDENCIES: no
+ DEPENDENCIES_VERSION: "latest" # |"pre-release" | "minimum-version"
+ TEST_TYPE: "standard" # | "coverage"
jobs:
- job: PyTest
@@ -20,12 +19,16 @@ jobs:
Python3.9:
python.version: '3.9'
Python3.11: {}
- minimal_tests:
+ minimal_dependencies:
TEST_EXTRA: 'test-min'
anndata_dev:
- ANNDATA_DEV: yes
- RUN_COVERAGE: yes
- PRERELEASE_DEPENDENCIES: yes
+ DEPENDENCIES_VERSION: "pre-release"
+ TEST_TYPE: "coverage"
+ minimum_versions:
+ python.version: '3.9'
+ DEPENDENCIES_VERSION: "minimum-version"
+ TEST_TYPE: "coverage"
+
steps:
- task: UsePythonVersion@0
@@ -52,19 +55,22 @@ jobs:
pip install wheel coverage
pip install .[dev,$(TEST_EXTRA)]
displayName: 'Install dependencies'
- condition: eq(variables['PRERELEASE_DEPENDENCIES'], 'no')
+ condition: eq(variables['DEPENDENCIES_VERSION'], 'latest')
- script: |
python -m pip install --pre --upgrade pip
pip install --pre wheel coverage
pip install --pre .[dev,$(TEST_EXTRA)]
+ pip install -v "anndata[dev,test] @ git+https://github.com/scverse/anndata"
displayName: 'Install dependencies release candidates'
- condition: eq(variables['PRERELEASE_DEPENDENCIES'], 'yes')
+ condition: eq(variables['DEPENDENCIES_VERSION'], 'pre-release')
- script: |
- pip install -v "anndata[dev,test] @ git+https://github.com/scverse/anndata"
- displayName: 'Install development anndata'
- condition: eq(variables['ANNDATA_DEV'], 'yes')
+ python -m pip install pip wheel tomli packaging pytest-cov
+ pip install `python3 ci/scripts/min-deps.py pyproject.toml --extra dev test`
+ pip install --no-deps .
+ displayName: 'Install dependencies minimum version'
+ condition: eq(variables['DEPENDENCIES_VERSION'], 'minimum-version')
- script: |
pip list
@@ -72,31 +78,31 @@ jobs:
- script: pytest
displayName: 'PyTest'
- condition: eq(variables['RUN_COVERAGE'], 'no')
+ condition: eq(variables['TEST_TYPE'], 'standard')
- script: |
coverage run -m pytest
coverage xml
displayName: 'PyTest (coverage)'
- condition: eq(variables['RUN_COVERAGE'], 'yes')
+ condition: eq(variables['TEST_TYPE'], 'coverage')
- task: PublishCodeCoverageResults@1
inputs:
codeCoverageTool: Cobertura
summaryFileLocation: 'test-data/coverage.xml'
failIfCoverageEmpty: true
- condition: eq(variables['RUN_COVERAGE'], 'yes')
+ condition: eq(variables['TEST_TYPE'], 'coverage')
- task: PublishTestResults@2
condition: succeededOrFailed()
inputs:
testResultsFiles: 'test-data/test-results.xml'
testResultsFormat: NUnit
- testRunTitle: 'Publish test results for Python $(python.version)'
+ testRunTitle: 'Publish test results for $(Agent.JobName)'
- script: bash <(curl -s https://codecov.io/bash)
displayName: 'Upload to codecov.io'
- condition: eq(variables['RUN_COVERAGE'], 'yes')
+ condition: eq(variables['TEST_TYPE'], 'coverage')
- job: CheckBuild
pool:
diff --git a/ci/scripts/min-deps.py b/ci/scripts/min-deps.py
new file mode 100755
index 0000000000..b3f393ea57
--- /dev/null
+++ b/ci/scripts/min-deps.py
@@ -0,0 +1,99 @@
+#!python3
+from __future__ import annotations
+
+import argparse
+import sys
+from collections import deque
+from pathlib import Path
+from typing import TYPE_CHECKING
+
+if sys.version_info >= (3, 11):
+ import tomllib
+else:
+ import tomli as tomllib
+
+from packaging.requirements import Requirement
+from packaging.version import Version
+
+if TYPE_CHECKING:
+ from collections.abc import Generator, Iterable
+
+
+def min_dep(req: Requirement) -> Requirement:
+ """
+ Given a requirement, return the minimum version specifier.
+
+ Example
+ -------
+
+ >>> min_dep(Requirement("numpy>=1.0"))
+ "numpy==1.0"
+ """
+ req_name = req.name
+ if req.extras:
+ req_name = f"{req_name}[{','.join(req.extras)}]"
+
+ if not req.specifier:
+ return Requirement(req_name)
+
+ min_version = Version("0.0.0.a1")
+ for spec in req.specifier:
+ if spec.operator in [">", ">=", "~="]:
+ min_version = max(min_version, Version(spec.version))
+ elif spec.operator == "==":
+ min_version = Version(spec.version)
+
+ return Requirement(f"{req_name}=={min_version}.*")
+
+
+def extract_min_deps(
+ dependencies: Iterable[Requirement], *, pyproject
+) -> Generator[Requirement, None, None]:
+ dependencies = deque(dependencies) # We'll be mutating this
+ project_name = pyproject["project"]["name"]
+
+ while len(dependencies) > 0:
+ req = dependencies.pop()
+
+ # If we are referring to other optional dependency lists, resolve them
+ if req.name == project_name:
+ assert req.extras, f"Project included itself as dependency, without specifying extras: {req}"
+ for extra in req.extras:
+ extra_deps = pyproject["project"]["optional-dependencies"][extra]
+ dependencies += map(Requirement, extra_deps)
+ else:
+ yield min_dep(req)
+
+
+def main():
+ parser = argparse.ArgumentParser(
+ prog="min-deps",
+ description="""Parse a pyproject.toml file and output a list of minimum dependencies.
+
+ Output is directly passable to `pip install`.""",
+ usage="pip install `python min-deps.py pyproject.toml`",
+ )
+ parser.add_argument(
+ "path", type=Path, help="pyproject.toml to parse minimum dependencies from"
+ )
+ parser.add_argument(
+ "--extras", type=str, nargs="*", default=(), help="extras to install"
+ )
+
+ args = parser.parse_args()
+
+ pyproject = tomllib.loads(args.path.read_text())
+
+ project_name = pyproject["project"]["name"]
+ deps = [
+ *map(Requirement, pyproject["project"]["dependencies"]),
+ *(Requirement(f"{project_name}[{extra}]") for extra in args.extras),
+ ]
+
+ min_deps = extract_min_deps(deps, pyproject=pyproject)
+
+ print(" ".join(map(str, min_deps)))
+
+
+if __name__ == "__main__":
+ main()
diff --git a/docs/release-notes/1.10.0.md b/docs/release-notes/1.10.0.md
index 37a6adf97f..7d48d4fd9d 100644
--- a/docs/release-notes/1.10.0.md
+++ b/docs/release-notes/1.10.0.md
@@ -35,6 +35,7 @@
* Fix setting `sc.settings.verbosity` in some cases {pr}`2605` {smaller}`P Angerer`
* Fix all remaining pandas warnings {pr}`2789` {smaller}`P Angerer`
* Fix some annoying plotting warnings around violin plots {pr}`2844` {smaller}`P Angerer`
+* Scanpy now has a test job which tests against the minumum versions of the dependencies. In the process of implementing this, many bugs associated with using older versions of `pandas`, `anndata`, `numpy`, and `matplotlib` were fixed. {pr}`2816` {smaller}`I Virshup`
```{rubric} Ecosystem
```
diff --git a/pyproject.toml b/pyproject.toml
index bd5e2b5215..fda07ea18b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -46,24 +46,25 @@ classifiers = [
"Topic :: Scientific/Engineering :: Visualization",
]
dependencies = [
- "anndata>=0.7.4",
+ "anndata>=0.8",
# numpy needs a version due to #1320
- "numpy>=1.17.0",
+ "numpy>=1.23",
"matplotlib>=3.6",
- "pandas >=2.1.3",
- "scipy>=1.4",
- "seaborn>=0.13.0",
- "h5py>=3",
+ "pandas >=1.5",
+ "scipy>=1.8",
+ "seaborn>=0.13",
+ "h5py>=3.1",
"tqdm",
"scikit-learn>=0.24",
- "statsmodels>=0.10.0rc2",
+ "statsmodels>=0.13",
"patsy",
- "networkx>=2.3",
+ "networkx>=2.7",
"natsort",
"joblib",
- "numba>=0.41.0",
+ "numba>=0.56",
"umap-learn>=0.3.10",
- "packaging",
+ "pynndescent>=0.5",
+ "packaging>=21.3",
"session-info",
"legacy-api-wrap>=1.4", # for positional API deprecations
"get-annotations; python_version < '3.10'",
@@ -132,8 +133,8 @@ dev = [
]
# Algorithms
paga = ["igraph"]
-louvain = ["igraph", "louvain>=0.6,!=0.6.2"] # Louvain community detection
-leiden = ["igraph>=0.10", "leidenalg>=0.9"] # Leiden community detection
+louvain = ["igraph", "louvain>=0.6.0,!=0.6.2"] # Louvain community detection
+leiden = ["igraph>=0.10", "leidenalg>=0.9.0"] # Leiden community detection
bbknn = ["bbknn"] # Batch balanced KNN (batch correction)
magic = ["magic-impute>=2.0"] # MAGIC imputation method
skmisc = ["scikit-misc>=0.1.3"] # highly_variable_genes method 'seurat_v3'
@@ -142,7 +143,7 @@ scanorama = ["scanorama"] # Scanorama dataset integration
scrublet = ["scikit-image"] # Doublet detection with automatic thresholds
# Acceleration
rapids = ["cudf>=0.9", "cuml>=0.9", "cugraph>=0.9"] # GPU accelerated calculation of neighbors
-dask = ["dask[array]!=2.17.0"] # Use the Dask parallelization engine
+dask = ["dask[array]>=2022.09.2"] # Use the Dask parallelization engine
dask-ml = ["dask-ml", "scanpy[dask]"] # Dask-ML for sklearn-like API
[tool.hatch.build]
@@ -166,6 +167,7 @@ nunit_attach_on = "fail"
markers = [
"internet: tests which rely on internet resources (enable with `--internet-tests`)",
"gpu: tests that use a GPU (currently unused, but needs to be specified here as we import anndata.tests.helpers, which uses it)",
+ "anndata_dask_support: tests that require dask support in anndata",
]
filterwarnings = [
# legacy-api-wrap: internal use of positional API
diff --git a/scanpy/get/get.py b/scanpy/get/get.py
index b51dc30a26..8ef58f4b51 100644
--- a/scanpy/get/get.py
+++ b/scanpy/get/get.py
@@ -260,7 +260,7 @@ def obs_df(
... )
>>> plotdf.columns
Index(['CD8B', 'n_genes', 'X_umap-0', 'X_umap-1'], dtype='object')
- >>> plotdf.plot.scatter("X_umap-0", "X_umap-1", c="CD8B")
+ >>> plotdf.plot.scatter("X_umap-0", "X_umap-1", c="CD8B") # doctest: +SKIP
<Axes: xlabel='X_umap-0', ylabel='X_umap-1'>
Calculating mean expression for marker genes by cluster:
diff --git a/scanpy/neighbors/_backends/rapids.py b/scanpy/neighbors/_backends/rapids.py
index 78a6bb7359..ef6b9b23f1 100644
--- a/scanpy/neighbors/_backends/rapids.py
+++ b/scanpy/neighbors/_backends/rapids.py
@@ -3,8 +3,9 @@
from typing import TYPE_CHECKING, Any, Literal
import numpy as np
-from sklearn.base import BaseEstimator, TransformerMixin, check_is_fitted
+from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.exceptions import NotFittedError
+from sklearn.utils.validation import check_is_fitted
from ..._settings import settings
from ._common import TransformerChecksMixin
diff --git a/scanpy/plotting/_baseplot_class.py b/scanpy/plotting/_baseplot_class.py
index 22ff04913e..10329f16e4 100644
--- a/scanpy/plotting/_baseplot_class.py
+++ b/scanpy/plotting/_baseplot_class.py
@@ -347,7 +347,7 @@ def add_totals(
>>> adata = sc.datasets.pbmc68k_reduced()
>>> markers = {'T-cell': 'CD3D', 'B-cell': 'CD79A', 'myeloid': 'CST3'}
>>> plot = sc.pl._baseplot_class.BasePlot(adata, markers, groupby='bulk_labels').add_totals()
- >>> plot.plot_group_extra['counts_df']
+ >>> plot.plot_group_extra['counts_df'] # doctest: +SKIP
bulk_labels
CD4+/CD25 T Reg 68
CD4+/CD45RA+/CD25- Naive T 8
diff --git a/scanpy/plotting/_matrixplot.py b/scanpy/plotting/_matrixplot.py
index d811a12a8a..a65332be65 100644
--- a/scanpy/plotting/_matrixplot.py
+++ b/scanpy/plotting/_matrixplot.py
@@ -168,7 +168,15 @@ def __init__(
if values_df is None:
# compute mean value
- values_df = self.obs_tidy.groupby(level=0, observed=True).mean()
+ values_df = (
+ self.obs_tidy.groupby(level=0, observed=True)
+ .mean()
+ .loc[
+ self.categories_order
+ if self.categories_order is not None
+ else self.categories
+ ]
+ )
if standard_scale == "group":
values_df = values_df.sub(values_df.min(1), axis=0)
diff --git a/scanpy/plotting/_stacked_violin.py b/scanpy/plotting/_stacked_violin.py
index fed4893cde..6b9abfd49c 100644
--- a/scanpy/plotting/_stacked_violin.py
+++ b/scanpy/plotting/_stacked_violin.py
@@ -383,14 +383,17 @@ def _mainplot(self, ax):
if self.var_names_idx_order is not None:
_matrix = _matrix.iloc[:, self.var_names_idx_order]
- if self.categories_order is not None:
- _matrix.index = _matrix.index.reorder_categories(
- self.categories_order, ordered=True
- )
-
# get mean values for color and transform to color values
# using colormap
- _color_df = _matrix.groupby(level=0, observed=True).median()
+ _color_df = (
+ _matrix.groupby(level=0, observed=True)
+ .median()
+ .loc[
+ self.categories_order
+ if self.categories_order is not None
+ else self.categories
+ ]
+ )
if self.are_axes_swapped:
_color_df = _color_df.T
diff --git a/scanpy/plotting/_tools/scatterplots.py b/scanpy/plotting/_tools/scatterplots.py
index b1b8937ff5..59c828d517 100644
--- a/scanpy/plotting/_tools/scatterplots.py
+++ b/scanpy/plotting/_tools/scatterplots.py
@@ -20,6 +20,7 @@
from matplotlib.colors import Colormap, Normalize
from matplotlib.figure import Figure # noqa: TCH002
from numpy.typing import NDArray # noqa: TCH002
+from packaging.version import Version
from ... import logging as logg
from ..._settings import settings
@@ -1247,8 +1248,10 @@ def _color_vector(
}
# If color_map does not have unique values, this can be slow as the
# result is not categorical
- color_vector = pd.Categorical(values.map(color_map, na_action="ignore"))
-
+ if Version(pd.__version__) < Version("2.1.0"):
+ color_vector = pd.Categorical(values.map(color_map))
+ else:
+ color_vector = pd.Categorical(values.map(color_map, na_action="ignore"))
# Set color to 'missing color' for all missing values
if color_vector.isna().any():
color_vector = color_vector.add_categories([to_hex(na_color)])
diff --git a/scanpy/preprocessing/_highly_variable_genes.py b/scanpy/preprocessing/_highly_variable_genes.py
index e6f925a168..0dd2a33c51 100644
--- a/scanpy/preprocessing/_highly_variable_genes.py
+++ b/scanpy/preprocessing/_highly_variable_genes.py
@@ -252,6 +252,11 @@ def _highly_variable_genes_single_batch(
`highly_variable`, `means`, `dispersions`, and `dispersions_norm`.
"""
X = _get_obs_rep(adata, layer=layer)
+
+ if hasattr(X, "_view_args"): # AnnData array view
+ # For compatibility with anndata<0.9
+ X = X.copy() # Doesn't actually copy memory, just removes View class wrapper
+
if flavor == "seurat":
X = X.copy()
if "log1p" in adata.uns_keys() and adata.uns["log1p"].get("base") is not None:
diff --git a/scanpy/preprocessing/_pca.py b/scanpy/preprocessing/_pca.py
index 53377f1321..df88b57b19 100644
--- a/scanpy/preprocessing/_pca.py
+++ b/scanpy/preprocessing/_pca.py
@@ -3,6 +3,7 @@
import warnings
from warnings import warn
+import anndata as ad
import numpy as np
from anndata import AnnData
from packaging import version
@@ -188,6 +189,19 @@ def pca(
X = _get_obs_rep(adata_comp, layer=layer)
+ # See: https://github.com/scverse/scanpy/pull/2816#issuecomment-1932650529
+ if (
+ version.parse(ad.__version__) < version.parse("0.9")
+ and mask is not None
+ and isinstance(X, np.ndarray)
+ ):
+ warnings.warn(
+ "When using a mask parameter with anndata<0.9 on a dense array, the PCA"
+ "can have slightly different results due the array being column major "
+ "instead of row major.",
+ UserWarning,
+ )
+
is_dask = isinstance(X, DaskArray)
# check_random_state returns a numpy RandomState when passed an int but
diff --git a/scanpy/testing/_helpers/__init__.py b/scanpy/testing/_helpers/__init__.py
index 939ce60c53..c36ed5a2cb 100644
--- a/scanpy/testing/_helpers/__init__.py
+++ b/scanpy/testing/_helpers/__init__.py
@@ -20,9 +20,24 @@
# These functions can be used to check that functions are correctly using arugments like `layers`, `obsm`, etc.
+def anndata_v0_8_constructor_compat(X, *args, **kwargs):
+ """Constructor for anndata that uses dtype of X for test compatibility with older versions of AnnData.
+
+ Once the minimum version of AnnData is 0.9, this function can be replaced with the default constructor.
+ """
+ import anndata as ad
+ from packaging.version import Version
+
+ if Version(ad.__version__) < Version("0.9"):
+ return ad.AnnData(X=X, *args, **kwargs, dtype=X.dtype)
+ else:
+ return ad.AnnData(X=X, *args, **kwargs)
+
+
def check_rep_mutation(func, X, *, fields=("layer", "obsm"), **kwargs):
"""Check that only the array meant to be modified is modified."""
- adata = sc.AnnData(X=X.copy())
+ adata = anndata_v0_8_constructor_compat(X.copy())
+
for field in fields:
sc.get._set_obs_rep(adata, X, **{field: field})
X_array = asarray(X)
@@ -105,3 +120,16 @@ def _check_check_values_warnings(function, adata, expected_warning, kwargs={}):
function(adata.copy(), **kwargs, check_values=True)
warning_msgs = [w.message.args[0] for w in record]
assert expected_warning in warning_msgs
+
+
+# Delayed imports for case where we aren't using dask
+def as_dense_dask_array(*args, **kwargs):
+ from anndata.tests.helpers import as_dense_dask_array
+
+ return as_dense_dask_array(*args, **kwargs)
+
+
+def as_sparse_dask_array(*args, **kwargs):
+ from anndata.tests.helpers import as_sparse_dask_array
+
+ return as_sparse_dask_array(*args, **kwargs)
diff --git a/scanpy/testing/_pytest/__init__.py b/scanpy/testing/_pytest/__init__.py
index 1dfc3f78d2..ad42b8ec74 100644
--- a/scanpy/testing/_pytest/__init__.py
+++ b/scanpy/testing/_pytest/__init__.py
@@ -3,6 +3,7 @@
import os
import sys
+import warnings
from typing import TYPE_CHECKING
import pytest
@@ -71,10 +72,12 @@ def _fix_dask_df_warning():
import dask # noqa: F401
except ImportError:
return
- with pytest.warns(
- DeprecationWarning,
- match=r"The current Dask DataFrame implementation is deprecated",
- ):
+ with warnings.catch_warnings():
+ warnings.filterwarnings(
+ "ignore",
+ category=DeprecationWarning,
+ message=r"The current Dask DataFrame implementation is deprecated",
+ )
import dask.dataframe # noqa: F401
@@ -118,3 +121,18 @@ def _modify_doctests(request: pytest.FixtureRequest) -> None:
skip_reason: str | None
if skip_reason := getattr(func, "_doctest_skip_reason", None):
pytest.skip(reason=skip_reason)
+
+
+def pytest_itemcollected(item: pytest.Item) -> None:
+ # Dask AnnData tests require anndata > 0.10
+ import anndata
+ from packaging.version import Version
+
+ requires_anndata_dask_support = (
+ len([mark for mark in item.iter_markers(name="anndata_dask_support")]) > 0
+ )
+
+ if requires_anndata_dask_support and Version(anndata.__version__) < Version("0.10"):
+ item.add_marker(
+ pytest.mark.skip(reason="dask support requires anndata version > 0.10")
+ )
diff --git a/scanpy/testing/_pytest/params.py b/scanpy/testing/_pytest/params.py
index 3ff543bfa7..655daf6c7a 100644
--- a/scanpy/testing/_pytest/params.py
+++ b/scanpy/testing/_pytest/params.py
@@ -5,9 +5,10 @@
from typing import TYPE_CHECKING, Literal
import pytest
-from anndata.tests.helpers import as_dense_dask_array, as_sparse_dask_array, asarray
+from anndata.tests.helpers import asarray
from scipy import sparse
+from .._helpers import as_dense_dask_array, as_sparse_dask_array
from .._pytest.marks import needs
if TYPE_CHECKING:
@@ -35,10 +36,18 @@ def param_with(
pytest.param(sparse.csc_matrix, id="scipy_csc"),
),
("dask", "dense"): (
- pytest.param(as_dense_dask_array, marks=[needs.dask], id="dask_array_dense"),
+ pytest.param(
+ as_dense_dask_array,
+ marks=[needs.dask, pytest.mark.anndata_dask_support],
+ id="dask_array_dense",
+ ),
),
("dask", "sparse"): (
- pytest.param(as_sparse_dask_array, marks=[needs.dask], id="dask_array_sparse"),
+ pytest.param(
+ as_sparse_dask_array,
+ marks=[needs.dask, pytest.mark.anndata_dask_support],
+ id="dask_array_sparse",
+ ),
# probably not necessary to also do csc
),
}
diff --git a/scanpy/tests/test_get.py b/scanpy/tests/test_get.py
index bbdea8ce3d..b55064088a 100644
--- a/scanpy/tests/test_get.py
+++ b/scanpy/tests/test_get.py
@@ -11,6 +11,7 @@
import scanpy as sc
from scanpy.datasets._utils import filter_oldformatwarning
+from scanpy.testing._helpers import anndata_v0_8_constructor_compat
from scanpy.testing._helpers.data import pbmc68k_reduced
@@ -38,7 +39,7 @@ def adata():
adata.X is np.ones((2, 2))
adata.layers['double'] is sparse np.ones((2,2)) * 2 to also test sparse matrices
"""
- return AnnData(
+ return anndata_v0_8_constructor_compat(
X=np.ones((2, 2), dtype=int),
obs=pd.DataFrame(
{"obs1": [0, 1], "obs2": ["a", "b"]}, index=["cell1", "cell2"]
@@ -60,7 +61,7 @@ def test_obs_df(adata):
adata.obsm["sparse"] = sparse.csr_matrix(np.eye(2), dtype="float64")
# make raw with different genes than adata
- adata.raw = AnnData(
+ adata.raw = anndata_v0_8_constructor_compat(
X=np.array([[1, 2, 3], [2, 4, 6]], dtype=np.float64),
var=pd.DataFrame(
{"gene_symbols": ["raw1", "raw2", "raw3"]},
diff --git a/scanpy/tests/test_metrics.py b/scanpy/tests/test_metrics.py
index 8d0acf3c33..226aecd7f6 100644
--- a/scanpy/tests/test_metrics.py
+++ b/scanpy/tests/test_metrics.py
@@ -5,10 +5,12 @@
from operator import eq
from string import ascii_letters
+import numba
import numpy as np
import pandas as pd
import pytest
import threadpoolctl
+from packaging.version import Version
from scipy import sparse
import scanpy as sc
@@ -77,7 +79,10 @@ def test_consistency(metric, threading):
all_genes = metric(pbmc, layer="raw")
first_gene = metric(pbmc, vals=pbmc.obs_vector(pbmc.var_names[0], layer="raw"))
- np.testing.assert_allclose(all_genes[0], first_gene, rtol=1e-9)
+ if Version(numba.__version__) < Version("0.57"):
+ np.testing.assert_allclose(all_genes[0], first_gene, rtol=1e-5)
+ else:
+ np.testing.assert_allclose(all_genes[0], first_gene, rtol=1e-9)
# Test that results are similar for sparse and dense reps of same data
equality_check(
diff --git a/scanpy/tests/test_neighbors.py b/scanpy/tests/test_neighbors.py
index dbefe5a006..d623682ff3 100644
--- a/scanpy/tests/test_neighbors.py
+++ b/scanpy/tests/test_neighbors.py
@@ -11,6 +11,7 @@
import scanpy as sc
from scanpy import Neighbors
+from scanpy.testing._helpers import anndata_v0_8_constructor_compat
if TYPE_CHECKING:
from pytest_mock import MockerFixture
@@ -113,7 +114,7 @@
def get_neighbors() -> Neighbors:
- return Neighbors(AnnData(np.array(X)))
+ return Neighbors(anndata_v0_8_constructor_compat(np.array(X)))
@pytest.fixture
diff --git a/scanpy/tests/test_pca.py b/scanpy/tests/test_pca.py
index 6e529b4320..c9b6d913c2 100644
--- a/scanpy/tests/test_pca.py
+++ b/scanpy/tests/test_pca.py
@@ -3,19 +3,20 @@
import warnings
from typing import Literal
+import anndata as ad
import numpy as np
import pytest
from anndata import AnnData
from anndata.tests.helpers import (
- as_dense_dask_array,
- as_sparse_dask_array,
asarray,
assert_equal,
)
+from packaging.version import Version
from scipy import sparse
from sklearn.utils import issparse
import scanpy as sc
+from scanpy.testing._helpers import as_dense_dask_array, as_sparse_dask_array
from scanpy.testing._helpers.data import pbmc3k_normalized
from scanpy.testing._pytest.marks import needs
from scanpy.testing._pytest.params import ARRAY_TYPES, ARRAY_TYPES_SUPPORTED, param_with
@@ -337,12 +338,20 @@ def test_mask_argument_equivalence(float_dtype, array_type):
)
-def test_mask(array_type):
+def test_mask(array_type, request):
if array_type is as_dense_dask_array:
pytest.xfail("TODO: Dask arrays are not supported")
adata = sc.datasets.blobs(n_variables=10, n_centers=3, n_observations=100)
adata.X = array_type(adata.X)
+ if isinstance(adata.X, np.ndarray) and Version(ad.__version__) < Version("0.9"):
+ request.node.add_marker(
+ pytest.mark.xfail(
+ reason="TODO: Previous version of anndata would return an F ordered array for one"
+ " case here, which suprisingly considerably changes the results of PCA. "
+ )
+ )
+
mask = np.random.choice([True, False], adata.shape[1])
adata_masked = adata[:, mask].copy()
@@ -359,6 +368,24 @@ def test_mask(array_type):
)
+def test_mask_order_warning(request):
+ if Version(ad.__version__) >= Version("0.9"):
+ request.node.add_marker(
+ pytest.mark.xfail(
+ reason="Not expected to warn in later versions of anndata"
+ )
+ )
+
+ adata = ad.AnnData(X=np.random.randn(50, 5))
+ mask = np.array([True, False, True, False, True])
+
+ with pytest.warns(
+ UserWarning,
+ match="When using a mask parameter with anndata<0.9 on a dense array",
+ ):
+ sc.pp.pca(adata, mask=mask)
+
+
def test_mask_defaults(array_type, float_dtype):
"""
Test if pca result is equal without highly variable and with-but mask is None
diff --git a/scanpy/tests/test_preprocessing.py b/scanpy/tests/test_preprocessing.py
index 3029068776..5ae3b3e08f 100644
--- a/scanpy/tests/test_preprocessing.py
+++ b/scanpy/tests/test_preprocessing.py
@@ -11,7 +11,11 @@
from scipy import sparse as sp
import scanpy as sc
-from scanpy.testing._helpers import check_rep_mutation, check_rep_results
+from scanpy.testing._helpers import (
+ anndata_v0_8_constructor_compat,
+ check_rep_mutation,
+ check_rep_results,
+)
from scanpy.testing._helpers.data import pbmc3k, pbmc68k_reduced
from scanpy.testing._pytest.params import ARRAY_TYPES_SUPPORTED
@@ -187,11 +191,11 @@ def test_scale_array(count_matrix_format, zero_center):
Test that running sc.pp.scale on an anndata object and an array returns the same results.
"""
X = count_matrix_format(sp.random(100, 200, density=0.3).toarray())
- adata = sc.AnnData(X=X.copy().astype(np.float64))
+ adata = anndata_v0_8_constructor_compat(X=X.copy())
sc.pp.scale(adata, zero_center=zero_center)
scaled_X = sc.pp.scale(X, zero_center=zero_center, copy=True)
- assert np.array_equal(asarray(scaled_X), asarray(adata.X))
+ np.testing.assert_equal(asarray(scaled_X), asarray(adata.X))
def test_recipe_plotting():
@@ -314,7 +318,7 @@ def test_downsample_counts_per_cell(count_matrix_format, replace, dtype):
TARGET = 1000
X = np.random.randint(0, 100, (1000, 100)) * np.random.binomial(1, 0.3, (1000, 100))
X = X.astype(dtype)
- adata = AnnData(X=count_matrix_format(X).astype(dtype))
+ adata = anndata_v0_8_constructor_compat(X=count_matrix_format(X).astype(dtype))
with pytest.raises(ValueError):
sc.pp.downsample_counts(
adata, counts_per_cell=TARGET, total_counts=TARGET, replace=replace
@@ -346,7 +350,7 @@ def test_downsample_counts_per_cell_multiple_targets(
TARGETS = np.random.randint(500, 1500, 1000)
X = np.random.randint(0, 100, (1000, 100)) * np.random.binomial(1, 0.3, (1000, 100))
X = X.astype(dtype)
- adata = AnnData(X=count_matrix_format(X).astype(dtype))
+ adata = anndata_v0_8_constructor_compat(X=count_matrix_format(X).astype(dtype))
initial_totals = np.ravel(adata.X.sum(axis=1))
with pytest.raises(ValueError):
sc.pp.downsample_counts(adata, counts_per_cell=[40, 10], replace=replace)
@@ -372,7 +376,7 @@ def test_downsample_counts_per_cell_multiple_targets(
def test_downsample_total_counts(count_matrix_format, replace, dtype):
X = np.random.randint(0, 100, (1000, 100)) * np.random.binomial(1, 0.3, (1000, 100))
X = X.astype(dtype)
- adata_orig = AnnData(X=count_matrix_format(X))
+ adata_orig = anndata_v0_8_constructor_compat(X=count_matrix_format(X))
total = X.sum()
target = np.floor_divide(total, 10)
initial_totals = np.ravel(adata_orig.X.sum(axis=1))
diff --git a/scanpy/tests/test_preprocessing_distributed.py b/scanpy/tests/test_preprocessing_distributed.py
index 6dfa78459d..5f7a7bf443 100644
--- a/scanpy/tests/test_preprocessing_distributed.py
+++ b/scanpy/tests/test_preprocessing_distributed.py
@@ -4,9 +4,10 @@
import numpy.testing as npt
import pytest
-from anndata import AnnData, OldFormatWarning, read_zarr
+from anndata import AnnData, read_zarr
from scanpy._compat import DaskArray, ZappyArray
+from scanpy.datasets._utils import filter_oldformatwarning
from scanpy.preprocessing import (
filter_cells,
filter_genes,
@@ -27,24 +28,24 @@
@pytest.fixture()
+@filter_oldformatwarning
def adata() -> AnnData:
- with pytest.warns(OldFormatWarning):
- a = read_zarr(input_file) # regular anndata
+ a = read_zarr(input_file)
a.var_names_make_unique()
a.X = a.X[:] # convert to numpy array
return a
+@filter_oldformatwarning
@pytest.fixture(
params=[
pytest.param("direct", marks=[needs.zappy]),
- pytest.param("dask", marks=[needs.dask]),
+ pytest.param("dask", marks=[needs.dask, pytest.mark.anndata_dask_support]),
]
)
def adata_dist(request: pytest.FixtureRequest) -> AnnData:
# regular anndata except for X, which we replace on the next line
- with pytest.warns(OldFormatWarning):
- a = read_zarr(input_file)
+ a = read_zarr(input_file)
a.var_names_make_unique()
a.uns["dist-mode"] = request.param
input_file_X = f"{input_file}/X"
@@ -133,6 +134,7 @@ def test_filter_genes(adata: AnnData, adata_dist: AnnData):
npt.assert_allclose(result, adata.X)
+@filter_oldformatwarning
def test_write_zarr(adata: AnnData, adata_dist: AnnData):
import zarr
@@ -153,7 +155,7 @@ def test_write_zarr(adata: AnnData, adata_dist: AnnData):
assert False, "add branch for new dist-mode"
# read back as zarr directly and check it is the same as adata.X
- with pytest.warns(OldFormatWarning, match="without encoding metadata"):
- adata_log1p = read_zarr(temp_store)
+ adata_log1p = read_zarr(temp_store)
+
log1p(adata)
npt.assert_allclose(adata_log1p.X, adata.X)
diff --git a/scanpy/tools/_dendrogram.py b/scanpy/tools/_dendrogram.py
index 31fe6564fc..68902a6632 100644
--- a/scanpy/tools/_dendrogram.py
+++ b/scanpy/tools/_dendrogram.py
@@ -111,7 +111,7 @@ def dendrogram(
>>> import scanpy as sc
>>> adata = sc.datasets.pbmc68k_reduced()
>>> sc.tl.dendrogram(adata, groupby='bulk_labels')
- >>> sc.pl.dendrogram(adata, groupby='bulk_labels')
+ >>> sc.pl.dendrogram(adata, groupby='bulk_labels') # doctest: +SKIP
<Axes: >
>>> markers = ['C1QA', 'PSAP', 'CD79A', 'CD79B', 'CST3', 'LYZ']
>>> sc.pl.dotplot(adata, markers, groupby='bulk_labels', dendrogram=True)
@@ -155,7 +155,11 @@ def dendrogram(
)
# aggregate values within categories using 'mean'
- mean_df = rep_df.groupby(level=0, observed=True).mean()
+ mean_df = (
+ rep_df.groupby(level=0, observed=True)
+ .mean()
+ .loc[categories] # Fixed ordering for pandas < 2
+ )
import scipy.cluster.hierarchy as sch
from scipy.spatial import distance