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test = de.test.pairwise(
data=adata,
grouping='group_label',
test='t-test',
lazy=False,
noise_model=None,
keep_full_test_objs=True
)
yields
File [~/mambaforge/envs/ENV/lib/python3.10/site-packages/diffxpy/testing/det.py:1567](https://file+.vscode-resource.vscode-cdn.net/Users/USER/Projects/PROJECT/examples/~/mambaforge/envs/ENV/lib/python3.10/site-packages/diffxpy/testing/det.py:1567), in DifferentialExpressionTestTT.__init__(self, data, sample_description, grouping, gene_names, is_logged, is_sig_zerovar)
1564 x0, x1 = split_x(data, grouping)
1566 # Only compute p-values for genes with non-zero observations and non-zero group-wise variance.
-> 1567 mean_x0 = np.asarray(np.mean(x0, axis=0)).flatten().astype(dtype=np.float)
1568 mean_x1 = np.asarray(np.mean(x1, axis=0)).flatten().astype(dtype=np.float)
1569 # Avoid unnecessary mean computation:
File [~/mambaforge/envs/ENV/lib/python3.10/site-packages/numpy/__init__.py:305](https://file+.vscode-resource.vscode-cdn.net/Users/USER/Projects/PROJECT/examples/~/mambaforge/envs/ENV/lib/python3.10/site-packages/numpy/__init__.py:305), in __getattr__(attr)
300 warnings.warn(
301 f"In the future `np.{attr}` will be defined as the "
302 "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
304 if attr in __former_attrs__:
--> 305 raise AttributeError(__former_attrs__[attr])
307 # Importing Tester requires importing all of UnitTest which is not a
308 # cheap import Since it is mainly used in test suits, we lazy import it
309 # here to save on the order of 10 ms of import time for most users
310 #
311 # The previous way Tester was imported also had a side effect of adding
312 # the full `numpy.testing` namespace
313 if attr == 'testing':
AttributeError: module 'numpy' has no attribute 'float'.
`np.float` was a deprecated alias for the builtin `float`. To avoid this error in existing code, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.
The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
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