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... or when the input isn't/cannot be a set. I left a few usages, e.g.
in random sampling, where "set" is reasonable as informal description of
an array as the order doesn't matter; however, for e.g. np.gradient the
order of the returned list is clearly important, so "set" is wrong.
Also some other minor doc edits noticed during the grepping: using
`shape` instead of `form` in `cov` is consistent with most other places;
the wording in `Polynomial.trim` now matches other methods on the same
class.
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[travis skip]
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counterparts (#17222)
* DOC: redistribute docstring-only content from numpy/doc
* DOC: post-transition clean-up
* DOC, MAINT: reskip doctests, fix a few easy ones
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compare already returns sets, so conversion is redundant
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The refguide_check (which currently runs as part of the CI) fails on :doc:
roles in the documentation. Added :doc: to the set of whitelisted role
names as it is a valid role in sphinx.
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* DOC: skip f2py fortran and arrays.nditer cython tests for refguide_check
* DOC: fix formatting of doc tests and improper use of nditer
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Signed-off-by: Changqing Li <changqing.li@windriver.com>
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TST: check exception details in refguide_check.py
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xref GH-12548
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MAINT: cleanup unused imports; avoid redefinition of imports
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* Cleanup unused imports (F401) of mostly standard Python modules,
or some internal but unlikely referenced modules
* Where internal imports are potentially used, mark with noqa
* Avoid redefinition of imports (F811)
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MAINT: Use contextmanager in _run_doctests
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MAINT: Cleanup duplicate line in refguide_check
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MAINT: Cleanup references to python2
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Inheriting from object was necessary for Python 2 compatibility to use
new-style classes. In Python 3, this is unnecessary as there are no
old-style classes.
Dropping the object is more idiomatic Python.
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As numpy is Python 3 only, these import statements are now unnecessary
and don't alter runtime behavior.
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empty_like (#13771)
* DOC: Change "random" to "uninitialized" in doctests
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Example behavior:
>>> x = np.array([1, 2, 3])
>>> y = np.array([1, 2, 3.0001])
>>> np.testing.assert_allclose(x, y)
AssertionError:
Not equal to tolerance rtol=1e-07, atol=0
Mismatch: 33.3%
Max absolute difference: 0.0001
Max relative difference: 3.33322223e-05
x: array([1, 2, 3])
y: array([1. , 2. , 3.0001])
Motivation: when writing numerical algorithms, I frequently find myself
experimenting to pick the right value of `atol` and `rtol` for
`np.testing.assert_allclose()`. If I make the tolerance too generous, I risk
missing regressions in accuracy, so I usually try to pick the smallest values
for which tests pass. This change immediately reveals appropriate values to
use for these parameters, so I don't need to guess and check.
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* added lib.recfunctions to refguide
PUBLIC_SUBMODULES, as the doctests
were otherwise not getting executed
* fixed a failing doctest in
recfunctions after above activation
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* ported the refguide_check module from SciPy for usage
in NumPy docstring execution/ verification; added the
refguide_check run to Azure Mac OS CI
* adjusted NumPy docstrings such that refguide_check passes
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