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BUG: Don't convert inputs to `np.float64` in digitize
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This converts digitize to a pure-python function that falls back on searchsorted.
Performance doesn't really matter here anyway - if you care about performance, then you should just call searchsorted directly, rather than checking the order of the bins.
Partially fixes gh-11022
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ENH: Add density argument to histogramdd.
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Fixes gh-4371
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TST: Added regression test for #11395
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Values like NaN and inf would result in wrong interpolated values on
exactly matching sampling points. To produce the correct behavior, we
add an additional check to avoid interpolation when handling such a
point.
Closes #11439
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BUG: Fix incorrect deprecation logic for histogram(normed=...) (master)
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Fixes #11426, which was introduced in #11323 and #11352
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TST: Show that histogramdd's normed argument is histogram's density
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Relevant to gh-4371
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Documenting this argument at such length gives it authenticity it does not deserve.
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BUG,MAINT: Ensure masked elements can be tested against nan and inf.
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This brought to light two bugs in tests, which are fixed here, viz.,
that a sample ndarray subclass that tested propagation of an added
parameter was incomplete, in that in propagating the parameter in
__array_wrap__ it assumed it was there on self, but that assumption
could be broken when a view of self was taken (as is done by
x[~flagged] in the test routine), since there was no
__array_finalize__ defined.
The other subclass bug counted, incorrectly, on only needing to provide
one type of comparison, the __lt__ being explicitly tested. But flags
are compared with __eq__ and those flags will have the same subclass.
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Fixes #11028
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np.ogrid was not tested anyway, so this was not caught.
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ENH: Add (put|take)_along_axis
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This is the reduced version that does not allow any insertion of extra dimensions
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* added function commonpts1d
* Update arraysetops.py
* Update arraysetops.py
* modified intersect1d to return common indices
Proposed this idea in previous pull request (https://github.com/numpy/numpy/pull/10683) and made suggested changes for implementing this idea to have relevant common indices that correspond to the values in the intersection of the two arrays.
* update intersect1d with suggested changes
* implemented return_indices options for intersect1d
I've tested out the above code and for the ~10 different test sets I've tried it's worked thus far. It's probably not the cleanest implementation but it works and is vectorized.
* cleaned up structure for intersect1d
* fixed copy-paste error, added second test, changed a conditional
* Testing return_indices in intersect1d
* formatting
* created separate test function for intersect1d indices, added spaces after commas
* fixed up example and some style
* fixed style
* style change
* removed one example
* removed extra space
* added version number for return_indices
* added 'return_indices' keyword for np.intersect1d
* fixed formatting
* updated return_indices entry
* fixed some typos and style
* added bit about first instance of a value being used
* STY: Fix comment formats
* DOC: missing space
* DOC: correct parameter names in docstring
* made suggested changes
* fixed a mistake from previous update
also added documentation for comm1, comm2 to match doc from np.unique
* added in tests for 2d inputs
* STY: Add missing spaces around commas
* TST: Correct array to actually be unique
* STY: Spaces at beginning of comments
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Move remaining Matrix tests to matrixlib
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BUG: np.histogramdd loses precision on its inputs, leading to incorrect results
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This is due to gh-11022.
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Previously a fuzzy rounded comparison was used for the rightmost bin of histogramdd.
It's not clear why this was done, and it resulted in surprising behavior.
This also removes the restriction that bin edges must be floats, and allows integer arrays to be passed (and returned)
Fixes gh-10864
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The old way of creating the padded array padded with wrong values for
large integers because the new prepended / appended array was implicitly
created with dtype float64:
>>> (np.zeros(1) + (2 ** 64 - 1)).astype(np.uint64)
array([0], np.uint64)
>>> (np.zeros(1) + (2 ** 63 - 1)).astype(np.int64)
array([-9223372036854775808])
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User- and non-user-facing typos.
Some source typos fixes as well.
Found via `codespell`.
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ENH: Quantile
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* fixed doc typo
* fixed lib typos
* fixed lapack_lite typos
* Revert "fixed lapack_lite typos"
This reverts commit e7dada860cb73af190234402508ab79965ecd079.
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TST: Extract a helper function to test for reference cycles
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This also means we can now test that our test is actually able to detect the type of failure we expect
Trying to give myself some tools to debug the failure at https://github.com/numpy/numpy/pull/10882/files#r180813166
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Closes #10847
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Fixes gh-10780
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BUG: fix savetxt, loadtxt for '+-' in complex
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Now falls back on sturges estimator when the IQR is zero
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That function is nose specific and has not worked since `__init__` files
were added to the tests directories.
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Use standard pytest markers everywhere in the numpy tests. At this point
there should be no nose dependency. However, nose is required to test
the legacy decorators if so desired.
At this point, numpy test cannot be run in the way with runtests, rather
installed numpy can be tested with `pytest --pyargs numpy` as long as
that is not run from the repo. Run it from the tools directory or some
such.
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The renamed C modules provide low level functions for testing. The
rename marks them as private functions and makes them invisible to
pytest by default.
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This removes a few left over uses of unittest. The main changes apart
from removal of Test case are:
* `setUp` replaced by nose and pytest compatible `setup`
* `tearDown` replaced by nose and pytest compatible `teardown`
* `assertRaises` replaced by `assert_raises`
* `assertEqual` replaced by `assert_equal`
The last two are in `numpy/testings/tests/test_utils.py`, so may seem a
but circular, but at least are limited to those two functions.
The use of `setup` and `teardown`, can be fixed up with the pytest
equivalents after we have switched to pytest.
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BUG: fix complex casting error in cov with aweights
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When using cov with a complex input and with aweights specified, cov
will error as a result of trying to cast a complex value into a float64.
This comes about since average is used to calculate the sum of the
weights from aweights. average returns the sum of weights as the same
type as its result, not the weights type. For a complex input m, and
any type for aweights, this would result in a complex value for fact.
It appears the primary purpose of np.float64(fact) is to provide a NaN
value from the divide when fact is an integer zero. This has been
replaced by using numpy.divide to replicate the same behavior, but to
also handle complex types.
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Fixes #10183
Documentation is copied from np.histogram
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* TST: skip refcount-requiring tests if sys.refcount is missing
* ENH: io: add refcheck=False to a safe .resize() call
The array is allocated immediately above, and the resize always succeeds
so it is not necessary to check it. Fixes Pypy compatibility.
* TST: remove unused code
* TST: factor skipif(not HAS_REFCOUNT) into a separate decorator
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These are now handled generically by the underlying C function
This fixes the period argument for 0d arrays.
Now never returns a pure-python scalar, which matches the behaviour of most of numpy.
Rework of b66a200a4a1e98f1955c8a774e4ebfb4588dab5b
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