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* DEP: remove deprecated casting in np.clipMatti Picus2023-03-161-67/+4
| | | | Signed-off-by: Matti Picus <matti.picus@gmail.com>
* API: Add leading underscore to `no_nep50_warning` and `get/set_promotion_state`Sebastian Berg2022-06-151-4/+4
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* MAINT: Ignore warning rather than forcing output dtypeSebastian Berg2022-06-151-8/+10
| | | | | | | | | | Forcing the output dtype does not work here, since it actually can be integral (not that this is usually a good idea). In practice, the change we are doing here is forcing 64bit (or 32bit depending on platform) of precision for the calculation. This means that the change will only ever increase precision mildly compared to the current situation.
* MAINT: Fortify methods (in-place division) against promotion changesSebastian Berg2022-06-151-3/+5
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* PERF: Use python integer on _count_reduce_items (#21465)Pieter Eendebak2022-05-111-1/+2
| | | | Small micro-optimization, since using Python integers is generally a bit faster in the loop. The effect is small, so this could undone if anyone figures it is cleaner the other way.
* BUG: Fixed an issue wherein `var` would raise for 0d object arraysBas van Beek2021-09-091-1/+3
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* ENH: Use new argument parsing for array creation functionsSebastian Berg2021-03-181-1/+1
| | | | | | | | | | | | | | | | | | The array creation functions have the most to gain: 1. np.asarray is 4 times faster and commonly used. 2. Other functions are wrapped using __array_function__ in Python making it more difficult This commit (unfortunatly) has to do a few things: * Modify __array_function__ C-side dispatching to accomodate the fastcall argument convention. * Move asarray, etc. to C after removing all "fast paths" from np.array (simplifying the code) * Fixup imports, since asarray was imported directly in a few places * Replace some places where `np.array` was probably used for speed instead of np.asarray or similar. (or by accident in 1 or 2 places)
* BUG: Fixed ``where`` keyword for ``np.mean`` & ``np.var`` methods (gh-18560)Mateusz Sokół2021-03-111-2/+2
| | | | | | | | | | | * Fixed keyword bug * Added test case * Reverted to original notation * Added tests for var and std Closes gh-18552
* MAINT: Replace `contextlib_nullcontext` with `contextlib.nullcontext`Bas van Beek2020-12-051-2/+3
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* ENH: Micro-optimize where=True path for mean, var, any, and allSebastian Berg2020-12-021-2/+8
| | | | | | | | | | | This removes a 20%-30% overhead, and thus the largest chunk of slowdown incurred by adding the `where` argument. Most other places have fast-paths for `where=True`, this one also should have it. The additional argument does slow down the function versions a bit more than this, but that is to be expected probably (it has to build a new argument dict, at some point we might want to move this to C, but that seems worth much more with FASTCALL logic).
* ENH: Add where argument to several functionsSimon Gasse2020-07-181-29/+51
| | | | | | Harmonize the signature of np.mean, np.var np.std, np.any, np.all, and their respective nd.array methods with np.sum by adding a where argument, see gh-15818.
* BUG: Fix IndexError for illegal axis in np.meansgasse2020-03-261-1/+1
| | | | | Catch IndexError in _count_reduce_items used in np.mean and np.var for illegal axis and reraise as AxisError, see gh-15817.
* MAINT: Remove non-native byte order from _var check.Ross Barnowski2020-03-101-4/+0
| | | | | | | | Removes unnecessary code introduced in #15696. Non-native byte orders were explicitly added to the fast-path check in _var for complex numbers. However, the non-native path is unreachable due to coercion in upstream ufuncs.
* ENH: Adds a fast path to var for complex inputRoss Barnowski2020-03-091-0/+23
| | | | | | | | | | var currently has a conditional that results in conjugate being called for the variance calculation of complex inputs. This leg of the computation is slow. This PR avoids this computational leg for complex inputs via a type check. Closes #15684
* MAINT: Remove unnecessary 'from __future__ import ...' statementsJon Dufresne2020-01-031-2/+0
| | | | | As numpy is Python 3 only, these import statements are now unnecessary and don't alter runtime behavior.
* BUG: Move ndarray.dump to python and make it close the file it opensEric Wieser2019-06-011-0/+12
| | | | As a side-effect, this makes it support kwargs.
* ENH/DEP: Use a ufunc under the hood for ndarray.clipEric Wieser2019-05-141-0/+75
| | | | | | | | | This includes: * The addition of 3-input PyObject inner loop * The removal of `->f->fastclip` for builtin types, which now use ufuncs instead * A deprecation in `PyArray_Clip` for third-party types that still use `->f->fastclip` * A deprecation of the unusual casting behavior of `clip` * A deprecation of the broken `nan`-behavior of `clip`, which was previously dependent on dimensionality and byte-order.
* MAINT: Move asarray helpers into their own moduleEric Wieser2019-04-131-1/+1
| | | | | | | | This is a direct move, with some tweaks to imports. This breaks a cyclic imports between `core.numeric` and `core.fromnumeric`. This doesn't affect the value of `np.core.numeric.__all__` which keeps code doing `from numpy.core.numeric import *` working.
* BUG: Fix of `var` method for complex object arraysChirag Nighut2019-04-011-3/+4
| | | | | Previously, an object array which contained complex numbers would give complex output, as it was simply multiplied with itself instead of with its conjugate.
* ENH: allow where in reduce operations.Marten van Kerkwijk2019-01-031-8/+8
| | | | | In this implementation, if the ufunc does not have an identity, it needs an initial vavlue to be supplied.
* ENH: port __array_function__ overrides to CStephan Hoyer2018-12-191-12/+0
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* ENH: refactor __array_function__ pure Python implementationStephan Hoyer2018-12-191-1/+1
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* MAINT: Allow subclasses in ndarray.__array_function__.Marten van Kerkwijk2018-12-021-8/+5
| | | | The Liskov substitution principle suggests it should.
* CLN: optimize ndarray.__array_function__Stephan Hoyer2018-09-241-3/+6
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* ENH: initial implementation of core __array_function__ machineryStephan Hoyer2018-09-201-0/+12
| | | | | | | | | | | | | I'd like to implement NEP-18 in a multi-step process: 1. (This PR) Pure Python implementation of `__array_function__` machinery, based on the prototype implementation from NEP-18. 2. Rewrite this machinery in C as needed to improve performance. 3. Implement overrides on NumPy functions. Steps 2 and 3 should be able to happen in parallel (and with other people contributing!). This PR still needs more tests, especially for `ndarray.__array_function__`
* ENH: Implement initial kwarg for ufunc.add.reduceHameer Abbasi2018-04-241-8/+13
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* ENH: Allow ptp to take an axis tuple and keepdimsEric Wieser2017-12-281-0/+7
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* BUG: fix mean for float 16 non-array inputsJulian Taylor2017-01-241-2/+2
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* Keep the subclass and update the docstring per the comment.Frederic Bastien2016-11-031-1/+1
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* Simplify and still reuse out with float16 inputs.Frederic Bastien2016-10-311-4/+5
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* [ENH]Make numpy.mean() do more precise computation without changing the ↵Frederic Bastien2016-10-281-3/+12
| | | | output dtype that stay in float16.
* ENH: Add stacklevel to all (or almost all) our function callsSebastian Berg2016-09-021-2/+3
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* STY: PEP8 fixes for numpy/core/*.pyCharles Harris2015-07-051-1/+0
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* ENH: avoid keyword arguments for some ufunc.reduce wrappersJulian Taylor2014-05-151-15/+19
| | | | | | PyArg_ParseTupleAndKeywords is pretty slow for keywords as it needs to create python strings first. Using positional arguments avoids this and gains 15-20% performance for small reductions.
* BUG: Fix mean, var, std methods for object arrays.Charles Harris2014-02-101-3/+9
| | | | | | | | This takes care to preserve the object type for scalar returns and fixes the error that resulted when the scalar did not have a dtype attribute. Closes #4063.
* MAINT: typoLars Buitinck2013-10-191-1/+1
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* BUG: Make ndarray.{var, std} work for scalars.Charles Harris2013-10-041-2/+3
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* STY: Giant whitespace cleanup.Charles Harris2013-08-181-1/+0
| | | | Now is as good a time as any with open PR's at a low.
* ENH: Make var and std methods raise error when ddof too big.Charles Harris2013-08-121-23/+29
| | | | | Currently the results may be infinite or negative. Instead, raise a ValueError in this case.
* BUG: Make mean, sum, var, std methods return correct scalar type.Charles Harris2013-08-121-10/+10
| | | | | The return type could differ depending on whether or not the value was a scalar.
* MAINT: Clean up core/_methods.py and core/fromnumeric.pyCharles Harris2013-08-121-9/+10
| | | | | | | | Use issubclass instead of issubdtype. Add some blank lines. Remove trailing whitespace. Remove uneeded float casts since true_divide is default. Clean up documentation a bit.
* MAINT: Separate nan functions into their own module.Charles Harris2013-08-121-83/+1
| | | | | | | | | | | | | | New files lib/nanfunctions.py and lib/tests/test_nanfunctions.py are added and both the previous and new nan functions and tests are moved into them. The existing nan functions moved from lib/function_base are: nansum, nanmin, nanmax, nanargmin, nanargmax The added nan functions moved from core/numeric are: nanmean, nanvar, nanstd
* TST: Added tests for nanmean(), nanvar(), nanstd()Benjamin Root2013-08-121-10/+19
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* ENH: Adding np.nanmean(), np.nanstd(), np.nanvar()Benjamin Root2013-08-121-1/+78
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* 2to3: Apply `print` fixer.Charles Harris2013-04-061-1/+1
| | | | | | | Add `print_function` to all `from __future__ import ...` statements and use the python3 print function syntax everywhere. Closes #3078.
* 2to3: Use absolute imports.Charles Harris2013-03-281-1/+1
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | The new import `absolute_import` is added the `from __future__ import` statement and The 2to3 `import` fixer is run to make the imports compatible. There are several things that need to be dealt with to make this work. 1) Files meant to be run as scripts run in a different environment than files imported as part of a package, and so changes to those files need to be skipped. The affected script files are: * all setup.py files * numpy/core/code_generators/generate_umath.py * numpy/core/code_generators/generate_numpy_api.py * numpy/core/code_generators/generate_ufunc_api.py 2) Some imported modules are not available as they are created during the build process and consequently 2to3 is unable to handle them correctly. Files that import those modules need a bit of extra work. The affected files are: * core/__init__.py, * core/numeric.py, * core/_internal.py, * core/arrayprint.py, * core/fromnumeric.py, * numpy/__init__.py, * lib/npyio.py, * lib/function_base.py, * fft/fftpack.py, * random/__init__.py Closes #3172
* 2to3: Replace xrange by range and use list(range(...)) where neededCharles Harris2013-03-271-1/+1
| | | | | | | | | | | | | | | In python3 range is an iterator and `xrange` has been removed. This has two consequence for code: 1) Where a list is needed `list(range(...))` must be used. 2) `xrange` must be replaced by `range` Both of these changes also work in python2 and this patch makes both. There are three places fixed that do not need it, but I left them in so that the result would be `xrange` clean. Closes #3092
* 2to3: Put `from __future__ import division in every python file.Charles Harris2013-03-011-2/+6
| | | | | | | | This should be harmless, as we already are division clean. However, placement of this import takes some care. In the future a script can be used to append new features without worry, at least until such time as it exceeds a single line. Having that ability will make it easier to deal with absolute imports and printing updates.
* Remove maskna API from ndarray, and all (and only) the code supporting itNathaniel J. Smith2012-06-161-26/+38
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | The original masked-NA-NEP branch contained a large number of changes in addition to the core NA support. For example: - ufunc.__call__ support for where= argument - nditer support for arbitrary masks (in support of where=) - ufunc.reduce support for simultaneous reduction over multiple axes - a new "array assignment API" - ndarray.diagonal() returning a view in all cases - bug-fixes in __array_priority__ handling - datetime test changes etc. There's no consensus yet on what should be done with the maskna-related part of this branch, but the rest is generally useful and uncontroversial, so the goal of this branch is to identify exactly which code changes are involved in maskna support. The basic strategy used to create this patch was: - Remove the new masking-related fields from ndarray, so no arrays are masked - Go through and remove all the code that this makes dead/inaccessible/irrelevant, in a largely mechanical fashion. So for example, if I saw 'if (PyArray_HASMASK(a)) { ... }' then that whole block was obviously just dead code if no arrays have masks, and I removed it. Likewise for function arguments like skipna that are useless if there aren't any NAs to skip. This changed the signature of a number of functions that were newly exposed in the numpy public API. I've removed all such functions from the public API, since releasing them with the NA-less signature in 1.7 would create pointless compatibility hassles later if and when we add back the NA-related functionality. Most such functions are removed by this commit; the exception is PyArray_ReduceWrapper, which requires more extensive surgery, and will be handled in followup commits. I also removed the new ndarray.setasflat method. Reason: a comment noted that the only reason this was added was to allow easier testing of one branch of PyArray_CopyAsFlat. That branch is now the main branch, so that isn't an issue. Nonetheless this function is arguably useful, so perhaps it should have remained, but I judged that since numpy's API is already hairier than we would like, it's not a good idea to add extra hair "just in case". (Also AFAICT the test for this method in test_maskna was actually incorrect, as noted here: https://github.com/njsmith/numpyNEP/blob/master/numpyNEP.py so I'm not confident that it ever worked in master, though I haven't had a chance to follow-up on this.) I also removed numpy.count_reduce_items, since without skipna it became trivial. I believe that these are the only exceptions to the "remove dead code" strategy.
* BLD: core: onefile build fix and Python3 compatibility changeMark Wiebe2011-08-271-3/+3
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