| Commit message (Collapse) | Author | Age | Files | Lines |
|---|
| ... | |
| |
|
|
|
|
|
|
| |
The companion matrices returned by the various polynomial types was
a scalar in the degree one case instead of a 2-D array. Fix that and
add a test to check for that result.
Closes #3459.
|
| |
|
|
|
|
|
| |
Add `print_function` to all `from __future__ import ...` statements
and use the python3 print function syntax everywhere.
Closes #3078.
|
| |\
| |
| | |
DOC: Formatting fixes using regex
|
| | |
| |
| |
| | |
also other spacing or formatting mistakes
|
| |/
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| |
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
|
| |
|
|
| |
This changes the `exec` command to the `exec` function.
|
| |
|
|
|
|
|
|
|
|
| |
Instead of
if lhs.dtype.char in np.typecodes['Complex']:
use
if issubclass(lhs.dtype.type, np.complexfloating):
|
| |
|
|
|
|
|
| |
The columns should be scaled using their 2-norm, but in the complex case
that was being incorrectly computed as the square root of the sum of the
squared elements rather than as the square root of the sum of their squared
real and imaginary parts.
|
| |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| |
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.
|
| |
|
|
| |
Use divmod instead of // and % separately.
|
| |
|
|
|
|
|
|
|
| |
The original method was overly sensitive to roundoff. Of the two
approaches considered, gauss integration or binary subdivision of
the roots, the latter is more compatible with using other number
representations such as mpmath. No method is going to be suitable
for large numbers of arbitrary zeros but the current method is a
significant improvement.
|
| | |
|
| | |
|
| | |
|
| | |
|
| | |
|
| |
|
|
| |
The old functions could use a review, but that isn't pressing.
|
| | |
|
| |
|
|
| |
Step 1 in the polynomial package documentation revisions.
|
| |
|
|
|
|
|
|
|
| |
The new companion matrices are related to the old by a
similarity transformation that makes them better conditioned
for root finding. In particular, the companion matrices for
the orthogonal polynomials are symmetric when the zeros of a
single polynomial term is wanted. This produces better zeros
for use in Gauss quadrature.
|
| |
|
|
|
|
|
|
| |
Where xxx is one of poly, cheb, leg, lag, herm, herme:
Refactor xxxval2d, xxxval3d, xxxgrid2d, and xxxgrid3d for clarity.
Check that coordinate arrays are compatible in xxxval2d, xxxval3d.
Work around einsum bug that affected xxxvander3d.
|
| |
|
|
|
|
|
|
|
|
| |
An axis keyword was added to the function signatures of xxxder and
xxxint, where xxx is any of poly, cheb, leg, lag, herm, herme. The
evaluation method for the Chebeshev series was also changed to avoid
using z_series and to more closely resemble the other implementations.
At some point the z_series will be removed from the chebyshev module
and only used for trigonometric series.
|
| |
|
|
|
|
|
|
| |
are useful for least squares fits to data depending on two or three variables using the various polynomial basis.
The new functions have names polyvander2d, and polyvander3d,
where 'poly' can be replaced by any of 'leg', 'cheb', 'lag',
'herm', or 'herme'.
|
| |
|
|
|
|
|
|
|
|
| |
coefficient arrays can be used. Add functions for evaluation of 2D and 3D polynomial series evaluated either on a specified set of points or on a cartesian product of 1D points.
The new functions have names polyval2d, polygrid2d, polyval3d, and
polygrid3d, where 'poly' can be replaced by any of 'leg', 'cheb', 'lag',
'herm', or 'herme'. These additional functions should cover the common
multidimensional cases and provide examples for anyone who wants to go to
higher dimensions.
|
| | |
|
| | |
|
| | |
|
| |
|
|
|
|
| |
Conflicts:
numpy/polynomial/chebyshev.py
numpy/polynomial/polynomial.py
|
| |
|
|
| |
Fix some documentation.
|
| |
|
|
|
|
| |
Remove checks that prevent use of foreign scalar types for lower
bounds and integration constants.
Cleanup code a bit.
|
| | |
|
| |
|
|
|
|
|
| |
1) Let {poly,cheb}int accept 0 for the number of integrations.
2) Let {poly,cheb}(int,der} accept floating integers for number
of integrations or derivations, raise ValueError otherwise.
3) Add tests for same.
|
| | |
|
| | |
|
|
|
New modules chebyshev and polynomial are added. The new polynomial module
is not compatible with the current polynomial support in numpy, but is much
like the new chebyshev module. The most noticeable difference to most will
be that coefficients are specified from low to high power, that the low
level functions do *not* accept the Chebyshev and Polynomial classes as
arguements, and that the Chebyshev and Polynomial classes include a domain.
Mapping between domains is a linear substitution and the two classes can be
converted one to the other, allowing, for instance, a Chebyshev series in
one domain to be expanded as a polynomial in another domain.
The new modules are not automatically imported into the numpy namespace,
they must be explicitly brought in with a "import numpy.polynomial"
statement.
|
