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128 files changed, 3923 insertions, 2342 deletions
diff --git a/.gitignore b/.gitignore index abba1fdc0..3952be1fe 100644 --- a/.gitignore +++ b/.gitignore @@ -15,6 +15,12 @@ *.tmp *.vim tags +cscope.out +# gnu global +GPATH +GRTAGS +GSYMS +GTAGS # Compiled source # ################### @@ -123,7 +129,7 @@ numpy/core/src/private/npy_partition.h numpy/core/src/private/scalarmathmodule.h numpy/core/src/scalarmathmodule.c numpy/core/src/umath/funcs.inc -numpy/core/src/umath/loops.c +numpy/core/src/umath/loops.[ch] numpy/core/src/umath/operand_flag_tests.c numpy/core/src/umath/simd.inc numpy/core/src/umath/struct_ufunc_test.c diff --git a/.travis.yml b/.travis.yml index deabf611e..85c216a2f 100644 --- a/.travis.yml +++ b/.travis.yml @@ -14,12 +14,8 @@ matrix: env: USE_DEBUG=1 - python: 2.7 env: NPY_SEPARATE_COMPILATION=0 - - python: 3.3 - env: NPY_SEPARATE_COMPILATION=0 - - python: 2.7 - env: NPY_RELAXED_STRIDES_CHECKING=1 - - python: 3.3 - env: NPY_RELAXED_STRIDES_CHECKING=1 + - python: 3.4 + env: NPY_RELAXED_STRIDES_CHECKING=0 - python: 2.7 env: USE_BENTO=1 - python: 2.7 diff --git a/doc/HOWTO_DOCUMENT.rst.txt b/doc/HOWTO_DOCUMENT.rst.txt index 2854b6b90..650f7d35c 100644 --- a/doc/HOWTO_DOCUMENT.rst.txt +++ b/doc/HOWTO_DOCUMENT.rst.txt @@ -30,14 +30,14 @@ A Guide to NumPy/SciPy Documentation Overview -------- -In general, we follow the standard Python style conventions as described here: - * `Style Guide for C Code <http://www.python.org/peps/pep-0007.html>`_ - * `Style Guide for Python Code <http://www.python.org/peps/pep-0008.html>`_ - * `Docstring Conventions <http://www.python.org/peps/pep-0257.html>`_ +We mostly follow the standard Python style conventions as described here: + * `Style Guide for C Code <http://python.org/dev/peps/pep-0007/>`_ + * `Style Guide for Python Code <http://python.org/dev/peps/pep-0008/>`_ + * `Docstring Conventions <http://python.org/dev/peps/pep-0257/>`_ Additional PEPs of interest regarding documentation of code: - * `Docstring Processing Framework <http://www.python.org/peps/pep-0256.html>`_ - * `Docutils Design Specification <http://www.python.org/peps/pep-0258.html>`_ + * `Docstring Processing Framework <http://python.org/dev/peps/pep-0256/>`_ + * `Docutils Design Specification <http://python.org/dev/peps/pep-0258/>`_ Use a code checker: * `pylint <http://www.logilab.org/857>`_ diff --git a/doc/neps/return-of-revenge-of-matmul-pep.rst b/doc/neps/return-of-revenge-of-matmul-pep.rst new file mode 100644 index 000000000..b19f07d85 --- /dev/null +++ b/doc/neps/return-of-revenge-of-matmul-pep.rst @@ -0,0 +1,1380 @@ +PEP: 465 +Title: A dedicated infix operator for matrix multiplication +Version: $Revision$ +Last-Modified: $Date$ +Author: Nathaniel J. Smith <njs@pobox.com> +Status: Draft +Type: Standards Track +Content-Type: text/x-rst +Created: 20-Feb-2014 +Python-Version: 3.5 +Post-History: 13-Mar-2014 + +Abstract +======== + +This PEP proposes a new binary operator to be used for matrix +multiplication, called ``@``. (Mnemonic: ``@`` is ``*`` for +mATrices.) + + +Specification +============= + +A new binary operator is added to the Python language, together +with the corresponding in-place version: + +======= ========================= =============================== + Op Precedence/associativity Methods +======= ========================= =============================== +``@`` Same as ``*`` ``__matmul__``, ``__rmatmul__`` +``@=`` n/a ``__imatmul__`` +======= ========================= =============================== + +No implementations of these methods are added to the builtin or +standard library types. However, a number of projects have reached +consensus on the recommended semantics for these operations; see +`Intended usage details`_ below for details. + +For details on how this operator will be implemented in CPython, see +`Implementation details`_. + + +Motivation +========== + +Executive summary +----------------- + +In numerical code, there are two important operations which compete +for use of Python's ``*`` operator: elementwise multiplication, and +matrix multiplication. In the nearly twenty years since the Numeric +library was first proposed, there have been many attempts to resolve +this tension [#hugunin]_; none have been really satisfactory. +Currently, most numerical Python code uses ``*`` for elementwise +multiplication, and function/method syntax for matrix multiplication; +however, this leads to ugly and unreadable code in common +circumstances. The problem is bad enough that significant amounts of +code continue to use the opposite convention (which has the virtue of +producing ugly and unreadable code in *different* circumstances), and +this API fragmentation across codebases then creates yet more +problems. There does not seem to be any *good* solution to the +problem of designing a numerical API within current Python syntax -- +only a landscape of options that are bad in different ways. The +minimal change to Python syntax which is sufficient to resolve these +problems is the addition of a single new infix operator for matrix +multiplication. + +Matrix multiplication has a singular combination of features which +distinguish it from other binary operations, which together provide a +uniquely compelling case for the addition of a dedicated infix +operator: + +* Just as for the existing numerical operators, there exists a vast + body of prior art supporting the use of infix notation for matrix + multiplication across all fields of mathematics, science, and + engineering; ``@`` harmoniously fills a hole in Python's existing + operator system. + +* ``@`` greatly clarifies real-world code. + +* ``@`` provides a smoother onramp for less experienced users, who are + particularly harmed by hard-to-read code and API fragmentation. + +* ``@`` benefits a substantial and growing portion of the Python user + community. + +* ``@`` will be used frequently -- in fact, evidence suggests it may + be used more frequently than ``//`` or the bitwise operators. + +* ``@`` allows the Python numerical community to reduce fragmentation, + and finally standardize on a single consensus duck type for all + numerical array objects. + + +Background: What's wrong with the status quo? +--------------------------------------------- + +When we crunch numbers on a computer, we usually have lots and lots of +numbers to deal with. Trying to deal with them one at a time is +cumbersome and slow -- especially when using an interpreted language. +Instead, we want the ability to write down simple operations that +apply to large collections of numbers all at once. The *n-dimensional +array* is the basic object that all popular numeric computing +environments use to make this possible. Python has several libraries +that provide such arrays, with numpy being at present the most +prominent. + +When working with n-dimensional arrays, there are two different ways +we might want to define multiplication. One is elementwise +multiplication:: + + [[1, 2], [[11, 12], [[1 * 11, 2 * 12], + [3, 4]] x [13, 14]] = [3 * 13, 4 * 14]] + +and the other is `matrix multiplication`_: + +.. _matrix multiplication: https://en.wikipedia.org/wiki/Matrix_multiplication + +:: + + [[1, 2], [[11, 12], [[1 * 11 + 2 * 13, 1 * 12 + 2 * 14], + [3, 4]] x [13, 14]] = [3 * 11 + 4 * 13, 3 * 12 + 4 * 14]] + +Elementwise multiplication is useful because it lets us easily and +quickly perform many multiplications on a large collection of values, +without writing a slow and cumbersome ``for`` loop. And this works as +part of a very general schema: when using the array objects provided +by numpy or other numerical libraries, all Python operators work +elementwise on arrays of all dimensionalities. The result is that one +can write functions using straightforward code like ``a * b + c / d``, +treating the variables as if they were simple values, but then +immediately use this function to efficiently perform this calculation +on large collections of values, while keeping them organized using +whatever arbitrarily complex array layout works best for the problem +at hand. + +Matrix multiplication is more of a special case. It's only defined on +2d arrays (also known as "matrices"), and multiplication is the only +operation that has an important "matrix" version -- "matrix addition" +is the same as elementwise addition; there is no such thing as "matrix +bitwise-or" or "matrix floordiv"; "matrix division" and "matrix +to-the-power-of" can be defined but are not very useful, etc. +However, matrix multiplication is still used very heavily across all +numerical application areas; mathematically, it's one of the most +fundamental operations there is. + +Because Python syntax currently allows for only a single +multiplication operator ``*``, libraries providing array-like objects +must decide: either use ``*`` for elementwise multiplication, or use +``*`` for matrix multiplication. And, unfortunately, it turns out +that when doing general-purpose number crunching, both operations are +used frequently, and there are major advantages to using infix rather +than function call syntax in both cases. Thus it is not at all clear +which convention is optimal, or even acceptable; often it varies on a +case-by-case basis. + +Nonetheless, network effects mean that it is very important that we +pick *just one* convention. In numpy, for example, it is technically +possible to switch between the conventions, because numpy provides two +different types with different ``__mul__`` methods. For +``numpy.ndarray`` objects, ``*`` performs elementwise multiplication, +and matrix multiplication must use a function call (``numpy.dot``). +For ``numpy.matrix`` objects, ``*`` performs matrix multiplication, +and elementwise multiplication requires function syntax. Writing code +using ``numpy.ndarray`` works fine. Writing code using +``numpy.matrix`` also works fine. But trouble begins as soon as we +try to integrate these two pieces of code together. Code that expects +an ``ndarray`` and gets a ``matrix``, or vice-versa, may crash or +return incorrect results. Keeping track of which functions expect +which types as inputs, and return which types as outputs, and then +converting back and forth all the time, is incredibly cumbersome and +impossible to get right at any scale. Functions that defensively try +to handle both types as input and DTRT, find themselves floundering +into a swamp of ``isinstance`` and ``if`` statements. + +PEP 238 split ``/`` into two operators: ``/`` and ``//``. Imagine the +chaos that would have resulted if it had instead split ``int`` into +two types: ``classic_int``, whose ``__div__`` implemented floor +division, and ``new_int``, whose ``__div__`` implemented true +division. This, in a more limited way, is the situation that Python +number-crunchers currently find themselves in. + +In practice, the vast majority of projects have settled on the +convention of using ``*`` for elementwise multiplication, and function +call syntax for matrix multiplication (e.g., using ``numpy.ndarray`` +instead of ``numpy.matrix``). This reduces the problems caused by API +fragmentation, but it doesn't eliminate them. The strong desire to +use infix notation for matrix multiplication has caused a number of +specialized array libraries to continue to use the opposing convention +(e.g., scipy.sparse, pyoperators, pyviennacl) despite the problems +this causes, and ``numpy.matrix`` itself still gets used in +introductory programming courses, often appears in StackOverflow +answers, and so forth. Well-written libraries thus must continue to +be prepared to deal with both types of objects, and, of course, are +also stuck using unpleasant funcall syntax for matrix multiplication. +After nearly two decades of trying, the numerical community has still +not found any way to resolve these problems within the constraints of +current Python syntax (see `Rejected alternatives to adding a new +operator`_ below). + +This PEP proposes the minimum effective change to Python syntax that +will allow us to drain this swamp. It splits ``*`` into two +operators, just as was done for ``/``: ``*`` for elementwise +multiplication, and ``@`` for matrix multiplication. (Why not the +reverse? Because this way is compatible with the existing consensus, +and because it gives us a consistent rule that all the built-in +numeric operators also apply in an elementwise manner to arrays; the +reverse convention would lead to more special cases.) + +So that's why matrix multiplication doesn't and can't just use ``*``. +Now, in the the rest of this section, we'll explain why it nonetheless +meets the high bar for adding a new operator. + + +Why should matrix multiplication be infix? +------------------------------------------ + +Right now, most numerical code in Python uses syntax like +``numpy.dot(a, b)`` or ``a.dot(b)`` to perform matrix multiplication. +This obviously works, so why do people make such a fuss about it, even +to the point of creating API fragmentation and compatibility swamps? + +Matrix multiplication shares two features with ordinary arithmetic +operations like addition and multiplication on numbers: (a) it is used +very heavily in numerical programs -- often multiple times per line of +code -- and (b) it has an ancient and universally adopted tradition of +being written using infix syntax. This is because, for typical +formulas, this notation is dramatically more readable than any +function call syntax. Here's an example to demonstrate: + +One of the most useful tools for testing a statistical hypothesis is +the linear hypothesis test for OLS regression models. It doesn't +really matter what all those words I just said mean; if we find +ourselves having to implement this thing, what we'll do is look up +some textbook or paper on it, and encounter many mathematical formulas +that look like: + +.. math:: + + S = (H \beta - r)^T (H V H^T)^{-1} (H \beta - r) + +Here the various variables are all vectors or matrices (details for +the curious: [#lht]_). + +Now we need to write code to perform this calculation. In current +numpy, matrix multiplication can be performed using either the +function or method call syntax. Neither provides a particularly +readable translation of the formula:: + + import numpy as np + from numpy.linalg import inv, solve + + # Using dot function: + S = np.dot((np.dot(H, beta) - r).T, + np.dot(inv(np.dot(np.dot(H, V), H.T)), np.dot(H, beta) - r)) + + # Using dot method: + S = (H.dot(beta) - r).T.dot(inv(H.dot(V).dot(H.T))).dot(H.dot(beta) - r) + +With the ``@`` operator, the direct translation of the above formula +becomes:: + + S = (H @ beta - r).T @ inv(H @ V @ H.T) @ (H @ beta - r) + +Notice that there is now a transparent, 1-to-1 mapping between the +symbols in the original formula and the code that implements it. + +Of course, an experienced programmer will probably notice that this is +not the best way to compute this expression. The repeated computation +of :math:`H \beta - r` should perhaps be factored out; and, +expressions of the form ``dot(inv(A), B)`` should almost always be +replaced by the more numerically stable ``solve(A, B)``. When using +``@``, performing these two refactorings gives us:: + + # Version 1 (as above) + S = (H @ beta - r).T @ inv(H @ V @ H.T) @ (H @ beta - r) + + # Version 2 + trans_coef = H @ beta - r + S = trans_coef.T @ inv(H @ V @ H.T) @ trans_coef + + # Version 3 + S = trans_coef.T @ solve(H @ V @ H.T, trans_coef) + +Notice that when comparing between each pair of steps, it's very easy +to see exactly what was changed. If we apply the equivalent +transformations to the code using the .dot method, then the changes +are much harder to read out or verify for correctness:: + + # Version 1 (as above) + S = (H.dot(beta) - r).T.dot(inv(H.dot(V).dot(H.T))).dot(H.dot(beta) - r) + + # Version 2 + trans_coef = H.dot(beta) - r + S = trans_coef.T.dot(inv(H.dot(V).dot(H.T))).dot(trans_coef) + + # Version 3 + S = trans_coef.T.dot(solve(H.dot(V).dot(H.T)), trans_coef) + +Readability counts! The statements using ``@`` are shorter, contain +more whitespace, can be directly and easily compared both to each +other and to the textbook formula, and contain only meaningful +parentheses. This last point is particularly important for +readability: when using function-call syntax, the required parentheses +on every operation create visual clutter that makes it very difficult +to parse out the overall structure of the formula by eye, even for a +relatively simple formula like this one. Eyes are terrible at parsing +non-regular languages. I made and caught many errors while trying to +write out the 'dot' formulas above. I know they still contain at +least one error, maybe more. (Exercise: find it. Or them.) The +``@`` examples, by contrast, are not only correct, they're obviously +correct at a glance. + +If we are even more sophisticated programmers, and writing code that +we expect to be reused, then considerations of speed or numerical +accuracy might lead us to prefer some particular order of evaluation. +Because ``@`` makes it possible to omit irrelevant parentheses, we can +be certain that if we *do* write something like ``(H @ V) @ H.T``, +then our readers will know that the parentheses must have been added +intentionally to accomplish some meaningful purpose. In the ``dot`` +examples, it's impossible to know which nesting decisions are +important, and which are arbitrary. + +Infix ``@`` dramatically improves matrix code usability at all stages +of programmer interaction. + + +Transparent syntax is especially crucial for non-expert programmers +------------------------------------------------------------------- + +A large proportion of scientific code is written by people who are +experts in their domain, but are not experts in programming. And +there are many university courses run each year with titles like "Data +analysis for social scientists" which assume no programming +background, and teach some combination of mathematical techniques, +introduction to programming, and the use of programming to implement +these mathematical techniques, all within a 10-15 week period. These +courses are more and more often being taught in Python rather than +special-purpose languages like R or Matlab. + +For these kinds of users, whose programming knowledge is fragile, the +existence of a transparent mapping between formulas and code often +means the difference between succeeding and failing to write that code +at all. This is so important that such classes often use the +``numpy.matrix`` type which defines ``*`` to mean matrix +multiplication, even though this type is buggy and heavily +disrecommended by the rest of the numpy community for the +fragmentation that it causes. This pedagogical use case is, in fact, +the *only* reason ``numpy.matrix`` remains a supported part of numpy. +Adding ``@`` will benefit both beginning and advanced users with +better syntax; and furthermore, it will allow both groups to +standardize on the same notation from the start, providing a smoother +on-ramp to expertise. + + +But isn't matrix multiplication a pretty niche requirement? +----------------------------------------------------------- + +The world is full of continuous data, and computers are increasingly +called upon to work with it in sophisticated ways. Arrays are the +lingua franca of finance, machine learning, 3d graphics, computer +vision, robotics, operations research, econometrics, meteorology, +computational linguistics, recommendation systems, neuroscience, +astronomy, bioinformatics (including genetics, cancer research, drug +discovery, etc.), physics engines, quantum mechanics, geophysics, +network analysis, and many other application areas. In most or all of +these areas, Python is rapidly becoming a dominant player, in large +part because of its ability to elegantly mix traditional discrete data +structures (hash tables, strings, etc.) on an equal footing with +modern numerical data types and algorithms. + +We all live in our own little sub-communities, so some Python users +may be surprised to realize the sheer extent to which Python is used +for number crunching -- especially since much of this particular +sub-community's activity occurs outside of traditional Python/FOSS +channels. So, to give some rough idea of just how many numerical +Python programmers are actually out there, here are two numbers: In +2013, there were 7 international conferences organized specifically on +numerical Python [#scipy-conf]_ [#pydata-conf]_. At PyCon 2014, ~20% +of the tutorials appear to involve the use of matrices +[#pycon-tutorials]_. + +To quantify this further, we used Github's "search" function to look +at what modules are actually imported across a wide range of +real-world code (i.e., all the code on Github). We checked for +imports of several popular stdlib modules, a variety of numerically +oriented modules, and various other extremely high-profile modules +like django and lxml (the latter of which is the #1 most downloaded +package on PyPI). Starred lines indicate packages which export array- +or matrix-like objects which will adopt ``@`` if this PEP is +approved:: + + Count of Python source files on Github matching given search terms + (as of 2014-04-10, ~21:00 UTC) + ================ ========== =============== ======= =========== + module "import X" "from X import" total total/numpy + ================ ========== =============== ======= =========== + sys 2374638 63301 2437939 5.85 + os 1971515 37571 2009086 4.82 + re 1294651 8358 1303009 3.12 + numpy ************** 337916 ********** 79065 * 416981 ******* 1.00 + warnings 298195 73150 371345 0.89 + subprocess 281290 63644 344934 0.83 + django 62795 219302 282097 0.68 + math 200084 81903 281987 0.68 + threading 212302 45423 257725 0.62 + pickle+cPickle 215349 22672 238021 0.57 + matplotlib 119054 27859 146913 0.35 + sqlalchemy 29842 82850 112692 0.27 + pylab *************** 36754 ********** 41063 ** 77817 ******* 0.19 + scipy *************** 40829 ********** 28263 ** 69092 ******* 0.17 + lxml 19026 38061 57087 0.14 + zlib 40486 6623 47109 0.11 + multiprocessing 25247 19850 45097 0.11 + requests 30896 560 31456 0.08 + jinja2 8057 24047 32104 0.08 + twisted 13858 6404 20262 0.05 + gevent 11309 8529 19838 0.05 + pandas ************** 14923 *********** 4005 ** 18928 ******* 0.05 + sympy 2779 9537 12316 0.03 + theano *************** 3654 *********** 1828 *** 5482 ******* 0.01 + ================ ========== =============== ======= =========== + +These numbers should be taken with several grains of salt (see +footnote for discussion: [#github-details]_), but, to the extent they +can be trusted, they suggest that ``numpy`` might be the single +most-imported non-stdlib module in the entire Pythonverse; it's even +more-imported than such stdlib stalwarts as ``subprocess``, ``math``, +``pickle``, and ``threading``. And numpy users represent only a +subset of the broader numerical community that will benefit from the +``@`` operator. Matrices may once have been a niche data type +restricted to Fortran programs running in university labs and military +clusters, but those days are long gone. Number crunching is a +mainstream part of modern Python usage. + +In addition, there is some precedence for adding an infix operator to +handle a more-specialized arithmetic operation: the floor division +operator ``//``, like the bitwise operators, is very useful under +certain circumstances when performing exact calculations on discrete +values. But it seems likely that there are many Python programmers +who have never had reason to use ``//`` (or, for that matter, the +bitwise operators). ``@`` is no more niche than ``//``. + + +So ``@`` is good for matrix formulas, but how common are those really? +---------------------------------------------------------------------- + +We've seen that ``@`` makes matrix formulas dramatically easier to +work with for both experts and non-experts, that matrix formulas +appear in many important applications, and that numerical libraries +like numpy are used by a substantial proportion of Python's user base. +But numerical libraries aren't just about matrix formulas, and being +important doesn't necessarily mean taking up a lot of code: if matrix +formulas only occured in one or two places in the average +numerically-oriented project, then it still wouldn't be worth adding a +new operator. So how common is matrix multiplication, really? + +When the going gets tough, the tough get empirical. To get a rough +estimate of how useful the ``@`` operator will be, the table below +shows the rate at which different Python operators are actually used +in the stdlib, and also in two high-profile numerical packages -- the +scikit-learn machine learning library, and the nipy neuroimaging +library -- normalized by source lines of code (SLOC). Rows are sorted +by the 'combined' column, which pools all three code bases together. +The combined column is thus strongly weighted towards the stdlib, +which is much larger than both projects put together (stdlib: 411575 +SLOC, scikit-learn: 50924 SLOC, nipy: 37078 SLOC). [#sloc-details]_ + +The ``dot`` row (marked ``******``) counts how common matrix multiply +operations are in each codebase. + +:: + + ==== ====== ============ ==== ======== + op stdlib scikit-learn nipy combined + ==== ====== ============ ==== ======== + = 2969 5536 4932 3376 / 10,000 SLOC + - 218 444 496 261 + + 224 201 348 231 + == 177 248 334 196 + * 156 284 465 192 + % 121 114 107 119 + ** 59 111 118 68 + != 40 56 74 44 + / 18 121 183 41 + > 29 70 110 39 + += 34 61 67 39 + < 32 62 76 38 + >= 19 17 17 18 + <= 18 27 12 18 + dot ***** 0 ********** 99 ** 74 ****** 16 + | 18 1 2 15 + & 14 0 6 12 + << 10 1 1 8 + // 9 9 1 8 + -= 5 21 14 8 + *= 2 19 22 5 + /= 0 23 16 4 + >> 4 0 0 3 + ^ 3 0 0 3 + ~ 2 4 5 2 + |= 3 0 0 2 + &= 1 0 0 1 + //= 1 0 0 1 + ^= 1 0 0 0 + **= 0 2 0 0 + %= 0 0 0 0 + <<= 0 0 0 0 + >>= 0 0 0 0 + ==== ====== ============ ==== ======== + +These two numerical packages alone contain ~780 uses of matrix +multiplication. Within these packages, matrix multiplication is used +more heavily than most comparison operators (``<`` ``!=`` ``<=`` +``>=``). Even when we dilute these counts by including the stdlib +into our comparisons, matrix multiplication is still used more often +in total than any of the bitwise operators, and 2x as often as ``//``. +This is true even though the stdlib, which contains a fair amount of +integer arithmetic and no matrix operations, makes up more than 80% of +the combined code base. + +By coincidence, the numeric libraries make up approximately the same +proportion of the 'combined' codebase as numeric tutorials make up of +PyCon 2014's tutorial schedule, which suggests that the 'combined' +column may not be *wildly* unrepresentative of new Python code in +general. While it's impossible to know for certain, from this data it +seems entirely possible that across all Python code currently being +written, matrix multiplication is already used more often than ``//`` +and the bitwise operations. + + +But isn't it weird to add an operator with no stdlib uses? +---------------------------------------------------------- + +It's certainly unusual (though extended slicing existed for some time +builtin types gained support for it, ``Ellipsis`` is still unused +within the stdlib, etc.). But the important thing is whether a change +will benefit users, not where the software is being downloaded from. +It's clear from the above that ``@`` will be used, and used heavily. +And this PEP provides the critical piece that will allow the Python +numerical community to finally reach consensus on a standard duck type +for all array-like objects, which is a necessary precondition to ever +adding a numerical array type to the stdlib. + + +Compatibility considerations +============================ + +Currently, the only legal use of the ``@`` token in Python code is at +statement beginning in decorators. The new operators are both infix; +the one place they can never occur is at statement beginning. +Therefore, no existing code will be broken by the addition of these +operators, and there is no possible parsing ambiguity between +decorator-@ and the new operators. + +Another important kind of compatibility is the mental cost paid by +users to update their understanding of the Python language after this +change, particularly for users who do not work with matrices and thus +do not benefit. Here again, ``@`` has minimal impact: even +comprehensive tutorials and references will only need to add a +sentence or two to fully document this PEP's changes for a +non-numerical audience. + + +Intended usage details +====================== + +This section is informative, rather than normative -- it documents the +consensus of a number of libraries that provide array- or matrix-like +objects on how ``@`` will be implemented. + +This section uses the numpy terminology for describing arbitrary +multidimensional arrays of data, because it is a superset of all other +commonly used models. In this model, the *shape* of any array is +represented by a tuple of integers. Because matrices are +two-dimensional, they have len(shape) == 2, while 1d vectors have +len(shape) == 1, and scalars have shape == (), i.e., they are "0 +dimensional". Any array contains prod(shape) total entries. Notice +that `prod(()) == 1`_ (for the same reason that sum(()) == 0); scalars +are just an ordinary kind of array, not a special case. Notice also +that we distinguish between a single scalar value (shape == (), +analogous to ``1``), a vector containing only a single entry (shape == +(1,), analogous to ``[1]``), a matrix containing only a single entry +(shape == (1, 1), analogous to ``[[1]]``), etc., so the dimensionality +of any array is always well-defined. Other libraries with more +restricted representations (e.g., those that support 2d arrays only) +might implement only a subset of the functionality described here. + +.. _prod(()) == 1: https://en.wikipedia.org/wiki/Empty_product + +Semantics +--------- + +The recommended semantics for ``@`` for different inputs are: + +* 2d inputs are conventional matrices, and so the semantics are + obvious: we apply conventional matrix multiplication. If we write + ``arr(2, 3)`` to represent an arbitrary 2x3 array, then ``arr(2, 3) + @ arr(3, 4)`` returns an array with shape (2, 4). + +* 1d vector inputs are promoted to 2d by prepending or appending a '1' + to the shape, the operation is performed, and then the added + dimension is removed from the output. The 1 is always added on the + "outside" of the shape: prepended for left arguments, and appended + for right arguments. The result is that matrix @ vector and vector + @ matrix are both legal (assuming compatible shapes), and both + return 1d vectors; vector @ vector returns a scalar. This is + clearer with examples. + + * ``arr(2, 3) @ arr(3, 1)`` is a regular matrix product, and returns + an array with shape (2, 1), i.e., a column vector. + + * ``arr(2, 3) @ arr(3)`` performs the same computation as the + previous (i.e., treats the 1d vector as a matrix containing a + single *column*, shape = (3, 1)), but returns the result with + shape (2,), i.e., a 1d vector. + + * ``arr(1, 3) @ arr(3, 2)`` is a regular matrix product, and returns + an array with shape (1, 2), i.e., a row vector. + + * ``arr(3) @ arr(3, 2)`` performs the same computation as the + previous (i.e., treats the 1d vector as a matrix containing a + single *row*, shape = (1, 3)), but returns the result with shape + (2,), i.e., a 1d vector. + + * ``arr(1, 3) @ arr(3, 1)`` is a regular matrix product, and returns + an array with shape (1, 1), i.e., a single value in matrix form. + + * ``arr(3) @ arr(3)`` performs the same computation as the + previous, but returns the result with shape (), i.e., a single + scalar value, not in matrix form. So this is the standard inner + product on vectors. + + An infelicity of this definition for 1d vectors is that it makes + ``@`` non-associative in some cases (``(Mat1 @ vec) @ Mat2`` != + ``Mat1 @ (vec @ Mat2)``). But this seems to be a case where + practicality beats purity: non-associativity only arises for strange + expressions that would never be written in practice; if they are + written anyway then there is a consistent rule for understanding + what will happen (``Mat1 @ vec @ Mat2`` is parsed as ``(Mat1 @ vec) + @ Mat2``, just like ``a - b - c``); and, not supporting 1d vectors + would rule out many important use cases that do arise very commonly + in practice. No-one wants to explain to new users why to solve the + simplest linear system in the obvious way, they have to type + ``(inv(A) @ b[:, np.newaxis]).flatten()`` instead of ``inv(A) @ b``, + or perform an ordinary least-squares regression by typing + ``solve(X.T @ X, X @ y[:, np.newaxis]).flatten()`` instead of + ``solve(X.T @ X, X @ y)``. No-one wants to type ``(a[np.newaxis, :] + @ b[:, np.newaxis])[0, 0]`` instead of ``a @ b`` every time they + compute an inner product, or ``(a[np.newaxis, :] @ Mat @ b[:, + np.newaxis])[0, 0]`` for general quadratic forms instead of ``a @ + Mat @ b``. In addition, sage and sympy (see below) use these + non-associative semantics with an infix matrix multiplication + operator (they use ``*``), and they report that they haven't + experienced any problems caused by it. + +* For inputs with more than 2 dimensions, we treat the last two + dimensions as being the dimensions of the matrices to multiply, and + 'broadcast' across the other dimensions. This provides a convenient + way to quickly compute many matrix products in a single operation. + For example, ``arr(10, 2, 3) @ arr(10, 3, 4)`` performs 10 separate + matrix multiplies, each of which multiplies a 2x3 and a 3x4 matrix + to produce a 2x4 matrix, and then returns the 10 resulting matrices + together in an array with shape (10, 2, 4). The intuition here is + that we treat these 3d arrays of numbers as if they were 1d arrays + *of matrices*, and then apply matrix multiplication in an + elementwise manner, where now each 'element' is a whole matrix. + Note that broadcasting is not limited to perfectly aligned arrays; + in more complicated cases, it allows several simple but powerful + tricks for controlling how arrays are aligned with each other; see + [#broadcasting]_ for details. (In particular, it turns out that + when broadcasting is taken into account, the standard scalar * + matrix product is a special case of the elementwise multiplication + operator ``*``.) + + If one operand is >2d, and another operand is 1d, then the above + rules apply unchanged, with 1d->2d promotion performed before + broadcasting. E.g., ``arr(10, 2, 3) @ arr(3)`` first promotes to + ``arr(10, 2, 3) @ arr(3, 1)``, then broadcasts the right argument to + create the aligned operation ``arr(10, 2, 3) @ arr(10, 3, 1)``, + multiplies to get an array with shape (10, 2, 1), and finally + removes the added dimension, returning an array with shape (10, 2). + Similarly, ``arr(2) @ arr(10, 2, 3)`` produces an intermediate array + with shape (10, 1, 3), and a final array with shape (10, 3). + +* 0d (scalar) inputs raise an error. Scalar * matrix multiplication + is a mathematically and algorithmically distinct operation from + matrix @ matrix multiplication, and is already covered by the + elementwise ``*`` operator. Allowing scalar @ matrix would thus + both require an unnecessary special case, and violate TOOWTDI. + + +Adoption +-------- + +We group existing Python projects which provide array- or matrix-like +types based on what API they currently use for elementwise and matrix +multiplication. + +**Projects which currently use * for elementwise multiplication, and +function/method calls for matrix multiplication:** + +The developers of the following projects have expressed an intention +to implement ``@`` on their array-like types using the above +semantics: + +* numpy +* pandas +* blaze +* theano + +The following projects have been alerted to the existence of the PEP, +but it's not yet known what they plan to do if it's accepted. We +don't anticipate that they'll have any objections, though, since +everything proposed here is consistent with how they already do +things: + +* pycuda +* panda3d + +**Projects which currently use * for matrix multiplication, and +function/method calls for elementwise multiplication:** + +The following projects have expressed an intention, if this PEP is +accepted, to migrate from their current API to the elementwise-``*``, +matmul-``@`` convention (i.e., this is a list of projects whose API +fragmentation will probably be eliminated if this PEP is accepted): + +* numpy (``numpy.matrix``) +* scipy.sparse +* pyoperators +* pyviennacl + +The following projects have been alerted to the existence of the PEP, +but it's not known what they plan to do if it's accepted (i.e., this +is a list of projects whose API fragmentation may or may not be +eliminated if this PEP is accepted): + +* cvxopt + +**Projects which currently use * for matrix multiplication, and which +don't really care about elementwise multiplication of matrices:** + +There are several projects which implement matrix types, but from a +very different perspective than the numerical libraries discussed +above. These projects focus on computational methods for analyzing +matrices in the sense of abstract mathematical objects (i.e., linear +maps over free modules over rings), rather than as big bags full of +numbers that need crunching. And it turns out that from the abstract +math point of view, there isn't much use for elementwise operations in +the first place; as discussed in the Background section above, +elementwise operations are motivated by the bag-of-numbers approach. +So these projects don't encounter the basic problem that this PEP +exists to address, making it mostly irrelevant to them; while they +appear superficially similar to projects like numpy, they're actually +doing something quite different. They use ``*`` for matrix +multiplication (and for group actions, and so forth), and if this PEP +is accepted, their expressed intention is to continue doing so, while +perhaps adding ``@`` as an alias. These projects include: + +* sympy +* sage + + +Implementation details +====================== + +New functions ``operator.matmul`` and ``operator.__matmul__`` are +added to the standard library, with the usual semantics. + +A corresponding function ``PyObject* PyObject_MatrixMultiply(PyObject +*o1, PyObject o2)`` is added to the C API. + +A new AST node is added named ``MatMult``, along with a new token +``ATEQUAL`` and new bytecode opcodes ``BINARY_MATRIX_MULTIPLY`` and +``INPLACE_MATRIX_MULTIPLY``. + +Two new type slots are added; whether this is to ``PyNumberMethods`` +or a new ``PyMatrixMethods`` struct remains to be determined. + + +Rationale for specification details +=================================== + +Choice of operator +------------------ + +Why ``@`` instead of some other spelling? There isn't any consensus +across other programming languages about how this operator should be +named [#matmul-other-langs]_; here we discuss the various options. + +Restricting ourselves only to symbols present on US English keyboards, +the punctuation characters that don't already have a meaning in Python +expression context are: ``@``, backtick, ``$``, ``!``, and ``?``. Of +these options, ``@`` is clearly the best; ``!`` and ``?`` are already +heavily freighted with inapplicable meanings in the programming +context, backtick has been banned from Python by BDFL pronouncement +(see PEP 3099), and ``$`` is uglier, even more dissimilar to ``*`` and +:math:`\cdot`, and has Perl/PHP baggage. ``$`` is probably the +second-best option of these, though. + +Symbols which are not present on US English keyboards start at a +significant disadvantage (having to spend 5 minutes at the beginning +of every numeric Python tutorial just going over keyboard layouts is +not a hassle anyone really wants). Plus, even if we somehow overcame +the typing problem, it's not clear there are any that are actually +better than ``@``. Some options that have been suggested include: + +* U+00D7 MULTIPLICATION SIGN: ``A × B`` +* U+22C5 DOT OPERATOR: ``A ⋅ B`` +* U+2297 CIRCLED TIMES: ``A ⊗ B`` +* U+00B0 DEGREE: ``A ° B`` + +What we need, though, is an operator that means "matrix +multiplication, as opposed to scalar/elementwise multiplication". +There is no conventional symbol with this meaning in either +programming or mathematics, where these operations are usually +distinguished by context. (And U+2297 CIRCLED TIMES is actually used +conventionally to mean exactly the wrong things: elementwise +multiplication -- the "Hadamard product" -- or outer product, rather +than matrix/inner product like our operator). ``@`` at least has the +virtue that it *looks* like a funny non-commutative operator; a naive +user who knows maths but not programming couldn't look at ``A * B`` +versus ``A × B``, or ``A * B`` versus ``A ⋅ B``, or ``A * B`` versus +``A ° B`` and guess which one is the usual multiplication, and which +one is the special case. + +Finally, there is the option of using multi-character tokens. Some +options: + +* Matlab and Julia use a ``.*`` operator. Aside from being visually + confusable with ``*``, this would be a terrible choice for us + because in Matlab and Julia, ``*`` means matrix multiplication and + ``.*`` means elementwise multiplication, so using ``.*`` for matrix + multiplication would make us exactly backwards from what Matlab and + Julia users expect. + +* APL apparently used ``+.×``, which by combining a multi-character + token, confusing attribute-access-like . syntax, and a unicode + character, ranks somewhere below U+2603 SNOWMAN on our candidate + list. If we like the idea of combining addition and multiplication + operators as being evocative of how matrix multiplication actually + works, then something like ``+*`` could be used -- though this may + be too easy to confuse with ``*+``, which is just multiplication + combined with the unary ``+`` operator. + +* PEP 211 suggested ``~*``. This has the downside that it sort of + suggests that there is a unary ``*`` operator that is being combined + with unary ``~``, but it could work. + +* R uses ``%*%`` for matrix multiplication. In R this forms part of a + general extensible infix system in which all tokens of the form + ``%foo%`` are user-defined binary operators. We could steal the + token without stealing the system. + +* Some other plausible candidates that have been suggested: ``><`` (= + ascii drawing of the multiplication sign ×); the footnote operator + ``[*]`` or ``|*|`` (but when used in context, the use of vertical + grouping symbols tends to recreate the nested parentheses visual + clutter that was noted as one of the major downsides of the function + syntax we're trying to get away from); ``^*``. + +So, it doesn't matter much, but ``@`` seems as good or better than any +of the alternatives: + +* It's a friendly character that Pythoneers are already used to typing + in decorators, but the decorator usage and the math expression + usage are sufficiently dissimilar that it would be hard to confuse + them in practice. + +* It's widely accessible across keyboard layouts (and thanks to its + use in email addresses, this is true even of weird keyboards like + those in phones). + +* It's round like ``*`` and :math:`\cdot`. + +* The mATrices mnemonic is cute. + +* The swirly shape is reminiscent of the simultaneous sweeps over rows + and columns that define matrix multiplication + +* Its asymmetry is evocative of its non-commutative nature. + +* Whatever, we have to pick something. + + +Precedence and associativity +---------------------------- + +There was a long discussion [#associativity-discussions]_ about +whether ``@`` should be right- or left-associative (or even something +more exotic [#group-associativity]_). Almost all Python operators are +left-associative, so following this convention would be the simplest +approach, but there were two arguments that suggested matrix +multiplication might be worth making right-associative as a special +case: + +First, matrix multiplication has a tight conceptual association with +function application/composition, so many mathematically sophisticated +users have an intuition that an expression like :math:`R S x` proceeds +from right-to-left, with first :math:`S` transforming the vector +:math:`x`, and then :math:`R` transforming the result. This isn't +universally agreed (and not all number-crunchers are steeped in the +pure-math conceptual framework that motivates this intuition +[#oil-industry-versus-right-associativity]_), but at the least this +intuition is more common than for other operations like :math:`2 \cdot +3 \cdot 4` which everyone reads as going from left-to-right. + +Second, if expressions like ``Mat @ Mat @ vec`` appear often in code, +then programs will run faster (and efficiency-minded programmers will +be able to use fewer parentheses) if this is evaluated as ``Mat @ (Mat +@ vec)`` then if it is evaluated like ``(Mat @ Mat) @ vec``. + +However, weighing against these arguments are the following: + +Regarding the efficiency argument, empirically, we were unable to find +any evidence that ``Mat @ Mat @ vec`` type expressions actually +dominate in real-life code. Parsing a number of large projects that +use numpy, we found that when forced by numpy's current funcall syntax +to choose an order of operations for nested calls to ``dot``, people +actually use left-associative nesting slightly *more* often than +right-associative nesting [#numpy-associativity-counts]_. And anyway, +writing parentheses isn't so bad -- if an efficiency-minded programmer +is going to take the trouble to think through the best way to evaluate +some expression, they probably *should* write down the parentheses +regardless of whether they're needed, just to make it obvious to the +next reader that they order of operations matter. + +In addition, it turns out that other languages, including those with +much more of a focus on linear algebra, overwhelmingly make their +matmul operators left-associative. Specifically, the ``@`` equivalent +is left-associative in R, Matlab, Julia, IDL, and Gauss. The only +exceptions we found are Mathematica, in which ``a @ b @ c`` would be +parsed non-associatively as ``dot(a, b, c)``, and APL, in which all +operators are right-associative. There do not seem to exist any +languages that make ``@`` right-associative and ``*`` +left-associative. And these decisions don't seem to be controversial +-- I've never seen anyone complaining about this particular aspect of +any of these other languages, and the left-associativity of ``*`` +doesn't seem to bother users of the existing Python libraries that use +``*`` for matrix multiplication. So, at the least we can conclude from +this that making ``@`` left-associative will certainly not cause any +disasters. Making ``@`` right-associative, OTOH, would be exploring +new and uncertain ground. + +And another advantage of left-associativity is that it is much easier +to learn and remember that ``@`` acts like ``*``, than it is to +remember first that ``@`` is unlike other Python operators by being +right-associative, and then on top of this, also have to remember +whether it is more tightly or more loosely binding than +``*``. (Right-associativity forces us to choose a precedence, and +intuitions were about equally split on which precedence made more +sense. So this suggests that no matter which choice we made, no-one +would be able to guess or remember it.) + +On net, therefore, the general consensus of the numerical community is +that while matrix multiplication is something of a special case, it's +not special enough to break the rules, and ``@`` should parse like +``*`` does. + + +(Non)-Definitions for built-in types +------------------------------------ + +No ``__matmul__`` or ``__matpow__`` are defined for builtin numeric +types (``float``, ``int``, etc.) or for the ``numbers.Number`` +hierarchy, because these types represent scalars, and the consensus +semantics for ``@`` are that it should raise an error on scalars. + +We do not -- for now -- define a ``__matmul__`` method on the standard +``memoryview`` or ``array.array`` objects, for several reasons. Of +course this could be added if someone wants it, but these types would +require quite a bit of additional work beyond ``__matmul__`` before +they could be used for numeric work -- e.g., they have no way to do +addition or scalar multiplication either! -- and adding such +functionality is beyond the scope of this PEP. In addition, providing +a quality implementation of matrix multiplication is highly +non-trivial. Naive nested loop implementations are very slow and +shipping such an implementation in CPython would just create a trap +for users. But the alternative -- providing a modern, competitive +matrix multiply -- would require that CPython link to a BLAS library, +which brings a set of new complications. In particular, several +popular BLAS libraries (including the one that ships by default on +OS X) currently break the use of ``multiprocessing`` [#blas-fork]_. +Together, these considerations mean that the cost/benefit of adding +``__matmul__`` to these types just isn't there, so for now we'll +continue to delegate these problems to numpy and friends, and defer a +more systematic solution to a future proposal. + +There are also non-numeric Python builtins which define ``__mul__`` +(``str``, ``list``, ...). We do not define ``__matmul__`` for these +types either, because why would we even do that. + + +Non-definition of matrix power +------------------------------ + +Earlier versions of this PEP also proposed a matrix power operator, +``@@``, analogous to ``**``. But on further consideration, it was +decided that the utility of this was sufficiently unclear that it +would be better to leave it out for now, and only revisit the issue if +-- once we have more experience with ``@`` -- it turns out that ``@@`` +is truly missed. [#atat-discussion]_ + + +Rejected alternatives to adding a new operator +============================================== + +Over the past few decades, the Python numeric community has explored a +variety of ways to resolve the tension between matrix and elementwise +multiplication operations. PEP 211 and PEP 225, both proposed in 2000 +and last seriously discussed in 2008 [#threads-2008]_, were early +attempts to add new operators to solve this problem, but suffered from +serious flaws; in particular, at that time the Python numerical +community had not yet reached consensus on the proper API for array +objects, or on what operators might be needed or useful (e.g., PEP 225 +proposes 6 new operators with unspecified semantics). Experience +since then has now led to consensus that the best solution, for both +numeric Python and core Python, is to add a single infix operator for +matrix multiply (together with the other new operators this implies +like ``@=``). + +We review some of the rejected alternatives here. + +**Use a second type that defines __mul__ as matrix multiplication:** +As discussed above (`Background: What's wrong with the status quo?`_), +this has been tried this for many years via the ``numpy.matrix`` type +(and its predecessors in Numeric and numarray). The result is a +strong consensus among both numpy developers and developers of +downstream packages that ``numpy.matrix`` should essentially never be +used, because of the problems caused by having conflicting duck types +for arrays. (Of course one could then argue we should *only* define +``__mul__`` to be matrix multiplication, but then we'd have the same +problem with elementwise multiplication.) There have been several +pushes to remove ``numpy.matrix`` entirely; the only counter-arguments +have come from educators who find that its problems are outweighed by +the need to provide a simple and clear mapping between mathematical +notation and code for novices (see `Transparent syntax is especially +crucial for non-expert programmers`_). But, of course, starting out +newbies with a dispreferred syntax and then expecting them to +transition later causes its own problems. The two-type solution is +worse than the disease. + +**Add lots of new operators, or add a new generic syntax for defining +infix operators:** In addition to being generally un-Pythonic and +repeatedly rejected by BDFL fiat, this would be using a sledgehammer +to smash a fly. The scientific python community has consensus that +adding one operator for matrix multiplication is enough to fix the one +otherwise unfixable pain point. (In retrospect, we all think PEP 225 +was a bad idea too -- or at least far more complex than it needed to +be.) + +**Add a new @ (or whatever) operator that has some other meaning in +general Python, and then overload it in numeric code:** This was the +approach taken by PEP 211, which proposed defining ``@`` to be the +equivalent of ``itertools.product``. The problem with this is that +when taken on its own terms, it's pretty clear that +``itertools.product`` doesn't actually need a dedicated operator. It +hasn't even been deemed worth of a builtin. (During discussions of +this PEP, a similar suggestion was made to define ``@`` as a general +purpose function composition operator, and this suffers from the same +problem; ``functools.compose`` isn't even useful enough to exist.) +Matrix multiplication has a uniquely strong rationale for inclusion as +an infix operator. There almost certainly don't exist any other +binary operations that will ever justify adding any other infix +operators to Python. + +**Add a .dot method to array types so as to allow "pseudo-infix" +A.dot(B) syntax:** This has been in numpy for some years, and in many +cases it's better than dot(A, B). But it's still much less readable +than real infix notation, and in particular still suffers from an +extreme overabundance of parentheses. See `Why should matrix +multiplication be infix?`_ above. + +**Use a 'with' block to toggle the meaning of * within a single code +block**: E.g., numpy could define a special context object so that +we'd have:: + + c = a * b # element-wise multiplication + with numpy.mul_as_dot: + c = a * b # matrix multiplication + +However, this has two serious problems: first, it requires that every +array-like type's ``__mul__`` method know how to check some global +state (``numpy.mul_is_currently_dot`` or whatever). This is fine if +``a`` and ``b`` are numpy objects, but the world contains many +non-numpy array-like objects. So this either requires non-local +coupling -- every numpy competitor library has to import numpy and +then check ``numpy.mul_is_currently_dot`` on every operation -- or +else it breaks duck-typing, with the above code doing radically +different things depending on whether ``a`` and ``b`` are numpy +objects or some other sort of object. Second, and worse, ``with`` +blocks are dynamically scoped, not lexically scoped; i.e., any +function that gets called inside the ``with`` block will suddenly find +itself executing inside the mul_as_dot world, and crash and burn +horribly -- if you're lucky. So this is a construct that could only +be used safely in rather limited cases (no function calls), and which +would make it very easy to shoot yourself in the foot without warning. + +**Use a language preprocessor that adds extra numerically-oriented +operators and perhaps other syntax:** (As per recent BDFL suggestion: +[#preprocessor]_) This suggestion seems based on the idea that +numerical code needs a wide variety of syntax additions. In fact, +given ``@``, most numerical users don't need any other operators or +syntax; it solves the one really painful problem that cannot be solved +by other means, and that causes painful reverberations through the +larger ecosystem. Defining a new language (presumably with its own +parser which would have to be kept in sync with Python's, etc.), just +to support a single binary operator, is neither practical nor +desireable. In the numerical context, Python's competition is +special-purpose numerical languages (Matlab, R, IDL, etc.). Compared +to these, Python's killer feature is exactly that one can mix +specialized numerical code with code for XML parsing, web page +generation, database access, network programming, GUI libraries, and +so forth, and we also gain major benefits from the huge variety of +tutorials, reference material, introductory classes, etc., which use +Python. Fragmenting "numerical Python" from "real Python" would be a +major source of confusion. A major motivation for this PEP is to +*reduce* fragmentation. Having to set up a preprocessor would be an +especially prohibitive complication for unsophisticated users. And we +use Python because we like Python! We don't want +almost-but-not-quite-Python. + +**Use overloading hacks to define a "new infix operator" like *dot*, +as in a well-known Python recipe:** (See: [#infix-hack]_) Beautiful is +better than ugly. This is... not beautiful. And not Pythonic. And +especially unfriendly to beginners, who are just trying to wrap their +heads around the idea that there's a coherent underlying system behind +these magic incantations that they're learning, when along comes an +evil hack like this that violates that system, creates bizarre error +messages when accidentally misused, and whose underlying mechanisms +can't be understood without deep knowledge of how object oriented +systems work. + +**Use a special "facade" type to support syntax like arr.M * arr:** +This is very similar to the previous proposal, in that the ``.M`` +attribute would basically return the same object as ``arr *dot` would, +and thus suffers the same objections about 'magicalness'. This +approach also has some non-obvious complexities: for example, while +``arr.M * arr`` must return an array, ``arr.M * arr.M`` and ``arr * +arr.M`` must return facade objects, or else ``arr.M * arr.M * arr`` +and ``arr * arr.M * arr`` will not work. But this means that facade +objects must be able to recognize both other array objects and other +facade objects (which creates additional complexity for writing +interoperating array types from different libraries who must now +recognize both each other's array types and their facade types). It +also creates pitfalls for users who may easily type ``arr * arr.M`` or +``arr.M * arr.M`` and expect to get back an array object; instead, +they will get a mysterious object that throws errors when they attempt +to use it. Basically with this approach users must be careful to +think of ``.M*`` as an indivisible unit that acts as an infix operator +-- and as infix-operator-like token strings go, at least ``*dot*`` +is prettier looking (look at its cute little ears!). + + +Discussions of this PEP +======================= + +Collected here for reference: + +* Github pull request containing much of the original discussion and + drafting: https://github.com/numpy/numpy/pull/4351 + +* sympy mailing list discussions of an early draft: + + * https://groups.google.com/forum/#!topic/sympy/22w9ONLa7qo + * https://groups.google.com/forum/#!topic/sympy/4tGlBGTggZY + +* sage-devel mailing list discussions of an early draft: + https://groups.google.com/forum/#!topic/sage-devel/YxEktGu8DeM + +* 13-Mar-2014 python-ideas thread: + https://mail.python.org/pipermail/python-ideas/2014-March/027053.html + +* numpy-discussion thread on whether to keep ``@@``: + http://mail.scipy.org/pipermail/numpy-discussion/2014-March/069448.html + +* numpy-discussion threads on precedence/associativity of ``@``: + * http://mail.scipy.org/pipermail/numpy-discussion/2014-March/069444.html + * http://mail.scipy.org/pipermail/numpy-discussion/2014-March/069605.html + + +References +========== + +.. [#preprocessor] From a comment by GvR on a G+ post by GvR; the + comment itself does not seem to be directly linkable: https://plus.google.com/115212051037621986145/posts/hZVVtJ9bK3u +.. [#infix-hack] http://code.activestate.com/recipes/384122-infix-operators/ + http://www.sagemath.org/doc/reference/misc/sage/misc/decorators.html#sage.misc.decorators.infix_operator +.. [#scipy-conf] http://conference.scipy.org/past.html +.. [#pydata-conf] http://pydata.org/events/ +.. [#lht] In this formula, :math:`\beta` is a vector or matrix of + regression coefficients, :math:`V` is the estimated + variance/covariance matrix for these coefficients, and we want to + test the null hypothesis that :math:`H\beta = r`; a large :math:`S` + then indicates that this hypothesis is unlikely to be true. For + example, in an analysis of human height, the vector :math:`\beta` + might contain one value which was the the average height of the + measured men, and another value which was the average height of the + measured women, and then setting :math:`H = [1, -1], r = 0` would + let us test whether men and women are the same height on + average. Compare to eq. 2.139 in + http://sfb649.wiwi.hu-berlin.de/fedc_homepage/xplore/tutorials/xegbohtmlnode17.html + + Example code is adapted from https://github.com/rerpy/rerpy/blob/0d274f85e14c3b1625acb22aed1efa85d122ecb7/rerpy/incremental_ls.py#L202 + +.. [#pycon-tutorials] Out of the 36 tutorials scheduled for PyCon 2014 + (https://us.pycon.org/2014/schedule/tutorials/), we guess that the + 8 below will almost certainly deal with matrices: + + * Dynamics and control with Python + + * Exploring machine learning with Scikit-learn + + * How to formulate a (science) problem and analyze it using Python + code + + * Diving deeper into Machine Learning with Scikit-learn + + * Data Wrangling for Kaggle Data Science Competitions – An etude + + * Hands-on with Pydata: how to build a minimal recommendation + engine. + + * Python for Social Scientists + + * Bayesian statistics made simple + + In addition, the following tutorials could easily involve matrices: + + * Introduction to game programming + + * mrjob: Snakes on a Hadoop *("We'll introduce some data science + concepts, such as user-user similarity, and show how to calculate + these metrics...")* + + * Mining Social Web APIs with IPython Notebook + + * Beyond Defaults: Creating Polished Visualizations Using Matplotlib + + This gives an estimated range of 8 to 12 / 36 = 22% to 33% of + tutorials dealing with matrices; saying ~20% then gives us some + wiggle room in case our estimates are high. + +.. [#sloc-details] SLOCs were defined as physical lines which contain + at least one token that is not a COMMENT, NEWLINE, ENCODING, + INDENT, or DEDENT. Counts were made by using ``tokenize`` module + from Python 3.2.3 to examine the tokens in all files ending ``.py`` + underneath some directory. Only tokens which occur at least once + in the source trees are included in the table. The counting script + is available `in the PEP repository + <http://hg.python.org/peps/file/tip/pep-0465/scan-ops.py>`_. + + Matrix multiply counts were estimated by counting how often certain + tokens which are used as matrix multiply function names occurred in + each package. This creates a small number of false positives for + scikit-learn, because we also count instances of the wrappers + around ``dot`` that this package uses, and so there are a few dozen + tokens which actually occur in ``import`` or ``def`` statements. + + All counts were made using the latest development version of each + project as of 21 Feb 2014. + + 'stdlib' is the contents of the Lib/ directory in commit + d6aa3fa646e2 to the cpython hg repository, and treats the following + tokens as indicating matrix multiply: n/a. + + 'scikit-learn' is the contents of the sklearn/ directory in commit + 69b71623273ccfc1181ea83d8fb9e05ae96f57c7 to the scikit-learn + repository (https://github.com/scikit-learn/scikit-learn), and + treats the following tokens as indicating matrix multiply: ``dot``, + ``fast_dot``, ``safe_sparse_dot``. + + 'nipy' is the contents of the nipy/ directory in commit + 5419911e99546401b5a13bd8ccc3ad97f0d31037 to the nipy repository + (https://github.com/nipy/nipy/), and treats the following tokens as + indicating matrix multiply: ``dot``. + +.. [#blas-fork] BLAS libraries have a habit of secretly spawning + threads, even when used from single-threaded programs. And threads + play very poorly with ``fork()``; the usual symptom is that + attempting to perform linear algebra in a child process causes an + immediate deadlock. + +.. [#threads-2008] http://fperez.org/py4science/numpy-pep225/numpy-pep225.html + +.. [#broadcasting] http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html + +.. [#matmul-other-langs] http://mail.scipy.org/pipermail/scipy-user/2014-February/035499.html + +.. [#github-details] Counts were produced by manually entering the + string ``"import foo"`` or ``"from foo import"`` (with quotes) into + the Github code search page, e.g.: + https://github.com/search?q=%22import+numpy%22&ref=simplesearch&type=Code + on 2014-04-10 at ~21:00 UTC. The reported values are the numbers + given in the "Languages" box on the lower-left corner, next to + "Python". This also causes some undercounting (e.g., leaving out + Cython code, and possibly one should also count HTML docs and so + forth), but these effects are negligible (e.g., only ~1% of numpy + usage appears to occur in Cython code, and probably even less for + the other modules listed). The use of this box is crucial, + however, because these counts appear to be stable, while the + "overall" counts listed at the top of the page ("We've found ___ + code results") are highly variable even for a single search -- + simply reloading the page can cause this number to vary by a factor + of 2 (!!). (They do seem to settle down if one reloads the page + repeatedly, but nonetheless this is spooky enough that it seemed + better to avoid these numbers.) + + These numbers should of course be taken with multiple grains of + salt; it's not clear how representative Github is of Python code in + general, and limitations of the search tool make it impossible to + get precise counts. AFAIK this is the best data set currently + available, but it'd be nice if it were better. In particular: + + * Lines like ``import sys, os`` will only be counted in the ``sys`` + row. + + * A file containing both ``import X`` and ``from X import`` will be + counted twice + + * Imports of the form ``from X.foo import ...`` are missed. We + could catch these by instead searching for "from X", but this is + a common phrase in English prose, so we'd end up with false + positives from comments, strings, etc. For many of the modules + considered this shouldn't matter too much -- for example, the + stdlib modules have flat namespaces -- but it might especially + lead to undercounting of django, scipy, and twisted. + + Also, it's possible there exist other non-stdlib modules we didn't + think to test that are even more-imported than numpy -- though we + tried quite a few of the obvious suspects. If you find one, let us + know! The modules tested here were chosen based on a combination + of intuition and the top-100 list at pypi-ranking.info. + + Fortunately, it doesn't really matter if it turns out that numpy + is, say, merely the *third* most-imported non-stdlib module, since + the point is just that numeric programming is a common and + mainstream activity. + + Finally, we should point out the obvious: whether a package is + import**ed** is rather different from whether it's import**ant**. + No-one's claiming numpy is "the most important package" or anything + like that. Certainly more packages depend on distutils, e.g., then + depend on numpy -- and far fewer source files import distutils than + import numpy. But this is fine for our present purposes. Most + source files don't import distutils because most source files don't + care how they're distributed, so long as they are; these source + files thus don't care about details of how distutils' API works. + This PEP is in some sense about changing how numpy's and related + packages' APIs work, so the relevant metric is to look at source + files that are choosing to directly interact with that API, which + is sort of like what we get by looking at import statements. + +.. [#hugunin] The first such proposal occurs in Jim Hugunin's very + first email to the matrix SIG in 1995, which lays out the first + draft of what became Numeric. He suggests using ``*`` for + elementwise multiplication, and ``%`` for matrix multiplication: + https://mail.python.org/pipermail/matrix-sig/1995-August/000002.html + +.. [#atat-discussion] http://mail.scipy.org/pipermail/numpy-discussion/2014-March/069502.html + +.. [#associativity-discussions] + http://mail.scipy.org/pipermail/numpy-discussion/2014-March/069444.html + http://mail.scipy.org/pipermail/numpy-discussion/2014-March/069605.html + +.. [#oil-industry-versus-right-associativity] + http://mail.scipy.org/pipermail/numpy-discussion/2014-March/069610.html + +.. [#numpy-associativity-counts] + http://mail.scipy.org/pipermail/numpy-discussion/2014-March/069578.html + +.. [#group-associativity] + http://mail.scipy.org/pipermail/numpy-discussion/2014-March/069530.html + + +Copyright +========= + +This document has been placed in the public domain. diff --git a/doc/release/1.10.0-notes.rst b/doc/release/1.10.0-notes.rst new file mode 100644 index 000000000..3f52bd5af --- /dev/null +++ b/doc/release/1.10.0-notes.rst @@ -0,0 +1,38 @@ +NumPy 1.10.0 Release Notes +************************** + +This release supports Python 2.6 - 2.7 and 3.2 - 3.4. + + +Highlights +========== + + +Dropped Support +=============== +* The polytemplate.py file has been removed. + + +Future Changes +============== + + +Compatibility notes +=================== +NPY_RELAXED_STRIDE_CHECKING is now true by default. + + +New Features +============ + + +Improvements +============ + + +Changes +======= + + +Deprecations +============ diff --git a/doc/release/1.9.0-notes.rst b/doc/release/1.9.0-notes.rst index c00f7f9d6..e6bedcdf8 100644 --- a/doc/release/1.9.0-notes.rst +++ b/doc/release/1.9.0-notes.rst @@ -35,6 +35,8 @@ Future Changes * String version checks will break because, e.g., '1.9' > '1.10' is True. A NumpyVersion class has been added that can be used for such comparisons. * The diagonal and diag functions will return writeable views in 1.10.0 +* The `S` and/or `a` dtypes may be changed to represent Python strings + instead of bytes, in Python 3 these two types are very different. Compatibility notes @@ -176,6 +178,11 @@ introduced in advanced indexing operations: * Indexing with more then one ellipsis (``...``) is deprecated. +Non-integer reduction axis indexes are deprecated +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Non-integer axis indexes to reduction ufuncs like `add.reduce` or `sum` are +deprecated. + ``promote_types`` and string dtype ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ``promote_types`` function now returns a valid string length when given an @@ -336,6 +343,12 @@ in either an error being raised, or wrong results computed. Improvements ============ +Better numerical stability for sum in some cases +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Pairwise summation is now used in the sum method, but only along the fast +axis and for groups of the values <= 8192 in length. This should also +improve the accuracy of var and std in some common cases. + Percentile implemented in terms of ``np.partition`` ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ``np.percentile`` has been implemented in terms of ``np.partition`` which diff --git a/doc/source/reference/arrays.indexing.rst b/doc/source/reference/arrays.indexing.rst index d04f89897..ef0180e0f 100644 --- a/doc/source/reference/arrays.indexing.rst +++ b/doc/source/reference/arrays.indexing.rst @@ -31,9 +31,9 @@ integer, or a tuple of slice objects and integers. :const:`Ellipsis` and :const:`newaxis` objects can be interspersed with these as well. In order to remain backward compatible with a common usage in Numeric, basic slicing is also initiated if the selection object is -any sequence (such as a :class:`list`) containing :class:`slice` +any non-ndarray sequence (such as a :class:`list`) containing :class:`slice` objects, the :const:`Ellipsis` object, or the :const:`newaxis` object, -but no integer arrays or other embedded sequences. +but not for integer arrays or other embedded sequences. .. index:: triple: ndarray; special methods; getslice @@ -46,8 +46,8 @@ scalar <arrays.scalars>` representing the corresponding item. As in Python, all indices are zero-based: for the *i*-th index :math:`n_i`, the valid range is :math:`0 \le n_i < d_i` where :math:`d_i` is the *i*-th element of the shape of the array. Negative indices are -interpreted as counting from the end of the array (*i.e.*, if *i < 0*, -it means :math:`n_i + i`). +interpreted as counting from the end of the array (*i.e.*, if +:math:`n_i < 0`, it means :math:`n_i + d_i`). All arrays generated by basic slicing are always :term:`views <view>` @@ -84,7 +84,7 @@ concepts to remember include: - Assume *n* is the number of elements in the dimension being sliced. Then, if *i* is not given it defaults to 0 for *k > 0* and - *n* for *k < 0* . If *j* is not given it defaults to *n* for *k > 0* + *n - 1* for *k < 0* . If *j* is not given it defaults to *n* for *k > 0* and -1 for *k < 0* . If *k* is not given it defaults to 1. Note that ``::`` is the same as ``:`` and means select all indices along this axis. diff --git a/doc/source/reference/c-api.array.rst b/doc/source/reference/c-api.array.rst index 23355bc91..baf804378 100644 --- a/doc/source/reference/c-api.array.rst +++ b/doc/source/reference/c-api.array.rst @@ -1632,11 +1632,11 @@ Conversion Shape Manipulation ^^^^^^^^^^^^^^^^^^ -.. cfunction:: PyObject* PyArray_Newshape(PyArrayObject* self, PyArray_Dims* newshape) +.. cfunction:: PyObject* PyArray_Newshape(PyArrayObject* self, PyArray_Dims* newshape, NPY_ORDER order) Result will be a new array (pointing to the same memory location - as *self* if possible), but having a shape given by *newshape* - . If the new shape is not compatible with the strides of *self*, + as *self* if possible), but having a shape given by *newshape*. + If the new shape is not compatible with the strides of *self*, then a copy of the array with the new specified shape will be returned. @@ -1645,6 +1645,7 @@ Shape Manipulation Equivalent to :meth:`ndarray.reshape` (*self*, *shape*) where *shape* is a sequence. Converts *shape* to a :ctype:`PyArray_Dims` structure and calls :cfunc:`PyArray_Newshape` internally. + For back-ward compatability -- Not recommended .. cfunction:: PyObject* PyArray_Squeeze(PyArrayObject* self) diff --git a/doc/source/reference/c-api.types-and-structures.rst b/doc/source/reference/c-api.types-and-structures.rst index f1e216a5c..95272c151 100644 --- a/doc/source/reference/c-api.types-and-structures.rst +++ b/doc/source/reference/c-api.types-and-structures.rst @@ -244,7 +244,7 @@ PyArrayDescr_Type Indicates that items of this data-type must be reference counted (using :cfunc:`Py_INCREF` and :cfunc:`Py_DECREF` ). - .. cvar:: NPY_ITEM_LISTPICKLE + .. cvar:: NPY_LIST_PICKLE Indicates arrays of this data-type must be converted to a list before pickling. @@ -646,9 +646,9 @@ PyUFunc_Type void **data; int ntypes; int check_return; - char *name; + const char *name; char *types; - char *doc; + const char *doc; void *ptr; PyObject *obj; PyObject *userloops; diff --git a/doc/source/reference/routines.array-creation.rst b/doc/source/reference/routines.array-creation.rst index 23b35243b..c7c6ab815 100644 --- a/doc/source/reference/routines.array-creation.rst +++ b/doc/source/reference/routines.array-creation.rst @@ -20,6 +20,8 @@ Ones and zeros ones_like zeros zeros_like + full + full_like From existing data ------------------ diff --git a/doc/source/reference/routines.array-manipulation.rst b/doc/source/reference/routines.array-manipulation.rst index ca97bb479..81af0a315 100644 --- a/doc/source/reference/routines.array-manipulation.rst +++ b/doc/source/reference/routines.array-manipulation.rst @@ -54,6 +54,8 @@ Changing kind of array asmatrix asfarray asfortranarray + ascontiguousarray + asarray_chkfinite asscalar require diff --git a/doc/source/reference/routines.ma.rst b/doc/source/reference/routines.ma.rst index 5cb38e83f..66bcb1f1c 100644 --- a/doc/source/reference/routines.ma.rst +++ b/doc/source/reference/routines.ma.rst @@ -65,6 +65,8 @@ Inspecting the array ma.nonzero ma.shape ma.size + ma.is_masked + ma.is_mask ma.MaskedArray.data ma.MaskedArray.mask @@ -141,6 +143,7 @@ Joining arrays ma.column_stack ma.concatenate + ma.append ma.dstack ma.hstack ma.vstack diff --git a/doc/source/reference/routines.maskna.rst b/doc/source/reference/routines.maskna.rst deleted file mode 100644 index 2910acbac..000000000 --- a/doc/source/reference/routines.maskna.rst +++ /dev/null @@ -1,11 +0,0 @@ -NA-Masked Array Routines -======================== - -.. currentmodule:: numpy - -NA Values ---------- -.. autosummary:: - :toctree: generated/ - - isna diff --git a/doc/source/reference/routines.polynomials.classes.rst b/doc/source/reference/routines.polynomials.classes.rst index 14729f08b..c40795434 100644 --- a/doc/source/reference/routines.polynomials.classes.rst +++ b/doc/source/reference/routines.polynomials.classes.rst @@ -211,7 +211,7 @@ constant are 0, but both can be specified.:: In the first case the lower bound of the integration is set to -1 and the integration constant is 0. In the second the constant of integration is set to 1 as well. Differentiation is simpler since the only option is the -number times the polynomial is differentiated:: +number of times the polynomial is differentiated:: >>> p = P([1, 2, 3]) >>> p.deriv(1) @@ -270,7 +270,7 @@ polynomials up to degree 5 are plotted below. >>> import matplotlib.pyplot as plt >>> from numpy.polynomial import Chebyshev as T >>> x = np.linspace(-1, 1, 100) - >>> for i in range(6): ax = plt.plot(x, T.basis(i)(x), lw=2, label="T_%d"%i) + >>> for i in range(6): ax = plt.plot(x, T.basis(i)(x), lw=2, label="$T_%d$"%i) ... >>> plt.legend(loc="upper left") <matplotlib.legend.Legend object at 0x3b3ee10> @@ -284,7 +284,7 @@ The same plots over the range -2 <= `x` <= 2 look very different: >>> import matplotlib.pyplot as plt >>> from numpy.polynomial import Chebyshev as T >>> x = np.linspace(-2, 2, 100) - >>> for i in range(6): ax = plt.plot(x, T.basis(i)(x), lw=2, label="T_%d"%i) + >>> for i in range(6): ax = plt.plot(x, T.basis(i)(x), lw=2, label="$T_%d$"%i) ... >>> plt.legend(loc="lower right") <matplotlib.legend.Legend object at 0x3b3ee10> diff --git a/doc/source/reference/routines.sort.rst b/doc/source/reference/routines.sort.rst index 2b36aec75..c22fa0cd6 100644 --- a/doc/source/reference/routines.sort.rst +++ b/doc/source/reference/routines.sort.rst @@ -39,4 +39,3 @@ Counting :toctree: generated/ count_nonzero - count_reduce_items diff --git a/doc/source/reference/ufuncs.rst b/doc/source/reference/ufuncs.rst index 2ae794f59..3d6112058 100644 --- a/doc/source/reference/ufuncs.rst +++ b/doc/source/reference/ufuncs.rst @@ -313,16 +313,15 @@ advanced usage and will not typically be used. .. versionadded:: 1.6 + May be 'no', 'equiv', 'safe', 'same_kind', or 'unsafe'. + See :func:`can_cast` for explanations of the parameter values. + Provides a policy for what kind of casting is permitted. For compatibility - with previous versions of NumPy, this defaults to 'unsafe'. May be 'no', - 'equiv', 'safe', 'same_kind', or 'unsafe'. See :func:`can_cast` for - explanations of the parameter values. - - In a future version of numpy, this argument will default to - 'same_kind'. As part of this transition, starting in version 1.7, - ufuncs will produce a DeprecationWarning for calls which are - allowed under the 'unsafe' rules, but not under the 'same_kind' - rules. + with previous versions of NumPy, this defaults to 'unsafe' for numpy < 1.7. + In numpy 1.7 a transition to 'same_kind' was begun where ufuncs produce a + DeprecationWarning for calls which are allowed under the 'unsafe' + rules, but not under the 'same_kind' rules. In numpy 1.10 the default + will be 'same_kind'. *order* diff --git a/doc/source/release.rst b/doc/source/release.rst index eb366661f..4df0631d4 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -2,6 +2,7 @@ Release Notes ************* +.. include:: ../release/1.10.0-notes.rst .. include:: ../release/1.9.0-notes.rst .. include:: ../release/1.8.0-notes.rst .. include:: ../release/1.7.2-notes.rst diff --git a/doc/source/user/c-info.how-to-extend.rst b/doc/source/user/c-info.how-to-extend.rst index db6c8e118..4d54c0eef 100644 --- a/doc/source/user/c-info.how-to-extend.rst +++ b/doc/source/user/c-info.how-to-extend.rst @@ -82,7 +82,7 @@ a variable indicating whether the method uses keyword arguments or not, and docstrings. These are explained in the next section. If you want to add constants to the module, then you store the returned value from Py_InitModule which is a module object. The most general way to -add itmes to the module is to get the module dictionary using +add items to the module is to get the module dictionary using PyModule_GetDict(module). With the module dictionary, you can add whatever you like to the module manually. An easier way to add objects to the module is to use one of three additional Python C-API calls @@ -212,7 +212,7 @@ special attention to the difference between 'N' and 'O' in the format string or you can easily create memory leaks. The 'O' format string increments the reference count of the :ctype:`PyObject *` C-variable it corresponds to, while the 'N' format string steals a reference to the -corresponding :ctype:`PyObject *` C-variable. You should use 'N' if you ave +corresponding :ctype:`PyObject *` C-variable. You should use 'N' if you have already created a reference for the object and just want to give that reference to the tuple. You should use 'O' if you only have a borrowed reference to an object and need to create one to provide for the diff --git a/doc/sphinxext b/doc/sphinxext -Subproject 447dd0b59c2fe91ca9643701036d3d04919ddc7 +Subproject 84cc897d266e0afc28fc5296edf01afb0800547 diff --git a/numpy/add_newdocs.py b/numpy/add_newdocs.py index 86ea4b8b6..09311a536 100644 --- a/numpy/add_newdocs.py +++ b/numpy/add_newdocs.py @@ -4459,12 +4459,12 @@ add_newdoc('numpy.core.multiarray', 'ndarray', ('tolist', tobytesdoc = """ - a.tostring(order='C') + a.{name}(order='C') - Construct a Python string containing the raw data bytes in the array. + Construct Python bytes containing the raw data bytes in the array. - Constructs a Python string showing a copy of the raw contents of - data memory. The string can be produced in either 'C' or 'Fortran', + Constructs Python bytes showing a copy of the raw contents of + data memory. The bytes object can be produced in either 'C' or 'Fortran', or 'Any' order (the default is 'C'-order). 'Any' order means C-order unless the F_CONTIGUOUS flag in the array is set, in which case it means 'Fortran' order. @@ -4479,29 +4479,31 @@ tobytesdoc = """ Returns ------- - s : str - A Python string exhibiting a copy of `a`'s raw data. + s : bytes + Python bytes exhibiting a copy of `a`'s raw data. Examples -------- >>> x = np.array([[0, 1], [2, 3]]) >>> x.tobytes() - '\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x03\\x00\\x00\\x00' + b'\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x03\\x00\\x00\\x00' >>> x.tobytes('C') == x.tobytes() True >>> x.tobytes('F') - '\\x00\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x03\\x00\\x00\\x00' + b'\\x00\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x03\\x00\\x00\\x00' """ add_newdoc('numpy.core.multiarray', 'ndarray', - ('tostring', tobytesdoc.format(deprecated= + ('tostring', tobytesdoc.format(name='tostring', + deprecated= 'This function is a compatibility ' 'alias for tobytes. Despite its ' 'name it returns bytes not ' 'strings.'))) add_newdoc('numpy.core.multiarray', 'ndarray', - ('tobytes', tobytesdoc.format(deprecated='.. versionadded:: 1.9.0'))) + ('tobytes', tobytesdoc.format(name='tobytes', + deprecated='.. versionadded:: 1.9.0'))) add_newdoc('numpy.core.multiarray', 'ndarray', ('trace', """ @@ -5519,6 +5521,8 @@ add_newdoc('numpy.core', 'ufunc', ('reduce', in the result as dimensions with size one. With this option, the result will broadcast correctly against the original `arr`. + .. versionadded:: 1.7.0 + Returns ------- r : ndarray diff --git a/numpy/compat/_inspect.py b/numpy/compat/_inspect.py index 6a499e727..c1aa22ec4 100644 --- a/numpy/compat/_inspect.py +++ b/numpy/compat/_inspect.py @@ -20,7 +20,9 @@ def ismethod(object): __name__ name with which this method was defined im_class class object in which this method belongs im_func function object containing implementation of method - im_self instance to which this method is bound, or None""" + im_self instance to which this method is bound, or None + + """ return isinstance(object, types.MethodType) def isfunction(object): @@ -33,7 +35,9 @@ def isfunction(object): func_defaults tuple of any default values for arguments func_doc (same as __doc__) func_globals global namespace in which this function was defined - func_name (same as __name__)""" + func_name (same as __name__) + + """ return isinstance(object, types.FunctionType) def iscode(object): @@ -51,7 +55,9 @@ def iscode(object): co_names tuple of names of local variables co_nlocals number of local variables co_stacksize virtual machine stack space required - co_varnames tuple of names of arguments and local variables""" + co_varnames tuple of names of arguments and local variables + + """ return isinstance(object, types.CodeType) # ------------------------------------------------ argument list extraction @@ -63,51 +69,23 @@ def getargs(co): Three things are returned: (args, varargs, varkw), where 'args' is a list of argument names (possibly containing nested lists), and - 'varargs' and 'varkw' are the names of the * and ** arguments or None.""" + 'varargs' and 'varkw' are the names of the * and ** arguments or None. + + """ if not iscode(co): raise TypeError('arg is not a code object') - code = co.co_code nargs = co.co_argcount names = co.co_varnames args = list(names[:nargs]) - step = 0 # The following acrobatics are for anonymous (tuple) arguments. + # Which we do not need to support, so remove to avoid importing + # the dis module. for i in range(nargs): if args[i][:1] in ['', '.']: - stack, remain, count = [], [], [] - while step < len(code): - op = ord(code[step]) - step = step + 1 - if op >= dis.HAVE_ARGUMENT: - opname = dis.opname[op] - value = ord(code[step]) + ord(code[step+1])*256 - step = step + 2 - if opname in ['UNPACK_TUPLE', 'UNPACK_SEQUENCE']: - remain.append(value) - count.append(value) - elif opname == 'STORE_FAST': - stack.append(names[value]) - - # Special case for sublists of length 1: def foo((bar)) - # doesn't generate the UNPACK_TUPLE bytecode, so if - # `remain` is empty here, we have such a sublist. - if not remain: - stack[0] = [stack[0]] - break - else: - remain[-1] = remain[-1] - 1 - while remain[-1] == 0: - remain.pop() - size = count.pop() - stack[-size:] = [stack[-size:]] - if not remain: break - remain[-1] = remain[-1] - 1 - if not remain: break - args[i] = stack[0] - + raise TypeError("tuple function arguments are not supported") varargs = None if co.co_flags & CO_VARARGS: varargs = co.co_varnames[nargs] @@ -124,6 +102,7 @@ def getargspec(func): 'args' is a list of the argument names (it may contain nested lists). 'varargs' and 'varkw' are the names of the * and ** arguments or None. 'defaults' is an n-tuple of the default values of the last n arguments. + """ if ismethod(func): @@ -139,7 +118,9 @@ def getargvalues(frame): A tuple of four things is returned: (args, varargs, varkw, locals). 'args' is a list of the argument names (it may contain nested lists). 'varargs' and 'varkw' are the names of the * and ** arguments or None. - 'locals' is the locals dictionary of the given frame.""" + 'locals' is the locals dictionary of the given frame. + + """ args, varargs, varkw = getargs(frame.f_code) return args, varargs, varkw, frame.f_locals @@ -150,7 +131,9 @@ def joinseq(seq): return '(' + ', '.join(seq) + ')' def strseq(object, convert, join=joinseq): - """Recursively walk a sequence, stringifying each element.""" + """Recursively walk a sequence, stringifying each element. + + """ if type(object) in [list, tuple]: return join([strseq(_o, convert, join) for _o in object]) else: @@ -167,7 +150,9 @@ def formatargspec(args, varargs=None, varkw=None, defaults=None, The first four arguments are (args, varargs, varkw, defaults). The other four arguments are the corresponding optional formatting functions that are called to turn names and values into strings. The ninth - argument is an optional function to format the sequence of arguments.""" + argument is an optional function to format the sequence of arguments. + + """ specs = [] if defaults: firstdefault = len(args) - len(defaults) @@ -193,7 +178,9 @@ def formatargvalues(args, varargs, varkw, locals, The first four arguments are (args, varargs, varkw, locals). The next four arguments are the corresponding optional formatting functions that are called to turn names and values into strings. The ninth - argument is an optional function to format the sequence of arguments.""" + argument is an optional function to format the sequence of arguments. + + """ def convert(name, locals=locals, formatarg=formatarg, formatvalue=formatvalue): return formatarg(name) + formatvalue(locals[name]) @@ -204,18 +191,4 @@ def formatargvalues(args, varargs, varkw, locals, specs.append(formatvarargs(varargs) + formatvalue(locals[varargs])) if varkw: specs.append(formatvarkw(varkw) + formatvalue(locals[varkw])) - return '(' + string.join(specs, ', ') + ')' - -if __name__ == '__main__': - import inspect - def foo(x, y, z=None): - return None - - print(inspect.getargs(foo.__code__)) - print(getargs(foo.__code__)) - - print(inspect.getargspec(foo)) - print(getargspec(foo)) - - print(inspect.formatargspec(*inspect.getargspec(foo))) - print(formatargspec(*getargspec(foo))) + return '(' + ', '.join(specs) + ')' diff --git a/numpy/compat/py3k.py b/numpy/compat/py3k.py index f5ac3f9f8..d95a362ca 100644 --- a/numpy/compat/py3k.py +++ b/numpy/compat/py3k.py @@ -36,7 +36,7 @@ if sys.version_info[0] >= 3: return str(s) def isfileobj(f): - return isinstance(f, (io.FileIO, io.BufferedReader)) + return isinstance(f, (io.FileIO, io.BufferedReader, io.BufferedWriter)) def open_latin1(filename, mode='r'): return open(filename, mode=mode, encoding='iso-8859-1') @@ -57,7 +57,6 @@ else: asstr = str strchar = 'S' - def isfileobj(f): return isinstance(f, file) diff --git a/numpy/compat/setup.py b/numpy/compat/setup.py index c163bcaf9..26161f330 100644 --- a/numpy/compat/setup.py +++ b/numpy/compat/setup.py @@ -8,5 +8,5 @@ def configuration(parent_package='',top_path=None): return config if __name__ == '__main__': - from numpy.distutils.core import setup + from numpy.distutils.core import setup setup(configuration=configuration) diff --git a/numpy/compat/tests/test_compat.py b/numpy/compat/tests/test_compat.py new file mode 100644 index 000000000..f2bba59e6 --- /dev/null +++ b/numpy/compat/tests/test_compat.py @@ -0,0 +1,19 @@ +from os.path import join + +from numpy.compat import isfileobj +from numpy.testing import assert_ +from numpy.testing.utils import tempdir + + +def test_isfileobj(): + with tempdir(prefix="numpy_test_compat_") as folder: + filename = join(folder, 'a.bin') + + with open(filename, 'wb') as f: + assert_(isfileobj(f)) + + with open(filename, 'ab') as f: + assert_(isfileobj(f)) + + with open(filename, 'rb') as f: + assert_(isfileobj(f)) diff --git a/numpy/core/__init__.py b/numpy/core/__init__.py index 79bc72a8c..0b8d5bb17 100644 --- a/numpy/core/__init__.py +++ b/numpy/core/__init__.py @@ -52,7 +52,11 @@ bench = Tester().bench # The name numpy.core._ufunc_reconstruct must be # available for unpickling to work. def _ufunc_reconstruct(module, name): - mod = __import__(module) + # The `fromlist` kwarg is required to ensure that `mod` points to the + # inner-most module rather than the parent package when module name is + # nested. This makes it possible to pickle non-toplevel ufuncs such as + # scipy.special.expit for instance. + mod = __import__(module, fromlist=[name]) return getattr(mod, name) def _ufunc_reduce(func): diff --git a/numpy/core/bscript b/numpy/core/bscript index 5230aa428..416e16524 100644 --- a/numpy/core/bscript +++ b/numpy/core/bscript @@ -28,7 +28,7 @@ from setup_common \ MANDATORY_FUNCS, C_ABI_VERSION, C_API_VERSION ENABLE_SEPARATE_COMPILATION = (os.environ.get('NPY_SEPARATE_COMPILATION', "1") != "0") -NPY_RELAXED_STRIDES_CHECKING = (os.environ.get('NPY_RELAXED_STRIDES_CHECKING', "0") != "0") +NPY_RELAXED_STRIDES_CHECKING = (os.environ.get('NPY_RELAXED_STRIDES_CHECKING', "1") != "0") NUMPYCONFIG_SYM = [] diff --git a/numpy/core/code_generators/cversions.txt b/numpy/core/code_generators/cversions.txt index d62115224..acfced812 100644 --- a/numpy/core/code_generators/cversions.txt +++ b/numpy/core/code_generators/cversions.txt @@ -28,4 +28,4 @@ # Version 9 (NumPy 1.9) Added function annotations. # The interface has not changed, but the hash is different due to # the annotations, so keep the previous version number. -0x00000009 = 49b27dc2dc7206a775a7376fdbc3b80c +0x00000009 = 982c4ebb6e7e4c194bf46b1535b4ef1b diff --git a/numpy/core/code_generators/genapi.py b/numpy/core/code_generators/genapi.py index 5ab60a37c..84bd042f5 100644 --- a/numpy/core/code_generators/genapi.py +++ b/numpy/core/code_generators/genapi.py @@ -473,9 +473,9 @@ def fullapi_hash(api_dicts): of the list of items in the API (as a string).""" a = [] for d in api_dicts: - for name, index in order_dict(d): + for name, data in order_dict(d): a.extend(name) - a.extend(str(index)) + a.extend(','.join(map(str, data))) return md5new(''.join(a).encode('ascii')).hexdigest() diff --git a/numpy/core/code_generators/generate_numpy_api.py b/numpy/core/code_generators/generate_numpy_api.py index a590cfb48..415cbf7fc 100644 --- a/numpy/core/code_generators/generate_numpy_api.py +++ b/numpy/core/code_generators/generate_numpy_api.py @@ -8,8 +8,9 @@ from genapi import \ import numpy_api +# use annotated api when running under cpychecker h_template = r""" -#ifdef _MULTIARRAYMODULE +#if defined(_MULTIARRAYMODULE) || defined(WITH_CPYCHECKER_STEALS_REFERENCE_TO_ARG_ATTRIBUTE) typedef struct { PyObject_HEAD diff --git a/numpy/core/code_generators/generate_umath.py b/numpy/core/code_generators/generate_umath.py index e3c9cf28b..9f8d4c688 100644 --- a/numpy/core/code_generators/generate_umath.py +++ b/numpy/core/code_generators/generate_umath.py @@ -20,6 +20,12 @@ ReorderableNone = "PyUFunc_ReorderableNone" class FullTypeDescr(object): pass +class FuncNameSuffix(object): + """Stores the suffix to append when generating functions names. + """ + def __init__(self, suffix): + self.suffix = suffix + class TypeDescription(object): """Type signature for a ufunc. @@ -795,6 +801,30 @@ defdict = { None, TD(flts), ), +'ldexp' : + Ufunc(2, 1, None, + docstrings.get('numpy.core.umath.ldexp'), + None, + [TypeDescription('e', None, 'ei', 'e'), + TypeDescription('f', None, 'fi', 'f'), + TypeDescription('e', FuncNameSuffix('long'), 'el', 'e'), + TypeDescription('f', FuncNameSuffix('long'), 'fl', 'f'), + TypeDescription('d', None, 'di', 'd'), + TypeDescription('d', FuncNameSuffix('long'), 'dl', 'd'), + TypeDescription('g', None, 'gi', 'g'), + TypeDescription('g', FuncNameSuffix('long'), 'gl', 'g'), + ], + ), +'frexp' : + Ufunc(1, 2, None, + docstrings.get('numpy.core.umath.frexp'), + None, + [TypeDescription('e', None, 'e', 'ei'), + TypeDescription('f', None, 'f', 'fi'), + TypeDescription('d', None, 'd', 'di'), + TypeDescription('g', None, 'g', 'gi'), + ], + ) } if sys.version_info[0] >= 3: @@ -854,7 +884,7 @@ def make_arrays(funcdict): thedict = chartotype1 # one input and one output for t in uf.type_descriptions: - if t.func_data not in (None, FullTypeDescr): + if t.func_data not in (None, FullTypeDescr) and not isinstance(t.func_data, FuncNameSuffix): funclist.append('NULL') astype = '' if not t.astype is None: @@ -880,6 +910,10 @@ def make_arrays(funcdict): tname = english_upper(chartoname[t.type]) datalist.append('(void *)NULL') funclist.append('%s_%s_%s_%s' % (tname, t.in_, t.out, name)) + elif isinstance(t.func_data, FuncNameSuffix): + datalist.append('(void *)NULL') + tname = english_upper(chartoname[t.type]) + funclist.append('%s_%s_%s' % (tname, name, t.func_data.suffix)) else: datalist.append('(void *)NULL') tname = english_upper(chartoname[t.type]) diff --git a/numpy/core/code_generators/ufunc_docstrings.py b/numpy/core/code_generators/ufunc_docstrings.py index 4d302969e..804108397 100644 --- a/numpy/core/code_generators/ufunc_docstrings.py +++ b/numpy/core/code_generators/ufunc_docstrings.py @@ -3324,9 +3324,6 @@ add_newdoc('numpy.core.umath', 'true_divide', """) -# This doc is not currently used, but has been converted to a C string -# that can be found in numpy/core/src/umath/umathmodule.c where the -# frexp ufunc is constructed. add_newdoc('numpy.core.umath', 'frexp', """ Decompose the elements of x into mantissa and twos exponent. @@ -3372,9 +3369,6 @@ add_newdoc('numpy.core.umath', 'frexp', """) -# This doc is not currently used, but has been converted to a C string -# that can be found in numpy/core/src/umath/umathmodule.c where the -# ldexp ufunc is constructed. add_newdoc('numpy.core.umath', 'ldexp', """ Returns x1 * 2**x2, element-wise. diff --git a/numpy/core/function_base.py b/numpy/core/function_base.py index 400e75eb5..0bf93390e 100644 --- a/numpy/core/function_base.py +++ b/numpy/core/function_base.py @@ -36,6 +36,8 @@ def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None): The type of the output array. If `dtype` is not given, infer the data type from the other input arguments. + .. versionadded:: 1.9.0 + Returns ------- samples : ndarray diff --git a/numpy/core/include/numpy/ndarraytypes.h b/numpy/core/include/numpy/ndarraytypes.h index 21ff8cd1a..78f79d5fe 100644 --- a/numpy/core/include/numpy/ndarraytypes.h +++ b/numpy/core/include/numpy/ndarraytypes.h @@ -203,12 +203,6 @@ typedef enum { NPY_SAME_KIND_CASTING=3, /* Allow any casts */ NPY_UNSAFE_CASTING=4, - - /* - * Temporary internal definition only, will be removed in upcoming - * release, see below - * */ - NPY_INTERNAL_UNSAFE_CASTING_BUT_WARN_UNLESS_SAME_KIND = 100, } NPY_CASTING; typedef enum { diff --git a/numpy/core/include/numpy/npy_endian.h b/numpy/core/include/numpy/npy_endian.h index 3ba03d0e3..a8ec57245 100644 --- a/numpy/core/include/numpy/npy_endian.h +++ b/numpy/core/include/numpy/npy_endian.h @@ -10,10 +10,22 @@ /* Use endian.h if available */ #include <endian.h> - #define NPY_BYTE_ORDER __BYTE_ORDER - #define NPY_LITTLE_ENDIAN __LITTLE_ENDIAN - #define NPY_BIG_ENDIAN __BIG_ENDIAN -#else + #if defined(BYTE_ORDER) && defined(BIG_ENDIAN) && defined(LITTLE_ENDIAN) + #define NPY_BYTE_ORDER BYTE_ORDER + #define NPY_LITTLE_ENDIAN LITTLE_ENDIAN + #define NPY_BIG_ENDIAN BIG_ENDIAN + #elif defined(_BYTE_ORDER) && defined(_BIG_ENDIAN) && defined(_LITTLE_ENDIAN) + #define NPY_BYTE_ORDER _BYTE_ORDER + #define NPY_LITTLE_ENDIAN _LITTLE_ENDIAN + #define NPY_BIG_ENDIAN _BIG_ENDIAN + #elif defined(__BYTE_ORDER) && defined(__BIG_ENDIAN) && defined(__LITTLE_ENDIAN) + #define NPY_BYTE_ORDER __BYTE_ORDER + #define NPY_LITTLE_ENDIAN __LITTLE_ENDIAN + #define NPY_BIG_ENDIAN __BIG_ENDIAN + #endif +#endif + +#ifndef NPY_BYTE_ORDER /* Set endianness info using target CPU */ #include "npy_cpu.h" diff --git a/numpy/core/include/numpy/npy_math.h b/numpy/core/include/numpy/npy_math.h index b7920460d..855ddf7fa 100644 --- a/numpy/core/include/numpy/npy_math.h +++ b/numpy/core/include/numpy/npy_math.h @@ -118,10 +118,6 @@ double npy_tanh(double x); double npy_asin(double x); double npy_acos(double x); double npy_atan(double x); -double npy_aexp(double x); -double npy_alog(double x); -double npy_asqrt(double x); -double npy_afabs(double x); double npy_log(double x); double npy_log10(double x); @@ -147,6 +143,8 @@ double npy_log2(double x); double npy_atan2(double x, double y); double npy_pow(double x, double y); double npy_modf(double x, double* y); +double npy_frexp(double x, int* y); +double npy_ldexp(double n, int y); double npy_copysign(double x, double y); double npy_nextafter(double x, double y); @@ -251,6 +249,8 @@ float npy_powf(float x, float y); float npy_fmodf(float x, float y); float npy_modff(float x, float* y); +float npy_frexpf(float x, int* y); +float npy_ldexpf(float x, int y); float npy_copysignf(float x, float y); float npy_nextafterf(float x, float y); @@ -292,6 +292,8 @@ npy_longdouble npy_powl(npy_longdouble x, npy_longdouble y); npy_longdouble npy_fmodl(npy_longdouble x, npy_longdouble y); npy_longdouble npy_modfl(npy_longdouble x, npy_longdouble* y); +npy_longdouble npy_frexpl(npy_longdouble x, int* y); +npy_longdouble npy_ldexpl(npy_longdouble x, int y); npy_longdouble npy_copysignl(npy_longdouble x, npy_longdouble y); npy_longdouble npy_nextafterl(npy_longdouble x, npy_longdouble y); diff --git a/numpy/core/include/numpy/ufuncobject.h b/numpy/core/include/numpy/ufuncobject.h index 38e3dcf0f..a24a0d837 100644 --- a/numpy/core/include/numpy/ufuncobject.h +++ b/numpy/core/include/numpy/ufuncobject.h @@ -152,13 +152,13 @@ typedef struct _tagPyUFuncObject { int check_return; /* The name of the ufunc */ - char *name; + const char *name; /* Array of type numbers, of size ('nargs' * 'ntypes') */ char *types; /* Documentation string */ - char *doc; + const char *doc; void *ptr; PyObject *obj; diff --git a/numpy/core/numeric.py b/numpy/core/numeric.py index 57784a51f..5d7407ce0 100644 --- a/numpy/core/numeric.py +++ b/numpy/core/numeric.py @@ -6,9 +6,11 @@ import warnings import collections from . import multiarray from . import umath -from .umath import * +from .umath import (invert, sin, UFUNC_BUFSIZE_DEFAULT, ERR_IGNORE, + ERR_WARN, ERR_RAISE, ERR_CALL, ERR_PRINT, ERR_LOG, + ERR_DEFAULT, PINF, NAN) from . import numerictypes -from .numerictypes import * +from .numerictypes import longlong, intc, int_, float_, complex_, bool_ if sys.version_info[0] >= 3: import pickle @@ -358,9 +360,6 @@ def extend_all(module): if a not in adict: __all__.append(a) -extend_all(umath) -extend_all(numerictypes) - newaxis = None @@ -2834,6 +2833,10 @@ nan = NaN = NAN False_ = bool_(False) True_ = bool_(True) +from .umath import * +from .numerictypes import * from . import fromnumeric from .fromnumeric import * extend_all(fromnumeric) +extend_all(umath) +extend_all(numerictypes) diff --git a/numpy/core/setup.py b/numpy/core/setup.py index 5da042413..15f66fa6c 100644 --- a/numpy/core/setup.py +++ b/numpy/core/setup.py @@ -19,7 +19,7 @@ from setup_common import * ENABLE_SEPARATE_COMPILATION = (os.environ.get('NPY_SEPARATE_COMPILATION', "1") != "0") # Set to True to enable relaxed strides checking. This (mostly) means # that `strides[dim]` is ignored if `shape[dim] == 1` when setting flags. -NPY_RELAXED_STRIDES_CHECKING = (os.environ.get('NPY_RELAXED_STRIDES_CHECKING', "0") != "0") +NPY_RELAXED_STRIDES_CHECKING = (os.environ.get('NPY_RELAXED_STRIDES_CHECKING', "1") != "0") # XXX: ugly, we use a class to avoid calling twice some expensive functions in # config.h/numpyconfig.h. I don't see a better way because distutils force @@ -176,12 +176,11 @@ def check_math_capabilities(config, moredefs, mathlibs): moredefs.append((fname2def(f), 1)) for dec, fn in OPTIONAL_FUNCTION_ATTRIBUTES: - if config.check_func(fn, decl='int %s %s(void *);' % (dec, fn), - call=False): + if config.check_gcc_function_attribute(dec, fn): moredefs.append((fname2def(fn), 1)) for fn in OPTIONAL_VARIABLE_ATTRIBUTES: - if config.check_func(fn, decl='int %s a;' % (fn), call=False): + if config.check_gcc_variable_attribute(fn): m = fn.replace("(", "_").replace(")", "_") moredefs.append((fname2def(m), 1)) diff --git a/numpy/core/src/multiarray/arraytypes.c.src b/numpy/core/src/multiarray/arraytypes.c.src index 86e719a44..876529a1f 100644 --- a/numpy/core/src/multiarray/arraytypes.c.src +++ b/numpy/core/src/multiarray/arraytypes.c.src @@ -3923,7 +3923,8 @@ NPY_NO_EXPORT PyArray_Descr @from@_Descr = { /* elsize */ @num@ * sizeof(@fromtype@), /* alignment */ - @num@ * _ALIGN(@fromtype@), + @num@ * _ALIGN(@fromtype@) > NPY_MAX_COPY_ALIGNMENT ? + NPY_MAX_COPY_ALIGNMENT : @num@ * _ALIGN(@fromtype@), /* subarray */ NULL, /* fields */ @@ -4265,7 +4266,8 @@ set_typeinfo(PyObject *dict) #endif NPY_@name@, NPY_BITSOF_@name@, - @num@ * _ALIGN(@type@), + @num@ * _ALIGN(@type@) > NPY_MAX_COPY_ALIGNMENT ? + NPY_MAX_COPY_ALIGNMENT : @num@ * _ALIGN(@type@), (PyObject *) &Py@Name@ArrType_Type)); Py_DECREF(s); diff --git a/numpy/core/src/multiarray/calculation.c b/numpy/core/src/multiarray/calculation.c index 50938be4c..5563a2515 100644 --- a/numpy/core/src/multiarray/calculation.c +++ b/numpy/core/src/multiarray/calculation.c @@ -1182,7 +1182,7 @@ PyArray_Clip(PyArrayObject *self, PyObject *min, PyObject *max, PyArrayObject *o NPY_NO_EXPORT PyObject * PyArray_Conjugate(PyArrayObject *self, PyArrayObject *out) { - if (PyArray_ISCOMPLEX(self)) { + if (PyArray_ISCOMPLEX(self) || PyArray_ISOBJECT(self)) { if (out == NULL) { return PyArray_GenericUnaryFunction(self, n_ops.conjugate); diff --git a/numpy/core/src/multiarray/common.c b/numpy/core/src/multiarray/common.c index 2b3d3c3d2..35b705aff 100644 --- a/numpy/core/src/multiarray/common.c +++ b/numpy/core/src/multiarray/common.c @@ -84,7 +84,7 @@ PyArray_GetAttrString_SuppressException(PyObject *obj, char *name) -NPY_NO_EXPORT NPY_CASTING NPY_DEFAULT_ASSIGN_CASTING = NPY_INTERNAL_UNSAFE_CASTING_BUT_WARN_UNLESS_SAME_KIND; +NPY_NO_EXPORT NPY_CASTING NPY_DEFAULT_ASSIGN_CASTING = NPY_SAME_KIND_CASTING; NPY_NO_EXPORT PyArray_Descr * @@ -676,7 +676,7 @@ _IsAligned(PyArrayObject *ap) /* alignment 1 types should have a efficient alignment for copy loops */ if (PyArray_ISFLEXIBLE(ap) || PyArray_ISSTRING(ap)) { - alignment = 16; + alignment = NPY_MAX_COPY_ALIGNMENT; } if (alignment == 1) { diff --git a/numpy/core/src/multiarray/conversion_utils.c b/numpy/core/src/multiarray/conversion_utils.c index b84dff864..d32fcabd2 100644 --- a/numpy/core/src/multiarray/conversion_utils.c +++ b/numpy/core/src/multiarray/conversion_utils.c @@ -16,6 +16,11 @@ #include "conversion_utils.h" +static int +PyArray_PyIntAsInt_ErrMsg(PyObject *o, const char * msg) NPY_GCC_NONNULL(2); +static npy_intp +PyArray_PyIntAsIntp_ErrMsg(PyObject *o, const char * msg) NPY_GCC_NONNULL(2); + /**************************************************************** * Useful function for conversion when used with PyArg_ParseTuple ****************************************************************/ @@ -215,8 +220,9 @@ PyArray_AxisConverter(PyObject *obj, int *axis) *axis = NPY_MAXDIMS; } else { - *axis = PyArray_PyIntAsInt(obj); - if (PyErr_Occurred()) { + *axis = PyArray_PyIntAsInt_ErrMsg(obj, + "an integer is required for the axis"); + if (error_converting(*axis)) { return NPY_FAIL; } } @@ -251,7 +257,8 @@ PyArray_ConvertMultiAxis(PyObject *axis_in, int ndim, npy_bool *out_axis_flags) } for (i = 0; i < naxes; ++i) { PyObject *tmp = PyTuple_GET_ITEM(axis_in, i); - int axis = PyArray_PyIntAsInt(tmp); + int axis = PyArray_PyIntAsInt_ErrMsg(tmp, + "integers are required for the axis tuple elements"); int axis_orig = axis; if (error_converting(axis)) { return NPY_FAIL; @@ -281,7 +288,8 @@ PyArray_ConvertMultiAxis(PyObject *axis_in, int ndim, npy_bool *out_axis_flags) memset(out_axis_flags, 0, ndim); - axis = PyArray_PyIntAsInt(axis_in); + axis = PyArray_PyIntAsInt_ErrMsg(axis_in, + "an integer is required for the axis"); axis_orig = axis; if (error_converting(axis)) { @@ -736,13 +744,12 @@ PyArray_CastingConverter(PyObject *obj, NPY_CASTING *casting) * Other conversion functions *****************************/ -/*NUMPY_API*/ -NPY_NO_EXPORT int -PyArray_PyIntAsInt(PyObject *o) +static int +PyArray_PyIntAsInt_ErrMsg(PyObject *o, const char * msg) { npy_intp long_value; /* This assumes that NPY_SIZEOF_INTP >= NPY_SIZEOF_INT */ - long_value = PyArray_PyIntAsIntp(o); + long_value = PyArray_PyIntAsIntp_ErrMsg(o, msg); #if (NPY_SIZEOF_INTP > NPY_SIZEOF_INT) if ((long_value < INT_MIN) || (long_value > INT_MAX)) { @@ -754,8 +761,14 @@ PyArray_PyIntAsInt(PyObject *o) } /*NUMPY_API*/ -NPY_NO_EXPORT npy_intp -PyArray_PyIntAsIntp(PyObject *o) +NPY_NO_EXPORT int +PyArray_PyIntAsInt(PyObject *o) +{ + return PyArray_PyIntAsInt_ErrMsg(o, "an integer is required"); +} + +static npy_intp +PyArray_PyIntAsIntp_ErrMsg(PyObject *o, const char * msg) { #if (NPY_SIZEOF_LONG < NPY_SIZEOF_INTP) long long long_value = -1; @@ -763,7 +776,6 @@ PyArray_PyIntAsIntp(PyObject *o) long long_value = -1; #endif PyObject *obj, *err; - static char *msg = "an integer is required"; if (!o) { PyErr_SetString(PyExc_TypeError, msg); @@ -909,6 +921,13 @@ PyArray_PyIntAsIntp(PyObject *o) return long_value; } +/*NUMPY_API*/ +NPY_NO_EXPORT npy_intp +PyArray_PyIntAsIntp(PyObject *o) +{ + return PyArray_PyIntAsIntp_ErrMsg(o, "an integer is required"); +} + /* * PyArray_IntpFromIndexSequence diff --git a/numpy/core/src/multiarray/convert_datatype.c b/numpy/core/src/multiarray/convert_datatype.c index 1db3bfe85..fa5fb6b67 100644 --- a/numpy/core/src/multiarray/convert_datatype.c +++ b/numpy/core/src/multiarray/convert_datatype.c @@ -624,63 +624,19 @@ type_num_unsigned_to_signed(int type_num) } } -/* - * NOTE: once the UNSAFE_CASTING -> SAME_KIND_CASTING transition is over, - * we should remove NPY_INTERNAL_UNSAFE_CASTING_BUT_WARN_UNLESS_SAME_KIND - * and PyArray_CanCastTypeTo_impl should be renamed back to - * PyArray_CanCastTypeTo. - */ -static npy_bool -PyArray_CanCastTypeTo_impl(PyArray_Descr *from, PyArray_Descr *to, - NPY_CASTING casting); - /*NUMPY_API * Returns true if data of type 'from' may be cast to data of type * 'to' according to the rule 'casting'. */ NPY_NO_EXPORT npy_bool PyArray_CanCastTypeTo(PyArray_Descr *from, PyArray_Descr *to, - NPY_CASTING casting) -{ - /* fast path for basic types */ - if (NPY_LIKELY(from->type_num < NPY_OBJECT) && - NPY_LIKELY(from->type_num == to->type_num) && - NPY_LIKELY(from->byteorder == to->byteorder)) { - return 1; - } - else if (casting == NPY_INTERNAL_UNSAFE_CASTING_BUT_WARN_UNLESS_SAME_KIND) { - npy_bool unsafe_ok, same_kind_ok; - unsafe_ok = PyArray_CanCastTypeTo_impl(from, to, NPY_UNSAFE_CASTING); - same_kind_ok = PyArray_CanCastTypeTo_impl(from, to, - NPY_SAME_KIND_CASTING); - if (unsafe_ok && !same_kind_ok) { - char * msg = "Implicitly casting between incompatible kinds. In " - "a future numpy release, this will raise an error. " - "Use casting=\"unsafe\" if this is intentional."; - if (DEPRECATE(msg) < 0) { - /* We have no way to propagate an exception :-( */ - PyErr_Clear(); - PySys_WriteStderr("Sorry, you requested this warning " - "be raised as an error, but we couldn't " - "do it. (See issue #3806 in the numpy " - "bug tracker.) So FYI, it was: " - "DeprecationWarning: %s\n", - msg); - } - } - return unsafe_ok; - } - else { - return PyArray_CanCastTypeTo_impl(from, to, casting); - } -} - -static npy_bool -PyArray_CanCastTypeTo_impl(PyArray_Descr *from, PyArray_Descr *to, NPY_CASTING casting) { - /* If unsafe casts are allowed */ - if (casting == NPY_UNSAFE_CASTING) { + /* Fast path for unsafe casts or basic types */ + if (casting == NPY_UNSAFE_CASTING || + (NPY_LIKELY(from->type_num < NPY_OBJECT) && + NPY_LIKELY(from->type_num == to->type_num) && + NPY_LIKELY(from->byteorder == to->byteorder))) { return 1; } /* Equivalent types can be cast with any value of 'casting' */ diff --git a/numpy/core/src/multiarray/ctors.c b/numpy/core/src/multiarray/ctors.c index d93995c8a..c57df147a 100644 --- a/numpy/core/src/multiarray/ctors.c +++ b/numpy/core/src/multiarray/ctors.c @@ -12,6 +12,7 @@ #include "npy_config.h" #include "npy_pycompat.h" +#include "multiarraymodule.h" #include "common.h" #include "ctors.h" @@ -1054,12 +1055,12 @@ PyArray_NewFromDescr_int(PyTypeObject *subtype, PyArray_Descr *descr, int nd, fa->data = data; /* - * If the strides were provided to the function, need to - * update the flags to get the right CONTIGUOUS, ALIGN properties + * always update the flags to get the right CONTIGUOUS, ALIGN properties + * not owned data and input strides may not be aligned and on some + * platforms (debian sparc) malloc does not provide enough alignment for + * long double types */ - if (strides != NULL) { - PyArray_UpdateFlags((PyArrayObject *)fa, NPY_ARRAY_UPDATE_ALL); - } + PyArray_UpdateFlags((PyArrayObject *)fa, NPY_ARRAY_UPDATE_ALL); /* * call the __array_finalize__ @@ -1069,7 +1070,7 @@ PyArray_NewFromDescr_int(PyTypeObject *subtype, PyArray_Descr *descr, int nd, if ((subtype != &PyArray_Type)) { PyObject *res, *func, *args; - func = PyObject_GetAttrString((PyObject *)fa, "__array_finalize__"); + func = PyObject_GetAttr((PyObject *)fa, npy_ma_str_array_finalize); if (func && func != Py_None) { if (NpyCapsule_Check(func)) { /* A C-function is stored here */ @@ -3368,7 +3369,7 @@ PyArray_FromBuffer(PyObject *buf, PyArray_Descr *type, #endif ) { PyObject *newbuf; - newbuf = PyObject_GetAttrString(buf, "__buffer__"); + newbuf = PyObject_GetAttr(buf, npy_ma_str_buffer); if (newbuf == NULL) { Py_DECREF(type); return NULL; diff --git a/numpy/core/src/multiarray/descriptor.c b/numpy/core/src/multiarray/descriptor.c index 8b55c9fbd..238077b36 100644 --- a/numpy/core/src/multiarray/descriptor.c +++ b/numpy/core/src/multiarray/descriptor.c @@ -2369,11 +2369,8 @@ arraydescr_setstate(PyArray_Descr *self, PyObject *args) { int elsize = -1, alignment = -1; int version = 4; -#if defined(NPY_PY3K) - int endian; -#else char endian; -#endif + PyObject *endian_obj; PyObject *subarray, *fields, *names = NULL, *metadata=NULL; int incref_names = 1; int int_dtypeflags = 0; @@ -2390,68 +2387,39 @@ arraydescr_setstate(PyArray_Descr *self, PyObject *args) } switch (PyTuple_GET_SIZE(PyTuple_GET_ITEM(args,0))) { case 9: -#if defined(NPY_PY3K) -#define _ARGSTR_ "(iCOOOiiiO)" -#else -#define _ARGSTR_ "(icOOOiiiO)" -#endif - if (!PyArg_ParseTuple(args, _ARGSTR_, &version, &endian, + if (!PyArg_ParseTuple(args, "(iOOOOiiiO)", &version, &endian_obj, &subarray, &names, &fields, &elsize, &alignment, &int_dtypeflags, &metadata)) { + PyErr_Clear(); return NULL; -#undef _ARGSTR_ } break; case 8: -#if defined(NPY_PY3K) -#define _ARGSTR_ "(iCOOOiii)" -#else -#define _ARGSTR_ "(icOOOiii)" -#endif - if (!PyArg_ParseTuple(args, _ARGSTR_, &version, &endian, + if (!PyArg_ParseTuple(args, "(iOOOOiii)", &version, &endian_obj, &subarray, &names, &fields, &elsize, &alignment, &int_dtypeflags)) { return NULL; -#undef _ARGSTR_ } break; case 7: -#if defined(NPY_PY3K) -#define _ARGSTR_ "(iCOOOii)" -#else -#define _ARGSTR_ "(icOOOii)" -#endif - if (!PyArg_ParseTuple(args, _ARGSTR_, &version, &endian, + if (!PyArg_ParseTuple(args, "(iOOOOii)", &version, &endian_obj, &subarray, &names, &fields, &elsize, &alignment)) { return NULL; -#undef _ARGSTR_ } break; case 6: -#if defined(NPY_PY3K) -#define _ARGSTR_ "(iCOOii)" -#else -#define _ARGSTR_ "(icOOii)" -#endif - if (!PyArg_ParseTuple(args, _ARGSTR_, &version, - &endian, &subarray, &fields, + if (!PyArg_ParseTuple(args, "(iOOOii)", &version, + &endian_obj, &subarray, &fields, &elsize, &alignment)) { - PyErr_Clear(); -#undef _ARGSTR_ + return NULL; } break; case 5: version = 0; -#if defined(NPY_PY3K) -#define _ARGSTR_ "(COOii)" -#else -#define _ARGSTR_ "(cOOii)" -#endif - if (!PyArg_ParseTuple(args, _ARGSTR_, - &endian, &subarray, &fields, &elsize, + if (!PyArg_ParseTuple(args, "(OOOii)", + &endian_obj, &subarray, &fields, &elsize, &alignment)) { -#undef _ARGSTR_ return NULL; } break; @@ -2494,11 +2462,55 @@ arraydescr_setstate(PyArray_Descr *self, PyObject *args) } } + /* Parse endian */ + if (PyUnicode_Check(endian_obj) || PyBytes_Check(endian_obj)) { + PyObject *tmp = NULL; + char *str; + Py_ssize_t len; + + if (PyUnicode_Check(endian_obj)) { + tmp = PyUnicode_AsASCIIString(endian_obj); + if (tmp == NULL) { + return NULL; + } + endian_obj = tmp; + } + + if (PyBytes_AsStringAndSize(endian_obj, &str, &len) == -1) { + Py_XDECREF(tmp); + return NULL; + } + if (len != 1) { + PyErr_SetString(PyExc_ValueError, + "endian is not 1-char string in Numpy dtype unpickling"); + Py_XDECREF(tmp); + return NULL; + } + endian = str[0]; + Py_XDECREF(tmp); + } + else { + PyErr_SetString(PyExc_ValueError, + "endian is not a string in Numpy dtype unpickling"); + return NULL; + } if ((fields == Py_None && names != Py_None) || (names == Py_None && fields != Py_None)) { PyErr_Format(PyExc_ValueError, - "inconsistent fields and names"); + "inconsistent fields and names in Numpy dtype unpickling"); + return NULL; + } + + if (names != Py_None && !PyTuple_Check(names)) { + PyErr_Format(PyExc_ValueError, + "non-tuple names in Numpy dtype unpickling"); + return NULL; + } + + if (fields != Py_None && !PyDict_Check(fields)) { + PyErr_Format(PyExc_ValueError, + "non-dict fields in Numpy dtype unpickling"); return NULL; } @@ -2563,13 +2575,82 @@ arraydescr_setstate(PyArray_Descr *self, PyObject *args) } if (fields != Py_None) { - Py_XDECREF(self->fields); - self->fields = fields; - Py_INCREF(fields); - Py_XDECREF(self->names); - self->names = names; - if (incref_names) { - Py_INCREF(names); + /* + * Ensure names are of appropriate string type + */ + Py_ssize_t i; + int names_ok = 1; + PyObject *name; + + for (i = 0; i < PyTuple_GET_SIZE(names); ++i) { + name = PyTuple_GET_ITEM(names, i); + if (!PyUString_Check(name)) { + names_ok = 0; + break; + } + } + + if (names_ok) { + Py_XDECREF(self->fields); + self->fields = fields; + Py_INCREF(fields); + Py_XDECREF(self->names); + self->names = names; + if (incref_names) { + Py_INCREF(names); + } + } + else { +#if defined(NPY_PY3K) + /* + * To support pickle.load(f, encoding='bytes') for loading Py2 + * generated pickles on Py3, we need to be more lenient and convert + * field names from byte strings to unicode. + */ + PyObject *tmp, *new_name, *field; + + tmp = PyDict_New(); + if (tmp == NULL) { + return NULL; + } + Py_XDECREF(self->fields); + self->fields = tmp; + + tmp = PyTuple_New(PyTuple_GET_SIZE(names)); + if (tmp == NULL) { + return NULL; + } + Py_XDECREF(self->names); + self->names = tmp; + + for (i = 0; i < PyTuple_GET_SIZE(names); ++i) { + name = PyTuple_GET_ITEM(names, i); + field = PyDict_GetItem(fields, name); + if (!field) { + return NULL; + } + + if (PyUnicode_Check(name)) { + new_name = name; + Py_INCREF(new_name); + } + else { + new_name = PyUnicode_FromEncodedObject(name, "ASCII", "strict"); + if (new_name == NULL) { + return NULL; + } + } + + PyTuple_SET_ITEM(self->names, i, new_name); + if (PyDict_SetItem(self->fields, new_name, field) != 0) { + return NULL; + } + } +#else + PyErr_Format(PyExc_ValueError, + "non-string names in Numpy dtype unpickling"); + return NULL; +#endif } } diff --git a/numpy/core/src/multiarray/lowlevel_strided_loops.c.src b/numpy/core/src/multiarray/lowlevel_strided_loops.c.src index b9063273f..38e7656f3 100644 --- a/numpy/core/src/multiarray/lowlevel_strided_loops.c.src +++ b/numpy/core/src/multiarray/lowlevel_strided_loops.c.src @@ -1490,7 +1490,9 @@ mapiter_@name@(PyArrayMapIterObject *mit) /* Constant information */ npy_intp fancy_dims[NPY_MAXDIMS]; npy_intp fancy_strides[NPY_MAXDIMS]; +#if @isget@ int iteraxis; +#endif char *baseoffset = mit->baseoffset; char **outer_ptrs = mit->outer_ptrs; @@ -1498,7 +1500,9 @@ mapiter_@name@(PyArrayMapIterObject *mit) PyArrayObject *array= mit->array; /* Fill constant information */ +#if @isget@ iteraxis = mit->iteraxes[0]; +#endif for (i = 0; i < numiter; i++) { fancy_dims[i] = mit->fancy_dims[i]; fancy_strides[i] = mit->fancy_strides[i]; diff --git a/numpy/core/src/multiarray/mapping.c b/numpy/core/src/multiarray/mapping.c index e2b8ef700..9be2683e6 100644 --- a/numpy/core/src/multiarray/mapping.c +++ b/numpy/core/src/multiarray/mapping.c @@ -1047,7 +1047,7 @@ array_boolean_subscript(PyArrayObject *self, Py_INCREF(dtype); ret = (PyArrayObject *)PyArray_NewFromDescr(Py_TYPE(self), dtype, 1, &size, PyArray_STRIDES(ret), PyArray_BYTES(ret), - 0, (PyObject *)self); + PyArray_FLAGS(self), (PyObject *)self); if (ret == NULL) { Py_DECREF(tmp); @@ -1221,7 +1221,7 @@ array_assign_boolean_subscript(PyArrayObject *self, if (needs_api) { /* - * FIXME?: most assignment operations stop after the first occurance + * FIXME?: most assignment operations stop after the first occurrence * of an error. Boolean does not currently, but should at least * report the error. (This is only relevant for things like str->int * casts which call into python) @@ -1436,7 +1436,7 @@ array_subscript(PyArrayObject *self, PyObject *op) /* * TODO: Should this be a view or not? The only reason not would be * optimization (i.e. of array[...] += 1) I think. - * Before, it was just self for a single Ellipis. + * Before, it was just self for a single ellipsis. */ result = PyArray_View(self, NULL, NULL); /* A single ellipsis, so no need to decref */ @@ -1569,7 +1569,7 @@ array_subscript(PyArrayObject *self, PyObject *op) PyArray_SHAPE(tmp_arr), PyArray_STRIDES(tmp_arr), PyArray_BYTES(tmp_arr), - 0, /* TODO: Flags? */ + PyArray_FLAGS(self), (PyObject *)self); if (result == NULL) { @@ -1656,6 +1656,58 @@ array_assign_item(PyArrayObject *self, Py_ssize_t i, PyObject *op) /* + * This fallback takes the old route of `arr.flat[index] = values` + * for one dimensional `arr`. The route can sometimes fail slightly + * differently (ValueError instead of IndexError), in which case we + * warn users about the change. But since it does not actually care *at all* + * about shapes, it should only fail for out of bound indexes or + * casting errors. + */ +NPY_NO_EXPORT int +attempt_1d_fallback(PyArrayObject *self, PyObject *ind, PyObject *op) +{ + PyObject *err = PyErr_Occurred(); + PyArrayIterObject *self_iter = NULL; + + Py_INCREF(err); + PyErr_Clear(); + + self_iter = (PyArrayIterObject *)PyArray_IterNew((PyObject *)self); + if (self_iter == NULL) { + goto fail; + } + if (iter_ass_subscript(self_iter, ind, op) < 0) { + goto fail; + } + + Py_XDECREF((PyObject *)self_iter); + Py_DECREF(err); + + if (DEPRECATE( + "assignment will raise an error in the future, most likely " + "because your index result shape does not match the value array " + "shape. You can use `arr.flat[index] = values` to keep the old " + "behaviour.") < 0) { + return -1; + } + return 0; + + fail: + if (!PyErr_ExceptionMatches(err)) { + PyObject *err, *val, *tb; + PyErr_Fetch(&err, &val, &tb); + DEPRECATE_FUTUREWARNING( + "assignment exception type will change in the future"); + PyErr_Restore(err, val, tb); + } + + Py_XDECREF((PyObject *)self_iter); + Py_DECREF(err); + return -1; +} + + +/* * General assignment with python indexing objects. */ static int @@ -1746,9 +1798,21 @@ array_assign_subscript(PyArrayObject *self, PyObject *ind, PyObject *op) Py_INCREF(op); tmp_arr = (PyArrayObject *)op; } + if (array_assign_boolean_subscript(self, (PyArrayObject *)indices[0].object, tmp_arr, NPY_CORDER) < 0) { + /* + * Deprecated case. The old boolean indexing seemed to have some + * check to allow wrong dimensional boolean arrays in all cases. + */ + if (PyArray_NDIM(tmp_arr) > 1) { + if (attempt_1d_fallback(self, indices[0].object, + (PyObject*)tmp_arr) < 0) { + goto fail; + } + goto success; + } goto fail; } goto success; @@ -1899,14 +1963,36 @@ array_assign_subscript(PyArrayObject *self, PyObject *ind, PyObject *op) tmp_arr, descr); if (mit == NULL) { - goto fail; + /* + * This is a deprecated special case to allow non-matching shapes + * for the index and value arrays. + */ + if (index_type != HAS_FANCY || index_num != 1) { + /* This is not a "flat like" 1-d special case */ + goto fail; + } + if (attempt_1d_fallback(self, indices[0].object, op) < 0) { + goto fail; + } + goto success; } if (tmp_arr == NULL) { /* Fill extra op */ if (PyArray_CopyObject(mit->extra_op, op) < 0) { - goto fail; + /* + * This is a deprecated special case to allow non-matching shapes + * for the index and value arrays. + */ + if (index_type != HAS_FANCY || index_num != 1) { + /* This is not a "flat like" 1-d special case */ + goto fail; + } + if (attempt_1d_fallback(self, indices[0].object, op) < 0) { + goto fail; + } + goto success; } } @@ -2357,7 +2443,7 @@ PyArray_MapIterCheckIndices(PyArrayMapIterObject *mit) NPY_BEGIN_THREADS_DEF; if (mit->size == 0) { - /* All indices got broadcasted away, do *not* check as it always was */ + /* All indices got broadcast away, do *not* check as it always was */ return 0; } @@ -2580,7 +2666,7 @@ PyArray_MapIterNew(npy_index_info *indices , int index_num, int index_type, * 1. No subspace iteration is necessary, so the extra_op can * be included into the index iterator (it will be buffered) * 2. Subspace iteration is necessary, so the extra op is iterated - * independendly, and the iteration order is fixed at C (could + * independently, and the iteration order is fixed at C (could * also use Fortran order if the array is Fortran order). * In this case the subspace iterator is not buffered. * @@ -2773,7 +2859,7 @@ PyArray_MapIterNew(npy_index_info *indices , int index_num, int index_type, NPY_ITER_GROWINNER; /* - * For a single 1-d operand, guarantee itertion order + * For a single 1-d operand, guarantee iteration order * (scipy used this). Note that subspace may be used. */ if ((mit->numiter == 1) && (PyArray_NDIM(index_arrays[0]) == 1)) { @@ -2985,7 +3071,7 @@ PyArray_MapIterNew(npy_index_info *indices , int index_num, int index_type, fail: /* - * Check whether the operand was not broadcastable and replace the error + * Check whether the operand could not be broadcast and replace the error * in that case. This should however normally be found early with a * direct goto to broadcast_error */ @@ -3000,7 +3086,7 @@ PyArray_MapIterNew(npy_index_info *indices , int index_num, int index_type, /* (j < 0 is currently impossible, extra_op is reshaped) */ j >= 0 && PyArray_DIM(extra_op, i) != mit->dimensions[j]) { - /* extra_op cannot be broadcasted to the indexing result */ + /* extra_op cannot be broadcast to the indexing result */ goto broadcast_error; } } @@ -3060,7 +3146,7 @@ PyArray_MapIterNew(npy_index_info *indices , int index_num, int index_type, * that most of this public API is currently not guaranteed * to stay the same between versions. If you plan on using * it, please consider adding more utility functions here - * to accomodate new features. + * to accommodate new features. */ NPY_NO_EXPORT PyObject * PyArray_MapIterArray(PyArrayObject * a, PyObject * index) diff --git a/numpy/core/src/multiarray/methods.c b/numpy/core/src/multiarray/methods.c index 5fab174ba..bfd3bc3c1 100644 --- a/numpy/core/src/multiarray/methods.c +++ b/numpy/core/src/multiarray/methods.c @@ -1670,6 +1670,13 @@ array_setstate(PyArrayObject *self, PyObject *args) tmp = PyUnicode_AsLatin1String(rawdata); Py_DECREF(rawdata); rawdata = tmp; + if (tmp == NULL) { + /* More informative error message */ + PyErr_SetString(PyExc_ValueError, + ("Failed to encode latin1 string when unpickling a Numpy array. " + "pickle.load(a, encoding='latin1') is assumed.")); + return NULL; + } } #endif @@ -1894,6 +1901,19 @@ array_dumps(PyArrayObject *self, PyObject *args) static PyObject * +array_sizeof(PyArrayObject *self) +{ + /* object + dimension and strides */ + Py_ssize_t nbytes = NPY_SIZEOF_PYARRAYOBJECT + + PyArray_NDIM(self) * sizeof(npy_intp) * 2; + if (PyArray_CHKFLAGS(self, NPY_ARRAY_OWNDATA)) { + nbytes += PyArray_NBYTES(self); + } + return PyLong_FromSsize_t(nbytes); +} + + +static PyObject * array_transpose(PyArrayObject *self, PyObject *args) { PyObject *shape = Py_None; @@ -2301,6 +2321,11 @@ NPY_NO_EXPORT PyMethodDef array_methods[] = { (PyCFunction)array_wraparray, METH_VARARGS, NULL}, + /* for the sys module */ + {"__sizeof__", + (PyCFunction) array_sizeof, + METH_NOARGS, NULL}, + /* for the copy module */ {"__copy__", (PyCFunction)array_copy_keeporder, diff --git a/numpy/core/src/multiarray/multiarray_tests.c.src b/numpy/core/src/multiarray/multiarray_tests.c.src index bd0366bd5..a22319cfe 100644 --- a/numpy/core/src/multiarray/multiarray_tests.c.src +++ b/numpy/core/src/multiarray/multiarray_tests.c.src @@ -556,6 +556,42 @@ fail: return NULL; } +/* check no elison for avoided increfs */ +static PyObject * +incref_elide(PyObject *dummy, PyObject *args) +{ + PyObject *arg = NULL, *res, *tup; + if (!PyArg_ParseTuple(args, "O", &arg)) { + return NULL; + } + + /* refcount 1 array but should not be elided */ + arg = PyArray_NewCopy((PyArrayObject*)arg, NPY_KEEPORDER); + res = PyNumber_Add(arg, arg); + + /* return original copy, should be equal to input */ + tup = PyTuple_Pack(2, arg, res); + Py_DECREF(arg); + Py_DECREF(res); + return tup; +} + +/* check no elison for get from list without incref */ +static PyObject * +incref_elide_l(PyObject *dummy, PyObject *args) +{ + PyObject *arg = NULL, *r, *res; + if (!PyArg_ParseTuple(args, "O", &arg)) { + return NULL; + } + /* get item without increasing refcount, item may still be on the python + * stack but above the inaccessible top */ + r = PyList_GetItem(arg, 4); + res = PyNumber_Add(r, r); + + return res; +} + #if !defined(NPY_PY3K) static PyObject * @@ -839,6 +875,12 @@ static PyMethodDef Multiarray_TestsMethods[] = { {"test_inplace_increment", inplace_increment, METH_VARARGS, NULL}, + {"incref_elide", + incref_elide, + METH_VARARGS, NULL}, + {"incref_elide_l", + incref_elide_l, + METH_VARARGS, NULL}, #if !defined(NPY_PY3K) {"test_int_subclass", int_subclass, diff --git a/numpy/core/src/multiarray/multiarraymodule.c b/numpy/core/src/multiarray/multiarraymodule.c index 682705a1b..8e9b656cf 100644 --- a/numpy/core/src/multiarray/multiarraymodule.c +++ b/numpy/core/src/multiarray/multiarraymodule.c @@ -56,6 +56,7 @@ NPY_NO_EXPORT int NPY_NUMUSERTYPES = 0; #include "common.h" #include "ufunc_override.h" #include "scalarmathmodule.h" /* for npy_mul_with_overflow_intp */ +#include "multiarraymodule.h" /* Only here for API compatibility */ NPY_NO_EXPORT PyTypeObject PyBigArray_Type; @@ -576,6 +577,12 @@ PyArray_Concatenate(PyObject *op, int axis) PyArrayObject **arrays; PyArrayObject *ret; + if (!PySequence_Check(op)) { + PyErr_SetString(PyExc_TypeError, + "The first input argument needs to be a sequence"); + return NULL; + } + /* Convert the input list into arrays */ narrays = PySequence_Size(op); if (narrays < 0) { @@ -1615,6 +1622,73 @@ _array_fromobject(PyObject *NPY_UNUSED(ignored), PyObject *args, PyObject *kws) "only 2 non-keyword arguments accepted"); return NULL; } + + /* super-fast path for ndarray argument calls */ + if (PyTuple_GET_SIZE(args) == 0) { + goto full_path; + } + op = PyTuple_GET_ITEM(args, 0); + if (PyArray_CheckExact(op)) { + PyObject * dtype_obj = Py_None; + oparr = (PyArrayObject *)op; + /* get dtype which can be positional */ + if (PyTuple_GET_SIZE(args) == 2) { + dtype_obj = PyTuple_GET_ITEM(args, 1); + } + else if (kws) { + dtype_obj = PyDict_GetItem(kws, npy_ma_str_dtype); + if (dtype_obj == NULL) { + dtype_obj = Py_None; + } + } + if (dtype_obj != Py_None) { + goto full_path; + } + + /* array(ndarray) */ + if (kws == NULL) { + ret = (PyArrayObject *)PyArray_NewCopy(oparr, order); + goto finish; + } + else { + /* fast path for copy=False rest default (np.asarray) */ + PyObject * copy_obj, * order_obj, *ndmin_obj; + copy_obj = PyDict_GetItem(kws, npy_ma_str_copy); + if (copy_obj != Py_False) { + goto full_path; + } + copy = NPY_FALSE; + + /* order does not matter for contiguous 1d arrays */ + if (PyArray_NDIM((PyArrayObject*)op) > 1 || + !PyArray_IS_C_CONTIGUOUS((PyArrayObject*)op)) { + order_obj = PyDict_GetItem(kws, npy_ma_str_order); + if (order_obj != Py_None && order_obj != NULL) { + goto full_path; + } + } + + ndmin_obj = PyDict_GetItem(kws, npy_ma_str_ndmin); + if (ndmin_obj) { + ndmin = PyLong_AsLong(ndmin_obj); + if (ndmin == -1 && PyErr_Occurred()) { + goto clean_type; + } + else if (ndmin > NPY_MAXDIMS) { + goto full_path; + } + } + + /* copy=False with default dtype, order and ndim */ + if (STRIDING_OK(oparr, order)) { + ret = oparr; + Py_INCREF(ret); + goto finish; + } + } + } + +full_path: if(!PyArg_ParseTupleAndKeywords(args, kws, "O|O&O&O&O&i", kwd, &op, PyArray_DescrConverter2, &type, @@ -1839,7 +1913,7 @@ array_scalar(PyObject *NPY_UNUSED(ignored), PyObject *args, PyObject *kwds) static char *kwlist[] = {"dtype","obj", NULL}; PyArray_Descr *typecode; - PyObject *obj = NULL; + PyObject *obj = NULL, *tmpobj = NULL; int alloc = 0; void *dptr; PyObject *ret; @@ -1871,14 +1945,31 @@ array_scalar(PyObject *NPY_UNUSED(ignored), PyObject *args, PyObject *kwds) alloc = 1; } else { +#if defined(NPY_PY3K) + /* Backward compatibility with Python 2 Numpy pickles */ + if (PyUnicode_Check(obj)) { + tmpobj = PyUnicode_AsLatin1String(obj); + obj = tmpobj; + if (tmpobj == NULL) { + /* More informative error message */ + PyErr_SetString(PyExc_ValueError, + ("Failed to encode Numpy scalar data string to latin1. " + "pickle.load(a, encoding='latin1') is assumed if unpickling.")); + return NULL; + } + } +#endif + if (!PyString_Check(obj)) { PyErr_SetString(PyExc_TypeError, "initializing object must be a string"); + Py_XDECREF(tmpobj); return NULL; } if (PyString_GET_SIZE(obj) < typecode->elsize) { PyErr_SetString(PyExc_ValueError, "initialization string is too small"); + Py_XDECREF(tmpobj); return NULL; } dptr = PyString_AS_STRING(obj); @@ -1890,6 +1981,7 @@ array_scalar(PyObject *NPY_UNUSED(ignored), PyObject *args, PyObject *kwds) if (alloc) { PyArray_free(dptr); } + Py_XDECREF(tmpobj); return ret; } @@ -4004,6 +4096,38 @@ set_flaginfo(PyObject *d) return; } +NPY_VISIBILITY_HIDDEN PyObject * npy_ma_str_array = NULL; +NPY_VISIBILITY_HIDDEN PyObject * npy_ma_str_array_prepare = NULL; +NPY_VISIBILITY_HIDDEN PyObject * npy_ma_str_array_wrap = NULL; +NPY_VISIBILITY_HIDDEN PyObject * npy_ma_str_array_finalize = NULL; +NPY_VISIBILITY_HIDDEN PyObject * npy_ma_str_buffer = NULL; +NPY_VISIBILITY_HIDDEN PyObject * npy_ma_str_ufunc = NULL; +NPY_VISIBILITY_HIDDEN PyObject * npy_ma_str_order = NULL; +NPY_VISIBILITY_HIDDEN PyObject * npy_ma_str_copy = NULL; +NPY_VISIBILITY_HIDDEN PyObject * npy_ma_str_dtype = NULL; +NPY_VISIBILITY_HIDDEN PyObject * npy_ma_str_ndmin = NULL; + +static int +intern_strings(void) +{ + npy_ma_str_array = PyUString_InternFromString("__array__"); + npy_ma_str_array_prepare = PyUString_InternFromString("__array_prepare__"); + npy_ma_str_array_wrap = PyUString_InternFromString("__array_wrap__"); + npy_ma_str_array_finalize = PyUString_InternFromString("__array_finalize__"); + npy_ma_str_buffer = PyUString_InternFromString("__buffer__"); + npy_ma_str_ufunc = PyUString_InternFromString("__numpy_ufunc__"); + npy_ma_str_order = PyUString_InternFromString("order"); + npy_ma_str_copy = PyUString_InternFromString("copy"); + npy_ma_str_dtype = PyUString_InternFromString("dtype"); + npy_ma_str_ndmin = PyUString_InternFromString("ndmin"); + + return npy_ma_str_array && npy_ma_str_array_prepare && + npy_ma_str_array_wrap && npy_ma_str_array_finalize && + npy_ma_str_array_finalize && npy_ma_str_ufunc && + npy_ma_str_order && npy_ma_str_copy && npy_ma_str_dtype && + npy_ma_str_ndmin; +} + #if defined(NPY_PY3K) static struct PyModuleDef moduledef = { PyModuleDef_HEAD_INIT, @@ -4176,6 +4300,10 @@ PyMODINIT_FUNC initmultiarray(void) { set_flaginfo(d); + if (!intern_strings()) { + goto err; + } + if (set_typeinfo(d) != 0) { goto err; } diff --git a/numpy/core/src/multiarray/multiarraymodule.h b/numpy/core/src/multiarray/multiarraymodule.h index 5a3b14b0b..82ae24845 100644 --- a/numpy/core/src/multiarray/multiarraymodule.h +++ b/numpy/core/src/multiarray/multiarraymodule.h @@ -1,4 +1,15 @@ #ifndef _NPY_MULTIARRAY_H_ #define _NPY_MULTIARRAY_H_ +NPY_VISIBILITY_HIDDEN extern PyObject * npy_ma_str_array; +NPY_VISIBILITY_HIDDEN extern PyObject * npy_ma_str_array_prepare; +NPY_VISIBILITY_HIDDEN extern PyObject * npy_ma_str_array_wrap; +NPY_VISIBILITY_HIDDEN extern PyObject * npy_ma_str_array_finalize; +NPY_VISIBILITY_HIDDEN extern PyObject * npy_ma_str_buffer; +NPY_VISIBILITY_HIDDEN extern PyObject * npy_ma_str_ufunc; +NPY_VISIBILITY_HIDDEN extern PyObject * npy_ma_str_order; +NPY_VISIBILITY_HIDDEN extern PyObject * npy_ma_str_copy; +NPY_VISIBILITY_HIDDEN extern PyObject * npy_ma_str_dtype; +NPY_VISIBILITY_HIDDEN extern PyObject * npy_ma_str_ndmin; + #endif diff --git a/numpy/core/src/multiarray/scalartypes.c.src b/numpy/core/src/multiarray/scalartypes.c.src index 110bef248..54f57bcf4 100644 --- a/numpy/core/src/multiarray/scalartypes.c.src +++ b/numpy/core/src/multiarray/scalartypes.c.src @@ -1078,6 +1078,24 @@ gentype_richcompare(PyObject *self, PyObject *other, int cmp_op) { PyObject *arr, *ret; + /* + * If the other object is None, False is always right. This avoids + * the array None comparison, at least until deprecation it is fixed. + * After that, this may be removed and numpy false would be returned. + * + * NOTE: np.equal(NaT, None) evaluates to TRUE! This is an + * an inconsistency, which may has to be considered + * when the deprecation is finished. + */ + if (other == Py_None) { + if (cmp_op == Py_EQ) { + Py_RETURN_FALSE; + } + if (cmp_op == Py_NE) { + Py_RETURN_TRUE; + } + } + arr = PyArray_FromScalar(self, NULL); if (arr == NULL) { return NULL; @@ -1175,6 +1193,20 @@ gentype_size_get(PyObject *NPY_UNUSED(self)) return PyInt_FromLong(1); } +static PyObject * +gentype_sizeof(PyObject *self) +{ + Py_ssize_t nbytes; + PyObject * isz = gentype_itemsize_get(self); + if (isz == NULL) { + return NULL; + } + nbytes = PyLong_AsLong(isz) + Py_TYPE(self)->tp_basicsize + + Py_SIZE(self) * Py_TYPE(self)->tp_itemsize; + Py_DECREF(isz); + return PyLong_FromSsize_t(nbytes); +} + #if PY_VERSION_HEX >= 0x03000000 NPY_NO_EXPORT void gentype_struct_free(PyObject *ptr) @@ -1903,6 +1935,11 @@ static PyMethodDef gentype_methods[] = { (PyCFunction)gentype_wraparray, METH_VARARGS, doc_sc_wraparray}, + /* for the sys module */ + {"__sizeof__", + (PyCFunction)gentype_sizeof, + METH_NOARGS, NULL}, + /* for the copy module */ {"__copy__", (PyCFunction)gentype_copy, diff --git a/numpy/core/src/multiarray/shape.c b/numpy/core/src/multiarray/shape.c index 2278b5d5b..e2acd3997 100644 --- a/numpy/core/src/multiarray/shape.c +++ b/numpy/core/src/multiarray/shape.c @@ -613,7 +613,7 @@ PyArray_SqueezeSelected(PyArrayObject *self, npy_bool *axis_flags) else { PyErr_SetString(PyExc_ValueError, "cannot select an axis to squeeze out " - "which has size greater than one"); + "which has size not equal to one"); return NULL; } } @@ -780,7 +780,8 @@ PyArray_Transpose(PyArrayObject *ap, PyArray_Dims *permute) PyArray_DIMS(ret)[i] = PyArray_DIMS(ap)[permutation[i]]; PyArray_STRIDES(ret)[i] = PyArray_STRIDES(ap)[permutation[i]]; } - PyArray_UpdateFlags(ret, NPY_ARRAY_C_CONTIGUOUS | NPY_ARRAY_F_CONTIGUOUS); + PyArray_UpdateFlags(ret, NPY_ARRAY_C_CONTIGUOUS | NPY_ARRAY_F_CONTIGUOUS | + NPY_ARRAY_ALIGNED); return (PyObject *)ret; } diff --git a/numpy/core/src/npymath/npy_math.c.src b/numpy/core/src/npymath/npy_math.c.src index 05af0b132..3a1be3745 100644 --- a/numpy/core/src/npymath/npy_math.c.src +++ b/numpy/core/src/npymath/npy_math.c.src @@ -343,6 +343,7 @@ double npy_log2(double x) * asinh, acosh, atanh * * hypot, atan2, pow, fmod, modf + * ldexp, frexp * * We assume the above are always available in their double versions. * @@ -405,6 +406,26 @@ double npy_log2(double x) } #endif +#ifdef ldexp@c@ +#undef ldexp@c@ +#endif +#ifndef HAVE_LDEXP@C@ +@type@ npy_ldexp@c@(@type@ x, int exp) +{ + return (@type@) npy_ldexp((double)x, exp); +} +#endif + +#ifdef frexp@c@ +#undef frexp@c@ +#endif +#ifndef HAVE_FREXP@C@ +@type@ npy_frexp@c@(@type@ x, int* exp) +{ + return (@type@) npy_frexp(x, exp); +} +#endif + /**end repeat**/ @@ -451,6 +472,20 @@ double npy_log2(double x) } #endif +#ifdef HAVE_LDEXP@C@ +@type@ npy_ldexp@c@(@type@ x, int exp) +{ + return ldexp@c@(x, exp); +} +#endif + +#ifdef HAVE_FREXP@C@ +@type@ npy_frexp@c@(@type@ x, int* exp) +{ + return frexp@c@(x, exp); +} +#endif + /**end repeat**/ diff --git a/numpy/core/src/npysort/heapsort.c.src b/numpy/core/src/npysort/heapsort.c.src index 84c9d7bd4..ba6c27f48 100644 --- a/numpy/core/src/npysort/heapsort.c.src +++ b/numpy/core/src/npysort/heapsort.c.src @@ -28,9 +28,9 @@ #define NPY_NO_DEPRECATED_API NPY_API_VERSION -#include <stdlib.h> #include "npy_sort.h" #include "npysort_common.h" +#include <stdlib.h> #define NOT_USED NPY_UNUSED(unused) #define PYA_QS_STACK 100 diff --git a/numpy/core/src/npysort/mergesort.c.src b/numpy/core/src/npysort/mergesort.c.src index 7f98c4016..c99c0e614 100644 --- a/numpy/core/src/npysort/mergesort.c.src +++ b/numpy/core/src/npysort/mergesort.c.src @@ -28,9 +28,9 @@ #define NPY_NO_DEPRECATED_API NPY_API_VERSION -#include <stdlib.h> #include "npy_sort.h" #include "npysort_common.h" +#include <stdlib.h> #define NOT_USED NPY_UNUSED(unused) #define PYA_QS_STACK 100 diff --git a/numpy/core/src/npysort/quicksort.c.src b/numpy/core/src/npysort/quicksort.c.src index 272615ab3..a27530eb4 100644 --- a/numpy/core/src/npysort/quicksort.c.src +++ b/numpy/core/src/npysort/quicksort.c.src @@ -28,9 +28,9 @@ #define NPY_NO_DEPRECATED_API NPY_API_VERSION -#include <stdlib.h> #include "npy_sort.h" #include "npysort_common.h" +#include <stdlib.h> #define NOT_USED NPY_UNUSED(unused) #define PYA_QS_STACK 100 diff --git a/numpy/core/src/npysort/selection.c.src b/numpy/core/src/npysort/selection.c.src index 920c07ec6..4167b2694 100644 --- a/numpy/core/src/npysort/selection.c.src +++ b/numpy/core/src/npysort/selection.c.src @@ -390,7 +390,10 @@ int /* move pivot into position */ SWAP(SORTEE(low), SORTEE(hh)); - store_pivot(hh, kth, pivots, npiv); + /* kth pivot stored later */ + if (hh != kth) { + store_pivot(hh, kth, pivots, npiv); + } if (hh >= kth) high = hh - 1; @@ -400,10 +403,11 @@ int /* two elements */ if (high == low + 1) { - if (@TYPE@_LT(v[IDX(high)], v[IDX(low)])) + if (@TYPE@_LT(v[IDX(high)], v[IDX(low)])) { SWAP(SORTEE(high), SORTEE(low)) - store_pivot(low, kth, pivots, npiv); + } } + store_pivot(kth, kth, pivots, npiv); return 0; } diff --git a/numpy/core/src/private/npy_config.h b/numpy/core/src/private/npy_config.h index 453dbd065..71d448ee9 100644 --- a/numpy/core/src/private/npy_config.h +++ b/numpy/core/src/private/npy_config.h @@ -10,6 +10,17 @@ #undef HAVE_HYPOT #endif +/* + * largest alignment the copy loops might require + * required as string, void and complex types might get copied using larger + * instructions than required to operate on them. E.g. complex float is copied + * in 8 byte moves but arithmetic on them only loads in 4 byte moves. + * the sparc platform may need that alignment for long doubles. + * amd64 is not harmed much by the bloat as the system provides 16 byte + * alignment by default. + */ +#define NPY_MAX_COPY_ALIGNMENT 16 + /* Safe to use ldexp and frexp for long double for MSVC builds */ #if (NPY_SIZEOF_LONGDOUBLE == NPY_SIZEOF_DOUBLE) || defined(_MSC_VER) #ifdef HAVE_LDEXP diff --git a/numpy/core/src/private/ufunc_override.h b/numpy/core/src/private/ufunc_override.h index 6b0f73fcf..c47c46a66 100644 --- a/numpy/core/src/private/ufunc_override.h +++ b/numpy/core/src/private/ufunc_override.h @@ -26,6 +26,7 @@ normalize___call___args(PyUFuncObject *ufunc, PyObject *args, else { obj = PyTuple_GetSlice(args, nin, nargs); PyDict_SetItemString(*normal_kwds, "out", obj); + Py_DECREF(obj); } } } diff --git a/numpy/core/src/umath/funcs.inc.src b/numpy/core/src/umath/funcs.inc.src index 9df39e41f..3aad44c9f 100644 --- a/numpy/core/src/umath/funcs.inc.src +++ b/numpy/core/src/umath/funcs.inc.src @@ -55,8 +55,6 @@ npy_ObjectPower(PyObject *x, PyObject *y) return PyNumber_Power(x, y, Py_None); } - -#if defined(NPY_PY3K) /**begin repeat * #Kind = Max, Min# * #OP = Py_GE, Py_LE# @@ -82,33 +80,6 @@ npy_Object@Kind@(PyObject *i1, PyObject *i2) } /**end repeat**/ -#else -/**begin repeat - * #Kind = Max, Min# - * #OP = >=, <=# - */ -static PyObject * -npy_Object@Kind@(PyObject *i1, PyObject *i2) -{ - PyObject *result; - int cmp; - - if (PyObject_Cmp(i1, i2, &cmp) < 0) { - return NULL; - } - if (cmp @OP@ 0) { - result = i1; - } - else { - result = i2; - } - Py_INCREF(result); - return result; -} -/**end repeat**/ -#endif - - /* Emulates Python's 'a or b' behavior */ static PyObject * npy_ObjectLogicalOr(PyObject *i1, PyObject *i2) diff --git a/numpy/core/src/umath/loops.c.src b/numpy/core/src/umath/loops.c.src index 89f1206b4..d747864f8 100644 --- a/numpy/core/src/umath/loops.c.src +++ b/numpy/core/src/umath/loops.c.src @@ -1743,25 +1743,22 @@ NPY_NO_EXPORT void } } -#ifdef HAVE_FREXP@C@ NPY_NO_EXPORT void @TYPE@_frexp(char **args, npy_intp *dimensions, npy_intp *steps, void *NPY_UNUSED(func)) { UNARY_LOOP_TWO_OUT { const @type@ in1 = *(@type@ *)ip1; - *((@type@ *)op1) = frexp@c@(in1, (int *)op2); + *((@type@ *)op1) = npy_frexp@c@(in1, (int *)op2); } } -#endif -#ifdef HAVE_LDEXP@C@ NPY_NO_EXPORT void @TYPE@_ldexp(char **args, npy_intp *dimensions, npy_intp *steps, void *NPY_UNUSED(func)) { BINARY_LOOP { const @type@ in1 = *(@type@ *)ip1; const int in2 = *(int *)ip2; - *((@type@ *)op1) = ldexp@c@(in1, in2); + *((@type@ *)op1) = npy_ldexp@c@(in1, in2); } } @@ -1778,7 +1775,7 @@ NPY_NO_EXPORT void const long in2 = *(long *)ip2; if (((int)in2) == in2) { /* Range OK */ - *((@type@ *)op1) = ldexp@c@(in1, ((int)in2)); + *((@type@ *)op1) = npy_ldexp@c@(in1, ((int)in2)); } else { /* @@ -1786,15 +1783,14 @@ NPY_NO_EXPORT void * given that exponent has less bits than npy_int. */ if (in2 > 0) { - *((@type@ *)op1) = ldexp@c@(in1, NPY_MAX_INT); + *((@type@ *)op1) = npy_ldexp@c@(in1, NPY_MAX_INT); } else { - *((@type@ *)op1) = ldexp@c@(in1, NPY_MIN_INT); + *((@type@ *)op1) = npy_ldexp@c@(in1, NPY_MIN_INT); } } } } -#endif #define @TYPE@_true_divide @TYPE@_divide @@ -2059,25 +2055,22 @@ HALF_modf(char **args, npy_intp *dimensions, npy_intp *steps, void *NPY_UNUSED(f } } -#ifdef HAVE_FREXPF NPY_NO_EXPORT void HALF_frexp(char **args, npy_intp *dimensions, npy_intp *steps, void *NPY_UNUSED(func)) { UNARY_LOOP_TWO_OUT { const float in1 = npy_half_to_float(*(npy_half *)ip1); - *((npy_half *)op1) = npy_float_to_half(frexpf(in1, (int *)op2)); + *((npy_half *)op1) = npy_float_to_half(npy_frexpf(in1, (int *)op2)); } } -#endif -#ifdef HAVE_LDEXPF NPY_NO_EXPORT void HALF_ldexp(char **args, npy_intp *dimensions, npy_intp *steps, void *NPY_UNUSED(func)) { BINARY_LOOP { const float in1 = npy_half_to_float(*(npy_half *)ip1); const int in2 = *(int *)ip2; - *((npy_half *)op1) = npy_float_to_half(ldexpf(in1, in2)); + *((npy_half *)op1) = npy_float_to_half(npy_ldexpf(in1, in2)); } } @@ -2094,7 +2087,7 @@ HALF_ldexp_long(char **args, npy_intp *dimensions, npy_intp *steps, void *NPY_UN const long in2 = *(long *)ip2; if (((int)in2) == in2) { /* Range OK */ - *((npy_half *)op1) = npy_float_to_half(ldexpf(in1, ((int)in2))); + *((npy_half *)op1) = npy_float_to_half(npy_ldexpf(in1, ((int)in2))); } else { /* @@ -2102,15 +2095,14 @@ HALF_ldexp_long(char **args, npy_intp *dimensions, npy_intp *steps, void *NPY_UN * given that exponent has less bits than npy_int. */ if (in2 > 0) { - *((npy_half *)op1) = npy_float_to_half(ldexpf(in1, NPY_MAX_INT)); + *((npy_half *)op1) = npy_float_to_half(npy_ldexpf(in1, NPY_MAX_INT)); } else { - *((npy_half *)op1) = npy_float_to_half(ldexpf(in1, NPY_MIN_INT)); + *((npy_half *)op1) = npy_float_to_half(npy_ldexpf(in1, NPY_MIN_INT)); } } } } -#endif #define HALF_true_divide HALF_divide @@ -2572,6 +2564,7 @@ OBJECT_@kind@(char **args, npy_intp *dimensions, npy_intp *steps, void *NPY_UNUS return; } ret = PyObject_IsTrue(ret_obj); + Py_DECREF(ret_obj); if (ret == -1) { #if @identity@ != -1 if (in1 == in2) { @@ -2621,6 +2614,7 @@ OBJECT_sign(char **args, npy_intp *dimensions, npy_intp *steps, void *NPY_UNUSED } ret = PyLong_FromLong(v); if (PyErr_Occurred()) { + Py_DECREF(zero); return; } Py_XDECREF(*out); @@ -2635,6 +2629,7 @@ OBJECT_sign(char **args, npy_intp *dimensions, npy_intp *steps, void *NPY_UNUSED PyObject *ret = PyInt_FromLong( PyObject_Compare(in1 ? in1 : Py_None, zero)); if (PyErr_Occurred()) { + Py_DECREF(zero); return; } Py_XDECREF(*out); diff --git a/numpy/core/src/umath/loops.h.src b/numpy/core/src/umath/loops.h.src index fdc9230de..a6e775a3a 100644 --- a/numpy/core/src/umath/loops.h.src +++ b/numpy/core/src/umath/loops.h.src @@ -248,17 +248,13 @@ NPY_NO_EXPORT void NPY_NO_EXPORT void @TYPE@_modf(char **args, npy_intp *dimensions, npy_intp *steps, void *NPY_UNUSED(func)); -#ifdef HAVE_FREXP@C@ NPY_NO_EXPORT void @TYPE@_frexp(char **args, npy_intp *dimensions, npy_intp *steps, void *NPY_UNUSED(func)); -#endif -#ifdef HAVE_LDEXP@C@ NPY_NO_EXPORT void @TYPE@_ldexp(char **args, npy_intp *dimensions, npy_intp *steps, void *NPY_UNUSED(func)); NPY_NO_EXPORT void @TYPE@_ldexp_long(char **args, npy_intp *dimensions, npy_intp *steps, void *NPY_UNUSED(func)); -#endif #define @TYPE@_true_divide @TYPE@_divide diff --git a/numpy/core/src/umath/operand_flag_tests.c.src b/numpy/core/src/umath/operand_flag_tests.c.src index 4fb428bfc..046c37595 100644 --- a/numpy/core/src/umath/operand_flag_tests.c.src +++ b/numpy/core/src/umath/operand_flag_tests.c.src @@ -1,11 +1,11 @@ #define NPY_NO_DEPRECATED_API NPY_API_VERSION -#include <math.h> #include <Python.h> -#include <structmember.h> #include <numpy/arrayobject.h> #include <numpy/ufuncobject.h> #include "numpy/npy_3kcompat.h" +#include <math.h> +#include <structmember.h> static PyMethodDef TestMethods[] = { diff --git a/numpy/core/src/umath/simd.inc.src b/numpy/core/src/umath/simd.inc.src index 92dc0c659..5b111eb0d 100644 --- a/numpy/core/src/umath/simd.inc.src +++ b/numpy/core/src/umath/simd.inc.src @@ -37,7 +37,9 @@ ((abs(args[1] - args[0]) >= (vsize)) || ((abs(args[1] - args[0]) == 0)))) #define IS_BLOCKABLE_REDUCE(esize, vsize) \ - (steps[1] == (esize) && abs(args[1] - args[0]) >= (vsize)) + (steps[1] == (esize) && abs(args[1] - args[0]) >= (vsize) && \ + npy_is_aligned(args[1], (esize)) && \ + npy_is_aligned(args[0], (esize))) #define IS_BLOCKABLE_BINARY(esize, vsize) \ (steps[0] == steps[1] && steps[1] == steps[2] && steps[2] == (esize) && \ @@ -480,14 +482,18 @@ sse2_binary_scalar2_@kind@_@TYPE@(@type@ * op, @type@ * ip1, @type@ * ip2, npy_i /**end repeat1**/ -/* compress 4 vectors to 4/8 bytes in op with filled with 0 or 1 */ +/* + * compress 4 vectors to 4/8 bytes in op with filled with 0 or 1 + * the last vector is passed as a pointer as MSVC 2010 is unable to ignore the + * calling convention leading to C2719 on 32 bit, see #4795 + */ static NPY_INLINE void -sse2_compress4_to_byte_@TYPE@(@vtype@ r1, @vtype@ r2, @vtype@ r3, @vtype@ r4, +sse2_compress4_to_byte_@TYPE@(@vtype@ r1, @vtype@ r2, @vtype@ r3, @vtype@ * r4, npy_bool * op) { const __m128i mask = @vpre@_set1_epi8(0x1); __m128i ir1 = @vpre@_packs_epi32(@cast@(r1), @cast@(r2)); - __m128i ir2 = @vpre@_packs_epi32(@cast@(r3), @cast@(r4)); + __m128i ir2 = @vpre@_packs_epi32(@cast@(r3), @cast@(*r4)); __m128i rr = @vpre@_packs_epi16(ir1, ir2); #if @double@ rr = @vpre@_packs_epi16(rr, rr); @@ -535,7 +541,7 @@ sse2_binary_@kind@_@TYPE@(npy_bool * op, @type@ * ip1, @type@ * ip2, npy_intp n) @vtype@ r2 = @vpre@_@VOP@_@vsuf@(b, b); @vtype@ r3 = @vpre@_@VOP@_@vsuf@(c, c); @vtype@ r4 = @vpre@_@VOP@_@vsuf@(d, d); - sse2_compress4_to_byte_@TYPE@(r1, r2, r3, r4, &op[i]); + sse2_compress4_to_byte_@TYPE@(r1, r2, r3, &r4, &op[i]); } } else { @@ -552,7 +558,7 @@ sse2_binary_@kind@_@TYPE@(npy_bool * op, @type@ * ip1, @type@ * ip2, npy_intp n) @vtype@ r2 = @vpre@_@VOP@_@vsuf@(b1, b2); @vtype@ r3 = @vpre@_@VOP@_@vsuf@(c1, c2); @vtype@ r4 = @vpre@_@VOP@_@vsuf@(d1, d2); - sse2_compress4_to_byte_@TYPE@(r1, r2, r3, r4, &op[i]); + sse2_compress4_to_byte_@TYPE@(r1, r2, r3, &r4, &op[i]); } } LOOP_BLOCKED_END { @@ -577,7 +583,7 @@ sse2_binary_scalar1_@kind@_@TYPE@(npy_bool * op, @type@ * ip1, @type@ * ip2, npy @vtype@ r2 = @vpre@_@VOP@_@vsuf@(s, b); @vtype@ r3 = @vpre@_@VOP@_@vsuf@(s, c); @vtype@ r4 = @vpre@_@VOP@_@vsuf@(s, d); - sse2_compress4_to_byte_@TYPE@(r1, r2, r3, r4, &op[i]); + sse2_compress4_to_byte_@TYPE@(r1, r2, r3, &r4, &op[i]); } LOOP_BLOCKED_END { op[i] = sse2_ordered_cmp_@kind@_@TYPE@(ip1[0], ip2[i]); @@ -601,7 +607,7 @@ sse2_binary_scalar2_@kind@_@TYPE@(npy_bool * op, @type@ * ip1, @type@ * ip2, npy @vtype@ r2 = @vpre@_@VOP@_@vsuf@(b, s); @vtype@ r3 = @vpre@_@VOP@_@vsuf@(c, s); @vtype@ r4 = @vpre@_@VOP@_@vsuf@(d, s); - sse2_compress4_to_byte_@TYPE@(r1, r2, r3, r4, &op[i]); + sse2_compress4_to_byte_@TYPE@(r1, r2, r3, &r4, &op[i]); } LOOP_BLOCKED_END { op[i] = sse2_ordered_cmp_@kind@_@TYPE@(ip1[i], ip2[0]); diff --git a/numpy/core/src/umath/test_rational.c.src b/numpy/core/src/umath/test_rational.c.src index 5ce792266..5c4f29f73 100644 --- a/numpy/core/src/umath/test_rational.c.src +++ b/numpy/core/src/umath/test_rational.c.src @@ -2,12 +2,12 @@ #define NPY_NO_DEPRECATED_API NPY_API_VERSION -#include <math.h> #include <Python.h> #include <structmember.h> #include <numpy/arrayobject.h> #include <numpy/ufuncobject.h> #include <numpy/npy_3kcompat.h> +#include <math.h> /* Relevant arithmetic exceptions */ diff --git a/numpy/core/src/umath/ufunc_object.c b/numpy/core/src/umath/ufunc_object.c index d825f15e9..385d59f88 100644 --- a/numpy/core/src/umath/ufunc_object.c +++ b/numpy/core/src/umath/ufunc_object.c @@ -73,7 +73,7 @@ static int _does_loop_use_arrays(void *data); static int -_extract_pyvals(PyObject *ref, char *name, int *bufsize, +_extract_pyvals(PyObject *ref, const char *name, int *bufsize, int *errmask, PyObject **errobj); static int @@ -237,7 +237,7 @@ static int PyUFunc_NUM_NODEFAULTS = 0; #endif static PyObject * -_get_global_ext_obj(char * name) +get_global_ext_obj(void) { PyObject *thedict; PyObject *ref = NULL; @@ -259,12 +259,12 @@ _get_global_ext_obj(char * name) static int -_get_bufsize_errmask(PyObject * extobj, char * ufunc_name, +_get_bufsize_errmask(PyObject * extobj, const char *ufunc_name, int *buffersize, int *errormask) { /* Get the buffersize and errormask */ if (extobj == NULL) { - extobj = _get_global_ext_obj(ufunc_name); + extobj = get_global_ext_obj(); } if (_extract_pyvals(extobj, ufunc_name, buffersize, errormask, NULL) < 0) { @@ -430,7 +430,7 @@ _find_array_prepare(PyObject *args, PyObject *kwds, * if an error handling method is 'call' */ static int -_extract_pyvals(PyObject *ref, char *name, int *bufsize, +_extract_pyvals(PyObject *ref, const char *name, int *bufsize, int *errmask, PyObject **errobj) { PyObject *retval; @@ -518,41 +518,41 @@ _extract_pyvals(PyObject *ref, char *name, int *bufsize, NPY_NO_EXPORT int PyUFunc_GetPyValues(char *name, int *bufsize, int *errmask, PyObject **errobj) { - PyObject *ref = _get_global_ext_obj(name); + PyObject *ref = get_global_ext_obj(); return _extract_pyvals(ref, name, bufsize, errmask, errobj); } -#define _GETATTR_(str, rstr) do {if (strcmp(name, #str) == 0) \ +#define GETATTR(str, rstr) do {if (strcmp(name, #str) == 0) \ return PyObject_HasAttrString(op, "__" #rstr "__");} while (0); static int -_has_reflected_op(PyObject *op, char *name) +_has_reflected_op(PyObject *op, const char *name) { - _GETATTR_(add, radd); - _GETATTR_(subtract, rsub); - _GETATTR_(multiply, rmul); - _GETATTR_(divide, rdiv); - _GETATTR_(true_divide, rtruediv); - _GETATTR_(floor_divide, rfloordiv); - _GETATTR_(remainder, rmod); - _GETATTR_(power, rpow); - _GETATTR_(left_shift, rlshift); - _GETATTR_(right_shift, rrshift); - _GETATTR_(bitwise_and, rand); - _GETATTR_(bitwise_xor, rxor); - _GETATTR_(bitwise_or, ror); + GETATTR(add, radd); + GETATTR(subtract, rsub); + GETATTR(multiply, rmul); + GETATTR(divide, rdiv); + GETATTR(true_divide, rtruediv); + GETATTR(floor_divide, rfloordiv); + GETATTR(remainder, rmod); + GETATTR(power, rpow); + GETATTR(left_shift, rlshift); + GETATTR(right_shift, rrshift); + GETATTR(bitwise_and, rand); + GETATTR(bitwise_xor, rxor); + GETATTR(bitwise_or, ror); /* Comparisons */ - _GETATTR_(equal, eq); - _GETATTR_(not_equal, ne); - _GETATTR_(greater, lt); - _GETATTR_(less, gt); - _GETATTR_(greater_equal, le); - _GETATTR_(less_equal, ge); + GETATTR(equal, eq); + GETATTR(not_equal, ne); + GETATTR(greater, lt); + GETATTR(less, gt); + GETATTR(greater_equal, le); + GETATTR(less_equal, ge); return 0; } -#undef _GETATTR_ +#undef GETATTR /* Return the position of next non-white-space char in the string */ @@ -779,7 +779,7 @@ static int get_ufunc_arguments(PyUFuncObject *ufunc, int i, nargs, nin = ufunc->nin; PyObject *obj, *context; PyObject *str_key_obj = NULL; - char *ufunc_name; + const char *ufunc_name; int type_num; int any_flexible = 0, any_object = 0, any_flexible_userloops = 0; @@ -1762,7 +1762,7 @@ make_arr_prep_args(npy_intp nin, PyObject *args, PyObject *kwds) * - ufunc_name: name of ufunc */ static int -_check_ufunc_fperr(int errmask, PyObject *extobj, char* ufunc_name) { +_check_ufunc_fperr(int errmask, PyObject *extobj, const char *ufunc_name) { int fperr; PyObject *errobj = NULL; int ret; @@ -1778,7 +1778,7 @@ _check_ufunc_fperr(int errmask, PyObject *extobj, char* ufunc_name) { /* Get error object globals */ if (extobj == NULL) { - extobj = _get_global_ext_obj(ufunc_name); + extobj = get_global_ext_obj(); } if (_extract_pyvals(extobj, ufunc_name, NULL, NULL, &errobj) < 0) { @@ -1800,7 +1800,7 @@ PyUFunc_GeneralizedFunction(PyUFuncObject *ufunc, { int nin, nout; int i, j, idim, nop; - char *ufunc_name; + const char *ufunc_name; int retval = -1, subok = 1; int needs_api = 0; @@ -2325,7 +2325,7 @@ PyUFunc_GenericFunction(PyUFuncObject *ufunc, { int nin, nout; int i, nop; - char *ufunc_name; + const char *ufunc_name; int retval = -1, subok = 1; int need_fancy = 0; @@ -2640,7 +2640,7 @@ reduce_type_resolver(PyUFuncObject *ufunc, PyArrayObject *arr, int i, retcode; PyArrayObject *op[3] = {arr, arr, NULL}; PyArray_Descr *dtypes[3] = {NULL, NULL, NULL}; - char *ufunc_name = ufunc->name ? ufunc->name : "(unknown)"; + const char *ufunc_name = ufunc->name ? ufunc->name : "(unknown)"; PyObject *type_tup = NULL; *out_dtype = NULL; @@ -2816,7 +2816,7 @@ PyUFunc_Reduce(PyUFuncObject *ufunc, PyArrayObject *arr, PyArrayObject *out, PyArray_Descr *dtype; PyArrayObject *result; PyArray_AssignReduceIdentityFunc *assign_identity = NULL; - char *ufunc_name = ufunc->name ? ufunc->name : "(unknown)"; + const char *ufunc_name = ufunc->name ? ufunc->name : "(unknown)"; /* These parameters come from a TLS global */ int buffersize = 0, errormask = 0; @@ -2912,7 +2912,7 @@ PyUFunc_Accumulate(PyUFuncObject *ufunc, PyArrayObject *arr, PyArrayObject *out, PyUFuncGenericFunction innerloop = NULL; void *innerloopdata = NULL; - char *ufunc_name = ufunc->name ? ufunc->name : "(unknown)"; + const char *ufunc_name = ufunc->name ? ufunc->name : "(unknown)"; /* These parameters come from extobj= or from a TLS global */ int buffersize = 0, errormask = 0; @@ -3265,7 +3265,7 @@ PyUFunc_Reduceat(PyUFuncObject *ufunc, PyArrayObject *arr, PyArrayObject *ind, PyUFuncGenericFunction innerloop = NULL; void *innerloopdata = NULL; - char *ufunc_name = ufunc->name ? ufunc->name : "(unknown)"; + const char *ufunc_name = ufunc->name ? ufunc->name : "(unknown)"; char *opname = "reduceat"; /* These parameters come from extobj= or from a TLS global */ @@ -3750,7 +3750,7 @@ PyUFunc_GenericReduction(PyUFuncObject *ufunc, PyObject *args, } for (i = 0; i < naxes; ++i) { PyObject *tmp = PyTuple_GET_ITEM(axes_in, i); - long axis = PyInt_AsLong(tmp); + int axis = PyArray_PyIntAsInt(tmp); if (axis == -1 && PyErr_Occurred()) { Py_XDECREF(otype); Py_DECREF(mp); @@ -3771,7 +3771,7 @@ PyUFunc_GenericReduction(PyUFuncObject *ufunc, PyObject *args, } /* Try to interpret axis as an integer */ else { - long axis = PyInt_AsLong(axes_in); + int axis = PyArray_PyIntAsInt(axes_in); /* TODO: PyNumber_Index would be good to use here */ if (axis == -1 && PyErr_Occurred()) { Py_XDECREF(otype); @@ -4305,7 +4305,7 @@ NPY_NO_EXPORT PyObject * PyUFunc_FromFuncAndData(PyUFuncGenericFunction *func, void **data, char *types, int ntypes, int nin, int nout, int identity, - char *name, char *doc, int check_return) + const char *name, const char *doc, int check_return) { return PyUFunc_FromFuncAndDataAndSignature(func, data, types, ntypes, nin, nout, identity, name, doc, check_return, NULL); @@ -4316,7 +4316,7 @@ NPY_NO_EXPORT PyObject * PyUFunc_FromFuncAndDataAndSignature(PyUFuncGenericFunction *func, void **data, char *types, int ntypes, int nin, int nout, int identity, - char *name, char *doc, + const char *name, const char *doc, int check_return, const char *signature) { PyUFuncObject *ufunc; diff --git a/numpy/core/src/umath/ufunc_type_resolution.c b/numpy/core/src/umath/ufunc_type_resolution.c index ffdb15bbe..3beb25cf1 100644 --- a/numpy/core/src/umath/ufunc_type_resolution.c +++ b/numpy/core/src/umath/ufunc_type_resolution.c @@ -58,7 +58,7 @@ PyUFunc_ValidateCasting(PyUFuncObject *ufunc, PyArray_Descr **dtypes) { int i, nin = ufunc->nin, nop = nin + ufunc->nout; - char *ufunc_name; + const char *ufunc_name; ufunc_name = ufunc->name ? ufunc->name : "<unnamed ufunc>"; @@ -186,7 +186,7 @@ PyUFunc_SimpleBinaryComparisonTypeResolver(PyUFuncObject *ufunc, PyArray_Descr **out_dtypes) { int i, type_num1, type_num2; - char *ufunc_name; + const char *ufunc_name; ufunc_name = ufunc->name ? ufunc->name : "<unnamed ufunc>"; @@ -292,7 +292,7 @@ PyUFunc_SimpleUnaryOperationTypeResolver(PyUFuncObject *ufunc, PyArray_Descr **out_dtypes) { int i, type_num1; - char *ufunc_name; + const char *ufunc_name; ufunc_name = ufunc->name ? ufunc->name : "<unnamed ufunc>"; @@ -433,7 +433,7 @@ PyUFunc_SimpleBinaryOperationTypeResolver(PyUFuncObject *ufunc, PyArray_Descr **out_dtypes) { int i, type_num1, type_num2; - char *ufunc_name; + const char *ufunc_name; ufunc_name = ufunc->name ? ufunc->name : "<unnamed ufunc>"; @@ -591,7 +591,7 @@ PyUFunc_AdditionTypeResolver(PyUFuncObject *ufunc, { int type_num1, type_num2; int i; - char *ufunc_name; + const char *ufunc_name; ufunc_name = ufunc->name ? ufunc->name : "<unnamed ufunc>"; @@ -781,7 +781,7 @@ PyUFunc_SubtractionTypeResolver(PyUFuncObject *ufunc, { int type_num1, type_num2; int i; - char *ufunc_name; + const char *ufunc_name; ufunc_name = ufunc->name ? ufunc->name : "<unnamed ufunc>"; @@ -963,7 +963,7 @@ PyUFunc_MultiplicationTypeResolver(PyUFuncObject *ufunc, { int type_num1, type_num2; int i; - char *ufunc_name; + const char *ufunc_name; ufunc_name = ufunc->name ? ufunc->name : "<unnamed ufunc>"; @@ -1106,7 +1106,7 @@ PyUFunc_DivisionTypeResolver(PyUFuncObject *ufunc, { int type_num1, type_num2; int i; - char *ufunc_name; + const char *ufunc_name; ufunc_name = ufunc->name ? ufunc->name : "<unnamed ufunc>"; @@ -1875,7 +1875,7 @@ linear_search_type_resolver(PyUFuncObject *self, { npy_intp i, j, nin = self->nin, nop = nin + self->nout; int types[NPY_MAXARGS]; - char *ufunc_name; + const char *ufunc_name; int no_castable_output, use_min_scalar; /* For making a better error message on coercion error */ @@ -1984,7 +1984,7 @@ type_tuple_type_resolver(PyUFuncObject *self, npy_intp i, j, n, nin = self->nin, nop = nin + self->nout; int n_specified = 0; int specified_types[NPY_MAXARGS], types[NPY_MAXARGS]; - char *ufunc_name; + const char *ufunc_name; int no_castable_output, use_min_scalar; /* For making a better error message on coercion error */ diff --git a/numpy/core/src/umath/umathmodule.c b/numpy/core/src/umath/umathmodule.c index 3ed7ee771..57b2bb239 100644 --- a/numpy/core/src/umath/umathmodule.c +++ b/numpy/core/src/umath/umathmodule.c @@ -54,16 +54,15 @@ object_ufunc_type_resolver(PyUFuncObject *ufunc, PyArray_Descr **out_dtypes) { int i, nop = ufunc->nin + ufunc->nout; - PyArray_Descr *obj_dtype; - obj_dtype = PyArray_DescrFromType(NPY_OBJECT); - if (obj_dtype == NULL) { + out_dtypes[0] = PyArray_DescrFromType(NPY_OBJECT); + if (out_dtypes[0] == NULL) { return -1; } - for (i = 0; i < nop; ++i) { - Py_INCREF(obj_dtype); - out_dtypes[i] = obj_dtype; + for (i = 1; i < nop; ++i) { + Py_INCREF(out_dtypes[0]); + out_dtypes[i] = out_dtypes[0]; } return 0; @@ -202,182 +201,6 @@ ufunc_frompyfunc(PyObject *NPY_UNUSED(dummy), PyObject *args, PyObject *NPY_UNUS ***************************************************************************** */ -/* Less automated additions to the ufuncs */ - -static PyUFuncGenericFunction frexp_functions[] = { -#ifdef HAVE_FREXPF - HALF_frexp, - FLOAT_frexp, -#endif - DOUBLE_frexp -#ifdef HAVE_FREXPL - ,LONGDOUBLE_frexp -#endif -}; - -static void * blank3_data[] = { (void *)NULL, (void *)NULL, (void *)NULL}; -static void * blank6_data[] = { (void *)NULL, (void *)NULL, (void *)NULL, - (void *)NULL, (void *)NULL, (void *)NULL}; -static char frexp_signatures[] = { -#ifdef HAVE_FREXPF - NPY_HALF, NPY_HALF, NPY_INT, - NPY_FLOAT, NPY_FLOAT, NPY_INT, -#endif - NPY_DOUBLE, NPY_DOUBLE, NPY_INT -#ifdef HAVE_FREXPL - ,NPY_LONGDOUBLE, NPY_LONGDOUBLE, NPY_INT -#endif -}; - -#if NPY_SIZEOF_LONG == NPY_SIZEOF_INT -#define LDEXP_LONG(typ) typ##_ldexp -#else -#define LDEXP_LONG(typ) typ##_ldexp_long -#endif - -static PyUFuncGenericFunction ldexp_functions[] = { -#ifdef HAVE_LDEXPF - HALF_ldexp, - FLOAT_ldexp, - LDEXP_LONG(HALF), - LDEXP_LONG(FLOAT), -#endif - DOUBLE_ldexp, - LDEXP_LONG(DOUBLE) -#ifdef HAVE_LDEXPL - , - LONGDOUBLE_ldexp, - LDEXP_LONG(LONGDOUBLE) -#endif -}; - -static const char frdoc[] = - " Decompose the elements of x into mantissa and twos exponent.\n" - "\n" - " Returns (`mantissa`, `exponent`), where `x = mantissa * 2**exponent``.\n" - " The mantissa is lies in the open interval(-1, 1), while the twos\n" - " exponent is a signed integer.\n" - "\n" - " Parameters\n" - " ----------\n" - " x : array_like\n" - " Array of numbers to be decomposed.\n" - " out1: ndarray, optional\n" - " Output array for the mantissa. Must have the same shape as `x`.\n" - " out2: ndarray, optional\n" - " Output array for the exponent. Must have the same shape as `x`.\n" - "\n" - " Returns\n" - " -------\n" - " (mantissa, exponent) : tuple of ndarrays, (float, int)\n" - " `mantissa` is a float array with values between -1 and 1.\n" - " `exponent` is an int array which represents the exponent of 2.\n" - "\n" - " See Also\n" - " --------\n" - " ldexp : Compute ``y = x1 * 2**x2``, the inverse of `frexp`.\n" - "\n" - " Notes\n" - " -----\n" - " Complex dtypes are not supported, they will raise a TypeError.\n" - "\n" - " Examples\n" - " --------\n" - " >>> x = np.arange(9)\n" - " >>> y1, y2 = np.frexp(x)\n" - " >>> y1\n" - " array([ 0. , 0.5 , 0.5 , 0.75 , 0.5 , 0.625, 0.75 , 0.875,\n" - " 0.5 ])\n" - " >>> y2\n" - " array([0, 1, 2, 2, 3, 3, 3, 3, 4])\n" - " >>> y1 * 2**y2\n" - " array([ 0., 1., 2., 3., 4., 5., 6., 7., 8.])\n" - "\n"; - - -static char ldexp_signatures[] = { -#ifdef HAVE_LDEXPF - NPY_HALF, NPY_INT, NPY_HALF, - NPY_FLOAT, NPY_INT, NPY_FLOAT, - NPY_HALF, NPY_LONG, NPY_HALF, - NPY_FLOAT, NPY_LONG, NPY_FLOAT, -#endif - NPY_DOUBLE, NPY_INT, NPY_DOUBLE, - NPY_DOUBLE, NPY_LONG, NPY_DOUBLE -#ifdef HAVE_LDEXPL - ,NPY_LONGDOUBLE, NPY_INT, NPY_LONGDOUBLE - ,NPY_LONGDOUBLE, NPY_LONG, NPY_LONGDOUBLE -#endif -}; - -static const char lddoc[] = - " Returns x1 * 2**x2, element-wise.\n" - "\n" - " The mantissas `x1` and twos exponents `x2` are used to construct\n" - " floating point numbers ``x1 * 2**x2``.\n" - "\n" - " Parameters\n" - " ----------\n" - " x1 : array_like\n" - " Array of multipliers.\n" - " x2 : array_like, int\n" - " Array of twos exponents.\n" - " out : ndarray, optional\n" - " Output array for the result.\n" - "\n" - " Returns\n" - " -------\n" - " y : ndarray or scalar\n" - " The result of ``x1 * 2**x2``.\n" - "\n" - " See Also\n" - " --------\n" - " frexp : Return (y1, y2) from ``x = y1 * 2**y2``, inverse to `ldexp`.\n" - "\n" - " Notes\n" - " -----\n" - " Complex dtypes are not supported, they will raise a TypeError.\n" - "\n" - " `ldexp` is useful as the inverse of `frexp`, if used by itself it is\n" - " more clear to simply use the expression ``x1 * 2**x2``.\n" - "\n" - " Examples\n" - " --------\n" - " >>> np.ldexp(5, np.arange(4))\n" - " array([ 5., 10., 20., 40.], dtype=float32)\n" - "\n" - " >>> x = np.arange(6)\n" - " >>> np.ldexp(*np.frexp(x))\n" - " array([ 0., 1., 2., 3., 4., 5.])\n" - "\n"; - - -static void -InitOtherOperators(PyObject *dictionary) { - PyObject *f; - int num; - - num = sizeof(frexp_functions) / sizeof(frexp_functions[0]); - f = PyUFunc_FromFuncAndData(frexp_functions, blank3_data, - frexp_signatures, num, - 1, 2, PyUFunc_None, "frexp", frdoc, 0); - PyDict_SetItemString(dictionary, "frexp", f); - Py_DECREF(f); - - num = sizeof(ldexp_functions) / sizeof(ldexp_functions[0]); - f = PyUFunc_FromFuncAndData(ldexp_functions, blank6_data, - ldexp_signatures, num, - 2, 1, PyUFunc_None, "ldexp", lddoc, 0); - PyDict_SetItemString(dictionary, "ldexp", f); - Py_DECREF(f); - -#if defined(NPY_PY3K) - f = PyDict_GetItemString(dictionary, "true_divide"); - PyDict_SetItemString(dictionary, "divide", f); -#endif - return; -} - NPY_VISIBILITY_HIDDEN PyObject * npy_um_str_out = NULL; NPY_VISIBILITY_HIDDEN PyObject * npy_um_str_subok = NULL; NPY_VISIBILITY_HIDDEN PyObject * npy_um_str_array_prepare = NULL; @@ -493,8 +316,6 @@ PyMODINIT_FUNC initumath(void) /* Load the ufunc operators into the array module's namespace */ InitOperators(d); - InitOtherOperators(d); - PyDict_SetItemString(d, "pi", s = PyFloat_FromDouble(NPY_PI)); Py_DECREF(s); PyDict_SetItemString(d, "e", s = PyFloat_FromDouble(NPY_E)); @@ -537,6 +358,11 @@ PyMODINIT_FUNC initumath(void) PyModule_AddObject(m, "NZERO", PyFloat_FromDouble(NPY_NZERO)); PyModule_AddObject(m, "NAN", PyFloat_FromDouble(NPY_NAN)); +#if defined(NPY_PY3K) + s = PyDict_GetItemString(d, "true_divide"); + PyDict_SetItemString(d, "divide", s); +#endif + s = PyDict_GetItemString(d, "conjugate"); s2 = PyDict_GetItemString(d, "remainder"); /* Setup the array object's numerical structures with appropriate diff --git a/numpy/core/tests/test_deprecations.py b/numpy/core/tests/test_deprecations.py index a1f4664a5..ef56766f5 100644 --- a/numpy/core/tests/test_deprecations.py +++ b/numpy/core/tests/test_deprecations.py @@ -12,7 +12,7 @@ from nose.plugins.skip import SkipTest import numpy as np from numpy.testing import (dec, run_module_suite, assert_raises, - assert_warns, assert_array_equal) + assert_warns, assert_array_equal, assert_) class _DeprecationTestCase(object): @@ -249,6 +249,14 @@ class TestFloatNonIntegerArgumentDeprecation(_DeprecationTestCase): self.assert_not_deprecated(mult, args=([1], np.int_(3))) + def test_reduce_axis_float_index(self): + d = np.zeros((3,3,3)) + self.assert_deprecated(np.min, args=(d, 0.5)) + self.assert_deprecated(np.min, num=1, args=(d, (0.5, 1))) + self.assert_deprecated(np.min, num=1, args=(d, (1, 2.2))) + self.assert_deprecated(np.min, num=2, args=(d, (.2, 1.2))) + + class TestBooleanArgumentDeprecation(_DeprecationTestCase): """This tests that using a boolean as integer argument/indexing is deprecated. @@ -426,6 +434,26 @@ class TestComparisonDepreactions(_DeprecationTestCase): assert_raises(FutureWarning, operator.eq, np.arange(3), None) assert_raises(FutureWarning, operator.ne, np.arange(3), None) + def test_scalar_none_comparison(self): + # Scalars should still just return false and not give a warnings. + with warnings.catch_warnings(record=True) as w: + warnings.filterwarnings('always', '', FutureWarning) + assert_(not np.float32(1) == None) + assert_(not np.str_('test') == None) + # This is dubious (see below): + assert_(not np.datetime64('NaT') == None) + + assert_(np.float32(1) != None) + assert_(np.str_('test') != None) + # This is dubious (see below): + assert_(np.datetime64('NaT') != None) + assert_(len(w) == 0) + + # For documentaiton purpose, this is why the datetime is dubious. + # At the time of deprecation this was no behaviour change, but + # it has to be considered when the deprecations is done. + assert_(np.equal(np.datetime64('NaT'), None)) + class TestIdentityComparisonDepreactions(_DeprecationTestCase): """This tests the equal and not_equal object ufuncs identity check diff --git a/numpy/core/tests/test_indexing.py b/numpy/core/tests/test_indexing.py index 6b0b0a0b5..bb1341455 100644 --- a/numpy/core/tests/test_indexing.py +++ b/numpy/core/tests/test_indexing.py @@ -147,7 +147,7 @@ class TestIndexing(TestCase): def test_boolean_assignment_value_mismatch(self): # A boolean assignment should fail when the shape of the values - # cannot be broadcasted to the subscription. (see also gh-3458) + # cannot be broadcast to the subscription. (see also gh-3458) a = np.arange(4) def f(a, v): a[a > -1] = v @@ -188,12 +188,12 @@ class TestIndexing(TestCase): # If the strides are not reversed, the 0 in the arange comes last. assert_equal(a[0], 0) - # This also tests that the subspace buffer is initiliazed: + # This also tests that the subspace buffer is initialized: a = np.ones((5, 2)) c = np.arange(10).reshape(5, 2)[::-1] a[b, :] = c assert_equal(a[0], [0, 1]) - + def test_reversed_strides_result_allocation(self): # Test a bug when calculating the output strides for a result array # when the subspace size was 1 (and test other cases as well) @@ -285,6 +285,17 @@ class TestIndexing(TestCase): assert_((a == 1).all()) + def test_subclass_writeable(self): + d = np.rec.array([('NGC1001', 11), ('NGC1002', 1.), ('NGC1003', 1.)], + dtype=[('target', 'S20'), ('V_mag', '>f4')]) + ind = np.array([False, True, True], dtype=bool) + assert_(d[ind].flags.writeable) + ind = np.array([0, 1]) + assert_(d[ind].flags.writeable) + assert_(d[...].flags.writeable) + assert_(d[0].flags.writeable) + + def test_memory_order(self): # This is not necessary to preserve. Memory layouts for # more complex indices are not as simple. @@ -335,7 +346,7 @@ class TestIndexing(TestCase): # Reference count of intp for index checks a = np.array([0]) refcount = sys.getrefcount(np.dtype(np.intp)) - # item setting always checks indices in seperate function: + # item setting always checks indices in separate function: a[np.array([0], dtype=np.intp)] = 1 a[np.array([0], dtype=np.uint8)] = 1 assert_raises(IndexError, a.__setitem__, @@ -402,8 +413,14 @@ class TestBroadcastedAssignments(TestCase): # Too large and not only ones. assert_raises(ValueError, assign, a, s_[...], np.ones((2, 1))) - assert_raises(ValueError, assign, a, s_[[1, 2, 3],], np.ones((2, 1))) - assert_raises(ValueError, assign, a, s_[[[1], [2]],], np.ones((2,2,1))) + + with warnings.catch_warnings(): + # Will be a ValueError as well. + warnings.simplefilter("error", DeprecationWarning) + assert_raises(DeprecationWarning, assign, a, s_[[1, 2, 3],], + np.ones((2, 1))) + assert_raises(DeprecationWarning, assign, a, s_[[[1], [2]],], + np.ones((2,2,1))) def test_simple_broadcasting_errors(self): @@ -520,11 +537,11 @@ class TestMultiIndexingAutomated(TestCase): These test use code to mimic the C-Code indexing for selection. NOTE: * This still lacks tests for complex item setting. - * If you change behavoir of indexing, you might want to modify + * If you change behavior of indexing, you might want to modify these tests to try more combinations. * Behavior was written to match numpy version 1.8. (though a first version matched 1.7.) - * Only tuple indicies are supported by the mimicing code. + * Only tuple indices are supported by the mimicking code. (and tested as of writing this) * Error types should match most of the time as long as there is only one error. For multiple errors, what gets raised @@ -547,7 +564,7 @@ class TestMultiIndexingAutomated(TestCase): slice(4, -1, -2), slice(None, None, -3), # Some Fancy indexes: - np.empty((0, 1, 1), dtype=np.intp), # empty broadcastable + np.empty((0, 1, 1), dtype=np.intp), # empty and can be broadcast np.array([0, 1, -2]), np.array([[2], [0], [1]]), np.array([[0, -1], [0, 1]], dtype=np.dtype('intp').newbyteorder()), @@ -594,7 +611,7 @@ class TestMultiIndexingAutomated(TestCase): fancy_dim = 0 # NOTE: This is a funny twist (and probably OK to change). # The boolean array has illegal indexes, but this is - # allowed if the broadcasted fancy-indices are 0-sized. + # allowed if the broadcast fancy-indices are 0-sized. # This variable is to catch that case. error_unless_broadcast_to_empty = False @@ -639,7 +656,7 @@ class TestMultiIndexingAutomated(TestCase): if arr.ndim - ndim < 0: # we can't take more dimensions then we have, not even for 0-d arrays. # since a[()] makes sense, but not a[(),]. We will raise an error - # lateron, unless a broadcasting error occurs first. + # later on, unless a broadcasting error occurs first. raise IndexError if ndim == 0 and not None in in_indices: @@ -651,7 +668,7 @@ class TestMultiIndexingAutomated(TestCase): for ax, indx in enumerate(in_indices): if isinstance(indx, slice): - # convert to an index array anways: + # convert to an index array indx = np.arange(*indx.indices(arr.shape[ax])) indices.append(['s', indx]) continue @@ -684,7 +701,7 @@ class TestMultiIndexingAutomated(TestCase): indx = flat_indx else: # This could be changed, a 0-d boolean index can - # make sense (even outide the 0-d indexed array case) + # make sense (even outside the 0-d indexed array case) # Note that originally this is could be interpreted as # integer in the full integer special case. raise IndexError @@ -736,7 +753,7 @@ class TestMultiIndexingAutomated(TestCase): arr = arr.transpose(*(fancy_axes + axes)) # We only have one 'f' index now and arr is transposed accordingly. - # Now handle newaxes by reshaping... + # Now handle newaxis by reshaping... ax = 0 for indx in indices: if indx[0] == 'f': @@ -754,7 +771,7 @@ class TestMultiIndexingAutomated(TestCase): res = np.broadcast(*indx[1:]) # raises ValueError... else: res = indx[1] - # unfortunatly the indices might be out of bounds. So check + # unfortunately the indices might be out of bounds. So check # that first, and use mode='wrap' then. However only if # there are any indices... if res.size != 0: @@ -892,7 +909,7 @@ class TestMultiIndexingAutomated(TestCase): # spot and the simple ones in one other spot. with warnings.catch_warnings(): # This is so that np.array(True) is not accepted in a full integer - # index, when running the file seperatly. + # index, when running the file separately. warnings.filterwarnings('error', '', DeprecationWarning) for simple_pos in [0, 2, 3]: tocheck = [self.fill_indices, self.complex_indices, diff --git a/numpy/core/tests/test_multiarray.py b/numpy/core/tests/test_multiarray.py index e460a0b87..96db8cde3 100644 --- a/numpy/core/tests/test_multiarray.py +++ b/numpy/core/tests/test_multiarray.py @@ -246,6 +246,43 @@ class TestArrayConstruction(TestCase): r = np.array([[True, False], [True, False], [False, True]]) assert_equal(r, tgt.T) + def test_array_empty(self): + assert_raises(TypeError, np.array) + + def test_array_copy_false(self): + d = np.array([1, 2, 3]) + e = np.array(d, copy=False) + d[1] = 3 + assert_array_equal(e, [1, 3, 3]) + e = np.array(d, copy=False, order='F') + d[1] = 4 + assert_array_equal(e, [1, 4, 3]) + e[2] = 7 + assert_array_equal(d, [1, 4, 7]) + + def test_array_copy_true(self): + d = np.array([[1,2,3], [1, 2, 3]]) + e = np.array(d, copy=True) + d[0, 1] = 3 + e[0, 2] = -7 + assert_array_equal(e, [[1, 2, -7], [1, 2, 3]]) + assert_array_equal(d, [[1, 3, 3], [1, 2, 3]]) + e = np.array(d, copy=True, order='F') + d[0, 1] = 5 + e[0, 2] = 7 + assert_array_equal(e, [[1, 3, 7], [1, 2, 3]]) + assert_array_equal(d, [[1, 5, 3], [1,2,3]]) + + def test_array_cont(self): + d = np.ones(10)[::2] + assert_(np.ascontiguousarray(d).flags.c_contiguous) + assert_(np.ascontiguousarray(d).flags.f_contiguous) + assert_(np.asfortranarray(d).flags.c_contiguous) + assert_(np.asfortranarray(d).flags.f_contiguous) + d = np.ones((10, 10))[::2,::2] + assert_(np.ascontiguousarray(d).flags.c_contiguous) + assert_(np.asfortranarray(d).flags.f_contiguous) + class TestAssignment(TestCase): def test_assignment_broadcasting(self): @@ -1400,6 +1437,12 @@ class TestMethods(TestCase): d[i:].partition(0, kind=k) assert_array_equal(d, tgt) + d = np.array([0, 1, 2, 3, 4, 5, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, + 7, 7, 7, 7, 7, 9]) + kth = [0, 3, 19, 20] + assert_equal(np.partition(d, kth, kind=k)[kth], (0, 3, 7, 7)) + assert_equal(d[np.argpartition(d, kth, kind=k)][kth], (0, 3, 7, 7)) + d = np.array([2, 1]) d.partition(0, kind=k) assert_raises(ValueError, d.partition, 2) @@ -1595,6 +1638,18 @@ class TestMethods(TestCase): assert_raises(ValueError, d.partition, 2, kind=k) assert_raises(ValueError, d.argpartition, 2, kind=k) + def test_partition_fuzz(self): + # a few rounds of random data testing + for j in range(10, 30): + for i in range(1, j - 2): + d = np.arange(j) + np.random.shuffle(d) + d = d % np.random.randint(2, 30) + idx = np.random.randint(d.size) + kth = [0, idx, i, i + 1] + tgt = np.sort(d)[kth] + assert_array_equal(np.partition(d, kth)[kth], tgt, + err_msg="data: %r\n kth: %r" % (d, kth)) def test_flatten(self): x0 = np.array([[1, 2, 3], [4, 5, 6]], np.int32) @@ -1719,8 +1774,105 @@ class TestMethods(TestCase): assert_equal(a.ravel(order='K'), [2, 3, 0, 1]) assert_(a.ravel(order='K').flags.owndata) + def test_conjugate(self): + a = np.array([1-1j, 1+1j, 23+23.0j]) + ac = a.conj() + assert_equal(a.real, ac.real) + assert_equal(a.imag, -ac.imag) + assert_equal(ac, a.conjugate()) + assert_equal(ac, np.conjugate(a)) + + a = np.array([1-1j, 1+1j, 23+23.0j], 'F') + ac = a.conj() + assert_equal(a.real, ac.real) + assert_equal(a.imag, -ac.imag) + assert_equal(ac, a.conjugate()) + assert_equal(ac, np.conjugate(a)) + + a = np.array([1, 2, 3]) + ac = a.conj() + assert_equal(a, ac) + assert_equal(ac, a.conjugate()) + assert_equal(ac, np.conjugate(a)) + + a = np.array([1.0, 2.0, 3.0]) + ac = a.conj() + assert_equal(a, ac) + assert_equal(ac, a.conjugate()) + assert_equal(ac, np.conjugate(a)) + + a = np.array([1-1j, 1+1j, 1, 2.0], object) + ac = a.conj() + assert_equal(ac, [k.conjugate() for k in a]) + assert_equal(ac, a.conjugate()) + assert_equal(ac, np.conjugate(a)) + + a = np.array([1-1j, 1, 2.0, 'f'], object) + assert_raises(AttributeError, lambda: a.conj()) + assert_raises(AttributeError, lambda: a.conjugate()) + class TestBinop(object): + def test_inplace(self): + # test refcount 1 inplace conversion + assert_array_almost_equal(np.array([0.5]) * np.array([1.0, 2.0]), + [0.5, 1.0]) + + d = np.array([0.5, 0.5])[::2] + assert_array_almost_equal(d * (d * np.array([1.0, 2.0])), + [0.25, 0.5]) + + a = np.array([0.5]) + b = np.array([0.5]) + c = a + b + c = a - b + c = a * b + c = a / b + assert_equal(a, b) + assert_almost_equal(c, 1.) + + c = a + b * 2. / b * a - a / b + assert_equal(a, b) + assert_equal(c, 0.5) + + # true divide + a = np.array([5]) + b = np.array([3]) + c = (a * a) / b + + assert_almost_equal(c, 25 / 3) + assert_equal(a, 5) + assert_equal(b, 3) + + def test_extension_incref_elide(self): + # test extension (e.g. cython) calling PyNumber_* slots without + # increasing the reference counts + # + # def incref_elide(a): + # d = input.copy() # refcount 1 + # return d, d + d # PyNumber_Add without increasing refcount + from numpy.core.multiarray_tests import incref_elide + d = np.ones(5) + orig, res = incref_elide(d) + # the return original should not be changed to an inplace operation + assert_array_equal(orig, d) + assert_array_equal(res, d + d) + + def test_extension_incref_elide_stack(self): + # scanning if the refcount == 1 object is on the python stack to check + # that we are called directly from python is flawed as object may still + # be above the stack pointer and we have no access to the top of it + # + # def incref_elide_l(d): + # return l[4] + l[4] # PyNumber_Add without increasing refcount + from numpy.core.multiarray_tests import incref_elide_l + # padding with 1 makes sure the object on the stack is not overwriten + l = [1, 1, 1, 1, np.ones(5)] + res = incref_elide_l(l) + # the return original should not be changed to an inplace operation + assert_array_equal(l[4], np.ones(5)) + assert_array_equal(res, l[4] + l[4]) + def test_ufunc_override_rop_precedence(self): # Check that __rmul__ and other right-hand operations have # precedence over __numpy_ufunc__ @@ -4420,5 +4572,51 @@ class TestWhere(TestCase): assert_equal(np.where(False, b, a), "abcd") +class TestSizeOf(TestCase): + + def test_empty_array(self): + x = np.array([]) + assert_(sys.getsizeof(x) > 0) + + def check_array(self, dtype): + elem_size = dtype(0).itemsize + + for length in [10, 50, 100, 500]: + x = np.arange(length, dtype=dtype) + assert_(sys.getsizeof(x) > length * elem_size) + + def test_array_int32(self): + self.check_array(np.int32) + + def test_array_int64(self): + self.check_array(np.int64) + + def test_array_float32(self): + self.check_array(np.float32) + + def test_array_float64(self): + self.check_array(np.float64) + + def test_view(self): + d = np.ones(100) + assert_(sys.getsizeof(d[...]) < sys.getsizeof(d)) + + def test_reshape(self): + d = np.ones(100) + assert_(sys.getsizeof(d) < sys.getsizeof(d.reshape(100, 1, 1).copy())) + + def test_resize(self): + d = np.ones(100) + old = sys.getsizeof(d) + d.resize(50) + assert_(old > sys.getsizeof(d)) + d.resize(150) + assert_(old < sys.getsizeof(d)) + + def test_error(self): + d = np.ones(100) + assert_raises(TypeError, d.__sizeof__, "a") + + if __name__ == "__main__": run_module_suite() diff --git a/numpy/core/tests/test_numeric.py b/numpy/core/tests/test_numeric.py index 40bbe5aec..483484467 100644 --- a/numpy/core/tests/test_numeric.py +++ b/numpy/core/tests/test_numeric.py @@ -5,6 +5,7 @@ import platform from decimal import Decimal import warnings import itertools +import platform import numpy as np from numpy.core import * @@ -931,6 +932,7 @@ class TestNonzero(TestCase): assert_equal(np.nonzero(x['a']), ([0, 1, 1, 2], [2, 0, 1, 1])) assert_equal(np.nonzero(x['b']), ([0, 0, 1, 2, 2], [0, 2, 0, 1, 2])) + assert_(not x['a'].T.flags.aligned) assert_equal(np.count_nonzero(x['a'].T), 4) assert_equal(np.count_nonzero(x['b'].T), 5) assert_equal(np.nonzero(x['a'].T), ([0, 1, 1, 2], [1, 1, 2, 0])) @@ -1047,8 +1049,17 @@ class TestArrayComparisons(TestCase): def assert_array_strict_equal(x, y): assert_array_equal(x, y) - # Check flags - assert_(x.flags == y.flags) + # Check flags, debian sparc and win32 don't provide 16 byte alignment + if (x.dtype.alignment > 8 and + 'sparc' not in platform.platform().lower() and + sys.platform != 'win32'): + assert_(x.flags == y.flags) + else: + assert_(x.flags.owndata == y.flags.owndata) + assert_(x.flags.writeable == y.flags.writeable) + assert_(x.flags.c_contiguous == y.flags.c_contiguous) + assert_(x.flags.f_contiguous == y.flags.f_contiguous) + assert_(x.flags.updateifcopy == y.flags.updateifcopy) # check endianness assert_(x.dtype.isnative == y.dtype.isnative) diff --git a/numpy/core/tests/test_regression.py b/numpy/core/tests/test_regression.py index 9f40d7b54..c7eaad984 100644 --- a/numpy/core/tests/test_regression.py +++ b/numpy/core/tests/test_regression.py @@ -181,7 +181,7 @@ class TestRegression(TestCase): assert_(np.all(b[yb] > 0.5)) def test_endian_where(self,level=rlevel): - """GitHuB issue #369""" + """GitHub issue #369""" net = np.zeros(3, dtype='>f4') net[1] = 0.00458849 net[2] = 0.605202 @@ -290,7 +290,7 @@ class TestRegression(TestCase): def test_tobytes_FORTRANORDER_discontiguous(self,level=rlevel): """Fix in r2836""" - # Create discontiguous Fortran-ordered array + # Create non-contiguous Fortran ordered array x = np.array(np.random.rand(3, 3), order='F')[:, :2] assert_array_almost_equal(x.ravel(), np.fromstring(x.tobytes())) @@ -311,7 +311,7 @@ class TestRegression(TestCase): self.assertRaises(ValueError, bfb) def test_nonarray_assignment(self): - # See also Issue gh-2870, test for nonarray assignment + # See also Issue gh-2870, test for non-array assignment # and equivalent unsafe casted array assignment a = np.arange(10) b = np.ones(10, dtype=bool) @@ -398,6 +398,41 @@ class TestRegression(TestCase): assert_raises(KeyError, np.lexsort, BuggySequence()) + def test_pickle_py2_bytes_encoding(self): + # Check that arrays and scalars pickled on Py2 are + # unpickleable on Py3 using encoding='bytes' + + test_data = [ + # (original, py2_pickle) + (np.unicode_('\u6f2c'), + asbytes("cnumpy.core.multiarray\nscalar\np0\n(cnumpy\ndtype\np1\n" + "(S'U1'\np2\nI0\nI1\ntp3\nRp4\n(I3\nS'<'\np5\nNNNI4\nI4\n" + "I0\ntp6\nbS',o\\x00\\x00'\np7\ntp8\nRp9\n.")), + + (np.array([9e123], dtype=np.float64), + asbytes("cnumpy.core.multiarray\n_reconstruct\np0\n(cnumpy\nndarray\n" + "p1\n(I0\ntp2\nS'b'\np3\ntp4\nRp5\n(I1\n(I1\ntp6\ncnumpy\ndtype\n" + "p7\n(S'f8'\np8\nI0\nI1\ntp9\nRp10\n(I3\nS'<'\np11\nNNNI-1\nI-1\n" + "I0\ntp12\nbI00\nS'O\\x81\\xb7Z\\xaa:\\xabY'\np13\ntp14\nb.")), + + (np.array([(9e123,)], dtype=[('name', float)]), + asbytes("cnumpy.core.multiarray\n_reconstruct\np0\n(cnumpy\nndarray\np1\n" + "(I0\ntp2\nS'b'\np3\ntp4\nRp5\n(I1\n(I1\ntp6\ncnumpy\ndtype\np7\n" + "(S'V8'\np8\nI0\nI1\ntp9\nRp10\n(I3\nS'|'\np11\nN(S'name'\np12\ntp13\n" + "(dp14\ng12\n(g7\n(S'f8'\np15\nI0\nI1\ntp16\nRp17\n(I3\nS'<'\np18\nNNNI-1\n" + "I-1\nI0\ntp19\nbI0\ntp20\nsI8\nI1\nI0\ntp21\n" + "bI00\nS'O\\x81\\xb7Z\\xaa:\\xabY'\np22\ntp23\nb.")), + ] + + if sys.version_info[:2] >= (3, 4): + # encoding='bytes' was added in Py3.4 + for original, data in test_data: + result = pickle.loads(data, encoding='bytes') + assert_equal(result, original) + + if isinstance(result, np.ndarray) and result.dtype.names: + for name in result.dtype.names: + assert_(isinstance(name, str)) def test_pickle_dtype(self,level=rlevel): """Ticket #251""" @@ -560,7 +595,7 @@ class TestRegression(TestCase): assert_(a.reshape(5, 1).strides[0] == 0) def test_reshape_zero_size(self, level=rlevel): - """Github Issue #2700, setting shape failed for 0-sized arrays""" + """GitHub Issue #2700, setting shape failed for 0-sized arrays""" a = np.ones((0, 2)) a.shape = (-1, 2) @@ -568,7 +603,7 @@ class TestRegression(TestCase): # With NPY_RELAXED_STRIDES_CHECKING the test becomes superfluous. @dec.skipif(np.ones(1).strides[0] == np.iinfo(np.intp).max) def test_reshape_trailing_ones_strides(self): - # Github issue gh-2949, bad strides for trailing ones of new shape + # GitHub issue gh-2949, bad strides for trailing ones of new shape a = np.zeros(12, dtype=np.int32)[::2] # not contiguous strides_c = (16, 8, 8, 8) strides_f = (8, 24, 48, 48) @@ -756,8 +791,12 @@ class TestRegression(TestCase): s = np.ones(10, dtype=float) x = np.array((15,), dtype=float) def ia(x, s, v): x[(s>0)]=v - self.assertRaises(ValueError, ia, x, s, np.zeros(9, dtype=float)) - self.assertRaises(ValueError, ia, x, s, np.zeros(11, dtype=float)) + # After removing deprecation, the following are ValueErrors. + # This might seem odd as compared to the value error below. This + # is due to the fact that the new code always uses "nonzero" logic + # and the boolean special case is not taken. + self.assertRaises(IndexError, ia, x, s, np.zeros(9, dtype=float)) + self.assertRaises(IndexError, ia, x, s, np.zeros(11, dtype=float)) # Old special case (different code path): self.assertRaises(ValueError, ia, x.flat, s, np.zeros(9, dtype=float)) @@ -844,7 +883,7 @@ class TestRegression(TestCase): cnt0_b = cnt(b) cnt0_c = cnt(c) - # -- 0d -> 1d broadcasted slice assignment + # -- 0d -> 1-d broadcast slice assignment arr = np.zeros(5, dtype=np.object_) @@ -861,7 +900,7 @@ class TestRegression(TestCase): del arr - # -- 1d -> 2d broadcasted slice assignment + # -- 1-d -> 2-d broadcast slice assignment arr = np.zeros((5, 2), dtype=np.object_) arr0 = np.zeros(2, dtype=np.object_) @@ -880,7 +919,7 @@ class TestRegression(TestCase): del arr, arr0 - # -- 2d copying + flattening + # -- 2-d copying + flattening arr = np.zeros((5, 2), dtype=np.object_) @@ -1025,8 +1064,8 @@ class TestRegression(TestCase): b = np.zeros((2, 1), dtype = np.single) try: a.compress([True, False], axis = 1, out = b) - raise AssertionError("compress with an out which cannot be " \ - "safely casted should not return "\ + raise AssertionError("compress with an out which cannot be " + "safely casted should not return " "successfully") except TypeError: pass @@ -1794,6 +1833,67 @@ class TestRegression(TestCase): bytestring = "\x01 ".encode('ascii') assert_equal(bytestring[0:1], '\x01'.encode('ascii')) + def test_pickle_py2_array_latin1_hack(self): + # Check that unpickling hacks in Py3 that support + # encoding='latin1' work correctly. + + # Python2 output for pickle.dumps(numpy.array([129], dtype='b')) + data = asbytes("cnumpy.core.multiarray\n_reconstruct\np0\n(cnumpy\nndarray\np1\n(I0\n" + "tp2\nS'b'\np3\ntp4\nRp5\n(I1\n(I1\ntp6\ncnumpy\ndtype\np7\n(S'i1'\np8\n" + "I0\nI1\ntp9\nRp10\n(I3\nS'|'\np11\nNNNI-1\nI-1\nI0\ntp12\nbI00\nS'\\x81'\n" + "p13\ntp14\nb.") + if sys.version_info[0] >= 3: + # This should work: + result = pickle.loads(data, encoding='latin1') + assert_array_equal(result, np.array([129], dtype='b')) + # Should not segfault: + assert_raises(Exception, pickle.loads, data, encoding='koi8-r') + + def test_pickle_py2_scalar_latin1_hack(self): + # Check that scalar unpickling hack in Py3 that supports + # encoding='latin1' work correctly. + + # Python2 output for pickle.dumps(...) + datas = [ + # (original, python2_pickle, koi8r_validity) + (np.unicode_('\u6bd2'), + asbytes("cnumpy.core.multiarray\nscalar\np0\n(cnumpy\ndtype\np1\n" + "(S'U1'\np2\nI0\nI1\ntp3\nRp4\n(I3\nS'<'\np5\nNNNI4\nI4\nI0\n" + "tp6\nbS'\\xd2k\\x00\\x00'\np7\ntp8\nRp9\n."), + 'invalid'), + + (np.float64(9e123), + asbytes("cnumpy.core.multiarray\nscalar\np0\n(cnumpy\ndtype\np1\n(S'f8'\n" + "p2\nI0\nI1\ntp3\nRp4\n(I3\nS'<'\np5\nNNNI-1\nI-1\nI0\ntp6\n" + "bS'O\\x81\\xb7Z\\xaa:\\xabY'\np7\ntp8\nRp9\n."), + 'invalid'), + + (np.bytes_(asbytes('\x9c')), # different 8-bit code point in KOI8-R vs latin1 + asbytes("cnumpy.core.multiarray\nscalar\np0\n(cnumpy\ndtype\np1\n(S'S1'\np2\n" + "I0\nI1\ntp3\nRp4\n(I3\nS'|'\np5\nNNNI1\nI1\nI0\ntp6\nbS'\\x9c'\np7\n" + "tp8\nRp9\n."), + 'different'), + ] + if sys.version_info[0] >= 3: + for original, data, koi8r_validity in datas: + result = pickle.loads(data, encoding='latin1') + assert_equal(result, original) + + # Decoding under non-latin1 encoding (e.g.) KOI8-R can + # produce bad results, but should not segfault. + if koi8r_validity == 'different': + # Unicode code points happen to lie within latin1, + # but are different in koi8-r, resulting to silent + # bogus results + result = pickle.loads(data, encoding='koi8-r') + assert_(result != original) + elif koi8r_validity == 'invalid': + # Unicode code points outside latin1, so results + # to an encoding exception + assert_raises(ValueError, pickle.loads, data, encoding='koi8-r') + else: + raise ValueError(koi8r_validity) + def test_structured_type_to_object(self): a_rec = np.array([(0, 1), (3, 2)], dtype='i4,i8') a_obj = np.empty((2,), dtype=object) diff --git a/numpy/core/tests/test_scalarmath.py b/numpy/core/tests/test_scalarmath.py index d823e963f..3ba3beff9 100644 --- a/numpy/core/tests/test_scalarmath.py +++ b/numpy/core/tests/test_scalarmath.py @@ -83,6 +83,18 @@ class TestBaseMath(TestCase): np.add(1, inp2, out=out) assert_almost_equal(out, exp1, err_msg=msg) + def test_lower_align(self): + # check data that is not aligned to element size + # i.e doubles are aligned to 4 bytes on i386 + d = np.zeros(23 * 8, dtype=np.int8)[4:-4].view(np.float64) + o = np.zeros(23 * 8, dtype=np.int8)[4:-4].view(np.float64) + assert_almost_equal(d + d, d * 2) + np.add(d, d, out=o) + np.add(np.ones_like(d), d, out=o) + np.add(d, np.ones_like(d), out=o) + np.add(np.ones_like(d), d) + np.add(d, np.ones_like(d)) + class TestPower(TestCase): def test_small_types(self): @@ -259,5 +271,18 @@ class TestRepr(object): for t in [np.float32, np.float64]: yield self._test_type_repr, t + +class TestSizeOf(TestCase): + + def test_equal_nbytes(self): + for type in types: + x = type(0) + assert_(sys.getsizeof(x) > x.nbytes) + + def test_error(self): + d = np.float32() + assert_raises(TypeError, d.__sizeof__, "a") + + if __name__ == "__main__": run_module_suite() diff --git a/numpy/core/tests/test_ufunc.py b/numpy/core/tests/test_ufunc.py index 080606dce..eacc266be 100644 --- a/numpy/core/tests/test_ufunc.py +++ b/numpy/core/tests/test_ufunc.py @@ -14,6 +14,10 @@ class TestUfunc(TestCase): import pickle assert pickle.loads(pickle.dumps(np.sin)) is np.sin + # Check that ufunc not defined in the top level numpy namespace such as + # numpy.core.test_rational.test_add can also be pickled + assert pickle.loads(pickle.dumps(test_add)) is test_add + def test_pickle_withstring(self): import pickle astring = asbytes("cnumpy.core\n_ufunc_reconstruct\np0\n" @@ -647,7 +651,6 @@ class TestUfunc(TestCase): a = np.array(1).view(MyArray) assert_(type(np.any(a)) is MyArray) - def test_casting_out_param(self): # Test that it's possible to do casts on output a = np.ones((200, 100), np.int64) @@ -834,45 +837,20 @@ class TestUfunc(TestCase): def test_safe_casting(self): # In old versions of numpy, in-place operations used the 'unsafe' - # casting rules. In some future version, 'same_kind' will become the - # default. + # casting rules. In versions >= 1.10, 'same_kind' is the + # default and an exception is raised instead of a warning. + # when 'same_kind' is not satisfied. a = np.array([1, 2, 3], dtype=int) # Non-in-place addition is fine assert_array_equal(assert_no_warnings(np.add, a, 1.1), [2.1, 3.1, 4.1]) - assert_warns(DeprecationWarning, np.add, a, 1.1, out=a) - assert_array_equal(a, [2, 3, 4]) + assert_raises(TypeError, np.add, a, 1.1, out=a) def add_inplace(a, b): a += b - assert_warns(DeprecationWarning, add_inplace, a, 1.1) - assert_array_equal(a, [3, 4, 5]) - # Make sure that explicitly overriding the warning is allowed: + assert_raises(TypeError, add_inplace, a, 1.1) + # Make sure that explicitly overriding the exception is allowed: assert_no_warnings(np.add, a, 1.1, out=a, casting="unsafe") - assert_array_equal(a, [4, 5, 6]) - - # There's no way to propagate exceptions from the place where we issue - # this deprecation warning, so we must throw the exception away - # entirely rather than cause it to be raised at some other point, or - # trigger some other unsuspecting if (PyErr_Occurred()) { ...} at some - # other location entirely. - import warnings - import sys - if sys.version_info[0] >= 3: - from io import StringIO - else: - from StringIO import StringIO - with warnings.catch_warnings(): - warnings.simplefilter("error") - old_stderr = sys.stderr - try: - sys.stderr = StringIO() - # No error, but dumps to stderr - a += 1.1 - # No error on the next bit of code executed either - 1 + 1 - assert_("Implicitly casting" in sys.stderr.getvalue()) - finally: - sys.stderr = old_stderr + assert_array_equal(a, [2, 3, 4]) def test_ufunc_custom_out(self): # Test ufunc with built in input types and custom output type @@ -1087,5 +1065,64 @@ class TestUfunc(TestCase): self.assertRaises(TypeError, np.add.at, values, [0, 1], 1) assert_array_equal(values, np.array(['a', 1], dtype=np.object)) + def test_reduce_arguments(self): + f = np.add.reduce + d = np.ones((5,2), dtype=int) + o = np.ones((2,), dtype=d.dtype) + r = o * 5 + assert_equal(f(d), r) + # a, axis=0, dtype=None, out=None, keepdims=False + assert_equal(f(d, axis=0), r) + assert_equal(f(d, 0), r) + assert_equal(f(d, 0, dtype=None), r) + assert_equal(f(d, 0, dtype='i'), r) + assert_equal(f(d, 0, 'i'), r) + assert_equal(f(d, 0, None), r) + assert_equal(f(d, 0, None, out=None), r) + assert_equal(f(d, 0, None, out=o), r) + assert_equal(f(d, 0, None, o), r) + assert_equal(f(d, 0, None, None), r) + assert_equal(f(d, 0, None, None, keepdims=False), r) + assert_equal(f(d, 0, None, None, True), r.reshape((1,) + r.shape)) + # multiple keywords + assert_equal(f(d, axis=0, dtype=None, out=None, keepdims=False), r) + assert_equal(f(d, 0, dtype=None, out=None, keepdims=False), r) + assert_equal(f(d, 0, None, out=None, keepdims=False), r) + + # too little + assert_raises(TypeError, f) + # too much + assert_raises(TypeError, f, d, 0, None, None, False, 1) + # invalid axis + assert_raises(TypeError, f, d, "invalid") + assert_raises(TypeError, f, d, axis="invalid") + assert_raises(TypeError, f, d, axis="invalid", dtype=None, + keepdims=True) + # invalid dtype + assert_raises(TypeError, f, d, 0, "invalid") + assert_raises(TypeError, f, d, dtype="invalid") + assert_raises(TypeError, f, d, dtype="invalid", out=None) + # invalid out + assert_raises(TypeError, f, d, 0, None, "invalid") + assert_raises(TypeError, f, d, out="invalid") + assert_raises(TypeError, f, d, out="invalid", dtype=None) + # keepdims boolean, no invalid value + # assert_raises(TypeError, f, d, 0, None, None, "invalid") + # assert_raises(TypeError, f, d, keepdims="invalid", axis=0, dtype=None) + # invalid mix + assert_raises(TypeError, f, d, 0, keepdims="invalid", dtype="invalid", + out=None) + + # invalid keyord + assert_raises(TypeError, f, d, 0, keepdims=True, invalid="invalid", + out=None) + assert_raises(TypeError, f, d, invalid=0) + assert_raises(TypeError, f, d, axis=0, dtype=None, keepdims=True, + out=None, invalid=0) + assert_raises(TypeError, f, d, axis=0, dtype=None, + out=None, invalid=0) + assert_raises(TypeError, f, d, axis=0, dtype=None, invalid=0) + + if __name__ == "__main__": run_module_suite() diff --git a/numpy/core/tests/test_umath.py b/numpy/core/tests/test_umath.py index b3ddc2398..3030c68ab 100644 --- a/numpy/core/tests/test_umath.py +++ b/numpy/core/tests/test_umath.py @@ -540,6 +540,17 @@ class TestMaximum(_FilterInvalids): out = np.array([nan, nan, nan]) assert_equal(np.maximum(arg1, arg2), out) + def test_object_nans(self): + # Multiple checks to give this a chance to + # fail if cmp is used instead of rich compare. + # Failure cannot be guaranteed. + for i in range(1): + x = np.array(float('nan'), np.object) + y = 1.0 + z = np.array(float('nan'), np.object) + assert_(np.maximum(x, y) == 1.0) + assert_(np.maximum(z, y) == 1.0) + def test_complex_nans(self): nan = np.nan for cnan in [complex(nan, 0), complex(0, nan), complex(nan, nan)] : @@ -587,6 +598,17 @@ class TestMinimum(_FilterInvalids): out = np.array([nan, nan, nan]) assert_equal(np.minimum(arg1, arg2), out) + def test_object_nans(self): + # Multiple checks to give this a chance to + # fail if cmp is used instead of rich compare. + # Failure cannot be guaranteed. + for i in range(1): + x = np.array(float('nan'), np.object) + y = 1.0 + z = np.array(float('nan'), np.object) + assert_(np.minimum(x, y) == 1.0) + assert_(np.minimum(z, y) == 1.0) + def test_complex_nans(self): nan = np.nan for cnan in [complex(nan, 0), complex(0, nan), complex(nan, nan)] : @@ -753,6 +775,13 @@ class TestMinMax(TestCase): inp[i] = -1e10 assert_equal(inp.min(), -1e10, err_msg=msg) + def test_lower_align(self): + # check data that is not aligned to element size + # i.e doubles are aligned to 4 bytes on i386 + d = np.zeros(23 * 8, dtype=np.int8)[4:-4].view(np.float64) + assert_equal(d.max(), d[0]) + assert_equal(d.min(), d[0]) + class TestAbsoluteNegative(TestCase): def test_abs_neg_blocked(self): @@ -785,6 +814,17 @@ class TestAbsoluteNegative(TestCase): np.negative(inp, out=out) assert_array_equal(out, -1*inp, err_msg=msg) + def test_lower_align(self): + # check data that is not aligned to element size + # i.e doubles are aligned to 4 bytes on i386 + d = np.zeros(23 * 8, dtype=np.int8)[4:-4].view(np.float64) + assert_equal(np.abs(d), d) + assert_equal(np.negative(d), -d) + np.negative(d, out=d) + np.negative(np.ones_like(d), out=d) + np.abs(d, out=d) + np.abs(np.ones_like(d), out=d) + class TestSpecialMethods(TestCase): def test_wrap(self): diff --git a/numpy/distutils/command/autodist.py b/numpy/distutils/command/autodist.py index 1b9b1dd57..5a9470b9b 100644 --- a/numpy/distutils/command/autodist.py +++ b/numpy/distutils/command/autodist.py @@ -41,3 +41,37 @@ main() } """ return cmd.try_compile(body, None, None) + + +def check_gcc_function_attribute(cmd, attribute, name): + """Return True if the given function attribute is supported.""" + cmd._check_compiler() + body = """ +#pragma GCC diagnostic error "-Wattributes" +#pragma clang diagnostic error "-Wattributes" + +int %s %s(void*); + +int +main() +{ +} +""" % (attribute, name) + return cmd.try_compile(body, None, None) != 0 + +def check_gcc_variable_attribute(cmd, attribute): + """Return True if the given variable attribute is supported.""" + cmd._check_compiler() + body = """ +#pragma GCC diagnostic error "-Wattributes" +#pragma clang diagnostic error "-Wattributes" + +int %s foo; + +int +main() +{ + return 0; +} +""" % (attribute, ) + return cmd.try_compile(body, None, None) != 0 diff --git a/numpy/distutils/command/config.py b/numpy/distutils/command/config.py index 0086e3632..4c407bee0 100644 --- a/numpy/distutils/command/config.py +++ b/numpy/distutils/command/config.py @@ -16,7 +16,10 @@ from distutils.ccompiler import CompileError, LinkError import distutils from numpy.distutils.exec_command import exec_command from numpy.distutils.mingw32ccompiler import generate_manifest -from numpy.distutils.command.autodist import check_inline, check_compiler_gcc4 +from numpy.distutils.command.autodist import (check_gcc_function_attribute, + check_gcc_variable_attribute, + check_inline, + check_compiler_gcc4) from numpy.distutils.compat import get_exception LANG_EXT['f77'] = '.f' @@ -59,17 +62,28 @@ class config(old_config): e = get_exception() msg = """\ Could not initialize compiler instance: do you have Visual Studio -installed ? If you are trying to build with mingw, please use python setup.py -build -c mingw32 instead ). If you have Visual Studio installed, check it is -correctly installed, and the right version (VS 2008 for python 2.6, VS 2003 for -2.5, etc...). Original exception was: %s, and the Compiler -class was %s +installed? If you are trying to build with MinGW, please use "python setup.py +build -c mingw32" instead. If you have Visual Studio installed, check it is +correctly installed, and the right version (VS 2008 for python 2.6, 2.7 and 3.2, +VS 2010 for >= 3.3). + +Original exception was: %s, and the Compiler class was %s ============================================================================""" \ % (e, self.compiler.__class__.__name__) print ("""\ ============================================================================""") raise distutils.errors.DistutilsPlatformError(msg) + # After MSVC is initialized, add an explicit /MANIFEST to linker + # flags. See issues gh-4245 and gh-4101 for details. Also + # relevant are issues 4431 and 16296 on the Python bug tracker. + from distutils import msvc9compiler + if msvc9compiler.get_build_version() >= 10: + for ldflags in [self.compiler.ldflags_shared, + self.compiler.ldflags_shared_debug]: + if '/MANIFEST' not in ldflags: + ldflags.append('/MANIFEST') + if not isinstance(self.fcompiler, FCompiler): self.fcompiler = new_fcompiler(compiler=self.fcompiler, dry_run=self.dry_run, force=1, @@ -402,6 +416,12 @@ int main () """Return True if the C compiler is gcc >= 4.""" return check_compiler_gcc4(self) + def check_gcc_function_attribute(self, attribute, name): + return check_gcc_function_attribute(self, attribute, name) + + def check_gcc_variable_attribute(self, attribute): + return check_gcc_variable_attribute(self, attribute) + def get_output(self, body, headers=None, include_dirs=None, libraries=None, library_dirs=None, lang="c", use_tee=None): diff --git a/numpy/distutils/fcompiler/gnu.py b/numpy/distutils/fcompiler/gnu.py index b786c0a46..368506470 100644 --- a/numpy/distutils/fcompiler/gnu.py +++ b/numpy/distutils/fcompiler/gnu.py @@ -220,6 +220,9 @@ class GnuFCompiler(FCompiler): def get_flags_arch(self): return [] + def runtime_library_dir_option(self, dir): + return '-Wl,-rpath="%s"' % dir + class Gnu95FCompiler(GnuFCompiler): compiler_type = 'gnu95' compiler_aliases = ('gfortran',) @@ -252,12 +255,13 @@ class Gnu95FCompiler(GnuFCompiler): possible_executables = ['gfortran', 'f95'] executables = { 'version_cmd' : ["<F90>", "--version"], - 'compiler_f77' : [None, "-Wall", "-ffixed-form", + 'compiler_f77' : [None, "-Wall", "-g", "-ffixed-form", + "-fno-second-underscore"] + _EXTRAFLAGS, + 'compiler_f90' : [None, "-Wall", "-g", "-fno-second-underscore"] + _EXTRAFLAGS, - 'compiler_f90' : [None, "-Wall", "-fno-second-underscore"] + _EXTRAFLAGS, - 'compiler_fix' : [None, "-Wall", "-ffixed-form", + 'compiler_fix' : [None, "-Wall", "-g","-ffixed-form", "-fno-second-underscore"] + _EXTRAFLAGS, - 'linker_so' : ["<F90>", "-Wall"], + 'linker_so' : ["<F90>", "-Wall", "-g"], 'archiver' : ["ar", "-cr"], 'ranlib' : ["ranlib"], 'linker_exe' : [None, "-Wall"] diff --git a/numpy/f2py/tests/test_array_from_pyobj.py b/numpy/f2py/tests/test_array_from_pyobj.py index 3a148e72c..2dcb9e834 100644 --- a/numpy/f2py/tests/test_array_from_pyobj.py +++ b/numpy/f2py/tests/test_array_from_pyobj.py @@ -4,6 +4,7 @@ import unittest import os import sys import copy +import platform import nose @@ -81,37 +82,45 @@ class Intent(object): intent = Intent() -class Type(object): - _type_names = ['BOOL', 'BYTE', 'UBYTE', 'SHORT', 'USHORT', 'INT', 'UINT', - 'LONG', 'ULONG', 'LONGLONG', 'ULONGLONG', - 'FLOAT', 'DOUBLE', 'LONGDOUBLE', 'CFLOAT', 'CDOUBLE', - 'CLONGDOUBLE'] - _type_cache = {} - - _cast_dict = {'BOOL':['BOOL']} - _cast_dict['BYTE'] = _cast_dict['BOOL'] + ['BYTE'] - _cast_dict['UBYTE'] = _cast_dict['BOOL'] + ['UBYTE'] - _cast_dict['BYTE'] = ['BYTE'] - _cast_dict['UBYTE'] = ['UBYTE'] - _cast_dict['SHORT'] = _cast_dict['BYTE'] + ['UBYTE', 'SHORT'] - _cast_dict['USHORT'] = _cast_dict['UBYTE'] + ['BYTE', 'USHORT'] - _cast_dict['INT'] = _cast_dict['SHORT'] + ['USHORT', 'INT'] - _cast_dict['UINT'] = _cast_dict['USHORT'] + ['SHORT', 'UINT'] - - _cast_dict['LONG'] = _cast_dict['INT'] + ['LONG'] - _cast_dict['ULONG'] = _cast_dict['UINT'] + ['ULONG'] - - _cast_dict['LONGLONG'] = _cast_dict['LONG'] + ['LONGLONG'] - _cast_dict['ULONGLONG'] = _cast_dict['ULONG'] + ['ULONGLONG'] - - _cast_dict['FLOAT'] = _cast_dict['SHORT'] + ['USHORT', 'FLOAT'] - _cast_dict['DOUBLE'] = _cast_dict['INT'] + ['UINT', 'FLOAT', 'DOUBLE'] - _cast_dict['LONGDOUBLE'] = _cast_dict['LONG'] + ['ULONG', 'FLOAT', 'DOUBLE', 'LONGDOUBLE'] - - _cast_dict['CFLOAT'] = _cast_dict['FLOAT'] + ['CFLOAT'] +_type_names = ['BOOL', 'BYTE', 'UBYTE', 'SHORT', 'USHORT', 'INT', 'UINT', + 'LONG', 'ULONG', 'LONGLONG', 'ULONGLONG', + 'FLOAT', 'DOUBLE', 'CFLOAT'] + +_cast_dict = {'BOOL':['BOOL']} +_cast_dict['BYTE'] = _cast_dict['BOOL'] + ['BYTE'] +_cast_dict['UBYTE'] = _cast_dict['BOOL'] + ['UBYTE'] +_cast_dict['BYTE'] = ['BYTE'] +_cast_dict['UBYTE'] = ['UBYTE'] +_cast_dict['SHORT'] = _cast_dict['BYTE'] + ['UBYTE', 'SHORT'] +_cast_dict['USHORT'] = _cast_dict['UBYTE'] + ['BYTE', 'USHORT'] +_cast_dict['INT'] = _cast_dict['SHORT'] + ['USHORT', 'INT'] +_cast_dict['UINT'] = _cast_dict['USHORT'] + ['SHORT', 'UINT'] + +_cast_dict['LONG'] = _cast_dict['INT'] + ['LONG'] +_cast_dict['ULONG'] = _cast_dict['UINT'] + ['ULONG'] + +_cast_dict['LONGLONG'] = _cast_dict['LONG'] + ['LONGLONG'] +_cast_dict['ULONGLONG'] = _cast_dict['ULONG'] + ['ULONGLONG'] + +_cast_dict['FLOAT'] = _cast_dict['SHORT'] + ['USHORT', 'FLOAT'] +_cast_dict['DOUBLE'] = _cast_dict['INT'] + ['UINT', 'FLOAT', 'DOUBLE'] + +_cast_dict['CFLOAT'] = _cast_dict['FLOAT'] + ['CFLOAT'] + +# (debian) sparc system malloc does not provide the alignment required by +# 16 byte long double types this means the inout intent cannot be satisfied and +# several tests fail as the alignment flag can be randomly true or fals +# when numpy gains an aligned allocator the tests could be enabled again +if 'sparc' not in platform.platform().lower() and sys.platform != 'win32': + _type_names.extend(['LONGDOUBLE', 'CDOUBLE', 'CLONGDOUBLE']) + _cast_dict['LONGDOUBLE'] = _cast_dict['LONG'] + \ + ['ULONG', 'FLOAT', 'DOUBLE', 'LONGDOUBLE'] + _cast_dict['CLONGDOUBLE'] = _cast_dict['LONGDOUBLE'] + \ + ['CFLOAT', 'CDOUBLE', 'CLONGDOUBLE'] _cast_dict['CDOUBLE'] = _cast_dict['DOUBLE'] + ['CFLOAT', 'CDOUBLE'] - _cast_dict['CLONGDOUBLE'] = _cast_dict['LONGDOUBLE'] + ['CFLOAT', 'CDOUBLE', 'CLONGDOUBLE'] +class Type(object): + _type_cache = {} def __new__(cls, name): if isinstance(name, dtype): @@ -138,15 +147,15 @@ class Type(object): self.dtypechar = typeinfo[self.NAME][0] def cast_types(self): - return [self.__class__(_m) for _m in self._cast_dict[self.NAME]] + return [self.__class__(_m) for _m in _cast_dict[self.NAME]] def all_types(self): - return [self.__class__(_m) for _m in self._type_names] + return [self.__class__(_m) for _m in _type_names] def smaller_types(self): bits = typeinfo[self.NAME][3] types = [] - for name in self._type_names: + for name in _type_names: if typeinfo[name][3]<bits: types.append(Type(name)) return types @@ -154,7 +163,7 @@ class Type(object): def equal_types(self): bits = typeinfo[self.NAME][3] types = [] - for name in self._type_names: + for name in _type_names: if name==self.NAME: continue if typeinfo[name][3]==bits: types.append(Type(name)) @@ -163,7 +172,7 @@ class Type(object): def larger_types(self): bits = typeinfo[self.NAME][3] types = [] - for name in self._type_names: + for name in _type_names: if typeinfo[name][3]>bits: types.append(Type(name)) return types @@ -532,7 +541,7 @@ class _test_shared_memory: assert_(obj.dtype.type is self.type.dtype) # obj type is changed inplace! -for t in Type._type_names: +for t in _type_names: exec('''\ class test_%s_gen(unittest.TestCase, _test_shared_memory diff --git a/numpy/fft/fftpack.py b/numpy/fft/fftpack.py index 706fcdd2f..4efb2a9a0 100644 --- a/numpy/fft/fftpack.py +++ b/numpy/fft/fftpack.py @@ -43,7 +43,7 @@ _fft_cache = {} _real_fft_cache = {} def _raw_fft(a, n=None, axis=-1, init_function=fftpack.cffti, - work_function=fftpack.cfftf, fft_cache = _fft_cache ): + work_function=fftpack.cfftf, fft_cache=_fft_cache): a = asarray(a) if n is None: diff --git a/numpy/fft/setup.py b/numpy/fft/setup.py index 79f681e55..cd99a82d7 100644 --- a/numpy/fft/setup.py +++ b/numpy/fft/setup.py @@ -12,7 +12,6 @@ def configuration(parent_package='',top_path=None): sources=['fftpack_litemodule.c', 'fftpack.c'] ) - return config if __name__ == '__main__': diff --git a/numpy/fft/tests/test_helper.py b/numpy/fft/tests/test_helper.py index 7eaa99fdb..1a51f8e3a 100644 --- a/numpy/fft/tests/test_helper.py +++ b/numpy/fft/tests/test_helper.py @@ -30,8 +30,8 @@ class TestFFTShift(TestCase): assert_array_almost_equal(fft.ifftshift(fft.fftshift(x)), x) def test_axes_keyword(self): - freqs = [[ 0, 1, 2], [ 3, 4, -4], [-3, -2, -1]] - shifted = [[-1, -3, -2], [ 2, 0, 1], [-4, 3, 4]] + freqs = [[0, 1, 2], [3, 4, -4], [-3, -2, -1]] + shifted = [[-1, -3, -2], [2, 0, 1], [-4, 3, 4]] assert_array_almost_equal(fft.fftshift(freqs, axes=(0, 1)), shifted) assert_array_almost_equal(fft.fftshift(freqs, axes=0), fft.fftshift(freqs, axes=(0,))) diff --git a/numpy/lib/_iotools.py b/numpy/lib/_iotools.py index 1b1180893..9108b2e4c 100644 --- a/numpy/lib/_iotools.py +++ b/numpy/lib/_iotools.py @@ -687,7 +687,7 @@ class StringConverter(object): def upgrade(self, value): """ - Rind the best converter for a given string, and return the result. + Find the best converter for a given string, and return the result. The supplied string `value` is converted by testing different converters in order. First the `func` method of the diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index 087d1cbb5..257057666 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -337,6 +337,11 @@ def histogramdd(sample, bins=10, range=None, normed=False, weights=None): smin[i] = smin[i] - .5 smax[i] = smax[i] + .5 + # avoid rounding issues for comparisons when dealing with inexact types + if np.issubdtype(sample.dtype, np.inexact): + edge_dt = sample.dtype + else: + edge_dt = float # Create edge arrays for i in arange(D): if isscalar(bins[i]): @@ -345,9 +350,9 @@ def histogramdd(sample, bins=10, range=None, normed=False, weights=None): "Element at index %s in `bins` should be a positive " "integer." % i) nbin[i] = bins[i] + 2 # +2 for outlier bins - edges[i] = linspace(smin[i], smax[i], nbin[i]-1) + edges[i] = linspace(smin[i], smax[i], nbin[i]-1, dtype=edge_dt) else: - edges[i] = asarray(bins[i], float) + edges[i] = asarray(bins[i], edge_dt) nbin[i] = len(edges[i]) + 1 # +1 for outlier bins dedges[i] = diff(edges[i]) if np.any(np.asarray(dedges[i]) <= 0): diff --git a/numpy/lib/index_tricks.py b/numpy/lib/index_tricks.py index 98c6b291b..f83024961 100644 --- a/numpy/lib/index_tricks.py +++ b/numpy/lib/index_tricks.py @@ -727,6 +727,7 @@ def fill_diagonal(a, val, wrap=False): # tall matrices no wrap >>> a = np.zeros((5, 3),int) >>> fill_diagonal(a, 4) + >>> a array([[4, 0, 0], [0, 4, 0], [0, 0, 4], @@ -735,7 +736,8 @@ def fill_diagonal(a, val, wrap=False): # tall matrices wrap >>> a = np.zeros((5, 3),int) - >>> fill_diagonal(a, 4) + >>> fill_diagonal(a, 4, wrap=True) + >>> a array([[4, 0, 0], [0, 4, 0], [0, 0, 4], @@ -744,7 +746,8 @@ def fill_diagonal(a, val, wrap=False): # wide matrices >>> a = np.zeros((3, 5),int) - >>> fill_diagonal(a, 4) + >>> fill_diagonal(a, 4, wrap=True) + >>> a array([[4, 0, 0, 0, 0], [0, 4, 0, 0, 0], [0, 0, 4, 0, 0]]) diff --git a/numpy/lib/nanfunctions.py b/numpy/lib/nanfunctions.py index f5ac35e54..7260a35b8 100644 --- a/numpy/lib/nanfunctions.py +++ b/numpy/lib/nanfunctions.py @@ -33,6 +33,10 @@ def _replace_nan(a, val): marking the locations where NaNs were present. If `a` is not of inexact type, do nothing and return `a` together with a mask of None. + Note that scalars will end up as array scalars, which is important + for using the result as the value of the out argument in some + operations. + Parameters ---------- a : array-like @@ -1037,7 +1041,7 @@ def nanvar(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False): avg = _divide_by_count(avg, cnt) # Compute squared deviation from mean. - arr -= avg + np.subtract(arr, avg, out=arr, casting='unsafe') arr = _copyto(arr, 0, mask) if issubclass(arr.dtype.type, np.complexfloating): sqr = np.multiply(arr, arr.conj(), out=arr).real diff --git a/numpy/lib/npyio.py b/numpy/lib/npyio.py index fe855a71a..21d98efe7 100644 --- a/numpy/lib/npyio.py +++ b/numpy/lib/npyio.py @@ -288,8 +288,7 @@ def load(file, mmap_mode=None): Parameters ---------- file : file-like object or string - The file to read. Compressed files with the filename extension - ``.gz`` are acceptable. File-like objects must support the + The file to read. File-like objects must support the ``seek()`` and ``read()`` methods. Pickled files require that the file-like object support the ``readline()`` method as well. mmap_mode : {None, 'r+', 'r', 'w+', 'c'}, optional @@ -1519,7 +1518,9 @@ def genfromtxt(fname, dtype=float, comments='#', delimiter=None, # Process the filling_values ............................... # Rename the input for convenience - user_filling_values = filling_values or [] + user_filling_values = filling_values + if user_filling_values is None: + user_filling_values = [] # Define the default filling_values = [None] * nbcols # We have a dictionary : update each entry individually diff --git a/numpy/lib/tests/test_function_base.py b/numpy/lib/tests/test_function_base.py index f70d7dda0..ee3000547 100644 --- a/numpy/lib/tests/test_function_base.py +++ b/numpy/lib/tests/test_function_base.py @@ -1070,6 +1070,13 @@ class TestHistogram(TestCase): h, b = histogram(a, weights=np.ones(10, float)) assert_(issubdtype(h.dtype, float)) + def test_f32_rounding(self): + # gh-4799, check that the rounding of the edges works with float32 + x = np.array([276.318359 , -69.593948 , 21.329449], dtype=np.float32) + y = np.array([5005.689453, 4481.327637, 6010.369629], dtype=np.float32) + counts_hist, xedges, yedges = np.histogram2d(x, y, bins=100) + assert_equal(counts_hist.sum(), 3.) + def test_weights(self): v = rand(100) w = np.ones(100) * 5 diff --git a/numpy/lib/tests/test_io.py b/numpy/lib/tests/test_io.py index 49ad1ba5b..40a229d14 100644 --- a/numpy/lib/tests/test_io.py +++ b/numpy/lib/tests/test_io.py @@ -4,9 +4,7 @@ import sys import gzip import os import threading -import shutil -import contextlib -from tempfile import mkstemp, mkdtemp, NamedTemporaryFile +from tempfile import mkstemp, NamedTemporaryFile import time import warnings import gc @@ -24,13 +22,7 @@ from numpy.ma.testutils import ( assert_raises, assert_raises_regex, run_module_suite ) from numpy.testing import assert_warns, assert_, build_err_msg - - -@contextlib.contextmanager -def tempdir(change_dir=False): - tmpdir = mkdtemp() - yield tmpdir - shutil.rmtree(tmpdir) +from numpy.testing.utils import tempdir class TextIO(BytesIO): @@ -202,7 +194,7 @@ class TestSavezLoad(RoundtripTest, TestCase): def test_big_arrays(self): L = (1 << 31) + 100000 a = np.empty(L, dtype=np.uint8) - with tempdir() as tmpdir: + with tempdir(prefix="numpy_test_big_arrays_") as tmpdir: tmp = os.path.join(tmpdir, "file.npz") np.savez(tmp, a=a) del a @@ -311,7 +303,7 @@ class TestSavezLoad(RoundtripTest, TestCase): # Check that zipfile owns file and can close it. # This needs to pass a file name to load for the # test. - with tempdir() as tmpdir: + with tempdir(prefix="numpy_test_closing_zipfile_after_load_") as tmpdir: fd, tmp = mkstemp(suffix='.npz', dir=tmpdir) os.close(fd) np.savez(tmp, lab='place holder') @@ -1308,6 +1300,16 @@ M 33 21.99 ctrl = np.array([(0, 3), (4, -999)], dtype=[(_, int) for _ in "ac"]) assert_equal(test, ctrl) + data2 = "1,2,*,4\n5,*,7,8\n" + test = np.genfromtxt(TextIO(data2), delimiter=',', dtype=int, + missing_values="*", filling_values=0) + ctrl = np.array([[1, 2, 0, 4], [5, 0, 7, 8]]) + assert_equal(test, ctrl) + test = np.genfromtxt(TextIO(data2), delimiter=',', dtype=int, + missing_values="*", filling_values=-1) + ctrl = np.array([[1, 2, -1, 4], [5, -1, 7, 8]]) + assert_equal(test, ctrl) + def test_withmissing_float(self): data = TextIO('A,B\n0,1.5\n2,-999.00') test = np.mafromtxt(data, dtype=None, delimiter=',', diff --git a/numpy/lib/tests/test_twodim_base.py b/numpy/lib/tests/test_twodim_base.py index e9dbef70f..739061a5d 100644 --- a/numpy/lib/tests/test_twodim_base.py +++ b/numpy/lib/tests/test_twodim_base.py @@ -311,6 +311,40 @@ def test_tril_triu_ndim3(): yield assert_equal, a_triu_observed.dtype, a.dtype yield assert_equal, a_tril_observed.dtype, a.dtype +def test_tril_triu_with_inf(): + # Issue 4859 + arr = np.array([[1, 1, np.inf], + [1, 1, 1], + [np.inf, 1, 1]]) + out_tril = np.array([[1, 0, 0], + [1, 1, 0], + [np.inf, 1, 1]]) + out_triu = out_tril.T + assert_array_equal(np.triu(arr), out_triu) + assert_array_equal(np.tril(arr), out_tril) + + +def test_tril_triu_dtype(): + # Issue 4916 + # tril and triu should return the same dtype as input + for c in np.typecodes['All']: + if c == 'V': + continue + arr = np.zeros((3, 3), dtype=c) + assert_equal(np.triu(arr).dtype, arr.dtype) + assert_equal(np.tril(arr).dtype, arr.dtype) + + # check special cases + arr = np.array([['2001-01-01T12:00', '2002-02-03T13:56'], + ['2004-01-01T12:00', '2003-01-03T13:45']], + dtype='datetime64') + assert_equal(np.triu(arr).dtype, arr.dtype) + assert_equal(np.tril(arr).dtype, arr.dtype) + + arr = np.zeros((3,3), dtype='f4,f4') + assert_equal(np.triu(arr).dtype, arr.dtype) + assert_equal(np.tril(arr).dtype, arr.dtype) + def test_mask_indices(): # simple test without offset diff --git a/numpy/lib/twodim_base.py b/numpy/lib/twodim_base.py index 2861e1c4a..40a140b6b 100644 --- a/numpy/lib/twodim_base.py +++ b/numpy/lib/twodim_base.py @@ -387,7 +387,6 @@ def tri(N, M=None, k=0, dtype=float): dtype : dtype, optional Data type of the returned array. The default is float. - Returns ------- tri : ndarray of shape (N, M) @@ -452,7 +451,9 @@ def tril(m, k=0): """ m = asanyarray(m) - return multiply(tri(*m.shape[-2:], k=k, dtype=bool), m, dtype=m.dtype) + mask = tri(*m.shape[-2:], k=k, dtype=bool) + + return where(mask, m, zeros(1, m.dtype)) def triu(m, k=0): @@ -478,7 +479,9 @@ def triu(m, k=0): """ m = asanyarray(m) - return multiply(~tri(*m.shape[-2:], k=k-1, dtype=bool), m, dtype=m.dtype) + mask = tri(*m.shape[-2:], k=k-1, dtype=bool) + + return where(mask, zeros(1, m.dtype), m) # Originally borrowed from John Hunter and matplotlib diff --git a/numpy/ma/core.py b/numpy/ma/core.py index 5c566b92c..00164b851 100644 --- a/numpy/ma/core.py +++ b/numpy/ma/core.py @@ -843,8 +843,7 @@ class _MaskedUnaryOperation: d = getdata(a) # Case 1.1. : Domained function if self.domain is not None: - with np.errstate(): - np.seterr(divide='ignore', invalid='ignore') + with np.errstate(divide='ignore', invalid='ignore'): result = self.f(d, *args, **kwargs) # Make a mask m = ~umath.isfinite(result) @@ -932,8 +931,7 @@ class _MaskedBinaryOperation: else: m = umath.logical_or(ma, mb) # Get the result - with np.errstate(): - np.seterr(divide='ignore', invalid='ignore') + with np.errstate(divide='ignore', invalid='ignore'): result = self.f(da, db, *args, **kwargs) # check it worked if result is NotImplemented: @@ -945,11 +943,8 @@ class _MaskedBinaryOperation: return result # Case 2. : array # Revert result to da where masked - if m.any(): - np.copyto(result, 0, casting='unsafe', where=m) - # This only makes sense if the operation preserved the dtype - if result.dtype == da.dtype: - result += m * da + if m is not nomask: + np.copyto(result, da, casting='unsafe', where=m) # Transforms to a (subclass of) MaskedArray result = result.view(get_masked_subclass(a, b)) result._mask = m @@ -1073,8 +1068,7 @@ class _DomainedBinaryOperation: (da, db) = (getdata(a, subok=False), getdata(b, subok=False)) (ma, mb) = (getmask(a), getmask(b)) # Get the result - with np.errstate(): - np.seterr(divide='ignore', invalid='ignore') + with np.errstate(divide='ignore', invalid='ignore'): result = self.f(da, db, *args, **kwargs) # check it worked if result is NotImplemented: @@ -1094,8 +1088,7 @@ class _DomainedBinaryOperation: else: return result # When the mask is True, put back da - np.copyto(result, 0, casting='unsafe', where=m) - result += m * da + np.copyto(result, da, casting='unsafe', where=m) result = result.view(get_masked_subclass(a, b)) result._mask = m if isinstance(b, MaskedArray): @@ -3840,8 +3833,7 @@ class MaskedArray(ndarray): "Raise self to the power other, in place." other_data = getdata(other) other_mask = getmask(other) - with np.errstate(): - np.seterr(divide='ignore', invalid='ignore') + with np.errstate(divide='ignore', invalid='ignore'): ndarray.__ipow__(self._data, np.where(self._mask, 1, other_data)) invalid = np.logical_not(np.isfinite(self._data)) if invalid.any(): @@ -5029,6 +5021,10 @@ class MaskedArray(ndarray): endwith : {True, False}, optional Whether missing values (if any) should be forced in the upper indices (at the end of the array) (True) or lower indices (at the beginning). + When the array contains unmasked values of the largest (or smallest if + False) representable value of the datatype the ordering of these values + and the masked values is undefined. To enforce the masked values are + at the end (beginning) in this case one must sort the mask. fill_value : {var}, optional Value used internally for the masked values. If ``fill_value`` is not None, it supersedes ``endwith``. @@ -5594,9 +5590,8 @@ class mvoid(MaskedArray): """ # def __new__(self, data, mask=nomask, dtype=None, fill_value=None, - hardmask=False): - dtype = dtype or data.dtype - _data = np.array(data, dtype=dtype) + hardmask=False, copy=False, subok=True): + _data = np.array(data, copy=copy, subok=subok, dtype=dtype) _data = _data.view(self) _data._hardmask = hardmask if mask is not nomask: @@ -6116,8 +6111,7 @@ def power(a, b, third=None): else: basetype = MaskedArray # Get the result and view it as a (subclass of) MaskedArray - with np.errstate(): - np.seterr(divide='ignore', invalid='ignore') + with np.errstate(divide='ignore', invalid='ignore'): result = np.where(m, fa, umath.power(fa, fb)).view(basetype) result._update_from(a) # Find where we're in trouble w/ NaNs and Infs diff --git a/numpy/ma/tests/test_core.py b/numpy/ma/tests/test_core.py index e6f659041..34951875d 100644 --- a/numpy/ma/tests/test_core.py +++ b/numpy/ma/tests/test_core.py @@ -194,8 +194,7 @@ class TestMaskedArray(TestCase): def test_fix_invalid(self): # Checks fix_invalid. - with np.errstate(): - np.seterr(invalid='ignore') + with np.errstate(invalid='ignore'): data = masked_array([np.nan, 0., 1.], mask=[0, 0, 1]) data_fixed = fix_invalid(data) assert_equal(data_fixed._data, [data.fill_value, 0., 1.]) @@ -815,7 +814,7 @@ class TestMaskedArrayArithmetic(TestCase): res = count(ott) self.assertTrue(res.dtype.type is np.intp) assert_equal(3, res) - + ott = ott.reshape((2, 2)) res = count(ott) assert_(res.dtype.type is np.intp) @@ -3523,8 +3522,15 @@ class TestMaskedFields(TestCase): assert_equal_records(a[-2]._mask, a._mask[-2]) def test_setitem(self): - # Issue 2403 + # Issue 4866: check that one can set individual items in [record][col] + # and [col][record] order ndtype = np.dtype([('a', float), ('b', int)]) + ma = np.ma.MaskedArray([(1.0, 1), (2.0, 2)], dtype=ndtype) + ma['a'][1] = 3.0 + assert_equal(ma['a'], np.array([1.0, 3.0])) + ma[1]['a'] = 4.0 + assert_equal(ma['a'], np.array([1.0, 4.0])) + # Issue 2403 mdtype = np.dtype([('a', bool), ('b', bool)]) # soft mask control = np.array([(False, True), (True, True)], dtype=mdtype) diff --git a/numpy/ma/tests/test_old_ma.py b/numpy/ma/tests/test_old_ma.py index 87c2133d7..047f91c77 100644 --- a/numpy/ma/tests/test_old_ma.py +++ b/numpy/ma/tests/test_old_ma.py @@ -607,8 +607,7 @@ class TestMa(TestCase): def test_testScalarArithmetic(self): xm = array(0, mask=1) #TODO FIXME: Find out what the following raises a warning in r8247 - with np.errstate(): - np.seterr(divide='ignore') + with np.errstate(divide='ignore'): self.assertTrue((1 / array(0)).mask) self.assertTrue((1 + xm).mask) self.assertTrue((-xm).mask) diff --git a/numpy/matrixlib/defmatrix.py b/numpy/matrixlib/defmatrix.py index 0fd5db66a..22ff4790b 100644 --- a/numpy/matrixlib/defmatrix.py +++ b/numpy/matrixlib/defmatrix.py @@ -454,6 +454,96 @@ class matrix(N.ndarray): """ return N.ndarray.sum(self, axis, dtype, out, keepdims=True)._collapse(axis) + + # To update docstring from array to matrix... + def squeeze(self, axis=None): + """ + Return a possibly reshaped matrix. + + Refer to `numpy.squeeze` for more documentation. + + Parameters + ---------- + axis : None or int or tuple of ints, optional + Selects a subset of the single-dimensional entries in the shape. + If an axis is selected with shape entry greater than one, + an error is raised. + + Returns + ------- + squeezed : matrix + The matrix, but as a (1, N) matrix if it had shape (N, 1). + + See Also + -------- + numpy.squeeze : related function + + Notes + ----- + If `m` has a single column then that column is returned + as the single row of a matrix. Otherwise `m` is returned. + The returned matrix is always either `m` itself or a view into `m`. + Supplying an axis keyword argument will not affect the returned matrix + but it may cause an error to be raised. + + Examples + -------- + >>> c = np.matrix([[1], [2]]) + >>> c + matrix([[1], + [2]]) + >>> c.squeeze() + matrix([[1, 2]]) + >>> r = c.T + >>> r + matrix([[1, 2]]) + >>> r.squeeze() + matrix([[1, 2]]) + >>> m = np.matrix([[1, 2], [3, 4]]) + >>> m.squeeze() + matrix([[1, 2], + [3, 4]]) + + """ + return N.ndarray.squeeze(self, axis=axis) + + + # To update docstring from array to matrix... + def flatten(self, order='C'): + """ + Return a flattened copy of the matrix. + + All `N` elements of the matrix are placed into a single row. + + Parameters + ---------- + order : {'C', 'F', 'A'}, optional + Whether to flatten in C (row-major), Fortran (column-major) order, + or preserve the C/Fortran ordering from `m`. + The default is 'C'. + + Returns + ------- + y : matrix + A copy of the matrix, flattened to a `(1, N)` matrix where `N` + is the number of elements in the original matrix. + + See Also + -------- + ravel : Return a flattened array. + flat : A 1-D flat iterator over the matrix. + + Examples + -------- + >>> m = np.matrix([[1,2], [3,4]]) + >>> m.flatten() + matrix([[1, 2, 3, 4]]) + >>> m.flatten('F') + matrix([[1, 3, 2, 4]]) + + """ + return N.ndarray.flatten(self, order=order) + def mean(self, axis=None, dtype=None, out=None): """ Returns the average of the matrix elements along the given axis. @@ -925,6 +1015,46 @@ class matrix(N.ndarray): """ return self.__array__().ravel() + + def ravel(self, order='C'): + """ + Return a flattened matrix. + + Refer to `numpy.ravel` for more documentation. + + Parameters + ---------- + order : {'C', 'F', 'A', 'K'}, optional + The elements of `m` are read using this index order. 'C' means to + index the elements in C-like order, with the last axis index + changing fastest, back to the first axis index changing slowest. + 'F' means to index the elements in Fortran-like index order, with + the first index changing fastest, and the last index changing + slowest. Note that the 'C' and 'F' options take no account of the + memory layout of the underlying array, and only refer to the order + of axis indexing. 'A' means to read the elements in Fortran-like + index order if `m` is Fortran *contiguous* in memory, C-like order + otherwise. 'K' means to read the elements in the order they occur + in memory, except for reversing the data when strides are negative. + By default, 'C' index order is used. + + Returns + ------- + ret : matrix + Return the matrix flattened to shape `(1, N)` where `N` + is the number of elements in the original matrix. + A copy is made only if necessary. + + See Also + -------- + matrix.flatten : returns a similar output matrix but always a copy + matrix.flat : a flat iterator on the array. + numpy.ravel : related function which returns an ndarray + + """ + return N.ndarray.ravel(self, order=order) + + def getT(self): """ Returns the transpose of the matrix. diff --git a/numpy/matrixlib/tests/test_defmatrix.py b/numpy/matrixlib/tests/test_defmatrix.py index a06a564aa..d2a89bd51 100644 --- a/numpy/matrixlib/tests/test_defmatrix.py +++ b/numpy/matrixlib/tests/test_defmatrix.py @@ -386,6 +386,7 @@ class TestNewScalarIndexing(TestCase): assert_array_equal(x[:, [1, 0]], x[:, ::-1]) assert_array_equal(x[[2, 1, 0],:], x[::-1,:]) + class TestPower(TestCase): def test_returntype(self): a = array([[0, 1], [0, 0]]) @@ -396,5 +397,51 @@ class TestPower(TestCase): def test_list(self): assert_array_equal(matrix_power([[0, 1], [0, 0]], 2), [[0, 0], [0, 0]]) + +class TestShape(TestCase): + def setUp(self): + self.a = array([[1], [2]]) + self.m = matrix([[1], [2]]) + + def test_shape(self): + assert_equal(self.a.shape, (2, 1)) + assert_equal(self.m.shape, (2, 1)) + + def test_numpy_ravel(self): + assert_equal(np.ravel(self.a).shape, (2,)) + assert_equal(np.ravel(self.m).shape, (2,)) + + def test_member_ravel(self): + assert_equal(self.a.ravel().shape, (2,)) + assert_equal(self.m.ravel().shape, (1, 2)) + + def test_member_flatten(self): + assert_equal(self.a.flatten().shape, (2,)) + assert_equal(self.m.flatten().shape, (1, 2)) + + def test_numpy_ravel_order(self): + for t in array, matrix: + x = t([[1, 2, 3], [4, 5, 6]]) + assert_equal(np.ravel(x), [1, 2, 3, 4, 5, 6]) + assert_equal(np.ravel(x, order='F'), [1, 4, 2, 5, 3, 6]) + assert_equal(np.ravel(x.T), [1, 4, 2, 5, 3, 6]) + assert_equal(np.ravel(x.T, order='A'), [1, 2, 3, 4, 5, 6]) + + def test_matrix_ravel_order(self): + x = matrix([[1, 2, 3], [4, 5, 6]]) + assert_equal(x.ravel(), [[1, 2, 3, 4, 5, 6]]) + assert_equal(x.ravel(order='F'), [[1, 4, 2, 5, 3, 6]]) + assert_equal(x.T.ravel(), [[1, 4, 2, 5, 3, 6]]) + assert_equal(x.T.ravel(order='A'), [[1, 2, 3, 4, 5, 6]]) + + def test_array_memory_sharing(self): + assert_(np.may_share_memory(self.a, self.a.ravel())) + assert_(not np.may_share_memory(self.a, self.a.flatten())) + + def test_matrix_memory_sharing(self): + assert_(np.may_share_memory(self.m, self.m.ravel())) + assert_(not np.may_share_memory(self.m, self.m.flatten())) + + if __name__ == "__main__": run_module_suite() diff --git a/numpy/polynomial/__init__.py b/numpy/polynomial/__init__.py index e9ca387c3..1200d1c8d 100644 --- a/numpy/polynomial/__init__.py +++ b/numpy/polynomial/__init__.py @@ -15,8 +15,6 @@ information can be found in the docstring for the module of interest. """ from __future__ import division, absolute_import, print_function -import warnings - from .polynomial import Polynomial from .chebyshev import Chebyshev from .legendre import Legendre diff --git a/numpy/polynomial/_polybase.py b/numpy/polynomial/_polybase.py index 23608c74a..234b509aa 100644 --- a/numpy/polynomial/_polybase.py +++ b/numpy/polynomial/_polybase.py @@ -374,7 +374,7 @@ class ABCPolyBase(object): return quo, rem def __pow__(self, other): - coef = self._pow(self.coef, other, maxpower = self.maxpower) + coef = self._pow(self.coef, other, maxpower=self.maxpower) res = self.__class__(coef, self.domain, self.window) return res @@ -721,8 +721,6 @@ class ABCPolyBase(object): y = self(x) return x, y - - @classmethod def fit(cls, x, y, deg, domain=None, rcond=None, full=False, w=None, window=None): diff --git a/numpy/polynomial/chebyshev.py b/numpy/polynomial/chebyshev.py index b4acbbeab..f213ab3fd 100644 --- a/numpy/polynomial/chebyshev.py +++ b/numpy/polynomial/chebyshev.py @@ -94,13 +94,14 @@ import numpy.linalg as la from . import polyutils as pu from ._polybase import ABCPolyBase -__all__ = ['chebzero', 'chebone', 'chebx', 'chebdomain', 'chebline', - 'chebadd', 'chebsub', 'chebmulx', 'chebmul', 'chebdiv', 'chebpow', - 'chebval', 'chebder', 'chebint', 'cheb2poly', 'poly2cheb', - 'chebfromroots', 'chebvander', 'chebfit', 'chebtrim', 'chebroots', - 'chebpts1', 'chebpts2', 'Chebyshev', 'chebval2d', 'chebval3d', - 'chebgrid2d', 'chebgrid3d', 'chebvander2d', 'chebvander3d', - 'chebcompanion', 'chebgauss', 'chebweight'] +__all__ = [ + 'chebzero', 'chebone', 'chebx', 'chebdomain', 'chebline', 'chebadd', + 'chebsub', 'chebmulx', 'chebmul', 'chebdiv', 'chebpow', 'chebval', + 'chebder', 'chebint', 'cheb2poly', 'poly2cheb', 'chebfromroots', + 'chebvander', 'chebfit', 'chebtrim', 'chebroots', 'chebpts1', + 'chebpts2', 'Chebyshev', 'chebval2d', 'chebval3d', 'chebgrid2d', + 'chebgrid3d', 'chebvander2d', 'chebvander3d', 'chebcompanion', + 'chebgauss', 'chebweight'] chebtrim = pu.trimcoef @@ -109,7 +110,7 @@ chebtrim = pu.trimcoef # functions and do minimal error checking. # -def _cseries_to_zseries(c) : +def _cseries_to_zseries(c): """Covert Chebyshev series to z-series. Covert a Chebyshev series to the equivalent z-series. The result is @@ -134,7 +135,7 @@ def _cseries_to_zseries(c) : return zs + zs[::-1] -def _zseries_to_cseries(zs) : +def _zseries_to_cseries(zs): """Covert z-series to a Chebyshev series. Covert a z series to the equivalent Chebyshev series. The result is @@ -159,7 +160,7 @@ def _zseries_to_cseries(zs) : return c -def _zseries_mul(z1, z2) : +def _zseries_mul(z1, z2): """Multiply two z-series. Multiply two z-series to produce a z-series. @@ -186,7 +187,7 @@ def _zseries_mul(z1, z2) : return np.convolve(z1, z2) -def _zseries_div(z1, z2) : +def _zseries_div(z1, z2): """Divide the first z-series by the second. Divide `z1` by `z2` and return the quotient and remainder as z-series. @@ -223,19 +224,19 @@ def _zseries_div(z1, z2) : z2 = z2.copy() len1 = len(z1) len2 = len(z2) - if len2 == 1 : + if len2 == 1: z1 /= z2 return z1, z1[:1]*0 - elif len1 < len2 : + elif len1 < len2: return z1[:1]*0, z1 - else : + else: dlen = len1 - len2 scl = z2[0] z2 /= scl quo = np.empty(dlen + 1, dtype=z1.dtype) i = 0 j = dlen - while i < j : + while i < j: r = z1[i] quo[i] = z1[i] quo[dlen - i] = r @@ -253,7 +254,7 @@ def _zseries_div(z1, z2) : return quo, rem -def _zseries_der(zs) : +def _zseries_der(zs): """Differentiate a z-series. The derivative is with respect to x, not z. This is achieved using the @@ -285,7 +286,7 @@ def _zseries_der(zs) : return d -def _zseries_int(zs) : +def _zseries_int(zs): """Integrate a z-series. The integral is with respect to x, not z. This is achieved by a change @@ -323,7 +324,7 @@ def _zseries_int(zs) : # -def poly2cheb(pol) : +def poly2cheb(pol): """ Convert a polynomial to a Chebyshev series. @@ -368,12 +369,12 @@ def poly2cheb(pol) : [pol] = pu.as_series([pol]) deg = len(pol) - 1 res = 0 - for i in range(deg, -1, -1) : + for i in range(deg, -1, -1): res = chebadd(chebmulx(res), pol[i]) return res -def cheb2poly(c) : +def cheb2poly(c): """ Convert a Chebyshev series to a polynomial. @@ -427,7 +428,7 @@ def cheb2poly(c) : c0 = c[-2] c1 = c[-1] # i is the current degree of c1 - for i in range(n - 1, 1, -1) : + for i in range(n - 1, 1, -1): tmp = c0 c0 = polysub(c[i - 2], c1) c1 = polyadd(tmp, polymulx(c1)*2) @@ -452,7 +453,7 @@ chebone = np.array([1]) chebx = np.array([0, 1]) -def chebline(off, scl) : +def chebline(off, scl): """ Chebyshev series whose graph is a straight line. @@ -482,13 +483,13 @@ def chebline(off, scl) : -3.0 """ - if scl != 0 : + if scl != 0: return np.array([off, scl]) - else : + else: return np.array([off]) -def chebfromroots(roots) : +def chebfromroots(roots): """ Generate a Chebyshev series with given roots. @@ -537,9 +538,9 @@ def chebfromroots(roots) : array([ 1.5+0.j, 0.0+0.j, 0.5+0.j]) """ - if len(roots) == 0 : + if len(roots) == 0: return np.ones(1) - else : + else: [roots] = pu.as_series([roots], trim=False) roots.sort() p = [chebline(-r, 1) for r in roots] @@ -595,10 +596,10 @@ def chebadd(c1, c2): """ # c1, c2 are trimmed copies [c1, c2] = pu.as_series([c1, c2]) - if len(c1) > len(c2) : + if len(c1) > len(c2): c1[:c2.size] += c2 ret = c1 - else : + else: c2[:c1.size] += c1 ret = c2 return pu.trimseq(ret) @@ -647,10 +648,10 @@ def chebsub(c1, c2): """ # c1, c2 are trimmed copies [c1, c2] = pu.as_series([c1, c2]) - if len(c1) > len(c2) : + if len(c1) > len(c2): c1[:c2.size] -= c2 ret = c1 - else : + else: c2 = -c2 c2[:c1.size] += c1 ret = c2 @@ -794,16 +795,16 @@ def chebdiv(c1, c2): """ # c1, c2 are trimmed copies [c1, c2] = pu.as_series([c1, c2]) - if c2[-1] == 0 : + if c2[-1] == 0: raise ZeroDivisionError() lc1 = len(c1) lc2 = len(c2) - if lc1 < lc2 : + if lc1 < lc2: return c1[:1]*0, c1 - elif lc2 == 1 : + elif lc2 == 1: return c1/c2[-1], c1[:1]*0 - else : + else: z1 = _cseries_to_zseries(c1) z2 = _cseries_to_zseries(c2) quo, rem = _zseries_div(z1, z2) @@ -812,7 +813,7 @@ def chebdiv(c1, c2): return quo, rem -def chebpow(c, pow, maxpower=16) : +def chebpow(c, pow, maxpower=16): """Raise a Chebyshev series to a power. Returns the Chebyshev series `c` raised to the power `pow`. The @@ -846,25 +847,25 @@ def chebpow(c, pow, maxpower=16) : # c is a trimmed copy [c] = pu.as_series([c]) power = int(pow) - if power != pow or power < 0 : + if power != pow or power < 0: raise ValueError("Power must be a non-negative integer.") - elif maxpower is not None and power > maxpower : + elif maxpower is not None and power > maxpower: raise ValueError("Power is too large") - elif power == 0 : + elif power == 0: return np.array([1], dtype=c.dtype) - elif power == 1 : + elif power == 1: return c - else : + else: # This can be made more efficient by using powers of two # in the usual way. zs = _cseries_to_zseries(c) prd = zs - for i in range(2, power + 1) : + for i in range(2, power + 1): prd = np.convolve(prd, zs) return _zseries_to_cseries(prd) -def chebder(c, m=1, scl=1, axis=0) : +def chebder(c, m=1, scl=1, axis=0): """ Differentiate a Chebyshev series. @@ -1057,9 +1058,9 @@ def chebint(c, m=1, k=[], lbnd=0, scl=1, axis=0): if cnt != m: raise ValueError("The order of integration must be integer") - if cnt < 0 : + if cnt < 0: raise ValueError("The order of integration must be non-negative") - if len(k) > cnt : + if len(k) > cnt: raise ValueError("Too many integration constants") if iaxis != axis: raise ValueError("The axis must be integer") @@ -1073,7 +1074,7 @@ def chebint(c, m=1, k=[], lbnd=0, scl=1, axis=0): c = np.rollaxis(c, iaxis) k = list(k) + [0]*(cnt - len(k)) - for i in range(cnt) : + for i in range(cnt): n = len(c) c *= scl if n == 1 and np.all(c[0] == 0): @@ -1162,19 +1163,19 @@ def chebval(x, c, tensor=True): if isinstance(x, (tuple, list)): x = np.asarray(x) if isinstance(x, np.ndarray) and tensor: - c = c.reshape(c.shape + (1,)*x.ndim) + c = c.reshape(c.shape + (1,)*x.ndim) - if len(c) == 1 : + if len(c) == 1: c0 = c[0] c1 = 0 - elif len(c) == 2 : + elif len(c) == 2: c0 = c[0] c1 = c[1] - else : + else: x2 = 2*x c0 = c[-2] c1 = c[-1] - for i in range(3, len(c) + 1) : + for i in range(3, len(c) + 1): tmp = c0 c0 = c[-i] - c1 c1 = tmp + c1*x2 @@ -1410,7 +1411,7 @@ def chebgrid3d(x, y, z, c): return c -def chebvander(x, deg) : +def chebvander(x, deg): """Pseudo-Vandermonde matrix of given degree. Returns the pseudo-Vandermonde matrix of degree `deg` and sample points @@ -1457,15 +1458,15 @@ def chebvander(x, deg) : v = np.empty(dims, dtype=dtyp) # Use forward recursion to generate the entries. v[0] = x*0 + 1 - if ideg > 0 : + if ideg > 0: x2 = 2*x v[1] = x - for i in range(2, ideg + 1) : + for i in range(2, ideg + 1): v[i] = v[i-1]*x2 - v[i-2] return np.rollaxis(v, 0, v.ndim) -def chebvander2d(x, y, deg) : +def chebvander2d(x, y, deg): """Pseudo-Vandermonde matrix of given degrees. Returns the pseudo-Vandermonde matrix of degrees `deg` and sample @@ -1528,7 +1529,7 @@ def chebvander2d(x, y, deg) : return v.reshape(v.shape[:-2] + (-1,)) -def chebvander3d(x, y, z, deg) : +def chebvander3d(x, y, z, deg): """Pseudo-Vandermonde matrix of given degrees. Returns the pseudo-Vandermonde matrix of degrees `deg` and sample @@ -1714,13 +1715,13 @@ def chebfit(x, y, deg, rcond=None, full=False, w=None): y = np.asarray(y) + 0.0 # check arguments. - if deg < 0 : + if deg < 0: raise ValueError("expected deg >= 0") if x.ndim != 1: raise TypeError("expected 1D vector for x") if x.size == 0: raise TypeError("expected non-empty vector for x") - if y.ndim < 1 or y.ndim > 2 : + if y.ndim < 1 or y.ndim > 2: raise TypeError("expected 1D or 2D array for y") if len(x) != len(y): raise TypeError("expected x and y to have same length") @@ -1740,7 +1741,7 @@ def chebfit(x, y, deg, rcond=None, full=False, w=None): rhs = rhs * w # set rcond - if rcond is None : + if rcond is None: rcond = len(x)*np.finfo(x.dtype).eps # Determine the norms of the design matrix columns. @@ -1759,9 +1760,9 @@ def chebfit(x, y, deg, rcond=None, full=False, w=None): msg = "The fit may be poorly conditioned" warnings.warn(msg, pu.RankWarning) - if full : + if full: return c, [resids, rank, s, rcond] - else : + else: return c @@ -1916,8 +1917,8 @@ def chebweight(x): The weight function of the Chebyshev polynomials. The weight function is :math:`1/\sqrt{1 - x^2}` and the interval of - integration is :math:`[-1, 1]`. The Chebyshev polynomials are orthogonal, but - not normalized, with respect to this weight function. + integration is :math:`[-1, 1]`. The Chebyshev polynomials are + orthogonal, but not normalized, with respect to this weight function. Parameters ---------- diff --git a/numpy/polynomial/hermite.py b/numpy/polynomial/hermite.py index 43ede58ac..1fd49d774 100644 --- a/numpy/polynomial/hermite.py +++ b/numpy/polynomial/hermite.py @@ -66,18 +66,18 @@ import numpy.linalg as la from . import polyutils as pu from ._polybase import ABCPolyBase -__all__ = ['hermzero', 'hermone', 'hermx', 'hermdomain', 'hermline', - 'hermadd', 'hermsub', 'hermmulx', 'hermmul', 'hermdiv', 'hermpow', - 'hermval', 'hermder', 'hermint', 'herm2poly', 'poly2herm', - 'hermfromroots', 'hermvander', 'hermfit', 'hermtrim', 'hermroots', - 'Hermite', 'hermval2d', 'hermval3d', 'hermgrid2d', 'hermgrid3d', - 'hermvander2d', 'hermvander3d', 'hermcompanion', 'hermgauss', - 'hermweight'] +__all__ = [ + 'hermzero', 'hermone', 'hermx', 'hermdomain', 'hermline', 'hermadd', + 'hermsub', 'hermmulx', 'hermmul', 'hermdiv', 'hermpow', 'hermval', + 'hermder', 'hermint', 'herm2poly', 'poly2herm', 'hermfromroots', + 'hermvander', 'hermfit', 'hermtrim', 'hermroots', 'Hermite', + 'hermval2d', 'hermval3d', 'hermgrid2d', 'hermgrid3d', 'hermvander2d', + 'hermvander3d', 'hermcompanion', 'hermgauss', 'hermweight'] hermtrim = pu.trimcoef -def poly2herm(pol) : +def poly2herm(pol): """ poly2herm(pol) @@ -118,12 +118,12 @@ def poly2herm(pol) : [pol] = pu.as_series([pol]) deg = len(pol) - 1 res = 0 - for i in range(deg, -1, -1) : + for i in range(deg, -1, -1): res = hermadd(hermmulx(res), pol[i]) return res -def herm2poly(c) : +def herm2poly(c): """ Convert a Hermite series to a polynomial. @@ -174,7 +174,7 @@ def herm2poly(c) : c0 = c[-2] c1 = c[-1] # i is the current degree of c1 - for i in range(n - 1, 1, -1) : + for i in range(n - 1, 1, -1): tmp = c0 c0 = polysub(c[i - 2], c1*(2*(i - 1))) c1 = polyadd(tmp, polymulx(c1)*2) @@ -198,7 +198,7 @@ hermone = np.array([1]) hermx = np.array([0, 1/2]) -def hermline(off, scl) : +def hermline(off, scl): """ Hermite series whose graph is a straight line. @@ -228,13 +228,13 @@ def hermline(off, scl) : 5.0 """ - if scl != 0 : + if scl != 0: return np.array([off, scl/2]) - else : + else: return np.array([off]) -def hermfromroots(roots) : +def hermfromroots(roots): """ Generate a Hermite series with given roots. @@ -284,9 +284,9 @@ def hermfromroots(roots) : array([ 0.+0.j, 0.+0.j]) """ - if len(roots) == 0 : + if len(roots) == 0: return np.ones(1) - else : + else: [roots] = pu.as_series([roots], trim=False) roots.sort() p = [hermline(-r, 1) for r in roots] @@ -340,10 +340,10 @@ def hermadd(c1, c2): """ # c1, c2 are trimmed copies [c1, c2] = pu.as_series([c1, c2]) - if len(c1) > len(c2) : + if len(c1) > len(c2): c1[:c2.size] += c2 ret = c1 - else : + else: c2[:c1.size] += c1 ret = c2 return pu.trimseq(ret) @@ -388,10 +388,10 @@ def hermsub(c1, c2): """ # c1, c2 are trimmed copies [c1, c2] = pu.as_series([c1, c2]) - if len(c1) > len(c2) : + if len(c1) > len(c2): c1[:c2.size] -= c2 ret = c1 - else : + else: c2 = -c2 c2[:c1.size] += c1 ret = c2 @@ -501,13 +501,13 @@ def hermmul(c1, c2): elif len(c) == 2: c0 = c[0]*xs c1 = c[1]*xs - else : + else: nd = len(c) c0 = c[-2]*xs c1 = c[-1]*xs - for i in range(3, len(c) + 1) : + for i in range(3, len(c) + 1): tmp = c0 - nd = nd - 1 + nd = nd - 1 c0 = hermsub(c[-i]*xs, c1*(2*(nd - 1))) c1 = hermadd(tmp, hermmulx(c1)*2) return hermadd(c0, hermmulx(c1)*2) @@ -560,16 +560,16 @@ def hermdiv(c1, c2): """ # c1, c2 are trimmed copies [c1, c2] = pu.as_series([c1, c2]) - if c2[-1] == 0 : + if c2[-1] == 0: raise ZeroDivisionError() lc1 = len(c1) lc2 = len(c2) - if lc1 < lc2 : + if lc1 < lc2: return c1[:1]*0, c1 - elif lc2 == 1 : + elif lc2 == 1: return c1/c2[-1], c1[:1]*0 - else : + else: quo = np.empty(lc1 - lc2 + 1, dtype=c1.dtype) rem = c1 for i in range(lc1 - lc2, - 1, -1): @@ -580,7 +580,7 @@ def hermdiv(c1, c2): return quo, pu.trimseq(rem) -def hermpow(c, pow, maxpower=16) : +def hermpow(c, pow, maxpower=16): """Raise a Hermite series to a power. Returns the Hermite series `c` raised to the power `pow`. The @@ -617,24 +617,24 @@ def hermpow(c, pow, maxpower=16) : # c is a trimmed copy [c] = pu.as_series([c]) power = int(pow) - if power != pow or power < 0 : + if power != pow or power < 0: raise ValueError("Power must be a non-negative integer.") - elif maxpower is not None and power > maxpower : + elif maxpower is not None and power > maxpower: raise ValueError("Power is too large") - elif power == 0 : + elif power == 0: return np.array([1], dtype=c.dtype) - elif power == 1 : + elif power == 1: return c - else : + else: # This can be made more efficient by using powers of two # in the usual way. prd = c - for i in range(2, power + 1) : + for i in range(2, power + 1): prd = hermmul(prd, c) return prd -def hermder(c, m=1, scl=1, axis=0) : +def hermder(c, m=1, scl=1, axis=0): """ Differentiate a Hermite series. @@ -712,7 +712,7 @@ def hermder(c, m=1, scl=1, axis=0) : n = len(c) if cnt >= n: c = c[:1]*0 - else : + else: for i in range(cnt): n = n - 1 c *= scl @@ -816,9 +816,9 @@ def hermint(c, m=1, k=[], lbnd=0, scl=1, axis=0): if cnt != m: raise ValueError("The order of integration must be integer") - if cnt < 0 : + if cnt < 0: raise ValueError("The order of integration must be non-negative") - if len(k) > cnt : + if len(k) > cnt: raise ValueError("Too many integration constants") if iaxis != axis: raise ValueError("The axis must be integer") @@ -832,7 +832,7 @@ def hermint(c, m=1, k=[], lbnd=0, scl=1, axis=0): c = np.rollaxis(c, iaxis) k = list(k) + [0]*(cnt - len(k)) - for i in range(cnt) : + for i in range(cnt): n = len(c) c *= scl if n == 1 and np.all(c[0] == 0): @@ -924,22 +924,22 @@ def hermval(x, c, tensor=True): if isinstance(x, (tuple, list)): x = np.asarray(x) if isinstance(x, np.ndarray) and tensor: - c = c.reshape(c.shape + (1,)*x.ndim) + c = c.reshape(c.shape + (1,)*x.ndim) x2 = x*2 - if len(c) == 1 : + if len(c) == 1: c0 = c[0] c1 = 0 - elif len(c) == 2 : + elif len(c) == 2: c0 = c[0] c1 = c[1] - else : + else: nd = len(c) c0 = c[-2] c1 = c[-1] - for i in range(3, len(c) + 1) : + for i in range(3, len(c) + 1): tmp = c0 - nd = nd - 1 + nd = nd - 1 c0 = c[-i] - c1*(2*(nd - 1)) c1 = tmp + c1*x2 return c0 + c1*x2 @@ -1174,7 +1174,7 @@ def hermgrid3d(x, y, z, c): return c -def hermvander(x, deg) : +def hermvander(x, deg): """Pseudo-Vandermonde matrix of given degree. Returns the pseudo-Vandermonde matrix of degree `deg` and sample points @@ -1229,15 +1229,15 @@ def hermvander(x, deg) : dtyp = x.dtype v = np.empty(dims, dtype=dtyp) v[0] = x*0 + 1 - if ideg > 0 : + if ideg > 0: x2 = x*2 v[1] = x2 - for i in range(2, ideg + 1) : + for i in range(2, ideg + 1): v[i] = (v[i-1]*x2 - v[i-2]*(2*(i - 1))) return np.rollaxis(v, 0, v.ndim) -def hermvander2d(x, y, deg) : +def hermvander2d(x, y, deg): """Pseudo-Vandermonde matrix of given degrees. Returns the pseudo-Vandermonde matrix of degrees `deg` and sample @@ -1300,7 +1300,7 @@ def hermvander2d(x, y, deg) : return v.reshape(v.shape[:-2] + (-1,)) -def hermvander3d(x, y, z, deg) : +def hermvander3d(x, y, z, deg): """Pseudo-Vandermonde matrix of given degrees. Returns the pseudo-Vandermonde matrix of degrees `deg` and sample @@ -1491,13 +1491,13 @@ def hermfit(x, y, deg, rcond=None, full=False, w=None): y = np.asarray(y) + 0.0 # check arguments. - if deg < 0 : + if deg < 0: raise ValueError("expected deg >= 0") if x.ndim != 1: raise TypeError("expected 1D vector for x") if x.size == 0: raise TypeError("expected non-empty vector for x") - if y.ndim < 1 or y.ndim > 2 : + if y.ndim < 1 or y.ndim > 2: raise TypeError("expected 1D or 2D array for y") if len(x) != len(y): raise TypeError("expected x and y to have same length") @@ -1517,7 +1517,7 @@ def hermfit(x, y, deg, rcond=None, full=False, w=None): rhs = rhs * w # set rcond - if rcond is None : + if rcond is None: rcond = len(x)*np.finfo(x.dtype).eps # Determine the norms of the design matrix columns. @@ -1536,9 +1536,9 @@ def hermfit(x, y, deg, rcond=None, full=False, w=None): msg = "The fit may be poorly conditioned" warnings.warn(msg, pu.RankWarning) - if full : + if full: return c, [resids, rank, s, rcond] - else : + else: return c @@ -1568,7 +1568,6 @@ def hermcompanion(c): .. versionadded::1.7.0 """ - accprod = np.multiply.accumulate # c is a trimmed copy [c] = pu.as_series([c]) if len(c) < 2: @@ -1636,9 +1635,9 @@ def hermroots(c): """ # c is a trimmed copy [c] = pu.as_series([c]) - if len(c) <= 1 : + if len(c) <= 1: return np.array([], dtype=c.dtype) - if len(c) == 2 : + if len(c) == 2: return np.array([-.5*c[0]/c[1]]) m = hermcompanion(c) diff --git a/numpy/polynomial/hermite_e.py b/numpy/polynomial/hermite_e.py index 874b42470..6e33dc0bc 100644 --- a/numpy/polynomial/hermite_e.py +++ b/numpy/polynomial/hermite_e.py @@ -66,18 +66,19 @@ import numpy.linalg as la from . import polyutils as pu from ._polybase import ABCPolyBase -__all__ = ['hermezero', 'hermeone', 'hermex', 'hermedomain', 'hermeline', - 'hermeadd', 'hermesub', 'hermemulx', 'hermemul', 'hermediv', 'hermpow', - 'hermeval', - 'hermeder', 'hermeint', 'herme2poly', 'poly2herme', 'hermefromroots', - 'hermevander', 'hermefit', 'hermetrim', 'hermeroots', 'HermiteE', - 'hermeval2d', 'hermeval3d', 'hermegrid2d', 'hermegrid3d', 'hermevander2d', - 'hermevander3d', 'hermecompanion', 'hermegauss', 'hermeweight'] +__all__ = [ + 'hermezero', 'hermeone', 'hermex', 'hermedomain', 'hermeline', + 'hermeadd', 'hermesub', 'hermemulx', 'hermemul', 'hermediv', + 'hermepow', 'hermeval', 'hermeder', 'hermeint', 'herme2poly', + 'poly2herme', 'hermefromroots', 'hermevander', 'hermefit', 'hermetrim', + 'hermeroots', 'HermiteE', 'hermeval2d', 'hermeval3d', 'hermegrid2d', + 'hermegrid3d', 'hermevander2d', 'hermevander3d', 'hermecompanion', + 'hermegauss', 'hermeweight'] hermetrim = pu.trimcoef -def poly2herme(pol) : +def poly2herme(pol): """ poly2herme(pol) @@ -118,12 +119,12 @@ def poly2herme(pol) : [pol] = pu.as_series([pol]) deg = len(pol) - 1 res = 0 - for i in range(deg, -1, -1) : + for i in range(deg, -1, -1): res = hermeadd(hermemulx(res), pol[i]) return res -def herme2poly(c) : +def herme2poly(c): """ Convert a Hermite series to a polynomial. @@ -173,7 +174,7 @@ def herme2poly(c) : c0 = c[-2] c1 = c[-1] # i is the current degree of c1 - for i in range(n - 1, 1, -1) : + for i in range(n - 1, 1, -1): tmp = c0 c0 = polysub(c[i - 2], c1*(i - 1)) c1 = polyadd(tmp, polymulx(c1)) @@ -197,7 +198,7 @@ hermeone = np.array([1]) hermex = np.array([0, 1]) -def hermeline(off, scl) : +def hermeline(off, scl): """ Hermite series whose graph is a straight line. @@ -228,13 +229,13 @@ def hermeline(off, scl) : 5.0 """ - if scl != 0 : + if scl != 0: return np.array([off, scl]) - else : + else: return np.array([off]) -def hermefromroots(roots) : +def hermefromroots(roots): """ Generate a HermiteE series with given roots. @@ -284,9 +285,9 @@ def hermefromroots(roots) : array([ 0.+0.j, 0.+0.j]) """ - if len(roots) == 0 : + if len(roots) == 0: return np.ones(1) - else : + else: [roots] = pu.as_series([roots], trim=False) roots.sort() p = [hermeline(-r, 1) for r in roots] @@ -340,10 +341,10 @@ def hermeadd(c1, c2): """ # c1, c2 are trimmed copies [c1, c2] = pu.as_series([c1, c2]) - if len(c1) > len(c2) : + if len(c1) > len(c2): c1[:c2.size] += c2 ret = c1 - else : + else: c2[:c1.size] += c1 ret = c2 return pu.trimseq(ret) @@ -388,10 +389,10 @@ def hermesub(c1, c2): """ # c1, c2 are trimmed copies [c1, c2] = pu.as_series([c1, c2]) - if len(c1) > len(c2) : + if len(c1) > len(c2): c1[:c2.size] -= c2 ret = c1 - else : + else: c2 = -c2 c2[:c1.size] += c1 ret = c2 @@ -501,13 +502,13 @@ def hermemul(c1, c2): elif len(c) == 2: c0 = c[0]*xs c1 = c[1]*xs - else : + else: nd = len(c) c0 = c[-2]*xs c1 = c[-1]*xs - for i in range(3, len(c) + 1) : + for i in range(3, len(c) + 1): tmp = c0 - nd = nd - 1 + nd = nd - 1 c0 = hermesub(c[-i]*xs, c1*(nd - 1)) c1 = hermeadd(tmp, hermemulx(c1)) return hermeadd(c0, hermemulx(c1)) @@ -558,16 +559,16 @@ def hermediv(c1, c2): """ # c1, c2 are trimmed copies [c1, c2] = pu.as_series([c1, c2]) - if c2[-1] == 0 : + if c2[-1] == 0: raise ZeroDivisionError() lc1 = len(c1) lc2 = len(c2) - if lc1 < lc2 : + if lc1 < lc2: return c1[:1]*0, c1 - elif lc2 == 1 : + elif lc2 == 1: return c1/c2[-1], c1[:1]*0 - else : + else: quo = np.empty(lc1 - lc2 + 1, dtype=c1.dtype) rem = c1 for i in range(lc1 - lc2, - 1, -1): @@ -578,7 +579,7 @@ def hermediv(c1, c2): return quo, pu.trimseq(rem) -def hermepow(c, pow, maxpower=16) : +def hermepow(c, pow, maxpower=16): """Raise a Hermite series to a power. Returns the Hermite series `c` raised to the power `pow`. The @@ -615,24 +616,24 @@ def hermepow(c, pow, maxpower=16) : # c is a trimmed copy [c] = pu.as_series([c]) power = int(pow) - if power != pow or power < 0 : + if power != pow or power < 0: raise ValueError("Power must be a non-negative integer.") - elif maxpower is not None and power > maxpower : + elif maxpower is not None and power > maxpower: raise ValueError("Power is too large") - elif power == 0 : + elif power == 0: return np.array([1], dtype=c.dtype) - elif power == 1 : + elif power == 1: return c - else : + else: # This can be made more efficient by using powers of two # in the usual way. prd = c - for i in range(2, power + 1) : + for i in range(2, power + 1): prd = hermemul(prd, c) return prd -def hermeder(c, m=1, scl=1, axis=0) : +def hermeder(c, m=1, scl=1, axis=0): """ Differentiate a Hermite_e series. @@ -710,7 +711,7 @@ def hermeder(c, m=1, scl=1, axis=0) : n = len(c) if cnt >= n: return c[:1]*0 - else : + else: for i in range(cnt): n = n - 1 c *= scl @@ -814,9 +815,9 @@ def hermeint(c, m=1, k=[], lbnd=0, scl=1, axis=0): if cnt != m: raise ValueError("The order of integration must be integer") - if cnt < 0 : + if cnt < 0: raise ValueError("The order of integration must be non-negative") - if len(k) > cnt : + if len(k) > cnt: raise ValueError("Too many integration constants") if iaxis != axis: raise ValueError("The axis must be integer") @@ -830,7 +831,7 @@ def hermeint(c, m=1, k=[], lbnd=0, scl=1, axis=0): c = np.rollaxis(c, iaxis) k = list(k) + [0]*(cnt - len(k)) - for i in range(cnt) : + for i in range(cnt): n = len(c) c *= scl if n == 1 and np.all(c[0] == 0): @@ -922,21 +923,21 @@ def hermeval(x, c, tensor=True): if isinstance(x, (tuple, list)): x = np.asarray(x) if isinstance(x, np.ndarray) and tensor: - c = c.reshape(c.shape + (1,)*x.ndim) + c = c.reshape(c.shape + (1,)*x.ndim) - if len(c) == 1 : + if len(c) == 1: c0 = c[0] c1 = 0 - elif len(c) == 2 : + elif len(c) == 2: c0 = c[0] c1 = c[1] - else : + else: nd = len(c) c0 = c[-2] c1 = c[-1] - for i in range(3, len(c) + 1) : + for i in range(3, len(c) + 1): tmp = c0 - nd = nd - 1 + nd = nd - 1 c0 = c[-i] - c1*(nd - 1) c1 = tmp + c1*x return c0 + c1*x @@ -1171,7 +1172,7 @@ def hermegrid3d(x, y, z, c): return c -def hermevander(x, deg) : +def hermevander(x, deg): """Pseudo-Vandermonde matrix of given degree. Returns the pseudo-Vandermonde matrix of degree `deg` and sample points @@ -1226,14 +1227,14 @@ def hermevander(x, deg) : dtyp = x.dtype v = np.empty(dims, dtype=dtyp) v[0] = x*0 + 1 - if ideg > 0 : + if ideg > 0: v[1] = x - for i in range(2, ideg + 1) : + for i in range(2, ideg + 1): v[i] = (v[i-1]*x - v[i-2]*(i - 1)) return np.rollaxis(v, 0, v.ndim) -def hermevander2d(x, y, deg) : +def hermevander2d(x, y, deg): """Pseudo-Vandermonde matrix of given degrees. Returns the pseudo-Vandermonde matrix of degrees `deg` and sample @@ -1296,7 +1297,7 @@ def hermevander2d(x, y, deg) : return v.reshape(v.shape[:-2] + (-1,)) -def hermevander3d(x, y, z, deg) : +def hermevander3d(x, y, z, deg): """Pseudo-Vandermonde matrix of given degrees. Returns the pseudo-Vandermonde matrix of degrees `deg` and sample @@ -1487,13 +1488,13 @@ def hermefit(x, y, deg, rcond=None, full=False, w=None): y = np.asarray(y) + 0.0 # check arguments. - if deg < 0 : + if deg < 0: raise ValueError("expected deg >= 0") if x.ndim != 1: raise TypeError("expected 1D vector for x") if x.size == 0: raise TypeError("expected non-empty vector for x") - if y.ndim < 1 or y.ndim > 2 : + if y.ndim < 1 or y.ndim > 2: raise TypeError("expected 1D or 2D array for y") if len(x) != len(y): raise TypeError("expected x and y to have same length") @@ -1513,7 +1514,7 @@ def hermefit(x, y, deg, rcond=None, full=False, w=None): rhs = rhs * w # set rcond - if rcond is None : + if rcond is None: rcond = len(x)*np.finfo(x.dtype).eps # Determine the norms of the design matrix columns. @@ -1532,9 +1533,9 @@ def hermefit(x, y, deg, rcond=None, full=False, w=None): msg = "The fit may be poorly conditioned" warnings.warn(msg, pu.RankWarning) - if full : + if full: return c, [resids, rank, s, rcond] - else : + else: return c @@ -1565,7 +1566,6 @@ def hermecompanion(c): .. versionadded::1.7.0 """ - accprod = np.multiply.accumulate # c is a trimmed copy [c] = pu.as_series([c]) if len(c) < 2: @@ -1633,9 +1633,9 @@ def hermeroots(c): """ # c is a trimmed copy [c] = pu.as_series([c]) - if len(c) <= 1 : + if len(c) <= 1: return np.array([], dtype=c.dtype) - if len(c) == 2 : + if len(c) == 2: return np.array([-c[0]/c[1]]) m = hermecompanion(c) diff --git a/numpy/polynomial/laguerre.py b/numpy/polynomial/laguerre.py index 9d88162ce..8d2705d5d 100644 --- a/numpy/polynomial/laguerre.py +++ b/numpy/polynomial/laguerre.py @@ -66,17 +66,18 @@ import numpy.linalg as la from . import polyutils as pu from ._polybase import ABCPolyBase -__all__ = ['lagzero', 'lagone', 'lagx', 'lagdomain', 'lagline', - 'lagadd', 'lagsub', 'lagmulx', 'lagmul', 'lagdiv', 'lagpow', - 'lagval', 'lagder', 'lagint', 'lag2poly', 'poly2lag', 'lagfromroots', - 'lagvander', 'lagfit', 'lagtrim', 'lagroots', 'Laguerre', 'lagval2d', - 'lagval3d', 'laggrid2d', 'laggrid3d', 'lagvander2d', 'lagvander3d', - 'lagcompanion', 'laggauss', 'lagweight'] +__all__ = [ + 'lagzero', 'lagone', 'lagx', 'lagdomain', 'lagline', 'lagadd', + 'lagsub', 'lagmulx', 'lagmul', 'lagdiv', 'lagpow', 'lagval', 'lagder', + 'lagint', 'lag2poly', 'poly2lag', 'lagfromroots', 'lagvander', + 'lagfit', 'lagtrim', 'lagroots', 'Laguerre', 'lagval2d', 'lagval3d', + 'laggrid2d', 'laggrid3d', 'lagvander2d', 'lagvander3d', 'lagcompanion', + 'laggauss', 'lagweight'] lagtrim = pu.trimcoef -def poly2lag(pol) : +def poly2lag(pol): """ poly2lag(pol) @@ -117,12 +118,12 @@ def poly2lag(pol) : [pol] = pu.as_series([pol]) deg = len(pol) - 1 res = 0 - for i in range(deg, -1, -1) : + for i in range(deg, -1, -1): res = lagadd(lagmulx(res), pol[i]) return res -def lag2poly(c) : +def lag2poly(c): """ Convert a Laguerre series to a polynomial. @@ -194,7 +195,7 @@ lagone = np.array([1]) lagx = np.array([1, -1]) -def lagline(off, scl) : +def lagline(off, scl): """ Laguerre series whose graph is a straight line. @@ -224,13 +225,13 @@ def lagline(off, scl) : 5.0 """ - if scl != 0 : + if scl != 0: return np.array([off + scl, -scl]) - else : + else: return np.array([off]) -def lagfromroots(roots) : +def lagfromroots(roots): """ Generate a Laguerre series with given roots. @@ -280,9 +281,9 @@ def lagfromroots(roots) : array([ 0.+0.j, 0.+0.j]) """ - if len(roots) == 0 : + if len(roots) == 0: return np.ones(1) - else : + else: [roots] = pu.as_series([roots], trim=False) roots.sort() p = [lagline(-r, 1) for r in roots] @@ -337,10 +338,10 @@ def lagadd(c1, c2): """ # c1, c2 are trimmed copies [c1, c2] = pu.as_series([c1, c2]) - if len(c1) > len(c2) : + if len(c1) > len(c2): c1[:c2.size] += c2 ret = c1 - else : + else: c2[:c1.size] += c1 ret = c2 return pu.trimseq(ret) @@ -385,10 +386,10 @@ def lagsub(c1, c2): """ # c1, c2 are trimmed copies [c1, c2] = pu.as_series([c1, c2]) - if len(c1) > len(c2) : + if len(c1) > len(c2): c1[:c2.size] -= c2 ret = c1 - else : + else: c2 = -c2 c2[:c1.size] += c1 ret = c2 @@ -499,13 +500,13 @@ def lagmul(c1, c2): elif len(c) == 2: c0 = c[0]*xs c1 = c[1]*xs - else : + else: nd = len(c) c0 = c[-2]*xs c1 = c[-1]*xs - for i in range(3, len(c) + 1) : + for i in range(3, len(c) + 1): tmp = c0 - nd = nd - 1 + nd = nd - 1 c0 = lagsub(c[-i]*xs, (c1*(nd - 1))/nd) c1 = lagadd(tmp, lagsub((2*nd - 1)*c1, lagmulx(c1))/nd) return lagadd(c0, lagsub(c1, lagmulx(c1))) @@ -556,16 +557,16 @@ def lagdiv(c1, c2): """ # c1, c2 are trimmed copies [c1, c2] = pu.as_series([c1, c2]) - if c2[-1] == 0 : + if c2[-1] == 0: raise ZeroDivisionError() lc1 = len(c1) lc2 = len(c2) - if lc1 < lc2 : + if lc1 < lc2: return c1[:1]*0, c1 - elif lc2 == 1 : + elif lc2 == 1: return c1/c2[-1], c1[:1]*0 - else : + else: quo = np.empty(lc1 - lc2 + 1, dtype=c1.dtype) rem = c1 for i in range(lc1 - lc2, - 1, -1): @@ -576,7 +577,7 @@ def lagdiv(c1, c2): return quo, pu.trimseq(rem) -def lagpow(c, pow, maxpower=16) : +def lagpow(c, pow, maxpower=16): """Raise a Laguerre series to a power. Returns the Laguerre series `c` raised to the power `pow`. The @@ -613,24 +614,24 @@ def lagpow(c, pow, maxpower=16) : # c is a trimmed copy [c] = pu.as_series([c]) power = int(pow) - if power != pow or power < 0 : + if power != pow or power < 0: raise ValueError("Power must be a non-negative integer.") - elif maxpower is not None and power > maxpower : + elif maxpower is not None and power > maxpower: raise ValueError("Power is too large") - elif power == 0 : + elif power == 0: return np.array([1], dtype=c.dtype) - elif power == 1 : + elif power == 1: return c - else : + else: # This can be made more efficient by using powers of two # in the usual way. prd = c - for i in range(2, power + 1) : + for i in range(2, power + 1): prd = lagmul(prd, c) return prd -def lagder(c, m=1, scl=1, axis=0) : +def lagder(c, m=1, scl=1, axis=0): """ Differentiate a Laguerre series. @@ -708,7 +709,7 @@ def lagder(c, m=1, scl=1, axis=0) : n = len(c) if cnt >= n: c = c[:1]*0 - else : + else: for i in range(cnt): n = n - 1 c *= scl @@ -815,9 +816,9 @@ def lagint(c, m=1, k=[], lbnd=0, scl=1, axis=0): if cnt != m: raise ValueError("The order of integration must be integer") - if cnt < 0 : + if cnt < 0: raise ValueError("The order of integration must be non-negative") - if len(k) > cnt : + if len(k) > cnt: raise ValueError("Too many integration constants") if iaxis != axis: raise ValueError("The axis must be integer") @@ -831,7 +832,7 @@ def lagint(c, m=1, k=[], lbnd=0, scl=1, axis=0): c = np.rollaxis(c, iaxis) k = list(k) + [0]*(cnt - len(k)) - for i in range(cnt) : + for i in range(cnt): n = len(c) c *= scl if n == 1 and np.all(c[0] == 0): @@ -924,22 +925,21 @@ def lagval(x, c, tensor=True): if isinstance(x, (tuple, list)): x = np.asarray(x) if isinstance(x, np.ndarray) and tensor: - c = c.reshape(c.shape + (1,)*x.ndim) + c = c.reshape(c.shape + (1,)*x.ndim) - - if len(c) == 1 : + if len(c) == 1: c0 = c[0] c1 = 0 - elif len(c) == 2 : + elif len(c) == 2: c0 = c[0] c1 = c[1] - else : + else: nd = len(c) c0 = c[-2] c1 = c[-1] - for i in range(3, len(c) + 1) : + for i in range(3, len(c) + 1): tmp = c0 - nd = nd - 1 + nd = nd - 1 c0 = c[-i] - (c1*(nd - 1))/nd c1 = tmp + (c1*((2*nd - 1) - x))/nd return c0 + c1*(1 - x) @@ -1174,7 +1174,7 @@ def laggrid3d(x, y, z, c): return c -def lagvander(x, deg) : +def lagvander(x, deg): """Pseudo-Vandermonde matrix of given degree. Returns the pseudo-Vandermonde matrix of degree `deg` and sample points @@ -1229,14 +1229,14 @@ def lagvander(x, deg) : dtyp = x.dtype v = np.empty(dims, dtype=dtyp) v[0] = x*0 + 1 - if ideg > 0 : + if ideg > 0: v[1] = 1 - x - for i in range(2, ideg + 1) : + for i in range(2, ideg + 1): v[i] = (v[i-1]*(2*i - 1 - x) - v[i-2]*(i - 1))/i return np.rollaxis(v, 0, v.ndim) -def lagvander2d(x, y, deg) : +def lagvander2d(x, y, deg): """Pseudo-Vandermonde matrix of given degrees. Returns the pseudo-Vandermonde matrix of degrees `deg` and sample @@ -1299,7 +1299,7 @@ def lagvander2d(x, y, deg) : return v.reshape(v.shape[:-2] + (-1,)) -def lagvander3d(x, y, z, deg) : +def lagvander3d(x, y, z, deg): """Pseudo-Vandermonde matrix of given degrees. Returns the pseudo-Vandermonde matrix of degrees `deg` and sample @@ -1490,13 +1490,13 @@ def lagfit(x, y, deg, rcond=None, full=False, w=None): y = np.asarray(y) + 0.0 # check arguments. - if deg < 0 : + if deg < 0: raise ValueError("expected deg >= 0") if x.ndim != 1: raise TypeError("expected 1D vector for x") if x.size == 0: raise TypeError("expected non-empty vector for x") - if y.ndim < 1 or y.ndim > 2 : + if y.ndim < 1 or y.ndim > 2: raise TypeError("expected 1D or 2D array for y") if len(x) != len(y): raise TypeError("expected x and y to have same length") @@ -1516,7 +1516,7 @@ def lagfit(x, y, deg, rcond=None, full=False, w=None): rhs = rhs * w # set rcond - if rcond is None : + if rcond is None: rcond = len(x)*np.finfo(x.dtype).eps # Determine the norms of the design matrix columns. @@ -1535,9 +1535,9 @@ def lagfit(x, y, deg, rcond=None, full=False, w=None): msg = "The fit may be poorly conditioned" warnings.warn(msg, pu.RankWarning) - if full : + if full: return c, [resids, rank, s, rcond] - else : + else: return c @@ -1566,7 +1566,6 @@ def lagcompanion(c): .. versionadded::1.7.0 """ - accprod = np.multiply.accumulate # c is a trimmed copy [c] = pu.as_series([c]) if len(c) < 2: @@ -1634,9 +1633,9 @@ def lagroots(c): """ # c is a trimmed copy [c] = pu.as_series([c]) - if len(c) <= 1 : + if len(c) <= 1: return np.array([], dtype=c.dtype) - if len(c) == 2 : + if len(c) == 2: return np.array([1 + c[0]/c[1]]) m = lagcompanion(c) @@ -1651,8 +1650,8 @@ def laggauss(deg): Computes the sample points and weights for Gauss-Laguerre quadrature. These sample points and weights will correctly integrate polynomials of - degree :math:`2*deg - 1` or less over the interval :math:`[0, \inf]` with the - weight function :math:`f(x) = \exp(-x)`. + degree :math:`2*deg - 1` or less over the interval :math:`[0, \inf]` + with the weight function :math:`f(x) = \exp(-x)`. Parameters ---------- diff --git a/numpy/polynomial/legendre.py b/numpy/polynomial/legendre.py index 58c130b7e..d2de28269 100644 --- a/numpy/polynomial/legendre.py +++ b/numpy/polynomial/legendre.py @@ -90,17 +90,18 @@ import numpy.linalg as la from . import polyutils as pu from ._polybase import ABCPolyBase -__all__ = ['legzero', 'legone', 'legx', 'legdomain', 'legline', - 'legadd', 'legsub', 'legmulx', 'legmul', 'legdiv', 'legpow', 'legval', - 'legder', 'legint', 'leg2poly', 'poly2leg', 'legfromroots', - 'legvander', 'legfit', 'legtrim', 'legroots', 'Legendre', 'legval2d', - 'legval3d', 'leggrid2d', 'leggrid3d', 'legvander2d', 'legvander3d', - 'legcompanion', 'leggauss', 'legweight'] +__all__ = [ + 'legzero', 'legone', 'legx', 'legdomain', 'legline', 'legadd', + 'legsub', 'legmulx', 'legmul', 'legdiv', 'legpow', 'legval', 'legder', + 'legint', 'leg2poly', 'poly2leg', 'legfromroots', 'legvander', + 'legfit', 'legtrim', 'legroots', 'Legendre', 'legval2d', 'legval3d', + 'leggrid2d', 'leggrid3d', 'legvander2d', 'legvander3d', 'legcompanion', + 'leggauss', 'legweight'] legtrim = pu.trimcoef -def poly2leg(pol) : +def poly2leg(pol): """ Convert a polynomial to a Legendre series. @@ -143,12 +144,12 @@ def poly2leg(pol) : [pol] = pu.as_series([pol]) deg = len(pol) - 1 res = 0 - for i in range(deg, -1, -1) : + for i in range(deg, -1, -1): res = legadd(legmulx(res), pol[i]) return res -def leg2poly(c) : +def leg2poly(c): """ Convert a Legendre series to a polynomial. @@ -202,7 +203,7 @@ def leg2poly(c) : c0 = c[-2] c1 = c[-1] # i is the current degree of c1 - for i in range(n - 1, 1, -1) : + for i in range(n - 1, 1, -1): tmp = c0 c0 = polysub(c[i - 2], (c1*(i - 1))/i) c1 = polyadd(tmp, (polymulx(c1)*(2*i - 1))/i) @@ -226,7 +227,7 @@ legone = np.array([1]) legx = np.array([0, 1]) -def legline(off, scl) : +def legline(off, scl): """ Legendre series whose graph is a straight line. @@ -256,13 +257,13 @@ def legline(off, scl) : -3.0 """ - if scl != 0 : + if scl != 0: return np.array([off, scl]) - else : + else: return np.array([off]) -def legfromroots(roots) : +def legfromroots(roots): """ Generate a Legendre series with given roots. @@ -311,9 +312,9 @@ def legfromroots(roots) : array([ 1.33333333+0.j, 0.00000000+0.j, 0.66666667+0.j]) """ - if len(roots) == 0 : + if len(roots) == 0: return np.ones(1) - else : + else: [roots] = pu.as_series([roots], trim=False) roots.sort() p = [legline(-r, 1) for r in roots] @@ -369,10 +370,10 @@ def legadd(c1, c2): """ # c1, c2 are trimmed copies [c1, c2] = pu.as_series([c1, c2]) - if len(c1) > len(c2) : + if len(c1) > len(c2): c1[:c2.size] += c2 ret = c1 - else : + else: c2[:c1.size] += c1 ret = c2 return pu.trimseq(ret) @@ -421,10 +422,10 @@ def legsub(c1, c2): """ # c1, c2 are trimmed copies [c1, c2] = pu.as_series([c1, c2]) - if len(c1) > len(c2) : + if len(c1) > len(c2): c1[:c2.size] -= c2 ret = c1 - else : + else: c2 = -c2 c2[:c1.size] += c1 ret = c2 @@ -533,13 +534,13 @@ def legmul(c1, c2): elif len(c) == 2: c0 = c[0]*xs c1 = c[1]*xs - else : + else: nd = len(c) c0 = c[-2]*xs c1 = c[-1]*xs - for i in range(3, len(c) + 1) : + for i in range(3, len(c) + 1): tmp = c0 - nd = nd - 1 + nd = nd - 1 c0 = legsub(c[-i]*xs, (c1*(nd - 1))/nd) c1 = legadd(tmp, (legmulx(c1)*(2*nd - 1))/nd) return legadd(c0, legmulx(c1)) @@ -593,16 +594,16 @@ def legdiv(c1, c2): """ # c1, c2 are trimmed copies [c1, c2] = pu.as_series([c1, c2]) - if c2[-1] == 0 : + if c2[-1] == 0: raise ZeroDivisionError() lc1 = len(c1) lc2 = len(c2) - if lc1 < lc2 : + if lc1 < lc2: return c1[:1]*0, c1 - elif lc2 == 1 : + elif lc2 == 1: return c1/c2[-1], c1[:1]*0 - else : + else: quo = np.empty(lc1 - lc2 + 1, dtype=c1.dtype) rem = c1 for i in range(lc1 - lc2, - 1, -1): @@ -613,7 +614,7 @@ def legdiv(c1, c2): return quo, pu.trimseq(rem) -def legpow(c, pow, maxpower=16) : +def legpow(c, pow, maxpower=16): """Raise a Legendre series to a power. Returns the Legendre series `c` raised to the power `pow`. The @@ -647,24 +648,24 @@ def legpow(c, pow, maxpower=16) : # c is a trimmed copy [c] = pu.as_series([c]) power = int(pow) - if power != pow or power < 0 : + if power != pow or power < 0: raise ValueError("Power must be a non-negative integer.") - elif maxpower is not None and power > maxpower : + elif maxpower is not None and power > maxpower: raise ValueError("Power is too large") - elif power == 0 : + elif power == 0: return np.array([1], dtype=c.dtype) - elif power == 1 : + elif power == 1: return c - else : + else: # This can be made more efficient by using powers of two # in the usual way. prd = c - for i in range(2, power + 1) : + for i in range(2, power + 1): prd = legmul(prd, c) return prd -def legder(c, m=1, scl=1, axis=0) : +def legder(c, m=1, scl=1, axis=0): """ Differentiate a Legendre series. @@ -747,7 +748,7 @@ def legder(c, m=1, scl=1, axis=0) : n = len(c) if cnt >= n: c = c[:1]*0 - else : + else: for i in range(cnt): n = n - 1 c *= scl @@ -857,9 +858,9 @@ def legint(c, m=1, k=[], lbnd=0, scl=1, axis=0): if cnt != m: raise ValueError("The order of integration must be integer") - if cnt < 0 : + if cnt < 0: raise ValueError("The order of integration must be non-negative") - if len(k) > cnt : + if len(k) > cnt: raise ValueError("Too many integration constants") if iaxis != axis: raise ValueError("The axis must be integer") @@ -873,7 +874,7 @@ def legint(c, m=1, k=[], lbnd=0, scl=1, axis=0): c = np.rollaxis(c, iaxis) k = list(k) + [0]*(cnt - len(k)) - for i in range(cnt) : + for i in range(cnt): n = len(c) c *= scl if n == 1 and np.all(c[0] == 0): @@ -964,19 +965,19 @@ def legval(x, c, tensor=True): if isinstance(x, np.ndarray) and tensor: c = c.reshape(c.shape + (1,)*x.ndim) - if len(c) == 1 : + if len(c) == 1: c0 = c[0] c1 = 0 - elif len(c) == 2 : + elif len(c) == 2: c0 = c[0] c1 = c[1] - else : + else: nd = len(c) c0 = c[-2] c1 = c[-1] - for i in range(3, len(c) + 1) : + for i in range(3, len(c) + 1): tmp = c0 - nd = nd - 1 + nd = nd - 1 c0 = c[-i] - (c1*(nd - 1))/nd c1 = tmp + (c1*x*(2*nd - 1))/nd return c0 + c1*x @@ -1211,7 +1212,7 @@ def leggrid3d(x, y, z, c): return c -def legvander(x, deg) : +def legvander(x, deg): """Pseudo-Vandermonde matrix of given degree. Returns the pseudo-Vandermonde matrix of degree `deg` and sample points @@ -1259,14 +1260,14 @@ def legvander(x, deg) : # Use forward recursion to generate the entries. This is not as accurate # as reverse recursion in this application but it is more efficient. v[0] = x*0 + 1 - if ideg > 0 : + if ideg > 0: v[1] = x - for i in range(2, ideg + 1) : + for i in range(2, ideg + 1): v[i] = (v[i-1]*x*(2*i - 1) - v[i-2]*(i - 1))/i return np.rollaxis(v, 0, v.ndim) -def legvander2d(x, y, deg) : +def legvander2d(x, y, deg): """Pseudo-Vandermonde matrix of given degrees. Returns the pseudo-Vandermonde matrix of degrees `deg` and sample @@ -1329,7 +1330,7 @@ def legvander2d(x, y, deg) : return v.reshape(v.shape[:-2] + (-1,)) -def legvander3d(x, y, z, deg) : +def legvander3d(x, y, z, deg): """Pseudo-Vandermonde matrix of given degrees. Returns the pseudo-Vandermonde matrix of degrees `deg` and sample @@ -1515,13 +1516,13 @@ def legfit(x, y, deg, rcond=None, full=False, w=None): y = np.asarray(y) + 0.0 # check arguments. - if deg < 0 : + if deg < 0: raise ValueError("expected deg >= 0") if x.ndim != 1: raise TypeError("expected 1D vector for x") if x.size == 0: raise TypeError("expected non-empty vector for x") - if y.ndim < 1 or y.ndim > 2 : + if y.ndim < 1 or y.ndim > 2: raise TypeError("expected 1D or 2D array for y") if len(x) != len(y): raise TypeError("expected x and y to have same length") @@ -1541,7 +1542,7 @@ def legfit(x, y, deg, rcond=None, full=False, w=None): rhs = rhs * w # set rcond - if rcond is None : + if rcond is None: rcond = len(x)*np.finfo(x.dtype).eps # Determine the norms of the design matrix columns. @@ -1560,9 +1561,9 @@ def legfit(x, y, deg, rcond=None, full=False, w=None): msg = "The fit may be poorly conditioned" warnings.warn(msg, pu.RankWarning) - if full : + if full: return c, [resids, rank, s, rcond] - else : + else: return c @@ -1637,11 +1638,11 @@ def legroots(c): ----- The root estimates are obtained as the eigenvalues of the companion matrix, Roots far from the origin of the complex plane may have large - errors due to the numerical instability of the series for such - values. Roots with multiplicity greater than 1 will also show larger - errors as the value of the series near such points is relatively - insensitive to errors in the roots. Isolated roots near the origin can - be improved by a few iterations of Newton's method. + errors due to the numerical instability of the series for such values. + Roots with multiplicity greater than 1 will also show larger errors as + the value of the series near such points is relatively insensitive to + errors in the roots. Isolated roots near the origin can be improved by + a few iterations of Newton's method. The Legendre series basis polynomials aren't powers of ``x`` so the results of this function may seem unintuitive. @@ -1649,7 +1650,7 @@ def legroots(c): Examples -------- >>> import numpy.polynomial.legendre as leg - >>> leg.legroots((1, 2, 3, 4)) # 4L_3 + 3L_2 + 2L_1 + 1L_0 has only real roots + >>> leg.legroots((1, 2, 3, 4)) # 4L_3 + 3L_2 + 2L_1 + 1L_0, all real roots array([-0.85099543, -0.11407192, 0.51506735]) """ diff --git a/numpy/polynomial/polynomial.py b/numpy/polynomial/polynomial.py index 60aaff83f..60e339a1d 100644 --- a/numpy/polynomial/polynomial.py +++ b/numpy/polynomial/polynomial.py @@ -55,11 +55,12 @@ See Also """ from __future__ import division, absolute_import, print_function -__all__ = ['polyzero', 'polyone', 'polyx', 'polydomain', 'polyline', - 'polyadd', 'polysub', 'polymulx', 'polymul', 'polydiv', 'polypow', - 'polyval', 'polyder', 'polyint', 'polyfromroots', 'polyvander', - 'polyfit', 'polytrim', 'polyroots', 'Polynomial', 'polyval2d', - 'polyval3d', 'polygrid2d', 'polygrid3d', 'polyvander2d', 'polyvander3d'] +__all__ = [ + 'polyzero', 'polyone', 'polyx', 'polydomain', 'polyline', 'polyadd', + 'polysub', 'polymulx', 'polymul', 'polydiv', 'polypow', 'polyval', + 'polyder', 'polyint', 'polyfromroots', 'polyvander', 'polyfit', + 'polytrim', 'polyroots', 'Polynomial', 'polyval2d', 'polyval3d', + 'polygrid2d', 'polygrid3d', 'polyvander2d', 'polyvander3d'] import warnings import numpy as np @@ -92,7 +93,7 @@ polyx = np.array([0, 1]) # -def polyline(off, scl) : +def polyline(off, scl): """ Returns an array representing a linear polynomial. @@ -113,20 +114,20 @@ def polyline(off, scl) : Examples -------- - >>> from numpy import polynomial as P + >>> from numpy.polynomial import polynomial as P >>> P.polyline(1,-1) array([ 1, -1]) >>> P.polyval(1, P.polyline(1,-1)) # should be 0 0.0 """ - if scl != 0 : + if scl != 0: return np.array([off, scl]) - else : + else: return np.array([off]) -def polyfromroots(roots) : +def polyfromroots(roots): """ Generate a monic polynomial with given roots. @@ -176,7 +177,7 @@ def polyfromroots(roots) : Examples -------- - >>> import numpy.polynomial as P + >>> from numpy.polynomial import polynomial as P >>> P.polyfromroots((-1,0,1)) # x(x - 1)(x + 1) = x^3 - x array([ 0., -1., 0., 1.]) >>> j = complex(0,1) @@ -184,9 +185,9 @@ def polyfromroots(roots) : array([ 1.+0.j, 0.+0.j, 1.+0.j]) """ - if len(roots) == 0 : + if len(roots) == 0: return np.ones(1) - else : + else: [roots] = pu.as_series([roots], trim=False) roots.sort() p = [polyline(-r, 1) for r in roots] @@ -225,7 +226,7 @@ def polyadd(c1, c2): Examples -------- - >>> from numpy import polynomial as P + >>> from numpy.polynomial import polynomial as P >>> c1 = (1,2,3) >>> c2 = (3,2,1) >>> sum = P.polyadd(c1,c2); sum @@ -236,10 +237,10 @@ def polyadd(c1, c2): """ # c1, c2 are trimmed copies [c1, c2] = pu.as_series([c1, c2]) - if len(c1) > len(c2) : + if len(c1) > len(c2): c1[:c2.size] += c2 ret = c1 - else : + else: c2[:c1.size] += c1 ret = c2 return pu.trimseq(ret) @@ -270,7 +271,7 @@ def polysub(c1, c2): Examples -------- - >>> from numpy import polynomial as P + >>> from numpy.polynomial import polynomial as P >>> c1 = (1,2,3) >>> c2 = (3,2,1) >>> P.polysub(c1,c2) @@ -281,10 +282,10 @@ def polysub(c1, c2): """ # c1, c2 are trimmed copies [c1, c2] = pu.as_series([c1, c2]) - if len(c1) > len(c2) : + if len(c1) > len(c2): c1[:c2.size] -= c2 ret = c1 - else : + else: c2 = -c2 c2[:c1.size] += c1 ret = c2 @@ -352,7 +353,7 @@ def polymul(c1, c2): Examples -------- - >>> import numpy.polynomial as P + >>> from numpy.polynomial import polynomial as P >>> c1 = (1,2,3) >>> c2 = (3,2,1) >>> P.polymul(c1,c2) @@ -389,7 +390,7 @@ def polydiv(c1, c2): Examples -------- - >>> import numpy.polynomial as P + >>> from numpy.polynomial import polynomial as P >>> c1 = (1,2,3) >>> c2 = (3,2,1) >>> P.polydiv(c1,c2) @@ -400,29 +401,29 @@ def polydiv(c1, c2): """ # c1, c2 are trimmed copies [c1, c2] = pu.as_series([c1, c2]) - if c2[-1] == 0 : + if c2[-1] == 0: raise ZeroDivisionError() len1 = len(c1) len2 = len(c2) - if len2 == 1 : + if len2 == 1: return c1/c2[-1], c1[:1]*0 - elif len1 < len2 : + elif len1 < len2: return c1[:1]*0, c1 - else : + else: dlen = len1 - len2 scl = c2[-1] - c2 = c2[:-1]/scl + c2 = c2[:-1]/scl i = dlen j = len1 - 1 - while i >= 0 : + while i >= 0: c1[i:j] -= c2*c1[j] i -= 1 j -= 1 return c1[j+1:]/scl, pu.trimseq(c1[:j+1]) -def polypow(c, pow, maxpower=None) : +def polypow(c, pow, maxpower=None): """Raise a polynomial to a power. Returns the polynomial `c` raised to the power `pow`. The argument @@ -456,19 +457,19 @@ def polypow(c, pow, maxpower=None) : # c is a trimmed copy [c] = pu.as_series([c]) power = int(pow) - if power != pow or power < 0 : + if power != pow or power < 0: raise ValueError("Power must be a non-negative integer.") - elif maxpower is not None and power > maxpower : + elif maxpower is not None and power > maxpower: raise ValueError("Power is too large") - elif power == 0 : + elif power == 0: return np.array([1], dtype=c.dtype) - elif power == 1 : + elif power == 1: return c - else : + else: # This can be made more efficient by using powers of two # in the usual way. prd = c - for i in range(2, power + 1) : + for i in range(2, power + 1): prd = np.convolve(prd, c) return prd @@ -513,7 +514,7 @@ def polyder(c, m=1, scl=1, axis=0): Examples -------- - >>> from numpy import polynomial as P + >>> from numpy.polynomial import polynomial as P >>> c = (1,2,3,4) # 1 + 2x + 3x**2 + 4x**3 >>> P.polyder(c) # (d/dx)(c) = 2 + 6x + 12x**2 array([ 2., 6., 12.]) @@ -550,7 +551,7 @@ def polyder(c, m=1, scl=1, axis=0): n = len(c) if cnt >= n: c = c[:1]*0 - else : + else: for i in range(cnt): n = n - 1 c *= scl @@ -624,7 +625,7 @@ def polyint(c, m=1, k=[], lbnd=0, scl=1, axis=0): Examples -------- - >>> from numpy import polynomial as P + >>> from numpy.polynomial import polynomial as P >>> c = (1,2,3) >>> P.polyint(c) # should return array([0, 1, 1, 1]) array([ 0., 1., 1., 1.]) @@ -650,9 +651,9 @@ def polyint(c, m=1, k=[], lbnd=0, scl=1, axis=0): if cnt != m: raise ValueError("The order of integration must be integer") - if cnt < 0 : + if cnt < 0: raise ValueError("The order of integration must be non-negative") - if len(k) > cnt : + if len(k) > cnt: raise ValueError("Too many integration constants") if iaxis != axis: raise ValueError("The axis must be integer") @@ -661,7 +662,6 @@ def polyint(c, m=1, k=[], lbnd=0, scl=1, axis=0): if iaxis < 0: iaxis += c.ndim - if cnt == 0: return c @@ -775,7 +775,7 @@ def polyval(x, c, tensor=True): c = c.reshape(c.shape + (1,)*x.ndim) c0 = c[-1] + x*0 - for i in range(2, len(c) + 1) : + for i in range(2, len(c) + 1): c0 = c[-i] + c0*x return c0 @@ -1010,7 +1010,7 @@ def polygrid3d(x, y, z, c): return c -def polyvander(x, deg) : +def polyvander(x, deg): """Vandermonde matrix of given degree. Returns the Vandermonde matrix of degree `deg` and sample points @@ -1059,14 +1059,14 @@ def polyvander(x, deg) : dtyp = x.dtype v = np.empty(dims, dtype=dtyp) v[0] = x*0 + 1 - if ideg > 0 : + if ideg > 0: v[1] = x - for i in range(2, ideg + 1) : + for i in range(2, ideg + 1): v[i] = v[i-1]*x return np.rollaxis(v, 0, v.ndim) -def polyvander2d(x, y, deg) : +def polyvander2d(x, y, deg): """Pseudo-Vandermonde matrix of given degrees. Returns the pseudo-Vandermonde matrix of degrees `deg` and sample @@ -1126,7 +1126,7 @@ def polyvander2d(x, y, deg) : return v.reshape(v.shape[:-2] + (-1,)) -def polyvander3d(x, y, z, deg) : +def polyvander3d(x, y, z, deg): """Pseudo-Vandermonde matrix of given degrees. Returns the pseudo-Vandermonde matrix of degrees `deg` and sample @@ -1254,7 +1254,7 @@ def polyfit(x, y, deg, rcond=None, full=False, w=None): rcond -- value of `rcond`. For more details, see `linalg.lstsq`. - + Raises ------ RankWarning @@ -1310,7 +1310,7 @@ def polyfit(x, y, deg, rcond=None, full=False, w=None): Examples -------- - >>> from numpy import polynomial as P + >>> from numpy.polynomial import polynomial as P >>> x = np.linspace(-1,1,51) # x "data": [-1, -0.96, ..., 0.96, 1] >>> y = x**3 - x + np.random.randn(len(x)) # x^3 - x + N(0,1) "noise" >>> c, stats = P.polyfit(x,y,3,full=True) @@ -1337,13 +1337,13 @@ def polyfit(x, y, deg, rcond=None, full=False, w=None): y = np.asarray(y) + 0.0 # check arguments. - if deg < 0 : + if deg < 0: raise ValueError("expected deg >= 0") if x.ndim != 1: raise TypeError("expected 1D vector for x") if x.size == 0: raise TypeError("expected non-empty vector for x") - if y.ndim < 1 or y.ndim > 2 : + if y.ndim < 1 or y.ndim > 2: raise TypeError("expected 1D or 2D array for y") if len(x) != len(y): raise TypeError("expected x and y to have same length") @@ -1363,7 +1363,7 @@ def polyfit(x, y, deg, rcond=None, full=False, w=None): rhs = rhs * w # set rcond - if rcond is None : + if rcond is None: rcond = len(x)*np.finfo(x.dtype).eps # Determine the norms of the design matrix columns. @@ -1382,9 +1382,9 @@ def polyfit(x, y, deg, rcond=None, full=False, w=None): msg = "The fit may be poorly conditioned" warnings.warn(msg, pu.RankWarning) - if full : + if full: return c, [resids, rank, s, rcond] - else : + else: return c @@ -1415,7 +1415,7 @@ def polycompanion(c): """ # c is a trimmed copy [c] = pu.as_series([c]) - if len(c) < 2 : + if len(c) < 2: raise ValueError('Series must have maximum degree of at least 1.') if len(c) == 2: return np.array([[-c[0]/c[1]]]) diff --git a/numpy/polynomial/polytemplate.py b/numpy/polynomial/polytemplate.py deleted file mode 100644 index e68dd18ef..000000000 --- a/numpy/polynomial/polytemplate.py +++ /dev/null @@ -1,927 +0,0 @@ -""" -Template for the Chebyshev and Polynomial classes. - -This module houses a Python string module Template object (see, e.g., -http://docs.python.org/library/string.html#template-strings) used by -the `polynomial` and `chebyshev` modules to implement their respective -`Polynomial` and `Chebyshev` classes. It provides a mechanism for easily -creating additional specific polynomial classes (e.g., Legendre, Jacobi, -etc.) in the future, such that all these classes will have a common API. - -""" -from __future__ import division, absolute_import, print_function - -import string -import sys -import warnings -from number import Number - -from numpy import ModuleDeprecationWarning - -warnings.warn("The polytemplate module will be removed in Numpy 1.10.0.", - ModuleDeprecationWarning) - -polytemplate = string.Template(''' -from __future__ import division, absolute_import, print_function -import numpy as np -import warnings -from . import polyutils as pu - -class $name(pu.PolyBase) : - """A $name series class. - - $name instances provide the standard Python numerical methods '+', - '-', '*', '//', '%', 'divmod', '**', and '()' as well as the listed - methods. - - Parameters - ---------- - coef : array_like - $name coefficients, in increasing order. For example, - ``(1, 2, 3)`` implies ``P_0 + 2P_1 + 3P_2`` where the - ``P_i`` are a graded polynomial basis. - domain : (2,) array_like, optional - Domain to use. The interval ``[domain[0], domain[1]]`` is mapped to - the interval ``[window[0], window[1]]`` by shifting and scaling. - The default value is $domain. - window : (2,) array_like, optional - Window, see ``domain`` for its use. The default value is $domain. - .. versionadded:: 1.6.0 - - Attributes - ---------- - coef : (N,) ndarray - $name coefficients, from low to high. - domain : (2,) ndarray - Domain that is mapped to ``window``. - window : (2,) ndarray - Window that ``domain`` is mapped to. - - Class Attributes - ---------------- - maxpower : int - Maximum power allowed, i.e., the largest number ``n`` such that - ``p(x)**n`` is allowed. This is to limit runaway polynomial size. - domain : (2,) ndarray - Default domain of the class. - window : (2,) ndarray - Default window of the class. - - Notes - ----- - It is important to specify the domain in many cases, for instance in - fitting data, because many of the important properties of the - polynomial basis only hold in a specified interval and consequently - the data must be mapped into that interval in order to benefit. - - Examples - -------- - - """ - # Limit runaway size. T_n^m has degree n*2^m - maxpower = 16 - # Default domain - domain = np.array($domain) - # Default window - window = np.array($domain) - # Don't let participate in array operations. Value doesn't matter. - __array_priority__ = 1000 - # Not hashable - __hash__ = None - - def has_samecoef(self, other): - """Check if coefficients match. - - Parameters - ---------- - other : class instance - The other class must have the ``coef`` attribute. - - Returns - ------- - bool : boolean - True if the coefficients are the same, False otherwise. - - Notes - ----- - .. versionadded:: 1.6.0 - - """ - if len(self.coef) != len(other.coef): - return False - elif not np.all(self.coef == other.coef): - return False - else: - return True - - def has_samedomain(self, other): - """Check if domains match. - - Parameters - ---------- - other : class instance - The other class must have the ``domain`` attribute. - - Returns - ------- - bool : boolean - True if the domains are the same, False otherwise. - - Notes - ----- - .. versionadded:: 1.6.0 - - """ - return np.all(self.domain == other.domain) - - def has_samewindow(self, other): - """Check if windows match. - - Parameters - ---------- - other : class instance - The other class must have the ``window`` attribute. - - Returns - ------- - bool : boolean - True if the windows are the same, False otherwise. - - Notes - ----- - .. versionadded:: 1.6.0 - - """ - return np.all(self.window == other.window) - - def has_sametype(self, other): - """Check if types match. - - Parameters - ---------- - other : object - Class instance. - - Returns - ------- - bool : boolean - True if other is same class as self - - Notes - ----- - .. versionadded:: 1.7.0 - - """ - return isinstance(other, self.__class__) - - def __init__(self, coef, domain=$domain, window=$domain) : - [coef, dom, win] = pu.as_series([coef, domain, window], trim=False) - if len(dom) != 2 : - raise ValueError("Domain has wrong number of elements.") - if len(win) != 2 : - raise ValueError("Window has wrong number of elements.") - self.coef = coef - self.domain = dom - self.window = win - - def __repr__(self): - format = "%s(%s, %s, %s)" - coef = repr(self.coef)[6:-1] - domain = repr(self.domain)[6:-1] - window = repr(self.window)[6:-1] - return format % ('$name', coef, domain, window) - - def __str__(self) : - format = "%s(%s)" - coef = str(self.coef) - return format % ('$nick', coef) - - # Pickle and copy - - def __getstate__(self) : - ret = self.__dict__.copy() - ret['coef'] = self.coef.copy() - ret['domain'] = self.domain.copy() - ret['window'] = self.window.copy() - return ret - - def __setstate__(self, dict) : - self.__dict__ = dict - - # Call - - def __call__(self, arg) : - off, scl = pu.mapparms(self.domain, self.window) - arg = off + scl*arg - return ${nick}val(arg, self.coef) - - def __iter__(self) : - return iter(self.coef) - - def __len__(self) : - return len(self.coef) - - # Numeric properties. - - def __neg__(self) : - return self.__class__(-self.coef, self.domain, self.window) - - def __pos__(self) : - return self - - def __add__(self, other) : - """Returns sum""" - if isinstance(other, pu.PolyBase): - if not self.has_sametype(other): - raise TypeError("Polynomial types differ") - elif not self.has_samedomain(other): - raise TypeError("Domains differ") - elif not self.has_samewindow(other): - raise TypeError("Windows differ") - else: - coef = ${nick}add(self.coef, other.coef) - else : - try : - coef = ${nick}add(self.coef, other) - except : - return NotImplemented - return self.__class__(coef, self.domain, self.window) - - def __sub__(self, other) : - """Returns difference""" - if isinstance(other, pu.PolyBase): - if not self.has_sametype(other): - raise TypeError("Polynomial types differ") - elif not self.has_samedomain(other): - raise TypeError("Domains differ") - elif not self.has_samewindow(other): - raise TypeError("Windows differ") - else: - coef = ${nick}sub(self.coef, other.coef) - else : - try : - coef = ${nick}sub(self.coef, other) - except : - return NotImplemented - return self.__class__(coef, self.domain, self.window) - - def __mul__(self, other) : - """Returns product""" - if isinstance(other, pu.PolyBase): - if not self.has_sametype(other): - raise TypeError("Polynomial types differ") - elif not self.has_samedomain(other): - raise TypeError("Domains differ") - elif not self.has_samewindow(other): - raise TypeError("Windows differ") - else: - coef = ${nick}mul(self.coef, other.coef) - else : - try : - coef = ${nick}mul(self.coef, other) - except : - return NotImplemented - return self.__class__(coef, self.domain, self.window) - - def __div__(self, other): - # set to __floordiv__, /, for now. - return self.__floordiv__(other) - - def __truediv__(self, other) : - # there is no true divide if the rhs is not a Number, although it - # could return the first n elements of an infinite series. - # It is hard to see where n would come from, though. - if not isinstance(other, Number) or isinstance(other, bool): - form = "unsupported types for true division: '%s', '%s'" - raise TypeError(form % (type(self), type(other))) - return self.__floordiv__(other) - - def __floordiv__(self, other) : - """Returns the quotient.""" - if isinstance(other, pu.PolyBase): - if not self.has_sametype(other): - raise TypeError("Polynomial types differ") - elif not self.has_samedomain(other): - raise TypeError("Domains differ") - elif not self.has_samewindow(other): - raise TypeError("Windows differ") - else: - quo, rem = ${nick}div(self.coef, other.coef) - else : - try : - quo, rem = ${nick}div(self.coef, other) - except : - return NotImplemented - return self.__class__(quo, self.domain, self.window) - - def __mod__(self, other) : - """Returns the remainder.""" - if isinstance(other, pu.PolyBase): - if not self.has_sametype(other): - raise TypeError("Polynomial types differ") - elif not self.has_samedomain(other): - raise TypeError("Domains differ") - elif not self.has_samewindow(other): - raise TypeError("Windows differ") - else: - quo, rem = ${nick}div(self.coef, other.coef) - else : - try : - quo, rem = ${nick}div(self.coef, other) - except : - return NotImplemented - return self.__class__(rem, self.domain, self.window) - - def __divmod__(self, other) : - """Returns quo, remainder""" - if isinstance(other, self.__class__) : - if not self.has_samedomain(other): - raise TypeError("Domains are not equal") - elif not self.has_samewindow(other): - raise TypeError("Windows are not equal") - else: - quo, rem = ${nick}div(self.coef, other.coef) - else : - try : - quo, rem = ${nick}div(self.coef, other) - except : - return NotImplemented - quo = self.__class__(quo, self.domain, self.window) - rem = self.__class__(rem, self.domain, self.window) - return quo, rem - - def __pow__(self, other) : - try : - coef = ${nick}pow(self.coef, other, maxpower = self.maxpower) - except : - raise - return self.__class__(coef, self.domain, self.window) - - def __radd__(self, other) : - try : - coef = ${nick}add(other, self.coef) - except : - return NotImplemented - return self.__class__(coef, self.domain, self.window) - - def __rsub__(self, other): - try : - coef = ${nick}sub(other, self.coef) - except : - return NotImplemented - return self.__class__(coef, self.domain, self.window) - - def __rmul__(self, other) : - try : - coef = ${nick}mul(other, self.coef) - except : - return NotImplemented - return self.__class__(coef, self.domain, self.window) - - def __rdiv__(self, other): - # set to __floordiv__ /. - return self.__rfloordiv__(other) - - def __rtruediv__(self, other) : - # An instance of PolyBase is not considered a - # Number. - return NotImplemented - - def __rfloordiv__(self, other) : - try : - quo, rem = ${nick}div(other, self.coef) - except: - return NotImplemented - return self.__class__(quo, self.domain, self.window) - - def __rmod__(self, other) : - try : - quo, rem = ${nick}div(other, self.coef) - except : - return NotImplemented - return self.__class__(rem, self.domain, self.window) - - def __rdivmod__(self, other) : - try : - quo, rem = ${nick}div(other, self.coef) - except : - return NotImplemented - quo = self.__class__(quo, self.domain, self.window) - rem = self.__class__(rem, self.domain, self.window) - return quo, rem - - # Enhance me - # some augmented arithmetic operations could be added here - - def __eq__(self, other) : - res = isinstance(other, self.__class__) \ - and self.has_samecoef(other) \ - and self.has_samedomain(other) \ - and self.has_samewindow(other) - return res - - def __ne__(self, other) : - return not self.__eq__(other) - - # - # Extra methods. - # - - def copy(self) : - """Return a copy. - - Return a copy of the current $name instance. - - Returns - ------- - new_instance : $name - Copy of current instance. - - """ - return self.__class__(self.coef, self.domain, self.window) - - def degree(self) : - """The degree of the series. - - Notes - ----- - .. versionadded:: 1.5.0 - - """ - return len(self) - 1 - - def cutdeg(self, deg) : - """Truncate series to the given degree. - - Reduce the degree of the $name series to `deg` by discarding the - high order terms. If `deg` is greater than the current degree a - copy of the current series is returned. This can be useful in least - squares where the coefficients of the high degree terms may be very - small. - - Parameters - ---------- - deg : non-negative int - The series is reduced to degree `deg` by discarding the high - order terms. The value of `deg` must be a non-negative integer. - - Returns - ------- - new_instance : $name - New instance of $name with reduced degree. - - Notes - ----- - .. versionadded:: 1.5.0 - - """ - return self.truncate(deg + 1) - - def trim(self, tol=0) : - """Remove small leading coefficients - - Remove leading coefficients until a coefficient is reached whose - absolute value greater than `tol` or the beginning of the series is - reached. If all the coefficients would be removed the series is set to - ``[0]``. A new $name instance is returned with the new coefficients. - The current instance remains unchanged. - - Parameters - ---------- - tol : non-negative number. - All trailing coefficients less than `tol` will be removed. - - Returns - ------- - new_instance : $name - Contains the new set of coefficients. - - """ - coef = pu.trimcoef(self.coef, tol) - return self.__class__(coef, self.domain, self.window) - - def truncate(self, size) : - """Truncate series to length `size`. - - Reduce the $name series to length `size` by discarding the high - degree terms. The value of `size` must be a positive integer. This - can be useful in least squares where the coefficients of the - high degree terms may be very small. - - Parameters - ---------- - size : positive int - The series is reduced to length `size` by discarding the high - degree terms. The value of `size` must be a positive integer. - - Returns - ------- - new_instance : $name - New instance of $name with truncated coefficients. - - """ - isize = int(size) - if isize != size or isize < 1 : - raise ValueError("size must be a positive integer") - if isize >= len(self.coef) : - coef = self.coef - else : - coef = self.coef[:isize] - return self.__class__(coef, self.domain, self.window) - - def convert(self, domain=None, kind=None, window=None) : - """Convert to different class and/or domain. - - Parameters - ---------- - domain : array_like, optional - The domain of the converted series. If the value is None, - the default domain of `kind` is used. - kind : class, optional - The polynomial series type class to which the current instance - should be converted. If kind is None, then the class of the - current instance is used. - window : array_like, optional - The window of the converted series. If the value is None, - the default window of `kind` is used. - - Returns - ------- - new_series_instance : `kind` - The returned class can be of different type than the current - instance and/or have a different domain. - - Notes - ----- - Conversion between domains and class types can result in - numerically ill defined series. - - Examples - -------- - - """ - if kind is None: - kind = $name - if domain is None: - domain = kind.domain - if window is None: - window = kind.window - return self(kind.identity(domain, window=window)) - - def mapparms(self) : - """Return the mapping parameters. - - The returned values define a linear map ``off + scl*x`` that is - applied to the input arguments before the series is evaluated. The - map depends on the ``domain`` and ``window``; if the current - ``domain`` is equal to the ``window`` the resulting map is the - identity. If the coefficients of the ``$name`` instance are to be - used by themselves outside this class, then the linear function - must be substituted for the ``x`` in the standard representation of - the base polynomials. - - Returns - ------- - off, scl : floats or complex - The mapping function is defined by ``off + scl*x``. - - Notes - ----- - If the current domain is the interval ``[l_1, r_1]`` and the window - is ``[l_2, r_2]``, then the linear mapping function ``L`` is - defined by the equations:: - - L(l_1) = l_2 - L(r_1) = r_2 - - """ - return pu.mapparms(self.domain, self.window) - - def integ(self, m=1, k=[], lbnd=None) : - """Integrate. - - Return an instance of $name that is the definite integral of the - current series. Refer to `${nick}int` for full documentation. - - Parameters - ---------- - m : non-negative int - The number of integrations to perform. - k : array_like - Integration constants. The first constant is applied to the - first integration, the second to the second, and so on. The - list of values must less than or equal to `m` in length and any - missing values are set to zero. - lbnd : Scalar - The lower bound of the definite integral. - - Returns - ------- - integral : $name - The integral of the series using the same domain. - - See Also - -------- - ${nick}int : similar function. - ${nick}der : similar function for derivative. - - """ - off, scl = self.mapparms() - if lbnd is None : - lbnd = 0 - else : - lbnd = off + scl*lbnd - coef = ${nick}int(self.coef, m, k, lbnd, 1./scl) - return self.__class__(coef, self.domain, self.window) - - def deriv(self, m=1): - """Differentiate. - - Return an instance of $name that is the derivative of the current - series. Refer to `${nick}der` for full documentation. - - Parameters - ---------- - m : non-negative int - The number of integrations to perform. - - Returns - ------- - derivative : $name - The derivative of the series using the same domain. - - See Also - -------- - ${nick}der : similar function. - ${nick}int : similar function for integration. - - """ - off, scl = self.mapparms() - coef = ${nick}der(self.coef, m, scl) - return self.__class__(coef, self.domain, self.window) - - def roots(self) : - """Return list of roots. - - Return ndarray of roots for this series. See `${nick}roots` for - full documentation. Note that the accuracy of the roots is likely to - decrease the further outside the domain they lie. - - See Also - -------- - ${nick}roots : similar function - ${nick}fromroots : function to go generate series from roots. - - """ - roots = ${nick}roots(self.coef) - return pu.mapdomain(roots, self.window, self.domain) - - def linspace(self, n=100, domain=None): - """Return x,y values at equally spaced points in domain. - - Returns x, y values at `n` linearly spaced points across domain. - Here y is the value of the polynomial at the points x. By default - the domain is the same as that of the $name instance. This method - is intended mostly as a plotting aid. - - Parameters - ---------- - n : int, optional - Number of point pairs to return. The default value is 100. - domain : {None, array_like} - If not None, the specified domain is used instead of that of - the calling instance. It should be of the form ``[beg,end]``. - The default is None. - - Returns - ------- - x, y : ndarrays - ``x`` is equal to linspace(self.domain[0], self.domain[1], n) - ``y`` is the polynomial evaluated at ``x``. - - .. versionadded:: 1.5.0 - - """ - if domain is None: - domain = self.domain - x = np.linspace(domain[0], domain[1], n) - y = self(x) - return x, y - - - - @staticmethod - def fit(x, y, deg, domain=None, rcond=None, full=False, w=None, - window=$domain): - """Least squares fit to data. - - Return a `$name` instance that is the least squares fit to the data - `y` sampled at `x`. Unlike `${nick}fit`, the domain of the returned - instance can be specified and this will often result in a superior - fit with less chance of ill conditioning. Support for NA was added - in version 1.7.0. See `${nick}fit` for full documentation of the - implementation. - - Parameters - ---------- - x : array_like, shape (M,) - x-coordinates of the M sample points ``(x[i], y[i])``. - y : array_like, shape (M,) or (M, K) - y-coordinates of the sample points. Several data sets of sample - points sharing the same x-coordinates can be fitted at once by - passing in a 2D-array that contains one dataset per column. - deg : int - Degree of the fitting polynomial. - domain : {None, [beg, end], []}, optional - Domain to use for the returned $name instance. If ``None``, - then a minimal domain that covers the points `x` is chosen. If - ``[]`` the default domain ``$domain`` is used. The default - value is $domain in numpy 1.4.x and ``None`` in later versions. - The ``[]`` value was added in numpy 1.5.0. - rcond : float, optional - Relative condition number of the fit. Singular values smaller - than this relative to the largest singular value will be - ignored. The default value is len(x)*eps, where eps is the - relative precision of the float type, about 2e-16 in most - cases. - full : bool, optional - Switch determining nature of return value. When it is False - (the default) just the coefficients are returned, when True - diagnostic information from the singular value decomposition is - also returned. - w : array_like, shape (M,), optional - Weights. If not None the contribution of each point - ``(x[i],y[i])`` to the fit is weighted by `w[i]`. Ideally the - weights are chosen so that the errors of the products - ``w[i]*y[i]`` all have the same variance. The default value is - None. - .. versionadded:: 1.5.0 - window : {[beg, end]}, optional - Window to use for the returned $name instance. The default - value is ``$domain`` - .. versionadded:: 1.6.0 - - Returns - ------- - least_squares_fit : instance of $name - The $name instance is the least squares fit to the data and - has the domain specified in the call. - - [residuals, rank, singular_values, rcond] : only if `full` = True - Residuals of the least squares fit, the effective rank of the - scaled Vandermonde matrix and its singular values, and the - specified value of `rcond`. For more details, see - `linalg.lstsq`. - - See Also - -------- - ${nick}fit : similar function - - """ - if domain is None: - domain = pu.getdomain(x) - elif type(domain) is list and len(domain) == 0: - domain = $domain - - if type(window) is list and len(window) == 0: - window = $domain - - xnew = pu.mapdomain(x, domain, window) - res = ${nick}fit(xnew, y, deg, w=w, rcond=rcond, full=full) - if full : - [coef, status] = res - return $name(coef, domain=domain, window=window), status - else : - coef = res - return $name(coef, domain=domain, window=window) - - @staticmethod - def fromroots(roots, domain=$domain, window=$domain) : - """Return $name instance with specified roots. - - Returns an instance of $name representing the product - ``(x - r[0])*(x - r[1])*...*(x - r[n-1])``, where ``r`` is the - list of roots. - - Parameters - ---------- - roots : array_like - List of roots. - domain : {array_like, None}, optional - Domain for the resulting instance of $name. If none the domain - is the interval from the smallest root to the largest. The - default is $domain. - window : array_like, optional - Window for the resulting instance of $name. The default value - is $domain. - - Returns - ------- - object : $name instance - Series with the specified roots. - - See Also - -------- - ${nick}fromroots : equivalent function - - """ - [roots] = pu.as_series([roots], trim=False) - if domain is None : - domain = pu.getdomain(roots) - deg = len(roots) - off, scl = pu.mapparms(domain, window) - rnew = off + scl*roots - coef = ${nick}fromroots(rnew) / scl**deg - return $name(coef, domain=domain, window=window) - - @staticmethod - def identity(domain=$domain, window=$domain) : - """Identity function. - - If ``p`` is the returned $name object, then ``p(x) == x`` for all - values of x. - - Parameters - ---------- - domain : array_like - The resulting array must be of the form ``[beg, end]``, where - ``beg`` and ``end`` are the endpoints of the domain. - window : array_like - The resulting array must be if the form ``[beg, end]``, where - ``beg`` and ``end`` are the endpoints of the window. - - Returns - ------- - identity : $name instance - - """ - off, scl = pu.mapparms(window, domain) - coef = ${nick}line(off, scl) - return $name(coef, domain, window) - - @staticmethod - def basis(deg, domain=$domain, window=$domain): - """$name polynomial of degree `deg`. - - Returns an instance of the $name polynomial of degree `d`. - - Parameters - ---------- - deg : int - Degree of the $name polynomial. Must be >= 0. - domain : array_like - The resulting array must be of the form ``[beg, end]``, where - ``beg`` and ``end`` are the endpoints of the domain. - window : array_like - The resulting array must be if the form ``[beg, end]``, where - ``beg`` and ``end`` are the endpoints of the window. - - Returns - p : $name instance - - Notes - ----- - .. versionadded:: 1.7.0 - - """ - ideg = int(deg) - if ideg != deg or ideg < 0: - raise ValueError("deg must be non-negative integer") - return $name([0]*ideg + [1], domain, window) - - @staticmethod - def cast(series, domain=$domain, window=$domain): - """Convert instance to equivalent $name series. - - The `series` is expected to be an instance of some polynomial - series of one of the types supported by by the numpy.polynomial - module, but could be some other class that supports the convert - method. - - Parameters - ---------- - series : series - The instance series to be converted. - domain : array_like - The resulting array must be of the form ``[beg, end]``, where - ``beg`` and ``end`` are the endpoints of the domain. - window : array_like - The resulting array must be if the form ``[beg, end]``, where - ``beg`` and ``end`` are the endpoints of the window. - - Returns - p : $name instance - A $name series equal to the `poly` series. - - See Also - -------- - convert -- similar instance method - - Notes - ----- - .. versionadded:: 1.7.0 - - """ - return series.convert(domain, $name, window) - -''') diff --git a/numpy/polynomial/polyutils.py b/numpy/polynomial/polyutils.py index 99f508521..9348559ed 100644 --- a/numpy/polynomial/polyutils.py +++ b/numpy/polynomial/polyutils.py @@ -45,27 +45,25 @@ Functions """ from __future__ import division, absolute_import, print_function -__all__ = ['RankWarning', 'PolyError', 'PolyDomainError', 'as_series', - 'trimseq', 'trimcoef', 'getdomain', 'mapdomain', 'mapparms', - 'PolyBase'] - -import warnings import numpy as np -import sys + +__all__ = [ + 'RankWarning', 'PolyError', 'PolyDomainError', 'as_series', 'trimseq', + 'trimcoef', 'getdomain', 'mapdomain', 'mapparms', 'PolyBase'] # # Warnings and Exceptions # -class RankWarning(UserWarning) : +class RankWarning(UserWarning): """Issued by chebfit when the design matrix is rank deficient.""" pass -class PolyError(Exception) : +class PolyError(Exception): """Base class for errors in this module.""" pass -class PolyDomainError(PolyError) : +class PolyDomainError(PolyError): """Issued by the generic Poly class when two domains don't match. This is raised when an binary operation is passed Poly objects with @@ -78,7 +76,7 @@ class PolyDomainError(PolyError) : # Base class for all polynomial types # -class PolyBase(object) : +class PolyBase(object): """ Base class for all polynomial types. @@ -93,7 +91,7 @@ class PolyBase(object) : # # Helper functions to convert inputs to 1-D arrays # -def trimseq(seq) : +def trimseq(seq): """Remove small Poly series coefficients. Parameters @@ -114,16 +112,16 @@ def trimseq(seq) : Do not lose the type info if the sequence contains unknown objects. """ - if len(seq) == 0 : + if len(seq) == 0: return seq - else : - for i in range(len(seq) - 1, -1, -1) : - if seq[i] != 0 : + else: + for i in range(len(seq) - 1, -1, -1): + if seq[i] != 0: break return seq[:i+1] -def as_series(alist, trim=True) : +def as_series(alist, trim=True): """ Return argument as a list of 1-d arrays. @@ -165,32 +163,32 @@ def as_series(alist, trim=True) : """ arrays = [np.array(a, ndmin=1, copy=0) for a in alist] - if min([a.size for a in arrays]) == 0 : + if min([a.size for a in arrays]) == 0: raise ValueError("Coefficient array is empty") - if any([a.ndim != 1 for a in arrays]) : + if any([a.ndim != 1 for a in arrays]): raise ValueError("Coefficient array is not 1-d") - if trim : + if trim: arrays = [trimseq(a) for a in arrays] - if any([a.dtype == np.dtype(object) for a in arrays]) : + if any([a.dtype == np.dtype(object) for a in arrays]): ret = [] - for a in arrays : - if a.dtype != np.dtype(object) : + for a in arrays: + if a.dtype != np.dtype(object): tmp = np.empty(len(a), dtype=np.dtype(object)) tmp[:] = a[:] ret.append(tmp) - else : + else: ret.append(a.copy()) - else : - try : + else: + try: dtype = np.common_type(*arrays) - except : + except: raise ValueError("Coefficient arrays have no common type") ret = [np.array(a, copy=1, dtype=dtype) for a in arrays] return ret -def trimcoef(c, tol=0) : +def trimcoef(c, tol=0): """ Remove "small" "trailing" coefficients from a polynomial. @@ -234,17 +232,17 @@ def trimcoef(c, tol=0) : array([ 0.0003+0.j , 0.0010-0.001j]) """ - if tol < 0 : + if tol < 0: raise ValueError("tol must be non-negative") [c] = as_series([c]) [ind] = np.where(np.abs(c) > tol) - if len(ind) == 0 : + if len(ind) == 0: return c[:1]*0 - else : + else: return c[:ind[-1] + 1].copy() -def getdomain(x) : +def getdomain(x): """ Return a domain suitable for given abscissae. @@ -283,14 +281,14 @@ def getdomain(x) : """ [x] = as_series([x], trim=False) - if x.dtype.char in np.typecodes['Complex'] : + if x.dtype.char in np.typecodes['Complex']: rmin, rmax = x.real.min(), x.real.max() imin, imax = x.imag.min(), x.imag.max() return np.array((complex(rmin, imin), complex(rmax, imax))) - else : + else: return np.array((x.min(), x.max())) -def mapparms(old, new) : +def mapparms(old, new): """ Linear map parameters between domains. @@ -337,7 +335,7 @@ def mapparms(old, new) : scl = newlen/oldlen return off, scl -def mapdomain(x, old, new) : +def mapdomain(x, old, new): """ Apply linear map to input points. diff --git a/numpy/polynomial/tests/test_chebyshev.py b/numpy/polynomial/tests/test_chebyshev.py index 82c3ba9ea..a596905f6 100644 --- a/numpy/polynomial/tests/test_chebyshev.py +++ b/numpy/polynomial/tests/test_chebyshev.py @@ -400,14 +400,14 @@ class TestFitting(TestCase): return x*(x - 1)*(x - 2) # Test exceptions - assert_raises(ValueError, cheb.chebfit, [1], [1], -1) - assert_raises(TypeError, cheb.chebfit, [[1]], [1], 0) - assert_raises(TypeError, cheb.chebfit, [], [1], 0) - assert_raises(TypeError, cheb.chebfit, [1], [[[1]]], 0) - assert_raises(TypeError, cheb.chebfit, [1, 2], [1], 0) - assert_raises(TypeError, cheb.chebfit, [1], [1, 2], 0) - assert_raises(TypeError, cheb.chebfit, [1], [1], 0, w=[[1]]) - assert_raises(TypeError, cheb.chebfit, [1], [1], 0, w=[1, 1]) + assert_raises(ValueError, cheb.chebfit, [1], [1], -1) + assert_raises(TypeError, cheb.chebfit, [[1]], [1], 0) + assert_raises(TypeError, cheb.chebfit, [], [1], 0) + assert_raises(TypeError, cheb.chebfit, [1], [[[1]]], 0) + assert_raises(TypeError, cheb.chebfit, [1, 2], [1], 0) + assert_raises(TypeError, cheb.chebfit, [1], [1, 2], 0) + assert_raises(TypeError, cheb.chebfit, [1], [1], 0, w=[[1]]) + assert_raises(TypeError, cheb.chebfit, [1], [1], 0, w=[1, 1]) # Test fit x = np.linspace(0, 2) @@ -532,7 +532,7 @@ class TestMisc(TestCase): assert_almost_equal(cheb.chebpts1(2), tgt) tgt = [-0.86602540378443871, 0, 0.86602540378443871] assert_almost_equal(cheb.chebpts1(3), tgt) - tgt = [-0.9238795325, -0.3826834323, 0.3826834323, 0.9238795325] + tgt = [-0.9238795325, -0.3826834323, 0.3826834323, 0.9238795325] assert_almost_equal(cheb.chebpts1(4), tgt) def test_chebpts2(self): diff --git a/numpy/polynomial/tests/test_classes.py b/numpy/polynomial/tests/test_classes.py index f9134b8c1..cd5a54687 100644 --- a/numpy/polynomial/tests/test_classes.py +++ b/numpy/polynomial/tests/test_classes.py @@ -10,12 +10,10 @@ from numbers import Number import numpy as np from numpy.polynomial import ( - Polynomial, Legendre, Chebyshev, Laguerre, - Hermite, HermiteE) + Polynomial, Legendre, Chebyshev, Laguerre, Hermite, HermiteE) from numpy.testing import ( - TestCase, assert_almost_equal, assert_raises, - assert_equal, assert_, run_module_suite, dec) -from numpy.testing.noseclasses import KnownFailure + assert_almost_equal, assert_raises, assert_equal, assert_, + run_module_suite) from numpy.compat import long @@ -410,6 +408,9 @@ def check_roots(Poly): d = Poly.domain + random((2,))*.25 w = Poly.window + random((2,))*.25 tgt = np.sort(random((5,))) + res = np.sort(Poly.fromroots(tgt, domain=d, window=w).roots()) + assert_almost_equal(res, tgt) + # default domain and window res = np.sort(Poly.fromroots(tgt).roots()) assert_almost_equal(res, tgt) @@ -468,6 +469,12 @@ def check_deriv(Poly): p3 = p1.integ(1, k=[1]) assert_almost_equal(p2.deriv(1).coef, p3.coef) assert_almost_equal(p2.deriv(2).coef, p1.coef) + # default domain and window + p1 = Poly([1, 2, 3]) + p2 = p1.integ(2, k=[1, 2]) + p3 = p1.integ(1, k=[1]) + assert_almost_equal(p2.deriv(1).coef, p3.coef) + assert_almost_equal(p2.deriv(2).coef, p1.coef) def check_linspace(Poly): @@ -491,11 +498,18 @@ def check_linspace(Poly): def check_pow(Poly): d = Poly.domain + random((2,))*.25 w = Poly.window + random((2,))*.25 - tgt = Poly([1], domain=d, window=d) - tst = Poly([1, 2, 3], domain=d, window=d) + tgt = Poly([1], domain=d, window=w) + tst = Poly([1, 2, 3], domain=d, window=w) + for i in range(5): + assert_poly_almost_equal(tst**i, tgt) + tgt = tgt * tst + # default domain and window + tgt = Poly([1]) + tst = Poly([1, 2, 3]) for i in range(5): assert_poly_almost_equal(tst**i, tgt) tgt = tgt * tst + # check error for invalid powers assert_raises(ValueError, op.pow, tgt, 1.5) assert_raises(ValueError, op.pow, tgt, -1) diff --git a/numpy/polynomial/tests/test_hermite.py b/numpy/polynomial/tests/test_hermite.py index ac60007d1..e67625a88 100644 --- a/numpy/polynomial/tests/test_hermite.py +++ b/numpy/polynomial/tests/test_hermite.py @@ -119,7 +119,6 @@ class TestEvaluation(TestCase): y = [polyval(x, c) for c in Hlist] for i in range(10): msg = "At i=%d" % i - ser = np.zeros tgt = y[i] res = herm.hermval(x, [0]*i + [1]) assert_almost_equal(res, tgt, err_msg=msg) @@ -389,14 +388,14 @@ class TestFitting(TestCase): return x*(x - 1)*(x - 2) # Test exceptions - assert_raises(ValueError, herm.hermfit, [1], [1], -1) - assert_raises(TypeError, herm.hermfit, [[1]], [1], 0) - assert_raises(TypeError, herm.hermfit, [], [1], 0) - assert_raises(TypeError, herm.hermfit, [1], [[[1]]], 0) - assert_raises(TypeError, herm.hermfit, [1, 2], [1], 0) - assert_raises(TypeError, herm.hermfit, [1], [1, 2], 0) - assert_raises(TypeError, herm.hermfit, [1], [1], 0, w=[[1]]) - assert_raises(TypeError, herm.hermfit, [1], [1], 0, w=[1, 1]) + assert_raises(ValueError, herm.hermfit, [1], [1], -1) + assert_raises(TypeError, herm.hermfit, [[1]], [1], 0) + assert_raises(TypeError, herm.hermfit, [], [1], 0) + assert_raises(TypeError, herm.hermfit, [1], [[[1]]], 0) + assert_raises(TypeError, herm.hermfit, [1, 2], [1], 0) + assert_raises(TypeError, herm.hermfit, [1], [1, 2], 0) + assert_raises(TypeError, herm.hermfit, [1], [1], 0, w=[[1]]) + assert_raises(TypeError, herm.hermfit, [1], [1], 0, w=[1, 1]) # Test fit x = np.linspace(0, 2) diff --git a/numpy/polynomial/tests/test_hermite_e.py b/numpy/polynomial/tests/test_hermite_e.py index 5341dc7ff..f8601a828 100644 --- a/numpy/polynomial/tests/test_hermite_e.py +++ b/numpy/polynomial/tests/test_hermite_e.py @@ -6,7 +6,9 @@ from __future__ import division, absolute_import, print_function import numpy as np import numpy.polynomial.hermite_e as herme from numpy.polynomial.polynomial import polyval -from numpy.testing import * +from numpy.testing import ( + TestCase, assert_almost_equal, assert_raises, + assert_equal, assert_, run_module_suite) He0 = np.array([1]) He1 = np.array([0, 1]) @@ -117,7 +119,6 @@ class TestEvaluation(TestCase): y = [polyval(x, c) for c in Helist] for i in range(10): msg = "At i=%d" % i - ser = np.zeros tgt = y[i] res = herme.hermeval(x, [0]*i + [1]) assert_almost_equal(res, tgt, err_msg=msg) @@ -388,14 +389,14 @@ class TestFitting(TestCase): return x*(x - 1)*(x - 2) # Test exceptions - assert_raises(ValueError, herme.hermefit, [1], [1], -1) - assert_raises(TypeError, herme.hermefit, [[1]], [1], 0) - assert_raises(TypeError, herme.hermefit, [], [1], 0) - assert_raises(TypeError, herme.hermefit, [1], [[[1]]], 0) - assert_raises(TypeError, herme.hermefit, [1, 2], [1], 0) - assert_raises(TypeError, herme.hermefit, [1], [1, 2], 0) - assert_raises(TypeError, herme.hermefit, [1], [1], 0, w=[[1]]) - assert_raises(TypeError, herme.hermefit, [1], [1], 0, w=[1, 1]) + assert_raises(ValueError, herme.hermefit, [1], [1], -1) + assert_raises(TypeError, herme.hermefit, [[1]], [1], 0) + assert_raises(TypeError, herme.hermefit, [], [1], 0) + assert_raises(TypeError, herme.hermefit, [1], [[[1]]], 0) + assert_raises(TypeError, herme.hermefit, [1, 2], [1], 0) + assert_raises(TypeError, herme.hermefit, [1], [1, 2], 0) + assert_raises(TypeError, herme.hermefit, [1], [1], 0, w=[[1]]) + assert_raises(TypeError, herme.hermefit, [1], [1], 0, w=[1, 1]) # Test fit x = np.linspace(0, 2) diff --git a/numpy/polynomial/tests/test_laguerre.py b/numpy/polynomial/tests/test_laguerre.py index b3d8fe5ee..1dc57a960 100644 --- a/numpy/polynomial/tests/test_laguerre.py +++ b/numpy/polynomial/tests/test_laguerre.py @@ -116,7 +116,6 @@ class TestEvaluation(TestCase): y = [polyval(x, c) for c in Llist] for i in range(7): msg = "At i=%d" % i - ser = np.zeros tgt = y[i] res = lag.lagval(x, [0]*i + [1]) assert_almost_equal(res, tgt, err_msg=msg) @@ -386,14 +385,14 @@ class TestFitting(TestCase): return x*(x - 1)*(x - 2) # Test exceptions - assert_raises(ValueError, lag.lagfit, [1], [1], -1) - assert_raises(TypeError, lag.lagfit, [[1]], [1], 0) - assert_raises(TypeError, lag.lagfit, [], [1], 0) - assert_raises(TypeError, lag.lagfit, [1], [[[1]]], 0) - assert_raises(TypeError, lag.lagfit, [1, 2], [1], 0) - assert_raises(TypeError, lag.lagfit, [1], [1, 2], 0) - assert_raises(TypeError, lag.lagfit, [1], [1], 0, w=[[1]]) - assert_raises(TypeError, lag.lagfit, [1], [1], 0, w=[1, 1]) + assert_raises(ValueError, lag.lagfit, [1], [1], -1) + assert_raises(TypeError, lag.lagfit, [[1]], [1], 0) + assert_raises(TypeError, lag.lagfit, [], [1], 0) + assert_raises(TypeError, lag.lagfit, [1], [[[1]]], 0) + assert_raises(TypeError, lag.lagfit, [1, 2], [1], 0) + assert_raises(TypeError, lag.lagfit, [1], [1, 2], 0) + assert_raises(TypeError, lag.lagfit, [1], [1], 0, w=[[1]]) + assert_raises(TypeError, lag.lagfit, [1], [1], 0, w=[1, 1]) # Test fit x = np.linspace(0, 2) diff --git a/numpy/polynomial/tests/test_legendre.py b/numpy/polynomial/tests/test_legendre.py index e248f005d..8ac1feb58 100644 --- a/numpy/polynomial/tests/test_legendre.py +++ b/numpy/polynomial/tests/test_legendre.py @@ -120,7 +120,6 @@ class TestEvaluation(TestCase): y = [polyval(x, c) for c in Llist] for i in range(10): msg = "At i=%d" % i - ser = np.zeros tgt = y[i] res = leg.legval(x, [0]*i + [1]) assert_almost_equal(res, tgt, err_msg=msg) @@ -390,14 +389,14 @@ class TestFitting(TestCase): return x*(x - 1)*(x - 2) # Test exceptions - assert_raises(ValueError, leg.legfit, [1], [1], -1) - assert_raises(TypeError, leg.legfit, [[1]], [1], 0) - assert_raises(TypeError, leg.legfit, [], [1], 0) - assert_raises(TypeError, leg.legfit, [1], [[[1]]], 0) - assert_raises(TypeError, leg.legfit, [1, 2], [1], 0) - assert_raises(TypeError, leg.legfit, [1], [1, 2], 0) - assert_raises(TypeError, leg.legfit, [1], [1], 0, w=[[1]]) - assert_raises(TypeError, leg.legfit, [1], [1], 0, w=[1, 1]) + assert_raises(ValueError, leg.legfit, [1], [1], -1) + assert_raises(TypeError, leg.legfit, [[1]], [1], 0) + assert_raises(TypeError, leg.legfit, [], [1], 0) + assert_raises(TypeError, leg.legfit, [1], [[[1]]], 0) + assert_raises(TypeError, leg.legfit, [1, 2], [1], 0) + assert_raises(TypeError, leg.legfit, [1], [1, 2], 0) + assert_raises(TypeError, leg.legfit, [1], [1], 0, w=[[1]]) + assert_raises(TypeError, leg.legfit, [1], [1], 0, w=[1, 1]) # Test fit x = np.linspace(0, 2) diff --git a/numpy/polynomial/tests/test_polynomial.py b/numpy/polynomial/tests/test_polynomial.py index 77092cd2f..c806a8497 100644 --- a/numpy/polynomial/tests/test_polynomial.py +++ b/numpy/polynomial/tests/test_polynomial.py @@ -420,14 +420,14 @@ class TestMisc(TestCase): return x*(x - 1)*(x - 2) # Test exceptions - assert_raises(ValueError, poly.polyfit, [1], [1], -1) - assert_raises(TypeError, poly.polyfit, [[1]], [1], 0) - assert_raises(TypeError, poly.polyfit, [], [1], 0) - assert_raises(TypeError, poly.polyfit, [1], [[[1]]], 0) - assert_raises(TypeError, poly.polyfit, [1, 2], [1], 0) - assert_raises(TypeError, poly.polyfit, [1], [1, 2], 0) - assert_raises(TypeError, poly.polyfit, [1], [1], 0, w=[[1]]) - assert_raises(TypeError, poly.polyfit, [1], [1], 0, w=[1, 1]) + assert_raises(ValueError, poly.polyfit, [1], [1], -1) + assert_raises(TypeError, poly.polyfit, [[1]], [1], 0) + assert_raises(TypeError, poly.polyfit, [], [1], 0) + assert_raises(TypeError, poly.polyfit, [1], [[[1]]], 0) + assert_raises(TypeError, poly.polyfit, [1, 2], [1], 0) + assert_raises(TypeError, poly.polyfit, [1], [1, 2], 0) + assert_raises(TypeError, poly.polyfit, [1], [1], 0, w=[[1]]) + assert_raises(TypeError, poly.polyfit, [1], [1], 0, w=[1, 1]) # Test fit x = np.linspace(0, 2) diff --git a/numpy/polynomial/tests/test_polyutils.py b/numpy/polynomial/tests/test_polyutils.py index c77ee2435..974e2e09a 100644 --- a/numpy/polynomial/tests/test_polyutils.py +++ b/numpy/polynomial/tests/test_polyutils.py @@ -5,7 +5,9 @@ from __future__ import division, absolute_import, print_function import numpy as np import numpy.polynomial.polyutils as pu -from numpy.testing import * +from numpy.testing import ( + TestCase, assert_almost_equal, assert_raises, + assert_equal, assert_, run_module_suite) class TestMisc(TestCase): @@ -101,3 +103,7 @@ class TestDomain(TestCase): tgt = [-1 + 1j, 1 - 1j] res = pu.mapparms(dom1, dom2) assert_almost_equal(res, tgt) + + +if __name__ == "__main__": + run_module_suite() diff --git a/numpy/random/mtrand/mtrand.pyx b/numpy/random/mtrand/mtrand.pyx index c2603543d..6a2e303d2 100644 --- a/numpy/random/mtrand/mtrand.pyx +++ b/numpy/random/mtrand/mtrand.pyx @@ -1068,7 +1068,7 @@ cdef class RandomState: if pop_size is 0: raise ValueError("a must be non-empty") - if None != p: + if p is not None: d = len(p) p = <ndarray>PyArray_ContiguousFromObject(p, NPY_DOUBLE, 1, 1) pix = <double*>PyArray_DATA(p) @@ -1090,7 +1090,7 @@ cdef class RandomState: # Actual sampling if replace: - if None != p: + if p is not None: cdf = p.cumsum() cdf /= cdf[-1] uniform_samples = self.random_sample(shape) @@ -1103,7 +1103,7 @@ cdef class RandomState: raise ValueError("Cannot take a larger sample than " "population when 'replace=False'") - if None != p: + if p is not None: if np.count_nonzero(p > 0) < size: raise ValueError("Fewer non-zero entries in p than size") n_uniq = 0 @@ -3752,8 +3752,9 @@ cdef class RandomState: Parameters ---------- - lam : float - Expectation of interval, should be >= 0. + lam : float or sequence of float + Expectation of interval, should be >= 0. A sequence of expectation + intervals must be broadcastable over the requested size. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn. Default is None, in which case a @@ -3793,6 +3794,10 @@ cdef class RandomState: >>> count, bins, ignored = plt.hist(s, 14, normed=True) >>> plt.show() + Draw each 100 values for lambda 100 and 500: + + >>> s = np.random.poisson(lam=(100., 500.), size=(100, 2)) + """ cdef ndarray olam cdef double flam diff --git a/numpy/random/setup.py b/numpy/random/setup.py index 55cca69da..33c12975b 100644 --- a/numpy/random/setup.py +++ b/numpy/random/setup.py @@ -45,12 +45,11 @@ def configuration(parent_package='',top_path=None): ['mtrand.c', 'randomkit.c', 'initarray.c', 'distributions.c']]+[generate_libraries], libraries=libs, - depends = [join('mtrand', '*.h'), - join('mtrand', '*.pyx'), - join('mtrand', '*.pxi'), - ], - define_macros = defs, - ) + depends=[join('mtrand', '*.h'), + join('mtrand', '*.pyx'), + join('mtrand', '*.pxi'),], + define_macros=defs, + ) config.add_data_files(('.', join('mtrand', 'randomkit.h'))) config.add_data_dir('tests') diff --git a/numpy/random/tests/test_random.py b/numpy/random/tests/test_random.py index b64c9d6cd..5a8f7a3d7 100644 --- a/numpy/random/tests/test_random.py +++ b/numpy/random/tests/test_random.py @@ -60,7 +60,7 @@ class TestMultinomial(TestCase): random.multinomial(100, [0.2, 0.8, 0.0, 0.0, 0.0]) def test_int_negative_interval(self): - assert_( -5 <= random.randint(-5, -1) < -1) + assert_(-5 <= random.randint(-5, -1) < -1) x = random.randint(-5, -1, 5) assert_(np.all(-5 <= x)) assert_(np.all(x < -1)) @@ -68,15 +68,15 @@ class TestMultinomial(TestCase): def test_size(self): # gh-3173 p = [0.5, 0.5] - assert_equal(np.random.multinomial(1 ,p, np.uint32(1)).shape, (1, 2)) - assert_equal(np.random.multinomial(1 ,p, np.uint32(1)).shape, (1, 2)) - assert_equal(np.random.multinomial(1 ,p, np.uint32(1)).shape, (1, 2)) - assert_equal(np.random.multinomial(1 ,p, [2, 2]).shape, (2, 2, 2)) - assert_equal(np.random.multinomial(1 ,p, (2, 2)).shape, (2, 2, 2)) - assert_equal(np.random.multinomial(1 ,p, np.array((2, 2))).shape, + assert_equal(np.random.multinomial(1, p, np.uint32(1)).shape, (1, 2)) + assert_equal(np.random.multinomial(1, p, np.uint32(1)).shape, (1, 2)) + assert_equal(np.random.multinomial(1, p, np.uint32(1)).shape, (1, 2)) + assert_equal(np.random.multinomial(1, p, [2, 2]).shape, (2, 2, 2)) + assert_equal(np.random.multinomial(1, p, (2, 2)).shape, (2, 2, 2)) + assert_equal(np.random.multinomial(1, p, np.array((2, 2))).shape, (2, 2, 2)) - assert_raises(TypeError, np.random.multinomial, 1 , p, + assert_raises(TypeError, np.random.multinomial, 1, p, np.float(1)) @@ -93,17 +93,16 @@ class TestSetState(TestCase): assert_(np.all(old == new)) def test_gaussian_reset(self): - """ Make sure the cached every-other-Gaussian is reset. - """ + # Make sure the cached every-other-Gaussian is reset. old = self.prng.standard_normal(size=3) self.prng.set_state(self.state) new = self.prng.standard_normal(size=3) assert_(np.all(old == new)) def test_gaussian_reset_in_media_res(self): - """ When the state is saved with a cached Gaussian, make sure the cached - Gaussian is restored. - """ + # When the state is saved with a cached Gaussian, make sure the + # cached Gaussian is restored. + self.prng.standard_normal() state = self.prng.get_state() old = self.prng.standard_normal(size=3) @@ -112,9 +111,8 @@ class TestSetState(TestCase): assert_(np.all(old == new)) def test_backwards_compatibility(self): - """ Make sure we can accept old state tuples that do not have the cached - Gaussian value. - """ + # Make sure we can accept old state tuples that do not have the + # cached Gaussian value. old_state = self.state[:-2] x1 = self.prng.standard_normal(size=16) self.prng.set_state(old_state) @@ -125,56 +123,55 @@ class TestSetState(TestCase): assert_(np.all(x1 == x3)) def test_negative_binomial(self): - """ Ensure that the negative binomial results take floating point - arguments without truncation. - """ + # Ensure that the negative binomial results take floating point + # arguments without truncation. self.prng.negative_binomial(0.5, 0.5) class TestRandomDist(TestCase): - """ Make sure the random distrobution return the correct value for a - given seed - """ + # Make sure the random distrobution return the correct value for a + # given seed + def setUp(self): self.seed = 1234567890 def test_rand(self): np.random.seed(self.seed) actual = np.random.rand(3, 2) - desired = np.array([[ 0.61879477158567997, 0.59162362775974664], - [ 0.88868358904449662, 0.89165480011560816], - [ 0.4575674820298663, 0.7781880808593471 ]]) + desired = np.array([[0.61879477158567997, 0.59162362775974664], + [0.88868358904449662, 0.89165480011560816], + [0.4575674820298663, 0.7781880808593471]]) np.testing.assert_array_almost_equal(actual, desired, decimal=15) def test_randn(self): np.random.seed(self.seed) actual = np.random.randn(3, 2) - desired = np.array([[ 1.34016345771863121, 1.73759122771936081], - [ 1.498988344300628, -0.2286433324536169 ], - [ 2.031033998682787, 2.17032494605655257]]) + desired = np.array([[1.34016345771863121, 1.73759122771936081], + [1.498988344300628, -0.2286433324536169], + [2.031033998682787, 2.17032494605655257]]) np.testing.assert_array_almost_equal(actual, desired, decimal=15) def test_randint(self): np.random.seed(self.seed) actual = np.random.randint(-99, 99, size=(3, 2)) - desired = np.array([[ 31, 3], - [-52, 41], - [-48, -66]]) + desired = np.array([[31, 3], + [-52, 41], + [-48, -66]]) np.testing.assert_array_equal(actual, desired) def test_random_integers(self): np.random.seed(self.seed) actual = np.random.random_integers(-99, 99, size=(3, 2)) - desired = np.array([[ 31, 3], - [-52, 41], - [-48, -66]]) + desired = np.array([[31, 3], + [-52, 41], + [-48, -66]]) np.testing.assert_array_equal(actual, desired) def test_random_sample(self): np.random.seed(self.seed) actual = np.random.random_sample((3, 2)) - desired = np.array([[ 0.61879477158567997, 0.59162362775974664], - [ 0.88868358904449662, 0.89165480011560816], - [ 0.4575674820298663, 0.7781880808593471 ]]) + desired = np.array([[0.61879477158567997, 0.59162362775974664], + [0.88868358904449662, 0.89165480011560816], + [0.4575674820298663, 0.7781880808593471]]) np.testing.assert_array_almost_equal(actual, desired, decimal=15) def test_choice_uniform_replace(self): @@ -304,9 +301,10 @@ class TestRandomDist(TestCase): def test_beta(self): np.random.seed(self.seed) actual = np.random.beta(.1, .9, size=(3, 2)) - desired = np.array([[ 1.45341850513746058e-02, 5.31297615662868145e-04], - [ 1.85366619058432324e-06, 4.19214516800110563e-03], - [ 1.58405155108498093e-04, 1.26252891949397652e-04]]) + desired = np.array( + [[1.45341850513746058e-02, 5.31297615662868145e-04], + [1.85366619058432324e-06, 4.19214516800110563e-03], + [1.58405155108498093e-04, 1.26252891949397652e-04]]) np.testing.assert_array_almost_equal(actual, desired, decimal=15) def test_binomial(self): @@ -320,26 +318,26 @@ class TestRandomDist(TestCase): def test_chisquare(self): np.random.seed(self.seed) actual = np.random.chisquare(50, size=(3, 2)) - desired = np.array([[ 63.87858175501090585, 68.68407748911370447], - [ 65.77116116901505904, 47.09686762438974483], - [ 72.3828403199695174, 74.18408615260374006]]) + desired = np.array([[63.87858175501090585, 68.68407748911370447], + [65.77116116901505904, 47.09686762438974483], + [72.3828403199695174, 74.18408615260374006]]) np.testing.assert_array_almost_equal(actual, desired, decimal=13) def test_dirichlet(self): np.random.seed(self.seed) - alpha = np.array([51.72840233779265162, 39.74494232180943953]) + alpha = np.array([51.72840233779265162, 39.74494232180943953]) actual = np.random.mtrand.dirichlet(alpha, size=(3, 2)) - desired = np.array([[[ 0.54539444573611562, 0.45460555426388438], - [ 0.62345816822039413, 0.37654183177960598]], - [[ 0.55206000085785778, 0.44793999914214233], - [ 0.58964023305154301, 0.41035976694845688]], - [[ 0.59266909280647828, 0.40733090719352177], - [ 0.56974431743975207, 0.43025568256024799]]]) + desired = np.array([[[0.54539444573611562, 0.45460555426388438], + [0.62345816822039413, 0.37654183177960598]], + [[0.55206000085785778, 0.44793999914214233], + [0.58964023305154301, 0.41035976694845688]], + [[0.59266909280647828, 0.40733090719352177], + [0.56974431743975207, 0.43025568256024799]]]) np.testing.assert_array_almost_equal(actual, desired, decimal=15) def test_dirichlet_size(self): # gh-3173 - p = np.array([51.72840233779265162, 39.74494232180943953]) + p = np.array([51.72840233779265162, 39.74494232180943953]) assert_equal(np.random.dirichlet(p, np.uint32(1)).shape, (1, 2)) assert_equal(np.random.dirichlet(p, np.uint32(1)).shape, (1, 2)) assert_equal(np.random.dirichlet(p, np.uint32(1)).shape, (1, 2)) @@ -352,49 +350,49 @@ class TestRandomDist(TestCase): def test_exponential(self): np.random.seed(self.seed) actual = np.random.exponential(1.1234, size=(3, 2)) - desired = np.array([[ 1.08342649775011624, 1.00607889924557314], - [ 2.46628830085216721, 2.49668106809923884], - [ 0.68717433461363442, 1.69175666993575979]]) + desired = np.array([[1.08342649775011624, 1.00607889924557314], + [2.46628830085216721, 2.49668106809923884], + [0.68717433461363442, 1.69175666993575979]]) np.testing.assert_array_almost_equal(actual, desired, decimal=15) def test_f(self): np.random.seed(self.seed) actual = np.random.f(12, 77, size=(3, 2)) - desired = np.array([[ 1.21975394418575878, 1.75135759791559775], - [ 1.44803115017146489, 1.22108959480396262], - [ 1.02176975757740629, 1.34431827623300415]]) + desired = np.array([[1.21975394418575878, 1.75135759791559775], + [1.44803115017146489, 1.22108959480396262], + [1.02176975757740629, 1.34431827623300415]]) np.testing.assert_array_almost_equal(actual, desired, decimal=15) def test_gamma(self): np.random.seed(self.seed) actual = np.random.gamma(5, 3, size=(3, 2)) - desired = np.array([[ 24.60509188649287182, 28.54993563207210627], - [ 26.13476110204064184, 12.56988482927716078], - [ 31.71863275789960568, 33.30143302795922011]]) + desired = np.array([[24.60509188649287182, 28.54993563207210627], + [26.13476110204064184, 12.56988482927716078], + [31.71863275789960568, 33.30143302795922011]]) np.testing.assert_array_almost_equal(actual, desired, decimal=14) def test_geometric(self): np.random.seed(self.seed) actual = np.random.geometric(.123456789, size=(3, 2)) - desired = np.array([[ 8, 7], - [17, 17], - [ 5, 12]]) + desired = np.array([[8, 7], + [17, 17], + [5, 12]]) np.testing.assert_array_equal(actual, desired) def test_gumbel(self): np.random.seed(self.seed) - actual = np.random.gumbel(loc = .123456789, scale = 2.0, size = (3, 2)) - desired = np.array([[ 0.19591898743416816, 0.34405539668096674], - [-1.4492522252274278, -1.47374816298446865], - [ 1.10651090478803416, -0.69535848626236174]]) + actual = np.random.gumbel(loc=.123456789, scale=2.0, size=(3, 2)) + desired = np.array([[0.19591898743416816, 0.34405539668096674], + [-1.4492522252274278, -1.47374816298446865], + [1.10651090478803416, -0.69535848626236174]]) np.testing.assert_array_almost_equal(actual, desired, decimal=15) def test_hypergeometric(self): np.random.seed(self.seed) actual = np.random.hypergeometric(10.1, 5.5, 14, size=(3, 2)) desired = np.array([[10, 10], - [10, 10], - [ 9, 9]]) + [10, 10], + [9, 9]]) np.testing.assert_array_equal(actual, desired) # Test nbad = 0 @@ -418,49 +416,49 @@ class TestRandomDist(TestCase): def test_laplace(self): np.random.seed(self.seed) actual = np.random.laplace(loc=.123456789, scale=2.0, size=(3, 2)) - desired = np.array([[ 0.66599721112760157, 0.52829452552221945], - [ 3.12791959514407125, 3.18202813572992005], - [-0.05391065675859356, 1.74901336242837324]]) + desired = np.array([[0.66599721112760157, 0.52829452552221945], + [3.12791959514407125, 3.18202813572992005], + [-0.05391065675859356, 1.74901336242837324]]) np.testing.assert_array_almost_equal(actual, desired, decimal=15) def test_logistic(self): np.random.seed(self.seed) actual = np.random.logistic(loc=.123456789, scale=2.0, size=(3, 2)) - desired = np.array([[ 1.09232835305011444, 0.8648196662399954 ], - [ 4.27818590694950185, 4.33897006346929714], - [-0.21682183359214885, 2.63373365386060332]]) + desired = np.array([[1.09232835305011444, 0.8648196662399954], + [4.27818590694950185, 4.33897006346929714], + [-0.21682183359214885, 2.63373365386060332]]) np.testing.assert_array_almost_equal(actual, desired, decimal=15) def test_lognormal(self): np.random.seed(self.seed) actual = np.random.lognormal(mean=.123456789, sigma=2.0, size=(3, 2)) - desired = np.array([[ 16.50698631688883822, 36.54846706092654784], - [ 22.67886599981281748, 0.71617561058995771], - [ 65.72798501792723869, 86.84341601437161273]]) + desired = np.array([[16.50698631688883822, 36.54846706092654784], + [22.67886599981281748, 0.71617561058995771], + [65.72798501792723869, 86.84341601437161273]]) np.testing.assert_array_almost_equal(actual, desired, decimal=13) def test_logseries(self): np.random.seed(self.seed) actual = np.random.logseries(p=.923456789, size=(3, 2)) - desired = np.array([[ 2, 2], - [ 6, 17], - [ 3, 6]]) + desired = np.array([[2, 2], + [6, 17], + [3, 6]]) np.testing.assert_array_equal(actual, desired) def test_multinomial(self): np.random.seed(self.seed) actual = np.random.multinomial(20, [1/6.]*6, size=(3, 2)) desired = np.array([[[4, 3, 5, 4, 2, 2], - [5, 2, 8, 2, 2, 1]], - [[3, 4, 3, 6, 0, 4], - [2, 1, 4, 3, 6, 4]], - [[4, 4, 2, 5, 2, 3], - [4, 3, 4, 2, 3, 4]]]) + [5, 2, 8, 2, 2, 1]], + [[3, 4, 3, 6, 0, 4], + [2, 1, 4, 3, 6, 4]], + [[4, 4, 2, 5, 2, 3], + [4, 3, 4, 2, 3, 4]]]) np.testing.assert_array_equal(actual, desired) def test_multivariate_normal(self): np.random.seed(self.seed) - mean= (.123456789, 10) + mean = (.123456789, 10) # Hmm... not even symmetric. cov = [[1, 0], [1, 0]] size = (3, 2) @@ -470,7 +468,7 @@ class TestRandomDist(TestCase): [[-2.29186329304599745, 10.], [-1.77505606019580053, 10.]], [[-0.54970369430044119, 10.], - [ 0.29768848031692957, 10.]]]) + [0.29768848031692957, 10.]]]) np.testing.assert_array_almost_equal(actual, desired, decimal=15) # Check for default size, was raising deprecation warning @@ -479,50 +477,50 @@ class TestRandomDist(TestCase): np.testing.assert_array_almost_equal(actual, desired, decimal=15) # Check that non positive-semidefinite covariance raises warning - mean= [0, 0] + mean = [0, 0] cov = [[1, 1 + 1e-10], [1 + 1e-10, 1]] - rng = np.random.multivariate_normal assert_warns(RuntimeWarning, np.random.multivariate_normal, mean, cov) def test_negative_binomial(self): np.random.seed(self.seed) actual = np.random.negative_binomial(n=100, p=.12345, size=(3, 2)) desired = np.array([[848, 841], - [892, 611], - [779, 647]]) + [892, 611], + [779, 647]]) np.testing.assert_array_equal(actual, desired) def test_noncentral_chisquare(self): np.random.seed(self.seed) - actual = np.random.noncentral_chisquare(df = 5, nonc = 5, size = (3, 2)) - desired = np.array([[ 23.91905354498517511, 13.35324692733826346], - [ 31.22452661329736401, 16.60047399466177254], - [ 5.03461598262724586, 17.94973089023519464]]) + actual = np.random.noncentral_chisquare(df=5, nonc=5, size=(3, 2)) + desired = np.array([[23.91905354498517511, 13.35324692733826346], + [31.22452661329736401, 16.60047399466177254], + [5.03461598262724586, 17.94973089023519464]]) np.testing.assert_array_almost_equal(actual, desired, decimal=14) def test_noncentral_f(self): np.random.seed(self.seed) - actual = np.random.noncentral_f(dfnum = 5, dfden = 2, nonc = 1, - size = (3, 2)) - desired = np.array([[ 1.40598099674926669, 0.34207973179285761], - [ 3.57715069265772545, 7.92632662577829805], - [ 0.43741599463544162, 1.1774208752428319 ]]) + actual = np.random.noncentral_f(dfnum=5, dfden=2, nonc=1, + size=(3, 2)) + desired = np.array([[1.40598099674926669, 0.34207973179285761], + [3.57715069265772545, 7.92632662577829805], + [0.43741599463544162, 1.1774208752428319]]) np.testing.assert_array_almost_equal(actual, desired, decimal=14) def test_normal(self): np.random.seed(self.seed) - actual = np.random.normal(loc = .123456789, scale = 2.0, size = (3, 2)) - desired = np.array([[ 2.80378370443726244, 3.59863924443872163], - [ 3.121433477601256, -0.33382987590723379], - [ 4.18552478636557357, 4.46410668111310471]]) + actual = np.random.normal(loc=.123456789, scale=2.0, size=(3, 2)) + desired = np.array([[2.80378370443726244, 3.59863924443872163], + [3.121433477601256, -0.33382987590723379], + [4.18552478636557357, 4.46410668111310471]]) np.testing.assert_array_almost_equal(actual, desired, decimal=15) def test_pareto(self): np.random.seed(self.seed) - actual = np.random.pareto(a =.123456789, size = (3, 2)) - desired = np.array([[ 2.46852460439034849e+03, 1.41286880810518346e+03], - [ 5.28287797029485181e+07, 6.57720981047328785e+07], - [ 1.40840323350391515e+02, 1.98390255135251704e+05]]) + actual = np.random.pareto(a=.123456789, size=(3, 2)) + desired = np.array( + [[2.46852460439034849e+03, 1.41286880810518346e+03], + [5.28287797029485181e+07, 6.57720981047328785e+07], + [1.40840323350391515e+02, 1.98390255135251704e+05]]) # For some reason on 32-bit x86 Ubuntu 12.10 the [1, 0] entry in this # matrix differs by 24 nulps. Discussion: # http://mail.scipy.org/pipermail/numpy-discussion/2012-September/063801.html @@ -533,7 +531,7 @@ class TestRandomDist(TestCase): def test_poisson(self): np.random.seed(self.seed) - actual = np.random.poisson(lam = .123456789, size=(3, 2)) + actual = np.random.poisson(lam=.123456789, size=(3, 2)) desired = np.array([[0, 0], [1, 0], [0, 0]]) @@ -549,84 +547,83 @@ class TestRandomDist(TestCase): def test_power(self): np.random.seed(self.seed) - actual = np.random.power(a =.123456789, size = (3, 2)) - desired = np.array([[ 0.02048932883240791, 0.01424192241128213], - [ 0.38446073748535298, 0.39499689943484395], - [ 0.00177699707563439, 0.13115505880863756]]) + actual = np.random.power(a=.123456789, size=(3, 2)) + desired = np.array([[0.02048932883240791, 0.01424192241128213], + [0.38446073748535298, 0.39499689943484395], + [0.00177699707563439, 0.13115505880863756]]) np.testing.assert_array_almost_equal(actual, desired, decimal=15) def test_rayleigh(self): np.random.seed(self.seed) - actual = np.random.rayleigh(scale = 10, size = (3, 2)) - desired = np.array([[ 13.8882496494248393, 13.383318339044731 ], - [ 20.95413364294492098, 21.08285015800712614], - [ 11.06066537006854311, 17.35468505778271009]]) + actual = np.random.rayleigh(scale=10, size=(3, 2)) + desired = np.array([[13.8882496494248393, 13.383318339044731], + [20.95413364294492098, 21.08285015800712614], + [11.06066537006854311, 17.35468505778271009]]) np.testing.assert_array_almost_equal(actual, desired, decimal=14) def test_standard_cauchy(self): np.random.seed(self.seed) - actual = np.random.standard_cauchy(size = (3, 2)) - desired = np.array([[ 0.77127660196445336, -6.55601161955910605], - [ 0.93582023391158309, -2.07479293013759447], - [-4.74601644297011926, 0.18338989290760804]]) + actual = np.random.standard_cauchy(size=(3, 2)) + desired = np.array([[0.77127660196445336, -6.55601161955910605], + [0.93582023391158309, -2.07479293013759447], + [-4.74601644297011926, 0.18338989290760804]]) np.testing.assert_array_almost_equal(actual, desired, decimal=15) def test_standard_exponential(self): np.random.seed(self.seed) - actual = np.random.standard_exponential(size = (3, 2)) - desired = np.array([[ 0.96441739162374596, 0.89556604882105506], - [ 2.1953785836319808, 2.22243285392490542], - [ 0.6116915921431676, 1.50592546727413201]]) + actual = np.random.standard_exponential(size=(3, 2)) + desired = np.array([[0.96441739162374596, 0.89556604882105506], + [2.1953785836319808, 2.22243285392490542], + [0.6116915921431676, 1.50592546727413201]]) np.testing.assert_array_almost_equal(actual, desired, decimal=15) def test_standard_gamma(self): np.random.seed(self.seed) - actual = np.random.standard_gamma(shape = 3, size = (3, 2)) - desired = np.array([[ 5.50841531318455058, 6.62953470301903103], - [ 5.93988484943779227, 2.31044849402133989], - [ 7.54838614231317084, 8.012756093271868 ]]) + actual = np.random.standard_gamma(shape=3, size=(3, 2)) + desired = np.array([[5.50841531318455058, 6.62953470301903103], + [5.93988484943779227, 2.31044849402133989], + [7.54838614231317084, 8.012756093271868]]) np.testing.assert_array_almost_equal(actual, desired, decimal=14) def test_standard_normal(self): np.random.seed(self.seed) - actual = np.random.standard_normal(size = (3, 2)) - desired = np.array([[ 1.34016345771863121, 1.73759122771936081], - [ 1.498988344300628, -0.2286433324536169 ], - [ 2.031033998682787, 2.17032494605655257]]) + actual = np.random.standard_normal(size=(3, 2)) + desired = np.array([[1.34016345771863121, 1.73759122771936081], + [1.498988344300628, -0.2286433324536169], + [2.031033998682787, 2.17032494605655257]]) np.testing.assert_array_almost_equal(actual, desired, decimal=15) def test_standard_t(self): np.random.seed(self.seed) - actual = np.random.standard_t(df = 10, size = (3, 2)) - desired = np.array([[ 0.97140611862659965, -0.08830486548450577], - [ 1.36311143689505321, -0.55317463909867071], - [-0.18473749069684214, 0.61181537341755321]]) + actual = np.random.standard_t(df=10, size=(3, 2)) + desired = np.array([[0.97140611862659965, -0.08830486548450577], + [1.36311143689505321, -0.55317463909867071], + [-0.18473749069684214, 0.61181537341755321]]) np.testing.assert_array_almost_equal(actual, desired, decimal=15) def test_triangular(self): np.random.seed(self.seed) - actual = np.random.triangular(left = 5.12, mode = 10.23, right = 20.34, - size = (3, 2)) - desired = np.array([[ 12.68117178949215784, 12.4129206149193152 ], - [ 16.20131377335158263, 16.25692138747600524], - [ 11.20400690911820263, 14.4978144835829923 ]]) + actual = np.random.triangular(left=5.12, mode=10.23, right=20.34, + size=(3, 2)) + desired = np.array([[12.68117178949215784, 12.4129206149193152], + [16.20131377335158263, 16.25692138747600524], + [11.20400690911820263, 14.4978144835829923]]) np.testing.assert_array_almost_equal(actual, desired, decimal=14) def test_uniform(self): np.random.seed(self.seed) - actual = np.random.uniform(low = 1.23, high=10.54, size = (3, 2)) - desired = np.array([[ 6.99097932346268003, 6.73801597444323974], - [ 9.50364421400426274, 9.53130618907631089], - [ 5.48995325769805476, 8.47493103280052118]]) + actual = np.random.uniform(low=1.23, high=10.54, size=(3, 2)) + desired = np.array([[6.99097932346268003, 6.73801597444323974], + [9.50364421400426274, 9.53130618907631089], + [5.48995325769805476, 8.47493103280052118]]) np.testing.assert_array_almost_equal(actual, desired, decimal=15) - def test_vonmises(self): np.random.seed(self.seed) - actual = np.random.vonmises(mu = 1.23, kappa = 1.54, size = (3, 2)) - desired = np.array([[ 2.28567572673902042, 2.89163838442285037], - [ 0.38198375564286025, 2.57638023113890746], - [ 1.19153771588353052, 1.83509849681825354]]) + actual = np.random.vonmises(mu=1.23, kappa=1.54, size=(3, 2)) + desired = np.array([[2.28567572673902042, 2.89163838442285037], + [0.38198375564286025, 2.57638023113890746], + [1.19153771588353052, 1.83509849681825354]]) np.testing.assert_array_almost_equal(actual, desired, decimal=15) def test_vonmises_small(self): @@ -637,31 +634,31 @@ class TestRandomDist(TestCase): def test_wald(self): np.random.seed(self.seed) - actual = np.random.wald(mean = 1.23, scale = 1.54, size = (3, 2)) - desired = np.array([[ 3.82935265715889983, 5.13125249184285526], - [ 0.35045403618358717, 1.50832396872003538], - [ 0.24124319895843183, 0.22031101461955038]]) + actual = np.random.wald(mean=1.23, scale=1.54, size=(3, 2)) + desired = np.array([[3.82935265715889983, 5.13125249184285526], + [0.35045403618358717, 1.50832396872003538], + [0.24124319895843183, 0.22031101461955038]]) np.testing.assert_array_almost_equal(actual, desired, decimal=14) def test_weibull(self): np.random.seed(self.seed) - actual = np.random.weibull(a = 1.23, size = (3, 2)) - desired = np.array([[ 0.97097342648766727, 0.91422896443565516], - [ 1.89517770034962929, 1.91414357960479564], - [ 0.67057783752390987, 1.39494046635066793]]) + actual = np.random.weibull(a=1.23, size=(3, 2)) + desired = np.array([[0.97097342648766727, 0.91422896443565516], + [1.89517770034962929, 1.91414357960479564], + [0.67057783752390987, 1.39494046635066793]]) np.testing.assert_array_almost_equal(actual, desired, decimal=15) def test_zipf(self): np.random.seed(self.seed) - actual = np.random.zipf(a = 1.23, size = (3, 2)) + actual = np.random.zipf(a=1.23, size=(3, 2)) desired = np.array([[66, 29], - [ 1, 1], - [ 3, 13]]) + [1, 1], + [3, 13]]) np.testing.assert_array_equal(actual, desired) -class TestThread: - """ make sure each state produces the same sequence even in threads """ +class TestThread(object): + # make sure each state produces the same sequence even in threads def setUp(self): self.seeds = range(4) diff --git a/numpy/random/tests/test_regression.py b/numpy/random/tests/test_regression.py index 1bba5d91d..ccffd033e 100644 --- a/numpy/random/tests/test_regression.py +++ b/numpy/random/tests/test_regression.py @@ -44,7 +44,7 @@ class TestRegression(TestCase): b = np.random.permutation(long(12)) assert_array_equal(a, b) - def test_randint_range(self) : + def test_randint_range(self): # Test for ticket #1690 lmax = np.iinfo('l').max lmin = np.iinfo('l').min diff --git a/numpy/testing/utils.py b/numpy/testing/utils.py index ddf21e2bc..bd184d922 100644 --- a/numpy/testing/utils.py +++ b/numpy/testing/utils.py @@ -10,6 +10,9 @@ import re import operator import warnings from functools import partial +import shutil +import contextlib +from tempfile import mkdtemp from .nosetester import import_nose from numpy.core import float32, empty, arange, array_repr, ndarray @@ -219,7 +222,7 @@ def build_err_msg(arrays, err_msg, header='Items are not equal:', def assert_equal(actual,desired,err_msg='',verbose=True): """ - Raise an assertion if two objects are not equal. + Raises an AssertionError if two objects are not equal. Given two objects (scalars, lists, tuples, dictionaries or numpy arrays), check that all elements of these objects are equal. An exception is raised @@ -371,7 +374,8 @@ def print_assert_equal(test_string, actual, desired): def assert_almost_equal(actual,desired,decimal=7,err_msg='',verbose=True): """ - Raise an assertion if two items are not equal up to desired precision. + Raises an AssertionError if two items are not equal up to desired + precision. .. note:: It is recommended to use one of `assert_allclose`, `assert_array_almost_equal_nulp` or `assert_array_max_ulp` @@ -488,7 +492,8 @@ def assert_almost_equal(actual,desired,decimal=7,err_msg='',verbose=True): def assert_approx_equal(actual,desired,significant=7,err_msg='',verbose=True): """ - Raise an assertion if two items are not equal up to significant digits. + Raises an AssertionError if two items are not equal up to significant + digits. .. note:: It is recommended to use one of `assert_allclose`, `assert_array_almost_equal_nulp` or `assert_array_max_ulp` @@ -669,7 +674,7 @@ def assert_array_compare(comparison, x, y, err_msg='', verbose=True, def assert_array_equal(x, y, err_msg='', verbose=True): """ - Raise an assertion if two array_like objects are not equal. + Raises an AssertionError if two array_like objects are not equal. Given two array_like objects, check that the shape is equal and all elements of these objects are equal. An exception is raised at @@ -735,7 +740,8 @@ def assert_array_equal(x, y, err_msg='', verbose=True): def assert_array_almost_equal(x, y, decimal=6, err_msg='', verbose=True): """ - Raise an assertion if two objects are not equal up to desired precision. + Raises an AssertionError if two objects are not equal up to desired + precision. .. note:: It is recommended to use one of `assert_allclose`, `assert_array_almost_equal_nulp` or `assert_array_max_ulp` @@ -838,7 +844,8 @@ def assert_array_almost_equal(x, y, decimal=6, err_msg='', verbose=True): def assert_array_less(x, y, err_msg='', verbose=True): """ - Raise an assertion if two array_like objects are not ordered by less than. + Raises an AssertionError if two array_like objects are not ordered by less + than. Given two array_like objects, check that the shape is equal and all elements of the first object are strictly smaller than those of the @@ -1240,7 +1247,8 @@ def _assert_valid_refcount(op): def assert_allclose(actual, desired, rtol=1e-7, atol=0, err_msg='', verbose=True): """ - Raise an assertion if two objects are not equal up to desired tolerance. + Raises an AssertionError if two objects are not equal up to desired + tolerance. The test is equivalent to ``allclose(actual, desired, rtol, atol)``. It compares the difference between `actual` and `desired` to @@ -1692,3 +1700,16 @@ def _gen_alignment_data(dtype=float32, type='binary', max_size=24): class IgnoreException(Exception): "Ignoring this exception due to disabled feature" + + +@contextlib.contextmanager +def tempdir(*args, **kwargs): + """Context manager to provide a temporary test folder. + + All arguments are passed as this to the underlying tempfile.mkdtemp + function. + + """ + tmpdir = mkdtemp(*args, **kwargs) + yield tmpdir + shutil.rmtree(tmpdir) diff --git a/pavement.py b/pavement.py index c0b5cb2d4..9e3cb065c 100644 --- a/pavement.py +++ b/pavement.py @@ -99,10 +99,10 @@ finally: #----------------------------------- # Source of the release notes -RELEASE_NOTES = 'doc/release/1.9.0-notes.rst' +RELEASE_NOTES = 'doc/release/1.10.0-notes.rst' # Start/end of the log (from git) -LOG_START = 'v1.8.0b1' +LOG_START = 'v1.9.0b1' LOG_END = 'master' @@ -48,7 +48,7 @@ Operating System :: MacOS """ MAJOR = 1 -MINOR = 9 +MINOR = 10 MICRO = 0 ISRELEASED = False VERSION = '%d.%d.%d' % (MAJOR, MINOR, MICRO) diff --git a/site.cfg.example b/site.cfg.example index 714ab6311..4a59f10e2 100644 --- a/site.cfg.example +++ b/site.cfg.example @@ -126,7 +126,7 @@ # better performance. Note that the AMD library has nothing to do with AMD # (Advanced Micro Devices), the CPU company. # -# UMFPACK is not needed for numpy or scipy. +# UMFPACK is not used by numpy. # # http://www.cise.ufl.edu/research/sparse/umfpack/ # http://www.cise.ufl.edu/research/sparse/amd/ @@ -141,7 +141,7 @@ # FFT libraries # ------------- # There are two FFT libraries that we can configure here: FFTW (2 and 3) and djbfft. -# Note that these libraries are not needed for numpy or scipy. +# Note that these libraries are not used by for numpy or scipy. # # http://fftw.org/ # http://cr.yp.to/djbfft.html diff --git a/tools/allocation_tracking/track_allocations.py b/tools/allocation_tracking/track_allocations.py index 2006217c2..dfc354eb5 100644 --- a/tools/allocation_tracking/track_allocations.py +++ b/tools/allocation_tracking/track_allocations.py @@ -1,6 +1,7 @@ from __future__ import division, absolute_import, print_function import numpy as np +import gc import inspect from alloc_hook import NumpyAllocHook @@ -35,12 +36,21 @@ class AllocationTracker(object): self.numpy_hook.__exit__() def hook(self, inptr, outptr, size): + # minimize the chances that the garbage collector kicks in during a + # cython __dealloc__ call and causes a double delete of the current + # object. To avoid this fully the hook would have to avoid all python + # api calls, e.g. by being implemented in C like python 3.4's + # tracemalloc module + gc_on = gc.isenabled() + gc.disable() if outptr == 0: # it's a free self.free_cb(inptr) elif inptr != 0: # realloc self.realloc_cb(inptr, outptr, size) else: # malloc self.alloc_cb(outptr, size) + if gc_on: + gc.enable() def alloc_cb(self, ptr, size): if size >= self.threshold: diff --git a/tools/swig/numpy.i b/tools/swig/numpy.i index e250e78bf..217acd5bf 100644 --- a/tools/swig/numpy.i +++ b/tools/swig/numpy.i @@ -872,7 +872,7 @@ (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[2] = { -1, -1 }; - array = obj_to_array_contiguous_allow_conversion($input, + array = obj_to_array_fortran_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 2) || @@ -1106,7 +1106,7 @@ (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[3] = { -1, -1, -1 }; - array = obj_to_array_contiguous_allow_conversion($input, + array = obj_to_array_fortran_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 3) || @@ -1345,7 +1345,7 @@ (PyArrayObject* array=NULL, int is_new_object=0) { npy_intp size[4] = { -1, -1, -1 , -1 }; - array = obj_to_array_contiguous_allow_conversion($input, DATA_TYPECODE, + array = obj_to_array_fortran_allow_conversion($input, DATA_TYPECODE, &is_new_object); if (!array || !require_dimensions(array, 4) || !require_size(array, size, 4) || !require_fortran(array)) SWIG_fail; diff --git a/tools/test-installed-numpy.py b/tools/test-installed-numpy.py index 0174e0708..26a50b2fa 100644 --- a/tools/test-installed-numpy.py +++ b/tools/test-installed-numpy.py @@ -38,7 +38,7 @@ import numpy # Check that NPY_RELAXED_STRIDES_CHECKING is active when set. # The same flags check is also used in the tests to switch behavior. -if (os.environ.get('NPY_RELAXED_STRIDES_CHECKING', "0") != "0"): +if (os.environ.get('NPY_RELAXED_STRIDES_CHECKING', "1") != "0"): if not numpy.ones((10, 1), order='C').flags.f_contiguous: print('NPY_RELAXED_STRIDES_CHECKING set, but not active.') sys.exit(1) @@ -25,7 +25,7 @@ # installed and that they can be run as 'python2.7', 'python3.3', etc. [tox] -envlist = py26,py27,py32,py33,py27-monolithic,py33-monolithic,py27-relaxed-strides,py33-relaxed-strides +envlist = py26,py27,py32,py33,py27-monolithic,py33-monolithic,py27-not-relaxed-strides,py33-not-relaxed-strides [testenv] deps= @@ -41,13 +41,13 @@ env=NPY_SEPARATE_COMPILATION=0 basepython=python3.3 env=NPY_SEPARATE_COMPILATION=0 -[testenv:py27-relaxed-strides] +[testenv:py27-not-relaxed-strides] basepython=python2.7 -env=NPY_RELAXED_STRIDES_CHECKING=1 +env=NPY_RELAXED_STRIDES_CHECKING=0 -[testenv:py33-relaxed-strides] +[testenv:py33-not-relaxed-strides] basepython=python3.3 -env=NPY_RELAXED_STRIDES_CHECKING=1 +env=NPY_RELAXED_STRIDES_CHECKING=0 # Not run by default. Set up the way you want then use 'tox -e debug' # if you want it: |