Mailman 3 July 2018 - NumPy-Discussion

Invalid value encoutered : how to prevent numpy.where to do this?
by Eric Emsellem 18 Feb '23

18 Feb '23

Dear all, I have a code using lots of "numpy.where" to make some constrained calculations as in: data = arange(10) result = np.where(data == 0, 0., 1./data) # or data1 = arange(10) data2 = arange(10)+1.0 result = np.where(data1 > data2, np.sqrt(data1-data2), np.sqrt(data2-data2)) which then produces warnings like: /usr/bin/ipython:1: RuntimeWarning: invalid value encountered in sqrt or for the first example: /usr/bin/ipython:1: RuntimeWarning: divide by zero encountered in divide How do I avoid these messages to appear? I know that I could in principle use numpy.seterr. However, I do NOT want to remove these warnings for other potential divide/multiply/sqrt etc errors. Only when I am using a "where", to in fact avoid such warnings! Note that the warnings only happen once, but since I am going to release that code, I would like to avoid the user to get such messages which are irrelevant here (because I am testing, with the where, when NOT to divide by zero or take a sqrt of a negative number). thanks! Eric

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Re: [Numpy-discussion] example reading binary Fortran file
by Neil Martinsen-Burrell 22 Jul '21

22 Jul '21

David Froger <david.froger.info <at> gmail.com> writes: > Hy,My question is about reading Fortran binary file (oh no this question > again...) I've posted this before, but I finally got it cleaned up for the Cookbook. For this purpose I use a subclass of file that has methods for reading unformatted Fortran data. See http://www.scipy.org/Cookbook/FortranIO/FortranFile. I'd gladly see this in numpy or scipy somewhere, but I'm not sure where it belongs. > program makeArray > implicit none > integer,parameter:: nx=10,ny=20 > real(4),dimension(nx,ny):: ux,uy,p > integer :: i,j > open(11,file='uxuyp.bin',form='unformatted') > do i = 1,nx > do j = 1,ny > ux(i,j) = real(i*j) > uy(i,j) = real(i)/real(j) > p (i,j) = real(i) + real(j) > enddo > enddo > write(11) ux,uy > write(11) p > close(11) > end program makeArray When I run the above program compiled with gfortran on my Intel Mac, I can read it back with:: >>> import numpy as np >>> from fortranfile import FortranFile >>> f=FortranFile('uxuyp.bin', endian='<') >>> uxuy = f.readReals(prec='f') # 'f' for default reals >>> len(uxuy) 400 >>> ux = np.array(uxuy[:200]).reshape((20,10)).T >>> uy = np.array(uxuy[200:]).reshape((20,10)).T >>> p = f.readReals('f').reshape((20,10)).T >>> ux array([[ 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20.], [ 2., 4., 6., 8., 10., 12., 14., 16., 18., 20., 22., 24., 26., 28., 30., 32., 34., 36., 38., 40.], [ 3., 6., 9., 12., 15., 18., 21., 24., 27., 30., 33., 36., 39., 42., 45., 48., 51., 54., 57., 60.], [ 4., 8., 12., 16., 20., 24., 28., 32., 36., 40., 44., 48., 52., 56., 60., 64., 68., 72., 76., 80.], [ 5., 10., 15., 20., 25., 30., 35., 40., 45., 50., 55., 60., 65., 70., 75., 80., 85., 90., 95., 100.], [ 6., 12., 18., 24., 30., 36., 42., 48., 54., 60., 66., 72., 78., 84., 90., 96., 102., 108., 114., 120.], [ 7.,Proxy-Connection: keep-alive Cache-Control: max-age=0 14., 21., 28., 35., 42., 49., 56., 63., 70., 77., 84., 91., 98., 105., 112., 119., 126., 133., 140.], [ 8., 16., 24., 32., 40., 48., 56., 64., 72., 80., 88., 96., 104., 112., 120., 128., 136., 144., 152., 160.], [ 9., 18., 27., 36., 45., 54., 63., 72., 81., 90., 99., 108., 117., 126., 135., 144., 153., 162., 171., 180.], [ 10., 20., 30., 40., 50., 60., 70., 80., 90., 100., 110., 120., 130., 140., 150., 160., 170., 180., 190., 200.]]) >>> uy array([[ 1. , 0.5 , 0.33333334, 0.25 , 0.2 , 0.16666667, 0.14285715, 0.125 , 0.11111111, 0.1 , 0.09090909, 0.08333334, 0.07692308, 0.07142857, 0.06666667, 0.0625 , 0.05882353, 0.05555556, 0.05263158, 0.05 ], [ 2. , 1. , 0.66666669, 0.5 , 0.40000001, 0.33333334, 0.2857143 , 0.25 , 0.22222222, 0.2 , 0.18181819, 0.16666667, 0.15384616, 0.14285715, 0.13333334, 0.125 , 0.11764706, 0.11111111, 0.10526316, 0.1 ], [ 3. , 1.5 , 1. , 0.75 , 0.60000002, 0.5 , 0.42857143, 0.375 , 0.33333334, 0.30000001, 0.27272728, 0.25 , 0.23076923, 0.21428572, 0.2 , 0.1875 , 0.17647059, 0.16666667, 0.15789473, 0.15000001], [ 4. , 2. , 1.33333337, 1. , 0.80000001, 0.66666669, 0.5714286 , 0.5 , 0.44444445, 0.40000001, 0.36363637, 0.33333334, 0.30769232, 0.2857143 , 0.26666668, 0.25 , 0.23529412, 0.22222222, 0.21052632, 0.2 ], [ 5. , 2.5 , 1.66666663, 1.25 , 1. , 0.83333331, 0.71428573, 0.625 , 0.55555558, 0.5 , 0.45454547, 0.41666666, 0.38461539, 0.35714287, 0.33333334, 0.3125 , 0.29411766, 0.27777779, 0.2631579 , 0.25 ], [ 6. , 3. , 2. , 1.5 , 1.20000005, 1. , 0.85714287, 0.75 , 0.66666669, 0.60000002, 0.54545456, 0.5 , 0.46153846, 0.42857143, 0.40000001, 0.375 , 0.35294119, 0.33333334, 0.31578946, 0.30000001], [ 7. , 3.5 , 2.33333325, 1.75 , 1.39999998, 1.16666663, 1. , 0.875 , 0.77777779, 0.69999999, 0.63636363, 0.58333331, 0.53846157, 0.5 , 0.46666667, 0.4375 , 0.41176471, 0.3888889 , 0.36842105, 0.34999999], [ 8. , 4. , 2.66666675, 2. , 1.60000002, 1.33333337, 1.14285719, 1. , 0.8888889 , 0.80000001, 0.72727275, 0.66666669, 0.61538464, 0.5714286 , 0.53333336, 0.5 , 0.47058824, 0.44444445, 0.42105263, 0.40000001], [ 9. , 4.5 , 3. , 2.25 , 1.79999995, 1.5 , 1.28571427, 1.125 , 1. , 0.89999998, 0.81818181, 0.75 , 0.69230771, 0.64285713, 0.60000002, 0.5625 , 0.52941179, 0.5 , 0.47368422, 0.44999999], [ 10. , 5. , 3.33333325, 2.5 , 2. , 1.66666663, 1.42857146, 1.25 , 1.11111116, 1. , 0.90909094, 0.83333331, 0.76923078, 0.71428573, 0.66666669, 0.625 , 0.58823532, 0.55555558, 0.52631581, 0.5 ]]) >>> p array([[ 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21.], [ 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22.], [ 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23.], [ 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24.], [ 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25.], [ 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26.], [ 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27.], [ 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28.], [ 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29.], [ 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30.]]) Note that you have to provide the shape information for ux and uy because fortran writes them together as a stream of 400 numbers. -Neil

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Adoption of a Code of Conduct
by Stefan van der Walt 03 Aug '18

03 Aug '18

Hi everyone, A while ago, SciPy (the library) adopted its Code of Conduct: https://docs.scipy.org/doc/scipy/reference/dev/conduct/code_of_conduct.html We worked hard to make that document friendly, while at the same time stating clearly the kinds of behavior that would and would not be tolerated. I propose that we adopt the SciPy code of conduct for NumPy as well. It is a good way to signal to newcomers that this is a community that cares about how people are treated. And I think we should do anything in our power to make NumPy as attractive as possible! If we adopt this document as policy, we will need to select a Code of Conduct committee, to whom potential transgressions can be reported. The individuals doing this for SciPy may very well be happy to do the same for NumPy, but the community should decide whom will best serve those roles. Let me know your thoughts. Thanks! Stéfan

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Roadmap proposal, v3
by Stefan van der Walt 02 Aug '18

02 Aug '18

Hi everyone, Please take a look at the latest roadmap proposal: https://github.com/numpy/numpy/pull/11611 This is a living document, so can easily be modified in the future, but we'd like to get in place a document that corresponds fairly closely with current community priorities. Best regards, Stéfan

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backwards compatibility and deprecation policy NEP
by Ralf Gommers 31 Jul '18

31 Jul '18

Hi all, Here is a first draft of a NEP on backwards compatibility and deprecation policy. This I think mostly formalized what we've done for the last couple of years, however I'm sure opinions and wish lists will differ here. Pull request: https://github.com/numpy/numpy/pull/11596 Rendered version: https://github.com/rgommers/numpy/blob/nep-backcompat/doc/neps/nep-0023-bac… Full text below (ducks). Cheers, Ralf ======================================================= NEP 23 - Backwards compatibility and deprecation policy ======================================================= :Author: Ralf Gommers <ralf.gommers(a)gmail.com> :Status: Draft :Type: Process :Created: 2018-07-14 :Resolution: <url> (required for Accepted | Rejected | Withdrawn) Abstract -------- In this NEP we describe NumPy's approach to backwards compatibility, its deprecation and removal policy, and the trade-offs and decision processes for individual cases where breaking backwards compatibility is considered. Detailed description -------------------- NumPy has a very large user base. Those users rely on NumPy being stable and the code they write that uses NumPy functionality to keep working. NumPy is also actively maintained and improved -- and sometimes improvements require, or are made much easier, by breaking backwards compatibility. Finally, there are trade-offs in stability for existing users vs. avoiding errors or having a better user experience for new users. These competing needs often give rise to heated debates and delays in accepting or rejecting contributions. This NEP tries to address that by providing a policy as well as examples and rationales for when it is or isn't a good idea to break backwards compatibility. General principles: - Aim not to break users' code unnecessarily. - Aim never to change code in ways that can result in users silently getting incorrect results from their previously working code. - Backwards incompatible changes can be made, provided the benefits outweigh the costs. - When assessing the costs, keep in mind that most users do not read the mailing list, do not look at deprecation warnings, and sometimes wait more than one or two years before upgrading from their old version. And that NumPy has many hundreds of thousands or even a couple of million users, so "no one will do or use this" is very likely incorrect. - Benefits include improved functionality, usability and performance (in order of importance), as well as lower maintenance cost and improved future extensibility. - Bug fixes are exempt from the backwards compatibility policy. However in case of serious impact on users (e.g. a downstream library doesn't build anymore), even bug fixes may have to be delayed for one or more releases. - The Python API and the C API will be treated in the same way. Examples ^^^^^^^^ We now discuss a number of concrete examples to illustrate typical issues and trade-offs. **Changing the behavior of a function** ``np.histogram`` is probably the most infamous example. First, a new keyword ``new=False`` was introduced, this was then switched over to None one release later, and finally it was removed again. Also, it has a ``normed`` keyword that had behavior that could be considered either suboptimal or broken (depending on ones opinion on the statistics). A new keyword ``density`` was introduced to replace it; ``normed`` started giving ``DeprecationWarning`` only in v.1.15.0. Evolution of ``histogram``:: def histogram(a, bins=10, range=None, normed=False): # v1.0.0 def histogram(a, bins=10, range=None, normed=False, weights=None, new=False): #v1.1.0 def histogram(a, bins=10, range=None, normed=False, weights=None, new=None): #v1.2.0 def histogram(a, bins=10, range=None, normed=False, weights=None): #v1.5.0 def histogram(a, bins=10, range=None, normed=False, weights=None, density=None): #v1.6.0 def histogram(a, bins=10, range=None, normed=None, weights=None, density=None): #v1.15.0 # v1.15.0 was the first release where `normed` started emitting # DeprecationWarnings The ``new`` keyword was planned from the start to be temporary; such a plan forces users to change their code more than once. Such keywords (there have been other instances proposed, e.g. ``legacy_index`` in `NEP 21 <http://www.numpy.org/neps/nep-0021-advanced-indexing.html>`_) are not desired. The right thing to have done here would probably have been to deprecate ``histogram`` and introduce a new function ``hist`` in its place. **Returning a view rather than a copy** The ``ndarray.diag`` method used to return a copy. A view would be better for both performance and design consistency. This change was warned about (``FutureWarning``) in v.8.0, and in v1.9.0 ``diag`` was changed to return a *read-only* view. The planned change to a writeable view in v1.10.0 was postponed due to backwards compatibility concerns, and is still an open issue (gh-7661). What should have happened instead: nothing. This change resulted in a lot of discussions and wasted effort, did not achieve its final goal, and was not that important in the first place. Finishing the change to a *writeable* view in the future is not desired, because it will result in users silently getting different results if they upgraded multiple versions or simply missed the warnings. **Disallowing indexing with floats** Indexing an array with floats is asking for something ambiguous, and can be a sign of a bug in user code. After some discussion, it was deemed a good idea to deprecate indexing with floats. This was first tried for the v1.8.0 release, however in pre-release testing it became clear that this would break many libraries that depend on NumPy. Therefore it was reverted before release, to give those libraries time to fix their code first. It was finally introduced for v1.11.0 and turned into a hard error for v1.12.0. This change was disruptive, however it did catch real bugs in e.g. SciPy and scikit-learn. Overall the change was worth the cost, and introducing it in master first to allow testing, then removing it again before a release, is a useful strategy. Similar recent deprecations also look like good examples of cleanups/improvements: - removing deprecated boolean indexing (gh-8312) - deprecating truth testing on empty arrays (gh-9718) - deprecating ``np.sum(generator)`` (gh-10670, one issue with this one is that its warning message is wrong - this should error in the future). **Removing the financial functions** The financial functions (e.g. ``np.pmt``) are badly named, are present in the main NumPy namespace, and don't really fit well with NumPy's scope. They were added in 2008 after `a discussion < https://mail.python.org/pipermail/numpy-discussion/2008-April/032353.html>`_ on the mailing list where opinion was divided (but a majority in favor). At the moment these functions don't cause a lot of overhead, however there are multiple issues and PRs a year for them which cost maintainer time to deal with. And they clutter up the ``numpy`` namespace. Discussion in 2013 happened on removing them again (gh-2880). This case is borderline, but given that they're clearly out of scope, deprecation and removal out of at least the main ``numpy`` namespace can be proposed. Alternatively, document clearly that new features for financial functions are unwanted, to keep the maintenance costs to a minimum. **Examples of features not added because of backwards compatibility** TODO: do we have good examples here? Possibly subclassing related? Removing complete submodules ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ This year there have been suggestions to consider removing some or all of ``numpy.distutils``, ``numpy.f2py``, ``numpy.linalg``, and ``numpy.random``. The motivation was that all these cost maintenance effort, and that they slow down work on the core of Numpy (ndarrays, dtypes and ufuncs). The import on downstream libraries and users would be very large, and maintenance of these modules would still have to happen. Therefore this is simply not a good idea; removing these submodules should not happen even for a new major version of NumPy. Subclassing of ndarray ^^^^^^^^^^^^^^^^^^^^^^ Subclassing of ``ndarray`` is a pain point. ``ndarray`` was not (or at least not well) designed to be subclassed. Despite that, a lot of subclasses have been created even within the NumPy code base itself, and some of those (e.g. ``MaskedArray``, ``astropy.units.Quantity``) are quite popular. The main problems with subclasses are: - They make it hard to change ``ndarray`` in ways that would otherwise be backwards compatible. - Some of them change the behavior of ndarray methods, making it difficult to write code that accepts array duck-types. Subclassing ``ndarray`` has been officially discouraged for a long time. Of the most important subclasses, ``np.matrix`` will be deprecated (see gh-10142) and ``MaskedArray`` will be kept in NumPy (`NEP 17 <http://www.numpy.org/neps/nep-0017-split-out-maskedarray.html>`_). ``MaskedArray`` will ideally be rewritten in a way such that it uses only public NumPy APIs. For subclasses outside of NumPy, more work is needed to provide alternatives (e.g. mixins, see gh-9016 and gh-10446) or better support for custom dtypes (see gh-2899). Until that is done, subclasses need to be taken into account when making change to the NumPy code base. A future change in NumPy to not support subclassing will certainly need a major version increase. Policy ------ 1. Code changes that have the potential to silently change the results of a users' code must never be made (except in the case of clear bugs). 2. Code changes that break users' code (i.e. the user will see a clear exception) can be made, *provided the benefit is worth the cost* and suitable deprecation warnings have been raised first. 3. Deprecation warnings are in all cases warnings that functionality will be removed. If there is no intent to remove functionlity, then deprecation in documentation only or other types of warnings shall be used. 4. Deprecations for stylistic reasons (e.g. consistency between functions) are strongly discouraged. Deprecations: - shall include the version numbers of both when the functionality was deprecated and when it will be removed (either two releases after the warning is introduced, or in the next major version). - shall include information on alternatives to the deprecated functionality, or a reason for the deprecation if no clear alternative is available. - shall use ``VisibleDeprecationWarning`` rather than ``DeprecationWarning`` for cases of relevance to end users (as opposed to cases only relevant to libraries building on top of NumPy). - shall be listed in the release notes of the release where the deprecation happened. Removal of deprecated functionality: - shall be done after 2 releases (assuming a 6-monthly release cycle; if that changes, there shall be at least 1 year between deprecation and removal), unless the impact of the removal is such that a major version number increase is warranted. - shall be listed in the release notes of the release where the removal happened. Versioning: - removal of deprecated code can be done in any minor (but not bugfix) release. - for heavily used functionality (e.g. removal of ``np.matrix``, of a whole submodule, or significant changes to behavior for subclasses) the major version number shall be increased. In concrete cases where this policy needs to be applied, decisions are made according to the `NumPy governance model <https://docs.scipy.org/doc/numpy/dev/governance/index.html>`_. Functionality with more strict policies: - ``numpy.random`` has its own backwards compatibility policy, see `NEP 19 <http://www.numpy.org/neps/nep-0019-rng-policy.html>`_. - The file format for ``.npy`` and ``.npz`` files must not be changed in a backwards incompatible way. Alternatives ------------ **Being more agressive with deprecations.** The goal of being more agressive is to allow NumPy to move forward faster. This would avoid others inventing their own solutions (often in multiple places), as well as be a benefit to users without a legacy code base. We reject this alternative because of the place NumPy has in the scientific Python ecosystem - being fairly conservative is required in order to not increase the extra maintenance for downstream libraries and end users to an unacceptable level. **Semantic versioning.** This would change the versioning scheme for code removals; those could then only be done when the major version number is increased. Rationale for rejection: semantic versioning is relatively common in software engineering, however it is not at all common in the Python world. Also, it would mean that NumPy's version number simply starts to increase faster, which would be more confusing than helpful. gh-10156 contains more discussion on this alternative. Discussion ---------- TODO This section may just be a bullet list including links to any discussions regarding the NEP: - This includes links to mailing list threads or relevant GitHub issues. References and Footnotes ------------------------ .. [1] TODO Copyright --------- This document has been placed in the public domain. [1]_

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NEP 15, 20 implementations waiting for review
by Matti Picus 27 Jul '18

27 Jul '18

Two largish pull requests that implement approved NEPS are waiting for review: https://github.com/numpy/numpy/pull/11175 for expanded gufunc signatures (NEP 20) https://github.com/numpy/numpy/pull/10915 for merging multiarray and umath c-extension modules (NEP 15) I realize reviewer time is precious, and appreciate that these PRs are perhaps more complex to review than others, but it would be nice to keep the good momentum we have in the NEP process moving forward. Thanks, Matti

1 0

update to numpy-1.5.0 gives new warnings from scipy
by Neal Becker 26 Jul '18

26 Jul '18

After update to numpy-1.5.0, I'm getting warnings from scipy. These probably come from my code using convolve. Does scipy need updating? /home/nbecker/.local/lib/python3.6/site-packages/scipy/fftpack/basic.py:160: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result. z[index] = x /home/nbecker/.local/lib/python3.6/site-packages/scipy/signal/signaltools.py:491: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result. return x[reverse].conj() /home/nbecker/.local/lib/python3.6/site-packages/scipy/signal/signaltools.py:251: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result. in1zpadded[sc] = in1.copy()

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Commit privileges
by Charles R Harris 24 Jul '18

24 Jul '18

Hi All, The NumPy Steering Council has been looking at commit rights for the NumPy developers hired at BIDS. We would like them to be able to label PRs, close issues, and merge simple fixes; doing that requires commit privileges. OTOH, it is also the case that people paid to work on NumPy don't automatically receive commit privileges. So it is a bit a quandary and we don't seem to have an official document codifying the giving of commit privileges, and the Github privileges are rather coarse grained, pretty much all or nothing for a given repository. The situation has also caused us to rethink commit privileges in general, perhaps we are being too selective. So there is some interest in offering commit privileges more freely, with the understanding that they are needed for many of the mundane tasks required to maintain NumPy, but that new people should be conservative in their exercise of the privilege. Given the reality of the Github environment, such a system needs be honor based, but would allow more people an opportunity to participate at a deeper level. So in line with that, we are going to give both of the BIDS workers commit privileges, but also extend the option of commit privileges for issue triage and other such things to the community at large. If you have contributed to NumPy and are interested in having commit rights, please respond to this post, but bear in mind that this is an experiment and that things might change if the system is abused. Chuck

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mplcairo 0.1 release
by Antony Lee 23 Jul '18

23 Jul '18

Dear all, I am pleased to announce the release of mplcairo 0.1 # Description mplcairo is a Matplotlib backend based on the well-known cairo library, supporting output to both raster (including interactively) and vector formats. In other words, it provides the functionality of Matplotlib's {,qt5,gtk3,wx,tk,macos}{agg,cairo}, pdf, ps, and svg backends. Per Matplotlib's standard API, the backend can be selected by calling matplotlib.use("module://mplcairo.qt") or setting your MPLBACKEND environment variable to `module://mplcairo.qt` for Qt5, and similarly for other toolkits. The source tarball, and Py3.6 manylinux and Windows wheels, are available on PyPI (I am looking for help to generate the OSX wheels). See the README for more details. # Why a new backend? Compared to Matplotlib's builtin Agg and cairo backends, mplcairo presents the following features: - Improved accuracy (e.g., with marker positioning, quad meshes, and text kerning). - Support for a wider variety of font formats, such as otf and pfb, for vector (PDF, PS, SVG) backends (Matplotlib's Agg backend also supports such fonts). - Optional support for complex text layout (right-to-left languages, etc.) using Raqm. **Note** that Raqm depends on Fribidi, which is licensed under the LGPLv2.1+. - Support for embedding URLs in PDF (but not SVG) output (requires cairo≥1.15.4). - Support for multi-page output both for PDF and PS (Matplotlib only supports multi-page PDF). - Support for custom blend modes (see `examples/operators.py`). See the README for more details. # Changelog from mplcairo 0.1a1 to mplcairo 0.1 - Integration with libraqm now occurs via dlopen() rather than being selected at compile-time. - Various rendering and performance improvements. - On Travis, we now run Matplotlib's test suite with mplcairo patching the default Agg renderer. Enjoy, Antony Lee

2 1

NumPy 1.15.0 released.
by Charles R Harris 23 Jul '18

23 Jul '18

Hi All, On behalf of the NumPy team I'm pleased to announce the release of NumPy 1.15.0rc2. This release has an unusual number of cleanups, many deprecations of old functions, and improvements to many existing functions. A total of 438 pull reguests were merged for this release, please look at the release notes <https://github.com/numpy/numpy/releases/tag/v1.15.0>for details. Some highlights are: - NumPy has switched to pytest for testing. - A new `numpy.printoptions` context manager. - Many improvements to the histogram functions. - Support for unicode field names in python 2.7. - Improved support for PyPy. - Fixes and improvements to `numpy.einsum`. The Python versions supported by this release are 2.7, 3.4-3.7. The wheels are linked with OpenBLAS v0.3.0, which should fix some of the linalg problems reported for NumPy 1.14. Wheels for this release can be downloaded from PyPI <https://pypi.org/project/numpy/1.15.0/>, source archives are available from Github <https://github.com/numpy/numpy/releases/tag/v1.15.0>. *Contributors* A total of 133 people contributed to this release. People with a "+" by their names contributed a patch for the first time. * Aaron Critchley + * Aarthi + * Aarthi Agurusa + * Alex Thomas + * Alexander Belopolsky * Allan Haldane * Anas Khan + * Andras Deak * Andrey Portnoy + * Anna Chiara * Aurelien Jarno + * Baurzhan Muftakhidinov * Berend Kapelle + * Bernhard M. Wiedemann * Bjoern Thiel + * Bob Eldering * Cenny Wenner + * Charles Harris * ChloeColeongco + * Chris Billington + * Christopher + * Chun-Wei Yuan + * Claudio Freire + * Daniel Smith * Darcy Meyer + * David Abdurachmanov + * David Freese * Deepak Kumar Gouda + * Dennis Weyland + * Derrick Williams + * Dmitriy Shalyga + * Eric Cousineau + * Eric Larson * Eric Wieser * Evgeni Burovski * Frederick Lefebvre + * Gaspar Karm + * Geoffrey Irving * Gerhard Hobler + * Gerrit Holl * Guo Ci + * Hameer Abbasi + * Han Shen * Hiroyuki V. Yamazaki + * Hong Xu * Ihor Melnyk + * Jaime Fernandez * Jake VanderPlas + * James Tocknell + * Jarrod Millman * Jeff VanOss + * John Kirkham * Jonas Rauber + * Jonathan March + * Joseph Fox-Rabinovitz * Julian Taylor * Junjie Bai + * Juris Bogusevs + * Jörg Döpfert * Kenichi Maehashi + * Kevin Sheppard * Kimikazu Kato + * Kirit Thadaka + * Kritika Jalan + * Kyle Sunden + * Lakshay Garg + * Lars G + * Licht Takeuchi * Louis Potok + * Luke Zoltan Kelley * MSeifert04 + * Mads R. B. Kristensen + * Malcolm Smith + * Mark Harfouche + * Marten H. van Kerkwijk + * Marten van Kerkwijk * Matheus Vieira Portela + * Mathieu Lamarre * Mathieu Sornay + * Matthew Brett * Matthew Rocklin + * Matthias Bussonnier * Matti Picus * Michael Droettboom * Miguel Sánchez de León Peque + * Mike Toews + * Milo + * Nathaniel J. Smith * Nelle Varoquaux * Nicholas Nadeau, P.Eng., AVS + * Nick Minkyu Lee + * Nikita + * Nikita Kartashov + * Nils Becker + * Oleg Zabluda * Orestis Floros + * Pat Gunn + * Paul van Mulbregt + * Pauli Virtanen * Pierre Chanial + * Ralf Gommers * Raunak Shah + * Robert Kern * Russell Keith-Magee + * Ryan Soklaski + * Samuel Jackson + * Sebastian Berg * Siavash Eliasi + * Simon Conseil * Simon Gibbons * Stefan Krah + * Stefan van der Walt * Stephan Hoyer * Subhendu + * Subhendu Ranjan Mishra + * Tai-Lin Wu + * Tobias Fischer + * Toshiki Kataoka + * Tyler Reddy + * Unknown + * Varun Nayyar * Victor Rodriguez + * Warren Weckesser * William D. Irons + * Zane Bradley + * cclauss + * fo40225 + * lapack_lite code generator + * lumbric + * luzpaz + * mamrehn + * tynn + * xoviat Cheers, Charles Harris

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NumPy-Discussion July 2018