Mailman 3 June 2018 - NumPy-Discussion

Updated 1.15.0 release notes
by Charles R Harris June 14, 2018

June 14, 2018

Hi All, There is a PR for the updated NumPy 1.15.0 release notes <https://github.com/numpy/numpy/pull/11327> . I would appreciate it if all those involved in the thatn release would have a look and fix incorrect or missing notes. Cheers, Chuck

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NumPy 1.14.5 released
by Matti Picus June 13, 2018

June 13, 2018

Hi All, I am pleased to announce the release of NumPy 14.4.5. This is a bugfix release for bugs reported following the 1.14.4 release. The most significant fixes are: * fixes for compilation errors on alpine and NetBSD The Python versions supported in this release are 2.7 and 3.4 - 3.6. The Python 3.6 wheels available from PIP are built with Python 3.6.2 and should be compatible with all previous versions of Python 3.6. The source releases were cythonized with Cython 0.28.2 and should work for the upcoming Python 3.7. Contributors ============ A total of 1 person contributed to this release. People with a "+" by their names contributed a patch for the first time. * Charles Harris Pull requests merged ==================== A total of 2 pull requests were merged for this release. * `#11274 <https://github.com/numpy/numpy/pull/11274>`__: BUG: Correct use of NPY_UNUSED. * `#11294 <https://github.com/numpy/numpy/pull/11294>`__: BUG: Remove extra trailing parentheses. Cheers, Matti

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NumPy 1.15.x branched.
by Charles R Harris June 12, 2018

June 12, 2018

Hi All, NumPy 1.15.x has been branched and master is now open for 1.16 development. If there are any remaining PRs that *just have to be in 1.15*, please complain here :0 Chuck

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Permissions to upload to PyPI
by Matti Picus June 12, 2018

June 12, 2018

Almost ready to finish the 1.14.5 release, but it seems I need permissions to upload to PyPI (makes sense). My user name there is mattip. Can someone help out? Matti

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SciPy 2018
by Charles R Harris June 12, 2018

June 12, 2018

Hi All, Thought I'd raise the topic of meeting up at SciPy 2018. I wasn't planning on registering for the main conference, but would be happy to fly down for a couple of days if we plan on a meetup during sprints or some other point in the conference schedule. Chuck

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1.14.5 bugfix release
by Matti Picus June 11, 2018

June 11, 2018

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Re: [Numpy-discussion] NEP: Random Number Generator Policy (Robert Kern)
by Kevin Sheppard June 11, 2018

June 11, 2018

Maybe a good place for a stable, testing focused generator would be in numpy.random.testing. This could host a default implementation of StableGenerator, although a better name might be TestingGenerator. It would also help users decide that this is not the generator they are looking for (I think many people might think StableGenerator is a good thing, after all, who wants an UnstableGenerator).

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NEP: Dispatch Mechanism for NumPy’s high level API
by Stephan Hoyer June 9, 2018

June 9, 2018

Matthew Rocklin and I have written NEP-18, which proposes a new dispatch mechanism for NumPy's high level API: http://www.numpy.org/neps/nep-0018-array-function-protocol.html There has already been a little bit of scattered discussion on the pull request (https://github.com/numpy/numpy/pull/11189), but per NEP-0 let's try to keep high-level discussion here on the mailing list. The full text of the NEP is reproduced below: ================================================== NEP: Dispatch Mechanism for NumPy's high level API ================================================== :Author: Stephan Hoyer <shoyer(a)google.com> :Author: Matthew Rocklin <mrocklin(a)gmail.com> :Status: Draft :Type: Standards Track :Created: 2018-05-29 Abstact ------- We propose a protocol to allow arguments of numpy functions to define how that function operates on them. This allows other libraries that implement NumPy's high level API to reuse Numpy functions. This allows libraries that extend NumPy's high level API to apply to more NumPy-like libraries. Detailed description -------------------- Numpy's high level ndarray API has been implemented several times outside of NumPy itself for different architectures, such as for GPU arrays (CuPy), Sparse arrays (scipy.sparse, pydata/sparse) and parallel arrays (Dask array) as well as various Numpy-like implementations in the deep learning frameworks, like TensorFlow and PyTorch. Similarly there are several projects that build on top of the Numpy API for labeled and indexed arrays (XArray), automatic differentation (Autograd, Tangent), higher order array factorizations (TensorLy), etc. that add additional functionality on top of the Numpy API. We would like to be able to use these libraries together, for example we would like to be able to place a CuPy array within XArray, or perform automatic differentiation on Dask array code. This would be easier to accomplish if code written for NumPy ndarrays could also be used by other NumPy-like projects. For example, we would like for the following code example to work equally well with any Numpy-like array object: .. code:: python def f(x): y = np.tensordot(x, x.T) return np.mean(np.exp(y)) Some of this is possible today with various protocol mechanisms within Numpy. - The ``np.exp`` function checks the ``__array_ufunc__`` protocol - The ``.T`` method works using Python's method dispatch - The ``np.mean`` function explicitly checks for a ``.mean`` method on the argument However other functions, like ``np.tensordot`` do not dispatch, and instead are likely to coerce to a Numpy array (using the ``__array__``) protocol, or err outright. To achieve enough coverage of the NumPy API to support downstream projects like XArray and autograd we want to support *almost all* functions within Numpy, which calls for a more reaching protocol than just ``__array_ufunc__``. We would like a protocol that allows arguments of a NumPy function to take control and divert execution to another function (for example a GPU or parallel implementation) in a way that is safe and consistent across projects. Implementation -------------- We propose adding support for a new protocol in NumPy, ``__array_function__``. This protocol is intended to be a catch-all for NumPy functionality that is not covered by existing protocols, like reductions (like ``np.sum``) or universal functions (like ``np.exp``). The semantics are very similar to ``__array_ufunc__``, except the operation is specified by an arbitrary callable object rather than a ufunc instance and method. The interface ~~~~~~~~~~~~~ We propose the following signature for implementations of ``__array_function__``: .. code-block:: python def __array_function__(self, func, types, args, kwargs) - ``func`` is an arbitrary callable exposed by NumPy's public API, which was called in the form ``func(*args, **kwargs)``. - ``types`` is a list of types for all arguments to the original NumPy function call that will be checked for an ``__array_function__`` implementation. - The tuple ``args`` and dict ``**kwargs`` are directly passed on from the original call. Unlike ``__array_ufunc__``, there are no high-level guarantees about the type of ``func``, or about which of ``args`` and ``kwargs`` may contain objects implementing the array API. As a convenience for ``__array_function__`` implementors of the NumPy API, the ``types`` keyword contains a list of all types that implement the ``__array_function__`` protocol. This allows downstream implementations to quickly determine if they are likely able to support the operation. Still be determined: what guarantees can we offer for ``types``? Should we promise that types are unique, and appear in the order in which they are checked? Example for a project implementing the NumPy API ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Most implementations of ``__array_function__`` will start with two checks: 1. Is the given function something that we know how to overload? 2. Are all arguments of a type that we know how to handle? If these conditions hold, ``__array_function__`` should return the result from calling its implementation for ``func(*args, **kwargs)``. Otherwise, it should return the sentinel value ``NotImplemented``, indicating that the function is not implemented by these types. .. code:: python class MyArray: def __array_function__(self, func, types, args, kwargs): if func not in HANDLED_FUNCTIONS: return NotImplemented if not all(issubclass(t, MyArray) for t in types): return NotImplemented return HANDLED_FUNCTIONS[func](*args, **kwargs) HANDLED_FUNCTIONS = { np.concatenate: my_concatenate, np.broadcast_to: my_broadcast_to, np.sum: my_sum, ... } Necessary changes within the Numpy codebase itself ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ This will require two changes within the Numpy codebase: 1. A function to inspect available inputs, look for the ``__array_function__`` attribute on those inputs, and call those methods appropriately until one succeeds. This needs to be fast in the common all-NumPy case. This is one additional function of moderate complexity. 2. Calling this function within all relevant Numpy functions. This affects many parts of the Numpy codebase, although with very low complexity. Finding and calling the right ``__array_function__`` ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Given a Numpy function, ``*args`` and ``**kwargs`` inputs, we need to search through ``*args`` and ``**kwargs`` for all appropriate inputs that might have the ``__array_function__`` attribute. Then we need to select among those possible methods and execute the right one. Negotiating between several possible implementations can be complex. Finding arguments ''''''''''''''''' Valid arguments may be directly in the ``*args`` and ``**kwargs``, such as in the case for ``np.tensordot(left, right, out=out)``, or they may be nested within lists or dictionaries, such as in the case of ``np.concatenate([x, y, z])``. This can be problematic for two reasons: 1. Some functions are given long lists of values, and traversing them might be prohibitively expensive 2. Some function may have arguments that we don't want to inspect, even if they have the ``__array_function__`` method To resolve these we ask the functions to provide an explicit list of arguments that should be traversed. This is the ``relevant_arguments=`` keyword in the examples below. Trying ``__array_function__`` methods until the right one works ''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''' Many arguments may implement the ``__array_function__`` protocol. Some of these may decide that, given the available inputs, they are unable to determine the correct result. How do we call the right one? If several are valid then which has precedence? The rules for dispatch with ``__array_function__`` match those for ``__array_ufunc__`` (see `NEP-13 <http://www.numpy.org/neps/nep-0013-ufunc-overrides.html>`_). In particular: - NumPy will gather implementations of ``__array_function__`` from all specified inputs and call them in order: subclasses before superclasses, and otherwise left to right. Note that in some edge cases, this differs slightly from the `current behavior <https://bugs.python.org/issue30140>`_ of Python. - Implementations of ``__array_function__`` indicate that they can handle the operation by returning any value other than ``NotImplemented``. - If all ``__array_function__`` methods return ``NotImplemented``, NumPy will raise ``TypeError``. Changes within Numpy functions ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Given a function defined above, for now call it ``do_array_function_dance``, we now need to call that function from within every relevant Numpy function. This is a pervasive change, but of fairly simple and innocuous code that should complete quickly and without effect if no arguments implement the ``__array_function__`` protocol. Let us consider a few examples of NumPy functions and how they might be affected by this change: .. code:: python def broadcast_to(array, shape, subok=False): success, value = do_array_function_dance( func=broadcast_to, relevant_arguments=[array], args=(array,), kwargs=dict(shape=shape, subok=subok)) if success: return value ... # continue with the definition of broadcast_to def concatenate(arrays, axis=0, out=None) success, value = do_array_function_dance( func=concatenate, relevant_arguments=[arrays, out], args=(arrays,), kwargs=dict(axis=axis, out=out)) if success: return value ... # continue with the definition of concatenate The list of objects passed to ``relevant_arguments`` are those that should be inspected for ``__array_function__`` implementations. Alternatively, we could write these overloads with a decorator, e.g., .. code:: python @overload_for_array_function(['array']) def broadcast_to(array, shape, subok=False): ... # continue with the definition of broadcast_to @overload_for_array_function(['arrays', 'out']) def concatenate(arrays, axis=0, out=None): ... # continue with the definition of concatenate The decorator ``overload_for_array_function`` would be written in terms of ``do_array_function_dance``. The downside of this approach would be a loss of introspection capability for NumPy functions on Python 2, since this requires the use of ``inspect.Signature`` (only available on Python 3). However, NumPy won't be supporting Python 2 for `very much longer < http://www.numpy.org/neps/nep-0014-dropping-python2.7-proposal.html>`_. Use outside of NumPy ~~~~~~~~~~~~~~~~~~~~ Nothing about this protocol that is particular to NumPy itself. Should we enourage use of the same ``__array_function__`` protocol third-party libraries for overloading non-NumPy functions, e.g., for making array-implementation generic functionality in SciPy? This would offer significant advantages (SciPy wouldn't need to invent its own dispatch system) and no downsides that we can think of, because every function that dispatches with ``__array_function__`` already needs to be explicitly recognized. Libraries like Dask, CuPy, and Autograd already wrap a limited subset of SciPy functionality (e.g., ``scipy.linalg``) similarly to how they wrap NumPy. If we want to do this, we should consider exposing the helper function ``do_array_function_dance()`` above as a public API. Non-goals --------- We are aiming for basic strategy that can be relatively mechanistically applied to almost all functions in NumPy's API in a relatively short period of time, the development cycle of a single NumPy release. We hope to get both the ``__array_function__`` protocol and all specific overloads right on the first try, but our explicit aim here is to get something that mostly works (and can be iterated upon), rather than to wait for an optimal implementation. The price of moving fast is that for now **this protocol should be considered strictly experimental**. We reserve the right to change the details of this protocol and how specific NumPy functions use it at any time in the future -- even in otherwise bug-fix only releases of NumPy. In particular, we don't plan to write additional NEPs that list all specific functions to overload, with exactly how they should be overloaded. We will leave this up to the discretion of committers on individual pull requests, trusting that they will surface any controversies for discussion by interested parties. However, we already know several families of functions that should be explicitly exclude from ``__array_function__``. These will need their own protocols: - universal functions, which already have their own protocol. - ``array`` and ``asarray``, because they are explicitly intended for coercion to actual ``numpy.ndarray`` object. - dispatch for methods of any kind, e.g., methods on ``np.random.RandomState`` objects. As a concrete example of how we expect to break behavior in the future, some functions such as ``np.where`` are currently not NumPy universal functions, but conceivably could become universal functions in the future. When/if this happens, we will change such overloads from using ``__array_function__`` to the more specialized ``__array_ufunc__``. Backward compatibility ---------------------- This proposal does not change existing semantics, except for those arguments that currently have ``__array_function__`` methods, which should be rare. Alternatives ------------ Specialized protocols ~~~~~~~~~~~~~~~~~~~~~ We could (and should) continue to develop protocols like ``__array_ufunc__`` for cohesive subsets of Numpy functionality. As mentioned above, if this means that some functions that we overload with ``__array_function__`` should switch to a new protocol instead, that is explicitly OK for as long as ``__array_function__`` retains its experimental status. Separate namespace ~~~~~~~~~~~~~~~~~~ A separate namespace for overloaded functions is another possibility, either inside or outside of NumPy. This has the advantage of alleviating any possible concerns about backwards compatibility and would provide the maximum freedom for quick experimentation. In the long term, it would provide a clean abstration layer, separating NumPy's high level API from default implementations on ``numpy.ndarray`` objects. The downsides are that this would require an explicit opt-in from all existing code, e.g., ``import numpy.api as np``, and in the long term would result in the maintainence of two separate NumPy APIs. Also, many functions from ``numpy`` itself are already overloaded (but inadequately), so confusion about high vs. low level APIs in NumPy would still persist. Multiple dispatch ~~~~~~~~~~~~~~~~~ An alternative to our suggestion of the ``__array_function__`` protocol would be implementing NumPy's core functions as `multi-methods <https://en.wikipedia.org/wiki/Multiple_dispatch>`_. Although one of us wrote a `multiple dispatch library <https://github.com/mrocklin/multipledispatch>`_ for Python, we don't think this approach makes sense for NumPy in the near term. The main reason is that NumPy already has a well-proven dispatching mechanism with ``__array_ufunc__``, based on Python's own dispatching system for arithemtic, and it would be confusing to add another mechanism that works in a very different way. This would also be more invasive change to NumPy itself, which would need to gain a multiple dispatch implementation. It is possible that multiple dispatch implementation for NumPy's high level API could make sense in the future. Fortunately, ``__array_function__`` does not preclude this possibility, because it would be straightforward to write a shim for a default ``__array_function__`` implementation in terms of multiple dispatch. Implementations in terms of a limited core API ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The internal implemenations of some NumPy functions is extremely simple. For example: - ``np.stack()`` is implemented in only a few lines of code by combining indexing with ``np.newaxis``, ``np.concatenate`` and the ``shape`` attribute. - ``np.mean()`` is implemented internally in terms of ``np.sum()``, ``np.divide()``, ``.astype()`` and ``.shape``. This suggests the possibility of defining a minimal "core" ndarray interface, and relying upon it internally in NumPy to implement the full API. This is an attractive option, because it could significantly reduce the work required for new array implementations. However, this also comes with several downsides: 1. The details of how NumPy implements a high-level function in terms of overloaded functions now becomes an implicit part of NumPy's public API. For example, refactoring ``stack`` to use ``np.block()`` instead of ``np.concatenate()`` internally would now become a breaking change. 2. Array libraries may prefer to implement high level functions differently than NumPy. For example, a library might prefer to implement a fundamental operations like ``mean()`` directly rather than relying on ``sum()`` followed by division. More generally, it's not clear yet what exactly qualifies as core functionality, and figuring this out could be a large project. 3. We don't yet have an overloading system for attributes and methods on array objects, e.g., for accessing ``.dtype`` and ``.shape``. This should be the subject of a future NEP, but until then we should be reluctant to rely on these properties. Given these concerns, we encourage relying on this approach only in limited cases. Coersion to a NumPy array as a catch-all fallback ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ With the current design, classes that implement ``__array_function__`` to overload at least one function implicitly declare an intent to implement the entire NumPy API. It's not possible to implement *only* ``np.concatenate()`` on a type, but fall back to NumPy's default behavior of casting with ``np.asarray()`` for all other functions. This could present a backwards compatibility concern that would discourage libraries from adopting ``__array_function__`` in an incremental fashion. For example, currently most numpy functions will implicitly convert ``pandas.Series`` objects into NumPy arrays, behavior that assuredly many pandas users rely on. If pandas implemented ``__array_function__`` only for ``np.concatenate``, unrelated NumPy functions like ``np.nanmean`` would suddenly break on pandas objects by raising TypeError. With ``__array_ufunc__``, it's possible to alleviate this concern by casting all arguments to numpy arrays and re-calling the ufunc, but the heterogeneous function signatures supported by ``__array_function__`` make it impossible to implement this generic fallback behavior for ``__array_function__``. We could resolve this issue by change the handling of return values in ``__array_function__`` in either of two possible ways: 1. Change the meaning of all arguments returning ``NotImplemented`` to indicate that all arguments should be coerced to NumPy arrays instead. However, many array libraries (e.g., scipy.sparse) really don't want implicit conversions to NumPy arrays, and often avoid implementing ``__array__`` for exactly this reason. Implicit conversions can result in silent bugs and performance degradation. 2. Use another sentinel value of some sort to indicate that a class implementing part of the higher level array API is coercible as a fallback, e.g., a return value of ``np.NotImplementedButCoercible`` from ``__array_function__``. If we take this second approach, we would need to define additional rules for how coercible array arguments are coerced, e.g., - Would we try for ``__array_function__`` overloads again after coercing coercible arguments? - If so, would we coerce coercible arguments one-at-a-time, or all-at-once? These are slightly tricky design questions, so for now we propose to defer this issue. We can always implement ``np.NotImplementedButCoercible`` at some later time if it proves critical to the numpy community in the future. Importantly, we don't think this will stop critical libraries that desire to implement most of the high level NumPy API from adopting this proposal. NOTE: If you are reading this NEP in its draft state and disagree, please speak up on the mailing list! Drawbacks of this approach -------------------------- Future difficulty extending NumPy's API ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ One downside of passing on all arguments directly on to ``__array_function__`` is that it makes it hard to extend the signatures of overloaded NumPy functions with new arguments, because adding even an optional keyword argument would break existing overloads. This is not a new problem for NumPy. NumPy has occasionally changed the signature for functions in the past, including functions like ``numpy.sum`` which support overloads. For adding new keyword arguments that do not change default behavior, we would only include these as keyword arguments when they have changed from default values. This is similar to `what NumPy already has done < https://github.com/numpy/numpy/blob/v1.14.2/numpy/core/fromnumeric.py#L1865… >`_, e.g., for the optional ``keepdims`` argument in ``sum``: .. code:: python def sum(array, ..., keepdims=np._NoValue): kwargs = {} if keepdims is not np._NoValue: kwargs['keepdims'] = keepdims return array.sum(..., **kwargs) In other cases, such as deprecated arguments, preserving the existing behavior of overloaded functions may not be possible. Libraries that use ``__array_function__`` should be aware of this risk: we don't propose to freeze NumPy's API in stone any more than it already is. Difficulty adding implementation specific arguments ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Some array implementations generally follow NumPy's API, but have additional optional keyword arguments (e.g., ``dask.array.sum()`` has ``split_every`` and ``tensorflow.reduce_sum()`` has ``name``). A generic dispatching library could potentially pass on all unrecognized keyword argument directly to the implementation, but extending ``np.sum()`` to pass on ``**kwargs`` would entail public facing changes in NumPy. Customizing the detailed behavior of array libraries will require using library specific functions, which could be limiting in the case of libraries that consume the NumPy API such as xarray. Discussion ---------- Various alternatives to this proposal were discussed in a few Github issues: 1. `pydata/sparse #1 <https://github.com/pydata/sparse/issues/1>`_ 2. `numpy/numpy #11129 <https://github.com/numpy/numpy/issues/11129>`_ Additionally it was the subject of `a blogpost <http://matthewrocklin.com/blog/work/2018/05/27/beyond-numpy>`_ Following this it was discussed at a `NumPy developer sprint <https://scisprints.github.io/#may-numpy-developer-sprint>`_ at the `UC Berkeley Institute for Data Science (BIDS) <https://bids.berkeley.edu/>`_. References and Footnotes ------------------------ .. [1] Each NEP must either be explicitly labeled as placed in the public domain (see this NEP as an example) or licensed under the `Open Publication License`_. .. _Open Publication License: http://www.opencontent.org/openpub/ Copyright --------- This document has been placed in the public domain. [1]_

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NumPy 1.14.4 released.
by Charles R Harris June 6, 2018

June 6, 2018

Hi All, On behalf of the NumPy team, I am pleased to announce the release of NumPy 1.14.4. This is a bugfix release for bugs reported following the 1.14.3 release. The most significant fixes are: * fixes for compiler instruction reordering that resulted in NaN's not being properly propagated in `np.max` and `np.min`, * fixes for bus faults on SPARC and older ARM due to incorrect alignment checks. There are also improvements to printing of long doubles on PPC platforms. All is not yet perfect on that platform, the whitespace padding is still incorrect and is to be fixed in numpy 1.15, consequently NumPy still fails some printing-related (and other) unit tests on ppc systems. However, the printed values are now correct. Note that NumPy will error on import if it detects incorrect float32 `dot` results. This problem has been seen on the Mac when working in the Anaconda enviroment and is due to a subtle interaction between MKL and PyQt5. It is not strictly a NumPy problem, but it is best that users be aware of it. See the gh-8577 NumPy issue for more information. The Python versions supported in this release are 2.7 and 3.4 - 3.6. Wheels for all supported versions are available from PIP <https://pypi.org/project/numpy/> and source releases are available on github <https://github.com/numpy/numpy/releases/tag/v1.14.4>. The source releases were cythonized with Cython 0.28.2 and should be compatible with the upcoming Python 3.7. Contributors ============ A total of 7 people contributed to this release. People with a "+" by their names contributed a patch for the first time. * Allan Haldane * Charles Harris * Marten van Kerkwijk * Matti Picus * Pauli Virtanen * Ryan Soklaski + * Sebastian Berg Pull requests merged ==================== A total of 11 pull requests were merged for this release. * #11104: BUG: str of DOUBLE_DOUBLE format wrong on ppc64 * #11170: TST: linalg: add regression test for gh-8577 * #11174: MAINT: add sanity-checks to be run at import time * #11181: BUG: void dtype setup checked offset not actual pointer for alignment * #11194: BUG: Python2 doubles don't print correctly in interactive shell. * #11198: BUG: optimizing compilers can reorder call to npy_get_floatstatus * #11199: BUG: reduce using SSE only warns if inside SSE loop * #11203: BUG: Bytes delimiter/comments in genfromtxt should be decoded Cheers, Chuck

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2018 John Hunter Excellence in Plotting Contest
by Nelle Varoquaux June 6, 2018

June 6, 2018

Hello everyone, Sorry about the cross-posting. There's a couple more days to submit to the John Hunter Excellence in Plotting Competition! If you have any scientific plot worth sharing, submit an entry before June 8th. For more information, see below. Thanks, Nelle In memory of John Hunter, we are pleased to be reviving the SciPy John Hunter Excellence in Plotting Competition for 2018. This open competition aims to highlight the importance of data visualization to scientific progress and showcase the capabilities of open source software. Participants are invited to submit scientific plots to be judged by a panel. The winning entries will be announced and displayed at the conference. John Hunter’s family and NumFocus are graciously sponsoring cash prizes for the winners in the following amounts: - 1st prize: $1000 - 2nd prize: $750 - 3rd prize: $500 - Entries must be submitted by June, 8th to the form at https://goo.gl/forms/7q86zgu5OYUOjODH3 <https://goo.gl/forms/7q86zgu5OYUOjODH3>. - Winners will be announced at Scipy 2018 in Austin, TX. - Participants do not need to attend the Scipy conference. - Entries may take the definition of “visualization” rather broadly. Entries may be, for example, a traditional printed plot, an interactive visualization for the web, or an animation. - Source code for the plot must be provided, in the form of Python code and/or a Jupyter notebook, along with a rendering of the plot in a widely used format. This may be, for example, PDF for print, standalone HTML and Javascript for an interactive plot, or MPEG-4 for a video. If the original data can not be shared for reasons of size or licensing, "fake" data may be substituted, along with an image of the plot using real data. - Each entry must include a 300-500 word abstract describing the plot and its importance for a general scientific audience. - Entries will be judged on their clarity, innovation and aesthetics, but most importantly for their effectiveness in communicating a real-world problem. Entrants are encouraged to submit plots that were used during the course of research or work, rather than merely being hypothetical. - SciPy reserves the right to display any and all entries, whether prize-winning or not, at the conference, use in any materials or on its website, with attribution to the original author(s). SciPy John Hunter Excellence in Plotting Competition Co-Chairs Thomas Caswell Michael Droettboom Nelle Varoquaux

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