Mailman 3 March 2015 - NumPy-Discussion

Rename arguments to np.clip and np.put
by Allan Haldane March 31, 2015

March 31, 2015

Hello everyone, What does the list think of renaming the arguments of np.clip and np.put to match those of ndarray.clip/put? Currently the signatures are np.clip(a, a_min, a_max, out=None) ndarray.clip(a, min=None, max=None, out=None) np.put(a, ind, v, mode='raise') ndarray.put(indices, values, mode='raise') (The docstring for ndarray.clip is incorrect, too). I suggest the signatures might be changed to this: np.clip(a, min=None, max=None, out=None, **kwargs) np.put(a, indices, values, mode='raise') We can still take care of the old argument names for np.clip using **kwards, while showing a deprecation warning. I think that would be fully back-compatible. Note this makes np.clip more flexible as only one of min or max are needed now, just like ndarray.clip. np.put is trickier to keep back-compatible as it has two positional arguments. Someone who called `np.put(a, v=0, ind=1)` would be in trouble with this change, although I didn't find anyone on github doing so. I suppose to maintain back-compatibility we could make indices and values keyword args, and use the same kwargs trick as in np.clip, but that might be confusing since they're both required args. Any opinions or suggestions? Allan

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ANN: pyMIC v0.5 released
by Klemm, Michael March 30, 2015

March 30, 2015

Announcement: pyMIC v0.5 ========================= I'm happy to announce the release of pyMIC v0.5. pyMIC is a Python module to offload computation in a Python program to the Intel Xeon Phi coprocessor. It contains offloadable arrays and device management functions. It supports invocation of native kernels (C/C++, Fortran) and blends in with Numpy's array types for float, complex, and int data types. For more information and downloads please visit pyMIC's Github page: https://github.com/01org/pyMIC. You can find pyMIC's mailinglist at https://lists.01.org/mailman/listinfo/pymic. Full change log: ================= Version 0.5 ---------------------------- - Introduced new kernel API that avoids insane pointer unpacking. - pyMIC now uses libxstreams as the offload back-end (https://github.com/hfp/libxstream). - Added smart pointers to make handling of fake pointers easier. Version 0.4 ---------------------------- - New low-level API to allocate, deallocate, and transfer data (see OffloadStream). - Support for in-place binary operators. - New internal design to handle offloads. Version 0.3 ---------------------------- - Improved handling of libraries and kernel invocation. - Trace collection (PYMIC_TRACE=1, PYMIC_TRACE_STACKS={none,compact,full}). - Replaced the device-centric API with a stream API. - Refactoring to better match PEP8 recommendations. - Added support for int(int64) and complex(complex128) data types. - Reworked the benchmarks and examples to fit the new API. - Bugfix: fixed syntax errors in OffloadArray. Version 0.2 ---------------------------- - Small improvements to the README files. - New example: Singular Value Decomposition. - Some documentation for the API functions. - Added a basic testsuite for unit testing (WIP). - Bugfix: benchmarks now use the latest interface. - Bugfix: numpy.ndarray does not offer an attribute 'order'. - Bugfix: number_of_devices was not visible after import. - Bugfix: member offload_array.device is now initialized. - Bugfix: use exception for errors w/ invoke_kernel & load_library. Version 0.1 ---------------------------- Initial release. Intel GmbH Dornacher Strasse 1 85622 Feldkirchen/Muenchen, Deutschland Sitz der Gesellschaft: Feldkirchen bei Muenchen Geschaeftsfuehrer: Christian Lamprechter, Hannes Schwaderer, Douglas Lusk Registergericht: Muenchen HRB 47456 Ust.-IdNr./VAT Registration No.: DE129385895 Citibank Frankfurt a.M. (BLZ 502 109 00) 600119052

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reaktionsschema
by Till Stensitzki March 28, 2015

March 28, 2015

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Announcing Theano 0.7
by Pascal Lamblin March 27, 2015

March 27, 2015

=========================== Announcing Theano 0.7 =========================== This is a release for a major version, with lots of new features, bug fixes, and some interface changes (deprecated or potentially misleading features were removed). Upgrading to Theano 0.7 is recommended for everyone, but you should first make sure that your code does not raise deprecation warnings with the version you are currently using. For those using the bleeding edge version in the git repository, we encourage you to update to the `rel-0.7` tag. What's New ---------- Highlights: * Integration of CuDNN for 2D convolutions and pooling on supported GPUs * Too many optimizations and new features to count * Various fixes and improvements to scan * Better support for GPU on Windows * On Mac OS X, clang is used by default * Many crash fixes * Some bug fixes as well Description ----------- Theano is a Python library that allows you to define, optimize, and efficiently evaluate mathematical expressions involving multi-dimensional arrays. It is built on top of NumPy. Theano features: * tight integration with NumPy: a similar interface to NumPy's. numpy.ndarrays are also used internally in Theano-compiled functions. * transparent use of a GPU: perform data-intensive computations up to 140x faster than on a CPU (support for float32 only). * efficient symbolic differentiation: Theano can compute derivatives for functions of one or many inputs. * speed and stability optimizations: avoid nasty bugs when computing expressions such as log(1+ exp(x)) for large values of x. * dynamic C code generation: evaluate expressions faster. * extensive unit-testing and self-verification: includes tools for detecting and diagnosing bugs and/or potential problems. Theano has been powering large-scale computationally intensive scientific research since 2007, but it is also approachable enough to be used in the classroom (IFT6266 at the University of Montreal). Resources --------- About Theano: http://deeplearning.net/software/theano/ Related projects: http://github.com/Theano/Theano/wiki/Related-projects About NumPy: http://numpy.scipy.org/ About SciPy: http://www.scipy.org/ Machine Learning Tutorial with Theano on Deep Architectures: http://deeplearning.net/tutorial/ Acknowledgments --------------- I would like to thank all contributors of Theano. For this particular release, many people have helped, and to list them all would be impractical. I would also like to thank users who submitted bug reports. Also, thank you to all NumPy and Scipy developers as Theano builds on their strengths. All questions/comments are always welcome on the Theano mailing-lists ( http://deeplearning.net/software/theano/#community ) -- Pascal

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Mathematical functions in Numpy
by Shubhankar Mohapatra March 27, 2015

March 27, 2015

Hello all,I am a undergraduate and i am trying to do a project this time on numppy in gsoc. This project is about integrating vector math library classes of sleef and yeppp into numpy to make the mathematical functions faster. I have already studied the new library classes but i am unable to find the sin , cos function definitions in the numpy souce code.Can someone please help me find the functions in the source code so that i can implement the new library class into numpy.Thanking you,Shubhankar Mohapatra

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Re: [Numpy-discussion] Asking proposal review/feedback for GSOC 15
by Oğuzhan Ünlü March 26, 2015

March 26, 2015

Hi, Sorry for a bit late reply. I will express my thoughts for Ralf's suggestions, respectively. > Regarding your schedule: > - I would remove the parts related to benchmarks. There's no nice benchmark > infrastructure in numpy itself at the moment (that's a separate GSoC idea), > so the two times 1 week that you have are likely not enough to get > something off the ground there. > - I think we can do a sample/demo benchmark only based on a library' speed performance over some basic set of data sets. Couldn't we? Instead of speed, it could be any other performance parameter, we can decide together. > - The "implement a flexible interface" part will need some discussion, > probably it makes sense to first draft a document (call it a NEP - Numpy > Enhancement Proposal) that lays out the options and makes a proposal. > To be realistic, I don't think I have enough time to complete an enhancement proposal. Maybe we can talk about it in the first half of April? - I wouldn't put "investigate accuracy differences" at the end. What if you > find out there that you've been working on something for the whole summer > that's not accurate enough? > However, we can't examine possible accuracy differences without having seen their real performance (in my case it is 'implementing an interface to libraries'). Isn't investigating possible libraries for numpy the fountain head of this project? Integrating chosen library can be possible by a small set of wrapping functions. > - The "researching possible options" I would do in the community bonding > period - when the coding period starts you should have a fairly > well-defined plan. > I agree with you at this point. After moving this to community bounding period, I can put a milestone like 'integrating chosen library to numpy' for 2 weeks. And we decide it would be better to remove benchmark part, then I would use that part for interface, probably. > - 3 weeks for implementing the interface looks optimistic. > It was an estimated time, I asked Julian's opinion about it and waiting his answer. You could be right, I am not familiar with codebase and exact set of functions to be improved. Since I prepared my schedule to serve as basis, I think it is understandable if something takes a bit longer or shorter as compared to what is written on schedule. > Cheers, > Ralf Your suggestions made me able think about project better. Thank you, Ralf. If you could share your opinions for my thoughts as well, I appreciate. My proposal is at https://gist.github.com/oguzhanunlu/1f8bf3ffc6ac5c420dd1 Cheers, Oguzhan

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Proposal feedback
by Marcos . March 26, 2015

March 26, 2015

Hello, I'm Marcos from Brazil! I've recently finalized a sketch of my proposal (on porting core functions of numpy from c to cython) and I would like some feedback from you. It can be seen on the following link: https://github.com/marcospc6/GSoC-Proposal/blob/master/gsocproposal.md Couldn't get the formatation right on github, but you get the idea. Thank you all

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flags attribute of ndarrays`
by Jaime Fernández del Río March 26, 2015

March 26, 2015

Hi, I have just submitted a longish issue on the github repo, #5721 <https://github.com/numpy/numpy/issues/5721>. After going in quite some detail over the flags attribute of ndarray's I have found several inconsistencies with the C API, and would like to make some changes. The details are in gh, but as a high level summary: 1. arr.flags.farray is almost certainly broken, and returns some useless result. I would like to make it behave like the C API's PyArray_ISFARRAY, even though that doesn't seem to be the intention of the original coder. 2. arr.flags.fortran is inconsitent with the C API's PyArray_ISFORTRAN. I think it should be modified to match it, but understand this may be too much of a backwards compatibility breach. 3. I would like for `arr.flags` to truly behave as a mutable property of `arr`. An explanation can be found on another thread's discussion with Benjamin Root earlier today. 4. I would like to match the Python and C version's of these as much as possible, and avoid future deviation, by actually using the C versions in the Python ones, even if this may introduce subtle behavior changes. Feedback is very welcome. Jaime -- (\__/) ( O.o) ( > <) Este es Conejo. Copia a Conejo en tu firma y ayúdale en sus planes de dominación mundial.

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Do you find this behavior surprising?
by Jaime Fernández del Río March 25, 2015

March 25, 2015

>>> import numpy as np >>> a = np.arange(10) >>> flags = a.flags >>> flags C_CONTIGUOUS : True F_CONTIGUOUS : True OWNDATA : True WRITEABLE : True ALIGNED : True UPDATEIFCOPY : False >>> flags.writeable = False >>> a.flags C_CONTIGUOUS : True F_CONTIGUOUS : True OWNDATA : True WRITEABLE : False <--- WTF!!?? ALIGNED : True UPDATEIFCOPY : False I understand why this is happening, and that there is no other obvious way to make a.flags.writeable = False work than to have the return of a.flags linked to a under the hood. But I don't think this is documented anywhere, and wonder if perhaps it should. Jaime -- (\__/) ( O.o) ( > <) Este es Conejo. Copia a Conejo en tu firma y ayúdale en sus planes de dominación mundial.

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Improve Numpy Datetime Functionality for Gsoc
by Saprative Jana March 25, 2015

March 25, 2015

hi, I am Saprative .I am new to numpy devlopment. I want to work on the project of improving datetime functionality numpy project .I want to solve some related bugs and get started with the basics. As there is no irc channel for numpy so i am facing a problem of contacting with the mentors moreover there is no mentors mentioned for this project. So anybody who can help me out please contact with me. from, Saprative Jana (Mob: +919477325233)

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