Mailman 3 April 2020 - SciPy-Dev

Re: [SciPy-Dev] Discontinued Rackspace Open Compute Discount
by Robert Lucente 05 Oct '20

05 Oct '20

Several CI services are free for open source projects although there are throughput limits https://about.gitlab.com https://github.com/features/actions https://travis-ci.org https://www.appveyor.com -----Original Message----- >From: Olivier Grisel <olivier.grisel(a)ensta.org> >Sent: Feb 8, 2020 11:30 AM >To: SciPy Developers List <scipy-dev(a)python.org> >Subject: [SciPy-Dev] Discontinued Rackspace Open Compute Discount > >Hi all, > >I received an email end of January that I just discovered today >telling me that the Rackspace Open Compute Discount grant we use to >host http://wheels.scipy.org for nightly builds and release management >of our community has been discontinued on December 31st 2019. > >I would like to take down the Rackspace hosted cloudfiles as soon as >possible but we need to find a replacement as this will break all the >CIs that rely on nightly wheels and also all the wheel release >infrastructure for several project in the ecosystem. > >Apparently the fees for January and the beginning of February already >sum to $936.51. This is a large cost because there were a bunch of >unused left-over block volumes that I already deleted. > >To host wheels for nightly builds and release automation, a possible >alternatives include using the github release pages but they are not >really designed for this kind of workflow. > >Alternatively there is a PyPI repo hosting feature in Azure Pipelines >in public preview but I am not sure how it works, if it's free for >open source projects and if we could use it as a replacement for the >Rackspace cloud storage we currently use to host >http://wheels.scipy.org . > >https://docs.microsoft.com/en-us/azure/devops/pipelines/artifacts/pypi?view… > >Important: if you have uploaded any important files to one of the >cloud file storage / blob containers on Rackspace, please download a >copy as soon as possible. > >But please do not download things you do not really want to keep to >avoid adding any unnecessary bandwidth costs. If you have file write >permission on one of those containers, please delete the files you own >and do not need anymore. > >Feel free to join this room to discuss the matter interactively: >https://gitter.im/scikit-learn/nightly-build-hosting > >Feel free to reply to this email on the scipy-dev mailing list if you >have suggestions for setting up a free replacement. > >-- >Olivier >http://twitter.com/ogrisel - http://github.com/ogrisel >_______________________________________________ >SciPy-Dev mailing list >SciPy-Dev(a)python.org >https://mail.python.org/mailman/listinfo/scipy-dev

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Tweedie distributions in scipy.stats
by Christian Lorentzen 17 Jul '20

17 Jul '20

Dear Scipy Developers and mailing list Readers I'd like to address the issue [1] to implement Tweedie distributions [2] in scipy.stats. Purpose The family of Tweedie distributions contains many known distributions like the Poisson and the Gamma distribution, but also distributions between them, aka compound poisson gamma distribution, see [3]. These are often appropriate for insurance claims and other fields, where one has a (Poisson) random count process of events and every event has a (Gamma) random size/amount. The distribution would enable simulations, maximum likelihood estimation of all parameters, choice and visualization of distributions, etc. Implementation I started PR [4] for Wrights generalized Bessel functions as a private function in scipy.special. Once this is ready, the pdf follows immediately. For the range of interest of Y ~ compound poisson gamma distribution, the distribution of Y has a point mass at zero and is otherwise continuous for Y>0. As already discussed in the issue [1], Tweedie might best fit as |rv_generic|. As such, it would be the first one, all others are either |rv_discrete| or |rv_continuous|. Without a template, I would need guidance how to implement a new rv_generic. References: [1] https://github.com/scipy/scipy/issues/11291 [2] https://en.wikipedia.org/wiki/Tweedie_distribution [3] https://en.wikipedia.org/wiki/Compound_Poisson_distribution [4] https://github.com/scipy/scipy/pull/11313 I'm looking forward to your feeback, thoughts and insights. Kind regards, Christian

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SciPy Contribution: Gammatone Filters in scipy.signal
by Shashaank Narayanan 23 May '20

23 May '20

Hello SciPy Team, I am new to this mailing list, and I am interested in contributing to SciPy. I would like to suggest a new feature to be added to the scipy.signal module: gammatone filters. Gammatone filters are becoming increasingly popular in the fields of digital signal processing and music analysis as it effectively models the auditory filters of the human auditory system. Currently, there are very few implementations of gammatone filters available for Python, and these implementations are not generalized to basic finite impulse response (FIR) and infinite impulse response (IIR) filters like SciPy has. I have written my own gammatone FIR filter using NumPy based on Malcolm Slaney's 1993 paper on the topic ( https://engineering.purdue.edu/~malcolm/apple/tr35/PattersonsEar.pdf). This paper was used for Matlab's implementation of gammatone filters. I am in the process of writing a gammatone IIR filter with NumPy and SciPy. Please let me know if this feature will fit with the scipy.signal module. Appreciate your time and guidance. Thanks, Shashaank

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Using global minimizer methods from `optimize.minimize` function
by Andrew Nelson 30 Apr '20

30 Apr '20

`optimize.minimize` offers a choice of many different methods for multivariate scalar minimisation. These methods are chosen using the `method` keyword. There are also different global minimisation routines that one can use (differential_evolution, basinhopping, dual_annealing, shgo). These minimisers have the same overall objective as `minimize`, just with a different approach to finding a minimum. The global minimiser routines are called individually, and are not accessible through the `minimize` function as different methods. A PR is open at https://github.com/scipy/scipy/pull/10778 which proposes to add a `differential-evolution` method to `minimize` that would permit this. This is a fairly straightforward change as the call interfaces are almost identical, and the problems are posed in similar ways. There are obviously pros and cons to this: Pros ------ - One could call any of the multivariate scalar minimizers through one function. - In user code this could simplify code significantly (code that offers all the different minimizers has to use if/elif constructs to call different functions depending on the method to be used). Cons ------- - A user may not appreciate the differences of how local and global minimisers work. e.g. a lot of the global minimisers are stochastic and some use local minimisers to polish the end solution. Could we have a discussion as to whether people think this is a good/bad idea? Would it confuse users, would it make `minimize` too convoluted, etc? A.

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gh-11787
by Władysław Raczek 25 Apr '20

25 Apr '20

Hi all, There is this issue on gh: https://github.com/scipy/scipy/issues/11787 Because I don't know who is the maintainer of the module, I'm writing hear to ask for (subjective) opinion (it would be great if the maintainer will answer): 1) Would it really be that useful? 2) Is there a reason we don't do that yet? I haven't deeply looked into issue, but I rather find that interesting and am ready to work on it. Kind regards, Vladyslav Rachek

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Regarding Contribution: a new image edge detection technique
by Sujoy Kumar Goswami 25 Apr '20

25 Apr '20

Hello Sir/ Madam, I want to contribute to SciPy (Python) with a new image edge detection technique which is more generic & better than the similar popular techniques for the same. The paper that I have published is attached here with this email. Please go through it. If you like it, please tell me the process to move next. Thanks & Regards, Sujoy K Goswami Principal Data Scientist TVS Motor Company India

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NumPy 1.18.3 released.
by Charles R Harris 19 Apr '20

19 Apr '20

Hi All, On behalf of the NumPy team I am pleased to announce that NumPy 1.18.3 has been released. This release contains various bug/regression fixes for the 1.18 series The Python versions supported in this release are 3.5-3.8. Downstream developers should use Cython >= 0.29.15 for Python 3.8 support and OpenBLAS >= 3.7 to avoid errors on the Skylake architecture. Wheels for this release can be downloaded from PyPI <https://pypi.org/project/numpy/1.18.3>, source archives and release notes are available from Github <https://github.com/numpy/numpy/releases/tag/v1.18.3>. *Highlights* Fix for the method='eigh' and method='cholesky' options in numpy.random.multivariate_normal. Those were producing samples from the wrong distribution. *Contributors* A total of 6 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - Charles Harris - Max Balandat + - @Mibu287 + - Pan Jan + - Sebastian Berg - @panpiort8 + *Pull requests merged* A total of 5 pull requests were merged for this release. - #15916: BUG: Fix eigh and cholesky methods of numpy.random.multivariate_normal - #15929: BUG,MAINT: Remove incorrect special case in string to number... - #15930: BUG: Guarantee array is in valid state after memory error occurs... - #15954: BUG: Check that pvals is 1D in _generator.multinomial. - #16017: BUG: Alpha parameter must be 1D in _generator.dirichlet Cheers, Charles Harris

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Run just the test functions that match a given pattern?
by Warren Weckesser 19 Apr '20

19 Apr '20

With runtests.py, we can run the tests for a specific module (e.g. `-s stats`), a specific file (e.g. `-t scipy.stats.tests.test_stats`), or a specific class or function within a file (e.g. `-s scipy.stats.tests.test_stats::TestCorrPearsonr`, `-s scipy.stats.tests.test_stats::test_kendalltau`). Is there a way to run the tests whose names match a given pattern? For example, I'd like to use the pattern `*ttest_rel*` to run any test that has `ttest_rel` in its name. I can hack this by adding the line python_functions = *ttest_rel* to pytest.ini, and then comment out that line when I want to run a larger set of tests, but it would be nice if a pattern could be given in the command line. Warren

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pyproject.toml for scipy dependent project
by Roman Yurchak 07 Apr '20

07 Apr '20

Hello, a quick question: for a project that requires scipy at build time (e.g. because it using its the Cython BLAS API), how should the associated pyproject.toml look like? i.e. a) is specifying scipy>=min_version enough b) or does one need to pin minimal supported scipy for each python version as it's currently done e.g. for numpy https://github.com/scipy/scipy/blob/7ac26a178f385e4fa88d5c22951797a32ec8f38… ? I think I have seen suggestions on conda-forge in the past that b) makes sense, in a similar situation. Can someone confirm that this is also something that should be done in `pyproject.toml`? Or would any supported version work? Thanks, -- Roman

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Specifying SciPy requirement in .toml file
by Benjamin Liu 02 Apr '20

02 Apr '20

Hello all, Wanna to know if it is necessary to specify SciPy version for each python version in .toml file. Like what pandas project do with numpy: https://github.com/pandas-dev/pandas/blob/master/pyproject.toml Many Thanks, Ben

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