From Alexander Tue Apr 2 04:35:20 2019 From: Alexander (Alexander) Date: Tue, 2 Apr 2019 10:35:20 +0200 Subject: [SciPy-User] EuroSciPy 2019 - CfP is open Message-ID: <085A8F3C-33DC-4E2B-B65C-E9DE0C7A0FC8@hendorf.com> ===================================================== *** Apologise for multiple posting *** Dear Colleagues, We are delighted to invite you to join us for the 12th European Conference on Python in Science. The EuroSciPy 2019 Conference will take place from September 2 to September 6 in Bilbao, Basque Country, Spain. The EuroSciPy meeting is a cross-disciplinary gathering focused on the use and development of the Python language in scientific research. This event strives to bring together both users and developers of scientific tools, as well as academic research and state of the art industry. The conference will be structured as it follows: Sep, 2-3 : Tutorials and Hands-on Sep, 4-5 : Main Conference Sep, 6 : Sprints ------------------------------------------------------------------ TOPICS OF INTEREST: Presentations of scientific tools and libraries using the Python language, including but not limited to: Algorithms implemented or exposed in Python Astronomy Data Visualisation Deep Learning & AI Earth, Ocean and Geo Science General-purpose Python tools that can be of special interest to the scientific community. Image Processing Materials Science Parallel computing Political and Social Sciences Project Jupyter Reports on the use of Python in scientific achievements or ongoing projects. Robotics & IoT Scientific data flow and persistence Scientific visualization Simulation Statistics Vector and array manipulation Web applications and portals for science and engineering 3D Printing ------------------------------------------------------------------- CALL FOR PROPOSALS: EuroScipy will accept three different kinds of contributions: Regular Talks: standard talks for oral presentations, allocated in time slots of `15`, or `30` minutes, depending on your preference and scheduling constraints. Each time slot considers a Q&A session at the end of the talk (at least, 5 mins). Hands-on Tutorials: These are beginner or advanced training sessions to dive into the subject with all details. These sessions are 90 minutes long, we expect participants to bring their own laptop for the tutorials. For a sneak peak of last years tutorials, here are the videos: https://www.youtube.com/channel/UCruMegFU9dg2doEGOUaAWTg Poster: EuroScipy will host two poster sessions during the two days of Main Conference. We warmly encourage students and participants to present their work and/or preliminary results as posters. Proposals should be submitted using the EuroScipy submission system at https://pretalx.com/euroscipy-2019/cfp. Submission deadline is April, 28 2019. ------------------------------------------------------------------ REGISTRATION & FEES: The registration fees will be as moderate as in previous years, we'll keep you posted. ------------------------------------------------------------------ IMPORTANT DATES: Mar 26 Call for talks, posters & tutorials Apr 28 Submission deadline for talks and tutorials May 20 Notifications of submission acceptance Sept 2 Start of EuroSciPy Tutorial Sept 4 Start of EuroSciPy Main Conference Sept 6 EuroSciPy Sprints ------------------------------------------------------------------ Best regards, EuroScipy 2019 Organising Committee, Email: info at euroscipy.org Website: http://www.euroscipy.org/2019 twitter: @euroscipy From pengyu.ut at gmail.com Sat Apr 6 09:01:58 2019 From: pengyu.ut at gmail.com (Peng Yu) Date: Sat, 6 Apr 2019 08:01:58 -0500 Subject: [SciPy-User] Why stats.fisher_exact gives results different from R? Message-ID: See below for the output of python and R. Why there is a difference in the odds ratio? Given that R is most widely used by statisticians, I'd prefer the python version print the same results. Also, confidence intervals are missing in the python's output. >>> stats.fisher_exact([[8, 2], [1, 5]]) (20.0, 0.03496503496503495) R> fisher.test(rbind(c(8, 2), c(1,5))) Fisher's Exact Test for Count Data data: rbind(c(8, 2), c(1, 5)) p-value = 0.03497 alternative hypothesis: true odds ratio is not equal to 1 95 percent confidence interval: 1.008849 1049.791446 sample estimates: odds ratio 15.46969 -- Regards, Peng From josef.pktd at gmail.com Sat Apr 6 09:21:47 2019 From: josef.pktd at gmail.com (josef.pktd at gmail.com) Date: Sat, 6 Apr 2019 09:21:47 -0400 Subject: [SciPy-User] Why stats.fisher_exact gives results different from R? In-Reply-To: References: Message-ID: On Sat, Apr 6, 2019 at 9:02 AM Peng Yu wrote: > > See below for the output of python and R. > > Why there is a difference in the odds ratio? Given that R is most > widely used by statisticians, I'd prefer the python version print the > same results. Also, confidence intervals are missing in the python's > output. > > >>> stats.fisher_exact([[8, 2], [1, 5]]) > (20.0, 0.03496503496503495) > > R> fisher.test(rbind(c(8, 2), c(1,5))) > > Fisher's Exact Test for Count Data > > data: rbind(c(8, 2), c(1, 5)) > p-value = 0.03497 > alternative hypothesis: true odds ratio is not equal to 1 > 95 percent confidence interval: > 1.008849 1049.791446 > sample estimates: > odds ratio > 15.46969 estimate an estimate of the odds ratio. Note that the conditional Maximum Likelihood Estimate (MLE) rather than the unconditional MLE (the sample odds ratio) is used. Only present in the 2 by 2 case. https://stat.ethz.ch/R-manual/R-devel/library/stats/html/fisher.test.html scipy shows sample odds ratio The R help page does not show which confidence interval method they use, and I don't remember or never knew. Josef > > -- > Regards, > Peng > _______________________________________________ > SciPy-User mailing list > SciPy-User at python.org > https://mail.python.org/mailman/listinfo/scipy-user From stuart at stuartreynolds.net Sat Apr 6 15:37:15 2019 From: stuart at stuartreynolds.net (Stuart Reynolds) Date: Sat, 6 Apr 2019 12:37:15 -0700 Subject: [SciPy-User] Why stats.fisher_exact gives results different from R? In-Reply-To: References: Message-ID: Also see: https://github.com/brentp/fishers_exact_test/issues/18 .... it's a **much** faster implementation of fisher's exact, and also differs from scipy.stats.fisher_exact. I think the brentp implementation is the same as R's -- actually would be good to test this and update the issue. - Stuart On Sat, Apr 6, 2019 at 6:22 AM wrote: > > On Sat, Apr 6, 2019 at 9:02 AM Peng Yu wrote: > > > > See below for the output of python and R. > > > > Why there is a difference in the odds ratio? Given that R is most > > widely used by statisticians, I'd prefer the python version print the > > same results. Also, confidence intervals are missing in the python's > > output. > > > > >>> stats.fisher_exact([[8, 2], [1, 5]]) > > (20.0, 0.03496503496503495) > > > > R> fisher.test(rbind(c(8, 2), c(1,5))) > > > > Fisher's Exact Test for Count Data > > > > data: rbind(c(8, 2), c(1, 5)) > > p-value = 0.03497 > > alternative hypothesis: true odds ratio is not equal to 1 > > 95 percent confidence interval: > > 1.008849 1049.791446 > > sample estimates: > > odds ratio > > 15.46969 > > estimate > an estimate of the odds ratio. Note that the conditional Maximum > Likelihood Estimate (MLE) rather than the unconditional MLE (the > sample odds ratio) is used. Only present in the 2 by 2 case. > https://stat.ethz.ch/R-manual/R-devel/library/stats/html/fisher.test.html > > scipy shows sample odds ratio > > The R help page does not show which confidence interval method they > use, and I don't remember or never knew. > > Josef > > > > > -- > > Regards, > > Peng > > _______________________________________________ > > SciPy-User mailing list > > SciPy-User at python.org > > https://mail.python.org/mailman/listinfo/scipy-user > _______________________________________________ > SciPy-User mailing list > SciPy-User at python.org > https://mail.python.org/mailman/listinfo/scipy-user From stuart at stuartreynolds.net Sat Apr 6 15:38:05 2019 From: stuart at stuartreynolds.net (Stuart Reynolds) Date: Sat, 6 Apr 2019 12:38:05 -0700 Subject: [SciPy-User] Why stats.fisher_exact gives results different from R? In-Reply-To: References: Message-ID: Also: BUG: stats: fisher_exact returns incorrect p-value https://github.com/scipy/scipy/issues/4130 - Stu On Sat, Apr 6, 2019 at 12:37 PM Stuart Reynolds wrote: > > Also see: > https://github.com/brentp/fishers_exact_test/issues/18 > .... it's a **much** faster implementation of fisher's exact, and also > differs from scipy.stats.fisher_exact. > I think the brentp implementation is the same as R's -- actually would > be good to test this and update the issue. > > - Stuart > > > On Sat, Apr 6, 2019 at 6:22 AM wrote: > > > > On Sat, Apr 6, 2019 at 9:02 AM Peng Yu wrote: > > > > > > See below for the output of python and R. > > > > > > Why there is a difference in the odds ratio? Given that R is most > > > widely used by statisticians, I'd prefer the python version print the > > > same results. Also, confidence intervals are missing in the python's > > > output. > > > > > > >>> stats.fisher_exact([[8, 2], [1, 5]]) > > > (20.0, 0.03496503496503495) > > > > > > R> fisher.test(rbind(c(8, 2), c(1,5))) > > > > > > Fisher's Exact Test for Count Data > > > > > > data: rbind(c(8, 2), c(1, 5)) > > > p-value = 0.03497 > > > alternative hypothesis: true odds ratio is not equal to 1 > > > 95 percent confidence interval: > > > 1.008849 1049.791446 > > > sample estimates: > > > odds ratio > > > 15.46969 > > > > estimate > > an estimate of the odds ratio. Note that the conditional Maximum > > Likelihood Estimate (MLE) rather than the unconditional MLE (the > > sample odds ratio) is used. Only present in the 2 by 2 case. > > https://stat.ethz.ch/R-manual/R-devel/library/stats/html/fisher.test.html > > > > scipy shows sample odds ratio > > > > The R help page does not show which confidence interval method they > > use, and I don't remember or never knew. > > > > Josef > > > > > > > > -- > > > Regards, > > > Peng > > > _______________________________________________ > > > SciPy-User mailing list > > > SciPy-User at python.org > > > https://mail.python.org/mailman/listinfo/scipy-user > > _______________________________________________ > > SciPy-User mailing list > > SciPy-User at python.org > > https://mail.python.org/mailman/listinfo/scipy-user From pengyu.ut at gmail.com Sat Apr 6 17:42:58 2019 From: pengyu.ut at gmail.com (Peng Yu) Date: Sat, 6 Apr 2019 16:42:58 -0500 Subject: [SciPy-User] Why stats.fisher_exact gives results different from R? In-Reply-To: References: Message-ID: But I don't see the computation of the odds ratio. On 4/6/19, Stuart Reynolds wrote: > Also: > > BUG: stats: fisher_exact returns incorrect p-value > https://github.com/scipy/scipy/issues/4130 > > - Stu -- Regards, Peng From charlesr.harris at gmail.com Sun Apr 21 22:39:15 2019 From: charlesr.harris at gmail.com (Charles R Harris) Date: Sun, 21 Apr 2019 20:39:15 -0600 Subject: [SciPy-User] NumPy 1.16. 3 release Message-ID: Hi All, On behalf of the NumPy team I am pleased to announce the release of NumPy 1.16.3 which contains several fixes for newly reported bugs. The Python versions supported in this release are 2.7 and 3.5-3.7. There are several notable changes. - Unpickling object arrays when loading *.npy or *.npz files now requires an explicit opt-in. This backwards incompatible change was made in response to CVE-2019-6446 . - The covariance in 'random.mvnormal' is cast to double. This should make the tolerance more useful when checking the singular values of the covariance matrix. - The '__array_interface__' offset now works as documented. The offset value was being mistakenly ignored. Downstream developers building this release should use Cython >= 0.29.2 and, if using OpenBLAS, OpenBLAS > v0.3.4. Wheels for this release can be downloaded from PyPI , source archives and release notes are available from Github . If you are installing using pip, you may encounter a problem with older installed versions of NumPy that pip did not delete becoming mixed with the current version, resulting in an ``ImportError``. That problem is particularly common on Debian derived distributions due to a modified pip. The fix is to make sure all previous NumPy versions installed by pip have been removed. See #12736 for discussion of the issue. *Contributors* A total of 16 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - Andreas Schwab - Bharat Raghunathan + - Bran + - Charles Harris - Eric Wieser - Jakub Wilk - Kevin Sheppard - Marten van Kerkwijk - Matti Picus - Paul Ivanov - Ralf Gommers - Sebastian Berg - Tyler Reddy - Warren Weckesser - Yu Feng - adeak + *Pull requests merged* A total of 26 pull requests were merged for this release. - gh-13072 : BUG: Fixes to numpy.distutils.Configuration.get_version (#13056) - gh-13082 : BUG: Fix errors in string formatting while producing an error - gh-13083 : BUG: Convert fortran flags in environment variable - gh-13084 : BUG: Remove error-prone borrowed reference handling - gh-13085 : BUG: Add error checks when converting integers to datetime types - gh-13091 : BUG: Remove our patched version of `distutils.split_quoted` - gh-13141 : BUG: Fix testsuite failures on ppc and riscv - gh-13142 : BUG: Fix parameter validity checks in ``random.choice`` - gh-13143 : BUG: Ensure linspace works on object input. - gh-13144 : BLD: fix include list for sdist building. - gh-13145 : BUG: __array_interface__ offset was always ignored - gh-13274 : MAINT: f2py: Add a cast to avoid a compiler warning. - gh-13275 : BUG, MAINT: fix reference count error on invalid input to ndarray.flat - gh-13276 : ENH: Cast covariance to double in random mvnormal - gh-13278 : BUG: Fix null pointer dereference in PyArray_DTypeFromObjectHelper - gh-13339 : BUG: Use C call to sysctlbyname for AVX detection on MacOS. - gh-13340 : BUG: Fix crash when calling savetxt on a padded array - gh-13341 : BUG: ufunc.at iteration variable size fix - gh-13342 : DOC: Add as_ctypes_type to the documentation - gh-13350 : BUG: Return the coefficients array directly - gh-13351 : BUG/MAINT: Tidy typeinfo.h and .c - gh-13359 : BUG: Make allow_pickle=False the default for loading - gh-13360 : DOC: fix some doctest failures - gh-13363 : BUG/MAINT: Tidy typeinfo.h and .c - gh-13381 : BLD: address mingw-w64 issue. Follow-up to gh-9977 - gh-13382 : REL: Prepare for the NumPy release. Cheers, Charles Harris -------------- next part -------------- An HTML attachment was scrubbed... URL: