Mailman 3 February 2023 - SciPy-User

Re: Textbook Recommendations
by Kevin Dunn April 3, 2023

April 3, 2023

(Sent with wrong subject earlier due to user error. Resending. Apologies for the noise) -- I am a bit biased of course, but my online book could also meet your needs. Process Improvement using Data. https://learnche.org/pid/ Chapter 4 is about Regression, chapter 2 on fundamental stats, and chapter 5 on designed experiments (including a full online course on Coursera). It's been a free, Creative Commons licensed resource since 2010, and used by quite a number of industrial companies and universities daily. I'm working on converting the R code to Python, so both are available side by side. The repo is available on GitHub if anyone is interested in helping speed that up ;) https://github.com/kgdunn/pid-book Kevin

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How to display a RGB image (MFISH)?
by jp April 3, 2023

April 3, 2023

Hi, I have written a script to try to combine five images into a three channels RGB images<http://dip4fish.blogspot.com/2011/05/trying-to-combine-mfish-images-into-rg…> . The resulting RGB image looks like a grey scale image instead of a color one. I have three np.array Rnorm, Gnorm, Bnorm which are copied into a rgb array: *rgb = np.zeros((shape[0],shape[1],3),dtype=float) mxr=np.max(R) mxg=np.max(G) mxb=np.max(B) Rnorm=np.uint8((255*(R/mxr)))Gnorm=np.uint8((255*(R/mxg)))Bnorm=np.uint8((255*(R/mxb)))#copy each RGB component in an RGB array rgb[:,:,0]=Rnorm rgb[:,:,1]=Gnorm rgb[:,:,2]=Bnorm* *pylab.subplot(224, aspect='equal',frameon=False, xticks=[], yticks=[]) pylab.imshow(rgb) pylab.show()* Any advice? Thank you Jean-Patrick

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NumPy 1.23.2 release
by Charles R Harris March 22, 2023

March 22, 2023

Hi All, On behalf of the NumPy team, I am pleased to announce the release of NumPy 1.23.2. NumPy 1.23.2 is a maintenance release that fixes bugs discovered after the 1.23.1 release. Notable features are: - Typing changes needed for Python 3.11 - Wheels for Python 3.11.0rc1 The Python versions supported for this release are 3.8-3.11. Wheels can be downloaded from PyPI <https://pypi.org/project/numpy/1.23.2>; source archives, release notes, and wheel hashes are available on Github <https://github.com/numpy/numpy/releases/tag/v1.23.2>. *Contributors* A total of 9 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - Alexander Grund + - Bas van Beek - Charles Harris - Jon Cusick + - Matti Picus - Michael Osthege + - Pal Barta + - Ross Barnowski - Sebastian Berg *Pull requests merged* A total of 15 pull requests were merged for this release. - #22030: ENH: Add ``__array_ufunc__`` typing support to the ``nin=1`` ufuncs - #22031: MAINT, TYP: Fix ``np.angle`` dtype-overloads - #22032: MAINT: Do not let ``_GenericAlias`` wrap the underlying classes'... - #22033: TYP,MAINT: Allow ``einsum`` subscripts to be passed via integer... - #22034: MAINT,TYP: Add object-overloads for the ``np.generic`` rich comparisons - #22035: MAINT,TYP: Allow the ``squeeze`` and ``transpose`` method to... - #22036: BUG: Fix subarray to object cast ownership details - #22037: BUG: Use ``Popen`` to silently invoke f77 -v - #22038: BUG: Avoid errors on NULL during deepcopy - #22039: DOC: Add versionchanged for converter callable behavior. - #22057: MAINT: Quiet the anaconda uploads. - #22078: ENH: reorder includes for testing on top of system installations... - #22106: TST: fix test_linear_interpolation_formula_symmetric - #22107: BUG: Fix skip condition for test_loss_of_precision[complex256] - #22115: BLD: Build python3.11.0rc1 wheels. Cheers, Charles Harris

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Textbook Recommendations
by Britta Smith Feb. 25, 2023

Feb. 25, 2023

Dear SciPy community, What are your recommendations for textbooks concerning statistics and data science? That hopefully go into depth about regression, and hopefully python. Many thanks, Britta

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Re: SciPy-User Digest, Vol 232, Issue 3
by Kevin Dunn Feb. 15, 2023

Feb. 15, 2023

I am a bit biased of course, but my online book could also meet your needs. Process Improvement using Data. https://learnche.org/pid/ Chapter 4 is about Regression, chapter 2 on fundamental stats, and chapter 5 on designed experiments (including a full online course on Coursera). It's been a free, Creative Commons licensed resource since 2010, and used by quite a number of industrial companies and universities daily. I'm working on converting the R code to Python, so both are available side by side. The repo is available on GitHub if anyone is interested in helping speed that up ;) https://github.com/kgdunn/pid-book Kevin Date: Wed, 15 Feb 2023 12:42:48 -0500 > From: David Nicholson <nicholdav(a)gmail.com> > Subject: [SciPy-User] Re: Textbook Recommendations > To: SciPy Users List <scipy-user(a)python.org> > > fwiw, some books from the Executable Books gallery ( > https://executablebooks.org/en/latest/gallery/) > > https://gulinan.github.io/mat244e/ > http://theoryandpractice.org/stats-ds-book/intro.html > > See also this translating Danielle Navarro's Learning Stats with R to > Python: > https://ethanweed.github.io/pythonbook/landingpage.html > > David Nicholson, Ph.D. > https://nicholdav.info/ > https://github.com/NickleDave > > > On Wed, Feb 15, 2023 at 11:47 AM Albert Steppi <albert.steppi(a)gmail.com> > wrote: > > > Hi Britta, > > > > I haven't read anything Python specific, but I like the following books: > > > > For basic introductory stats, Freedman, Pisani, and Purves > > <https://wwnorton.com/books/Statistics/> > > > > As a follow up, Freedman's Statistical Models: Theory and Practice > > < > https://www.cambridge.org/core/books/statistical-models/68F8872C7788AF62BD6… > >, > > teaches one to think critically about regression modeling and its > > limitations. > > > > Efron and Hasties Computer Age Statistical Inference > > <https://hastie.su.domains/CASI_files/PDF/casi.pdf>, for a very broad > > overview of a wide range of computational statistics topics. > > > > Hastie, Tibshirani, and Friedman's Elements of Statistical Learning > > <https://hastie.su.domains/Papers/ESLII.pdf>, classic introduction to > > statistical machine learning. > > > > Gelman et al's Bayesian Data Analysis > > <http://www.stat.columbia.edu/~gelman/book/BDA3.pdf> for Bayesian > > modeling, but I haven't read any other books on this topic. > > > > I tend to just read the relevant documentation (pymc, pyro, pytorch, > > scikit-learn, scipy, statsmodels) to figure out how to do things in > Python. > > Hopefully someone else can chime in with more Python specific books. > > > > Best, > > Albert > > > > > > > > On Wed, Feb 15, 2023 at 10:31 AM Britta Smith <bretana1226(a)gmail.com> > > wrote: > > > >> Dear SciPy community, > >> What are your recommendations for textbooks concerning statistics and > >> data science? That hopefully go into depth about regression, and > hopefully > >> python. > >> Many thanks, > >> Britta > >

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ANN: pvlib-0.9.4 released
by Dr. Mark Alexander Mikofski PhD Feb. 15, 2023

Feb. 15, 2023

Dear Pythonistas and solar power enthusiasts, The maintainers are happy to announce a new release of pvlib python: software for simulating performance of photovoltaic solar energy systems. *See what's new for v0.9.4:* ** *https://pvlib-python.readthedocs.io/en/stable/whatsnew.html *Releases are available from PyPI and the conda-forge channel:* * https://pypi.org/project/pvlib/ * https://anaconda.org/conda-forge/pvlib and https://anaconda.org/conda-forge/pvlib-python *Read the Documentation:* * https://pvlib-python.readthedocs.io/en/stable/index.html *Report issues & contribute:* * https://github.com/pvlib/pvlib-python *v0.9.4 Highlights:* * Fitting <https://pvlib-python.readthedocs.io/en/stable/reference/pv_modeling/generat…> and prediction <https://pvlib-python.readthedocs.io/en/stable/reference/pv_modeling/generat…> functions for the ADR PV efficiency model, along with several gallery examples <https://pvlib-python.readthedocs.io/en/stable/gallery/index.html#adr-model-…> . * An extension of the Faiman temperature model <https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.tem…> that includes a radiative loss term. * Efficient direct <https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.iam…> and diffuse <https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.iam…> IAM models based on the Schlick approximation to the Fresnel equations. * A convenience function <https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.irr…> to calculate one of DNI, DHI, GHI from the other two. * The Hay-Davies <https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.irr…> transposition model can now return individual components in addition to global tilted irradiance. * An implementation of ASTM E1036 <https://pvlib-python.readthedocs.io/en/stable/reference/generated/pvlib.ivt…> for extracting the basic characteristics of an I-V curve. * There were several new contributors. *Thank you for using pvlib python!* -- Mark Mikofski, PhD (2005) *Fiat Lux*

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NumPy 1.24. 2 Release
by Charles R Harris Feb. 5, 2023

Feb. 5, 2023

Hi All, On behalf of the NumPy team, I'm pleased to announce the release of NumPy 1.24.2. NumPy 1.24.2 is a maintenance release that fixes bugs and regressions discovered after the 1.24.1 release. The Python versions supported by this release are 3.8-3.11 Note that 32 bit wheels are only provided for Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit support. Wheels can be downloaded from PyPI <https://pypi.org/project/numpy/1.24.2/>; source archives, release notes, and wheel hashes are available on Github <https://github.com/numpy/numpy/releases/tag/v1.24.2>. *Contributors* A total of 14 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - Bas van Beek - Charles Harris - Khem Raj + - Mark Harfouche - Matti Picus - Panagiotis Zestanakis + - Peter Hawkins - Pradipta Ghosh - Ross Barnowski - Sayed Adel - Sebastian Berg - Syam Gadde + - dmbelov + - pkubaj + *Pull requests merged* A total of 17 pull requests were merged for this release. - #22965: MAINT: Update python 3.11-dev to 3.11. - #22966: DOC: Remove dangling deprecation warning - #22967: ENH: Detect CPU features on FreeBSD/powerpc64* - #22968: BUG: np.loadtxt cannot load text file with quoted fields separated... - #22969: TST: Add fixture to avoid issue with randomizing test order. - #22970: BUG: Fix fill violating read-only flag. (#22959) - #22971: MAINT: Add additional information to missing scalar AttributeError - #22972: MAINT: Move export for scipy arm64 helper into main module - #22976: BUG, SIMD: Fix spurious invalid exception for sin/cos on arm64/clang - #22989: BUG: Ensure correct loop order in sin, cos, and arctan2 - #23030: DOC: Add version added information for the strict parameter in... - #23031: BUG: use ``_Alignof`` rather than ``offsetof()`` on most compilers - #23147: BUG: Fix for npyv__trunc_s32_f32 (VXE) - #23148: BUG: Fix integer / float scalar promotion - #23149: BUG: Add missing <type_traits> header. - #23150: TYP, MAINT: Add a missing explicit ``Any`` parameter to the ``npt.ArrayLike``... - #23161: BLD: remove redundant definition of npy_nextafter [wheel build] Cheers, Charles Harris

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raw values or distributions as input to wasserstein_distance
by mhfh.kvd5＠gmail.com Feb. 1, 2023

Feb. 1, 2023

I'd like to use the wasserstein_distance function to compute the similarity between two time series (x and y below). The documentation says that the input data are "Values observed in the (empirical) distribution". My data arrays x and y have been standardized to mean 0: x = array([-1.50849069, -0.38884367, -0.48630848, -0.921818 , 1.5075886 , -0.07742729, 0.73485371, 0.87245738, 0.11869032, -0.18608133, 1.13576794, 0.59285469, -1.43974886, -1.30714982, -1.48295675, 2.06252624, 0.8560106 , -0.28539087, -0.16960491, 0.37307118]) y = array([-1.58132851, -1.49493426, -0.75626285, -0.37475104, 0.00193676, 1.54513722, 0.77022117, 0.74651951, 0.04927758, -0.41780599, 1.41896231, 0.25463222, 1.06646509, -1.28262476, -2.11766631, 0.92243796, 0.53390812, -0.10711093, 0.35730549, 0.46568122]) I initially thought I should create an empirical distribution of x and y: from statsmodels.distributions.empirical_distribution import ECDF ecdf_x = ECDF(x) x_ecdf = ecdf_y.y ecdf_y = ECDF(y) y_ecdf = ecdf_y.y wasserstein_distance(x_ecdf, y_ecdf) But an answer in stackoverflow (https://stackoverflow.com/questions/75237101/empirical-distribution-from-da…) suggests I should simply use the actual data without converting x and y to distributions. Would you recommend one approach over the other? Are there disadvantages to using the actual data as input to wasserstein_distance? The tutorials I read so far have always mentioned distributions as input data, not raw values. However, as the SO answer from the link above points out, even the examples in the scipy documentation seem to use raw values. So I'm a little confused how I should go on with this.

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