Mailman 3 September 2021 - SciPy-User

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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Invitation to connect on LinkedIn
by Jelle Feringa via LinkedIn July 28, 2022

July 28, 2022

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Help required regding Eigenvectors
by Sudheer Phani March 14, 2022

March 14, 2022

Hello All, I have been encountering some strange problems with the calculation of eigen vectors using python. The problem is with the sign of the eigen vectors calculated. On Very few occasions the eigen vectors calculated using python have an opposite sign than the actual eigenvectors, though the magnitude is correct. Can any one of you plz help me in this regard and suggest me some solution if you have encoutered similar problems . Any help in this regd is appreciated Thanks in Advace regds Sudheer

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ANN: SfePy 2021.3
by Robert Cimrman Sept. 29, 2021

Sept. 29, 2021

I am pleased to announce the release of SfePy 2021.3. Description ----------- SfePy (simple finite elements in Python) is a software for solving systems of coupled partial differential equations by finite element methods. It is distributed under the new BSD license. Home page: https://sfepy.org Mailing list: https://mail.python.org/mm3/mailman3/lists/sfepy.python.org/ Git (source) repository, issue tracker: https://github.com/sfepy/sfepy Highlights of this release -------------------------- - unified volume and surface integration terms - improved pyvista-based visualization script For full release notes see [1]. Cheers, Robert Cimrman [1] http://docs.sfepy.org/doc/release_notes.html#id1 --- Contributors to this release in alphabetical order: Robert Cimrman Hugo Levy Vladimir Lukes

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Subtraction between Boolean sparse matrices
by kaleueno＠gmail.com Sept. 22, 2021

Sept. 22, 2021

What operation is performed when I subtract B from A, where both A and B are sparse matrices of Boolean elements? This operation is barred in numpy with regular arrays, but allowed in scipy.sparse. I believe it is XOR, but I could not follow the code to confirm this hypothesis.

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new searchable mailing list archives
by Ralf Gommers Sept. 21, 2021

Sept. 21, 2021

Hi all, Thanks to the friendly folks who maintain the python.org mailing lists, we now have nice searchable mailing list archives for NumPy and SciPy: https://mail.python.org/archives/list/numpy-discussion@python.org/ https://mail.python.org/archives/list/scipy-dev@python.org/ https://mail.python.org/archives/list/scipy-user@python.org/ Cheers, Ralf

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Review of new/fixed quantile and percentile methods in NumPy
by Sebastian Berg Sept. 21, 2021

Sept. 21, 2021

Hi all, I thought the SciPy community is more likely to have an opinion on quantile calculation/estimation methods than the NumPy one. We would like to refactor the quantile function in NumPy: https://github.com/numpy/numpy/pull/19857/files And comments about the API choice (or math) would be very welcome! The PR basically introduces the "9 types" that R has (Which are from the Hyndman and Fan paper [1]. The R code is also by Hyndman. The paper was a largely a review of methods implemented at the time.). I pinged Hyndman, because I was wondering if there is a more minimal set or better naming for the methods. This did not reduce the number but gave us the current methods and names (all of them, plus the ones NumPy currently has), the Numbers map to the R/Hyndman "type": 1. inverted cdf 2. averaged inverted cdf 3. closest observation 4. interpolated inverted cdf 5. hazen (these names come from wolfram) 6. weibull 7. inclusive (default! Name from `statistics.quantiles`: rename?!) 8. median unbiased 9. normal unbiased And the ones NumPy (unfortunately) currently has: * lower * higher * nearest * midpoint Note that Python's `statistics.quantiles` also has `exclusive` which is none of those methods :(. Any review of the math and the API would be great! I am still considering asking for smaller changes [2]. But in general, I think this is a necessary step. Help with documenting this better could also be great! Cheers, Sebastian [1] Hyndman, R. J., & Fan, Y. (1996). Sample quantiles in statistical packages. The American Statistician, 50(4), 361-365 [2] Some thoughts of things that maybe we should consider: * Rename `interpolation` to `method` * Rename any of the methods? Especially the default? * Rename the current "lower", "nearest", etc. to "legacy-lower" or so to discourage their use. * Expose or do not expose the `QuantileInterpolation` enum? * If I knew a way to pick 4 of those 9 methods only, I would consider doing that :). Right now I can't, and as much as I dislike having 13 methods, it seems better than what we have now. And making the parameters public does not feel helpful either...

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scipy.optimize: SHGO - local minimization parameters
by Olivier B. Sept. 19, 2021

Sept. 19, 2021

Hi everyone, I have a question regarding the local minimization in SHGO from the scipy.optimize module. We are trying to apply a local Nelder-Mead optimization to the minimizer list of all vertex points from a sobol sequence. We would like the local minimization to terminate when either a maximum number of steps is reached or when the difference between iterations is sufficiently small. However, we are struggling to implement this. We are using the lmfit interface to scipy. We did the following: minner = Minimizer(func, params, fcn_args=arg),sampling_method='sobol',n=600,minimizer_kwargs={'method':'Nelder-Mead','options':{'disp':'True','maxiter':10000}}) result = minner.minimize(method='shgo') minner.minimize will pass all the specified options to lmfit. However 'maxiter' specified to Nelder-Mead was filtered out by the '_restrict_to_keys' function. So our question is, why is maxiter not included in the accepted keys? This seems to us the easiest way to tune the local minimization procedure. We have also discussed this at the lmfit mailing list (https://groups.google.com/g/lmfit-py/c/Fm43gjFoTCI). What would be the easiest solution for what we are trying to achieve? Kind regards, Olivier

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Calculating circulation around a latitude circle
by ashwin .D Sept. 10, 2021

Sept. 10, 2021

Hello, It probably is a very trivial question but I think (and hope) that there is no harm in asking for a clarification. I have grid coordinates and their associated velocities arranged along a latitude circle(https://en.wikipedia.org/wiki/Circle_of_latitude_ . I am wanting to calculate the circulation around that latitude circle. I have seen this example - http://www.joshtheengineer.com/2019/04/01/compute-circulation-of-a-vector-f… and the associated video - https://www.youtube.com/watch?v=b8EnhiSjL3o . But I think my case is a trivial case of what is being explained in those links. A straightforward application of Stokes integral in my case as shown below Numerical integration of (u_i d (lambda) + v . (d theta)). In my case d.theta is zero since there is no change of latitude along a latitude circle . Am I right on those assumptions ? Or do I need to interpolate my grid coordinates to an ellipse as shown in that example ? Thanks and regards, Ashwin.

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ANN: pvlib-0.9.0 released
by Dr. Mark Alexander Mikofski PhD Sept. 3, 2021

Sept. 3, 2021

Dear Pythonistas and solar power enthusiasts, On behalf of the maintainers, we're happy to announce a new release of pvlib python: software for simulating performance of photovoltaic solar energy systems. *See what's new for v0.9.0:* * 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-python *Read the Documentation:* * https://pvlib-python.readthedocs.io/en/stable/index.html *Report issues & contribute:* * https://github.com/pvlib/pvlib-python *Highlights:* * There are some breaking changes <https://pvlib-python.readthedocs.io/en/stable/whatsnew.html#breaking-changes>, so please read the Release Notes carefully before updating. * pvlib now has a new Array class <https://pvlib-python.readthedocs.io/en/stable/generated/pvlib.pvsystem.Arra…> that represents groups of identical modules that are part of a PVSystem but have the same orientation and mounting type. * The PVSystem class <https://pvlib-python.readthedocs.io/en/stable/generated/pvlib.pvsystem.PVSy…> can now model a combination of arrays each with different orientations, modules, and mounting types. * pvlib completed a very successful GSoC <https://summerofcode.withgoogle.com/>, which added several new iotools like get_bsrn() <https://pvlib-python.readthedocs.io/en/stable/generated/pvlib.iotools.get_b…> , get_pvgis_hourly() <https://pvlib-python.readthedocs.io/en/stable/generated/pvlib.iotools.read_…> , get_cams() <https://pvlib-python.readthedocs.io/en/stable/generated/pvlib.iotools.get_c…>, and more. Special thanks to Adam R. Jensen <https://predictablysunny.com/>, Kevin Anderson, and many reviewers who helped! * Many bug fixes <https://pvlib-python.readthedocs.io/en/stable/whatsnew.html#bug-fixes> including a fix to Perez <https://github.com/pvlib/pvlib-python/issues/1238> contributed by Clean Power Research. Read their blog <https://www.solaranywhere.com/2021/pvlib-models-via-solaranywhere-joins-pvl…> to learn more about how they're contributing to pvlib. Thanks! *The maintainers thank you for using pvlib python!* -- Mark Mikofski, PhD (2005) *Fiat Lux*

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