Mailman 3 November 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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Issue solving ode with solve_ivp
by Andreas Scholz Nov. 24, 2021

Nov. 24, 2021

Hi everyone, I have an issue with integrate.solve_ivp. In a matlab example with matlabs function [~,x] = ode45(@(t,x) g(t,x,a,b,c,d),tspan,ic), I am able to create this output, visible the 4 solution lines from the saddle point. [cid:0b1985d9-c2c1-4011-b08b-4c5ef63d70c5] The problematic parts are the last 4 blocks calculating solutions from the saddle point at [1,1], the unstable node [0,0] and the stable nodes [0,c] and [a,0]. This function is defined as follows: def lotkavolterra_variant(a=3, b=2, c=2, d=1, xmin=0, xmax=4, ymin=0, ymax=4, stepsize=.25): ## Lotka-Volterra system with intraspecific competition # Skript Figure 4.27 x = np.arange(xmin, xmax+stepsize, stepsize) y = np.arange(ymin, ymax+stepsize, stepsize) yval = np.arange(ymin, ymax+stepsize*0.1, stepsize*0.1) # evaluate curve 4 times more often to get smoothness [X, Y] = np.meshgrid(x, y) dx = X*(a - X - b*Y) dy = Y*(c - Y - d*X) f = lambda t, x,a,b,c,d : [x[0]*(a - x[0] - b*x[1]), x[1]*(c - x[1] - d*x[0])] g = lambda t, x,a,b,c,d : [-x[0]*(a - x[0] - b*x[1]), -x[1]*(c - x[1] - d*x[0])] plt.figure(figsize = (13,13)) plt.quiver(X, Y, dx, dy, angles = 'xy', color='#0086b3', width=0.0015) plt.grid() plt.xlabel('x') plt.ylabel('y') plt.axis([xmin,xmax,ymin,ymax]) tspan=[0,50] x0stepsize = .0001 for x0 in np.arange(0, .01+x0stepsize, x0stepsize): ic = [x0, .01-x0] solution = solve_ivp(f, [xmin, xmax], ic, method='RK45', t_eval=yval, dense_output=True, args=(a,b,c,d,)) plt.plot(solution.y[0], solution.y[1],'r') x0stepsize = .1 for x0 in np.arange(0, 10+x0stepsize, x0stepsize): ic = [x0, 10-x0] solution = solve_ivp(f, [xmin, xmax], ic, method='RK45', t_eval=yval, dense_output=True, args=(a,b,c,d,)) plt.plot(solution.y[0], solution.y[1],'r') x4 = (a - b*c)/(1 - b*d) y4 = (c - a*d)/(1 - b*d) plt.plot( 0, 0 ,'go',fillstyle='none', ms=15, zorder=100)[0].set_clip_on(False) plt.plot (0, c ,'yo',fillstyle='full', ms=15, zorder=100)[0].set_clip_on(False) plt.plot( a, 0 ,'mo',fillstyle='full', ms=15, zorder=100)[0].set_clip_on(False) plt.plot(x4,y4 ,'bo',fillstyle='none', ms=15, zorder=100)[0].set_clip_on(False) epsilon = 0.00001 ic = [x4-epsilon,y4+epsilon] solution = solve_ivp(f, [xmin, xmax], ic, method='RK45', t_eval=yval, dense_output=True, args=(a,b,c,d,)) plt.plot(solution.y[0], solution.y[1],'g--', linewidth=2, zorder=80) ic = [x4+epsilon,y4-epsilon] solution = solve_ivp(f, [xmin, xmax], ic, method='RK45', t_eval=yval, dense_output=True, args=(a,b,c,d,)) plt.plot(solution.y[0], solution.y[1],'m--', linewidth=2, zorder=81) ic = [x4-epsilon,y4-epsilon] solution = solve_ivp(g, [ymin, ymax], ic, method='RK45', t_eval=yval, dense_output=True, args=(a,b,c,d,)) plt.plot(solution.y[0], solution.y[1], 'y', linewidth=2, zorder=82) ic = [x4+epsilon,y4+epsilon] solution = solve_ivp(g, [xmin, xmax], ic, method='RK45', t_eval=yval, dense_output=True, args=(a,b,c,d,)) plt.plot(solution.y[0], solution.y[1], 'b', linewidth=2, zorder=83) While Matlab is able to compute them, the Scipy implementation failed with lots of variations of methods, first_step, max_step (I even tried to set it to 0.0001), rtol and atol adjustments. The result is always like this, so the computaion stops after about 40 steps and a short distance [cid:cbbec946-971a-4768-965c-086199212999] unless I set epsilon to something quite high like 0.1. Maybe I'm missing something, so I hope someone can help me resolve to a setting, which calculates the solution for those ode's like the Matlab implementation. Thanks in advance and kind regards

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NumPy 1.22.0rc1 released.
by Charles R Harris Nov. 23, 2021

Nov. 23, 2021

Hi All, On behalf of the NumPy team, I'm pleased to announce the release of NumPy 1.22.0rc1. NumPy 1.22.0rc1 is a big release featuring the work of 150 contributers spread over 575 pull requests. There have been many improvements, highlights are: - Annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release. - A preliminary version of the proposed Array-API is provided. This is a step in creating a standard collection of functions that can be used across applications such as CuPy and JAX. - NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data. - New methods for `quantile`, `percentile`, and related functions. Thenew methods provide a complete set of the methods commonly found in the literature. - A new configurable allocator for use by downstream projects. These are in addition to the ongoing work to provide SIMD support for commonly used functions, improvements to F2PY, and better documentation. The Python versions supported in this release are 3.8-3.10, Python 3.7 has been dropped. Note that 32 bit wheels are only provided for Python 3.8 and 3.9 on Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit support. All 64 bit wheels are also linked with 64 bit OpenBLAS, which should fix the occasional problems encountered by folks using truly huge arrays. Wheels can be downloaded from PyPI <https://pypi.org/project/numpy/1.22.0rc1/>; source archives, release notes, and wheel hashes are available on Github <https://github.com/numpy/numpy/releases/tag/v1.22.0rc1>. Linux users will need pip >= 0.19.3 in order to install the manylinux2014 wheels. A recent version of pip is needed to install the universal2 macos wheels. *Contributors* A total of 150 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - @DWesl - @Illviljan - @h-vetinari - @yan-wyb + - Aaron Meurer - Abel Aoun + - Adrian Gao + - Ahmet Can Solak + - Ajay DS + - Alban Colley + - Alberto Rubiales + - Alessia Marcolini + - Amit Kumar + - Andrei Batomunkuev + - Andrew Watson + - Anirudh Dagar + - Ankit Dwivedi + - Antony Lee - Arfy Slowy + - Arryan Singh + - Arun Palaniappen + - Arushi Sharma + - Bas van Beek - Brent Brewington + - Carl Johnsen + - Carl Michal + - Charles Harris - Chiara Marmo - Chris Fu (傅立业) + - Christoph Buchner + - Christoph Reiter + - Chunlin Fang - Clément Robert + - Constanza Fierro - Damien Caliste - Daniel Ching - David Badnar + - David Cortes + - David Okpare + - Derek Huang + - Developer-Ecosystem-Engineering + - Dima Pasechnik - Dimitri Papadopoulos + - Dmitriy Fishman + - Eero Vaher + - Elias Koromilas + - Eliaz Bobadilla + - Elisha Hollander + - Eric Wieser - Eskild Eriksen + - Fayas Noushad + - Gagandeep Singh + - Ganesh Kathiresan - Ghiles Meddour + - Greg Lucas - Gregory R. Lee - Guo Shuai + - Gwyn Ciesla + - Hameer Abbasi - Hector Martin + - Henry Schreiner + - Himanshu + - Hood Chatham + - Hugo Defois + - Hugo van Kemenade - I-Shen Leong + - Imen Rajhi + - Irina Maria Mocan + - Irit Katriel + - Isuru Fernando - Jakob Jakobson - Jerry Morrison + - Jessi J Zhao + - Joe Marshall + - Johan von Forstner + - Jonas I. Liechti + - Jonathan Reichelt Gjertsen + - Joshua Himmens + - Jérome Eertmans - Jérôme Kieffer + - KIU Shueng Chuan + - Kenichi Maehashi - Kenny Huynh + - Kent R. Spillner + - Kevin Granados + - Kevin Modzelewski + - Kevin Sheppard - Lalit Musmade + - Malik Idrees Hasan Khan + - Marco Aurelio da Costa + - Margret Pax + - Mars Lee + - Marten van Kerkwijk - Matthew Barber + - Matthew Brett - Matthias Bussonnier - Matthieu Dartiailh - Matti Picus - Melissa Weber Mendonça - Michael McCann + - Mike Jarvis + - Mike McCann + - Mike Toews - Mukulika Pahari - Nick Pope + - Nick Wogan + - Niels Dunnewind + - Niko Savola + - Nikola Forró - Niyas Sait + - Pamphile ROY - Paul Ganssle + - Pauli Virtanen - Pearu Peterson - Peter Hawkins + - Peter Tillema + - Prathmesh Shirsat + - Raghuveer Devulapalli - Ralf Gommers - Robert Kern - Rohit Goswami + - Ronan Lamy - Ross Barnowski - Roy Jacobson + - Samyak S Sarnayak + - Sayantika Banik + - Sayed Adel - Sebastian Berg - Sebastian Schleehauf + - Serge Guelton - Shriraj Hegde + - Shubham Gupta + - Sista Seetaram + - Stefan van der Walt - Stephannie Jimenez Gacha + - Tania Allard - Theodoros Nikolaou + - Thomas J. Fan - Thomas Li + - Tim Hoffmann - Tom Tan + - Tyler Reddy - Vijay Arora + - Vinith Kishore + - Warren Weckesser - Yang Hau - Yashasvi Misra - Yuval Ofek + - Zac Hatfield-Dodds - Zhang Na + Cheers, Charles Harris

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how to treat different kind of errors when using numeric solvers?
by Schuldei, Andreas Nov. 13, 2021

Nov. 13, 2021

I would like some help from experienced users of numeric solvers (like optimize.minimize()). I have a system of vector equations that I want to solve numerically. [cid:6d69c188-653b-4b2b-9f36-69bed2a70a15] How should I combine the errors, so the solver can use the feedback best? The resulting errors of the first two equations are about 10 orders of magnitude smaller then the third. What is a "natural" mathematical way to combine such errors? this is my code: def detect_cable(data: np.ndarray): b_measured = np.average(data, axis=1) sensor_coordinates = np.array([[.265, 0, .382], [0, .712, .764], [0, .712, 0], [.752, .712, .382]]) sensor_num = b_measured.shape[0] # print(b_measured) guess_b_earth = np.array( [1.7921, 4.6676, -1.013]) # https://ngdc.noaa.gov/geomag/calculators/magcalc.shtml#igrfwmm # Latitude: 54° 08' 47" N # Longitude: 8° 48' 50" E guess_n = np.array([0, 0, 1]) guess_a = np.array([.2, 0, 0]) guess_r = np.array([0, 2.7, 0]) guess_i = 1000 p0 = np.hstack([guess_b_earth, guess_n, guess_r, guess_a, guess_i]) def error_func_geometry_vs_measurement(x, b_measured): b_earth = x[0:3] n = x[3:6] r = x[6:9] a = x[9:12] i = x[12] k = i * MU_0 / np.pi for sensor_cnt in range(0, sensor_num): r_relativ = 0 if sensor_cnt: # only do this for sensor 1,2 and 3, not 0. # that means that the algorithm will find the projection of 0's point on to the cable on its own. # The other r_relativ vectors depend on r r_relativ = r - n * np.matmul((r - sensor_coordinates[sensor_cnt]), n) / np.linalg.norm(n) else: # for sensor 0 we take r as the projection point on to the cable r_relativ = r - sensor_coordinates[sensor_cnt] r_magnitude = np.linalg.norm(r_relativ, keepdims=True) # print("r: {}".format(r_relativ)) error_0 = -k * np.cross(a - 2 * (r_relativ.T * np.matmul(r_relativ, a)).T, n) / r_magnitude ** 4 + b_earth - b_measured error_0 = np.linalg.norm(error_0, axis=1) error_0 = np.dot(error_0, error_0) # error_1 = np.dot(n, a) ** 2 # error_2 = np.linalg.norm(np.matmul(r, n)) ** 2 error_3 = error_0 # + error_1 + error_2 # print("error_3 {}, error_0 {}, error_1 {}, error_2 {}".format(error_3, error_0, error_1, error_2)) return error_3

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ANN: PyWavelets 1.2.0
by Gregory Lee Nov. 9, 2021

Nov. 9, 2021

Greetings, We are pleased to announce the release of PyWavelets 1.2.0. This release adds support for Python 3.10 and now also provides pre-built wheels for linux aarch64 and Apple's M1 architecture. There is also a new set of discrete wavelet transform functions suitable for multiresolution analysis <https://pywavelets.readthedocs.io/en/latest/ref/mra.html>. A summary of changes and contributors is given below. A more comprehensive list of specific issues and pull requests closed is available in the release notes at: https://github.com/PyWavelets/pywt/blob/master/doc/release/1.2.0-notes.rst <https://github.com/PyWavelets/pywt/blob/master/doc/release/1.0.0-notes.rst> New features - There is a new series of multilevel stationary wavelet transforms (mra, mra2 and mran) suited for multiresolution analysis of 1D, 2D or nD signals, respectively. This MRA analysis is also known as the additive wavelet decomposition because the corresponding inverse functions (imra, imra2 or imran) reconstruct the original signal by simple addition of the components. These are a good alternative to the use of the existing SWT functions when it is important to have features aligned across wavelet scales (see the new demo in demo/mra_vs_swt.py). - There is now an n-dimensional implementation available for the wavelet packet transforms (see class WaveletPacketND). Backwards incompatible changes - The image returned by pywt.data.camera has been replaced by a similar, CC0-licensed image because the original image was determined to only be licensed for non-commercial use. Any users who still need the prior camera image for non-commercial use can find it many places online by performing a web search for "cameraman test image". Bugs Fixed - Add input length check in dwt_single for reflect modes. - Demos were updated for compatibility with recent Matplotlib versions. - Removed deprecated import from imp. Other changes - PyWavelets has dropped support for Python 3.5 and 3.6 in this release. Authors - ElConno + - Ralf Gommers - Gregory R. Lee - Jakub Mandula + - nperraud + - ossdev07 + A total of 6 people contributed to this release. People with a "+" by their names contributed a patch for the first time. This list of names is automatically generated, and may not be fully complete.

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NumPy 1.21.4 release.
by Charles R Harris Nov. 5, 2021

Nov. 5, 2021

Hi All, On behalf of the NumPy team I am pleased to announce the release of NumPy 1.21.4. NumPy 1.21.4 is a maintenance release that fixes a few bugs discovered after 1.21.3. The most important fix here is for the NumPy header files to make them work for both x86_64 and M1 hardware when included in the Mac universal2 wheels. Previously, the header files only worked for M1 and this caused problems for folks building x86_64 extensions. This problem was not seen before Python 3.10 because there were thin wheels for x86_64 that had precedence. This release also provides thin x86_64 Mac wheels for Python 3.10. Note that there are *no* manylinux1 wheels, the needed CentOS 5 repos have gone missing. Make sure you upgrade your pip version if you have problems downloading the correct wheels. The Python versions supported in this release are 3.7-3.10. If you want to compile your own version using gcc-11 you will need to use gcc-11.2+ to avoid problems. Wheels can be downloaded from PyPI <https://pypi.org/project/numpy/1.21.4/>; source archives, release notes, and wheel hashes are available on Github <https://github.com/numpy/numpy/releases/tag/v1.21.4>. Linux users will need pip >= 0.19.3 in order to install manylinux2010 and manylinux2014 wheels. A recent version of pip is needed to install the universal2 macos wheels. *Contributors* A total of 7 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - Bas van Beek - Charles Harris - Isuru Fernando - Matthew Brett - Sayed Adel - Sebastian Berg - 傅立业（Chris Fu） + *Pull requests merged* A total of 9 pull requests were merged for this release. - #20278: BUG: Fix shadowed reference of `dtype` in type stub - #20293: BUG: Fix headers for universal2 builds - #20294: BUG: ``VOID_nonzero`` could sometimes mutate alignment flag - #20295: BUG: Do not use nonzero fastpath on unaligned arrays - #20296: BUG: Distutils patch to allow for 2 as a minor version (!) - #20297: BUG, SIMD: Fix 64-bit/8-bit integer division by a scalar - #20298: BUG, SIMD: Workaround broadcasting SIMD 64-bit integers on MSVC... - #20300: REL: Prepare for the NumPy 1.21.4 release. - #20302: TST: Fix a ``Arrayterator`` typing test failure Cheers, Charles Harris

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