Mailman 3 November 2015 - 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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Using ctypes with scipy.integrate
by Rodrigo Araújo Nov. 25, 2015

Nov. 25, 2015

Hello everyone, I'm using Python and SciPy to develop a reservoir flow simulator for my master thesis and part of the solution includes solving some integrals in order to build a coefficient matrix. This solution is based on using the solution for a source point acting at an observation point and integrating its value considering that the source is a plane instead of a point, as shown below: Point solution: [image: Inline image 3] Plane solution: [image: Inline image 1] where [image: Inline image 2] I'm using scipy.integrate.nquad to integrate based on [1] using the following python function, which receives multiple arguments. def deriv_green_2d(self, x, y, sx, sy, sz, ox, oy, oz, ori, sqrt_u, dn): """Calculates Green's function derivative value on two dimensions. Keyword arguments: x,y -- integration variables sx, sy, sz -- source point coordinates ox, oy, oz -- observation point coordinates ori -- source orientation (wether it's a xy, xz or yz plane) sqrt_u -- square root of Laplace's variable value dn -- normal distance between the points """ # linear distance if ori == 0: r = sqrt((sx-ox)**2 + (sy-oy+x)**2 + (sz-oz+y)**2) elif ori == 1: r = sqrt((sx-ox+x)**2 + (sy-oy)**2 + (sz-oz+y)**2) else: r = sqrt((sx-ox+x)**2 + (sy-oy+y)**2 + (sz-oz)**2) return dn/r**2 * (sqrt_u + 1/r) * exp(-r*sqrt_u) As this integral gets solved multiple times for each timestep, it slows down the simulator performance. In order to improve the speed, I'm trying to use ctypes as shown in [2], but in the example provided, the only parameters for the c function are the integration variables and, as show above, I need multiple different arguments besides the integration variables (sx, sy, sz, ox, oy, oz, ori, sqrt_u and dn). Does anyone know if this possible on SciPy v0.16.1? Best regards and thanks in advance, Rodrigo Araújo [1]: http://docs.scipy.org/doc/scipy/reference/tutorial/integrate.html#general-m… [2]: http://docs.scipy.org/doc/scipy/reference/tutorial/integrate.html#faster-in…

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ANN: New edition of the IPython minibook
by Cyrille Rossant Nov. 23, 2015

Nov. 23, 2015

Hi all, The second edition of the book *Learning IPython for Interactive Computing and Data Visualization* has been released (Packt Publishing). The book now targets Python 3, IPython 4, and the Jupyter Notebook. There is a new introduction to the Python programming language for complete beginners, as well as new code examples covering pandas and NumPy for data analysis and numerical computing. There are also contents for more advanced users, like parallel computing with IPython and high-performance computing with Numba and Cython. All code examples are available on GitHub as Jupyter notebooks. You'll find more information here: * IPython Books website: <http://ipython-books.github.io/> * Book page on Packt's website: <https://www.packtpub.com/big-data-and-business-intelligence/learning-ipytho…> * GitHub repo: <https://github.com/ipython-books/minibook-2nd-code> Cyrille

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Finding Derivatives of a 2D Interpolation Using RectBivariateSpline.__call__
by Delaram Ghoreishi Nov. 22, 2015

Nov. 22, 2015

Hi. I need to find partial derivatives of 2d interpolation using RectBivariateSpline.__call__ from scipy. This is the code that I have so far: import numpy as np from scipy import interpolate xm = np.arange(-180.,182.,2) #rank-1 array length 181 ym = np.arange(-180.,182.,2) #rank-1 array length 181 zm = np.loadtxt('data.dat', dtype = np.double) #rank-2 array length (181x181) V = interpolate.RectBivariateSpline(xm, ym, zm, s=0) this code works fine when I need to know the value of the interpolated function at a given point, now how should I proceed from here to also find the derivatives at any specific point. Thanks a lot for the help. -- Delaram Ghoreishi Ph.D. Student Department of Physics University of Florida P.O. Box 118440 Gainesville, Florida 32611

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ANN: pandas v0.17.1 Released
by Jeff Reback Nov. 21, 2015

Nov. 21, 2015

Hi, We are proud to announce that *pandas* has become a sponsored project of the NUMFocus organization <http://numfocus.org/news/2015/10/09/numfocus-announces-new-fiscally-sponsor…> This will help ensure the success of development of *pandas* as a world-class open-source project. This is a minor bug-fix release from 0.17.0 and includes a large number of bug fixes along several new features, enhancements, and performance improvements. We recommend that all users upgrade to this version. This was a release of 5 weeks with 176 commits by 61 authors encompassing 84 issues and 128 pull-requests. *What is it:* *pandas* is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. *Highlights*: - Support for Conditional HTML Formatting, see here <http://pandas.pydata.org/pandas-docs/version/0.17.1/whatsnew.html#whatsnew-…> - Releasing the GIL on the csv reader & other ops, see here <http://pandas.pydata.org/pandas-docs/version/0.17.1/whatsnew.html#whatsnew-…> - Fixed regression in DataFrame.drop_duplicates from 0.16.2, causing incorrect results on integer values see Issue 11376 See the Whatsnew <http://pandas.pydata.org/pandas-docs/version/0.17.1/whatsnew.html> for much more information and the full Documentation <http://pandas.pydata.org/pandas-docs/stable/> link. *How to get it:* Source tarballs, windows wheels, and macosx wheels are available on PyPI <https://pypi.python.org/pypi/pandas> Installation via conda is: - conda install pandas windows wheels are courtesy of Christoph Gohlke and are built on Numpy 1.9 macosx wheels are courtesy of Matthew Brett *Issues:* Please report any issues on our issue tracker <https://github.com/pydata/pandas/issues>: Jeff *Thanks to all of the contributors* * - Aleksandr Drozd - Alex Chase - Anthonios Partheniou - BrenBarn - Brian J. McGuirk - Chris - Christian Berendt - Christian Perez - Cody Piersall - Data & Code Expert Experimenting with Code on Data - DrIrv - Evan Wright - Guillaume Gay - Hamed Saljooghinejad - Iblis Lin - Jake VanderPlas - Jan Schulz - Jean-Mathieu Deschenes - Jeff Reback - Jimmy Callin - Joris Van den Bossche - K.-Michael Aye - Ka Wo Chen - Loïc Séguin-C - Luo Yicheng - Magnus Jöud - Manuel Leonhardt - Matthew Gilbert - Maximilian Roos - Michael - Nicholas Stahl - Nicolas Bonnotte - Pastafarianist - Petra Chong - Phil Schaf - Philipp A - Rob deCarvalho - Roman Khomenko - Rémy Léone - Sebastian Bank - Thierry Moisan - Tom Augspurger - Tux1 - Varun - Wieland Hoffmann - Winterflower - Yoav Ram - Younggun Kim - Zeke - ajcr - azuranski - behzad nouri - cel4 - emilydolson - hironow - lexual - llllllllll - rockg - silentquasar - sinhrks - taeold *

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Gmres preconditionner
by Puiseux Pierre Nov. 20, 2015

Nov. 20, 2015

Hello, i try to solve a linear sparse system using scipy.sparse.linalg.gmres. >>> w, v = gmres(A, b) It works fine. Now i’m trying to improve the iterations, using scipy.sparse.linalg.spilu preconditionner : >>> invA = spilu(A) >>> w,v = gmres(A, b, M=invA) File "<string>", line 2, in gmres File "/usr/local/lib/python2.7/site-packages/scipy/sparse/linalg/isolve/iterative.py", line 85, in non_reentrant return func(*a, **kw) File "/usr/local/lib/python2.7/site-packages/scipy/sparse/linalg/isolve/iterative.py", line 422, in gmres A,M,x,b,postprocess = make_system(A,M,x0,b,xtype) File "/usr/local/lib/python2.7/site-packages/scipy/sparse/linalg/isolve/utils.py", line 131, in make_system M = aslinearoperator(M) File "/usr/local/lib/python2.7/site-packages/scipy/sparse/linalg/interface.py", line 682, in aslinearoperator raise TypeError('type not understood') TypeError: type not understood Do i mistake somewhere ? Thanks for your answer. Pierre Puiseux

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linear objective, non-linear constraint
by Gustavo Goretkin Nov. 19, 2015

Nov. 19, 2015

I have what is a linear program, except for a non-linear constraint of the form S^2 + C^2 = 1. The other constraints are linear, as is the objective. I'm trying to use optimize.minimize interface and the SLSQP solver (which I believe is the only solver that supports non-linear and equality constraints), but I get status: 6 success: False njev: 1 nfev: 1 fun: -0.0 x: array([ 5., 5., 1., 0., 0., 6., 6., 4., 4., 6., 4., 4., 6.]) message: 'Singular matrix C in LSQ subproblem' jac: array([-0., -0., -0., -0., -1., -0., -0., -0., -0., -0., -0., -0., -0., 0.]) nit: 1 Is this because the objective is not positive definite? Or is there another reason? I've looked below, but I don't see where in the fortan code that the mode gets set to 6 https://github.com/scipy/scipy/blob/v0.16.1/scipy/optimize/slsqp/slsqp_optm… https://github.com/scipy/scipy/blob/a9fb36bc44bad4bbd2c1a41cb43c6f10925b38a…

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Access to objective function and gradient in minimize callback
by Mark vdw Nov. 19, 2015

Nov. 19, 2015

Hi all, I want to keep track of the optimisation history when using minimize. I want to keep track of the objective function value and the gradient. However, the callback function is only given the current parameters, not the actual value of the objective function and its gradient. So whenever I want to store the current fval and gradient, I have to recompute them in the callback, which is wasteful, especially in the case where I want to store the values at every iteration. Is there a way to get the fval and gradient that have already been computed inside the optimiser to the callback function? Many thanks, Mark

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Numpy 1.10.2rc1
by Charles R Harris Nov. 12, 2015

Nov. 12, 2015

Hi All, I am pleased to announce the release of Numpy 1.10.2rc1. This release should fix the problems exposed in 1.10.1, which is not to say there are no remaining problems. Please test this thoroughly, exspecially if you experienced problems with 1.10.1. Julian Taylor has opened an issue relating to cblas detection on Debian (and probably Debian derived distributions) that is not dealt with in this release. Hopefully a solution will be available before the final. To all who reported issues with 1.10.1 and to those who helped close them, a big thank you. Source and binary files may be found on Sourceforge <https://sourceforge.net/projects/numpy/files/NumPy/1.10.1/>. Chuck

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ANN: properscoring: proper scoring rules in Python
by Stephan Hoyer Nov. 12, 2015

Nov. 12, 2015

I'm pleased to announce the release of a new open source package, properscoring, for calculating proper scoring rules in Python: https://github.com/TheClimateCorporation/properscoring Evaluation methods that are "strictly proper" cannot be artificially improved through hedging, which makes them fair methods for accessing the accuracy of probabilistic forecasts. These methods are useful for evaluating machine learning or statistical models that produce probabilities instead of point estimates. In particular, these rules are often used for evaluating weather forecasts. properscoring currently contains optimized and extensively tested routines for calculating the Continuous Ranked Probability Score (CRPS) and the Brier Score: - CRPS for an ensemble forecast - CRPS for a Gaussian distribution - CRPS for an arbitrary cumulative distribution function - Brier score for binary probability forecasts - Brier score for threshold exceedances with an ensemble forecast If you're interested in these types of metrics, we'd love to hear your thoughts on this package. Cheers, Stephan

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array creation
by elmar werling Nov. 12, 2015

Nov. 12, 2015

Hi, is there a function foo(ncols, min, max, delta) in mumpy/scipy to create an array such as 0 100 0 0 0 100 10 90 0 10 80 10 10 70 20 10 60 30 10 50 40 10 40 50 10 30 60 20 20 70 20 10 80 20 0 90 20 80 0 ... ... ... 90 10 0 90 0 10 100 0 0 Any help or link is welcome Elmar

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"Order of items" optimization
by Andrea Gavana Nov. 10, 2015

Nov. 10, 2015

Hello List, I am sure the problem I am trying to solve is fairly known but my English/Google skills are failing me today. The issue I am facing is to find the "best" (or "optimal") ordering of some items (and the order is important). These items can only be taken at most once - no repetition, although an item can also be deemed unimportant and not taken at all. So basically it would be a 0-1 coefficient for each item, but the order in which these items are taken is fundamental. I don't have big issues in writing the objective function for this kind of problem, but I am unclear about the solution method: I have looked at the Knapsack problem and bin packing problem, but I am not sure they completely apply to my situation. Any pointer, suggestion (and maybe if you have a link with some Python code somewhere) would be very appreciated :-) . Thank you in advance for your help :-) . Andrea.

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LTI simulation with time delay
by Jason Sachs Nov. 5, 2015

Nov. 5, 2015

Does anyone know if there's a straightforward way to simulate LTI systems in Python with numpy/scipy if one of the systems contains a time delay? I have a SISO system like the one below where G(s) and H(s) are regular rational transfer functions, but F(s) = e^(-sT)/(tau*s+1), and I would like to simulate the step response. [image: Inline image 1]

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Numerical precision: Scipy Vs Numpy
by Sergio Rojas Nov. 5, 2015

Nov. 5, 2015

""" Having fun with numerical precision/errors, a solution of the quadratic equation [a*x**2 + b*x + c == 0] with small values for the constant a is given an unexpected result when comparing SciPy and SymPy numerical results, which I hope an earthly kind soul could explain. The output is as follows (the bothering result is on the second root property): SciPy a*x1**2 + b*x1 + c = 16777217.0 a*x2**2 + b*x2 + c = 1.11022302463e-16 Root properties: (x1 + x2) + b/a = 0.0 x1*x2 - c/a = 0.0 ---- SymPy a*x1**2 + b*x1 + c = 16777217.0000000 a*x2**2 + b*x2 + c = 0 Root properties: (x1 + x2) + b/a = 0 x1*x2 - c/a = 0.125000000000000 """ import scipy def myprint(a, b, c, x1, x2): print('a*x1**2 + b*x1 + c = '), print(a*x1**2 + b*x1 + c) print('a*x2**2 + b*x2 + c = '), print(a*x2**2 + b*x2 + c) print("\t Root properties: ") print('(x1 + x2) + b/a = '), print((x1+x2) + float(b)/float(a)) print(' x1*x2 - c/a = '), print(x1*x2 - float(c)/float(a)) a = 1.0e-15 b = 10000.0 c = 1.0 coeff = [a, b, c] x1, x2 = scipy.roots(coeff) print("\t SciPy ") myprint(a, b, c, x1, x2) from sympy import * var('x') x1, x2 = solve(a*x**2 + b*x + c, x) print(" \t ---- \n \t SymPy ") myprint(a, b, c, x1, x2) # Thanks in advance, # Sergio

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