Mailman 3 November 2015 - SciPy-User

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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