Mailman 3 April 2017 - SciPy-User

Multivariate linear (bilinear) fit
by Daniele Nicolodi April 20, 2017

April 20, 2017

Hello, before I write my own, does SciPy come with a function for multivariate (often also called bilinear, in the case of two variables) linear fits? I would like to fit a function if the form: y = c + p1 * x1 + p2 * x2 + ... + pn * xn which is a simple least squares problem, but I would like to avoid to write the code :-) Thanks. Cheers, Daniele

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Re: [SciPy-User] Multivariate linear (bilinear) fit
by Sergio Rojas April 19, 2017

April 19, 2017

---------------------------------------------------------------------- Message: 1 Date: Tue, 18 Apr 2017 11:17:26 -0600 From: Daniele Nicolodi <daniele(a)grinta.net> To: scipy-user(a)python.org Subject: [SciPy-User] Multivariate linear (bilinear) fit Message-ID: <7728d109-52a4-50f5-1289-e75961a7a5d6(a)grinta.net> Content-Type: text/plain; charset=utf-8 Hello, before I write my own, does SciPy come with a function for multivariate (often also called bilinear, in the case of two variables) linear fits? I would like to fit a function if the form: y = c + p1 * x1 + p2 * x2 + ... + pn * xn which is a simple least squares problem, but I would like to avoid to write the code :-) Thanks. Cheers, Daniele ===================== Take a look at the optimization tools from SciPy http://www.scipy-lectures.org/advanced/mathematical_optimization/index.html Sergio Enhance your #MachineLearning and #BigData skills via #Python #SciPy: 1) https://www.packtpub.com/big-data-and-business-intelligence/numerical-and-s… 2) https://www.packtpub.com/big-data-and-business-intelligence/learning-scipy-…

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Molecular Index Calculation
by Stephen P. Molnar April 16, 2017

April 16, 2017

I am a newcomer to Python and am attempting to translate a FORTRAN program that I wrote about 20 years ago into Python, specifically v-3.6. Let me say that I am not asking someone to write the program for me, but only to point me is the right direction. So far, I have bumbled my way to the point that I can get all of the input data resulting from a quantum mechanical calculation of a very simple organic molecule in to a Python program, but am encountering problems with processing the data. The equation that I a want to evaluate (the one that I programmed in FORTRAN) is equation (7) in the attached scan of one of the pages of the following document. Stephen P. Molnar and James W. King, Theory and Applications of the Integrated Molecular Transform and the Normalized Molecular Moment Structure Descriptors: QSAR and QSPR Paradigms, Int. J Quantum Chem., 85, 662 (2001). the equation is I(s) = sum from i=2toN sum from j=1toi-1 Z_sub_i*Z_sub_j * sin s*r_sub_ij ----------------- Equation (7) s*r_sub_ij What I have managed to do so far is for a simple organic molecule containing 5 atoms (CHFClBr): import numpy as np import numpy, pandas, scipy.spatial.distance as dist r=[] #distances between pairs of atoms z = [] #atomic numbers of atoms Z_dist = [] #products of successive atomic numbers I = [] #vector calculated in equation (7) start=1 finish=31 points=300 s = np.linspace(start,finish,points) name = input("Enter Molecule ID: ") name = str(name) name_in = name+'.dat' df = pandas.read_table(name_in, skiprows=2, sep=" ", skipinitialspace=True) z = numpy.array(df["ZA"]) N = numpy.ma.size(z) ## of atoms in molecule a = numpy.array(df[["X", "Y", "Z"]]) dist.squareform(dist.pdist(a, "euclidean")) anrows, ancols = np.shape(a) a_new = a.reshape(anrows, 1, ancols) diff = a_new - a r = (diff ** 2).sum(2) r = np.sqrt(r) for j in range(0,N-1): for k in range(j+1,N): Z_diff = (z[j]*z[k]) For the test molecule I get the following: MASS = [ 12.011 1.008 79.9 35.453 18.998] (Atomic weights of atoms) r: [ 0. 2.059801 3.60937686 3.32591826 2.81569212] [ 2.059801 0. 4.71452879 4.45776445 4.00467382] [ 3.60937686 4.71452879 0. 5.66500917 5.26602175] [ 3.32591826 4.45776445 5.66500917 0. 5.02324896] [ 2.81569212 4.00467382 5.26602175 5.02324896 0. ] z: 6.0 210.0 102.0 54.0 35.0 17.0 9.0 595.0 315.0 153.0 I have checked these calculations with a spreadsheet calculation, consequently I'm correct as far as I have gotten. However, my problem is calculating the value of I for the 300 x-coordinate values required for the molecular index calculation. The problem that I am currently having is calculating the product of the pre-sine function and the sine term for the 300 values of s. Unfortunately, the wealth of function in Python makes it difficult for me to ascertain just how to proceed. It seems that I have the problem of seeing the trees because of the size of the forest. At this point Goggling only makes my confusion deeper. Any pointers in the correct direction will be greatly appreciated. Thanks in advance. -- Stephen P. Molnar, Ph.D. Life is a fuzzy set www.molecular-modeling.net Stochastic and multivariate (614)312-7528 (c) Skype: smolnar1

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Hi everyone , I wanna consult one question about linear calculate
by Matthew Shawn April 7, 2017

April 7, 2017

I have little basic knowledge about python, can build CMS with Django ,install,upgrade,uninstall python modules. I occur one question in fact work, and I fee Scipy can fix the question ,but I can't understand how to use Scipy and using which module or function even I have read two books about Numpy and Scipy in two weeks. So I have to seek help form somebody. Here is the question's description :: We have 50 numbers [A1,A2,A3,A4,A5,A6,A7,A8,A9...A50] and we also know the average of these numbers , for example the average is 3219, we will group the 50 numbers into 11 groups,So the average number 3218 and the sum of each group also will have 11 subtractions , and in the sum of 11 subtract abs value , the minimize one is our target. we wanna find the minimize sum of the 11 subtractions, and the group style of the 50 numbers. 1. We have 50 numbers [A1, A2, A3, ..., A49, A50] and the avg of these numbers: 3219; 2. Group these numbers in 11 groups : S1 = x1_1*A1 + x2_1*A2 + x3_1*A3 + ... + x49_1*A49 + x50_1*A50; S2 = x1_2*A1 + x2_2*A2 + x3_2*A3 + ... + x49_2*A49 + x50_2*A50; S3 = x1_3*A1 + x2_3*A2 + x3_3*A3 + ... + x49_3*A49 + x50_3*A50; ..... S11 = x1_11*A1 + x2_11*A2 + x3_11*A3 + ... + x49_11*A49 + x50_11*A50; 3. F(X) = abs(S1-3219) + abs(S2-3219) + abs(S3-3219) + ... +abs(S11-3219) 4. LIMIT each one of the 50 numbers only in one group: x1_1 + x1_2 + x1_3+ x1_4 + x1_5 + x1_6 + x1_7 + x1_8 + x1_9 + x1_10 + x1_11 = 1 x2_1 + x2_2 + x2_3+ x2_4 + x2_5 + x2_6 + x2_7 + x2_8 + x2_9 + x2_10 + x2_11 = 1 x3_1 + ...... + x2_11 = 1 ... x50_1 + ...... + x50_11 = 1 5. LIMIT each one of the x variables only can be 0 or 1; x1_1 = {0,1} x1_2 = {0,1} ..... x50_11 = {0,1} 6.solve the minimize function: minimize F(X) Will anyone teach me how to solve this? I will be very thankful for your reply.

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Re: [SciPy-User] Three-term gaussian fit to gaussian data using scipy (Otto Ngeke)
by Sergio Rojas April 7, 2017

April 7, 2017

------------------------------ Message: 3 Date: Thu, 6 Apr 2017 08:56:56 +0200 From: Otto Ngeke <lieemlieem(a)gmail.com> To: SciPy Users List <scipy-user(a)python.org> Subject: Re: [SciPy-User] Three-term gaussian fit to gaussian data using scipy Message-ID: <CAO7y-mt2KekFx+1-=M5m_+A=+-n-JmTQ3jCYQbLwB8bF3cZrKw(a)mail.gmail.com> Content-Type: text/plain; charset="utf-8" I tried to pay more attention to the initial guess as suggested, and found a very nearly close fit! The new initial guess I used is p0=[V.max(), std_dev, V.max(), 2] See attached for new fit. ... ... ------------------- In case a set of gaussians aren't a constraint to your fit, you might want to play a bit with something like, return ps1*np.exp(-(x/ps2)**2)*np.sin(x)**2 + ps2*np.exp(-(x/ps3)**2) + ps3*np.exp(-(x/ps4)**2) and the set of parameters: p0=[V.max(), std_dev, V.min(), 2] Reagrds, Sergio Non-linear fitting stuff: https://www.packtpub.com/big-data-and-business-intelligence/numerical-and-s…

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Three-term gaussian fit to gaussian data using scipy
by Otto Ngeke April 6, 2017

April 6, 2017

Hi ALl, I am trying to a three-term gaussian with a specific form to fit a data set. The fit isn't working out as I nicely. Could you help me out? Here attached is my code and the data, along with the fit I obtain: import numpy as np #from scipy.optimize import curve_fit import scipy.optimize as optimize import matplotlib.pyplot as plt #r=np.linspace(0.0e-15,4e-15, 100) data = np.loadtxt('V_lambda_n.dat') r = data[:, 0] V = data[:, 1] std_dev=np.std(data) def func(x, ps1, ps2, ps3, ps4): return ps1*np.exp(-(x/ps2)**2) + ps2*np.exp(-(x/ps3)**2) + ps3*np.exp(-(x/ps4)**2) popt, pcov = optimize.curve_fit(func, r, V, p0=[50, std_dev, 50, std_dev], maxfev=10000) #params = optimize.curve_fit(func, ps1, ps2, ps3, ps4) #[ps1, ps2, ps2, ps4] = params[0] p1=plt.plot(r, V, 'bo', label='data') p2=plt.plot(r, func(r, *popt), 'r-', label='fit') plt.xticks(np.linspace(0, 4, 9, endpoint=True)) plt.yticks(np.linspace(-50, 150, 9, endpoint=True)) plt.show()

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A contribution for enhancing your SciPy skills
by Sergio Rojas April 5, 2017

April 5, 2017

Hello Guys, I am just very happy to have finished my video project with Pack on a brief introduction to Machine Learning via SciPy : https://www.packtpub.com/big-data-and-business-intelligence/numerical-and-s… Previously, as you might know, we finished this one: https://www.packtpub.com/big-data-and-business-intelligence/learning-scipy-… https://github.com/rojassergio/Learning-Scipy Hope you can spread the word. Salut, Sergio

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preserving sparse matrix type under operations
by Gideon Simpson April 3, 2017

April 3, 2017

I was wondering if there’s a way to (cheaply) retain sparsity type of matrices which are of the same sparsity pattern? For instance, if I have two DIA formatted matrices with the same banding, and I want to compute A + s * B, where s is a scalar, it seems to convert everything over to CSR. Is there a way to avoid this? -gideon

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ANN: PyMeasure version 0.4.3 released
by Colin Jermain April 3, 2017

April 3, 2017

Hey Scipy users, We've recently released a new version of PyMeasure <https://github.com/ralph-group/pymeasure>, a library that makes it easy to build measurement programs with live-plotting. In under 150 lines of code you get produce a fully-fledged program for running scientific measurements. PyMeasure is accessible to beginner programmers, and programs are easy to maintain. The library also supports 22 scientific instruments <http://pymeasure.readthedocs.io/en/latest/api/instruments/index.html> out of the box. There are number of great examples <https://github.com/ralph-group/pymeasure/tree/master/examples> and a full set of documentation <http://pymeasure.readthedocs.io/en/latest/> if you are interested. We would love to hear your feedback and suggestions for improvement. Feel free to email me, or strike up an issue on GitHub <https://github.com/ralph-group/pymeasure/issues>. Happy coding, Colin GitHub: https://github.com/ralph-group/pymeasure Documentation: http://pymeasure.readthedocs.io/en/latest/ Examples: https://github.com/ralph-group/pymeasure/tree/master/examples

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extracting diagonals efficiently from DIA matrix
by Gideon Simpson April 3, 2017

April 3, 2017

If I have a matrix that was constructed using spdiags is there a routine that will efficiently extract the diagonals? -gideon

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