I’m not aware of any pre-existing implementation in Python, but it’s easy to take a look at the MATLAB code using edit procrustes.m and port it to Numpy: def procrustes(X, Y, scaling=True, reflection=’best’): “”” A port of MATLAB’s `procrustes` function to Numpy. Procrustes analysis determines a linear transformation (translation, reflection, orthogonal rotation and scaling) of … Read more
Your closed path can be considered as a parametric curve, x=f(u), y=g(u) where u is distance along the curve, bounded on the interval [0, 1). You can use scipy.interpolate.splprep with per=True to treat your x and y points as periodic, then evaluate the fitted splines using scipy.interpolate.splev: import numpy as np from scipy import interpolate … Read more
Try installing using Scipy wheel file. Download it from here: http://www.lfd.uci.edu/~gohlke/pythonlibs/#scipy Make sure to download the one that’s compatible with your Python version and your laptop bit. Then install it like this: pip install “path\to\your\wheel\file\scipy-0.18.1-cp27-cp27m-win_amd64.whl”
You should use stride_tricks. When I first saw this, the word ‘magic’ did spring to mind. It’s simple and is by far the fastest method. >>> as_strided = numpy.lib.stride_tricks.as_strided >>> a = numpy.arange(1,15) >>> a array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]) >>> b = as_strided(a, (11,4), … Read more
In the same ticket you have linked, there is an example implementation of what they call tensor product interpolation, showing the proper way to nest recursive calls to interp1d. This is equivalent to quadrilinear interpolation if you choose the default kind=’linear’ parameter for your interp1d‘s. While this may be good enough, this is not linear … Read more
Sure! There are two options that do different things but both exploit the regularly-gridded nature of the original data. The first is scipy.ndimage.zoom. If you just want to produce a denser regular grid based on interpolating the original data, this is the way to go. The second is scipy.ndimage.map_coordinates. If you’d like to interpolate a … Read more
I don’t see what the advantages of csr format are in this case. Sure, all the nonzero values are collected in one .data array, with the corresponding column indexes in .indices. But they are in blocks of varying length. And that means they can’t be processed in parallel or with numpy array strides. One solution … Read more
Refer to https://docs.scipy.org/doc/scipy-0.18.1/reference/tutorial/optimize.html and scroll down to Constrained minimization of multivariate scalar functions (minimize), you can find that This algorithm allows to deal with constrained minimization problems of the form: where the inequalities are of the form C_j(x) >= 0. So when you define the constraint as def constraint1(x): return x[0]+x[1]+x[2]+x[3]-1 and specify the type … Read more
Here a simulation with scipy tools : from pylab import * from scipy.optimize import curve_fit data=concatenate((normal(1,.2,5000),normal(2,.2,2500))) y,x,_=hist(data,100,alpha=.3,label=”data”) x=(x[1:]+x[:-1])/2 # for len(x)==len(y) def gauss(x,mu,sigma,A): return A*exp(-(x-mu)**2/2/sigma**2) def bimodal(x,mu1,sigma1,A1,mu2,sigma2,A2): return gauss(x,mu1,sigma1,A1)+gauss(x,mu2,sigma2,A2) expected=(1,.2,250,2,.2,125) params,cov=curve_fit(bimodal,x,y,expected) sigma=sqrt(diag(cov)) plot(x,bimodal(x,*params),color=”red”,lw=3,label=”model”) legend() print(params,’\n’,sigma) The data is the superposition of two normal samples, the model a sum of Gaussian curves. we obtain : And … Read more
scipy.optimize.least_squares in scipy 0.17 (January 2016) handles bounds; use that, not this hack. Bound constraints can easily be made quadratic, and minimized by leastsq along with the rest. Say you want to minimize a sum of 10 squares Σ f_i(p)^2, so your func(p) is a 10-vector [f0(p) … f9(p)], and also want 0 <= p_i … Read more