By default, “import scipy” does not import any subpackage. There are too many subpackages with large Fortran extension modules that are slow to load. I do not recommend doing import scipy or the abbreviated import scipy as sp. It’s just not very useful. Use from scipy import special, from scipy import linalg, etc.
Can we assume they are the same shape? In [202]: a=sparse.csr_matrix([[0,1],[1,0]]) In [203]: b=sparse.csr_matrix([[0,1],[1,1]]) In [204]: (a!=b).nnz==0 Out[204]: False This checks the sparsity of the inequality array. It will give you an efficiency warning if you try a==b (at least the 1st time you use it). That’s because it has to test all those zeros. … Read more
You don’t need root privileges to install packages in your home directory. You can do that with a command such as pip install –user numpy or from source python setup.py install –user See https://stackoverflow.com/a/7143496/284795 The first alternative is much more convenient, so if the server doesn’t have pip or easy_install, you should politely ask the … Read more
First comment: since a*exp(b – c*x) = (a*exp(b))*exp(-c*x) = A*exp(-c*x), a or b is redundant. I’ll drop b and use: import matplotlib.pyplot as plt def func(x, a, c, d): return a*np.exp(-c*x)+d That isn’t the main issue. The problem is simply that curve_fit fails to converge to a solution to this problem when you use the … Read more
I think you don’t need to do fftshift(), and you can pass sampling period to fftfreq(): import scipy import scipy.fftpack import pylab from scipy import pi t = scipy.linspace(0,120,4000) acc = lambda t: 10*scipy.sin(2*pi*2.0*t) + 5*scipy.sin(2*pi*8.0*t) + 2*scipy.random.random(len(t)) signal = acc(t) FFT = abs(scipy.fft(signal)) freqs = scipy.fftpack.fftfreq(signal.size, t[1]-t[0]) pylab.subplot(211) pylab.plot(t, signal) pylab.subplot(212) pylab.plot(freqs,20*scipy.log10(FFT),’x’) pylab.show() from … Read more
To get the distance you can use the norm method of the linalg module in numpy: np.linalg.norm(x – y)
You have to import the module: import scipy.ndimage
I found an answer to my question, just sharing for everyone who stumbles upon this. There is an algorithm called “Asymmetric Least Squares Smoothing” by P. Eilers and H. Boelens in 2005. The paper is free and you can find it on google. def baseline_als(y, lam, p, niter=10): L = len(y) D = sparse.csc_matrix(np.diff(np.eye(L), 2)) … Read more
If you are looking to print the ± symbol, just use: print(u”\u00B1″)
After discussions on the same issue on the Numpy Github page (https://github.com/numpy/numpy/issues/2995) it has been brought to my attention that Numpy/Scipy will not support such a large number of non-zeros in the resulting sparse matrix. Basically, W is a sparse matrix, and Q (or np.log(Q)-1) is a dense matrix. When multiplying a dense matrix with … Read more