Referring to the source code of scipy.misc.comb, the update routine of the result is: val = 1 for j in xrange(min(k, N-k)): val = (val*(N-j))//(j+1) return val whereas the update routine you suggested is: ntok = 1 ktok = 1 for t in xrange(1, min(k, n – k) + 1): ntok *= n ktok *= … Read more
After some trial and error I found out how to interprete the results of scipy.signal.deconvolve() and I post my findings as an answer. Let’s start with a working example code import numpy as np import scipy.signal import matplotlib.pyplot as plt # let the signal be box-like signal = np.repeat([0., 1., 0.], 100) # and use … Read more
You could certainly wrap whatever FFT implementation that you wanted to test using Cython or other like-minded tools that allow you to access external libraries. GPU-based If you’re going to test FFT implementations, you might also take a look at GPU-based codes (if you have access to the proper hardware). There are several: reikna.fft, scikits.cuda. … Read more
Here are some examples of a different (loop free) way to work with blocks: import numpy as np from numpy.lib.stride_tricks import as_strided as ast A= np.arange(36).reshape(6, 6) print A #[[ 0 1 2 3 4 5] # [ 6 7 8 9 10 11] # … # [30 31 32 33 34 35]] # 2×2 … Read more
Using scipy.optimize.leastsq: import numpy as np import matplotlib.pyplot as plt import scipy.optimize def sigmoid(p,x): x0,y0,c,k=p y = c / (1 + np.exp(-k*(x-x0))) + y0 return y def residuals(p,x,y): return y – sigmoid(p,x) def resize(arr,lower=0.0,upper=1.0): arr=arr.copy() if lower>upper: lower,upper=upper,lower arr -= arr.min() arr *= (upper-lower)/arr.max() arr += lower return arr # raw data x = np.array([821,576,473,377,326],dtype=”float”) … Read more
If I’m understanding your question correctly, your input “observation” data is regularly gridded? If so, scipy.ndimage.map_coordinates does exactly what you want. It’s a bit hard to understand at first pass, but essentially, you just feed it a sequence of coordinates that you want to interpolate the values of the grid at in pixel/voxel/n-dimensional-index coordinates. As … Read more
There are several things going on every time you make a call to scipy.interpolate.griddata: First, a call to sp.spatial.qhull.Delaunay is made to triangulate the irregular grid coordinates. Then, for each point in the new grid, the triangulation is searched to find in which triangle (actually, in which simplex, which in your 3D case will be … Read more
numpy.argmax only returns the index of the first occurrence. You could apply argmax to a reversed view of the array: import numpy as np a = np.array([0,0,4,4,4,4,2,2,2,2]) b = a[::-1] i = len(b) – np.argmax(b) – 1 i # 5 a[i:] # array([4, 2, 2, 2, 2]) Note numpy doesn’t copy the array but instead … Read more
You can install scipy and numpy using their wheels. First install wheel package if it’s already not there… pip install wheel Just select the package you want from http://www.lfd.uci.edu/~gohlke/pythonlibs/#scipy Example: if you’re running python3.5 32 bit on Windows choose scipy-0.18.1-cp35-cp35m-win_amd64.whl then it will automatically download. Then go to the command line and change the directory … Read more
I found that a particular sparse matrix constructor can achieve the desired result very efficiently. It’s a bit obscure but we can abuse it for this purpose. The function below can be used in nearly the same way as scipy.stats.binned_statistic but can be orders of magnitude faster import numpy as np from scipy.sparse import csr_matrix … Read more