You can pass a numpy array or matrix as an argument when initializing a sparse matrix. For a CSR matrix, for example, you can do the following. >>> import numpy as np >>> from scipy import sparse >>> A = np.array([[1,2,0],[0,0,3],[1,0,4]]) >>> B = np.matrix([[1,2,0],[0,0,3],[1,0,4]]) >>> A array([[1, 2, 0], [0, 0, 3], [1, 0, … Read more
Just to elaborate on my comment above, matplotlib’s 3D plotting really isn’t intended for something as complex as isosurfaces. It’s meant to produce nice, publication-quality vector output for really simple 3D plots. It can’t handle complex 3D polygons, so even if implemented marching cubes yourself to create the isosurface, it wouldn’t render it properly. However, … Read more
Five years later, when I Google “how to create a kernel density plot using python”, this thread still shows up at the top! Today, a much easier way to do this is to use seaborn, a package that provides many convenient plotting functions and good style management. import numpy as np import seaborn as sns … Read more
I prefer a Savitzky-Golay filter. It uses least squares to regress a small window of your data onto a polynomial, then uses the polynomial to estimate the point in the center of the window. Finally the window is shifted forward by one data point and the process repeats. This continues until every point has been … Read more
I was originally going to show you how much of a difference it makes for 2d interpolation if your input data are oriented along the coordinate axes rather than in some general direction, but it turns out that the result would be even messier than I had anticipated. I tried using a random dataset over … Read more
I think the problem is that you did not do what SciPy implemented. I won’t dwell on the details or the foundations but only provide you with a solution: Reverse the kernel. >>> import numpy as np >>> arr = np.array([[0, 0, 0], [1, 1, 2], [1, 3, 0]]) >>> kernel = np.array([[4, 1, 1], … Read more
From the response of user1318806 to cphlewis: Thank you. Actually I wanted a combination of log+linear on the x axis not y. But I assume your code should be easily adaptable. Hello! If you wanted a combination of log+linear on the x-axis (patterned from the code of Duncan Watts and CubeJockey): import matplotlib.pyplot as plt … Read more
I don’t think you can use nnls directly as the Fortran code it calls doesn’t allow extra constraints. However, the constraint that the equation sums to one can be introduced as a third equation, so your example system is of the form, 60 x1 + 90 x2 + 120 x3 = 67.5 30 x1 + … Read more
This seems to be a problem common to py2exe and pyinstaller with scipy 0.11.0 as discussed here. The temporal solution given in that thread is to import the file manually: adding the following codes into your program def dependencies_for_myprogram(): from scipy.sparse.csgraph import _validation Problem solved for both pyInstaller and py2exe You can alternatively try including … Read more