I want to use numpy.exp like this: cc = np.array([ [0.120,0.34,-1234.1] ]) print 1/(1+np.exp(-cc)) But this gives me error: /usr/local/lib/python2.7/site-packages/ipykernel/__main__.py:5: RuntimeWarning: overflow encountered in exp I can’t understand why? How can I fix this? It seems the problem is with third ...

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I think this is the most popular way to do it before: https://pytools.codeplex.com/wikipage?title=NumPy%20and%20SciPy%20for%20.Net But this link is no longer exist: https://store.enthought.com/repo/.iron/ I recently found a clone for the instruction, and also found a clone of ironpkg-1.0.0.py on github. ...

I read an image with ndimage, which results in a binary image like this: I would like to invert the image such that white turns into black, and vice versa. Help is ...

I am looking for a way to do a plus/minus operation in python 2 or 3. I do not know the command or operator, and I cannot find a command or operator to do this. Am I missing something?

I am using the scipy.optimize module to find optimal input weights that would minimize my output. From the examples I’ve seen, we define the constraint with a one-sided equation; then we create a variable that’s of the type ...

I’m trying to take a second derivative in python with two numpy arrays of data. For example, the arrays in question look like this: import numpy as np x = np.array([ 120. , 121.5, 122. , ...

I am trying to install “pulp” module in Anaconda Navigator’s Environment tabs. But when I search in “All” packages I can’t find it. It happened with other packages too. Is there ...

I have one set of data in python. I am plotting this as a histogram, this plot shows a bimodal distribution, therefore I am trying to plot two gaussian profiles over each peak in the bimodality. If i use ...

I am using nibabel lib to load data from nii file. I read the document of the lib at http://nipy.org/nibabel/gettingstarted.html, and found that This information is available without the need to load anything of the main image ...

I receive this error in scipy interp1d function. Normally, this error would be generated if the x was not monotonically increasing. import scipy.interpolate as spi def refine(coarsex,coarsey,step): finex = np.arange(min(coarsex),max(coarsex)+step,step) intfunc = spi.interp1d(coarsex, coarsey,axis=0) ...