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IMPORT SCIPY AS CODE
Right now, my code starts with: import numpy as np What is the recommended way to work with SciPy and NumPy? Being a scientist, sqrt(-1) should return a complex number, so I'm inclined to go with SciPy only. The sub-package signal can be replaced by other modules concerned with scipy. Python Numpy is required for most of the sub-packages. We can import any sub-package in the similar manner. This is a basic scipy code where the sub-package signal is being imported. On the other hand, SciPy imports every Numpy functions in its main namespace, such that scipy.array() is the same thing as numpy.array() ( see this question), so NumPy should only be used when SciPy is not being used, as they are duplicates. Import numpy as np From scipy import signal. So NumPy should be used for array operation and SciPy for everything else. I know there is no strict guideline and I can do it the way I want, but from time to time, I still find contradictory instructions.įor example, I've read somewhere that NumPy is meant to only implement the array object, while SciPy is there for every other scientific algorithms. In an effort to clean up my code, I have been looking for a standard convention for importing SciPy and NumPy in my programs.
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BFGS, Nelder-Mead simplex, Newton Conjugate Gradient, COBYLA or SLSQP. Unconstrained and constrained minimization of multivariate scalar functions (minimize ()) using a variety of algorithms (e.g. This module contains the following aspects. I trust you'll let me know if I missed something! The scipy.optimize package provides several commonly used optimization algorithms. Notice: I checked for duplicate and nothing clearly answers my question.