Category Archives: NumPy and SciPy

Installing PyPardiso

The PyPardiso package provides an interface to the Intel MKL Pardiso library to solve large sparse linear systems of equations. Trying to install this package with Conda raises the message: To search for alternate channels that may provide the conda package you’relooking … Continue reading

Posted in Excel, Finite Element Analysis, Frame Analysis, Link to Python, Newton, NumPy and SciPy, PyXLL, UDFs | Tagged , , , , , , | Leave a comment

Making Finite Element Analysis go faster – Update and PyPardiso

The previous post on this topic looked at the performance of alternative Scipy sparse equation solvers. This post updates those results with the current Scipy version (1.7.1), with very different results. It also compares the Scipy solver performance with the … Continue reading

Posted in Arrays, Excel, Finite Element Analysis, Frame Analysis, Link to Python, Newton, NumPy and SciPy, PyXLL | Tagged , , , , , , | 1 Comment

More on Numba

Following my recent post Making Finite Element Analysis go faster … I have been having a closer look at the options in the Numba just-in-time compiler for improving the performance of Python code. The Numba docs include a series of short … Continue reading

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A Numpy trap – correction

In my post of 30th May this year (here) I said that: As a check that the functions were working correctly, the Python functions were modified to return the sum of the largest array in the first row, revealing that … Continue reading

Posted in Arrays, Excel, Link to Python, NumPy and SciPy, PyXLL, UDFs | Tagged , , , , , | Leave a comment

Making Finite Element Analysis go faster …

… with Numba and Scipy sparse matrix functions. As promised in the previous post in this series, I will be looking at speeding up the formation and solving of matrix functions for the analysis of structural frames, using Excel and … Continue reading

Posted in Arrays, Excel, Finite Element Analysis, Frame Analysis, Link to Python, NumPy and SciPy, PyXLL | Tagged , , , , , , , , | 2 Comments