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Category Archives: Link to Python
Installing PyPardiso
Update 5th June 2022: The PyPardiso package may now be installed simply with pip (see Installing PyPardiso and speed of Scipy spsolve): Install the MKL library: pip install mkl Install PyPardiso: pip install pypardiso The PyPardiso package provides an interface to … Continue reading
Posted in Excel, Finite Element Analysis, Frame Analysis, Link to Python, Newton, NumPy and SciPy, PyXLL, UDFs
Tagged Exceel, Intel MKL library, PyPardiso, PyXLL, SciPy, Sparse matrix solver, UDFs
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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 Excel, PyPardiso, Python, PyXLL, SciPy, sparse matrices, sparse solvers
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Upgrade to Python 3.10?
Python 3.10 was released on October 4th, rapidly followed by a new release of pyxll (5.3.0), but the Anaconda site does not seem to have it yet, so is it time to upgrade or not. A search on that question … Continue reading
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
Posted in Excel, Link to Python, NumPy and SciPy, PyXLL, UDFs
Tagged Excel, Faster python code, Numba, Numpy, Python, PyXLL, UDFs
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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 arange data limit, int32, int64, Numpy, Numpy bug, Python
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