## Pete Brown

Peter Ronald Brown (25 December 1940 – 19 May 2023) was an English performance poet, lyricist, and singer best known for his collaborations with Cream and Jack Bruce.

https://en.m.wikipedia.org/wiki/Pete_Brown

I spent my last year of high school in England as a boarder, and the “upper sixth” boarders were privileged to have their own common room (located in a cardboard box in the middle of the road) where we could huddle around the record player and listen to the latest progressive rock releases, including the works of Cream, featuring the music of Eric Clapton, Ginger Baker, and Jack Bruce, and the words of Pete Brown; such as:

## Scipy linear algebra functions in Excel

The file

pyLinAlgfuncs3.zip

has open source Python code allowing the Scipy linear algebra functions to be called from Excel using the pyxll add-in. It also includes all the Python code to perform LU decomposition, as discussed in the previous post., and a spreadsheet with examples of all the available functions.

The spreadsheet has 64 functions, listed on the first sheet:

The solver functions are demonstrated solving a stiffness matrix representing a small structural frame:

The first 10 rows of the solution to the matrix for the example loading are presented for a wide range of different functions:

The problem presented in the previous post is also included, with the associated python code:

Future post will look at the use of the different solver functions in more detail.

## LU Decomposition with Python and Scipy

This post was prompted by a question at the Eng-Tips forum:

LU Decomposition

which asked why Mathcad was generating LU matrices different to those calculated from the basic theory. The short answer was that there is more than one way to do LU decomposition. This post looks at the question in more detail, including examples of the basic procedures, and how and why the variations are used.

LU decomposition is an efficient method for solving systems of linear equations of the form Ax = b, where A is a square matrix and b is a vector. A is decomposed into a lower triangular matrix, L, and an Upper triangular matrix, U, such that LU = A. It is then a simple process to find the vector x such that LUx = b, for any values of b, without having to repeat the decomposition process.

Many of the on-line articles on the procedures for decomposition (including Wikipedia) are very hard to follow, but I found a clear and comprehensive tutorial at:

LU Decomposition for Solving Linear Equations

The code listed at the end of this post is a modified version of code at the link above.

The basic process to find x such that Ax = b is:

• Decompose the matrix A into a lower triangle, L, with a leading diagonal of 1s, and an upper triangle, U.
• Use forward substitution to find y such that Ly = b
• Use backward substitution to find x such that Ux = y

Python code for this procedure is shown below. Applying this procedure to a 3×3 matrix gives the results shown in the screenshot below, under Python unpivoted results:

• Triangular matrices L and U are generated from Matrix A.
• Multiplying L by U gives LU which is equal to A.
• The lu_solve function follows the full process to generate the solution with A and b as input.
• The lu_solve2 function has L, U, and b as input, and returns the same result.
• The Scipy function LUSolve returns the same results, but the Scipy decomposition functions generate additional P (pivot) and PL matrices. (Octave returns the same results).

The reason for the introduction of the additional matrices can be seen in the screenshot below:

The top left value of the Matrix A has been changed to zero. The calculation of the unpivoted L matrix now requires a division by zero, giving invalid results. The pivoting process moves the matrix rows and/or columns to avoid the zero at the top left, and remove the division by zero.

There are alternative methods of pivoting, as can be seen in the Scipy and Python pivoted results above, but both result in a PLU matrix identical to the original Matrix A, and the same results. Python code for the whole decomposition and solving process is shown below, without and with pivoting.

Without pivoting
Decompose Matrix A to Lower and Upper triangular matrices:

``````def lu_decomp(A):
"""(L, U) = lu_decomp(A) is the LU decomposition A = L U
A is any matrix
L will be a lower-triangular matrix with 1 on the diagonal, the same shape as A
U will be an upper-triangular matrix, the same shape as A
"""
n = A.shape[0]
if n == 1:
L = np.array([[1]])
U = A.copy()
return (L, U)

A11 = A[0,0]
A12 = A[0,1:]
A21 = A[1:,0]
A22 = A[1:,1:]

L11 = 1
U11 = A11

L12 = np.zeros(n-1)
U12 = A12.copy()

L21 = A21.copy() / U11
U21 = np.zeros(n-1)

S22 = A22 - np.outer(L21, U12)
(L22, U22) = lu_decomp(S22)

L = np.zeros((n,n))
U = np.zeros((n,n))
L[0,0] = L11
L[0,1:] = L12
L[1:,0] = L21
L[1:,1:] = L22

U[0,0] = U11
U[0,1:] = U12
U[1:,0] = U21
U[1:,1:] = U22

return (L, U)``````

Find y such that Ly = b

``````def forward_sub(L, b):
"""y = forward_sub(L, b) is the solution to L y = b
L must be a lower-triangular matrix
b must be a vector of the same leading dimension as L
"""
n = L.shape[0]
y = np.zeros(n)
for i in range(n):
tmp = b[i]
for j in range(i):
tmp -= L[i,j] * y[j]
y[i] = tmp / L[i,i]
return y``````

Find x such that Ux = y

``````def back_sub(U, y):
"""x = back_sub(U, y) is the solution to U x = y
U must be an upper-triangular matrix
y must be a vector of the same leading dimension as U
"""
n = U.shape[0]
x = np.zeros(n)
for i in range(n-1, -1, -1):
tmp = y[i]
for j in range(i+1, n):
tmp -= U[i,j] * x[j]
x[i] = tmp / U[i,i]
return x``````

Combine the process to solve Ax = b

``````def lu_solve(A, b):
"""x = lu_solve(A, b) is the solution to A x = b
A must be a square matrix
b must be a vector of the same leading dimension as L
"""
L, U = lu_decomp(A)
y = forward_sub(L, b)
x = back_sub(U, y)
return x``````

With pivoting
Form pivot matrix, and pivoted L and U matrices

``````def lup_decomp(A, out = 0):
"""(L, U, P) = lup_decomp(A) is the LUP decomposition P A = L U
A is any matrix
L will be a lower-triangular matrix with 1 on the diagonal, the same shape as A
U will be an upper-triangular matrix, the same shape as A
P will be a permutation matrix, the same shape as A
"""
n = A.shape[0]
if n == 1:
L = np.array([[1]])
U = A.copy()
P = np.array([[1]])
return (L, U, P)
# Form pivot matrix, P
P = np.identity(n, dtype = np.int)
A_bar = np.copy(A)
for row in range(0, n):
i = np.abs(A_bar[row:,row]).argmax()
if i > 0:
temp = A_bar[row+i,:].copy()
A_bar[row+1:row+i+1,:] = A_bar[row:row+i,:].copy()
A_bar[row,:] = temp
temp = P[row+i,:].copy()
P[row+1:row+i+1,:] = P[row:row+i,:].copy()
P[row,:] = temp
if out == 3: return P
if out == 4: return A_bar

(L, U) = lu_decomp(A_bar)
if out == 5: return L
if out == 6: return U

return (L, U, P)``````

Solve by forward and back substitution:

``````def lup_solve(A, b):
"""x = lup_solve(A, b) is the solution to A x = P b
A square matrix to be solved
b must be a vector of the same leading dimension as A
"""
L, U, P = lup_decomp(A)
z = np.dot(P, b)
y = forward_sub(L, z)
x = back_sub(U, y)
return x``````

Full code to link the Scipy linear algebra functions, and the code above, to Excel via pyxll will be posted in the near future.

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## py_Numpy update and using Numba

The py_Numpy spreadsheet presented at Calling Numpy polynomial functions from Excel has had a minor upgrade to the py_Polyfit function, which was previously returning an error message if the optional “full” argument was set to False. The new version can be downloaded from:

py_Numpy.zip

The recent post More long numbers with Python was also supposed to link to py_Numpy, but I had linked to the wrong file, which has now been fixed. That post noted that the Numba JIT compiler was not working with Python release 11. That has now been fixed, and Numba release 0.57 is working with the latest Python without a problem.

As before the py_Numpy.zip file contains two versions, py_Numpy-JIT.py and py_Numpy-noJIT.py. Just copy whichever one you want to the py_Numpy.py file.

The screen-shot below shows some of the polynomial functions available from py_Numpy. See the link at the top of the post for more details.

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## Two views on VBA

First from Microsoft:

Microsoft are continuing their quest to make downloading spreadsheets with macros ever more difficult, and now any downloaded spreadsheet has VBA code disabled by default. Unblocking the code (if you know and trust the source of the file) is quite easy. Find the file in File Explorer, right-click and select Properties, then select “unblock” down the bottom:

If you leave the file blocked however, and try to enable the macros, Microsoft will take you to a page on their website that says:

A potentially dangerous macro has been blocked
Macros can add a lot of functionality to Microsoft 365, but they are often used by people with bad intentions to distribute malware to unsuspecting victims.

Which is fair enough, but then it says:

Macros aren’t required for everyday use like reading or editing a document in Word or using Excel workbooks. In most cases you can do everything you need to do in Microsoft 365 without allowing macros to run.

Which is rather like saying you can do everything you need to do in your car without enabling top gear.

Coincidentally, the same day I had the problem with the download I found a new website dealing with using Excel with VBA for structural engineering design:

Lucas Beattie

where the very first article was: