Getting Excel Solver working

Recently I found that for unknown reasons the Excel Solver add-in was not working. Opening the add-ins list from the Devloper tab showed that it had become deactivated (also accessible from File-Options-Add-ins):

Clicking the Solver check box returned a message that the Solver file cold not be accessed. Several similar problems were reported on Stackoverflow and elsewhere, but none of the suggested solutions worked for me, and a lengthy session with Microsoft support, ending up with reinstalling Office, also had no effect.

I finally discovered that when the Excel add-ins are accessed through File-Options-Addins the dialog box has a list of “inactive add-ins”, then a list of “Disabled Application Add-ins”, which listed the Solver add-in. I was able to remove Solver from the list (leaving it empty), after which it could be enabled in the usual way.

I don’t recall when or how Solver became disabled, but for anyone with a similar problem, checking the disabled add-ins list may provide a simple fix.

Posted in Computing - general, Excel | Tagged , , , | Leave a comment

Listing Python modules and getting help docs from Excel

Python functions include detailed help documentation but to access this you need the full path to the function, including the names of all code modules and submodules. This post looks at how this information can be found using Excel with pyxll, using the Scipy and Numpy libraries as examples.

As well as pyxll the code requires the inspect, importlib, pkgutil, and numpy libraries:

from inspect import getmembers, isfunction, getdoc, ismodule, signature
import importlib as imp
import pkgutil

import numpy as np

from pyxll import xl_func, xl_arg, xl_return

Example code samples for listing functions from modules include the use of the functions inspect.getmembers and pkgutil.iter_modules. The code below allows either of these functions to be used, and returns either the full output, or just the names of listed functions:

def get_modlist(modname, out=1, out2 = 1):
    mod = imp.import_module(modname)
    if out == 1:
        memb = getmembers(mod, ismodule)
        namelist =  [submod[0] for submod in memb if ismodule(submod[1])] 
        memb = list(pkgutil.iter_modules(mod.__path__))
        namelist =  [submod[1] for submod in memb if submod[2] == True  ]
    if out2 == 1:
        return memb
        return namelist

This function was found to give different results with Numpy and Scipy. Using getmembers on the top level Scipy module returned no results, but Numpy returned all available module names:

Using pkgutil.iter_module returns the available modules in Scipy and are indicated with TRUE in the third column, but for Numpy all the modules are indicated as FALSE:

The code below uses pkgutil.iter for the top level Scipy module, and getmembers for all other cases:

def get_modules(modname):
        mod = imp.import_module(modname)
        return []
    if modname == 'scipy': 
        memb = list(pkgutil.iter_modules(mod.__path__))
        namelist =  [submod[1] for submod in memb if submod[2] == True  ]
        memb = getmembers(mod, ismodule)
        namelist =  [submod[0] for submod in memb] 
    return namelist

The screenshot below shows this function displaying scipy modules and 5 levels of submodule:

For any selected submodule all the available functions can be listed with the get_funcs function:

def get_funcs(modname,  searchstring =''):
    lentxt = len(searchstring)
    mod = imp.import_module(modname)
    memb = getmembers(mod)
    namelist =  [func[0] for func in memb if ((isfunction(func[1]) or type(func[1]) == np.ufunc) and func[0][0:lentxt] == searchstring)]
    return namelist

This function will display all functions included in the sub-module, or optionally a search string may be used:

From the list of functions, one can be chosen to display the built-in help with the Get_Docs function:

@xl_arg('afunc', 'str')
@xl_arg('modname', 'str')
def get_docs(afunc, modname):
        mod = imp.import_module(modname)
        afun = getattr(mod, afunc)
        doc = getdoc(afun).split('\n')
        doc = ''
    return np.array([doc])

With recent versions of Excel this function will return a dynamic array, automatically resizing to display the full extent of the text:

The code and spreadsheet may be downloaded from:

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

Pint, MPmath and implied units, working with Excel

Spreadsheets linking to the Python Pint and MPmath libraries have been presented here before at:

Units and solvers with Pint and Sympy

mpmath for Excel

I have now updated the spreadsheet to work with pyxll, and with some new functions and examples. The spreadsheet and associated Python code can be downloaded from:

New functions include:

py_Quant creates a Pint Quantity object, which may be conveniently used to convert between any compatible units:

py_AddUnits adds units to the Pint Unit Registry, and py_UnitDefined checks if a unit yet exists:

A new example has been added, illustrating how to work with formulae that have constants with implied units, using as an example finding the tensile strength of concrete based on a constant times the concrete’s compressive strength:

Posted in Concrete, Excel, Link to Python, Newton, PyXLL, UDFs | Tagged , , , , , , , , | Leave a comment

3D Matplotlib Plots in Excel

As well as Excel, the code shown in this post requires current versions of Python, Numpy, Matplotlib, and pyxll. The required import statements at the head of the Python code are:

import numpy as np

import matplotlib as mpl
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d.axes3d as axes3d
from matplotlib import colors

import pyxll
from pyxll import xl_func, xl_arg, xl_return, plot

The spreadsheets and Python code described below may be downloaded from

The last post on creating Matplotlib animations in Excel had examples of 3D plots which either plotted a single line, or a single surface defined by points on a regular grid. The code below is a simple example of the latter, using test data included in the Matplotlib library:

@xl_arg('rtn', 'int')
def PlotWireFrame(val = 0.05, rtn = 2):
    fig = plt.figure()
    ax = fig.add_subplot(111, projection='3d')
    ax.set_title('Wireframe Plot')

    #getting test data
    X, Y, Z = axes3d.get_test_data(val)

    #drawing wireframe plot
    cb = ax.plot_wireframe(X, Y, Z, cstride=10,
                            rstride=10, color="green")

    dat = X, Y, Z
    return dat[rtn] 

This generates the graph below when called from Excel:

The Excel PlotWireFrame UDF generates the wireframe graph and also returns the data for the selected axis.

The plot_wireframe function plots a 3D surface, but for my purposes I more frequently need to plot a 3D frame with the following features:

  • The plot should allow for a large number of straight line segments with any orientation and connections.
  • It should be possible to plot different groups of elements with different colours.
  • All three axes should be plotted to the same scale.
  • It should be possible to specify the viewpoint angles and the centre and extent of the plotted image.

The code below performs this task. The input data is specified in two ranges:

  • The lines are specified as separate straight line segments with a “material” number, start node number and end node number.
  • The nodes are specified in a 3 column range with X Y and Z coordinates.

The code combines all the lines of the same material into 3 arrays with X, Y and Z coordinates. Each line is specified with the start and end coordinates, then None, so that lines that are not connected are plotted with a gap between them:

@xl_arg('Nodes', 'numpy_array')
@xl_arg('Beams', 'numpy_array<int>')
@xl_arg('CenXYZ', 'numpy_array', ndim=1)
@xl_arg('ViewAng', 'numpy_array', ndim=1)
@xl_arg('DisplayAx', 'bool')
@xl_arg('Xrange', 'float')
@xl_arg('LineWidth', 'float')
def Plot3D(Nodes, Beams, ViewAng = None, CenXYZ = None, Xrange = 0., DisplayAx = False, LineWidth = 1):
    fig = mpl.figure.Figure()
    ax = axes3d.Axes3D(fig)
    ax.set_box_aspect([1, 1, 1])

    # Set viewing angle if specified, or create axes with defaults
        ax = axes3d.Axes3D(fig, azim = ViewAng[0], elev = ViewAng[1]) # roll = ViewAng[2] to be added in next release
        ax = axes3d.Axes3D(fig)
    # Set axis limits
    if Xrange == 0:
        Xrange = (np.max(Nodes[:,0]) - np.min(Nodes[:,0]))/2
    rng = Xrange/2
        X, Y, Z = CenXYZ[0:3]
        X = (np.max(Nodes[:,0]) + np.min(Nodes[:,0]))/2
        Y = (np.max(Nodes[:,1]) + np.min(Nodes[:,1]))/2 
        Z = (np.max(Nodes[:,2]) + np.min(Nodes[:,2]))/2    
    ax.set_zlim3d(Z-rng, Z+rng)
    # Read beams and coordinates
    rows = Beams.shape[0]
    mats = np.max(Beams[:,0])
    nummata = np.zeros(mats, dtype = int)
    for i in range(0, mats):
        nummata[i] = np.count_nonzero(Beams[:,0] == i+1)

    colors =['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan']
    if DisplayAx == False:

    # Create lines for each material
    for mat in range(0, mats):
        rowsm = nummata[mat]    
        rows2 = rowsm*3
        x_line = np.empty(rows2)
        y_line = np.empty(rows2)
        z_line = np.empty(rows2)
        matcol = np.mod(mat, 10)
        col = colors[matcol]
        j = 0
        for i in range(0, rows):
            if Beams[i, 0] == mat+1:
                n1 = Beams[i,1]-1
                n2 = Beams[i, 2]-1
                x_line[j] = Nodes[n1,0]
                x_line[j+1] = Nodes[n2,0]
                x_line[j+2] = None
                y_line[j] = Nodes[n1,1]
                y_line[j+1] = Nodes[n2,1]
                y_line[j+2] = None
                z_line[j] = Nodes[n1,2]
                z_line[j+1] = Nodes[n2,2]
                z_line[j+2] = None
                j = j+3
        ax.plot3D(x_line, y_line, z_line, col, linewidth=LineWidth)

The code was checked by plotting three circles centred at the origin and with radius 10, in the XY, XZ, and YZ planes:

The default plot looks along the X axis with the Y axis to the right, and the Z axis vertical. In the next plot the view point is rotated 45 degrees about the Z axis (azimuth), with a vertical deflection of 30 degrees:

The next example shows a much more complex plot; a 3D image of the Sydney Harbour Bridge with the axes display turned off and the line width reduced to 1:

The data for this image was taken from a Strand7 file, available from the Strand7 web site to licenced Strand7 users. The top of the data range is shown below. In all there are 11,473 beams and 7737 nodes.

The next example shows the same data with a different view angle and centre coordinates:

Finally the same data viewed in the Strand7 FEA program, showing a very similar image to the Matplotlib/Excel plot with the same view angles:

The current Matplotlib code does not allow for rotation about the line of sight (“roll”), but this feature is under development and is expected to be included in the next release.

Posted in Charts, Charts, Coordinate Geometry, Drawing, Excel, Link to Python, Newton, NumPy and SciPy, PyXLL, UDFs | Tagged , , , , , , | Leave a comment

Free and Simple Tools for Editing Videos

I recently needed to extract 15 minutes from a 45 minute long video, in 5 separate clips. I discovered that there are free tools to do this simply and efficiently, although they are not widely advertised. Here’s how:

By default, videos on my computer open with the Windows 10 “Film & TV” app, which has an icon in the bottom right corner to “Edit with Photos”. Clicking that lists several options, including Trim:

Alternatively the video can be opened directly in Photos, which has an Edit-Trim icon at top right:

Either option opens the video in the Photos Editor, where the section of the video to be trimmed can be selected by moving the start and end sliders at the bottom of the window. Then click Save-as at the top right:

This process is repeated for each clip by re-opening the original video, in my case producing 5 video clips. These can be simply combined into a single video with the Adobe online Merge Videos page:

Posted in Bach, Computing - general, Newton | Tagged , , , | 2 Comments