Archive for the ‘python’ Category

January 19th, 2017

There are lots of Widgets in ipywidgets. Here’s how to list them

from ipywidgets import *

At the time of writing, this gave me

{'Jupyter.Accordion': ipywidgets.widgets.widget_selectioncontainer.Accordion,
 'Jupyter.BoundedFloatText': ipywidgets.widgets.widget_float.BoundedFloatText,
 'Jupyter.BoundedIntText': ipywidgets.widgets.widget_int.BoundedIntText,
 'Jupyter.Box': ipywidgets.widgets.widget_box.Box,
 'Jupyter.Button': ipywidgets.widgets.widget_button.Button,
 'Jupyter.Checkbox': ipywidgets.widgets.widget_bool.Checkbox,
 'Jupyter.ColorPicker': ipywidgets.widgets.widget_color.ColorPicker,
 'Jupyter.Controller': ipywidgets.widgets.widget_controller.Controller,
 'Jupyter.ControllerAxis': ipywidgets.widgets.widget_controller.Axis,
 'Jupyter.ControllerButton': ipywidgets.widgets.widget_controller.Button,
 'Jupyter.Dropdown': ipywidgets.widgets.widget_selection.Dropdown,
 'Jupyter.FlexBox': ipywidgets.widgets.widget_box.FlexBox,
 'Jupyter.FloatProgress': ipywidgets.widgets.widget_float.FloatProgress,
 'Jupyter.FloatRangeSlider': ipywidgets.widgets.widget_float.FloatRangeSlider,
 'Jupyter.FloatSlider': ipywidgets.widgets.widget_float.FloatSlider,
 'Jupyter.FloatText': ipywidgets.widgets.widget_float.FloatText,
 'Jupyter.HTML': ipywidgets.widgets.widget_string.HTML,
 'Jupyter.Image': ipywidgets.widgets.widget_image.Image,
 'Jupyter.IntProgress': ipywidgets.widgets.widget_int.IntProgress,
 'Jupyter.IntRangeSlider': ipywidgets.widgets.widget_int.IntRangeSlider,
 'Jupyter.IntSlider': ipywidgets.widgets.widget_int.IntSlider,
 'Jupyter.IntText': ipywidgets.widgets.widget_int.IntText,
 'Jupyter.Label': ipywidgets.widgets.widget_string.Label,
 'Jupyter.PlaceProxy': ipywidgets.widgets.widget_box.PlaceProxy,
 'Jupyter.Play': ipywidgets.widgets.widget_int.Play,
 'Jupyter.Proxy': ipywidgets.widgets.widget_box.Proxy,
 'Jupyter.RadioButtons': ipywidgets.widgets.widget_selection.RadioButtons,
 'Jupyter.Select': ipywidgets.widgets.widget_selection.Select,
 'Jupyter.SelectMultiple': ipywidgets.widgets.widget_selection.SelectMultiple,
 'Jupyter.SelectionSlider': ipywidgets.widgets.widget_selection.SelectionSlider,
 'Jupyter.Tab': ipywidgets.widgets.widget_selectioncontainer.Tab,
 'Jupyter.Text': ipywidgets.widgets.widget_string.Text,
 'Jupyter.Textarea': ipywidgets.widgets.widget_string.Textarea,
 'Jupyter.ToggleButton': ipywidgets.widgets.widget_bool.ToggleButton,
 'Jupyter.ToggleButtons': ipywidgets.widgets.widget_selection.ToggleButtons,
 'Jupyter.Valid': ipywidgets.widgets.widget_bool.Valid,
 'jupyter.DirectionalLink': ipywidgets.widgets.widget_link.DirectionalLink,
 'jupyter.Link': ipywidgets.widgets.widget_link.Link}
August 11th, 2016

This is my rant on import *. There are many like it, but this one is mine.

I tend to work with scientists so I’ll use something from mathematics as my example.  What is the result of executing the following line of Python code?

result = sqrt(-1)

Of course, you have no idea if you don’t know which module sqrt came from. Let’s look at a few possibilities. Perhaps you’ll get an exception:

In [1]: import math
In [2]: math.sqrt(-1)
ValueError Traceback (most recent call last)
in ()
----> 1 math.sqrt(-1)

ValueError: math domain error

Or maybe you’ll just get a warning and a nan

In [3]: import numpy
In [4]: numpy.sqrt(-1)
/Users/walkingrandomly/anaconda/bin/ipython:1: RuntimeWarning: invalid value encountered in sqrt
#!/bin/bash /Users/walkingrandomly/anaconda/bin/
Out[4]: nan

You might get an answer but the datatype of your answer could be all sorts of strange and wonderful stuff.

In [5]: import cmath
In [6]: cmath.sqrt(-1)
Out[6]: 1j
In [7]: type(cmath.sqrt(-1))
Out[7]: complex

In [8]: import scipy
In [9]: scipy.sqrt(-1)
Out[9]: 1j
In [10]: type(scipy.sqrt(-1))
Out[10]: numpy.complex128

In [11]: import sympy
In [12]: sympy.sqrt(-1)
Out[12]: I
In [13]: type(sympy.sqrt(-1))
Out[13]: sympy.core.numbers.ImaginaryUnit

Even the humble square root function behaves very differently when imported from different modules! There are probably other sqrt functions, with yet more behaviours that I’ve missed.

Sometimes, they seem to behave in very similar ways:-

In [16]: math.sqrt(2)
Out[16]: 1.4142135623730951

In [17]: numpy.sqrt(2)
Out[17]: 1.4142135623730951

In [18]: scipy.sqrt(2)
Out[18]: 1.4142135623730951

Let’s invent some trivial code.

from scipy import sqrt

x = float(input('enter a number\n'))
y = sqrt(x)

# important things happen after here. Complex numbers are fine!

I can input -1 just fine. Then, someone comes along and decides that they need a function from math in the ‘important bit’. They use import *

from scipy import sqrt
from math import *

x = float(input('enter a number\n'))
y = sqrt(x)

# important things happen after here. Complex numbers are fine!

They test using inputs like 2 and 4 and everything works (we don’t have automated tests — we suck!). Of course it breaks for -1 now though. This is easy to diagnose when you’ve got a few lines of code but it causes a lot of grief when there’s hundreds…or, horror of horrors, if the ‘from math import *’ was done somewhere in the middle of the source file!

I’m sometimes accused of being obsessive and maybe I’m labouring the point a little but I see this stuff, in various guises, all the time!

So, yeah, don’t use import *.

February 8th, 2016

While waiting for the rain to stop before heading home, I started messing around with the heart equation described in an old WalkingRandomly post. Playing code golf with myself, I worked to get the code tweetable. In Python:

In R:

I liked the look of the default plot in R so animated it by turning 200 into a parameter that ranged from 1 to 200. The result was this animation:

The code for the above isn’t quite tweetable:

for(num in seq(1,200,1))
    filename = paste("rplot" ,sprintf("%03d", num),'.jpg',sep='')

This produces a lot of .jpg files which I turned into the animated gif with ImageMagick:

convert -delay 12 -layers OptimizeTransparency -colors 8 -loop 0 *.jpg animated.gif 
September 5th, 2015

The test suite of a project I’m working on is poking around at the extreme edges of the range of double precision numbers. I noticed a difference between Windows and other platforms that I can’t yet fully explain. On Windows, the test suite was pumping out RuntimeWarnings that we don’t see in Linux or Mac. I’ve distilled the issue down to a single numpy command:


On Windows 7 Anaconda Python 2.3, this gives a RuntimeWarning and returns inf whereas on Linux and Mac OS X it evaluates to 709.78-ish

Numpy version is 1.9.2 in all cases.

64 bit Windows 7

Python 2.7.10 |Continuum Analytics, Inc.| (default, May 28 2015, 16:44:52) [MSC
v.1500 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
Anaconda is brought to you by Continuum Analytics.
Please check out: and
>>> import numpy as np
>>> np.log1p(1.7976931348622732e+308)
__main__:1: RuntimeWarning: overflow encountered in log1p

64 bit Linux

Python 2.7.9 (default, Apr  2 2015, 15:33:21) 
[GCC 4.9.2] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import numpy as np
>>> np.log1p(1.7976931348622732e+308)

Mac OS X

Python 2.7.10 |Anaconda 2.3.0 (x86_64)| (default, May 28 2015, 17:04:42) 
[GCC 4.2.1 (Apple Inc. build 5577)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
Anaconda is brought to you by Continuum Analytics.
Please check out: and
>>> import numpy as np
>>> np.log1p(1.7976931348622732e+308)

The argument to log1p is getting close to the largest double precision number:

>>> sys.float_info.max
June 4th, 2015

The ever-superb John D. Cook recently found this lovely looking curve in a book he’s currently readingmystery_curve


John posted some Python code that reproduced this curve. I stole borrowed his code, put it in a Jupyter notebook and wrapped it in an interactive widget to allow me to play with the parameters and see what other curves I could come up with. The result looks like this.

Screen Shot 2015-06-04 at 15.09.26

If you’d like something where those sliders work, you need to run the notebook I’ve created in Project Jupyter. Here are 2 ways to do that.

Once you have the notebook open, click on Cell->Run All and play with the sliders that pop up.

Other posts about these curves:

April 28th, 2015

I recently found myself in need of a portable install of the Jupyter notebook which made use of a portable install of R as the compute kernel. When you work in institutions that have locked-down managed Windows desktops, such portable installs can be a life-saver! This is particularly true when you are working with rapidly developing projects such as Jupyter and IRKernel.

It’s not perfect but it works for the fairly modest requirements I had for it. Here are the steps I took to get it working.

Download and install Portable Python

I downloaded Portable Python from and installed into a directory called Portable Python

Update IPython and install the extra modules we need

This version of Portable Python comes with a portable IPython instance but it is too old to support alternative kernels. As such, we need to install a newer version.

Open a cmd.exe command prompt and navigate to Portable Python\App\Scripts.

Enter the command

easy_install ipython.exe

You’ll now find that you can launch the ipython.exe terminal from within this directory:

C:\Users\walkingrandomly\Desktop\Portable Python\App\Scripts>ipython
Python 2.7.6 (default, Nov 10 2013, 19:24:18) [MSC v.1500 32 bit (Intel)]
Type "copyright", "credits" or "license" for more information.

IPython 3.1.0 -- An enhanced Interactive Python.
?         -> Introduction and overview of IPython's features.
%quickref -> Quick reference.
help      -> Python's own help system.
object?   -> Details about 'object', use 'object??' for extra details.

In [1]: exit()

If you try to launch the notebook, however, you’ll get error messages. This is because we haven’t taken care of all the dependencies. Let’s do that now. Ensuring you are still in the Portable Python\App\Scripts folder, execute the following commands.

easy_install pyzmq
easy_install jinja2
easy_install tornado
easy_install jsonschema

You should now be able to launch the notebook using

ipython notebook

Install portable R and IRKernel

  • I downloaded Portable R 3.2 from and installed into a directory called R-Portable
  • Move this directory into the Portable Python directory. It needs to go inside Portable Python\App (see this discussion to learn how I discovered that this location was the correct one)
  • Launch the Portable R executable which should be at Portable Python\App\R-Portable\R-portable.exe and install the IRKernel packages by doing
install.packages(c("rzmq","repr","IRkernel","IRdisplay"), repos="")

Install additional R packages

The version of Portable R I used didn’t include various necessary packages. Here’s how I fixed that.

  • Launch the Portable R executable which should be at Portable Python\App\R-Portable\R-portable.exe and install the following packages 

Install the R kernel file
Create the directory structure Portable Python\App\share\jupyter\kernels\R_kernel

Create a file called kernel.json that contains the following

{"argv": ["R-Portable/App/R-Portable/bin/i386/R.exe","-e","IRkernel::main()",
 "display_name":"Portable R"

This file needs to go in the R_kernel directory created earlier. Note that the kernel location specified in kernel.json uses Linux style forward slashes in the path rather than the backslashes that Windows users are used to. I found that this was necessary for the kernel to work –it was ignored by the notebook otherwise.

Finishing off

Everything created so far, including R, is in the folder Portable Python 2.7.6

I created a folder called PortableJupyter and put the Portable Python 2.7.6 folder inside it. I also created the folder PortableJupyter\notebooks to allow me to carry my notebooks around with the software that runs them.

There is a bug in Portable Python relating to scripts like IPython.exe that have been installed using easy_install. In short, they stop working if you move the directory they’re installed in – breaking portability somewhat! (Details here)

The workaround is to launch Ipython by running the script Portable Python\App\Scripts\

I didn’t want to bother with that so created a shortcut in my PortableJupyter folder called Launch notebook. The target of this shortcut was the following line

%windir%\system32\cmd.exe /c "cd notebooks && "%CD%/Portable Python\python.exe" "%CD%/Portable Python\App\Scripts\" notebook"

This starts the notebook using the default web browser and puts you in the notebooks directory.

The pay off

My folder looks like this:


If I click on the Launch Notebook shortcut, I get a Jupyter session with 2 kernel options


I can choose the Portable R kernel and start using R in the notebook!


March 4th, 2015

If you really want to learn the differences between Python 2 and Python 3, I suggest you try converting a non-trivial software project. I’m in the middle of doing one now and am learning all kinds of little gotchas over and above the standard stuff that everyone knows such as changes to print, integer division and removal of xrange.

The most recent one I learned about (about 10 minutes ago) amounted to this

#Python 2.7.6 (default, Mar 22 2014, 22:59:56) 
[GCC 4.8.2] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> [x for x in range(10)]
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> x

compared to

Python 3.4.0 (default, Apr 11 2014, 13:05:11) 
[GCC 4.8.2] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> [x for x in range(10)]
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> x
Traceback (most recent call last):
  File "", line 1, in 
NameError: name 'x' is not defined

This is well documented (This StackOverflow Q+A is great!) but I didn’t know about it and, in the code I was looking at, there was a heck of a lot of complication between the list comprehension and when ‘x’ was used. As such, it took me a while to figure out!

Another change that had me scratching my head for a while is the fact that Python 3 ignores the __metaclass__ hook. I didn’t know this little fact but discovered it while debugging failing tests!

Of course, once you know these little gotchas, you’ll probably not be caught out by them again in your next Python 2->Python 3 porting project but they got me wondering…..

What changes from Python 2->Python 3 have really caught you out at some point?

January 27th, 2015

Linear Algebra – Foundations to Frontiers (or LAFF to its friends) is a popular, high quality and free MOOC that, as the title suggests, teaches aspects of linear algebra in a way that takes the student from the very basics through to some cutting edge techniques. I worked through much of it last year and thoroughly enjoyed the approach it took — focusing on programming aspects from the very beginning. The course authors are also among the developers of the FLAME project, a high performance linear algebra library, and one of the interesting aspects of the LAFF course (for me at least) was that it taught linear algebra in a way that also allowed you to understand the approaches used in the algorithms behind FLAME.

Last year, all of the programming assignments in LAFF were done in Python, making use of the IPython notebook. This year, the software stack will be different and will be based on MATLAB. I understand that everyone who signs up to LAFF will be able to get a free MATLAB license from Mathworks for the duration of the course. Understandably, this caused quite a bit of discussion between the LAFF team and software/language geeks like me. In a recent Facebook thread, I asked about the switch and received the reply

‘MATLAB will be free during the course. There are open source equivalents, but Mathworks staff is supporting the use of MATLAB (staff for us). There were some who never got the IPython notebooks to work properly. We are really excited at the opportunity to innovate again and perhaps clear up snags in the programming issues we had. It was complicated to support IPython on all of the operating systems and machines that participants use. MATLAB promises to be easier and will allow us again to concentrate on the Linear Algebra’ – LAFF UTx

I’m sufficiently interested in this change from IPython to MATLAB that I’ll be signing up for the course again this year and I encourage you to do the same — I believe that the programming-centric teaching approach taken by LAFF is extremely well done and your time would be well-spent working through the course.

The course starts on 28th January 2015 so sign up now!

Here’s the trailer for last year’s course.

November 17th, 2014

Given a symmetric matrix such as

    \light \[ \left( \begin{array}{ccc} 1 & 1 & 0 \\ 1 & 1 & 1 \\ 0 & 1 & 1 \end{array} \right)\]

What’s the nearest correlation matrix? A 2002 paper by Manchester University’s Nick Higham which answered this question has turned out to be rather popular! At the time of writing, Google tells me that it’s been cited 394 times.

Last year, Nick wrote a blog post about the algorithm he used and included some MATLAB code. He also included links to applications of this algorithm and implementations of various NCM algorithms in languages such as MATLAB, R and SAS as well as details of the superb commercial implementation by The Numerical algorithms group.

I noticed that there was no Python implementation of Nick’s code so I ported it myself.

Here’s an example IPython session using the module

In [1]: from nearest_correlation import nearcorr

In [2]: import numpy as np

In [3]: A = np.array([[2, -1, 0, 0], 
   ...:               [-1, 2, -1, 0],
   ...:               [0, -1, 2, -1], 
   ...:               [0, 0, -1, 2]])

In [4]: X = nearcorr(A)

In [5]: X
array([[ 1.        , -0.8084125 ,  0.1915875 ,  0.10677505],
       [-0.8084125 ,  1.        , -0.65623269,  0.1915875 ],
       [ 0.1915875 , -0.65623269,  1.        , -0.8084125 ],
       [ 0.10677505,  0.1915875 , -0.8084125 ,  1.        ]])

This module is in the early stages and there is a lot of work to be done. For example, I’d like to include a lot more examples in the test suite, add support for the commercial routines from NAG and implement other algorithms such as the one by Qi and Sun among other things.

Hopefully, however, it is just good enough to be useful to someone. Help yourself and let me know if there are any problems. Thanks to Vedran Sego for many useful comments and suggestions.

September 22nd, 2014

The Mathematics department at The University of Manchester runs a third year undergraduate module called ‘Problem solving by computer’ which invites students to solve complex mathematical problems by doing a little programming. Along with some interesting mathematics, the course exposes students to a wide variety of languages and numerical libraries including MATLAB, Octave, NAG, Mathematica and, most recently, Python.

Earlier this year, Python was introduced as an option for students who wanted to use it for a project in this course and, despite only being given two lectures in the language, quite a few people chose to use it. Much of this success must be attributed to the Python for MATLABers notes written by Manchester PhD student, Sophia Coban which is why I’m providing links to them here.