blog.dmcdougall.co.uk

Damon's blog

Page 1 of 2. Older Posts →. Parallel Monte Carlo using python and NumPy. I occassionally read Darren Wilkinson's blog, and you should too. He talks about some pretty interesting stuff in the scientific and statistical computing fields. I've taken ». Improving matplotlib turnaround time with IPython. When I produce a plot, I'm working in vim on a python script that I can save and reuse later for reproducibility. My workflow goes something ». Generic accumulator functions using numpy. Page 1 of 2.

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Page 1 of 2. Older Posts →. Parallel Monte Carlo using python and NumPy. I occassionally read Darren Wilkinson's blog, and you should too. He talks about some pretty interesting stuff in the scientific and statistical computing fields. I've taken ». Improving matplotlib turnaround time with IPython. When I produce a plot, I'm working in vim on a python script that I can save and reuse later for reproducibility. My workflow goes something ». Generic accumulator functions using numpy. Page 1 of 2.

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Damon's blog | blog.dmcdougall.co.uk Reviews

https://blog.dmcdougall.co.uk

Page 1 of 2. Older Posts →. Parallel Monte Carlo using python and NumPy. I occassionally read Darren Wilkinson's blog, and you should too. He talks about some pretty interesting stuff in the scientific and statistical computing fields. I've taken ». Improving matplotlib turnaround time with IPython. When I produce a plot, I'm working in vim on a python script that I can save and reuse later for reproducibility. My workflow goes something ». Generic accumulator functions using numpy. Page 1 of 2.

INTERNAL PAGES

blog.dmcdougall.co.uk

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Improving matplotlib turnaround time with IPython

http://blog.dmcdougall.co.uk/improving-matplotlib-turnaround-time-with-ipython

Improving matplotlib turnaround time with IPython. When I produce a plot, I'm working in. Script that I can save and reuse later for reproducibility. My workflow goes something like this:. Write some basic plotting script and have the script save the plot as a. Open the plot and decide something needs tweaking (colours, fonts, labels, etc). And make the tweak. Save the script. This is what a typical session might look like:. Turns out, there's an. Magic function that does this. Research Associate at UT A...

2

Parallel Monte Carlo using python and NumPy

http://blog.dmcdougall.co.uk/parallel-monte-carlo-using-python-and-numpy

Parallel Monte Carlo using python and NumPy. I occassionally read Darren Wilkinson's blog. And you should too. He talks about some pretty interesting stuff in the scientific and statistical computing fields. I've taken a keen interest in some of the parallel implementations of Monte Carlo algorithms he's written about, most recently he wrote an implementation using Scala. You can check out the post here. And here are the timings. I just used the linux. Command, so take these with a pinch of salt. Yields ...

3

Damon's blog - Page 2

http://blog.dmcdougall.co.uk/page/2

Larr; Newer Posts. Page 2 of 2. Getting started with matplotlib. Bear with me; this is my first blog post. I want to write about something that I care about. I care about software and I care ». Larr; Newer Posts. Page 2 of 2. Proudly published with Ghost.

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Automatic differentiation: What is it and why should I care?

http://blog.dmcdougall.co.uk/ad-with-python

Automatic differentiation: What is it and why should I care? Given a function $f$, it is often useful to have an understanding of the derivative $f'$. Derivatives are useful in all fields of science, but they are not necessarily easy to calculate. The most naive algorithm one can implement to differentiate. Def derivative(f, x, h=1e-8): return (f(x) - f(x h) / h. From the definition of the derivative,. F'(x) = text{lim} {h to 0} frac{f(x h) - f(x)}{h},$. The smaller you make. Note that $ varepsilon$ is.

5

Publication-ready the first time: Beautiful, reproducible plots with Matplotlib

http://blog.dmcdougall.co.uk/publication-ready-the-first-time-beautiful-reproducible-plots-with-matplotlib

Publication-ready the first time: Beautiful, reproducible plots with Matplotlib. First things first, mad props to Rhys Ulerich. For coming up with this title. In my last post. I gave a very brief introduction into matplotlib. This time I'd like to cover some of the more finicky details. Details that pertain to making figures look good in your journal publications. To wrap up, I'm going to try package all these touches into a single file. This should make it easy to create reproducible figures. As long as...

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Page 1 of 2. Older Posts →. Parallel Monte Carlo using python and NumPy. I occassionally read Darren Wilkinson's blog, and you should too. He talks about some pretty interesting stuff in the scientific and statistical computing fields. I've taken ». Improving matplotlib turnaround time with IPython. When I produce a plot, I'm working in vim on a python script that I can save and reuse later for reproducibility. My workflow goes something ». Generic accumulator functions using numpy. Page 1 of 2.

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