5 things I learned at SciPy

Posted on Tue 01 November 2016 in open science • Tagged with python, open science, programming, conferences

I've finally decompressed after my first go-around with Scipy. For those who haven't heard of this conference before, Scipy is an annual meeting where members of scientific community get together to discuss their love of Python, scientific programming, and open science. It spans both academics and people from industry, making it a unique place in terms of how software interfaces with scientific research. (if you're interested the full set of Scipy conferences, check out here
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The beauty of computational efficiency: The Fast Fourier Transform.

Posted on Sat 02 July 2016 in computation • Tagged with python, programming, computation, efficiency, fft

When we discuss "computational efficiency", you often hear people throw around phrases like $O(n^2)$ or $O(nlogn)$. We talk about them in the abstract, and it can be hard to appreciate what these distinctions mean and how important they are. So let's take a quick look at what computational efficiency looks like in the context of a very famous algorithm: The Fourier Transform.

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Scraping craigslist

Posted on Sun 30 August 2015 in computation, munging, scraping • Tagged with python, programming


In this notebook, I'll show you how to make a simple query on Craigslist using some nifty python modules. You can take advantage of all the structure data that exists on webpages to collect interesting datasets.

Coherence correlation

Posted on Wed 27 May 2015 in neuroscience • Tagged with python, programming, timeseries, correlation

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Note - you can find the nbviewer of this post here

Coherence vs. Correlation - a simple simulation

A big question that I've always wrestled with is the difference between correlation and coherence. Intuitively, I think of these two things as very similar to one another. Correlation is a way to determine the extent to which two variables covary (normalized to be between -1 and 1). Coherence is similar, but instead assesses "similarity" by looking at the similarity for two variables in frequency space, rather than time space.

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