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Doug Burke
An astronomer who spends way too much time on the computer.
An astronomer who spends way too much time on the computer.

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Astroinformatics: the big data of the universe:

"In astrophysics we like to think that our field was the originator of big data, back when it had to be carried around in big sky charts and books full of tables. These days, it's easier to move astrophysics data around, but we still have a lot of it, and upcoming telescope  facilities will generate even more. I discuss how astrophysicists approach big data in general, and give examples from some Western Physics & Astronomy research projects.  I also give an overview of how the astrophysics community approaches related issues  such as  data sharing and citation, as well as credit for software contributions."

#astronomical #observations

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When writing last week's 'simulating and fitting 2D data in Sherpa' IPython notebook, I realised that I can try out the object API of the pyBLoCXS module (Bayesian Low Count X-ray Spectral), and so compare the error estimates from the "standard" X-ray Astronomy techniques (covariance and confidence) with that from a Monte Carlo Markov Chain (MCMC) analysis. So I threw this notebook together today (mainly to show how to do it, rather than discussing why or interpreting the results).

As a reminder, the other notebooks in the series, of using Sherpa for modelling and fitting data in Python - can be found at

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Continuing my IPython notebook blitz, explaining parts of Sherpa, our Python fitting and modelling package which is on GitHub ( This on e shows how you can simulate and fit a 2D (i.e. image) data set using the object API, and do some error analysis on the parameters. It probably best makes sense when read as part of the series, which can be found at

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Another IPython notebook showin off some of the features in Sherpa, our Python fitting and modelling package. This time I talk about how to build it so that you can use the models from XSPEC and, thanks to a discussion with a colleague, how you can write a model that extends (or changes) the behavior of an existing model (rather than having to rewrite it from scratch).

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More publicity for the IPython notebooks I've been writing recently to show how to use our Python-based fitting and modeling library now that we've made it an "open" project (it was always GPL based, but now the code is on GitHub and we welcome contributors).

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To round the week off, here's a notebook (building off of yesterday's one, which is linked to at the start of this one, for those who need some context) showing how to create plots with Sherpa when using the low-level API for fitting and modelling. It also shows that you can still use matplotlib commands with these "pre-canned" plots.

Another "Yay" for IPython notebooks.

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A colleague - on reading my recent post on how to add a user model to our Python-based modelling and fitting environment (Sherpa) - asked why I was bothering with the CDF rather than use the PDF. This is a good example of expanding the example, since it covers writing an "integrated" model and how to write a "guess" function.

It also meant I could avoid writing the documentation I'm supposed to be writing, by writing other documentation!

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Finally got around to writing up a simple example of adding your own model to Sherpa. In this case I wanted to fit a cumulative distribution with a Gamma CDF. It also shows an example of using the under-documented "direct access" API.

This works with the version we distribute as part of CIAO as well as the version on GitHub

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It's a rather big ditch
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I'm in a link-sharing kinda mood today: #DataScience for #Astronomy school in La Serena.
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