Tag Archives: idv

To read: EnsembleMatrix paper

EnsembleMatrix: Interactive Visualization to Support Machine Learning with Multiple Classifiers http://research.microsoft.com/en-us/um/redmond/groups/cue/publications/CHI2009-EnsembleMatrix.pdf

I want one

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Filed under dataviz, machine learning

IDV in Python: Interactive Legend in MPLD3

We got a nice pull request for mpld3 recently, interactive legends. Another chance for me to use my new GIF animation recorder: https://github.com/jakevdp/mpld3/pull/299#issuecomment-110434953

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Unhide a toolbar in MPLD3

Did you ever want to know how to unhide the toolbar in MPLD3? Here is how: http://stackoverflow.com/questions/29976625/unhide-toolbar-in-mpld3

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Filed under dataviz, Uncategorized

IDV in Python: Retrieve Data From Dynamic mpld3 plot in python

Mpld3 questions show up on Stack Overflow from time to time, too, and they can get really informative answers if they pull in the javascript experts. This one got a comprehensive answer that was perhaps too expert, and so this follow up was an opportunity to show off my interactive plot call-out plugin yet again.

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Digging into the GBD 2010 Risk Factors II

A follow-up on my recent adventure digging into the GBD 2010 risk factors, wherein I helped a fellow researcher find the specific diseases to which the risk factor DALYs are attributed (http://ihmeuw.org/2c6v ):

Capture

I helped find where to look at the risk factor attribution, but what if you want to download the numbers? The “download chart data as CSV” link gives you something, but it is not the risk factors, it is just the disease fractions of total DALYs:

Capture

Fortunately, a javascript guru with a few minute told me the incantation to get the risk factor fractions out, recorded here for posterity:

$.fileDownload(app.settings.baseURL+'php/risk_data.php',{ httpMethod: "POST", data:{ measure: app.getMeasure(1, 'percent'), sex: app.getSex(1), age: app.getAge(1), year: app.getYear(1), location: app.getLocation(1), cause: d3.keys(app.metadata.causes), risk: app.getRisk(1), format: 'csv', title: app.charts[1].getTitle(), } });

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Anyone want to fix things in mpld3?

People are actually using mpld3. It would be great if there was more progress addressing the many issues that this use has uncovered. Interested?

Thanks for your interest in this project. I think that all of these points can be addressed, but it would be helpful to have a minimal example of python code that generates the issue in point (1) reliably. The github issue tracker has discussions related to points (3) and (4), and something that might be related to point (1). For point (2), it would be great to have a specific example in mind also, so that we can be sure any solution reduces file size substantially without compromising graphically accuracy.

  1. https://github.com/jakevdp/mpld3/issues/226 https://github.com/jakevdp/mpld3/issues/250
  2. Would be good to add an issue: https://github.com/jakevdp/mpld3/issues/new
  3. https://github.com/jakevdp/mpld3/issues/247
  4. https://github.com/jakevdp/mpld3/issues/198

As far as when all of these issues will be addressed, that is a pitfall of certain open-source projects that you might already be familiar with from your work with [related project]… I suspect that each fix will require a few hours of debugging at least, with (2) being easiest and (4) being hardest. I have a long list of issues to address, and although I’m happy to put these on it, I never seem to make progress on any of them.

Pull requests are certainly welcome, and if you or your collaborators wants to make these improvements, the mpld3 project will be happy to incorporate them into the codebase.

–Abie

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Digging into GBD 2010 Risk Factors

I have to make the old DisMod-III website disappear (is it still here?). It is beautiful, but it is not available, and so I have to turn away researchers who want to use it for their own work. Fortunately, I can send them to a GitHub repository of DisMod code that they can use. But recently, it was not really DisMod that the emailing researcher wanted. I think they were really interested in digging into the details behind a figure like this one:
rf

For that, there is a non-dead website I can offer: GBD Compare. Finding your way around it can be a bit of a challenge, though, so here is a link straight to the relative contribution of each nutritional risk factor for Germany and USA: http://ihmeuw.org/2c6t ; to see from which specific diseases the risk factor DALYs are attributed, you can use a different part of this tool, linked to here: http://ihmeuw.org/2c6v

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