Dean Malmgren deanmalmgren

no idea if this will work or not.

This little gist is used to demonstrate how svg-crowbar can be refactored for use beyond a bookmarklet. Click on the download png button to download a PNG image that is styled from stylesheets rather than attributes on the SVG elements themselves.

This is a quick visualization of the Scialog pre-conference network. Orange nodes are theorists, blue nodes are experimentalists, and yellow circles are both experimentalists and theorists. For simplicity, this only displays the bidirectional links between pairs of scientists where the thickness of the line joining two scientists corresponds with how strongly they know each other.

To run this in development, just use python's simple server:

python -m SimpleHTTPServer

This gist has some quick analysis related to the outcomes from an elongated dice experiment. analyze_roll_data.py is a script to conduct the analysis. p16_aspect_ratio.agr contains the results of this analysis in an xmgrace file.

We tried a few quick things to work out a theoretical relationship between the

When I saw this on twitter, it really made me wonder whether the reported statistics had any significance. Based on the numbers reported, I wrote a quick little program to estimate the 95% confidence interval of the percent of CEOs hired internally. Alongside each regions name is the lower and upper cutoff on the 95% confidence interval.

[unix]$ python simulate.py
          USA/Canada	0.679	0.859
      Western Europe	0.625	0.854
               Japan	0.897	1.000
        Other mature	0.565	0.826
               China	0.710	0.968
Brazil, Russia, India	0.517	0.828

	"""Export a json of the-hat-rack board and run this script to print out all of the names of the internal hats:

	hats.py trello_export.json
	"""
	import sys
	import json
	from pprint import pprint

	with open(sys.argv[1]) as stream:
	data = json.loads(stream.read().decode("utf-8", "ignore"))

	5 function calls in 6.465 seconds

	Ordered by: internal time

	ncalls tottime percall cumtime percall filename:lineno(function)
	1 6.461 6.461 6.465 6.465 similarity_profiling.py:20(print_approach)
	1 0.004 0.004 0.004 0.004 {open}
	1 0.000 0.000 0.000 0.000 {range}
	1 0.000 0.000 6.465 6.465 <string>:1(<module>)
	1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}

	sample_a.dat
	sample_b.dat