> > > > [2] C6XTY @ sa: this Thursday: > https://flic.kr/p/HnGeut > > (sa: is where I've taught Martian Math before) > http://www.4dsolutions.net/satacad/martianmath/toc.html > http://wikieducator.org/Martian_Math > >
In this post I'm merging two threads: (i) introduce some of the high end 3rd party stuff early e.g. science stack numpy and pandas (ii) segue a geometry that requires some rethinking of familiar orthodoxies. Example: pd.options.display.float_format = '{:,.3f}'.format data = pd.DataFrame(vectors, columns = index) print(data) Vtype ivm xyz Coords a b c d x y z 0 0.000 1.000 1.000 2.000 0.000 -0.707 -0.707 1 1.000 0.000 1.000 2.000 0.707 0.000 -0.707 2 2.000 0.000 1.000 1.000 0.707 0.707 0.000 3 0.000 2.000 1.000 1.000 -0.707 -0.707 0.000 4 0.000 1.000 2.000 1.000 -0.707 0.000 -0.707 5 1.000 2.000 1.000 0.000 -0.707 0.000 0.707 6 1.000 1.000 2.000 0.000 -0.707 0.707 0.000 7 2.000 1.000 1.000 0.000 0.000 0.707 0.707 8 1.000 0.000 2.000 1.000 0.000 0.707 -0.707 9 1.000 2.000 0.000 1.000 0.000 -0.707 0.707 10 2.000 1.000 0.000 1.000 0.707 0.000 0.707 11 1.000 1.000 0.000 2.000 0.707 -0.707 0.000 Getting such multi-indexing requires such as: arrays = [['ivm', 'ivm', 'ivm', 'ivm', 'xyz', 'xyz', 'xyz'], ['a', 'b', 'c', 'd', 'x', 'y', 'z',]] tuples = list(zip(*arrays)) index = pd.MultiIndex.from_tuples(tuples, names=['Vtype', 'Coords']) Call it business math if you like, micro-management of data tables via some command line interface. https://en.wikipedia.org/wiki/In_the_Beginning..._Was_the_Command_Line That's using the multi-indexing phenomenon (object) the pandas dataframes provide. Above, I'm showing vectors in two notations, side by side, with a link to their colorful depictions, in terms of ball packings, at this still improving Jupyter Notebook: https://github.com/4dsolutions/Python5/blob/master/Generating%20the%20FCC.ipynb Next challenge is to segment the dataframe vertically, into nucleus (1) plus successive layers (12, 42, 92...). Kirby
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