{"componentChunkName":"component---src-templates-blog-post-js","path":"/eight_bit_gauges/","result":{"data":{"site":{"siteMetadata":{"title":"tigue.com"}},"markdownRemark":{"id":"9540d859-27f6-597e-bac3-012cc2eb4857","excerpt":"Over the last few weeks I have written so many variants on “eight bit\ngauges” in Jupyter that I thought I might as well corral them all\ntogether in a menagerie…","html":"\n

Over the last few weeks I have written so many variants on “eight bit\ngauges” in Jupyter that I thought I might as well corral them all\ntogether in a menagerie. By eight big gauges I mean histograms with\n256 bins.

\n

For example, the above image is for grayscale images as\nimplied by the gray gradient on the face of the histogram. The red\nline is the same data but as a cumulative distribution, starting at\nzero and marching monotonically to 100% total luminance.

\n

\n \n \n The code is all Python using common plotting libraries (Bokeh, Plotly,\nMatplotlib, etc.). In particular, the code has been well tested on\nColab, including working out full-screenable versions which perform well\nas “slides.” I’ve licensed the code under the Apache 2 license. So, peruse\nthe menagerie and maybe take one home.

\n\n \n \"simple\n \n

There is a pre-run and runnable copy on Colab,\neight_bit_gauges.ipynb,\nwhich can also be found on GitHub in the shell repo. To\nload it from from GitHub and run your own copy:

\n
\n\n
","frontmatter":{"title":"Eight Bit Gauges in Jupyter","date":"April 27, 2020","description":"TODO: add discription in md's header"}}},"pageContext":{"isCreatedByStatefulCreatePages":false,"slug":"/eight_bit_gauges/","previous":{"fields":{"slug":"/jupyter_book_notes/"},"frontmatter":{"title":"Jupyter Books' Many Manifestations of a Notebook"}},"next":{"fields":{"slug":"/whiteboarder_a_two_bit_photo_processor/"},"frontmatter":{"title":"Whiteboarder: A Two Bit Image Processor"}}}}}