jeremy9959

---
title: Chapter 1 Section 1
---


\newcommand{\Z}{\mathbb{Z}}
\newcommand{\N}{\mathbb{N}}
\newcommand{\R}{\mathbb{R}}
\newcommand{\Q}{\mathbb{Q}}

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div = {
  var div = html``
  var doc = new Bokeh.Document() ; 
  doc.add_root(P) ; 
  await Bokeh.embed.add_document_standalone(doc, div)
  return div ; 
}

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P = {
  var p = Bokeh.Plotting.figure({x_range: new Bokeh.FactorRange({factors: x, range_padding: 0.1}),
    plot_height: 300, plot_width:800, toolbar_location: null, title: "Fruit Counts By Year"})
  p.vbar({x, top: counts, width:0.9}) ;
  p.xaxis.map((axis) => axis.major_label_orientation = 1) ;
  p.xgrid.map((grid) => grid.grid_line_color = null) ; 
  p.y_range_start = 0 ; 
  return p ;
}

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viewof Bokeh = 
{
  const ver = '2.0.2';
  const message = html`Loaded Bokeh Version ${ver}...` ;

  if (!document.getElementById('bokehjs-css'))
    document.head.appendChild(
      html`<link id='bokehjs-css' 
      href='https://cdn.pydata.org/bokeh/release/bokeh-${ver}.min.css' 
      rel=stylesheet>`);

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# withdrawn pending further study!

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{"directed": false, "multigraph": false, "graph": {}, "nodes": [{"bipartite": 1, "name": "Razaaq Adoti", "id": 1040389}, {"bipartite": 1, "name": "Tom Fisher", "id": 1024010}, {"bipartite": 1, "name": "Ronald Guttman", "id": 1007630}, {"bipartite": 1, "name": "Beverly D'Angelo", "id": 1040399}, {"bipartite": 1, "name": "H\u00e9ctor Elizondo", "id": 1024017}, {"bipartite": 1, "name": "Richard Bright", "id": 1015826}, {"bipartite": 1, "name": "Andre B. Blake", "id": 1007635}, {"bipartite": 1, "name": "George Buck Flower", "id": 1024020}, {"bipartite": 1, "name": "Chevy Chase", "id": 1007640}, {"bipartite": 1, "name": "Gary Oldman", "id": 1007642}, {"bipartite": 1, "name": "Matthew Cornwell", "id": 1032224}, {"bipartite": 1, "name": "Eileen Ryan", "id": 1040417}, {"bipartite": 1, "name": "Mia Farrow", "id": 1007648}, {"bipartite": 1, "name": "Donnie Wahlberg", "id": 1024033}, {"bipartite": 1, "name": "Richard Kiel", "id": 1024036}, {"bipartite": 1, "name": "Muse Watson", "id": 1007652}, {"bipartite": 1, "name": 

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import numpy as np


class _nqueue(list):
    """A simple fixed length priority queue of tuples used by the vptree class to track
    the closest points found so far.  Contains pairs (dist, node) where node refers to the
    vptree structure and dist is the distance from the given point to the corresponding node.
    Only the smallest max_size examples are kept.

    Does a simple append followed by sort, so is intended for short lists.

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jeremy9959

license: bsd-3-clause

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This is an example from "Data Visualization with Python and Javascript" by Kyran Dale, O'Reilly, 2016. The goal was just to see how blocks work.