Douglas Duhaime duhaime

## cheatsheet.rb
# Basic key operators to query the JSON objects :

# #> : Get the JSON object at that path (if you need to do something fancy)
# -> : Get the JSON object at that path (if you don't)
# ->> : Get the JSON object at that path as text
# {obj, n} : Get the nth item in that object

# https://www.postgresql.org/docs/9.4/functions-json.html#FUNCTIONS-JSONB-OP-TABLE
# Date
# date before today

## imagenet1000_clsidx_to_labels.txt
{0: 'tench, Tinca tinca',
 1: 'goldfish, Carassius auratus',
 2: 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias',
 3: 'tiger shark, Galeocerdo cuvieri',
 4: 'hammerhead, hammerhead shark',
 5: 'electric ray, crampfish, numbfish, torpedo',
 6: 'stingray',
 7: 'cock',
 8: 'hen',
 9: 'ostrich, Struthio camelus',

## separate_points.py
def lat_lon_hex_mesh(bounds, d=3):
    """Creates a hexagonal lat/lon mesh within bounds that has a radial separation of d"""

    lone, lonw, lats, latn = bounds

    # heigt of equilatral triangle from radial distance sqrt(r^2 - (r/2)^2)
    h = np.sqrt(0.75) * d
    w = d / 2

    lat_vals = np.arange(lats, latn, h)

## bezier_curves.py
import matplotlib

matplotlib.use('webagg')

import numpy as np
from scipy.special import binom

import matplotlib.pyplot as plt

from matplotlib.lines import Line2D

## .block
license: gpl-3.0
border: no
scrolling: no
height: 500

## index.html
<html>
    <head>
        <title>
            [Visualizing Movement Data with D3.js]
        </title>
        <script src="https://d3js.org/d3.v4.min.js"></script>
        <script src="https://omid.al/Mova-Viz/MovaViz-v0.1.2.js"></script>
        <style>
            * {
                box-sizing: border-box

## README.md

      
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                duhaime
                / README.md
            
            
              Created
              October 1, 2018 01:12
                — forked from armollica/README.md
            
              
                K-Nearest Neighbors
              
          
    K-nearest neighbors search of a 2D set of points.
Move the slider or scroll to change k.
The algorithm was adapted from this block

  
## README.md

      
              4 files
            
          
              0 forks
            
          
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              0 stars
            
          
                duhaime
                / README.md
            
            
              Created
              October 1, 2018 01:09
                — forked from armollica/README.md
            
              
                K-D Tree Nearest Neighbors (k-NN)
              
          
    k-d tree nearest neighbors search
(k-NN). The red dots are the 10 nearest neighbors.
Orange dots are scanned and not selected.
Compare with this block that implements
a k nearest neighbor search using a quadtree instead of a k-d tree.
The algorithm for this search came from
this course handout.
Note that the "k" in k-d tree need not be the same number as the "k" in

  
## .block
license: gpl-3.0

## .block
license: mit
	# Basic key operators to query the JSON objects :

	# #> : Get the JSON object at that path (if you need to do something fancy)
	# -> : Get the JSON object at that path (if you don't)
	# ->> : Get the JSON object at that path as text
	# {obj, n} : Get the nth item in that object

	# https://www.postgresql.org/docs/9.4/functions-json.html#FUNCTIONS-JSONB-OP-TABLE
	# Date
	# date before today
	{0: 'tench, Tinca tinca',
	1: 'goldfish, Carassius auratus',
	2: 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias',
	3: 'tiger shark, Galeocerdo cuvieri',
	4: 'hammerhead, hammerhead shark',
	5: 'electric ray, crampfish, numbfish, torpedo',
	6: 'stingray',
	7: 'cock',
	8: 'hen',
	9: 'ostrich, Struthio camelus',
	def lat_lon_hex_mesh(bounds, d=3):
	"""Creates a hexagonal lat/lon mesh within bounds that has a radial separation of d"""

	lone, lonw, lats, latn = bounds

	# heigt of equilatral triangle from radial distance sqrt(r^2 - (r/2)^2)
	h = np.sqrt(0.75) * d
	w = d / 2

	lat_vals = np.arange(lats, latn, h)
	import matplotlib

	matplotlib.use('webagg')

	import numpy as np
	from scipy.special import binom

	import matplotlib.pyplot as plt

	from matplotlib.lines import Line2D
	<html>
	<head>
	<title>
	[Visualizing Movement Data with D3.js]
	</title>
	<script src="https://d3js.org/d3.v4.min.js"></script>
	<script src="https://omid.al/Mova-Viz/MovaViz-v0.1.2.js"></script>
	<style>
	* {
	box-sizing: border-box