jjdelc

[
    {
        "hex": "#EFDECD", 
        "name": "Almond", 
        "rgb": "(239, 222, 205)"
    }, 
    {
        "hex": "#CD9575", 
        "name": "Antique Brass", 
        "rgb": "(205, 149, 117)"

cpbotha

Look ma, those drop shadows are breathing!

Your eyes want you to believe that the blocks are hovering up and down, when in fact only the drop shadow is being translated out and in again.

Follow me on https://twitter.com/cpbotha for even more fun, or see my other d3 blocks: http://bl.ocks.org/cpbotha

andrewgiessel

# good discussion here:  http://stackoverflow.com/questions/4308168/sigmoidal-regression-with-scipy-numpy-python-etc
# curve_fit() example from here: http://permalink.gmane.org/gmane.comp.python.scientific.user/26238
# other sigmoid functions here: http://en.wikipedia.org/wiki/Sigmoid_function

import numpy as np
import pylab
from scipy.optimize import curve_fit

def sigmoid(x, x0, k):
     y = 1 / (1 + np.exp(-k*(x-x0)))

mdml

A dendrogram is a common way to represent hierarchical data. For Python users, Scipy has a hierarchical clustering module that performs hierarchical clustering and outputs the results as dendrogram plots via matplotlib. When it's time to make a prettier, more customized, or web-version of the dendogram, however, it can be tricky to use Scipy's dendrogram to create a suitable visualization. My preferred method of visualizing data -- especially on the web -- is D3. This example includes a script to convert a Scipy dendrogram into JSON format used by D3's cluster method.

In the example, I cluster six genes by their expression values from two experiments. You can easily replace that data with your own, larger data set, to harness the power of both Scipy and D3 for analyzing hierarchical data. The D3 code I used to generate this example is straigh

erogol

import numpy as np
import numpy
import theano
import theano.tensor as T
from theano import function, config, shared, sandbox
from theano import ProfileMode
from sklearn import cluster, datasets
import matplotlib.pyplot as plt

def rsom(data, cluster_num, alpha, epochs = -1, batch = 1, verbose = False):   

urschrei

import math


def calculate_polygons(startx, starty, endx, endy, radius):
    """ 
    Calculate a grid of hexagon coordinates of the given radius
    given lower-left and upper-right coordinates 
    Returns a list of lists containing 6 tuples of x, y point coordinates
    These can be used to construct valid regular hexagonal polygons
    

cornhundred

/* prevents horizontal scrolling */
html {
  /*min-width: 1040px;*/
  max-width: 100%;
  overflow-x: hidden;
}


h1 {
  font-size: 45px;

urschrei

# sample invocation:
# pdftotext -f 5 /tmp/The_Complete_Works_of_H.P._Lovecraft.pdf - | python3 horror.py | uniq > lovecraft_word_pairs_sorted.txt

import re
import sys
import nltk
import wordfreq

re_word = re.compile(r'[^\-\w]')