erikbern

n, k = len(te), 0
ts, ys = [], []
p = 1.0
for t, e in te:
    if e:  # whether the event was "observed" (converted) or not observed (may convert in the future)
        p *= (n-1) / n
    n -= 1
    ts.append(t)
    ys.append(100. * (1-p))
pyplot.plot(ts, ys, 'b')

erikbern

def ll_to_3d(lat, lon):
    lat *= math.pi / 180
    lon *= math.pi / 180
    x = math.cos(lat) * math.cos(lon)
    z = math.cos(lat) * math.sin(lon)
    y = math.sin(lat)
    return numpy.array([x, y, z])

erikbern

import matplotlib.pyplot, numpy, scipy.stats, seaborn

for i in range(50):
    p, = scipy.stats.uniform.rvs(size=1)
    a, b = scipy.stats.geom.rvs(p, size=2)
    if a == b == 1:
        continue
    print(a, b)
    x = numpy.linspace(0, 1, 1000)
    y = scipy.stats.beta.pdf(x, a, b)

erikbern

from num2words import num2words
from matplotlib import pyplot

lang = 'de'
words = [num2words(i, lang=lang) for i in range(1000000)]
fig = pyplot.figure()
ax = fig.add_subplot(111)

ax.semilogx([len(word) for word in words], color='green')

erikbern

import urllib2, csv
import matplotlib.pyplot as plt
import datetime
import seaborn
import numpy, scipy.stats, math

f = urllib2.urlopen('https://raw.githubusercontent.com/datasets/s-and-p-500/master/data/data.csv')
csv = csv.reader(f)
csv.next() # headers

erikbern

import numpy
import random
from matplotlib import pyplot

percentiles = [50, 75, 90, 95, 99]
latencies = [[] for p in percentiles]
loads = []

n = 100000
for k in numpy.linspace(0.01, 1.0, 100):

erikbern

import pymc3, numpy, sys, seaborn, re

def get_dist(fn):
    y = [0, 0, 0, 0, 0]
    for line in open(fn):
        try:
            num = re.split('\D', line)[0]
            y[int(num) - 1] += 1
        except:
            print fn, 'can not parse:', line

erikbern

import numpy
import scipy.optimize
from matplotlib import pyplot

cs = numpy.linspace(0.01, 0.99, 100)
ks = []

for c in cs:
    def f(log_k):
        k = numpy.exp(log_k)

erikbern

import random
import numpy
from matplotlib import pyplot
rs = numpy.random.randn(1000)

xs = rs[1:-1] - rs[:-2]
ys = rs[2:] - rs[1:-1]
pyplot.scatter(xs, ys)
pyplot.show()

erikbern

import pymc, pymc.graph
import matplotlib.pyplot as plt
import numpy as np
import sys

channels = [
    ('A', 2292.04, 9),
    ('B', 1276.85, 2),
    ('C', 139.59, 3),
    ('D', 954.98, 5)