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# Johannes BuchnerJohannesBuchner

Last active Aug 29, 2015
Toy linefitting: bootstrapped estimator
View toybootstrap.py
 import numpy from numpy import log, log10, sin, cos, tan, arctan, arccos, arcsin, abs, any, pi import sys import matplotlib.pyplot as plt data = numpy.loadtxt(sys.argv, dtype=[(colname, 'f') for colname in 'x', 'x_err', 'y', 'y_err', 'cor'], skiprows=1) plt.figure(figsize=(7,7))
Last active Aug 29, 2015
Toy linefitting
View toy.py
 import numpy from numpy import log, isnan, isfinite, sin, cos, tan, abs, any, pi import scipy, scipy.stats import pymultinest import json import sys import matplotlib.pyplot as plt numpy.random.seed(1) outputfiles_basename = "mnchains_toy_"
Last active Aug 29, 2015
Toy linefitting: new test data with known true values
View gen.tab
 # "x" "x_err" "y" "y_err" "cor" 10.191 0.125 20.128 0.125 0.731 9.808 0.050 20.286 0.050 0.662 9.700 0.039 20.437 0.039 0.580 9.831 0.065 20.058 0.065 0.720 9.912 0.058 20.194 0.058 0.502 9.861 0.083 19.989 0.083 0.769 9.971 0.060 20.229 0.060 0.563 9.859 0.060 20.164 0.060 0.752 9.720 0.044 20.318 0.044 0.646
Created Feb 6, 2015
birthday problem for 4 people
View bd.py
 import numpy import matplotlib.pyplot as plt def prob(M): # for M people, compute the probability of having more than 4 with same birthday hits = 0 # number of simulation instances N = 1000 I = numpy.arange(365).reshape((1,-1)) for j in range(N):
Created Feb 11, 2015
View gist:f05bf324a6d6d7e035e7
 def generateTuple(): if numpy.random.uniform() > 0.05: # generate from normal data set, e.g. normal distribution around some values -- here, a line k = 1.16 d = 8.9 x = numpy.random.uniform(6, 12) y = k * (x - 11) + d return numpy.random.norm(x, 1), numpy.random.norm(y, 3) else: # generate from outlier distribution, e.g. uniform distribution over full parameter space
Created Mar 31, 2015
View optpath.py
 import numpy from numpy import cos, sin, exp, log, pi, tan, arccos, arcsin, arctan import matplotlib.pyplot as plt # make a quadratic figure plt.figure(figsize=(6, 6)) # generate 400 points between 0 and 1 t = numpy.linspace(0, 1, 40) print 't = ', t
Last active Aug 29, 2015
For tests/builds that should only re-run when code or data files have changed (memoized tests)
View codememoize.py