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# Johannes Buchner JohannesBuchner

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[1], 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: 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
Created Jun 20, 2015
Probability that two measurements actually have the same value
View overlapgauss.py
 import numpy import matplotlib.pyplot as plt import scipy.stats # two gaussian uncertainties with width sigma # at distance delta # what is the probability that they actually have the same value? def compute_bayes(delta, border=5): a = scipy.stats.norm()
Created Jun 20, 2015
p-value reliability
View pvalue.py
 import matplotlib.pyplot as plt import numpy import scipy.stats # http://www.medpagetoday.com/Blogs/TheMethodsMan/52171 def calc_reliability(p, power=0.8, frac_true=0.1): """ Given this p-value, power of the test and fraction of hypotheses that are actually true.
Last active Aug 29, 2015
Console progress bar -- takes stdin from arbitrary commands and plots a progress bar
View console-progress.py
 """ SYNOPSIS: ./myprog | python console-progress.py example for myprog: #!/bin/bash echo 100 for i in \$(seq 1 100) do sleep 1
Last active Jul 31, 2016
ArXiV minimal statistics checklist
View statistics-minimal.rst

# ArXiV minimal statistics checklist

This checklist help you identify and fix common errors/misinterpretation in your analysis, or of a paper you are refereeing.

1. If you use p-values (from a KS test, Pearson correlation, etc.).
1. What do you think a low p-value says?
1. You have absolutely disproved the null hypothesis (e.g. "no correlation" is ruled out, the data are not sampled from this model, there is no difference between the population means).