def compare(m, Nobs, Ncodes, Nfeatures):
obs = RandomArray.normal(0., 1., (Nobs, Nfeatures))
codes = RandomArray.normal(0., 1., (Ncodes, Nfeatures))
import scipy.cluster.vq
scipy.cluster.vq
print(
'vq with %d observation, %d features and %d codes for %d iterations' %
(Nobs, Nfeatures, Ncodes, m))
t1 = time.time()
for i in range(m):
code, dist = scipy.cluster.vq.py_vq(obs, codes)
t2 = time.time()
py = (t2 - t1)
print(' speed in python:', (t2 - t1) / m)
print(code[:2], dist[:2])
t1 = time.time()
for i in range(m):
code, dist = scipy.cluster.vq.vq(obs, codes)
t2 = time.time()
print(' speed in standard c:', (t2 - t1) / m)
print(code[:2], dist[:2])
print(' speed up: %3.2f' % (py / (t2 - t1)))
# load into cache
b = vq(obs, codes)
t1 = time.time()
for i in range(m):
code, dist = vq(obs, codes)
t2 = time.time()
print(' speed inline/blitz:', (t2 - t1) / m)
print(code[:2], dist[:2])
print(' speed up: %3.2f' % (py / (t2 - t1)))
# load into cache
b = vq2(obs, codes)
t1 = time.time()
for i in range(m):
code, dist = vq2(obs, codes)
t2 = time.time()
print(' speed inline/blitz2:', (t2 - t1) / m)
print(code[:2], dist[:2])
print(' speed up: %3.2f' % (py / (t2 - t1)))
# load into cache
b = vq3(obs, codes)
t1 = time.time()
for i in range(m):
code, dist = vq3(obs, codes)
t2 = time.time()
print(' speed using C arrays:', (t2 - t1) / m)
print(code[:2], dist[:2])
print(' speed up: %3.2f' % (py / (t2 - t1)))
def compare(m, Nobs, Ncodes, Nfeatures):
obs = RandomArray.normal(0., 1., (Nobs, Nfeatures))
codes = RandomArray.normal(0., 1., (Ncodes, Nfeatures))
import scipy.cluster.vq
scipy.cluster.vq
print 'vq with %d observation, %d features and %d codes for %d iterations' % \
(Nobs,Nfeatures,Ncodes,m)
t1 = time.time()
for i in range(m):
code, dist = scipy.cluster.vq.py_vq(obs, codes)
t2 = time.time()
py = (t2 - t1)
print ' speed in python:', (t2 - t1) / m
print code[:2], dist[:2]
t1 = time.time()
for i in range(m):
code, dist = scipy.cluster.vq.vq(obs, codes)
t2 = time.time()
print ' speed in standard c:', (t2 - t1) / m
print code[:2], dist[:2]
print ' speed up: %3.2f' % (py / (t2 - t1))
# load into cache
b = vq(obs, codes)
t1 = time.time()
for i in range(m):
code, dist = vq(obs, codes)
t2 = time.time()
print ' speed inline/blitz:', (t2 - t1) / m
print code[:2], dist[:2]
print ' speed up: %3.2f' % (py / (t2 - t1))
# load into cache
b = vq2(obs, codes)
t1 = time.time()
for i in range(m):
code, dist = vq2(obs, codes)
t2 = time.time()
print ' speed inline/blitz2:', (t2 - t1) / m
print code[:2], dist[:2]
print ' speed up: %3.2f' % (py / (t2 - t1))
# load into cache
b = vq3(obs, codes)
t1 = time.time()
for i in range(m):
code, dist = vq3(obs, codes)
t2 = time.time()
print ' speed using C arrays:', (t2 - t1) / m
print code[:2], dist[:2]
print ' speed up: %3.2f' % (py / (t2 - t1))
def ThermalizingTransformer(atoms, T):
"""
Thermalizes velocities to m v^2 / 2 = kB T/2, with kB = 1
"""
#
vRMS = numpy.sqrt(T / atoms.mass)
atoms.velocities = RandomArray.normal(0, vRMS,
numpy.shape(atoms.velocities))
def statistics():
pd = stats.norm(loc=1, scale=0.5) # normal distribution N(1,0.5)
n=10000
r = pd.rvs(n) # random variates
import RandomArray
r = RandomArray.normal(1, 0.1, n)
s = stats.stats
print pd.stats()
print 'mean=%g stdev=%g skewness=%g kurtosis=%g' % \
(s.mean(r), s.variation(r), s.skew(r), s.kurtosis(r))
bin_counts, bin_min, min_width, noutside = s.histogram(r, numbins=50)
def statistics():
pd = stats.norm(loc=1, scale=0.5) # normal distribution N(1,0.5)
n = 10000
r = pd.rvs(n) # random variates
import RandomArray
r = RandomArray.normal(1, 0.1, n)
s = stats.stats
print pd.stats()
print 'mean=%g stdev=%g skewness=%g kurtosis=%g' % \
(s.mean(r), s.variation(r), s.skew(r), s.kurtosis(r))
bin_counts, bin_min, min_width, noutside = s.histogram(r, numbins=50)
nbins = self.array.shape[0]
histo = N.add.reduce(weights*N.equal(N.arange(nbins)[:,N.NewAxis],
data), -1)
histo[-1] = histo[-1] + N.add.reduce(N.repeat(weights,
N.equal(nbins, data)))
self.array[:, 1] = self.array[:, 1] + histo
if __name__ == '__main__':
if N.package == 'Numeric':
import RandomArray as random
elif N.package == 'NumPy':
from numpy import random
nsamples = 1000
random.seed(12,13)
data = random.normal(1.0, 0.5, nsamples)
h = Histogram(data, 50) # use 50 bins between min & max samples
h.normalizeArea() # make probabilities in histogram
x = h.getBinIndices()
y = h.getBinCounts()
# add many more samples:
nsamples2 = nsamples*100
data = random.normal(1.0, 0.5, nsamples2)
h.addData(data)
h.normalizeArea()
x2 = h.getBinIndices()
y2 = h.getBinCounts()
# and more:
nsamples3 = nsamples*1000
from Numeric import dot,sum import sys,numeric_version import RandomArray import LinearAlgebra print sys.version print "Numeric version:",numeric_version.version RandomArray.seed(123,456) a = RandomArray.normal(0,1,(100,10)) f = RandomArray.normal(0,1,(10,30)) e = RandomArray.normal(0,0.1,(100,30)) print "Got to seed:",RandomArray.get_seed() b = dot(a,f)+e (x,res,rank,s)=LinearAlgebra.linear_least_squares(a,b) f_res = sum((b-dot(a,f))**2) x_res = sum((b-dot(a,x))**2) print "'Planted' residues, upper bound for optimal residues:" print f_res print "Claimed residues:" print res print "Actual residues:" print x_res print "Ratio between actual and claimed (shoudl be 1):" print x_res/res print "Ratio between actual and planted (should be <1):" print x_res/f_res
sampleRate = 44100.
c = 300. # meters per second
spectralRange = sampleRate / 2
import numpy
import math
import RandomArray
import stats
T=r/c
spectrum = numpy.zeros( nBins-1, numpy.complex)
for x in RandomArray.normal(0,standardMicrophoneDeviation,(nSpeakers,)) :
t1root = 1+x
t2root = 1-x
print t1root, t2root
spectrum += numpy.array([
complex(1, t1root*w*T) * math.e**complex(math.cos(w*T*t1root), math.sin(w*T*t1root)) /x / w / w / T / T -
complex(1, t2root*w*T) * math.e**complex(math.cos(w*T*t2root), math.sin(w*T*t2root)) /x / w / w / T / T
for w in [ math.pi*2*normfreq*spectralRange/nBins
for normfreq in xrange(1,nBins) ] ])
import Gnuplot
gp=Gnuplot.Gnuplot(persist=1)
gp('set data style lines')
gp.plot(abs(spectrum), [bin.real for bin in spectrum], [bin.imag for bin in spectrum], numpy.zeros(nBins))
gp.hardcopy(filename="IncoherenceSimulation.png",terminal="png")
import RandomArray
import numpy
from numpy.random import normal
import sys
import fcs
import pylab
sys.path.append('../')
import flow
if __name__ == '__main__':
data = numpy.concatenate((RandomArray.normal(5, 1, (2000, 2)),
RandomArray.normal(7, 1, (2000, 2)),
RandomArray.normal(9, 1, (3000, 2)),
RandomArray.normal(11, 1, (2000, 2)),
RandomArray.normal(13, 1, (1000, 2))), axis=0)
# f = fcs.FCSReader("../data/3FITC-4PE.004.fcs")
# print f.data.keys()
# m = 10000
# x1 = numpy.array((f.data['FSC-H'])[:m], 'd')
# x2 = numpy.array((f.data['SSC-H'])[:m], 'd')
# x3 = numpy.array((f.data['FL1-H'])[:m], 'd')
# x4 = numpy.array((f.data['FL2-H'])[:m], 'd')
# print min(x1), max(x1)
# print min(x2), max(x2)
# print min(x3), max(x3)
# print min(x4), max(x4)
# data_unscaled = numpy.transpose([x1, x2, x3, x4])
# #data = numpy.transpose([(x1-min(x1))/max(x1), (x2-min(x2))/max(x2), (x3-min(x3))/max(x3), (x4-min(x4))/max(x4)])
def NumPy_random(n):
import RandomArray
return RandomArray.normal(0,1,n)
nbins = self.array.shape[0]
histo = N.add.reduce(weights*N.equal(N.arange(nbins)[:,N.NewAxis],
data), -1)
histo[-1] = histo[-1] + N.add.reduce(N.repeat(weights,
N.equal(nbins, data)))
self.array[:, 1] = self.array[:, 1] + histo
if __name__ == '__main__':
if N.package == 'Numeric':
import RandomArray as random
elif N.package == 'NumPy':
from numpy import random
nsamples = 1000
random.seed(12,13)
data = random.normal(1.0, 0.5, nsamples)
h = Histogram(data, 50) # use 50 bins between min & max samples
h.normalizeArea() # make probabilities in histogram
x = h.getBinIndices()
y = h.getBinCounts()
# add many more samples:
nsamples2 = nsamples*100
data = random.normal(1.0, 0.5, nsamples2)
h.addData(data)
h.normalizeArea()
x2 = h.getBinIndices()
y2 = h.getBinCounts()
# and more:
nsamples3 = nsamples*1000
