def random_tree(labels):
"""
Given a list of labels, create a list of leaf nodes, and then one
by one pop them off, randomly grafting them on to the growing tree.
Return the root node.
"""
assert len(labels) > 2
import RandomArray; RandomArray.seed()
leaves = []
for label in labels:
leaves.append(Fnode(istip=1, label=label))
leaf_indices = list(RandomArray.permutation(len(leaves)))
joined = [leaves[leaf_indices.pop()]]
remaining = leaf_indices
while remaining:
i = RandomArray.randint(0, len(joined)-1)
c1 = joined[i]
if c1.back:
n = c1.bisect()
else:
n = InternalNode()
n.add_child(c1)
c = leaves[remaining.pop()]
n.add_child(c)
joined.append(c)
joined.append(n)
for node in joined:
if not node.back:
node.isroot = 1
return node
def main():
""" A simple example. Note that the Tkinter lines are there only
because this code will be run standalone. On the interpreter,
simply invoking surf and view would do the job."""
import Tkinter
r = Tkinter.Tk()
r.withdraw()
def f(x, y):
return Numeric.sin(x*y)/(x*y)
x = Numeric.arange(-7., 7.05, 0.1)
y = Numeric.arange(-5., 5.05, 0.05)
v = surf(x, y, f)
import RandomArray
z = RandomArray.random((50, 25))
v1 = view(z)
v2 = view(z, warp=1)
z_large = RandomArray.random((1024, 512))
v3 = viewi(z_large)
# A hack for stopping Python when all windows are closed.
v.master = r
v1.master = r
v2.master = r
#v3.master = r
r.mainloop()
def randomArray(shape, seed=None, range=(0, 1), type=Float):
"""Utility to generate a Numeric array full of pseudorandom numbers in the given range.
This will attempt to use the RandomArray module, but fall back on using the standard
random module in a loop.
"""
global globalSeed
if not seed:
if not globalSeed:
globalSeed = int(time.time())
seed = globalSeed
# Keep our global seed mixed up enough that many requests for
# random arrays consecutively still gives random-looking output.
globalSeed = (globalSeed + random.randint(1, 0xFFFFF)) & 0x7FFFFFF
try:
import RandomArray
RandomArray.seed(seed + 1, seed + 1)
return (RandomArray.random(shape) * (range[1] - range[0]) + range[0]).astype(type)
except ImportError:
random.seed(seed)
a = zeros(multiply.reduce(shape), Float)
for i in xrange(a.shape[0]):
a[i] = random.random() * (range[1] - range[0]) + range[0]
return reshape(a, shape).astype(type)
def setUp(self):
self.number = 50
X = RandomArray.random(self.number)
Y = RandomArray.random(self.number)
Z = RandomArray.random(self.number)
co = Numeric.array([X, Y, Z])
self.points = []
for i in range(len(co[0])):
self.points.append(tuple(co[:,i].tolist()))
def setUp(self):
self.number = 50
X = RandomArray.random(self.number)
Y = RandomArray.random(self.number)
Z = RandomArray.random(self.number)
co = Numeric.array([X, Y, Z])
self.points = []
for i in range(len(co[0])):
self.points.append(tuple(co[:, i].tolist()))
def MakeRandomPartitionProblem(N, M):
"""
Returns a random series of N integers in the range 1 < p < 2**M, guaranteed
to sum to an even number. Use RandomArray.randint to generate a length N
vector S of the appropriate range. While sum(S) mod 2 is not zero,
re-generate S.
"""
intSize = 2**M
S = RandomArray.randint(1, intSize + 1, N)
while sum(S) % 2 != 0:
S = RandomArray.randint(1, intSize + 1, N)
return S
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 RunMovie(self,event = None):
import RandomArray
start = clock()
shift = RandomArray.randint(0,0,(2,))
NumFrames = 50
for i in range(NumFrames):
points = self.LEs.Points
shift = RandomArray.randint(-5,5,(2,))
points += shift
self.LEs.SetPoints(points)
self.Canvas.Draw()
print "running the movie took %f seconds to disply %i frames"%((clock() - start),NumFrames)
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 RunMovie(self, event=None):
import RandomArray
start = clock()
shift = RandomArray.randint(0, 0, (2, ))
NumFrames = 50
for i in range(NumFrames):
points = self.LEs.Points
shift = RandomArray.randint(-5, 5, (2, ))
points += shift
self.LEs.SetPoints(points)
self.Canvas.Draw()
print "running the movie took %f seconds to disply %i frames" % (
(clock() - start), NumFrames)
def test_sparse_vs_dense(self):
RandomArray.seed(0) # For reproducability
for s, l in (100, 100000), (10000, 100000), (100000, 100000):
small = Numeric.sort(RandomArray.randint(0, 100000, (s,)))
large = Numeric.sort(RandomArray.randint(0, 100000, (l,)))
sparse1 = soomfunc.sparse_intersect(small, large)
sparse2 = soomfunc.sparse_intersect(large, small)
dense1 = soomfunc.dense_intersect(small, large)
dense2 = soomfunc.dense_intersect(large, small)
self.assertEqual(sparse1, sparse2)
self.assertEqual(dense1, dense2)
self.assertEqual(sparse1, dense1)
def _synth(self, freq, msdur, vol, risefall):
t = arange(0, msdur / 1000.0, 1.0 / _Beeper._dafreq)
s = zeros((t.shape[0], 2))
# use trapezoidal envelope with risefall (below) time
if msdur < 40:
risefall = msdur / 2.0
env = -abs((t - (t[-1] / 2)) / (risefall / 1000.0))
env = env - min(env)
env = where(less(env, 1.0), env, 1.0)
bits = _Beeper._bits
if bits < 0:
bits = -bits
signed = 1
else:
signed = 0
fullrange = power(2, bits - 1)
if freq is None:
y = (env * vol * fullrange * \
RandomArray.random(t.shape)).astype(Int16)
else:
y = (env * vol * fullrange * \
sin(2.0 * pi * t * freq)).astype(Int16)
if _Beeper._chans == 2:
y = transpose(array([y, y]))
s = pygame.sndarray.make_sound(y)
return s
def readfiles(self):
ncopy = 2
for j in range(ncopy):
infile = open('/Users/rfinn/SDSS/fieldDR4/myclusters.cat', 'r')
for line in infile:
if line.find('#') > -1:
continue
t = line.split()
self.id.append(float(t[0])) #C4 id name
self.r200.append(float(t[3])) #R200 in Mpc
self.sigma.append(float(t[4])) #sigma in km/s
self.z.append(float(t[1]))
c1 = Rand.randint(0, len(
g.x1all)) #center on random galaxy in gal catalog
#c2=Rand.randint(0,len(g.x1))#center on random galaxy in gal catalog
#c3=Rand.randint(0,len(g.x1))#center on random galaxy in gal catalog
self.x1.append(g.x1all[c1])
self.x2.append(g.x2all[c1])
self.x3.append(g.x3all[c1])
#c1=Rand.random()#center on random position in simulation
#c2=Rand.random()#center on random position in simulation
#c3=Rand.random()#center on random
#self.x1.append(c1*simL)
#self.x2.append(c2*simL)
#self.x3.append(c3*simL)
infile.close()
self.r200 = N.array(self.r200, 'd')
self.sigma = N.array(self.sigma, 'd')
self.z = N.array(self.z, 'd')
self.x1 = N.array(self.x1, 'd')
self.x2 = N.array(self.x2, 'd')
self.x3 = N.array(self.x3, 'd')
def _synth(self, freq, msdur, vol, risefall): t = arange(0, msdur / 1000.0, 1.0 / _Beeper._dafreq) s = zeros((t.shape[0], 2)) # use trapezoidal envelope with risefall (below) time if msdur < 40: risefall = msdur / 2.0 env = -abs((t - (t[-1] / 2)) / (risefall/1000.0)) env = env - min(env) env = where(less(env, 1.0), env, 1.0) bits = _Beeper._bits if bits < 0: bits = -bits signed = 1 else: signed = 0 fullrange = power(2, bits-1) if freq is None: y = (env * vol * fullrange * \ RandomArray.random(t.shape)).astype(Int16) else: y = (env * vol * fullrange * \ sin(2.0 * pi * t * freq)).astype(Int16) if _Beeper._chans == 2: y = transpose(array([y,y])) s = pygame.sndarray.make_sound(y) return s
def test2(shape=(100,100)):
dl = DynamicLattice.DynamicLattice(shape)
a = RandomArray.randint(0, 2, shape)
dl.display(a)
for i in range(shape[0]/2):
for j in range(shape[0]/2):
a[i,j] = 0
dl.display(a, (i,j))
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 __init__(self, rows, cols, size):
self.rows = rows
self.cols = cols
self.vectorLen = size
self.weight = RandomArray.random((rows, cols, size))
self.input = []
self.loadOrder = []
self.step = 0
self.maxStep = 1000.0
def sampled_ds(parent_dataset, sample, name=None, filter_label=None, **kwargs):
parent_len = len(parent_dataset)
samp_len = int(parent_len * sample)
record_ids = Numeric.sort(RandomArray.randint(0, parent_len, samp_len))
if name is None:
name = 'samp%02d_%s' % (sample * 100, parent_dataset.name)
if filter_label is None:
filter_label = '%.3g%% sample' % (sample * 100)
return FilteredDataset(parent_dataset, record_ids, name=name,
filter_label=filter_label, **kwargs)
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)
def sampled_ds(parent_dataset, sample, name=None, filter_label=None, **kwargs):
parent_len = len(parent_dataset)
samp_len = int(parent_len * sample)
record_ids = Numeric.sort(RandomArray.randint(0, parent_len, samp_len))
if name is None:
name = 'samp%02d_%s' % (sample * 100, parent_dataset.name)
if filter_label is None:
filter_label = '%.3g%% sample' % (sample * 100)
return FilteredDataset(parent_dataset,
record_ids,
name=name,
filter_label=filter_label,
**kwargs)
def RandomNonoverlappingListOfAtoms(L,
neighborLocator,
minDist=1.0,
nAtoms=10,
temperature=1.0,
maxTriesQuiet=1000,
mass=1.0,
radius=0.5,
color=vi.color.green):
"""
Put atoms of given radius in box (0,L)^dim, at random except without
overlaps less than minDist.
"""
pos = RandomArray.uniform(radius, L - radius, (nAtoms, dim))
vel = numpy.zeros((nAtoms, dim))
atoms = ListOfAtoms(mass, radius, color, pos, vel)
# Enforce no overlaps
# neighborLocator.HalfNeighbors returns (n1, n2, r, dr)
# with n1 > n2: remove n1 atoms
overlappingAtoms = neighborLocator.HalfNeighbors(atoms, minDist)[0]
nPairOverlap = len(overlappingAtoms)
tries = 0
while nPairOverlap > 0:
tries += 1
if tries % maxTriesQuiet == 0:
print "After ", tries, "attempts, still have ", nPairOverlap, \
"overlapping pairs of atoms: may need fewer atoms or larger box"
posNew = [pos for n, pos in enumerate(atoms.positions) \
if n not in overlappingAtoms]
nOverlap = nAtoms - len(posNew)
newAtomPositions = RandomArray.uniform(radius, L - radius,
(nOverlap, dim))
posNew.extend(newAtomPositions)
atoms.positions = numpy.array(posNew)
overlappingAtoms = neighborLocator.HalfNeighbors(atoms, minDist)[0]
nPairOverlap = len(overlappingAtoms)
ThermalizingTransformer(atoms, temperature)
return atoms
def RandomListOfAtoms(L,
nAtoms=1000,
temperature=1.0,
mass=1.0,
radius=0.5,
color=vi.color.green):
"""
Put atoms of radius r in box (0,L)^dim
"""
positions = RandomArray.uniform(radius, L - radius, (nAtoms, dim))
velocities = numpy.zeros((nAtoms, dim))
atoms = ListOfAtoms(mass, radius, color, positions, velocities)
ThermalizingTransformer(atoms, temperature)
return atoms
def init_velocities(T, ma, ndf=None):
# Simple equipartition, no Maxwell-Boltzmann
# For Maxwell-Boltzmann, use RandomArray.standard_normal()
import RandomArray
v = RandomArray.random((len(ma), 3)) - 0.5
# Make the C.M. static
vcm = v_cm(v, ma)
for xyz in range(3):
v[:, xyz] = v[:, xyz] - vcm[xyz]
# Re-scale for correct kinetic energy
kin = ekin(v, ma)
# This will take into account the reduced no of degrees of freedom
if ndf is None:
ndf = 3 * len(ma) - 3
temp = temp_from_ekin(kin, ndf)
v = v * math.sqrt(T / temp)
return v
def __init__(self, *args):
apply(QWidget.__init__, (self,) + args)
# make a QwtPlot widget
self.plot = QwtPlot('A PyQwt and MinPack Demonstration', self)
# initialize the noisy data
scatter = 0.05
x = arrayrange(-5.0, 5.0, 0.1)
y = RandomArray.uniform(1.0-scatter, 1.0+scatter, shape(x)) * \
function([1.0, 1.0, -2.0, 2.0], x)
# fit from a reasonable initial guess
guess = asarray([0.5, 1.5, -1.0, 3.0])
yGuess = function(guess, x)
solution = leastsq(function, guess, args=(x, y))
yFit = function(solution[0], x)
print solution
# insert a few curves
c1 = self.plot.insertCurve('data')
c2 = self.plot.insertCurve('guess')
c3 = self.plot.insertCurve('fit')
# set curve styles
self.plot.setCurvePen(c1, QPen(Qt.black))
self.plot.setCurvePen(c2, QPen(Qt.red))
self.plot.setCurvePen(c3, QPen(Qt.green))
# copy the data
self.plot.setCurveData(c1, x, y)
self.plot.setCurveData(c2, x, yGuess)
self.plot.setCurveData(c3, x, yFit)
# set axis titles
self.plot.setAxisTitle(QwtPlot.xBottom, 'x -->')
self.plot.setAxisTitle(QwtPlot.yLeft, 'y -->')
self.plot.enableLegend(1)
self.plot.replot()
def PlotFit(traj=SandPConstantDollar,
times=SandPTime,
nRandomTrajs=0,
a=0.04,
output=False):
plotEm = []
eta, trajFlattened = RemoveMeanGrowth(traj, times)
plotEm.append(times)
plotEm.append(trajFlattened)
randomTimes = scipy.arange(times[0], times[-1] + 1.e-10,
(times[-1] - times[0]) / (len(times) - 1))
randomTrajs = []
for i in range(nRandomTrajs):
randomTrajs.append(
scipy.exp(scipy.cumsum(a *
(RandomArray.random(len(times)) - 0.5))))
randomTrajsFlattened = randomTrajs[:]
for rF in randomTrajsFlattened:
eta, rF = RemoveMeanGrowth(rF, randomTimes)
plotEm.append(randomTimes)
plotEm.append(rF)
pylab.plot(*plotEm)
pylab.show()
if output:
outputSandP = file("SandPFlattened.dat", "w")
for t, data in zip(times, trajFlattened):
outputSandP.write("%s %s\n" % (t, data))
outputSandP.close()
outputRandom = file("OneDRandomFlattened.dat", "w")
for rF in randomTrajs:
eta, rF = RemoveMeanGrowth(rF, randomTimes)
for t, data in zip(randomTimes, rF):
outputRandom.write("%s %s\n" % (t, data))
outputRandom.write("\n")
outputRandom.close()
import RandomArray, time, sys
from tables import Filters
import tables.netcdf3 as NetCDF
import Scientific.IO.NetCDF
# create an n1dim by n2dim random array.
n1dim = 1000
n2dim = 10000
print 'reading and writing a %s by %s random array ..'%(n1dim,n2dim)
array = RandomArray.random((n1dim,n2dim))
filters = Filters(complevel=0,complib='zlib',shuffle=0)
# create a file, put a random array in it.
# no compression is used.
# first, use Scientific.IO.NetCDF
t1 = time.time()
file = Scientific.IO.NetCDF.NetCDFFile('test.nc','w')
file.createDimension('n1', None)
file.createDimension('n2', n2dim)
foo = file.createVariable('data', 'd', ('n1','n2',))
for n in range(n1dim):
foo[n] = array[n]
file.close()
print 'Scientific.IO.NetCDF took',time.time()-t1,'seconds'
# now use pytables NetCDF emulation layer.
t1 = time.time()
file = NetCDF.NetCDFFile('test.h5','w')
file.createDimension('n1', None)
file.createDimension('n2', n2dim)
# no compression (override default filters instance).
foo = file.createVariable('data', 'd', ('n1','n2',),filters=filters)
# this is faster
foo.append(array)
sigma_A = sigma_y * Numeric.sqrt(Numeric.sum(x**2) / DELTA)
sigma_B = sigma_y * Numeric.sqrt(N / DELTA)
return A, B, sigma_y, sigma_A, sigma_B
# Test program
if __name__ == '__main__':
# Use latex text formatting
matplotlib.rc('text', usetex=True)
# Create a test data set
x = Numeric.arange(20)
dy = RandomArray.standard_normal(20)
y = 0.5 * x + 3 + dy
A, B, dy, dA, dB = lregress(x, y)
y2 = A + B * x
print A, B, dy, dA, dB
# pylab.plot(x,y,'o')
# pylab.plot(x,y2,linewidth=3)
# pylab.xlabel('x',fontsize='large')
# pylab.ylabel('y',fontsize='large')
# pylab.text(2, 13, 'A = %.1f $\pm$ %.1f (true: 3.0)' % (A,dA) )
# pylab.text(2, 12, 'B = %.2f $\pm$ %.2f (true: 0.5)' % (B,dB) )
# pylab.text(2, 11, 'dy = %.1f (true: 1.0)' % (dy) )
def OnTimerFraction( self, event ):
"""Perform the particle-system simulation calculations"""
points = self.points.coord.point
colors = self.points.color.color
'''Our calculations are going to need to know how much time
has passed since our last event. This is complicated by the
fact that a "fraction" event is cyclic, returning to 0.0 after
1.0.'''
f = event.fraction()
if f < self.lastFraction:
f += 1.0
deltaFraction = (f-self.lastFraction)
self.lastFraction = event.fraction()
'''If we have received an event which is so soon after a
previous event as to have a 0.0s delta (this does happen
on some platforms), then we need to ignore this simulation
tick.'''
if not deltaFraction:
return
'''Each droplet has been moving at their current velocity
for deltaFraction seconds, update their position with the
results of this speed * time. You'll note that this is not
precisely accurate for a body under acceleration, but it
makes for easy calculations. Two machines running
the same simulation will get *different* results here, as
a faster machine will apply acceleration more frequently,
resulting in a faster total velocity.'''
points = points + (self.velocities*deltaFraction)
'''We also cycle the droplet's colour value, though with
the applied texture it's somewhat hard to see.'''
colors = colors + (self.colorVelocities*deltaFraction)
'''Now, apply acceleration to the current velocities such
that the droplets have a new velocity for the next simulation
tick.'''
self.velocities[:,1] = self.velocities[:,1] + (gravity * deltaFraction)
'''Find all droplets which have "retired" by falling below the
y==0.0 plane.'''
below = less_equal( points[:,1], 0.0)
dead = nonzero(below)
if isinstance( dead, tuple ):
# weird numpy change here...
dead = dead[0]
if len(dead):
'''Move all dead droplets back to the emitter.'''
def put( a, ind, b ):
for i in ind:
a[i] = b
put( points, dead, emitter)
'''Re-spawn up to half of the droplets...'''
dead = dead[:(len(dead)//2)+1]
if len(dead):
'''Reset color to initialColor, as we are sending out
these droplets right now.'''
put( colors, dead, initialColor)
'''Assign slightly randomized versions of our initial
velocity for each of the re-spawned droplets. Replace
the current velocities with the new velocities.'''
if RandomArray:
velocities = (RandomArray.random( (len(dead),3) ) + [-.5, 0.0, -.5 ]) * initialVelocityVector
else:
velocities = [
array( (random.random()-.5, random.random(), random.random()-.5), 'f')* initialVelocityVector
for x in xrange(len(dead))
]
def copy( a, ind, b ):
for x in xrange(len(ind)):
i = ind[x]
a[i] = b[x]
copy( self.velocities, dead, velocities)
'''Now re-set the point/color fields so that the nodes notice
the array has changed and they update the GL with the changed
values.'''
self.points.coord.point = points
self.points.color.color = colors
import BSTIterative
import AVLIterative
import RandomArray
bt = BSTIterative.BST()
at = AVLIterative.AVL()
arraylength = 10000
l = RandomArray.getRandomArray(arraylength)
print("Processing random case")
for i in range(len(l)):
bt.insertIter(l[i])
at.insertIter(l[i])
print("BST level traversals: ", bt.traversecount)
print("AVL level traversals: ", at.traversecount)
print("Done")
bt = BSTIterative.BST()
at = AVLIterative.AVL()
print("Processing worst case")
for i in range(arraylength):
bt.insertIter(i)
at.insertIter(i)
print("BST level traversals: ", bt.traversecount)
def rand(*args):
"""rand(d1,...,dn) returns a matrix of the given dimensions
which is initialized to random numbers from a uniform distribution
in the range [0,1).
"""
return RandomArray.random(args)
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 _maxwellboltzmanndistribution(masses, temp):
xi = RandomArray.standard_normal(shape=(len(masses),3))
momenta = xi * Numeric.sqrt(masses * temp)[:,Numeric.NewAxis]
return momenta
def test(shape=(100,100)):
dl = DynamicLattice.DynamicLattice(shape)
for n in range(20):
a = RandomArray.randint(0, 2, shape)
dl.display(a)
import sys, cPickle, time, commands, os, re
import RandomArray
from Numeric import *
from Asap.testtools import ReportTest
# cpu time: time.clock(). Wall clock time: time.time()
host = commands.getoutput("hostname")
timesteps = 20
dbfilename = "bigtiming.dat"
selfcheckfilename = "bigtiming-selfcheck.dat"
logfilename = "bigtiming.log"
asapversion = GetVersion()
when = time.strftime("%a %d %b %Y %H:%M", time.localtime(time.time()))
RandomArray.seed(42, 12345)
PrintVersion(1)
print "Running ASAP timing on "+host+"."
if re.match("^n\d\d\d.dcsc.fysik.dtu.dk$", host):
print " This is a d512 node on Niflheim."
fullhost = "niflheim-d512/%s" % (host.split(".")[0])
host = "niflheim-d512"
elif re.match("^[stu]\d\d\d.dcsc.fysik.dtu.dk$", host):
print " This is an s50 node on Niflheim."
fullhost = "niflheim-s50/%s" % (host.split(".")[0])
host = "niflheim-s50"
else:
fullhost = host
print "Current time is "+when
print ""
def randomBits(length):
address = RandomArray.randint(0, 2, length)
return address
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")
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
data = N.floor((data - self.min)/self.bin_width).astype(N.Int)
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:
import RandomArray, time, sys
from tables import Filters
import tables.netcdf3 as NetCDF
import Scientific.IO.NetCDF
# create an n1dim by n2dim random array.
n1dim = 1000
n2dim = 10000
print 'reading and writing a %s by %s random array ..' % (n1dim, n2dim)
array = RandomArray.random((n1dim, n2dim))
filters = Filters(complevel=0, complib='zlib', shuffle=0)
# create a file, put a random array in it.
# no compression is used.
# first, use Scientific.IO.NetCDF
t1 = time.time()
file = Scientific.IO.NetCDF.NetCDFFile('test.nc', 'w')
file.createDimension('n1', None)
file.createDimension('n2', n2dim)
foo = file.createVariable('data', 'd', (
'n1',
'n2',
))
for n in range(n1dim):
foo[n] = array[n]
file.close()
print 'Scientific.IO.NetCDF took', time.time() - t1, 'seconds'
# now use pytables NetCDF emulation layer.
t1 = time.time()
file = NetCDF.NetCDFFile('test.h5', 'w')
file.createDimension('n1', None)
file.createDimension('n2', n2dim)
# no compression (override default filters instance).
def _random_norm(shape):
matrix = asarray(RandomArray.random(shape), MATCODE)
return _normalize(matrix)
import RandomArray
import random
def SortedArray(n):
A = n
for i in range(len(A) - 1):
for z in range(len(A) - i - 1):
print(A)
if A[z] > A[z + 1]:
A[z], A[z + 1] = A[z + 1], A[z]
#n = input('Input the number:')
#Ar = [5,6,7,9,0,1]
n = random.randint(2, 20)
SortedArray(RandomArray.RandomArray(n))
Classes:
MarkovModel Holds the description of a markov model
"""
import math
from Numeric import *
import RandomArray
import StringIO # StringIO is in Numeric's namespace, so import this after.
from Bio import listfns
#RandomArray.seed(0, 0) # use 0 for debugging
RandomArray.seed()
VERY_SMALL_NUMBER = 1E-300
LOG0 = log(VERY_SMALL_NUMBER)
MATCODE = Float64
class MarkovModel:
def __init__(self, states, alphabet,
p_initial=None, p_transition=None, p_emission=None):
self.states = states
self.alphabet = alphabet
self.p_initial = p_initial
self.p_transition = p_transition
self.p_emission = p_emission
def __str__(self):
sigma_A = sigma_y * Numeric.sqrt( Numeric.sum(x**2)/DELTA )
sigma_B = sigma_y * Numeric.sqrt( N/DELTA )
return A,B,sigma_y,sigma_A,sigma_B
# Test program
if __name__== '__main__':
# Use latex text formatting
matplotlib.rc('text', usetex=True)
# Create a test data set
x = Numeric.arange(20)
dy = RandomArray.standard_normal(20)
y = 0.5*x+3 + dy
A,B,dy,dA,dB = lregress(x,y)
y2 = A + B*x
print A, B, dy ,dA,dB
# pylab.plot(x,y,'o')
# pylab.plot(x,y2,linewidth=3)
# pylab.xlabel('x',fontsize='large')
# pylab.ylabel('y',fontsize='large')
# pylab.text(2, 13, 'A = %.1f $\pm$ %.1f (true: 3.0)' % (A,dA) )
# pylab.text(2, 12, 'B = %.2f $\pm$ %.2f (true: 0.5)' % (B,dB) )
# pylab.text(2, 11, 'dy = %.1f (true: 1.0)' % (dy) )
B.append(max_value)
i, j = 0, 0
C = [None]*size
for k in range(0,size):
if A[i] <= B[j]:
C[k] = A[i]
i += 1
else:
C[k] = B[j]
j += 1
#print "merged %s and %s and got %s"%(A,B,C)
return C
times = []
for i in range(0,10):
A = RandomArray.getRandomArray(10000)
start = time.clock()
print (Mergesort(A))
stop = time.clock()
print ("Process Finished in %s seconds"%(stop-start))
times.append(stop-start)
for i in range(0,10):
print ("%s"%(times[i]))
def rand(*args):
"""rand(d1,...,dn) returns a matrix of the given dimensions
which is initialized to random numbers from a uniform distribution
in the range [0,1).
"""
return RandomArray.random(args)
assert allclose(computeResiduals(As, None, lmbd, Q), zeros(kconv), 0.0, tol)
assert allclose(lmbd, lmbd_exact, tol*tol, 0.0)
print 'OK'
#-------------------------------------------------------------------------------
# Test 2: K = None
print 'Test 2',
lmbd_exact = zeros(ncv, 'd')
for k in xrange(ncv):
lmbd_exact[k] = A[k,k]/M[k,k]
X0 = RandomArray.random((n,ncv))
kconv, lmbd, Q, it, it_inner = jdsym.jdsym(As, Ms, None, ncv, 0.0, tol, 150, itsolvers.qmrs,
jmin=5, jmax=10, eps_tr=1e-4, clvl=1)
assert ncv == kconv
assert allclose(computeResiduals(As, Ms, lmbd, Q), zeros(kconv), 0.0, normM*tol)
assert allclose(lmbd, lmbd_exact, normM*tol*tol, 0.0)
print 'OK'
#-------------------------------------------------------------------------------
# Test 3: general case
print 'Test 3',
def randn(*args):
"""u = randn(d0,d1,...,dn) returns zero-mean, unit-variance Gaussian
random numbers in an array of size (d0,d1,...,dn)."""
x1 = RandomArray.random(args)
x2 = RandomArray.random(args)
return sqrt(-2*log(x1))*cos(2*pi*x2)
data = N.floor((data - self.min)/self.bin_width).astype(N.Int)
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:
x = []
y = []
for l in yprof.readlines():
l = l.split()
x.append(float(l[0]))
y.append(float(l[1]))
i = 0
while i * delta[1] <= x[0]:
vartopg[0, i, :] = y[0]
i = i + 1
if i == numx:
break
for d in range(0, len(x) - 1):
slope = (y[d + 1] - y[d]) / (x[d + 1] - x[d])
if i == numx:
break
while i * delta[1] <= x[d + 1]:
vartopg[0, i, :] = y[d] + (i * delta[1] - x[d]) * slope
i = i + 1
if i == numx:
break
for i in range(i, numx):
vartopg[0, i, :] = y[d]
else:
vartopg[0, :, :] = 0.0
vartopg[0, :, :] = vartopg[0, :, :] + RandomArray.uniform(0.0, amplitude, (numy, numx)).astype(Numeric.Float32)
cffile.close()
def randn(*args):
"""u = randn(d0,d1,...,dn) returns zero-mean, unit-variance Gaussian
random numbers in an array of size (d0,d1,...,dn)."""
x1 = RandomArray.random(args)
x2 = RandomArray.random(args)
return sqrt(-2*log(x1))*cos(2*pi*x2)
print "Error =", error
def trainPattern(self, pattern):
# will depend on self.step
x, y, d = self.winner(pattern)
error += self.updateMap(pattern, x, y)
print "Winner is weight at (", x, y, ") (diff was", d, ") error = ", \
error
def test(self):
import numpy.oldnumeric as Numeric
self.loadOrder = range(len(self.input))
histogram = Numeric.zeros((self.cols, self.rows), 'i')
for p in self.loadOrder:
x, y, d = self.winner(self.input[p])
# print "Input[%d] =" % p, self.input[p],"(%d, %d)" % (x, y)
histogram[x][y] += 1
for r in range(self.rows):
for c in range(self.cols):
print "%5d" % histogram[c][r],
print ""
print ""
if __name__ == '__main__':
import numpy.oldnumeric as Numeric
s = SOM(5, 7, 5) # rows, cols; length of high-dimensional input
s.setInputs( RandomArray.random((100, 5)))
s.maxStep = 100
s.train()
s.test()
from pysparse.spmatrix import *
import RandomArray
import time
n = 1000
nnz = 50000
A = ll_mat(n, n, nnz)
R = RandomArray.randint(0, n, (nnz,2))
t1 = time.clock()
for k in xrange(nnz):
A[R[k,0],R[k,1]] = k
print 'Time for populating matrix: %8.2f sec' % (time.clock() - t1, )
print A.nnz
B = A[:,:]
A.shift(-1.0, B)
print A
if show:
p.show()
LAST = p
return p
def psprint(dest="-"):
if TABLE:
TABLE.write_eps(dest)
if __name__=='__main__' :
import sys, Numeric, RandomArray
x = Numeric.arrayrange(-10,10);
y = x**2;
e = y/4
a = RandomArray.random([20,20,3])
imagesc(a, x=range(-10,10), y=range(-10,10))
drawnow()
sys.stdin.readline()
a = Numeric.array([[[1, 0, 0],
[0, 1, 0],
[0, 0, 1],
[0, 0, 0]],
[[0, 0, 0],
[0, 0, 0],
[0, 0, 0],
[0, 0, 0]],
[[0, 0, 0],
[0, 0, 0],
