def phase(filtered, carf, sampf,tarr,bitclock,baudrate):
uphasearr = [] # Establishing arrays to hold the entire unfiltered phase
lphasearr = [] # in both the upper and lower sideband frequencies
deltaf = 50 # This is determined from baudrate and modulation scheme (MSK)
window = 125 # This is the window the phase is calculated and averaged over for a single bit (1/6 of a full bit). This bit phase is in turn averaged over the whole second later on
phasebitsize = len(filtered[0])/window/baudrate # data points in a bit in phase time series (6)
rawbitsize = len(filtered[0])/baudrate # data points in a bit in raw signal time series (750)
bins = len(filtered[0])/window - phasebitsize # Lose a full bits worth of data points(6) to start in sync with bitclock
time = np.array(tarr) # Just to not f up the 'tarr' array created a 'time' array
for k in range(0,len(filtered)):
modu = carf[k] + deltaf # The sideband frequencies used in the
modl = carf[k] - deltaf # MSK modulation scheme
startbin = (np.abs(time - bitclock[k])).argmin() # Start measuring the phase at start of measured bitclock
endbin = startbin - rawbitsize # Endbin will be negative to make sure it is even splitting the time series into chunks 1/6 of a bit in length
uy = filtered[k]*sin((2.0)*(pi)*modu*time) # Crunching the phase in segments
ux = filtered[k]*cos((2.0)*(pi)*modu*time) # 1/6 of a bit in length
uysum = np.split(uy[startbin:endbin],bins) # Summed over this whole segment for
uxsum = np.split(ux[startbin:endbin],bins) # phase measurement
uphase = -arctan((sum(uysum, axis = 1))/(sum(uxsum, axis = 1))) # a phase for upper and lower sidebands in MSK modulation
ly = filtered[k]*sin((2.0)*(pi)*modl*time) # Crunching the phase in segments
lx = filtered[k]*cos((2.0)*(pi)*modl*time) # 1/6 of a bit in length
lysum = np.split(ly[startbin:endbin],bins) # Summed over this whole segment for
lxsum = np.split(lx[startbin:endbin],bins) # phase measurement
lphase = -arctan((sum(lysum, axis = 1))/(sum(lxsum, axis = 1))) # this is the lower sidebands phase
lphasearr.extend([lphase]) # Adding the arrays of uppper phase
uphasearr.extend([uphase]) # and lower phase for each frequency
return uphasearr, lphasearr # Each element in array has 1194 datapoints
def calculateFDunc(self):
#Calculates the uncertainty of the FFT according to:
# - J. M. Fornies-Marquina, J. Letosa, M. Garcia-Garcia, J. M. Artacho, "Error Propagation for the transformation of time domain into frequency domain", IEEE Trans. Magn, Vol. 33, No. 2, March 1997, pp. 1456-1459
#return asarray _tdData
#Assumes tha the amplitude of each time sample is statistically independent from the amplitude of the other time
#samples
# Calculates uncertainty of the real and imaginary part of the FFT and ther covariance
unc_E_real = []
unc_E_imag = []
cov = []
for f in self.getfreqs():
unc_E_real.append(py.sum((py.cos(2*py.pi*f*self._tdData.getTimes())*self._tdData.getUncEX())**2))
unc_E_imag.append(py.sum((py.sin(2*py.pi*f*self._tdData.getTimes())*self._tdData.getUncEX())**2))
cov.append(-0.5*sum(py.sin(4*py.pi*f*self._tdData.getTimes())*self._tdData.getUncEX()**2))
unc_E_real = py.sqrt(py.asarray(unc_E_real))
unc_E_imag = py.sqrt(py.asarray(unc_E_imag))
cov = py.asarray(cov)
# Calculates the uncertainty of the modulus and phase of the FFT
unc_E_abs = py.sqrt((self.getFReal()**2*unc_E_real**2+self.getFImag()**2*unc_E_imag**2+2*self.getFReal()*self.getFImag()*cov)/self.getFAbs()**2)
unc_E_ph = py.sqrt((self.getFImag()**2*unc_E_real**2+self.getFReal()**2*unc_E_imag**2-2*self.getFReal()*self.getFImag()*cov)/self.getFAbs()**4)
t=py.column_stack((self.getfreqs(),unc_E_real,unc_E_imag,unc_E_abs,unc_E_ph))
return self.getcroppedData(t)
def calc_a(x_point, y_point):
A = pylab.sum(pylab.square(x_point)) * 30
B = pylab.square(pylab.sum(x_point))
C = pylab.sum(x_point * y_point) * 30
D = pylab.sum(x_point) * pylab.sum(y_point)
a = (C - D) / (A - B)
print(a)
def likelihood(self, x0, X, Y, U):
"""returns the log likelihood of the states `X` and observations `Y`
under the current model p(X,Y|M)
Parameters
----------
x0 : matrix
initial state
X : list of matrix
state sequence
Y : list of matrix
observation sequence
U : list of matrix
input sequence
Notes
----------
This calculates
p(X,Y|M) = p(x0)\prod_{t=1}^Tp(y_t|x_t)\prod_{t=1}^Tp(x_t|x_{t-1})
using the model currently defined in self.
"""
l1 = pb.sum([pb.log(self.observation_dist(x,y)) for (x,y) in zip(X,Y)])
l2 = pb.sum([
pb.log(self.transition_dist(x,u,xdash)) for (x,u,xdash) in zip(X[:-1],U[:-1],X[1:])])
l3 = self.init_dist(x0)
l = l1 + l2 + l3
assert not pb.isinf(l).any(), (l1,l2,l3)
return l
def FG(self,kList): """ recursive function for the F and G kernels kList is a List of numarray vectors """ if len(kList) == 1: return (1.0,1.0) sgg = 0.0 sgf = 0.0 n = len(kList) for m in range(1,n): k1 = M.sum(kList[:m]) k2 = M.sum(kList[m:]) k1Sqr = M.sum(k1*k1) k2Sqr = M.sum(k2*k2) print k1,k2,k1Sqr,k2Sqr if k1Sqr > self.eps and k2Sqr > self.eps: locF1,locG1 = self.FG(kList[:m]) locF2,locG2 = self.FG(kList[m:]) sgg += locG1*locG2*self.beta(k1,k2) sgf += locG1*locF2*self.alpha(k1,k2) return (((2*n+1.)*sgf+2*sgg)/(2*n+3.)/(n-1.), (3*sgf+2*n*sgg)/(2*n+3.)/(n-1.))
def CleanImage(im, bkgr_rad=100):
'''
Removes DC background level from image. Averages value in lower-right corner of image within user-specified radius
and subtracts from total image matrix.
'''
center = (200, 200)
im = im.astype(float)
imrows, imcolumns = pl.shape(im)
a = pl.arange(imrows * imcolumns)
imflat = im.flatten()
bkgrmask = (((divmod(a, imcolumns)[1] - center[1])**2 +
(divmod(a, imcolumns)[0] - center[0])**2) < bkgr_rad**2)
immasked = imflat * bkgrmask
noise = pl.sum(immasked) / pl.sum(bkgrmask)
print 'sum of background counts:', pl.sum(immasked)
print 'area of quarter circle:', pl.pi * bkgr_rad**2 / 4.
print 'average noise (in counts):', noise
im -= noise
#Bulldozer noise flattener:
threshold = 200
im = im * (im > threshold)
return im
def r_squared(y, estimated):
"""
Calculate the R-squared error term.
Args:
y: 1-d pylab array with length N, representing the y-coordinates of the
N sample points
estimated: an 1-d pylab array of values estimated by the regression
model
Returns:
a float for the R-squared error term
"""
# TODO
y_mean = pylab.mean(y)
#R^2 = 1 - sum((y_i-e_i)^2)/sum((y_i-y_mean)^2)
#first calculate sum of (y_i - e_i)^2
y_minus_e = y - estimated #list of y_i - e_i
y_minus_e_sq = y_minus_e**2 #list of (y_i - e_i)^2
numerator = pylab.sum(y_minus_e_sq) #the numerator term sum((y_i-e_i)^2)
#next calculate sum of (y_i - y_mean)^2
y_minus_mean = y - y_mean #list of y_i - y_mean
y_minus_mean_sq = y_minus_mean**2 #list of (y_i - y_mean)^2
denom = pylab.sum(
y_minus_mean_sq) #the denominator term sum((y_i-y_mean)^2)
R_sq = 1 - numerator / denom
return R_sq
def computeMomentumSpreading(self):
spreading = []
meanPosMomentum = []
for i in range(len(self.variableArray)):
profile = self.profileArray[i] / max(self.profileArray[i])
maxLocation = []
maxVal = []
for x0x1 in self.coords:
x0 = x0x1[0]
x1 = x0x1[1]
maxLocation.append(x0 + py.argmax(profile[x0:x1]))
maxVal.append(max(profile[x0:x1]))
maxLocation = py.array(maxLocation)
maxVal = py.array(maxVal)
zeroIndex = int(len(maxLocation) / 2)
momemtum = (py.array(range(len(profile))) -
maxLocation[zeroIndex]) / self.h_d_ratio
maxLocationMomentum = (py.array(maxLocation) -
maxLocation[zeroIndex]) / self.h_d_ratio
meanPosMomentum.append(
py.sum(maxLocationMomentum * maxVal) / py.sum(maxVal))
spreading.append(
py.sum(maxVal *
(maxLocationMomentum - meanPosMomentum[-1])**2))
spreading = py.array(spreading)
spreading = spreading / max(spreading)
self.spreading = spreading
self.meanPosMomentum = meanPosMomentum
def test_good_model(self):
vars = models.latent_simplex(self.X)
assert pl.all(
pl.sum(vars['pi'].value, 1) <= 1.0
), 'pi values should sum to at most 1, (%s found)' % pl.sum(
vars['pi'].value, 1)
m = mc.MCMC(vars)
m.sample(10)
def lin_reg_UU(x, y):
import numpy as np
from numpy import arange
from pylab import plot, legend, xlabel, ylabel
from pylab import figure, mean, exp, sqrt, sum
from scipy import stats
# Based on "Least Squares fitting" equations from Math World website.
# This version checked against data on the Wikipedia "Simple Linear Regression" pages.
# It also calculates the +/- 1 sigma confidence limits in the regression [xfit,yband]
# IN THIS VERSION THE YBAND PREDICTION ERRORS ARE CALCULATED
# ACCORDING TO DAVID PEARSON (AS USED IN COX ET AL., 2013)
nx = len(x)
ny = len(y)
xm = mean(x)
ym = mean(y)
x2 = x * x
y2 = y * y
xy = x * y
ssxx = sum(x2) - nx * xm * xm
ssyy = sum(y2) - ny * ym * ym
ssxy = sum(xy) - ny * xm * ym
b = ssxy / ssxx
a = ym - b * xm
yf = a + b * x
r2 = ssxy**2 / (ssxx * ssyy)
e2 = (y - yf)**2
s2 = sum(e2) / (nx - 2)
s = sqrt(s2)
da = s * sqrt(1.0 / nx + xm * xm / ssxx)
db = s / sqrt(ssxx)
# Calculate confidence limits on fit (see Wikipedia page on "Simple Linear Regression")
minx = min(x) - 0.1 * (max(x) - min(x))
maxx = max(x) + 0.1 * (max(x) - min(x))
nfit = 200
dx = (maxx - minx) / nfit
xfit = minx + dx * arange(0, nfit)
yfit = a + b * xfit
yband = np.zeros(nfit)
# David Pearson's formula for "Prediction Error"
for n in range(0, nfit):
yband[n] = sqrt(s2 * (1.0 + 1.0 / nx + (xfit[n] - xm)**2 / ssxx))
pass
return yf, a, b, da, db, xfit, yfit, yband
def alpha(self,k1,k2): """ alpha coupling function k1,k2 are numarray vectors """ if M.sum(k1*k1) < self.eps or M.sum(k2*k2) < self.eps: raise DivergentPTKernelError return M.sum((k1+k2)*k1)/M.sum(k1*k1)
def rsquared(fitfunc, param, x, y):
''' The R^2 value
'''
yhat = fitfunc(param, x)
ymean = pylab.mean(y)
ssreg = pylab.sum((yhat - ymean)**2)
sstot = pylab.sum((y - ymean)**2)
return (ssreg / sstot)**2
def mean_score_per_attempt(num_attempts, num_attempt_limit, maxscores, first_20_scores):
#create mask, so we only include people who play more than some number of games
played_more_than = pb.array(num_attempts) > num_attempt_limit
# this mask means we only look at people who got roughly equivalent scores on plays 1 and 2
equate_mask1bot=pb.array(first_20_scores[:,0]>7500)
equate_mask1top=pb.array(first_20_scores[:,0]<12500)
equate_mask2bot=pb.array(first_20_scores[:,1]>7500)
equate_mask2top=pb.array(first_20_scores[:,1]<12500)
equate_mask3bot=pb.array(first_20_scores[:,2]>7500)
equate_mask3top=pb.array(first_20_scores[:,2]<12500)
#can't figure out the all function to do this elegantly
#equate_mask=equate_mask1bot & equate_mask1top & equate_mask2bot & equate_mask2top & equate_mask3bot & equate_mask3top
equate_mask=equate_mask1bot & equate_mask1top
#eligible_mask = equate_mask & played_more_than
eligible_mask = played_more_than
print str(sum(eligible_mask))+' players in analysis'
#now rank everyone according to their top score
#this calculates the boundaries
percentiles = [stats.scoreatpercentile(maxscores[eligible_mask], per) for per in range(20,120,20)]
#this assigns to percentiles (actually quintile) groups
#NB assignment is to ALL players, but based on boundaries of only those who are eligible
#non-eligible players get excluded later (because we reuse the eligible mask)
player_percentiles = [bisect.bisect(percentiles, maxscore) for maxscore in maxscores]
#this sets up some holding variables for the means
mean_score_at_attempt_i_for_percentile_j = pb.zeros((20,5))
mean_agg_score_at_attempt_i_for_percentile_j = pb.zeros((20,5))
count_at_attempt_i_for_percentile_j = pb.zeros((20,5))
std_at_attempt_i_for_percentile_j = pb.zeros((20,5))
# -------------
for j in range(5):
print "looking at percentile group %s"%j
# this second mask chooses only those players whose max score is in the jth percentile
scores_in_this_percentile = pb.array(player_percentiles) == j
# we AND these together to get the mask
#mask = played_more_than & scores_in_this_percentile & equate_mask
percentile_mask = scores_in_this_percentile & eligible_mask
# Then add up all the scores that satisfy the mask (everyone in this percentile who played more than 19 times)
# and we divide them by the number of people maskwho satisfy the mask
## NOTE THAT WE ASSUME THAT NOONE SCORES ZERO!
mean_score_at_attempt_i_for_percentile_j[:,j] = pb.sum(first_20_scores[percentile_mask ,:],0) / sum(first_20_scores[percentile_mask ,:] > 0, 0)
mean_agg_score_at_attempt_i_for_percentile_j[:,j] = pb.sum(agg_scores[percentile_mask ,:],0) / sum(first_20_scores[percentile_mask ,:] > 0, 0)
#
#tom added this
count_at_attempt_i_for_percentile_j[:,j] = sum(first_20_scores[percentile_mask ,:] > 0, 0)
# the square route of the average squared difference from the mean
std_at_attempt_i_for_percentile_j[:,j] = np.sqrt(sum(((first_20_scores[percentile_mask ,:]-mean_score_at_attempt_i_for_percentile_j[:,j])**2)*(first_20_scores[percentile_mask,:] > 0),0)/count_at_attempt_i_for_percentile_j[:,j])
pickle.dump(count_at_attempt_i_for_percentile_j, open('save_count_at_attempt_i_for_percentile_j.p', 'wb'))
pickle.dump(std_at_attempt_i_for_percentile_j, open('save_std_at_attempt_i_for_percentile_j.p', 'wb'))
pickle.dump(mean_agg_score_at_attempt_i_for_percentile_j, open('save_mean_agg_score_at_attempt_i_for_percentile_j.p', 'wb'))
return mean_score_at_attempt_i_for_percentile_j
def getParamCovMat(prefix,dlogpower = 2, theoconstmult = 1.,dlogfilenames = ['dlogpnldloga.dat'],volume=256.**3,startki = 0, endki = 0, veff = [0.]):
"""
Calculates parameter covariance matrix from the power spectrum covariance matrix and derivative term
in the prefix directory
"""
nparams = len(dlogfilenames)
kpnl = M.load(prefix+'pnl.dat')
k = kpnl[startki:,0]
nk = len(k)
if (endki == 0):
endki = nk
pnl = M.array(kpnl[startki:,1],M.Float64)
covarwhole = M.load(prefix+'covar.dat')
covar = covarwhole[startki:,startki:]
if len(veff) > 1:
sqrt_veff = M.sqrt(veff[startki:])
else:
sqrt_veff = M.sqrt(volume*M.ones(nk))
dlogs = M.reshape(M.ones(nparams*nk,M.Float64),(nparams,nk))
paramFishMat = M.reshape(M.zeros(nparams*nparams*(endki-startki),M.Float64),(nparams,nparams,endki-startki))
paramCovMat = paramFishMat * 0.
# Covariance matrices of dlog's
for param in range(nparams):
if len(dlogfilenames[param]) > 0:
dlogs[param,:] = M.load(prefix+dlogfilenames[param])[startki:,1]
normcovar = M.zeros(M.shape(covar),M.Float64)
for i in range(nk):
normcovar[i,:] = covar[i,:]/(pnl*pnl[i])
M.save(prefix+'normcovar.dat',normcovar)
f = k[1]/k[0]
if (volume == -1.):
volume = (M.pi/k[0])**3
#theoconst = volume * k[1]**3 * f**(-1.5)/(12.*M.pi**2) #1 not 0 since we're starting at 1
for ki in range(1,endki-startki):
for p1 in range(nparams):
for p2 in range(nparams):
paramFishMat[p1,p2,ki] = M.sum(M.sum(\
M.inverse(normcovar[:ki+1,:ki+1]) *
M.outerproduct(dlogs[p1,:ki+1]*sqrt_veff[:ki+1],\
dlogs[p2,:ki+1]*sqrt_veff[:ki+1])))
paramCovMat[:,:,ki] = M.inverse(paramFishMat[:,:,ki])
return k[1:],paramCovMat[:,:,1:]
def findAreaOfInterest(self):
"""
Argument :
- None
Return :
- None
Use to detect the AOI by an edge detection technique.
"""
OD = self.findNoisyArea()
max = 0
bestAngle = None
for angle in range(-10, 10, 1):
# the best angle is the one for wich the maximum peak is reached
# on the yProfile (integral on the horizontal direction) ...
image = rotate(OD, angle)
yProfile = py.sum(image, axis=1)
newMax = yProfile.max()
if newMax > max:
max = newMax
bestAngle = angle
# ... once found, the resulting OD image is kept and used to find the
# the top and bottom bounds by edge detection.
bestOD = rotate(OD, bestAngle)
YProfile = py.sum(bestOD, axis=1)
derivative = py.gradient(YProfile)
N = 10
# because the derivative is usually very noisy, a sliding average is
# performed in order to smooth the signal. This is done by a
# convolution with a gate function of size "N".
res = py.convolve(derivative, py.ones((N, )) / N, mode='valid')
mean = res.mean()
# index of the maximum value of the signal.
i = res.argmax()
while res[i] > mean:
# once "i" is greater or equal to the mean of the derivative,
# we have found the upper bound of the AOI.
i -= 1
# for security we take an extra 50 pixels.
y0 = int(i - 50)
# index of the minimum value of the signal.
i = res.argmin()
while res[i] < mean:
# once "i" is smaller or equal to the mean of the derivative,
# we have found the lower bound of the AOI.
i += 1
# Again, for security, we take an extra 50 pixels.
y1 = int(i + 50)
# The horizontal bound are taken to be maximal, but for security, we
# take an extra 50 pixels.
x0 = 50
x1 = py.shape(OD)[0] - 50
self.setAreaOfInterest(area=(x0, x1, y0, y1), angle=bestAngle)
return bestOD[y0:y1, x0:x1]
def r_squared(y, estimated):
"""
Calculate the R-squared error term.
Args:
y: list with length N, representing the y-coords of N sample points
estimated: a list of values estimated by the regression model
Returns:
a float for the R-squared error term
"""
mean = pylab.mean(y)
R_sq = 1 - pylab.sum((y - estimated)**2)/pylab.sum((y - mean)**2)
return R_sq
def Moving_Avg_Filter(self):
self.stance_hip_filtered = pl.sum(
self.stance_hip_arr, axis=0) / self.num_of_samples_in_buffer
self.swing_foot_filtered = pl.sum(
self.swing_foot_arr, axis=0) / self.num_of_samples_in_buffer
self.swing_hip_filtered = pl.sum(
self.swing_hip_arr, axis=0) / self.num_of_samples_in_buffer
self.pelvis_m_filtered = pl.sum(self.pelvis_m_arr,
axis=0) / self.num_of_samples_in_buffer
self.com_m_filtered = pl.sum(self.com_m_arr,
axis=0) / self.num_of_samples_in_buffer
return ()
def CreateFromAliFile(self):
self.LoadAligments(self.AliFile)
printStr = ''
self._lst_ignored_files = []
self.NumFrames = 0
created_means = False
for index, (file_name, utterance_id) in \
enumerate(zip(self.RawFileList, self.UtteranceIds)):
printStrNew = '\b' * (len(printStr)+1)
printStr = "Loading data for utterance #: " + str(index+1)
printString = printStrNew + printStr
print printString,
sys.stdout.flush()
data = HTK.ReadHTKWithDeltas(file_name)
if sum(isnan(data)) != 0 or sum(isinf(data)) != 0:
self._lst_ignored_files.append(index)
continue
if not created_means:
created_means = True
self.data_dim = data.shape[0]
self.__CreateMeansAndStdevs()
self.DataSumSq += (data**2).sum(axis=1).reshape(-1,1)
self.DataSum += data.sum(axis=1).reshape(-1,1)
self.NumFrames += data.shape[1]
if self.Utt2Speaker != None:
speaker = self.Utt2Speaker[utterance_id]
self.SpeakerMeans[speaker] += data.sum(axis=1).reshape(-1,1)
self.SpeakerStds[speaker] += (data**2).sum(axis=1).reshape(-1,1)
self.SpeakerNumFrames[speaker] += data.shape[1]
sys.stdout.write("\n")
for file_num in self._lst_ignored_files:
sys.stdout.write("File # " + str(file_num) + " was ignored \
because of errors\n")
if self.Utt2Speaker != None:
for speaker in self.Speaker2Utt.keys():
self.SpeakerMeans[speaker] /= (1.0 *self.SpeakerNumFrames[speaker])
self.SpeakerStds[speaker] -= self.SpeakerNumFrames[speaker] * \
(self.SpeakerMeans[speaker]**2)
self.SpeakerStds[speaker] /= (1.0 *self.SpeakerNumFrames[speaker]-1)
self.SpeakerStds[speaker][self.SpeakerStds[speaker] < 1e-8] = 1e-8
self.SpeakerStds[speaker] = sqrt(self.SpeakerStds[speaker])
self.DataMeanVect = self.DataSum/self.NumFrames
variances = (self.DataSumSq - self.NumFrames*(self.DataMeanVect**2))/(self.NumFrames-1)
variances[variances < 1e-8] = 1e-8
self.DataStdVect = sqrt(variances)
def getDissim(data, atype, vbose=0, minRank=0, maxRank=50, NPOZ=50):
ks = data.keys()
matr = pylab.ones(len(ks)**2)
matr = pylab.reshape(matr, (len(ks), len(ks)))
scs = []
names = []
for ik, k_con in enumerate(ks):
name = ik
if not k_con in names:
names.append(k_con)
for jk, k_pl in enumerate(ks):
ss1 = computeSimSc(data, k_con, k_pl, vbose=vbose, minRank=minRank, maxRank=maxRank, NPOZ=NPOZ)
ss2 = computeSimSc(data, k_pl, k_con, vbose=vbose, minRank=minRank, maxRank=maxRank, NPOZ=NPOZ)
if atype=='abs':
sc1 = sum(ss1)
sc2 = sum(ss2)
elif atype=='rms':
sc1 = pylab.sqrt(pylab.sum(ss1**2))
sc2 = pylab.sqrt(pylab.sum(ss2**2))
elif atype=='met':
sc1 = sum(pylab.logical_and(ss1!=0, True))
sc2 = sum(pylab.logical_and(ss2!=0, True))
if vbose>=1:
print 'score for ', k_con, k_pl, ss1, sc1, ss2, sc2
oldsc = sc1 + sc2
oldsc *= 0.5
l1 = len(data[k_con])
l2 = len(data[k_pl])
iscale = min(l1, l2)
nsc = oldsc/(1.0*iscale)
if vbose>=1:
print k_con, k_pl, 'oldsc', oldsc, l1, l2, iscale, 'nsc', nsc
matr[ik][jk] = nsc
if jk<=ik:
continue
print nsc, 'xx', ik, k_con, jk, k_pl
scs.append(nsc)
return names, pylab.array(scs), matr
def adaptIntPlot(f,a,b):
"""
Adaptive (doubling partition) integration.
Minimizes function evaluations at the expense of some tedious
array minipulations.
"""
maxiter = 20
miniter = 5
tolerance = 0.1
maxnx = 2**maxiter
minnx = 2**miniter
x = 0.*M.zeros(maxnx)
dx = (b-a)/2.#**minsteps
nx = 2
x[0] = a
x[1] = a+dx
integral = M.sum(f(x[1:2]))*dx # 1 so we don't include the first endpt
dx /= 2.
newintegral = integral/2. + M.sum(f(x[:nx]+dx))*dx
for i in range(nx-1,-1,-1):
x[2*i] = x[i]
x[2*i+1] = x[i] + dx
nx *= 2
keepgoing = 1
while keepgoing == 1:
integral = newintegral
dx /= 2.
eff = f(x[:nx]+dx)
M.plot(x[:nx]+dx,f(x[:nx]+dx))
newintegral = integral/2. + M.sum(eff)*dx#M.sum(f(x[:nx]+dx))*dx
print newintegral*nx/(nx-1)
for i in range(nx-1,-1,-1):
x[2*i] = x[i]
x[2*i+1] = x[i] + dx
nx *= 2
keepgoing = 0
if integral*newintegral > 0.:
if ((M.fabs(M.log(integral*(nx/2)/(nx/2-1)/(newintegral*nx/(nx-1)))) >\
tolerance) and (nx < maxnx/2)) or (nx < minnx):
keepgoing = 1
elif integral*newintegral == 0.:
print "Hmmm, we have a zero integral here. Assuming convergence."
else:
keepgoing = 1
M.show()
print nx,
if nx == maxnx/2:
print 'No convergence in utils.adaptInt!'
return newintegral*nx/(nx-1)
def EM(self,Y,U,eps = 0.000000001): converged = False l = [1000] while not converged: # E step X, P, K, M = self.rtssmooth(Y, U) Xi11 = pb.sum([Pt + x*x.T for Pt,x in zip(P,X)],0) Xi10 = pb.sum([Mt + x1*x.T for Mt,x1,x in zip(M[1:],X[:-1],X[1:])],0) # M step self.A = pb.inv(Xi11)*Xi10 l.append(self.likelihood(self.x0,X,Y,U)) converged = abs(l[-1] - l[-2]) < eps print l[-1] return l, X, P
def mingumbel_mle_cfun(p,data): """ Returns the mingumbel mle log cost function (normalized) The gumbel CDF is 1 - exp( -exp( (x - m)/bt ) ). """ m = p[0] bt = p[1] n = len(data) cost = 0 cost = cost - pylab.log(bt) cost = cost + pylab.sum( (data - m)/bt)/n cost = cost - pylab.sum( pylab.exp( (data - m)/bt))/n return -cost
def duxbury_mle_cfun(p,L,data): """ Returns the duxbury mle log cost function (normalized) The duxbury CDF is 1 - exp( -(L^2)*exp( - (s/x)^2 ) ) """ s = p[0] n = len(data) cost = 0 cost = cost + 2*pylab.log(s) cost = cost - 3*pylab.sum( pylab.log(data) )/n cost = cost - L*L*pylab.sum( pylab.exp(-((s/data)**2)))/n cost = cost - pylab.sum( (s/data)**2)/n return -cost
def weibull_mle_cfun(p,data): """ Returns the weibull mle log cost function (normalized) The weibull CDF is 1 - exp(-(x/l)^k). """ k = p[0] l = p[1] n = len(data) cost = 0 cost = cost + pylab.log(k/l) cost = cost + (k-1)*pylab.sum(pylab.log(data/l))/n cost = cost - pylab.sum( (data/l)**k)/n return -cost
def groupConfidence(AllData):
"""
treat all data as group
"""
correct, confA, confB, RTs = getMatrix(AllData)
answ = [1.5,2,3,4,5.5,6.5,7.5,8.5,9.5,10,11.5,12,13.5,14.5,15.5,16.5]
hundy = py.ones(len(answ))
hundy = hundy*50
fig = py.figure()
ax1 = fig.add_subplot(111)
m,n = py.shape(correct)
ANS0 = []
ANS1 = []
for i in range(m):
ans0, ans1 = [], []
for j in range(n):
ans0.append(confA[i,j])
ans1.append(confB[i,j])
ANS0.append(py.sum(ans0))
ANS1.append(py.sum(ans1))
# get democratic % correct
collective = []
for i in range(len(ANS0)):
if ANS0[i] > ANS1[i]:
collective.append(0)
else:
collective.append(1)
right = []
print(len(collective))
for i in range(len(answers)):
if collective[i] == answers[i]:
right.append(1)
else:
right.append(0)
colmean = py.mean(right)
print('Collective mean: %.3f' % colmean)
prange = range(1,17)
prange2 = [prange[i]+0.25 for i in range(len(prange))]
# print(ANS0, ANS1)
rects0 = ax1.bar(prange, ANS0, color='b', width=0.35)
rects1 = ax1.bar(prange2, ANS1, color='r', width=0.35)
ax1.set_title('Democratic decisions')
ax1.set_xlabel('Question No.')
ax1.set_ylabel('Confidence')
ax1.plot(answ, hundy, 'k*')
def gen_cities_avg(climate, multi_cities, years):
"""
Compute the average annual temperature over multiple cities.
Args:
climate: instance of Climate
multi_cities: the names of cities we want to average over (list of str)
years: the range of years of the yearly averaged temperature (list of
int)
Returns:
a pylab 1-d array of floats with length = len(years). Each element in
this array corresponds to the average annual temperature over the given
cities for a given year.
"""
res = []
for year in years:
temperature = []
for city in multi_cities:
yearly_temp = pylab.sum(climate.get_yearly_temp(city, year))
temperature.append(yearly_temp /
(366 if (year % 4 == 0 and year % 100 != 0) or
(year % 400 == 0) else 365))
res.append(sum(temperature) / len(multi_cities))
return pylab.array(res)
def psp_parameter_estimate_fixmem(time, value):
smoothing_kernel = 10
smoothed_value = p.convolve(
value,
p.ones(smoothing_kernel) / float(smoothing_kernel),
"same")
mean_est_part = int(len(value) * .1)
mean_estimate = p.mean(smoothed_value[-mean_est_part:])
noise_estimate = p.std(value[-mean_est_part:])
integral = p.sum(smoothed_value - mean_estimate) * (time[1] - time[0])
f = 1.
A_estimate = (max(smoothed_value) - mean_estimate) / (1. / 4.)
min_A = noise_estimate
if A_estimate < min_A:
A_estimate = min_A
t1_est = integral / A_estimate * f
t2_est = 2 * t1_est
tmax_est = time[p.argmax(smoothed_value)] + p.log(t2_est / t1_est) * (t1_est * t2_est) / (t1_est - t2_est)
return p.array([
tmax_est,
A_estimate,
t1_est,
mean_estimate])
def Logistic(self):
'''Create logistic model from data'''
tStep = self.time[1] - self.time[0]
# Time vector for calculating lag phase
timevec = py.arange(self.time[0], self.time[-1], tStep / 2)
# Try using to find logistic model with optimal lag phase
# y = p2 + (A-p2) / (1 + exp(( (um/A) * (L-t) ) + 2))
sse = 0
sseF = 0
# Attempt to use every possible value in the time vector as the lag
# Choose lag that creates best-fit model
for idx, lag in enumerate(timevec):
logDataTemp = [self.startOD + ((self.asymptote - self.startOD) /
(1 + py.exp((((self.maxgrowth /
self.asymptote) *
(lag - t)) + 2)
))
) for t in self.time]
sse = py.sum([((self.data[i] - logDataTemp[i]) ** 2)
for i in xrange(len(self.data) - 1)])
if idx == 0 or sse < sseF:
logisticData = logDataTemp
lagF = lag
sseF = sse
return logisticData, lagF, sseF
def exclude_boundary_annotations(_signal, _annotation, _excluded_annotations):
"""
method removes boundary annotations from _annototion array and
corresponding items from _signal array, returns named tuple
SignalNoBoundaryAnnotation which includes new signal, new annotation
array and indexes of remaining annotation values from _annontation
array; what values are annotations is defined by _excluded_annotations
parameter
"""
if _annotation == None or \
pl.sum(_annotation, dtype=int) == 0:
return SignalNoBoundaryAnnotation(_signal, _annotation, None)
#removing nonsinus beats from the beginning
while (_annotation[0] != 0
and (_excluded_annotations == ALL_ANNOTATIONS
or _annotation[0] in _excluded_annotations)):
_signal = _signal[1:]
_annotation = _annotation[1:]
if len(_signal) == 0:
break
if len(_signal) > 0:
#removing nonsinus beats from the end
while (_annotation[-1] != 0
and (_excluded_annotations == ALL_ANNOTATIONS
or _annotation[-1] in _excluded_annotations)):
_signal = _signal[:-1]
_annotation = _annotation[:-1]
if len(_signal) == 0:
break
annotation_indexes = get_annotation_indexes(_annotation,
_excluded_annotations)
return SignalNoBoundaryAnnotation(_signal, _annotation, annotation_indexes)
def _filter(self,Y): ## initialise xf=self.x0 Pf=self.P0 # filter quantities xfStore =[] PfStore=[] #calculate the weights Wm_i,Wc_i=self.sigma_vectors_weights() for y in Y: #calculate the sigma points matrix, each column is a sigma vector Xi_f_=self.sigma_vectors(xf,Pf) #propogate sigma verctors through non-linearity Xi_f=self.state_equation(Xi_f_) #pointwise multiply by weights and sum along y-axis xf_=pb.sum(Wm_i*Xi_f,1) xf_=xf_.reshape(self.nx,1) #purturbation Xi_purturbation=Xi_f-xf_ weighted_Xi_purturbation=Wc_i*Xi_purturbation Pf_=pb.dot(Xi_purturbation,weighted_Xi_purturbation.T)+self.Sigma_e #measurement update equation Pyy=dots(self.C,Pf_,self.C.T)+self.Sigma_varepsilon Pxy=pb.dot(Pf_,self.C.T) K=pb.dot(Pxy,pb.inv(Pyy)) yf_=pb.dot(self.C,xf_) xf=xf_+pb.dot(K,(y-yf_)) Pf=pb.dot((pb.eye(self.nx)-pb.dot(K,self.C)),Pf_) xfStore.append(xf) PfStore.append(Pf) return xfStore,PfStore
def optimize_segment(data, dt, interval_start, ftol=1e-3):
"""
Estimate the recurrence time of a periodic signal within data.
data : 1d numpy.ndarray
the periodic signal
dt : float
sampling interval for data
interval_start : float
the interval that is taken as initial guess for
the optimization (in the same unit as dt)
ftol : float
tolerance on the solution (passed to scipy.optimize.fmin)
"""
i = fmin(
lambda interval: p.sum(
p.var(
segment(data, dt, interval),
axis=0)),
interval_start,
ftol=ftol)
return i[0]
def check(self, _data_vector):
"""
if there are no annotations, a message is returned
"""
if _data_vector.annotation == None or \
pl.sum(_data_vector.annotation, dtype=int) == 0:
return "No annotations found in signal data !"
def getEazyPz(idx,
MAIN_OUTPUT_FILE='photz',
OUTPUT_DIRECTORY='./OUTPUT',
CACHE_FILE='Same'):
"""
zgrid, pz = getEazyPz(idx, \
MAIN_OUTPUT_FILE='photz', \
OUTPUT_DIRECTORY='./OUTPUT', \
CACHE_FILE='Same')
Get Eazy p(z) for object #idx.
"""
tempfilt, coeffs, temp_seds, pz = readEazyBinary(MAIN_OUTPUT_FILE=MAIN_OUTPUT_FILE, \
OUTPUT_DIRECTORY=OUTPUT_DIRECTORY, \
CACHE_FILE = CACHE_FILE)
if pz is None:
return None, None
###### Get p(z|m) from prior grid
kidx = pz['kidx'][idx]
#print kidx, pz['priorzk'].shape
if (kidx > 0) & (kidx < pz['priorzk'].shape[1]):
prior = pz['priorzk'][:, kidx]
else:
prior = pylab.ones(pz['NZ'])
###### Convert Chi2 to p(z)
pzi = pylab.exp(
-0.5 * (pz['chi2fit'][:, idx] - min(pz['chi2fit'][:, idx]))) * prior
if pylab.sum(pzi) > 0:
pzi /= pylab.trapz(pzi, tempfilt['zgrid'])
###### Done
return tempfilt['zgrid'], pzi
def collectAlleles(at, thresh = 0.05):
lineageGrps = at["lineageGrp"].unique()
at_piv = pd.pivot_table(at, index="cellBC", columns="intBC", values="UMI", aggfunc="count")
at_piv.fillna(value = 0, inplace=True)
at_piv[at_piv > 0] = 1
lgs = []
for i in tqdm(lineageGrps):
lg = at[at["lineageGrp"] == i]
cells = lg["cellBC"].unique()
lg_pivot = at_piv.loc[cells]
props = lg_pivot.apply(lambda x: pylab.sum(x) / len(x)).to_frame().reset_index()
props.columns = ["iBC", "prop"]
props = props.sort_values(by="prop", ascending=False)
props.index = props["iBC"]
p_bc = props[(props["prop"] > thresh) & (props["iBC"] != "NC")]
lg_group = lg.loc[np.in1d(lg["intBC"], p_bc["iBC"])]
lgs.append(lg_group)
return lgs
def Main():
options, _ = MakeOpts().parse_args(sys.argv)
assert options.input_filename
f = open(options.input_filename)
r = csv.DictReader(f)
orgs = r.fieldnames[1:]
names = []
data = []
for i, row in enumerate(r):
names.append(row.get('Enzyme Function'))
a = pylab.array([float(row[k]) for k in orgs])
data.append(a / 10000.0)
fig = pylab.figure()
ytick_formatter = FormatStrFormatter('%d%%')
m = pylab.array(data).T
rows, cols = m.shape
cur_bottom = pylab.zeros(rows)
left = range(rows)
colors = ColorMap(range(cols))
for i in xrange(cols):
heights = m[:,i]
pylab.bar(left, heights, bottom=cur_bottom, color=colors[i],
width=0.5, align='center', label=names[i])
cur_bottom += heights
pylab.ylim((0.0, cur_bottom.max()))
pcts = pylab.sum(m, 1)
ticks = ['%s \n%.1f%%' % (o,p) for o,p in zip(orgs,pcts)]
pylab.xticks(pylab.arange(rows), ticks, ha='center')
ax = fig.get_axes()
ax[0].yaxis.set_major_formatter(ytick_formatter)
pylab.legend()
pylab.show()
def plotDensityMap(bins,binnedData,xscale='log',yscale='log',normaliseX=True,logScale=True):
if logScale:
l, b, r, t = 0.1, 0.12, 1.0, 0.970
else:
l, b, r, t = 0.1, 0.12, 1.05, 0.970
axes_rect = [l,b,r-l,t-b]
fig=p.figure()
fig.subplots_adjust(left=0.01, bottom=.05, right=.985, top=.95, wspace=.005, hspace=.05)
ax = fig.add_axes(axes_rect)
ax.set_xscale(xscale)
ax.set_yscale(yscale)
X,Y=bins.edge_grids
if normaliseX:
ySum=p.sum(binnedData,1)
ySum=p.ma.masked_array(ySum,ySum==0)
Z=binnedData.transpose()/ySum
else:
Z=binnedData.transpose()
if logScale:
mappable=ax.pcolor(X,Y,p.ma.array(Z,mask=Z==0),cmap=p.get_cmap("jet"),norm=matplotlib.colors.LogNorm())
else:
mappable=ax.pcolor(X,Y,p.ma.array(Z,mask=Z==0),cmap=p.get_cmap("jet"))
markersize=5.0
linewidth=2.0
fig.colorbar(mappable)
#ax.set_ylim((T/2.0/bins2D.centers[0][-1],T/2.0*2))
#ax.set_xlim(bins2D.centers[0][0]*day,bins2D.centers[0][-1]*day)
return fig,ax
def smooth_gamma(gamma=flat_gamma, knots=knots, tau=smoothing**-2):
# the following is to include a "noise floor" so that level value
# zero prior does not exert undue influence on age pattern
# smoothing
gamma = gamma.clip(pl.log(pl.exp(gamma).mean()/10.), pl.inf) # only include smoothing on values within 10x of mean
return mc.normal_like(pl.sqrt(pl.sum(pl.diff(gamma)**2 / pl.diff(knots))), 0, tau)
def alpha_psp_parameter_estimate(time, value, smoothing_samples=10):
t1_est_min = time[1] - time[0]
mean_est_part = len(value) * .1
mean_estimate = p.mean(value[-mean_est_part:])
noise_estimate = p.std(value[-mean_est_part:])
smoothed_value = p.convolve(
value - mean_estimate,
p.ones(smoothing_samples) / float(smoothing_samples),
"same") + mean_estimate
integral = p.sum(smoothed_value - mean_estimate) * (time[1] - time[0])
f = 1.
height_estimate = (max(smoothed_value) - mean_estimate)
min_height = noise_estimate
if height_estimate < min_height:
height_estimate = min_height
t1_est = integral / height_estimate * f
if t1_est < t1_est_min:
t1_est = t1_est_min
t2_est = 2 * t1_est
tmax_est = time[p.argmax(smoothed_value)]
tstart_est = tmax_est + p.log(t2_est / t1_est) \
* (t1_est * t2_est) / (t1_est - t2_est)
return p.array(
[height_estimate, t1_est, t2_est, tstart_est, mean_estimate])
def int(self, integrand):
"""
Integrates over second argument of an array
with Gaussian Quadrature weights
"""
return M.sum(M.outer(1.*M.ones(integrand.shape[0]),self.weights) * \
integrand,1)
def computeEvolutionOfOrder(self):
"""
Argument :
- None.
Return :
- None.
Evaluate the population of each order for a given optical density
image."""
# number of orders
n = len(self.coords)
# number of values for each order
m = len(self.profileArray)
indexesOfOrder = [i - int(n / 2) for i in range(n)]
self.orders = {i: py.zeros(m) for i in indexesOfOrder}
# Compute the integral of each order
for j, profile in enumerate(self.profileArray):
normalizeFactor = 0
for x0x1, i in zip(self.coords, indexesOfOrder):
x0, x1 = x0x1[0], x0x1[1]
integral = py.sum(profile[x0:x1], axis=0)
self.orders[i][j] = integral
normalizeFactor += integral
# normalization for the current profile
for i in indexesOfOrder:
self.orders[i][j] /= normalizeFactor
def log_likelihood(theta, x, y, xerr, yerr):
m, b, s = theta
model = m * x + b
sigma2 = s**2 + yerr**2 + m**2 * xerr**2
return -0.5 * pyl.sum(pyl.log(2 * pyl.pi * sigma2) + (y - model)**2 /
sigma2)
def get_test_layup2():
fiber_layup = {}
th = py.array([1.0] * 6) * 1e-3
angle = py.array([0.0] * len(th))
E1 = 40000e6
E2 = 11500e6
v12 = 0.3
G12 = 4500e6
thickness = py.sum(th)
th_cur = -thickness / 2.0
fiber_layup["thickness"] = thickness
fiber_layup["fiber_nr"] = len(th)
for i_lay in range(1, len(th) + 1):
fiber_layup[i_lay] = {}
fiber_layup[i_lay]["E1"] = E1
fiber_layup[i_lay]["E2"] = E2
fiber_layup[i_lay]["nu12"] = v12
fiber_layup[i_lay]["G12"] = G12
fiber_layup[i_lay]["angle"] = angle[i_lay - 1] * py.pi / 180
fiber_layup[i_lay]["thickness"] = th[i_lay - 1]
fiber_layup[i_lay]["z_start"] = th_cur
th_cur += th[i_lay - 1]
fiber_layup[i_lay]["z_end"] = th_cur
return (fiber_layup)
def pick_handler(event):
artist = event.artist
mouseevent = event.mouseevent
i = argmin(
sum((array(artist.get_data()).T -
array([mouseevent.xdata, mouseevent.ydata]))**2,
axis=1))
x, y = array(artist.get_data()).T[i]
Nelements = len(artist.get_data()[0])
d = str(artist.get_url()[i]) if Nelements > 1 else str(artist.get_url())
ax = artist.axes
if not hasattr(ax, "annotation"):
ax.annotation = ax.annotate(
d,
xy=(x, y),
xycoords='data',
xytext=(0, 30),
textcoords="offset points",
size="larger",
va="bottom",
ha="center",
bbox=dict(boxstyle="round", fc="w", alpha=0.5),
arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=-0.2"),
)
ax.annotation.xy = (x, y)
ax.annotation.set_text(d)
artist.figure.canvas.draw()
def addDataVectorAccessor(self, data_vector_accessor):
self.__data_vectors_accessors__.append(data_vector_accessor)
_sum = pl.sum(data_vector_accessor.signal)
_min = pl.amin(data_vector_accessor.signal)
_max = pl.amax(data_vector_accessor.signal)
if self.__minimal_signal__ == None:
self.__minimal_signal__ = _sum
self.__minimal_data_vector_accessor__ = data_vector_accessor
self.__min_signal__ = _min
self.__max_signal__ = _max
if _sum < self.__minimal_signal__:
self.__minimal_data_vector_accessor__ = data_vector_accessor
self.__minimal_signal__ = _sum
if _min < self.__min_signal__:
self.__min_signal__ = _min
if _max > self.__max_signal__:
self.__max_signal__ = _max
#collects unique annotations (>0) as a set
if not data_vector_accessor.annotation == None:
unique_annotations = pl.unique(data_vector_accessor.annotation[
pl.where(data_vector_accessor.annotation > 0)])
if len(unique_annotations) > 0:
#union of sets
self.__unique_annotations__ |= set(unique_annotations)
def Logistic(self):
'''Create logistic model from data'''
tStep = self.time[1] - self.time[0]
# Time vector for calculating lag phase
timevec = py.arange(self.time[0], self.time[-1], tStep / 2)
# Try using to find logistic model with optimal lag phase
# y = p2 + (A-p2) / (1 + exp(( (um/A) * (L-t) ) + 2))
sse = 0
sseF = 0
# Attempt to use every possible value in the time vector as the lag
# Choose lag that creates best-fit model
for idx, lag in enumerate(timevec):
logDataTemp = [
self.startOD + ((self.asymptote - self.startOD) / (1 + py.exp(
(((self.maxgrowth / self.asymptote) * (lag - t)) + 2))))
for t in self.time
]
sse = py.sum([((self.data[i] - logDataTemp[i])**2)
for i in xrange(len(self.data) - 1)])
if idx == 0 or sse < sseF:
logisticData = logDataTemp
lagF = lag
sseF = sse
return logisticData, lagF, sseF
def calcNewGrowth(logistic, asym, y0):
"""
Calculate growth level using an adjusted harmonic mean
using a logistic model, its asymptote, and its starting OD value
"""
diff = asym - y0
return len(logistic) / py.sum((1 / (logistic + diff)))
def calcAUC(data, y0, lag, mgr, asym, time):
"""
Calculate the area under the curve of the logistic function
using its integrated formula
[ A( [A-y0] log[ exp( [4m(l-t)/A]+2 )+1 ]) / 4m ] + At
"""
# First check that max growth rate is not zero
# If so, calculate using the data instead of the equation
if mgr == 0:
auc = calcAUCData(data, time)
else:
timeS = time[0]
timeE = time[-1]
t1 = asym - y0
#try:
t2_s = py.log(py.exp((4 * mgr * (lag - timeS) / asym) + 2) + 1)
t2_e = py.log(py.exp((4 * mgr * (lag - timeE) / asym) + 2) + 1)
#except RuntimeWarning as rw:
# Exponent is too large, setting to 10^3
# newexp = 1000
# t2_s = py.log(newexp + 1)
# t2_e = py.log(newexp + 1)
t3 = 4 * mgr
t4_s = asym * timeS
t4_e = asym * timeE
start = (asym * (t1 * t2_s) / t3) + t4_s
end = (asym * (t1 * t2_e) / t3) + t4_e
auc = end - start
if py.absolute(auc) == float('Inf'):
x = py.diff(time)
auc = py.sum(x * data[1:])
return auc
def r_squared(y, estimated):
"""
Calculate the R-squared error term.
Args:
y: 1-d pylab array with length N, representing the y-coordinates of the
N sample points
estimated: an 1-d pylab array of values estimated by the regression
model
Returns:
a float for the R-squared error term
"""
#R^2 = 1 - E (actual - estimate)^2 / E (actual - mean)^2
r_sq = 1 - pylab.sum((y - estimated)**2)/pylab.sum((y - pylab.mean(y))**2)
return r_sq
def fBM(n, H):
"""
creates fractional Brownian motion
parameters: length of sample path;
Hurst exponent
this method uses another computational approach than fBM
http://en.wikipedia.org/wiki/Fractional_Brownian_motion#Method_2_of_simulation
I should somewhen look that up with proper references
- proper scaling is not implemented. Look up the article to implement it!
"""
gammaH = gamma(H + .5)
def KH(t, s):
"""
accordint to the article
"""
return (t - s)**(H - .5) / gammaH * hyp2f1(H - .5, .5 - H, H + .5,
1. - float(t) / float(s))
incs = randn(n + 1)
path = zeros(n + 1)
for pos in arange(n) + 1:
path[pos] = sum([KH(pos, x) * incs[x] for x in arange(pos) + 1])
return path[1:]
def mean_score_per_attempt(num_attempts, num_attempt_limit, maxscores, first_20_scores):
played_more_than = pb.array(num_attempts) > num_attempt_limit
percentiles = [stats.scoreatpercentile(maxscores[played_more_than], per) for per in range(20,120,20)]
player_percentiles = [bisect.bisect(percentiles, maxscore) for maxscore in maxscores]
mean_score_at_attempt_i_for_percentile_j = pb.zeros((20,5))
count_at_attempt_i_for_percentile_j = pb.zeros((20,5))
std_at_attempt_i_for_percentile_j = pb.zeros((20,5))
for j in range(5):
print "looking at percentile group %s"%j
# this second mask chooses only those players whose max score is in the jth percentile
scores_in_this_percentile = pb.array(player_percentiles) == j
# we AND these together to get the mask
mask = played_more_than & scores_in_this_percentile
# Then add up all the scores that satisfy the mask (everyone in this percentile who played more than 19 times)
# and we divide them by the number of people maskwho satisfy the mask
## NOTE THAT WE ASSUME THAT NOONE SCORES ZERO!
mean_score_at_attempt_i_for_percentile_j[:,j] = pb.sum(first_20_scores[mask,:],0) / sum(first_20_scores[mask,:] > 0, 0)
#
#tom added this
count_at_attempt_i_for_percentile_j[:,j] = sum(first_20_scores[mask,:] > 0, 0)
# the square route of the average squared difference from the mean
std_at_attempt_i_for_percentile_j[:,j] = np.sqrt(sum(((first_20_scores[mask,:]-mean_score_at_attempt_i_for_percentile_j[:,j])**2)*(first_20_scores[mask,:] > 0),0)/count_at_attempt_i_for_percentile_j[:,j])
pickle.dump(count_at_attempt_i_for_percentile_j, open('save_count_at_attempt_i_for_percentile_j.p', 'wb'))
pickle.dump(std_at_attempt_i_for_percentile_j, open('save_std_at_attempt_i_for_percentile_j.p', 'wb'))
return mean_score_at_attempt_i_for_percentile_j
def __calculate__(self):
global USE_IDENTITY_LINE
sd1 = (self.signal_plus - self.signal_minus) / pl.sqrt(2)
if USE_IDENTITY_LINE:
return pl.sqrt(pl.sum((sd1**2)) / len(self.signal_plus))
else:
return pl.sqrt(pl.var(sd1))
def classify(self, x):
d = self.X - tile(x.reshape(self.n,1), self.N);
dsq = sum(d*d,0)
# Get N nearest neighbors
minindex = np.argsort(dsq)[0:self.k]
# Group sum by value
return Counter(self.c[minindex]).most_common()[0][0]
def findNoisyArea(self):
"""
Argument :
- None
Return :
- None
Use to detect the noisy area (the area without atoms) by an edge
detection technique.
"""
OD = self.computeFirstOD()
yProfile = py.sum(OD, axis=1)
derivative = py.gradient(yProfile)
N = 10
# because the derivative is usually very noisy, a sliding average is
# performed in order to smooth the signal. This is done by a
# convolution with a gate function of size "N".
res = py.convolve(derivative, py.ones((N, )) / N, mode='valid')
mean = res.mean()
# index of the maximum value of the signal.
i = res.argmax()
while res[i] >= mean:
# once "i" is greater or equal to the mean of the derivative,
# we have found the upper bound of the noisy area.
i -= 1
# index of the minimum value of the signal.
upBound = i - 50
i = res.argmin()
while res[i] < mean:
# once "i" is smaller or equal to the mean of the derivative,
# we have found the lower bound of the noisy area.
i += 1
downBound = i + 50
self.setNoisyArea((upBound, downBound))
return OD
def __calculate__(self):
global USE_IDENTITY_LINE
sd1 = (self.signal_plus - self.signal_minus) / pl.sqrt(2)
if USE_IDENTITY_LINE:
return pl.sqrt(pl.sum((sd1 ** 2)) / len(self.signal_plus))
else:
return pl.sqrt(pl.var(sd1))