def check_preamble_properties(preamble, x_preamble):
x_1st = x_preamble[0:len(x_preamble) // 2]
x_2nd = x_preamble[-len(x_preamble) // 2:]
if not np.all(np.abs(x_1st - x_2nd) < 1e-12):
print np.abs(x_1st - x_2nd)
raise ValueError('preamble timeslots do not repeat!')
from correlation import cross_correlate_naive, auto_correlate_halfs
from utils import calculate_signal_energy
x_ampl = np.sqrt(calculate_signal_energy(x_preamble))
preamble *= x_ampl
x_preamble *= x_ampl
x_energy = calculate_signal_energy(x_preamble)
if np.abs(2. * auto_correlate_halfs(x_preamble) / x_energy) -1. > 1e-10:
raise ValueError('auto correlating halfs of preamble fails!')
print 'normalized preamble xcorr val: ', np.correlate(x_preamble, x_preamble) / x_energy
print 'windowed normalized preamble: ', np.correlate(preamble[-len(x_preamble):], x_preamble) / x_energy
fxc = np.correlate(preamble, x_preamble, 'full') / x_energy
vxc = np.correlate(preamble, x_preamble, 'valid') / x_energy
nxc = cross_correlate_naive(preamble, x_preamble) / x_energy
import matplotlib.pyplot as plt
plt.plot(np.abs(fxc))
plt.plot(np.abs(vxc))
plt.plot(np.abs(nxc))
plt.show()
def ccovf(x, y, unbiased=True, demean=True):
''' crosscovariance for 1D
Parameters
----------
x, y : arrays
time series data
unbiased : boolean
if True, then denominators is n-k, otherwise n
Returns
-------
ccovf : array
autocovariance function
Notes
-----
This uses np.correlate which does full convolution. For very long time
series it is recommended to use fft convolution instead.
'''
n = len(x)
if demean:
xo = x - x.mean()
yo = y - y.mean()
else:
xo = x
yo = y
if unbiased:
xi = np.ones(n)
d = np.correlate(xi, xi, 'full')
else:
d = n
return (np.correlate(xo, yo, 'full') / d)[n - 1:]
def _updateBuffer(self, v):
"""
Keep a buffer of the running data and process it to determine if there is
a peak.
"""
self._rtData.append(v)
wndwCenter = int(np.floor(self._window / 2.0))
# pop the end of the buffer
if len(self._rtData) > self._window:
self._rtData = self._rtData[1:]
if self._isPeak:
lm = self._rtData.findPeaks()
for l in lm:
if l[0] == wndwCenter and l[1] > self._cutoff:
if self.doCorr:
corrVal = np.correlate(self._rtData.normalize(), self._template)
thresh = self.corrThresh[0] - self.corrStdMult * self.corrThresh[1]
if corrVal[0] > thresh:
self.count += 1
else:
self.count += 1
else:
lm = self._rtData.findValleys()
for l in lm:
if l[0] == wndwCenter and l[1] < self._cutoff:
if self.doCorr:
corrVal = np.correlate(self._rtData.normalize(), self._template)
thresh = self.corrThresh[0] - self.corrStdMult * self.corrThresh[1]
if corrVal[0] > thresh:
self.count += 1
else:
self.count += 1
return self.count
def plot_wavenvelope(self, ax, w_start, w_end):
""" This function plots the envelope of the recording.
:param ax: The axis in which you wish to plot.
:param w_start: Start of the best window.
:param w_end: End of the best window.
"""
window_size = int(0.05 * self._sample_rate) # 0.050 are 50 milliseconds for the envelope window!
w = 1.0 * np.ones(window_size) / window_size
envelope = (np.sqrt((np.correlate(self._eod ** 2, w, mode='same') -
np.correlate(self._eod, w, mode='same') ** 2)).ravel()) * np.sqrt(2.)
upper_bound = np.max(envelope) + np.percentile(envelope, 1)
ax.fill_between(self._time[::500], y1=-envelope[::500], y2=envelope[::500], color='purple', alpha=0.5)
ax.plot((w_start, w_start), (-upper_bound, upper_bound), 'k--', linewidth=2)
ax.plot((w_end, w_end), (-upper_bound, upper_bound), 'k--', linewidth=2)
ax.text((w_start + w_end) / 2., upper_bound - np.percentile(envelope, 10), 'Analysis Window',
rotation='horizontal', horizontalalignment='center', verticalalignment='center', fontsize=14)
ax.set_ylim(-upper_bound, upper_bound)
ax.set_xlabel('Time [s]', fontsize=16)
ax.set_ylabel('Signal Amplitude [au]', fontsize=16)
ax.tick_params(axis='both', which='major', labelsize=14)
pass
def get_best_time_window(data, samplerate, fundamental_frequency, eod_cycles):
eod_peaks1, eod_peak_idx1, _, _ = peakdet(data)
max_time = len(data) / samplerate
time_for_eod_cycles_in_window = eod_cycles / fundamental_frequency
if time_for_eod_cycles_in_window > max_time * .2:
time_for_eod_cycles_in_window = max_time * .2
warnings.warn("You are reqeusting a window that is too long. Using T=%f" % (time_for_eod_cycles_in_window,))
sample_points_in_window = int(fundamental_frequency * time_for_eod_cycles_in_window)
tApp = np.arange(len(data)) / samplerate
w1 = np.ones(sample_points_in_window) / sample_points_in_window
local_mean = np.correlate(eod_peaks1, w1, mode='valid')
local_std = np.sqrt(np.correlate(eod_peaks1 ** 2., w1, mode='valid') - local_mean ** 2.)
COV = local_std / local_mean
mi = min(COV)
for ind, j in enumerate(COV):
if j == mi:
v = (eod_peak_idx1[ind])
idx = (tApp >= tApp[v]) & (tApp < tApp[v] + time_for_eod_cycles_in_window)
tApp = tApp[idx]
dat_app = data[idx]
tApp = tApp - tApp[0]
return tApp, dat_app
def linearCouplingCoeff2(dataH, dataX, timeH, timeX, transFnXtoH, segStartTime,
segEndTime, timeShift, samplFreq, logFid, debugLevel):
# LINEARCOUPLINGCOEFF - calculate the cross correlation coeff b/w the gravitational
# ave channel H and the "projected" instrumental channel X. The noise in the
# instrumental channel X is projected to the domain of the H using a linear coupling
# function Txh
rXH = np.asarray([])
rMaxXH = np.asarray([])
if((len(dataH)==0) | (len(dataX)==0)):
logFid.write('Error: One or more data vectors are empty..\n')
logFid.write('Error: len(dataH) = %d len(dataX) = %d..\n' %(len(dataH), len(dataX[0])))
elif(len(dataH)!=len(dataX[0])):
logFid.write('Error: Different lengths. len(dataH) = %d len(dataX) = %d..\n'%(len(dataH), len(dataX[0])))
else:
dataH = dataH #- np.mean(dataH)
dataX = dataX[0] #- np.mean(dataX[0])
segIdxH = np.intersect1d(np.where(timeH>=segStartTime)[0], np.where(timeH<segEndTime)[0])
dataH = dataH[segIdxH]
segIdxX = np.intersect1d(np.where(timeX + timeShift >= segStartTime)[0], np.where(timeX + timeShift < segEndTime)[0])
dataX = dataX[segIdxX]
a = np.correlate(dataH, dataX)/(np.sqrt(np.correlate(dataH, dataH)*np.correlate(dataX, dataX)))
rXH = np.append(rXH, a)
rMaxXH = np.append(rMaxXH, a)
return [rXH, rMaxXH]
def determineDelay(source, target, maxdel=2**16, ax=None):
'''
Determine the delay between two signals
(based on correlation extrema)
Parameters:
* Signals
- source
- target
* maxdel: maximum delay to look for (in both directions)
'''
sample_start = 0
xd = source[sample_start:sample_start+maxdel]
yd = target[sample_start:sample_start+maxdel]
Cxx = np.correlate(xd, xd, 'full')
Cxy = np.correlate(yd, xd, 'full')
Pkx = np.argmax(np.abs(Cxx))
Pky = np.argmax(np.abs(Cxy))
if ax:
try:
ax.plot(Cxx)
except AttributeError:
fig, ax = pl.subplots(1)
ax.plot(Cxx)
ax.plot(Cxy)
ax.axvline(Pkx, color='red')
ax.plot(Pky, Cxy[Pky], 'o')
delay = Pky-Pkx
return delay
def calculate_maxcrosscorrelation(reference_signal, unknown_signal):
'''
function:
---------
given a reference signal and an unknown signal, calculate the max cross correlation score. the higher the score,
the more similar two signals are.
the max cross correlation score will be used to identify events.
parameters:
-----------
@reference_signal: 150 unit numpy array, representing reference signal.
@unknown_signal: 150 unit numpy array
returns:
--------
@score: int between [0,1]; represents similarity between two curves.
'''
# https://stackoverflow.com/questions/1289415/what-is-a-good-r-value-when-comparing-2-signals-using-cross-correlation
x = max(np.correlate(reference_signal, reference_signal, 'full'))
y = max(np.correlate(unknown_signal, unknown_signal, 'full'))
z = max(np.correlate(reference_signal, unknown_signal, 'full'))
score = (z ** 2) / float(x * y)
return score
def correlation (results = [], bin_size = 100, N =1000): wait_time=0. print 'N = ',N nr_datasets = results ['max_ind'] ind = 0 for counter in np.arange(nr_datasets): dati = results [str(counter)] if (len(dati)>2*N+1): if (bin_size>1 ): b = bin_data (data = dati, bin_size = bin_size) else: b = dati t = np.arange(len(b))*bin_size*(20e-6+wait_time*1e-6) mu = np.mean(b) sigma = np.std(b) corr = np.correlate (b-mu, b-mu, 'full')/(np.correlate(b-mu, b-mu)+0.) t_corr = (np.arange (len(corr))-len(corr)/2.)*(wait_time+20.)*1e-6*bin_size nn = len(corr) corr2 = corr [nn/2-N:nn/2+N] t_corr2 = t_corr [nn/2-N:nn/2+N] if (ind == 0): avg_corr = corr2 else: avg_corr = avg_corr+corr2 ind = ind + 1 avg_corr[N] = 0 avg_corr = avg_corr/max(avg_corr) return t_corr2, avg_corr
def correlateData(self,frameLimit): sample = [] self.fh.seek((self.startFrame)*4128,0) steps = frameLimit/10 totalTime = datetime.now() print 'Correlating [ ]', print '\b'*12, sys.stdout.flush() for p in xrange(frameLimit): startTime = datetime.now() frame = drx.readFrame(self.fh) if frame.parseID()[1] == 1: self.realTune1 = self.realTune1 + numpy.correlate(frame.data.iq.real,self.template).tolist() self.imagTune1 = self.imagTune1 + numpy.correlate(frame.data.iq.imag,self.template).tolist() else: self.realTune2 = self.realTune2 + numpy.correlate(frame.data.iq.real,self.template).tolist() self.imagTune2 = self.imagTune2 + numpy.correlate(frame.data.iq.imag,self.template).tolist() if p%steps == 0: print '\b=', sys.stdout.flush() print '\b] Done' self.startFrame += frameLimit #self.fh.close() print 'Read time: ' + str(datetime.now() - totalTime)
def correlationIndividual(data, idx = (0,1), cls = -1, delay = (-100, 100)):
"""Calculate corrs and auto correlation in time between the various measures"""
n = len(idx);
means = np.mean(data[:,:-1], axis = 0);
nd = delay[1] - delay[0] + 1;
cc = np.zeros((nd,n,n))
for i in range(n):
for j in range(n):
if delay[0] < 0:
cm = np.correlate(data[:, i] - means[i], data[-delay[0]:, j] - means[j]);
else:
cm = [0];
if delay[1] > 0:
cp = np.correlate(data[:, j] - means[j], data[delay[1]:, i] - means[i]);
else:
cp = [0];
ca = np.concatenate((cm[1:], cp[::-1]));
if delay[0] > 0:
cc[:,i,j] = ca[delay[0]:];
elif delay[1] < 0:
cc[:,i,j] = ca[:-delay[1]];
else:
cc[:,i,j] = ca;
return cc;
def _corr_ax1(input_image):
"""
Internal helper function that finds the best estimate for the
location of the vertical mirror plane. For each row the maximum
of the correlating with it's mirror is found. The most common value
is reported back as the location of the mirror plane.
Parameters
----------
input_image : ndarray
The input image
Returns
-------
vals : ndarray
histogram of what pixel has the highest correlation
bins : ndarray
Bin edges for the vals histogram
"""
dim = input_image.shape[1]
m_ones = np.ones(dim)
norm_mask = np.correlate(m_ones, m_ones, mode='full')
# not sure that the /2 is the correct correction
est_by_row = [np.argmax(np.correlate(v, v[::-1],
mode='full')/norm_mask) / 2
for v in input_image]
return np.histogram(est_by_row, bins=np.arange(0, dim + 1))
def find(self, target):
if len(target) == 4:
#check pattern d
sum = 0
for i in range(len(target)):
sum += np.correlate(target[i], self.pd[i])[0]
if sum >= self.threshold_expand['pd']:
return True
else:
return False
elif len(target) == 3:
if len(target[0]) == 4:
#check pattern c
sum = 0
for i in range(len(target)):
sum += np.correlate(target[i], self.pc[i])[0]
if sum >= self.threshold_expand['pc']:
return True
else:
return False
elif len(target[0]) == 3:
# common cases
for k in self.threshold:
sum = 0
pt = k[0]
tr = k[1]
for i in range(len(target)):
sum += np.correlate(target[i], pt[i])[0]
if sum >= tr:
return True
return False
def chickling_corr(shotno, date=time.strftime("%Y%m%d"), bandwidth=40000):
fname, data = file_finder(shotno,date)
samplesize = int(np.unwrap(data[0]['phasediff_co2']).size/bandwidth)
phase_avr_co2 = np.zeros(samplesize)
phase_avr_hene = np.zeros(samplesize)
#reshape the array of x points (20M for 1s) into a 2d array each with 40k segments.
phasediff_co2 = np.reshape(np.unwrap(data[0]['phasediff_co2'][0:(samplesize*bandwidth)]),(samplesize,bandwidth))
phasediff_hene = np.reshape(np.unwrap(data[0]['phasediff_hene'][0:(samplesize*bandwidth)]),(samplesize,bandwidth))
#for each horizontal column perform an average
for i in range(0,samplesize):
phase_avr_co2[i] = np.mean(phasediff_co2[i])
phase_avr_hene[i] = np.mean(phasediff_hene[i])
x = np.linspace(0,1,samplesize)
plt.figure("2 Channels | Blue = Scene | Orange = Reference | Green = Cross-Correlation | shot " + str(shotno) + " Date " + str(date))
plt.xlabel("Time, s")
plt.ylabel("Phase Difference, Radians")
plt.plot(x,phase_avr_co2-np.average(phase_avr_co2))
plt.plot(x,phase_avr_hene-np.average(phase_avr_hene))
a = (phase_avr_co2 - np.mean(phase_avr_co2)) / (np.std(phase_avr_co2) * len(phase_avr_co2))
b = (phase_avr_hene - np.mean(phase_avr_hene)) / (np.std(phase_avr_hene))
yc = np.correlate(a, b, 'full')
print(np.correlate(a, b, 'valid'))
xc = np.linspace(0,1,yc.size)
plt.plot(xc,yc)#,'o',ms=0.4)
def findInserts(self):
#interpolate to the base phantom slice thickness
#X=numpy.linspace(0,self.phBase.slicethk*(self.slices*self.slicethk/self.phBase.slicethk),(self.slices*self.slicethk)/self.phBase.slicethk+1)
X=numpy.arange(0,self.slices*self.slicethk,self.phBase.slicethk)
Xp=numpy.linspace(0,(self.slices-1)*self.slicethk,self.slices)
profileResc=numpy.interp(X,Xp,self.profile)
profileRescMirror=numpy.fliplr([profileResc,numpy.zeros(len(profileResc))])[0,:]
#find order of acquisition
fwdcor=numpy.correlate(self.phBase.profile[:,1],profileResc,'full')
rwdcor=numpy.correlate(self.phBase.profile[:,1],profileRescMirror,'full')
reverse=False
if numpy.amax(fwdcor)>=numpy.amax(rwdcor):
shift=numpy.argmax(fwdcor)
else:
reverse=True
shift=numpy.argmax(rwdcor)
#align profile and base profile
#get index of slices
Xcor=(X/self.phBase.slicethk)-len(X)+1+shift
#find phantom slice nearest to base inserts
Inserts=["resolution","sliceThk","uniform","dgp"]
for insert in Inserts:
if (Xcor==self.phBase.inserts[insert][0]).any() or (Xcor==self.phBase.inserts[insert][1]).any():
f=max(self.phBase.inserts[insert][0],Xcor[0])
s=min(self.phBase.inserts[insert][1],Xcor[len(Xcor)-1])
self.inserts[insert]=numpy.round(((numpy.array([f,s])+len(X)-1-shift)*float(self.phBase.slicethk))/float(self.slicethk))
if reverse:
self.inserts[insert]=numpy.abs(self.inserts[insert]-self.slices+1)
(self.inserts[insert]).sort()
def msd_fast(trajs):
T = trajs.shape[1]
N = trajs.shape[0]
trajs2 = trajs ** 2
msd = np.zeros((T))
for n in xrange(N):
r2 = np.zeros((T))
rtau2 = np.zeros((T))
# compute sums over squares of positions for r(t) and r(t+tau)
for tau in xrange(T):
r2[tau] = np.sum(trajs2[n][: (T - tau), 0] + trajs2[n][: (T - tau), 1])
rtau2[tau] = np.sum(trajs2[n][tau:, 0] + trajs2[n][tau:, 1])
# compute auto correlation
corx = np.correlate(trajs[n][:, 0], trajs[n][:, 0], mode="full")[T - 1 :]
cory = np.correlate(trajs[n][:, 1], trajs[n][:, 1], mode="full")[T - 1 :]
cor = corx + cory
msd += (rtau2 - 2 * cor + r2) / np.arange(T, 0, -1)
msd = msd / N
return msd
def plot_acorr(x, ax=None, title="", xlabel="Shift", ylabel="",
append_analysis=True):
"""Plot the autocorrelation
If variance is too small (i.e. for a deterministic process),
falls back to plotting autocovariance
"""
x_centered = x - np.mean(x)
x_var = np.var(x)
x_len = len(x)
x_centered_sample = x_centered[:int(x_len//2)]
if len(np.unique(x.round(decimals=12))) > 1:
# compute autocorrelation
x_acorr = np.correlate(x_centered, x_centered_sample, 'valid')/x_var
analysis_mode = "Autocorrelation"
else:
# if process is deterministic, autocorrelation is undefined
# use the autocovariance instead
x_acorr = np.correlate(x_centered, x_centered_sample, 'valid')
analysis_mode = "Autocovariance"
if ax is None:
fig, ax = plt.subplots(nrows=1, figsize=(12,3))
ax.plot(x_acorr[:100], 'o')
if append_analysis:
ax.set_title(title+analysis_mode)
else:
ax.set_title(title)
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
limit_ylim(ax)
return ax
def aligndata(baselineremoved, brightest, pulsar):
nbins = baselineremoved.shape[0]
nprofiles = baselineremoved.shape[1]
template = baselineremoved[:,brightest]
# rotate template to put peak at 1/4
peakbin = np.argmax(template)
fixedlag = int(nbins/4)-peakbin
aligned = np.zeros((nbins,nprofiles))
newtemplate = np.roll(template, fixedlag)
template = newtemplate
plt.plot(newtemplate)
plt.savefig('./{0}/{0}_brightest.png' .format(pulsar))
plt.clf()
for i in range(nprofiles):
xcorr = np.correlate(template,baselineremoved[:,i],"full")
lag = np.argmax(xcorr)
aligned[:,i] = np.roll(baselineremoved[:,i],lag)
template = np.median(aligned,1)
# repeat with better template now and shift peak to 1/4 of the profile
peakbin = np.argmax(template)
fixedlag = int(nbins/4)-peakbin
double = np.zeros(2*nbins)
for i in range(nprofiles):
double[0:nbins] = baselineremoved[:,i]
double[nbins:2*nbins] = baselineremoved[:,i]
# xcorr = np.correlate(template,baselineremoved[:,i],"full")
xcorr = np.correlate(template,double,"full")
lag = np.argmax(xcorr) + fixedlag
aligned[:,i] = np.roll(baselineremoved[:,i],lag)
newtemplate = np.median(aligned,1)
return np.array(aligned), np.array(newtemplate)
def dbpsk_demod(rx_data, sample_rate, L):
print "Demodulating Data...@", sample_rate
time_seq = np.arange(0, len(rx_data), 1, dtype=float) / sample_rate
two_pi_fc_t = 2 * np.pi * CenterFreq * time_seq
# Filter out-of-band noise
rx_inband = np.convolve(rx_data, bp_filt)
N = len(rx_inband)
# Downconvert I/Q channels into baseband signals
rx_bb_i = np.multiply(rx_inband[SamplesPerSymbol / 2 : N - SamplesPerSymbol / 2], np.cos(two_pi_fc_t))
rx_bb_q = np.multiply(rx_inband[SamplesPerSymbol / 2 : N - SamplesPerSymbol / 2], np.sin(two_pi_fc_t))
# Filter any high frequency remnants
audio_bb_i = np.convolve(rx_bb_i, lp_filt)[: L * SamplesPerSymbol * BitsPerChar]
audio_bb_q = np.convolve(rx_bb_q, lp_filt)[: L * SamplesPerSymbol * BitsPerChar]
decoded_bits = np.zeros(L * BitsPerChar)
# Previous Phase and decode bit
pp = 0
pb = 0
detected_bitstream = np.zeros(L * BitsPerChar, dtype=int)
T = SamplesPerSymbol
# Matched filter is just a rectangular pulse
rect_pulse = np.ones(T)
for demod in np.arange(L * BitsPerChar):
sym_i = np.correlate(audio_bb_i[demod * T : (demod + 1) * T], rect_pulse, "full")[T]
sym_q = np.correlate(audio_bb_q[demod * T : (demod + 1) * T], rect_pulse, "full")[T]
cp = np.arctan(sym_q / sym_i)
# print "Phase Diff:", cp-pp
if np.abs(cp - pp) > 0.1:
detected_bitstream[demod] = pb ^ 1
else:
detected_bitstream[demod] = detected_bitstream[demod - 1]
pb = detected_bitstream[demod]
pp = cp
return detected_bitstream
def plotSpectrum(data,timeR,Fourier=1,Corr=1,index = array([5,6]),figname='a.pdf',ind=1):
[ind1,ind2] = index
k = min([60*5,len(data[:,0])/3])
y = data[k:,ind1]
x = data[k:,ind2]
time = timeR[k:]
if Fourier:
figure(1)
subplot(2,1,1)
hold(True)
plot(time,y)
plot(time,x)
xlabel('Time')
ylabel('Amplitude')
subplot(2,1,2)
Y = fft(y)
Y = Y[1:]
n= len(Y)
#Crazy math in here
powerY = abs(Y[arange(0,floor(n/2),dtype='int')])**2
nyquist = 1./2.
freq = arange(0,n/2,dtype='float')/(n/2)*nyquist
period=1./freq
# a is the number of points per time step (Depends on the interpolation in process - interp)
a = 1
plot(period/a,powerY)
ylabel('Power')
xlabel('Period (Min/oscillation)')
#savefig(''.join([figname,".png"]), bbox_inches=0 ,dpi=100)
if Corr:
modeC = "same"
x = (x - mean(x))/std(x)
y = (y - mean(y))/std(y)
timeInt = time[1]-time[0]
numPoints = len(time)
fig2 = figure(2)
fig2.set_size_inches(6*0.9,3.7*0.9*0.906)
fig2.set_facecolor('white')
ax2 = fig2.add_subplot(1,1,ind)
n= correlate(y,y,modeC)
d= correlate(y,x,modeC)
lagR = (nonzero(n == max(n))[0] - nonzero(d == max(d))[0])*timeR[1]
title("CC Act-DS %f Max corr: %f" %(lagR, max(d)/numPoints))
ax2.plot(linspace(len(x)/2*timeInt,-len(x)/2*timeInt,len(x)),d/numPoints)
xlim([-180,180])
#ylim([-0.25,0.75])
#savefig(''.join([figname,".png"]), bbox_inches=0 ,dpi=100)
show()
def get_scientist_series_from_txt(scientist_dict, dir):
series_dict = {}
correlation_list = []
for scientist in scientist_dict:
year_dict = {}
scientist_series = []
scientist = scientist.rstrip().split('/')[-1]
filename = os.path.join(dir + '\\' + scientist + '.txt')
try:
f = open(filename)
for line in f:
time_list = map(float, line.split(','))
year = int(time_list.pop(0))
if year>2004 and year<2016:
year_dict.update({year:len(time_list)})
scientist_series += time_list
f.close()
except IOError:
# print scientist
continue
if year_dict!={}:
start_year = min(list(year_dict.keys()))
end_year = max(list(year_dict.keys()))
d = datetime.date(start_year, 1, 1)
# 0 = Monday, 1=Tuesday, 2=Wednesday... We use Sunday as in Google Trends csv file every week starts from Sunday
start_point = str(start_year) + '-01-01'
end_point = datetime.datetime(int(end_year), 1, 1) + datetime.timedelta(year_dict[end_year]-1)
#Generate periods
rng = pd.date_range(start_point, end_point, freq='D')
#print scientist, start_point, len(rng)
ts = pd.Series(scientist_series, index=rng)
param_ts = numpy.array(ts.asfreq('W', method='pad').values, dtype = float)
gt_series = get_google_trends_series(scientist)
if gt_series!=[]:
rng = pd.date_range('2004-01-04', '2016-06-26', freq='W')
ts = pd.Series(gt_series, index=rng)
rng = pd.date_range(start_point, end_point, freq='W')
gtrends_ts = numpy.array(pd.Series(ts, index=rng).values, dtype = float)
param_ts = running_mean(param_ts, 4)
gtrends_ts = running_mean(gtrends_ts, 4)
# Plotting
plt.figure()
plt.title(scientist)
plt.plot(param_ts, label = 'edits')
plt.plot(gtrends_ts, label = 'google_trends')
plt.legend(loc='upper left')
param_ts = (param_ts - numpy.mean(param_ts)) / (numpy.std(param_ts)* len(param_ts))
gtrends_ts = (gtrends_ts - numpy.mean(gtrends_ts)) / (numpy.std(gtrends_ts) )
print scientist, numpy.correlate(param_ts, gtrends_ts)[0]
correlation_list.append(numpy.correlate(param_ts, gtrends_ts)[0])
print 'average correlation', numpy.mean(numpy.absolute(correlation_list))
print 'min correlation', format(numpy.min(numpy.absolute(correlation_list)),'f')
print 'max correlation',format(numpy.max(numpy.absolute(correlation_list)),'f')
plt.show()
return
def adjust_for_phase_and_lag(self):
# store a list of the l2-norms and the lags for each phase value
norm_list = np.zeros((self.phases_vec.size,2))
# loop through each phase value
for pp in range(self.phases_vec.size):
y = self.cur_signal_up.copy()
y = y*np.exp(1j*self.phases_vec[pp])
# Adjust for magnitude setting
if self.sig_type == 'm':
y = self.sig_mag(y)
# compute autocorrelation
if self.filter_on:
cur_corr = np.correlate(y,self.avg_complex_cir,mode='full')
else:
cur_corr = np.correlate(y,self.ref_complex_cir,mode='full')
opt_lag = -self.lag_vec[np.argmax(self.sig_mag(cur_corr)).flatten()[0]]
norm_list[pp,0] = opt_lag
# Shift the signal to adjust for any lag
if opt_lag > 0:
y = np.array(((0.+1j*0.)*np.ones(opt_lag)).tolist() + y[0:-opt_lag].tolist())
elif opt_lag < 0:
y = np.array(y[-opt_lag:].tolist() + ((0.+1j*0.)*np.ones(-opt_lag)).tolist())
# Adjust for magnitude setting
if self.sig_type == 'm':
y = self.sig_mag(y)
# Compute the l2-norm
if self.filter_on:
tmp = y - self.avg_complex_cir
else:
tmp = y - self.ref_complex_cir
# Save l2-norm to list
norm_list[pp,1] = self.sig_mag(tmp).sum()
# Get the index of the smallest l2-norm
min_idx = np.argmin(norm_list[:,1]).flatten()[0]
# Adjust for phase and lag
y = self.cur_signal_up.copy()
y = y*np.exp(1j*self.phases_vec[min_idx])
opt_lag = norm_list[min_idx,0]
# Shift the signal to adjust for any lag
if opt_lag > 0:
self.cur_signal_up = np.array(((0+1j*0)*np.ones(opt_lag)).tolist() + y[0:-opt_lag].tolist())
elif opt_lag < 0:
self.cur_signal_up = np.array(y[-opt_lag:].tolist() + ((0+1j*0)*np.ones(-opt_lag)).tolist())
else:
self.cur_signal_up = y.copy()
# Adjust for magnitude setting
if self.sig_type == 'm':
self.cur_signal_up = self.sig_mag(self.cur_signal_up)
def acf(y): '''Calculate the autocorrelation function of an array, returning an array of lags and an array with the acf.''' a = np.correlate(y,y,'full') trimmed = a[len(a)/2:] lag = np.arange(len(trimmed)) return lag, trimmed/np.correlate(y,y)
def sync_index_correlate( a, b ):
a = a - a.mean( )
b = b - b.mean( )
a = a / a.max()
b = b / b.max( )
same = np.correlate( a, a )[0]
other = np.correlate( a, b)[0]
return other / same
def correlate(X, Y):
m = X.size
n = Y.size
correction = np.correlate(np.ones((m,)), np.ones((n,)), "full")
rXY = np.correlate(X, Y, "full")
rXYc = rXY / correction
rXYC = rXYc[m - 1 :]
return rXYc
def makeCausalWiener(template, rawPulse, nTaps=50):
#template = template[tempOffs:tempOffs+nTaps*2]
#rawPulse = rawPulse[tempOffs:tempOffs+nTaps*2]
rawPulse = rawPulse/np.max(np.abs(rawPulse))
crossCorr = np.correlate(template, rawPulse[0:-nTaps+1])
autoCorr = np.correlate(rawPulse, rawPulse[0:-nTaps+1])
firCoeffs = sp.linalg.solve_toeplitz(autoCorr, crossCorr)
return firCoeffs/np.max(np.abs(firCoeffs))
def max_ccors(X):
N = np.correlate(np.ones(X.shape[1]),np.ones(X.shape[1]),'full')
ac = [np.correlate(X[i],X[i])[0] for i in range(X.shape[0])]
mcc = []
for i in range(X.shape[0]):
for j in range(i):
cc = np.correlate(X[i],X[j],'full')/N*1./np.sqrt(ac[i]*ac[j]+1e-6)
mcc.append(cc.max())
return np.array(mcc)
def parameter_autocor(sessions, population_fit, param = 'side'):
''' Evaluate within and cross subject variability in
specified parameter and autocorrelation across sessions.
'''
assert len(population_fit['MAP_fits']) == len(sessions), \
'Population fit does not match number of sessions.'
param_index = population_fit['param_names'].index(param)
for i, MAP_fit in enumerate(population_fit['MAP_fits']):
sessions[i].side_loading = MAP_fit['params_U'][param_index]
sIDs = list(set([s.subject_ID for s in sessions]))
p.figure(1)
p.clf()
p.subplot2grid((2,2),(0,0), colspan = 2)
subject_means = []
subject_SDs = []
cor_len = 20
subject_autocorrelations = np.zeros([len(sIDs), 2 * cor_len + 1])
subject_shuffled_autocor = np.zeros([len(sIDs), 2 * cor_len + 1, 1000])
for i, sID in enumerate(sIDs):
a_sessions = sorted([s for s in sessions if s.subject_ID == sID],
key = lambda s:s.day)
sl = [s.side_loading for s in a_sessions]
p.plot(sl)
subject_means.append(np.mean(sl))
subject_SDs.append(np.std(sl))
sl = (np.array(sl) - np.mean(sl)) / np.std(sl)
autocor = np.correlate(sl, sl, 'full') / len(sl)
subject_autocorrelations[i,:] = autocor[autocor.size/2 - cor_len:
autocor.size/2 + cor_len + 1]
for j in range(1000):
shuffle(sl)
autocor = np.correlate(sl, sl, 'full') / len(sl)
subject_shuffled_autocor[i,:,j] = autocor[autocor.size/2 - cor_len:
autocor.size/2 + cor_len + 1]
mean_shuffled_autocors = np.mean(subject_shuffled_autocor,0)
mean_shuffled_autocors.sort(1)
p.xlabel('Day')
p.ylabel('Subject rotational bias')
p.subplot2grid((2,2),(1,0))
p.fill_between(range(-cor_len, cor_len + 1),mean_shuffled_autocors[:,10],
mean_shuffled_autocors[:,-10], color = 'k', alpha = 0.2)
p.plot(range(-cor_len, cor_len + 1),np.mean(subject_autocorrelations,0),'b.-', markersize = 5)
p.xlabel('Lag (days)')
p.ylabel('Correlation')
p.subplot2grid((2,2),(1,1))
p.bar([0.5,1.5], [np.mean(subject_SDs), np.sqrt(np.var(subject_means))])
p.xticks([1,2], ['Within subject', 'Cross subject'])
p.xlim(0.25,2.5)
p.ylabel('Standard deviation')
def test_correlation(self):
numpy.set_printoptions(precision=3,suppress=True)
# Create 2 vectors of scores, zero everywhere except a random position
N = 10
x = numpy.zeros(N)
y = numpy.zeros(N)
xpeak = numpy.random.randint(0,N)
ypeak = numpy.random.randint(0,N)
x[xpeak] = 10
y[ypeak] = 10
x = (x-numpy.mean(x))/numpy.std(x)
y = (y-numpy.mean(y))/numpy.std(y)
# Make tracks out of them and compute cross-correlation with our own function
X = [('chr',k,k+1,s) for k,s in enumerate(x)]
Y = [('chr',k,k+1,s) for k,s in enumerate(y)]
X = fstream(iter(X),fields=['chr','start','end','score'])
Y = fstream(iter(Y),fields=['chr','start','end','score'])
corr = correlation([X,Y], regions=(0,N))#, limits=[-N+1,N-1])
# Compute cross-correlation "by hand" and using numpy.correlate(mode='valid')
raw = []
np_corr_valid = []
for k in range(N):
"""
X |- - - - -| k=0
Y <- |- - - - -|
up to
X |- - - - -| k=4
Y |- - - - -|
"""
raw.append(numpy.dot(x[-k-1:],y[:k+1]) / N)
np_corr_valid.extend(numpy.correlate(x[-k-1:],y[:k+1],mode='valid'))
for k in range(N-1,0,-1):
"""
X |- - - - -| k=4
Y <- |- - - - -|
up to
X |- - - - -| k=1
Y |- - - - -|
"""
raw.append(numpy.dot(x[:k],y[-k:]) / N)
np_corr_valid.extend(numpy.correlate(x[:k],y[-k:],mode='valid'))
# Compute cross-correlation using numpy.correlate(mode='full')
np_corr_full = numpy.correlate(x,y,mode="full")[::-1] / N
np_corr_valid = numpy.asarray(np_corr_valid) / N
# Test if all methods yield the same result
assert_almost_equal(corr, numpy.asarray(raw))
assert_almost_equal(corr, np_corr_full)
assert_almost_equal(corr, np_corr_valid)
# Test if the lag between the two tracks is correcty detected
self.assertEqual(numpy.argmax(corr)-(N-1), ypeak-xpeak)
def correlate_envelope_signal(signal, amp_envelope, n_surrogates=100,
random_state=None):
""" Correlate the amplitude envelope with the signal.
Parameters
----------
signal : ndarrray (n_times)
Low freq filtered signal.
amp_envelope: ndarray (n_times)
Amplitude envelope of high freq signal.
n_surrogates : int
Number of surrogates to be computed.
random_state : int
Seed value for random generator.
Returns
-------
xcorr : ndarray (n_times)
Cross correlation of the two signals.
xcorr_surrogates : ndarray (n_surrogates)
Cross correlation of the surrogate signals.
max_surr : ndarray (n_surrogates)
Maximum value of surrogate cross correlation.
z_threshold : float
Threshold value after z-scoring.
"""
from sklearn.utils import check_random_state
xcorr = np.correlate(signal, amp_envelope, 'full')
xcorr_surrogates = np.zeros((n_surrogates, xcorr.size))
max_surr = np.zeros((n_surrogates, 1))
rng = check_random_state(random_state) # initialize random generator
for i in range(0, n_surrogates):
order = np.argsort(rng.randn(len(amp_envelope)))
xcorr_surrogates[i, :] = np.correlate(signal, amp_envelope[order], 'full')
max_surr[i] = np.max(np.abs(xcorr_surrogates[i, :]))
# compute some statistics
#NOTE Needs to be rechecked. I want to check if the xcorr values
# can come from the surrogates values (i.e. reject the null hypothesis
# that xcorr computed is random.
max_surr_mean = np.mean(max_surr)
max_surr_std = np.std(max_surr)
# compute zscores
zscores = (xcorr - max_surr_mean) / max_surr_std
from scipy import stats
p_values = stats.norm.pdf(zscores)
# perform fdr correction and compute threshold
accept, _ = mne.stats.fdr_correction(p_values, alpha=0.001)
z_threshold = np.abs(zscores[accept]).min()
return xcorr, xcorr_surrogates, max_surr, z_threshold
def spec_flex_shift(obj_skyspec, arx_skyspec, mxshft=20):
""" Calculate shift between object sky spectrum and archive sky spectrum
Args:
obj_skyspec (:class:`linetools.spectra.xspectrum1d.XSpectrum1d`):
Spectrum of the sky related to our object
arx_skyspec (:class:`linetools.spectra.xspectrum1d.XSpectrum1d`):
Archived sky spectrum
mxshft (float, optional):
Maximum allowed shift from flexure; note there are cases that
have been known to exceed even 30 pixels..
Returns:
dict: Contains flexure info
"""
# TODO None of these routines should have dependencies on XSpectrum1d!
# Determine the brightest emission lines
msgs.warn("If we use Paranal, cut down on wavelength early on")
arx_amp, arx_amp_cont, arx_cent, arx_wid, _, arx_w, arx_yprep, nsig \
= arc.detect_lines(arx_skyspec.flux.value)
obj_amp, obj_amp_cont, obj_cent, obj_wid, _, obj_w, obj_yprep, nsig_obj \
= arc.detect_lines(obj_skyspec.flux.value)
# Keep only 5 brightest amplitude lines (xxx_keep is array of
# indices within arx_w of the 5 brightest)
arx_keep = np.argsort(arx_amp[arx_w])[-5:]
obj_keep = np.argsort(obj_amp[obj_w])[-5:]
# Calculate wavelength (Angstrom per pixel)
arx_disp = np.append(
arx_skyspec.wavelength.value[1] - arx_skyspec.wavelength.value[0],
arx_skyspec.wavelength.value[1:] - arx_skyspec.wavelength.value[:-1])
obj_disp = np.append(
obj_skyspec.wavelength.value[1] - obj_skyspec.wavelength.value[0],
obj_skyspec.wavelength.value[1:] - obj_skyspec.wavelength.value[:-1])
# Calculate resolution (lambda/delta lambda_FWHM)..maybe don't need
# this? can just use sigmas
arx_idx = (arx_cent + 0.5).astype(
np.int)[arx_w][arx_keep] # The +0.5 is for rounding
arx_res = arx_skyspec.wavelength.value[arx_idx]/\
(arx_disp[arx_idx]*(2*np.sqrt(2*np.log(2)))*arx_wid[arx_w][arx_keep])
obj_idx = (obj_cent + 0.5).astype(
np.int)[obj_w][obj_keep] # The +0.5 is for rounding
obj_res = obj_skyspec.wavelength.value[obj_idx]/ \
(obj_disp[obj_idx]*(2*np.sqrt(2*np.log(2)))*obj_wid[obj_w][obj_keep])
if not np.all(np.isfinite(obj_res)):
msgs.warn(
'Failed to measure the resolution of the object spectrum, likely due to error '
'in the wavelength image.')
return None
msgs.info("Resolution of Archive={0} and Observation={1}".format(
np.median(arx_res), np.median(obj_res)))
# Determine sigma of gaussian for smoothing
arx_sig2 = np.power(arx_disp[arx_idx] * arx_wid[arx_w][arx_keep], 2)
obj_sig2 = np.power(obj_disp[obj_idx] * obj_wid[obj_w][obj_keep], 2)
arx_med_sig2 = np.median(arx_sig2)
obj_med_sig2 = np.median(obj_sig2)
if obj_med_sig2 >= arx_med_sig2:
smooth_sig = np.sqrt(obj_med_sig2 - arx_med_sig2) # Ang
smooth_sig_pix = smooth_sig / np.median(arx_disp[arx_idx])
arx_skyspec = arx_skyspec.gauss_smooth(smooth_sig_pix * 2 *
np.sqrt(2 * np.log(2)))
else:
msgs.warn("Prefer archival sky spectrum to have higher resolution")
smooth_sig_pix = 0.
msgs.warn("New Sky has higher resolution than Archive. Not smoothing")
#smooth_sig = np.sqrt(arx_med_sig**2-obj_med_sig**2)
#Determine region of wavelength overlap
min_wave = max(np.amin(arx_skyspec.wavelength.value),
np.amin(obj_skyspec.wavelength.value))
max_wave = min(np.amax(arx_skyspec.wavelength.value),
np.amax(obj_skyspec.wavelength.value))
#Smooth higher resolution spectrum by smooth_sig (flux is conserved!)
# if np.median(obj_res) >= np.median(arx_res):
# msgs.warn("New Sky has higher resolution than Archive. Not smoothing")
#obj_sky_newflux = ndimage.gaussian_filter(obj_sky.flux, smooth_sig)
# else:
#tmp = ndimage.gaussian_filter(arx_sky.flux, smooth_sig)
# arx_skyspec = arx_skyspec.gauss_smooth(smooth_sig_pix*2*np.sqrt(2*np.log(2)))
#arx_sky.flux = ndimage.gaussian_filter(arx_sky.flux, smooth_sig)
# Define wavelengths of overlapping spectra
keep_idx = np.where((obj_skyspec.wavelength.value >= min_wave)
& (obj_skyspec.wavelength.value <= max_wave))[0]
#keep_wave = [i for i in obj_sky.wavelength.value if i>=min_wave if i<=max_wave]
#Rebin both spectra onto overlapped wavelength range
if len(keep_idx) <= 50:
msgs.warn("Not enough overlap between sky spectra")
return None
# rebin onto object ALWAYS
keep_wave = obj_skyspec.wavelength[keep_idx]
arx_skyspec = arx_skyspec.rebin(keep_wave)
obj_skyspec = obj_skyspec.rebin(keep_wave)
# Trim edges (rebinning is junk there)
arx_skyspec.data['flux'][0, :2] = 0.
arx_skyspec.data['flux'][0, -2:] = 0.
obj_skyspec.data['flux'][0, :2] = 0.
obj_skyspec.data['flux'][0, -2:] = 0.
# Normalize spectra to unit average sky count
norm = np.sum(obj_skyspec.flux.value) / obj_skyspec.npix
obj_skyspec.flux = obj_skyspec.flux / norm
norm2 = np.sum(arx_skyspec.flux.value) / arx_skyspec.npix
arx_skyspec.flux = arx_skyspec.flux / norm2
if norm < 0:
msgs.warn("Bad normalization of object in flexure algorithm")
msgs.warn("Will try the median")
norm = np.median(obj_skyspec.flux.value)
if norm < 0:
msgs.warn("Improper sky spectrum for flexure. Is it too faint??")
return None
if norm2 < 0:
msgs.warn(
'Bad normalization of archive in flexure. You are probably using wavelengths '
'well beyond the archive.')
return None
# Deal with bad pixels
msgs.work("Need to mask bad pixels")
# Deal with underlying continuum
msgs.work("Consider taking median first [5 pixel]")
everyn = obj_skyspec.npix // 20
bspline_par = dict(everyn=everyn)
mask, ct = utils.robust_polyfit(obj_skyspec.wavelength.value,
obj_skyspec.flux.value,
3,
function='bspline',
sigma=3.,
bspline_par=bspline_par)
obj_sky_cont = utils.func_val(ct, obj_skyspec.wavelength.value, 'bspline')
obj_sky_flux = obj_skyspec.flux.value - obj_sky_cont
mask, ct_arx = utils.robust_polyfit(arx_skyspec.wavelength.value,
arx_skyspec.flux.value,
3,
function='bspline',
sigma=3.,
bspline_par=bspline_par)
arx_sky_cont = utils.func_val(ct_arx, arx_skyspec.wavelength.value,
'bspline')
arx_sky_flux = arx_skyspec.flux.value - arx_sky_cont
# Consider sharpness filtering (e.g. LowRedux)
msgs.work("Consider taking median first [5 pixel]")
#Cross correlation of spectra
#corr = np.correlate(arx_skyspec.flux, obj_skyspec.flux, "same")
corr = np.correlate(arx_sky_flux, obj_sky_flux, "same")
#Create array around the max of the correlation function for fitting for subpixel max
# Restrict to pixels within maxshift of zero lag
lag0 = corr.size // 2
#mxshft = settings.argflag['reduce']['flexure']['maxshift']
max_corr = np.argmax(corr[lag0 - mxshft:lag0 + mxshft]) + lag0 - mxshft
subpix_grid = np.linspace(max_corr - 3., max_corr + 3., 7)
#Fit a 2-degree polynomial to peak of correlation function. JFH added this if/else to not crash for bad slits
if np.any(np.isfinite(corr[subpix_grid.astype(np.int)])):
fit = utils.func_fit(subpix_grid, corr[subpix_grid.astype(np.int)],
'polynomial', 2)
success = True
max_fit = -0.5 * fit[1] / fit[2]
else:
fit = utils.func_fit(subpix_grid, 0.0 * subpix_grid, 'polynomial', 2)
success = False
max_fit = 0.0
msgs.warn('Flexure compensation failed for one of your objects')
#Calculate and apply shift in wavelength
shift = float(max_fit) - lag0
msgs.info("Flexure correction of {:g} pixels".format(shift))
#model = (fit[2]*(subpix_grid**2.))+(fit[1]*subpix_grid)+fit[0]
return dict(polyfit=fit,
shift=shift,
subpix=subpix_grid,
corr=corr[subpix_grid.astype(np.int)],
sky_spec=obj_skyspec,
arx_spec=arx_skyspec,
corr_cen=corr.size / 2,
smooth=smooth_sig_pix,
success=success)
def time_correlate(self, size1, size2, mode):
np.correlate(self.x1, self.x2, mode=mode)
def calc_offset_plot(filename, resolution=1, scale=1.0):
Load(Filename='/SNS/CORELLI/IPTS-12008/nexus/' + filename + '.nxs.h5',
OutputWorkspace=filename,
LoadMonitors='1',
MonitorsAsEvents='1')
LoadInstrument(Workspace=filename,
Filename='/SNS/users/rwp/CORELLI_Definition.xml')
ScaleX(InputWorkspace=filename + '_monitors',
OutputWorkspace=filename + '_monitors',
Factor=str(scale))
Rebin(InputWorkspace=filename + '_monitors',
OutputWorkspace=filename + '_monitors',
Params=str(resolution))
w = mtd[filename]
sequence = map(
float,
w.getInstrument().getComponentByName(
'correlation-chopper').getStringParameter('sequence')[0].split())
freq = round(
w.getRun().getProperty("BL9:Chop:Skf4:MotorSpeed").timeAverageValue())
delay = w.getRun().getProperty(
"BL9:Chop:Skf4:PhaseTimeDelaySet").timeAverageValue()
print filename, 'MotorSpeed =', freq, 'Hz', 'PhaseTimeDelaySet =', delay, 'uS'
if freq % 60 != 0:
print 'Frequency not a multiple of 60.'
return
sequence2 = sequence
for i in range(int(freq / 60) - 1):
sequence2 = np.append(sequence2, sequence)
m = mtd[filename + '_monitors']
x = m.extractX()[1]
y = m.extractY()[1]
s = np.cumsum(sequence2)
chopper = np.zeros(len(x) - 1)
l = len(chopper)
for n in range(l):
if np.searchsorted(s, (
(x[n] + x[n + 1]) / 2 / 16666.67) * 360. * freq / 60.) % 2 == 1:
chopper[n] = 1
chopper2 = np.zeros(len(chopper) * 2)
chopper2 = np.append(chopper, chopper)
#y=np.roll(chopper,1337)
corr = np.correlate(y, chopper2)
r = np.argmax(corr)
r2 = (x[r] + x[r + 1]) / 2
#print x[-1]
print "Chopper sequence offset = ", r2, "uS"
chopper = np.roll(chopper, r)
chopper[0] = 0
chopper[-1] = 0
lb = 2500
ub = 13500
fig = plt.figure(1, figsize=(14, 6))
plt.title(filename + ' (' + str(freq) + 'Hz)')
#plt.plot(x[lb:ub],y[lb:ub])
#plt.plot(x[lb:ub],chopper[lb:ub]*y.max()*0.5)
plt.plot(x[:-1], y)
plt.plot(x[:-1], chopper * y.max() * 0.5)
plt.xlabel('ToF (uS)')
plt.xlim([lb, ub])
fig.savefig('/SNS/users/rwp/' + filename + '.png')
fig.clf()
#chopper[lb]=0.0
#chopper[ub-1]=0.0
fig = plt.figure(1, figsize=(14, 6))
plt.title(filename + ' (' + str(freq) + 'Hz)')
#plt.fill(x[lb:ub],chopper[lb:ub]*y.max(),color = '0.85')
#plt.plot(x[lb:ub],y[lb:ub])
plt.fill(x[:-1], chopper * y.max(), color='0.85')
plt.plot(x[:-1], y)
plt.xlabel('ToF (uS)')
plt.xlim([lb, ub])
fig.savefig('/SNS/users/rwp/' + filename + '_fill.png')
fig.clf()
lb = 5000
ub = 8000
fig = plt.figure(1, figsize=(14, 6))
plt.title(filename + ' (' + str(freq) + 'Hz)')
plt.plot(x[:-1], y)
plt.plot(x[:-1], chopper * y.max() * 0.5)
plt.xlabel('ToF (uS)')
plt.xlim([lb, ub])
fig.savefig('/SNS/users/rwp/' + filename + '_2.png')
fig.clf()
#chopper[lb]=0.0
#chopper[ub-1]=0.0
fig = plt.figure(1, figsize=(14, 6))
plt.title(filename + ' (' + str(freq) + 'Hz)')
plt.fill(x[:-1], chopper * y.max(), color='0.85')
plt.plot(x[:-1], y)
plt.xlabel('ToF (uS)')
plt.xlim([lb, ub])
fig.savefig('/SNS/users/rwp/' + filename + '_fill_2.png')
fig.clf()
Rebin(InputWorkspace=filename + '_monitors',
OutputWorkspace=filename + '_monitors',
Params='5')
Load(Filename=r'/SNS/CORELLI/IPTS-12008/nexus/CORELLI_306.nxs.h5',
OutputWorkspace='CORELLI_306',
LoadMonitors='1',
MonitorsAsEvents='1')
ScaleX(InputWorkspace='CORELLI_306_monitors',
OutputWorkspace='CORELLI_306_monitors',
Factor=str(scale))
Rebin(InputWorkspace='CORELLI_306_monitors',
OutputWorkspace='CORELLI_306_monitors',
Params='5')
Divide(LHSWorkspace=filename + '_monitors',
RHSWorkspace='CORELLI_306_monitors',
OutputWorkspace='out',
WarnOnZeroDivide='0')
norm = mtd['out']
#ox=norm.extractX()[1]
#oy=norm.extractY()[1]
#lb=3000
#ub=13000
#fig = plt.figure(1,figsize=(14,6))
#plt.title(filename+' ('+str(freq)+'Hz) Normalized')
#plt.fill(x[lb:ub],chopper[lb:ub]*y[lb:ub].max(),color = '0.85')
#plt.plot(ox[lb:ub],oy[lb:ub])
#plt.xlabel('ToF (uS)')
#plt.xlim([lb,ub])
#fig.savefig('/SNS/users/rwp/'+filename+'_norm.png')
#fig.clf()
#return (freq,delay,r2,corr.max())
return (x, y, chopper)
def main():
'''Main Driver routine for pdcompare - reads a collection of spreadsheets with spectra and compares the spectra'''
def show_usage():
'''Show pdcompare usage'''
print('pdcompare file1 file2...')
print(
'pdcompare: Calculates the cross-correlation of spectra calculated using pdielec'
)
print(
' -column column Take the data for cross correlation from column'
)
print(' C Averaged')
print(' D MG ')
print(' E Mie/0.1 ')
print(' F Mie/1.0 ')
print(' G Mie/2.0 ')
print(' H Mie/3.0 ')
print(' -rmax rmax Use rows from rmin to rmax ')
print(
' -rmin rmin Use rows from rmin to rmax (rows start from 2)'
)
print(' -sheet [molar/absorption/real/imaginary/atr]')
print(' -excel filename')
return
# check usage
if len(sys.argv) <= 1:
show_usage()
exit()
sheet_dict = {
"molar": "Molar Absorption",
"absorption": "Absorption",
"real": "Real Permittivity",
"imaginary": "Imaginary Permittivity",
"atr": "ATR Reflectance",
}
# Begin processing of command line
command_line = ' '.join(sys.argv)
tokens = sys.argv[1:]
ntokens = len(tokens)
itoken = 0
names = []
rmin = 0
rmax = 0
column = 'D'
sheet = 'molar'
excefile = ''
while itoken < ntokens:
token = tokens[itoken]
if token == '-rmin':
itoken += 1
rmin = int(tokens[itoken])
elif token == '-rmax':
itoken += 1
rmax = int(tokens[itoken])
elif token == '-excel':
itoken += 1
excelfile = tokens[itoken]
elif token == '-column':
itoken += 1
column = tokens[itoken]
elif token == '-sheet':
itoken += 1
sheet = tokens[itoken]
else:
names.append(token)
itoken += 1
# end loop over tokens
if len(names) <= 0:
print('No files were specified')
show_usage()
exit(1)
print('Comparison based on ', sheet, sheet_dict[sheet])
sheet = sheet_dict[sheet]
print('Comparision of spectra based on column: ', column)
if excelfile != '':
print('Output will be sent to the excel file: ', excelfile)
size = len(names)
columns = []
lags = np.zeros((size, size))
correlations = np.eye(size)
# Use the first file name to define the frequency range
# and the range of rows to be treated
wb1 = load_workbook(filename=names[0], read_only=True)
ws1 = wb1[sheet]
max_rows1 = ws1.max_row
max_cols1 = ws1.max_column
print('Maximum number of rows', max_rows1)
print('Maximum number of cols', max_cols1)
# rmax and rmin are set by the first spread sheet
if rmin == 0:
rmin = 2
if rmax == 0:
rmax = max_rows1
range1 = '{}{}'.format('B', rmin)
range2 = '{}{}'.format('B', rmax)
frequencies = np.array([[i.value for i in j] for j in ws1[range1:range2]])
frequencies = frequencies[:, 0]
freq_scale = (frequencies[1] - frequencies[0])
print('Frequencies', frequencies)
print('Frequency scale', freq_scale)
# Go through the file names and store the required column of numbers
for f1_name in names:
print('Loading work book ', f1_name)
wb1 = load_workbook(filename=f1_name, read_only=True)
# print('Work sheet names for ',f1_name)
# print(wb1.get_sheet_names())
ws1 = wb1[sheet]
range1 = '{}{}'.format(column, rmin)
range2 = '{}{}'.format(column, rmax)
col1 = np.array([[i.value for i in j] for j in ws1[range1:range2]])
# Convert to a 1D array
col1 = col1[:, 0]
# Store the normalised signal
col1 = (col1 - np.mean(col1)) / (np.std(col1) * np.sqrt(len(col1)))
columns.append(col1)
# print(columns[-1])
# Print the new row min and max
print('Final rmin is ', rmin)
print('Final rmax is ', rmax)
for i, col1 in enumerate(columns):
for j, col2 in enumerate(columns):
if i > j:
#print('Correlation',i,j)
# Calculate correlation using same and full seem to produce the same results
correlation = np.correlate(col1, col2, mode='full')
lag = np.argmax(correlation) - (len(correlation) - 1) / 2
#print('Old lag',lag,len(correlation))
lags[i, j] = lag
lags[j, i] = lags[i, j]
correlations[i, j] = np.max(correlation)
correlations[j, i] = correlations[i, j]
#print(correlation)
#print('Lag = ',lags[i,j])
#print('Maximum = ',correlations[i,j])
# end for j,f2
# end for i, f1
print('Lags (steps)')
print(lags)
lags = freq_scale * lags
print('Lags (cm-1)')
print(lags)
print('correlations')
print(correlations)
if excelfile != "":
import xlsxwriter as xlsx
workbook = xlsx.Workbook(excelfile)
worksheet = workbook.add_worksheet('Lags_cm1')
for i, name1 in enumerate(names):
worksheet.write(0, i + 1, name1)
worksheet.write(i + 1, 0, name1)
for j, name2 in enumerate(names):
worksheet.write(j + 1, i + 1, lags[j, i])
worksheet.write(i + 1, j + 1, lags[i, j])
worksheet = workbook.add_worksheet('Correlations')
for i, name1 in enumerate(names):
worksheet.write(0, i + 1, name1)
worksheet.write(i + 1, 0, name1)
for j, name2 in enumerate(names):
worksheet.write(j + 1, i + 1, correlations[j, i])
worksheet.write(i + 1, j + 1, correlations[i, j])
print('Finished write the spread sheet to ', excelfile)
workbook.close()
def autocorrelation(x):
n = x.shape[0]
x = x - x.mean()
r = np.correlate(x, x, mode='full')[-n:]
return r / (x.var() * np.arange(n, 0, -1))
def corr_preamble(self, buf):
corr = np.correlate(buf, self.search_preamble)
return np.argmax(corr)
def test_twosided(self):
'''Test the two-sided version of ``corr``.'''
a = np.arange(10)
c_cpp = asignal.acorr(a, mode='twosided', max_lag=5)
c_np = np.correlate(a, a, mode='full')[::-1][a.size - 6:a.size + 5]
np.testing.assert_allclose(c_cpp, c_np, rtol=RTOL)
def pltxcorr(self,
x,
y,
normed=True,
detrend=detrend_none,
usevlines=True,
maxlags=10,
**kwargs):
"""
call signature::
def xcorr(self, x, y, normed=True, detrend=detrend_none,
usevlines=True, maxlags=10, **kwargs):
Plot the cross correlation between *x* and *y*. If *normed* =
*True*, normalize the data by the cross correlation at 0-th
lag. *x* and y are detrended by the *detrend* callable
(default no normalization). *x* and *y* must be equal length.
Data are plotted as ``plot(lags, c, **kwargs)``
Return value is a tuple (*lags*, *c*, *line*) where:
- *lags* are a length ``2*maxlags+1`` lag vector
- *c* is the ``2*maxlags+1`` auto correlation vector
- *line* is a :class:`~matplotlib.lines.Line2D` instance
returned by :func:`~matplotlib.pyplot.plot`.
The default *linestyle* is *None* and the default *marker* is
'o', though these can be overridden with keyword args. The
cross correlation is performed with :func:`numpy.correlate`
with *mode* = 2.
If *usevlines* is *True*:
:func:`~matplotlib.pyplot.vlines`
rather than :func:`~matplotlib.pyplot.plot` is used to draw
vertical lines from the origin to the xcorr. Otherwise the
plotstyle is determined by the kwargs, which are
:class:`~matplotlib.lines.Line2D` properties.
The return value is a tuple (*lags*, *c*, *linecol*, *b*)
where *linecol* is the
:class:`matplotlib.collections.LineCollection` instance and
*b* is the *x*-axis.
*maxlags* is a positive integer detailing the number of lags to show.
The default value of *None* will return all ``(2*len(x)-1)`` lags.
**Example:**
:func:`~matplotlib.pyplot.xcorr` above, and
:func:`~matplotlib.pyplot.acorr` below.
**Example:**
.. plot:: mpl_examples/pylab_examples/xcorr_demo.py
"""
Nx = len(x)
if Nx != len(y):
raise ValueError('x and y must be equal length')
x = detrend(np.asarray(x))
y = detrend(np.asarray(y))
c = np.correlate(x, y, mode=2)
if normed:
c /= np.sqrt(np.dot(x, x) * np.dot(y, y))
if maxlags is None:
maxlags = Nx - 1
if maxlags >= Nx or maxlags < 1:
raise ValueError('maxlags must be None or strictly '
'positive < %d' % Nx)
lags = np.arange(-maxlags, maxlags + 1)
c = c[Nx - 1 - maxlags:Nx + maxlags]
if usevlines:
a = self.vlines(lags, [0], c, **kwargs)
b = self.axhline(**kwargs)
kwargs.setdefault('marker', 'o')
kwargs.setdefault('linestyle', 'None')
d = self.plot(lags, c, **kwargs)
else:
kwargs.setdefault('marker', 'o')
kwargs.setdefault('linestyle', 'None')
a, = self.plot(lags, c, **kwargs)
b = None
return lags, c, a, b
# header = list(data)
# del header[0]
#
# for name in header:
# data[name + '_sq'] = np.square(data[name])
# test_data[name + '_sq'] = np.square(test_data[name])
#
# data.to_csv('ls_data_squared.csv', index=False)
# test_data.to_csv('ls_test_data_squared.csv', index=False)
#
# """
# Square root data
# """
#
# data = pd.read_csv(path + 'ls_data.csv')
# test_data = pd.read_csv(path + 'ls_test_data.csv')
#
# for name in header:
# data[name + '_sq'] = np.sqrt(data[name])
# test_data[name + '_sq'] = np.sqrt(test_data[name])
#
# data.to_csv('ls_data_sqrt.csv', index=False)
# test_data.to_csv('ls_test_data_sqrt.csv', index=False)
data = pd.read_csv(path + 'test_data.csv')
numbers = np.random.normal(size=(100, 1))
numbers2 = np.random.normal(size=(100, 1))
a = np.correlate(numbers[:, 0], numbers[:, 0], "same")
def _correlate(arr1, arr2):
"""Use ``np.correlate()`` on ``mode='same'`` on two selected arrays
from one input.
"""
return np.correlate(arr1, arr2, mode='same')
for x in range(num_of_vals):
val = x - len(second) + 1
for i in range(len(first)):
if len(second) > (i + val) >= 0:
p = first[i] * second[i + val]
corr[index] += p
index += 1
return corr
vectora = np.array([3, 2, 1])
vectorb = np.array([6, 5, 4])
# testing that np.correlate and our code produces same result
our_xcorr_arr = np.array(cross_correlation(vectora, vectorb))
builtin_xcorr_arr = np.correlate([6, 5, 4], [3, 2, 1], 'full')
np.testing.assert_equal(our_xcorr_arr, builtin_xcorr_arr)
#3
# plotting w/noise
t = np.arange(0, 2, 0.01)
x = np.sin(t * (20 * np.pi)) + np.random.rand(len(t))
fig = plt.figure()
plt.xlabel('Time (Seconds)')
plt.ylabel('Amplitude')
plt.title('sin(20pi*t + rand(1, length(t))')
plt.plot(x[:len(t)])
plt.show()
nmaxlist.append(nmax)
#Thus giving us the best lag time
maxstime = stlist[maxnorm]
smax.append(maxstime)
#SO now we run the unnormalised cross correlation, which requires mode to be set to full
#First changing our time windows
slow1 = t71 + maxstime
shi1 = t71 + maxstime + 800
low2 = t72
hi2 = t72 + 800
#Need to trim the traces and taper them before correlating, using a DPSS this time
taparr1, sigwindow = sleptap(trorig, slow1, shi1, slepper, slepwid)
taparr2, sigwindow2 = sleptap(tr2orig, low2, hi2, slepper, slepwid)
sigunnorm.append(correlate(taparr1, taparr2, mode='full'))
#And we also want to run unnormalised cross correlations on noise windows, placing each one in a list of lists
nois1low1 = t71 + maxstime - 2700
nois1hi1 = t71 + maxstime - 1900
nois1low2 = t72 - 2700
nois1hi2 = t72 - 1900
nois2low1 = t71 + maxstime - 1800
nois2hi1 = t71 + maxstime - 1000
nois2low2 = t72 - 1800
nois2hi2 = t72 - 1000
nois3low1 = t71 + maxstime - 900
nois3hi1 = t71 + maxstime - 100
nois3low2 = t72 - 900
nois3hi2 = t72 - 100
#Performing trimming and tapering on noise windows
cD = abs(cD[::(2**(levels - loop - 1))])
cD = cD - numpy.mean(cD)
# 6) Recombine the signal before ACF
# essentially, each level I concatenate
# the detail coefs (i.e. the HPF values)
# to the beginning of the array
cD_sum = cD[0:cD_minlen] + cD_sum
cA = signal.lfilter([0.01], [1 - 0.99], cA)
cA = abs(cA)
cA = cA - numpy.mean(cA)
cD_sum = cA[0:cD_minlen] + cD_sum
# ACF
correl = numpy.correlate(cD_sum, cD_sum, 'full')
# integration...
if window_ndx == 1:
accum_correl = numpy.zeros(len(correl))
accum_correl = (correl + accum_correl) / len(correl)
#iterate at the end of the loop
window_ndx = window_ndx + 1
samps_ndx = samps_ndx + window_step_size
# Peak detection
accum_zero = len(correl) / 2
correl_final = accum_correl[accum_zero:]
min_ndx = 60. / 220 * (fs / max_decimation)
max_ndx = 60. / 40 * (fs / max_decimation)
def test_default_params(self):
'''Test default parameters.'''
a = np.arange(10)
c_cpp = asignal.acorr(a)
c_np = np.correlate(a, a, mode='full')[::-1][a.size - 1:]
np.testing.assert_allclose(c_cpp, c_np, rtol=RTOL)
def test_twosided(self):
'''Test the two-sided version of ``corr``.'''
for a1, a2 in _data_generator(self.maxLoops, self.maxN):
c_cpp = asignal.corr(a1, a2, mode='twosided')
c_np = np.correlate(a1, a2, mode='full')[::-1]
np.testing.assert_allclose(c_cpp, c_np, rtol=RTOL)
def norm_corr(x, y, mode='valid'):
"""Returns the correlation between two ndarrays, by calling np.correlate in
'same' mode and normalizing the result by the std of the arrays and by
their lengths. This results in a correlation = 1 for an auto-correlation"""
return (np.correlate(x, y, mode) / (np.std(x) * np.std(y) * (x.shape[-1])))
def calc_rv_template(spect,wave,sig, template_dir,bad_intervals,smooth_distance=101, \
gaussian_offset=1e-4,nwave_log=1e4,oversamp=1,fig_fn='',convolve_template=True,\
starnumber=0, plotit=False, save_figures=False, save_dir='./', heliocentric_correction=0.):
"""Compute a radial velocity based on an best fitting template spectrum.
Teff is estimated at the same time.
Parameters
----------
spect: array-like
The reduced WiFeS spectrum
wave: array-like
The wavelengths corresponding to the reduced WiFeS spectrum
template_conv_dir: string
The directory containing template spectra convolved to 0.1 Angstrom resolution
bad_intervals:
List of wavelength intervals where e.g. telluric absorption is bad.
smooth_distance: float
Distance to smooth for "continuum" correction
oversamp: float
Oversampling of the input wavelength scale. The slit is assumed 2 pixels wide.
gaussian_offset: float
Offset for the likelihood function from a Gaussian normalised to 1.
Returns
-------
rv: float
Radial velocity in km/s
rv_sig: float
Uncertainty in radial velocity (NB assumes good model fit)
temp: int
Temperature of model spectrum used for cross-correlation.
"""
if isinstance(template_dir, list):
template_fns = template_dir
else:
template_fns = glob.glob(template_dir)
#ADD IN HELIOCENTRIC CORRECTION SOMEWHERE:
#Make the Heliocentric correction...
#rv += h['RADVEL']
#Interpolate the target and template spectra.
(wave_log, spect_int, sig_int,
template_ints) = interpolate_spectra_onto_log_grid(
spect,
wave,
sig,
template_fns,
bad_intervals=bad_intervals,
smooth_distance=smooth_distance,
convolve_template=convolve_template,
nwave_log=nwave_log)
#Do a cross-correlation to the nearest "spectral pixel" for each template
drv = np.log(wave_log[1] / wave_log[0]) * 2.998e5
rvs = np.zeros(len(template_fns))
peaks = np.zeros(len(template_fns))
for i, template_fn in enumerate(template_fns):
template_int = template_ints[i]
if save_figures == True:
plt.clf()
plt.plot(wave_log, template_int, label='template')
plt.plot(wave_log, spect_int, label='spectrum')
plt.title('Template no.' + str(i + 1))
plt.savefig(save_dir + 'spectrum_vs_template_' +
template_fns[i].split('/')[-1].split('.fits')[0] +
'.png')
plt.clf()
cor = np.correlate(spect_int, template_int, 'same')
##here it's a good idea to limit where the peak Xcorrelation can be, only search for a peak within 1000 of rv=0
## that's and RV range of -778 to 778 for the default spacings in the code
peaks[i] = np.max(cor[int(nwave_log / 2) - 100:int(nwave_log / 2) +
100]) / np.sqrt(np.sum(np.abs(template_int)**2))
rvs[i] = (np.argmax(cor[int(nwave_log / 2) - 100:int(nwave_log / 2) +
100]) - 100) * drv
if starnumber == 0:
print('Correlating Template ' + str(i + 1) + ' out of ' +
str(len(template_fns)))
if starnumber > 0:
print('Correlating Template ' + str(i + 1) + ' out of ' +
str(len(template_fns)) + ' for star ' + str(starnumber))
this_rvs = drv * (np.arange(2 * smooth_distance) - smooth_distance)
correlation = cor[int(nwave_log / 2) - 100:int(nwave_log / 2) +
100] / np.sqrt(np.sum(np.abs(template_int)**2))
best_ind = np.argmax(correlation)
print("best RV for template " + str(i + 1) + " is " +
str(this_rvs[best_ind + 1] + heliocentric_correction))
if save_figures == True:
plt.clf()
plt.plot(this_rvs[1:-1], correlation / np.max(correlation))
plt.title('Correlation_with_template_no.' + str(i + 1))
plt.savefig(save_dir + 'Correlation_with_template_no' +
str(i + 1) + '.png')
plt.clf()
#Find the best cross-correlation.
ix = np.argmax(peaks)
print("BEST TEMPLATE:" + template_fns[ix].split('/')[-1])
#Recompute and plot the best cross-correlation
template_int = template_ints[ix, :]
cor = np.correlate(spect_int, template_int, 'same')
plt.clf()
plt.plot(
drv * (np.arange(2 * smooth_distance) - smooth_distance),
cor[int(nwave_log / 2) - smooth_distance:int(nwave_log / 2) +
smooth_distance])
##store the figure data for later use
outsave = np.array([
drv * (np.arange(2 * smooth_distance) - smooth_distance),
cor[int(nwave_log / 2) - smooth_distance:int(nwave_log / 2) +
smooth_distance]
])
saveoutname = fig_fn.split('.png')[0] + "_figdat.pkl"
pickle.dump(outsave, open(saveoutname, "wb"))
plt.xlabel('Velocity (km/s)')
plt.ylabel('X Correlation')
#plt.show()
fn_ix = template_fns[ix].rfind('/')
#Dodgy! Need a better way to find a name for the template.
fn_ix_delta = template_fns[ix][fn_ix:].find(':')
if fn_ix_delta > 0:
name = template_fns[ix][fn_ix + 1:fn_ix + fn_ix_delta]
name_string = name
#A little messy !!!
if name[0] == 'p':
name = name[1:]
name_string = 'T = ' + name + ' K'
name_string = template_fns[ix][fn_ix + 1:]
#pdb.set_trace()
#Fit for a precise RV... note that minimize (rather than minimize_scalar) failed more
#often for spectra that were not good matches.
modft = np.fft.rfft(template_int)
#res = op.minimize(rv_fit_mlnlike,rvs[ix]/drv,args=(modft,spect_int,sig_int,gaussian_offset))
#x = res.x[0]
#res = op.minimize_scalar(rv_fit_mlnlike,args=(modft,spect_int,sig_int,gaussian_offset),bounds=((rvs[ix]-1)/drv,(rvs[ix]+1)/drv))
#x = res.x
#fval = res.fun
x, fval, ierr, numfunc = op.fminbound(rv_fit_mlnlike,
rvs[ix] / drv - 5 / drv,
rvs[ix] / drv + 5 / drv,
args=(modft, spect_int, sig_int,
gaussian_offset),
full_output=True)
rv = x * drv
rv += heliocentric_correction
##best model
shifted_mod = np.fft.irfft(
modft *
np.exp(-2j * np.pi * np.arange(len(modft)) / len(spect_int) * x))
#pdb.set_trace()
fplus = rv_fit_mlnlike(x + 0.5, modft, spect_int, sig_int, gaussian_offset)
fminus = rv_fit_mlnlike(x - 0.5, modft, spect_int, sig_int,
gaussian_offset)
hess_inv = 0.5**2 / (fplus + fminus - 2 * fval)
if (hess_inv < 0) | (fplus < fval) | (fminus < fval):
#If you get here, then there is a problem with the input spectrum or fitting.
#raise UserWarning
print(
"WARNING: Radial velocity fit did not work - trying again with wider range for: "
+ fig_fn)
x, fval, ierr, numfunc = op.fminbound(rv_fit_mlnlike,
rvs[ix] / drv - 10 / drv,
rvs[ix] / drv + 10 / drv,
args=(modft, spect_int, sig_int,
gaussian_offset),
full_output=True)
rv = x * drv
#print("RV ="+str(rv)+", fval ="+str(fval))
fplus = rv_fit_mlnlike(x + 0.5, modft, spect_int, sig_int,
gaussian_offset)
#print("fplus ="+str(fplus))
fminus = rv_fit_mlnlike(x - 0.5, modft, spect_int, sig_int,
gaussian_offset)
#print("fminus ="+str(fminus))
hess_inv = 0.5**2 / (fplus + fminus - 2 * fval)
#print("hess_inv ="+str(hess_inv))
#import pdb
#pdb.set_trace()
if (hess_inv < 0) | (fplus < fval) | (fminus < fval):
print(
"WARNING: Radial velocity fit did not work, giving up with NaN uncertainty"
)
rv_sig = np.sqrt(hess_inv * nwave_log / len(spect) / oversamp) * drv
plt.title('RV, RV_sigma:' + str(rv) + ',' + str(rv_sig))
plt.savefig(save_dir + 'Best_correlation_temp_' +
template_fns[ix].split('/')[-1] + '.png')
plt.title(name_string +
', RV = {0:4.1f}+/-{1:4.1f} km/s'.format(rv, rv_sig))
if len(fig_fn) > 0:
plt.savefig(fig_fn)
plt.clf()
plt.plot(wave_log, spect_int)
plt.plot(wave_log, shifted_mod)
plt.xlim([6400.0, 6700.0])
plt.title(name_string +
', RV = {0:4.1f}+/-{1:4.1f} km/s'.format(rv, rv_sig))
if len(fig_fn) > 0:
fig_fn_new = fig_fn.split('_xcor.png')[0] + 'fitplot.png'
plt.savefig(fig_fn_new)
#again save the figure data for use later in making nicer plots with IDL
outsave = np.array([wave_log, spect_int, shifted_mod])
saveoutname = fig_fn.split('_xcor.png')[0] + 'fitplot_figdat.pkl'
pickle.dump(outsave, open(saveoutname, "wb"))
# pdb.set_trace()
return rv, rv_sig, template_fns[ix].split('/')[-1]
s = np.cumsum(sequence2)
chopper = np.zeros(len(x) - 1)
chopper_n = np.zeros(len(x) - 1)
l = len(chopper)
for n in range(l):
i = np.searchsorted(s * scale_m2,
((x[n] + x[n + 1]) / 2 / 16666.67) * 360. * freq / 60.)
chopper_n[n] = i
if i % 2 == 1:
chopper[n] = 1
chopper2 = np.zeros(len(chopper) * 2)
chopper2 = np.append(chopper, chopper)
corr = np.correlate(y, chopper2)
r = np.argmax(corr)
r2 = (x[r] + x[r + 1]) / 2
print "Chopper sequence offset = ", r2, "uS"
s = np.cumsum(sequence2)
m3chopper = np.zeros(len(x) - 1)
m3chopper_n = np.zeros(len(x) - 1)
l = len(m3chopper)
for n in range(l):
i = np.searchsorted(s * scale_m3,
((x[n] + x[n + 1]) / 2 / 16666.67) * 360. * freq / 60.)
m3chopper_n[n] = i
if i % 2 == 1:
import matplotlib as mpl
mpl.rc('font', size=14,
weight='bold') #set default font size and weight for plots
plt.figure(figsize=(20, 5))
plt.plot(x)
plt.xlabel("Observation")
plt.title("Time Series of Random Normal Data")
plt.show()
# We can calculate the autocorrelation function using the `np.correlate()` function. Note that this function computes the autocovariance (except it does not divide by $N$), not the autocorrelation, so we have to make some adjustments to convert to autocorrelation. In this case, we simply divide by $N$, because we are using random data drawn from a standard normal distribution, i.e. $\sigma$ = 1.
# In[3]:
# calculate the autocorrelation of x
acorr = np.correlate(x, x / len(x), 'same')
# To visualize the autocorrelation function, we plot it as a function of lag, $\tau$.
# In[4]:
# plot the autocorrelation as a function of lag
fig = plt.figure(figsize=(10, 5))
# define a variable for lag
t = np.arange(0, len(x), 1)
lag = [i - len(t) / 2 for i in t]
# plot acorr
plt.axhline(0)
plt.axvline(0)
def synchronize(signal1=None, signal2=None):
"""Align two signals based on cross-correlation.
Parameters
----------
signal1 : array
First input signal.
signal2 : array
Second input signal.
Returns
-------
delay : int
Delay (number of samples) of 'signal1' in relation to 'signal2';
if 'delay' < 0 , 'signal1' is ahead in relation to 'signal2';
if 'delay' > 0 , 'signal1' is delayed in relation to 'signal2'.
corr : float
Value of maximum correlation.
synch1 : array
Biggest possible portion of 'signal1' in synchronization.
synch2 : array
Biggest possible portion of 'signal2' in synchronization.
"""
# check inputs
if signal1 is None:
raise TypeError("Please specify the first input signal.")
if signal2 is None:
raise TypeError("Please specify the second input signal.")
n1 = len(signal1)
n2 = len(signal2)
# correlate
corr = np.correlate(signal1, signal2, mode='full')
x = np.arange(-n2 + 1, n1, dtype='int')
ind = np.argmax(corr)
delay = x[ind]
maxCorr = corr[ind]
# get synchronization overlap
if delay < 0:
c = min([n1, len(signal2[-delay:])])
synch1 = signal1[:c]
synch2 = signal2[-delay:-delay + c]
elif delay > 0:
c = min([n2, len(signal1[delay:])])
synch1 = signal1[delay:delay + c]
synch2 = signal2[:c]
else:
c = min([n1, n2])
synch1 = signal1[:c]
synch2 = signal2[:c]
# output
args = (delay, maxCorr, synch1, synch2)
names = ('delay', 'corr', 'synch1', 'synch2')
return utils.ReturnTuple(args, names)
def get_y_shift(x_corrected_flat):
max_shift, extent, up_sampling = 50, 20, 10
no_of_vertical_pixels = x_corrected_flat.shape[0]
vertical_indices = np.arange(no_of_vertical_pixels)
shift_horizontal = np.zeros(no_of_vertical_pixels)
shift_horizontal_fit = np.zeros(no_of_vertical_pixels)
weights = np.ones(no_of_vertical_pixels)
display = copy.deepcopy(x_corrected_flat)
plt.figure('Click on the line profile to trace')
plt.imshow(display, cmap='gray', origin='lower')
point = plt.ginput(1)
plt.clf()
plt.cla()
point_as_a_list = list(map(int, point[0]))
reference_row = int(point_as_a_list[1])
x_beg = int(point_as_a_list[0] - extent / 2)
x_end = int(point_as_a_list[0] + extent / 2)
slit_reference = np.mean(display[reference_row - 10:reference_row + 10,
x_beg:x_end],
axis=0)
normalised_slit = (slit_reference -
slit_reference.mean()) / slit_reference.std()
for j in vertical_indices:
this_slit = display[j, x_beg - max_shift:x_end + max_shift]
weights[j] = np.sqrt(this_slit.mean()**2)
this_slit_normalised = (this_slit - this_slit.mean()) / this_slit.std()
correlation = np.correlate(scipy.ndimage.zoom(this_slit_normalised,
up_sampling),
scipy.ndimage.zoom(normalised_slit,
up_sampling),
mode='valid')
shift_horizontal[j] = np.argmax(correlation)
shift_horizontal = shift_horizontal / up_sampling - max_shift
valid_x_points = np.argwhere(np.abs(shift_horizontal) < max_shift)
shifts_for_valid_points = shift_horizontal[valid_x_points]
c = np.polyfit(valid_x_points.ravel(),
shifts_for_valid_points.ravel(),
2,
w=np.nan_to_num(weights)[valid_x_points].ravel())
shift_horizontal_fit = c[0] * vertical_indices**2 + \
c[1] * vertical_indices + c[2]
shift_horizontal_apply = -shift_horizontal_fit
plt.plot(valid_x_points, shifts_for_valid_points, 'k-',
shift_horizontal_fit, 'k-')
plt.show()
return shift_horizontal_apply
# Lee archivo de entrada
fs, d = wavfile.read('Tamara_Laurel_-_Sweet_extract.wav')
d = np.float32(d)
# Simula un canal
u, w_true = simulate_channel(d, 80);
# Extrae segmentos de un segundo de ambas señales
s_start = 8;
d = d[s_start * fs:(s_start + 1) * fs];
u = u[s_start * fs:(s_start + 1) * fs];
# Estima la autocorrelación y correlación cruzada
N_THETA = 6;
r = np.correlate(u, u, 'full') / len(u);
r = r[(len(u) - 1):len(u) - 1 + N_THETA];
R = toeplitz(r);
p = np.correlate(d, u, 'full') / len(u);
p = p[(len(u) - 1):len(u) - 1 + N_THETA];
# Determina el filtro óptimo Wiener
w_wiener = inv(R).dot(p);
# Encuentra el filtro óptimo Wiener con descenso por gradiente
mus = [1e-10, 1e-9, 1e-8, 1e-7]; # Diferentes tamaños de paso
w0 = np.zeros(N_THETA);
for mu in mus:
N = 5000; # Número de iteraciones
def measure_offset_correlate(samples_a: List[float], samples_b: List[float],
sampling_rate: float, mode: str = "parabolic") -> float:
auto = np.correlate(samples_a, samples_b, mode="full")
best_offset = np.argmax(auto)
return interpolate(auto, best_offset, mode) * (1/sampling_rate) # offset in sec
def calculateCorr(s0, s1):
"""
Calculates the correlation between the fourier transformation of the time series
"""
return np.correlate(dct(s0, norm='ortho'), dct(s1, norm='ortho'))
def calc_offset(filename, resolution=1, scale=1.0, plot=False):
Load(Filename='/SNS/CORELLI/IPTS-12008/nexus/' + filename + '.nxs.h5',
OutputWorkspace=filename,
LoadMonitors='1',
MonitorsAsEvents='1')
LoadInstrument(Workspace=filename,
Filename='/SNS/users/rwp/CORELLI_Definition.xml')
ScaleX(InputWorkspace=filename + '_monitors',
OutputWorkspace=filename + '_monitors',
Factor=str(scale))
Rebin(InputWorkspace=filename + '_monitors',
OutputWorkspace=filename + '_monitors',
Params=str(resolution))
w = mtd[filename]
sequence = map(
float,
w.getInstrument().getComponentByName(
'correlation-chopper').getStringParameter('sequence')[0].split())
freq = round(
w.getRun().getProperty("BL9:Chop:Skf4:MotorSpeed").timeAverageValue())
delay = w.getRun().getProperty(
"BL9:Chop:Skf4:PhaseTimeDelaySet").timeAverageValue()
print filename, 'MotorSpeed =', freq, 'Hz', 'PhaseTimeDelaySet =', delay, 'uS'
if freq % 60 != 0:
print 'Frequency not a multiple of 60.'
return
sequence2 = sequence
for i in range(int(freq / 60) - 1):
sequence2 = np.append(sequence2, sequence)
m = mtd[filename + '_monitors']
x = m.extractX()[1]
y = m.extractY()[1]
s = np.cumsum(sequence2)
chopper = np.zeros(len(x) - 1)
l = len(chopper)
for n in range(l):
if np.searchsorted(s, (
(x[n] + x[n + 1]) / 2 / 16666.67) * 360. * freq / 60.) % 2 == 1:
chopper[n] = 1
chopper2 = np.zeros(len(chopper) * 2)
chopper2 = np.append(chopper, chopper)
#y=np.roll(chopper,1337)
corr = np.correlate(y, chopper2)
r = np.argmax(corr)
r2 = (x[r] + x[r + 1]) / 2
#print x[-1]
print "Chopper sequence offset = ", r2, "uS"
chopper = np.roll(chopper, r)
if plot:
plt.plot(x[:-1], y)
plt.plot(x[:-1], chopper * y.max() * 0.5)
plt.show()
return (freq, delay, r2, corr.max())
def SSA(s, m, rtnRC=1, pad='linear', **kwargs):
"""Implement Singular Spectrum Analysis for pandas Series
Parameters
----------
s : pandas.Series
Input data, in series or single columns of a data frame. Any necessary
normalization (e.g. for anomolies from baseline) should be done.
m : int
Order or number of time lags to calculate over. A larger number gives
more smoothing in the first returned column.
**Optionals**
rtnRC : int
Number of reconstructed principles to return. Set to None to get all
of them. Most smoothing is done in first returned column, but other
columns may be useful to see periodicities.
pad : str ['linear' | 'mirror' | None] default 'linear'
Type of padding to use. If no padding desired, use ``None``.
Returns
-------
pandas.DataFrame containing the reconstructed principles (or just the first
one if allRC is True).
Notes
-----
Computes the first m principle components (PCs) using Singular Spectrum
Analysis. Most of the trend information is in the first reconstructed PC
(RC), so the function returns just the first RC by default. This RC will
look like smoothed data, and the amount of smoothing is determined by how
large `m` is. Note that padding is added to prevent a drop towards 0 at
beginning and end.
Examples
--------
from:
http://environnement.ens.fr/IMG/file/DavidPDF/SSA_beginners_guide_v9.pdf
::
%precision 2
import pandas as pd
import numpy as np
import matplotlib as plt
y = [1.0135518, - 0.7113242, - 0.3906069, 1.565203, 0.0439317,
- 1.1656093, 1.0701692, 1.0825379, - 1.2239744, - 0.0321446,
1.1815997, - 1.4969448, - 0.7455299, 1.0973884, - 0.2188716,
- 1.0719573, 0.9922009, 0.4374216, - 1.6880219, 0.2609807]
rc = SSA(pd.Series(y), 4, allRC=None, pad=None)
plt.plot(rc)
plt.show()
rc[0].values.flatten()
array([ 0.3 , -0.31, -0.33, 0.82, -0.06, -0.82, 0.54, 0.53, -0.88,
0.07, 0.83, -0.66, -0.36, 0.83, -0.18, -0.72, 0.63, 0.23,
-0.68, 0.24])
"""
if pad:
ps = Padded(s, m * 2, ptype=pad)
y = np.array(ps.values)
else:
y = np.array(s.values)
n = len(y)
mr = range(m)
ys = np.ones((n, m)) # time shifted y-values
for i in mr:
ys[:n - i, i] = y[i:]
# get autocorrelation at first `order` time lags
cor = np.correlate(y, y, mode='full')[n - 1:n - 1 + m] / n
# make toeplitz matrix (diagonal, symmetric)
c = np.array([[cor[abs(i - j)] for i in mr] for j in mr])
# get eigenvalues and eigenvectors
lam, rho = np.linalg.eig(c)
pc = ys.dot(rho) # principle components
# reconstruct the components in proper time frame
if rtnRC is None:
desired = m
else:
desired = min(m, rtnRC)
rc = np.zeros((n, desired))
for j in range(desired):
z = np.zeros((n, m))
for i in mr: # make time shifted principle component matrix
z[i:, i] = pc[:n - i, j]
rc[:, j] = z.dot(rho[:, j]) / m
if pad:
rc = rc[m:n - m]
return pd.DataFrame(rc, index=s.index)
def update_figure(event):
if icontainer.stage == 0 and icontainer.i < 7 and icontainer.drawdist:
i = icontainer.i
icontainer.drawdist = False
# Remove previous lines
for l in icontainer.remove_lines:
ax.lines.remove(l)
icontainer.remove_lines = []
if i % 2 == 0:
line = ax.axhline(y=tt[i, 1], linestyle='--', color='k')
icontainer.remove_lines.append(line)
line, = ax.plot(Y1,
tt[i, 1] +
stats.norm.pdf(Y1,
loc=y1 + r * (tt[i, 1] - y2),
scale=np.sqrt((1 - r**2))),
color='#377eb8')
icontainer.remove_lines.append(line)
if i == 0:
ax.legend((el_legend, samp_legend, pdfline_legend),
('90% HPD', 'Starting point',
r'Conditional density given $\theta_2$'),
numpoints=1,
loc='lower right')
else:
ax.legend((el_legend, samp_legend, pdfline_legend),
('90% HPD', 'Samples from the chain',
r'Conditional density given $\theta_2$'),
loc='lower right')
else:
line = ax.axvline(x=tt[i, 0], linestyle='--', color='k')
icontainer.remove_lines.append(line)
line, = ax.plot(tt[i, 0] + stats.norm.pdf(
Y2, loc=y2 + r * (tt[i, 0] - y1), scale=np.sqrt((1 - r**2))),
Y2,
color='#377eb8')
icontainer.remove_lines.append(line)
ax.legend((el_legend, samp_legend, pdfline_legend),
('90% HPD', 'Samples from the chain',
r'Conditional density given $\theta_1$'),
loc='lower right')
fig.canvas.draw()
elif icontainer.stage == 0 and icontainer.i < 7 and not icontainer.drawdist:
icontainer.i += 1
i = icontainer.i
if i == 6:
icontainer.stage += 1
icontainer.drawdist = True
sampi, = ax.plot(tt[i, 0],
tt[i, 1],
'o',
markerfacecolor='none',
markeredgecolor='#377eb8')
icontainer.samps.append(sampi)
if i == 1:
ax.legend((el_legend, samp_legend, pdfline_legend),
('90% HPD', 'Samples from the chain',
r'Conditional density given $\theta_2$'),
loc='lower right')
fig.canvas.draw()
elif icontainer.stage == 1:
icontainer.stage += 1
for l in icontainer.remove_lines:
ax.lines.remove(l)
icontainer.remove_lines = []
ax.legend((el_legend, samp_legend),
('90% HPD', 'Samples from the chain'),
loc='lower right')
fig.canvas.draw()
elif icontainer.stage == 2:
icontainer.stage += 1
for s in icontainer.samps:
ax.lines.remove(s)
icontainer.samps = []
line, = ax.plot(tt[:icontainer.i + 1, 0],
tt[:icontainer.i + 1, 1],
color='#377eb8')
icontainer.samps.append(line)
line, = ax.plot(tt[:icontainer.i + 1:2, 0],
tt[:icontainer.i + 1:2, 1],
'o',
markerfacecolor='none',
markeredgecolor='#377eb8')
icontainer.samps.append(line)
ax.legend((el_legend, chain_legend), ('90% HPD', 'Markov chain'),
loc='lower right')
fig.canvas.draw()
elif icontainer.stage == 3:
icontainer.stage += 1
# modify helper text
htext.set_text('Gibbs sampling\npress `q` to skip animation')
# start the timer
anim_thread.start()
elif icontainer.stage == 4 and event.key == 'q':
# stop the animation
stop_anim.set()
elif icontainer.stage == 5:
icontainer.stage += 1
for s in icontainer.samps:
ax.lines.remove(s)
icontainer.samps = []
# remove helper text
icontainer.itertext.remove()
line, = ax.plot(tt[:burnin, 0], tt[:burnin, 1], color='m')
icontainer.samps.append(line)
line, = ax.plot(tt[:burnin:2, 0],
tt[:burnin:2, 1],
'o',
markerfacecolor='none',
markeredgecolor='m')
icontainer.samps.append(line)
line, = ax.plot(tt[burnin:nanim + 1, 0],
tt[burnin:nanim + 1, 1],
color='#377eb8')
icontainer.samps.append(line)
line, = ax.plot(tt[burnin:nanim + 1:2, 0],
tt[burnin:nanim + 1:2, 1],
'o',
markerfacecolor='none',
markeredgecolor='#377eb8')
icontainer.samps.append(line)
ax.legend((el_legend, chain_legend, burnchain_legend),
('90% HPD', 'Markov chain', 'warm-up'),
loc='lower right')
fig.canvas.draw()
elif icontainer.stage == 6:
icontainer.stage += 1
for s in icontainer.samps:
ax.lines.remove(s)
icontainer.samps = []
line, = ax.plot(tt[burnin:nanim + 1:2, 0],
tt[burnin:nanim + 1:2, 1],
'o',
markerfacecolor='none',
markeredgecolor='#377eb8')
icontainer.samps.append(line)
ax.legend((el_legend, samp_legend),
('90% HPD', 'samples from the chain after warm-up'),
loc='lower right')
fig.canvas.draw()
elif icontainer.stage == 7:
icontainer.stage += 1
for s in icontainer.samps:
ax.lines.remove(s)
icontainer.samps = []
points = ax.scatter(tt[burnin::2, 0],
tt[burnin::2, 1],
10,
alpha=0.5,
color='#377eb8')
icontainer.samps.append(points)
ax.legend((el_legend, points),
('90% HPD', '950 samples from the chain'),
loc='lower right')
fig.canvas.draw()
elif icontainer.stage == 8:
icontainer.stage += 1
fig.clear()
indexes = np.arange(burnin, M, 2)
samps = tt[indexes]
ax1 = fig.add_subplot(3, 1, 1)
ax1.axhline(y=0, linewidth=1, color='gray')
line1, line2, = ax1.plot(indexes / 2, samps, linewidth=1)
ax1.legend((line1, line2), (r'$\theta_1$', r'$\theta_2$'))
ax1.set_xlabel('iteration')
ax1.set_title('trends')
ax1.set_xlim([burnin / 2, 1000])
ax2 = fig.add_subplot(3, 1, 2)
ax2.axhline(y=0, linewidth=1, color='gray')
ax2.plot(indexes / 2,
np.cumsum(samps, axis=0) / np.arange(1,
len(samps) + 1)[:, None],
linewidth=1.5)
ax2.set_xlabel('iteration')
ax2.set_title('cumulative average')
ax2.set_xlim([burnin / 2, 1000])
ax3 = fig.add_subplot(3, 1, 3)
maxlag = 20
sampsc = samps - np.mean(samps, axis=0)
acorlags = np.arange(maxlag + 1)
ax3.axhline(y=0, linewidth=1, color='gray')
for i in [0, 1]:
t = np.correlate(sampsc[:, i], sampsc[:, i], 'full')
t = t[-len(sampsc):-len(sampsc) + maxlag + 1] / t[-len(sampsc)]
ax3.plot(acorlags, t)
ax3.set_xlabel('lag')
ax3.set_title('estimate of the autocorrelation function')
fig.suptitle('Gibbs sampling - press any key to continue...',
fontsize=18)
fig.subplots_adjust(hspace=0.6)
fig.canvas.draw()
elif icontainer.stage == 9:
icontainer.stage += 1
fig.clear()
indexes = np.arange(burnin, M, 2)
samps = tt[indexes]
nsamps = np.arange(1, len(samps) + 1)
ax1 = fig.add_subplot(1, 1, 1)
ax1.axhline(y=0, linewidth=1, color='gray')
line1, line2, = ax1.plot(indexes / 2,
np.cumsum(samps, axis=0) / nsamps[:, None],
linewidth=1.5)
er1, = ax1.plot(indexes / 2,
1.96 / np.sqrt(nsamps / 4),
'k--',
linewidth=1)
ax1.plot(indexes / 2, -1.96 / np.sqrt(nsamps / 4), 'k--', linewidth=1)
er2, = ax1.plot(indexes / 2, 1.96 / np.sqrt(nsamps), 'k:', linewidth=1)
ax1.plot(indexes / 2, -1.96 / np.sqrt(nsamps), 'k:', linewidth=1)
ax1.set_xlabel('iteration')
ax1.set_title('Gibbs sampling\ncumulative average')
ax1.legend(
(line1, line2, er1, er2),
(r'$\theta_1$', r'$\theta_2$', '95% interval for MCMC error',
'95% interval for independent MC'))
ax1.set_xlim([burnin / 2, 1000])
ax1.set_ylim([-2, 2])
fig.canvas.draw()
def smart_align(template, observations):
aligned_obs = np.zeros((observations.shape[1], observations.shape[0]))
aligned_obs_norm = np.zeros((observations.shape[1], observations.shape[0]))
for n in tqdm_notebook(range(observations.shape[1])):
#print('Aligning observation',n+1,'of',observations.shape[1])
obs = observations[:, n] / np.max(observations[:, n])
bins = template.shape[0]
first_try = 0
no_scale_incr = 100
template_noise_list = []
obs_noise_list = []
bins_with_signal_test = []
list_of_means = []
list_of_stds = []
list_list_means = []
list_list_stds = []
list_list_no_points = []
min_arg_list = []
min_val_list = []
std_min_val_list = []
mean_times_points = []
# Correlate to find rough alignment and then start with a fractional offset before rolling the observations past each other
# make sure observations don't span the edge
# rotate template to put peak at 1/4
peak_bin = np.argmax(obs)
initial_shift = int(bins / 4) - peak_bin
obs = np.roll(obs, initial_shift)
xcorr = np.correlate(template, obs, "full")
lag = np.argmax(xcorr)
obs = np.roll(obs, lag)
# obs = np.roll(obs,-int(bins/7.0))
# Break the template into 8 parts and find the rms of each. Then find the smallest. Do with the observation too.
for z in range(8):
template_noise_list.append(
np.std(template[z * int(bins / 8.0):(z + 1) *
int(bins / 8.0)]))
obs_noise_list.append(
np.std(obs[z * int(bins / 8.0):(z + 1) * int(bins / 8.0)]))
# Find the approximate peaks of template and observation so give an idea of the range over which to scale the observations.
temp_peak = np.mean(np.sort(template)[-10:])
obs_peak = np.mean(np.sort(obs)[-10:])
rough_scale = temp_peak / obs_peak
rough_scale_upp = rough_scale * 1.1
rough_scale_low = rough_scale * 0.9
scale_incr = (rough_scale_upp - rough_scale_low) / no_scale_incr
# Keep a copy of the observation in its original state.
obs_original = obs[:]
# Phase shift over all bins.
for roll in range(int(bins / 3.5)):
# if (roll+1)%100 == 0:
# print( 'Bin',roll+1,'out of',int(bins/3.5)
closest_to_one = 1e10
bins_with_signal = []
bins_with_signal_test = []
list_mean_each_scale_shift = []
list_std_each_scale_shift = []
list_points_each_scale_shift = []
# No shift needed for first try.
if roll != 0:
obs = np.roll(obs, 1)
# If the level is too low in either template or observation, don't include the bin in further analysis.
for r in range(bins):
#print( r,obs[r],obs_peak,np.min(obs_noise_list),template[r],temp_peak,np.min(template_noise_list)
if obs[r] > obs_peak / 3. and template[r] > temp_peak / 3.:
bins_with_signal.append(r)
bins_with_signal_test.append(1)
else:
bins_with_signal_test.append(0)
# For each roll, only proceed if there are more than 20 bins that have signal in them.
if len(bins_with_signal) >= 10.0:
# Loop over each of the 100 scale attempts to find which is the best fit.
for s in range(no_scale_incr):
if s == 0:
first_scale_val = rough_scale_low + s * scale_incr
if s == no_scale_incr - 1:
last_scale_val = rough_scale_low + s * scale_incr
diff = []
escape = 0
scaled_obs = obs * (rough_scale_low + s * scale_incr)
# Loop over all the bins with signal and find the absolute difference between template and observation.
for each in bins_with_signal:
diff.append(abs(scaled_obs[each] - template[each]))
# Save this difference list before outliers are removed.
orig_diff = diff[:]
# Remove outliers (over 2 sigma) and re-evaluate the mean. If mean doesn't change much, exit the loop. Record the last set of data that had outliers removed.
while escape == 0:
diff_mean = np.mean(diff)
diff_std = np.std(diff)
outlier_list = []
for y in range(len(diff)):
if abs(diff[y] - diff_mean) > 2 * diff_std:
outlier_list.append(y)
latest_diff_list = diff[:]
for index in sorted(outlier_list, reverse=True):
del diff[index]
if np.mean(diff) == 0:
escape = 1
diff = latest_diff_list[:]
else:
if np.mean(diff) / diff_mean < 1.001 and np.mean(
diff
) / diff_mean > 0.999 and first_try == 1:
escape = 1
diff = latest_diff_list[:]
first_try = 1
# In lists - For any phase, record the mean and std and number of data points after all outliers removed at each scale attempt.
list_mean_each_scale_shift.append(abs(np.mean(diff)))
list_std_each_scale_shift.append(np.std(diff))
list_points_each_scale_shift.append(len(diff))
# Make a list containing the above lists. 1 per phase shift.
list_list_means.append(list_mean_each_scale_shift)
list_list_stds.append(list_std_each_scale_shift)
list_list_no_points.append(list_points_each_scale_shift)
else:
# If the number of bins with signal is not high enough, just put 1s into the list of lists. We will find minimum later, and 1 is >>.
list_list_means.append([1] * no_scale_incr)
list_list_stds.append([1] * no_scale_incr)
list_list_no_points.append([1] * no_scale_incr)
# Calculate the mean / number of points. This should be minimised to find the best fit.
for h in range(len(list_list_means)):
for y in range(len(list_list_means[0])):
mean_times_points.append(list_list_means[h][y] /
list_list_no_points[h][y])
min_arg_final = np.argmin(mean_times_points)
the_scale = min_arg_final % no_scale_incr
the_roll = min_arg_final / no_scale_incr
min_val_final = np.min(mean_times_points)
std_min_val_final = list_list_stds[int(
min_arg_final / no_scale_incr)][int(min_arg_final % no_scale_incr)]
# Return the aligned and scaled observations.
aligned_obs_norm[n, :] = np.roll(
obs_original * (rough_scale_low + the_scale * scale_incr),
int(the_roll))
# obs = observations[:,n]/np.max(observations[:,n])
aligned_obs[n, :] = np.roll(obs_original * np.max(observations[:, n]),
int(the_roll))
aligned_obs = np.transpose(aligned_obs)
aligned_obs_norm = np.transpose(aligned_obs_norm)
return aligned_obs_norm, aligned_obs
plt.plot(
np.mean(temperature[nlabel * (house_i - 1):nlabel * house_i]),
np.mean(energy_production[nlabel * (house_i - 1):nlabel * house_i]),
'.')
plt.subplot(1, 3, 3)
plt.plot(
np.mean(daylight[nlabel * (house_i - 1):nlabel * house_i]),
np.mean(energy_production[nlabel * (house_i - 1):nlabel * house_i]),
'.')
# raw_input('Here.')
# plt.draw()
mode = 'full'
tt = np.correlate(temperature[nlabel * (house_i - 1):nlabel * house_i],
temperature[nlabel * (house_i - 1):nlabel * house_i],
mode=mode)
# tt = tt[len(tt)/2:]
dd = np.correlate(daylight[nlabel * (house_i - 1):nlabel * house_i],
daylight[nlabel * (house_i - 1):nlabel * house_i],
mode=mode)
dd = dd[len(dd) / 2:]
ee = np.correlate(
energy_production[nlabel * (house_i - 1):nlabel * house_i],
energy_production[nlabel * (house_i - 1):nlabel * house_i],
mode=mode)
ee = ee[len(ee) / 2:]
td = np.correlate(temperature[nlabel * (house_i - 1):nlabel * house_i],
daylight[nlabel * (house_i - 1):nlabel * house_i],
mode=mode)
td = td[len(td) / 2:]
def autocorr(x):
result = numpy.correlate(x, x, mode='full')
return result[result.size / 2:]
