def find_dominant(seg):
"""Finds the dominant frequency of a given ThinkDSP Wave object
by Meg
Arguments
---------
seg: ThinkDSP Wave Obj
a segment of Wave
Returns
-------
dominant_freq: a Number
the dominant frequency, relative to the framerate parameter specified in the Wave.
"""
lags, corrs = autocorr(seg)
#argmax() gedom_freq_index = numpy.argmax(corrs[1:len(corrs)])+1
dom_freq_index = argmax(corrs[1:len(corrs)])+1
period = dom_freq_index/seg.framerate
dom_freq = 1/period
return dom_freq
def dfluct_auto(energy, eels):
"""
Computes the charge-density autocorrelation function using the eels
spectrum
Args
----
energy : array-like
energy scale
eels : array-like
electron energy-loss spectrum
Returns
-------
array-like
real part of autocorrelation function associated with charge-density
fluctuations
"""
# Following prefactor has to do with units in equation, i.e. converting
# to eV and incorporating Planck constant
return float(6.582118487e-16) * autocorr(dfluct_spectrum(energy, eels),
method="pow").real
nTlp[l,:] = np.real(Tlp[l,:])/np.sqrt(cl[l])
else:
nTlp[l,:] = np.real(Tlp[l,:])/np.sqrt(cl[l])
nTlp = nTlp[2:,:]
t1 = time.time()-t0
ftxt.write(' - CPU time for computing normalized T_{l,p}: %.4f\n' % t1)
# Autocorrelation and AC discrepancy
thres = 2/np.sqrt(L-1); # threshold for autocorrelation
# compute autocorrelation and AC discrepancy
acf = np.zeros((lag_acd+1,Npix_acd),dtype='float64')
acd = np.zeros(Npix_acd,dtype='float64')
t0 = time.time()
for i in range(Npix_acd):
acf[:,i] = autocorr(nTlp[:,i],lag_acd)
acd[i] = np.sum(np.maximum(np.absolute(acf[1:,i])-thres,0))
t2 = time.time()-t0
ftxt.write(' - CPU time for computing AC discrepancy: %.4f\n' % t2)
# find max AC discrepancy
jmax = np.argmax(acd)
nTlp_mx = nTlp[:,jmax]
acd_mx = acd[jmax]
mx_coor = hp.pix2vec(Nside_acd,jmax)
ftxt.write(' - total CPU time for computing AC discrepancy: %.4f\n' % (t1+t2))
#%% save data
ftxt.write(' - saving data: acd,jmax,nalp_mx,mx_coor\n')
sio.savemat(sv_acd, mdict={'acd':acd,'jmax':jmax,'nTlp_mx':nTlp_mx,'mx_coor':mx_coor})
import numpy as np
import autocorr
print "Sandvik:"
print autocorr.autocorr(autocorr.readfromfile('Sand.csv'))
print "ABB: "
print autocorr.autocorr(autocorr.readfromfile('ABB.csv'))
print "Fing: "
print autocorr.autocorr(autocorr.readfromfile('Fing.csv'))
print "HM: "
print autocorr.autocorr(autocorr.readfromfile('HM.csv'))
print autocorr.smallchanges(autocorr.readfromfile('Sand.csv'))
def n_best_slice_widths(image,n): xcor_signal = autocorr(abs(diff(abs(array(column_distances(image)))))) indexes = range(len(xcor_signal)) return nlargest(n,indexes[1:],key=lambda i: xcor_signal[i])
def test_autoccorr_output_type_method_pos(self):
"""autocorr should return type np.ndarray
"""
self.assertIsInstance(autocorr(self.input_data, method="pow"),
np.ndarray)