The following are code examples for showing how to use . They are extracted from open source Python projects. You can vote up the examples you like or vote down the exmaples you don’t like. You can also save this page to your account.

Example 1

def get_timepixel_g2( oned_count ):

n = len( oned_count )

norm = ( np.arange( n, 0, -1) *

np.array( [np.average(oned_count[i:]) for i in range( n )] ) *

np.array( [np.average(oned_count[0:n-i]) for i in range( n )] ) )

return np.correlate(oned_count, oned_count, mode = 'full')[-n:]/norm

#########################################

Example 2

def get_timepixel_g2( oned_count ):

n = len( oned_count )

norm = ( np.arange( n, 0, -1) *

np.array( [np.average(oned_count[i:]) for i in range( n )] ) *

np.array( [np.average(oned_count[0:n-i]) for i in range( n )] ) )

return np.correlate(oned_count, oned_count, mode = 'full')[-n:]/norm

#########################################

Example 3

def get_timepixel_g2( oned_count ):

n = len( oned_count )

norm = ( np.arange( n, 0, -1) *

np.array( [np.average(oned_count[i:]) for i in range( n )] ) *

np.array( [np.average(oned_count[0:n-i]) for i in range( n )] ) )

return np.correlate(oned_count, oned_count, mode = 'full')[-n:]/norm

#########################################

Example 4

def gi_correlation(self, p_gi_len, p_symbol_len, signal):

''' Mode Erkennung '''

corr_delay = p_symbol_len - p_gi_len

# Verzögertes Signal / Delay

corr_A = signal[0:p_gi_len-1];

corr_B = signal[corr_delay:len(signal)-1]

# Normierung

corr_A = corr_A / np.sqrt( np.sum(np.square(np.abs(corr_A))) )

corr_B = corr_B / np.sqrt( np.sum(np.square(np.abs(corr_B))) )

# Korrelation

erg_corr = np.correlate(corr_A,corr_B)

return erg_corr[0]

Example 5

def crosscorr(data, fb, fs, pairs=None):

"""

Parameters

----------

Returns

-------

"""

n_channels, n_samples = np.shape(data)

filtered, _, _ = analytic_signal(data, fb, fs)

r = np.zeros([n_channels, n_channels], dtype=np.float32)

for i in range(n_channels):

for ii in range(n_channels):

r[i, ii] = np.correlate(filtered[i, ], filtered[ii, ], mode='valid')

return r

Example 6

def estimate_range(tx,rx,fs,quiet=False):

"""

tx: the known, noise-free, undelayed transmit signal (bistatic radars agree beforehand on the psuedorandom sequence)

rx: the noisy, corrupted, interference, jammed signal to estimate distance from

fs: baseband sample frequency

"""

Rxy = np.correlate(tx, rx, 'full')

lags = np.arange(Rxy.size) - Rxy.size // 2

pklag = lags[Rxy.argmax()]

distest_m = -pklag / fs / 2 * c

mR = abs(Rxy) # magnitude of complex cross-correlation

if not quiet and figure is not None:

ax = figure().gca()

ax.plot(lags,mR)

ax.plot(pklag,mR[mR.argmax()], color='red', marker='*')

ax.set_title('cross-correlation of receive waveform with transmit waveform')

ax.set_ylabel('$|R_{xy}|$')

ax.set_xlabel('lags')

ax.set_xlim(pklag-100,pklag+100)

return distest_m

Example 7

def procchunk(rx, tx, P:dict):

if P['rxfn'] is not None:

rx = scipy.signal.resample_poly(rx,

P['resample'].numerator,

P['resample'].denominator)

fs = P['txfs']

# %% resamples parameters

NrxPRI = int(fs * P['pri']) # Number of RX samples per PRI (resampled)

assert NrxPRI >= tx.size,'PRI must be longer than chirp length!'

NrxChirp = rx.size // NrxPRI # number of complete PRIs received in this data

assert NrxChirp == P['Nchirp']

Rxy = 0.

for i in range(P['Nchirp']):

r = rx[i*NrxPRI:(i+1)*NrxPRI]

Rxy += np.correlate(tx, r,'same')

if P['verbose']:

plotxcor(Rxy, fs)

draw()

pause(0.1)

return Rxy

Example 8

def find_audio_period(aclip, t_min=.1, t_max=2, t_res=.01):

""" Finds the period, in seconds of an audioclip.

The beat is then given by bpm = 60/T

t_min and _tmax are bounds for the returned value, t_res

is the numerical precision

"""

chunksize = int(t_res*aclip.fps)

chunk_duration = 1.0*chunksize/aclip.fps

# v denotes the list of volumes

v = np.array([(c**2).sum() for c in

aclip.iter_chunks(chunksize)])

v = v-v.mean()

corrs = np.correlate(v, v, mode = 'full')[-len(v):]

corrs[:int(t_min/chunk_duration)]=0

corrs[int(t_max/chunk_duration):]=0

return chunk_duration*np.argmax(corrs)

Example 9

def extractMseq(cover, stego, secret_length, m, tau=1):

u"""Extract secret informations by spread spectrum using m-sequence.

@param cover : cover data (2 dimensional np.ndarray)

@param stego : stego data (2 dimension np.ndarray)

@param secret_length : length of secret information

@param m : M-Sequence

@param tau : embed shift interval

@return secret : extracted secret information

"""

cover = _image2vrctor(cover)

stego = _image2vrctor(stego)

m_length = len(m)

data = stego - cover

data = data[:m_length:tau]

secret_data = correlate(m, data, cycle=CYCLE)

center = ((m_length-1)*2+1)//2

secret_data = secret_data[center:center+secret_length]

secret_data = list(map(_checkData, secret_data))

return secret_data

Example 10

def _convolve_or_correlate(f, a, v, mode, propagate_mask):

"""

Helper function for ma.correlate and ma.convolve

"""

if propagate_mask:

# results which are contributed to by either item in any pair being invalid

mask = (

f(getmaskarray(a), np.ones(np.shape(v), dtype=np.bool), mode=mode)

| f(np.ones(np.shape(a), dtype=np.bool), getmaskarray(v), mode=mode)

)

data = f(getdata(a), getdata(v), mode=mode)

else:

# results which are not contributed to by any pair of valid elements

mask = ~f(~getmaskarray(a), ~getmaskarray(v))

data = f(filled(a, 0), filled(v, 0), mode=mode)

return masked_array(data, mask=mask)

Example 11

def plot_correlate():

"""Plots the autocorrelation function computed by numpy.

"""

wave = thinkdsp.read_wave('28042__bcjordan__voicedownbew.wav')

wave.normalize()

segment = wave.segment(start=0.2, duration=0.01)

lags, corrs = autocorr(segment)

corrs2 = numpy.correlate(segment.ys, segment.ys, mode='same')

thinkplot.plot(corrs2)

thinkplot.config(xlabel='lag',

ylabel='correlation',

xlim=[0, len(corrs2)])

thinkplot.save(root='autocorr9')

N = len(corrs2)

half = corrs2[N//2:]

lengths = range(N, N//2, -1)

half /= lengths

half /= half[0]

Example 12

def find_audio_period(aclip, t_min=.1, t_max=2, t_res=.01):

""" Finds the period, in seconds of an audioclip.

The beat is then given by bpm = 60/T

t_min and _tmax are bounds for the returned value, t_res

is the numerical precision

"""

chunksize = int(t_res*aclip.fps)

chunk_duration = 1.0*chunksize/aclip.fps

# v denotes the list of volumes

v = np.array([(c**2).sum() for c in

aclip.iter_chunks(chunksize)])

v = v-v.mean()

corrs = np.correlate(v, v, mode = 'full')[-len(v):]

corrs[:int(t_min/chunk_duration)]=0

corrs[int(t_max/chunk_duration):]=0

return chunk_duration*np.argmax(corrs)

Example 13

def find_audio_period(aclip, t_min=.1, t_max=2, t_res=.01):

""" Finds the period, in seconds of an audioclip.

The beat is then given by bpm = 60/T

t_min and _tmax are bounds for the returned value, t_res

is the numerical precision

"""

chunksize = int(t_res*aclip.fps)

chunk_duration = 1.0*chunksize/aclip.fps

# v denotes the list of volumes

v = np.array([(c**2).sum() for c in

aclip.iter_chunks(chunksize)])

v = v-v.mean()

corrs = np.correlate(v, v, mode = 'full')[-len(v):]

corrs[:int(t_min/chunk_duration)]=0

corrs[int(t_max/chunk_duration):]=0

return chunk_duration*np.argmax(corrs)

Example 14

def estimated_autocorrelation(x):

n = len(x)

variance = x.var()

x = x - x.mean()

r = np.correlate(x, x, mode='full')[-n:]

assert np.allclose(r, np.array([(x[:n - k] * x[-(n - k):]).sum() for k in range(n)]))

result = r / (variance * (np.arange(n, 0, -1)))

return result

Example 15

def _delta(x, window):

return np.correlate(x, window, mode="same")

Example 16

def test_float(self):

self._setup(np.float)

z = np.correlate(self.x, self.y, 'full')

assert_array_almost_equal(z, self.z1)

z = np.correlate(self.x, self.y[:-1], 'full')

assert_array_almost_equal(z, self.z1_4)

z = np.correlate(self.y, self.x, 'full')

assert_array_almost_equal(z, self.z2)

z = np.correlate(self.x[::-1], self.y, 'full')

assert_array_almost_equal(z, self.z1r)

z = np.correlate(self.y, self.x[::-1], 'full')

assert_array_almost_equal(z, self.z2r)

z = np.correlate(self.xs, self.y, 'full')

assert_array_almost_equal(z, self.zs)

Example 17

def test_object(self):

self._setup(Decimal)

z = np.correlate(self.x, self.y, 'full')

assert_array_almost_equal(z, self.z1)

z = np.correlate(self.y, self.x, 'full')

assert_array_almost_equal(z, self.z2)

Example 18

def test_no_overwrite(self):

d = np.ones(100)

k = np.ones(3)

np.correlate(d, k)

assert_array_equal(d, np.ones(100))

assert_array_equal(k, np.ones(3))

Example 19

def test_complex(self):

x = np.array([1, 2, 3, 4+1j], dtype=np.complex)

y = np.array([-1, -2j, 3+1j], dtype=np.complex)

r_z = np.array([3-1j, 6, 8+1j, 11+5j, -5+8j, -4-1j], dtype=np.complex)

r_z = r_z[::-1].conjugate()

z = np.correlate(y, x, mode='full')

assert_array_almost_equal(z, r_z)

Example 20

def align(l1, l2, axis):

if axis == 1: #horizontal alignment, we do not care about the right line end

#cw = min(l2.shape[1],l1.shape[1])

#l1 = l1[:,:cw]

#l2 = l2[:,:cw]

#compute correlation

sc1 = np.sum(l1, axis=1-axis)

sc2 = np.sum(l2, axis=1-axis)

cor = np.correlate(sc1,sc2,"same")

posErr = np.argmax(cor)-sc1.shape[0]/2

#place at right position

if posErr > 0:

l2c = l2.copy()

l2c[:]=0

l2c[:,posErr:] = l2[:,:-posErr]

l2 = l2c

elif posErr < 0:

l1c = l1.copy()

l1c[:]=0

l1c[:,-posErr:] = l1[:,:posErr]

l1=l1c

else: #vertical alignment, we cate about both ends

#compute correlation

sc1 = np.sum(l1, axis=1-axis)

sc2 = np.sum(l2, axis=1-axis)

cor = np.correlate(sc1,sc2,"same")

posErr = np.argmax(cor)-sc1.shape[0]/2

#place at right position

if posErr > 0:

l2c=l2.copy()

l2c[:]=0

l2c[posErr:,:] = l2[:-posErr,:]

l2 = l2c

elif posErr < 0:

l1c=l1.copy()

l1c[:]=0

l1c[-posErr:,:]=l1[:posErr,:]

l1 = l1c

return posErr, l1, l2

Example 21

def autocorr(x):

x=x-np.mean(x)

y=np.correlate(x,x,mode='full')

return y[y.size/2:]/y[y.size/2]

Example 22

def time_delay_func_paralel(start, end, outs, multi):

for idx in range(start, end):

print 'locating ...', getpid()

c = numpy.correlate(multi[0, ][:, 0], multi[idx, ][:, 0], "full")

C, I = c.max(0), c.argmax(0)

outs[idx] = ((float(len(c))+1.0)/2.0 - I)/44100.0

Example 23

def time_delay_func(x, y):

print 'locating ...'

c = numpy.correlate(x[:, 0], y[:, 0], "full")

C, I = c.max(0), c.argmax(0)

out = ((float(len(c))+1.0)/2.0 - I)/44100.0

return out

Example 24

def fst_delay_snd(fst, snd, samp_rate, max_delay):

# Verify argument shape.

s1, s2 = fst.shape, snd.shape

if len(s1) != 1 or len(s2) != 1 or s1[0] != s2[0]:

raise Exception("Argument shape invalid, in 'fst_delay_snd' function")

half_len = int(s1[0]/2)

a = numpy.array(fst, dtype=numpy.double)

b = numpy.array(snd, dtype=numpy.double)

corr = numpy.correlate(a, b, 'same')

max_pos = numpy.argmax(corr)

# plot(s1[0], samp_rate, a, b, corr)

return corr, (max_pos - half_len) / samp_rate

Example 25

def fst_delay_snd(fst, snd, samp_rate):

# Verify argument shape.

s1, s2 = fst.shape, snd.shape

if len(s1) != 1 or len(s2) != 1 or s1[0] != s2[0]:

raise Exception("Argument shape invalid, in 'fst_delay_snd' function")

half_len = int(s1[0]/2)

corre = numpy.correlate(fst, snd, 'same')

max_pos = numpy.argmax(corre)

return (max_pos - half_len)/samp_rate

Example 26

def rolling_mean(X, window_size):

w = 1.0 / window_size * np.ones((window_size))

return np.correlate(X, w, 'valid')

Example 27

def rolling_mean(X, window_size):

w = 1.0 / window_size * np.ones((window_size))

return np.correlate(X, w, 'valid')

Example 28

def test_float(self):

self._setup(np.float)

z = np.correlate(self.x, self.y, 'full')

assert_array_almost_equal(z, self.z1)

z = np.correlate(self.x, self.y[:-1], 'full')

assert_array_almost_equal(z, self.z1_4)

z = np.correlate(self.y, self.x, 'full')

assert_array_almost_equal(z, self.z2)

z = np.correlate(self.x[::-1], self.y, 'full')

assert_array_almost_equal(z, self.z1r)

z = np.correlate(self.y, self.x[::-1], 'full')

assert_array_almost_equal(z, self.z2r)

z = np.correlate(self.xs, self.y, 'full')

assert_array_almost_equal(z, self.zs)

Example 29

def test_object(self):

self._setup(Decimal)

z = np.correlate(self.x, self.y, 'full')

assert_array_almost_equal(z, self.z1)

z = np.correlate(self.y, self.x, 'full')

assert_array_almost_equal(z, self.z2)

Example 30

def test_no_overwrite(self):

d = np.ones(100)

k = np.ones(3)

np.correlate(d, k)

assert_array_equal(d, np.ones(100))

assert_array_equal(k, np.ones(3))

Example 31

def test_complex(self):

x = np.array([1, 2, 3, 4+1j], dtype=np.complex)

y = np.array([-1, -2j, 3+1j], dtype=np.complex)

r_z = np.array([3-1j, 6, 8+1j, 11+5j, -5+8j, -4-1j], dtype=np.complex)

r_z = r_z[::-1].conjugate()

z = np.correlate(y, x, mode='full')

assert_array_almost_equal(z, r_z)

Example 32

def autoCorrelation(s):

def ac1d(x):

var = x.var()

x = x - x.mean()

r = np.correlate(x[-x.size:], x, mode='full')

return r[r.size//2:] / (var * np.arange(x.shape[0], 0, -1))

return np.apply_along_axis(ac1d, 0, s)

Example 33

def dodemod(rx, P:dict):

aud = None

fs = P['rxfs']

if P['demod']=='chirp':

tx = loadbin(P['txfn'], P['txfs'])

if tx is None:

warnings.warn('simulated chirp reception')

tx = rx

rx = 0.05*rx + 0.1*rx.max()*(np.random.randn(rx.size) + 1j*np.random.randn(rx.size))

txfs = fs

else:

rx = scipy.signal.resample_poly(rx, UP, DOWN)

fs = txfs = P['txfs']

txsec = tx.size/txfs # length of TX in seconds

if P['pri'] is None:

pri=txsec

print(f'Using {pri*1000} ms PRI and {P["Npulse"]} pulses incoherently integrated')

# %% integration

NrxPRI = int(fs * pri) # Number of RX samples per PRI

NrxStack = rx.size // NrxPRI # number of complete PRIs received in this data

Nint = NrxStack // P['Npulse'] # Number of steps we'll take iterating

Nextract = P['Npulse'] * NrxPRI # total number of samples to extract (in general part of one PRI is discarded after numerous PRIs)

ax=None

for i in range(Nint):

ci = slice(i*Nextract, (i+1)*Nextract)

rxint = rx[ci].reshape((NrxPRI, P['Npulse'])).mean(axis=1)

Rxy = np.correlate(tx, rxint, 'full')

ax = plotxcor(Rxy, txfs, ax)

draw(); pause(0.5)

elif P['demod']=='am':

aud = am_demod(P['again']*rx, fs, fsaudio, P['fc'], p.audiobw, frumble=p.frumble, verbose=True)

elif P['demod']=='ssb':

aud = ssb_demod(P['again']*rx, fs, fsaudio, P['fc'], p.audiobw,verbose=True)

return aud,fs

Example 34

def plot(seq):

"""Plot the autocorrelation of the given sequence."""

import matplotlib.pyplot as plt

bipolar = np.where(seq, 1.0, -1.0)

autocorr = np.correlate(bipolar, bipolar, 'same')

plt.figure()

plt.title("Length {} Gold code autocorrelation".format(len(seq)))

xdata = np.arange(len(seq)) - len(seq) // 2

plt.plot(xdata, autocorr, '.-')

plt.show()

Example 35

def _print_stats(seq):

bipolar = np.where(seq, 1.0, -1.0)

autocorr = np.correlate(bipolar, bipolar, 'same')

peaks = np.sort(np.abs(autocorr))

peak = peaks[-1]

noise = np.sqrt(np.mean(peaks[:-1]**2))

peak_to_peak2 = peak / peaks[-2]

peak_to_noise = peak / noise

print("Peak amplitude: {:.0f}".format(peak))

print("Largest non-peak amplitude: {:.0f}".format(peaks[-2]))

print("Peak-to-max: {:.2f}".format(peak_to_peak2))

print("Peak-to-noise: {:.2f}".format(peak_to_noise))

Example 36

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;

Example 37

def correlationIndividualStages(data, idx = (0,1), cls = -1, delay = (-100, 100)):

"""Calculate correlation functions in time between the various measures in each stage"""

stages = stageIndex(data, cls = cls);

stages = np.insert(np.append(stages, data.shape[0]), 0, 0);

ns = len(stages) - 1;

n = len(idx);

means = np.mean(data[:,:-1], axis = 0);

nd = delay[1] - delay[0] + 1;

cc = np.zeros((nd,n,n,ns))

for s in range(ns):

dat = data[stages[s]:stages[s+1],:];

for i in range(n):

for j in range(n):

if delay[0] < 0:

cm = np.correlate(dat[:, i] - means[i], dat[-delay[0]:, j] - means[j]);

else:

cm = [0];

if delay[1] > 0:

cp = np.correlate(dat[:, j] - means[j], dat[delay[1]:, i] - means[i]);

else:

cp = [0];

ca = np.concatenate((cm[1:], cp[::-1]));

if delay[0] > 0:

cc[:,i,j,s] = ca[delay[0]:];

elif delay[1] < 0:

cc[:,i,j,s] = ca[:-delay[1]];

else:

cc[:,i,j,s] = ca;

return cc;

Example 38

def correlogram(experiment):

left = experiment.getresults('left')

right = experiment.getresults('right')

corr_trials = [np.correlate(l,r,'same') for l,r in zip(left,right)]

return np.mean(corr_trials,axis=0)

Example 39

def five_group_stats(group):

sales = np.array(group['Demanda_uni_equil'].values)

samana = group['Semana'].values

max_index = np.argmax(samana)

returns = group['Dev_proxima'].mean()

#this is signature on when slaes happens

sorted_samana_index = np.argsort(samana)

sorted_sales = sales[sorted_samana_index]

signature = np.sum([ math.pow(2,s-3) for s in samana])

kurtosis = fillna_and_inf(scipy.stats.kurtosis(sorted_sales))

hmean = fillna_and_inf(scipy.stats.hmean(np.where(sales <0, 0.1, sales)))

entropy = fillna_and_inf(scipy.stats.entropy(sales))

std = fillna_and_inf(np.std(sales))

N = len(sales)

ci = fillna_and_inf(calculate_ci(std, N))

corr = fillna_and_inf(scipy.stats.pearsonr(range(N), sorted_sales)[0])

autocorr_list = np.correlate(sorted_sales, sorted_sales, mode='same')

mean_autocorr = fillna_and_inf(np.mean(autocorr_list))

mean = np.mean(sales)

mean_corss_points_count = 0

if N > 1:

high_than_mean = mean < sorted_sales[0]

for i in range(1,N):

if (high_than_mean and mean > sorted_sales[i]) or (not high_than_mean and mean > sorted_sales[i]):

mean_corss_points_count += mean_corss_points_count

high_than_mean = mean < sorted_sales[i]

return mean, N, std, np.median(sales), sales[max_index], samana[max_index], \

returns, signature, kurtosis, hmean, entropy, ci, corr, mean_autocorr, mean_corss_points_count

Example 40

def test_float(self):

self._setup(np.float)

z = np.correlate(self.x, self.y, 'full')

assert_array_almost_equal(z, self.z1)

z = np.correlate(self.x, self.y[:-1], 'full')

assert_array_almost_equal(z, self.z1_4)

z = np.correlate(self.y, self.x, 'full')

assert_array_almost_equal(z, self.z2)

z = np.correlate(self.x[::-1], self.y, 'full')

assert_array_almost_equal(z, self.z1r)

z = np.correlate(self.y, self.x[::-1], 'full')

assert_array_almost_equal(z, self.z2r)

z = np.correlate(self.xs, self.y, 'full')

assert_array_almost_equal(z, self.zs)

Example 41

def test_object(self):

self._setup(Decimal)

z = np.correlate(self.x, self.y, 'full')

assert_array_almost_equal(z, self.z1)

z = np.correlate(self.y, self.x, 'full')

assert_array_almost_equal(z, self.z2)

Example 42

def test_no_overwrite(self):

d = np.ones(100)

k = np.ones(3)

np.correlate(d, k)

assert_array_equal(d, np.ones(100))

assert_array_equal(k, np.ones(3))

Example 43

def test_complex(self):

x = np.array([1, 2, 3, 4+1j], dtype=np.complex)

y = np.array([-1, -2j, 3+1j], dtype=np.complex)

r_z = np.array([3-1j, 6, 8+1j, 11+5j, -5+8j, -4-1j], dtype=np.complex)

r_z = r_z[::-1].conjugate()

z = np.correlate(y, x, mode='full')

assert_array_almost_equal(z, r_z)

Example 44

def test_float(self):

self._setup(np.float)

z = np.correlate(self.x, self.y, 'full')

assert_array_almost_equal(z, self.z1)

z = np.correlate(self.x, self.y[:-1], 'full')

assert_array_almost_equal(z, self.z1_4)

z = np.correlate(self.y, self.x, 'full')

assert_array_almost_equal(z, self.z2)

z = np.correlate(self.x[::-1], self.y, 'full')

assert_array_almost_equal(z, self.z1r)

z = np.correlate(self.y, self.x[::-1], 'full')

assert_array_almost_equal(z, self.z2r)

z = np.correlate(self.xs, self.y, 'full')

assert_array_almost_equal(z, self.zs)

Example 45

def test_object(self):

self._setup(Decimal)

z = np.correlate(self.x, self.y, 'full')

assert_array_almost_equal(z, self.z1)

z = np.correlate(self.y, self.x, 'full')

assert_array_almost_equal(z, self.z2)

Example 46

def test_no_overwrite(self):

d = np.ones(100)

k = np.ones(3)

np.correlate(d, k)

assert_array_equal(d, np.ones(100))

assert_array_equal(k, np.ones(3))

Example 47

def test_complex(self):

x = np.array([1, 2, 3, 4+1j], dtype=np.complex)

y = np.array([-1, -2j, 3+1j], dtype=np.complex)

r_z = np.array([3-1j, 6, 8+1j, 11+5j, -5+8j, -4-1j], dtype=np.complex)

r_z = r_z[::-1].conjugate()

z = np.correlate(y, x, mode='full')

assert_array_almost_equal(z, r_z)

Example 48

def angle_from_audio(self, file_name, chunks):

[rate, wave] = wavfile.read(file_name)

raw_0 = wave[:, 0].astype(np.float64)

raw_1 = wave[:, 1].astype(np.float64)

for i in range(1, chunks):

start = i*chunks

end = (i+1)*chunks

left = raw_0[start:end]

right = raw_1[start-self.buffer:end+self.buffer]

corr_arr = np.correlate(right, left, 'valid')

max_index = (len(corr_arr)/2)-np.argmax(corr_arr)

time_d = max_index/float(rate)

signal_dist = time_d*self.sound_speed

if (signal_dist != 0 and abs(signal_dist)<=self.mic_dist):

angle = math.degrees(math.asin( signal_dist / self.mic_dist))

self.angles.append(angle)

a = np.array(self.angles)

hist, bins = np.histogram(a, bins=10)

# width = 0.7 * (bins[1] - bins[0])

# center = (bins[:-1] + bins[1:]) / 2

# plt.bar(center, hist, align='center', width=width)

# plt.xlabel('Angle (degrees)', fontsize=16)

# plt.show()

index = np.argmax(hist)

self.angle_pred = bins[index]

print self.angle_pred

Example 49

def autocorrelation(x,lags): #Temporal correlation

n = len(x)

x = np.array(x)

result = [np.correlate(x[i:] - x[i:].mean(),x[:n-i]-x[:n-i].mean())[0]\

/(x[i:].std()*x[:n-i].std()*(n-i)) for i in range(1,lags+1)]

return result

Example 50

def autocorrelation(x,lags): #Temporal correlation

n = len(x)

x = np.array(x)

result = [np.correlate(x[i:] - x[i:].mean(),x[:n-i]-x[:n-i].mean())[0]\

/(x[i:].std()*x[:n-i].std()*(n-i)) for i in range(1,lags+1)]

return result