def FFTs(f, dir):
"""fft with shifting"""
what = np.ascontiguousarray(f)
if dir == 1:
return np.fftshift(np.fftn(what))
elif dir == -1:
return np.fftshift(np.ifftn(np.ifftshift(what)))
else:
print('Select direction: 1 -> Direct FFT; -1 -> Inverse FFT')
quit()
return 0
def plot_pspec(history):
#need to modify
field=history['field']
rho=history['rho']
detune=history['detune']
plt.figure()
fieldFFT = np.fftshift(np.fft(field.T)) # unconjugate complex transpose by .'
Pspec=fieldFFT*np.conj(fieldFFT)
plt.plot(detune*2*rho,Pspec)
plt.axis([-0.06,+0.01,0,1.2*np.max(Pspec)])
plt.xlabel('{(\Delta\omega)/\omega_r}')
plt.ylabel('{output spectral power} (a.u.)')
def periodogram(time_series, taper=None, L=None):
'Implements the periodogram spectral estimator.'
#If we don't have a taper, just use a rectangular one (array of ones)
if not taper:
taper = N.ones_like(time_series)
#Other wise, make sure the sizes are compatible
elif taper.size != time_series.size and taper.size != time_series.shape[-1]:
raise ValueError('Data and Taper must have the same size')
#Zero pad if necessary
if L is None:
L = time_series.shape[-1]
elif L > time_series.shape[-1]:
num_zeros = L - time_series.shape[-1]
zeropad = N.zeros(time_series.shape[:-1] + (num_zeros,),
dtype=time_series.dtype)
time_series = N.concatenate((time_series, zero_pad), axis=-1)
elif L < time_series.shape[-1]:
time_series = time_series[...,:L]
Z = fftshift(fft(taper * time_series))
return N.abs(Z)*N.abs(Z)/time_series.size
def correlogram(time_series, scale='biased'):
'''Implements the correlogram spectral estimator. This is often known as the
Blackman-Tukey estimator with a rectangular window.'''
R = xcorr(time_series, scale=scale)
return N.abs(fftshift(fft(R)))
# Objective: observe bubble cloud migration on high-speed camera and compare to
# ACE peak arrival (and edge detection) signal.
import numpy as np
import scipi.io as sio
import matplotlib.pyplot as plt
class array():
def __init__(self):
coords = sio.loadmat('256x2cm_Hemispherical_Array_CAD_Coordinates.mat')
X = coords['XCAD'][7:]
Y = coords['YCAD'][7:]
self.z = coords['ZCAD'][7:]
t1 = np.arctan2(Y,X)-0.75*pi
rr1 = (X**2+Y**2)**0.5
self.x = rr1*np.cos(t1)
self.y = rr1*np.sin(t1)
self.z = rr1*np.tan(t1)
class bSignals():
def FTsig(self,signal,interval)
insig = signal(interval)
ft = np.fftshift(np.fft(insig))
return ft
fig = plt.figure()
plt.plot(ft)
plt.show()