def correlation_retrieval(data,K=None,ARGS=False):
f_data = np.fft.fft2(data)
kx = np.fft.fftfreq(f_data.shape[0])
ky = np.fft.fftfreq(f_data.shape[1])
# get k-vector, if not give
if K is None:
K = get_wave(f_data)
# get the frequency data information
Kfreq = kx[K[0]],ky[K[1]]
# make an X-Y grid
pi = np.pi
nx,ny = np.shape(data)
Y,X = np.meshgrid(np.arange(ny),np.arange(nx),\
sparse=False,indexing='xy')
print Kfreq
# produce a correlation function
phplot.imageshow(wave_function(X,Y,Kfreq))
zsum = np.zeros(data.shape)
Nphi = 1.
a_phi = np.linspace(0,2*np.pi,Nphi)
for phi in a_phi:
zsum += correlate2d(data,wave_function(X,Y,K,phi),mode='same')
phplot.imageshow(zsum)
return data
def __init__(self): nx,ny,nz = 100,100,100 lx,ly,lz = 1.,1.,1. x,y,z = np.linspace(-lx,lx,nx),np.linspace(-ly,ly,ny),np.linspace(-lz,0,nz) self.X,self.Y,self.Z = np.meshgrid(x,y,z) nt = 100 lt = 10 self.t = np.linspace(0.,lt,nt) self.I = self.T_impulse(1.,1.,0.,0.,0.,self.t[1]) phplot.imageshow(np.sum(self.I,2)) self.S = self.T_symb(1.,1.,0.,self.t[99]) phplot.imageshow(self.S)
def subtract_field(data,ref_data): # subtract the max point of the data data_max = np.max(np.max(data)) data_n = data - data_max phplot.imageshow(data_n) phplot.imageshow(phplot.spectrogram(data_n)) # subtract the reference field # data_n = data_n - ref_data wx = np.hanning(data.shape[0]) wy = np.hanning(data.shape[1]) w2 = np.sqrt(np.outer(wx,wy)) phplot.imageshow(data_n) phplot.imageshow(phplot.spectrogram(data_n*w2)) # set 0-points of reference data to some non-0 value (median) # this is important for the division step ref_data[ref_data == 0] = np.median(ref_data) # divide by reference field data_n /= 2*np.sqrt(ref_data*data_max) return data_n