ind = np.where(np.abs(x) > 1e-20)

ph = np.ones_like(x)

ph[ind] = x[ind]/np.abs(x[ind])

sz = pyramids[0].highpasses[level].size

length = len(pyramids)

ph = np.empty((length,sz),pyramids[0].highpasses[level].dtype)

ph[0,:] = phase(pyramids[0].highpasses[level].flatten())

ph_prev = ph[0,:]

for i in range(1,length):

ph_cur = phase(pyramids[i].highpasses[level].flatten())

ph[i,:] = ph_cur / ph_prev

ph_prev = ph_cur

ang = np.angle(ph)

ang =np.cumsum(ang, axis =0)

window_size = round(width/0.2327)

window = signal.flattop(window_size)

window = window/np.sum(window)

result = ndimage.convolve1d(data, window, axis = axis, mode = 'reflect')

nlevels = 8

tr = dtcwt.Transform2d()

pyramids =list()

n = np.shape(data)[0]

print('Forward DTCWT...', end = ' ', flush = 1)

for i in range(0,n):

pyramids.append(tr.forward(data[i,:,:],nlevels =nlevels))

print('DONE')

print('Modifying phase...', end = ' ', flush = 1)

for level in range(0,nlevels):

phase = get_phases(pyramids,level)

phase0 = flattop_filter1d(phase,width, axis = 0, mode = 'reflect')

phase = phase0 + (phase - phase0)*k

phase = flattop_filter1d(phase,2.0, axis = 0, mode = 'reflect')

for i in range(0,n):

h = pyramids[i].highpasses[level]

abs_value = np.abs(h).flatten()

h = abs_value * np.exp(1j*phase[i,:])

pyramids[i].highpasses[level][:] = np.reshape(h,np.shape(pyramids[i].highpasses[level][:]))

result = np.empty_like(data)

print('DONE')

print('Inverse DTCWT...', end = ' ', flush = 1)

for i in range(0,n):

result[i,:,:] = tr.inverse(pyramids[i])

print('DONE')

f = open(file,'rb')

dim = np.fromfile(f,np.int32,3)

data = np.fromfile(f,np.float64,np.product(dim))

data = data.reshape((dim[2],dim[1],dim[0]))

f = open(file,'wb')

dim = np.flipud(cube.shape).astype(np.int32)

dim.tofile(f)