def phaseshift_regular_grid(A, speed):
"""
Phase shift data for wave-frame moving averages
Parameters
----------
A: DataArray
The input data array can have any number of dimensions, but it must
satisfy some simple properties:
- periodicity in the 'x' direction
- 'time' is the first dimension of the dataset
- 'x', and 'time' are defined on a regular grid
- the units of speed are [x]/[time]
speed: float
The speed of waves to follow.
Returns
-------
C : The phase shifted data array
"""
from scipy.ndimage.interpolation import shift as ndshift
C = A.copy()
# Grid spacing
dx = A.x[1] - A.x[0]
dt = (A.time[1] - A.time[0])
# shift = (-c * t, 0) = (- c * dt * i / dx)
def indshift(i):
shift = [0]*(C.ndim-1)
shift[C.get_axis_num('x')-1] = float((-speed *dt * i/dx).values)
return shift
# shift data
for it, t in enumerate(A.time):
ndshift(A.values[it,...], indshift(it), output=C.values[it,...], mode='wrap')
return C
def test_normfunc2():
norm_func = norm.set_normfunc(bg, dk)
res1 = norm_func(im)
res2 = norm_func(im, bp=bp)
res3 = norm_func(im, bp=bp, bc=bc)
real1 = np.zeros(im.shape, dtype=im.dtype)+r1
real2 = np.zeros(im.shape, dtype=im.dtype)+r2
real3 = ndshift(np.zeros(im.shape, dtype=im.dtype)+r2, bc, mode='constant', cval=r3)
assert_allclose(real1, res1)
assert_allclose(real2, res2)
assert_allclose(real3, res3)
def shift2d(data, t=0.0):
'''
t: float or sequence
'''
ret = None
if isvector(t):
map_obj = map(ndshift, data, t)
ret = fromiter(map_obj)
else:
ret = ndshift(data, (0.0, t))
return ret
def test_corr1d():
A = np.array([1, 3, 5, 3, 1], dtype=np.double)
A = np.pad(A, 10, mode='constant', constant_values=1)
ran = np.zeros(ntry, dtype=np.double)
cor = np.zeros(ntry, dtype=np.double)
for i in range(ntry):
ran[i] = np.ceil(np.random.random()*50.-100.)/10.
B = ndshift(A, (ran[i]), mode='constant', cval=1.0)
cor[i] = corr.corr1d(A, B)
assert_allclose(ran, cor, rtol=0, atol=0.1)
def normfunc(im, bp=None, bc=None):
'''
im: 2d image
bp: beam power value
bc: beam center
'''
_im = im
_bg = bg
_dk = dk
if dk is not None:
_im = _im - _dk
_bg = _bg - _dk
if bp is not None:
_im = _im - bp
if bc is not None:
_bg = ndshift(_bg, bc, mode='constant', cval=1.0)
res = (_im / _bg)
return res
def test_corr2d():
A = np.array([[1, 1, 1, 1, 1],
[1, 3, 3, 3, 1],
[1, 3, 5, 3, 1],
[1, 3, 3, 3, 1],
[1, 1, 1, 1, 1]], dtype=np.double)
A = np.pad(A, 10, mode='constant', constant_values=1)
ran_dx = np.zeros(ntry, dtype=np.double)
ran_dy = np.zeros(ntry, dtype=np.double)
cor_dx = np.zeros(ntry, dtype=np.double)
cor_dy = np.zeros(ntry, dtype=np.double)
for i in range(ntry):
ran_dy[i] = np.ceil(np.random.random()*50.-100.)/10.
ran_dx[i] = np.ceil(np.random.random()*50.-100.)/10.
B = ndshift(A, (ran_dy[i], ran_dx[i]), mode='constant', cval=1.0)
cor_dy[i], cor_dx[i] = corr.corr2d(A, B)
assert_allclose(ran_dx, cor_dx, rtol=0, atol=0.1)
assert_allclose(ran_dy, cor_dy, rtol=0, atol=0.1)
def test_norm_all3():
norm_func = norm.set_normfunc(bg, dk)
res = norm.norm_all(np.array([im, im]), bg, dk=dk, beam_power=np.array([bp,bp]), beam_center=np.array([bc, bc]), crop=True)
real = ndshift(np.zeros(im.shape, dtype=im.dtype)+r2, bc, mode='constant', cval=r3)
real = np.array([real, real])
assert_allclose(real[:,2:,1:], res)
