def psf__wPSF(parm, t):
"""
parm: s0, c, d, A, B
example (72, 125, 246, 1, 1)
"""
s0, c, d, A, B = parm
com = (t - c) / d
sq = 1 + N.power(com, 2) + A * N.power(com, 3) + B * N.power(com, 4)
return s0 * N.sqrt(sq)
def psf__wPSF_fromSigma(t, parm):
"""
t: now this is sigma that you got
parm: s0, c, d, A, B (use parm form psf__wPSF fit)
"""
s0, c, d, A, B = parm
com = (t - c) / d
sq = 1 + N.power(com, 2) + A * N.power(com, 3) + B * N.power(com, 4)
return s0 * N.sqrt(1 + sq)
def psf__wPSF_fromSigma(t, parm):
"""
t: now this is sigma that you got
parm: s0, c, d, A, B (use parm form psf__wPSF fit)
"""
s0, c, d, A, B = parm
com = (t - c) / d
sq = 1 + N.power(com, 2) + A * N.power(com, 3) + B * N.power(com, 4)
return s0 * N.sqrt( 1 + sq)
def psf__wPSF(parm, t):
"""
parm: s0, c, d, A, B
example (72, 125, 246, 1, 1)
"""
s0, c, d, A, B = parm
com = (t - c) / d
sq = 1 + N.power(com, 2) + A * N.power(com, 3) + B * N.power(com, 4)
return s0 * N.sqrt(sq)
def radialaverage(data, center=None, useMaxShape=False):
"""
data: ND array
center: coordinate of center of radii
useMinShape: the output uses the maximum shape available
return 1D array
"""
if center is None:
center = N.array(data.shape) // 2
if len(center) != data.ndim:
raise ValueError(
'dimension of center (%i) does not match the dimension of data (%i)'
% (len(center), data.ndim))
zyx = N.indices((data.shape))
r = N.zeros(data.shape, N.float32)
for i, t in enumerate(zyx):
r += (t - center[i])**2
r = N.sqrt(r)
#y, x = N.indices((data.shape))
#r = N.sqrt((x - center[0])**2 + (y - center[1])**2) # distance from the center
r = r.astype(N.int)
if data.dtype.type in (N.complex64, N.complex128):
rbin = N.bincount(r.ravel(), data.real.ravel())
ibin = N.bincount(r.ravel(), data.imag.ravel())
tbin = N.empty(rbin.shape, data.dtype.type)
tbin.real = rbin
tbin.imag = ibin
else:
tbin = N.bincount(r.ravel(), data.ravel())
nr = N.bincount(r.ravel())
radialprofile = tbin / nr.astype(N.float32)
if not useMaxShape:
minShape = min(list(N.array(data.shape) - center) + list(center))
radialprofile = radialprofile[:minShape]
return radialprofile
def radialaverage(data, center=None, useMaxShape=False):
"""
data: ND array
center: coordinate of center of radii
useMinShape: the output uses the maximum shape available
return 1D array
"""
if center is None:
center = N.array(data.shape) // 2
if len(center) != data.ndim:
raise ValueError('dimension of center (%i) does not match the dimension of data (%i)' % (len(center), data.ndim))
zyx = N.indices((data.shape))
r = N.zeros(data.shape, N.float32)
for i, t in enumerate(zyx):
r += (t - center[i])**2
r = N.sqrt(r)
#y, x = N.indices((data.shape))
#r = N.sqrt((x - center[0])**2 + (y - center[1])**2) # distance from the center
r = r.astype(N.int)
if data.dtype.type in (N.complex64, N.complex128):
rbin = N.bincount(r.ravel(), data.real.ravel())
ibin = N.bincount(r.ravel(), data.imag.ravel())
tbin = N.empty(rbin.shape, data.dtype.type)
tbin.real = rbin
tbin.imag = ibin
else:
tbin = N.bincount(r.ravel(), data.ravel())
nr = N.bincount(r.ravel())
radialprofile = tbin / nr.astype(N.float32)
if not useMaxShape:
minShape = min(list(N.array(data.shape) - center) + list(center))
radialprofile = radialprofile[:minShape]
return radialprofile
def cart2polar2D(y, x):
r = N.sqrt(y**2 + x**2)
theta = N.arctan2(y, x)
return r, theta
