def get_min_gradient(image, cen, mask=None, start_exc=25):
"""Return the radial coordinate with the largest negative gradient.
Needed for center refinement with sector or ring methods.
Arguments:
`image`: Image to use.
`cen`: Tuple with the center coordinates.
`mask`: Mask giving the pixels to be used in determination.
`start_exc`: Number of points to exclude from the start (center) of the
image radial gradient array
"""
[I, n1] = c.radbin(image, cen, mask=mask)
r = np.arange(len(I))
yerr = np.sqrt(I)
nzerr = yerr.copy()
zrepl = np.abs(np.median(yerr))
if zrepl == 0.0:
zrepl = 1.0 # give up
nzerr[nzerr <= 0.0] = zrepl
sp = ip.UnivariateSpline(r, I, w=1/nzerr)
# smoo = [ sp.derivatives(x)[0] for x in r ]
grad = [ sp.derivatives(x)[1] for x in r ]
gradmin = np.argmin(grad[start_exc:]) + start_exc
# plt.plot(r, smoo, r, I, r, grad)
# print("Min gradient: %d" % gradmin)
return gradmin
def ofun_sectors(cen):
"""Calculate two chi2s from two opposing sectors and multiply"""
sect_cens = [0, 0.5*np.pi, np.pi, 1.5*np.pi]
seclims = [ np.array([a - hwidth, a + hwidth])+np.pi/4.0 for a in sect_cens ]
rints = [ c.radbin(image, cen, radrange=np.linspace(rstart,rstop), phirange=x, mask=mask)[0] for x in seclims ]
chi2_1 = chivectors(rints[0], rints[2])
chi2_2 = chivectors(rints[1], rints[3])
return chi2_1*chi2_2
def ofun_ringvariance(cen):
"""Calculate STD from counts in a (narrow) ring in the image"""
[r, n] = c.radbin(image, cen, \
radrange=np.array([ringradius, ringradius+ringwidth]), \
phirange=np.linspace(0, 2*np.pi, \
np.floor(2*np.pi*ringradius)), mask=mask)
r = r[r != 0]
w = np.std(r)
return w
def ofun_ring(cen):
"""Return width of a Debye ring with the given center"""
[r, n] = c.radbin(image, cen, peakrange, mask=mask)
w = fwhm(x, r)
# 2nd moment is not very stable
# w = secondmoment(x, r)
# print cen, w
# sys.stdout.flush()
return w
def ofun_cylsymm(cen):
"""Calculate STD from counts in a (narrow) ring in the image"""
R = 100
[r, n] = c.radbin(image, cen, radrange=np.array([R,R+2]), phirange=np.linspace(0, 2*np.pi, np.floor(2*np.pi*R)), mask=mask)
r = r[r != 0]
w = np.std(r)
# plt.hold(0)
# plt.plot(r)
# plt.show()
# plt.waitforbuttonpress()
# print cen, w
# sys.stdout.flush()
return w
def coordinate_test():
m = np.zeros((9,9))
m[0,4] = 1
m[8,4]= 1
m[4,0] = 1
m[4,8]= 1
(I,n) = r.radbin(m, (4.5, 4.5), radrange=np.arange(0,10), norm=0)
# plt.plot(I)
# plt.show()
# plt.waitforbuttonpress()
# print(I[4])
assert(I[4] == 4.0)
m = np.zeros((10,5))
m[0,3] = 1
(I,n) = r.radbin(m, (3.5, 5.5), radrange=np.arange(0,10), norm=0)
# plt.plot(I)
# plt.show()
assert(I[5] == 1.0)
I[5] = 0.0
assert(not I.all())
(I,n) = r.radbin(m, (3.5, 0.5), radrange=np.arange(0,10), norm=0)
assert(I[0] == 1.0)
I[0] = 0.0
assert(not I.all())
def ofun_sectors(cen):
"""Calculate two chi2s from two opposing sectors and multiply"""
rstart = 120
rstop = 220
hwidth = 10 * (np.pi/180) # width in rads
sect_cens = [0, 0.5*np.pi, np.pi, 1.5*np.pi]
seclims = [ np.array([a - hwidth, a + hwidth])+np.pi/4.0 for a in sect_cens ]
rints = [ c.radbin(image, cen, radrange=np.linspace(rstart,rstop), phirange=x, mask=mask)[0] for x in seclims ]
chi2_1 = chivectors(rints[0], rints[2])
chi2_2 = chivectors(rints[1], rints[3])
# print(cen, chi2_1, chi2_2)
# sys.stdout.flush()
# plt.hold(0)
# plt.plot(rints[0], 'b')
# plt.hold(1)
# plt.plot(rints[2], 'b')
# plt.plot(rints[1], 'r')
# plt.plot(rints[3], 'r')
# plt.waitforbuttonpress()
return chi2_1*chi2_2
def centerfit_peakwidth(image, startcen=None, peakrange=None, baseline=None, mask=None, plotit=False):
"""Return center from an image with a ring.
Minimizes the width of the peak obtained from radial averaging
in the given range as a function of the center position."""
if peakrange is None:
raise ValueError("peakrange must be supplied")
if startcen is None:
startcen = get_im_max(image)
[r1, n1] = c.radbin(image, startcen, peakrange, mask=mask)
if baseline == None:
baseline = r1.min()
x = np.arange(peakrange.size - 1)
if(plotit):
plt.hold(0)
plt.plot(x, r1)
plt.show()
# plt.waitforbuttonpress()
def ofun_ring(cen):
"""Return width of a Debye ring with the given center"""
[r, n] = c.radbin(image, cen, peakrange, mask=mask)
w = fwhm(x, r)
# 2nd moment is not very stable
# w = secondmoment(x, r)
# print cen, w
# sys.stdout.flush()
return w
def ofun_cylsymm(cen):
"""Calculate STD from counts in a (narrow) ring in the image"""
R = 100
[r, n] = c.radbin(image, cen, radrange=np.array([R,R+2]), phirange=np.linspace(0, 2*np.pi, np.floor(2*np.pi*R)), mask=mask)
r = r[r != 0]
w = np.std(r)
# plt.hold(0)
# plt.plot(r)
# plt.show()
# plt.waitforbuttonpress()
# print cen, w
# sys.stdout.flush()
return w
def ofun_sectors(cen):
"""Calculate two chi2s from two opposing sectors and multiply"""
rstart = 120
rstop = 220
hwidth = 10 * (np.pi/180) # width in rads
sect_cens = [0, 0.5*np.pi, np.pi, 1.5*np.pi]
seclims = [ np.array([a - hwidth, a + hwidth])+np.pi/4.0 for a in sect_cens ]
rints = [ c.radbin(image, cen, radrange=np.linspace(rstart,rstop), phirange=x, mask=mask)[0] for x in seclims ]
chi2_1 = chivectors(rints[0], rints[2])
chi2_2 = chivectors(rints[1], rints[3])
# print(cen, chi2_1, chi2_2)
# sys.stdout.flush()
# plt.hold(0)
# plt.plot(rints[0], 'b')
# plt.hold(1)
# plt.plot(rints[2], 'b')
# plt.plot(rints[1], 'r')
# plt.plot(rints[3], 'r')
# plt.waitforbuttonpress()
return chi2_1*chi2_2
optcen = optim.fmin(ofun_sectors, startcen, disp=True)
[r2, n2] = c.radbin(image, optcen, peakrange, mask=mask)
if(plotit):
plt.plot(x, r1, x, r2)
plt.show()
return optcen
