def annular_av(data):
"""
Using annular averging, it converts data to 1D (Q vs. I)
**Inputs**
data (sans2d): data in
**Returns**
output (sans1d): converted to I vs. Q
2016-04-08 Brian Maranville
"""
""" """
from draw_annulus_aa import annular_mask_antialiased
#annular_mask_antialiased(shape, center, inner_radius, outer_radius, background_value=0.0, mask_value=1.0, oversampling=8)
# calculate the change in q that corresponds to a change in pixel of 1
q_per_pixel = data.qx[1,0]-data.qx[0,0] / 1.0
# for now, we'll make the q-bins have the same width as a single pixel
step = q_per_pixel
shape1 = data.data.x.shape
x0=data.metadata['det.beamx'] #should be close to 64
y0=data.metadata['det.beamy'] #should be close to 64
center = (x0, y0)
Qmax = data.q.max()
Q = np.arange(0,Qmax,step)
I = []
I_error = []
for i in Q:
# inner radius is the q we're at right now, converted to pixel dimensions:
inner_r = i * (1.0/q_per_pixel)
# outer radius is the q of the next bin, also converted to pixel dimensions:
outer_r = (i + step) * (1.0/q_per_pixel)
mask = annular_mask_antialiased(shape1,center,inner_r,outer_r)
if IGNORE_CORNER_PIXELS == True:
mask[0,0] = mask[-1,0] = mask[-1,-1] = mask[0,-1] = 0.0
#print "Mask: ",mask
integrated_intensity = np.sum(mask.T*data.data.x)
#error = getPoissonUncertainty(integrated_intensity)
error = np.sqrt(integrated_intensity)
mask_sum = np.sum(mask)
if (integrated_intensity != 0.0):
norm_integrated_intensity = integrated_intensity / mask_sum
#error['yupper'] /= mask_sum
#error['ylower'] /= mask_sum
error /= mask_sum
else:
norm_integrated_intensity = integrated_intensity
I.append(norm_integrated_intensity) # not multiplying by step anymore
I_error.append(error)
output = Sans1dData(Q, I, dx=0, dv=I_error, xlabel="Q", vlabel="I", xunits="inv. A", vunits="neutrons")
output.metadata=deepcopy(data.metadata)
return output
def annular_av(sansdata):
"""Using annular averging, it converts data to 1D (Q vs. I) """
#annular_mask_antialiased(shape, center, inner_radius, outer_radius, background_value=0.0, mask_value=1.0, oversampling=8)
#x0=sansdata.metadata['det.beamx'] #should be close to 64
#y0=sansdata.metadata['det.beamy'] #should be close to 64
#shape=sansdata.data.x.shape
#x,y = np.indices(shape)
#X = PIXEL_SIZE_X_CM*(x-x0)
#Y=PIXEL_SIZE_Y_CM*(y-y0)
#alpha=np.arctan2(Y,X)
#q = sansdata.q
#qx=q*np.cos(alpha)
#print sansdata.q
# calculate the change in q that corresponds to a change in pixel of 1
q_per_pixel = sansdata.qx[1, 0] - sansdata.qx[0, 0] / 1.0
# for now, we'll make the q-bins have the same width as a single pixel
step = q_per_pixel
#print "Step: ", step
shape1 = (128, 128)
center = (sansdata.metadata['det.beamx'], sansdata.metadata['det.beamy'])
Qmax = sansdata.q.max()
#print "QMax: ",Qmax
Q = np.arange(0, Qmax, step)
#print "Q=",Q
I = []
for i in Q:
# inner radius is the q we're at right now, converted to pixel dimensions:
inner_r = i * (1.0 / q_per_pixel)
# outer radius is the q of the next bin, also converted to pixel dimensions:
outer_r = (i + step) * (1.0 / q_per_pixel)
mask = annular_mask_antialiased(shape1, center, inner_r, outer_r)
if IGNORE_CORNER_PIXELS == True:
mask[0, 0] = mask[-1, 0] = mask[-1, -1] = mask[0, -1] = 0.0
#print "Mask: ",mask
norm_integrated_intensity = np.sum(mask * sansdata.data.x)
if (norm_integrated_intensity != 0.0):
norm_integrated_intensity /= np.sum(mask)
I.append(norm_integrated_intensity) #not multiplying by step anymore
Q = Q.tolist()
I = (np.array(I)).tolist()
metadata = deepcopy(sansdata.metadata)
plot1 = plot1D(Q, I, metadata)
#print "Q is : ", Q
#print "I is : ", I
Qlog = np.log10(Q).tolist()
Ilog = np.log10(I).tolist()
#print "Qlog: ", Qlog
#print "Ilog: ", Ilog
#plt.plot(Q,I,'ro')
#plt.title('1D')
#plt.xlabel('q(A^-1)')
#plt.ylabel('I(q)')
#plt.show()
#sys.exit()
return plot1
def annular_av(sansdata):
"""Using annular averging, it converts data to 1D (Q vs. I) """
#annular_mask_antialiased(shape, center, inner_radius, outer_radius, background_value=0.0, mask_value=1.0, oversampling=8)
#x0=sansdata.metadata['det.beamx'] #should be close to 64
#y0=sansdata.metadata['det.beamy'] #should be close to 64
#shape=sansdata.data.x.shape
#x,y = np.indices(shape)
#X = PIXEL_SIZE_X_CM*(x-x0)
#Y=PIXEL_SIZE_Y_CM*(y-y0)
#alpha=np.arctan2(Y,X)
#q = sansdata.q
#qx=q*np.cos(alpha)
#print sansdata.q
# calculate the change in q that corresponds to a change in pixel of 1
q_per_pixel = sansdata.qx[1,0]-sansdata.qx[0,0] / 1.0
# for now, we'll make the q-bins have the same width as a single pixel
step = q_per_pixel
#print "Step: ", step
shape1 = (128,128)
center = (sansdata.metadata['det.beamx'],sansdata.metadata['det.beamy'])
Qmax = sansdata.q.max()
#print "QMax: ",Qmax
Q = np.arange(0,Qmax,step)
#print "Q=",Q
I = []
for i in Q:
# inner radius is the q we're at right now, converted to pixel dimensions:
inner_r = i * (1.0/q_per_pixel)
# outer radius is the q of the next bin, also converted to pixel dimensions:
outer_r = (i + step) * (1.0/q_per_pixel)
mask = annular_mask_antialiased(shape1,center,inner_r,outer_r)
if IGNORE_CORNER_PIXELS == True:
mask[0,0] = mask[-1,0] = mask[-1,-1] = mask[0,-1] = 0.0
#print "Mask: ",mask
norm_integrated_intensity = np.sum(mask*sansdata.data.x)
if (norm_integrated_intensity != 0.0):
norm_integrated_intensity/=np.sum(mask)
I.append(norm_integrated_intensity)#not multiplying by step anymore
Q = Q.tolist()
I = (np.array(I)).tolist()
metadata = deepcopy(sansdata.metadata)
plot1 = plot1D(Q,I,metadata)
#print "Q is : ", Q
#print "I is : ", I
Qlog = np.log10(Q).tolist()
Ilog = np.log10(I).tolist()
#print "Qlog: ", Qlog
#print "Ilog: ", Ilog
#plt.plot(Q,I,'ro')
#plt.title('1D')
#plt.xlabel('q(A^-1)')
#plt.ylabel('I(q)')
#plt.show()
#sys.exit()
return plot1
def annular_av(sansdata):
# annular_mask_antialiased(shape, center, inner_radius, outer_radius, background_value=0.0, mask_value=1.0, oversampling=8)
x0 = sansdata.metadata["det.beamx"] # should be close to 64
y0 = sansdata.metadata["det.beamy"] # should be close to 64
shape = sansdata.data.x.shape
x, y = np.indices(shape)
X = PIXEL_SIZE_X_CM * (x - x0)
Y = PIXEL_SIZE_Y_CM * (y - y0)
alpha = np.arctan2(Y, X)
q = sansdata.q
qx = q * np.cos(alpha)
print q
# calculate the change in q that corresponds to a change in pixel of 1
q_per_pixel = qx[1, 0] - qx[0, 0] / 1.0
# for now, we'll make the q-bins have the same width as a single pixel
step = q_per_pixel
# print "Step: ",step
shape1 = (128, 128)
center = (sansdata.metadata["det.beamx"], sansdata.metadata["det.beamy"])
Qmax = q.max()
# print "QMax: ",Qmax
Q = np.arange(0, Qmax, step)
# print "Q=",Q
I = []
for i in Q:
# inner radius is the q we're at right now, converted to pixel dimensions:
inner_r = i * (1.0 / q_per_pixel)
# outer radius is the q of the next bin, also converted to pixel dimensions:
outer_r = (i + step) * (1.0 / q_per_pixel)
mask = annular_mask_antialiased(shape1, center, inner_r, outer_r)
# my handwriting may have been illegible here: I was trying to write the product of mask and data,
# not mask.data (sorry - Brian)
# print "Mask: ",mask
norm_integrated_intensity = np.sum(mask * sansdata.data.x)
if norm_integrated_intensity != 0.0:
norm_integrated_intensity /= np.sum(mask)
I.append(norm_integrated_intensity)
print "Q is : ", Q
print "I is : ", I