def angularIntegrationBackDetector(n_bins=100,center=(265,655),min_bin=40,max_bin=800,threshold=(0.001,10000)): ''' Does the circular integration and q-calibration on the back detector, with an additional threshold masking ''' # -- parameters Ldet = 5080. # detector to sample distance dpix = 0.055 # um (pixel size) energy = 8.4 #keV # -- constants h = 4.135667516*1e-18 #kev*sec c = 3*1e8 #m/s lambda_ = h*c/energy*1e10 # wavelength of the X-rays # -- calulate q-range width,spacing = float(max_bin-min_bin)/float(n_bins),0 edges = roi.ring_edges(min_bin, width, spacing, n_bins) two_theta = utils.radius_to_twotheta(Ldet, edges*dpix) q_val = utils.twotheta_to_q(two_theta, lambda_) q = np.mean(q_val, axis=1) # -- apply threshold mask data self.data[data<threshold[0]]=0 self.data[data>threshold[1]]=0 # -- angular average Iq= circularAverage(self.data,calibrated_center=center,nx=n_bins,min_x=min_bin,max_x=max_bin) print q.shape,Iq[1].shape return Iq[0],Iq[1]
def test_circular_average():
image = np.zeros((12, 12))
calib_center = (5, 5)
inner_radius = 1
edges = roi.ring_edges(inner_radius, width=1, spacing=1, num_rings=2)
labels = roi.rings(edges, calib_center, image.shape)
image[labels == 1] = 10
image[labels == 2] = 10
bin_cen, ring_avg = roi.circular_average(image, calib_center, nx=6)
assert_array_almost_equal(bin_cen, [0.70710678, 2.12132034,
3.53553391, 4.94974747, 6.36396103,
7.77817459], decimal=6)
assert_array_almost_equal(ring_avg, [8., 2.5, 5.55555556, 0.,
0., 0.], decimal=6)
bin_cen1, ring_avg1 = roi.circular_average(image, calib_center, min_x=0,
max_x=10, nx=None)
assert_array_almost_equal(bin_cen1, [0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5,
7.5, 8.5])
mask = np.ones_like(image)
mask[4:6, 2:3] = 0
bin_cen_masked, ring_avg_masked = roi.circular_average(image, calib_center,
min_x=0, max_x=10,
nx=6, mask=mask)
assert_array_almost_equal(bin_cen_masked, [0.83333333, 2.5, 4.16666667,
5.83333333, 7.5, 9.16666667])
assert_array_almost_equal(ring_avg_masked, [8.88888889, 3.84615385, 2.5,
0., 0., 0.])
def test_segmented_rings():
center = (75, 75)
img_dim = (150, 140)
first_q = 5
delta_q = 5
num_rings = 4 # number of Q rings
slicing = 4
edges = roi.ring_edges(first_q, width=delta_q, spacing=4,
num_rings=num_rings)
print("edges", edges)
label_array = roi.segmented_rings(edges, slicing, center,
img_dim, offset_angle=0)
print("label_array for segmented_rings", label_array)
# Did we draw the right number of ROIs?
label_list = np.unique(label_array.ravel())
actual_num_labels = len(label_list) - 1
num_labels = num_rings * slicing
assert_equal(actual_num_labels, num_labels)
# Did we draw the right ROIs? (1-16 with some zeros around too)
assert_array_equal(label_list, np.arange(num_labels + 1))
# A brittle test to make sure the exactly number of pixels per label
# is never accidentally changed:
# number of pixels per ROI
num_pixels = np.bincount(label_array.ravel())
expected_num_pixels = [18372, 59, 59, 59, 59, 129, 129, 129,
129, 200, 200, 200, 200, 269, 269, 269, 269]
assert_array_equal(num_pixels, expected_num_pixels)
def radial_integration_back_detector(data,n_bins=300,mask=None,center=(265,655),min_bin=-np.pi,max_bin=np.pi,threshold=(0.001,10000)):
'''
Does the circular integration and q-calibration on the back
detector, with an additional threshold masking
'''
# -- parameters
Ldet = 5080. # detector to sample distance
dpix = 0.055 # um (pixel size)
energy = 8.4 #keV
inner_radius,width,spacing,num_rings=180,120,0,1
# -- constants
h = 4.135667516*1e-18 #kev*sec
c = 3*1e8 #m/s
lambda_ = h*c/energy*1e10 # wavelength of the X-rays
# -- ring mask
edges = roi.ring_edges(inner_radius, width, spacing, num_rings)
ring_mask = roi.rings(edges, center, data.shape)
# -- apply threshold mask data
data[data<threshold[0]]=0
data[data>threshold[1]]=0
# -- radial average
Iphi= radial_average(data,calibrated_center=center,mask=mask*ring_mask,nx=n_bins,min_x=min_bin,max_x=max_bin)
return Iphi[0]*180./np.pi+180.,Iphi[1]
def test_segmented_rings():
center = (75, 75)
img_dim = (150, 140)
first_q = 5
delta_q = 5
num_rings = 4 # number of Q rings
slicing = 4
edges = roi.ring_edges(first_q, width=delta_q, spacing=4,
num_rings=num_rings)
print("edges", edges)
label_array = roi.segmented_rings(edges, slicing, center,
img_dim, offset_angle=0)
print("label_array for segmented_rings", label_array)
# Did we draw the right number of ROIs?
label_list = np.unique(label_array.ravel())
actual_num_labels = len(label_list) - 1
num_labels = num_rings * slicing
assert_equal(actual_num_labels, num_labels)
# Did we draw the right ROIs? (1-16 with some zeros around too)
assert_array_equal(label_list, np.arange(num_labels + 1))
# A brittle test to make sure the exactly number of pixels per label
# is never accidentally changed:
# number of pixels per ROI
num_pixels = np.bincount(label_array.ravel())
expected_num_pixels = [18372, 59, 59, 59, 59, 129, 129, 129,
129, 200, 200, 200, 200, 269, 269, 269, 269]
assert_array_equal(num_pixels, expected_num_pixels)
def ring_mask(data,center,inner_radius=0,width=1,spacing=0,num_rings=10):
'''
Creates a ring mask with cocentric rings of given radius, width and spacing
center = (cx, cy)
'''
edges = roi.ring_edges(inner_radius, width, spacing, num_rings)
ring_mask = roi.rings(edges, center, data.shape)
return ring_mask
def test_construct_circ_avg_image():
# need to test with center and dims and without
# and test anisotropic pixels
image = np.zeros((12, 12))
calib_center = (2, 2)
inner_radius = 1
edges = roi.ring_edges(inner_radius, width=1, spacing=1, num_rings=2)
labels = roi.rings(edges, calib_center, image.shape)
image[labels == 1] = 10
image[labels == 2] = 10
bin_cen, ring_avg = roi.circular_average(image, calib_center, nx=6)
# check that the beam center and dims yield the circavg in the right place
cimg = nimage.construct_circ_avg_image(bin_cen,
ring_avg,
dims=image.shape,
center=calib_center)
assert_array_almost_equal(
cimg[2],
np.array([
5.0103283, 6.15384615, 6.15384615, 6.15384615, 5.0103283,
3.79296498, 2.19422113, 0.51063356, 0., 0., 0., 0.
]))
# check that the default values center in the same place
cimg2 = nimage.construct_circ_avg_image(bin_cen, ring_avg)
assert_array_almost_equal(
cimg2[12],
np.array([
0., 0., 0., 0., 0., 0., 0., 0.51063356, 2.19422113, 3.79296498,
5.0103283, 6.15384615, 6.15384615, 6.15384615, 5.0103283,
3.79296498, 2.19422113, 0.51063356, 0., 0., 0., 0., 0., 0., 0.
]))
# check that anisotropic pixels are treated properly
cimg3 = nimage.construct_circ_avg_image(bin_cen,
ring_avg,
dims=image.shape,
pixel_size=(2, 1))
assert_array_almost_equal(
cimg3[5],
np.array([
0., 1.16761618, 2.80022015, 4.16720388, 5.250422, 6.08400137,
6.08400137, 5.250422, 4.16720388, 2.80022015, 1.16761618, 0.
]))
with assert_raises(ValueError):
nimage.construct_circ_avg_image(bin_cen,
ring_avg,
center=calib_center,
pixel_size=(2, 1))
def radialIntegrationBackDetector(self,
n_bins=300,
center=(265, 655),
min_bin=-np.pi,
max_bin=np.pi,
threshold=(0.001, 10000)):
"""
--------------------------------------------
Method: SpecklePattern.radialIntegrationBackDetector
--------------------------------------------
Wraps radialAverage to perform circular integration and q-calibration on the back
detector, with an additional threshold masking with parameters
for Lambda l02 detector at P10, PETRA III
--------------------------------------------
Usage: if mySpeckles.radialIntegrationBackDetector()
--------------------------------------------
Arguments (optional):
title - string that contains track title
to search for, preferentially unicode
object with UTF-8 encoding
--------------------------------------------
Returns: boolean value if the query was
successful (True) or not (False)
--------------------------------------------
"""
# -- parameters
Ldet = 5080. # detector to sample distance
dpix = 0.055 # um (pixel size)
energy = 8.4 #keV
inner_radius, width, spacing, num_rings = 180, 120, 0, 1
# -- constants
h = 4.135667516 * 1e-18 #kev*sec
c = 3 * 1e8 #m/s
lambda_ = h * c / energy * 1e10 # wavelength of the X-rays
# -- ring mask
edges = roi.ring_edges(inner_radius, width, spacing, num_rings)
ring_mask = roi.rings(edges, center, data.shape)
# -- apply threshold mask data
data[data < threshold[0]] = 0
data[data > threshold[1]] = 0
# -- radial average
Iphi = self.radialAverage(self.data,
calibrated_center=center,
mask=self.mask * ring_mask,
nx=n_bins,
min_x=min_bin,
max_x=max_bin)
return Iphi[0] * 180. / np.pi + 180., Iphi[1]
def calculate_g2(data,mask=None,g2q=180,center = [265,655]):
'''
Calculates the intensity-intensity temporal autocorrelation using
scikit-beam packages on the back detector
for a q-range (g2q) and width (width, num_rings,spacing)
'''
# -- parameters
inner_radius = g2q#180 # radius of the first ring
width = 1
spacing = 0
num_rings = 10
#center = (273, 723) # center of the speckle pattern
dpix = 0.055 # The physical size of the pixels
energy = 8.4 #keV
h = 4.135667516*1e-18#kev*sec
c = 3*1e8 #m/s
lambda_ = h*c/energy*1e10 # wavelength of the X-rays
Ldet = 5080. # # detector to sample distance
# -- average and mask data
avg_data = np.average(data,axis=0)#*mask
# -- ring array
edges = roi.ring_edges(inner_radius, width, spacing, num_rings)
# -- convert ring to q
two_theta = utils.radius_to_twotheta(Ldet, edges*dpix)
q_val = utils.twotheta_to_q(two_theta, lambda_)
q_ring = np.mean(q_val, axis=1)
# -- ring mask
rings = roi.rings(edges, center, data[0].shape)
ring_mask = rings*mask
# -- calulate g2
num_levels = 1#7
num_bufs = 100#2
g2, lag_steps = corr.multi_tau_auto_corr(num_levels,num_bufs,ring_mask,mask*data)
# -- average
g2_avg = np.average(g2,axis=1)
qt = np.average(q_ring)
# -- standard error
g2_err = np.std(g2,axis=1)/np.sqrt(len(g2[0,:]))
return qt,lag_steps[1:],g2_avg[1:],g2_err[1:]
def radialIntegrationBackDetector(self, n_bins=300, center=(265,655), min_bin=-np.pi, max_bin=np.pi,threshold=(0.001,10000)):
"""
--------------------------------------------
Method: SpecklePattern.radialIntegrationBackDetector
--------------------------------------------
Wraps radialAverage to perform circular integration and q-calibration on the back
detector, with an additional threshold masking with parameters
for Lambda l02 detector at P10, PETRA III
--------------------------------------------
Usage: if mySpeckles.radialIntegrationBackDetector()
--------------------------------------------
Arguments (optional):
title - string that contains track title
to search for, preferentially unicode
object with UTF-8 encoding
--------------------------------------------
Returns: boolean value if the query was
successful (True) or not (False)
--------------------------------------------
"""
# -- parameters
Ldet = 5080. # detector to sample distance
dpix = 0.055 # um (pixel size)
energy = 8.4 #keV
inner_radius, width, spacing, num_rings = 180, 120, 0, 1
# -- constants
h = 4.135667516*1e-18 #kev*sec
c = 3*1e8 #m/s
lambda_ = h*c/energy*1e10 # wavelength of the X-rays
# -- ring mask
edges = roi.ring_edges(inner_radius, width, spacing, num_rings)
ring_mask = roi.rings(edges, center, data.shape)
# -- apply threshold mask data
data[data<threshold[0]]=0
data[data>threshold[1]]=0
# -- radial average
Iphi= self.radialAverage(self.data, calibrated_center=center, mask=self.mask*ring_mask, nx=n_bins, min_x=min_bin, max_x=max_bin)
return Iphi[0]*180./np.pi+180.,Iphi[1]
def test_construct_circ_avg_image():
# need to test with center and dims and without
# and test anisotropic pixels
image = np.zeros((12, 12))
calib_center = (2, 2)
inner_radius = 1
edges = roi.ring_edges(inner_radius, width=1, spacing=1, num_rings=2)
labels = roi.rings(edges, calib_center, image.shape)
image[labels == 1] = 10
image[labels == 2] = 10
bin_cen, ring_avg = roi.circular_average(image, calib_center, nx=6)
# check that the beam center and dims yield the circavg in the right place
cimg = nimage.construct_circ_avg_image(bin_cen, ring_avg, dims=image.shape,
center=calib_center)
assert_array_almost_equal(cimg[2], np.array([5.0103283, 6.15384615,
6.15384615, 6.15384615, 5.0103283, 3.79296498,
2.19422113, 0.51063356, 0., 0., 0., 0.]))
# check that the default values center in the same place
cimg2 = nimage.construct_circ_avg_image(bin_cen, ring_avg)
assert_array_almost_equal(cimg2[12], np.array([0., 0., 0., 0., 0.,
0., 0., 0.51063356, 2.19422113, 3.79296498,
5.0103283, 6.15384615, 6.15384615, 6.15384615,
5.0103283, 3.79296498, 2.19422113, 0.51063356,
0., 0., 0., 0., 0., 0., 0.]))
# check that anisotropic pixels are treated properly
cimg3 = nimage.construct_circ_avg_image(bin_cen, ring_avg,
dims=image.shape,
pixel_size=(2, 1))
assert_array_almost_equal(cimg3[5], np.array([0., 1.16761618, 2.80022015,
4.16720388, 5.250422, 6.08400137, 6.08400137,
5.250422, 4.16720388, 2.80022015,
1.16761618, 0.]))
with assert_raises(ValueError):
nimage.construct_circ_avg_image(bin_cen, ring_avg, center=calib_center,
pixel_size=(2, 1))
def angular_integration_back_detector(data,
n_bins=100,
mask=None,
center=(265, 655),
min_bin=40,
max_bin=800,
threshold=(0.001, 10000)):
'''
Does the circular integration and q-calibration on the back
detector, with an additional threshold masking
'''
# -- parameters
Ldet = 5080. # detector to sample distance
dpix = 0.055 # um (pixel size)
energy = 8.4 #keV
# -- constants
h = 4.135667516 * 1e-18 #kev*sec
c = 3 * 1e8 #m/s
lambda_ = h * c / energy * 1e10 # wavelength of the X-rays
# -- calulate q-range
width, spacing = float(max_bin - min_bin) / float(n_bins), 0
edges = roi.ring_edges(min_bin, width, spacing, n_bins)
two_theta = utils.radius_to_twotheta(Ldet, edges * dpix)
q_val = utils.twotheta_to_q(two_theta, lambda_)
q = np.mean(q_val, axis=1)
# -- apply threshold mask data
data[data < threshold[0]] = 0
data[data > threshold[1]] = 0
# -- angular average
Iq = circular_average(data,
calibrated_center=center,
mask=mask,
nx=n_bins,
min_x=min_bin,
max_x=max_bin)
print q.shape, Iq[1].shape
return Iq[0], Iq[1]
def test_roi_pixel_values():
images = morphology.diamond(8)
# width incompatible with num_rings
label_array = np.zeros((256, 256))
# different shapes for the images and labels
assert_raises(ValueError,
lambda: roi.roi_pixel_values(images, label_array))
# create a label mask
center = (8., 8.)
inner_radius = 2.
width = 1
spacing = 1
edges = roi.ring_edges(inner_radius, width, spacing, num_rings=5)
rings = roi.rings(edges, center, images.shape)
intensity_data, index = roi.roi_pixel_values(images, rings)
assert_array_equal(intensity_data[0], ([1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1]))
assert_array_equal([1, 2, 3, 4, 5], index)
def test_roi_pixel_values():
images = morphology.diamond(8)
# width incompatible with num_rings
label_array = np.zeros((256, 256))
# different shapes for the images and labels
assert_raises(ValueError,
lambda: roi.roi_pixel_values(images, label_array))
# create a label mask
center = (8., 8.)
inner_radius = 2.
width = 1
spacing = 1
edges = roi.ring_edges(inner_radius, width, spacing, num_rings=5)
rings = roi.rings(edges, center, images.shape)
intensity_data, index = roi.roi_pixel_values(images, rings)
assert_array_equal(intensity_data[0],
([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]))
assert_array_equal([1, 2, 3, 4, 5], index)
def test_kymograph():
calib_center = (25, 25)
inner_radius = 5
edges = roi.ring_edges(inner_radius, width=2, num_rings=1)
labels = roi.rings(edges, calib_center, (50, 50))
images = []
num_images = 100
for i in range(num_images):
int_array = i * np.ones(labels.shape)
images.append(int_array)
kymograph_data = roi.kymograph(np.asarray(images), labels, num=1)
# make sure the the return array has the expected dimensions
expected_shape = (num_images, np.sum(labels[labels == 1]))
assert kymograph_data.shape[0] == expected_shape[0]
assert kymograph_data.shape[1] == expected_shape[1]
# make sure we got one element from each image
assert np.all(kymograph_data[:, 0] == np.arange(num_images))
# given the input data, every row of kymograph_data should be the same
# number
for row in kymograph_data:
assert np.all(row == row[0])
def test_kymograph():
calib_center = (25, 25)
inner_radius = 5
edges = roi.ring_edges(inner_radius, width=2, num_rings=1)
labels = roi.rings(edges, calib_center, (50, 50))
images = []
num_images = 100
for i in range(num_images):
int_array = i*np.ones(labels.shape)
images.append(int_array)
kymograph_data = roi.kymograph(np.asarray(images), labels, num=1)
# make sure the the return array has the expected dimensions
expected_shape = (num_images, np.sum(labels[labels == 1]))
assert kymograph_data.shape[0] == expected_shape[0]
assert kymograph_data.shape[1] == expected_shape[1]
# make sure we got one element from each image
assert np.all(kymograph_data[:, 0] == np.arange(num_images))
# given the input data, every row of kymograph_data should be the same
# number
for row in kymograph_data:
assert np.all(row == row[0])
def calculateG2(self, g2q=180, center=[265, 655]):
"""
--------------------------------------------
Method: SpecklePattern.calculateG2
--------------------------------------------
Calculates the intensity-intensity temporal autocorrelation using
scikit-beam packages on the back detector
for a q-range (g2q) and width (width, num_rings,spacing)
--------------------------------------------
Usage: mySpeckles.calculateG2()
--------------------------------------------
Prerequisites:
data - data stored as numpy 3D array
--------------------------------------------
Arguments: (optional)
g2q - at which q (pixels) you wanna calculate g2
center - beam position in the data array (y, x)
--------------------------------------------
Returns tuple with:
qt - q (in Ang-1) at which you calculated g2
lag_steps[1:] - time array
g2_avg[1:] - g2 average array
g2_err[1:] - g2 uncertainty array
--------------------------------------------
"""
# -- parameters
inner_radius = g2q #180 # radius of the first ring
width = 1
spacing = 0
num_rings = 10
#center = (273, 723) # center of the speckle pattern
dpix = 0.055 # The physical size of the pixels
energy = 8.4 #keV
h = 4.135667516 * 1e-18 #kev*sec
c = 3 * 1e8 #m/s
lambda_ = h * c / energy * 1e10 # wavelength of the X-rays
Ldet = 5080. # # detector to sample distance
# -- average and mask data
avg_data = np.average(self.data, axis=0) #*mask
# -- ring array
edges = roi.ring_edges(inner_radius, width, spacing, num_rings)
# -- convert ring to q
two_theta = utils.radius_to_twotheta(Ldet, edges * dpix)
q_val = utils.twotheta_to_q(two_theta, lambda_)
q_ring = np.mean(q_val, axis=1)
# -- ring mask
rings = roi.rings(edges, center, self.data[0].shape)
ring_mask = rings * mask
# -- calulate g2
num_levels = 1 #7
num_bufs = 100 #2
g2, lag_steps = corr.multi_tau_auto_corr(num_levels, num_bufs,
ring_mask,
self.mask * self.data)
# -- average
g2_avg = np.average(g2, axis=1)
qt = np.average(q_ring)
# -- standard error
g2_err = np.std(g2, axis=1) / np.sqrt(len(g2[0, :]))
return qt, lag_steps[1:], g2_avg[1:], g2_err[1:]
import skbeam.core.roi as roi
inner_radius = 3
width = 1
spacing = 1
num_rings = 2
center = (13, 14)
num_levels = 5
num_bufs = 4 # must be even
xdim = 25
ydim = 25
stack_size = 10
synthetic_data = np.random.randint(1, 10, (stack_size, xdim, ydim))
edges = roi.ring_edges(inner_radius, width, spacing, num_rings)
rings = roi.rings(edges, center, (xdim, ydim))
#(label_array, pixel_list, num_rois, num_pixels, lag_steps, buf,
# img_per_level, track_level, cur,
# norm, lev_len) = _validate_and_transform_inputs(num_bufs,
# num_levels, rings)
for i in range(num_rings):
if rank==i:
lazy_one_time(synthetic_data, num_levels, num_bufs, rings,
internal_state=None)
def testCrossCorrelator2d():
''' Test the 2D case of the cross correlator.
With non-binary labels.
'''
np.random.seed(123)
# test 2D data
Npoints2 = 10
x2 = np.linspace(-10, 10, Npoints2)
X, Y = np.meshgrid(x2, x2)
Z = np.random.random((Npoints2, Npoints2))
np.random.seed(123)
sigma = .2
# purposely have sparsely filled values (with lots of zeros)
# place peaks in random positions
peak_positions = (np.random.random((2, 10))-.5)*20
for peak_position in peak_positions:
Z += np.exp(-((X - peak_position[0])**2 +
(Y - peak_position[1])**2)/2./sigma**2)
mask_2D = np.ones_like(Z)
mask_2D[1:2, 1:2] = 0
mask_2D[7:9, 4:6] = 0
mask_2D[1:2, 9:] = 0
# Compute with segmented rings
edges = ring_edges(1, 3, num_rings=2)
segments = 5
x0, y0 = np.array(mask_2D.shape)//2
maskids = segmented_rings(edges, segments, (y0, x0), mask_2D.shape)
cc2D_ids = CrossCorrelator(mask_2D.shape, mask=maskids)
cc2D_ids_symavg = CrossCorrelator(mask_2D.shape, mask=maskids,
normalization='symavg')
# 10 ids
assert_equal(cc2D_ids.nids, 10)
ycorr_ids_2D = cc2D_ids(Z)
ycorr_ids_2D_symavg = cc2D_ids_symavg(Z)
index = 0
ycorr_ids_2D[index][ycorr_ids_2D[index].shape[0]//2]
assert_array_almost_equal(ycorr_ids_2D[index]
[ycorr_ids_2D[index].shape[0]//2],
np.array([1.22195059, 1.08685771,
1.43246508, 1.08685771, 1.22195059
])
)
index = 1
ycorr_ids_2D[index][ycorr_ids_2D[index].shape[0]//2]
assert_array_almost_equal(ycorr_ids_2D[index]
[ycorr_ids_2D[index].shape[0]//2],
np.array([1.24324268, 0.80748997,
1.35790022, 0.80748997, 1.24324268
])
)
index = 0
ycorr_ids_2D_symavg[index][ycorr_ids_2D[index].shape[0]//2]
assert_array_almost_equal(ycorr_ids_2D_symavg[index]
[ycorr_ids_2D[index].shape[0]//2],
np.array([0.84532695, 1.16405848, 1.43246508,
1.16405848, 0.84532695])
)
index = 1
ycorr_ids_2D_symavg[index][ycorr_ids_2D[index].shape[0]//2]
assert_array_almost_equal(ycorr_ids_2D_symavg[index]
[ycorr_ids_2D[index].shape[0]//2],
np.array([0.94823482, 0.8629459, 1.35790022,
0.8629459, 0.94823482])
)
def calculateG2(self, g2q=180, center=[265,655]):
"""
--------------------------------------------
Method: SpecklePattern.calculateG2
--------------------------------------------
Calculates the intensity-intensity temporal autocorrelation using
scikit-beam packages on the back detector
for a q-range (g2q) and width (width, num_rings,spacing)
--------------------------------------------
Usage: mySpeckles.calculateG2()
--------------------------------------------
Prerequisites:
data - data stored as numpy 3D array
--------------------------------------------
Arguments: (optional)
g2q - at which q (pixels) you wanna calculate g2
center - beam position in the data array (y, x)
--------------------------------------------
Returns tuple with:
qt - q (in Ang-1) at which you calculated g2
lag_steps[1:] - time array
g2_avg[1:] - g2 average array
g2_err[1:] - g2 uncertainty array
--------------------------------------------
"""
# -- parameters
inner_radius = g2q#180 # radius of the first ring
width = 1
spacing = 0
num_rings = 10
#center = (273, 723) # center of the speckle pattern
dpix = 0.055 # The physical size of the pixels
energy = 8.4 #keV
h = 4.135667516*1e-18#kev*sec
c = 3*1e8 #m/s
lambda_ = h*c/energy*1e10 # wavelength of the X-rays
Ldet = 5080. # # detector to sample distance
# -- average and mask data
avg_data = np.average(self.data,axis=0)#*mask
# -- ring array
edges = roi.ring_edges(inner_radius, width, spacing, num_rings)
# -- convert ring to q
two_theta = utils.radius_to_twotheta(Ldet, edges*dpix)
q_val = utils.twotheta_to_q(two_theta, lambda_)
q_ring = np.mean(q_val, axis=1)
# -- ring mask
rings = roi.rings(edges, center, self.data[0].shape)
ring_mask = rings*mask
# -- calulate g2
num_levels = 1 #7
num_bufs = 100 #2
g2, lag_steps = corr.multi_tau_auto_corr(num_levels,num_bufs,ring_mask,self.mask*self.data)
# -- average
g2_avg = np.average(g2,axis=1)
qt = np.average(q_ring)
# -- standard error
g2_err = np.std(g2,axis=1)/np.sqrt(len(g2[0,:]))
return qt, lag_steps[1:], g2_avg[1:], g2_err[1:]
def testCrossCorrelator2d():
''' Test the 2D case of the cross correlator.
With non-binary labels.
'''
np.random.seed(123)
# test 2D data
Npoints2 = 10
x2 = np.linspace(-10, 10, Npoints2)
X, Y = np.meshgrid(x2, x2)
Z = np.random.random((Npoints2, Npoints2))
np.random.seed(123)
sigma = .2
# purposely have sparsely filled values (with lots of zeros)
# place peaks in random positions
peak_positions = (np.random.random((2, 10)) - .5) * 20
for peak_position in peak_positions:
Z += np.exp(-((X - peak_position[0])**2 + (Y - peak_position[1])**2) /
2. / sigma**2)
mask_2D = np.ones_like(Z)
mask_2D[1:2, 1:2] = 0
mask_2D[7:9, 4:6] = 0
mask_2D[1:2, 9:] = 0
# Compute with segmented rings
edges = ring_edges(1, 3, num_rings=2)
segments = 5
x0, y0 = np.array(mask_2D.shape) // 2
maskids = segmented_rings(edges, segments, (y0, x0), mask_2D.shape)
cc2D_ids = CrossCorrelator(mask_2D.shape, mask=maskids)
cc2D_ids_symavg = CrossCorrelator(mask_2D.shape,
mask=maskids,
normalization='symavg')
# 10 ids
assert_equal(cc2D_ids.nids, 10)
ycorr_ids_2D = cc2D_ids(Z)
ycorr_ids_2D_symavg = cc2D_ids_symavg(Z)
index = 0
ycorr_ids_2D[index][ycorr_ids_2D[index].shape[0] // 2]
assert_array_almost_equal(
ycorr_ids_2D[index][ycorr_ids_2D[index].shape[0] // 2],
np.array([1.22195059, 1.08685771, 1.43246508, 1.08685771, 1.22195059]))
index = 1
ycorr_ids_2D[index][ycorr_ids_2D[index].shape[0] // 2]
assert_array_almost_equal(
ycorr_ids_2D[index][ycorr_ids_2D[index].shape[0] // 2],
np.array([1.24324268, 0.80748997, 1.35790022, 0.80748997, 1.24324268]))
index = 0
ycorr_ids_2D_symavg[index][ycorr_ids_2D[index].shape[0] // 2]
assert_array_almost_equal(
ycorr_ids_2D_symavg[index][ycorr_ids_2D[index].shape[0] // 2],
np.array([0.84532695, 1.16405848, 1.43246508, 1.16405848, 0.84532695]))
index = 1
ycorr_ids_2D_symavg[index][ycorr_ids_2D[index].shape[0] // 2]
assert_array_almost_equal(
ycorr_ids_2D_symavg[index][ycorr_ids_2D[index].shape[0] // 2],
np.array([0.94823482, 0.8629459, 1.35790022, 0.8629459, 0.94823482]))
# plot the first image to make sure the data loaded correctly
fig, ax = plt.subplots()
ax.imshow(img_stack[0])
ax.set_title("NIPA_GEL_250K")
# define the ROIs
roi_start = 65 # in pixels
roi_width = 9 # in pixels
roi_spacing = (5.0, 4.0)
x_center = 7. # in pixels
y_center = (129.) # in pixels
num_rings = 3
# get the edges of the rings
edges = roi.ring_edges(roi_start, width=roi_width,
spacing=roi_spacing, num_rings=num_rings)
# get the label array from the ring shaped 3 region of interests(ROI's)
labeled_roi_array = roi.rings(
edges, (y_center, x_center), img_stack.shape[1:])
# extarct the ROI's lables and pixel indices corresponding to those labels
roi_indices, pixel_list = roi.extract_label_indices(labeled_roi_array)
# define the ROIs
roi_start = 65 # in pixels
roi_width = 9 # in pixels
roi_spacing = (5.0, 4.0)
x_center = 7. # in pixels
y_center = (129.) # in pixels
def test_rings():
center = (100., 100.)
img_dim = (200, 205)
first_q = 10.
delta_q = 5.
num_rings = 7 # number of Q rings
one_step_q = 5.0
step_q = [2.5, 3.0, 5.8]
# test when there is same spacing between rings
edges = roi.ring_edges(first_q, width=delta_q, spacing=one_step_q,
num_rings=num_rings)
print("edges there is same spacing between rings ", edges)
label_array = roi.rings(edges, center, img_dim)
print("label_array there is same spacing between rings", label_array)
label_mask, pixel_list = roi.extract_label_indices(label_array)
# number of pixels per ROI
num_pixels = np.bincount(label_mask, minlength=(np.max(label_mask)+1))
num_pixels = num_pixels[1:]
# test when there is same spacing between rings
edges = roi.ring_edges(first_q, width=delta_q, spacing=2.5,
num_rings=num_rings)
print("edges there is same spacing between rings ", edges)
label_array = roi.rings(edges, center, img_dim)
print("label_array there is same spacing between rings", label_array)
label_mask, pixel_list = roi.extract_label_indices(label_array)
# number of pixels per ROI
num_pixels = np.bincount(label_mask, minlength=(np.max(label_mask)+1))
num_pixels = num_pixels[1:]
# test when there is different spacing between rings
edges = roi.ring_edges(first_q, width=delta_q, spacing=step_q,
num_rings=4)
print("edges when there is different spacing between rings", edges)
label_array = roi.rings(edges, center, img_dim)
print("label_array there is different spacing between rings", label_array)
label_mask, pixel_list = roi.extract_label_indices(label_array)
# number of pixels per ROI
num_pixels = np.bincount(label_mask, minlength=(np.max(label_mask)+1))
num_pixels = num_pixels[1:]
# test when there is no spacing between rings
edges = roi.ring_edges(first_q, width=delta_q, num_rings=num_rings)
print("edges", edges)
label_array = roi.rings(edges, center, img_dim)
print("label_array", label_array)
label_mask, pixel_list = roi.extract_label_indices(label_array)
# number of pixels per ROI
num_pixels = np.bincount(label_mask, minlength=(np.max(label_mask)+1))
num_pixels = num_pixels[1:]
# Did we draw the right number of rings?
print(np.unique(label_array))
actual_num_rings = len(np.unique(label_array)) - 1
assert_equal(actual_num_rings, num_rings)
# Does each ring have more pixels than the last, being larger?
ring_areas = np.bincount(label_array.ravel())[1:]
area_comparison = np.diff(ring_areas)
print(area_comparison)
areas_monotonically_increasing = np.all(area_comparison > 0)
assert_true(areas_monotonically_increasing)
# Test various illegal inputs
assert_raises(ValueError,
lambda: roi.ring_edges(1, 2)) # need num_rings
# width incompatible with num_rings
assert_raises(ValueError,
lambda: roi.ring_edges(1, [1, 2, 3], num_rings=2))
# too few spacings
assert_raises(ValueError,
lambda: roi.ring_edges(1, [1, 2, 3], [1]))
# too many spacings
assert_raises(ValueError,
lambda: roi.ring_edges(1, [1, 2, 3], [1, 2, 3]))
# num_rings conflicts with width, spacing
assert_raises(ValueError,
lambda: roi.ring_edges(1, [1, 2, 3], [1, 2], 5))
w_edges = [[5, 7], [1, 2]]
assert_raises(ValueError, roi.rings, w_edges, center=(4, 4),
shape=(20, 20))
def test_rings():
center = (100., 100.)
img_dim = (200, 205)
first_q = 10.
delta_q = 5.
num_rings = 7 # number of Q rings
one_step_q = 5.0
step_q = [2.5, 3.0, 5.8]
# test when there is same spacing between rings
edges = roi.ring_edges(first_q,
width=delta_q,
spacing=one_step_q,
num_rings=num_rings)
print("edges there is same spacing between rings ", edges)
label_array = roi.rings(edges, center, img_dim)
print("label_array there is same spacing between rings", label_array)
label_mask, pixel_list = roi.extract_label_indices(label_array)
# number of pixels per ROI
num_pixels = np.bincount(label_mask, minlength=(np.max(label_mask) + 1))
num_pixels = num_pixels[1:]
# test when there is same spacing between rings
edges = roi.ring_edges(first_q,
width=delta_q,
spacing=2.5,
num_rings=num_rings)
print("edges there is same spacing between rings ", edges)
label_array = roi.rings(edges, center, img_dim)
print("label_array there is same spacing between rings", label_array)
label_mask, pixel_list = roi.extract_label_indices(label_array)
# number of pixels per ROI
num_pixels = np.bincount(label_mask, minlength=(np.max(label_mask) + 1))
num_pixels = num_pixels[1:]
# test when there is different spacing between rings
edges = roi.ring_edges(first_q, width=delta_q, spacing=step_q, num_rings=4)
print("edges when there is different spacing between rings", edges)
label_array = roi.rings(edges, center, img_dim)
print("label_array there is different spacing between rings", label_array)
label_mask, pixel_list = roi.extract_label_indices(label_array)
# number of pixels per ROI
num_pixels = np.bincount(label_mask, minlength=(np.max(label_mask) + 1))
num_pixels = num_pixels[1:]
# test when there is no spacing between rings
edges = roi.ring_edges(first_q, width=delta_q, num_rings=num_rings)
print("edges", edges)
label_array = roi.rings(edges, center, img_dim)
print("label_array", label_array)
label_mask, pixel_list = roi.extract_label_indices(label_array)
# number of pixels per ROI
num_pixels = np.bincount(label_mask, minlength=(np.max(label_mask) + 1))
num_pixels = num_pixels[1:]
# Did we draw the right number of rings?
print(np.unique(label_array))
actual_num_rings = len(np.unique(label_array)) - 1
assert_equal(actual_num_rings, num_rings)
# Does each ring have more pixels than the last, being larger?
ring_areas = np.bincount(label_array.ravel())[1:]
area_comparison = np.diff(ring_areas)
print(area_comparison)
areas_monotonically_increasing = np.all(area_comparison > 0)
assert areas_monotonically_increasing
# Test various illegal inputs
assert_raises(ValueError, lambda: roi.ring_edges(1, 2)) # need num_rings
# width incompatible with num_rings
assert_raises(ValueError,
lambda: roi.ring_edges(1, [1, 2, 3], num_rings=2))
# too few spacings
assert_raises(ValueError, lambda: roi.ring_edges(1, [1, 2, 3], [1]))
# too many spacings
assert_raises(ValueError, lambda: roi.ring_edges(1, [1, 2, 3], [1, 2, 3]))
# num_rings conflicts with width, spacing
assert_raises(ValueError, lambda: roi.ring_edges(1, [1, 2, 3], [1, 2], 5))
w_edges = [[5, 7], [1, 2]]
assert_raises(ValueError,
roi.rings,
w_edges,
center=(4, 4),
shape=(20, 20))
