def xsvs(image_sets,
label_array,
number_of_img,
timebin_num=2,
time_bin=None,
max_cts=None,
bad_images=None,
threshold=None):
"""
This function will provide the probability density of detecting photons
for different integration times.
The experimental probability density P(K) of detecting photons K is
obtained by histogramming the speckle counts over an ensemble of
equivalent pixels and over a number of speckle patterns recorded
with the same integration time T under the same condition.
Parameters
----------
image_sets : array
sets of images
label_array : array
labeled array; 0 is background.
Each ROI is represented by a distinct label (i.e., integer).
number_of_img : int
number of images (how far to go with integration times when finding
the time_bin, using skxray.utils.geometric function)
timebin_num : int, optional
integration time; default is 2
max_cts : int, optional
the brightest pixel in any ROI in any image in the image set.
defaults to using skxray.core.roi.roi_max_counts to determine
the brightest pixel in any of the ROIs
bad_images: array, optional
the bad images number list, the XSVS will not analyze the binning image groups which involve any bad images
threshold: float, optional
If one image involves a pixel with intensity above threshold, such image will be considered as a bad image.
Returns
-------
prob_k_all : array
probability density of detecting photons
prob_k_std_dev : array
standard deviation of probability density of detecting photons
Notes
-----
These implementation is based on following references
References: text [1]_, text [2]_
.. [1] L. Li, P. Kwasniewski, D. Oris, L Wiegart, L. Cristofolini,
C. Carona and A. Fluerasu , "Photon statistics and speckle visibility
spectroscopy with partially coherent x-rays" J. Synchrotron Rad.,
vol 21, p 1288-1295, 2014.
.. [2] R. Bandyopadhyay, A. S. Gittings, S. S. Suh, P.K. Dixon and
D.J. Durian "Speckle-visibilty Spectroscopy: A tool to study
time-varying dynamics" Rev. Sci. Instrum. vol 76, p 093110, 2005.
There is an example in https://github.com/scikit-xray/scikit-xray-examples
It will demonstrate the use of these functions in this module for
experimental data.
"""
if max_cts is None:
max_cts = roi.roi_max_counts(image_sets, label_array)
# find the label's and pixel indices for ROI's
labels, indices = roi.extract_label_indices(label_array)
nopixels = len(indices)
# number of ROI's
u_labels = list(np.unique(labels))
num_roi = len(u_labels)
# create integration times
if time_bin is None:
time_bin = geometric_series(timebin_num, number_of_img)
# number of times in the time bin
num_times = len(time_bin)
# number of pixels per ROI
num_pixels = np.bincount(labels, minlength=(num_roi + 1))[1:]
# probability density of detecting photons
prob_k_all = np.zeros([num_times, num_roi], dtype=np.object)
# square of probability density of detecting photons
prob_k_pow_all = np.zeros_like(prob_k_all)
# standard deviation of probability density of detecting photons
prob_k_std_dev = np.zeros_like(prob_k_all)
# get the bin edges for each time bin for each ROI
bin_edges = np.zeros(prob_k_all.shape[0], dtype=prob_k_all.dtype)
for i in range(num_times):
bin_edges[i] = np.arange(max_cts * 2**i)
start_time = time.time() # used to log the computation time (optionally)
for i, images in enumerate(image_sets):
# Ring buffer, a buffer with periodic boundary conditions.
# Images must be keep for up to maximum delay in buf.
#buf = np.zeros([num_times, timebin_num], dtype=np.object) # matrix of buffers
buf = np.ma.zeros([num_times, timebin_num, nopixels])
buf.mask = True
# to track processing each time level
track_level = np.zeros(num_times)
track_bad_level = np.zeros(num_times)
# to increment buffer
cur = np.full(num_times, timebin_num)
# to track how many images processed in each level
img_per_level = np.zeros(num_times, dtype=np.int64)
prob_k = np.zeros_like(prob_k_all)
prob_k_pow = np.zeros_like(prob_k_all)
try:
noframes = len(images)
except:
noframes = images.length
#Num= { key: [0]* len( dict_dly[key] ) for key in list(dict_dly.keys()) }
for n, img in enumerate(images):
cur[0] = 1 + cur[0] % timebin_num
# read each frame
# Put the image into the ring buffer.
img_ = (np.ravel(img))[indices]
if threshold is not None:
if img_.max() >= threshold:
print('bad image: %s here!' % n)
img_ = np.ma.zeros(len(img_))
img_.mask = True
if bad_images is not None:
if n in bad_images:
print('bad image: %s here!' % n)
img_ = np.ma.zeros(len(img_))
img_.mask = True
buf[0, cur[0] - 1] = img_
_process(num_roi, 0, cur[0] - 1, buf, img_per_level, labels,
max_cts, bin_edges[0], prob_k, prob_k_pow,
track_bad_level)
#print (0, img_per_level)
# check whether the number of levels is one, otherwise
# continue processing the next level
level = 1
processing = 1
#print ('track_level: %s'%track_level)
#while level < num_times:
#if not track_level[level]:
#track_level[level] = 1
while processing:
if track_level[level]:
prev = 1 + (cur[level - 1] - 2) % timebin_num
cur[level] = 1 + cur[level] % timebin_num
bufa = buf[level - 1, prev - 1]
bufb = buf[level - 1, cur[level - 1] - 1]
if (bufa.data == 0).all():
buf[level, cur[level] - 1] = bufa
elif (bufb.data == 0).all():
buf[level, cur[level] - 1] = bufb
else:
buf[level, cur[level] - 1] = bufa + bufb
#print (level, cur[level]-1)
track_level[level] = 0
_process(num_roi, level, cur[level] - 1, buf,
img_per_level, labels, max_cts, bin_edges[level],
prob_k, prob_k_pow, track_bad_level)
level += 1
if level < num_times: processing = 1
else: processing = 0
else:
track_level[level] = 1
processing = 0
#print ('track_level: %s'%track_level)
if noframes >= 10 and n % (int(noframes / 10)) == 0:
sys.stdout.write("#")
sys.stdout.flush()
prob_k_all += (prob_k - prob_k_all) / (i + 1)
prob_k_pow_all += (prob_k_pow - prob_k_pow_all) / (i + 1)
prob_k_std_dev = np.power((prob_k_pow_all - np.power(prob_k_all, 2)), .5)
for i in range(num_times):
if isinstance(prob_k_all[i, 0], float):
for j in range(len(u_labels)):
prob_k_all[i, j] = np.array([0] * (len(bin_edges[i]) - 1))
prob_k_std_dev[i, j] = np.array([0] * (len(bin_edges[i]) - 1))
logger.info("Processing time for XSVS took %s seconds."
"", (time.time() - start_time))
elapsed_time = time.time() - start_time
#print (Num)
print('Total time: %.2f min' % (elapsed_time / 60.))
#print (img_per_level - track_bad_level)
#print (buf)
return prob_k_all, prob_k_std_dev
def test_geometric_series():
time_series = core.geometric_series(common_ratio=5, number_of_images=150)
assert_array_equal(time_series, [1, 5, 25, 125])
def xsvs(image_sets, label_array, number_of_img, timebin_num=2,
max_cts=None, bad_images = None, threshold=None):
"""
This function will provide the probability density of detecting photons
for different integration times.
The experimental probability density P(K) of detecting photons K is
obtained by histogramming the speckle counts over an ensemble of
equivalent pixels and over a number of speckle patterns recorded
with the same integration time T under the same condition.
Parameters
----------
image_sets : array
sets of images
label_array : array
labeled array; 0 is background.
Each ROI is represented by a distinct label (i.e., integer).
number_of_img : int
number of images (how far to go with integration times when finding
the time_bin, using skxray.utils.geometric function)
timebin_num : int, optional
integration time; default is 2
max_cts : int, optional
the brightest pixel in any ROI in any image in the image set.
defaults to using skxray.core.roi.roi_max_counts to determine
the brightest pixel in any of the ROIs
bad_images: array, optional
the bad images number list, the XSVS will not analyze the binning image groups which involve any bad images
threshold: float, optional
If one image involves a pixel with intensity above threshold, such image will be considered as a bad image.
Returns
-------
prob_k_all : array
probability density of detecting photons
prob_k_std_dev : array
standard deviation of probability density of detecting photons
Notes
-----
These implementation is based on following references
References: text [1]_, text [2]_
.. [1] L. Li, P. Kwasniewski, D. Oris, L Wiegart, L. Cristofolini,
C. Carona and A. Fluerasu , "Photon statistics and speckle visibility
spectroscopy with partially coherent x-rays" J. Synchrotron Rad.,
vol 21, p 1288-1295, 2014.
.. [2] R. Bandyopadhyay, A. S. Gittings, S. S. Suh, P.K. Dixon and
D.J. Durian "Speckle-visibilty Spectroscopy: A tool to study
time-varying dynamics" Rev. Sci. Instrum. vol 76, p 093110, 2005.
There is an example in https://github.com/scikit-xray/scikit-xray-examples
It will demonstrate the use of these functions in this module for
experimental data.
"""
if max_cts is None:
max_cts = roi.roi_max_counts(image_sets, label_array)
# find the label's and pixel indices for ROI's
labels, indices = roi.extract_label_indices(label_array)
# number of ROI's
u_labels = list(np.unique(labels))
num_roi = len(u_labels)
# create integration times
time_bin = geometric_series(timebin_num, number_of_img)
# number of times in the time bin
num_times = len(time_bin)
# number of pixels per ROI
num_pixels = np.bincount(labels, minlength=(num_roi+1))[1:]
# probability density of detecting photons
prob_k_all = np.zeros([num_times, num_roi], dtype=np.object)
# square of probability density of detecting photons
prob_k_pow_all = np.zeros_like(prob_k_all)
# standard deviation of probability density of detecting photons
prob_k_std_dev = np.zeros_like(prob_k_all)
# get the bin edges for each time bin for each ROI
bin_edges = np.zeros(prob_k_all.shape[0], dtype=prob_k_all.dtype)
for i in range(num_times):
bin_edges[i] = np.arange(max_cts*2**i)
start_time = time.time() # used to log the computation time (optionally)
for i, images in enumerate(image_sets):
# Ring buffer, a buffer with periodic boundary conditions.
# Images must be keep for up to maximum delay in buf.
buf = np.zeros([num_times, timebin_num],
dtype=np.object) # matrix of buffers
# to track processing each time level
track_level = np.zeros( num_times )
# to increment buffer
cur = np.full(num_times, timebin_num)
# to track how many images processed in each level
img_per_level = np.zeros(num_times, dtype=np.int64)
prob_k = np.zeros_like(prob_k_all)
prob_k_pow = np.zeros_like(prob_k_all)
try:
noframes= len(images)
except:
noframes= images.length
#Num= { key: [0]* len( dict_dly[key] ) for key in list(dict_dly.keys()) }
for n, img in enumerate(images):
cur[0] = 1 + cur[0]% timebin_num
# read each frame
# Put the image into the ring buffer.
buf[0, cur[0] - 1] = (np.ravel(img))[indices]
#print (n, cur[0]-1)
#print (buf.shape)
_process(num_roi, 0, cur[0] - 1, buf, img_per_level, labels,
max_cts, bin_edges[0], prob_k, prob_k_pow)
#print (0, img_per_level)
# check whether the number of levels is one, otherwise
# continue processing the next level
level = 1
processing=1
#print ('track_level: %s'%track_level)
#while level < num_times:
#if not track_level[level]:
#track_level[level] = 1
while processing:
if track_level[level]:
prev = 1 + (cur[level - 1] - 2) % timebin_num
cur[level] = 1 + cur[level] % timebin_num
buf[level, cur[level]-1] = (buf[level-1,
prev-1] +
buf[level-1,
cur[level - 1] - 1])
#print (level, cur[level]-1)
track_level[level] = 0
_process(num_roi, level, cur[level]-1, buf, img_per_level,
labels, max_cts, bin_edges[level], prob_k,
prob_k_pow)
level += 1
if level < num_times:processing = 1
else:processing = 0
else:
track_level[level] = 1
processing = 0
#print ('track_level: %s'%track_level)
if n %( int(noframes/10) ) ==0:
sys.stdout.write("#")
sys.stdout.flush()
prob_k_all += (prob_k - prob_k_all)/(i + 1)
prob_k_pow_all += (prob_k_pow - prob_k_pow_all)/(i + 1)
prob_k_std_dev = np.power((prob_k_pow_all -
np.power(prob_k_all, 2)), .5)
logger.info("Processing time for XSVS took %s seconds."
"", (time.time() - start_time))
elapsed_time = time.time() - start_time
#print (Num)
print ('Total time: %.2f min' %(elapsed_time/60.))
print (img_per_level)
#print (buf)
return prob_k_all, prob_k_std_dev