def normalize_bin_edges(num_times, num_rois, mean_roi, max_cts):
"""
This will provide the normalized bin edges and bin centers for each
integration time.
Parameters
----------
num_times : int
number of integration times for XSVS
num_rois : int
number of ROI's
mean_roi : array
mean intensity of each ROI
shape (number of ROI's)
max_cts : int
maximum pixel counts
Returns
-------
norm_bin_edges : array
normalized speckle count bin edges
shape (num_times, num_rois)
norm_bin_centers :array
normalized speckle count bin centers
shape (num_times, num_rois)
"""
norm_bin_edges = np.zeros((num_times, num_rois), dtype=object)
norm_bin_centers = np.zeros_like(norm_bin_edges)
for i in range(num_times):
for j in range(num_rois):
norm_bin_edges[i, j] = np.arange(
max_cts * 2**i) / (mean_roi[j] * 2**i)
norm_bin_centers[i, j] = bin_edges_to_centers(norm_bin_edges[i, j])
return norm_bin_edges, norm_bin_centers
def normalize_bin_edges(num_times, num_rois, mean_roi, max_cts):
"""
This will provide the normalized bin edges and bin centers for each
integration time.
Parameters
----------
num_times : int
number of integration times for XSVS
num_rois : int
number of ROI's
mean_roi : array
mean intensity of each ROI
shape (number of ROI's)
max_cts : int
maximum pixel counts
Returns
-------
norm_bin_edges : array
normalized speckle count bin edges
shape (num_times, num_rois)
norm_bin_centers :array
normalized speckle count bin centers
shape (num_times, num_rois)
"""
norm_bin_edges = np.zeros((num_times, num_rois), dtype=object)
norm_bin_centers = np.zeros_like(norm_bin_edges)
for i in range(num_times):
for j in range(num_rois):
norm_bin_edges[i, j] = np.arange(max_cts*2**i)/(mean_roi[j]*2**i)
norm_bin_centers[i, j] = bin_edges_to_centers(norm_bin_edges[i, j])
return norm_bin_edges, norm_bin_centers
def circular_average(image,
calibrated_center,
threshold=-1,
nx=None,
pixel_size=None,
mask=None):
"""Circular average of the the image data
The circular average is also known as the radial integration
Parameters
----------
image : array
Image to compute the average as a function of radius
calibrated_center : tuple
The center of the image in pixel units
argument order should be (row, col)
threshold : int, optional
Ignore counts above `threshold`
nx : int, optional
Number of bins in R. Defaults to 100
pixel_size : tuple, optional
The size of a pixel (in a real unit, like mm).
argument order should be (pixel_height, pixel_width)
Returns
-------
bin_centers : array
The center of each bin in R. shape is (nx, )
ring_averages : array
Radial average of the image. shape is (nx, ).
"""
radial_val = utils.radial_grid(calibrated_center, image.shape, pixel_size)
if nx is None:
ps = np.min(pixel_size)
max_x = np.max(radial_val) / ps
min_x = np.min(radial_val) / ps
nx = int(max_x - min_x)
#print (nx)
if mask is None: mask = 1
bin_edges, sums, counts = bin_1D(np.ravel(radial_val * mask),
np.ravel(image * mask), nx)
th_mask = counts > threshold
ring_averages = sums[th_mask] / counts[th_mask]
bin_centers = utils.bin_edges_to_centers(bin_edges)[th_mask]
return bin_centers, ring_averages
def circular_average(image, calibrated_center, threshold=-1, nx=None,
pixel_size=None, mask=None):
"""Circular average of the the image data
The circular average is also known as the radial integration
Parameters
----------
image : array
Image to compute the average as a function of radius
calibrated_center : tuple
The center of the image in pixel units
argument order should be (row, col)
threshold : int, optional
Ignore counts above `threshold`
nx : int, optional
Number of bins in R. Defaults to 100
pixel_size : tuple, optional
The size of a pixel (in a real unit, like mm).
argument order should be (pixel_height, pixel_width)
Returns
-------
bin_centers : array
The center of each bin in R. shape is (nx, )
ring_averages : array
Radial average of the image. shape is (nx, ).
"""
radial_val = utils.radial_grid(calibrated_center, image.shape, pixel_size )
if nx is None:
ps =np.min( pixel_size )
max_x = np.max(radial_val)/ps
min_x = np.min(radial_val)/ps
nx = int(max_x - min_x)
#print (nx)
if mask is None: mask =1
bin_edges, sums, counts = bin_1D(np.ravel(radial_val * mask ),
np.ravel(image * mask), nx)
th_mask = counts > threshold
ring_averages = sums[th_mask] / counts[th_mask]
bin_centers = utils.bin_edges_to_centers(bin_edges)[th_mask]
return bin_centers, ring_averages
def test_bin_edge2center():
test_edges = np.arange(11)
centers = core.bin_edges_to_centers(test_edges)
assert_array_almost_equal(.5, centers % 1)
assert_equal(10, len(centers))
