def radial_average(image,
mask=None,
center=[1364, 1504],
threshold=0,
nx=300,
pixel_size=(1, 1),
min_x=-np.pi,
max_x=np.pi):
'''
Does the radial average over a given range, including threshold masking
'''
# - create angular grid
phi_val = utils.angle_grid(center, image.shape,
pixel_size) #*180./np.pi+180.
# - create unitary mask (in case it's none)
if mask is None:
mask = np.ones(image.shape)
# - bin values of image based on the angular coordinates
bin_edges, sums, counts = utils.bin_1D(np.ravel(phi_val * mask),
np.ravel(image * mask),
nx,
min_x=min_x,
max_x=max_x)
th_mask = counts > threshold
phi_averages = np.array(sums[th_mask], dtype=float) / np.array(
counts[th_mask], dtype=float)
bin_centers = utils.bin_edges_to_centers(bin_edges)[th_mask]
return bin_centers * 180 / np.pi + 180., phi_averages
def circular_average(calibrated_center,
threshold=0,
nx=100,
pixel_size=(1, 1),
min_x=None,
max_x=None):
"""Circular average of the the image data
The circular average is also known as the radial integration
(adapted from scikit-beam.roi)
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)
mask : arrayint, optional
Boolean array with 1s (and 0s) to be used (or not) in the average
threshold : int, optional
Ignore counts above `threshold`
default is zero
nx : int, optional
number of bins in x
defaults is 100 bins
pixel_size : tuple, optional
The size of a pixel (in a real unit, like mm).
argument order should be (pixel_height, pixel_width)
default is (1, 1)
min_x : float, optional number of pixels
Left edge of first bin defaults to minimum value of x
max_x : float, optional number of pixels
Right edge of last bin defaults to maximum value of x
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, ).
"""
# - create radial grid
radial_val = utils.radial_grid(calibrated_center, self.data.shape,
pixel_size)
# - bin values of image based on the radial coordinates
bin_edges, sums, counts = utils.bin_1D(np.ravel(radial_val *
self.mask),
np.ravel(self.data * self.mask),
nx,
min_x=min_x,
max_x=max_x)
th_mask = counts > threshold
ring_averages = sums[th_mask] / counts[th_mask]
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 radialAverage(calibrated_center, threshold=0, nx=100, pixel_size=(1, 1), min_x=None, max_x=None):
"""
--------------------------------------------
Method: SpecklePattern.radialAverage
--------------------------------------------
Radial average of the the image data
The radial average is also known as the azimuthal integration
(adapted from scikit-beam.roi)
--------------------------------------------
Usage: tracksToUse = mySpeckles.findTracksToUse(message)
--------------------------------------------
Arguments:
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)
mask : arrayint, optional
Boolean array with 1s (and 0s) to be used (or not) in the average
threshold : int, optional
Ignore counts above `threshold`
default is zero
nx : int, optional
number of bins in x
defaults is 100 bins
pixel_size : tuple, optional
The size of a pixel (in a real unit, like mm).
argument order should be (pixel_height, pixel_width)
default is (1, 1)
min_x : float, optional number of pixels
Left edge of first bin defaults to minimum value of x
max_x : float, optional number of pixels
Right edge of last bin defaults to maximum value of x
--------------------------------------------
Returns:
bin_centers : array
The center of each bin in R. shape is (nx, )
phi_averages : array
Radial average of the image. shape is (nx, ).
--------------------------------------------
"""
# - create angular grid
phi_val = utils.angle_grid(calibrated_center, self.data.shape,pixel_size)#*180./np.pi+180.
# - bin values of self.data based on the angular coordinates
bin_edges, sums, counts = utils.bin_1D(np.ravel(phi_val*self.mask),
np.ravel(self.data*self.mask), nx,
min_x=min_x,
max_x=max_x)
th_mask = counts > threshold
phi_averages = sums[th_mask]/ counts[th_mask]
bin_centers = utils.bin_edges_to_centers(bin_edges)[th_mask]
return bin_centers, phi_averages
def test_bin_1D():
# set up simple data
x = np.linspace(0, 1, 100)
y = np.arange(100)
nx = 10
# make call
edges, val, count = core.bin_1D(x, y, nx)
# check that values are as expected
assert_array_almost_equal(edges, np.linspace(0, 1, nx + 1, endpoint=True))
assert_array_almost_equal(val, np.sum(y.reshape(nx, -1), axis=1))
assert_array_equal(count, np.ones(nx) * 10)
def angular_average(image, calibrated_center,mask,threshold=0, nx=100,
pixel_size=(1, 1), min_x=None, max_x=None):
"""Circular average of the the image data
The circular average is also known as the radial integration
(adapted from scikit-beam.roi)
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)
mask : arrayint, optional
Boolean array with 1s (and 0s) to be used (or not) in the average
threshold : int, optional
Ignore counts above `threshold`
default is zero
nx : int, optional
number of bins in x
defaults is 100 bins
pixel_size : tuple, optional
The size of a pixel (in a real unit, like mm).
argument order should be (pixel_height, pixel_width)
default is (1, 1)
min_x : float, optional number of pixels
Left edge of first bin defaults to minimum value of x
max_x : float, optional number of pixels
Right edge of last bin defaults to maximum value of x
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, ).
"""
# - create radial grid
radial_val = utils.radial_grid(calibrated_center, image.shape, pixel_size)
# - create unitary mask (in case it's none)
if mask is None:
mask=np.ones(image.shape)
# - bin values of image based on the radial coordinates
bin_edges, sums, counts = utils.bin_1D(np.ravel(radial_val*mask),
np.ravel(image*mask), nx,
min_x=min_x,
max_x=max_x)
th_mask = counts > threshold
ring_averages = sums[th_mask] / counts[th_mask]
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_1D():
# set up simple data
x = np.linspace(0, 1, 100)
y = np.arange(100)
nx = 10
# make call
edges, val, count = core.bin_1D(x, y, nx)
# check that values are as expected
assert_array_almost_equal(edges,
np.linspace(0, 1, nx + 1, endpoint=True))
assert_array_almost_equal(val,
np.sum(y.reshape(nx, -1), axis=1))
assert_array_equal(count,
np.ones(nx) * 10)
def test_bin_1D_2():
"""
Test for appropriate default value handling
"""
# set up simple data
x = np.linspace(0, 1, 100)
y = np.arange(100)
nx = core._defaults["bins"]
min_x = None
max_x = None
# make call
edges, val, count = core.bin_1D(x=x, y=y, nx=nx, min_x=min_x, max_x=max_x)
# check that values are as expected
assert_array_almost_equal(edges, np.linspace(0, 1, nx + 1, endpoint=True))
assert_array_almost_equal(val, np.sum(y.reshape(nx, -1), axis=1))
assert_array_equal(count, np.ones(nx))
def test_bin_1D_2():
"""
Test for appropriate default value handling
"""
# set up simple data
x = np.linspace(0, 1, 100)
y = np.arange(100)
nx = core._defaults["bins"]
min_x = None
max_x = None
# make call
edges, val, count = core.bin_1D(x=x, y=y, nx=nx, min_x=min_x, max_x=max_x)
# check that values are as expected
assert_array_almost_equal(edges,
np.linspace(0, 1, nx + 1, endpoint=True))
assert_array_almost_equal(val,
np.sum(y.reshape(nx, -1), axis=1))
assert_array_equal(count,
np.ones(nx))
def radialAverage(calibrated_center,
threshold=0,
nx=100,
pixel_size=(1, 1),
min_x=None,
max_x=None):
"""
--------------------------------------------
Method: SpecklePattern.radialAverage
--------------------------------------------
Radial average of the the image data
The radial average is also known as the azimuthal integration
(adapted from scikit-beam.roi)
--------------------------------------------
Usage: tracksToUse = mySpeckles.findTracksToUse(message)
--------------------------------------------
Arguments:
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)
mask : arrayint, optional
Boolean array with 1s (and 0s) to be used (or not) in the average
threshold : int, optional
Ignore counts above `threshold`
default is zero
nx : int, optional
number of bins in x
defaults is 100 bins
pixel_size : tuple, optional
The size of a pixel (in a real unit, like mm).
argument order should be (pixel_height, pixel_width)
default is (1, 1)
min_x : float, optional number of pixels
Left edge of first bin defaults to minimum value of x
max_x : float, optional number of pixels
Right edge of last bin defaults to maximum value of x
--------------------------------------------
Returns:
bin_centers : array
The center of each bin in R. shape is (nx, )
phi_averages : array
Radial average of the image. shape is (nx, ).
--------------------------------------------
"""
# - create angular grid
phi_val = utils.angle_grid(calibrated_center, self.data.shape,
pixel_size) #*180./np.pi+180.
# - bin values of self.data based on the angular coordinates
bin_edges, sums, counts = utils.bin_1D(np.ravel(phi_val * self.mask),
np.ravel(self.data * self.mask),
nx,
min_x=min_x,
max_x=max_x)
th_mask = counts > threshold
phi_averages = sums[th_mask] / counts[th_mask]
bin_centers = utils.bin_edges_to_centers(bin_edges)[th_mask]
return bin_centers, phi_averages