def profile_line(img, src=None, dst=None, linewidth=1, order=1, mode="constant", cval=0.0, constrain=True, **kargs):
"""Wrapper for sckit-image method of the same name to get a line_profile.
Parameters:
img(ImageArray): Image data to take line section of
src, dst (2-tuple of int or float): start and end of line profile. If the co-ordinates
are given as intergers then they are assumed to be pxiel co-ordinates, floats are
assumed to be real-space co-ordinates using the embedded metadata.
linewidth (int): the wideth of the profile to be taken.
order (int 1-3): Order of interpolation used to find image data when not aligned to a point
mode (str): How to handle data outside of the image.
cval (float): The constant value to assume for data outside of the image is mode is "constant"
constrain (bool): Ensure the src and dst are within the image (default True).
Returns:
A :py:class:`Stoner.Data` object containing the line profile data and the metadata from the image.
"""
scale = img.get("MicronsPerPixel", 1.0)
r, c = img.shape
if src is None and dst is None:
if "x" in kargs:
src = (kargs["x"], 0)
dst = (kargs["x"], r)
if "y" in kargs:
src = (0, kargs["y"])
dst = (c, kargs["y"])
if isinstance(src, float):
src = (src, src)
if isinstance(dst, float):
dst = (dst, dst)
dst = _scale(dst, scale)
src = _scale(src, scale)
if not istuple(src, int, int):
raise ValueError("src co-ordinates are not a 2-tuple of ints.")
if not istuple(dst, int, int):
raise ValueError("dst co-ordinates are not a 2-tuple of ints.")
if constrain:
fix = lambda x, mx: int(round(sorted([0, x, mx])[1]))
r, c = img.shape
src = list(src)
src = (fix(src[0], r), fix(src[1], c))
dst = (fix(dst[0], r), fix(dst[1], c))
result = measure.profile_line(img, src, dst, linewidth, order, mode, cval)
points = measure.profile._line_profile_coordinates(src, dst, linewidth)[:, :, 0]
ret = Data()
ret.data = points.T
ret.setas = "xy"
ret &= np.sqrt(ret.x ** 2 + ret.y ** 2) * scale
ret &= result
ret.column_headers = ["X", "Y", "Distance", "Intensity"]
ret.setas = "..xy"
ret.metadata = img.metadata.copy()
return ret
def profile_line(img, src=None, dst=None, linewidth=1, order=1, mode="constant", cval=0.0, constrain=True, **kargs):
"""Wrapper for sckit-image method of the same name to get a line_profile.
Parameters:
img(ImageArray): Image data to take line section of
src, dst (2-tuple of int or float): start and end of line profile. If the co-ordinates
are given as intergers then they are assumed to be pxiel co-ordinates, floats are
assumed to be real-space co-ordinates using the embedded metadata.
linewidth (int): the wideth of the profile to be taken.
order (int 1-3): Order of interpolation used to find image data when not aligned to a point
mode (str): How to handle data outside of the image.
cval (float): The constant value to assume for data outside of the image is mode is "constant"
constrain (bool): Ensure the src and dst are within the image (default True).
Returns:
A :py:class:`Stoner.Data` object containing the line profile data and the metadata from the image.
"""
scale = img.get("MicronsPerPixel", 1.0)
r, c = img.shape
if src is None and dst is None:
if "x" in kargs:
src = (kargs["x"], 0)
dst = (kargs["x"], r)
if "y" in kargs:
src = (0, kargs["y"])
dst = (c, kargs["y"])
if isinstance(src, float):
src = (src, src)
if isinstance(dst, float):
dst = (dst, dst)
dst = _scale(dst, scale)
src = _scale(src, scale)
if not istuple(src, int, int):
raise ValueError("src co-ordinates are not a 2-tuple of ints.")
if not istuple(dst, int, int):
raise ValueError("dst co-ordinates are not a 2-tuple of ints.")
if constrain:
fix = lambda x, mx: int(round(sorted([0, x, mx])[1]))
r, c = img.shape
src = list(src)
src = (fix(src[0], r), fix(src[1], c))
dst = (fix(dst[0], r), fix(dst[1], c))
result = measure.profile_line(img, src, dst, linewidth, order, mode, cval)
points = measure.profile._line_profile_coordinates(src, dst, linewidth)[:, :, 0]
ret = Data()
ret.data = points.T
ret.setas = "xy"
ret &= np.sqrt(ret.x ** 2 + ret.y ** 2) * scale
ret &= result
ret.column_headers = ["X", "Y", "Distance", "Intensity"]
ret.setas = "..xy"
ret.metadata = img.metadata.copy()
return ret
def normalise(im, scale=None, sample=None, limits=(0.0, 1.0)):
"""Norm alise the data to a fixed scale.
Keyword Arguements:
scale (2-tuple): The range to scale the image to, defaults to -1 to 1.
saple (box): Only use a section of the input image to calculate the new scale over.
limits (low,high): Take the input range from the *high* and *low* fraction of the input when sorted.
Returns:
A scaled version of the data. The ndarray min and max methods are used to allow masked images
to be operated on only on the unmasked areas.
Notes:
The *sample* keyword controls the area in which the range of input values is calculated, the actual scaling is
done on the whole image.
The *limits* parameter is used to set the input scale being normalised from - if an image has a few outliers then
this setting can be used to clip the input range before normalising. The parameters in the limit are the values at
the *low* and *high* fractions of the cumulative distribution functions.
"""
mask = im.mask
cls = im.__class__
im = im.astype(float)
if scale is None:
scale = (-1.0, 1.0)
if sample is not None:
section = im[im._box(sample)]
else:
section = im
if limits != (0.0, 1.0):
low, high = limits
low = np.sort(section.ravel())[int(low * section.size)]
high = np.sort(section.ravel())[int(high * section.size)]
im.clip_intensity(limits=(low, high))
else:
high = section.max()
low = section.min()
if not istuple(scale, float, float, strict=False):
raise ValueError("scale should be a 2-tuple of floats.")
scaled = (im.data - low) / (high - low)
delta = scale[1] - scale[0]
offset = scale[0]
im = scaled * delta + offset
im = im.view(cls)
im.mask = mask
return im
def normalise(im, scale=None, sample=None, limits=(0.0, 1.0)):
"""Norm alise the data to a fixed scale.
Keyword Arguements:
scale (2-tuple): The range to scale the image to, defaults to -1 to 1.
saple (box): Only use a section of the input image to calculate the new scale over.
limits (low,high): Take the input range from the *high* and *low* fraction of the input when sorted.
Returns:
A scaled version of the data. The ndarray min and max methods are used to allow masked images
to be operated on only on the unmasked areas.
Notes:
The *sample* keyword controls the area in which the range of input values is calculated, the actual scaling is
done on the whole image.
The *limits* parameter is used to set the input scale being normalised from - if an image has a few outliers then
this setting can be used to clip the input range before normalising. The parameters in the limit are the values at
the *low* and *high* fractions of the cumulative distribution functions.
"""
mask = im.mask
cls = im.__class__
im = im.astype(float)
if scale is None:
scale = (-1.0, 1.0)
if sample is not None:
section = im[im._box(sample)]
else:
section = im
if limits != (0.0, 1.0):
low, high = limits
low = np.sort(section.ravel())[int(low * section.size)]
high = np.sort(section.ravel())[int(high * section.size)]
im.clip_intensity(limits=(low, high))
else:
high = section.max()
low = section.min()
if not istuple(scale, float, float, strict=False):
raise ValueError("scale should be a 2-tuple of floats.")
scaled = (im.data - low) / (high - low)
delta = scale[1] - scale[0]
offset = scale[0]
im = scaled * delta + offset
im = im.view(cls)
im.mask = mask
return im