def distance(shape, center=None, scale=1, dtype=float, out=None):
"""
Returns an array whose values are the distances to a given center.
Parameters
----------
shape : tuple of integer
dimensions of the output array. For a 2d array, the first integer
is for the Y-axis and the second one for the X-axis.
center : array-like, optional
The coordinates (x0, y0, ...) of the point from which the distance is
calculated, assuming a zero-based coordinate indexing. Default value
is the array center.
scale : float or array-like, optional
Inter-pixel distance (dx, dy, ...). If scale is a Quantity, its
unit will be carried over to the returned distance array
dtype : np.dtype, optional
The output data type.
Example
-------
nx, ny = 3, 3
print(distance((ny,nx)))
[[ 1.41421356 1. 1.41421356]
[ 1. 0. 1. ]
[ 1.41421356 1. 1.41421356]]
"""
shape = tointtuple(shape)
dtype = np.dtype(dtype)
unit = getattr(scale, '_unit', None)
if out is None:
out = np.empty(shape, dtype)
ndim = out.ndim
dtype = float_intrinsic_dtype(out.dtype)
if ndim in (1, 2) and dtype != out.dtype:
out_ = np.empty(shape, dtype)
else:
out_ = out
if center is None:
center = (np.array(shape[::-1]) - 1) / 2
else:
center = np.ascontiguousarray(center, dtype)
if isscalarlike(scale):
scale = np.resize(scale, out.ndim)
scale = np.ascontiguousarray(scale, dtype)
if ndim in (1, 2):
fname = 'distance_{0}d_r{1}'.format(ndim, dtype.itemsize)
func = getattr(flib.datautils, fname)
if ndim == 1:
func(out_, center[0], scale[0])
else:
func(out_.T, center, scale)
if not isalias(out, out_):
out[...] = out_
else:
_distance_slow(shape, center, scale, dtype, out)
return Map(out, copy=False, unit=unit)
def distance(shape, center=None, scale=1, dtype=float, out=None):
"""
Returns an array whose values are the distances to a given center.
Parameters
----------
shape : tuple of integer
dimensions of the output array. For a 2d array, the first integer
is for the Y-axis and the second one for the X-axis.
center : array-like, optional
The coordinates (x0, y0, ...) of the point from which the distance is
calculated, assuming a zero-based coordinate indexing. Default value
is the array center.
scale : float or array-like, optional
Inter-pixel distance (dx, dy, ...). If scale is a Quantity, its
unit will be carried over to the returned distance array
dtype : np.dtype, optional
The output data type.
Example
-------
nx, ny = 3, 3
print(distance((ny,nx)))
[[ 1.41421356 1. 1.41421356]
[ 1. 0. 1. ]
[ 1.41421356 1. 1.41421356]]
"""
shape = tointtuple(shape)
dtype = np.dtype(dtype)
unit = getattr(scale, '_unit', None)
if out is None:
out = np.empty(shape, dtype)
ndim = out.ndim
dtype = float_intrinsic_dtype(out.dtype)
if ndim in (1, 2) and dtype != out.dtype:
out_ = np.empty(shape, dtype)
else:
out_ = out
if center is None:
center = (np.array(shape[::-1]) - 1) / 2
else:
center = np.ascontiguousarray(center, dtype)
if isscalarlike(scale):
scale = np.resize(scale, out.ndim)
scale = np.ascontiguousarray(scale, dtype)
if ndim in (1, 2):
fname = 'distance_{0}d_r{1}'.format(ndim, dtype.itemsize)
func = getattr(flib.datautils, fname)
if ndim == 1:
func(out_, center[0], scale[0])
else:
func(out_.T, center, scale)
if not isalias(out, out_):
out[...] = out_
else:
_distance_slow(shape, center, scale, dtype, out)
return Map(out, copy=False, unit=unit)
def profile(input, bin=1, nbins=None, histogram=False, center=None, scale=1):
"""
Returns axisymmetric profile of a 2d image.
x, y[, n] = profile(image, [center, bin, nbins, histogram=True])
Parameters
----------
input: array
2d input array
bin: number, optional
Width of the profile bins.
nbins: integer, optional
Number of profile bins.
histogram: boolean, optional
If set to True, return the histogram.
center : array-like, optional
Center (x0, y0) of the profile, in pixel coordinates. By convention,
the coordinates of the center of the pixel [0, 0] are (0, 0).
Default is the image center.
scale : float, optional
The inter-pixel distance.
"""
input = np.array(input, order='c', copy=False)
dtype = float_intrinsic_dtype(input.dtype)
input = np.array(input, dtype=dtype, copy=False)
if center is None:
center = (np.array(input.shape[::-1], dtype) - 1) / 2
else:
center = np.ascontiguousarray(center, dtype)
bin_scaled = bin / scale
if nbins is None:
nbins = int(
max(input.shape[0] - center[1], center[1],
input.shape[1] - center[0], center[0]) / bin_scaled)
fname = 'profile_axisymmetric_2d_r{}'.format(dtype.itemsize)
func = getattr(flib.datautils, fname)
x, y, n = func(input.T, center, bin_scaled, nbins)
x *= scale
if histogram:
return x, y, n
else:
return x, y
def profile(input, bin=1, nbins=None, histogram=False, center=None, scale=1):
"""
Returns axisymmetric profile of a 2d image.
x, y[, n] = profile(image, [center, bin, nbins, histogram=True])
Parameters
----------
input: array
2d input array
bin: number, optional
Width of the profile bins.
nbins: integer, optional
Number of profile bins.
histogram: boolean, optional
If set to True, return the histogram.
center : array-like, optional
Center (x0, y0) of the profile, in pixel coordinates. By convention,
the coordinates of the center of the pixel [0, 0] are (0, 0).
Default is the image center.
scale : float, optional
The inter-pixel distance.
"""
input = np.array(input, order='c', copy=False)
dtype = float_intrinsic_dtype(input.dtype)
input = np.array(input, dtype=dtype, copy=False)
if center is None:
center = (np.array(input.shape[::-1], dtype) - 1) / 2
else:
center = np.ascontiguousarray(center, dtype)
bin_scaled = bin / scale
if nbins is None:
nbins = int(max(input.shape[0]-center[1], center[1],
input.shape[1]-center[0], center[0]) / bin_scaled)
fname = 'profile_axisymmetric_2d_r{}'.format(dtype.itemsize)
func = getattr(flib.datautils, fname)
x, y, n = func(input.T, center, bin_scaled, nbins)
x *= scale
if histogram:
return x, y, n
else:
return x, y
