def angular_integration(IM, origin=None, Jacobian=True, dr=1, dt=None):
"""Angular integration of the image.
Returns the one-dimensional intensity profile as a function of the
radial coordinate.
Note: the use of Jacobian=True applies the correct Jacobian for the
integration of a 3D object in spherical coordinates.
Parameters
----------
IM : 2D numpy.array
The data image.
origin : tuple
Image center coordinate relative to *bottom-left* corner
defaults to ``rows//2, cols//2``.
Jacobian : boolean
Include :math:`r\sin\\theta` in the angular sum (integration).
Also, ``Jacobian=True`` is passed to
:func:`abel.tools.polar.reproject_image_into_polar`,
which includes another value of ``r``, thus providing the appropriate
total Jacobian of :math:`r^2\sin\\theta`.
dr : float
Radial coordinate grid spacing, in pixels (default 1). `dr=0.5` may
reduce pixel granularity of the speed profile.
dt : float
Theta coordinate grid spacing in radians.
if ``dt=None``, dt will be set such that the number of theta values
is equal to the height of the image (which should typically ensure
good sampling.)
Returns
------
r : 1D numpy.array
radial coordinates
speeds : 1D numpy.array
Integrated intensity array (vs radius).
"""
polarIM, R, T = reproject_image_into_polar(
IM, origin, Jacobian=Jacobian, dr=dr, dt=dt)
dt = T[0, 1] - T[0, 0]
if Jacobian: # x r sinθ
polarIM = polarIM * R * np.abs(np.sin(T))
speeds = np.trapz(polarIM, axis=1, dx=dt)
n = speeds.shape[0]
return R[:n, 0], speeds # limit radial coordinates range to match speed
def angular_integration(IM, origin=None, Jacobian=True, dr=1, dt=None):
"""
Angular integration of the image.
Returns the one-dimentional intensity profile as a function of the
radial coordinate.
Note: the use of Jacobian=True applies the correct Jacobian for the integration of a 3D object in spherical coordinates.
Parameters
----------
IM : 2D numpy.array
The data image.
origin : tuple
Image center coordinate relative to *bottom-left* corner
defaults to ``rows//2+rows%2,cols//2+cols%2``.
Jacobian : boolean
Include :math:`r\sin\\theta` in the angular sum (integration).
Also, ``Jacobian=True`` is passed to
:func:`abel.tools.polar.reproject_image_into_polar`,
which includes another value of ``r``, thus providing the appropriate
total Jacobian of :math:`r^2\sin\\theta`.
dr : float
Radial coordinate grid spacing, in pixels (default 1). `dr=0.5` may
reduce pixel granularity of the speed profile.
dt : float
Theta coordinate grid spacing in degrees.
if ``dt=None``, dt will be set such that the number of theta values
is equal to the height of the image (which should typically ensure
good sampling.)
Returns
-------
r : 1D numpy.array
radial coordinates
speeds : 1D numpy.array
Integrated intensity array (vs radius).
"""
polarIM, R, T = reproject_image_into_polar(
IM, origin, Jacobian=Jacobian, dr=dr, dt=dt)
dt = T[0,1] - T[0,0]
if Jacobian: # x r sinθ
polarIM = polarIM * R * np.abs(np.sin(T))
speeds = np.trapz(polarIM, axis=1, dx=dt)
n = speeds.shape[0]
return R[:n, 0], speeds # limit radial coordinates range to match speed
def radial_integration(IM, radial_ranges=None):
""" Intensity variation in the angular coordinate.
This function is the :math:`\\theta`-coordinate complement to
:func:`abel.tools.vmi.angular_integration`
(optionally and more useful) returning intensity vs angle for defined
radial ranges, to evaluate the anisotropy parameter.
See :doc:`examples/example_O2_PES_PAD.py <examples>`
Parameters
----------
IM : 2D numpy.array
Image data
radial_ranges : list of tuple ranges or int step
tuple integration ranges
``[(r0, r1), (r2, r3), ...]``
evaluates the intensity vs angle
for the radial ranges ``r0_r1``, ``r2_r3``, etc.
int - the whole radial range ``(r0, r_step), (r_step, r_2step), ..)``
Returns
-------
intensity_vs_theta: 2D numpy.array
Intensity vs angle distribution for each selected radial range.
theta: 1D numpy.array
Angle coordinates, referenced to vertical direction.
radial_midpt: numpy.array
Array of radial positions of the mid-point of the integration range
"""
polarIM, r_grid, theta_grid = reproject_image_into_polar(IM)
theta = theta_grid[0, :] # theta coordinates
r = r_grid[:, 0] # radial coordinates
if radial_ranges is None:
radial_ranges = 1
if isinstance(radial_ranges, int):
radial_ranges = list(zip(r[:-radial_ranges], r[radial_ranges:]))
intensity_vs_theta_at_R = []
radial_midpt = []
for rr in radial_ranges:
subr = np.logical_and(r >= rr[0], r <= rr[1])
# sum intensity across radius of spectral feature
intensity_vs_theta_at_R.append(np.sum(polarIM[subr], axis=0))
radial_midpt.append(np.mean(rr))
return np.array(intensity_vs_theta_at_R), theta, radial_midpt
def calculate_speeds(IM, origin=None, Jacobian=False, dr=1, dt=None):
""" Angular integration of the image.
Returning the one-dimentional intensity profile as a function of the
radial coordinate.
Parameters
----------
IM : rows x cols 2D np.array
The data image.
origin : tuple
Image center coordinate relative to *bottom-left* corner
defaults to (rows//2+rows%2,cols//2+cols%2).
Jacobian : boolean
Include r*sinθ in the angular sum (integration).
dr : float
Radial coordinate grid spacing, in pixels (default 1).
dt : float
Theta coordinate grid spacing in degrees, defaults to rows//2.
Returns
-------
speeds : 1D np.array
Integrated intensity array (vs radius).
r : 1D np.array
radial coordinates
"""
polarIM, r_grid, theta_grid = reproject_image_into_polar(IM,
origin,
Jacobian=Jacobian,
dr=dr,
dt=dt)
theta = theta_grid[0, :] # theta coordinates
r = r_grid[:, 0] # radial coordinates
if Jacobian: # x r sinθ
sintheta = np.abs(np.sin(theta))
polarIM = polarIM * sintheta[np.newaxis, :]
polarIM = polarIM * r[:, np.newaxis]
speeds = np.sum(polarIM, axis=1)
n = speeds.shape[0]
return speeds, r[:n] # limit radial coordinates range to match speed
def radial_integration(IM, radial_ranges=None):
""" Intensity variation in the angular coordinate.
This function is the :math:`\\theta`-coordinate complement to
:func:`abel.tools.vmi.angular_integration`
(optionally and more useful) returning intensity vs angle for defined
radial ranges, to evaluate the anisotropy parameter.
See :doc:`examples/example_O2_PES_PAD.py <examples>`
Parameters
----------
IM : 2D np.array
Image data
radial_ranges : list of tuples
integration ranges
``[(r0, r1), (r2, r3), ...]``
Evaluate the intensity vs angle
for the radial ranges ``r0_r1``, ``r2_r3``, etc.
Returns
-------
intensity_vs_theta: 2D np.array
Intensity vs angle distribution for each selected radial range.
theta: 1D np.array
Angle coordinates, referenced to vertical direction.
"""
polarIM, r_grid, theta_grid = reproject_image_into_polar(IM)
theta = theta_grid[0, :] # theta coordinates
r = r_grid[:, 0] # radial coordinates
if radial_ranges is None:
radial_ranges = [
(0, r[-1]),
]
intensity_vs_theta_at_R = []
for rr in radial_ranges:
subr = np.logical_and(r >= rr[0], r <= rr[1])
# sum intensity across radius of spectral feature
intensity_vs_theta_at_R.append(np.sum(polarIM[subr], axis=0))
return np.array(intensity_vs_theta_at_R), theta
def radial_integration(IM, radial_ranges=None):
""" Intensity variation in the angular coordinate.
This function is the :math:`\\theta`-coordinate complement to
:func:`abel.tools.vmi.angular_integration`
(optionally and more useful) returning intensity vs angle for defined
radial ranges, to evaluate the anisotropy parameter.
See :doc:`examples/example_O2_PES_PAD.py <examples>`
Parameters
----------
IM : 2D np.array
Image data
radial_ranges : list of tuples
integration ranges
``[(r0, r1), (r2, r3), ...]``
Evaluate the intensity vs angle
for the radial ranges ``r0_r1``, ``r2_r3``, etc.
Returns
-------
intensity_vs_theta: 2D np.array
Intensity vs angle distribution for each selected radial range.
theta: 1D np.array
Angle coordinates, referenced to vertical direction.
"""
polarIM, r_grid, theta_grid = reproject_image_into_polar(IM)
theta = theta_grid[0, :] # theta coordinates
r = r_grid[:, 0] # radial coordinates
if radial_ranges is None:
radial_ranges = [(0, r[-1]), ]
intensity_vs_theta_at_R = []
for rr in radial_ranges:
subr = np.logical_and(r >= rr[0], r <= rr[1])
# sum intensity across radius of spectral feature
intensity_vs_theta_at_R.append(np.sum(polarIM[subr], axis=0))
return np.array(intensity_vs_theta_at_R), theta
def calculate_speeds(IM, origin=None, Jacobian=False, dr=1, dt=None):
""" Angular integration of the image.
Returning the one-dimentional intensity profile as a function of the
radial coordinate.
Parameters
----------
IM : rows x cols 2D np.array
The data image.
origin : tuple
Image center coordinate relative to *bottom-left* corner
defaults to (rows//2+rows%2,cols//2+cols%2).
Jacobian : boolean
Include r*sinθ in the angular sum (integration).
dr : float
Radial coordinate grid spacing, in pixels (default 1).
dt : float
Theta coordinate grid spacing in degrees, defaults to rows//2.
Returns
-------
speeds : 1D np.array
Integrated intensity array (vs radius).
r : 1D np.array
radial coordinates
"""
polarIM, r_grid, theta_grid = reproject_image_into_polar(IM, origin,
Jacobian=Jacobian, dr=dr, dt=dt)
theta = theta_grid[0, :] # theta coordinates
r = r_grid[:, 0] # radial coordinates
if Jacobian: # x r sinθ
sintheta = np.abs(np.sin(theta))
polarIM = polarIM*sintheta[np.newaxis, :]
polarIM = polarIM*r[:, np.newaxis]
speeds = np.sum(polarIM, axis=1)
n = speeds.shape[0]
return speeds, r[:n] # limit radial coordinates range to match speed
def calculate_angular_distribution(IM, radial_ranges=None):
""" Intensity variation in the angular coordinate, theta.
This function is the theta-coordinate complement to 'calculate_speeds(IM)'
(optionally and more useful) returning intensity vs angle for defined
radial ranges.
Parameters
----------
IM : 2D np.array
Image data
radial_ranges : list of tuples
[(r0, r1), (r2, r3), ...]
Evaluate the intensity vs angle for the radial ranges r0_r1, r2_r3, etc.
Returns
--------
intensity_vs_theta: 2D np.array
Intensity vs angle distribution for each selected radial range.
theta: 1D np.array
Angle coordinates, referenced to vertical direction.
"""
polarIM, r_grid, theta_grid = reproject_image_into_polar(IM)
theta = theta_grid[0, :] # theta coordinates
r = r_grid[:, 0] # radial coordinates
if radial_ranges is None:
radial_ranges = [
(0, r[-1]),
]
intensity_vs_theta_at_R = []
for rr in radial_ranges:
subr = np.logical_and(r >= rr[0], r <= rr[1])
# sum intensity across radius of spectral feature
intensity_vs_theta_at_R.append(np.sum(polarIM[subr], axis=0))
return intensity_vs_theta_at_R, theta
def calculate_angular_distribution(IM, radial_ranges=None):
""" Intensity variation in the angular coordinate, theta.
This function is the theta-coordinate complement to 'calculate_speeds(IM)'
(optionally and more useful) returning intensity vs angle for defined
radial ranges.
Parameters
----------
IM : 2D np.array
Image data
radial_ranges : list of tuples
[(r0, r1), (r2, r3), ...]
Evaluate the intensity vs angle for the radial ranges r0_r1, r2_r3, etc.
Returns
--------
intensity_vs_theta: 2D np.array
Intensity vs angle distribution for each selected radial range.
theta: 1D np.array
Angle coordinates, referenced to vertical direction.
"""
polarIM, r_grid, theta_grid = reproject_image_into_polar(IM)
theta = theta_grid[0, :] # theta coordinates
r = r_grid[:, 0] # radial coordinates
if radial_ranges is None:
radial_ranges = [(0, r[-1]), ]
intensity_vs_theta_at_R = []
for rr in radial_ranges:
subr = np.logical_and(r >= rr[0], r <= rr[1])
# sum intensity across radius of spectral feature
intensity_vs_theta_at_R.append(np.sum(polarIM[subr], axis=0))
return intensity_vs_theta_at_R, theta
def angular_integration(IM, origin=None, Jacobian=True, dr=1, dt=None):
r"""Angular integration of the image.
Returns the one-dimensional intensity profile as a function of the
radial coordinate.
Note: the use of ``Jacobian=True`` applies the correct Jacobian for the
integration of a 3D object in spherical coordinates.
Parameters
----------
IM : 2D numpy.array
the image data
origin : tuple or None
image origin in the (row, column) format. If ``None``, the geometric
center of the image (``rows // 2, cols // 2``) is used.
Jacobian : bool
Include :math:`r\sin\theta` in the angular sum (integration).
Also, ``Jacobian=True`` is passed to
:func:`abel.tools.polar.reproject_image_into_polar`,
which includes another value of `r`, thus providing the appropriate
total Jacobian of :math:`r^2\sin\theta`.
dr : float
radial grid spacing in pixels (default 1). ``dr=0.5`` may
reduce pixel granularity of the speed profile.
dt : float or None
angular grid spacing in radians.
If ``None``, the number of theta values will be set to largest
dimension (the height or the width) of the image, which should
typically ensure good sampling.
Returns
------
r : 1D numpy.array
radial coordinates
speeds : 1D numpy.array
integrated intensity array (vs radius).
"""
polarIM, R, T = reproject_image_into_polar(IM,
origin,
Jacobian=Jacobian,
dr=dr,
dt=dt)
dt = T[0, 1] - T[0, 0]
if Jacobian: # × r sinθ
polarIM *= R * np.abs(np.sin(T))
speeds = np.trapz(polarIM, axis=1, dx=dt)
n = speeds.shape[0]
return R[:n, 0], speeds # limit radial coordinates range to match speed
def radial_integration(IM, origin=None, radial_ranges=None):
r""" Intensity variation in the angular coordinate.
This function is the :math:`\theta`-coordinate complement to
:func:`abel.tools.vmi.angular_integration`.
Evaluates intensity vs angle for defined radial ranges.
Determines the anisotropy parameter for each radial range.
See :doc:`examples/example_O2_PES_PAD.py <example_O2_PES_PAD>`.
Parameters
----------
IM : 2D numpy.array
the image data
origin : tuple or None
image origin in the (row, column) format. If ``None``, the geometric
center of the image (``rows // 2, cols // 2``) is used.
radial_ranges : list of tuple ranges or int step
tuple
integration ranges
``[(r0, r1), (r2, r3), ...]``
evaluates the intensity vs angle
for the radial ranges ``r0_r1``, ``r2_r3``, etc.
int
the whole radial range ``(0, step), (step, 2*step), ..``
Returns
-------
Beta : array of tuples
(beta0, error_beta_fit0), (beta1, error_beta_fit1), ...
corresponding to the radial ranges
Amplitude : array of tuples
(amp0, error_amp_fit0), (amp1, error_amp_fit1), ...
corresponding to the radial ranges
Rmidpt : numpy float 1D array
radial mid-point of each radial range
Intensity_vs_theta: 2D numpy.array
intensity vs angle distribution for each selected radial range
theta: 1D numpy.array
angle coordinates, referenced to vertical direction
"""
if origin is not None and not isinstance(origin, tuple):
_deprecate('radial_integration() has 2nd argument "origin", '
'use keyword argument "radial_ranges" or insert "None".')
radial_ranges = origin
origin = None
polarIM, r_grid, theta_grid = reproject_image_into_polar(IM, origin)
theta = theta_grid[0, :] # theta coordinates
r = r_grid[:, 0] # radial coordinates
if radial_ranges is None:
radial_ranges = 1
if isinstance(radial_ranges, int):
rr = np.arange(0, r[-1], radial_ranges)
# @DanHickstein clever code to map ranges
radial_ranges = list(zip(rr[:-1], rr[1:]))
Intensity_vs_theta = []
radial_midpt = []
Beta = []
Amp = []
for rr in radial_ranges:
subr = np.logical_and(r >= rr[0], r <= rr[1])
# sum intensity across radius of spectral feature
intensity_vs_theta_at_R = np.sum(polarIM[subr], axis=0)
Intensity_vs_theta.append(intensity_vs_theta_at_R)
radial_midpt.append(np.mean(rr))
beta, amp = anisotropy_parameter(theta, intensity_vs_theta_at_R)
Beta.append(beta)
Amp.append(amp)
return Beta, Amp, radial_midpt, Intensity_vs_theta, theta
def radial_integration(IM, radial_ranges=None):
""" Intensity variation in the angular coordinate.
This function is the :math:`\\theta`-coordinate complement to
:func:`abel.tools.vmi.angular_integration`
Evaluates intensity vs angle for defined radial ranges.
Determines the anisotropy parameter for each radial range.
See :doc:`examples/example_PAD.py <examples>`
Parameters
----------
IM : 2D numpy.array
Image data
radial_ranges : list of tuple ranges or int step
tuple
integration ranges
``[(r0, r1), (r2, r3), ...]``
evaluates the intensity vs angle
for the radial ranges ``r0_r1``, ``r2_r3``, etc.
int
the whole radial range ``(0, step), (step, 2*step), ..``
Returns
-------
Beta : array of tuples
(beta0, error_beta_fit0), (beta1, error_beta_fit1), ...
corresponding to the radial ranges
Amplitude : array of tuples
(amp0, error_amp_fit0), (amp1, error_amp_fit1), ...
corresponding to the radial ranges
Rmidpt : numpy float 1d array
radial-mid point of each radial range
Intensity_vs_theta: 2D numpy.array
Intensity vs angle distribution for each selected radial range.
theta: 1D numpy.array
Angle coordinates, referenced to vertical direction.
"""
polarIM, r_grid, theta_grid = reproject_image_into_polar(IM)
theta = theta_grid[0, :] # theta coordinates
r = r_grid[:, 0] # radial coordinates
if radial_ranges is None:
radial_ranges = 1
if isinstance(radial_ranges, int):
rr = np.arange(0, r[-1], radial_ranges)
# @DanHickstein clever code to map ranges
radial_ranges = list(zip(rr[:-1], rr[1:]))
Intensity_vs_theta = []
radial_midpt = []
Beta = []
Amp = []
for rr in radial_ranges:
subr = np.logical_and(r >= rr[0], r <= rr[1])
# sum intensity across radius of spectral feature
intensity_vs_theta_at_R = np.sum(polarIM[subr], axis=0)
Intensity_vs_theta.append(intensity_vs_theta_at_R)
radial_midpt.append(np.mean(rr))
beta, amp = anisotropy_parameter(theta, intensity_vs_theta_at_R)
Beta.append(beta)
Amp.append(amp)
return Beta, Amp, radial_midpt, Intensity_vs_theta, theta
def radial_integration(IM, radial_ranges=None):
""" Intensity variation in the angular coordinate.
This function is the :math:`\\theta`-coordinate complement to
:func:`abel.tools.vmi.angular_integration`
Evaluates intensity vs angle for defined radial ranges.
Determines the anisotropy parameter for each radial range.
See :doc:`examples/example_PAD.py <examples>`
Parameters
----------
IM : 2D numpy.array
Image data
radial_ranges : list of tuple ranges or int step
tuple
integration ranges
``[(r0, r1), (r2, r3), ...]``
evaluates the intensity vs angle
for the radial ranges ``r0_r1``, ``r2_r3``, etc.
int
the whole radial range ``(0, step), (step, 2*step), ..``
Returns
-------
Beta : array of tuples
(beta0, error_beta_fit0), (beta1, error_beta_fit1), ...
corresponding to the radial ranges
Amplitude : array of tuples
(amp0, error_amp_fit0), (amp1, error_amp_fit1), ...
corresponding to the radial ranges
Rmidpt : numpy float 1d array
radial-mid point of each radial range
Intensity_vs_theta: 2D numpy.array
Intensity vs angle distribution for each selected radial range.
theta: 1D numpy.array
Angle coordinates, referenced to vertical direction.
"""
polarIM, r_grid, theta_grid = reproject_image_into_polar(IM)
theta = theta_grid[0, :] # theta coordinates
r = r_grid[:, 0] # radial coordinates
if radial_ranges is None:
radial_ranges = 1
if isinstance(radial_ranges, int):
rr = np.arange(0, r[-1], radial_ranges)
# @DanHickstein clever code to map ranges
radial_ranges = list(zip(rr[:-1], rr[1:]))
Intensity_vs_theta = []
radial_midpt = []
Beta = []
Amp = []
for rr in radial_ranges:
subr = np.logical_and(r >= rr[0], r <= rr[1])
# sum intensity across radius of spectral feature
intensity_vs_theta_at_R = np.sum(polarIM[subr], axis=0)
Intensity_vs_theta.append(intensity_vs_theta_at_R)
radial_midpt.append(np.mean(rr))
beta, amp = anisotropy_parameter(theta, intensity_vs_theta_at_R)
Beta.append(beta)
Amp.append(amp)
return Beta, Amp, radial_midpt, Intensity_vs_theta, theta
