"""
Synthetic pseudo-Voigt function
+++++++++++++++++++++++++++++++++++++++
EXAMPLES:
.. code-block:: python
:caption: Simple example of SynPseudoVoigt().
:linenos:
from apstools.devices import SynPseudoVoigt
from ophyd.sim import motor
det = SynPseudoVoigt('det', motor, 'motor',
center=0, eta=0.5, scale=1, sigma=1, bkg=0)
# scan the "det" peak with the "motor" positioner
# RE(bp.scan([det], motor, -2, 2, 41))
.. code-block:: python
:caption: Example of SynPseudoVoigt() with randomized values.
:linenos:
import numpy as np
from apstools.devices import SynPseudoVoigt
synthetic_pseudovoigt = SynPseudoVoigt(
'synthetic_pseudovoigt', m1, 'm1',
center=-1.5 + 0.5*np.random.uniform(),
eta=0.2 + 0.5*np.random.uniform(),
sigma=0.001 + 0.05*np.random.uniform(),
scale=1e5,
bkg=0.01*np.random.uniform())
# scan the "synthetic_pseudovoigt" peak with the "m1" positioner
# RE(bp.scan([synthetic_pseudovoigt], m1, -2, 0, 219))
.. autosummary::
~SynPseudoVoigt
"""
import ophyd.sim
import numpy as np
[docs]class SynPseudoVoigt(ophyd.sim.SynSignal): # lgtm [py/missing-call-to-init]
"""
Evaluate a point on a pseudo-Voigt based on the value of a motor.
.. index:: Ophyd Signal; SynPseudoVoigt
Provides a signal to be measured.
Acts like a detector.
:see: https://en.wikipedia.org/wiki/Voigt_profile
PARAMETERS
name *str* :
name of detector signal
motor positioner :
The independent coordinate
motor_field *str* :
name of `motor`
center *float* :
(optional)
location of maximum value, default=0
eta *float* :
(optional)
0 <= eta < 1.0: Lorentzian fraction, default=0.5
scale *float* :
(optional)
scale >= 1 : scale factor, default=1
sigma *float* :
(optional)
sigma > 0 : width, default=1
bkg *float* :
(optional)
bkg >= 0 : constant background, default=0
noise ``"poisson"`` or ``"uniform"`` or ``None`` :
Add noise to the result.
noise_multiplier *float* :
Only relevant for 'uniform' noise. Multiply the random amount of
noise by 'noise_multiplier'
"""
def __init__(
# fmt: off
self,
name, motor, motor_field,
center=0, eta=0.5, scale=1, sigma=1, bkg=0,
noise=None, noise_multiplier=1,
**kwargs
# fmt: on
):
if eta < 0.0 or eta > 1.0:
raise ValueError("eta={} must be between 0 and 1".format(eta))
if scale < 1.0:
raise ValueError("scale must be >= 1")
if sigma <= 0.0:
raise ValueError("sigma must be > 0")
if bkg < 0.0:
raise ValueError("bkg must be >= 0")
# remember these terms for later access by user
self.name = name
self.motor = motor
self.center = center
self.eta = eta
self.scale = scale
self.sigma = sigma
self.bkg = bkg
self.noise = noise
self.noise_multiplier = noise_multiplier
def f_lorentzian(x, gamma):
# return gamma / np.pi / (x**2 + gamma**2)
return 1 / np.pi / gamma / (1 + (x / gamma) ** 2)
def f_gaussian(x, sigma):
numerator = np.exp(-0.5 * (x / sigma) ** 2)
denominator = sigma * np.sqrt(2 * np.pi)
return numerator / denominator
def pvoigt():
m = motor.read()[motor_field]["value"]
g_max = f_gaussian(0, sigma) # peak normalization
l_max = f_lorentzian(0, sigma)
v = bkg
if eta > 0:
v += eta * f_lorentzian(m - center, sigma) / l_max
if eta < 1:
v += (1 - eta) * f_gaussian(m - center, sigma) / g_max
v *= scale
if noise == "poisson":
v = int(np.random.poisson(np.round(v), 1))
elif noise == "uniform":
v += np.random.uniform(-1, 1) * noise_multiplier
return v
ophyd.sim.SynSignal.__init__(
self, name=name, func=pvoigt, **kwargs
)
# -----------------------------------------------------------------------------
# :author: Pete R. Jemian
# :email: jemian@anl.gov
# :copyright: (c) 2017-2022, UChicago Argonne, LLC
#
# Distributed under the terms of the Creative Commons Attribution 4.0 International Public License.
#
# The full license is in the file LICENSE.txt, distributed with this software.
# -----------------------------------------------------------------------------