def fit_muon(self, centre_x, centre_y, radius, pixel_x, pixel_y, image):
"""
Parameters
----------
centre_x: float
Centre of muon ring in the field of view from circle fitting
centre_y: float
Centre of muon ring in the field of view from circle fitting
radius: float
Radius of muon ring from circle fitting
pixel_x: ndarray
X position of pixels in image from circle fitting
pixel_y: ndarray
Y position of pixel in image from circle fitting
image: ndarray
Amplitude of image pixels
Returns
-------
MuonIntensityParameters
"""
# First store these parameters in the class so we can use them in minimisation
self.image = image
self.pixel_x = pixel_x.to(u.deg)
self.pixel_y = pixel_y.to(u.deg)
self.unit = pixel_x.unit
radius.to(u.deg)
centre_x.to(u.deg)
centre_y.to(u.deg)
# Return interesting stuff
fitoutput = MuonIntensityParameter()
init_params = {}
init_errs = {}
init_constrain = {}
init_params['impact_parameter'] = 4.
init_params['phi'] = 0.
init_params['radius'] = radius.value
init_params['centre_x'] = centre_x.value
init_params['centre_y'] = centre_y.value
init_params['ring_width'] = 0.1
init_params['optical_efficiency_muon'] = 0.1
init_errs['error_impact_parameter'] = 2.
init_constrain['limit_impact_parameter'] = (0.,25.)
init_errs['error_phi'] = 0.1
init_errs['error_ring_width'] = 0.001 * radius.value
init_errs['error_optical_efficiency_muon'] = 0.05
init_constrain['limit_phi'] = (-np.pi,np.pi)
init_constrain['fix_radius'] = True
init_constrain['fix_centre_x'] = True
init_constrain['fix_centre_y'] = True
init_constrain['limit_ring_width'] = (0.,1.)
init_constrain['limit_optical_efficiency_muon'] = (0.,1.)
print("radius =",radius," pre migrad")
parameter_names = init_params.keys()
# Create Minuit object with first guesses at parameters
# strip away the units as Minuit doesnt like them
minuit = Minuit(
self.likelihood,
#forced_parameters=parameter_names,
**init_params,
**init_errs,
**init_constrain,
errordef=1.,
print_level=1
#pedantic=False
)
# Perform minimisation
minuit.migrad()
# Get fitted values
fit_params = minuit.values
fitoutput.impact_parameter = fit_params['impact_parameter']*u.m
#fitoutput.phi = fit_params['phi']*u.rad
fitoutput.impact_parameter_pos_x = fit_params['impact_parameter'] * np.cos(fit_params['phi'] * u.rad) * u.m
fitoutput.impact_parameter_pos_y = fit_params['impact_parameter'] * np.sin(fit_params['phi'] * u.rad) * u.m
fitoutput.ring_width = fit_params['ring_width']*self.unit
fitoutput.optical_efficiency_muon = fit_params['optical_efficiency_muon']
fitoutput.prediction = self.prediction
return fitoutput
def fit_muon(self, centre_x, centre_y, radius, pixel_x, pixel_y, image):
"""
Parameters
----------
centre_x: float
Centre of muon ring in the field of view from circle fitting
centre_y: float
Centre of muon ring in the field of view from circle fitting
radius: float
Radius of muon ring from circle fitting
pixel_x: ndarray
X position of pixels in image from circle fitting
pixel_y: ndarray
Y position of pixel in image from circle fitting
image: ndarray
Amplitude of image pixels
Returns
-------
MuonIntensityParameters
"""
# First store these parameters in the class so we can use them in minimisation
self.image = image
self.pixel_x = pixel_x.to(u.deg)
self.pixel_y = pixel_y.to(u.deg)
self.unit = pixel_x.unit
radius.to(u.deg)
centre_x.to(u.deg)
centre_y.to(u.deg)
# Return interesting stuff
fitoutput = MuonIntensityParameter()
init_params = {}
init_errs = {}
init_constrain = {}
init_params['impact_parameter'] = 4.
init_params['phi'] = 0.
init_params['radius'] = radius.value
init_params['centre_x'] = centre_x.value
init_params['centre_y'] = centre_y.value
init_params['ring_width'] = 0.1
init_params['optical_efficiency_muon'] = 0.1
init_errs['error_impact_parameter'] = 2.
init_constrain['limit_impact_parameter'] = (0., 25.)
init_errs['error_phi'] = 0.1
init_errs['error_ring_width'] = 0.001 * radius.value
init_errs['error_optical_efficiency_muon'] = 0.05
init_constrain['limit_phi'] = (-np.pi, np.pi)
init_constrain['fix_radius'] = True
init_constrain['fix_centre_x'] = True
init_constrain['fix_centre_y'] = True
init_constrain['limit_ring_width'] = (0., 1.)
#init_constrain['limit_optical_efficiency_muon'] = (0., 1.) # Unneeded constraint - and misleading in case of changes leading to >1 values!
logger.debug("radius = %3.3f pre migrad", radius)
# Create Minuit object with first guesses at parameters
# strip away the units as Minuit doesnt like them
minuit = Minuit(
self.likelihood,
# forced_parameters=parameter_names,
**init_params,
**init_errs,
**init_constrain,
errordef=0.1,
print_level=0,
pedantic=False
)
# Perform minimisation
minuit.migrad()
# Get fitted values
fit_params = minuit.values
fitoutput.impact_parameter = fit_params['impact_parameter'] * u.m
# fitoutput.phi = fit_params['phi']*u.rad
fitoutput.impact_parameter_pos_x = fit_params['impact_parameter'] * np.cos(
fit_params['phi'] * u.rad) * u.m
fitoutput.impact_parameter_pos_y = fit_params['impact_parameter'] * np.sin(
fit_params['phi'] * u.rad) * u.m
fitoutput.ring_width = fit_params['ring_width'] * self.unit
fitoutput.optical_efficiency_muon = fit_params['optical_efficiency_muon']
fitoutput.prediction = self.prediction
return fitoutput
def fit_muon(self, centre_x, centre_y, radius, pixel_x, pixel_y, image):
"""
Parameters
----------
centre_x: float
Centre of muon ring in the field of view from circle fitting
centre_y: float
Centre of muon ring in the field of view from circle fitting
radius: float
Radius of muon ring from circle fitting
pixel_x: ndarray
X position of pixels in image from circle fitting
pixel_y: ndarray
Y position of pixel in image from circle fitting
image: ndarray
Amplitude of image pixels
Returns
-------
MuonIntensityParameters
"""
# First store these parameters in the class so we can use them in minimisation
self.image = image
self.pixel_x = pixel_x.to(u.deg)
self.pixel_y = pixel_y.to(u.deg)
self.unit = pixel_x.unit
radius.to(u.deg)
centre_x.to(u.deg)
centre_y.to(u.deg)
# Return interesting stuff
fitoutput = MuonIntensityParameter()
# Create Minuit object with first guesses at parameters
# strip away the units as Minuit doesnt like them
minuit = Minuit(
self.likelihood,
impact_parameter=4,
limit_impact_parameter=(0, 25),
error_impact_parameter=5,
phi=0,
limit_phi=(-np.pi, np.pi),
error_phi=0.1,
radius=radius.value,
fix_radius=True,
error_radius=0.,
centre_x=centre_x.value,
fix_centre_x=True,
error_centre_x=0.,
centre_y=centre_y.value,
fix_centre_y=True,
error_centre_y=0.,
ring_width=0.1,
error_ring_width=0.001*radius.value,
limit_ring_width=(0, 1),
optical_efficiency_muon=0.1,
error_optical_efficiency_muon=0.05,
limit_optical_efficiency_muon=(0, 1),
throw_nan=False,
print_level=0,
pedantic=False,
errordef=1,
)
# Perform minimisation
minuit.migrad()
# Get fitted values
fit_params = minuit.values
fitoutput.impact_parameter = fit_params['impact_parameter']*u.m
#fitoutput.phi = fit_params['phi']*u.rad
fitoutput.ring_width = fit_params['ring_width']*self.unit
fitoutput.optical_efficiency_muon = fit_params['optical_efficiency_muon']
#embed()
#
fitoutput.prediction = self.prediction
return fitoutput
def fit_muon(self, centre_x, centre_y, radius, pixel_x, pixel_y, image):
"""
Parameters
----------
centre_x: float
Centre of muon ring in the field of view from circle fitting
centre_y: float
Centre of muon ring in the field of view from circle fitting
radius: float
Radius of muon ring from circle fitting
pixel_x: ndarray
X position of pixels in image from circle fitting
pixel_y: ndarray
Y position of pixel in image from circle fitting
image: ndarray
Amplitude of image pixels
Returns
-------
MuonIntensityParameters
"""
# First store these parameters in the class so we can use them in minimisation
self.image = image
self.pixel_x = pixel_x.to(u.deg)
self.pixel_y = pixel_y.to(u.deg)
self.unit = pixel_x.unit
radius.to(u.deg)
centre_x.to(u.deg)
centre_y.to(u.deg)
# Return interesting stuff
fitoutput = MuonIntensityParameter()
# Create Minuit object with first guesses at parameters
# strip away the units as Minuit doesnt like them
minuit = Minuit(
self.likelihood,
impact_parameter=4,
limit_impact_parameter=(0, 25),
error_impact_parameter=5,
phi=0,
limit_phi=(-np.pi, np.pi),
error_phi=0.1,
radius=radius.value,
fix_radius=True,
error_radius=0.,
centre_x=centre_x.value,
fix_centre_x=True,
error_centre_x=0.,
centre_y=centre_y.value,
fix_centre_y=True,
error_centre_y=0.,
ring_width=0.1,
error_ring_width=0.001 * radius.value,
limit_ring_width=(0, 1),
optical_efficiency_muon=0.1,
error_optical_efficiency_muon=0.05,
limit_optical_efficiency_muon=(0, 1),
throw_nan=False,
print_level=0,
pedantic=False,
errordef=1,
)
# Perform minimisation
minuit.migrad()
# Get fitted values
fit_params = minuit.values
fitoutput.impact_parameter = fit_params['impact_parameter'] * u.m
#fitoutput.phi = fit_params['phi']*u.rad
fitoutput.ring_width = fit_params['ring_width'] * self.unit
fitoutput.optical_efficiency_muon = fit_params[
'optical_efficiency_muon']
#embed()
#
fitoutput.prediction = self.prediction
return fitoutput