def worker(channel):
channel_mask = total_poly_energies == channel
# Mask background events and current channel
# poly_chan_mask = np.logical_and(poly_mask, channel_mask)
# Select the masked events
current_events = total_poly_events[channel_mask]
polynomial, _ = unbinned_polyfit(
current_events,
self._optimal_polynomial_grade,
t_start,
t_stop,
poly_exposure,
bayes=bayes)
return polynomial
def _unbinned_fit_global_and_determine_optimum_grade(self, events, exposure):
"""
Provides the ability to find the optimum polynomial grade for *unbinned* events by fitting the
total (all channels) to 0-4 order polynomials and then comparing them via a likelihood ratio test.
:param events: an event list
:param exposure: the exposure per event
:return: polynomial grade
"""
# Fit the sum of all the channels to determine the optimal polynomial
# grade
min_grade = 0
max_grade = 4
log_likelihoods = []
t_start = self._poly_intervals.start_times
t_stop = self._poly_intervals.stop_times
for grade in range(min_grade, max_grade + 1):
polynomial, log_like = unbinned_polyfit(events, grade, t_start, t_stop, exposure)
log_likelihoods.append(log_like)
# Found the best one
delta_loglike = np.array([2 * (x[0] - x[1]) for x in zip(log_likelihoods[:-1], log_likelihoods[1:])])
delta_threshold = 9.0
mask = (delta_loglike >= delta_threshold)
if (len(mask.nonzero()[0]) == 0):
# best grade is zero!
best_grade = 0
else:
best_grade = mask.nonzero()[0][-1] + 1
return best_grade
def _unbinned_fit_global_and_determine_optimum_grade(self, events, exposure):
"""
Provides the ability to find the optimum polynomial grade for *unbinned* events by fitting the
total (all channels) to 0-4 order polynomials and then comparing them via a likelihood ratio test.
:param events: an event list
:param exposure: the exposure per event
:return: polynomial grade
"""
# Fit the sum of all the channels to determine the optimal polynomial
# grade
min_grade = 0
max_grade = 4
log_likelihoods = []
t_start = self._poly_intervals.start_times
t_stop = self._poly_intervals.stop_times
for grade in range(min_grade, max_grade + 1):
polynomial, log_like = unbinned_polyfit(events, grade, t_start, t_stop, exposure)
log_likelihoods.append(log_like)
# Found the best one
delta_loglike = np.array(map(lambda x: 2 * (x[0] - x[1]), zip(log_likelihoods[:-1], log_likelihoods[1:])))
delta_threshold = 9.0
mask = (delta_loglike >= delta_threshold)
if (len(mask.nonzero()[0]) == 0):
# best grade is zero!
best_grade = 0
else:
best_grade = mask.nonzero()[0][-1] + 1
return best_grade
def _unbinned_fit_polynomials(self):
self._poly_fit_exists = True
# inform the type of fit we have
self._fit_method_info['bin type'] = 'Unbinned'
self._fit_method_info['fit method'] = threeML_config['event list'][
'unbinned fit method']
# Select all the events that are in the background regions
# and make a mask
all_bkg_masks = []
total_duration = 0.
poly_exposure = 0
for selection in self._poly_intervals:
total_duration += selection.duration
poly_exposure += self.exposure_over_interval(
selection.start_time, selection.stop_time)
all_bkg_masks.append(
np.logical_and(self._arrival_times >= selection.start_time,
self._arrival_times <= selection.stop_time))
poly_mask = all_bkg_masks[0]
# If there are multiple masks:
if len(all_bkg_masks) > 1:
for mask in all_bkg_masks[1:]:
poly_mask = np.logical_or(poly_mask, mask)
# Select the all the events in the poly selections
# We only need to do this once
total_poly_events = self._arrival_times[poly_mask]
# For the channel energies we will need to down select again.
# We can go ahead and do this to avoid repeated computations
total_poly_energies = self._measurement[poly_mask]
# Now we will find the the best poly order unless the use specified one
# The total cnts (over channels) is binned to .1 sec intervals
if self._user_poly_order == -1:
self._optimal_polynomial_grade = self._unbinned_fit_global_and_determine_optimum_grade(
total_poly_events, poly_exposure)
if self._verbose:
print("Auto-determined polynomial order: %d" %
self._optimal_polynomial_grade)
print('\n')
else:
self._optimal_polynomial_grade = self._user_poly_order
channels = range(self._first_channel,
self._n_channels + self._first_channel)
# Check whether we are parallelizing or not
t_start = self._poly_intervals.start_times
t_stop = self._poly_intervals.stop_times
polynomials = []
with progress_bar(self._n_channels,
title="Fitting %s background" %
self._instrument) as p:
for channel in channels:
channel_mask = total_poly_energies == channel
# Mask background events and current channel
# poly_chan_mask = np.logical_and(poly_mask, channel_mask)
# Select the masked events
current_events = total_poly_events[channel_mask]
polynomial, _ = unbinned_polyfit(
current_events, self._optimal_polynomial_grade, t_start,
t_stop, poly_exposure)
polynomials.append(polynomial)
p.increase()
# We are now ready to return the polynomials
self._polynomials = polynomials
def _unbinned_fit_polynomials(self, bayes=False):
self._poly_fit_exists = True
# Select all the events that are in the background regions
# and make a mask
all_bkg_masks = []
total_duration = 0.0
poly_exposure = 0
for selection in self._poly_intervals:
total_duration += selection.duration
poly_exposure += self.exposure_over_interval(
selection.start_time, selection.stop_time)
all_bkg_masks.append(
np.logical_and(
self._arrival_times >= selection.start_time,
self._arrival_times <= selection.stop_time,
))
poly_mask = all_bkg_masks[0]
# If there are multiple masks:
if len(all_bkg_masks) > 1:
for mask in all_bkg_masks[1:]:
poly_mask = np.logical_or(poly_mask, mask)
# Select the all the events in the poly selections
# We only need to do this once
total_poly_events = self._arrival_times[poly_mask]
# For the channel energies we will need to down select again.
# We can go ahead and do this to avoid repeated computations
total_poly_energies = self._measurement[poly_mask]
# Now we will find the the best poly order unless the use specified one
# The total cnts (over channels) is binned to .1 sec intervals
if self._user_poly_order == -1:
self._optimal_polynomial_grade = (
self._unbinned_fit_global_and_determine_optimum_grade(
total_poly_events, poly_exposure, bayes=bayes))
log.info("Auto-determined polynomial order: %d" %
self._optimal_polynomial_grade)
else:
self._optimal_polynomial_grade = self._user_poly_order
channels = list(
range(self._first_channel, self._n_channels + self._first_channel))
# Check whether we are parallelizing or not
t_start = self._poly_intervals.start_times
t_stop = self._poly_intervals.stop_times
if threeML_config["parallel"]["use_parallel"]:
def worker(channel):
channel_mask = total_poly_energies == channel
# Mask background events and current channel
# poly_chan_mask = np.logical_and(poly_mask, channel_mask)
# Select the masked events
current_events = total_poly_events[channel_mask]
polynomial, _ = unbinned_polyfit(
current_events,
self._optimal_polynomial_grade,
t_start,
t_stop,
poly_exposure,
bayes=bayes)
return polynomial
client = ParallelClient()
polynomials = client.execute_with_progress_bar(
worker,
channels,
name=f"Fitting {self._instrument} background")
else:
polynomials = []
for channel in tqdm(channels,
desc=f"Fitting {self._instrument} background"):
channel_mask = total_poly_energies == channel
# Mask background events and current channel
# poly_chan_mask = np.logical_and(poly_mask, channel_mask)
# Select the masked events
current_events = total_poly_events[channel_mask]
polynomial, _ = unbinned_polyfit(
current_events,
self._optimal_polynomial_grade,
t_start,
t_stop,
poly_exposure,
bayes=bayes)
polynomials.append(polynomial)
# We are now ready to return the polynomials
self._polynomials = polynomials
def _unbinned_fit_global_and_determine_optimum_grade(
self, events, exposure, bayes=False):
"""
Provides the ability to find the optimum polynomial grade for *unbinned* events by fitting the
total (all channels) to 0-2 order polynomials and then comparing them via a likelihood ratio test.
:param events: an event list
:param exposure: the exposure per event
:return: polynomial grade
"""
# Fit the sum of all the channels to determine the optimal polynomial
# grade
min_grade = 0
max_grade = 2
log_likelihoods = []
t_start = self._poly_intervals.start_times
t_stop = self._poly_intervals.stop_times
log.debug("attempting to find best fit poly with unbinned")
if threeML_config["parallel"]["use_parallel"]:
def worker(grade):
polynomial, log_like = unbinned_polyfit(events,
grade,
t_start,
t_stop,
exposure,
bayes=bayes)
return log_like
client = ParallelClient()
log_likelihoods = client.execute_with_progress_bar(
worker,
list(range(min_grade, max_grade + 1)),
name="Finding best polynomial Order")
else:
for grade in trange(min_grade,
max_grade + 1,
desc="Finding best polynomial Order"):
polynomial, log_like = unbinned_polyfit(events,
grade,
t_start,
t_stop,
exposure,
bayes=bayes)
log_likelihoods.append(log_like)
# Found the best one
delta_loglike = np.array([
2 * (x[0] - x[1])
for x in zip(log_likelihoods[:-1], log_likelihoods[1:])
])
log.debug(f"log likes {log_likelihoods}")
log.debug(f" delta loglikes {delta_loglike}")
delta_threshold = 9.0
mask = delta_loglike >= delta_threshold
if len(mask.nonzero()[0]) == 0:
# best grade is zero!
best_grade = 0
else:
best_grade = mask.nonzero()[0][-1] + 1
return best_grade