def _step1d(self, steps1):
log_likes = np.zeros_like(steps1)
with progress_bar(len(steps1), title='Profiling likelihood') as p:
for i, step in enumerate(steps1):
if self._n_free_parameters > 0:
# Profile out the free parameters
self._wrapper.set_fixed_values(step)
_, this_log_like = self._optimizer.minimize(
compute_covar=False)
else:
# No free parameters, just compute the likelihood
this_log_like = self._function(step)
log_likes[i] = this_log_like
p.increase()
return log_likes
def _get_errors(self):
"""
Override this method if the minimizer provide a function to get all errors at once. If instead it provides
a method to get one error at the time, override the _get_one_error method
:return: a ordered dictionary parameter_path -> (negative_error, positive_error)
"""
# TODO: options for other significance levels
target_delta_log_like = 0.5
errors = collections.OrderedDict()
with progress_bar(2 * len(self.parameters),
title='Computing errors') as p:
for parameter_name in self.parameters:
negative_error = self._get_one_error(parameter_name,
target_delta_log_like, -1)
p.increase()
positive_error = self._get_one_error(parameter_name,
target_delta_log_like, +1)
p.increase()
errors[parameter_name] = (negative_error, positive_error)
return errors
def _get_errors(self):
"""
Override this method if the minimizer provide a function to get all errors at once. If instead it provides
a method to get one error at the time, override the _get_one_error method
:return: a ordered dictionary parameter_path -> (negative_error, positive_error)
"""
# TODO: options for other significance levels
target_delta_log_like = 0.5
errors = collections.OrderedDict()
with progress_bar(2 * len(self.parameters), title='Computing errors') as p:
for parameter_name in self.parameters:
negative_error = self._get_one_error(parameter_name, target_delta_log_like, -1)
p.increase()
positive_error = self._get_one_error(parameter_name, target_delta_log_like, +1)
p.increase()
errors[parameter_name] = (negative_error, positive_error)
return errors
def _step1d(self, steps1):
log_likes = np.zeros_like(steps1)
with progress_bar(len(steps1), title='Profiling likelihood') as p:
for i, step in enumerate(steps1):
if self._n_free_parameters > 0:
# Profile out the free parameters
self._wrapper.set_fixed_values(step)
_, this_log_like = self._optimizer.minimize(compute_covar=False)
else:
# No free parameters, just compute the likelihood
this_log_like = self._function(step)
log_likes[i] = this_log_like
p.increase()
return log_likes
def _evaluate(self):
"""
calculate the best or mean fit of the new function or
quantity
:return:
"""
# if there are independent variables
if self._independent_variable_range:
variates = []
# scroll through the independent variables
n_iterations = np.product(self._out_shape)
with progress_bar(n_iterations, title="Propagating errors") as p:
with use_astromodels_memoization(False):
for variables in itertools.product(*self._independent_variable_range):
variates.append(self._propagated_function(*variables))
p.increase()
# otherwise just evaluate
else:
variates = self._propagated_function()
# create a variates container
self._propagated_variates = VariatesContainer(variates, self._out_shape, self._cl, self._transform, self._equal_tailed)
def execute_with_progress_bar(self, worker, items, chunk_size=None):
# Let's make a wrapper which will allow us to recover the order
def wrapper(x):
(id, item) = x
return (id, worker(item))
items_wrapped = [(i, item) for i, item in enumerate(items)]
n_iterations = len(items)
with progress_bar(n_iterations) as p:
amr = self._interactive_map(wrapper, items_wrapped, ordered=False, chunk_size=chunk_size)
results = []
for i, res in enumerate(amr):
results.append(res)
p.increase()
# Reorder the list according to the id
return map(lambda x:x[1], sorted(results, key=lambda x:x[0]))
def execute_with_progress_bar(self, worker, items, chunk_size=None):
# Let's make a wrapper which will allow us to recover the order
def wrapper(x):
(id, item) = x
return (id, worker(item))
items_wrapped = [(i, item) for i, item in enumerate(items)]
n_iterations = len(items)
with progress_bar(n_iterations) as p:
amr = self._interactive_map(wrapper,
items_wrapped,
ordered=False,
chunk_size=chunk_size)
results = []
for i, res in enumerate(amr):
results.append(res)
p.increase()
# Reorder the list according to the id
return list(
map(lambda x: x[1], sorted(results, key=lambda x: x[0])))
def go(self, continue_on_failure=True, compute_covariance=False, verbose=False, **options_for_parallel_computation):
# Generate the data frame which will contain all results
if verbose:
log.setLevel(logging.INFO)
self._continue_on_failure = continue_on_failure
self._compute_covariance = compute_covariance
# let's iterate, perform the fit and fill the data frame
if threeML_config['parallel']['use-parallel']:
# Parallel computation
client = ParallelClient(**options_for_parallel_computation)
results = client.execute_with_progress_bar(self.worker, range(self._n_iterations))
else:
# Serial computation
results = []
with progress_bar(self._n_iterations, title='Goodness of fit computation') as p:
for i in range(self._n_iterations):
results.append(self.worker(i))
p.increase()
assert len(results) == self._n_iterations, "Something went wrong, I have %s results " \
"for %s intervals" % (len(results), self._n_iterations)
# Store the results in the data frames
parameter_frames = pd.concat(map(lambda x: x[0], results), keys=range(self._n_iterations))
like_frames = pd.concat(map(lambda x: x[1], results), keys=range(self._n_iterations))
# Store a list with all results (this is a list of lists, each list contains the results for the different
# iterations for the same model)
self._all_results = []
for i in range(self._n_models):
this_model_results = map(lambda x: x[2][i], results)
self._all_results.append(AnalysisResultsSet(this_model_results))
return parameter_frames, like_frames
def go(self):
if is_parallel_computation_active():
client = ParallelClient()
if self._n_decs % client.get_number_of_engines() != 0:
warnings.warn(
"The number of Dec bands is not a multiple of the number of engine. Make it so for optimal performances.",
RuntimeWarning)
res = client.execute_with_progress_bar(
self.worker,
list(range(len(self._points))),
chunk_size=self._n_ras)
else:
n_points = len(self._points)
with progress_bar(n_points) as p:
res = np.zeros(n_points)
for i, point in enumerate(self._points):
res[i] = self.worker(i)
p.increase()
TS = 2 * (-np.array(res) - self._like0)
#self._debug_map = {k:v for v,k in zip(self._points, TS)}
# Get maximum of TS
idx = TS.argmax()
self._max_ts = (TS[idx], self._points[idx])
print("Maximum TS is %.2f at (R.A., Dec) = (%.3f, %.3f)" %
(self._max_ts[0], self._max_ts[1][0], self._max_ts[1][1]))
self._ts_map = TS.reshape(self._n_decs, self._n_ras)
return self._ts_map
def sample_with_progress(title, p0, sampler, n_samples, **kwargs):
# Loop collecting n_samples samples
pos, prob, state = [None, None, None]
# This is only for producing the progress bar
with progress_bar(n_samples, title=title) as progress:
for i, result in enumerate(sampler.sample(p0, iterations=n_samples, **kwargs)):
# Show progress
progress.animate((i + 1))
# Get the vectors with the results
pos, prob, state = result
return pos, prob, state
def sample_with_progress(title, p0, sampler, n_samples, **kwargs):
# Loop collecting n_samples samples
pos, prob, state = [None, None, None]
# This is only for producing the progress bar
with progress_bar(n_samples, title=title) as progress:
for i, result in enumerate(sampler.sample(p0, iterations=n_samples, **kwargs)):
# Show progress
progress.animate((i + 1))
# Get the vectors with the results
pos, prob, state = result
return pos, prob, state
def _step2d(self, steps1, steps2):
log_likes = np.zeros((len(steps1), len(steps2)))
with progress_bar(len(steps1) * len(steps2),
title='Profiling likelihood') as p:
for i, step1 in enumerate(steps1):
for j, step2 in enumerate(steps2):
if self._n_free_parameters > 0:
# Profile out the free parameters
self._wrapper.set_fixed_values([step1, step2])
try:
_, this_log_like = self._optimizer.minimize(
compute_covar=False)
except FitFailed:
# If the user is stepping too far it might be that the fit fails. It is usually not a
# problem
this_log_like = np.nan
else:
# No free parameters, just compute the likelihood
this_log_like = self._function(step1, step2)
log_likes[i, j] = this_log_like
p.increase()
return log_likes
def _evaluate(self):
"""
calculate the best or mean fit of the new function or
quantity
:return:
"""
# if there are independent variables
if self._independent_variable_range:
variates = []
# scroll through the independent variables
n_iterations = np.product(self._out_shape)
with progress_bar(n_iterations, title="Propagating errors") as p:
with use_astromodels_memoization(False):
for variables in itertools.product(
*self._independent_variable_range):
variates.append(self._propagated_function(*variables))
p.increase()
# otherwise just evaluate
else:
variates = self._propagated_function()
# create a variates container
self._propagated_variates = VariatesContainer(variates,
self._out_shape,
self._cl,
self._transform,
self._equal_tailed)
def _step2d(self, steps1, steps2):
log_likes = np.zeros((len(steps1), len(steps2)))
with progress_bar(len(steps1) * len(steps2), title='Profiling likelihood') as p:
for i, step1 in enumerate(steps1):
for j,step2 in enumerate(steps2):
if self._n_free_parameters > 0:
# Profile out the free parameters
self._wrapper.set_fixed_values([step1, step2])
try:
_, this_log_like = self._optimizer.minimize(compute_covar=False)
except FitFailed:
# If the user is stepping too far it might be that the fit fails. It is usually not a
# problem
this_log_like = np.nan
else:
# No free parameters, just compute the likelihood
this_log_like = self._function(step1, step2)
log_likes[i,j] = this_log_like
p.increase()
return log_likes
def __init__(self,
pha_file_or_instance,
file_type='observed',
rsp_file=None,
arf_file=None):
"""
A spectrum with dispersion build from an OGIP-compliant PHA FITS file. Both Type I & II files can be read. Type II
spectra are selected either by specifying the spectrum_number or via the {spectrum_number} file name convention used
in XSPEC. If the file_type is background, a 3ML InstrumentResponse or subclass must be passed so that the energy
bounds can be obtained.
:param pha_file_or_instance: either a PHA file name or threeML.plugins.OGIP.pha.PHAII instance
:param spectrum_number: (optional) the spectrum number of the TypeII file to be used
:param file_type: observed or background
:param rsp_file: RMF filename or threeML.plugins.OGIP.response.InstrumentResponse instance
:param arf_file: (optional) and ARF filename
"""
# extract the spectrum number if needed
assert isinstance(pha_file_or_instance, str) or isinstance(
pha_file_or_instance,
PHAII), 'Must provide a FITS file name or PHAII instance'
with fits.open(pha_file_or_instance) as f:
try:
HDUidx = f.index_of("SPECTRUM")
except:
raise RuntimeError("The input file %s is not in PHA format" %
(pha2_file))
spectrum = f[HDUidx]
data = spectrum.data
if "COUNTS" in data.columns.names:
has_rates = False
data_column_name = "COUNTS"
elif "RATE" in data.columns.names:
has_rates = True
data_column_name = "RATE"
else:
raise RuntimeError(
"This file does not contain a RATE nor a COUNTS column. "
"This is not a valid PHA file")
# Determine if this is a PHA I or PHA II
if len(data.field(data_column_name).shape) == 2:
num_spectra = data.field(data_column_name).shape[0]
else:
raise RuntimeError(
"This appears to be a PHA I and not PHA II file")
pha_information = _read_pha_or_pha2_file(pha_file_or_instance,
None,
file_type,
rsp_file,
arf_file,
treat_as_time_series=True)
# default the grouping to all open bins
# this will only be altered if the spectrum is rebinned
self._grouping = np.ones_like(pha_information['counts'])
# this saves the extra properties to the class
self._gathered_keywords = pha_information['gathered_keywords']
self._file_type = file_type
# need to see if we have count errors, tstart, tstop
# if not, we create an list of None
if pha_information['count_errors'] is None:
count_errors = [None] * num_spectra
else:
count_errors = pha_information['count_errors']
if pha_information['tstart'] is None:
tstart = [None] * num_spectra
else:
tstart = pha_information['tstart']
if pha_information['tstop'] is None:
tstop = [None] * num_spectra
else:
tstop = pha_information['tstop']
# now build the list of binned spectra
list_of_binned_spectra = []
with progress_bar(num_spectra, title='Loading PHAII spectra') as p:
for i in range(num_spectra):
list_of_binned_spectra.append(
BinnedSpectrumWithDispersion(
counts=pha_information['counts'][i],
exposure=pha_information['exposure'][i, 0],
response=pha_information['rsp'],
count_errors=count_errors[i],
sys_errors=pha_information['sys_errors'][i],
is_poisson=pha_information['is_poisson'],
quality=pha_information['quality'].get_slice(i),
mission=pha_information['gathered_keywords']
['mission'],
instrument=pha_information['gathered_keywords']
['instrument'],
tstart=tstart[i],
tstop=tstop[i]))
p.increase()
# now get the time intervals
start_times = data.field('TIME')
stop_times = data.field('ENDTIME')
time_intervals = TimeIntervalSet.from_starts_and_stops(
start_times, stop_times)
reference_time = 0
# see if there is a reference time in the file
if 'TRIGTIME' in spectrum.header:
reference_time = spectrum.header['TRIGTIME']
for t_number in range(spectrum.header['TFIELDS']):
if 'TZERO%d' % t_number in spectrum.header:
reference_time = spectrum.header['TZERO%d' % t_number]
super(PHASpectrumSet, self).__init__(list_of_binned_spectra,
reference_time=reference_time,
time_intervals=time_intervals)
def download_files_from_directory_ftp(ftp_url,
destination_directory,
filenames=None,
namefilter=None):
# Parse url
tokens = urllib.parse.urlparse(ftp_url)
serverAddress = tokens.netloc
directory = tokens.path
# if no filename has been specified, connect first to retrieve the list of files to download
if filenames == None:
# Connect to server and log in
ftp = ftplib.FTP(serverAddress, "anonymous", '', '', timeout=60)
try:
ftp.login()
except:
# Maybe we are already logged in
try:
ftp.cwd('/')
except:
# nope! don't know what is happening
raise
# Move to origin directory
ftp.cwd(directory)
# Retrieve list of files
filenames = []
ftp.retrlines('NLST', filenames.append)
# Close connection (will reopen later)
ftp.close()
# Download files with progress report
downloaded_files = []
with progress_bar(len(filenames)) as p:
for i, filename in enumerate(filenames):
if namefilter != None and filename.find(namefilter) < 0:
p.increase()
# Filename does not match, do not download it
continue
else:
local_filename = os.path.join(destination_directory, filename)
urllib.request.urlretrieve(
"ftp://%s/%s/%s" % (serverAddress, directory, filename),
local_filename)
urllib.request.urlcleanup()
downloaded_files.append(local_filename)
return downloaded_files
def _fit_polynomials(self):
"""
fits a polynomial to all channels over the input time intervals
:param fit_intervals: str input intervals
:return:
"""
# mark that we have fit a poly now
self._poly_fit_exists = True
# set the fit method
self._fit_method_info['bin type'] = 'Binned'
self._fit_method_info['fit method'] = threeML_config['event list']['binned fit method']
# we need to adjust the selection to the true intervals of the time-binned spectra
tmp_poly_intervals = self._poly_intervals
poly_intervals = self._adjust_to_true_intervals(tmp_poly_intervals)
self._poly_intervals = poly_intervals
# now lets get all the counts, exposure and midpoints for the
# selection
selected_counts = []
selected_exposure = []
selected_midpoints = []
for selection in poly_intervals:
# get the mask of these bins
mask = self._select_bins(selection.start_time,selection.stop_time)
# the counts will be (time, channel) here,
# so the mask is selecting time.
# a sum along axis=0 is a sum in time, while axis=1 is a sum in energy
selected_counts.extend(self._binned_spectrum_set.counts_per_bin[mask])
selected_exposure.extend(self._binned_spectrum_set.exposure_per_bin[mask])
selected_midpoints.extend(self._binned_spectrum_set.time_intervals.mid_points[mask])
selected_counts = np.array(selected_counts)
selected_midpoints = np.array(selected_midpoints)
selected_exposure = np.array(selected_exposure)
# Now we will find the the best poly order unless the use specified one
# The total cnts (over channels) is binned
if self._user_poly_order == -1:
self._optimal_polynomial_grade = self._fit_global_and_determine_optimum_grade(selected_counts.sum(axis=1),
selected_midpoints,
selected_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
polynomials = []
# now fit the light curve of each channel
# and save the estimated polynomial
with progress_bar(self._n_channels, title="Fitting background") as p:
for counts in selected_counts.T:
polynomial, _ = polyfit(selected_midpoints,
counts,
self._optimal_polynomial_grade,
selected_exposure)
polynomials.append(polynomial)
p.increase()
self._polynomials = polynomials
def bayesian_blocks_not_unique(tt, ttstart, ttstop, p0):
# Verify that the input array is one-dimensional
tt = np.asarray(tt, dtype=float)
assert tt.ndim == 1
# Now create the array of unique times
unique_t = np.unique(tt)
t = tt
tstart = ttstart
tstop = ttstop
# Create initial cell edges (Voronoi tessellation) using the unique time stamps
edges = np.concatenate([[tstart],
0.5 * (unique_t[1:] + unique_t[:-1]),
[tstop]])
# The last block length is 0 by definition
block_length = tstop - edges
if np.sum((block_length <= 0)) > 1:
raise RuntimeError("Events appears to be out of order! Check for order, or duplicated events.")
N = unique_t.shape[0]
# arrays to store the best configuration
best = np.zeros(N, dtype=float)
last = np.zeros(N, dtype=int)
# Pre-computed priors (for speed)
# eq. 21 from Scargle 2012
priors = 4 - np.log(73.53 * p0 * np.power(np.arange(1, N + 1), -0.478))
# Count how many events are in each Voronoi cell
x, _ = np.histogram(t, edges)
# Speed tricks: resolve once for all the functions which will be used
# in the loop
cumsum = np.cumsum
log = np.log
argmax = np.argmax
numexpr_evaluate = numexpr.evaluate
arange = np.arange
# Decide the step for reporting progress
incr = max(int(float(N) / 100.0 * 10), 1)
logger.debug("Finding blocks...")
# This is where the computation happens. Following Scargle et al. 2012.
# This loop has been optimized for speed:
# * the expression for the fitness function has been rewritten to
# avoid multiple log computations, and to avoid power computations
# * the use of scipy.weave and numexpr has been evaluated. The latter
# gives a big gain (~40%) if used for the fitness function. No other
# gain is obtained by using it anywhere else
# Set numexpr precision to low (more than enough for us), which is
# faster than high
oldaccuracy = numexpr.set_vml_accuracy_mode('low')
numexpr.set_num_threads(1)
numexpr.set_vml_num_threads(1)
with progress_bar(N) as progress:
for R in range(N):
br = block_length[R + 1]
T_k = block_length[:R + 1] - br
# N_k: number of elements in each block
# This expression has been simplified for the case of
# unbinned events (i.e., one element in each block)
# It was:
N_k = cumsum(x[:R + 1][::-1])[::-1]
# Now it is:
# N_k = arange(R + 1, 0, -1)
# Evaluate fitness function
# This is the slowest part, which I'm speeding up by using
# numexpr. It provides a ~40% gain in execution speed.
fit_vec = numexpr_evaluate('''N_k * log(N_k/ T_k) ''',
optimization='aggressive',
local_dict={'N_k': N_k, 'T_k': T_k})
p = priors[R]
A_R = fit_vec - p
A_R[1:] += best[:R]
i_max = argmax(A_R)
last[R] = i_max
best[R] = A_R[i_max]
progress.increase()
numexpr.set_vml_accuracy_mode(oldaccuracy)
logger.debug("Done\n")
# Now find blocks
change_points = np.zeros(N, dtype=int)
i_cp = N
ind = N
while True:
i_cp -= 1
change_points[i_cp] = ind
if ind == 0:
break
ind = last[ind - 1]
change_points = change_points[i_cp:]
finalEdges = edges[change_points]
return np.asarray(finalEdges)
def to_spectrumlike(self, from_bins=False, start=None, stop=None, interval_name='_interval', extract_measured_background=False):
"""
Create plugin(s) from either the current active selection or the time bins.
If creating from an event list, the
bins are from create_time_bins. If using a pre-time binned time series, the bins are those
native to the data. Start and stop times can be used to control which bins are used.
:param from_bins: choose to create plugins from the time bins
:param start: optional start time of the bins
:param stop: optional stop time of the bins
:param extract_measured_background: Use the selected background rather than a polynomial fit to the background
:param interval_name: the name of the interval
:return: SpectrumLike plugin(s)
"""
# we can use either the modeled or the measured background. In theory, all the information
# in the background spectrum should propagate to the likelihood
if extract_measured_background:
this_background_spectrum = self._measured_background_spectrum
else:
this_background_spectrum = self._background_spectrum
# this is for a single interval
if not from_bins:
assert self._observed_spectrum is not None, 'Must have selected an active time interval'
assert isinstance(self._observed_spectrum, BinnedSpectrum), 'You are attempting to create a SpectrumLike plugin from the wrong data type'
if this_background_spectrum is None:
custom_warnings.warn('No background selection has been made. This plugin will contain no background!')
if self._response is None:
return SpectrumLike(name=self._name,
observation=self._observed_spectrum,
background=this_background_spectrum,
verbose=self._verbose,
tstart=self._tstart,
tstop=self._tstop)
else:
return DispersionSpectrumLike(name=self._name,
observation=self._observed_spectrum,
background=this_background_spectrum,
verbose=self._verbose,
tstart = self._tstart,
tstop = self._tstop
)
else:
# this is for a set of intervals.
assert self._time_series.bins is not None, 'This time series does not have any bins!'
# save the original interval if there is one
old_interval = copy.copy(self._active_interval)
old_verbose = copy.copy(self._verbose)
# we will keep it quiet to keep from being annoying
self._verbose = False
list_of_speclikes = []
# get the bins from the time series
# for event lists, these are from created bins
# for binned spectra sets, these are the native bines
these_bins = self._time_series.bins # type: TimeIntervalSet
if start is not None:
assert stop is not None, 'must specify a start AND a stop time'
if stop is not None:
assert stop is not None, 'must specify a start AND a stop time'
these_bins = these_bins.containing_interval(start, stop, inner=False)
# loop through the intervals and create spec likes
with progress_bar(len(these_bins), title='Creating plugins') as p:
for i, interval in enumerate(these_bins):
self.set_active_time_interval(interval.to_string())
assert isinstance(self._observed_spectrum, BinnedSpectrum), 'You are attempting to create a SpectrumLike plugin from the wrong data type'
if extract_measured_background:
this_background_spectrum = self._measured_background_spectrum
else:
this_background_spectrum = self._background_spectrum
if this_background_spectrum is None:
custom_warnings.warn(
'No bakckground selection has been made. This plugin will contain no background!')
try:
if self._response is None:
sl = SpectrumLike(name="%s%s%d" % (self._name, interval_name, i),
observation=self._observed_spectrum,
background=this_background_spectrum,
verbose=self._verbose,
tstart=self._tstart,
tstop=self._tstop)
else:
sl = DispersionSpectrumLike(name="%s%s%d" % (self._name, interval_name, i),
observation=self._observed_spectrum,
background=this_background_spectrum,
verbose=self._verbose,
tstart=self._tstart,
tstop=self._tstop)
list_of_speclikes.append(sl)
except(NegativeBackground):
custom_warnings.warn('Something is wrong with interval %s. skipping.' % interval)
p.increase()
# restore the old interval
if old_interval is not None:
self.set_active_time_interval(*old_interval)
else:
self._active_interval = None
self._verbose = old_verbose
return list_of_speclikes
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 download(
self,
remote_filename,
destination_path,
new_filename=None,
progress=True,
compress=False,
):
assert remote_filename in self.files, (
"File %s is not contained in this directory (%s)" %
(remote_filename, self._request_result.url))
destination_path = sanitize_filename(destination_path, abspath=True)
assert path_exists_and_is_directory(destination_path), (
"Provided destination does not exist or "
"is not a directory" % destination_path)
# If no filename is specified, use the same name that the file has on the remote server
if new_filename is None:
new_filename = remote_filename.split("/")[-1]
# Get the fully qualified path for the remote and the local file
remote_path = self._request_result.url + remote_filename
local_path = os.path.join(destination_path, new_filename)
# Ask the server for the file, but do not download it just yet
# (stream=True will get the HTTP header but nothing else)
# Use stream=True for two reasons:
# * so that the file is not downloaded all in memory before being written to the disk
# * so that we can report progress is requested
this_request = requests.get(remote_path, stream=True)
# Figure out the size of the file
file_size = int(this_request.headers["Content-Length"])
# Now check if we really need to download this file
if compress:
# Add a .gz at the end of the file path
local_path += ".gz"
if file_existing_and_readable(local_path):
local_size = os.path.getsize(local_path)
if local_size == file_size or compress:
# if the compressed file already exists
# it will have a smaller size
# No need to download it again
return local_path
# Chunk size shouldn't bee too small otherwise we are causing a bottleneck in the download speed
chunk_size = 1024 * 10
# If the user wants to compress the file, use gzip, otherwise the normal opener
if compress:
import gzip
opener = gzip.open
else:
opener = open
if progress:
# Set a title for the progress bar
bar_title = "Downloading %s" % new_filename
with progress_bar(file_size,
scale=1024 * 1024,
units="Mb",
title=bar_title) as bar: # type: ProgressBarBase
with opener(local_path, "wb") as f:
for chunk in this_request.iter_content(
chunk_size=chunk_size):
if chunk: # filter out keep-alive new chunks
f.write(chunk)
bar.increase(len(chunk))
this_request.close()
else:
with opener(local_path, "wb") as f:
for chunk in this_request.iter_content(chunk_size=chunk_size):
if chunk: # filter out keep-alive new chunks
f.write(chunk)
this_request.close()
return local_path
def _minimize(self):
# Gather the setup
islands = self._setup_dict['islands']
pop_size = self._setup_dict['population_size']
evolution_cycles = self._setup_dict['evolution_cycles']
# Print some info
print("\nPAGMO setup:")
print("------------")
print("- Number of islands: %i" % islands)
print("- Population size per island: %i" % pop_size)
print("- Evolutions cycles per island: %i\n" % evolution_cycles)
Npar = len(self._internal_parameters)
if is_parallel_computation_active():
wrapper = PAGMOWrapper(function=self.function, parameters=self._internal_parameters, dim=Npar)
# use the archipelago, which uses the ipyparallel computation
archi = pg.archipelago(udi=pg.ipyparallel_island(), n=islands,
algo=self._setup_dict['algorithm'], prob=wrapper, pop_size=pop_size)
archi.wait()
# Display some info
print("\nSetup before parallel execution:")
print("--------------------------------\n")
print(archi)
# Evolve populations on islands
print("Evolving... (progress not available for parallel execution)")
# For some weird reason, ipyparallel looks for _winreg on Linux (where does
# not exist, being a Windows module). Let's mock it with an empty module'
mocked = False
if os.path.exists("_winreg.py") is False:
with open("_winreg.py", "w+") as f:
f.write("pass")
mocked = True
archi.evolve()
# Wait for completion (evolve() is async)
archi.wait_check()
# Now remove _winreg.py if needed
if mocked:
os.remove("_winreg.py")
# Find best and worst islands
fOpts = np.array(map(lambda x:x[0], archi.get_champions_f()))
xOpts = archi.get_champions_x()
else:
# do not use ipyparallel. Evolve populations on islands serially
wrapper = PAGMOWrapper(function=self.function, parameters=self._internal_parameters, dim=Npar)
xOpts = []
fOpts = np.zeros(islands)
with progress_bar(iterations=islands, title="pygmo minimization") as p:
for island_id in range(islands):
pop = pg.population(prob=wrapper, size=pop_size)
for i in range(evolution_cycles):
pop = self._setup_dict['algorithm'].evolve(pop)
# Gather results
xOpts.append(pop.champion_x)
fOpts[island_id] = pop.champion_f[0]
p.increase()
# Find best and worst islands
min_idx = fOpts.argmin()
max_idx = fOpts.argmax()
fOpt = fOpts[min_idx]
fWorse = fOpts[max_idx]
xOpt = np.array(xOpts)[min_idx]
# Some information
print("\nSummary of evolution:")
print("---------------------")
print("Best population has minimum %.3f" % (fOpt))
print("Worst population has minimum %.3f" % (fWorse))
print("")
# Transform to numpy.array
best_fit_values = np.array(xOpt)
return best_fit_values, fOpt
def display_fit(self, smoothing_kernel_sigma=0.1, display_colorbar=False):
"""
Make a figure containing 4 maps for each active analysis bins with respectively model, data,
background and residuals. The model, data and residual maps are smoothed, the background
map is not.
:param smoothing_kernel_sigma: sigma for the Gaussian smoothing kernel, for all but
background maps
:param display_colorbar: whether or not to display the colorbar in the residuals
:return: a matplotlib.Figure
"""
n_point_sources = self._likelihood_model.get_number_of_point_sources()
n_ext_sources = self._likelihood_model.get_number_of_extended_sources()
# This is the resolution (i.e., the size of one pixel) of the image
resolution = 3.0 # arcmin
# The image is going to cover the diameter plus 20% padding
xsize = self._get_optimal_xsize(resolution)
n_active_planes = len(self._active_planes)
n_columns = 4
fig, subs = plt.subplots(n_active_planes, n_columns,
figsize=(2.7 * n_columns, n_active_planes * 2), squeeze=False)
with progress_bar(len(self._active_planes), title='Smoothing maps') as prog_bar:
images = ['None'] * n_columns
for i, plane_id in enumerate(self._active_planes):
data_analysis_bin = self._maptree[plane_id]
# Get the center of the projection for this plane
this_ra, this_dec = self._roi.ra_dec_center
# Make a full healpix map for a second
whole_map = self._get_model_map(plane_id, n_point_sources, n_ext_sources).as_dense()
# Healpix uses longitude between -180 and 180, while R.A. is between 0 and 360. We need to fix that:
longitude = ra_to_longitude(this_ra)
# Declination is already between -90 and 90
latitude = this_dec
# Background and excess maps
bkg_subtracted, _, background_map = self._get_excess(data_analysis_bin, all_maps=True)
# Make all the projections: model, excess, background, residuals
proj_model = self._represent_healpix_map(fig, whole_map,
longitude, latitude,
xsize, resolution, smoothing_kernel_sigma)
# Here we removed the background otherwise nothing is visible
# Get background (which is in a way "part of the model" since the uncertainties are neglected)
proj_data = self._represent_healpix_map(fig, bkg_subtracted,
longitude, latitude,
xsize, resolution, smoothing_kernel_sigma)
# No smoothing for this one (because a goal is to check it is smooth).
proj_bkg = self._represent_healpix_map(fig, background_map,
longitude, latitude,
xsize, resolution, None)
proj_residuals = proj_data - proj_model
# Common color scale range for model and excess maps
vmin = min(np.nanmin(proj_model), np.nanmin(proj_data))
vmax = max(np.nanmax(proj_model), np.nanmax(proj_data))
# Plot model
images[0] = subs[i][0].imshow(proj_model, origin='lower', vmin=vmin, vmax=vmax)
subs[i][0].set_title('model, bin {}'.format(data_analysis_bin.name))
# Plot data map
images[1] = subs[i][1].imshow(proj_data, origin='lower', vmin=vmin, vmax=vmax)
subs[i][1].set_title('excess, bin {}'.format(data_analysis_bin.name))
# Plot background map.
images[2] = subs[i][2].imshow(proj_bkg, origin='lower')
subs[i][2].set_title('background, bin {}'.format(data_analysis_bin.name))
# Now residuals
images[3] = subs[i][3].imshow(proj_residuals, origin='lower')
subs[i][3].set_title('residuals, bin {}'.format(data_analysis_bin.name))
# Remove numbers from axis
for j in range(n_columns):
subs[i][j].axis('off')
if display_colorbar:
for j, image in enumerate(images):
plt.colorbar(image, ax=subs[i][j])
prog_bar.increase()
fig.set_tight_layout(True)
return fig
def go(args):
# Define the spectral and spatial models for the source
spectrum = Log_parabola()
source = PointSource("CrabNebula",
ra=args.RA,
dec=args.Dec,
spectral_shape=spectrum)
# NOTE: if you use units, you have to set up the values for the parameters
# AFTER you create the source, because during creation the function Log_parabola
# gets its units
spectrum.piv = 10 * u.TeV # Pivot energy
spectrum.piv.fix = True
spectrum.K = 1e-14 / (u.TeV * u.cm**2 * u.s) # norm (in 1/(keV cm2 s))
spectrum.K.bounds = (1e-25, 1e-21) # without units energies are in keV
spectrum.beta = 0 # log parabolic beta
spectrum.beta.bounds = (-4., 2.)
spectrum.alpha = -2.5 # log parabolic alpha (index)
spectrum.alpha.bounds = (-4., 2.)
print(source(1 * u.keV))
# Set up a likelihood model using the source.
# Then create a HAWCLike object using the model, the maptree, and detector
# response.
lm = Model(source)
llh = HAWCLike("CrabNebula", args.maptreefile, args.responsefile)
llh.set_active_measurements(args.startBin, args.stopBin)
# Double check the free parameters
print("Likelihood model:\n")
print(lm)
# Set up the likelihood and run the fit
print("Performing likelihood fit...\n")
datalist = DataList(llh)
jl = JointLikelihood(lm, datalist)
# Pre-fit with root
# The ROOT minimizer is still under dev and does
# not provide a printer. This is the reason for the try/except
try:
jl.set_minimizer("ROOT")
jl.fit()
except AttributeError:
pass
jl.set_minimizer("minuit")
result = jl.fit()
# Print up the TS, and fit parameters, and then plot stuff
print("\nTest statistic:")
TS = llh.calc_TS()
print("Test statistic: %g" % TS)
# Get the differential flux at 1 TeV
diff_flux = spectrum(1 * u.TeV)
# Convert it to 1 / (TeV cm2 s)
diff_flux_TeV = diff_flux.to(1 / (u.TeV * u.cm**2 * u.s))
print("Norm @ 1 TeV: %s \n" % diff_flux_TeV)
spectrum.display()
E = np.logspace(np.log10(1), np.log10(100), 100) * u.TeV
fML = spectrum(E)
fMin68 = None
fMax68 = None
fMin95 = None
fMax95 = None
# Get a flux uncertainty envelope from MCMC samples
if args.nMCMC > 0:
print("Running MCMC...")
# The set_uninformative_prior will use the given prior type
# and the current minimum and maximum to set the prior
spectrum.K.set_uninformative_prior(Log_uniform_prior)
spectrum.beta.set_uninformative_prior(Uniform_prior)
spectrum.alpha.set_uninformative_prior(Uniform_prior)
ba = BayesianAnalysis(lm, datalist)
nW = 10 # number of MCMC walkers
nB = 100 # number of samples for burning in
nS = args.nMCMC # number of samples
samples = ba.sample(nW, nB, nS)
sigmas = [1., 2., 3.]
levels = [1. - np.exp(-0.5 * s**2) for s in sigmas]
# Make a corner plot of the sampled parameters from the MCMC
fig = ba.corner_plot(
labels=[
r"$K$ [keV$^{-1}$ cm$^{-2}$ s$^{-1}$]", r"$\alpha$", r"$\beta$"
],
#range=[[-2.5, -2.], [-0.4, 0.], [-10.6, -10.45]],
show_titles=True,
title_kwargs={"fontsize": 10},
quantiles=[0.16, 0.5, 0.84],
levels=levels,
verbose=True)
fig.savefig("crab_logParabola_MCMC.png")
# Construct the uncertainty envelope on the flux using the sampled
# parameter space
fMin68, fMax68 = [], []
fMin95, fMax95 = [], []
# Just to be fancy, let's use the progress bar
print("\nMaking spectrum plot...\n")
from threeML.io.progress_bar import progress_bar
with progress_bar(E.shape[0], title='Plotting') as progress:
for i, energy in enumerate(E):
progress.animate(i)
flux = []
for (kk, a, b) in ba.raw_samples:
spectrum.K = kk
spectrum.alpha = a
spectrum.beta = b
# Note that here energy has units, so the computation
# would be made with units and the output will be a
# astropy.Quantity instance. However that is slow,
# so let's use the value instead (the computation without
# unit is much faster)
this_diff_flux = spectrum(energy.to('keV').value)
flux.append(this_diff_flux)
# Evaluate the percentiles of the flux
f02, f16, f84, f97 = np.percentile(np.asarray(flux),
[2.5, 16, 84, 97.5])
fMin68.append(f16)
fMax68.append(f84)
fMin95.append(f02)
fMax95.append(f97)
print(" done")
# Save the percentiles with units
# y_unit is the unit of the output if the function is 1d,
# as in y = f(x)
fMin68 = np.asarray(fMin68) * spectrum.y_unit
fMax68 = np.asarray(fMax68) * spectrum.y_unit
fMin95 = np.asarray(fMin95) * spectrum.y_unit
fMax95 = np.asarray(fMax95) * spectrum.y_unit
fig, ax = plt.subplots(1, 1, figsize=(8, 6))
conv = (E)**2.5
if fMin95 is not None and fMax95 is not None:
# Plot the 95% C.I. on the flux as a function of energy
ax.fill_between(
E.to(u.TeV).value,
(conv * fMin95).to(u.TeV**2.5 / (u.TeV * u.cm**2 * u.s)).value,
(conv * fMax95).to(u.TeV**2.5 / (u.TeV * u.cm**2 * u.s)).value,
color="royalblue",
alpha=0.3,
label="95% C.I.")
if fMin68 is not None and fMax68 is not None:
# Plot the 68% C.I. on the flux as a function of energy
ax.fill_between(
E.to(u.TeV).value,
(conv * fMin68).to(u.TeV**2.5 / (u.TeV * u.cm**2 * u.s)).value,
(conv * fMax68).to(u.TeV**2.5 / (u.TeV * u.cm**2 * u.s)).value,
color="royalblue",
alpha=0.5,
label="68% C.I.")
ax.plot(E.to(u.TeV).value,
(conv * fML).to(u.TeV**2.5 / (u.TeV * u.cm**2 * u.s)).value,
"r--",
lw=2,
label="Max LL")
ax.set(xscale="log",
xlabel="energy [TeV]",
yscale="log",
ylabel=r"$E^{2.5}$ flux [TeV$^{1.5}$ cm$^{-2}$ s$^{-1}$]",
ylim=[5e-12, 5e-11],
title="Crab Nebula")
ax.xaxis.set_major_formatter(mpl.ticker.FormatStrFormatter("%g"))
h, l = ax.get_legend_handles_labels()
leg = ax.legend(h, l, loc=3)
leg.get_frame().set_linewidth(0)
fig.tight_layout()
fig.savefig("crab_logParabola.png")
plt.show()
def display_fit(self, smoothing_kernel_sigma=0.1):
n_point_sources = self._likelihood_model.get_number_of_point_sources()
n_ext_sources = self._likelihood_model.get_number_of_extended_sources()
# This is the resolution (i.e., the size of one pixel) of the image
resolution = 3.0 # arcmin
# The image is going to cover the diameter plus 20% padding
xsize = self._get_optimal_xsize(resolution)
n_active_planes = len(self._active_planes)
fig, subs = plt.subplots(n_active_planes,
3,
figsize=(8, n_active_planes * 2))
with progress_bar(len(self._active_planes),
title='Smoothing maps') as prog_bar:
for i, plane_id in enumerate(self._active_planes):
data_analysis_bin = self._maptree[plane_id]
# Get the center of the projection for this plane
this_ra, this_dec = self._roi.ra_dec_center
this_model_map_hpx = self._get_expectation(
data_analysis_bin, plane_id, n_point_sources,
n_ext_sources)
# Make a full healpix map for a second
whole_map = SparseHealpix(
this_model_map_hpx, self._active_pixels[plane_id],
data_analysis_bin.observation_map.nside).as_dense()
# Healpix uses longitude between -180 and 180, while R.A. is between 0 and 360. We need to fix that:
longitude = ra_to_longitude(this_ra)
# Declination is already between -90 and 90
latitude = this_dec
# Plot model
proj_m = self._represent_healpix_map(fig, whole_map, longitude,
latitude, xsize,
resolution,
smoothing_kernel_sigma)
subs[i][0].imshow(proj_m, origin='lower')
# Remove numbers from axis
subs[i][0].axis('off')
# Plot data map
# Here we removed the background otherwise nothing is visible
# Get background (which is in a way "part of the model" since the uncertainties are neglected)
background_map = data_analysis_bin.background_map.as_dense()
bkg_subtracted = data_analysis_bin.observation_map.as_dense(
) - background_map
proj_d = self._represent_healpix_map(fig, bkg_subtracted,
longitude, latitude,
xsize, resolution,
smoothing_kernel_sigma)
subs[i][1].imshow(proj_d, origin='lower')
# Remove numbers from axis
subs[i][1].axis('off')
# Now residuals
res = proj_d - proj_m
# proj_res = self._represent_healpix_map(fig, res,
# longitude, latitude,
# xsize, resolution, smoothing_kernel_sigma)
subs[i][2].imshow(res, origin='lower')
# Remove numbers from axis
subs[i][2].axis('off')
prog_bar.increase()
fig.set_tight_layout(True)
return fig
def bayesian_blocks_not_unique(tt, ttstart, ttstop, p0):
# Verify that the input array is one-dimensional
tt = np.asarray(tt, dtype=float)
assert tt.ndim == 1
# Now create the array of unique times
unique_t = np.unique(tt)
t = tt
tstart = ttstart
tstop = ttstop
# Create initial cell edges (Voronoi tessellation) using the unique time stamps
edges = np.concatenate([[tstart], 0.5 * (unique_t[1:] + unique_t[:-1]),
[tstop]])
# The last block length is 0 by definition
block_length = tstop - edges
if np.sum((block_length <= 0)) > 1:
raise RuntimeError(
"Events appears to be out of order! Check for order, or duplicated events."
)
N = unique_t.shape[0]
# arrays to store the best configuration
best = np.zeros(N, dtype=float)
last = np.zeros(N, dtype=int)
# Pre-computed priors (for speed)
# eq. 21 from Scargle 2012
priors = 4 - np.log(73.53 * p0 * np.power(np.arange(1, N + 1), -0.478))
# Count how many events are in each Voronoi cell
x, _ = np.histogram(t, edges)
# Speed tricks: resolve once for all the functions which will be used
# in the loop
cumsum = np.cumsum
log = np.log
argmax = np.argmax
numexpr_evaluate = numexpr.evaluate
arange = np.arange
# Decide the step for reporting progress
incr = max(int(float(N) / 100.0 * 10), 1)
logger.debug("Finding blocks...")
# This is where the computation happens. Following Scargle et al. 2012.
# This loop has been optimized for speed:
# * the expression for the fitness function has been rewritten to
# avoid multiple log computations, and to avoid power computations
# * the use of scipy.weave and numexpr has been evaluated. The latter
# gives a big gain (~40%) if used for the fitness function. No other
# gain is obtained by using it anywhere else
# Set numexpr precision to low (more than enough for us), which is
# faster than high
oldaccuracy = numexpr.set_vml_accuracy_mode('low')
numexpr.set_num_threads(1)
numexpr.set_vml_num_threads(1)
with progress_bar(N) as progress:
for R in range(N):
br = block_length[R + 1]
T_k = block_length[:R + 1] - br
# N_k: number of elements in each block
# This expression has been simplified for the case of
# unbinned events (i.e., one element in each block)
# It was:
N_k = cumsum(x[:R + 1][::-1])[::-1]
# Now it is:
# N_k = arange(R + 1, 0, -1)
# Evaluate fitness function
# This is the slowest part, which I'm speeding up by using
# numexpr. It provides a ~40% gain in execution speed.
fit_vec = numexpr_evaluate('''N_k * log(N_k/ T_k) ''',
optimization='aggressive',
local_dict={
'N_k': N_k,
'T_k': T_k
})
p = priors[R]
A_R = fit_vec - p
A_R[1:] += best[:R]
i_max = argmax(A_R)
last[R] = i_max
best[R] = A_R[i_max]
progress.increase()
numexpr.set_vml_accuracy_mode(oldaccuracy)
logger.debug("Done\n")
# Now find blocks
change_points = np.zeros(N, dtype=int)
i_cp = N
ind = N
while True:
i_cp -= 1
change_points[i_cp] = ind
if ind == 0:
break
ind = last[ind - 1]
change_points = change_points[i_cp:]
finalEdges = edges[change_points]
return np.asarray(finalEdges)
def bin_by_significance(
cls,
arrival_times,
background_getter,
background_error_getter=None,
sigma_level=10,
min_counts=1,
tstart=None,
tstop=None,
):
"""
Bin the data to a given significance level for a given background method and sigma
method. If a background error function is given then it is assumed that the error distribution
is gaussian. Otherwise, the error distribution is assumed to be Poisson.
:param background_getter: function of a start and stop time that returns background counts
:param background_error_getter: function of a start and stop time that returns background count errors
:param sigma_level: the sigma level of the intervals
:param min_counts: the minimum counts per bin
:return:
"""
if tstart is None:
tstart = arrival_times.min()
else:
tstart = float(tstart)
if tstop is None:
tstop = arrival_times.max()
else:
tstop = float(tstop)
starts = []
stops = []
# Switching to a fast search
# Idea inspired by Damien Begue
# these factors change the time steps
# in the fast search. should experiment
if sigma_level > 25:
increase_factor = 0.5
decrease_factor = 0.5
else:
increase_factor = 0.25
decrease_factor = 0.25
current_start = arrival_times[0]
# first we need to see if the interval provided has enough counts
_, counts = TemporalBinner._select_events(arrival_times, current_start,
arrival_times[-1])
# if it does not, the flag for the big loop never gets set
end_all_search = not TemporalBinner._check_exceeds_sigma_interval(
current_start,
arrival_times[-1],
counts,
sigma_level,
background_getter,
background_error_getter,
)
# We will start the search at the mid point of the whole interval
mid_point = 0.5 * (arrival_times[-1] + current_start)
current_stop = mid_point
# initialize the fast search flag
end_fast_search = False
# resolve once for functions used in the loop
searchsorted = np.searchsorted
# this is the main loop
# as long as we have not reached the end of the interval
# the loop will run
with progress_bar(arrival_times.shape[0]) as pbar:
while not end_all_search:
# start of the fast search
# we reset the flag for the interval
# having been decreased in the last pass
decreased_interval = False
while not end_fast_search:
# we calculate the sigma of the current region
_, counts = TemporalBinner._select_events(
arrival_times, current_start, current_stop)
sigma_exceeded = TemporalBinner._check_exceeds_sigma_interval(
current_start,
current_stop,
counts,
sigma_level,
background_getter,
background_error_getter,
)
time_step = abs(current_stop - current_start)
# if we do not exceed the sigma
# we need to increase the time interval
if not sigma_exceeded:
# however, if in the last pass we had to decrease
# the interval, it means we have found where we
# we need to start the slow search
if decreased_interval:
# mark where we are in the list
start_idx = searchsorted(arrival_times,
current_stop)
# end the fast search
end_fast_search = True
# otherwise we increase the interval
else:
# unless, we would increase it too far
if (current_stop + time_step *
increase_factor) >= arrival_times[-1]:
# mark where we are in the interval
start_idx = searchsorted(
arrival_times, current_stop)
# then we also want to go ahead and get out of the fast search
end_fast_search = True
else:
# increase the interval
current_stop += time_step * increase_factor
# if we did exceede the sigma level we will need to step
# back in time to find where it was NOT exceeded
else:
# decrease the interval
current_stop -= time_step * decrease_factor
# inform the loop that we have been back stepping
decreased_interval = True
# Now we are ready for the slow forward search
# where we count up all the photons
# we have already counted up the photons to this point
total_counts = counts
# start searching from where the fast search ended
pbar.increase(counts)
for time in arrival_times[start_idx:]:
total_counts += 1
pbar.increase()
if total_counts < min_counts:
continue
else:
# first use the background function to know the number of background counts
bkg = background_getter(current_start, time)
sig = Significance(total_counts, bkg)
if background_error_getter is not None:
bkg_error = background_error_getter(
current_start, time)
sigma = sig.li_and_ma_equivalent_for_gaussian_background(
bkg_error)[0]
else:
sigma = sig.li_and_ma()[0]
# now test if we have enough sigma
if sigma >= sigma_level:
# if we succeeded we want to mark the time bins
stops.append(time)
starts.append(current_start)
# set up the next fast search
# by looking past this interval
current_start = time
current_stop = 0.5 * (arrival_times[-1] + time)
end_fast_search = False
# get out of the for loop
break
# if we never exceeded the sigma level by the
# end of the search, we never will
if end_fast_search:
# so lets kill the main search
end_all_search = True
if not starts:
print(
"The requested sigma level could not be achieved in the interval. Try decreasing it."
)
else:
return cls.from_starts_and_stops(starts, stops)
def _minimize(self):
assert len(
self._grid
) > 0, "You need to set up a grid using add_parameter_to_grid"
if self._2nd_minimization is None:
raise RuntimeError(
"You did not setup this global minimizer (GRID). You need to use the .setup() method"
)
# For each point in the grid, perform a fit
parameters = self._grid.keys()
overall_minimum = 1e20
internal_best_fit_values = None
n_iterations = np.prod([x.shape for x in self._grid.values()])
with progress_bar(n_iterations, title='Grid minimization') as progress:
for values_tuple in itertools.product(*self._grid.values()):
# Reset everything to the original values, so that the fit will always start
# from there, instead that from the values obtained in the last iterations, which
# might have gone completely awry
for par_name, par_value in self._original_values.items():
self.parameters[par_name].value = par_value
# Now set the parameters in the grid to their starting values
for i, this_value in enumerate(values_tuple):
self.parameters[parameters[i]].value = this_value
# Get a new instance of the minimizer. We need to do this instead of reusing an existing instance
# because some minimizers (like iminuit) keep internal track of their status, so that reusing
# a minimizer will create correlation between the different points
# NOTE: this line necessarily needs to be after the values of the parameters has been set to the
# point, because the init method of the minimizer instance will use those values to set the starting
# point for the fit
_minimizer = self._2nd_minimization.get_instance(
self.function, self.parameters, verbosity=0)
# Perform fit
try:
# We call _minimize() and not minimize() so that the best fit values are
# in the internal system.
this_best_fit_values_internal, this_minimum = _minimizer._minimize(
)
except:
# A failure is not a problem here, only if all of the fit fail then we have a problem
# but this case is handled later
continue
# If this minimum is the overall minimum, save the result
if this_minimum < overall_minimum:
overall_minimum = this_minimum
internal_best_fit_values = this_best_fit_values_internal
# Use callbacks (if any)
for callback in self._callbacks:
callback(values_tuple, this_minimum)
progress.increase()
if internal_best_fit_values is None:
raise AllFitFailed(
"All fit starting from values in the grid have failed!")
return internal_best_fit_values, overall_minimum
def __init__(self, pha_file_or_instance, file_type='observed',rsp_file=None, arf_file=None):
"""
A spectrum with dispersion build from an OGIP-compliant PHA FITS file. Both Type I & II files can be read. Type II
spectra are selected either by specifying the spectrum_number or via the {spectrum_number} file name convention used
in XSPEC. If the file_type is background, a 3ML InstrumentResponse or subclass must be passed so that the energy
bounds can be obtained.
:param pha_file_or_instance: either a PHA file name or threeML.plugins.OGIP.pha.PHAII instance
:param spectrum_number: (optional) the spectrum number of the TypeII file to be used
:param file_type: observed or background
:param rsp_file: RMF filename or threeML.plugins.OGIP.response.InstrumentResponse instance
:param arf_file: (optional) and ARF filename
"""
# extract the spectrum number if needed
assert isinstance(pha_file_or_instance, str) or isinstance(pha_file_or_instance,
PHAII), 'Must provide a FITS file name or PHAII instance'
with fits.open(pha_file_or_instance) as f:
try:
HDUidx = f.index_of("SPECTRUM")
except:
raise RuntimeError("The input file %s is not in PHA format" % (pha2_file))
spectrum = f[HDUidx]
data = spectrum.data
if "COUNTS" in data.columns.names:
has_rates = False
data_column_name = "COUNTS"
elif "RATE" in data.columns.names:
has_rates = True
data_column_name = "RATE"
else:
raise RuntimeError("This file does not contain a RATE nor a COUNTS column. "
"This is not a valid PHA file")
# Determine if this is a PHA I or PHA II
if len(data.field(data_column_name).shape) == 2:
num_spectra = data.field(data_column_name).shape[0]
else:
raise RuntimeError("This appears to be a PHA I and not PHA II file")
pha_information = _read_pha_or_pha2_file(pha_file_or_instance,
None,
file_type,
rsp_file,
arf_file,
treat_as_time_series=True)
# default the grouping to all open bins
# this will only be altered if the spectrum is rebinned
self._grouping = np.ones_like(pha_information['counts'])
# this saves the extra properties to the class
self._gathered_keywords = pha_information['gathered_keywords']
self._file_type = file_type
# need to see if we have count errors, tstart, tstop
# if not, we create an list of None
if pha_information['count_errors'] is None:
count_errors = [None]*num_spectra
else:
count_errors = pha_information['count_errors']
if pha_information['tstart'] is None:
tstart = [None] * num_spectra
else:
tstart = pha_information['tstart']
if pha_information['tstop'] is None:
tstop = [None] * num_spectra
else:
tstop = pha_information['tstop']
# now build the list of binned spectra
list_of_binned_spectra = []
with progress_bar(num_spectra,title='Loading PHAII spectra') as p:
for i in xrange(num_spectra):
list_of_binned_spectra.append(BinnedSpectrumWithDispersion(counts=pha_information['counts'][i],
exposure=pha_information['exposure'][i,0],
response=pha_information['rsp'],
count_errors=count_errors[i],
sys_errors=pha_information['sys_errors'][i],
is_poisson=pha_information['is_poisson'],
quality=pha_information['quality'].get_slice(i),
mission=pha_information['gathered_keywords']['mission'],
instrument=pha_information['gathered_keywords']['instrument'],
tstart=tstart[i],
tstop=tstop[i]))
p.increase()
# now get the time intervals
start_times = data.field('TIME')
stop_times = data.field('ENDTIME')
time_intervals = TimeIntervalSet.from_starts_and_stops(start_times, stop_times)
reference_time = 0
# see if there is a reference time in the file
if 'TRIGTIME' in spectrum.header:
reference_time = spectrum.header['TRIGTIME']
for t_number in range(spectrum.header['TFIELDS']):
if 'TZERO%d' % t_number in spectrum.header:
reference_time = spectrum.header['TZERO%d' % t_number]
super(PHASpectrumSet, self).__init__(list_of_binned_spectra,
reference_time=reference_time,
time_intervals=time_intervals)
def download(self, remote_filename, destination_path, new_filename=None, progress=True, compress=False):
assert remote_filename in self.files, "File %s is not contained in this directory (%s)" % (remote_filename,
self._request_result.url)
destination_path = sanitize_filename(destination_path, abspath=True)
assert path_exists_and_is_directory(destination_path), "Provided destination does not exist or " \
"is not a directory" % destination_path
# If no filename is specified, use the same name that the file has on the remote server
if new_filename is None:
new_filename = remote_filename.split("/")[-1]
# Get the fully qualified path for the remote and the local file
remote_path = self._request_result.url + remote_filename
local_path = os.path.join(destination_path, new_filename)
# Ask the server for the file, but do not download it just yet
# (stream=True will get the HTTP header but nothing else)
# Use stream=True for two reasons:
# * so that the file is not downloaded all in memory before being written to the disk
# * so that we can report progress is requested
this_request = requests.get(remote_path, stream=True)
# Figure out the size of the file
file_size = int(this_request.headers['Content-Length'])
# Now check if we really need to download this file
if compress:
# Add a .gz at the end of the file path
local_path += '.gz'
if file_existing_and_readable(local_path):
local_size = os.path.getsize(local_path)
if local_size == file_size or compress:
# if the compressed file already exists
# it will have a smaller size
# No need to download it again
return local_path
# Chunk size shouldn't bee too small otherwise we are causing a bottleneck in the download speed
chunk_size = 1024 * 10
# If the user wants to compress the file, use gzip, otherwise the normal opener
if compress:
import gzip
opener = gzip.open
else:
opener = open
if progress:
# Set a title for the progress bar
bar_title = "Downloading %s" % new_filename
with progress_bar(file_size, scale=1024 * 1024, units='Mb',
title=bar_title) as bar: # type: ProgressBarBase
with opener(local_path, 'wb') as f:
for chunk in this_request.iter_content(chunk_size=chunk_size):
if chunk: # filter out keep-alive new chunks
f.write(chunk)
bar.increase(len(chunk))
this_request.close()
else:
with opener(local_path, 'wb') as f:
for chunk in this_request.iter_content(chunk_size=chunk_size):
if chunk: # filter out keep-alive new chunks
f.write(chunk)
this_request.close()
return local_path
def _fit_polynomials(self):
"""
Binned fit to each channel. Sets the polynomial array that will be used to compute
counts over an interval
:return:
"""
self._poly_fit_exists = True
self._fit_method_info['bin type'] = 'Binned'
self._fit_method_info['fit method'] = threeML_config['event list'][
'binned fit method']
# Select all the events that are in the background regions
# and make a mask
all_bkg_masks = []
for selection in self._poly_intervals:
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]
# This calculation removes the unselected portion of the light curve
# so that we are not fitting zero counts. It will be used in the channel calculations
# as well
bin_width = 1. # seconds
these_bins = np.arange(self._start_time, self._stop_time, bin_width)
cnts, bins = np.histogram(total_poly_events, bins=these_bins)
# Find the mean time of the bins and calculate the exposure in each bin
mean_time = []
exposure_per_bin = []
for i in xrange(len(bins) - 1):
m = np.mean((bins[i], bins[i + 1]))
mean_time.append(m)
exposure_per_bin.append(
self.exposure_over_interval(bins[i], bins[i + 1]))
mean_time = np.array(mean_time)
exposure_per_bin = np.array(exposure_per_bin)
# Remove bins with zero counts
all_non_zero_mask = []
for selection in self._poly_intervals:
all_non_zero_mask.append(
np.logical_and(mean_time >= selection.start_time,
mean_time <= selection.stop_time))
non_zero_mask = all_non_zero_mask[0]
if len(all_non_zero_mask) > 1:
for mask in all_non_zero_mask[1:]:
non_zero_mask = np.logical_or(mask, non_zero_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._fit_global_and_determine_optimum_grade(
cnts[non_zero_mask], mean_time[non_zero_mask],
exposure_per_bin[non_zero_mask])
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)
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]
# now bin the selected channel counts
cnts, bins = np.histogram(current_events, bins=these_bins)
# Put data to fit in an x vector and y vector
polynomial, _ = polyfit(mean_time[non_zero_mask],
cnts[non_zero_mask],
self._optimal_polynomial_grade,
exposure_per_bin[non_zero_mask])
polynomials.append(polynomial)
p.increase()
# We are now ready to return the polynomials
self._polynomials = polynomials
def to_polarlike(self,
from_bins=False,
start=None,
stop=None,
interval_name='_interval',
extract_measured_background=False):
assert has_polarpy, 'you must have the polarpy module installed'
assert issubclass(
self._container_type, BinnedModulationCurve
), 'You are attempting to create a POLARLike plugin from the wrong data type'
if extract_measured_background:
this_background_spectrum = self._measured_background_spectrum
else:
this_background_spectrum = self._background_spectrum
if isinstance(self._response, str):
self._response = PolarResponse(self._response)
if not from_bins:
assert self._observed_spectrum is not None, 'Must have selected an active time interval'
if this_background_spectrum is None:
custom_warnings.warn(
'No background selection has been made. This plugin will contain no background!'
)
return PolarLike(
name=self._name,
observation=self._observed_spectrum,
background=this_background_spectrum,
response=self._response,
verbose=self._verbose,
# tstart=self._tstart,
# tstop=self._tstop
)
else:
# this is for a set of intervals.
assert self._time_series.bins is not None, 'This time series does not have any bins!'
# save the original interval if there is one
old_interval = copy.copy(self._active_interval)
old_verbose = copy.copy(self._verbose)
# we will keep it quiet to keep from being annoying
self._verbose = False
list_of_polarlikes = []
# now we make one response to save time
# get the bins from the time series
# for event lists, these are from created bins
# for binned spectra sets, these are the native bines
these_bins = self._time_series.bins # type: TimeIntervalSet
if start is not None:
assert stop is not None, 'must specify a start AND a stop time'
if stop is not None:
assert stop is not None, 'must specify a start AND a stop time'
these_bins = these_bins.containing_interval(start,
stop,
inner=False)
# loop through the intervals and create spec likes
with progress_bar(len(these_bins), title='Creating plugins') as p:
for i, interval in enumerate(these_bins):
self.set_active_time_interval(interval.to_string())
if extract_measured_background:
this_background_spectrum = self._measured_background_spectrum
else:
this_background_spectrum = self._background_spectrum
if this_background_spectrum is None:
custom_warnings.warn(
'No bakckground selection has been made. This plugin will contain no background!'
)
try:
pl = PolarLike(
name="%s%s%d" % (self._name, interval_name, i),
observation=self._observed_spectrum,
background=this_background_spectrum,
response=self._response,
verbose=self._verbose,
# tstart=self._tstart,
# tstop=self._tstop
)
list_of_polarlikes.append(pl)
except (NegativeBackground):
custom_warnings.warn(
'Something is wrong with interval %s. skipping.' %
interval)
p.increase()
# restore the old interval
if old_interval is not None:
self.set_active_time_interval(*old_interval)
else:
self._active_interval = None
self._verbose = old_verbose
return list_of_polarlikes
def to_polarlike(self, from_bins=False, start=None, stop=None, interval_name='_interval', extract_measured_background=False):
assert has_polarpy, 'you must have the polarpy module installed'
assert issubclass(self._container_type, BinnedModulationCurve), 'You are attempting to create a POLARLike plugin from the wrong data type'
if extract_measured_background:
this_background_spectrum = self._measured_background_spectrum
else:
this_background_spectrum = self._background_spectrum
if isinstance(self._response,str):
self._response = PolarResponse(self._response)
if not from_bins:
assert self._observed_spectrum is not None, 'Must have selected an active time interval'
if this_background_spectrum is None:
custom_warnings.warn('No background selection has been made. This plugin will contain no background!')
return PolarLike(name=self._name,
observation=self._observed_spectrum,
background=this_background_spectrum,
response=self._response,
verbose=self._verbose,
# tstart=self._tstart,
# tstop=self._tstop
)
else:
# this is for a set of intervals.
assert self._time_series.bins is not None, 'This time series does not have any bins!'
# save the original interval if there is one
old_interval = copy.copy(self._active_interval)
old_verbose = copy.copy(self._verbose)
# we will keep it quiet to keep from being annoying
self._verbose = False
list_of_polarlikes = []
# now we make one response to save time
# get the bins from the time series
# for event lists, these are from created bins
# for binned spectra sets, these are the native bines
these_bins = self._time_series.bins # type: TimeIntervalSet
if start is not None:
assert stop is not None, 'must specify a start AND a stop time'
if stop is not None:
assert stop is not None, 'must specify a start AND a stop time'
these_bins = these_bins.containing_interval(start, stop, inner=False)
# loop through the intervals and create spec likes
with progress_bar(len(these_bins), title='Creating plugins') as p:
for i, interval in enumerate(these_bins):
self.set_active_time_interval(interval.to_string())
if extract_measured_background:
this_background_spectrum = self._measured_background_spectrum
else:
this_background_spectrum = self._background_spectrum
if this_background_spectrum is None:
custom_warnings.warn(
'No bakckground selection has been made. This plugin will contain no background!')
try:
pl = PolarLike(name="%s%s%d" % (self._name, interval_name, i),
observation=self._observed_spectrum,
background=this_background_spectrum,
response=self._response,
verbose=self._verbose,
# tstart=self._tstart,
# tstop=self._tstop
)
list_of_polarlikes.append(pl)
except(NegativeBackground):
custom_warnings.warn('Something is wrong with interval %s. skipping.' % interval)
p.increase()
# restore the old interval
if old_interval is not None:
self.set_active_time_interval(*old_interval)
else:
self._active_interval = None
self._verbose = old_verbose
return list_of_polarlikes
def to_spectrumlike(self,
from_bins=False,
start=None,
stop=None,
interval_name='_interval',
extract_measured_background=False):
"""
Create plugin(s) from either the current active selection or the time bins.
If creating from an event list, the
bins are from create_time_bins. If using a pre-time binned time series, the bins are those
native to the data. Start and stop times can be used to control which bins are used.
:param from_bins: choose to create plugins from the time bins
:param start: optional start time of the bins
:param stop: optional stop time of the bins
:param extract_measured_background: Use the selected background rather than a polynomial fit to the background
:param interval_name: the name of the interval
:return: SpectrumLike plugin(s)
"""
# we can use either the modeled or the measured background. In theory, all the information
# in the background spectrum should propagate to the likelihood
if extract_measured_background:
this_background_spectrum = self._measured_background_spectrum
else:
this_background_spectrum = self._background_spectrum
# this is for a single interval
if not from_bins:
assert self._observed_spectrum is not None, 'Must have selected an active time interval'
assert isinstance(
self._observed_spectrum, BinnedSpectrum
), 'You are attempting to create a SpectrumLike plugin from the wrong data type'
if this_background_spectrum is None:
custom_warnings.warn(
'No background selection has been made. This plugin will contain no background!'
)
if self._response is None:
return SpectrumLike(name=self._name,
observation=self._observed_spectrum,
background=this_background_spectrum,
verbose=self._verbose,
tstart=self._tstart,
tstop=self._tstop)
else:
return DispersionSpectrumLike(
name=self._name,
observation=self._observed_spectrum,
background=this_background_spectrum,
verbose=self._verbose,
tstart=self._tstart,
tstop=self._tstop)
else:
# this is for a set of intervals.
assert self._time_series.bins is not None, 'This time series does not have any bins!'
# save the original interval if there is one
old_interval = copy.copy(self._active_interval)
old_verbose = copy.copy(self._verbose)
# we will keep it quiet to keep from being annoying
self._verbose = False
list_of_speclikes = []
# get the bins from the time series
# for event lists, these are from created bins
# for binned spectra sets, these are the native bines
these_bins = self._time_series.bins # type: TimeIntervalSet
if start is not None:
assert stop is not None, 'must specify a start AND a stop time'
if stop is not None:
assert stop is not None, 'must specify a start AND a stop time'
these_bins = these_bins.containing_interval(start,
stop,
inner=False)
# loop through the intervals and create spec likes
with progress_bar(len(these_bins), title='Creating plugins') as p:
for i, interval in enumerate(these_bins):
self.set_active_time_interval(interval.to_string())
assert isinstance(
self._observed_spectrum, BinnedSpectrum
), 'You are attempting to create a SpectrumLike plugin from the wrong data type'
if extract_measured_background:
this_background_spectrum = self._measured_background_spectrum
else:
this_background_spectrum = self._background_spectrum
if this_background_spectrum is None:
custom_warnings.warn(
'No bakckground selection has been made. This plugin will contain no background!'
)
try:
if self._response is None:
sl = SpectrumLike(
name="%s%s%d" % (self._name, interval_name, i),
observation=self._observed_spectrum,
background=this_background_spectrum,
verbose=self._verbose,
tstart=self._tstart,
tstop=self._tstop)
else:
sl = DispersionSpectrumLike(
name="%s%s%d" % (self._name, interval_name, i),
observation=self._observed_spectrum,
background=this_background_spectrum,
verbose=self._verbose,
tstart=self._tstart,
tstop=self._tstop)
list_of_speclikes.append(sl)
except (NegativeBackground):
custom_warnings.warn(
'Something is wrong with interval %s. skipping.' %
interval)
p.increase()
# restore the old interval
if old_interval is not None:
self.set_active_time_interval(*old_interval)
else:
self._active_interval = None
self._verbose = old_verbose
return list_of_speclikes
def _minimize(self):
# Gather the setup
islands = self._setup_dict['islands']
pop_size = self._setup_dict['population_size']
evolution_cycles = self._setup_dict['evolution_cycles']
# Print some info
print("\nPAGMO setup:")
print("------------")
print("- Number of islands: %i" % islands)
print("- Population size per island: %i" % pop_size)
print("- Evolutions cycles per island: %i\n" % evolution_cycles)
Npar = len(self._internal_parameters)
if is_parallel_computation_active():
wrapper = PAGMOWrapper(function=self.function,
parameters=self._internal_parameters,
dim=Npar)
# use the archipelago, which uses the ipyparallel computation
archi = pg.archipelago(udi=pg.ipyparallel_island(),
n=islands,
algo=self._setup_dict['algorithm'],
prob=wrapper,
pop_size=pop_size)
archi.wait()
# Display some info
print("\nSetup before parallel execution:")
print("--------------------------------\n")
print(archi)
# Evolve populations on islands
print(
"Evolving... (progress not available for parallel execution)")
# For some weird reason, ipyparallel looks for _winreg on Linux (where does
# not exist, being a Windows module). Let's mock it with an empty module'
mocked = False
if os.path.exists("_winreg.py") is False:
with open("_winreg.py", "w+") as f:
f.write("pass")
mocked = True
archi.evolve()
# Wait for completion (evolve() is async)
archi.wait_check()
# Now remove _winreg.py if needed
if mocked:
os.remove("_winreg.py")
# Find best and worst islands
fOpts = np.array(map(lambda x: x[0], archi.get_champions_f()))
xOpts = archi.get_champions_x()
else:
# do not use ipyparallel. Evolve populations on islands serially
wrapper = PAGMOWrapper(function=self.function,
parameters=self._internal_parameters,
dim=Npar)
xOpts = []
fOpts = np.zeros(islands)
with progress_bar(iterations=islands,
title="pygmo minimization") as p:
for island_id in range(islands):
pop = pg.population(prob=wrapper, size=pop_size)
for i in range(evolution_cycles):
pop = self._setup_dict['algorithm'].evolve(pop)
# Gather results
xOpts.append(pop.champion_x)
fOpts[island_id] = pop.champion_f[0]
p.increase()
# Find best and worst islands
min_idx = fOpts.argmin()
max_idx = fOpts.argmax()
fOpt = fOpts[min_idx]
fWorse = fOpts[max_idx]
xOpt = np.array(xOpts)[min_idx]
# Some information
print("\nSummary of evolution:")
print("---------------------")
print("Best population has minimum %.3f" % (fOpt))
print("Worst population has minimum %.3f" % (fWorse))
print("")
# Transform to numpy.array
best_fit_values = np.array(xOpt)
return best_fit_values, fOpt
def _minimize(self):
assert len(self._grid) > 0, "You need to set up a grid using add_parameter_to_grid"
if self._2nd_minimization is None:
raise RuntimeError("You did not setup this global minimizer (GRID). You need to use the .setup() method")
# For each point in the grid, perform a fit
parameters = self._grid.keys()
overall_minimum = 1e20
internal_best_fit_values = None
n_iterations = np.prod([x.shape for x in self._grid.values()])
with progress_bar(n_iterations, title='Grid minimization') as progress:
for values_tuple in itertools.product(*self._grid.values()):
# Reset everything to the original values, so that the fit will always start
# from there, instead that from the values obtained in the last iterations, which
# might have gone completely awry
for par_name, par_value in self._original_values.items():
self.parameters[par_name].value = par_value
# Now set the parameters in the grid to their starting values
for i, this_value in enumerate(values_tuple):
self.parameters[parameters[i]].value = this_value
# Get a new instance of the minimizer. We need to do this instead of reusing an existing instance
# because some minimizers (like iminuit) keep internal track of their status, so that reusing
# a minimizer will create correlation between the different points
# NOTE: this line necessarily needs to be after the values of the parameters has been set to the
# point, because the init method of the minimizer instance will use those values to set the starting
# point for the fit
_minimizer = self._2nd_minimization.get_instance(self.function, self.parameters, verbosity=0)
# Perform fit
try:
# We call _minimize() and not minimize() so that the best fit values are
# in the internal system.
this_best_fit_values_internal, this_minimum = _minimizer._minimize()
except:
# A failure is not a problem here, only if all of the fit fail then we have a problem
# but this case is handled later
continue
# If this minimum is the overall minimum, save the result
if this_minimum < overall_minimum:
overall_minimum = this_minimum
internal_best_fit_values = this_best_fit_values_internal
# Use callbacks (if any)
for callback in self._callbacks:
callback(values_tuple, this_minimum)
progress.increase()
if internal_best_fit_values is None:
raise AllFitFailed("All fit starting from values in the grid have failed!")
return internal_best_fit_values, overall_minimum
def go(args):
spectrum = Powerlaw()
if args.shape == 'ps':
source = PointSource("TestSource",
args.RA,
args.Dec,
spectral_shape=spectrum)
elif args.shape == 'disk':
shape = Disk_on_sphere()
source = ExtendedSource("TestSource",
spatial_shape=shape,
spectral_shape=spectrum)
shape.lon0 = args.RA * u.degree
shape.lon0.fix = True
shape.lat0 = args.Dec * u.degree
shape.lat0.fix = True
shape.radius = 1. * u.degree
shape.radius.bounds = (0.05 * u.degree, 1.5 * u.degree)
elif args.shape == 'gauss':
shape = Gaussian_on_sphere()
source = ExtendedSource("TestSource",
spatial_shape=shape,
spectral_shape=spectrum)
shape.lon0 = args.RA * u.degree
shape.lon0.fix = True
shape.lat0 = args.Dec * u.degree
shape.lat0.fix = True
shape.sigma = 1.0 * u.degree
shape.sigma.fix = False
shape.sigma.max_value = 5.
elif args.shape == 'diffusion':
shape = Continuous_injection_diffusion()
source = ExtendedSource("TestSource",
spatial_shape=shape,
spectral_shape=spectrum)
shape.lon0 = args.RA * u.degree
shape.lon0.fix = True
shape.lat0 = args.Dec * u.degree
shape.lat0.fix = True
shape.rdiff0 = 6.0 * u.degree
shape.rdiff0.fix = False
shape.rdiff0.max_value = 12.
shape.delta = 0.33
shape.delta.fix = True
shape.uratio = 0.5 # Diffusion spectral index (0.3 to 0.6)
shape.uratio.fix = True
shape.piv = 2e10
shape.piv.fix = True
shape.piv2 = 1e9
shape.piv2.fix = True
elif args.shape == 'powerlaw':
shape = Power_law_on_sphere()
source = ExtendedSource("TestSource",
spatial_shape=shape,
spectral_shape=spectrum)
shape.lon0 = args.RA * u.degree
shape.lon0.fix = True
shape.lat0 = args.Dec * u.degree
shape.lat0.fix = True
shape.index = -1.1
shape.index.fix = False
else:
print 'Error, invalid shape:', args.shape
sys.exit(0)
fluxUnit = 1. / (u.TeV * u.cm**2 * u.s)
# Set spectral parameters (do it after the source definition to make sure
# the units are handled correctly)
spectrum.K = 1e-14 * fluxUnit
spectrum.K.bounds = (1e-16 * fluxUnit, 1e-12*fluxUnit) # Factor 1e9 to what's output
spectrum.piv = 10 * u.TeV
spectrum.piv.fix = True
spectrum.index = -2.5
spectrum.index.bounds = (-4., -1.)
spectrum.index.fix = (not args.freeindex)
spectrum.display()
if args.shape == 'ps':
print("Morphology: point source.")
else:
shape.display()
# Set up a likelihood model using the source
lm = Model(source)
llh = HAWCLike("TheLikelikood", args.maptreefile, args.responsefile)
llh.set_active_measurements(args.startBin, args.stopBin)
llh.set_ROI(args.RA, args.Dec, args.roiradius, True)
# Double check the free parameters
print("Likelihood model:\n")
print(lm)
# Set up the likelihood and run the fit
print("Performing likelihood fit...\n")
datalist = DataList(llh)
jl = JointLikelihood(lm, datalist, verbose=True)
try:
jl.set_minimizer("ROOT")
jl.fit()
except AttributeError:
jl.set_minimizer("minuit")
result = jl.fit()
# Print the TS
TS = llh.calc_TS()
print("\nTest statistic: %g" % TS)
# Write model and residual maps if needed
if args.outmodel is not None:
llh.write_model_map(args.outmodel)
if args.outresidual is not None:
llh.write_residual_map(args.outresidual)
# Get a flux uncertainty envelope from MCMC samples and plot it
if args.nMCMC > 0 and len(lm.free_parameters)>1:
print("Running MCMC...")
# The set_uninformative_prior will use the given prior type
# and the current minimum and maximum to set the prior
# Set all priors to uniform, except the spectrum.K
for parameter_name, parameter in lm.free_parameters.iteritems():
parameter.set_uninformative_prior(Uniform_prior)
spectrum.K.set_uninformative_prior(Log_uniform_prior)
ba = BayesianAnalysis(lm, datalist)
nW = 10 # number of MCMC walkers
nB = 100 # number of samples for burning in
nS = args.nMCMC # number of samples
print("Sampling")
samples = ba.sample(nW, nB, nS)
print("Sampling complete")
print("\nMaking corner plot...\n")
# Make a corner plot of the sampled parameters from the MCMC
sigmas = [1., 2., 3.]
levels = [1. - np.exp(-0.5 * s ** 2) for s in sigmas]
fig = ba.corner_plot(show_titles=True,
title_kwargs={"fontsize": 10},
quantiles=[0.16, 0.5, 0.84],
levels=levels,
verbose=True
)
filename = "%s/corner_%s.png" %(args.outplotdir, args.outplottag)
fig.savefig(filename,dpi=200)
print("Save corner plot in: %s" %filename)
print("\nMaking spectrum plot...\n")
E = np.logspace(np.log10(1), np.log10(50), 100) * u.TeV
fML = spectrum(E)
# Construct the uncertainty envelope on the flux using the sampled
# parameter space
fMin68, fMax68 = [], []
fMin95, fMax95 = [], []
# Just to be fancy, let's use the progress bar
from threeML.io.progress_bar import progress_bar
with progress_bar(E.shape[0], title='Plotting') as progress:
for i, energy in enumerate(E):
progress.animate(i)
flux = []
for rsamp in ba.raw_samples:
# Dirty, but it looks like the morphology parameter come
# first. Is there a safe way to do this?
spectrum.K = rsamp[-2]
spectrum.index = rsamp[-1]
# Note that here energy has units, so the computation
# would be made with units and the output will be a
# astropy.Quantity instance. However that is slow,
# so let's use the value instead (the computation without
# unit is much faster)
this_diff_flux = spectrum(energy.to('keV').value)
flux.append( this_diff_flux )
# Evaluate the percentiles of the flux
f02, f16, f84, f97 = np.percentile(np.asarray(flux),
[2.5, 16, 84, 97.5])
fMin68.append(f16)
fMax68.append(f84)
fMin95.append(f02)
fMax95.append(f97)
print(" done")
# Save the percentiles with units
# y_unit is the unit of the output if the function is 1d,
# as in y = f(x)
fMin68 = np.asarray(fMin68) * spectrum.y_unit
fMax68 = np.asarray(fMax68) * spectrum.y_unit
fMin95 = np.asarray(fMin95) * spectrum.y_unit
fMax95 = np.asarray(fMax95) * spectrum.y_unit
fig, ax = plt.subplots(1, 1, figsize=(8, 6))
energyPower = 2.0 # To plot E**eP dN/dE
conv = (E)**energyPower
if fMin95 is not None and fMax95 is not None:
# Plot the 95% C.I. on the flux as a function of energy
ax.fill_between(E.to(u.TeV).value,
(conv * fMin95).to(u.TeV**energyPower / (u.TeV*u.cm**2*u.s)).value,
(conv * fMax95).to(u.TeV**energyPower / (u.TeV*u.cm**2*u.s)).value,
color="royalblue",
alpha=0.3,
label="95% C.I.")
if fMin68 is not None and fMax68 is not None:
# Plot the 68% C.I. on the flux as a function of energy
ax.fill_between(E.to(u.TeV).value,
(conv * fMin68).to(u.TeV**energyPower / (u.TeV*u.cm**2*u.s)).value,
(conv * fMax68).to(u.TeV**energyPower / (u.TeV*u.cm**2*u.s)).value,
color="royalblue",
alpha=0.5,
label="68% C.I.")
ax.plot(E.to(u.TeV).value,
(conv * fML).to(u.TeV**energyPower / (u.TeV*u.cm**2*u.s)).value,
"r--", lw=2, label="Max LL")
ax.set(xscale="log",
xlabel="energy [TeV]",
yscale="log",
ylabel=r"$E^{%.1f}$ flux [TeV$^{%.1f}$ cm$^{-2}$ s$^{-1}$]" %(energyPower, energyPower-1),
title="Spectrum for %s shape" %args.shape
)
ax.xaxis.set_major_formatter(mpl.ticker.FormatStrFormatter("%g"))
h, l = ax.get_legend_handles_labels()
leg = ax.legend(h, l)
fig.tight_layout()
filename = "%s/spectrum_%s.png" %(args.outplotdir, args.outplottag)
fig.savefig(filename,dpi=200)
print("Save spectrum plot in: %s" %filename)
plt.show()
def download_files_from_directory_ftp(ftp_url, destination_directory, filenames=None, namefilter=None):
# Parse url
tokens = urlparse.urlparse(ftp_url)
serverAddress = tokens.netloc
directory = tokens.path
# if no filename has been specified, connect first to retrieve the list of files to download
if filenames == None:
# Connect to server and log in
ftp = ftplib.FTP(serverAddress, "anonymous", '', '', timeout=60)
try:
ftp.login()
except:
# Maybe we are already logged in
try:
ftp.cwd('/')
except:
# nope! don't know what is happening
raise
# Move to origin directory
ftp.cwd(directory)
# Retrieve list of files
filenames = []
ftp.retrlines('NLST', filenames.append)
# Close connection (will reopen later)
ftp.close()
# Download files with progress report
downloaded_files = []
with progress_bar(len(filenames)) as p:
for i, filename in enumerate(filenames):
if namefilter != None and filename.find(namefilter) < 0:
p.increase()
# Filename does not match, do not download it
continue
else:
local_filename = os.path.join(destination_directory, filename)
urllib.urlretrieve("ftp://%s/%s/%s" % (serverAddress, directory, filename), local_filename)
urllib.urlcleanup()
downloaded_files.append(local_filename)
return downloaded_files
