def test_analysis_set_input_output(xy_fitted_joint_likelihood):
# Collect twice the same analysis results just to see if we can
# save them in a file as set of results
jl, _, _ = xy_fitted_joint_likelihood # type: JointLikelihood, None, None
jl.restore_best_fit()
ar = jl.results # type: MLEResults
ar2 = jl.results
analysis_set = AnalysisResultsSet([ar, ar2])
analysis_set.set_bins("testing", [-1, 1], [3, 5], unit = 's')
temp_file = "_analysis_set_test"
analysis_set.write_to(temp_file, overwrite=True)
analysis_set_reloaded = load_analysis_results(temp_file)
os.remove(temp_file)
# Test they are the same
assert len(analysis_set_reloaded) == len(analysis_set)
for res1, res2 in zip(analysis_set, analysis_set_reloaded):
_results_are_same(res1, res2)
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
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
with silence_console_log(and_progress_bars=False):
client = ParallelClient(**options_for_parallel_computation)
results = client.execute_with_progress_bar(
self.worker, list(range(self._n_iterations)))
else:
# Serial computation
results = []
with silence_console_log(and_progress_bars=False):
for i in trange(self._n_iterations,
desc="Goodness of fit computation"):
results.append(self.worker(i))
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([x[0] for x in results],
keys=list(range(self._n_iterations)))
like_frames = pd.concat([x[1] for x in results],
keys=list(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 = [x[2][i] for x in results]
self._all_results.append(AnalysisResultsSet(this_model_results))
return parameter_frames, like_frames
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 test_analysis_set_input_output(xy_fitted_joint_likelihood):
# Collect twice the same analysis results just to see if we can
# save them in a file as set of results
jl, _, _ = xy_fitted_joint_likelihood # type: JointLikelihood, None, None
jl.restore_best_fit()
ar = jl.results # type: MLEResults
ar2 = jl.results
analysis_set = AnalysisResultsSet([ar, ar2])
analysis_set.set_bins("testing", [-1, 1], [3, 5], unit='s')
temp_file = "_analysis_set_test"
analysis_set.write_to(temp_file, overwrite=True)
analysis_set_reloaded = load_analysis_results(temp_file)
os.remove(temp_file)
# Test they are the same
assert len(analysis_set_reloaded) == len(analysis_set)
for res1, res2 in zip(analysis_set, analysis_set_reloaded):
_results_are_same(res1, res2)
def test_conversion_fits2hdf(xy_fitted_joint_likelihood):
jl, _, _ = xy_fitted_joint_likelihood # type: JointLikelihood, None, None
jl.restore_best_fit()
ar = jl.results # type: MLEResults
ar2 = jl.results
analysis_set = AnalysisResultsSet([ar, ar2])
analysis_set.set_bins("testing", [-1, 1], [3, 5], unit="s")
temp_file = "_analysis_set_test.fits"
analysis_set.write_to(temp_file, overwrite=True)
convert_fits_analysis_result_to_hdf(temp_file)
analysis_set_reloaded = load_analysis_results_hdf("_analysis_set_test.h5")
# Test they are the same
assert len(analysis_set_reloaded) == len(analysis_set)
for res1, res2 in zip(analysis_set, analysis_set_reloaded):
_results_are_same(res1, res2)