def test_one_free_parameter_input_output():
fluxUnit = 1. / (u.TeV * u.cm**2 * u.s)
temp_file = "__test_mle.fits"
spectrum=Powerlaw()
source = PointSource( "tst", ra=100 , dec=20, spectral_shape=spectrum)
model = Model(source)
spectrum.piv = 7*u.TeV
spectrum.index = -2.3
spectrum.K = 1e-15*fluxUnit
spectrum.piv.fix = True
#two free parameters (one with units)
spectrum.index.fix = False
spectrum.K.fix = False
cov_matrix = np.diag([0.001]*2)
ar = MLEResults(model, cov_matrix, {})
ar.write_to(temp_file, overwrite=True)
ar_reloaded = load_analysis_results(temp_file)
os.remove(temp_file)
_results_are_same(ar, ar_reloaded)
#one free parameter with units
spectrum.index.fix = True
spectrum.K.fix = False
cov_matrix = np.diag([0.001]*1)
ar = MLEResults(model, cov_matrix, {})
ar.write_to(temp_file, overwrite=True)
ar_reloaded = load_analysis_results(temp_file)
os.remove(temp_file)
_results_are_same(ar, ar_reloaded)
#one free parameter without units
spectrum.index.fix = False
spectrum.K.fix = True
cov_matrix = np.diag([0.001]*1)
ar = MLEResults(model, cov_matrix, {})
ar.write_to(temp_file, overwrite=True)
ar_reloaded = load_analysis_results(temp_file)
os.remove(temp_file)
_results_are_same(ar, ar_reloaded)
def test_one_free_parameter_input_output():
fluxUnit = 1. / (u.TeV * u.cm**2 * u.s)
temp_file = "__test_mle.fits"
spectrum = Powerlaw()
source = PointSource("tst", ra=100, dec=20, spectral_shape=spectrum)
model = Model(source)
spectrum.piv = 7 * u.TeV
spectrum.index = -2.3
spectrum.K = 1e-15 * fluxUnit
spectrum.piv.fix = True
#two free parameters (one with units)
spectrum.index.fix = False
spectrum.K.fix = False
cov_matrix = np.diag([0.001] * 2)
ar = MLEResults(model, cov_matrix, {})
ar.write_to(temp_file, overwrite=True)
ar_reloaded = load_analysis_results(temp_file)
os.remove(temp_file)
_results_are_same(ar, ar_reloaded)
#one free parameter with units
spectrum.index.fix = True
spectrum.K.fix = False
cov_matrix = np.diag([0.001] * 1)
ar = MLEResults(model, cov_matrix, {})
ar.write_to(temp_file, overwrite=True)
ar_reloaded = load_analysis_results(temp_file)
os.remove(temp_file)
_results_are_same(ar, ar_reloaded)
#one free parameter without units
spectrum.index.fix = False
spectrum.K.fix = True
cov_matrix = np.diag([0.001] * 1)
ar = MLEResults(model, cov_matrix, {})
ar.write_to(temp_file, overwrite=True)
ar_reloaded = load_analysis_results(temp_file)
os.remove(temp_file)
_results_are_same(ar, ar_reloaded)
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_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 __init__(self,
analysis_path,
trigdat_path,
real_coord=None,
det_list=all_det_list,
save_path=None,
sun=True):
"""
:param analysis_file: path of the file with the bayesian analysis result.
:param trigdat_path: path of the trigdat file.
:param real_coord: real or reference equatorial coordinates for the source in the format [ra,dec] (in deg). Default is none.
:param det_list: list of the detector you wish to plot, in the format ['n1','n5','b0']. Default is all detectors.
:param save_path: path and name for the plotted skymap (default is 'skymap.pdf' in cwd)
:param sun: plot sun position in the skymap (default is True)
"""
self._analysis_path = analysis_path
self._trigdat_path = trigdat_path
if real_coord != None:
self._real_coord = [
np.radians(-real_coord[0] + 180.),
np.radians(real_coord[1])
]
else:
self._real_coord = None
self._det_list = det_list
if save_path == None:
self._save_path = getcwd() + '/skymap.pdf'
else:
self._save_path = save_path
results = load_analysis_results(self._analysis_path)
self._ra_samples = np.radians(-np.array(results.samples[0]) + 180.)
self._dec_samples = np.radians(np.array(results.samples[1]))
ra_fit = np.radians(-results.get_data_frame().value[0] + 180.)
dec_fit = np.radians(results.get_data_frame().value[1])
self._fit_coord = [ra_fit, dec_fit]
ra_cl_50 = results.get_equal_tailed_interval('test.position.ra',
cl=0.5)
dec_cl_50 = results.get_equal_tailed_interval('test.position.dec',
cl=0.5)
ra_cl_90 = results.get_equal_tailed_interval('test.position.ra',
cl=0.9)
dec_cl_90 = results.get_equal_tailed_interval('test.position.dec',
cl=0.9)
self._cl_50 = [np.radians(ra_cl_50), np.radians(dec_cl_50)]
self._cl_90 = [np.radians(ra_cl_90), np.radians(dec_cl_90)]
self._detectors = self._get_gbm_geometry()
self._earth_circle = self._get_earth_circle()
def test_bayesian_input_output(xy_completed_bayesian_analysis):
bs, _ = xy_completed_bayesian_analysis
rb1 = bs.results
temp_file = "_test_bayes.fits"
rb1.write_to(temp_file, overwrite=True)
rb2 = load_analysis_results(temp_file)
os.remove(temp_file)
_results_are_same(rb1, rb2, bayes=True)
def test_bayesian_input_output(xy_completed_bayesian_analysis):
bs, _ = xy_completed_bayesian_analysis
rb1 = bs.results
temp_file = "_test_bayes.fits"
rb1.write_to(temp_file, overwrite=True)
rb2 = load_analysis_results(temp_file)
os.remove(temp_file)
_results_are_same(rb1, rb2, bayes=True)
def test_analysis_results_input_output(xy_fitted_joint_likelihood):
jl, _, _ = xy_fitted_joint_likelihood # type: JointLikelihood, None, None
jl.restore_best_fit()
ar = jl.results # type: MLEResults
temp_file = "__test_mle.fits"
ar.write_to(temp_file, overwrite=True)
ar_reloaded = load_analysis_results(temp_file)
os.remove(temp_file)
_results_are_same(ar, ar_reloaded)
def test_analysis_results_input_output(xy_fitted_joint_likelihood):
jl, _, _ = xy_fitted_joint_likelihood # type: JointLikelihood, None, None
jl.restore_best_fit()
ar = jl.results # type: MLEResults
temp_file = "__test_mle.fits"
ar.write_to(temp_file, overwrite=True)
ar_reloaded = load_analysis_results(temp_file)
os.remove(temp_file)
_results_are_same(ar, ar_reloaded)