def check_edisp_normalisation():
import matplotlib.pyplot as plt
obs = SpectrumObservation.read(obs_path)
p = obs.edisp.data.data.sum(axis=1)
e = obs.edisp.data.axis("e_true").nodes
plt.plot(e, p)
plt.semilogx()
plt.show()
def setup(self):
self.model = PowerLaw(
index=Quantity(2, ""), amplitude=Quantity(1e-11, "m-2 s-1 TeV-1"), reference=Quantity(1, "TeV")
)
# TODO: simulate known spectrum instead of using this example:
filename = "$GAMMAPY_EXTRA/datasets/hess-crab4_pha/pha_obs23523.fits"
self.obs = SpectrumObservation.read(filename)
self.seg = SpectrumEnergyGroupMaker(obs=self.obs)
ebounds = [0.3, 1.001, 3, 10.001, 30] * u.TeV
self.seg.compute_range_safe()
self.seg.compute_groups_fixed(ebounds=ebounds)
self.groups = self.seg.groups
def setup(self):
self.model = PowerLaw(
index=Quantity(2, ''),
amplitude=Quantity(1e-11, 'm-2 s-1 TeV-1'),
reference=Quantity(1, 'TeV'),
)
# TODO: simulate known spectrum instead of using this example:
filename = '$GAMMAPY_EXTRA/datasets/hess-crab4_pha/pha_obs23523.fits'
self.obs = SpectrumObservation.read(filename)
self.seg = SpectrumEnergyGroupMaker(obs=self.obs)
ebounds = [0.3, 1.001, 3, 10.001, 30] * u.TeV
self.seg.compute_range_safe()
self.seg.compute_groups_fixed(ebounds=ebounds)
self.groups = self.seg.groups
def fit_gammapy():
"""
Current results
Parameters:
name value error unit min max frozen
--------- --------- --------- --------------- --- --- ------
index 2.602e+00 1.555e-01 nan nan False
amplitude 2.441e-11 3.452e-12 1 / (cm2 s TeV) nan nan False
reference 1.000e+00 0.000e+00 TeV nan nan True
Covariance:
name/name index amplitude
--------- -------- ---------
index 0.0242 3.79e-13
amplitude 3.79e-13 1.19e-23
Statistic: -157.719 (cash)
Fit Range: [1.0000000e+09 2.7825594e+10] keV
"""
obs = SpectrumObservation.read(obs_path)
# obs.peek()
# plt.show()
model = PowerLaw(
amplitude=1.23 * 1e-11 * u.Unit("cm-2 s-1 TeV-1"),
reference=1 * u.Unit("TeV"),
index=2.14 * u.Unit(""),
)
fit = SpectrumFit(obs_list=obs, model=model, fit_range=energy_range, stat="cash")
fit.run()
print(fit.result[0])
pprint(fit.__dict__)
obs = fit.obs_list[0]
print(obs)
print("This is fit_gammapy")
obs.peek()
import matplotlib.pyplot as plt
plt.savefig("fit_gammapy.png")
def setup(self):
pha = gammapy_extra.filename("datasets/hess-crab4_pha/pha_obs23592.fits")
self.obs = SpectrumObservation.read(pha)
self.best_fit_model = models.PowerLaw(index=2 * u.Unit(''),
amplitude=1e-11 * u.Unit('cm-2 s-1 TeV-1'),
reference=1 * u.TeV)
self.npred = self.obs.predicted_counts(self.best_fit_model).data.value
self.covar_axis = ['index', 'amplitude']
self.covar = np.diag([0.1 ** 2, 1e-12 ** 2])
self.fit_range = [0.1, 50] * u.TeV
self.fit_result = SpectrumFitResult(
model=self.best_fit_model,
covariance=self.covar,
covar_axis=self.covar_axis,
fit_range=self.fit_range,
statname='wstat',
statval=42,
npred=self.npred,
obs=self.obs,
)
"""Compute flux points
This is an example script that show how to compute flux points in Gammapy.
TODO: Refactor and add to FluxPointsComputation class or so
"""
from gammapy.spectrum import (SpectrumObservation, SpectrumFit, FluxPoints,
SpectrumResult)
import astropy.units as u
from astropy.table import Table
import numpy as np
import copy
import matplotlib.pyplot as plt
plt.style.use('ggplot')
obs = SpectrumObservation.read(
'$GAMMAPY_EXTRA/datasets/hess-crab4_pha/pha_obs23523.fits')
fit = SpectrumFit(obs)
fit.run()
best_fit = copy.deepcopy(fit.result[0].fit)
# Define Flux points binning
emin = np.log10(obs.lo_threshold.to('TeV').value)
emax = np.log10(40)
binning = np.logspace(emin, emax, 8) * u.TeV
# Fix index
fit.model.gamma.freeze()
# Fit norm in bands
diff_flux = list()
"""Test spectrum energy grouping.
"""
import warnings
import astropy.units as u
from gammapy.spectrum import SpectrumObservation, SpectrumEnergyGroupMaker
warnings.filterwarnings('ignore')
filename = '$GAMMAPY_EXTRA/datasets/hess-crab4_pha/pha_obs23523.fits'
obs = SpectrumObservation.read(filename)
table = obs.stats_table()
seg = SpectrumEnergyGroupMaker(obs=obs)
ebounds = [0.03, 1, 3, 10, 30] * u.TeV
seg.compute_range_safe()
seg.compute_groups_fixed(ebounds=ebounds)
print('\nTable of original energy bins:')
seg.table[['energy_group_idx', 'bin_idx', 'energy_min', 'energy_max']].pprint(max_lines=-1)
print('\nTable of grouped energy bins:')
seg.groups.to_group_table().pprint(max_lines=-1)
print('\nTable of grouped energy bins:')
seg.groups.to_total_table().pprint(max_lines=-1)
print(seg)
from gammapy.spectrum import (
SpectrumObservation,
SpectrumFit,
DifferentialFluxPoints,
SpectrumFitResult,
SpectrumResult
)
import astropy.units as u
import numpy as np
import copy
import matplotlib.pyplot as plt
plt.style.use('ggplot')
obs = SpectrumObservation.read('$GAMMAPY_EXTRA/datasets/hess-crab4_pha/pha_obs23523.fits')
fit = SpectrumFit(obs)
fit.run()
best_fit = copy.deepcopy(fit.result[0].fit)
# Define Flux points binning
emin = np.log10(obs.lo_threshold.to('TeV').value)
emax = np.log10(40)
binning = np.logspace(emin, emax, 8) * u.TeV
# Fix index
fit.model.gamma.freeze()
# Fit norm in bands
diff_flux = list()
def check_energy_binning_effects():
"""Check how spectral fit results change with energy binnings.
Actually this is still using the default:
In [14]: print(analysis.extraction.observations[0].edisp)
EnergyDispersion
NDDataArray summary info
e_true : size = 108, min = 0.010 TeV, max = 301.416 TeV
e_reco : size = 72, min = 0.011 TeV, max = 93.804 TeV
Data : size = 7776, min = 0.000, max = 1.000
But now, the fit results from SpectrumAnalysisIACT, which is just
driving Fitspectrum, are different, almost the same as Sherpa.
Current results
Parameters:
name value error unit min max frozen
--------- --------- --------- --------------- --- --- ------
index 2.620e+00 1.540e-01 nan nan False
amplitude 3.849e-11 5.407e-12 1 / (cm2 s TeV) nan nan False
reference 1.000e+00 0.000e+00 TeV nan nan True
Covariance:
name/name index amplitude
--------- -------- ---------
index 0.0237 5.85e-13
amplitude 5.85e-13 2.92e-23
Statistic: -157.166 (cash)
Fit Range: [ 1. 27.82559402] TeV
???
"""
data_store = DataStore.from_dir("data/hess")
obs_list = data_store.obs_list([23523])
on_region = CircleSkyRegion(conf.crab_position, conf.on_radius["hess"])
fp_binning = [1, 10, 30] * u.TeV
exclusion_mask = get_exclusion_mask(conf.crab_position)
model = PowerLaw(
amplitude=1.23 * 1e-11 * u.Unit("cm-2 s-1 TeV-1"),
reference=1 * u.Unit("TeV"),
index=2.14 * u.Unit(""),
)
cfg = dict(
outdir=None,
background=dict(
on_region=on_region,
exclusion_mask=exclusion_mask,
# min_distance=0.1 * u.rad,
),
extraction=dict(containment_correction=True),
fit=dict(
model=model,
stat="cash",
# forward_folded=True,
fit_range=energy_range,
),
fp_binning=fp_binning,
)
analysis = SpectrumAnalysisIACT(obs_list, cfg)
analysis.run()
analysis.fit.est_errors()
print("BEFORE", analysis.fit.result[0])
# print(analysis.extraction.observations[0].edisp)
# pprint(analysis.fit.__dict__)
# obs = analysis.fit.obs_list[0]
# print(obs)
# # import IPython; IPython.embed()
# obs.peek()
# import matplotlib.pyplot as plt
# plt.savefig('check_energy_binning_effects.png')
# print('This is check_energy_binning_effects')
# Try to see if the I/O causes a change in results.
analysis.extraction.observations.write("temp123")
analysis.extraction.observations.write("temp123", use_sherpa=True)
obs = SpectrumObservation.read("temp123/pha_obs23523.fits")
model = PowerLaw(
amplitude=1.23 * 1e-11 * u.Unit("cm-2 s-1 TeV-1"),
reference=1 * u.Unit("TeV"),
index=2.14 * u.Unit(""),
)
fit = SpectrumFit(obs_list=obs, model=model, fit_range=energy_range, stat="cash")
fit.run()
print("AFTER", fit.result[0])
from gammapy.spectrum import SpectrumFit, SpectrumObservation, SpectrumFitResult
import matplotlib.pyplot as plt
import astropy.units as u
obs = SpectrumObservation.read('pha_obs31415.fits')
fit = SpectrumFit(obs)
obs.peek()
plt.savefig('observation.png')
plt.cla()
fit.run()
fit.result[0].plot_fit()
plt.savefig('debug_fit.png')
# TODO: implement properly
plt.cla()
fig = plt.figure()
ax = fig.add_subplot(111)
fit.result[0].fit.plot_butterfly(ax=ax, label='Fit result')
input_parameters = dict(index = 2.3 * u.Unit(''),
norm = 2.5 * 1e-12 * u.Unit('cm-2 s-1 TeV-1'),
reference = 1 * u.TeV)
input_parameter_errors = dict(index = 0 * u.TeV,
norm = 0 * u.Unit('cm-2 s-1 TeV-1'),
reference = 0 * u.TeV)
input_model = SpectrumFitResult(spectral_model = 'PowerLaw',
parameters = input_parameters,
parameter_errors = input_parameter_errors)
"""Test spectrum energy grouping.
"""
import warnings
import astropy.units as u
from gammapy.spectrum import SpectrumObservation, SpectrumEnergyGroupMaker
warnings.filterwarnings('ignore')
filename = '$GAMMAPY_EXTRA/datasets/hess-crab4_pha/pha_obs23523.fits'
obs = SpectrumObservation.read(filename)
table = obs.stats_table()
seg = SpectrumEnergyGroupMaker(obs=obs)
ebounds = [0.03, 1, 3, 10, 30] * u.TeV
seg.compute_range_safe()
seg.compute_groups_fixed(ebounds=ebounds)
print('\nTable of original energy bins:')
seg.table[['energy_group_idx', 'bin_idx', 'energy_min',
'energy_max']].pprint(max_lines=-1)
print('\nTable of grouped energy bins:')
seg.groups.to_group_table().pprint(max_lines=-1)
print('\nTable of grouped energy bins:')
seg.groups.to_total_table().pprint(max_lines=-1)
print(seg)
lw=2,
label=name)
ax.step(x[start:stop],
bkg_counts[start:stop],
where='post',
lw=1,
color=line.get_color(),
alpha=0.5)
return ax
fig, ax = plt.subplots(1, 1)
# filenames = glob.glob('./plots/data/joint_crab/spectra/magic/pha_*.fits')
filenames = glob.glob('./build/pymc_results/spectra/magic/pha_*.fits')
obs = [SpectrumObservation.read(f) for f in filenames]
plot_counts(ax, obs, 'MAGIC')
filenames = glob.glob('./build/pymc_results/spectra/fact/pha_*.fits')
obs = [SpectrumObservation.read(f) for f in filenames]
plot_counts(ax, obs, 'FACT')
filenames = glob.glob('./build/pymc_results/spectra/veritas/pha_*.fits')
obs = [SpectrumObservation.read(f) for f in filenames]
plot_counts(ax, obs, 'VERITAS')
filenames = glob.glob('./build/pymc_results/spectra/hess/pha_*.fits')
obs = [SpectrumObservation.read(f) for f in filenames]
plot_counts(ax, obs, 'H.E.S.S')
ax.legend()
