コード例 #1
0
ファイル: test_xspec_fake.py プロジェクト: astrofrog/gammapy
def make_test_arf():
    from gammapy.irf import np_to_arf
    from gammapy.irf import abramowski_effective_area

    effective_area = abramowski_effective_area(np.diff(EBOUNDS))
    arf = np_to_arf(effective_area, EBOUNDS)

    filename = 'xspec_test_arf.fits'
    logging.info('Writing {0}'.format(filename))
    arf.writeto(filename, clobber=True)
コード例 #2
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ファイル: xspec_fake.py プロジェクト: zblz/gammapy
def make_test_arf():
    from gammapy.irf import np_to_arf
    from gammapy.irf import abramowski_effective_area

    effective_area = abramowski_effective_area(np.diff(EBOUNDS))
    arf = np_to_arf(effective_area, EBOUNDS)

    filename = 'xspec_test_arf.fits'
    logging.info('Writing {0}'.format(filename))
    arf.writeto(filename, clobber=True)
コード例 #3
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ファイル: simluate.py プロジェクト: OlgaVorokh/gammapy-extra
from gammapy.spectrum.models import PowerLaw
from gammapy.spectrum import calculate_predicted_counts, SpectrumExtraction, PHACountsSpectrum, SpectrumObservation
from gammapy.utils.random import get_random_state
import numpy as np
import astropy.units as u
import matplotlib.pyplot as plt

e_true = SpectrumExtraction.DEFAULT_TRUE_ENERGY
e_reco = SpectrumExtraction.DEFAULT_RECO_ENERGY

# EDISP
edisp = EnergyDispersion.from_gauss(e_true=e_true, e_reco=e_true, sigma=0.2)

# AEFF
nodes = np.sqrt(e_true[:-1] * e_true[1:])
data = abramowski_effective_area(energy=nodes)
aeff = EffectiveAreaTable(data=data, energy=e_true)
lo_threshold = aeff.find_energy(0.1 * aeff.max_area)


# MODEL
model = PowerLaw(index=2.3 * u.Unit(""), amplitude=2.5 * 1e-12 * u.Unit("cm-2 s-1 TeV-1"), reference=1 * u.TeV)

# COUNTS
livetime = 2 * u.h
npred = calculate_predicted_counts(model=model, aeff=aeff, edisp=edisp, livetime=livetime)

bkg = 0.2 * npred.data
alpha = 0.1
counts_kwargs = dict(
    energy=npred.energy,
コード例 #4
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"""Plot approximate effective area for HESS, HESS2 and CTA."""
import numpy as np
import matplotlib.pyplot as plt
from astropy.units import Quantity
from gammapy.irf import abramowski_effective_area

energy = Quantity(np.logspace(-3, 3, 100), 'TeV')

for instrument in ['HESS', 'HESS2', 'CTA']:
    a_eff = abramowski_effective_area(energy, instrument)
    plt.plot(energy.value, a_eff.value, label=instrument)

plt.loglog()
plt.xlabel('Energy (TeV)')
plt.ylabel('Effective Area (cm^2)')
plt.xlim([1e-3, 1e3])
plt.ylim([1e3, 1e12])
plt.legend(loc='best')
plt.show()
コード例 #5
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ファイル: simluate.py プロジェクト: msjacob/gammapy-extra
from gammapy.spectrum import (calculate_predicted_counts, SpectrumExtraction,
                              PHACountsSpectrum, SpectrumObservation)
from gammapy.utils.random import get_random_state
import numpy as np
import astropy.units as u
import matplotlib.pyplot as plt

e_true = SpectrumExtraction.DEFAULT_TRUE_ENERGY
e_reco = SpectrumExtraction.DEFAULT_RECO_ENERGY

# EDISP
edisp = EnergyDispersion.from_gauss(e_true=e_true, e_reco=e_true, sigma=0.2)

# AEFF
nodes = np.sqrt(e_true[:-1] * e_true[1:])
data = abramowski_effective_area(energy=nodes)
aeff = EffectiveAreaTable(data=data, energy=e_true)
lo_threshold = aeff.find_energy(0.1 * aeff.max_area)

# MODEL
model = PowerLaw(index=2.3 * u.Unit(''),
                 amplitude=2.5 * 1e-12 * u.Unit('cm-2 s-1 TeV-1'),
                 reference=1 * u.TeV)

# COUNTS
livetime = 2 * u.h
npred = calculate_predicted_counts(model=model,
                                   aeff=aeff,
                                   edisp=edisp,
                                   livetime=livetime)
コード例 #6
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ファイル: plot_effective_area.py プロジェクト: zblz/gammapy
# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""Plot approximate effective area for HESS, HESS2 and CTA.
"""
import numpy as np
import matplotlib.pyplot as plt
from astropy.units import Quantity
from gammapy.irf import abramowski_effective_area

energy = Quantity(np.logspace(-3, 3, 100), 'TeV')

for instrument in ['HESS', 'HESS2', 'CTA']:
    a_eff = abramowski_effective_area(energy, instrument)
    plt.plot(energy.value, a_eff.value, label=instrument)

plt.loglog()
plt.xlabel('Energy (TeV)')
plt.ylabel('Effective Area (cm^2)')
plt.xlim([1e-3, 1e3])
plt.ylim([1e3, 1e12])
plt.legend(loc='best')
plt.show()