def plot_result(results):
import matplotlib.pyplot as plt
# Plot model
energy = Energy.equal_log_spacing(1e-3, 1e2, nbins=100, unit='TeV')
flux = results['model'](energy.to('TeV').value)
flux = Quantity(flux, 'erg cm-2 s-1')
plt.plot(energy, flux, label='model')
# Plot data points
data = FermiData()
plt.errorbar(x=data.x,
y=data.y,
yerr=data.staterror,
fmt='ro',
label='data')
data = IACTData()
plt.errorbar(x=data.x,
y=data.y,
yerr=data.staterror,
fmt='ro',
label='data')
# Make it pretty
plt.loglog()
plt.xlabel('Energy (TeV)')
plt.ylabel('E^2 * dN / dE (TeV cm^-2 s^-1)')
plt.legend()
filename = 'example_sherpa_mwl.png'
print('Writing {}'.format(filename))
plt.savefig(filename)
def plot_spectral_models(self):
for component in self.get_components(tags=['pwn', 'composite']):
fig, ax = plt.subplots()
table = component['table']
tag = component['tag']
vals = []
idx = 0
energies = Energy.equal_log_spacing(emin=0.02,
emax=100,
unit='TeV',
nbins=40)
for row in table:
idx += 1
if (idx < 100):
spec = LogParabola(
amplitude=row['spec_norm'] * u.Unit('MeV-1 s-1 cm-2'),
alpha=row['spec_alpha'],
reference=1 * u.TeV,
beta=row['spec_beta'],
)
fluxes = []
e2dnde = []
for energy in energies:
dnde = spec.evaluate(
energy=energy,
amplitude=row['spec_norm'] *
u.Unit('MeV-1 s-1 cm-2'),
alpha=row['spec_alpha'],
reference=1 * u.TeV,
beta=row['spec_beta'],
)
fluxes.append(dnde.value)
e2dnde.append(
((energy**2 * dnde).to('erg cm-2 s-1')).value)
ax.plot(energies.value,
e2dnde,
color='black',
alpha=0.2,
lw=2)
else:
break
ax.set_title('{} spectra'.format(component['tag']))
ax.loglog()
ax.set_xlabel('Energy (TeV)')
ax.set_ylabel('e2dnde (erg cm-2 s-1)')
ax.set_ylim(2e-18, 5e-10)
fig.tight_layout()
filename = 'ctadc_skymodel_gps_sources_spectra_{}.png'.format(
component['tag'])
log.info('Writing {}'.format(filename))
fig.savefig(filename)
plt.close(fig)
def _get_ref_cube(self, enumbins=11):
p = self.parameters
wcs = self.reference.wcs.deepcopy()
shape = (enumbins, ) + self.reference.data.shape
data = np.zeros(shape)
energy = Energy.equal_log_spacing(p['emin'], p['emax'], enumbins,
'TeV')
energy_axis = LogEnergyAxis(energy, mode='center')
return SkyCube(data=data, wcs=wcs, energy_axis=energy_axis)
def plot_spectral_model(model, ax, plot_kwargs):
energies = Energy.equal_log_spacing(emin=0.02,
emax=100,
unit='TeV',
nbins=40)
fluxes = []
for energy in energies:
energy_gammalib = gammalib.GEnergy(energy.value, 'TeV')
fluxes.append(model.eval(energy_gammalib))
dnde = u.Quantity(fluxes, 'cm-2 s-1 MeV-1')
e2dnde = (energies**2 * dnde).to('erg cm-2 s-1')
ax.plot(energies.value, e2dnde.value, **plot_kwargs)
def plot_result(results):
import matplotlib.pyplot as plt
# Plot model
energy = Energy.equal_log_spacing(1e-3, 1e2, nbins=100, unit='TeV')
flux = results['model'](energy.to('TeV').value)
flux = Quantity(flux, 'erg cm-2 s-1')
plt.plot(energy, flux, label='model')
# Plot data points
data = FermiData()
plt.errorbar(x=data.x, y=data.y, yerr=data.staterror, fmt='ro', label='data')
data = IACTData()
plt.errorbar(x=data.x, y=data.y, yerr=data.staterror, fmt='ro', label='data')
# Make it pretty
plt.loglog()
plt.xlabel('Energy (TeV)')
plt.ylabel('E^2 * dN / dE (TeV cm^-2 s^-1)')
plt.legend()
filename = 'example_sherpa_mwl.png'
print('Writing {}'.format(filename))
plt.savefig(filename)
from gammapy.utils.nddata import NDDataArray, DataAxis, BinnedDataAxis
from gammapy.utils.energy import Energy, EnergyBounds
import numpy as np
import astropy.units as u
# ## 1D example
#
# Let's start with a simple example. A one dimensional array storing an exposure in ``cm-2 s-1`` as a function of energy. The energy axis is log spaced and thus also the interpolation shall take place in log.
# In[ ]:
energies = Energy.equal_log_spacing(10, 100, 10, unit=u.TeV)
x_axis = DataAxis(energies, name="energy", interpolation_mode="log")
data = np.arange(20, 0, -2) / u.cm ** 2 / u.s
nddata = NDDataArray(axes=[x_axis], data=data)
print(nddata)
print(nddata.axis("energy"))
# In[ ]:
eval_energies = np.linspace(2, 6, 20) * 1e4 * u.GeV
eval_exposure = nddata.evaluate(energy=eval_energies, method="linear")
plt.plot(
nddata.axis("energy").nodes.value,