def __init__(
self,
energy_lo,
energy_hi,
offset_lo,
offset_hi,
data,
meta=None,
interp_kwargs=None,
):
if interp_kwargs is None:
interp_kwargs = self.default_interp_kwargs
e_edges = edges_from_lo_hi(energy_lo, energy_hi)
energy_axis = MapAxis.from_edges(e_edges,
interp="log",
name="energy_true")
# TODO: for some reason the H.E.S.S. DL3 files contain the same values for offset_hi and offset_lo
if np.allclose(offset_lo.to_value("deg"), offset_hi.to_value("deg")):
offset_axis = MapAxis.from_nodes(offset_lo,
interp="lin",
name="offset")
else:
offset_edges = edges_from_lo_hi(offset_lo, offset_hi)
offset_axis = MapAxis.from_edges(offset_edges,
interp="lin",
name="offset")
self.data = NDDataArray(axes=[energy_axis, offset_axis],
data=data,
interp_kwargs=interp_kwargs)
self.meta = meta or {}
def __init__(
self,
e_true_lo,
e_true_hi,
migra_lo,
migra_hi,
offset_lo,
offset_hi,
data,
interp_kwargs=None,
meta=None,
):
if interp_kwargs is None:
interp_kwargs = self.default_interp_kwargs
e_true_edges = edges_from_lo_hi(e_true_lo, e_true_hi)
e_true_axis = MapAxis.from_edges(e_true_edges, interp="log", name="e_true")
migra_edges = edges_from_lo_hi(migra_lo, migra_hi)
migra_axis = MapAxis.from_edges(
migra_edges, interp="log", name="migra", unit=""
)
# TODO: for some reason the H.E.S.S. DL3 files contain the same values for offset_hi and offset_lo
if np.allclose(offset_lo.to_value("deg"), offset_hi.to_value("deg")):
offset_axis = MapAxis.from_nodes(offset_lo, interp="lin", name="offset")
else:
offset_edges = edges_from_lo_hi(offset_lo, offset_hi)
offset_axis = MapAxis.from_edges(offset_edges, interp="lin", name="offset")
axes = [e_true_axis, migra_axis, offset_axis]
self.data = NDDataArray(axes=axes, data=data, interp_kwargs=interp_kwargs)
self.meta = OrderedDict(meta) if meta else OrderedDict()
def __init__(
self,
energy_lo,
energy_hi,
offset_lo,
offset_hi,
data,
meta=None,
interp_kwargs=None,
):
if interp_kwargs is None:
interp_kwargs = self.default_interp_kwargs
e_edges = edges_from_lo_hi(energy_lo, energy_hi)
energy_axis = MapAxis.from_edges(e_edges, interp="log", name="energy")
offset_edges = edges_from_lo_hi(offset_lo, offset_hi)
offset_axis = MapAxis.from_edges(offset_edges,
interp="lin",
name="offset")
self.data = NDDataArray(axes=[energy_axis, offset_axis],
data=data,
interp_kwargs=interp_kwargs)
self.meta = meta or {}
def __init__(
self,
e_true_lo,
e_true_hi,
e_reco_lo,
e_reco_hi,
data,
interp_kwargs=None,
meta=None,
):
if interp_kwargs is None:
interp_kwargs = self.default_interp_kwargs
e_true_edges = edges_from_lo_hi(e_true_lo, e_true_hi)
e_true_axis = MapAxis.from_edges(e_true_edges,
interp="log",
name="e_true")
e_reco_edges = edges_from_lo_hi(e_reco_lo, e_reco_hi)
e_reco_axis = MapAxis.from_edges(e_reco_edges,
interp="log",
name="e_reco")
self.data = NDDataArray(axes=[e_true_axis, e_reco_axis],
data=data,
interp_kwargs=interp_kwargs)
self.meta = meta or {}
def __init__(
self,
energy_lo,
energy_hi,
fov_lon_lo,
fov_lon_hi,
fov_lat_lo,
fov_lat_hi,
data,
meta=None,
interp_kwargs=None,
):
if interp_kwargs is None:
interp_kwargs = self.default_interp_kwargs
e_edges = edges_from_lo_hi(energy_lo, energy_hi)
energy_axis = MapAxis.from_edges(e_edges, interp="log", name="energy")
fov_lon_edges = edges_from_lo_hi(fov_lon_lo, fov_lon_hi)
fov_lon_axis = MapAxis.from_edges(fov_lon_edges,
interp="lin",
name="fov_lon")
fov_lat_edges = edges_from_lo_hi(fov_lat_lo, fov_lat_hi)
fov_lat_axis = MapAxis.from_edges(fov_lat_edges,
interp="lin",
name="fov_lat")
self.data = NDDataArray(
axes=[energy_axis, fov_lon_axis, fov_lat_axis],
data=data,
interp_kwargs=interp_kwargs,
)
self.meta = meta or {}
def __init__(self, rad_axis, data, interp_kwargs=None):
interp_kwargs = interp_kwargs or {}
rad_axis.assert_name("rad")
self.data = NDDataArray(axes=[rad_axis],
data=u.Quantity(data).to("sr^-1"),
interp_kwargs=interp_kwargs)
def __init__(self, energy_axis_true, data, meta=None):
interp_kwargs = {"extrapolate": False, "bounds_error": False}
self.data = NDDataArray(axes=[energy_axis_true],
data=data,
interp_kwargs=interp_kwargs)
self.data.axes.assert_names(["energy_true"])
self.meta = meta or {}
def __init__(self, energy_lo, energy_hi, data):
axes = [
BinnedDataAxis(energy_lo,
energy_hi,
interpolation_mode='log',
name='energy'),
]
self.data = NDDataArray(axes=axes, data=data)
def __init__(
self, e_true, e_reco, data, interp_kwargs=None, meta=None,
):
if interp_kwargs is None:
interp_kwargs = self.default_interp_kwargs
self.data = NDDataArray(
axes=[e_true, e_reco], data=data, interp_kwargs=interp_kwargs
)
self.meta = meta or {}
def __init__(
self, energy_axis_true, energy_axis, data, interp_kwargs=None, meta=None,
):
if interp_kwargs is None:
interp_kwargs = self.default_interp_kwargs
self.data = NDDataArray(
axes=[energy_axis_true, energy_axis], data=data, interp_kwargs=interp_kwargs
)
self.meta = meta or {}
def __init__(self, energy_lo, energy_hi, data, meta=None):
e_edges = edges_from_lo_hi(energy_lo, energy_hi)
energy_axis = MapAxis.from_edges(e_edges, interp="log", name="energy")
interp_kwargs = {"extrapolate": False, "bounds_error": False}
self.data = NDDataArray(axes=[energy_axis],
data=data,
interp_kwargs=interp_kwargs)
self.meta = meta or {}
def __init__(
self, energy_axis, offset_axis, data, meta=None, interp_kwargs=None,
):
if interp_kwargs is None:
interp_kwargs = self.default_interp_kwargs
self.data = NDDataArray(
axes=[energy_axis, offset_axis], data=data, interp_kwargs=interp_kwargs
)
self.data.axes.assert_names(["energy", "offset"])
self.meta = meta or {}
def __init__(
self, energy_axis, offset_axis, data, meta=None, interp_kwargs=None,
):
if interp_kwargs is None:
interp_kwargs = self.default_interp_kwargs
assert offset_axis.name == "offset"
self.data = NDDataArray(
axes=[energy_axis, offset_axis], data=data, interp_kwargs=interp_kwargs
)
self.meta = meta or {}
def load_irf(self):
filename = os.path.join(self.outdir, "irf.fits.gz")
with fits.open(filename, memmap=False) as hdulist:
aeff = EffectiveAreaTable2D.from_hdulist(hdulist=hdulist)
edisp = EnergyDispersion2D.read(filename, hdu="ENERGY DISPERSION")
bkg_fits_table = hdulist["BACKGROUND"]
bkg_table = Table.read(bkg_fits_table)
energy_lo = bkg_table["ENERG_LO"].quantity
energy_hi = bkg_table["ENERG_HI"].quantity
bkg = bkg_table["BGD"].quantity
axes = [
BinnedDataAxis(energy_lo,
energy_hi,
interpolation_mode="log",
name="energy")
]
bkg = BkgData(data=NDDataArray(axes=axes, data=bkg))
# Create rmf with appropriate dimensions (e_reco->bkg, e_true->area)
e_reco_min = bkg.energy.lo[0]
e_reco_max = bkg.energy.hi[-1]
e_reco_bin = bkg.energy.nbins
e_reco_axis = EnergyBounds.equal_log_spacing(e_reco_min, e_reco_max,
e_reco_bin, "TeV")
e_true_min = aeff.data.axes[0].lo[0]
e_true_max = aeff.data.axes[0].hi[-1]
e_true_bin = len(aeff.data.axes[0].bins) - 1
e_true_axis = EnergyBounds.equal_log_spacing(e_true_min, e_true_max,
e_true_bin, "TeV")
# Fake offset...
rmf = edisp.to_energy_dispersion(offset=0.5 * u.deg,
e_reco=e_reco_axis,
e_true=e_true_axis)
# This is required because in gammapy v0.8
# gammapy.spectrum.utils.integrate_model
# calls the attribute aeff.energy which is an attribute of
# EffectiveAreaTable and not of EffectiveAreaTable2D
# WARNING the angle is not important, but only because we started with
# on-axis data! TO UPDATE
aeff = aeff.to_effective_area_table(Angle("1d"))
self.irf = Irf(bkg=bkg, aeff=aeff, rmf=rmf)
def __init__(
self,
energy_axis_true,
offset_axis,
rad_axis,
data,
meta=None,
interp_kwargs=None,
):
interp_kwargs = interp_kwargs or {}
axes = MapAxes([energy_axis_true, offset_axis, rad_axis])
axes.assert_names(["energy_true", "offset", "rad"])
self.data = NDDataArray(axes=axes,
data=u.Quantity(data).to("sr^-1"),
interp_kwargs=interp_kwargs)
self.meta = meta or {}
def __init__(
self,
energy_axis_true,
rad_axis,
exposure=None,
data=None,
interp_kwargs=None,
):
interp_kwargs = interp_kwargs or {}
axes = MapAxes([energy_axis_true, rad_axis])
axes.assert_names(["energy_true", "rad"])
self.data = NDDataArray(axes=axes,
data=u.Quantity(data).to("sr^-1"),
interp_kwargs=interp_kwargs)
if exposure is None:
self.exposure = u.Quantity(np.ones(self.energy_axis_true.nbin),
"cm^2 s")
else:
self.exposure = u.Quantity(exposure).to("cm^2 s")
def load_irf(self):
filename = os.path.join(self.outdir, 'irf.fits.gz')
with fits.open(filename, memmap=False) as hdulist:
aeff = EffectiveAreaTable.from_hdulist(hdulist=hdulist)
edisp = EnergyDispersion2D.read(filename, hdu="ENERGY DISPERSION")
bkg_fits_table = hdulist["BACKGROUND"]
bkg_table = Table.read(bkg_fits_table)
energy_lo = bkg_table["ENERG_LO"].quantity
energy_hi = bkg_table["ENERG_HI"].quantity
bkg = bkg_table["BGD"].quantity
axes = [
BinnedDataAxis(
energy_lo, energy_hi, interpolation_mode="log", name="energy"
)
]
bkg = BkgData(data=NDDataArray(axes=axes, data=bkg))
# Create rmf with appropriate dimensions (e_reco->bkg, e_true->area)
e_reco_min = bkg.energy.lo[0]
e_reco_max = bkg.energy.hi[-1]
e_reco_bin = bkg.energy.nbins
e_reco_axis = EnergyBounds.equal_log_spacing(
e_reco_min, e_reco_max, e_reco_bin, "TeV"
)
e_true_min = aeff.energy.lo[0]
e_true_max = aeff.energy.hi[-1]
e_true_bin = aeff.energy.nbins
e_true_axis = EnergyBounds.equal_log_spacing(
e_true_min, e_true_max, e_true_bin, "TeV"
)
# Fake offset...
rmf = edisp.to_energy_dispersion(
offset=0.5 * u.deg, e_reco=e_reco_axis, e_true=e_true_axis
)
self.irf = Irf(bkg=bkg, aeff=aeff, rmf=rmf)
def nddata_1d(axis_x):
return NDDataArray(
axes=[axis_x],
data=[1, -1, 2],
interp_kwargs=dict(bounds_error=False, fill_value=None),
)
def nddata_2d(axis_energy, axis_offset):
return NDDataArray(
axes=[axis_energy, axis_offset],
data=np.arange(8).reshape(2, 4) * u.cm * u.cm,
interp_kwargs=dict(bounds_error=False, fill_value=None),
)
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,
nddata.data.value,
".",
label="Interpolation nodes",
def test_init_error(self):
with pytest.raises(ValueError):
NDDataArray(
axes=[MapAxis.from_nodes([1, 3, 6], name="x")],
data=np.arange(8).reshape(4, 2),
)