# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""HESS Galactic plane survey (HGPS) catalog."""
import numpy as np
import astropy.units as u
from astropy.coordinates import Angle
from astropy.modeling.models import Gaussian1D
from astropy.table import Table
from gammapy.estimators import FluxPoints
from gammapy.maps import MapAxis, RegionGeom
from gammapy.modeling.models import Model, Models, SkyModel
from gammapy.utils.interpolation import ScaledRegularGridInterpolator
from gammapy.utils.scripts import make_path
from gammapy.utils.table import table_row_to_dict
from .core import SourceCatalog, SourceCatalogObject, format_flux_points_table
__all__ = [
"SourceCatalogHGPS",
"SourceCatalogLargeScaleHGPS",
"SourceCatalogObjectHGPS",
"SourceCatalogObjectHGPSComponent",
]
# Flux factor, used for printing
FF = 1e-12
# Multiplicative factor to go from cm^-2 s^-1 to % Crab for integral flux > 1 TeV
# Here we use the same Crab reference that's used in the HGPS paper
# CRAB = crab_integral_flux(energy_min=1, reference='hess_ecpl')
FLUX_TO_CRAB = 100 / 2.26e-11
FLUX_TO_CRAB_DIFF = 100 / 3.5060459323111307e-11
[docs]
class SourceCatalogObjectHGPSComponent(SourceCatalogObject):
"""One Gaussian component from the HGPS catalog.
See Also
--------
SourceCatalogHGPS, SourceCatalogObjectHGPS
"""
_source_name_key = "Component_ID"
def __init__(self, data):
self.data = data
def __repr__(self):
return f"{self.__class__.__name__}({self.name!r})"
def __str__(self):
"""Pretty-print source data."""
d = self.data
ss = "Component {}:\n".format(d["Component_ID"])
fmt = "{:<20s} : {:8.3f} +/- {:.3f} deg\n"
ss += fmt.format("GLON", d["GLON"].value, d["GLON_Err"].value)
ss += fmt.format("GLAT", d["GLAT"].value, d["GLAT_Err"].value)
fmt = "{:<20s} : {:.3f} +/- {:.3f} deg\n"
ss += fmt.format("Size", d["Size"].value, d["Size_Err"].value)
val, err = d["Flux_Map"].value, d["Flux_Map_Err"].value
fmt = "{:<20s} : ({:.2f} +/- {:.2f}) x 10^-12 cm^-2 s^-1 = ({:.1f} +/- {:.1f}) % Crab"
ss += fmt.format(
"Flux (>1 TeV)", val / FF, err / FF, val * FLUX_TO_CRAB, err * FLUX_TO_CRAB
)
return ss
@property
def name(self):
"""Source name as a string."""
return self.data[self._source_name_key]
[docs]
def spatial_model(self):
"""Component spatial model as a `~gammapy.modeling.models.GaussianSpatialModel` object."""
d = self.data
tag = "GaussianSpatialModel"
pars = {
"lon_0": d["GLON"],
"lat_0": d["GLAT"],
"sigma": d["Size"],
"frame": "galactic",
}
errs = {"lon_0": d["GLON_Err"], "lat_0": d["GLAT_Err"], "sigma": d["Size_Err"]}
model = Model.create(tag, "spatial", **pars)
for name, value in errs.items():
model.parameters[name].error = value
return model
[docs]
class SourceCatalogObjectHGPS(SourceCatalogObject):
"""One object from the HGPS catalog.
The catalog is represented by `SourceCatalogHGPS`.
Examples are given there.
"""
def __repr__(self):
return f"{self.__class__.__name__}({self.name!r})"
def __str__(self):
return self.info()
@property
def flux_points(self):
"""Flux points as a `~gammapy.estimators.FluxPoints` object."""
reference_model = SkyModel(spectral_model=self.spectral_model(), name=self.name)
return FluxPoints.from_table(
self.flux_points_table,
reference_model=reference_model,
)
[docs]
def info(self, info="all"):
"""Information string.
Parameters
----------
info : {'all', 'basic', 'map', 'spec', 'flux_points', 'components', 'associations', 'id'}
Comma separated list of options.
"""
if info == "all":
info = "basic,associations,id,map,spec,flux_points,components"
ss = ""
ops = info.split(",")
if "basic" in ops:
ss += self._info_basic()
if "map" in ops:
ss += self._info_map()
if "spec" in ops:
ss += self._info_spec()
if "flux_points" in ops:
ss += self._info_flux_points()
if "components" in ops and hasattr(self, "components"):
ss += self._info_components()
if "associations" in ops:
ss += self._info_associations()
if "id" in ops and hasattr(self, "identification_info"):
ss += self._info_id()
return ss
def _info_basic(self):
"""Print basic information."""
d = self.data
ss = "\n*** Basic info ***\n\n"
ss += "Catalog row index (zero-based) : {}\n".format(self.row_index)
ss += "{:<20s} : {}\n".format("Source name", self.name)
ss += "{:<20s} : {}\n".format("Analysis reference", d["Analysis_Reference"])
ss += "{:<20s} : {}\n".format("Source class", d["Source_Class"])
ss += "{:<20s} : {}\n".format("Identified object", d["Identified_Object"])
ss += "{:<20s} : {}\n".format("Gamma-Cat id", d["Gamma_Cat_Source_ID"])
ss += "\n"
return ss
def _info_associations(self):
ss = "\n*** Source associations info ***\n\n"
lines = self.associations.pformat(max_width=-1, max_lines=-1)
ss += "\n".join(lines)
return ss + "\n"
def _info_id(self):
ss = "\n*** Source identification info ***\n\n"
ss += "\n".join(f"{k}: {v}" for k, v in self.identification_info.items())
return ss + "\n"
def _info_map(self):
"""Print information from map analysis."""
d = self.data
ss = "\n*** Info from map analysis ***\n\n"
ra_str = Angle(d["RAJ2000"], "deg").to_string(unit="hour", precision=0)
dec_str = Angle(d["DEJ2000"], "deg").to_string(unit="deg", precision=0)
ss += "{:<20s} : {:8.3f} = {}\n".format("RA", d["RAJ2000"], ra_str)
ss += "{:<20s} : {:8.3f} = {}\n".format("DEC", d["DEJ2000"], dec_str)
ss += "{:<20s} : {:8.3f} +/- {:.3f} deg\n".format(
"GLON", d["GLON"].value, d["GLON_Err"].value
)
ss += "{:<20s} : {:8.3f} +/- {:.3f} deg\n".format(
"GLAT", d["GLAT"].value, d["GLAT_Err"].value
)
ss += "{:<20s} : {:.3f}\n".format("Position Error (68%)", d["Pos_Err_68"])
ss += "{:<20s} : {:.3f}\n".format("Position Error (95%)", d["Pos_Err_95"])
ss += "{:<20s} : {:.0f}\n".format("ROI number", d["ROI_Number"])
ss += "{:<20s} : {}\n".format("Spatial model", d["Spatial_Model"])
ss += "{:<20s} : {}\n".format("Spatial components", d["Components"])
ss += "{:<20s} : {:.1f}\n".format("TS", d["Sqrt_TS"] ** 2)
ss += "{:<20s} : {:.1f}\n".format("sqrt(TS)", d["Sqrt_TS"])
ss += "{:<20s} : {:.3f} +/- {:.3f} (UL: {:.3f}) deg\n".format(
"Size", d["Size"].value, d["Size_Err"].value, d["Size_UL"].value
)
ss += "{:<20s} : {:.3f}\n".format("R70", d["R70"])
ss += "{:<20s} : {:.3f}\n".format("RSpec", d["RSpec"])
ss += "{:<20s} : {:.1f}\n".format("Total model excess", d["Excess_Model_Total"])
ss += "{:<20s} : {:.1f}\n".format("Excess in RSpec", d["Excess_RSpec"])
ss += "{:<20s} : {:.1f}\n".format(
"Model Excess in RSpec", d["Excess_RSpec_Model"]
)
ss += "{:<20s} : {:.1f}\n".format("Background in RSpec", d["Background_RSpec"])
ss += "{:<20s} : {:.1f} hours\n".format("Livetime", d["Livetime"].value)
ss += "{:<20s} : {:.2f}\n".format("Energy threshold", d["Energy_Threshold"])
val, err = d["Flux_Map"].value, d["Flux_Map_Err"].value
ss += "{:<20s} : ({:.3f} +/- {:.3f}) x 10^-12 cm^-2 s^-1 = ({:.2f} +/- {:.2f}) % Crab\n".format( # noqa: 501
"Source flux (>1 TeV)",
val / FF,
err / FF,
val * FLUX_TO_CRAB,
err * FLUX_TO_CRAB,
)
ss += "\nFluxes in RSpec (> 1 TeV):\n"
ss += "{:<30s} : {:.3f} x 10^-12 cm^-2 s^-1 = {:5.2f} % Crab\n".format(
"Map measurement",
d["Flux_Map_RSpec_Data"].value / FF,
d["Flux_Map_RSpec_Data"].value * FLUX_TO_CRAB,
)
ss += "{:<30s} : {:.3f} x 10^-12 cm^-2 s^-1 = {:5.2f} % Crab\n".format(
"Source model",
d["Flux_Map_RSpec_Source"].value / FF,
d["Flux_Map_RSpec_Source"].value * FLUX_TO_CRAB,
)
ss += "{:<30s} : {:.3f} x 10^-12 cm^-2 s^-1 = {:5.2f} % Crab\n".format(
"Other component model",
d["Flux_Map_RSpec_Other"].value / FF,
d["Flux_Map_RSpec_Other"].value * FLUX_TO_CRAB,
)
ss += "{:<30s} : {:.3f} x 10^-12 cm^-2 s^-1 = {:5.2f} % Crab\n".format(
"Large scale component model",
d["Flux_Map_RSpec_LS"].value / FF,
d["Flux_Map_RSpec_LS"].value * FLUX_TO_CRAB,
)
ss += "{:<30s} : {:.3f} x 10^-12 cm^-2 s^-1 = {:5.2f} % Crab\n".format(
"Total model",
d["Flux_Map_RSpec_Total"].value / FF,
d["Flux_Map_RSpec_Total"].value * FLUX_TO_CRAB,
)
ss += "{:<35s} : {:5.1f} %\n".format(
"Containment in RSpec", 100 * d["Containment_RSpec"]
)
ss += "{:<35s} : {:5.1f} %\n".format(
"Contamination in RSpec", 100 * d["Contamination_RSpec"]
)
label, val = (
"Flux correction (RSpec -> Total)",
100 * d["Flux_Correction_RSpec_To_Total"],
)
ss += f"{label:<35s} : {val:5.1f} %\n"
label, val = (
"Flux correction (Total -> RSpec)",
100 * (1 / d["Flux_Correction_RSpec_To_Total"]),
)
ss += f"{label:<35s} : {val:5.1f} %\n"
return ss
def _info_spec(self):
"""Print information from spectral analysis."""
d = self.data
ss = "\n*** Info from spectral analysis ***\n\n"
ss += "{:<20s} : {:.1f} hours\n".format("Livetime", d["Livetime_Spec"].value)
lo = d["Energy_Range_Spec_Min"].value
hi = d["Energy_Range_Spec_Max"].value
ss += "{:<20s} : {:.2f} to {:.2f} TeV\n".format("Energy range:", lo, hi)
ss += "{:<20s} : {:.1f}\n".format("Background", d["Background_Spec"])
ss += "{:<20s} : {:.1f}\n".format("Excess", d["Excess_Spec"])
ss += "{:<20s} : {}\n".format("Spectral model", d["Spectral_Model"])
val = d["TS_ECPL_over_PL"]
ss += "{:<20s} : {:.1f}\n".format("TS ECPL over PL", val)
val = d["Flux_Spec_Int_1TeV"].value
err = d["Flux_Spec_Int_1TeV_Err"].value
ss += "{:<20s} : ({:.3f} +/- {:.3f}) x 10^-12 cm^-2 s^-1 = ({:.2f} +/- {:.2f}) % Crab\n".format( # noqa: E501
"Best-fit model flux(> 1 TeV)",
val / FF,
err / FF,
val * FLUX_TO_CRAB,
err * FLUX_TO_CRAB,
)
val = d["Flux_Spec_Energy_1_10_TeV"].value
err = d["Flux_Spec_Energy_1_10_TeV_Err"].value
ss += "{:<20s} : ({:.3f} +/- {:.3f}) x 10^-12 erg cm^-2 s^-1\n".format(
"Best-fit model energy flux(1 to 10 TeV)", val / FF, err / FF
)
ss += self._info_spec_pl()
ss += self._info_spec_ecpl()
return ss
def _info_spec_pl(self):
d = self.data
ss = "{:<20s} : {:.2f}\n".format("Pivot energy", d["Energy_Spec_PL_Pivot"])
val = d["Flux_Spec_PL_Diff_Pivot"].value
err = d["Flux_Spec_PL_Diff_Pivot_Err"].value
ss += "{:<20s} : ({:.3f} +/- {:.3f}) x 10^-12 cm^-2 s^-1 TeV^-1 = ({:.2f} +/- {:.2f}) % Crab\n".format( # noqa: E501
"Flux at pivot energy",
val / FF,
err / FF,
val * FLUX_TO_CRAB,
err * FLUX_TO_CRAB_DIFF,
)
val = d["Flux_Spec_PL_Int_1TeV"].value
err = d["Flux_Spec_PL_Int_1TeV_Err"].value
ss += "{:<20s} : ({:.3f} +/- {:.3f}) x 10^-12 cm^-2 s^-1 = ({:.2f} +/- {:.2f}) % Crab\n".format( # noqa: E501
"PL Flux(> 1 TeV)",
val / FF,
err / FF,
val * FLUX_TO_CRAB,
err * FLUX_TO_CRAB,
)
val = d["Flux_Spec_PL_Diff_1TeV"].value
err = d["Flux_Spec_PL_Diff_1TeV_Err"].value
ss += "{:<20s} : ({:.3f} +/- {:.3f}) x 10^-12 cm^-2 s^-1 TeV^-1 = ({:.2f} +/- {:.2f}) % Crab\n".format( # noqa: E501
"PL Flux(@ 1 TeV)",
val / FF,
err / FF,
val * FLUX_TO_CRAB,
err * FLUX_TO_CRAB_DIFF,
)
val = d["Index_Spec_PL"]
err = d["Index_Spec_PL_Err"]
ss += "{:<20s} : {:.2f} +/- {:.2f}\n".format("PL Index", val, err)
return ss
def _info_spec_ecpl(self):
d = self.data
ss = ""
# ss = '{:<20s} : {:.1f}\n'.format('Pivot energy', d['Energy_Spec_ECPL_Pivot'])
val = d["Flux_Spec_ECPL_Diff_1TeV"].value
err = d["Flux_Spec_ECPL_Diff_1TeV_Err"].value
ss += "{:<20s} : ({:.3f} +/- {:.3f}) x 10^-12 cm^-2 s^-1 TeV^-1 = ({:.2f} +/- {:.2f}) % Crab\n".format( # noqa: E501
"ECPL Flux(@ 1 TeV)",
val / FF,
err / FF,
val * FLUX_TO_CRAB,
err * FLUX_TO_CRAB_DIFF,
)
val = d["Flux_Spec_ECPL_Int_1TeV"].value
err = d["Flux_Spec_ECPL_Int_1TeV_Err"].value
ss += "{:<20s} : ({:.3f} +/- {:.3f}) x 10^-12 cm^-2 s^-1 = ({:.2f} +/- {:.2f}) % Crab\n".format( # noqa: E501
"ECPL Flux(> 1 TeV)",
val / FF,
err / FF,
val * FLUX_TO_CRAB,
err * FLUX_TO_CRAB,
)
val = d["Index_Spec_ECPL"]
err = d["Index_Spec_ECPL_Err"]
ss += "{:<20s} : {:.2f} +/- {:.2f}\n".format("ECPL Index", val, err)
val = d["Lambda_Spec_ECPL"].value
err = d["Lambda_Spec_ECPL_Err"].value
ss += "{:<20s} : {:.3f} +/- {:.3f} TeV^-1\n".format("ECPL Lambda", val, err)
# Use Gaussian analytical error propagation,
# tested against the uncertainties package
err = err / val**2
val = 1.0 / val
ss += "{:<20s} : {:.2f} +/- {:.2f} TeV\n".format("ECPL E_cut", val, err)
return ss
def _info_flux_points(self):
"""Print flux point results."""
d = self.data
ss = "\n*** Flux points info ***\n\n"
ss += "Number of flux points: {}\n".format(d["N_Flux_Points"])
ss += "Flux points table: \n\n"
lines = format_flux_points_table(self.flux_points_table).pformat(
max_width=-1, max_lines=-1
)
ss += "\n".join(lines)
return ss + "\n"
def _info_components(self):
"""Print information about the components."""
ss = "\n*** Gaussian component info ***\n\n"
ss += "Number of components: {}\n".format(len(self.components))
ss += "{:<20s} : {}\n\n".format("Spatial components", self.data["Components"])
for component in self.components:
ss += str(component)
ss += "\n\n"
return ss
@property
def energy_range(self):
"""Spectral model energy range as a `~astropy.units.Quantity` with length 2."""
energy_min, energy_max = (
self.data["Energy_Range_Spec_Min"],
self.data["Energy_Range_Spec_Max"],
)
if np.isnan(energy_min):
energy_min = u.Quantity(0.2, "TeV")
if np.isnan(energy_max):
energy_max = u.Quantity(50, "TeV")
return u.Quantity([energy_min, energy_max], "TeV")
[docs]
def spectral_model(self, which="best"):
"""Spectral model as a `~gammapy.modeling.models.SpectralModel` object.
One of the following models (given by ``Spectral_Model`` in the catalog):
- ``PL`` : `~gammapy.modeling.models.PowerLawSpectralModel`
- ``ECPL`` : `~gammapy.modeling.models.ExpCutoffPowerLawSpectralModel`
Parameters
----------
which : {'best', 'pl', 'ecpl'}
Which spectral model.
"""
data = self.data
if which == "best":
spec_type = self.data["Spectral_Model"].strip().lower()
elif which in {"pl", "ecpl"}:
spec_type = which
else:
raise ValueError(f"Invalid selection: which = {which!r}")
if spec_type == "pl":
tag = "PowerLawSpectralModel"
pars = {
"index": data["Index_Spec_PL"],
"amplitude": data["Flux_Spec_PL_Diff_Pivot"],
"reference": data["Energy_Spec_PL_Pivot"],
}
errs = {
"amplitude": data["Flux_Spec_PL_Diff_Pivot_Err"],
"index": data["Index_Spec_PL_Err"],
}
elif spec_type == "ecpl":
tag = "ExpCutoffPowerLawSpectralModel"
pars = {
"index": data["Index_Spec_ECPL"],
"amplitude": data["Flux_Spec_ECPL_Diff_Pivot"],
"reference": data["Energy_Spec_ECPL_Pivot"],
"lambda_": data["Lambda_Spec_ECPL"],
}
errs = {
"index": data["Index_Spec_ECPL_Err"],
"amplitude": data["Flux_Spec_ECPL_Diff_Pivot_Err"],
"lambda_": data["Lambda_Spec_ECPL_Err"],
}
else:
raise ValueError(f"Invalid spec_type: {spec_type}")
model = Model.create(tag, "spectral", **pars)
errs["reference"] = 0 * u.TeV
for name, value in errs.items():
model.parameters[name].error = value
return model
[docs]
def spatial_model(self):
"""Spatial model as a `~gammapy.modeling.models.SpatialModel` object.
One of the following models (given by ``Spatial_Model`` in the catalog):
- ``Point-Like`` or has a size upper limit : `~gammapy.modeling.models.PointSpatialModel`
- ``Gaussian``: `~gammapy.modeling.models.GaussianSpatialModel`
- ``2-Gaussian`` or ``3-Gaussian``: composite model (using ``+`` with Gaussians)
- ``Shell``: `~gammapy.modeling.models.ShellSpatialModel`
"""
d = self.data
pars = {"lon_0": d["GLON"], "lat_0": d["GLAT"], "frame": "galactic"}
errs = {"lon_0": d["GLON_Err"], "lat_0": d["GLAT_Err"]}
spatial_type = self.data["Spatial_Model"]
if spatial_type == "Point-Like":
tag = "PointSpatialModel"
elif spatial_type == "Gaussian":
tag = "GaussianSpatialModel"
pars["sigma"] = d["Size"]
errs["sigma"] = d["Size_Err"]
elif spatial_type in {"2-Gaussian", "3-Gaussian"}:
raise ValueError("For Gaussian or Multi-Gaussian models, use sky_model()!")
elif spatial_type == "Shell":
# HGPS contains no information on shell width
# Here we assume a 5% shell width for all shells.
tag = "ShellSpatialModel"
pars["radius"] = 0.95 * d["Size"]
pars["width"] = d["Size"] - pars["radius"]
errs["radius"] = d["Size_Err"]
else:
raise ValueError(f"Invalid spatial_type: {spatial_type}")
model = Model.create(tag, "spatial", **pars)
for name, value in errs.items():
model.parameters[name].error = value
return model
[docs]
def sky_model(self, which="best"):
"""Source sky model.
Parameters
----------
which : {'best', 'pl', 'ecpl'}
Which spectral model.
Returns
-------
sky_model : `~gammapy.modeling.models.Models`
Models of the catalog object.
"""
models = self.components_models(which=which)
if len(models) > 1:
geom = self._get_components_geom(models)
return models.to_template_sky_model(geom=geom, name=self.name)
else:
return models[0]
[docs]
def components_models(self, which="best", linked=False):
"""Models of the source components.
Parameters
----------
which : {'best', 'pl', 'ecpl'}
Which spectral model.
linked : bool
Each subcomponent of a source is given as a different `SkyModel`.
If True, the spectral parameters except the normalisation are linked.
Default is False.
Returns
-------
sky_model : `~gammapy.modeling.models.Models`
Models of the catalog object.
"""
spatial_type = self.data["Spatial_Model"]
missing_size = (
spatial_type == "Gaussian" and self.spatial_model().sigma.value == 0
)
if spatial_type in {"2-Gaussian", "3-Gaussian"} or missing_size:
models = []
spectral_model = self.spectral_model(which=which)
for component in self.components:
spec_component = spectral_model.copy()
weight = component.data["Flux_Map"] / self.data["Flux_Map"]
spec_component.parameters["amplitude"].value *= weight
if linked:
for name in spec_component.parameters.names:
if name not in ["norm", "amplitude"]:
spec_component.__dict__[name] = spectral_model.parameters[
name
]
model = SkyModel(
spatial_model=component.spatial_model(),
spectral_model=spec_component,
name=component.name,
)
models.append(model)
else:
models = [
SkyModel(
spatial_model=self.spatial_model(),
spectral_model=self.spectral_model(which=which),
name=self.name,
)
]
return Models(models)
@staticmethod
def _get_components_geom(models):
energy_axis = MapAxis.from_energy_bounds(
"100 GeV", "100 TeV", nbin=10, per_decade=True, name="energy_true"
)
regions = [m.spatial_model.evaluation_region for m in models]
geom = RegionGeom.from_regions(
regions, binsz_wcs="0.02 deg", axes=[energy_axis]
)
return geom.to_wcs_geom()
@property
def flux_points_table(self):
"""Flux points table as a `~astropy.table.Table`."""
table = Table()
table.meta["sed_type_init"] = "dnde"
table.meta["n_sigma_ul"] = 2
table.meta["n_sigma"] = 1
table.meta["sqrt_ts_threshold_ul"] = 1
mask = ~np.isnan(self.data["Flux_Points_Energy"])
table["e_ref"] = self.data["Flux_Points_Energy"][mask]
table["e_min"] = self.data["Flux_Points_Energy_Min"][mask]
table["e_max"] = self.data["Flux_Points_Energy_Max"][mask]
table["dnde"] = self.data["Flux_Points_Flux"][mask]
table["dnde_errn"] = self.data["Flux_Points_Flux_Err_Lo"][mask]
table["dnde_errp"] = self.data["Flux_Points_Flux_Err_Hi"][mask]
table["dnde_ul"] = self.data["Flux_Points_Flux_UL"][mask]
table["is_ul"] = self.data["Flux_Points_Flux_Is_UL"][mask].astype("bool")
return table
[docs]
class SourceCatalogHGPS(SourceCatalog):
"""HESS Galactic plane survey (HGPS) source catalog.
Reference: https://www.mpi-hd.mpg.de/hfm/HESS/hgps/
One source is represented by `SourceCatalogObjectHGPS`.
Examples
--------
Let's assume you have downloaded the HGPS catalog FITS file, e.g. via:
.. code-block:: bash
curl -O https://www.mpi-hd.mpg.de/hfm/HESS/hgps/data/hgps_catalog_v1.fits.gz
Then you can load it like this:
>>> import matplotlib.pyplot as plt
>>> from gammapy.catalog import SourceCatalogHGPS
>>> filename = '$GAMMAPY_DATA/catalogs/hgps_catalog_v1.fits.gz'
>>> cat = SourceCatalogHGPS(filename)
Access a source by name:
>>> source = cat['HESS J1843-033']
>>> print(source)
<BLANKLINE>
*** Basic info ***
<BLANKLINE>
Catalog row index (zero-based) : 64
Source name : HESS J1843-033
Analysis reference : HGPS
Source class : Unid
Identified object : --
Gamma-Cat id : 126
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*** Info from map analysis ***
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RA : 280.952 deg = 18h43m48s
DEC : -3.554 deg = -3d33m15s
GLON : 28.899 +/- 0.072 deg
GLAT : 0.075 +/- 0.036 deg
Position Error (68%) : 0.122 deg
Position Error (95%) : 0.197 deg
ROI number : 3
Spatial model : 2-Gaussian
Spatial components : HGPSC 083, HGPSC 084
TS : 256.6
sqrt(TS) : 16.0
Size : 0.239 +/- 0.063 (UL: 0.000) deg
R70 : 0.376 deg
RSpec : 0.376 deg
Total model excess : 979.5
Excess in RSpec : 775.6
Model Excess in RSpec : 690.2
Background in RSpec : 1742.4
Livetime : 41.8 hours
Energy threshold : 0.38 TeV
Source flux (>1 TeV) : (2.882 +/- 0.305) x 10^-12 cm^-2 s^-1 = (12.75 +/- 1.35) % Crab
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Fluxes in RSpec (> 1 TeV):
Map measurement : 2.267 x 10^-12 cm^-2 s^-1 = 10.03 % Crab
Source model : 2.018 x 10^-12 cm^-2 s^-1 = 8.93 % Crab
Other component model : 0.004 x 10^-12 cm^-2 s^-1 = 0.02 % Crab
Large scale component model : 0.361 x 10^-12 cm^-2 s^-1 = 1.60 % Crab
Total model : 2.383 x 10^-12 cm^-2 s^-1 = 10.54 % Crab
Containment in RSpec : 70.0 %
Contamination in RSpec : 15.3 %
Flux correction (RSpec -> Total) : 121.0 %
Flux correction (Total -> RSpec) : 82.7 %
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*** Info from spectral analysis ***
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Livetime : 16.8 hours
Energy range: : 0.22 to 61.90 TeV
Background : 5126.9
Excess : 980.1
Spectral model : PL
TS ECPL over PL : --
Best-fit model flux(> 1 TeV) : (3.043 +/- 0.196) x 10^-12 cm^-2 s^-1 = (13.47 +/- 0.87) % Crab
Best-fit model energy flux(1 to 10 TeV) : (10.918 +/- 0.733) x 10^-12 erg cm^-2 s^-1
Pivot energy : 1.87 TeV
Flux at pivot energy : (0.914 +/- 0.058) x 10^-12 cm^-2 s^-1 TeV^-1 = (4.04 +/- 0.17) % Crab
PL Flux(> 1 TeV) : (3.043 +/- 0.196) x 10^-12 cm^-2 s^-1 = (13.47 +/- 0.87) % Crab
PL Flux(@ 1 TeV) : (3.505 +/- 0.247) x 10^-12 cm^-2 s^-1 TeV^-1 = (15.51 +/- 0.70) % Crab
PL Index : 2.15 +/- 0.05
ECPL Flux(@ 1 TeV) : (0.000 +/- 0.000) x 10^-12 cm^-2 s^-1 TeV^-1 = (0.00 +/- 0.00) % Crab
ECPL Flux(> 1 TeV) : (0.000 +/- 0.000) x 10^-12 cm^-2 s^-1 = (0.00 +/- 0.00) % Crab
ECPL Index : -- +/- --
ECPL Lambda : 0.000 +/- 0.000 TeV^-1
ECPL E_cut : inf +/- nan TeV
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*** Flux points info ***
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Number of flux points: 6
Flux points table:
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e_ref e_min e_max dnde dnde_errn dnde_errp dnde_ul is_ul
TeV TeV TeV 1 / (TeV s cm2) 1 / (TeV s cm2) 1 / (TeV s cm2) 1 / (TeV s cm2)
------ ------ ------ --------------- --------------- --------------- --------------- -----
0.332 0.215 0.511 3.048e-11 6.890e-12 7.010e-12 4.455e-11 False
0.787 0.511 1.212 5.383e-12 6.655e-13 6.843e-13 6.739e-12 False
1.957 1.212 3.162 9.160e-13 9.732e-14 1.002e-13 1.120e-12 False
4.870 3.162 7.499 1.630e-13 2.001e-14 2.097e-14 2.054e-13 False
12.115 7.499 19.573 1.648e-14 3.124e-15 3.348e-15 2.344e-14 False
30.142 19.573 46.416 7.777e-16 4.468e-16 5.116e-16 1.883e-15 False
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*** Gaussian component info ***
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Number of components: 2
Spatial components : HGPSC 083, HGPSC 084
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Component HGPSC 083:
GLON : 29.047 +/- 0.026 deg
GLAT : 0.244 +/- 0.027 deg
Size : 0.125 +/- 0.021 deg
Flux (>1 TeV) : (1.34 +/- 0.36) x 10^-12 cm^-2 s^-1 = (5.9 +/- 1.6) % Crab
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Component HGPSC 084:
GLON : 28.770 +/- 0.059 deg
GLAT : -0.073 +/- 0.069 deg
Size : 0.229 +/- 0.046 deg
Flux (>1 TeV) : (1.54 +/- 0.47) x 10^-12 cm^-2 s^-1 = (6.8 +/- 2.1) % Crab
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*** Source associations info ***
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Source_Name Association_Catalog Association_Name Separation
deg
---------------- ------------------- --------------------- ----------
HESS J1843-033 3FGL 3FGL J1843.7-0322 0.178442
HESS J1843-033 3FGL 3FGL J1844.3-0344 0.242835
HESS J1843-033 SNR G28.6-0.1 0.330376
Access source spectral data and plot it:
>>> ax = plt.subplot()
>>> source.spectral_model().plot(source.energy_range, ax=ax) # doctest: +SKIP
>>> source.spectral_model().plot_error(source.energy_range, ax=ax) # doctest: +SKIP
>>> source.flux_points.plot(ax=ax) # doctest: +SKIP
Gaussian component information can be accessed as well,
either via the source, or via the catalog:
>>> source.components
[SourceCatalogObjectHGPSComponent('HGPSC 083'), SourceCatalogObjectHGPSComponent('HGPSC 084')]
>>> cat.gaussian_component(83)
SourceCatalogObjectHGPSComponent('HGPSC 084')
"""
tag = "hgps"
"""Source catalog name (str)."""
description = "H.E.S.S. Galactic plane survey (HGPS) source catalog"
"""Source catalog description (str)."""
source_object_class = SourceCatalogObjectHGPS
def __init__(
self,
filename="$GAMMAPY_DATA/catalogs/hgps_catalog_v1.fits.gz",
hdu="HGPS_SOURCES",
):
filename = make_path(filename)
table = Table.read(filename, hdu=hdu)
source_name_alias = ("Identified_Object",)
super().__init__(table=table, source_name_alias=source_name_alias)
self._table_components = Table.read(filename, hdu="HGPS_GAUSS_COMPONENTS")
self._table_associations = Table.read(filename, hdu="HGPS_ASSOCIATIONS")
self._table_associations["Separation"].format = ".6f"
self._table_identifications = Table.read(filename, hdu="HGPS_IDENTIFICATIONS")
self._table_large_scale_component = Table.read(
filename, hdu="HGPS_LARGE_SCALE_COMPONENT"
)
@property
def table_components(self):
"""Gaussian component table as a `~astropy.table.Table`."""
return self._table_components
@property
def table_associations(self):
"""Source association table as a `~astropy.table.Table`."""
return self._table_associations
@property
def table_identifications(self):
"""Source identification table as a `~astropy.table.Table`."""
return self._table_identifications
@property
def table_large_scale_component(self):
"""Large scale component table as a `~astropy.table.Table`."""
return self._table_large_scale_component
@property
def large_scale_component(self):
"""Large scale component model as a `~gammapy.catalog.SourceCatalogLargeScaleHGPS` object."""
return SourceCatalogLargeScaleHGPS(self.table_large_scale_component)
def _make_source_object(self, index):
"""Make `SourceCatalogObject` for given row index."""
source = super()._make_source_object(index)
if source.data["Components"] != "":
source.components = list(self._get_gaussian_components(source))
self._attach_association_info(source)
if source.data["Source_Class"] != "Unid":
self._attach_identification_info(source)
return source
def _get_gaussian_components(self, source):
for name in source.data["Components"].split(", "):
row_index = int(name.split()[-1]) - 1
yield self.gaussian_component(row_index)
def _attach_association_info(self, source):
t = self.table_associations
mask = source.data["Source_Name"] == t["Source_Name"]
source.associations = t[mask]
def _attach_identification_info(self, source):
t = self._table_identifications
idx = np.nonzero(source.name == t["Source_Name"])[0][0]
source.identification_info = table_row_to_dict(t[idx])
[docs]
def gaussian_component(self, row_idx):
"""Gaussian component as a `SourceCatalogObjectHGPSComponent` object."""
data = table_row_to_dict(self.table_components[row_idx])
data[SourceCatalogObject._row_index_key] = row_idx
return SourceCatalogObjectHGPSComponent(data=data)
[docs]
def to_models(self, which="best", components_status="independent"):
"""Create Models object from catalog.
Parameters
----------
which : {'best', 'pl', 'ecpl'}
Which spectral model.
components_status : {'independent', 'linked', 'merged'}
Relation between the sources components. Available options are:
* 'independent': each subcomponent of a source is given as a different `SkyModel` (Default).
* 'linked': each subcomponent of a source is given as a different `SkyModel` but the spectral
parameters except the normalisation are linked.
* 'merged': the subcomponents are merged into a single `SkyModel` given as a
`~gammapy.modeling.models.TemplateSpatialModel` with a `~gammapy.modeling.models.PowerLawNormSpectralModel`.
In that case the relative weights between the components cannot be adjusted.
Returns
-------
models : `~gammapy.modeling.models.Models`
Models of the catalog.
"""
models = []
for source in self:
if components_status == "merged":
m = [source.sky_model(which=which)]
else:
m = source.components_models(
which=which, linked=components_status == "linked"
)
models.extend(m)
return Models(models)
[docs]
class SourceCatalogLargeScaleHGPS:
"""Gaussian band model.
This 2-dimensional model is Gaussian in ``y`` for a given ``x``,
and the Gaussian parameters can vary in ``x``.
One application of this model is the diffuse emission along the
Galactic plane, i.e. ``x = GLON`` and ``y = GLAT``.
Parameters
----------
table : `~astropy.table.Table`
Table of Gaussian parameters.
``x``, ``amplitude``, ``mean``, ``stddev``.
interp_kwargs : dict
Keyword arguments passed to `ScaledRegularGridInterpolator`.
"""
def __init__(self, table, interp_kwargs=None):
interp_kwargs = interp_kwargs or {}
interp_kwargs.setdefault("values_scale", "lin")
self.table = table
glon = Angle(self.table["GLON"]).wrap_at("180d")
interps = {}
for column in table.colnames:
values = self.table[column].quantity
interp = ScaledRegularGridInterpolator((glon,), values, **interp_kwargs)
interps[column] = interp
self._interp = interps
def _interpolate_parameter(self, parname, glon):
glon = glon.wrap_at("180d")
return self._interp[parname]((np.asanyarray(glon),), clip=False)
[docs]
def peak_brightness(self, glon):
"""Peak brightness at a given longitude.
Parameters
----------
glon : `~astropy.coordinates.Angle`
Galactic Longitude.
"""
return self._interpolate_parameter("Surface_Brightness", glon)
[docs]
def peak_brightness_error(self, glon):
"""Peak brightness error at a given longitude.
Parameters
----------
glon : `~astropy.coordinates.Angle`
Galactic Longitude.
"""
return self._interpolate_parameter("Surface_Brightness_Err", glon)
[docs]
def width(self, glon):
"""Width at a given longitude.
Parameters
----------
glon : `~astropy.coordinates.Angle`
Galactic Longitude.
"""
return self._interpolate_parameter("Width", glon)
[docs]
def width_error(self, glon):
"""Width error at a given longitude.
Parameters
----------
glon : `~astropy.coordinates.Angle`
Galactic Longitude.
"""
return self._interpolate_parameter("Width_Err", glon)
[docs]
def peak_latitude(self, glon):
"""Peak position at a given longitude.
Parameters
----------
glon : `~astropy.coordinates.Angle`
Galactic Longitude.
"""
return self._interpolate_parameter("GLAT", glon)
[docs]
def peak_latitude_error(self, glon):
"""Peak position error at a given longitude.
Parameters
----------
glon : `~astropy.coordinates.Angle`
Galactic Longitude.
"""
return self._interpolate_parameter("GLAT_Err", glon)
[docs]
def evaluate(self, position):
"""Evaluate model at a given position.
Parameters
----------
position : `~astropy.coordinates.SkyCoord`
Position on the sky.
"""
glon, glat = position.galactic.l, position.galactic.b
width = self.width(glon)
amplitude = self.peak_brightness(glon)
mean = self.peak_latitude(glon)
return Gaussian1D.evaluate(glat, amplitude=amplitude, mean=mean, stddev=width)
