# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""Gammacat open TeV source catalog.
https://github.com/gammapy/gamma-cat
"""
from collections import OrderedDict, namedtuple
import functools
import logging
import json
import numpy as np
from astropy import units as u
from astropy.table import Table
from ..utils.scripts import make_path
from ..spectrum import FluxPoints
from ..spectrum.models import PowerLaw, PowerLaw2, ExponentialCutoffPowerLaw
from ..image.models import SkyPointSource, SkyGaussian, SkyShell
from ..cube.models import SkyModel, SkyModels
from .core import SourceCatalog, SourceCatalogObject
__all__ = [
"SourceCatalogGammaCat",
"SourceCatalogObjectGammaCat",
"GammaCatDataCollection",
"GammaCatResource",
"GammaCatResourceIndex",
]
log = logging.getLogger(__name__)
class NoDataAvailableError(LookupError):
"""Generic error used in Gammapy, when some data isn't available."""
class GammaCatNotFoundError(OSError):
"""The gamma-cat repo is not available."""
[docs]class SourceCatalogObjectGammaCat(SourceCatalogObject):
"""One object from the gamma-cat source catalog.
Catalog is represented by `~gammapy.catalog.SourceCatalogGammaCat`.
"""
_source_name_key = "common_name"
_source_index_key = "catalog_row_index"
def __str__(self):
return self.info()
[docs] def info(self, info="all"):
"""Info string.
Parameters
----------
info : {'all', 'basic', 'position, 'model'}
Comma separated list of options
"""
if info == "all":
info = "basic,position,model"
ss = ""
ops = info.split(",")
if "basic" in ops:
ss += self._info_basic()
if "position" in ops:
ss += self._info_position()
if "model" in ops:
ss += self._info_morph()
ss += self._info_spectral_fit()
ss += self._info_spectral_points()
return ss
def _info_basic(self):
"""Print basic info."""
d = self.data
ss = "\n*** Basic info ***\n\n"
ss += "Catalog row index (zero-based) : {}\n".format(d["catalog_row_index"])
ss += "{:<15s} : {}\n".format("Common name", d["common_name"])
# ss += '{:<15s} : {}\n'.format('Gamma names', d['gamma_names'])
# ss += '{:<15s} : {}\n'.format('Fermi names', d['fermi_names'])
# ss += '{:<15s} : {}\n'.format('Other names', d['other_names'])
def get_nonentry_keys(keys):
vals = [d[_].strip() for _ in keys]
return ",".join([_ for _ in vals if _ != ""])
keys = ["gamma_names", "fermi_names", "other_names"]
other_names = get_nonentry_keys(keys)
ss += "{:<15s} : {}\n".format("Other names", other_names)
ss += "{:<15s} : {}\n".format("Location", d["where"])
ss += "{:<15s} : {}\n".format("Class", d["classes"])
ss += "\n{:<15s} : {}\n".format("TeVCat ID", d["tevcat_id"])
ss += "{:<15s} : {}\n".format("TeVCat 2 ID", d["tevcat2_id"])
ss += "{:<15s} : {}\n".format("TeVCat name", d["tevcat_name"])
ss += "\n{:<15s} : {}\n".format("TGeVCat ID", d["tgevcat_id"])
ss += "{:<15s} : {}\n".format("TGeVCat name", d["tgevcat_name"])
ss += "\n{:<15s} : {}\n".format("Discoverer", d["discoverer"])
ss += "{:<15s} : {}\n".format("Discovery date", d["discovery_date"])
ss += "{:<15s} : {}\n".format("Seen by", d["seen_by"])
ss += "{:<15s} : {}\n".format("Reference", d["reference_id"])
return ss
def _info_position(self):
"""Print position info."""
d = self.data
ss = "\n*** Position info ***\n\n"
ss += "SIMBAD:\n"
ss += "{:<20s} : {:.3f}\n".format("RA", d["ra"])
ss += "{:<20s} : {:.3f}\n".format("DEC", d["dec"])
ss += "{:<20s} : {:.3f}\n".format("GLON", d["glon"])
ss += "{:<20s} : {:.3f}\n".format("GLAT", d["glat"])
ss += "\nMeasurement:\n"
ss += "{:<20s} : {:.3f}\n".format("RA", d["pos_ra"])
ss += "{:<20s} : {:.3f}\n".format("DEC", d["pos_dec"])
ss += "{:<20s} : {:.3f}\n".format("GLON", d["pos_glon"])
ss += "{:<20s} : {:.3f}\n".format("GLAT", d["pos_glat"])
ss += "{:<20s} : {:.3f}\n".format("Position error", d["pos_err"])
return ss
def _info_morph(self):
"""Print morphology info."""
ss = "\n*** Morphology info ***\n\n"
d = self.data
ss += "{:<25s} : {}\n".format("Morphology model type", d["morph_type"])
# TODO: change to morphology model dependent printout
# (see spectra printout and `spatial_model` property)
ss += "{:<25s} : {:.3f}\n".format("Sigma", d["morph_sigma"])
ss += "{:<25s} : {:.3f}\n".format("Sigma error", d["morph_sigma_err"])
ss += "{:<25s} : {:.3f}\n".format("Sigma2", d["morph_sigma2"])
ss += "{:<25s} : {:.3f}\n".format("Sigma2 error", d["morph_sigma2_err"])
ss += "{:<25s} : {:.3f}\n".format("Position angle", d["morph_pa"])
ss += "{:<25s} : {:.3f}\n".format("Position angle error", d["morph_pa_err"])
ss += "{:<25s} : {}\n".format("Position angle frame", d["morph_pa_frame"])
return ss
def _info_spectral_fit(self):
"""Print spectral info."""
d = self.data
ss = "\n*** Spectral info ***\n\n"
ss += "{:<15s} : {:.3f}\n".format("Significance", d["significance"])
ss += "{:<15s} : {:.3f}\n".format("Livetime", d["livetime"])
spec_type = d["spec_type"]
ss += "\n{:<15s} : {}\n".format("Spectrum type", spec_type)
# Spectral model parameters
if spec_type == "pl":
ss += "{:<15s} : {:.3} +- {:.3} (stat) +- {:.3} (sys) {}\n".format(
"norm",
d["spec_pl_norm"].value,
d["spec_pl_norm_err"].value,
d["spec_pl_norm_err_sys"].value,
"cm-2 s-1 TeV-1",
)
ss += "{:<15s} : {:.3} +- {:.3} (stat) +- {:.3} (sys)\n".format(
"index",
d["spec_pl_index"],
d["spec_pl_index_err"],
d["spec_pl_index_err_sys"],
)
ss += "{:<15s} : {:.3}\n".format("reference", d["spec_pl_e_ref"])
elif spec_type == "pl2":
ss += "{:<15s} : {:.3} +- {:.3} (stat) +- {:.3} (sys) {}\n".format(
"flux",
d["spec_pl2_flux"].value,
d["spec_pl2_flux_err"].value,
d["spec_pl2_flux_err_sys"].value,
"cm-2 s-1",
)
ss += "{:<15s} : {:.3} +- {:.3} (stat) +- {:.3} (sys)\n".format(
"index",
d["spec_pl2_index"],
d["spec_pl2_index_err"],
d["spec_pl2_index_err_sys"],
)
ss += "{:<15s} : {:.3}\n".format("e_min", d["spec_pl2_e_min"])
ss += "{:<15s} : {:.3}\n".format("e_max", d["spec_pl2_e_max"])
elif spec_type == "ecpl":
ss += "{:<15s} : {:.3g} +- {:.3g} (stat) +- {:.03g} (sys) {}\n".format(
"norm",
d["spec_ecpl_norm"].value,
d["spec_ecpl_norm_err"].value,
d["spec_ecpl_norm_err_sys"].value,
"cm-2 s-1 TeV-1",
)
ss += "{:<15s} : {:.3} +- {:.3} (stat) +- {:.3} (sys)\n".format(
"index",
d["spec_ecpl_index"],
d["spec_ecpl_index_err"],
d["spec_ecpl_index_err_sys"],
)
ss += "{:<15s} : {:.3} +- {:.3} (stat) +- {:.3} (stat) {}\n".format(
"e_cut",
d["spec_ecpl_e_cut"].value,
d["spec_ecpl_e_cut_err"].value,
d["spec_ecpl_e_cut_err_sys"].value,
"TeV",
)
ss += "{:<15s} : {:.3}\n".format("reference", d["spec_ecpl_e_ref"])
else:
# raise ValueError('Spectral model printout not implemented: {}'.format(spec))
ss += "\nSpectral model printout not yet implemented.\n"
ss += "\n{:<20s} : ({:.3}, {:.3}) TeV\n".format(
"Energy range", d["spec_erange_min"].value, d["spec_erange_max"].value
)
ss += "{:<20s} : {:.3}\n".format("theta", d["spec_theta"])
ss += "\n\nDerived fluxes:\n"
ss += "{:<30s} : {:.3} +- {:.3} (stat) {}\n".format(
"Spectral model norm (1 TeV)",
d["spec_dnde_1TeV"].value,
d["spec_dnde_1TeV_err"].value,
"cm-2 s-1 TeV-1",
)
ss += "{:<30s} : {:.3} +- {:.3} (stat) {}\n".format(
"Integrated flux (>1 TeV)",
d["spec_flux_1TeV"].value,
d["spec_flux_1TeV_err"].value,
"cm-2 s-1",
)
ss += "{:<30s} : {:.3f} +- {:.3f} {}\n".format(
"Integrated flux (>1 TeV)",
d["spec_flux_1TeV_crab"],
d["spec_flux_1TeV_crab_err"],
"(% Crab)",
)
ss += "{:<30s} : {:.3} +- {:.3} (stat) {}\n".format(
"Integrated flux (1-10 TeV)",
d["spec_eflux_1TeV_10TeV"].value,
d["spec_eflux_1TeV_10TeV_err"].value,
"erg cm-2 s-1",
)
return ss
def _info_spectral_points(self):
"""Print spectral points info."""
d = self.data
ss = "\n*** Spectral points ***\n\n"
ss += "{:<25s} : {}\n".format("SED reference id", d["sed_reference_id"])
ss += "{:<25s} : {}\n".format("Number of spectral points", d["sed_n_points"])
ss += "{:<25s} : {}\n\n".format("Number of upper limits", d["sed_n_ul"])
try:
lines = self.flux_points.table_formatted.pformat(max_width=-1, max_lines=-1)
ss += "\n".join(lines)
except NoDataAvailableError:
ss += "\nNo spectral points available for this source."
return ss + "\n"
@property
def spectral_model(self):
"""Source spectral model (`~gammapy.spectrum.models.SpectralModel`).
Parameter errors are statistical errors only.
"""
data = self.data
spec_type = data["spec_type"]
pars, errs = {}, {}
if spec_type == "pl":
model_class = PowerLaw
pars["amplitude"] = data["spec_pl_norm"]
errs["amplitude"] = data["spec_pl_norm_err"]
pars["index"] = data["spec_pl_index"]
errs["index"] = data["spec_pl_index_err"]
pars["reference"] = data["spec_pl_e_ref"]
elif spec_type == "pl2":
model_class = PowerLaw2
pars["amplitude"] = data["spec_pl2_flux"]
errs["amplitude"] = data["spec_pl2_flux_err"]
pars["index"] = data["spec_pl2_index"]
errs["index"] = data["spec_pl2_index_err"]
pars["emin"] = data["spec_pl2_e_min"]
e_max = data["spec_pl2_e_max"]
DEFAULT_E_MAX = u.Quantity(1e5, "TeV")
if np.isnan(e_max.value):
e_max = DEFAULT_E_MAX
pars["emax"] = e_max
elif spec_type == "ecpl":
model_class = ExponentialCutoffPowerLaw
pars["amplitude"] = data["spec_ecpl_norm"]
errs["amplitude"] = data["spec_ecpl_norm_err"]
pars["index"] = data["spec_ecpl_index"]
errs["index"] = data["spec_ecpl_index_err"]
pars["lambda_"] = 1.0 / data["spec_ecpl_e_cut"]
errs["lambda_"] = data["spec_ecpl_e_cut_err"] / data["spec_ecpl_e_cut"] ** 2
pars["reference"] = data["spec_ecpl_e_ref"]
else:
raise ValueError("Invalid spec_type: {}".format(spec_type))
model = model_class(**pars)
model.parameters.set_parameter_errors(errs)
return model
@property
def spatial_model(self):
"""Source spatial model (`~gammapy.image.models.SkySpatialModel`).
TODO: add parameter errors!
"""
d = self.data
morph_type = d["morph_type"]
glon = d["glon"]
glat = d["glat"]
if morph_type == "point":
return SkyPointSource(lon_0=glon, lat_0=glat)
elif morph_type == "gauss":
# TODO: add infos back once model support elongation
# pars['x_stddev'] = d['morph_sigma']
# pars['y_stddev'] = d['morph_sigma']
# if not np.isnan(d['morph_sigma2']):
# pars['y_stddev'] = d['morph_sigma2']
# if not np.isnan(d['morph_pa']):
# # TODO: handle reference frame for rotation angle
# pars['theta'] = Angle(d['morph_pa'], 'deg').rad
return SkyGaussian(lon_0=glon, lat_0=glat, sigma=d["morph_sigma"])
elif morph_type == "shell":
return SkyShell(
lon_0=glon,
lat_0=glat,
# TODO: probably we shouldn't guess a shell width here!
radius=0.8 * d["morph_sigma"],
width=0.2 * d["morph_sigma"],
)
elif morph_type == "none":
raise NoDataAvailableError(
"No spatial model available: {}".format(self.name)
)
else:
raise NotImplementedError("Unknown spatial model: {!r}".format(morph_type))
@property
def sky_model(self):
"""Source sky model (`~gammapy.cube.models.SkyModel`)."""
spatial_model = self.spatial_model
spectral_model = self.spectral_model
return SkyModel(spatial_model, spectral_model, name=self.name)
def _add_source_meta(self, table):
"""Copy over some info to table.meta"""
d = self.data
m = table.meta
m["origin"] = "Data from gamma-cat"
m["source_id"] = d["source_id"]
m["common_name"] = d["common_name"]
m["reference_id"] = d["reference_id"]
@property
def flux_points(self):
"""Differential flux points (`~gammapy.spectrum.FluxPoints`)."""
d = self.data
table = Table()
table.meta["SED_TYPE"] = "dnde"
self._add_source_meta(table)
valid = np.isfinite(d["sed_e_ref"].value)
if valid.sum() == 0:
raise NoDataAvailableError("No flux points available: {}".format(self.name))
table["e_ref"] = d["sed_e_ref"]
table["e_min"] = d["sed_e_min"]
table["e_max"] = d["sed_e_max"]
table["dnde"] = d["sed_dnde"]
table["dnde_err"] = d["sed_dnde_err"]
table["dnde_errn"] = d["sed_dnde_errn"]
table["dnde_errp"] = d["sed_dnde_errp"]
table["dnde_ul"] = d["sed_dnde_ul"]
# Only keep rows that actually contain information
table = table[valid]
# Only keep columns that actually contain information
def _del_nan_col(table, colname):
if np.isfinite(table[colname]).sum() == 0:
del table[colname]
for colname in table.colnames:
_del_nan_col(table, colname)
return FluxPoints(table)
@property
def is_pointlike(self):
"""
Source is pointlike.
"""
return self.data["morph_type"] == "point"
[docs]class SourceCatalogGammaCat(SourceCatalog):
"""Gammacat open TeV source catalog.
See: https://github.com/gammapy/gamma-cat
One source is represented by `~gammapy.catalog.SourceCatalogObjectGammaCat`.
Parameters
----------
filename : str
Path to the gamma-cat fits file.
Examples
--------
Load the catalog data:
>>> from gammapy.catalog import SourceCatalogGammaCat
>>> cat = SourceCatalogGammaCat()
Access a source by name:
>>> source = cat['Vela Junior']
Access source spectral data and plot it:
>>> source.spectral_model.plot()
>>> source.spectral_model.plot_error()
>>> source.flux_points.plot()
"""
name = "gamma-cat"
description = "An open catalog of gamma-ray sources"
source_object_class = SourceCatalogObjectGammaCat
def __init__(self, filename="$GAMMAPY_DATA/catalogs/gammacat/gammacat.fits.gz"):
filename = str(make_path(filename))
table = Table.read(filename, hdu=1)
self.filename = filename
source_name_key = "common_name"
source_name_alias = ("other_names", "gamma_names")
super().__init__(
table=table,
source_name_key=source_name_key,
source_name_alias=source_name_alias,
)
[docs] def to_sky_models(self):
"""Convert to a `~gammapy.cube.models.SkyModels`.
TODO: add an option whether to skip or raise on missing models or data.
"""
source_list = []
for source_idx in range(len(self.table)):
source = self[source_idx]
try:
source_list.append(source.sky_model)
except NoDataAvailableError:
log.warning(
"Skipping source {} (missing data in gamma-cat)".format(source.name)
)
continue
return SkyModels(source_list)
[docs]class GammaCatDataCollection:
"""Data store for gamma-cat.
Gives access to all data from https://github.com/gammapy/gamma-cat .
Holds a `GammaCatResourceIndex` to locate resources,
but also more info about gamma-cat, as well as methods to create
Gammapy objects (spectral models, flux points, lightcurves) from the datasets.
"""
def __init__(self, data_index):
self.data_index = data_index
[docs] @classmethod
def from_index_file(
cls, filename="$GAMMAPY_DATA/catalogs/gammacat/gammacat-datasets.json"
):
"""Create from index file."""
path = make_path(filename)
# TODO: make a list of `GammaCatResource`, as well as a dict by ``resource_id`` for lookup!
data_index = json.load(path.read_text())
return cls(data_index=data_index)
def __str__(self):
ss = "version = {}".format(self.data_index["info"]["version"])
return ss
[docs]@functools.total_ordering
class GammaCatResource:
"""Reference for a single resource in gamma-cat.
This can be considered an implementation detail,
used to assign ``global_id`` and to load resources.
TODO: explain how ``global_id``, ``type`` and ``location`` work.
Uses the Python ``hash`` function on the tuple ``(source_id, reference_id, file_id)``
Parameters
----------
source_id : int
Gamma-cat source ID
reference_id : str
Gamma-cat reference ID (usually the ADS paper bibcode)
file_id : int
File ID (a counter for cases with multiple measurements per reference / source)
(use integer -1 if missing)
type : str
Resource type (use string 'none' if missing)
location : str
Resource location (use string 'none' if missing)
Examples
--------
>>> from gammapy.catalog.gammacat import GammaCatResource
>>> resource = GammaCatResource(source_id=42, reference_id='2010A&A...516A..62A', file_id=2)
>>> resource
GammaCatResource(source_id=42, reference_id='2010A&A...516A..62A', file_id=2, type='none', location='none')
"""
_NA_FILL = dict(file_id=-1, type="none", location="none")
def __init__(
self, source_id, reference_id, file_id=-1, type="none", location="none"
):
self.source_id = int(source_id)
self.reference_id = str(reference_id)
self.file_id = int(file_id)
self.type = str(type)
self.location = str(location)
@property
def global_id(self):
"""Globally unique (within gamma-cat) resource ID (str).
(see class docstring for explanation and example).
"""
return "|".join(
(str(self.source_id), self.reference_id, str(self.file_id), self.type)
)
def __repr__(self):
fmt = "{}(source_id={!r}, reference_id={!r}, file_id={!r}, type={!r}, location={!r})"
return fmt.format(
self.__class__.__name__,
self.source_id,
str(self.reference_id),
self.file_id,
str(self.type),
str(self.location),
)
def __eq__(self, other):
return self.to_namedtuple() == other.to_namedtuple()
def __lt__(self, other):
return self.to_namedtuple() < other.to_namedtuple()
[docs] def to_namedtuple(self):
"""Convert to `collections.namedtuple`."""
d = self.to_dict()
return namedtuple("GammaCatResourceNamedTuple", d.keys())(**d)
[docs] def to_dict(self):
"""Convert to `collections.OrderedDict`."""
data = OrderedDict()
data["source_id"] = self.source_id
data["reference_id"] = self.reference_id
data["file_id"] = self.file_id
data["type"] = self.type
data["location"] = self.location
return data
[docs] @classmethod
def from_dict(cls, data):
"""Create from dict."""
return cls(
source_id=data["source_id"],
reference_id=data["reference_id"],
file_id=data.get("file_id", cls._NA_FILL["file_id"]),
type=data.get("type", cls._NA_FILL["type"]),
location=data.get("location", cls._NA_FILL["location"]),
)
[docs]class GammaCatResourceIndex:
"""Resource index for gamma-cat.
Parameters
----------
resources : list
List of `GammaCatResource` objects
"""
def __init__(self, resources):
self.resources = resources
def __repr__(self):
return "{}(n_resources={})".format(self.__class__.__name__, len(self.resources))
def __eq__(self, other):
if len(self.resources) != len(other.resources):
return False
return all(a == b for (a, b) in zip(self.resources, other.resources))
@property
def unique_source_ids(self):
"""Sorted list of unique source IDs (list of int)."""
return sorted({resource.source_id for resource in self.resources})
@property
def unique_reference_ids(self):
"""Sorted list of unique source IDs (list of str)."""
return sorted({resource.reference_id for resource in self.resources})
@property
def global_ids(self):
"""List of global resource IDs (list of str).
In original order, not sorted.
"""
return [resource.global_id for resource in self.resources]
[docs] def sort(self):
"""Return a sorted copy (leave self unchanged)."""
return self.__class__(sorted(self.resources))
[docs] def to_list(self):
"""Convert to list of dict."""
return [resource.to_dict() for resource in self.resources]
[docs] @classmethod
def from_list(cls, data):
"""Create from list of dicts."""
return cls([GammaCatResource.from_dict(_) for _ in data])
[docs] def to_table(self):
"""Convert to `~astropy.table.Table`."""
rows = self.to_list()
return Table(rows=rows, names=list(rows[0].keys()))
[docs] @classmethod
def from_table(cls, table):
"""Create from `~astropy.table.Table`."""
resources = []
for row in table:
data = OrderedDict((k, row[k]) for k in table.colnames)
resources.append(GammaCatResource.from_dict(data))
return cls(resources=resources)
[docs] def to_pandas(self):
"""Convert to `pandas.DataFrame`."""
# This is inefficient. Could implement direct conversion if needed.
table = self.to_table()
return table.to_pandas()
[docs] @classmethod
def from_pandas(cls, dataframe):
"""Create from `pandas.DataFrame`."""
table = Table.from_pandas(dataframe)
return cls.from_table(table)
[docs] def query(self, *args, **kwargs):
"""Query to select subset of resources.
Calls `pandas.DataFrame.query` and passes arguments to that method.
Examples
--------
>>> resource_index = GammaCatResourceIndex(...)
>>> resource_index2 = resource_index.query('type == "sed" and source_id == 42')
"""
df = self.to_pandas()
df2 = df.query(*args, **kwargs)
return self.from_pandas(df2)