def test_validation_checks():
config = AnalysisConfig()
analysis = Analysis(config)
analysis.settings["observations"]["datastore"] = "other"
with pytest.raises(FileNotFoundError):
analysis.get_observations()
config = AnalysisConfig.from_template("1d")
analysis = Analysis(config)
assert analysis.get_flux_points() is False
assert analysis.run_fit() is False
assert analysis.set_model() is False
assert analysis.get_datasets() is False
analysis.get_observations()
analysis.settings["datasets"]["dataset-type"] = "not assigned"
assert analysis.get_datasets() is False
analysis.settings["datasets"]["dataset-type"] = "SpectrumDatasetOnOff"
analysis.get_observations()
analysis.get_datasets()
model_str = Path(MODEL_FILE).read_text()
analysis.set_model(model=model_str)
assert isinstance(analysis.model, SkyModels) is True
assert analysis.set_model() is False
analysis.run_fit()
del analysis.settings["flux-points"]
assert analysis.get_flux_points() is False
def run_3d(name):
"""Run 3D analysis for one source."""
logging.info(f"run3d: {name}")
mode = "3d"
config_file = f"config{mode}.yaml"
target_config_file = f"targets.yaml"
model_file = f"model{mode}_{name}.yaml"
outdir = f"results/{name}"
config = target_config3d(config_file, target_config_file, name)
analysis = Analysis(config)
analysis.get_observations()
conf = config.settings["observations"]["filters"][0]
nb, lon, lat, rad = (
len(analysis.observations.ids),
conf["lon"],
conf["lat"],
conf["radius"],
)
logging.info(f"{nb} observations found in {rad} around {lon}, {lat} ")
analysis.get_datasets()
# test
plt.figure(figsize=(5, 5))
analysis.datasets["stacked"].counts.sum_over_axes().plot(add_cbar=True)
plt.savefig(f"{outdir}/{name}_{mode}_counts.png", bbox_inches="tight")
analysis.set_model(filename=model_file)
logging.info(analysis.model)
analysis.run_fit()
logging.info(analysis.fit_result.parameters.to_table())
analysis.fit_result.parameters.to_table().write(
f"{outdir}/{name}_{mode}_bestfit.dat", format="ascii", overwrite=True)
analysis.get_flux_points(source=f"{name}")
analysis.flux_points.write(f"{outdir}/{name}_{mode}_fluxpoints.fits")
plt.figure(figsize=(5, 5))
analysis.datasets["stacked"].counts.sum_over_axes().plot(add_cbar=True)
plt.savefig(f"{outdir}/{name}_{mode}_counts.png", bbox_inches="tight")
plt.figure(figsize=(5, 5))
analysis.datasets["stacked"].plot_residuals(method="diff/sqrt(model)",
vmin=-0.5,
vmax=0.5)
plt.savefig(f"{outdir}/{name}_{mode}_residuals.png", bbox_inches="tight")
plt.figure(figsize=(8, 5))
ax_sed, ax_residuals = analysis.flux_points.peek()
plt.savefig(f"{outdir}/{name}_{mode}_fluxpoints.png", bbox_inches="tight")
def test_analysis_1d_stacked():
config = AnalysisConfig.from_template("1d")
analysis = Analysis(config)
analysis.settings["datasets"]["stack-datasets"] = True
analysis.get_observations()
analysis.get_datasets()
analysis.set_model(filename=MODEL_FILE)
analysis.run_fit()
assert len(analysis.datasets) == 1
assert_allclose(analysis.datasets["stacked"].counts.data.sum(), 404)
pars = analysis.fit_result.parameters
assert_allclose(pars["index"].value, 2.689559, rtol=1e-3)
assert_allclose(pars["amplitude"].value, 2.81629e-11, rtol=1e-3)
def test_analysis_1d(config_analysis_data):
config = AnalysisConfig.from_template("1d")
analysis = Analysis(config)
analysis.config.update_settings(config_analysis_data)
analysis.get_observations()
analysis.get_datasets()
analysis.set_model(filename=MODEL_FILE)
analysis.run_fit()
analysis.get_flux_points()
assert len(analysis.datasets) == 2
assert len(analysis.flux_points.data.table) == 4
dnde = analysis.flux_points.data.table["dnde"].quantity
assert dnde.unit == "cm-2 s-1 TeV-1"
assert_allclose(dnde[0].value, 8.03604e-12, rtol=1e-2)
assert_allclose(dnde[-1].value, 4.780021e-21, rtol=1e-2)
def test_analysis_3d():
config = AnalysisConfig.from_template("3d")
analysis = Analysis(config)
analysis.get_observations()
analysis.get_datasets()
analysis.set_model(filename=MODEL_FILE)
analysis.datasets["stacked"].background_model.tilt.frozen = False
analysis.run_fit()
analysis.get_flux_points()
assert len(analysis.datasets) == 1
assert len(analysis.fit_result.parameters) == 8
res = analysis.fit_result.parameters
assert res[3].unit == "cm-2 s-1 TeV-1"
assert len(analysis.flux_points.data.table) == 2
dnde = analysis.flux_points.data.table["dnde"].quantity
assert_allclose(dnde[0].value, 1.182768e-11, rtol=1e-1)
assert_allclose(dnde[-1].value, 4.051367e-13, rtol=1e-1)
assert_allclose(res["index"].value, 2.76607, rtol=1e-1)
assert_allclose(res["tilt"].value, -0.143204, rtol=1e-1)
def run_analysis_3d(target_dict):
"""Run 3D analysis for the selected target"""
tag = target_dict["tag"]
name = target_dict["name"]
log.info(f"running 3d analysis, {tag}")
path_res = Path(tag + "/results/")
ra = target_dict["ra"]
dec = target_dict["dec"]
e_decorr = target_dict["e_decorr"]
config_str = f"""
general:
logging:
level: INFO
outdir: .
observations:
datastore: $GAMMAPY_DATA/hess-dl3-dr1/
filters:
- filter_type: par_value
value_param: {name}
variable: TARGET_NAME
datasets:
dataset-type: MapDataset
stack-datasets: true
offset-max: 2.5 deg
geom:
skydir: [{ra}, {dec}]
width: [5, 5]
binsz: 0.02
coordsys: CEL
proj: TAN
axes:
- name: energy
hi_bnd: 100
lo_bnd: 0.1
nbin: 24
interp: log
node_type: edges
unit: TeV
energy-axis-true:
name: energy
hi_bnd: 100
lo_bnd: 0.1
nbin: 72
interp: log
node_type: edges
unit: TeV
"""
print(config_str)
config = AnalysisConfig(config_str)
# Observation selection
analysis = Analysis(config)
analysis.get_observations()
if DEBUG is True:
analysis.observations.list = [analysis.observations.list[0]]
# Data reduction
analysis.get_datasets()
# Set runwise energy threshold. See reference paper, section 5.1.1.
for dataset in analysis.datasets:
# energy threshold given by the 10% edisp criterium
e_thr_bias = dataset.edisp.get_bias_energy(0.1)
# energy at which the background peaks
background_model = dataset.background_model
bkg_spectrum = background_model.map.get_spectrum()
peak = bkg_spectrum.data.max()
idx = list(bkg_spectrum.data).index(peak)
e_thr_bkg = bkg_spectrum.energy.center[idx]
esafe = max(e_thr_bias, e_thr_bkg)
dataset.mask_fit = dataset.counts.geom.energy_mask(emin=esafe)
# Model fitting
spatial_model = target_dict["spatial_model"]
model_config = f"""
components:
- name: {tag}
type: SkyModel
spatial:
type: {spatial_model}
frame: icrs
parameters:
- name: lon_0
value: {ra}
unit: deg
- name: lat_0
value: {dec}
unit: deg
spectral:
type: PowerLawSpectralModel
parameters:
- name: amplitude
value: 1.0e-12
unit: cm-2 s-1 TeV-1
- name: index
value: 2.0
unit: ''
- name: reference
value: {e_decorr}
unit: TeV
frozen: true
"""
model_npars = 5
if spatial_model == "DiskSpatialModel":
model_config = yaml.load(model_config)
parameters = model_config["components"][0]["spatial"]["parameters"]
parameters.append(
{
"name": "r_0",
"value": 0.2,
"unit": "deg",
"frozen": False
}
)
parameters.append(
{
"name": "e",
"value": 0.8,
"unit": "",
"frozen": False
}
)
parameters.append(
{
"name": "phi",
"value": 150,
"unit": "deg",
"frozen": False
}
)
parameters.append(
{
"name": "edge",
"value": 0.01,
"unit": "deg",
"frozen": True
}
)
model_npars += 4
analysis.set_model(model=model_config)
for dataset in analysis.datasets:
dataset.background_model.norm.frozen = False
analysis.run_fit()
parameters = analysis.model.parameters
parameters.covariance = analysis.fit_result.parameters.covariance[0:model_npars, 0:model_npars]
write_fit_summary(parameters, str(path_res / "results-summary-fit-3d.yaml"))
# Flux points
# TODO: This is a workaround to re-optimize the bkg in each energy bin. Add has to be added to the Analysis class
datasets = analysis.datasets.copy()
for dataset in datasets:
for par in dataset.parameters:
if par is not dataset.background_model.norm:
par.frozen = True
reoptimize = True if DEBUG is False else False
fpe = FluxPointsEstimator(
datasets=datasets, e_edges=FLUXP_EDGES, source=tag, reoptimize=reoptimize
)
flux_points = fpe.run()
flux_points.table["is_ul"] = flux_points.table["ts"] < 4
keys = ["e_ref", "e_min", "e_max", "dnde", "dnde_errp", "dnde_errn"]
flux_points.table_formatted[keys].write(
path_res / "flux-points-3d.ecsv", format="ascii.ecsv"
)
