def test_get_observations_obs_ids():
config = AnalysisConfig()
analysis = Analysis(config)
analysis.config.observations.datastore = "$GAMMAPY_DATA/cta-1dc/index/gps/"
analysis.config.observations.obs_ids = ["110380"]
analysis.get_observations()
assert len(analysis.observations) == 1
def test_get_observations_missing_irf():
config = AnalysisConfig()
analysis = Analysis(config)
analysis.config.observations.datastore = "$GAMMAPY_DATA/joint-crab/dl3/magic/"
analysis.config.observations.obs_ids = ["05029748"]
analysis.config.observations.required_irf = ["aeff", "edisp"]
analysis.get_observations()
assert len(analysis.observations) == 1
def test_set_models():
config = get_example_config("1d")
analysis = Analysis(config)
analysis.get_observations()
analysis.get_datasets()
models_str = Path(MODEL_FILE).read_text()
analysis.set_models(models=models_str)
assert isinstance(analysis.models, Models) is True
with pytest.raises(TypeError):
analysis.set_models(0)
def test_usage_errors():
config = get_example_config("1d")
analysis = Analysis(config)
with pytest.raises(RuntimeError):
analysis.get_datasets()
with pytest.raises(RuntimeError):
analysis.read_models(MODEL_FILE)
with pytest.raises(RuntimeError):
analysis.run_fit()
with pytest.raises(RuntimeError):
analysis.get_flux_points()
def run_analysis(estimate):
"""Run analysis from observation selection to model fitting."""
config = AnalysisConfig.read(f"{estimate}/config.yaml")
analysis = Analysis(config)
analysis.get_observations()
analysis.get_datasets()
models = Models.read(f"{estimate}/models.yaml")
analysis.set_models(models)
analysis.run_fit()
return analysis
def test_get_observations_obs_cone():
config = AnalysisConfig()
analysis = Analysis(config)
analysis.config.observations.datastore = "$GAMMAPY_DATA/hess-dl3-dr1"
analysis.config.observations.obs_cone = {
"frame": "icrs",
"lon": "83d",
"lat": "22d",
"radius": "5d",
}
analysis.get_observations()
assert len(analysis.observations) == 4
def test_geom_analysis_1d():
cfg = """
observations:
datastore: $GAMMAPY_DATA/hess-dl3-dr1
obs_ids: [23523]
datasets:
type: 1d
background:
method: reflected
on_region: {frame: icrs, lon: 83.633 deg, lat: 22.014 deg, radius: 0.11 deg}
geom:
axes:
energy: {min: 0.1 TeV, max: 30 TeV, nbins: 20}
energy_true: {min: 0.03 TeV, max: 100 TeV, nbins: 50}
containment_correction: false
flux_points:
energy: {min: 1 TeV, max: 50 TeV, nbins: 4}
"""
config = get_example_config("1d")
analysis = Analysis(config)
analysis.update_config(cfg)
analysis.get_observations()
analysis.get_datasets()
assert len(analysis.datasets) == 1
axis = analysis.datasets[0].exposure.geom.axes["energy_true"]
assert axis.nbin == 50
assert_allclose(axis.edges[0].to_value("TeV"), 0.03)
assert_allclose(axis.edges[-1].to_value("TeV"), 100)
def data_reduction(instrument):
log.info(f"data_reduction: {instrument}")
config = AnalysisConfig.read(f"config.yaml")
config.observations.datastore = f"$JOINT_CRAB/data/{instrument}"
config.datasets.stack = instrument_opts[instrument]['stack']
config.datasets.containment_correction = instrument_opts[instrument][
'containment']
config.datasets.on_region.radius = instrument_opts[instrument]['on_radius']
analysis = Analysis(config)
analysis.get_observations()
analysis.get_datasets()
# TODO remove when safe mask can be set on config
if instrument is 'fact':
from gammapy.datasets import SpectrumDatasetOnOff
stacked = SpectrumDatasetOnOff.create(
e_reco=analysis.datasets[0]._energy_axis.edges,
e_true=analysis.datasets[0]._energy_axis.edges,
region=None)
for ds in analysis.datasets:
ds.mask_safe[:] = True
stacked.stack(ds)
analysis.datasets = Datasets([stacked])
analysis.datasets.write(f"reduced_{instrument}", overwrite=True)
def test_config():
config = AnalysisConfig()
assert config.settings["general"]["logging"]["level"] == "INFO"
cfg = {"general": {"outdir": "test"}}
config.update_settings(cfg)
assert config.settings["general"]["logging"]["level"] == "INFO"
assert config.settings["general"]["outdir"] == "test"
with pytest.raises(ValueError):
Analysis()
assert "AnalysisConfig" in str(config)
def test_analysis_3d_joint_datasets():
config = get_example_config("3d")
config.datasets.stack = False
analysis = Analysis(config)
analysis.get_observations()
analysis.get_datasets()
assert len(analysis.datasets) == 2
def test_analysis_3d_joint_datasets():
config = AnalysisConfig.from_template("3d")
config.settings["datasets"]["stack-datasets"] = False
analysis = Analysis(config)
analysis.get_observations()
analysis.get_datasets()
assert len(analysis.datasets) == 4
def test_analysis_3d_no_geom_irf():
config = AnalysisConfig.from_template("3d")
analysis = Analysis(config)
analysis.get_observations()
analysis.get_datasets()
assert len(analysis.datasets) == 1
def setup_analysis(target):
log.info(f"analysis_3d_data_reduction: {target}")
opts = yaml.safe_load(open("targets.yaml"))[target]
txt = Path("config_template.yaml").read_text()
print(opts)
txt = txt.format_map(opts)
config = AnalysisConfig.from_yaml(txt)
config.flux_points.source = target
config.datasets.safe_mask.parameters = {"offset_max": 5 * u.deg}
return Analysis(config)
def test_analysis_ring_3d():
config = get_example_config("3d")
config.datasets.background.method = "ring"
config.datasets.background.parameters = {"r_in": "0.7 deg", "width": "0.7 deg"}
analysis = Analysis(config)
analysis.get_observations()
with pytest.raises(ValueError):
analysis.get_datasets()
def cli_run_analysis(filename, out, overwrite):
"""Performs automated data reduction process."""
config = AnalysisConfig.read(filename)
analysis = Analysis(config)
analysis.get_observations()
analysis.get_datasets()
analysis.datasets.write(out, overwrite=overwrite)
log.info(f"Datasets stored in {out} folder.")
def test_get_observations_obs_file(tmp_path):
config = AnalysisConfig()
analysis = Analysis(config)
analysis.get_observations()
filename = tmp_path / "obs_ids.txt"
filename.write_text("20136\n47829\n")
analysis.config.observations.obs_file = filename
analysis.get_observations()
assert len(analysis.observations) == 2
def test_analysis_no_bkg_1d(caplog):
config = get_example_config("1d")
analysis = Analysis(config)
analysis.get_observations()
analysis.get_datasets()
assert isinstance(analysis.datasets[0], SpectrumDatasetOnOff) is False
assert caplog.records[-1].levelname == "WARNING"
assert caplog.records[
-1].message == "No background maker set. Check configuration."
def test_analysis_ring_background():
config = get_example_config("3d")
config.datasets.background.method = "ring"
config.datasets.background.parameters = {"r_in": "0.7 deg", "width": "0.7 deg"}
config.datasets.geom.axes.energy.nbins = 1
analysis = Analysis(config)
analysis.get_observations()
analysis.get_datasets()
assert isinstance(analysis.datasets[0], MapDataset)
assert_allclose(analysis.datasets[0].background_model.map.data[0, 10, 10], 0.091552, rtol=1e-5)
def test_analysis_no_bkg_1d(caplog):
config = get_example_config("1d")
analysis = Analysis(config)
analysis.get_observations()
analysis.get_datasets()
assert not isinstance(analysis.datasets[0], SpectrumDatasetOnOff)
assert "WARNING" in [_.levelname for _ in caplog.records]
assert "No background maker set. Check configuration." in [
_.message for _ in caplog.records
]
def test_analysis_no_bkg_3d(caplog):
config = get_example_config("3d")
config.datasets.background.method = None
analysis = Analysis(config)
analysis.get_observations()
analysis.get_datasets()
assert isinstance(analysis.datasets[0], MapDataset) is True
assert caplog.records[-1].levelname == "WARNING"
assert caplog.records[
-1].message == "No background maker set. Check configuration."
def test_analysis_ring_background():
config = get_example_config("3d")
config.datasets.background.method = "ring"
config.datasets.background.parameters = {
"r_in": "0.7 deg",
"width": "0.7 deg"
}
config.datasets.geom.axes.energy.nbins = 1
analysis = Analysis(config)
analysis.get_observations()
analysis.get_datasets()
analysis.get_excess_map()
assert isinstance(analysis.datasets[0], MapDataset)
assert_allclose(analysis.datasets[0].npred_background().data[0, 10, 10],
0.091799,
rtol=1e-2)
assert isinstance(analysis.excess_map["sqrt_ts"], WcsNDMap)
assert_allclose(analysis.excess_map["excess"].data[0, 62, 62], 134.12389)
def test_analysis_no_bkg_3d(caplog):
config = get_example_config("3d")
config.datasets.background.method = None
analysis = Analysis(config)
with caplog.at_level(logging.WARNING):
analysis.get_observations()
analysis.get_datasets()
assert isinstance(analysis.datasets[0], MapDataset)
assert "No background maker set. Check configuration." in [
_.message for _ in caplog.records
]
def test_get_observations_obs_time(tmp_path):
config = AnalysisConfig()
analysis = Analysis(config)
analysis.config.observations.obs_time = {
"start": "2004-03-26",
"stop": "2004-05-26",
}
analysis.get_observations()
assert len(analysis.observations) == 40
analysis.config.observations.obs_ids = [0]
with pytest.raises(ValueError):
analysis.get_observations()
def test_analysis_3d_joint_datasets():
config = get_example_config("3d")
config.datasets.stack = False
analysis = Analysis(config)
analysis.get_observations()
analysis.get_datasets()
assert len(analysis.datasets) == 2
assert_allclose(analysis.datasets[0].background_model.norm.value,
1.031743694988066)
assert_allclose(analysis.datasets[0].background_model.tilt.value, 0.0)
assert_allclose(analysis.datasets[1].background_model.norm.value,
0.9776349021876344)
def data_prep():
# source_pos = SkyCoord.from_name("MSH 15-52")
source_pos = SkyCoord(228.32, -59.08, unit="deg")
config = AnalysisConfig()
# Select observations - 2.5 degrees from the source position
config.observations.datastore = "$GAMMAPY_DATA/hess-dl3-dr1/"
config.observations.obs_cone = {
"frame": "icrs",
"lon": source_pos.ra,
"lat": source_pos.dec,
"radius": 2.5 * u.deg,
}
config.datasets.type = "3d"
config.datasets.geom.wcs.skydir = {
"lon": source_pos.ra,
"lat": source_pos.dec,
"frame": "icrs",
}
# The WCS geometry - centered on MSH 15-52
config.datasets.geom.wcs.width = {"width": "3 deg", "height": "3 deg"}
# The FoV radius to use for cutouts
config.datasets.geom.wcs.binsize = "0.02 deg"
config.datasets.geom.selection.offset_max = 3.5 * u.deg
# We now fix the energy axis for the counts map - (the reconstructed
# energy binning)
config.datasets.geom.axes.energy.min = "0.5 TeV"
config.datasets.geom.axes.energy.max = "5 TeV"
config.datasets.geom.axes.energy.nbins = 10
# We need to extract the ring for each observation separately, hence, no
# stacking at this stage
config.datasets.stack = False
# create the config
analysis = Analysis(config)
# for this specific case,w e do not need fine bins in true energy
analysis.config.datasets.geom.axes.energy_true = (
analysis.config.datasets.geom.axes.energy)
# `First get the required observations
analysis.get_observations()
# Analysis extraction
analysis.get_datasets()
return analysis
def test_exclusion_region(tmp_path):
config = get_example_config("1d")
analysis = Analysis(config)
exclusion_region = CircleSkyRegion(center=SkyCoord("85d 23d"),
radius=1 * u.deg)
exclusion_mask = Map.create(npix=(150, 150),
binsz=0.05,
skydir=SkyCoord("83d 22d"))
mask = exclusion_mask.geom.region_mask([exclusion_region], inside=False)
exclusion_mask.data = mask.astype(int)
filename = tmp_path / "exclusion.fits"
exclusion_mask.write(filename)
config.datasets.background.exclusion = filename
analysis.get_observations()
analysis.get_datasets()
assert len(analysis.datasets) == 2
def data_reduction(instrument):
log.info(f"data_reduction: {instrument}")
config = AnalysisConfig.read(f"config.yaml")
config.observations.datastore = str(Path().resolve().parent / "data" /
"joint-crab" / instrument)
config.datasets.stack = instrument_opts[instrument]["stack"]
config.datasets.containment_correction = instrument_opts[instrument][
"containment"]
config.datasets.on_region.radius = instrument_opts[instrument]["on_radius"]
if instrument == "fact":
config.datasets.safe_mask.methods = ["aeff-default"]
analysis = Analysis(config)
analysis.get_observations()
analysis.get_datasets()
if instrument == "fact":
counts = analysis.datasets[0].counts
data = counts.geom.energy_mask(emin=0.4 * u.TeV)
analysis.datasets[0].mask_safe = counts.copy(data=data)
analysis.datasets.write(f"reduced_{instrument}", overwrite=True)
# In[ ]:
config.write("config.yaml", overwrite=True)
# In[ ]:
config = AnalysisConfig.read("config.yaml")
print(config)
# ## Running the analysis
#
# We first create an `~gammapy.analysis.Analysis` object from our configuration.
# In[ ]:
analysis = Analysis(config)
# ### Observation selection
#
# We can directly select and load the observations from disk using `~gammapy.analysis.Analysis.get_observations()`:
# In[ ]:
analysis.get_observations()
# The observations are now available on the `Analysis` object. The selection corresponds to the following ids:
# In[ ]:
analysis.observations.ids
def test_get_observations_no_datastore():
config = AnalysisConfig()
analysis = Analysis(config)
analysis.config.observations.datastore = "other"
with pytest.raises(FileNotFoundError):
analysis.get_observations()
def test_analysis_3d():
config = get_example_config("3d")
analysis = Analysis(config)
analysis.get_observations()
analysis.get_datasets()
analysis.read_models(MODEL_FILE)
analysis.datasets[
"stacked"].background_model.spectral_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["amplitude"].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, 1.340073e-11, rtol=1e-2)
assert_allclose(dnde[-1].value, 2.776611e-13, rtol=1e-2)
assert_allclose(res["index"].value, 3.097613, rtol=1e-2)
assert_allclose(res["tilt"].value, -0.207792, rtol=1e-2)
