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 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_update_config():
analysis = Analysis(AnalysisConfig())
data = {"general": {"outdir": "test"}}
config = AnalysisConfig(**data)
analysis.update_config(config)
assert analysis.config.general.outdir == "test"
analysis = Analysis(AnalysisConfig())
data = """
general:
outdir: test
"""
analysis.update_config(data)
assert analysis.config.general.outdir == "test"
analysis = Analysis(AnalysisConfig())
with pytest.raises(TypeError):
analysis.update_config(0)
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_validate_astropy_quantities():
config = AnalysisConfig()
cfg = {
"observations": {
"filters": [{
"filter_type": "all",
"lon": "1 deg"
}]
}
}
config.update_settings(cfg)
assert config.validate() is None
def test_get_observations_obs_cone():
config = AnalysisConfig()
analysis = Analysis(config)
analysis.config.observations.datastore = "$GAMMAPY_DATA/hess-dl3-dr1"
analysis.config.observations.obs_cone = {
"frame": "icrs",
"lon": "83d",
"lat": "22d",
"radius": "5d",
}
analysis.get_observations()
assert len(analysis.observations) == 4
def test_get_observations_obs_time(tmp_path):
config = AnalysisConfig()
analysis = Analysis(config)
analysis.config.observations.obs_time = {
"start": "2004-03-26",
"stop": "2004-05-26",
}
analysis.get_observations()
assert len(analysis.observations) == 40
analysis.config.observations.obs_ids = [0]
with pytest.raises(ValueError):
analysis.get_observations()
def test_config():
config = AnalysisConfig()
assert config.settings["general"]["logging"]["level"] == "INFO"
cfg = {"general": {"outdir": "test"}}
config.update_settings(cfg)
assert config.settings["general"]["logging"]["level"] == "INFO"
assert config.settings["general"]["outdir"] == "test"
with pytest.raises(ValueError):
Analysis()
assert "AnalysisConfig" in str(config)
def data_prep():
# source_pos = SkyCoord.from_name("MSH 15-52")
source_pos = SkyCoord(228.32, -59.08, unit="deg")
config = AnalysisConfig()
# Select observations - 2.5 degrees from the source position
config.observations.datastore = "$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
from gammapy.modeling.models import PointSpatialModel from gammapy.modeling.models import SkyModel, SkyModels from gammapy.time import LightCurveEstimator from gammapy.analysis import Analysis, AnalysisConfig # ## Analysis configuration # For the 1D and 3D extraction, we will use the same CrabNebula configuration than in the notebook analysis_1.ipynb using the high level interface of Gammapy. # # From the high level interface, the data reduction for those observations is performed as followed # ### Building the 3D analysis configuration # # In[ ]: conf_3d = AnalysisConfig() # #### Definition of the data selection # # Here we use the Crab runs from the HESS DL3 data release 1 # In[ ]: conf_3d.observations.obs_ids = [23523, 23526, 23559, 23592] # #### Definition of the dataset geometry # In[ ]: # We want a 3D analysis conf_3d.datasets.type = "3d"
def test_validate_config():
config = AnalysisConfig()
assert config.validate() is None
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_help():
config = AnalysisConfig()
assert config.help() is None
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"
)
def cli_make_config(filename, overwrite):
"""Writes default configuration file."""
config = AnalysisConfig()
config.write(filename, overwrite=overwrite)
log.info(f"Configuration file produced: {filename}")
def test_config_to_yaml(tmp_path):
config = AnalysisConfig()
config.settings["general"]["outdir"] = tmp_path
config.to_yaml(overwrite=True)
text = (tmp_path / config.filename).read_text()
assert "stack-datasets" in text
# In[ ]: from pathlib import Path from astropy import units as u from gammapy.analysis import Analysis, AnalysisConfig from gammapy.modeling.models import create_crab_spectral_model # ## Analysis configuration # # For configuration of the analysis we use the [YAML](https://en.wikipedia.org/wiki/YAML) data format. YAML is a machine readable serialisation format, that is also friendly for humans to read. In this tutorial we will write the configuration file just using Python strings, but of course the file can be created and modified with any text editor of your choice. # # Here is what the configuration for our analysis looks like: # In[ ]: config = AnalysisConfig() # the AnalysisConfig gives access to the various parameters used from logging to reduced dataset geometries print(config) # ### Setting the data to use # We want to use Crab runs from the H.E.S.S. DL3-DR1. We define here the datastore and a cone search of observations pointing with 5 degrees of the Crab nebula. # Parameters can be set directly or as a python dict. # In[ ]: # We define the datastore containing the data config.observations.datastore = "$GAMMAPY_DATA/hess-dl3-dr1" # We define the cone search parameters config.observations.obs_cone.frame = "icrs"
def test_get_observations(config_obs):
config = AnalysisConfig()
analysis = Analysis(config)
analysis.config.update_settings(config_obs)
analysis.get_observations()
assert len(analysis.observations) == config_obs["result"]