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test_flux_point_estimator.py
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test_flux_point_estimator.py
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# Licensed under a 3-clause BSD style license - see LICENSE.rst
import pytest
import numpy as np
from numpy.testing import assert_allclose
from astropy import units as u
from astropy.coordinates import SkyCoord
from ...utils.testing import requires_dependency, requires_data
from ...irf import EffectiveAreaTable, load_cta_irfs
from ..models import PowerLaw, ExponentialCutoffPowerLaw
from ..simulation import SpectrumSimulation
from ..flux_point import FluxPointsEstimator
from ...cube import simulate_dataset
from ...cube.models import SkyModel
from ...image.models import SkyGaussian
from ...maps import MapAxis, WcsGeom
# TODO: use pregenerate data instead
def simulate_spectrum_dataset(model):
energy = np.logspace(-0.5, 1.5, 21) * u.TeV
aeff = EffectiveAreaTable.from_parametrization(energy=energy)
bkg_model = PowerLaw(index=2.5, amplitude="1e-12 cm-2 s-1 TeV-1")
sim = SpectrumSimulation(
aeff=aeff,
source_model=model,
livetime=100 * u.h,
background_model=bkg_model,
alpha=0.2,
)
sim.run(seed=[0])
obs = sim.result[0]
return obs.to_spectrum_dataset()
def create_fpe(model):
dataset = simulate_spectrum_dataset(model)
e_edges = [0.1, 1, 10, 100] * u.TeV
dataset.model = model
return FluxPointsEstimator(datasets=[dataset], e_edges=e_edges, norm_n_values=11)
def simulate_map_dataset():
irfs = load_cta_irfs(
"$GAMMAPY_DATA/cta-1dc/caldb/data/cta/1dc/bcf/South_z20_50h/irf_file.fits"
)
skydir = SkyCoord("0 deg", "0 deg", frame="galactic")
edges = np.logspace(-1, 2, 15) * u.TeV
energy_axis = MapAxis.from_edges(edges=edges, name="energy")
geom = WcsGeom.create(
skydir=skydir, width=(4, 4), binsz=0.1, axes=[energy_axis], coordsys="GAL"
)
gauss = SkyGaussian("0 deg", "0 deg", "0.4 deg", frame="galactic")
pwl = PowerLaw(amplitude="1e-11 cm-2 s-1 TeV-1")
skymodel = SkyModel(spatial_model=gauss, spectral_model=pwl, name="source")
dataset = simulate_dataset(
skymodel=skymodel, geom=geom, pointing=skydir, irfs=irfs, random_state=0
)
return dataset
@pytest.fixture(scope="session")
def fpe_map_pwl():
dataset = simulate_map_dataset()
e_edges = [0.1, 1, 10, 100] * u.TeV
return FluxPointsEstimator(
datasets=[dataset], e_edges=e_edges, norm_n_values=3, source="source"
)
@pytest.fixture(scope="session")
def fpe_map_pwl_reoptimize():
dataset = simulate_map_dataset()
e_edges = [1, 10] * u.TeV
dataset.parameters["lon_0"].frozen = True
dataset.parameters["lat_0"].frozen = True
dataset.parameters["index"].frozen = True
return FluxPointsEstimator(
datasets=[dataset],
e_edges=e_edges,
norm_values=[1],
reoptimize=True,
source="source",
)
@pytest.fixture(scope="session")
def fpe_pwl():
return create_fpe(PowerLaw())
@pytest.fixture(scope="session")
def fpe_ecpl():
return create_fpe(ExponentialCutoffPowerLaw(lambda_="1 TeV-1"))
class TestFluxPointsEstimator:
@staticmethod
def test_str(fpe_pwl):
assert "FluxPointsEstimator" in str(fpe_pwl)
@staticmethod
@requires_dependency("iminuit")
def test_run_pwl(fpe_pwl):
fp = fpe_pwl.run()
actual = fp.table["norm"].data
assert_allclose(actual, [1.080933, 0.910776, 0.922278], rtol=1e-5)
actual = fp.table["norm_err"].data
assert_allclose(actual, [0.066364, 0.061025, 0.179742], rtol=1e-5)
actual = fp.table["norm_errn"].data
assert_allclose(actual, [0.065305, 0.060409, 0.17148], rtol=1e-5)
actual = fp.table["norm_errp"].data
assert_allclose(actual, [0.067454, 0.061646, 0.188288], rtol=1e-5)
actual = fp.table["norm_ul"].data
assert_allclose(actual, [1.219995, 1.037478, 1.321045], rtol=1e-5)
actual = fp.table["sqrt_ts"].data
assert_allclose(actual, [18.568429, 18.054651, 7.057121], rtol=1e-5)
actual = fp.table["norm_scan"][0][[0, 5, -1]]
assert_allclose(actual, [0.2, 1, 5], rtol=1e-5)
actual = fp.table["dloglike_scan"][0][[0, 5, -1]]
assert_allclose(actual, [220.368653, 4.301011, 1881.626454], rtol=1e-5)
@staticmethod
@requires_dependency("iminuit")
def test_run_ecpl(fpe_ecpl):
fp = fpe_ecpl.estimate_flux_point(fpe_ecpl.e_groups[1])
assert_allclose(fp["norm"], 1, rtol=1e-1)
@staticmethod
@requires_dependency("iminuit")
@requires_data("gammapy-data")
def test_run_map_pwl(fpe_map_pwl):
fp = fpe_map_pwl.run(steps=["err", "norm-scan", "ts"])
actual = fp.table["norm"].data
assert_allclose(actual, [0.97922, 0.94081, 1.074426], rtol=1e-3)
actual = fp.table["norm_err"].data
assert_allclose(actual, [0.069963, 0.052605, 0.09297], rtol=1e-3)
actual = fp.table["sqrt_ts"].data
assert_allclose(actual, [16.165806, 27.121415, 22.04104], rtol=1e-3)
actual = fp.table["norm_scan"][0]
assert_allclose(actual, [0.2, 1, 5], rtol=1e-3)
actual = fp.table["dloglike_scan"][0] - fp.table["loglike"][0]
assert_allclose(actual, [1.536452e02, 8.762343e-02, 1.883447e03], rtol=1e-3)
@staticmethod
@requires_dependency("iminuit")
@requires_data("gammapy-data")
def test_run_map_pwl_reoptimize(fpe_map_pwl_reoptimize):
fp = fpe_map_pwl_reoptimize.run(steps=["err", "norm-scan", "ts"])
actual = fp.table["norm"].data
assert_allclose(actual, 0.884621, rtol=1e-3)
actual = fp.table["norm_err"].data
assert_allclose(actual, 0.058067, rtol=1e-3)
actual = fp.table["sqrt_ts"].data
assert_allclose(actual, 23.971251, rtol=1e-3)
actual = fp.table["norm_scan"][0]
assert_allclose(actual, 1, rtol=1e-3)
actual = fp.table["dloglike_scan"][0] - fp.table["loglike"][0]
assert_allclose(actual, 3.698882, rtol=1e-3)