def test_lightcurve_estimator_spectrum_datasets_withmaskfit():
# Doing a LC on one hour bin
datasets = get_spectrum_datasets()
time_intervals = [
Time(["2010-01-01T00:00:00", "2010-01-01T01:00:00"]),
Time(["2010-01-01T01:00:00", "2010-01-01T02:00:00"]),
]
e_min_fit = 1 * u.TeV
e_max_fit = 3 * u.TeV
for dataset in datasets:
mask_fit = dataset.counts.energy_mask(emin=e_min_fit, emax=e_max_fit)
dataset.mask_fit = mask_fit
steps = ["err", "counts", "ts", "norm-scan"]
estimator = LightCurveEstimator(
datasets, norm_n_values=3, time_intervals=time_intervals
)
lightcurve = estimator.run(
e_ref=10 * u.TeV, e_min=1 * u.TeV, e_max=100 * u.TeV, steps=steps
)
assert_allclose(lightcurve.table["time_min"], [55197.0, 55197.041667])
assert_allclose(lightcurve.table["time_max"], [55197.041667, 55197.083333])
assert_allclose(lightcurve.table["stat"], [6.60304, 0.421047], rtol=1e-5)
assert_allclose(lightcurve.table["norm"], [0.885082, 0.967022], rtol=1e-5)
def test_lightcurve_estimator_map_datasets():
datasets = get_map_datasets()
estimator = LightCurveEstimator(datasets, source="source")
steps = ["err", "counts", "ts", "norm-scan"]
lightcurve = estimator.run(e_ref=10 * u.TeV,
e_min=1 * u.TeV,
e_max=100 * u.TeV,
steps=steps)
assert_allclose(lightcurve.table["time_min"], [55197.0, 55197.041667])
assert_allclose(lightcurve.table["time_max"], [55197.041667, 55197.083333])
assert_allclose(lightcurve.table["e_ref"], [10, 10])
assert_allclose(lightcurve.table["e_min"], [1, 1])
assert_allclose(lightcurve.table["e_max"], [100, 100])
assert_allclose(lightcurve.table["ref_dnde"], [1e-13, 1e-13])
assert_allclose(lightcurve.table["ref_flux"], [9.9e-12, 9.9e-12])
assert_allclose(lightcurve.table["ref_eflux"], [4.60517e-11, 4.60517e-11])
assert_allclose(lightcurve.table["ref_e2dnde"], [1e-11, 1e-11])
assert_allclose(lightcurve.table["loglike"],
[-86541.447142, -89740.436161])
assert_allclose(lightcurve.table["norm_err"], [0.042729, 0.042469],
rtol=1e-4)
assert_allclose(lightcurve.table["counts"], [46648, 47321])
assert_allclose(lightcurve.table["sqrt_ts"], [54.034112, 53.920883],
rtol=1e-5)
assert_allclose(lightcurve.table["ts"], [2919.685309, 2907.461611],
rtol=1e-5)
def test_lightcurve_estimator_spectrum_datasets():
# Doing a LC on one hour bin
datasets = get_spectrum_datasets()
time_intervals = [
Time(["2010-01-01T00:00:00", "2010-01-01T01:00:00"]),
Time(["2010-01-01T01:00:00", "2010-01-01T02:00:00"]),
]
estimator = LightCurveEstimator(
datasets, norm_n_values=3, time_intervals=time_intervals
)
lightcurve = estimator.run(e_ref=10 * u.TeV, e_min=1 * u.TeV, e_max=100 * u.TeV)
assert_allclose(lightcurve.table["time_min"], [55197.0, 55197.041667])
assert_allclose(lightcurve.table["time_max"], [55197.041667, 55197.083333])
assert_allclose(lightcurve.table["e_ref"], [10, 10])
assert_allclose(lightcurve.table["e_min"], [1, 1])
assert_allclose(lightcurve.table["e_max"], [100, 100])
assert_allclose(lightcurve.table["ref_dnde"], [1e-14, 1e-14])
assert_allclose(lightcurve.table["ref_flux"], [9.9e-13, 9.9e-13])
assert_allclose(lightcurve.table["ref_eflux"], [4.60517e-12, 4.60517e-12])
assert_allclose(lightcurve.table["ref_e2dnde"], [1e-12, 1e-12])
assert_allclose(lightcurve.table["stat"], [23.302288, 22.457766], rtol=1e-5)
assert_allclose(lightcurve.table["norm"], [0.988127, 0.948108], rtol=1e-5)
assert_allclose(lightcurve.table["norm_err"], [0.043985, 0.043498], rtol=1e-4)
assert_allclose(lightcurve.table["counts"], [2281, 2222])
assert_allclose(lightcurve.table["norm_errp"], [0.044231, 0.04377], rtol=1e-5)
assert_allclose(lightcurve.table["norm_errn"], [0.04374, 0.043226], rtol=1e-5)
assert_allclose(lightcurve.table["norm_ul"], [1.077213, 1.036237], rtol=1e-5)
assert_allclose(lightcurve.table["sqrt_ts"], [26.773925, 25.796426], rtol=1e-4)
assert_allclose(lightcurve.table["ts"], [716.843084, 665.455601], rtol=1e-4)
assert_allclose(lightcurve.table[0]["norm_scan"], [0.2, 1.0, 5.0])
assert_allclose(
lightcurve.table[0]["stat_scan"],
[444.426957, 23.375417, 3945.382802],
rtol=1e-5,
)
def test_lightcurve_estimator_spectrum_datasets():
datasets = get_spectrum_datasets()
estimator = LightCurveEstimator(datasets, norm_n_values=3)
lightcurve = estimator.run(e_ref=10 * u.TeV, e_min=1 * u.TeV, e_max=100 * u.TeV)
assert_allclose(lightcurve.table["time_min"], [55197.0, 55197.041667])
assert_allclose(lightcurve.table["time_max"], [55197.041667, 55197.083333])
assert_allclose(lightcurve.table["e_ref"], [10, 10])
assert_allclose(lightcurve.table["e_min"], [1, 1])
assert_allclose(lightcurve.table["e_max"], [100, 100])
assert_allclose(lightcurve.table["ref_dnde"], [1e-14, 1e-14])
assert_allclose(lightcurve.table["ref_flux"], [9.9e-13, 9.9e-13])
assert_allclose(lightcurve.table["ref_eflux"], [4.60517e-12, 4.60517e-12])
assert_allclose(lightcurve.table["ref_e2dnde"], [1e-12, 1e-12])
assert_allclose(lightcurve.table["loglike"], [23.302288, 22.457766], rtol=1e-5)
assert_allclose(lightcurve.table["norm"], [0.988127, 0.948108], rtol=1e-5)
assert_allclose(lightcurve.table["norm_err"], [0.043985, 0.043498], rtol=1e-4)
assert_allclose(lightcurve.table["counts"], [2281, 2222])
assert_allclose(lightcurve.table["norm_errp"], [0.044231, 0.04377], rtol=1e-5)
assert_allclose(lightcurve.table["norm_errn"], [0.04374, 0.043226], rtol=1e-5)
assert_allclose(lightcurve.table["norm_ul"], [1.077213, 1.036237], rtol=1e-5)
assert_allclose(lightcurve.table["sqrt_ts"], [37.16802, 37.168033], rtol=1e-5)
assert_allclose(lightcurve.table["ts"], [1381.461738, 1381.462675], rtol=1e-5)
assert_allclose(lightcurve.table[0]["norm_scan"], [0.2, 1.0, 5.0])
assert_allclose(
lightcurve.table[0]["dloglike_scan"],
[444.426957, 23.375417, 3945.382802],
rtol=1e-5,
)
def test_lightcurve_estimator_spectrum_datasets_largerbin():
# Test all dataset in a single LC bin, here two hours
datasets = get_spectrum_datasets()
time_intervals = [Time(["2010-01-01T00:00:00", "2010-01-01T02:00:00"])]
estimator = LightCurveEstimator(
datasets, norm_n_values=3, time_intervals=time_intervals
)
steps = ["err", "counts", "ts", "norm-scan"]
lightcurve = estimator.run(
e_ref=10 * u.TeV, e_min=1 * u.TeV, e_max=100 * u.TeV, steps=steps
)
assert_allclose(lightcurve.table["time_min"], [55197.0])
assert_allclose(lightcurve.table["time_max"], [55197.083333])
assert_allclose(lightcurve.table["e_ref"], [10])
assert_allclose(lightcurve.table["e_min"], [1])
assert_allclose(lightcurve.table["e_max"], [100])
assert_allclose(lightcurve.table["ref_dnde"], [1e-14])
assert_allclose(lightcurve.table["ref_flux"], [9.9e-13])
assert_allclose(lightcurve.table["ref_eflux"], [4.60517e-12])
assert_allclose(lightcurve.table["ref_e2dnde"], [1e-12])
assert_allclose(lightcurve.table["stat"], [46.177981], rtol=1e-5)
assert_allclose(lightcurve.table["norm"], [0.968049], rtol=1e-5)
assert_allclose(lightcurve.table["norm_err"], [0.030929], rtol=1e-4)
assert_allclose(lightcurve.table["ts"], [1381.880757], rtol=1e-4)
def data_fit(datasets):
lc_maker_1d = LightCurveEstimator(datasets,
source="crab",
reoptimize=False)
lc_1d = lc_maker_1d.run(e_ref=1 * u.TeV,
e_min=1.0 * u.TeV,
e_max=10.0 * u.TeV)
def test_lightcurve_estimator_map_datasets():
datasets = get_map_datasets()
estimator = LightCurveEstimator(datasets, source="source")
steps = ["err", "counts", "ts", "norm-scan"]
lightcurve = estimator.run(
e_ref=10 * u.TeV, e_min=1 * u.TeV, e_max=100 * u.TeV, steps=steps
)
assert_allclose(lightcurve.table["time_min"], [55197.0, 55197.041667])
assert_allclose(lightcurve.table["time_max"], [55197.041667, 55197.083333])
assert_allclose(lightcurve.table["e_ref"], [10, 10])
assert_allclose(lightcurve.table["e_min"], [1, 1])
assert_allclose(lightcurve.table["e_max"], [100, 100])
assert_allclose(lightcurve.table["ref_dnde"], [1e-13, 1e-13])
assert_allclose(lightcurve.table["ref_flux"], [9.9e-12, 9.9e-12])
assert_allclose(lightcurve.table["ref_eflux"], [4.60517e-11, 4.60517e-11])
assert_allclose(lightcurve.table["ref_e2dnde"], [1e-11, 1e-11])
assert_allclose(
lightcurve.table["loglike"], [-86845.74348, -90386.086642], rtol=1e-5
)
assert_allclose(lightcurve.table["norm_err"], [0.041529, 0.041785], rtol=1e-3)
assert_allclose(lightcurve.table["counts"], [46702, 47508])
assert_allclose(lightcurve.table["sqrt_ts"], [54.503321, 54.296488], atol=0.01)
assert_allclose(lightcurve.table["ts"], [2970.61202, 2948.108572], atol=0.01)
def test_lightcurve_estimator_map_datasets():
datasets = get_map_datasets()
estimator = LightCurveEstimator(datasets, source="source")
steps = ["err", "counts", "ts", "norm-scan"]
lightcurve = estimator.run(e_ref=10 * u.TeV,
e_min=1 * u.TeV,
e_max=100 * u.TeV,
steps=steps)
assert_allclose(lightcurve.table["time_min"], [55197.0, 55197.041667])
assert_allclose(lightcurve.table["time_max"], [55197.041667, 55197.083333])
assert_allclose(lightcurve.table["e_ref"], [10, 10])
assert_allclose(lightcurve.table["e_min"], [1, 1])
assert_allclose(lightcurve.table["e_max"], [100, 100])
assert_allclose(lightcurve.table["ref_dnde"], [1e-13, 1e-13])
assert_allclose(lightcurve.table["ref_flux"], [9.9e-12, 9.9e-12])
assert_allclose(lightcurve.table["ref_eflux"], [4.60517e-11, 4.60517e-11])
assert_allclose(lightcurve.table["ref_e2dnde"], [1e-11, 1e-11])
assert_allclose(lightcurve.table["loglike"],
[-87130.477068, -89848.520203],
rtol=1e-5)
assert_allclose(lightcurve.table["norm_err"], [0.042941, 0.040918],
rtol=1e-3)
assert_allclose(lightcurve.table["counts"], [46804, 47417])
assert_allclose(lightcurve.table["sqrt_ts"], [55.505477, 55.342541],
atol=0.01)
assert_allclose(lightcurve.table["ts"], [3080.857998, 3062.796807],
atol=0.01)
def test_lightcurve_estimator_spectrum_datasets_default():
# Test default time interval: each time interval is equal to the gti of each dataset, here one hour
datasets = get_spectrum_datasets()
estimator = LightCurveEstimator(datasets, norm_n_values=3)
steps = ["err", "counts", "ts", "norm-scan"]
lightcurve = estimator.run(
e_ref=10 * u.TeV, e_min=1 * u.TeV, e_max=100 * u.TeV, steps=steps
)
assert_allclose(lightcurve.table["time_min"], [55197.0, 55197.041667])
assert_allclose(lightcurve.table["time_max"], [55197.041667, 55197.083333])
assert_allclose(lightcurve.table["norm"], [0.988127, 0.948108], rtol=1e-5)
def test_group_datasets_in_time_interval_outflows():
datasets = get_spectrum_datasets()
# Check Overflow
time_intervals = [
Time(["2010-01-01T00:00:00", "2010-01-01T00:55:00"]),
Time(["2010-01-01T01:00:00", "2010-01-01T02:00:00"]),
]
estimator = LightCurveEstimator(
datasets, norm_n_values=3, time_intervals=time_intervals
)
steps = ["err", "counts", "ts", "norm-scan"]
estimator.run(e_ref=10 * u.TeV, e_min=1 * u.TeV, e_max=100 * u.TeV, steps=steps)
assert estimator.group_table_info["Bin_type"][0] == "Overflow"
# Check underflow
time_intervals = [
Time(["2010-01-01T00:05:00", "2010-01-01T01:00:00"]),
Time(["2010-01-01T01:00:00", "2010-01-01T02:00:00"]),
]
estimator = LightCurveEstimator(
datasets, norm_n_values=3, time_intervals=time_intervals
)
steps = ["err", "counts", "ts", "norm-scan"]
estimator.run(e_ref=10 * u.TeV, e_min=1 * u.TeV, e_max=100 * u.TeV, steps=steps)
assert estimator.group_table_info["Bin_type"][0] == "Underflow"
def test_lightcurve_estimator_spectrum_datasets_gti_not_include_in_time_intervals():
# Check that it returns a ValueError if the time intervals are smaller than the dataset GTI.
datasets = get_spectrum_datasets()
time_intervals = [
Time(["2010-01-01T00:00:00", "2010-01-01T00:05:00"]),
Time(["2010-01-01T01:00:00", "2010-01-01T01:05:00"]),
]
estimator = LightCurveEstimator(
datasets, norm_n_values=3, time_intervals=time_intervals
)
with pytest.raises(ValueError) as excinfo:
steps = ["err", "counts", "ts", "norm-scan"]
estimator.run(e_ref=10 * u.TeV, e_min=1 * u.TeV, e_max=100 * u.TeV, steps=steps)
msg = "LightCurveEstimator: No datasets in time intervals"
assert str(excinfo.value) == msg
def test_lightcurve_estimator_spectrum_datasets_notordered():
# Test that if the time intervals given are not ordered in time, it is first ordered correctly and then
# compute as expected
datasets = get_spectrum_datasets()
time_intervals = [
Time(["2010-01-01T01:00:00", "2010-01-01T02:00:00"]),
Time(["2010-01-01T00:00:00", "2010-01-01T01:00:00"]),
]
estimator = LightCurveEstimator(
datasets, norm_n_values=3, time_intervals=time_intervals
)
steps = ["err", "counts", "ts", "norm-scan"]
lightcurve = estimator.run(
e_ref=10 * u.TeV, e_min=1 * u.TeV, e_max=100 * u.TeV, steps=steps
)
assert_allclose(lightcurve.table["time_min"], [55197.0, 55197.041667])
assert_allclose(lightcurve.table["time_max"], [55197.041667, 55197.083333])
assert_allclose(lightcurve.table["norm"], [0.988127, 0.948108], rtol=1e-5)
def test_lightcurve_estimator_spectrum_datasets_timeoverlaped():
# Check that it returns a ValueError if the time intervals overlapped
datasets = get_spectrum_datasets()
time_intervals = [
Time(["2010-01-01T00:00:00", "2010-01-01T01:30:00"]),
Time(["2010-01-01T01:00:00", "2010-01-01T02:00:00"]),
]
with pytest.raises(ValueError) as excinfo:
LightCurveEstimator(datasets, norm_n_values=3, time_intervals=time_intervals)
msg = "LightCurveEstimator requires non-overlapping time bins."
assert str(excinfo.value) == msg
def test_group_datasets_in_time_interval():
# Doing a LC on one hour bin
datasets = get_spectrum_datasets()
time_intervals = [
Time(["2010-01-01T00:00:00", "2010-01-01T01:00:00"]),
Time(["2010-01-01T01:00:00", "2010-01-01T02:00:00"]),
]
estimator = LightCurveEstimator(
datasets, norm_n_values=3, time_intervals=time_intervals
)
steps = ["err", "counts", "ts", "norm-scan"]
estimator.run(e_ref=10 * u.TeV, e_min=1 * u.TeV, e_max=100 * u.TeV, steps=steps)
assert len(estimator.group_table_info) == 2
assert estimator.group_table_info["Name"][0] == "dataset_1"
assert_allclose(estimator.group_table_info["Tstart"], [55197.0, 55197.04166666667])
assert_allclose(
estimator.group_table_info["Tstop"], [55197.04166666667, 55197.083333333336]
)
assert_allclose(estimator.group_table_info["Group_ID"], [0, 1])
def test_lightcurve_estimator_map_datasets():
datasets = get_map_datasets()
time_intervals = [
Time(["2010-01-01T00:00:00", "2010-01-01T01:00:00"]),
Time(["2010-01-01T01:00:00", "2010-01-01T02:00:00"]),
]
estimator = LightCurveEstimator(datasets,
source="source",
time_intervals=time_intervals)
steps = ["err", "counts", "ts", "norm-scan"]
lightcurve = estimator.run(e_ref=10 * u.TeV,
e_min=1 * u.TeV,
e_max=100 * u.TeV,
steps=steps)
assert_allclose(lightcurve.table["time_min"], [55197.0, 55197.041667])
assert_allclose(lightcurve.table["time_max"], [55197.041667, 55197.083333])
assert_allclose(lightcurve.table["e_ref"], [10, 10])
assert_allclose(lightcurve.table["e_min"], [1, 1])
assert_allclose(lightcurve.table["e_max"], [100, 100])
assert_allclose(lightcurve.table["ref_dnde"], [1e-13, 1e-13])
assert_allclose(lightcurve.table["ref_flux"], [9.9e-12, 9.9e-12])
assert_allclose(lightcurve.table["ref_eflux"], [4.60517e-11, 4.60517e-11])
assert_allclose(lightcurve.table["ref_e2dnde"], [1e-11, 1e-11])
assert_allclose(lightcurve.table["stat"], [-86845.74348, -90386.086642],
rtol=1e-5)
assert_allclose(lightcurve.table["norm_err"], [0.041529, 0.041785],
rtol=1e-3)
assert_allclose(lightcurve.table["sqrt_ts"], [39.016115, 37.766553],
atol=0.01)
assert_allclose(lightcurve.table["ts"], [1522.257266, 1426.31252],
atol=0.01)
datasets = get_map_datasets()
time_intervals2 = [Time(["2010-01-01T00:00:00", "2010-01-01T02:00:00"])]
estimator2 = LightCurveEstimator(datasets,
source="source",
time_intervals=time_intervals2)
lightcurve2 = estimator2.run(e_ref=10 * u.TeV,
e_min=1 * u.TeV,
e_max=100 * u.TeV)
assert_allclose(lightcurve2.table["time_min"], [55197.0])
assert_allclose(lightcurve2.table["time_max"], [55197.083333])
assert_allclose(lightcurve2.table["e_ref"], [10])
assert_allclose(lightcurve2.table["e_min"], [1])
assert_allclose(lightcurve2.table["e_max"], [100])
assert_allclose(lightcurve2.table["ref_dnde"], [1e-13])
assert_allclose(lightcurve2.table["ref_flux"], [9.9e-12])
assert_allclose(lightcurve2.table["ref_eflux"], [4.60517e-11])
assert_allclose(lightcurve2.table["ref_e2dnde"], [1e-11])
assert_allclose(lightcurve2.table["stat"], [-177231.653683], rtol=1e-5)
assert_allclose(lightcurve2.table["norm_err"], [0.029441], rtol=1e-3)
assert_allclose(lightcurve.table["counts"], [46702, 47508])
assert_allclose(lightcurve2.table["ts"], [2948.403853], atol=0.01)
def test_lightcurve_estimator_map_datasets():
datasets = get_map_datasets()
time_intervals = [
Time(["2010-01-01T00:00:00", "2010-01-01T01:00:00"]),
Time(["2010-01-01T01:00:00", "2010-01-01T02:00:00"]),
]
estimator = LightCurveEstimator(
datasets, source="source", time_intervals=time_intervals
)
steps = ["err", "counts", "ts", "norm-scan"]
lightcurve = estimator.run(
e_ref=10 * u.TeV, e_min=1 * u.TeV, e_max=100 * u.TeV, steps=steps
)
assert_allclose(lightcurve.table["time_min"], [55197.0, 55197.041667])
assert_allclose(lightcurve.table["time_max"], [55197.041667, 55197.083333])
assert_allclose(lightcurve.table["e_ref"], [10, 10])
assert_allclose(lightcurve.table["e_min"], [1, 1])
assert_allclose(lightcurve.table["e_max"], [100, 100])
assert_allclose(lightcurve.table["ref_dnde"], [1e-13, 1e-13])
assert_allclose(lightcurve.table["ref_flux"], [9.9e-12, 9.9e-12])
assert_allclose(lightcurve.table["ref_eflux"], [4.60517e-11, 4.60517e-11])
assert_allclose(lightcurve.table["ref_e2dnde"], [1e-11, 1e-11])
assert_allclose(lightcurve.table["stat"], [-87412.393367, -89856.129206], rtol=1e-5)
assert_allclose(lightcurve.table["norm_err"], [0.042259, 0.043614], rtol=1e-3)
assert_allclose(lightcurve.table["sqrt_ts"], [38.527512, 39.489968], rtol=1e-4)
assert_allclose(lightcurve.table["ts"], [1484.369159, 1559.457547], rtol=1e-4)
datasets = get_map_datasets()
time_intervals2 = [Time(["2010-01-01T00:00:00", "2010-01-01T02:00:00"])]
estimator2 = LightCurveEstimator(
datasets, source="source", time_intervals=time_intervals2
)
lightcurve2 = estimator2.run(e_ref=10 * u.TeV, e_min=1 * u.TeV, e_max=100 * u.TeV)
assert_allclose(lightcurve2.table["time_min"], [55197.0])
assert_allclose(lightcurve2.table["time_max"], [55197.083333])
assert_allclose(lightcurve2.table["e_ref"], [10])
assert_allclose(lightcurve2.table["e_min"], [1])
assert_allclose(lightcurve2.table["e_max"], [100])
assert_allclose(lightcurve2.table["ref_dnde"], [1e-13])
assert_allclose(lightcurve2.table["ref_flux"], [9.9e-12])
assert_allclose(lightcurve2.table["ref_eflux"], [4.60517e-11])
assert_allclose(lightcurve2.table["ref_e2dnde"], [1e-11])
assert_allclose(lightcurve2.table["stat"], [-177267.775615], rtol=1e-5)
assert_allclose(lightcurve2.table["norm_err"], [0.030358], rtol=1e-3)
assert_allclose(lightcurve.table["counts"], [46794, 47388])
assert_allclose(lightcurve2.table["ts"], [3042.893291], rtol=1e-4)
reference=1 * u.TeV,
)
spectral_model.parameters["index"].frozen = False
sky_model = SkyModel(spatial_model=None,
spectral_model=spectral_model,
name="crab")
# We affect to each dataset it spectral model
# In[ ]:
for dataset in datasets:
dataset.models = sky_model
# In[ ]:
lc_maker_1d = LightCurveEstimator(datasets, source="crab")
# In[ ]:
lc_1d = lc_maker_1d.run(e_ref=1 * u.TeV, e_min=1.0 * u.TeV, e_max=10.0 * u.TeV)
# Finally we plot the result for the 1D lightcurve:
# In[ ]:
lc_1d.plot(marker="o")
# In[ ]:
#
# Now that we have created the datasets we assign them the model to be fitted:
# In[ ]:
for dataset in datasets:
# Copy the source model
model = sky_model.copy(name="crab")
dataset.model = model
# We can now create the light curve estimator by passing it the list of datasets.
# We can optionally ask for parameters reoptimization during fit, e.g. to fit background normalization in each time bin.
# In[ ]:
lc_maker = LightCurveEstimator(datasets, source="crab", reoptimize=True)
# We now run the estimator once we pass it the energy interval on which to compute the integral flux of the source.
# In[ ]:
get_ipython().run_cell_magic(
'time', '',
'lc = lc_maker.run(e_ref=1 * u.TeV, e_min=1.0 * u.TeV, e_max=10.0 * u.TeV)'
)
# The LightCurve object contains a table which we can explore.
# In[ ]:
lc.table["time_min", "time_max", "flux", "flux_err"]
for obs in extraction.obs_list:
intervals.append([obs.events.time[0], obs.events.time[-1]])
# In[8]:
# Model to compute the expected counts (generally, parameters come from the fit)
model = PowerLaw(
index=2. * u.Unit(''),
amplitude=2.e-11 * u.Unit('1 / (cm2 s TeV)'),
reference=1 * u.TeV,
)
# In[9]:
# Estimation of the light curve
lc_estimator = LightCurveEstimator(extraction)
lc = lc_estimator.light_curve(
time_intervals=intervals,
spectral_model=model,
energy_range=[0.7, 100] * u.TeV,
)
# ## Results
#
# The light curve measurement result is stored in a table. Let's have a look at the results:
# In[10]:
print(lc.table.colnames)
# In[11]:
analysis_3d.set_models(models)
# ## Light Curve estimation: by observation
#
# We can now create the light curve estimator.
#
# We pass it the list of datasets and the name of the model component for which we want to build the light curve.
# We can optionally ask for parameters reoptimization during fit, that is most of the time to fit background normalization in each time bin.
#
# If we don't set any time interval, the `~gammapy.time.LightCurveEstimator` is determines the flux of each dataset and places it at the corresponding time in the light curve.
# Here one dataset equals to one observing run.
# In[ ]:
lc_maker_3d = LightCurveEstimator(analysis_3d.datasets,
source="crab",
reoptimize=False)
lc_3d = lc_maker_3d.run(e_ref=1 * u.TeV, e_min=1.0 * u.TeV, e_max=10.0 * u.TeV)
# The LightCurve object contains a table which we can explore.
# In[ ]:
lc_3d.table["time_min", "time_max", "e_min", "e_max", "flux", "flux_err"]
# ## Running the light curve extraction in 1D
# ### Building the 1D analysis configuration
#
# In[ ]: