def test_compute_ts_map(input_maps):
"""Minimal test of compute_ts_image"""
kernel = Gaussian2DKernel(5)
ts_estimator = TSMapEstimator(method="leastsq iter", threshold=1)
result = ts_estimator.run(input_maps, kernel=kernel)
assert "leastsq iter" in repr(ts_estimator)
assert_allclose(result["ts"].data[99, 99], 1714.23, rtol=1e-2)
assert_allclose(result["niter"].data[99, 99], 3)
assert_allclose(result["flux"].data[99, 99], 1.02e-09, rtol=1e-2)
assert_allclose(result["flux_err"].data[99, 99], 3.84e-11, rtol=1e-2)
assert_allclose(result["flux_ul"].data[99, 99], 1.10e-09, rtol=1e-2)
def test_compute_ts_map_downsampled(input_maps):
"""Minimal test of compute_ts_image"""
kernel = Gaussian2DKernel(2.5)
ts_estimator = TSMapEstimator(method="root brentq",
error_method="conf",
ul_method="conf")
result = ts_estimator.run(input_maps, kernel=kernel, downsampling_factor=2)
assert_allclose(result["ts"].data[99, 99], 1675.28, rtol=1e-2)
assert_allclose(result["niter"].data[99, 99], 7)
assert_allclose(result["flux"].data[99, 99], 1.02e-09, rtol=1e-2)
assert_allclose(result["flux_err"].data[99, 99], 3.84e-11, rtol=1e-2)
assert_allclose(result["flux_ul"].data[99, 99], 1.10e-09, rtol=1e-2)
def test_compute_ts_map_newton(input_dataset):
"""Minimal test of compute_ts_image"""
kernel = Gaussian2DKernel(5)
ts_estimator = TSMapEstimator(method="root newton", threshold=1)
result = ts_estimator.run(input_dataset, kernel=kernel)
assert "root newton" in repr(ts_estimator)
assert_allclose(result["ts"].data[99, 99], 1714.23, rtol=1e-2)
assert_allclose(result["niter"].data[99, 99], 0)
assert_allclose(result["flux"].data[99, 99], 1.02e-09, rtol=1e-2)
assert_allclose(result["flux_err"].data[99, 99], 3.84e-11, rtol=1e-2)
assert_allclose(result["flux_ul"].data[99, 99], 1.10e-09, rtol=1e-2)
assert result["flux"].unit == u.Unit("cm-2s-1")
assert result["flux_err"].unit == u.Unit("cm-2s-1")
assert result["flux_ul"].unit == u.Unit("cm-2s-1")
# Check mask is correctly taken into account
assert np.isnan(result["ts"].data[30, 40])
def test_compute_ts_map_downsampled(input_dataset):
"""Minimal test of compute_ts_image"""
kernel = Gaussian2DKernel(2.5)
ts_estimator = TSMapEstimator(method="root brentq",
error_method="conf",
ul_method="conf")
result = ts_estimator.run(input_dataset,
kernel=kernel,
downsampling_factor=2)
assert_allclose(result["ts"].data[99, 99], 1675.28, rtol=1e-2)
assert_allclose(result["niter"].data[99, 99], 7)
assert_allclose(result["flux"].data[99, 99], 1.02e-09, rtol=1e-2)
assert_allclose(result["flux_err"].data[99, 99], 3.84e-11, rtol=1e-2)
assert_allclose(result["flux_ul"].data[99, 99], 1.10e-09, rtol=1e-2)
assert result["flux"].unit == u.Unit("cm-2s-1")
assert result["flux_err"].unit == u.Unit("cm-2s-1")
assert result["flux_ul"].unit == u.Unit("cm-2s-1")
# Check mask is correctly taken into account
assert np.isnan(result["ts"].data[30, 40])
def test_incorrect_method():
with pytest.raises(ValueError):
TSMapEstimator(method="bad")
with pytest.raises(ValueError):
TSMapEstimator(error_method="bad")