def test_eqwith_err1(make_data_path, restore_xspec_settings):
def check1(e0, e1, e2):
assert e0 == pytest.approx(0.028335201547206704, rel=1.0e-3)
assert e1 == pytest.approx(-0.00744118799274448756, rel=1.0e-3)
assert e2 == pytest.approx(0.0706249544851336, rel=1.0e-3)
ui.set_xsabund('angr')
ui.set_xsxsect('bcmc')
ui.load_pha(make_data_path('3c273.pi'))
ui.notice(0.5, 7.0)
ui.set_stat("chi2datavar")
ui.set_method("simplex")
ui.set_model('powlaw1d.p1+gauss1d.g1')
g1.fwhm = 0.1
g1.pos = 2.0
ui.freeze(g1.pos, g1.fwhm)
ui.fit()
np.random.seed(2345)
e = ui.eqwidth(p1, p1 + g1, error=True, niter=100)
check1(e[0], e[1], e[2])
params = e[3]
np.random.seed(2345)
e = ui.eqwidth(p1, p1 + g1, error=True, params=params, niter=100)
check1(e[0], e[1], e[2])
parvals = ui.get_fit_results().parvals
assert parvals[0] == pytest.approx(1.9055272902160334, rel=1.0e-3)
assert parvals[1] == pytest.approx(0.00017387966749772638, rel=1.0e-3)
assert parvals[2] == pytest.approx(1.279415076070516e-05, rel=1.0e-3)
def test_eqwith_err1(make_data_path, restore_xspec_settings):
def check1(e0, e1, e2):
assert e0 == approx(0.028335201547206704, rel=1.0e-3)
assert e1 == approx(-0.00744118799274448756, rel=1.0e-3)
assert e2 == approx(0.0706249544851336, rel=1.0e-3)
ui.set_xsabund('angr')
ui.set_xsxsect('bcmc')
ui.load_pha(make_data_path('3c273.pi'))
ui.notice(0.5, 7.0)
ui.set_stat("chi2datavar")
ui.set_method("simplex")
ui.set_model('powlaw1d.p1+gauss1d.g1')
g1.fwhm = 0.1
g1.pos = 2.0
ui.freeze(g1.pos, g1.fwhm)
ui.fit()
numpy.random.seed(2345)
e = ui.eqwidth(p1, p1 + g1, error=True, niter=100)
check1(e[0], e[1], e[2])
params = e[3]
numpy.random.seed(2345)
e = ui.eqwidth(p1, p1 + g1, error=True, params=params, niter=100)
check1(e[0], e[1], e[2])
parvals = ui.get_fit_results().parvals
assert parvals[0] == approx(1.9055272902160334, rel=1.0e-3)
assert parvals[1] == approx(0.00017387966749772638, rel=1.0e-3)
assert parvals[2] == approx(1.279415076070516e-05, rel=1.0e-3)
def test_pha_intro(self):
self.run_thread('pha_intro')
# astro.ui imported as ui, instead of
# being in global namespace
self.assertEqualWithinTol(ui.get_fit_results().statval, 37.9079, 1e-4)
self.assertEqualWithinTol(ui.get_fit_results().rstat, 0.902569, 1e-4)
self.assertEqualWithinTol(ui.get_fit_results().qval, 0.651155, 1e-4)
self.assertEqualWithinTol(self.locals['p1'].gamma.val, 2.15852, 1e-4)
self.assertEqualWithinTol(self.locals['p1'].ampl.val, 0.00022484, 1e-4)
self.assertEqualWithinTol(ui.calc_photon_flux(), 0.000469964, 1e-4)
self.assertEqualWithinTol(ui.calc_energy_flux(), 9.614847e-13, 1e-4)
self.assertEqualWithinTol(ui.calc_data_sum(), 706.85714092, 1e-4)
self.assertEqualWithinTol(ui.calc_model_sum(), 638.45693377, 1e-4)
self.assertEqualWithinTol(ui.calc_source_sum(), 0.046996409, 1e-4)
self.assertEqualWithinTol(
ui.eqwidth(self.locals['p1'], ui.get_source()), -0.57731725, 1e-4)
self.assertEqualWithinTol(
ui.calc_kcorr([1, 1.2, 1.4, 1.6, 1.8, 2], 0.5, 2), [
0.93341286, 0.93752836, 0.94325233, 0.94990140, 0.95678054,
0.96393515
], 1e-4)
self.assertEqual(ui.get_fit_results().nfev, 22)
self.assertEqual(ui.get_fit_results().numpoints, 44)
self.assertEqual(ui.get_fit_results().dof, 42)
def cmp_pha_intro(fit_result, p1, covarerr):
assert fit_result.statval == approx(37.9079, rel=1e-4)
assert fit_result.rstat == approx(0.902569, rel=1e-4)
assert fit_result.qval == approx(0.651155, rel=1e-4)
self.assertEqual(fit_result.nfev, 22)
self.assertEqual(fit_result.numpoints, 44)
self.assertEqual(fit_result.dof, 42)
p1.gamma.val == approx(2.15852, rel=1e-4)
p1.ampl.val == approx(0.00022484, rel=1e-4)
assert ui.calc_photon_flux() == approx(0.000469964, rel=1e-4)
assert ui.calc_energy_flux() == approx(9.614847e-13, rel=1e-4)
assert ui.calc_data_sum() == approx(706.85714092, rel=1e-4)
assert ui.calc_model_sum() == approx(638.45693377, rel=1e-4)
assert ui.calc_source_sum() == approx(0.046996409, rel=1e-4)
calc = ui.eqwidth(self.locals['p1'], ui.get_source())
assert calc == approx(-0.57731725, rel=1e-4)
calc = ui.calc_kcorr([1, 1.2, 1.4, 1.6, 1.8, 2], 0.5, 2)
# Prior to fixing #619 the expected values were
# expected = [0.93341286, 0.93752836, 0.94325233,
# 0.94990140, 0.95678054, 0.96393515]
expected = [0.93132747, 0.9352768, 0.94085917,
0.94738472, 0.95415463, 0.96121113]
assert calc == approx(expected, rel=1e-4)
def test_eqwidth_err_needs_fit(clean_astro_ui):
"""We get an error if fit has not been called"""
ui.load_arrays(2, [1, 5], [1, 12], ui.Data1D)
cmdl = ui.const1d.cmdl
pmdl = ui.polynom1d.pmdl
cmdl.c0 = 1.5
pmdl.c0 = -5
pmdl.c1 = 2
ui.set_source(2, cmdl + pmdl)
with pytest.raises(SessionErr) as exc:
ui.eqwidth(cmdl, cmdl + pmdl, id=2, error=True)
assert str(exc.value) == 'no fit has been performed'
def test_pha_intro(self):
self.run_thread("pha_intro")
# astro.ui imported as ui, instead of
# being in global namespace
self.assertEqualWithinTol(ui.get_fit_results().statval, 37.9079, 1e-4)
self.assertEqualWithinTol(ui.get_fit_results().rstat, 0.902569, 1e-4)
self.assertEqualWithinTol(ui.get_fit_results().qval, 0.651155, 1e-4)
self.assertEqualWithinTol(self.locals["p1"].gamma.val, 2.15852, 1e-4)
self.assertEqualWithinTol(self.locals["p1"].ampl.val, 0.00022484, 1e-4)
self.assertEqualWithinTol(ui.calc_photon_flux(), 0.000469964, 1e-4)
self.assertEqualWithinTol(ui.calc_energy_flux(), 9.614847e-13, 1e-4)
self.assertEqualWithinTol(ui.calc_data_sum(), 706.85714092, 1e-4)
self.assertEqualWithinTol(ui.calc_model_sum(), 638.45693377, 1e-4)
self.assertEqualWithinTol(ui.calc_source_sum(), 0.046996409, 1e-4)
self.assertEqualWithinTol(ui.eqwidth(self.locals["p1"], ui.get_source()), -0.57731725, 1e-4)
self.assertEqualWithinTol(
ui.calc_kcorr([1, 1.2, 1.4, 1.6, 1.8, 2], 0.5, 2),
[0.93341286, 0.93752836, 0.94325233, 0.94990140, 0.95678054, 0.96393515],
1e-4,
)
self.assertEqual(ui.get_fit_results().nfev, 22)
self.assertEqual(ui.get_fit_results().numpoints, 44)
self.assertEqual(ui.get_fit_results().dof, 42)
def cmp_thread(fit_result, p1, covarerr):
assert fit_result.statval == approx(37.9079, rel=1e-4)
assert fit_result.rstat == approx(0.902569, rel=1e-4)
assert fit_result.qval == approx(0.651155, rel=1e-4)
assert fit_result.nfev == 22
assert fit_result.numpoints == 44
assert fit_result.dof == 42
p1.gamma.val == approx(2.15852, rel=1e-4)
p1.ampl.val == approx(0.00022484, rel=1e-4)
assert ui.calc_photon_flux() == approx(0.000469964, rel=1e-4)
assert ui.calc_energy_flux() == approx(9.614847e-13, rel=1e-4)
assert ui.calc_data_sum() == approx(706.85714092, rel=1e-4)
assert ui.calc_model_sum() == approx(638.45693377, rel=1e-4)
assert ui.calc_source_sum() == approx(0.046996409, rel=1e-4)
calc = ui.eqwidth(p1, ui.get_source())
assert calc == approx(-0.57731725, rel=1e-4)
calc = ui.calc_kcorr([1, 1.2, 1.4, 1.6, 1.8, 2], 0.5, 2)
expected = [
0.93132747, 0.9352768, 0.94085917, 0.94738472, 0.95415463,
0.96121113
]
assert calc == approx(expected, rel=1e-4)
def test_pha_intro(self):
self.run_thread('pha_intro')
# astro.ui imported as ui, instead of
# being in global namespace
fit_results = ui.get_fit_results()
covarerr = sqrt(fit_results.extra_output['covar'].diagonal())
assert covarerr[0] == approx(0.0790393, rel=1e-4)
assert covarerr[1] == approx(1.4564e-05, rel=1e-4)
assert fit_results.statval == approx(37.9079, rel=1e-4)
assert fit_results.rstat == approx(0.902569, rel=1e-4)
assert fit_results.qval == approx(0.651155, rel=1e-4)
assert self.locals['p1'].gamma.val == approx(2.15852, rel=1e-4)
assert self.locals['p1'].ampl.val == approx(0.00022484, rel=1e-4)
assert ui.calc_photon_flux() == approx(0.000469964, rel=1e-4)
assert ui.calc_energy_flux() == approx(9.614847e-13, rel=1e-4)
assert ui.calc_data_sum() == approx(706.85714092, rel=1e-4)
assert ui.calc_model_sum() == approx(638.45693377, rel=1e-4)
assert ui.calc_source_sum() == approx(0.046996409, rel=1e-4)
calc = ui.eqwidth(self.locals['p1'], ui.get_source())
assert calc == approx(-0.57731725, rel=1e-4)
calc = ui.calc_kcorr([1, 1.2, 1.4, 1.6, 1.8, 2], 0.5, 2)
expected = [0.93341286, 0.93752836, 0.94325233,
0.94990140, 0.95678054, 0.96393515]
assert calc == approx(expected, rel=1e-4)
self.assertEqual(ui.get_fit_results().nfev, 22)
self.assertEqual(ui.get_fit_results().numpoints, 44)
self.assertEqual(ui.get_fit_results().dof, 42)
def test_eqwith_err(make_data_path, restore_xspec_settings):
def check(a0, a1, a2):
assert a0 == approx(0.16443033244310976, rel=1e-3)
assert a1 == approx(0.09205564216156815, rel=1e-3)
assert a2 == approx(0.23933118287470895, rel=1e-3)
ui.set_method('neldermead')
ui.set_stat('cstat')
ui.set_xsabund('angr')
ui.set_xsxsect('bcmc')
ui.load_data(make_data_path('12845.pi'))
ui.notice(0.5, 7)
ui.set_model("xsphabs.gal*xszphabs.zabs*(powlaw1d.p1+xszgauss.g1)")
ui.set_par(gal.nh, 0.08)
ui.freeze(gal)
ui.set_par(zabs.redshift, 0.518)
ui.set_par(g1.redshift, 0.518)
ui.set_par(g1.Sigma, 0.01)
ui.freeze(g1.Sigma)
ui.set_par(g1.LineE, min=6.0, max=7.0)
ui.fit()
numpy.random.seed(12345)
result = ui.eqwidth(p1, p1 + g1, error=True, niter=100)
check(result[0], result[1], result[2])
params = result[3]
numpy.random.seed(12345)
result = ui.eqwidth(p1, p1 + g1, error=True, params=params, niter=100)
check(result[0], result[1], result[2])
parvals = ui.get_fit_results().parvals
assert parvals[0] == approx(0.6111340686157877, rel=1.0e-3)
assert parvals[1] == approx(1.6409785803466297, rel=1.0e-3)
assert parvals[2] == approx(8.960926761312153e-05, rel=1.0e-3)
assert parvals[3] == approx(6.620017726014523, rel=1.0e-3)
assert parvals[4] == approx(1.9279114810359657e-06, rel=1.0e-3)
def test_eqwith_err(make_data_path, restore_xspec_settings):
def check(a0, a1, a2):
assert a0 == pytest.approx(0.16443033244310976, rel=1e-3)
assert a1 == pytest.approx(0.09205564216156815, rel=1e-3)
assert a2 == pytest.approx(0.23933118287470895, rel=1e-3)
ui.set_method('neldermead')
ui.set_stat('cstat')
ui.set_xsabund('angr')
ui.set_xsxsect('bcmc')
ui.load_data(make_data_path('12845.pi'))
ui.notice(0.5, 7)
ui.set_model("xsphabs.gal*xszphabs.zabs*(powlaw1d.p1+xszgauss.g1)")
ui.set_par(gal.nh, 0.08)
ui.freeze(gal)
ui.set_par(zabs.redshift, 0.518)
ui.set_par(g1.redshift, 0.518)
ui.set_par(g1.Sigma, 0.01)
ui.freeze(g1.Sigma)
ui.set_par(g1.LineE, min=6.0, max=7.0)
ui.fit()
np.random.seed(12345)
result = ui.eqwidth(p1, p1 + g1, error=True, niter=100)
check(result[0], result[1], result[2])
params = result[3]
np.random.seed(12345)
result = ui.eqwidth(p1, p1 + g1, error=True, params=params, niter=100)
check(result[0], result[1], result[2])
parvals = ui.get_fit_results().parvals
assert parvals[0] == pytest.approx(0.6111340686157877, rel=1.0e-3)
assert parvals[1] == pytest.approx(1.6409785803466297, rel=1.0e-3)
assert parvals[2] == pytest.approx(8.960926761312153e-05, rel=1.0e-3)
assert parvals[3] == pytest.approx(6.620017726014523, rel=1.0e-3)
assert parvals[4] == pytest.approx(1.9279114810359657e-06, rel=1.0e-3)
def test_eqwidth_err_arg_is_numpy(arg, clean_astro_ui):
"""Ensure argument is a NumPy argument."""
ui.load_arrays('bob', [1, 5], [1, 12], ui.Data1D)
cmdl = ui.const1d.cmdl
pmdl = ui.polynom1d.pmdl
cmdl.c0 = 1.5
pmdl.c0 = -5
pmdl.c1 = 2
ui.set_source('bob', cmdl + pmdl)
ui.fit('bob')
arglist = {'id': 'bob', 'error': True, 'niter': 100, arg: 2.3}
with pytest.raises(IOErr) as exc:
ui.eqwidth(cmdl, cmdl + pmdl, **arglist)
assert str(exc.value) == f'{arg} must be of type numpy.ndarray'
def test_eqwidth_err_arg_size(arg, clean_astro_ui):
"""Ensure argument is the correct size."""
ui.load_arrays('bob', [1, 5], [1, 12], ui.Data1D)
cmdl = ui.const1d.cmdl
pmdl = ui.polynom1d.pmdl
cmdl.c0 = 1.5
pmdl.c0 = -5
pmdl.c1 = 2
ui.set_source('bob', cmdl + pmdl)
ui.fit('bob')
arglist = {'id': 'bob', 'error': True, 'niter': 100,
arg: np.arange(12).reshape(3, 4)}
with pytest.raises(IOErr) as exc:
ui.eqwidth(cmdl, cmdl + pmdl, **arglist)
assert str(exc.value) == f'{arg} must be of dimension (2, x)'
def test_eqwidth_err_arg_square(clean_astro_ui):
"""Ensure argument is square.
It looks like this is only checked for with covar_matrix argument
"""
ui.load_arrays('bob', [1, 5], [1, 12], ui.Data1D)
cmdl = ui.const1d.cmdl
pmdl = ui.polynom1d.pmdl
cmdl.c0 = 1.5
pmdl.c0 = -5
pmdl.c1 = 2
ui.set_source('bob', cmdl + pmdl)
ui.fit('bob')
arglist = {'id': 'bob', 'error': True, 'niter': 100,
'covar_matrix': np.arange(12).reshape(2, 6)}
with pytest.raises(IOErr) as exc:
ui.eqwidth(cmdl, cmdl + pmdl, **arglist)
assert str(exc.value) == 'covar_matrix must be of dimension (2, 2)'
def test_numpy_histogram_density_vs_normed():
from sherpa.astro import ui
ui.load_arrays(1, [1, 2, 3], [1, 2, 3])
ui.set_source('const1d.c')
c = ui.get_model_component('c')
ui.fit()
res = ui.eqwidth(c, c+c, error=True)
ui.plot_pdf(res[4])
plot = ui.get_pdf_plot()
expected_x = numpy.linspace(2.5, 3.5, 13)
expected_xlo, expected_xhi = expected_x[:-1], expected_x[1:]
expected_y = [0, 0, 0, 0, 0, 0, 12, 0, 0, 0, 0, 0]
assert plot.y == pytest.approx(expected_y)
assert plot.xlo == pytest.approx(expected_xlo)
assert plot.xhi == pytest.approx(expected_xhi)
