def test_instrument_response_set_function_and_convolve():
# A very basic test. More tests will be made against XSpec later
matrix, mc_energies, ebounds = get_matrix_elements()
rsp = InstrumentResponse(matrix, ebounds, mc_energies)
# Integral of a constant, so we know easily what the output should be
integral_function = lambda e1, e2: e2 - e1
rsp.set_function(integral_function)
folded_counts = rsp.convolve()
assert np.all(folded_counts == [1.0, 2.0, 3.0])
def test_response_against_xspec():
# Make a response and write to a FITS OGIP file
matrix, mc_energies, ebounds = get_matrix_elements()
rsp = InstrumentResponse(matrix, ebounds, mc_energies)
temp_file = "__test.rsp"
rsp.to_fits(temp_file, "TEST", "TEST", overwrite=True)
# Test for various photon indexes
for index in np.linspace(-2.0, 2.0, 10):
if index == 1.0:
# This would make the integral of the power law different, so let's just
# skip it
continue
# First reset xspec
xspec.AllData.clear()
# Create a model in XSpec
mo = xspec.Model("po")
# Change the default value for the photon index
# (remember that in XSpec the definition of the powerlaw is norm * E^(-PhoIndex),
# so PhoIndex is positive normally. This is the opposite of astromodels.
mo.powerlaw.PhoIndex = index
mo.powerlaw.norm = 12.2
# Now repeat the same in 3ML
# Generate the astromodels function and set it to the same values as the XSpec power law
# (the pivot in XSpec is set to 1). Remember also that the definition in xspec has the
# sign of the photon index opposite
powerlaw = Powerlaw()
powerlaw.piv = 1.0
powerlaw.index = -mo.powerlaw.PhoIndex.values[0]
powerlaw.K = mo.powerlaw.norm.values[0]
# Exploit the fact that the power law integral is analytic
powerlaw_integral = Powerlaw()
powerlaw_integral.K._transformation = None
powerlaw_integral.K.bounds = (None, None)
powerlaw_integral.index = powerlaw.index.value + 1
powerlaw_integral.K = old_div(powerlaw.K.value, (powerlaw.index.value + 1))
integral_function = lambda e1, e2: powerlaw_integral(e2) - powerlaw_integral(e1)
# Now check that the two convoluted model give the same number of counts in each channel
# Fake a spectrum so we can actually compute the convoluted model
# Get path of response file
fs1 = xspec.FakeitSettings(
temp_file, exposure=1.0, fileName="_fake_spectrum.pha"
)
xspec.AllData.fakeit(noWrite=True, applyStats=False, settings=fs1)
# Get the expected counts
xspec_counts = mo.folded(1)
# Now get the convolution from 3ML
rsp.set_function(integral_function)
threeML_counts = rsp.convolve()
# Compare them
assert np.allclose(xspec_counts, threeML_counts)
os.remove(temp_file)
def test_response_against_xspec():
# Make a response and write to a FITS OGIP file
matrix, mc_energies, ebounds = get_matrix_elements()
rsp = InstrumentResponse(matrix, ebounds, mc_energies)
temp_file = "__test.rsp"
rsp.to_fits(temp_file, "TEST", "TEST", overwrite=True)
# Test for various photon indexes
for index in np.linspace(-2.0, 2.0, 10):
if index == 1.0:
# This would make the integral of the power law different, so let's just
# skip it
continue
# First reset xspec
xspec.AllData.clear()
# Create a model in XSpec
mo = xspec.Model("po")
# Change the default value for the photon index
# (remember that in XSpec the definition of the powerlaw is norm * E^(-PhoIndex),
# so PhoIndex is positive normally. This is the opposite of astromodels.
mo.powerlaw.PhoIndex = index
mo.powerlaw.norm = 12.2
# Now repeat the same in 3ML
# Generate the astromodels function and set it to the same values as the XSpec power law
# (the pivot in XSpec is set to 1). Remember also that the definition in xspec has the
# sign of the photon index opposite
powerlaw = Powerlaw()
powerlaw.piv = 1.0
powerlaw.index = -mo.powerlaw.PhoIndex.values[0]
powerlaw.K = mo.powerlaw.norm.values[0]
# Exploit the fact that the power law integral is analytic
powerlaw_integral = Powerlaw()
powerlaw_integral.K._transformation = None
powerlaw_integral.K.bounds = (None, None)
powerlaw_integral.index = powerlaw.index.value + 1
powerlaw_integral.K = powerlaw.K.value / (powerlaw.index.value + 1)
integral_function = lambda e1, e2: powerlaw_integral(e2) - powerlaw_integral(e1)
# Now check that the two convoluted model give the same number of counts in each channel
# Fake a spectrum so we can actually compute the convoluted model
# Get path of response file
fs1 = xspec.FakeitSettings(temp_file, exposure=1.0, fileName="_fake_spectrum.pha")
xspec.AllData.fakeit(noWrite=True, applyStats=False, settings=fs1)
# Get the expected counts
xspec_counts = mo.folded(1)
# Now get the convolution from 3ML
rsp.set_function(integral_function)
threeML_counts = rsp.convolve()
# Compare them
assert np.allclose(xspec_counts, threeML_counts)
os.remove(temp_file)
