def test_effective_area_interpolation():
'''
This test checks if the interpolated effective area is
in a sensible range of values. The data mus have the following
structure:
# comment
# comment
# comment
# log10(E/TeV), A_eff/cm^2
data, data
data, data
.
.
.
'''
a_eff_list = get_effective_area_list()
for a_eff in a_eff_list:
# check that log10(energy/TeV) is > 1 MeV
assert a_eff.x.min() > -6
# check that log10(energy/TeV) is < 1000000 TeV
assert a_eff.x.max() < 7
# check that effective area/cm^2 is positive
assert a_eff.y.min() >= 0
# check that effective area/cm^2 is smaller than earth's surface
assert a_eff.y.max() < 5e18
def test_get_useful_e_0():
'''
This tests if the get_useful_e_0 function
returns sensible values
'''
a_eff_list = get_effective_area_list()
for a_eff in a_eff_list:
e_0 = gls.get_useful_e_0(a_eff)
assert e_0 in [0.1, 1.0, 100000.0]
def test_get_effective_area():
'''
This test checks if the effective area is an interpolated function
'''
# build path to test files
a_eff_list = get_effective_area_list()
for a_eff in a_eff_list:
# check that it is interpolation function
assert isinstance(a_eff, scipy.interpolate.interpolate.interp1d)
def test_get_energy_range():
'''
Test if the function really returns sensible energy ranges
in units of TeV
'''
a_eff_list = get_effective_area_list()
for a_eff in a_eff_list:
e_range = gls.get_energy_range(a_eff)
assert e_range[0] > 5e-6 # all supplied effective areas start > 5 MeV
assert e_range[1] < 1e7 # all supplied effective areas stop < 1e7 TeV
def test_get_effective_area_figure():
'''
Test to check if the above function really returns a matplotlib figure
'''
a_eff_list = get_effective_area_list()
a_eff_figures = [
gls.get_effective_area_figure(a_eff_interpol)
for a_eff_interpol in a_eff_list
]
for plot in a_eff_figures:
assert isinstance(plot, matplotlib.figure.Figure)
def test_inverse_sensitive_energy():
'''
Test if the calculation of the inverse
sensitive energy (calculation of Gamma) makes sense.
'''
gamma_test = -2.6
a_eff = get_effective_area_list()[2] # Veritas V5 lowZd
sensitive_e = gls.sensitive_energy(gamma_test, a_eff)
gamma_inverse = gls.get_gamma_from_sensitive_energy(sensitive_e, a_eff)
assert np.abs(gamma_inverse-gamma_test)/gamma_test < 1e-4
def test_sensitive_energy():
'''
Test if the calculation of the sensitive energy
makes sense.
'''
gamma_test = -2.6
a_eff = get_effective_area_list()[2] # Veritas V5 lowZd
sensitive_e = gls.sensitive_energy(gamma_test, a_eff)
assert sensitive_e < 1.
assert sensitive_e > 0.1
def test_get_ul_spectrum_figure():
'''
Test to check if the above function really returns a matplotlib figure
'''
a_eff_list = get_effective_area_list()
__a, __b, __c, lambda_lim = get_random_on_off_experiment_no_source()
ul_spectrum_figures, energy_xs, dn_de_ys = zip(
*(gls.get_ul_spectrum_figure(
1. * 3600., lambda_lim, a_eff_interpol, e_0=1., n_points_to_plot=2)
for a_eff_interpol in a_eff_list))
for plot in ul_spectrum_figures:
assert isinstance(plot, matplotlib.figure.Figure)
def test_get_ul_phasespace_figure():
'''
Test to check if the above function really returns a matplotlib figure
'''
a_eff_list = get_effective_area_list()
__a, __b, __c, lambda_lim = get_random_on_off_experiment_no_source()
ul_phasespace_figures = [
gls.get_ul_phasespace_figure(1. * 3600.,
lambda_lim,
a_eff_interpol,
pixels_per_line=2)
for a_eff_interpol in a_eff_list
]
for plot in ul_phasespace_figures:
assert isinstance(plot, matplotlib.figure.Figure)
def test_get_sens_spectrum_figure():
'''
Test to check if the above function returns matplotlib figure
'''
a_eff_list = get_effective_area_list()
sens_phasespace_figures, energy_xs, dn_de_ys = zip(
*(gls.get_sens_spectrum_figure(sigma_bg=10. / 3600.,
alpha=0.2,
t_obs=10. * 3600,
a_eff_interpol=a_eff_interpol,
e_0=1.,
n_points_to_plot=2)
for a_eff_interpol in a_eff_list))
for plot in sens_phasespace_figures:
assert isinstance(plot, matplotlib.figure.Figure)
def test_get_sens_phasespace_figure():
'''
Test to check if the above function really returns a matplotlib figure
'''
a_eff_list = get_effective_area_list()
sens_phasespace_figures = [
gls.get_sens_phasespace_figure(sigma_bg=10. / 3600.,
alpha=0.2,
t_obs=100. * 3600,
a_eff_interpol=a_eff_interpol,
pixels_per_line=2)
for a_eff_interpol in a_eff_list
]
for plot in sens_phasespace_figures:
assert isinstance(plot, matplotlib.figure.Figure)
def test_get_ul_f_0():
'''
Test if the calculation of f_0 from the implicit condition
lambda_s = lambda_limi is correct.
This also tests effective_area_averaged_flux() and power_law()
'''
gamma_test = -2.6
a_eff = get_effective_area_list()[2] # Veritas V5 lowZd
f_0_calc = gls.get_ul_f_0(
t_obs=7.*3600.,
lambda_lim=11.3,
a_eff_interpol=a_eff,
e_0=1.,
gamma=gamma_test
)
assert f_0_calc < 2e-13
assert f_0_calc > 1e-13
def test_get_predict_phasespace_figure():
'''
A simple test for the get_phasespace_figure() function
'''
a_eff_list = get_effective_area_list()
chosen_index = 1
predict_phasespace_figure = gls.get_predict_phasespace_figure(
sigma_bg=7. / 3600.,
alpha=0.2,
f_0=1e-12,
df_0=1e-13,
gamma=-2.6,
dgamma=0.1,
e_0=1.,
a_eff_interpol=a_eff_list[chosen_index],
pixels_per_line=2)
assert isinstance(predict_phasespace_figure, matplotlib.figure.Figure)
def test_get_predict_spectrum_figure():
'''
A simple test for the get_spectrum_figure() function
'''
a_eff_list = get_effective_area_list()
chosen_index = 0
predict_spectrum_figure, __a, __b = gls.get_predict_spectrum_figure(
sigma_bg=7. / 3600.,
alpha=0.2,
t_obs_est=[1. * 3600., 2. * 3600., 3. * 3600.],
f_0=1e-12,
df_0=1e-13,
gamma=-2.6,
dgamma=0.1,
e_0=1.,
a_eff_interpol=a_eff_list[chosen_index],
n_points_to_plot=2)
assert isinstance(predict_spectrum_figure, matplotlib.figure.Figure)
def test_integral_spectral_exclusion_zone():
'''
Test the function for calculating the integral spectral exclusion zone
'''
energy_test = 1.2 # TeV
lambda_lim_test = 11.3
t_obs_test = 7.*3600. # in s
a_eff = get_effective_area_list()[2] # Veritas V5 lowZd
f_0_result, gamma_result = gls.integral_spectral_exclusion_zone_parameters(
energy_test, lambda_lim_test, a_eff, t_obs_test)
# test results
assert f_0_result > 1e-14
assert f_0_result < 1e-10
assert gamma_result > -10
assert gamma_result < 0.5
# cross check that the sensitive energy for inferred gamma is actually
# equal (to one permil) to the one calculated with the Lagrangian result
sensitive_e = gls.sensitive_energy(gamma_result, a_eff)
assert np.abs(sensitive_e-energy_test)/energy_test < 1e-4
def test_get_t_obs_samples():
'''
Test if this function retruns a sensible array of t_obs_est
'''
a_eff_list = get_effective_area_list()
chosen_index = 2
a_eff = a_eff_list[chosen_index]
t_obs_samples = gls.get_t_obs_samples(
sigma_bg=7./3600.,
alpha=0.2,
f_0=1e-12,
df_0=1e-13,
gamma=-2.6,
dgamma=0.1,
e_0=1.,
a_eff_interpol=a_eff,
n_samples=5,
)
for t_obs_est in t_obs_samples:
assert t_obs_est > 0.
