def test_ford1984_fixed_sampling():
"""
Compare EDP-calculation to published data of Ford1984 using fixed
samplingpoints.
"""
u_begin = 0.1
u_end = 1e4
u_sampling = 30
a_range_ford = AngularRange(
quantity="u",
unit="None",
begin=u_begin,
end=u_end,
sampling_points=u_sampling,
scale="log")
wl_ford = 633.
edp = EDP(stack_ford(), wl_ford, a_range_ford)
edp.calc()
expected_us = np.logspace(
np.log10(u_begin), np.log10(u_end), u_sampling)
# Depending on the used integration rule for presampled data the expeced
# results change. The choices for integration rules are
# 'simpson rule' or 'trapezoidal rule'. We use simpson rule.
expected_fu_simp = np.array([
2.26251490e-10, 7.48538564e-10, 2.49354914e-09, 8.44135783e-09,
2.97679978e-08, 1.19471423e-07, 4.12958094e-08, 4.59544802e-09,
5.02343278e-09, 7.98190669e-09, 1.51456092e-08, 3.13764552e-08,
6.74498466e-08, 1.46902522e-07, 3.20916627e-07, 6.99828263e-07,
1.51834593e-06, 3.26581994e-06, 6.93147240e-06, 1.44198875e-05,
2.91154047e-05, 5.62299028e-05, 1.01641970e-04, 1.66506172e-04,
2.35615633e-04, 2.68167554e-04, 2.20773058e-04, 1.12272195e-04,
2.78883745e-05, 2.38642019e-06
])
# expected_fu_trapz = np.array([
# 2.2209952640e-10, 7.3480208673e-10, 2.4477898665e-09,
# 8.2864494495e-09, 2.9221722578e-08, 1.1727899292e-07,
# 4.0537987838e-08, 4.5111162433e-09, 4.9312469264e-09,
# 7.8354303257e-09, 1.4867671080e-08, 3.0800661873e-08,
# 6.6212065183e-08, 1.4420670611e-07, 3.1502742945e-07,
# 6.8698569979e-07, 1.4904825231e-06, 3.2058884131e-06,
# 6.8042720600e-06, 1.4155266001e-05, 2.8581105859e-05,
# 5.5198022429e-05, 9.9776726635e-05, 1.6345060860e-04,
# 2.3129182464e-04, 2.6324636722e-04, 2.1672163426e-04,
# 1.1021187688e-04, 2.7376590798e-05, 2.3426268963e-06
# ])
np.testing.assert_allclose(expected_us, edp.us, rtol=3e-07, atol=1e-16)
np.testing.assert_allclose(
expected_fu_simp, edp.f_u, rtol=3e-07, atol=1e-16)
def run_EDP(self):
edp = EDP(self.stack, self.sp_sys.value, self.ar_sys)
edp.calc()
self.ang = AngularArray(self.angularrange_user.quantity,
self.angularrange_user.unit,
self.ang_conv.num_s_to_u(edp.us))
ang_qtty = self.angularrange_user.quantity
jcb_u_to_ang = self.ang_conv.jcb_s_to_u(edp.us)
self.f_ang = QunttyArray("emission propability density", "?",
"f({0})".format(ang_qtty),
edp.f_u * jcb_u_to_ang)
self.f_hTE_ang = QunttyArray("emission propability density hTE", "?",
"f_hTE({0})".format(ang_qtty),
edp.f_hTE_u * jcb_u_to_ang)
self.f_hTM_ang = QunttyArray("emission propability density hTM", "?",
"f_hTM({0})".format(ang_qtty),
edp.f_hTM_u * jcb_u_to_ang)
self.f_vTM_ang = QunttyArray("emission propability density vTM", "?",
"f_vTM({0})".format(ang_qtty),
edp.f_vTM_u * jcb_u_to_ang)
self.p_ang = QunttyArray("power density", "?",
"p({0})".format(ang_qtty),
edp.p_u * jcb_u_to_ang)
self.p_hTE_ang = QunttyArray("power density hTE", "?",
"p_hTE({0})".format(ang_qtty),
edp.p_hTE_u * jcb_u_to_ang)
self.p_hTM_ang = QunttyArray("power density hTM", "?",
"p_hTM({0})".format(ang_qtty),
edp.p_hTM_u * jcb_u_to_ang)
self.p_vTM_ang = QunttyArray("power density vTM", "?",
"p_vTM({0})".format(ang_qtty),
edp.p_vTM_u * jcb_u_to_ang)
self.Re_w = QunttyArray("Re(w)", "None", "Re(w)", edp.we_u.real)
self.Im_w = QunttyArray("Im(w)", "None", "Im(w)", edp.we_u.imag)
self.PinP0 = QunttyValue("emitted power", "P0", "P", edp.P)