def test_case118_data(self):
    net = pn.case118()
    self.converter.set_sn_mva(net.sn_mva)
    self.converter.set_f_hz(net.f_hz)
    (line_r, line_x, line_h) = self.converter.get_line_param((net.line['r_ohm_per_km'].values * net.line['length_km'].values), (net.line['x_ohm_per_km'].values * net.line['length_km'].values), (net.line['c_nf_per_km'].values * net.line['length_km'].values), (net.line['g_us_per_km'].values * net.line['length_km'].values), net.bus.loc[net.line['from_bus']]['vn_kv'], net.bus.loc[net.line['to_bus']]['vn_kv'])
    res_r = np.array([0.000303, 0.000129, 5.95e-05, 8.82e-05, 0.000488, 0.000446, 8.66e-05, 0.000401, 0.000428, 0.000405, 0.000123, 0.000444, 0.000309, 0.000187, 0.000601, 3.76e-05, 5.46e-05, 0.00017, 0.000294, 0.000156, 0.000298, 0.000112, 0.000625, 0.00043, 0.000484, 0.000302, 0.00035, 0.0002, 0.000239, 0.000139, 0.000518, 0.000238, 0.000254, 9.9e-05, 0.000393, 8.62e-05, 0.000387, 0.000258, 0.000481, 0.000223, 0.000132, 0.000356, 0.000162, 0.000269, 0.000183, 0.000238, 0.0002225, 0.000454, 0.000648, 0.000178, 0.000171, 0.000173, 0.000397, 0.00018, 0.000277, 0.000123, 0.000246, 0.000215, 0.00016, 0.000451, 0.000466, 0.000535, 0.000605, 9.94e-05, 0.00014, 0.00053, 0.000261, 0.00053, 0.000744, 0.000105, 0.0003906, 0.000278, 0.00022, 0.000247, 9.13e-05, 0.000615, 0.000135, 0.000164, 2.3e-05, 0.000595, 0.000329, 0.000145, 0.000164, 0.000212, 0.000132, 1.76e-05, 0.000454, 0.000123, 0.0001119, 0.000252, 0.00012, 0.000183, 0.000209, 0.000342, 0.000135, 0.000156, 0.000241, 0.000318, 0.0001913, 0.000237, 4.31e-05, 7.99e-05, 0.000474, 0.000108, 0.000317, 0.000298, 0.000229, 0.000119, 0.00038, 0.000752, 2.24e-05, 0.00011, 0.000415, 8.71e-05, 2.56e-05, 0.000321, 0.000593, 4.64e-05, 4.59e-05, 0.000184, 0.000145, 0.000555, 0.00041, 0.000608, 0.000413, 0.000224, 0.0004, 0.00038, 0.000601, 2.44e-05, 0.000191, 0.000715, 0.000715, 0.000684, 0.000179, 0.000267, 0.000486, 0.000203, 0.000405, 0.000263, 2.58e-05, 0.00073, 0.000869, 0.000169, 2.75e-05, 4.88e-05, 0.000343, 0.000474, 0.000343, 0.000255, 0.000503, 0.000209, 0.000825, 0.000803, 0.0004739, 0.000317, 0.000328, 2.64e-05, 0.000123, 8.24e-05, 1.72e-05, 9.01e-05, 0.000203, 2.69e-05, 0.00018, 0.00018, 0.000482, 0.000258, 0.000224, 0.000844, 0.000985, 0.0003, 2.21e-05])
    res_x = np.array([0.000999, 0.000424, 0.000196, 0.000355, 0.00196, 0.0018, 0.000454, 0.001323, 0.00141, 0.00122, 0.000406, 0.00148, 0.00101, 0.000616, 0.001999, 0.000124, 0.000244, 0.000485, 0.00105, 0.000704, 0.000853, 0.0003665, 0.00132, 0.00148, 0.0016, 0.000641, 0.00123, 0.00102, 0.00173, 0.000712, 0.00188, 0.000997, 0.000836, 0.000505, 0.001581, 0.00034, 0.001272, 0.000848, 0.00158, 0.000732, 0.000434, 0.00182, 0.00053, 0.000869, 0.000934, 0.00108, 0.000731, 0.00206, 0.00295, 0.00058, 0.000547, 0.000885, 0.00179, 0.000813, 0.001262, 0.000559, 0.00112, 0.000707, 0.000525, 0.00204, 0.001584, 0.001625, 0.00229, 0.000378, 0.000547, 0.00183, 0.000703, 0.00183, 0.002444, 0.000288, 0.001813, 0.000762, 0.000755, 0.00064, 0.000301, 0.00203, 0.000612, 0.000741, 0.000104, 0.00195, 0.0014, 0.000481, 0.000544, 0.000834, 0.000437, 7.98e-05, 0.001801, 0.000505, 0.000493, 0.00117, 0.000394, 0.000849, 0.00097, 0.00159, 0.000492, 0.0008, 0.00108, 0.00163, 0.000855, 0.000943, 0.000504, 0.00086, 0.001563, 0.000331, 0.001153, 0.000985, 0.000755, 0.00054, 0.001244, 0.00247, 0.000102, 0.000497, 0.00142, 0.000268, 9.4e-05, 0.00106, 0.00168, 0.00054, 0.000208, 0.000605, 0.000487, 0.00183, 0.00135, 0.002454, 0.001681, 0.000901, 0.001356, 0.00127, 0.00189, 0.000305, 0.000625, 0.00323, 0.00323, 0.00186, 0.000505, 0.000752, 0.00137, 0.000588, 0.001635, 0.00122, 0.000322, 0.00289, 0.00291, 0.000707, 9.55e-05, 0.000151, 0.000966, 0.00134, 0.000966, 0.000719, 0.002293, 0.000688, 0.00251, 0.00239, 0.002158, 0.00145, 0.0015, 0.000135, 0.000561, 0.000376, 0.0002, 0.000986, 0.000682, 0.000302, 0.000919, 0.000919, 0.00218, 0.00117, 0.001015, 0.002778, 0.00324, 0.00127, 0.004115])
    res_h = np.array([(2.54 + 0j), (1.082 + 0j), (0.502 + 0j), (0.878 + 0j), (4.88 + 0j), (4.444 + 0j), (1.178 + 0j), (3.368 + 0j), (3.6 + 0j), (12.4 + 0j), (1.034 + 0j), (3.68 + 0j), (10.38 + 0j), (1.572 + 0j), (4.978 + 0j), (1.264 + 0j), (0.648 + 0j), (4.72 + 0j), (2.28 + 0j), (1.87 + 0j), (8.174 + 0j), (3.796 + 0j), (2.58 + 0j), (3.48 + 0j), (4.06 + 0j), (1.234 + 0j), (2.76 + 0j), (2.76 + 0j), (4.7 + 0j), (1.934 + 0j), (5.28 + 0j), (10.6 + 0j), (2.14 + 0j), (5.48 + 0j), (4.14 + 0j), (0.874 + 0j), (3.268 + 0j), (2.18 + 0j), (4.06 + 0j), (1.876 + 0j), (1.11 + 0j), (4.94 + 0j), (5.44 + 0j), (2.3 + 0j), (2.54 + 0j), (2.86 + 0j), (1.876 + 0j), (5.46 + 0j), (4.72 + 0j), (6.04 + 0j), (1.474 + 0j), (2.4 + 0j), (4.76 + 0j), (2.16 + 0j), (3.28 + 0j), (1.464 + 0j), (2.94 + 0j), (1.816 + 0j), (5.36 + 0j), (5.41 + 0j), (4.07 + 0j), (4.08 + 0j), (6.2 + 0j), (0.986 + 0j), (1.434 + 0j), (4.72 + 0j), (1.844 + 0j), (4.72 + 0j), (6.268 + 0j), (0.76 + 0j), (4.61 + 0j), (2.02 + 0j), (2.0 + 0j), (6.2 + 0j), (0.768 + 0j), (5.18 + 0j), (1.628 + 0j), (1.972 + 0j), (0.276 + 0j), (5.02 + 0j), (3.58 + 0j), (1.198 + 0j), (1.356 + 0j), (2.14 + 0j), (4.44 + 0j), (0.21 + 0j), (4.66 + 0j), (1.298 + 0j), (1.142 + 0j), (2.98 + 0j), (1.01 + 0j), (2.16 + 0j), (2.46 + 0j), (4.04 + 0j), (4.98 + 0j), (8.64 + 0j), (2.84 + 0j), (17.64 + 0j), (2.16 + 0j), (2.38 + 0j), (51.4 + 0j), (90.8 + 0j), (3.99 + 0j), (0.83 + 0j), (11.73 + 0j), (2.51 + 0j), (1.926 + 0j), (1.426 + 0j), (3.194 + 0j), (6.32 + 0j), (0.268 + 0j), (1.318 + 0j), (3.66 + 0j), (0.568 + 0j), (0.984 + 0j), (2.7 + 0j), (4.2 + 0j), (42.2 + 0j), (0.55 + 0j), (1.552 + 0j), (1.222 + 0j), (4.66 + 0j), (3.44 + 0j), (6.068 + 0j), (4.226 + 0j), (2.24 + 0j), (3.32 + 0j), (3.16 + 0j), (4.72 + 0j), (116.2 + 0j), (1.604 + 0j), (8.6 + 0j), (8.6 + 0j), (4.44 + 0j), (1.258 + 0j), (1.874 + 0j), (3.42 + 0j), (1.396 + 0j), (4.058 + 0j), (3.1 + 0j), (123.0 + 0j), (7.38 + 0j), (7.3 + 0j), (2.02 + 0j), (0.732 + 0j), (0.374 + 0j), (2.42 + 0j), (3.32 + 0j), (2.42 + 0j), (1.788 + 0j), (5.98 + 0j), (1.748 + 0j), (5.69 + 0j), (5.36 + 0j), (5.646 + 0j), (3.76 + 0j), (3.88 + 0j), (1.456 + 0j), (1.468 + 0j), (0.98 + 0j), (21.6 + 0j), (104.6 + 0j), (1.738 + 0j), (38.0 + 0j), (2.48 + 0j), (2.48 + 0j), (5.78 + 0j), (3.1 + 0j), (2.682 + 0j), (7.092 + 0j), (8.28 + 0j), (12.2 + 0j), (10.198 + 0j)])
    self.assert_equal(line_r, res_r)
    self.assert_equal(line_x, res_x)
    self.assert_equal(line_h, res_h)
    pp_net = net
    tap_step_pct = (1.0 * pp_net.trafo['tap_step_percent'].values)
    tap_step_pct[(~ np.isfinite(tap_step_pct))] = 0.0
    tap_pos = (1.0 * pp_net.trafo['tap_pos'].values)
    tap_pos[(~ np.isfinite(tap_pos))] = 0.0
    is_tap_hv_side = (pp_net.trafo['tap_side'].values == 'hv')
    is_tap_hv_side[(~ np.isfinite(is_tap_hv_side))] = True
    tap_angles_ = (1.0 * pp_net.trafo['tap_step_degree'].values)
    tap_angles_[(~ np.isfinite(tap_angles_))] = 0.0
    tap_angles_ = np.deg2rad(tap_angles_)
    (trafo_r, trafo_x, trafo_b) = self.converter.get_trafo_param(tap_step_pct, tap_pos, tap_angles_, is_tap_hv_side, pp_net.bus.loc[pp_net.trafo['hv_bus']]['vn_kv'], pp_net.bus.loc[pp_net.trafo['lv_bus']]['vn_kv'], pp_net.trafo['vk_percent'].values, pp_net.trafo['vkr_percent'].values, pp_net.trafo['sn_mva'].values, pp_net.trafo['pfe_kw'].values, pp_net.trafo['i0_percent'].values)
    trafo_r_res = np.array([0.0, 0.0, 0.0, 0.000281494977, 3.39887086e-06, 0.0, 0.0, 0.0, 0.0, 0.0, 1.37295648e-05, 0.0, 1.7357186e-05])
    trafo_x_res = np.array([0.000267, 0.000382, 0.00037, 0.00206930358, 4.04933224e-05, 0.000388, 0.000375, 0.000386, 0.000268, 0.00037, 0.000159594718, 0.00037, 0.000201181945])
    trafo_h_res = np.array([(0.0 - 0j), (0.0 - 0j), (0.0 - 0j), (4.4602909 - 0.00140652j), (16.40272367 - 0.00022869j), (0.0 - 0j), (0.0 - 0j), (0.0 - 0j), (0.0 - 0j), (0.0 - 0j), (63.96323106 - 0.01411497j), (0.0 - 0j), (81.1310369 - 0.02879733j)])
    self.assert_equal(trafo_r, trafo_r_res)
    self.assert_equal(trafo_x, trafo_x_res)
    self.assert_equal(trafo_b, trafo_h_res)
def test_case118_data(self):
    net = pn.case118()
    self.converter.set_sn_mva(net.sn_mva)
    self.converter.set_f_hz(net.f_hz)
    (line_r, line_x, line_h) = self.converter.get_line_param((net.line['r_ohm_per_km'].values * net.line['length_km'].values), (net.line['x_ohm_per_km'].values * net.line['length_km'].values), (net.line['c_nf_per_km'].values * net.line['length_km'].values), (net.line['g_us_per_km'].values * net.line['length_km'].values), net.bus.loc[net.line['from_bus']]['vn_kv'], net.bus.loc[net.line['to_bus']]['vn_kv'])
    res_r = np.array([0.000303, 0.000129, 5.95e-05, 8.82e-05, 0.000488, 0.000446, 8.66e-05, 0.000401, 0.000428, 0.000405, 0.000123, 0.000444, 0.000309, 0.000187, 0.000601, 3.76e-05, 5.46e-05, 0.00017, 0.000294, 0.000156, 0.000298, 0.000112, 0.000625, 0.00043, 0.000484, 0.000302, 0.00035, 0.0002, 0.000239, 0.000139, 0.000518, 0.000238, 0.000254, 9.9e-05, 0.000393, 8.62e-05, 0.000387, 0.000258, 0.000481, 0.000223, 0.000132, 0.000356, 0.000162, 0.000269, 0.000183, 0.000238, 0.0002225, 0.000454, 0.000648, 0.000178, 0.000171, 0.000173, 0.000397, 0.00018, 0.000277, 0.000123, 0.000246, 0.000215, 0.00016, 0.000451, 0.000466, 0.000535, 0.000605, 9.94e-05, 0.00014, 0.00053, 0.000261, 0.00053, 0.000744, 0.000105, 0.0003906, 0.000278, 0.00022, 0.000247, 9.13e-05, 0.000615, 0.000135, 0.000164, 2.3e-05, 0.000595, 0.000329, 0.000145, 0.000164, 0.000212, 0.000132, 1.76e-05, 0.000454, 0.000123, 0.0001119, 0.000252, 0.00012, 0.000183, 0.000209, 0.000342, 0.000135, 0.000156, 0.000241, 0.000318, 0.0001913, 0.000237, 4.31e-05, 7.99e-05, 0.000474, 0.000108, 0.000317, 0.000298, 0.000229, 0.000119, 0.00038, 0.000752, 2.24e-05, 0.00011, 0.000415, 8.71e-05, 2.56e-05, 0.000321, 0.000593, 4.64e-05, 4.59e-05, 0.000184, 0.000145, 0.000555, 0.00041, 0.000608, 0.000413, 0.000224, 0.0004, 0.00038, 0.000601, 2.44e-05, 0.000191, 0.000715, 0.000715, 0.000684, 0.000179, 0.000267, 0.000486, 0.000203, 0.000405, 0.000263, 2.58e-05, 0.00073, 0.000869, 0.000169, 2.75e-05, 4.88e-05, 0.000343, 0.000474, 0.000343, 0.000255, 0.000503, 0.000209, 0.000825, 0.000803, 0.0004739, 0.000317, 0.000328, 2.64e-05, 0.000123, 8.24e-05, 1.72e-05, 9.01e-05, 0.000203, 2.69e-05, 0.00018, 0.00018, 0.000482, 0.000258, 0.000224, 0.000844, 0.000985, 0.0003, 2.21e-05])
    res_x = np.array([0.000999, 0.000424, 0.000196, 0.000355, 0.00196, 0.0018, 0.000454, 0.001323, 0.00141, 0.00122, 0.000406, 0.00148, 0.00101, 0.000616, 0.001999, 0.000124, 0.000244, 0.000485, 0.00105, 0.000704, 0.000853, 0.0003665, 0.00132, 0.00148, 0.0016, 0.000641, 0.00123, 0.00102, 0.00173, 0.000712, 0.00188, 0.000997, 0.000836, 0.000505, 0.001581, 0.00034, 0.001272, 0.000848, 0.00158, 0.000732, 0.000434, 0.00182, 0.00053, 0.000869, 0.000934, 0.00108, 0.000731, 0.00206, 0.00295, 0.00058, 0.000547, 0.000885, 0.00179, 0.000813, 0.001262, 0.000559, 0.00112, 0.000707, 0.000525, 0.00204, 0.001584, 0.001625, 0.00229, 0.000378, 0.000547, 0.00183, 0.000703, 0.00183, 0.002444, 0.000288, 0.001813, 0.000762, 0.000755, 0.00064, 0.000301, 0.00203, 0.000612, 0.000741, 0.000104, 0.00195, 0.0014, 0.000481, 0.000544, 0.000834, 0.000437, 7.98e-05, 0.001801, 0.000505, 0.000493, 0.00117, 0.000394, 0.000849, 0.00097, 0.00159, 0.000492, 0.0008, 0.00108, 0.00163, 0.000855, 0.000943, 0.000504, 0.00086, 0.001563, 0.000331, 0.001153, 0.000985, 0.000755, 0.00054, 0.001244, 0.00247, 0.000102, 0.000497, 0.00142, 0.000268, 9.4e-05, 0.00106, 0.00168, 0.00054, 0.000208, 0.000605, 0.000487, 0.00183, 0.00135, 0.002454, 0.001681, 0.000901, 0.001356, 0.00127, 0.00189, 0.000305, 0.000625, 0.00323, 0.00323, 0.00186, 0.000505, 0.000752, 0.00137, 0.000588, 0.001635, 0.00122, 0.000322, 0.00289, 0.00291, 0.000707, 9.55e-05, 0.000151, 0.000966, 0.00134, 0.000966, 0.000719, 0.002293, 0.000688, 0.00251, 0.00239, 0.002158, 0.00145, 0.0015, 0.000135, 0.000561, 0.000376, 0.0002, 0.000986, 0.000682, 0.000302, 0.000919, 0.000919, 0.00218, 0.00117, 0.001015, 0.002778, 0.00324, 0.00127, 0.004115])
    res_h = np.array([(2.54 + 0j), (1.082 + 0j), (0.502 + 0j), (0.878 + 0j), (4.88 + 0j), (4.444 + 0j), (1.178 + 0j), (3.368 + 0j), (3.6 + 0j), (12.4 + 0j), (1.034 + 0j), (3.68 + 0j), (10.38 + 0j), (1.572 + 0j), (4.978 + 0j), (1.264 + 0j), (0.648 + 0j), (4.72 + 0j), (2.28 + 0j), (1.87 + 0j), (8.174 + 0j), (3.796 + 0j), (2.58 + 0j), (3.48 + 0j), (4.06 + 0j), (1.234 + 0j), (2.76 + 0j), (2.76 + 0j), (4.7 + 0j), (1.934 + 0j), (5.28 + 0j), (10.6 + 0j), (2.14 + 0j), (5.48 + 0j), (4.14 + 0j), (0.874 + 0j), (3.268 + 0j), (2.18 + 0j), (4.06 + 0j), (1.876 + 0j), (1.11 + 0j), (4.94 + 0j), (5.44 + 0j), (2.3 + 0j), (2.54 + 0j), (2.86 + 0j), (1.876 + 0j), (5.46 + 0j), (4.72 + 0j), (6.04 + 0j), (1.474 + 0j), (2.4 + 0j), (4.76 + 0j), (2.16 + 0j), (3.28 + 0j), (1.464 + 0j), (2.94 + 0j), (1.816 + 0j), (5.36 + 0j), (5.41 + 0j), (4.07 + 0j), (4.08 + 0j), (6.2 + 0j), (0.986 + 0j), (1.434 + 0j), (4.72 + 0j), (1.844 + 0j), (4.72 + 0j), (6.268 + 0j), (0.76 + 0j), (4.61 + 0j), (2.02 + 0j), (2.0 + 0j), (6.2 + 0j), (0.768 + 0j), (5.18 + 0j), (1.628 + 0j), (1.972 + 0j), (0.276 + 0j), (5.02 + 0j), (3.58 + 0j), (1.198 + 0j), (1.356 + 0j), (2.14 + 0j), (4.44 + 0j), (0.21 + 0j), (4.66 + 0j), (1.298 + 0j), (1.142 + 0j), (2.98 + 0j), (1.01 + 0j), (2.16 + 0j), (2.46 + 0j), (4.04 + 0j), (4.98 + 0j), (8.64 + 0j), (2.84 + 0j), (17.64 + 0j), (2.16 + 0j), (2.38 + 0j), (51.4 + 0j), (90.8 + 0j), (3.99 + 0j), (0.83 + 0j), (11.73 + 0j), (2.51 + 0j), (1.926 + 0j), (1.426 + 0j), (3.194 + 0j), (6.32 + 0j), (0.268 + 0j), (1.318 + 0j), (3.66 + 0j), (0.568 + 0j), (0.984 + 0j), (2.7 + 0j), (4.2 + 0j), (42.2 + 0j), (0.55 + 0j), (1.552 + 0j), (1.222 + 0j), (4.66 + 0j), (3.44 + 0j), (6.068 + 0j), (4.226 + 0j), (2.24 + 0j), (3.32 + 0j), (3.16 + 0j), (4.72 + 0j), (116.2 + 0j), (1.604 + 0j), (8.6 + 0j), (8.6 + 0j), (4.44 + 0j), (1.258 + 0j), (1.874 + 0j), (3.42 + 0j), (1.396 + 0j), (4.058 + 0j), (3.1 + 0j), (123.0 + 0j), (7.38 + 0j), (7.3 + 0j), (2.02 + 0j), (0.732 + 0j), (0.374 + 0j), (2.42 + 0j), (3.32 + 0j), (2.42 + 0j), (1.788 + 0j), (5.98 + 0j), (1.748 + 0j), (5.69 + 0j), (5.36 + 0j), (5.646 + 0j), (3.76 + 0j), (3.88 + 0j), (1.456 + 0j), (1.468 + 0j), (0.98 + 0j), (21.6 + 0j), (104.6 + 0j), (1.738 + 0j), (38.0 + 0j), (2.48 + 0j), (2.48 + 0j), (5.78 + 0j), (3.1 + 0j), (2.682 + 0j), (7.092 + 0j), (8.28 + 0j), (12.2 + 0j), (10.198 + 0j)])
    self.assert_equal(line_r, res_r)
    self.assert_equal(line_x, res_x)
    self.assert_equal(line_h, res_h)
    pp_net = net
    tap_step_pct = (1.0 * pp_net.trafo['tap_step_percent'].values)
    tap_step_pct[(~ np.isfinite(tap_step_pct))] = 0.0
    tap_pos = (1.0 * pp_net.trafo['tap_pos'].values)
    tap_pos[(~ np.isfinite(tap_pos))] = 0.0
    is_tap_hv_side = (pp_net.trafo['tap_side'].values == 'hv')
    is_tap_hv_side[(~ np.isfinite(is_tap_hv_side))] = True
    tap_angles_ = (1.0 * pp_net.trafo['tap_step_degree'].values)
    tap_angles_[(~ np.isfinite(tap_angles_))] = 0.0
    tap_angles_ = np.deg2rad(tap_angles_)
    (trafo_r, trafo_x, trafo_b) = self.converter.get_trafo_param(tap_step_pct, tap_pos, tap_angles_, is_tap_hv_side, pp_net.bus.loc[pp_net.trafo['hv_bus']]['vn_kv'], pp_net.bus.loc[pp_net.trafo['lv_bus']]['vn_kv'], pp_net.trafo['vk_percent'].values, pp_net.trafo['vkr_percent'].values, pp_net.trafo['sn_mva'].values, pp_net.trafo['pfe_kw'].values, pp_net.trafo['i0_percent'].values)
    trafo_r_res = np.array([0.0, 0.0, 0.0, 0.000281494977, 3.39887086e-06, 0.0, 0.0, 0.0, 0.0, 0.0, 1.37295648e-05, 0.0, 1.7357186e-05])
    trafo_x_res = np.array([0.000267, 0.000382, 0.00037, 0.00206930358, 4.04933224e-05, 0.000388, 0.000375, 0.000386, 0.000268, 0.00037, 0.000159594718, 0.00037, 0.000201181945])
    trafo_h_res = np.array([(0.0 - 0j), (0.0 - 0j), (0.0 - 0j), (4.4602909 - 0.00140652j), (16.40272367 - 0.00022869j), (0.0 - 0j), (0.0 - 0j), (0.0 - 0j), (0.0 - 0j), (0.0 - 0j), (63.96323106 - 0.01411497j), (0.0 - 0j), (81.1310369 - 0.02879733j)])
    self.assert_equal(trafo_r, trafo_r_res)
    self.assert_equal(trafo_x, trafo_x_res)
    self.assert_equal(trafo_b, trafo_h_res)