#!/usr/bin/env python """ Tests of xray interface """ import time import pytest import numpy as np from numpy.testing import assert_allclose from xraydb import (chemparse, material_mu, material_mu_components, get_material, add_material, f0, f0_ions, chantler_energies, f1_chantler, f2_chantler, mu_chantler, mu_elam, coherent_cross_section_elam, incoherent_cross_section_elam, atomic_number, atomic_symbol, atomic_mass, atomic_density, xray_edges, xray_edge, xray_lines, xray_line, fluor_yield, ck_probability, core_width, guess_edge, xray_delta_beta, darwin_width, mirror_reflectivity, ionchamber_fluxes, XrayDB) from xraydb.xray import (chantler_data, formula_to_mass_fracs, _validate_mass_fracs, mass_fracs_to_molar_fracs, transmission_sample) def test_atomic_data(): assert atomic_number('zn') == 30 assert atomic_symbol(26) == 'Fe' assert atomic_mass(45) > 102.8 assert atomic_mass(45) < 103.0 assert atomic_density(29) == atomic_density('cu') assert atomic_density(22) > 4.50 assert atomic_density(79) > 19.2 def test_edge_energies(): from xraydb.xray import _edge_energies assert xray_edge(30, 'K') == xray_edge('Zn', 'K') for edge in ('k', 'l3', 'l2'): for iz in range(1, 98): _edge = xray_edge(iz, edge) if _edge is not None: en = _edge.energy assert _edge_energies[edge][iz] < en + 0.5 assert _edge_energies[edge][iz] > en - 0.5 assert_allclose(xray_edge('Zn', 'K', energy_only=True), 9660, rtol=0.01) sr_edges = xray_edges('Sr') assert_allclose(sr_edges['K'].energy, 16105.0, rtol=0.001) assert_allclose(sr_edges['L1'].energy, 2216.0, rtol=0.001) assert_allclose(sr_edges['L2'].energy, 2007.0, rtol=0.001) assert_allclose(sr_edges['L3'].energy, 1940.0, rtol=0.001) assert_allclose(sr_edges['M1'].energy, 358.7, rtol=0.001) assert_allclose(sr_edges['M2'].energy, 280.3, rtol=0.001) assert_allclose(sr_edges['M3'].energy, 270.0, rtol=0.001) assert_allclose(sr_edges['M4'].energy, 136.0, rtol=0.001) assert_allclose(sr_edges['M5'].energy, 134.2, rtol=0.001) assert_allclose(sr_edges['N1'].energy, 38.9, rtol=0.001) assert_allclose(sr_edges['N2'].energy, 21.6, rtol=0.001) assert_allclose(sr_edges['N3'].energy, 20.1, rtol=0.001) assert_allclose(sr_edges['K'].fyield, 0.664652, rtol=0.001) assert_allclose(sr_edges['L1'].fyield, 0.0051, rtol=0.001) assert_allclose(sr_edges['L2'].fyield, 0.024, rtol=0.001) assert_allclose(sr_edges['L3'].fyield, 0.026, rtol=0.001) assert_allclose(sr_edges['M1'].fyield, 5.95e-05, rtol=0.001) assert_allclose(sr_edges['M2'].fyield, 3.7e-05, rtol=0.001) assert_allclose(sr_edges['M3'].fyield, 0.000105, rtol=0.001) assert_allclose(sr_edges['M4'].fyield, 0.0027, rtol=0.001) assert_allclose(sr_edges['M5'].fyield, 0.0, rtol=0.001) assert_allclose(sr_edges['N1'].fyield, 1.2e-05, rtol=0.001) assert_allclose(sr_edges['N2'].fyield, 0.013, rtol=0.001) assert_allclose(sr_edges['N3'].fyield, 0.013, rtol=0.001) assert_allclose(sr_edges['K'].jump_ratio, 6.888, rtol=0.01) assert_allclose(sr_edges['L1'].jump_ratio, 1.1415, rtol=0.01) assert_allclose(sr_edges['L2'].jump_ratio, 1.4, rtol=0.01) assert_allclose(sr_edges['L3'].jump_ratio, 3.982, rtol=0.01) assert_allclose(sr_edges['M1'].jump_ratio, 1.04, rtol=0.01) assert_allclose(sr_edges['M2'].jump_ratio, 1.058, rtol=0.01) assert_allclose(sr_edges['M3'].jump_ratio, 1.12491, rtol=0.01) assert_allclose(sr_edges['M4'].jump_ratio, 1.139, rtol=0.01) assert_allclose(sr_edges['M5'].jump_ratio, 1.808, rtol=0.01) assert_allclose(sr_edges['N1'].jump_ratio, 1.0, rtol=0.01) assert_allclose(sr_edges['N2'].jump_ratio, 1.0, rtol=0.01) assert_allclose(sr_edges['N3'].jump_ratio, 1.0, rtol=0.01) def test_emission_lines(): cu_lines = xray_lines('Cu') for lname in ('Ka3', 'Ka2', 'Ka1', 'Kb3', 'Kb1', 'Kb5', 'Lb4', 'Lb3', 'Ln', 'Lb1', 'Ll', 'La2', 'La1'): assert(lname in cu_lines) assert cu_lines['Ka1'].energy > 8045.0 assert cu_lines['Ka1'].energy < 8047.0 assert cu_lines['Ka1'].initial_level == 'K' assert cu_lines['Ka1'].final_level == 'L3' def test_f0(): q = np.linspace(0, 5, 21) known_f = np.array([21.998, 14.569, 9.142, 6.958, 5.743, 4.639, 3.664, 2.880, 2.296, 1.890, 1.621, 1.445, 1.326, 1.240, 1.170, 1.108, 1.050, 0.992, 0.936, 0.880, 0.826]) assert_allclose(known_f, f0('Ti', q), rtol=0.005) assert_allclose(known_f, f0(22, q), rtol=0.005) with pytest.raises(ValueError): f = f0('Ti5+', q) def test_f0_ions(): ions = f0_ions() assert len(ions) > 200 for ion in ('Li', 'Cl', 'Fe2+', 'Sr2+', 'Cd2+', 'Nd', 'Lu3+', 'Pb', 'Pu'): assert ion in ions ions = f0_ions('Eu') assert len(ions) == 3 ions = f0_ions('Ti') assert len(ions) == 4 def test_mu_elam(): en = np.linspace(13000, 17000, 51) mu_total = np.array([67.46, 160.62, 158.03, 155.50, 153.03, 150.61, 148.24, 145.92, 143.66, 141.44, 139.27, 137.14, 135.06, 133.02, 131.03, 129.07, 127.16, 125.28, 123.45, 121.65, 119.88, 118.15, 116.46, 114.80, 113.17, 111.57, 110.03, 108.52, 147.60, 145.79, 144.01, 142.26, 140.54, 138.86, 137.21, 135.58, 154.36, 152.41, 150.50, 148.63, 146.79, 144.97, 143.19, 141.45, 139.73, 138.04, 136.37, 134.74, 133.13, 131.55, 130.00]) mu_photo = np.array([63.54, 156.73, 154.16, 151.66, 149.21, 146.81, 144.46, 142.17, 139.93, 137.73, 135.58, 133.48, 131.42, 129.40, 127.43, 125.50, 123.61, 121.75, 119.94, 118.16, 116.41, 114.71, 113.03, 111.39, 109.78, 108.21, 106.69, 105.19, 144.30, 142.50, 140.74, 139.01, 137.31, 135.65, 134.01, 132.41, 151.20, 149.27, 147.38, 145.52, 143.70, 141.90, 140.14, 138.41, 136.71, 135.03, 133.39, 131.77, 130.18, 128.62, 127.08]) mu_coh = np.array([3.86, 3.84, 3.82, 3.79, 3.77, 3.74, 3.72, 3.70, 3.67, 3.65, 3.63, 3.60, 3.58, 3.56, 3.54, 3.52, 3.49, 3.47, 3.45, 3.43, 3.41, 3.39, 3.37, 3.35, 3.33, 3.31, 3.29, 3.27, 3.25, 3.23, 3.21, 3.19, 3.17, 3.15, 3.13, 3.12, 3.10, 3.08, 3.06, 3.04, 3.03, 3.01, 2.99, 2.97, 2.96, 2.94, 2.92, 2.91, 2.89, 2.87, 2.86]) mu_incoh = np.array([0.05432, 0.05453, 0.05474, 0.05495, 0.05515, 0.05536, 0.05556, 0.05576, 0.05596, 0.05616, 0.05636, 0.05656, 0.05675, 0.05695, 0.05714, 0.05734, 0.05753, 0.05772, 0.05791, 0.05810, 0.05828, 0.05847, 0.05865, 0.05884, 0.05902, 0.05920, 0.05939, 0.05957, 0.05974, 0.05992, 0.06010, 0.06028, 0.06045, 0.06063, 0.06080, 0.06097, 0.06114, 0.06132, 0.06149, 0.06166, 0.06182, 0.06199, 0.06216, 0.06232, 0.06249, 0.06265, 0.06282, 0.06298, 0.06314, 0.06330, 0.06346]) assert_allclose(mu_total, mu_elam('Pb', en), rtol=0.01) assert_allclose(mu_photo, mu_elam('Pb', en, kind='photo'), rtol=0.01) assert_allclose(mu_incoh, mu_elam('Pb', en, kind='incoh'), rtol=0.01) assert_allclose(mu_coh, mu_elam('Pb', en, kind='coh'), rtol=0.01) with pytest.raises(ValueError): mu_elam('Pb', en, kind='all') # Check that array in -> array out, and scalar in -> scalar out assert mu_elam('Pb', [1000]).shape == (1,) assert isinstance(mu_elam('Pb', 1000), float) def test_elam_coh_incoh(): en = np.linspace(13000, 17000, 51) incoh = np.array([0.08085165, 0.08112906, 0.08140468, 0.08167853, 0.08195061, 0.08222095, 0.08248954, 0.0827564 , 0.08302154, 0.08328497, 0.0835467 , 0.08380673, 0.08406508, 0.08432176, 0.08457678, 0.08483014, 0.08508186, 0.08533195, 0.08558041, 0.08582725, 0.08607249, 0.08631613, 0.08655818, 0.08679866, 0.08703756, 0.08727491, 0.0875107 , 0.08774495, 0.08797767, 0.08820887, 0.08843856, 0.08866675, 0.08889345, 0.08911867, 0.08934242, 0.08956471, 0.08978554, 0.09000494, 0.09022291, 0.09043946, 0.09065459, 0.09086832, 0.09108066, 0.09129161, 0.09150119, 0.0917094 , 0.09191626, 0.09212176, 0.09232593, 0.09252877, 0.09273028]) coh = np.array([1.34896099, 1.33854813, 1.32825911, 1.31809228, 1.308046 , 1.29811866, 1.28830866, 1.27861442, 1.26903438, 1.259567 , 1.25021075, 1.24096413, 1.23182566, 1.22279385, 1.21386727, 1.20504448, 1.19632407, 1.18770464, 1.17918482, 1.17076326, 1.1624386 , 1.15420954, 1.14607477, 1.138033 , 1.13008296, 1.12222341, 1.11445296, 1.10677039, 1.09917435, 1.09166353, 1.08423663, 1.07689238, 1.06962952, 1.06244685, 1.05534314, 1.04831724, 1.04136798, 1.03449422, 1.02769486, 1.02096879, 1.01431496, 1.0077323 , 1.00121977, 0.99477638, 0.98840111, 0.98209299, 0.97585106, 0.96967437, 0.96356201, 0.95751305, 0.9515266 ]) assert_allclose(coherent_cross_section_elam('Br', en), coh, rtol=0.01) assert_allclose(incoherent_cross_section_elam('Br', en), incoh, rtol=0.01) def test_mu_chantler(): en = np.linspace(13000, 17000, 51) mu_total = np.array([ 67.24736219, 156.95328746, 155.78747252, 153.64333955, 151.35899169, 149.0524903 , 146.74603265, 144.48912501, 142.28067496, 140.09062866, 137.93546991, 135.8273229 , 133.76088713, 131.73138628, 129.74341174, 127.79595645, 125.88827932, 124.01945648, 122.18963972, 120.39733297, 118.64336375, 116.92801058, 115.25227237, 113.61866015, 112.02400421, 110.52333492, 109.0762108 , 108.31373956, 146.25741077, 144.9280078 , 143.11197774, 141.21399554, 139.35518911, 137.5608917 , 135.82234118, 134.4535719 , 148.93741603, 149.77263523, 148.14548539, 146.37740382, 144.62801998, 142.87203507, 141.14422981, 139.43965338, 137.76256418, 136.10754019, 134.47907574, 132.8679473 , 131.28344089, 129.71407476, 128.1707394 ]) mu_photo = np.array([63.33, 153.06, 151.92, 149.80, 147.54, 145.26, 142.98, 140.74, 138.56, 136.39, 134.26, 132.17, 130.13, 128.12, 126.15, 124.23, 122.34, 120.50, 118.69, 116.92, 115.18, 113.49, 111.83, 110.22, 108.64, 107.16, 105.74, 104.99, 142.95, 141.64, 139.85, 137.97, 136.13, 134.35, 132.63, 131.28, 145.78, 146.64, 145.03, 143.28, 141.54, 139.80, 138.09, 136.41, 134.75, 133.11, 131.49, 129.90, 128.33, 126.78, 125.25]) mu_incoh = np.array([3.92, 3.89, 3.87, 3.84, 3.82, 3.79, 3.77, 3.75, 3.72, 3.70, 3.68, 3.66, 3.63, 3.61, 3.59, 3.57, 3.55, 3.52, 3.50, 3.48, 3.46, 3.44, 3.42, 3.40, 3.38, 3.36, 3.34, 3.32, 3.30, 3.28, 3.26, 3.25, 3.23, 3.21, 3.19, 3.17, 3.15, 3.14, 3.12, 3.10, 3.08, 3.07, 3.05, 3.03, 3.02, 3.00, 2.98, 2.97, 2.95, 2.94, 2.92]) assert_allclose(mu_total, mu_chantler('Pb', en), rtol=0.01) assert_allclose(mu_photo, mu_chantler('Pb', en, photo=True), rtol=0.01) assert_allclose(mu_incoh, mu_chantler('Pb', en, incoh=True), rtol=0.01) assert_allclose(mu_incoh, chantler_data('Pb', en, 'mu_incoh'), rtol=0.01) assert_allclose(mu_photo, chantler_data('Pb', en, 'mu_photo'), rtol=0.01) assert_allclose(mu_total, chantler_data('Pb', en, 'mu_total'), rtol=0.01) assert_allclose(mu_total, chantler_data('Pb', en, 'mu'), rtol=0.01) assert_allclose(67.25, chantler_data('Pb', 13000, 'mu'), rtol=0.01) def test_f1f2_chantler(): en = np.linspace(10000, 15000, 51) f1 = np.array([-5.97, -6.08, -6.20, -6.33, -6.46, -6.60, -6.75, -6.91, -7.09, -7.28, -7.49, -7.72, -7.99, -8.28, -8.64, -9.06, -9.60, -10.33, -11.54, -15.14, -12.27, -10.64, -9.78, -9.18, -8.76, -8.43, -8.18, -7.98, -7.83, -7.73, -7.66, -7.64, -7.66, -7.73, -7.87, -8.13, -8.58, -9.91, -9.18, -8.19, -7.73, -7.46, -7.36, -7.61, -7.43, -6.61, -6.13, -5.77, -5.46, -5.20, -4.96 ]) f2 = np.array([5.12, 5.04, 4.96, 4.87, 4.80, 4.72, 4.64, 4.57, 4.50, 4.43, 4.37, 4.30, 4.24, 4.18, 4.12, 4.06, 4.01, 3.97, 3.93, 4.15, 9.94, 9.83, 9.70, 9.57, 9.43, 9.30, 9.17, 9.05, 8.92, 8.80, 8.69, 8.57, 8.46, 8.35, 8.25, 8.14, 8.05, 8.02, 10.94, 10.83, 10.71, 10.59, 10.47, 10.39, 11.75, 11.67, 11.56, 11.44, 11.33, 11.21, 11.10]) assert_allclose(f1, f1_chantler('Au', en), rtol=0.01) assert_allclose(f2, f2_chantler('Au', en), rtol=0.01) assert_allclose(f1, chantler_data('Au', en, 'f1'), rtol=0.01) assert_allclose(f2, chantler_data('Au', en, 'f2'), rtol=0.01) def test_f1f2_chantler_mo(): en = np.linspace(1000, 500000, 501) f1 = f1_chantler('Mo', en) assert(len(f1) > 400) def test_chantler_energies(): en = np.array([6876.64, 6945.41, 7014.86, 7085.01, 7155.86, 7227.42, 7299.69, 7372.69, 7446.42, 7520.88, 7596.09, 7672.05, 7748.77, 7826.26, 7904.52, 7983.56, 8063.40, 8144.03, 8225.47, 8307.73, 8390.81, 8474.71, 8559.46, 8645.06, 8731.51, 8818.82, 8907.01, 8996.08, 9086.04, 9176.90, 9268.67, 9361.36, 9454.97, 9549.52, 9609.00, 9633.80, 9645.01, 9646.20, 9652.40, 9655.50, 9657.10, 9660.10, 9661.70, 9664.80, 9671.00, 9683.40, 9708.20, 9741.46, 9838.88, 9937.27, 10036.64, 10137.01, 10238.38, 10340.76, 10444.17, 10548.61, 10654.09, 10760.64, 10868.24, 10976.92, 11086.69, 11197.56, 11309.54, 11422.63, 11536.86, 11652.23, 11768.75, 11886.44, 12005.30, 12125.35]) assert_allclose(en, chantler_energies('Zn', emin=7000, emax=12000), rtol=0.01) eall_ge = chantler_energies('Ge') assert len(eall_ge) > 1425 assert eall_ge[0] < 1.2 assert eall_ge[0] > 0.9 assert eall_ge[-1] < 1e6 assert eall_ge[-1] > 9.5e5 def test_guess_edge(): vals = ((1607.22, 'Al', 'K'), (2260.35, 'P', 'K'), (2411.97, 'S', 'K'), (2559.74, 'S', 'K'), (2867.76, 'Mo', 'L1'), (3696.58, 'K', 'K'), (3971.10, 'Ca', 'K'), (4421.73, 'Sc', 'K'), (4765.58, 'Xe', 'L3'), (6278.32, 'La', 'L1'), (6375.02, 'Mn', 'K'), (6447.24, 'Pr', 'L2'), (6745.03, 'Nd', 'L2'), (6831.54, 'Pr', 'L1'), (6897.72, 'Fe', 'K'), (7074.41, 'Fe', 'K'), (7297.04, 'Sm', 'L2'), (7981.74, 'Gd', 'L2'), (8111.00, 'Ho', 'L3'), (8289.68, 'Ni', 'K'), (8787.12, 'Cu', 'K'), (8893.41, 'Cu', 'K'), (9032.98, 'Cu', 'K'), (9690.75, 'Zn', 'K'), (9694.53, 'Zn', 'K'), (10354.47, 'Ga', 'K'), (10442.38, 'Ga', 'K'), (10455.03, 'Ga', 'K'), (11150.30, 'Ge', 'K'), (11963.31, 'Re', 'L2'), (12774.25, 'Se', 'K'), (13268.59, 'Pt', 'L2'), (13971.48, 'Po', 'L3'), (14009.96, 'Kr', 'K'), (14192.88, 'Hg', 'L2'), (14248.22, 'At', 'L3'), (14601.50, 'Rn', 'L3'), (14754.45, 'Tl', 'L2'), (15058.15, 'Fr', 'L3'), (15529.31, 'Rb', 'K'), (15846.94, 'Ac', 'L3'), (16800.91, 'At', 'L2'), (16923.31, 'Y', 'K'), (17235.13, 'Y', 'K'), (17452.53, 'Y', 'K'), (17455.89, 'Y', 'K'), (18327.81, 'Zr', 'K'), (18478.71, 'Ra', 'L2'), (18895.14, 'Nb', 'K'), (18917.70, 'Nb', 'K'), (19015.14, 'Nb', 'K'), (20259.03, 'Mo', 'K'), (20325.21, 'Pa', 'L2'), (20329.70, 'Pa', 'L2'), (20489.15, 'Th', 'L1'), (20520.48, 'Th', 'L1'), (20905.52, 'Tc', 'K'), (20997.74, 'Tc', 'K'), (21606.63, 'Np', 'L2'), (21894.47, 'Ru', 'K'), (22424.97, 'Np', 'L1'), (23101.67, 'Pu', 'L1'), (24929.69, 'Pd', 'K'), (25507.23, 'Ag', 'K'), (26291.15, 'Cd', 'K'), (26523.45, 'Cd', 'K'), (26730.45, 'Cd', 'K'), (27496.51, 'In', 'K'), (27534.86, 'In', 'K'), (28524.18, 'In', 'K'), (29220.68, 'Sn', 'K'), (29563.18, 'Sn', 'K'), (30148.46, 'Sb', 'K'), (30178.67, 'Sb', 'K'), (30692.73, 'Sb', 'K'), (31006.32, 'Sb', 'K'), (31375.27, 'Te', 'K'), (31511.43, 'Te', 'K'), (31539.46, 'Te', 'K'), (31549.18, 'Te', 'K'), (31781.82, 'Te', 'K'), (32113.33, 'Te', 'K'), (32529.16, 'I', 'K'), (32769.91, 'I', 'K'), (32945.96, 'I', 'K'), (33162.58, 'I', 'K'), (33861.76, 'I', 'K'), (34282.18, 'Xe', 'K'), (34616.82, 'Xe', 'K'), (35048.42, 'Xe', 'K'), (35451.22, 'Cs', 'K'), (35732.84, 'Cs', 'K'), (36226.88, 'Cs', 'K'), (37011.63, 'Ba', 'K'), (37119.11, 'Ba', 'K'), (37687.55, 'Ba', 'K'), (38255.21, 'La', 'K'), (38534.03, 'La', 'K'), (39383.09, 'La', 'K'), (39597.61, 'La', 'K'), (39985.10, 'Ce', 'K'), (40189.85, 'Ce', 'K'), (40299.94, 'Ce', 'K'), (40803.37, 'Ce', 'K'), (41062.46, 'Ce', 'K'), (41563.00, 'Pr', 'K'), (41604.41, 'Pr', 'K'), (41914.19, 'Pr', 'K'), (41947.64, 'Pr', 'K'), (42090.33, 'Pr', 'K'), (42995.34, 'Nd', 'K'), (43296.60, 'Nd', 'K'), (44306.16, 'Nd', 'K'), (44470.03, 'Pm', 'K'), (45936.49, 'Pm', 'K'), (45992.17, 'Pm', 'K'), (45998.94, 'Pm', 'K'), (47495.02, 'Sm', 'K'), (47674.10, 'Sm', 'K'), (47942.94, 'Eu', 'K'), (48996.75, 'Eu', 'K'), (49840.90, 'Gd', 'K'), (50129.53, 'Gd', 'K'), (50525.35, 'Gd', 'K'), (51211.59, 'Tb', 'K'), (51574.05, 'Tb', 'K'), (51872.86, 'Tb', 'K'), (52198.93, 'Tb', 'K'), (52448.10, 'Tb', 'K'), (53243.79, 'Dy', 'K'), (53409.52, 'Dy', 'K'), (53945.61, 'Dy', 'K'), (53970.83, 'Dy', 'K'), (55202.28, 'Ho', 'K'), (55573.56, 'Ho', 'K'), (56150.05, 'Ho', 'K'), (56361.18, 'Ho', 'K'), (56372.13, 'Ho', 'K'), (58278.64, 'Er', 'K'), (58712.03, 'Tm', 'K'), (58804.02, 'Tm', 'K'), (59170.93, 'Tm', 'K'), (59295.42, 'Tm', 'K'), (59777.61, 'Tm', 'K'), (59795.79, 'Tm', 'K'), (60196.31, 'Tm', 'K'), (60733.44, 'Yb', 'K'), (60829.65, 'Yb', 'K'), (60877.42, 'Yb', 'K'), (60958.45, 'Yb', 'K'), (64355.51, 'Hf', 'K'), (64528.43, 'Hf', 'K'), (64807.76, 'Hf', 'K'), (67037.59, 'Ta', 'K'), (67684.42, 'Ta', 'K'), (68153.83, 'Ta', 'K'), (68190.34, 'Ta', 'K'), (68223.59, 'Ta', 'K'), (68281.45, 'Ta', 'K'), (68454.23, 'Ta', 'K'), (68645.09, 'W', 'K'), (69098.00, 'W', 'K'), (69375.45, 'W', 'K'), (69629.24, 'W', 'K'), (69917.35, 'W', 'K'), (70279.29, 'W', 'K'), (71077.63, 'Re', 'K'), (71181.95, 'Re', 'K'), (71218.00, 'Re', 'K'), (71726.69, 'Re', 'K'), (72635.91, 'Re', 'K'), (73524.97, 'Os', 'K'), (73609.73, 'Os', 'K'), (73924.75, 'Os', 'K'), (74544.48, 'Os', 'K'), (75531.12, 'Ir', 'K'), (76370.20, 'Ir', 'K'), (76460.23, 'Ir', 'K'), (77749.62, 'Pt', 'K'), (78466.63, 'Pt', 'K'), (79382.39, 'Pt', 'K'), (79752.40, 'Au', 'K'), (80068.68, 'Au', 'K'), (81540.62, 'Au', 'K'), (82353.66, 'Hg', 'K'), (82571.70, 'Hg', 'K'), (82672.30, 'Hg', 'K'), (82932.53, 'Hg', 'K'), (83155.18, 'Hg', 'K'), (83452.92, 'Hg', 'K'), (83669.34, 'Hg', 'K'), (83823.59, 'Hg', 'K'), (84319.11, 'Tl', 'K'), (84434.41, 'Tl', 'K'), (84538.14, 'Tl', 'K'), (85183.11, 'Tl', 'K'), (85213.28, 'Tl', 'K'), (85276.74, 'Tl', 'K'), (85352.80, 'Tl', 'K'), (85377.82, 'Tl', 'K')) for en, elem, edge in vals: _elem, _edge = guess_edge(en) assert elem == _elem assert edge == _edge _elem, _edge = guess_edge(1608, edges=['K', 'L1', 'L2', 'L3', 'M5', 'M3']) assert _elem == 'Tb' assert _edge == 'M3' def test_fluor_yeild(): fy1 = (0.351, 6400.75, 0.8746) assert_allclose(fy1, fluor_yield('Fe', 'K', 'Ka', 8000), rtol=0.001) fy2 = 0.0, 6400.752, 0.8746 assert_allclose(fy2, fluor_yield('Fe', 'K', 'Ka', 6000), rtol=0.001) fy3 = 0.052, 2982.13, 0.8619 assert_allclose(fy3, fluor_yield('Ag', 'L3', 'La', 6000), rtol=0.001) def test_core_width(): kwid_known = np.array([0.0201, 0.0269, 0.036, 0.0483, 0.0647, 0.0868, 0.1163, 0.1559, 0.2089, 0.24, 0.3, 0.36, 0.42, 0.48, 0.53, 0.59, 0.64, 0.68, 0.74, 0.81, 0.86, 0.94, 1.01, 1.08, 1.16, 1.25, 1.33, 1.44, 1.55, 1.67, 1.82, 1.96, 2.14, 2.33, 2.52, 2.75, 2.99, 3.25, 3.52, 3.84, 4.14, 4.52, 4.91, 5.33, 5.77, 6.24, 6.75, 7.28, 7.91, 8.49, 9.16, 9.89, 10.6, 11.4, 12.3, 13.2, 14.1, 15.1, 16.2, 17.3, 18.5, 19.7, 21.0, 22.3, 23.8, 25.2, 26.8, 28.4, 30.1, 31.9, 33.7, 35.7, 37.7, 39.9, 42.1, 44.4, 46.8, 49.3, 52.0, 54.6, 57.4, 60.4, 63.4, 66.6, 69.8, 73.3, 76.8, 80.4, 84.1, 88.0, 91.9, 96.1, 100.0, 105.0, 109.0, 114.0, 119.0, 124.0]) kwid = np.array([core_width(i, 'K') for i in range(1, 99)]) assert_allclose(kwid, kwid_known, rtol=0.01) l3wid_known = np.array([0.17, 0.19, 0.22, 0.24, 0.27, 0.32, 0.36, 0.43, 0.48, 0.56, 0.65, 0.76, 0.82, 0.94, 1.0, 1.08, 1.17, 1.27, 1.39, 1.5, 1.57, 1.66, 1.78, 1.91, 2.0, 2.13, 2.25, 2.4, 2.5, 2.65, 2.75, 2.87, 2.95, 3.08, 3.13, 3.25, 3.32, 3.41, 3.48, 3.6, 3.65, 3.75, 3.86, 3.91, 4.01, 4.12, 4.17, 4.26, 4.35, 4.48, 4.6, 4.68, 4.8, 4.88, 4.98, 5.04, 5.16, 5.25, 5.31, 5.41, 5.5, 5.65, 5.81, 5.98, 6.13, 6.29, 6.41, 6.65, 6.82, 6.98, 7.13, 7.33, 7.43, 7.59, 7.82, 8.04, 8.26, 8.55, 8.75]) l3wid = np.array([core_width(i, 'L3') for i in range(20, 99)]) assert_allclose(l3wid, l3wid_known, rtol=0.01) def test_xray_line(): cuk = xray_line('Cu', 'K') assert_allclose(cuk.energy, 8039.626, rtol=0.001) assert_allclose(cuk.intensity, 0.8716833, rtol=0.001) assert cuk.final_level == 'L' cuka = xray_line('Cu', 'Ka1') assert_allclose(cuka.energy, 8046.3, rtol=0.001) assert_allclose(cuka.intensity, 0.5771, rtol=0.001) assert cuka.final_level == 'L3' ti_klines = xray_lines('Ti', 'K') for line in ('Ka1', 'Ka2', 'Ka3', 'Kb1', 'Kb3', 'Kb5'): assert ti_klines[line].initial_level == 'K' assert_allclose(ti_klines['Ka1'].energy, 4512.2, rtol=0.001) assert_allclose(ti_klines['Ka2'].energy, 4505.8, rtol=0.001) assert_allclose(ti_klines['Ka3'].energy, 4405.1, rtol=0.001) assert_allclose(ti_klines['Kb1'].energy, 4933.4, rtol=0.001) assert_allclose(ti_klines['Kb3'].energy, 4933.4, rtol=0.001) assert_allclose(ti_klines['Kb5'].energy, 4964.0, rtol=0.001) assert_allclose(ti_klines['Ka1'].intensity, 0.58455, rtol=0.001) assert_allclose(ti_klines['Ka2'].intensity, 0.29369, rtol=0.001) assert_allclose(ti_klines['Ka3'].intensity, 0.000235, rtol=0.01) assert_allclose(ti_klines['Kb1'].intensity, 0.07965, rtol=0.001) assert_allclose(ti_klines['Kb3'].intensity, 0.04126, rtol=0.001) assert_allclose(ti_klines['Kb5'].intensity, 0.000607, rtol=0.01) assert_allclose(ti_klines['Ka1'].intensity, 0.58455, rtol=0.001) assert ti_klines['Ka1'].final_level == 'L3' assert ti_klines['Ka2'].final_level == 'L2' assert ti_klines['Ka3'].final_level == 'L1' assert ti_klines['Kb1'].final_level == 'M3' assert ti_klines['Kb3'].final_level == 'M2' assert ti_klines['Kb5'].final_level == 'M4,5' def test_xray_lines_levels(): assert len(xray_lines('Hg', excitation_energy=2800)) == 2 assert len(xray_lines('Hg', excitation_energy=12000)) == 3 assert len(xray_lines('Hg', excitation_energy=12500)) == 9 assert len(xray_lines('Hg', excitation_energy=14300)) == 13 assert len(xray_lines('Hg', excitation_energy=16000)) == 17 def test_ck_probability(): assert_allclose(ck_probability('W', 'L1', 'L3'), 0.3, rtol=0.01) def test_delta_beta(): delta = np.array([2.31879798e-05, 2.30263614e-05, 2.28655690e-05, 2.27055461e-05, 2.25462258e-05, 2.23875379e-05, 2.22294125e-05, 2.20717621e-05, 2.19144854e-05, 2.17574831e-05, 2.16006447e-05, 2.14438386e-05, 2.12869338e-05, 2.11297790e-05, 2.09721149e-05, 2.08136556e-05, 2.06541214e-05, 2.04932245e-05, 2.03306727e-05, 2.01661788e-05, 1.99990853e-05, 1.98278335e-05, 1.96507644e-05, 1.94662663e-05, 1.92728472e-05, 1.90690943e-05, 1.88535304e-05, 1.86137126e-05, 1.83166396e-05, 1.79263695e-05, 1.72644728e-05, 1.68846677e-05, 1.75514931e-05, 1.77905451e-05, 1.79131927e-05, 1.79732450e-05, 1.79928595e-05, 1.79928689e-05, 1.79824646e-05, 1.79627078e-05, 1.79344673e-05, 1.78986834e-05, 1.78566159e-05, 1.78095782e-05, 1.77588480e-05, 1.77052893e-05, 1.76492014e-05, 1.75908356e-05, 1.75304432e-05, 1.74683253e-05, 1.74048109e-05]) beta = np.array([4.15330456e-07, 4.10533886e-07, 4.05807206e-07, 4.01142710e-07, 3.96544748e-07, 3.92013242e-07, 3.87546123e-07, 3.83142560e-07, 3.78802101e-07, 3.74526554e-07, 3.70319235e-07, 3.66171556e-07, 3.62082498e-07, 3.58040827e-07, 3.54055453e-07, 3.50126039e-07, 3.46263146e-07, 3.42462535e-07, 3.38714592e-07, 3.35014122e-07, 3.31343582e-07, 3.27723749e-07, 3.24153777e-07, 3.20645679e-07, 3.17190186e-07, 3.13781696e-07, 3.10416648e-07, 3.07089178e-07, 3.03806706e-07, 3.00558985e-07, 2.97367605e-07, 2.10914637e-06, 2.08861169e-06, 2.06826314e-06, 2.04838399e-06, 2.02875052e-06, 2.00935901e-06, 1.99017262e-06, 1.97120233e-06, 1.95246442e-06, 1.93395484e-06, 1.91563532e-06, 1.89753871e-06, 1.87966170e-06, 1.86198608e-06, 1.84449823e-06, 1.82722124e-06, 1.81015203e-06, 1.79327486e-06, 1.77659378e-06, 1.76011187e-06]) beta_photo = np.array([3.90526978e-07, 3.86063935e-07, 3.81665372e-07, 3.77336646e-07, 3.73071089e-07, 3.68866519e-07, 3.64729098e-07, 3.60655470e-07, 3.56639426e-07, 3.52684594e-07, 3.48796990e-07, 3.44963677e-07, 3.41183740e-07, 3.37480438e-07, 3.33829590e-07, 3.30228863e-07, 3.26702653e-07, 3.23242941e-07, 3.19829864e-07, 3.16477854e-07, 3.13244987e-07, 3.10054443e-07, 3.06905547e-07, 3.03998036e-07, 3.01199959e-07, 2.98435746e-07, 2.96106878e-07, 2.94958918e-07, 2.93820876e-07, 2.93903973e-07, 3.11478882e-07, 2.10154368e-06, 2.11105335e-06, 2.09391918e-06, 2.07224074e-06, 2.05084613e-06, 2.02973085e-06, 2.00827838e-06, 1.98675441e-06, 1.96551940e-06, 1.94456638e-06, 1.92374766e-06, 1.90320773e-06, 1.88294210e-06, 1.86292819e-06, 1.84314908e-06, 1.82363251e-06, 1.80437431e-06, 1.78600492e-06, 1.76816730e-06, 1.75055477e-06]) atten = np.array([0.00376372, 0.00379486, 0.00382616, 0.00385755, 0.00388909, 0.00392079, 0.00395256, 0.00398442, 0.00401644, 0.00404858, 0.00408074, 0.00411306, 0.00414555, 0.00417787, 0.00421033, 0.00424294, 0.0042754 , 0.00430777, 0.00434029, 0.00437275, 0.00440432, 0.00443603, 0.00446788, 0.00449701, 0.00452518, 0.00455344, 0.00457579, 0.00458077, 0.00458572, 0.0045724 , 0.00431164, 0.00065601, 0.00065126, 0.00065474, 0.00065973, 0.00066474, 0.00066978, 0.00067505, 0.00068046, 0.0006859 , 0.00069138, 0.00069693, 0.00070251, 0.00070812, 0.00071377, 0.00071946, 0.00072517, 0.00073092, 0.00073644, 0.00074186, 0.0007473 ]) en = np.linspace(6500, 7500, 51) d, b, a = xray_delta_beta('Fe2O3', 5.25, en) assert_allclose(delta, d, rtol=0.005) assert_allclose(beta_photo, b, rtol=0.005) assert_allclose(atten, a, rtol=0.005) def test_mirror_reflectivity(): rh1 = np.array([0.97199571, 0.97748356, 0.95360848, 0.92357963, 0.93588329, 0.94249572, 0.94771043, 0.95137756, 0.95416567, 0.95693941, 0.95906606, 0.9606963 , 0.96193867, 0.96298571, 0.9637671 , 0.96434822, 0.96458685, 0.96443861, 0.963873 , 0.96273628, 0.9607325 , 0.95705164, 0.94578432, 0.69383757, 0.65937538, 0.57222587, 0.40829232, 0.26919575, 0.18904094, 0.14024917]) r1 = mirror_reflectivity('Rh', 0.0025, np.arange(1000, 31000, 1000)) assert_allclose(rh1, r1, rtol=0.005) assert_allclose(mirror_reflectivity('Pt', 0.001, 80000), 0.74, rtol=0.005) def test_darwin_width(): def dw_dide(energy, crystal, hkl): """estimate darwin width in energy from min/max derivative""" dat = darwin_width(energy, crystal=crystal, hkl=hkl) dide = np.gradient(dat.intensity) / np.gradient(dat.denergy) return (dat.denergy[np.where(dide==dide.min())] - dat.denergy[np.where(dide==dide.max())])[0] def dw_fwhm(energy, crystal, hkl): """as-calculated darwin width in energy (eV)""" return darwin_width(energy, crystal=crystal, hkl=hkl).energy_fwhm # fwhm from derivatives: assert_allclose(dw_dide( 5000, 'Si', (1, 1, 1)), 0.685, rtol=0.01) assert_allclose(dw_dide(10000, 'Si', (1, 1, 1)), 1.340, rtol=0.01) assert_allclose(dw_dide(10000, 'Si', (2, 2, 0)), 0.595, rtol=0.01) assert_allclose(dw_dide(15000, 'Si', (2, 2, 0)), 0.891, rtol=0.01) assert_allclose(dw_dide(10000, 'Si', (3, 1, 1)), 0.288, rtol=0.01) assert_allclose(dw_dide(20000, 'Si', (3, 1, 1)), 0.565, rtol=0.01) # fwhm as cacluated assert_allclose(dw_fwhm( 5000, 'Si', (1, 1, 1)), 0.723, rtol=0.01) assert_allclose(dw_fwhm(10000, 'Si', (1, 1, 1)), 1.418, rtol=0.01) assert_allclose(dw_fwhm(10000, 'Si', (2, 2, 0)), 0.624, rtol=0.01) assert_allclose(dw_fwhm(15000, 'Si', (2, 2, 0)), 0.927, rtol=0.01) assert_allclose(dw_fwhm(10000, 'Si', (3, 1, 1)), 0.301, rtol=0.01) assert_allclose(dw_fwhm(20000, 'Si', (3, 1, 1)), 0.593, rtol=0.01) def test_ionchamber_fluxes(): ic1 = ionchamber_fluxes(gas='helium', volts=1.25, length=20.0, energy=15000.0, sensitivity=1.e-9) ic2 = ionchamber_fluxes(gas='nitrogen', volts=1.25, length=20.0, energy=15000.0, sensitivity=1.e-6) ic3 = ionchamber_fluxes(gas={'nitrogen':0.5, 'helium': 0.5}, volts=1.25, length=20.0, energy=15000.0, sensitivity=1.e-6) ic4 = ionchamber_fluxes(gas='Ar', volts=1.25, length=20.0, energy=15000.0, sensitivity=1.e-6) assert_allclose(ic1.transmitted/ic1.incident, 0.999, rtol=0.03) assert_allclose(ic1.incident, 2.503e11, rtol=0.03) assert_allclose(ic2.transmitted/ic2.incident, 0.970, rtol=0.03) assert_allclose(ic2.incident, 3.75e11, rtol=0.03) assert_allclose(ic3.transmitted/ic3.incident, 0.984, rtol=0.03) assert_allclose(ic3.incident, 8.40e11, rtol=0.03) assert_allclose(ic4.transmitted/ic4.incident, 0.493, rtol=0.03) assert_allclose(ic4.incident, 1.394e10, rtol=0.03) def test_formula_to_mass_fracs(): mf1 = formula_to_mass_fracs('Fe2O3') desired1 = {'Fe': 0.69943, 'O': 0.30056} assert_allclose(mf1['Fe'], desired1['Fe'], rtol=0.001) assert_allclose(mf1['O'], desired1['O'], rtol=0.001) assert_allclose(sum(mf1.values()), 1.0, rtol=0.001) mf2 = formula_to_mass_fracs({'Fe2O3':1, 'FeO':2}) desired2 = {'Fe': 0.7363, 'O': 0.2637} assert_allclose(mf2['Fe'], desired2['Fe'], rtol=0.001) assert_allclose(mf2['O'], desired2['O'], rtol=0.001) assert_allclose(sum(mf2.values()), 1.0, rtol=0.001) def test_validate_mass_fracs(): mf = _validate_mass_fracs({'Fe2O3': 0.75, 'FeO': 0.25}) desired = {'Fe': 0.7189, 'O': 0.2811} assert_allclose(mf['Fe'], desired['Fe'], rtol=0.001) assert_allclose(mf['O'], desired['O'], rtol=0.001) assert_allclose(sum(mf.values()), 1.0, rtol=0.001) def test_mass_fracs_to_molar_fracs(): """Circular test for consistency formula->mass fracs and vice versa.""" formula = {'Fe':1, 'FeO': 3} mass_fracs = formula_to_mass_fracs(formula) molar_fracs = mass_fracs_to_molar_fracs(mass_fracs) assert_allclose(molar_fracs['Fe'], 4 / 7, rtol=0.001) assert_allclose(molar_fracs['O'], 3 / 7, rtol=0.001) def test_transmission_sample(): # Validate against Hephaestus result, see PR #16 result1 = transmission_sample( sample='Fe2O3', energy=7162, absorp_total=1, area=1, density=5.24 ) assert_allclose(result1.mass_total_mg, 3.506, rtol=0.001) assert_allclose(result1.absorption_length_um, 6.7, rtol=0.01) # Validate against Hephaestus result, see PR #16 result2 = transmission_sample( sample='Cu', energy=9029, absorp_total=1, area=1, density=8.94 ) assert_allclose(result2.mass_total_mg, 3.640, rtol=0.001) assert_allclose(result2.absorption_length_um, 4.1, rtol=0.01) # Validate against XAFSmass result, see PR #16 result3 = transmission_sample( sample={'Fe': 0.05, 'SiO2': -1}, energy=7162, absorp_total=2.6, area=1.33, density=2.65 ) assert_allclose(result3.mass_components_mg['Fe'], 2.58, rtol=0.03) assert_allclose(result3.absorbance_steps['Fe'], 0.706, rtol=0.06) assert_allclose(result3.mass_total_mg, 51.7, rtol=0.02) assert_allclose(result3.thickness_mm, 0.1466, rtol=0.02)