def test_get_sigma_a_n1():
# Test example with one entry
sigma_a_n = xs.get_sigma_a_n(10010)
expected = np.array([
9.96360000e-01, 6.07160000e-01, 4.72250000e-01,
3.99360000e-01, 3.52130000e-01, 3.18550000e-01,
2.93030000e-01, 2.69290000e-01, 2.46390000e-01,
2.27390000e-01, 2.11380000e-01, 1.95050000e-01,
1.76480000e-01, 1.50820000e-01, 1.20850000e-01,
9.42780000e-02, 7.26880000e-02, 5.65930000e-02,
4.38660000e-02, 3.42250000e-02, 2.68640000e-02,
2.08190000e-02, 1.61410000e-02, 1.27460000e-02,
9.89720000e-03, 7.70440000e-03, 5.88870000e-03,
4.59450000e-03, 3.57770000e-03, 2.78980000e-03,
2.18600000e-03, 1.74630000e-03, 1.32340000e-03,
1.13220000e-03, 1.04010000e-03, 9.54700000e-04,
7.95210000e-04, 6.03360000e-04, 4.53900000e-04,
3.60530000e-04, 2.99660000e-04, 2.36340000e-04,
1.71240000e-04, 1.19680000e-04, 8.19370000e-05,
5.64050000e-05, 4.48700000e-05, 3.98850000e-05,
3.71800000e-05, 3.54070000e-05, 3.45980000e-05,
3.44370000e-05, 3.43310000e-05, 3.43120000e-05,
3.49470000e-05, 3.58100000e-05, 3.62750000e-05,
3.60950000e-05, 3.48160000e-05, 2.97130000e-05,
2.89150000e-05, 2.67690000e-05, 2.60400000e-05])
assert_array_equal(sigma_a_n, expected)
def test_get_sigma_a_n2():
# Test example with multiple entries but not that have reaction_type = 'c'
sigma_a_n = xs.get_sigma_a_n(10020)
expected = np.array([
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0.0011439, 0.019188 , 0.047752 , 0.087012 , 0.18032 ,
0.18847 , 0.20148 , 0.2053 ])
expected += np.array([
1.52240000e-03, 9.30720000e-04, 7.23200000e-04,
6.13370000e-04, 5.40020000e-04, 4.89210000e-04,
4.49150000e-04, 4.13780000e-04, 3.78790000e-04,
3.48940000e-04, 3.24070000e-04, 2.99220000e-04,
2.70960000e-04, 2.31220000e-04, 1.85480000e-04,
1.44650000e-04, 1.11350000e-04, 8.69150000e-05,
6.73760000e-05, 5.26030000e-05, 4.15620000e-05,
3.20750000e-05, 2.48770000e-05, 1.95970000e-05,
1.52790000e-05, 1.17130000e-05, 9.09390000e-06,
7.05040000e-06, 5.53650000e-06, 4.39520000e-06,
3.43240000e-06, 2.68440000e-06, 2.00390000e-06,
1.70200000e-06, 1.58790000e-06, 1.46300000e-06,
1.28160000e-06, 1.09490000e-06, 1.01380000e-06,
1.04050000e-06, 1.07440000e-06, 1.17800000e-06,
1.41250000e-06, 1.77080000e-06, 2.30510000e-06,
2.96130000e-06, 3.51820000e-06, 3.92870000e-06,
4.43470000e-06, 4.95710000e-06, 5.57910000e-06,
6.32150000e-06, 7.01920000e-06, 7.69640000e-06,
8.43000000e-06, 9.14600000e-06, 9.83610000e-06,
1.03010000e-05, 1.06530000e-05, 9.44650000e-06,
9.18780000e-06, 8.38510000e-06, 8.10500000e-06])
assert_array_equal(sigma_a_n, expected)
def test_get_sigma_a_n4():
# Test that a zeros array is returned for an entry that is not in the table
sigma_a_n = xs.get_sigma_a_n(10420)
expected = np.zeros(63)
assert_array_equal(sigma_a_n, expected)
def test_get_sigma_a_n3():
# Test example with multiple entries including one that has reaction_type = 'c'
sigma_a_n = xs.get_sigma_a_n(20030)
expected = np.array([
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0.0043036, 0.060056 , 0.11588 , 0.15215 ,
0.15165 , 0.12801 , 0.112 ])
expected += np.array([
1.59870000e+04, 9.74200000e+03, 7.57740000e+03,
6.40770000e+03, 5.65000000e+03, 5.11100000e+03,
4.70140000e+03, 4.32040000e+03, 3.95290000e+03,
3.64790000e+03, 3.39100000e+03, 3.12890000e+03,
2.83090000e+03, 2.41910000e+03, 1.93810000e+03,
1.51180000e+03, 1.16550000e+03, 9.07320000e+02,
7.03200000e+02, 5.48580000e+02, 4.30550000e+02,
3.33630000e+02, 2.58630000e+02, 2.04220000e+02,
1.58550000e+02, 1.23410000e+02, 9.43150000e+01,
7.36280000e+01, 5.73090000e+01, 4.46360000e+01,
3.50230000e+01, 2.79830000e+01, 2.10090000e+01,
1.78020000e+01, 1.61630000e+01, 1.46700000e+01,
1.20900000e+01, 9.13540000e+00, 6.93580000e+00,
5.58270000e+00, 4.72330000e+00, 3.83980000e+00,
2.91150000e+00, 2.19690000e+00, 1.63240000e+00,
1.22460000e+00, 1.05240000e+00, 9.59460000e-01,
9.20880000e-01, 9.06540000e-01, 8.90440000e-01,
8.79710000e-01, 8.66060000e-01, 8.25060000e-01,
7.46760000e-01, 6.08280000e-01, 4.47760000e-01,
3.38630000e-01, 2.50370000e-01, 1.21070000e-01,
1.11410000e-01, 8.97170000e-02, 8.20000000e-02])
expected += np.array([
1.40840000e-03, 1.10660000e-03, 8.04470000e-04,
5.02330000e-04, 2.00200000e-04, 3.15200000e-05,
3.10000000e-05, 3.10000000e-05, 3.10000000e-05,
3.10000000e-05, 3.10000000e-05, 3.10000000e-05,
3.10000000e-05, 3.10000000e-05, 3.10000000e-05,
3.10000000e-05, 3.10000000e-05, 3.10000000e-05,
3.10000000e-05, 3.10000000e-05, 3.10000000e-05,
3.10000000e-05, 3.09990000e-05, 3.09990000e-05,
3.09980000e-05, 3.09970000e-05, 3.09950000e-05,
3.09920000e-05, 3.09860000e-05, 3.09770000e-05,
3.09630000e-05, 3.09390000e-05, 3.08990000e-05,
3.08620000e-05, 3.08380000e-05, 3.08080000e-05,
3.07250000e-05, 3.05460000e-05, 3.02520000e-05,
2.99180000e-05, 2.95930000e-05, 2.89430000e-05,
2.76470000e-05, 2.54710000e-05, 2.18830000e-05,
1.59690000e-05, 9.60350000e-06, 3.53490000e-06,
5.38530000e-07, 1.74800000e-06, 3.50630000e-06,
5.50360000e-06, 7.86570000e-06, 1.11590000e-05,
1.53870000e-05, 2.08170000e-05, 2.86970000e-05,
3.76470000e-05, 5.65300000e-05, 8.97390000e-05,
1.15640000e-04, 1.34700000e-04, 1.46310000e-04])
assert_array_equal(sigma_a_n, expected)
