def test_select_freq(self):
self.methyloxirane_freq.parse_frequency()
self.methyloxirane_freq.parse_frequency_ext()
delta = gen_delta(delta_type=2,
freq=self.methyloxirane_freq.frequency.copy())
va_corr = VA()
path = sep.join(['methyloxirane', '*'])
va_corr.roa = get_data(path=path,
attr='roa',
soft=Output,
f_start='va-roa-methyloxirane-def2tzvp-488.9-',
f_end='.out')
va_corr.roa['exc_freq'] = np.tile(488.9, len(va_corr.roa))
va_corr.gradient = get_data(
path=path,
attr='gradient',
soft=Output,
f_start='va-roa-methyloxirane-def2tzvp-488.9-',
f_end='.out')
va_corr.gradient['exc_freq'] = np.tile(488.9, len(va_corr.gradient))
va_corr.vroa(uni=self.methyloxirane_freq, delta=delta['delta'].values)
scatter_data = np.array(
[[
1.12639199e+03, 1.00000000e+01, -6.15736884e+01,
-1.53103521e+01, -1.68892383e-01, -6.40100535e-03,
-8.61815897e-04, 4.88900000e+02
],
[
1.15100631e+03, 1.10000000e+01, -1.06898371e+02,
-2.76794343e+01, 3.53857297e+00, -1.11479855e-02,
1.28026956e-03, 4.88900000e+02
],
[
1.24937064e+03, 1.20000000e+01, 5.23431984e+01,
5.17874012e+00, -8.24615516e+00, 5.19066673e-03,
-5.18260038e-03, 4.88900000e+02
],
[
1.37094149e+03, 1.30000000e+01, 3.49746537e+01,
-9.43653998e+00, -8.72689935e-02, 3.05559747e-03,
6.47745423e-04, 4.88900000e+02
],
[
1.39064221e+03, 1.40000000e+01, -5.31532967e+01,
-5.65151249e+00, -6.11336634e+00, -5.28356488e-03,
-5.16165231e-03, 4.88900000e+02
],
[
1.44754882e+03, 1.50000000e+01, -1.25064010e+02,
-2.49033712e+01, -8.12931706e-02, -1.28030529e-02,
-1.66110131e-03, 4.88900000e+02
]])
raman_data = np.array(
[[
1.12639199e+03, 1.00000000e+01, 3.61528920e-04, 5.94192417e-01,
1.90792325e+01, 4.88900000e+02
],
[
1.15100631e+03, 1.10000000e+01, 3.36862711e-02,
1.62723253e-01, 1.12706729e+01, 4.88900000e+02
],
[
1.24937064e+03, 1.20000000e+01, 3.10356963e-01,
1.61670230e+00, 1.07598727e+02, 4.88900000e+02
],
[
1.37094149e+03, 1.30000000e+01, 6.63217766e-03,
2.37302109e-01, 8.78745947e+00, 4.88900000e+02
],
[
1.39064221e+03, 1.40000000e+01, 6.30361373e-02,
8.04145907e-01, 3.70791737e+01, 4.88900000e+02
],
[
1.44754882e+03, 1.50000000e+01, 5.36564516e-05,
1.07944901e+00, 3.45520265e+01, 4.88900000e+02
]])
scatter_data = scatter_data.T
raman_data = raman_data.T
# test all columns of the respective dataframe to get a better sense of what is broken
self.assertTrue(
np.allclose(va_corr.scatter['freq'].values,
scatter_data[0],
rtol=5e-4))
self.assertTrue(
np.allclose(va_corr.scatter['freqdx'].values,
scatter_data[1],
rtol=5e-4))
self.assertTrue(
np.allclose(va_corr.scatter['beta_g*1e6'].values,
scatter_data[2],
rtol=5e-4))
self.assertTrue(
np.allclose(va_corr.scatter['beta_A*1e6'].values,
scatter_data[3],
rtol=5e-4))
self.assertTrue(
np.allclose(va_corr.scatter['alpha_g*1e6'].values,
scatter_data[4],
rtol=5e-4))
self.assertTrue(
np.allclose(va_corr.scatter['backscatter'].values,
scatter_data[5],
rtol=5e-4))
self.assertTrue(
np.allclose(va_corr.scatter['forwardscatter'].values,
scatter_data[6],
rtol=5e-4))
self.assertTrue(
np.allclose(va_corr.scatter['exc_freq'].values,
scatter_data[7],
rtol=5e-4))
self.assertTrue(
np.allclose(va_corr.raman['freq'].values, raman_data[0],
rtol=5e-4))
self.assertTrue(
np.allclose(va_corr.raman['freqdx'].values,
raman_data[1],
rtol=5e-4))
self.assertTrue(
np.allclose(va_corr.raman['alpha_squared'].values,
raman_data[2],
rtol=5e-4))
self.assertTrue(
np.allclose(va_corr.raman['beta_alpha'].values,
raman_data[3],
rtol=5e-4))
self.assertTrue(
np.allclose(va_corr.raman['raman_int'].values,
raman_data[4],
rtol=5e-4))
self.assertTrue(
np.allclose(va_corr.raman['exc_freq'].values,
raman_data[5],
rtol=5e-4))
def test_zpvc(self):
self.nitro_freq.parse_frequency()
self.nitro_freq.parse_frequency_ext()
path = sep.join(['nitromalonamide_nmr', '*'])
va_corr = VA()
va_corr.gradient = get_data(path=path,
attr='gradient',
soft=gOutput,
f_start='nitromal_grad_',
f_end='.out')
va_corr.property = get_data(
path=path,
attr='nmr_shielding',
soft=gOutput,
f_start='nitromal_prop',
f_end='.out').groupby('atom').get_group(0)[[
'isotropic', 'file'
]].reset_index(drop=True)
delta = gen_delta(delta_type=2, freq=self.nitro_freq.frequency.copy())
va_corr.zpvc(uni=self.nitro_freq,
delta=delta['delta'].values,
temperature=[0, 200])
zpvc_results = np.array([[
13.9329, -1.80706136, 12.12583864, -2.65173195, 0.84467059, 0.
], [13.9329, -1.48913965, 12.44376035, -2.39264653, 0.90350687, 200.]])
eff_coord = np.array([[1., 0.43933078, -4.1685104, 0.],
[1., -5.89314484, -2.18542914, 0.],
[1., -4.92050271, 1.02704248, 0.],
[1., 6.27207499, -0.61188352, 0.],
[1., 4.51177692, 2.23712277, 0.],
[6., 2.50712078, -1.03455595, 0.],
[8., 2.68478934, -3.43995936, 0.],
[7., 4.63712862, 0.33562593, 0.],
[6., -0.00921467, 0.10958486, 0.],
[6., -2.14821065, -1.60648521, 0.],
[8., -1.72316811, -4.00875859, 0.],
[7., -0.356593, 2.76254918, 0.],
[8., 1.51924782, 4.1859399, 0.],
[8., -2.53953864, 3.65511737, 0.],
[7., -4.55751742, -0.8483932, 0.],
[1., 0.42984441, -4.17018127, 0.],
[1., -5.88480085, -2.18035498, 0.],
[1., -4.91515264, 1.02280195, 0.],
[1., 6.26195233, -0.60839022, 0.],
[1., 4.50575843, 2.22964105, 0.],
[6., 2.50646903, -1.03456634, 0.],
[8., 2.68667123, -3.43354071, 0.],
[7., 4.62935451, 0.33263776, 0.],
[6., -0.00919927, 0.1084463, 0.],
[6., -2.14622625, -1.60477806, 0.],
[8., -1.72498822, -4.00524579, 0.],
[7., -0.35655368, 2.76183552, 0.],
[8., 1.50986822, 4.18058144, 0.],
[8., -2.52931977, 3.65383296, 0.],
[7., -4.55110836, -0.84857983, 0.]])
vib_average = np.array(
[[
8.50080000e+01, 0.00000000e+00, -0.00000000e+00,
7.20197387e-03, 7.20197387e-03, 0.00000000e+00
],
[
8.92980000e+01, 1.00000000e+00, -0.00000000e+00,
-1.92131709e-03, -1.92131709e-03, 0.00000000e+00
],
[
1.46093800e+02, 2.00000000e+00, -0.00000000e+00,
2.20218342e-02, 2.20218342e-02, 0.00000000e+00
],
[
2.17704400e+02, 3.00000000e+00, -0.00000000e+00,
9.06126467e-04, 9.06126467e-04, 0.00000000e+00
],
[
3.21218700e+02, 4.00000000e+00, -1.04570441e+00,
7.43587697e-02, -9.71345643e-01, 0.00000000e+00
],
[
3.54578000e+02, 5.00000000e+00, -1.69193968e-01,
1.02299102e-02, -1.58964058e-01, 0.00000000e+00
],
[
4.01846100e+02, 6.00000000e+00, -9.95359531e-04,
3.02869005e-03, 2.03333052e-03, 0.00000000e+00
],
[
4.18516500e+02, 7.00000000e+00, -0.00000000e+00,
-1.59091073e-02, -1.59091073e-02, 0.00000000e+00
],
[
4.25136100e+02, 8.00000000e+00, 9.96865969e-02,
9.60161049e-03, 1.09288207e-01, 0.00000000e+00
],
[
4.33864900e+02, 9.00000000e+00, -0.00000000e+00,
-2.24181573e-02, -2.24181573e-02, 0.00000000e+00
],
[
4.61383400e+02, 1.00000000e+01, 3.02755804e-01,
7.35128407e-02, 3.76268645e-01, 0.00000000e+00
],
[
4.85255900e+02, 1.10000000e+01, 5.73683252e-04,
3.43416159e-03, 4.00784484e-03, 0.00000000e+00
],
[
6.09549200e+02, 1.20000000e+01, 8.95579596e-02,
9.59664081e-03, 9.91546004e-02, 0.00000000e+00
],
[
6.66622400e+02, 1.30000000e+01, -0.00000000e+00,
-2.76336851e-03, -2.76336851e-03, 0.00000000e+00
],
[
6.85145800e+02, 1.40000000e+01, -0.00000000e+00,
-7.69008204e-03, -7.69008204e-03, 0.00000000e+00
],
[
7.03986700e+02, 1.50000000e+01, -3.59227410e-01,
2.76777550e-02, -3.31549655e-01, 0.00000000e+00
],
[
7.14892300e+02, 1.60000000e+01, -0.00000000e+00,
2.59325707e-03, 2.59325707e-03, 0.00000000e+00
],
[
7.25846000e+02, 1.70000000e+01, -0.00000000e+00,
-6.41058056e-03, -6.41058056e-03, 0.00000000e+00
],
[
7.62762300e+02, 1.80000000e+01, -0.00000000e+00,
-5.35324542e-03, -5.35324542e-03, 0.00000000e+00
],
[
8.46200900e+02, 1.90000000e+01, -3.55846141e-03,
-8.75803015e-04, -4.43426443e-03, 0.00000000e+00
],
[
1.07527990e+03, 2.00000000e+01, -1.84207554e-02,
4.72086557e-03, -1.36998899e-02, 0.00000000e+00
],
[
1.09465730e+03, 2.10000000e+01, -3.15295434e-02,
5.46069862e-03, -2.60688448e-02, 0.00000000e+00
],
[
1.10619190e+03, 2.20000000e+01, -0.00000000e+00,
4.49794220e-02, 4.49794220e-02, 0.00000000e+00
],
[
1.16155690e+03, 2.30000000e+01, -1.07394061e-02,
2.19152295e-03, -8.54788314e-03, 0.00000000e+00
],
[
1.17408590e+03, 2.40000000e+01, 2.00299542e-02,
9.48213114e-03, 2.95120853e-02, 0.00000000e+00
],
[
1.26700700e+03, 2.50000000e+01, -5.20609316e-01,
1.81355739e-01, -3.39253577e-01, 0.00000000e+00
],
[
1.31668580e+03, 2.60000000e+01, -3.86074967e-03,
2.99473191e-03, -8.66017757e-04, 0.00000000e+00
],
[
1.39527270e+03, 2.70000000e+01, -1.48603969e-03,
3.70156572e-03, 2.21552603e-03, 0.00000000e+00
],
[
1.45205880e+03, 2.80000000e+01, -2.50446596e-03,
-1.17089006e-03, -3.67535602e-03, 0.00000000e+00
],
[
1.55570980e+03, 2.90000000e+01, -6.91414901e-04,
-9.62715516e-04, -1.65413042e-03, 0.00000000e+00
],
[
1.57585090e+03, 3.00000000e+01, 4.04075730e-03,
-1.72319285e-03, 2.31756445e-03, 0.00000000e+00
],
[
1.59816940e+03, 3.10000000e+01, -3.02221180e-03,
-1.26461431e-02, -1.56683549e-02, 0.00000000e+00
],
[
1.63134120e+03, 3.20000000e+01, -3.85968122e-02,
1.18178497e-02, -2.67789625e-02, 0.00000000e+00
],
[
1.71103720e+03, 3.30000000e+01, -5.30081127e-02,
9.07966487e-03, -4.39284478e-02, 0.00000000e+00
],
[
2.26025580e+03, 3.40000000e+01, -9.00414250e-01,
4.04124232e-01, -4.96290017e-01, 0.00000000e+00
],
[
3.52007010e+03, 3.50000000e+01, -3.79625326e-04,
7.67211701e-04, 3.87586375e-04, 0.00000000e+00
],
[
3.54188550e+03, 3.60000000e+01, -2.40654106e-03,
-3.52958626e-04, -2.75949969e-03, 0.00000000e+00
],
[
3.68688910e+03, 3.70000000e+01, -1.40898432e-03,
3.54638132e-04, -1.05434619e-03, 0.00000000e+00
],
[
3.69638850e+03, 3.80000000e+01, -6.18866036e-04,
-3.25687908e-04, -9.44553944e-04, 0.00000000e+00
],
[
8.50080000e+01, 0.00000000e+00, -0.00000000e+00,
2.42846696e-02, 2.42846696e-02, 2.00000000e+02
],
[
8.92980000e+01, 1.00000000e+00, -0.00000000e+00,
-6.18637794e-03, -6.18637794e-03, 2.00000000e+02
],
[
1.46093800e+02, 2.00000000e+00, -0.00000000e+00,
4.56979114e-02, 4.56979114e-02, 2.00000000e+02
],
[
2.17704400e+02, 3.00000000e+00, -0.00000000e+00,
1.38459170e-03, 1.38459170e-03, 2.00000000e+02
],
[
3.21218700e+02, 4.00000000e+00, -1.01880224e+00,
9.07354499e-02, -9.28066794e-01, 2.00000000e+02
],
[
3.54578000e+02, 5.00000000e+00, -1.47884125e-01,
1.19615757e-02, -1.35922549e-01, 2.00000000e+02
],
[
4.01846100e+02, 6.00000000e+00, -1.06537672e-03,
3.38490386e-03, 2.31952714e-03, 2.00000000e+02
],
[
4.18516500e+02, 7.00000000e+00, -0.00000000e+00,
-1.75578234e-02, -1.75578234e-02, 2.00000000e+02
],
[
4.25136100e+02, 8.00000000e+00, 1.13889092e-01,
1.05481068e-02, 1.24437199e-01, 2.00000000e+02
],
[
4.33864900e+02, 9.00000000e+00, -0.00000000e+00,
-2.44873679e-02, -2.44873679e-02, 2.00000000e+02
],
[
4.61383400e+02, 1.00000000e+01, 3.50247546e-01,
7.90337919e-02, 4.29281338e-01, 2.00000000e+02
],
[
4.85255900e+02, 1.10000000e+01, 1.71633606e-04,
3.65009840e-03, 3.82173201e-03, 2.00000000e+02
],
[
6.09549200e+02, 1.20000000e+01, 7.81791456e-02,
9.83892770e-03, 8.80180732e-02, 2.00000000e+02
],
[
6.66622400e+02, 1.30000000e+01, -0.00000000e+00,
-2.80944797e-03, -2.80944797e-03, 2.00000000e+02
],
[
6.85145800e+02, 1.40000000e+01, -0.00000000e+00,
-7.80220122e-03, -7.80220122e-03, 2.00000000e+02
],
[
7.03986700e+02, 1.50000000e+01, -2.69187645e-01,
2.80298166e-02, -2.41157829e-01, 2.00000000e+02
],
[
7.14892300e+02, 1.60000000e+01, -0.00000000e+00,
2.62373990e-03, 2.62373990e-03, 2.00000000e+02
],
[
7.25846000e+02, 1.70000000e+01, -0.00000000e+00,
-6.48019409e-03, -6.48019409e-03, 2.00000000e+02
],
[
7.62762300e+02, 1.80000000e+01, -0.00000000e+00,
-5.39776338e-03, -5.39776338e-03, 2.00000000e+02
],
[
8.46200900e+02, 1.90000000e+01, 2.39979858e-03,
-8.79791824e-04, 1.52000675e-03, 2.00000000e+02
],
[
1.07527990e+03, 2.00000000e+01, -1.08417544e-02,
4.72499608e-03, -6.11675836e-03, 2.00000000e+02
],
[
1.09465730e+03, 2.10000000e+01, -2.64528256e-03,
5.46485456e-03, 2.81957200e-03, 2.00000000e+02
],
[
1.10619190e+03, 2.20000000e+01, -0.00000000e+00,
4.50109276e-02, 4.50109276e-02, 2.00000000e+02
],
[
1.16155690e+03, 2.30000000e+01, -6.76838012e-03,
2.19255359e-03, -4.57582653e-03, 2.00000000e+02
],
[
1.17408590e+03, 2.40000000e+01, 9.29716984e-03,
9.48620604e-03, 1.87833759e-02, 2.00000000e+02
],
[
1.26700700e+03, 2.50000000e+01, -5.14657884e-01,
1.81395679e-01, -3.33262205e-01, 2.00000000e+02
],
[
1.31668580e+03, 2.60000000e+01, -2.24279961e-02,
2.99519325e-03, -1.94328029e-02, 2.00000000e+02
],
[
1.39527270e+03, 2.70000000e+01, -9.34932877e-04,
3.70188971e-03, 2.76695683e-03, 2.00000000e+02
],
[
1.45205880e+03, 2.80000000e+01, 3.53202807e-03,
-1.17095817e-03, 2.36106990e-03, 2.00000000e+02
],
[
1.55570980e+03, 2.90000000e+01, -2.49842213e-04,
-9.62742087e-04, -1.21258430e-03, 2.00000000e+02
],
[
1.57585090e+03, 3.00000000e+01, 2.39535538e-03,
-1.72323400e-03, 6.72121388e-04, 2.00000000e+02
],
[
1.59816940e+03, 3.10000000e+01, 4.90707509e-03,
-1.26464003e-02, -7.73932517e-03, 2.00000000e+02
],
[
1.63134120e+03, 3.20000000e+01, -3.00698127e-02,
1.18180390e-02, -1.82517738e-02, 2.00000000e+02
],
[
1.71103720e+03, 3.30000000e+01, -3.95916986e-02,
9.07974685e-03, -3.05119518e-02, 2.00000000e+02
],
[
2.26025580e+03, 3.40000000e+01, -8.86090407e-01,
4.04124303e-01, -4.81966104e-01, 2.00000000e+02
],
[
3.52007010e+03, 3.50000000e+01, -5.63472032e-04,
7.67211701e-04, 2.03739669e-04, 2.00000000e+02
],
[
3.54188550e+03, 3.60000000e+01, -3.39925478e-03,
-3.52958626e-04, -3.75221341e-03, 2.00000000e+02
],
[
3.68688910e+03, 3.70000000e+01, -1.73391855e-03,
3.54638132e-04, -1.37928042e-03, 2.00000000e+02
],
[
3.69638850e+03, 3.80000000e+01, -7.51344750e-04,
-3.25687908e-04, -1.07703266e-03, 2.00000000e+02
]])
cols = ['property', 'zpvc', 'zpva', 'tot_anharm', 'tot_curva', 'temp']
self.assertTrue(
np.allclose(va_corr.zpvc_results[cols].values,
zpvc_results,
rtol=5e-4))
va_corr.eff_coord['Z'] = va_corr.eff_coord['Z'].astype(int)
self.assertTrue(
np.allclose(va_corr.eff_coord[['Z', 'x', 'y', 'z']].values,
eff_coord,
atol=5e-5))
cols = ['freq', 'freqdx', 'anharm', 'curva', 'sum', 'temp']
def test_vroa(self):
h2o2_freq.parse_frequency()
h2o2_freq.parse_frequency_ext()
delta = gen_delta(delta_type=2, freq=h2o2_freq.frequency.copy())
va_corr = VA()
path = sep.join([TMPDIR, 'h2o2', '*'])
va_corr.roa = get_data(path=path, attr='roa', soft=Output, f_start='va-roa-h2o2-def2tzvp-514.5-',
f_end='.out')
va_corr.roa['exc_freq'] = np.tile(514.5, len(va_corr.roa))
va_corr.gradient = get_data(path=path, attr='gradient', soft=Output,
f_start='va-roa-h2o2-def2tzvp-514.5-', f_end='.out')
va_corr.gradient['exc_freq'] = np.tile(514.5, len(va_corr.gradient))
va_corr.vroa(uni=h2o2_freq, delta=delta['delta'].values)
scatter_data = np.array([[ 3.47311779e+02, 0.00000000e+00, -3.27390198e+02,
-8.44921542e+01, -4.22102267e-02, -3.41332079e-02,
-3.91676006e-03, 5.14500000e+02],
[ 8.60534577e+02, 1.00000000e+00, 1.75268228e+02,
-8.09603043e+00, -8.26286589e+00, 1.65666769e-02,
-3.01543530e-03, 5.14500000e+02],
[ 1.24319010e+03, 2.00000000e+00, -3.35605422e+02,
-7.26516978e+01, 1.06370293e-06, -3.45429748e-02,
-4.20725882e-03, 5.14500000e+02],
[ 1.37182002e+03, 3.00000000e+00, 2.20210484e+02,
5.78999940e+01, -4.46257676e+00, 2.29930063e-02,
-6.16087427e-04, 5.14500000e+02],
[ 3.59750268e+03, 4.00000000e+00, -3.50819253e+03,
-3.90826518e+02, 5.90325028e-02, -3.49292932e-01,
-4.98353528e-02, 5.14500000e+02],
[ 3.59821746e+03, 5.00000000e+00, 5.05236006e+03,
4.00023286e+02, 6.60389814e+00, 4.97827311e-01,
7.91921951e-02, 5.14500000e+02]])
raman_data = np.array([[3.47311779e+02, 0.00000000e+00, 3.42307581e-05, 6.38955258e-01,
2.04527298e+01, 5.14500000e+02],
[8.60534577e+02, 1.00000000e+00, 1.90190339e-01, 1.07090867e+00,
6.85033386e+01, 5.14500000e+02],
[1.24319010e+03, 2.00000000e+00, 1.27606294e-08, 3.46909710e-01,
1.11011130e+01, 5.14500000e+02],
[1.37182002e+03, 3.00000000e+00, 3.94139588e-02, 1.19286864e+00,
4.52663091e+01, 5.14500000e+02],
[3.59750268e+03, 4.00000000e+00, 1.52822910e-02, 6.21166773e+00,
2.01524180e+02, 5.14500000e+02],
[3.59821746e+03, 5.00000000e+00, 1.60412161e+00, 5.19841596e+00,
4.55091201e+02, 5.14500000e+02]])
scatter_data = scatter_data.T.copy()
raman_data = raman_data.T.copy()
# test all columns of the respective dataframe to get a better sense of what is broken
self.assertTrue(np.allclose(va_corr.scatter['freq'].values, scatter_data[0], rtol=5e-4))
self.assertTrue(np.allclose(va_corr.scatter['freqdx'].values, scatter_data[1], rtol=5e-4))
self.assertTrue(np.allclose(va_corr.scatter['beta_g*1e6'].values, scatter_data[2], rtol=5e-4))
self.assertTrue(np.allclose(va_corr.scatter['beta_A*1e6'].values, scatter_data[3], rtol=5e-4))
self.assertTrue(np.allclose(va_corr.scatter['alpha_g*1e6'].values, scatter_data[4], rtol=5e-4))
self.assertTrue(np.allclose(va_corr.scatter['backscatter'].values, scatter_data[5], rtol=5e-4))
self.assertTrue(np.allclose(va_corr.scatter['forwardscatter'].values, scatter_data[6], rtol=5e-4))
self.assertTrue(np.allclose(va_corr.scatter['exc_freq'].values, scatter_data[7], rtol=5e-4))
self.assertTrue(np.allclose(va_corr.raman['freq'].values, raman_data[0], rtol=5e-4))
self.assertTrue(np.allclose(va_corr.raman['freqdx'].values, raman_data[1], rtol=5e-4))
self.assertTrue(np.allclose(va_corr.raman['alpha_squared'].values, raman_data[2], rtol=5e-4))
self.assertTrue(np.allclose(va_corr.raman['beta_alpha'].values, raman_data[3], rtol=5e-4))
self.assertTrue(np.allclose(va_corr.raman['raman_int'].values, raman_data[4], rtol=5e-4))
self.assertTrue(np.allclose(va_corr.raman['exc_freq'].values, raman_data[5], rtol=5e-4))