def test_init_npt(self): # Diffusion vasprun.xmls are rather large. We are only going to use a # very small preprocessed run for testing. Note that the results are # unreliable for short runs. with open(os.path.join(test_dir, "DiffusionAnalyzer_NPT.json"), 'r') as f: dd = json.load(f) d = DiffusionAnalyzer.from_dict(dd) # large tolerance because scipy constants changed between 0.16.1 and 0.17 self.assertAlmostEqual(d.conductivity, 499.15058192970508, 4) self.assertAlmostEqual(d.chg_conductivity, 1219.59633107, 4) self.assertAlmostEqual(d.diffusivity, 8.40265434771e-06, 7) self.assertAlmostEqual(d.chg_diffusivity, 2.05305709033e-05, 6) self.assertAlmostEqual(d.conductivity_std_dev, 0.10368477696021029, 7) self.assertAlmostEqual(d.diffusivity_std_dev, 9.1013023085561779e-09, 7) self.assertAlmostEqual(d.chg_diffusivity_std_dev, 1.20834853646e-08, 6) self.assertAlmostEqual(d.haven_ratio, 0.409275240679, 7) self.assertArrayAlmostEqual( d.conductivity_components, [455.178101, 602.252644, 440.0210014], 3) self.assertArrayAlmostEqual( d.diffusivity_components, [7.66242570e-06, 1.01382648e-05, 7.40727250e-06]) self.assertArrayAlmostEqual( d.conductivity_components_std_dev, [0.1196577, 0.0973347, 0.1525400] ) self.assertArrayAlmostEqual( d.diffusivity_components_std_dev, [2.0143072e-09, 1.6385239e-09, 2.5678445e-09] ) self.assertArrayAlmostEqual( d.max_ion_displacements, [1.13147881, 0.79899554, 1.04153733, 0.96061850, 0.83039864, 0.70246715, 0.61365911, 0.67965179, 1.91973907, 1.69127386, 1.60568746, 1.35587641, 1.03280378, 0.99202692, 2.03359655, 1.03760269, 1.40228350, 1.36315080, 1.27414979, 1.26742035, 0.88199589, 0.97700804, 1.11323184, 1.00139511, 2.94164403, 0.89438909, 1.41508334, 1.23660358, 0.39322939, 0.54264064, 1.25291806, 0.62869809, 0.40846708, 1.43415505, 0.88891241, 0.56259128, 0.81712740, 0.52700441, 0.51011733, 0.55557882, 0.49131002, 0.66740277, 0.57798671, 0.63521025, 0.50277142, 0.52878021, 0.67803443, 0.81161269, 0.46486345, 0.47132761, 0.74301293, 0.79285519, 0.48789600, 0.61776836, 0.60695847, 0.67767756, 0.70972268, 1.08232442, 0.87871177, 0.84674206, 0.45694693, 0.60417985, 0.61652272, 0.66444583, 0.52211986, 0.56544134, 0.43311443, 0.43027547, 1.10730439, 0.59829728, 0.52270635, 0.72327608, 1.02919775, 0.84423208, 0.61694764, 0.72795752, 0.72957755, 0.55491631, 0.68507454, 0.76745343, 0.96346584, 0.66672645, 1.06810107, 0.65705843]) self.assertEqual(d.sq_disp_ions.shape, (84, 217)) self.assertEqual(d.lattices.shape, (1001, 3, 3)) self.assertEqual(d.mscd.shape, (217,)) self.assertEqual(d.mscd.shape, d.msd.shape) self.assertAlmostEqual(d.max_framework_displacement, 1.43415505156) ss = list(d.get_drift_corrected_structures(10, 1000, 20)) self.assertEqual(len(ss), 50) n = random.randint(0, 49) n_orig = n * 20 + 10 self.assertArrayAlmostEqual( ss[n].cart_coords - d.structure.cart_coords + d.drift[:, n_orig, :], d.disp[:, n_orig, :]) d = DiffusionAnalyzer.from_dict(d.as_dict()) self.assertIsInstance(d, DiffusionAnalyzer) # Ensure summary dict is json serializable. json.dumps(d.get_summary_dict(include_msd_t=True)) d = DiffusionAnalyzer(d.structure, d.disp, d.specie, d.temperature, d.time_step, d.step_skip, smoothed="max") self.assertAlmostEqual(d.conductivity, 499.15058192970508, 4) self.assertAlmostEqual(d.diffusivity, 8.40265434771e-06, 7) self.assertAlmostEqual(d.haven_ratio, 0.409275240679, 7) self.assertAlmostEqual(d.chg_diffusivity, 2.05305709033e-05, 7) d = DiffusionAnalyzer(d.structure, d.disp, d.specie, d.temperature, d.time_step, d.step_skip, smoothed=False) self.assertAlmostEqual(d.conductivity, 406.5965396, 4) self.assertAlmostEqual(d.diffusivity, 6.8446082e-06, 7) self.assertAlmostEqual(d.chg_diffusivity, 1.03585877962e-05, 6) self.assertAlmostEqual(d.haven_ratio, 0.6607665413, 6) d = DiffusionAnalyzer(d.structure, d.disp, d.specie, d.temperature, d.time_step, d.step_skip, smoothed="constant", avg_nsteps=100) self.assertAlmostEqual(d.conductivity, 425.7789898, 4) self.assertAlmostEqual(d.diffusivity, 7.167523809142514e-06, 7) self.assertAlmostEqual(d.chg_diffusivity, 9.33480892187e-06, 6) self.assertAlmostEqual(d.haven_ratio, 0.767827586952, 6) self.assertAlmostEqual(d.chg_conductivity, 554.524214937, 6) # Can't average over 2000 steps because this is a 1000-step run. self.assertRaises(ValueError, DiffusionAnalyzer, d.structure, d.disp, d.specie, d.temperature, d.time_step, d.step_skip, smoothed="constant", avg_nsteps=2000) d = DiffusionAnalyzer.from_structures( list(d.get_drift_corrected_structures()), d.specie, d.temperature, d.time_step, d.step_skip, smoothed=d.smoothed, avg_nsteps=100) self.assertAlmostEqual(d.conductivity, 425.77898986201302, 4) self.assertAlmostEqual(d.diffusivity, 7.1675238091425148e-06, 7) self.assertAlmostEqual(d.haven_ratio, 0.767827586952, 7) self.assertAlmostEqual(d.chg_conductivity, 554.524214937, 6) d.export_msdt("test.csv") with open("test.csv") as f: data = [] for row in csv.reader(f): if row: data.append(row) data.pop(0) data = np.array(data, dtype=np.float64) self.assertArrayAlmostEqual(data[:, 1], d.msd) self.assertArrayAlmostEqual(data[:, -1], d.mscd) os.remove("test.csv")
def test_init(self): # Diffusion vasprun.xmls are rather large. We are only going to use a # very small preprocessed run for testing. Note that the results are # unreliable for short runs. with open(os.path.join(test_dir, "DiffusionAnalyzer.json")) as f: dd = json.load(f) d = DiffusionAnalyzer.from_dict(dd) # large tolerance because scipy constants changed between 0.16.1 and 0.17 self.assertAlmostEqual(d.conductivity, 74.165372613735684, 4) self.assertAlmostEqual(d.chg_conductivity, 232.827958801, 4) self.assertAlmostEqual(d.diffusivity, 1.16083658794e-06, 7) self.assertAlmostEqual(d.chg_diffusivity, 3.64565578208e-06, 7) self.assertAlmostEqual(d.conductivity_std_dev, 0.0097244677795984488, 7) self.assertAlmostEqual(d.diffusivity_std_dev, 9.1013023085561779e-09, 7) self.assertAlmostEqual(d.chg_diffusivity_std_dev, 7.20911399729e-10, 5) self.assertAlmostEqual(d.haven_ratio, 0.31854161048867402, 7) self.assertArrayAlmostEqual( d.conductivity_components, [45.7903694, 26.1651956, 150.5406140], 3) self.assertArrayAlmostEqual( d.diffusivity_components, [7.49601236e-07, 4.90254273e-07, 2.24649255e-06]) self.assertArrayAlmostEqual( d.conductivity_components_std_dev, [0.0063566, 0.0180854, 0.0217918] ) self.assertArrayAlmostEqual( d.diffusivity_components_std_dev, [8.9465670e-09, 2.4931224e-08, 2.2636384e-08] ) self.assertArrayAlmostEqual( d.mscd[0:4], [0.69131064, 0.71794072, 0.74315283, 0.76703961] ) self.assertArrayAlmostEqual( d.max_ion_displacements, [1.4620659693989553, 1.2787303484445025, 3.419618540097756, 2.340104469126246, 2.6080973517594233, 1.3928579365672844, 1.3561505956708932, 1.6699242923686253, 1.0352389639563648, 1.1662520093955808, 1.2322019205885841, 0.8094210554832534, 1.9917808504954169, 1.2684148391206396, 2.392633794162402, 2.566313049232671, 1.3175030435622759, 1.4628945430952793, 1.0984921286753002, 1.2864482076554093, 0.655567027815413, 0.5986961164605746, 0.5639091444309045, 0.6166004192954059, 0.5997911580422605, 0.4374606277579815, 1.1865683960470783, 0.9017064371676591, 0.6644840367853767, 1.0346375380664645, 0.6177630142863979, 0.7952002051914302, 0.7342686123054011, 0.7858047956905577, 0.5570732369065661, 1.0942937746885417, 0.6509372395308788, 1.0876687380413455, 0.7058162184725, 0.8298306317598585, 0.7813913747621343, 0.7337655232056153, 0.9057161616236746, 0.5979093093186919, 0.6830333586985015, 0.7926500894084628, 0.6765180009988608, 0.8555866032968998, 0.713087091642237, 0.7621007695790749]) self.assertEqual(d.sq_disp_ions.shape, (50, 206)) self.assertEqual(d.lattices.shape, (1, 3, 3)) self.assertEqual(d.mscd.shape, (206,)) self.assertEqual(d.mscd.shape, d.msd.shape) self.assertAlmostEqual(d.max_framework_displacement, 1.18656839605) ss = list(d.get_drift_corrected_structures(10, 1000, 20)) self.assertEqual(len(ss), 50) n = random.randint(0, 49) n_orig = n * 20 + 10 self.assertArrayAlmostEqual( ss[n].cart_coords - d.structure.cart_coords + d.drift[:, n_orig, :], d.disp[:, n_orig, :]) d = DiffusionAnalyzer.from_dict(d.as_dict()) self.assertIsInstance(d, DiffusionAnalyzer) #Ensure summary dict is json serializable. json.dumps(d.get_summary_dict(include_msd_t=True)) d = DiffusionAnalyzer(d.structure, d.disp, d.specie, d.temperature, d.time_step, d.step_skip, smoothed="max") self.assertAlmostEqual(d.conductivity, 74.165372613735684, 4) self.assertAlmostEqual(d.diffusivity, 1.14606446822e-06, 7) self.assertAlmostEqual(d.haven_ratio, 0.318541610489, 6) self.assertAlmostEqual(d.chg_conductivity, 232.827958801, 4) self.assertAlmostEqual(d.chg_diffusivity, 3.64565578208e-06, 7) d = DiffusionAnalyzer(d.structure, d.disp, d.specie, d.temperature, d.time_step, d.step_skip, smoothed=False) self.assertAlmostEqual(d.conductivity, 27.20479170406027, 4) self.assertAlmostEqual(d.diffusivity, 4.25976905436e-07, 7) self.assertAlmostEqual(d.chg_diffusivity, 1.6666666666666667e-17, 3) d = DiffusionAnalyzer(d.structure, d.disp, d.specie, d.temperature, d.time_step, d.step_skip, smoothed="constant", avg_nsteps=100) self.assertAlmostEqual(d.conductivity, 47.404056230438741, 4) self.assertAlmostEqual(d.diffusivity, 7.4226016496716148e-07, 7) self.assertAlmostEqual(d.chg_conductivity, 1.06440821953e-09, 4) # Can't average over 2000 steps because this is a 1000-step run. self.assertRaises(ValueError, DiffusionAnalyzer, d.structure, d.disp, d.specie, d.temperature, d.time_step, d.step_skip, smoothed="constant", avg_nsteps=2000) d = DiffusionAnalyzer.from_structures( list(d.get_drift_corrected_structures()), d.specie, d.temperature, d.time_step, d.step_skip, smoothed=d.smoothed, avg_nsteps=100) self.assertAlmostEqual(d.conductivity, 47.404056230438741, 4) self.assertAlmostEqual(d.diffusivity, 7.4226016496716148e-07, 7) d.export_msdt("test.csv") with open("test.csv") as f: data = [] for row in csv.reader(f): if row: data.append(row) data.pop(0) data = np.array(data, dtype=np.float64) self.assertArrayAlmostEqual(data[:, 1], d.msd) self.assertArrayAlmostEqual(data[:, -1], d.mscd) os.remove("test.csv")
def test_init(self): # Diffusion vasprun.xmls are rather large. We are only going to use a # very small preprocessed run for testing. Note that the results are # unreliable for short runs. with open( os.path.join(PymatgenTest.TEST_FILES_DIR, "DiffusionAnalyzer.json")) as f: dd = json.load(f) d = DiffusionAnalyzer.from_dict(dd) # large tolerance because scipy constants changed between 0.16.1 and 0.17 self.assertAlmostEqual(d.conductivity, 74.165372613735684, 4) self.assertAlmostEqual(d.chg_conductivity, 232.8278799754324, 4) self.assertAlmostEqual(d.diffusivity, 1.16083658794e-06, 7) self.assertAlmostEqual(d.chg_diffusivity, 3.64565578208e-06, 7) self.assertAlmostEqual(d.conductivity_std_dev, 0.0097244677795984488, 7) self.assertAlmostEqual(d.diffusivity_std_dev, 9.1013023085561779e-09, 7) self.assertAlmostEqual(d.chg_diffusivity_std_dev, 7.20911399729e-10, 5) self.assertAlmostEqual(d.haven_ratio, 0.31854161048867402, 7) self.assertArrayAlmostEqual(d.conductivity_components, [45.7903694, 26.1651956, 150.5406140], 3) self.assertArrayAlmostEqual( d.diffusivity_components, [7.49601236e-07, 4.90254273e-07, 2.24649255e-06], ) self.assertArrayAlmostEqual(d.conductivity_components_std_dev, [0.0063566, 0.0180854, 0.0217918]) self.assertArrayAlmostEqual( d.diffusivity_components_std_dev, [8.9465670e-09, 2.4931224e-08, 2.2636384e-08], ) self.assertArrayAlmostEqual( d.mscd[0:4], [0.69131064, 0.71794072, 0.74315283, 0.76703961]) self.assertArrayAlmostEqual( d.max_ion_displacements, [ 1.4620659693989553, 1.2787303484445025, 3.419618540097756, 2.340104469126246, 2.6080973517594233, 1.3928579365672844, 1.3561505956708932, 1.6699242923686253, 1.0352389639563648, 1.1662520093955808, 1.2322019205885841, 0.8094210554832534, 1.9917808504954169, 1.2684148391206396, 2.392633794162402, 2.566313049232671, 1.3175030435622759, 1.4628945430952793, 1.0984921286753002, 1.2864482076554093, 0.655567027815413, 0.5986961164605746, 0.5639091444309045, 0.6166004192954059, 0.5997911580422605, 0.4374606277579815, 1.1865683960470783, 0.9017064371676591, 0.6644840367853767, 1.0346375380664645, 0.6177630142863979, 0.7952002051914302, 0.7342686123054011, 0.7858047956905577, 0.5570732369065661, 1.0942937746885417, 0.6509372395308788, 1.0876687380413455, 0.7058162184725, 0.8298306317598585, 0.7813913747621343, 0.7337655232056153, 0.9057161616236746, 0.5979093093186919, 0.6830333586985015, 0.7926500894084628, 0.6765180009988608, 0.8555866032968998, 0.713087091642237, 0.7621007695790749, ], ) self.assertEqual(d.sq_disp_ions.shape, (50, 206)) self.assertEqual(d.lattices.shape, (1, 3, 3)) self.assertEqual(d.mscd.shape, (206, )) self.assertEqual(d.mscd.shape, d.msd.shape) self.assertAlmostEqual(d.max_framework_displacement, 1.18656839605) ss = list(d.get_drift_corrected_structures(10, 1000, 20)) self.assertEqual(len(ss), 50) n = random.randint(0, 49) n_orig = n * 20 + 10 self.assertArrayAlmostEqual( ss[n].cart_coords - d.structure.cart_coords + d.drift[:, n_orig, :], d.disp[:, n_orig, :], ) d = DiffusionAnalyzer.from_dict(d.as_dict()) self.assertIsInstance(d, DiffusionAnalyzer) # Ensure summary dict is json serializable. json.dumps(d.get_summary_dict(include_msd_t=True)) d = DiffusionAnalyzer( d.structure, d.disp, d.specie, d.temperature, d.time_step, d.step_skip, smoothed="max", ) self.assertAlmostEqual(d.conductivity, 74.165372613735684, 4) self.assertAlmostEqual(d.diffusivity, 1.14606446822e-06, 7) self.assertAlmostEqual(d.haven_ratio, 0.318541610489, 6) self.assertAlmostEqual(d.chg_conductivity, 232.8278799754324, 4) self.assertAlmostEqual(d.chg_diffusivity, 3.64565578208e-06, 7) d = DiffusionAnalyzer( d.structure, d.disp, d.specie, d.temperature, d.time_step, d.step_skip, smoothed=False, ) self.assertAlmostEqual(d.conductivity, 27.20479170406027, 4) self.assertAlmostEqual(d.diffusivity, 4.25976905436e-07, 7) self.assertAlmostEqual(d.chg_diffusivity, 1.6666666666666667e-17, 3) d = DiffusionAnalyzer( d.structure, d.disp, d.specie, d.temperature, d.time_step, d.step_skip, smoothed="constant", avg_nsteps=100, ) self.assertAlmostEqual(d.conductivity, 47.404056230438741, 4) self.assertAlmostEqual(d.diffusivity, 7.4226016496716148e-07, 7) self.assertAlmostEqual(d.chg_conductivity, 1.06440821953e-09, 4) # Can't average over 2000 steps because this is a 1000-step run. self.assertRaises( ValueError, DiffusionAnalyzer, d.structure, d.disp, d.specie, d.temperature, d.time_step, d.step_skip, smoothed="constant", avg_nsteps=2000, ) d = DiffusionAnalyzer.from_structures( list(d.get_drift_corrected_structures()), d.specie, d.temperature, d.time_step, d.step_skip, smoothed=d.smoothed, avg_nsteps=100, ) self.assertAlmostEqual(d.conductivity, 47.404056230438741, 4) self.assertAlmostEqual(d.diffusivity, 7.4226016496716148e-07, 7) d.export_msdt("test.csv") with open("test.csv") as f: data = [] for row in csv.reader(f): if row: data.append(row) data.pop(0) data = np.array(data, dtype=np.float64) self.assertArrayAlmostEqual(data[:, 1], d.msd) self.assertArrayAlmostEqual(data[:, -1], d.mscd) os.remove("test.csv")
def test_init_npt(self): # Diffusion vasprun.xmls are rather large. We are only going to use a # very small preprocessed run for testing. Note that the results are # unreliable for short runs. with open( os.path.join(PymatgenTest.TEST_FILES_DIR, "DiffusionAnalyzer_NPT.json"), "r") as f: dd = json.load(f) d = DiffusionAnalyzer.from_dict(dd) # large tolerance because scipy constants changed between 0.16.1 and 0.17 self.assertAlmostEqual(d.conductivity, 499.1504129387108, 4) self.assertAlmostEqual(d.chg_conductivity, 1219.5959181678043, 4) self.assertAlmostEqual(d.diffusivity, 8.40265434771e-06, 7) self.assertAlmostEqual(d.chg_diffusivity, 2.05305709033e-05, 6) self.assertAlmostEqual(d.conductivity_std_dev, 0.10368477696021029, 7) self.assertAlmostEqual(d.diffusivity_std_dev, 9.1013023085561779e-09, 7) self.assertAlmostEqual(d.chg_diffusivity_std_dev, 1.20834853646e-08, 6) self.assertAlmostEqual(d.haven_ratio, 0.409275240679, 7) self.assertArrayAlmostEqual(d.conductivity_components, [455.178101, 602.252644, 440.0210014], 3) self.assertArrayAlmostEqual( d.diffusivity_components, [7.66242570e-06, 1.01382648e-05, 7.40727250e-06], ) self.assertArrayAlmostEqual(d.conductivity_components_std_dev, [0.1196577, 0.0973347, 0.1525400]) self.assertArrayAlmostEqual( d.diffusivity_components_std_dev, [2.0143072e-09, 1.6385239e-09, 2.5678445e-09], ) self.assertArrayAlmostEqual( d.max_ion_displacements, [ 1.13147881, 0.79899554, 1.04153733, 0.96061850, 0.83039864, 0.70246715, 0.61365911, 0.67965179, 1.91973907, 1.69127386, 1.60568746, 1.35587641, 1.03280378, 0.99202692, 2.03359655, 1.03760269, 1.40228350, 1.36315080, 1.27414979, 1.26742035, 0.88199589, 0.97700804, 1.11323184, 1.00139511, 2.94164403, 0.89438909, 1.41508334, 1.23660358, 0.39322939, 0.54264064, 1.25291806, 0.62869809, 0.40846708, 1.43415505, 0.88891241, 0.56259128, 0.81712740, 0.52700441, 0.51011733, 0.55557882, 0.49131002, 0.66740277, 0.57798671, 0.63521025, 0.50277142, 0.52878021, 0.67803443, 0.81161269, 0.46486345, 0.47132761, 0.74301293, 0.79285519, 0.48789600, 0.61776836, 0.60695847, 0.67767756, 0.70972268, 1.08232442, 0.87871177, 0.84674206, 0.45694693, 0.60417985, 0.61652272, 0.66444583, 0.52211986, 0.56544134, 0.43311443, 0.43027547, 1.10730439, 0.59829728, 0.52270635, 0.72327608, 1.02919775, 0.84423208, 0.61694764, 0.72795752, 0.72957755, 0.55491631, 0.68507454, 0.76745343, 0.96346584, 0.66672645, 1.06810107, 0.65705843, ], ) self.assertEqual(d.sq_disp_ions.shape, (84, 217)) self.assertEqual(d.lattices.shape, (1001, 3, 3)) self.assertEqual(d.mscd.shape, (217, )) self.assertEqual(d.mscd.shape, d.msd.shape) self.assertAlmostEqual(d.max_framework_displacement, 1.43415505156) ss = list(d.get_drift_corrected_structures(10, 1000, 20)) self.assertEqual(len(ss), 50) n = random.randint(0, 49) n_orig = n * 20 + 10 self.assertArrayAlmostEqual( ss[n].cart_coords - d.structure.cart_coords + d.drift[:, n_orig, :], d.disp[:, n_orig, :], ) d = DiffusionAnalyzer.from_dict(d.as_dict()) self.assertIsInstance(d, DiffusionAnalyzer) # Ensure summary dict is json serializable. json.dumps(d.get_summary_dict(include_msd_t=True)) d = DiffusionAnalyzer( d.structure, d.disp, d.specie, d.temperature, d.time_step, d.step_skip, smoothed="max", ) self.assertAlmostEqual(d.conductivity, 499.1504129387108, 4) self.assertAlmostEqual(d.diffusivity, 8.40265434771e-06, 7) self.assertAlmostEqual(d.haven_ratio, 0.409275240679, 7) self.assertAlmostEqual(d.chg_diffusivity, 2.05305709033e-05, 7) d = DiffusionAnalyzer( d.structure, d.disp, d.specie, d.temperature, d.time_step, d.step_skip, smoothed=False, ) self.assertAlmostEqual(d.conductivity, 406.5964019770787, 4) self.assertAlmostEqual(d.diffusivity, 6.8446082e-06, 7) self.assertAlmostEqual(d.chg_diffusivity, 1.03585877962e-05, 6) self.assertAlmostEqual(d.haven_ratio, 0.6607665413, 6) d = DiffusionAnalyzer( d.structure, d.disp, d.specie, d.temperature, d.time_step, d.step_skip, smoothed="constant", avg_nsteps=100, ) self.assertAlmostEqual(d.conductivity, 425.77884571149525, 4) self.assertAlmostEqual(d.diffusivity, 7.167523809142514e-06, 7) self.assertAlmostEqual(d.chg_diffusivity, 9.33480892187e-06, 6) self.assertAlmostEqual(d.haven_ratio, 0.767827586952, 6) self.assertAlmostEqual(d.chg_conductivity, 554.5240271992852, 6) # Can't average over 2000 steps because this is a 1000-step run. self.assertRaises( ValueError, DiffusionAnalyzer, d.structure, d.disp, d.specie, d.temperature, d.time_step, d.step_skip, smoothed="constant", avg_nsteps=2000, ) d = DiffusionAnalyzer.from_structures( list(d.get_drift_corrected_structures()), d.specie, d.temperature, d.time_step, d.step_skip, smoothed=d.smoothed, avg_nsteps=100, ) self.assertAlmostEqual(d.conductivity, 425.7788457114952, 4) self.assertAlmostEqual(d.diffusivity, 7.1675238091425148e-06, 7) self.assertAlmostEqual(d.haven_ratio, 0.767827586952, 7) self.assertAlmostEqual(d.chg_conductivity, 554.5240271992852, 6) d.export_msdt("test.csv") with open("test.csv") as f: data = [] for row in csv.reader(f): if row: data.append(row) data.pop(0) data = np.array(data, dtype=np.float64) self.assertArrayAlmostEqual(data[:, 1], d.msd) self.assertArrayAlmostEqual(data[:, -1], d.mscd) os.remove("test.csv")
def test_init(self): # Diffusion vasprun.xmls are rather large. We are only going to use a # very small preprocessed run for testing. Note that the results are # unreliable for short runs. with open(os.path.join(test_dir, "DiffusionAnalyzer.json")) as f: dd = json.load(f) d = DiffusionAnalyzer.from_dict(dd) self.assertAlmostEqual(d.conductivity, 74.165372208150615, 7) self.assertAlmostEqual(d.diffusivity, 1.16083658794e-06, 7) self.assertAlmostEqual(d.conductivity_std_dev, 0.0097244677795984488, 7) self.assertAlmostEqual(d.diffusivity_std_dev, 9.1013023085561779e-09, 7) self.assertArrayAlmostEqual(d.conductivity_components, [45.9109701, 26.28563, 150.5405718]) self.assertArrayAlmostEqual( d.diffusivity_components, [7.49601236e-07, 4.90254273e-07, 2.24649255e-06]) self.assertArrayAlmostEqual(d.conductivity_components_std_dev, [0.0063579, 0.0180862, 0.0217917]) self.assertArrayAlmostEqual( d.diffusivity_components_std_dev, [8.9465670e-09, 2.4931224e-08, 2.2636384e-08]) self.assertArrayAlmostEqual(d.max_ion_displacements, [ 1.4620659693989553, 1.2787303484445025, 3.419618540097756, 2.340104469126246, 2.6080973517594233, 1.3928579365672844, 1.3561505956708932, 1.6699242923686253, 1.0352389639563648, 1.1662520093955808, 1.2322019205885841, 0.8094210554832534, 1.9917808504954169, 1.2684148391206396, 2.392633794162402, 2.566313049232671, 1.3175030435622759, 1.4628945430952793, 1.0984921286753002, 1.2864482076554093, 0.655567027815413, 0.5986961164605746, 0.5639091444309045, 0.6166004192954059, 0.5997911580422605, 0.4374606277579815, 1.1865683960470783, 0.9017064371676591, 0.6644840367853767, 1.0346375380664645, 0.6177630142863979, 0.7952002051914302, 0.7342686123054011, 0.7858047956905577, 0.5570732369065661, 1.0942937746885417, 0.6509372395308788, 1.0876687380413455, 0.7058162184725, 0.8298306317598585, 0.7813913747621343, 0.7337655232056153, 0.9057161616236746, 0.5979093093186919, 0.6830333586985015, 0.7926500894084628, 0.6765180009988608, 0.8555866032968998, 0.713087091642237, 0.7621007695790749 ]) self.assertEqual(d.sq_disp_ions.shape, (50, 206)) self.assertAlmostEqual(d.max_framework_displacement, 1.18656839605) ss = list(d.get_drift_corrected_structures()) self.assertEqual(len(ss), 1000) n = random.randint(0, 999) self.assertArrayAlmostEqual( ss[n].cart_coords - d.structure.cart_coords + d.drift[:, n, :], d.disp[:, n, :]) d = DiffusionAnalyzer.from_dict(d.as_dict()) self.assertIsInstance(d, DiffusionAnalyzer) #Ensure summary dict is json serializable. json.dumps(d.get_summary_dict(include_msd_t=True)) d = DiffusionAnalyzer(d.structure, d.disp, d.specie, d.temperature, d.time_step, d.step_skip, smoothed="max") self.assertAlmostEqual(d.conductivity, 74.16537220815061, 7) self.assertAlmostEqual(d.diffusivity, 1.14606446822e-06, 7) d = DiffusionAnalyzer(d.structure, d.disp, d.specie, d.temperature, d.time_step, d.step_skip, smoothed=False) self.assertAlmostEqual(d.conductivity, 27.2047915553, 7) self.assertAlmostEqual(d.diffusivity, 4.25976905436e-07, 7) d = DiffusionAnalyzer(d.structure, d.disp, d.specie, d.temperature, d.time_step, d.step_skip, smoothed="constant", avg_nsteps=100) self.assertAlmostEqual(d.conductivity, 47.404055971202155, 7) self.assertAlmostEqual(d.diffusivity, 7.4226016496716148e-07, 7) # Can't average over 2000 steps because this is a 1000-step run. self.assertRaises(ValueError, DiffusionAnalyzer, d.structure, d.disp, d.specie, d.temperature, d.time_step, d.step_skip, smoothed="constant", avg_nsteps=2000) d = DiffusionAnalyzer.from_structures(list( d.get_drift_corrected_structures()), d.specie, d.temperature, d.time_step, d.step_skip, d.smoothed, avg_nsteps=100) self.assertAlmostEqual(d.conductivity, 47.404055971202155, 7) self.assertAlmostEqual(d.diffusivity, 7.4226016496716148e-07, 7)
def __init__(self, diffusion_analyzer: DiffusionAnalyzer, avg_nsteps: int = 50, ngrid: int = 101, rmax: float = 10.0, step_skip: int = 50, sigma: float = 0.1, cell_range: int = 1, species: Union[Tuple, List] = ("Li", "Na"), reference_species: Union[Tuple, List] = None, indices: List = None): """ Initiation. Args: diffusion_analyzer (DiffusionAnalyzer): A pymatgen.analysis.diffusion_analyzer.DiffusionAnalyzer object avg_nsteps (int): Number of t0 used for statistical average ngrid (int): Number of radial grid points rmax (float): Maximum of radial grid (the minimum is always set zero) step_skip (int): # of time steps skipped during analysis. It defines the resolution of the reduced time grid sigma (float): Smearing of a Gaussian function cell_range (int): Range of translational vector elements associated with supercell. Default is 1, i.e. including the adjacent image cells along all three directions. species ([string]): a list of specie symbols of interest. reference_species ([string]): Set this option along with 'species' parameter to calculate the distinct-part of van Hove function. Note that the self-part of van Hove function is always computed only for those in "species" parameter. indices (list of int): If not None, only a subset of atomic indices will be selected for the analysis. If this is given, "species" parameter will be ignored. """ # initial check if step_skip <= 0: raise ValueError("skip_step should be >=1!") n_ions, nsteps, ndim = diffusion_analyzer.disp.shape if nsteps <= avg_nsteps: raise ValueError("Number of timesteps is too small!") ntsteps = nsteps - avg_nsteps if ngrid - 1 <= 0: raise ValueError("Ntot should be greater than 1!") if sigma <= 0.0: raise ValueError("sigma should be > 0!") dr = rmax / (ngrid - 1) interval = np.linspace(0.0, rmax, ngrid) reduced_nt = int(ntsteps / float(step_skip)) + 1 lattice = diffusion_analyzer.structure.lattice structure = diffusion_analyzer.structure if indices is None: indices = [ j for j, site in enumerate(structure) if site.specie.symbol in species ] ref_indices = indices if reference_species: ref_indices = [ j for j, site in enumerate(structure) if site.specie.symbol in reference_species ] rho = float(len(indices)) / lattice.volume # reduced time grid rtgrid = np.arange(0.0, reduced_nt) # van Hove functions gsrt = np.zeros((reduced_nt, ngrid), dtype=np.double) gdrt = np.zeros((reduced_nt, ngrid), dtype=np.double) tracking_ions = [] ref_ions = [] # auxiliary factor for 4*\pi*r^2 aux_factor = 4.0 * np.pi * interval**2 aux_factor[0] = np.pi * dr**2 for i, ss in enumerate( diffusion_analyzer.get_drift_corrected_structures()): all_fcoords = np.array(ss.frac_coords) tracking_ions.append(all_fcoords[indices, :]) ref_ions.append(all_fcoords[ref_indices, :]) tracking_ions = np.array(tracking_ions) ref_ions = np.array(ref_ions) gaussians = norm.pdf(interval[:, None], interval[None, :], sigma) / float(avg_nsteps) / float( len(ref_indices)) # calculate self part of van Hove function image = np.array([0, 0, 0]) for it in range(reduced_nt): dns = Counter() it0 = min(it * step_skip, ntsteps) for it1 in range(avg_nsteps): dists = [ lattice.get_distance_and_image(tracking_ions[it1][u], tracking_ions[it0 + it1][u], jimage=image)[0] for u in range(len(indices)) ] dists = filter(lambda e: e < rmax, dists) r_indices = [int(dist / dr) for dist in dists] dns.update(r_indices) for indx, dn in dns.most_common(ngrid): gsrt[it, :] += gaussians[indx, :] * dn # calculate distinct part of van Hove function of species r = np.arange(-cell_range, cell_range + 1) arange = r[:, None] * np.array([1, 0, 0])[None, :] brange = r[:, None] * np.array([0, 1, 0])[None, :] crange = r[:, None] * np.array([0, 0, 1])[None, :] images = arange[:, None, None] + brange[None, :, None] + crange[None, None, :] images = images.reshape((len(r)**3, 3)) # find the zero image vector zd = np.sum(images**2, axis=1) indx0 = np.argmin(zd) for it in range(reduced_nt): dns = Counter() it0 = min(it * step_skip, ntsteps) for it1 in range(avg_nsteps): dcf = (tracking_ions[it0 + it1, :, None, None, :] + images[None, None, :, :] - ref_ions[it1, None, :, None, :]) dcc = lattice.get_cartesian_coords(dcf) d2 = np.sum(dcc**2, axis=3) dists = [ d2[u, v, j]**0.5 for u in range(len(indices)) for v in range(len(ref_indices)) for j in range(len(r)**3) if u != v or j != indx0 ] dists = filter(lambda e: e < rmax, dists) r_indices = [int(dist / dr) for dist in dists] dns.update(r_indices) for indx, dn in dns.most_common(ngrid): gdrt[it, :] += gaussians[indx, :] * dn / aux_factor[indx] / rho self.obj = diffusion_analyzer self.avg_nsteps = avg_nsteps self.step_skip = step_skip self.rtgrid = rtgrid self.interval = interval self.gsrt = gsrt self.gdrt = gdrt # time interval (in ps) in gsrt and gdrt. self.timeskip = self.obj.time_step * self.obj.step_skip * step_skip / 1000.0
def test_init(self): # Diffusion vasprun.xmls are rather large. We are only going to use a # very small preprocessed run for testing. Note that the results are # unreliable for short runs. with open(os.path.join(test_dir, "DiffusionAnalyzer.json")) as f: dd = json.load(f) d = DiffusionAnalyzer.from_dict(dd) self.assertAlmostEqual(d.conductivity, 74.165372208150615, 7) self.assertAlmostEqual(d.diffusivity, 1.16083658794e-06, 7) self.assertAlmostEqual(d.conductivity_std_dev, 0.0097244677795984488, 7) self.assertAlmostEqual(d.diffusivity_std_dev, 9.1013023085561779e-09, 7) self.assertArrayAlmostEqual( d.conductivity_components, [45.9109701, 26.28563 , 150.5405718]) self.assertArrayAlmostEqual( d.diffusivity_components, [7.49601236e-07, 4.90254273e-07, 2.24649255e-06]) self.assertArrayAlmostEqual( d.conductivity_components_std_dev, [0.0063579, 0.0180862, 0.0217917] ) self.assertArrayAlmostEqual( d.diffusivity_components_std_dev, [8.9465670e-09, 2.4931224e-08, 2.2636384e-08] ) self.assertArrayAlmostEqual( d.max_ion_displacements, [1.4620659693989553, 1.2787303484445025, 3.419618540097756, 2.340104469126246, 2.6080973517594233, 1.3928579365672844, 1.3561505956708932, 1.6699242923686253, 1.0352389639563648, 1.1662520093955808, 1.2322019205885841, 0.8094210554832534, 1.9917808504954169, 1.2684148391206396, 2.392633794162402, 2.566313049232671, 1.3175030435622759, 1.4628945430952793, 1.0984921286753002, 1.2864482076554093, 0.655567027815413, 0.5986961164605746, 0.5639091444309045, 0.6166004192954059, 0.5997911580422605, 0.4374606277579815, 1.1865683960470783, 0.9017064371676591, 0.6644840367853767, 1.0346375380664645, 0.6177630142863979, 0.7952002051914302, 0.7342686123054011, 0.7858047956905577, 0.5570732369065661, 1.0942937746885417, 0.6509372395308788, 1.0876687380413455, 0.7058162184725, 0.8298306317598585, 0.7813913747621343, 0.7337655232056153, 0.9057161616236746, 0.5979093093186919, 0.6830333586985015, 0.7926500894084628, 0.6765180009988608, 0.8555866032968998, 0.713087091642237, 0.7621007695790749]) self.assertEqual(d.sq_disp_ions.shape, (50, 206)) self.assertAlmostEqual(d.max_framework_displacement, 1.18656839605) ss = list(d.get_drift_corrected_structures()) self.assertEqual(len(ss), 1000) n = random.randint(0, 999) self.assertArrayAlmostEqual( ss[n].cart_coords - d.structure.cart_coords + d.drift[:, n, :], d.disp[:, n, :]) d = DiffusionAnalyzer.from_dict(d.as_dict()) self.assertIsInstance(d, DiffusionAnalyzer) #Ensure summary dict is json serializable. json.dumps(d.get_summary_dict(include_msd_t=True)) d = DiffusionAnalyzer(d.structure, d.disp, d.specie, d.temperature, d.time_step, d.step_skip, smoothed="max") self.assertAlmostEqual(d.conductivity, 74.16537220815061, 7) self.assertAlmostEqual(d.diffusivity, 1.14606446822e-06, 7) d = DiffusionAnalyzer(d.structure, d.disp, d.specie, d.temperature, d.time_step, d.step_skip, smoothed=False) self.assertAlmostEqual(d.conductivity, 27.2047915553, 7) self.assertAlmostEqual(d.diffusivity, 4.25976905436e-07, 7) d = DiffusionAnalyzer(d.structure, d.disp, d.specie, d.temperature, d.time_step, d.step_skip, smoothed="constant", avg_nsteps=100) self.assertAlmostEqual(d.conductivity, 47.404055971202155, 7) self.assertAlmostEqual(d.diffusivity, 7.4226016496716148e-07, 7) # Can't average over 2000 steps because this is a 1000-step run. self.assertRaises(ValueError, DiffusionAnalyzer, d.structure, d.disp, d.specie, d.temperature, d.time_step, d.step_skip, smoothed="constant", avg_nsteps=2000) d = DiffusionAnalyzer.from_structures( list(d.get_drift_corrected_structures()), d.specie, d.temperature, d.time_step, d.step_skip, d.smoothed, avg_nsteps=100) self.assertAlmostEqual(d.conductivity, 47.404055971202155, 7) self.assertAlmostEqual(d.diffusivity, 7.4226016496716148e-07, 7)