def test_from_structure_NPT(self): from pymatgen import Lattice, Structure coords1 = np.array([[0.0, 0.0, 0.0], [0.5, 0.5, 0.5]]) coords2 = np.array([[0.0, 0.0, 0.0], [0.6, 0.6, 0.6]]) coords3 = np.array([[0.0, 0.0, 0.0], [0.7, 0.7, 0.7]]) lattice1 = Lattice.from_parameters( a=2.0, b=2.0, c=2.0, alpha=90, beta=90, gamma=90 ) lattice2 = Lattice.from_parameters( a=2.1, b=2.1, c=2.1, alpha=90, beta=90, gamma=90 ) lattice3 = Lattice.from_parameters( a=2.0, b=2.0, c=2.0, alpha=90, beta=90, gamma=90 ) s1 = Structure(coords=coords1, lattice=lattice1, species=["F", "Li"]) s2 = Structure(coords=coords2, lattice=lattice2, species=["F", "Li"]) s3 = Structure(coords=coords3, lattice=lattice3, species=["F", "Li"]) structures = [s1, s2, s3] d = DiffusionAnalyzer.from_structures( structures, specie="Li", temperature=500.0, time_step=2.0, step_skip=1, smoothed=None, ) self.assertArrayAlmostEqual( d.disp[1], np.array([[0.0, 0.0, 0.0], [0.21, 0.21, 0.21], [0.40, 0.40, 0.40]]), )
def test_from_structure_NPT( self ): from pymatgen import Structure, Lattice coords1 = np.array([[0.0, 0.0, 0.0], [0.5, 0.5, 0.5]] ) coords2 = np.array([[0.0, 0.0, 0.0], [0.6, 0.6, 0.6]] ) coords3 = np.array([[0.0, 0.0, 0.0], [0.7, 0.7, 0.7]] ) lattice1 = Lattice.from_parameters(a=2.0, b=2.0, c=2.0, alpha=90, beta=90, gamma=90) lattice2 = Lattice.from_parameters(a=2.1, b=2.1, c=2.1, alpha=90, beta=90, gamma=90) lattice3 = Lattice.from_parameters(a=2.0, b=2.0, c=2.0, alpha=90, beta=90, gamma=90) s1 = Structure(coords=coords1, lattice=lattice1, species=['F', 'Li']) s2 = Structure(coords=coords2, lattice=lattice2, species=['F', 'Li']) s3 = Structure(coords=coords3, lattice=lattice3, species=['F', 'Li']) structures = [s1, s2, s3] d = DiffusionAnalyzer.from_structures( structures, specie='Li', temperature=500.0, time_step=2.0, step_skip=1, smoothed=None ) self.assertArrayAlmostEqual(d.disp[1], np.array([[0., 0., 0. ], [0.21, 0.21, 0.21], [0.40, 0.40, 0.40]]))
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_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")
分析AIMD结果,计算MSD 和 conductivity ''' import os from pymatgen.core.trajectory import Trajectory from pymatgen.io.vasp.outputs import Xdatcar from pymatgen import Structure from pymatgen.analysis.diffusion_analyzer import DiffusionAnalyzer import numpy as np import pickle # 这一步是读取 XDATCAR,得到一系列结构信息 traj = Trajectory.from_file('XDATCAR') # 这一步是实例化 DiffusionAnalyzer 的类 # 并用 from_structures 方法初始化这个类; 900 是温度,2 是POTIM 的值,1是间隔步数 # 间隔步数(step_skip)不太容易理解,但是根据官方教程: # dt = timesteps * self.time_step * self.step_skip diff = DiffusionAnalyzer.from_structures(traj,'Li',900,2,1) # 可以用内置的 plot_msd 方法画出 MSD 图像 # 有些终端不能显示图像,这时候可以调用 export_msdt() 方法,得到数据后再自己作图 diff.plot_msd() # 接下来直接得到 离子迁移率, 单位是 mS/cm C = diff.conductivity with open('result.dat','w') as f: f.write('# AIMD result for Li-ion\n') f.write('temp\tconductivity\n') f.write('%d\t%.2f\n' %(900,C))
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)
#%% from pymatgen import Molecule, Structure, Lattice from pymatgen.analysis.diffusion_analyzer import DiffusionAnalyzer import os os.chdir('/home/jinho93/oxides/perobskite/lanthanum-aluminate/slab/gulp/stoichiometric/more') # os.chdir('/home/jinho93/new/oxides/perobskite/lanthanum-aluminate/slab/gulp/nostochio/La-vac') with open('lao.xyz') as f: lines = f.readlines() ss = [] atoms_number = 802 for i in range(len(lines) // atoms_number // 5): mole = Molecule.from_str(''.join(lines[atoms_number * i:atoms_number * (i + 1)]), fmt='xyz') l = Lattice([[15.2882,0,0], [0,15.2882, 0], [0,0,50]]) s = Structure(l, mole.species, mole.cart_coords, coords_are_cartesian=True) ss.append(s) from pymatgen.core import Element, Specie d = DiffusionAnalyzer.from_structures(ss, 'La', 300, len(ss) // 2, step_skip=2) d.get_msd_plot() #%% import numpy as np np.savetxt('msd.dat', d.dt)
#%% from pymatgen.core.lattice import Lattice from pymatgen.io.xyz import XYZ from pymatgen import Structure from pymatgen.analysis.diffusion_analyzer import DiffusionAnalyzer import os import numpy as np os.chdir('/home/jinho93/oxides/wurtzite/zno/cp2k/1.aimd/3.16A/30/3.fix') x = XYZ.from_file('526.xyz') lat = Lattice(np.diag([16.02, 16.67, 40.67])) s = [ Structure(lat, r.species, r.cart_coords, coords_are_cartesian=True) for r in x.all_molecules ] #%% from pymatgen.analysis.diffusion_analyzer import DiffusionAnalyzer d = DiffusionAnalyzer.from_structures(s, 'Zn', 600, 500, 2) d.get_msd_plot()
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)