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ovito软件分析Voronoi
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使用ovito分析Voronoi，输入参数后，怎么得到结果？不明白？有会的吗？ 发自小木虫Android客户端 |
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老师好，我把路径改了，在ovito图形界面，run scripts，但是ovito图形界面只是闪了下，没报错，也没结果，啥也没有，是脚本的问题吗？我用的是vasp的POSAR文件，下面是ovito计算voro的脚本，我直接复制的，不知道对不对 # Import OVITO modules. from ovito.io import * from ovito.modifiers import * # Import NumPy module. import numpy # Load a simulation snapshot of a Co-B metallic glass. node = import_file("E:/ovito/Co65B35/POSCAR"  # Set atomic radii (required for polydisperse Voronoi tessellation). atypes = node.source.particle_properties.particle_type.type_list atypes[0].radius = 1.25 # Co atomic radius (atom type 1 in input file) atypes[1].radius = 0.82 # B atomic radius (atom type 2 in input file) # Set up the Voronoi analysis modifier. voro = VoronoiAnalysisModifier( compute_indices = True, use_radii = True, edge_count = 6, # Length after which Voronoi index vectors are truncated edge_threshold = 0.1 ) node.modifiers.append(voro) # Let OVITO compute the results. node.compute() # Make sure we did not lose information due to truncated Voronoi index vectors. if voro.max_face_order > voro.edge_count: print("Warning: Maximum face order in Voronoi tessellation is {0}, " "but computed Voronoi indices are truncated after {1} entries. " "You should consider increasing the 'edge_count' parameter to {0}." .format(voro.max_face_order, voro.edge_count)) # Note that it would be possible to automatically increase the 'edge_count' # parameter to 'max_face_order' here and recompute the Voronoi tessellation: # voro.edge_count = voro.max_face_order # node.compute() # Access computed Voronoi indices as NumPy array. # This is an (N)x(edge_count) array. voro_indices = node.output.particle_properties['Voronoi Index'].array # This helper function takes a two-dimensional array and computes a frequency # histogram of the data rows using some NumPy magic. # It returns two arrays (of equal length): # 1. The list of unique data rows from the input array # 2. The number of occurences of each unique row # Both arrays are sorted in descending order such that the most frequent rows # are listed first. def row_histogram(a): ca = numpy.ascontiguousarray(a).view([('', a.dtype)] * a.shape[1]) unique, indices, inverse = numpy.unique(ca, return_index=True, return_inverse=True) counts = numpy.bincount(inverse) sort_indices = numpy.argsort(counts)[::-1] return (a[indices[sort_indices]], counts[sort_indices]) # Compute frequency histogram. unique_indices, counts = row_histogram(voro_indices) # Print the ten most frequent histogram entries. for i in range(10): print("%s\t%i\t(%.1f %%)" % (tuple(unique_indices), counts, 100.0*float(counts)/len(voro_indices))) |
4楼2017-03-09 14:30:58
valenhou001
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meizhixin: 金币+10 2017-02-28 13:32:37
meizhixin: 金币+10 2017-02-28 13:32:37
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采用python script来计算吧： https://www.ovito.org/manual/pyt ... ute-voronoi-indices |
2楼2017-02-28 12:52:46
3楼2017-02-28 13:31:52
tongaigao
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5楼2017-05-18 15:49:20