Question

我有一个粘贴在下面的python代码。它可以很好地满足我的需要。您会注意到我加载了一个转储文件。如何遍历所有具有* .dump结束模式的转储文件，并让每个新文件仅向输出文件添加新的数据列？本质上，我想添加一个循环，这样就不必手动为每个转储文件重新编写代码。

from ovito.io import *
from ovito.data import *
from ovito.modifiers import *
import numpy as np

node = import_file("../200eV.dump",multiple_frames = True)

# Perform Wigner-Seitz analysis:
ws = WignerSeitzAnalysisModifier(
    per_type_occupancies = True, 
    eliminate_cell_deformation = True)
ws.reference.load("../../../WS_Ref/ws.dump")
node.modifiers.append(ws)

# Define a modifier function that selects sites of type A=1 which
# are occupied by exactly one atom of type B=2.
def modify(frame, input, output):

    # Retrieve the two-dimensional Numpy array with the site occupancy numbers.
    occupancies = input.particle_properties['Occupancy'].array

    # Get the site types as additional input:
    site_type = input.particle_properties.particle_type.array

    # Calculate total occupancy of every site:
    total_occupancy = np.sum(occupancies, axis=1)

    # Set up a particle selection by creating the Selection property:

    selection1 = (site_type == 1) & (occupancies[:,0] == 0) & (occupancies[:,1] == 0)

    output.attributes['Ca_Vac'] = np.count_nonzero(selection1)


# Insert Python modifier into the data pipeline.
node.modifiers.append(PythonScriptModifier(function = modify))

# Let OVITO do the computation and export the number of identified 
# antisites as a function of simulation time to a text file:
export_file(node, "defects_200.txt", "txt", 
    columns = ['Timestep', 'Ca_Vac'],
    multiple_frames = True)

Answer 1

import numpy as np
from ovito.data import *
from ovito.io import *
from ovito.modifiers import *

ws = WignerSeitzAnalysisModifier(
    per_type_occupancies=True,
    eliminate_cell_deformation=True)
ws.reference.load("../../../WS_Ref/ws.dump")


def modify(frame, input, output):
    occupancies = input.particle_properties['Occupancy'].array

    site_type = input.particle_properties.particle_type.array

    total_occupancy = np.sum(occupancies, axis=1)  # you are also not using, also not using the frame parameter

    selection1 = (site_type == 1) & (occupancies[:, 0] == 0) & (occupancies[:, 1] == 0)

    output.attributes['Ca_Vac'] = np.count_nonzero(selection1)


import glob

for file in glob.glob('../*.glob'):
    node = import_file(file, multiple_frames=True)
    node.modifiers.append(ws)
    node.modifiers.append(PythonScriptModifier(function=modify))
    export_file(
        node, "defects_200.txt", "txt",
        columns=['Timestep', 'Ca_Vac'],
        multiple_frames=True
    )

我不知道这是我能想到的最好的方法，我希望它能起作用！

根据OP的要求添加此部分。

for index, file in enumerate(glob.glob('../*.glob')):
    node = import_file(file, multiple_frames=True)
    node.modifiers.append(ws)
    node.modifiers.append(PythonScriptModifier(function=modify))
    export_file(
        node, "defects_{}.txt".format(index), "txt",
        columns=['Timestep', 'Ca_Vac'],
        multiple_frames=True
    )

再次，这是关于库如何工作的猜测，并且只有在原始代码产生defetcs_200.txt作为结果时才能起作用。

Answer 2

尝试使用python glob软件包。

>>> import glob
>>> glob.glob('./[0-9].*')
['./1.gif', './2.txt']
>>> glob.glob('*.gif')
['1.gif', 'card.gif']
>>> glob.glob('?.gif')
['1.gif']

python遍历文件

2 个答案: