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我是编程和分子动力学模拟的新手。我正在使用 LAMMPS 来模拟物理气相沉积 (PVD) 过程并确定不同时间步长中原子之间的相互作用。

在我执行分子动力学模拟后，LAMMPS 为我提供了一个输出键文件，其中包含每个原子的记录（作为原子 ID）、它们的类型（与特定元素交互的数字）以及与这些特定原子键合的其他原子的信息. 典型的债券文件如下所示。

我的目标是通过考虑来自键输出文件的键合信息并计算每个时间步长的组数，根据它们的类型（如 Group1：Oxygen-Hydrogen-Hydrogen）在三组中对原子进行分类。我使用 pandas 并为每个时间步创建了一个数据框。

df = pd.read_table(directory, comment="#", delim_whitespace= True, header=None, usecols=[0,1,2,3,4,5,6] )
headers= ["ID","Type","NofB","bondID_1","bondID_2","bondID_3","bondID_4"]
df.columns = headers
df.fillna(0,inplace=True)
df = df.astype(int)

timestep = int(input("Number of Timesteps: ")) #To display desired number of timesteps.
total_atom_number = 53924 #Total number of atoms in the simulation.

t= 0 #code starts from 0th timestep.
firstTime = []
while(t <= timestep):
    open('file.txt', 'w').close() #In while loop = displays every timestep individually, Out of the while loop = displays results cumulatively.
    i = 0
    df_tablo =(df[total_atom_number*t:total_atom_number*(t+1)]) #Creates a new dataframe that inlucdes only t'th timestep.
    df_tablo.reset_index(inplace=True)


    print(df_tablo)

请参阅此示例，该示例说明了我对 3 个原子进行分组的算法。键列显示与其行中的原子键合在一起的不同原子（按原子 ID）。例如，通过使用该算法，我们可以将 [1,2,5] 和 [1,2,6] 分组，但不能将 [1,2,1] 分组，因为原子不能与自身建立键。此外，我们可以在分组后将这些原子 ID（第一列）转换为它们的原子类型（第二列），例如 [1,3,7] 到 [1,1,3]。

通过遵循上面提到的键，1)我可以成功地根据它们的 ID 对原子进行分组，2)将它们转换为它们的原子类型，以及3)分别计算每个时间步中存在多少个组。第一个while 循环（上图）计算每个时间步的组数，而第二个while 循环（下图）将每一行的原子（等于存在的每个原子 ID）与数据帧中不同行的相应键合原子分组。 

 while i < total_atom_number:
    atom1_ID = df_tablo["ID"][i]  # atom ID of i'th row was defined.
    atom1_NB = df_tablo["NofB"][i]  # number of bonds of the above atom ID was defined, but not used.
    atom1_bond1 = df_tablo["bondID_1"][i]  #bond ID1 of above atom was defined.
        # bondIDs and atom types  of 1,2,3 and 4 for atom1_bond1 were defined respectively.
    if atom1_bond1 != 0:
        atom2_index = (df_tablo.set_index('ID').index.get_loc(atom1_bond1))
        atom2_ID = df_tablo["ID"][atom2_index]
        atom2_bond1 = df_tablo["bondID_1"][atom2_index]
        atom2_bond2 = df_tablo["bondID_2"][atom2_index]
        atom2_bond3 = df_tablo["bondID_3"][atom2_index]
        atom2_bond4 = df_tablo["bondID_4"][atom2_index]


        type_atom1 = df_tablo["Type"][i]
        type_atom2 = df_tablo["Type"][atom2_index]
        #If the desired conditions are satisfied, atom types  are combined as [atom at i'th row, bondID1 at'ith row, 4 bondIDs respectively at the row which is equal to bondID1's row ]
        if atom1_ID != atom2_bond1 and atom2_bond1 != 0:
            set = [atom1_ID, atom2_ID, atom2_bond1]
            atom2_bond1_index = (df_tablo.set_index('ID').index.get_loc(atom2_bond1))
            type_atom2_bond1 = df_tablo["Type"][atom2_bond1_index]
            print("{}{}{}".format(type_atom1, type_atom2, type_atom2_bond1), file=open("file.txt", "a"))
            # print(set)

        if atom1_ID != atom2_bond2 and atom2_bond2 != 0:
            set = [atom1_ID, atom2_ID, atom2_bond2]
            atom2_bond2_index = (df_tablo.set_index('ID').index.get_loc(atom2_bond2))
            type_atom2_bond2 = df_tablo["Type"][atom2_bond2_index]
            print("{}{}{}".format(type_atom1, type_atom2, type_atom2_bond2), file=open("file.txt", "a"))
            # print(set)

        if atom1_ID != atom2_bond3 and atom2_bond3 != 0:
            set = [atom1_ID, atom2_ID, atom2_bond3]
            atom2_bond3_index = (df_tablo.set_index('ID').index.get_loc(atom2_bond3))
            type_atom2_bond3 = df_tablo["Type"][atom2_bond3_index]
            print("{}{}{}".format(type_atom1, type_atom2, type_atom2_bond3), file=open("file.txt", "a"))
            # print(set)
        if atom1_ID != atom2_bond4 and atom2_bond4 != 0:
            set = [atom1_ID, atom2_ID, atom2_bond4]
            atom2_bond4_index = (df_tablo.set_index('ID').index.get_loc(atom2_bond4))
            type_atom2_bond4 = df_tablo["Type"][atom2_bond4_index]
            print("{}{}{}".format(type_atom1, type_atom2, type_atom2_bond4), file=open("file.txt", "a"))
            # print(set)
    # bondIDs and atom types  of 1,2,3 and 4 for atom1_bond2 were defined respectively.
    atom1_bond2 = df_tablo["bondID_2"][i]
    if atom1_bond2 != 0:
        atom1_bond2_index = (df_tablo.set_index('ID').index.get_loc(atom1_bond2) + 6)
        atom1_bond2_ID = df_tablo["ID"][atom1_bond2_index]
        atom1_bond2_bond1 = df_tablo["bondID_1"][atom1_bond2_index]
        atom1_bond2_bond2 = df_tablo["bondID_2"][atom1_bond2_index]
        atom1_bond2_bond3 = df_tablo["bondID_3"][atom1_bond2_index]
        atom1_bond2_bond4 = df_tablo["bondID_4"][atom1_bond2_index]

        type_atom1_bond2 = df_tablo["Type"][atom1_bond2_index] # If the desired conditions are satisfied, atom types  are combined as [atom at i'th row, bondID2 at'ith row, and 4 bondIDs respectively at the row which is equal to bondID2's row ]

        if atom1_ID != atom1_bond2_bond1 and atom1_bond2_bond1 != 0:
            set = [atom1_ID, atom1_bond2, atom1_bond2_bond1]
            atom1_bond2_bond1_index = (df_tablo.set_index('ID').index.get_loc(atom1_bond2_bond1))
            type_atom1_bond2_bond1 = df_tablo["Type"][atom1_bond2_bond1_index]
            print("{}{}{}".format(type_atom1, type_atom1_bond2, type_atom1_bond2_bond1), file=open("file.txt", "a"))
            # print(set)

        if atom1_ID != atom1_bond2_bond2 and atom1_bond2_bond2 != 0:
            set = [atom1_ID, atom1_bond2, atom1_bond2_bond2]
            atom1_bond2_bond2_index = (df_tablo.set_index('ID').index.get_loc(atom1_bond2_bond2))
            type_atom1_bond2_bond2 = df_tablo["Type"][atom1_bond2_bond2_index]
            print("{}{}{}".format(type_atom1, type_atom1_bond2, type_atom1_bond2_bond2), file=open("file.txt", "a"))
            # print(set)

        if atom1_ID != atom1_bond2_bond3 and atom1_bond2_bond3 != 0:
            set = [atom1_ID, atom1_bond2, atom1_bond2_bond3]
            atom1_bond2_bond3_index = (df_tablo.set_index('ID').index.get_loc(atom1_bond2_bond3))
            type_atom1_bond2_bond3 = df_tablo["Type"][atom1_bond2_bond3_index]
            print("{}{}{}".format(type_atom1, type_atom1_bond2, type_atom1_bond2_bond3), file=open("file.txt", "a"))
            # print(set)
        if atom1_ID != atom1_bond2_bond4 and atom1_bond2_bond4 != 0:
            set = [atom1_ID, atom1_bond2, atom1_bond2_bond4]
            atom1_bond2_bond4_index = (df_tablo.set_index('ID').index.get_loc(atom1_bond2_bond4))
            type_atom1_bond2_bond4 = df_tablo["Type"][atom1_bond2_bond4_index]
            print("{}{}{}".format(type_atom1, type_atom1_bond2, type_atom1_bond2_bond4), file=open("file.txt", "a"))
            # print(set)

    # bondIDs and atom types  of 1,2,3 and 4 for atom1_bond3 were defined respectively.
    atom1_bond3 = df_tablo["bondID_3"][i]
    if atom1_bond3 != 0:
        atom1_bond3_index = (df_tablo.set_index('ID').index.get_loc(atom1_bond3))
        atom1_bond3_ID = df_tablo["ID"][atom1_bond3_index]
        atom1_bond3_bond1 = df_tablo["bondID_1"][atom1_bond3_index]
        atom1_bond3_bond2 = df_tablo["bondID_2"][atom1_bond3_index]
        atom1_bond3_bond3 = df_tablo["bondID_3"][atom1_bond3_index]
        atom1_bond3_bond4 = df_tablo["bondID_4"][atom1_bond3_index]

        type_atom1_bond3 = df_tablo["Type"][atom1_bond3_index]
        # If the desired conditions are satisfied, atom types  are combined as [atom at i'th row, bondID3 at'ith row, and 4 bondIDs respectively at the row which is equal to bondID3's row ]
        if atom1_ID != atom1_bond3_bond1 and atom1_bond3_bond1 != 0:
            atom1_bond3_bond1_index = (df_tablo.set_index('ID').index.get_loc(atom1_bond3_bond1))
            type_atom1_bond3_bond1 = df_tablo["Type"][atom1_bond3_bond1_index]
            print("{}{}{}".format(type_atom1, type_atom1_bond3, type_atom1_bond3_bond1), file=open("file.txt", "a"))
            set = [atom1_ID, atom1_bond3, atom1_bond3_bond1]
            # print(set)

        if atom1_ID != atom1_bond3_bond2 and atom1_bond3_bond2 != 0:
            set = [atom1_ID, atom1_bond3, atom1_bond3_bond2]
            atom1_bond3_bond2_index = (df_tablo.set_index('ID').index.get_loc(atom1_bond3_bond2))
            type_atom1_bond3_bond2 = df_tablo["Type"][atom1_bond3_bond2_index]
            print("{}{}{}".format(type_atom1, type_atom1_bond3, type_atom1_bond3_bond2), file=open("file.txt", "a"))
            # print(set)

        if atom1_ID != atom1_bond3_bond3 and atom1_bond3_bond3 != 0:
            set = [atom1_ID, atom1_bond3, atom1_bond3_bond3]
            atom1_bond3_bond3_index = (df_tablo.set_index('ID').index.get_loc(atom1_bond3_bond3))
            type_atom1_bond3_bond3 = df_tablo["Type"][atom1_bond3_bond3_index]
            print("{}{}{}".format(type_atom1, type_atom1_bond3, type_atom1_bond3_bond3), file=open("file.txt", "a"))
            # print(set)
        if atom1_ID != atom1_bond3_bond4 and atom1_bond3_bond4 != 0:
            set = [atom1_ID, atom1_bond3, atom1_bond3_bond4]
            atom1_bond3_bond4_index = (df_tablo.set_index('ID').index.get_loc(atom1_bond3_bond4))
            type_atom1_bond3_bond4 = df_tablo["Type"][atom1_bond3_bond4_index]
            print("{}{}{}".format(type_atom1, type_atom1_bond3, type_atom1_bond3_bond4), file=open("file.txt", "a"))
            # print(set)

    atom1_bond4 = df_tablo["bondID_4"][i]
    # bondIDs and atom types of 1,2,3 and 4 for atom1_bond4 were defined respectively.
    if atom1_bond4 != 0:

        atom1_bond4_index = (df_tablo.set_index('ID').index.get_loc(atom1_bond4))
        atom1_bond4_ID = df_tablo["ID"][atom1_bond4_index]
        atom1_bond4_bond1 = df_tablo["bondID_1"][atom1_bond4_index]
        atom1_bond4_bond2 = df_tablo["bondID_2"][atom1_bond4_index]
        atom1_bond4_bond3 = df_tablo["bondID_3"][atom1_bond4_index]
        atom1_bond4_bond4 = df_tablo["bondID_4"][atom1_bond4_index]

        type_atom1_bond4 = df_tablo["Type"][atom1_bond4_index]
        # If the desired conditions are satisfied, atom types are combined as [atom at i'th row, bondID4 at'ith row, and 4 bondIDs respectively at the row which is equal to bondID4's row ]
        if atom1_ID != atom1_bond4_bond1 and atom1_bond4_bond1 != 0:
            set = [atom1_ID, atom1_bond4, atom1_bond4_bond1]
            atom1_bond4_bond1_index = (df_tablo.set_index('ID').index.get_loc(atom1_bond4_bond1))
            type_atom1_bond4_bond1 = df_tablo["Type"][atom1_bond4_bond1_index]
            print("{}{}{}".format(type_atom1, type_atom1_bond4, type_atom1_bond4_bond1), file=open("file.txt", "a"))
            # print(set)

        if atom1_ID != atom1_bond4_bond2 and atom1_bond4_bond2 != 0:
            set = [atom1_ID, atom1_bond4, atom1_bond4_bond2]
            atom1_bond4_bond2_index = (df_tablo.set_index('ID').index.get_loc(atom1_bond4_bond2))
            type_atom1_bond4_bond2 = df_tablo["Type"][atom1_bond4_bond2_index]
            print("{}{}{}".format(type_atom1, type_atom1_bond4, type_atom1_bond4_bond2), file=open("file.txt", "a"))
            # print(set)

        if atom1_ID != atom1_bond4_bond3 and atom1_bond4_bond3 != 0:
            set = [atom1_ID, atom1_bond4, atom1_bond4_bond3]
            atom1_bond4_bond3_index = (df_tablo.set_index('ID').index.get_loc(atom1_bond4_bond3))
            type_atom1_bond4_bond3 = df_tablo["Type"][atom1_bond4_bond3_index]
            print("{}{}{}".format(type_atom1, type_atom1_bond4, type_atom1_bond4_bond3), file=open("file.txt", "a"))
            # print(set)
        if atom1_ID != atom1_bond4_bond4 and atom1_bond4_bond4 != 0:
            set = [atom1_ID, atom1_bond4, atom1_bond4_bond4]
            atom1_bond4_bond4_index = (df_tablo.set_index('ID').index.get_loc(atom1_bond4_bond4))
            type_atom1_bond4_bond4 = df_tablo["Type"][atom1_bond4_bond4_index]
            print("{}{}{}".format(type_atom1, type_atom1_bond4, type_atom1_bond4_bond4), file=open("file.txt", "a"))
            # print(set)


    print(i,".step" )
    print(time.time() - start_time, "seconds")


    i = i + 1

print("#timestep", t, file=open("file.txt", "a"))
print("#timestep", t)

df_veri = pd.read_table('file.txt', comment="#", header=None)
df_veri.columns = ["timestep %d" % (t)]
#Created a dictionary that corresponds to type of bonds
df_veri["timestep %d" % (t)] = df_veri["timestep %d" % (t)].astype(str).replace(
    {'314': 'NCO', '312': 'NCH', '412': 'OCH', '214': 'HCO', '431': 'ONC', '414': 'OCO', '212': 'HCH',
     '344': 'NOO', '343': 'NON', '441': 'OOC', '144': 'COO', '421': 'OHC', '434': 'ONO', '444': 'OOO', '121': 'CHC',
     '141': 'COC'
     })
# To calculate the number of  3-atom combinations
ndf = df_veri.apply(pd.Series.value_counts).fillna(0)
ndfy = pd.DataFrame(ndf)
ndfy1 = ndfy.transpose()

# To write the number of 3-atom combinations in first timestep with headers and else without headers.
if firstTime == []:
    ndfy1.to_csv('filename8.csv', mode='a', header=True)
    firstTime.append('Not Empty')
else:

    ndfy1.to_csv('filename8.csv', mode='a', header=False)

t = t + 1

这是我的 csv 格式代码的典型输出文件

尽管代码有效，但效率不高；

  • 对于每个原子 ID，它只能迭代超过 4 个键原子（但是，模拟结果最多可以达到应该计算的 12 个键原子。）

  • 该程序运行缓慢。（我使用超过 50000 个原子，计算每个时间步可能需要长达 88 分钟。）

你能推荐我一个更有效的方法吗？由于我是编程新手，我不知道是否有任何其他 Python 迭代工具或包可以适用于我的情况。我相信如果我可以用更少的代码行来执行这些操作（特别是如果我可以摆脱重复的if语句），它会更有效率。

谢谢你的时间。

4

1 回答 1

0

我不确定我是否理解逻辑，看看这是否有帮助。

对于 100000 个三重奏，需要 41 秒。

loc、get_loc 是非常广泛的操作，因此请将您的表放在字典中，而不是验证所有内容都是唯一的，而是将其放在一个集合中

import pandas as pd
import random
from collections import defaultdict as dd
from collections import Counter
import time

# create 100000 unique trios of numbers
ids = list(range(50000))
trios_set = set()
while len(trios_set)<100000:
    trio = random.sample(ids,3)
    trios_set.add(frozenset(trio))

ids_dict = dd(list)  # a dictionery where id is the key and value is all the id who are partner with it in a list

for s in trios_set:
    for id in s:
        for other_id in s:
            if id!= other_id:
                ids_dict[id].append(other_id)

ids_dict = dict(ids_dict)
for_df = []
type_list = ["a","b","c","d","e","f","g","h","i","j","k","l","m","n"] 
for id in ids_dict:
    massage = {}
    massage["id"] = id
    other_id_index = 1
    for other_id in ids_dict[id]:
        massage["id_"+str(other_id_index)] =  other_id
        other_id_index+=1
    massage["type"] = random.choice(type_list)
    for_df.append(massage)

df = pd.DataFrame(for_df) # a table with id column and all ids who are with it in trios in id_1 id_2.. and type column with a letter

#------------------------------------------------------------------
#till here we built the input table
start_time = time.time() #till here we build the input table, now check the time for 100000 atoms
type_dict = {}
from_df = dd(set)

for i,r in df.iterrows(): #move the dataframe to a dict of id as key and value as list of ids who connected to it
    for col in df:
        if "id_"in col and str(r[col])!="nan":
            from_df[r["id"]].add(r[col])
    type_dict[r["id"]] = r["type"] #save the type of id in a dictionery

from_df = dict(from_df)
out_trio_set = set() 

for id in from_df:
    for other_id in from_df[id]:
        if other_id!= id and str(other_id)!="nan":
            for third_id in from_df[other_id]:
                current_trio = frozenset([id, other_id,third_id])
                if len(current_trio)==3:
                    out_trio_set.add(current_trio)

type_conter = Counter()

for trio in out_trio_set:
    type_list = []
    for id in trio:
        type_list.append(type_dict[id])
    type_list = sorted(type_list)
    atom_type = "".join(type_list)
    type_conter[atom_type] +=1

out_df = pd.DataFrame(type_conter, index = [1]) # in here put index as timestamp

out_df.to_excel(r"D:\atom.xlsx")
print("--- %s seconds ---" % (time.time() - start_time))
于 2020-04-11T18:13:11.947 回答