如何根据通过Pandas从LAMMPS输出文件中导入的键合数据将原子细分为三组？

Question

我是编程和分子动力学模拟方面的新手。我使用LAMMPS模拟物理气相沉积(PVD)过程，并确定不同时间步骤中原子之间的相互作用。

在我进行了分子动力学模拟之后，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(与特定元素相互作用的数字)，以及与这些特定原子结合的其他原子的信息。文件看上去像这样。

I通过考虑键输出文件中的键合信息，根据原子的类型(如Group1:氧-氢-氢)对原子进行分类，并计算每个时间步骤的组数。我使用了熊猫，并为每一个时间步骤创建了一个数据。

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)

请看这个例子，它说明了我的3原子组算法。。键列显示与其行中的原子结合在一起的不同原子(通过原子in )。例如，利用这个算法，我们可以分组1,2,5和1,2,6，但不是1,2,1，因为一个原子不能与自己形成键。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。

通过上面提到的键，1) I可以成功地按它们的in对原子进行分组，2)将它们转换成原子类型，3)分别计算每个时间步骤中存在多少组。第一个while循环(上面)计算每个时间步骤的组，而第二个while循环(下面)将来自每一行的原子(等于存在的每个原子ID )与数据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。

虽然代码有效，但由于；

• 对于每个原子ID，它只能迭代4个以上的键原子(然而，模拟结果可以达到多达12个应该被计算的键原子)。
• 这个程序运行缓慢。(我与50000多个原子一起工作，每一步的计算时间可达88分钟。)

你能给我推荐一种更有效的方法吗？由于我是编程新手，我不知道是否还有其他的python迭代工具或包可以适用于我的情况。我相信，如果我能够用更少的代码来执行这些操作(特别是如果我能够摆脱重复的if语句)，那么它会更有效率。

耽误您时间，实在对不起。

Answer 1

我不确定我是否理解逻辑，看看这是否有帮助。

100000次三重奏花了41秒。

，get_loc是非常广泛的操作，所以将表放在字典中，而不是验证所有事物都是唯一的，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))