如何根据通过 Pandas 从 LAMMPS 输出文件导入的成键数据将原子细分为三个一组？
How to subdivide atoms into groups of three according to their bonding data that is imported from a LAMMPS output file via Pandas?
我是编程和分子动力学模拟方面的新手。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 bonds 文件，其中包含每个原子的记录（作为原子 ID）、它们的类型（往复特定的数字）元素），以及与这些特定原子结合的其他原子的信息。
A typical bonds file looks like this.
我的目标 通过考虑来自 bond[ 的成键信息，根据原子的类型（如第 1 组：氧-氢-氢）将原子分为三组=51=] 输出文件并计算每个时间步的组数。 我使用 pandas 并为每个时间步创建了一个数据帧。
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)
Please see this example that illustrates my algorithm to group 3 atoms。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] 归为一组，但不能将 [1,2,1] 归为一组，因为原子无法与自身形成键。此外，我们可以将这些原子 ID（第一列）分组后转换为它们的原子类型（第二列），例如 [1,3,7] 转换为 [1,1,3].
通过按照上面提到的键，1) 我可以根据原子的 ID 成功地对原子进行分组，2) 将它们转换为它们的原子类型和 3) 分别计算每个时间步长中存在多少组。第一个 while 循环（上图）计算每个时间步长的组数，而第二个 while 循环（下图）对每一行的原子进行分组（这是等于存在的每个原子 ID）及其来自数据框中不同行的相应键合原子。
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
This is a typical output file of my code in csv format
尽管代码有效，但效率不高；
每个原子ID只能迭代4个键原子（但是，模拟结果最多可以达到12个键原子，应该算的。）
程序运行缓慢。 （我处理超过 50000 个原子，每个时间步的计算可能需要长达 88 分钟。）
你能给我推荐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，那将会更有效率（特别是如果我可以摆脱重复 if 语句）。
感谢您的宝贵时间。
我不确定我是否理解其中的逻辑，看看是否有帮助。
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 table 放入字典中，而不是验证所有内容是否唯一，而是将其放入集合中
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))
我是编程和分子动力学模拟方面的新手。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 bonds 文件，其中包含每个原子的记录（作为原子 ID）、它们的类型（往复特定的数字）元素），以及与这些特定原子结合的其他原子的信息。 A typical bonds file looks like this.
我的目标 通过考虑来自 bond[ 的成键信息，根据原子的类型（如第 1 组：氧-氢-氢）将原子分为三组=51=] 输出文件并计算每个时间步的组数。 我使用 pandas 并为每个时间步创建了一个数据帧。
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)
Please see this example that illustrates my algorithm to group 3 atoms。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] 归为一组，但不能将 [1,2,1] 归为一组，因为原子无法与自身形成键。此外，我们可以将这些原子 ID（第一列）分组后转换为它们的原子类型（第二列），例如 [1,3,7] 转换为 [1,1,3].
通过按照上面提到的键，1) 我可以根据原子的 ID 成功地对原子进行分组，2) 将它们转换为它们的原子类型和 3) 分别计算每个时间步长中存在多少组。第一个 while 循环（上图）计算每个时间步长的组数，而第二个 while 循环（下图）对每一行的原子进行分组（这是等于存在的每个原子 ID）及其来自数据框中不同行的相应键合原子。
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
This is a typical output file of my code in csv format
尽管代码有效，但效率不高；
每个原子ID只能迭代4个键原子（但是，模拟结果最多可以达到12个键原子，应该算的。）
程序运行缓慢。 （我处理超过 50000 个原子，每个时间步的计算可能需要长达 88 分钟。）
你能给我推荐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，那将会更有效率（特别是如果我可以摆脱重复 if 语句）。
感谢您的宝贵时间。
我不确定我是否理解其中的逻辑，看看是否有帮助。
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 table 放入字典中，而不是验证所有内容是否唯一，而是将其放入集合中
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))