如何根据通过熊猫从LAMMPS输出文件导入的键合数据将原子细分为三个组？

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。

在执行分子动力学模拟之后，LAMMPS为我提供了一个输出bonds文件，其中包含每个单独原子的记录（作为原子ID），它们的类型（往复到特定元素的数字）以及其他原子的信息与那些特定的原子键合。 A typical bonds file looks like this.

我的目标通过考虑来自[[bond输出文件的键合信息，并根据原子的类型（如第1组：氧-氢-氢）将原子按三组进行排序，并计算每个时间步的基团数。 我使用大熊猫，并为每个时间步创建一个数据框。

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)

Please see this example that illustrates my algorithm to group 3 atoms。键列显示与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，因为原子无法与其自身创建键。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]。
通过如上所述的键，
1）

我可以根据原子的ID成功地对原子进行分组， 2）将其转换为原子类型，并且 3）计算存在的基团数在每个时间步分别。第一个 while循环（上方）为每个时间步计数组，而第二个while循环（下方）将每行中的原子（等于存在的每个原子ID）及其对应的键合原子分组来自数据框中的不同行。 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，但由于效率不高；

每个原子ID只能迭代4个以上的键原子（但是，模拟结果最多可以计算12个应计的键原子。] >>

程序运行缓慢。 （我处理了超过50000个原子，每个时间步长可能需要88分钟才能计算出来。）

您能推荐我一种更有效的方法吗？由于我是编程新手，所以我不知道是否有其他适用于我的案例的python迭代工具或软件包。我相信，如果我可以用更少的代码行执行那些操作（特别是如果我可以摆脱重复的[[if

语句）。

感谢您的时间。

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，并确定不同原子之间的相互作用...