我是编程和分子动力学模拟的新手。我使用LAMMPS来模拟物理气相沉积(PVD)过程,并确定不同时间段中原子之间的相互作用
在我执行分子动力学模拟之后,LAMMPS为我提供了一个输出bonds文件,其中包含每个原子(作为原子ID)的记录、它们的类型(与特定元素往复的数量)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.
我的目标是通过考虑键输出文件中的键信息,将原子按其类型(如组1:氧-氢-氢)分为三组,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。键列显示不同的原子(通过原子ID),这些原子与行中的原子键合在一起。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]
通过遵循上面提到的键,1)我可以成功地根据原子ID对原子进行分组,2)将它们转换为原子类型,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)对每个时间步的组进行计数,而第二个while循环(下图)将来自每行(等于存在的每个原子ID)的原子与其来自数据帧中不同行的对应键合原子进行分组
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
This is a typical output file of my code in csv format
虽然代码可以工作,但它没有效率,因为
对于每个原子ID,它只能迭代4个键原子(但是,模拟结果最多可以达到12个本应计数的键原子)。
程序运行缓慢。(我处理50000多个原子,计算每个时间步最多需要88分钟。)
你能给我推荐一种更有效的方法吗?由于我是一名编程新手,我不知道是否还有其他python迭代工具或包可以用于我的案例。我相信,如果我能用更少的代码行执行这些操作(特别是如果我能避免重复if语句),效率会更高
谢谢你抽出时间
我不确定我是否理解逻辑,看看这是否有帮助
100000个三人组花了41秒
loc、get_loc是非常扩展的操作,所以将表放在字典中,而不是验证每件事都是唯一的,将它放在一个集合中
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