如何选择要从python字典中打印出的内容并将其写到另一个文件中？

Question

I have a file I read in called 'peaks_ee.xpk' and I have a dictionary in which the atom name is the key and the chemical shift is the value.

This is a sample of my peaks_ee.xpk file:

label dataset sw sf
1H 1H_2
NOESY_F1eF2e.nv
4807.69238281 4803.07373047
600.402832031 600.402832031
1H.L 1H.P 1H.W 1H.B 1H.E 1H.J 1H.U 1H_2.L 1H_2.P 1H_2.W 1H_2.B 1H_2.E 1H_2.J 1H_2.U vol int stat comment flag0 flag8 flag9
0 {1.H1'} 5.82020 0.05000 0.10000 ++ {0.0} {} {2.H8} 7.61004 0.05000 0.10000 ++ {0.0} {} 0.0 100.0000 0 {} 0 0 0
1 {2.H8} 7.61004 0.05000 0.10000 ++ {0.0} {} {1.H1'} 5.82020 0.05000 0.10000 ++ {0.0} {} 0.0 100.0000 0 {} 0 0 0
2 {1.H8} 8.13712 0.05000 0.10000 ++ {0.0} {} {1.H1'} 5.82020 0.05000 0.10000 ++ {0.0} {} 0.0 100.0000 0 {} 0 0 0
3 {1.H1'} 5.82020 0.05000 0.10000 ++ {0.0} {} {1.H8} 8.13712 0.05000 0.10000 ++ {0.0} {} 0.0 100.0000 0 {} 0 0 0
4 {2.H8} 7.61004 0.05000 0.10000 ++ {0.0} {} {2.H1'} 5.90291 0.05000 0.10000 ++ {0.0} {} 0.0 100.0000 0 {} 0 0 0
5 {2.H1'} 5.90291 0.05000 0.10000 ++ {0.0} {} {2.H8} 7.61004 0.05000 0.10000 ++ {0.0} {} 0.0 100.0000 0 {} 0 0 0
6 {2.H8} 7.61004 0.05000 0.10000 ++ {0.0} {} {1.H1'} 5.82020 0.05000 0.10000 ++ {0.0} {} 0.0 100.0000 0 {} 0 0 0
7 {2.H8} 7.61004 0.05000 0.10000 ++ {0.0} {} {1.H8} 8.13712 0.05000 0.10000 ++ {0.0} {} 0.0 100.0000 0 {} 0 0 0
8 {1.H1'} 5.82020 0.05000 0.10000 ++ {0.0} {} {2.H8} 7.61004 0.05000 0.10000 ++ {0.0} {} 0.0 100.0000 0 {} 0 0 0
9 {1.H8} 8.13712 0.05000 0.10000 ++ {0.0} {} {2.H8} 7.61004 0.05000 0.10000 ++ {0.0} {} 0.0 100.0000 0 {} 0 0 0

This is the python code:

import pandas as pd

result = {}
text = 'fe'
filename = 'fe_yellow.xpk'

if text == 'ee':
    df = pd.read_csv('peaks_ee.xpk', sep=" ",skiprows=5)

    shift1= df["1H.P"]
    shift2= df["1H_2.P"]

    if filename == 'ee_pinkH1.xpk':
        mask = ((shift1>5.1) & (shift1<6)) & ((shift2>7) & (shift2<8.25))
    elif filename == 'ee_pinkH2.xpk':
    mask = ((shift1>3.25)&(shift1<5))&((shift2>7)&(shift2<8.5))

    result = df[mask]
    result = result[["1H.L","1H_2.L"]]

    tclust_atom = open("tclust.txt","a")

    tclust_atom.write(str(result))

The output is:

         1H.L    1H_2.L
25    {5.H2'}   {5.H1'}
26    {5.H2'}   {5.H1'}
27    {5.H2'}    {6.H5}
42    {7.H2'}   {7.H1'}
43    {7.H2'}   {7.H1'}
44    {7.H2'}    {8.H5}
60    {9.H2'}   {9.H1'}
61    {9.H2'}   {9.H1'}
62    {9.H2'}   {10.H5}
87   {12.H2'}  {12.H1'}
88   {12.H2'}  {12.H1'}
89   {12.H2'}   {13.H5}
132  {18.H2'}  {18.H1'}
133  {18.H2'}  {18.H1'}
146  {20.H2'}  {20.H1'}
147  {20.H2'}  {20.H1'}
154  {21.H2'}  {21.H1'}
155  {21.H2'}  {21.H1'}
169  {23.H2'}  {23.H1'}
170  {23.H2'}  {23.H1'}
171  {23.H2'}   {24.H5}

Instead, I want the output to look like: Atom 1 5.H2' 5.H1' Atom 2 5.H2' 5.H1' Atom 3 5.H2' 6.H5 Atom 4 7.H2' 7.H1' Atom 5 7.H2' 7.H1' Atom 6 7.H2' 8.H5 Atom 7 9.H2' 9.H1' Atom 8 9.H2' 9.H1' Atom 9 9.H2' 10.H5 Atom 10 12.H2' 12.H1' Atom 11 12.H2' 12.H1' Atom 12 12.H2' 13.H5 Atom 13 18.H2' 18.H1' Atom 14 18.H2' 18.H1' Atom 15 20.H2' 20.H1' Atom 16 20.H2' 20.H1' Atom 17 21.H2' 21.H1' Atom 18 21.H2' 21.H1' Atom 19 23.H2' 23.H1' Atom 20 23.H2' 23.H1' Atom 21 23.H2' 24.H5

So I want to get rid of the first line and get rid of the curly braces in the file I currently have, and I want to add the word "Atom" next to each line along with the number (starting from 1 going to n)

And for example, atom 1 and atom 2 are the same, how can I only print it once instead of twice?

Answer 1

Check if this helps you. Replace the last two lines in your code with this code:

for col in result.columns:
    result[col] = result[col].str.strip('{} ')
result.drop_duplicates(keep='first', inplace=True)
result = result.set_index([['Atom '+str(i) for i in range(1,len(result)+1)]])
tclust_atom = open("tclust.txt", "a")
result.to_string(tclust_atom, header=False)

The for loop strips the extra spaces and curly braces from all the series in your DF. drop_duplicates , as the name suggests, drops duplicate rows from the DF. And finally set_index replaces the integer index with the index where each entry is of the form 'Atom #'.