IO

1. Get file names

We can get the file names in a specific directory using pp.io.find_from_folder.

Python

import pypropel as pp

df = pp.io.find_from_folder(
    file_path=to('data/ex/xml/'),
    suffix='.xml',
    flag=1,
    sv_fpn=None,
    # sv_fpn=to('data/find.txt'),
)
print(df)

Output

29/07/2024 02:31:43 logger: ======>0. Find file (like "Q86V85"): 1a11
29/07/2024 02:31:43 logger: ======>1. Find file (like "Q86V85"): 1a91
29/07/2024 02:31:43 logger: ======>2. Find file (like "Q86V85"): 1afo
...
29/07/2024 02:31:43 logger: ======>8954. Find file (like "Q86V85"): 8wbx
29/07/2024 02:31:43 logger: ======>8955. Find file (like "Q86V85"): 8wd6
         0
0     1a11
1     1a91
2     1afo
3     1aig
4     1aij
...    ...
8951  8u5b
8952  8wam
8953  8wba
8954  8wbx
8955  8wd6

[8956 rows x 1 columns]

Info

flag: which method used to suit file names. Default value: 1

• 1 - a general function for finding the prefixes of files

• 2 - separate protein names and chains from file prefixes, like 1atz_A, PDBTM format

• 3 - separate protein names and chains from file prefixes, like 1atzA

• 4 - separate protein names and multiple chains from file prefixes, like 1atz_ABCD

• 5 - separate protein names and multiple chains from file prefixes with regular expression

2. Different and repeated files between 1D lists

To identify different and repeated files between two lists data/pdbtm_alpha_10.02.2023.txt and df[0], we can do

Python

import pypropel as pp

df_lg = pfreader().generic(to('data/pdbtm_alpha_10.02.2023.txt'), df_sep='\t')
df_lg[1], df_lg[2] = zip(*df_lg[0].apply(lambda x: (x.split("_")[0], x.split("_")[1])))
series_prot_names = pd.Series(df_lg[1].unique())

pds_diff, psd_rept = pp.io.list_diff_unipartite(
    pds_lg=series_prot_names,
    pds_sm=df[0],
    sv_diff_fpn=to('data/diff.txt'),
    sv_rept_fpn=to('data/repeat.txt'),
)
print(pds_diff)
print(psd_rept)

Output

# print(pds_diff)
Series([], dtype: object)

# print(psd_rept)
0       3zmj
1       5mur
2       7f92
3       7zdv
4       2fyn
        ... 
8814    8a1v
8815    7wu9
8816    5iou
8817    3rce
8818    7f1s
Length: 8819, dtype: object

In addition, we can find newly added proteins between two versions of PDBTM protein lists.

Python

import pypropel as pp

df_lg = pfreader().generic(to('data/pdbtm_alpha_10.02.2023.txt'), df_sep='\t')
df_sm = pfreader().generic(to('data/pdbtm_alpha_06.30.2023.txt'), df_sep='\t')

pds_diff, psd_rept = pp.io.list_diff_unipartite(
    pds_lg=df_lg[0],
    pds_sm=df_sm[0],
    sv_diff_fpn=to('data/diff.txt'),
    sv_rept_fpn=to('data/repeat.txt'),
)
print(pds_diff)
print(psd_rept)

Output

0       6s7o_A
1       8ha2_A
2       8j5p_P
3       1izl_A
4       8p3w_H
         ...  
2951    8t6u_A
2952    8hju_B
2953    6zjy_H
2954    4qkm_B
2955    6hjr_D
Length: 2956, dtype: object
0        5va3_A
1        8f4f_T
2        6yto_F
3        4knf_D
4        2xq3_A
          ...  
33371    6nt7_B
33372    6jlo_Z
33373    8h9j_8
33374    6idf_E
33375    5jtg_E
Length: 33376, dtype: object

3. Different and repeated files between 2D lists

We can get the list of Different and repeated files between 2D lists. It drops duplicates by considering two columns in lists.

Python

import pypropel as pp

df = pd.DataFrame()
df1 = pd.DataFrame()

df[0], df[1] = zip(*pds_diff.apply(lambda x: (x.split("_")[0], x.split("_")[1])))
df1[0], df1[1] = zip(*df_lg[0].apply(lambda x: (x.split("_")[0], x.split("_")[1])))
print(df)
print(df1)

df_differ, df_repeat = pp.io.list_diff_bipartite(
    pds_lg_1=df[0],
    pds_lg_2=df[1],
    pds_sm_1=df1[0],
    pds_sm_2=df1[1],
    sv_diff_fpn=to('data/diff1.txt'),
    sv_rept_fpn=to('data/repeat1.txt'),
)
print(df_differ)
print(df_repeat)

Output

# df_differ
Empty DataFrame
Columns: []
Index: []

# df_repeat
         0  1
0     8t2v  A
1     6b8h  3
2     8p3s  H
3     7thu  K
4     8bx5  C
...    ... ..
2951  7a24  V
2952  6vam  B
2953  8go3  C
2954  8c29  n
2955  6psn  F

[2956 rows x 2 columns]

4. move, copy, remove, rename, create

PyPropel also provides a few move, copy, remove, rename, create operations on a list of IDs rendered in pandas Series format.

move_files
copy_files
delete_files
rename_file_suffix
rename_file_prefix
makedir

Below shows an example that a list of proteins are moved from data/isoform/ to data/isoform/transmembrane/.

Python

df_cano = pp.io.find_from_folder(
    file_path='data/canonical/',
    suffix='.fasta',
    flag=1,
    sv_fpn=None,
    # sv_fpn=to('data/find.txt'),
)
print(df_cano)

df_isof = pp.io.find_from_folder(
    file_path='data/isoform/',
    suffix='.fasta',
    flag=1,
    sv_fpn=None,
    # sv_fpn=to('data/find.txt'),
)
print(df_isof)

df_isof['canonical'] = df_isof[0].apply(lambda x: x.split('-')[0])
print(df_isof)

df_isof_tm = df_isof.loc[df_isof['canonical'].isin(df_cano[0].values)]
print(df_isof_tm)

pp.io.move_files(
    pds_mv=df_isof_tm[0],
    mv_from_fp='data/isoform/',
    mv_to_fp='data/isoform/transmembrane/',
    suffix='.fasta',
)