mglcmdtools 0.0.9

Description:

mglcmdtools 0.0.9

mglcmdtools
1 Introduction
mglcmdtools is a collection of common cmd tools intended to be used in Python3 scripts. By Guanliang MENG, see https://github.com/linzhi2013/mglcmdtools.
2 Installation
pip install mglcmdtools

3 Usage
from mglcmdtools import rm_and_mkdir, runcmd, longStrings_not_match_shortStrings, read_fastaLike, read_fastaLike2, csv2dict, csv2tupe, split_fasta_to_equal_size


rm_and_mkdir('Newdirectory')

rm_and_mkdir('Newdirectory', force=True)


cmd = 'ls -lhtr /'
runcmd(cmd)

runcmd(cmd, verbose=True)


Long_strings = ['AABB', 'CCDD', 'EEFF']
Short_strings = ['AA', 'EE']
longStrings_not_match_shortStrings(Long_strings, Short_strings)
# ['CCDD']

seq.fa file has following content:
>scaffold512 Locus_1222_0 8.3 LINEAR length=1717 score=20.785
COX2 2 649 45 643 + 4
COX3 897 1691 18 784 + 4
>C7676 18.0 length=1633 score=19.113
DNA afd
COX1 34 1580 12 1530 - 4
>C7536 14.0 length=1185 score=13.529
CYTB 178 1185 25 1008 + 4
>scaffold619 Locus_1559_0 5.0 LINEAR length=803 score=3.515
ND4 27 764 515 1185 + 2
>scaffold367 Locus_808_0 4.6 LINEAR length=652 score=2.296
ATP6 1 306 324 620 - 4
AAA adfjkaj

Then read each record:
for rec in read_fastaLike('seq.fa'):
print('seqid line:', rec[0])
print('sequence line 1:', rec[1])

function csv2dict(file=None, header=None, nrows=None, index_col=0, rm_self=True, **kwargs):
targeted file: a csv file containing a matrix.

by default, assuming the csv file does not have header row, and the first column (index 0) is the row names.

you must specify how many rows to be read.

1. read data from a csv file into a pandas Dataframe;
2. change the up triangular and low triangular to dictionary 'triu_dict' and 'tril_dict', respectively.

Parameter:
rm_self: remove the pair of self-to-self, default True.


Return:
(triu_dict, tril_dict)

function csv2tupe(file=None, header=None, nrows=None, index_col=0, rm_self=True, **kwargs):
targeted file: a csv file containing a matrix.

by default, assuming the csv file does not have header row, and the first column (index 0) is the row names.

you must specify how many rows to be read.

1. read data from a csv file into a pandas Dataframe;
2. change the up triangular and low triangular to LIST of tupes 'triu' and 'tril', respectively.

Parameter:
rm_self: remove the pair of self-to-self, default True.


Return:
(triu, tril)

function split_fasta_to_equal_size(fastafile=None, tot_file_num=10, outdir='./'):
Split a fasta file to `tot_file_num` subfiles, and all subfiles have
appropximately equal size.

Return:
A list of the subfiles' abspath

function extend_ambiguous_dna(seq=None, get_a_random_seq=False, get_first_seq=False):
return a `map` iterator of all possible sequences given an ambiguous
DNA input.

if `get_a_random_seq=True`, return a randomly chosen sequence. Beware, if the seq is too long, and there are too many ambiguous sites,this can take
a lot of memory. It is at your own risk to use `get_a_random_seq=True`. I
would suggest you use `get_first_seq=True` instead.

if `get_first_seq=True`, return only the first sequence of the `map`
iterator. the result should always be the same for one input DNA.

if `get_a_random_seq=True` and `get_first_seq=True` at the same time,
only `get_first_seq=True` will work.

cannot deal with 'U' in RNA sequences.

the lower case or upper case of each base will be the same with input DNA.

modified from:
https://stackoverflow.com/questions/27551921/how-to-extend-ambiguous-dna-sequence

function extend_ambiguous_dna_randomly(seq=None):
return one sequence by randomly extending the input ambiguous DNA.

the lower case or upper case of each base will be the same with input DNA.

cannot deal with 'U' in RNA sequences.

4 Author
Guanliang MENG
5 Citation
Currently I have no plan to publish mglcmdtools.