135 lines
4.5 KiB
Python
Executable file
135 lines
4.5 KiB
Python
Executable file
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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'''
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Created on Tue Aug 6 12:56:03 2019
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@author: tanu
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'''
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#=============================================================================
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# Task: Residue depth (rd) processing to generate a df with residue_depth(rd)
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# values
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# FIXME
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# Input: '.tsv' i.e residue depth txt file (output from .zip file manually
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# downloaded from the website).
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# This should be integrated into the pipeline
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# Output: .csv with 3 cols i.e position, rd_values & 3-letter wt aa code(caps)
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#=============================================================================
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#%% load packages
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import sys, os
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import pandas as pd
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#import numpy as np
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#=============================================================================
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#%% specify input and curr dir
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homedir = os.path.expanduser('~')
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# set working dir
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os.getcwd()
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os.chdir(homedir + '/git/LSHTM_analysis/meta_data_analysis')
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os.getcwd()
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#=============================================================================
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#%% variable assignment: input and output
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drug = 'pyrazinamide'
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gene = 'pncA'
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gene_match = gene + '_p.'
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#==========
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# data dir
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#==========
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#indir = 'git/Data/pyrazinamide/input/original'
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datadir = homedir + '/' + 'git/Data'
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#=======
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# input
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#=======
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#indir = 'git/Data/pyrazinamide/input/original'
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indir = datadir + '/' + drug + '/' + 'output'
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in_filename = '3pl1_rd.tsv'
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infile = indir + '/' + in_filename
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print('Input filename:', in_filename
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, '\nInput path:', indir
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, '\n=============================================================')
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#=======
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# output
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#=======
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outdir = datadir + '/' + drug + '/' + 'output'
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out_filename = gene.lower() + '_rd.csv'
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outfile = outdir + '/' + out_filename
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print('Output filename:', out_filename
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, '\nOutput path:', outdir
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, '\n=============================================================')
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#%% end of variable assignment for input and output files
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#=======================================================================
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#%% Read input file
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rd_data = pd.read_csv(infile, sep = '\t')
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print('Reading input file:', infile
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, '\nNo. of rows:', len(rd_data)
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, '\nNo. of cols:', len(rd_data.columns))
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print('Column names:', rd_data.columns
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, '\n===============================================================')
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#========================
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# creating position col
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#========================
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# Extracting residue number from index and assigning
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# the values to a column [position]. Then convert the position col to numeric.
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rd_data['position'] = rd_data.index.str.extract('([0-9]+)').values
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# converting position to numeric
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rd_data['position'] = pd.to_numeric(rd_data['position'])
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rd_data['position'].dtype
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print('Extracted residue num from index and assigned as a column:'
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, '\ncolumn name: position'
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, '\ntotal no. of cols now:', len(rd_data.columns)
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, '\n=============================================================')
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#========================
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# Renaming amino-acid
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# and all-atom cols
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#========================
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print('Renaming columns:'
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, '\ncolname==> # chain:residue: wt_3letter_caps'
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, '\nYES... the column name *actually* contains a # ..!'
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, '\ncolname==> all-atom: rd_values'
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, '\n=============================================================')
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rd_data.rename(columns = {'# chain:residue':'wt_3letter_caps', 'all-atom':'rd_values'}, inplace = True)
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print('Column names:', rd_data.columns)
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#========================
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# extracting df with the
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# desired columns
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#========================
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print('Extracting relevant columns for writing df as csv')
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rd_df = rd_data[['position','rd_values','wt_3letter_caps']]
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if len(rd_df) == len(rd_data):
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print('PASS: extracted df has expected no. of rows'
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,'\nExtracted df dim:'
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,'\nNo. of rows:', len(rd_df)
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,'\nNo. of cols:', len(rd_df.columns))
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else:
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print('FAIL: no. of rows mimatch'
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, '\nExpected no. of rows:', len(rd_data)
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, '\nGot no. of rows:', len(rd_df)
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, '\n=========================================================')
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#%% write file
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print('Writing file:'
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, '\nFilename:', out_filename
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, '\nPath:', outdir
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, '\n=============================================================')
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rd_df.to_csv(outfile, header = True, index = False)
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print('Finished writing:', out_filename
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, '\nNo. of rows:', len(rd_df)
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, '\nNo. of cols:', len(rd_df.columns)
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, '\n=============================================================')
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#%% end of script
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#=======================================================================
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