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Tagger.py
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Tagger.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Module metadata variables
__author__ = "Rafael Barrero Rodriguez"
__credits__ = ["Rafael Barrero Rodriguez", "Jose Rodriguez", "Jesus Vazquez"]
__license__ = "Creative Commons Attribution-NonCommercial-NoDerivs 4.0 Unported License https://creativecommons.org/licenses/by-nc-nd/4.0/"
__version__ = "0.0.1"
__maintainer__ = "Jose Rodriguez"
__email__ = "[email protected];[email protected]"
__status__ = "Development"
# Import modules
import os
import sys
import argparse
import configparser
import logging
from pathlib import Path
import numpy as np
import pandas as pd
import re
from multiprocessing import Pool, cpu_count
import pdb
###################
# Local functions #
###################
def readInfile(infile, row):
'''
Input:
- infile: Path where the file is located
- row: Index (0-based) of column headers, where the table starts
Output:
- df: Pandas dataframe with the table
'''
log_str = f'Reading input file: {str(Path(infile))}'
logging.info(log_str)
try:
df = pd.read_excel(infile, header=row)
except:
log_str = f'Error when reading {str(Path(infile))}'
logging.info(log_str)
# Log error class and message
exctype, value = sys.exc_info()[:2]
log_str = f'{exctype}: {value}'
logging.info(log_str)
sys.exit()
log_str = f'{str(Path(infile))} was read'
logging.info(log_str)
return df
def readFoodTable(path):
'''
Input:
- path: String containing the path with the food containing table
Output:
- food_list: Numpy array of strings containing all food compounds in the table
'''
logging.info(f"Reading Food Table: {path}")
df = pd.read_csv(path, header=0, sep="\t", dtype=str)
food_list = np.array(df.iloc[:, 0].drop_duplicates(keep='first', inplace=False))
return food_list
def getNameColumnIndex(column_names):
'''
Input:
- column_names: Pandas series containing the names of the columns in the infile table
Output:
- An integer indicating the position of the Name column
'''
return int(np.where(column_names == "Name")[0][0])
def foodTaggerBatch(df, food_list):
'''
Input:
- df: Pandas dataframe containing batch of the total content
- food_list: String Numpy Array with all food compounds extracted from the database
Output:
- df: Pandas dataframe with the "Food" tag added in a new column
'''
# Get numpy array with compound names in the dataframe
compound_names = np.array(df.loc[:, 'Name'])
# Tag corresponding compounds using food list
food_tag_from_db = ["Food" if compound in food_list else "" for compound in compound_names]
# Tag compounds that fits regular expression
food_tag_from_regex = ["Food" if re.search('^[Ee]nt-', compound) else "" for compound in compound_names]
# Combine Food tags
food_tag = ["Food" if "Food" in tag else "" for tag in zip(food_tag_from_db, food_tag_from_regex)]
# Add Food tag column to the dataframe
name_column_index = getNameColumnIndex(df.columns)
df.insert(name_column_index+1, "Food", food_tag, True)
return df
def foodTagger(df, n_cores):
'''
Input:
- df: Pandas dataframe containing the whole content
- n_cores: Integer indicating the number of cores used in the multiprocessing
Output:
- df_out: Pandas dataframe with the whole content and the added tag
'''
logging.info("Start food tagging")
# Split dataframe so that each batch is processed by one core
df_split = np.array_split(df, n_cores)
# Get numpy array with food compounds in database
food_list = readFoodTable(args.foodList)
# Create list of tuples. Each tuple contains arguments received by foodTaggerBatch in each subprocess
subprocess_args = [(df_i, food_list) for df_i in df_split]
with Pool(n_cores) as p:
logging.info("Tagging food compounds")
result = p.starmap(foodTaggerBatch, subprocess_args)
df_out = pd.concat(result)
logging.info("Finished food tagging")
return df_out
def readDrugTable(path):
'''
Input:
- path: String containing the path to the Drug database
Output:
- drug_list: String Numpy Array containing drugs name extracted from the database
'''
logging.info(f"Reading Drug Table: {path}")
# Import drug table as a pandas dataframe
df = pd.read_csv(path, header=0, sep="\t", dtype=str)
# Extract drug list name from df as a numpy array
drug_list = np.array(df.iloc[:, 0])
return drug_list
def drugTaggerBatch(df, drug_list):
'''
Input:
- df: Pandas dataframe containing a batch of the whole infile dataframe
- drug_list: String Numpy Array containing all drug compounds in the database
Output:
- df: Pandas dataframe with the drug tag added in a new column
'''
# Get numpy array with compound in input table
compound_names = np.array(df.loc[:, 'Name'])
# Tag corresponding compounds
drug_tag = ["Drug" if compound in drug_list else "" for compound in compound_names]
# Add Drug tag column to the dataframe
name_column_index = getNameColumnIndex(df.columns)
df.insert(name_column_index+1, "Drug", drug_tag, True)
return df
def drugTagger(df, n_cores):
'''
Input:
- df: Pandas dataframe containing the whole infile content
- n_cores: Integer indicating the number of cores used in the multiprocessing
Output: df_out: Pandas dataframe containing the whole infile content with the Drug Tag
added in a new column
'''
logging.info("Start drug tagging")
# Split dataframe so that each batch is processed by one core
df_split = np.array_split(df, n_cores)
# Get numpy array with drug list
drug_list = readDrugTable(args.drugList)
# Create list with parameters received by each drugTaggerBatch function in each subprocess
subprocess_args = [(df_i, drug_list) for df_i in df_split]
with Pool(n_cores) as p:
logging.info("Tagging drug compounds")
result = p.starmap(drugTaggerBatch, subprocess_args)
df_out = pd.concat(result)
logging.info("Finished drug tagging")
return df_out
def halogenatedTaggerBatch(df, halogen_regex):
'''
Input:
- df: Pandas dataframe corresponding to a batch of the infile table
- halogen_regex: String corresponding to the regular expression used to identify halogenated compounds
Output:
- df: Pandas dataframe with Halogenated tag added in a new column
'''
# Get numpy array with compound in input table
compound_names = np.array(df.loc[:, 'Name'])
# Tag corresponding compounds
halogenated_tag = ["x" if re.search(halogen_regex, compound) else "" for compound in compound_names]
# Add Drug tag column to the dataframe
name_column_index = getNameColumnIndex(df.columns)
df.insert(name_column_index+1, "Halogenated", halogenated_tag, True)
return df
def halogenatedTagger(df, n_cores):
'''
Input:
- df: Pandas dataframe containing the whole content present in infile table
- n_cores: Integer indicating the number of cores used in the multiprocessing
Output:
- df_out: Pandas dataframe with halogenated tag added in a new column
'''
logging.info("Start halogenated compounds tagging")
# Split dataframe so that each batch is processed by one core
df_split = np.array_split(df, n_cores)
# Get string with the regular expression used to identify halogenated compounds
halogen_regex = config_param.get('Parameters', 'HalogenatedRegex')
# Create list with parameters received by halogenatedTaggerBatch in each subprocess
subprocess_args = [(df_i, halogen_regex) for df_i in df_split]
with Pool(n_cores) as p:
logging.info("Tagging halogenated compounds")
result = p.starmap(halogenatedTaggerBatch, subprocess_args)
df_out = pd.concat(result)
logging.info("Finished halogenated compounds tagging")
return df_out
def getOutputFilename():
'''
Output:
- filename: String containing the name of the output file
'''
filename = config_param.get('Parameters', 'OutputName')
if not filename:
filename = 'tagged_' + os.path.basename(args.infile)
elif not os.path.splitext(filename)[1]:
filename += '.xls'
return filename
def getOutputColumns(df_columns):
'''
Input:
- df_columns: Pandas series containing the name of the columns in the output table
Output:
- selected_columns: List of strings with the name of the columns selected by the user
'''
selected_columns = config_param.get('Parameters', 'OutputColumns')
if selected_columns:
selected_columns = [column.strip() for column in selected_columns.split(',') if column.strip() in df_columns]
else:
selected_columns = list(df_columns)
return selected_columns
def writeDataFrame(df, path):
'''
Description: Function used to export pandas dataframe with the tags added
Input:
- df: Pandas dataframe that will be exported
- path: String containing the path to infile. The new file will be saved in the
same folder.
'''
# Build output file path
# output_path = os.path.join(os.path.dirname(path), "results")
output_path = os.path.join(os.path.dirname(path))
if not os.path.exists(output_path):
os.mkdir(output_path)
# Get output file name
filename = getOutputFilename()
output_file = os.path.join(output_path, filename)
# Get output columns
output_columns = getOutputColumns(df.columns)
# Handle errors in exception case
try:
df.to_excel(output_file, index=False, columns=output_columns)
except:
log_str = f'Error when writing {str(Path(output_file))}'
logging.info(log_str)
# Log error class and message
exctype, value = sys.exc_info()[:2]
log_str = f'{exctype}: {value}'
logging.info(log_str)
sys.exit()
log_str = f'{str(Path(output_file))} was written'
logging.info(log_str)
#################
# Main function #
#################
def main(args):
'''
Main function
'''
# Number of cores used
n_cores = cpu_count() - 1
logging.info(f"Using {n_cores} cores")
# Read infile
df = readInfile(args.infile, 0)
# Check user selection
if re.search('(?i)true', config_param['TagSelection']['Food']):
df = foodTagger(df, n_cores)
if re.search('(?i)true', config_param['TagSelection']['Drug']):
df = drugTagger(df, n_cores)
if re.search('(?i)true', config_param['TagSelection']['Halogenated']):
df = halogenatedTagger(df, n_cores)
# Export dataframe as excel file
writeDataFrame(df, args.infile)
if __name__=="__main__":
# parse arguments
parser = argparse.ArgumentParser(
description='Tagger',
epilog='''
Example:
python Tagger.py
'''
)
# Set default values
default_config_path = os.path.join(os.path.dirname(__file__), "config/configTagger/configTagger.ini")
default_food_list_path = os.path.join(os.path.dirname(__file__), "Data/food_database.tsv")
default_drug_list_path = os.path.join(os.path.dirname(__file__), "Data/drug_database.tsv")
# default_microbial_compound_list_path = os.path.join(os.path.dirname(__file__), "Data/TaggerData/microbial_compound_database.tsv")
# Parse arguments corresponding to path files
parser.add_argument('-i', '--infile', help="Path to input file", type=str, required=True)
parser.add_argument('-c', '--config', help="Path to configTagger.ini file", type=str, default=default_config_path)
parser.add_argument('-fL', '--foodList', help="Path to food compounds list", type=str, default=default_food_list_path)
parser.add_argument('-dL', '--drugList', help="Path to drug compounds list", type=str, default=default_drug_list_path)
# parser.add_argument('-mL', '--microbialList', help="Path to microbial compounds list", type=str, default=default_microbial_compound_list_path)
parser.add_argument('-o', '--output', help="Name of output table", type=str)
parser.add_argument('-oc', '--outCol', help='Name of columns present in output table. By default, all columns will be displayed', type=str)
# Parser arguments indicating which tags are going to be added
parser.add_argument('-f', '--food', help="Add food tag to compounds", action='store_true', default=False)
parser.add_argument('-d', '--drug', help="Add drug tag to compounds", action='store_true', default=False)
parser.add_argument('-m', '--microbial', help="Add 'microbial compound' tag to compounds", action='store_true', default=False)
parser.add_argument('-ha', '--halogenated', help="Add 'halogenated compound' tag to compounds", action='store_true', default=False)
parser.add_argument('-v', dest='verbose', action='store_true', help='Increase output verbosity')
args = parser.parse_args()
# parse config with user selection
config_param = configparser.ConfigParser(inline_comment_prefixes='#')
config_param.read(Path(args.config))
# Parameters introduced in the execution replace those in the .ini file
if args.food:
config_param.set('TagSelection', 'Food', str(args.food))
if args.drug:
config_param.set('TagSelection', 'Drug', str(args.drug))
if args.microbial:
config_param.set('TagSelection', 'MicrobialCompound', str(args.microbial))
if args.halogenated:
config_param.set('TagSelection', 'Halogenated', str(args.halogenated))
if args.output:
config_param.set('Parameters', 'OutputName', args.output)
if args.outCol:
config_param.set('Parameters', 'OutputColumns', args.outCol)
# logging debug level. By default, info level
if args.infile:
log_file = outfile = os.path.splitext(args.infile)[0] + '_log.txt'
log_file_debug = outfile = os.path.splitext(args.infile)[0] + '_log_debug.txt'
else:
log_file = outfile = 'log.txt'
log_file_debug = outfile = 'log_debug.txt'
if args.verbose:
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s - %(levelname)s - %(message)s',
datefmt='%m/%d/%Y %I:%M:%S %p',
handlers=[logging.FileHandler(log_file_debug),
logging.StreamHandler()])
else:
logging.basicConfig(level=logging.INFO,
format='%(asctime)s - %(levelname)s - %(message)s',
datefmt='%m/%d/%Y %I:%M:%S %p',
handlers=[logging.FileHandler(log_file),
logging.StreamHandler()])
# start main function
logging.info('start script: '+"{0}".format(" ".join([x for x in sys.argv])))
main(args)
logging.info('end script')