report.py

# -*- coding: utf-8 -*-
"""
This is script file for generating output after running alignment
fastq files (raw read) to genome graph
"""

#from Bio import SeqIO
import pandas as pd
import os
import csv
import numpy as np
import matplotlib.pyplot as mpl


path=os.getcwd()

# read csv file that contain number of protein per species in support_files folder

#file = path+'/support_files/access_vs_prot.csv'
#with open(file) as fh:
#    rd = csv.DictReader(fh, delimiter=',') 

# create data frame and the split gene column to accession_id and protein name
report = pd.read_csv("summarise.csv",sep="\t",names=['gene',"read_count","length","coverage"])

# dropping null value columns to avoid errors
report.dropna(inplace = True)

# new data frame with split value columns
new = report["gene"].str.split("_", n = 1, expand = True)

# making seperate first name column from new data frame
report["accession_id"] = new[0]

# making seperate last name column from new data frame
report["prot"] = new[1]

# dictionary to get number of mature peptides extracted for each species
acc_no_vs_prot = pd.read_csv("support_files/access_vs_prot.csv",sep=",",names=[''])

acc_no_vs_prot.head()

d = acc_no_vs_prot.to_dict()['']

total_prot = []

for i in report['accession_id']:
  total_prot.append(d[i])

report['total_prot'] = total_prot

table1 = pd.crosstab(report['accession_id'],report['accession_id'],margins=False)





def report(align_result_file, table_name):
  table = pd.read_csv(align_result_file,sep="\t",names=['gene',"read_count","length","coverage"])
  # dropping null value columns to avoid errors
  table.dropna(inplace = True)
  # new data frame with split value columns
  new = table["gene"].str.split("_", n = 1, expand = True)
  # making seperate first name column from new data frame
  table1["accession_id"] = new[0]
  # making seperate last name column from new data frame
  table["prot"] = new[1]
  total_prot = []
  # check dictionary
  for i in table['accession_id']:
    total_prot.append(d[i])
  # assign total protein by accession_id
  table['total_prot'] = total_prot
  # output
  table_name = pd.crosstab(table['accession_id'],table['accession_id'],margins=False)