#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Aug 19 14:33:51 2020

@author: tanu
"""
#%% load packages
import os,sys
import subprocess
import argparse
import requests
import re
import time
from bs4 import BeautifulSoup
import pandas as pd
from pandas.api.types import is_string_dtype
from pandas.api.types import is_numeric_dtype
#%%============================================================================
host = 'http://biosig.unimelb.edu.au'
pred_dynamut_batch = '/dynamut/results_prediction/161287964015'
batch_result_url = host + pred_dynamut_batch
batch_result_url 

# build a single url with a given mutation
result_id = re.search( r"([0-9]+)$", pred_dynamut_batch).group(0)
mut = 'S2C'
single_url = host + '/single_results/' + str(result_id)
single_result_url = host + '/single_results/' + str(result_id) + '/' + mut
print(single_result_url)

#%%============================================================================
param_dict = {}

result_response = requests.get(single_result_url)
if result_response.status_code == 200:
        print('Fetching results')
        # extract results using the html parser          
        soup = BeautifulSoup(result_response.text, features = 'html.parser')
        #web_result_raw = soup.find(id = 'predictions').get_text()
        ddg_dynamut = soup.find(id = 'ddg_dynamut').get_text()
        ddg_encom = soup.find(id = 'ddg_encom').get_text()
        ddg_mcsm = soup.find(id = 'ddg_mcsm').get_text()
        ddg_sdm = soup.find(id = 'ddg_sdm').get_text()
        ddg_duet = soup.find(id = 'ddg_duet').get_text()
        dds_encom = soup.find(id = 'dds_encom').get_text()
        
        param_dict = {"mutationinformation" : mut
            , "ddg_dynamut" : ddg_dynamut
            , "ddg_encom"   : ddg_encom
            , "ddg_mcsm"    : ddg_mcsm
            , "ddg_sdm"     : ddg_sdm
            , "ddg_duet"    : ddg_duet
            , "dds_encom"   : dds_encom
            
            }
        results_df = pd.DataFrame.from_dict(param_dict, orient = "index").T  

print(results_df)

#%% looping over mutation
single_url = host + '/single_results/' + str(result_id)    
muts = ["S2C", "S2F"]

# initilialise empty df
dynamut_results_df = pd.DataFrame()

for i, mut in enumerate(muts):
    #param_dict = {}
    print('Running mutation', i+1, ':', mut)
    snp = mut
    single_result_url = single_url + '/' + snp
    print('Getting results from:',  single_result_url)
    
    result_response = requests.get(single_result_url)
    if result_response.status_code == 200:
            print('Fetching results')
            # extract results using the html parser          
            soup = BeautifulSoup(result_response.text, features = 'html.parser')
            #web_result_raw = soup.find(id = 'predictions').get_text()
            ddg_dynamut = soup.find(id = 'ddg_dynamut').get_text()
            ddg_encom = soup.find(id = 'ddg_encom').get_text()
            ddg_mcsm = soup.find(id = 'ddg_mcsm').get_text()
            ddg_sdm = soup.find(id = 'ddg_sdm').get_text()
            ddg_duet = soup.find(id = 'ddg_duet').get_text()
            dds_encom = soup.find(id = 'dds_encom').get_text()
            
            param_dict = {"mutationinformation" : snp
                , "ddg_dynamut" : ddg_dynamut
                , "ddg_encom"   : ddg_encom
                , "ddg_mcsm"    : ddg_mcsm
                , "ddg_sdm"     : ddg_sdm
                , "ddg_duet"    : ddg_duet
                , "dds_encom"   : dds_encom 
                }
            results_df = pd.DataFrame.from_dict(param_dict, orient = "index").T
            print(results_df)
            dynamut_results_df = dynamut_results_df.append(results_df)
            print(dynamut_results_df)
            
#%% Derive the single url from the batch result itself
# get request from a batch url
# corresponding to href
batch_result_url 
batch_response = requests.get(batch_result_url)
batch_soup = BeautifulSoup(batch_response.text, features = 'html.parser')
print(batch_soup)
#table = batch_soup.find('table', attrs = {'class':'table table-striped table-bordered table-responsive'})
#btn = batch_soup.find_all(href = True, attrs = {'class':'btn btn-default btn-sm'})
#print(btn)


# initilialise empty df
dynamut_results_df = pd.DataFrame()
for a in batch_soup.find_all('a', href=True, attrs = {'class':'btn btn-default btn-sm'}):
    print ("Found the URL:", a['href']) 
    single_result_url = host + a['href']
    snp = re.search(r'([A-Z]+[0-9]+[A-Z]+$)', single_result_url).group(0)
    print(snp)
    print('\nGetting results from:',  single_result_url)
    
    result_response = requests.get(single_result_url)
    if result_response.status_code == 200:
            print('\nFetching results for SNP:', snp)
            # extract results using the html parser          
            soup = BeautifulSoup(result_response.text, features = 'html.parser')
            #web_result_raw = soup.find(id = 'predictions').get_text()
            ddg_dynamut = soup.find(id = 'ddg_dynamut').get_text()
            ddg_encom = soup.find(id = 'ddg_encom').get_text()
            ddg_mcsm = soup.find(id = 'ddg_mcsm').get_text()
            ddg_sdm = soup.find(id = 'ddg_sdm').get_text()
            ddg_duet = soup.find(id = 'ddg_duet').get_text()
            dds_encom = soup.find(id = 'dds_encom').get_text()
            
            param_dict = {"mutationinformation" : snp
                , "ddg_dynamut" : ddg_dynamut
                , "ddg_encom"   : ddg_encom
                , "ddg_mcsm"    : ddg_mcsm
                , "ddg_sdm"     : ddg_sdm
                , "ddg_duet"    : ddg_duet
                , "dds_encom"   : dds_encom 
                }
            results_df = pd.DataFrame.from_dict(param_dict, orient = "index").T
            print(results_df)
            dynamut_results_df = dynamut_results_df.append(results_df)
            print(dynamut_results_df)
            print('\nWriting dynamut results df')
            dynamut_results_df.to_csv('test_dynamut.csv', index = False)
            print('\nResults File:'
                  , '\nNo. of rows:', dynamut_results_df.shape[0]
                  , '\nNo. of cols:', dynamut_results_df.shape[1])