105 lines
3.7 KiB
Python
Executable file
105 lines
3.7 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 Wed Aug 19 14:33:51 2020
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@author: tanu
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"""
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#%% load packages
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import os,sys
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import subprocess
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import argparse
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import requests
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import re
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import time
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from bs4 import BeautifulSoup
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import pandas as pd
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from pandas.api.types import is_string_dtype
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from pandas.api.types import is_numeric_dtype
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#%%============================================================================
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#streptomycin/gid_complex.pdb
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host_dynamut = 'http://biosig.unimelb.edu.au/dynamut'
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pred_dynamut_batch = '/results_prediction/161287964015'
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result_id = re.search( r"([0-9]+)$", pred_dynamut).group(0)
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batch_result_url = host_dynamut + pred_dynamut_batch
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mut = 'S2C'
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single_url = host_dynamut + '/single_results/' + str(result_id)
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single_result_url = host_dynamut + '/single_results/' + str(result_id) + '/' + mut
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print(single_result_url)
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#%%============================================================================
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param_dict = {}
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result_response = requests.get(single_result_url)
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if result_response.status_code == 200:
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print('Fetching results')
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# extract results using the html parser
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soup = BeautifulSoup(result_response.text, features = 'html.parser')
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#web_result_raw = soup.find(id = 'predictions').get_text()
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ddg_dynamut = soup.find(id = 'ddg_dynamut').get_text()
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ddg_encom = soup.find(id = 'ddg_encom').get_text()
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ddg_mcsm = soup.find(id = 'ddg_mcsm').get_text()
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ddg_sdm = soup.find(id = 'ddg_sdm').get_text()
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ddg_duet = soup.find(id = 'ddg_duet').get_text()
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dds_encom = soup.find(id = 'dds_encom').get_text()
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param_dict = {"mutationinformation" : mut
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, "ddg_dynamut" : ddg_dynamut
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, "ddg_encom" : ddg_encom
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, "ddg_mcsm" : ddg_mcsm
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, "ddg_sdm" : ddg_sdm
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, "ddg_duet" : ddg_duet
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, "dds_encom" : dds_encom
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}
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results_df = pd.DataFrame.from_dict(param_dict, orient = "index").T
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print(results_df)
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#%% for loop
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single_url = host_dynamut + '/single_results/' + str(result_id)
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muts = ["S2C", "S2F"]
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# initilialise empty df
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dynamut_results_df = pd.DataFrame()
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for i, mut in enumerate(muts):
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#param_dict = {}
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print('Running mutation', i+1, ':', mut)
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snp = mut
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single_result_url = single_url + '/' + snp
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print('Getting results from:', single_result_url)
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result_response = requests.get(single_result_url)
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if result_response.status_code == 200:
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print('Fetching results')
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# extract results using the html parser
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soup = BeautifulSoup(result_response.text, features = 'html.parser')
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#web_result_raw = soup.find(id = 'predictions').get_text()
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ddg_dynamut = soup.find(id = 'ddg_dynamut').get_text()
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ddg_encom = soup.find(id = 'ddg_encom').get_text()
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ddg_mcsm = soup.find(id = 'ddg_mcsm').get_text()
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ddg_sdm = soup.find(id = 'ddg_sdm').get_text()
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ddg_duet = soup.find(id = 'ddg_duet').get_text()
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dds_encom = soup.find(id = 'dds_encom').get_text()
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param_dict = {"mutationinformation" : snp
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, "ddg_dynamut" : ddg_dynamut
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, "ddg_encom" : ddg_encom
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, "ddg_mcsm" : ddg_mcsm
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, "ddg_sdm" : ddg_sdm
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, "ddg_duet" : ddg_duet
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, "dds_encom" : dds_encom
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}
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results_df = pd.DataFrame.from_dict(param_dict, orient = "index").T
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print(results_df)
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dynamut_results_df = dynamut_results_df.append(results_df)
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print(dynamut_results_df)
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