separated defs and calls and added a separate script to test examples
This commit is contained in:
parent
6c458f8883
commit
deb0aa8e58
13 changed files with 281 additions and 517 deletions
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#!/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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#1) define muts batch
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#take mcsm file
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#split into 'n' batches
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#write output file with suffix of batch number
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#********** done this par ****************
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#2) get results for a batch url
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# read file
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# store batch url
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#extract number
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#build single url
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#build single results urls
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#get results and store them in df
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#update df
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#dim of df = no. of muts in batch
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#3) format results
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# store unit measurements separtely
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# omit unit measurements from cols
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# create extra columns '_outcome' suffix by splitting numerical output
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# create separate col for mcsm as it doesn't have output text
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#%%============================================================================
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7
dynamut/dynamut_results/dynamut_output_multiple.csv
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7
dynamut/dynamut_results/dynamut_output_multiple.csv
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@ -0,0 +1,7 @@
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mutationinformation,ddg_dynamut,ddg_encom,ddg_mcsm,ddg_sdm,ddg_duet,dds_encom
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G13V,0.006 kcal/mol (Stabilizing),-0.053 kcal/mol (Destabilizing),-0.261 kcal/mol (Destabilizing),-0.120 kcal/mol (Destabilizing),0.120 kcal/mol (Stabilizing),0.066 kcal.mol-1.K-1 (Increase of molecule flexibility)
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A19T,-0.077 kcal/mol (Destabilizing),0.224 kcal/mol (Destabilizing),-0.631 kcal/mol (Destabilizing),-2.620 kcal/mol (Destabilizing),-0.758 kcal/mol (Destabilizing),-0.280 kcal.mol-1.K-1 (Decrease of molecule flexibility)
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I4N,-0.239 kcal/mol (Destabilizing),-0.720 kcal/mol (Destabilizing),-0.728 kcal/mol (Destabilizing),-0.550 kcal/mol (Destabilizing),-0.461 kcal/mol (Destabilizing),0.900 kcal.mol-1.K-1 (Increase of molecule flexibility)
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P3S,0.727 kcal/mol (Stabilizing),0.334 kcal/mol (Destabilizing),-0.672 kcal/mol (Destabilizing),0.010 kcal/mol (Stabilizing),-0.252 kcal/mol (Destabilizing),-0.418 kcal.mol-1.K-1 (Decrease of molecule flexibility)
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F12S,-0.270 kcal/mol (Destabilizing),0.048 kcal/mol (Destabilizing),-1.028 kcal/mol (Destabilizing),-0.930 kcal/mol (Destabilizing),-0.993 kcal/mol (Destabilizing),-0.060 kcal.mol-1.K-1 (Decrease of molecule flexibility)
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A19V,2.389 kcal/mol (Stabilizing),0.450 kcal/mol (Destabilizing),0.659 kcal/mol (Stabilizing),-0.170 kcal/mol (Destabilizing),1.040 kcal/mol (Stabilizing),-0.562 kcal.mol-1.K-1 (Decrease of molecule flexibility)
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3
dynamut/dynamut_results/dynamut_output_single.csv
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3
dynamut/dynamut_results/dynamut_output_single.csv
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mutationinformation,ddg_dynamut,ddg_encom,ddg_mcsm,ddg_sdm,ddg_duet,dds_encom
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G13V,0.006 kcal/mol (Stabilizing),-0.053 kcal/mol (Destabilizing),-0.261 kcal/mol (Destabilizing),-0.120 kcal/mol (Destabilizing),0.120 kcal/mol (Stabilizing),0.066 kcal.mol-1.K-1 (Increase of molecule flexibility)
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A19T,-0.077 kcal/mol (Destabilizing),0.224 kcal/mol (Destabilizing),-0.631 kcal/mol (Destabilizing),-2.620 kcal/mol (Destabilizing),-0.758 kcal/mol (Destabilizing),-0.280 kcal.mol-1.K-1 (Decrease of molecule flexibility)
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2
dynamut/example_input/snp_test1.csv
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2
dynamut/example_input/snp_test1.csv
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F12S
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A19V
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2
dynamut/example_input/snp_test2.csv
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2
dynamut/example_input/snp_test2.csv
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G13V
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A19T
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71
dynamut/examples.py
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71
dynamut/examples.py
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Fri Feb 12 12:15:26 2021
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@author: tanu
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"""
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import os
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homedir = os.path.expanduser('~')
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os.chdir (homedir + '/git/LSHTM_analysis/dynamut')
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from get_results import *
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from submit import *
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#%%#####################################################################
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#EXAMPLE RUN for different stages
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#=====================
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# STAGE: submit.py
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#=====================
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my_host = 'http://biosig.unimelb.edu.au'
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my_prediction_url = f"{host}/dynamut/prediction_list"
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print(prediction_url)
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my_outdir = homedir + '/git/LSHTM_analysis/dynamut'
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my_chain = 'A'
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my_email = 'tanushree.tunstall@lshtm.ac.uk'
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my_pdb_file = homedir + '/git/Data/streptomycin/input/gid_complex.pdb'
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my_mutation_list = homedir + '/git/LSHTM_analysis/dynamut/example_input/snp_test1.csv'
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my_suffix = 'gid_test1'
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#----------------------------------------------
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# example 1: 2 snps in a file
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#----------------------------------------------
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submit_dynamut(host_url = my_host
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, pdb_file = my_pdb_file
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, mutation_list = my_mutation_list
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, chain = my_chain
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, email_address = my_email
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, prediction_url = my_prediction_url
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, output_dir = my_outdir
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, outfile_suffix = my_suffix)
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#%%###################################################################
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#=====================
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# STAGE:get_results.py
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#=====================
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my_host = 'http://biosig.unimelb.edu.au'
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my_outdir = homedir + '/git/LSHTM_analysis/dynamut'
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#----------------------------------------------
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# example 1: multiple urls in a single file
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#----------------------------------------------
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my_url_file_multiple = homedir + '/git/LSHTM_analysis/dynamut/dynamut_temp/dynamut_result_url_batch_multiple.txt'
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print(my_url_file_multiple)
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my_suffix = 'multiple'
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get_results(url_file = my_url_file_multiple
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, host_url = my_host
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, output_dir = my_outdir
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, outfile_suffix = my_suffix)
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#----------------------------------------------
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# example 2: single url in a file
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#----------------------------------------------
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my_url_file_single = homedir + '/git/LSHTM_analysis/dynamut/dynamut_temp/dynamut_result_url_batch_single.txt'
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print(my_url_file_multiple)
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my_suffix = 'single'
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get_results(my_url_file_single
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, host_url = my_host
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, output_dir = my_outdir
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, outfile_suffix = my_suffix)
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#%%###################################################################
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194
dynamut/get_results.py
Executable file → Normal file
194
dynamut/get_results.py
Executable file → Normal file
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@ -16,140 +16,68 @@ 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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host = 'http://biosig.unimelb.edu.au'
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pred_dynamut_batch = '/dynamut/results_prediction/161287964015'
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batch_result_url = host + pred_dynamut_batch
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batch_result_url
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# build a single url with a given mutation
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result_id = re.search( r"([0-9]+)$", pred_dynamut_batch).group(0)
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mut = 'S2C'
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single_url = host + '/single_results/' + str(result_id)
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single_result_url = host + '/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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#%% looping over mutation
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single_url = host + '/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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#%% Derive the single url from the batch result itself
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# get request from a batch url
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# corresponding to href
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batch_result_url
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batch_response = requests.get(batch_result_url)
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batch_soup = BeautifulSoup(batch_response.text, features = 'html.parser')
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print(batch_soup)
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#table = batch_soup.find('table', attrs = {'class':'table table-striped table-bordered table-responsive'})
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#btn = batch_soup.find_all(href = True, attrs = {'class':'btn btn-default btn-sm'})
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#print(btn)
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# initilialise empty df
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dynamut_results_df = pd.DataFrame()
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for a in batch_soup.find_all('a', href=True, attrs = {'class':'btn btn-default btn-sm'}):
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print ("Found the URL:", a['href'])
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single_result_url = host + a['href']
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snp = re.search(r'([A-Z]+[0-9]+[A-Z]+$)', single_result_url).group(0)
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print(snp)
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print('\nGetting 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('\nFetching results for SNP:', snp)
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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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print('\nWriting dynamut results df')
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dynamut_results_df.to_csv('test_dynamut.csv', index = False)
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print('\nResults File:'
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, '\nNo. of rows:', dynamut_results_df.shape[0]
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, '\nNo. of cols:', dynamut_results_df.shape[1])
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#%%#####################################################################
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def get_results(url_file, host_url, output_dir, outfile_suffix):
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# initilialise empty df
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dynamut_results_out_df = pd.DataFrame()
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with open(url_file, 'r') as f:
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for count, line in enumerate(f):
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line = line.strip()
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print('URL no.', count+1, '\n', line)
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#batch_response = requests.get(line, headers=headers)
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batch_response = requests.get(line)
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batch_soup = BeautifulSoup(batch_response.text, features = 'html.parser')
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# initilialise empty df
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#dynamut_results_df = pd.DataFrame()
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for a in batch_soup.find_all('a', href=True, attrs = {'class':'btn btn-default btn-sm'}):
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print ("Found the URL:", a['href'])
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single_result_url = host_url + a['href']
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snp = re.search(r'([A-Z]+[0-9]+[A-Z]+$)', single_result_url).group(0)
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print(snp)
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print('\nGetting 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('\nFetching results for SNP:', snp)
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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('Result DF:', results_df, 'for URL:', line)
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#dynamut_results_df = dynamut_results_df.append(results_df)#!1 too many!:-)
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dynamut_results_out_df = dynamut_results_out_df.append(results_df)
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#print(dynamut_results_out_df)
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#============================
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# Writing results file: csv
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#============================
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dynamut_results_dir = output_dir + '/dynamut_results'
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if not os.path.exists(dynamut_results_dir):
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print('\nCreating dir: dynamut_results within:', output_dir )
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os.makedirs(dynamut_results_dir)
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print('\nWriting dynamut results df')
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print('\nResults File:'
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, '\nNo. of rows:', dynamut_results_out_df.shape[0]
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, '\nNo. of cols:', dynamut_results_out_df.shape[1])
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print(dynamut_results_out_df)
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#dynamut_results_out_df.to_csv('/tmp/test_dynamut.csv', index = False)
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# build out filename
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out_filename = dynamut_results_dir + '/dynamut_output_' + outfile_suffix + '.csv'
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dynamut_results_out_df.to_csv(out_filename, index = False)
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#%%#####################################################################
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#!/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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#%%
|
||||
def get_results(url_file, host_url, output_dir, outfile_suffix):
|
||||
# initilialise empty df
|
||||
dynamut_results_out_df = pd.DataFrame()
|
||||
with open(url_file, 'r') as f:
|
||||
for count, line in enumerate(f):
|
||||
line = line.strip()
|
||||
print('URL no.', count+1, '\n', line)
|
||||
#batch_response = requests.get(line, headers=headers)
|
||||
batch_response = requests.get(line)
|
||||
batch_soup = BeautifulSoup(batch_response.text, features = 'html.parser')
|
||||
|
||||
# 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_url + 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('Result DF:', results_df, 'for URL:', line)
|
||||
#dynamut_results_df = dynamut_results_df.append(results_df)#!1 too many!:-)
|
||||
dynamut_results_out_df = dynamut_results_out_df.append(results_df)
|
||||
#print(dynamut_results_out_df)
|
||||
#============================
|
||||
# Writing results file: csv
|
||||
#============================
|
||||
dynamut_results_dir = output_dir + '/dynamut_results'
|
||||
if not os.path.exists(dynamut_results_dir):
|
||||
print('\nCreating dir: dynamut_results within:', output_dir )
|
||||
os.makedirs(dynamut_results_dir)
|
||||
print('\nWriting dynamut results df')
|
||||
print('\nResults File:'
|
||||
, '\nNo. of rows:', dynamut_results_out_df.shape[0]
|
||||
, '\nNo. of cols:', dynamut_results_out_df.shape[1])
|
||||
print(dynamut_results_out_df)
|
||||
#dynamut_results_out_df.to_csv('/tmp/test_dynamut.csv', index = False)
|
||||
|
||||
# build out filename
|
||||
out_filename = dynamut_results_dir + '/dynamut_output_' + outfile_suffix + '.csv'
|
||||
dynamut_results_out_df.to_csv(out_filename, index = False)
|
||||
#%% EXAMPLE RUN
|
||||
# globals
|
||||
#homedir = os.path.expanduser('~')
|
||||
#my_host = 'http://biosig.unimelb.edu.au'
|
||||
#my_outdir = homedir + '/git/LSHTM_analysis/dynamut'
|
||||
|
||||
#=============================================
|
||||
# example 1: multiple urls in a single file
|
||||
#=============================================
|
||||
#my_url_file_multiple = homedir + '/git/LSHTM_analysis/dynamut/dynamut_temp/dynamut_result_url_batch_multiple.txt'
|
||||
#print(my_url_file_multiple)
|
||||
#get_results(url_file = my_url_file_multiple
|
||||
# , host_url = my_host
|
||||
# , output_dir = my_outdir
|
||||
# , outfile_suffix='multiple')
|
||||
|
||||
#=============================================
|
||||
# example 2: single url in a file
|
||||
#=============================================
|
||||
#my_url_file_single = homedir + '/git/LSHTM_analysis/dynamut/dynamut_temp/dynamut_result_url_batch_single.txt'
|
||||
#print(my_url_file_multiple)
|
||||
#get_results(my_url_file_single
|
||||
# , host_url = my_host
|
||||
# , output_dir = my_outdir
|
||||
# , outfile_suffix = 'single')
|
||||
#%%
|
||||
|
|
@ -1,36 +0,0 @@
|
|||
#!/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
|
||||
import numpy as np
|
||||
from pandas.api.types import is_string_dtype
|
||||
from pandas.api.types import is_numeric_dtype
|
||||
#%%============================================================================
|
||||
# read mutation file
|
||||
|
||||
all_muts = pd.read_csv("/home/tanu/git/Data/streptomycin/output/snp_batches/snps_batch_00", header=None)
|
||||
#https://gist.github.com/jrivero/1085501
|
||||
n = 20
|
||||
chunks = [all_muts[i:i+n] for i in range(0,all_muts.shape[0],n)]
|
||||
#%%
|
||||
|
||||
muts_list = all_muts[0].values.tolist()
|
||||
|
||||
host = 'http://biosig.unimelb.edu.au/dynamut'
|
||||
mut_prediction = '/prediction'
|
||||
|
||||
submit_url = host + mut_prediction
|
||||
38
dynamut/run_results.py
Normal file
38
dynamut/run_results.py
Normal file
|
|
@ -0,0 +1,38 @@
|
|||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Wed Aug 19 14:33:51 2020
|
||||
|
||||
@author: tanu
|
||||
"""
|
||||
#%% load packages
|
||||
import os
|
||||
homedir = os.path.expanduser('~')
|
||||
os.chdir (homedir + '/git/LSHTM_analysis/dynamut')
|
||||
from get_results import *
|
||||
########################################################################
|
||||
# variables
|
||||
my_host = 'http://biosig.unimelb.edu.au'
|
||||
# Needed if things try to block the 'requests' user agent
|
||||
#headers = {"User-Agent":"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4103.97 Safari/537.36"}
|
||||
|
||||
# TODO: add cmd line args
|
||||
#gene = 'gid'
|
||||
drug = 'streptomycin'
|
||||
datadir = homedir + '/git/Data'
|
||||
indir = datadir + '/' + drug + '/input'
|
||||
outdir = datadir + '/' + drug + '/output'
|
||||
|
||||
my_url_file = outdir + '/dynamut_temp/dynamut_result_url_gid_b1.txt'
|
||||
my_suffix = 'gid_b1'
|
||||
#==========================
|
||||
# CALL: get_results()
|
||||
# Data: gid+streptomycin
|
||||
#==========================
|
||||
print(my_url_file, 'suffix:', my_suffix)
|
||||
|
||||
get_results(url_file = my_url_file
|
||||
, host_url = my_host
|
||||
, output_dir = outdir
|
||||
, outfile_suffix = my_suffix)
|
||||
########################################################################
|
||||
58
dynamut/run_submit.py
Normal file
58
dynamut/run_submit.py
Normal file
|
|
@ -0,0 +1,58 @@
|
|||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Wed Aug 19 14:33:51 2020
|
||||
|
||||
@author: tanu
|
||||
"""
|
||||
#%% load packages
|
||||
import os
|
||||
homedir = os.path.expanduser('~')
|
||||
os.chdir (homedir + '/git/LSHTM_analysis/dynamut')
|
||||
from submit import *
|
||||
########################################################################
|
||||
# variables
|
||||
my_host = 'http://biosig.unimelb.edu.au'
|
||||
my_prediction_url = f"{my_host}/dynamut/prediction_list"
|
||||
print(my_prediction_url)
|
||||
|
||||
# TODO: add cmd line args
|
||||
#gene = 'gid'
|
||||
drug = 'streptomycin'
|
||||
datadir = homedir + '/git/Data'
|
||||
indir = datadir + '/' + drug + '/input'
|
||||
outdir = datadir + '/' + drug + '/output'
|
||||
|
||||
my_chain = 'A'
|
||||
my_email = 'tanushree.tunstall@lshtm.ac.uk'
|
||||
|
||||
my_pdb_file = homedir + '/git/Data/streptomycin/input/gid_complex.pdb'
|
||||
|
||||
# batch 1: 00.txt
|
||||
#my_mutation_list =outdir + '/snp_batches/50/snp_batch_00.txt'
|
||||
#my_suffix = 'gid_b1'
|
||||
#RAN: 11 Feb, ~14:00 pm * RETRIEVED
|
||||
|
||||
# batch 2: 01.txt
|
||||
#my_mutation_list = outdir + '/snp_batches/50/snp_batch_01.txt'
|
||||
#my_suffix = 'gid_b2'
|
||||
#RAN: 12 Feb, ~10:00 am, AWAITING
|
||||
|
||||
# batch 3: 02.txt
|
||||
my_mutation_list = outdir + '/snp_batches/50/snp_batch_02.txt'
|
||||
my_suffix = 'gid_b3'
|
||||
#RAN: 12 Feb, ~12:40 pm, AWAITING
|
||||
|
||||
#==========================
|
||||
# CALL: submit_dynamut()
|
||||
# Data: gid+streptomycin
|
||||
#==========================
|
||||
submit_dynamut(host_url = my_host
|
||||
, pdb_file = my_pdb_file
|
||||
, mutation_list = my_mutation_list
|
||||
, chain = my_chain
|
||||
, email_address = my_email
|
||||
, prediction_url = my_prediction_url
|
||||
, output_dir = outdir
|
||||
, outfile_suffix = my_suffix)
|
||||
#%%#####################################################################
|
||||
104
dynamut/submit.py
Executable file → Normal file
104
dynamut/submit.py
Executable file → Normal file
|
|
@ -16,68 +16,21 @@ from bs4 import BeautifulSoup
|
|||
import pandas as pd
|
||||
from pandas.api.types import is_string_dtype
|
||||
from pandas.api.types import is_numeric_dtype
|
||||
#%% homedir
|
||||
homedir = os.path.expanduser('~')
|
||||
print('My homedir is:', homedir)
|
||||
|
||||
#%%
|
||||
host = 'http://biosig.unimelb.edu.au'
|
||||
prediction_url = f"{host}/dynamut/prediction_list"
|
||||
print(prediction_url)
|
||||
|
||||
#%% example params
|
||||
gene_name = 'gid'
|
||||
drug = 'streptomycin'
|
||||
datadir = homedir + '/git/Data'
|
||||
indir = datadir + '/' + drug + '/input'
|
||||
#outdir = datadir + '/' + drug + '/output'
|
||||
outdir = homedir + '/git/LSHTM_analysis/dynamut' # for example
|
||||
|
||||
dynamut_temp_dir = outdir + '/dynamut_temp'
|
||||
|
||||
if not os.path.exists(dynamut_temp_dir):
|
||||
print('Creating dynamut_temp in outdir', outdir )
|
||||
os.makedirs(dynamut_temp_dir)
|
||||
|
||||
batch_no = 1
|
||||
out_url_file = dynamut_temp_dir + '/dynamut_result_url_batch_' + str(batch_no) + '.txt'
|
||||
|
||||
|
||||
#%% request calculation (no def)
|
||||
with open("/home/tanu/git/Data/streptomycin/input/gid_complex.pdb", "rb") as pdb_file, open ("/home/tanu/git/LSHTM_analysis/dynamut/snp_test2.csv", "rb") as mutation_list:
|
||||
files = {"wild": pdb_file
|
||||
, "mutation_list": mutation_list}
|
||||
body = {"chain": 'A'
|
||||
, "email": 'tanushree.tunstall@lshtm.ac.uk'}
|
||||
|
||||
response = requests.post(prediction_url, files = files, data = body)
|
||||
print(response.status_code)
|
||||
if response.history:
|
||||
print('PASS: valid mutation submitted. Fetching result url')
|
||||
url_match = re.search('/dynamut/results_prediction/.+(?=")', response.text)
|
||||
url = host + url_match.group()
|
||||
print(url)
|
||||
|
||||
#===============
|
||||
# writing file: result urls
|
||||
#===============
|
||||
out_url_file = dynamut_temp_dir + '/dynamut_result_url_batch_' + str(batch_no) + '.txt'
|
||||
print('Writing output url file:', out_url_file)
|
||||
myfile = open(out_url_file, 'a')
|
||||
myfile.write(url)
|
||||
myfile.close()
|
||||
|
||||
#%%
|
||||
def request_calculation(pdb_file, mutation_list
|
||||
#%%#####################################################################
|
||||
def submit_dynamut(host_url
|
||||
, pdb_file
|
||||
, mutation_list
|
||||
, chain
|
||||
, my_email
|
||||
, email_address
|
||||
, prediction_url
|
||||
, output_dir
|
||||
, gene_name
|
||||
, batch_no
|
||||
, out_url_file):
|
||||
, outfile_suffix
|
||||
):
|
||||
"""
|
||||
Makes a POST request for a ligand affinity prediction.
|
||||
Makes a POST request for dynamut predictions.
|
||||
|
||||
@param host_url: valid host url for submitting the job
|
||||
@type string
|
||||
|
||||
@param pdb_file: valid path to pdb structure
|
||||
@type string
|
||||
|
|
@ -91,31 +44,44 @@ def request_calculation(pdb_file, mutation_list
|
|||
@param prediction_url: dynamut url for prediction
|
||||
@type string
|
||||
|
||||
@return txt file containing batch no. of snps processed
|
||||
@param output_dir: output dir
|
||||
@type string
|
||||
|
||||
@param outfile_suffix: outfile_suffix
|
||||
@type string, default is batch no.
|
||||
|
||||
@param outfile_suffix: to append to outfile
|
||||
@type string
|
||||
|
||||
@return writes a .txt file containing url for the snps processed with user provided suffix in filename
|
||||
@type string
|
||||
"""
|
||||
|
||||
with open(pdb_file, "rb") as pdb_file, open (mutation_list) as mutation_list:
|
||||
with open(pdb_file, "rb") as pdb_file, open (mutation_list, "rb") as mutation_list:
|
||||
files = {"wild": pdb_file
|
||||
, "mutation_list": mutation_list}
|
||||
body = {"chain": 'A'
|
||||
, "email": 'tanushree.tunstall@lshtm.ac.uk'}
|
||||
body = {"chain": chain
|
||||
, "email": email_address}
|
||||
|
||||
response = requests.post(prediction_url, files = files, data = body)
|
||||
print(response.status_code)
|
||||
if response.history:
|
||||
print('PASS: valid mutation submitted. Fetching result url')
|
||||
print('\nPASS: valid submission. Fetching result url')
|
||||
url_match = re.search('/dynamut/results_prediction/.+(?=")', response.text)
|
||||
url = host + url_match.group()
|
||||
print(url)
|
||||
url = host_url + url_match.group()
|
||||
print('\nURL for snp batch no ', str(outfile_suffix), ':', url)
|
||||
|
||||
#===============
|
||||
# writing file: result urls
|
||||
#===============
|
||||
out_url_file = dynamut_temp_dir + '/dynamut_result_url_batch_' + str(batch_no) + '.txt'
|
||||
print('Writing output url file:', out_url_file)
|
||||
dynamut_temp_dir = output_dir + '/dynamut_temp' # creates a temp dir within output_dir
|
||||
if not os.path.exists(dynamut_temp_dir):
|
||||
print('\nCreating dynamut_temp in output_dir', output_dir )
|
||||
os.makedirs(dynamut_temp_dir)
|
||||
|
||||
out_url_file = dynamut_temp_dir + '/dynamut_result_url_' + str(outfile_suffix) + '.txt'
|
||||
print('\nWriting output url file:', out_url_file)
|
||||
myfile = open(out_url_file, 'a')
|
||||
myfile.write(url)
|
||||
myfile.close()
|
||||
#====================
|
||||
# Submit first batch
|
||||
#%%#####################################################################
|
||||
|
|
|
|||
|
|
@ -1,121 +0,0 @@
|
|||
#!/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
|
||||
#%% homedir
|
||||
homedir = os.path.expanduser('~')
|
||||
print('My homedir is:', homedir)
|
||||
#%%
|
||||
def request_calculation(pdb_file
|
||||
, mutation_list
|
||||
, batch_no
|
||||
, chain
|
||||
, my_email
|
||||
, prediction_url
|
||||
, output_dir
|
||||
#, gene_name
|
||||
#, out_url_file
|
||||
):
|
||||
"""
|
||||
Makes a POST request for dynamut predictions.
|
||||
|
||||
@param pdb_file: valid path to pdb structure
|
||||
@type string
|
||||
|
||||
@param mutation_list: list of mutations (1 per line) of the format: {WT}<POS>{Mut}
|
||||
@type string
|
||||
|
||||
@param batch_no: batch no so it can be added as a suffix to the the outfile
|
||||
@type int
|
||||
|
||||
@param chain: single-letter(caps)
|
||||
@type chr
|
||||
|
||||
@param prediction_url: dynamut url for prediction
|
||||
@type string
|
||||
|
||||
@param output_dir: output dir
|
||||
@type string
|
||||
|
||||
@param gene_name: name of gene
|
||||
@type string
|
||||
|
||||
#@param out_url_file: name of output file with batch no. as suffix
|
||||
@type string
|
||||
|
||||
@return txt file containing batch no. of snps processed (i.e out_url_file)
|
||||
@type string
|
||||
"""
|
||||
|
||||
with open(pdb_file, "rb") as pdb_file, open (mutation_list, "rb") as mutation_list:
|
||||
files = {"wild": pdb_file
|
||||
, "mutation_list": mutation_list}
|
||||
body = {"chain": chain
|
||||
, "email": my_email}
|
||||
|
||||
response = requests.post(prediction_url, files = files, data = body)
|
||||
print(response.status_code)
|
||||
if response.history:
|
||||
print('\nPASS: valid submission. Fetching result url')
|
||||
url_match = re.search('/dynamut/results_prediction/.+(?=")', response.text)
|
||||
url = host + url_match.group()
|
||||
print('\nURL for snp batch no ', str(batch_no), ':', url)
|
||||
|
||||
#===============
|
||||
# writing file: result urls
|
||||
#===============
|
||||
dynamut_temp_dir = outdir + '/dynamut_temp'
|
||||
if not os.path.exists(dynamut_temp_dir):
|
||||
print('\nCreating dynamut_temp in outdir', outdir )
|
||||
os.makedirs(dynamut_temp_dir)
|
||||
|
||||
out_url_file = dynamut_temp_dir + '/dynamut_result_url_batch_' + str(batch_no) + '.txt'
|
||||
print('\nWriting output url file:', out_url_file)
|
||||
myfile = open(out_url_file, 'a')
|
||||
myfile.write(url)
|
||||
myfile.close()
|
||||
#%%globals!?
|
||||
host = 'http://biosig.unimelb.edu.au'
|
||||
#prediction_url = f"{host}/dynamut/prediction_list"
|
||||
#print(prediction_url)
|
||||
|
||||
#gene = 'gid'
|
||||
drug = 'streptomycin'
|
||||
datadir = homedir + '/git/Data'
|
||||
indir = datadir + '/' + drug + '/input'
|
||||
outdir = datadir + '/' + drug + '/output'
|
||||
#outdir = homedir + '/git/LSHTM_analysis/dynamut' # for example
|
||||
|
||||
my_chain = 'A'
|
||||
my_pdb_file = homedir + '/git/Data/streptomycin/input/gid_complex.pdb'
|
||||
|
||||
# batch 1: 00.txt
|
||||
#my_mutation_list = homedir + '/git/Data/streptomycin/output/snp_batches/50/snp_batch_00.txt'
|
||||
#my_batch = 1
|
||||
|
||||
# batch 2: 01.txt
|
||||
my_mutation_list = outdir + '/snp_batches/50/snp_batch_01.txt'
|
||||
my_batch = 2
|
||||
|
||||
# %% call this function
|
||||
request_calculation (pdb_file = my_pdb_file
|
||||
, mutation_list = my_mutation_list
|
||||
, chain = my_chain
|
||||
, my_email = 'tanushree.tunstall@lshtm.ac.uk'
|
||||
, prediction_url = f"{host}/dynamut/prediction_list"
|
||||
, output_dir = outdir
|
||||
, batch_no = my_batch)
|
||||
Loading…
Add table
Add a link
Reference in a new issue