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UKBscrape.py
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UKBscrape.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
It is function to scrape the UKBiobank website:
1) It helps you find intersection between variables available through your
application and (almost) all other UKB variables.
2) It can act as a dictionary, you can pass a variable Field ID and it will
return the variable name and its category (by category I mean *UKbiobank*
pre-defined categories.)
To kick start do the following:
import UKBscrape as ukbs
ukbs.update()
You need to be connected to internet for ukbs.update() to work. Once you
have done it, there is a new sub-directory ~/IDLabCat which contains
scrapped text files from the UKBiobank.
See usage/help for individual functions.
Created on Mon Jan 7 14:32:45 2019
@author: sorooshafyouni
University of Oxford, 2019
srafyouni@gmail.com
"""
################################################################################################
################################################################################################
import re
import numpy as np
import os
import pandas as pd
def update():
"""
REQUIREMENTS:
packages: os
This function uses inner bash script 'UKBscrape.sh' to scrape UKBB website.
Need to be called on regularly bases to be updated with the latest version
of variables.
SA, Ox, 2019
srafyouni@gmail.com
"""
import os
if not os.path.isdir("IDLabCat"):
os.makedirs('IDLabCat')
os.system('sh UKBscrape.sh')
print('==DONE UPDATES==')
return
################################################################################################
################################################################################################
def match(UKBCSVFile = 'ukb25120_Headers.txt',\
Report2 = ''):
"""
ukbs.match(UKBCSVFile = '',Report2 = '')
REQUIREMENTS:
packages: os, re, numpy
INPUTS:
UKBCSVFile: path to a text file which contains *your* UKBiobank headers.
On you linux/Mac terminal do as following to make such file:
$ head -n 1 /path/2/your/UKB/ukbXXXXX.csv > ukbXXXX_Headers.txt
Report2 [optional]: if you need to save the reports, set a path for the results.
This will produce two csv files:
YouDontHave.csv, FieldID, label and category of the variables that you don't have
WasntParsed.csv, the variables that may have missed in parsing but your application has!
SA, Ox, 2019
srafyouni@gmail.com
"""
# 1) Should check the match folder, it creates a dictory a / before the target
# 2) Also, skip if the field doesn't exist in the UKB scrapped.
#######INPUT CHECK ##############
if UKBCSVFile == '':
print('HALTED: Specify the path to your UKBB CSV file...')
return
if not os.path.isdir("IDLabCat"):
update()
print('You requested a match between your current version of UKBB variables at:\n'\
+ str(UKBCSVFile) + ' \n and the UKB available data at:\nhttps://biobank.ctsu.ox.ac.uk/crystal/list.cgi')
################################
# Extract what we have:
#UKBCSVFile = 'ukb25120_Headers.txt';
ScrapedCSVFile = 'IDLabCat/IDs_All.txt';
WeHave = open(UKBCSVFile, 'r').read()
A = WeHave.split(sep=',');
IDRegEx = '^\"([0-9]*).*\"'
IDsWeHave = list()
for i in range(0,np.size(A)):
IDsWeHave.append(re.findall(IDRegEx,A[i])[0])
del IDsWeHave[0] # because the first element is always eid, right?
IDsWeHave = list(set(IDsWeHave))
################################
UKBHave = open(ScrapedCSVFile, 'r').read()
IDsUKBHave = UKBHave.split(sep='\n')
WhatWeDontHave = list(set(IDsUKBHave)-set(IDsWeHave))
WhatUKBDontHave = list(set(IDsWeHave)-set(IDsUKBHave))
if Report2!='':
Report2_dir = os.path.dirname(Report2)
Report2_dir = Report2_dir + '/matchreport/';
dictionary(UKBCSVFile=UKBCSVFile, FieldID = WhatWeDontHave, SaveMe2=Report2_dir + 'YouDontHave.csv')
dictionary(UKBCSVFile=UKBCSVFile, FieldID = WhatUKBDontHave, SaveMe2=Report2_dir + 'WasntParsed.csv')
return WhatWeDontHave,WhatUKBDontHave
################################################################################################
################################################################################################
def dictionary(\
UKBCSVFile = 'ukb25120_Headers.txt',\
FieldID = '',\
SaveMe2 = ''):
"""
ukbs.dictionary(UKBCSVFile = '', FieldID = '' ,SaveMe2 = '')
REQUIREMENTS:
packages: os, numpy, pandas
INPUTS:
UKBCSVFile: path to a text file which contains *your* UKBiobank headers.
On you linux/Mac terminal do as following to make such file:
$ head -n 1 /path/2/your/UKB/ukbXXXXX.csv > ukbXXXX_Headers.txt
FieldID: Field IDs as a list of strings.
For example, labels and categories for field IDs 4056 & 26412 can be found as following:
In [80]: ukbs.dictionary(UKBCSVFile = 'ukb25120_Headers.txt',FieldID=['4056','26412']);
You requested dictionary for 2 data fields.
Label Category
4056 Age stroke diagnosed Medical conditions
26412 Employment score (England) Indices of Multiple Deprivation
SaveMe2 [optional]: path to a directory to save the results as tsv file.
saves the FieldID, labels and categories of passed FieldIDs
SA, Ox, 2019
srafyouni@gmail.com
"""
#######INPUT CHECK ##############
if FieldID=='':
print('HALTED: You have to specify which data field you need...')
return
headPath, tailName = os.path.split(UKBCSVFile)
if headPath != '':
headPath = headPath + '/'
print("I am looking at this directory: " + headPath)
if not os.path.isdir(headPath + "IDLabCat"):
update()
print('You requested dictionary for ' + str(np.size(FieldID)) + ' data fields.')
################################
DataDict = pd.DataFrame(index=FieldID,columns=('Label','Category','WeHave'))
# Can't I just simply use np.loadtxt?!
ScrapedCSVFile = headPath + 'IDLabCat/IDs_All.txt';
UKBHave = open(ScrapedCSVFile, 'r').read()
IDsUKBHave = UKBHave.split(sep='\n')
Path2CatFile = headPath + 'IDLabCat/Cats_All.txt'
CatFile = open(Path2CatFile, 'r').read()
Cats = CatFile.split(sep='\n')
Path2LabelFile = headPath + 'IDLabCat/Labels_All.txt'
LabFile = open(Path2LabelFile, 'r').read()
Labs = LabFile.split(sep='\n')
for i in range(0,np.size(FieldID)):
WeHaveFlag = False
if not (FieldID[i] in IDsUKBHave):
print('*****Field ID: ' + str(FieldID[i]) + ' is missing in the UKBB scrapped files.*****')
Cats_tmp = np.nan
Labs_tmp = np.nan
else:
Idx = IDsUKBHave.index(FieldID[i])
Cats_tmp = Cats[Idx]
Labs_tmp = Labs[Idx]
################################
# Extract what we have:
#UKBCSVFile = 'ukb25120_Headers.txt';
ScrapedCSVFile = headPath + 'IDLabCat/IDs_All.txt';
WeHave = open(UKBCSVFile, 'r').read()
A = WeHave.split(sep=',');
IDRegEx = '^\"([0-9]*).*\"'
IDsWeHave = list()
for ii in range(0,np.size(A)):
IDsWeHave.append(re.findall(IDRegEx,A[ii])[0])
del IDsWeHave[0] # because the first element is always eid, right?
IDsWeHave = list(set(IDsWeHave))
################################
if FieldID[i] in IDsWeHave:
WeHaveFlag = True
DataDict['Category'][i] = Cats_tmp
DataDict['Label'][i] = Labs_tmp
DataDict['WeHave'][i] = WeHaveFlag
print(DataDict)
if SaveMe2!='':
if not os.path.isdir(SaveMe2):
print('Report dictory was created: ' + str(SaveMe2))
SaveMe2_dir = os.path.dirname(SaveMe2)
os.makedirs(SaveMe2_dir,exist_ok=True)
DataDict.to_csv(SaveMe2)
return DataDict