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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Wiki links from obo descriptions
Step1:  urllib2 to read page in html
Step2:  Fuzzy logic
Step3:  Wikipedia search engine
Step4:  [name for name in names if len(re.split(' ', name)) > 3]
pub-med
Step5:  def find_synonym(s_ref, s)
Step6:  Assymetric distance
Step7:  Length statistics
Step8:  Unique words
Step9:  Threading
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Python Code:
import wiki
lst = wiki.get_links_from_ontology(ontology)
print r'example:{:}'.format(repr(lst[10]))
Explanation: Wiki links from obo descriptions
End of explanation
page = wiki.get_html(lst[101])
page[:1000]
Explanation: urllib2 to read page in html
End of explanation
import fuzzywuzzy.process as fuzzy_process
from fuzzywuzzy import fuzz
string = "ventricular arrhythmia"
names = np.sort(name2doid.keys())
print fuzzy_process.extractOne(string, names, scorer=fuzz.token_set_ratio)
string = "Complete remission of hairy cell leukemia variant (HCL-v) complicated by red cell aplasia post treatment with rituximab."
print fuzzy_process.extractOne(string, names, scorer=fuzz.partial_ratio)
Explanation: Fuzzy logic
End of explanation
query = "ventricular arrhythmia"
top = wiki.get_top_headers(query)
top
for header in top:
    results = fuzzy_process.extractOne(header, names, scorer=fuzz.token_set_ratio)
    print results
page = wikipedia.WikipediaPage(title='Cell_proliferation')
page.summary
Explanation: Wikipedia search engine: headers
End of explanation
import pubmed
query = 'hcl-v'
titles = pubmed.get(query)
titles_len = [len(title) for title in titles] 
for i, string in enumerate(titles):
    print("%d) %s" % (i+1, string))
    print fuzzy_process.extractOne(string, names, scorer=fuzz.partial_ratio)
    print 
Explanation: [name for name in names if len(re.split(' ', name)) > 3]
pub-med
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import utils
print utils.find_synonym('Wolff-Parkinson-White syndrome (WPW) and athletes: Darwin at play?', 'WPW')
print utils.find_synonym('Complete remission of hairy cell leukemia variant (HCL-v)...', 'hcl-v')
Explanation: def find_synonym(s_ref, s):
    last = s_ref.find('(' + s + ')')
    if last == -1:
        return None
n_upper = len(''.join([c for c in s if c.isupper()]))
first = [(i,c) for i, c in enumerate(s_ref[:last]) if c.isupper()][-n_upper][0]
return s_ref[first:last-1]
print find_synonym('Wolff-Parkinson-White syndrome (WPW) and athletes: Darwin at play?',
             'WPW')
synonyms
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s_ref = 'artery disease'
s = 'nonartery'
print utils.assym_dist(s, s_ref)
Explanation: Assymetric distance
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print 'Mean term name length:', np.mean([len(term.name) for term in ontology.get_terms()])
print 'Mean article title length:', np.mean(titles_len)
Explanation: Length statistics
End of explanation
words = [re.split(' |-', term.name) for term in ontology.get_terms()]
words = np.unique([l for sublist in words for l in sublist if len(l) > 0])
words = [w for w in words if len(w) >= 4]
words[:10]
Explanation: Unique words
End of explanation
from threading import Thread
from time import sleep
from ontology import get_ontology
query_results = None
def fn_get_q(query):
    global query_results
    query_results = fuzzy_process.extractOne(query, names, scorer=fuzz.ratio)
    return True
wiki_results = None
def fn_get_wiki(query):
    global wiki_results
    header = wiki.get_top_headers(query, 1)[0]
    wiki_results = fuzzy_process.extractOne(header, names, scorer=fuzz.ratio)
    #sleep(0.1)
    return True
pubmed_results = None
def fn_get_pubmed(query):
    global pubmed_results
    string = pubmed.get(query, topK=1)
    if string is not None:
        string = string[0]
        print string
        pubmed_results = fuzzy_process.extractOne(string, names, scorer=fuzz.partial_ratio)
        return True
    else:
        return False
'''main'''
## from bot
query = 'valve disease'
def find_answer(query):
    query = query.lower()
    
    # load ontology
    ontology = get_ontology('../data/doid.obo')
    name2doid = {term.name: term.id for term in ontology.get_terms()}
    doid2name = {term.id: term.name for term in ontology.get_terms()}
    
    ## exact match
    if query in name2doid.keys():
        doid = name2doid[query]
    else:
        # exact match -- no
        th_get_q = Thread(target = fn_get_q, args = (query,))
        th_get_wiki = Thread(target = fn_get_wiki, args = (query,))
        th_get_pubmed = Thread(target = fn_get_pubmed, args = (query,))
        th_get_q.start()
        th_get_wiki.start()
        th_get_pubmed.start()
        ## search engine query --> vertices, p=100(NLP??); synonyms
        ## new thread for synonyms???
        ## synonyms NLP
        ## new thread for NLP
        ## tree search on vertices (returned + synonyms)
        ## sleep ?
        th_get_q.join()
        print query_results
        th_get_wiki.join()
        print wiki_results
        th_get_pubmed.join()
        print pubmed_results
        ## final answer
        ## draw graph
        doid = None
    
    graph = None
    return doid, graph
Explanation: Threading
End of explanation
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