def res_kmeans(query):
form = visOptions()
base_url = (app.config['BASE_URL'])
if request.method == 'POST':
kmr_conn = connection() #k-means results database connection
pmid_list = query.split('+') #list of string pmids
pmid = pmid_list[0]
prefix = pmid[0:3]
suffix = pmid[3:6]
filename2 = str(prefix) + '/' + str(suffix) + '/' + "lemma_samples_" + str(query) + ".pickle"
lemma_file = os.path.join((app.config['PATH_TO_CACHE']), filename2)
with open(lemma_file, "rb") as l:
lemma_samples = pickle.load(l)
k_clusters = form.k_val.data #2,3,4,or 5
logging.info("the k value is " + str(k_clusters))
x0_coordinates, y0_coordinates, z0_coordinates, x1_coordinates, y1_coordinates, z1_coordinates, x2_coordinates, y2_coordinates, z2_coordinates, x3_coordinates, y3_coordinates, z3_coordinates, x4_coordinates, y4_coordinates, z4_coordinates, titles0, titles1, titles2, titles3, titles4 = vis_kmeans(lemma_samples, k_clusters, kmr_conn)
kmr_conn.close()
return render_template('res_kmeans1.html', base_url=base_url, query=query,
x0_coordinates=x0_coordinates, y0_coordinates=y0_coordinates, z0_coordinates=z0_coordinates,
x1_coordinates=x1_coordinates, y1_coordinates=y1_coordinates, z1_coordinates=z1_coordinates,
x2_coordinates=x2_coordinates, y2_coordinates=y2_coordinates, z2_coordinates=z2_coordinates,
x3_coordinates=x3_coordinates, y3_coordinates=y3_coordinates, z3_coordinates=z3_coordinates,
x4_coordinates=x4_coordinates, y4_coordinates=y4_coordinates, z4_coordinates=z4_coordinates,
titles0=titles0, titles1=titles1, titles2=titles2, titles3=titles3, titles4=titles4)
else:
#Not running any default vis because its slow
return render_template('res_kmeans1.html', query=query, base_url=base_url)
def cogeclustermap():
query = '18952863+18269575'
form = nesOptions()
if request.method == 'POST':
cm_conn = connection()
nes_categories = request.form.getlist('n_categories')
logging.info(nes_categories)
w_number = form.w_words.data
logging.info("the w value is " + str(w_number))
filename1 = "189/528/nes_18952863+18269575.pickle"
nes_file = os.path.join((app.config['PATH_TO_CACHE']), filename1)
with open(nes_file, "rb") as f:
nes_samples = pickle.load(f)
filename2 = "189/528/lemma_samples_18952863+18269575.pickle"
lemma_file = os.path.join((app.config['PATH_TO_CACHE']), filename2)
with open(lemma_file, "rb") as f:
lemma_samples = pickle.load(f)
saveName = vis_clustermap(lemma_samples, nes_samples, nes_categories, w_number, query, cm_conn)
#Fix.... why bother returning full file path if html only looks in "static" dir?
image = "clustermaps/cm_"+str(query)+".png"
cm_conn.close()
return render_template('coge_clustermap.html', image=image)
else:
#show the default data :D
image = "clustermaps/cm_18952863+18269575.png"
return render_template('coge_clustermap.html', image=image)
def cogeheatmap():
form = nesOptions()
query = '18952863+18269575'
if request.method == 'POST':
hm_conn = connection()
# nes_categories = request.form.getlist('check')
#TODO: need to get complete list of stuff
nes_categories = request.form.getlist('n_categories')
logging.info(nes_categories)
w_number = form.w_words.data
logging.info("the w value is "+str(w_number))
filename1 = "189/528/nes_18952863+18269575.pickle"
nes_file = os.path.join((app.config['PATH_TO_CACHE']), filename1)
with open(nes_file, "rb") as f:
nes_samples = pickle.load(f)
filename2 = "189/528/lemma_samples_18952863+18269575.pickle"
lemma_file = os.path.join((app.config['PATH_TO_CACHE']), filename2)
with open(lemma_file, "rb") as f:
lemma_samples = pickle.load(f)
x_docs, y_words, z_counts, titles = vis_heatmap(lemma_samples, nes_samples, nes_categories, w_number, hm_conn)
len_x_docs = list(range(len(x_docs)))
popup = '<div class="alert alert-warning alert-dismissible" role="alert"><button type="button" class="close" data-dismiss="alert" aria-label="Close"><span aria-hidden="true">×</span></button><strong>[ ! ]</strong>Displaying results for N= '+str(w_number)+' from categories: '+str(nes_categories)+'</div>'
hm_conn.close()
return render_template('coge_heatmap2.html', z_counts=z_counts, x_docs=x_docs, y_words=y_words, titles=titles, len_x_docs=len_x_docs, popup=popup)
else:
#display the default data :D
return render_template('coge_heatmap1.html')
def cogecrawl():
query = '18952863+18269575'
coge_conn = connection()
citations_with_links = db_unique_citations_retrieval(query, coge_conn) #unique
unique_publications = db_unique_citations_number(query, coge_conn)
coge_conn.close()
base_url = (app.config['BASE_URL'])
return render_template("dashboard.html", citations_with_links=citations_with_links, unique_publications=unique_publications,
base_url=base_url)
def res_heatmap(query):
form = nesOptions()
base_url = (app.config['BASE_URL'])
hmr_conn = connection() #heatmap results database connection
if request.method == 'POST':
nes_categories = request.form.getlist('n_categories')
logging.info(nes_categories)
w_number = form.w_words.data
logging.info("the w value is "+str(w_number))
pmid_list = query.split('+')
pmid = pmid_list[0]
prefix = pmid[0:3]
suffix = pmid[3:6]
filename1 = str(prefix) + '/' + str(suffix) + '/' + "nes_" + str(query) + ".pickle"
nes_file = os.path.join((app.config['PATH_TO_CACHE']), filename1)
with open(nes_file, "rb") as f:
nes_samples = pickle.load(f)
filename2 = str(prefix) + '/' + str(suffix) + '/' + "lemma_samples_" + str(query) + ".pickle"
lemma_file = os.path.join((app.config['PATH_TO_CACHE']), filename2)
with open(lemma_file, "rb") as l:
lemma_samples = pickle.load(l)
x_docs, y_words, z_counts, titles = vis_heatmap(lemma_samples, nes_samples, nes_categories, w_number, hmr_conn)
popup = '<div class="alert alert-warning alert-dismissible" role="alert"><button type="button" class="close" data-dismiss="alert" aria-label="Close"><span aria-hidden="true">×</span></button><strong>[ ! ]</strong>Displaying results for N= '+str(w_number)+' from categories: '+str(nes_categories)+'</div>'
hmr_conn.close()
return render_template('results_heatmap.html', query=query, z_counts=z_counts, x_docs=x_docs, y_words=y_words, popup=popup, titles=titles, base_url=base_url)
else:
nes_categories= ['BioProcess', 'CellLine', 'Cellular_component', 'Family', 'Gene_or_gene_product', 'Organ', 'Simple_chemical', 'Site', 'Species', 'TissueType']
w_number = 10
logging.info("the w value is "+str(w_number))
pmid_list = query.split('+')
pmid = pmid_list[0]
prefix = pmid[0:3]
suffix = pmid[3:6]
filename1 = str(prefix) + '/' + str(suffix) + '/' + "nes_" + str(query) + ".pickle"
nes_file = os.path.join((app.config['PATH_TO_CACHE']), filename1)
with open(nes_file, "rb") as f:
nes_samples = pickle.load(f)
filename2 = str(prefix) + '/' + str(suffix) + '/' + "lemma_samples_" + str(query) + ".pickle"
lemma_file = os.path.join((app.config['PATH_TO_CACHE']), filename2)
with open(lemma_file, "rb") as l:
lemma_samples = pickle.load(l)
x_docs, y_words, z_counts, titles = vis_heatmap(lemma_samples, nes_samples, nes_categories, w_number, hmr_conn)
popup = '<div class="alert alert-warning alert-dismissible" role="alert"><button type="button" class="close" data-dismiss="alert" aria-label="Close"><span aria-hidden="true">×</span></button><strong>[ ! ]</strong> Default: N=10, from all categories.</div>'
hmr_conn.close()
return render_template('results_heatmap.html', query=query, z_counts=z_counts, x_docs=x_docs, y_words=y_words, popup=popup, titles=titles, base_url=base_url)
def cogejournals():
j_conn = connection()
filename = "189/528/journals_18952863+18269575.json"
filepath = os.path.join((app.config['PATH_TO_JOURNALS']), filename)
with open(filepath) as load_data:
journals = json.load(load_data)
query = '18952863+18269575'
range_years, unique_pubs, unique_journals = getJournalsVis(query, j_conn)
years_list = range_years.split('+')
s_year = years_list[0]
e_year = years_list[1]
j_conn.close()
return render_template('coge_journals.html', journals=journals, unique_pubs=unique_pubs,
unique_journals=unique_journals, s_year=s_year, e_year=e_year)
def coge_stats():
s_conn = connection()
query = "18952863+18269575"
input_click_citations = statsSelfInfo(query, s_conn)
statistics = get_statistics(query, s_conn) #actually a lot of these are "None" right now. Will need to populate.
sum_total = statistics[0]
unique = statistics[1]
sum_abstracts = statistics[2]
sum_whole = statistics[3]
sum_sents = statistics[4]
sum_tokens = statistics[5]
s_conn.close()
return render_template('coge_stats.html', input_click_citations=input_click_citations,
sum_total=sum_total, unique=unique, sum_abstracts=sum_abstracts, sum_whole=sum_whole,
sum_sents=sum_sents, sum_tokens=sum_tokens)
def coge_scifi():
form = corpusOptions()
path_to_eligible_paper = os.path.join((app.config['PATH_TO_CACHE']), '259/367/2593677.txt')
eligible_papers = [('paper1', '18952863', path_to_eligible_paper, '2008, Lyons')]
if request.method == 'POST':
csf_conn = connection()
logging.info("posted a thing in scifi!")
corpus = form.corpus.data
logging.info(corpus)
query = "18952863+18269575"
if corpus == 'darwin':
title = 'On The Origin of Species'
if corpus == 'yeast':
title = 'Yeast by Thomas Henry Huxley'
if corpus == 'mouse':
title = 'The Dancing Mouse, a Study in Animal Behavior'
if corpus == 'brain_speech':
title = 'The Brain & The Voice in Speech & Song'
if corpus == 'grecoroman':
title = 'Outlines of Greek and Roman Medicine'
if corpus == 'startrek':
title = 'Star Trek: The Next Generation'
if corpus == 'mars':
title = 'Gulliver of Mars'
if corpus == 'last_evolution':
title = 'The Last Evolution by John W Campbell'
if corpus == 'youth':
title = 'Youth by Isaac Asimov'
if corpus == 'frankenstein':
title = 'Frankenstein; or, The Modern Prometheus'
if corpus == 'sherlock':
title = 'Sherlock Holmes by Sir Arthur Conan Doyle'
if corpus == 'austen':
title = 'Pride and Prejudice'
if corpus == 'bible':
title = 'The Bible'
if corpus == 'paper1':
title = 'Lyons et al., 2008'
x, y, names, color = vis_scifi(corpus, query, eligible_papers, csf_conn)
csf_conn.close()
return render_template('coge_scifi2.html', x=x, y=y, title=title, color=color, names=names, eligible_papers=eligible_papers)
else:
flash('Some input paper(s) are not avaliable for TextCompare')
#Default data!
return render_template('coge_scifi.html', eligible_papers=eligible_papers)
def cogekmeans():
form = visOptions()
if request.method == 'POST':
k_conn = connection()
filename = "189/528/lemma_samples_18952863+18269575.pickle"
lemma_file = os.path.join((app.config['PATH_TO_CACHE']), filename)
with open(lemma_file, "rb") as f:
lemma_samples = pickle.load(f)
k_clusters = form.k_val.data #2,3,4,or 5
logging.info("the k value is " + str(k_clusters))
x0_coordinates, y0_coordinates, z0_coordinates, x1_coordinates, y1_coordinates, z1_coordinates, x2_coordinates, y2_coordinates, z2_coordinates, x3_coordinates, y3_coordinates, z3_coordinates, x4_coordinates, y4_coordinates, z4_coordinates, titles0, titles1, titles2, titles3, titles4 = vis_kmeans(lemma_samples, k_clusters, k_conn)
k_conn.close()
return render_template('coge_kmeans2.html', x0_coordinates=x0_coordinates, y0_coordinates=y0_coordinates, z0_coordinates=z0_coordinates,
x1_coordinates=x1_coordinates, y1_coordinates=y1_coordinates, z1_coordinates=z1_coordinates,
x2_coordinates=x2_coordinates, y2_coordinates=y2_coordinates, z2_coordinates=z2_coordinates,
x3_coordinates=x3_coordinates, y3_coordinates=y3_coordinates, z3_coordinates=z3_coordinates,
x4_coordinates=x4_coordinates, y4_coordinates=y4_coordinates, z4_coordinates=z4_coordinates,
titles0=titles0, titles1=titles1, titles2=titles2, titles3=titles3, titles4=titles4)
else:
#show the default data!!
return render_template('coge_kmeans.html')
def res_stats(query):
sr_conn = connection() #statistics results db connection
pmid_list = query.split('+') #list of string pmids
venn_data = make_venn(pmid_list, sr_conn)
input_click_citations = statsSelfInfo(query, sr_conn)
statistics = get_statistics(query, sr_conn)
if None in statistics:
logging.info("There were some 'None's in our stastics! Let's get this right and make sure the db is populated...")
db_query_update_statistics(query, sr_conn) #do it again
logging.info("Done updating the db with the statistics")
statistics = get_statistics(query, sr_conn)
sum_total = statistics[0]
unique = statistics[1]
sum_abstracts = statistics[2]
sum_whole = statistics[3]
sum_sents = statistics[4]
sum_tokens = statistics[5]
else:
sum_total = statistics[0]
unique = statistics[1]
sum_abstracts = statistics[2]
sum_whole = statistics[3]
sum_sents = statistics[4]
sum_tokens = statistics[5]
#get x, y coordinates for pubs x year bar chart.
#max 5 papers
x0, x1, x2, x3, x4, y0, y1, y2, y3, y4, n0, n1, n2, n3, n4 = stats_barchart(query, sr_conn)
sr_conn.close()
return render_template('results_stats.html', input_click_citations=input_click_citations,
venn_data=venn_data, sum_total=sum_total,
unique=unique, sum_abstracts=sum_abstracts, sum_whole=sum_whole,
sum_sents=sum_sents, sum_tokens=sum_tokens,
x0=x0, x1=x1, x2=x2, x3=x3, x4=x4,
y0=y0, y1=y1, y2=y2, y3=y3, y4=y4,
n0=n0, n1=n1, n2=n2, n3=n3, n4=n4)
def res_clustermap(query):
form = nesOptions()
base_url = (app.config['BASE_URL'])
if request.method == 'POST':
cmr_conn = connection() #clustermap results database connection
nes_categories = request.form.getlist('n_categories')
logging.info(nes_categories)
w_number = form.w_words.data
logging.info("the w value is "+str(w_number))
pmid_list = query.split('+')
pmid = pmid_list[0]
prefix = pmid[0:3]
suffix = pmid[3:6]
filename1 = str(prefix) + '/' + str(suffix) + '/' + "nes_" + str(query) + ".pickle"
nes_file = os.path.join((app.config['PATH_TO_CACHE']), filename1)
with open(nes_file, "rb") as f:
nes_samples = pickle.load(f)
filename2 = str(prefix) + '/' + str(suffix) + '/' + "lemma_samples_" + str(query) + ".pickle"
lemma_file = os.path.join((app.config['PATH_TO_CACHE']), filename2)
with open(lemma_file, "rb") as l:
lemma_samples = pickle.load(l)
saveName = vis_clustermap(lemma_samples, nes_samples, nes_categories, w_number, query, cmr_conn)
image = '/clustermaps/' + saveName
cmr_conn.close()
return render_template('results_clustermap.html',image=image, query=query, base_url=base_url)
else:
popup = '<div class="alert alert-warning alert-dismissible" role="alert"><button type="button" class="close" data-dismiss="alert" aria-label="Close"><span aria-hidden="true">×</span></button><strong>[ ! ]</strong> Choose N and categories to run clustermap.</div>'
#No default visualization
return render_template('results_clustermapH.html', query=query, popup=popup, base_url=base_url)
def resjournals(query, update_check):
logging.info("in routine res-journals")
jr_conn = connection() #journal results connection
needed_to_annotate_check = [update_check]
range_years, unique_publications, unique_journals = print_journalvis(query, needed_to_annotate_check, jr_conn)
logging.info("YEARS RANGE: " +str(range_years))
#Need years for range
### AHHHH if range is not in db yet, it will get it without the "+" as a tuple!
try:
years_list = range_years.split('+')
s_year = years_list[0]
e_year = years_list[1]
except Exception as e:
s_year = range_years[0]
e_year = range_years[1]
#only want to load the json for the LAST id in the query (so includes all)
pmid_list = query.split('+') #list of string pmids
pmid = pmid_list[0]
prefix = pmid[0:3]
suffix = pmid[3:6]
filename = str(prefix)+'/'+str(suffix)+'/'+"journals_"+str(query)+".json"
logging.info("last entry's JOURNAL is named: " + str(filename))
savePath = (app.config['PATH_TO_JOURNALS'])
completeName = os.path.join(savePath, filename)
logging.info("complete file: " + str(completeName))
with open(completeName) as load_data:
journals = json.load(load_data)
jr_conn.close()
return render_template('results_journals.html', journals=journals, s_year=s_year, e_year=e_year,
unique_journals=unique_journals, unique_publications=unique_publications)
def results():
logging.info("In app route RESULTS")
time_start = time.time()
form = pmidForm()
r_conn = connection() #results_connection to db
base_url = (app.config['BASE_URL'])
try:
if request.method == 'GET':
logging.info("Request method is GET")
#entry = form.pmid.data #If method is POST #str
entry = (request.args.getlist('pmid'))[0] #str
if len(entry) == 0:
logging.info("someone pushed enter without entering anything....default entry go!")
query = '18952863+18269575'
citations_with_links = db_unique_citations_retrieval(query, r_conn) # unique
unique_publications = db_unique_citations_number(query, r_conn)
r_conn.close()
base_url = (app.config['BASE_URL'])
return render_template("dashboard.html", citations_with_links=citations_with_links,
unique_publications=unique_publications,
base_url=base_url)
pmid_list = multiple_pmid_input(entry) #list for handling multiple pmids
logging.info(pmid_list)
#URL TO RE-VISIT THIS ANALYSIS
url_data = {}
url_data['pmid'] = entry
url_values = urllib.parse.urlencode(url_data)
scke_url = (app.config['BASE_URL'])
full_url = scke_url + '?' + url_values
logging.info(full_url)
# If the user inputs more than 5 PMIDs, return the home page and flash a warning
# Need 5 PMIDs or less
if len(pmid_list) > 5:
flash('You have entered more than 5 PMIDs. Please reduce your query to 5 PMIDs or less to continue.')
base_url = (app.config['BASE_URL'])
r_conn.close()
return render_template("home.html", base_url=base_url)
q = '+'
query = str(q.join(pmid_list))
logging.info("query: " + str(query))
needed_to_annotate_check = []
for user_input in pmid_list:
logging.info(str(user_input))
user_input = str(user_input)
############################################
#Check database for pmid #Does the entry exists in the db already?
#conn = connection()
s = inputPapers.select().\
where(inputPapers.c.pmid == user_input)
c = r_conn.execute(s)
check1 = c.fetchone()
c.close()
#if the entry does NOT exist in the db already, will need to retrieve text, annotate it, and populate cache
if check1 is None:
update_check = "yes"
#flash('new pubmedid!')
#Information Retireval of citing pmcids and info about them
number_of_citations = run_IR_not_db(user_input, r_conn)
logging.info(number_of_citations)
#Throws an exception if there are no citations for something
if number_of_citations == 0:
flash('PubMed has no citations for PMID: ' + str(user_input) + '. Please try again with a different PMID or without this PMID in your query. If you had other PMIDs in your query before this, those are fine :)')
citations_with_links = db_unique_citations_retrieval('18952863+18269575', r_conn) # unique
unique_publications = db_unique_citations_number('18952863+18269575', r_conn)
r_conn.close()
return render_template("dashboard.html", citations_with_links=citations_with_links,
unique_publications=unique_publications)
else:
#Annotate
logging.info("beginning multi-preprocessing")
biodoc_data = do_multi_preprocessing(user_input, r_conn)
needed_to_annotate_check.append("yes")
logging.info("done with new document multi_preprocessing")
#Before moving on, lets double check that we have EVERYTHING.
#If some texts were coppied because they are in the database but actually missing, that's a problemo!
logging.info("Just checking that everything (texts and jsons) is there.... ")
needed_to_rescrape = check_for_texts(user_input, r_conn) # returns yes or no
logging.info("needed_to_rescrape/re_annotate: " + str(needed_to_rescrape))
logging.info("writing the BIODOC LEMMAS")
#Populate cache (lemmas and nes)
need_to_annotate = "yes" #of course we need to annotate, its a new pmid!
print_lemma_nes_samples(user_input, biodoc_data, need_to_annotate)
logging.info("* wrote lemme and nes samples to cache!!!")
#After all citations have been processed, now we can do the analyses:
if user_input == pmid_list[-1]: #if its the last pmid
logging.info("last pmid in the query")
#Lemma_samples and nes_samples for entire query here:
logging.info("concatting lemma nes samples for query")
#Populate cache
need_to_update = "yes" #of course we do, something is new!
concat_lemma_nes_samples(query, need_to_update)
#Update "queries" table of db here!!
logging.info("STARTING db_query_update_statistics")
db_query_update_statistics(query, r_conn)
logging.info("finishe db_query_update_statistics")
#if the entry IS in the db, no need to retrieve text from Entrez, just grab from db
#MAYBE need to annotate some new documents, maybe not
#If new citations do need to be retireved, annotated, etc then DO NEED to re-populate cache
if check1 is not None:
update_check = "no" #no by default
#flash("alreay exists in database :) ")
#Using user_input for Information Retireval - checks if any new papers have been added that we need to scrape
need_to_annotate = run_IR_in_db(user_input, r_conn)
# if there's nothing to annotate, still double check that all things are there
# if papers or annotations are missing, then fix that!
needed_to_rescrape = check_for_texts(user_input, r_conn) #returns yes or no
logging.info("needed_to_rescrape: " + str(needed_to_rescrape))
needed_to_annotate_check.append(needed_to_rescrape) #if we needed to re-scrape anything, lets say we need to update
if needed_to_rescrape == 'yes':
logging.info("updated needed_to_annotate_check ")
if need_to_annotate == 'yes':
needed_to_annotate_check.append('yes')
logging.info("need to annotate new documents")
#Annotate
biodoc_data = do_multi_preprocessing(user_input, r_conn)
logging.info("done with new document multi_preprocessing")
#If need_to_annotate is "yes", will re-populate :)
#Make cache
print_lemma_nes_samples(user_input, biodoc_data, need_to_annotate)
logging.info("repopulated lemmas and nes cache")
#Make sure that the lemma and nes cache exists before moving on!!!
if need_to_annotate == 'no':
logging.info("dont need to annotate any new documents")
if exists_lemma(user_input) and exists_nes(user_input):
logging.info("lemmas and nes cache exist so pass :)")
needed_to_annotate_check.append('no')
else:
logging.info("lemmas and nes cache didn't exist so gotta make them!!!")
biodoc_data = do_multi_preprocessing(user_input, r_conn)
logging.info("done with new document multi_preprocessing")
nonexistant = "yes"
print_lemma_nes_samples(user_input, biodoc_data, nonexistant)
needed_to_annotate_check.append('yes')
## Now that we have all the data, do the topic model
## Only want to save final topic model (not running topic model)
if user_input == pmid_list[-1]:
logging.info("last pmid in the query")
#If ANY user_inputs in the query needed to update, we must update the query's comprehensive cache.
if 'yes' in needed_to_annotate_check:
need_to_update = 'yes'
#will over-ride existing file :)
concat_lemma_nes_samples(query, need_to_update)
db_query_update_statistics(query, r_conn)
update_check = "yes"
if 'yes' not in needed_to_annotate_check:
need_to_update = 'no'
concat_lemma_nes_samples(query, need_to_update) #this should check for file
db_query_update_statistics(query, r_conn)
update_check = "no"
# Update "queries" table of db here!!
logging.info("STARTING db_query_update_statistics")
db_query_update_statistics(query, r_conn)
logging.info("finishe db_query_update_statistics")
#Housekeeping
gc.collect() #garbage collector for cleaning up unneeded stuff
session['entered_id'] = True
session['engaged'] = 'engaged'
citations_with_links = db_unique_citations_retrieval(query, r_conn) #unique
r_conn.close()
logging.info("BENCHMARK TIME: done in %0.3fs." % (time.time() - time_start))
return render_template('results.html', form=form, citations_with_links=citations_with_links,
query=query, update_check=update_check, base_url=base_url)
except Exception as e:
return(str(e))
r_conn.close()
def results_scifi(query):
form = corpusOptions()
base_url = (app.config['BASE_URL'])
pmid_list = query.split('+') # list of string pmids
#decide eligible papers:
sfr_conn = connection() #scifi results db connection
eligible_papers = inputEligible(query, sfr_conn)
logging.info("eligible papers: " +str(eligible_papers))
if request.method == 'POST':
logging.info("posted a thing in scifi!")
corpus = form.corpus.data
logging.info(corpus)
if corpus == 'darwin':
title = 'On The Origin of Species'
if corpus == 'yeast':
title = 'Yeast by Thomas Henry Huxley'
if corpus == 'mouse':
title = 'The Dancing Mouse, a Study in Animal Behavior'
if corpus == 'brain_speech':
title = 'The Brain and The Voice in Speech and Song'
if corpus == 'grecoroman':
title = 'Outlines of Greek and Roman Medicine'
if corpus == 'startrek':
title = 'Star Trek: The Next Generation'
if corpus == 'mars':
title = 'Guilliver of Mars'
if corpus == 'last_evolution':
title = 'The Last Evolution by John W Campbell'
if corpus == 'youth':
title = 'Youth by Isaac Asimov'
if corpus == 'frankenstein':
title = 'Frankenstein; or, The Modern Prometheus'
if corpus == 'sherlock':
title = 'Sherlock Holmes by Sir Arthur Conan Doyle'
if corpus == 'austen':
title = 'Pride and Prejudice'
if corpus == 'bible':
title = 'The Bible'
if corpus == 'paper1':
title = str('PMID: '+ str(eligible_papers[0][1]))
if corpus == 'paper2':
title = str('PMID: '+ str(eligible_papers[1][1]))
if corpus == 'paper3':
title = str('PMID: ' + str(eligible_papers[2][1]))
if corpus == 'paper4':
title = str('PMID: ' + str(eligible_papers[3][1]))
if corpus == 'paper5':
title = str('PMID: ' + str(eligible_papers[4][1]))
x, y, names, color = vis_scifi(corpus, query, eligible_papers, sfr_conn)
sfr_conn.close()
return render_template('results_scifi.html', base_url=base_url, x=x, y=y, title=title, color=color, query=query, names=names, eligible_papers=eligible_papers)
else:
logging.info("scifi analysis")
corpus = 'darwin'
title = 'On The Origin of Species'
x, y, names, color = vis_scifi(corpus, query, eligible_papers, sfr_conn)
logging.info("done with x and y")
if len(eligible_papers) < len(pmid_list):
flash('Some input paper(s) are not avaliable for TextCompare')
sfr_conn.close()
return render_template('results_scifi.html',base_url=base_url, x=x, y=y, title=title, color=color, query=query, names=names, eligible_papers=eligible_papers)
