def resolve_first_degree(left, right):
left_numbers, left_x = utils.extract_numbers(left, 1)
left_numbers = utils.clean_list(left_numbers, 'left')
left_x = utils.clean_vars(left_x, 'left')
right_numbers, right_x = utils.extract_numbers(right, 1)
right_numbers = utils.clean_list(right_numbers, 'right')
right_x = utils.clean_vars(right_x, 'right')
unkowns_cleaned, numbers_cleaned = utils.final_clean(
left_x, right_x, left_numbers + right_numbers, 1)
final_solver_first_degree(numbers_cleaned, unkowns_cleaned[1])
def resolve_second_degree(left, right):
left_numbers, left_x = utils.extract_numbers(left, 2)
left_numbers = utils.clean_list(left_numbers, 'left')
left_x = utils.clean_vars(left_x, 'left')
right_numbers, right_x = utils.extract_numbers(right, 2)
right_numbers = utils.clean_list(right_numbers, 'right')
right_x = utils.clean_vars(right_x, 'right')
unkown_cleaned, numbers_cleaned = utils.final_clean(
left_x, right_x, right_numbers + left_numbers, 1)
if unkown_cleaned[2] == 0:
final_solver_first_degree(numbers_cleaned, unkown_cleaned[1])
else:
final_solver_second_degree(unkown_cleaned[2], unkown_cleaned[1],
numbers_cleaned)
return 1
def resolve_zero_degree(left, right):
left_numbers = utils.extract_numbers(left, 0)
left_numbers = utils.clean_list(left_numbers, 'left')
right_numbers = utils.extract_numbers(right, 0)
right_numbers = utils.clean_list(right_numbers, 'right')
left_numbers = utils.final_clean({}, {}, left_numbers, 0)
right_numbers = utils.final_clean({}, {}, right_numbers, 0)
right_numbers *= -1
if right_numbers == left_numbers:
print(str(left_numbers) + '* X^0 = ' + str(right_numbers) + ' * X^0')
print('The solution is: ')
print('All real numbers')
else:
print(
str(left_numbers) + ' is not equal to' + ' ' + str(right_numbers))
print('There is no solution.')
def data_from_csv(self, filename):
f = open(filename, 'rU')
rows = []
for line in f:
row = line.split(',')
row = clean_list(row)
rows.append(row)
return rows
def write_text(event_file):
"""Takes event file as input, writes text from all queries contained in
event file to TEXT_DIR, and returns a list of documents written"""
for line in open(event_file):
query = line.strip()
logger.info('Writing query from %s: "%s"' % (current_process(), query))
qi = QueryIterator('http://search-s11.prod.wikia.net:8983/solr/main/', {'query': query, 'fields': 'id,wid,html_en,indexed', 'sort': 'id asc'})
for doc in qi:
# Sanitize and write text
text = '\n'.join(clean_list(doc.get('html_en', '')))
localpath = os.path.join(TEXT_DIR, doc['id'])
logger.debug('Writing text from %s to %s' % (doc['id'], localpath))
with open(localpath, 'w') as f:
f.write(text)
return 'Finished event file %s' % event_file
k.set_contents_from_filename(tar_file)
os.remove(tar_file)
# send post requests for each wid covered in this batch
for wid in wids:
requests.post('http://nlp-s1:5000/wiki/%i' % wid)
wids = []
batch_count += 1
dest_dir = ensure_dir_exists(TEXT_DIR + '%s_%i' % (os.path.basename(qqfile), batch_count))
# open tarball for writing
tar_file = dest_dir + '.tgz'
tar = tarfile.open(tar_file, 'w:gz')
wid = int(doc['wid'])
if wid not in wids:
wids.append(wid)
# sanitize and write text
text = '\n'.join(clean_list(doc.get('html_en', '')))
localpath = os.path.join(dest_dir, doc['id'])
with open(localpath, 'w') as f:
f.write(text)
# add text file to tarball
tar.add(localpath, doc['id'])
doc_count += 1
# tar the final batch and send to aws
if tar:
tar.close()
shutil.rmtree(dest_dir)
if aws:
k.key = 'text_events/%s' % os.path.basename(tar_file)
k.set_contents_from_filename(tar_file)
os.remove(tar_file)
for wid in wids:
'I-ARG0': 4,
'I-ARG1': 4,
'I-ARG2': 4,
'I-ARG3': 4
}
number_of_num = len(set(labeles_to_num.values()))
empty_tag = [labeles_to_num['O'] for i in range(max_len)]
sents_srl_features = {}
for sent in pos_sents_srl:
srl = pos_sents_srl[sent]
k = 0
srl_features = []
for v in srl['verbs']:
tags = utils.clean_list(v['tags'], important_tags, 'O')
tags = tags[:max_len] + ['pad' for i in range(max_len - len(tags))]
tags_num = [labeles_to_num[i] for i in tags]
srl_features.append(tags_num)
k += 1
if k == number_of_verbs:
break
srl_features = srl_features + [
empty_tag for i in range(number_of_verbs - len(srl_features))
]
sents_srl_features[sent] = tf.keras.utils.to_categorical(
np.array(srl_features), num_classes=number_of_num)
sents_srl_features[sent] = sents_srl_features[sent].reshape(
30, number_of_num * number_of_verbs)
###parse sentences
def add_each_matching_item_to_results(items, search_terms,
interaction_mappings = {}):
''' Will add each item with a field value that matches a search term to the
results, and will add the matched values to the matches
'''
results = []
matches = []
studies_to_be_checked = []
phenotypes_to_be_checked = []
genotypes_to_be_checked = []
panels_to_be_checked = []
''' 'study_id_to_interaction_id_mapping' is filled when searching through
interactions.
Having that dict saves a for-loop later on in the template. '''
study_id_to_interaction_id_mapping = {}
for item in items:
if type(item) == Interaction:
''' Make sure every item referenced by the interaction is checked
to see if it should be included in the search result
'''
studies_to_be_checked.append(item.study)
if not study_id_to_interaction_id_mapping.has_key(item.study.id):
study_id_to_interaction_id_mapping[item.study.id] = []
study_id_to_interaction_id_mapping[item.study.id].append(item.id)
phenotypes_to_be_checked.append(
interaction_mappings[item.id]['phenotypes'])
genotypes_to_be_checked.append(
interaction_mappings[item.id]['genotypes'])
panels_to_be_checked.append(
interaction_mappings[item.id]['panels'])
else:
if not type(item) == Study:
if not study_id_to_interaction_id_mapping.has_key(item.study.id):
study_id_to_interaction_id_mapping[item.study.id] = []
study_id_to_interaction_id_mapping[item.study.id].append(item.id)
# Group field names and values together
list_of_name_value_pairs = [
(field.name, getattr(item,field.name))
for field in item._meta.fields]
for li in list_of_name_value_pairs:
if not (li[0]=='id' or li[0]=='study'):
# Handle unicode strings to lowercase (since str(u'abc') with a unicode string raises an error)
val = ''
if isinstance( li[1], str ) or isinstance( li[1], unicode ):
val = li[1].lower()
else:
val = str(li[1]).lower()
for term in search_terms:
if term in val:
# A search term has been found in the field value
results.append(item)
matches.append(val)
if type(item) == Interaction:
''' Add the study, genotype, phenotype and
panel related to this interaction '''
if item.study != None:
results.append(item.study)
if len(item.phenotypes.all())!=0:
results.append(item.phenotypes.all()[0])
if len(item.genotypes.all())!=0:
results.append(item.genotypes.all()[0])
if len(item.panels.all())!=0:
results.append(item.panels.all()[0])
return {'results': results,
'matches': matches,
'studies_to_be_checked': utils.clean_list(
studies_to_be_checked),
'phenotypes_to_be_checked': utils.clean_list(
phenotypes_to_be_checked),
'genotypes_to_be_checked': utils.clean_list(
genotypes_to_be_checked),
'panels_to_be_checked': utils.clean_list(panels_to_be_checked),
'study_id_to_interaction_id_mapping':
study_id_to_interaction_id_mapping
}
