mirror of
https://gitlab.ub.uni-bielefeld.de/sfb1288inf/nopaque.git
synced 2025-11-04 12:22:47 +00:00
Remove wrapper
This commit is contained in:
Patrick Jentsch
@@ -1,321 +0,0 @@
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from .api import APIClient
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from .api.specification import CONST_FIELD_MATCH, CONST_FIELD_MATCHEND
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import time
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class CQiWrapper(APIClient):
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'''
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CQIiWrapper object
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High level wrapper that groups and renames some functions of CQiClient
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for ease of use. Also structures recieved data into python dictionaries.
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Keyword arguments:
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host -- host IP adress or hostname wher the cqp server is running
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port -- port of the cqp server
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username -- username used to connect to the cqp server
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password -- password of the user to connect to the cqp server
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'''
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SUBCORPUS_NAMES = []
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def __init__(self, host='127.0.0.1', port=4877, username='anonymous',
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password=''):
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super(CQiWrapper, self).__init__(host, port=port)
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self.username = username
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self.password = password
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def connect(self):
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'''
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Connect with CQP server
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Connects via socket to the CQP server using the given username and
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password from class initiation.
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'''
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self.ctrl_connect(self.username, self.password)
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def __create_attribute_strings(self):
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'''
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Creates all needed attribute strings to query for word, lemma etc. in
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the given corpus.
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For example: CORPUS_NAME.word to query words
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Automaticalle creates strings for all pre defined tags.
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'''
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p_attrs = self.corpus_positional_attributes(self.corpus_name)
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struct_attrs = self.corpus_structural_attributes(self.corpus_name)
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self.attr_strings = {}
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self.attr_strings['positional_attrs'] = {}
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self.attr_strings['struct_attrs'] = {}
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for p_attr in p_attrs:
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self.attr_strings['positional_attrs'][p_attr] = (self.corpus_name
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+ '.'
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+ p_attr)
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for struct_attr in struct_attrs:
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self.attr_strings['struct_attrs'][struct_attr] = (self.corpus_name
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+ '.'
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+ struct_attr)
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print(('All positional and '
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'structural attributes: {}').format(self.attr_strings))
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def select_corpus(self, corpus_name):
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'''
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Checks if given copus name exists. If it exists set it as the main
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corpus name used to create the needed query attribute strings like
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CORPUS_NAME.word.
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'''
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if corpus_name in self.corpus_list_coprora():
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self.corpus_name = corpus_name
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self.__create_attribute_strings()
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print('{} does exist.'.format(corpus_name))
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else:
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print('{} does not exist.'.format(corpus_name))
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raise Exception('Given Corpus Name is not in corpora list.')
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def disconnect(self):
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'''
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Disconnect from CQP server
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Disconnects from the CQP server. Closes used socket after disconnect.
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'''
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self.ctrl_bye()
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print('Disconnected from cqp server.')
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def query_subcorpus(self, query, result_subcorpus_name='Query-results'):
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'''
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Create subcorpus
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Input query will be used to create a subcorpus holding all cpos match
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positions for that query.
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Keyword arguments:
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result_subcorpus_name -- set name of the subcorpus which holds all
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cpos match positions, produced by the query
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query -- query written in cqp query language
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'''
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self.query = query
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self.cqp_query(self.corpus_name, result_subcorpus_name, query)
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self.result_subcorpus = (self.corpus_name
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+ ':'
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+ result_subcorpus_name)
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self.SUBCORPUS_NAMES.append(self.result_subcorpus)
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self.match_count = self.cqp_subcorpus_size(self.result_subcorpus)
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print('Nr of all matches is: {}'.format(self.match_count))
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def show_subcorpora(self):
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'''
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Show all subcorpora currently saved by the cqp server.
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'''
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return self.cqp_list_subcorpora(self.corpus_name)
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def show_query_results(self,
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context_len=10,
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result_len=1000,
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result_offset=0):
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'''
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Show query results
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Shows the actual matched strings produce by the query. Uses the cpos
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match indexes to grab those strings. saves them into an orderd
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dictionary. Also saves coresponding tags, lemmas and context. Gets those
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informations using the corresponding cpos.
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Keyword arguments:
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context_len -- defines how many words before and after a match will be
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shown (default 10)
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result_len -- defines for how many matches all informations like lemma
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and POS are being grabbed
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result_offset -- defines the offset of the matches being requested. If
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the offset is 100 informations for matches 100 to result_len are being
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grabbed
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'''
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t0 = time.time()
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self.context_len = context_len
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self.corpus_max_len = self.cl_attribute_size(
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self.attr_strings['positional_attrs']['word']
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)
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self.nr_matches = min(result_len, self.match_count)
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if self.match_count == 0:
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print('Query resulted in 0 matches.')
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self.results = {'code': 0,
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'result': {'matches': [],
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'match_count': self.match_count,
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'cpos_lookup': {},
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'text_lookup': {}}
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}
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return self.results
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else:
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# Get match cpos boundries
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# match_boundries shows the start and end cpos of one match as a
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# pair of cpositions
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# [(1355, 1357), (1477, 1479)] Example for two boundry pairs
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offset_start = 0 if result_offset == 0 else result_offset
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print('Offset start is: {}'.format(offset_start))
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offset_end = min((self.nr_matches + result_offset - 1), self.match_count - 1)
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print('Offset end is: {}'.format(offset_end))
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match_boundaries = zip(self.cqp_dump_subcorpus(self.result_subcorpus,
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CONST_FIELD_MATCH,
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offset_start,
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offset_end),
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self.cqp_dump_subcorpus(self.result_subcorpus,
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CONST_FIELD_MATCHEND,
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offset_start,
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offset_end))
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# Generate all cpos between match boundries including start and end
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# boundries.
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# Also generate cpos for left and right context.
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# Save those cpos into dict as lists for the keys 'lc', 'hit' and 'rc'
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# Also collect all cpos together in one list for the final request of
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# all cpos informations
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all_matches = []
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all_cpos = []
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for start, end in match_boundaries:
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end += 1
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lc_cpos = list(range(max([0, start - self.context_len]), start))
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lc = {'lc': lc_cpos}
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match_cpos = list(range(start, end))
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match = {'hit': match_cpos}
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rc_cpos = list(range(end, min([self.corpus_max_len,
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end + self.context_len])))
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rc = {'rc': rc_cpos}
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lc.update(match)
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lc.update(rc)
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all_cpos.extend(lc_cpos + match_cpos + rc_cpos)
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all_matches.append(lc)
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all_cpos = list(set(all_cpos)) # get rid of cpos duplicates
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len_all_cpos = len(all_cpos)
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t1 = time.time()
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t_total = t1 - t0
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print('Time to create all CPOS for query: {}'.format(t_total))
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print('Requesting {} CPOS with one query.'.format(len_all_cpos))
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# Get cpos informations like CORPUS_NAME.word or CORPUS_NAME.lemma for
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# all cpos entries in all_cpos_list
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# Also saves these informations into self.results dict
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t2 = time.time()
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all_cpos_infos, text_lookup = self.get_cpos_infos(all_cpos)
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t3 = time.time()
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t_final = t3 - t2
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print('Got infos for {} CPOS in {} seconds:'.format(len_all_cpos,
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t_final))
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self.results = {'code': 0,
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'result': {'matches': all_matches,
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'match_count': self.match_count,
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'cpos_lookup': all_cpos_infos,
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'text_lookup': text_lookup}
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}
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return self.results
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def get_cpos_infos(self, all_cpos):
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'''
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Get cpos informations like CORPUS_NAME.word or CORPUS_NAME.lemma for
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all cpos entries specified in the parameter all_cpos.
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'''
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# Get all positional attribute informations
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cpos_infos = {}
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for p_attr_key in self.attr_strings['positional_attrs'].keys():
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match_strs = self.cl_cpos2str(self.attr_strings['positional_attrs'][p_attr_key], all_cpos)
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cpos_infos[p_attr_key] = match_strs
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# Get all strucutural attribute informations
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tmp_info = {}
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structs_to_check = []
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for struct_attr_key in self.attr_strings['struct_attrs'].keys():
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key = self.attr_strings['struct_attrs'][struct_attr_key]
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has_value = self.corpus_structural_attribute_has_values(key)
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struct_ids = self.cl_cpos2struc(key, all_cpos)
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if has_value is False: # Get IDs of strucutural elements without values (this means get IDs of XML tags. Struct elements only have values if they are XML attributes)
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tmp_info[struct_attr_key] = []
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for id in struct_ids:
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tmp_info[struct_attr_key].append(id)
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else:
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structs_to_check.append({key: struct_attr_key})
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print('Structs to check: {}'.format(structs_to_check))
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struct_attr_values = list(tmp_info.values())
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# print('Struct attr value list: {}'.format(struct_attr_values))
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struct_attr_keys = list(tmp_info.keys())
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# print('Struct attr key list: {}'.format(struct_attr_keys))
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# Build textlookup dictionary
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text_lookup_ids = list(set(struct_attr_values[0])) # every CPOS is associated with one text id. A set is build to only gather text_lookup informations for every unique text id
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text_lookup = {} # final dict containing all info of one text identified by its id
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for d in structs_to_check:
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s_key, s_value = zip(*d.items())
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print('dict entries: {}: {}'.format(s_key, s_value))
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s_value = s_value[0].split('_', 1)[-1]
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print('S_VALUE: {}'.format(s_value))
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struct_values = self.cl_struc2str(s_key[0], text_lookup_ids)
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print('Extracted Value with key {}: {}'.format(s_key[0], struct_values))
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zipped = dict(zip(text_lookup_ids, struct_values))
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for zip_key, zip_value in zipped.items():
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print('Text id as key is: {}'.format(zip_key))
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print('Value of this text is: {}'.format(zip_value))
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check = text_lookup.get(zip_key)
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print('check: {}'.format(check))
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if check is None:
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text_lookup[zip_key] = {s_value: zip_value}
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else:
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text_lookup[zip_key].update({s_value: zip_value})
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# zip keys and values together
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attr_values_list = []
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attr_keys_list = []
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for key in cpos_infos.keys():
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attr_values_list.append(cpos_infos[key])
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attr_keys_list.append(key)
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attr_keys_list.extend(struct_attr_keys)
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attr_values_list.extend(struct_attr_values)
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joined_cpos_infos = zip(all_cpos, *attr_values_list)
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dict_cpos_infos = {}
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for info in joined_cpos_infos:
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dict_cpos_infos[info[0]] = dict(zip(attr_keys_list, info[1:]))
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return dict_cpos_infos, text_lookup
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def get_sentences(self,
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match_cpos_list,
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get_surrounding_s=False,
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l_r_s_context_additional_len=1):
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'''
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Get sentence informations for one match also set if and how much left
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right context sentences should be grabbed surrounding the given CPOS.
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'''
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t0 = time.time()
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key = self.corpus_name + '.s'
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first_cpos, last_cpos = match_cpos_list[0], match_cpos_list[-1]
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context_sentences = {}
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s_ids = self.cl_cpos2struc(key, [first_cpos, last_cpos])
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print('s id match: {}'.format(s_ids))
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for s_id in s_ids:
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s_start, s_end = self.cl_struc2cpos(key, s_id)
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s_cpos = list(range(s_start, s_end + 1))
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context_sentences[s_id] = s_cpos
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if get_surrounding_s:
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max_s_id = self.cl_attribute_size(key) - 1
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print('max sid: {}'.format(max_s_id))
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additional_s_ids = []
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additional_s = list(range(max(s_ids[0]
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- l_r_s_context_additional_len,
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0),
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min(s_ids[-1]
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+ l_r_s_context_additional_len,
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max_s_id) + 1))
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additional_s_ids.extend(additional_s)
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for s_id in additional_s_ids:
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print('s id additional: {}'.format(s_id))
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s_start, s_end = self.cl_struc2cpos(key, s_id)
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s_cpos = list(range(s_start, s_end + 1))
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context_sentences[s_id] = s_cpos
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all_cpos = []
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for key in context_sentences.keys():
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all_cpos.extend(context_sentences[key])
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all_cpos = list(set(all_cpos))
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all_cpos_infos, text_lookup = self.get_cpos_infos(all_cpos)
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t1 = time.time()
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t_total = t1 - t0
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print('Got all sentences informations in {} seconds'. format(t_total))
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match_context = {'context_s_cpos': context_sentences,
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'cpos_lookup': all_cpos_infos,
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'text_lookup': text_lookup,
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'match_cpos_list': match_cpos_list}
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return match_context
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