--- a/lexmapr/pipeline_helpers.py	Wed Aug 31 14:32:14 2022 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,281 +0,0 @@
-"""Helper functions for main pipeline"""
-
-import inflection, re, unicodedata, sqlite3
-from collections import OrderedDict
-from itertools import combinations
-from dateutil.parser import parse
-from lexmapr.definitions import synonym_db
-from nltk import pos_tag
-from nltk.tokenize import word_tokenize
-from nltk.tokenize.treebank import TreebankWordDetokenizer
-
-
-def _lookup_correction(sample, lookup_table, lookup_x, micro_status, status_title):
-    '''Apply corections, if available in resource'''
-    sample = ' ' + sample + ' '
-    for x in lookup_table[lookup_x]:
-        find_x = re.findall(' '+x+' ', sample)
-        if find_x != []:
-            micro_status.append(status_title + x)
-            sample = sample.replace(' '+x+' ', ' '+lookup_table[lookup_x][x]+' ')
-    return(' '.join(sample.split()), micro_status)
-
-
-def _remove_annotated_synonyms(input_annotations):
-    '''Remove annotations to see original phrase'''
-    output_sample = ''
-    copy_char = True
-    for x in input_annotations:
-        if x == '{':
-            copy_char = False
-        elif x == '}':
-            copy_char = True
-        else:
-            if copy_char == True:
-                output_sample += x
-    while re.search('  ', output_sample):
-        output_sample = output_sample.replace('  ', ' ')
-    return(output_sample)
-
-
-def _retrieve_map_id(search_results, c):
-  '''Get resource id from database'''
-  return_list = []
-  for x in search_results:
-    c.execute('SELECT * FROM non_standard_resource_ids WHERE key=:key', {'key':x[1]})
-    for y in c.fetchall():
-      result_dic = {'term':y[1], 'id':y[0], 'status':[]}
-      if not result_dic in return_list:
-        return_list.append(result_dic)
-  return(return_list)
-
-
-def _map_term_helper(term, c):
-    '''Maps term to resource or resource permutation'''
-    c.execute('SELECT * FROM standard_resource_labels WHERE key=:key', {'key':term})
-    search_results = c.fetchall()
-    if len(search_results) == 0:
-        c.execute('SELECT * FROM standard_resource_permutations WHERE key=:key', {'key':term})
-        search_results = c.fetchall()
-        if len(search_results) != 0:
-            return(_retrieve_map_id(search_results, c))
-    else:
-        return(_retrieve_map_id(search_results, c))
-    return(None)
-
-
-def _ngrams(input_phrase, gram_value):
-    '''Get ngrams with a given value of gram_value'''
-    input_phrase = input_phrase.split()
-    output = []
-    for i in range(len(input_phrase) - gram_value + 1):
-        output.append(input_phrase[i:i + gram_value])
-    return(output)
-
-
-def process_sample(sample, lookup_table, micro_status):
-    '''Apply corrections to input sample'''
-    sample, micro_status = _lookup_correction(sample, lookup_table, 'spelling_mistakes',
-                                          micro_status, 'Spelling Correction Treatment: ')
-    sample, micro_status = _lookup_correction(sample, lookup_table, 'abbreviations',
-                                          micro_status, 'Abbreviation-Acronym Treatment: ')
-    sample, micro_status = _lookup_correction(sample, lookup_table, 'non_english_words',
-                                          micro_status, 'Non English Language Words Treatment: ')
-    return(sample, micro_status)
-
-
-def punctuation_treatment(untreated_term):
-    '''Remove punctuations from term'''
-    punctuations_regex_char_class = '[~`!@#$%^*()_\|/{}:;,.<>?]'
-    ret_term = ''
-    for word_token in untreated_term.split():
-        if word_token.count('-') > 1:
-            ret_term += word_token.replace('-',' ') + ' '
-        else:
-            ret_term += word_token + ' '
-    ret_term = ret_term.lower().replace('\"','').replace('\'ve','').replace('\'m','')
-    ret_term = ret_term.replace('\'s','').replace('\'t','').replace('\'ll','').replace('\'re','')
-    ret_term = ret_term.replace('\'','').replace('-','').replace('[','').replace(']','')
-    ret_term = ret_term.replace('&',' and ').replace('+',' and ').replace('=',' is ')
-    ret_term = re.sub(punctuations_regex_char_class, ' ', ret_term).lower()
-    return(' '.join(ret_term.split()))
-
-
-def further_cleanup(sample_text):
-    '''Remove terms indicated to not be relevant and some compound words'''
-    new_text = []
-    neg_words = [r'no ',r'non',r'not',r'neither',r'nor',r'without']
-    stt_words = ['animal','cb','chicken','environmental','food','human','large','medium','necropsy',
-                 'organic','other','poultry','product','sausage','small','stool','swab','wild',]
-    end_words = ['aspirate','culture','environmental','fluid','food','intestine','large','meal','medium',
-                 'mixed','necropsy','other','poultry','product','research','sample','sausage','slaughter',
-                 'small','swab','water','wild',]
-    not_replace = ['agriculture','apiculture','aquaculture','aquiculture','aviculture',
-                   'coculture','hemoculture','mariculture','monoculture','sericulture',
-                   'subculture','viniculture','viticulture',
-                   'semifluid','subfluid','superfluid',
-                   'superlarge','reenlarge','enlarge','overlarge','largemouth','larges',
-                   'bonemeal','cornmeal','fishmeal','inchmeal','oatmeal','piecemeal','premeal',
-                   'wholemeal','biosample','ensample','resample','subsample','backwater',
-                   'another','bother','brother','foremother','frother','godmother','grandmother',
-                   'housemother','mother','otherguess','otherness','othernesse','otherwhere',
-                   'otherwhile','otherworld','pother','soother','smoother','smother','stepbrother',
-                   'stepmother','tother',
-                   'byproduct','coproduct','production','productive','subproduct',
-                   'ultrasmall','smaller','smallmouth','smalltime','smallpox','smallpoxe',
-                   'smallsword','smallsholder','mediumship',
-                   'bathwater','bilgewater','blackwater','breakwater','cutwater','deepwater',
-                   'dewater','dishwater','eyewater','firewater','floodwater','freshwater',
-                   'graywater','groundwater','headwater','jerkwater','limewater','meltwater',
-                   'overwater','polywater','rainwater','rosewater','saltwater','seawater',
-                   'shearwater','springwater','tailwater','tidewater','underwater','wastewater',
-                   'semiwild','wildcard','wildcat','wildcatter','wildcatted','wildebeest','wilded',
-                   'wilder','wilderment','wilderness','wildernesse','wildest','wildfire','wildflower',
-                   'wildfowl','wildfowler','wildish','wildland','wildling','wildlife','wildwood',
-                  ]
-
-    found_comp = []
-    for comp_word in stt_words:
-        found_comp.extend(re.findall(f'({comp_word})(\w+)', sample_text))
-    for comp_word in end_words:
-        found_comp.extend(re.findall(f'(\w+)({comp_word})', sample_text))
-    for x in found_comp:
-        if x[0]+x[1] not in not_replace and x[0]+x[1]+'s' not in not_replace:
-            sample_text = sample_text.replace(x[0]+x[1], x[0]+' '+x[1])
-
-    for sample_word in sample_text.split():
-        if len(sample_word) > 1:
-            new_text.append(sample_word.strip())
-
-    if 'nor' in new_text:
-        if 'neither' not in new_text:
-            word_ind = new_text.index('nor')
-            new_text.insert(max[0,word_ind-2], 'neither')
-
-    for neg_word in neg_words:
-        if neg_word in new_text:
-            word_ind = new_text.index(neg_word)
-            del(new_text[word_ind:word_ind+2])
-    return(' '.join(new_text))
-
-
-def is_number(input_string):
-    '''Determine whether a string is a number'''
-    try:
-        unicodedata.numeric(input_string)
-        return(True)
-    except(TypeError, ValueError):
-        return(False)
-
-
-def is_date(input_string):
-    '''Determine  whether a string is a date or day'''
-    try:
-        parse(input_string)
-        return(True)
-    except(ValueError, OverflowError):
-        return(False)
-
-
-def singularize_token(token, lookup_table, micro_status, c):
-    '''Singularize the string token, if applicable'''
-    if token in lookup_table['inflection_exceptions']:
-        return(token, micro_status)
-
-    exception_tail_chars_list = ['us', 'ia', 'ta', 'ss'] # TODO: add as, is?
-    for char in exception_tail_chars_list:
-        if token.endswith(char):
-            return(token, micro_status)
-
-    taxon_names = c.execute('''SELECT * FROM standard_resource_labels WHERE key LIKE :key AND 
-                                                                           value LIKE :value''',
-                           {'key':'% '+token,'value':'NCBITaxon%'}).fetchall()
-    remove_finds = []
-    for x in taxon_names:
-        if len(x[0].split()) > 2:
-            remove_finds.append(x)
-    for x in remove_finds:
-        taxon_names.remove(x)
-    if taxon_names != []:
-        return(token, micro_status)
-
-    lemma = inflection.singularize(token)
-    micro_status.append('Inflection (Plural) Treatment: ' + token)
-    return(lemma, micro_status)
-
-
-def get_cleaned_sample(input_sample, token, lookup_table):
-    '''Prepare the cleaned sample phrase using the input token'''
-    if input_sample == '' and token not in lookup_table['stop_words']:
-        return(token)
-    elif token not in lookup_table['stop_words']:
-        return(input_sample + ' ' + token)
-    else:
-        return(input_sample)
-
-
-def get_annotated_sample(annotated_sample, lemma):
-    '''Embed synonyms in the sample, if available'''
-    # TODO: able to annotate permuatations instead of just left to right?
-    synonym_map = {}
-    if not annotated_sample:
-        annotated_sample = lemma
-    else:
-        annotated_sample = f'{annotated_sample} {lemma}'
-
-    conn_syn = sqlite3.connect(synonym_db)
-    d = conn_syn.cursor()
-    for y in [lemma, _remove_annotated_synonyms(annotated_sample)]:
-        d.execute('SELECT * FROM label_synonyms WHERE key=:key', {'key':y})
-        for x in d.fetchall():
-            if not re.search(x[1], annotated_sample):
-                annotated_sample = annotated_sample+' {'+x[1]+'}'
-                synonym_map[y] = x[1]
-    conn_syn.close()
-    return(annotated_sample, synonym_map)
-
-
-def map_term(term, lookup_table, c, consider_suffixes=False):
-    '''Map term to some resource in database'''
-    if consider_suffixes:
-        for suffix in lookup_table['suffixes']:
-            mapping = _map_term_helper(term+' '+suffix, c)
-            if mapping:
-                for x in mapping:
-                    x['status'].insert(-2, 'Suffix Addition')
-                return(mapping)
-    else:
-        mapping = _map_term_helper(term, c)
-        if mapping:
-            return(mapping)
-    return([])
-
-
-def annotation_reduce(annotated_sample, synonym_map):
-    '''Remove annotations on shorter phrases included in longer phrases with annotations'''
-    remove_list = []
-    for x in list(synonym_map.keys()):
-        for y in list(synonym_map.keys()):
-            if x != y:
-                if x.startswith(y) or x.endswith(y) == True:
-                    remove_list.append(y)
-    for x in remove_list:
-        annotated_sample = annotated_sample.replace('{'+synonym_map[x]+'}',' ')
-    return(' '.join(annotated_sample.split()))
-
-
-def get_annotated_synonyms(input_annotations):
-    '''Get list of the annotations'''
-    synonym_list = []
-    for x in input_annotations.split('{')[1:]:
-        synonym_list.append(x.split('}')[0])
-    return(synonym_list)
-
-
-def get_gram_chunks(input_phrase, num):
-    '''Make num-gram chunks from input'''
-    input_tokens = input_phrase.split()
-    if len(input_tokens) < 15:
-        return(list(combinations(input_tokens, num)))
-    else:
-        return(_ngrams(input_phrase, num))