本文整理汇总了Python中elastic.search.Search.get_mapping方法的典型用法代码示例。如果您正苦于以下问题：Python Search.get_mapping方法的具体用法？Python Search.get_mapping怎么用？Python Search.get_mapping使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类elastic.search.Search的用法示例。
在下文中一共展示了Search.get_mapping方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_gene_history_loader
# 需要导入模块: from elastic.search import Search [as 别名]
# 或者: from elastic.search.Search import get_mapping [as 别名]
def test_gene_history_loader(self):
""" Test the gene history loading. """
call_command("pipeline", "--steps", "load", sections="GENE_HISTORY", dir=TEST_DATA_DIR, ini=MY_INI_FILE)
INI_CONFIG = IniParser().read_ini(MY_INI_FILE)
idx = INI_CONFIG["GENE_HISTORY"]["index"]
idx_type = INI_CONFIG["GENE_HISTORY"]["index_type"]
elastic = Search(idx=idx, idx_type=idx_type)
Search.index_refresh(idx)
self.assertTrue(elastic.get_count()["count"] > 1, "Count documents in the index")
map1_props = Gene.gene_history_mapping(idx, idx_type, test_mode=True).mapping_properties
map2_props = elastic.get_mapping()
if idx not in map2_props:
logger.error("MAPPING ERROR: " + json.dumps(map2_props))
self._cmpMappings(map2_props[idx]["mappings"], map1_props, idx_type)示例2: _search_engine
# 需要导入模块: from elastic.search import Search [as 别名]
# 或者: from elastic.search.Search import get_mapping [as 别名]
def _search_engine(query_dict, user_filters, user):
''' Carry out a search and add results to the context object. '''
user_query = query_dict.get("query")
query = _gene_lookup(user_query)
source_filter = [
'symbol', 'synonyms', "dbxrefs.*", 'biotype', 'description', # gene
'id', 'rscurrent', 'rshigh', # marker
'journal', 'title', 'tags.disease', # publication
'name', 'code', # disease
'study_id', 'study_name', # study
'region_name', 'marker'] # regions
if re.compile(r'^[0-9 ]+$').findall(query):
source_filter.append('pmid') # publication - possible PMID(s)
search_fields = []
maxsize = 20
if user_filters.getlist("maxsize"):
maxsize = int(user_filters.get("maxsize"))
# build search_fields from user input filter fields
for it in user_filters.items():
if len(it) == 2:
if it[0] == 'query':
continue
parts = it[1].split(":")
if len(parts) == 3:
search_fields.append(parts[1]+"."+parts[2])
elif len(parts) == 2:
search_fields.append(parts[1])
if len(search_fields) == 0:
search_fields = list(source_filter)
search_fields.extend(['abstract', 'authors.name', # publication
'authors', 'pmids', # study
'markers', 'genes']) # study/region
source_filter.extend(['date', 'pmid', 'build_id', 'ref', 'alt', 'chr_band',
'disease_locus', 'disease_loci', 'region_id'])
idx_name = query_dict.get("idx")
idx_dict = ElasticSettings.search_props(idx_name, user)
query_filters = _get_query_filters(user_filters, user)
highlight = Highlight(search_fields, pre_tags="<strong>", post_tags="</strong>", number_of_fragments=0)
sub_agg = Agg('idx_top_hits', 'top_hits', {"size": maxsize, "_source": source_filter,
"highlight": highlight.highlight['highlight']})
aggs = Aggs([Agg("idxs", "terms", {"field": "_index"}, sub_agg=sub_agg),
Agg("biotypes", "terms", {"field": "biotype", "size": 0}),
Agg("categories", "terms", {"field": "_type", "size": 0})])
# create score functions
score_fns = _build_score_functions(idx_dict)
equery = BoolQuery(must_arr=Query.query_string(query, fields=search_fields),
should_arr=_auth_arr(user),
b_filter=query_filters,
minimum_should_match=1)
search_query = ElasticQuery(FunctionScoreQuery(equery, score_fns, boost_mode='replace'))
elastic = Search(search_query=search_query, aggs=aggs, size=0,
idx=idx_dict['idx'], idx_type=idx_dict['idx_type'])
result = elastic.search()
mappings = elastic.get_mapping()
_update_mapping_filters(mappings, result.aggs)
_update_biotypes(user_filters, result)
return {'data': _top_hits(result), 'aggs': result.aggs,
'query': user_query, 'idx_name': idx_name,
'fields': search_fields, 'mappings': mappings,
'hits_total': result.hits_total,
'maxsize': maxsize, 'took': result.took}示例3: test_gene_pipeline
# 需要导入模块: from elastic.search import Search [as 别名]
# 或者: from elastic.search.Search import get_mapping [as 别名]
def test_gene_pipeline(self):
""" Test gene pipeline. """
INI_CONFIG = IniParser().read_ini(MY_INI_FILE)
idx = INI_CONFIG["ENSEMBL_GENE_GTF"]["index"]
idx_type = INI_CONFIG["ENSEMBL_GENE_GTF"]["index_type"]
""" 1. Test ensembl GTF loading. """
call_command(
"pipeline", "--steps", "stage", "load", sections="ENSEMBL_GENE_GTF", dir=TEST_DATA_DIR, ini=MY_INI_FILE
)
Search.index_refresh(idx)
elastic = Search(idx=idx, idx_type=idx_type)
self.assertGreaterEqual(elastic.get_count()["count"], 1, "Count documents in the index")
map1_props = Gene.gene_mapping(idx, idx_type, test_mode=True).mapping_properties
map2_props = elastic.get_mapping()
if idx not in map2_props:
logger.error("MAPPING ERROR: " + json.dumps(map2_props))
self._cmpMappings(map2_props[idx]["mappings"], map1_props, idx_type)
""" 2. Test adding entrez ID to documents """
call_command("pipeline", "--steps", "load", sections="GENE2ENSEMBL", dir=TEST_DATA_DIR, ini=MY_INI_FILE)
Search.index_refresh(idx)
query = ElasticQuery.query_string("PTPN22", fields=["symbol"])
elastic = Search(query, idx=idx)
docs = elastic.search().docs
self.assertEqual(len(docs), 1)
self.assertTrue("entrez" in getattr(docs[0], "dbxrefs"))
self.assertEqual(getattr(docs[0], "dbxrefs")["entrez"], "26191")
""" 3. Add uniprot and fill in missing entrez fields. """
call_command(
"pipeline", "--steps", "download", "load", sections="ENSMART_GENE", dir=TEST_DATA_DIR, ini=MY_INI_FILE
)
Search.index_refresh(idx)
query = ElasticQuery.query_string("DNMT3L", fields=["symbol"])
elastic = Search(query, idx=idx)
docs = elastic.search().docs
self.assertTrue("entrez" in getattr(docs[0], "dbxrefs"))
self.assertTrue("swissprot" in getattr(docs[0], "dbxrefs"))
""" 4. Add gene synonyms and dbxrefs. """
call_command("pipeline", "--steps", "load", sections="GENE_INFO", dir=TEST_DATA_DIR, ini=MY_INI_FILE)
Search.index_refresh(idx)
query = ElasticQuery.query_string("PTPN22", fields=["symbol"])
elastic = Search(query, idx=idx)
docs = elastic.search().docs
self.assertTrue("PTPN8" in getattr(docs[0], "synonyms"))
""" 5. Add PMIDs to gene docs. """
call_command("pipeline", "--steps", "load", sections="GENE_PUBS", dir=TEST_DATA_DIR, ini=MY_INI_FILE)
Search.index_refresh(idx)
query = ElasticQuery.query_string("PTPN22", fields=["symbol"])
elastic = Search(query, idx=idx)
docs = elastic.search().docs
self.assertGreater(len(getattr(docs[0], "pmids")), 0)
""" 6. Add ortholog data. """
call_command("pipeline", "--steps", "load", sections="ENSMART_HOMOLOG", dir=TEST_DATA_DIR, ini=MY_INI_FILE)
Search.index_refresh(idx)
query = ElasticQuery.query_string("PTPN22", fields=["symbol"])
elastic = Search(query, idx=idx)
docs = elastic.search().docs
dbxrefs = getattr(docs[0], "dbxrefs")
self.assertTrue("orthologs" in dbxrefs, dbxrefs)
self.assertTrue("mmusculus" in dbxrefs["orthologs"], dbxrefs)
self.assertEqual("ENSMUSG00000027843", dbxrefs["orthologs"]["mmusculus"]["ensembl"])
query = ElasticQuery.filtered(
Query.match_all(),
TermsFilter.get_terms_filter("dbxrefs.orthologs.mmusculus.ensembl", ["ENSMUSG00000027843"]),
)
docs = Search(query, idx=idx, size=1).search().docs
self.assertEqual(len(docs), 1)
""" 7. Add mouse ortholog link to MGI """
call_command("pipeline", "--steps", "load", sections="ENSEMBL2MGI", dir=TEST_DATA_DIR, ini=MY_INI_FILE)
Search.index_refresh(idx)
docs = Search(query, idx=idx, size=1).search().docs
dbxrefs = getattr(docs[0], "dbxrefs")
self.assertEqual("ENSMUSG00000027843", dbxrefs["orthologs"]["mmusculus"]["ensembl"])
self.assertEqual("107170", dbxrefs["orthologs"]["mmusculus"]["MGI"])