def single_schema(species_id, schema_id, virtuoso_graph, local_sparql, base_url):
"""
"""
start = time.time()
start_date = dt.datetime.now()
start_date_str = dt.datetime.strftime(start_date, '%Y-%m-%dT%H:%M:%S')
logging.info('Started determination of loci and alleles counts at: {0}'.format(start_date_str))
# create species uri
species_uri = '{0}species/{1}'.format(base_url, species_id)
species_result = aux.get_data(SPARQLWrapper(local_sparql),
sparql_queries.SELECT_SINGLE_SPECIES.format(virtuoso_graph, species_uri))
result_data = species_result['results']['bindings']
if len(result_data) == 0:
logging.warning('Could not find species with identifier {0}. '
'Aborting.\n\n'.format(species_id))
sys.exit(1)
schema_uri = '{0}/schemas/{1}'.format(species_uri, schema_id)
schema_info = aux.get_data(SPARQLWrapper(local_sparql),
(sparql_queries.SELECT_SPECIES_SCHEMA.format(virtuoso_graph, schema_uri)))
schema_properties = schema_info['results']['bindings']
if len(schema_properties) == 0:
logging.warning('Could not find properties values for schema with identifier {0}. '
'Aborting.\n\n'.format(schema_id))
sys.exit(1)
last_modified = schema_properties[0]['last_modified']['value']
# list files in folder
computed_dir = Config.PRE_COMPUTE
computed_files = os.listdir(computed_dir)
# check if folder with schema alleles lengths files exists
lengths_dir = '{0}_{1}_lengths'.format(species_id, schema_id)
# get files with species prefix
species_prefix = 'loci_{0}'.format(species_id)
species_files = [f for f in computed_files if f.startswith(species_prefix)]
species_file = os.path.join(computed_dir, '{0}.json'.format(species_prefix))
if len(species_files) == 0:
create_file(species_file, {'message': []})
if lengths_dir in computed_files:
lengths_dir = os.path.join(computed_dir, lengths_dir)
fast_update(schema_uri, last_modified, species_file, lengths_dir)
else:
full_update(schema_uri, last_modified, species_file, virtuoso_graph,
local_sparql)
end = time.time()
delta = end - start
print(delta)
def full_update(schema, last_modified, file, virtuoso_graph,
local_sparql):
"""
"""
schema_id = int(schema.split('/')[-1])
current_file = file
# read current file
with open(current_file, 'r') as json_file:
json_data = json.load(json_file)
json_schemas = json_data['message']
schemas_indexes = {int(s['schema'].split('/')[-1]): i for i, s in enumerate(json_schemas)}
# if the schema is in the json file
if schema_id in schemas_indexes:
current_schema = json_schemas[schemas_indexes[schema_id]]
# get modification date in json file
json_date = current_schema['last_modified']
virtuoso_date = last_modified
if json_date == virtuoso_date:
logging.info('Information about number of loci and number of alleles for schema {0} is up-to-date.'.format(schema))
elif json_date != virtuoso_date:
result = aux.get_data(SPARQLWrapper(local_sparql),
(sparql_queries.COUNT_SINGLE_SCHEMA_LOCI_ALLELE.format(virtuoso_graph, schema)))
result_data = result['results']['bindings']
loci_data = [{'locus': r['locus']['value'], 'nr_alleles': r['nr_alleles']['value']} for r in result_data]
proc_data = {'schema': schema,
'last_modified': virtuoso_date,
'loci': loci_data}
json_data['message'][schemas_indexes[schema_id]] = proc_data
with open(current_file, 'w') as json_outfile:
json.dump(json_data, json_outfile)
logging.info('Updated data for schema {0}'.format())
# new schema that is not in the json file
elif schema_id not in schemas_indexes:
result = aux.get_data(SPARQLWrapper(local_sparql),
(sparql_queries.COUNT_SINGLE_SCHEMA_LOCI_ALLELE.format(virtuoso_graph, schema)))
result_data = result['results']['bindings']
loci_data = [{'locus': r['locus']['value'], 'nr_alleles': r['nr_alleles']['value']} for r in result_data]
proc_data = {'schema': schema,
'last_modified': last_modified,
'loci': loci_data}
if len(result_data) > 0:
json_data['message'].append(proc_data)
with open(current_file, 'w') as json_outfile:
json.dump(json_data, json_outfile)
def schema_loci(schema_uri, local_sparql, virtuoso_graph):
""" Gets the list of loci for a schema.
Parameters
----------
schema_uri : str
The URI of the schema in the Chewie-NS.
Returns
-------
loci_list : list of tup
A list with tuples. Each tuple has two
elements, a locus name and a locus URI.
"""
# get loci
loci_result = aux.get_data(
SPARQLWrapper(local_sparql),
(sq.SELECT_SCHEMA_LOCI.format(virtuoso_graph, schema_uri)))
# check if schema has loci
loci_list = loci_result['results']['bindings']
if loci_list != []:
loci_list = [(l['name']['value'], l['locus']['value'])
for l in loci_list]
return loci_list
def get_species(local_sparql, virtuoso_graph):
""" Gets the list of species in the Chewie-NS.
This function has no arguments but expects
that the SPARQL endpoint and default Virtuoso
Graph be set as OS environment variables.
Returns
-------
species_list : dict
A dictionary with species URIs as keys and species
names as values. None if species has no schemas.
"""
# get the list of species in NS
species_result = aux.get_data(
SPARQLWrapper(local_sparql),
(sq.SELECT_SPECIES.format(virtuoso_graph, ' typon:name ?name. ')))
species = species_result['results']['bindings']
if len(species) == 0:
species_list = None
else:
species_list = {
s['species']['value']: s['name']['value']
for s in species
}
return species_list
def determine_date(schema_uri, local_sparql, virtuoso_graph):
""" Gets the last modification date for a schema.
Parameters
----------
schema_uri : str
The URI of the schema in the Chewie-NS.
Returns
-------
A list with the following variables:
- last_date (str): The last modification date in
the format YYYY-MM-DDTHH:MM:SS.f.
- lock_state (str): Locking state of the schema.
- schema_info (dict): A dictionary with schema
properties values.
"""
# get schema last modification date
date_result = aux.get_data(
SPARQLWrapper(local_sparql),
(sq.SELECT_SPECIES_SCHEMA.format(virtuoso_graph, schema_uri)))
schema_info = date_result['results']['bindings'][0]
lock_state = schema_info['Schema_lock']['value']
last_date = schema_info['last_modified']['value']
return [last_date, lock_state, schema_info]
def rm_loci(identifier, virtuoso_graph, local_sparql, base_url, virtuoso_user,
virtuoso_pass):
"""
"""
total_triples = 0
# check input type
if os.path.isfile(identifier) is False:
if ',' in identifier:
loci_ids = identifier.split(',')
else:
loci_ids = [identifier]
else:
with open(identifier, 'r') as ids:
loci_ids = [l.strip() for l in ids.readlines()]
# create loci URIs
loci_uris = ['{0}loci/{1}'.format(base_url, i) for i in loci_ids]
logging.info('Started rm process for loci: {0}.'.format(loci_ids))
# check if loci exist
invalid = []
for locus in loci_uris:
locus_result = aux.get_data(SPARQLWrapper(local_sparql),
(sq.ASK_LOCUS.format(locus)))
if locus_result['boolean'] is not True:
invalid.append(locus)
logging.info('Could not find locus {0}.\n'.format(locus))
# exclude invalid URIs
loci_uris = [l for l in loci_uris if l not in invalid]
print('\nLoci to delete: {0}\n'.format(loci_uris))
results = collapse_loci(loci_uris, virtuoso_graph, local_sparql,
virtuoso_user, virtuoso_pass)
total_triples += results[0]
print('\nDeleted a total of {0} triples.'.format(total_triples))
logging.info('Deleted a total of {0} triples.'.format(total_triples))
print('({0} loci, {1} species links, {2} schema links, '
'{3} alleles)'.format(results[2], results[3], results[4],
results[1]))
logging.info('({0} loci, {1} species links, {2} schema links, '
'{3} alleles)'.format(results[2], results[3], results[4],
results[1]))
return_dict = {
'loci': results[2],
'splinks': results[3],
'sclinks': results[4],
'alleles': results[1],
'total_triples': total_triples
}
return return_dict
def create_queries(locus_file, virtuoso_graph, local_sparql, base_url):
"""
"""
# get sequences sent by user
with open(locus_file, 'rb') as f:
locus_data = pickle.load(f)
locus_url = locus_data[0]
locus_id = locus_url.split('/')[-1]
# get sequences in the NS
sequences = fasta_sequences(locus_url, local_sparql, virtuoso_graph)
ns_seqs = fasta_seqs = {
f['nucSeq']['value']: f['allele_id']['value']
for f in sequences
}
# count number of alleles for locus
count_query = (sq.COUNT_LOCUS_ALLELES.format(virtuoso_graph, locus_url))
count_res = aux.get_data(SPARQLWrapper(local_sparql), count_query)
start_id = int(count_res['results']['bindings'][0]['count']['value']) + 1
spec_name = locus_data[1]
user_url = locus_data[2]
alleles = locus_data[3]
novel = [a for a in alleles if a not in ns_seqs]
repeated = {
hashlib.sha256(a.encode('utf-8')).hexdigest(): ns_seqs[a]
for a in alleles if a in ns_seqs
}
attributed = {}
if len(novel) > 0:
max_length = max([len(a) for a in novel])
if max_length < 7000:
queries, attributed = create_multiple_insert(
novel, spec_name, locus_url, user_url, start_id, base_url,
virtuoso_graph, attributed)
else:
queries, attributed = create_single_insert(novel, spec_name,
locus_url, user_url,
start_id, base_url,
virtuoso_graph,
attributed)
queries_file = '{0}_queries'.format(locus_file.split('alleles')[0])
with open(queries_file, 'wb') as qf:
pickle.dump(queries, qf)
return [queries_file, locus_id, repeated, attributed]
else:
return [None, locus_id, repeated, attributed]
def rm_loci_links(mode, identifier, virtuoso_graph, local_sparql, base_url,
virtuoso_user, virtuoso_pass):
"""
"""
total_triples = 0
# check input type
if os.path.isfile(identifier) is False:
if ',' in identifier:
loci_ids = identifier.split(',')
else:
loci_ids = [identifier]
else:
with open(identifier, 'r') as ids:
loci_ids = [l.strip() for l in ids.readlines()]
# create loci URIs
loci_uris = ['{0}loci/{1}'.format(base_url, i) for i in loci_ids]
logging.info('Started rm process to delete {0} for loci: {1}.'.format(
mode, loci_uris))
# check if loci exist
invalid = []
for locus in loci_uris:
locus_result = aux.get_data(SPARQLWrapper(local_sparql),
(sq.ASK_LOCUS.format(locus)))
if locus_result['boolean'] is not True:
invalid.append(locus)
logging.info('Could not find locus {0}.\n'.format(locus))
# exclude invalid URIs
loci_uris = [[l] for l in loci_uris if l not in invalid]
if mode == 'splinks':
statement = sq.DELETE_SPECIES_LOCUS
elif mode == 'sclinks':
statement = sq.DELETE_SCHEMA_LOCUS
# delete loci links to species
print('Deleting loci {0}...'.format(mode))
deleted, stderr, noeffect, triples = \
multiple_delete(statement, loci_uris, virtuoso_graph,
local_sparql, virtuoso_user, virtuoso_pass)
total_links = int(triples) if mode == 'splinks' else int(triples / 4)
stdout_text = 'Deleted {0} {1} ({2} triples).'.format(
deleted, mode, triples)
log_results(stdout_text, stderr, noeffect)
return_dict = {'{0}'.format(mode): total_links, 'total_triples': triples}
return return_dict
def count_alleles(schema, virtuoso_graph, local_sparql):
"""
"""
# get total number of alleles
loci = aux.get_data(
SPARQLWrapper(local_sparql),
sparql_queries.COUNT_SCHEMA_ALLELES.format(virtuoso_graph, schema))
loci = loci['results']['bindings']
total_alleles = sum(map(int, [a['nr_allele']['value'] for a in loci]))
return total_alleles
def global_species(virtuoso_graph, local_sparql, base_url):
"""
"""
# get all species in the NS
species_result = aux.get_data(SPARQLWrapper(local_sparql),
sparql_queries.SELECT_SPECIES.format(virtuoso_graph, ' typon:name ?name. '))
result_data = species_result['results']['bindings']
ns_species = {s['species']['value']: s['name']['value'] for s in result_data}
species_ids = [s.split('/')[-1] for s in ns_species]
for i in species_ids:
single_species(i, virtuoso_graph, local_sparql, base_url)
def alleles_lengths(total_alleles, schema, offset, limit, virtuoso_graph,
local_sparql):
"""
"""
limit = limit
offset = offset
count = 0
result = []
while count != total_alleles:
alleles = aux.get_data(
SPARQLWrapper(local_sparql),
sparql_queries.SELECT_ALLELES_LENGTH.format(
virtuoso_graph, schema, offset, limit))
data = alleles['results']['bindings']
result.extend(data)
count += len(data)
offset += limit
return result
def loci_annotations(schema, virtuoso_graph, local_sparql):
"""
"""
# get total number of alleles
tries = 0
bah = False
while bah is False:
loci = aux.get_data(
SPARQLWrapper(local_sparql),
sparql_queries.SELECT_SCHEMA_LOCI_ANNOTATIONS.format(
virtuoso_graph, schema))
try:
annotations = loci['results']['bindings']
bah = True
except:
print(
sparql_queries.SELECT_SCHEMA_LOCI_ANNOTATIONS.format(
virtuoso_graph, schema))
logging.warning('Could not get annotations.')
logging.warning(loci)
tries += 1
if tries == 5:
sys.exit('This schema makes no sense!')
annotations = [{
'locus': l['locus']['value'],
'name': l['name']['value'],
'original_name': l['original_name']['value'],
'UniprotName': l['UniprotName']['value'],
'UniprotURI': l['UniprotURI']['value'],
'UserAnnotation': l['UserAnnotation']['value'],
'CustomAnnotation': l['CustomAnnotation']['value']
} for l in annotations]
return annotations
def determine_date(schema_uri, local_sparql, virtuoso_graph):
""" Gets the last modification date for a schema.
Parameters
----------
schema_uri : str
The URI of the schema in the Chewie-NS.
Returns
--------
insertion_date : str
The insertion date in the format YYYY-MM-DDTHH:MM:SS.f.
"""
# get schema last modification date
date_result = aux.get_data(
SPARQLWrapper(local_sparql),
(sq.SELECT_SPECIES_SCHEMA.format(virtuoso_graph, schema_uri)))
schema_info = date_result['results']['bindings'][0]
insertion_date = schema_info['dateEntered']['value']
return insertion_date
def species_schemas(species_uri, schemas, local_sparql, virtuoso_graph):
""" Gets the list of schemas for a species.
Parameters
----------
species_uri : str
The URI of the species in the Chewie-NS.
schemas : dict
An empty dictionary to store schemas' data.
Returns
-------
A list with the following variables:
- status (int): status code of the response.
- schemas (dict): A dictionary with the species
URI as key and a list of tuples as value.
Each tuple has a schema URI and the name of
that schema.
"""
result = aux.get_data(
SPARQLWrapper(local_sparql),
(sq.SELECT_SPECIES_SCHEMAS.format(virtuoso_graph, species_uri)))
try:
ns_schemas = result['results']['bindings']
if len(ns_schemas) > 0:
for schema in ns_schemas:
schemas.setdefault(species_uri, []).append(
(schema['schemas']['value'], schema['name']['value']))
except Exception:
logging.warning('Could not retrieve schemas for '
'{0}. Exception:\n{1}'.format(species_uri, result))
return schemas
def main(temp_dir, graph, sparql, base_url, user, password, c_user):
start = time.time()
# get species and schema identifiers
species_id = os.path.basename(temp_dir).split('_')[0]
schema_id = os.path.basename(temp_dir).split('_')[1]
# create schema URI
schema_uri = '{0}species/{1}/schemas/{2}'.format(base_url, species_id,
schema_id)
# count total loci
count_schema_loci = (sq.COUNT_SINGLE_SCHEMA_LOCI_ALLELES.format(
graph, schema_uri))
count_schema_loci_res = aux.get_data(SPARQLWrapper(sparql),
count_schema_loci)
total_loci = int(
count_schema_loci_res["results"]["bindings"][0]["nr_loci"]["value"])
post_files = [
os.path.join(temp_dir, file) for file in os.listdir(temp_dir)
]
# extract files
schema_files = []
for file in post_files:
dest_dir = os.path.dirname(file)
locus_file = unzip(file, dest_dir)
locus_file = os.path.join(temp_dir, locus_file)
schema_files.append(locus_file)
new_alleles_per_locus = []
# create SPARQL multiple INSERT queries
new_seqs = 0
identifiers = {}
queries_files = []
with concurrent.futures.ThreadPoolExecutor(max_workers=5) as executor:
for res in executor.map(create_queries, schema_files, repeat(graph),
repeat(sparql), repeat(base_url)):
if res[0] is not None:
queries_files.append(res[0])
identifiers[res[1]] = [res[2], res[3]]
new_seqs += len(res[3])
new_alleles_per_locus.append(
{"locus_{0}".format(res[1]): {
"newAlleles": [len(res[3])]
}})
# create/update allele contributions file
allele_contrib_path = os.path.join(
"pre-computed-data",
"allele_contributions_{0}_{1}.json".format(species_id, schema_id))
if os.path.exists(allele_contrib_path):
update_contributions_file(allele_contrib_path, schema_uri, c_user,
new_alleles_per_locus, new_seqs)
else:
create_contributions_file(allele_contrib_path, schema_uri, c_user,
new_alleles_per_locus, new_seqs, total_loci)
start = time.time()
# insert data
# sort reponses to include summary in log file
# create lock file
with open(sync_lock, 'w') as lf:
lf.write('{0}\n{1}'.format(temp_dir, user))
post_results = send_alleles(queries_files, sparql, user, password)
# remove lock file after insertion
os.remove(sync_lock)
# create file with identifiers
identifiers_file = os.path.join(temp_dir, 'identifiers')
with open(identifiers_file, 'wb') as rf:
pickle.dump(identifiers, rf)
end = time.time()
delta = end - start
print('Insertion: {0}'.format(delta), flush=True)
# change last_modified date
if new_seqs > 0:
modification_date = str(
dt.datetime.now().strftime('%Y-%m-%dT%H:%M:%S.%f'))
change_date(schema_uri, 'last_modified', modification_date, graph,
sparql, user, password)
# change_schema_version(schema_uri, '2.5.1',
# graph, sparql, user, password)
# create pre-computed frontend files
os.system('python schema_totals.py -m single_schema '
'--sp {0} --sc {1} --g {2} --s {3} --b {4}'
''.format(species_id, schema_id, graph, sparql, base_url))
os.system('python loci_totals.py -m single_schema '
'--sp {0} --sc {1} --g {2} --s {3} --b {4}'
''.format(species_id, schema_id, graph, sparql, base_url))
os.system('python loci_mode.py -m single_schema '
'--sp {0} --sc {1} --g {2} --s {3} --b {4}'
''.format(species_id, schema_id, graph, sparql, base_url))
os.system('python annotations.py -m single_schema '
'--sp {0} --sc {1} --g {2} --s {3} --b {4}'
''.format(species_id, schema_id, graph, sparql, base_url))
os.system('python loci_boxplot.py -m single_schema '
'--sp {0} --sc {1} --g {2} --s {3} --b {4}'
''.format(species_id, schema_id, graph, sparql, base_url))
# unlock schema
change_lock(schema_uri, 'Unlocked', graph, sparql, user, password)
def main(temp_dir, graph, sparql, base_url, user, password):
# get species and schema identifiers
species_id = os.path.basename(temp_dir).split('_')[0]
schema_id = os.path.basename(temp_dir).split('_')[1]
# create species and schema URIs
species_uri = '{0}species/{1}'.format(base_url, species_id)
schema_uri = '{0}/schemas/{1}'.format(species_uri, schema_id)
logging.info('Started loci insertion for ' 'schema {0}'.format(schema_uri))
# define path to file with loci data
loci_file = os.path.join(temp_dir,
'{0}_{1}_loci'.format(species_id, schema_id))
# read loci data
if os.path.isfile(loci_file) is True:
with open(loci_file, 'rb') as lf:
loci_prefix, loci_data = pickle.load(lf)
logging.info('Loci prefix is {0}.'.format(loci_prefix))
else:
logging.warning('Could not find file {0}. '
'Aborting\n\n'.format(loci_file))
sys.exit(1)
# determine locus with highest identifier
result = aux.get_data(SPARQLWrapper(sparql),
(sq.SELECT_HIGHEST_LOCUS.format(graph)))
highest_locus = result['results']['bindings']
# if there are no loci
if highest_locus != []:
highest_locus = highest_locus[0]['locus']['value']
highest_id = int(highest_locus.split('/')[-1])
start_id = highest_id + 1
elif highest_locus == []:
start_id = 1
# define path to file with schema upload status data
hashes_file = os.path.join(temp_dir,
'{0}_{1}_hashes'.format(species_id, schema_id))
# read schema upload status data
if os.path.isfile(hashes_file) is True:
with open(hashes_file, 'rb') as hf:
schema_hashes = pickle.load(hf)
else:
logging.warning(
'Could not find schema upload status file. Aborting.\n\n')
sys.exit(1)
# assign identifiers to new loci based on the total number of loci in the Chewie-NS
response, hash_to_uri, loci_data, = assign_identifiers(
loci_data, schema_hashes, loci_prefix, start_id, base_url)
# insert loci
insert_queries, insert = create_insert_queries(loci_data, schema_hashes,
graph)
logging.info('{0} loci to insert out of {1} total loci '
'in schema.'.format(insert, len(loci_data)))
logging.info('Loci integer identifiers interval: [{0} .. {1}]'
''.format(start_id, start_id + insert))
# insert data to create loci
if len(insert_queries) > 0:
loci_insertion = send_queries(insert_queries, sparql, user, password)
insert_status, success, failed = results_status(loci_insertion)
for h, v in insert_status.items():
if v is True:
schema_hashes[h][1][0] = hash_to_uri[h]
response[h].append(v)
elif v is False:
response[h].append(v)
logging.info('Successfully inserted {0} loci. '
'Failed {1}'.format(success, failed))
# halt process if it could not insert all loci
if failed > 0:
logging.warning('Could not insert all loci. Aborting.\n\n')
sys.exit(1)
# link loci to species
sp_queries, link = species_link_queries(loci_data, schema_hashes,
species_uri, graph)
logging.info('{0} loci to link to species out of {1} total loci '
'in schema.'.format(link, len(loci_data)))
if len(sp_queries) > 0:
species_links = send_queries(sp_queries, sparql, user, password)
link_status, success, failed = results_status(species_links)
for h, v in link_status.items():
if v is True:
schema_hashes[h][1][1] = True
response[h].append(v)
elif v is False:
response[h].append(v)
logging.info('Successfully linked {0} loci to species. '
'Failed {1}'.format(success, failed))
# link loci to schema
sc_queries, link = schema_link_queries(loci_data, schema_hashes,
schema_uri, graph)
logging.info('{0} loci to link to schema out of {1} total loci '
'in schema.'.format(link, len(loci_data)))
if len(sc_queries) > 0:
schema_links = send_queries(sc_queries, sparql, user, password)
link_status, success, failed = results_status(schema_links)
for h, v in link_status.items():
if v is True:
schema_hashes[h][1][2] = True
response[h].append(v)
elif v is False:
response[h].append(v)
logging.info('Successfully linked {0} loci to schema. '
'Failed {1}'.format(success, failed))
# save updated schema hashes
with open(hashes_file, 'wb') as hf:
pickle.dump(schema_hashes, hf)
# write response to file
response_file = os.path.join(
temp_dir, '{0}_{1}_loci_response'.format(species_id, schema_id))
with open(response_file, 'wb') as rf:
pickle.dump(response, rf)
# remove temp file
os.remove(loci_file)
logging.info('Finished loci insertion for '
'schema {0}\n\n'.format(schema_uri))
return response_file
def rm_schema(identifier, species_id, virtuoso_graph, local_sparql, base_url,
virtuoso_user, virtuoso_pass):
"""
"""
total_triples = 0
# create schema URI
schema_uri = ('{0}species/{1}/schemas/{2}'
'').format(base_url, species_id, identifier)
logging.info('Started rm process for schema {0}.'.format(schema_uri))
# check if schema exists
schema_result = aux.get_data(SPARQLWrapper(local_sparql),
(sq.ASK_SCHEMA.format(schema_uri)))
if schema_result['boolean'] is not True:
logging.info('Could not find schema.\n')
sys.exit('\nThere is no schema with specified ID.')
print('\nDeleting loci and alleles for schema: {0}'.format(schema_uri))
# get schema's loci
schema_result = aux.get_data(
SPARQLWrapper(local_sparql),
(sq.SELECT_SCHEMA_LOCI.format(virtuoso_graph, schema_uri)))
schema_result = schema_result['results']['bindings']
results = [0, 0, 0, 0, 0]
if len(schema_result) == 0:
logging.info('{0} has no loci.'.format(schema_uri))
else:
loci_uris = [l['locus']['value'] for l in schema_result]
print('Loci to delete: {0}\n'.format(len(loci_uris)))
logging.info('{0} loci to delete'.format(len(loci_uris)))
# collapse all loci (sequences are not deleted)
results = collapse_loci(loci_uris, virtuoso_graph, local_sparql,
virtuoso_user, virtuoso_pass)
total_triples += results[0]
# delete description
#schema_desc = aux.get_data(SPARQLWrapper(local_sparql),
# (sq.SELECT_SCHEMA_DESCRIPTION.format(virtuoso_graph, schema_uri)))
#schema_desc = schema_desc['results']['bindings'][0]['description']['value']
#desc_file = '{0}/{1}'.format(Config.PRE_COMPUTE, schema_desc)
#if os.path.isfile(desc_file) is True:
# subprocess.call(['rm', desc_file])
# delete compressed version
zip_file = [
f for f in os.listdir(Config.SCHEMAS_ZIP)
if f.startswith('{0}_{1}'.format(species_id, identifier))
]
if len(zip_file) > 0:
zip_file = '{0}/{1}'.format(Config.SCHEMAS_ZIP, zip_file[0])
subprocess.call(['rm', zip_file])
print('Deleted compressed version ({0})'.format(zip_file))
logging.info('Deleted compressed version ({0})'.format(zip_file))
# delete pre-computed files
length_files = '{0}/{1}_{2}_lengths'.format(Config.PRE_COMPUTE, species_id,
identifier)
if os.path.isdir(length_files) is True:
subprocess.call(['rm', '-rf', length_files])
print(
'Deleted directory with length values ({0})'.format(length_files))
logging.info(
'Deleted directory with length values ({0})'.format(length_files))
annotation_file = '{0}/annotations_{1}_{2}.json'.format(
Config.PRE_COMPUTE, species_id, identifier)
if os.path.isfile(annotation_file) is True:
subprocess.call(['rm', annotation_file])
print('Deleted pre-computed annotations ({0})'.format(annotation_file))
logging.info(
'Deleted pre-computed annotations ({0})'.format(annotation_file))
mode_file = '{0}/mode_{1}_{2}.json'.format(Config.PRE_COMPUTE, species_id,
identifier)
if os.path.isfile(mode_file) is True:
subprocess.call(['rm', mode_file])
print('Deleted pre-computed modes ({0})'.format(mode_file))
logging.info('Deleted pre-computed modes ({0})'.format(mode_file))
boxplot_file = '{0}/boxplot_{1}_{2}.json'.format(Config.PRE_COMPUTE,
species_id, identifier)
if os.path.isfile(boxplot_file) is True:
subprocess.call(['rm', boxplot_file])
print('Deleted pre-computed boxplot values ({0})'.format(boxplot_file))
logging.info(
'Deleted pre-computed boxplot values ({0})'.format(boxplot_file))
# remove schema data from pre-computed files
loci_file = '{0}/loci_{1}.json'.format(Config.PRE_COMPUTE, species_id)
if os.path.isfile(loci_file) is True:
with open(loci_file, 'r') as json_file:
json_data = json.load(json_file)
schemas = json_data['message']
schemas = [s for s in schemas if s['schema'] != schema_uri]
json_data['message'] = schemas
with open(loci_file, 'w') as json_outfile:
json.dump(json_data, json_outfile)
print('Deleted pre-computed values from file with loci values ({0})'.
format(loci_file))
logging.info(
'Deleted pre-computed values from file with loci values ({0})'.
format(loci_file))
totals_file = '{0}/totals_{1}.json'.format(Config.PRE_COMPUTE, species_id)
if os.path.isfile(totals_file) is True:
with open(totals_file, 'r') as json_file:
json_data = json.load(json_file)
schemas = json_data['message']
schemas = [s for s in schemas if s['uri'] != schema_uri]
json_data['message'] = schemas
with open(totals_file, 'w') as json_outfile:
json.dump(json_data, json_outfile)
print('Deleted pre-computed values from file with schema totals ({0})'.
format(totals_file))
logging.info(
'Deleted pre-computed values from file with schema totals ({0})'.
format(totals_file))
# delete schema
status_code, message = single_delete(sq.DELETE_SCHEMA, [schema_uri],
virtuoso_graph, local_sparql,
virtuoso_user, virtuoso_pass)
schema_triples = int(extract_triples(message))
schema_del = 0
if status_code in [200, 201]:
if schema_triples > 0:
schema_del = 1
print('Deleted {0}'.format(schema_uri))
logging.info('Deleted {0}'.format(schema_uri))
total_triples += schema_triples
else:
print('Could not delete triples for {0}'.format(schema_uri))
logging.info('Could not delete triples for {0}'.format(schema_uri))
else:
print('Failed to delete schema: {0}'.format(schema_uri))
logging.info('Failed to delete {0}'.format(schema_uri))
logging.info('Failed stderr:\n{0}'.format(message))
print('\nDeleted a total of {0} triples.'.format(total_triples))
print('({0} loci, {1} species links, {2} schema links, '
'{3} alleles)'.format(results[2], results[3], results[4],
results[1]))
return_dict = {
'schema': schema_del,
'loci': results[2],
'splinks': results[3],
'sclinks': results[4],
'alleles': results[1],
'total_triples': total_triples
}
return return_dict
def single_species(species_id, virtuoso_graph, local_sparql, base_url):
"""
"""
start_date = dt.datetime.now()
start_date_str = dt.datetime.strftime(start_date, '%Y-%m-%dT%H:%M:%S')
logging.info(
'Started determination of loci and alleles counts at: {0}'.format(
start_date_str))
# create species uri
species_uri = '{0}species/{1}'.format(base_url, species_id)
species_result = aux.get_data(
SPARQLWrapper(local_sparql),
sparql_queries.SELECT_SINGLE_SPECIES.format(virtuoso_graph,
species_uri))
result_data = species_result['results']['bindings']
if len(result_data) == 0:
logging.warning('Could not find species with identifier {0}. '
'Aborting.\n\n'.format(species_id))
# get all schemas for the species
species_result = aux.get_data(
SPARQLWrapper(local_sparql),
sparql_queries.SELECT_SPECIES_SCHEMAS.format(virtuoso_graph,
species_uri))
result_data = species_result['results']['bindings']
if len(result_data) == 0:
logging.info('Species has no schemas.')
schemas = [s['schemas']['value'] for s in result_data]
# sort by integer identifier to be able to fetch schemas by index
schemas = sorted(schemas, key=lambda x: int(x.split('/')[-1]))
# list files in folder
computed_dir = Config.PRE_COMPUTE
computed_files = os.listdir(computed_dir)
for schema in schemas:
schema_id = schema.split('/')[-1]
schema_prefix = 'mode_{0}_{1}'.format(species_id, schema_id)
schema_files = [
f for f in computed_files if f == '{0}.json'.format(schema_prefix)
]
schema_file = os.path.join(computed_dir,
'{0}.json'.format(schema_prefix))
# check if schema is locked
schema_lock = aux.get_data(
SPARQLWrapper(local_sparql),
(sparql_queries.ASK_SCHEMA_LOCK.format(schema)))
lock_status = schema_lock['boolean']
if lock_status is True:
schema_info = aux.get_data(
SPARQLWrapper(local_sparql),
(sparql_queries.SELECT_SPECIES_SCHEMA.format(
virtuoso_graph, schema)))
schema_properties = schema_info['results']['bindings']
if len(schema_properties) == 0:
logging.warning(
'Could not find properties values for schema with identifier {0}. '
'Aborting.\n\n'.format(schema_id))
continue
last_modified = schema_properties[0]['last_modified']['value']
if len(schema_files) == 0:
create_file(schema_file, {
'mode': [],
'total_alleles': [],
'scatter_data': []
})
lengths_dir = '{0}_{1}_lengths'.format(species_id, schema_id)
if lengths_dir in computed_files:
lengths_dir = os.path.join(computed_dir, lengths_dir)
fast_update(schema, last_modified, schema_file, lengths_dir,
virtuoso_graph, local_sparql)
else:
full_update(schema, last_modified, schema_file, virtuoso_graph,
local_sparql)
else:
logging.warning('Schema {0} is locked. Aborting.'.format(schema))
def fast_update(schema, last_modified, file, lengths_dir, virtuoso_graph,
local_sparql):
"""
"""
schema_id = int(schema.split('/')[-1])
current_file = file
# read current file
with open(current_file, 'r') as json_file:
json_data = json.load(json_file)
loci_modes = json_data['mode']
loci_alleles = json_data['total_alleles']
loci_scatter = json_data['scatter_data']
# get schema loci
loci = aux.get_data(
SPARQLWrapper(local_sparql),
sparql_queries.SELECT_SCHEMA_LOCI.format(virtuoso_graph, schema))
loci = loci['results']['bindings']
loci_names = {l['locus']['value']: l['name']['value'] for l in loci}
if len(loci_modes) == 0:
length_files = [
os.path.join(lengths_dir, f) for f in os.listdir(lengths_dir)
]
loci_stats = []
for locus_file in length_files:
with open(locus_file, 'rb') as lf:
locus_data = pickle.load(lf)
locus_uri = list(locus_data.keys())[0]
locus_name = loci_names[locus_uri]
locus_id = locus_name.split('-')[-1]
alleles_lengths = [v for k, v in locus_data[locus_uri].items()]
nr_alleles = len(alleles_lengths)
locus_mode = Counter(alleles_lengths).most_common()[0][0]
locus_mean = round(sum(alleles_lengths) / nr_alleles)
locus_median = round(statistics.median(alleles_lengths))
locus_min = min(alleles_lengths)
locus_max = max(alleles_lengths)
loci_stats.append((locus_name, locus_id, nr_alleles, locus_mode,
locus_mean, locus_median, locus_min, locus_max))
modes = determine_modes(loci_stats)
total_alleles = loci_total_alleles(loci_stats)
scatter_data = get_scatter_data(loci_stats)
json_to_file = {
'schema': schema,
'last_modified': last_modified,
'mode': modes,
'total_alleles': total_alleles,
'scatter_data': scatter_data
}
with open(file, 'w') as json_outfile:
json.dump(json_to_file, json_outfile)
# if the schema is in the json file
elif len(loci_modes) > 0:
# get modification date in json file
json_date = json_data['last_modified']
virtuoso_date = last_modified
if json_date == virtuoso_date:
logging.info(
'Information about number for schema {0} is up-to-date.'.
format(schema))
elif json_date != virtuoso_date:
length_files = [
os.path.join(lengths_dir, f) for f in os.listdir(lengths_dir)
]
loci_stats = []
for locus_file in length_files:
with open(locus_file, 'rb') as f:
locus_data = pickle.load(f)
locus_uri = list(locus_data.keys())[0]
locus_name = loci_names[locus_uri]
locus_id = locus_name.split('-')[-1]
alleles_lengths = [v for k, v in locus_data[locus_uri].items()]
nr_alleles = len(alleles_lengths)
locus_mode = Counter(alleles_lengths).most_common()[0][0]
locus_mean = round(sum(alleles_lengths) / nr_alleles)
locus_median = round(statistics.median(alleles_lengths))
locus_min = min(alleles_lengths)
locus_max = max(alleles_lengths)
loci_stats.append(
(locus_name, locus_id, nr_alleles, locus_mode, locus_mean,
locus_median, locus_min, locus_max))
modes = determine_modes(loci_stats)
total_alleles = loci_total_alleles(loci_stats)
scatter_data = get_scatter_data(loci_stats)
json_to_file = {
'schema': schema,
'last_modified': last_modified,
'mode': modes,
'total_alleles': total_alleles,
'scatter_data': scatter_data
}
with open(file, 'w') as json_outfile:
json.dump(json_to_file, json_outfile)
logging.info('Updated data for schema {0}'.format(schema))
def fast_update(schema, last_modified, file, lengths_dir, virtuoso_graph,
local_sparql):
"""
"""
schema_id = int(schema.split('/')[-1])
current_file = file
# read current file
with open(current_file, 'r') as json_file:
json_data = json.load(json_file)
loci_info = json_data['message']
# get schema loci
loci = aux.get_data(
SPARQLWrapper(local_sparql),
sparql_queries.SELECT_SCHEMA_LOCI.format(virtuoso_graph, schema))
loci = loci['results']['bindings']
loci_names = {l['locus']['value']: l['name']['value'] for l in loci}
if len(loci_info) == 0:
length_files = [
os.path.join(lengths_dir, f) for f in os.listdir(lengths_dir)
]
loci_stats = {}
for locus_file in length_files:
with open(locus_file, 'rb') as lf:
locus_data = pickle.load(lf)
locus_uri = list(locus_data.keys())[0]
locus_name = loci_names[locus_uri]
alleles_lengths = [v for k, v in locus_data[locus_uri].items()]
total_alleles = len(alleles_lengths)
locus_mode = Counter(alleles_lengths).most_common()[0][0]
locus_min = min(alleles_lengths)
locus_max = max(alleles_lengths)
loci_stats[locus_name] = [
locus_mode, total_alleles, locus_min, locus_max
]
annotations = loci_annotations(schema, virtuoso_graph, local_sparql)
for a in annotations:
locus = a['name']
a['mode'] = loci_stats[locus][0]
a['nr_alleles'] = loci_stats[locus][1]
a['min'] = loci_stats[locus][2]
a['max'] = loci_stats[locus][3]
json_to_file = {
'schema': schema,
'last_modified': last_modified,
'message': annotations
}
with open(file, 'w') as json_outfile:
json.dump(json_to_file, json_outfile)
# if the schema is in the json file
elif len(loci_info) > 0:
# get modification date in json file
json_date = json_data['last_modified']
virtuoso_date = last_modified
if json_date == virtuoso_date:
logging.info(
'Information about loci annotations and length modes for schema {0} is up-to-date.'
.format(schema))
elif json_date != virtuoso_date:
length_files = [
os.path.join(lengths_dir, f) for f in os.listdir(lengths_dir)
]
loci_stats = {}
for locus_file in length_files:
with open(locus_file, 'rb') as lf:
locus_data = pickle.load(lf)
locus_uri = list(locus_data.keys())[0]
locus_name = loci_names[locus_uri]
alleles_lengths = [v for k, v in locus_data[locus_uri].items()]
total_alleles = len(alleles_lengths)
locus_mode = Counter(alleles_lengths).most_common()[0][0]
locus_min = min(alleles_lengths)
locus_max = max(alleles_lengths)
loci_stats[locus_name] = [
locus_mode, total_alleles, locus_min, locus_max
]
annotations = loci_annotations(schema, virtuoso_graph,
local_sparql)
for a in annotations:
locus = a['name']
a['mode'] = loci_stats[locus][0]
a['nr_alleles'] = loci_stats[locus][1]
a['min'] = loci_stats[locus][2]
a['max'] = loci_stats[locus][3]
json_to_file = {
'schema': schema,
'last_modified': last_modified,
'message': annotations
}
with open(file, 'w') as json_outfile:
json.dump(json_to_file, json_outfile)
logging.info('Updated data for schema {0}'.format(schema))
def fasta_sequences(locus, date, local_sparql, virtuoso_graph):
""" Get the DNA sequences of all alleles of a locus.
Parameters
----------
locus : str
The URI of the locus in the Chewie-NS.
date : str
Last modification date of the schema in
the format YYYY-MM-DDTHH:MM:SS.f.
Returns
-------
fasta_seqs : list of dict
A list with one dictionary per allele.
Each dictionary has the identifier and the DNA
sequence of an allele.
"""
# setting [SPARQL] ResultSetMaxRows = 400000 in virtuoso.ini
# is important to return all sequences at once
fasta_result = aux.get_data(
SPARQLWrapper(local_sparql),
(sq.SELECT_LOCUS_FASTA_BY_DATE.format(virtuoso_graph, locus, date)))
# virtuoso returned an error because request length exceeded maximum value of Temp Col
# get each allele separately
try:
fasta_seqs = fasta_result['results']['bindings']
# virtuoso returned an error
# probably because sequence/request length exceeded maximum value
except:
logging.warning('Could not retrieve FASTA records for locus {0}\n'
'Response content:\n{1}\nTrying to get each sequence '
'separately...\n'.format(locus, fasta_result))
# get each allele separately
result = aux.get_data(
SPARQLWrapper(local_sparql),
(sq.SELECT_LOCUS_SEQS_BY_DATE.format(virtuoso_graph, locus, date)))
try:
fasta_seqs = result['results']['bindings']
if len(fasta_seqs) == 0:
logging.warning('Locus {0} has 0 sequences.'.format(locus))
return False
except:
logging.warning('Could not retrieve sequences hashes '
'for locus {0}.'.format(locus))
return False
total = 0
hashes = []
for s in range(len(fasta_seqs)):
# get the sequence corresponding to the hash
result2 = aux.get_data(
SPARQLWrapper(local_sparql), (sq.SELECT_SEQ_FASTA.format(
virtuoso_graph, fasta_seqs[s]['sequence']['value'])))
hashes.append(fasta_seqs[s]['sequence']['value'])
fasta_seqs[s]['nucSeq'] = result2['results']['bindings'][0][
'nucSeq']
total += 1
return fasta_seqs
def full_update(schema_uri, file, schema_data, virtuoso_graph, local_sparql,
base_url):
"""
"""
schema_id = int(schema_uri.split('/')[-1])
current_file = file
# read current file
with open(current_file, 'r') as json_file:
json_data = json.load(json_file)
json_schemas = json_data['message']
schemas_indexes = {
int(s['uri'].split('/')[-1]): i
for i, s in enumerate(json_schemas)
}
# if the schema is in the json file
if schema_id in schemas_indexes:
current_schema = json_schemas[schemas_indexes[schema_id]]
# get modification date in json file
json_date = current_schema['last_modified']
virtuoso_date = schema_data['last_modified']
if json_date == virtuoso_date:
logging.info(
'Information about number of loci and number of alleles for schema {0} is up-to-date.'
.format(schema_uri))
elif json_date != virtuoso_date:
result = aux.get_data(SPARQLWrapper(local_sparql),
(sq.COUNT_SINGLE_SCHEMA_LOCI_ALLELES.format(
virtuoso_graph, schema_uri)))
result_data = result['results']['bindings'][0]
current_schema['last_modified'] = virtuoso_date
current_schema['nr_loci'] = result_data['nr_loci']['value']
current_schema['nr_alleles'] = result_data['nr_alleles']['value']
json_data['message'][schemas_indexes[schema_id]] = current_schema
with open(current_file, 'w') as json_outfile:
json.dump(json_data, json_outfile)
logging.info('Updated data for schema {0}'.format(schema_uri))
# new schema that is not in the json file
elif schema_id not in schemas_indexes:
result = aux.get_data(SPARQLWrapper(local_sparql),
(sq.COUNT_SINGLE_SCHEMA_LOCI_ALLELES.format(
virtuoso_graph, schema_uri)))
result_data = result['results']['bindings'][0]
# determine user that uploaded the file
admin = aux.get_data(
SPARQLWrapper(local_sparql),
sq.SELECT_SCHEMA_ADMIN.format(virtuoso_graph, schema_uri))
admin = admin['results']['bindings'][0]['admin']['value']
new_schema = schema_data
new_schema['user'] = admin
new_schema['uri'] = schema_uri
new_schema['nr_loci'] = result_data['nr_loci']['value']
new_schema['nr_alleles'] = result_data['nr_alleles']['value']
del (new_schema['Schema_lock'])
json_data['message'].append(new_schema)
with open(current_file, 'w') as json_outfile:
json.dump(json_data, json_outfile)
def fast_update(schema, last_modified, file, lengths_dir, virtuoso_graph,
local_sparql):
"""
"""
current_file = file
# read current file
with open(current_file, 'r') as json_file:
json_data = json.load(json_file)
loci_list = json_data['loci']
# get schema loci
loci = aux.get_data(
SPARQLWrapper(local_sparql),
sparql_queries.SELECT_SCHEMA_LOCI.format(virtuoso_graph, schema))
loci = loci['results']['bindings']
loci_names = {l['locus']['value']: l['name']['value'] for l in loci}
if len(loci_list) == 0:
length_files = [
os.path.join(lengths_dir, f) for f in os.listdir(lengths_dir)
]
# sort by locus id
length_files = sorted(length_files,
key=lambda x: int(x.split('_')[-1]))
loci_list = []
loci_min = []
loci_q1 = []
loci_median = []
loci_q3 = []
loci_max = []
loci_mean = []
loci_sd = []
alleles_counts = []
for locus_file in length_files:
with open(locus_file, 'rb') as lf:
locus_data = pickle.load(lf)
locus_uri = list(locus_data.keys())[0]
# get name of locus
locus_name = loci_names[locus_uri]
loci_list.append(locus_name)
alleles_lengths = [v for k, v in locus_data[locus_uri].items()]
alleles_lengths.sort()
nr_alleles = len(alleles_lengths)
alleles_counts.append(nr_alleles)
# minimum and maximum values
loci_min.append(min(alleles_lengths))
loci_max.append(max(alleles_lengths))
# standard deviation
if nr_alleles > 1:
locus_sd = statistics.stdev(alleles_lengths)
else:
locus_sd = 0.0
loci_sd.append(locus_sd)
# mean
locus_mean = round(sum(alleles_lengths) / nr_alleles)
loci_mean.append(locus_mean)
# median
locus_median = round(statistics.median(alleles_lengths))
loci_median.append(locus_median)
# q1 and q3
if nr_alleles > 1:
half = int(nr_alleles // 2)
q1 = statistics.median(alleles_lengths[:half])
q3 = statistics.median(alleles_lengths[-half:])
else:
q1 = alleles_lengths[0]
q3 = alleles_lengths[0]
loci_q1.append(q1)
loci_q3.append(q3)
json_to_file = {
'schema': schema,
'last_modified': last_modified,
'loci': loci_list,
'min': loci_min,
'q1': loci_q1,
'median': loci_median,
'q3': loci_q3,
'max': loci_max,
'mean': loci_mean,
'sd': loci_sd,
'nr_alleles': alleles_counts
}
with open(file, 'w') as json_outfile:
json.dump(json_to_file, json_outfile)
# if the schema is in the json file
elif len(loci_list) > 0:
# get modification date in json file
json_date = json_data['last_modified']
virtuoso_date = last_modified
if json_date == virtuoso_date:
logging.info(
'Information for schema {0} is up-to-date.'.format(schema))
elif json_date != virtuoso_date:
length_files = [
os.path.join(lengths_dir, f) for f in os.listdir(lengths_dir)
]
# sort by locus id
length_files = sorted(length_files,
key=lambda x: int(x.split('_')[-1]))
loci_list = []
loci_min = []
loci_q1 = []
loci_median = []
loci_q3 = []
loci_max = []
loci_mean = []
loci_sd = []
alleles_counts = []
for locus_file in length_files:
with open(locus_file, 'rb') as f:
locus_data = pickle.load(f)
locus_uri = list(locus_data.keys())[0]
# get name of locus
locus_name = loci_names[locus_uri]
loci_list.append(locus_name)
alleles_lengths = [v for k, v in locus_data[locus_uri].items()]
alleles_lengths.sort()
nr_alleles = len(alleles_lengths)
alleles_counts.append(nr_alleles)
# minimum and maximum values
loci_min.append(min(alleles_lengths))
loci_max.append(max(alleles_lengths))
# standard deviation
if nr_alleles > 1:
locus_sd = statistics.stdev(alleles_lengths)
else:
locus_sd = 0.0
loci_sd.append(locus_sd)
# mean
locus_mean = round(sum(alleles_lengths) / nr_alleles)
loci_mean.append(locus_mean)
# median
locus_median = round(statistics.median(alleles_lengths))
loci_median.append(locus_median)
# q1 and q3
if nr_alleles > 1:
half = int(nr_alleles // 2)
q1 = statistics.median(alleles_lengths[:half])
q3 = statistics.median(alleles_lengths[-half:])
else:
q1 = alleles_lengths[0]
q3 = alleles_lengths[0]
loci_q1.append(q1)
loci_q3.append(q3)
json_to_file = {
'schema': schema,
'last_modified': last_modified,
'loci': loci_list,
'min': loci_min,
'q1': loci_q1,
'median': loci_median,
'q3': loci_q3,
'max': loci_max,
'mean': loci_mean,
'sd': loci_sd,
'nr_alleles': alleles_counts
}
with open(file, 'w') as json_outfile:
json.dump(json_to_file, json_outfile)
logging.info('Updated data for schema {0}'.format(schema))
def single_compressor(species_id, schema_id, graph, sparql, base_url, user,
password):
""" Determines if a schema needs to be compressed and
generates a compressed version if needed.
"""
logging.info('Started single compressor for schema {0} '
'of species {1}'.format(schema_id, species_id))
# check if species exists
species_uri = '{0}species/{1}'.format(base_url, species_id)
species_result = aux.get_data(
SPARQLWrapper(sparql),
sq.SELECT_SINGLE_SPECIES.format(graph, species_uri))
result_data = species_result['results']['bindings']
if len(result_data) == 0:
logging.warning('Could not find species with identifier {0}. '
'Aborting schema compression.\n\n'.format(species_id))
sys.exit(1)
sp_name = result_data[0]['name']['value']
sp_name = '_'.join(sp_name.split(' '))
# get schema info
# construct schema URI
schema_uri = '{0}/schemas/{1}'.format(species_uri, schema_id)
schema_info = aux.get_data(
SPARQLWrapper(sparql),
(sq.SELECT_SPECIES_SCHEMA.format(graph, schema_uri)))
schema_properties = schema_info['results']['bindings']
if len(schema_properties) == 0:
logging.warning(
'Could not find properties values for schema with identifier {0}. '
'Aborting schema compression.\n\n'.format(schema_id))
sys.exit(1)
schema_name = schema_properties[0]['name']['value']
schemas = [(schema_uri, schema_name)]
# list compressed schemas
compressed_schemas = os.listdir(Config.SCHEMAS_ZIP)
to_compress = []
old_zips = {}
to_compress, old_zip = compress_determiner(schemas, species_id, sp_name,
compressed_schemas, to_compress,
old_zips, sparql, graph)
if len(to_compress) == 0:
logging.info('Aborting schema compression.\n\n')
sys.exit(0)
else:
schemas = ['{0} ({1})'.format(s[0], s[-2]) for s in to_compress]
logging.info('Schema to compress: {0}'.format(';'.join(schemas)))
# check if schema is locked
schema_lock = aux.get_data(SPARQLWrapper(sparql),
(sq.ASK_SCHEMA_LOCK.format(schema_uri)))
lock_status = schema_lock['boolean']
if lock_status is True:
# lock schema
locked = change_lock(schema_uri, 'LOCKED', graph, sparql, user,
password)
if isinstance(locked, list) is True:
logging.warning('Could not lock schema {0}. Response:'
'\n{1}\n\n'.format(schema_uri, locked[1]))
sys.exit(1)
single_schema_name = to_compress[0][-2]
if old_zip[schema_uri] is not None:
old_zip[schema_uri] = os.path.join(Config.SCHEMAS_ZIP,
old_zip[schema_uri])
# adapt and compress schema
response = compress_schema(to_compress[0], old_zip[schema_uri], sparql,
graph)
if response == 0:
logging.info('Successfully compressed schema {0} '
'({1})'.format(schema_uri, single_schema_name))
else:
logging.info('Could not compress schema {0} '
'({1})'.format(schema_uri, single_schema_name))
# unlock schema
unlocked = change_lock(schema_uri, 'Unlocked', graph, sparql, user,
password)
if isinstance(unlocked, list) is True:
logging.warning(
'Could not unlock schema at the end of compression process.')
logging.info('Finished single compressor for schema {0} '
'of species {1}'.format(schema_id, species_id))
def fast_update(species_file, files_dir, schema_uri, schema_data,
virtuoso_graph, local_sparql, base_url):
"""
"""
schema_id = int(schema_uri.split('/')[-1])
current_file = species_file
# read current file
with open(current_file, 'r') as json_file:
json_data = json.load(json_file)
json_schemas = json_data['message']
schemas_indexes = {
int(s['uri'].split('/')[-1]): i
for i, s in enumerate(json_schemas)
}
# if the schema is in the json file
if schema_id in schemas_indexes:
current_schema = json_schemas[schemas_indexes[schema_id]]
# get modification date in json file
json_date = current_schema['last_modified']
# get schema info that is in Virtuoso
virtuoso_date = schema_data['last_modified']
if json_date == virtuoso_date:
logging.info(
'Information about number of loci and number of alleles for schema {0} is up-to-date.'
.format(schema_uri))
elif json_date != virtuoso_date:
length_files = [
os.path.join(files_dir, file) for file in os.listdir(files_dir)
]
total_loci = len(length_files)
total_alleles = 0
for file in length_files:
locus_id = os.path.basename(file).split('_')[-1]
locus_uri = '{0}loci/{1}'.format(base_url, locus_id)
with open(file, 'rb') as f:
locus_data = pickle.load(f)
total_alleles += len(locus_data[locus_uri])
current_schema['last_modified'] = virtuoso_date
current_schema['nr_loci'] = str(total_loci)
current_schema['nr_alleles'] = str(total_alleles)
json_data['message'][schemas_indexes[schema_id]] = current_schema
with open(current_file, 'w') as json_outfile:
json.dump(json_data, json_outfile)
logging.info('Updated data for schema {0}'.format(schema_uri))
# new schema that is not in the json file
elif schema_id not in schemas_indexes:
length_files = [
os.path.join(files_dir, file) for file in os.listdir(files_dir)
]
total_loci = len(length_files)
total_alleles = 0
for file in length_files:
locus_id = os.path.basename(file).split('_')[-1]
locus_uri = '{0}loci/{1}'.format(base_url, locus_id)
with open(file, 'rb') as f:
locus_data = pickle.load(f)
total_alleles += len(locus_data[locus_uri])
# determine user that uploaded the file
admin = aux.get_data(
SPARQLWrapper(local_sparql),
sq.SELECT_SCHEMA_ADMIN.format(virtuoso_graph, schema_uri))
admin = admin['results']['bindings'][0]['admin']['value']
new_schema = schema_data
new_schema['user'] = admin
new_schema['uri'] = schema_uri
new_schema['nr_loci'] = str(total_loci)
new_schema['nr_alleles'] = str(total_alleles)
del (new_schema['Schema_lock'])
json_data['message'].append(new_schema)
with open(current_file, 'w') as json_outfile:
json.dump(json_data, json_outfile)