0\\n \\\"\\\"\\\"\\n sql = f\\\"\\\"\\\"update {self.schemaRepo}.tablediff set result = 'ok',\\n server1_status = 'done',server2_status = 'done' where step = 0\\n and lower(table_name) = lower('{tablename}')\\n and server1_rows = server2_rows and server1_rows>0\\n and not exists (select 1 from {self.schemaRepo}.tablediff\\n where step>0 and server1_status = 'done'\\n and server2_status = 'done' and result = 'nok'\\n and lower(table_name) =lower('{tablename}'))\\\"\\\"\\\"\\n with conn:\\n with conn.cursor() as curs:\\n try:\\n curs.execute(sql)\\n except conn.DatabaseError as exc:\\n error, = exc.args\\n logging.error(f\\\"\\\"\\\"error executing {sql} : {error.code}\\\"\\\"\\\")\\n\\n \\\"\\\"\\\" qry2 : table has been processed : one step > 0 is not ok so the\\n global result is nok\\n \\\"\\\"\\\"\\n sql = f\\\"\\\"\\\"update {schemaRepo}.tablediff set result = 'nok',\\n server1_status = 'done',server2_status = 'done' where step = 0\\n and lower(table_name) = lower('{tablename}')\\n and exists (select 1 from {schemaRepo}.tablediff where step>0\\n and server1_status = 'done'\\n and server2_status = 'done' and result = 'nok'\\n and lower(table_name) =lower('{tablename}'))\\\"\\\"\\\"\\n with conn:\\n with conn.cursor() as curs:\\n curs.execute(sql)\\n\\n \\\"\\\"\\\" qry3 : table has been processed : server1_rows and server2_rows\\n are <> so the global result is nok\\n \\\"\\\"\\\"\\n sql = f\\\"\\\"\\\"update {schemaRepo}.tablediff set result = 'nok',\\n server1_status = 'done',server2_status = 'done'\\n where step = 0 and lower(table_name) = lower('{tablename}')\\n and server1_rows<>server2_rows and server1_status = 'ready'\\n and server2_status = 'ready'\\\"\\\"\\\"\\n with conn:\\n with conn.cursor() as curs:\\n try:\\n curs.execute(sql)\\n except conn.DatabaseError as exc:\\n error, = exc.args\\n logging.error(f\\\"\\\"\\\"error executing {sql} : {error}\\\"\\\"\\\")\\n\\n \\\"\\\"\\\" qry4 : table has been processed : server1_rows = server2_rows = 0\\n (because fo filter) so the global result is ok\\n \\\"\\\"\\\"\\n sql = f\\\"\\\"\\\"update {schemaRepo}.tablediff set result = 'ok',\\n server1_status = 'done',server2_status = 'done' where step = 0\\n and lower(table_name) = lower('{tablename}')\\n and server1_rows = server2_rows and server1_rows = 0\\n and server1_status = 'ready' and server2_status = 'ready'\\\"\\\"\\\"\\n with conn:\\n with conn.cursor() as curs:\\n try:\\n curs.execute(sql)\\n except conn.DatabaseError as exc:\\n error, = exc.args\\n logging.error(f\\\"\\\"\\\"error executing {sql} : {error}\\\"\\\"\\\")\",\n \"def slow_transaction_data(self):\\n\\n # XXX This method no longer appears to be used. Being replaced\\n # by the transaction_trace_data() method.\\n\\n if not self.__settings:\\n return []\\n\\n if not self.__slow_transaction:\\n return []\\n\\n maximum = self.__settings.agent_limits.transaction_traces_nodes\\n\\n transaction_trace = self.__slow_transaction.transaction_trace(\\n self, maximum)\\n\\n data = [transaction_trace,\\n list(self.__slow_transaction.string_table.values())]\\n\\n if self.__settings.debug.log_transaction_trace_payload:\\n _logger.debug('Encoding slow transaction data where '\\n 'payload=%r.', data)\\n\\n json_data = json_encode(data)\\n\\n level = self.__settings.agent_limits.data_compression_level\\n level = level or zlib.Z_DEFAULT_COMPRESSION\\n\\n zlib_data = zlib.compress(six.b(json_data), level)\\n\\n pack_data = base64.standard_b64encode(zlib_data)\\n\\n if six.PY3:\\n pack_data = pack_data.decode('Latin-1')\\n\\n root = transaction_trace.root\\n\\n trace_data = [[root.start_time,\\n root.end_time - root.start_time,\\n self.__slow_transaction.path,\\n self.__slow_transaction.request_uri,\\n pack_data]]\\n\\n return trace_data\",\n \"def mine(self):\\n if not self.unconfirmed_transactions: \\n return False\\n \\n last_block = self.last_block\\n \\n new_block = Block(index= last_block.index + 1, \\n transactions = self.unconfirmed_transactions,\\n timestamp = time.time(),\\n previous_hash = last_block.hash)\\n\\n proof = self.proof_of_work(new_block)\\n self.add_block(new_block, proof)\\n self.unconfirmed_transactions = []\\n return new_block.index\",\n \"def transactional():\\n return {\\\"transactional\\\": bool(salt.utils.path.which(\\\"transactional-update\\\"))}\",\n \"def is_spent(tx_hash, index):\\n try:\\n response = make_request('http://tbtc.blockr.io/api/v1/tx/info/' + tx_hash)\\n data = json.loads(response)\\n result = bool(data['data']['vouts'][index]['is_spent'])\\n except Exception as e:\\n result = True\\n\\n return result\",\n \"def is_updated(self):\\n return self.timestamp > 0\",\n \"def sync_update(self):\\n for rec in self:\\n if rec.ks_last_exported_date and rec.ks_sync_date:\\n ks_reduced_ks_sync_time = rec.ks_last_exported_date - datetime.timedelta(seconds=30)\\n ks_increased_ks_sync_time = rec.ks_last_exported_date + datetime.timedelta(seconds=30)\\n if rec.ks_sync_date > ks_reduced_ks_sync_time and rec.ks_sync_date < ks_increased_ks_sync_time:\\n rec.ks_sync_status = True\\n else:\\n rec.ks_sync_status = False\\n else:\\n rec.ks_sync_status = False\",\n \"def checksum_for_changes(self, single_trx=False):\\n if self.eliminate_dups:\\n log.warning(\\\"Skip checksum, because --eliminate-duplicate \\\" \\\"specified\\\")\\n return\\n elif not self.need_checksum_for_changes():\\n return\\n # Because chunk checksum use old pk combination for searching row\\n # If we don't have a pk/uk on old table then it'll be very slow, so we\\n # have to skip here\\n elif self.is_full_table_dump:\\n return\\n else:\\n log.info(\\n \\\"Running checksum for rows have been changed since \\\"\\n \\\"last checksum from change ID: {}\\\".format(self.last_checksumed_id)\\n )\\n start_time = time.time()\\n old_table_checksum = self.checksum_by_replay_chunk(self.table_name)\\n # Checksum for the __new table should be issued inside the transaction\\n # too. Otherwise those invisible gaps in the __chg table will show\\n # up when calculating checksums\\n new_table_checksum = self.checksum_by_replay_chunk(self.new_table_name)\\n # After calculation checksums from both tables, we now can close the\\n # transaction, if we want\\n if not single_trx:\\n self.commit()\\n self.compare_checksum(old_table_checksum, new_table_checksum)\\n self.last_checksumed_id = self.last_replayed_id\\n self.stats[\\\"time_in_delta_checksum\\\"] = self.stats.setdefault(\\n \\\"time_in_delta_checksum\\\", 0\\n ) + (time.time() - start_time)\\n\\n self.record_checksum()\",\n \"def is_update(self) -> bool:\\n return self.statement.is_dml and self.statement.is_update\",\n \"def isTx(self):\\n\\t\\treturn self.extension == '.tx'\",\n \"def verify_transaction(transaction):\\n sender_balance = get_balance(transaction['sender'])\\n return sender_balance >= transaction['amount']\",\n \"def rollback_block(self, block):\\n # TODO: 0.007-12c changes\\n current_length = self.db.get('length')\\n if block['length'] != current_length:\\n # Block is not at the top the chain\\n return False\\n\\n for tx in block['txs']:\\n tx_owner_address = tools.tx_owner_address(tx)\\n owner_account = self.get_account(tx_owner_address)\\n if tx['type'] == 'mint':\\n owner_account['amount'] -= tools.block_reward(block['length'])\\n self.db.put(tx_owner_address, owner_account)\\n elif tx['type'] == 'spend':\\n owner_account['amount'] += tx['amount']\\n owner_account['count'] -= 1\\n owner_account['tx_blocks'].remove(block['length'])\\n\\n receiver_account = self.db.get(tx['to'])\\n receiver_account['amount'] -= tx['amount']\\n receiver_account['tx_blocks'].remove(block['length'])\\n\\n self.db.put(tx_owner_address, owner_account)\\n self.db.put(tx['to'], receiver_account)\",\n \"def test_update_loop(self):\\n self.create_org(provider='qbo')\\n old_task_count = 0\\n\\n while True:\\n update_call = self.app.post('/adapter/qbo/test/update')\\n self.assertEqual(update_call.status_code, 204)\\n\\n new_task_count = len(self.taskqueue.get_filtered_tasks())\\n\\n if new_task_count == old_task_count:\\n break\\n\\n if new_task_count > 100:\\n self.fail(\\\"too many adapter calls, infinite loop maybe???\\\")\\n\\n old_task_count = new_task_count\\n\\n self.assertEqual(new_task_count, 20)\",\n \"def _pair_stale(self, pair):\\r\\n (_conn, return_time) = pair\\r\\n now = time.time()\\r\\n return return_time + ConnectionPool.STALE_DURATION < now\",\n \"def iter_mempool_from_best_index(self) -> Iterator[Transaction]:\\n assert self.indexes is not None\\n if self.indexes.mempool_tips is not None:\\n yield from self.indexes.mempool_tips.iter_all(self)\\n else:\\n yield from self.iter_mempool_from_tx_tips()\",\n \"def slower(self):\\n self._prepare()\\n rate = self._eng.getProperty(\\\"rate\\\")\\n newrate = rate - 50\\n logging.debug(\\\"slower %d => %d\\\" %(rate, newrate))\\n self._eng.setProperty(\\\"rate\\\", newrate)\\n self._eng.runAndWait()\\n self.say(\\\"slower\\\")\",\n \"def check_transaction(self):\\n if self.transaction_valid():\\n transid = self._request.var(\\\"_transid\\\")\\n if transid and transid != \\\"-1\\\":\\n self._invalidate(transid)\\n return True\\n else:\\n return False\",\n \"def _save_or_verify_genesis(self) -> None:\\n self._saving_genesis = True\\n for tx in self._get_genesis_from_settings():\\n try:\\n assert tx.hash is not None\\n tx2 = self.get_transaction(tx.hash)\\n assert tx == tx2\\n except TransactionDoesNotExist:\\n self.save_transaction(tx)\\n self.add_to_indexes(tx)\\n tx2 = tx\\n assert tx2.hash is not None\\n self._genesis_cache[tx2.hash] = tx2\\n self._saving_genesis = False\",\n \"def faster():\\n try:\\n ttsEng.faster()\\n except Exception, e:\\n logging.error(e)\",\n \"def improve(update, close, guess=1, max_updates=100):\\n k = 0\\n while not close(guess) and k < max_updates:\\n guess = update(guess)\\n k = k + 1\\n return guess\",\n \"def ifAlreadyDone(self, cxRepo, schemaRepo, schema, tablename):\\n logging.debug(f\\\"\\\"\\\"check if {schema}.{tablename} has been analyzed\\\"\\\"\\\")\\n conn = self.connect(cxRepo)\\n sql = f\\\"\\\"\\\"select table_name from {schemaRepo}.tablediff where lower\\n (table_name) = lower('{tablename}') and schema1 = '{schema}' and\\n server1_status = 'ready' and server1_status = 'ready' and result in\\n ('ready', 'init')\\\"\\\"\\\"\\n with conn:\\n with conn.cursor() as curs:\\n curs.execute(sql)\\n row = curs.fetchone()\\n if row is None:\\n return 1\\n else:\\n return 0\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5796732","0.56013685","0.5567382","0.5438813","0.52951","0.527724","0.5276468","0.52692777","0.51808035","0.5144416","0.5136642","0.51107734","0.5102024","0.5087963","0.50874853","0.5072691","0.5062309","0.503474","0.49916357","0.49607188","0.4943801","0.4930255","0.4902352","0.49019596","0.48956656","0.48263434","0.48068967","0.4802907","0.4801067","0.47867498","0.47809693","0.47674438","0.47640356","0.47615427","0.4758213","0.47550946","0.4747565","0.47402883","0.4700052","0.4699536","0.469953","0.46855187","0.46836972","0.46790412","0.46787927","0.46652392","0.46599016","0.465931","0.46401915","0.46377778","0.46316016","0.4630753","0.46261266","0.4622053","0.46165088","0.4614489","0.4614411","0.4612617","0.46059397","0.4600536","0.4597893","0.4597668","0.4596869","0.4587794","0.4580221","0.45791888","0.45789686","0.45687062","0.45637774","0.45580825","0.4552644","0.45512924","0.4540829","0.45375812","0.45375556","0.45335963","0.4531187","0.4529453","0.45292464","0.45280516","0.45278555","0.4526291","0.4520214","0.45161885","0.4515844","0.45055905","0.45048964","0.45044515","0.45033613","0.45030642","0.4496469","0.44960496","0.44929412","0.4491187","0.4490119","0.44854057","0.44814512","0.44795266","0.44774947","0.44744924"],"string":"[\n \"0.5796732\",\n \"0.56013685\",\n \"0.5567382\",\n \"0.5438813\",\n \"0.52951\",\n \"0.527724\",\n \"0.5276468\",\n \"0.52692777\",\n \"0.51808035\",\n \"0.5144416\",\n \"0.5136642\",\n \"0.51107734\",\n \"0.5102024\",\n \"0.5087963\",\n \"0.50874853\",\n \"0.5072691\",\n \"0.5062309\",\n \"0.503474\",\n \"0.49916357\",\n \"0.49607188\",\n \"0.4943801\",\n \"0.4930255\",\n \"0.4902352\",\n \"0.49019596\",\n \"0.48956656\",\n \"0.48263434\",\n \"0.48068967\",\n \"0.4802907\",\n \"0.4801067\",\n \"0.47867498\",\n \"0.47809693\",\n \"0.47674438\",\n \"0.47640356\",\n \"0.47615427\",\n \"0.4758213\",\n \"0.47550946\",\n \"0.4747565\",\n \"0.47402883\",\n \"0.4700052\",\n \"0.4699536\",\n \"0.469953\",\n \"0.46855187\",\n \"0.46836972\",\n \"0.46790412\",\n \"0.46787927\",\n \"0.46652392\",\n \"0.46599016\",\n \"0.465931\",\n \"0.46401915\",\n \"0.46377778\",\n \"0.46316016\",\n \"0.4630753\",\n \"0.46261266\",\n \"0.4622053\",\n \"0.46165088\",\n \"0.4614489\",\n \"0.4614411\",\n \"0.4612617\",\n \"0.46059397\",\n \"0.4600536\",\n \"0.4597893\",\n \"0.4597668\",\n \"0.4596869\",\n \"0.4587794\",\n \"0.4580221\",\n \"0.45791888\",\n \"0.45789686\",\n \"0.45687062\",\n \"0.45637774\",\n \"0.45580825\",\n \"0.4552644\",\n \"0.45512924\",\n \"0.4540829\",\n \"0.45375812\",\n \"0.45375556\",\n \"0.45335963\",\n \"0.4531187\",\n \"0.4529453\",\n \"0.45292464\",\n \"0.45280516\",\n \"0.45278555\",\n \"0.4526291\",\n \"0.4520214\",\n \"0.45161885\",\n \"0.4515844\",\n \"0.45055905\",\n \"0.45048964\",\n \"0.45044515\",\n \"0.45033613\",\n \"0.45030642\",\n \"0.4496469\",\n \"0.44960496\",\n \"0.44929412\",\n \"0.4491187\",\n \"0.4490119\",\n \"0.44854057\",\n \"0.44814512\",\n \"0.44795266\",\n \"0.44774947\",\n \"0.44744924\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.78206486"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":3687,"cells":{"query":{"kind":"string","value":"Check if transaction is a synthetics trace and save it to __synthetics_transactions."},"document":{"kind":"string","value":"def _update_synthetics_transaction(self, transaction):\n\n settings = self.__settings\n\n if not transaction.synthetics_resource_id:\n return\n\n maximum = settings.agent_limits.synthetics_transactions\n if len(self.__synthetics_transactions) < maximum:\n self.__synthetics_transactions.append(transaction)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def isSStx(tx):\n try:\n checkSStx(tx)\n\n except Exception as e:\n log.debug(\"isSStx: {}\".format(e))\n\n else:\n return True","def save(self, trade: Trade) -> Trade:\n\n pass # pragma: no cover","def is_transaction(self) -> bool:\n return False","def isTx(self):\n\t\treturn self.extension == '.tx'","def trackTrans(self):\n self.data_struct['_trackTrans'] = True","def _is_trace_on():\n return AceQLHttpApi.is_trace_on()","def is_transaction(self):\n return self._request.has_var(\"_transid\")","def isSTraced(self):\n try:\n return self.sTraced\n except AttributeError:\n self.sTraced = False\n return False","def report(self, trade, is_entry):\n pass","def transaction_trace_data(self, connections):\n\n _logger.debug('Generating transaction trace data.')\n\n if not self.__settings:\n return []\n\n # Create a set 'traces' that is a union of slow transaction,\n # and Synthetics transactions. This ensures we don't send\n # duplicates of a transaction.\n\n traces = set()\n if self.__slow_transaction:\n traces.add(self.__slow_transaction)\n traces.update(self.__synthetics_transactions)\n\n # Return an empty list if no transactions were captured.\n\n if not traces:\n return []\n\n # We want to limit the number of explain plans we do across\n # these. So work out what were the slowest and tag them.\n # Later the explain plan will only be run on those which are\n # tagged.\n\n agent_limits = self.__settings.agent_limits\n explain_plan_limit = agent_limits.sql_explain_plans_per_harvest\n maximum_nodes = agent_limits.transaction_traces_nodes\n\n database_nodes = []\n\n if explain_plan_limit != 0:\n for trace in traces:\n for node in trace.slow_sql:\n # Make sure we clear any flag for explain plans on\n # the nodes in case a transaction trace was merged\n # in from previous harvest period.\n\n node.generate_explain_plan = False\n\n # Node should be excluded if not for an operation\n # that we can't do an explain plan on. Also should\n # not be one which would not be included in the\n # transaction trace because limit was reached.\n\n if (node.node_count < maximum_nodes and\n node.connect_params and node.statement.operation in\n node.statement.database.explain_stmts):\n database_nodes.append(node)\n\n database_nodes = sorted(database_nodes,\n key=lambda x: x.duration)[-explain_plan_limit:]\n\n for node in database_nodes:\n node.generate_explain_plan = True\n\n else:\n for trace in traces:\n for node in trace.slow_sql:\n node.generate_explain_plan = True\n database_nodes.append(node)\n\n # Now generate the transaction traces. We need to cap the\n # number of nodes capture to the specified limit.\n\n trace_data = []\n\n for trace in traces:\n transaction_trace = trace.transaction_trace(\n self, maximum_nodes, connections)\n\n data = [transaction_trace,\n list(trace.string_table.values())]\n\n if self.__settings.debug.log_transaction_trace_payload:\n _logger.debug('Encoding slow transaction data where '\n 'payload=%r.', data)\n\n json_data = json_encode(data)\n\n level = self.__settings.agent_limits.data_compression_level\n level = level or zlib.Z_DEFAULT_COMPRESSION\n\n zlib_data = zlib.compress(six.b(json_data), level)\n\n pack_data = base64.standard_b64encode(zlib_data)\n\n if six.PY3:\n pack_data = pack_data.decode('Latin-1')\n\n root = transaction_trace.root\n\n if trace.record_tt:\n force_persist = True\n else:\n force_persist = False\n\n if trace.include_transaction_trace_request_uri:\n request_uri = trace.request_uri\n else:\n request_uri = None\n\n trace_data.append([transaction_trace.start_time,\n root.end_time - root.start_time,\n trace.path,\n request_uri,\n pack_data,\n trace.guid,\n None,\n force_persist,\n None,\n trace.synthetics_resource_id, ])\n\n return trace_data","def _apply_trx_trade_to_allocation(cls, allocation, block_trade):\n try:\n allocation.TrxTrade(block_trade)\n allocation.Commit()\n except Exception as e:\n error_message = 'Failed to stamp TrxTrade {0} on Allocation Trade: {1} , {2}'\n LOGGER.exception(error_message.format(block_trade.Oid(), allocation.Oid(), e))\n return False\n\n return True","def sign_transaction_essence(self, prepared_transaction_data):\n return self._call_account_method(\n 'signTransactionEssence', {\n 'preparedTransactionData': prepared_transaction_data\n }\n )","def is_sed(self) -> bool:\n return False","def signSign(self):\r\n if \"signature\" in self: # or \"signatures\" in self ?\r\n self.pop(\"id\", False)\r\n try:\r\n self[\"signSignature\"] = dposlib.core.crypto.getSignature(\r\n self, self._secondPrivateKey,\r\n exclude_second_sig=True,\r\n )\r\n except AttributeError:\r\n raise Exception(\"no second private Key available\")\r\n else:\r\n raise Exception(\"transaction not signed\")","async def check_trustline(\n cls, transaction: Transaction, server: Server, locks: Dict\n ):\n try:\n _, account = await get_account_obj_async(\n Keypair.from_public_key(transaction.to_address), server\n )\n except BaseRequestError:\n logger.exception(f\"Failed to load account {transaction.to_address}\")\n transaction.pending_execution_attempt = False\n await sync_to_async(transaction.save)()\n return\n trustline_found = False\n for balance in account[\"balances\"]:\n if balance.get(\"asset_type\") == \"native\":\n continue\n if (\n balance[\"asset_code\"] == transaction.asset.code\n and balance[\"asset_issuer\"] == transaction.asset.issuer\n ):\n trustline_found = True\n break\n if trustline_found:\n logger.debug(\n f\"detected transaction {transaction.id} is no longer pending trust\"\n )\n await cls.process_deposit(transaction, server, locks)\n else:\n transaction.pending_execution_attempt = False\n await sync_to_async(transaction.save)()","def _save_internal_transactions(self, blocks_traces):\n docs = [\n self._preprocess_internal_transaction(transaction)\n for transaction in blocks_traces\n if transaction[\"transactionHash\"]\n ]\n if docs:\n for chunk in bulk_chunks(docs, None, BYTES_PER_CHUNK):\n self.client.bulk_index(docs=chunk, index=self.indices[\"internal_transaction\"], doc_type=\"itx\",\n id_field=\"hash\", refresh=True)","def trace(self, trace=...):\n ...","def _save_miner_transactions(self, blocks_traces):\n docs = [self._preprocess_internal_transaction(transaction) for transaction in blocks_traces if\n not transaction[\"transactionHash\"]]\n self.client.bulk_index(docs=docs, index=self.indices[\"miner_transaction\"], doc_type=\"tx\", id_field=\"hash\",\n refresh=True)","def sign_trx(self, signture):\n self.trx_signature = signture","def record_transaction(self, transaction):\n\n if not self.__settings:\n return\n\n settings = self.__settings\n\n # Record the apdex, value and time metrics generated from the\n # transaction. Whether time metrics are reported as distinct\n # metrics or into a rollup is in part controlled via settings\n # for minimum number of unique metrics to be reported and thence\n # whether over a time threshold calculated as percentage of\n # overall request time, up to a maximum number of unique\n # metrics. This is intended to limit how many metrics are\n # reported for each transaction and try and cut down on an\n # explosion of unique metric names. The limits and thresholds\n # are applied after the metrics are reverse sorted based on\n # exclusive times for each metric. This ensures that the metrics\n # with greatest exclusive time are retained over those with\n # lesser time. Such metrics get reported into the performance\n # breakdown tab for specific web transactions.\n\n self.record_apdex_metrics(transaction.apdex_metrics(self))\n\n self.merge_custom_metrics(transaction.custom_metrics.metrics())\n\n self.record_time_metrics(transaction.time_metrics(self))\n\n # Capture any errors if error collection is enabled.\n # Only retain maximum number allowed per harvest.\n\n error_collector = settings.error_collector\n\n if (error_collector.enabled and settings.collect_errors and\n len(self.__transaction_errors) <\n settings.agent_limits.errors_per_harvest):\n self.__transaction_errors.extend(transaction.error_details())\n\n self.__transaction_errors = self.__transaction_errors[:\n settings.agent_limits.errors_per_harvest]\n\n if (error_collector.capture_events and\n error_collector.enabled and\n settings.collect_error_events):\n events = transaction.error_events(self.__stats_table)\n for event in events:\n self._error_events.add(event, priority=transaction.priority)\n\n # Capture any sql traces if transaction tracer enabled.\n\n if settings.slow_sql.enabled and settings.collect_traces:\n for node in transaction.slow_sql_nodes(self):\n self.record_slow_sql_node(node)\n\n # Remember as slowest transaction if transaction tracer\n # is enabled, it is over the threshold and slower than\n # any existing transaction seen for this period and in\n # the historical snapshot of slow transactions, plus\n # recording of transaction trace for this transaction\n # has not been suppressed.\n\n transaction_tracer = settings.transaction_tracer\n\n if (not transaction.suppress_transaction_trace and\n transaction_tracer.enabled and settings.collect_traces):\n\n # Transactions saved for Synthetics transactions\n # do not depend on the transaction threshold.\n\n self._update_synthetics_transaction(transaction)\n\n threshold = transaction_tracer.transaction_threshold\n\n if threshold is None:\n threshold = transaction.apdex_t * 4\n\n if transaction.duration >= threshold:\n self._update_slow_transaction(transaction)\n\n # Create the transaction event and add it to the\n # appropriate \"bucket.\" Synthetic requests are saved in one,\n # while transactions from regular requests are saved in another.\n\n if transaction.synthetics_resource_id:\n event = transaction.transaction_event(self.__stats_table)\n self._synthetics_events.add(event)\n\n elif (settings.collect_analytics_events and\n settings.transaction_events.enabled):\n\n event = transaction.transaction_event(self.__stats_table)\n self._transaction_events.add(event, priority=transaction.priority)\n\n # Merge in custom events\n\n if (settings.collect_custom_events and\n settings.custom_insights_events.enabled):\n self.custom_events.merge(transaction.custom_events)\n\n # Merge in span events\n\n if (settings.distributed_tracing.enabled and\n settings.span_events.enabled and settings.collect_span_events):\n if settings.infinite_tracing.enabled:\n for event in transaction.span_protos(settings):\n self._span_stream.put(event)\n elif transaction.sampled:\n for event in transaction.span_events(self.__settings):\n self._span_events.add(event, priority=transaction.priority)","def hasTx(self):\n\t\tif self.isTx:\n\t\t\treturn True\n\t\treturn textureFile( self.path.replace( self.extension, '.tx' ) ).exists","def _is_transaction_isolation_error(self, error):\n from psycopg2.extensions import TransactionRollbackError\n\n # Django can wrap errors, adding it to the `__cause__` attribute\n for e in (error, getattr(error, '__cause__', None)):\n if isinstance(e, TransactionRollbackError):\n return True\n return False","def save_transcription(trs_fname, trs):\n existed = os.path.exists(trs_fname)\n if not trs.endswith('\\n'):\n trs += '\\n'\n with codecs.open(trs_fname, 'w+', encoding='UTF-8') as trs_file:\n trs_file.write(trs)\n return existed","def record_trace(self):\n\n tfname = str(int(time.time())) + \".obd2_reader.trace\"\n self.tf_out = open(tfname, 'a')\n self.RecordTrace = 1\n print \"Recoding trace to:\", tfname","def isSSGen(tx):\n try:\n checkSSGen(tx)\n\n except Exception as e:\n log.debug(\"isSSGen: {}\".format(e))\n\n else:\n return True","def check(transaction):\n if not isinstance(transaction, Transaction):\n transaction = Transaction.objects.get(id=transaction)\n\n r = requests.post(\"https://www.blockonomics.co/api/searchhistory\",\n data=json.dumps({\"addr\": transaction.to_address}))\n try:\n history_data = json.loads(r.content.decode('utf-8'))['history'][0]\n except:\n return\n\n set_tx_details(history_data, transaction)","def is_sedes(obj):\n return hasattr(obj, 'serialize') and hasattr(obj, 'deserialize')","def save(self, *args, **kwargs):\n\n # Call the \"real\" save() method.\n super(Product, self).save(*args, **kwargs)\n\n # If sold and is a Widget\n if self.sale and self.kind == self.WIDGET:\n # But has not stamp\n try:\n self.stamp\n except:\n s = Stamp(owned_by=self.sale.customer, obtained_with=self)\n s.save()","def supports_transactions(self):\n return False","def save(self):\n self.lock()\n\n trader = self.strategy.trader()\n\n for trade in self.trades:\n t_data = trade.dumps()\n ops_data = [operation.dumps() for operation in trade.operations]\n\n # store per trade\n Database.inst().store_user_trade((trader.name, trader.account.name, self.instrument.market_id,\n self.strategy.identifier, trade.id, trade.trade_type, t_data, ops_data))\n\n # dumps of regions\n trader_data = {}\n regions_data = [region.dumps() for region in self.regions]\n\n Database.inst().store_user_trader((trader.name, trader.account.name, self.instrument.market_id,\n self.strategy.identifier, self.activity, trader_data, regions_data))\n\n self.unlock()","def is_tx(self):\n return self._pin_name in TX_CHANNELS","def _save_transactions(self):\r\n\t\tlogger.debug(\"Enter\")\r\n\t\t\r\n\t\twith open(self._state_file, 'wb') as tmp:\r\n\t\t\tlogger.debug(\"Dumping transactions: %r\" % self.transactions)\r\n\t\t\tpickle.dump(self.transactions, tmp)\r\n\t\t\r\n\t\tlogger.debug(\"Exit\")","def add_transaction(self,transaction):\n if type(transaction) != PoWGenericTransaction:\n raise Exception('TYPEERROR','transaction should be type of \"PoWGenericTransaction\" but got {}'.format(type(transaction)))\n if not transaction.is_validation_passed():\n print 'The transaction is not valid. Skipped...'\n return\n self.transactions.append(transaction)","def in_transaction(self):\n # We likely just changed data - give it a second to catch up\n time.sleep(0.1) # I think I keep reading journal watermark too soon without this\n \n # Get relevant data\n water_mark = pos.read_journal_watermark()\n self.log.info(f\"Watermark: [{water_mark}]\")\n balance = pos.read_balance()['Total']\n self.log.info(f\"Balance: [{balance}]\")\n \n # Decide if we need more checks based on watermark\n if water_mark == \"TRANSACTION IN PROGRESS\":\n self.log.info(\"In Transaction: In Transaction Watermark found\")\n return True\n elif water_mark == \"TRANSACTION COMPLETE\" or water_mark == \"TRANSACTION VOIDED\":\n self.log.info(\"Not in Transaction: Transaction Complete/Voided watermarks found\")\n return False\n else:\n # No watermark - decide based on balance\n if balance == \"$0.00\":\n self.log.info(\"Not in Transaction: $0 balance with no watermark\")\n return False\n else:\n self.log.info(\"In Transaction: Non-$0 balance with no watermark\")\n return True","def istraceback(object):\r\n return isinstance(object, types.TracebackType)","def serialize(\n self,\n transactions: Sequence[SnubaTransaction],\n errors: Sequence[SnubaError],\n roots: Sequence[SnubaTransaction],\n warning_extra: Dict[str, str],\n event_id: Optional[str],\n detailed: bool = False,\n ) -> Sequence[FullResponse]:\n parent_map = self.construct_parent_map(transactions)\n error_map = self.construct_error_map(errors)\n parent_events: Dict[str, TraceEvent] = {}\n results_map: Dict[Optional[str], List[TraceEvent]] = defaultdict(list)\n to_check: Deque[SnubaTransaction] = deque()\n # The root of the orphan tree we're currently navigating through\n orphan_root: Optional[SnubaTransaction] = None\n if roots:\n results_map[None] = []\n for root in roots:\n root_event = TraceEvent(root, None, 0)\n parent_events[root[\"id\"]] = root_event\n results_map[None].append(root_event)\n to_check.append(root)\n\n with sentry_sdk.start_span(op=\"building.trace\", description=\"full trace\"):\n iteration = 0\n has_orphans = False\n while parent_map or to_check:\n if len(to_check) == 0:\n has_orphans = True\n # Grab any set of events from the parent map\n parent_span_id, current_events = parent_map.popitem()\n\n current_event, *siblings = current_events\n # If there were any siblings put them back\n if siblings:\n parent_map[parent_span_id] = siblings\n\n previous_event = parent_events[current_event[\"id\"]] = TraceEvent(\n current_event, None, 0\n )\n\n # Used to avoid removing the orphan from results entirely if we loop\n orphan_root = current_event\n results_map[parent_span_id].append(previous_event)\n else:\n current_event = to_check.popleft()\n previous_event = parent_events[current_event[\"id\"]]\n\n # We've found the event for the trace navigator so we can remove everything in the deque\n # As they're unrelated ancestors now\n if event_id and current_event[\"id\"] == event_id:\n # Remove any remaining events so we don't think they're orphans\n while to_check:\n to_remove = to_check.popleft()\n if to_remove[\"trace.parent_span\"] in parent_map:\n del parent_map[to_remove[\"trace.parent_span\"]]\n to_check = deque()\n\n # This is faster than doing a call to get_events, since get_event_by_id only makes a call to snuba\n # when non transaction events are included.\n with sentry_sdk.start_span(op=\"nodestore\", description=\"get_event_by_id\"):\n nodestore_event = eventstore.get_event_by_id(\n current_event[\"project.id\"], current_event[\"id\"]\n )\n\n previous_event.nodestore_event = nodestore_event\n\n spans: NodeSpans = nodestore_event.data.get(\"spans\", [])\n\n # Need to include the transaction as a span as well\n #\n # Important that we left pad the span id with 0s because\n # the span id is stored as an UInt64 and converted into\n # a hex string when quering. However, the conversion does\n # not ensure that the final span id is 16 chars long since\n # it's a naive base 10 to base 16 conversion.\n spans.append({\"span_id\": previous_event.event[\"trace.span\"].rjust(16, \"0\")})\n\n for child in spans:\n if child[\"span_id\"] in error_map:\n previous_event.errors.extend(\n [\n self.serialize_error(error)\n for error in error_map.pop(child[\"span_id\"])\n ]\n )\n # We need to connect back to an existing orphan trace\n if (\n has_orphans\n and\n # The child event has already been checked\n child[\"span_id\"] in results_map\n and orphan_root is not None\n and\n # In the case of a span loop popping the current root removes the orphan subtrace\n child[\"span_id\"] != orphan_root[\"trace.parent_span\"]\n ):\n orphan_subtraces = results_map.pop(child[\"span_id\"])\n for orphan_subtrace in orphan_subtraces:\n orphan_subtrace.parent_event_id = previous_event.event[\"id\"]\n previous_event.children.extend(orphan_subtraces)\n if child[\"span_id\"] not in parent_map:\n continue\n # Avoid potential span loops by popping, so we don't traverse the same nodes twice\n child_events = parent_map.pop(child[\"span_id\"])\n\n for child_event in child_events:\n parent_events[child_event[\"id\"]] = TraceEvent(\n child_event,\n current_event[\"id\"],\n previous_event.generation + 1\n if previous_event.generation is not None\n else None,\n )\n # Add this event to its parent's children\n previous_event.children.append(parent_events[child_event[\"id\"]])\n\n to_check.append(child_event)\n # Limit iterations just to be safe\n iteration += 1\n if iteration > MAX_TRACE_SIZE:\n sentry_sdk.set_tag(\"discover.trace-view.warning\", \"surpassed-trace-limit\")\n logger.warning(\n \"discover.trace-view.surpassed-trace-limit\",\n extra=warning_extra,\n )\n break\n\n root_traces: List[TraceEvent] = []\n orphans: List[TraceEvent] = []\n for index, result in enumerate(results_map.values()):\n for subtrace in result:\n self.update_children(subtrace)\n if index > 0 or len(roots) == 0:\n orphans.extend(result)\n elif len(roots) > 0:\n root_traces = result\n # We sort orphans and roots separately because we always want the root(s) as the first element(s)\n root_traces.sort(key=child_sort_key)\n orphans.sort(key=child_sort_key)\n\n if len(orphans) > 0:\n sentry_sdk.set_tag(\"discover.trace-view.contains-orphans\", \"yes\")\n logger.warning(\"discover.trace-view.contains-orphans\", extra=warning_extra)\n\n return [trace.full_dict(detailed) for trace in root_traces] + [\n orphan.full_dict(detailed) for orphan in orphans\n ]","def traceXml(self, traceConfXml):\r\n if core.FW_conf['tracing_enabled']:\r\n core.FW_conf['trace'].traceActivation(traceConfXml, scriptActivation = True )","def _is_valid_sbl_settlement(settlement):\n if not settlement.IsKindOf(acm.FSettlement):\n return False\n\n if not settlement.Trade():\n return False\n \n trade = settlement.Trade()\n acquirer = trade.Acquirer().Name()\n instrument = trade.Instrument().InsType()\n delivery_type = trade.AddInfoValue(\"SL_SWIFT\")\n if instrument != 'SecurityLoan':\n return False\n if acquirer != 'SECURITY LENDINGS DESK':\n return False\n if delivery_type != 'SWIFT':\n return False\n if settlement.Type() not in ['Security Nominal', 'End Security']:\n return False\n\n return True","def transcription(self, sid):\r\n return recordings.Transcription(self, sid)","def isStakeBase(tx):\n # A stake base (SSGen) must only have two transaction inputs.\n if len(tx.txIn) != 2:\n return False\n\n # The previous output of a coin base must have a max value index and\n # a zero hash, as well as null fraud proofs.\n if not isNullOutpoint(tx):\n return False\n\n if not isNullFraudProof(tx):\n return False\n\n return True","def serialize(\n self,\n transactions: Sequence[SnubaTransaction],\n errors: Sequence[SnubaError],\n roots: Sequence[SnubaTransaction],\n warning_extra: Dict[str, str],\n event_id: Optional[str],\n detailed: bool = False,\n ) -> Sequence[LightResponse]:\n if event_id is None:\n raise ParseError(detail=\"An event_id is required for the light trace\")\n snuba_event, nodestore_event = self.get_current_transaction(transactions, errors, event_id)\n parent_map = self.construct_parent_map(transactions)\n error_map = self.construct_error_map(errors)\n trace_results: List[TraceEvent] = []\n current_generation: Optional[int] = None\n root_id: Optional[str] = None\n\n with sentry_sdk.start_span(op=\"building.trace\", description=\"light trace\"):\n # Going to nodestore is more expensive than looping twice so check if we're on the root first\n for root in roots:\n if root[\"id\"] == snuba_event[\"id\"]:\n current_generation = 0\n break\n\n if current_generation is None:\n for root in roots:\n # We might not be necessarily connected to the root if we're on an orphan event\n if root[\"id\"] != snuba_event[\"id\"]:\n # Get the root event and see if the current event's span is in the root event\n root_event = eventstore.get_event_by_id(root[\"project.id\"], root[\"id\"])\n root_spans: NodeSpans = root_event.data.get(\"spans\", [])\n root_span = find_event(\n root_spans,\n lambda item: item is not None\n and item[\"span_id\"] == snuba_event[\"trace.parent_span\"],\n )\n\n # We only know to add the root if its the direct parent\n if root_span is not None:\n # For the light response, the parent will be unknown unless it is a direct descendent of the root\n root_id = root[\"id\"]\n trace_results.append(\n TraceEvent(\n root,\n None,\n 0,\n )\n )\n current_generation = 1\n break\n\n current_event = TraceEvent(snuba_event, root_id, current_generation)\n trace_results.append(current_event)\n\n spans: NodeSpans = nodestore_event.data.get(\"spans\", [])\n # Need to include the transaction as a span as well\n #\n # Important that we left pad the span id with 0s because\n # the span id is stored as an UInt64 and converted into\n # a hex string when quering. However, the conversion does\n # not ensure that the final span id is 16 chars long since\n # it's a naive base 10 to base 16 conversion.\n spans.append({\"span_id\": snuba_event[\"trace.span\"].rjust(16, \"0\")})\n\n for span in spans:\n if span[\"span_id\"] in error_map:\n current_event.errors.extend(\n [self.serialize_error(error) for error in error_map.pop(span[\"span_id\"])]\n )\n if span[\"span_id\"] in parent_map:\n child_events = parent_map.pop(span[\"span_id\"])\n trace_results.extend(\n [\n TraceEvent(\n child_event,\n snuba_event[\"id\"],\n (\n current_event.generation + 1\n if current_event.generation is not None\n else None\n ),\n )\n for child_event in child_events\n ]\n )\n\n return [result.to_dict() for result in trace_results]","def _save_or_verify_genesis(self) -> None:\n self._saving_genesis = True\n for tx in self._get_genesis_from_settings():\n try:\n assert tx.hash is not None\n tx2 = self.get_transaction(tx.hash)\n assert tx == tx2\n except TransactionDoesNotExist:\n self.save_transaction(tx)\n self.add_to_indexes(tx)\n tx2 = tx\n assert tx2.hash is not None\n self._genesis_cache[tx2.hash] = tx2\n self._saving_genesis = False","def on_trade(self, trade: TradeData):\n pass","def is_trashed(self):\n return self.has_label(TRASHED_LABEL)","def is_transient_error(self):\n return self._tag == 'transient_error'","def checkSStx(tx):\n # Check to make sure there aren't too many inputs.\n # CheckTransactionSanity already makes sure that number of inputs is\n # greater than 0, so no need to check that.\n if len(tx.txIn) > MaxInputsPerSStx:\n raise DecredError(\"SStx has too many inputs\")\n\n # Check to make sure there aren't too many outputs.\n if len(tx.txOut) > MaxOutputsPerSStx:\n raise DecredError(\"SStx has too many outputs\")\n\n # Check to make sure there are some outputs.\n if len(tx.txOut) == 0:\n raise DecredError(\"SStx has no outputs\")\n\n # Check to make sure that all output scripts are the consensus version.\n for idx, txOut in enumerate(tx.txOut):\n if txOut.version != consensusVersion:\n raise DecredError(\"invalid script version found in txOut idx %d\" % idx)\n\n # Ensure that the first output is tagged OP_SSTX.\n if getScriptClass(tx.txOut[0].version, tx.txOut[0].pkScript) != StakeSubmissionTy:\n raise DecredError(\n \"First SStx output should have been OP_SSTX tagged, but it was not\"\n )\n\n # Ensure that the number of outputs is equal to the number of inputs\n # + 1.\n if (len(tx.txIn) * 2 + 1) != len(tx.txOut):\n raise DecredError(\n \"The number of inputs in the SStx tx was not the number of outputs/2 - 1\"\n )\n\n # Ensure that the rest of the odd outputs are 28-byte OP_RETURN pushes that\n # contain putative pubkeyhashes, and that the rest of the odd outputs are\n # OP_SSTXCHANGE tagged.\n for outTxIndex in range(1, len(tx.txOut)):\n scrVersion = tx.txOut[outTxIndex].version\n rawScript = tx.txOut[outTxIndex].pkScript\n\n # Check change outputs.\n if outTxIndex % 2 == 0:\n if getScriptClass(scrVersion, rawScript) != StakeSubChangeTy:\n raise DecredError(\n \"SStx output at output index %d was not an sstx change output\",\n outTxIndex,\n )\n continue\n\n # Else (odd) check commitment outputs. The script should be a\n # NullDataTy output.\n if getScriptClass(scrVersion, rawScript) != NullDataTy:\n raise DecredError(\n \"SStx output at output index %d was not a NullData (OP_RETURN) push\",\n outTxIndex,\n )\n\n # The length of the output script should be between 32 and 77 bytes long.\n if len(rawScript) < SStxPKHMinOutSize or len(rawScript) > SStxPKHMaxOutSize:\n raise DecredError(\n \"SStx output at output index %d was a NullData (OP_RETURN) push\"\n \" of the wrong size\",\n outTxIndex,\n )","def on_trade(self, trade: TradeData):\n # self.on_event(EVENT_TRADE, trade)\n # self.on_event(EVENT_TRADE + trade.vt_symbol, trade)\n pass","def _save_to_weakref(self, tx: BaseTransaction) -> None:\n if self._tx_weakref_disabled:\n return\n assert tx.hash is not None\n tx2 = self._tx_weakref.get(tx.hash, None)\n if tx2 is None:\n self._tx_weakref[tx.hash] = tx\n else:\n assert tx is tx2, 'There are two instances of the same transaction in memory ({})'.format(tx.hash_hex)","def has_save_strategy(self):\r\n return self.save_strategy is not None","def isTransmitted(self) -> bool:\r\n\r\n return self.__is_transmitted","def _handle_reference_enabled_save(self, error):\n err_id = error.get('id', None)\n if err_id is None:\n return False\n\n # this is a kind of signature of the error\n tail = filtered_error_tail(error)\n\n # check all errors in the storage\n for existing_id, existing_error in self._storage.items():\n error_tail = filtered_error_tail(existing_error)\n\n # error signature is the same\n if error_tail == tail:\n info = dict(referencedError=existing_id,\n time=error.get('time'),\n userid=error.get('userid') or 'anon')\n self._index[err_id] = info\n return True\n\n # save it\n return self._handle_save(error)","def save_axs_table(self, tblname, calculate_zone=False):\n from axs.catalog import AxsCatalog\n AxsCatalog(None).save_axs_table(self, tblname, True, calculate_zone)","def transaction_exists(self, transaction: \"Transaction\") -> bool:\n try:\n super().inspect_transaction(transaction=transaction)\n return True\n except grpc.RpcError as err:\n err: grpc.Call\n if err.code() == grpc.StatusCode.NOT_FOUND:\n return False\n raise err","def verify_trace_roundtrip(\n sch: Schedule,\n mod: Union[PrimFunc, IRModule],\n *,\n debug_mask: Union[str, int] = \"all\",\n text_format: Union[str, Sequence[str]] = [\"python\", \"json\"],\n) -> Schedule:\n from tvm.script import tir as T # pylint: disable=import-outside-toplevel\n\n if not isinstance(text_format, str):\n for opt in text_format:\n new_sch = verify_trace_roundtrip(sch, mod, debug_mask=debug_mask, text_format=opt)\n return new_sch\n\n trace = sch.trace\n assert trace is not None\n\n # Step 1. Perform a round-trip through the text-format\n new_sch = Schedule(mod=mod, debug_mask=debug_mask)\n if text_format == \"json\":\n json_obj = trace.as_json()\n Trace.apply_json_to_schedule(json_obj=json_obj, sch=new_sch)\n elif text_format == \"python\":\n py_trace = \"\\n\".join(trace.as_python())\n vars_dict = {\"T\": T}\n vars_dict.update(tvm.tir.__dict__)\n exec(py_trace, vars_dict, {\"sch\": new_sch}) # pylint: disable=exec-used\n else:\n assert text_format in (\"json\", \"python\"), f\"Unknown text format: {text_format}\"\n\n # Step 2. Verify that the round-trip produced the same scheduling\n assert_structural_equal(new_sch.mod, sch.mod)\n\n # Step 3. Check the consistency of the text format between the old and new traces\n py_repr = \"\\n\".join(trace.as_python())\n new_py_repr = \"\\n\".join(new_sch.trace.as_python())\n assert py_repr == new_py_repr\n\n # Step 4. Return the new schedule in case it could be useful\n return new_sch","def save(self, strm):\n raise NotImplementedError(\n '\"save\" should be implemented in the' \" class derived from SurrogateBase\"\n )","def record_transaction(self, transaction: Transaction) -> bool:\n if self._locked:\n print('Failed to record transaction! Your account has been locked!'\n )\n return False\n\n if transaction.amount > self.bank_balance:\n print('Failed to record transaction! Not enough balance!')\n return False\n\n budget = self.budget_manager.get_budget(transaction.budget_category)\n if budget.locked:\n print('Failed to record transaction! This budget has been locked!')\n return False\n\n self.transactions.append(transaction)\n self.bank_balance -= transaction.amount\n budget.amount_spent += transaction.amount\n self._warn_and_lock_if_needed(transaction)\n return True","def set_signal_trace(self):\n\t\t\n\t\tassert self.signal_trace_file is not None, \"Need to set \"\\\n\t\t\t\"'signal_trace_file' var before calling set_signal_trace; \"\\\n\t\t\t\"var should be set without extension, which must be .dat\"\n\t\t\n\t\tsignal_data = load_signal_trace_from_file(self.signal_trace_file)\n\t\tprint 'Signal time trace from file %s.dat loaded\\n' \\\n\t\t\t\t% self.signal_trace_file\n\t\tself.signal_trace_Tt = signal_data[:, 0]\n\t\tself.signal_trace = (signal_data[:, 1] + self.signal_trace_offset)*\\\n\t\t\t\t\t\t\t\tself.signal_trace_multiplier\n\t\t\n\t\tif self.signal_trace_file_2 is not None:\n\t\t\tsignal_data_2 = load_signal_trace_from_file(self.signal_trace_file_2)\n\t\t\tprint 'Signal time trace 2 from file %s.dat loaded\\n' \\\n\t\t\t\t% self.signal_trace_file_2\n\t\t\tassert len(self.signal_trace_Tt) == len(signal_data_2[:, 0]), \\\n\t\t\t\t\"signal_trace_file_2 must be same length as signal_trace_file\"\n\t\t\tassert sp.allclose(self.signal_trace_Tt, signal_data_2[:, 0], \n\t\t\t\t1e-6), \"signal_trace_file_2 must have same time array as \"\\\n\t\t\t\t\"signal_trace_file\"\n\t\t\tself.signal_trace_2 = (signal_data_2[:, 1] + \\\n\t\t\t\t\t\t\t\t\tself.signal_trace_offset_2)*\\\n\t\t\t\t\t\t\t\t\tself.signal_trace_multiplier_2","def store(self, timeseries_dict: ty.Dict[str, TimeSeries]):\n raise NotImplementedError(\"read_forecast\")","def add_transaction(self, tx_json):\n recv_tx = Transaction.from_json(tx_json)\n if not recv_tx.verify():\n raise Exception(\"New transaction failed signature verification.\")\n with self.all_tx_lock:\n if tx_json in self._all_transactions:\n print(f\"{self.name} - Transaction already exist in pool.\")\n return\n self._all_transactions.add(tx_json)","def _handle_save(self, error):\n err_id = error.get('id', None)\n if err_id is None:\n return False\n # save each error under its id\n self._storage[err_id] = error\n # notify new error\n event.notify(NotifyTraceback(error))\n return True","def flag_excitation_type(self, tracks: pd.DataFrame):\n # FIXME: Don't force convert to int, but raise an error (?)\n # First, the track method needs to preserve the data type of the time\n # column\n eseq = self.frame_selector.eval_seq()\n frames = tracks[\"donor\", self.columns[\"time\"]].to_numpy(dtype=int)\n et = pd.Series(eseq[frames % len(eseq)], dtype=\"category\")\n # Assignment to dataframe is done by matching indices, not line-wise\n # Thus copy index\n et.index = tracks.index\n tracks[\"fret\", \"exc_type\"] = et","def symptomatic_infection(self, s):\n if self.disease_status == 0:\n self.disease_status = 1\n self.time_since_infection = 0\n s.number_of_symptomatic += 1\n s.infected.add(self.identifier)","def is_tentative(self):\n return self.state == TrackState.Tentative","def is_tentative(self):\n return self.state == TrackState.Tentative","def _update_voided(self, tx: BaseTransaction) -> None:\n tx_meta = tx.get_metadata()\n assert tx.hash is not None\n assert tx_meta.voided_by\n log = self.log.new(tx=tx.hash_hex)\n log.debug('update voided')\n # remove all outputs\n for index, tx_output in enumerate(tx.outputs):\n log_it = log.new(index=index)\n if _should_skip_output(tx_output):\n log_it.debug('ignore output')\n continue\n log_it.debug('remove voided output')\n self._remove_utxo(UtxoIndexItem.from_tx_output(tx, index, tx_output))\n # re-add inputs that aren't voided\n for tx_input in tx.inputs:\n spent_tx = tx.get_spent_tx(tx_input)\n spent_tx_output = spent_tx.outputs[tx_input.index]\n log_it = log.new(tx_id=spent_tx.hash_hex, index=tx_input.index)\n if _should_skip_output(spent_tx_output):\n log_it.debug('ignore input')\n continue\n if spent_tx.get_metadata().voided_by:\n log_it.debug('do not re-add input that spend voided')\n continue\n spent_tx_meta = spent_tx.get_metadata()\n spent_by = spent_tx_meta.get_output_spent_by(tx_input.index)\n if spent_by is not None and spent_by != tx.hash:\n log_it.debug('do not re-add input that is spent by other tx', spent_by=spent_by.hex())\n continue\n log_it.debug('re-add input that became unspent')\n self._add_utxo(UtxoIndexItem.from_tx_output(spent_tx, tx_input.index, spent_tx_output))","def _store_transaction(account, transaction):\n tr_tx = transaction['tx']\n meta = transaction.get('meta', {})\n\n if meta.get('TransactionResult') != 'tesSUCCESS':\n return\n\n amount = meta.get('delivered_amount') or tr_tx.get('Amount', {})\n\n is_unprocessed = (\n tr_tx['TransactionType'] == 'Payment' and\n tr_tx['Destination'] == account and\n isinstance(amount, dict) and\n not Transaction.objects.filter(hash=tr_tx['hash'])\n )\n if is_unprocessed:\n logger.info(\n format_log_message(\n 'Saving transaction: %s', transaction\n )\n )\n\n transaction_object = Transaction.objects.create(\n account=tr_tx['Account'],\n hash=tr_tx['hash'],\n destination=account,\n ledger_index=tr_tx['ledger_index'],\n destination_tag=tr_tx.get('DestinationTag'),\n source_tag=tr_tx.get('SourceTag'),\n status=Transaction.RECEIVED,\n currency=amount['currency'],\n issuer=amount['issuer'],\n value=amount['value']\n )\n\n logger.info(\n format_log_message(\n \"Transaction saved: %s\", transaction_object\n )\n )","def transform_perfetto_trace_to_systrace(path_to_perfetto_trace: str,\n path_to_tmp_systrace: str) -> None:\n cmd_utils.run_command_nofail([str(TRACECONV_BIN),\n 'systrace',\n path_to_perfetto_trace,\n path_to_tmp_systrace])","def market_snap(self, timestamp = -1):\n # refresh account states\n self.trading_acc1.sync_account_with_exh()\n self.trading_acc2.sync_account_with_exh()\n\n signal__arbitrage_delta = self.get_signal__arbitrage_delta()\n signal__gdax_has_usd = self.get_signal__gdax_has_usd()\n signal__gdax_has_eth = self.get_signal__gdax_has_eth()\n signal__cex_has_eth = self.get_signal__cex_has_eth()\n\n def mk_audit_js():\n gdax_account = self.trading_acc1.get_account()\n cex_account = self.trading_acc2.get_account()\n transaction_t = epoch.current_milli_time() if timestamp == -1 else timestamp\n\n audit_js = OrderedDict()\n audit_js['strategy_run_id'] = self.run_id\n audit_js['timestamp'] = epoch.to_str(transaction_t)\n audit_js['timestamp__long'] = transaction_t\n audit_js['ticker'] = self.ticker\n audit_js['strategy_info'] = self._strategy_info\n\n audit_js['signal'] = OrderedDict()\n audit_js['signal']['signal__gdax_has_usd'] = signal__gdax_has_usd\n audit_js['signal']['signal__gdax_has_eth'] = signal__gdax_has_eth\n audit_js['signal']['signal__cex_has_eth'] = signal__cex_has_eth\n audit_js['signal']['signal__arbitrage_delta'] = signal__arbitrage_delta\n\n audit_js['total_usd__num'] = gdax_account.js['usd__num'] + cex_account.js['usd__num']\n audit_js['total_eth__num'] = gdax_account.js['eth__num'] + cex_account.js['eth__num']\n audit_js['gdax_account'] = gdax_account.js\n audit_js['cex_account'] = cex_account.js\n return audit_js\n\n snap_again = False # Only repeat if we have an gdax buy action\n if signal__gdax_has_usd['signal'] and signal__arbitrage_delta['signal']:\n exec_context = self.exec_gdax_buy(timestamp)\n snap_again = True\n\n # Audit\n audit_js = mk_audit_js()\n audit_js['action'] = exec_context\n audit = AuditTradeModel.build(audit_js)\n logger.info('-----Executed GDAX Buy-----')\n logger.info(audit)\n logger.info('---------------------------')\n audit.db_save(es)\n\n if signal__gdax_has_eth['signal']:\n exec_context = self.exec_eth_transfer()\n\n # Audit\n audit_js = mk_audit_js()\n audit_js['action'] = exec_context\n audit = AuditTradeModel.build(audit_js)\n logger.info('-----Executed ETH TRANSFER-----')\n logger.info(audit)\n logger.info('---------------------------')\n audit.db_save(es)\n\n if signal__cex_has_eth['signal']:\n exec_context = self.exec_cex_sell(timestamp)\n\n # Audit\n audit_js = mk_audit_js()\n audit_js['action'] = exec_context\n audit = AuditTradeModel.build(audit_js)\n logger.info('-----Executed CEX Sell-----')\n logger.info(audit)\n logger.info('---------------------------')\n audit.db_save(es)\n\n # Extra logging\n audit_js = mk_audit_js()\n logger.info('post-snapping states: \\n' + json.dumps(audit_js, indent=2))\n\n return snap_again","def save(self, ts):\n with open(self, 'w') as f:\n Timestamp.wrap(ts).dump(f)","def save_transaction(record, bucket=None):\n if not isinstance(record, _TransactionRecord):\n raise TypeError(\"You can only write TransactionRecord objects \"\n \"to the ledger!\")\n\n if not record.is_null():\n if bucket is None:\n bucket = _login_to_service_account()\n\n _ObjectStore.set_object_from_json(bucket,\n Ledger.get_key(record.uid()),\n record.to_data())","def save_scopeTraces(fileName, scope, channel, noPulses):\n scope._get_preamble(channel)\n results = utils.PickleFile(fileName, 1)\n results.add_meta_data(\"timeform_1\", scope.get_timeform(channel))\n\n #ct = scope.acquire_time_check()\n #if ct == False:\n # print 'No triggers for this data point. Will skip and set data to 0.'\n # results.save()\n # results.close()\n # return False\n\n t_start, loopStart = time.time(),time.time()\n for i in range(noPulses):\n try:\n ct = scope.acquire_time_check(timeout=.4)\n results.add_data(scope.get_waveform(channel), 1)\n except Exception, e:\n print \"Scope died, acquisition lost.\"\n print e\n if i % 100 == 0 and i > 0:\n print \"%d traces collected - This loop took : %1.1f s\" % (i, time.time()-loopStart)\n loopStart = time.time()\n print \"%d traces collected TOTAL - took : %1.1f s\" % (i, (time.time()-t_start))\n results.save()\n results.close()\n return True","def writeSTH(seis, STH_Key, newSTH):\n traces = seis.traces\n for i, trace in enumerate(traces):\n trace.header.__setattr__(STH_Key, newSTH[i])","def _before_stockpyle_deserialize(self, obj):\n \n # only merge SA objects\n if _is_sqlalchemy_object(obj):\n self.__session.merge(obj, load=False)","def recordTransaction(self, loop, transaction):\n\n a = {}\n a['time'] = transaction.transactionTime\n a['atm'] = transaction.transactionATM.atmID\n a['transaction'] = transaction.transactionType\n a['cash'] = transaction.transactionATM.atmCash\n a['status'] = transaction.transactionStatus\n self._atmDict[loop] = a\n\n c = {}\n c['time'] = transaction.transactionTime\n c['client'] = transaction.transactionCard.cardAccount.accountClient.clientID\n c['account'] = transaction.transactionCard.cardAccount.accountNumber\n c['transaction'] = transaction.transactionType\n c['balance'] = transaction.transactionCard.cardAccount.accountBalance\n c['status'] = transaction.transactionStatus\n self._clientDict[loop] = c\n\n t = {}\n t['time'] = transaction.transactionTime\n t['transaction'] = transaction.transactionType\n t['amount'] = transaction.transactionAmount\n t['status'] = transaction.transactionStatus\n self._transactionDict[loop] = t","def _save(self):\n if self.character:\n self.character.db_command_stack = dumps(self)","def process_trace(self, trace):\n # type: (List[Span]) -> Optional[List[Span]]\n pass","def test_log_track_with_transform(self):\n tf_id = self.add_transform(cond_artist=True, pattern_artist='Artist',\n change_artist=True, to_artist='Artist 2')\n self.assertNotEqual(tf_id, 0)\n self.app.load_data()\n\n track = self.app.log_track(self.track_obj('silence.mp3'))\n self.assertEqual(self.get_track_count(), 1)\n track_row = self.get_track_by_id(track.pk)\n self.assertNotEqual(track_row, None)\n self.assertEqual(track_row['lasttransform'], tf_id)\n self.assertEqual(track_row['artist'], 'Artist 2')\n self.assertEqual(track_row['album'], 'Album')\n self.assertEqual(track_row['title'], 'Track')\n self.assertEqual(track_row['source'], 'xmms')","def is_stair(game_object: GameObject) -> bool:\n return CommonObjectTagUtils.has_game_tags(game_object, (\n CommonGameTag.BUILD_STAIR,\n ))","def change_TTS_engine(self):\n\t\t\n\t\tif self.isActiveDualTTS:\n\t\t\t#dual TTS\n\t\t\tpath_to_file = '/var/lib/snips/skills/snips_app_pilldispenser/settings/dual_TTS.sh'\n\t\t\tsubprocess.call([path_to_file])\n\t\t\tprint('Dual TTS is enabled. Using Amazon Polly TTS in case of internet connection, else use offline Picotts TTS.')\n\t\t\t\n\t\telse:\n\t\t\t#go back to single offline Picotts TTS\n\t\t\tpath_to_file = '/var/lib/snips/skills/snips_app_pilldispenser/settings/single_TTS.sh'\n\t\t\tsubprocess.call([path_to_file])\n\t\t\tprint('Dual TTS is disabled. Using offline Picotts TTS regardless of internect connection.')","def supports_transactions(self):\n return self._mysql_storage_engine != \"MyISAM\"","def process_transaction(self, transaction):\n instrument = transaction.instrument\n if isinstance(instrument, Future):\n try:\n old_price = self._payout_last_sale_prices[instrument]\n except KeyError:\n self._payout_last_sale_prices[instrument] = transaction.price\n else:\n position = self.position_tracker.positions[instrument]\n amount = position.amount\n price = transaction.price\n\n self._cash_flow(\n self._calculate_payout(\n instrument.multiplier,\n amount,\n old_price,\n price,\n ),\n )\n\n if amount + transaction.amount == 0:\n del self._payout_last_sale_prices[instrument]\n else:\n self._payout_last_sale_prices[instrument] = price\n else:\n self._cash_flow(-(transaction.price * transaction.amount))\n\n self.position_tracker.execute_transaction(transaction)\n\n # we only ever want the dict form from now on\n transaction_dict = transaction.to_dict()\n try:\n self._processed_transactions[transaction.dt].append(\n transaction_dict,\n )\n except KeyError:\n self._processed_transactions[transaction.dt] = [transaction_dict]","def test_save_unjsonable_subs_to_store(self):\r\n with self.assertRaises(NotFoundError):\r\n contentstore().find(self.content_location_unjsonable)\r\n\r\n with self.assertRaises(TypeError):\r\n transcripts_utils.save_subs_to_store(\r\n self.unjsonable_subs,\r\n self.unjsonable_subs_id,\r\n self.course)\r\n\r\n with self.assertRaises(NotFoundError):\r\n contentstore().find(self.content_location_unjsonable)","def _is_valid_trade(self, trade):\n if not trade:\n return False\n\n if trade.Status() in VALID_TRADE_STATUSES:\n if acm.Time().AsDate(trade.TradeTime()) > self.start_date:\n return False\n print '1'\n ins_type = trade.Instrument().InsType()\n if ins_type == 'Curr':\n if trade.ValueDay() > self.start_date:\n return True\n elif ins_type == 'Combination':\n for comb_ins in trade.Instrument().Instruments():\n trades = comb_ins.Trades()\n if trades and trades[0] in VALID_TRADE_STATUSES:\n trade = trades[0]\n ins_type = trade.Instrument().InsType()\n if (self._is_basis_trade(trade) and\n ins_type in ('Swap', 'FRA')):\n return True\n elif ins_type == 'CurrSwap':\n if trade.Instrument().ExpiryDateOnly() > self.start_date:\n return True\n else:\n if trade.Instrument().ExpiryDateOnly() > self.start_date:\n if (self._is_basis_trade(trade) and\n ins_type in ('Swap', 'FRA')):\n return True\n\n return False","def visualize_transcription(self):\n # # Test if current nwb file contains Survey table\n # if 'behavior' in self.model.nwb.processing:\n # list_surveys = [v for v in self.model.nwb.processing['behavior'].data_interfaces.values()\n # if v.neurodata_type == 'SurveyTable']\n # if len(list_surveys) > 0:\n # ShowSurveyDialog(nwbfile=self.model.nwb)\n ShowTranscriptionDialog(nwbfile=self.model.nwb)\n pass","def _set_types(self):\n if isinstance(self.elastic_span, elasticapm.traces.Transaction):\n attributes = self.elastic_span.context.get(\"otel_attributes\", {})\n span_kind = self.elastic_span.context[\"otel_spankind\"]\n is_rpc = bool(attributes.get(\"rpc.system\"))\n is_http = bool(attributes.get(\"http.url\")) or bool(attributes.get(\"http.scheme\"))\n is_messaging = bool(attributes.get(\"messaging.system\"))\n if span_kind == SpanKind.SERVER.name and (is_rpc or is_http):\n transaction_type = \"request\"\n elif span_kind == SpanKind.CONSUMER.name and is_messaging:\n transaction_type = \"messaging\"\n else:\n transaction_type = \"unknown\"\n self.elastic_span.transaction_type = transaction_type\n else:\n attributes = self.elastic_span.context.get(\"otel_attributes\", {})\n span_type = None\n span_subtype = None\n resource = None\n\n def http_port_from_scheme(scheme: str):\n if scheme == \"http\":\n return 80\n elif scheme == \"https\":\n return 443\n return None\n\n def parse_net_name(url: str):\n u = urllib.parse.urlparse(url)\n if u.port:\n return u.netloc\n else:\n port = http_port_from_scheme(u.scheme)\n return u.netloc if not port else \"{}:{}\".format(u.netloc, port)\n\n net_port = attributes.get(\"net.peer.port\", -1)\n net_name = net_peer = attributes.get(\"net.peer.name\", attributes.get(\"net.peer.ip\", \"\"))\n\n if net_name and (net_port > 0):\n net_name = f\"{net_name}:{net_port}\"\n\n if attributes.get(\"db.system\"):\n span_type = \"db\"\n span_subtype = attributes.get(\"db.system\")\n resource = net_name or span_subtype\n if attributes.get(\"db.name\"):\n resource = \"{}/{}\".format(resource, attributes.get(\"db.name\"))\n elif attributes.get(\"messaging.system\"):\n span_type = \"messaging\"\n span_subtype = attributes.get(\"messaging.system\")\n if not net_name and attributes.get(\"messaging.url\"):\n net_name = parse_net_name(attributes.get(\"messaging.url\"))\n resource = net_name or span_subtype\n if attributes.get(\"messaging.destination\"):\n resource = \"{}/{}\".format(resource, attributes.get(\"messaging.destination\"))\n elif attributes.get(\"rpc.system\"):\n span_type = \"external\"\n span_subtype = attributes.get(\"rpc.system\")\n resource = net_name or span_subtype\n if attributes.get(\"rpc.service\"):\n resource = \"{}/{}\".format(resource, attributes.get(\"rpc.service\"))\n elif attributes.get(\"http.url\") or attributes.get(\"http.scheme\"):\n span_type = \"external\"\n span_subtype = \"http\"\n http_host = attributes.get(\"http.host\", net_peer)\n if http_host:\n if net_port < 0:\n net_port = http_port_from_scheme(attributes.get(\"http.scheme\"))\n resource = http_host if net_port < 0 else f\"{http_host}:{net_port}\"\n elif attributes.get(\"http.url\"):\n resource = parse_net_name(attributes[\"http.url\"])\n\n if not span_type:\n span_kind = self.elastic_span.context[\"otel_spankind\"]\n if span_kind == SpanKind.INTERNAL.name:\n span_type = \"app\"\n span_subtype = \"internal\"\n else:\n span_type = \"unknown\"\n self.elastic_span.type = span_type\n self.elastic_span.subtype = span_subtype\n if resource:\n if \"destination\" not in self.elastic_span.context:\n self.elastic_span.context[\"destination\"] = {\"service\": {\"resource\": resource}}\n elif \"service\" not in self.elastic_span.context[\"destination\"]:\n self.elastic_span.context[\"destination\"][\"service\"] = {\"resource\": resource}\n else:\n self.elastic_span.context[\"destination\"][\"service\"][\"resource\"] = resource","def on_save_interpretation_button(self, event):\n if \"specimen_int_uT\" not in self.Data[self.s]['pars']:\n return\n if 'deleted' in self.Data[self.s]['pars']:\n self.Data[self.s]['pars'].pop('deleted')\n self.Data[self.s]['pars']['saved'] = True\n\n # collect all interpretation by sample\n sample = self.Data_hierarchy['specimens'][self.s]\n if sample not in list(self.Data_samples.keys()):\n self.Data_samples[sample] = {}\n if self.s not in list(self.Data_samples[sample].keys()):\n self.Data_samples[sample][self.s] = {}\n self.Data_samples[sample][self.s]['B'] = self.Data[self.s]['pars'][\"specimen_int_uT\"]\n\n # collect all interpretation by site\n # site=thellier_gui_lib.get_site_from_hierarchy(sample,self.Data_hierarchy)\n site = thellier_gui_lib.get_site_from_hierarchy(\n sample, self.Data_hierarchy)\n if site not in list(self.Data_sites.keys()):\n self.Data_sites[site] = {}\n if self.s not in list(self.Data_sites[site].keys()):\n self.Data_sites[site][self.s] = {}\n self.Data_sites[site][self.s]['B'] = self.Data[self.s]['pars'][\"specimen_int_uT\"]\n\n self.draw_sample_mean()\n self.write_sample_box()\n self.close_warning = True","def test_serialize_sinfo(self):\n self.assert_raises(RuntimeError, self.instance.serialize,\n self.testing_options['objects'][0],\n add_serializer_info=True)","def test_add_trace(self):\n payload = Payload()\n\n # Add falsey traces\n for val in (False, None, 0, '', [], dict()):\n payload.add_trace(val)\n self.assertEqual(payload.length, 0)\n self.assertTrue(payload.empty)\n\n # Add a single trace to the payload\n trace = [Span(self.tracer, name='root.span'), Span(self.tracer, name='child.span')]\n payload.add_trace(trace)\n\n self.assertEqual(payload.length, 1)\n self.assertFalse(payload.empty)","def filter_trace_variable_event(self, event):\n return isinstance(event, (TraceAccess, TraceAssign, TraceDelete))","def process_traces(st,positions):\n spti = {}\n isis = {}\n rav = {}\n\n nspti = {}\n nisis = {}\n nrav = {}\n\n for i,sti in enumerate(st.keys()):\n print('analysing trace nr %i'%i)\n cst = st[sti]\n cp = positions[sti]\n cisi = np.diff(cst)\n if len(cisi) > 0:\n spti[sti], isis[sti], positions[sti] = delete_artifacts(cst,cisi,cp)\n nspti[sti], nisis[sti] = fill_gaps(spti[sti][:-1],np.diff(spti[sti]))\n\n print('smooting traces')\n\n return smooth_traces(nspti,nisis,5), positions","def _add_stix_object(self, stix_object: _Observable):\n if stix_object.id in self._all_objects:\n if len(stix_object.serialize()) > len(self._all_objects[stix_object.id].serialize()):\n self._all_objects[stix_object.id] = stix_object\n else:\n self._all_objects[stix_object.id] = stix_object","def _check_duplicate_trans(self):\n transactions_set = set(self._transactions)\n return len(transactions_set) == len(self._transactions)","def loads_trade(self, trade_id, trade_type, data, operations):\n trade = None\n\n if trade_type == StrategyTrade.TRADE_BUY_SELL:\n trade = StrategyAssetTrade(0)\n elif trade_type == StrategyTrade.TRADE_MARGIN:\n trade = StrategyMarginTrade(0)\n elif trade_type == StrategyTrade.TRADE_POSITION:\n trade = StrategyPositionTrade(0)\n elif trade_type == StrategyTrade.TRADE_IND_MARGIN:\n trade = StrategyIndMarginTrade(0)\n else:\n error_logger.error(\"During loads, usupported trade type %i\" % (trade_type,))\n return\n\n trade.loads(data, self.strategy.service)\n\n # operations\n for op in operations:\n if op['name'] in self.strategy.service.tradeops:\n try: \n operation = self.strategy.service.tradeops[op['name']]()\n operation.loads(op)\n\n if operation.check(trade):\n # append the operation to the trade\n trade.add_operation(operation)\n else:\n error_logger.error(\"During loads, operation checking error %s\" % (op_name,))\n except Exception as e:\n error_logger.error(repr(e))\n else:\n error_logger.error(\"During loads, region checking error %s\" % (r['name'],))\n\n # ignored for now because need to check assets/positions/orders\n # self.add_trade(trade)","def create_incoming_transaction(transaction: IncomingTransactionCreate, db: Session = Depends(get_db), auth_user: User=Depends(manager)):\n try:\n transaction = transaction_service.create(db, auth_user, transaction)\n return transaction\n except ItensNotFound as err:\n\t raise HTTPException(status_code=404, detail=f\"Os seguintes produtos não foram encontrados no sistema: {str(err)}\")\n except ProductsNotFound as err:\n raise HTTPException(status_code=400, detail=\"A movimentação a ser registrada deve conter no minimo um produto.\")\n except ProviderNotFound as err:\n\t raise HTTPException(status_code=404, detail=f\"O fornecedor informado não foi encontrado: {str(err)}\")\n except InvalidStockQuantity as err:\n products_missing = transaction_service.make_response(db, str(err))\n raise HTTPException(status_code=400, detail={\n \"message\": \"A quantidade informada para os seguintes produtos deve ser maior do que zero.\",\n \"products_missing\": products_missing\n })","def saveTrans(self):\n modtranDataDir = os.getenv('MODTRAN_DATADIR')\n outputfile = '{0}/{1}_final.plt'.format(\n self.outfilename, self.outfilename)\n outputpath = os.path.join(modtranDataDir, outputfile)\n with open(outputpath, 'w') as transmf:\n transmf.write('$ FINAL ATMOSPHERE TRANSMISSION\\n')\n for val in range(len(self.modtran_wl)):\n data = '\\t'.join('{0:f}'.format(self.transmittance[run][val])\n for run in range(len(self.modtran_wl)))\n line = '{0}\\t{1}\\n'.format(self.modtran_wl[val], data)\n transmf.write(line)","def save(self):\n self.add_statements(self.triples())","def configureThisTrace(self):\n for spec in Trace.traceSpec:\n if (spec.compiledRegex.match(self.entityName)):\n self.traceLevel = spec.level\n break\n #endIf\n #endFor","def _is_transaction_isolation_error(self, error):\n return False","def is_serious(self, level=ERROR):\n return self.level >= level","def should_save(event) -> bool:\n schema = event[\"$schemaRef\"]\n if schema == \"https://eddn.edcd.io/schemas/commodity/3\":\n return True\n elif schema == \"https://eddn.edcd.io/schemas/journal/1\":\n return event[\"message\"][\"event\"] == \"Docked\""],"string":"[\n \"def isSStx(tx):\\n try:\\n checkSStx(tx)\\n\\n except Exception as e:\\n log.debug(\\\"isSStx: {}\\\".format(e))\\n\\n else:\\n return True\",\n \"def save(self, trade: Trade) -> Trade:\\n\\n pass # pragma: no cover\",\n \"def is_transaction(self) -> bool:\\n return False\",\n \"def isTx(self):\\n\\t\\treturn self.extension == '.tx'\",\n \"def trackTrans(self):\\n self.data_struct['_trackTrans'] = True\",\n \"def _is_trace_on():\\n return AceQLHttpApi.is_trace_on()\",\n \"def is_transaction(self):\\n return self._request.has_var(\\\"_transid\\\")\",\n \"def isSTraced(self):\\n try:\\n return self.sTraced\\n except AttributeError:\\n self.sTraced = False\\n return False\",\n \"def report(self, trade, is_entry):\\n pass\",\n \"def transaction_trace_data(self, connections):\\n\\n _logger.debug('Generating transaction trace data.')\\n\\n if not self.__settings:\\n return []\\n\\n # Create a set 'traces' that is a union of slow transaction,\\n # and Synthetics transactions. This ensures we don't send\\n # duplicates of a transaction.\\n\\n traces = set()\\n if self.__slow_transaction:\\n traces.add(self.__slow_transaction)\\n traces.update(self.__synthetics_transactions)\\n\\n # Return an empty list if no transactions were captured.\\n\\n if not traces:\\n return []\\n\\n # We want to limit the number of explain plans we do across\\n # these. So work out what were the slowest and tag them.\\n # Later the explain plan will only be run on those which are\\n # tagged.\\n\\n agent_limits = self.__settings.agent_limits\\n explain_plan_limit = agent_limits.sql_explain_plans_per_harvest\\n maximum_nodes = agent_limits.transaction_traces_nodes\\n\\n database_nodes = []\\n\\n if explain_plan_limit != 0:\\n for trace in traces:\\n for node in trace.slow_sql:\\n # Make sure we clear any flag for explain plans on\\n # the nodes in case a transaction trace was merged\\n # in from previous harvest period.\\n\\n node.generate_explain_plan = False\\n\\n # Node should be excluded if not for an operation\\n # that we can't do an explain plan on. Also should\\n # not be one which would not be included in the\\n # transaction trace because limit was reached.\\n\\n if (node.node_count < maximum_nodes and\\n node.connect_params and node.statement.operation in\\n node.statement.database.explain_stmts):\\n database_nodes.append(node)\\n\\n database_nodes = sorted(database_nodes,\\n key=lambda x: x.duration)[-explain_plan_limit:]\\n\\n for node in database_nodes:\\n node.generate_explain_plan = True\\n\\n else:\\n for trace in traces:\\n for node in trace.slow_sql:\\n node.generate_explain_plan = True\\n database_nodes.append(node)\\n\\n # Now generate the transaction traces. We need to cap the\\n # number of nodes capture to the specified limit.\\n\\n trace_data = []\\n\\n for trace in traces:\\n transaction_trace = trace.transaction_trace(\\n self, maximum_nodes, connections)\\n\\n data = [transaction_trace,\\n list(trace.string_table.values())]\\n\\n if self.__settings.debug.log_transaction_trace_payload:\\n _logger.debug('Encoding slow transaction data where '\\n 'payload=%r.', data)\\n\\n json_data = json_encode(data)\\n\\n level = self.__settings.agent_limits.data_compression_level\\n level = level or zlib.Z_DEFAULT_COMPRESSION\\n\\n zlib_data = zlib.compress(six.b(json_data), level)\\n\\n pack_data = base64.standard_b64encode(zlib_data)\\n\\n if six.PY3:\\n pack_data = pack_data.decode('Latin-1')\\n\\n root = transaction_trace.root\\n\\n if trace.record_tt:\\n force_persist = True\\n else:\\n force_persist = False\\n\\n if trace.include_transaction_trace_request_uri:\\n request_uri = trace.request_uri\\n else:\\n request_uri = None\\n\\n trace_data.append([transaction_trace.start_time,\\n root.end_time - root.start_time,\\n trace.path,\\n request_uri,\\n pack_data,\\n trace.guid,\\n None,\\n force_persist,\\n None,\\n trace.synthetics_resource_id, ])\\n\\n return trace_data\",\n \"def _apply_trx_trade_to_allocation(cls, allocation, block_trade):\\n try:\\n allocation.TrxTrade(block_trade)\\n allocation.Commit()\\n except Exception as e:\\n error_message = 'Failed to stamp TrxTrade {0} on Allocation Trade: {1} , {2}'\\n LOGGER.exception(error_message.format(block_trade.Oid(), allocation.Oid(), e))\\n return False\\n\\n return True\",\n \"def sign_transaction_essence(self, prepared_transaction_data):\\n return self._call_account_method(\\n 'signTransactionEssence', {\\n 'preparedTransactionData': prepared_transaction_data\\n }\\n )\",\n \"def is_sed(self) -> bool:\\n return False\",\n \"def signSign(self):\\r\\n if \\\"signature\\\" in self: # or \\\"signatures\\\" in self ?\\r\\n self.pop(\\\"id\\\", False)\\r\\n try:\\r\\n self[\\\"signSignature\\\"] = dposlib.core.crypto.getSignature(\\r\\n self, self._secondPrivateKey,\\r\\n exclude_second_sig=True,\\r\\n )\\r\\n except AttributeError:\\r\\n raise Exception(\\\"no second private Key available\\\")\\r\\n else:\\r\\n raise Exception(\\\"transaction not signed\\\")\",\n \"async def check_trustline(\\n cls, transaction: Transaction, server: Server, locks: Dict\\n ):\\n try:\\n _, account = await get_account_obj_async(\\n Keypair.from_public_key(transaction.to_address), server\\n )\\n except BaseRequestError:\\n logger.exception(f\\\"Failed to load account {transaction.to_address}\\\")\\n transaction.pending_execution_attempt = False\\n await sync_to_async(transaction.save)()\\n return\\n trustline_found = False\\n for balance in account[\\\"balances\\\"]:\\n if balance.get(\\\"asset_type\\\") == \\\"native\\\":\\n continue\\n if (\\n balance[\\\"asset_code\\\"] == transaction.asset.code\\n and balance[\\\"asset_issuer\\\"] == transaction.asset.issuer\\n ):\\n trustline_found = True\\n break\\n if trustline_found:\\n logger.debug(\\n f\\\"detected transaction {transaction.id} is no longer pending trust\\\"\\n )\\n await cls.process_deposit(transaction, server, locks)\\n else:\\n transaction.pending_execution_attempt = False\\n await sync_to_async(transaction.save)()\",\n \"def _save_internal_transactions(self, blocks_traces):\\n docs = [\\n self._preprocess_internal_transaction(transaction)\\n for transaction in blocks_traces\\n if transaction[\\\"transactionHash\\\"]\\n ]\\n if docs:\\n for chunk in bulk_chunks(docs, None, BYTES_PER_CHUNK):\\n self.client.bulk_index(docs=chunk, index=self.indices[\\\"internal_transaction\\\"], doc_type=\\\"itx\\\",\\n id_field=\\\"hash\\\", refresh=True)\",\n \"def trace(self, trace=...):\\n ...\",\n \"def _save_miner_transactions(self, blocks_traces):\\n docs = [self._preprocess_internal_transaction(transaction) for transaction in blocks_traces if\\n not transaction[\\\"transactionHash\\\"]]\\n self.client.bulk_index(docs=docs, index=self.indices[\\\"miner_transaction\\\"], doc_type=\\\"tx\\\", id_field=\\\"hash\\\",\\n refresh=True)\",\n \"def sign_trx(self, signture):\\n self.trx_signature = signture\",\n \"def record_transaction(self, transaction):\\n\\n if not self.__settings:\\n return\\n\\n settings = self.__settings\\n\\n # Record the apdex, value and time metrics generated from the\\n # transaction. Whether time metrics are reported as distinct\\n # metrics or into a rollup is in part controlled via settings\\n # for minimum number of unique metrics to be reported and thence\\n # whether over a time threshold calculated as percentage of\\n # overall request time, up to a maximum number of unique\\n # metrics. This is intended to limit how many metrics are\\n # reported for each transaction and try and cut down on an\\n # explosion of unique metric names. The limits and thresholds\\n # are applied after the metrics are reverse sorted based on\\n # exclusive times for each metric. This ensures that the metrics\\n # with greatest exclusive time are retained over those with\\n # lesser time. Such metrics get reported into the performance\\n # breakdown tab for specific web transactions.\\n\\n self.record_apdex_metrics(transaction.apdex_metrics(self))\\n\\n self.merge_custom_metrics(transaction.custom_metrics.metrics())\\n\\n self.record_time_metrics(transaction.time_metrics(self))\\n\\n # Capture any errors if error collection is enabled.\\n # Only retain maximum number allowed per harvest.\\n\\n error_collector = settings.error_collector\\n\\n if (error_collector.enabled and settings.collect_errors and\\n len(self.__transaction_errors) <\\n settings.agent_limits.errors_per_harvest):\\n self.__transaction_errors.extend(transaction.error_details())\\n\\n self.__transaction_errors = self.__transaction_errors[:\\n settings.agent_limits.errors_per_harvest]\\n\\n if (error_collector.capture_events and\\n error_collector.enabled and\\n settings.collect_error_events):\\n events = transaction.error_events(self.__stats_table)\\n for event in events:\\n self._error_events.add(event, priority=transaction.priority)\\n\\n # Capture any sql traces if transaction tracer enabled.\\n\\n if settings.slow_sql.enabled and settings.collect_traces:\\n for node in transaction.slow_sql_nodes(self):\\n self.record_slow_sql_node(node)\\n\\n # Remember as slowest transaction if transaction tracer\\n # is enabled, it is over the threshold and slower than\\n # any existing transaction seen for this period and in\\n # the historical snapshot of slow transactions, plus\\n # recording of transaction trace for this transaction\\n # has not been suppressed.\\n\\n transaction_tracer = settings.transaction_tracer\\n\\n if (not transaction.suppress_transaction_trace and\\n transaction_tracer.enabled and settings.collect_traces):\\n\\n # Transactions saved for Synthetics transactions\\n # do not depend on the transaction threshold.\\n\\n self._update_synthetics_transaction(transaction)\\n\\n threshold = transaction_tracer.transaction_threshold\\n\\n if threshold is None:\\n threshold = transaction.apdex_t * 4\\n\\n if transaction.duration >= threshold:\\n self._update_slow_transaction(transaction)\\n\\n # Create the transaction event and add it to the\\n # appropriate \\\"bucket.\\\" Synthetic requests are saved in one,\\n # while transactions from regular requests are saved in another.\\n\\n if transaction.synthetics_resource_id:\\n event = transaction.transaction_event(self.__stats_table)\\n self._synthetics_events.add(event)\\n\\n elif (settings.collect_analytics_events and\\n settings.transaction_events.enabled):\\n\\n event = transaction.transaction_event(self.__stats_table)\\n self._transaction_events.add(event, priority=transaction.priority)\\n\\n # Merge in custom events\\n\\n if (settings.collect_custom_events and\\n settings.custom_insights_events.enabled):\\n self.custom_events.merge(transaction.custom_events)\\n\\n # Merge in span events\\n\\n if (settings.distributed_tracing.enabled and\\n settings.span_events.enabled and settings.collect_span_events):\\n if settings.infinite_tracing.enabled:\\n for event in transaction.span_protos(settings):\\n self._span_stream.put(event)\\n elif transaction.sampled:\\n for event in transaction.span_events(self.__settings):\\n self._span_events.add(event, priority=transaction.priority)\",\n \"def hasTx(self):\\n\\t\\tif self.isTx:\\n\\t\\t\\treturn True\\n\\t\\treturn textureFile( self.path.replace( self.extension, '.tx' ) ).exists\",\n \"def _is_transaction_isolation_error(self, error):\\n from psycopg2.extensions import TransactionRollbackError\\n\\n # Django can wrap errors, adding it to the `__cause__` attribute\\n for e in (error, getattr(error, '__cause__', None)):\\n if isinstance(e, TransactionRollbackError):\\n return True\\n return False\",\n \"def save_transcription(trs_fname, trs):\\n existed = os.path.exists(trs_fname)\\n if not trs.endswith('\\\\n'):\\n trs += '\\\\n'\\n with codecs.open(trs_fname, 'w+', encoding='UTF-8') as trs_file:\\n trs_file.write(trs)\\n return existed\",\n \"def record_trace(self):\\n\\n tfname = str(int(time.time())) + \\\".obd2_reader.trace\\\"\\n self.tf_out = open(tfname, 'a')\\n self.RecordTrace = 1\\n print \\\"Recoding trace to:\\\", tfname\",\n \"def isSSGen(tx):\\n try:\\n checkSSGen(tx)\\n\\n except Exception as e:\\n log.debug(\\\"isSSGen: {}\\\".format(e))\\n\\n else:\\n return True\",\n \"def check(transaction):\\n if not isinstance(transaction, Transaction):\\n transaction = Transaction.objects.get(id=transaction)\\n\\n r = requests.post(\\\"https://www.blockonomics.co/api/searchhistory\\\",\\n data=json.dumps({\\\"addr\\\": transaction.to_address}))\\n try:\\n history_data = json.loads(r.content.decode('utf-8'))['history'][0]\\n except:\\n return\\n\\n set_tx_details(history_data, transaction)\",\n \"def is_sedes(obj):\\n return hasattr(obj, 'serialize') and hasattr(obj, 'deserialize')\",\n \"def save(self, *args, **kwargs):\\n\\n # Call the \\\"real\\\" save() method.\\n super(Product, self).save(*args, **kwargs)\\n\\n # If sold and is a Widget\\n if self.sale and self.kind == self.WIDGET:\\n # But has not stamp\\n try:\\n self.stamp\\n except:\\n s = Stamp(owned_by=self.sale.customer, obtained_with=self)\\n s.save()\",\n \"def supports_transactions(self):\\n return False\",\n \"def save(self):\\n self.lock()\\n\\n trader = self.strategy.trader()\\n\\n for trade in self.trades:\\n t_data = trade.dumps()\\n ops_data = [operation.dumps() for operation in trade.operations]\\n\\n # store per trade\\n Database.inst().store_user_trade((trader.name, trader.account.name, self.instrument.market_id,\\n self.strategy.identifier, trade.id, trade.trade_type, t_data, ops_data))\\n\\n # dumps of regions\\n trader_data = {}\\n regions_data = [region.dumps() for region in self.regions]\\n\\n Database.inst().store_user_trader((trader.name, trader.account.name, self.instrument.market_id,\\n self.strategy.identifier, self.activity, trader_data, regions_data))\\n\\n self.unlock()\",\n \"def is_tx(self):\\n return self._pin_name in TX_CHANNELS\",\n \"def _save_transactions(self):\\r\\n\\t\\tlogger.debug(\\\"Enter\\\")\\r\\n\\t\\t\\r\\n\\t\\twith open(self._state_file, 'wb') as tmp:\\r\\n\\t\\t\\tlogger.debug(\\\"Dumping transactions: %r\\\" % self.transactions)\\r\\n\\t\\t\\tpickle.dump(self.transactions, tmp)\\r\\n\\t\\t\\r\\n\\t\\tlogger.debug(\\\"Exit\\\")\",\n \"def add_transaction(self,transaction):\\n if type(transaction) != PoWGenericTransaction:\\n raise Exception('TYPEERROR','transaction should be type of \\\"PoWGenericTransaction\\\" but got {}'.format(type(transaction)))\\n if not transaction.is_validation_passed():\\n print 'The transaction is not valid. Skipped...'\\n return\\n self.transactions.append(transaction)\",\n \"def in_transaction(self):\\n # We likely just changed data - give it a second to catch up\\n time.sleep(0.1) # I think I keep reading journal watermark too soon without this\\n \\n # Get relevant data\\n water_mark = pos.read_journal_watermark()\\n self.log.info(f\\\"Watermark: [{water_mark}]\\\")\\n balance = pos.read_balance()['Total']\\n self.log.info(f\\\"Balance: [{balance}]\\\")\\n \\n # Decide if we need more checks based on watermark\\n if water_mark == \\\"TRANSACTION IN PROGRESS\\\":\\n self.log.info(\\\"In Transaction: In Transaction Watermark found\\\")\\n return True\\n elif water_mark == \\\"TRANSACTION COMPLETE\\\" or water_mark == \\\"TRANSACTION VOIDED\\\":\\n self.log.info(\\\"Not in Transaction: Transaction Complete/Voided watermarks found\\\")\\n return False\\n else:\\n # No watermark - decide based on balance\\n if balance == \\\"$0.00\\\":\\n self.log.info(\\\"Not in Transaction: $0 balance with no watermark\\\")\\n return False\\n else:\\n self.log.info(\\\"In Transaction: Non-$0 balance with no watermark\\\")\\n return True\",\n \"def istraceback(object):\\r\\n return isinstance(object, types.TracebackType)\",\n \"def serialize(\\n self,\\n transactions: Sequence[SnubaTransaction],\\n errors: Sequence[SnubaError],\\n roots: Sequence[SnubaTransaction],\\n warning_extra: Dict[str, str],\\n event_id: Optional[str],\\n detailed: bool = False,\\n ) -> Sequence[FullResponse]:\\n parent_map = self.construct_parent_map(transactions)\\n error_map = self.construct_error_map(errors)\\n parent_events: Dict[str, TraceEvent] = {}\\n results_map: Dict[Optional[str], List[TraceEvent]] = defaultdict(list)\\n to_check: Deque[SnubaTransaction] = deque()\\n # The root of the orphan tree we're currently navigating through\\n orphan_root: Optional[SnubaTransaction] = None\\n if roots:\\n results_map[None] = []\\n for root in roots:\\n root_event = TraceEvent(root, None, 0)\\n parent_events[root[\\\"id\\\"]] = root_event\\n results_map[None].append(root_event)\\n to_check.append(root)\\n\\n with sentry_sdk.start_span(op=\\\"building.trace\\\", description=\\\"full trace\\\"):\\n iteration = 0\\n has_orphans = False\\n while parent_map or to_check:\\n if len(to_check) == 0:\\n has_orphans = True\\n # Grab any set of events from the parent map\\n parent_span_id, current_events = parent_map.popitem()\\n\\n current_event, *siblings = current_events\\n # If there were any siblings put them back\\n if siblings:\\n parent_map[parent_span_id] = siblings\\n\\n previous_event = parent_events[current_event[\\\"id\\\"]] = TraceEvent(\\n current_event, None, 0\\n )\\n\\n # Used to avoid removing the orphan from results entirely if we loop\\n orphan_root = current_event\\n results_map[parent_span_id].append(previous_event)\\n else:\\n current_event = to_check.popleft()\\n previous_event = parent_events[current_event[\\\"id\\\"]]\\n\\n # We've found the event for the trace navigator so we can remove everything in the deque\\n # As they're unrelated ancestors now\\n if event_id and current_event[\\\"id\\\"] == event_id:\\n # Remove any remaining events so we don't think they're orphans\\n while to_check:\\n to_remove = to_check.popleft()\\n if to_remove[\\\"trace.parent_span\\\"] in parent_map:\\n del parent_map[to_remove[\\\"trace.parent_span\\\"]]\\n to_check = deque()\\n\\n # This is faster than doing a call to get_events, since get_event_by_id only makes a call to snuba\\n # when non transaction events are included.\\n with sentry_sdk.start_span(op=\\\"nodestore\\\", description=\\\"get_event_by_id\\\"):\\n nodestore_event = eventstore.get_event_by_id(\\n current_event[\\\"project.id\\\"], current_event[\\\"id\\\"]\\n )\\n\\n previous_event.nodestore_event = nodestore_event\\n\\n spans: NodeSpans = nodestore_event.data.get(\\\"spans\\\", [])\\n\\n # Need to include the transaction as a span as well\\n #\\n # Important that we left pad the span id with 0s because\\n # the span id is stored as an UInt64 and converted into\\n # a hex string when quering. However, the conversion does\\n # not ensure that the final span id is 16 chars long since\\n # it's a naive base 10 to base 16 conversion.\\n spans.append({\\\"span_id\\\": previous_event.event[\\\"trace.span\\\"].rjust(16, \\\"0\\\")})\\n\\n for child in spans:\\n if child[\\\"span_id\\\"] in error_map:\\n previous_event.errors.extend(\\n [\\n self.serialize_error(error)\\n for error in error_map.pop(child[\\\"span_id\\\"])\\n ]\\n )\\n # We need to connect back to an existing orphan trace\\n if (\\n has_orphans\\n and\\n # The child event has already been checked\\n child[\\\"span_id\\\"] in results_map\\n and orphan_root is not None\\n and\\n # In the case of a span loop popping the current root removes the orphan subtrace\\n child[\\\"span_id\\\"] != orphan_root[\\\"trace.parent_span\\\"]\\n ):\\n orphan_subtraces = results_map.pop(child[\\\"span_id\\\"])\\n for orphan_subtrace in orphan_subtraces:\\n orphan_subtrace.parent_event_id = previous_event.event[\\\"id\\\"]\\n previous_event.children.extend(orphan_subtraces)\\n if child[\\\"span_id\\\"] not in parent_map:\\n continue\\n # Avoid potential span loops by popping, so we don't traverse the same nodes twice\\n child_events = parent_map.pop(child[\\\"span_id\\\"])\\n\\n for child_event in child_events:\\n parent_events[child_event[\\\"id\\\"]] = TraceEvent(\\n child_event,\\n current_event[\\\"id\\\"],\\n previous_event.generation + 1\\n if previous_event.generation is not None\\n else None,\\n )\\n # Add this event to its parent's children\\n previous_event.children.append(parent_events[child_event[\\\"id\\\"]])\\n\\n to_check.append(child_event)\\n # Limit iterations just to be safe\\n iteration += 1\\n if iteration > MAX_TRACE_SIZE:\\n sentry_sdk.set_tag(\\\"discover.trace-view.warning\\\", \\\"surpassed-trace-limit\\\")\\n logger.warning(\\n \\\"discover.trace-view.surpassed-trace-limit\\\",\\n extra=warning_extra,\\n )\\n break\\n\\n root_traces: List[TraceEvent] = []\\n orphans: List[TraceEvent] = []\\n for index, result in enumerate(results_map.values()):\\n for subtrace in result:\\n self.update_children(subtrace)\\n if index > 0 or len(roots) == 0:\\n orphans.extend(result)\\n elif len(roots) > 0:\\n root_traces = result\\n # We sort orphans and roots separately because we always want the root(s) as the first element(s)\\n root_traces.sort(key=child_sort_key)\\n orphans.sort(key=child_sort_key)\\n\\n if len(orphans) > 0:\\n sentry_sdk.set_tag(\\\"discover.trace-view.contains-orphans\\\", \\\"yes\\\")\\n logger.warning(\\\"discover.trace-view.contains-orphans\\\", extra=warning_extra)\\n\\n return [trace.full_dict(detailed) for trace in root_traces] + [\\n orphan.full_dict(detailed) for orphan in orphans\\n ]\",\n \"def traceXml(self, traceConfXml):\\r\\n if core.FW_conf['tracing_enabled']:\\r\\n core.FW_conf['trace'].traceActivation(traceConfXml, scriptActivation = True )\",\n \"def _is_valid_sbl_settlement(settlement):\\n if not settlement.IsKindOf(acm.FSettlement):\\n return False\\n\\n if not settlement.Trade():\\n return False\\n \\n trade = settlement.Trade()\\n acquirer = trade.Acquirer().Name()\\n instrument = trade.Instrument().InsType()\\n delivery_type = trade.AddInfoValue(\\\"SL_SWIFT\\\")\\n if instrument != 'SecurityLoan':\\n return False\\n if acquirer != 'SECURITY LENDINGS DESK':\\n return False\\n if delivery_type != 'SWIFT':\\n return False\\n if settlement.Type() not in ['Security Nominal', 'End Security']:\\n return False\\n\\n return True\",\n \"def transcription(self, sid):\\r\\n return recordings.Transcription(self, sid)\",\n \"def isStakeBase(tx):\\n # A stake base (SSGen) must only have two transaction inputs.\\n if len(tx.txIn) != 2:\\n return False\\n\\n # The previous output of a coin base must have a max value index and\\n # a zero hash, as well as null fraud proofs.\\n if not isNullOutpoint(tx):\\n return False\\n\\n if not isNullFraudProof(tx):\\n return False\\n\\n return True\",\n \"def serialize(\\n self,\\n transactions: Sequence[SnubaTransaction],\\n errors: Sequence[SnubaError],\\n roots: Sequence[SnubaTransaction],\\n warning_extra: Dict[str, str],\\n event_id: Optional[str],\\n detailed: bool = False,\\n ) -> Sequence[LightResponse]:\\n if event_id is None:\\n raise ParseError(detail=\\\"An event_id is required for the light trace\\\")\\n snuba_event, nodestore_event = self.get_current_transaction(transactions, errors, event_id)\\n parent_map = self.construct_parent_map(transactions)\\n error_map = self.construct_error_map(errors)\\n trace_results: List[TraceEvent] = []\\n current_generation: Optional[int] = None\\n root_id: Optional[str] = None\\n\\n with sentry_sdk.start_span(op=\\\"building.trace\\\", description=\\\"light trace\\\"):\\n # Going to nodestore is more expensive than looping twice so check if we're on the root first\\n for root in roots:\\n if root[\\\"id\\\"] == snuba_event[\\\"id\\\"]:\\n current_generation = 0\\n break\\n\\n if current_generation is None:\\n for root in roots:\\n # We might not be necessarily connected to the root if we're on an orphan event\\n if root[\\\"id\\\"] != snuba_event[\\\"id\\\"]:\\n # Get the root event and see if the current event's span is in the root event\\n root_event = eventstore.get_event_by_id(root[\\\"project.id\\\"], root[\\\"id\\\"])\\n root_spans: NodeSpans = root_event.data.get(\\\"spans\\\", [])\\n root_span = find_event(\\n root_spans,\\n lambda item: item is not None\\n and item[\\\"span_id\\\"] == snuba_event[\\\"trace.parent_span\\\"],\\n )\\n\\n # We only know to add the root if its the direct parent\\n if root_span is not None:\\n # For the light response, the parent will be unknown unless it is a direct descendent of the root\\n root_id = root[\\\"id\\\"]\\n trace_results.append(\\n TraceEvent(\\n root,\\n None,\\n 0,\\n )\\n )\\n current_generation = 1\\n break\\n\\n current_event = TraceEvent(snuba_event, root_id, current_generation)\\n trace_results.append(current_event)\\n\\n spans: NodeSpans = nodestore_event.data.get(\\\"spans\\\", [])\\n # Need to include the transaction as a span as well\\n #\\n # Important that we left pad the span id with 0s because\\n # the span id is stored as an UInt64 and converted into\\n # a hex string when quering. However, the conversion does\\n # not ensure that the final span id is 16 chars long since\\n # it's a naive base 10 to base 16 conversion.\\n spans.append({\\\"span_id\\\": snuba_event[\\\"trace.span\\\"].rjust(16, \\\"0\\\")})\\n\\n for span in spans:\\n if span[\\\"span_id\\\"] in error_map:\\n current_event.errors.extend(\\n [self.serialize_error(error) for error in error_map.pop(span[\\\"span_id\\\"])]\\n )\\n if span[\\\"span_id\\\"] in parent_map:\\n child_events = parent_map.pop(span[\\\"span_id\\\"])\\n trace_results.extend(\\n [\\n TraceEvent(\\n child_event,\\n snuba_event[\\\"id\\\"],\\n (\\n current_event.generation + 1\\n if current_event.generation is not None\\n else None\\n ),\\n )\\n for child_event in child_events\\n ]\\n )\\n\\n return [result.to_dict() for result in trace_results]\",\n \"def _save_or_verify_genesis(self) -> None:\\n self._saving_genesis = True\\n for tx in self._get_genesis_from_settings():\\n try:\\n assert tx.hash is not None\\n tx2 = self.get_transaction(tx.hash)\\n assert tx == tx2\\n except TransactionDoesNotExist:\\n self.save_transaction(tx)\\n self.add_to_indexes(tx)\\n tx2 = tx\\n assert tx2.hash is not None\\n self._genesis_cache[tx2.hash] = tx2\\n self._saving_genesis = False\",\n \"def on_trade(self, trade: TradeData):\\n pass\",\n \"def is_trashed(self):\\n return self.has_label(TRASHED_LABEL)\",\n \"def is_transient_error(self):\\n return self._tag == 'transient_error'\",\n \"def checkSStx(tx):\\n # Check to make sure there aren't too many inputs.\\n # CheckTransactionSanity already makes sure that number of inputs is\\n # greater than 0, so no need to check that.\\n if len(tx.txIn) > MaxInputsPerSStx:\\n raise DecredError(\\\"SStx has too many inputs\\\")\\n\\n # Check to make sure there aren't too many outputs.\\n if len(tx.txOut) > MaxOutputsPerSStx:\\n raise DecredError(\\\"SStx has too many outputs\\\")\\n\\n # Check to make sure there are some outputs.\\n if len(tx.txOut) == 0:\\n raise DecredError(\\\"SStx has no outputs\\\")\\n\\n # Check to make sure that all output scripts are the consensus version.\\n for idx, txOut in enumerate(tx.txOut):\\n if txOut.version != consensusVersion:\\n raise DecredError(\\\"invalid script version found in txOut idx %d\\\" % idx)\\n\\n # Ensure that the first output is tagged OP_SSTX.\\n if getScriptClass(tx.txOut[0].version, tx.txOut[0].pkScript) != StakeSubmissionTy:\\n raise DecredError(\\n \\\"First SStx output should have been OP_SSTX tagged, but it was not\\\"\\n )\\n\\n # Ensure that the number of outputs is equal to the number of inputs\\n # + 1.\\n if (len(tx.txIn) * 2 + 1) != len(tx.txOut):\\n raise DecredError(\\n \\\"The number of inputs in the SStx tx was not the number of outputs/2 - 1\\\"\\n )\\n\\n # Ensure that the rest of the odd outputs are 28-byte OP_RETURN pushes that\\n # contain putative pubkeyhashes, and that the rest of the odd outputs are\\n # OP_SSTXCHANGE tagged.\\n for outTxIndex in range(1, len(tx.txOut)):\\n scrVersion = tx.txOut[outTxIndex].version\\n rawScript = tx.txOut[outTxIndex].pkScript\\n\\n # Check change outputs.\\n if outTxIndex % 2 == 0:\\n if getScriptClass(scrVersion, rawScript) != StakeSubChangeTy:\\n raise DecredError(\\n \\\"SStx output at output index %d was not an sstx change output\\\",\\n outTxIndex,\\n )\\n continue\\n\\n # Else (odd) check commitment outputs. The script should be a\\n # NullDataTy output.\\n if getScriptClass(scrVersion, rawScript) != NullDataTy:\\n raise DecredError(\\n \\\"SStx output at output index %d was not a NullData (OP_RETURN) push\\\",\\n outTxIndex,\\n )\\n\\n # The length of the output script should be between 32 and 77 bytes long.\\n if len(rawScript) < SStxPKHMinOutSize or len(rawScript) > SStxPKHMaxOutSize:\\n raise DecredError(\\n \\\"SStx output at output index %d was a NullData (OP_RETURN) push\\\"\\n \\\" of the wrong size\\\",\\n outTxIndex,\\n )\",\n \"def on_trade(self, trade: TradeData):\\n # self.on_event(EVENT_TRADE, trade)\\n # self.on_event(EVENT_TRADE + trade.vt_symbol, trade)\\n pass\",\n \"def _save_to_weakref(self, tx: BaseTransaction) -> None:\\n if self._tx_weakref_disabled:\\n return\\n assert tx.hash is not None\\n tx2 = self._tx_weakref.get(tx.hash, None)\\n if tx2 is None:\\n self._tx_weakref[tx.hash] = tx\\n else:\\n assert tx is tx2, 'There are two instances of the same transaction in memory ({})'.format(tx.hash_hex)\",\n \"def has_save_strategy(self):\\r\\n return self.save_strategy is not None\",\n \"def isTransmitted(self) -> bool:\\r\\n\\r\\n return self.__is_transmitted\",\n \"def _handle_reference_enabled_save(self, error):\\n err_id = error.get('id', None)\\n if err_id is None:\\n return False\\n\\n # this is a kind of signature of the error\\n tail = filtered_error_tail(error)\\n\\n # check all errors in the storage\\n for existing_id, existing_error in self._storage.items():\\n error_tail = filtered_error_tail(existing_error)\\n\\n # error signature is the same\\n if error_tail == tail:\\n info = dict(referencedError=existing_id,\\n time=error.get('time'),\\n userid=error.get('userid') or 'anon')\\n self._index[err_id] = info\\n return True\\n\\n # save it\\n return self._handle_save(error)\",\n \"def save_axs_table(self, tblname, calculate_zone=False):\\n from axs.catalog import AxsCatalog\\n AxsCatalog(None).save_axs_table(self, tblname, True, calculate_zone)\",\n \"def transaction_exists(self, transaction: \\\"Transaction\\\") -> bool:\\n try:\\n super().inspect_transaction(transaction=transaction)\\n return True\\n except grpc.RpcError as err:\\n err: grpc.Call\\n if err.code() == grpc.StatusCode.NOT_FOUND:\\n return False\\n raise err\",\n \"def verify_trace_roundtrip(\\n sch: Schedule,\\n mod: Union[PrimFunc, IRModule],\\n *,\\n debug_mask: Union[str, int] = \\\"all\\\",\\n text_format: Union[str, Sequence[str]] = [\\\"python\\\", \\\"json\\\"],\\n) -> Schedule:\\n from tvm.script import tir as T # pylint: disable=import-outside-toplevel\\n\\n if not isinstance(text_format, str):\\n for opt in text_format:\\n new_sch = verify_trace_roundtrip(sch, mod, debug_mask=debug_mask, text_format=opt)\\n return new_sch\\n\\n trace = sch.trace\\n assert trace is not None\\n\\n # Step 1. Perform a round-trip through the text-format\\n new_sch = Schedule(mod=mod, debug_mask=debug_mask)\\n if text_format == \\\"json\\\":\\n json_obj = trace.as_json()\\n Trace.apply_json_to_schedule(json_obj=json_obj, sch=new_sch)\\n elif text_format == \\\"python\\\":\\n py_trace = \\\"\\\\n\\\".join(trace.as_python())\\n vars_dict = {\\\"T\\\": T}\\n vars_dict.update(tvm.tir.__dict__)\\n exec(py_trace, vars_dict, {\\\"sch\\\": new_sch}) # pylint: disable=exec-used\\n else:\\n assert text_format in (\\\"json\\\", \\\"python\\\"), f\\\"Unknown text format: {text_format}\\\"\\n\\n # Step 2. Verify that the round-trip produced the same scheduling\\n assert_structural_equal(new_sch.mod, sch.mod)\\n\\n # Step 3. Check the consistency of the text format between the old and new traces\\n py_repr = \\\"\\\\n\\\".join(trace.as_python())\\n new_py_repr = \\\"\\\\n\\\".join(new_sch.trace.as_python())\\n assert py_repr == new_py_repr\\n\\n # Step 4. Return the new schedule in case it could be useful\\n return new_sch\",\n \"def save(self, strm):\\n raise NotImplementedError(\\n '\\\"save\\\" should be implemented in the' \\\" class derived from SurrogateBase\\\"\\n )\",\n \"def record_transaction(self, transaction: Transaction) -> bool:\\n if self._locked:\\n print('Failed to record transaction! Your account has been locked!'\\n )\\n return False\\n\\n if transaction.amount > self.bank_balance:\\n print('Failed to record transaction! Not enough balance!')\\n return False\\n\\n budget = self.budget_manager.get_budget(transaction.budget_category)\\n if budget.locked:\\n print('Failed to record transaction! This budget has been locked!')\\n return False\\n\\n self.transactions.append(transaction)\\n self.bank_balance -= transaction.amount\\n budget.amount_spent += transaction.amount\\n self._warn_and_lock_if_needed(transaction)\\n return True\",\n \"def set_signal_trace(self):\\n\\t\\t\\n\\t\\tassert self.signal_trace_file is not None, \\\"Need to set \\\"\\\\\\n\\t\\t\\t\\\"'signal_trace_file' var before calling set_signal_trace; \\\"\\\\\\n\\t\\t\\t\\\"var should be set without extension, which must be .dat\\\"\\n\\t\\t\\n\\t\\tsignal_data = load_signal_trace_from_file(self.signal_trace_file)\\n\\t\\tprint 'Signal time trace from file %s.dat loaded\\\\n' \\\\\\n\\t\\t\\t\\t% self.signal_trace_file\\n\\t\\tself.signal_trace_Tt = signal_data[:, 0]\\n\\t\\tself.signal_trace = (signal_data[:, 1] + self.signal_trace_offset)*\\\\\\n\\t\\t\\t\\t\\t\\t\\t\\tself.signal_trace_multiplier\\n\\t\\t\\n\\t\\tif self.signal_trace_file_2 is not None:\\n\\t\\t\\tsignal_data_2 = load_signal_trace_from_file(self.signal_trace_file_2)\\n\\t\\t\\tprint 'Signal time trace 2 from file %s.dat loaded\\\\n' \\\\\\n\\t\\t\\t\\t% self.signal_trace_file_2\\n\\t\\t\\tassert len(self.signal_trace_Tt) == len(signal_data_2[:, 0]), \\\\\\n\\t\\t\\t\\t\\\"signal_trace_file_2 must be same length as signal_trace_file\\\"\\n\\t\\t\\tassert sp.allclose(self.signal_trace_Tt, signal_data_2[:, 0], \\n\\t\\t\\t\\t1e-6), \\\"signal_trace_file_2 must have same time array as \\\"\\\\\\n\\t\\t\\t\\t\\\"signal_trace_file\\\"\\n\\t\\t\\tself.signal_trace_2 = (signal_data_2[:, 1] + \\\\\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tself.signal_trace_offset_2)*\\\\\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tself.signal_trace_multiplier_2\",\n \"def store(self, timeseries_dict: ty.Dict[str, TimeSeries]):\\n raise NotImplementedError(\\\"read_forecast\\\")\",\n \"def add_transaction(self, tx_json):\\n recv_tx = Transaction.from_json(tx_json)\\n if not recv_tx.verify():\\n raise Exception(\\\"New transaction failed signature verification.\\\")\\n with self.all_tx_lock:\\n if tx_json in self._all_transactions:\\n print(f\\\"{self.name} - Transaction already exist in pool.\\\")\\n return\\n self._all_transactions.add(tx_json)\",\n \"def _handle_save(self, error):\\n err_id = error.get('id', None)\\n if err_id is None:\\n return False\\n # save each error under its id\\n self._storage[err_id] = error\\n # notify new error\\n event.notify(NotifyTraceback(error))\\n return True\",\n \"def flag_excitation_type(self, tracks: pd.DataFrame):\\n # FIXME: Don't force convert to int, but raise an error (?)\\n # First, the track method needs to preserve the data type of the time\\n # column\\n eseq = self.frame_selector.eval_seq()\\n frames = tracks[\\\"donor\\\", self.columns[\\\"time\\\"]].to_numpy(dtype=int)\\n et = pd.Series(eseq[frames % len(eseq)], dtype=\\\"category\\\")\\n # Assignment to dataframe is done by matching indices, not line-wise\\n # Thus copy index\\n et.index = tracks.index\\n tracks[\\\"fret\\\", \\\"exc_type\\\"] = et\",\n \"def symptomatic_infection(self, s):\\n if self.disease_status == 0:\\n self.disease_status = 1\\n self.time_since_infection = 0\\n s.number_of_symptomatic += 1\\n s.infected.add(self.identifier)\",\n \"def is_tentative(self):\\n return self.state == TrackState.Tentative\",\n \"def is_tentative(self):\\n return self.state == TrackState.Tentative\",\n \"def _update_voided(self, tx: BaseTransaction) -> None:\\n tx_meta = tx.get_metadata()\\n assert tx.hash is not None\\n assert tx_meta.voided_by\\n log = self.log.new(tx=tx.hash_hex)\\n log.debug('update voided')\\n # remove all outputs\\n for index, tx_output in enumerate(tx.outputs):\\n log_it = log.new(index=index)\\n if _should_skip_output(tx_output):\\n log_it.debug('ignore output')\\n continue\\n log_it.debug('remove voided output')\\n self._remove_utxo(UtxoIndexItem.from_tx_output(tx, index, tx_output))\\n # re-add inputs that aren't voided\\n for tx_input in tx.inputs:\\n spent_tx = tx.get_spent_tx(tx_input)\\n spent_tx_output = spent_tx.outputs[tx_input.index]\\n log_it = log.new(tx_id=spent_tx.hash_hex, index=tx_input.index)\\n if _should_skip_output(spent_tx_output):\\n log_it.debug('ignore input')\\n continue\\n if spent_tx.get_metadata().voided_by:\\n log_it.debug('do not re-add input that spend voided')\\n continue\\n spent_tx_meta = spent_tx.get_metadata()\\n spent_by = spent_tx_meta.get_output_spent_by(tx_input.index)\\n if spent_by is not None and spent_by != tx.hash:\\n log_it.debug('do not re-add input that is spent by other tx', spent_by=spent_by.hex())\\n continue\\n log_it.debug('re-add input that became unspent')\\n self._add_utxo(UtxoIndexItem.from_tx_output(spent_tx, tx_input.index, spent_tx_output))\",\n \"def _store_transaction(account, transaction):\\n tr_tx = transaction['tx']\\n meta = transaction.get('meta', {})\\n\\n if meta.get('TransactionResult') != 'tesSUCCESS':\\n return\\n\\n amount = meta.get('delivered_amount') or tr_tx.get('Amount', {})\\n\\n is_unprocessed = (\\n tr_tx['TransactionType'] == 'Payment' and\\n tr_tx['Destination'] == account and\\n isinstance(amount, dict) and\\n not Transaction.objects.filter(hash=tr_tx['hash'])\\n )\\n if is_unprocessed:\\n logger.info(\\n format_log_message(\\n 'Saving transaction: %s', transaction\\n )\\n )\\n\\n transaction_object = Transaction.objects.create(\\n account=tr_tx['Account'],\\n hash=tr_tx['hash'],\\n destination=account,\\n ledger_index=tr_tx['ledger_index'],\\n destination_tag=tr_tx.get('DestinationTag'),\\n source_tag=tr_tx.get('SourceTag'),\\n status=Transaction.RECEIVED,\\n currency=amount['currency'],\\n issuer=amount['issuer'],\\n value=amount['value']\\n )\\n\\n logger.info(\\n format_log_message(\\n \\\"Transaction saved: %s\\\", transaction_object\\n )\\n )\",\n \"def transform_perfetto_trace_to_systrace(path_to_perfetto_trace: str,\\n path_to_tmp_systrace: str) -> None:\\n cmd_utils.run_command_nofail([str(TRACECONV_BIN),\\n 'systrace',\\n path_to_perfetto_trace,\\n path_to_tmp_systrace])\",\n \"def market_snap(self, timestamp = -1):\\n # refresh account states\\n self.trading_acc1.sync_account_with_exh()\\n self.trading_acc2.sync_account_with_exh()\\n\\n signal__arbitrage_delta = self.get_signal__arbitrage_delta()\\n signal__gdax_has_usd = self.get_signal__gdax_has_usd()\\n signal__gdax_has_eth = self.get_signal__gdax_has_eth()\\n signal__cex_has_eth = self.get_signal__cex_has_eth()\\n\\n def mk_audit_js():\\n gdax_account = self.trading_acc1.get_account()\\n cex_account = self.trading_acc2.get_account()\\n transaction_t = epoch.current_milli_time() if timestamp == -1 else timestamp\\n\\n audit_js = OrderedDict()\\n audit_js['strategy_run_id'] = self.run_id\\n audit_js['timestamp'] = epoch.to_str(transaction_t)\\n audit_js['timestamp__long'] = transaction_t\\n audit_js['ticker'] = self.ticker\\n audit_js['strategy_info'] = self._strategy_info\\n\\n audit_js['signal'] = OrderedDict()\\n audit_js['signal']['signal__gdax_has_usd'] = signal__gdax_has_usd\\n audit_js['signal']['signal__gdax_has_eth'] = signal__gdax_has_eth\\n audit_js['signal']['signal__cex_has_eth'] = signal__cex_has_eth\\n audit_js['signal']['signal__arbitrage_delta'] = signal__arbitrage_delta\\n\\n audit_js['total_usd__num'] = gdax_account.js['usd__num'] + cex_account.js['usd__num']\\n audit_js['total_eth__num'] = gdax_account.js['eth__num'] + cex_account.js['eth__num']\\n audit_js['gdax_account'] = gdax_account.js\\n audit_js['cex_account'] = cex_account.js\\n return audit_js\\n\\n snap_again = False # Only repeat if we have an gdax buy action\\n if signal__gdax_has_usd['signal'] and signal__arbitrage_delta['signal']:\\n exec_context = self.exec_gdax_buy(timestamp)\\n snap_again = True\\n\\n # Audit\\n audit_js = mk_audit_js()\\n audit_js['action'] = exec_context\\n audit = AuditTradeModel.build(audit_js)\\n logger.info('-----Executed GDAX Buy-----')\\n logger.info(audit)\\n logger.info('---------------------------')\\n audit.db_save(es)\\n\\n if signal__gdax_has_eth['signal']:\\n exec_context = self.exec_eth_transfer()\\n\\n # Audit\\n audit_js = mk_audit_js()\\n audit_js['action'] = exec_context\\n audit = AuditTradeModel.build(audit_js)\\n logger.info('-----Executed ETH TRANSFER-----')\\n logger.info(audit)\\n logger.info('---------------------------')\\n audit.db_save(es)\\n\\n if signal__cex_has_eth['signal']:\\n exec_context = self.exec_cex_sell(timestamp)\\n\\n # Audit\\n audit_js = mk_audit_js()\\n audit_js['action'] = exec_context\\n audit = AuditTradeModel.build(audit_js)\\n logger.info('-----Executed CEX Sell-----')\\n logger.info(audit)\\n logger.info('---------------------------')\\n audit.db_save(es)\\n\\n # Extra logging\\n audit_js = mk_audit_js()\\n logger.info('post-snapping states: \\\\n' + json.dumps(audit_js, indent=2))\\n\\n return snap_again\",\n \"def save(self, ts):\\n with open(self, 'w') as f:\\n Timestamp.wrap(ts).dump(f)\",\n \"def save_transaction(record, bucket=None):\\n if not isinstance(record, _TransactionRecord):\\n raise TypeError(\\\"You can only write TransactionRecord objects \\\"\\n \\\"to the ledger!\\\")\\n\\n if not record.is_null():\\n if bucket is None:\\n bucket = _login_to_service_account()\\n\\n _ObjectStore.set_object_from_json(bucket,\\n Ledger.get_key(record.uid()),\\n record.to_data())\",\n \"def save_scopeTraces(fileName, scope, channel, noPulses):\\n scope._get_preamble(channel)\\n results = utils.PickleFile(fileName, 1)\\n results.add_meta_data(\\\"timeform_1\\\", scope.get_timeform(channel))\\n\\n #ct = scope.acquire_time_check()\\n #if ct == False:\\n # print 'No triggers for this data point. Will skip and set data to 0.'\\n # results.save()\\n # results.close()\\n # return False\\n\\n t_start, loopStart = time.time(),time.time()\\n for i in range(noPulses):\\n try:\\n ct = scope.acquire_time_check(timeout=.4)\\n results.add_data(scope.get_waveform(channel), 1)\\n except Exception, e:\\n print \\\"Scope died, acquisition lost.\\\"\\n print e\\n if i % 100 == 0 and i > 0:\\n print \\\"%d traces collected - This loop took : %1.1f s\\\" % (i, time.time()-loopStart)\\n loopStart = time.time()\\n print \\\"%d traces collected TOTAL - took : %1.1f s\\\" % (i, (time.time()-t_start))\\n results.save()\\n results.close()\\n return True\",\n \"def writeSTH(seis, STH_Key, newSTH):\\n traces = seis.traces\\n for i, trace in enumerate(traces):\\n trace.header.__setattr__(STH_Key, newSTH[i])\",\n \"def _before_stockpyle_deserialize(self, obj):\\n \\n # only merge SA objects\\n if _is_sqlalchemy_object(obj):\\n self.__session.merge(obj, load=False)\",\n \"def recordTransaction(self, loop, transaction):\\n\\n a = {}\\n a['time'] = transaction.transactionTime\\n a['atm'] = transaction.transactionATM.atmID\\n a['transaction'] = transaction.transactionType\\n a['cash'] = transaction.transactionATM.atmCash\\n a['status'] = transaction.transactionStatus\\n self._atmDict[loop] = a\\n\\n c = {}\\n c['time'] = transaction.transactionTime\\n c['client'] = transaction.transactionCard.cardAccount.accountClient.clientID\\n c['account'] = transaction.transactionCard.cardAccount.accountNumber\\n c['transaction'] = transaction.transactionType\\n c['balance'] = transaction.transactionCard.cardAccount.accountBalance\\n c['status'] = transaction.transactionStatus\\n self._clientDict[loop] = c\\n\\n t = {}\\n t['time'] = transaction.transactionTime\\n t['transaction'] = transaction.transactionType\\n t['amount'] = transaction.transactionAmount\\n t['status'] = transaction.transactionStatus\\n self._transactionDict[loop] = t\",\n \"def _save(self):\\n if self.character:\\n self.character.db_command_stack = dumps(self)\",\n \"def process_trace(self, trace):\\n # type: (List[Span]) -> Optional[List[Span]]\\n pass\",\n \"def test_log_track_with_transform(self):\\n tf_id = self.add_transform(cond_artist=True, pattern_artist='Artist',\\n change_artist=True, to_artist='Artist 2')\\n self.assertNotEqual(tf_id, 0)\\n self.app.load_data()\\n\\n track = self.app.log_track(self.track_obj('silence.mp3'))\\n self.assertEqual(self.get_track_count(), 1)\\n track_row = self.get_track_by_id(track.pk)\\n self.assertNotEqual(track_row, None)\\n self.assertEqual(track_row['lasttransform'], tf_id)\\n self.assertEqual(track_row['artist'], 'Artist 2')\\n self.assertEqual(track_row['album'], 'Album')\\n self.assertEqual(track_row['title'], 'Track')\\n self.assertEqual(track_row['source'], 'xmms')\",\n \"def is_stair(game_object: GameObject) -> bool:\\n return CommonObjectTagUtils.has_game_tags(game_object, (\\n CommonGameTag.BUILD_STAIR,\\n ))\",\n \"def change_TTS_engine(self):\\n\\t\\t\\n\\t\\tif self.isActiveDualTTS:\\n\\t\\t\\t#dual TTS\\n\\t\\t\\tpath_to_file = '/var/lib/snips/skills/snips_app_pilldispenser/settings/dual_TTS.sh'\\n\\t\\t\\tsubprocess.call([path_to_file])\\n\\t\\t\\tprint('Dual TTS is enabled. Using Amazon Polly TTS in case of internet connection, else use offline Picotts TTS.')\\n\\t\\t\\t\\n\\t\\telse:\\n\\t\\t\\t#go back to single offline Picotts TTS\\n\\t\\t\\tpath_to_file = '/var/lib/snips/skills/snips_app_pilldispenser/settings/single_TTS.sh'\\n\\t\\t\\tsubprocess.call([path_to_file])\\n\\t\\t\\tprint('Dual TTS is disabled. Using offline Picotts TTS regardless of internect connection.')\",\n \"def supports_transactions(self):\\n return self._mysql_storage_engine != \\\"MyISAM\\\"\",\n \"def process_transaction(self, transaction):\\n instrument = transaction.instrument\\n if isinstance(instrument, Future):\\n try:\\n old_price = self._payout_last_sale_prices[instrument]\\n except KeyError:\\n self._payout_last_sale_prices[instrument] = transaction.price\\n else:\\n position = self.position_tracker.positions[instrument]\\n amount = position.amount\\n price = transaction.price\\n\\n self._cash_flow(\\n self._calculate_payout(\\n instrument.multiplier,\\n amount,\\n old_price,\\n price,\\n ),\\n )\\n\\n if amount + transaction.amount == 0:\\n del self._payout_last_sale_prices[instrument]\\n else:\\n self._payout_last_sale_prices[instrument] = price\\n else:\\n self._cash_flow(-(transaction.price * transaction.amount))\\n\\n self.position_tracker.execute_transaction(transaction)\\n\\n # we only ever want the dict form from now on\\n transaction_dict = transaction.to_dict()\\n try:\\n self._processed_transactions[transaction.dt].append(\\n transaction_dict,\\n )\\n except KeyError:\\n self._processed_transactions[transaction.dt] = [transaction_dict]\",\n \"def test_save_unjsonable_subs_to_store(self):\\r\\n with self.assertRaises(NotFoundError):\\r\\n contentstore().find(self.content_location_unjsonable)\\r\\n\\r\\n with self.assertRaises(TypeError):\\r\\n transcripts_utils.save_subs_to_store(\\r\\n self.unjsonable_subs,\\r\\n self.unjsonable_subs_id,\\r\\n self.course)\\r\\n\\r\\n with self.assertRaises(NotFoundError):\\r\\n contentstore().find(self.content_location_unjsonable)\",\n \"def _is_valid_trade(self, trade):\\n if not trade:\\n return False\\n\\n if trade.Status() in VALID_TRADE_STATUSES:\\n if acm.Time().AsDate(trade.TradeTime()) > self.start_date:\\n return False\\n print '1'\\n ins_type = trade.Instrument().InsType()\\n if ins_type == 'Curr':\\n if trade.ValueDay() > self.start_date:\\n return True\\n elif ins_type == 'Combination':\\n for comb_ins in trade.Instrument().Instruments():\\n trades = comb_ins.Trades()\\n if trades and trades[0] in VALID_TRADE_STATUSES:\\n trade = trades[0]\\n ins_type = trade.Instrument().InsType()\\n if (self._is_basis_trade(trade) and\\n ins_type in ('Swap', 'FRA')):\\n return True\\n elif ins_type == 'CurrSwap':\\n if trade.Instrument().ExpiryDateOnly() > self.start_date:\\n return True\\n else:\\n if trade.Instrument().ExpiryDateOnly() > self.start_date:\\n if (self._is_basis_trade(trade) and\\n ins_type in ('Swap', 'FRA')):\\n return True\\n\\n return False\",\n \"def visualize_transcription(self):\\n # # Test if current nwb file contains Survey table\\n # if 'behavior' in self.model.nwb.processing:\\n # list_surveys = [v for v in self.model.nwb.processing['behavior'].data_interfaces.values()\\n # if v.neurodata_type == 'SurveyTable']\\n # if len(list_surveys) > 0:\\n # ShowSurveyDialog(nwbfile=self.model.nwb)\\n ShowTranscriptionDialog(nwbfile=self.model.nwb)\\n pass\",\n \"def _set_types(self):\\n if isinstance(self.elastic_span, elasticapm.traces.Transaction):\\n attributes = self.elastic_span.context.get(\\\"otel_attributes\\\", {})\\n span_kind = self.elastic_span.context[\\\"otel_spankind\\\"]\\n is_rpc = bool(attributes.get(\\\"rpc.system\\\"))\\n is_http = bool(attributes.get(\\\"http.url\\\")) or bool(attributes.get(\\\"http.scheme\\\"))\\n is_messaging = bool(attributes.get(\\\"messaging.system\\\"))\\n if span_kind == SpanKind.SERVER.name and (is_rpc or is_http):\\n transaction_type = \\\"request\\\"\\n elif span_kind == SpanKind.CONSUMER.name and is_messaging:\\n transaction_type = \\\"messaging\\\"\\n else:\\n transaction_type = \\\"unknown\\\"\\n self.elastic_span.transaction_type = transaction_type\\n else:\\n attributes = self.elastic_span.context.get(\\\"otel_attributes\\\", {})\\n span_type = None\\n span_subtype = None\\n resource = None\\n\\n def http_port_from_scheme(scheme: str):\\n if scheme == \\\"http\\\":\\n return 80\\n elif scheme == \\\"https\\\":\\n return 443\\n return None\\n\\n def parse_net_name(url: str):\\n u = urllib.parse.urlparse(url)\\n if u.port:\\n return u.netloc\\n else:\\n port = http_port_from_scheme(u.scheme)\\n return u.netloc if not port else \\\"{}:{}\\\".format(u.netloc, port)\\n\\n net_port = attributes.get(\\\"net.peer.port\\\", -1)\\n net_name = net_peer = attributes.get(\\\"net.peer.name\\\", attributes.get(\\\"net.peer.ip\\\", \\\"\\\"))\\n\\n if net_name and (net_port > 0):\\n net_name = f\\\"{net_name}:{net_port}\\\"\\n\\n if attributes.get(\\\"db.system\\\"):\\n span_type = \\\"db\\\"\\n span_subtype = attributes.get(\\\"db.system\\\")\\n resource = net_name or span_subtype\\n if attributes.get(\\\"db.name\\\"):\\n resource = \\\"{}/{}\\\".format(resource, attributes.get(\\\"db.name\\\"))\\n elif attributes.get(\\\"messaging.system\\\"):\\n span_type = \\\"messaging\\\"\\n span_subtype = attributes.get(\\\"messaging.system\\\")\\n if not net_name and attributes.get(\\\"messaging.url\\\"):\\n net_name = parse_net_name(attributes.get(\\\"messaging.url\\\"))\\n resource = net_name or span_subtype\\n if attributes.get(\\\"messaging.destination\\\"):\\n resource = \\\"{}/{}\\\".format(resource, attributes.get(\\\"messaging.destination\\\"))\\n elif attributes.get(\\\"rpc.system\\\"):\\n span_type = \\\"external\\\"\\n span_subtype = attributes.get(\\\"rpc.system\\\")\\n resource = net_name or span_subtype\\n if attributes.get(\\\"rpc.service\\\"):\\n resource = \\\"{}/{}\\\".format(resource, attributes.get(\\\"rpc.service\\\"))\\n elif attributes.get(\\\"http.url\\\") or attributes.get(\\\"http.scheme\\\"):\\n span_type = \\\"external\\\"\\n span_subtype = \\\"http\\\"\\n http_host = attributes.get(\\\"http.host\\\", net_peer)\\n if http_host:\\n if net_port < 0:\\n net_port = http_port_from_scheme(attributes.get(\\\"http.scheme\\\"))\\n resource = http_host if net_port < 0 else f\\\"{http_host}:{net_port}\\\"\\n elif attributes.get(\\\"http.url\\\"):\\n resource = parse_net_name(attributes[\\\"http.url\\\"])\\n\\n if not span_type:\\n span_kind = self.elastic_span.context[\\\"otel_spankind\\\"]\\n if span_kind == SpanKind.INTERNAL.name:\\n span_type = \\\"app\\\"\\n span_subtype = \\\"internal\\\"\\n else:\\n span_type = \\\"unknown\\\"\\n self.elastic_span.type = span_type\\n self.elastic_span.subtype = span_subtype\\n if resource:\\n if \\\"destination\\\" not in self.elastic_span.context:\\n self.elastic_span.context[\\\"destination\\\"] = {\\\"service\\\": {\\\"resource\\\": resource}}\\n elif \\\"service\\\" not in self.elastic_span.context[\\\"destination\\\"]:\\n self.elastic_span.context[\\\"destination\\\"][\\\"service\\\"] = {\\\"resource\\\": resource}\\n else:\\n self.elastic_span.context[\\\"destination\\\"][\\\"service\\\"][\\\"resource\\\"] = resource\",\n \"def on_save_interpretation_button(self, event):\\n if \\\"specimen_int_uT\\\" not in self.Data[self.s]['pars']:\\n return\\n if 'deleted' in self.Data[self.s]['pars']:\\n self.Data[self.s]['pars'].pop('deleted')\\n self.Data[self.s]['pars']['saved'] = True\\n\\n # collect all interpretation by sample\\n sample = self.Data_hierarchy['specimens'][self.s]\\n if sample not in list(self.Data_samples.keys()):\\n self.Data_samples[sample] = {}\\n if self.s not in list(self.Data_samples[sample].keys()):\\n self.Data_samples[sample][self.s] = {}\\n self.Data_samples[sample][self.s]['B'] = self.Data[self.s]['pars'][\\\"specimen_int_uT\\\"]\\n\\n # collect all interpretation by site\\n # site=thellier_gui_lib.get_site_from_hierarchy(sample,self.Data_hierarchy)\\n site = thellier_gui_lib.get_site_from_hierarchy(\\n sample, self.Data_hierarchy)\\n if site not in list(self.Data_sites.keys()):\\n self.Data_sites[site] = {}\\n if self.s not in list(self.Data_sites[site].keys()):\\n self.Data_sites[site][self.s] = {}\\n self.Data_sites[site][self.s]['B'] = self.Data[self.s]['pars'][\\\"specimen_int_uT\\\"]\\n\\n self.draw_sample_mean()\\n self.write_sample_box()\\n self.close_warning = True\",\n \"def test_serialize_sinfo(self):\\n self.assert_raises(RuntimeError, self.instance.serialize,\\n self.testing_options['objects'][0],\\n add_serializer_info=True)\",\n \"def test_add_trace(self):\\n payload = Payload()\\n\\n # Add falsey traces\\n for val in (False, None, 0, '', [], dict()):\\n payload.add_trace(val)\\n self.assertEqual(payload.length, 0)\\n self.assertTrue(payload.empty)\\n\\n # Add a single trace to the payload\\n trace = [Span(self.tracer, name='root.span'), Span(self.tracer, name='child.span')]\\n payload.add_trace(trace)\\n\\n self.assertEqual(payload.length, 1)\\n self.assertFalse(payload.empty)\",\n \"def filter_trace_variable_event(self, event):\\n return isinstance(event, (TraceAccess, TraceAssign, TraceDelete))\",\n \"def process_traces(st,positions):\\n spti = {}\\n isis = {}\\n rav = {}\\n\\n nspti = {}\\n nisis = {}\\n nrav = {}\\n\\n for i,sti in enumerate(st.keys()):\\n print('analysing trace nr %i'%i)\\n cst = st[sti]\\n cp = positions[sti]\\n cisi = np.diff(cst)\\n if len(cisi) > 0:\\n spti[sti], isis[sti], positions[sti] = delete_artifacts(cst,cisi,cp)\\n nspti[sti], nisis[sti] = fill_gaps(spti[sti][:-1],np.diff(spti[sti]))\\n\\n print('smooting traces')\\n\\n return smooth_traces(nspti,nisis,5), positions\",\n \"def _add_stix_object(self, stix_object: _Observable):\\n if stix_object.id in self._all_objects:\\n if len(stix_object.serialize()) > len(self._all_objects[stix_object.id].serialize()):\\n self._all_objects[stix_object.id] = stix_object\\n else:\\n self._all_objects[stix_object.id] = stix_object\",\n \"def _check_duplicate_trans(self):\\n transactions_set = set(self._transactions)\\n return len(transactions_set) == len(self._transactions)\",\n \"def loads_trade(self, trade_id, trade_type, data, operations):\\n trade = None\\n\\n if trade_type == StrategyTrade.TRADE_BUY_SELL:\\n trade = StrategyAssetTrade(0)\\n elif trade_type == StrategyTrade.TRADE_MARGIN:\\n trade = StrategyMarginTrade(0)\\n elif trade_type == StrategyTrade.TRADE_POSITION:\\n trade = StrategyPositionTrade(0)\\n elif trade_type == StrategyTrade.TRADE_IND_MARGIN:\\n trade = StrategyIndMarginTrade(0)\\n else:\\n error_logger.error(\\\"During loads, usupported trade type %i\\\" % (trade_type,))\\n return\\n\\n trade.loads(data, self.strategy.service)\\n\\n # operations\\n for op in operations:\\n if op['name'] in self.strategy.service.tradeops:\\n try: \\n operation = self.strategy.service.tradeops[op['name']]()\\n operation.loads(op)\\n\\n if operation.check(trade):\\n # append the operation to the trade\\n trade.add_operation(operation)\\n else:\\n error_logger.error(\\\"During loads, operation checking error %s\\\" % (op_name,))\\n except Exception as e:\\n error_logger.error(repr(e))\\n else:\\n error_logger.error(\\\"During loads, region checking error %s\\\" % (r['name'],))\\n\\n # ignored for now because need to check assets/positions/orders\\n # self.add_trade(trade)\",\n \"def create_incoming_transaction(transaction: IncomingTransactionCreate, db: Session = Depends(get_db), auth_user: User=Depends(manager)):\\n try:\\n transaction = transaction_service.create(db, auth_user, transaction)\\n return transaction\\n except ItensNotFound as err:\\n\\t raise HTTPException(status_code=404, detail=f\\\"Os seguintes produtos não foram encontrados no sistema: {str(err)}\\\")\\n except ProductsNotFound as err:\\n raise HTTPException(status_code=400, detail=\\\"A movimentação a ser registrada deve conter no minimo um produto.\\\")\\n except ProviderNotFound as err:\\n\\t raise HTTPException(status_code=404, detail=f\\\"O fornecedor informado não foi encontrado: {str(err)}\\\")\\n except InvalidStockQuantity as err:\\n products_missing = transaction_service.make_response(db, str(err))\\n raise HTTPException(status_code=400, detail={\\n \\\"message\\\": \\\"A quantidade informada para os seguintes produtos deve ser maior do que zero.\\\",\\n \\\"products_missing\\\": products_missing\\n })\",\n \"def saveTrans(self):\\n modtranDataDir = os.getenv('MODTRAN_DATADIR')\\n outputfile = '{0}/{1}_final.plt'.format(\\n self.outfilename, self.outfilename)\\n outputpath = os.path.join(modtranDataDir, outputfile)\\n with open(outputpath, 'w') as transmf:\\n transmf.write('$ FINAL ATMOSPHERE TRANSMISSION\\\\n')\\n for val in range(len(self.modtran_wl)):\\n data = '\\\\t'.join('{0:f}'.format(self.transmittance[run][val])\\n for run in range(len(self.modtran_wl)))\\n line = '{0}\\\\t{1}\\\\n'.format(self.modtran_wl[val], data)\\n transmf.write(line)\",\n \"def save(self):\\n self.add_statements(self.triples())\",\n \"def configureThisTrace(self):\\n for spec in Trace.traceSpec:\\n if (spec.compiledRegex.match(self.entityName)):\\n self.traceLevel = spec.level\\n break\\n #endIf\\n #endFor\",\n \"def _is_transaction_isolation_error(self, error):\\n return False\",\n \"def is_serious(self, level=ERROR):\\n return self.level >= level\",\n \"def should_save(event) -> bool:\\n schema = event[\\\"$schemaRef\\\"]\\n if schema == \\\"https://eddn.edcd.io/schemas/commodity/3\\\":\\n return True\\n elif schema == \\\"https://eddn.edcd.io/schemas/journal/1\\\":\\n return event[\\\"message\\\"][\\\"event\\\"] == \\\"Docked\\\"\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.54398495","0.5149518","0.51403934","0.51361316","0.5022585","0.48563206","0.48534927","0.48511583","0.4807828","0.47867486","0.46448067","0.46145433","0.45834467","0.4583097","0.45755658","0.4567124","0.45389777","0.45228583","0.45225585","0.45150757","0.45129964","0.4472929","0.44627464","0.44578195","0.4440597","0.44364247","0.44289523","0.44144338","0.4412158","0.44016916","0.43960384","0.43888324","0.43816006","0.43731356","0.4363284","0.4361652","0.43608597","0.43204397","0.43175736","0.43163043","0.43141267","0.43092197","0.43065256","0.43032444","0.42844808","0.4249547","0.42486185","0.42334342","0.42308417","0.42304826","0.4226509","0.42261812","0.42091802","0.4205113","0.42026576","0.41993904","0.4199355","0.4192711","0.4183708","0.41762114","0.41716656","0.41671136","0.4164237","0.4164237","0.41612288","0.41561615","0.4154866","0.41522595","0.4148224","0.41455764","0.4143687","0.41236743","0.41112763","0.4109861","0.41051835","0.41039652","0.41023162","0.40989587","0.40866426","0.4084628","0.40788648","0.4075117","0.407139","0.40675253","0.40657637","0.4063273","0.4062249","0.40614104","0.40573442","0.405548","0.40554017","0.40541264","0.40516865","0.40495834","0.40349567","0.4034339","0.4026953","0.40243247","0.4020469","0.40203857"],"string":"[\n \"0.54398495\",\n \"0.5149518\",\n \"0.51403934\",\n \"0.51361316\",\n \"0.5022585\",\n \"0.48563206\",\n \"0.48534927\",\n \"0.48511583\",\n \"0.4807828\",\n \"0.47867486\",\n \"0.46448067\",\n \"0.46145433\",\n \"0.45834467\",\n \"0.4583097\",\n \"0.45755658\",\n \"0.4567124\",\n \"0.45389777\",\n \"0.45228583\",\n \"0.45225585\",\n \"0.45150757\",\n \"0.45129964\",\n \"0.4472929\",\n \"0.44627464\",\n \"0.44578195\",\n \"0.4440597\",\n \"0.44364247\",\n \"0.44289523\",\n \"0.44144338\",\n \"0.4412158\",\n \"0.44016916\",\n \"0.43960384\",\n \"0.43888324\",\n \"0.43816006\",\n \"0.43731356\",\n \"0.4363284\",\n \"0.4361652\",\n \"0.43608597\",\n \"0.43204397\",\n \"0.43175736\",\n \"0.43163043\",\n \"0.43141267\",\n \"0.43092197\",\n \"0.43065256\",\n \"0.43032444\",\n \"0.42844808\",\n \"0.4249547\",\n \"0.42486185\",\n \"0.42334342\",\n \"0.42308417\",\n \"0.42304826\",\n \"0.4226509\",\n \"0.42261812\",\n \"0.42091802\",\n \"0.4205113\",\n \"0.42026576\",\n \"0.41993904\",\n \"0.4199355\",\n \"0.4192711\",\n \"0.4183708\",\n \"0.41762114\",\n \"0.41716656\",\n \"0.41671136\",\n \"0.4164237\",\n \"0.4164237\",\n \"0.41612288\",\n \"0.41561615\",\n \"0.4154866\",\n \"0.41522595\",\n \"0.4148224\",\n \"0.41455764\",\n \"0.4143687\",\n \"0.41236743\",\n \"0.41112763\",\n \"0.4109861\",\n \"0.41051835\",\n \"0.41039652\",\n \"0.41023162\",\n \"0.40989587\",\n \"0.40866426\",\n \"0.4084628\",\n \"0.40788648\",\n \"0.4075117\",\n \"0.407139\",\n \"0.40675253\",\n \"0.40657637\",\n \"0.4063273\",\n \"0.4062249\",\n \"0.40614104\",\n \"0.40573442\",\n \"0.405548\",\n \"0.40554017\",\n \"0.40541264\",\n \"0.40516865\",\n \"0.40495834\",\n \"0.40349567\",\n \"0.4034339\",\n \"0.4026953\",\n \"0.40243247\",\n \"0.4020469\",\n \"0.40203857\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.64152426"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":3688,"cells":{"query":{"kind":"string","value":"Record any apdex and time metrics for the transaction as well as any errors which occurred for the transaction. If the transaction qualifies to become the slow transaction remember it for later."},"document":{"kind":"string","value":"def record_transaction(self, transaction):\n\n if not self.__settings:\n return\n\n settings = self.__settings\n\n # Record the apdex, value and time metrics generated from the\n # transaction. Whether time metrics are reported as distinct\n # metrics or into a rollup is in part controlled via settings\n # for minimum number of unique metrics to be reported and thence\n # whether over a time threshold calculated as percentage of\n # overall request time, up to a maximum number of unique\n # metrics. This is intended to limit how many metrics are\n # reported for each transaction and try and cut down on an\n # explosion of unique metric names. The limits and thresholds\n # are applied after the metrics are reverse sorted based on\n # exclusive times for each metric. This ensures that the metrics\n # with greatest exclusive time are retained over those with\n # lesser time. Such metrics get reported into the performance\n # breakdown tab for specific web transactions.\n\n self.record_apdex_metrics(transaction.apdex_metrics(self))\n\n self.merge_custom_metrics(transaction.custom_metrics.metrics())\n\n self.record_time_metrics(transaction.time_metrics(self))\n\n # Capture any errors if error collection is enabled.\n # Only retain maximum number allowed per harvest.\n\n error_collector = settings.error_collector\n\n if (error_collector.enabled and settings.collect_errors and\n len(self.__transaction_errors) <\n settings.agent_limits.errors_per_harvest):\n self.__transaction_errors.extend(transaction.error_details())\n\n self.__transaction_errors = self.__transaction_errors[:\n settings.agent_limits.errors_per_harvest]\n\n if (error_collector.capture_events and\n error_collector.enabled and\n settings.collect_error_events):\n events = transaction.error_events(self.__stats_table)\n for event in events:\n self._error_events.add(event, priority=transaction.priority)\n\n # Capture any sql traces if transaction tracer enabled.\n\n if settings.slow_sql.enabled and settings.collect_traces:\n for node in transaction.slow_sql_nodes(self):\n self.record_slow_sql_node(node)\n\n # Remember as slowest transaction if transaction tracer\n # is enabled, it is over the threshold and slower than\n # any existing transaction seen for this period and in\n # the historical snapshot of slow transactions, plus\n # recording of transaction trace for this transaction\n # has not been suppressed.\n\n transaction_tracer = settings.transaction_tracer\n\n if (not transaction.suppress_transaction_trace and\n transaction_tracer.enabled and settings.collect_traces):\n\n # Transactions saved for Synthetics transactions\n # do not depend on the transaction threshold.\n\n self._update_synthetics_transaction(transaction)\n\n threshold = transaction_tracer.transaction_threshold\n\n if threshold is None:\n threshold = transaction.apdex_t * 4\n\n if transaction.duration >= threshold:\n self._update_slow_transaction(transaction)\n\n # Create the transaction event and add it to the\n # appropriate \"bucket.\" Synthetic requests are saved in one,\n # while transactions from regular requests are saved in another.\n\n if transaction.synthetics_resource_id:\n event = transaction.transaction_event(self.__stats_table)\n self._synthetics_events.add(event)\n\n elif (settings.collect_analytics_events and\n settings.transaction_events.enabled):\n\n event = transaction.transaction_event(self.__stats_table)\n self._transaction_events.add(event, priority=transaction.priority)\n\n # Merge in custom events\n\n if (settings.collect_custom_events and\n settings.custom_insights_events.enabled):\n self.custom_events.merge(transaction.custom_events)\n\n # Merge in span events\n\n if (settings.distributed_tracing.enabled and\n settings.span_events.enabled and settings.collect_span_events):\n if settings.infinite_tracing.enabled:\n for event in transaction.span_protos(settings):\n self._span_stream.put(event)\n elif transaction.sampled:\n for event in transaction.span_events(self.__settings):\n self._span_events.add(event, priority=transaction.priority)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _update_slow_transaction(self, transaction):\n\n slowest = 0\n name = transaction.path\n\n if self.__slow_transaction:\n slowest = self.__slow_transaction.duration\n if name in self.__slow_transaction_map:\n slowest = max(self.__slow_transaction_map[name], slowest)\n\n if transaction.duration > slowest:\n # We are going to replace the prior slow transaction.\n # We need to be a bit tricky here. If we are overriding\n # an existing slow transaction for a different name,\n # then we need to restore in the transaction map what\n # the previous slowest duration was for that, or remove\n # it if there wasn't one. This is so we do not incorrectly\n # suppress it given that it was never actually reported\n # as the slowest transaction.\n\n if self.__slow_transaction:\n if self.__slow_transaction.path != name:\n if self.__slow_transaction_old_duration:\n self.__slow_transaction_map[\n self.__slow_transaction.path] = (\n self.__slow_transaction_old_duration)\n else:\n del self.__slow_transaction_map[\n self.__slow_transaction.path]\n\n if name in self.__slow_transaction_map:\n self.__slow_transaction_old_duration = (\n self.__slow_transaction_map[name])\n else:\n self.__slow_transaction_old_duration = None\n\n self.__slow_transaction = transaction\n self.__slow_transaction_map[name] = transaction.duration","def transaction_trace_data(self, connections):\n\n _logger.debug('Generating transaction trace data.')\n\n if not self.__settings:\n return []\n\n # Create a set 'traces' that is a union of slow transaction,\n # and Synthetics transactions. This ensures we don't send\n # duplicates of a transaction.\n\n traces = set()\n if self.__slow_transaction:\n traces.add(self.__slow_transaction)\n traces.update(self.__synthetics_transactions)\n\n # Return an empty list if no transactions were captured.\n\n if not traces:\n return []\n\n # We want to limit the number of explain plans we do across\n # these. So work out what were the slowest and tag them.\n # Later the explain plan will only be run on those which are\n # tagged.\n\n agent_limits = self.__settings.agent_limits\n explain_plan_limit = agent_limits.sql_explain_plans_per_harvest\n maximum_nodes = agent_limits.transaction_traces_nodes\n\n database_nodes = []\n\n if explain_plan_limit != 0:\n for trace in traces:\n for node in trace.slow_sql:\n # Make sure we clear any flag for explain plans on\n # the nodes in case a transaction trace was merged\n # in from previous harvest period.\n\n node.generate_explain_plan = False\n\n # Node should be excluded if not for an operation\n # that we can't do an explain plan on. Also should\n # not be one which would not be included in the\n # transaction trace because limit was reached.\n\n if (node.node_count < maximum_nodes and\n node.connect_params and node.statement.operation in\n node.statement.database.explain_stmts):\n database_nodes.append(node)\n\n database_nodes = sorted(database_nodes,\n key=lambda x: x.duration)[-explain_plan_limit:]\n\n for node in database_nodes:\n node.generate_explain_plan = True\n\n else:\n for trace in traces:\n for node in trace.slow_sql:\n node.generate_explain_plan = True\n database_nodes.append(node)\n\n # Now generate the transaction traces. We need to cap the\n # number of nodes capture to the specified limit.\n\n trace_data = []\n\n for trace in traces:\n transaction_trace = trace.transaction_trace(\n self, maximum_nodes, connections)\n\n data = [transaction_trace,\n list(trace.string_table.values())]\n\n if self.__settings.debug.log_transaction_trace_payload:\n _logger.debug('Encoding slow transaction data where '\n 'payload=%r.', data)\n\n json_data = json_encode(data)\n\n level = self.__settings.agent_limits.data_compression_level\n level = level or zlib.Z_DEFAULT_COMPRESSION\n\n zlib_data = zlib.compress(six.b(json_data), level)\n\n pack_data = base64.standard_b64encode(zlib_data)\n\n if six.PY3:\n pack_data = pack_data.decode('Latin-1')\n\n root = transaction_trace.root\n\n if trace.record_tt:\n force_persist = True\n else:\n force_persist = False\n\n if trace.include_transaction_trace_request_uri:\n request_uri = trace.request_uri\n else:\n request_uri = None\n\n trace_data.append([transaction_trace.start_time,\n root.end_time - root.start_time,\n trace.path,\n request_uri,\n pack_data,\n trace.guid,\n None,\n force_persist,\n None,\n trace.synthetics_resource_id, ])\n\n return trace_data","def recordTransaction(self, loop, transaction):\n\n a = {}\n a['time'] = transaction.transactionTime\n a['atm'] = transaction.transactionATM.atmID\n a['transaction'] = transaction.transactionType\n a['cash'] = transaction.transactionATM.atmCash\n a['status'] = transaction.transactionStatus\n self._atmDict[loop] = a\n\n c = {}\n c['time'] = transaction.transactionTime\n c['client'] = transaction.transactionCard.cardAccount.accountClient.clientID\n c['account'] = transaction.transactionCard.cardAccount.accountNumber\n c['transaction'] = transaction.transactionType\n c['balance'] = transaction.transactionCard.cardAccount.accountBalance\n c['status'] = transaction.transactionStatus\n self._clientDict[loop] = c\n\n t = {}\n t['time'] = transaction.transactionTime\n t['transaction'] = transaction.transactionType\n t['amount'] = transaction.transactionAmount\n t['status'] = transaction.transactionStatus\n self._transactionDict[loop] = t","def trackTrans(self):\n self.data_struct['_trackTrans'] = True","def slow_transaction_data(self):\n\n # XXX This method no longer appears to be used. Being replaced\n # by the transaction_trace_data() method.\n\n if not self.__settings:\n return []\n\n if not self.__slow_transaction:\n return []\n\n maximum = self.__settings.agent_limits.transaction_traces_nodes\n\n transaction_trace = self.__slow_transaction.transaction_trace(\n self, maximum)\n\n data = [transaction_trace,\n list(self.__slow_transaction.string_table.values())]\n\n if self.__settings.debug.log_transaction_trace_payload:\n _logger.debug('Encoding slow transaction data where '\n 'payload=%r.', data)\n\n json_data = json_encode(data)\n\n level = self.__settings.agent_limits.data_compression_level\n level = level or zlib.Z_DEFAULT_COMPRESSION\n\n zlib_data = zlib.compress(six.b(json_data), level)\n\n pack_data = base64.standard_b64encode(zlib_data)\n\n if six.PY3:\n pack_data = pack_data.decode('Latin-1')\n\n root = transaction_trace.root\n\n trace_data = [[root.start_time,\n root.end_time - root.start_time,\n self.__slow_transaction.path,\n self.__slow_transaction.request_uri,\n pack_data]]\n\n return trace_data","def transaction_time(self, transaction_time):\n\n self._transaction_time = transaction_time","def _update_time_delivered(self, time_delivered):\n # Update db record's time_delivered field\n update = {'time_delivered': time_delivered}\n datebase.update_transaction_record(filter=self.filter, update=update)\n \n # Update db record's estimated_time field\n datebase.update_transaction_record(filter=self.filter, {estimated_time:'0'})\n \n # Update db record's transaction status to delivered\n self._update_transaction_status(transaction_status='delivered')\n \t\t self.transaction_info.update(delivery_status='delivered')\n \n # Update object\n \t\tself.transaction_info.update(time_delivered=time_delivered)\n self.transaction_info.update(estimated_time=0)\n self.transaction_info(transaction_status='delivered')\n\n \tdef _update_transaction_status(self, transaction_status, photo=None):\n \"\"\"\n Update record's transaction_status and send sms msg to update seeker\n \"\"\"\n # Send text message when status changes \n self.send_text(message_type=transaction_status)\n\n # Update db record's transaction status\n update = {'transaction_status': transaction_status}\n datebase.update_transaction_record(filter=self.filter, update=update)\n\n # Update object\n self.transaction_info.update('transaction_seeker': transaction_status)\n\n # If delivered ... TODO: do we actually want to remove from db? \n \t\t# if transaction_status == 'delivered':\n # datebase.delete_transaction_record()\n # return 1 \n # arguments against: we wont be able to access delivered photo if we want to do that","def record_apdex_metrics(self, metrics):\n\n if not self.__settings:\n return\n\n for metric in metrics:\n self.record_apdex_metric(metric)","def transaction_failed_before_processing(self):","def _post_record(\n self, ret_record_args, error, cost, start_time, end_time, record\n ):\n\n ret_record_args['main_error'] = str(error)\n ret_record_args['calls'] = record\n ret_record_args['cost'] = cost\n ret_record_args['perf'] = Perf(start_time=start_time, end_time=end_time)\n ret_record_args['app_id'] = self.app_id\n ret_record_args['tags'] = self.tags\n\n ret_record = Record(**ret_record_args)\n\n if error is not None:\n if self.feedback_mode == FeedbackMode.WITH_APP:\n self._handle_error(record=ret_record, error=error)\n\n elif self.feedback_mode in [FeedbackMode.DEFERRED,\n FeedbackMode.WITH_APP_THREAD]:\n TP().runlater(\n self._handle_error, record=ret_record, error=error\n )\n\n raise error\n\n if self.feedback_mode == FeedbackMode.WITH_APP:\n self._handle_record(record=ret_record)\n\n elif self.feedback_mode in [FeedbackMode.DEFERRED,\n FeedbackMode.WITH_APP_THREAD]:\n TP().runlater(self._handle_record, record=ret_record)\n\n return ret_record","def record_slow_sql_node(self, node):\n\n if not self.__settings:\n return\n\n key = node.identifier\n stats = self.__sql_stats_table.get(key)\n if stats is None:\n # Only record slow SQL if not already over the limit on\n # how many can be collected in the harvest period.\n\n settings = self.__settings\n maximum = settings.agent_limits.slow_sql_data\n if len(self.__sql_stats_table) < maximum:\n stats = SlowSqlStats()\n self.__sql_stats_table[key] = stats\n\n if stats:\n stats.merge_slow_sql_node(node)\n\n return key","def transaction_failed(self):","async def test_all_transactions(self):\n response = await self.collect(get_request_text=self.GATLING_LOG)\n self.assert_measurement(response, value=\"2\")","def process_transaction(self, transaction):\n instrument = transaction.instrument\n if isinstance(instrument, Future):\n try:\n old_price = self._payout_last_sale_prices[instrument]\n except KeyError:\n self._payout_last_sale_prices[instrument] = transaction.price\n else:\n position = self.position_tracker.positions[instrument]\n amount = position.amount\n price = transaction.price\n\n self._cash_flow(\n self._calculate_payout(\n instrument.multiplier,\n amount,\n old_price,\n price,\n ),\n )\n\n if amount + transaction.amount == 0:\n del self._payout_last_sale_prices[instrument]\n else:\n self._payout_last_sale_prices[instrument] = price\n else:\n self._cash_flow(-(transaction.price * transaction.amount))\n\n self.position_tracker.execute_transaction(transaction)\n\n # we only ever want the dict form from now on\n transaction_dict = transaction.to_dict()\n try:\n self._processed_transactions[transaction.dt].append(\n transaction_dict,\n )\n except KeyError:\n self._processed_transactions[transaction.dt] = [transaction_dict]","def _log_update_time(self, *_):\n import time\n if not hasattr(self, '_time'):\n setattr(self, '_time', time.time())\n _time = time.time()\n debug('Time since last call: {:.6f}s'.format(_time - getattr(self, '_time')))\n setattr(self, '_time', _time)","def _record_current_time(self):\n now = time.time()\n delta = now - self._last_time\n self._last_time = now\n self._timing_recorder.append(delta)","def record_transaction(self, transaction: Transaction) -> bool:\n if self._locked:\n print('Failed to record transaction! Your account has been locked!'\n )\n return False\n\n if transaction.amount > self.bank_balance:\n print('Failed to record transaction! Not enough balance!')\n return False\n\n budget = self.budget_manager.get_budget(transaction.budget_category)\n if budget.locked:\n print('Failed to record transaction! This budget has been locked!')\n return False\n\n self.transactions.append(transaction)\n self.bank_balance -= transaction.amount\n budget.amount_spent += transaction.amount\n self._warn_and_lock_if_needed(transaction)\n return True","def update_isolation(self, time: int):","def audit(self):\n self.ping()","def Analyze(self):\n \n self._analyzeLogs()\n for user in self._start_times:\n self._result[user] = self._zipTimes(user)","def slow_update_duration(self):\n for i in range(len(self.data_file.sorted_data)):\n if self.data_file.sorted_data[i]['type'] == 'slow':\n slow_upd = self.data_file.sorted_data[i]['timestamp']\n Config.ANALYSIS.write(f\"slow at: {slow_upd}\\n\")\n if i == 0:\n after_slow = self.data_file.sorted_data[i + 1]['timestamp']\n Config.ANALYSIS.write(f\"after slow: ({slow_upd}, {after_slow}) \"\n f\"= {after_slow - slow_upd}\\n\\n\")\n elif i == len(self.data_file.sorted_data) - 1:\n before_slow = self.data_file.sorted_data[i - 1]['timestamp']\n Config.ANALYSIS.write(f\"before slow: ({before_slow}, {slow_upd}) \"\n f\"= {slow_upd - before_slow}\\n\\n\")\n else:\n before_slow = self.data_file.sorted_data[i - 1]['timestamp']\n after_slow = self.data_file.sorted_data[i + 1]['timestamp']\n Config.ANALYSIS.write(f\"before slow: ({before_slow}, {slow_upd}) \"\n f\"= {slow_upd - before_slow}\\n\")\n Config.ANALYSIS.write(f\"after slow: ({slow_upd}, {after_slow}) \"\n f\"= {after_slow - slow_upd}\\n\\n\")\n Config.ANALYSIS.write(\"\\n\\n\")","def output_stats(self):\n elapsed = self.timer.elapsed.total_seconds()\n count = self.copied + self.errored\n total = self.total\n # Time per key in milliseconds\n avg = round(elapsed / count * 1000, 3)\n # Time remaining in seconds\n remaining = 1.0 * elapsed / count * (total - count)\n # Time remaining in minutes\n remaining = round(remaining / 60.0, 1)\n # Time taken in minutes\n elapsed = round(elapsed / 60.0, 1)\n\n self.log.info(f\"{self.prefix}: {avg}ms avg, {elapsed}min passed, \"\n f\"{remaining}min remaining. ({count:,}/{total:,})\")","def test_avg_database_time(self):\n time = timeit(\n '''post(\"http://127.0.0.1:5000/database?format=json&merge=0\",\\\n data='{\"RUR\": 1.0, \"EUR\": 2.0, \"USD\": 3.0}')''',\n number=1000,\n globals=globals())\n print(time/1000, end=' ', flush=True)","def monitor_transactions(account):\n start_time = datetime.datetime.now()\n logger.info(\n format_log_message(\n 'Looking for new ripple transactions since last run'\n )\n )\n ledger_min_index = _get_min_ledger_index(account)\n marker = None\n has_results = True\n\n try:\n timeout = settings.RIPPLE_TIMEOUT\n except AttributeError:\n timeout = 5\n\n while has_results:\n try:\n response = account_tx(account,\n ledger_min_index,\n limit=PROCESS_TRANSACTIONS_LIMIT,\n marker=marker,\n timeout=timeout)\n except (RippleApiError, ConnectionError), e:\n logger.error(format_log_message(e))\n break\n\n transactions = response['transactions']\n marker = response.get('marker')\n has_results = bool(marker)\n\n for transaction in transactions:\n _store_transaction(account, transaction)\n\n transactions_timeout_reached = (\n datetime.datetime.now() - start_time >= datetime.timedelta(\n seconds=PROCESS_TRANSACTIONS_TIMEOUT\n )\n )\n\n if transactions_timeout_reached and has_results:\n has_results = False\n logger.error(\n 'Process_transactions command terminated because '\n '(%s seconds) timeout: %s',\n PROCESS_TRANSACTIONS_TIMEOUT, unicode(marker)\n )","def record_transaction(self) -> None:\n Menu.prompt_record_transaction()\n tx_data = Transaction.prompt_record_tx()\n new_tx = Transaction.generate_new_tx(tx_data)\n\n # Convert the user budget category int input to the enum\n budget_category_int = new_tx.budget_category\n budget_category = BudgetManager.category_mapping[budget_category_int]\n\n # Retrieve the budget object using the enum as the key\n budget = self.user.budget_manager.budget_dict[budget_category]\n\n # Validate the transaction before proceeding\n validated_tx, error_msg = self.validate_transaction_record(new_tx,\n budget)\n if not validated_tx:\n print(\"\\n[red]Warning:[/red] Unable to record transaction!\")\n print(error_msg)\n print(f\"{self.user.account}\\n\")\n print(budget)\n return\n\n # User has successfully recorded a transaction\n budget.add_amount_spent(new_tx.tx_amount)\n self.user.account.add_amount_spent(new_tx.tx_amount)\n self.user.tx_manager.add_transaction(new_tx)\n self.user.update_lock_status()\n print(\"\\nSuccessfully recorded the following transaction:\")\n print(new_tx)\n print(\"\\nTransaction has been recorded under the following budget \"\n \"category:\")\n print(budget)\n\n self.user.check_and_issue_user_warnings(budget)","def log_and_dispatch(self, state_manager, state_change):\n state_change_id = self.raiden.transaction_log.log(state_change)\n events = self.dispatch(state_manager, state_change)\n self.raiden.transaction_log.log_events(\n state_change_id,\n events,\n self.raiden.get_block_number()\n )","def log_results(self, filename=None):\n\n self.ad_log['train_auc'] = self.diag['train']['auc'][-1]\n self.ad_log['train_accuracy'] = self.diag['train']['acc'][-1]\n self.ad_log['train_time'] = self.train_time\n\n self.ad_log['test_auc'] = self.diag['test']['auc'][-1]\n self.ad_log['test_accuracy'] = self.diag['test']['acc'][-1]\n self.ad_log['test_time'] = self.test_time\n\n self.ad_log.save_to_file(filename=filename)","def record_time_metrics(self, metrics):\n\n if not self.__settings:\n return\n\n for metric in metrics:\n self.record_time_metric(metric)","def record_trace(self):\n\n tfname = str(int(time.time())) + \".obd2_reader.trace\"\n self.tf_out = open(tfname, 'a')\n self.RecordTrace = 1\n print \"Recoding trace to:\", tfname","def _log_progress(self, t):\n\n # Run the update only 2 step before the actual logging happens in order to\n # make sure that the most recent possible values will be stored in\n # self.summary. This is a hacky workaround in order to support OffPolicyAgent\n # which runs 2 threads without coordination\n if (t+2) % self.log_freq == 0 and self.learn_started:\n episode_rewards = self.env_monitor.get_episode_rewards()\n self.episode_rewards = np.asarray(episode_rewards)\n if self.episode_rewards.size > 0:\n self.mean_ep_rew = np.mean(episode_rewards[-self.stats_n:])\n self.best_mean_ep_rew = max(self.best_mean_ep_rew, self.mean_ep_rew)\n\n if t % self.log_freq == 0 and self.learn_started:\n stats_logger.info(\"\")\n for s, lambda_v in self.log_info:\n stats_logger.info(s.format(lambda_v(t)))\n stats_logger.info(\"\")\n\n if self.summary:\n # Log with TensorBoard\n self.tb_writer.add_summary(self.summary, global_step=t)","def _get_recordTtl(self):\n return self.__recordTtl","def get_post_stats(self):\n stats = self.stats\n stats.results = self.job.result().get_counts(stats.iteration)\n stats.datetime = str(datetime.now())","def _trace(self):\n self.__aceQLHttpApi.trace()","def report(self):\n self.last_contacted = time.time()","def report():\n _get_cached_tracker().report()","def flush_transaction(self):\n self.flush_split()\n if not self.transaction_cancelled:\n if self.transaction_info.account is None and self.account_info and self.account_info.name:\n self.transaction_info.account = self.account_info.name\n if self.transaction_info.is_valid():\n self.transaction_infos.append(self.transaction_info)\n self.transaction_cancelled = False\n self.transaction_info = TransactionInfo()","async def test_transaction_specific_response_time_target(self):\n self.set_source_parameter(\"transaction_specific_target_response_times\", [\"[Bb]ar:150\"])\n response = await self.collect(get_request_json_return_value=self.GATLING_JSON)\n self.assert_measurement(response, value=\"1\", entities=self.expected_entities[:1])","def _record_result(self, action, data, tags=None):\r\n if tags is None:\r\n tags = []\r\n\r\n tags.append(u'result:{}'.format(data.get('success', False)))\r\n tags.append(u'action:{}'.format(action))\r\n dog_stats_api.increment(self._metric_name('request.count'), tags=tags)","async def update_trade_stats(self):\n\n summary_keys = [base for base in config['min_base_volumes']] + ['global']\n summaries = {\n key: {\n 'open_count': 0,\n 'buys': 0,\n 'rebuys': 0,\n 'sells': 0,\n 'collect_sells': 0,\n 'soft_stop_sells': 0,\n 'total_profit': 0.0,\n 'total_loss': 0.0,\n 'total_fees': 0.0,\n 'balancer_refills': 0,\n 'balancer_remits': 0,\n 'balancer_stop_losses': 0,\n 'balancer_profit': 0.0,\n 'balancer_loss': 0.0,\n 'balancer_fees': 0.0,\n } for key in summary_keys\n }\n\n for pair in self.trades:\n if pair not in self.trade_stats[self.time_prefix]:\n continue\n\n base = pair.split('-', 1)[0]\n open_count = len(self.trades[pair]['open'])\n\n summaries[base]['open_count'] += open_count\n summaries[base]['buys'] += self.trade_stats[self.time_prefix][pair]['buys']\n summaries[base]['rebuys'] += self.trade_stats[self.time_prefix][pair]['rebuys']\n summaries[base]['sells'] += self.trade_stats[self.time_prefix][pair]['sells']\n summaries[base]['collect_sells'] += self.trade_stats[self.time_prefix][pair]['collect_sells']\n summaries[base]['soft_stop_sells'] += self.trade_stats[self.time_prefix][pair]['soft_stop_sells']\n summaries[base]['total_profit'] += self.trade_stats[self.time_prefix][pair]['total_profit']\n summaries[base]['total_loss'] += self.trade_stats[self.time_prefix][pair]['total_loss']\n summaries[base]['total_fees'] += self.trade_stats[self.time_prefix][pair]['total_fees']\n summaries[base]['balancer_refills'] += self.trade_stats[self.time_prefix][pair]['balancer_refills']\n summaries[base]['balancer_remits'] += self.trade_stats[self.time_prefix][pair]['balancer_remits']\n summaries[base]['balancer_profit'] += self.trade_stats[self.time_prefix][pair]['balancer_profit']\n summaries[base]['balancer_loss'] += self.trade_stats[self.time_prefix][pair]['balancer_loss']\n summaries[base]['balancer_fees'] += self.trade_stats[self.time_prefix][pair]['balancer_fees']\n\n summaries['global']['open_count'] += open_count\n summaries['global']['buys'] += self.trade_stats[self.time_prefix][pair]['buys']\n summaries['global']['rebuys'] += self.trade_stats[self.time_prefix][pair]['rebuys']\n summaries['global']['sells'] += self.trade_stats[self.time_prefix][pair]['sells']\n summaries['global']['collect_sells'] += self.trade_stats[self.time_prefix][pair]['collect_sells']\n summaries['global']['soft_stop_sells'] += self.trade_stats[self.time_prefix][pair]['soft_stop_sells']\n summaries['global']['total_profit'] += self.trade_stats[self.time_prefix][pair]['total_profit']\n summaries['global']['total_loss'] += self.trade_stats[self.time_prefix][pair]['total_loss']\n summaries['global']['total_fees'] += self.trade_stats[self.time_prefix][pair]['total_fees']\n summaries['global']['balancer_refills'] += self.trade_stats[self.time_prefix][pair]['balancer_refills']\n summaries['global']['balancer_remits'] += self.trade_stats[self.time_prefix][pair]['balancer_remits']\n summaries['global']['balancer_profit'] += self.trade_stats[self.time_prefix][pair]['balancer_profit']\n summaries['global']['balancer_loss'] += self.trade_stats[self.time_prefix][pair]['balancer_loss']\n summaries['global']['balancer_fees'] += self.trade_stats[self.time_prefix][pair]['balancer_fees']\n\n for key in summaries:\n self.trade_stats[self.time_prefix][key]['buys'] = summaries[key]['buys']\n self.trade_stats[self.time_prefix][key]['rebuys'] = summaries[key]['rebuys']\n self.trade_stats[self.time_prefix][key]['sells'] = summaries[key]['sells']\n self.trade_stats[self.time_prefix][key]['collect_sells'] = summaries[key]['collect_sells']\n self.trade_stats[self.time_prefix][key]['soft_stop_sells'] = summaries[key]['soft_stop_sells']\n self.trade_stats[self.time_prefix][key]['total_profit'] = summaries[key]['total_profit']\n self.trade_stats[self.time_prefix][key]['total_loss'] = summaries[key]['total_loss']\n self.trade_stats[self.time_prefix][key]['total_fees'] = summaries[key]['total_fees']\n self.trade_stats[self.time_prefix][key]['balancer_refills'] = summaries[key]['balancer_refills']\n self.trade_stats[self.time_prefix][key]['balancer_remits'] = summaries[key]['balancer_remits']\n self.trade_stats[self.time_prefix][key]['balancer_profit'] = summaries[key]['balancer_profit']\n self.trade_stats[self.time_prefix][key]['balancer_loss'] = summaries[key]['balancer_loss']\n self.trade_stats[self.time_prefix][key]['balancer_fees'] = summaries[key]['balancer_fees']\n\n if summaries[key]['open_count'] > self.trade_stats[self.time_prefix][key]['most_open']:\n self.trade_stats[self.time_prefix][key]['most_open'] = summaries[key]['open_count']\n\n filter_items = [pair for pair in self.trades] + [base for base in config['min_base_volumes']] + ['global']\n self.save_attr('trade_stats', max_depth=2, filter_items=filter_items, filter_keys=[self.time_prefix])","def set_cache_data(self) -> None:\n if isinstance(self.tx_storage, TransactionCacheStorage):\n hits = self.tx_storage.stats.get(\"hit\")\n misses = self.tx_storage.stats.get(\"miss\")\n if hits:\n self.transaction_cache_hits = hits\n if misses:\n self.transaction_cache_misses = misses","def report_total_usage(self):\n work_time = 0\n if self.type == 'normal':\n work_time = self.fwk.fwk_global_time - self.start_exec_time\n elif self.type == 'sandia_work':\n self.total_time += self.fwk.fwk_global_time - self.start_exec_time\n self.total_usage = self.total_time * self.nproc\n if self.state == \"running\":\n # update total work done\n self.sim.completed_work += self.fwk.fwk_global_time - self.start_exec_time\n elif self.state == \"failed\":\n # add this work to the work to be redone\n self.sim.rework_todo += self.fwk.fwk_global_time - self.start_exec_time\n self.state = \"not_ready\"\n self.num_faults += 1\n elif self.type == 'sandia_rework':\n self.total_rework_time += self.fwk.fwk_global_time - self.start_exec_time\n self.total_rework_usage = self.total_rework_time * self.nproc\n if self.state == \"running\":\n # update total work done\n self.sim.next_ckpt = self.sim.ckpt_interval - (self.fwk.fwk_global_time - self.start_exec_time)\n self.sim.rework_todo -= self.fwk.fwk_global_time - self.start_exec_time\n elif self.state == \"failed\":\n # add this work to the work to be redone\n self.state = \"not_ready\"\n self.num_faults += 1\n elif self.type == 'sandia_ckpt':\n self.total_ckpt_time += self.fwk.fwk_global_time - self.start_exec_time\n self.total_ckpt_usage = self.total_ckpt_time * self.nproc\n if self.state == \"running\":\n # update last ckpt\n self.sim.last_ckpt = self.sim.completed_work\n elif self.state == \"failed\":\n # add work to rework\n self.sim.rework_todo += self.sim.next_ckpt\n self.state = \"not_ready\"\n self.num_faults += 1\n elif self.type == 'sandia_restart':\n print(\"time spent in rework\", self.fwk.fwk_global_time - self.start_exec_time)\n self.total_restart_time += self.fwk.fwk_global_time - self.start_exec_time\n self.total_restart_usage = self.total_restart_time * self.nproc\n #if self.state == \"running\":\n # nothing to do?\n # pass\n if self.state == \"failed\":\n # gotta try again\n self.state = \"ready\"\n self.num_faults += 1\n else:\n print(\"problems updating state in report_total_usage\")\n raise\n if self.type == 'normal':\n if self.sim.state == 'rework':\n self.total_rework_time += work_time\n self.total_rework_usage = self.total_rework_time * self.nproc\n else: # sim.state == 'work'\n if self.retry:\n self.total_rework_time += work_time\n self.total_rework_usage = self.total_rework_time * self.nproc\n else:\n self.total_time += work_time\n self.total_usage = self.total_time * self.nproc","def record(self, time, increment):\n raise NotImplementedError(\"Abstract method not implemented.\")","def track_duration(self):\n # raise NotImplementedError\n self.out_schema.append(\"run_duration\")\n self._track_duration = True\n # self.runner = GridExecutor.timer(self.runner)","def emit_metrics(self):\n parse_time = time.perf_counter() - self._parsing_start_time\n Stats.gauge(\"dag_processing.total_parse_time\", parse_time)\n Stats.gauge(\"dagbag_size\", sum(stat.num_dags for stat in self._file_stats.values()))\n Stats.gauge(\n \"dag_processing.import_errors\", sum(stat.import_errors for stat in self._file_stats.values())\n )","def market_snap(self, timestamp = -1):\n # refresh account states\n self.trading_acc1.sync_account_with_exh()\n self.trading_acc2.sync_account_with_exh()\n\n signal__arbitrage_delta = self.get_signal__arbitrage_delta()\n signal__gdax_has_usd = self.get_signal__gdax_has_usd()\n signal__gdax_has_eth = self.get_signal__gdax_has_eth()\n signal__cex_has_eth = self.get_signal__cex_has_eth()\n\n def mk_audit_js():\n gdax_account = self.trading_acc1.get_account()\n cex_account = self.trading_acc2.get_account()\n transaction_t = epoch.current_milli_time() if timestamp == -1 else timestamp\n\n audit_js = OrderedDict()\n audit_js['strategy_run_id'] = self.run_id\n audit_js['timestamp'] = epoch.to_str(transaction_t)\n audit_js['timestamp__long'] = transaction_t\n audit_js['ticker'] = self.ticker\n audit_js['strategy_info'] = self._strategy_info\n\n audit_js['signal'] = OrderedDict()\n audit_js['signal']['signal__gdax_has_usd'] = signal__gdax_has_usd\n audit_js['signal']['signal__gdax_has_eth'] = signal__gdax_has_eth\n audit_js['signal']['signal__cex_has_eth'] = signal__cex_has_eth\n audit_js['signal']['signal__arbitrage_delta'] = signal__arbitrage_delta\n\n audit_js['total_usd__num'] = gdax_account.js['usd__num'] + cex_account.js['usd__num']\n audit_js['total_eth__num'] = gdax_account.js['eth__num'] + cex_account.js['eth__num']\n audit_js['gdax_account'] = gdax_account.js\n audit_js['cex_account'] = cex_account.js\n return audit_js\n\n snap_again = False # Only repeat if we have an gdax buy action\n if signal__gdax_has_usd['signal'] and signal__arbitrage_delta['signal']:\n exec_context = self.exec_gdax_buy(timestamp)\n snap_again = True\n\n # Audit\n audit_js = mk_audit_js()\n audit_js['action'] = exec_context\n audit = AuditTradeModel.build(audit_js)\n logger.info('-----Executed GDAX Buy-----')\n logger.info(audit)\n logger.info('---------------------------')\n audit.db_save(es)\n\n if signal__gdax_has_eth['signal']:\n exec_context = self.exec_eth_transfer()\n\n # Audit\n audit_js = mk_audit_js()\n audit_js['action'] = exec_context\n audit = AuditTradeModel.build(audit_js)\n logger.info('-----Executed ETH TRANSFER-----')\n logger.info(audit)\n logger.info('---------------------------')\n audit.db_save(es)\n\n if signal__cex_has_eth['signal']:\n exec_context = self.exec_cex_sell(timestamp)\n\n # Audit\n audit_js = mk_audit_js()\n audit_js['action'] = exec_context\n audit = AuditTradeModel.build(audit_js)\n logger.info('-----Executed CEX Sell-----')\n logger.info(audit)\n logger.info('---------------------------')\n audit.db_save(es)\n\n # Extra logging\n audit_js = mk_audit_js()\n logger.info('post-snapping states: \\n' + json.dumps(audit_js, indent=2))\n\n return snap_again","def report(self, trade, is_entry):\n pass","def store_info(self):\r\n _debug('Protocol: store_info' ) \r\n \r\n #Times\r\n if self.measure_type == '3PL':\r\n self.t_probe_p_s .append(self.t_probe) \r\n self.t_probe_m_s .append(self.t_probe) \r\n if self.measure_type == '4PL':\r\n self.t_probe_p_s .append(self.tp) \r\n self.t_probe_m_s .append(self.tm) \r\n \r\n self.t_pulseSequences_s.append(self.t_pulseSequences)\r\n self.t_process_s .append(self.t_process)\r\n #Total, accumulated, times\r\n self.t_tot_pulseSequences_s.append(self.t_tot_pulseSequences) \r\n self.t_tot_process_s .append(self.t_tot_process) \r\n #Rates\r\n self.Gp_guess_s .append(self.Gp_guess) #Mean of gamma+ \r\n self.Gm_guess_s .append(self.Gm_guess) #Mean of gamma- \r\n self.eGp_guess_s .append(self.eGp_guess) #Uncertainty of gamma+\r\n self.eGm_guess_s .append(self.eGm_guess) #Uncertainty of gamma- \r\n self.cov_GpGm_s .append(self.cov_GpGm) #Covariance of gamma- & gamma- \r\n #Other\r\n self.nb_iteration_s.append(self.iter)\r\n self.R_tot_s .append(self.R_tot)","def _store_transaction(account, transaction):\n tr_tx = transaction['tx']\n meta = transaction.get('meta', {})\n\n if meta.get('TransactionResult') != 'tesSUCCESS':\n return\n\n amount = meta.get('delivered_amount') or tr_tx.get('Amount', {})\n\n is_unprocessed = (\n tr_tx['TransactionType'] == 'Payment' and\n tr_tx['Destination'] == account and\n isinstance(amount, dict) and\n not Transaction.objects.filter(hash=tr_tx['hash'])\n )\n if is_unprocessed:\n logger.info(\n format_log_message(\n 'Saving transaction: %s', transaction\n )\n )\n\n transaction_object = Transaction.objects.create(\n account=tr_tx['Account'],\n hash=tr_tx['hash'],\n destination=account,\n ledger_index=tr_tx['ledger_index'],\n destination_tag=tr_tx.get('DestinationTag'),\n source_tag=tr_tx.get('SourceTag'),\n status=Transaction.RECEIVED,\n currency=amount['currency'],\n issuer=amount['issuer'],\n value=amount['value']\n )\n\n logger.info(\n format_log_message(\n \"Transaction saved: %s\", transaction_object\n )\n )","def tpc_finish(self, transaction):\n raise NotImplementedError","def time_metrics(self, stats, root, parent):\n\n product = self.product\n operation = self.operation or 'other'\n target = self.target\n\n # Determine the scoped metric\n\n statement_metric_name = 'Datastore/statement/%s/%s/%s' % (product,\n target, operation)\n\n operation_metric_name = 'Datastore/operation/%s/%s' % (product,\n operation)\n\n if target:\n scoped_metric_name = statement_metric_name\n else:\n scoped_metric_name = operation_metric_name\n\n yield TimeMetric(name=scoped_metric_name, scope=root.path,\n duration=self.duration, exclusive=self.exclusive)\n\n # Unscoped rollup metrics\n\n yield TimeMetric(name='Datastore/all', scope='',\n duration=self.duration, exclusive=self.exclusive)\n\n yield TimeMetric(name='Datastore/%s/all' % product, scope='',\n duration=self.duration, exclusive=self.exclusive)\n\n if root.type == 'WebTransaction':\n yield TimeMetric(name='Datastore/allWeb', scope='',\n duration=self.duration, exclusive=self.exclusive)\n\n yield TimeMetric(name='Datastore/%s/allWeb' % product, scope='',\n duration=self.duration, exclusive=self.exclusive)\n else:\n yield TimeMetric(name='Datastore/allOther', scope='',\n duration=self.duration, exclusive=self.exclusive)\n\n yield TimeMetric(name='Datastore/%s/allOther' % product, scope='',\n duration=self.duration, exclusive=self.exclusive)\n\n # Unscoped operation metric\n\n yield TimeMetric(name=operation_metric_name, scope='',\n duration=self.duration, exclusive=self.exclusive)\n\n # Unscoped statement metric\n\n if target:\n yield TimeMetric(name=statement_metric_name, scope='',\n duration=self.duration, exclusive=self.exclusive)\n\n # Unscoped instance Metric\n\n if self.instance_hostname and self.port_path_or_id:\n\n instance_metric_name = 'Datastore/instance/%s/%s/%s' % (product,\n self.instance_hostname, self.port_path_or_id)\n\n yield TimeMetric(name=instance_metric_name, scope='',\n duration=self.duration, exclusive=self.exclusive)","def performance_history(self, request, pk=None, **kwargs):\n # Get the goal even though we don't need it (we could just use the pk)\n # so we can ensure we have permission to do so.\n goal = self.get_object()\n\n # - Get all the transaction with this goal involved that are of reason 'Execution'.\n # We want the volume, ticker id, date ordered by date. [(date, {ticker: vol}, ...]\n qs = Transaction.objects.filter(Q(to_goal=goal) | Q(from_goal=goal),\n reason=Transaction.REASON_EXECUTION).order_by('executed')\n txs = qs.values_list('execution_distribution__execution__executed',\n 'execution_distribution__execution__asset__id',\n 'execution_distribution__volume')\n ts = []\n entry = (None,)\n aids = set()\n # If there were no transactions, there can be no performance\n if len(txs) == 0:\n return Response([])\n\n # Because executions are stored with timezone, but other things are just as date, we need to make datetimes\n # naive before doing date arithmetic on them.\n bd = timezone.make_naive(txs[0][0]).date()\n ed = timezone.make_naive(timezone.now()).date()\n for tx in txs:\n aids.add(tx[1])\n txd = timezone.make_naive(tx[0]).date()\n if txd == entry[0]:\n entry[1][tx[1]] += tx[2]\n else:\n if entry[0] is not None:\n ts.append(entry)\n entry = (txd, defaultdict(int))\n entry[1][tx[1]] = tx[2]\n ts.append(entry)\n\n # - Get the time-series of prices for each instrument from the first transaction date until now.\n # Fill empty dates with previous value [(date, {ticker: price}, ...]\n pqs = DailyPrice.objects.filter(date__range=(bd, ed),\n instrument_content_type=ContentType.objects.get_for_model(Ticker).id,\n instrument_object_id__in=aids)\n prices = pqs.to_timeseries(fieldnames=['price', 'date', 'instrument_object_id'],\n index='date',\n storage='long',\n pivot_columns='instrument_object_id',\n values='price')\n # Remove negative prices and fill missing values\n # We replace negs with None so they are interpolated.\n prices[prices <= 0] = None\n prices = prices.reindex(pd.date_range(bd, ed), method='ffill').fillna(method='bfill')\n\n # For each day, calculate the performance\n piter = prices.itertuples()\n res = []\n # Process the first day - it's special\n row = next(piter)\n p_m1 = row[1:]\n vols_m1 = [0] * len(prices.columns)\n tidlocs = {tid: ix for ix, tid in enumerate(prices.columns)}\n for tid, vd in ts.pop(0)[1].items():\n vols_m1[tidlocs[tid]] += vd\n res.append((dt2ed(row[0]), 0)) # First day has no performance as there wasn't a move\n # Process the rest\n for row in piter:\n # row[0] (a datetime) is a naive timestamp, so we don't need to convert it\n if ts and row[0].date() == ts[0][0]:\n vols = vols_m1.copy()\n dtrans = ts.pop(0)[1] # The transactions for the current processed day.\n for tid, vd in dtrans.items():\n vols[tidlocs[tid]] += vd\n # The exposed assets for the day. These are the assets we know for sure were exposed for the move.\n pvol = list(map(min, vols, vols_m1))\n else:\n vols = vols_m1\n pvol = vols\n pdelta = list(map(operator.sub, row[1:], p_m1)) # The change in price from yesterday\n impact = sum(map(operator.mul, pvol, pdelta)) # The total portfolio impact due to price moves for exposed assets.\n b_m1 = sum(map(operator.mul, pvol, p_m1)) # The total portfolio value yesterday for the exposed assets.\n perf = 0 if b_m1 == 0 else impact / b_m1\n # row[0] (a datetime) is a naive timestamp, so we don't need to convert it\n res.append((dt2ed(row[0]), decimal.Decimal.from_float(perf).quantize(decimal.Decimal('1.000000'))))\n p_m1 = row[1:]\n vols_m1 = vols[:]\n\n return Response(res)","def __preprocess_transactions(self):\n\n p_bar = tqdm(range(14), desc=\"Preprocessing transactions\", leave=False)\n\n try:\n # 0. If optional fields not in the transactions add missing\n optional_fields = [\n \"Sector\",\n \"Industry\",\n \"Country\",\n \"Region\",\n \"Fees\",\n \"Premium\",\n \"ISIN\",\n ]\n if not set(optional_fields).issubset(set(self.__transactions.columns)):\n for field in optional_fields:\n if field not in self.__transactions.columns:\n self.__transactions[field] = np.nan\n\n p_bar.n += 1\n p_bar.refresh()\n\n # 1. Convert Date to datetime\n self.__transactions[\"Date\"] = pd.to_datetime(self.__transactions[\"Date\"])\n\n p_bar.n += 1\n p_bar.refresh()\n\n # 2. Sort transactions by date\n self.__transactions = self.__transactions.sort_values(by=\"Date\")\n\n p_bar.n += 1\n p_bar.refresh()\n\n # 3. Capitalize Ticker and Type [of instrument...]\n self.__transactions[\"Ticker\"] = self.__transactions[\"Ticker\"].map(\n lambda x: x.upper() if isinstance(x, str) else x\n )\n self.__transactions[\"Type\"] = self.__transactions[\"Type\"].map(\n lambda x: x.upper() if isinstance(x, str) else x\n )\n\n p_bar.n += 1\n p_bar.refresh()\n\n # 4. Translate side: [\"deposit\", \"buy\"] -> 1 and [\"withdrawal\", \"sell\"] -> -1\n self.__transactions[\"Signal\"] = self.__transactions[\"Side\"].map(\n lambda x: 1\n if x.lower() in [\"deposit\", \"buy\"]\n else (-1 if x.lower() in [\"withdrawal\", \"sell\"] else 0)\n )\n\n p_bar.n += 1\n p_bar.refresh()\n\n # 5. Convert quantity to signed integer\n self.__transactions[\"Quantity\"] = (\n abs(self.__transactions[\"Quantity\"]) * self.__transactions[\"Signal\"]\n )\n\n # Adjust quantity and price for splits\n for ticker in self.__transactions[\"Ticker\"].unique():\n try:\n splits_df = get_splits(ticker)\n if not splits_df.empty:\n splits_df = splits_df.tz_localize(tz=None)\n for split_date in splits_df.index:\n self.__transactions[\"Quantity\"] = np.where(\n (self.__transactions[\"Ticker\"] == ticker)\n & (self.__transactions[\"Date\"] < split_date),\n self.__transactions[\"Quantity\"]\n * splits_df.loc[split_date].values,\n self.__transactions[\"Quantity\"],\n )\n self.__transactions[\"Price\"] = np.where(\n (self.__transactions[\"Ticker\"] == ticker)\n & (self.__transactions[\"Date\"] < split_date),\n self.__transactions[\"Price\"]\n / splits_df.loc[split_date].values,\n self.__transactions[\"Price\"],\n )\n\n except Exception:\n console.print(\"\\nCould not get splits adjusted\")\n\n p_bar.n += 1\n p_bar.refresh()\n\n # 6. Determining the investment/divestment value\n self.__transactions[\"Investment\"] = (\n self.__transactions[\"Quantity\"] * self.__transactions[\"Price\"]\n + self.__transactions[\"Fees\"]\n )\n\n p_bar.n += 1\n p_bar.refresh()\n\n # 7. Reformat crypto tickers to yfinance format (e.g. BTC -> BTC-USD)\n crypto_trades = self.__transactions[self.__transactions.Type == \"CRYPTO\"]\n self.__transactions.loc[\n (self.__transactions.Type == \"CRYPTO\"), \"Ticker\"\n ] = [\n f\"{crypto}-{currency}\"\n for crypto, currency in zip(\n crypto_trades.Ticker, crypto_trades.Currency\n )\n ]\n\n p_bar.n += 1\n p_bar.refresh()\n\n # 8. Reformat STOCK/ETF tickers to yfinance format if ISIN provided.\n\n # If isin not valid ticker is empty\n self.__transactions[\"yf_Ticker\"] = self.__transactions[\"ISIN\"].apply(\n lambda x: yf.utils.get_ticker_by_isin(x) if not pd.isna(x) else np.nan\n )\n\n empty_tickers = list(\n self.__transactions[\n (self.__transactions[\"yf_Ticker\"] == \"\")\n | (self.__transactions[\"yf_Ticker\"].isna())\n ][\"Ticker\"].unique()\n )\n\n # If ticker from isin is empty it is not valid in yfinance, so check if user provided ticker is supported\n removed_tickers = []\n for item in empty_tickers:\n with suppress_stdout():\n # Suppress yfinance failed download message if occurs\n valid_ticker = not (\n yf.download(\n item,\n start=datetime.datetime.now() + datetime.timedelta(days=-5),\n progress=False,\n ).empty\n )\n if valid_ticker:\n # Invalid ISIN but valid ticker\n self.__transactions.loc[\n self.__transactions[\"Ticker\"] == item, \"yf_Ticker\"\n ] = np.nan\n else:\n self.__transactions.loc[\n self.__transactions[\"Ticker\"] == item, \"yf_Ticker\"\n ] = \"\"\n removed_tickers.append(item)\n\n # Merge reformatted tickers into Ticker\n self.__transactions[\"Ticker\"] = self.__transactions[\"yf_Ticker\"].fillna(\n self.__transactions[\"Ticker\"]\n )\n\n p_bar.n += 1\n p_bar.refresh()\n\n # 9. Remove unsupported ISINs that came out empty\n self.__transactions.drop(\n self.__transactions[self.__transactions[\"Ticker\"] == \"\"].index,\n inplace=True,\n )\n\n p_bar.n += 1\n p_bar.refresh()\n\n # 10. Create tickers dictionary with structure {'Type': [Ticker]}\n unsupported_type = self.__transactions[\n (~self.__transactions[\"Type\"].isin([\"STOCK\", \"ETF\", \"CRYPTO\"]))\n ].index\n if unsupported_type.any():\n self.__transactions.drop(unsupported_type, inplace=True)\n console.print(\n \"[red]Unsupported transaction type detected and removed. Supported types: stock, etf or crypto.[/red]\"\n )\n\n for ticker_type in set(self.__transactions[\"Type\"]):\n self.tickers[ticker_type] = list(\n set(\n self.__transactions[\n self.__transactions[\"Type\"].isin([ticker_type])\n ][\"Ticker\"]\n )\n )\n\n p_bar.n += 1\n p_bar.refresh()\n\n # 11. Create list with tickers except cash\n self.tickers_list = list(set(self.__transactions[\"Ticker\"]))\n\n p_bar.n += 1\n p_bar.refresh()\n\n # 12. Save transactions inception date\n self.inception_date = self.__transactions[\"Date\"].iloc[0]\n\n p_bar.n += 1\n p_bar.refresh()\n\n # 13. Populate fields Sector, Industry and Country\n if (\n self.__transactions.loc[\n self.__transactions[\"Type\"] == \"STOCK\",\n optional_fields,\n ]\n .isnull()\n .values.any()\n ):\n # If any fields is empty for stocks (overwrites any info there)\n self.__load_company_data()\n\n p_bar.n += 1\n p_bar.refresh()\n\n # Warn user of removed ISINs\n if removed_tickers:\n p_bar.disable = True\n console.print(\n f\"\\n[red]The following tickers are not supported and were removed: {removed_tickers}.\"\n f\"\\nManually edit the 'Ticker' field with the proper Yahoo Finance suffix or provide a valid ISIN.\"\n f\"\\nSuffix info on 'Yahoo Finance market coverage':\"\n \" https://help.yahoo.com/kb/exchanges-data-providers-yahoo-finance-sln2310.html\"\n f\"\\nE.g. IWDA -> IWDA.AS[/red]\\n\"\n )\n except Exception:\n console.print(\"\\nCould not preprocess transactions.\")\n raise","def track_transaction(self, transaction, items):\n trans = Transaction()\n trans.order_id = transaction.get('order_id', None)\n trans.total = transaction.get('total', None)\n trans.tax = transaction.get('tax', None)\n trans.affiliation = transaction.get('affiliation', None)\n trans.shipping = transaction.get('shipping', None)\n trans.city = transaction.get('city', None)\n trans.state = transaction.get('state', None)\n trans.country = transaction.get('country', None)\n\n for item in items:\n gitem = gaItem()\n gitem.sku = item.get('sku', None)\n gitem.name = item.get('name', None)\n gitem.variation = item.get('variation', None)\n gitem.price = item.get('price', None)\n gitem.quantity = item.get('quantity', 1)\n trans.add_item(gitem)\n\n self.ga_tracker.track_transaction(transaction=trans,session=self.ga_session,visitor=self.ga_visitor)","def internal_event (self):\n self.clock_time += 1\n self.log()","def _analyze(self):\r\n if self.value is None or self.value == self.previous:\r\n pass\r\n elif self._operation == \"add\":\r\n self._additions = self.value\r\n elif self._operation == \"remove\":\r\n self._removals = self.value\r\n elif self.previous is None:\r\n self._assignments = self.value\r\n else:\r\n # partial update time\r\n self._additions = (self.value - self.previous) or None\r\n self._removals = (self.previous - self.value) or None\r\n self._analyzed = True","def track_tick_processing_performance(self, duration):\n\n self.parse_count += 1\n self.total_parse_time += duration\n self.mean_parse_time = self.total_parse_time / self.parse_count","def start_transaction(self) -> None:\n pass","def log_all(self):\n self.save_raw()\n self.log()","def test_peformance(self):\n timedeltas = []\n for file in os.listdir(settings.ANALYSIS_REPORT_FOLDER):\n _file = open(os.path.join(settings.ANALYSIS_REPORT_FOLDER, file), \"r\")\n report = json.loads(_file.read())\n timedeltas.append(\n parse_datetime(report['finish']) - parse_datetime(report['start']))\n _file.close()\n\n # number of queue\n print('NUMBER OF QUEUE = {}'.format(len(timedeltas)))\n\n # get average time\n average_timedelta = sum(timedeltas, datetime.timedelta(0)) / len(timedeltas)\n print('AVERAGE = {}'.format(average_timedelta))\n self.assertTrue(average_timedelta < datetime.timedelta(minutes=3))\n\n # get total process time\n total = timedeltas[0]\n for delta in timedeltas[:1]:\n total += delta\n print('TOTAL = {}'.format(total))\n self.assertTrue(total < datetime.timedelta(minutes=3 * len(timedeltas)))","def test_timeout_processing(self):\n # setup\n self.transaction_behaviour.processing_time = None\n\n # operation\n self.transaction_behaviour._timeout_processing()\n\n # after\n self.assert_quantity_in_outbox(0)","def _save_internal_transactions(self, blocks_traces):\n docs = [\n self._preprocess_internal_transaction(transaction)\n for transaction in blocks_traces\n if transaction[\"transactionHash\"]\n ]\n if docs:\n for chunk in bulk_chunks(docs, None, BYTES_PER_CHUNK):\n self.client.bulk_index(docs=chunk, index=self.indices[\"internal_transaction\"], doc_type=\"itx\",\n id_field=\"hash\", refresh=True)","def test_log_last_completed_datetime(self):\n initial_count = CostUsageReportStatus.objects.count()\n saver = ReportStatsDBAccessor(\"myreport\", self.manifest_id)\n saver.log_last_completed_datetime()\n self.assertIsNotNone(saver.get_last_completed_datetime())\n saver.delete()\n self.assertEqual(CostUsageReportStatus.objects.count(), initial_count)","def record_failure(self, now=None) -> None:\n logging.info('Recording failure at %r', now or int(time.time()))\n self.failure_timestamp = now or int(time.time())\n self.put()","def transaction(fn):\n @wraps(fn)\n def transaction_inner(*args, **kwargs): #1\n start = time()\n stmp_id = id_gen()\n session = operation.session\n sessionid = id(session)\n \n # set distributed transaction id to 0 for single transaction\n try:\n operation.id\n except: \n operation.id = str(uuid4())\n \n try:\n # get runtime info\n cp = current_process()\n ct = current_thread() \n \n # format request params\n params = []\n for item in args:\n params.append(unicode(item))\n for k,v in kwargs.iteritems():\n params.append(u\"'%s':'%s'\" % (k, v))\n \n # call internal function\n res = fn(*args, **kwargs)\n \n session.commit()\n elapsed = round(time() - start, 4)\n logger.debug(u'%s.%s - %s - transaction - %s - %s - OK - %s' % (\n operation.id, stmp_id, sessionid, fn.__name__, \n params, elapsed))\n \n return res\n except ModelError as ex:\n elapsed = round(time() - start, 4)\n logger.error(u'%s.%s - %s - transaction - %s - %s - KO - %s' % (\n operation.id, stmp_id, sessionid, fn.__name__, \n params, elapsed))\n if ex.code not in [409]:\n #logger.error(ex.desc, exc_info=1)\n logger.error(ex.desc)\n \n session.rollback()\n raise TransactionError(ex.desc, code=ex.code)\n except IntegrityError as ex:\n elapsed = round(time() - start, 4)\n logger.error(u'%s.%s - %s - transaction - %s - %s - KO - %s' % (\n operation.id, stmp_id, sessionid, fn.__name__, \n params, elapsed))\n logger.error(ex.orig, exc_info=1)\n logger.error(ex.orig)\n\n session.rollback()\n raise TransactionError(ex.orig)\n except DBAPIError as ex:\n elapsed = round(time() - start, 4)\n logger.error(u'%s.%s - %s - transaction - %s - %s - KO - %s' % (\n operation.id, stmp_id, sessionid, fn.__name__, \n params, elapsed))\n #logger.error(ex.orig, exc_info=1)\n logger.error(ex.orig)\n \n session.rollback()\n raise TransactionError(ex.orig)\n \n except Exception as ex:\n elapsed = round(time() - start, 4)\n logger.error(u'%s.%s - %s - transaction - %s - %s - KO - %s' % (\n operation.id, stmp_id, sessionid, fn.__name__, \n params, elapsed))\n #logger.error(ex, exc_info=1)\n logger.error(ex)\n \n session.rollback()\n raise TransactionError(ex)\n\n return transaction_inner","def report_store(analysis_store, helpers, timestamp_yesterday):\n case = analysis_store.get_cases()[0]\n helpers.add_analysis(\n analysis_store, case, pipeline=Pipeline.MIP_DNA, started_at=timestamp_yesterday\n )\n helpers.add_analysis(analysis_store, case, pipeline=Pipeline.MIP_DNA, started_at=datetime.now())\n # Mock sample dates to calculate processing times\n for family_sample in analysis_store.get_case_samples_by_case_id(\n case_internal_id=case.internal_id\n ):\n family_sample.sample.ordered_at = timestamp_yesterday - timedelta(days=2)\n family_sample.sample.received_at = timestamp_yesterday - timedelta(days=1)\n family_sample.sample.prepared_at = timestamp_yesterday\n family_sample.sample.sequenced_at = timestamp_yesterday\n family_sample.sample.delivered_at = datetime.now()\n return analysis_store","def transaction(self, context: InjectionContext = None) -> \"ProfileSession\":","def progress(transactionId, function):\n _log.info(f\"{transactionId.hex} PROGRESS {function}\")","def log_metrics_for_packet(self, packet: \"Packet\") -> None:\n client_timestamp = packet.client_timestamp\n router_incoming_timestamp = packet.router_incoming_timestamp\n router_outgoing_timestamp = packet.router_outgoing_timestamp\n server_timestamp = packet.server_timestamp\n response_timestamp = time.time()\n if router_outgoing_timestamp is None:\n router_outgoing_timestamp = server_timestamp\n if router_incoming_timestamp is None:\n router_incoming_timestamp = router_outgoing_timestamp\n if client_timestamp is None:\n client_timestamp = router_incoming_timestamp\n client_to_router = max(0, router_incoming_timestamp - client_timestamp)\n router_processing = max(0, router_outgoing_timestamp - router_incoming_timestamp)\n router_to_server = max(0, server_timestamp - router_outgoing_timestamp)\n server_processing = max(0, response_timestamp - server_timestamp)\n e2e_time = max(0, response_timestamp - client_timestamp)\n E2E_PACKET_LATENCY.labels(packet_type=packet.type, stage=\"client_to_router\").observe(\n client_to_router\n )\n E2E_PACKET_LATENCY.labels(packet_type=packet.type, stage=\"router_processing\").observe(\n router_processing\n )\n E2E_PACKET_LATENCY.labels(packet_type=packet.type, stage=\"router_to_server\").observe(\n router_to_server\n )\n E2E_PACKET_LATENCY.labels(packet_type=packet.type, stage=\"server_processing\").observe(\n server_processing\n )\n E2E_PACKET_LATENCY.labels(packet_type=packet.type, stage=\"e2e_time\").observe(e2e_time)","def internal_event(self):\n # log activity\n self.log_activity(LogEntry(\n sys_time=time(),\n logical_time=self.logical_clock,\n action=\"work\"\n ))","def report_performance(self):\n performance = self.amygdala.visualize(self.timestep, \n self.name, \n self.log_dir)\n print('Final performance is {0:.3}'.format(performance))\n self.backup()\n return performance","async def test_ignore_transaction(self):\n self.set_source_parameter(\"transactions_to_ignore\", [self.API2])\n response = await self.collect(get_request_json_return_value=self.JMETER_JSON)\n self.assert_measurement(response, value=\"123\", entities=[])","def rest_api_log(self):\n with self.resource_lock:\n pass","def report_trial(self):\n pass","def monitor_usage(records_output_csv, total_time, delay_time):\n total_time *= 60.0 # convert to seconds\n get_usage(total_time, delay_time, records_output_csv)","def _log_performance_metrics(\n self, task: \"tasks.ClassyTask\", local_variables: Dict[str, Any]\n ) -> None:\n phase_type = task.phase_type\n batches = len(task.losses)\n\n if self.start_time is None:\n logging.warning(\"start_time not initialized\")\n else:\n # Average batch time calculation\n total_batch_time = time.time() - self.start_time\n average_batch_time = total_batch_time / batches\n logging.info(\n \"Average %s batch time (ms) for %d batches: %d\"\n % (phase_type, batches, 1000.0 * average_batch_time)\n )\n\n # Train step time breakdown\n if local_variables.get(\"perf_stats\") is None:\n logging.warning('\"perf_stats\" not set in local_variables')\n elif task.train:\n logging.info(\n \"Train step time breakdown (rank {}):\\n{}\".format(\n get_rank(), local_variables[\"perf_stats\"].report_str()\n )\n )","async def _record_logs(self, report):\n\t\tif report.action == Frame.Report.PARSE:\n\t\t\t# Collects the tests parsing log for further writing to Test_Parser.log\n\t\t\tif report.success:\n\t\t\t\tself._parse_logs[\"success\"] += [report.log]\n\t\t\telse:\n\t\t\t\tself._parse_logs[\"failure\"] += [report.log]\n\t\telif report.action == Frame.Report.EXECUTE:\n\t\t\t# Writes a test log and dump to the results directory\n\t\t\ttest_log = (\"EXECUTE STATUS: SUCCESS\\n\\n\" if report.success else \"EXECUTE STATUS: FAILURE\\n\\n\") + report.log\n\t\t\tfor task in as_completed([self._event_loop.run_in_executor(self._thread_executor, FileSystem.dump_to, \n\t\t\t\t self._result_directory_name + \"/Log/\" + report.test_name + \".log\", test_log)]):\n\t\t\t\tawait task\n\t\t\tfor task in as_completed([self._event_loop.run_in_executor(self._thread_executor, TestLogger._write_test_dump, \n\t\t\t\t self._result_directory_name + \"/Dump/\" + report.test_name + \".pcap\", report.dump)]):\n\t\t\t\tawait task","def perform_patient_audit(self):\n \n # Continuous audit\n while True:\n \n # General patient audit (put audit results in dictionary and add to audit DataFrame)\n audit = dict()\n audit['day'] = self._env.now\n audit['negative'] = len(self._pop.negative_patients)\n audit['positive'] = len(self._pop.positive_patients)\n audit['recovered'] = len(self._pop.recovered_patients)\n audit['inpatient'] = len(self._pop.inpatients)\n audit['died'] = len(self._pop.died_patients)\n audit['total'] = len(self._pop.patients)\n audit['unallocated'] = len(self._pop.unallocated_patients)\n # Add dictionary to existing DataFrame\n self.patient_audit = \\\n self.patient_audit.append(audit, ignore_index=True)\n \n # Displaced patient audit (put audit results in dictionary and add to audit DataFrame)\n audit = dict()\n # Get displaced times\n additional_time = []\n for patient in self._pop.displaced_patients:\n additional_time.append(patient.displaced_additional_time)\n audit['day'] = self._env.now\n audit['number'] = len(self._pop.displaced_patients)\n if len(additional_time) > 0:\n # Dispalced patients exist, calculate statistics\n audit['add_time_min'] = np.min(additional_time)\n audit['add_time_1Q'] = np.quantile(additional_time, 0.25)\n audit['add_time_median'] = np.quantile(additional_time, 0.50)\n audit['add_time_3Q'] = np.quantile(additional_time, 0.75)\n audit['add_time_max'] = np.max(additional_time)\n audit['add_time_total'] = np.sum(additional_time)\n else:\n # No displaced patients exist, set all statistics to zero\n audit['add_time_min'] = 0\n audit['add_time_1Q'] = 0\n audit['add_time_median'] = 0\n audit['add_time_3Q'] = 0\n audit['add_time_max'] = 0\n audit['add_time_total'] = 0\n # Add dictionary to existing DataFrame\n self.displaced_audit = \\\n self.displaced_audit.append(audit, ignore_index=True)\n \n # Trigger next audit after interval\n yield self._env.timeout(self._params.audit_interval)","def _merge_report(self, target, new):\r\n time = None\r\n if 'ts' in new['parsed']:\r\n time = new['parsed']['ts']\r\n\r\n if (target.get('lastSeenDate', None) and\r\n time and\r\n target['lastSeenDate'] < time):\r\n target['lastSeenDate'] = time\r\n\r\n query_millis = int(new['parsed']['stats']['millis'])\r\n target['stats']['totalTimeMillis'] += query_millis\r\n target['stats']['count'] += 1\r\n target['stats']['avgTimeMillis'] = target['stats']['totalTimeMillis'] / target['stats']['count']","def _preprocess_internal_transaction(self, transaction):\n transaction = transaction.copy()\n for field in [\"action\", \"result\"]:\n if (field in transaction.keys()) and (transaction[field]):\n transaction.update(transaction[field])\n del transaction[field]\n for field in [\"value\", \"gasPrice\", \"gasUsed\"]:\n if (field in transaction.keys()) and (transaction[field]):\n value_string = transaction[field][0:2] + \"0\" + transaction[field][2:]\n transaction[field] = int(value_string, 0) / 1e18\n if \"gasUsed\" in transaction:\n transaction[\"gasUsed\"] = int(transaction[\"gasUsed\"] * 1e18)\n return transaction","def _assess(self):\n # get eruptions\n with open(os.sep.join(getfile(currentframe()).split(os.sep)[:-2]+['data','eruptive_periods.txt']),'r') as fp:\n self.tes = [datetimeify(ln.rstrip()) for ln in fp.readlines()]\n # check if data file exists\n self.exists = os.path.isfile(self.file)\n if not self.exists:\n t0 = datetime(2011,1,1)\n t1 = datetime(2011,1,2)\n self.update(t0,t1)\n # check date of latest data in file\n self.df = pd.read_csv(self.file, index_col=0, parse_dates=[0,], infer_datetime_format=True)\n self.ti = self.df.index[0]\n self.tf = self.df.index[-1]","def record_time_metric(self, metric):\n\n if not self.__settings:\n return\n\n # Scope is forced to be empty string if None as\n # scope of None is reserved for apdex metrics.\n\n key = (metric.name, metric.scope or '')\n stats = self.__stats_table.get(key)\n if stats is None:\n stats = TimeStats(call_count=1,\n total_call_time=metric.duration,\n total_exclusive_call_time=metric.exclusive,\n min_call_time=metric.duration,\n max_call_time=metric.duration,\n sum_of_squares=metric.duration ** 2)\n self.__stats_table[key] = stats\n else:\n stats.merge_time_metric(metric)\n\n return key","def CallLoggedAndAccounted(f):\n\n @functools.wraps(f)\n def Decorator(*args, **kwargs):\n try:\n start_time = time.time()\n result = f(*args, **kwargs)\n latency = time.time() - start_time\n\n DB_REQUEST_LATENCY.RecordEvent(latency, fields=[f.__name__])\n logging.debug(\"DB request %s SUCCESS (%.3fs)\", f.__name__, latency)\n\n return result\n except db.Error as e:\n DB_REQUEST_ERRORS.Increment(fields=[f.__name__, \"grr\"])\n logging.debug(\"DB request %s GRR ERROR: %s\", f.__name__,\n utils.SmartUnicode(e))\n raise\n except Exception as e:\n DB_REQUEST_ERRORS.Increment(fields=[f.__name__, \"db\"])\n logging.debug(\"DB request %s INTERNAL DB ERROR : %s\", f.__name__,\n utils.SmartUnicode(e))\n raise\n\n return Decorator","def trace(self, trace=...):\n ...","def __init__(self):\n self._profiling_mode = False\n self._total_time_ms = 0.0\n self._traced_records = []\n self._statistical_results = {}","def _merge_report(self, target, new):\n time = None\n if 'ts' in new['parsed']:\n time = new['parsed']['ts']\n\n if (target.get('lastSeenDate', None) and\n time and\n target['lastSeenDate'] < time):\n target['lastSeenDate'] = time\n\n query_millis = int(new['parsed']['stats']['millis'])\n target['stats']['totalTimeMillis'] += query_millis\n target['stats']['count'] += 1\n target['stats']['avgTimeMillis'] = target['stats']['totalTimeMillis'] / target['stats']['count']","def emit(self, record):\n self.eng.dispose()\n trys = 3\n info = {key: value for key, value in record.__dict__.items() if not key.startswith(\"__\")}\n for t in range(trys):\n # Having what i think is an issue to reflect so try a couple times and don't complain if breaks\n failed = False\n try:\n with self.eng.connect() as con:\n # formating values to convert from python or parsl to db standards\n info['task_fail_history'] = str(info.get('task_fail_history', None))\n info['timestamp'] = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(record.created))\n run_id = info['run_id']\n\n # if workflow or task has completed, update their entries with the time.\n if 'time_completed' in info.keys() and info['time_completed'] != 'None':\n workflows = self.meta.tables['workflows']\n up = workflows.update().values(time_completed=info['time_completed']).where(workflows.c.run_id == run_id)\n con.execute(up)\n return\n if 'task_time_returned' in info.keys() and info['task_time_returned'] is not None:\n workflow = self.meta.tables['task']\n up = workflow.update().values(task_time_returned=info['task_time_returned']).where(workflow.c.task_id == info['task_id'])\\\n .where(workflow.c.run_id == run_id)\n con.execute(up)\n\n # create workflows table if this is a new database without one\n if 'workflows' not in self.meta.tables.keys():\n workflows = create_workflows_table(self.meta)\n self.meta.create_all(con)\n # if this is the first sight of the workflow, add it to the workflows table\n if len(con.execute(self.meta.tables['workflows'].select(self.meta.tables['workflows'].c.run_id == run_id)).fetchall()) == 0:\n workflows = self.meta.tables['workflows']\n ins = workflows.insert().values(**{k: v for k, v in info.items() if k in workflows.c})\n con.execute(ins)\n\n # if log has task counts, update the workflow entry in the workflows table\n if 'tasks_completed_count' in info.keys():\n workflows = self.meta.tables['workflows']\n up = workflows.update().values(tasks_completed_count=info['tasks_completed_count']).where(workflows.c.run_id == run_id)\n con.execute(up)\n if 'tasks_failed_count' in info.keys():\n workflows = self.meta.tables['workflows']\n up = workflows.update().values(tasks_failed_count=info['tasks_failed_count']).where(workflows.c.run_id == run_id)\n con.execute(up)\n\n # create task table if this is a new run without one\n if 'task' not in self.meta.tables.keys():\n workflow = create_task_table(self.meta)\n self.meta.create_all(con)\n\n # check to make sure it is a task log and not just a workflow overview log\n if info.get('task_id', None) is not None:\n # if this is the first sight of the task in the workflow, add it to the workflow table\n if len(con.execute(self.meta.tables['task'].select(self.meta.tables['task'].c.task_id == info['task_id'])\n .where(self.meta.tables['task'].c.run_id == run_id)).fetchall()) == 0:\n if 'psutil_process_pid' in info.keys():\n # this is the race condition that a resource log is before a status log so ignore this resource update\n return\n\n workflow = self.meta.tables['task']\n ins = workflow.insert().values(**{k: v for k, v in info.items() if k in workflow.c})\n con.execute(ins)\n\n if 'task_status' in info.keys():\n # if this is the first sight of a task, create a task_status_table to hold this task's updates\n if 'task_status' not in self.meta.tables.keys():\n task_status_table = create_task_status_table(self.meta)\n self.meta.create_all(con)\n con.execute(task_status_table.insert().values(**{k: v for k, v in info.items() if k in task_status_table.c}))\n # if this status table already exists, just insert the update\n else:\n task_status_table = self.meta.tables['task_status']\n con.execute(task_status_table.insert().values(**{k: v for k, v in info.items() if k in task_status_table.c}))\n return\n\n if 'psutil_process_pid' in info.keys():\n # if this is a task resource update then handle that, if the resource table DNE then create it\n if 'task_resources' not in self.meta.tables.keys():\n task_resource_table = create_task_resource_table(self.meta)\n self.meta.create_all(con)\n con.execute(task_resource_table.insert().values(**{k: v for k, v in info.items() if k in task_resource_table.c}))\n # if this resource table already exists, just insert the update\n else:\n task_resource_table = self.meta.tables['task_resources']\n con.execute(task_resource_table.insert().values(**{k: v for k, v in info.items() if k in task_resource_table.c}))\n return\n\n except Exception as e:\n logger.error(\"Try a couple times since some known issues can occur. Number of Failures: {} Error: {}\".format(t, str(e)))\n failed = True\n time.sleep(5)\n if not failed:\n return","def test_upsert_metadata_for_rate_plan(self):\n pass","def do_timestamp_messages(self, messages):\n timestamp = self.env.now\n self.reception_records[timestamp] = messages\n log.debug(\"{} recorded {}\".format(self, self.reception_records))","def get_gdax_historical_data():\n \n start = None\n while not start:\n start,end,tid = getStartAndEndHistoric()\n if not start:\n time.sleep(60)\n #Todo: change this to 1min\n firsttimestamp = start\n engine = sa.create_engine(sql_address)\n products = [\"LTC-USD\",\"LTC-BTC\",\"ETH-USD\",\"ETH-BTC\",\"BTC-USD\"]\n public_client = gdax.PublicClient()\n deltat = datetime.timedelta(seconds = 200)\n timewindows = []\n while end - start > datetime.timedelta(seconds=0):\n if start + deltat > end:\n endx = end\n else:\n endx = start + deltat\n timewindows.append([start,endx])\n start += deltat\n results = []\n total = len(timewindows)\n current_idx = 0\n timeold = time.time()\n numofqueries = 0\n engine = sa.create_engine(sql_address)\n Base.metadata.bind = engine\n DBSession = sa.orm.sessionmaker()\n DBSession.bind = engine\n session = DBSession()\n for startx,endx in timewindows:\n\n current_idx += 1\n for i in products:\n repeat = True\n while repeat:\n\n #delay if ratelimts are close\n if numofqueries < 3:\n while time.time() - timeold < 1:\n time.sleep(0.05)\n \n timeold = time.time()\n numofqueries = 0\n try:\n alist = public_client.get_product_historic_rates(i, start = startx, end = endx, granularity = 1)\n except:\n time.sleep(30)\n public_client = gdax.PublicClient()\n alist = public_client.get_product_historic_rates(i, start = startx, end = endx, granularity = 1)\n\n alist = public_client.get_product_historic_rates(i, start = startx, end = endx, granularity = 1)\n\n numofqueries += 1\n\n #rate limit exceeded has 'message' as dict.\n if not 'message' in alist:\n repeat = False\n for a in alist:\n a[0] = datetime.datetime.fromtimestamp(float(a[0]))\n tmp = i.split('-')\n d = dict(coin = tmp[0], currency = tmp[1], timestamp = a[0], low=a[1], high=a[2], open=a[3], close=a[4], volume=a[5])\n results.append(d)\n lasttimestamp = a[0]\n\n #upload with batch size of 10000\n if len(results) > 10000:\n engine.execute(\n GADXHistoricalDataOneSecondOHLC.__table__.insert(),\n results\n )\n results = []\n \n update = session.query(historicalDataProgramState).filter(sa.and_(historicalDataProgramState.transaction_id == tid,historicalDataProgramState.entry_type == 'update')).first()\n if update:\n update.end = lasttimestamp\n session.commit()\n else:\n new_update = historicalDataProgramState(entry_type = 'update',transaction_id = tid,start=firsttimestamp,end=lasttimestamp,platform='GDAX',status='incomplete')\n session.add(new_update)\n session.commit()\n if len(results) > 0:\n engine.execute(\n GADXHistoricalDataOneSecondOHLC.__table__.insert(),\n results\n )\n results = []\n \n update = session.query(historicalDataProgramState).filter(sa.and_(historicalDataProgramState.transaction_id == tid,historicalDataProgramState.entry_type == 'update')).first()\n if update:\n update.end = lasttimestamp\n session.commit()\n else:\n new_update = historicalDataProgramState(entry_type = 'update',transaction_id = tid,start=firsttimestamp,end=lasttimestamp,platform='GDAX',status='incomplete')\n session.add(new_update)\n session.commit()\n\n update = session.query(historicalDataProgramState).filter(sa.and_(historicalDataProgramState.transaction_id == tid,historicalDataProgramState.entry_type == 'update')).first()\n update.status='complete'\n order = session.query(historicalDataProgramState).filter(sa.and_(historicalDataProgramState.transaction_id == tid,historicalDataProgramState.entry_type == 'order')).first()\n order.status='complete'\n session.commit()","def test_log_last_started_datetime(self):\n initial_count = CostUsageReportStatus.objects.count()\n saver = ReportStatsDBAccessor(\"myreport\", self.manifest_id)\n saver.log_last_started_datetime()\n self.assertIsNotNone(saver.get_last_started_datetime())\n saver.delete()\n self.assertEqual(CostUsageReportStatus.objects.count(), initial_count)","def commit_transaction(self) -> None:\n pass","def exec_eth_transfer(self, timestamp=-1):\n ticker = self.ticker\n gdax_trading_account = self.trading_acc1\n cex_trading_account = self.trading_acc2\n gdax_eth_amount = gdax_trading_account.get_balance(ticker)\n cex_deposit_address = cex_trading_account.get_crypto_deposit_address(ticker)\n\n # transfer\n gdax_trading_account.transfer_crypto(ticker, cex_deposit_address, gdax_eth_amount)\n\n # audit info\n audit_info = {\n 'ticker': ticker,\n 'action_type': 'exec_eth_transfer',\n 'gdax_eth_amount': gdax_eth_amount,\n 'cex_deposit_address': cex_deposit_address\n }\n return audit_info","def record_inference_stats(self, nms_step_time: float, inference_round_trip_time: Tuple[float, float, float], inference_step_time: float):\n\n # inference_round_trip_time is an average time needed for a step\n self.inference_times.append(inference_round_trip_time)\n # inference_step_time is the time taken to complete the step, and used to calculate the throughput\n inference_throughput = self.image_count/inference_step_time\n self.inference_throughputs.append(inference_throughput)\n\n self.nms_times.append(nms_step_time)\n\n total_step_time = inference_step_time + nms_step_time\n self.total_times.append(total_step_time)\n\n total_throughput = self.image_count/total_step_time\n self.total_throughputs.append(total_throughput)","def trace_time(func):\n\n @functools.wraps(func)\n def wrapper(*args, **kw):\n begin = time.time()\n logging.debug(\"begin at %s: %s()\" % (__format_time(begin), func.__name__))\n result = func(*args, **kw)\n end = time.time()\n logging.debug(\n \"end at %s, cost %.2fs: %s() -- return type: %s\"\n % (__format_time(end), end - begin, func.__name__, type(result).__name__))\n return result\n\n return wrapper","def logging(self, function):\n avg_nms_time_per_step = sum(self.nms_times)/len(self.nms_times)\n avg_total_time_per_step = sum(self.total_times)/len(self.total_times)\n\n avg_min_latency = [x[0] for x in self.inference_times]\n avg_max_latency = [x[1] for x in self.inference_times]\n avg_latency = [x[2] for x in self.inference_times]\n\n function(\"Inference stats: image size {}x{}, batches per step {}, batch size {}, {} steps\".format(\n self.cfg.model.image_size, self.cfg.model.image_size, self.cfg.ipuopts.batches_per_step, self.cfg.model.micro_batch_size, len(self.total_times)\n ))\n function(\"--------------------------------------------------\")\n function(\"Inference\")\n function(\"Average Min Latency per Batch: {:.3f} ms\".format(1000 * sum(avg_min_latency)/len(self.inference_times)))\n function(\"Average Max Latency per Batch: {:.3f} ms\".format(1000 * sum(avg_max_latency)/len(self.inference_times)))\n function(\"Average Latency per Batch: {:.3f} ms\".format(1000 * sum(avg_latency)/len(self.inference_times)))\n function(\"Average Inference Throughput: {:.3f} img/s\".format(sum(self.inference_throughputs)/len(self.inference_throughputs)))\n function(\"--------------------------------------------------\")\n # TODO remove the NMS and end-to-end time report once NMS is on device\n function(\"End-to-end\")\n function(\"Average NMS Latency per Batch: {:.3f} ms\".format(1000 * avg_nms_time_per_step/self.cfg.ipuopts.batches_per_step))\n function(\"Average End-to-end Latency per Batch: {:.3f} ms\".format(1000 * avg_total_time_per_step/self.cfg.ipuopts.batches_per_step))\n function(\"End-to-end Throughput: {:.3f} img/s\".format(sum(self.total_throughputs)/len(self.total_throughputs)))\n function(\"==================================================\")\n\n if self.cfg.eval.metrics:\n self.compute_and_print_eval_metrics()","def acc(ev):\n profData = getProfilingData(ev)\n if profData is not None:\n return profData.Tacc()\n return \"\"","def cache_txn_manage(database, table, action, trans=None, **kw):\n trace = kw['trace']\n cache = server.data[database].tables['cache']\n transaction = request.get_json() if trans == None else trans\n if 'txn' in transaction:\n txn_id = transaction['txn']\n tx=None\n wait_time = 0.0 # total time waiting to commit txn \n wait_interval = txn_default_wait_in_sec # amount of time to wait between checks - if multiple txns exist \n # Get transaction from cache db\n if action == 'commit':\n while True:\n txns = cache.select('id','timestamp',\n where={'table_name': table}\n )\n if not txn_id in {tx['id'] for tx in txns}:\n return {\"message\": trace.error(f\"{txn_id} does not exist in cache\")}, 500\n if len(txns) == 1:\n if not txns[0]['id'] == txn_id:\n warning = f\"txn with id {txn_id} does not exist for {database} {table}\"\n return {'warning': trace.warning(warning)}, 500\n # txn_id is only value inside\n tx = txns[0]\n break\n # multiple pending txns - need to check timestamp to verify if this txn can be commited yet\n txns = sorted(txns, key=lambda txn: txn['timestamp'])\n for ind, txn in enumerate(txns):\n if txn['id'] == txn_id:\n if ind == 0:\n tx = txns[0]\n break\n if wait_time > txn_max_wait_time_in_sec:\n warning = f\"timeout of {wait_time} reached while waiting to commit {txn_id} for {database} {table}, waiting on {txns[:ind]}\"\n trace.warning(warning)\n trace.warning(f\"removing txn with id {txns[0]['id']} maxWaitTime of {txn_max_wait_time_in_sec} reached\")\n cache.delete(where={'id': txns[0]['id']})\n break\n break\n if tx == None:\n trace.warning(f\"txn_id {txn_id} is behind txns {txns[:ind]} - waiting {wait_time} to retry\")\n time.sleep(wait_interval)\n wait_time+=wait_interval \n # wait_interval scales up to txn_max_wait_interval_in_sec\n wait_interval+=wait_interval \n if wait_interval >= txn_max_wait_interval_in_sec:\n wait_interval = txn_max_wait_interval_in_sec\n continue\n break\n # Should not have broken out of loop here without a tx\n if tx == None:\n trace.error(\"tx is None, this should not hppen\")\n return {\"error\": \"tx was none\"}, 500\n tx = cache.select('type','txn',\n where={'id': txn_id})[0]\n try:\n r, rc = server.actions[tx['type']](database, table, tx['txn'])\n trace.warning(f\"##cache {action} response {r} rc {rc}\")\n except Exception as e:\n r, rc = trace.exception(f\"Exception when performing cache {action}\"), 500\n \n del_txn = cache.delete(\n where={'id': txn_id}\n )\n if rc == 200:\n # update last txn id\n set_params = {\n 'set': {\n 'last_txn_uuid': txn_id,\n 'last_mod_time': float(time.time())\n },\n 'where': {\n 'table_name': table\n }\n }\n server.data['cluster'].tables['pyql'].update(\n **set_params['set'],\n where=set_params['where']\n )\n return {\"message\": r, \"status\": rc}, rc\n if action == 'cancel':\n del_txn = cache.delete(\n where={'id': txn_id}\n )\n return {'deleted': txn_id}, 200","def process_request(self, request):\n self.db.set_profiling_level(0)\n try:\n self.start_ts = self.db.system.profile.find()\\\n .sort(\"ts\", pymongo.DESCENDING)\\\n .limit(1)[0].get('ts')\n except IndexError:\n self.start_ts = None\n\n self.db.set_profiling_level(2)","def startTransaction(self) -> int:\n ...","def update_time(self):\n time_metrics = self._fetch_time_metrics_and_clear()\n self._logger.info('update_time. time_metrics = %s', build_metrics_times_data(time_metrics))"],"string":"[\n \"def _update_slow_transaction(self, transaction):\\n\\n slowest = 0\\n name = transaction.path\\n\\n if self.__slow_transaction:\\n slowest = self.__slow_transaction.duration\\n if name in self.__slow_transaction_map:\\n slowest = max(self.__slow_transaction_map[name], slowest)\\n\\n if transaction.duration > slowest:\\n # We are going to replace the prior slow transaction.\\n # We need to be a bit tricky here. If we are overriding\\n # an existing slow transaction for a different name,\\n # then we need to restore in the transaction map what\\n # the previous slowest duration was for that, or remove\\n # it if there wasn't one. This is so we do not incorrectly\\n # suppress it given that it was never actually reported\\n # as the slowest transaction.\\n\\n if self.__slow_transaction:\\n if self.__slow_transaction.path != name:\\n if self.__slow_transaction_old_duration:\\n self.__slow_transaction_map[\\n self.__slow_transaction.path] = (\\n self.__slow_transaction_old_duration)\\n else:\\n del self.__slow_transaction_map[\\n self.__slow_transaction.path]\\n\\n if name in self.__slow_transaction_map:\\n self.__slow_transaction_old_duration = (\\n self.__slow_transaction_map[name])\\n else:\\n self.__slow_transaction_old_duration = None\\n\\n self.__slow_transaction = transaction\\n self.__slow_transaction_map[name] = transaction.duration\",\n \"def transaction_trace_data(self, connections):\\n\\n _logger.debug('Generating transaction trace data.')\\n\\n if not self.__settings:\\n return []\\n\\n # Create a set 'traces' that is a union of slow transaction,\\n # and Synthetics transactions. This ensures we don't send\\n # duplicates of a transaction.\\n\\n traces = set()\\n if self.__slow_transaction:\\n traces.add(self.__slow_transaction)\\n traces.update(self.__synthetics_transactions)\\n\\n # Return an empty list if no transactions were captured.\\n\\n if not traces:\\n return []\\n\\n # We want to limit the number of explain plans we do across\\n # these. So work out what were the slowest and tag them.\\n # Later the explain plan will only be run on those which are\\n # tagged.\\n\\n agent_limits = self.__settings.agent_limits\\n explain_plan_limit = agent_limits.sql_explain_plans_per_harvest\\n maximum_nodes = agent_limits.transaction_traces_nodes\\n\\n database_nodes = []\\n\\n if explain_plan_limit != 0:\\n for trace in traces:\\n for node in trace.slow_sql:\\n # Make sure we clear any flag for explain plans on\\n # the nodes in case a transaction trace was merged\\n # in from previous harvest period.\\n\\n node.generate_explain_plan = False\\n\\n # Node should be excluded if not for an operation\\n # that we can't do an explain plan on. Also should\\n # not be one which would not be included in the\\n # transaction trace because limit was reached.\\n\\n if (node.node_count < maximum_nodes and\\n node.connect_params and node.statement.operation in\\n node.statement.database.explain_stmts):\\n database_nodes.append(node)\\n\\n database_nodes = sorted(database_nodes,\\n key=lambda x: x.duration)[-explain_plan_limit:]\\n\\n for node in database_nodes:\\n node.generate_explain_plan = True\\n\\n else:\\n for trace in traces:\\n for node in trace.slow_sql:\\n node.generate_explain_plan = True\\n database_nodes.append(node)\\n\\n # Now generate the transaction traces. We need to cap the\\n # number of nodes capture to the specified limit.\\n\\n trace_data = []\\n\\n for trace in traces:\\n transaction_trace = trace.transaction_trace(\\n self, maximum_nodes, connections)\\n\\n data = [transaction_trace,\\n list(trace.string_table.values())]\\n\\n if self.__settings.debug.log_transaction_trace_payload:\\n _logger.debug('Encoding slow transaction data where '\\n 'payload=%r.', data)\\n\\n json_data = json_encode(data)\\n\\n level = self.__settings.agent_limits.data_compression_level\\n level = level or zlib.Z_DEFAULT_COMPRESSION\\n\\n zlib_data = zlib.compress(six.b(json_data), level)\\n\\n pack_data = base64.standard_b64encode(zlib_data)\\n\\n if six.PY3:\\n pack_data = pack_data.decode('Latin-1')\\n\\n root = transaction_trace.root\\n\\n if trace.record_tt:\\n force_persist = True\\n else:\\n force_persist = False\\n\\n if trace.include_transaction_trace_request_uri:\\n request_uri = trace.request_uri\\n else:\\n request_uri = None\\n\\n trace_data.append([transaction_trace.start_time,\\n root.end_time - root.start_time,\\n trace.path,\\n request_uri,\\n pack_data,\\n trace.guid,\\n None,\\n force_persist,\\n None,\\n trace.synthetics_resource_id, ])\\n\\n return trace_data\",\n \"def recordTransaction(self, loop, transaction):\\n\\n a = {}\\n a['time'] = transaction.transactionTime\\n a['atm'] = transaction.transactionATM.atmID\\n a['transaction'] = transaction.transactionType\\n a['cash'] = transaction.transactionATM.atmCash\\n a['status'] = transaction.transactionStatus\\n self._atmDict[loop] = a\\n\\n c = {}\\n c['time'] = transaction.transactionTime\\n c['client'] = transaction.transactionCard.cardAccount.accountClient.clientID\\n c['account'] = transaction.transactionCard.cardAccount.accountNumber\\n c['transaction'] = transaction.transactionType\\n c['balance'] = transaction.transactionCard.cardAccount.accountBalance\\n c['status'] = transaction.transactionStatus\\n self._clientDict[loop] = c\\n\\n t = {}\\n t['time'] = transaction.transactionTime\\n t['transaction'] = transaction.transactionType\\n t['amount'] = transaction.transactionAmount\\n t['status'] = transaction.transactionStatus\\n self._transactionDict[loop] = t\",\n \"def trackTrans(self):\\n self.data_struct['_trackTrans'] = True\",\n \"def slow_transaction_data(self):\\n\\n # XXX This method no longer appears to be used. Being replaced\\n # by the transaction_trace_data() method.\\n\\n if not self.__settings:\\n return []\\n\\n if not self.__slow_transaction:\\n return []\\n\\n maximum = self.__settings.agent_limits.transaction_traces_nodes\\n\\n transaction_trace = self.__slow_transaction.transaction_trace(\\n self, maximum)\\n\\n data = [transaction_trace,\\n list(self.__slow_transaction.string_table.values())]\\n\\n if self.__settings.debug.log_transaction_trace_payload:\\n _logger.debug('Encoding slow transaction data where '\\n 'payload=%r.', data)\\n\\n json_data = json_encode(data)\\n\\n level = self.__settings.agent_limits.data_compression_level\\n level = level or zlib.Z_DEFAULT_COMPRESSION\\n\\n zlib_data = zlib.compress(six.b(json_data), level)\\n\\n pack_data = base64.standard_b64encode(zlib_data)\\n\\n if six.PY3:\\n pack_data = pack_data.decode('Latin-1')\\n\\n root = transaction_trace.root\\n\\n trace_data = [[root.start_time,\\n root.end_time - root.start_time,\\n self.__slow_transaction.path,\\n self.__slow_transaction.request_uri,\\n pack_data]]\\n\\n return trace_data\",\n \"def transaction_time(self, transaction_time):\\n\\n self._transaction_time = transaction_time\",\n \"def _update_time_delivered(self, time_delivered):\\n # Update db record's time_delivered field\\n update = {'time_delivered': time_delivered}\\n datebase.update_transaction_record(filter=self.filter, update=update)\\n \\n # Update db record's estimated_time field\\n datebase.update_transaction_record(filter=self.filter, {estimated_time:'0'})\\n \\n # Update db record's transaction status to delivered\\n self._update_transaction_status(transaction_status='delivered')\\n \\t\\t self.transaction_info.update(delivery_status='delivered')\\n \\n # Update object\\n \\t\\tself.transaction_info.update(time_delivered=time_delivered)\\n self.transaction_info.update(estimated_time=0)\\n self.transaction_info(transaction_status='delivered')\\n\\n \\tdef _update_transaction_status(self, transaction_status, photo=None):\\n \\\"\\\"\\\"\\n Update record's transaction_status and send sms msg to update seeker\\n \\\"\\\"\\\"\\n # Send text message when status changes \\n self.send_text(message_type=transaction_status)\\n\\n # Update db record's transaction status\\n update = {'transaction_status': transaction_status}\\n datebase.update_transaction_record(filter=self.filter, update=update)\\n\\n # Update object\\n self.transaction_info.update('transaction_seeker': transaction_status)\\n\\n # If delivered ... TODO: do we actually want to remove from db? \\n \\t\\t# if transaction_status == 'delivered':\\n # datebase.delete_transaction_record()\\n # return 1 \\n # arguments against: we wont be able to access delivered photo if we want to do that\",\n \"def record_apdex_metrics(self, metrics):\\n\\n if not self.__settings:\\n return\\n\\n for metric in metrics:\\n self.record_apdex_metric(metric)\",\n \"def transaction_failed_before_processing(self):\",\n \"def _post_record(\\n self, ret_record_args, error, cost, start_time, end_time, record\\n ):\\n\\n ret_record_args['main_error'] = str(error)\\n ret_record_args['calls'] = record\\n ret_record_args['cost'] = cost\\n ret_record_args['perf'] = Perf(start_time=start_time, end_time=end_time)\\n ret_record_args['app_id'] = self.app_id\\n ret_record_args['tags'] = self.tags\\n\\n ret_record = Record(**ret_record_args)\\n\\n if error is not None:\\n if self.feedback_mode == FeedbackMode.WITH_APP:\\n self._handle_error(record=ret_record, error=error)\\n\\n elif self.feedback_mode in [FeedbackMode.DEFERRED,\\n FeedbackMode.WITH_APP_THREAD]:\\n TP().runlater(\\n self._handle_error, record=ret_record, error=error\\n )\\n\\n raise error\\n\\n if self.feedback_mode == FeedbackMode.WITH_APP:\\n self._handle_record(record=ret_record)\\n\\n elif self.feedback_mode in [FeedbackMode.DEFERRED,\\n FeedbackMode.WITH_APP_THREAD]:\\n TP().runlater(self._handle_record, record=ret_record)\\n\\n return ret_record\",\n \"def record_slow_sql_node(self, node):\\n\\n if not self.__settings:\\n return\\n\\n key = node.identifier\\n stats = self.__sql_stats_table.get(key)\\n if stats is None:\\n # Only record slow SQL if not already over the limit on\\n # how many can be collected in the harvest period.\\n\\n settings = self.__settings\\n maximum = settings.agent_limits.slow_sql_data\\n if len(self.__sql_stats_table) < maximum:\\n stats = SlowSqlStats()\\n self.__sql_stats_table[key] = stats\\n\\n if stats:\\n stats.merge_slow_sql_node(node)\\n\\n return key\",\n \"def transaction_failed(self):\",\n \"async def test_all_transactions(self):\\n response = await self.collect(get_request_text=self.GATLING_LOG)\\n self.assert_measurement(response, value=\\\"2\\\")\",\n \"def process_transaction(self, transaction):\\n instrument = transaction.instrument\\n if isinstance(instrument, Future):\\n try:\\n old_price = self._payout_last_sale_prices[instrument]\\n except KeyError:\\n self._payout_last_sale_prices[instrument] = transaction.price\\n else:\\n position = self.position_tracker.positions[instrument]\\n amount = position.amount\\n price = transaction.price\\n\\n self._cash_flow(\\n self._calculate_payout(\\n instrument.multiplier,\\n amount,\\n old_price,\\n price,\\n ),\\n )\\n\\n if amount + transaction.amount == 0:\\n del self._payout_last_sale_prices[instrument]\\n else:\\n self._payout_last_sale_prices[instrument] = price\\n else:\\n self._cash_flow(-(transaction.price * transaction.amount))\\n\\n self.position_tracker.execute_transaction(transaction)\\n\\n # we only ever want the dict form from now on\\n transaction_dict = transaction.to_dict()\\n try:\\n self._processed_transactions[transaction.dt].append(\\n transaction_dict,\\n )\\n except KeyError:\\n self._processed_transactions[transaction.dt] = [transaction_dict]\",\n \"def _log_update_time(self, *_):\\n import time\\n if not hasattr(self, '_time'):\\n setattr(self, '_time', time.time())\\n _time = time.time()\\n debug('Time since last call: {:.6f}s'.format(_time - getattr(self, '_time')))\\n setattr(self, '_time', _time)\",\n \"def _record_current_time(self):\\n now = time.time()\\n delta = now - self._last_time\\n self._last_time = now\\n self._timing_recorder.append(delta)\",\n \"def record_transaction(self, transaction: Transaction) -> bool:\\n if self._locked:\\n print('Failed to record transaction! Your account has been locked!'\\n )\\n return False\\n\\n if transaction.amount > self.bank_balance:\\n print('Failed to record transaction! Not enough balance!')\\n return False\\n\\n budget = self.budget_manager.get_budget(transaction.budget_category)\\n if budget.locked:\\n print('Failed to record transaction! This budget has been locked!')\\n return False\\n\\n self.transactions.append(transaction)\\n self.bank_balance -= transaction.amount\\n budget.amount_spent += transaction.amount\\n self._warn_and_lock_if_needed(transaction)\\n return True\",\n \"def update_isolation(self, time: int):\",\n \"def audit(self):\\n self.ping()\",\n \"def Analyze(self):\\n \\n self._analyzeLogs()\\n for user in self._start_times:\\n self._result[user] = self._zipTimes(user)\",\n \"def slow_update_duration(self):\\n for i in range(len(self.data_file.sorted_data)):\\n if self.data_file.sorted_data[i]['type'] == 'slow':\\n slow_upd = self.data_file.sorted_data[i]['timestamp']\\n Config.ANALYSIS.write(f\\\"slow at: {slow_upd}\\\\n\\\")\\n if i == 0:\\n after_slow = self.data_file.sorted_data[i + 1]['timestamp']\\n Config.ANALYSIS.write(f\\\"after slow: ({slow_upd}, {after_slow}) \\\"\\n f\\\"= {after_slow - slow_upd}\\\\n\\\\n\\\")\\n elif i == len(self.data_file.sorted_data) - 1:\\n before_slow = self.data_file.sorted_data[i - 1]['timestamp']\\n Config.ANALYSIS.write(f\\\"before slow: ({before_slow}, {slow_upd}) \\\"\\n f\\\"= {slow_upd - before_slow}\\\\n\\\\n\\\")\\n else:\\n before_slow = self.data_file.sorted_data[i - 1]['timestamp']\\n after_slow = self.data_file.sorted_data[i + 1]['timestamp']\\n Config.ANALYSIS.write(f\\\"before slow: ({before_slow}, {slow_upd}) \\\"\\n f\\\"= {slow_upd - before_slow}\\\\n\\\")\\n Config.ANALYSIS.write(f\\\"after slow: ({slow_upd}, {after_slow}) \\\"\\n f\\\"= {after_slow - slow_upd}\\\\n\\\\n\\\")\\n Config.ANALYSIS.write(\\\"\\\\n\\\\n\\\")\",\n \"def output_stats(self):\\n elapsed = self.timer.elapsed.total_seconds()\\n count = self.copied + self.errored\\n total = self.total\\n # Time per key in milliseconds\\n avg = round(elapsed / count * 1000, 3)\\n # Time remaining in seconds\\n remaining = 1.0 * elapsed / count * (total - count)\\n # Time remaining in minutes\\n remaining = round(remaining / 60.0, 1)\\n # Time taken in minutes\\n elapsed = round(elapsed / 60.0, 1)\\n\\n self.log.info(f\\\"{self.prefix}: {avg}ms avg, {elapsed}min passed, \\\"\\n f\\\"{remaining}min remaining. ({count:,}/{total:,})\\\")\",\n \"def test_avg_database_time(self):\\n time = timeit(\\n '''post(\\\"http://127.0.0.1:5000/database?format=json&merge=0\\\",\\\\\\n data='{\\\"RUR\\\": 1.0, \\\"EUR\\\": 2.0, \\\"USD\\\": 3.0}')''',\\n number=1000,\\n globals=globals())\\n print(time/1000, end=' ', flush=True)\",\n \"def monitor_transactions(account):\\n start_time = datetime.datetime.now()\\n logger.info(\\n format_log_message(\\n 'Looking for new ripple transactions since last run'\\n )\\n )\\n ledger_min_index = _get_min_ledger_index(account)\\n marker = None\\n has_results = True\\n\\n try:\\n timeout = settings.RIPPLE_TIMEOUT\\n except AttributeError:\\n timeout = 5\\n\\n while has_results:\\n try:\\n response = account_tx(account,\\n ledger_min_index,\\n limit=PROCESS_TRANSACTIONS_LIMIT,\\n marker=marker,\\n timeout=timeout)\\n except (RippleApiError, ConnectionError), e:\\n logger.error(format_log_message(e))\\n break\\n\\n transactions = response['transactions']\\n marker = response.get('marker')\\n has_results = bool(marker)\\n\\n for transaction in transactions:\\n _store_transaction(account, transaction)\\n\\n transactions_timeout_reached = (\\n datetime.datetime.now() - start_time >= datetime.timedelta(\\n seconds=PROCESS_TRANSACTIONS_TIMEOUT\\n )\\n )\\n\\n if transactions_timeout_reached and has_results:\\n has_results = False\\n logger.error(\\n 'Process_transactions command terminated because '\\n '(%s seconds) timeout: %s',\\n PROCESS_TRANSACTIONS_TIMEOUT, unicode(marker)\\n )\",\n \"def record_transaction(self) -> None:\\n Menu.prompt_record_transaction()\\n tx_data = Transaction.prompt_record_tx()\\n new_tx = Transaction.generate_new_tx(tx_data)\\n\\n # Convert the user budget category int input to the enum\\n budget_category_int = new_tx.budget_category\\n budget_category = BudgetManager.category_mapping[budget_category_int]\\n\\n # Retrieve the budget object using the enum as the key\\n budget = self.user.budget_manager.budget_dict[budget_category]\\n\\n # Validate the transaction before proceeding\\n validated_tx, error_msg = self.validate_transaction_record(new_tx,\\n budget)\\n if not validated_tx:\\n print(\\\"\\\\n[red]Warning:[/red] Unable to record transaction!\\\")\\n print(error_msg)\\n print(f\\\"{self.user.account}\\\\n\\\")\\n print(budget)\\n return\\n\\n # User has successfully recorded a transaction\\n budget.add_amount_spent(new_tx.tx_amount)\\n self.user.account.add_amount_spent(new_tx.tx_amount)\\n self.user.tx_manager.add_transaction(new_tx)\\n self.user.update_lock_status()\\n print(\\\"\\\\nSuccessfully recorded the following transaction:\\\")\\n print(new_tx)\\n print(\\\"\\\\nTransaction has been recorded under the following budget \\\"\\n \\\"category:\\\")\\n print(budget)\\n\\n self.user.check_and_issue_user_warnings(budget)\",\n \"def log_and_dispatch(self, state_manager, state_change):\\n state_change_id = self.raiden.transaction_log.log(state_change)\\n events = self.dispatch(state_manager, state_change)\\n self.raiden.transaction_log.log_events(\\n state_change_id,\\n events,\\n self.raiden.get_block_number()\\n )\",\n \"def log_results(self, filename=None):\\n\\n self.ad_log['train_auc'] = self.diag['train']['auc'][-1]\\n self.ad_log['train_accuracy'] = self.diag['train']['acc'][-1]\\n self.ad_log['train_time'] = self.train_time\\n\\n self.ad_log['test_auc'] = self.diag['test']['auc'][-1]\\n self.ad_log['test_accuracy'] = self.diag['test']['acc'][-1]\\n self.ad_log['test_time'] = self.test_time\\n\\n self.ad_log.save_to_file(filename=filename)\",\n \"def record_time_metrics(self, metrics):\\n\\n if not self.__settings:\\n return\\n\\n for metric in metrics:\\n self.record_time_metric(metric)\",\n \"def record_trace(self):\\n\\n tfname = str(int(time.time())) + \\\".obd2_reader.trace\\\"\\n self.tf_out = open(tfname, 'a')\\n self.RecordTrace = 1\\n print \\\"Recoding trace to:\\\", tfname\",\n \"def _log_progress(self, t):\\n\\n # Run the update only 2 step before the actual logging happens in order to\\n # make sure that the most recent possible values will be stored in\\n # self.summary. This is a hacky workaround in order to support OffPolicyAgent\\n # which runs 2 threads without coordination\\n if (t+2) % self.log_freq == 0 and self.learn_started:\\n episode_rewards = self.env_monitor.get_episode_rewards()\\n self.episode_rewards = np.asarray(episode_rewards)\\n if self.episode_rewards.size > 0:\\n self.mean_ep_rew = np.mean(episode_rewards[-self.stats_n:])\\n self.best_mean_ep_rew = max(self.best_mean_ep_rew, self.mean_ep_rew)\\n\\n if t % self.log_freq == 0 and self.learn_started:\\n stats_logger.info(\\\"\\\")\\n for s, lambda_v in self.log_info:\\n stats_logger.info(s.format(lambda_v(t)))\\n stats_logger.info(\\\"\\\")\\n\\n if self.summary:\\n # Log with TensorBoard\\n self.tb_writer.add_summary(self.summary, global_step=t)\",\n \"def _get_recordTtl(self):\\n return self.__recordTtl\",\n \"def get_post_stats(self):\\n stats = self.stats\\n stats.results = self.job.result().get_counts(stats.iteration)\\n stats.datetime = str(datetime.now())\",\n \"def _trace(self):\\n self.__aceQLHttpApi.trace()\",\n \"def report(self):\\n self.last_contacted = time.time()\",\n \"def report():\\n _get_cached_tracker().report()\",\n \"def flush_transaction(self):\\n self.flush_split()\\n if not self.transaction_cancelled:\\n if self.transaction_info.account is None and self.account_info and self.account_info.name:\\n self.transaction_info.account = self.account_info.name\\n if self.transaction_info.is_valid():\\n self.transaction_infos.append(self.transaction_info)\\n self.transaction_cancelled = False\\n self.transaction_info = TransactionInfo()\",\n \"async def test_transaction_specific_response_time_target(self):\\n self.set_source_parameter(\\\"transaction_specific_target_response_times\\\", [\\\"[Bb]ar:150\\\"])\\n response = await self.collect(get_request_json_return_value=self.GATLING_JSON)\\n self.assert_measurement(response, value=\\\"1\\\", entities=self.expected_entities[:1])\",\n \"def _record_result(self, action, data, tags=None):\\r\\n if tags is None:\\r\\n tags = []\\r\\n\\r\\n tags.append(u'result:{}'.format(data.get('success', False)))\\r\\n tags.append(u'action:{}'.format(action))\\r\\n dog_stats_api.increment(self._metric_name('request.count'), tags=tags)\",\n \"async def update_trade_stats(self):\\n\\n summary_keys = [base for base in config['min_base_volumes']] + ['global']\\n summaries = {\\n key: {\\n 'open_count': 0,\\n 'buys': 0,\\n 'rebuys': 0,\\n 'sells': 0,\\n 'collect_sells': 0,\\n 'soft_stop_sells': 0,\\n 'total_profit': 0.0,\\n 'total_loss': 0.0,\\n 'total_fees': 0.0,\\n 'balancer_refills': 0,\\n 'balancer_remits': 0,\\n 'balancer_stop_losses': 0,\\n 'balancer_profit': 0.0,\\n 'balancer_loss': 0.0,\\n 'balancer_fees': 0.0,\\n } for key in summary_keys\\n }\\n\\n for pair in self.trades:\\n if pair not in self.trade_stats[self.time_prefix]:\\n continue\\n\\n base = pair.split('-', 1)[0]\\n open_count = len(self.trades[pair]['open'])\\n\\n summaries[base]['open_count'] += open_count\\n summaries[base]['buys'] += self.trade_stats[self.time_prefix][pair]['buys']\\n summaries[base]['rebuys'] += self.trade_stats[self.time_prefix][pair]['rebuys']\\n summaries[base]['sells'] += self.trade_stats[self.time_prefix][pair]['sells']\\n summaries[base]['collect_sells'] += self.trade_stats[self.time_prefix][pair]['collect_sells']\\n summaries[base]['soft_stop_sells'] += self.trade_stats[self.time_prefix][pair]['soft_stop_sells']\\n summaries[base]['total_profit'] += self.trade_stats[self.time_prefix][pair]['total_profit']\\n summaries[base]['total_loss'] += self.trade_stats[self.time_prefix][pair]['total_loss']\\n summaries[base]['total_fees'] += self.trade_stats[self.time_prefix][pair]['total_fees']\\n summaries[base]['balancer_refills'] += self.trade_stats[self.time_prefix][pair]['balancer_refills']\\n summaries[base]['balancer_remits'] += self.trade_stats[self.time_prefix][pair]['balancer_remits']\\n summaries[base]['balancer_profit'] += self.trade_stats[self.time_prefix][pair]['balancer_profit']\\n summaries[base]['balancer_loss'] += self.trade_stats[self.time_prefix][pair]['balancer_loss']\\n summaries[base]['balancer_fees'] += self.trade_stats[self.time_prefix][pair]['balancer_fees']\\n\\n summaries['global']['open_count'] += open_count\\n summaries['global']['buys'] += self.trade_stats[self.time_prefix][pair]['buys']\\n summaries['global']['rebuys'] += self.trade_stats[self.time_prefix][pair]['rebuys']\\n summaries['global']['sells'] += self.trade_stats[self.time_prefix][pair]['sells']\\n summaries['global']['collect_sells'] += self.trade_stats[self.time_prefix][pair]['collect_sells']\\n summaries['global']['soft_stop_sells'] += self.trade_stats[self.time_prefix][pair]['soft_stop_sells']\\n summaries['global']['total_profit'] += self.trade_stats[self.time_prefix][pair]['total_profit']\\n summaries['global']['total_loss'] += self.trade_stats[self.time_prefix][pair]['total_loss']\\n summaries['global']['total_fees'] += self.trade_stats[self.time_prefix][pair]['total_fees']\\n summaries['global']['balancer_refills'] += self.trade_stats[self.time_prefix][pair]['balancer_refills']\\n summaries['global']['balancer_remits'] += self.trade_stats[self.time_prefix][pair]['balancer_remits']\\n summaries['global']['balancer_profit'] += self.trade_stats[self.time_prefix][pair]['balancer_profit']\\n summaries['global']['balancer_loss'] += self.trade_stats[self.time_prefix][pair]['balancer_loss']\\n summaries['global']['balancer_fees'] += self.trade_stats[self.time_prefix][pair]['balancer_fees']\\n\\n for key in summaries:\\n self.trade_stats[self.time_prefix][key]['buys'] = summaries[key]['buys']\\n self.trade_stats[self.time_prefix][key]['rebuys'] = summaries[key]['rebuys']\\n self.trade_stats[self.time_prefix][key]['sells'] = summaries[key]['sells']\\n self.trade_stats[self.time_prefix][key]['collect_sells'] = summaries[key]['collect_sells']\\n self.trade_stats[self.time_prefix][key]['soft_stop_sells'] = summaries[key]['soft_stop_sells']\\n self.trade_stats[self.time_prefix][key]['total_profit'] = summaries[key]['total_profit']\\n self.trade_stats[self.time_prefix][key]['total_loss'] = summaries[key]['total_loss']\\n self.trade_stats[self.time_prefix][key]['total_fees'] = summaries[key]['total_fees']\\n self.trade_stats[self.time_prefix][key]['balancer_refills'] = summaries[key]['balancer_refills']\\n self.trade_stats[self.time_prefix][key]['balancer_remits'] = summaries[key]['balancer_remits']\\n self.trade_stats[self.time_prefix][key]['balancer_profit'] = summaries[key]['balancer_profit']\\n self.trade_stats[self.time_prefix][key]['balancer_loss'] = summaries[key]['balancer_loss']\\n self.trade_stats[self.time_prefix][key]['balancer_fees'] = summaries[key]['balancer_fees']\\n\\n if summaries[key]['open_count'] > self.trade_stats[self.time_prefix][key]['most_open']:\\n self.trade_stats[self.time_prefix][key]['most_open'] = summaries[key]['open_count']\\n\\n filter_items = [pair for pair in self.trades] + [base for base in config['min_base_volumes']] + ['global']\\n self.save_attr('trade_stats', max_depth=2, filter_items=filter_items, filter_keys=[self.time_prefix])\",\n \"def set_cache_data(self) -> None:\\n if isinstance(self.tx_storage, TransactionCacheStorage):\\n hits = self.tx_storage.stats.get(\\\"hit\\\")\\n misses = self.tx_storage.stats.get(\\\"miss\\\")\\n if hits:\\n self.transaction_cache_hits = hits\\n if misses:\\n self.transaction_cache_misses = misses\",\n \"def report_total_usage(self):\\n work_time = 0\\n if self.type == 'normal':\\n work_time = self.fwk.fwk_global_time - self.start_exec_time\\n elif self.type == 'sandia_work':\\n self.total_time += self.fwk.fwk_global_time - self.start_exec_time\\n self.total_usage = self.total_time * self.nproc\\n if self.state == \\\"running\\\":\\n # update total work done\\n self.sim.completed_work += self.fwk.fwk_global_time - self.start_exec_time\\n elif self.state == \\\"failed\\\":\\n # add this work to the work to be redone\\n self.sim.rework_todo += self.fwk.fwk_global_time - self.start_exec_time\\n self.state = \\\"not_ready\\\"\\n self.num_faults += 1\\n elif self.type == 'sandia_rework':\\n self.total_rework_time += self.fwk.fwk_global_time - self.start_exec_time\\n self.total_rework_usage = self.total_rework_time * self.nproc\\n if self.state == \\\"running\\\":\\n # update total work done\\n self.sim.next_ckpt = self.sim.ckpt_interval - (self.fwk.fwk_global_time - self.start_exec_time)\\n self.sim.rework_todo -= self.fwk.fwk_global_time - self.start_exec_time\\n elif self.state == \\\"failed\\\":\\n # add this work to the work to be redone\\n self.state = \\\"not_ready\\\"\\n self.num_faults += 1\\n elif self.type == 'sandia_ckpt':\\n self.total_ckpt_time += self.fwk.fwk_global_time - self.start_exec_time\\n self.total_ckpt_usage = self.total_ckpt_time * self.nproc\\n if self.state == \\\"running\\\":\\n # update last ckpt\\n self.sim.last_ckpt = self.sim.completed_work\\n elif self.state == \\\"failed\\\":\\n # add work to rework\\n self.sim.rework_todo += self.sim.next_ckpt\\n self.state = \\\"not_ready\\\"\\n self.num_faults += 1\\n elif self.type == 'sandia_restart':\\n print(\\\"time spent in rework\\\", self.fwk.fwk_global_time - self.start_exec_time)\\n self.total_restart_time += self.fwk.fwk_global_time - self.start_exec_time\\n self.total_restart_usage = self.total_restart_time * self.nproc\\n #if self.state == \\\"running\\\":\\n # nothing to do?\\n # pass\\n if self.state == \\\"failed\\\":\\n # gotta try again\\n self.state = \\\"ready\\\"\\n self.num_faults += 1\\n else:\\n print(\\\"problems updating state in report_total_usage\\\")\\n raise\\n if self.type == 'normal':\\n if self.sim.state == 'rework':\\n self.total_rework_time += work_time\\n self.total_rework_usage = self.total_rework_time * self.nproc\\n else: # sim.state == 'work'\\n if self.retry:\\n self.total_rework_time += work_time\\n self.total_rework_usage = self.total_rework_time * self.nproc\\n else:\\n self.total_time += work_time\\n self.total_usage = self.total_time * self.nproc\",\n \"def record(self, time, increment):\\n raise NotImplementedError(\\\"Abstract method not implemented.\\\")\",\n \"def track_duration(self):\\n # raise NotImplementedError\\n self.out_schema.append(\\\"run_duration\\\")\\n self._track_duration = True\\n # self.runner = GridExecutor.timer(self.runner)\",\n \"def emit_metrics(self):\\n parse_time = time.perf_counter() - self._parsing_start_time\\n Stats.gauge(\\\"dag_processing.total_parse_time\\\", parse_time)\\n Stats.gauge(\\\"dagbag_size\\\", sum(stat.num_dags for stat in self._file_stats.values()))\\n Stats.gauge(\\n \\\"dag_processing.import_errors\\\", sum(stat.import_errors for stat in self._file_stats.values())\\n )\",\n \"def market_snap(self, timestamp = -1):\\n # refresh account states\\n self.trading_acc1.sync_account_with_exh()\\n self.trading_acc2.sync_account_with_exh()\\n\\n signal__arbitrage_delta = self.get_signal__arbitrage_delta()\\n signal__gdax_has_usd = self.get_signal__gdax_has_usd()\\n signal__gdax_has_eth = self.get_signal__gdax_has_eth()\\n signal__cex_has_eth = self.get_signal__cex_has_eth()\\n\\n def mk_audit_js():\\n gdax_account = self.trading_acc1.get_account()\\n cex_account = self.trading_acc2.get_account()\\n transaction_t = epoch.current_milli_time() if timestamp == -1 else timestamp\\n\\n audit_js = OrderedDict()\\n audit_js['strategy_run_id'] = self.run_id\\n audit_js['timestamp'] = epoch.to_str(transaction_t)\\n audit_js['timestamp__long'] = transaction_t\\n audit_js['ticker'] = self.ticker\\n audit_js['strategy_info'] = self._strategy_info\\n\\n audit_js['signal'] = OrderedDict()\\n audit_js['signal']['signal__gdax_has_usd'] = signal__gdax_has_usd\\n audit_js['signal']['signal__gdax_has_eth'] = signal__gdax_has_eth\\n audit_js['signal']['signal__cex_has_eth'] = signal__cex_has_eth\\n audit_js['signal']['signal__arbitrage_delta'] = signal__arbitrage_delta\\n\\n audit_js['total_usd__num'] = gdax_account.js['usd__num'] + cex_account.js['usd__num']\\n audit_js['total_eth__num'] = gdax_account.js['eth__num'] + cex_account.js['eth__num']\\n audit_js['gdax_account'] = gdax_account.js\\n audit_js['cex_account'] = cex_account.js\\n return audit_js\\n\\n snap_again = False # Only repeat if we have an gdax buy action\\n if signal__gdax_has_usd['signal'] and signal__arbitrage_delta['signal']:\\n exec_context = self.exec_gdax_buy(timestamp)\\n snap_again = True\\n\\n # Audit\\n audit_js = mk_audit_js()\\n audit_js['action'] = exec_context\\n audit = AuditTradeModel.build(audit_js)\\n logger.info('-----Executed GDAX Buy-----')\\n logger.info(audit)\\n logger.info('---------------------------')\\n audit.db_save(es)\\n\\n if signal__gdax_has_eth['signal']:\\n exec_context = self.exec_eth_transfer()\\n\\n # Audit\\n audit_js = mk_audit_js()\\n audit_js['action'] = exec_context\\n audit = AuditTradeModel.build(audit_js)\\n logger.info('-----Executed ETH TRANSFER-----')\\n logger.info(audit)\\n logger.info('---------------------------')\\n audit.db_save(es)\\n\\n if signal__cex_has_eth['signal']:\\n exec_context = self.exec_cex_sell(timestamp)\\n\\n # Audit\\n audit_js = mk_audit_js()\\n audit_js['action'] = exec_context\\n audit = AuditTradeModel.build(audit_js)\\n logger.info('-----Executed CEX Sell-----')\\n logger.info(audit)\\n logger.info('---------------------------')\\n audit.db_save(es)\\n\\n # Extra logging\\n audit_js = mk_audit_js()\\n logger.info('post-snapping states: \\\\n' + json.dumps(audit_js, indent=2))\\n\\n return snap_again\",\n \"def report(self, trade, is_entry):\\n pass\",\n \"def store_info(self):\\r\\n _debug('Protocol: store_info' ) \\r\\n \\r\\n #Times\\r\\n if self.measure_type == '3PL':\\r\\n self.t_probe_p_s .append(self.t_probe) \\r\\n self.t_probe_m_s .append(self.t_probe) \\r\\n if self.measure_type == '4PL':\\r\\n self.t_probe_p_s .append(self.tp) \\r\\n self.t_probe_m_s .append(self.tm) \\r\\n \\r\\n self.t_pulseSequences_s.append(self.t_pulseSequences)\\r\\n self.t_process_s .append(self.t_process)\\r\\n #Total, accumulated, times\\r\\n self.t_tot_pulseSequences_s.append(self.t_tot_pulseSequences) \\r\\n self.t_tot_process_s .append(self.t_tot_process) \\r\\n #Rates\\r\\n self.Gp_guess_s .append(self.Gp_guess) #Mean of gamma+ \\r\\n self.Gm_guess_s .append(self.Gm_guess) #Mean of gamma- \\r\\n self.eGp_guess_s .append(self.eGp_guess) #Uncertainty of gamma+\\r\\n self.eGm_guess_s .append(self.eGm_guess) #Uncertainty of gamma- \\r\\n self.cov_GpGm_s .append(self.cov_GpGm) #Covariance of gamma- & gamma- \\r\\n #Other\\r\\n self.nb_iteration_s.append(self.iter)\\r\\n self.R_tot_s .append(self.R_tot)\",\n \"def _store_transaction(account, transaction):\\n tr_tx = transaction['tx']\\n meta = transaction.get('meta', {})\\n\\n if meta.get('TransactionResult') != 'tesSUCCESS':\\n return\\n\\n amount = meta.get('delivered_amount') or tr_tx.get('Amount', {})\\n\\n is_unprocessed = (\\n tr_tx['TransactionType'] == 'Payment' and\\n tr_tx['Destination'] == account and\\n isinstance(amount, dict) and\\n not Transaction.objects.filter(hash=tr_tx['hash'])\\n )\\n if is_unprocessed:\\n logger.info(\\n format_log_message(\\n 'Saving transaction: %s', transaction\\n )\\n )\\n\\n transaction_object = Transaction.objects.create(\\n account=tr_tx['Account'],\\n hash=tr_tx['hash'],\\n destination=account,\\n ledger_index=tr_tx['ledger_index'],\\n destination_tag=tr_tx.get('DestinationTag'),\\n source_tag=tr_tx.get('SourceTag'),\\n status=Transaction.RECEIVED,\\n currency=amount['currency'],\\n issuer=amount['issuer'],\\n value=amount['value']\\n )\\n\\n logger.info(\\n format_log_message(\\n \\\"Transaction saved: %s\\\", transaction_object\\n )\\n )\",\n \"def tpc_finish(self, transaction):\\n raise NotImplementedError\",\n \"def time_metrics(self, stats, root, parent):\\n\\n product = self.product\\n operation = self.operation or 'other'\\n target = self.target\\n\\n # Determine the scoped metric\\n\\n statement_metric_name = 'Datastore/statement/%s/%s/%s' % (product,\\n target, operation)\\n\\n operation_metric_name = 'Datastore/operation/%s/%s' % (product,\\n operation)\\n\\n if target:\\n scoped_metric_name = statement_metric_name\\n else:\\n scoped_metric_name = operation_metric_name\\n\\n yield TimeMetric(name=scoped_metric_name, scope=root.path,\\n duration=self.duration, exclusive=self.exclusive)\\n\\n # Unscoped rollup metrics\\n\\n yield TimeMetric(name='Datastore/all', scope='',\\n duration=self.duration, exclusive=self.exclusive)\\n\\n yield TimeMetric(name='Datastore/%s/all' % product, scope='',\\n duration=self.duration, exclusive=self.exclusive)\\n\\n if root.type == 'WebTransaction':\\n yield TimeMetric(name='Datastore/allWeb', scope='',\\n duration=self.duration, exclusive=self.exclusive)\\n\\n yield TimeMetric(name='Datastore/%s/allWeb' % product, scope='',\\n duration=self.duration, exclusive=self.exclusive)\\n else:\\n yield TimeMetric(name='Datastore/allOther', scope='',\\n duration=self.duration, exclusive=self.exclusive)\\n\\n yield TimeMetric(name='Datastore/%s/allOther' % product, scope='',\\n duration=self.duration, exclusive=self.exclusive)\\n\\n # Unscoped operation metric\\n\\n yield TimeMetric(name=operation_metric_name, scope='',\\n duration=self.duration, exclusive=self.exclusive)\\n\\n # Unscoped statement metric\\n\\n if target:\\n yield TimeMetric(name=statement_metric_name, scope='',\\n duration=self.duration, exclusive=self.exclusive)\\n\\n # Unscoped instance Metric\\n\\n if self.instance_hostname and self.port_path_or_id:\\n\\n instance_metric_name = 'Datastore/instance/%s/%s/%s' % (product,\\n self.instance_hostname, self.port_path_or_id)\\n\\n yield TimeMetric(name=instance_metric_name, scope='',\\n duration=self.duration, exclusive=self.exclusive)\",\n \"def performance_history(self, request, pk=None, **kwargs):\\n # Get the goal even though we don't need it (we could just use the pk)\\n # so we can ensure we have permission to do so.\\n goal = self.get_object()\\n\\n # - Get all the transaction with this goal involved that are of reason 'Execution'.\\n # We want the volume, ticker id, date ordered by date. [(date, {ticker: vol}, ...]\\n qs = Transaction.objects.filter(Q(to_goal=goal) | Q(from_goal=goal),\\n reason=Transaction.REASON_EXECUTION).order_by('executed')\\n txs = qs.values_list('execution_distribution__execution__executed',\\n 'execution_distribution__execution__asset__id',\\n 'execution_distribution__volume')\\n ts = []\\n entry = (None,)\\n aids = set()\\n # If there were no transactions, there can be no performance\\n if len(txs) == 0:\\n return Response([])\\n\\n # Because executions are stored with timezone, but other things are just as date, we need to make datetimes\\n # naive before doing date arithmetic on them.\\n bd = timezone.make_naive(txs[0][0]).date()\\n ed = timezone.make_naive(timezone.now()).date()\\n for tx in txs:\\n aids.add(tx[1])\\n txd = timezone.make_naive(tx[0]).date()\\n if txd == entry[0]:\\n entry[1][tx[1]] += tx[2]\\n else:\\n if entry[0] is not None:\\n ts.append(entry)\\n entry = (txd, defaultdict(int))\\n entry[1][tx[1]] = tx[2]\\n ts.append(entry)\\n\\n # - Get the time-series of prices for each instrument from the first transaction date until now.\\n # Fill empty dates with previous value [(date, {ticker: price}, ...]\\n pqs = DailyPrice.objects.filter(date__range=(bd, ed),\\n instrument_content_type=ContentType.objects.get_for_model(Ticker).id,\\n instrument_object_id__in=aids)\\n prices = pqs.to_timeseries(fieldnames=['price', 'date', 'instrument_object_id'],\\n index='date',\\n storage='long',\\n pivot_columns='instrument_object_id',\\n values='price')\\n # Remove negative prices and fill missing values\\n # We replace negs with None so they are interpolated.\\n prices[prices <= 0] = None\\n prices = prices.reindex(pd.date_range(bd, ed), method='ffill').fillna(method='bfill')\\n\\n # For each day, calculate the performance\\n piter = prices.itertuples()\\n res = []\\n # Process the first day - it's special\\n row = next(piter)\\n p_m1 = row[1:]\\n vols_m1 = [0] * len(prices.columns)\\n tidlocs = {tid: ix for ix, tid in enumerate(prices.columns)}\\n for tid, vd in ts.pop(0)[1].items():\\n vols_m1[tidlocs[tid]] += vd\\n res.append((dt2ed(row[0]), 0)) # First day has no performance as there wasn't a move\\n # Process the rest\\n for row in piter:\\n # row[0] (a datetime) is a naive timestamp, so we don't need to convert it\\n if ts and row[0].date() == ts[0][0]:\\n vols = vols_m1.copy()\\n dtrans = ts.pop(0)[1] # The transactions for the current processed day.\\n for tid, vd in dtrans.items():\\n vols[tidlocs[tid]] += vd\\n # The exposed assets for the day. These are the assets we know for sure were exposed for the move.\\n pvol = list(map(min, vols, vols_m1))\\n else:\\n vols = vols_m1\\n pvol = vols\\n pdelta = list(map(operator.sub, row[1:], p_m1)) # The change in price from yesterday\\n impact = sum(map(operator.mul, pvol, pdelta)) # The total portfolio impact due to price moves for exposed assets.\\n b_m1 = sum(map(operator.mul, pvol, p_m1)) # The total portfolio value yesterday for the exposed assets.\\n perf = 0 if b_m1 == 0 else impact / b_m1\\n # row[0] (a datetime) is a naive timestamp, so we don't need to convert it\\n res.append((dt2ed(row[0]), decimal.Decimal.from_float(perf).quantize(decimal.Decimal('1.000000'))))\\n p_m1 = row[1:]\\n vols_m1 = vols[:]\\n\\n return Response(res)\",\n \"def __preprocess_transactions(self):\\n\\n p_bar = tqdm(range(14), desc=\\\"Preprocessing transactions\\\", leave=False)\\n\\n try:\\n # 0. If optional fields not in the transactions add missing\\n optional_fields = [\\n \\\"Sector\\\",\\n \\\"Industry\\\",\\n \\\"Country\\\",\\n \\\"Region\\\",\\n \\\"Fees\\\",\\n \\\"Premium\\\",\\n \\\"ISIN\\\",\\n ]\\n if not set(optional_fields).issubset(set(self.__transactions.columns)):\\n for field in optional_fields:\\n if field not in self.__transactions.columns:\\n self.__transactions[field] = np.nan\\n\\n p_bar.n += 1\\n p_bar.refresh()\\n\\n # 1. Convert Date to datetime\\n self.__transactions[\\\"Date\\\"] = pd.to_datetime(self.__transactions[\\\"Date\\\"])\\n\\n p_bar.n += 1\\n p_bar.refresh()\\n\\n # 2. Sort transactions by date\\n self.__transactions = self.__transactions.sort_values(by=\\\"Date\\\")\\n\\n p_bar.n += 1\\n p_bar.refresh()\\n\\n # 3. Capitalize Ticker and Type [of instrument...]\\n self.__transactions[\\\"Ticker\\\"] = self.__transactions[\\\"Ticker\\\"].map(\\n lambda x: x.upper() if isinstance(x, str) else x\\n )\\n self.__transactions[\\\"Type\\\"] = self.__transactions[\\\"Type\\\"].map(\\n lambda x: x.upper() if isinstance(x, str) else x\\n )\\n\\n p_bar.n += 1\\n p_bar.refresh()\\n\\n # 4. Translate side: [\\\"deposit\\\", \\\"buy\\\"] -> 1 and [\\\"withdrawal\\\", \\\"sell\\\"] -> -1\\n self.__transactions[\\\"Signal\\\"] = self.__transactions[\\\"Side\\\"].map(\\n lambda x: 1\\n if x.lower() in [\\\"deposit\\\", \\\"buy\\\"]\\n else (-1 if x.lower() in [\\\"withdrawal\\\", \\\"sell\\\"] else 0)\\n )\\n\\n p_bar.n += 1\\n p_bar.refresh()\\n\\n # 5. Convert quantity to signed integer\\n self.__transactions[\\\"Quantity\\\"] = (\\n abs(self.__transactions[\\\"Quantity\\\"]) * self.__transactions[\\\"Signal\\\"]\\n )\\n\\n # Adjust quantity and price for splits\\n for ticker in self.__transactions[\\\"Ticker\\\"].unique():\\n try:\\n splits_df = get_splits(ticker)\\n if not splits_df.empty:\\n splits_df = splits_df.tz_localize(tz=None)\\n for split_date in splits_df.index:\\n self.__transactions[\\\"Quantity\\\"] = np.where(\\n (self.__transactions[\\\"Ticker\\\"] == ticker)\\n & (self.__transactions[\\\"Date\\\"] < split_date),\\n self.__transactions[\\\"Quantity\\\"]\\n * splits_df.loc[split_date].values,\\n self.__transactions[\\\"Quantity\\\"],\\n )\\n self.__transactions[\\\"Price\\\"] = np.where(\\n (self.__transactions[\\\"Ticker\\\"] == ticker)\\n & (self.__transactions[\\\"Date\\\"] < split_date),\\n self.__transactions[\\\"Price\\\"]\\n / splits_df.loc[split_date].values,\\n self.__transactions[\\\"Price\\\"],\\n )\\n\\n except Exception:\\n console.print(\\\"\\\\nCould not get splits adjusted\\\")\\n\\n p_bar.n += 1\\n p_bar.refresh()\\n\\n # 6. Determining the investment/divestment value\\n self.__transactions[\\\"Investment\\\"] = (\\n self.__transactions[\\\"Quantity\\\"] * self.__transactions[\\\"Price\\\"]\\n + self.__transactions[\\\"Fees\\\"]\\n )\\n\\n p_bar.n += 1\\n p_bar.refresh()\\n\\n # 7. Reformat crypto tickers to yfinance format (e.g. BTC -> BTC-USD)\\n crypto_trades = self.__transactions[self.__transactions.Type == \\\"CRYPTO\\\"]\\n self.__transactions.loc[\\n (self.__transactions.Type == \\\"CRYPTO\\\"), \\\"Ticker\\\"\\n ] = [\\n f\\\"{crypto}-{currency}\\\"\\n for crypto, currency in zip(\\n crypto_trades.Ticker, crypto_trades.Currency\\n )\\n ]\\n\\n p_bar.n += 1\\n p_bar.refresh()\\n\\n # 8. Reformat STOCK/ETF tickers to yfinance format if ISIN provided.\\n\\n # If isin not valid ticker is empty\\n self.__transactions[\\\"yf_Ticker\\\"] = self.__transactions[\\\"ISIN\\\"].apply(\\n lambda x: yf.utils.get_ticker_by_isin(x) if not pd.isna(x) else np.nan\\n )\\n\\n empty_tickers = list(\\n self.__transactions[\\n (self.__transactions[\\\"yf_Ticker\\\"] == \\\"\\\")\\n | (self.__transactions[\\\"yf_Ticker\\\"].isna())\\n ][\\\"Ticker\\\"].unique()\\n )\\n\\n # If ticker from isin is empty it is not valid in yfinance, so check if user provided ticker is supported\\n removed_tickers = []\\n for item in empty_tickers:\\n with suppress_stdout():\\n # Suppress yfinance failed download message if occurs\\n valid_ticker = not (\\n yf.download(\\n item,\\n start=datetime.datetime.now() + datetime.timedelta(days=-5),\\n progress=False,\\n ).empty\\n )\\n if valid_ticker:\\n # Invalid ISIN but valid ticker\\n self.__transactions.loc[\\n self.__transactions[\\\"Ticker\\\"] == item, \\\"yf_Ticker\\\"\\n ] = np.nan\\n else:\\n self.__transactions.loc[\\n self.__transactions[\\\"Ticker\\\"] == item, \\\"yf_Ticker\\\"\\n ] = \\\"\\\"\\n removed_tickers.append(item)\\n\\n # Merge reformatted tickers into Ticker\\n self.__transactions[\\\"Ticker\\\"] = self.__transactions[\\\"yf_Ticker\\\"].fillna(\\n self.__transactions[\\\"Ticker\\\"]\\n )\\n\\n p_bar.n += 1\\n p_bar.refresh()\\n\\n # 9. Remove unsupported ISINs that came out empty\\n self.__transactions.drop(\\n self.__transactions[self.__transactions[\\\"Ticker\\\"] == \\\"\\\"].index,\\n inplace=True,\\n )\\n\\n p_bar.n += 1\\n p_bar.refresh()\\n\\n # 10. Create tickers dictionary with structure {'Type': [Ticker]}\\n unsupported_type = self.__transactions[\\n (~self.__transactions[\\\"Type\\\"].isin([\\\"STOCK\\\", \\\"ETF\\\", \\\"CRYPTO\\\"]))\\n ].index\\n if unsupported_type.any():\\n self.__transactions.drop(unsupported_type, inplace=True)\\n console.print(\\n \\\"[red]Unsupported transaction type detected and removed. Supported types: stock, etf or crypto.[/red]\\\"\\n )\\n\\n for ticker_type in set(self.__transactions[\\\"Type\\\"]):\\n self.tickers[ticker_type] = list(\\n set(\\n self.__transactions[\\n self.__transactions[\\\"Type\\\"].isin([ticker_type])\\n ][\\\"Ticker\\\"]\\n )\\n )\\n\\n p_bar.n += 1\\n p_bar.refresh()\\n\\n # 11. Create list with tickers except cash\\n self.tickers_list = list(set(self.__transactions[\\\"Ticker\\\"]))\\n\\n p_bar.n += 1\\n p_bar.refresh()\\n\\n # 12. Save transactions inception date\\n self.inception_date = self.__transactions[\\\"Date\\\"].iloc[0]\\n\\n p_bar.n += 1\\n p_bar.refresh()\\n\\n # 13. Populate fields Sector, Industry and Country\\n if (\\n self.__transactions.loc[\\n self.__transactions[\\\"Type\\\"] == \\\"STOCK\\\",\\n optional_fields,\\n ]\\n .isnull()\\n .values.any()\\n ):\\n # If any fields is empty for stocks (overwrites any info there)\\n self.__load_company_data()\\n\\n p_bar.n += 1\\n p_bar.refresh()\\n\\n # Warn user of removed ISINs\\n if removed_tickers:\\n p_bar.disable = True\\n console.print(\\n f\\\"\\\\n[red]The following tickers are not supported and were removed: {removed_tickers}.\\\"\\n f\\\"\\\\nManually edit the 'Ticker' field with the proper Yahoo Finance suffix or provide a valid ISIN.\\\"\\n f\\\"\\\\nSuffix info on 'Yahoo Finance market coverage':\\\"\\n \\\" https://help.yahoo.com/kb/exchanges-data-providers-yahoo-finance-sln2310.html\\\"\\n f\\\"\\\\nE.g. IWDA -> IWDA.AS[/red]\\\\n\\\"\\n )\\n except Exception:\\n console.print(\\\"\\\\nCould not preprocess transactions.\\\")\\n raise\",\n \"def track_transaction(self, transaction, items):\\n trans = Transaction()\\n trans.order_id = transaction.get('order_id', None)\\n trans.total = transaction.get('total', None)\\n trans.tax = transaction.get('tax', None)\\n trans.affiliation = transaction.get('affiliation', None)\\n trans.shipping = transaction.get('shipping', None)\\n trans.city = transaction.get('city', None)\\n trans.state = transaction.get('state', None)\\n trans.country = transaction.get('country', None)\\n\\n for item in items:\\n gitem = gaItem()\\n gitem.sku = item.get('sku', None)\\n gitem.name = item.get('name', None)\\n gitem.variation = item.get('variation', None)\\n gitem.price = item.get('price', None)\\n gitem.quantity = item.get('quantity', 1)\\n trans.add_item(gitem)\\n\\n self.ga_tracker.track_transaction(transaction=trans,session=self.ga_session,visitor=self.ga_visitor)\",\n \"def internal_event (self):\\n self.clock_time += 1\\n self.log()\",\n \"def _analyze(self):\\r\\n if self.value is None or self.value == self.previous:\\r\\n pass\\r\\n elif self._operation == \\\"add\\\":\\r\\n self._additions = self.value\\r\\n elif self._operation == \\\"remove\\\":\\r\\n self._removals = self.value\\r\\n elif self.previous is None:\\r\\n self._assignments = self.value\\r\\n else:\\r\\n # partial update time\\r\\n self._additions = (self.value - self.previous) or None\\r\\n self._removals = (self.previous - self.value) or None\\r\\n self._analyzed = True\",\n \"def track_tick_processing_performance(self, duration):\\n\\n self.parse_count += 1\\n self.total_parse_time += duration\\n self.mean_parse_time = self.total_parse_time / self.parse_count\",\n \"def start_transaction(self) -> None:\\n pass\",\n \"def log_all(self):\\n self.save_raw()\\n self.log()\",\n \"def test_peformance(self):\\n timedeltas = []\\n for file in os.listdir(settings.ANALYSIS_REPORT_FOLDER):\\n _file = open(os.path.join(settings.ANALYSIS_REPORT_FOLDER, file), \\\"r\\\")\\n report = json.loads(_file.read())\\n timedeltas.append(\\n parse_datetime(report['finish']) - parse_datetime(report['start']))\\n _file.close()\\n\\n # number of queue\\n print('NUMBER OF QUEUE = {}'.format(len(timedeltas)))\\n\\n # get average time\\n average_timedelta = sum(timedeltas, datetime.timedelta(0)) / len(timedeltas)\\n print('AVERAGE = {}'.format(average_timedelta))\\n self.assertTrue(average_timedelta < datetime.timedelta(minutes=3))\\n\\n # get total process time\\n total = timedeltas[0]\\n for delta in timedeltas[:1]:\\n total += delta\\n print('TOTAL = {}'.format(total))\\n self.assertTrue(total < datetime.timedelta(minutes=3 * len(timedeltas)))\",\n \"def test_timeout_processing(self):\\n # setup\\n self.transaction_behaviour.processing_time = None\\n\\n # operation\\n self.transaction_behaviour._timeout_processing()\\n\\n # after\\n self.assert_quantity_in_outbox(0)\",\n \"def _save_internal_transactions(self, blocks_traces):\\n docs = [\\n self._preprocess_internal_transaction(transaction)\\n for transaction in blocks_traces\\n if transaction[\\\"transactionHash\\\"]\\n ]\\n if docs:\\n for chunk in bulk_chunks(docs, None, BYTES_PER_CHUNK):\\n self.client.bulk_index(docs=chunk, index=self.indices[\\\"internal_transaction\\\"], doc_type=\\\"itx\\\",\\n id_field=\\\"hash\\\", refresh=True)\",\n \"def test_log_last_completed_datetime(self):\\n initial_count = CostUsageReportStatus.objects.count()\\n saver = ReportStatsDBAccessor(\\\"myreport\\\", self.manifest_id)\\n saver.log_last_completed_datetime()\\n self.assertIsNotNone(saver.get_last_completed_datetime())\\n saver.delete()\\n self.assertEqual(CostUsageReportStatus.objects.count(), initial_count)\",\n \"def record_failure(self, now=None) -> None:\\n logging.info('Recording failure at %r', now or int(time.time()))\\n self.failure_timestamp = now or int(time.time())\\n self.put()\",\n \"def transaction(fn):\\n @wraps(fn)\\n def transaction_inner(*args, **kwargs): #1\\n start = time()\\n stmp_id = id_gen()\\n session = operation.session\\n sessionid = id(session)\\n \\n # set distributed transaction id to 0 for single transaction\\n try:\\n operation.id\\n except: \\n operation.id = str(uuid4())\\n \\n try:\\n # get runtime info\\n cp = current_process()\\n ct = current_thread() \\n \\n # format request params\\n params = []\\n for item in args:\\n params.append(unicode(item))\\n for k,v in kwargs.iteritems():\\n params.append(u\\\"'%s':'%s'\\\" % (k, v))\\n \\n # call internal function\\n res = fn(*args, **kwargs)\\n \\n session.commit()\\n elapsed = round(time() - start, 4)\\n logger.debug(u'%s.%s - %s - transaction - %s - %s - OK - %s' % (\\n operation.id, stmp_id, sessionid, fn.__name__, \\n params, elapsed))\\n \\n return res\\n except ModelError as ex:\\n elapsed = round(time() - start, 4)\\n logger.error(u'%s.%s - %s - transaction - %s - %s - KO - %s' % (\\n operation.id, stmp_id, sessionid, fn.__name__, \\n params, elapsed))\\n if ex.code not in [409]:\\n #logger.error(ex.desc, exc_info=1)\\n logger.error(ex.desc)\\n \\n session.rollback()\\n raise TransactionError(ex.desc, code=ex.code)\\n except IntegrityError as ex:\\n elapsed = round(time() - start, 4)\\n logger.error(u'%s.%s - %s - transaction - %s - %s - KO - %s' % (\\n operation.id, stmp_id, sessionid, fn.__name__, \\n params, elapsed))\\n logger.error(ex.orig, exc_info=1)\\n logger.error(ex.orig)\\n\\n session.rollback()\\n raise TransactionError(ex.orig)\\n except DBAPIError as ex:\\n elapsed = round(time() - start, 4)\\n logger.error(u'%s.%s - %s - transaction - %s - %s - KO - %s' % (\\n operation.id, stmp_id, sessionid, fn.__name__, \\n params, elapsed))\\n #logger.error(ex.orig, exc_info=1)\\n logger.error(ex.orig)\\n \\n session.rollback()\\n raise TransactionError(ex.orig)\\n \\n except Exception as ex:\\n elapsed = round(time() - start, 4)\\n logger.error(u'%s.%s - %s - transaction - %s - %s - KO - %s' % (\\n operation.id, stmp_id, sessionid, fn.__name__, \\n params, elapsed))\\n #logger.error(ex, exc_info=1)\\n logger.error(ex)\\n \\n session.rollback()\\n raise TransactionError(ex)\\n\\n return transaction_inner\",\n \"def report_store(analysis_store, helpers, timestamp_yesterday):\\n case = analysis_store.get_cases()[0]\\n helpers.add_analysis(\\n analysis_store, case, pipeline=Pipeline.MIP_DNA, started_at=timestamp_yesterday\\n )\\n helpers.add_analysis(analysis_store, case, pipeline=Pipeline.MIP_DNA, started_at=datetime.now())\\n # Mock sample dates to calculate processing times\\n for family_sample in analysis_store.get_case_samples_by_case_id(\\n case_internal_id=case.internal_id\\n ):\\n family_sample.sample.ordered_at = timestamp_yesterday - timedelta(days=2)\\n family_sample.sample.received_at = timestamp_yesterday - timedelta(days=1)\\n family_sample.sample.prepared_at = timestamp_yesterday\\n family_sample.sample.sequenced_at = timestamp_yesterday\\n family_sample.sample.delivered_at = datetime.now()\\n return analysis_store\",\n \"def transaction(self, context: InjectionContext = None) -> \\\"ProfileSession\\\":\",\n \"def progress(transactionId, function):\\n _log.info(f\\\"{transactionId.hex} PROGRESS {function}\\\")\",\n \"def log_metrics_for_packet(self, packet: \\\"Packet\\\") -> None:\\n client_timestamp = packet.client_timestamp\\n router_incoming_timestamp = packet.router_incoming_timestamp\\n router_outgoing_timestamp = packet.router_outgoing_timestamp\\n server_timestamp = packet.server_timestamp\\n response_timestamp = time.time()\\n if router_outgoing_timestamp is None:\\n router_outgoing_timestamp = server_timestamp\\n if router_incoming_timestamp is None:\\n router_incoming_timestamp = router_outgoing_timestamp\\n if client_timestamp is None:\\n client_timestamp = router_incoming_timestamp\\n client_to_router = max(0, router_incoming_timestamp - client_timestamp)\\n router_processing = max(0, router_outgoing_timestamp - router_incoming_timestamp)\\n router_to_server = max(0, server_timestamp - router_outgoing_timestamp)\\n server_processing = max(0, response_timestamp - server_timestamp)\\n e2e_time = max(0, response_timestamp - client_timestamp)\\n E2E_PACKET_LATENCY.labels(packet_type=packet.type, stage=\\\"client_to_router\\\").observe(\\n client_to_router\\n )\\n E2E_PACKET_LATENCY.labels(packet_type=packet.type, stage=\\\"router_processing\\\").observe(\\n router_processing\\n )\\n E2E_PACKET_LATENCY.labels(packet_type=packet.type, stage=\\\"router_to_server\\\").observe(\\n router_to_server\\n )\\n E2E_PACKET_LATENCY.labels(packet_type=packet.type, stage=\\\"server_processing\\\").observe(\\n server_processing\\n )\\n E2E_PACKET_LATENCY.labels(packet_type=packet.type, stage=\\\"e2e_time\\\").observe(e2e_time)\",\n \"def internal_event(self):\\n # log activity\\n self.log_activity(LogEntry(\\n sys_time=time(),\\n logical_time=self.logical_clock,\\n action=\\\"work\\\"\\n ))\",\n \"def report_performance(self):\\n performance = self.amygdala.visualize(self.timestep, \\n self.name, \\n self.log_dir)\\n print('Final performance is {0:.3}'.format(performance))\\n self.backup()\\n return performance\",\n \"async def test_ignore_transaction(self):\\n self.set_source_parameter(\\\"transactions_to_ignore\\\", [self.API2])\\n response = await self.collect(get_request_json_return_value=self.JMETER_JSON)\\n self.assert_measurement(response, value=\\\"123\\\", entities=[])\",\n \"def rest_api_log(self):\\n with self.resource_lock:\\n pass\",\n \"def report_trial(self):\\n pass\",\n \"def monitor_usage(records_output_csv, total_time, delay_time):\\n total_time *= 60.0 # convert to seconds\\n get_usage(total_time, delay_time, records_output_csv)\",\n \"def _log_performance_metrics(\\n self, task: \\\"tasks.ClassyTask\\\", local_variables: Dict[str, Any]\\n ) -> None:\\n phase_type = task.phase_type\\n batches = len(task.losses)\\n\\n if self.start_time is None:\\n logging.warning(\\\"start_time not initialized\\\")\\n else:\\n # Average batch time calculation\\n total_batch_time = time.time() - self.start_time\\n average_batch_time = total_batch_time / batches\\n logging.info(\\n \\\"Average %s batch time (ms) for %d batches: %d\\\"\\n % (phase_type, batches, 1000.0 * average_batch_time)\\n )\\n\\n # Train step time breakdown\\n if local_variables.get(\\\"perf_stats\\\") is None:\\n logging.warning('\\\"perf_stats\\\" not set in local_variables')\\n elif task.train:\\n logging.info(\\n \\\"Train step time breakdown (rank {}):\\\\n{}\\\".format(\\n get_rank(), local_variables[\\\"perf_stats\\\"].report_str()\\n )\\n )\",\n \"async def _record_logs(self, report):\\n\\t\\tif report.action == Frame.Report.PARSE:\\n\\t\\t\\t# Collects the tests parsing log for further writing to Test_Parser.log\\n\\t\\t\\tif report.success:\\n\\t\\t\\t\\tself._parse_logs[\\\"success\\\"] += [report.log]\\n\\t\\t\\telse:\\n\\t\\t\\t\\tself._parse_logs[\\\"failure\\\"] += [report.log]\\n\\t\\telif report.action == Frame.Report.EXECUTE:\\n\\t\\t\\t# Writes a test log and dump to the results directory\\n\\t\\t\\ttest_log = (\\\"EXECUTE STATUS: SUCCESS\\\\n\\\\n\\\" if report.success else \\\"EXECUTE STATUS: FAILURE\\\\n\\\\n\\\") + report.log\\n\\t\\t\\tfor task in as_completed([self._event_loop.run_in_executor(self._thread_executor, FileSystem.dump_to, \\n\\t\\t\\t\\t self._result_directory_name + \\\"/Log/\\\" + report.test_name + \\\".log\\\", test_log)]):\\n\\t\\t\\t\\tawait task\\n\\t\\t\\tfor task in as_completed([self._event_loop.run_in_executor(self._thread_executor, TestLogger._write_test_dump, \\n\\t\\t\\t\\t self._result_directory_name + \\\"/Dump/\\\" + report.test_name + \\\".pcap\\\", report.dump)]):\\n\\t\\t\\t\\tawait task\",\n \"def perform_patient_audit(self):\\n \\n # Continuous audit\\n while True:\\n \\n # General patient audit (put audit results in dictionary and add to audit DataFrame)\\n audit = dict()\\n audit['day'] = self._env.now\\n audit['negative'] = len(self._pop.negative_patients)\\n audit['positive'] = len(self._pop.positive_patients)\\n audit['recovered'] = len(self._pop.recovered_patients)\\n audit['inpatient'] = len(self._pop.inpatients)\\n audit['died'] = len(self._pop.died_patients)\\n audit['total'] = len(self._pop.patients)\\n audit['unallocated'] = len(self._pop.unallocated_patients)\\n # Add dictionary to existing DataFrame\\n self.patient_audit = \\\\\\n self.patient_audit.append(audit, ignore_index=True)\\n \\n # Displaced patient audit (put audit results in dictionary and add to audit DataFrame)\\n audit = dict()\\n # Get displaced times\\n additional_time = []\\n for patient in self._pop.displaced_patients:\\n additional_time.append(patient.displaced_additional_time)\\n audit['day'] = self._env.now\\n audit['number'] = len(self._pop.displaced_patients)\\n if len(additional_time) > 0:\\n # Dispalced patients exist, calculate statistics\\n audit['add_time_min'] = np.min(additional_time)\\n audit['add_time_1Q'] = np.quantile(additional_time, 0.25)\\n audit['add_time_median'] = np.quantile(additional_time, 0.50)\\n audit['add_time_3Q'] = np.quantile(additional_time, 0.75)\\n audit['add_time_max'] = np.max(additional_time)\\n audit['add_time_total'] = np.sum(additional_time)\\n else:\\n # No displaced patients exist, set all statistics to zero\\n audit['add_time_min'] = 0\\n audit['add_time_1Q'] = 0\\n audit['add_time_median'] = 0\\n audit['add_time_3Q'] = 0\\n audit['add_time_max'] = 0\\n audit['add_time_total'] = 0\\n # Add dictionary to existing DataFrame\\n self.displaced_audit = \\\\\\n self.displaced_audit.append(audit, ignore_index=True)\\n \\n # Trigger next audit after interval\\n yield self._env.timeout(self._params.audit_interval)\",\n \"def _merge_report(self, target, new):\\r\\n time = None\\r\\n if 'ts' in new['parsed']:\\r\\n time = new['parsed']['ts']\\r\\n\\r\\n if (target.get('lastSeenDate', None) and\\r\\n time and\\r\\n target['lastSeenDate'] < time):\\r\\n target['lastSeenDate'] = time\\r\\n\\r\\n query_millis = int(new['parsed']['stats']['millis'])\\r\\n target['stats']['totalTimeMillis'] += query_millis\\r\\n target['stats']['count'] += 1\\r\\n target['stats']['avgTimeMillis'] = target['stats']['totalTimeMillis'] / target['stats']['count']\",\n \"def _preprocess_internal_transaction(self, transaction):\\n transaction = transaction.copy()\\n for field in [\\\"action\\\", \\\"result\\\"]:\\n if (field in transaction.keys()) and (transaction[field]):\\n transaction.update(transaction[field])\\n del transaction[field]\\n for field in [\\\"value\\\", \\\"gasPrice\\\", \\\"gasUsed\\\"]:\\n if (field in transaction.keys()) and (transaction[field]):\\n value_string = transaction[field][0:2] + \\\"0\\\" + transaction[field][2:]\\n transaction[field] = int(value_string, 0) / 1e18\\n if \\\"gasUsed\\\" in transaction:\\n transaction[\\\"gasUsed\\\"] = int(transaction[\\\"gasUsed\\\"] * 1e18)\\n return transaction\",\n \"def _assess(self):\\n # get eruptions\\n with open(os.sep.join(getfile(currentframe()).split(os.sep)[:-2]+['data','eruptive_periods.txt']),'r') as fp:\\n self.tes = [datetimeify(ln.rstrip()) for ln in fp.readlines()]\\n # check if data file exists\\n self.exists = os.path.isfile(self.file)\\n if not self.exists:\\n t0 = datetime(2011,1,1)\\n t1 = datetime(2011,1,2)\\n self.update(t0,t1)\\n # check date of latest data in file\\n self.df = pd.read_csv(self.file, index_col=0, parse_dates=[0,], infer_datetime_format=True)\\n self.ti = self.df.index[0]\\n self.tf = self.df.index[-1]\",\n \"def record_time_metric(self, metric):\\n\\n if not self.__settings:\\n return\\n\\n # Scope is forced to be empty string if None as\\n # scope of None is reserved for apdex metrics.\\n\\n key = (metric.name, metric.scope or '')\\n stats = self.__stats_table.get(key)\\n if stats is None:\\n stats = TimeStats(call_count=1,\\n total_call_time=metric.duration,\\n total_exclusive_call_time=metric.exclusive,\\n min_call_time=metric.duration,\\n max_call_time=metric.duration,\\n sum_of_squares=metric.duration ** 2)\\n self.__stats_table[key] = stats\\n else:\\n stats.merge_time_metric(metric)\\n\\n return key\",\n \"def CallLoggedAndAccounted(f):\\n\\n @functools.wraps(f)\\n def Decorator(*args, **kwargs):\\n try:\\n start_time = time.time()\\n result = f(*args, **kwargs)\\n latency = time.time() - start_time\\n\\n DB_REQUEST_LATENCY.RecordEvent(latency, fields=[f.__name__])\\n logging.debug(\\\"DB request %s SUCCESS (%.3fs)\\\", f.__name__, latency)\\n\\n return result\\n except db.Error as e:\\n DB_REQUEST_ERRORS.Increment(fields=[f.__name__, \\\"grr\\\"])\\n logging.debug(\\\"DB request %s GRR ERROR: %s\\\", f.__name__,\\n utils.SmartUnicode(e))\\n raise\\n except Exception as e:\\n DB_REQUEST_ERRORS.Increment(fields=[f.__name__, \\\"db\\\"])\\n logging.debug(\\\"DB request %s INTERNAL DB ERROR : %s\\\", f.__name__,\\n utils.SmartUnicode(e))\\n raise\\n\\n return Decorator\",\n \"def trace(self, trace=...):\\n ...\",\n \"def __init__(self):\\n self._profiling_mode = False\\n self._total_time_ms = 0.0\\n self._traced_records = []\\n self._statistical_results = {}\",\n \"def _merge_report(self, target, new):\\n time = None\\n if 'ts' in new['parsed']:\\n time = new['parsed']['ts']\\n\\n if (target.get('lastSeenDate', None) and\\n time and\\n target['lastSeenDate'] < time):\\n target['lastSeenDate'] = time\\n\\n query_millis = int(new['parsed']['stats']['millis'])\\n target['stats']['totalTimeMillis'] += query_millis\\n target['stats']['count'] += 1\\n target['stats']['avgTimeMillis'] = target['stats']['totalTimeMillis'] / target['stats']['count']\",\n \"def emit(self, record):\\n self.eng.dispose()\\n trys = 3\\n info = {key: value for key, value in record.__dict__.items() if not key.startswith(\\\"__\\\")}\\n for t in range(trys):\\n # Having what i think is an issue to reflect so try a couple times and don't complain if breaks\\n failed = False\\n try:\\n with self.eng.connect() as con:\\n # formating values to convert from python or parsl to db standards\\n info['task_fail_history'] = str(info.get('task_fail_history', None))\\n info['timestamp'] = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(record.created))\\n run_id = info['run_id']\\n\\n # if workflow or task has completed, update their entries with the time.\\n if 'time_completed' in info.keys() and info['time_completed'] != 'None':\\n workflows = self.meta.tables['workflows']\\n up = workflows.update().values(time_completed=info['time_completed']).where(workflows.c.run_id == run_id)\\n con.execute(up)\\n return\\n if 'task_time_returned' in info.keys() and info['task_time_returned'] is not None:\\n workflow = self.meta.tables['task']\\n up = workflow.update().values(task_time_returned=info['task_time_returned']).where(workflow.c.task_id == info['task_id'])\\\\\\n .where(workflow.c.run_id == run_id)\\n con.execute(up)\\n\\n # create workflows table if this is a new database without one\\n if 'workflows' not in self.meta.tables.keys():\\n workflows = create_workflows_table(self.meta)\\n self.meta.create_all(con)\\n # if this is the first sight of the workflow, add it to the workflows table\\n if len(con.execute(self.meta.tables['workflows'].select(self.meta.tables['workflows'].c.run_id == run_id)).fetchall()) == 0:\\n workflows = self.meta.tables['workflows']\\n ins = workflows.insert().values(**{k: v for k, v in info.items() if k in workflows.c})\\n con.execute(ins)\\n\\n # if log has task counts, update the workflow entry in the workflows table\\n if 'tasks_completed_count' in info.keys():\\n workflows = self.meta.tables['workflows']\\n up = workflows.update().values(tasks_completed_count=info['tasks_completed_count']).where(workflows.c.run_id == run_id)\\n con.execute(up)\\n if 'tasks_failed_count' in info.keys():\\n workflows = self.meta.tables['workflows']\\n up = workflows.update().values(tasks_failed_count=info['tasks_failed_count']).where(workflows.c.run_id == run_id)\\n con.execute(up)\\n\\n # create task table if this is a new run without one\\n if 'task' not in self.meta.tables.keys():\\n workflow = create_task_table(self.meta)\\n self.meta.create_all(con)\\n\\n # check to make sure it is a task log and not just a workflow overview log\\n if info.get('task_id', None) is not None:\\n # if this is the first sight of the task in the workflow, add it to the workflow table\\n if len(con.execute(self.meta.tables['task'].select(self.meta.tables['task'].c.task_id == info['task_id'])\\n .where(self.meta.tables['task'].c.run_id == run_id)).fetchall()) == 0:\\n if 'psutil_process_pid' in info.keys():\\n # this is the race condition that a resource log is before a status log so ignore this resource update\\n return\\n\\n workflow = self.meta.tables['task']\\n ins = workflow.insert().values(**{k: v for k, v in info.items() if k in workflow.c})\\n con.execute(ins)\\n\\n if 'task_status' in info.keys():\\n # if this is the first sight of a task, create a task_status_table to hold this task's updates\\n if 'task_status' not in self.meta.tables.keys():\\n task_status_table = create_task_status_table(self.meta)\\n self.meta.create_all(con)\\n con.execute(task_status_table.insert().values(**{k: v for k, v in info.items() if k in task_status_table.c}))\\n # if this status table already exists, just insert the update\\n else:\\n task_status_table = self.meta.tables['task_status']\\n con.execute(task_status_table.insert().values(**{k: v for k, v in info.items() if k in task_status_table.c}))\\n return\\n\\n if 'psutil_process_pid' in info.keys():\\n # if this is a task resource update then handle that, if the resource table DNE then create it\\n if 'task_resources' not in self.meta.tables.keys():\\n task_resource_table = create_task_resource_table(self.meta)\\n self.meta.create_all(con)\\n con.execute(task_resource_table.insert().values(**{k: v for k, v in info.items() if k in task_resource_table.c}))\\n # if this resource table already exists, just insert the update\\n else:\\n task_resource_table = self.meta.tables['task_resources']\\n con.execute(task_resource_table.insert().values(**{k: v for k, v in info.items() if k in task_resource_table.c}))\\n return\\n\\n except Exception as e:\\n logger.error(\\\"Try a couple times since some known issues can occur. Number of Failures: {} Error: {}\\\".format(t, str(e)))\\n failed = True\\n time.sleep(5)\\n if not failed:\\n return\",\n \"def test_upsert_metadata_for_rate_plan(self):\\n pass\",\n \"def do_timestamp_messages(self, messages):\\n timestamp = self.env.now\\n self.reception_records[timestamp] = messages\\n log.debug(\\\"{} recorded {}\\\".format(self, self.reception_records))\",\n \"def get_gdax_historical_data():\\n \\n start = None\\n while not start:\\n start,end,tid = getStartAndEndHistoric()\\n if not start:\\n time.sleep(60)\\n #Todo: change this to 1min\\n firsttimestamp = start\\n engine = sa.create_engine(sql_address)\\n products = [\\\"LTC-USD\\\",\\\"LTC-BTC\\\",\\\"ETH-USD\\\",\\\"ETH-BTC\\\",\\\"BTC-USD\\\"]\\n public_client = gdax.PublicClient()\\n deltat = datetime.timedelta(seconds = 200)\\n timewindows = []\\n while end - start > datetime.timedelta(seconds=0):\\n if start + deltat > end:\\n endx = end\\n else:\\n endx = start + deltat\\n timewindows.append([start,endx])\\n start += deltat\\n results = []\\n total = len(timewindows)\\n current_idx = 0\\n timeold = time.time()\\n numofqueries = 0\\n engine = sa.create_engine(sql_address)\\n Base.metadata.bind = engine\\n DBSession = sa.orm.sessionmaker()\\n DBSession.bind = engine\\n session = DBSession()\\n for startx,endx in timewindows:\\n\\n current_idx += 1\\n for i in products:\\n repeat = True\\n while repeat:\\n\\n #delay if ratelimts are close\\n if numofqueries < 3:\\n while time.time() - timeold < 1:\\n time.sleep(0.05)\\n \\n timeold = time.time()\\n numofqueries = 0\\n try:\\n alist = public_client.get_product_historic_rates(i, start = startx, end = endx, granularity = 1)\\n except:\\n time.sleep(30)\\n public_client = gdax.PublicClient()\\n alist = public_client.get_product_historic_rates(i, start = startx, end = endx, granularity = 1)\\n\\n alist = public_client.get_product_historic_rates(i, start = startx, end = endx, granularity = 1)\\n\\n numofqueries += 1\\n\\n #rate limit exceeded has 'message' as dict.\\n if not 'message' in alist:\\n repeat = False\\n for a in alist:\\n a[0] = datetime.datetime.fromtimestamp(float(a[0]))\\n tmp = i.split('-')\\n d = dict(coin = tmp[0], currency = tmp[1], timestamp = a[0], low=a[1], high=a[2], open=a[3], close=a[4], volume=a[5])\\n results.append(d)\\n lasttimestamp = a[0]\\n\\n #upload with batch size of 10000\\n if len(results) > 10000:\\n engine.execute(\\n GADXHistoricalDataOneSecondOHLC.__table__.insert(),\\n results\\n )\\n results = []\\n \\n update = session.query(historicalDataProgramState).filter(sa.and_(historicalDataProgramState.transaction_id == tid,historicalDataProgramState.entry_type == 'update')).first()\\n if update:\\n update.end = lasttimestamp\\n session.commit()\\n else:\\n new_update = historicalDataProgramState(entry_type = 'update',transaction_id = tid,start=firsttimestamp,end=lasttimestamp,platform='GDAX',status='incomplete')\\n session.add(new_update)\\n session.commit()\\n if len(results) > 0:\\n engine.execute(\\n GADXHistoricalDataOneSecondOHLC.__table__.insert(),\\n results\\n )\\n results = []\\n \\n update = session.query(historicalDataProgramState).filter(sa.and_(historicalDataProgramState.transaction_id == tid,historicalDataProgramState.entry_type == 'update')).first()\\n if update:\\n update.end = lasttimestamp\\n session.commit()\\n else:\\n new_update = historicalDataProgramState(entry_type = 'update',transaction_id = tid,start=firsttimestamp,end=lasttimestamp,platform='GDAX',status='incomplete')\\n session.add(new_update)\\n session.commit()\\n\\n update = session.query(historicalDataProgramState).filter(sa.and_(historicalDataProgramState.transaction_id == tid,historicalDataProgramState.entry_type == 'update')).first()\\n update.status='complete'\\n order = session.query(historicalDataProgramState).filter(sa.and_(historicalDataProgramState.transaction_id == tid,historicalDataProgramState.entry_type == 'order')).first()\\n order.status='complete'\\n session.commit()\",\n \"def test_log_last_started_datetime(self):\\n initial_count = CostUsageReportStatus.objects.count()\\n saver = ReportStatsDBAccessor(\\\"myreport\\\", self.manifest_id)\\n saver.log_last_started_datetime()\\n self.assertIsNotNone(saver.get_last_started_datetime())\\n saver.delete()\\n self.assertEqual(CostUsageReportStatus.objects.count(), initial_count)\",\n \"def commit_transaction(self) -> None:\\n pass\",\n \"def exec_eth_transfer(self, timestamp=-1):\\n ticker = self.ticker\\n gdax_trading_account = self.trading_acc1\\n cex_trading_account = self.trading_acc2\\n gdax_eth_amount = gdax_trading_account.get_balance(ticker)\\n cex_deposit_address = cex_trading_account.get_crypto_deposit_address(ticker)\\n\\n # transfer\\n gdax_trading_account.transfer_crypto(ticker, cex_deposit_address, gdax_eth_amount)\\n\\n # audit info\\n audit_info = {\\n 'ticker': ticker,\\n 'action_type': 'exec_eth_transfer',\\n 'gdax_eth_amount': gdax_eth_amount,\\n 'cex_deposit_address': cex_deposit_address\\n }\\n return audit_info\",\n \"def record_inference_stats(self, nms_step_time: float, inference_round_trip_time: Tuple[float, float, float], inference_step_time: float):\\n\\n # inference_round_trip_time is an average time needed for a step\\n self.inference_times.append(inference_round_trip_time)\\n # inference_step_time is the time taken to complete the step, and used to calculate the throughput\\n inference_throughput = self.image_count/inference_step_time\\n self.inference_throughputs.append(inference_throughput)\\n\\n self.nms_times.append(nms_step_time)\\n\\n total_step_time = inference_step_time + nms_step_time\\n self.total_times.append(total_step_time)\\n\\n total_throughput = self.image_count/total_step_time\\n self.total_throughputs.append(total_throughput)\",\n \"def trace_time(func):\\n\\n @functools.wraps(func)\\n def wrapper(*args, **kw):\\n begin = time.time()\\n logging.debug(\\\"begin at %s: %s()\\\" % (__format_time(begin), func.__name__))\\n result = func(*args, **kw)\\n end = time.time()\\n logging.debug(\\n \\\"end at %s, cost %.2fs: %s() -- return type: %s\\\"\\n % (__format_time(end), end - begin, func.__name__, type(result).__name__))\\n return result\\n\\n return wrapper\",\n \"def logging(self, function):\\n avg_nms_time_per_step = sum(self.nms_times)/len(self.nms_times)\\n avg_total_time_per_step = sum(self.total_times)/len(self.total_times)\\n\\n avg_min_latency = [x[0] for x in self.inference_times]\\n avg_max_latency = [x[1] for x in self.inference_times]\\n avg_latency = [x[2] for x in self.inference_times]\\n\\n function(\\\"Inference stats: image size {}x{}, batches per step {}, batch size {}, {} steps\\\".format(\\n self.cfg.model.image_size, self.cfg.model.image_size, self.cfg.ipuopts.batches_per_step, self.cfg.model.micro_batch_size, len(self.total_times)\\n ))\\n function(\\\"--------------------------------------------------\\\")\\n function(\\\"Inference\\\")\\n function(\\\"Average Min Latency per Batch: {:.3f} ms\\\".format(1000 * sum(avg_min_latency)/len(self.inference_times)))\\n function(\\\"Average Max Latency per Batch: {:.3f} ms\\\".format(1000 * sum(avg_max_latency)/len(self.inference_times)))\\n function(\\\"Average Latency per Batch: {:.3f} ms\\\".format(1000 * sum(avg_latency)/len(self.inference_times)))\\n function(\\\"Average Inference Throughput: {:.3f} img/s\\\".format(sum(self.inference_throughputs)/len(self.inference_throughputs)))\\n function(\\\"--------------------------------------------------\\\")\\n # TODO remove the NMS and end-to-end time report once NMS is on device\\n function(\\\"End-to-end\\\")\\n function(\\\"Average NMS Latency per Batch: {:.3f} ms\\\".format(1000 * avg_nms_time_per_step/self.cfg.ipuopts.batches_per_step))\\n function(\\\"Average End-to-end Latency per Batch: {:.3f} ms\\\".format(1000 * avg_total_time_per_step/self.cfg.ipuopts.batches_per_step))\\n function(\\\"End-to-end Throughput: {:.3f} img/s\\\".format(sum(self.total_throughputs)/len(self.total_throughputs)))\\n function(\\\"==================================================\\\")\\n\\n if self.cfg.eval.metrics:\\n self.compute_and_print_eval_metrics()\",\n \"def acc(ev):\\n profData = getProfilingData(ev)\\n if profData is not None:\\n return profData.Tacc()\\n return \\\"\\\"\",\n \"def cache_txn_manage(database, table, action, trans=None, **kw):\\n trace = kw['trace']\\n cache = server.data[database].tables['cache']\\n transaction = request.get_json() if trans == None else trans\\n if 'txn' in transaction:\\n txn_id = transaction['txn']\\n tx=None\\n wait_time = 0.0 # total time waiting to commit txn \\n wait_interval = txn_default_wait_in_sec # amount of time to wait between checks - if multiple txns exist \\n # Get transaction from cache db\\n if action == 'commit':\\n while True:\\n txns = cache.select('id','timestamp',\\n where={'table_name': table}\\n )\\n if not txn_id in {tx['id'] for tx in txns}:\\n return {\\\"message\\\": trace.error(f\\\"{txn_id} does not exist in cache\\\")}, 500\\n if len(txns) == 1:\\n if not txns[0]['id'] == txn_id:\\n warning = f\\\"txn with id {txn_id} does not exist for {database} {table}\\\"\\n return {'warning': trace.warning(warning)}, 500\\n # txn_id is only value inside\\n tx = txns[0]\\n break\\n # multiple pending txns - need to check timestamp to verify if this txn can be commited yet\\n txns = sorted(txns, key=lambda txn: txn['timestamp'])\\n for ind, txn in enumerate(txns):\\n if txn['id'] == txn_id:\\n if ind == 0:\\n tx = txns[0]\\n break\\n if wait_time > txn_max_wait_time_in_sec:\\n warning = f\\\"timeout of {wait_time} reached while waiting to commit {txn_id} for {database} {table}, waiting on {txns[:ind]}\\\"\\n trace.warning(warning)\\n trace.warning(f\\\"removing txn with id {txns[0]['id']} maxWaitTime of {txn_max_wait_time_in_sec} reached\\\")\\n cache.delete(where={'id': txns[0]['id']})\\n break\\n break\\n if tx == None:\\n trace.warning(f\\\"txn_id {txn_id} is behind txns {txns[:ind]} - waiting {wait_time} to retry\\\")\\n time.sleep(wait_interval)\\n wait_time+=wait_interval \\n # wait_interval scales up to txn_max_wait_interval_in_sec\\n wait_interval+=wait_interval \\n if wait_interval >= txn_max_wait_interval_in_sec:\\n wait_interval = txn_max_wait_interval_in_sec\\n continue\\n break\\n # Should not have broken out of loop here without a tx\\n if tx == None:\\n trace.error(\\\"tx is None, this should not hppen\\\")\\n return {\\\"error\\\": \\\"tx was none\\\"}, 500\\n tx = cache.select('type','txn',\\n where={'id': txn_id})[0]\\n try:\\n r, rc = server.actions[tx['type']](database, table, tx['txn'])\\n trace.warning(f\\\"##cache {action} response {r} rc {rc}\\\")\\n except Exception as e:\\n r, rc = trace.exception(f\\\"Exception when performing cache {action}\\\"), 500\\n \\n del_txn = cache.delete(\\n where={'id': txn_id}\\n )\\n if rc == 200:\\n # update last txn id\\n set_params = {\\n 'set': {\\n 'last_txn_uuid': txn_id,\\n 'last_mod_time': float(time.time())\\n },\\n 'where': {\\n 'table_name': table\\n }\\n }\\n server.data['cluster'].tables['pyql'].update(\\n **set_params['set'],\\n where=set_params['where']\\n )\\n return {\\\"message\\\": r, \\\"status\\\": rc}, rc\\n if action == 'cancel':\\n del_txn = cache.delete(\\n where={'id': txn_id}\\n )\\n return {'deleted': txn_id}, 200\",\n \"def process_request(self, request):\\n self.db.set_profiling_level(0)\\n try:\\n self.start_ts = self.db.system.profile.find()\\\\\\n .sort(\\\"ts\\\", pymongo.DESCENDING)\\\\\\n .limit(1)[0].get('ts')\\n except IndexError:\\n self.start_ts = None\\n\\n self.db.set_profiling_level(2)\",\n \"def startTransaction(self) -> int:\\n ...\",\n \"def update_time(self):\\n time_metrics = self._fetch_time_metrics_and_clear()\\n self._logger.info('update_time. time_metrics = %s', build_metrics_times_data(time_metrics))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.57431966","0.56813395","0.5386816","0.5268158","0.52590114","0.5248265","0.51790476","0.51674783","0.51302767","0.5116144","0.5102775","0.5091443","0.50542235","0.5050009","0.5020564","0.50163","0.50032055","0.4916298","0.48824197","0.4858216","0.4846671","0.48407298","0.4829507","0.48275587","0.481562","0.4814692","0.4810644","0.48033282","0.47969192","0.47949752","0.47873122","0.47837195","0.47808605","0.47747633","0.4774446","0.47730988","0.47151595","0.47100452","0.4707107","0.4703334","0.47032672","0.46971434","0.46826646","0.46750718","0.46651492","0.46387458","0.4631215","0.4616959","0.46131918","0.461252","0.46110803","0.46048835","0.4600367","0.46002933","0.45998284","0.45987594","0.45985317","0.45939773","0.45909387","0.45868132","0.45790964","0.45719755","0.4568749","0.45644447","0.4562327","0.45622173","0.45621982","0.45553368","0.4545873","0.45452747","0.45441023","0.45418426","0.4539685","0.45349243","0.4534225","0.45304772","0.45269358","0.45267773","0.45234093","0.45226473","0.45220056","0.451876","0.45021942","0.44993764","0.44898534","0.44806254","0.4477325","0.44668064","0.44652733","0.44632363","0.44623008","0.44589537","0.44572914","0.44551933","0.44494697","0.44488522","0.44426656","0.44426435","0.4440055","0.4433733"],"string":"[\n \"0.57431966\",\n \"0.56813395\",\n \"0.5386816\",\n \"0.5268158\",\n \"0.52590114\",\n \"0.5248265\",\n \"0.51790476\",\n \"0.51674783\",\n \"0.51302767\",\n \"0.5116144\",\n \"0.5102775\",\n \"0.5091443\",\n \"0.50542235\",\n \"0.5050009\",\n \"0.5020564\",\n \"0.50163\",\n \"0.50032055\",\n \"0.4916298\",\n \"0.48824197\",\n \"0.4858216\",\n \"0.4846671\",\n \"0.48407298\",\n \"0.4829507\",\n \"0.48275587\",\n \"0.481562\",\n \"0.4814692\",\n \"0.4810644\",\n \"0.48033282\",\n \"0.47969192\",\n \"0.47949752\",\n \"0.47873122\",\n \"0.47837195\",\n \"0.47808605\",\n \"0.47747633\",\n \"0.4774446\",\n \"0.47730988\",\n \"0.47151595\",\n \"0.47100452\",\n \"0.4707107\",\n \"0.4703334\",\n \"0.47032672\",\n \"0.46971434\",\n \"0.46826646\",\n \"0.46750718\",\n \"0.46651492\",\n \"0.46387458\",\n \"0.4631215\",\n \"0.4616959\",\n \"0.46131918\",\n \"0.461252\",\n \"0.46110803\",\n \"0.46048835\",\n \"0.4600367\",\n \"0.46002933\",\n \"0.45998284\",\n \"0.45987594\",\n \"0.45985317\",\n \"0.45939773\",\n \"0.45909387\",\n \"0.45868132\",\n \"0.45790964\",\n \"0.45719755\",\n \"0.4568749\",\n \"0.45644447\",\n \"0.4562327\",\n \"0.45622173\",\n \"0.45621982\",\n \"0.45553368\",\n \"0.4545873\",\n \"0.45452747\",\n \"0.45441023\",\n \"0.45418426\",\n \"0.4539685\",\n \"0.45349243\",\n \"0.4534225\",\n \"0.45304772\",\n \"0.45269358\",\n \"0.45267773\",\n \"0.45234093\",\n \"0.45226473\",\n \"0.45220056\",\n \"0.451876\",\n \"0.45021942\",\n \"0.44993764\",\n \"0.44898534\",\n \"0.44806254\",\n \"0.4477325\",\n \"0.44668064\",\n \"0.44652733\",\n \"0.44632363\",\n \"0.44623008\",\n \"0.44589537\",\n \"0.44572914\",\n \"0.44551933\",\n \"0.44494697\",\n \"0.44488522\",\n \"0.44426656\",\n \"0.44426435\",\n \"0.4440055\",\n \"0.4433733\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7942365"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":3689,"cells":{"query":{"kind":"string","value":"Returns a list containing the low level metric data for sending to the core application pertaining to the reporting period. This consists of tuple pairs where first is dictionary with name and scope keys with corresponding values, or integer identifier if metric had an entry in dictionary mapping metric (name, scope) as supplied from core application. The second is the list of accumulated metric data, the list always being of length 6."},"document":{"kind":"string","value":"def metric_data(self, normalizer=None):\n\n if not self.__settings:\n return []\n\n result = []\n normalized_stats = {}\n\n # Metric Renaming and Re-Aggregation. After applying the metric\n # renaming rules, the metrics are re-aggregated to collapse the\n # metrics with same names after the renaming.\n\n if self.__settings.debug.log_raw_metric_data:\n _logger.info('Raw metric data for harvest of %r is %r.',\n self.__settings.app_name,\n list(six.iteritems(self.__stats_table)))\n\n if normalizer is not None:\n for key, value in six.iteritems(self.__stats_table):\n key = (normalizer(key[0])[0], key[1])\n stats = normalized_stats.get(key)\n if stats is None:\n normalized_stats[key] = copy.copy(value)\n else:\n stats.merge_stats(value)\n else:\n normalized_stats = self.__stats_table\n\n if self.__settings.debug.log_normalized_metric_data:\n _logger.info('Normalized metric data for harvest of %r is %r.',\n self.__settings.app_name,\n list(six.iteritems(normalized_stats)))\n\n for key, value in six.iteritems(normalized_stats):\n key = dict(name=key[0], scope=key[1])\n result.append((key, value))\n\n return result"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_metric_list(self) -> List[str]:\n ...","def get_all_metrics(self):\n up_time = self.uptime()\n down_time = self.downtime()\n customer_sla = self.sla()\n objective = self.slo()\n indicator = self.sli()\n avail_percentage = self.availability()\n mt_bf = self.mtbf(up_time)\n mt_tr = self.mttr(down_time)\n list_results = [up_time,down_time,customer_sla,objective,indicator,avail_percentage,mt_bf,mt_tr]\n return list_results","def __get_metrics_list(self):\n metrics = metrics_calculator.MetricsCalculator(self.processor)\n metric_list = []\n # Populate the list\n for key in metrics.get_raw_metrics().keys():\n name = metrics.get_raw_metrics()[key][\"NAME\"]\n formula = metrics.get_raw_metrics()[key][\"FORMULA\"]\n description = metrics.get_raw_metrics()[key][\"DESCRIPTION\"]\n metric = Metric(name, formula, description)\n metric_list.append(metric)\n return metric_list","def list_metrics(self):\n pass","def metrics(self):\n self.metrics = []\n \n self.clients()\n\n if len(self.metrics) > 0:\n return self.metrics\n else:\n return []","def metrics(self):\n metrics_registry = getattr(self._thread_local, \"klio_metrics\", None)\n if not metrics_registry:\n self._thread_local.klio_metrics = self._get_metrics_registry()\n return self._thread_local.klio_metrics","def get_metrics(self) -> Dict[str, base.Number]:\n return self._metrics","def list_definition(self):\n return self._get(path='metrics')","def metrics(self):\n\n return six.iteritems(self.__stats_table)","def build_metrics_gauge_data(gauge_metrics):\n return [{'name': name, 'value': value} for name, value in iteritems(gauge_metrics)]","def get_data(self):\n data = list(IgnitionRow.objects.all().order_by('-pub_date')[:self.num_ticks].values())\n two_hours = data[::-1]\n pct_flop_data = [[int(elem['pct_flop_{}'.format(key)]) for elem in two_hours] \n \tfor key in self.keys]\n pct_flop_data = [[min(elem, 100) for elem in arr] for arr in pct_flop_data] # Assume a max pot size of 2000 BBs\n return pct_flop_data","def get_metric_variables(self) -> List[Any]:\n with self._lock:\n return self._get_metric_variables()","def get_metrics(self):\n self.logger.debug(\"Fetching metrics.\")\n return self._api_query(\"metrics\")['metrics']","def metrics(self) -> list[dict[str, dict[str, float | int]]]:\n return self.performance[\"performances\"]","def metrics(self):\n\n data = requests.get(\n f\"http://{self.prometheus_host}:{self.prometheus_port}/metrics\"\n ).content.decode()\n lines = [line for line in data.split(\"\\n\") if not line.startswith(\"#\")]\n metrics = {}\n for line in lines:\n if not line:\n continue\n\n name, value = line.split(\" \")\n\n try:\n value = int(value) # type: ignore\n except ValueError:\n value = float(value) # type: ignore\n\n if \"{\" in name and \"}\" in name:\n base = name[: name.index(\"{\")]\n tags = name[name.index(\"{\") + 1 : -1]\n tags = [tag.split(\"=\") for tag in tags.split(\",\")]\n tags = [(key, val.replace('\"', \"\")) for key, val in tags]\n\n name = base + \"#\" + \",\".join(f\"{k}:{v}\" for k, v in sorted(tags))\n\n metrics[name] = value\n\n return metrics","def metrics(self) -> list:\n my_metrics = [\n FramesMetric(\"frames\"),\n FPSMetric(\"fps\"),\n EpisodeRewardMetric('PMM:episode_rewards'),\n EpisodeRewardMetricQuantile('P09:episode_rewards', quantile=0.9),\n EpisodeRewardMetricQuantile('P01:episode_rewards', quantile=0.1),\n EpisodeLengthMetric(\"episode_length\")\n ]\n\n return my_metrics + self.algo.metrics() + self.env_roller.metrics()","def tracked_metrics(self) -> list:\n metric_names = [\"loss\"]\n if self.metrics_map is not None:\n metric_names.extend([key for key in self.metrics_map.keys()])\n return metric_names","def stats(self):\r\n\t\tdata = self._get('global/', query=None)\r\n\t\treturn data","def metrics(self):\r\n url = '{0}/{1}'.format(self.get_url(), 'metrics')\r\n\r\n return http.Request('GET', url), parsers.parse_json","def metrics(self):\n if not self.df:\n return []\n\n column_metric_strings = [col.split(self.sep)[0] for col in self.df.columns]\n\n metrics = set()\n for colstring in column_metric_strings:\n try:\n metrics.add(Metric(colstring))\n except ValueError:\n continue\n\n return sorted(list(set(metrics)))","def get_metrics(self) -> dict:\n return self.metric_dict","def convert(report):\n M = []\n for row in report['data']['rows']:\n dimensions = row['dimensions']\n metrics = row['metrics'][0]['values']\n M.append(dimensions + metrics)\n return M","def metrics(self):\n return self.__metrics","def metrics(self):\n return self._metrics","def metrics(self):\n return self._metrics","def get_all_metrics(self):\n metrics = {}\n for item in self.list_metrics():\n metric_name = item[2]\n metric = self.get_metric(\n item,\n existing_dict=metrics.get(metric_name, None))\n metrics[metric_name] = metric\n return metrics","def get_measurements(self):\n metrics = {}\n for key in self.fields.keys():\n metrics[key] = []\n # What's in output:\n # proc_pid date virt res shrd cpu mem power gpus_power\n while not self.queue.empty():\n data = self.queue.get().strip().split()\n for field in self.fields:\n tp = self.fields[field]['type']\n idx = self.fields[field]['index']\n count = self.fields[field]['count']\n if count == -1:\n metrics[field].append(ResourceMonitor.str_to_type(data[idx], tp))\n elif count == 0:\n metrics[field].append([ResourceMonitor.str_to_type(data[idx], tp)])\n else:\n metrics[field].append([\n ResourceMonitor.str_to_type(data[index], tp) for index in xrange(idx, idx+count)\n ])\n return metrics","def metrics_group():","def metrics(self) -> List[Metric]:\n return self._metrics","def get_metrics(self):\n return None","def get_all_metrics():\n return get_overlap_metrics() + get_distance_metrics() + get_distance_metrics()","def list_metrics(self):\n results = []\n if self.r.exists(self.metrics_key):\n keys = self.r.smembers(self.metrics_key)\n for k in keys:\n # metric_key, metric_type, metric_name, metric_help = keys.split(\" \", 3)\n results.append(k.split(\" \", 3))\n return results","def get_metric_info(self):\n metric_data_object = self.client.get_metric_data(\n MetricDataQueries=[\n {\n \"Id\": \"cdbdata_invocations\",\n \"MetricStat\": {\n \"Metric\": {\n \"Namespace\": \"AWS/Lambda\",\n \"MetricName\": \"Invocations\",\n \"Dimensions\": [\n {\n \"Name\": \"FunctionName\",\n \"Value\": self.function_name\n }\n ]\n },\n \"Period\": 60,\n \"Stat\": \"Sum\"\n },\n \"ReturnData\": True\n },\n {\n \"Id\": \"cdbdata_errors\",\n \"MetricStat\": {\n \"Metric\": {\n \"Namespace\": \"AWS/Lambda\",\n \"MetricName\": \"Errors\",\n \"Dimensions\": [\n {\n \"Name\": \"FunctionName\",\n \"Value\": self.function_name\n }\n ]\n },\n \"Period\": 60,\n \"Stat\": \"Sum\"\n },\n \"ReturnData\": True\n },\n {\n \"Id\": \"cdbdata_throttles\",\n \"MetricStat\": {\n \"Metric\": {\n \"Namespace\": \"AWS/Lambda\",\n \"MetricName\": \"Throttles\",\n \"Dimensions\": [\n {\n \"Name\": \"FunctionName\",\n \"Value\": self.function_name\n }\n ]\n },\n \"Period\": 60,\n \"Stat\": \"Sum\"\n },\n \"ReturnData\": True\n },\n {\n \"Id\": \"cdbdata_concurrentexec\",\n \"MetricStat\": {\n \"Metric\": {\n \"Namespace\": \"AWS/Lambda\",\n \"MetricName\": \"ConcurrentExecutions\",\n \"Dimensions\": [\n {\n \"Name\": \"FunctionName\",\n \"Value\": self.function_name\n }\n ]\n },\n \"Period\": 60,\n \"Stat\": \"Sum\"\n },\n \"ReturnData\": True\n }\n ],\n StartTime=self.start_timestamp,\n EndTime=self.end_timestamp,\n ScanBy='TimestampDescending'\n )\n\n metric_data_points = metric_data_object[DataPointsCollector.RESPONSE_KEY]\n\n return metric_data_points","def getLineData(self):\n # Each view must have exactly one DateRange object\n date_range = DateRange.objects.filter(foreign_key=self.id).first()\n assert(date_range is not None)\n\n begin, end = date_range.getBeginEnd()\n return {\n \"begin\" : begin.strftime(\"%Y%m%d\"),\n \"end\" : end.strftime(\"%Y%m%d\"),\n \"data_sets\" : [\n {\n \"label\" : filter_set.label,\n \"color\" : filter_set.color,\n \"data\" : [\n {\n \"cnt\": row[\"cnt\"],\n \"date\": row[\"date\"].strftime(\"%Y%m%d\")\n } for row in filter_set.getMessageCountPerDay()\n ],\n } for filter_set in self.filterset_set.all()\n ]\n }","def collect(self): # pylint: disable=no-self-use\n start = time.time()\n for metric in metric_rq():\n yield metric\n\n gauge = GaugeMetricFamily(\n \"nautobot_rq_metrics_processing_ms\", \"Time in ms to generate the app metrics endpoint\"\n )\n duration = time.time() - start\n gauge.add_metric([], format(duration * 1000, \".5f\"))\n yield gauge","def supported_metrics(cls) -> List[str]:\n ...","def getReportMetrics(self):\n return self.__unwrapResults().reportMetrics","def metrics(self) -> dict:\n if not self.exp_metadata.metrics:\n self.exp_metadata.metrics = {}\n return self.exp_metadata.metrics","def metrics(self):\n raise NotImplementedError(\"metrics\")","def get_data(self):\n data = list(IgnitionRow.objects.all().order_by('-pub_date')[:self.num_ticks].values())\n two_hours = data[::-1] # The most recent two hours of data\n# print([elem['avg_pot_5'] for elem in two_hours])\n# avg_pot_data = [[float(elem['avg_pot_{}'.format(key)]) / (int(key) / 100) for elem in two_hours]\n avg_pot_data = [[float(elem['avg_pot_{}'.format(key)]) for elem in two_hours] \n \tfor key in self.keys]\n# print(avg_pot_data[0][-5:])\n avg_pot_data = [[max(min(elem, 100),0) for elem in arr] for arr in avg_pot_data] # Assume a max pot size of 2000 BBs\n avg_pot_data = [[elem if elem != 100 else 0 for elem in arr] for arr in avg_pot_data] # Assume a max pot size of 2000 BBs\n# print(avg_pot_data[0][-5:])\n return avg_pot_data","def _compute_group_stats():\n group_stats = []\n \n wmt16_group = Group.objects.filter(name='WMT16')\n wmt16_users = _get_active_users_for_group(wmt16_group)\n \n # Aggregate information about participating groups.\n groups = set()\n for user in wmt16_users:\n for group in _identify_groups_for_user(user):\n groups.add(group)\n \n # TODO: move this to property of evaluation group or add dedicated data model.\n # GOAL: should be configurable from within the Django admin backend.\n #\n # MINIMAL: move to local_settings.py?\n #\n # The following dictionary defines the number of HITs each group should\n # have completed during the WMT16 evaluation campaign.\n \n for group in groups:\n _name = group.name\n \n _group_stats = HIT.compute_status_for_group(group)\n _total = _group_stats[0]\n \n if _total > 0 and not _name in GROUP_HIT_REQUIREMENTS.keys():\n _required = 0\n elif _name in GROUP_HIT_REQUIREMENTS.keys():\n _required = GROUP_HIT_REQUIREMENTS[_name]\n _delta = _total - _required\n _data = (_total, _required, _delta)\n \n if _data[0] > 0:\n group_stats.append((_name, _data))\n \n # Sort by number of remaining HITs.\n group_stats.sort(key=lambda x: x[1][2])\n \n # Add totals at the bottom.\n global_total = sum([x[1][0] for x in group_stats])\n global_required = sum([x[1][1] for x in group_stats])\n global_delta = global_total - global_required\n global_data = (global_total, global_required, global_delta)\n group_stats.append((\"Totals\", global_data))\n \n return group_stats","def getMapData(self):\n return [\n {\n \"label\": filter_set.label,\n \"color\": filter_set.color,\n \"data\" : list(filter_set.getMessageCountPerCountry())\n } for filter_set in self.filterset_set.all()\n ]","def global_stats(self):\n\n glob_stats = []\n for job in self.jobs:\n\n g_stats = job.get_global_stats()\n if g_stats:\n glob_stats.append(\n {\n \"job_name\": job.name,\n \"globals\": g_stats\n }\n )\n\n return glob_stats","def metrics(self):\r\n if not hasattr(self, '_observable_metrics'):\r\n self._observable_metrics = Metrics()\r\n return self._observable_metrics","def gpu_metrics(self) -> List[ClaraGpuUtilization]:\r\n return self._gpu_metrics","def _monitor_metrics(self):\n metrics = [\"loss\"]\n try:\n m = U.metrics_from_model(self.model)\n if isinstance(m, list):\n metrics.extend(m)\n except:\n pass\n if self.val_data is not None:\n for m in metrics[:]:\n metrics.append(\"val_%s\" % (m))\n return metrics","def read_metric_values(self):\n inv_objs = self._inventory_mgr.current_inventory()\n monitored_metrics = self._metric_mgr.get_monitored_metrics()\n perf_manager = self._si.RetrieveServiceContent().perfManager\n for mor in inv_objs.keys():\n for inv_obj in inv_objs[mor]:\n inv_obj_metrics = inv_obj.metric_id_map\n desired_keys = list(set(inv_obj_metrics.keys()) & set(monitored_metrics[mor].keys()))\n if not len(desired_keys) == 0:\n metric_id_objs = [inv_obj_metrics[key] for key in desired_keys]\n query_spec = vim.PerformanceManager.QuerySpec(\n entity=inv_obj.mor, metricId=metric_id_objs,\n intervalId=inv_obj.INSTANT_INTERVAL,\n maxSample=1, format='normal'\n )\n try:\n results = perf_manager.QueryPerf(querySpec=[query_spec])\n except Exception as e:\n self._logger.error(\"Exception while making performance query : {0}\".format(e))\n if results:\n dps = self._parse_query(inv_obj, results, monitored_metrics[mor])\n payload = self._build_payload(dps)\n self._dispatch_metrics(payload)\n else:\n self._logger.warning(\"Empty result from query : {0}\".format(query_spec))","def collected_data(self) -> Iterable[Dict[str, float]]:\n return self._collected_data","def _hydro_metrics(self) -> list:\n\n return self._minimal() + [\n 'fdc_flv', 'fdc_fhv',\n 'kge', 'kge_np', 'kge_mod', 'kge_bound', 'kgeprime_c2m', 'kgenp_bound',\n 'nse', 'nse_alpha', 'nse_beta', 'nse_mod', 'nse_bound']","def collect(self): # pylint: disable=no-self-use\n start = time.time()\n\n if \"jobs\" in PLUGIN_SETTINGS and PLUGIN_SETTINGS[\"jobs\"]:\n for metric in metric_jobs():\n yield metric\n\n if \"models\" in PLUGIN_SETTINGS:\n for metric in metric_models(PLUGIN_SETTINGS[\"models\"]):\n yield metric\n\n # --------------------------------------------------------------\n # Extras Function defined in configuration.py or the Regristry\n # # --------------------------------------------------------------\n if \"extras\" in PLUGIN_SETTINGS:\n for metric in collect_extras_metric(PLUGIN_SETTINGS[\"extras\"]):\n yield metric\n\n for metric in collect_extras_metric(__REGISTRY__):\n yield metric\n\n gauge = GaugeMetricFamily(\n \"nautobot_app_metrics_processing_ms\", \"Time in ms to generate the app metrics endpoint\"\n )\n duration = time.time() - start\n gauge.add_metric([], format(duration * 1000, \".5f\"))\n yield gauge","def get_timestamped_metric_values_as_strings(self):\n ret_list = []\n i = 0\n while i < len(self.__metric_value_list):\n ret_list.append(self.__metric_value_list[i].timestamp.strftime(\"%Y-%m-%d %H:%M:%S\") + \" \" +\n str(self.__metric_value_list[i].value) +\n \"(\" + str(self.__metric_value_list[i].metric_def_ID) + \")\")\n i += 1\n return ret_list","def get_telemetry ():\n telemetry = OrderedDict()\n\n telemetry[\"ip_addr\"] = socket.gethostbyname(socket.gethostname())\n\n telemetry[\"mem_free\"] = psutil.virtual_memory().free\n\n telemetry[\"cpu_num\"] = psutil.NUM_CPUS\n\n x = psutil.cpu_times()\n telemetry[\"cpu_times\"] = OrderedDict([ (\"user\", x.user), (\"system\", x.system), (\"idle\", x.idle) ])\n\n x = psutil.disk_usage(\"/tmp\")\n telemetry[\"disk_usage\"] = OrderedDict([ (\"free\", x.free), (\"percent\", x.percent) ])\n\n x = psutil.disk_io_counters()\n telemetry[\"disk_io\"] = OrderedDict([ (\"read_count\", x.read_count), (\"write_count\", x.write_count), (\"read_bytes\", x.read_bytes), (\"write_bytes\", x.write_bytes), (\"read_time\", x.read_time), (\"write_time\", x.write_time) ])\n\n x = psutil.network_io_counters()\n telemetry[\"network_io\"] = OrderedDict([ (\"bytes_sent\", x.bytes_sent), (\"bytes_recv\", x.bytes_recv), (\"packets_sent\", x.packets_sent), (\"packets_recv\", x.packets_recv), (\"errin\", x.errin), (\"errout\", x.errout), (\"dropin\", x.dropin), (\"dropout\", x.dropout) ])\n\n return telemetry","def gather_metric(self):\n device_dict = {}\n # Delete first and last line of output of adb.\n output = self._shell.run(self.COMMAND).stdout\n\n # Example Line, Device Serial Num TAB Phone Status\n # 00bd977c7f504caf\toffline\n if output:\n for line in output.split('\\n'):\n spl_line = line.split('\\t')\n # spl_line[0] is serial, [1] is status. See example line.\n device_dict[spl_line[0]] = spl_line[1]\n\n return {self.DEVICES: device_dict}","def get_metric_data(config, metric_list, metric_grouping, start_time, end_time, collected_data_map):\n\n def format_data_entry(json_data_entry):\n metric_name = json_data_entry.get('metric')\n host_name = json_data_entry.get('tags', {}).get('host') or 'unknownHost'\n dps = json_data_entry.get('dps', {})\n metric_value = None\n header_field = normalize_key(metric_name) + \"[\" + host_name + \"]:\" + str(\n get_grouping_id(metric_name, metric_grouping))\n mtime = 0\n for stime, val in dps.items():\n if int(stime) > mtime:\n metric_value = val\n mtime = int(stime)\n\n epoch = mtime * 1000\n\n if epoch in collected_data_map:\n timestamp_value_map = collected_data_map[epoch]\n else:\n timestamp_value_map = {}\n\n timestamp_value_map[header_field] = str(metric_value)\n collected_data_map[epoch] = timestamp_value_map\n\n json_data = {\n \"token\": config['OPENTSDB_TOKEN'],\n \"start\": start_time,\n \"end\": end_time,\n \"queries\": map(lambda m: {\n \"aggregator\": \"avg\",\n \"downsample\": \"1m-avg\",\n \"metric\": m.encode('ascii')\n }, metric_list)\n }\n\n url = config[\"OPENTSDB_URL\"] + \"/api/query\"\n response = requests.post(url, data=json.dumps(json_data))\n if response.status_code == 200:\n rawdata_list = response.json()\n logger.debug(\"Get metric data from opentsdb: \" + str(len(rawdata_list)))\n\n # format metric and save to collected_data_map\n map(lambda d: format_data_entry(d), rawdata_list)","def get_aggregated_values(self):\n if not self._initialized:\n raise Exception(\"To readout you must first initialize, then\"\n \"process batches!\")\n else:\n ret_vals = [q.readout() for q in self.quantities]\n return dict(zip(self.quantity_names, ret_vals))","def build_metrics_times_data(time_metrics):\n return [{'name': name, 'latencies': latencies.get_latencies()}\n for name, latencies in iteritems(time_metrics)]","def get_stats(self):\n return {\n \"pings_sent\" : self.ping_count,\n \"measurements\" : self.measurements,\n }","def logs(self):\n dataTuples = list()\n try:\n with sqlite3.connect(self.db_file) as conn:\n cur = conn.cursor()\n cur.execute(\"SELECT * FROM curves\")\n dataTuples = cur.fetchall()\n return [{\"id\": a[0], \"name\": a[1], \"units\": a[2],\n \"descr\": a[3]} for a in dataTuples]\n except:\n return dataTuples","def collect(self) -> Metric:\n ret = self.source()\n if ret is None:\n LOGGER.warning('Statistics are not available')\n return\n gauge = GaugeMetricFamily('wemo_device_state', 'Status of Wemo device', labels=['address', 'parameter'])\n gauge.add_metric([ret.address, 'today_kwh'], ret.today_kwh, timestamp=ret.collection_time.timestamp())\n gauge.add_metric([ret.address, 'current_power_mW'], ret.current_power,\n timestamp=ret.collection_time.timestamp())\n gauge.add_metric([ret.address, 'today_on_time'], ret.today_on_time, timestamp=ret.collection_time.timestamp())\n gauge.add_metric([ret.address, 'on_for'], ret.on_for, timestamp=ret.collection_time.timestamp())\n gauge.add_metric([ret.address, 'today_standby_time'], ret.today_standby_time,\n timestamp=ret.collection_time.timestamp())\n\n yield gauge\n\n counter = CounterMetricFamily('wemo_power_usage', 'Today power consumption', labels=['address'])\n counter.add_metric([ret.address], ret.today_kwh, timestamp=ret.collection_time.timestamp())\n yield counter","def get_data(self):\n data = list(IgnitionRow.objects.all().order_by('-pub_date')[:self.num_ticks].values())\n two_hours = data[::-1]\n #two_hours = data\n num_players_data = [[max(min(elem['num_players_{}'.format(key)],50),0) for elem in two_hours] for key in self.keys]\n return num_players_data","def _aggregate_perf_data(perf_all_ordinals: List[str]):\n aggregate = {}\n\n pd = PerfData()\n for data in perf_all_ordinals:\n worker_pd = PerfData(**json.loads(data))\n if len(perf_all_ordinals) > 1:\n aggregate.setdefault(\"ordinals\", [])\n aggregate[\"ordinals\"].append(worker_pd.throughput_dict())\n\n pd.merge(worker_pd)\n\n aggregate.update(dataclasses.asdict(pd))\n return aggregate","def get_all_stats():\n\n return get_component(CachingPackage.COMPONENT_NAME).get_all_stats()","def get_metric_list(config):\n metric_list = []\n url = config[\"OPENTSDB_URL\"] + \"/api/suggest?type=metrics&q=\"\n response = requests.get(url)\n if response.status_code == 200:\n metric_list = response.json()\n logger.debug(\"Get metric list from opentsdb: \" + str(metric_list))\n return metric_list","def get_metrics_list(self, project, callback=None):\n metrics = sorted((yield gen.Task(self.client.smembers,\n self.get_metrics_key(project))))\n if callback:\n callback(metrics)","def get_data(self):\n return [10, 20, 30]","def _get_daily_statistics(self, cr, uid, ids, field_name, arg, context=None):\n obj = self.pool['mail.mail.statistics']\n res = {}\n for mailing in self.browse(cr, uid, ids, context=context):\n res[mailing.id] = {}\n date = mailing.sent_date if mailing.sent_date else mailing.create_date\n date_begin = datetime.strptime(date, tools.DEFAULT_SERVER_DATETIME_FORMAT)\n date_end = date_begin + relativedelta.relativedelta(days=self._period_number - 1)\n date_begin_str = date_begin.strftime(tools.DEFAULT_SERVER_DATETIME_FORMAT)\n date_end_str = date_end.strftime(tools.DEFAULT_SERVER_DATETIME_FORMAT)\n domain = [('mass_mailing_id', '=', mailing.id), ('opened', '>=', date_begin_str), ('opened', '<=', date_end_str)]\n res[mailing.id]['opened_daily'] = json.dumps(self.__get_bar_values(cr, uid, obj, domain, ['opened'], 'opened_count', 'opened:day', date_begin, context=context))\n domain = [('mass_mailing_id', '=', mailing.id), ('replied', '>=', date_begin_str), ('replied', '<=', date_end_str)]\n res[mailing.id]['replied_daily'] = json.dumps(self.__get_bar_values(cr, uid, obj, domain, ['replied'], 'replied_count', 'replied:day', date_begin, context=context))\n return res","def data_quartiles(self):\n data = []\n for graph in self._graphs.values():\n data += graph.data.values()\n data.sort()\n datalen = len(data)\n return(data[0], data[datalen/4], data[datalen/2],\n data[3*datalen/4], data[-1])","def get_stats(self):\n return utils.csv_to_dict(wait(self.proto.stat()))","def metrics_get(period):\n return flask.jsonify({\"message\": \"noop\"}), 200","def getAllMetrics(self):\n result = self.getReportMetrics()\n result.update(self.getOptimizationMetrics())\n return result","def build_metrics_counter_data(count_metrics):\n return [{'name': name, 'delta': delta} for name, delta in iteritems(count_metrics)]","def metrics(self, request):\n return OtterMetrics(self.store).app.resource()","def get_metrics(event):\n return tba_session.get(BASE_URL + '/event/%s/oprs' %event).json()","def listmetrics(namespace: str = None, Region_name: str = None) -> List:\r\n cloudwatch = client('cloudwatch', region_name=Region_name)\r\n paginator = cloudwatch.get_paginator('list_metrics')\r\n metrics=[] # type: List\r\n if namespace is not None:\r\n page = paginator.paginate(Namespace=namespace) \r\n else:\r\n page = paginator.paginate()\r\n for response in page:\r\n for metric in response['Metrics']:\r\n metrics.append(metric)\r\n return metrics","def metrics(self):\n return self.verificationRun.metrics()","def get_metric(ms):\n\treturn '['+','.join(str(m) for m in ms)+']'","def parse_response(response):\n data = []\n \n for report in response.get('reports', []):\n columnHeader = report.get('columnHeader', {})\n dimensionHeaders = columnHeader.get('dimensions', [])\n metricHeaders = columnHeader.get('metricHeader', {}).get('metricHeaderEntries', [])\n rows = report.get('data', {}).get('rows', [])\n \n row_count = 0 \n for row in rows:\n #print '\\n\\n', 'ROW_COUNT: ', row_count, '\\n'\n data.append({}) \n\n dimensions = row.get('dimensions', [])\n dateRangeValues = row.get('metrics', [])\n\n for header, dimension in zip(dimensionHeaders, dimensions):\n #print header + ': ' + dimension\n data[row_count][header[3:]] = dimension\n \n for i, values in enumerate(dateRangeValues):\n #print 'Date range (' + str(i) + ')'\n for metricHeader, value in zip(metricHeaders, values.get('values')):\n #print metricHeader.get('name') + ': ' + value\n data[row_count][metricHeader.get('name')[3:]] = value\n \n row_count += 1 \n \n return data","def calculate_metrics(metrics_data: List[Tuple[Metric, DataType]]) -> List[float]:\n pass","def collect(self):\n service = build_service('calendar', 'v3')\n data = []\n\n for time_window, suffix in zip(self._parameters['time_windows'],\n self._parameters['suffixes']):\n # TODO: Get Search prefix and calendar from configs\n events = get_calendar_entries_by_query(service, \"S-\", time_window, \"Tracking\")\n\n # If we don't have any events we set it to the window size (which is in hours)\n seconds_since = [time_window * 60 * 60]\n long_count = 0\n short_count = 0\n for event in events:\n start_time = parse(event['start'].get('dateTime'))\n end_time = parse(event['end'].get('dateTime'))\n seconds_since.append(\n (datetime.datetime.now(datetime.timezone.utc) - start_time).total_seconds())\n event_length = end_time - start_time\n if event_length.seconds <= 30*60:\n short_count += 1\n else:\n long_count += 1\n\n data.append(DataPoint(time.time(), self._base_name + 's_count.' + suffix, len(events)))\n data.append(DataPoint(time.time(), self._base_name + 's_seconds_since.' + suffix, min(seconds_since)))\n data.append(DataPoint(time.time(), self._base_name + 's_short_count.' + suffix, short_count))\n data.append(DataPoint(time.time(), self._base_name + 's_long_count.' + suffix, long_count))\n return data","def getAllMeasurement(self): \n return self.measurement","def getFormattedJobStatistics(self):\n\t\tformatted_job_stats = [self.name]\n\t\tformatted_job_stats.append(str(self.retry_count))\n\t\tif self.site is None:\n\t\t\tformatted_job_stats.append('-')\n\t\telse:\n\t\t\tformatted_job_stats.append(self.site)\n\t\tformatted_job_stats.append(round_to_str(self.kickstart))\n\t\tformatted_job_stats.append(round_to_str(self.post))\n\t\tformatted_job_stats.append(round_to_str(self.condor_delay))\n\t\tformatted_job_stats.append(round_to_str(self.resource))\n\t\tformatted_job_stats.append(round_to_str(self.runtime))\n\t\tformatted_job_stats.append(round_to_str(self.seqexec))\n\t\tformatted_job_stats.append(round_to_str(self.seqexec_delay))\n\t\treturn formatted_job_stats","def flush(self):\n with self._lock:\n batch = self._batch\n timestamps = self._timestamps\n\n items = []\n for identity, value in batch.items():\n metric = {}\n typ, name, tags = identity\n metric[\"name\"] = name\n if typ:\n metric[\"type\"] = typ\n else:\n metric[\"timestamp\"] = timestamps[identity]\n\n if tags:\n metric[\"attributes\"] = dict(tags)\n\n metric[\"value\"] = value\n items.append(metric)\n\n items = tuple(items)\n\n batch.clear()\n timestamps.clear()\n\n common = self._common.copy()\n common[\"timestamp\"] = self._interval_start\n now = int(time.time() * 1000.0)\n interval = now - self._interval_start\n common[\"interval.ms\"] = interval\n\n self._interval_start = now\n\n return items, common","def metrics(_):\r\n collector = BuildsCollector()\r\n build_metrics, headers = collector.get_metrics_table()\r\n print(tabulate(build_metrics, headers=headers))","def get_scoped_to_feature() -> Iterable[Feature] | Feature | None:\n return Feature.METRICS","def statistics(self):\n return self.get_statistics()","def get_general_stats() ->List[BaseStat]:\n return [PositionalTendencies(),\n SpeedTendencies(),\n ItemGoals(),\n DropshotGoals(),\n DropshotBallPhaseTimes(),\n DropshotStats()\n ]","def init_metric_definitions():\n metric_definitions = []\n\n # add info to list in memory, one by one, following signature values\n metric_def_ID = 1\n metric_def_name = \"Recovery Time\"\n metric_def_info = \"Measures time taken by ONAP to restore a VNF\"\n metric_definitions.append(RecoveryTimeDef(metric_def_ID, metric_def_name,\n metric_def_info))\n\n metric_def_ID = 2\n metric_def_name = \"Uptime Percentage\"\n metric_def_info = \"Measures ratio of uptime to reference time, not counting planned downtime\"\n metric_definitions.append(UptimePercentageDef(metric_def_ID, metric_def_name,\n metric_def_info))\n\n\n # write list to binary file\n write_list_bin(metric_definitions, FILE_METRIC_DEFINITIONS)\n\n return metric_definitions","def get_next_batch(self):\n\n metrics = {}\n for struct in self.metrics.values():\n metrics = {**metrics, **struct.get_next_batch()}\n\n return metrics","def get_data(self):\n data = list(IgnitionRowPredictionOLS.objects.all().order_by('-pub_date')[:self.num_ticks].values())\n two_hours = data[::-1]\n num_players_data = [[elem['num_players_{}'.format(key)] for elem in two_hours] for key in self.keys]\n return num_players_data","def get_stats(self): \n return dict(l.split('\\t', 1) \\\n for l in wait(self.proto.stat()).splitlines() if l)","def read_metrics_and_assertions(self) -> (\n typing.Iterable[typing.Tuple[str, float, metrics_pb2.Assertion]]):\n raise NotImplementedError","def metrics():\n\tmetrics = pd.DataFrame(index=['accuracy', 'precision', 'recall'],\n\t\t\t\t\t\t columns=['LogisticReg', 'NeuralNetwork'])\n\treturn metrics","def get_values(self, names):\n r = []\n for n in names:\n if n in self.raw_metrics:\n r.append(self.raw_metrics[n])\n else:\n return None\n return r","def core_stats():\n data = get_tsv_dataset(os.path.join(DATA_DIR, TOTALS_FILE))\n if data is None:\n return make_response(jsonify({'error': 'Data could not be read'}), 500)\n # parse up so we can manipulate things.\n dataset = [int(x) for x in data]\n annual_sightings = sum(dataset)\n # for each 'month' (selection of x4\n monthly_sightings = []\n max_sightings = 0\n max_month = 0\n\n # grab each month's data into its own list for post processing.\n # also calculate some other numbers as we go.\n for i in range(0, len(dataset), 4):\n # select 4x data points.\n this_month = dataset[i:i + 4]\n total_sightings_this_month = sum(this_month)\n monthly_sightings.append(total_sightings_this_month)\n old_max = max_sightings\n max_sightings = max(max_sightings, total_sightings_this_month)\n if old_max < max_sightings:\n # it could be the 0th month.\n max_month = len(monthly_sightings)\n\n mean_monthly_sightings = mean(monthly_sightings)\n month_name = list(calendar.month_name)[max_month]\n return make_response(jsonify({'annual_sightings': annual_sightings,\n 'max_sightings': max_sightings,\n 'max_sighting_month': month_name,\n 'mean_monthly_sightings': mean_monthly_sightings}), 200)","def stats(self):\r\n return {}","def mymetrics(): \n _update_metric_counters()\n logging.debug(prom_objects_seen.collect())\n return flask.Response(generate_latest(), mimetype='text/plain')","def get_metric_statistics(self, period, start_time, end_time, metric_name,\n statistics, dimensions=None):\n # Ugly heuristic to determine where to query a metric from\n if dimensions and dimensions.get('pid') and metric_name in self.app_metrics:\n query_type = 'application'\n index_by = 'pid'\n elif dimensions and dimensions.get('pid') and metric_name in self.host_metrics:\n query_type = 'host'\n index_by = 'pid'\n elif dimensions and dimensions.get('hostname') and metric_name in self.app_metrics:\n query_type = 'application'\n index_by = 'hostname'\n elif 'app_attributes' in metric_name:\n query_type = 'application'\n index_by = 'pid'\n else:\n query_type = 'host'\n index_by = 'hostname'\n\n if not isinstance(start_time, datetime):\n raise TypeError(\"start_time must be a datetime object\")\n if not isinstance(end_time, datetime):\n raise TypeError(\"end_time must be a datetime object\")\n\n tformat = \"%Y%m%d %H:%M\"\n interval = \"%s,%s\" % (start_time.strftime(tformat), end_time.strftime(tformat))\n time_group = int(period)\n\n if metric_name.startswith('app_attributes:'):\n metric_name, app_attribute = metric_name.split(':')\n else:\n app_attribute = ''\n if index_by == 'pid':\n query_fields = [metric_name, ]\n else:\n query_fields = [index_by, metric_name, ]\n\n script = self._build_script(query_fields, query_type, interval, time_group, dimensions)\n\n authenticate = 'basic' if self.username and self.password else None\n url = self._build_query_url(self.base_url, authenticate=authenticate,\n script=script)\n request = urllib2.Request(url)\n\n if self.username and self.password:\n auth_header = self._build_auth_header(self.username, self.password)\n request.add_header('Authorization', auth_header)\n\n reply = urllib2.urlopen(request)\n results = {}\n reader = csv.reader(reply)\n for metrics in reader:\n if metrics == []:\n continue\n\n if index_by == 'pid':\n match = re.search('pid=(.*?)&', metrics[0])\n if match:\n index = match.group(1)\n else:\n continue\n else:\n index = metrics.pop(0)\n if index == '':\n continue\n\n result = results.get(index, {Statistics.SERIES: []})\n for i, metric in enumerate(metrics):\n if metric_name == 'app_attributes' and app_attribute:\n metric = _extract_app_attribute(metric, app_attribute)\n\n result[Statistics.SERIES].append(metric)\n\n results[index] = result\n\n for index, metric in results.iteritems():\n series = metric[Statistics.SERIES]\n if Statistics.AVERAGE in statistics:\n try:\n metric[Statistics.AVERAGE] = sum(map(float, series)) / float(len(series))\n except (ZeroDivisionError, ValueError):\n metric[Statistics.AVERAGE] = 0.0\n if Statistics.SUM in statistics:\n metric[Statistics.SUM] = sum(map(float, series))\n if Statistics.SAMPLE_COUNT in statistics:\n metric[Statistics.SAMPLE_COUNT] = len(series)\n if Statistics.MAXIMUM in statistics:\n metric[Statistics.MAXIMUM] = max(map(float, series))\n if Statistics.MINIMUM in statistics:\n metric[Statistics.MINIMUM] = min(map(float, series))\n\n return results","def get_metric_for_all_dates(inargs, exp_id):\n date_list = []\n # Loop over dates and collect data\n for date in h.make_timelist(inargs.date_start, inargs.date_stop,\n inargs.hours_inc):\n date_list.append(get_metric_for_one_day(inargs, exp_id, date))\n return np.array(date_list)","def population_timeline_chart_data(matchid):\n ps = Match.query.get(matchid).populationstats.all()\n labels = []\n popcounts = []\n lowestPop = 100\n\n for snapshot in ps:\n labels.append(snapshot.time.strftime('%H:%M'))\n popcounts.append(snapshot.popcount)\n if snapshot.popcount is None or snapshot.popcount < lowestPop:\n lowestPop = snapshot.popcount\n\n return json.dumps(labels), json.dumps(popcounts), lowestPop","def get_data(self):\n data = list(IgnitionRowPredictionCVX.objects.all().order_by('-pub_date')[:self.num_ticks].values())\n two_hours = data[::-1]\n num_players_data = [[elem['num_players_{}'.format(key)] for elem in two_hours] for key in self.keys]\n return num_players_data"],"string":"[\n \"def get_metric_list(self) -> List[str]:\\n ...\",\n \"def get_all_metrics(self):\\n up_time = self.uptime()\\n down_time = self.downtime()\\n customer_sla = self.sla()\\n objective = self.slo()\\n indicator = self.sli()\\n avail_percentage = self.availability()\\n mt_bf = self.mtbf(up_time)\\n mt_tr = self.mttr(down_time)\\n list_results = [up_time,down_time,customer_sla,objective,indicator,avail_percentage,mt_bf,mt_tr]\\n return list_results\",\n \"def __get_metrics_list(self):\\n metrics = metrics_calculator.MetricsCalculator(self.processor)\\n metric_list = []\\n # Populate the list\\n for key in metrics.get_raw_metrics().keys():\\n name = metrics.get_raw_metrics()[key][\\\"NAME\\\"]\\n formula = metrics.get_raw_metrics()[key][\\\"FORMULA\\\"]\\n description = metrics.get_raw_metrics()[key][\\\"DESCRIPTION\\\"]\\n metric = Metric(name, formula, description)\\n metric_list.append(metric)\\n return metric_list\",\n \"def list_metrics(self):\\n pass\",\n \"def metrics(self):\\n self.metrics = []\\n \\n self.clients()\\n\\n if len(self.metrics) > 0:\\n return self.metrics\\n else:\\n return []\",\n \"def metrics(self):\\n metrics_registry = getattr(self._thread_local, \\\"klio_metrics\\\", None)\\n if not metrics_registry:\\n self._thread_local.klio_metrics = self._get_metrics_registry()\\n return self._thread_local.klio_metrics\",\n \"def get_metrics(self) -> Dict[str, base.Number]:\\n return self._metrics\",\n \"def list_definition(self):\\n return self._get(path='metrics')\",\n \"def metrics(self):\\n\\n return six.iteritems(self.__stats_table)\",\n \"def build_metrics_gauge_data(gauge_metrics):\\n return [{'name': name, 'value': value} for name, value in iteritems(gauge_metrics)]\",\n \"def get_data(self):\\n data = list(IgnitionRow.objects.all().order_by('-pub_date')[:self.num_ticks].values())\\n two_hours = data[::-1]\\n pct_flop_data = [[int(elem['pct_flop_{}'.format(key)]) for elem in two_hours] \\n \\tfor key in self.keys]\\n pct_flop_data = [[min(elem, 100) for elem in arr] for arr in pct_flop_data] # Assume a max pot size of 2000 BBs\\n return pct_flop_data\",\n \"def get_metric_variables(self) -> List[Any]:\\n with self._lock:\\n return self._get_metric_variables()\",\n \"def get_metrics(self):\\n self.logger.debug(\\\"Fetching metrics.\\\")\\n return self._api_query(\\\"metrics\\\")['metrics']\",\n \"def metrics(self) -> list[dict[str, dict[str, float | int]]]:\\n return self.performance[\\\"performances\\\"]\",\n \"def metrics(self):\\n\\n data = requests.get(\\n f\\\"http://{self.prometheus_host}:{self.prometheus_port}/metrics\\\"\\n ).content.decode()\\n lines = [line for line in data.split(\\\"\\\\n\\\") if not line.startswith(\\\"#\\\")]\\n metrics = {}\\n for line in lines:\\n if not line:\\n continue\\n\\n name, value = line.split(\\\" \\\")\\n\\n try:\\n value = int(value) # type: ignore\\n except ValueError:\\n value = float(value) # type: ignore\\n\\n if \\\"{\\\" in name and \\\"}\\\" in name:\\n base = name[: name.index(\\\"{\\\")]\\n tags = name[name.index(\\\"{\\\") + 1 : -1]\\n tags = [tag.split(\\\"=\\\") for tag in tags.split(\\\",\\\")]\\n tags = [(key, val.replace('\\\"', \\\"\\\")) for key, val in tags]\\n\\n name = base + \\\"#\\\" + \\\",\\\".join(f\\\"{k}:{v}\\\" for k, v in sorted(tags))\\n\\n metrics[name] = value\\n\\n return metrics\",\n \"def metrics(self) -> list:\\n my_metrics = [\\n FramesMetric(\\\"frames\\\"),\\n FPSMetric(\\\"fps\\\"),\\n EpisodeRewardMetric('PMM:episode_rewards'),\\n EpisodeRewardMetricQuantile('P09:episode_rewards', quantile=0.9),\\n EpisodeRewardMetricQuantile('P01:episode_rewards', quantile=0.1),\\n EpisodeLengthMetric(\\\"episode_length\\\")\\n ]\\n\\n return my_metrics + self.algo.metrics() + self.env_roller.metrics()\",\n \"def tracked_metrics(self) -> list:\\n metric_names = [\\\"loss\\\"]\\n if self.metrics_map is not None:\\n metric_names.extend([key for key in self.metrics_map.keys()])\\n return metric_names\",\n \"def stats(self):\\r\\n\\t\\tdata = self._get('global/', query=None)\\r\\n\\t\\treturn data\",\n \"def metrics(self):\\r\\n url = '{0}/{1}'.format(self.get_url(), 'metrics')\\r\\n\\r\\n return http.Request('GET', url), parsers.parse_json\",\n \"def metrics(self):\\n if not self.df:\\n return []\\n\\n column_metric_strings = [col.split(self.sep)[0] for col in self.df.columns]\\n\\n metrics = set()\\n for colstring in column_metric_strings:\\n try:\\n metrics.add(Metric(colstring))\\n except ValueError:\\n continue\\n\\n return sorted(list(set(metrics)))\",\n \"def get_metrics(self) -> dict:\\n return self.metric_dict\",\n \"def convert(report):\\n M = []\\n for row in report['data']['rows']:\\n dimensions = row['dimensions']\\n metrics = row['metrics'][0]['values']\\n M.append(dimensions + metrics)\\n return M\",\n \"def metrics(self):\\n return self.__metrics\",\n \"def metrics(self):\\n return self._metrics\",\n \"def metrics(self):\\n return self._metrics\",\n \"def get_all_metrics(self):\\n metrics = {}\\n for item in self.list_metrics():\\n metric_name = item[2]\\n metric = self.get_metric(\\n item,\\n existing_dict=metrics.get(metric_name, None))\\n metrics[metric_name] = metric\\n return metrics\",\n \"def get_measurements(self):\\n metrics = {}\\n for key in self.fields.keys():\\n metrics[key] = []\\n # What's in output:\\n # proc_pid date virt res shrd cpu mem power gpus_power\\n while not self.queue.empty():\\n data = self.queue.get().strip().split()\\n for field in self.fields:\\n tp = self.fields[field]['type']\\n idx = self.fields[field]['index']\\n count = self.fields[field]['count']\\n if count == -1:\\n metrics[field].append(ResourceMonitor.str_to_type(data[idx], tp))\\n elif count == 0:\\n metrics[field].append([ResourceMonitor.str_to_type(data[idx], tp)])\\n else:\\n metrics[field].append([\\n ResourceMonitor.str_to_type(data[index], tp) for index in xrange(idx, idx+count)\\n ])\\n return metrics\",\n \"def metrics_group():\",\n \"def metrics(self) -> List[Metric]:\\n return self._metrics\",\n \"def get_metrics(self):\\n return None\",\n \"def get_all_metrics():\\n return get_overlap_metrics() + get_distance_metrics() + get_distance_metrics()\",\n \"def list_metrics(self):\\n results = []\\n if self.r.exists(self.metrics_key):\\n keys = self.r.smembers(self.metrics_key)\\n for k in keys:\\n # metric_key, metric_type, metric_name, metric_help = keys.split(\\\" \\\", 3)\\n results.append(k.split(\\\" \\\", 3))\\n return results\",\n \"def get_metric_info(self):\\n metric_data_object = self.client.get_metric_data(\\n MetricDataQueries=[\\n {\\n \\\"Id\\\": \\\"cdbdata_invocations\\\",\\n \\\"MetricStat\\\": {\\n \\\"Metric\\\": {\\n \\\"Namespace\\\": \\\"AWS/Lambda\\\",\\n \\\"MetricName\\\": \\\"Invocations\\\",\\n \\\"Dimensions\\\": [\\n {\\n \\\"Name\\\": \\\"FunctionName\\\",\\n \\\"Value\\\": self.function_name\\n }\\n ]\\n },\\n \\\"Period\\\": 60,\\n \\\"Stat\\\": \\\"Sum\\\"\\n },\\n \\\"ReturnData\\\": True\\n },\\n {\\n \\\"Id\\\": \\\"cdbdata_errors\\\",\\n \\\"MetricStat\\\": {\\n \\\"Metric\\\": {\\n \\\"Namespace\\\": \\\"AWS/Lambda\\\",\\n \\\"MetricName\\\": \\\"Errors\\\",\\n \\\"Dimensions\\\": [\\n {\\n \\\"Name\\\": \\\"FunctionName\\\",\\n \\\"Value\\\": self.function_name\\n }\\n ]\\n },\\n \\\"Period\\\": 60,\\n \\\"Stat\\\": \\\"Sum\\\"\\n },\\n \\\"ReturnData\\\": True\\n },\\n {\\n \\\"Id\\\": \\\"cdbdata_throttles\\\",\\n \\\"MetricStat\\\": {\\n \\\"Metric\\\": {\\n \\\"Namespace\\\": \\\"AWS/Lambda\\\",\\n \\\"MetricName\\\": \\\"Throttles\\\",\\n \\\"Dimensions\\\": [\\n {\\n \\\"Name\\\": \\\"FunctionName\\\",\\n \\\"Value\\\": self.function_name\\n }\\n ]\\n },\\n \\\"Period\\\": 60,\\n \\\"Stat\\\": \\\"Sum\\\"\\n },\\n \\\"ReturnData\\\": True\\n },\\n {\\n \\\"Id\\\": \\\"cdbdata_concurrentexec\\\",\\n \\\"MetricStat\\\": {\\n \\\"Metric\\\": {\\n \\\"Namespace\\\": \\\"AWS/Lambda\\\",\\n \\\"MetricName\\\": \\\"ConcurrentExecutions\\\",\\n \\\"Dimensions\\\": [\\n {\\n \\\"Name\\\": \\\"FunctionName\\\",\\n \\\"Value\\\": self.function_name\\n }\\n ]\\n },\\n \\\"Period\\\": 60,\\n \\\"Stat\\\": \\\"Sum\\\"\\n },\\n \\\"ReturnData\\\": True\\n }\\n ],\\n StartTime=self.start_timestamp,\\n EndTime=self.end_timestamp,\\n ScanBy='TimestampDescending'\\n )\\n\\n metric_data_points = metric_data_object[DataPointsCollector.RESPONSE_KEY]\\n\\n return metric_data_points\",\n \"def getLineData(self):\\n # Each view must have exactly one DateRange object\\n date_range = DateRange.objects.filter(foreign_key=self.id).first()\\n assert(date_range is not None)\\n\\n begin, end = date_range.getBeginEnd()\\n return {\\n \\\"begin\\\" : begin.strftime(\\\"%Y%m%d\\\"),\\n \\\"end\\\" : end.strftime(\\\"%Y%m%d\\\"),\\n \\\"data_sets\\\" : [\\n {\\n \\\"label\\\" : filter_set.label,\\n \\\"color\\\" : filter_set.color,\\n \\\"data\\\" : [\\n {\\n \\\"cnt\\\": row[\\\"cnt\\\"],\\n \\\"date\\\": row[\\\"date\\\"].strftime(\\\"%Y%m%d\\\")\\n } for row in filter_set.getMessageCountPerDay()\\n ],\\n } for filter_set in self.filterset_set.all()\\n ]\\n }\",\n \"def collect(self): # pylint: disable=no-self-use\\n start = time.time()\\n for metric in metric_rq():\\n yield metric\\n\\n gauge = GaugeMetricFamily(\\n \\\"nautobot_rq_metrics_processing_ms\\\", \\\"Time in ms to generate the app metrics endpoint\\\"\\n )\\n duration = time.time() - start\\n gauge.add_metric([], format(duration * 1000, \\\".5f\\\"))\\n yield gauge\",\n \"def supported_metrics(cls) -> List[str]:\\n ...\",\n \"def getReportMetrics(self):\\n return self.__unwrapResults().reportMetrics\",\n \"def metrics(self) -> dict:\\n if not self.exp_metadata.metrics:\\n self.exp_metadata.metrics = {}\\n return self.exp_metadata.metrics\",\n \"def metrics(self):\\n raise NotImplementedError(\\\"metrics\\\")\",\n \"def get_data(self):\\n data = list(IgnitionRow.objects.all().order_by('-pub_date')[:self.num_ticks].values())\\n two_hours = data[::-1] # The most recent two hours of data\\n# print([elem['avg_pot_5'] for elem in two_hours])\\n# avg_pot_data = [[float(elem['avg_pot_{}'.format(key)]) / (int(key) / 100) for elem in two_hours]\\n avg_pot_data = [[float(elem['avg_pot_{}'.format(key)]) for elem in two_hours] \\n \\tfor key in self.keys]\\n# print(avg_pot_data[0][-5:])\\n avg_pot_data = [[max(min(elem, 100),0) for elem in arr] for arr in avg_pot_data] # Assume a max pot size of 2000 BBs\\n avg_pot_data = [[elem if elem != 100 else 0 for elem in arr] for arr in avg_pot_data] # Assume a max pot size of 2000 BBs\\n# print(avg_pot_data[0][-5:])\\n return avg_pot_data\",\n \"def _compute_group_stats():\\n group_stats = []\\n \\n wmt16_group = Group.objects.filter(name='WMT16')\\n wmt16_users = _get_active_users_for_group(wmt16_group)\\n \\n # Aggregate information about participating groups.\\n groups = set()\\n for user in wmt16_users:\\n for group in _identify_groups_for_user(user):\\n groups.add(group)\\n \\n # TODO: move this to property of evaluation group or add dedicated data model.\\n # GOAL: should be configurable from within the Django admin backend.\\n #\\n # MINIMAL: move to local_settings.py?\\n #\\n # The following dictionary defines the number of HITs each group should\\n # have completed during the WMT16 evaluation campaign.\\n \\n for group in groups:\\n _name = group.name\\n \\n _group_stats = HIT.compute_status_for_group(group)\\n _total = _group_stats[0]\\n \\n if _total > 0 and not _name in GROUP_HIT_REQUIREMENTS.keys():\\n _required = 0\\n elif _name in GROUP_HIT_REQUIREMENTS.keys():\\n _required = GROUP_HIT_REQUIREMENTS[_name]\\n _delta = _total - _required\\n _data = (_total, _required, _delta)\\n \\n if _data[0] > 0:\\n group_stats.append((_name, _data))\\n \\n # Sort by number of remaining HITs.\\n group_stats.sort(key=lambda x: x[1][2])\\n \\n # Add totals at the bottom.\\n global_total = sum([x[1][0] for x in group_stats])\\n global_required = sum([x[1][1] for x in group_stats])\\n global_delta = global_total - global_required\\n global_data = (global_total, global_required, global_delta)\\n group_stats.append((\\\"Totals\\\", global_data))\\n \\n return group_stats\",\n \"def getMapData(self):\\n return [\\n {\\n \\\"label\\\": filter_set.label,\\n \\\"color\\\": filter_set.color,\\n \\\"data\\\" : list(filter_set.getMessageCountPerCountry())\\n } for filter_set in self.filterset_set.all()\\n ]\",\n \"def global_stats(self):\\n\\n glob_stats = []\\n for job in self.jobs:\\n\\n g_stats = job.get_global_stats()\\n if g_stats:\\n glob_stats.append(\\n {\\n \\\"job_name\\\": job.name,\\n \\\"globals\\\": g_stats\\n }\\n )\\n\\n return glob_stats\",\n \"def metrics(self):\\r\\n if not hasattr(self, '_observable_metrics'):\\r\\n self._observable_metrics = Metrics()\\r\\n return self._observable_metrics\",\n \"def gpu_metrics(self) -> List[ClaraGpuUtilization]:\\r\\n return self._gpu_metrics\",\n \"def _monitor_metrics(self):\\n metrics = [\\\"loss\\\"]\\n try:\\n m = U.metrics_from_model(self.model)\\n if isinstance(m, list):\\n metrics.extend(m)\\n except:\\n pass\\n if self.val_data is not None:\\n for m in metrics[:]:\\n metrics.append(\\\"val_%s\\\" % (m))\\n return metrics\",\n \"def read_metric_values(self):\\n inv_objs = self._inventory_mgr.current_inventory()\\n monitored_metrics = self._metric_mgr.get_monitored_metrics()\\n perf_manager = self._si.RetrieveServiceContent().perfManager\\n for mor in inv_objs.keys():\\n for inv_obj in inv_objs[mor]:\\n inv_obj_metrics = inv_obj.metric_id_map\\n desired_keys = list(set(inv_obj_metrics.keys()) & set(monitored_metrics[mor].keys()))\\n if not len(desired_keys) == 0:\\n metric_id_objs = [inv_obj_metrics[key] for key in desired_keys]\\n query_spec = vim.PerformanceManager.QuerySpec(\\n entity=inv_obj.mor, metricId=metric_id_objs,\\n intervalId=inv_obj.INSTANT_INTERVAL,\\n maxSample=1, format='normal'\\n )\\n try:\\n results = perf_manager.QueryPerf(querySpec=[query_spec])\\n except Exception as e:\\n self._logger.error(\\\"Exception while making performance query : {0}\\\".format(e))\\n if results:\\n dps = self._parse_query(inv_obj, results, monitored_metrics[mor])\\n payload = self._build_payload(dps)\\n self._dispatch_metrics(payload)\\n else:\\n self._logger.warning(\\\"Empty result from query : {0}\\\".format(query_spec))\",\n \"def collected_data(self) -> Iterable[Dict[str, float]]:\\n return self._collected_data\",\n \"def _hydro_metrics(self) -> list:\\n\\n return self._minimal() + [\\n 'fdc_flv', 'fdc_fhv',\\n 'kge', 'kge_np', 'kge_mod', 'kge_bound', 'kgeprime_c2m', 'kgenp_bound',\\n 'nse', 'nse_alpha', 'nse_beta', 'nse_mod', 'nse_bound']\",\n \"def collect(self): # pylint: disable=no-self-use\\n start = time.time()\\n\\n if \\\"jobs\\\" in PLUGIN_SETTINGS and PLUGIN_SETTINGS[\\\"jobs\\\"]:\\n for metric in metric_jobs():\\n yield metric\\n\\n if \\\"models\\\" in PLUGIN_SETTINGS:\\n for metric in metric_models(PLUGIN_SETTINGS[\\\"models\\\"]):\\n yield metric\\n\\n # --------------------------------------------------------------\\n # Extras Function defined in configuration.py or the Regristry\\n # # --------------------------------------------------------------\\n if \\\"extras\\\" in PLUGIN_SETTINGS:\\n for metric in collect_extras_metric(PLUGIN_SETTINGS[\\\"extras\\\"]):\\n yield metric\\n\\n for metric in collect_extras_metric(__REGISTRY__):\\n yield metric\\n\\n gauge = GaugeMetricFamily(\\n \\\"nautobot_app_metrics_processing_ms\\\", \\\"Time in ms to generate the app metrics endpoint\\\"\\n )\\n duration = time.time() - start\\n gauge.add_metric([], format(duration * 1000, \\\".5f\\\"))\\n yield gauge\",\n \"def get_timestamped_metric_values_as_strings(self):\\n ret_list = []\\n i = 0\\n while i < len(self.__metric_value_list):\\n ret_list.append(self.__metric_value_list[i].timestamp.strftime(\\\"%Y-%m-%d %H:%M:%S\\\") + \\\" \\\" +\\n str(self.__metric_value_list[i].value) +\\n \\\"(\\\" + str(self.__metric_value_list[i].metric_def_ID) + \\\")\\\")\\n i += 1\\n return ret_list\",\n \"def get_telemetry ():\\n telemetry = OrderedDict()\\n\\n telemetry[\\\"ip_addr\\\"] = socket.gethostbyname(socket.gethostname())\\n\\n telemetry[\\\"mem_free\\\"] = psutil.virtual_memory().free\\n\\n telemetry[\\\"cpu_num\\\"] = psutil.NUM_CPUS\\n\\n x = psutil.cpu_times()\\n telemetry[\\\"cpu_times\\\"] = OrderedDict([ (\\\"user\\\", x.user), (\\\"system\\\", x.system), (\\\"idle\\\", x.idle) ])\\n\\n x = psutil.disk_usage(\\\"/tmp\\\")\\n telemetry[\\\"disk_usage\\\"] = OrderedDict([ (\\\"free\\\", x.free), (\\\"percent\\\", x.percent) ])\\n\\n x = psutil.disk_io_counters()\\n telemetry[\\\"disk_io\\\"] = OrderedDict([ (\\\"read_count\\\", x.read_count), (\\\"write_count\\\", x.write_count), (\\\"read_bytes\\\", x.read_bytes), (\\\"write_bytes\\\", x.write_bytes), (\\\"read_time\\\", x.read_time), (\\\"write_time\\\", x.write_time) ])\\n\\n x = psutil.network_io_counters()\\n telemetry[\\\"network_io\\\"] = OrderedDict([ (\\\"bytes_sent\\\", x.bytes_sent), (\\\"bytes_recv\\\", x.bytes_recv), (\\\"packets_sent\\\", x.packets_sent), (\\\"packets_recv\\\", x.packets_recv), (\\\"errin\\\", x.errin), (\\\"errout\\\", x.errout), (\\\"dropin\\\", x.dropin), (\\\"dropout\\\", x.dropout) ])\\n\\n return telemetry\",\n \"def gather_metric(self):\\n device_dict = {}\\n # Delete first and last line of output of adb.\\n output = self._shell.run(self.COMMAND).stdout\\n\\n # Example Line, Device Serial Num TAB Phone Status\\n # 00bd977c7f504caf\\toffline\\n if output:\\n for line in output.split('\\\\n'):\\n spl_line = line.split('\\\\t')\\n # spl_line[0] is serial, [1] is status. See example line.\\n device_dict[spl_line[0]] = spl_line[1]\\n\\n return {self.DEVICES: device_dict}\",\n \"def get_metric_data(config, metric_list, metric_grouping, start_time, end_time, collected_data_map):\\n\\n def format_data_entry(json_data_entry):\\n metric_name = json_data_entry.get('metric')\\n host_name = json_data_entry.get('tags', {}).get('host') or 'unknownHost'\\n dps = json_data_entry.get('dps', {})\\n metric_value = None\\n header_field = normalize_key(metric_name) + \\\"[\\\" + host_name + \\\"]:\\\" + str(\\n get_grouping_id(metric_name, metric_grouping))\\n mtime = 0\\n for stime, val in dps.items():\\n if int(stime) > mtime:\\n metric_value = val\\n mtime = int(stime)\\n\\n epoch = mtime * 1000\\n\\n if epoch in collected_data_map:\\n timestamp_value_map = collected_data_map[epoch]\\n else:\\n timestamp_value_map = {}\\n\\n timestamp_value_map[header_field] = str(metric_value)\\n collected_data_map[epoch] = timestamp_value_map\\n\\n json_data = {\\n \\\"token\\\": config['OPENTSDB_TOKEN'],\\n \\\"start\\\": start_time,\\n \\\"end\\\": end_time,\\n \\\"queries\\\": map(lambda m: {\\n \\\"aggregator\\\": \\\"avg\\\",\\n \\\"downsample\\\": \\\"1m-avg\\\",\\n \\\"metric\\\": m.encode('ascii')\\n }, metric_list)\\n }\\n\\n url = config[\\\"OPENTSDB_URL\\\"] + \\\"/api/query\\\"\\n response = requests.post(url, data=json.dumps(json_data))\\n if response.status_code == 200:\\n rawdata_list = response.json()\\n logger.debug(\\\"Get metric data from opentsdb: \\\" + str(len(rawdata_list)))\\n\\n # format metric and save to collected_data_map\\n map(lambda d: format_data_entry(d), rawdata_list)\",\n \"def get_aggregated_values(self):\\n if not self._initialized:\\n raise Exception(\\\"To readout you must first initialize, then\\\"\\n \\\"process batches!\\\")\\n else:\\n ret_vals = [q.readout() for q in self.quantities]\\n return dict(zip(self.quantity_names, ret_vals))\",\n \"def build_metrics_times_data(time_metrics):\\n return [{'name': name, 'latencies': latencies.get_latencies()}\\n for name, latencies in iteritems(time_metrics)]\",\n \"def get_stats(self):\\n return {\\n \\\"pings_sent\\\" : self.ping_count,\\n \\\"measurements\\\" : self.measurements,\\n }\",\n \"def logs(self):\\n dataTuples = list()\\n try:\\n with sqlite3.connect(self.db_file) as conn:\\n cur = conn.cursor()\\n cur.execute(\\\"SELECT * FROM curves\\\")\\n dataTuples = cur.fetchall()\\n return [{\\\"id\\\": a[0], \\\"name\\\": a[1], \\\"units\\\": a[2],\\n \\\"descr\\\": a[3]} for a in dataTuples]\\n except:\\n return dataTuples\",\n \"def collect(self) -> Metric:\\n ret = self.source()\\n if ret is None:\\n LOGGER.warning('Statistics are not available')\\n return\\n gauge = GaugeMetricFamily('wemo_device_state', 'Status of Wemo device', labels=['address', 'parameter'])\\n gauge.add_metric([ret.address, 'today_kwh'], ret.today_kwh, timestamp=ret.collection_time.timestamp())\\n gauge.add_metric([ret.address, 'current_power_mW'], ret.current_power,\\n timestamp=ret.collection_time.timestamp())\\n gauge.add_metric([ret.address, 'today_on_time'], ret.today_on_time, timestamp=ret.collection_time.timestamp())\\n gauge.add_metric([ret.address, 'on_for'], ret.on_for, timestamp=ret.collection_time.timestamp())\\n gauge.add_metric([ret.address, 'today_standby_time'], ret.today_standby_time,\\n timestamp=ret.collection_time.timestamp())\\n\\n yield gauge\\n\\n counter = CounterMetricFamily('wemo_power_usage', 'Today power consumption', labels=['address'])\\n counter.add_metric([ret.address], ret.today_kwh, timestamp=ret.collection_time.timestamp())\\n yield counter\",\n \"def get_data(self):\\n data = list(IgnitionRow.objects.all().order_by('-pub_date')[:self.num_ticks].values())\\n two_hours = data[::-1]\\n #two_hours = data\\n num_players_data = [[max(min(elem['num_players_{}'.format(key)],50),0) for elem in two_hours] for key in self.keys]\\n return num_players_data\",\n \"def _aggregate_perf_data(perf_all_ordinals: List[str]):\\n aggregate = {}\\n\\n pd = PerfData()\\n for data in perf_all_ordinals:\\n worker_pd = PerfData(**json.loads(data))\\n if len(perf_all_ordinals) > 1:\\n aggregate.setdefault(\\\"ordinals\\\", [])\\n aggregate[\\\"ordinals\\\"].append(worker_pd.throughput_dict())\\n\\n pd.merge(worker_pd)\\n\\n aggregate.update(dataclasses.asdict(pd))\\n return aggregate\",\n \"def get_all_stats():\\n\\n return get_component(CachingPackage.COMPONENT_NAME).get_all_stats()\",\n \"def get_metric_list(config):\\n metric_list = []\\n url = config[\\\"OPENTSDB_URL\\\"] + \\\"/api/suggest?type=metrics&q=\\\"\\n response = requests.get(url)\\n if response.status_code == 200:\\n metric_list = response.json()\\n logger.debug(\\\"Get metric list from opentsdb: \\\" + str(metric_list))\\n return metric_list\",\n \"def get_metrics_list(self, project, callback=None):\\n metrics = sorted((yield gen.Task(self.client.smembers,\\n self.get_metrics_key(project))))\\n if callback:\\n callback(metrics)\",\n \"def get_data(self):\\n return [10, 20, 30]\",\n \"def _get_daily_statistics(self, cr, uid, ids, field_name, arg, context=None):\\n obj = self.pool['mail.mail.statistics']\\n res = {}\\n for mailing in self.browse(cr, uid, ids, context=context):\\n res[mailing.id] = {}\\n date = mailing.sent_date if mailing.sent_date else mailing.create_date\\n date_begin = datetime.strptime(date, tools.DEFAULT_SERVER_DATETIME_FORMAT)\\n date_end = date_begin + relativedelta.relativedelta(days=self._period_number - 1)\\n date_begin_str = date_begin.strftime(tools.DEFAULT_SERVER_DATETIME_FORMAT)\\n date_end_str = date_end.strftime(tools.DEFAULT_SERVER_DATETIME_FORMAT)\\n domain = [('mass_mailing_id', '=', mailing.id), ('opened', '>=', date_begin_str), ('opened', '<=', date_end_str)]\\n res[mailing.id]['opened_daily'] = json.dumps(self.__get_bar_values(cr, uid, obj, domain, ['opened'], 'opened_count', 'opened:day', date_begin, context=context))\\n domain = [('mass_mailing_id', '=', mailing.id), ('replied', '>=', date_begin_str), ('replied', '<=', date_end_str)]\\n res[mailing.id]['replied_daily'] = json.dumps(self.__get_bar_values(cr, uid, obj, domain, ['replied'], 'replied_count', 'replied:day', date_begin, context=context))\\n return res\",\n \"def data_quartiles(self):\\n data = []\\n for graph in self._graphs.values():\\n data += graph.data.values()\\n data.sort()\\n datalen = len(data)\\n return(data[0], data[datalen/4], data[datalen/2],\\n data[3*datalen/4], data[-1])\",\n \"def get_stats(self):\\n return utils.csv_to_dict(wait(self.proto.stat()))\",\n \"def metrics_get(period):\\n return flask.jsonify({\\\"message\\\": \\\"noop\\\"}), 200\",\n \"def getAllMetrics(self):\\n result = self.getReportMetrics()\\n result.update(self.getOptimizationMetrics())\\n return result\",\n \"def build_metrics_counter_data(count_metrics):\\n return [{'name': name, 'delta': delta} for name, delta in iteritems(count_metrics)]\",\n \"def metrics(self, request):\\n return OtterMetrics(self.store).app.resource()\",\n \"def get_metrics(event):\\n return tba_session.get(BASE_URL + '/event/%s/oprs' %event).json()\",\n \"def listmetrics(namespace: str = None, Region_name: str = None) -> List:\\r\\n cloudwatch = client('cloudwatch', region_name=Region_name)\\r\\n paginator = cloudwatch.get_paginator('list_metrics')\\r\\n metrics=[] # type: List\\r\\n if namespace is not None:\\r\\n page = paginator.paginate(Namespace=namespace) \\r\\n else:\\r\\n page = paginator.paginate()\\r\\n for response in page:\\r\\n for metric in response['Metrics']:\\r\\n metrics.append(metric)\\r\\n return metrics\",\n \"def metrics(self):\\n return self.verificationRun.metrics()\",\n \"def get_metric(ms):\\n\\treturn '['+','.join(str(m) for m in ms)+']'\",\n \"def parse_response(response):\\n data = []\\n \\n for report in response.get('reports', []):\\n columnHeader = report.get('columnHeader', {})\\n dimensionHeaders = columnHeader.get('dimensions', [])\\n metricHeaders = columnHeader.get('metricHeader', {}).get('metricHeaderEntries', [])\\n rows = report.get('data', {}).get('rows', [])\\n \\n row_count = 0 \\n for row in rows:\\n #print '\\\\n\\\\n', 'ROW_COUNT: ', row_count, '\\\\n'\\n data.append({}) \\n\\n dimensions = row.get('dimensions', [])\\n dateRangeValues = row.get('metrics', [])\\n\\n for header, dimension in zip(dimensionHeaders, dimensions):\\n #print header + ': ' + dimension\\n data[row_count][header[3:]] = dimension\\n \\n for i, values in enumerate(dateRangeValues):\\n #print 'Date range (' + str(i) + ')'\\n for metricHeader, value in zip(metricHeaders, values.get('values')):\\n #print metricHeader.get('name') + ': ' + value\\n data[row_count][metricHeader.get('name')[3:]] = value\\n \\n row_count += 1 \\n \\n return data\",\n \"def calculate_metrics(metrics_data: List[Tuple[Metric, DataType]]) -> List[float]:\\n pass\",\n \"def collect(self):\\n service = build_service('calendar', 'v3')\\n data = []\\n\\n for time_window, suffix in zip(self._parameters['time_windows'],\\n self._parameters['suffixes']):\\n # TODO: Get Search prefix and calendar from configs\\n events = get_calendar_entries_by_query(service, \\\"S-\\\", time_window, \\\"Tracking\\\")\\n\\n # If we don't have any events we set it to the window size (which is in hours)\\n seconds_since = [time_window * 60 * 60]\\n long_count = 0\\n short_count = 0\\n for event in events:\\n start_time = parse(event['start'].get('dateTime'))\\n end_time = parse(event['end'].get('dateTime'))\\n seconds_since.append(\\n (datetime.datetime.now(datetime.timezone.utc) - start_time).total_seconds())\\n event_length = end_time - start_time\\n if event_length.seconds <= 30*60:\\n short_count += 1\\n else:\\n long_count += 1\\n\\n data.append(DataPoint(time.time(), self._base_name + 's_count.' + suffix, len(events)))\\n data.append(DataPoint(time.time(), self._base_name + 's_seconds_since.' + suffix, min(seconds_since)))\\n data.append(DataPoint(time.time(), self._base_name + 's_short_count.' + suffix, short_count))\\n data.append(DataPoint(time.time(), self._base_name + 's_long_count.' + suffix, long_count))\\n return data\",\n \"def getAllMeasurement(self): \\n return self.measurement\",\n \"def getFormattedJobStatistics(self):\\n\\t\\tformatted_job_stats = [self.name]\\n\\t\\tformatted_job_stats.append(str(self.retry_count))\\n\\t\\tif self.site is None:\\n\\t\\t\\tformatted_job_stats.append('-')\\n\\t\\telse:\\n\\t\\t\\tformatted_job_stats.append(self.site)\\n\\t\\tformatted_job_stats.append(round_to_str(self.kickstart))\\n\\t\\tformatted_job_stats.append(round_to_str(self.post))\\n\\t\\tformatted_job_stats.append(round_to_str(self.condor_delay))\\n\\t\\tformatted_job_stats.append(round_to_str(self.resource))\\n\\t\\tformatted_job_stats.append(round_to_str(self.runtime))\\n\\t\\tformatted_job_stats.append(round_to_str(self.seqexec))\\n\\t\\tformatted_job_stats.append(round_to_str(self.seqexec_delay))\\n\\t\\treturn formatted_job_stats\",\n \"def flush(self):\\n with self._lock:\\n batch = self._batch\\n timestamps = self._timestamps\\n\\n items = []\\n for identity, value in batch.items():\\n metric = {}\\n typ, name, tags = identity\\n metric[\\\"name\\\"] = name\\n if typ:\\n metric[\\\"type\\\"] = typ\\n else:\\n metric[\\\"timestamp\\\"] = timestamps[identity]\\n\\n if tags:\\n metric[\\\"attributes\\\"] = dict(tags)\\n\\n metric[\\\"value\\\"] = value\\n items.append(metric)\\n\\n items = tuple(items)\\n\\n batch.clear()\\n timestamps.clear()\\n\\n common = self._common.copy()\\n common[\\\"timestamp\\\"] = self._interval_start\\n now = int(time.time() * 1000.0)\\n interval = now - self._interval_start\\n common[\\\"interval.ms\\\"] = interval\\n\\n self._interval_start = now\\n\\n return items, common\",\n \"def metrics(_):\\r\\n collector = BuildsCollector()\\r\\n build_metrics, headers = collector.get_metrics_table()\\r\\n print(tabulate(build_metrics, headers=headers))\",\n \"def get_scoped_to_feature() -> Iterable[Feature] | Feature | None:\\n return Feature.METRICS\",\n \"def statistics(self):\\n return self.get_statistics()\",\n \"def get_general_stats() ->List[BaseStat]:\\n return [PositionalTendencies(),\\n SpeedTendencies(),\\n ItemGoals(),\\n DropshotGoals(),\\n DropshotBallPhaseTimes(),\\n DropshotStats()\\n ]\",\n \"def init_metric_definitions():\\n metric_definitions = []\\n\\n # add info to list in memory, one by one, following signature values\\n metric_def_ID = 1\\n metric_def_name = \\\"Recovery Time\\\"\\n metric_def_info = \\\"Measures time taken by ONAP to restore a VNF\\\"\\n metric_definitions.append(RecoveryTimeDef(metric_def_ID, metric_def_name,\\n metric_def_info))\\n\\n metric_def_ID = 2\\n metric_def_name = \\\"Uptime Percentage\\\"\\n metric_def_info = \\\"Measures ratio of uptime to reference time, not counting planned downtime\\\"\\n metric_definitions.append(UptimePercentageDef(metric_def_ID, metric_def_name,\\n metric_def_info))\\n\\n\\n # write list to binary file\\n write_list_bin(metric_definitions, FILE_METRIC_DEFINITIONS)\\n\\n return metric_definitions\",\n \"def get_next_batch(self):\\n\\n metrics = {}\\n for struct in self.metrics.values():\\n metrics = {**metrics, **struct.get_next_batch()}\\n\\n return metrics\",\n \"def get_data(self):\\n data = list(IgnitionRowPredictionOLS.objects.all().order_by('-pub_date')[:self.num_ticks].values())\\n two_hours = data[::-1]\\n num_players_data = [[elem['num_players_{}'.format(key)] for elem in two_hours] for key in self.keys]\\n return num_players_data\",\n \"def get_stats(self): \\n return dict(l.split('\\\\t', 1) \\\\\\n for l in wait(self.proto.stat()).splitlines() if l)\",\n \"def read_metrics_and_assertions(self) -> (\\n typing.Iterable[typing.Tuple[str, float, metrics_pb2.Assertion]]):\\n raise NotImplementedError\",\n \"def metrics():\\n\\tmetrics = pd.DataFrame(index=['accuracy', 'precision', 'recall'],\\n\\t\\t\\t\\t\\t\\t columns=['LogisticReg', 'NeuralNetwork'])\\n\\treturn metrics\",\n \"def get_values(self, names):\\n r = []\\n for n in names:\\n if n in self.raw_metrics:\\n r.append(self.raw_metrics[n])\\n else:\\n return None\\n return r\",\n \"def core_stats():\\n data = get_tsv_dataset(os.path.join(DATA_DIR, TOTALS_FILE))\\n if data is None:\\n return make_response(jsonify({'error': 'Data could not be read'}), 500)\\n # parse up so we can manipulate things.\\n dataset = [int(x) for x in data]\\n annual_sightings = sum(dataset)\\n # for each 'month' (selection of x4\\n monthly_sightings = []\\n max_sightings = 0\\n max_month = 0\\n\\n # grab each month's data into its own list for post processing.\\n # also calculate some other numbers as we go.\\n for i in range(0, len(dataset), 4):\\n # select 4x data points.\\n this_month = dataset[i:i + 4]\\n total_sightings_this_month = sum(this_month)\\n monthly_sightings.append(total_sightings_this_month)\\n old_max = max_sightings\\n max_sightings = max(max_sightings, total_sightings_this_month)\\n if old_max < max_sightings:\\n # it could be the 0th month.\\n max_month = len(monthly_sightings)\\n\\n mean_monthly_sightings = mean(monthly_sightings)\\n month_name = list(calendar.month_name)[max_month]\\n return make_response(jsonify({'annual_sightings': annual_sightings,\\n 'max_sightings': max_sightings,\\n 'max_sighting_month': month_name,\\n 'mean_monthly_sightings': mean_monthly_sightings}), 200)\",\n \"def stats(self):\\r\\n return {}\",\n \"def mymetrics(): \\n _update_metric_counters()\\n logging.debug(prom_objects_seen.collect())\\n return flask.Response(generate_latest(), mimetype='text/plain')\",\n \"def get_metric_statistics(self, period, start_time, end_time, metric_name,\\n statistics, dimensions=None):\\n # Ugly heuristic to determine where to query a metric from\\n if dimensions and dimensions.get('pid') and metric_name in self.app_metrics:\\n query_type = 'application'\\n index_by = 'pid'\\n elif dimensions and dimensions.get('pid') and metric_name in self.host_metrics:\\n query_type = 'host'\\n index_by = 'pid'\\n elif dimensions and dimensions.get('hostname') and metric_name in self.app_metrics:\\n query_type = 'application'\\n index_by = 'hostname'\\n elif 'app_attributes' in metric_name:\\n query_type = 'application'\\n index_by = 'pid'\\n else:\\n query_type = 'host'\\n index_by = 'hostname'\\n\\n if not isinstance(start_time, datetime):\\n raise TypeError(\\\"start_time must be a datetime object\\\")\\n if not isinstance(end_time, datetime):\\n raise TypeError(\\\"end_time must be a datetime object\\\")\\n\\n tformat = \\\"%Y%m%d %H:%M\\\"\\n interval = \\\"%s,%s\\\" % (start_time.strftime(tformat), end_time.strftime(tformat))\\n time_group = int(period)\\n\\n if metric_name.startswith('app_attributes:'):\\n metric_name, app_attribute = metric_name.split(':')\\n else:\\n app_attribute = ''\\n if index_by == 'pid':\\n query_fields = [metric_name, ]\\n else:\\n query_fields = [index_by, metric_name, ]\\n\\n script = self._build_script(query_fields, query_type, interval, time_group, dimensions)\\n\\n authenticate = 'basic' if self.username and self.password else None\\n url = self._build_query_url(self.base_url, authenticate=authenticate,\\n script=script)\\n request = urllib2.Request(url)\\n\\n if self.username and self.password:\\n auth_header = self._build_auth_header(self.username, self.password)\\n request.add_header('Authorization', auth_header)\\n\\n reply = urllib2.urlopen(request)\\n results = {}\\n reader = csv.reader(reply)\\n for metrics in reader:\\n if metrics == []:\\n continue\\n\\n if index_by == 'pid':\\n match = re.search('pid=(.*?)&', metrics[0])\\n if match:\\n index = match.group(1)\\n else:\\n continue\\n else:\\n index = metrics.pop(0)\\n if index == '':\\n continue\\n\\n result = results.get(index, {Statistics.SERIES: []})\\n for i, metric in enumerate(metrics):\\n if metric_name == 'app_attributes' and app_attribute:\\n metric = _extract_app_attribute(metric, app_attribute)\\n\\n result[Statistics.SERIES].append(metric)\\n\\n results[index] = result\\n\\n for index, metric in results.iteritems():\\n series = metric[Statistics.SERIES]\\n if Statistics.AVERAGE in statistics:\\n try:\\n metric[Statistics.AVERAGE] = sum(map(float, series)) / float(len(series))\\n except (ZeroDivisionError, ValueError):\\n metric[Statistics.AVERAGE] = 0.0\\n if Statistics.SUM in statistics:\\n metric[Statistics.SUM] = sum(map(float, series))\\n if Statistics.SAMPLE_COUNT in statistics:\\n metric[Statistics.SAMPLE_COUNT] = len(series)\\n if Statistics.MAXIMUM in statistics:\\n metric[Statistics.MAXIMUM] = max(map(float, series))\\n if Statistics.MINIMUM in statistics:\\n metric[Statistics.MINIMUM] = min(map(float, series))\\n\\n return results\",\n \"def get_metric_for_all_dates(inargs, exp_id):\\n date_list = []\\n # Loop over dates and collect data\\n for date in h.make_timelist(inargs.date_start, inargs.date_stop,\\n inargs.hours_inc):\\n date_list.append(get_metric_for_one_day(inargs, exp_id, date))\\n return np.array(date_list)\",\n \"def population_timeline_chart_data(matchid):\\n ps = Match.query.get(matchid).populationstats.all()\\n labels = []\\n popcounts = []\\n lowestPop = 100\\n\\n for snapshot in ps:\\n labels.append(snapshot.time.strftime('%H:%M'))\\n popcounts.append(snapshot.popcount)\\n if snapshot.popcount is None or snapshot.popcount < lowestPop:\\n lowestPop = snapshot.popcount\\n\\n return json.dumps(labels), json.dumps(popcounts), lowestPop\",\n \"def get_data(self):\\n data = list(IgnitionRowPredictionCVX.objects.all().order_by('-pub_date')[:self.num_ticks].values())\\n two_hours = data[::-1]\\n num_players_data = [[elem['num_players_{}'.format(key)] for elem in two_hours] for key in self.keys]\\n return num_players_data\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.63188374","0.61312807","0.59498423","0.59364164","0.59249073","0.58868825","0.5866983","0.5859181","0.57975364","0.5722001","0.57070863","0.57040983","0.5697402","0.5691452","0.5689769","0.5687561","0.5686129","0.5646755","0.56391305","0.5638463","0.5635187","0.5616114","0.5606177","0.5598606","0.5598606","0.55425334","0.55383384","0.5533974","0.5528502","0.551542","0.5487736","0.5486818","0.54844344","0.54571635","0.54278886","0.5421712","0.5421639","0.5405077","0.53709936","0.5367911","0.5354709","0.53222334","0.53190094","0.52956116","0.5294608","0.52773863","0.52704525","0.5257143","0.52420276","0.5241034","0.5234681","0.52256286","0.52200747","0.51918274","0.5176487","0.5160102","0.51492894","0.5149026","0.51411676","0.513769","0.51329356","0.5132127","0.51149696","0.511279","0.5111572","0.5111125","0.51075345","0.5103818","0.5102552","0.5097158","0.5093842","0.5091793","0.5087501","0.5086499","0.50805986","0.50791514","0.50786304","0.50695014","0.50573736","0.50434643","0.50434095","0.50417304","0.50390923","0.50360835","0.50340307","0.5033167","0.5032438","0.5029268","0.5021769","0.5021764","0.5013847","0.5007797","0.50061584","0.49977797","0.4997243","0.4994867","0.49878496","0.49873653","0.4973891","0.49721482"],"string":"[\n \"0.63188374\",\n \"0.61312807\",\n \"0.59498423\",\n \"0.59364164\",\n \"0.59249073\",\n \"0.58868825\",\n \"0.5866983\",\n \"0.5859181\",\n \"0.57975364\",\n \"0.5722001\",\n \"0.57070863\",\n \"0.57040983\",\n \"0.5697402\",\n \"0.5691452\",\n \"0.5689769\",\n \"0.5687561\",\n \"0.5686129\",\n \"0.5646755\",\n \"0.56391305\",\n \"0.5638463\",\n \"0.5635187\",\n \"0.5616114\",\n \"0.5606177\",\n \"0.5598606\",\n \"0.5598606\",\n \"0.55425334\",\n \"0.55383384\",\n \"0.5533974\",\n \"0.5528502\",\n \"0.551542\",\n \"0.5487736\",\n \"0.5486818\",\n \"0.54844344\",\n \"0.54571635\",\n \"0.54278886\",\n \"0.5421712\",\n \"0.5421639\",\n \"0.5405077\",\n \"0.53709936\",\n \"0.5367911\",\n \"0.5354709\",\n \"0.53222334\",\n \"0.53190094\",\n \"0.52956116\",\n \"0.5294608\",\n \"0.52773863\",\n \"0.52704525\",\n \"0.5257143\",\n \"0.52420276\",\n \"0.5241034\",\n \"0.5234681\",\n \"0.52256286\",\n \"0.52200747\",\n \"0.51918274\",\n \"0.5176487\",\n \"0.5160102\",\n \"0.51492894\",\n \"0.5149026\",\n \"0.51411676\",\n \"0.513769\",\n \"0.51329356\",\n \"0.5132127\",\n \"0.51149696\",\n \"0.511279\",\n \"0.5111572\",\n \"0.5111125\",\n \"0.51075345\",\n \"0.5103818\",\n \"0.5102552\",\n \"0.5097158\",\n \"0.5093842\",\n \"0.5091793\",\n \"0.5087501\",\n \"0.5086499\",\n \"0.50805986\",\n \"0.50791514\",\n \"0.50786304\",\n \"0.50695014\",\n \"0.50573736\",\n \"0.50434643\",\n \"0.50434095\",\n \"0.50417304\",\n \"0.50390923\",\n \"0.50360835\",\n \"0.50340307\",\n \"0.5033167\",\n \"0.5032438\",\n \"0.5029268\",\n \"0.5021769\",\n \"0.5021764\",\n \"0.5013847\",\n \"0.5007797\",\n \"0.50061584\",\n \"0.49977797\",\n \"0.4997243\",\n \"0.4994867\",\n \"0.49878496\",\n \"0.49873653\",\n \"0.4973891\",\n \"0.49721482\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5418441"},"document_rank":{"kind":"string","value":"37"}}},{"rowIdx":3690,"cells":{"query":{"kind":"string","value":"Returns a count of the number of unique metrics."},"document":{"kind":"string","value":"def metric_data_count(self):\n\n if not self.__settings:\n return 0\n\n return len(self.__stats_table)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def metrics_count(self):\n\n return len(self.__stats_table)","def get_count(cls):\n total = 0\n for counter in SimpleCounterShard.objects.all():\n total += counter.count\n return total","def count(self):\n return self._reduce_for_stat_function(F.count, only_numeric=False)","def number_of_running_metrics(self):\n try:\n return len(self.get_classads(\"OSGRSV==\\\"metrics\\\"\"))\n except TypeError:\n self.rsv.log(\"ERROR\", \"Classad parsing failed, unable to count running metrics\")","def Count(self) -> int:","def Count(self) -> int:","def Count(self) -> int:","def Count(self) -> int:","def test_task_unique_total(self):\r\n # from bookie.bcelery import tasks\r\n tasks.count_unique()\r\n\r\n stat = StatBookmark.query.first()\r\n self.assertEqual(stat.attrib, stats.UNIQUE_CT)\r\n self.assertEqual(stat.data, 3)","def count():","def count_unique_features(self):\n return N_UNIQUE_FEATS","def count(self, key):\n self._metrics[key] += 1","def count(self):\n return sum(1 for _ in self)","def count_unique(self) -> int:\n # Binary Search Tree == empty\n if self.root is None:\n return 0\n\n #count values\n q = Queue()\n return self.count_unique_helper(self.root, q)","def count(self):\n # TODO not implemented yet\n return 0","def _fetch_count_metrics_and_clear(self):\n with self._count_rlock:\n count_metrics = self._count_metrics\n self._count_metrics = defaultdict(int)\n\n return count_metrics","def count(self):\n return len([i for i in self.iteritems()])","def count(self) -> Tuple[groupable, pdarray]:\n repMsg = generic_msg(\n cmd=\"countReduction\",\n args={\"segments\": cast(pdarray, self.segments), \"size\": self.length},\n )\n self.logger.debug(repMsg)\n return self.unique_keys, create_pdarray(repMsg)","def get_counts(self):\n self._update_counts()\n return self.failures, self.warnings, self.infos","def count_unique():\r\n trans = transaction.begin()\r\n StatBookmarkMgr.count_unique_bookmarks()\r\n trans.commit()","def count_counts(self):\n count_counts = defaultdict(Counter)\n for token, followers in self._dict.items():\n for f, count in followers.items():\n count_counts[token][count] += 1\n count_counts[token][0] = len(self._dict) - sum(count_counts[token].values())\n return count_counts","def get_number_of_unique_students(self):\n unique_students = set()\n for row in self.responses:\n unique_students.add(row.student)\n return len(unique_students)","def count_unique(self) -> int:\n if self.root is None: # If tree is empty\n return 0\n\n q = Queue()\n return self.unique_helper(self.root, q)","def __uniqueCounts(rows):\n results = {} #Initialize a dictionary to store the results\n for row in rows: #Iterate over all rows of data\n #The result is the last column\n r = row[-1]\n if r not in results: results[r] = 0 #Start the count for each class at zero\n results[r] += 1 #Increment the count for this row's class by 1\n return results","def count_umls(self) -> int:\n return self._count_model(Umls)","def counts(self):\n return sum(self.counter.values()), len(self.visited)","def counts(self):\n return sum(self.counter.values()), len(self.visited)","async def test_nr_of_metrics(self):\n response = await self.collect(get_request_json_side_effect=[self.data_model, self.reports])\n self.assert_measurement(\n response,\n value=str(len(self.entities)),\n total=self.expected_software_metrics,\n entities=self.entities,\n )","def count(self):\n\n raise NotImplementedError","def count(self):\n return self.get_count()","def count() -> int:\n pass","def get_count():\n _check_init()\n return _pypm.CountDevices()","def count_barcodes(metrics_file):\n\n barcodes = pd.read_csv(metrics_file, sep=\"\\t\", header=0, names=[\"barcode\", \"randomer\", \"count\"])\n return Counter(dict(barcodes.groupby(\"barcode\")['count'].sum().iteritems()))","def count(self, tokens):\n return self.counts[tokens]","def test_counts(self):\n c = array([5,0,1,1,5,5])\n obs = counts(c)\n exp = array([1,2,0,0,0,3])\n self.assertEqual(obs, exp)\n d = array([2,2,1,0])\n obs = counts(d, obs)\n exp = array([2,3,2,0,0,3])\n self.assertEqual(obs, exp)","def uniqueNodeCount(eval):\n if not isEvaluator(eval):\n return 0\n return eval.ReferencedUniqueNodes().Size()","def count_elements_in_dataset(dataset):\n return dataset.count()","def count(self) -> int:\n return pulumi.get(self, \"count\")","def _get_count(results):\n return len(results)","def count(self):\n return int()","def count(self):\n return int()","def count(self) -> int:\n return self.__count","def count(self, cls=None):\n return len(self.all(cls))","def __len__(self):\n return self.data.index.get_level_values(0).to_series().nunique()","def Count(self):\r\n\t\treturn self._get_attribute('count')","def Count(self):\r\n\t\treturn self._get_attribute('count')","def get_count(name, key):\n total = 0\n query = CounterShard.all().filter('name = ', name).filter('reference_key = ', key)\n for counter in query:\n total += counter.count\n \n return total","def count(self):\n\n return self._get(\"count\", rtype=UInt)","def count(self):\n return self.size()","def get_number_of_unique_values(local_data, attr):\n\tvalues = []\n\tfor element in local_data:\n\t\tif element[attr] not in values:\n\t\t\tvalues.extend([element[attr]])\n\treturn len(values)","def count(self) -> float:\n return pulumi.get(self, \"count\")","def count(self):\n nreq, nres = 0, 0\n for entry in self.__history:\n if entry.oreq is not None:\n nreq += 1\n if entry.ores is not None:\n nres += 1\n return nreq, nres","def get_count(self):\n\n\t\treturn self.__count","def count_unique_bookmarks():\r\n total = BmarkMgr.count(distinct=True)\r\n stat = StatBookmark(attrib=UNIQUE_CT, data=total)\r\n DBSession.add(stat)","def count(self):\n\n if self.cluster:\n return self.cluster.count()\n\n return super().count()","def specht(mu):\n return StandardTableaux(mu).cardinality().n()","def tally(self):\n return self.count","def count(self):\n \n return self._count","def _count(self):\n if self._count_valid:\n return self._total_results\n\n result = self._cb.get_object(self._doc_class.urlobject.format(self._cb.credentials.org_key))\n results = result.get(\"results\", [])\n\n self._total_results = len(results)\n self._count_valid = True\n\n return self._total_results","def count(self) -> int:\n return self._count","def count(self) -> int:\n return self._count","def count(self) -> int:\n return self._count","def count(self, tokens):\n return self._count.get(tuple(tokens), 0)","def count(self, resource):\n return len(self.all(resource))","def observation_count(self):\n if not self.can_update():\n self._handle_error(910, [self.type])\n return self.tc_requests.observation_count(\n self.api_type, self.api_branch, self.unique_id, owner=self.owner\n )","def oneup_count(self):\n return self.oneups.filter(Oneup.state >= 0).count()","def count(self, axis=None):\n return self.data.count(axis=axis)","def count_benchmarks():\n return len(setup_storage().fetch_benchmark({}))","def get_num_measured_outputs(self):\n i = 0\n for o in self.outputs:\n if o.is_measured_output():\n i += 1\n return i","def Count(self):\n return self._get_attribute('count')","def totalhashes(self):\n return np.sum(self.counts)","def n_users(self):\n if self._n_users is None:\n self._n_users = len(self.user_unique_vals)\n return self._n_users","def fetch_counts_for_debug(stdout):\n test_names = TEST_NAMES_DEBUG_APP_PATTERN.findall(stdout)\n test_counts = collections.Counter(test_class for test_class, _ in test_names\n if test_class not in IGNORED_CLASSES)\n\n return test_counts","def count(self):\n return len(self)","def test_get_virtual_machine_count_metrics1(self):\n pass","def totalCount(self):\n return sum(self.values())","def totalCount(self):\n return sum(self.values())","def totalCount(self):\n return sum(self.values())","def get_untested_count(self):\n return sum(1 for outcome in (r.outcome for r in self.values()) if outcome == Result.UNTESTED)","def get_total_counts(self):\n ret = {}\n all_loggers_count = 0\n for logger, name_map in self.acc_map.items():\n cur_logger_count = 0\n ret[logger.name] = {}\n for name, status_map in name_map.items():\n cur_name_count = 0\n ret[logger.name][name] = {}\n for status, acc in status_map.items():\n cur_count = acc.total_count\n ret[logger.name][name][status] = cur_count\n cur_name_count += cur_count\n cur_logger_count += cur_count\n all_loggers_count += cur_count\n ret[logger.name][name]['__all__'] = cur_name_count\n ret[logger.name]['__all__'] = cur_logger_count\n ret['__all__'] = all_loggers_count\n return ret","def resultCounter(detections):\n counter = 0\n for attribute, value in classIterator(detections):\n if 'crease' in attribute:\n counter += len(value)\n return counter","def count(self):\n return len(self.names)","def snmpqosqos_sch_sessions_regulated_count(self) :\n\t\ttry :\n\t\t\treturn self._snmpqosqos_sch_sessions_regulated_count\n\t\texcept Exception as e:\n\t\t\traise e","def count(self) -> int:\n return self._adapter.count()","def Count():\n return CheckForError(lib.Generators_Get_Count())","def test_set_count(self) -> int:\n return pulumi.get(self, \"test_set_count\")","def num_humans(self):\n return len(self._human_list)","def num_humans(self):\n return len(self._human_list)","def count(self, tokens):\n return self._count[tuple(tokens)]","def count(self):\n return self._lift(\"count\")","def get_count(self):\r\n return self.count","def update_count(self):\n count_metrics = self._fetch_count_metrics_and_clear()\n self._logger.info('update_count. count_metrics = %s',\n build_metrics_counter_data(count_metrics))","def GetCount(name):\n counter = StrongCounter.get_or_insert(key_name=name)\n return counter.count","def count(self):\n return self._count","def count(self):\n return self._count","def count(self):\n return self._count","def count(self):\n return self._count","def count(self):\n return self._count","def count(self):\n return self._count","def count(self):\n return self._count"],"string":"[\n \"def metrics_count(self):\\n\\n return len(self.__stats_table)\",\n \"def get_count(cls):\\n total = 0\\n for counter in SimpleCounterShard.objects.all():\\n total += counter.count\\n return total\",\n \"def count(self):\\n return self._reduce_for_stat_function(F.count, only_numeric=False)\",\n \"def number_of_running_metrics(self):\\n try:\\n return len(self.get_classads(\\\"OSGRSV==\\\\\\\"metrics\\\\\\\"\\\"))\\n except TypeError:\\n self.rsv.log(\\\"ERROR\\\", \\\"Classad parsing failed, unable to count running metrics\\\")\",\n \"def Count(self) -> int:\",\n \"def Count(self) -> int:\",\n \"def Count(self) -> int:\",\n \"def Count(self) -> int:\",\n \"def test_task_unique_total(self):\\r\\n # from bookie.bcelery import tasks\\r\\n tasks.count_unique()\\r\\n\\r\\n stat = StatBookmark.query.first()\\r\\n self.assertEqual(stat.attrib, stats.UNIQUE_CT)\\r\\n self.assertEqual(stat.data, 3)\",\n \"def count():\",\n \"def count_unique_features(self):\\n return N_UNIQUE_FEATS\",\n \"def count(self, key):\\n self._metrics[key] += 1\",\n \"def count(self):\\n return sum(1 for _ in self)\",\n \"def count_unique(self) -> int:\\n # Binary Search Tree == empty\\n if self.root is None:\\n return 0\\n\\n #count values\\n q = Queue()\\n return self.count_unique_helper(self.root, q)\",\n \"def count(self):\\n # TODO not implemented yet\\n return 0\",\n \"def _fetch_count_metrics_and_clear(self):\\n with self._count_rlock:\\n count_metrics = self._count_metrics\\n self._count_metrics = defaultdict(int)\\n\\n return count_metrics\",\n \"def count(self):\\n return len([i for i in self.iteritems()])\",\n \"def count(self) -> Tuple[groupable, pdarray]:\\n repMsg = generic_msg(\\n cmd=\\\"countReduction\\\",\\n args={\\\"segments\\\": cast(pdarray, self.segments), \\\"size\\\": self.length},\\n )\\n self.logger.debug(repMsg)\\n return self.unique_keys, create_pdarray(repMsg)\",\n \"def get_counts(self):\\n self._update_counts()\\n return self.failures, self.warnings, self.infos\",\n \"def count_unique():\\r\\n trans = transaction.begin()\\r\\n StatBookmarkMgr.count_unique_bookmarks()\\r\\n trans.commit()\",\n \"def count_counts(self):\\n count_counts = defaultdict(Counter)\\n for token, followers in self._dict.items():\\n for f, count in followers.items():\\n count_counts[token][count] += 1\\n count_counts[token][0] = len(self._dict) - sum(count_counts[token].values())\\n return count_counts\",\n \"def get_number_of_unique_students(self):\\n unique_students = set()\\n for row in self.responses:\\n unique_students.add(row.student)\\n return len(unique_students)\",\n \"def count_unique(self) -> int:\\n if self.root is None: # If tree is empty\\n return 0\\n\\n q = Queue()\\n return self.unique_helper(self.root, q)\",\n \"def __uniqueCounts(rows):\\n results = {} #Initialize a dictionary to store the results\\n for row in rows: #Iterate over all rows of data\\n #The result is the last column\\n r = row[-1]\\n if r not in results: results[r] = 0 #Start the count for each class at zero\\n results[r] += 1 #Increment the count for this row's class by 1\\n return results\",\n \"def count_umls(self) -> int:\\n return self._count_model(Umls)\",\n \"def counts(self):\\n return sum(self.counter.values()), len(self.visited)\",\n \"def counts(self):\\n return sum(self.counter.values()), len(self.visited)\",\n \"async def test_nr_of_metrics(self):\\n response = await self.collect(get_request_json_side_effect=[self.data_model, self.reports])\\n self.assert_measurement(\\n response,\\n value=str(len(self.entities)),\\n total=self.expected_software_metrics,\\n entities=self.entities,\\n )\",\n \"def count(self):\\n\\n raise NotImplementedError\",\n \"def count(self):\\n return self.get_count()\",\n \"def count() -> int:\\n pass\",\n \"def get_count():\\n _check_init()\\n return _pypm.CountDevices()\",\n \"def count_barcodes(metrics_file):\\n\\n barcodes = pd.read_csv(metrics_file, sep=\\\"\\\\t\\\", header=0, names=[\\\"barcode\\\", \\\"randomer\\\", \\\"count\\\"])\\n return Counter(dict(barcodes.groupby(\\\"barcode\\\")['count'].sum().iteritems()))\",\n \"def count(self, tokens):\\n return self.counts[tokens]\",\n \"def test_counts(self):\\n c = array([5,0,1,1,5,5])\\n obs = counts(c)\\n exp = array([1,2,0,0,0,3])\\n self.assertEqual(obs, exp)\\n d = array([2,2,1,0])\\n obs = counts(d, obs)\\n exp = array([2,3,2,0,0,3])\\n self.assertEqual(obs, exp)\",\n \"def uniqueNodeCount(eval):\\n if not isEvaluator(eval):\\n return 0\\n return eval.ReferencedUniqueNodes().Size()\",\n \"def count_elements_in_dataset(dataset):\\n return dataset.count()\",\n \"def count(self) -> int:\\n return pulumi.get(self, \\\"count\\\")\",\n \"def _get_count(results):\\n return len(results)\",\n \"def count(self):\\n return int()\",\n \"def count(self):\\n return int()\",\n \"def count(self) -> int:\\n return self.__count\",\n \"def count(self, cls=None):\\n return len(self.all(cls))\",\n \"def __len__(self):\\n return self.data.index.get_level_values(0).to_series().nunique()\",\n \"def Count(self):\\r\\n\\t\\treturn self._get_attribute('count')\",\n \"def Count(self):\\r\\n\\t\\treturn self._get_attribute('count')\",\n \"def get_count(name, key):\\n total = 0\\n query = CounterShard.all().filter('name = ', name).filter('reference_key = ', key)\\n for counter in query:\\n total += counter.count\\n \\n return total\",\n \"def count(self):\\n\\n return self._get(\\\"count\\\", rtype=UInt)\",\n \"def count(self):\\n return self.size()\",\n \"def get_number_of_unique_values(local_data, attr):\\n\\tvalues = []\\n\\tfor element in local_data:\\n\\t\\tif element[attr] not in values:\\n\\t\\t\\tvalues.extend([element[attr]])\\n\\treturn len(values)\",\n \"def count(self) -> float:\\n return pulumi.get(self, \\\"count\\\")\",\n \"def count(self):\\n nreq, nres = 0, 0\\n for entry in self.__history:\\n if entry.oreq is not None:\\n nreq += 1\\n if entry.ores is not None:\\n nres += 1\\n return nreq, nres\",\n \"def get_count(self):\\n\\n\\t\\treturn self.__count\",\n \"def count_unique_bookmarks():\\r\\n total = BmarkMgr.count(distinct=True)\\r\\n stat = StatBookmark(attrib=UNIQUE_CT, data=total)\\r\\n DBSession.add(stat)\",\n \"def count(self):\\n\\n if self.cluster:\\n return self.cluster.count()\\n\\n return super().count()\",\n \"def specht(mu):\\n return StandardTableaux(mu).cardinality().n()\",\n \"def tally(self):\\n return self.count\",\n \"def count(self):\\n \\n return self._count\",\n \"def _count(self):\\n if self._count_valid:\\n return self._total_results\\n\\n result = self._cb.get_object(self._doc_class.urlobject.format(self._cb.credentials.org_key))\\n results = result.get(\\\"results\\\", [])\\n\\n self._total_results = len(results)\\n self._count_valid = True\\n\\n return self._total_results\",\n \"def count(self) -> int:\\n return self._count\",\n \"def count(self) -> int:\\n return self._count\",\n \"def count(self) -> int:\\n return self._count\",\n \"def count(self, tokens):\\n return self._count.get(tuple(tokens), 0)\",\n \"def count(self, resource):\\n return len(self.all(resource))\",\n \"def observation_count(self):\\n if not self.can_update():\\n self._handle_error(910, [self.type])\\n return self.tc_requests.observation_count(\\n self.api_type, self.api_branch, self.unique_id, owner=self.owner\\n )\",\n \"def oneup_count(self):\\n return self.oneups.filter(Oneup.state >= 0).count()\",\n \"def count(self, axis=None):\\n return self.data.count(axis=axis)\",\n \"def count_benchmarks():\\n return len(setup_storage().fetch_benchmark({}))\",\n \"def get_num_measured_outputs(self):\\n i = 0\\n for o in self.outputs:\\n if o.is_measured_output():\\n i += 1\\n return i\",\n \"def Count(self):\\n return self._get_attribute('count')\",\n \"def totalhashes(self):\\n return np.sum(self.counts)\",\n \"def n_users(self):\\n if self._n_users is None:\\n self._n_users = len(self.user_unique_vals)\\n return self._n_users\",\n \"def fetch_counts_for_debug(stdout):\\n test_names = TEST_NAMES_DEBUG_APP_PATTERN.findall(stdout)\\n test_counts = collections.Counter(test_class for test_class, _ in test_names\\n if test_class not in IGNORED_CLASSES)\\n\\n return test_counts\",\n \"def count(self):\\n return len(self)\",\n \"def test_get_virtual_machine_count_metrics1(self):\\n pass\",\n \"def totalCount(self):\\n return sum(self.values())\",\n \"def totalCount(self):\\n return sum(self.values())\",\n \"def totalCount(self):\\n return sum(self.values())\",\n \"def get_untested_count(self):\\n return sum(1 for outcome in (r.outcome for r in self.values()) if outcome == Result.UNTESTED)\",\n \"def get_total_counts(self):\\n ret = {}\\n all_loggers_count = 0\\n for logger, name_map in self.acc_map.items():\\n cur_logger_count = 0\\n ret[logger.name] = {}\\n for name, status_map in name_map.items():\\n cur_name_count = 0\\n ret[logger.name][name] = {}\\n for status, acc in status_map.items():\\n cur_count = acc.total_count\\n ret[logger.name][name][status] = cur_count\\n cur_name_count += cur_count\\n cur_logger_count += cur_count\\n all_loggers_count += cur_count\\n ret[logger.name][name]['__all__'] = cur_name_count\\n ret[logger.name]['__all__'] = cur_logger_count\\n ret['__all__'] = all_loggers_count\\n return ret\",\n \"def resultCounter(detections):\\n counter = 0\\n for attribute, value in classIterator(detections):\\n if 'crease' in attribute:\\n counter += len(value)\\n return counter\",\n \"def count(self):\\n return len(self.names)\",\n \"def snmpqosqos_sch_sessions_regulated_count(self) :\\n\\t\\ttry :\\n\\t\\t\\treturn self._snmpqosqos_sch_sessions_regulated_count\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"def count(self) -> int:\\n return self._adapter.count()\",\n \"def Count():\\n return CheckForError(lib.Generators_Get_Count())\",\n \"def test_set_count(self) -> int:\\n return pulumi.get(self, \\\"test_set_count\\\")\",\n \"def num_humans(self):\\n return len(self._human_list)\",\n \"def num_humans(self):\\n return len(self._human_list)\",\n \"def count(self, tokens):\\n return self._count[tuple(tokens)]\",\n \"def count(self):\\n return self._lift(\\\"count\\\")\",\n \"def get_count(self):\\r\\n return self.count\",\n \"def update_count(self):\\n count_metrics = self._fetch_count_metrics_and_clear()\\n self._logger.info('update_count. count_metrics = %s',\\n build_metrics_counter_data(count_metrics))\",\n \"def GetCount(name):\\n counter = StrongCounter.get_or_insert(key_name=name)\\n return counter.count\",\n \"def count(self):\\n return self._count\",\n \"def count(self):\\n return self._count\",\n \"def count(self):\\n return self._count\",\n \"def count(self):\\n return self._count\",\n \"def count(self):\\n return self._count\",\n \"def count(self):\\n return self._count\",\n \"def count(self):\\n return self._count\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.71416634","0.688916","0.6736414","0.65794754","0.6549508","0.6549508","0.6549508","0.6549508","0.6548206","0.65326285","0.64953864","0.64506423","0.64230627","0.64027196","0.6389793","0.6381835","0.6377822","0.6373317","0.6364511","0.63238037","0.62901294","0.6253662","0.62463033","0.624508","0.6204462","0.6198452","0.6198452","0.61811167","0.6174914","0.6163041","0.6151766","0.6147231","0.6133947","0.60912174","0.6071731","0.60696566","0.6054436","0.60463285","0.60330665","0.60213363","0.60213363","0.6017155","0.59825283","0.59763074","0.5969955","0.5969955","0.59678435","0.596004","0.595392","0.5943759","0.5931562","0.59295124","0.59247833","0.59237444","0.59171695","0.59169304","0.5916226","0.5903465","0.5897899","0.58902204","0.58902204","0.58902204","0.5889679","0.58847314","0.58793694","0.5874305","0.58727044","0.5870249","0.5857602","0.5856956","0.5853532","0.5850423","0.58494127","0.5848211","0.58432674","0.58417815","0.58417815","0.58417815","0.5840477","0.58390623","0.5838034","0.5830424","0.58255076","0.5825012","0.5819198","0.58173656","0.5815668","0.5815668","0.58103424","0.58102286","0.58098453","0.58083326","0.5800024","0.5797845","0.5797845","0.5797845","0.5797845","0.5797845","0.5797845","0.5797845"],"string":"[\n \"0.71416634\",\n \"0.688916\",\n \"0.6736414\",\n \"0.65794754\",\n \"0.6549508\",\n \"0.6549508\",\n \"0.6549508\",\n \"0.6549508\",\n \"0.6548206\",\n \"0.65326285\",\n \"0.64953864\",\n \"0.64506423\",\n \"0.64230627\",\n \"0.64027196\",\n \"0.6389793\",\n \"0.6381835\",\n \"0.6377822\",\n \"0.6373317\",\n \"0.6364511\",\n \"0.63238037\",\n \"0.62901294\",\n \"0.6253662\",\n \"0.62463033\",\n \"0.624508\",\n \"0.6204462\",\n \"0.6198452\",\n \"0.6198452\",\n \"0.61811167\",\n \"0.6174914\",\n \"0.6163041\",\n \"0.6151766\",\n \"0.6147231\",\n \"0.6133947\",\n \"0.60912174\",\n \"0.6071731\",\n \"0.60696566\",\n \"0.6054436\",\n \"0.60463285\",\n \"0.60330665\",\n \"0.60213363\",\n \"0.60213363\",\n \"0.6017155\",\n \"0.59825283\",\n \"0.59763074\",\n \"0.5969955\",\n \"0.5969955\",\n \"0.59678435\",\n \"0.596004\",\n \"0.595392\",\n \"0.5943759\",\n \"0.5931562\",\n \"0.59295124\",\n \"0.59247833\",\n \"0.59237444\",\n \"0.59171695\",\n \"0.59169304\",\n \"0.5916226\",\n \"0.5903465\",\n \"0.5897899\",\n \"0.58902204\",\n \"0.58902204\",\n \"0.58902204\",\n \"0.5889679\",\n \"0.58847314\",\n \"0.58793694\",\n \"0.5874305\",\n \"0.58727044\",\n \"0.5870249\",\n \"0.5857602\",\n \"0.5856956\",\n \"0.5853532\",\n \"0.5850423\",\n \"0.58494127\",\n \"0.5848211\",\n \"0.58432674\",\n \"0.58417815\",\n \"0.58417815\",\n \"0.58417815\",\n \"0.5840477\",\n \"0.58390623\",\n \"0.5838034\",\n \"0.5830424\",\n \"0.58255076\",\n \"0.5825012\",\n \"0.5819198\",\n \"0.58173656\",\n \"0.5815668\",\n \"0.5815668\",\n \"0.58103424\",\n \"0.58102286\",\n \"0.58098453\",\n \"0.58083326\",\n \"0.5800024\",\n \"0.5797845\",\n \"0.5797845\",\n \"0.5797845\",\n \"0.5797845\",\n \"0.5797845\",\n \"0.5797845\",\n \"0.5797845\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.67860687"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":3691,"cells":{"query":{"kind":"string","value":"Returns a to a list containing any errors collected during the reporting period."},"document":{"kind":"string","value":"def error_data(self):\n\n if not self.__settings:\n return []\n\n return self.__transaction_errors"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def getErrorsList(self):\n return self.__errors","def errors(self) -> List[Error]:","def getErrors(self) -> java.util.Collection:\n ...","def get_errors(self):\n return [result for result in self.values() if result.outcome == Result.ERROR]","def errors(self) -> List[Error]:\n return self._errors_files + list(self._errors.values())","def retrieve_error_messages(self):\n return self.errors_seen[:]","def getParseErrors(self):\n return [x for x in self.xeps if x.parseErrors]","def GetAll(self):\n return self._errors.copy()","def errors(self):\n return self._errors","def errors():\n return THE_LOGGER.errors","def errors (self):\n return self._errors","def errors (self):\n return self._errors","def getErrors(self):\n return self.errors","def get_reports(self):\r\n result = QtSql.QSqlQuery('''Select * FROM failures''')\r\n list = []\r\n while result.next():\r\n failure = Failure(unicode(result.value(0).toString()), # id\r\n unicode(result.value(1).toString()), # comment\r\n unicode(result.value(2).toString()), # indicator\r\n bool(result.value(3))) # release\r\n p = self.get_presentation(failure.talkId)\r\n r = Report(p, failure)\r\n list.append(r)\r\n return list","def errors(self):\n return self.__errors","def get_errors(self):\n df = self.get_events()\n return df[df.error.notnull()]","def errors(self) -> List[Error]:\n # May have inherited errors with a different path.\n for error in self._errors.values():\n error.path = self.path\n if self.is_removed: # Mark all of our errors as non-persistant.\n error.is_persistant = False\n return list(self._errors.values())","def all_errors(self) -> List[XMLSchemaParseError]:\n errors = []\n for comp in self.iter_components():\n if comp.errors:\n errors.extend(comp.errors)\n return errors","def formatErrors(self):\n errorlist = []\n xepsWithErrors = sorted(\n set(self.getParseErrors() + self.getBuildErrors()),\n key=lambda x: str(x))\n if self.getErrors() or xepsWithErrors:\n if self.getErrors():\n errorlist.append(\"********** Read errors **********\")\n for error in self.getErrors():\n errorlist.append(error)\n for xep in xepsWithErrors:\n errorlist.append(\n \"********** Error report for {} **********\".format(str(xep)))\n if xep.parseErrors:\n errorlist.append(\"********** Parsing Errors **********\")\n errors = list(set(xep.parseErrors))\n for error in errors:\n errorlist.append(error)\n if xep.buildErrors:\n errorlist.append(\"********** Build Errors **********\")\n for error in xep.buildErrors:\n if len(error.splitlines()) > 4:\n error = ''.join(error.splitlines()[:4])\n errorlist.append(error)\n return '\\n'.join(errorlist)\n else:\n return None","def Errors(self):\r\n\t\treturn self._get_attribute('errors')","def error(self) -> list:\n return self.__err","def getBuildErrors(self):\n return [x for x in self.xeps if x.buildErrors]","def errors_fatal(self) -> List[Error]:","def _filter_return_errors_list(self, url, har=None):\r\n if not har:\r\n har = self.har\r\n \r\n matches = []\r\n for entry in har[\"log\"][\"entries\"]:\r\n temp = entry[\"request\"][\"url\"].encode('ascii', 'ignore')\r\n if url in entry[\"request\"][\"url\"] and temp not in matches and entry[\"response\"][\"status\"] >= 400:\r\n print \"\\nRequest failed w/ \" + str(entry[\"response\"][\"status\"]) + \" error:\\n\" + entry[\"request\"][\"url\"]\r\n if entry[\"response\"][\"content\"].get(\"text\"):\r\n print \"RESPONSE: \" + str(entry[\"response\"][\"content\"][\"text\"].encode('ascii', 'ignore'))\r\n temp = entry[\"request\"][\"url\"].encode('ascii', 'ignore')\r\n matches.append([temp,entry[\"response\"][\"content\"].get(\"text\",\"\")])\r\n return matches","def Errcheck(self) -> list:\n\n myError = []\n\n ErrorList = self.myFieldFox.query(\"SYST:ERR?\").split(',')\n\n Error = ErrorList[0]\n\n if int(Error) == 0:\n\n print (\"+0, No Error!\")\n\n else:\n\n while int(Error)!=0:\n\n print (\"Error #: \" + ErrorList[0])\n\n print (\"Error Description: \" + ErrorList[1])\n\n myError.append(ErrorList[0])\n\n myError.append(ErrorList[1])\n\n ErrorList = self.myFieldFox.query(\"SYST:ERR?\").split(',')\n\n Error = ErrorList[0]\n\n myError = list(myError)\n\n return myError","def analysis_errors(self) -> str:\n errors = []\n\n # Get any callback errors\n for cid, callback in self._analysis_callbacks.items():\n if callback.status == AnalysisStatus.ERROR:\n errors.append(f\"\\n[Analysis Callback ID: {cid}]: {callback.error_msg}\")\n\n return \"\".join(errors)","def check_errors(self):\n\n errors = []\n while True:\n err = self.values(\"SYST:ERR?\")\n if int(err[0]) != 0:\n errmsg = \"Agilent 5313xA: {0}: {1}\".format(err[0], err[1])\n log.error(errmsg + '\\n')\n errors.append(errmsg)\n else:\n break\n\n return errors","def get_error(self) -> List[str]:\n return []","def get_error(self) -> List[str]:\n return []","def Errors(self):\n return self._get_attribute('errors')","def get_validation_errors(self):\n return [err.to_dict() for err in self._schema.validator.validation_errors]","def tasks_with_errors(self):\n errs = []\n while True:\n try:\n errs.append(self._errq.get_nowait())\n except Queue.Empty:\n break\n return errs","def _get_errors(exc):\n if hasattr(exc, 'message'):\n errors = exc.messages\n else:\n errors = [str(exc)]\n return errors","def _get_resends(self):\n if not self.has_error():\n return []\n\n errors = []\n i = 0\n for item in self.my_json['results']:\n if item.has_key('error') and item['error'] == 'Unavailable':\n errors.append((i, item['error']))\n i += 1\n return errors","def refined_errors(self):\r\n errs = []\r\n for err in self.errors:\r\n if err['typo'].lower() not in self.terms:\r\n errs.append(err)\r\n return errs","def flushErrors(exc_type: type) -> list[Exception]:\n # There is no public API for flushing logged errors if you're not\n # using one of trial's TestCase classes...\n from twisted.trial.runner import _logObserver # type: ignore[attr-defined]\n\n result = _logObserver.flushErrors(exc_type)\n assert isinstance(result, list)\n return result","def errors(self):\n return self._properties.get(\"errors\")","def report_transaction_error_messages(self):\n response = self.__get_transaction_response()\n\n # get response data from response object\n response_data = response.json()\n\n # get error messages\n response_error = response_data['Error']\n response_error_messages = response_error['messages']\n\n # add all error messages to the report\n error_messages_to_report = []\n for response_error_message in response_error_messages:\n error_description = response_error_message['description']\n error_messages_to_report.append(error_description)\n\n return error_messages_to_report","def validation_errors(self):\n return self._validation_errors","def date_errors(self):\r\n try:\r\n _date_errors = self._validate_date(self.sourceDateCol)\r\n return _date_errors\r\n except:\r\n return None","def error_report():\n db, c = connect(DBNAME)\n c.execute(\"select to_char(time,'FMMonth DD, YYYY') as date, \"\n \"round((sum(case when status = '200 OK' \"\n \"then 0 else 1 end)::decimal / count(*)) * 100,2) \"\n \"as percent_error from log group by date \"\n \"having (sum(case when status = '200 OK' \"\n \"then 0 else 1 end)::decimal / count(*)) * 100 > 1\")\n error_table = c.fetchall()\n db.close()\n print \"\\nDates on Which Over 1% of Requests Led to Errors:\"\n for error in error_table:\n if __name__ == '__main__':\n print str(error[0]) + \" - \" + str(error[1]) + \"%\"","def get_form_errors(form):\n all_errors = []\n for field in form.errors:\n all_errors += form.errors[field]\n return all_errors","def get_all_failures(self):\n return self._get_filtered_results(success=False)","def error_messages(self) -> List[str]:\n spatial_msgs = []\n temporal_msgs = []\n if self.spatial:\n spatial_msgs = [m for v, m in self.spatial_validations if not v(self.spatial)]\n if self.temporal:\n temporal_msgs = [m for v, m in self.temporal_validations if not v(self.temporal)]\n\n return spatial_msgs + temporal_msgs","def errors(self) -> Tuple[MqexsErrorInfo, ...]:\n return self.__errors","def get_error_messages(self):\n\n if len(self._sensor_results_list) == 0:\n return\n\n error_msgs = []\n\n for reading in self._sensor_results_list:\n if reading.is_error():\n error_msgs.append(reading.get_error_msg())\n\n if len(error_msgs) > 0:\n return error_msgs\n else:\n return \"No Error Readings\"","def errors(self):\r\n if not hasattr(self, '_errors_cache'):\r\n self._errors_cache = self.form.get_field_errors(self)\r\n return self._errors_cache","def get_diagnostics(self) -> List[Diagnostic]:\n raise NotImplementedError","def _get_errors(sheet, row, col):\n field = _FIELDS['primary data']\n val = sheet.cell(row + field['row'], col + field['column']).value\n if not val:\n return []\n final_row = row + field['row']\n error = sheet.cell(final_row, col + field['column']).value\n while error:\n final_row += 1\n error = sheet.cell(final_row, col + field['column']).value\n return [sheet.cell(i, col + field['column']).value\n for i in range(row + field['row'], final_row)]","def all_errata(self):\n return self._all_errata","def get_errors(self, path: str,\n is_ancillary: bool = False,\n is_system: bool = False,\n is_removed: bool = False) -> List[str]:","def output_errors(self) -> List[str]:\n output = list()\n for error in sorted(self.errors):\n if len(error) == 2:\n line = f\"{error[0]} - Could not find {error[1]} in map names!\"\n else:\n line = f\"{error[0]} - {error[1]}: Could not find {error[2]} in map names!\"\n logger.error(line)\n output.append(line)\n return output","def _pydantic_errors_to_validation_results(\n errors: list[dict | Exception] | ValidationError,\n file_path: Path,\n scope: Scope,\n) -> list[ValidationResult]:\n out = []\n for e in (\n errors.errors() if isinstance(errors, ValidationError) else cast(list, errors)\n ):\n if isinstance(e, Exception):\n message = getattr(e, \"message\", str(e))\n id = \"exception\"\n scope = Scope.FILE\n else:\n id = \".\".join(\n filter(\n bool,\n (\n \"dandischema\",\n e.get(\"type\", \"UNKNOWN\"),\n \"+\".join(e.get(\"loc\", [])),\n ),\n )\n )\n message = e.get(\"message\", e.get(\"msg\", None))\n out.append(\n ValidationResult(\n origin=ValidationOrigin(\n name=\"dandischema\",\n version=dandischema.__version__,\n ),\n severity=Severity.ERROR,\n id=id,\n scope=scope,\n path=file_path,\n message=message,\n # TODO? dataset_path=dataset_path,\n # TODO? dandiset_path=dandiset_path,\n )\n )\n return out","def get_errors(response):\n errors = response.get(\"error\")\n if errors:\n return [e.get(\"message\") for e in errors]\n return None","def errors_fatal(self) -> List[Error]:\n return self._errors_fatal_files + self._errors_fatal","def _get_retriable_errors(out: List[str]) -> List[str]:\n return [\n line for line in out\n if any(error in line for error in RETRIABLE_ERRORS)\n ]","def get_errors(self, request):\n\n value = request._get_parameter_value(self)\n return value.errors","def errors(self):\n raise NotImplementedError","def recorded_messages(self):\n messages = []\n for time in sorted(self.reception_records):\n messages.extend(self.reception_records[time])\n return messages","def warnings(self) -> List[Error]:","def get_aggregated_exceptions(self) -> Payload:\n return Payload(aggregated_errors=list(self._aggregated_exceptions.values()))","def get_errors(self, path: str,\n is_ancillary: bool = False,\n is_system: bool = False,\n is_removed: bool = False) -> List[str]:\n u_file = self.__api.files.get(path, is_ancillary=is_ancillary,\n is_system=is_system,\n is_removed=is_removed)\n return [e.message for e in u_file.errors]","def check_set_errors(self):\n response = self.read()\n return [] if response == \"\" else [response]","def getErrors(self):\n errorList = []\n\n # E0\n try:\n if not self.e0.isValid():\n errorList.append(\"Invalid first error axis in ErrorEllipse Class\")\n except (NameError, AttributeError):\n errorList.append(\"No first error axis in ErrorEllipse Class.\")\n\n # E1\n try:\n if not self.e1.isValid():\n errorList.append(\"Invalid second error axis in ErrorEllipse Class\")\n except (NameError, AttributeError):\n errorList.append(\"No second error axis in ErrorEllipse Class.\")\n\n # E2\n try:\n if not self.e2.isValid():\n errorList.append(\"Invalid third error axis in ErrorEllipse Class\")\n except (NameError, AttributeError):\n errorList.append(\"No third error axis in ErrorEllipse Class.\")\n\n # maximumHorizontalProjection\n try:\n self.maximumHorizontalProjection\n except (NameError, AttributeError):\n errorList.append(\"No MaximumHorizontalProjection in ErrorEllipse Class.\")\n\n # maximumVerticalProjection\n try:\n self.maximumVerticalProjection\n except (NameError, AttributeError):\n errorList.append(\"No MaximumVerticalProjection in ErrorEllipse Class\")\n\n # equivalentHorizontalRadius\n try:\n self.equivalentHorizontalRadius\n except (NameError, AttributeError):\n errorList.append(\"No EquivalentHorizontalRadius in ErrorEllipse class\")\n\n return errorList","def error_wrapper(x):\n errors = list()\n for error_key, error_list in list(x.items()):\n for error in error_list:\n if error_key == 'non_field_errors':\n errors.append(error)\n else:\n errors.append(\"%s: %s\" % (error_key, error))\n return errors","def err_rec(self):\n if len(self.dt_list) < self.err_len:\n self.err_len = len(self.dt_list)\n\n last_dt_sum = sum(self.dt_list[int(-1 * self.err_len):])\n last_dt = self.dt_list[-8]\n\n # Records the discrepancies\n self.lin_int_err.append(abs(self.lin_int_est_next[0] - last_dt_sum))\n self.lin_int_no_err.append(abs(self.lin_int_no_est_next[0] - last_dt_sum))\n self.average_int_err.append(abs(self.average_int_est_next[0] - last_dt_sum))\n self.average_int_no_err.append(abs(self.average_int_no_est_next[0] - last_dt_sum))\n\n self.lin_err.append(abs(self.lin_est_next[0] - last_dt_sum))\n self.lin_no_err.append(abs(self.lin_no_est_next[0] - last_dt_sum))\n self.average_err.append(abs(self.average_est_next[0] - last_dt_sum))\n self.average_no_err.append(abs(self.average_no_est_next[0] - last_dt_sum))\n\n # Removes the oldest entry\n self.lin_err.pop(0)\n self.average_err.pop(0)","def getLogs():\n # in flux, it may be possible to provide more structured information\n # like python Failure instances","def _filter_return_errors(self, url, har=None):\r\n if not har:\r\n har = self.har\r\n \r\n matches = []\r\n for entry in har[\"log\"][\"entries\"]:\r\n temp = entry[\"request\"][\"url\"].encode('ascii', 'ignore')\r\n if url in entry[\"request\"][\"url\"] and temp not in matches and entry[\"response\"][\"status\"] >= 400:\r\n print \"\\nRequest failed w/ \" + str(entry[\"response\"][\"status\"]) + \" error:\\n\" + entry[\"request\"][\"url\"]\r\n if entry[\"response\"][\"content\"].get(\"text\"):\r\n print \"RESPONSE: \" + str(entry[\"response\"][\"content\"][\"text\"].encode('ascii', 'ignore'))\r\n temp = entry[\"request\"][\"url\"].encode('ascii', 'ignore')\r\n matches.append(temp)\r\n return matches","def get_error_days():\n db = psycopg2.connect(database=DBNAME)\n c = db.cursor()\n # dividing views of bad requests and total request to get percentage\n c.execute(\"select bad_request.time, \"\n \"(bad_request.num * 1.0 / total_request.num) as errors \"\n \"from bad_request, total_request \"\n \"where bad_request.time = total_request.time \"\n \"and (bad_request.num * 1.0 / total_request.num) > 0.01\")\n results = c.fetchall()\n text_file = open(\"text.txt\", \"a+\") # append to text file\n text_file.write(\"Day(s) where more than 1 percent of requests were errors:\"\n \"\\n\\n\")\n for time, errors in results:\n text_file.write(time.strftime('%B %d, %Y') + \" - \" +\n str(errors * 100)[:3] + \"% errors\\n\")\n text_file.write(\"\\n\")\n text_file.close()\n db.close()","def getFatalErrors(self):\n global hadFatalErrors\n if hadFatalErrors:\n text = '\\n'.join(hadFatalErrors)\n hadFatalErrors = []\n return text","def time_errors(self):\r\n try:\r\n _time_errors = self._validate_time(self.sourceTimeCol)\r\n return _time_errors\r\n except:\r\n return None","def errors(self) -> pulumi.Output[Sequence['outputs.BatchAIErrorResponse']]:\n return pulumi.get(self, \"errors\")","def filter_draft_errors(result):\n error_messages = []\n for field, msgs in result.get('messages', {}).items():\n if msgs.get('state', None) == 'error':\n for m in msgs['messages']:\n error_messages.append(dict(\n field=field,\n message=m,\n code=error_codes['validation_error'],\n ))\n return error_messages","def _parse_file_errors(self, file):\n\n with open(file, 'r') as lines:\n errors = []\n\n for line in lines:\n if self.log_regexp.TRACEBACK.match(line):\n errors.append({})\n\n if self.log_regexp.ERROR_PLACE.match(line):\n code_line = re.search(self.log_regexp.ERROR_LINE, line)[0]\n error_file = re.search(self.log_regexp.ERROR_FILE, line)[0]\n errors[-1][self.output_params.LINE] = int(code_line.split()[1])\n errors[-1][self.output_params.ERROR_FILE] = error_file.strip('\"')\n\n if self.log_regexp.ERROR_TYPE.match(line):\n type_error = re.search(self.log_regexp.ERROR_TYPE, line)[0]\n message = line[len(type_error)+1:]\n errors[-1].update({self.output_params.TYPE: type_error})\n errors[-1].update({self.output_params.MESSAGE: message.strip()})\n\n return errors","def failing_periods(self) -> 'outputs.DynamicThresholdFailingPeriodsResponse':\n return pulumi.get(self, \"failing_periods\")","def error_dates():\n\n results = query_database(QUERIES[2])\n print('\\nOn which days did more than 1% of requests lead to errors?\\n')\n for date, rate in results:\n print(' * {} -- {:.2%}'.format(date, rate))","def _step_errors(sequence: Sequence) -> List[TransformationMetrics]:\n errors = [TransformationMetrics.calc(t_pred, t_gt, time)\n for t_pred, t_gt, time in zip(sequence.prediction.transforms, sequence.ground_truth.transforms,\n sequence.times)]\n return errors","def get_failed_spectra_list(diag_workspace):\n if type(diag_workspace) == str:\n diag_workspace = mtd[diag_workspace]\n\n if hasattr(diag_workspace, \"getAxis\") == False:\n raise ValueError(\"Invalid input to get_failed_spectra_list. \"\n \"A workspace handle or name is expected\")\n \n spectra_axis = diag_workspace.getAxis(1)\n failed_spectra = []\n for i in range(diag_workspace.getNumberHistograms()):\n try:\n det = diag_workspace.getDetector(i)\n except RuntimeError:\n continue\n if det.isMasked():\n failed_spectra.append(spectra_axis.spectraNumber(i))\n\n return failed_spectra","def get_validation_errors(\n self,\n schema_version: Optional[str] = None,\n devel_debug: bool = False,\n ) -> list[ValidationResult]:\n ...","def getFailedJobs(self):\n return self.__failedJobs","def listErrors(individual): #TODO: add other errors implemented elsewhere\n\n results = [birthBeforeMarriage(individual), birthBeforeDeath(individual), marriageBeforeDeath(individual), \n datesBeforeCurrentDate(individual), noBigamy(individual)]\n results = [x for x in results if x is not None]\n return not all(results)","def get_fails(self):\n return [result for result in self.values() if result.outcome == Result.FAIL]","def parse(self, errors, explicit_ignore):\n\n error_list = []\n if errors is None:\n return error_list\n\n errors.sort(key=linter.cmp_to_key(lambda a, b: a.lineno < b.lineno))\n for error in errors:\n error_level = 'W' if not hasattr(error, 'level') else error.level\n message = error.message.capitalize()\n\n error_data = {\n 'underline_range': False,\n 'level': error_level,\n 'lineno': error.lineno,\n 'message': message,\n 'raw_error': str(error)\n }\n if hasattr(error, 'offset'):\n error_data['offset'] = error.offset\n elif hasattr(error, 'col'):\n error_data['offset'] = error.col\n\n if (isinstance(error, (linter.OffsetError))):\n error_data['underline_range'] = True\n error_list.append(error_data)\n elif (isinstance(\n error, (\n pyflakes.messages.RedefinedWhileUnused,\n pyflakes.messages.RedefinedInListComp,\n pyflakes.messages.UndefinedName,\n pyflakes.messages.UndefinedExport,\n pyflakes.messages.UndefinedLocal,\n pyflakes.messages.UnusedVariable)) and\n error.__class__.__name__ not in explicit_ignore):\n\n error_data['len'] = len(error.message_args[0])\n error_data['regex'] = (\n r'((and|or|not|if|elif|while|in)\\s+|[+\\-*^%%<>=\\(\\{{])*\\s'\n '*(?P<underline>[\\w\\.]*{0}[\\w]*)'.format(re.escape(\n error.message_args[0]\n ))\n )\n error_list.append(error_data)\n elif isinstance(error, pyflakes.messages.ImportShadowedByLoopVar):\n regex = 'for\\s+(?P<underline>[\\w]*{0}[\\w*])'.format(\n re.escape(error.message_args[0])\n )\n error_data['regex'] = regex\n error_list.append(error_data)\n elif (isinstance(\n error, (\n pyflakes.messages.UnusedImport,\n pyflakes.messages.ImportStarUsed)) and\n error.__class__.__name__ not in explicit_ignore):\n if isinstance(error, pyflakes.messages.ImportStarUsed):\n word = '*'\n else:\n word = error.message_args[0]\n\n linematch = '(from\\s+[\\w_\\.]+\\s+)?import\\s+(?P<match>[^#;]+)'\n r = '(^|\\s+|,\\s*|as\\s+)(?P<underline>[\\w]*{0}[\\w]*)'.format(\n re.escape(word)\n )\n error_data['regex'] = r\n error_data['linematch'] = linematch\n error_list.append(error_data)\n elif (isinstance(error, pyflakes.messages.DuplicateArgument) and\n error.__class__.__name__ not in explicit_ignore):\n regex = 'def [\\w_]+\\(.*?(?P<underline>[\\w]*{0}[\\w]*)'.format(\n re.escape(error.message_args[0])\n )\n error_data['regex'] = regex\n error_list.append(error_data)\n elif isinstance(error, pyflakes.messages.LateFutureImport):\n pass\n elif isinstance(error, linter.PythonError):\n print(error)\n else:\n print(\n 'Ooops, we missed an error type for pyflakes', type(error)\n )\n\n return error_list","def failed_items(self) -> ItemLog:\n if self._failed_items is None:\n self._failed_items = ItemLog(self.dir / 'dropped-failed.log.csv', DROPPED_FAILED_FIELDNAMES, 'id')\n return self._failed_items","def errors(self):\n _errors = {}\n # pylint: disable=no-member\n for name, field in self._fields.items():\n if field.errors:\n _errors[name] = field.errors.pop()\n\n return _errors","def warnings(self) -> List[Error]:\n return self._get_warnings()","def validation_errors_to_error_messages(validation_errors):\n errorMessages = []\n for field in validation_errors:\n for error in validation_errors[field]:\n errorMessages.append(f\"{field} : {error}\")\n return errorMessages","def validation_errors_to_error_messages(validation_errors):\n errorMessages = []\n for field in validation_errors:\n for error in validation_errors[field]:\n errorMessages.append(f\"{field} : {error}\")\n return errorMessages","def validation_errors_to_error_messages(validation_errors):\n errorMessages = []\n for field in validation_errors:\n for error in validation_errors[field]:\n errorMessages.append(f\"{field} : {error}\")\n return errorMessages","def validation_errors_to_error_messages(validation_errors):\n errorMessages = []\n for field in validation_errors:\n for error in validation_errors[field]:\n errorMessages.append(f\"{field} : {error}\")\n return errorMessages","def validation_errors_to_error_messages(validation_errors):\n errorMessages = []\n for field in validation_errors:\n for error in validation_errors[field]:\n errorMessages.append(f\"{field} : {error}\")\n return errorMessages","def getErrorReport(self):\n return self.sError;","def getErrorReport(self):\n return self.sError;","def job_errors(self) -> str:\n errors = []\n\n # Get any job errors\n for job in self._jobs.values():\n if job and job.status() == JobStatus.ERROR:\n if hasattr(job, \"error_message\"):\n error_msg = job.error_message()\n else:\n error_msg = \"\"\n errors.append(f\"\\n[Job ID: {job.job_id()}]: {error_msg}\")\n\n # Get any job futures errors:\n for jid, fut in self._job_futures.items():\n if fut and fut.done() and fut.exception():\n ex = fut.exception()\n errors.append(\n f\"[Job ID: {jid}]\"\n \"\\n\".join(traceback.format_exception(type(ex), ex, ex.__traceback__))\n )\n return \"\".join(errors)","def get_alerts(self):\n url = 'http://www.p2000-online.net/p2000.php?%s&nofilterform=1'\n url = url % '&'.join(['%s=1' % x for x in self.regions])\n if self.verbose:\n print time.ctime(), url\n try:\n data = urllib.urlopen(url).read()\n except IOError:\n if self.verbose:\n import traceback\n traceback.print_exc()\n return []\n\n doc = soup(data)\n alerts = []\n table = doc.body('table', {'style': 'align:center'})[0]\n for tr in table('tr'):\n if tr.td.get('class', None) == 'DT':\n alerts.append(Alert(*[x.text for x in tr('td')]))\n else:\n recipient = tr('td')[-1].text\n if recipient != ' ':\n alerts[-1].recipients.append(recipient)\n return alerts","def __call__(self, errors: List[float]) -> List[float]:","def get_errors(self):\n errors = []\n\n if not self.title:\n msg = 'Title not found: {0}'.format(self.number)\n print(msg)\n errors.append(msg)\n\n if not self.ref:\n msg = 'Ref not found: {0}'.format(self.number)\n print(msg)\n errors.append(msg)\n\n chapter_index = int(self.number) - 1\n\n # get the expected number of frames for this chapter\n expected_frame_count = chapters_and_frames.frame_counts[chapter_index]\n\n for x in range(1, expected_frame_count + 1):\n\n # frame id is formatted like '01-01'\n frame_id = '{0}-{1}'.format(self.number.zfill(2), str(x).zfill(2))\n\n # get the next frame\n frame = next((f for f in self.frames if f['id'] == frame_id), None) # type: dict\n if not frame:\n msg = 'Frame not found: {0}'.format(frame_id)\n print(msg)\n errors.append(msg)\n else:\n # check the frame img and values\n if 'img' not in frame or not frame['img']:\n msg = 'Attribute \"img\" is missing for frame {0}'.format(frame_id)\n print(msg)\n errors.append(msg)\n\n if 'text' not in frame or not frame['text']:\n msg = 'Attribute \"text\" is missing for frame {0}'.format(frame_id)\n print(msg)\n errors.append(msg)\n\n return errors","def errors(self) -> str:\n return self.job_errors() + self.analysis_errors()","def errDays():\n c = db.cursor()\n c.execute(\"select date, percent from avg_error\\\n where percent > 1.00;\")\n results = c.fetchall()\n c.close()\n return results","def json(self):\n d = [err.json for err in self.errors]\n return d"],"string":"[\n \"def getErrorsList(self):\\n return self.__errors\",\n \"def errors(self) -> List[Error]:\",\n \"def getErrors(self) -> java.util.Collection:\\n ...\",\n \"def get_errors(self):\\n return [result for result in self.values() if result.outcome == Result.ERROR]\",\n \"def errors(self) -> List[Error]:\\n return self._errors_files + list(self._errors.values())\",\n \"def retrieve_error_messages(self):\\n return self.errors_seen[:]\",\n \"def getParseErrors(self):\\n return [x for x in self.xeps if x.parseErrors]\",\n \"def GetAll(self):\\n return self._errors.copy()\",\n \"def errors(self):\\n return self._errors\",\n \"def errors():\\n return THE_LOGGER.errors\",\n \"def errors (self):\\n return self._errors\",\n \"def errors (self):\\n return self._errors\",\n \"def getErrors(self):\\n return self.errors\",\n \"def get_reports(self):\\r\\n result = QtSql.QSqlQuery('''Select * FROM failures''')\\r\\n list = []\\r\\n while result.next():\\r\\n failure = Failure(unicode(result.value(0).toString()), # id\\r\\n unicode(result.value(1).toString()), # comment\\r\\n unicode(result.value(2).toString()), # indicator\\r\\n bool(result.value(3))) # release\\r\\n p = self.get_presentation(failure.talkId)\\r\\n r = Report(p, failure)\\r\\n list.append(r)\\r\\n return list\",\n \"def errors(self):\\n return self.__errors\",\n \"def get_errors(self):\\n df = self.get_events()\\n return df[df.error.notnull()]\",\n \"def errors(self) -> List[Error]:\\n # May have inherited errors with a different path.\\n for error in self._errors.values():\\n error.path = self.path\\n if self.is_removed: # Mark all of our errors as non-persistant.\\n error.is_persistant = False\\n return list(self._errors.values())\",\n \"def all_errors(self) -> List[XMLSchemaParseError]:\\n errors = []\\n for comp in self.iter_components():\\n if comp.errors:\\n errors.extend(comp.errors)\\n return errors\",\n \"def formatErrors(self):\\n errorlist = []\\n xepsWithErrors = sorted(\\n set(self.getParseErrors() + self.getBuildErrors()),\\n key=lambda x: str(x))\\n if self.getErrors() or xepsWithErrors:\\n if self.getErrors():\\n errorlist.append(\\\"********** Read errors **********\\\")\\n for error in self.getErrors():\\n errorlist.append(error)\\n for xep in xepsWithErrors:\\n errorlist.append(\\n \\\"********** Error report for {} **********\\\".format(str(xep)))\\n if xep.parseErrors:\\n errorlist.append(\\\"********** Parsing Errors **********\\\")\\n errors = list(set(xep.parseErrors))\\n for error in errors:\\n errorlist.append(error)\\n if xep.buildErrors:\\n errorlist.append(\\\"********** Build Errors **********\\\")\\n for error in xep.buildErrors:\\n if len(error.splitlines()) > 4:\\n error = ''.join(error.splitlines()[:4])\\n errorlist.append(error)\\n return '\\\\n'.join(errorlist)\\n else:\\n return None\",\n \"def Errors(self):\\r\\n\\t\\treturn self._get_attribute('errors')\",\n \"def error(self) -> list:\\n return self.__err\",\n \"def getBuildErrors(self):\\n return [x for x in self.xeps if x.buildErrors]\",\n \"def errors_fatal(self) -> List[Error]:\",\n \"def _filter_return_errors_list(self, url, har=None):\\r\\n if not har:\\r\\n har = self.har\\r\\n \\r\\n matches = []\\r\\n for entry in har[\\\"log\\\"][\\\"entries\\\"]:\\r\\n temp = entry[\\\"request\\\"][\\\"url\\\"].encode('ascii', 'ignore')\\r\\n if url in entry[\\\"request\\\"][\\\"url\\\"] and temp not in matches and entry[\\\"response\\\"][\\\"status\\\"] >= 400:\\r\\n print \\\"\\\\nRequest failed w/ \\\" + str(entry[\\\"response\\\"][\\\"status\\\"]) + \\\" error:\\\\n\\\" + entry[\\\"request\\\"][\\\"url\\\"]\\r\\n if entry[\\\"response\\\"][\\\"content\\\"].get(\\\"text\\\"):\\r\\n print \\\"RESPONSE: \\\" + str(entry[\\\"response\\\"][\\\"content\\\"][\\\"text\\\"].encode('ascii', 'ignore'))\\r\\n temp = entry[\\\"request\\\"][\\\"url\\\"].encode('ascii', 'ignore')\\r\\n matches.append([temp,entry[\\\"response\\\"][\\\"content\\\"].get(\\\"text\\\",\\\"\\\")])\\r\\n return matches\",\n \"def Errcheck(self) -> list:\\n\\n myError = []\\n\\n ErrorList = self.myFieldFox.query(\\\"SYST:ERR?\\\").split(',')\\n\\n Error = ErrorList[0]\\n\\n if int(Error) == 0:\\n\\n print (\\\"+0, No Error!\\\")\\n\\n else:\\n\\n while int(Error)!=0:\\n\\n print (\\\"Error #: \\\" + ErrorList[0])\\n\\n print (\\\"Error Description: \\\" + ErrorList[1])\\n\\n myError.append(ErrorList[0])\\n\\n myError.append(ErrorList[1])\\n\\n ErrorList = self.myFieldFox.query(\\\"SYST:ERR?\\\").split(',')\\n\\n Error = ErrorList[0]\\n\\n myError = list(myError)\\n\\n return myError\",\n \"def analysis_errors(self) -> str:\\n errors = []\\n\\n # Get any callback errors\\n for cid, callback in self._analysis_callbacks.items():\\n if callback.status == AnalysisStatus.ERROR:\\n errors.append(f\\\"\\\\n[Analysis Callback ID: {cid}]: {callback.error_msg}\\\")\\n\\n return \\\"\\\".join(errors)\",\n \"def check_errors(self):\\n\\n errors = []\\n while True:\\n err = self.values(\\\"SYST:ERR?\\\")\\n if int(err[0]) != 0:\\n errmsg = \\\"Agilent 5313xA: {0}: {1}\\\".format(err[0], err[1])\\n log.error(errmsg + '\\\\n')\\n errors.append(errmsg)\\n else:\\n break\\n\\n return errors\",\n \"def get_error(self) -> List[str]:\\n return []\",\n \"def get_error(self) -> List[str]:\\n return []\",\n \"def Errors(self):\\n return self._get_attribute('errors')\",\n \"def get_validation_errors(self):\\n return [err.to_dict() for err in self._schema.validator.validation_errors]\",\n \"def tasks_with_errors(self):\\n errs = []\\n while True:\\n try:\\n errs.append(self._errq.get_nowait())\\n except Queue.Empty:\\n break\\n return errs\",\n \"def _get_errors(exc):\\n if hasattr(exc, 'message'):\\n errors = exc.messages\\n else:\\n errors = [str(exc)]\\n return errors\",\n \"def _get_resends(self):\\n if not self.has_error():\\n return []\\n\\n errors = []\\n i = 0\\n for item in self.my_json['results']:\\n if item.has_key('error') and item['error'] == 'Unavailable':\\n errors.append((i, item['error']))\\n i += 1\\n return errors\",\n \"def refined_errors(self):\\r\\n errs = []\\r\\n for err in self.errors:\\r\\n if err['typo'].lower() not in self.terms:\\r\\n errs.append(err)\\r\\n return errs\",\n \"def flushErrors(exc_type: type) -> list[Exception]:\\n # There is no public API for flushing logged errors if you're not\\n # using one of trial's TestCase classes...\\n from twisted.trial.runner import _logObserver # type: ignore[attr-defined]\\n\\n result = _logObserver.flushErrors(exc_type)\\n assert isinstance(result, list)\\n return result\",\n \"def errors(self):\\n return self._properties.get(\\\"errors\\\")\",\n \"def report_transaction_error_messages(self):\\n response = self.__get_transaction_response()\\n\\n # get response data from response object\\n response_data = response.json()\\n\\n # get error messages\\n response_error = response_data['Error']\\n response_error_messages = response_error['messages']\\n\\n # add all error messages to the report\\n error_messages_to_report = []\\n for response_error_message in response_error_messages:\\n error_description = response_error_message['description']\\n error_messages_to_report.append(error_description)\\n\\n return error_messages_to_report\",\n \"def validation_errors(self):\\n return self._validation_errors\",\n \"def date_errors(self):\\r\\n try:\\r\\n _date_errors = self._validate_date(self.sourceDateCol)\\r\\n return _date_errors\\r\\n except:\\r\\n return None\",\n \"def error_report():\\n db, c = connect(DBNAME)\\n c.execute(\\\"select to_char(time,'FMMonth DD, YYYY') as date, \\\"\\n \\\"round((sum(case when status = '200 OK' \\\"\\n \\\"then 0 else 1 end)::decimal / count(*)) * 100,2) \\\"\\n \\\"as percent_error from log group by date \\\"\\n \\\"having (sum(case when status = '200 OK' \\\"\\n \\\"then 0 else 1 end)::decimal / count(*)) * 100 > 1\\\")\\n error_table = c.fetchall()\\n db.close()\\n print \\\"\\\\nDates on Which Over 1% of Requests Led to Errors:\\\"\\n for error in error_table:\\n if __name__ == '__main__':\\n print str(error[0]) + \\\" - \\\" + str(error[1]) + \\\"%\\\"\",\n \"def get_form_errors(form):\\n all_errors = []\\n for field in form.errors:\\n all_errors += form.errors[field]\\n return all_errors\",\n \"def get_all_failures(self):\\n return self._get_filtered_results(success=False)\",\n \"def error_messages(self) -> List[str]:\\n spatial_msgs = []\\n temporal_msgs = []\\n if self.spatial:\\n spatial_msgs = [m for v, m in self.spatial_validations if not v(self.spatial)]\\n if self.temporal:\\n temporal_msgs = [m for v, m in self.temporal_validations if not v(self.temporal)]\\n\\n return spatial_msgs + temporal_msgs\",\n \"def errors(self) -> Tuple[MqexsErrorInfo, ...]:\\n return self.__errors\",\n \"def get_error_messages(self):\\n\\n if len(self._sensor_results_list) == 0:\\n return\\n\\n error_msgs = []\\n\\n for reading in self._sensor_results_list:\\n if reading.is_error():\\n error_msgs.append(reading.get_error_msg())\\n\\n if len(error_msgs) > 0:\\n return error_msgs\\n else:\\n return \\\"No Error Readings\\\"\",\n \"def errors(self):\\r\\n if not hasattr(self, '_errors_cache'):\\r\\n self._errors_cache = self.form.get_field_errors(self)\\r\\n return self._errors_cache\",\n \"def get_diagnostics(self) -> List[Diagnostic]:\\n raise NotImplementedError\",\n \"def _get_errors(sheet, row, col):\\n field = _FIELDS['primary data']\\n val = sheet.cell(row + field['row'], col + field['column']).value\\n if not val:\\n return []\\n final_row = row + field['row']\\n error = sheet.cell(final_row, col + field['column']).value\\n while error:\\n final_row += 1\\n error = sheet.cell(final_row, col + field['column']).value\\n return [sheet.cell(i, col + field['column']).value\\n for i in range(row + field['row'], final_row)]\",\n \"def all_errata(self):\\n return self._all_errata\",\n \"def get_errors(self, path: str,\\n is_ancillary: bool = False,\\n is_system: bool = False,\\n is_removed: bool = False) -> List[str]:\",\n \"def output_errors(self) -> List[str]:\\n output = list()\\n for error in sorted(self.errors):\\n if len(error) == 2:\\n line = f\\\"{error[0]} - Could not find {error[1]} in map names!\\\"\\n else:\\n line = f\\\"{error[0]} - {error[1]}: Could not find {error[2]} in map names!\\\"\\n logger.error(line)\\n output.append(line)\\n return output\",\n \"def _pydantic_errors_to_validation_results(\\n errors: list[dict | Exception] | ValidationError,\\n file_path: Path,\\n scope: Scope,\\n) -> list[ValidationResult]:\\n out = []\\n for e in (\\n errors.errors() if isinstance(errors, ValidationError) else cast(list, errors)\\n ):\\n if isinstance(e, Exception):\\n message = getattr(e, \\\"message\\\", str(e))\\n id = \\\"exception\\\"\\n scope = Scope.FILE\\n else:\\n id = \\\".\\\".join(\\n filter(\\n bool,\\n (\\n \\\"dandischema\\\",\\n e.get(\\\"type\\\", \\\"UNKNOWN\\\"),\\n \\\"+\\\".join(e.get(\\\"loc\\\", [])),\\n ),\\n )\\n )\\n message = e.get(\\\"message\\\", e.get(\\\"msg\\\", None))\\n out.append(\\n ValidationResult(\\n origin=ValidationOrigin(\\n name=\\\"dandischema\\\",\\n version=dandischema.__version__,\\n ),\\n severity=Severity.ERROR,\\n id=id,\\n scope=scope,\\n path=file_path,\\n message=message,\\n # TODO? dataset_path=dataset_path,\\n # TODO? dandiset_path=dandiset_path,\\n )\\n )\\n return out\",\n \"def get_errors(response):\\n errors = response.get(\\\"error\\\")\\n if errors:\\n return [e.get(\\\"message\\\") for e in errors]\\n return None\",\n \"def errors_fatal(self) -> List[Error]:\\n return self._errors_fatal_files + self._errors_fatal\",\n \"def _get_retriable_errors(out: List[str]) -> List[str]:\\n return [\\n line for line in out\\n if any(error in line for error in RETRIABLE_ERRORS)\\n ]\",\n \"def get_errors(self, request):\\n\\n value = request._get_parameter_value(self)\\n return value.errors\",\n \"def errors(self):\\n raise NotImplementedError\",\n \"def recorded_messages(self):\\n messages = []\\n for time in sorted(self.reception_records):\\n messages.extend(self.reception_records[time])\\n return messages\",\n \"def warnings(self) -> List[Error]:\",\n \"def get_aggregated_exceptions(self) -> Payload:\\n return Payload(aggregated_errors=list(self._aggregated_exceptions.values()))\",\n \"def get_errors(self, path: str,\\n is_ancillary: bool = False,\\n is_system: bool = False,\\n is_removed: bool = False) -> List[str]:\\n u_file = self.__api.files.get(path, is_ancillary=is_ancillary,\\n is_system=is_system,\\n is_removed=is_removed)\\n return [e.message for e in u_file.errors]\",\n \"def check_set_errors(self):\\n response = self.read()\\n return [] if response == \\\"\\\" else [response]\",\n \"def getErrors(self):\\n errorList = []\\n\\n # E0\\n try:\\n if not self.e0.isValid():\\n errorList.append(\\\"Invalid first error axis in ErrorEllipse Class\\\")\\n except (NameError, AttributeError):\\n errorList.append(\\\"No first error axis in ErrorEllipse Class.\\\")\\n\\n # E1\\n try:\\n if not self.e1.isValid():\\n errorList.append(\\\"Invalid second error axis in ErrorEllipse Class\\\")\\n except (NameError, AttributeError):\\n errorList.append(\\\"No second error axis in ErrorEllipse Class.\\\")\\n\\n # E2\\n try:\\n if not self.e2.isValid():\\n errorList.append(\\\"Invalid third error axis in ErrorEllipse Class\\\")\\n except (NameError, AttributeError):\\n errorList.append(\\\"No third error axis in ErrorEllipse Class.\\\")\\n\\n # maximumHorizontalProjection\\n try:\\n self.maximumHorizontalProjection\\n except (NameError, AttributeError):\\n errorList.append(\\\"No MaximumHorizontalProjection in ErrorEllipse Class.\\\")\\n\\n # maximumVerticalProjection\\n try:\\n self.maximumVerticalProjection\\n except (NameError, AttributeError):\\n errorList.append(\\\"No MaximumVerticalProjection in ErrorEllipse Class\\\")\\n\\n # equivalentHorizontalRadius\\n try:\\n self.equivalentHorizontalRadius\\n except (NameError, AttributeError):\\n errorList.append(\\\"No EquivalentHorizontalRadius in ErrorEllipse class\\\")\\n\\n return errorList\",\n \"def error_wrapper(x):\\n errors = list()\\n for error_key, error_list in list(x.items()):\\n for error in error_list:\\n if error_key == 'non_field_errors':\\n errors.append(error)\\n else:\\n errors.append(\\\"%s: %s\\\" % (error_key, error))\\n return errors\",\n \"def err_rec(self):\\n if len(self.dt_list) < self.err_len:\\n self.err_len = len(self.dt_list)\\n\\n last_dt_sum = sum(self.dt_list[int(-1 * self.err_len):])\\n last_dt = self.dt_list[-8]\\n\\n # Records the discrepancies\\n self.lin_int_err.append(abs(self.lin_int_est_next[0] - last_dt_sum))\\n self.lin_int_no_err.append(abs(self.lin_int_no_est_next[0] - last_dt_sum))\\n self.average_int_err.append(abs(self.average_int_est_next[0] - last_dt_sum))\\n self.average_int_no_err.append(abs(self.average_int_no_est_next[0] - last_dt_sum))\\n\\n self.lin_err.append(abs(self.lin_est_next[0] - last_dt_sum))\\n self.lin_no_err.append(abs(self.lin_no_est_next[0] - last_dt_sum))\\n self.average_err.append(abs(self.average_est_next[0] - last_dt_sum))\\n self.average_no_err.append(abs(self.average_no_est_next[0] - last_dt_sum))\\n\\n # Removes the oldest entry\\n self.lin_err.pop(0)\\n self.average_err.pop(0)\",\n \"def getLogs():\\n # in flux, it may be possible to provide more structured information\\n # like python Failure instances\",\n \"def _filter_return_errors(self, url, har=None):\\r\\n if not har:\\r\\n har = self.har\\r\\n \\r\\n matches = []\\r\\n for entry in har[\\\"log\\\"][\\\"entries\\\"]:\\r\\n temp = entry[\\\"request\\\"][\\\"url\\\"].encode('ascii', 'ignore')\\r\\n if url in entry[\\\"request\\\"][\\\"url\\\"] and temp not in matches and entry[\\\"response\\\"][\\\"status\\\"] >= 400:\\r\\n print \\\"\\\\nRequest failed w/ \\\" + str(entry[\\\"response\\\"][\\\"status\\\"]) + \\\" error:\\\\n\\\" + entry[\\\"request\\\"][\\\"url\\\"]\\r\\n if entry[\\\"response\\\"][\\\"content\\\"].get(\\\"text\\\"):\\r\\n print \\\"RESPONSE: \\\" + str(entry[\\\"response\\\"][\\\"content\\\"][\\\"text\\\"].encode('ascii', 'ignore'))\\r\\n temp = entry[\\\"request\\\"][\\\"url\\\"].encode('ascii', 'ignore')\\r\\n matches.append(temp)\\r\\n return matches\",\n \"def get_error_days():\\n db = psycopg2.connect(database=DBNAME)\\n c = db.cursor()\\n # dividing views of bad requests and total request to get percentage\\n c.execute(\\\"select bad_request.time, \\\"\\n \\\"(bad_request.num * 1.0 / total_request.num) as errors \\\"\\n \\\"from bad_request, total_request \\\"\\n \\\"where bad_request.time = total_request.time \\\"\\n \\\"and (bad_request.num * 1.0 / total_request.num) > 0.01\\\")\\n results = c.fetchall()\\n text_file = open(\\\"text.txt\\\", \\\"a+\\\") # append to text file\\n text_file.write(\\\"Day(s) where more than 1 percent of requests were errors:\\\"\\n \\\"\\\\n\\\\n\\\")\\n for time, errors in results:\\n text_file.write(time.strftime('%B %d, %Y') + \\\" - \\\" +\\n str(errors * 100)[:3] + \\\"% errors\\\\n\\\")\\n text_file.write(\\\"\\\\n\\\")\\n text_file.close()\\n db.close()\",\n \"def getFatalErrors(self):\\n global hadFatalErrors\\n if hadFatalErrors:\\n text = '\\\\n'.join(hadFatalErrors)\\n hadFatalErrors = []\\n return text\",\n \"def time_errors(self):\\r\\n try:\\r\\n _time_errors = self._validate_time(self.sourceTimeCol)\\r\\n return _time_errors\\r\\n except:\\r\\n return None\",\n \"def errors(self) -> pulumi.Output[Sequence['outputs.BatchAIErrorResponse']]:\\n return pulumi.get(self, \\\"errors\\\")\",\n \"def filter_draft_errors(result):\\n error_messages = []\\n for field, msgs in result.get('messages', {}).items():\\n if msgs.get('state', None) == 'error':\\n for m in msgs['messages']:\\n error_messages.append(dict(\\n field=field,\\n message=m,\\n code=error_codes['validation_error'],\\n ))\\n return error_messages\",\n \"def _parse_file_errors(self, file):\\n\\n with open(file, 'r') as lines:\\n errors = []\\n\\n for line in lines:\\n if self.log_regexp.TRACEBACK.match(line):\\n errors.append({})\\n\\n if self.log_regexp.ERROR_PLACE.match(line):\\n code_line = re.search(self.log_regexp.ERROR_LINE, line)[0]\\n error_file = re.search(self.log_regexp.ERROR_FILE, line)[0]\\n errors[-1][self.output_params.LINE] = int(code_line.split()[1])\\n errors[-1][self.output_params.ERROR_FILE] = error_file.strip('\\\"')\\n\\n if self.log_regexp.ERROR_TYPE.match(line):\\n type_error = re.search(self.log_regexp.ERROR_TYPE, line)[0]\\n message = line[len(type_error)+1:]\\n errors[-1].update({self.output_params.TYPE: type_error})\\n errors[-1].update({self.output_params.MESSAGE: message.strip()})\\n\\n return errors\",\n \"def failing_periods(self) -> 'outputs.DynamicThresholdFailingPeriodsResponse':\\n return pulumi.get(self, \\\"failing_periods\\\")\",\n \"def error_dates():\\n\\n results = query_database(QUERIES[2])\\n print('\\\\nOn which days did more than 1% of requests lead to errors?\\\\n')\\n for date, rate in results:\\n print(' * {} -- {:.2%}'.format(date, rate))\",\n \"def _step_errors(sequence: Sequence) -> List[TransformationMetrics]:\\n errors = [TransformationMetrics.calc(t_pred, t_gt, time)\\n for t_pred, t_gt, time in zip(sequence.prediction.transforms, sequence.ground_truth.transforms,\\n sequence.times)]\\n return errors\",\n \"def get_failed_spectra_list(diag_workspace):\\n if type(diag_workspace) == str:\\n diag_workspace = mtd[diag_workspace]\\n\\n if hasattr(diag_workspace, \\\"getAxis\\\") == False:\\n raise ValueError(\\\"Invalid input to get_failed_spectra_list. \\\"\\n \\\"A workspace handle or name is expected\\\")\\n \\n spectra_axis = diag_workspace.getAxis(1)\\n failed_spectra = []\\n for i in range(diag_workspace.getNumberHistograms()):\\n try:\\n det = diag_workspace.getDetector(i)\\n except RuntimeError:\\n continue\\n if det.isMasked():\\n failed_spectra.append(spectra_axis.spectraNumber(i))\\n\\n return failed_spectra\",\n \"def get_validation_errors(\\n self,\\n schema_version: Optional[str] = None,\\n devel_debug: bool = False,\\n ) -> list[ValidationResult]:\\n ...\",\n \"def getFailedJobs(self):\\n return self.__failedJobs\",\n \"def listErrors(individual): #TODO: add other errors implemented elsewhere\\n\\n results = [birthBeforeMarriage(individual), birthBeforeDeath(individual), marriageBeforeDeath(individual), \\n datesBeforeCurrentDate(individual), noBigamy(individual)]\\n results = [x for x in results if x is not None]\\n return not all(results)\",\n \"def get_fails(self):\\n return [result for result in self.values() if result.outcome == Result.FAIL]\",\n \"def parse(self, errors, explicit_ignore):\\n\\n error_list = []\\n if errors is None:\\n return error_list\\n\\n errors.sort(key=linter.cmp_to_key(lambda a, b: a.lineno < b.lineno))\\n for error in errors:\\n error_level = 'W' if not hasattr(error, 'level') else error.level\\n message = error.message.capitalize()\\n\\n error_data = {\\n 'underline_range': False,\\n 'level': error_level,\\n 'lineno': error.lineno,\\n 'message': message,\\n 'raw_error': str(error)\\n }\\n if hasattr(error, 'offset'):\\n error_data['offset'] = error.offset\\n elif hasattr(error, 'col'):\\n error_data['offset'] = error.col\\n\\n if (isinstance(error, (linter.OffsetError))):\\n error_data['underline_range'] = True\\n error_list.append(error_data)\\n elif (isinstance(\\n error, (\\n pyflakes.messages.RedefinedWhileUnused,\\n pyflakes.messages.RedefinedInListComp,\\n pyflakes.messages.UndefinedName,\\n pyflakes.messages.UndefinedExport,\\n pyflakes.messages.UndefinedLocal,\\n pyflakes.messages.UnusedVariable)) and\\n error.__class__.__name__ not in explicit_ignore):\\n\\n error_data['len'] = len(error.message_args[0])\\n error_data['regex'] = (\\n r'((and|or|not|if|elif|while|in)\\\\s+|[+\\\\-*^%%<>=\\\\(\\\\{{])*\\\\s'\\n '*(?P<underline>[\\\\w\\\\.]*{0}[\\\\w]*)'.format(re.escape(\\n error.message_args[0]\\n ))\\n )\\n error_list.append(error_data)\\n elif isinstance(error, pyflakes.messages.ImportShadowedByLoopVar):\\n regex = 'for\\\\s+(?P<underline>[\\\\w]*{0}[\\\\w*])'.format(\\n re.escape(error.message_args[0])\\n )\\n error_data['regex'] = regex\\n error_list.append(error_data)\\n elif (isinstance(\\n error, (\\n pyflakes.messages.UnusedImport,\\n pyflakes.messages.ImportStarUsed)) and\\n error.__class__.__name__ not in explicit_ignore):\\n if isinstance(error, pyflakes.messages.ImportStarUsed):\\n word = '*'\\n else:\\n word = error.message_args[0]\\n\\n linematch = '(from\\\\s+[\\\\w_\\\\.]+\\\\s+)?import\\\\s+(?P<match>[^#;]+)'\\n r = '(^|\\\\s+|,\\\\s*|as\\\\s+)(?P<underline>[\\\\w]*{0}[\\\\w]*)'.format(\\n re.escape(word)\\n )\\n error_data['regex'] = r\\n error_data['linematch'] = linematch\\n error_list.append(error_data)\\n elif (isinstance(error, pyflakes.messages.DuplicateArgument) and\\n error.__class__.__name__ not in explicit_ignore):\\n regex = 'def [\\\\w_]+\\\\(.*?(?P<underline>[\\\\w]*{0}[\\\\w]*)'.format(\\n re.escape(error.message_args[0])\\n )\\n error_data['regex'] = regex\\n error_list.append(error_data)\\n elif isinstance(error, pyflakes.messages.LateFutureImport):\\n pass\\n elif isinstance(error, linter.PythonError):\\n print(error)\\n else:\\n print(\\n 'Ooops, we missed an error type for pyflakes', type(error)\\n )\\n\\n return error_list\",\n \"def failed_items(self) -> ItemLog:\\n if self._failed_items is None:\\n self._failed_items = ItemLog(self.dir / 'dropped-failed.log.csv', DROPPED_FAILED_FIELDNAMES, 'id')\\n return self._failed_items\",\n \"def errors(self):\\n _errors = {}\\n # pylint: disable=no-member\\n for name, field in self._fields.items():\\n if field.errors:\\n _errors[name] = field.errors.pop()\\n\\n return _errors\",\n \"def warnings(self) -> List[Error]:\\n return self._get_warnings()\",\n \"def validation_errors_to_error_messages(validation_errors):\\n errorMessages = []\\n for field in validation_errors:\\n for error in validation_errors[field]:\\n errorMessages.append(f\\\"{field} : {error}\\\")\\n return errorMessages\",\n \"def validation_errors_to_error_messages(validation_errors):\\n errorMessages = []\\n for field in validation_errors:\\n for error in validation_errors[field]:\\n errorMessages.append(f\\\"{field} : {error}\\\")\\n return errorMessages\",\n \"def validation_errors_to_error_messages(validation_errors):\\n errorMessages = []\\n for field in validation_errors:\\n for error in validation_errors[field]:\\n errorMessages.append(f\\\"{field} : {error}\\\")\\n return errorMessages\",\n \"def validation_errors_to_error_messages(validation_errors):\\n errorMessages = []\\n for field in validation_errors:\\n for error in validation_errors[field]:\\n errorMessages.append(f\\\"{field} : {error}\\\")\\n return errorMessages\",\n \"def validation_errors_to_error_messages(validation_errors):\\n errorMessages = []\\n for field in validation_errors:\\n for error in validation_errors[field]:\\n errorMessages.append(f\\\"{field} : {error}\\\")\\n return errorMessages\",\n \"def getErrorReport(self):\\n return self.sError;\",\n \"def getErrorReport(self):\\n return self.sError;\",\n \"def job_errors(self) -> str:\\n errors = []\\n\\n # Get any job errors\\n for job in self._jobs.values():\\n if job and job.status() == JobStatus.ERROR:\\n if hasattr(job, \\\"error_message\\\"):\\n error_msg = job.error_message()\\n else:\\n error_msg = \\\"\\\"\\n errors.append(f\\\"\\\\n[Job ID: {job.job_id()}]: {error_msg}\\\")\\n\\n # Get any job futures errors:\\n for jid, fut in self._job_futures.items():\\n if fut and fut.done() and fut.exception():\\n ex = fut.exception()\\n errors.append(\\n f\\\"[Job ID: {jid}]\\\"\\n \\\"\\\\n\\\".join(traceback.format_exception(type(ex), ex, ex.__traceback__))\\n )\\n return \\\"\\\".join(errors)\",\n \"def get_alerts(self):\\n url = 'http://www.p2000-online.net/p2000.php?%s&nofilterform=1'\\n url = url % '&'.join(['%s=1' % x for x in self.regions])\\n if self.verbose:\\n print time.ctime(), url\\n try:\\n data = urllib.urlopen(url).read()\\n except IOError:\\n if self.verbose:\\n import traceback\\n traceback.print_exc()\\n return []\\n\\n doc = soup(data)\\n alerts = []\\n table = doc.body('table', {'style': 'align:center'})[0]\\n for tr in table('tr'):\\n if tr.td.get('class', None) == 'DT':\\n alerts.append(Alert(*[x.text for x in tr('td')]))\\n else:\\n recipient = tr('td')[-1].text\\n if recipient != ' ':\\n alerts[-1].recipients.append(recipient)\\n return alerts\",\n \"def __call__(self, errors: List[float]) -> List[float]:\",\n \"def get_errors(self):\\n errors = []\\n\\n if not self.title:\\n msg = 'Title not found: {0}'.format(self.number)\\n print(msg)\\n errors.append(msg)\\n\\n if not self.ref:\\n msg = 'Ref not found: {0}'.format(self.number)\\n print(msg)\\n errors.append(msg)\\n\\n chapter_index = int(self.number) - 1\\n\\n # get the expected number of frames for this chapter\\n expected_frame_count = chapters_and_frames.frame_counts[chapter_index]\\n\\n for x in range(1, expected_frame_count + 1):\\n\\n # frame id is formatted like '01-01'\\n frame_id = '{0}-{1}'.format(self.number.zfill(2), str(x).zfill(2))\\n\\n # get the next frame\\n frame = next((f for f in self.frames if f['id'] == frame_id), None) # type: dict\\n if not frame:\\n msg = 'Frame not found: {0}'.format(frame_id)\\n print(msg)\\n errors.append(msg)\\n else:\\n # check the frame img and values\\n if 'img' not in frame or not frame['img']:\\n msg = 'Attribute \\\"img\\\" is missing for frame {0}'.format(frame_id)\\n print(msg)\\n errors.append(msg)\\n\\n if 'text' not in frame or not frame['text']:\\n msg = 'Attribute \\\"text\\\" is missing for frame {0}'.format(frame_id)\\n print(msg)\\n errors.append(msg)\\n\\n return errors\",\n \"def errors(self) -> str:\\n return self.job_errors() + self.analysis_errors()\",\n \"def errDays():\\n c = db.cursor()\\n c.execute(\\\"select date, percent from avg_error\\\\\\n where percent > 1.00;\\\")\\n results = c.fetchall()\\n c.close()\\n return results\",\n \"def json(self):\\n d = [err.json for err in self.errors]\\n return d\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7021791","0.70215106","0.6847071","0.6754863","0.6702647","0.6692272","0.65891427","0.65589136","0.65341383","0.65235007","0.65032333","0.65032333","0.6477152","0.6440303","0.642983","0.6413375","0.6399626","0.6374042","0.6354887","0.6273033","0.62713027","0.62526685","0.62447596","0.62249994","0.6223605","0.62194806","0.620146","0.6165684","0.6165684","0.6119376","0.6090197","0.60822266","0.607846","0.6057128","0.6053458","0.59767246","0.591607","0.5914257","0.5898345","0.58874625","0.5881139","0.58664495","0.58615905","0.5856585","0.5831075","0.5809601","0.5794592","0.57865477","0.5765051","0.57592994","0.57558554","0.5746094","0.57423496","0.57266444","0.57260984","0.5724483","0.5722599","0.5718919","0.57043016","0.56999606","0.567887","0.56754166","0.5673889","0.5672582","0.5659258","0.56456894","0.5644008","0.56153643","0.5607474","0.56057703","0.5600659","0.5588476","0.5578138","0.55776983","0.55689037","0.5560539","0.5550898","0.5548416","0.55364776","0.55314523","0.5522481","0.5513594","0.55006075","0.5498907","0.5474333","0.5464376","0.54519504","0.54519504","0.54519504","0.54519504","0.54519504","0.54490054","0.54490054","0.54457843","0.5445546","0.54447746","0.5437848","0.5430879","0.5413174","0.54126525"],"string":"[\n \"0.7021791\",\n \"0.70215106\",\n \"0.6847071\",\n \"0.6754863\",\n \"0.6702647\",\n \"0.6692272\",\n \"0.65891427\",\n \"0.65589136\",\n \"0.65341383\",\n \"0.65235007\",\n \"0.65032333\",\n \"0.65032333\",\n \"0.6477152\",\n \"0.6440303\",\n \"0.642983\",\n \"0.6413375\",\n \"0.6399626\",\n \"0.6374042\",\n \"0.6354887\",\n \"0.6273033\",\n \"0.62713027\",\n \"0.62526685\",\n \"0.62447596\",\n \"0.62249994\",\n \"0.6223605\",\n \"0.62194806\",\n \"0.620146\",\n \"0.6165684\",\n \"0.6165684\",\n \"0.6119376\",\n \"0.6090197\",\n \"0.60822266\",\n \"0.607846\",\n \"0.6057128\",\n \"0.6053458\",\n \"0.59767246\",\n \"0.591607\",\n \"0.5914257\",\n \"0.5898345\",\n \"0.58874625\",\n \"0.5881139\",\n \"0.58664495\",\n \"0.58615905\",\n \"0.5856585\",\n \"0.5831075\",\n \"0.5809601\",\n \"0.5794592\",\n \"0.57865477\",\n \"0.5765051\",\n \"0.57592994\",\n \"0.57558554\",\n \"0.5746094\",\n \"0.57423496\",\n \"0.57266444\",\n \"0.57260984\",\n \"0.5724483\",\n \"0.5722599\",\n \"0.5718919\",\n \"0.57043016\",\n \"0.56999606\",\n \"0.567887\",\n \"0.56754166\",\n \"0.5673889\",\n \"0.5672582\",\n \"0.5659258\",\n \"0.56456894\",\n \"0.5644008\",\n \"0.56153643\",\n \"0.5607474\",\n \"0.56057703\",\n \"0.5600659\",\n \"0.5588476\",\n \"0.5578138\",\n \"0.55776983\",\n \"0.55689037\",\n \"0.5560539\",\n \"0.5550898\",\n \"0.5548416\",\n \"0.55364776\",\n \"0.55314523\",\n \"0.5522481\",\n \"0.5513594\",\n \"0.55006075\",\n \"0.5498907\",\n \"0.5474333\",\n \"0.5464376\",\n \"0.54519504\",\n \"0.54519504\",\n \"0.54519504\",\n \"0.54519504\",\n \"0.54519504\",\n \"0.54490054\",\n \"0.54490054\",\n \"0.54457843\",\n \"0.5445546\",\n \"0.54447746\",\n \"0.5437848\",\n \"0.5430879\",\n \"0.5413174\",\n \"0.54126525\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.59131587"},"document_rank":{"kind":"string","value":"38"}}},{"rowIdx":3692,"cells":{"query":{"kind":"string","value":"Returns a list of slow transaction data collected during the reporting period."},"document":{"kind":"string","value":"def transaction_trace_data(self, connections):\n\n _logger.debug('Generating transaction trace data.')\n\n if not self.__settings:\n return []\n\n # Create a set 'traces' that is a union of slow transaction,\n # and Synthetics transactions. This ensures we don't send\n # duplicates of a transaction.\n\n traces = set()\n if self.__slow_transaction:\n traces.add(self.__slow_transaction)\n traces.update(self.__synthetics_transactions)\n\n # Return an empty list if no transactions were captured.\n\n if not traces:\n return []\n\n # We want to limit the number of explain plans we do across\n # these. So work out what were the slowest and tag them.\n # Later the explain plan will only be run on those which are\n # tagged.\n\n agent_limits = self.__settings.agent_limits\n explain_plan_limit = agent_limits.sql_explain_plans_per_harvest\n maximum_nodes = agent_limits.transaction_traces_nodes\n\n database_nodes = []\n\n if explain_plan_limit != 0:\n for trace in traces:\n for node in trace.slow_sql:\n # Make sure we clear any flag for explain plans on\n # the nodes in case a transaction trace was merged\n # in from previous harvest period.\n\n node.generate_explain_plan = False\n\n # Node should be excluded if not for an operation\n # that we can't do an explain plan on. Also should\n # not be one which would not be included in the\n # transaction trace because limit was reached.\n\n if (node.node_count < maximum_nodes and\n node.connect_params and node.statement.operation in\n node.statement.database.explain_stmts):\n database_nodes.append(node)\n\n database_nodes = sorted(database_nodes,\n key=lambda x: x.duration)[-explain_plan_limit:]\n\n for node in database_nodes:\n node.generate_explain_plan = True\n\n else:\n for trace in traces:\n for node in trace.slow_sql:\n node.generate_explain_plan = True\n database_nodes.append(node)\n\n # Now generate the transaction traces. We need to cap the\n # number of nodes capture to the specified limit.\n\n trace_data = []\n\n for trace in traces:\n transaction_trace = trace.transaction_trace(\n self, maximum_nodes, connections)\n\n data = [transaction_trace,\n list(trace.string_table.values())]\n\n if self.__settings.debug.log_transaction_trace_payload:\n _logger.debug('Encoding slow transaction data where '\n 'payload=%r.', data)\n\n json_data = json_encode(data)\n\n level = self.__settings.agent_limits.data_compression_level\n level = level or zlib.Z_DEFAULT_COMPRESSION\n\n zlib_data = zlib.compress(six.b(json_data), level)\n\n pack_data = base64.standard_b64encode(zlib_data)\n\n if six.PY3:\n pack_data = pack_data.decode('Latin-1')\n\n root = transaction_trace.root\n\n if trace.record_tt:\n force_persist = True\n else:\n force_persist = False\n\n if trace.include_transaction_trace_request_uri:\n request_uri = trace.request_uri\n else:\n request_uri = None\n\n trace_data.append([transaction_trace.start_time,\n root.end_time - root.start_time,\n trace.path,\n request_uri,\n pack_data,\n trace.guid,\n None,\n force_persist,\n None,\n trace.synthetics_resource_id, ])\n\n return trace_data"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def slow_transaction_data(self):\n\n # XXX This method no longer appears to be used. Being replaced\n # by the transaction_trace_data() method.\n\n if not self.__settings:\n return []\n\n if not self.__slow_transaction:\n return []\n\n maximum = self.__settings.agent_limits.transaction_traces_nodes\n\n transaction_trace = self.__slow_transaction.transaction_trace(\n self, maximum)\n\n data = [transaction_trace,\n list(self.__slow_transaction.string_table.values())]\n\n if self.__settings.debug.log_transaction_trace_payload:\n _logger.debug('Encoding slow transaction data where '\n 'payload=%r.', data)\n\n json_data = json_encode(data)\n\n level = self.__settings.agent_limits.data_compression_level\n level = level or zlib.Z_DEFAULT_COMPRESSION\n\n zlib_data = zlib.compress(six.b(json_data), level)\n\n pack_data = base64.standard_b64encode(zlib_data)\n\n if six.PY3:\n pack_data = pack_data.decode('Latin-1')\n\n root = transaction_trace.root\n\n trace_data = [[root.start_time,\n root.end_time - root.start_time,\n self.__slow_transaction.path,\n self.__slow_transaction.request_uri,\n pack_data]]\n\n return trace_data","def return_trade_history(\n self,\n start: Timestamp,\n end: Timestamp,\n ) -> list[dict[str, Any]]:\n limit = 100\n data: list[dict[str, Any]] = []\n start_ms = start * 1000\n end_ms = end * 1000\n while True:\n new_data = self.api_query_list('/trades', {\n 'startTime': start_ms,\n 'endTime': end_ms,\n 'limit': limit,\n })\n results_length = len(new_data)\n if data == [] and results_length < limit:\n return new_data # simple case - only one query needed\n\n latest_ts_ms = start_ms\n # add results to data and prepare for next query\n existing_ids = {x['id'] for x in data}\n for trade in new_data:\n try:\n timestamp_ms = trade['createTime']\n latest_ts_ms = max(latest_ts_ms, timestamp_ms)\n # since we query again from last ts seen make sure no duplicates make it in\n if trade['id'] not in existing_ids:\n data.append(trade)\n except (DeserializationError, KeyError) as e:\n msg = str(e)\n if isinstance(e, KeyError):\n msg = f'Missing key entry for {msg}.'\n self.msg_aggregator.add_warning(\n 'Error deserializing a poloniex trade. Check the logs for details',\n )\n log.error(\n 'Error deserializing poloniex trade',\n trade=trade,\n error=msg,\n )\n continue\n\n if results_length < limit:\n break # last query has less than limit. We are done.\n\n # otherwise we query again from the last ts seen in the last result\n start_ms = latest_ts_ms\n continue\n\n return data","def get_reports(self):\r\n return sorted(self._reports,\r\n key=lambda x: x['stats']['totalTimeMillis'],\r\n reverse=True)","def get_reports(self):\n return sorted(self._reports,\n key=lambda x: x['stats']['totalTimeMillis'],\n reverse=True)","def slow_queries(self):\n request = Request(method=\"get\", endpoint=\"/query/slow\")\n\n def response_handler(resp):\n if not resp.is_success:\n raise C8QLQueryListError(resp, request)\n return self._format_queries(resp.body)\n\n return self._execute(request, response_handler)","def showTransactions(self):\n self.scanTransactions()\n txns = []\n\n # Summarize the stats\n for x in range(len(self._trans)):\n stats = self._trans[x]\n trans_time = 0\n remote_calls = 0\n for name, stat in stats:\n trans_time += stat.total_tt\n remote_calls += 1\n txns.append((x, trans_time, remote_calls))\n\n results = [\"TX#\\tTime\\tCalls\",\n \"=\" * 22]\n\n for item in txns:\n results.append(\"%3d\\t%4f\\t%5d\" % item)\n \n return \"\\n\".join(results)","def fetch(self, daterange=(datetime.now() - timedelta(1), datetime.now())):\n cursor = self.conn.cursor()\n sql = 'SELECT measure_dt, ping, download, upload FROM speedlogs ' + \\\n ' WHERE measure_dt BETWEEN ? AND ?'\n cursor.execute(sql, daterange)\n return cursor.fetchall()","def gather_data(self, *args, **kwargs):\n instrument_arg = kwargs.get('instrument', 'EUR_USD')\n granularity_arg = kwargs.get('granularity', 'M1')\n candle_format = kwargs.get('candleFormat', 'bidask')\n start_time = kwargs.get('start', '2014-10-01T00:00:00.000000Z')\n count_arg = kwargs.get('count', 5000)\n out_data = []\n data_complete = False\n while(not data_complete):\n response = self.oanda.get_history(instrument=instrument_arg,\n granularity=granularity_arg,\n candleFormat=candle_format,\n start=start_time,\n count=count_arg)\n raw_data = response['candles']\n if (len(out_data) == 0):\n out_data = out_data + raw_data\n elif (len(out_data) > 1):\n # raw_data[0] is already in out_data as raw_data[-1] from last\n # iteration\n out_data = out_data + raw_data[1:]\n start_time = raw_data[-1]['time']\n if (len(raw_data) < 5000):\n data_complete = True\n\n out_data = self._list_to_df(out_data)\n return out_data","def get_gdax_historical_data():\n \n start = None\n while not start:\n start,end,tid = getStartAndEndHistoric()\n if not start:\n time.sleep(60)\n #Todo: change this to 1min\n firsttimestamp = start\n engine = sa.create_engine(sql_address)\n products = [\"LTC-USD\",\"LTC-BTC\",\"ETH-USD\",\"ETH-BTC\",\"BTC-USD\"]\n public_client = gdax.PublicClient()\n deltat = datetime.timedelta(seconds = 200)\n timewindows = []\n while end - start > datetime.timedelta(seconds=0):\n if start + deltat > end:\n endx = end\n else:\n endx = start + deltat\n timewindows.append([start,endx])\n start += deltat\n results = []\n total = len(timewindows)\n current_idx = 0\n timeold = time.time()\n numofqueries = 0\n engine = sa.create_engine(sql_address)\n Base.metadata.bind = engine\n DBSession = sa.orm.sessionmaker()\n DBSession.bind = engine\n session = DBSession()\n for startx,endx in timewindows:\n\n current_idx += 1\n for i in products:\n repeat = True\n while repeat:\n\n #delay if ratelimts are close\n if numofqueries < 3:\n while time.time() - timeold < 1:\n time.sleep(0.05)\n \n timeold = time.time()\n numofqueries = 0\n try:\n alist = public_client.get_product_historic_rates(i, start = startx, end = endx, granularity = 1)\n except:\n time.sleep(30)\n public_client = gdax.PublicClient()\n alist = public_client.get_product_historic_rates(i, start = startx, end = endx, granularity = 1)\n\n alist = public_client.get_product_historic_rates(i, start = startx, end = endx, granularity = 1)\n\n numofqueries += 1\n\n #rate limit exceeded has 'message' as dict.\n if not 'message' in alist:\n repeat = False\n for a in alist:\n a[0] = datetime.datetime.fromtimestamp(float(a[0]))\n tmp = i.split('-')\n d = dict(coin = tmp[0], currency = tmp[1], timestamp = a[0], low=a[1], high=a[2], open=a[3], close=a[4], volume=a[5])\n results.append(d)\n lasttimestamp = a[0]\n\n #upload with batch size of 10000\n if len(results) > 10000:\n engine.execute(\n GADXHistoricalDataOneSecondOHLC.__table__.insert(),\n results\n )\n results = []\n \n update = session.query(historicalDataProgramState).filter(sa.and_(historicalDataProgramState.transaction_id == tid,historicalDataProgramState.entry_type == 'update')).first()\n if update:\n update.end = lasttimestamp\n session.commit()\n else:\n new_update = historicalDataProgramState(entry_type = 'update',transaction_id = tid,start=firsttimestamp,end=lasttimestamp,platform='GDAX',status='incomplete')\n session.add(new_update)\n session.commit()\n if len(results) > 0:\n engine.execute(\n GADXHistoricalDataOneSecondOHLC.__table__.insert(),\n results\n )\n results = []\n \n update = session.query(historicalDataProgramState).filter(sa.and_(historicalDataProgramState.transaction_id == tid,historicalDataProgramState.entry_type == 'update')).first()\n if update:\n update.end = lasttimestamp\n session.commit()\n else:\n new_update = historicalDataProgramState(entry_type = 'update',transaction_id = tid,start=firsttimestamp,end=lasttimestamp,platform='GDAX',status='incomplete')\n session.add(new_update)\n session.commit()\n\n update = session.query(historicalDataProgramState).filter(sa.and_(historicalDataProgramState.transaction_id == tid,historicalDataProgramState.entry_type == 'update')).first()\n update.status='complete'\n order = session.query(historicalDataProgramState).filter(sa.and_(historicalDataProgramState.transaction_id == tid,historicalDataProgramState.entry_type == 'order')).first()\n order.status='complete'\n session.commit()","def timings(self):\r\n return self._timings","def _get_meas_times_from_db(self):\n meas_times = []\n if self._data['report_save_historical_instances_ind'] != 'Y':\n # for non historical reports take measurement time from saved dataset\n dataset = self._jfile.get_current_stored_dataset()\n try:\n meas_time = datetime.datetime.strptime(dataset['meas_time'], '%Y-%m-%d %H:%M:%S')\n except ValueError:\n raise Exception(\"Cannot unformat string %s to datetime\" % dataset['meas_time'])\n meas_times.append(meas_time)\n\n else:\n # for historical reports take measurement times from db datasets\n where_sql = ''\n where_sql_list = list()\n params = [self._id, self._segment_value_id]\n\n if self._process_dataset_ids:\n for dataset_id in self._process_dataset_ids:\n if type(dataset_id) == list:\n where_sql_list.append(\"(report_data_set_instance_id >= %s AND report_data_set_instance_id <= %s)\")\n if dataset_id[0] < dataset_id[1]:\n params.append(dataset_id[0])\n params.append(dataset_id[1])\n else:\n params.append(dataset_id[1])\n params.append(dataset_id[0])\n else:\n where_sql_list.append(\"report_data_set_instance_id = %s\")\n params.append(dataset_id)\n where_sql = ' AND (%s)' % ' OR '.join(where_sql_list)\n\n self._db.Query(\"\"\"SELECT measurement_time\n FROM report_data_set_instance\n WHERE\n `element_id`= %%s\n AND segment_value_id = %%s\n %s\n ORDER BY measurement_time ASC\"\"\" % where_sql, tuple(params))\n meas_times = [item['measurement_time'] for item in self._db.record]\n\n return meas_times","def getTransferListSummary(self):\n p_ids_and_prices = {}\n players = self.getAllPlayerInfoTransferlist()\n\n # Get IDs of all players\n log_event(self.queue, \"Gathering player prices... \")\n for p in players:\n p_bidstatus = p[1]\n p_id = p[8]\n # removed Filter for unlisted / expired players\n if p_id not in p_ids_and_prices:\n p_sellprice = self.getPlayerSellPrice(p_id)\n # If sell price returns 0, need to fetch from Futbin\n if p_sellprice == 0:\n p_sellprice = self.getFutbinPrice_opentab(p_id)\n self.sleep_approx(5) # Delay iteration to not anger futbin\n # Add player ID and price to dict\n p_ids_and_prices[p_id] = p_sellprice\n\n for p_id in p_ids_and_prices:\n p_price = p_ids_and_prices[p_id]\n p_name = self.getPlayerCardName(p_id)\n log_event(self.queue, str(p_name) + \" - #\" +\n str(p_id) + \" Price \" + str(p_price))\n\n num_p_sold = 0\n num_p_expired = 0\n num_p_unlisted = 0\n num_p_listed = 0\n\n sold_p_value = 0\n expired_p_value = 0\n unlisted_p_value = 0\n listed_p_value = 0\n\n for p in players:\n p_bidstatus = p[1]\n p_id = p[8]\n p_soldprice = p[5] # is 0 if unlisted\n p_sellprice = int(p_ids_and_prices[p_id])\n\n if \"won\" in p_bidstatus:\n num_p_sold += 1\n sold_p_value += p_soldprice\n if \"expired\" in p_bidstatus:\n num_p_expired += 1\n expired_p_value += p_sellprice\n if (p_bidstatus == \"listFUTItem\"):\n num_p_unlisted += 1\n unlisted_p_value += p_sellprice\n if (p_bidstatus == \"listFUTItem has-auction-data\"):\n num_p_listed += 1\n listed_p_value += p_sellprice\n\n log_event(self.queue, \"Players sold: \" + str(num_p_sold))\n log_event(self.queue, \"Players expired: \" + str(num_p_expired))\n log_event(self.queue, \"Players listed: \" + str(num_p_listed))\n log_event(self.queue, \"Players unlisted: \" + str(num_p_unlisted))\n log_event(self.queue, \" - - - \")\n log_event(self.queue, \"Sold players value: \" + str(sold_p_value))\n log_event(self.queue, \"Expired players value: \" +\n str(expired_p_value))\n log_event(self.queue, \"Unlisted players value: \" +\n str(unlisted_p_value))\n log_event(self.queue, \"Listed players value: \" + str(listed_p_value))\n\n # TODO subtract bought price\n self.user_players_won += int(num_p_unlisted)\n self.p_ids_and_prices = p_ids_and_prices\n intel = [p_ids_and_prices, num_p_sold, num_p_expired, num_p_unlisted,\n num_p_listed, sold_p_value, expired_p_value, unlisted_p_value, listed_p_value]\n return intel","def describe_slow_log_records(\n self,\n request: dds_20151201_models.DescribeSlowLogRecordsRequest,\n ) -> dds_20151201_models.DescribeSlowLogRecordsResponse:\n runtime = util_models.RuntimeOptions()\n return self.describe_slow_log_records_with_options(request, runtime)","def time_list(self):\n return (self.N_T * (np.arange(self.N_itr) + 1) /\n self.N_itr * 1000 * self.DT)","def get_new_data(self):\n\n # record bar parse performance\n self.logger.debug(\"Started parsing new ticks.\")\n start_parse = time.time()\n for exchange in self.exchanges:\n exchange.parse_ticks()\n end_parse = time.time()\n duration = round(end_parse - start_parse, 5)\n\n self.logger.debug(\n \"Parsed \" + str(self.total_instruments) +\n \" instruments' ticks in \" + str(duration) + \" seconds.\")\n self.track_tick_processing_performance(duration)\n\n # wrap new 1 min bars in market events\n new_market_events = []\n for exchange in self.exchanges:\n bars = exchange.get_new_bars()\n for symbol in exchange.get_symbols():\n for bar in bars[symbol]:\n event = MarketEvent(exchange.get_name(), bar)\n new_market_events.append(event)\n # add bars to save-to-db-later queue\n # TODO: store new bars concurrently with a processpool\n self.bars_save_to_db.put(event)\n return new_market_events","def timeseries_report(self):\n report = pd.DataFrame(index=self.price.index)\n report.loc[:, \"FR Energy Throughput (kWh)\"] = self.ene_results['ene']\n report.loc[:, \"FR Energy Throughput Up (Charging) (kWh)\"] = self.variables['regu_c']*self.krd_avg*self.dt*self.storage.rte\n report.loc[:, \"FR Energy Throughput Up (Discharging) (kWh)\"] = self.variables['regu_d']*self.krd_avg*self.dt\n report.loc[:, \"FR Energy Throughput Down (Charging) (kWh)\"] = self.variables['regd_c']*self.krd_avg*self.dt*self.storage.rte\n report.loc[:, \"FR Energy Throughput Down (Discharging) (kWh)\"] = self.variables['regd_d']*self.krd_avg*self.dt\n report.loc[:, \"FR Energy Settlement Price Signal ($/kWh)\"] = self.price\n report.loc[:, 'Regulation Up (Charging) (kW)'] = self.variables['regu_c']\n report.loc[:, 'Regulation Up (Discharging) (kW)'] = self.variables['regu_d']\n report.loc[:, 'Regulation Down (Charging) (kW)'] = self.variables['regd_c']\n report.loc[:, 'Regulation Down (Discharging) (kW)'] = self.variables['regd_d']\n report.loc[:, \"Regulation Up Price Signal ($/kW)\"] = self.p_regu\n report.loc[:, \"Regulation Down Price Signal ($/kW)\"] = self.p_regd\n\n return report","async def get_trades(self) -> List[TradeRequest]:\n data = j.dumps({\n 'startindex': 0,\n 'statustype': 'inbound'\n })\n r = await self.request.request(url='https://www.roblox.com/my/money.aspx/getmyitemtrades', data=data, method='POST')\n data = json.loads(r.json()['d'])[\"Data\"]\n trades = []\n for trade in data:\n t = json.loads(trade)\n trades.append(TradeRequest(self.request, t['Date'], t['Expires'], t['TradePartner'], t['TradePartnerID'], t['Status'], t['TradeSessionID']))\n return trades","def tobs():\n temps = engine.execute(\"SELECT date, tobs FROM Measurement WHERE date BETWEEN '2016-08-23' AND '2017-08-23'\").fetchall()\n\n # Convert list of tuples into normal list\n temps_list = list(np.ravel(temps))\n\n return jsonify(temps_list)","def get_pending_transactions():\n\n return History.get_pending().get()","def get_all_coins_history(self, end_date=None, start_date=None, verbose=True):\n infos = []\n for coin in self.get_coins():\n if verbose:\n print(\"Collecting data for >> \" + coin)\n if start_date:\n start_date = start_date\n else:\n start_date = '20130428'\n if end_date:\n end_date = end_date\n else:\n now = str(datetime.now().date()).replace('-', '')\n end_date = now\n coin_url = self.coins[coin]\n coin_url = coin_url + '/historical-data/?start=' + start_date + '&end=' + end_date\n content = urlopen(coin_url).read()\n soup = BeautifulSoup(content, 'html.parser')\n results = soup.find_all(\"tr\", class_=\"text-right\")\n\n for result in results:\n date = result.find_all('td')[0].text\n\n open_val = result.find_all('td')[1].text\n if open_val == '-':\n open_val = None\n else:\n open_val = float(result.find_all('td')[1].text.replace(',', ''))\n\n high_val = result.find_all('td')[2].text\n if high_val == '-':\n high_val = None\n else:\n high_val = float(result.find_all('td')[2].text.replace(',', ''))\n\n low_val = result.find_all('td')[3].text\n if low_val == '-':\n low_val = None\n else:\n low_val = float(result.find_all('td')[3].text.replace(',', ''))\n\n close_val = result.find_all('td')[4].text\n if close_val == '-':\n close_val = None\n else:\n close_val = float(result.find_all('td')[4].text.replace(',', ''))\n\n volume = result.find_all('td')[5].text\n if volume == '-':\n volume = None\n else:\n volume = float(result.find_all('td')[5].text.replace(',', ''))\n\n market_cap = result.find_all('td')[6].text\n if market_cap == '-':\n market_cap = None\n else:\n market_cap = float(result.find_all('td')[6].text.replace(',', ''))\n temp = {\n \"coin\": coin, # soup.title.text.split()[0],\n \"date\": date,\n \"symbol\": soup.title.text.split()[1].replace('(', '').replace(')', ''),\n \"open_val\": open_val,\n \"high_val\": high_val,\n \"low_val\": low_val,\n \"close_val\": close_val,\n \"volume\": volume,\n \"market_cap\": market_cap\n }\n infos.append(temp)\n df_all = pd.DataFrame.from_dict(infos)\n df_all['middle_val'] = (df_all.high_val + df_all.low_val) / 2\n df_all['datetime'] = pd.to_datetime(df_all['date'])\n df_all = df_all.sort_values(by='datetime')\n self.coins_history = df_all","def get_report_list(jtl_file):\n df = None\n try:\n df = pd.read_csv(jtl_file,\n low_memory=False,\n error_bad_lines=False,\n quoting=csv.QUOTE_NONE,\n encoding='utf-8')\n except Exception as e:\n err_msg = 'read jtl file error. detail:{e}'.format(e=e)\n LOGGER.error(err_msg)\n if df is None:\n return\n threads = int(jtl_file.split(os.sep)[-1].split('_')[0])\n success, elapsed, latency, sent_bytes, receive_bytes = [df.get(x) for x in\n ['success', 'elapsed', 'Latency', 'sentBytes', 'bytes']]\n samples = df.shape[0]\n error_count = success.value_counts().get(False)\n if not error_count:\n error_count = 0\n error_rate = str(float(error_count / samples) * 100) + '%'\n label = df.loc[0, 'label']\n start_time = df.iat[0, 0]\n end_time = df.iloc[-1, 0]\n last_req_time = df.iat[-1, 1]\n\n # 如果最后一行数据无效,则取上一行\n i = 1\n while not len(str(end_time)) == 13 and not re.findall('[\\d]{13}', str(end_time)):\n i += 1\n end_time = df.iloc[-i, 0]\n last_req_time = df.iat[-i, 1]\n samples -= 1\n\n if isinstance(start_time, str):\n start_time = int(start_time)\n if isinstance(end_time, str):\n end_time = int(end_time)\n\n local_start_time = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(start_time / 1000))\n local_end_time = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(end_time / 1000))\n\n durations = (end_time + last_req_time - start_time) / 1000\n throughput = samples / durations\n\n report_list = [label, local_start_time, local_end_time, durations, threads, throughput, error_rate,\n elapsed.min(), elapsed.max(), elapsed.mean(), samples, sent_bytes.mean(), receive_bytes.mean(),\n latency.mean(), latency.min(), latency.max()]\n\n return report_list","def TOBS():\n session = Session(engine)\n # Query all passengers\n\n TOBS = session.query(Measurement.date,Measurement.tobs).filter(Measurement.date >= '2010-08-23').all()\n\n # Convert list of tuples into normal list\n all_TOBS = list(np.ravel(TOBS))\n\n return jsonify(all_TOBS)","def get_trades_history(self, symbol, start_time, end_time, limit=1000):\n payload = {'symbol': symbol, 'start': start_time, 'end': end_time, 'limit': limit}\n return self.public_request('GET', '/api/v1/trades', **payload)","def get_data(symbol_id='BTC', period_id='1DAY', request_limit=1000, tdelta=30):\n now = datetime.utcnow()\n month = timedelta(days=tdelta)\n past_month = (now - month).isoformat()\n\n parameters = {'symbol_id': symbol_id, 'period_id': period_id, 'time_start': past_month[:-3], 'limit':request_limit}\n response = requests.get(HISTORY_URL, params=parameters, headers=header)\n\n while response.status_code != 200:\n time.sleep(5)\n response = requests.get(HISTORY_URL, params=parameters, headers=header)\n \n data = response.json()\n \n # this is a commnet\n csv_headers = ['time_period_start', 'time_period_end', 'price_high', 'price_low', 'price_close', 'price_open', 'trades_count', \n 'volume_traded', 'time_open', 'time_close']\n\n\n with open(str(datafolder / f'{symbol_id}_{tdelta}_day.csv'), 'w', newline='') as f:\n writer = csv.DictWriter(f, csv_headers)\n writer.writeheader()\n for item in data:\n writer.writerow(item)","def returnTradeHistory(self, time=1 * 60 * 60, limit=100):\n assert limit <= 100, \"'limit' has to be smaller than 100\"\n return self.dpay.rpc.get_trade_history(\n transactions.formatTimeFromNow(-time),\n transactions.formatTimeFromNow(),\n limit,\n api=\"market_history\"\n )","def timeseries_report(self):\n try:\n n = self.n.value\n except AttributeError:\n n = self.n\n results = pd.DataFrame(index=self.variables.index)\n results['ICE Generation (kW)'] = self.variables['ice_gen']\n results['ICE On (y/n)'] = self.variables['on_ice']\n results['ICE P_min (kW)'] = self.p_min\n results['ICE Genset P_max (kW)'] = self.rated_power * n\n return results","def read_daily_messages_report(self):\n from itertools import repeat\n\n self.ID_TWEET_ORANGE_FLOW = kpi_from_db_config.ID_TWEET_ORANGE_FLOW\n self.ID_PROCESSING_MESSAGES = kpi_from_db_config.ID_PROCESSING_MESSAGES\n self.ID_CANDIDATES_PROCESSED = kpi_from_db_config.ID_CANDIDATES_PROCESSED\n\n list_id = [self.ID_TWEET_ORANGE_FLOW, \n self.ID_PROCESSING_MESSAGES, \n self.ID_CANDIDATES_PROCESSED]\n len_need_list = [7, 8, 2]\n list_result = [[] for i in repeat(None,len(list_id))]\n\n for i in range(len(list_id)):\n self.cursor.execute('''\n SELECT value\n FROM public.kpi_report\n WHERE id = %s\n ORDER BY created_at DESC\n LIMIT %s\n ''', [list_id[i], len_need_list[i]])\n rows_count = self.cursor.rowcount\n\n if (rows_count == len_need_list[i]): # If rows_count as expected \n for doc in self.cursor:\n list_result[i].append(int(doc[0]))\n elif (rows_count > 0 and rows_count < len_need_list[i]):\n for doc in self.cursor:\n list_result[i].append(int(doc[0]))\n list_result[i] = list_result[i] + [0] * (len_need_list[i] - rows_count) \n else:\n list_result[i] = [0] * len_need_list[i]\n\n return list_result","def get_tiingo_prices(tickers, start, end, api_key=None):\n\n all_results = []\n if api_key is None:\n api_key = os.getenv('TIINGO_API_KEY')\n # Sort tickers so that error logging can be used to identify progress\n tickers = sorted(tickers)\n\n for i, ticker in enumerate(tickers):\n try:\n df = web.DataReader(name=ticker,\n data_source='tiingo',\n start=start,\n end=end,\n api_key=api_key)\n df = df[['adjClose']]\n except KeyError as e:\n if e.args[0] == 'date':\n # Patch to handle issue in pandas_datareader\n # where empty results cause a KeyError\n print(f'Got empty df for i={i}, ticker={tickers[i]}')\n df = pd.DataFrame()\n except Exception as e:\n print('Received an unexpected error:', e)\n print(f'Only fetched up to {i-1} inclusive. Returning.')\n return pd.concat(all_results)\n\n if (i % 50 == 0) and i > 0:\n # Sleep to avoid timeouts. Empirically found 20s to be sufficient\n time.sleep(20)\n\n all_results.append(df)\n return pd.concat(all_results)","def temps(): \n \n # Create session and save reference to table\n session = Session(engine)\n Measurement = Base.classes.measurement\n\n # Query\n tobs_query = session.query(Measurement.date, func.avg(Measurement.tobs).label('tobs'))\\\n .filter(Measurement.date >= '2016-08-23').group_by(Measurement.date)\n \n tobs_list = []\n for row in tobs_query:\n tobs_list.append(row._asdict())\n \n return jsonify(tobs_list)\n\n session.close()","def tobs ():\n # Query the last 12 months \n # session.query(func.max (Measurement.date)).all()f \n # temperature observation data for this station \n\n last = session.query(func.max (Measurement.date)).all()\n prev = dt.date(last) - dt.timedelta(days=365)\n\n\n #make a query that goes back 12 months before that date\n results = session.query(Measurement.date, Measurement.tobs).\\\n filter(Measurement.date >= prev).all()\n\n all_Tobs = list(np.ravel(results))\n\n return jsonify(all_Tobs)","def determineUnitHistory():\n\tunitTracker = Unitiser()\n\t\n\timport transactions\n\ttrades = transactions.allTransactions()\n\t\n\thistory = dict()\n\t\n\tfor date in timeline():\n\t\t#print(\"\\ntimelime:\", date.strftime('%Y-%m-%d %H:%M:%S'))\n\t\timport valuator\n\t\tvalue = valuator.getPortfolioValueAt(date)\n\t\tif date in trades:\n\t\t\tprior = getPortfolioBefore(date)\n\t\t\tprior_value = valuator.getPortfolioValueAt(date, portfolio = prior)\n\n\t\t\tinvested = Decimal('0.0')\n\t\t\tfor equity in trades[date]:\n\t\t\t\ttrade = trades[date][equity]\n\t\t\t\t#print(equity, trade)\n\t\t\t\tif trade['action'] == 'buy':\n\t\t\t\t\tinvested = invested + Decimal(trade['value'])\n\t\t\t\telif trade['action'] == 'sell':\n\t\t\t\t\tinvested = invested - Decimal(trade['value'])\n\n\t\t\tsince = getPortfolioAt(date)\n\t\t\tsince_value = valuator.getPortfolioValueAt(date, portfolio = since)\n\n\t\t\t#print(\"change amount is\", invested)\n\t\t\tif invested > 0:\n\t\t\t\tunitTracker.invest(invested, prior_value)\n\t\t\telif invested < 0:\n\t\t\t\tunitTracker.divest(abs(invested), prior_value)\n\n\t\thistory[date] = {\n\t\t\t 'date' : date,\n\t\t\t 'value' : value.quantize(TWOPLACES),\n\t\t\t 'units' : unitTracker.numberOfUnits().quantize(TWOPLACES),\n\t\t\t 'price' : unitTracker.pricePerUnit(value).quantize(TWOPLACES),\n\t\t\t 'invested' : unitTracker.invested\n\t\t\t }\n\t\n\treturn history","def get_transactions(self):\n # open a cursor object\n cur = self.get_cursor()\n\n # get transactions from database\n cur.execute(\"SELECT * FROM transactions\")\n transactions_data = cur.fetchall()\n\n # convert into a dict of values.\n transactions_list = []\n [transactions_list.append({'transaction_id': transaction[0],\n 'date': transaction[1],\n 'payee_id': transaction[2],\n 'description': transaction[3],\n 'amount': transaction[4]})\n for transaction in transactions_data]\n\n # close the cursor\n self.close_cursor()\n\n return transactions_list","def performance_history(self, request, pk=None, **kwargs):\n # Get the goal even though we don't need it (we could just use the pk)\n # so we can ensure we have permission to do so.\n goal = self.get_object()\n\n # - Get all the transaction with this goal involved that are of reason 'Execution'.\n # We want the volume, ticker id, date ordered by date. [(date, {ticker: vol}, ...]\n qs = Transaction.objects.filter(Q(to_goal=goal) | Q(from_goal=goal),\n reason=Transaction.REASON_EXECUTION).order_by('executed')\n txs = qs.values_list('execution_distribution__execution__executed',\n 'execution_distribution__execution__asset__id',\n 'execution_distribution__volume')\n ts = []\n entry = (None,)\n aids = set()\n # If there were no transactions, there can be no performance\n if len(txs) == 0:\n return Response([])\n\n # Because executions are stored with timezone, but other things are just as date, we need to make datetimes\n # naive before doing date arithmetic on them.\n bd = timezone.make_naive(txs[0][0]).date()\n ed = timezone.make_naive(timezone.now()).date()\n for tx in txs:\n aids.add(tx[1])\n txd = timezone.make_naive(tx[0]).date()\n if txd == entry[0]:\n entry[1][tx[1]] += tx[2]\n else:\n if entry[0] is not None:\n ts.append(entry)\n entry = (txd, defaultdict(int))\n entry[1][tx[1]] = tx[2]\n ts.append(entry)\n\n # - Get the time-series of prices for each instrument from the first transaction date until now.\n # Fill empty dates with previous value [(date, {ticker: price}, ...]\n pqs = DailyPrice.objects.filter(date__range=(bd, ed),\n instrument_content_type=ContentType.objects.get_for_model(Ticker).id,\n instrument_object_id__in=aids)\n prices = pqs.to_timeseries(fieldnames=['price', 'date', 'instrument_object_id'],\n index='date',\n storage='long',\n pivot_columns='instrument_object_id',\n values='price')\n # Remove negative prices and fill missing values\n # We replace negs with None so they are interpolated.\n prices[prices <= 0] = None\n prices = prices.reindex(pd.date_range(bd, ed), method='ffill').fillna(method='bfill')\n\n # For each day, calculate the performance\n piter = prices.itertuples()\n res = []\n # Process the first day - it's special\n row = next(piter)\n p_m1 = row[1:]\n vols_m1 = [0] * len(prices.columns)\n tidlocs = {tid: ix for ix, tid in enumerate(prices.columns)}\n for tid, vd in ts.pop(0)[1].items():\n vols_m1[tidlocs[tid]] += vd\n res.append((dt2ed(row[0]), 0)) # First day has no performance as there wasn't a move\n # Process the rest\n for row in piter:\n # row[0] (a datetime) is a naive timestamp, so we don't need to convert it\n if ts and row[0].date() == ts[0][0]:\n vols = vols_m1.copy()\n dtrans = ts.pop(0)[1] # The transactions for the current processed day.\n for tid, vd in dtrans.items():\n vols[tidlocs[tid]] += vd\n # The exposed assets for the day. These are the assets we know for sure were exposed for the move.\n pvol = list(map(min, vols, vols_m1))\n else:\n vols = vols_m1\n pvol = vols\n pdelta = list(map(operator.sub, row[1:], p_m1)) # The change in price from yesterday\n impact = sum(map(operator.mul, pvol, pdelta)) # The total portfolio impact due to price moves for exposed assets.\n b_m1 = sum(map(operator.mul, pvol, p_m1)) # The total portfolio value yesterday for the exposed assets.\n perf = 0 if b_m1 == 0 else impact / b_m1\n # row[0] (a datetime) is a naive timestamp, so we don't need to convert it\n res.append((dt2ed(row[0]), decimal.Decimal.from_float(perf).quantize(decimal.Decimal('1.000000'))))\n p_m1 = row[1:]\n vols_m1 = vols[:]\n\n return Response(res)","def get_stock_price(df_excld):\n\n ts = TimeSeries(os.environ['ALPHA_VANTAGE_KEY'])\n\n info = []\n symbols = []\n counter = 0\n\n for t in df_excld['Ticker']:\n\n if counter % 5 == 0:\n time.sleep(65)\n\n i, m = ts.get_daily(symbol=t, outputsize='full')\n info.append(i)\n symbols.append(m['2. Symbol'])\n counter += 1\n\n return info, symbols","async def get_blacklist_hist(self, search_time, limit=1000):\n\n start = search_time[0][0]\n end = search_time[0][1]\n\n url = f'https://{self.__api}/v3/blacklist/history'\n continuation = None\n full_resp = {}\n flag = True\n body = {\"filter[clientid]\": self.clientid, \"filter[start_time]\": start, \"filter[end_time]\": end,\n \"limit\": limit, \"continuation\": continuation}\n while True:\n with requests.get(url, params=body,\n headers={'X-WallarmAPI-UUID': self.__uuid,\n 'X-WallarmAPI-Secret': self.__secret}) as response:\n if response.status not in [200, 201, 202, 204, 304]:\n raise NonSuccessResponse(response.status, await response.text)\n continuation = response.json().get('body').get('continuation')\n\n if flag:\n full_resp = response.json()\n\n if continuation is not None:\n body['continuation'] = continuation\n if not flag:\n full_resp['body']['objects'].extend(response.json().get('body').get('objects'))\n else:\n break\n flag = False\n return full_resp","async def test_all_transactions(self):\n response = await self.collect(get_request_text=self.GATLING_LOG)\n self.assert_measurement(response, value=\"2\")","def slow_update_duration(self):\n for i in range(len(self.data_file.sorted_data)):\n if self.data_file.sorted_data[i]['type'] == 'slow':\n slow_upd = self.data_file.sorted_data[i]['timestamp']\n Config.ANALYSIS.write(f\"slow at: {slow_upd}\\n\")\n if i == 0:\n after_slow = self.data_file.sorted_data[i + 1]['timestamp']\n Config.ANALYSIS.write(f\"after slow: ({slow_upd}, {after_slow}) \"\n f\"= {after_slow - slow_upd}\\n\\n\")\n elif i == len(self.data_file.sorted_data) - 1:\n before_slow = self.data_file.sorted_data[i - 1]['timestamp']\n Config.ANALYSIS.write(f\"before slow: ({before_slow}, {slow_upd}) \"\n f\"= {slow_upd - before_slow}\\n\\n\")\n else:\n before_slow = self.data_file.sorted_data[i - 1]['timestamp']\n after_slow = self.data_file.sorted_data[i + 1]['timestamp']\n Config.ANALYSIS.write(f\"before slow: ({before_slow}, {slow_upd}) \"\n f\"= {slow_upd - before_slow}\\n\")\n Config.ANALYSIS.write(f\"after slow: ({slow_upd}, {after_slow}) \"\n f\"= {after_slow - slow_upd}\\n\\n\")\n Config.ANALYSIS.write(\"\\n\\n\")","def _get_meas_times(self, last_meas_time):\n meas_times = list()\n data = None\n \n if self._process_type == 'soft_gen':\n meas_times = self._get_meas_times_from_db()\n else:\n if self._data['data_fetch_method'] == 'sql':\n # get from outer sql db\n data = self._get_meas_times_sql(last_meas_time)\n elif self._data['data_fetch_method'] == 'web service':\n # get from web service\n data = self._get_meas_times_web_service(last_meas_time)\n\n\n if data:\n clear_data = [row[0] for row in data['data']]\n # check if we have values in list of datetime type\n if clear_data:\n if type(clear_data[0]) == datetime.datetime:\n meas_times = clear_data\n else:\n # it's a date type\n meas_times = [datetime.datetime.combine(d, datetime.time.min) for d in clear_data]\n\n \n\n\n # sort measurement times if they weren't sorted before\n meas_times.sort()\n # if do not save history, take only last element\n if self._data['report_save_historical_instances_ind'] != 'Y':\n if len(meas_times) > 1:\n del meas_times[:-1]\n \n return meas_times","def tobs():\n # calculate year ago from latest date in database\n latest_date = session.query(Measurement.date).order_by(Measurement.date.desc()).first()\n year_ago = dt.datetime.strptime(latest_date[0], \"%Y-%m-%d\") - dt.timedelta(days=366)\n\n # retrieve temp observations and convert to list\n temps = list(session.query(Measurement.date, Measurement.tobs).filter(Measurement.date >= year_ago).all())\n return jsonify(temps)","def tobs():\n # Query all all dates and temperature observations from the last year\n tobs_list = session.query(Measurement.date, Measurement.tobs).filter(Measurement.date >= \"2016-08-23\").filter(Measurement.station == 'USC00519281').order_by(Measurement.date).all()\n # Convert list of tuples into normal list\n all_tobs = list(np.ravel(tobs_list))\n\n return jsonify(all_tobs)","def aggregate_timetable_details(logger):\n df_array = []\n for page in range(0, MAX_PAGE+1): # MAX_PAGE inclusive\n filename = dirname + 'TimeTablePage_{}'.format(page) + '.csv'\n if os.exists(filename):\n # When database is ready, this info can be recorded there\n logger.info('File[{file}] already downloaded. Reading it'.format(file=filename))\n url = filename\n else:\n count = page * 20\n url = TIMETABLE_URL + '&count={count}&page={page}'.format(count=count, page=page)\n logger.info('Fetch TimeTableDetails from URL[{url}] page[{page}] into {directory}'.format(url=url, page=page, directory=dirname))\n \n try:\n df = pd.read_html(url)[1] # the table of interest from the list\n except Exception as e:\n logger.error('Exception when reading HTML:[{exception}]'.format(exception=e))\n exit(0)\n\n logger.info('Writing to [{file}]'.format(file=filename))\n df.to_csv(filename, index=False)\n df_array.append(df)\n\n df = pd.concat(df_array)\n print(df.head())\n print(df.tail())\n\n return 0","def temporal_database():\n return TimeHistory()","def trades_for(self, symbol, from_id=None, timestamp=None, limit=None):\n\t\tif self._session:\n\t\t\tdata = {'symbol': symbol}\n\n\t\t\tif from_id:\n\t\t\t\tdata['fromId'] = from_id\n\n\t\t\tif timestamp:\n\t\t\t\tdata['startTime'] = int(timestamp * 1000.0)\n\n\t\t\tif limit:\n\t\t\t\tdata['limit'] = limit\n\n\t\t\treturn self._session.get_my_trades(**data)\n\n\t\treturn []","def get_chartdata():\n callback = bottle.request.query.get('callback')\n y_axis = bottle.request.query.get('y_axis').strip()\n w_acts = [\"action='%s'\" % act for act in bottle.request.query.get('actions').strip().split(',')]\n w_acts = 'AND (%s)' % ' OR '.join(w_acts) if w_acts else ''\n f_value = 'AVG(latency)' if y_axis.startswith('avg') else 'COUNT(timestamp)'\n atomic = 1 if y_axis in ['aops', 'avgl'] else 0\n\n db_conn = tools.get_db_conn('%s.db' % bottle.request.query.test_run_id)\n sql = 'SELECT test_run_status, timestamp_started, timestamp_completed FROM info LIMIT 1'\n status, started, finished = tools.db_query(db_conn, sql)[1][0]\n progress = int(float(finished) - float(started)) if finished \\\n else int(tools.get_timestamp() - float(started))\n\n sql = 'SELECT substr(timestamp, 0, 11), code, %s FROM recs ' % f_value + \\\n 'WHERE atomic=%s %s GROUP BY code, substr(timestamp, 0, 11) ' % (atomic, w_acts) + \\\n 'ORDER BY id DESC LIMIT 3600' # last 1 hour activity\n\n result = tools.db_query(db_conn, sql)[1] if finished else tools.db_query(db_conn, sql)[1][:-1]\n result = list(reversed(result))\n results = {str(abs(int(item[0]) - int(float(started)))):\n {'failed': 0, 'passed': 0, 'incomplete': 0} for item in result}\n for item in result: # item[0] - timestamp, item[1] - code (None if incomplete), item[2] - value\n timestamp = str(int(item[0]) - int(float(started)))\n value = item[2] or 0\n results[timestamp]['failed'] += value if item[1] and item[1] != 200 else 0\n results[timestamp]['passed'] += value if item[1] == 200 else 0\n results[timestamp]['incomplete'] += value if item[1] == None else 0\n results = [{'timestamp': key, 'failed': value['failed'], 'passed': value['passed'],\n 'incomplete': value['incomplete']} for key, value in results.items()]\n result = {bottle.request.query.slave: results, 'status': status,\n 'started': started, 'finished': finished or '(not finished)', 'progress': progress}\n return '{0}({1})'.format(callback, result)","def tobs():\n\n # Query date and tobs values from a year ago\n one_year_from_last_results = session.query(Measurement.date, Measurement.tobs)\\\n .filter(Measurement.date >= '2016-08-23').all()\n\n # Create a all_last_year_tobs dictionary and adding a list of date and tob values\n all_last_year_tobs = []\n for result in one_year_from_last_results:\n tobs_result_dict = {}\n tobs_result_dict['date'] = result.date\n tobs_result_dict['tobs'] = result.tobs\n all_last_year_tobs.append(tobs_result_dict)\n\n return jsonify(all_last_year_tobs)","def get_all_files_to_instrument_for_live_session():\n sql=\"SELECT * FROM files WHERE should_instrument=1 AND is_history=0\"\n conn=sqlite3.connect(CONNECTION_STRING)\n c=conn.cursor()\n c.execute(sql)\n results=c.fetchall()\n conn.close()\n return results","def get_meter_data_for_time_slice(apt_no, start_time, end_time):\n if apt_no in ['102A', 102]:\n apt_no = '102A'\n\n logger.debug(\"sMap: Getting meter data for %s between %s and %s\", apt_no, start_time, end_time)\n\n query = (\"select data in ('\" + str(start_time) + \"','\" + str(end_time) + \"') \"\n \"limit 200000 \"\n \"where Metadata/LoadLocation/FlatNumber ='\" + str(apt_no) + \"' and \"\n \"Metadata/Extra/PhysicalParameter='Power'\")\n\n r = requests.post(url, data=query)\n # logger.debug (\"%s\",r)\n payload = r.json()\n # logger.debug(\"Payload:%s\", payload)\n\n if apt_no in ['102A', 102]:\n apt_no = 102\n meters = retrieve_meter_info(apt_no)\n logger.debug(\"Meters: %s\", meters)\n\n streams = []\n meter_type = []\n l_meters = range(0, len(meters))\n for i in l_meters:\n uuid = payload[i]['uuid']\n\n # Get meter type based on uuid\n for meter in meters:\n if meter['uuid'] == uuid:\n m_type = meter['type']\n # logger.debug (uuid, m_type)\n\n meter_type.append(m_type)\n streams.append(np.array(payload[i]['Readings']))\n # logger.debug(\"Streams: %s\", streams)\n\n if len(streams[0]) > 0:\n\n df = [pd.DataFrame({'time': readings[:, 0] / 1000, 'power': readings[:, 1],\n 'type': [meter_type[i]] * len(readings)},\n columns=['time', 'power', 'type']) for i, readings in enumerate(streams)]\n else:\n df = []\n\n return df","def get_market_inf_more_data(start=None, end=None, index=None, retry_count=3, pause=0.001):\n # data to be sent to post request\n data = {'startDate': start,\n 'endDate': end,\n 'searchRecentMarket': 'Search Recent Market'}\n\n for _ in range(retry_count):\n time.sleep(pause)\n try:\n r = requests.post(\n url=vs.DSE_URL+vs.DSE_MARKET_INF_MORE_URL, data=data)\n except Exception as e:\n print(e)\n else:\n #soup = BeautifulSoup(r.text, 'html.parser')\n soup = BeautifulSoup(r.content, 'html5lib')\n\n quotes = [] # a list to store quotes\n\n table = soup.find('table', attrs={\n 'class': 'table table-bordered background-white text-center'})\n\n for row in table.find_all('tr')[1:]:\n cols = row.find_all('td')\n quotes.append({'Date': cols[0].text.strip().replace(\",\", \"\"),\n 'Total Trade': int(cols[1].text.strip().replace(\",\", \"\")),\n 'Total Volume': int(cols[2].text.strip().replace(\",\", \"\")),\n 'Total Value in Taka(mn)': float(cols[3].text.strip().replace(\",\", \"\")),\n 'Total Market Cap. in Taka(mn)': float(cols[4].text.strip().replace(\",\", \"\")),\n 'DSEX Index': float(cols[5].text.strip().replace(\",\", \"\")),\n 'DSES Index': float(cols[6].text.strip().replace(\",\", \"\")),\n 'DS30 Index': float(cols[7].text.strip().replace(\",\", \"\")),\n 'DGEN Index': float(cols[8].text.strip().replace(\"-\", \"0\"))\n })\n df = pd.DataFrame(quotes)\n if 'date' in df.columns:\n if (index == 'date'):\n df = df.set_index('date')\n df = df.sort_index(ascending=True)\n df = df.sort_index(ascending=True)\n else:\n print('No data found')\n return df","def _historical_user_data_for_decisions(user: User, days: int) -> List:\n data = []\n for begin, end in _time_range_list(days):\n db_clicks_statements = DBDiscussionSession.query(ClickedStatement).filter(\n ClickedStatement.author_uid == user.uid,\n ClickedStatement.timestamp >= begin,\n ClickedStatement.timestamp < end).all()\n db_clicks_arguments = DBDiscussionSession.query(ClickedArgument).filter(\n ClickedArgument.author_uid == user.uid,\n ClickedArgument.timestamp >= begin,\n ClickedArgument.timestamp < end).all()\n clicks = len(db_clicks_statements) + len(db_clicks_arguments)\n data.append(clicks)\n\n return data","def get_timeseries_data(self, table, datetime_start, datetime_end, timechunk=datetime.timedelta(hours=1)):\n table_schema = LMTDB_TABLES.get(table.upper())\n if table_schema is None:\n raise KeyError(\"Table '%s' is not valid\" % table)\n else:\n result_columns = ['TIMESTAMP'] + table_schema['columns']\n format_dict = {\n 'schema': ', '.join(result_columns).replace(\"TS_ID,\", \"TIMESTAMP_INFO.TS_ID,\"),\n 'table': table,\n }\n\n index0 = len(self.saved_results.get(table, {'rows': []})['rows'])\n chunk_start = datetime_start\n while chunk_start < datetime_end:\n if timechunk is None:\n chunk_end = datetime_end\n else:\n chunk_end = chunk_start + timechunk\n if chunk_end > datetime_end:\n chunk_end = datetime_end\n start_stamp = chunk_start.strftime(\"%Y-%m-%d %H:%M:%S\")\n end_stamp = chunk_end.strftime(\"%Y-%m-%d %H:%M:%S\")\n\n query_str = \"\"\"SELECT\n %(schema)s\n FROM\n %(table)s\n INNER JOIN TIMESTAMP_INFO ON TIMESTAMP_INFO.TS_ID = %(table)s.TS_ID\n WHERE\n TIMESTAMP_INFO.TIMESTAMP >= %%(ps)s\n AND TIMESTAMP_INFO.TIMESTAMP < %%(ps)s\n \"\"\" % format_dict\n self.query(query_str, (start_stamp, end_stamp), table=table, table_schema=table_schema)\n if timechunk is not None:\n chunk_start += timechunk\n\n return self.saved_results[table]['rows'][index0:], result_columns","def returnOrderTrades(self, order_number):","def temp_monthly():\n # Calculate the date 1 year ago from last date in database\n prev_year = dt.date(2017, 8, 23) - dt.timedelta(days=365)\n results = session.query(Measurement.tobs).\\\n filter(Measurement.station == 'USC00519281').\\\n filter(Measurement.date >= prev_year).all()\n # Unravel results into a ID array and convert to a list\n temps = list(np.ravel(results))\n \n # Return the results\n return jsonify(temps)","def probe_times(self):\r\n probe_times = []\r\n for probe in self.__probes.values():\r\n if probe.complete():\r\n if probe.round_trip_time() > 20:\r\n \"Long probe: %s \" %self.__id\r\n probe_times.append(probe.round_trip_time())\r\n return probe_times","def transaction_data(self):\n return list(map(lambda transaction:transaction.to_json(), self.transaction_map.values()))","def get_latest_transactions(self):\n first_run = False\n if not self._transactions:\n first_run = True\n transactions = []\n for account in self.accounts:\n self._logger.debug('Getting transactions for account \"%s\"', account.ynab_account.name)\n for transaction in account.get_latest_transactions():\n if not self._filter_transaction(transaction):\n transactions.append(transaction)\n self._logger.debug('Caching %s transactions', len(transactions))\n self._transactions.extend(transactions)\n if first_run:\n self._logger.info('First run detected, discarding transactions until now')\n return []\n return transactions","def temp():\n \n #Query temp from a year from last data point\n query = session.query(func.max(Measurement.date)).first()\n maxDate = dt.datetime.strptime(query[0],'%Y-%m-%d')\n year_ago = maxDate - dt.timedelta(days=365)\n\n results = session.query(Measurement.date,Measurement.tobs).filter(Measurement.date>=year_ago).all()\n\n # convert list of tuples into normal list\n all_dates = (result[0] for result in results)\n all_tobs = (result[1] for result in results)\n\n # Convert result to dictionary using date as key and tobs as value\n tobs_dict = dict(zip(all_dates,all_tobs))\n\n # Return on webpage\n return jsonify(tobs_dict)","def get_timeseries():\n\n # generate the result files\n name = os.path.join(project.output_folder, project.scenario+'.sww')\n log.debug('get_timeseries: input SWW file=%s' % name)\n log.debug('get_timeseries: gauge file=%s' % project.gauge_file)\n anuga.sww2csv_gauges(name, project.gauge_file, quantities=project.layers_list,\n verbose=False)\n\n # since ANUGA code doesn't return a list of generated files,\n # look in output directory for 'gauge_*.csv' files.\n glob_mask = os.path.join(project.output_folder, 'gauge_*.csv')\n return glob.glob(glob_mask)","def report_data(self):\n report = [donor_obj.data for donor_obj in self.donor_list]\n return report","def hourly_data():\n return generate_df_for_tests(freq=\"H\", periods=24 * 500)","def get_target_timestamps(self):\n times=[]\n curr = self.begin_ts\n while curr<=self.end_ts:\n times.append(curr)\n curr = curr + 24 * 60 * 60\n return times","def spending_over_time_test_data():\n for i in range(30):\n # Define some values that are calculated and used multiple times\n transaction_id = i\n award_id = i + 1000\n awarding_agency_id = i + 2000\n toptier_awarding_agency_id = i + 3000\n subtier_awarding_agency_id = i + 4000\n funding_agency_id = i + 5000\n toptier_funding_agency_id = i + 6000\n subtier_funding_agency_id = i + 7000\n federal_action_obligation = i + 8000\n total_obligation = i + 9000\n federal_account_id = i + 10000\n treasury_account_id = i + 11000\n\n action_date = f\"20{i % 10 + 10}-{i % 9 + 1}-{i % 28 + 1}\"\n action_date_obj = datetime.datetime.strptime(action_date, \"%Y-%m-%d\")\n fiscal_month = generate_fiscal_month(action_date_obj)\n fiscal_year = generate_fiscal_year(action_date_obj)\n fiscal_action_date = f\"{fiscal_year}-{fiscal_month}-{i % 28 + 1}\"\n contract_award_type = [\"A\", \"B\", \"C\", \"D\"][i % 4]\n grant_award_type = [\"02\", \"03\", \"04\", \"05\"][i % 4]\n is_fpds = i % 2 == 0\n\n # Award\n baker.make(\n \"search.AwardSearch\",\n award_id=award_id,\n fain=f\"fain_{transaction_id}\" if not is_fpds else None,\n is_fpds=is_fpds,\n latest_transaction_id=transaction_id,\n piid=f\"piid_{transaction_id}\" if is_fpds else None,\n total_obligation=total_obligation,\n type=contract_award_type if is_fpds else grant_award_type,\n action_date=\"2020-01-01\",\n )\n\n # Federal, Treasury, and Financial Accounts\n baker.make(\n \"accounts.FederalAccount\",\n id=federal_account_id,\n parent_toptier_agency_id=toptier_awarding_agency_id,\n account_title=f\"federal_account_title_{transaction_id}\",\n federal_account_code=f\"federal_account_code_{transaction_id}\",\n )\n baker.make(\n \"accounts.TreasuryAppropriationAccount\",\n agency_id=f\"taa_aid_{transaction_id}\",\n allocation_transfer_agency_id=f\"taa_ata_{transaction_id}\",\n availability_type_code=f\"taa_a_{transaction_id}\",\n beginning_period_of_availability=f\"taa_bpoa_{transaction_id}\",\n ending_period_of_availability=f\"taa_epoa_{transaction_id}\",\n federal_account_id=federal_account_id,\n main_account_code=f\"taa_main_{transaction_id}\",\n sub_account_code=f\"taa_sub_{transaction_id}\",\n treasury_account_identifier=treasury_account_id,\n )\n tas_components = [\n f\"aid=taa_aid_{transaction_id}\"\n f\"main=taa_main_{transaction_id}\"\n f\"ata=taa_ata_{transaction_id}\"\n f\"sub=taa_sub_{transaction_id}\"\n f\"bpoa=taa_bpoa_{transaction_id}\"\n f\"epoa=taa_epoa_{transaction_id}\"\n f\"a=taa_a_{transaction_id}\"\n ]\n baker.make(\"awards.FinancialAccountsByAwards\", award_id=award_id, treasury_account_id=treasury_account_id)\n\n # Awarding Agency\n baker.make(\n \"references.Agency\",\n id=awarding_agency_id,\n subtier_agency_id=subtier_awarding_agency_id,\n toptier_agency_id=toptier_awarding_agency_id,\n )\n baker.make(\n \"references.ToptierAgency\",\n abbreviation=f\"toptier_awarding_agency_abbreviation_{transaction_id}\",\n name=f\"toptier_awarding_agency_agency_name_{transaction_id}\",\n toptier_agency_id=toptier_awarding_agency_id,\n toptier_code=f\"toptier_awarding_agency_code_{transaction_id}\",\n )\n baker.make(\n \"references.SubtierAgency\",\n abbreviation=f\"subtier_awarding_agency_abbreviation_{transaction_id}\",\n name=f\"subtier_awarding_agency_agency_name_{transaction_id}\",\n subtier_agency_id=subtier_awarding_agency_id,\n subtier_code=f\"subtier_awarding_agency_code_{transaction_id}\",\n )\n\n # Funding Agency\n baker.make(\n \"references.Agency\",\n id=funding_agency_id,\n subtier_agency_id=subtier_funding_agency_id,\n toptier_agency_id=toptier_funding_agency_id,\n )\n baker.make(\n \"references.ToptierAgency\",\n abbreviation=f\"toptier_funding_agency_abbreviation_{transaction_id}\",\n name=f\"toptier_funding_agency_agency_name_{transaction_id}\",\n toptier_agency_id=toptier_funding_agency_id,\n toptier_code=f\"toptier_funding_agency_code_{transaction_id}\",\n )\n baker.make(\n \"references.SubtierAgency\",\n abbreviation=f\"subtier_funding_agency_abbreviation_{transaction_id}\",\n name=f\"subtier_funding_agency_agency_name_{transaction_id}\",\n subtier_agency_id=subtier_funding_agency_id,\n subtier_code=f\"subtier_funding_agency_code_{transaction_id}\",\n )\n\n # Ref Country Code\n baker.make(\"references.RefCountryCode\", country_code=\"USA\", country_name=\"UNITED STATES\")\n\n # FPDS / FABS\n if is_fpds:\n baker.make(\n \"search.TransactionSearch\",\n transaction_id=transaction_id,\n is_fpds=is_fpds,\n action_date=action_date,\n fiscal_year=fiscal_year,\n fiscal_action_date=fiscal_action_date,\n award_id=award_id,\n awarding_agency_id=awarding_agency_id,\n business_categories=[f\"business_category_1_{transaction_id}\", f\"business_category_2_{transaction_id}\"],\n transaction_description=f\"This is a test description {transaction_id}\"\n if transaction_id % 2 == 0\n else None,\n federal_action_obligation=federal_action_obligation,\n generated_pragmatic_obligation=federal_action_obligation,\n award_amount=total_obligation,\n funding_agency_id=funding_agency_id,\n type=contract_award_type if is_fpds else grant_award_type,\n awarding_agency_code=f\"toptier_awarding_agency_code_{transaction_id}\",\n awarding_toptier_agency_name=f\"toptier_awarding_agency_agency_name_{transaction_id}\",\n awarding_toptier_agency_abbreviation=f\"toptier_awarding_agency_agency_name_{transaction_id}\",\n funding_agency_code=f\"toptier_funding_agency_code_{transaction_id}\",\n funding_toptier_agency_name=f\"toptier_funding_agency_agency_name_{transaction_id}\",\n funding_toptier_agency_abbreviation=f\"toptier_funding_agency_agency_name_{transaction_id}\",\n awarding_sub_tier_agency_c=f\"subtier_awarding_agency_code_{transaction_id}\",\n awarding_subtier_agency_name=f\"subtier_awarding_agency_agency_name_{transaction_id}\",\n funding_sub_tier_agency_co=f\"subtier_funding_agency_code_{transaction_id}\",\n funding_subtier_agency_name=f\"subtier_funding_agency_agency_name_{transaction_id}\",\n funding_subtier_agency_abbreviation=f\"subtier_funding_agency_agency_name_{transaction_id}\",\n recipient_name=f\"recipient_name_{transaction_id}\",\n recipient_unique_id=f\"{transaction_id:09d}\",\n recipient_hash=\"c687823d-10af-701b-1bad-650c6e680190\" if transaction_id == 21 else None,\n recipient_levels=[\"R\"] if i == 21 else [],\n extent_competed=f\"extent_competed_{transaction_id}\",\n recipient_location_country_code=\"USA\",\n recipient_location_country_name=\"USA\",\n recipient_location_state_code=f\"LE_STATE_CODE_{transaction_id}\",\n recipient_location_county_code=f\"{transaction_id:03d}\",\n recipient_location_county_name=f\"LE_COUNTY_NAME_{transaction_id}\",\n recipient_location_congressional_code=f\"{transaction_id:02d}\",\n recipient_location_zip5=f\"LE_ZIP5_{transaction_id}\",\n recipient_location_city_name=f\"LE_CITY_NAME_{transaction_id}\",\n naics_code=f\"{transaction_id}{transaction_id}\",\n naics_description=f\"naics_description_{transaction_id}\",\n piid=f\"piid_{transaction_id}\",\n pop_country_code=\"USA\",\n pop_country_name=\"UNITED STATES\",\n pop_state_code=f\"POP_STATE_CODE_{transaction_id}\",\n pop_county_code=f\"{transaction_id:03d}\",\n pop_county_name=f\"POP_COUNTY_NAME_{transaction_id}\",\n pop_zip5=f\"POP_ZIP5_{transaction_id}\",\n pop_congressional_code=f\"{transaction_id:02d}\",\n pop_city_name=f\"POP_CITY_NAME_{transaction_id}\",\n product_or_service_code=str(transaction_id).zfill(4),\n product_or_service_description=f\"psc_description_{transaction_id}\",\n type_of_contract_pricing=f\"type_of_contract_pricing_{transaction_id}\",\n type_set_aside=f\"type_set_aside_{transaction_id}\",\n tas_components=tas_components,\n )\n baker.make(\n \"references.NAICS\",\n code=f\"{transaction_id}\",\n description=f\"naics_description_{transaction_id}\",\n )\n baker.make(\n \"references.PSC\", code=str(transaction_id).zfill(4), description=f\"psc_description_{transaction_id}\"\n )\n else:\n baker.make(\n \"search.TransactionSearch\",\n transaction_id=transaction_id,\n is_fpds=is_fpds,\n action_date=action_date,\n fiscal_year=fiscal_year,\n fiscal_action_date=fiscal_action_date,\n award_id=award_id,\n awarding_agency_id=awarding_agency_id,\n business_categories=[f\"business_category_1_{transaction_id}\", f\"business_category_2_{transaction_id}\"],\n transaction_description=f\"This is a test description {transaction_id}\"\n if transaction_id % 2 == 0\n else None,\n federal_action_obligation=federal_action_obligation,\n generated_pragmatic_obligation=federal_action_obligation,\n award_amount=total_obligation,\n funding_agency_id=funding_agency_id,\n type=contract_award_type if is_fpds else grant_award_type,\n awarding_agency_code=f\"toptier_awarding_agency_code_{transaction_id}\",\n awarding_toptier_agency_name=f\"toptier_awarding_agency_agency_name_{transaction_id}\",\n awarding_toptier_agency_abbreviation=f\"toptier_awarding_agency_agency_name_{transaction_id}\",\n funding_agency_code=f\"toptier_funding_agency_code_{transaction_id}\",\n funding_toptier_agency_name=f\"toptier_funding_agency_agency_name_{transaction_id}\",\n funding_toptier_agency_abbreviation=f\"toptier_funding_agency_agency_name_{transaction_id}\",\n awarding_sub_tier_agency_c=f\"subtier_awarding_agency_code_{transaction_id}\",\n awarding_subtier_agency_name=f\"subtier_awarding_agency_agency_name_{transaction_id}\",\n funding_sub_tier_agency_co=f\"subtier_funding_agency_code_{transaction_id}\",\n funding_subtier_agency_name=f\"subtier_funding_agency_agency_name_{transaction_id}\",\n funding_subtier_agency_abbreviation=f\"subtier_funding_agency_agency_name_{transaction_id}\",\n recipient_name=f\"recipient_name_{transaction_id}\",\n recipient_unique_id=f\"{transaction_id:09d}\",\n recipient_hash=\"c687823d-10af-701b-1bad-650c6e680190\" if transaction_id == 21 else None,\n recipient_levels=[\"R\"] if i == 21 else [],\n cfda_number=f\"cfda_number_{transaction_id}\",\n fain=f\"fain_{transaction_id}\",\n recipient_location_country_code=\"USA\",\n recipient_location_country_name=\"USA\",\n recipient_location_state_code=f\"LE_STATE_CODE_{transaction_id}\",\n recipient_location_county_code=f\"{transaction_id:03d}\",\n recipient_location_county_name=f\"LE_COUNTY_NAME_{transaction_id}\",\n recipient_location_congressional_code=f\"{transaction_id:02d}\",\n recipient_location_zip5=f\"LE_ZIP5_{transaction_id}\",\n recipient_location_city_name=f\"LE_CITY_NAME_{transaction_id}\",\n pop_country_code=\"USA\",\n pop_country_name=\"UNITED STATES\",\n pop_state_code=f\"POP_STATE_CODE_{transaction_id}\",\n pop_county_code=f\"{transaction_id:03d}\",\n pop_county_name=f\"POP_COUNTY_NAME_{transaction_id}\",\n pop_zip5=f\"POP_ZIP5_{transaction_id}\",\n pop_congressional_code=f\"{transaction_id:02d}\",\n pop_city_name=f\"POP_CITY_NAME{transaction_id}\",\n tas_components=tas_components,\n )","def temperatures():\n hi_act= session.query(measurements.tobs,measurements.date,measurements.station).\\\n filter(measurements.station == 'USC00519281').\\\n filter(measurements.date >last_12).\\\n order_by(measurements.date).all()\n hi_act_df=pd.DataFrame(hi_act).set_index('date')\n hi_act_dict=hi_act_df.to_dict()\n return jsonify(hi_act_dict)","def frame(self):\n microseconds = np.array(self.results['times']) * 1e6\n return pd.DataFrame(self.results, index=microseconds)","def query_tickets(self):\n return self._call_txtrader_api('query_tickets', {})","async def describe_slow_log_records_async(\n self,\n request: dds_20151201_models.DescribeSlowLogRecordsRequest,\n ) -> dds_20151201_models.DescribeSlowLogRecordsResponse:\n runtime = util_models.RuntimeOptions()\n return await self.describe_slow_log_records_with_options_async(request, runtime)","def wem_market_value_all():\n __query = \"\"\"\n select\n date_trunc('month', wfs.trading_interval) AS trading_day,\n sum(wfs.eoi_quantity * wbs.price) as energy_interval,\n wf.fueltech_id\n from wem_facility_scada wfs\n left join wem_facility wf on wfs.facility_id = wf.code\n join wem_balancing_summary wbs on wfs.trading_interval = wbs.trading_interval\n where\n wf.fueltech_id is not null\n group by 1, wf.fueltech_id\n order by 1 desc, 2 asc\n \"\"\"\n\n query = __query.format()\n\n json_envelope = {}\n\n with engine.connect() as c:\n rows = c.execute(query)\n\n current_tech = None\n\n for row in rows:\n\n current_tech = row[2]\n\n if current_tech not in json_envelope.keys():\n json_envelope[current_tech] = {\n \"id\": f\"wem.fuel_tech.{current_tech}.market_value\",\n \"fuel_tech\": current_tech,\n \"region\": \"wa\",\n \"type\": \"market_value\",\n \"units\": \"AUD\",\n \"history\": {\n \"interval\": \"1M\",\n \"start\": None,\n \"last\": None,\n \"data\": [],\n },\n }\n\n if (\n json_envelope[current_tech][\"history\"][\"start\"] == None\n or row[0] < json_envelope[current_tech][\"history\"][\"start\"]\n ):\n json_envelope[current_tech][\"history\"][\"start\"] = row[0]\n\n if (\n json_envelope[current_tech][\"history\"][\"last\"] == None\n or row[0] > json_envelope[current_tech][\"history\"][\"last\"]\n ):\n json_envelope[current_tech][\"history\"][\"last\"] = row[0]\n\n json_envelope[current_tech][\"history\"][\"data\"].append(row[1])\n\n return [json_envelope[i] for i in json_envelope.keys()]","def tobs():\n # Create our session (link) from Python to the DB.\n session = Session(engine)\n\n # Calculate the date 1 year ago from the last data point in the database.\n last_measurement_data_point_tuple = session.query(Measurement.date).order_by(Measurement.date.desc()).first()\n (latest_date, ) = last_measurement_data_point_tuple\n latest_date = dt.datetime.strptime(latest_date, '%Y-%m-%d')\n latest_date = latest_date.date()\n date_year_ago = latest_date - relativedelta(years=1)\n\n # Perform a query to retrieve the data and temperature scores.\n data_from_last_year = session.query(Measurement.date, Measurement.tobs).filter(Measurement.date >= date_year_ago).all()\n\n session.close()\n\n # Convert results to a dictionary \n all_temperatures = []\n for date, temp in data_from_last_year:\n if temp != None:\n temp_dict = {}\n temp_dict[date] = temp\n all_temperatures.append(temp_dict)\n # Return JSON\n return jsonify(all_temperatures)","def test_get_events_history_filtering_by_timestamp(rotkehlchen_api_server: 'APIServer'):\n tx_hex = deserialize_evm_tx_hash('0xb226ddb8cbb286a7a998a35263ad258110eed5f923488f03a8d890572cd4608e') # noqa: E501\n ethereum_inquirer = rotkehlchen_api_server.rest_api.rotkehlchen.chains_aggregator.ethereum.node_inquirer # noqa: E501\n database = rotkehlchen_api_server.rest_api.rotkehlchen.data.db\n get_decoded_events_of_transaction(\n evm_inquirer=ethereum_inquirer,\n database=database,\n tx_hash=tx_hex,\n )\n # Call time range\n from_timestamp = 1627401169\n to_timestamp = 1627401170\n async_query = random.choice([False, True])\n with patch(\n 'rotkehlchen.chain.ethereum.modules.sushiswap.sushiswap.Sushiswap.get_balances',\n side_effect=lambda _: {},\n ):\n response = requests.get(\n api_url_for(\n rotkehlchen_api_server,\n 'modulestatsresource',\n module='sushiswap',\n ),\n json={\n 'async_query': async_query,\n 'from_timestamp': from_timestamp,\n 'to_timestamp': to_timestamp,\n },\n )\n if async_query:\n task_id = assert_ok_async_response(response)\n outcome = wait_for_async_task(rotkehlchen_api_server, task_id, timeout=120)\n assert outcome['message'] == ''\n result = outcome['result']\n else:\n result = assert_proper_response_with_result(response)\n\n events_balances = result[TEST_EVENTS_ADDRESS_1]\n\n assert len(events_balances) == 1","def get_stats(evts_perigee) -> Table:\n rows = []\n\n for evt in reversed(evts_perigee):\n rows.append(evt.info)\n\n out = Table(rows=rows)\n return out","def get_all(self):\r\n return [{ 'label': x[0], 'timing': x[1], 'is_tool': x[0] in self._tool_labels}\r\n for x in sorted(self._timings_by_path.items(), key=lambda x: x[1], reverse=True)]","def getDataWithTimeIndex(self, t):\n\n return self.sensorDf.iloc[t,:self.sensorChannels].values","def unbalanced(self):\n # TODO: Find a way to make a sql query to return all unbalanced transactions\n return []","def get_non_indexed_results(self):\n\n with sqlite3.connect(self.db_path) as conn:\n cursor = conn.cursor()\n\n cursor.execute(\"SELECT status_code, file_count, start_time, end_time, website_id\"\n \" FROM TaskResult WHERE indexed_time IS NULL\")\n db_result = cursor.fetchall()\n\n cursor.execute(\"UPDATE TaskResult SET indexed_time=CURRENT_TIMESTAMP WHERE indexed_time IS NULL\")\n conn.commit()\n\n return [TaskResult(r[0], r[1], r[2], r[3], r[4]) for r in db_result]","def tobs():\n # Query all tobs for the previous year\n results = session.query(Measurement.date, Measurement.tobs).\\\n filter(Measurement.date >= '2016-10-01').all()\n\n all_tobs = []\n\n for result in results:\n tobs_dict = {}\n tobs_dict[\"date\"] = result[0]\n tobs_dict[\"tobs\"] = result[1]\n all_tobs.append(tobs_dict)\n return jsonify(all_tobs)","def transactions(self):\n url = f'{self._ynab.api_url}/budgets/{self.id}/transactions'\n response = self._ynab.session.get(url)\n if not response.ok:\n self._logger.error('Error retrieving transactions, response was : %s with status code : %s',\n response.text,\n response.status_code)\n return []\n return [Transaction(self._ynab, transaction)\n for transaction in response.json().get('data', {}).get('transactions', [])]","def get_reports(self):\r\n result = QtSql.QSqlQuery('''Select * FROM failures''')\r\n list = []\r\n while result.next():\r\n failure = Failure(unicode(result.value(0).toString()), # id\r\n unicode(result.value(1).toString()), # comment\r\n unicode(result.value(2).toString()), # indicator\r\n bool(result.value(3))) # release\r\n p = self.get_presentation(failure.talkId)\r\n r = Report(p, failure)\r\n list.append(r)\r\n return list","def fetch_all_tx(self):\n transactions = []\n for block in self.chain:\n transactions.append(block.data)\n return transactions","def read_daily_qualified_report(self):\n from itertools import repeat\n\n self.ID_TOTAL_CANDIDATES = kpi_from_db_config.ID_TOTAL_CANDIDATES\n self.ID_TOTAL_PROCESSED = kpi_from_db_config.ID_TOTAL_PROCESSED\n self.ID_TOTAL_EXPORTED = kpi_from_db_config.ID_TOTAL_EXPORTED\n self.ID_TOTAL_CLASSIFIED = kpi_from_db_config.ID_TOTAL_CLASSIFIED\n self.ID_TOTAL_QUALIFIED = kpi_from_db_config.ID_TOTAL_QUALIFIED\n self.ID_TOTAL_DISQUALIFIED = kpi_from_db_config.ID_TOTAL_DISQUALIFIED\n\n list_id = [self.ID_TOTAL_CANDIDATES, \n self.ID_TOTAL_PROCESSED, \n self.ID_TOTAL_EXPORTED, \n self.ID_TOTAL_CLASSIFIED, \n self.ID_TOTAL_QUALIFIED, \n self.ID_TOTAL_DISQUALIFIED]\n list_result = [[] for i in repeat(None,len(list_id))]\n\n for i in range(len(list_id)):\n self.cursor.execute('''\n SELECT value\n FROM public.kpi_report\n WHERE id = %s\n ORDER BY created_at DESC\n LIMIT 2\n ''', [list_id[i]])\n\n rows_count = self.cursor.rowcount\n if (rows_count == 2):\n for doc in self.cursor:\n list_result[i].append(int(doc[0]))\n elif (rows_count == 1):\n for doc in self.cursor:\n list_result[i].append(int(doc[0]))\n list_result[i] = list_result[i] + [0]\n else:\n list_result[i] = [0] * 2 \n\n# print \"TESTING .... {}\".format(list_result)\n return list_result","def logs(self):\n dataTuples = list()\n try:\n with sqlite3.connect(self.db_file) as conn:\n cur = conn.cursor()\n cur.execute(\"SELECT * FROM curves\")\n dataTuples = cur.fetchall()\n return [{\"id\": a[0], \"name\": a[1], \"units\": a[2],\n \"descr\": a[3]} for a in dataTuples]\n except:\n return dataTuples","def trades(self) -> List[ClosedTrade]:\n return store.completed_trades.trades","def tobs():\n \n # Obtain the current year from the date and using that date determine the previous year appending 01-01 and 12-31\n compare_date = dt.date.today()\n start_date = f\"{compare_date.year - 1}-01-01\"\n end_date = f\"{compare_date.year - 1}-12-31\"\n \n tobs_result = session.query(Measurement.tobs).filter((Measurement.date >= start_date) & (Measurement.date <= end_date)\n ).order_by(Measurement.date).all()\n \n tobs = []\n tobs = list(np.ravel(tobs_result))\n return jsonify(tobs)","def gather_stock_data(tickers, save=True):\n prices = pd.DataFrame()\n ts = TimeSeries(key='EY2QBMV6MD9FX9CP', output_format='pandas')\n\n for ticker in tickers:\n successful_grab = False\n ticker_daily_adj = None\n\n while successful_grab is not True:\n try:\n ticker_daily_adj = ts.get_daily_adjusted(ticker, outputsize='full')[0]\n successful_grab = True\n except ValueError:\n print('Waiting for API to let me in')\n time.sleep(10)\n\n ticker_daily_adj.loc[:, '0. ticker'] = ticker\n ticker_daily_adj = ticker_daily_adj[sorted(ticker_daily_adj.columns)]\n\n prices = pd.concat([prices, ticker_daily_adj])\n\n prices.sort_index(inplace=True)\n prices.reset_index(inplace=True)\n prices['date'] = pd.to_datetime(prices['date'])\n if save:\n prices.to_csv('stockdata.csv', index=True)\n\n return prices","def test_get_settled_batch_list(self):\n self.trans_details.get_settled_batch_list(\n include_statistics = True,\n )\n\n self.trans_details.get_settled_batch_list(\n first_settlement_date=u\"2011-01-01T01:00:00\",\n )\n\n self.trans_details.get_settled_batch_list(\n last_settlement_date=u\"2011-01-01T01:00:00\",\n )\n\n # all three together\n self.trans_details.get_settled_batch_list(\n include_statistics = True,\n first_settlement_date=u\"2011-01-01T01:00:00\",\n last_settlement_date=u\"2011-01-02T01:00:00\"\n )","def tobsdata():\n # query for the dates and temperature observations from a year from the last data point.\n # * Return a JSON list of Temperature Observations (tobs) for the previous year.\n # as this should be a list, I'm just grabbing the station name\n session = Session(engine)\n results = session.query(Measurement.tobs).filter(Measurement.date>='2016-08-23').all()\n session.close()\n tobs_list = list(np.ravel(results))\n\n # * Return the JSON representation of your dictionary.\n return jsonify(tobs_list)","def get_data(self):\n data = list(IgnitionRow.objects.all().order_by('-pub_date')[:self.num_ticks].values())\n two_hours = data[::-1]\n pct_flop_data = [[int(elem['pct_flop_{}'.format(key)]) for elem in two_hours] \n \tfor key in self.keys]\n pct_flop_data = [[min(elem, 100) for elem in arr] for arr in pct_flop_data] # Assume a max pot size of 2000 BBs\n return pct_flop_data","def get_data(self,tickers,alpha,max_retries = 5):\n data = []\n skipped = []\n for ticker in tqdm(tickers,desc = \"Acquiring data\"):\n try:\n company = Company(ticker,alpha = alpha)\n data.append(company)\n except Exception as e:\n time.sleep(10)\n skipped.append(ticker)\n\n for i in range(max_retries):\n new_data,skipped = self.get_data(skipped,alpha = alpha,max_retries = 0)\n data.extend(new_data)\n if len(skipped) == 0:\n break\n\n return data,skipped","def timestamps(self):\n return self.source.timestamps[self._time_keep]","def timers(self):\n return self['timers']","def get_all_metrics(self):\n up_time = self.uptime()\n down_time = self.downtime()\n customer_sla = self.sla()\n objective = self.slo()\n indicator = self.sli()\n avail_percentage = self.availability()\n mt_bf = self.mtbf(up_time)\n mt_tr = self.mttr(down_time)\n list_results = [up_time,down_time,customer_sla,objective,indicator,avail_percentage,mt_bf,mt_tr]\n return list_results","def _get_trades(self):\n\n trade_url = self.trade_url % (self.date, self.instrument, self.exchange)\n self.trades = pd.read_csv(trade_url, parse_dates=[0],\n date_parser=lambda t: pd.to_datetime(str(t), format='%Y%m%dT%H%M%S'))\n\n self.trades.fillna(np.nan)\n self.trades.index = pd.to_datetime(self.trades.time, unit='s')\n self.trades.time = pd.to_datetime(self.trades.time, unit='s')\n self.trades.columns = ['time', 'price', 'volume', 'source', 'buyer', 'seller', 'initiator']\n # del self.trades['time']\n\n if self.exclude_derivative:\n self.trades = self.trades[(self.trades.source != 'Derivatives trade') & (self.trades.source != 'Official')]","def report(self):\n log = self._array.state()\n result = []\n for record in log:\n result.append(f\"{record.worker_name()}\\t${record.task_payment()}\")\n return \"\\n\".join(result)","def get_transactions(self):\n\n df = self.__transactions[\n [\n \"Date\",\n \"Type\",\n \"Ticker\",\n \"Side\",\n \"Price\",\n \"Quantity\",\n \"Fees\",\n \"Investment\",\n \"Currency\",\n \"Sector\",\n \"Industry\",\n \"Country\",\n \"Region\",\n ]\n ]\n df = df.replace(np.nan, \"-\")\n df[\"Date\"] = df[\"Date\"].dt.strftime(\"%Y-%m-%d\")\n df.sort_values(by=\"Date\", ascending=False, inplace=True)\n return df","def tobs():\n # query for the last day\n\n # Create our session (link) from Python to the DB\n session = Session(engine)\n \n last_day = session.query(Measurement.date).order_by(Measurement.date.desc()).first()[0]\n len_months = 12\n # convert result to datetime format\n last_day = datetime.datetime.strptime(last_day, \"%Y-%m-%d\")\n # calculate start day\n start_day = last_day - datetime.timedelta(days=365)\n start_day = \"{:%Y-%m-%d}\".format(start_day)\n\n # Design a query to retrieve the last 12 months of temperature data and plot the results\n results = session.query(Measurement.date, Measurement.tobs, Measurement.station).\\\n filter(Measurement.date >= start_day ).\\\n order_by(Measurement.date).all()\n\n session.close()\n \n temps = []\n for result in results:\n temp_dict = {}\n temp_dict[\"date\"] = result.date\n temp_dict[\"tobs\"] = result.tobs\n temp_dict[\"station\"] = result.station\n temps.append(temp_dict)\n \n return jsonify(temps)","def _get_unit_records(self, start_time):\r\n\r\n if self.optMTTF.get_active():\r\n _query = \"SELECT t2.fld_unit, t1.fld_incident_id, \\\r\n t1.fld_age_at_incident, t1.fld_failure, \\\r\n t1.fld_suspension, t1.fld_cnd_nff, \\\r\n t1.fld_occ_fault, t1.fld_initial_installation, \\\r\n t1.fld_interval_censored, t2.fld_request_date, \\\r\n t2.fld_hardware_id \\\r\n FROM rtk_incident_detail AS t1 \\\r\n INNER JOIN \\\r\n ( \\\r\n SELECT DISTINCT MIN(fld_unit, fld_request_date), \\\r\n fld_incident_id, fld_request_date, \\\r\n fld_unit, fld_hardware_id \\\r\n FROM rtk_incident \\\r\n GROUP BY fld_unit \\\r\n ) AS t2 \\\r\n ON t2.fld_incident_id=t1.fld_incident_id \\\r\n WHERE t1.fld_age_at_incident >= {0:f} \\\r\n ORDER BY t2.fld_unit ASC, \\\r\n t1.fld_age_at_incident ASC, \\\r\n t2.fld_request_date ASC\".format(start_time)\r\n\r\n elif self.optMTBBD.get_active():\r\n _query = \"SELECT t2.fld_unit, t1.fld_incident_id, \\\r\n t1.fld_age_at_incident, t1.fld_failure, \\\r\n t1.fld_suspension, t1.fld_cnd_nff, \\\r\n t1.fld_occ_fault, t1.fld_initial_installation, \\\r\n t1.fld_interval_censored, t2.fld_request_date, \\\r\n t2.fld_hardware_id \\\r\n FROM rtk_incident_detail AS t1 \\\r\n INNER JOIN \\\r\n ( \\\r\n SELECT fld_incident_id, fld_request_date, fld_unit, \\\r\n fld_hardware_id \\\r\n FROM rtk_incident \\\r\n GROUP BY fld_unit, fld_request_date \\\r\n ) AS t2 \\\r\n ON t2.fld_incident_id=t1.fld_incident_id \\\r\n WHERE t1.fld_age_at_incident >= {0:f} \\\r\n GROUP BY t2.fld_unit, t1.fld_age_at_incident \\\r\n ORDER BY t2.fld_unit ASC, \\\r\n t1.fld_age_at_incident ASC, \\\r\n t2.fld_request_date ASC\".format(start_time)\r\n\r\n elif self.optMTBF.get_active():\r\n _query = \"SELECT t2.fld_unit, t1.fld_incident_id, \\\r\n t1.fld_age_at_incident, t1.fld_failure, \\\r\n t1.fld_suspension, t1.fld_cnd_nff, \\\r\n t1.fld_occ_fault, t1.fld_initial_installation, \\\r\n t1.fld_interval_censored, t2.fld_request_date, \\\r\n t2.fld_hardware_id \\\r\n FROM rtk_incident_detail AS t1 \\\r\n INNER JOIN rtk_incident AS t2 \\\r\n ON t2.fld_incident_id=t1.fld_incident_id \\\r\n WHERE t1.fld_age_at_incident >= {0:f} \\\r\n ORDER BY t2.fld_unit ASC, \\\r\n t1.fld_age_at_incident ASC, \\\r\n t2.fld_request_date ASC\".format(start_time)\r\n\r\n (_results, _error_code, __) = self._dao.execute(_query, commit=False)\r\n\r\n return(_results, _error_code)","def get_stats(self, epg_dn):\n # Apic saves up to 95 different objects with statistic information\n traffic_list = []\n for i in range(10, -1, -1):\n traffic = self.moDir.lookupByDn(epg_dn + '/HDl2IngrBytesAg15min-%s' % str(i))\n if traffic is not None:\n traffic_list.append(traffic)\n return traffic_list","def process_ticket_data():\r\n c = conn.cursor()\r\n c.execute('Select Count(*) from raw_ticket_data')\r\n totalleft = c.fetchone()[0]\r\n print('{} total rows required'.format(totalleft))\r\n np.random.seed(1)\r\n df_total = pd.read_sql_query('Select Ticketnumber, TickIssueDate, TickIssueTime, ViolationDesc, '\r\n ' VehMake, TickRPPlate, TickStreetNo, TickMeter, Agency, TickBadgeIssued, '\r\n 'TickStreetName , TotalPaid, TotalAmtDue from raw_ticket_data ', conn)\r\n columnlist = df_total.columns.tolist()\r\n df_total.sort_values(by = 'TickIssueDate', inplace = True)\r\n n = 500000\r\n totalsize = df_total.shape[0]\r\n indexes = [i for i in range(0,totalsize, n)]\r\n columnlist = df_total.columns.tolist()\r\n columnlist.append('address')\r\n tqdm.pandas()\r\n j = 1\r\n for i in indexes:\r\n df = df_total[i:i+n]\r\n print('Iteration {} started at {}. {} records left'.format(j, dt.datetime.now().strftime(\"%H:%M\"), totalsize))\r\n df['TickStreetNo'] = df['TickStreetNo'].apply(return_num)\r\n df['ViolationDesc'] = df['ViolationDesc'].apply(lambda x: x.replace('METER DTN','MTR OUT DT'))\r\n df['TickStreetName'] = df['TickStreetName'].apply(replace_street)\r\n df['TickStreetName'] = df['TickStreetName'].apply(return_street)\r\n df['TotalPaid'] = df['TotalPaid'].apply(return_cost)\r\n df['TotalAmtDue'] = df['TotalAmtDue'].apply(lambda x: re.sub('[^1-9]', '', str(x)))\r\n df['TickRPPlate'] = df['TickRPPlate'].apply(lambda x: 'None' if len(re.findall('[\\w+]', str(x))) == 0 else str(x).replace('[^\\w+]', ''))\r\n df['Tdelt'] = df['TickIssueTime'].apply(return_time_delta)\r\n\r\n\r\n df_1 = df.merge(single_address, left_on = ['TickStreetNo', 'TickStreetName'], right_on = ['number', 'streetname'])\r\n df_2 = df.merge(double_address, left_on = ['TickStreetNo', 'TickStreetName'], right_on = ['number', 'streetname'])\r\n\r\n df_2 = df_2.merge(df_1, how = 'left', left_on = ['TickIssueDate', 'TickBadgeIssued', 'nhood'], right_on = ['TickIssueDate', 'TickBadgeIssued', 'nhood'])\r\n df_3 = df_2[pd.isnull(df_2['Tdelt_y'])]\r\n df_2.dropna(subset = ['Tdelt_y'], inplace = True)\r\n df_2['timedelta'] = df_2.apply(lambda x: np.abs(x['Tdelt_y'] - x['Tdelt_x']), axis = 1)\r\n df_2.sort_values(by = 'timedelta', inplace = True)\r\n\r\n df_2.columns = [col.replace('_x', '') for col in df_2.columns]\r\n df_3.columns = [col.replace('_x', '') for col in df_3.columns]\r\n df_2.drop_duplicates(subset = 'TicketNumber', inplace = True)\r\n print(\"Searching for unmatchable addresses\")\r\n df_3['address'] = df_3.progress_apply(return_address, axis = 1)\r\n\r\n df = df_1.append(df_2)\r\n df = df.append(df_3)\r\n df['TickIssueDate'] = df.apply(Time, axis = 1)\r\n df = df[columnlist]\r\n\r\n if i == 0:\r\n df.to_sql('ticket_data', if_exists = 'replace',con = conn)\r\n else:\r\n df.to_sql('ticket_data', if_exists = 'append',con = conn)\r\n\r\n totalsize -= n\r\n j+=1\r\n\r\n del c\r\n\r\n return","def get_all(self):\n total_expense_reports = []\n get_count = {\n 'query': {\n 'object': 'EEXPENSES',\n 'select': {\n 'field': 'RECORDNO'\n },\n 'pagesize': '1'\n }\n }\n\n response = self.format_and_send_request(get_count)\n count = int(response['data']['@totalcount'])\n pagesize = 2000\n offset = 0\n for i in range(0, count, pagesize):\n data = {\n 'query': {\n 'object': 'EEXPENSES',\n 'select': {\n 'field': [\n 'RECORDNO',\n 'RECORDID',\n 'WHENCREATED',\n 'WHENPOSTED',\n 'TOTALENTERED',\n 'STATE',\n 'TOTALDUE',\n 'DESCRIPTION',\n 'CURRENCY',\n 'BASECURR',\n 'MEMO'\n ]\n },\n 'pagesize': pagesize,\n 'offset': offset\n }\n }\n expense_reports = self.format_and_send_request(data)['data']['EEXPENSES']\n total_expense_reports = total_expense_reports + expense_reports\n offset = offset + pagesize\n return total_expense_reports","def get_ticker_details(self):\n\n ticker_data = sequential_data_scrape(\n scrape.download_ticker_details,\n [\n f\"https://finviz.com/quote.ashx?&t={row.get('Ticker')}\"\n for row in self.data\n ],\n self._user_agent,\n )\n\n for entry in ticker_data:\n for key, value in entry.items():\n for ticker_generic in self.data:\n if ticker_generic.get(\"Ticker\") == key:\n if \"Sales\" not in self.headers:\n self.headers.extend(list(value[0].keys()))\n\n ticker_generic.update(value[0])\n self.analysis.extend(value[1])\n\n return self.data","def get_send_statistics(self):\r\n return self._make_request('GetSendStatistics')","def GenerateTimeBlockData(self, time_ranges):\r\n # Reset the class data for the next log parsing.\r\n for time_range in time_ranges:\r\n time_range.Reset()\r\n\r\n self.ParseLog(time_ranges)\r\n\r\n daily_data = {}\r\n html_string = \"\"\r\n\r\n html_string += \"Summary: %s <br>\\n\" % self.station\r\n html_string += \"<table border='1'>\\n\"\r\n html_string += \"<tr><th></th><th>Completed</th><th>Canceled</th>\"\r\n html_string += \"<th>Errors</th>\"\r\n html_string += \"<th>Average Rig Scan Time</th>\"\r\n html_string += \"<th>Average Operator Scan Time</th>\"\r\n html_string += \"<th>Average Time Between Scans</th>\"\r\n html_string += \"<th>Total Idle Time</th></tr>\"\r\n\r\n # Filter the log data into different timeblocks\r\n for time_range in time_ranges:\r\n daily_data[str(time_range)] = time_range.total\r\n daily_data[str(time_range) + \"_canceled\"] = time_range.canceled\r\n daily_data[str(time_range) + \"_error\"] = time_range.errors\r\n\r\n html_string += self.GenerateTimeblockString(time_range)\r\n\r\n # Special case for total\r\n daily_data[\"Total\"] = self.total_time_range.total\r\n daily_data[\"Total_canceled\"] = self.total_time_range.canceled\r\n daily_data[\"Total_error\"] = self.total_time_range.errors\r\n\r\n html_string += self.GenerateTimeblockString(self.total_time_range)\r\n\r\n html_string += \"</table><br>\\n\"\r\n\r\n daily_data[\"html_string\"] = html_string\r\n\r\n return daily_data"],"string":"[\n \"def slow_transaction_data(self):\\n\\n # XXX This method no longer appears to be used. Being replaced\\n # by the transaction_trace_data() method.\\n\\n if not self.__settings:\\n return []\\n\\n if not self.__slow_transaction:\\n return []\\n\\n maximum = self.__settings.agent_limits.transaction_traces_nodes\\n\\n transaction_trace = self.__slow_transaction.transaction_trace(\\n self, maximum)\\n\\n data = [transaction_trace,\\n list(self.__slow_transaction.string_table.values())]\\n\\n if self.__settings.debug.log_transaction_trace_payload:\\n _logger.debug('Encoding slow transaction data where '\\n 'payload=%r.', data)\\n\\n json_data = json_encode(data)\\n\\n level = self.__settings.agent_limits.data_compression_level\\n level = level or zlib.Z_DEFAULT_COMPRESSION\\n\\n zlib_data = zlib.compress(six.b(json_data), level)\\n\\n pack_data = base64.standard_b64encode(zlib_data)\\n\\n if six.PY3:\\n pack_data = pack_data.decode('Latin-1')\\n\\n root = transaction_trace.root\\n\\n trace_data = [[root.start_time,\\n root.end_time - root.start_time,\\n self.__slow_transaction.path,\\n self.__slow_transaction.request_uri,\\n pack_data]]\\n\\n return trace_data\",\n \"def return_trade_history(\\n self,\\n start: Timestamp,\\n end: Timestamp,\\n ) -> list[dict[str, Any]]:\\n limit = 100\\n data: list[dict[str, Any]] = []\\n start_ms = start * 1000\\n end_ms = end * 1000\\n while True:\\n new_data = self.api_query_list('/trades', {\\n 'startTime': start_ms,\\n 'endTime': end_ms,\\n 'limit': limit,\\n })\\n results_length = len(new_data)\\n if data == [] and results_length < limit:\\n return new_data # simple case - only one query needed\\n\\n latest_ts_ms = start_ms\\n # add results to data and prepare for next query\\n existing_ids = {x['id'] for x in data}\\n for trade in new_data:\\n try:\\n timestamp_ms = trade['createTime']\\n latest_ts_ms = max(latest_ts_ms, timestamp_ms)\\n # since we query again from last ts seen make sure no duplicates make it in\\n if trade['id'] not in existing_ids:\\n data.append(trade)\\n except (DeserializationError, KeyError) as e:\\n msg = str(e)\\n if isinstance(e, KeyError):\\n msg = f'Missing key entry for {msg}.'\\n self.msg_aggregator.add_warning(\\n 'Error deserializing a poloniex trade. Check the logs for details',\\n )\\n log.error(\\n 'Error deserializing poloniex trade',\\n trade=trade,\\n error=msg,\\n )\\n continue\\n\\n if results_length < limit:\\n break # last query has less than limit. We are done.\\n\\n # otherwise we query again from the last ts seen in the last result\\n start_ms = latest_ts_ms\\n continue\\n\\n return data\",\n \"def get_reports(self):\\r\\n return sorted(self._reports,\\r\\n key=lambda x: x['stats']['totalTimeMillis'],\\r\\n reverse=True)\",\n \"def get_reports(self):\\n return sorted(self._reports,\\n key=lambda x: x['stats']['totalTimeMillis'],\\n reverse=True)\",\n \"def slow_queries(self):\\n request = Request(method=\\\"get\\\", endpoint=\\\"/query/slow\\\")\\n\\n def response_handler(resp):\\n if not resp.is_success:\\n raise C8QLQueryListError(resp, request)\\n return self._format_queries(resp.body)\\n\\n return self._execute(request, response_handler)\",\n \"def showTransactions(self):\\n self.scanTransactions()\\n txns = []\\n\\n # Summarize the stats\\n for x in range(len(self._trans)):\\n stats = self._trans[x]\\n trans_time = 0\\n remote_calls = 0\\n for name, stat in stats:\\n trans_time += stat.total_tt\\n remote_calls += 1\\n txns.append((x, trans_time, remote_calls))\\n\\n results = [\\\"TX#\\\\tTime\\\\tCalls\\\",\\n \\\"=\\\" * 22]\\n\\n for item in txns:\\n results.append(\\\"%3d\\\\t%4f\\\\t%5d\\\" % item)\\n \\n return \\\"\\\\n\\\".join(results)\",\n \"def fetch(self, daterange=(datetime.now() - timedelta(1), datetime.now())):\\n cursor = self.conn.cursor()\\n sql = 'SELECT measure_dt, ping, download, upload FROM speedlogs ' + \\\\\\n ' WHERE measure_dt BETWEEN ? AND ?'\\n cursor.execute(sql, daterange)\\n return cursor.fetchall()\",\n \"def gather_data(self, *args, **kwargs):\\n instrument_arg = kwargs.get('instrument', 'EUR_USD')\\n granularity_arg = kwargs.get('granularity', 'M1')\\n candle_format = kwargs.get('candleFormat', 'bidask')\\n start_time = kwargs.get('start', '2014-10-01T00:00:00.000000Z')\\n count_arg = kwargs.get('count', 5000)\\n out_data = []\\n data_complete = False\\n while(not data_complete):\\n response = self.oanda.get_history(instrument=instrument_arg,\\n granularity=granularity_arg,\\n candleFormat=candle_format,\\n start=start_time,\\n count=count_arg)\\n raw_data = response['candles']\\n if (len(out_data) == 0):\\n out_data = out_data + raw_data\\n elif (len(out_data) > 1):\\n # raw_data[0] is already in out_data as raw_data[-1] from last\\n # iteration\\n out_data = out_data + raw_data[1:]\\n start_time = raw_data[-1]['time']\\n if (len(raw_data) < 5000):\\n data_complete = True\\n\\n out_data = self._list_to_df(out_data)\\n return out_data\",\n \"def get_gdax_historical_data():\\n \\n start = None\\n while not start:\\n start,end,tid = getStartAndEndHistoric()\\n if not start:\\n time.sleep(60)\\n #Todo: change this to 1min\\n firsttimestamp = start\\n engine = sa.create_engine(sql_address)\\n products = [\\\"LTC-USD\\\",\\\"LTC-BTC\\\",\\\"ETH-USD\\\",\\\"ETH-BTC\\\",\\\"BTC-USD\\\"]\\n public_client = gdax.PublicClient()\\n deltat = datetime.timedelta(seconds = 200)\\n timewindows = []\\n while end - start > datetime.timedelta(seconds=0):\\n if start + deltat > end:\\n endx = end\\n else:\\n endx = start + deltat\\n timewindows.append([start,endx])\\n start += deltat\\n results = []\\n total = len(timewindows)\\n current_idx = 0\\n timeold = time.time()\\n numofqueries = 0\\n engine = sa.create_engine(sql_address)\\n Base.metadata.bind = engine\\n DBSession = sa.orm.sessionmaker()\\n DBSession.bind = engine\\n session = DBSession()\\n for startx,endx in timewindows:\\n\\n current_idx += 1\\n for i in products:\\n repeat = True\\n while repeat:\\n\\n #delay if ratelimts are close\\n if numofqueries < 3:\\n while time.time() - timeold < 1:\\n time.sleep(0.05)\\n \\n timeold = time.time()\\n numofqueries = 0\\n try:\\n alist = public_client.get_product_historic_rates(i, start = startx, end = endx, granularity = 1)\\n except:\\n time.sleep(30)\\n public_client = gdax.PublicClient()\\n alist = public_client.get_product_historic_rates(i, start = startx, end = endx, granularity = 1)\\n\\n alist = public_client.get_product_historic_rates(i, start = startx, end = endx, granularity = 1)\\n\\n numofqueries += 1\\n\\n #rate limit exceeded has 'message' as dict.\\n if not 'message' in alist:\\n repeat = False\\n for a in alist:\\n a[0] = datetime.datetime.fromtimestamp(float(a[0]))\\n tmp = i.split('-')\\n d = dict(coin = tmp[0], currency = tmp[1], timestamp = a[0], low=a[1], high=a[2], open=a[3], close=a[4], volume=a[5])\\n results.append(d)\\n lasttimestamp = a[0]\\n\\n #upload with batch size of 10000\\n if len(results) > 10000:\\n engine.execute(\\n GADXHistoricalDataOneSecondOHLC.__table__.insert(),\\n results\\n )\\n results = []\\n \\n update = session.query(historicalDataProgramState).filter(sa.and_(historicalDataProgramState.transaction_id == tid,historicalDataProgramState.entry_type == 'update')).first()\\n if update:\\n update.end = lasttimestamp\\n session.commit()\\n else:\\n new_update = historicalDataProgramState(entry_type = 'update',transaction_id = tid,start=firsttimestamp,end=lasttimestamp,platform='GDAX',status='incomplete')\\n session.add(new_update)\\n session.commit()\\n if len(results) > 0:\\n engine.execute(\\n GADXHistoricalDataOneSecondOHLC.__table__.insert(),\\n results\\n )\\n results = []\\n \\n update = session.query(historicalDataProgramState).filter(sa.and_(historicalDataProgramState.transaction_id == tid,historicalDataProgramState.entry_type == 'update')).first()\\n if update:\\n update.end = lasttimestamp\\n session.commit()\\n else:\\n new_update = historicalDataProgramState(entry_type = 'update',transaction_id = tid,start=firsttimestamp,end=lasttimestamp,platform='GDAX',status='incomplete')\\n session.add(new_update)\\n session.commit()\\n\\n update = session.query(historicalDataProgramState).filter(sa.and_(historicalDataProgramState.transaction_id == tid,historicalDataProgramState.entry_type == 'update')).first()\\n update.status='complete'\\n order = session.query(historicalDataProgramState).filter(sa.and_(historicalDataProgramState.transaction_id == tid,historicalDataProgramState.entry_type == 'order')).first()\\n order.status='complete'\\n session.commit()\",\n \"def timings(self):\\r\\n return self._timings\",\n \"def _get_meas_times_from_db(self):\\n meas_times = []\\n if self._data['report_save_historical_instances_ind'] != 'Y':\\n # for non historical reports take measurement time from saved dataset\\n dataset = self._jfile.get_current_stored_dataset()\\n try:\\n meas_time = datetime.datetime.strptime(dataset['meas_time'], '%Y-%m-%d %H:%M:%S')\\n except ValueError:\\n raise Exception(\\\"Cannot unformat string %s to datetime\\\" % dataset['meas_time'])\\n meas_times.append(meas_time)\\n\\n else:\\n # for historical reports take measurement times from db datasets\\n where_sql = ''\\n where_sql_list = list()\\n params = [self._id, self._segment_value_id]\\n\\n if self._process_dataset_ids:\\n for dataset_id in self._process_dataset_ids:\\n if type(dataset_id) == list:\\n where_sql_list.append(\\\"(report_data_set_instance_id >= %s AND report_data_set_instance_id <= %s)\\\")\\n if dataset_id[0] < dataset_id[1]:\\n params.append(dataset_id[0])\\n params.append(dataset_id[1])\\n else:\\n params.append(dataset_id[1])\\n params.append(dataset_id[0])\\n else:\\n where_sql_list.append(\\\"report_data_set_instance_id = %s\\\")\\n params.append(dataset_id)\\n where_sql = ' AND (%s)' % ' OR '.join(where_sql_list)\\n\\n self._db.Query(\\\"\\\"\\\"SELECT measurement_time\\n FROM report_data_set_instance\\n WHERE\\n `element_id`= %%s\\n AND segment_value_id = %%s\\n %s\\n ORDER BY measurement_time ASC\\\"\\\"\\\" % where_sql, tuple(params))\\n meas_times = [item['measurement_time'] for item in self._db.record]\\n\\n return meas_times\",\n \"def getTransferListSummary(self):\\n p_ids_and_prices = {}\\n players = self.getAllPlayerInfoTransferlist()\\n\\n # Get IDs of all players\\n log_event(self.queue, \\\"Gathering player prices... \\\")\\n for p in players:\\n p_bidstatus = p[1]\\n p_id = p[8]\\n # removed Filter for unlisted / expired players\\n if p_id not in p_ids_and_prices:\\n p_sellprice = self.getPlayerSellPrice(p_id)\\n # If sell price returns 0, need to fetch from Futbin\\n if p_sellprice == 0:\\n p_sellprice = self.getFutbinPrice_opentab(p_id)\\n self.sleep_approx(5) # Delay iteration to not anger futbin\\n # Add player ID and price to dict\\n p_ids_and_prices[p_id] = p_sellprice\\n\\n for p_id in p_ids_and_prices:\\n p_price = p_ids_and_prices[p_id]\\n p_name = self.getPlayerCardName(p_id)\\n log_event(self.queue, str(p_name) + \\\" - #\\\" +\\n str(p_id) + \\\" Price \\\" + str(p_price))\\n\\n num_p_sold = 0\\n num_p_expired = 0\\n num_p_unlisted = 0\\n num_p_listed = 0\\n\\n sold_p_value = 0\\n expired_p_value = 0\\n unlisted_p_value = 0\\n listed_p_value = 0\\n\\n for p in players:\\n p_bidstatus = p[1]\\n p_id = p[8]\\n p_soldprice = p[5] # is 0 if unlisted\\n p_sellprice = int(p_ids_and_prices[p_id])\\n\\n if \\\"won\\\" in p_bidstatus:\\n num_p_sold += 1\\n sold_p_value += p_soldprice\\n if \\\"expired\\\" in p_bidstatus:\\n num_p_expired += 1\\n expired_p_value += p_sellprice\\n if (p_bidstatus == \\\"listFUTItem\\\"):\\n num_p_unlisted += 1\\n unlisted_p_value += p_sellprice\\n if (p_bidstatus == \\\"listFUTItem has-auction-data\\\"):\\n num_p_listed += 1\\n listed_p_value += p_sellprice\\n\\n log_event(self.queue, \\\"Players sold: \\\" + str(num_p_sold))\\n log_event(self.queue, \\\"Players expired: \\\" + str(num_p_expired))\\n log_event(self.queue, \\\"Players listed: \\\" + str(num_p_listed))\\n log_event(self.queue, \\\"Players unlisted: \\\" + str(num_p_unlisted))\\n log_event(self.queue, \\\" - - - \\\")\\n log_event(self.queue, \\\"Sold players value: \\\" + str(sold_p_value))\\n log_event(self.queue, \\\"Expired players value: \\\" +\\n str(expired_p_value))\\n log_event(self.queue, \\\"Unlisted players value: \\\" +\\n str(unlisted_p_value))\\n log_event(self.queue, \\\"Listed players value: \\\" + str(listed_p_value))\\n\\n # TODO subtract bought price\\n self.user_players_won += int(num_p_unlisted)\\n self.p_ids_and_prices = p_ids_and_prices\\n intel = [p_ids_and_prices, num_p_sold, num_p_expired, num_p_unlisted,\\n num_p_listed, sold_p_value, expired_p_value, unlisted_p_value, listed_p_value]\\n return intel\",\n \"def describe_slow_log_records(\\n self,\\n request: dds_20151201_models.DescribeSlowLogRecordsRequest,\\n ) -> dds_20151201_models.DescribeSlowLogRecordsResponse:\\n runtime = util_models.RuntimeOptions()\\n return self.describe_slow_log_records_with_options(request, runtime)\",\n \"def time_list(self):\\n return (self.N_T * (np.arange(self.N_itr) + 1) /\\n self.N_itr * 1000 * self.DT)\",\n \"def get_new_data(self):\\n\\n # record bar parse performance\\n self.logger.debug(\\\"Started parsing new ticks.\\\")\\n start_parse = time.time()\\n for exchange in self.exchanges:\\n exchange.parse_ticks()\\n end_parse = time.time()\\n duration = round(end_parse - start_parse, 5)\\n\\n self.logger.debug(\\n \\\"Parsed \\\" + str(self.total_instruments) +\\n \\\" instruments' ticks in \\\" + str(duration) + \\\" seconds.\\\")\\n self.track_tick_processing_performance(duration)\\n\\n # wrap new 1 min bars in market events\\n new_market_events = []\\n for exchange in self.exchanges:\\n bars = exchange.get_new_bars()\\n for symbol in exchange.get_symbols():\\n for bar in bars[symbol]:\\n event = MarketEvent(exchange.get_name(), bar)\\n new_market_events.append(event)\\n # add bars to save-to-db-later queue\\n # TODO: store new bars concurrently with a processpool\\n self.bars_save_to_db.put(event)\\n return new_market_events\",\n \"def timeseries_report(self):\\n report = pd.DataFrame(index=self.price.index)\\n report.loc[:, \\\"FR Energy Throughput (kWh)\\\"] = self.ene_results['ene']\\n report.loc[:, \\\"FR Energy Throughput Up (Charging) (kWh)\\\"] = self.variables['regu_c']*self.krd_avg*self.dt*self.storage.rte\\n report.loc[:, \\\"FR Energy Throughput Up (Discharging) (kWh)\\\"] = self.variables['regu_d']*self.krd_avg*self.dt\\n report.loc[:, \\\"FR Energy Throughput Down (Charging) (kWh)\\\"] = self.variables['regd_c']*self.krd_avg*self.dt*self.storage.rte\\n report.loc[:, \\\"FR Energy Throughput Down (Discharging) (kWh)\\\"] = self.variables['regd_d']*self.krd_avg*self.dt\\n report.loc[:, \\\"FR Energy Settlement Price Signal ($/kWh)\\\"] = self.price\\n report.loc[:, 'Regulation Up (Charging) (kW)'] = self.variables['regu_c']\\n report.loc[:, 'Regulation Up (Discharging) (kW)'] = self.variables['regu_d']\\n report.loc[:, 'Regulation Down (Charging) (kW)'] = self.variables['regd_c']\\n report.loc[:, 'Regulation Down (Discharging) (kW)'] = self.variables['regd_d']\\n report.loc[:, \\\"Regulation Up Price Signal ($/kW)\\\"] = self.p_regu\\n report.loc[:, \\\"Regulation Down Price Signal ($/kW)\\\"] = self.p_regd\\n\\n return report\",\n \"async def get_trades(self) -> List[TradeRequest]:\\n data = j.dumps({\\n 'startindex': 0,\\n 'statustype': 'inbound'\\n })\\n r = await self.request.request(url='https://www.roblox.com/my/money.aspx/getmyitemtrades', data=data, method='POST')\\n data = json.loads(r.json()['d'])[\\\"Data\\\"]\\n trades = []\\n for trade in data:\\n t = json.loads(trade)\\n trades.append(TradeRequest(self.request, t['Date'], t['Expires'], t['TradePartner'], t['TradePartnerID'], t['Status'], t['TradeSessionID']))\\n return trades\",\n \"def tobs():\\n temps = engine.execute(\\\"SELECT date, tobs FROM Measurement WHERE date BETWEEN '2016-08-23' AND '2017-08-23'\\\").fetchall()\\n\\n # Convert list of tuples into normal list\\n temps_list = list(np.ravel(temps))\\n\\n return jsonify(temps_list)\",\n \"def get_pending_transactions():\\n\\n return History.get_pending().get()\",\n \"def get_all_coins_history(self, end_date=None, start_date=None, verbose=True):\\n infos = []\\n for coin in self.get_coins():\\n if verbose:\\n print(\\\"Collecting data for >> \\\" + coin)\\n if start_date:\\n start_date = start_date\\n else:\\n start_date = '20130428'\\n if end_date:\\n end_date = end_date\\n else:\\n now = str(datetime.now().date()).replace('-', '')\\n end_date = now\\n coin_url = self.coins[coin]\\n coin_url = coin_url + '/historical-data/?start=' + start_date + '&end=' + end_date\\n content = urlopen(coin_url).read()\\n soup = BeautifulSoup(content, 'html.parser')\\n results = soup.find_all(\\\"tr\\\", class_=\\\"text-right\\\")\\n\\n for result in results:\\n date = result.find_all('td')[0].text\\n\\n open_val = result.find_all('td')[1].text\\n if open_val == '-':\\n open_val = None\\n else:\\n open_val = float(result.find_all('td')[1].text.replace(',', ''))\\n\\n high_val = result.find_all('td')[2].text\\n if high_val == '-':\\n high_val = None\\n else:\\n high_val = float(result.find_all('td')[2].text.replace(',', ''))\\n\\n low_val = result.find_all('td')[3].text\\n if low_val == '-':\\n low_val = None\\n else:\\n low_val = float(result.find_all('td')[3].text.replace(',', ''))\\n\\n close_val = result.find_all('td')[4].text\\n if close_val == '-':\\n close_val = None\\n else:\\n close_val = float(result.find_all('td')[4].text.replace(',', ''))\\n\\n volume = result.find_all('td')[5].text\\n if volume == '-':\\n volume = None\\n else:\\n volume = float(result.find_all('td')[5].text.replace(',', ''))\\n\\n market_cap = result.find_all('td')[6].text\\n if market_cap == '-':\\n market_cap = None\\n else:\\n market_cap = float(result.find_all('td')[6].text.replace(',', ''))\\n temp = {\\n \\\"coin\\\": coin, # soup.title.text.split()[0],\\n \\\"date\\\": date,\\n \\\"symbol\\\": soup.title.text.split()[1].replace('(', '').replace(')', ''),\\n \\\"open_val\\\": open_val,\\n \\\"high_val\\\": high_val,\\n \\\"low_val\\\": low_val,\\n \\\"close_val\\\": close_val,\\n \\\"volume\\\": volume,\\n \\\"market_cap\\\": market_cap\\n }\\n infos.append(temp)\\n df_all = pd.DataFrame.from_dict(infos)\\n df_all['middle_val'] = (df_all.high_val + df_all.low_val) / 2\\n df_all['datetime'] = pd.to_datetime(df_all['date'])\\n df_all = df_all.sort_values(by='datetime')\\n self.coins_history = df_all\",\n \"def get_report_list(jtl_file):\\n df = None\\n try:\\n df = pd.read_csv(jtl_file,\\n low_memory=False,\\n error_bad_lines=False,\\n quoting=csv.QUOTE_NONE,\\n encoding='utf-8')\\n except Exception as e:\\n err_msg = 'read jtl file error. detail:{e}'.format(e=e)\\n LOGGER.error(err_msg)\\n if df is None:\\n return\\n threads = int(jtl_file.split(os.sep)[-1].split('_')[0])\\n success, elapsed, latency, sent_bytes, receive_bytes = [df.get(x) for x in\\n ['success', 'elapsed', 'Latency', 'sentBytes', 'bytes']]\\n samples = df.shape[0]\\n error_count = success.value_counts().get(False)\\n if not error_count:\\n error_count = 0\\n error_rate = str(float(error_count / samples) * 100) + '%'\\n label = df.loc[0, 'label']\\n start_time = df.iat[0, 0]\\n end_time = df.iloc[-1, 0]\\n last_req_time = df.iat[-1, 1]\\n\\n # 如果最后一行数据无效,则取上一行\\n i = 1\\n while not len(str(end_time)) == 13 and not re.findall('[\\\\d]{13}', str(end_time)):\\n i += 1\\n end_time = df.iloc[-i, 0]\\n last_req_time = df.iat[-i, 1]\\n samples -= 1\\n\\n if isinstance(start_time, str):\\n start_time = int(start_time)\\n if isinstance(end_time, str):\\n end_time = int(end_time)\\n\\n local_start_time = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(start_time / 1000))\\n local_end_time = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(end_time / 1000))\\n\\n durations = (end_time + last_req_time - start_time) / 1000\\n throughput = samples / durations\\n\\n report_list = [label, local_start_time, local_end_time, durations, threads, throughput, error_rate,\\n elapsed.min(), elapsed.max(), elapsed.mean(), samples, sent_bytes.mean(), receive_bytes.mean(),\\n latency.mean(), latency.min(), latency.max()]\\n\\n return report_list\",\n \"def TOBS():\\n session = Session(engine)\\n # Query all passengers\\n\\n TOBS = session.query(Measurement.date,Measurement.tobs).filter(Measurement.date >= '2010-08-23').all()\\n\\n # Convert list of tuples into normal list\\n all_TOBS = list(np.ravel(TOBS))\\n\\n return jsonify(all_TOBS)\",\n \"def get_trades_history(self, symbol, start_time, end_time, limit=1000):\\n payload = {'symbol': symbol, 'start': start_time, 'end': end_time, 'limit': limit}\\n return self.public_request('GET', '/api/v1/trades', **payload)\",\n \"def get_data(symbol_id='BTC', period_id='1DAY', request_limit=1000, tdelta=30):\\n now = datetime.utcnow()\\n month = timedelta(days=tdelta)\\n past_month = (now - month).isoformat()\\n\\n parameters = {'symbol_id': symbol_id, 'period_id': period_id, 'time_start': past_month[:-3], 'limit':request_limit}\\n response = requests.get(HISTORY_URL, params=parameters, headers=header)\\n\\n while response.status_code != 200:\\n time.sleep(5)\\n response = requests.get(HISTORY_URL, params=parameters, headers=header)\\n \\n data = response.json()\\n \\n # this is a commnet\\n csv_headers = ['time_period_start', 'time_period_end', 'price_high', 'price_low', 'price_close', 'price_open', 'trades_count', \\n 'volume_traded', 'time_open', 'time_close']\\n\\n\\n with open(str(datafolder / f'{symbol_id}_{tdelta}_day.csv'), 'w', newline='') as f:\\n writer = csv.DictWriter(f, csv_headers)\\n writer.writeheader()\\n for item in data:\\n writer.writerow(item)\",\n \"def returnTradeHistory(self, time=1 * 60 * 60, limit=100):\\n assert limit <= 100, \\\"'limit' has to be smaller than 100\\\"\\n return self.dpay.rpc.get_trade_history(\\n transactions.formatTimeFromNow(-time),\\n transactions.formatTimeFromNow(),\\n limit,\\n api=\\\"market_history\\\"\\n )\",\n \"def timeseries_report(self):\\n try:\\n n = self.n.value\\n except AttributeError:\\n n = self.n\\n results = pd.DataFrame(index=self.variables.index)\\n results['ICE Generation (kW)'] = self.variables['ice_gen']\\n results['ICE On (y/n)'] = self.variables['on_ice']\\n results['ICE P_min (kW)'] = self.p_min\\n results['ICE Genset P_max (kW)'] = self.rated_power * n\\n return results\",\n \"def read_daily_messages_report(self):\\n from itertools import repeat\\n\\n self.ID_TWEET_ORANGE_FLOW = kpi_from_db_config.ID_TWEET_ORANGE_FLOW\\n self.ID_PROCESSING_MESSAGES = kpi_from_db_config.ID_PROCESSING_MESSAGES\\n self.ID_CANDIDATES_PROCESSED = kpi_from_db_config.ID_CANDIDATES_PROCESSED\\n\\n list_id = [self.ID_TWEET_ORANGE_FLOW, \\n self.ID_PROCESSING_MESSAGES, \\n self.ID_CANDIDATES_PROCESSED]\\n len_need_list = [7, 8, 2]\\n list_result = [[] for i in repeat(None,len(list_id))]\\n\\n for i in range(len(list_id)):\\n self.cursor.execute('''\\n SELECT value\\n FROM public.kpi_report\\n WHERE id = %s\\n ORDER BY created_at DESC\\n LIMIT %s\\n ''', [list_id[i], len_need_list[i]])\\n rows_count = self.cursor.rowcount\\n\\n if (rows_count == len_need_list[i]): # If rows_count as expected \\n for doc in self.cursor:\\n list_result[i].append(int(doc[0]))\\n elif (rows_count > 0 and rows_count < len_need_list[i]):\\n for doc in self.cursor:\\n list_result[i].append(int(doc[0]))\\n list_result[i] = list_result[i] + [0] * (len_need_list[i] - rows_count) \\n else:\\n list_result[i] = [0] * len_need_list[i]\\n\\n return list_result\",\n \"def get_tiingo_prices(tickers, start, end, api_key=None):\\n\\n all_results = []\\n if api_key is None:\\n api_key = os.getenv('TIINGO_API_KEY')\\n # Sort tickers so that error logging can be used to identify progress\\n tickers = sorted(tickers)\\n\\n for i, ticker in enumerate(tickers):\\n try:\\n df = web.DataReader(name=ticker,\\n data_source='tiingo',\\n start=start,\\n end=end,\\n api_key=api_key)\\n df = df[['adjClose']]\\n except KeyError as e:\\n if e.args[0] == 'date':\\n # Patch to handle issue in pandas_datareader\\n # where empty results cause a KeyError\\n print(f'Got empty df for i={i}, ticker={tickers[i]}')\\n df = pd.DataFrame()\\n except Exception as e:\\n print('Received an unexpected error:', e)\\n print(f'Only fetched up to {i-1} inclusive. Returning.')\\n return pd.concat(all_results)\\n\\n if (i % 50 == 0) and i > 0:\\n # Sleep to avoid timeouts. Empirically found 20s to be sufficient\\n time.sleep(20)\\n\\n all_results.append(df)\\n return pd.concat(all_results)\",\n \"def temps(): \\n \\n # Create session and save reference to table\\n session = Session(engine)\\n Measurement = Base.classes.measurement\\n\\n # Query\\n tobs_query = session.query(Measurement.date, func.avg(Measurement.tobs).label('tobs'))\\\\\\n .filter(Measurement.date >= '2016-08-23').group_by(Measurement.date)\\n \\n tobs_list = []\\n for row in tobs_query:\\n tobs_list.append(row._asdict())\\n \\n return jsonify(tobs_list)\\n\\n session.close()\",\n \"def tobs ():\\n # Query the last 12 months \\n # session.query(func.max (Measurement.date)).all()f \\n # temperature observation data for this station \\n\\n last = session.query(func.max (Measurement.date)).all()\\n prev = dt.date(last) - dt.timedelta(days=365)\\n\\n\\n #make a query that goes back 12 months before that date\\n results = session.query(Measurement.date, Measurement.tobs).\\\\\\n filter(Measurement.date >= prev).all()\\n\\n all_Tobs = list(np.ravel(results))\\n\\n return jsonify(all_Tobs)\",\n \"def determineUnitHistory():\\n\\tunitTracker = Unitiser()\\n\\t\\n\\timport transactions\\n\\ttrades = transactions.allTransactions()\\n\\t\\n\\thistory = dict()\\n\\t\\n\\tfor date in timeline():\\n\\t\\t#print(\\\"\\\\ntimelime:\\\", date.strftime('%Y-%m-%d %H:%M:%S'))\\n\\t\\timport valuator\\n\\t\\tvalue = valuator.getPortfolioValueAt(date)\\n\\t\\tif date in trades:\\n\\t\\t\\tprior = getPortfolioBefore(date)\\n\\t\\t\\tprior_value = valuator.getPortfolioValueAt(date, portfolio = prior)\\n\\n\\t\\t\\tinvested = Decimal('0.0')\\n\\t\\t\\tfor equity in trades[date]:\\n\\t\\t\\t\\ttrade = trades[date][equity]\\n\\t\\t\\t\\t#print(equity, trade)\\n\\t\\t\\t\\tif trade['action'] == 'buy':\\n\\t\\t\\t\\t\\tinvested = invested + Decimal(trade['value'])\\n\\t\\t\\t\\telif trade['action'] == 'sell':\\n\\t\\t\\t\\t\\tinvested = invested - Decimal(trade['value'])\\n\\n\\t\\t\\tsince = getPortfolioAt(date)\\n\\t\\t\\tsince_value = valuator.getPortfolioValueAt(date, portfolio = since)\\n\\n\\t\\t\\t#print(\\\"change amount is\\\", invested)\\n\\t\\t\\tif invested > 0:\\n\\t\\t\\t\\tunitTracker.invest(invested, prior_value)\\n\\t\\t\\telif invested < 0:\\n\\t\\t\\t\\tunitTracker.divest(abs(invested), prior_value)\\n\\n\\t\\thistory[date] = {\\n\\t\\t\\t 'date' : date,\\n\\t\\t\\t 'value' : value.quantize(TWOPLACES),\\n\\t\\t\\t 'units' : unitTracker.numberOfUnits().quantize(TWOPLACES),\\n\\t\\t\\t 'price' : unitTracker.pricePerUnit(value).quantize(TWOPLACES),\\n\\t\\t\\t 'invested' : unitTracker.invested\\n\\t\\t\\t }\\n\\t\\n\\treturn history\",\n \"def get_transactions(self):\\n # open a cursor object\\n cur = self.get_cursor()\\n\\n # get transactions from database\\n cur.execute(\\\"SELECT * FROM transactions\\\")\\n transactions_data = cur.fetchall()\\n\\n # convert into a dict of values.\\n transactions_list = []\\n [transactions_list.append({'transaction_id': transaction[0],\\n 'date': transaction[1],\\n 'payee_id': transaction[2],\\n 'description': transaction[3],\\n 'amount': transaction[4]})\\n for transaction in transactions_data]\\n\\n # close the cursor\\n self.close_cursor()\\n\\n return transactions_list\",\n \"def performance_history(self, request, pk=None, **kwargs):\\n # Get the goal even though we don't need it (we could just use the pk)\\n # so we can ensure we have permission to do so.\\n goal = self.get_object()\\n\\n # - Get all the transaction with this goal involved that are of reason 'Execution'.\\n # We want the volume, ticker id, date ordered by date. [(date, {ticker: vol}, ...]\\n qs = Transaction.objects.filter(Q(to_goal=goal) | Q(from_goal=goal),\\n reason=Transaction.REASON_EXECUTION).order_by('executed')\\n txs = qs.values_list('execution_distribution__execution__executed',\\n 'execution_distribution__execution__asset__id',\\n 'execution_distribution__volume')\\n ts = []\\n entry = (None,)\\n aids = set()\\n # If there were no transactions, there can be no performance\\n if len(txs) == 0:\\n return Response([])\\n\\n # Because executions are stored with timezone, but other things are just as date, we need to make datetimes\\n # naive before doing date arithmetic on them.\\n bd = timezone.make_naive(txs[0][0]).date()\\n ed = timezone.make_naive(timezone.now()).date()\\n for tx in txs:\\n aids.add(tx[1])\\n txd = timezone.make_naive(tx[0]).date()\\n if txd == entry[0]:\\n entry[1][tx[1]] += tx[2]\\n else:\\n if entry[0] is not None:\\n ts.append(entry)\\n entry = (txd, defaultdict(int))\\n entry[1][tx[1]] = tx[2]\\n ts.append(entry)\\n\\n # - Get the time-series of prices for each instrument from the first transaction date until now.\\n # Fill empty dates with previous value [(date, {ticker: price}, ...]\\n pqs = DailyPrice.objects.filter(date__range=(bd, ed),\\n instrument_content_type=ContentType.objects.get_for_model(Ticker).id,\\n instrument_object_id__in=aids)\\n prices = pqs.to_timeseries(fieldnames=['price', 'date', 'instrument_object_id'],\\n index='date',\\n storage='long',\\n pivot_columns='instrument_object_id',\\n values='price')\\n # Remove negative prices and fill missing values\\n # We replace negs with None so they are interpolated.\\n prices[prices <= 0] = None\\n prices = prices.reindex(pd.date_range(bd, ed), method='ffill').fillna(method='bfill')\\n\\n # For each day, calculate the performance\\n piter = prices.itertuples()\\n res = []\\n # Process the first day - it's special\\n row = next(piter)\\n p_m1 = row[1:]\\n vols_m1 = [0] * len(prices.columns)\\n tidlocs = {tid: ix for ix, tid in enumerate(prices.columns)}\\n for tid, vd in ts.pop(0)[1].items():\\n vols_m1[tidlocs[tid]] += vd\\n res.append((dt2ed(row[0]), 0)) # First day has no performance as there wasn't a move\\n # Process the rest\\n for row in piter:\\n # row[0] (a datetime) is a naive timestamp, so we don't need to convert it\\n if ts and row[0].date() == ts[0][0]:\\n vols = vols_m1.copy()\\n dtrans = ts.pop(0)[1] # The transactions for the current processed day.\\n for tid, vd in dtrans.items():\\n vols[tidlocs[tid]] += vd\\n # The exposed assets for the day. These are the assets we know for sure were exposed for the move.\\n pvol = list(map(min, vols, vols_m1))\\n else:\\n vols = vols_m1\\n pvol = vols\\n pdelta = list(map(operator.sub, row[1:], p_m1)) # The change in price from yesterday\\n impact = sum(map(operator.mul, pvol, pdelta)) # The total portfolio impact due to price moves for exposed assets.\\n b_m1 = sum(map(operator.mul, pvol, p_m1)) # The total portfolio value yesterday for the exposed assets.\\n perf = 0 if b_m1 == 0 else impact / b_m1\\n # row[0] (a datetime) is a naive timestamp, so we don't need to convert it\\n res.append((dt2ed(row[0]), decimal.Decimal.from_float(perf).quantize(decimal.Decimal('1.000000'))))\\n p_m1 = row[1:]\\n vols_m1 = vols[:]\\n\\n return Response(res)\",\n \"def get_stock_price(df_excld):\\n\\n ts = TimeSeries(os.environ['ALPHA_VANTAGE_KEY'])\\n\\n info = []\\n symbols = []\\n counter = 0\\n\\n for t in df_excld['Ticker']:\\n\\n if counter % 5 == 0:\\n time.sleep(65)\\n\\n i, m = ts.get_daily(symbol=t, outputsize='full')\\n info.append(i)\\n symbols.append(m['2. Symbol'])\\n counter += 1\\n\\n return info, symbols\",\n \"async def get_blacklist_hist(self, search_time, limit=1000):\\n\\n start = search_time[0][0]\\n end = search_time[0][1]\\n\\n url = f'https://{self.__api}/v3/blacklist/history'\\n continuation = None\\n full_resp = {}\\n flag = True\\n body = {\\\"filter[clientid]\\\": self.clientid, \\\"filter[start_time]\\\": start, \\\"filter[end_time]\\\": end,\\n \\\"limit\\\": limit, \\\"continuation\\\": continuation}\\n while True:\\n with requests.get(url, params=body,\\n headers={'X-WallarmAPI-UUID': self.__uuid,\\n 'X-WallarmAPI-Secret': self.__secret}) as response:\\n if response.status not in [200, 201, 202, 204, 304]:\\n raise NonSuccessResponse(response.status, await response.text)\\n continuation = response.json().get('body').get('continuation')\\n\\n if flag:\\n full_resp = response.json()\\n\\n if continuation is not None:\\n body['continuation'] = continuation\\n if not flag:\\n full_resp['body']['objects'].extend(response.json().get('body').get('objects'))\\n else:\\n break\\n flag = False\\n return full_resp\",\n \"async def test_all_transactions(self):\\n response = await self.collect(get_request_text=self.GATLING_LOG)\\n self.assert_measurement(response, value=\\\"2\\\")\",\n \"def slow_update_duration(self):\\n for i in range(len(self.data_file.sorted_data)):\\n if self.data_file.sorted_data[i]['type'] == 'slow':\\n slow_upd = self.data_file.sorted_data[i]['timestamp']\\n Config.ANALYSIS.write(f\\\"slow at: {slow_upd}\\\\n\\\")\\n if i == 0:\\n after_slow = self.data_file.sorted_data[i + 1]['timestamp']\\n Config.ANALYSIS.write(f\\\"after slow: ({slow_upd}, {after_slow}) \\\"\\n f\\\"= {after_slow - slow_upd}\\\\n\\\\n\\\")\\n elif i == len(self.data_file.sorted_data) - 1:\\n before_slow = self.data_file.sorted_data[i - 1]['timestamp']\\n Config.ANALYSIS.write(f\\\"before slow: ({before_slow}, {slow_upd}) \\\"\\n f\\\"= {slow_upd - before_slow}\\\\n\\\\n\\\")\\n else:\\n before_slow = self.data_file.sorted_data[i - 1]['timestamp']\\n after_slow = self.data_file.sorted_data[i + 1]['timestamp']\\n Config.ANALYSIS.write(f\\\"before slow: ({before_slow}, {slow_upd}) \\\"\\n f\\\"= {slow_upd - before_slow}\\\\n\\\")\\n Config.ANALYSIS.write(f\\\"after slow: ({slow_upd}, {after_slow}) \\\"\\n f\\\"= {after_slow - slow_upd}\\\\n\\\\n\\\")\\n Config.ANALYSIS.write(\\\"\\\\n\\\\n\\\")\",\n \"def _get_meas_times(self, last_meas_time):\\n meas_times = list()\\n data = None\\n \\n if self._process_type == 'soft_gen':\\n meas_times = self._get_meas_times_from_db()\\n else:\\n if self._data['data_fetch_method'] == 'sql':\\n # get from outer sql db\\n data = self._get_meas_times_sql(last_meas_time)\\n elif self._data['data_fetch_method'] == 'web service':\\n # get from web service\\n data = self._get_meas_times_web_service(last_meas_time)\\n\\n\\n if data:\\n clear_data = [row[0] for row in data['data']]\\n # check if we have values in list of datetime type\\n if clear_data:\\n if type(clear_data[0]) == datetime.datetime:\\n meas_times = clear_data\\n else:\\n # it's a date type\\n meas_times = [datetime.datetime.combine(d, datetime.time.min) for d in clear_data]\\n\\n \\n\\n\\n # sort measurement times if they weren't sorted before\\n meas_times.sort()\\n # if do not save history, take only last element\\n if self._data['report_save_historical_instances_ind'] != 'Y':\\n if len(meas_times) > 1:\\n del meas_times[:-1]\\n \\n return meas_times\",\n \"def tobs():\\n # calculate year ago from latest date in database\\n latest_date = session.query(Measurement.date).order_by(Measurement.date.desc()).first()\\n year_ago = dt.datetime.strptime(latest_date[0], \\\"%Y-%m-%d\\\") - dt.timedelta(days=366)\\n\\n # retrieve temp observations and convert to list\\n temps = list(session.query(Measurement.date, Measurement.tobs).filter(Measurement.date >= year_ago).all())\\n return jsonify(temps)\",\n \"def tobs():\\n # Query all all dates and temperature observations from the last year\\n tobs_list = session.query(Measurement.date, Measurement.tobs).filter(Measurement.date >= \\\"2016-08-23\\\").filter(Measurement.station == 'USC00519281').order_by(Measurement.date).all()\\n # Convert list of tuples into normal list\\n all_tobs = list(np.ravel(tobs_list))\\n\\n return jsonify(all_tobs)\",\n \"def aggregate_timetable_details(logger):\\n df_array = []\\n for page in range(0, MAX_PAGE+1): # MAX_PAGE inclusive\\n filename = dirname + 'TimeTablePage_{}'.format(page) + '.csv'\\n if os.exists(filename):\\n # When database is ready, this info can be recorded there\\n logger.info('File[{file}] already downloaded. Reading it'.format(file=filename))\\n url = filename\\n else:\\n count = page * 20\\n url = TIMETABLE_URL + '&count={count}&page={page}'.format(count=count, page=page)\\n logger.info('Fetch TimeTableDetails from URL[{url}] page[{page}] into {directory}'.format(url=url, page=page, directory=dirname))\\n \\n try:\\n df = pd.read_html(url)[1] # the table of interest from the list\\n except Exception as e:\\n logger.error('Exception when reading HTML:[{exception}]'.format(exception=e))\\n exit(0)\\n\\n logger.info('Writing to [{file}]'.format(file=filename))\\n df.to_csv(filename, index=False)\\n df_array.append(df)\\n\\n df = pd.concat(df_array)\\n print(df.head())\\n print(df.tail())\\n\\n return 0\",\n \"def temporal_database():\\n return TimeHistory()\",\n \"def trades_for(self, symbol, from_id=None, timestamp=None, limit=None):\\n\\t\\tif self._session:\\n\\t\\t\\tdata = {'symbol': symbol}\\n\\n\\t\\t\\tif from_id:\\n\\t\\t\\t\\tdata['fromId'] = from_id\\n\\n\\t\\t\\tif timestamp:\\n\\t\\t\\t\\tdata['startTime'] = int(timestamp * 1000.0)\\n\\n\\t\\t\\tif limit:\\n\\t\\t\\t\\tdata['limit'] = limit\\n\\n\\t\\t\\treturn self._session.get_my_trades(**data)\\n\\n\\t\\treturn []\",\n \"def get_chartdata():\\n callback = bottle.request.query.get('callback')\\n y_axis = bottle.request.query.get('y_axis').strip()\\n w_acts = [\\\"action='%s'\\\" % act for act in bottle.request.query.get('actions').strip().split(',')]\\n w_acts = 'AND (%s)' % ' OR '.join(w_acts) if w_acts else ''\\n f_value = 'AVG(latency)' if y_axis.startswith('avg') else 'COUNT(timestamp)'\\n atomic = 1 if y_axis in ['aops', 'avgl'] else 0\\n\\n db_conn = tools.get_db_conn('%s.db' % bottle.request.query.test_run_id)\\n sql = 'SELECT test_run_status, timestamp_started, timestamp_completed FROM info LIMIT 1'\\n status, started, finished = tools.db_query(db_conn, sql)[1][0]\\n progress = int(float(finished) - float(started)) if finished \\\\\\n else int(tools.get_timestamp() - float(started))\\n\\n sql = 'SELECT substr(timestamp, 0, 11), code, %s FROM recs ' % f_value + \\\\\\n 'WHERE atomic=%s %s GROUP BY code, substr(timestamp, 0, 11) ' % (atomic, w_acts) + \\\\\\n 'ORDER BY id DESC LIMIT 3600' # last 1 hour activity\\n\\n result = tools.db_query(db_conn, sql)[1] if finished else tools.db_query(db_conn, sql)[1][:-1]\\n result = list(reversed(result))\\n results = {str(abs(int(item[0]) - int(float(started)))):\\n {'failed': 0, 'passed': 0, 'incomplete': 0} for item in result}\\n for item in result: # item[0] - timestamp, item[1] - code (None if incomplete), item[2] - value\\n timestamp = str(int(item[0]) - int(float(started)))\\n value = item[2] or 0\\n results[timestamp]['failed'] += value if item[1] and item[1] != 200 else 0\\n results[timestamp]['passed'] += value if item[1] == 200 else 0\\n results[timestamp]['incomplete'] += value if item[1] == None else 0\\n results = [{'timestamp': key, 'failed': value['failed'], 'passed': value['passed'],\\n 'incomplete': value['incomplete']} for key, value in results.items()]\\n result = {bottle.request.query.slave: results, 'status': status,\\n 'started': started, 'finished': finished or '(not finished)', 'progress': progress}\\n return '{0}({1})'.format(callback, result)\",\n \"def tobs():\\n\\n # Query date and tobs values from a year ago\\n one_year_from_last_results = session.query(Measurement.date, Measurement.tobs)\\\\\\n .filter(Measurement.date >= '2016-08-23').all()\\n\\n # Create a all_last_year_tobs dictionary and adding a list of date and tob values\\n all_last_year_tobs = []\\n for result in one_year_from_last_results:\\n tobs_result_dict = {}\\n tobs_result_dict['date'] = result.date\\n tobs_result_dict['tobs'] = result.tobs\\n all_last_year_tobs.append(tobs_result_dict)\\n\\n return jsonify(all_last_year_tobs)\",\n \"def get_all_files_to_instrument_for_live_session():\\n sql=\\\"SELECT * FROM files WHERE should_instrument=1 AND is_history=0\\\"\\n conn=sqlite3.connect(CONNECTION_STRING)\\n c=conn.cursor()\\n c.execute(sql)\\n results=c.fetchall()\\n conn.close()\\n return results\",\n \"def get_meter_data_for_time_slice(apt_no, start_time, end_time):\\n if apt_no in ['102A', 102]:\\n apt_no = '102A'\\n\\n logger.debug(\\\"sMap: Getting meter data for %s between %s and %s\\\", apt_no, start_time, end_time)\\n\\n query = (\\\"select data in ('\\\" + str(start_time) + \\\"','\\\" + str(end_time) + \\\"') \\\"\\n \\\"limit 200000 \\\"\\n \\\"where Metadata/LoadLocation/FlatNumber ='\\\" + str(apt_no) + \\\"' and \\\"\\n \\\"Metadata/Extra/PhysicalParameter='Power'\\\")\\n\\n r = requests.post(url, data=query)\\n # logger.debug (\\\"%s\\\",r)\\n payload = r.json()\\n # logger.debug(\\\"Payload:%s\\\", payload)\\n\\n if apt_no in ['102A', 102]:\\n apt_no = 102\\n meters = retrieve_meter_info(apt_no)\\n logger.debug(\\\"Meters: %s\\\", meters)\\n\\n streams = []\\n meter_type = []\\n l_meters = range(0, len(meters))\\n for i in l_meters:\\n uuid = payload[i]['uuid']\\n\\n # Get meter type based on uuid\\n for meter in meters:\\n if meter['uuid'] == uuid:\\n m_type = meter['type']\\n # logger.debug (uuid, m_type)\\n\\n meter_type.append(m_type)\\n streams.append(np.array(payload[i]['Readings']))\\n # logger.debug(\\\"Streams: %s\\\", streams)\\n\\n if len(streams[0]) > 0:\\n\\n df = [pd.DataFrame({'time': readings[:, 0] / 1000, 'power': readings[:, 1],\\n 'type': [meter_type[i]] * len(readings)},\\n columns=['time', 'power', 'type']) for i, readings in enumerate(streams)]\\n else:\\n df = []\\n\\n return df\",\n \"def get_market_inf_more_data(start=None, end=None, index=None, retry_count=3, pause=0.001):\\n # data to be sent to post request\\n data = {'startDate': start,\\n 'endDate': end,\\n 'searchRecentMarket': 'Search Recent Market'}\\n\\n for _ in range(retry_count):\\n time.sleep(pause)\\n try:\\n r = requests.post(\\n url=vs.DSE_URL+vs.DSE_MARKET_INF_MORE_URL, data=data)\\n except Exception as e:\\n print(e)\\n else:\\n #soup = BeautifulSoup(r.text, 'html.parser')\\n soup = BeautifulSoup(r.content, 'html5lib')\\n\\n quotes = [] # a list to store quotes\\n\\n table = soup.find('table', attrs={\\n 'class': 'table table-bordered background-white text-center'})\\n\\n for row in table.find_all('tr')[1:]:\\n cols = row.find_all('td')\\n quotes.append({'Date': cols[0].text.strip().replace(\\\",\\\", \\\"\\\"),\\n 'Total Trade': int(cols[1].text.strip().replace(\\\",\\\", \\\"\\\")),\\n 'Total Volume': int(cols[2].text.strip().replace(\\\",\\\", \\\"\\\")),\\n 'Total Value in Taka(mn)': float(cols[3].text.strip().replace(\\\",\\\", \\\"\\\")),\\n 'Total Market Cap. in Taka(mn)': float(cols[4].text.strip().replace(\\\",\\\", \\\"\\\")),\\n 'DSEX Index': float(cols[5].text.strip().replace(\\\",\\\", \\\"\\\")),\\n 'DSES Index': float(cols[6].text.strip().replace(\\\",\\\", \\\"\\\")),\\n 'DS30 Index': float(cols[7].text.strip().replace(\\\",\\\", \\\"\\\")),\\n 'DGEN Index': float(cols[8].text.strip().replace(\\\"-\\\", \\\"0\\\"))\\n })\\n df = pd.DataFrame(quotes)\\n if 'date' in df.columns:\\n if (index == 'date'):\\n df = df.set_index('date')\\n df = df.sort_index(ascending=True)\\n df = df.sort_index(ascending=True)\\n else:\\n print('No data found')\\n return df\",\n \"def _historical_user_data_for_decisions(user: User, days: int) -> List:\\n data = []\\n for begin, end in _time_range_list(days):\\n db_clicks_statements = DBDiscussionSession.query(ClickedStatement).filter(\\n ClickedStatement.author_uid == user.uid,\\n ClickedStatement.timestamp >= begin,\\n ClickedStatement.timestamp < end).all()\\n db_clicks_arguments = DBDiscussionSession.query(ClickedArgument).filter(\\n ClickedArgument.author_uid == user.uid,\\n ClickedArgument.timestamp >= begin,\\n ClickedArgument.timestamp < end).all()\\n clicks = len(db_clicks_statements) + len(db_clicks_arguments)\\n data.append(clicks)\\n\\n return data\",\n \"def get_timeseries_data(self, table, datetime_start, datetime_end, timechunk=datetime.timedelta(hours=1)):\\n table_schema = LMTDB_TABLES.get(table.upper())\\n if table_schema is None:\\n raise KeyError(\\\"Table '%s' is not valid\\\" % table)\\n else:\\n result_columns = ['TIMESTAMP'] + table_schema['columns']\\n format_dict = {\\n 'schema': ', '.join(result_columns).replace(\\\"TS_ID,\\\", \\\"TIMESTAMP_INFO.TS_ID,\\\"),\\n 'table': table,\\n }\\n\\n index0 = len(self.saved_results.get(table, {'rows': []})['rows'])\\n chunk_start = datetime_start\\n while chunk_start < datetime_end:\\n if timechunk is None:\\n chunk_end = datetime_end\\n else:\\n chunk_end = chunk_start + timechunk\\n if chunk_end > datetime_end:\\n chunk_end = datetime_end\\n start_stamp = chunk_start.strftime(\\\"%Y-%m-%d %H:%M:%S\\\")\\n end_stamp = chunk_end.strftime(\\\"%Y-%m-%d %H:%M:%S\\\")\\n\\n query_str = \\\"\\\"\\\"SELECT\\n %(schema)s\\n FROM\\n %(table)s\\n INNER JOIN TIMESTAMP_INFO ON TIMESTAMP_INFO.TS_ID = %(table)s.TS_ID\\n WHERE\\n TIMESTAMP_INFO.TIMESTAMP >= %%(ps)s\\n AND TIMESTAMP_INFO.TIMESTAMP < %%(ps)s\\n \\\"\\\"\\\" % format_dict\\n self.query(query_str, (start_stamp, end_stamp), table=table, table_schema=table_schema)\\n if timechunk is not None:\\n chunk_start += timechunk\\n\\n return self.saved_results[table]['rows'][index0:], result_columns\",\n \"def returnOrderTrades(self, order_number):\",\n \"def temp_monthly():\\n # Calculate the date 1 year ago from last date in database\\n prev_year = dt.date(2017, 8, 23) - dt.timedelta(days=365)\\n results = session.query(Measurement.tobs).\\\\\\n filter(Measurement.station == 'USC00519281').\\\\\\n filter(Measurement.date >= prev_year).all()\\n # Unravel results into a ID array and convert to a list\\n temps = list(np.ravel(results))\\n \\n # Return the results\\n return jsonify(temps)\",\n \"def probe_times(self):\\r\\n probe_times = []\\r\\n for probe in self.__probes.values():\\r\\n if probe.complete():\\r\\n if probe.round_trip_time() > 20:\\r\\n \\\"Long probe: %s \\\" %self.__id\\r\\n probe_times.append(probe.round_trip_time())\\r\\n return probe_times\",\n \"def transaction_data(self):\\n return list(map(lambda transaction:transaction.to_json(), self.transaction_map.values()))\",\n \"def get_latest_transactions(self):\\n first_run = False\\n if not self._transactions:\\n first_run = True\\n transactions = []\\n for account in self.accounts:\\n self._logger.debug('Getting transactions for account \\\"%s\\\"', account.ynab_account.name)\\n for transaction in account.get_latest_transactions():\\n if not self._filter_transaction(transaction):\\n transactions.append(transaction)\\n self._logger.debug('Caching %s transactions', len(transactions))\\n self._transactions.extend(transactions)\\n if first_run:\\n self._logger.info('First run detected, discarding transactions until now')\\n return []\\n return transactions\",\n \"def temp():\\n \\n #Query temp from a year from last data point\\n query = session.query(func.max(Measurement.date)).first()\\n maxDate = dt.datetime.strptime(query[0],'%Y-%m-%d')\\n year_ago = maxDate - dt.timedelta(days=365)\\n\\n results = session.query(Measurement.date,Measurement.tobs).filter(Measurement.date>=year_ago).all()\\n\\n # convert list of tuples into normal list\\n all_dates = (result[0] for result in results)\\n all_tobs = (result[1] for result in results)\\n\\n # Convert result to dictionary using date as key and tobs as value\\n tobs_dict = dict(zip(all_dates,all_tobs))\\n\\n # Return on webpage\\n return jsonify(tobs_dict)\",\n \"def get_timeseries():\\n\\n # generate the result files\\n name = os.path.join(project.output_folder, project.scenario+'.sww')\\n log.debug('get_timeseries: input SWW file=%s' % name)\\n log.debug('get_timeseries: gauge file=%s' % project.gauge_file)\\n anuga.sww2csv_gauges(name, project.gauge_file, quantities=project.layers_list,\\n verbose=False)\\n\\n # since ANUGA code doesn't return a list of generated files,\\n # look in output directory for 'gauge_*.csv' files.\\n glob_mask = os.path.join(project.output_folder, 'gauge_*.csv')\\n return glob.glob(glob_mask)\",\n \"def report_data(self):\\n report = [donor_obj.data for donor_obj in self.donor_list]\\n return report\",\n \"def hourly_data():\\n return generate_df_for_tests(freq=\\\"H\\\", periods=24 * 500)\",\n \"def get_target_timestamps(self):\\n times=[]\\n curr = self.begin_ts\\n while curr<=self.end_ts:\\n times.append(curr)\\n curr = curr + 24 * 60 * 60\\n return times\",\n \"def spending_over_time_test_data():\\n for i in range(30):\\n # Define some values that are calculated and used multiple times\\n transaction_id = i\\n award_id = i + 1000\\n awarding_agency_id = i + 2000\\n toptier_awarding_agency_id = i + 3000\\n subtier_awarding_agency_id = i + 4000\\n funding_agency_id = i + 5000\\n toptier_funding_agency_id = i + 6000\\n subtier_funding_agency_id = i + 7000\\n federal_action_obligation = i + 8000\\n total_obligation = i + 9000\\n federal_account_id = i + 10000\\n treasury_account_id = i + 11000\\n\\n action_date = f\\\"20{i % 10 + 10}-{i % 9 + 1}-{i % 28 + 1}\\\"\\n action_date_obj = datetime.datetime.strptime(action_date, \\\"%Y-%m-%d\\\")\\n fiscal_month = generate_fiscal_month(action_date_obj)\\n fiscal_year = generate_fiscal_year(action_date_obj)\\n fiscal_action_date = f\\\"{fiscal_year}-{fiscal_month}-{i % 28 + 1}\\\"\\n contract_award_type = [\\\"A\\\", \\\"B\\\", \\\"C\\\", \\\"D\\\"][i % 4]\\n grant_award_type = [\\\"02\\\", \\\"03\\\", \\\"04\\\", \\\"05\\\"][i % 4]\\n is_fpds = i % 2 == 0\\n\\n # Award\\n baker.make(\\n \\\"search.AwardSearch\\\",\\n award_id=award_id,\\n fain=f\\\"fain_{transaction_id}\\\" if not is_fpds else None,\\n is_fpds=is_fpds,\\n latest_transaction_id=transaction_id,\\n piid=f\\\"piid_{transaction_id}\\\" if is_fpds else None,\\n total_obligation=total_obligation,\\n type=contract_award_type if is_fpds else grant_award_type,\\n action_date=\\\"2020-01-01\\\",\\n )\\n\\n # Federal, Treasury, and Financial Accounts\\n baker.make(\\n \\\"accounts.FederalAccount\\\",\\n id=federal_account_id,\\n parent_toptier_agency_id=toptier_awarding_agency_id,\\n account_title=f\\\"federal_account_title_{transaction_id}\\\",\\n federal_account_code=f\\\"federal_account_code_{transaction_id}\\\",\\n )\\n baker.make(\\n \\\"accounts.TreasuryAppropriationAccount\\\",\\n agency_id=f\\\"taa_aid_{transaction_id}\\\",\\n allocation_transfer_agency_id=f\\\"taa_ata_{transaction_id}\\\",\\n availability_type_code=f\\\"taa_a_{transaction_id}\\\",\\n beginning_period_of_availability=f\\\"taa_bpoa_{transaction_id}\\\",\\n ending_period_of_availability=f\\\"taa_epoa_{transaction_id}\\\",\\n federal_account_id=federal_account_id,\\n main_account_code=f\\\"taa_main_{transaction_id}\\\",\\n sub_account_code=f\\\"taa_sub_{transaction_id}\\\",\\n treasury_account_identifier=treasury_account_id,\\n )\\n tas_components = [\\n f\\\"aid=taa_aid_{transaction_id}\\\"\\n f\\\"main=taa_main_{transaction_id}\\\"\\n f\\\"ata=taa_ata_{transaction_id}\\\"\\n f\\\"sub=taa_sub_{transaction_id}\\\"\\n f\\\"bpoa=taa_bpoa_{transaction_id}\\\"\\n f\\\"epoa=taa_epoa_{transaction_id}\\\"\\n f\\\"a=taa_a_{transaction_id}\\\"\\n ]\\n baker.make(\\\"awards.FinancialAccountsByAwards\\\", award_id=award_id, treasury_account_id=treasury_account_id)\\n\\n # Awarding Agency\\n baker.make(\\n \\\"references.Agency\\\",\\n id=awarding_agency_id,\\n subtier_agency_id=subtier_awarding_agency_id,\\n toptier_agency_id=toptier_awarding_agency_id,\\n )\\n baker.make(\\n \\\"references.ToptierAgency\\\",\\n abbreviation=f\\\"toptier_awarding_agency_abbreviation_{transaction_id}\\\",\\n name=f\\\"toptier_awarding_agency_agency_name_{transaction_id}\\\",\\n toptier_agency_id=toptier_awarding_agency_id,\\n toptier_code=f\\\"toptier_awarding_agency_code_{transaction_id}\\\",\\n )\\n baker.make(\\n \\\"references.SubtierAgency\\\",\\n abbreviation=f\\\"subtier_awarding_agency_abbreviation_{transaction_id}\\\",\\n name=f\\\"subtier_awarding_agency_agency_name_{transaction_id}\\\",\\n subtier_agency_id=subtier_awarding_agency_id,\\n subtier_code=f\\\"subtier_awarding_agency_code_{transaction_id}\\\",\\n )\\n\\n # Funding Agency\\n baker.make(\\n \\\"references.Agency\\\",\\n id=funding_agency_id,\\n subtier_agency_id=subtier_funding_agency_id,\\n toptier_agency_id=toptier_funding_agency_id,\\n )\\n baker.make(\\n \\\"references.ToptierAgency\\\",\\n abbreviation=f\\\"toptier_funding_agency_abbreviation_{transaction_id}\\\",\\n name=f\\\"toptier_funding_agency_agency_name_{transaction_id}\\\",\\n toptier_agency_id=toptier_funding_agency_id,\\n toptier_code=f\\\"toptier_funding_agency_code_{transaction_id}\\\",\\n )\\n baker.make(\\n \\\"references.SubtierAgency\\\",\\n abbreviation=f\\\"subtier_funding_agency_abbreviation_{transaction_id}\\\",\\n name=f\\\"subtier_funding_agency_agency_name_{transaction_id}\\\",\\n subtier_agency_id=subtier_funding_agency_id,\\n subtier_code=f\\\"subtier_funding_agency_code_{transaction_id}\\\",\\n )\\n\\n # Ref Country Code\\n baker.make(\\\"references.RefCountryCode\\\", country_code=\\\"USA\\\", country_name=\\\"UNITED STATES\\\")\\n\\n # FPDS / FABS\\n if is_fpds:\\n baker.make(\\n \\\"search.TransactionSearch\\\",\\n transaction_id=transaction_id,\\n is_fpds=is_fpds,\\n action_date=action_date,\\n fiscal_year=fiscal_year,\\n fiscal_action_date=fiscal_action_date,\\n award_id=award_id,\\n awarding_agency_id=awarding_agency_id,\\n business_categories=[f\\\"business_category_1_{transaction_id}\\\", f\\\"business_category_2_{transaction_id}\\\"],\\n transaction_description=f\\\"This is a test description {transaction_id}\\\"\\n if transaction_id % 2 == 0\\n else None,\\n federal_action_obligation=federal_action_obligation,\\n generated_pragmatic_obligation=federal_action_obligation,\\n award_amount=total_obligation,\\n funding_agency_id=funding_agency_id,\\n type=contract_award_type if is_fpds else grant_award_type,\\n awarding_agency_code=f\\\"toptier_awarding_agency_code_{transaction_id}\\\",\\n awarding_toptier_agency_name=f\\\"toptier_awarding_agency_agency_name_{transaction_id}\\\",\\n awarding_toptier_agency_abbreviation=f\\\"toptier_awarding_agency_agency_name_{transaction_id}\\\",\\n funding_agency_code=f\\\"toptier_funding_agency_code_{transaction_id}\\\",\\n funding_toptier_agency_name=f\\\"toptier_funding_agency_agency_name_{transaction_id}\\\",\\n funding_toptier_agency_abbreviation=f\\\"toptier_funding_agency_agency_name_{transaction_id}\\\",\\n awarding_sub_tier_agency_c=f\\\"subtier_awarding_agency_code_{transaction_id}\\\",\\n awarding_subtier_agency_name=f\\\"subtier_awarding_agency_agency_name_{transaction_id}\\\",\\n funding_sub_tier_agency_co=f\\\"subtier_funding_agency_code_{transaction_id}\\\",\\n funding_subtier_agency_name=f\\\"subtier_funding_agency_agency_name_{transaction_id}\\\",\\n funding_subtier_agency_abbreviation=f\\\"subtier_funding_agency_agency_name_{transaction_id}\\\",\\n recipient_name=f\\\"recipient_name_{transaction_id}\\\",\\n recipient_unique_id=f\\\"{transaction_id:09d}\\\",\\n recipient_hash=\\\"c687823d-10af-701b-1bad-650c6e680190\\\" if transaction_id == 21 else None,\\n recipient_levels=[\\\"R\\\"] if i == 21 else [],\\n extent_competed=f\\\"extent_competed_{transaction_id}\\\",\\n recipient_location_country_code=\\\"USA\\\",\\n recipient_location_country_name=\\\"USA\\\",\\n recipient_location_state_code=f\\\"LE_STATE_CODE_{transaction_id}\\\",\\n recipient_location_county_code=f\\\"{transaction_id:03d}\\\",\\n recipient_location_county_name=f\\\"LE_COUNTY_NAME_{transaction_id}\\\",\\n recipient_location_congressional_code=f\\\"{transaction_id:02d}\\\",\\n recipient_location_zip5=f\\\"LE_ZIP5_{transaction_id}\\\",\\n recipient_location_city_name=f\\\"LE_CITY_NAME_{transaction_id}\\\",\\n naics_code=f\\\"{transaction_id}{transaction_id}\\\",\\n naics_description=f\\\"naics_description_{transaction_id}\\\",\\n piid=f\\\"piid_{transaction_id}\\\",\\n pop_country_code=\\\"USA\\\",\\n pop_country_name=\\\"UNITED STATES\\\",\\n pop_state_code=f\\\"POP_STATE_CODE_{transaction_id}\\\",\\n pop_county_code=f\\\"{transaction_id:03d}\\\",\\n pop_county_name=f\\\"POP_COUNTY_NAME_{transaction_id}\\\",\\n pop_zip5=f\\\"POP_ZIP5_{transaction_id}\\\",\\n pop_congressional_code=f\\\"{transaction_id:02d}\\\",\\n pop_city_name=f\\\"POP_CITY_NAME_{transaction_id}\\\",\\n product_or_service_code=str(transaction_id).zfill(4),\\n product_or_service_description=f\\\"psc_description_{transaction_id}\\\",\\n type_of_contract_pricing=f\\\"type_of_contract_pricing_{transaction_id}\\\",\\n type_set_aside=f\\\"type_set_aside_{transaction_id}\\\",\\n tas_components=tas_components,\\n )\\n baker.make(\\n \\\"references.NAICS\\\",\\n code=f\\\"{transaction_id}\\\",\\n description=f\\\"naics_description_{transaction_id}\\\",\\n )\\n baker.make(\\n \\\"references.PSC\\\", code=str(transaction_id).zfill(4), description=f\\\"psc_description_{transaction_id}\\\"\\n )\\n else:\\n baker.make(\\n \\\"search.TransactionSearch\\\",\\n transaction_id=transaction_id,\\n is_fpds=is_fpds,\\n action_date=action_date,\\n fiscal_year=fiscal_year,\\n fiscal_action_date=fiscal_action_date,\\n award_id=award_id,\\n awarding_agency_id=awarding_agency_id,\\n business_categories=[f\\\"business_category_1_{transaction_id}\\\", f\\\"business_category_2_{transaction_id}\\\"],\\n transaction_description=f\\\"This is a test description {transaction_id}\\\"\\n if transaction_id % 2 == 0\\n else None,\\n federal_action_obligation=federal_action_obligation,\\n generated_pragmatic_obligation=federal_action_obligation,\\n award_amount=total_obligation,\\n funding_agency_id=funding_agency_id,\\n type=contract_award_type if is_fpds else grant_award_type,\\n awarding_agency_code=f\\\"toptier_awarding_agency_code_{transaction_id}\\\",\\n awarding_toptier_agency_name=f\\\"toptier_awarding_agency_agency_name_{transaction_id}\\\",\\n awarding_toptier_agency_abbreviation=f\\\"toptier_awarding_agency_agency_name_{transaction_id}\\\",\\n funding_agency_code=f\\\"toptier_funding_agency_code_{transaction_id}\\\",\\n funding_toptier_agency_name=f\\\"toptier_funding_agency_agency_name_{transaction_id}\\\",\\n funding_toptier_agency_abbreviation=f\\\"toptier_funding_agency_agency_name_{transaction_id}\\\",\\n awarding_sub_tier_agency_c=f\\\"subtier_awarding_agency_code_{transaction_id}\\\",\\n awarding_subtier_agency_name=f\\\"subtier_awarding_agency_agency_name_{transaction_id}\\\",\\n funding_sub_tier_agency_co=f\\\"subtier_funding_agency_code_{transaction_id}\\\",\\n funding_subtier_agency_name=f\\\"subtier_funding_agency_agency_name_{transaction_id}\\\",\\n funding_subtier_agency_abbreviation=f\\\"subtier_funding_agency_agency_name_{transaction_id}\\\",\\n recipient_name=f\\\"recipient_name_{transaction_id}\\\",\\n recipient_unique_id=f\\\"{transaction_id:09d}\\\",\\n recipient_hash=\\\"c687823d-10af-701b-1bad-650c6e680190\\\" if transaction_id == 21 else None,\\n recipient_levels=[\\\"R\\\"] if i == 21 else [],\\n cfda_number=f\\\"cfda_number_{transaction_id}\\\",\\n fain=f\\\"fain_{transaction_id}\\\",\\n recipient_location_country_code=\\\"USA\\\",\\n recipient_location_country_name=\\\"USA\\\",\\n recipient_location_state_code=f\\\"LE_STATE_CODE_{transaction_id}\\\",\\n recipient_location_county_code=f\\\"{transaction_id:03d}\\\",\\n recipient_location_county_name=f\\\"LE_COUNTY_NAME_{transaction_id}\\\",\\n recipient_location_congressional_code=f\\\"{transaction_id:02d}\\\",\\n recipient_location_zip5=f\\\"LE_ZIP5_{transaction_id}\\\",\\n recipient_location_city_name=f\\\"LE_CITY_NAME_{transaction_id}\\\",\\n pop_country_code=\\\"USA\\\",\\n pop_country_name=\\\"UNITED STATES\\\",\\n pop_state_code=f\\\"POP_STATE_CODE_{transaction_id}\\\",\\n pop_county_code=f\\\"{transaction_id:03d}\\\",\\n pop_county_name=f\\\"POP_COUNTY_NAME_{transaction_id}\\\",\\n pop_zip5=f\\\"POP_ZIP5_{transaction_id}\\\",\\n pop_congressional_code=f\\\"{transaction_id:02d}\\\",\\n pop_city_name=f\\\"POP_CITY_NAME{transaction_id}\\\",\\n tas_components=tas_components,\\n )\",\n \"def temperatures():\\n hi_act= session.query(measurements.tobs,measurements.date,measurements.station).\\\\\\n filter(measurements.station == 'USC00519281').\\\\\\n filter(measurements.date >last_12).\\\\\\n order_by(measurements.date).all()\\n hi_act_df=pd.DataFrame(hi_act).set_index('date')\\n hi_act_dict=hi_act_df.to_dict()\\n return jsonify(hi_act_dict)\",\n \"def frame(self):\\n microseconds = np.array(self.results['times']) * 1e6\\n return pd.DataFrame(self.results, index=microseconds)\",\n \"def query_tickets(self):\\n return self._call_txtrader_api('query_tickets', {})\",\n \"async def describe_slow_log_records_async(\\n self,\\n request: dds_20151201_models.DescribeSlowLogRecordsRequest,\\n ) -> dds_20151201_models.DescribeSlowLogRecordsResponse:\\n runtime = util_models.RuntimeOptions()\\n return await self.describe_slow_log_records_with_options_async(request, runtime)\",\n \"def wem_market_value_all():\\n __query = \\\"\\\"\\\"\\n select\\n date_trunc('month', wfs.trading_interval) AS trading_day,\\n sum(wfs.eoi_quantity * wbs.price) as energy_interval,\\n wf.fueltech_id\\n from wem_facility_scada wfs\\n left join wem_facility wf on wfs.facility_id = wf.code\\n join wem_balancing_summary wbs on wfs.trading_interval = wbs.trading_interval\\n where\\n wf.fueltech_id is not null\\n group by 1, wf.fueltech_id\\n order by 1 desc, 2 asc\\n \\\"\\\"\\\"\\n\\n query = __query.format()\\n\\n json_envelope = {}\\n\\n with engine.connect() as c:\\n rows = c.execute(query)\\n\\n current_tech = None\\n\\n for row in rows:\\n\\n current_tech = row[2]\\n\\n if current_tech not in json_envelope.keys():\\n json_envelope[current_tech] = {\\n \\\"id\\\": f\\\"wem.fuel_tech.{current_tech}.market_value\\\",\\n \\\"fuel_tech\\\": current_tech,\\n \\\"region\\\": \\\"wa\\\",\\n \\\"type\\\": \\\"market_value\\\",\\n \\\"units\\\": \\\"AUD\\\",\\n \\\"history\\\": {\\n \\\"interval\\\": \\\"1M\\\",\\n \\\"start\\\": None,\\n \\\"last\\\": None,\\n \\\"data\\\": [],\\n },\\n }\\n\\n if (\\n json_envelope[current_tech][\\\"history\\\"][\\\"start\\\"] == None\\n or row[0] < json_envelope[current_tech][\\\"history\\\"][\\\"start\\\"]\\n ):\\n json_envelope[current_tech][\\\"history\\\"][\\\"start\\\"] = row[0]\\n\\n if (\\n json_envelope[current_tech][\\\"history\\\"][\\\"last\\\"] == None\\n or row[0] > json_envelope[current_tech][\\\"history\\\"][\\\"last\\\"]\\n ):\\n json_envelope[current_tech][\\\"history\\\"][\\\"last\\\"] = row[0]\\n\\n json_envelope[current_tech][\\\"history\\\"][\\\"data\\\"].append(row[1])\\n\\n return [json_envelope[i] for i in json_envelope.keys()]\",\n \"def tobs():\\n # Create our session (link) from Python to the DB.\\n session = Session(engine)\\n\\n # Calculate the date 1 year ago from the last data point in the database.\\n last_measurement_data_point_tuple = session.query(Measurement.date).order_by(Measurement.date.desc()).first()\\n (latest_date, ) = last_measurement_data_point_tuple\\n latest_date = dt.datetime.strptime(latest_date, '%Y-%m-%d')\\n latest_date = latest_date.date()\\n date_year_ago = latest_date - relativedelta(years=1)\\n\\n # Perform a query to retrieve the data and temperature scores.\\n data_from_last_year = session.query(Measurement.date, Measurement.tobs).filter(Measurement.date >= date_year_ago).all()\\n\\n session.close()\\n\\n # Convert results to a dictionary \\n all_temperatures = []\\n for date, temp in data_from_last_year:\\n if temp != None:\\n temp_dict = {}\\n temp_dict[date] = temp\\n all_temperatures.append(temp_dict)\\n # Return JSON\\n return jsonify(all_temperatures)\",\n \"def test_get_events_history_filtering_by_timestamp(rotkehlchen_api_server: 'APIServer'):\\n tx_hex = deserialize_evm_tx_hash('0xb226ddb8cbb286a7a998a35263ad258110eed5f923488f03a8d890572cd4608e') # noqa: E501\\n ethereum_inquirer = rotkehlchen_api_server.rest_api.rotkehlchen.chains_aggregator.ethereum.node_inquirer # noqa: E501\\n database = rotkehlchen_api_server.rest_api.rotkehlchen.data.db\\n get_decoded_events_of_transaction(\\n evm_inquirer=ethereum_inquirer,\\n database=database,\\n tx_hash=tx_hex,\\n )\\n # Call time range\\n from_timestamp = 1627401169\\n to_timestamp = 1627401170\\n async_query = random.choice([False, True])\\n with patch(\\n 'rotkehlchen.chain.ethereum.modules.sushiswap.sushiswap.Sushiswap.get_balances',\\n side_effect=lambda _: {},\\n ):\\n response = requests.get(\\n api_url_for(\\n rotkehlchen_api_server,\\n 'modulestatsresource',\\n module='sushiswap',\\n ),\\n json={\\n 'async_query': async_query,\\n 'from_timestamp': from_timestamp,\\n 'to_timestamp': to_timestamp,\\n },\\n )\\n if async_query:\\n task_id = assert_ok_async_response(response)\\n outcome = wait_for_async_task(rotkehlchen_api_server, task_id, timeout=120)\\n assert outcome['message'] == ''\\n result = outcome['result']\\n else:\\n result = assert_proper_response_with_result(response)\\n\\n events_balances = result[TEST_EVENTS_ADDRESS_1]\\n\\n assert len(events_balances) == 1\",\n \"def get_stats(evts_perigee) -> Table:\\n rows = []\\n\\n for evt in reversed(evts_perigee):\\n rows.append(evt.info)\\n\\n out = Table(rows=rows)\\n return out\",\n \"def get_all(self):\\r\\n return [{ 'label': x[0], 'timing': x[1], 'is_tool': x[0] in self._tool_labels}\\r\\n for x in sorted(self._timings_by_path.items(), key=lambda x: x[1], reverse=True)]\",\n \"def getDataWithTimeIndex(self, t):\\n\\n return self.sensorDf.iloc[t,:self.sensorChannels].values\",\n \"def unbalanced(self):\\n # TODO: Find a way to make a sql query to return all unbalanced transactions\\n return []\",\n \"def get_non_indexed_results(self):\\n\\n with sqlite3.connect(self.db_path) as conn:\\n cursor = conn.cursor()\\n\\n cursor.execute(\\\"SELECT status_code, file_count, start_time, end_time, website_id\\\"\\n \\\" FROM TaskResult WHERE indexed_time IS NULL\\\")\\n db_result = cursor.fetchall()\\n\\n cursor.execute(\\\"UPDATE TaskResult SET indexed_time=CURRENT_TIMESTAMP WHERE indexed_time IS NULL\\\")\\n conn.commit()\\n\\n return [TaskResult(r[0], r[1], r[2], r[3], r[4]) for r in db_result]\",\n \"def tobs():\\n # Query all tobs for the previous year\\n results = session.query(Measurement.date, Measurement.tobs).\\\\\\n filter(Measurement.date >= '2016-10-01').all()\\n\\n all_tobs = []\\n\\n for result in results:\\n tobs_dict = {}\\n tobs_dict[\\\"date\\\"] = result[0]\\n tobs_dict[\\\"tobs\\\"] = result[1]\\n all_tobs.append(tobs_dict)\\n return jsonify(all_tobs)\",\n \"def transactions(self):\\n url = f'{self._ynab.api_url}/budgets/{self.id}/transactions'\\n response = self._ynab.session.get(url)\\n if not response.ok:\\n self._logger.error('Error retrieving transactions, response was : %s with status code : %s',\\n response.text,\\n response.status_code)\\n return []\\n return [Transaction(self._ynab, transaction)\\n for transaction in response.json().get('data', {}).get('transactions', [])]\",\n \"def get_reports(self):\\r\\n result = QtSql.QSqlQuery('''Select * FROM failures''')\\r\\n list = []\\r\\n while result.next():\\r\\n failure = Failure(unicode(result.value(0).toString()), # id\\r\\n unicode(result.value(1).toString()), # comment\\r\\n unicode(result.value(2).toString()), # indicator\\r\\n bool(result.value(3))) # release\\r\\n p = self.get_presentation(failure.talkId)\\r\\n r = Report(p, failure)\\r\\n list.append(r)\\r\\n return list\",\n \"def fetch_all_tx(self):\\n transactions = []\\n for block in self.chain:\\n transactions.append(block.data)\\n return transactions\",\n \"def read_daily_qualified_report(self):\\n from itertools import repeat\\n\\n self.ID_TOTAL_CANDIDATES = kpi_from_db_config.ID_TOTAL_CANDIDATES\\n self.ID_TOTAL_PROCESSED = kpi_from_db_config.ID_TOTAL_PROCESSED\\n self.ID_TOTAL_EXPORTED = kpi_from_db_config.ID_TOTAL_EXPORTED\\n self.ID_TOTAL_CLASSIFIED = kpi_from_db_config.ID_TOTAL_CLASSIFIED\\n self.ID_TOTAL_QUALIFIED = kpi_from_db_config.ID_TOTAL_QUALIFIED\\n self.ID_TOTAL_DISQUALIFIED = kpi_from_db_config.ID_TOTAL_DISQUALIFIED\\n\\n list_id = [self.ID_TOTAL_CANDIDATES, \\n self.ID_TOTAL_PROCESSED, \\n self.ID_TOTAL_EXPORTED, \\n self.ID_TOTAL_CLASSIFIED, \\n self.ID_TOTAL_QUALIFIED, \\n self.ID_TOTAL_DISQUALIFIED]\\n list_result = [[] for i in repeat(None,len(list_id))]\\n\\n for i in range(len(list_id)):\\n self.cursor.execute('''\\n SELECT value\\n FROM public.kpi_report\\n WHERE id = %s\\n ORDER BY created_at DESC\\n LIMIT 2\\n ''', [list_id[i]])\\n\\n rows_count = self.cursor.rowcount\\n if (rows_count == 2):\\n for doc in self.cursor:\\n list_result[i].append(int(doc[0]))\\n elif (rows_count == 1):\\n for doc in self.cursor:\\n list_result[i].append(int(doc[0]))\\n list_result[i] = list_result[i] + [0]\\n else:\\n list_result[i] = [0] * 2 \\n\\n# print \\\"TESTING .... {}\\\".format(list_result)\\n return list_result\",\n \"def logs(self):\\n dataTuples = list()\\n try:\\n with sqlite3.connect(self.db_file) as conn:\\n cur = conn.cursor()\\n cur.execute(\\\"SELECT * FROM curves\\\")\\n dataTuples = cur.fetchall()\\n return [{\\\"id\\\": a[0], \\\"name\\\": a[1], \\\"units\\\": a[2],\\n \\\"descr\\\": a[3]} for a in dataTuples]\\n except:\\n return dataTuples\",\n \"def trades(self) -> List[ClosedTrade]:\\n return store.completed_trades.trades\",\n \"def tobs():\\n \\n # Obtain the current year from the date and using that date determine the previous year appending 01-01 and 12-31\\n compare_date = dt.date.today()\\n start_date = f\\\"{compare_date.year - 1}-01-01\\\"\\n end_date = f\\\"{compare_date.year - 1}-12-31\\\"\\n \\n tobs_result = session.query(Measurement.tobs).filter((Measurement.date >= start_date) & (Measurement.date <= end_date)\\n ).order_by(Measurement.date).all()\\n \\n tobs = []\\n tobs = list(np.ravel(tobs_result))\\n return jsonify(tobs)\",\n \"def gather_stock_data(tickers, save=True):\\n prices = pd.DataFrame()\\n ts = TimeSeries(key='EY2QBMV6MD9FX9CP', output_format='pandas')\\n\\n for ticker in tickers:\\n successful_grab = False\\n ticker_daily_adj = None\\n\\n while successful_grab is not True:\\n try:\\n ticker_daily_adj = ts.get_daily_adjusted(ticker, outputsize='full')[0]\\n successful_grab = True\\n except ValueError:\\n print('Waiting for API to let me in')\\n time.sleep(10)\\n\\n ticker_daily_adj.loc[:, '0. ticker'] = ticker\\n ticker_daily_adj = ticker_daily_adj[sorted(ticker_daily_adj.columns)]\\n\\n prices = pd.concat([prices, ticker_daily_adj])\\n\\n prices.sort_index(inplace=True)\\n prices.reset_index(inplace=True)\\n prices['date'] = pd.to_datetime(prices['date'])\\n if save:\\n prices.to_csv('stockdata.csv', index=True)\\n\\n return prices\",\n \"def test_get_settled_batch_list(self):\\n self.trans_details.get_settled_batch_list(\\n include_statistics = True,\\n )\\n\\n self.trans_details.get_settled_batch_list(\\n first_settlement_date=u\\\"2011-01-01T01:00:00\\\",\\n )\\n\\n self.trans_details.get_settled_batch_list(\\n last_settlement_date=u\\\"2011-01-01T01:00:00\\\",\\n )\\n\\n # all three together\\n self.trans_details.get_settled_batch_list(\\n include_statistics = True,\\n first_settlement_date=u\\\"2011-01-01T01:00:00\\\",\\n last_settlement_date=u\\\"2011-01-02T01:00:00\\\"\\n )\",\n \"def tobsdata():\\n # query for the dates and temperature observations from a year from the last data point.\\n # * Return a JSON list of Temperature Observations (tobs) for the previous year.\\n # as this should be a list, I'm just grabbing the station name\\n session = Session(engine)\\n results = session.query(Measurement.tobs).filter(Measurement.date>='2016-08-23').all()\\n session.close()\\n tobs_list = list(np.ravel(results))\\n\\n # * Return the JSON representation of your dictionary.\\n return jsonify(tobs_list)\",\n \"def get_data(self):\\n data = list(IgnitionRow.objects.all().order_by('-pub_date')[:self.num_ticks].values())\\n two_hours = data[::-1]\\n pct_flop_data = [[int(elem['pct_flop_{}'.format(key)]) for elem in two_hours] \\n \\tfor key in self.keys]\\n pct_flop_data = [[min(elem, 100) for elem in arr] for arr in pct_flop_data] # Assume a max pot size of 2000 BBs\\n return pct_flop_data\",\n \"def get_data(self,tickers,alpha,max_retries = 5):\\n data = []\\n skipped = []\\n for ticker in tqdm(tickers,desc = \\\"Acquiring data\\\"):\\n try:\\n company = Company(ticker,alpha = alpha)\\n data.append(company)\\n except Exception as e:\\n time.sleep(10)\\n skipped.append(ticker)\\n\\n for i in range(max_retries):\\n new_data,skipped = self.get_data(skipped,alpha = alpha,max_retries = 0)\\n data.extend(new_data)\\n if len(skipped) == 0:\\n break\\n\\n return data,skipped\",\n \"def timestamps(self):\\n return self.source.timestamps[self._time_keep]\",\n \"def timers(self):\\n return self['timers']\",\n \"def get_all_metrics(self):\\n up_time = self.uptime()\\n down_time = self.downtime()\\n customer_sla = self.sla()\\n objective = self.slo()\\n indicator = self.sli()\\n avail_percentage = self.availability()\\n mt_bf = self.mtbf(up_time)\\n mt_tr = self.mttr(down_time)\\n list_results = [up_time,down_time,customer_sla,objective,indicator,avail_percentage,mt_bf,mt_tr]\\n return list_results\",\n \"def _get_trades(self):\\n\\n trade_url = self.trade_url % (self.date, self.instrument, self.exchange)\\n self.trades = pd.read_csv(trade_url, parse_dates=[0],\\n date_parser=lambda t: pd.to_datetime(str(t), format='%Y%m%dT%H%M%S'))\\n\\n self.trades.fillna(np.nan)\\n self.trades.index = pd.to_datetime(self.trades.time, unit='s')\\n self.trades.time = pd.to_datetime(self.trades.time, unit='s')\\n self.trades.columns = ['time', 'price', 'volume', 'source', 'buyer', 'seller', 'initiator']\\n # del self.trades['time']\\n\\n if self.exclude_derivative:\\n self.trades = self.trades[(self.trades.source != 'Derivatives trade') & (self.trades.source != 'Official')]\",\n \"def report(self):\\n log = self._array.state()\\n result = []\\n for record in log:\\n result.append(f\\\"{record.worker_name()}\\\\t${record.task_payment()}\\\")\\n return \\\"\\\\n\\\".join(result)\",\n \"def get_transactions(self):\\n\\n df = self.__transactions[\\n [\\n \\\"Date\\\",\\n \\\"Type\\\",\\n \\\"Ticker\\\",\\n \\\"Side\\\",\\n \\\"Price\\\",\\n \\\"Quantity\\\",\\n \\\"Fees\\\",\\n \\\"Investment\\\",\\n \\\"Currency\\\",\\n \\\"Sector\\\",\\n \\\"Industry\\\",\\n \\\"Country\\\",\\n \\\"Region\\\",\\n ]\\n ]\\n df = df.replace(np.nan, \\\"-\\\")\\n df[\\\"Date\\\"] = df[\\\"Date\\\"].dt.strftime(\\\"%Y-%m-%d\\\")\\n df.sort_values(by=\\\"Date\\\", ascending=False, inplace=True)\\n return df\",\n \"def tobs():\\n # query for the last day\\n\\n # Create our session (link) from Python to the DB\\n session = Session(engine)\\n \\n last_day = session.query(Measurement.date).order_by(Measurement.date.desc()).first()[0]\\n len_months = 12\\n # convert result to datetime format\\n last_day = datetime.datetime.strptime(last_day, \\\"%Y-%m-%d\\\")\\n # calculate start day\\n start_day = last_day - datetime.timedelta(days=365)\\n start_day = \\\"{:%Y-%m-%d}\\\".format(start_day)\\n\\n # Design a query to retrieve the last 12 months of temperature data and plot the results\\n results = session.query(Measurement.date, Measurement.tobs, Measurement.station).\\\\\\n filter(Measurement.date >= start_day ).\\\\\\n order_by(Measurement.date).all()\\n\\n session.close()\\n \\n temps = []\\n for result in results:\\n temp_dict = {}\\n temp_dict[\\\"date\\\"] = result.date\\n temp_dict[\\\"tobs\\\"] = result.tobs\\n temp_dict[\\\"station\\\"] = result.station\\n temps.append(temp_dict)\\n \\n return jsonify(temps)\",\n \"def _get_unit_records(self, start_time):\\r\\n\\r\\n if self.optMTTF.get_active():\\r\\n _query = \\\"SELECT t2.fld_unit, t1.fld_incident_id, \\\\\\r\\n t1.fld_age_at_incident, t1.fld_failure, \\\\\\r\\n t1.fld_suspension, t1.fld_cnd_nff, \\\\\\r\\n t1.fld_occ_fault, t1.fld_initial_installation, \\\\\\r\\n t1.fld_interval_censored, t2.fld_request_date, \\\\\\r\\n t2.fld_hardware_id \\\\\\r\\n FROM rtk_incident_detail AS t1 \\\\\\r\\n INNER JOIN \\\\\\r\\n ( \\\\\\r\\n SELECT DISTINCT MIN(fld_unit, fld_request_date), \\\\\\r\\n fld_incident_id, fld_request_date, \\\\\\r\\n fld_unit, fld_hardware_id \\\\\\r\\n FROM rtk_incident \\\\\\r\\n GROUP BY fld_unit \\\\\\r\\n ) AS t2 \\\\\\r\\n ON t2.fld_incident_id=t1.fld_incident_id \\\\\\r\\n WHERE t1.fld_age_at_incident >= {0:f} \\\\\\r\\n ORDER BY t2.fld_unit ASC, \\\\\\r\\n t1.fld_age_at_incident ASC, \\\\\\r\\n t2.fld_request_date ASC\\\".format(start_time)\\r\\n\\r\\n elif self.optMTBBD.get_active():\\r\\n _query = \\\"SELECT t2.fld_unit, t1.fld_incident_id, \\\\\\r\\n t1.fld_age_at_incident, t1.fld_failure, \\\\\\r\\n t1.fld_suspension, t1.fld_cnd_nff, \\\\\\r\\n t1.fld_occ_fault, t1.fld_initial_installation, \\\\\\r\\n t1.fld_interval_censored, t2.fld_request_date, \\\\\\r\\n t2.fld_hardware_id \\\\\\r\\n FROM rtk_incident_detail AS t1 \\\\\\r\\n INNER JOIN \\\\\\r\\n ( \\\\\\r\\n SELECT fld_incident_id, fld_request_date, fld_unit, \\\\\\r\\n fld_hardware_id \\\\\\r\\n FROM rtk_incident \\\\\\r\\n GROUP BY fld_unit, fld_request_date \\\\\\r\\n ) AS t2 \\\\\\r\\n ON t2.fld_incident_id=t1.fld_incident_id \\\\\\r\\n WHERE t1.fld_age_at_incident >= {0:f} \\\\\\r\\n GROUP BY t2.fld_unit, t1.fld_age_at_incident \\\\\\r\\n ORDER BY t2.fld_unit ASC, \\\\\\r\\n t1.fld_age_at_incident ASC, \\\\\\r\\n t2.fld_request_date ASC\\\".format(start_time)\\r\\n\\r\\n elif self.optMTBF.get_active():\\r\\n _query = \\\"SELECT t2.fld_unit, t1.fld_incident_id, \\\\\\r\\n t1.fld_age_at_incident, t1.fld_failure, \\\\\\r\\n t1.fld_suspension, t1.fld_cnd_nff, \\\\\\r\\n t1.fld_occ_fault, t1.fld_initial_installation, \\\\\\r\\n t1.fld_interval_censored, t2.fld_request_date, \\\\\\r\\n t2.fld_hardware_id \\\\\\r\\n FROM rtk_incident_detail AS t1 \\\\\\r\\n INNER JOIN rtk_incident AS t2 \\\\\\r\\n ON t2.fld_incident_id=t1.fld_incident_id \\\\\\r\\n WHERE t1.fld_age_at_incident >= {0:f} \\\\\\r\\n ORDER BY t2.fld_unit ASC, \\\\\\r\\n t1.fld_age_at_incident ASC, \\\\\\r\\n t2.fld_request_date ASC\\\".format(start_time)\\r\\n\\r\\n (_results, _error_code, __) = self._dao.execute(_query, commit=False)\\r\\n\\r\\n return(_results, _error_code)\",\n \"def get_stats(self, epg_dn):\\n # Apic saves up to 95 different objects with statistic information\\n traffic_list = []\\n for i in range(10, -1, -1):\\n traffic = self.moDir.lookupByDn(epg_dn + '/HDl2IngrBytesAg15min-%s' % str(i))\\n if traffic is not None:\\n traffic_list.append(traffic)\\n return traffic_list\",\n \"def process_ticket_data():\\r\\n c = conn.cursor()\\r\\n c.execute('Select Count(*) from raw_ticket_data')\\r\\n totalleft = c.fetchone()[0]\\r\\n print('{} total rows required'.format(totalleft))\\r\\n np.random.seed(1)\\r\\n df_total = pd.read_sql_query('Select Ticketnumber, TickIssueDate, TickIssueTime, ViolationDesc, '\\r\\n ' VehMake, TickRPPlate, TickStreetNo, TickMeter, Agency, TickBadgeIssued, '\\r\\n 'TickStreetName , TotalPaid, TotalAmtDue from raw_ticket_data ', conn)\\r\\n columnlist = df_total.columns.tolist()\\r\\n df_total.sort_values(by = 'TickIssueDate', inplace = True)\\r\\n n = 500000\\r\\n totalsize = df_total.shape[0]\\r\\n indexes = [i for i in range(0,totalsize, n)]\\r\\n columnlist = df_total.columns.tolist()\\r\\n columnlist.append('address')\\r\\n tqdm.pandas()\\r\\n j = 1\\r\\n for i in indexes:\\r\\n df = df_total[i:i+n]\\r\\n print('Iteration {} started at {}. {} records left'.format(j, dt.datetime.now().strftime(\\\"%H:%M\\\"), totalsize))\\r\\n df['TickStreetNo'] = df['TickStreetNo'].apply(return_num)\\r\\n df['ViolationDesc'] = df['ViolationDesc'].apply(lambda x: x.replace('METER DTN','MTR OUT DT'))\\r\\n df['TickStreetName'] = df['TickStreetName'].apply(replace_street)\\r\\n df['TickStreetName'] = df['TickStreetName'].apply(return_street)\\r\\n df['TotalPaid'] = df['TotalPaid'].apply(return_cost)\\r\\n df['TotalAmtDue'] = df['TotalAmtDue'].apply(lambda x: re.sub('[^1-9]', '', str(x)))\\r\\n df['TickRPPlate'] = df['TickRPPlate'].apply(lambda x: 'None' if len(re.findall('[\\\\w+]', str(x))) == 0 else str(x).replace('[^\\\\w+]', ''))\\r\\n df['Tdelt'] = df['TickIssueTime'].apply(return_time_delta)\\r\\n\\r\\n\\r\\n df_1 = df.merge(single_address, left_on = ['TickStreetNo', 'TickStreetName'], right_on = ['number', 'streetname'])\\r\\n df_2 = df.merge(double_address, left_on = ['TickStreetNo', 'TickStreetName'], right_on = ['number', 'streetname'])\\r\\n\\r\\n df_2 = df_2.merge(df_1, how = 'left', left_on = ['TickIssueDate', 'TickBadgeIssued', 'nhood'], right_on = ['TickIssueDate', 'TickBadgeIssued', 'nhood'])\\r\\n df_3 = df_2[pd.isnull(df_2['Tdelt_y'])]\\r\\n df_2.dropna(subset = ['Tdelt_y'], inplace = True)\\r\\n df_2['timedelta'] = df_2.apply(lambda x: np.abs(x['Tdelt_y'] - x['Tdelt_x']), axis = 1)\\r\\n df_2.sort_values(by = 'timedelta', inplace = True)\\r\\n\\r\\n df_2.columns = [col.replace('_x', '') for col in df_2.columns]\\r\\n df_3.columns = [col.replace('_x', '') for col in df_3.columns]\\r\\n df_2.drop_duplicates(subset = 'TicketNumber', inplace = True)\\r\\n print(\\\"Searching for unmatchable addresses\\\")\\r\\n df_3['address'] = df_3.progress_apply(return_address, axis = 1)\\r\\n\\r\\n df = df_1.append(df_2)\\r\\n df = df.append(df_3)\\r\\n df['TickIssueDate'] = df.apply(Time, axis = 1)\\r\\n df = df[columnlist]\\r\\n\\r\\n if i == 0:\\r\\n df.to_sql('ticket_data', if_exists = 'replace',con = conn)\\r\\n else:\\r\\n df.to_sql('ticket_data', if_exists = 'append',con = conn)\\r\\n\\r\\n totalsize -= n\\r\\n j+=1\\r\\n\\r\\n del c\\r\\n\\r\\n return\",\n \"def get_all(self):\\n total_expense_reports = []\\n get_count = {\\n 'query': {\\n 'object': 'EEXPENSES',\\n 'select': {\\n 'field': 'RECORDNO'\\n },\\n 'pagesize': '1'\\n }\\n }\\n\\n response = self.format_and_send_request(get_count)\\n count = int(response['data']['@totalcount'])\\n pagesize = 2000\\n offset = 0\\n for i in range(0, count, pagesize):\\n data = {\\n 'query': {\\n 'object': 'EEXPENSES',\\n 'select': {\\n 'field': [\\n 'RECORDNO',\\n 'RECORDID',\\n 'WHENCREATED',\\n 'WHENPOSTED',\\n 'TOTALENTERED',\\n 'STATE',\\n 'TOTALDUE',\\n 'DESCRIPTION',\\n 'CURRENCY',\\n 'BASECURR',\\n 'MEMO'\\n ]\\n },\\n 'pagesize': pagesize,\\n 'offset': offset\\n }\\n }\\n expense_reports = self.format_and_send_request(data)['data']['EEXPENSES']\\n total_expense_reports = total_expense_reports + expense_reports\\n offset = offset + pagesize\\n return total_expense_reports\",\n \"def get_ticker_details(self):\\n\\n ticker_data = sequential_data_scrape(\\n scrape.download_ticker_details,\\n [\\n f\\\"https://finviz.com/quote.ashx?&t={row.get('Ticker')}\\\"\\n for row in self.data\\n ],\\n self._user_agent,\\n )\\n\\n for entry in ticker_data:\\n for key, value in entry.items():\\n for ticker_generic in self.data:\\n if ticker_generic.get(\\\"Ticker\\\") == key:\\n if \\\"Sales\\\" not in self.headers:\\n self.headers.extend(list(value[0].keys()))\\n\\n ticker_generic.update(value[0])\\n self.analysis.extend(value[1])\\n\\n return self.data\",\n \"def get_send_statistics(self):\\r\\n return self._make_request('GetSendStatistics')\",\n \"def GenerateTimeBlockData(self, time_ranges):\\r\\n # Reset the class data for the next log parsing.\\r\\n for time_range in time_ranges:\\r\\n time_range.Reset()\\r\\n\\r\\n self.ParseLog(time_ranges)\\r\\n\\r\\n daily_data = {}\\r\\n html_string = \\\"\\\"\\r\\n\\r\\n html_string += \\\"Summary: %s <br>\\\\n\\\" % self.station\\r\\n html_string += \\\"<table border='1'>\\\\n\\\"\\r\\n html_string += \\\"<tr><th></th><th>Completed</th><th>Canceled</th>\\\"\\r\\n html_string += \\\"<th>Errors</th>\\\"\\r\\n html_string += \\\"<th>Average Rig Scan Time</th>\\\"\\r\\n html_string += \\\"<th>Average Operator Scan Time</th>\\\"\\r\\n html_string += \\\"<th>Average Time Between Scans</th>\\\"\\r\\n html_string += \\\"<th>Total Idle Time</th></tr>\\\"\\r\\n\\r\\n # Filter the log data into different timeblocks\\r\\n for time_range in time_ranges:\\r\\n daily_data[str(time_range)] = time_range.total\\r\\n daily_data[str(time_range) + \\\"_canceled\\\"] = time_range.canceled\\r\\n daily_data[str(time_range) + \\\"_error\\\"] = time_range.errors\\r\\n\\r\\n html_string += self.GenerateTimeblockString(time_range)\\r\\n\\r\\n # Special case for total\\r\\n daily_data[\\\"Total\\\"] = self.total_time_range.total\\r\\n daily_data[\\\"Total_canceled\\\"] = self.total_time_range.canceled\\r\\n daily_data[\\\"Total_error\\\"] = self.total_time_range.errors\\r\\n\\r\\n html_string += self.GenerateTimeblockString(self.total_time_range)\\r\\n\\r\\n html_string += \\\"</table><br>\\\\n\\\"\\r\\n\\r\\n daily_data[\\\"html_string\\\"] = html_string\\r\\n\\r\\n return daily_data\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7311632","0.5812507","0.571646","0.57079303","0.5684339","0.55696833","0.55493486","0.553089","0.5528401","0.5502346","0.548358","0.544936","0.544525","0.54149175","0.5411108","0.54054636","0.5394847","0.5392003","0.53741133","0.5357601","0.5319892","0.5315574","0.5306587","0.5294445","0.5268564","0.5268471","0.52652085","0.5246509","0.5229982","0.5213194","0.52094626","0.5193845","0.51731986","0.51707786","0.5167521","0.5164851","0.5157847","0.5154265","0.51487666","0.51421916","0.5126859","0.51257807","0.51206225","0.5117641","0.5113356","0.5107234","0.51052964","0.51018983","0.51013494","0.50983304","0.5089383","0.5087617","0.50871193","0.5077727","0.5075697","0.50666744","0.50664437","0.5065563","0.5059428","0.50571084","0.50521255","0.50519127","0.50451815","0.50408965","0.5040734","0.5034725","0.50309044","0.5028216","0.5027254","0.5022196","0.5015484","0.5015203","0.50140107","0.50135046","0.501185","0.5005963","0.5002926","0.500187","0.49993232","0.4993229","0.49838415","0.4979748","0.4978617","0.49767402","0.49660423","0.49655992","0.49651027","0.4963297","0.4958102","0.4958062","0.49552363","0.4948401","0.49470198","0.4946902","0.49459326","0.49453542","0.49379748","0.49336943","0.49332044","0.4929667"],"string":"[\n \"0.7311632\",\n \"0.5812507\",\n \"0.571646\",\n \"0.57079303\",\n \"0.5684339\",\n \"0.55696833\",\n \"0.55493486\",\n \"0.553089\",\n \"0.5528401\",\n \"0.5502346\",\n \"0.548358\",\n \"0.544936\",\n \"0.544525\",\n \"0.54149175\",\n \"0.5411108\",\n \"0.54054636\",\n \"0.5394847\",\n \"0.5392003\",\n \"0.53741133\",\n \"0.5357601\",\n \"0.5319892\",\n \"0.5315574\",\n \"0.5306587\",\n \"0.5294445\",\n \"0.5268564\",\n \"0.5268471\",\n \"0.52652085\",\n \"0.5246509\",\n \"0.5229982\",\n \"0.5213194\",\n \"0.52094626\",\n \"0.5193845\",\n \"0.51731986\",\n \"0.51707786\",\n \"0.5167521\",\n \"0.5164851\",\n \"0.5157847\",\n \"0.5154265\",\n \"0.51487666\",\n \"0.51421916\",\n \"0.5126859\",\n \"0.51257807\",\n \"0.51206225\",\n \"0.5117641\",\n \"0.5113356\",\n \"0.5107234\",\n \"0.51052964\",\n \"0.51018983\",\n \"0.51013494\",\n \"0.50983304\",\n \"0.5089383\",\n \"0.5087617\",\n \"0.50871193\",\n \"0.5077727\",\n \"0.5075697\",\n \"0.50666744\",\n \"0.50664437\",\n \"0.5065563\",\n \"0.5059428\",\n \"0.50571084\",\n \"0.50521255\",\n \"0.50519127\",\n \"0.50451815\",\n \"0.50408965\",\n \"0.5040734\",\n \"0.5034725\",\n \"0.50309044\",\n \"0.5028216\",\n \"0.5027254\",\n \"0.5022196\",\n \"0.5015484\",\n \"0.5015203\",\n \"0.50140107\",\n \"0.50135046\",\n \"0.501185\",\n \"0.5005963\",\n \"0.5002926\",\n \"0.500187\",\n \"0.49993232\",\n \"0.4993229\",\n \"0.49838415\",\n \"0.4979748\",\n \"0.4978617\",\n \"0.49767402\",\n \"0.49660423\",\n \"0.49655992\",\n \"0.49651027\",\n \"0.4963297\",\n \"0.4958102\",\n \"0.4958062\",\n \"0.49552363\",\n \"0.4948401\",\n \"0.49470198\",\n \"0.4946902\",\n \"0.49459326\",\n \"0.49453542\",\n \"0.49379748\",\n \"0.49336943\",\n \"0.49332044\",\n \"0.4929667\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6642906"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":3693,"cells":{"query":{"kind":"string","value":"Returns a list containing any slow transaction data collected during the reporting period. NOTE Currently only the slowest transaction for the reporting period is retained."},"document":{"kind":"string","value":"def slow_transaction_data(self):\n\n # XXX This method no longer appears to be used. Being replaced\n # by the transaction_trace_data() method.\n\n if not self.__settings:\n return []\n\n if not self.__slow_transaction:\n return []\n\n maximum = self.__settings.agent_limits.transaction_traces_nodes\n\n transaction_trace = self.__slow_transaction.transaction_trace(\n self, maximum)\n\n data = [transaction_trace,\n list(self.__slow_transaction.string_table.values())]\n\n if self.__settings.debug.log_transaction_trace_payload:\n _logger.debug('Encoding slow transaction data where '\n 'payload=%r.', data)\n\n json_data = json_encode(data)\n\n level = self.__settings.agent_limits.data_compression_level\n level = level or zlib.Z_DEFAULT_COMPRESSION\n\n zlib_data = zlib.compress(six.b(json_data), level)\n\n pack_data = base64.standard_b64encode(zlib_data)\n\n if six.PY3:\n pack_data = pack_data.decode('Latin-1')\n\n root = transaction_trace.root\n\n trace_data = [[root.start_time,\n root.end_time - root.start_time,\n self.__slow_transaction.path,\n self.__slow_transaction.request_uri,\n pack_data]]\n\n return trace_data"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def transaction_trace_data(self, connections):\n\n _logger.debug('Generating transaction trace data.')\n\n if not self.__settings:\n return []\n\n # Create a set 'traces' that is a union of slow transaction,\n # and Synthetics transactions. This ensures we don't send\n # duplicates of a transaction.\n\n traces = set()\n if self.__slow_transaction:\n traces.add(self.__slow_transaction)\n traces.update(self.__synthetics_transactions)\n\n # Return an empty list if no transactions were captured.\n\n if not traces:\n return []\n\n # We want to limit the number of explain plans we do across\n # these. So work out what were the slowest and tag them.\n # Later the explain plan will only be run on those which are\n # tagged.\n\n agent_limits = self.__settings.agent_limits\n explain_plan_limit = agent_limits.sql_explain_plans_per_harvest\n maximum_nodes = agent_limits.transaction_traces_nodes\n\n database_nodes = []\n\n if explain_plan_limit != 0:\n for trace in traces:\n for node in trace.slow_sql:\n # Make sure we clear any flag for explain plans on\n # the nodes in case a transaction trace was merged\n # in from previous harvest period.\n\n node.generate_explain_plan = False\n\n # Node should be excluded if not for an operation\n # that we can't do an explain plan on. Also should\n # not be one which would not be included in the\n # transaction trace because limit was reached.\n\n if (node.node_count < maximum_nodes and\n node.connect_params and node.statement.operation in\n node.statement.database.explain_stmts):\n database_nodes.append(node)\n\n database_nodes = sorted(database_nodes,\n key=lambda x: x.duration)[-explain_plan_limit:]\n\n for node in database_nodes:\n node.generate_explain_plan = True\n\n else:\n for trace in traces:\n for node in trace.slow_sql:\n node.generate_explain_plan = True\n database_nodes.append(node)\n\n # Now generate the transaction traces. We need to cap the\n # number of nodes capture to the specified limit.\n\n trace_data = []\n\n for trace in traces:\n transaction_trace = trace.transaction_trace(\n self, maximum_nodes, connections)\n\n data = [transaction_trace,\n list(trace.string_table.values())]\n\n if self.__settings.debug.log_transaction_trace_payload:\n _logger.debug('Encoding slow transaction data where '\n 'payload=%r.', data)\n\n json_data = json_encode(data)\n\n level = self.__settings.agent_limits.data_compression_level\n level = level or zlib.Z_DEFAULT_COMPRESSION\n\n zlib_data = zlib.compress(six.b(json_data), level)\n\n pack_data = base64.standard_b64encode(zlib_data)\n\n if six.PY3:\n pack_data = pack_data.decode('Latin-1')\n\n root = transaction_trace.root\n\n if trace.record_tt:\n force_persist = True\n else:\n force_persist = False\n\n if trace.include_transaction_trace_request_uri:\n request_uri = trace.request_uri\n else:\n request_uri = None\n\n trace_data.append([transaction_trace.start_time,\n root.end_time - root.start_time,\n trace.path,\n request_uri,\n pack_data,\n trace.guid,\n None,\n force_persist,\n None,\n trace.synthetics_resource_id, ])\n\n return trace_data","def get_reports(self):\r\n return sorted(self._reports,\r\n key=lambda x: x['stats']['totalTimeMillis'],\r\n reverse=True)","def get_reports(self):\n return sorted(self._reports,\n key=lambda x: x['stats']['totalTimeMillis'],\n reverse=True)","def return_trade_history(\n self,\n start: Timestamp,\n end: Timestamp,\n ) -> list[dict[str, Any]]:\n limit = 100\n data: list[dict[str, Any]] = []\n start_ms = start * 1000\n end_ms = end * 1000\n while True:\n new_data = self.api_query_list('/trades', {\n 'startTime': start_ms,\n 'endTime': end_ms,\n 'limit': limit,\n })\n results_length = len(new_data)\n if data == [] and results_length < limit:\n return new_data # simple case - only one query needed\n\n latest_ts_ms = start_ms\n # add results to data and prepare for next query\n existing_ids = {x['id'] for x in data}\n for trade in new_data:\n try:\n timestamp_ms = trade['createTime']\n latest_ts_ms = max(latest_ts_ms, timestamp_ms)\n # since we query again from last ts seen make sure no duplicates make it in\n if trade['id'] not in existing_ids:\n data.append(trade)\n except (DeserializationError, KeyError) as e:\n msg = str(e)\n if isinstance(e, KeyError):\n msg = f'Missing key entry for {msg}.'\n self.msg_aggregator.add_warning(\n 'Error deserializing a poloniex trade. Check the logs for details',\n )\n log.error(\n 'Error deserializing poloniex trade',\n trade=trade,\n error=msg,\n )\n continue\n\n if results_length < limit:\n break # last query has less than limit. We are done.\n\n # otherwise we query again from the last ts seen in the last result\n start_ms = latest_ts_ms\n continue\n\n return data","def get_latest_transactions(self):\n first_run = False\n if not self._transactions:\n first_run = True\n transactions = []\n for account in self.accounts:\n self._logger.debug('Getting transactions for account \"%s\"', account.ynab_account.name)\n for transaction in account.get_latest_transactions():\n if not self._filter_transaction(transaction):\n transactions.append(transaction)\n self._logger.debug('Caching %s transactions', len(transactions))\n self._transactions.extend(transactions)\n if first_run:\n self._logger.info('First run detected, discarding transactions until now')\n return []\n return transactions","def slow_queries(self):\n request = Request(method=\"get\", endpoint=\"/query/slow\")\n\n def response_handler(resp):\n if not resp.is_success:\n raise C8QLQueryListError(resp, request)\n return self._format_queries(resp.body)\n\n return self._execute(request, response_handler)","def timings(self):\r\n return self._timings","def get_pending_transactions():\n\n return History.get_pending().get()","def returnTradeHistory(self, time=1 * 60 * 60, limit=100):\n assert limit <= 100, \"'limit' has to be smaller than 100\"\n return self.dpay.rpc.get_trade_history(\n transactions.formatTimeFromNow(-time),\n transactions.formatTimeFromNow(),\n limit,\n api=\"market_history\"\n )","def unbalanced(self):\n # TODO: Find a way to make a sql query to return all unbalanced transactions\n return []","def trades(self) -> List[ClosedTrade]:\n return store.completed_trades.trades","def get_all_latest_transactions(self):\n transactions = []\n for account in self.accounts:\n self._logger.debug('Getting transactions for account \"%s\"', account.ynab_account.name)\n for transaction in account.get_latest_transactions():\n if not self._filter_transaction(transaction):\n transactions.append(transaction)\n return transactions","def showTransactions(self):\n self.scanTransactions()\n txns = []\n\n # Summarize the stats\n for x in range(len(self._trans)):\n stats = self._trans[x]\n trans_time = 0\n remote_calls = 0\n for name, stat in stats:\n trans_time += stat.total_tt\n remote_calls += 1\n txns.append((x, trans_time, remote_calls))\n\n results = [\"TX#\\tTime\\tCalls\",\n \"=\" * 22]\n\n for item in txns:\n results.append(\"%3d\\t%4f\\t%5d\" % item)\n \n return \"\\n\".join(results)","def getTransferListSummary(self):\n p_ids_and_prices = {}\n players = self.getAllPlayerInfoTransferlist()\n\n # Get IDs of all players\n log_event(self.queue, \"Gathering player prices... \")\n for p in players:\n p_bidstatus = p[1]\n p_id = p[8]\n # removed Filter for unlisted / expired players\n if p_id not in p_ids_and_prices:\n p_sellprice = self.getPlayerSellPrice(p_id)\n # If sell price returns 0, need to fetch from Futbin\n if p_sellprice == 0:\n p_sellprice = self.getFutbinPrice_opentab(p_id)\n self.sleep_approx(5) # Delay iteration to not anger futbin\n # Add player ID and price to dict\n p_ids_and_prices[p_id] = p_sellprice\n\n for p_id in p_ids_and_prices:\n p_price = p_ids_and_prices[p_id]\n p_name = self.getPlayerCardName(p_id)\n log_event(self.queue, str(p_name) + \" - #\" +\n str(p_id) + \" Price \" + str(p_price))\n\n num_p_sold = 0\n num_p_expired = 0\n num_p_unlisted = 0\n num_p_listed = 0\n\n sold_p_value = 0\n expired_p_value = 0\n unlisted_p_value = 0\n listed_p_value = 0\n\n for p in players:\n p_bidstatus = p[1]\n p_id = p[8]\n p_soldprice = p[5] # is 0 if unlisted\n p_sellprice = int(p_ids_and_prices[p_id])\n\n if \"won\" in p_bidstatus:\n num_p_sold += 1\n sold_p_value += p_soldprice\n if \"expired\" in p_bidstatus:\n num_p_expired += 1\n expired_p_value += p_sellprice\n if (p_bidstatus == \"listFUTItem\"):\n num_p_unlisted += 1\n unlisted_p_value += p_sellprice\n if (p_bidstatus == \"listFUTItem has-auction-data\"):\n num_p_listed += 1\n listed_p_value += p_sellprice\n\n log_event(self.queue, \"Players sold: \" + str(num_p_sold))\n log_event(self.queue, \"Players expired: \" + str(num_p_expired))\n log_event(self.queue, \"Players listed: \" + str(num_p_listed))\n log_event(self.queue, \"Players unlisted: \" + str(num_p_unlisted))\n log_event(self.queue, \" - - - \")\n log_event(self.queue, \"Sold players value: \" + str(sold_p_value))\n log_event(self.queue, \"Expired players value: \" +\n str(expired_p_value))\n log_event(self.queue, \"Unlisted players value: \" +\n str(unlisted_p_value))\n log_event(self.queue, \"Listed players value: \" + str(listed_p_value))\n\n # TODO subtract bought price\n self.user_players_won += int(num_p_unlisted)\n self.p_ids_and_prices = p_ids_and_prices\n intel = [p_ids_and_prices, num_p_sold, num_p_expired, num_p_unlisted,\n num_p_listed, sold_p_value, expired_p_value, unlisted_p_value, listed_p_value]\n return intel","def time_list(self):\n return (self.N_T * (np.arange(self.N_itr) + 1) /\n self.N_itr * 1000 * self.DT)","def _get_meas_times_from_db(self):\n meas_times = []\n if self._data['report_save_historical_instances_ind'] != 'Y':\n # for non historical reports take measurement time from saved dataset\n dataset = self._jfile.get_current_stored_dataset()\n try:\n meas_time = datetime.datetime.strptime(dataset['meas_time'], '%Y-%m-%d %H:%M:%S')\n except ValueError:\n raise Exception(\"Cannot unformat string %s to datetime\" % dataset['meas_time'])\n meas_times.append(meas_time)\n\n else:\n # for historical reports take measurement times from db datasets\n where_sql = ''\n where_sql_list = list()\n params = [self._id, self._segment_value_id]\n\n if self._process_dataset_ids:\n for dataset_id in self._process_dataset_ids:\n if type(dataset_id) == list:\n where_sql_list.append(\"(report_data_set_instance_id >= %s AND report_data_set_instance_id <= %s)\")\n if dataset_id[0] < dataset_id[1]:\n params.append(dataset_id[0])\n params.append(dataset_id[1])\n else:\n params.append(dataset_id[1])\n params.append(dataset_id[0])\n else:\n where_sql_list.append(\"report_data_set_instance_id = %s\")\n params.append(dataset_id)\n where_sql = ' AND (%s)' % ' OR '.join(where_sql_list)\n\n self._db.Query(\"\"\"SELECT measurement_time\n FROM report_data_set_instance\n WHERE\n `element_id`= %%s\n AND segment_value_id = %%s\n %s\n ORDER BY measurement_time ASC\"\"\" % where_sql, tuple(params))\n meas_times = [item['measurement_time'] for item in self._db.record]\n\n return meas_times","def fetch(self, daterange=(datetime.now() - timedelta(1), datetime.now())):\n cursor = self.conn.cursor()\n sql = 'SELECT measure_dt, ping, download, upload FROM speedlogs ' + \\\n ' WHERE measure_dt BETWEEN ? AND ?'\n cursor.execute(sql, daterange)\n return cursor.fetchall()","async def get_trades(self) -> List[TradeRequest]:\n data = j.dumps({\n 'startindex': 0,\n 'statustype': 'inbound'\n })\n r = await self.request.request(url='https://www.roblox.com/my/money.aspx/getmyitemtrades', data=data, method='POST')\n data = json.loads(r.json()['d'])[\"Data\"]\n trades = []\n for trade in data:\n t = json.loads(trade)\n trades.append(TradeRequest(self.request, t['Date'], t['Expires'], t['TradePartner'], t['TradePartnerID'], t['Status'], t['TradeSessionID']))\n return trades","def get_trades_for_symbol(self, symbol, time_range=5):\n return [trade for trade in self\n if trade.symbol == symbol and (trade.timestamp > (int(time.time())-time_range*60))]","def get_new_data(self):\n\n # record bar parse performance\n self.logger.debug(\"Started parsing new ticks.\")\n start_parse = time.time()\n for exchange in self.exchanges:\n exchange.parse_ticks()\n end_parse = time.time()\n duration = round(end_parse - start_parse, 5)\n\n self.logger.debug(\n \"Parsed \" + str(self.total_instruments) +\n \" instruments' ticks in \" + str(duration) + \" seconds.\")\n self.track_tick_processing_performance(duration)\n\n # wrap new 1 min bars in market events\n new_market_events = []\n for exchange in self.exchanges:\n bars = exchange.get_new_bars()\n for symbol in exchange.get_symbols():\n for bar in bars[symbol]:\n event = MarketEvent(exchange.get_name(), bar)\n new_market_events.append(event)\n # add bars to save-to-db-later queue\n # TODO: store new bars concurrently with a processpool\n self.bars_save_to_db.put(event)\n return new_market_events","def get_gdax_historical_data():\n \n start = None\n while not start:\n start,end,tid = getStartAndEndHistoric()\n if not start:\n time.sleep(60)\n #Todo: change this to 1min\n firsttimestamp = start\n engine = sa.create_engine(sql_address)\n products = [\"LTC-USD\",\"LTC-BTC\",\"ETH-USD\",\"ETH-BTC\",\"BTC-USD\"]\n public_client = gdax.PublicClient()\n deltat = datetime.timedelta(seconds = 200)\n timewindows = []\n while end - start > datetime.timedelta(seconds=0):\n if start + deltat > end:\n endx = end\n else:\n endx = start + deltat\n timewindows.append([start,endx])\n start += deltat\n results = []\n total = len(timewindows)\n current_idx = 0\n timeold = time.time()\n numofqueries = 0\n engine = sa.create_engine(sql_address)\n Base.metadata.bind = engine\n DBSession = sa.orm.sessionmaker()\n DBSession.bind = engine\n session = DBSession()\n for startx,endx in timewindows:\n\n current_idx += 1\n for i in products:\n repeat = True\n while repeat:\n\n #delay if ratelimts are close\n if numofqueries < 3:\n while time.time() - timeold < 1:\n time.sleep(0.05)\n \n timeold = time.time()\n numofqueries = 0\n try:\n alist = public_client.get_product_historic_rates(i, start = startx, end = endx, granularity = 1)\n except:\n time.sleep(30)\n public_client = gdax.PublicClient()\n alist = public_client.get_product_historic_rates(i, start = startx, end = endx, granularity = 1)\n\n alist = public_client.get_product_historic_rates(i, start = startx, end = endx, granularity = 1)\n\n numofqueries += 1\n\n #rate limit exceeded has 'message' as dict.\n if not 'message' in alist:\n repeat = False\n for a in alist:\n a[0] = datetime.datetime.fromtimestamp(float(a[0]))\n tmp = i.split('-')\n d = dict(coin = tmp[0], currency = tmp[1], timestamp = a[0], low=a[1], high=a[2], open=a[3], close=a[4], volume=a[5])\n results.append(d)\n lasttimestamp = a[0]\n\n #upload with batch size of 10000\n if len(results) > 10000:\n engine.execute(\n GADXHistoricalDataOneSecondOHLC.__table__.insert(),\n results\n )\n results = []\n \n update = session.query(historicalDataProgramState).filter(sa.and_(historicalDataProgramState.transaction_id == tid,historicalDataProgramState.entry_type == 'update')).first()\n if update:\n update.end = lasttimestamp\n session.commit()\n else:\n new_update = historicalDataProgramState(entry_type = 'update',transaction_id = tid,start=firsttimestamp,end=lasttimestamp,platform='GDAX',status='incomplete')\n session.add(new_update)\n session.commit()\n if len(results) > 0:\n engine.execute(\n GADXHistoricalDataOneSecondOHLC.__table__.insert(),\n results\n )\n results = []\n \n update = session.query(historicalDataProgramState).filter(sa.and_(historicalDataProgramState.transaction_id == tid,historicalDataProgramState.entry_type == 'update')).first()\n if update:\n update.end = lasttimestamp\n session.commit()\n else:\n new_update = historicalDataProgramState(entry_type = 'update',transaction_id = tid,start=firsttimestamp,end=lasttimestamp,platform='GDAX',status='incomplete')\n session.add(new_update)\n session.commit()\n\n update = session.query(historicalDataProgramState).filter(sa.and_(historicalDataProgramState.transaction_id == tid,historicalDataProgramState.entry_type == 'update')).first()\n update.status='complete'\n order = session.query(historicalDataProgramState).filter(sa.and_(historicalDataProgramState.transaction_id == tid,historicalDataProgramState.entry_type == 'order')).first()\n order.status='complete'\n session.commit()","def pending_transactions(self):\n return self._call_account_method(\n 'pendingTransactions'\n )","def transactions(self):\n url = f'{self._ynab.api_url}/budgets/{self.id}/transactions'\n response = self._ynab.session.get(url)\n if not response.ok:\n self._logger.error('Error retrieving transactions, response was : %s with status code : %s',\n response.text,\n response.status_code)\n return []\n return [Transaction(self._ynab, transaction)\n for transaction in response.json().get('data', {}).get('transactions', [])]","def get_trades_history(self, symbol, start_time, end_time, limit=1000):\n payload = {'symbol': symbol, 'start': start_time, 'end': end_time, 'limit': limit}\n return self.public_request('GET', '/api/v1/trades', **payload)","def get_transactions(self):\n # open a cursor object\n cur = self.get_cursor()\n\n # get transactions from database\n cur.execute(\"SELECT * FROM transactions\")\n transactions_data = cur.fetchall()\n\n # convert into a dict of values.\n transactions_list = []\n [transactions_list.append({'transaction_id': transaction[0],\n 'date': transaction[1],\n 'payee_id': transaction[2],\n 'description': transaction[3],\n 'amount': transaction[4]})\n for transaction in transactions_data]\n\n # close the cursor\n self.close_cursor()\n\n return transactions_list","def describe_slow_log_records(\n self,\n request: dds_20151201_models.DescribeSlowLogRecordsRequest,\n ) -> dds_20151201_models.DescribeSlowLogRecordsResponse:\n runtime = util_models.RuntimeOptions()\n return self.describe_slow_log_records_with_options(request, runtime)","async def get_blacklist_hist(self, search_time, limit=1000):\n\n start = search_time[0][0]\n end = search_time[0][1]\n\n url = f'https://{self.__api}/v3/blacklist/history'\n continuation = None\n full_resp = {}\n flag = True\n body = {\"filter[clientid]\": self.clientid, \"filter[start_time]\": start, \"filter[end_time]\": end,\n \"limit\": limit, \"continuation\": continuation}\n while True:\n with requests.get(url, params=body,\n headers={'X-WallarmAPI-UUID': self.__uuid,\n 'X-WallarmAPI-Secret': self.__secret}) as response:\n if response.status not in [200, 201, 202, 204, 304]:\n raise NonSuccessResponse(response.status, await response.text)\n continuation = response.json().get('body').get('continuation')\n\n if flag:\n full_resp = response.json()\n\n if continuation is not None:\n body['continuation'] = continuation\n if not flag:\n full_resp['body']['objects'].extend(response.json().get('body').get('objects'))\n else:\n break\n flag = False\n return full_resp","def _get_meas_times(self, last_meas_time):\n meas_times = list()\n data = None\n \n if self._process_type == 'soft_gen':\n meas_times = self._get_meas_times_from_db()\n else:\n if self._data['data_fetch_method'] == 'sql':\n # get from outer sql db\n data = self._get_meas_times_sql(last_meas_time)\n elif self._data['data_fetch_method'] == 'web service':\n # get from web service\n data = self._get_meas_times_web_service(last_meas_time)\n\n\n if data:\n clear_data = [row[0] for row in data['data']]\n # check if we have values in list of datetime type\n if clear_data:\n if type(clear_data[0]) == datetime.datetime:\n meas_times = clear_data\n else:\n # it's a date type\n meas_times = [datetime.datetime.combine(d, datetime.time.min) for d in clear_data]\n\n \n\n\n # sort measurement times if they weren't sorted before\n meas_times.sort()\n # if do not save history, take only last element\n if self._data['report_save_historical_instances_ind'] != 'Y':\n if len(meas_times) > 1:\n del meas_times[:-1]\n \n return meas_times","def transactions(self, dt=None):\n if dt is None:\n # flatten the by-day transactions\n return [\n txn\n for by_day in itervalues(self._processed_transactions)\n for txn in by_day\n ]\n\n return self._processed_transactions.get(dt, [])","def fetch_all_tx(self):\n transactions = []\n for block in self.chain:\n transactions.append(block.data)\n return transactions","def gather_data(self, *args, **kwargs):\n instrument_arg = kwargs.get('instrument', 'EUR_USD')\n granularity_arg = kwargs.get('granularity', 'M1')\n candle_format = kwargs.get('candleFormat', 'bidask')\n start_time = kwargs.get('start', '2014-10-01T00:00:00.000000Z')\n count_arg = kwargs.get('count', 5000)\n out_data = []\n data_complete = False\n while(not data_complete):\n response = self.oanda.get_history(instrument=instrument_arg,\n granularity=granularity_arg,\n candleFormat=candle_format,\n start=start_time,\n count=count_arg)\n raw_data = response['candles']\n if (len(out_data) == 0):\n out_data = out_data + raw_data\n elif (len(out_data) > 1):\n # raw_data[0] is already in out_data as raw_data[-1] from last\n # iteration\n out_data = out_data + raw_data[1:]\n start_time = raw_data[-1]['time']\n if (len(raw_data) < 5000):\n data_complete = True\n\n out_data = self._list_to_df(out_data)\n return out_data","def timestamps(self):\n return self.source.timestamps[self._time_keep]","def timings(self):\n if self._C_timings is None:\n raise RuntimeError(\"Cannot extract timings with non-finalized Profiler.\")\n return {field: max(getattr(self._C_timings, field), 10**-6)\n for field, _ in self._C_timings._fields_}","def query_tickets(self):\n return self._call_txtrader_api('query_tickets', {})","def probe_times(self):\r\n probe_times = []\r\n for probe in self.__probes.values():\r\n if probe.complete():\r\n if probe.round_trip_time() > 20:\r\n \"Long probe: %s \" %self.__id\r\n probe_times.append(probe.round_trip_time())\r\n return probe_times","def get_non_indexed_results(self):\n\n with sqlite3.connect(self.db_path) as conn:\n cursor = conn.cursor()\n\n cursor.execute(\"SELECT status_code, file_count, start_time, end_time, website_id\"\n \" FROM TaskResult WHERE indexed_time IS NULL\")\n db_result = cursor.fetchall()\n\n cursor.execute(\"UPDATE TaskResult SET indexed_time=CURRENT_TIMESTAMP WHERE indexed_time IS NULL\")\n conn.commit()\n\n return [TaskResult(r[0], r[1], r[2], r[3], r[4]) for r in db_result]","def timeseries_report(self):\n report = pd.DataFrame(index=self.price.index)\n report.loc[:, \"FR Energy Throughput (kWh)\"] = self.ene_results['ene']\n report.loc[:, \"FR Energy Throughput Up (Charging) (kWh)\"] = self.variables['regu_c']*self.krd_avg*self.dt*self.storage.rte\n report.loc[:, \"FR Energy Throughput Up (Discharging) (kWh)\"] = self.variables['regu_d']*self.krd_avg*self.dt\n report.loc[:, \"FR Energy Throughput Down (Charging) (kWh)\"] = self.variables['regd_c']*self.krd_avg*self.dt*self.storage.rte\n report.loc[:, \"FR Energy Throughput Down (Discharging) (kWh)\"] = self.variables['regd_d']*self.krd_avg*self.dt\n report.loc[:, \"FR Energy Settlement Price Signal ($/kWh)\"] = self.price\n report.loc[:, 'Regulation Up (Charging) (kW)'] = self.variables['regu_c']\n report.loc[:, 'Regulation Up (Discharging) (kW)'] = self.variables['regu_d']\n report.loc[:, 'Regulation Down (Charging) (kW)'] = self.variables['regd_c']\n report.loc[:, 'Regulation Down (Discharging) (kW)'] = self.variables['regd_d']\n report.loc[:, \"Regulation Up Price Signal ($/kW)\"] = self.p_regu\n report.loc[:, \"Regulation Down Price Signal ($/kW)\"] = self.p_regd\n\n return report","def read_daily_messages_report(self):\n from itertools import repeat\n\n self.ID_TWEET_ORANGE_FLOW = kpi_from_db_config.ID_TWEET_ORANGE_FLOW\n self.ID_PROCESSING_MESSAGES = kpi_from_db_config.ID_PROCESSING_MESSAGES\n self.ID_CANDIDATES_PROCESSED = kpi_from_db_config.ID_CANDIDATES_PROCESSED\n\n list_id = [self.ID_TWEET_ORANGE_FLOW, \n self.ID_PROCESSING_MESSAGES, \n self.ID_CANDIDATES_PROCESSED]\n len_need_list = [7, 8, 2]\n list_result = [[] for i in repeat(None,len(list_id))]\n\n for i in range(len(list_id)):\n self.cursor.execute('''\n SELECT value\n FROM public.kpi_report\n WHERE id = %s\n ORDER BY created_at DESC\n LIMIT %s\n ''', [list_id[i], len_need_list[i]])\n rows_count = self.cursor.rowcount\n\n if (rows_count == len_need_list[i]): # If rows_count as expected \n for doc in self.cursor:\n list_result[i].append(int(doc[0]))\n elif (rows_count > 0 and rows_count < len_need_list[i]):\n for doc in self.cursor:\n list_result[i].append(int(doc[0]))\n list_result[i] = list_result[i] + [0] * (len_need_list[i] - rows_count) \n else:\n list_result[i] = [0] * len_need_list[i]\n\n return list_result","def getScanList(self):\n \n scanList = []\n for row in self._calData:\n if str(row.calType()) == 'CAL_DELAY':\n scanList.append(row.scanSet()[0])\n return scanList","def get_tiingo_prices(tickers, start, end, api_key=None):\n\n all_results = []\n if api_key is None:\n api_key = os.getenv('TIINGO_API_KEY')\n # Sort tickers so that error logging can be used to identify progress\n tickers = sorted(tickers)\n\n for i, ticker in enumerate(tickers):\n try:\n df = web.DataReader(name=ticker,\n data_source='tiingo',\n start=start,\n end=end,\n api_key=api_key)\n df = df[['adjClose']]\n except KeyError as e:\n if e.args[0] == 'date':\n # Patch to handle issue in pandas_datareader\n # where empty results cause a KeyError\n print(f'Got empty df for i={i}, ticker={tickers[i]}')\n df = pd.DataFrame()\n except Exception as e:\n print('Received an unexpected error:', e)\n print(f'Only fetched up to {i-1} inclusive. Returning.')\n return pd.concat(all_results)\n\n if (i % 50 == 0) and i > 0:\n # Sleep to avoid timeouts. Empirically found 20s to be sufficient\n time.sleep(20)\n\n all_results.append(df)\n return pd.concat(all_results)","def trades(self) -> list[TradeOffer]:\n return self._connection.trades","def get_target_timestamps(self):\n times=[]\n curr = self.begin_ts\n while curr<=self.end_ts:\n times.append(curr)\n curr = curr + 24 * 60 * 60\n return times","def transactions(self):\n url = f'{self._ynab.api_url}/budgets/{self.budget.id}/accounts/{self.id}/transactions'\n response = self._ynab.session.get(url)\n if not response.ok:\n self._logger.error('Error retrieving transactions, response was : %s with status code : %s',\n response.text,\n response.status_code)\n return []\n return [Transaction(self._ynab, transaction)\n for transaction in response.json().get('data', {}).get('transactions', [])]","def related_reports(self):\n return Report.objects.exclude(contact_email__isnull=True).filter(contact_email__iexact=self.contact_email).order_by('status', '-create_date')[:1000]","def determineUnitHistory():\n\tunitTracker = Unitiser()\n\t\n\timport transactions\n\ttrades = transactions.allTransactions()\n\t\n\thistory = dict()\n\t\n\tfor date in timeline():\n\t\t#print(\"\\ntimelime:\", date.strftime('%Y-%m-%d %H:%M:%S'))\n\t\timport valuator\n\t\tvalue = valuator.getPortfolioValueAt(date)\n\t\tif date in trades:\n\t\t\tprior = getPortfolioBefore(date)\n\t\t\tprior_value = valuator.getPortfolioValueAt(date, portfolio = prior)\n\n\t\t\tinvested = Decimal('0.0')\n\t\t\tfor equity in trades[date]:\n\t\t\t\ttrade = trades[date][equity]\n\t\t\t\t#print(equity, trade)\n\t\t\t\tif trade['action'] == 'buy':\n\t\t\t\t\tinvested = invested + Decimal(trade['value'])\n\t\t\t\telif trade['action'] == 'sell':\n\t\t\t\t\tinvested = invested - Decimal(trade['value'])\n\n\t\t\tsince = getPortfolioAt(date)\n\t\t\tsince_value = valuator.getPortfolioValueAt(date, portfolio = since)\n\n\t\t\t#print(\"change amount is\", invested)\n\t\t\tif invested > 0:\n\t\t\t\tunitTracker.invest(invested, prior_value)\n\t\t\telif invested < 0:\n\t\t\t\tunitTracker.divest(abs(invested), prior_value)\n\n\t\thistory[date] = {\n\t\t\t 'date' : date,\n\t\t\t 'value' : value.quantize(TWOPLACES),\n\t\t\t 'units' : unitTracker.numberOfUnits().quantize(TWOPLACES),\n\t\t\t 'price' : unitTracker.pricePerUnit(value).quantize(TWOPLACES),\n\t\t\t 'invested' : unitTracker.invested\n\t\t\t }\n\t\n\treturn history","def getTempBanIps(self):\n banned = []\n q = \"\"\"SELECT clients.ip AS target_ip FROM penalties INNER JOIN clients ON penalties.client_id = clients.id\n WHERE penalties.type = 'TempBan' AND penalties.inactive = 0 AND penalties.time_expire > %s\n GROUP BY clients.ip\"\"\" % int(time())\n cursor = self.query(q)\n if cursor:\n while not cursor.EOF:\n banned.append(cursor.getValue('target_ip'))\n cursor.moveNext()\n cursor.close()\n return banned","def timers(self):\n return self['timers']","def get_stock_price(df_excld):\n\n ts = TimeSeries(os.environ['ALPHA_VANTAGE_KEY'])\n\n info = []\n symbols = []\n counter = 0\n\n for t in df_excld['Ticker']:\n\n if counter % 5 == 0:\n time.sleep(65)\n\n i, m = ts.get_daily(symbol=t, outputsize='full')\n info.append(i)\n symbols.append(m['2. Symbol'])\n counter += 1\n\n return info, symbols","def get_all_coins_history(self, end_date=None, start_date=None, verbose=True):\n infos = []\n for coin in self.get_coins():\n if verbose:\n print(\"Collecting data for >> \" + coin)\n if start_date:\n start_date = start_date\n else:\n start_date = '20130428'\n if end_date:\n end_date = end_date\n else:\n now = str(datetime.now().date()).replace('-', '')\n end_date = now\n coin_url = self.coins[coin]\n coin_url = coin_url + '/historical-data/?start=' + start_date + '&end=' + end_date\n content = urlopen(coin_url).read()\n soup = BeautifulSoup(content, 'html.parser')\n results = soup.find_all(\"tr\", class_=\"text-right\")\n\n for result in results:\n date = result.find_all('td')[0].text\n\n open_val = result.find_all('td')[1].text\n if open_val == '-':\n open_val = None\n else:\n open_val = float(result.find_all('td')[1].text.replace(',', ''))\n\n high_val = result.find_all('td')[2].text\n if high_val == '-':\n high_val = None\n else:\n high_val = float(result.find_all('td')[2].text.replace(',', ''))\n\n low_val = result.find_all('td')[3].text\n if low_val == '-':\n low_val = None\n else:\n low_val = float(result.find_all('td')[3].text.replace(',', ''))\n\n close_val = result.find_all('td')[4].text\n if close_val == '-':\n close_val = None\n else:\n close_val = float(result.find_all('td')[4].text.replace(',', ''))\n\n volume = result.find_all('td')[5].text\n if volume == '-':\n volume = None\n else:\n volume = float(result.find_all('td')[5].text.replace(',', ''))\n\n market_cap = result.find_all('td')[6].text\n if market_cap == '-':\n market_cap = None\n else:\n market_cap = float(result.find_all('td')[6].text.replace(',', ''))\n temp = {\n \"coin\": coin, # soup.title.text.split()[0],\n \"date\": date,\n \"symbol\": soup.title.text.split()[1].replace('(', '').replace(')', ''),\n \"open_val\": open_val,\n \"high_val\": high_val,\n \"low_val\": low_val,\n \"close_val\": close_val,\n \"volume\": volume,\n \"market_cap\": market_cap\n }\n infos.append(temp)\n df_all = pd.DataFrame.from_dict(infos)\n df_all['middle_val'] = (df_all.high_val + df_all.low_val) / 2\n df_all['datetime'] = pd.to_datetime(df_all['date'])\n df_all = df_all.sort_values(by='datetime')\n self.coins_history = df_all","def slow_update_duration(self):\n for i in range(len(self.data_file.sorted_data)):\n if self.data_file.sorted_data[i]['type'] == 'slow':\n slow_upd = self.data_file.sorted_data[i]['timestamp']\n Config.ANALYSIS.write(f\"slow at: {slow_upd}\\n\")\n if i == 0:\n after_slow = self.data_file.sorted_data[i + 1]['timestamp']\n Config.ANALYSIS.write(f\"after slow: ({slow_upd}, {after_slow}) \"\n f\"= {after_slow - slow_upd}\\n\\n\")\n elif i == len(self.data_file.sorted_data) - 1:\n before_slow = self.data_file.sorted_data[i - 1]['timestamp']\n Config.ANALYSIS.write(f\"before slow: ({before_slow}, {slow_upd}) \"\n f\"= {slow_upd - before_slow}\\n\\n\")\n else:\n before_slow = self.data_file.sorted_data[i - 1]['timestamp']\n after_slow = self.data_file.sorted_data[i + 1]['timestamp']\n Config.ANALYSIS.write(f\"before slow: ({before_slow}, {slow_upd}) \"\n f\"= {slow_upd - before_slow}\\n\")\n Config.ANALYSIS.write(f\"after slow: ({slow_upd}, {after_slow}) \"\n f\"= {after_slow - slow_upd}\\n\\n\")\n Config.ANALYSIS.write(\"\\n\\n\")","def get_consumption_transactions(self, exclude_inferred_receipts=False):\n from casexml.apps.stock.const import (\n TRANSACTION_TYPE_STOCKONHAND,\n TRANSACTION_TYPE_RECEIPTS,\n TRANSACTION_TYPE_CONSUMPTION\n )\n transactions = [\n ConsumptionTransaction(\n TRANSACTION_TYPE_RECEIPTS if self.delta > 0 else TRANSACTION_TYPE_CONSUMPTION,\n abs(self.delta),\n self.report_date\n )\n ]\n if self.type == LedgerTransaction.TYPE_BALANCE:\n if self.delta > 0 and exclude_inferred_receipts:\n transactions = []\n\n transactions.append(\n ConsumptionTransaction(\n TRANSACTION_TYPE_STOCKONHAND,\n self.updated_balance,\n self.report_date\n )\n )\n return transactions","def trades_for(self, symbol, from_id=None, timestamp=None, limit=None):\n\t\tif self._session:\n\t\t\tdata = {'symbol': symbol}\n\n\t\t\tif from_id:\n\t\t\t\tdata['fromId'] = from_id\n\n\t\t\tif timestamp:\n\t\t\t\tdata['startTime'] = int(timestamp * 1000.0)\n\n\t\t\tif limit:\n\t\t\t\tdata['limit'] = limit\n\n\t\t\treturn self._session.get_my_trades(**data)\n\n\t\treturn []","def deposits_for_period(self):\n cash_transaction = CashTransaction(self.user)\n limits = self.user.limits\n deposits = 0\n if limits.exists():\n deposit_limit = self.user.limits.get(type=Limit.DEPOSIT)\n deposits = \\\n cash_transaction.get_all_deposits(date_range=deposit_limit.time_period_boundaries)[\n 'amount__sum']\n return deposits","def get_reports(self):\r\n result = QtSql.QSqlQuery('''Select * FROM failures''')\r\n list = []\r\n while result.next():\r\n failure = Failure(unicode(result.value(0).toString()), # id\r\n unicode(result.value(1).toString()), # comment\r\n unicode(result.value(2).toString()), # indicator\r\n bool(result.value(3))) # release\r\n p = self.get_presentation(failure.talkId)\r\n r = Report(p, failure)\r\n list.append(r)\r\n return list","def get_all(self):\r\n return [{ 'label': x[0], 'timing': x[1], 'is_tool': x[0] in self._tool_labels}\r\n for x in sorted(self._timings_by_path.items(), key=lambda x: x[1], reverse=True)]","def get_report_list(jtl_file):\n df = None\n try:\n df = pd.read_csv(jtl_file,\n low_memory=False,\n error_bad_lines=False,\n quoting=csv.QUOTE_NONE,\n encoding='utf-8')\n except Exception as e:\n err_msg = 'read jtl file error. detail:{e}'.format(e=e)\n LOGGER.error(err_msg)\n if df is None:\n return\n threads = int(jtl_file.split(os.sep)[-1].split('_')[0])\n success, elapsed, latency, sent_bytes, receive_bytes = [df.get(x) for x in\n ['success', 'elapsed', 'Latency', 'sentBytes', 'bytes']]\n samples = df.shape[0]\n error_count = success.value_counts().get(False)\n if not error_count:\n error_count = 0\n error_rate = str(float(error_count / samples) * 100) + '%'\n label = df.loc[0, 'label']\n start_time = df.iat[0, 0]\n end_time = df.iloc[-1, 0]\n last_req_time = df.iat[-1, 1]\n\n # 如果最后一行数据无效,则取上一行\n i = 1\n while not len(str(end_time)) == 13 and not re.findall('[\\d]{13}', str(end_time)):\n i += 1\n end_time = df.iloc[-i, 0]\n last_req_time = df.iat[-i, 1]\n samples -= 1\n\n if isinstance(start_time, str):\n start_time = int(start_time)\n if isinstance(end_time, str):\n end_time = int(end_time)\n\n local_start_time = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(start_time / 1000))\n local_end_time = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(end_time / 1000))\n\n durations = (end_time + last_req_time - start_time) / 1000\n throughput = samples / durations\n\n report_list = [label, local_start_time, local_end_time, durations, threads, throughput, error_rate,\n elapsed.min(), elapsed.max(), elapsed.mean(), samples, sent_bytes.mean(), receive_bytes.mean(),\n latency.mean(), latency.min(), latency.max()]\n\n return report_list","def get_ser_spktimes(self):\n\n spktimes = []\n for sweep_no in range(self.get_no_sweeps()):\n spktimes_singlesweep = []\n for cell_no in range(self.get_no_ser_neurons()):\n spktimes_singlesweep.append(\n np.where(self.ser_spktrains[sweep_no, cell_no, :] > 0.5)[0]\n * self.get_dt()\n )\n spktimes.append(spktimes_singlesweep)\n return spktimes","def get_all_files_to_instrument_for_live_session():\n sql=\"SELECT * FROM files WHERE should_instrument=1 AND is_history=0\"\n conn=sqlite3.connect(CONNECTION_STRING)\n c=conn.cursor()\n c.execute(sql)\n results=c.fetchall()\n conn.close()\n return results","def transaction_data(self):\n return list(map(lambda transaction:transaction.to_json(), self.transaction_map.values()))","def timers(self):\n return self.client.call('GET', self.name + '/timers')","def get_send_statistics(self):\r\n return self._make_request('GetSendStatistics')","def get_all(self):\n total_expense_reports = []\n get_count = {\n 'query': {\n 'object': 'EEXPENSES',\n 'select': {\n 'field': 'RECORDNO'\n },\n 'pagesize': '1'\n }\n }\n\n response = self.format_and_send_request(get_count)\n count = int(response['data']['@totalcount'])\n pagesize = 2000\n offset = 0\n for i in range(0, count, pagesize):\n data = {\n 'query': {\n 'object': 'EEXPENSES',\n 'select': {\n 'field': [\n 'RECORDNO',\n 'RECORDID',\n 'WHENCREATED',\n 'WHENPOSTED',\n 'TOTALENTERED',\n 'STATE',\n 'TOTALDUE',\n 'DESCRIPTION',\n 'CURRENCY',\n 'BASECURR',\n 'MEMO'\n ]\n },\n 'pagesize': pagesize,\n 'offset': offset\n }\n }\n expense_reports = self.format_and_send_request(data)['data']['EEXPENSES']\n total_expense_reports = total_expense_reports + expense_reports\n offset = offset + pagesize\n return total_expense_reports","def performance_history(self, request, pk=None, **kwargs):\n # Get the goal even though we don't need it (we could just use the pk)\n # so we can ensure we have permission to do so.\n goal = self.get_object()\n\n # - Get all the transaction with this goal involved that are of reason 'Execution'.\n # We want the volume, ticker id, date ordered by date. [(date, {ticker: vol}, ...]\n qs = Transaction.objects.filter(Q(to_goal=goal) | Q(from_goal=goal),\n reason=Transaction.REASON_EXECUTION).order_by('executed')\n txs = qs.values_list('execution_distribution__execution__executed',\n 'execution_distribution__execution__asset__id',\n 'execution_distribution__volume')\n ts = []\n entry = (None,)\n aids = set()\n # If there were no transactions, there can be no performance\n if len(txs) == 0:\n return Response([])\n\n # Because executions are stored with timezone, but other things are just as date, we need to make datetimes\n # naive before doing date arithmetic on them.\n bd = timezone.make_naive(txs[0][0]).date()\n ed = timezone.make_naive(timezone.now()).date()\n for tx in txs:\n aids.add(tx[1])\n txd = timezone.make_naive(tx[0]).date()\n if txd == entry[0]:\n entry[1][tx[1]] += tx[2]\n else:\n if entry[0] is not None:\n ts.append(entry)\n entry = (txd, defaultdict(int))\n entry[1][tx[1]] = tx[2]\n ts.append(entry)\n\n # - Get the time-series of prices for each instrument from the first transaction date until now.\n # Fill empty dates with previous value [(date, {ticker: price}, ...]\n pqs = DailyPrice.objects.filter(date__range=(bd, ed),\n instrument_content_type=ContentType.objects.get_for_model(Ticker).id,\n instrument_object_id__in=aids)\n prices = pqs.to_timeseries(fieldnames=['price', 'date', 'instrument_object_id'],\n index='date',\n storage='long',\n pivot_columns='instrument_object_id',\n values='price')\n # Remove negative prices and fill missing values\n # We replace negs with None so they are interpolated.\n prices[prices <= 0] = None\n prices = prices.reindex(pd.date_range(bd, ed), method='ffill').fillna(method='bfill')\n\n # For each day, calculate the performance\n piter = prices.itertuples()\n res = []\n # Process the first day - it's special\n row = next(piter)\n p_m1 = row[1:]\n vols_m1 = [0] * len(prices.columns)\n tidlocs = {tid: ix for ix, tid in enumerate(prices.columns)}\n for tid, vd in ts.pop(0)[1].items():\n vols_m1[tidlocs[tid]] += vd\n res.append((dt2ed(row[0]), 0)) # First day has no performance as there wasn't a move\n # Process the rest\n for row in piter:\n # row[0] (a datetime) is a naive timestamp, so we don't need to convert it\n if ts and row[0].date() == ts[0][0]:\n vols = vols_m1.copy()\n dtrans = ts.pop(0)[1] # The transactions for the current processed day.\n for tid, vd in dtrans.items():\n vols[tidlocs[tid]] += vd\n # The exposed assets for the day. These are the assets we know for sure were exposed for the move.\n pvol = list(map(min, vols, vols_m1))\n else:\n vols = vols_m1\n pvol = vols\n pdelta = list(map(operator.sub, row[1:], p_m1)) # The change in price from yesterday\n impact = sum(map(operator.mul, pvol, pdelta)) # The total portfolio impact due to price moves for exposed assets.\n b_m1 = sum(map(operator.mul, pvol, p_m1)) # The total portfolio value yesterday for the exposed assets.\n perf = 0 if b_m1 == 0 else impact / b_m1\n # row[0] (a datetime) is a naive timestamp, so we don't need to convert it\n res.append((dt2ed(row[0]), decimal.Decimal.from_float(perf).quantize(decimal.Decimal('1.000000'))))\n p_m1 = row[1:]\n vols_m1 = vols[:]\n\n return Response(res)","def get_staking_transaction_history(address, page=0, page_size=1000, include_full_tx=False, tx_type='ALL',\n order='ASC', endpoint=_default_endpoint, timeout=_default_timeout\n ) -> list:\n params = [\n {\n 'address': address,\n 'pageIndex': page,\n 'pageSize': page_size,\n 'fullTx': include_full_tx,\n 'txType': tx_type,\n 'order': order\n }\n ]\n # Using v2 API, because getStakingTransactionHistory not implemented in v1\n method = 'hmyv2_getStakingTransactionsHistory'\n stx_history = rpc_request(method, params=params, endpoint=endpoint, timeout=timeout)['result']\n try:\n return stx_history['staking_transactions']\n except KeyError as e:\n raise InvalidRPCReplyError(method, endpoint) from e","def get_extension_list(self, timespan, db_lookup):\n\n if (self.search_list_extension != None):\n return [self.search_list_extension]\n \n t1 = time.time()\n\n # Get UTC offset\n stop_struct = time.localtime(timespan.stop)\n utc_offset = (calendar.timegm(stop_struct) - calendar.timegm(time.gmtime(time.mktime(stop_struct))))/60\n\n # Get our start time, numDays days ago but aligned with start of day\n # first get the start of today\n _ts = startOfDay(timespan.stop)\n # then go back numDays days\n _ts_dt = datetime.datetime.fromtimestamp(_ts)\n _start_dt = _ts_dt - datetime.timedelta(days=self.numDays)\n _start_ts = time.mktime(_start_dt.timetuple())\n\n if (self.numDays == 3):\n _start_ts = time.mktime((_ts_dt - datetime.timedelta(days=0)).timetuple())\n _end_ts = time.mktime((_ts_dt + datetime.timedelta(days=3)).timetuple())\n timespan = TimeSpan(_start_ts, _end_ts)\n \n # Get our temperature vector\n (time_start_vt, time_stop_vt, outTemp_vt) = db_lookup().getSqlVectors(TimeSpan(_start_ts, timespan.stop),'outTemp')\n # Convert our temperature vector\n outTemp_vt = self.generator.converter.convert(outTemp_vt)\n # Can't use ValueHelper so round our results manually\n # Get the number of decimal points\n tempRound = int(self.generator.skin_dict['Units']['StringFormats'].get(outTemp_vt[1], \"1f\")[-2])\n # Do the rounding\n outTempRound_vt = [roundNone(x,tempRound) for x in outTemp_vt[0]]\n # Get our time vector in ms (w34highcharts requirement)\n # Need to do it for each getSqlVectors result as they might be different\n outTemp_time_ms = [time_stop_vt[0][0] if (x == 0) else time_stop_vt[0][x] - time_stop_vt[0][0] for x in range(len(time_stop_vt[0]))]\n \n # Get our dewpoint vector\n (time_start_vt, time_stop_vt, dewpoint_vt) = db_lookup().getSqlVectors(TimeSpan(_start_ts, timespan.stop),'dewpoint')\n dewpoint_vt = self.generator.converter.convert(dewpoint_vt)\n # Can't use ValueHelper so round our results manually\n # Get the number of decimal points\n dewpointRound = int(self.generator.skin_dict['Units']['StringFormats'].get(dewpoint_vt[1], \"1f\")[-2])\n # Do the rounding\n dewpointRound_vt = [roundNone(x,dewpointRound) for x in dewpoint_vt[0]]\n # Get our time vector in ms (w34highcharts requirement)\n # Need to do it for each getSqlVectors result as they might be different\n dewpoint_time_ms = [time_stop_vt[0][0] if (x == 0) else time_stop_vt[0][x] - time_stop_vt[0][0] for x in range(len(time_stop_vt[0]))]\n\n # Get our humidity vector\n (time_start_vt, time_stop_vt, outHumidity_vt) = db_lookup().getSqlVectors(TimeSpan(_start_ts, timespan.stop),'outHumidity')\n # Can't use ValueHelper so round our results manually\n # Get the number of decimal points\n outHumidityRound = int(self.generator.skin_dict['Units']['StringFormats'].get(outHumidity_vt[1], \"1f\")[-2])\n # Do the rounding\n outHumidityRound_vt = [roundNone(x,outHumidityRound) for x in outHumidity_vt[0]]\n # Get our time vector in ms (w34highcharts requirement)\n # Need to do it for each getSqlVectors result as they might be different\n outHumidity_time_ms = [time_stop_vt[0][0] if (x == 0) else time_stop_vt[0][x] - time_stop_vt[0][0] for x in range(len(time_stop_vt[0]))]\n # Format our vectors in json format. Need the zip() to get time/value pairs\n # Assumes all vectors have the same number of elements\n outTemp_json = json.dumps(zip(outTemp_time_ms, outTempRound_vt))\n dewpoint_json = json.dumps(zip(dewpoint_time_ms, dewpointRound_vt))\n outHumidity_json = json.dumps(zip(outHumidity_time_ms, outHumidityRound_vt))\n # Put into a dictionary to return\n self.search_list_extension = {\n 'outTempWeekjson' : outTemp_json,\n 'dewpointWeekjson' : dewpoint_json,\n 'outHumidityWeekjson' : outHumidity_json,\n 'utcOffset': utc_offset,\n 'weekPlotStart' : _start_ts * 1000,\n 'weekPlotEnd' : timespan.stop * 1000}\n t2 = time.time()\n logdbg2(\"w34highcharts_temp_week SLE executed in %0.3f seconds\" % (t2 - t1))\n\n # Return our json data\n return [self.search_list_extension]","def TOBS():\n session = Session(engine)\n # Query all passengers\n\n TOBS = session.query(Measurement.date,Measurement.tobs).filter(Measurement.date >= '2010-08-23').all()\n\n # Convert list of tuples into normal list\n all_TOBS = list(np.ravel(TOBS))\n\n return jsonify(all_TOBS)","def recorded_messages(self):\n messages = []\n for time in sorted(self.reception_records):\n messages.extend(self.reception_records[time])\n return messages","def returnTradeHistory(self,\n currency_pair=\"all\",\n start=datetime.now() - timedelta(days=1),\n end=datetime.now()):\n pass","def returnTradeHistory(self,\n currency_pair=\"all\",\n start=datetime.now() - timedelta(days=1),\n end=datetime.now()):\n pass","def aggregated_results(self, limit=2000) -> List[dict]:\n stored_events = []\n for events in self._iter_events():\n stored_events.extend(events)\n if len(stored_events) >= limit:\n return stored_events[:limit]\n return stored_events","def get_transactions(self):\n\n df = self.__transactions[\n [\n \"Date\",\n \"Type\",\n \"Ticker\",\n \"Side\",\n \"Price\",\n \"Quantity\",\n \"Fees\",\n \"Investment\",\n \"Currency\",\n \"Sector\",\n \"Industry\",\n \"Country\",\n \"Region\",\n ]\n ]\n df = df.replace(np.nan, \"-\")\n df[\"Date\"] = df[\"Date\"].dt.strftime(\"%Y-%m-%d\")\n df.sort_values(by=\"Date\", ascending=False, inplace=True)\n return df","def getDataWithTimeIndex(self, t):\n\n return self.sensorDf.iloc[t,:self.sensorChannels].values","def aggregate_timetable_details(logger):\n df_array = []\n for page in range(0, MAX_PAGE+1): # MAX_PAGE inclusive\n filename = dirname + 'TimeTablePage_{}'.format(page) + '.csv'\n if os.exists(filename):\n # When database is ready, this info can be recorded there\n logger.info('File[{file}] already downloaded. Reading it'.format(file=filename))\n url = filename\n else:\n count = page * 20\n url = TIMETABLE_URL + '&count={count}&page={page}'.format(count=count, page=page)\n logger.info('Fetch TimeTableDetails from URL[{url}] page[{page}] into {directory}'.format(url=url, page=page, directory=dirname))\n \n try:\n df = pd.read_html(url)[1] # the table of interest from the list\n except Exception as e:\n logger.error('Exception when reading HTML:[{exception}]'.format(exception=e))\n exit(0)\n\n logger.info('Writing to [{file}]'.format(file=filename))\n df.to_csv(filename, index=False)\n df_array.append(df)\n\n df = pd.concat(df_array)\n print(df.head())\n print(df.tail())\n\n return 0","def find_all(self) -> List[Trade]:\n\n pass # pragma: no cover","def service_times(self):\r\n service_times = []\r\n for task in self.__tasks.values():\r\n if task.complete():\r\n x = task.service_time()\r\n service_times.append(task.service_time())\r\n return service_times","def get_data(symbol_id='BTC', period_id='1DAY', request_limit=1000, tdelta=30):\n now = datetime.utcnow()\n month = timedelta(days=tdelta)\n past_month = (now - month).isoformat()\n\n parameters = {'symbol_id': symbol_id, 'period_id': period_id, 'time_start': past_month[:-3], 'limit':request_limit}\n response = requests.get(HISTORY_URL, params=parameters, headers=header)\n\n while response.status_code != 200:\n time.sleep(5)\n response = requests.get(HISTORY_URL, params=parameters, headers=header)\n \n data = response.json()\n \n # this is a commnet\n csv_headers = ['time_period_start', 'time_period_end', 'price_high', 'price_low', 'price_close', 'price_open', 'trades_count', \n 'volume_traded', 'time_open', 'time_close']\n\n\n with open(str(datafolder / f'{symbol_id}_{tdelta}_day.csv'), 'w', newline='') as f:\n writer = csv.DictWriter(f, csv_headers)\n writer.writeheader()\n for item in data:\n writer.writerow(item)","def get_timeseries():\n\n # generate the result files\n name = os.path.join(project.output_folder, project.scenario+'.sww')\n log.debug('get_timeseries: input SWW file=%s' % name)\n log.debug('get_timeseries: gauge file=%s' % project.gauge_file)\n anuga.sww2csv_gauges(name, project.gauge_file, quantities=project.layers_list,\n verbose=False)\n\n # since ANUGA code doesn't return a list of generated files,\n # look in output directory for 'gauge_*.csv' files.\n glob_mask = os.path.join(project.output_folder, 'gauge_*.csv')\n return glob.glob(glob_mask)","def to_exclude(self):\n reporting_period = self.message_number - 3\n one_day_ago = now() - datetime.timedelta(hours=24)\n\n return PollingReport.objects.filter(\n period_number=reporting_period,\n creation_date__gte=one_day_ago,\n ).values_list('phone_number', flat=True)","def _parse_transactions_file(self, path_to_transactions_file: str) -> List[Transaction]:\n ticker_params_to_ticker = {\n (ticker.name, ticker.security_type, ticker.point_value): ticker for ticker in self.tickers\n }\n\n def get_matching_ticker(row: QFSeries) -> Ticker:\n \"\"\" Returns the matching specific ticker. In case if the ticker does not belong to the list of tickers\n passed as the parameter, the transaction is excluded. \"\"\"\n ticker_str = row.loc[\"Contract symbol\"]\n name = row.loc[\"Asset Name\"]\n sec_type = SecurityType(row.loc[\"Security type\"])\n point_value = row.loc[\"Contract size\"]\n ticker = ticker_params_to_ticker.get((name, sec_type, point_value), None)\n if isinstance(ticker, FutureTicker):\n ticker_type = ticker.supported_ticker_type()\n ticker = ticker_type(ticker_str, sec_type, point_value)\n return ticker\n\n transactions_df = pd.read_csv(path_to_transactions_file)\n transactions = [Transaction(pd.to_datetime(row.loc[\"Timestamp\"]),\n get_matching_ticker(row),\n row.loc[\"Quantity\"],\n row.loc[\"Price\"],\n row.loc[\"Commission\"]) for _, row in transactions_df.iterrows()]\n transactions = [t for t in transactions if t.ticker is not None]\n return transactions","def temporal_database():\n return TimeHistory()","def get_relevant_reports(self):\r\n split_reports = [x.split(' ') for x in self.get_directory_list()]\r\n formatted_reps = [list(filter(None, line)) for line in split_reports]\r\n recent_reports = [line for line in formatted_reps if self.is_report_recent(line)]\r\n return recent_reports","def fetch_bank_transactions(self):\n return self.fetch('/bank_transactions')","def scrape_validator_withdrawals(\n validator_index: int,\n last_known_timestamp: Timestamp,\n) -> list[tuple[Timestamp, ChecksumEvmAddress, FVal]]:\n withdrawals = []\n now = ts_now()\n start = 0\n page_length = 10\n stop_iterating = False\n\n while True:\n url = f'{BEACONCHAIN_ROOT_URL}/validator/{validator_index}/withdrawals?draw=1&columns%5B0%5D%5Bdata%5D=0&columns%5B0%5D%5Bname%5D=&columns%5B0%5D%5Bsearchable%5D=true&columns%5B0%5D%5Borderable%5D=true&columns%5B0%5D%5Bsearch%5D%5Bvalue%5D=&columns%5B0%5D%5Bsearch%5D%5Bregex%5D=false&columns%5B1%5D%5Bdata%5D=1&columns%5B1%5D%5Bname%5D=&columns%5B1%5D%5Bsearchable%5D=true&columns%5B1%5D%5Borderable%5D=true&columns%5B1%5D%5Bsearch%5D%5Bvalue%5D=&columns%5B1%5D%5Bsearch%5D%5Bregex%5D=false&columns%5B2%5D%5Bdata%5D=2&columns%5B2%5D%5Bname%5D=&columns%5B2%5D%5Bsearchable%5D=true&columns%5B2%5D%5Borderable%5D=true&columns%5B2%5D%5Bsearch%5D%5Bvalue%5D=&columns%5B2%5D%5Bsearch%5D%5Bregex%5D=false&columns%5B3%5D%5Bdata%5D=3&columns%5B3%5D%5Bname%5D=&columns%5B3%5D%5Bsearchable%5D=true&columns%5B3%5D%5Borderable%5D=true&columns%5B3%5D%5Bsearch%5D%5Bvalue%5D=&columns%5B3%5D%5Bsearch%5D%5Bregex%5D=false&columns%5B4%5D%5Bdata%5D=4&columns%5B4%5D%5Bname%5D=&columns%5B4%5D%5Bsearchable%5D=true&columns%5B4%5D%5Borderable%5D=true&columns%5B4%5D%5Bsearch%5D%5Bvalue%5D=&columns%5B4%5D%5Bsearch%5D%5Bregex%5D=false&order%5B0%5D%5Bcolumn%5D=0&order%5B0%5D%5Bdir%5D=desc&start={start}&length={page_length}&search%5Bvalue%5D=&search%5Bregex%5D=false&_={now}' # noqa: E501\n response = _query_page(url, 'withdrawals')\n try:\n result = response.json()\n except json.JSONDecodeError as e:\n raise RemoteError(f'Could not parse {response.text} from beaconchain as json') from e\n\n for entry in result['data']: # data appears in descending time\n epoch_match = EPOCH_PARSE_REGEX.match(entry[0])\n if epoch_match is None:\n log.error(f'Failed to match epoch regex for {entry[0]}')\n raise RemoteError('Failed to parse withdrawals response from beaconchain. Check logs') # noqa: E501\n groups = epoch_match.groups()\n if len(groups) != 1:\n log.error(f'Failed to match epoch regex for {entry[0]}')\n raise RemoteError('Failed to parse withdrawals response from beaconchain. Check logs') # noqa: E501\n epoch = deserialize_int_from_str(groups[0], location='beaconchain epoch')\n timestamp = epoch_to_timestamp(epoch)\n if timestamp <= last_known_timestamp:\n stop_iterating = True\n break # we already know about this withdrawal\n\n address_match = ADDRESS_PARSE_REGEX.match(entry[3])\n if address_match is None:\n log.error(f'Failed to match address regex for {entry[3]}')\n raise RemoteError('Failed to parse withdrawals response from beaconchain. Check logs') # noqa: E501\n groups = address_match.groups()\n if len(groups) != 1:\n log.error(f'Failed to match address regex for {entry[3]}')\n raise RemoteError('Failed to parse withdrawals response from beaconchain. Check logs') # noqa: E501\n address = deserialize_evm_address(groups[0])\n\n eth_match = ETH_PARSE_REGEX.match(entry[4])\n if eth_match is None:\n log.error(f'Failed to match eth regex for {entry[4]}')\n raise RemoteError('Failed to parse withdrawals response from beaconchain. Check logs') # noqa: E501\n groups = eth_match.groups()\n if len(groups) != 1:\n log.error(f'Failed to match eth regex for {entry[4]}')\n raise RemoteError('Failed to parse withdrawals response from beaconchain. Check logs') # noqa: E501\n eth_amount = deserialize_fval(groups[0], name='withdrawal ETH', location='beaconchain query') # noqa: E501\n\n withdrawals.append((timestamp, address, eth_amount))\n\n if stop_iterating or len(withdrawals) >= result['recordsTotal']:\n break # reached the end\n start += page_length\n\n return withdrawals","def get_df_transactions():\n\n _, res = DBX.files_download(c.io.FILE_TRANSACTIONS)\n return pd.read_excel(io.BytesIO(res.content), index_col=0)","def wem_market_value_all():\n __query = \"\"\"\n select\n date_trunc('month', wfs.trading_interval) AS trading_day,\n sum(wfs.eoi_quantity * wbs.price) as energy_interval,\n wf.fueltech_id\n from wem_facility_scada wfs\n left join wem_facility wf on wfs.facility_id = wf.code\n join wem_balancing_summary wbs on wfs.trading_interval = wbs.trading_interval\n where\n wf.fueltech_id is not null\n group by 1, wf.fueltech_id\n order by 1 desc, 2 asc\n \"\"\"\n\n query = __query.format()\n\n json_envelope = {}\n\n with engine.connect() as c:\n rows = c.execute(query)\n\n current_tech = None\n\n for row in rows:\n\n current_tech = row[2]\n\n if current_tech not in json_envelope.keys():\n json_envelope[current_tech] = {\n \"id\": f\"wem.fuel_tech.{current_tech}.market_value\",\n \"fuel_tech\": current_tech,\n \"region\": \"wa\",\n \"type\": \"market_value\",\n \"units\": \"AUD\",\n \"history\": {\n \"interval\": \"1M\",\n \"start\": None,\n \"last\": None,\n \"data\": [],\n },\n }\n\n if (\n json_envelope[current_tech][\"history\"][\"start\"] == None\n or row[0] < json_envelope[current_tech][\"history\"][\"start\"]\n ):\n json_envelope[current_tech][\"history\"][\"start\"] = row[0]\n\n if (\n json_envelope[current_tech][\"history\"][\"last\"] == None\n or row[0] > json_envelope[current_tech][\"history\"][\"last\"]\n ):\n json_envelope[current_tech][\"history\"][\"last\"] = row[0]\n\n json_envelope[current_tech][\"history\"][\"data\"].append(row[1])\n\n return [json_envelope[i] for i in json_envelope.keys()]","def timeseries_report(self):\n try:\n n = self.n.value\n except AttributeError:\n n = self.n\n results = pd.DataFrame(index=self.variables.index)\n results['ICE Generation (kW)'] = self.variables['ice_gen']\n results['ICE On (y/n)'] = self.variables['on_ice']\n results['ICE P_min (kW)'] = self.p_min\n results['ICE Genset P_max (kW)'] = self.rated_power * n\n return results","def timestamps(self) -> List[T]:\n return self._timestamps","def get_pnl_trades(self):\n\n if self._pnl_trades is None:\n tsc = TimeSeriesCalcs()\n self._pnl_trades = tsc.calculate_individual_trade_gains(self._signal, self._pnl)\n\n return self._pnl_trades","def get_all_stats():\n\n return get_component(CachingPackage.COMPONENT_NAME).get_all_stats()","def listunspent(self, minconf=1, maxconf=999999):\n return [TransactionInfo(**tx) for tx in\n self.proxy.listunspent(minconf, maxconf)]","def get_stats(self, epg_dn):\n # Apic saves up to 95 different objects with statistic information\n traffic_list = []\n for i in range(10, -1, -1):\n traffic = self.moDir.lookupByDn(epg_dn + '/HDl2IngrBytesAg15min-%s' % str(i))\n if traffic is not None:\n traffic_list.append(traffic)\n return traffic_list","def main_looper(df: pd.DataFrame()) -> list:\n for_sale_dictionary = {}\n sell_dictionary = {}\n repeat_sale = {}\n unrecorded_sale = []\n for idx, row in df.iterrows():\n if row['function_call']['function'] == 'createSaleAuction':\n # TODO - change for different NFT\n # entry into dict is kitty ID (unique to cryptokitties)\n # checks if already posted to be sold, continues if this is a reentry to sell, recording it\n if row['function_call']['_kittyId'] in for_sale_dictionary:\n if row['function_call']['_kittyId'] in repeat_sale:\n repeat_sale[row['function_call']['_kittyId']] += 1\n continue\n else:\n repeat_sale[row['function_call']['_kittyId']] = 1 \n continue\n for_sale_dictionary.update({str(row['function_call']['_kittyId']) : row['block_timestamp']})\n \n if row['function_call']['function'] == 'transfer':\n # must have posted for sale in an earlier block, cant find interval\n if not row['function_call']['_tokenId'] in for_sale_dictionary:\n unrecorded_sale.append([row['hash'], row['function_call']['_tokenId']])\n # print(f\"token not recoreded for sale: {row['function_call']['_tokenId']}\")\n # print(row['hash'])\n continue\n if for_sale_dictionary[row['function_call']['_tokenId']] == -1:\n print('missed for sale block, somehow??')\n continue\n # TODO find a smarter way to track times transfered!!\n sell_entry = str(row['function_call']['_tokenId']) + '_' + str(row['block_number'])\n print('')\n print('')\n print('')\n print(row['block_timestamp'])\n print(for_sale_dictionary['_tokenId'])\n time_interval = row['block_timestamp'] - for_sale_dictionary['_tokenId']\n for_sale_dictionary[row['function_call']['_tokenId']] = -1\n print(time_interval)\n sell_dictionary.update({sell_entry : row['block_timestamp']})\n print(len(unrecorded_sale))\n if len(unrecorded_sale) > 0:\n rng = min(7, len(unrecorded_sale))\n for ii in range(rng):\n print(unrecorded_sale[ii])\n for jj in range(rng):\n print(unrecorded_sale[len(unrecorded_sale)-jj-1])\n \n\n # print(f'Token IDs posted for sale:')\n # for key in for_sale_dictionary:\n # print(f'{key} : {for_sale_dictionary[key]}')","def _get_current_session_tiling_list(self) -> List:\n return self._data[-1][History._TILINGS]","def get_open_transactions(self):\n return self.__open_transactions[:]","def _get_tweets(self):\n auth = tweepy.OAuthHandler(CONSUMER_KEY, CONSUMER_SECRET)\n auth.set_access_token(ACCESS_TOKEN, ACCESS_SECRET)\n api = tweepy.API(auth)\n search = api.search(self.term, lang='en', count=100)\n\n print(f\"Getting tweets that mention '{self.term}', \"\n f\"this may take a while...\")\n\n save_tweet_text = [tweet._json['text'] for tweet in search]\n while len(save_tweet_text) < 1000:\n try:\n oldest = search[-1].id - 1\n search = api.search(self.term, lang='en', count=100, max_id=oldest)\n new_tweets = [tweet._json['text'] for tweet in search]\n save_tweet_text.extend(new_tweets)\n\n # Turn into a set to remove duplicated tweets, then back to list\n save_tweet_text = list(set(save_tweet_text))\n except IndexError:\n break\n\n print(f\"Done. {len(save_tweet_text)} Tweets received.\")\n return save_tweet_text","def _historical_user_data_for_decisions(user: User, days: int) -> List:\n data = []\n for begin, end in _time_range_list(days):\n db_clicks_statements = DBDiscussionSession.query(ClickedStatement).filter(\n ClickedStatement.author_uid == user.uid,\n ClickedStatement.timestamp >= begin,\n ClickedStatement.timestamp < end).all()\n db_clicks_arguments = DBDiscussionSession.query(ClickedArgument).filter(\n ClickedArgument.author_uid == user.uid,\n ClickedArgument.timestamp >= begin,\n ClickedArgument.timestamp < end).all()\n clicks = len(db_clicks_statements) + len(db_clicks_arguments)\n data.append(clicks)\n\n return data","def get_blocking_times(individuals):\n ambulance_patients_times = []\n other_patients_times = []\n patients_still_in_system = []\n\n for ind in individuals:\n if ind.data_records[0].node == 1 and len(ind.data_records) == 2:\n ambulance_patients_times.append(\n ind.data_records[0].time_blocked + ind.data_records[1].time_blocked\n )\n elif ind.data_records[0].node == 2 and len(ind.data_records) == 1:\n other_patients_times.append(ind.data_records[0].time_blocked)\n else:\n patients_still_in_system.append(ind)\n return [ambulance_patients_times, other_patients_times, patients_still_in_system]","def get_extension_list(self, timespan, db_lookup):\n\n if (self.search_list_extension != None):\n return [self.search_list_extension]\n \n t1 = time.time()\n\n # Get UTC offset\n stop_struct = time.localtime(timespan.stop)\n utc_offset = (calendar.timegm(stop_struct) - calendar.timegm(time.gmtime(time.mktime(stop_struct))))/60\n\n # Get our start time, numDays days ago but aligned with start of day\n # first get the start of today\n _ts = startOfDay(timespan.stop)\n # then go back numDays days\n _ts_dt = datetime.datetime.fromtimestamp(_ts)\n _start_dt = _ts_dt - datetime.timedelta(days=self.numDays)\n _start_ts = time.mktime(_start_dt.timetuple())\n\n if (self.numDays == 3):\n _start_ts = time.mktime((_ts_dt - datetime.timedelta(days=0)).timetuple())\n _end_ts = time.mktime((_ts_dt + datetime.timedelta(days=3)).timetuple())\n timespan = TimeSpan(_start_ts, _end_ts)\n \n # Get our barometer vector\n (time_start_vt, time_stop_vt, barometer_vt) = db_lookup().getSqlVectors(TimeSpan(_start_ts, timespan.stop),\n 'barometer')\n barometer_vt = self.generator.converter.convert(barometer_vt)\n # Can't use ValueHelper so round our results manually\n # Get the number of decimal points\n barometerRound = int(self.generator.skin_dict['Units']['StringFormats'].get(barometer_vt[1], \"1f\")[-2])\n # Do the rounding\n barometerRound_vt = [roundNone(x,barometerRound) for x in barometer_vt[0]]\n # Get our time vector in ms (w34highcharts requirement)\n # Need to do it for each getSqlVectors result as they might be different\n barometer_time_ms = [time_stop_vt[0][0] if (x == 0) else time_stop_vt[0][x] - time_stop_vt[0][0] for x in range(len(time_stop_vt[0]))]\n\n # Get our rain vector, need to sum over the hour\n (time_start_vt, time_stop_vt, rain_vt) = db_lookup().getSqlVectors(TimeSpan(_start_ts, timespan.stop),'rain')\n (time_start_vt, time_stop_vt, rainRate_vt) = db_lookup().getSqlVectors(TimeSpan(_start_ts, timespan.stop),'rainRate')\n # Check if we have a partial hour at the end\n # If we do then set the last time in the time vector to the hour\n # Avoids display issues with the column chart\n # Need to make sure we have at least 2 records though\n #if len(time_stop_vt[0]) > 1:\n # if time_stop_vt[0][-1] < time_stop_vt[0][-2] + 3600:\n # time_stop_vt[0][-1] = time_stop_vt[0][-2] + 3600\n # Convert our rain vector\n rain_vt = self.generator.converter.convert(rain_vt)\n rainRate_vt = self.generator.converter.convert(rainRate_vt)\n # Can't use ValueHelper so round our results manually\n # Get the number of decimal points\n rainRound = int(self.generator.skin_dict['Units']['StringFormats'].get(rain_vt[1], \"1f\")[-2])\n rainRateRound = int(self.generator.skin_dict['Units']['StringFormats'].get(rainRate_vt[1], \"1f\")[-2])\n # Do the rounding\n rainRound_vt = [roundNone(x,rainRound) for x in rain_vt[0]]\n rainRateRound_vt = [roundNone(x,rainRateRound) for x in rainRate_vt[0]]\n # Get our time vector in ms (w34highcharts requirement)\n # Need to do it for each getSqlVectors result as they might be different\n timeRain_ms = [time_stop_vt[0][0] if (x == 0) else time_stop_vt[0][x] - time_stop_vt[0][0] for x in range(len(time_stop_vt[0]))]\n\n # Format our vectors in json format. Need the zip() to get time/value pairs\n # Assumes all vectors have the same number of elements\n barometer_json = json.dumps(zip(barometer_time_ms, barometerRound_vt))\n rain_json = json.dumps(zip(timeRain_ms, rainRound_vt))\n rainRate_json = json.dumps(zip(timeRain_ms, rainRateRound_vt))\n\n # Put into a dictionary to return\n self.search_list_extension = {\n 'barometerWeekjson' : barometer_json,\n 'rainWeekjson' : rain_json,\n 'rainRateWeekjson' : rainRate_json,\n 'utcOffset': utc_offset,\n 'weekPlotStart' : _start_ts * 1000,\n 'weekPlotEnd' : timespan.stop * 1000}\n\n t2 = time.time()\n logdbg2(\"w34highcharts_bar_rain_week SLE executed in %0.3f seconds\" % (t2 - t1))\n\n # Return our json data\n return [self.search_list_extension]","def report_data(self):\n report = [donor_obj.data for donor_obj in self.donor_list]\n return report","def test_get_events_history_filtering_by_timestamp(rotkehlchen_api_server: 'APIServer'):\n tx_hex = deserialize_evm_tx_hash('0xb226ddb8cbb286a7a998a35263ad258110eed5f923488f03a8d890572cd4608e') # noqa: E501\n ethereum_inquirer = rotkehlchen_api_server.rest_api.rotkehlchen.chains_aggregator.ethereum.node_inquirer # noqa: E501\n database = rotkehlchen_api_server.rest_api.rotkehlchen.data.db\n get_decoded_events_of_transaction(\n evm_inquirer=ethereum_inquirer,\n database=database,\n tx_hash=tx_hex,\n )\n # Call time range\n from_timestamp = 1627401169\n to_timestamp = 1627401170\n async_query = random.choice([False, True])\n with patch(\n 'rotkehlchen.chain.ethereum.modules.sushiswap.sushiswap.Sushiswap.get_balances',\n side_effect=lambda _: {},\n ):\n response = requests.get(\n api_url_for(\n rotkehlchen_api_server,\n 'modulestatsresource',\n module='sushiswap',\n ),\n json={\n 'async_query': async_query,\n 'from_timestamp': from_timestamp,\n 'to_timestamp': to_timestamp,\n },\n )\n if async_query:\n task_id = assert_ok_async_response(response)\n outcome = wait_for_async_task(rotkehlchen_api_server, task_id, timeout=120)\n assert outcome['message'] == ''\n result = outcome['result']\n else:\n result = assert_proper_response_with_result(response)\n\n events_balances = result[TEST_EVENTS_ADDRESS_1]\n\n assert len(events_balances) == 1"],"string":"[\n \"def transaction_trace_data(self, connections):\\n\\n _logger.debug('Generating transaction trace data.')\\n\\n if not self.__settings:\\n return []\\n\\n # Create a set 'traces' that is a union of slow transaction,\\n # and Synthetics transactions. This ensures we don't send\\n # duplicates of a transaction.\\n\\n traces = set()\\n if self.__slow_transaction:\\n traces.add(self.__slow_transaction)\\n traces.update(self.__synthetics_transactions)\\n\\n # Return an empty list if no transactions were captured.\\n\\n if not traces:\\n return []\\n\\n # We want to limit the number of explain plans we do across\\n # these. So work out what were the slowest and tag them.\\n # Later the explain plan will only be run on those which are\\n # tagged.\\n\\n agent_limits = self.__settings.agent_limits\\n explain_plan_limit = agent_limits.sql_explain_plans_per_harvest\\n maximum_nodes = agent_limits.transaction_traces_nodes\\n\\n database_nodes = []\\n\\n if explain_plan_limit != 0:\\n for trace in traces:\\n for node in trace.slow_sql:\\n # Make sure we clear any flag for explain plans on\\n # the nodes in case a transaction trace was merged\\n # in from previous harvest period.\\n\\n node.generate_explain_plan = False\\n\\n # Node should be excluded if not for an operation\\n # that we can't do an explain plan on. Also should\\n # not be one which would not be included in the\\n # transaction trace because limit was reached.\\n\\n if (node.node_count < maximum_nodes and\\n node.connect_params and node.statement.operation in\\n node.statement.database.explain_stmts):\\n database_nodes.append(node)\\n\\n database_nodes = sorted(database_nodes,\\n key=lambda x: x.duration)[-explain_plan_limit:]\\n\\n for node in database_nodes:\\n node.generate_explain_plan = True\\n\\n else:\\n for trace in traces:\\n for node in trace.slow_sql:\\n node.generate_explain_plan = True\\n database_nodes.append(node)\\n\\n # Now generate the transaction traces. We need to cap the\\n # number of nodes capture to the specified limit.\\n\\n trace_data = []\\n\\n for trace in traces:\\n transaction_trace = trace.transaction_trace(\\n self, maximum_nodes, connections)\\n\\n data = [transaction_trace,\\n list(trace.string_table.values())]\\n\\n if self.__settings.debug.log_transaction_trace_payload:\\n _logger.debug('Encoding slow transaction data where '\\n 'payload=%r.', data)\\n\\n json_data = json_encode(data)\\n\\n level = self.__settings.agent_limits.data_compression_level\\n level = level or zlib.Z_DEFAULT_COMPRESSION\\n\\n zlib_data = zlib.compress(six.b(json_data), level)\\n\\n pack_data = base64.standard_b64encode(zlib_data)\\n\\n if six.PY3:\\n pack_data = pack_data.decode('Latin-1')\\n\\n root = transaction_trace.root\\n\\n if trace.record_tt:\\n force_persist = True\\n else:\\n force_persist = False\\n\\n if trace.include_transaction_trace_request_uri:\\n request_uri = trace.request_uri\\n else:\\n request_uri = None\\n\\n trace_data.append([transaction_trace.start_time,\\n root.end_time - root.start_time,\\n trace.path,\\n request_uri,\\n pack_data,\\n trace.guid,\\n None,\\n force_persist,\\n None,\\n trace.synthetics_resource_id, ])\\n\\n return trace_data\",\n \"def get_reports(self):\\r\\n return sorted(self._reports,\\r\\n key=lambda x: x['stats']['totalTimeMillis'],\\r\\n reverse=True)\",\n \"def get_reports(self):\\n return sorted(self._reports,\\n key=lambda x: x['stats']['totalTimeMillis'],\\n reverse=True)\",\n \"def return_trade_history(\\n self,\\n start: Timestamp,\\n end: Timestamp,\\n ) -> list[dict[str, Any]]:\\n limit = 100\\n data: list[dict[str, Any]] = []\\n start_ms = start * 1000\\n end_ms = end * 1000\\n while True:\\n new_data = self.api_query_list('/trades', {\\n 'startTime': start_ms,\\n 'endTime': end_ms,\\n 'limit': limit,\\n })\\n results_length = len(new_data)\\n if data == [] and results_length < limit:\\n return new_data # simple case - only one query needed\\n\\n latest_ts_ms = start_ms\\n # add results to data and prepare for next query\\n existing_ids = {x['id'] for x in data}\\n for trade in new_data:\\n try:\\n timestamp_ms = trade['createTime']\\n latest_ts_ms = max(latest_ts_ms, timestamp_ms)\\n # since we query again from last ts seen make sure no duplicates make it in\\n if trade['id'] not in existing_ids:\\n data.append(trade)\\n except (DeserializationError, KeyError) as e:\\n msg = str(e)\\n if isinstance(e, KeyError):\\n msg = f'Missing key entry for {msg}.'\\n self.msg_aggregator.add_warning(\\n 'Error deserializing a poloniex trade. Check the logs for details',\\n )\\n log.error(\\n 'Error deserializing poloniex trade',\\n trade=trade,\\n error=msg,\\n )\\n continue\\n\\n if results_length < limit:\\n break # last query has less than limit. We are done.\\n\\n # otherwise we query again from the last ts seen in the last result\\n start_ms = latest_ts_ms\\n continue\\n\\n return data\",\n \"def get_latest_transactions(self):\\n first_run = False\\n if not self._transactions:\\n first_run = True\\n transactions = []\\n for account in self.accounts:\\n self._logger.debug('Getting transactions for account \\\"%s\\\"', account.ynab_account.name)\\n for transaction in account.get_latest_transactions():\\n if not self._filter_transaction(transaction):\\n transactions.append(transaction)\\n self._logger.debug('Caching %s transactions', len(transactions))\\n self._transactions.extend(transactions)\\n if first_run:\\n self._logger.info('First run detected, discarding transactions until now')\\n return []\\n return transactions\",\n \"def slow_queries(self):\\n request = Request(method=\\\"get\\\", endpoint=\\\"/query/slow\\\")\\n\\n def response_handler(resp):\\n if not resp.is_success:\\n raise C8QLQueryListError(resp, request)\\n return self._format_queries(resp.body)\\n\\n return self._execute(request, response_handler)\",\n \"def timings(self):\\r\\n return self._timings\",\n \"def get_pending_transactions():\\n\\n return History.get_pending().get()\",\n \"def returnTradeHistory(self, time=1 * 60 * 60, limit=100):\\n assert limit <= 100, \\\"'limit' has to be smaller than 100\\\"\\n return self.dpay.rpc.get_trade_history(\\n transactions.formatTimeFromNow(-time),\\n transactions.formatTimeFromNow(),\\n limit,\\n api=\\\"market_history\\\"\\n )\",\n \"def unbalanced(self):\\n # TODO: Find a way to make a sql query to return all unbalanced transactions\\n return []\",\n \"def trades(self) -> List[ClosedTrade]:\\n return store.completed_trades.trades\",\n \"def get_all_latest_transactions(self):\\n transactions = []\\n for account in self.accounts:\\n self._logger.debug('Getting transactions for account \\\"%s\\\"', account.ynab_account.name)\\n for transaction in account.get_latest_transactions():\\n if not self._filter_transaction(transaction):\\n transactions.append(transaction)\\n return transactions\",\n \"def showTransactions(self):\\n self.scanTransactions()\\n txns = []\\n\\n # Summarize the stats\\n for x in range(len(self._trans)):\\n stats = self._trans[x]\\n trans_time = 0\\n remote_calls = 0\\n for name, stat in stats:\\n trans_time += stat.total_tt\\n remote_calls += 1\\n txns.append((x, trans_time, remote_calls))\\n\\n results = [\\\"TX#\\\\tTime\\\\tCalls\\\",\\n \\\"=\\\" * 22]\\n\\n for item in txns:\\n results.append(\\\"%3d\\\\t%4f\\\\t%5d\\\" % item)\\n \\n return \\\"\\\\n\\\".join(results)\",\n \"def getTransferListSummary(self):\\n p_ids_and_prices = {}\\n players = self.getAllPlayerInfoTransferlist()\\n\\n # Get IDs of all players\\n log_event(self.queue, \\\"Gathering player prices... \\\")\\n for p in players:\\n p_bidstatus = p[1]\\n p_id = p[8]\\n # removed Filter for unlisted / expired players\\n if p_id not in p_ids_and_prices:\\n p_sellprice = self.getPlayerSellPrice(p_id)\\n # If sell price returns 0, need to fetch from Futbin\\n if p_sellprice == 0:\\n p_sellprice = self.getFutbinPrice_opentab(p_id)\\n self.sleep_approx(5) # Delay iteration to not anger futbin\\n # Add player ID and price to dict\\n p_ids_and_prices[p_id] = p_sellprice\\n\\n for p_id in p_ids_and_prices:\\n p_price = p_ids_and_prices[p_id]\\n p_name = self.getPlayerCardName(p_id)\\n log_event(self.queue, str(p_name) + \\\" - #\\\" +\\n str(p_id) + \\\" Price \\\" + str(p_price))\\n\\n num_p_sold = 0\\n num_p_expired = 0\\n num_p_unlisted = 0\\n num_p_listed = 0\\n\\n sold_p_value = 0\\n expired_p_value = 0\\n unlisted_p_value = 0\\n listed_p_value = 0\\n\\n for p in players:\\n p_bidstatus = p[1]\\n p_id = p[8]\\n p_soldprice = p[5] # is 0 if unlisted\\n p_sellprice = int(p_ids_and_prices[p_id])\\n\\n if \\\"won\\\" in p_bidstatus:\\n num_p_sold += 1\\n sold_p_value += p_soldprice\\n if \\\"expired\\\" in p_bidstatus:\\n num_p_expired += 1\\n expired_p_value += p_sellprice\\n if (p_bidstatus == \\\"listFUTItem\\\"):\\n num_p_unlisted += 1\\n unlisted_p_value += p_sellprice\\n if (p_bidstatus == \\\"listFUTItem has-auction-data\\\"):\\n num_p_listed += 1\\n listed_p_value += p_sellprice\\n\\n log_event(self.queue, \\\"Players sold: \\\" + str(num_p_sold))\\n log_event(self.queue, \\\"Players expired: \\\" + str(num_p_expired))\\n log_event(self.queue, \\\"Players listed: \\\" + str(num_p_listed))\\n log_event(self.queue, \\\"Players unlisted: \\\" + str(num_p_unlisted))\\n log_event(self.queue, \\\" - - - \\\")\\n log_event(self.queue, \\\"Sold players value: \\\" + str(sold_p_value))\\n log_event(self.queue, \\\"Expired players value: \\\" +\\n str(expired_p_value))\\n log_event(self.queue, \\\"Unlisted players value: \\\" +\\n str(unlisted_p_value))\\n log_event(self.queue, \\\"Listed players value: \\\" + str(listed_p_value))\\n\\n # TODO subtract bought price\\n self.user_players_won += int(num_p_unlisted)\\n self.p_ids_and_prices = p_ids_and_prices\\n intel = [p_ids_and_prices, num_p_sold, num_p_expired, num_p_unlisted,\\n num_p_listed, sold_p_value, expired_p_value, unlisted_p_value, listed_p_value]\\n return intel\",\n \"def time_list(self):\\n return (self.N_T * (np.arange(self.N_itr) + 1) /\\n self.N_itr * 1000 * self.DT)\",\n \"def _get_meas_times_from_db(self):\\n meas_times = []\\n if self._data['report_save_historical_instances_ind'] != 'Y':\\n # for non historical reports take measurement time from saved dataset\\n dataset = self._jfile.get_current_stored_dataset()\\n try:\\n meas_time = datetime.datetime.strptime(dataset['meas_time'], '%Y-%m-%d %H:%M:%S')\\n except ValueError:\\n raise Exception(\\\"Cannot unformat string %s to datetime\\\" % dataset['meas_time'])\\n meas_times.append(meas_time)\\n\\n else:\\n # for historical reports take measurement times from db datasets\\n where_sql = ''\\n where_sql_list = list()\\n params = [self._id, self._segment_value_id]\\n\\n if self._process_dataset_ids:\\n for dataset_id in self._process_dataset_ids:\\n if type(dataset_id) == list:\\n where_sql_list.append(\\\"(report_data_set_instance_id >= %s AND report_data_set_instance_id <= %s)\\\")\\n if dataset_id[0] < dataset_id[1]:\\n params.append(dataset_id[0])\\n params.append(dataset_id[1])\\n else:\\n params.append(dataset_id[1])\\n params.append(dataset_id[0])\\n else:\\n where_sql_list.append(\\\"report_data_set_instance_id = %s\\\")\\n params.append(dataset_id)\\n where_sql = ' AND (%s)' % ' OR '.join(where_sql_list)\\n\\n self._db.Query(\\\"\\\"\\\"SELECT measurement_time\\n FROM report_data_set_instance\\n WHERE\\n `element_id`= %%s\\n AND segment_value_id = %%s\\n %s\\n ORDER BY measurement_time ASC\\\"\\\"\\\" % where_sql, tuple(params))\\n meas_times = [item['measurement_time'] for item in self._db.record]\\n\\n return meas_times\",\n \"def fetch(self, daterange=(datetime.now() - timedelta(1), datetime.now())):\\n cursor = self.conn.cursor()\\n sql = 'SELECT measure_dt, ping, download, upload FROM speedlogs ' + \\\\\\n ' WHERE measure_dt BETWEEN ? AND ?'\\n cursor.execute(sql, daterange)\\n return cursor.fetchall()\",\n \"async def get_trades(self) -> List[TradeRequest]:\\n data = j.dumps({\\n 'startindex': 0,\\n 'statustype': 'inbound'\\n })\\n r = await self.request.request(url='https://www.roblox.com/my/money.aspx/getmyitemtrades', data=data, method='POST')\\n data = json.loads(r.json()['d'])[\\\"Data\\\"]\\n trades = []\\n for trade in data:\\n t = json.loads(trade)\\n trades.append(TradeRequest(self.request, t['Date'], t['Expires'], t['TradePartner'], t['TradePartnerID'], t['Status'], t['TradeSessionID']))\\n return trades\",\n \"def get_trades_for_symbol(self, symbol, time_range=5):\\n return [trade for trade in self\\n if trade.symbol == symbol and (trade.timestamp > (int(time.time())-time_range*60))]\",\n \"def get_new_data(self):\\n\\n # record bar parse performance\\n self.logger.debug(\\\"Started parsing new ticks.\\\")\\n start_parse = time.time()\\n for exchange in self.exchanges:\\n exchange.parse_ticks()\\n end_parse = time.time()\\n duration = round(end_parse - start_parse, 5)\\n\\n self.logger.debug(\\n \\\"Parsed \\\" + str(self.total_instruments) +\\n \\\" instruments' ticks in \\\" + str(duration) + \\\" seconds.\\\")\\n self.track_tick_processing_performance(duration)\\n\\n # wrap new 1 min bars in market events\\n new_market_events = []\\n for exchange in self.exchanges:\\n bars = exchange.get_new_bars()\\n for symbol in exchange.get_symbols():\\n for bar in bars[symbol]:\\n event = MarketEvent(exchange.get_name(), bar)\\n new_market_events.append(event)\\n # add bars to save-to-db-later queue\\n # TODO: store new bars concurrently with a processpool\\n self.bars_save_to_db.put(event)\\n return new_market_events\",\n \"def get_gdax_historical_data():\\n \\n start = None\\n while not start:\\n start,end,tid = getStartAndEndHistoric()\\n if not start:\\n time.sleep(60)\\n #Todo: change this to 1min\\n firsttimestamp = start\\n engine = sa.create_engine(sql_address)\\n products = [\\\"LTC-USD\\\",\\\"LTC-BTC\\\",\\\"ETH-USD\\\",\\\"ETH-BTC\\\",\\\"BTC-USD\\\"]\\n public_client = gdax.PublicClient()\\n deltat = datetime.timedelta(seconds = 200)\\n timewindows = []\\n while end - start > datetime.timedelta(seconds=0):\\n if start + deltat > end:\\n endx = end\\n else:\\n endx = start + deltat\\n timewindows.append([start,endx])\\n start += deltat\\n results = []\\n total = len(timewindows)\\n current_idx = 0\\n timeold = time.time()\\n numofqueries = 0\\n engine = sa.create_engine(sql_address)\\n Base.metadata.bind = engine\\n DBSession = sa.orm.sessionmaker()\\n DBSession.bind = engine\\n session = DBSession()\\n for startx,endx in timewindows:\\n\\n current_idx += 1\\n for i in products:\\n repeat = True\\n while repeat:\\n\\n #delay if ratelimts are close\\n if numofqueries < 3:\\n while time.time() - timeold < 1:\\n time.sleep(0.05)\\n \\n timeold = time.time()\\n numofqueries = 0\\n try:\\n alist = public_client.get_product_historic_rates(i, start = startx, end = endx, granularity = 1)\\n except:\\n time.sleep(30)\\n public_client = gdax.PublicClient()\\n alist = public_client.get_product_historic_rates(i, start = startx, end = endx, granularity = 1)\\n\\n alist = public_client.get_product_historic_rates(i, start = startx, end = endx, granularity = 1)\\n\\n numofqueries += 1\\n\\n #rate limit exceeded has 'message' as dict.\\n if not 'message' in alist:\\n repeat = False\\n for a in alist:\\n a[0] = datetime.datetime.fromtimestamp(float(a[0]))\\n tmp = i.split('-')\\n d = dict(coin = tmp[0], currency = tmp[1], timestamp = a[0], low=a[1], high=a[2], open=a[3], close=a[4], volume=a[5])\\n results.append(d)\\n lasttimestamp = a[0]\\n\\n #upload with batch size of 10000\\n if len(results) > 10000:\\n engine.execute(\\n GADXHistoricalDataOneSecondOHLC.__table__.insert(),\\n results\\n )\\n results = []\\n \\n update = session.query(historicalDataProgramState).filter(sa.and_(historicalDataProgramState.transaction_id == tid,historicalDataProgramState.entry_type == 'update')).first()\\n if update:\\n update.end = lasttimestamp\\n session.commit()\\n else:\\n new_update = historicalDataProgramState(entry_type = 'update',transaction_id = tid,start=firsttimestamp,end=lasttimestamp,platform='GDAX',status='incomplete')\\n session.add(new_update)\\n session.commit()\\n if len(results) > 0:\\n engine.execute(\\n GADXHistoricalDataOneSecondOHLC.__table__.insert(),\\n results\\n )\\n results = []\\n \\n update = session.query(historicalDataProgramState).filter(sa.and_(historicalDataProgramState.transaction_id == tid,historicalDataProgramState.entry_type == 'update')).first()\\n if update:\\n update.end = lasttimestamp\\n session.commit()\\n else:\\n new_update = historicalDataProgramState(entry_type = 'update',transaction_id = tid,start=firsttimestamp,end=lasttimestamp,platform='GDAX',status='incomplete')\\n session.add(new_update)\\n session.commit()\\n\\n update = session.query(historicalDataProgramState).filter(sa.and_(historicalDataProgramState.transaction_id == tid,historicalDataProgramState.entry_type == 'update')).first()\\n update.status='complete'\\n order = session.query(historicalDataProgramState).filter(sa.and_(historicalDataProgramState.transaction_id == tid,historicalDataProgramState.entry_type == 'order')).first()\\n order.status='complete'\\n session.commit()\",\n \"def pending_transactions(self):\\n return self._call_account_method(\\n 'pendingTransactions'\\n )\",\n \"def transactions(self):\\n url = f'{self._ynab.api_url}/budgets/{self.id}/transactions'\\n response = self._ynab.session.get(url)\\n if not response.ok:\\n self._logger.error('Error retrieving transactions, response was : %s with status code : %s',\\n response.text,\\n response.status_code)\\n return []\\n return [Transaction(self._ynab, transaction)\\n for transaction in response.json().get('data', {}).get('transactions', [])]\",\n \"def get_trades_history(self, symbol, start_time, end_time, limit=1000):\\n payload = {'symbol': symbol, 'start': start_time, 'end': end_time, 'limit': limit}\\n return self.public_request('GET', '/api/v1/trades', **payload)\",\n \"def get_transactions(self):\\n # open a cursor object\\n cur = self.get_cursor()\\n\\n # get transactions from database\\n cur.execute(\\\"SELECT * FROM transactions\\\")\\n transactions_data = cur.fetchall()\\n\\n # convert into a dict of values.\\n transactions_list = []\\n [transactions_list.append({'transaction_id': transaction[0],\\n 'date': transaction[1],\\n 'payee_id': transaction[2],\\n 'description': transaction[3],\\n 'amount': transaction[4]})\\n for transaction in transactions_data]\\n\\n # close the cursor\\n self.close_cursor()\\n\\n return transactions_list\",\n \"def describe_slow_log_records(\\n self,\\n request: dds_20151201_models.DescribeSlowLogRecordsRequest,\\n ) -> dds_20151201_models.DescribeSlowLogRecordsResponse:\\n runtime = util_models.RuntimeOptions()\\n return self.describe_slow_log_records_with_options(request, runtime)\",\n \"async def get_blacklist_hist(self, search_time, limit=1000):\\n\\n start = search_time[0][0]\\n end = search_time[0][1]\\n\\n url = f'https://{self.__api}/v3/blacklist/history'\\n continuation = None\\n full_resp = {}\\n flag = True\\n body = {\\\"filter[clientid]\\\": self.clientid, \\\"filter[start_time]\\\": start, \\\"filter[end_time]\\\": end,\\n \\\"limit\\\": limit, \\\"continuation\\\": continuation}\\n while True:\\n with requests.get(url, params=body,\\n headers={'X-WallarmAPI-UUID': self.__uuid,\\n 'X-WallarmAPI-Secret': self.__secret}) as response:\\n if response.status not in [200, 201, 202, 204, 304]:\\n raise NonSuccessResponse(response.status, await response.text)\\n continuation = response.json().get('body').get('continuation')\\n\\n if flag:\\n full_resp = response.json()\\n\\n if continuation is not None:\\n body['continuation'] = continuation\\n if not flag:\\n full_resp['body']['objects'].extend(response.json().get('body').get('objects'))\\n else:\\n break\\n flag = False\\n return full_resp\",\n \"def _get_meas_times(self, last_meas_time):\\n meas_times = list()\\n data = None\\n \\n if self._process_type == 'soft_gen':\\n meas_times = self._get_meas_times_from_db()\\n else:\\n if self._data['data_fetch_method'] == 'sql':\\n # get from outer sql db\\n data = self._get_meas_times_sql(last_meas_time)\\n elif self._data['data_fetch_method'] == 'web service':\\n # get from web service\\n data = self._get_meas_times_web_service(last_meas_time)\\n\\n\\n if data:\\n clear_data = [row[0] for row in data['data']]\\n # check if we have values in list of datetime type\\n if clear_data:\\n if type(clear_data[0]) == datetime.datetime:\\n meas_times = clear_data\\n else:\\n # it's a date type\\n meas_times = [datetime.datetime.combine(d, datetime.time.min) for d in clear_data]\\n\\n \\n\\n\\n # sort measurement times if they weren't sorted before\\n meas_times.sort()\\n # if do not save history, take only last element\\n if self._data['report_save_historical_instances_ind'] != 'Y':\\n if len(meas_times) > 1:\\n del meas_times[:-1]\\n \\n return meas_times\",\n \"def transactions(self, dt=None):\\n if dt is None:\\n # flatten the by-day transactions\\n return [\\n txn\\n for by_day in itervalues(self._processed_transactions)\\n for txn in by_day\\n ]\\n\\n return self._processed_transactions.get(dt, [])\",\n \"def fetch_all_tx(self):\\n transactions = []\\n for block in self.chain:\\n transactions.append(block.data)\\n return transactions\",\n \"def gather_data(self, *args, **kwargs):\\n instrument_arg = kwargs.get('instrument', 'EUR_USD')\\n granularity_arg = kwargs.get('granularity', 'M1')\\n candle_format = kwargs.get('candleFormat', 'bidask')\\n start_time = kwargs.get('start', '2014-10-01T00:00:00.000000Z')\\n count_arg = kwargs.get('count', 5000)\\n out_data = []\\n data_complete = False\\n while(not data_complete):\\n response = self.oanda.get_history(instrument=instrument_arg,\\n granularity=granularity_arg,\\n candleFormat=candle_format,\\n start=start_time,\\n count=count_arg)\\n raw_data = response['candles']\\n if (len(out_data) == 0):\\n out_data = out_data + raw_data\\n elif (len(out_data) > 1):\\n # raw_data[0] is already in out_data as raw_data[-1] from last\\n # iteration\\n out_data = out_data + raw_data[1:]\\n start_time = raw_data[-1]['time']\\n if (len(raw_data) < 5000):\\n data_complete = True\\n\\n out_data = self._list_to_df(out_data)\\n return out_data\",\n \"def timestamps(self):\\n return self.source.timestamps[self._time_keep]\",\n \"def timings(self):\\n if self._C_timings is None:\\n raise RuntimeError(\\\"Cannot extract timings with non-finalized Profiler.\\\")\\n return {field: max(getattr(self._C_timings, field), 10**-6)\\n for field, _ in self._C_timings._fields_}\",\n \"def query_tickets(self):\\n return self._call_txtrader_api('query_tickets', {})\",\n \"def probe_times(self):\\r\\n probe_times = []\\r\\n for probe in self.__probes.values():\\r\\n if probe.complete():\\r\\n if probe.round_trip_time() > 20:\\r\\n \\\"Long probe: %s \\\" %self.__id\\r\\n probe_times.append(probe.round_trip_time())\\r\\n return probe_times\",\n \"def get_non_indexed_results(self):\\n\\n with sqlite3.connect(self.db_path) as conn:\\n cursor = conn.cursor()\\n\\n cursor.execute(\\\"SELECT status_code, file_count, start_time, end_time, website_id\\\"\\n \\\" FROM TaskResult WHERE indexed_time IS NULL\\\")\\n db_result = cursor.fetchall()\\n\\n cursor.execute(\\\"UPDATE TaskResult SET indexed_time=CURRENT_TIMESTAMP WHERE indexed_time IS NULL\\\")\\n conn.commit()\\n\\n return [TaskResult(r[0], r[1], r[2], r[3], r[4]) for r in db_result]\",\n \"def timeseries_report(self):\\n report = pd.DataFrame(index=self.price.index)\\n report.loc[:, \\\"FR Energy Throughput (kWh)\\\"] = self.ene_results['ene']\\n report.loc[:, \\\"FR Energy Throughput Up (Charging) (kWh)\\\"] = self.variables['regu_c']*self.krd_avg*self.dt*self.storage.rte\\n report.loc[:, \\\"FR Energy Throughput Up (Discharging) (kWh)\\\"] = self.variables['regu_d']*self.krd_avg*self.dt\\n report.loc[:, \\\"FR Energy Throughput Down (Charging) (kWh)\\\"] = self.variables['regd_c']*self.krd_avg*self.dt*self.storage.rte\\n report.loc[:, \\\"FR Energy Throughput Down (Discharging) (kWh)\\\"] = self.variables['regd_d']*self.krd_avg*self.dt\\n report.loc[:, \\\"FR Energy Settlement Price Signal ($/kWh)\\\"] = self.price\\n report.loc[:, 'Regulation Up (Charging) (kW)'] = self.variables['regu_c']\\n report.loc[:, 'Regulation Up (Discharging) (kW)'] = self.variables['regu_d']\\n report.loc[:, 'Regulation Down (Charging) (kW)'] = self.variables['regd_c']\\n report.loc[:, 'Regulation Down (Discharging) (kW)'] = self.variables['regd_d']\\n report.loc[:, \\\"Regulation Up Price Signal ($/kW)\\\"] = self.p_regu\\n report.loc[:, \\\"Regulation Down Price Signal ($/kW)\\\"] = self.p_regd\\n\\n return report\",\n \"def read_daily_messages_report(self):\\n from itertools import repeat\\n\\n self.ID_TWEET_ORANGE_FLOW = kpi_from_db_config.ID_TWEET_ORANGE_FLOW\\n self.ID_PROCESSING_MESSAGES = kpi_from_db_config.ID_PROCESSING_MESSAGES\\n self.ID_CANDIDATES_PROCESSED = kpi_from_db_config.ID_CANDIDATES_PROCESSED\\n\\n list_id = [self.ID_TWEET_ORANGE_FLOW, \\n self.ID_PROCESSING_MESSAGES, \\n self.ID_CANDIDATES_PROCESSED]\\n len_need_list = [7, 8, 2]\\n list_result = [[] for i in repeat(None,len(list_id))]\\n\\n for i in range(len(list_id)):\\n self.cursor.execute('''\\n SELECT value\\n FROM public.kpi_report\\n WHERE id = %s\\n ORDER BY created_at DESC\\n LIMIT %s\\n ''', [list_id[i], len_need_list[i]])\\n rows_count = self.cursor.rowcount\\n\\n if (rows_count == len_need_list[i]): # If rows_count as expected \\n for doc in self.cursor:\\n list_result[i].append(int(doc[0]))\\n elif (rows_count > 0 and rows_count < len_need_list[i]):\\n for doc in self.cursor:\\n list_result[i].append(int(doc[0]))\\n list_result[i] = list_result[i] + [0] * (len_need_list[i] - rows_count) \\n else:\\n list_result[i] = [0] * len_need_list[i]\\n\\n return list_result\",\n \"def getScanList(self):\\n \\n scanList = []\\n for row in self._calData:\\n if str(row.calType()) == 'CAL_DELAY':\\n scanList.append(row.scanSet()[0])\\n return scanList\",\n \"def get_tiingo_prices(tickers, start, end, api_key=None):\\n\\n all_results = []\\n if api_key is None:\\n api_key = os.getenv('TIINGO_API_KEY')\\n # Sort tickers so that error logging can be used to identify progress\\n tickers = sorted(tickers)\\n\\n for i, ticker in enumerate(tickers):\\n try:\\n df = web.DataReader(name=ticker,\\n data_source='tiingo',\\n start=start,\\n end=end,\\n api_key=api_key)\\n df = df[['adjClose']]\\n except KeyError as e:\\n if e.args[0] == 'date':\\n # Patch to handle issue in pandas_datareader\\n # where empty results cause a KeyError\\n print(f'Got empty df for i={i}, ticker={tickers[i]}')\\n df = pd.DataFrame()\\n except Exception as e:\\n print('Received an unexpected error:', e)\\n print(f'Only fetched up to {i-1} inclusive. Returning.')\\n return pd.concat(all_results)\\n\\n if (i % 50 == 0) and i > 0:\\n # Sleep to avoid timeouts. Empirically found 20s to be sufficient\\n time.sleep(20)\\n\\n all_results.append(df)\\n return pd.concat(all_results)\",\n \"def trades(self) -> list[TradeOffer]:\\n return self._connection.trades\",\n \"def get_target_timestamps(self):\\n times=[]\\n curr = self.begin_ts\\n while curr<=self.end_ts:\\n times.append(curr)\\n curr = curr + 24 * 60 * 60\\n return times\",\n \"def transactions(self):\\n url = f'{self._ynab.api_url}/budgets/{self.budget.id}/accounts/{self.id}/transactions'\\n response = self._ynab.session.get(url)\\n if not response.ok:\\n self._logger.error('Error retrieving transactions, response was : %s with status code : %s',\\n response.text,\\n response.status_code)\\n return []\\n return [Transaction(self._ynab, transaction)\\n for transaction in response.json().get('data', {}).get('transactions', [])]\",\n \"def related_reports(self):\\n return Report.objects.exclude(contact_email__isnull=True).filter(contact_email__iexact=self.contact_email).order_by('status', '-create_date')[:1000]\",\n \"def determineUnitHistory():\\n\\tunitTracker = Unitiser()\\n\\t\\n\\timport transactions\\n\\ttrades = transactions.allTransactions()\\n\\t\\n\\thistory = dict()\\n\\t\\n\\tfor date in timeline():\\n\\t\\t#print(\\\"\\\\ntimelime:\\\", date.strftime('%Y-%m-%d %H:%M:%S'))\\n\\t\\timport valuator\\n\\t\\tvalue = valuator.getPortfolioValueAt(date)\\n\\t\\tif date in trades:\\n\\t\\t\\tprior = getPortfolioBefore(date)\\n\\t\\t\\tprior_value = valuator.getPortfolioValueAt(date, portfolio = prior)\\n\\n\\t\\t\\tinvested = Decimal('0.0')\\n\\t\\t\\tfor equity in trades[date]:\\n\\t\\t\\t\\ttrade = trades[date][equity]\\n\\t\\t\\t\\t#print(equity, trade)\\n\\t\\t\\t\\tif trade['action'] == 'buy':\\n\\t\\t\\t\\t\\tinvested = invested + Decimal(trade['value'])\\n\\t\\t\\t\\telif trade['action'] == 'sell':\\n\\t\\t\\t\\t\\tinvested = invested - Decimal(trade['value'])\\n\\n\\t\\t\\tsince = getPortfolioAt(date)\\n\\t\\t\\tsince_value = valuator.getPortfolioValueAt(date, portfolio = since)\\n\\n\\t\\t\\t#print(\\\"change amount is\\\", invested)\\n\\t\\t\\tif invested > 0:\\n\\t\\t\\t\\tunitTracker.invest(invested, prior_value)\\n\\t\\t\\telif invested < 0:\\n\\t\\t\\t\\tunitTracker.divest(abs(invested), prior_value)\\n\\n\\t\\thistory[date] = {\\n\\t\\t\\t 'date' : date,\\n\\t\\t\\t 'value' : value.quantize(TWOPLACES),\\n\\t\\t\\t 'units' : unitTracker.numberOfUnits().quantize(TWOPLACES),\\n\\t\\t\\t 'price' : unitTracker.pricePerUnit(value).quantize(TWOPLACES),\\n\\t\\t\\t 'invested' : unitTracker.invested\\n\\t\\t\\t }\\n\\t\\n\\treturn history\",\n \"def getTempBanIps(self):\\n banned = []\\n q = \\\"\\\"\\\"SELECT clients.ip AS target_ip FROM penalties INNER JOIN clients ON penalties.client_id = clients.id\\n WHERE penalties.type = 'TempBan' AND penalties.inactive = 0 AND penalties.time_expire > %s\\n GROUP BY clients.ip\\\"\\\"\\\" % int(time())\\n cursor = self.query(q)\\n if cursor:\\n while not cursor.EOF:\\n banned.append(cursor.getValue('target_ip'))\\n cursor.moveNext()\\n cursor.close()\\n return banned\",\n \"def timers(self):\\n return self['timers']\",\n \"def get_stock_price(df_excld):\\n\\n ts = TimeSeries(os.environ['ALPHA_VANTAGE_KEY'])\\n\\n info = []\\n symbols = []\\n counter = 0\\n\\n for t in df_excld['Ticker']:\\n\\n if counter % 5 == 0:\\n time.sleep(65)\\n\\n i, m = ts.get_daily(symbol=t, outputsize='full')\\n info.append(i)\\n symbols.append(m['2. Symbol'])\\n counter += 1\\n\\n return info, symbols\",\n \"def get_all_coins_history(self, end_date=None, start_date=None, verbose=True):\\n infos = []\\n for coin in self.get_coins():\\n if verbose:\\n print(\\\"Collecting data for >> \\\" + coin)\\n if start_date:\\n start_date = start_date\\n else:\\n start_date = '20130428'\\n if end_date:\\n end_date = end_date\\n else:\\n now = str(datetime.now().date()).replace('-', '')\\n end_date = now\\n coin_url = self.coins[coin]\\n coin_url = coin_url + '/historical-data/?start=' + start_date + '&end=' + end_date\\n content = urlopen(coin_url).read()\\n soup = BeautifulSoup(content, 'html.parser')\\n results = soup.find_all(\\\"tr\\\", class_=\\\"text-right\\\")\\n\\n for result in results:\\n date = result.find_all('td')[0].text\\n\\n open_val = result.find_all('td')[1].text\\n if open_val == '-':\\n open_val = None\\n else:\\n open_val = float(result.find_all('td')[1].text.replace(',', ''))\\n\\n high_val = result.find_all('td')[2].text\\n if high_val == '-':\\n high_val = None\\n else:\\n high_val = float(result.find_all('td')[2].text.replace(',', ''))\\n\\n low_val = result.find_all('td')[3].text\\n if low_val == '-':\\n low_val = None\\n else:\\n low_val = float(result.find_all('td')[3].text.replace(',', ''))\\n\\n close_val = result.find_all('td')[4].text\\n if close_val == '-':\\n close_val = None\\n else:\\n close_val = float(result.find_all('td')[4].text.replace(',', ''))\\n\\n volume = result.find_all('td')[5].text\\n if volume == '-':\\n volume = None\\n else:\\n volume = float(result.find_all('td')[5].text.replace(',', ''))\\n\\n market_cap = result.find_all('td')[6].text\\n if market_cap == '-':\\n market_cap = None\\n else:\\n market_cap = float(result.find_all('td')[6].text.replace(',', ''))\\n temp = {\\n \\\"coin\\\": coin, # soup.title.text.split()[0],\\n \\\"date\\\": date,\\n \\\"symbol\\\": soup.title.text.split()[1].replace('(', '').replace(')', ''),\\n \\\"open_val\\\": open_val,\\n \\\"high_val\\\": high_val,\\n \\\"low_val\\\": low_val,\\n \\\"close_val\\\": close_val,\\n \\\"volume\\\": volume,\\n \\\"market_cap\\\": market_cap\\n }\\n infos.append(temp)\\n df_all = pd.DataFrame.from_dict(infos)\\n df_all['middle_val'] = (df_all.high_val + df_all.low_val) / 2\\n df_all['datetime'] = pd.to_datetime(df_all['date'])\\n df_all = df_all.sort_values(by='datetime')\\n self.coins_history = df_all\",\n \"def slow_update_duration(self):\\n for i in range(len(self.data_file.sorted_data)):\\n if self.data_file.sorted_data[i]['type'] == 'slow':\\n slow_upd = self.data_file.sorted_data[i]['timestamp']\\n Config.ANALYSIS.write(f\\\"slow at: {slow_upd}\\\\n\\\")\\n if i == 0:\\n after_slow = self.data_file.sorted_data[i + 1]['timestamp']\\n Config.ANALYSIS.write(f\\\"after slow: ({slow_upd}, {after_slow}) \\\"\\n f\\\"= {after_slow - slow_upd}\\\\n\\\\n\\\")\\n elif i == len(self.data_file.sorted_data) - 1:\\n before_slow = self.data_file.sorted_data[i - 1]['timestamp']\\n Config.ANALYSIS.write(f\\\"before slow: ({before_slow}, {slow_upd}) \\\"\\n f\\\"= {slow_upd - before_slow}\\\\n\\\\n\\\")\\n else:\\n before_slow = self.data_file.sorted_data[i - 1]['timestamp']\\n after_slow = self.data_file.sorted_data[i + 1]['timestamp']\\n Config.ANALYSIS.write(f\\\"before slow: ({before_slow}, {slow_upd}) \\\"\\n f\\\"= {slow_upd - before_slow}\\\\n\\\")\\n Config.ANALYSIS.write(f\\\"after slow: ({slow_upd}, {after_slow}) \\\"\\n f\\\"= {after_slow - slow_upd}\\\\n\\\\n\\\")\\n Config.ANALYSIS.write(\\\"\\\\n\\\\n\\\")\",\n \"def get_consumption_transactions(self, exclude_inferred_receipts=False):\\n from casexml.apps.stock.const import (\\n TRANSACTION_TYPE_STOCKONHAND,\\n TRANSACTION_TYPE_RECEIPTS,\\n TRANSACTION_TYPE_CONSUMPTION\\n )\\n transactions = [\\n ConsumptionTransaction(\\n TRANSACTION_TYPE_RECEIPTS if self.delta > 0 else TRANSACTION_TYPE_CONSUMPTION,\\n abs(self.delta),\\n self.report_date\\n )\\n ]\\n if self.type == LedgerTransaction.TYPE_BALANCE:\\n if self.delta > 0 and exclude_inferred_receipts:\\n transactions = []\\n\\n transactions.append(\\n ConsumptionTransaction(\\n TRANSACTION_TYPE_STOCKONHAND,\\n self.updated_balance,\\n self.report_date\\n )\\n )\\n return transactions\",\n \"def trades_for(self, symbol, from_id=None, timestamp=None, limit=None):\\n\\t\\tif self._session:\\n\\t\\t\\tdata = {'symbol': symbol}\\n\\n\\t\\t\\tif from_id:\\n\\t\\t\\t\\tdata['fromId'] = from_id\\n\\n\\t\\t\\tif timestamp:\\n\\t\\t\\t\\tdata['startTime'] = int(timestamp * 1000.0)\\n\\n\\t\\t\\tif limit:\\n\\t\\t\\t\\tdata['limit'] = limit\\n\\n\\t\\t\\treturn self._session.get_my_trades(**data)\\n\\n\\t\\treturn []\",\n \"def deposits_for_period(self):\\n cash_transaction = CashTransaction(self.user)\\n limits = self.user.limits\\n deposits = 0\\n if limits.exists():\\n deposit_limit = self.user.limits.get(type=Limit.DEPOSIT)\\n deposits = \\\\\\n cash_transaction.get_all_deposits(date_range=deposit_limit.time_period_boundaries)[\\n 'amount__sum']\\n return deposits\",\n \"def get_reports(self):\\r\\n result = QtSql.QSqlQuery('''Select * FROM failures''')\\r\\n list = []\\r\\n while result.next():\\r\\n failure = Failure(unicode(result.value(0).toString()), # id\\r\\n unicode(result.value(1).toString()), # comment\\r\\n unicode(result.value(2).toString()), # indicator\\r\\n bool(result.value(3))) # release\\r\\n p = self.get_presentation(failure.talkId)\\r\\n r = Report(p, failure)\\r\\n list.append(r)\\r\\n return list\",\n \"def get_all(self):\\r\\n return [{ 'label': x[0], 'timing': x[1], 'is_tool': x[0] in self._tool_labels}\\r\\n for x in sorted(self._timings_by_path.items(), key=lambda x: x[1], reverse=True)]\",\n \"def get_report_list(jtl_file):\\n df = None\\n try:\\n df = pd.read_csv(jtl_file,\\n low_memory=False,\\n error_bad_lines=False,\\n quoting=csv.QUOTE_NONE,\\n encoding='utf-8')\\n except Exception as e:\\n err_msg = 'read jtl file error. detail:{e}'.format(e=e)\\n LOGGER.error(err_msg)\\n if df is None:\\n return\\n threads = int(jtl_file.split(os.sep)[-1].split('_')[0])\\n success, elapsed, latency, sent_bytes, receive_bytes = [df.get(x) for x in\\n ['success', 'elapsed', 'Latency', 'sentBytes', 'bytes']]\\n samples = df.shape[0]\\n error_count = success.value_counts().get(False)\\n if not error_count:\\n error_count = 0\\n error_rate = str(float(error_count / samples) * 100) + '%'\\n label = df.loc[0, 'label']\\n start_time = df.iat[0, 0]\\n end_time = df.iloc[-1, 0]\\n last_req_time = df.iat[-1, 1]\\n\\n # 如果最后一行数据无效,则取上一行\\n i = 1\\n while not len(str(end_time)) == 13 and not re.findall('[\\\\d]{13}', str(end_time)):\\n i += 1\\n end_time = df.iloc[-i, 0]\\n last_req_time = df.iat[-i, 1]\\n samples -= 1\\n\\n if isinstance(start_time, str):\\n start_time = int(start_time)\\n if isinstance(end_time, str):\\n end_time = int(end_time)\\n\\n local_start_time = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(start_time / 1000))\\n local_end_time = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(end_time / 1000))\\n\\n durations = (end_time + last_req_time - start_time) / 1000\\n throughput = samples / durations\\n\\n report_list = [label, local_start_time, local_end_time, durations, threads, throughput, error_rate,\\n elapsed.min(), elapsed.max(), elapsed.mean(), samples, sent_bytes.mean(), receive_bytes.mean(),\\n latency.mean(), latency.min(), latency.max()]\\n\\n return report_list\",\n \"def get_ser_spktimes(self):\\n\\n spktimes = []\\n for sweep_no in range(self.get_no_sweeps()):\\n spktimes_singlesweep = []\\n for cell_no in range(self.get_no_ser_neurons()):\\n spktimes_singlesweep.append(\\n np.where(self.ser_spktrains[sweep_no, cell_no, :] > 0.5)[0]\\n * self.get_dt()\\n )\\n spktimes.append(spktimes_singlesweep)\\n return spktimes\",\n \"def get_all_files_to_instrument_for_live_session():\\n sql=\\\"SELECT * FROM files WHERE should_instrument=1 AND is_history=0\\\"\\n conn=sqlite3.connect(CONNECTION_STRING)\\n c=conn.cursor()\\n c.execute(sql)\\n results=c.fetchall()\\n conn.close()\\n return results\",\n \"def transaction_data(self):\\n return list(map(lambda transaction:transaction.to_json(), self.transaction_map.values()))\",\n \"def timers(self):\\n return self.client.call('GET', self.name + '/timers')\",\n \"def get_send_statistics(self):\\r\\n return self._make_request('GetSendStatistics')\",\n \"def get_all(self):\\n total_expense_reports = []\\n get_count = {\\n 'query': {\\n 'object': 'EEXPENSES',\\n 'select': {\\n 'field': 'RECORDNO'\\n },\\n 'pagesize': '1'\\n }\\n }\\n\\n response = self.format_and_send_request(get_count)\\n count = int(response['data']['@totalcount'])\\n pagesize = 2000\\n offset = 0\\n for i in range(0, count, pagesize):\\n data = {\\n 'query': {\\n 'object': 'EEXPENSES',\\n 'select': {\\n 'field': [\\n 'RECORDNO',\\n 'RECORDID',\\n 'WHENCREATED',\\n 'WHENPOSTED',\\n 'TOTALENTERED',\\n 'STATE',\\n 'TOTALDUE',\\n 'DESCRIPTION',\\n 'CURRENCY',\\n 'BASECURR',\\n 'MEMO'\\n ]\\n },\\n 'pagesize': pagesize,\\n 'offset': offset\\n }\\n }\\n expense_reports = self.format_and_send_request(data)['data']['EEXPENSES']\\n total_expense_reports = total_expense_reports + expense_reports\\n offset = offset + pagesize\\n return total_expense_reports\",\n \"def performance_history(self, request, pk=None, **kwargs):\\n # Get the goal even though we don't need it (we could just use the pk)\\n # so we can ensure we have permission to do so.\\n goal = self.get_object()\\n\\n # - Get all the transaction with this goal involved that are of reason 'Execution'.\\n # We want the volume, ticker id, date ordered by date. [(date, {ticker: vol}, ...]\\n qs = Transaction.objects.filter(Q(to_goal=goal) | Q(from_goal=goal),\\n reason=Transaction.REASON_EXECUTION).order_by('executed')\\n txs = qs.values_list('execution_distribution__execution__executed',\\n 'execution_distribution__execution__asset__id',\\n 'execution_distribution__volume')\\n ts = []\\n entry = (None,)\\n aids = set()\\n # If there were no transactions, there can be no performance\\n if len(txs) == 0:\\n return Response([])\\n\\n # Because executions are stored with timezone, but other things are just as date, we need to make datetimes\\n # naive before doing date arithmetic on them.\\n bd = timezone.make_naive(txs[0][0]).date()\\n ed = timezone.make_naive(timezone.now()).date()\\n for tx in txs:\\n aids.add(tx[1])\\n txd = timezone.make_naive(tx[0]).date()\\n if txd == entry[0]:\\n entry[1][tx[1]] += tx[2]\\n else:\\n if entry[0] is not None:\\n ts.append(entry)\\n entry = (txd, defaultdict(int))\\n entry[1][tx[1]] = tx[2]\\n ts.append(entry)\\n\\n # - Get the time-series of prices for each instrument from the first transaction date until now.\\n # Fill empty dates with previous value [(date, {ticker: price}, ...]\\n pqs = DailyPrice.objects.filter(date__range=(bd, ed),\\n instrument_content_type=ContentType.objects.get_for_model(Ticker).id,\\n instrument_object_id__in=aids)\\n prices = pqs.to_timeseries(fieldnames=['price', 'date', 'instrument_object_id'],\\n index='date',\\n storage='long',\\n pivot_columns='instrument_object_id',\\n values='price')\\n # Remove negative prices and fill missing values\\n # We replace negs with None so they are interpolated.\\n prices[prices <= 0] = None\\n prices = prices.reindex(pd.date_range(bd, ed), method='ffill').fillna(method='bfill')\\n\\n # For each day, calculate the performance\\n piter = prices.itertuples()\\n res = []\\n # Process the first day - it's special\\n row = next(piter)\\n p_m1 = row[1:]\\n vols_m1 = [0] * len(prices.columns)\\n tidlocs = {tid: ix for ix, tid in enumerate(prices.columns)}\\n for tid, vd in ts.pop(0)[1].items():\\n vols_m1[tidlocs[tid]] += vd\\n res.append((dt2ed(row[0]), 0)) # First day has no performance as there wasn't a move\\n # Process the rest\\n for row in piter:\\n # row[0] (a datetime) is a naive timestamp, so we don't need to convert it\\n if ts and row[0].date() == ts[0][0]:\\n vols = vols_m1.copy()\\n dtrans = ts.pop(0)[1] # The transactions for the current processed day.\\n for tid, vd in dtrans.items():\\n vols[tidlocs[tid]] += vd\\n # The exposed assets for the day. These are the assets we know for sure were exposed for the move.\\n pvol = list(map(min, vols, vols_m1))\\n else:\\n vols = vols_m1\\n pvol = vols\\n pdelta = list(map(operator.sub, row[1:], p_m1)) # The change in price from yesterday\\n impact = sum(map(operator.mul, pvol, pdelta)) # The total portfolio impact due to price moves for exposed assets.\\n b_m1 = sum(map(operator.mul, pvol, p_m1)) # The total portfolio value yesterday for the exposed assets.\\n perf = 0 if b_m1 == 0 else impact / b_m1\\n # row[0] (a datetime) is a naive timestamp, so we don't need to convert it\\n res.append((dt2ed(row[0]), decimal.Decimal.from_float(perf).quantize(decimal.Decimal('1.000000'))))\\n p_m1 = row[1:]\\n vols_m1 = vols[:]\\n\\n return Response(res)\",\n \"def get_staking_transaction_history(address, page=0, page_size=1000, include_full_tx=False, tx_type='ALL',\\n order='ASC', endpoint=_default_endpoint, timeout=_default_timeout\\n ) -> list:\\n params = [\\n {\\n 'address': address,\\n 'pageIndex': page,\\n 'pageSize': page_size,\\n 'fullTx': include_full_tx,\\n 'txType': tx_type,\\n 'order': order\\n }\\n ]\\n # Using v2 API, because getStakingTransactionHistory not implemented in v1\\n method = 'hmyv2_getStakingTransactionsHistory'\\n stx_history = rpc_request(method, params=params, endpoint=endpoint, timeout=timeout)['result']\\n try:\\n return stx_history['staking_transactions']\\n except KeyError as e:\\n raise InvalidRPCReplyError(method, endpoint) from e\",\n \"def get_extension_list(self, timespan, db_lookup):\\n\\n if (self.search_list_extension != None):\\n return [self.search_list_extension]\\n \\n t1 = time.time()\\n\\n # Get UTC offset\\n stop_struct = time.localtime(timespan.stop)\\n utc_offset = (calendar.timegm(stop_struct) - calendar.timegm(time.gmtime(time.mktime(stop_struct))))/60\\n\\n # Get our start time, numDays days ago but aligned with start of day\\n # first get the start of today\\n _ts = startOfDay(timespan.stop)\\n # then go back numDays days\\n _ts_dt = datetime.datetime.fromtimestamp(_ts)\\n _start_dt = _ts_dt - datetime.timedelta(days=self.numDays)\\n _start_ts = time.mktime(_start_dt.timetuple())\\n\\n if (self.numDays == 3):\\n _start_ts = time.mktime((_ts_dt - datetime.timedelta(days=0)).timetuple())\\n _end_ts = time.mktime((_ts_dt + datetime.timedelta(days=3)).timetuple())\\n timespan = TimeSpan(_start_ts, _end_ts)\\n \\n # Get our temperature vector\\n (time_start_vt, time_stop_vt, outTemp_vt) = db_lookup().getSqlVectors(TimeSpan(_start_ts, timespan.stop),'outTemp')\\n # Convert our temperature vector\\n outTemp_vt = self.generator.converter.convert(outTemp_vt)\\n # Can't use ValueHelper so round our results manually\\n # Get the number of decimal points\\n tempRound = int(self.generator.skin_dict['Units']['StringFormats'].get(outTemp_vt[1], \\\"1f\\\")[-2])\\n # Do the rounding\\n outTempRound_vt = [roundNone(x,tempRound) for x in outTemp_vt[0]]\\n # Get our time vector in ms (w34highcharts requirement)\\n # Need to do it for each getSqlVectors result as they might be different\\n outTemp_time_ms = [time_stop_vt[0][0] if (x == 0) else time_stop_vt[0][x] - time_stop_vt[0][0] for x in range(len(time_stop_vt[0]))]\\n \\n # Get our dewpoint vector\\n (time_start_vt, time_stop_vt, dewpoint_vt) = db_lookup().getSqlVectors(TimeSpan(_start_ts, timespan.stop),'dewpoint')\\n dewpoint_vt = self.generator.converter.convert(dewpoint_vt)\\n # Can't use ValueHelper so round our results manually\\n # Get the number of decimal points\\n dewpointRound = int(self.generator.skin_dict['Units']['StringFormats'].get(dewpoint_vt[1], \\\"1f\\\")[-2])\\n # Do the rounding\\n dewpointRound_vt = [roundNone(x,dewpointRound) for x in dewpoint_vt[0]]\\n # Get our time vector in ms (w34highcharts requirement)\\n # Need to do it for each getSqlVectors result as they might be different\\n dewpoint_time_ms = [time_stop_vt[0][0] if (x == 0) else time_stop_vt[0][x] - time_stop_vt[0][0] for x in range(len(time_stop_vt[0]))]\\n\\n # Get our humidity vector\\n (time_start_vt, time_stop_vt, outHumidity_vt) = db_lookup().getSqlVectors(TimeSpan(_start_ts, timespan.stop),'outHumidity')\\n # Can't use ValueHelper so round our results manually\\n # Get the number of decimal points\\n outHumidityRound = int(self.generator.skin_dict['Units']['StringFormats'].get(outHumidity_vt[1], \\\"1f\\\")[-2])\\n # Do the rounding\\n outHumidityRound_vt = [roundNone(x,outHumidityRound) for x in outHumidity_vt[0]]\\n # Get our time vector in ms (w34highcharts requirement)\\n # Need to do it for each getSqlVectors result as they might be different\\n outHumidity_time_ms = [time_stop_vt[0][0] if (x == 0) else time_stop_vt[0][x] - time_stop_vt[0][0] for x in range(len(time_stop_vt[0]))]\\n # Format our vectors in json format. Need the zip() to get time/value pairs\\n # Assumes all vectors have the same number of elements\\n outTemp_json = json.dumps(zip(outTemp_time_ms, outTempRound_vt))\\n dewpoint_json = json.dumps(zip(dewpoint_time_ms, dewpointRound_vt))\\n outHumidity_json = json.dumps(zip(outHumidity_time_ms, outHumidityRound_vt))\\n # Put into a dictionary to return\\n self.search_list_extension = {\\n 'outTempWeekjson' : outTemp_json,\\n 'dewpointWeekjson' : dewpoint_json,\\n 'outHumidityWeekjson' : outHumidity_json,\\n 'utcOffset': utc_offset,\\n 'weekPlotStart' : _start_ts * 1000,\\n 'weekPlotEnd' : timespan.stop * 1000}\\n t2 = time.time()\\n logdbg2(\\\"w34highcharts_temp_week SLE executed in %0.3f seconds\\\" % (t2 - t1))\\n\\n # Return our json data\\n return [self.search_list_extension]\",\n \"def TOBS():\\n session = Session(engine)\\n # Query all passengers\\n\\n TOBS = session.query(Measurement.date,Measurement.tobs).filter(Measurement.date >= '2010-08-23').all()\\n\\n # Convert list of tuples into normal list\\n all_TOBS = list(np.ravel(TOBS))\\n\\n return jsonify(all_TOBS)\",\n \"def recorded_messages(self):\\n messages = []\\n for time in sorted(self.reception_records):\\n messages.extend(self.reception_records[time])\\n return messages\",\n \"def returnTradeHistory(self,\\n currency_pair=\\\"all\\\",\\n start=datetime.now() - timedelta(days=1),\\n end=datetime.now()):\\n pass\",\n \"def returnTradeHistory(self,\\n currency_pair=\\\"all\\\",\\n start=datetime.now() - timedelta(days=1),\\n end=datetime.now()):\\n pass\",\n \"def aggregated_results(self, limit=2000) -> List[dict]:\\n stored_events = []\\n for events in self._iter_events():\\n stored_events.extend(events)\\n if len(stored_events) >= limit:\\n return stored_events[:limit]\\n return stored_events\",\n \"def get_transactions(self):\\n\\n df = self.__transactions[\\n [\\n \\\"Date\\\",\\n \\\"Type\\\",\\n \\\"Ticker\\\",\\n \\\"Side\\\",\\n \\\"Price\\\",\\n \\\"Quantity\\\",\\n \\\"Fees\\\",\\n \\\"Investment\\\",\\n \\\"Currency\\\",\\n \\\"Sector\\\",\\n \\\"Industry\\\",\\n \\\"Country\\\",\\n \\\"Region\\\",\\n ]\\n ]\\n df = df.replace(np.nan, \\\"-\\\")\\n df[\\\"Date\\\"] = df[\\\"Date\\\"].dt.strftime(\\\"%Y-%m-%d\\\")\\n df.sort_values(by=\\\"Date\\\", ascending=False, inplace=True)\\n return df\",\n \"def getDataWithTimeIndex(self, t):\\n\\n return self.sensorDf.iloc[t,:self.sensorChannels].values\",\n \"def aggregate_timetable_details(logger):\\n df_array = []\\n for page in range(0, MAX_PAGE+1): # MAX_PAGE inclusive\\n filename = dirname + 'TimeTablePage_{}'.format(page) + '.csv'\\n if os.exists(filename):\\n # When database is ready, this info can be recorded there\\n logger.info('File[{file}] already downloaded. Reading it'.format(file=filename))\\n url = filename\\n else:\\n count = page * 20\\n url = TIMETABLE_URL + '&count={count}&page={page}'.format(count=count, page=page)\\n logger.info('Fetch TimeTableDetails from URL[{url}] page[{page}] into {directory}'.format(url=url, page=page, directory=dirname))\\n \\n try:\\n df = pd.read_html(url)[1] # the table of interest from the list\\n except Exception as e:\\n logger.error('Exception when reading HTML:[{exception}]'.format(exception=e))\\n exit(0)\\n\\n logger.info('Writing to [{file}]'.format(file=filename))\\n df.to_csv(filename, index=False)\\n df_array.append(df)\\n\\n df = pd.concat(df_array)\\n print(df.head())\\n print(df.tail())\\n\\n return 0\",\n \"def find_all(self) -> List[Trade]:\\n\\n pass # pragma: no cover\",\n \"def service_times(self):\\r\\n service_times = []\\r\\n for task in self.__tasks.values():\\r\\n if task.complete():\\r\\n x = task.service_time()\\r\\n service_times.append(task.service_time())\\r\\n return service_times\",\n \"def get_data(symbol_id='BTC', period_id='1DAY', request_limit=1000, tdelta=30):\\n now = datetime.utcnow()\\n month = timedelta(days=tdelta)\\n past_month = (now - month).isoformat()\\n\\n parameters = {'symbol_id': symbol_id, 'period_id': period_id, 'time_start': past_month[:-3], 'limit':request_limit}\\n response = requests.get(HISTORY_URL, params=parameters, headers=header)\\n\\n while response.status_code != 200:\\n time.sleep(5)\\n response = requests.get(HISTORY_URL, params=parameters, headers=header)\\n \\n data = response.json()\\n \\n # this is a commnet\\n csv_headers = ['time_period_start', 'time_period_end', 'price_high', 'price_low', 'price_close', 'price_open', 'trades_count', \\n 'volume_traded', 'time_open', 'time_close']\\n\\n\\n with open(str(datafolder / f'{symbol_id}_{tdelta}_day.csv'), 'w', newline='') as f:\\n writer = csv.DictWriter(f, csv_headers)\\n writer.writeheader()\\n for item in data:\\n writer.writerow(item)\",\n \"def get_timeseries():\\n\\n # generate the result files\\n name = os.path.join(project.output_folder, project.scenario+'.sww')\\n log.debug('get_timeseries: input SWW file=%s' % name)\\n log.debug('get_timeseries: gauge file=%s' % project.gauge_file)\\n anuga.sww2csv_gauges(name, project.gauge_file, quantities=project.layers_list,\\n verbose=False)\\n\\n # since ANUGA code doesn't return a list of generated files,\\n # look in output directory for 'gauge_*.csv' files.\\n glob_mask = os.path.join(project.output_folder, 'gauge_*.csv')\\n return glob.glob(glob_mask)\",\n \"def to_exclude(self):\\n reporting_period = self.message_number - 3\\n one_day_ago = now() - datetime.timedelta(hours=24)\\n\\n return PollingReport.objects.filter(\\n period_number=reporting_period,\\n creation_date__gte=one_day_ago,\\n ).values_list('phone_number', flat=True)\",\n \"def _parse_transactions_file(self, path_to_transactions_file: str) -> List[Transaction]:\\n ticker_params_to_ticker = {\\n (ticker.name, ticker.security_type, ticker.point_value): ticker for ticker in self.tickers\\n }\\n\\n def get_matching_ticker(row: QFSeries) -> Ticker:\\n \\\"\\\"\\\" Returns the matching specific ticker. In case if the ticker does not belong to the list of tickers\\n passed as the parameter, the transaction is excluded. \\\"\\\"\\\"\\n ticker_str = row.loc[\\\"Contract symbol\\\"]\\n name = row.loc[\\\"Asset Name\\\"]\\n sec_type = SecurityType(row.loc[\\\"Security type\\\"])\\n point_value = row.loc[\\\"Contract size\\\"]\\n ticker = ticker_params_to_ticker.get((name, sec_type, point_value), None)\\n if isinstance(ticker, FutureTicker):\\n ticker_type = ticker.supported_ticker_type()\\n ticker = ticker_type(ticker_str, sec_type, point_value)\\n return ticker\\n\\n transactions_df = pd.read_csv(path_to_transactions_file)\\n transactions = [Transaction(pd.to_datetime(row.loc[\\\"Timestamp\\\"]),\\n get_matching_ticker(row),\\n row.loc[\\\"Quantity\\\"],\\n row.loc[\\\"Price\\\"],\\n row.loc[\\\"Commission\\\"]) for _, row in transactions_df.iterrows()]\\n transactions = [t for t in transactions if t.ticker is not None]\\n return transactions\",\n \"def temporal_database():\\n return TimeHistory()\",\n \"def get_relevant_reports(self):\\r\\n split_reports = [x.split(' ') for x in self.get_directory_list()]\\r\\n formatted_reps = [list(filter(None, line)) for line in split_reports]\\r\\n recent_reports = [line for line in formatted_reps if self.is_report_recent(line)]\\r\\n return recent_reports\",\n \"def fetch_bank_transactions(self):\\n return self.fetch('/bank_transactions')\",\n \"def scrape_validator_withdrawals(\\n validator_index: int,\\n last_known_timestamp: Timestamp,\\n) -> list[tuple[Timestamp, ChecksumEvmAddress, FVal]]:\\n withdrawals = []\\n now = ts_now()\\n start = 0\\n page_length = 10\\n stop_iterating = False\\n\\n while True:\\n url = f'{BEACONCHAIN_ROOT_URL}/validator/{validator_index}/withdrawals?draw=1&columns%5B0%5D%5Bdata%5D=0&columns%5B0%5D%5Bname%5D=&columns%5B0%5D%5Bsearchable%5D=true&columns%5B0%5D%5Borderable%5D=true&columns%5B0%5D%5Bsearch%5D%5Bvalue%5D=&columns%5B0%5D%5Bsearch%5D%5Bregex%5D=false&columns%5B1%5D%5Bdata%5D=1&columns%5B1%5D%5Bname%5D=&columns%5B1%5D%5Bsearchable%5D=true&columns%5B1%5D%5Borderable%5D=true&columns%5B1%5D%5Bsearch%5D%5Bvalue%5D=&columns%5B1%5D%5Bsearch%5D%5Bregex%5D=false&columns%5B2%5D%5Bdata%5D=2&columns%5B2%5D%5Bname%5D=&columns%5B2%5D%5Bsearchable%5D=true&columns%5B2%5D%5Borderable%5D=true&columns%5B2%5D%5Bsearch%5D%5Bvalue%5D=&columns%5B2%5D%5Bsearch%5D%5Bregex%5D=false&columns%5B3%5D%5Bdata%5D=3&columns%5B3%5D%5Bname%5D=&columns%5B3%5D%5Bsearchable%5D=true&columns%5B3%5D%5Borderable%5D=true&columns%5B3%5D%5Bsearch%5D%5Bvalue%5D=&columns%5B3%5D%5Bsearch%5D%5Bregex%5D=false&columns%5B4%5D%5Bdata%5D=4&columns%5B4%5D%5Bname%5D=&columns%5B4%5D%5Bsearchable%5D=true&columns%5B4%5D%5Borderable%5D=true&columns%5B4%5D%5Bsearch%5D%5Bvalue%5D=&columns%5B4%5D%5Bsearch%5D%5Bregex%5D=false&order%5B0%5D%5Bcolumn%5D=0&order%5B0%5D%5Bdir%5D=desc&start={start}&length={page_length}&search%5Bvalue%5D=&search%5Bregex%5D=false&_={now}' # noqa: E501\\n response = _query_page(url, 'withdrawals')\\n try:\\n result = response.json()\\n except json.JSONDecodeError as e:\\n raise RemoteError(f'Could not parse {response.text} from beaconchain as json') from e\\n\\n for entry in result['data']: # data appears in descending time\\n epoch_match = EPOCH_PARSE_REGEX.match(entry[0])\\n if epoch_match is None:\\n log.error(f'Failed to match epoch regex for {entry[0]}')\\n raise RemoteError('Failed to parse withdrawals response from beaconchain. Check logs') # noqa: E501\\n groups = epoch_match.groups()\\n if len(groups) != 1:\\n log.error(f'Failed to match epoch regex for {entry[0]}')\\n raise RemoteError('Failed to parse withdrawals response from beaconchain. Check logs') # noqa: E501\\n epoch = deserialize_int_from_str(groups[0], location='beaconchain epoch')\\n timestamp = epoch_to_timestamp(epoch)\\n if timestamp <= last_known_timestamp:\\n stop_iterating = True\\n break # we already know about this withdrawal\\n\\n address_match = ADDRESS_PARSE_REGEX.match(entry[3])\\n if address_match is None:\\n log.error(f'Failed to match address regex for {entry[3]}')\\n raise RemoteError('Failed to parse withdrawals response from beaconchain. Check logs') # noqa: E501\\n groups = address_match.groups()\\n if len(groups) != 1:\\n log.error(f'Failed to match address regex for {entry[3]}')\\n raise RemoteError('Failed to parse withdrawals response from beaconchain. Check logs') # noqa: E501\\n address = deserialize_evm_address(groups[0])\\n\\n eth_match = ETH_PARSE_REGEX.match(entry[4])\\n if eth_match is None:\\n log.error(f'Failed to match eth regex for {entry[4]}')\\n raise RemoteError('Failed to parse withdrawals response from beaconchain. Check logs') # noqa: E501\\n groups = eth_match.groups()\\n if len(groups) != 1:\\n log.error(f'Failed to match eth regex for {entry[4]}')\\n raise RemoteError('Failed to parse withdrawals response from beaconchain. Check logs') # noqa: E501\\n eth_amount = deserialize_fval(groups[0], name='withdrawal ETH', location='beaconchain query') # noqa: E501\\n\\n withdrawals.append((timestamp, address, eth_amount))\\n\\n if stop_iterating or len(withdrawals) >= result['recordsTotal']:\\n break # reached the end\\n start += page_length\\n\\n return withdrawals\",\n \"def get_df_transactions():\\n\\n _, res = DBX.files_download(c.io.FILE_TRANSACTIONS)\\n return pd.read_excel(io.BytesIO(res.content), index_col=0)\",\n \"def wem_market_value_all():\\n __query = \\\"\\\"\\\"\\n select\\n date_trunc('month', wfs.trading_interval) AS trading_day,\\n sum(wfs.eoi_quantity * wbs.price) as energy_interval,\\n wf.fueltech_id\\n from wem_facility_scada wfs\\n left join wem_facility wf on wfs.facility_id = wf.code\\n join wem_balancing_summary wbs on wfs.trading_interval = wbs.trading_interval\\n where\\n wf.fueltech_id is not null\\n group by 1, wf.fueltech_id\\n order by 1 desc, 2 asc\\n \\\"\\\"\\\"\\n\\n query = __query.format()\\n\\n json_envelope = {}\\n\\n with engine.connect() as c:\\n rows = c.execute(query)\\n\\n current_tech = None\\n\\n for row in rows:\\n\\n current_tech = row[2]\\n\\n if current_tech not in json_envelope.keys():\\n json_envelope[current_tech] = {\\n \\\"id\\\": f\\\"wem.fuel_tech.{current_tech}.market_value\\\",\\n \\\"fuel_tech\\\": current_tech,\\n \\\"region\\\": \\\"wa\\\",\\n \\\"type\\\": \\\"market_value\\\",\\n \\\"units\\\": \\\"AUD\\\",\\n \\\"history\\\": {\\n \\\"interval\\\": \\\"1M\\\",\\n \\\"start\\\": None,\\n \\\"last\\\": None,\\n \\\"data\\\": [],\\n },\\n }\\n\\n if (\\n json_envelope[current_tech][\\\"history\\\"][\\\"start\\\"] == None\\n or row[0] < json_envelope[current_tech][\\\"history\\\"][\\\"start\\\"]\\n ):\\n json_envelope[current_tech][\\\"history\\\"][\\\"start\\\"] = row[0]\\n\\n if (\\n json_envelope[current_tech][\\\"history\\\"][\\\"last\\\"] == None\\n or row[0] > json_envelope[current_tech][\\\"history\\\"][\\\"last\\\"]\\n ):\\n json_envelope[current_tech][\\\"history\\\"][\\\"last\\\"] = row[0]\\n\\n json_envelope[current_tech][\\\"history\\\"][\\\"data\\\"].append(row[1])\\n\\n return [json_envelope[i] for i in json_envelope.keys()]\",\n \"def timeseries_report(self):\\n try:\\n n = self.n.value\\n except AttributeError:\\n n = self.n\\n results = pd.DataFrame(index=self.variables.index)\\n results['ICE Generation (kW)'] = self.variables['ice_gen']\\n results['ICE On (y/n)'] = self.variables['on_ice']\\n results['ICE P_min (kW)'] = self.p_min\\n results['ICE Genset P_max (kW)'] = self.rated_power * n\\n return results\",\n \"def timestamps(self) -> List[T]:\\n return self._timestamps\",\n \"def get_pnl_trades(self):\\n\\n if self._pnl_trades is None:\\n tsc = TimeSeriesCalcs()\\n self._pnl_trades = tsc.calculate_individual_trade_gains(self._signal, self._pnl)\\n\\n return self._pnl_trades\",\n \"def get_all_stats():\\n\\n return get_component(CachingPackage.COMPONENT_NAME).get_all_stats()\",\n \"def listunspent(self, minconf=1, maxconf=999999):\\n return [TransactionInfo(**tx) for tx in\\n self.proxy.listunspent(minconf, maxconf)]\",\n \"def get_stats(self, epg_dn):\\n # Apic saves up to 95 different objects with statistic information\\n traffic_list = []\\n for i in range(10, -1, -1):\\n traffic = self.moDir.lookupByDn(epg_dn + '/HDl2IngrBytesAg15min-%s' % str(i))\\n if traffic is not None:\\n traffic_list.append(traffic)\\n return traffic_list\",\n \"def main_looper(df: pd.DataFrame()) -> list:\\n for_sale_dictionary = {}\\n sell_dictionary = {}\\n repeat_sale = {}\\n unrecorded_sale = []\\n for idx, row in df.iterrows():\\n if row['function_call']['function'] == 'createSaleAuction':\\n # TODO - change for different NFT\\n # entry into dict is kitty ID (unique to cryptokitties)\\n # checks if already posted to be sold, continues if this is a reentry to sell, recording it\\n if row['function_call']['_kittyId'] in for_sale_dictionary:\\n if row['function_call']['_kittyId'] in repeat_sale:\\n repeat_sale[row['function_call']['_kittyId']] += 1\\n continue\\n else:\\n repeat_sale[row['function_call']['_kittyId']] = 1 \\n continue\\n for_sale_dictionary.update({str(row['function_call']['_kittyId']) : row['block_timestamp']})\\n \\n if row['function_call']['function'] == 'transfer':\\n # must have posted for sale in an earlier block, cant find interval\\n if not row['function_call']['_tokenId'] in for_sale_dictionary:\\n unrecorded_sale.append([row['hash'], row['function_call']['_tokenId']])\\n # print(f\\\"token not recoreded for sale: {row['function_call']['_tokenId']}\\\")\\n # print(row['hash'])\\n continue\\n if for_sale_dictionary[row['function_call']['_tokenId']] == -1:\\n print('missed for sale block, somehow??')\\n continue\\n # TODO find a smarter way to track times transfered!!\\n sell_entry = str(row['function_call']['_tokenId']) + '_' + str(row['block_number'])\\n print('')\\n print('')\\n print('')\\n print(row['block_timestamp'])\\n print(for_sale_dictionary['_tokenId'])\\n time_interval = row['block_timestamp'] - for_sale_dictionary['_tokenId']\\n for_sale_dictionary[row['function_call']['_tokenId']] = -1\\n print(time_interval)\\n sell_dictionary.update({sell_entry : row['block_timestamp']})\\n print(len(unrecorded_sale))\\n if len(unrecorded_sale) > 0:\\n rng = min(7, len(unrecorded_sale))\\n for ii in range(rng):\\n print(unrecorded_sale[ii])\\n for jj in range(rng):\\n print(unrecorded_sale[len(unrecorded_sale)-jj-1])\\n \\n\\n # print(f'Token IDs posted for sale:')\\n # for key in for_sale_dictionary:\\n # print(f'{key} : {for_sale_dictionary[key]}')\",\n \"def _get_current_session_tiling_list(self) -> List:\\n return self._data[-1][History._TILINGS]\",\n \"def get_open_transactions(self):\\n return self.__open_transactions[:]\",\n \"def _get_tweets(self):\\n auth = tweepy.OAuthHandler(CONSUMER_KEY, CONSUMER_SECRET)\\n auth.set_access_token(ACCESS_TOKEN, ACCESS_SECRET)\\n api = tweepy.API(auth)\\n search = api.search(self.term, lang='en', count=100)\\n\\n print(f\\\"Getting tweets that mention '{self.term}', \\\"\\n f\\\"this may take a while...\\\")\\n\\n save_tweet_text = [tweet._json['text'] for tweet in search]\\n while len(save_tweet_text) < 1000:\\n try:\\n oldest = search[-1].id - 1\\n search = api.search(self.term, lang='en', count=100, max_id=oldest)\\n new_tweets = [tweet._json['text'] for tweet in search]\\n save_tweet_text.extend(new_tweets)\\n\\n # Turn into a set to remove duplicated tweets, then back to list\\n save_tweet_text = list(set(save_tweet_text))\\n except IndexError:\\n break\\n\\n print(f\\\"Done. {len(save_tweet_text)} Tweets received.\\\")\\n return save_tweet_text\",\n \"def _historical_user_data_for_decisions(user: User, days: int) -> List:\\n data = []\\n for begin, end in _time_range_list(days):\\n db_clicks_statements = DBDiscussionSession.query(ClickedStatement).filter(\\n ClickedStatement.author_uid == user.uid,\\n ClickedStatement.timestamp >= begin,\\n ClickedStatement.timestamp < end).all()\\n db_clicks_arguments = DBDiscussionSession.query(ClickedArgument).filter(\\n ClickedArgument.author_uid == user.uid,\\n ClickedArgument.timestamp >= begin,\\n ClickedArgument.timestamp < end).all()\\n clicks = len(db_clicks_statements) + len(db_clicks_arguments)\\n data.append(clicks)\\n\\n return data\",\n \"def get_blocking_times(individuals):\\n ambulance_patients_times = []\\n other_patients_times = []\\n patients_still_in_system = []\\n\\n for ind in individuals:\\n if ind.data_records[0].node == 1 and len(ind.data_records) == 2:\\n ambulance_patients_times.append(\\n ind.data_records[0].time_blocked + ind.data_records[1].time_blocked\\n )\\n elif ind.data_records[0].node == 2 and len(ind.data_records) == 1:\\n other_patients_times.append(ind.data_records[0].time_blocked)\\n else:\\n patients_still_in_system.append(ind)\\n return [ambulance_patients_times, other_patients_times, patients_still_in_system]\",\n \"def get_extension_list(self, timespan, db_lookup):\\n\\n if (self.search_list_extension != None):\\n return [self.search_list_extension]\\n \\n t1 = time.time()\\n\\n # Get UTC offset\\n stop_struct = time.localtime(timespan.stop)\\n utc_offset = (calendar.timegm(stop_struct) - calendar.timegm(time.gmtime(time.mktime(stop_struct))))/60\\n\\n # Get our start time, numDays days ago but aligned with start of day\\n # first get the start of today\\n _ts = startOfDay(timespan.stop)\\n # then go back numDays days\\n _ts_dt = datetime.datetime.fromtimestamp(_ts)\\n _start_dt = _ts_dt - datetime.timedelta(days=self.numDays)\\n _start_ts = time.mktime(_start_dt.timetuple())\\n\\n if (self.numDays == 3):\\n _start_ts = time.mktime((_ts_dt - datetime.timedelta(days=0)).timetuple())\\n _end_ts = time.mktime((_ts_dt + datetime.timedelta(days=3)).timetuple())\\n timespan = TimeSpan(_start_ts, _end_ts)\\n \\n # Get our barometer vector\\n (time_start_vt, time_stop_vt, barometer_vt) = db_lookup().getSqlVectors(TimeSpan(_start_ts, timespan.stop),\\n 'barometer')\\n barometer_vt = self.generator.converter.convert(barometer_vt)\\n # Can't use ValueHelper so round our results manually\\n # Get the number of decimal points\\n barometerRound = int(self.generator.skin_dict['Units']['StringFormats'].get(barometer_vt[1], \\\"1f\\\")[-2])\\n # Do the rounding\\n barometerRound_vt = [roundNone(x,barometerRound) for x in barometer_vt[0]]\\n # Get our time vector in ms (w34highcharts requirement)\\n # Need to do it for each getSqlVectors result as they might be different\\n barometer_time_ms = [time_stop_vt[0][0] if (x == 0) else time_stop_vt[0][x] - time_stop_vt[0][0] for x in range(len(time_stop_vt[0]))]\\n\\n # Get our rain vector, need to sum over the hour\\n (time_start_vt, time_stop_vt, rain_vt) = db_lookup().getSqlVectors(TimeSpan(_start_ts, timespan.stop),'rain')\\n (time_start_vt, time_stop_vt, rainRate_vt) = db_lookup().getSqlVectors(TimeSpan(_start_ts, timespan.stop),'rainRate')\\n # Check if we have a partial hour at the end\\n # If we do then set the last time in the time vector to the hour\\n # Avoids display issues with the column chart\\n # Need to make sure we have at least 2 records though\\n #if len(time_stop_vt[0]) > 1:\\n # if time_stop_vt[0][-1] < time_stop_vt[0][-2] + 3600:\\n # time_stop_vt[0][-1] = time_stop_vt[0][-2] + 3600\\n # Convert our rain vector\\n rain_vt = self.generator.converter.convert(rain_vt)\\n rainRate_vt = self.generator.converter.convert(rainRate_vt)\\n # Can't use ValueHelper so round our results manually\\n # Get the number of decimal points\\n rainRound = int(self.generator.skin_dict['Units']['StringFormats'].get(rain_vt[1], \\\"1f\\\")[-2])\\n rainRateRound = int(self.generator.skin_dict['Units']['StringFormats'].get(rainRate_vt[1], \\\"1f\\\")[-2])\\n # Do the rounding\\n rainRound_vt = [roundNone(x,rainRound) for x in rain_vt[0]]\\n rainRateRound_vt = [roundNone(x,rainRateRound) for x in rainRate_vt[0]]\\n # Get our time vector in ms (w34highcharts requirement)\\n # Need to do it for each getSqlVectors result as they might be different\\n timeRain_ms = [time_stop_vt[0][0] if (x == 0) else time_stop_vt[0][x] - time_stop_vt[0][0] for x in range(len(time_stop_vt[0]))]\\n\\n # Format our vectors in json format. Need the zip() to get time/value pairs\\n # Assumes all vectors have the same number of elements\\n barometer_json = json.dumps(zip(barometer_time_ms, barometerRound_vt))\\n rain_json = json.dumps(zip(timeRain_ms, rainRound_vt))\\n rainRate_json = json.dumps(zip(timeRain_ms, rainRateRound_vt))\\n\\n # Put into a dictionary to return\\n self.search_list_extension = {\\n 'barometerWeekjson' : barometer_json,\\n 'rainWeekjson' : rain_json,\\n 'rainRateWeekjson' : rainRate_json,\\n 'utcOffset': utc_offset,\\n 'weekPlotStart' : _start_ts * 1000,\\n 'weekPlotEnd' : timespan.stop * 1000}\\n\\n t2 = time.time()\\n logdbg2(\\\"w34highcharts_bar_rain_week SLE executed in %0.3f seconds\\\" % (t2 - t1))\\n\\n # Return our json data\\n return [self.search_list_extension]\",\n \"def report_data(self):\\n report = [donor_obj.data for donor_obj in self.donor_list]\\n return report\",\n \"def test_get_events_history_filtering_by_timestamp(rotkehlchen_api_server: 'APIServer'):\\n tx_hex = deserialize_evm_tx_hash('0xb226ddb8cbb286a7a998a35263ad258110eed5f923488f03a8d890572cd4608e') # noqa: E501\\n ethereum_inquirer = rotkehlchen_api_server.rest_api.rotkehlchen.chains_aggregator.ethereum.node_inquirer # noqa: E501\\n database = rotkehlchen_api_server.rest_api.rotkehlchen.data.db\\n get_decoded_events_of_transaction(\\n evm_inquirer=ethereum_inquirer,\\n database=database,\\n tx_hash=tx_hex,\\n )\\n # Call time range\\n from_timestamp = 1627401169\\n to_timestamp = 1627401170\\n async_query = random.choice([False, True])\\n with patch(\\n 'rotkehlchen.chain.ethereum.modules.sushiswap.sushiswap.Sushiswap.get_balances',\\n side_effect=lambda _: {},\\n ):\\n response = requests.get(\\n api_url_for(\\n rotkehlchen_api_server,\\n 'modulestatsresource',\\n module='sushiswap',\\n ),\\n json={\\n 'async_query': async_query,\\n 'from_timestamp': from_timestamp,\\n 'to_timestamp': to_timestamp,\\n },\\n )\\n if async_query:\\n task_id = assert_ok_async_response(response)\\n outcome = wait_for_async_task(rotkehlchen_api_server, task_id, timeout=120)\\n assert outcome['message'] == ''\\n result = outcome['result']\\n else:\\n result = assert_proper_response_with_result(response)\\n\\n events_balances = result[TEST_EVENTS_ADDRESS_1]\\n\\n assert len(events_balances) == 1\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6520807","0.5953189","0.5941957","0.5698872","0.56704205","0.55845326","0.55000305","0.54975384","0.53581554","0.5356301","0.5327412","0.53010184","0.5276863","0.52720934","0.5257284","0.52468723","0.5164559","0.5162055","0.51554185","0.5149581","0.5126841","0.51238465","0.512099","0.5117233","0.5102619","0.5096786","0.5094895","0.50867134","0.50841445","0.5080421","0.50669205","0.5043826","0.5013088","0.5007789","0.5005582","0.5003696","0.499864","0.4992441","0.499195","0.49833015","0.4983003","0.4982164","0.497292","0.49664688","0.49629444","0.49527597","0.49433863","0.49418467","0.49315026","0.49312994","0.49193215","0.49047247","0.49017963","0.489988","0.48939842","0.4880181","0.48619732","0.48574516","0.4830393","0.48298204","0.48180303","0.48159268","0.48088557","0.48060074","0.48047212","0.4799049","0.47962448","0.4793121","0.4793121","0.4793023","0.47926268","0.47911507","0.47899738","0.4788828","0.47856945","0.4781686","0.477621","0.47705534","0.47657394","0.4764844","0.47592068","0.47548464","0.47517413","0.4749733","0.47467735","0.4745751","0.47418064","0.47402418","0.47398","0.4738336","0.47336483","0.47329706","0.47326845","0.4732064","0.47302482","0.47302148","0.47300956","0.4726414","0.4725063","0.47225544"],"string":"[\n \"0.6520807\",\n \"0.5953189\",\n \"0.5941957\",\n \"0.5698872\",\n \"0.56704205\",\n \"0.55845326\",\n \"0.55000305\",\n \"0.54975384\",\n \"0.53581554\",\n \"0.5356301\",\n \"0.5327412\",\n \"0.53010184\",\n \"0.5276863\",\n \"0.52720934\",\n \"0.5257284\",\n \"0.52468723\",\n \"0.5164559\",\n \"0.5162055\",\n \"0.51554185\",\n \"0.5149581\",\n \"0.5126841\",\n \"0.51238465\",\n \"0.512099\",\n \"0.5117233\",\n \"0.5102619\",\n \"0.5096786\",\n \"0.5094895\",\n \"0.50867134\",\n \"0.50841445\",\n \"0.5080421\",\n \"0.50669205\",\n \"0.5043826\",\n \"0.5013088\",\n \"0.5007789\",\n \"0.5005582\",\n \"0.5003696\",\n \"0.499864\",\n \"0.4992441\",\n \"0.499195\",\n \"0.49833015\",\n \"0.4983003\",\n \"0.4982164\",\n \"0.497292\",\n \"0.49664688\",\n \"0.49629444\",\n \"0.49527597\",\n \"0.49433863\",\n \"0.49418467\",\n \"0.49315026\",\n \"0.49312994\",\n \"0.49193215\",\n \"0.49047247\",\n \"0.49017963\",\n \"0.489988\",\n \"0.48939842\",\n \"0.4880181\",\n \"0.48619732\",\n \"0.48574516\",\n \"0.4830393\",\n \"0.48298204\",\n \"0.48180303\",\n \"0.48159268\",\n \"0.48088557\",\n \"0.48060074\",\n \"0.48047212\",\n \"0.4799049\",\n \"0.47962448\",\n \"0.4793121\",\n \"0.4793121\",\n \"0.4793023\",\n \"0.47926268\",\n \"0.47911507\",\n \"0.47899738\",\n \"0.4788828\",\n \"0.47856945\",\n \"0.4781686\",\n \"0.477621\",\n \"0.47705534\",\n \"0.47657394\",\n \"0.4764844\",\n \"0.47592068\",\n \"0.47548464\",\n \"0.47517413\",\n \"0.4749733\",\n \"0.47467735\",\n \"0.4745751\",\n \"0.47418064\",\n \"0.47402418\",\n \"0.47398\",\n \"0.4738336\",\n \"0.47336483\",\n \"0.47329706\",\n \"0.47326845\",\n \"0.4732064\",\n \"0.47302482\",\n \"0.47302148\",\n \"0.47300956\",\n \"0.4726414\",\n \"0.4725063\",\n \"0.47225544\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.72200763"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":3694,"cells":{"query":{"kind":"string","value":"Resets the accumulated statistics back to initial state and associates the application settings object with the stats engine. This should be called when application is first activated and combined application settings incorporating server side settings are available. Would also be called on any forced restart of agent or a reconnection due to loss of connection."},"document":{"kind":"string","value":"def reset_stats(self, settings, reset_stream=False):\n\n self.__settings = settings\n self.__stats_table = {}\n self.__sql_stats_table = {}\n self.__slow_transaction = None\n self.__slow_transaction_map = {}\n self.__slow_transaction_old_duration = None\n self.__transaction_errors = []\n self.__synthetics_transactions = []\n\n self.reset_transaction_events()\n self.reset_error_events()\n self.reset_custom_events()\n self.reset_span_events()\n self.reset_synthetics_events()\n # streams are never reset after instantiation\n if reset_stream:\n self._span_stream = StreamBuffer(\n settings.infinite_tracing.span_queue_size)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def stats_reset(self):\n self.stats.reset()","def stats_reset(self):\n self.stats.reset()","def reset_settings():\n settings = Settings()\n settings.reset()\n settings.save()","def initialize(self):\n super(Stats, self).initialize()\n if not hasattr(self.application, 'rabbitmq'):\n setattr(self.application, 'rabbitmq', dict())\n if not hasattr(self.application, 'host'):\n setattr(self.application, 'host',\n socket.gethostname())","def update(self, settings):\n self.settings.cache_clear()\n self._settings = settings\n log.info(\"Updated settings to %s\", self._settings)","def reset(self):\n self.stats = {}","def reset(self):\n reset_system_health_series()","def update_settings(self):\n\n self.sim.account.set_balance(int(self.balance_str.get()))\n\n self.sim.config.set_base_bet(int(self.base_bet_str.get()))\n self.sim.config.set_payout(float(self.payout_str.get()))\n self.sim.config.set_iterations(int(self.iterations_str.get()))\n self.sim.config.set_loss_adder(int(self.loss_adder_str.get()))","def reset_metric_stats(self):\n\n self.__stats_table = {}","async def _reset_settings(self, ctx):\n data = await self.get_data(ctx)\n await data.Settings.clear()\n msg = (\"{0.name} ({0.id}) reset all \"\n \"casino settings.\").format(ctx.author)\n await ctx.send(msg)","async def _reset_all_settings(self, ctx):\n await self._reset_settings(ctx)\n await self._reset_memberships(ctx)\n await self._reset_games(ctx)\n await self._reset_cooldowns(ctx)","def reset_metric_stats(self):\n self.__stats_table = {}","def reset(self):\n self.manager.delete_all()\n for name, val in DEFAULT_SETTINGS.items():\n val['name'] = name\n val['default_value'] = val['value']\n self.manager.from_dict(val)","def resetSettings(self):\n\n # it does this 4 times because for some reason it would not grab everything one time through. Investigate\n for i in range(4):\n\n networkNode = self.returnNetworkNode\n attrs = cmds.listAttr(networkNode, ud=True)\n\n for attr in attrs:\n attrType = str(cmds.getAttr(networkNode + \".\" + attr, type=True))\n\n if attrType == \"double\":\n cmds.setAttr(networkNode + \".\" + attr, lock=False)\n cmds.setAttr(networkNode + \".\" + attr, 0, lock=True)\n\n if attrType == \"bool\":\n cmds.setAttr(networkNode + \".\" + attr, lock=False)\n cmds.setAttr(networkNode + \".\" + attr, True, lock=True)\n\n if attrType == \"enum\":\n cmds.setAttr(networkNode + \".\" + attr, lock=False)\n cmds.setAttr(networkNode + \".\" + attr, 0, lock=True)\n\n # relaunch the UI\n self.updateSettingsUI()\n self.applyModuleChanges(self)","def initial_config(self, server_id):\n\n if server_id not in self.settings:\n self.settings[server_id] = {'inactive': True,\n 'output': [],\n 'cleanup': False,\n 'usercache': [],\n 'multiout': False\n }\n self.save_json()","def reset(self):\n self.sum_metric = 0.\n self.num_inst = 0.\n self.metrics.reset_stats()","def __load_settings(self):\n\n self.app_settings = sublime.load_settings(self.SETTINGS_FILE)\n self.__refresh_settings(True)\n\n # The settings may change during execution so we need to listen for changes\n self.app_settings.add_on_change(self.SETTINGS_CALLBACK_KEY, self.__refresh_settings)","def set_unhandled_settings(self):\n # Set any custom settings\n # which where not setted (ex. on some widget's state changed)\n\n # Save all settings\n settings.save()","def clear_settings(site_name): # untested - do I need/want this?\n return update_settings(site_name, {})","def reset(self) -> None:\n self.statistics = defaultdict(self._mp_hack)\n self._ddp_backend = get_backend()","def reset(self) -> None:\n self.statistics = defaultdict(self._mp_hack)\n self._ddp_backend = get_backend()","def reset(self) -> None:\n self.statistics = defaultdict(self._mp_hack)\n self._ddp_backend = get_backend()","def on_settings(self):\n\n # Pull the current app state from the relay Observer object\n status, interval, ntfc_status, ntfc_state = settings_state.get_state()\n\n # Pass it to the Observable object in order to render the Settings window\n settings_changed, update_interval, ntfc_changed, ntfc_selected = render_settings_window(\n status, interval, ntfc_status, ntfc_state, settings_state)\n\n # Register any state changes\n settings_state.update_state(settings_changed, update_interval, ntfc_changed, ntfc_selected)\n\n # If the interval has changed, reprogram scheduler to run at the new interval\n if settings_state.intrvl_change_trig:\n modify_scheduler(JOB_ID, settings_state.settings_interval)\n\n if settings_state.notification_change_trig:\n NewsIndicator.notifications = False if not settings_state.notification_state else True","def reset(self) -> None:\n self.statistics = defaultdict(int)","async def reset(self, ctx):\n await self.config.clear_all_guilds()\n await ctx.send(\"Reset all settings to default values.\")","def __init__(self, settings):\n self._settings = settings\n self._stats = None","def _create_config_and_reset_app(cls):\n cls.server_configuration = cls.create_configuration()\n utils.copy_resources(cls.server_configuration.file_server_root)\n server.reset_app(cls.server_configuration)\n\n cls._set_hash_mechanism_to_plaintext()","def merge_settings(self, settings):\n self.settings.merge_settings(settings)","def refresh_config(self):\n self._user_config = UserConfig(None)","def reset(self) -> None:\n self.statistics = defaultdict(float)","def add_settings_early(self):\n pass","def load_settings(self):\n # Set the default settings. In case in a later version of this script the settings change, new default variables will be added automatically\n self.settings = {\n # Connection settings to OBS Studio websockets plugin\n \"host\": \"localhost\",\n \"port\": 4444,\n \"password\": \"\",\n \"update_frequency\": 1, # seconds, how often the script loads the SC2 UI location\n }\n if os.path.isfile(self.settings_path):\n with open(self.settings_path) as f:\n self.settings.update(json.load(f))","def open_settings(self, event):\n settings_dialog = cfg.SettingsDialog(parent=self, exclude=['window'])\n res = settings_dialog.ShowModal()\n if res == wx.ID_OK:\n # Reload relevant parts of app\n restart_monitor_timer = False\n restart_gui_timer = False\n reload_correlations = False\n reload_logger = False\n reload_graph = False\n\n for setting in settings_dialog.changed_settings:\n # If any 'monitor.' settings except 'monitor.divergence_threshold have changed then restart\n # monitoring timer with new settings.\n # If 'monitor.interval has changed then restart gui timer.\n # If 'monitor.monitoring_threshold' has changed, then refresh correlation data.\n # If any 'logging.' settings have changed, then reload logger config.\n if setting.startswith('monitor.') and setting != 'monitor.divergence_threshold':\n restart_monitor_timer = True\n if setting == 'monitor.interval':\n restart_gui_timer = True\n if setting == 'monitor.monitoring_threshold':\n reload_correlations = True\n if setting.startswith('logging.'):\n reload_logger = True\n if setting.startswith('monitor.calculations'):\n reload_graph = True\n\n # Now perform the actions\n if restart_monitor_timer:\n self.__log.info(\"Settings updated. Reloading monitoring timer.\")\n self.__cor.stop_monitor()\n\n # Build calculation params and start monitor\n calculation_params = [self.__config.get('monitor.calculations.long'),\n self.__config.get('monitor.calculations.medium'),\n self.__config.get('monitor.calculations.short')]\n\n self.__cor.start_monitor(interval=self.__config.get('monitor.interval'),\n calculation_params=calculation_params,\n cache_time=self.__config.get('monitor.tick_cache_time'),\n autosave=self.__config.get('monitor.autosave'),\n filename=self.__opened_filename)\n\n if restart_gui_timer:\n self.__log.info(\"Settings updated. Restarting gui timer.\")\n self.timer.Stop()\n self.timer.Start(self.__config.get('monitor.interval') * 1000)\n\n if reload_correlations:\n self.__log.info(\"Settings updated. Updating monitoring threshold and reloading grid.\")\n self.__cor.monitoring_threshold = self.__config.get(\"monitor.monitoring_threshold\")\n self.__refresh_grid()\n\n if reload_logger:\n self.__log.info(\"Settings updated. Reloading logger.\")\n log_config = cfg.Config().get('logging')\n logging.config.dictConfig(log_config)\n\n if reload_graph:\n self.__log.info(\"Settings updated. Reloading graph.\")\n if len(self.__selected_correlation) == 2:\n self.show_graph(symbol1=self.__selected_correlation[0], symbol2=self.__selected_correlation[1])","def reset_active_settings(self):\n self.compute = yacman.YacAttMap()\n return True","def clearAllSettings(self) -> None:\n ...","def init_settings(self):\n self.app.config.setdefault('SIMPLE_DOMAINS', [])\n self.app.config.setdefault('AWS_ACCESS_KEY_ID', environ.get('AWS_ACCESS_KEY_ID'))\n self.app.config.setdefault('AWS_SECRET_ACCESS_KEY', environ.get('AWS_SECRET_ACCESS_KEY'))\n self.app.config.setdefault('AWS_REGION', environ.get('AWS_REGION', self.DEFAULT_REGION))","def reset( self ):\n self.conf = self.defaults","def refresh(self) -> None:\n self.data = {}\n self.load_settings_file(self.default_settings_path / \"settings.yaml\", file_key=\"internal\")\n self.load_systems(self.default_settings_path / \"systems\")\n self.load_settings_file(self.personal_dir / \"settings.yaml\", file_key=\"user\")\n self.load_systems(self.personal_dir / \"systems\")","def reset_stats(self):\n self.ships_left = self.ai_settings.ship_limit\n self.score = 0\n # Never reset the high score!\n self.high_score = self.read_score()\n self.level = 1","def settings(self, settings):\n\n self._settings = settings","def override_appsettings(**settings):\n\n def _dec(func):\n @wraps(func)\n def _inner(*args, **kwargs):\n # Apply new settings, backup old, clear caches\n old_values = {}\n for key, new_value in settings.items():\n old_values[key] = getattr(appsettings, key)\n setattr(appsettings, key, new_value)\n _reset_setting_caches()\n\n func(*args, **kwargs)\n for key, old_value in old_values.items():\n setattr(appsettings, key, old_value)\n\n # reset caches\n _reset_setting_caches()\n\n return _inner\n\n return _dec","def reset(self):\n self.start_times = {}\n self.stats = defaultdict(OnlineMeter) # float defaults to 0","def _post_initialisations(self):\n # Init the settings module.\n self.dummy_for_settings = SectionConfig(self.app.id, self.__class__.__name__)\n global settings\n settings = self.dummy_for_settings\n\n self.dummy_for_options = OptionConfig(self.app.id)\n global options\n options = self.dummy_for_options\n\n # Bind message boxes.\n self.MessageBox = MessageBox(self)\n self.msg = self.MessageBox.Message\n self.are_you_sure = self.MessageBox.are_you_sure\n\n # Set previous size and state.\n width = settings.get('width', 350)\n height = settings.get('height', 350)\n self.set_title(self.app.localizedname)\n self.resize(width, height)\n if settings.get_bool('maximized', False):\n self.maximize()\n # Load any other settings here.\n self.load_xinput_devices()","def add_settings_early(self):\n\n # config settings\n config = {\n # some generic settings for every site, to point to location of some stuff\n mconst.DEF_SETTINGNAME_pkgdirimps_sitempacks: [pkgdirimp_sitempacks],\n mconst.DEF_SETTINGNAME_controllerroot: pkgdirimp_controllers,\n mconst.DEF_SETTINGNAME_sitefilepath: misc.calc_modulefiledirpath(__file__),\n # should we also load mewlo site installed setuptools plugins\n mconst.DEF_SETTINGNAME_flag_importsetuptoolspacks: True,\n mconst.DEF_SETTINGNAME_replaceshadowpath: '${sitefilepath}/replaceshadow',\n }\n self.settings.merge_settings_key(mconst.DEF_SETTINGSEC_config, config)\n\n # config settings\n config = {\n # Name of site\n mconst.DEF_SETTINGNAME_sitename: 'Mewlo',\n # Specify where this site serves from\n # these siteurls should not end in / so if you are serving a site at root just use relative of '' and absolute of 'http://sitename.com'\n mconst.DEF_SETTINGNAME_siteurl_relative: '',\n mconst.DEF_SETTINGNAME_siteurl_absolute: 'http://127.0.0.1:8080',\n #mconst.DEF_SETTINGNAME_siteurl_relative: '/public/publicity',\n #mconst.DEF_SETTINGNAME_siteurl_absolute: 'http://127.0.0.1:8080/public/publicity',\n }\n self.settings.merge_settings_key(mconst.DEF_SETTINGSEC_config, config)\n\n # config settings\n config = {\n # online status information\n mconst.DEF_SETTINGNAME_isenabled: True,\n mconst.DEF_SETTINGNAME_isonline: True,\n mconst.DEF_SETTINGNAME_offline_mode: 'maintenance',\n mconst.DEF_SETTINGNAME_offline_message: 'We are down for leap-year maintenance; we will be back soon.',\n mconst.DEF_SETTINGNAME_offline_allowadmin: False,\n }\n self.settings.merge_settings_key(mconst.DEF_SETTINGSEC_config, config)\n\n\n\n # extension pack config -- we need to explicitly enable plugins\n packconfig = {\n 'mouser.mewlotestplug' : {\n 'isenabled': False,\n },\n 'mouser.testpack' : {\n 'isenabled': False,\n },\n 'mewlo.siteaddon.account' : {\n 'isenabled': True,\n },\n 'mewlo.siteaddon.group' : {\n 'isenabled': True,\n },\n }\n self.settings.merge_settings_key(mconst.DEF_SETTINGSEC_packs, packconfig)\n\n\n # database config\n databaseconfig = {\n 'settings' : {\n 'sqlalchemy_loglevel' : logging.NOTSET,\n #'sqlalchemy_loglevel' : logging.INFO,\n },\n 'default' : {\n 'url' : 'sqlite:///${dbfilepath}/mewlo_testsite1.sqlite',\n #'tablename_prefix': 'mewlo_',\n 'flag_echologging' : False,\n },\n 'mysql_unused' : {\n # Sample configuration for mysql\n 'url' : 'mysql://mewlo_user:mewlo_pass@localhost:3306/mewlo_testsite1',\n 'tablename_prefix': 'mewlo_'\n },\n }\n self.settings.merge_settings_key(mconst.DEF_SETTINGSEC_database, databaseconfig)\n self.settings.listappend_settings_key(mconst.DEF_SETTINGSEC_make_dirs, '${dbfilepath}')\n\n # email config settings\n mailconfig = {\n # online status information\n 'smtp_host': self.get_configval('mail_smtp_host'),\n 'smtp_login': self.get_configval('mail_smtp_login'),\n 'smtp_port': self.get_configval('mail_smtp_port'),\n 'smtp_mode': self.get_configval('mail_smtp_mode'),\n 'smtp_password': self.get_configval('mail_smtp_password'),\n 'mail_from' : self.get_configval('mail_from'),\n }\n self.settings.merge_settings_key(mconst.DEF_SETTINGSEC_mail, mailconfig)\n\n\n # account siteaddon settings\n siteaddonconfig = {\n # online status information\n 'registration_mode': 'immediate',\n 'flag_require_email_verified_before_login': False,\n }\n self.settings.merge_settings_key('siteaddon_account', siteaddonconfig)\n\n\n\n # ATTN: UNFINISHED\n # asset mounts config\n if (False):\n assetmountconfig = {\n 'default' : {\n # an internal assetmount just needs a url route\n 'type': 'internal',\n 'routeid': 'static_files',\n },\n 'external' : {\n 'type': 'external',\n 'filepath': '${mewlofilepath}/public_assets',\n 'urlpath': 'http://127.0.0.1/mewlo/public_assets',\n },\n }\n self.settings.merge_settings_key(mconst.DEF_SETTINGSEC_asset_mounts, assetmountconfig)\n\n\n\n\n\n #print \"TESTING CONFIG1:\"\n #self.run_configfunc('sayhello',1,2,3)\n #print \"TESTING CONFIG2:\"\n #self.run_allconfigfuncs('sayhello',1,2,3)","def reload_settings(self):\n importlib.reload(sys.modules['micromasters.settings'])\n # Restore settings to original settings after test\n self.addCleanup(importlib.reload, sys.modules['micromasters.settings'])\n return vars(sys.modules['micromasters.settings'])","def _reset_server_settings(self, server_id):\n\t\tself._remove_cfg_from_list(server_id)\n\t\tself._remove_settings_file(server_id)","def set_config(self): # called from button_set object \n self.settings['lights_on'] = self.lights_on.get()\n self.settings['lights_off'] = self.lights_off.get()\n self.settings['ambient_min'] = self.ambient_min.get()\n self.settings['soil_1'] = self.smc1.get()\n self.settings['soil_2'] = self.smc2.get()\n self.settings['soil_3'] = self.smc3.get()\n self.settings['soil_4'] = self.smc4.get()\n self.settings['overhead_level'] = self.overhead_level.get()\n\n # Save settings to config file in case of reboot / power-loss\n print \"UPDATING SETTINGS FILE\"\n with open(self.settings_path, 'w') as jsonfile:\n jsonfile.write(json.dumps(self.settings, indent=4))\n self.active_changes = True # (flag) changes are active!","def action_settings(self):\n\n cur_datadir = self.config.starbound_data_dir\n settings = SettingsDialog(self)\n settings.exec()\n new_datadir = self.config.starbound_data_dir\n if new_datadir:\n if cur_datadir != new_datadir:\n self.load_data()\n self.scene.refresh(self.data)\n else:\n self.close_world()\n\n # Make sure our menus are enabled/disabled as appropriate\n self.enforce_menu_state()\n\n # Re-focus the main window\n self.activateWindow()","def reset_stats(self):\r\n self.ships_left = self.ai_settings.ship_limit\r\n self.score = 0\r\n self.level = 1","def reset_stats(self):\n self.ships_left= self.settings.ship_limit\n self.score = 0\n self.level = 1","def reload(self):\n self.load_config()\n # Seems we need to explicitly refresh this\n if self.main_instance:\n self.main_instance.config = self.config","def _setup_stats(self) -> None:\n\n # Save statistics\n self.mass = np.array([0])\n self.mass_balance = np.array([0])\n self.mass_balance_trend = np.array([0])","def merge_onto(cls, settings):\r\n for key, value in cls.SETTINGS.iteritems():\r\n setattr(settings, key, value)","def _soft_reset(self):\n self._reset_specific_envs(self.episodes_done)\n self._update_other_info()","def clear_stats(self):\n self._stats = None","def reset_metric_variables(self) -> None:\n with self._lock:\n self._reset_metric_variables()","def reset_stats(self):\n print(\"Reseting stats\")\n self.player_lives = self.ai_stts.player_lives\n self.score = 0\n self.level = 1","def user_settings(self, user_settings):\n\n self._user_settings = user_settings","def reset_stats(self):\n self.lives_left = self.game_settings.lives\n self.score = 0\n self.level = 1","def reset(self):\n self.settings = None\n self.sublime_settings = None\n self.settings_base = \"Javatar.sublime-settings\"\n self.sublime_base = \"Preferences.sublime-settings\"","def onSettings(self):\n pass","def _clear_gui_settings(self):\n self._settings.clear()\n self._settings.sync()\n self.gui_settings_clear_button.setEnabled(False)\n self.cleared = True","def __setSettingsToStorage(value):\n AccountSettings.setSettings(NEW_SETTINGS_COUNTER, value)","def set_app_defaults(self):\n self.curve_render = 0\n self.image_render = 0\n self.image_height = 200\n self.image_data = []\n self.auto_scale = True\n\n self.create_actions()\n self.setup_signals()\n self.reset_graph()\n\n self.fps = utils.SimpleFPS()\n\n # Click the live button\n self.ui.actionContinue_Live_Updates.trigger()","def reset_stats() -> None:\n STATS[\"cleaned\"] = 0\n STATS[\"null\"] = 0\n STATS[\"unknown\"] = 0","def reset_hl_stats(self):\n\n self.ships_left = self.settings.ship_limit\n self.score = 0\n self.level = 1","def update(self):\n registry = getUtility(IRegistry)\n site_settings = registry.forInterface(\n ISiteSchema, prefix=\"plone\", check=False)\n try:\n if site_settings.webstats_js:\n self.webstats_js = site_settings.webstats_js\n except AttributeError:\n pass","def add_earlydefault_settings(self):\n self.add_default_settings_config()\n self.add_default_settings_aliases()","def reset_stats(self):\r\n self.ship_left = self.settings.ship_limit\r\n self.score = 0\r\n self.level = 1","def merge_into_settings(self, settings):\n if not self._meta_dict:\n self._load_from_file()\n\n settings.chat_name = self._meta_dict[DumpMetadata.CHAT_NAME]\n settings.last_message_id = self._meta_dict[DumpMetadata.LAST_MESSAGE_ID]\n settings.exporter = self._meta_dict[DumpMetadata.EXPORTER]","def _apply_settings(self):\n if 'fixed_delta_seconds' in self.dict_settings:\n self.settings.fixed_delta_seconds = self.dict_settings['fixed_delta_seconds']\n\n self.world.apply_settings(self.settings)","def reset(self):\n self._config = Config()\n self._router = Router(())\n self._middleware = []\n self._start_response = None","def reset(cls):\n cls._options = None\n cls._scoped_instances = {}","def refresh_configuration(self):\n pass","def _onSettings(self, event):\n dialog = sc.SettingsDialog(self)\n if dialog.ShowModal() == wx.ID_OK:\n dialog.saveSettings()\n dialog.Destroy()","def reset(frequency=None):\n assert state.profile_level == 0, b\"Can't reset() while statprof is running\"\n CodeSite.cache.clear()\n state.reset(frequency)","def reset_stats(self):\n self.ships_left = self.sett.ship_limit\n self.score = 0\n self.level = 1","async def reset_config(self):\n self.config = {\"enable_auto_gen\": False, \"enable_world_barrier\": False}\n await shared.event_handler.call_async(\"world:reset_config\")\n self.gamerule_handler = mcpython.common.world.GameRule.GameRuleHandler(self)","def reload_settings():\n global settings, cancel_thread\n\n # cancel the thread if the settings say so\n if cancel_thread is None:\n if settings.get('disabled') is False:\n cancel_thread = start_thread()\n else:\n if settings.get('disabled') is True:\n light_scheme_set = None\n current_timeout = 0\n cancel_thread()\n cancel_thread = None","def reset_el_stats(self):\n\n # Number of available ships.\n self.ships_left = self.settings.ship_limit\n self.score = 0 # Score.\n self.level = 1 # Level.","def resetSelfWithDefaults( self ):\n self.__dict__.update( self._defDict )","def reset_stats(self):\r\n self.pepes_left = self.ai_settings.pepe_limit\r\n self.score = 0\r\n self.level = 1","def __init__(self) -> None:\n self._settings = {}\n\n # Load values from global_settings (only uppercase)\n self.filter_and_set(global_settings)\n\n settings_env_value: str = os.environ.get(SETTINGS_ENV)\n if settings_env_value:\n # Load values from custom settings\n try:\n module = importlib.import_module(settings_env_value)\n except ModuleNotFoundError:\n msg = \"Can't import custom settings. Is it under PYTHONPATH?\"\n raise ModuleError(msg)\n self.filter_and_set(module)","def reset_env(env, num_active_adv=0):\n if hasattr(env, 'domain_randomization'):\n env.domain_randomization = False\n if num_active_adv > 0:\n env.adversary_range = env.advs_per_strength * env.num_adv_strengths","def gatherActiveDataStats(self, config):\n gatherWMDataMiningStats(config.wmstats_url, config.reqmgrdb_url, \n config.wmdatamining_url, False, log = cherrypy.log)\n return","def on_actionSettings_triggered(self):\n self.start_app(SettingsApp)","def kill_all(self):\n self.settings['lights_on'] = 12\n self.settings['lights_off'] = 12\n self.settings['overhead_level'] = 0\n self.settings['soil_1'] = 0\n self.settings['soil_2'] = 0\n self.settings['soil_3'] = 0\n self.settings['soil_4'] = 0\n self.scale_overhead_level.set(self.settings['overhead_level'])\n self.scale_smc1.set(self.settings['soil_1'])\n self.scale_smc2.set(self.settings['soil_2'])\n self.scale_smc3.set(self.settings['soil_3'])\n self.scale_smc4.set(self.settings['soil_4'])\n self.active_changes = True # (flag) Once changes are retrieved, we assume that they will be sent to the controller","def requestConfig(self):\n self.radioConfig = None\n self.channels = None\n self.partialChannels = [] # We keep our channels in a temp array until finished\n\n self._requestSettings()","def reset(self):\n\n for value in self.__dict__.itervalues():\n if isinstance(value, EnvParm):\n value._value = 'use_env'\n getattr(value, 'value')","def load_rule_settings():\n logger.info('Loading the default rule settings')\n with create_app().app_context():\n sess = GlobalDB.db().session\n # Clearing the current defaults before reloading them\n sess.query(RuleSetting).filter(RuleSetting.agency_code.is_(None)).delete(synchronize_session=False)\n sess.commit()\n load_default_rule_settings(sess)","def set_settings_devices(self):\n self.set_thermostat, self.set_humidifier, self.set_sprinklers, self.set_ventilation = self.settings[3:]","def init(self):\n\n if self.has_settings:\n print(\n TERM.bold_red('Error:'),\n 'Settings file already exists. Doing nothing.'\n )\n return\n\n new_settings = {\n 'strategy': self.ns.strategy,\n 'branch': self.ns.branch,\n 'scoring': self.ns.scoring,\n }\n\n with open(self.settings, 'w') as f:\n f.write(yaml.dump(new_settings, default_flow_style=False))\n\n print(\n TERM.bold_green('Yay!'),\n 'Wrote settings file {0}'.format(self.settings)\n )","def afterSetUp(self):\n self.load_config = {}\n self.load_config['monitor_interval'] = 1\n self.load_config['limit_number_request'] = 100\n self.load_config['limit_memory_used'] = 500","def save_settings(self):\n logger.info(f'Saving settings: {self.settings_dict}')\n for k, section in self.settings_dict.items():\n for setting_name in section.keys():\n value = self.get_control_value(setting_name)\n if value is not None:\n section[setting_name] = value\n\n write_settings(self.settings_dict)","def tearDownConfig(self):\n print time.ctime(), 'enter tearDownConfig'\n\n self.site1 = self.globalCfg['site1']\n self.site2 = self.globalCfg['site2']\n self.site3 = self.globalCfg['site3']\n\n self.site1.databaseLandscapeInfo()\n self.site2.databaseLandscapeInfo()\n self.site3.databaseLandscapeInfo()\n self.site1.systemReplicationStatus()\n\n if self.globalCfg['sync_mode'] == 'sync' and self.site1.fullSync:\n try:\n self.site1.srDisableFullSync(self.site1.getHost(\"WORKER1\"))\n self.site1.fullSync = False\n except Exception, e:\n print 'disable full_sync in tearDownConfig failed: %s' % e\n\n for h in range(1, self.site1.getHostNo()):\n self.site1.setConfigParameter(h, \"daemon.ini\", \"ConfigMgrPy.HOST\", \"indexserver.c\", \"instanceids\", None)\n for h in range(1, self.site2.getHostNo()):\n self.site2.setConfigParameter(h, \"daemon.ini\", \"ConfigMgrPy.HOST\", \"indexserver.c\", \"instanceids\", None)\n for h in range(1, self.site3.getHostNo()):\n self.site3.setConfigParameter(h, \"daemon.ini\", \"ConfigMgrPy.HOST\", \"indexserver.c\", \"instanceids\", None)\n\n self.site1.resetStatXSToMaster(self.globalCfg['multiDB'])\n self.site2.resetStatXSToMaster(self.globalCfg['multiDB'])\n self.site3.resetStatXSToMaster(self.globalCfg['multiDB'])\n\n self.site1.setTraceLevel(self.site1.getHost(\"WORKER1\"), \"global.ini\", \"sr_nameserver\", None)\n self.site1.setTraceLevel(self.site1.getHost(\"WORKER1\"), \"global.ini\", \"sr_dataaccess\", None)\n self.site1.setTraceLevel(self.site1.getHost(\"WORKER1\"), \"global.ini\", \"sr_log_retention\", None)\n self.site1.setTraceLevel(self.site1.getHost(\"WORKER1\"), \"global.ini\", \"pitrestart\", None)\n self.site1.setTraceLevel(self.site1.getHost(\"WORKER1\"), \"global.ini\", \"warm_upper\", None)\n self.site1.setTraceLevel(self.site1.getHost(\"WORKER1\"), \"global.ini\", \"sr_spcoordinator\", None)\n self.site1.setTraceLevel(self.site1.getHost(\"WORKER1\"), \"global.ini\", \"persistencelayer\", None)\n self.site1.setTraceLevel(self.site1.getHost(\"WORKER1\"), \"nameserver.ini\", \"nameserver\", None)\n\n self.site2.setTraceLevel(self.site2.getHost(\"WORKER1\"), \"global.ini\", \"sr_nameserver\", None)\n self.site2.setTraceLevel(self.site2.getHost(\"WORKER1\"), \"global.ini\", \"sr_dataaccess\", None)\n self.site2.setTraceLevel(self.site2.getHost(\"WORKER1\"), \"global.ini\", \"sr_log_retention\", None)\n self.site2.setTraceLevel(self.site2.getHost(\"WORKER1\"), \"global.ini\", \"pitrestart\", None)\n self.site2.setTraceLevel(self.site2.getHost(\"WORKER1\"), \"global.ini\", \"warm_upper\", None)\n self.site2.setTraceLevel(self.site2.getHost(\"WORKER1\"), \"global.ini\", \"sr_spcoordinator\", None)\n self.site2.setTraceLevel(self.site2.getHost(\"WORKER1\"), \"global.ini\", \"persistencelayer\", None)\n self.site2.setTraceLevel(self.site2.getHost(\"WORKER1\"), \"nameserver.ini\", \"nameserver\", None)\n\n self.site3.setTraceLevel(self.site3.getHost(\"WORKER1\"), \"global.ini\", \"sr_nameserver\", None)\n self.site3.setTraceLevel(self.site3.getHost(\"WORKER1\"), \"global.ini\", \"sr_dataaccess\", None)\n self.site3.setTraceLevel(self.site3.getHost(\"WORKER1\"), \"global.ini\", \"sr_log_retention\", None)\n self.site3.setTraceLevel(self.site3.getHost(\"WORKER1\"), \"global.ini\", \"pitrestart\", None)\n self.site3.setTraceLevel(self.site3.getHost(\"WORKER1\"), \"global.ini\", \"warm_upper\", None)\n self.site3.setTraceLevel(self.site3.getHost(\"WORKER1\"), \"global.ini\", \"sr_spcoordinator\", None)\n self.site3.setTraceLevel(self.site3.getHost(\"WORKER1\"), \"global.ini\", \"persistencelayer\", None)\n self.site3.setTraceLevel(self.site3.getHost(\"WORKER1\"), \"nameserver.ini\", \"nameserver\", None)\n\n # for normal tear down(unregister/disable), the steps should be in order\n # the primary cannot be disabled if there's secondary attached\n # so there's no need to use multi-thread\n # executing here means the landscape has been resorded to site1--(sync/syncmem)--site2--(async)--site3\n #pdb.set_trace()\n self.site3.tearDown()\n self.site2.tearDown()\n self.site1.tearDown()","async def admin_reset(self, ctx: commands.Context):\n await self.config.clear_all()\n await self.initialize_internals()\n await ctx.send('Global team management factory reset complete.')","def updateSettings(self):\n self.parser.read(self.file)\n self.showTicker = self.parser.getboolean('Settings', 'showTicker')\n self.verbose = self.parser.getboolean('Settings', 'verbose')\n self.sleepTime = self.parser.getint('Settings', 'sleeptime')\n self.saveGraph = self.parser.getboolean('Settings', 'saveGraph')\n self.graphDPI = self.parser.getint('Settings', 'graphDPI')","def reset():\n global HEALTH_AGGREGATOR\n\n HEALTH_AGGREGATOR.cleanup()\n HEALTH_AGGREGATOR = HealthAggregator()\n\n return jsonify({}), 200","def _reset(cls):\r\n cls._CONFIGURED = False\r\n cls._ENABLED = {}","def reset_cfg(self, server):\n\t\tserver = valid_server(server)\n\t\tself._reset_server_settings(server)"],"string":"[\n \"def stats_reset(self):\\n self.stats.reset()\",\n \"def stats_reset(self):\\n self.stats.reset()\",\n \"def reset_settings():\\n settings = Settings()\\n settings.reset()\\n settings.save()\",\n \"def initialize(self):\\n super(Stats, self).initialize()\\n if not hasattr(self.application, 'rabbitmq'):\\n setattr(self.application, 'rabbitmq', dict())\\n if not hasattr(self.application, 'host'):\\n setattr(self.application, 'host',\\n socket.gethostname())\",\n \"def update(self, settings):\\n self.settings.cache_clear()\\n self._settings = settings\\n log.info(\\\"Updated settings to %s\\\", self._settings)\",\n \"def reset(self):\\n self.stats = {}\",\n \"def reset(self):\\n reset_system_health_series()\",\n \"def update_settings(self):\\n\\n self.sim.account.set_balance(int(self.balance_str.get()))\\n\\n self.sim.config.set_base_bet(int(self.base_bet_str.get()))\\n self.sim.config.set_payout(float(self.payout_str.get()))\\n self.sim.config.set_iterations(int(self.iterations_str.get()))\\n self.sim.config.set_loss_adder(int(self.loss_adder_str.get()))\",\n \"def reset_metric_stats(self):\\n\\n self.__stats_table = {}\",\n \"async def _reset_settings(self, ctx):\\n data = await self.get_data(ctx)\\n await data.Settings.clear()\\n msg = (\\\"{0.name} ({0.id}) reset all \\\"\\n \\\"casino settings.\\\").format(ctx.author)\\n await ctx.send(msg)\",\n \"async def _reset_all_settings(self, ctx):\\n await self._reset_settings(ctx)\\n await self._reset_memberships(ctx)\\n await self._reset_games(ctx)\\n await self._reset_cooldowns(ctx)\",\n \"def reset_metric_stats(self):\\n self.__stats_table = {}\",\n \"def reset(self):\\n self.manager.delete_all()\\n for name, val in DEFAULT_SETTINGS.items():\\n val['name'] = name\\n val['default_value'] = val['value']\\n self.manager.from_dict(val)\",\n \"def resetSettings(self):\\n\\n # it does this 4 times because for some reason it would not grab everything one time through. Investigate\\n for i in range(4):\\n\\n networkNode = self.returnNetworkNode\\n attrs = cmds.listAttr(networkNode, ud=True)\\n\\n for attr in attrs:\\n attrType = str(cmds.getAttr(networkNode + \\\".\\\" + attr, type=True))\\n\\n if attrType == \\\"double\\\":\\n cmds.setAttr(networkNode + \\\".\\\" + attr, lock=False)\\n cmds.setAttr(networkNode + \\\".\\\" + attr, 0, lock=True)\\n\\n if attrType == \\\"bool\\\":\\n cmds.setAttr(networkNode + \\\".\\\" + attr, lock=False)\\n cmds.setAttr(networkNode + \\\".\\\" + attr, True, lock=True)\\n\\n if attrType == \\\"enum\\\":\\n cmds.setAttr(networkNode + \\\".\\\" + attr, lock=False)\\n cmds.setAttr(networkNode + \\\".\\\" + attr, 0, lock=True)\\n\\n # relaunch the UI\\n self.updateSettingsUI()\\n self.applyModuleChanges(self)\",\n \"def initial_config(self, server_id):\\n\\n if server_id not in self.settings:\\n self.settings[server_id] = {'inactive': True,\\n 'output': [],\\n 'cleanup': False,\\n 'usercache': [],\\n 'multiout': False\\n }\\n self.save_json()\",\n \"def reset(self):\\n self.sum_metric = 0.\\n self.num_inst = 0.\\n self.metrics.reset_stats()\",\n \"def __load_settings(self):\\n\\n self.app_settings = sublime.load_settings(self.SETTINGS_FILE)\\n self.__refresh_settings(True)\\n\\n # The settings may change during execution so we need to listen for changes\\n self.app_settings.add_on_change(self.SETTINGS_CALLBACK_KEY, self.__refresh_settings)\",\n \"def set_unhandled_settings(self):\\n # Set any custom settings\\n # which where not setted (ex. on some widget's state changed)\\n\\n # Save all settings\\n settings.save()\",\n \"def clear_settings(site_name): # untested - do I need/want this?\\n return update_settings(site_name, {})\",\n \"def reset(self) -> None:\\n self.statistics = defaultdict(self._mp_hack)\\n self._ddp_backend = get_backend()\",\n \"def reset(self) -> None:\\n self.statistics = defaultdict(self._mp_hack)\\n self._ddp_backend = get_backend()\",\n \"def reset(self) -> None:\\n self.statistics = defaultdict(self._mp_hack)\\n self._ddp_backend = get_backend()\",\n \"def on_settings(self):\\n\\n # Pull the current app state from the relay Observer object\\n status, interval, ntfc_status, ntfc_state = settings_state.get_state()\\n\\n # Pass it to the Observable object in order to render the Settings window\\n settings_changed, update_interval, ntfc_changed, ntfc_selected = render_settings_window(\\n status, interval, ntfc_status, ntfc_state, settings_state)\\n\\n # Register any state changes\\n settings_state.update_state(settings_changed, update_interval, ntfc_changed, ntfc_selected)\\n\\n # If the interval has changed, reprogram scheduler to run at the new interval\\n if settings_state.intrvl_change_trig:\\n modify_scheduler(JOB_ID, settings_state.settings_interval)\\n\\n if settings_state.notification_change_trig:\\n NewsIndicator.notifications = False if not settings_state.notification_state else True\",\n \"def reset(self) -> None:\\n self.statistics = defaultdict(int)\",\n \"async def reset(self, ctx):\\n await self.config.clear_all_guilds()\\n await ctx.send(\\\"Reset all settings to default values.\\\")\",\n \"def __init__(self, settings):\\n self._settings = settings\\n self._stats = None\",\n \"def _create_config_and_reset_app(cls):\\n cls.server_configuration = cls.create_configuration()\\n utils.copy_resources(cls.server_configuration.file_server_root)\\n server.reset_app(cls.server_configuration)\\n\\n cls._set_hash_mechanism_to_plaintext()\",\n \"def merge_settings(self, settings):\\n self.settings.merge_settings(settings)\",\n \"def refresh_config(self):\\n self._user_config = UserConfig(None)\",\n \"def reset(self) -> None:\\n self.statistics = defaultdict(float)\",\n \"def add_settings_early(self):\\n pass\",\n \"def load_settings(self):\\n # Set the default settings. In case in a later version of this script the settings change, new default variables will be added automatically\\n self.settings = {\\n # Connection settings to OBS Studio websockets plugin\\n \\\"host\\\": \\\"localhost\\\",\\n \\\"port\\\": 4444,\\n \\\"password\\\": \\\"\\\",\\n \\\"update_frequency\\\": 1, # seconds, how often the script loads the SC2 UI location\\n }\\n if os.path.isfile(self.settings_path):\\n with open(self.settings_path) as f:\\n self.settings.update(json.load(f))\",\n \"def open_settings(self, event):\\n settings_dialog = cfg.SettingsDialog(parent=self, exclude=['window'])\\n res = settings_dialog.ShowModal()\\n if res == wx.ID_OK:\\n # Reload relevant parts of app\\n restart_monitor_timer = False\\n restart_gui_timer = False\\n reload_correlations = False\\n reload_logger = False\\n reload_graph = False\\n\\n for setting in settings_dialog.changed_settings:\\n # If any 'monitor.' settings except 'monitor.divergence_threshold have changed then restart\\n # monitoring timer with new settings.\\n # If 'monitor.interval has changed then restart gui timer.\\n # If 'monitor.monitoring_threshold' has changed, then refresh correlation data.\\n # If any 'logging.' settings have changed, then reload logger config.\\n if setting.startswith('monitor.') and setting != 'monitor.divergence_threshold':\\n restart_monitor_timer = True\\n if setting == 'monitor.interval':\\n restart_gui_timer = True\\n if setting == 'monitor.monitoring_threshold':\\n reload_correlations = True\\n if setting.startswith('logging.'):\\n reload_logger = True\\n if setting.startswith('monitor.calculations'):\\n reload_graph = True\\n\\n # Now perform the actions\\n if restart_monitor_timer:\\n self.__log.info(\\\"Settings updated. Reloading monitoring timer.\\\")\\n self.__cor.stop_monitor()\\n\\n # Build calculation params and start monitor\\n calculation_params = [self.__config.get('monitor.calculations.long'),\\n self.__config.get('monitor.calculations.medium'),\\n self.__config.get('monitor.calculations.short')]\\n\\n self.__cor.start_monitor(interval=self.__config.get('monitor.interval'),\\n calculation_params=calculation_params,\\n cache_time=self.__config.get('monitor.tick_cache_time'),\\n autosave=self.__config.get('monitor.autosave'),\\n filename=self.__opened_filename)\\n\\n if restart_gui_timer:\\n self.__log.info(\\\"Settings updated. Restarting gui timer.\\\")\\n self.timer.Stop()\\n self.timer.Start(self.__config.get('monitor.interval') * 1000)\\n\\n if reload_correlations:\\n self.__log.info(\\\"Settings updated. Updating monitoring threshold and reloading grid.\\\")\\n self.__cor.monitoring_threshold = self.__config.get(\\\"monitor.monitoring_threshold\\\")\\n self.__refresh_grid()\\n\\n if reload_logger:\\n self.__log.info(\\\"Settings updated. Reloading logger.\\\")\\n log_config = cfg.Config().get('logging')\\n logging.config.dictConfig(log_config)\\n\\n if reload_graph:\\n self.__log.info(\\\"Settings updated. Reloading graph.\\\")\\n if len(self.__selected_correlation) == 2:\\n self.show_graph(symbol1=self.__selected_correlation[0], symbol2=self.__selected_correlation[1])\",\n \"def reset_active_settings(self):\\n self.compute = yacman.YacAttMap()\\n return True\",\n \"def clearAllSettings(self) -> None:\\n ...\",\n \"def init_settings(self):\\n self.app.config.setdefault('SIMPLE_DOMAINS', [])\\n self.app.config.setdefault('AWS_ACCESS_KEY_ID', environ.get('AWS_ACCESS_KEY_ID'))\\n self.app.config.setdefault('AWS_SECRET_ACCESS_KEY', environ.get('AWS_SECRET_ACCESS_KEY'))\\n self.app.config.setdefault('AWS_REGION', environ.get('AWS_REGION', self.DEFAULT_REGION))\",\n \"def reset( self ):\\n self.conf = self.defaults\",\n \"def refresh(self) -> None:\\n self.data = {}\\n self.load_settings_file(self.default_settings_path / \\\"settings.yaml\\\", file_key=\\\"internal\\\")\\n self.load_systems(self.default_settings_path / \\\"systems\\\")\\n self.load_settings_file(self.personal_dir / \\\"settings.yaml\\\", file_key=\\\"user\\\")\\n self.load_systems(self.personal_dir / \\\"systems\\\")\",\n \"def reset_stats(self):\\n self.ships_left = self.ai_settings.ship_limit\\n self.score = 0\\n # Never reset the high score!\\n self.high_score = self.read_score()\\n self.level = 1\",\n \"def settings(self, settings):\\n\\n self._settings = settings\",\n \"def override_appsettings(**settings):\\n\\n def _dec(func):\\n @wraps(func)\\n def _inner(*args, **kwargs):\\n # Apply new settings, backup old, clear caches\\n old_values = {}\\n for key, new_value in settings.items():\\n old_values[key] = getattr(appsettings, key)\\n setattr(appsettings, key, new_value)\\n _reset_setting_caches()\\n\\n func(*args, **kwargs)\\n for key, old_value in old_values.items():\\n setattr(appsettings, key, old_value)\\n\\n # reset caches\\n _reset_setting_caches()\\n\\n return _inner\\n\\n return _dec\",\n \"def reset(self):\\n self.start_times = {}\\n self.stats = defaultdict(OnlineMeter) # float defaults to 0\",\n \"def _post_initialisations(self):\\n # Init the settings module.\\n self.dummy_for_settings = SectionConfig(self.app.id, self.__class__.__name__)\\n global settings\\n settings = self.dummy_for_settings\\n\\n self.dummy_for_options = OptionConfig(self.app.id)\\n global options\\n options = self.dummy_for_options\\n\\n # Bind message boxes.\\n self.MessageBox = MessageBox(self)\\n self.msg = self.MessageBox.Message\\n self.are_you_sure = self.MessageBox.are_you_sure\\n\\n # Set previous size and state.\\n width = settings.get('width', 350)\\n height = settings.get('height', 350)\\n self.set_title(self.app.localizedname)\\n self.resize(width, height)\\n if settings.get_bool('maximized', False):\\n self.maximize()\\n # Load any other settings here.\\n self.load_xinput_devices()\",\n \"def add_settings_early(self):\\n\\n # config settings\\n config = {\\n # some generic settings for every site, to point to location of some stuff\\n mconst.DEF_SETTINGNAME_pkgdirimps_sitempacks: [pkgdirimp_sitempacks],\\n mconst.DEF_SETTINGNAME_controllerroot: pkgdirimp_controllers,\\n mconst.DEF_SETTINGNAME_sitefilepath: misc.calc_modulefiledirpath(__file__),\\n # should we also load mewlo site installed setuptools plugins\\n mconst.DEF_SETTINGNAME_flag_importsetuptoolspacks: True,\\n mconst.DEF_SETTINGNAME_replaceshadowpath: '${sitefilepath}/replaceshadow',\\n }\\n self.settings.merge_settings_key(mconst.DEF_SETTINGSEC_config, config)\\n\\n # config settings\\n config = {\\n # Name of site\\n mconst.DEF_SETTINGNAME_sitename: 'Mewlo',\\n # Specify where this site serves from\\n # these siteurls should not end in / so if you are serving a site at root just use relative of '' and absolute of 'http://sitename.com'\\n mconst.DEF_SETTINGNAME_siteurl_relative: '',\\n mconst.DEF_SETTINGNAME_siteurl_absolute: 'http://127.0.0.1:8080',\\n #mconst.DEF_SETTINGNAME_siteurl_relative: '/public/publicity',\\n #mconst.DEF_SETTINGNAME_siteurl_absolute: 'http://127.0.0.1:8080/public/publicity',\\n }\\n self.settings.merge_settings_key(mconst.DEF_SETTINGSEC_config, config)\\n\\n # config settings\\n config = {\\n # online status information\\n mconst.DEF_SETTINGNAME_isenabled: True,\\n mconst.DEF_SETTINGNAME_isonline: True,\\n mconst.DEF_SETTINGNAME_offline_mode: 'maintenance',\\n mconst.DEF_SETTINGNAME_offline_message: 'We are down for leap-year maintenance; we will be back soon.',\\n mconst.DEF_SETTINGNAME_offline_allowadmin: False,\\n }\\n self.settings.merge_settings_key(mconst.DEF_SETTINGSEC_config, config)\\n\\n\\n\\n # extension pack config -- we need to explicitly enable plugins\\n packconfig = {\\n 'mouser.mewlotestplug' : {\\n 'isenabled': False,\\n },\\n 'mouser.testpack' : {\\n 'isenabled': False,\\n },\\n 'mewlo.siteaddon.account' : {\\n 'isenabled': True,\\n },\\n 'mewlo.siteaddon.group' : {\\n 'isenabled': True,\\n },\\n }\\n self.settings.merge_settings_key(mconst.DEF_SETTINGSEC_packs, packconfig)\\n\\n\\n # database config\\n databaseconfig = {\\n 'settings' : {\\n 'sqlalchemy_loglevel' : logging.NOTSET,\\n #'sqlalchemy_loglevel' : logging.INFO,\\n },\\n 'default' : {\\n 'url' : 'sqlite:///${dbfilepath}/mewlo_testsite1.sqlite',\\n #'tablename_prefix': 'mewlo_',\\n 'flag_echologging' : False,\\n },\\n 'mysql_unused' : {\\n # Sample configuration for mysql\\n 'url' : 'mysql://mewlo_user:mewlo_pass@localhost:3306/mewlo_testsite1',\\n 'tablename_prefix': 'mewlo_'\\n },\\n }\\n self.settings.merge_settings_key(mconst.DEF_SETTINGSEC_database, databaseconfig)\\n self.settings.listappend_settings_key(mconst.DEF_SETTINGSEC_make_dirs, '${dbfilepath}')\\n\\n # email config settings\\n mailconfig = {\\n # online status information\\n 'smtp_host': self.get_configval('mail_smtp_host'),\\n 'smtp_login': self.get_configval('mail_smtp_login'),\\n 'smtp_port': self.get_configval('mail_smtp_port'),\\n 'smtp_mode': self.get_configval('mail_smtp_mode'),\\n 'smtp_password': self.get_configval('mail_smtp_password'),\\n 'mail_from' : self.get_configval('mail_from'),\\n }\\n self.settings.merge_settings_key(mconst.DEF_SETTINGSEC_mail, mailconfig)\\n\\n\\n # account siteaddon settings\\n siteaddonconfig = {\\n # online status information\\n 'registration_mode': 'immediate',\\n 'flag_require_email_verified_before_login': False,\\n }\\n self.settings.merge_settings_key('siteaddon_account', siteaddonconfig)\\n\\n\\n\\n # ATTN: UNFINISHED\\n # asset mounts config\\n if (False):\\n assetmountconfig = {\\n 'default' : {\\n # an internal assetmount just needs a url route\\n 'type': 'internal',\\n 'routeid': 'static_files',\\n },\\n 'external' : {\\n 'type': 'external',\\n 'filepath': '${mewlofilepath}/public_assets',\\n 'urlpath': 'http://127.0.0.1/mewlo/public_assets',\\n },\\n }\\n self.settings.merge_settings_key(mconst.DEF_SETTINGSEC_asset_mounts, assetmountconfig)\\n\\n\\n\\n\\n\\n #print \\\"TESTING CONFIG1:\\\"\\n #self.run_configfunc('sayhello',1,2,3)\\n #print \\\"TESTING CONFIG2:\\\"\\n #self.run_allconfigfuncs('sayhello',1,2,3)\",\n \"def reload_settings(self):\\n importlib.reload(sys.modules['micromasters.settings'])\\n # Restore settings to original settings after test\\n self.addCleanup(importlib.reload, sys.modules['micromasters.settings'])\\n return vars(sys.modules['micromasters.settings'])\",\n \"def _reset_server_settings(self, server_id):\\n\\t\\tself._remove_cfg_from_list(server_id)\\n\\t\\tself._remove_settings_file(server_id)\",\n \"def set_config(self): # called from button_set object \\n self.settings['lights_on'] = self.lights_on.get()\\n self.settings['lights_off'] = self.lights_off.get()\\n self.settings['ambient_min'] = self.ambient_min.get()\\n self.settings['soil_1'] = self.smc1.get()\\n self.settings['soil_2'] = self.smc2.get()\\n self.settings['soil_3'] = self.smc3.get()\\n self.settings['soil_4'] = self.smc4.get()\\n self.settings['overhead_level'] = self.overhead_level.get()\\n\\n # Save settings to config file in case of reboot / power-loss\\n print \\\"UPDATING SETTINGS FILE\\\"\\n with open(self.settings_path, 'w') as jsonfile:\\n jsonfile.write(json.dumps(self.settings, indent=4))\\n self.active_changes = True # (flag) changes are active!\",\n \"def action_settings(self):\\n\\n cur_datadir = self.config.starbound_data_dir\\n settings = SettingsDialog(self)\\n settings.exec()\\n new_datadir = self.config.starbound_data_dir\\n if new_datadir:\\n if cur_datadir != new_datadir:\\n self.load_data()\\n self.scene.refresh(self.data)\\n else:\\n self.close_world()\\n\\n # Make sure our menus are enabled/disabled as appropriate\\n self.enforce_menu_state()\\n\\n # Re-focus the main window\\n self.activateWindow()\",\n \"def reset_stats(self):\\r\\n self.ships_left = self.ai_settings.ship_limit\\r\\n self.score = 0\\r\\n self.level = 1\",\n \"def reset_stats(self):\\n self.ships_left= self.settings.ship_limit\\n self.score = 0\\n self.level = 1\",\n \"def reload(self):\\n self.load_config()\\n # Seems we need to explicitly refresh this\\n if self.main_instance:\\n self.main_instance.config = self.config\",\n \"def _setup_stats(self) -> None:\\n\\n # Save statistics\\n self.mass = np.array([0])\\n self.mass_balance = np.array([0])\\n self.mass_balance_trend = np.array([0])\",\n \"def merge_onto(cls, settings):\\r\\n for key, value in cls.SETTINGS.iteritems():\\r\\n setattr(settings, key, value)\",\n \"def _soft_reset(self):\\n self._reset_specific_envs(self.episodes_done)\\n self._update_other_info()\",\n \"def clear_stats(self):\\n self._stats = None\",\n \"def reset_metric_variables(self) -> None:\\n with self._lock:\\n self._reset_metric_variables()\",\n \"def reset_stats(self):\\n print(\\\"Reseting stats\\\")\\n self.player_lives = self.ai_stts.player_lives\\n self.score = 0\\n self.level = 1\",\n \"def user_settings(self, user_settings):\\n\\n self._user_settings = user_settings\",\n \"def reset_stats(self):\\n self.lives_left = self.game_settings.lives\\n self.score = 0\\n self.level = 1\",\n \"def reset(self):\\n self.settings = None\\n self.sublime_settings = None\\n self.settings_base = \\\"Javatar.sublime-settings\\\"\\n self.sublime_base = \\\"Preferences.sublime-settings\\\"\",\n \"def onSettings(self):\\n pass\",\n \"def _clear_gui_settings(self):\\n self._settings.clear()\\n self._settings.sync()\\n self.gui_settings_clear_button.setEnabled(False)\\n self.cleared = True\",\n \"def __setSettingsToStorage(value):\\n AccountSettings.setSettings(NEW_SETTINGS_COUNTER, value)\",\n \"def set_app_defaults(self):\\n self.curve_render = 0\\n self.image_render = 0\\n self.image_height = 200\\n self.image_data = []\\n self.auto_scale = True\\n\\n self.create_actions()\\n self.setup_signals()\\n self.reset_graph()\\n\\n self.fps = utils.SimpleFPS()\\n\\n # Click the live button\\n self.ui.actionContinue_Live_Updates.trigger()\",\n \"def reset_stats() -> None:\\n STATS[\\\"cleaned\\\"] = 0\\n STATS[\\\"null\\\"] = 0\\n STATS[\\\"unknown\\\"] = 0\",\n \"def reset_hl_stats(self):\\n\\n self.ships_left = self.settings.ship_limit\\n self.score = 0\\n self.level = 1\",\n \"def update(self):\\n registry = getUtility(IRegistry)\\n site_settings = registry.forInterface(\\n ISiteSchema, prefix=\\\"plone\\\", check=False)\\n try:\\n if site_settings.webstats_js:\\n self.webstats_js = site_settings.webstats_js\\n except AttributeError:\\n pass\",\n \"def add_earlydefault_settings(self):\\n self.add_default_settings_config()\\n self.add_default_settings_aliases()\",\n \"def reset_stats(self):\\r\\n self.ship_left = self.settings.ship_limit\\r\\n self.score = 0\\r\\n self.level = 1\",\n \"def merge_into_settings(self, settings):\\n if not self._meta_dict:\\n self._load_from_file()\\n\\n settings.chat_name = self._meta_dict[DumpMetadata.CHAT_NAME]\\n settings.last_message_id = self._meta_dict[DumpMetadata.LAST_MESSAGE_ID]\\n settings.exporter = self._meta_dict[DumpMetadata.EXPORTER]\",\n \"def _apply_settings(self):\\n if 'fixed_delta_seconds' in self.dict_settings:\\n self.settings.fixed_delta_seconds = self.dict_settings['fixed_delta_seconds']\\n\\n self.world.apply_settings(self.settings)\",\n \"def reset(self):\\n self._config = Config()\\n self._router = Router(())\\n self._middleware = []\\n self._start_response = None\",\n \"def reset(cls):\\n cls._options = None\\n cls._scoped_instances = {}\",\n \"def refresh_configuration(self):\\n pass\",\n \"def _onSettings(self, event):\\n dialog = sc.SettingsDialog(self)\\n if dialog.ShowModal() == wx.ID_OK:\\n dialog.saveSettings()\\n dialog.Destroy()\",\n \"def reset(frequency=None):\\n assert state.profile_level == 0, b\\\"Can't reset() while statprof is running\\\"\\n CodeSite.cache.clear()\\n state.reset(frequency)\",\n \"def reset_stats(self):\\n self.ships_left = self.sett.ship_limit\\n self.score = 0\\n self.level = 1\",\n \"async def reset_config(self):\\n self.config = {\\\"enable_auto_gen\\\": False, \\\"enable_world_barrier\\\": False}\\n await shared.event_handler.call_async(\\\"world:reset_config\\\")\\n self.gamerule_handler = mcpython.common.world.GameRule.GameRuleHandler(self)\",\n \"def reload_settings():\\n global settings, cancel_thread\\n\\n # cancel the thread if the settings say so\\n if cancel_thread is None:\\n if settings.get('disabled') is False:\\n cancel_thread = start_thread()\\n else:\\n if settings.get('disabled') is True:\\n light_scheme_set = None\\n current_timeout = 0\\n cancel_thread()\\n cancel_thread = None\",\n \"def reset_el_stats(self):\\n\\n # Number of available ships.\\n self.ships_left = self.settings.ship_limit\\n self.score = 0 # Score.\\n self.level = 1 # Level.\",\n \"def resetSelfWithDefaults( self ):\\n self.__dict__.update( self._defDict )\",\n \"def reset_stats(self):\\r\\n self.pepes_left = self.ai_settings.pepe_limit\\r\\n self.score = 0\\r\\n self.level = 1\",\n \"def __init__(self) -> None:\\n self._settings = {}\\n\\n # Load values from global_settings (only uppercase)\\n self.filter_and_set(global_settings)\\n\\n settings_env_value: str = os.environ.get(SETTINGS_ENV)\\n if settings_env_value:\\n # Load values from custom settings\\n try:\\n module = importlib.import_module(settings_env_value)\\n except ModuleNotFoundError:\\n msg = \\\"Can't import custom settings. Is it under PYTHONPATH?\\\"\\n raise ModuleError(msg)\\n self.filter_and_set(module)\",\n \"def reset_env(env, num_active_adv=0):\\n if hasattr(env, 'domain_randomization'):\\n env.domain_randomization = False\\n if num_active_adv > 0:\\n env.adversary_range = env.advs_per_strength * env.num_adv_strengths\",\n \"def gatherActiveDataStats(self, config):\\n gatherWMDataMiningStats(config.wmstats_url, config.reqmgrdb_url, \\n config.wmdatamining_url, False, log = cherrypy.log)\\n return\",\n \"def on_actionSettings_triggered(self):\\n self.start_app(SettingsApp)\",\n \"def kill_all(self):\\n self.settings['lights_on'] = 12\\n self.settings['lights_off'] = 12\\n self.settings['overhead_level'] = 0\\n self.settings['soil_1'] = 0\\n self.settings['soil_2'] = 0\\n self.settings['soil_3'] = 0\\n self.settings['soil_4'] = 0\\n self.scale_overhead_level.set(self.settings['overhead_level'])\\n self.scale_smc1.set(self.settings['soil_1'])\\n self.scale_smc2.set(self.settings['soil_2'])\\n self.scale_smc3.set(self.settings['soil_3'])\\n self.scale_smc4.set(self.settings['soil_4'])\\n self.active_changes = True # (flag) Once changes are retrieved, we assume that they will be sent to the controller\",\n \"def requestConfig(self):\\n self.radioConfig = None\\n self.channels = None\\n self.partialChannels = [] # We keep our channels in a temp array until finished\\n\\n self._requestSettings()\",\n \"def reset(self):\\n\\n for value in self.__dict__.itervalues():\\n if isinstance(value, EnvParm):\\n value._value = 'use_env'\\n getattr(value, 'value')\",\n \"def load_rule_settings():\\n logger.info('Loading the default rule settings')\\n with create_app().app_context():\\n sess = GlobalDB.db().session\\n # Clearing the current defaults before reloading them\\n sess.query(RuleSetting).filter(RuleSetting.agency_code.is_(None)).delete(synchronize_session=False)\\n sess.commit()\\n load_default_rule_settings(sess)\",\n \"def set_settings_devices(self):\\n self.set_thermostat, self.set_humidifier, self.set_sprinklers, self.set_ventilation = self.settings[3:]\",\n \"def init(self):\\n\\n if self.has_settings:\\n print(\\n TERM.bold_red('Error:'),\\n 'Settings file already exists. Doing nothing.'\\n )\\n return\\n\\n new_settings = {\\n 'strategy': self.ns.strategy,\\n 'branch': self.ns.branch,\\n 'scoring': self.ns.scoring,\\n }\\n\\n with open(self.settings, 'w') as f:\\n f.write(yaml.dump(new_settings, default_flow_style=False))\\n\\n print(\\n TERM.bold_green('Yay!'),\\n 'Wrote settings file {0}'.format(self.settings)\\n )\",\n \"def afterSetUp(self):\\n self.load_config = {}\\n self.load_config['monitor_interval'] = 1\\n self.load_config['limit_number_request'] = 100\\n self.load_config['limit_memory_used'] = 500\",\n \"def save_settings(self):\\n logger.info(f'Saving settings: {self.settings_dict}')\\n for k, section in self.settings_dict.items():\\n for setting_name in section.keys():\\n value = self.get_control_value(setting_name)\\n if value is not None:\\n section[setting_name] = value\\n\\n write_settings(self.settings_dict)\",\n \"def tearDownConfig(self):\\n print time.ctime(), 'enter tearDownConfig'\\n\\n self.site1 = self.globalCfg['site1']\\n self.site2 = self.globalCfg['site2']\\n self.site3 = self.globalCfg['site3']\\n\\n self.site1.databaseLandscapeInfo()\\n self.site2.databaseLandscapeInfo()\\n self.site3.databaseLandscapeInfo()\\n self.site1.systemReplicationStatus()\\n\\n if self.globalCfg['sync_mode'] == 'sync' and self.site1.fullSync:\\n try:\\n self.site1.srDisableFullSync(self.site1.getHost(\\\"WORKER1\\\"))\\n self.site1.fullSync = False\\n except Exception, e:\\n print 'disable full_sync in tearDownConfig failed: %s' % e\\n\\n for h in range(1, self.site1.getHostNo()):\\n self.site1.setConfigParameter(h, \\\"daemon.ini\\\", \\\"ConfigMgrPy.HOST\\\", \\\"indexserver.c\\\", \\\"instanceids\\\", None)\\n for h in range(1, self.site2.getHostNo()):\\n self.site2.setConfigParameter(h, \\\"daemon.ini\\\", \\\"ConfigMgrPy.HOST\\\", \\\"indexserver.c\\\", \\\"instanceids\\\", None)\\n for h in range(1, self.site3.getHostNo()):\\n self.site3.setConfigParameter(h, \\\"daemon.ini\\\", \\\"ConfigMgrPy.HOST\\\", \\\"indexserver.c\\\", \\\"instanceids\\\", None)\\n\\n self.site1.resetStatXSToMaster(self.globalCfg['multiDB'])\\n self.site2.resetStatXSToMaster(self.globalCfg['multiDB'])\\n self.site3.resetStatXSToMaster(self.globalCfg['multiDB'])\\n\\n self.site1.setTraceLevel(self.site1.getHost(\\\"WORKER1\\\"), \\\"global.ini\\\", \\\"sr_nameserver\\\", None)\\n self.site1.setTraceLevel(self.site1.getHost(\\\"WORKER1\\\"), \\\"global.ini\\\", \\\"sr_dataaccess\\\", None)\\n self.site1.setTraceLevel(self.site1.getHost(\\\"WORKER1\\\"), \\\"global.ini\\\", \\\"sr_log_retention\\\", None)\\n self.site1.setTraceLevel(self.site1.getHost(\\\"WORKER1\\\"), \\\"global.ini\\\", \\\"pitrestart\\\", None)\\n self.site1.setTraceLevel(self.site1.getHost(\\\"WORKER1\\\"), \\\"global.ini\\\", \\\"warm_upper\\\", None)\\n self.site1.setTraceLevel(self.site1.getHost(\\\"WORKER1\\\"), \\\"global.ini\\\", \\\"sr_spcoordinator\\\", None)\\n self.site1.setTraceLevel(self.site1.getHost(\\\"WORKER1\\\"), \\\"global.ini\\\", \\\"persistencelayer\\\", None)\\n self.site1.setTraceLevel(self.site1.getHost(\\\"WORKER1\\\"), \\\"nameserver.ini\\\", \\\"nameserver\\\", None)\\n\\n self.site2.setTraceLevel(self.site2.getHost(\\\"WORKER1\\\"), \\\"global.ini\\\", \\\"sr_nameserver\\\", None)\\n self.site2.setTraceLevel(self.site2.getHost(\\\"WORKER1\\\"), \\\"global.ini\\\", \\\"sr_dataaccess\\\", None)\\n self.site2.setTraceLevel(self.site2.getHost(\\\"WORKER1\\\"), \\\"global.ini\\\", \\\"sr_log_retention\\\", None)\\n self.site2.setTraceLevel(self.site2.getHost(\\\"WORKER1\\\"), \\\"global.ini\\\", \\\"pitrestart\\\", None)\\n self.site2.setTraceLevel(self.site2.getHost(\\\"WORKER1\\\"), \\\"global.ini\\\", \\\"warm_upper\\\", None)\\n self.site2.setTraceLevel(self.site2.getHost(\\\"WORKER1\\\"), \\\"global.ini\\\", \\\"sr_spcoordinator\\\", None)\\n self.site2.setTraceLevel(self.site2.getHost(\\\"WORKER1\\\"), \\\"global.ini\\\", \\\"persistencelayer\\\", None)\\n self.site2.setTraceLevel(self.site2.getHost(\\\"WORKER1\\\"), \\\"nameserver.ini\\\", \\\"nameserver\\\", None)\\n\\n self.site3.setTraceLevel(self.site3.getHost(\\\"WORKER1\\\"), \\\"global.ini\\\", \\\"sr_nameserver\\\", None)\\n self.site3.setTraceLevel(self.site3.getHost(\\\"WORKER1\\\"), \\\"global.ini\\\", \\\"sr_dataaccess\\\", None)\\n self.site3.setTraceLevel(self.site3.getHost(\\\"WORKER1\\\"), \\\"global.ini\\\", \\\"sr_log_retention\\\", None)\\n self.site3.setTraceLevel(self.site3.getHost(\\\"WORKER1\\\"), \\\"global.ini\\\", \\\"pitrestart\\\", None)\\n self.site3.setTraceLevel(self.site3.getHost(\\\"WORKER1\\\"), \\\"global.ini\\\", \\\"warm_upper\\\", None)\\n self.site3.setTraceLevel(self.site3.getHost(\\\"WORKER1\\\"), \\\"global.ini\\\", \\\"sr_spcoordinator\\\", None)\\n self.site3.setTraceLevel(self.site3.getHost(\\\"WORKER1\\\"), \\\"global.ini\\\", \\\"persistencelayer\\\", None)\\n self.site3.setTraceLevel(self.site3.getHost(\\\"WORKER1\\\"), \\\"nameserver.ini\\\", \\\"nameserver\\\", None)\\n\\n # for normal tear down(unregister/disable), the steps should be in order\\n # the primary cannot be disabled if there's secondary attached\\n # so there's no need to use multi-thread\\n # executing here means the landscape has been resorded to site1--(sync/syncmem)--site2--(async)--site3\\n #pdb.set_trace()\\n self.site3.tearDown()\\n self.site2.tearDown()\\n self.site1.tearDown()\",\n \"async def admin_reset(self, ctx: commands.Context):\\n await self.config.clear_all()\\n await self.initialize_internals()\\n await ctx.send('Global team management factory reset complete.')\",\n \"def updateSettings(self):\\n self.parser.read(self.file)\\n self.showTicker = self.parser.getboolean('Settings', 'showTicker')\\n self.verbose = self.parser.getboolean('Settings', 'verbose')\\n self.sleepTime = self.parser.getint('Settings', 'sleeptime')\\n self.saveGraph = self.parser.getboolean('Settings', 'saveGraph')\\n self.graphDPI = self.parser.getint('Settings', 'graphDPI')\",\n \"def reset():\\n global HEALTH_AGGREGATOR\\n\\n HEALTH_AGGREGATOR.cleanup()\\n HEALTH_AGGREGATOR = HealthAggregator()\\n\\n return jsonify({}), 200\",\n \"def _reset(cls):\\r\\n cls._CONFIGURED = False\\r\\n cls._ENABLED = {}\",\n \"def reset_cfg(self, server):\\n\\t\\tserver = valid_server(server)\\n\\t\\tself._reset_server_settings(server)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5851397","0.5851397","0.5782384","0.57592905","0.56791097","0.5615239","0.5549352","0.5518156","0.5513039","0.54954475","0.54496795","0.54446083","0.54427564","0.54313743","0.5414981","0.5411828","0.5359997","0.5353914","0.5333628","0.53336173","0.53336173","0.53336173","0.5309062","0.52863175","0.5286004","0.52740467","0.526341","0.52472097","0.5238809","0.5224572","0.52174425","0.5216299","0.51908445","0.5179103","0.5168972","0.51673394","0.51620424","0.5158321","0.5149918","0.51469815","0.5140568","0.51253647","0.512178","0.5106764","0.5105461","0.5102273","0.50912786","0.50823903","0.50636727","0.5056766","0.5053086","0.5043886","0.5032688","0.5025051","0.5023266","0.5014213","0.50134873","0.50077343","0.5001891","0.50008655","0.49986854","0.49972528","0.49856833","0.49840286","0.49810112","0.49803767","0.4970735","0.49671605","0.49631295","0.49618486","0.49591592","0.4952905","0.49467528","0.49416956","0.49380693","0.49368733","0.49245253","0.49237898","0.4918992","0.49160004","0.4915216","0.4908733","0.4905474","0.4896978","0.48875263","0.488396","0.4880871","0.48739615","0.48630226","0.485475","0.4845176","0.48421398","0.48334652","0.48331612","0.48301736","0.48291224","0.48269537","0.4826309","0.48174787","0.48165002"],"string":"[\n \"0.5851397\",\n \"0.5851397\",\n \"0.5782384\",\n \"0.57592905\",\n \"0.56791097\",\n \"0.5615239\",\n \"0.5549352\",\n \"0.5518156\",\n \"0.5513039\",\n \"0.54954475\",\n \"0.54496795\",\n \"0.54446083\",\n \"0.54427564\",\n \"0.54313743\",\n \"0.5414981\",\n \"0.5411828\",\n \"0.5359997\",\n \"0.5353914\",\n \"0.5333628\",\n \"0.53336173\",\n \"0.53336173\",\n \"0.53336173\",\n \"0.5309062\",\n \"0.52863175\",\n \"0.5286004\",\n \"0.52740467\",\n \"0.526341\",\n \"0.52472097\",\n \"0.5238809\",\n \"0.5224572\",\n \"0.52174425\",\n \"0.5216299\",\n \"0.51908445\",\n \"0.5179103\",\n \"0.5168972\",\n \"0.51673394\",\n \"0.51620424\",\n \"0.5158321\",\n \"0.5149918\",\n \"0.51469815\",\n \"0.5140568\",\n \"0.51253647\",\n \"0.512178\",\n \"0.5106764\",\n \"0.5105461\",\n \"0.5102273\",\n \"0.50912786\",\n \"0.50823903\",\n \"0.50636727\",\n \"0.5056766\",\n \"0.5053086\",\n \"0.5043886\",\n \"0.5032688\",\n \"0.5025051\",\n \"0.5023266\",\n \"0.5014213\",\n \"0.50134873\",\n \"0.50077343\",\n \"0.5001891\",\n \"0.50008655\",\n \"0.49986854\",\n \"0.49972528\",\n \"0.49856833\",\n \"0.49840286\",\n \"0.49810112\",\n \"0.49803767\",\n \"0.4970735\",\n \"0.49671605\",\n \"0.49631295\",\n \"0.49618486\",\n \"0.49591592\",\n \"0.4952905\",\n \"0.49467528\",\n \"0.49416956\",\n \"0.49380693\",\n \"0.49368733\",\n \"0.49245253\",\n \"0.49237898\",\n \"0.4918992\",\n \"0.49160004\",\n \"0.4915216\",\n \"0.4908733\",\n \"0.4905474\",\n \"0.4896978\",\n \"0.48875263\",\n \"0.488396\",\n \"0.4880871\",\n \"0.48739615\",\n \"0.48630226\",\n \"0.485475\",\n \"0.4845176\",\n \"0.48421398\",\n \"0.48334652\",\n \"0.48331612\",\n \"0.48301736\",\n \"0.48291224\",\n \"0.48269537\",\n \"0.4826309\",\n \"0.48174787\",\n \"0.48165002\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5501668"},"document_rank":{"kind":"string","value":"9"}}},{"rowIdx":3695,"cells":{"query":{"kind":"string","value":"Resets the accumulated statistics back to initial state for metric data."},"document":{"kind":"string","value":"def reset_metric_stats(self):\n\n self.__stats_table = {}"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def reset(self):\n self.sum_metric = 0.\n self.num_inst = 0.\n self.metrics.reset_stats()","def reset_metric_stats(self):\n self.__stats_table = {}","def stats_reset(self):\n self.stats.reset()","def stats_reset(self):\n self.stats.reset()","def reset(self) -> None:\n self.statistics = defaultdict(float)","def reset(self) -> None:\n self.statistics = defaultdict(int)","def reset(self):\n self.stats = {}","def reset(self):\n self.num_inst = 0\n self.sum_metric = 0.0","def reset_state(self):\n for name in self.metrics:\n self.metrics[name].reset_state()","def reset_stats() -> None:\n STATS[\"cleaned\"] = 0\n STATS[\"null\"] = 0\n STATS[\"unknown\"] = 0","def reset(self):\n self.start_times = {}\n self.stats = defaultdict(OnlineMeter) # float defaults to 0","def reset(self):\n self.avg = 0\n self.sum = 0\n self.cnt = 0","def reset_metric_variables(self) -> None:\n with self._lock:\n self._reset_metric_variables()","def clear_stats(self):\n self._stats = None","def reset(self):\n self.accumulation = None","def reset(self):\n self.accumulation = None","def reset(self):\n self.val = 0\n self.avg = 0\n self.sum = 0\n self.count = 0","def reset_metrics(self):\n self.metrics['loss'] = 0.0\n self.metrics['num_tokens'] = 0\n self.metrics['correct_tokens'] = 0\n self.metrics['correct_pred'] = 0\n self.metrics['pred_count'] = 0","def reset(self) -> None:\n self.statistics = defaultdict(self._mp_hack)\n self._ddp_backend = get_backend()","def reset(self) -> None:\n self.statistics = defaultdict(self._mp_hack)\n self._ddp_backend = get_backend()","def reset(self) -> None:\n self.statistics = defaultdict(self._mp_hack)\n self._ddp_backend = get_backend()","def reset(self):\n self.sum = [0.] * len(self.topk)\n self.data_num = 0\n self.pfm = [0.] * len(self.topk)","def reset(self) -> None:\n self.statistics = defaultdict(self._mp_hack)\n self._is_ddp = get_rank() > -1","def reset(self):\n self._total_value = 0.0\n self._count = 0","def reset_all(self) -> None:\n for metric in self:\n metric.reset()","def reset(self):\n self.ref_value = 0.0\n self._average = 0.0\n self.num_samples = 0","def reset(self):\n for i in range(0, len(self.__counts)):\n self.__counts[i] = 0\n self.__overflow = 0\n self.__total_count = 0\n self.__total_values = 0\n self.__min = None\n self.__max = None","def reset(self):\n self._value_estimates[:] = self.prior\n self.action_attempts[:] = 0\n self.last_action = None\n self.t = 0","def reset(self):\n self._accumulated_time.clear()\n self._hit_count.clear()","def reset(self):\n super().reset()\n self.m_n = 1\n self.m_num_errors = 0\n self.m_d = 0\n self.m_lastd = 0\n self.m_mean = 0.0\n self.m_std_temp = 0.0\n self.m_m2s_max = 0.0\n self.estimation = 0.0","def _reset(self):\n\n # Checking one attribute is enough, because they are all set together\n # in partial_fit\n if hasattr(self, 'scale_'):\n del self.scale_\n del self.n_samples_seen_\n del self.mean_\n del self.var_","def reset_turn_stats(self):\n\n # Set the attribute value to 0\n self._current_score = 0","def reset() -> None:\n Stat._cache = SortedDict()","def reset(self):\n\n self.scaler = None\n self.isFitted = False\n self.__create_scaler()","def reset(self):\n self.reset_count += 1\n self._init_data()","def reset(self):\n super().reset()\n self.sample_count = 1\n self.miss_prob = 1.0\n self.miss_std = 0.0\n self.miss_prob_sd_min = float(\"inf\")\n self.miss_prob_min = float(\"inf\")\n self.miss_sd_min = float(\"inf\")","def reset(self):\n self.current_exposure = None\n self.scores = {}","def reset(self):\n reset_system_health_series()","def reset_average(self):\n self._total_time = 0\n self._average_time = 0\n self._calls = 0","def reset(self):\n self.damage_dealt = 0\n self.kills = 0\n self.got_killed = False\n self.fitness = 0","def reset(self):\n self.values.clear()\n\n self.on_reset()","def reset(self):\n self.loss = 0\n self.cnt = 0","def _reset(self):\n\n # Checking one attribute is enough, becase they are all set together\n # in partial_fit\n if hasattr(self, 'scale_'):\n del self.scale_\n del self.mean_\n del self.var_","def clear(self):\n self.sum_hit_at_one = 0.0\n self.sum_perr = 0.0\n self.sum_loss = 0.0\n self.map_calculator.clear()\n self.global_ap_calculator.clear()\n self.num_examples = 0","def reset_memory_statistics(sender, **kwargs): # pylint: disable=unused-argument\n MemoryUsageData.start_counting()","def reset(self):\n self.m = normalize(self.m0)\n self.t = 0.0","def reset(self, complete=False):\n self.sum = 0\n self.n = 0\n if complete:\n self.running_avg = []","def reset(self):\n previous_solution_values = tf.constant(np.tile((self._action_lower_bound + self._action_upper_bound) / 2,\n [self._planning_horizon * self._num_agents, 1]), dtype=tf.float32)\n previous_solution_values = tf.reshape(previous_solution_values, [-1])\n solution_variance_values = tf.constant(\n np.tile(np.square(self._action_lower_bound - self._action_upper_bound) / 16,\n [self._planning_horizon * self._num_agents, 1]), dtype=tf.float32)\n solution_variance_values = tf.reshape(solution_variance_values, [-1])\n self._m.assign(previous_solution_values)\n self._sigma.assign(tf.math.sqrt(solution_variance_values))","def clear(self):\r\n\r\n\t\tself.ITerm = 0.0\r\n\t\tself.DTerm = 0.0\r\n\t\tself.last_error = 0.0\r\n\r\n\t\tself.state_history = []\r\n\t\tself.error_history = []\r\n\t\tself.output_history = []\r\n\t\tself.sample_times = []\r\n\t\t\r\n\t\tself.OutputValue = 0.0","def reset(self):\n self.total_pulls = 0\n self.total_score = 0\n self.npulls = np.zeros(self.k)\n self.score = np.zeros(self.k)","def reset(self):\n self.temp_data.clear()","def _reset(self):\n self._values = {}","def reset(self):\n self.acc_loss = 0\n self.norm_term = 0","def reset(self):\n if self.monotonic_energy is not None:\n self.monotonic_energy.reset()\n if self.chunk_energy is not None:\n self.chunk_energy.reset()\n self.bd_L_prev = 0\n self.key_tail = None","def reset(self):\n\n self.results = []\n self._plot()","def reset(self):\n self.observation = None\n self.history.clear()\n for i in range(len(self.answers)):\n self.answers[i] = None\n self.reset_metrics()","def reset(self):\n self.state = copy.copy(self.mu)","def reset(self):\n self.state = copy.copy(self.mu)","def reset(self):\n self.state = copy.copy(self.mu)","def reset(self):\n self.state = copy.copy(self.mu)","def reset(self):\n self.state = copy.copy(self.mu)","def reset(self):\n self.state = copy.copy(self.mu)","def reset(self):\n self.state = copy.copy(self.mu)","def reset(self):\n self.state = copy.copy(self.mu)","def reset(self):\n self.state = copy.copy(self.mu)","def reset(self):\n self.state = copy.copy(self.mu)","def reset(self):\n self.state = copy.copy(self.mu)","def reset(self):\n self.score = None\n self.true = None\n self.meta = None","def reset_data_recorder(self):\n\n self.t_values = []\n self.x_values = []\n self.tau_values = []","def reset(self):\n self.data = {}\n self.is_bound = False\n self._errors = None","def reset(self):\n self.error_p = 0.0\n self.error_i = 0.0\n self.error_d = 0.0\n self.errors = [ 0.0 ] * self.samples\n if callable(self.debug_callback):\n self.debug_callback(\"reset\")","def reset_stats(self):\n self.ships_left = self.sett.ship_limit\n self.score = 0\n self.level = 1","def reset(self):\n self.table[:, :] = 0\n self.counts[:] = 0\n self.names = []\n self.hashesperid.resize(0)\n self.dirty = True","def reset(self):\r\n self.state = copy.copy(self.mu)","def reset(self):\n self.reset_image_estimate()\n self.init_m_aux()\n self.reset_hessian_and_bias()\n self.reset_adadelta_variables()","def reset(self):\n self.visited = False\n self.calculated = False\n self.past_value = self.value\n self.value = 0","def reset(self):\n self.pred_classes.clear()\n self.gold_classes.clear()\n self.pred_probas.clear()\n self.gold_probas.clear()\n self.loss = 0\n self.nb_batches = 0\n self.prec_rec_f1 = None\n self.acc = None\n self.mcc = None","def reset(self):\n self.data = self._defaults","def reset(self):\n self.mode = 0\n self.graphs = [[], [], []]\n self.coefficients = []\n self.sample = []","def reset_wm(self):\n\n self.plan = []\n self.hist = []","def reset(self):\n\t\tself._initial = None\n\t\tself._start = None\n\t\tself._time = 0\n\t\tself._total = 0\n\t\treturn self","def reset(self):\n self._proportional = 0\n self._integral = 0\n self._derivative = 0\n\n self._last_time = self._current_time()\n self._last_output = None\n self._last_input = None","def reset(self):\n self.__sets = []\n self._computed = False","def reset(self):\n self._stat = CardMeta()","def reset(self):\n self._hist.reset()\n return self","def reset(self):\n self._clusters = {}\n self._clusters_val = {}\n self._centroids = {}\n self.store()","def reset_stats(self):\n print(\"Reseting stats\")\n self.player_lives = self.ai_stts.player_lives\n self.score = 0\n self.level = 1","def reset(self):\n self.edges = None\n self.chi = None\n self.k = None\n self.n_bins = None\n self.classes = None\n self.n_params = None","def clear_all_accumulators(self):\n self._require_state(\"INITIALIZING\")\n for mi,accum in self._accums.items():\n accum.clear()","def reset(self):\n self.work_state = work_state[\"Measuring\"]\n self.report_mode = report_mode[\"Initiative\"]\n self.duty_cycle = 0\n self.logger.info(\"{}: sensor resetted.\".format(self.sensor_name))","def reset(self):\n self.x_prev = np.zeros_like(self.mu)","def reset(self):\n self.baseline = None\n self.cut = None\n self.manual_push = 0","def reset(self):\n self.head_pos = 0\n self.left_expands = 0\n self.memory = np.zeros(\n (\n self.max_memory if self.fixed_size else 1,\n self.memory_unit_size\n )\n )\n self.previous_read = np.zeros(self.memory_unit_size)\n if self.history is not None:\n self.history = defaultdict(list)","def __reset(self):\n\t\tself.__highest = -float('inf')\n\t\tself.__lowest = float('inf')\n\t\tself.__total = 0\n\t\tself.__steps = 0\n\t\tself.__cold_days = 0","def reset(self):\n self.reserve.reset()\n self.revenue.reset()\n self.transfers.reset()\n self.missions.reset()\n self.debt.reset()\n self.genfund.reset()\n self.macro.reset(self.pop[self.start_yr],self.eco_first)\n self.summary = pd.DataFrame(index=self.names,columns=[t for t in range(self.start_yr,self.stop_yr)])\n self.year = self.start_yr","def reset(self):\n self.values = None\n self.keys = None\n self.mask = None","def _reset(self):\n\n # Checking one attribute is enough, becase they are all set together\n # in partial_fit\n if hasattr(self, 'scale_'):\n del self.scale_","def _reset(self):\n\n # Checking one attribute is enough, becase they are all set together\n # in partial_fit\n if hasattr(self, 'scale_'):\n del self.scale_","def reset(self):\n self.train_loss.reset_states()\n self.train_accuracy.reset_states()\n self.val_loss.reset_states()\n self.val_accuracy.reset_states()\n self.train_mIoU.reset_states()\n self.val_mIoU.reset_states()","def reset(self):\n # Attempt to reset data loader\n self.data_loader_iter = iter(self.data_loader)\n self.num_batches = 0\n\n # Make sure calibrator will check the cache again when reset.\n self.cache_contents = None"],"string":"[\n \"def reset(self):\\n self.sum_metric = 0.\\n self.num_inst = 0.\\n self.metrics.reset_stats()\",\n \"def reset_metric_stats(self):\\n self.__stats_table = {}\",\n \"def stats_reset(self):\\n self.stats.reset()\",\n \"def stats_reset(self):\\n self.stats.reset()\",\n \"def reset(self) -> None:\\n self.statistics = defaultdict(float)\",\n \"def reset(self) -> None:\\n self.statistics = defaultdict(int)\",\n \"def reset(self):\\n self.stats = {}\",\n \"def reset(self):\\n self.num_inst = 0\\n self.sum_metric = 0.0\",\n \"def reset_state(self):\\n for name in self.metrics:\\n self.metrics[name].reset_state()\",\n \"def reset_stats() -> None:\\n STATS[\\\"cleaned\\\"] = 0\\n STATS[\\\"null\\\"] = 0\\n STATS[\\\"unknown\\\"] = 0\",\n \"def reset(self):\\n self.start_times = {}\\n self.stats = defaultdict(OnlineMeter) # float defaults to 0\",\n \"def reset(self):\\n self.avg = 0\\n self.sum = 0\\n self.cnt = 0\",\n \"def reset_metric_variables(self) -> None:\\n with self._lock:\\n self._reset_metric_variables()\",\n \"def clear_stats(self):\\n self._stats = None\",\n \"def reset(self):\\n self.accumulation = None\",\n \"def reset(self):\\n self.accumulation = None\",\n \"def reset(self):\\n self.val = 0\\n self.avg = 0\\n self.sum = 0\\n self.count = 0\",\n \"def reset_metrics(self):\\n self.metrics['loss'] = 0.0\\n self.metrics['num_tokens'] = 0\\n self.metrics['correct_tokens'] = 0\\n self.metrics['correct_pred'] = 0\\n self.metrics['pred_count'] = 0\",\n \"def reset(self) -> None:\\n self.statistics = defaultdict(self._mp_hack)\\n self._ddp_backend = get_backend()\",\n \"def reset(self) -> None:\\n self.statistics = defaultdict(self._mp_hack)\\n self._ddp_backend = get_backend()\",\n \"def reset(self) -> None:\\n self.statistics = defaultdict(self._mp_hack)\\n self._ddp_backend = get_backend()\",\n \"def reset(self):\\n self.sum = [0.] * len(self.topk)\\n self.data_num = 0\\n self.pfm = [0.] * len(self.topk)\",\n \"def reset(self) -> None:\\n self.statistics = defaultdict(self._mp_hack)\\n self._is_ddp = get_rank() > -1\",\n \"def reset(self):\\n self._total_value = 0.0\\n self._count = 0\",\n \"def reset_all(self) -> None:\\n for metric in self:\\n metric.reset()\",\n \"def reset(self):\\n self.ref_value = 0.0\\n self._average = 0.0\\n self.num_samples = 0\",\n \"def reset(self):\\n for i in range(0, len(self.__counts)):\\n self.__counts[i] = 0\\n self.__overflow = 0\\n self.__total_count = 0\\n self.__total_values = 0\\n self.__min = None\\n self.__max = None\",\n \"def reset(self):\\n self._value_estimates[:] = self.prior\\n self.action_attempts[:] = 0\\n self.last_action = None\\n self.t = 0\",\n \"def reset(self):\\n self._accumulated_time.clear()\\n self._hit_count.clear()\",\n \"def reset(self):\\n super().reset()\\n self.m_n = 1\\n self.m_num_errors = 0\\n self.m_d = 0\\n self.m_lastd = 0\\n self.m_mean = 0.0\\n self.m_std_temp = 0.0\\n self.m_m2s_max = 0.0\\n self.estimation = 0.0\",\n \"def _reset(self):\\n\\n # Checking one attribute is enough, because they are all set together\\n # in partial_fit\\n if hasattr(self, 'scale_'):\\n del self.scale_\\n del self.n_samples_seen_\\n del self.mean_\\n del self.var_\",\n \"def reset_turn_stats(self):\\n\\n # Set the attribute value to 0\\n self._current_score = 0\",\n \"def reset() -> None:\\n Stat._cache = SortedDict()\",\n \"def reset(self):\\n\\n self.scaler = None\\n self.isFitted = False\\n self.__create_scaler()\",\n \"def reset(self):\\n self.reset_count += 1\\n self._init_data()\",\n \"def reset(self):\\n super().reset()\\n self.sample_count = 1\\n self.miss_prob = 1.0\\n self.miss_std = 0.0\\n self.miss_prob_sd_min = float(\\\"inf\\\")\\n self.miss_prob_min = float(\\\"inf\\\")\\n self.miss_sd_min = float(\\\"inf\\\")\",\n \"def reset(self):\\n self.current_exposure = None\\n self.scores = {}\",\n \"def reset(self):\\n reset_system_health_series()\",\n \"def reset_average(self):\\n self._total_time = 0\\n self._average_time = 0\\n self._calls = 0\",\n \"def reset(self):\\n self.damage_dealt = 0\\n self.kills = 0\\n self.got_killed = False\\n self.fitness = 0\",\n \"def reset(self):\\n self.values.clear()\\n\\n self.on_reset()\",\n \"def reset(self):\\n self.loss = 0\\n self.cnt = 0\",\n \"def _reset(self):\\n\\n # Checking one attribute is enough, becase they are all set together\\n # in partial_fit\\n if hasattr(self, 'scale_'):\\n del self.scale_\\n del self.mean_\\n del self.var_\",\n \"def clear(self):\\n self.sum_hit_at_one = 0.0\\n self.sum_perr = 0.0\\n self.sum_loss = 0.0\\n self.map_calculator.clear()\\n self.global_ap_calculator.clear()\\n self.num_examples = 0\",\n \"def reset_memory_statistics(sender, **kwargs): # pylint: disable=unused-argument\\n MemoryUsageData.start_counting()\",\n \"def reset(self):\\n self.m = normalize(self.m0)\\n self.t = 0.0\",\n \"def reset(self, complete=False):\\n self.sum = 0\\n self.n = 0\\n if complete:\\n self.running_avg = []\",\n \"def reset(self):\\n previous_solution_values = tf.constant(np.tile((self._action_lower_bound + self._action_upper_bound) / 2,\\n [self._planning_horizon * self._num_agents, 1]), dtype=tf.float32)\\n previous_solution_values = tf.reshape(previous_solution_values, [-1])\\n solution_variance_values = tf.constant(\\n np.tile(np.square(self._action_lower_bound - self._action_upper_bound) / 16,\\n [self._planning_horizon * self._num_agents, 1]), dtype=tf.float32)\\n solution_variance_values = tf.reshape(solution_variance_values, [-1])\\n self._m.assign(previous_solution_values)\\n self._sigma.assign(tf.math.sqrt(solution_variance_values))\",\n \"def clear(self):\\r\\n\\r\\n\\t\\tself.ITerm = 0.0\\r\\n\\t\\tself.DTerm = 0.0\\r\\n\\t\\tself.last_error = 0.0\\r\\n\\r\\n\\t\\tself.state_history = []\\r\\n\\t\\tself.error_history = []\\r\\n\\t\\tself.output_history = []\\r\\n\\t\\tself.sample_times = []\\r\\n\\t\\t\\r\\n\\t\\tself.OutputValue = 0.0\",\n \"def reset(self):\\n self.total_pulls = 0\\n self.total_score = 0\\n self.npulls = np.zeros(self.k)\\n self.score = np.zeros(self.k)\",\n \"def reset(self):\\n self.temp_data.clear()\",\n \"def _reset(self):\\n self._values = {}\",\n \"def reset(self):\\n self.acc_loss = 0\\n self.norm_term = 0\",\n \"def reset(self):\\n if self.monotonic_energy is not None:\\n self.monotonic_energy.reset()\\n if self.chunk_energy is not None:\\n self.chunk_energy.reset()\\n self.bd_L_prev = 0\\n self.key_tail = None\",\n \"def reset(self):\\n\\n self.results = []\\n self._plot()\",\n \"def reset(self):\\n self.observation = None\\n self.history.clear()\\n for i in range(len(self.answers)):\\n self.answers[i] = None\\n self.reset_metrics()\",\n \"def reset(self):\\n self.state = copy.copy(self.mu)\",\n \"def reset(self):\\n self.state = copy.copy(self.mu)\",\n \"def reset(self):\\n self.state = copy.copy(self.mu)\",\n \"def reset(self):\\n self.state = copy.copy(self.mu)\",\n \"def reset(self):\\n self.state = copy.copy(self.mu)\",\n \"def reset(self):\\n self.state = copy.copy(self.mu)\",\n \"def reset(self):\\n self.state = copy.copy(self.mu)\",\n \"def reset(self):\\n self.state = copy.copy(self.mu)\",\n \"def reset(self):\\n self.state = copy.copy(self.mu)\",\n \"def reset(self):\\n self.state = copy.copy(self.mu)\",\n \"def reset(self):\\n self.state = copy.copy(self.mu)\",\n \"def reset(self):\\n self.score = None\\n self.true = None\\n self.meta = None\",\n \"def reset_data_recorder(self):\\n\\n self.t_values = []\\n self.x_values = []\\n self.tau_values = []\",\n \"def reset(self):\\n self.data = {}\\n self.is_bound = False\\n self._errors = None\",\n \"def reset(self):\\n self.error_p = 0.0\\n self.error_i = 0.0\\n self.error_d = 0.0\\n self.errors = [ 0.0 ] * self.samples\\n if callable(self.debug_callback):\\n self.debug_callback(\\\"reset\\\")\",\n \"def reset_stats(self):\\n self.ships_left = self.sett.ship_limit\\n self.score = 0\\n self.level = 1\",\n \"def reset(self):\\n self.table[:, :] = 0\\n self.counts[:] = 0\\n self.names = []\\n self.hashesperid.resize(0)\\n self.dirty = True\",\n \"def reset(self):\\r\\n self.state = copy.copy(self.mu)\",\n \"def reset(self):\\n self.reset_image_estimate()\\n self.init_m_aux()\\n self.reset_hessian_and_bias()\\n self.reset_adadelta_variables()\",\n \"def reset(self):\\n self.visited = False\\n self.calculated = False\\n self.past_value = self.value\\n self.value = 0\",\n \"def reset(self):\\n self.pred_classes.clear()\\n self.gold_classes.clear()\\n self.pred_probas.clear()\\n self.gold_probas.clear()\\n self.loss = 0\\n self.nb_batches = 0\\n self.prec_rec_f1 = None\\n self.acc = None\\n self.mcc = None\",\n \"def reset(self):\\n self.data = self._defaults\",\n \"def reset(self):\\n self.mode = 0\\n self.graphs = [[], [], []]\\n self.coefficients = []\\n self.sample = []\",\n \"def reset_wm(self):\\n\\n self.plan = []\\n self.hist = []\",\n \"def reset(self):\\n\\t\\tself._initial = None\\n\\t\\tself._start = None\\n\\t\\tself._time = 0\\n\\t\\tself._total = 0\\n\\t\\treturn self\",\n \"def reset(self):\\n self._proportional = 0\\n self._integral = 0\\n self._derivative = 0\\n\\n self._last_time = self._current_time()\\n self._last_output = None\\n self._last_input = None\",\n \"def reset(self):\\n self.__sets = []\\n self._computed = False\",\n \"def reset(self):\\n self._stat = CardMeta()\",\n \"def reset(self):\\n self._hist.reset()\\n return self\",\n \"def reset(self):\\n self._clusters = {}\\n self._clusters_val = {}\\n self._centroids = {}\\n self.store()\",\n \"def reset_stats(self):\\n print(\\\"Reseting stats\\\")\\n self.player_lives = self.ai_stts.player_lives\\n self.score = 0\\n self.level = 1\",\n \"def reset(self):\\n self.edges = None\\n self.chi = None\\n self.k = None\\n self.n_bins = None\\n self.classes = None\\n self.n_params = None\",\n \"def clear_all_accumulators(self):\\n self._require_state(\\\"INITIALIZING\\\")\\n for mi,accum in self._accums.items():\\n accum.clear()\",\n \"def reset(self):\\n self.work_state = work_state[\\\"Measuring\\\"]\\n self.report_mode = report_mode[\\\"Initiative\\\"]\\n self.duty_cycle = 0\\n self.logger.info(\\\"{}: sensor resetted.\\\".format(self.sensor_name))\",\n \"def reset(self):\\n self.x_prev = np.zeros_like(self.mu)\",\n \"def reset(self):\\n self.baseline = None\\n self.cut = None\\n self.manual_push = 0\",\n \"def reset(self):\\n self.head_pos = 0\\n self.left_expands = 0\\n self.memory = np.zeros(\\n (\\n self.max_memory if self.fixed_size else 1,\\n self.memory_unit_size\\n )\\n )\\n self.previous_read = np.zeros(self.memory_unit_size)\\n if self.history is not None:\\n self.history = defaultdict(list)\",\n \"def __reset(self):\\n\\t\\tself.__highest = -float('inf')\\n\\t\\tself.__lowest = float('inf')\\n\\t\\tself.__total = 0\\n\\t\\tself.__steps = 0\\n\\t\\tself.__cold_days = 0\",\n \"def reset(self):\\n self.reserve.reset()\\n self.revenue.reset()\\n self.transfers.reset()\\n self.missions.reset()\\n self.debt.reset()\\n self.genfund.reset()\\n self.macro.reset(self.pop[self.start_yr],self.eco_first)\\n self.summary = pd.DataFrame(index=self.names,columns=[t for t in range(self.start_yr,self.stop_yr)])\\n self.year = self.start_yr\",\n \"def reset(self):\\n self.values = None\\n self.keys = None\\n self.mask = None\",\n \"def _reset(self):\\n\\n # Checking one attribute is enough, becase they are all set together\\n # in partial_fit\\n if hasattr(self, 'scale_'):\\n del self.scale_\",\n \"def _reset(self):\\n\\n # Checking one attribute is enough, becase they are all set together\\n # in partial_fit\\n if hasattr(self, 'scale_'):\\n del self.scale_\",\n \"def reset(self):\\n self.train_loss.reset_states()\\n self.train_accuracy.reset_states()\\n self.val_loss.reset_states()\\n self.val_accuracy.reset_states()\\n self.train_mIoU.reset_states()\\n self.val_mIoU.reset_states()\",\n \"def reset(self):\\n # Attempt to reset data loader\\n self.data_loader_iter = iter(self.data_loader)\\n self.num_batches = 0\\n\\n # Make sure calibrator will check the cache again when reset.\\n self.cache_contents = None\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8448358","0.83412653","0.83299094","0.83299094","0.8270503","0.8153123","0.79735655","0.7772603","0.77270293","0.77003264","0.76665866","0.7614711","0.75709176","0.7540221","0.75036174","0.75036174","0.7490715","0.7471787","0.74488425","0.74488425","0.74488425","0.7399214","0.7371752","0.7350859","0.73405546","0.73228246","0.7312936","0.72774065","0.7229525","0.71553284","0.7075319","0.706154","0.7059099","0.70363504","0.702773","0.70184267","0.70123726","0.70105654","0.70089495","0.6993825","0.69876575","0.6979957","0.69488895","0.69401044","0.68768877","0.686644","0.6866367","0.6860591","0.68563175","0.6852236","0.68487674","0.6847574","0.68278927","0.6826811","0.6824579","0.6819496","0.6812596","0.6812596","0.6812596","0.6812596","0.6812596","0.6812596","0.6812596","0.6812596","0.6812596","0.6812596","0.6812596","0.6810124","0.6801448","0.6796431","0.6786861","0.6774378","0.676991","0.6768513","0.6748076","0.674789","0.6747709","0.6743537","0.67379457","0.67296934","0.67281467","0.6725452","0.67183644","0.67178744","0.6709824","0.6704403","0.6679279","0.6678569","0.66781825","0.6674983","0.66681176","0.6668089","0.6666285","0.66623515","0.6658285","0.66577923","0.66570085","0.66570085","0.66526616","0.665174"],"string":"[\n \"0.8448358\",\n \"0.83412653\",\n \"0.83299094\",\n \"0.83299094\",\n \"0.8270503\",\n \"0.8153123\",\n \"0.79735655\",\n \"0.7772603\",\n \"0.77270293\",\n \"0.77003264\",\n \"0.76665866\",\n \"0.7614711\",\n \"0.75709176\",\n \"0.7540221\",\n \"0.75036174\",\n \"0.75036174\",\n \"0.7490715\",\n \"0.7471787\",\n \"0.74488425\",\n \"0.74488425\",\n \"0.74488425\",\n \"0.7399214\",\n \"0.7371752\",\n \"0.7350859\",\n \"0.73405546\",\n \"0.73228246\",\n \"0.7312936\",\n \"0.72774065\",\n \"0.7229525\",\n \"0.71553284\",\n \"0.7075319\",\n \"0.706154\",\n \"0.7059099\",\n \"0.70363504\",\n \"0.702773\",\n \"0.70184267\",\n \"0.70123726\",\n \"0.70105654\",\n \"0.70089495\",\n \"0.6993825\",\n \"0.69876575\",\n \"0.6979957\",\n \"0.69488895\",\n \"0.69401044\",\n \"0.68768877\",\n \"0.686644\",\n \"0.6866367\",\n \"0.6860591\",\n \"0.68563175\",\n \"0.6852236\",\n \"0.68487674\",\n \"0.6847574\",\n \"0.68278927\",\n \"0.6826811\",\n \"0.6824579\",\n \"0.6819496\",\n \"0.6812596\",\n \"0.6812596\",\n \"0.6812596\",\n \"0.6812596\",\n \"0.6812596\",\n \"0.6812596\",\n \"0.6812596\",\n \"0.6812596\",\n \"0.6812596\",\n \"0.6812596\",\n \"0.6812596\",\n \"0.6810124\",\n \"0.6801448\",\n \"0.6796431\",\n \"0.6786861\",\n \"0.6774378\",\n \"0.676991\",\n \"0.6768513\",\n \"0.6748076\",\n \"0.674789\",\n \"0.6747709\",\n \"0.6743537\",\n \"0.67379457\",\n \"0.67296934\",\n \"0.67281467\",\n \"0.6725452\",\n \"0.67183644\",\n \"0.67178744\",\n \"0.6709824\",\n \"0.6704403\",\n \"0.6679279\",\n \"0.6678569\",\n \"0.66781825\",\n \"0.6674983\",\n \"0.66681176\",\n \"0.6668089\",\n \"0.6666285\",\n \"0.66623515\",\n \"0.6658285\",\n \"0.66577923\",\n \"0.66570085\",\n \"0.66570085\",\n \"0.66526616\",\n \"0.665174\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.84143066"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":3696,"cells":{"query":{"kind":"string","value":"Resets the accumulated statistics back to initial state for sample analytics data."},"document":{"kind":"string","value":"def reset_transaction_events(self):\n\n if self.__settings is not None:\n self._transaction_events = SampledDataSet(\n self.__settings.event_harvest_config.\n harvest_limits.analytic_event_data)\n else:\n self._transaction_events = SampledDataSet()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def stats_reset(self):\n self.stats.reset()","def stats_reset(self):\n self.stats.reset()","def reset(self) -> None:\n self.statistics = defaultdict(float)","def reset(self) -> None:\n self.statistics = defaultdict(int)","def reset(self):\n self.stats = {}","def reset(self):\n self.sum_metric = 0.\n self.num_inst = 0.\n self.metrics.reset_stats()","def reset_metric_stats(self):\n\n self.__stats_table = {}","def reset_metric_stats(self):\n self.__stats_table = {}","def clear_stats(self):\n self._stats = None","def reset_stats() -> None:\n STATS[\"cleaned\"] = 0\n STATS[\"null\"] = 0\n STATS[\"unknown\"] = 0","def reset(self):\n self.avg = 0\n self.sum = 0\n self.cnt = 0","def reset(self):\n self.start_times = {}\n self.stats = defaultdict(OnlineMeter) # float defaults to 0","def reset(self):\n self.ref_value = 0.0\n self._average = 0.0\n self.num_samples = 0","def reset(self) -> None:\n self.statistics = defaultdict(self._mp_hack)\n self._ddp_backend = get_backend()","def reset(self) -> None:\n self.statistics = defaultdict(self._mp_hack)\n self._ddp_backend = get_backend()","def reset(self) -> None:\n self.statistics = defaultdict(self._mp_hack)\n self._ddp_backend = get_backend()","def reset(self):\n self.num_inst = 0\n self.sum_metric = 0.0","def reset(self):\n self.val = 0\n self.avg = 0\n self.sum = 0\n self.count = 0","def reset(self) -> None:\n self.statistics = defaultdict(self._mp_hack)\n self._is_ddp = get_rank() > -1","def reset(self):\n super().reset()\n self.sample_count = 1\n self.miss_prob = 1.0\n self.miss_std = 0.0\n self.miss_prob_sd_min = float(\"inf\")\n self.miss_prob_min = float(\"inf\")\n self.miss_sd_min = float(\"inf\")","def reset(self):\n self.accumulation = None","def reset(self):\n self.accumulation = None","def reset(self):\n self._total_value = 0.0\n self._count = 0","def reset(self):\n for i in range(0, len(self.__counts)):\n self.__counts[i] = 0\n self.__overflow = 0\n self.__total_count = 0\n self.__total_values = 0\n self.__min = None\n self.__max = None","def reset(self):\n self._accumulated_time.clear()\n self._hit_count.clear()","def reset(self):\n reset_system_health_series()","def reset_data_recorder(self):\n\n self.t_values = []\n self.x_values = []\n self.tau_values = []","def reset_average(self):\n self._total_time = 0\n self._average_time = 0\n self._calls = 0","def reset(self):\n self.sum = [0.] * len(self.topk)\n self.data_num = 0\n self.pfm = [0.] * len(self.topk)","def reset(self):\n self._value_estimates[:] = self.prior\n self.action_attempts[:] = 0\n self.last_action = None\n self.t = 0","def clear(self):\r\n\r\n\t\tself.state_history = []\r\n\t\tself.error_history = []\r\n\t\tself.output_history = []\r\n\t\tself.sample_times = []\r\n\t\t\r\n\t\tself.OutputValue = 0.0","def reset_stats(self):\n print(\"Reseting stats\")\n self.player_lives = self.ai_stts.player_lives\n self.score = 0\n self.level = 1","def reset(self):\n self.current_exposure = None\n self.scores = {}","def clear(self):\r\n\r\n\t\tself.state_history = []\r\n\t\tself.error_history = []\r\n\t\tself.output_history = []\r\n\t\tself.sample_times = []\r\n\t\t\r\n\t\tself.LastOutputValue = 0.0\r\n\t\tself.OutputValue = 0.0","def clear(self):\r\n\r\n\t\tself.state_history = []\r\n\t\tself.error_history = []\r\n\t\tself.output_history = []\r\n\t\tself.sample_times = []\r\n\t\t\r\n\t\tself.LastOutputValue = 0.0\r\n\t\tself.OutputValue = 0.0","def reinit(self):\n self.logger.info(\"Reinit called. Clear the population.\")\n self.set_init_population([], perf_name=None)\n self._gt_rollouts = []\n self._gt_scores = []","def reset(self):\n self.reset_count += 1\n self._init_data()","def clear(self):\r\n\r\n\t\tself.ITerm = 0.0\r\n\t\tself.DTerm = 0.0\r\n\t\tself.last_error = 0.0\r\n\r\n\t\tself.state_history = []\r\n\t\tself.error_history = []\r\n\t\tself.output_history = []\r\n\t\tself.sample_times = []\r\n\t\t\r\n\t\tself.OutputValue = 0.0","def reset_state(self):\n for name in self.metrics:\n self.metrics[name].reset_state()","def reset(self):\n self.error_p = 0.0\n self.error_i = 0.0\n self.error_d = 0.0\n self.errors = [ 0.0 ] * self.samples\n if callable(self.debug_callback):\n self.debug_callback(\"reset\")","def reset(self):\n super().reset()\n self.m_n = 1\n self.m_num_errors = 0\n self.m_d = 0\n self.m_lastd = 0\n self.m_mean = 0.0\n self.m_std_temp = 0.0\n self.m_m2s_max = 0.0\n self.estimation = 0.0","def clear(self):\n self.sum_hit_at_one = 0.0\n self.sum_perr = 0.0\n self.sum_loss = 0.0\n self.map_calculator.clear()\n self.global_ap_calculator.clear()\n self.num_examples = 0","def reset_turn_stats(self):\n\n # Set the attribute value to 0\n self._current_score = 0","def clearstats(self, clearstats) :\n\t\ttry :\n\t\t\tself._clearstats = clearstats\n\t\texcept Exception as e:\n\t\t\traise e","def clearstats(self, clearstats) :\n\t\ttry :\n\t\t\tself._clearstats = clearstats\n\t\texcept Exception as e:\n\t\t\traise e","def clearstats(self, clearstats) :\n\t\ttry :\n\t\t\tself._clearstats = clearstats\n\t\texcept Exception as e:\n\t\t\traise e","def clearstats(self, clearstats) :\n\t\ttry :\n\t\t\tself._clearstats = clearstats\n\t\texcept Exception as e:\n\t\t\traise e","def reset(self):\n self.curr_idx = 0\n # shuffle data in each bucket\n random.shuffle(self.idx)\n for i, buck in enumerate(self.utterances):\n self.indices[i], self.utterances[i], self.intents[i] = shuffle(self.indices[i],\n self.utterances[i],\n self.intents[i])\n self.ndindex = []\n self.ndsent = []\n self.ndlabel = []\n\n # append the lists with an array\n for i, buck in enumerate(self.utterances):\n self.ndindex.append(ndarray.array(self.indices[i], dtype=self.dtype))\n self.ndsent.append(ndarray.array(self.utterances[i], dtype=self.dtype))\n self.ndlabel.append(ndarray.array(self.intents[i], dtype=self.dtype))","def _reset(self):\n\n # Checking one attribute is enough, because they are all set together\n # in partial_fit\n if hasattr(self, 'scale_'):\n del self.scale_\n del self.n_samples_seen_\n del self.mean_\n del self.var_","def reset_stats(self, settings, reset_stream=False):\n\n self.__settings = settings\n self.__stats_table = {}\n self.__sql_stats_table = {}\n self.__slow_transaction = None\n self.__slow_transaction_map = {}\n self.__slow_transaction_old_duration = None\n self.__transaction_errors = []\n self.__synthetics_transactions = []\n\n self.reset_transaction_events()\n self.reset_error_events()\n self.reset_custom_events()\n self.reset_span_events()\n self.reset_synthetics_events()\n # streams are never reset after instantiation\n if reset_stream:\n self._span_stream = StreamBuffer(\n settings.infinite_tracing.span_queue_size)","def reset(self):\n self.mode = 0\n self.graphs = [[], [], []]\n self.coefficients = []\n self.sample = []","def reset(self):\n self.st = segment_tree.SegmentTreeSampler(self.n, np.ones(self.n) * self.reg, self.random_state)","def reset(self):\r\n self.buffer = np.zeros(self.nBins)\r\n self.counter = 0","def reset(self):\n self.algo_state = {}\n self.actual_repetitions = 0\n self.next_session = -1\n self.last_session = -1\n self.past_quality = []","def reset(self):\n self.temp_data.clear()","def reset(self, complete=False):\n self.sum = 0\n self.n = 0\n if complete:\n self.running_avg = []","def reset(self):\n self.correct_count = 0\n self.total_count = 0","def reset_metrics(self):\n self.metrics['loss'] = 0.0\n self.metrics['num_tokens'] = 0\n self.metrics['correct_tokens'] = 0\n self.metrics['correct_pred'] = 0\n self.metrics['pred_count'] = 0","def _reset_histories(self):\n self.train_loss_history = []\n self.train_pos_dist_history = []\n self.train_neg_dist_history = []\n self.val_loss_history = []\n self.val_pos_dist_history = []\n self.val_neg_dist_history = []","def reset(self):\n # Sample random state from initial state distribution\n self._cur_state = self._sample_state(self._mdp.I)\n self._prev_state = self._cur_state","def reset(self):\n\n self.results = []\n self._plot()","def reset(self):\n self.test = 0\n self.hit = 0","def reset(self):\n self.test = 0\n self.hit = 0","def reset(self):\n self.test = 0\n self.hit = 0","def reset() -> None:\n Stat._cache = SortedDict()","def reset(self):\n\n self.scaler = None\n self.isFitted = False\n self.__create_scaler()","def _reset_histories(self):\n\t\tself.train_loss_history = []\n\t\tself.train_acc_history = []\n\t\tself.val_acc_history = []\n\t\tself.val_loss_history = []","def reset(self):\n self.observation = None\n self.history.clear()\n for i in range(len(self.answers)):\n self.answers[i] = None\n self.reset_metrics()","def reset_memory_statistics(sender, **kwargs): # pylint: disable=unused-argument\n MemoryUsageData.start_counting()","def reset_stats(self):\n self.lives_left = self.game_settings.lives\n self.score = 0\n self.level = 1","def clear(self):\n self.__indexclusters[:] = []\n self.__sample_size = 0\n self.__samples[:] = []\n self.__simifunc = None","def reset(self):\n self.damage_dealt = 0\n self.kills = 0\n self.got_killed = False\n self.fitness = 0","def reset(self):\n if not self._data_writer.is_ran_last():\n if not self._data_writer.is_ran_ever():\n logger.error(\"Ignoring the reset before the run\")\n else:\n logger.error(\"Ignoring the repeated reset call\")\n return\n for population in self._data_writer.iterate_populations():\n population._cache_data() # pylint: disable=protected-access\n\n # Call superclass implementation\n AbstractSpinnakerBase.reset(self)","def reset(self):\n self.reserve.reset()\n self.revenue.reset()\n self.transfers.reset()\n self.missions.reset()\n self.debt.reset()\n self.genfund.reset()\n self.macro.reset(self.pop[self.start_yr],self.eco_first)\n self.summary = pd.DataFrame(index=self.names,columns=[t for t in range(self.start_yr,self.stop_yr)])\n self.year = self.start_yr","def reset(self):\n self._proportional = 0\n self._integral = 0\n self._derivative = 0\n\n self._last_time = self._current_time()\n self._last_output = None\n self._last_input = None","def _reset(self):\n self._values = {}","def reset(self):\n if self.monotonic_energy is not None:\n self.monotonic_energy.reset()\n if self.chunk_energy is not None:\n self.chunk_energy.reset()\n self.bd_L_prev = 0\n self.key_tail = None","def reset(self):\n self.score = None\n self.true = None\n self.meta = None","def reset(self):\n self.curr_idx = 0\n #shuffle data in each bucket\n random.shuffle(self.idx)\n for i, buck in enumerate(self.sentences):\n self.indices[i], self.sentences[i], self.characters[i], self.label[i] = shuffle(self.indices[i],\n self.sentences[i],\n self.characters[i],\n self.label[i])\n\n self.ndindex = []\n self.ndsent = []\n self.ndchar = []\n self.ndlabel = []\n\n #for each bucket of data\n for i, buck in enumerate(self.sentences):\n #append the lists with an array\n self.ndindex.append(ndarray.array(self.indices[i], dtype=self.dtype))\n self.ndsent.append(ndarray.array(self.sentences[i], dtype=self.dtype))\n self.ndchar.append(ndarray.array(self.characters[i], dtype=self.dtype))\n self.ndlabel.append(ndarray.array(self.label[i], dtype=self.dtype))","def _reset(self):\n\n # Checking one attribute is enough, becase they are all set together\n # in partial_fit\n if hasattr(self, 'scale_'):\n del self.scale_\n del self.mean_\n del self.var_","def reset(self):\n self.x_prev = np.zeros_like(self.mu)","def reset_stats(self):\r\n self.pepes_left = self.ai_settings.pepe_limit\r\n self.score = 0\r\n self.level = 1","def reset(self):\n self.total_pulls = 0\n self.total_score = 0\n self.npulls = np.zeros(self.k)\n self.score = np.zeros(self.k)","def clear(self):\n self.counts = [0] * len(self.values)\n if HAS_NUMPY:\n self.counts = numpy.array(self.counts)","def _reset(self):\n self._set(\"_n_init_features\", None)\n self._set(\"_n_output_features\", None)\n self._set(\"_n_intervals\", None)\n self._set(\"_mapper\", {})\n self._set(\"_cpp_preprocessor\", None)\n self._set(\"_fitted\", False)","def reset_results_arrays(self):\n raw_dim0 = self._dataset.raw_dims[0]\n \n if len(self.data) != raw_dim0:\n self.freq_current = np.zeros(raw_dim0, complex)","def reset(self):\n\n self.results = {}","def reset(self):\n self.baseline = None\n self.cut = None\n self.manual_push = 0","def reset_metric_variables(self) -> None:\n with self._lock:\n self._reset_metric_variables()","def reset(self):\n self.values.clear()\n\n self.on_reset()","def reset(self):\n self.tot = 0\n self.cnt = [0.0 for _ in range( self.alpha.getLen() )]","def reset(self):\n self.reset_image_estimate()\n self.init_m_aux()\n self.reset_hessian_and_bias()\n self.reset_adadelta_variables()","def reset(self):\n self.table[:, :] = 0\n self.counts[:] = 0\n self.names = []\n self.hashesperid.resize(0)\n self.dirty = True","def reset(self):\n self.loss = 0\n self.cnt = 0","def restore_auxiliary_stats(self):\n self.rms.restore_rms()","def test_reset(self):\n # Set tracked variables to some non-zero values\n base_gcmc_sampler.n_accepted = 1\n base_gcmc_sampler.n_moves = 1\n base_gcmc_sampler.Ns = [1]\n\n # Reset base_gcmc_sampler\n base_gcmc_sampler.reset()\n\n # Check that the values have been reset\n assert base_gcmc_sampler.n_accepted == 0\n assert base_gcmc_sampler.n_moves == 0\n assert len(base_gcmc_sampler.Ns) == 0\n\n return None","def reset_for_new_run(\n self,\n state: State\n ):\n\n super().reset_for_new_run(state)\n\n if self.Q is None:\n self.Q = {\n a: IncrementalSampleAverager(\n initial_value=self.initial_q_value,\n alpha=self.alpha\n )\n for a in self.most_recent_state.AA\n }\n else:\n for averager in self.Q.values():\n averager.reset()","def reset(self):\n self.__sets = []\n self._computed = False","def reset(self):\n self.edges = None\n self.chi = None\n self.k = None\n self.n_bins = None\n self.classes = None\n self.n_params = None","def reset(self) -> None:\n self.current = 0\n self.num_cuts = 0","def reset(self):\n self._hist.reset()\n return self"],"string":"[\n \"def stats_reset(self):\\n self.stats.reset()\",\n \"def stats_reset(self):\\n self.stats.reset()\",\n \"def reset(self) -> None:\\n self.statistics = defaultdict(float)\",\n \"def reset(self) -> None:\\n self.statistics = defaultdict(int)\",\n \"def reset(self):\\n self.stats = {}\",\n \"def reset(self):\\n self.sum_metric = 0.\\n self.num_inst = 0.\\n self.metrics.reset_stats()\",\n \"def reset_metric_stats(self):\\n\\n self.__stats_table = {}\",\n \"def reset_metric_stats(self):\\n self.__stats_table = {}\",\n \"def clear_stats(self):\\n self._stats = None\",\n \"def reset_stats() -> None:\\n STATS[\\\"cleaned\\\"] = 0\\n STATS[\\\"null\\\"] = 0\\n STATS[\\\"unknown\\\"] = 0\",\n \"def reset(self):\\n self.avg = 0\\n self.sum = 0\\n self.cnt = 0\",\n \"def reset(self):\\n self.start_times = {}\\n self.stats = defaultdict(OnlineMeter) # float defaults to 0\",\n \"def reset(self):\\n self.ref_value = 0.0\\n self._average = 0.0\\n self.num_samples = 0\",\n \"def reset(self) -> None:\\n self.statistics = defaultdict(self._mp_hack)\\n self._ddp_backend = get_backend()\",\n \"def reset(self) -> None:\\n self.statistics = defaultdict(self._mp_hack)\\n self._ddp_backend = get_backend()\",\n \"def reset(self) -> None:\\n self.statistics = defaultdict(self._mp_hack)\\n self._ddp_backend = get_backend()\",\n \"def reset(self):\\n self.num_inst = 0\\n self.sum_metric = 0.0\",\n \"def reset(self):\\n self.val = 0\\n self.avg = 0\\n self.sum = 0\\n self.count = 0\",\n \"def reset(self) -> None:\\n self.statistics = defaultdict(self._mp_hack)\\n self._is_ddp = get_rank() > -1\",\n \"def reset(self):\\n super().reset()\\n self.sample_count = 1\\n self.miss_prob = 1.0\\n self.miss_std = 0.0\\n self.miss_prob_sd_min = float(\\\"inf\\\")\\n self.miss_prob_min = float(\\\"inf\\\")\\n self.miss_sd_min = float(\\\"inf\\\")\",\n \"def reset(self):\\n self.accumulation = None\",\n \"def reset(self):\\n self.accumulation = None\",\n \"def reset(self):\\n self._total_value = 0.0\\n self._count = 0\",\n \"def reset(self):\\n for i in range(0, len(self.__counts)):\\n self.__counts[i] = 0\\n self.__overflow = 0\\n self.__total_count = 0\\n self.__total_values = 0\\n self.__min = None\\n self.__max = None\",\n \"def reset(self):\\n self._accumulated_time.clear()\\n self._hit_count.clear()\",\n \"def reset(self):\\n reset_system_health_series()\",\n \"def reset_data_recorder(self):\\n\\n self.t_values = []\\n self.x_values = []\\n self.tau_values = []\",\n \"def reset_average(self):\\n self._total_time = 0\\n self._average_time = 0\\n self._calls = 0\",\n \"def reset(self):\\n self.sum = [0.] * len(self.topk)\\n self.data_num = 0\\n self.pfm = [0.] * len(self.topk)\",\n \"def reset(self):\\n self._value_estimates[:] = self.prior\\n self.action_attempts[:] = 0\\n self.last_action = None\\n self.t = 0\",\n \"def clear(self):\\r\\n\\r\\n\\t\\tself.state_history = []\\r\\n\\t\\tself.error_history = []\\r\\n\\t\\tself.output_history = []\\r\\n\\t\\tself.sample_times = []\\r\\n\\t\\t\\r\\n\\t\\tself.OutputValue = 0.0\",\n \"def reset_stats(self):\\n print(\\\"Reseting stats\\\")\\n self.player_lives = self.ai_stts.player_lives\\n self.score = 0\\n self.level = 1\",\n \"def reset(self):\\n self.current_exposure = None\\n self.scores = {}\",\n \"def clear(self):\\r\\n\\r\\n\\t\\tself.state_history = []\\r\\n\\t\\tself.error_history = []\\r\\n\\t\\tself.output_history = []\\r\\n\\t\\tself.sample_times = []\\r\\n\\t\\t\\r\\n\\t\\tself.LastOutputValue = 0.0\\r\\n\\t\\tself.OutputValue = 0.0\",\n \"def clear(self):\\r\\n\\r\\n\\t\\tself.state_history = []\\r\\n\\t\\tself.error_history = []\\r\\n\\t\\tself.output_history = []\\r\\n\\t\\tself.sample_times = []\\r\\n\\t\\t\\r\\n\\t\\tself.LastOutputValue = 0.0\\r\\n\\t\\tself.OutputValue = 0.0\",\n \"def reinit(self):\\n self.logger.info(\\\"Reinit called. Clear the population.\\\")\\n self.set_init_population([], perf_name=None)\\n self._gt_rollouts = []\\n self._gt_scores = []\",\n \"def reset(self):\\n self.reset_count += 1\\n self._init_data()\",\n \"def clear(self):\\r\\n\\r\\n\\t\\tself.ITerm = 0.0\\r\\n\\t\\tself.DTerm = 0.0\\r\\n\\t\\tself.last_error = 0.0\\r\\n\\r\\n\\t\\tself.state_history = []\\r\\n\\t\\tself.error_history = []\\r\\n\\t\\tself.output_history = []\\r\\n\\t\\tself.sample_times = []\\r\\n\\t\\t\\r\\n\\t\\tself.OutputValue = 0.0\",\n \"def reset_state(self):\\n for name in self.metrics:\\n self.metrics[name].reset_state()\",\n \"def reset(self):\\n self.error_p = 0.0\\n self.error_i = 0.0\\n self.error_d = 0.0\\n self.errors = [ 0.0 ] * self.samples\\n if callable(self.debug_callback):\\n self.debug_callback(\\\"reset\\\")\",\n \"def reset(self):\\n super().reset()\\n self.m_n = 1\\n self.m_num_errors = 0\\n self.m_d = 0\\n self.m_lastd = 0\\n self.m_mean = 0.0\\n self.m_std_temp = 0.0\\n self.m_m2s_max = 0.0\\n self.estimation = 0.0\",\n \"def clear(self):\\n self.sum_hit_at_one = 0.0\\n self.sum_perr = 0.0\\n self.sum_loss = 0.0\\n self.map_calculator.clear()\\n self.global_ap_calculator.clear()\\n self.num_examples = 0\",\n \"def reset_turn_stats(self):\\n\\n # Set the attribute value to 0\\n self._current_score = 0\",\n \"def clearstats(self, clearstats) :\\n\\t\\ttry :\\n\\t\\t\\tself._clearstats = clearstats\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"def clearstats(self, clearstats) :\\n\\t\\ttry :\\n\\t\\t\\tself._clearstats = clearstats\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"def clearstats(self, clearstats) :\\n\\t\\ttry :\\n\\t\\t\\tself._clearstats = clearstats\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"def clearstats(self, clearstats) :\\n\\t\\ttry :\\n\\t\\t\\tself._clearstats = clearstats\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"def reset(self):\\n self.curr_idx = 0\\n # shuffle data in each bucket\\n random.shuffle(self.idx)\\n for i, buck in enumerate(self.utterances):\\n self.indices[i], self.utterances[i], self.intents[i] = shuffle(self.indices[i],\\n self.utterances[i],\\n self.intents[i])\\n self.ndindex = []\\n self.ndsent = []\\n self.ndlabel = []\\n\\n # append the lists with an array\\n for i, buck in enumerate(self.utterances):\\n self.ndindex.append(ndarray.array(self.indices[i], dtype=self.dtype))\\n self.ndsent.append(ndarray.array(self.utterances[i], dtype=self.dtype))\\n self.ndlabel.append(ndarray.array(self.intents[i], dtype=self.dtype))\",\n \"def _reset(self):\\n\\n # Checking one attribute is enough, because they are all set together\\n # in partial_fit\\n if hasattr(self, 'scale_'):\\n del self.scale_\\n del self.n_samples_seen_\\n del self.mean_\\n del self.var_\",\n \"def reset_stats(self, settings, reset_stream=False):\\n\\n self.__settings = settings\\n self.__stats_table = {}\\n self.__sql_stats_table = {}\\n self.__slow_transaction = None\\n self.__slow_transaction_map = {}\\n self.__slow_transaction_old_duration = None\\n self.__transaction_errors = []\\n self.__synthetics_transactions = []\\n\\n self.reset_transaction_events()\\n self.reset_error_events()\\n self.reset_custom_events()\\n self.reset_span_events()\\n self.reset_synthetics_events()\\n # streams are never reset after instantiation\\n if reset_stream:\\n self._span_stream = StreamBuffer(\\n settings.infinite_tracing.span_queue_size)\",\n \"def reset(self):\\n self.mode = 0\\n self.graphs = [[], [], []]\\n self.coefficients = []\\n self.sample = []\",\n \"def reset(self):\\n self.st = segment_tree.SegmentTreeSampler(self.n, np.ones(self.n) * self.reg, self.random_state)\",\n \"def reset(self):\\r\\n self.buffer = np.zeros(self.nBins)\\r\\n self.counter = 0\",\n \"def reset(self):\\n self.algo_state = {}\\n self.actual_repetitions = 0\\n self.next_session = -1\\n self.last_session = -1\\n self.past_quality = []\",\n \"def reset(self):\\n self.temp_data.clear()\",\n \"def reset(self, complete=False):\\n self.sum = 0\\n self.n = 0\\n if complete:\\n self.running_avg = []\",\n \"def reset(self):\\n self.correct_count = 0\\n self.total_count = 0\",\n \"def reset_metrics(self):\\n self.metrics['loss'] = 0.0\\n self.metrics['num_tokens'] = 0\\n self.metrics['correct_tokens'] = 0\\n self.metrics['correct_pred'] = 0\\n self.metrics['pred_count'] = 0\",\n \"def _reset_histories(self):\\n self.train_loss_history = []\\n self.train_pos_dist_history = []\\n self.train_neg_dist_history = []\\n self.val_loss_history = []\\n self.val_pos_dist_history = []\\n self.val_neg_dist_history = []\",\n \"def reset(self):\\n # Sample random state from initial state distribution\\n self._cur_state = self._sample_state(self._mdp.I)\\n self._prev_state = self._cur_state\",\n \"def reset(self):\\n\\n self.results = []\\n self._plot()\",\n \"def reset(self):\\n self.test = 0\\n self.hit = 0\",\n \"def reset(self):\\n self.test = 0\\n self.hit = 0\",\n \"def reset(self):\\n self.test = 0\\n self.hit = 0\",\n \"def reset() -> None:\\n Stat._cache = SortedDict()\",\n \"def reset(self):\\n\\n self.scaler = None\\n self.isFitted = False\\n self.__create_scaler()\",\n \"def _reset_histories(self):\\n\\t\\tself.train_loss_history = []\\n\\t\\tself.train_acc_history = []\\n\\t\\tself.val_acc_history = []\\n\\t\\tself.val_loss_history = []\",\n \"def reset(self):\\n self.observation = None\\n self.history.clear()\\n for i in range(len(self.answers)):\\n self.answers[i] = None\\n self.reset_metrics()\",\n \"def reset_memory_statistics(sender, **kwargs): # pylint: disable=unused-argument\\n MemoryUsageData.start_counting()\",\n \"def reset_stats(self):\\n self.lives_left = self.game_settings.lives\\n self.score = 0\\n self.level = 1\",\n \"def clear(self):\\n self.__indexclusters[:] = []\\n self.__sample_size = 0\\n self.__samples[:] = []\\n self.__simifunc = None\",\n \"def reset(self):\\n self.damage_dealt = 0\\n self.kills = 0\\n self.got_killed = False\\n self.fitness = 0\",\n \"def reset(self):\\n if not self._data_writer.is_ran_last():\\n if not self._data_writer.is_ran_ever():\\n logger.error(\\\"Ignoring the reset before the run\\\")\\n else:\\n logger.error(\\\"Ignoring the repeated reset call\\\")\\n return\\n for population in self._data_writer.iterate_populations():\\n population._cache_data() # pylint: disable=protected-access\\n\\n # Call superclass implementation\\n AbstractSpinnakerBase.reset(self)\",\n \"def reset(self):\\n self.reserve.reset()\\n self.revenue.reset()\\n self.transfers.reset()\\n self.missions.reset()\\n self.debt.reset()\\n self.genfund.reset()\\n self.macro.reset(self.pop[self.start_yr],self.eco_first)\\n self.summary = pd.DataFrame(index=self.names,columns=[t for t in range(self.start_yr,self.stop_yr)])\\n self.year = self.start_yr\",\n \"def reset(self):\\n self._proportional = 0\\n self._integral = 0\\n self._derivative = 0\\n\\n self._last_time = self._current_time()\\n self._last_output = None\\n self._last_input = None\",\n \"def _reset(self):\\n self._values = {}\",\n \"def reset(self):\\n if self.monotonic_energy is not None:\\n self.monotonic_energy.reset()\\n if self.chunk_energy is not None:\\n self.chunk_energy.reset()\\n self.bd_L_prev = 0\\n self.key_tail = None\",\n \"def reset(self):\\n self.score = None\\n self.true = None\\n self.meta = None\",\n \"def reset(self):\\n self.curr_idx = 0\\n #shuffle data in each bucket\\n random.shuffle(self.idx)\\n for i, buck in enumerate(self.sentences):\\n self.indices[i], self.sentences[i], self.characters[i], self.label[i] = shuffle(self.indices[i],\\n self.sentences[i],\\n self.characters[i],\\n self.label[i])\\n\\n self.ndindex = []\\n self.ndsent = []\\n self.ndchar = []\\n self.ndlabel = []\\n\\n #for each bucket of data\\n for i, buck in enumerate(self.sentences):\\n #append the lists with an array\\n self.ndindex.append(ndarray.array(self.indices[i], dtype=self.dtype))\\n self.ndsent.append(ndarray.array(self.sentences[i], dtype=self.dtype))\\n self.ndchar.append(ndarray.array(self.characters[i], dtype=self.dtype))\\n self.ndlabel.append(ndarray.array(self.label[i], dtype=self.dtype))\",\n \"def _reset(self):\\n\\n # Checking one attribute is enough, becase they are all set together\\n # in partial_fit\\n if hasattr(self, 'scale_'):\\n del self.scale_\\n del self.mean_\\n del self.var_\",\n \"def reset(self):\\n self.x_prev = np.zeros_like(self.mu)\",\n \"def reset_stats(self):\\r\\n self.pepes_left = self.ai_settings.pepe_limit\\r\\n self.score = 0\\r\\n self.level = 1\",\n \"def reset(self):\\n self.total_pulls = 0\\n self.total_score = 0\\n self.npulls = np.zeros(self.k)\\n self.score = np.zeros(self.k)\",\n \"def clear(self):\\n self.counts = [0] * len(self.values)\\n if HAS_NUMPY:\\n self.counts = numpy.array(self.counts)\",\n \"def _reset(self):\\n self._set(\\\"_n_init_features\\\", None)\\n self._set(\\\"_n_output_features\\\", None)\\n self._set(\\\"_n_intervals\\\", None)\\n self._set(\\\"_mapper\\\", {})\\n self._set(\\\"_cpp_preprocessor\\\", None)\\n self._set(\\\"_fitted\\\", False)\",\n \"def reset_results_arrays(self):\\n raw_dim0 = self._dataset.raw_dims[0]\\n \\n if len(self.data) != raw_dim0:\\n self.freq_current = np.zeros(raw_dim0, complex)\",\n \"def reset(self):\\n\\n self.results = {}\",\n \"def reset(self):\\n self.baseline = None\\n self.cut = None\\n self.manual_push = 0\",\n \"def reset_metric_variables(self) -> None:\\n with self._lock:\\n self._reset_metric_variables()\",\n \"def reset(self):\\n self.values.clear()\\n\\n self.on_reset()\",\n \"def reset(self):\\n self.tot = 0\\n self.cnt = [0.0 for _ in range( self.alpha.getLen() )]\",\n \"def reset(self):\\n self.reset_image_estimate()\\n self.init_m_aux()\\n self.reset_hessian_and_bias()\\n self.reset_adadelta_variables()\",\n \"def reset(self):\\n self.table[:, :] = 0\\n self.counts[:] = 0\\n self.names = []\\n self.hashesperid.resize(0)\\n self.dirty = True\",\n \"def reset(self):\\n self.loss = 0\\n self.cnt = 0\",\n \"def restore_auxiliary_stats(self):\\n self.rms.restore_rms()\",\n \"def test_reset(self):\\n # Set tracked variables to some non-zero values\\n base_gcmc_sampler.n_accepted = 1\\n base_gcmc_sampler.n_moves = 1\\n base_gcmc_sampler.Ns = [1]\\n\\n # Reset base_gcmc_sampler\\n base_gcmc_sampler.reset()\\n\\n # Check that the values have been reset\\n assert base_gcmc_sampler.n_accepted == 0\\n assert base_gcmc_sampler.n_moves == 0\\n assert len(base_gcmc_sampler.Ns) == 0\\n\\n return None\",\n \"def reset_for_new_run(\\n self,\\n state: State\\n ):\\n\\n super().reset_for_new_run(state)\\n\\n if self.Q is None:\\n self.Q = {\\n a: IncrementalSampleAverager(\\n initial_value=self.initial_q_value,\\n alpha=self.alpha\\n )\\n for a in self.most_recent_state.AA\\n }\\n else:\\n for averager in self.Q.values():\\n averager.reset()\",\n \"def reset(self):\\n self.__sets = []\\n self._computed = False\",\n \"def reset(self):\\n self.edges = None\\n self.chi = None\\n self.k = None\\n self.n_bins = None\\n self.classes = None\\n self.n_params = None\",\n \"def reset(self) -> None:\\n self.current = 0\\n self.num_cuts = 0\",\n \"def reset(self):\\n self._hist.reset()\\n return self\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8175733","0.8175733","0.81559753","0.81235486","0.78766066","0.78731924","0.77236235","0.76826066","0.7648331","0.7643053","0.75607705","0.7420023","0.73855245","0.735026","0.735026","0.735026","0.7306659","0.7305569","0.72605497","0.72255266","0.7146157","0.7146157","0.71064705","0.70541924","0.70354277","0.7002376","0.6995929","0.698548","0.69429284","0.69302046","0.69075143","0.6902323","0.6900659","0.6899984","0.6899984","0.68919635","0.6878506","0.6862571","0.68519914","0.6809108","0.67935514","0.67851716","0.67811215","0.6769244","0.6769244","0.6769244","0.6769244","0.67565143","0.67392087","0.67041886","0.67033416","0.6702633","0.6696533","0.66853565","0.66830385","0.66795826","0.66770935","0.667698","0.66607165","0.66598594","0.6652083","0.66517764","0.66517764","0.66517764","0.6633372","0.6620441","0.66171205","0.66151804","0.66147643","0.6590698","0.6590537","0.65893716","0.65869385","0.6565601","0.6563439","0.65514684","0.6551448","0.65482515","0.6542173","0.6540907","0.6536822","0.65345055","0.6529401","0.6519701","0.6516039","0.6505992","0.6504654","0.6504634","0.65002054","0.6497957","0.6494268","0.6486475","0.64846194","0.64806986","0.6479931","0.646618","0.64621705","0.64560664","0.6455684","0.6452345","0.64394677"],"string":"[\n \"0.8175733\",\n \"0.8175733\",\n \"0.81559753\",\n \"0.81235486\",\n \"0.78766066\",\n \"0.78731924\",\n \"0.77236235\",\n \"0.76826066\",\n \"0.7648331\",\n \"0.7643053\",\n \"0.75607705\",\n \"0.7420023\",\n \"0.73855245\",\n \"0.735026\",\n \"0.735026\",\n \"0.735026\",\n \"0.7306659\",\n \"0.7305569\",\n \"0.72605497\",\n \"0.72255266\",\n \"0.7146157\",\n \"0.7146157\",\n \"0.71064705\",\n \"0.70541924\",\n \"0.70354277\",\n \"0.7002376\",\n \"0.6995929\",\n \"0.698548\",\n \"0.69429284\",\n \"0.69302046\",\n \"0.69075143\",\n \"0.6902323\",\n \"0.6900659\",\n \"0.6899984\",\n \"0.6899984\",\n \"0.68919635\",\n \"0.6878506\",\n \"0.6862571\",\n \"0.68519914\",\n \"0.6809108\",\n \"0.67935514\",\n \"0.67851716\",\n \"0.67811215\",\n \"0.6769244\",\n \"0.6769244\",\n \"0.6769244\",\n \"0.6769244\",\n \"0.67565143\",\n \"0.67392087\",\n \"0.67041886\",\n \"0.67033416\",\n \"0.6702633\",\n \"0.6696533\",\n \"0.66853565\",\n \"0.66830385\",\n \"0.66795826\",\n \"0.66770935\",\n \"0.667698\",\n \"0.66607165\",\n \"0.66598594\",\n \"0.6652083\",\n \"0.66517764\",\n \"0.66517764\",\n \"0.66517764\",\n \"0.6633372\",\n \"0.6620441\",\n \"0.66171205\",\n \"0.66151804\",\n \"0.66147643\",\n \"0.6590698\",\n \"0.6590537\",\n \"0.65893716\",\n \"0.65869385\",\n \"0.6565601\",\n \"0.6563439\",\n \"0.65514684\",\n \"0.6551448\",\n \"0.65482515\",\n \"0.6542173\",\n \"0.6540907\",\n \"0.6536822\",\n \"0.65345055\",\n \"0.6529401\",\n \"0.6519701\",\n \"0.6516039\",\n \"0.6505992\",\n \"0.6504654\",\n \"0.6504634\",\n \"0.65002054\",\n \"0.6497957\",\n \"0.6494268\",\n \"0.6486475\",\n \"0.64846194\",\n \"0.64806986\",\n \"0.6479931\",\n \"0.646618\",\n \"0.64621705\",\n \"0.64560664\",\n \"0.6455684\",\n \"0.6452345\",\n \"0.64394677\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":3697,"cells":{"query":{"kind":"string","value":"Resets the accumulated statistics back to initial state for Synthetics events data."},"document":{"kind":"string","value":"def reset_synthetics_events(self):\n if self.__settings is not None:\n self._synthetics_events = LimitedDataSet(\n self.__settings.agent_limits.synthetics_events)\n else:\n self._synthetics_events = LimitedDataSet()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def reset(self) -> None:\n self.statistics = defaultdict(float)","def stats_reset(self):\n self.stats.reset()","def stats_reset(self):\n self.stats.reset()","def reset(self) -> None:\n self.statistics = defaultdict(int)","def reset(self):\n self.stats = {}","def reset_stats() -> None:\n STATS[\"cleaned\"] = 0\n STATS[\"null\"] = 0\n STATS[\"unknown\"] = 0","def reset(self):\n self.sum_metric = 0.\n self.num_inst = 0.\n self.metrics.reset_stats()","def reset(self):\n self.start_times = {}\n self.stats = defaultdict(OnlineMeter) # float defaults to 0","def reset_metric_stats(self):\n\n self.__stats_table = {}","def reset_metric_stats(self):\n self.__stats_table = {}","def reset(self):\n reset_system_health_series()","def reset(self):\n self.avg = 0\n self.sum = 0\n self.cnt = 0","def clear_stats(self):\n self._stats = None","def reset(self):\n self.sum = [0.] * len(self.topk)\n self.data_num = 0\n self.pfm = [0.] * len(self.topk)","def reset(self) -> None:\n self.statistics = defaultdict(self._mp_hack)\n self._ddp_backend = get_backend()","def reset(self) -> None:\n self.statistics = defaultdict(self._mp_hack)\n self._ddp_backend = get_backend()","def reset(self) -> None:\n self.statistics = defaultdict(self._mp_hack)\n self._ddp_backend = get_backend()","def reset(self):\n self.num_inst = 0\n self.sum_metric = 0.0","def reset(self):\n self.ref_value = 0.0\n self._average = 0.0\n self.num_samples = 0","def reset_average(self):\n self._total_time = 0\n self._average_time = 0\n self._calls = 0","def reset(self):\n self.val = 0\n self.avg = 0\n self.sum = 0\n self.count = 0","def reset_data_recorder(self):\n\n self.t_values = []\n self.x_values = []\n self.tau_values = []","def reset(self) -> None:\n self.statistics = defaultdict(self._mp_hack)\n self._is_ddp = get_rank() > -1","def reset(self):\n super().reset()\n self.m_n = 1\n self.m_num_errors = 0\n self.m_d = 0\n self.m_lastd = 0\n self.m_mean = 0.0\n self.m_std_temp = 0.0\n self.m_m2s_max = 0.0\n self.estimation = 0.0","def reset(self):\n self.accumulation = None","def reset(self):\n self.accumulation = None","def reset(self):\n self._accumulated_time.clear()\n self._hit_count.clear()","def reset(self):\n self.reset_count += 1\n self._init_data()","def reset(self):\n super().reset()\n self.sample_count = 1\n self.miss_prob = 1.0\n self.miss_std = 0.0\n self.miss_prob_sd_min = float(\"inf\")\n self.miss_prob_min = float(\"inf\")\n self.miss_sd_min = float(\"inf\")","def reset(self):\n self.error_p = 0.0\n self.error_i = 0.0\n self.error_d = 0.0\n self.errors = [ 0.0 ] * self.samples\n if callable(self.debug_callback):\n self.debug_callback(\"reset\")","def reset(self):\n self.current_exposure = None\n self.scores = {}","def reset_state(self):\n for name in self.metrics:\n self.metrics[name].reset_state()","def reset(self):\n self.work_state = work_state[\"Measuring\"]\n self.report_mode = report_mode[\"Initiative\"]\n self.duty_cycle = 0\n self.logger.info(\"{}: sensor resetted.\".format(self.sensor_name))","def reset_stats(self, settings, reset_stream=False):\n\n self.__settings = settings\n self.__stats_table = {}\n self.__sql_stats_table = {}\n self.__slow_transaction = None\n self.__slow_transaction_map = {}\n self.__slow_transaction_old_duration = None\n self.__transaction_errors = []\n self.__synthetics_transactions = []\n\n self.reset_transaction_events()\n self.reset_error_events()\n self.reset_custom_events()\n self.reset_span_events()\n self.reset_synthetics_events()\n # streams are never reset after instantiation\n if reset_stream:\n self._span_stream = StreamBuffer(\n settings.infinite_tracing.span_queue_size)","def resetSimulatedData(self):\n self.space = 0*self.space # numpy array that represents the physical space\n self.susceptible = self.space.size # the current number of healthy people in the population\n self.infected = 0 # the current number of infected people in the population\n self.recovered = 0 # the current number of infected people in the population\n self.snapshots = [] # this will contain numpy arrays that represent each time step\n self.SIR = np.zeros((3,1)) #this contains a time series of the number of susceptible, infected, recovered\n self.time = np.zeros(1) # this array will be the time array\n self.total_infections = 0 # set the number of total infections back to zero","def reset(self):\n for i in range(0, len(self.__counts)):\n self.__counts[i] = 0\n self.__overflow = 0\n self.__total_count = 0\n self.__total_values = 0\n self.__min = None\n self.__max = None","def reset(self):\n\n self.scaler = None\n self.isFitted = False\n self.__create_scaler()","def reset(self):\n self._total_value = 0.0\n self._count = 0","def reset(self):\n self.temp_data.clear()","def reset(self,prepare=True):\n if self.lowmem:\n self.load(self.filename,prepare=prepare)\n else:\n self.data = np.copy(self.data_orig)\n self.weights = np.copy(self.weights_orig)\n self.durations = self.getSubintinfo('TSUBINT')\n #if prepare:\n # self.scrunch()","def reset_state(self):\n self.s = np.copy(self.s_i)","def reset(self):\n self.data = {}\n self.is_bound = False\n self._errors = None","def reset(self):\n self.baseline = None\n self.cut = None\n self.manual_push = 0","def reset(self):\n self.dims.clear()\n self.xlabels.clear()\n self.annotators.clear()\n self._figTitle = None\n self.tbmTitle = None\n self._isSubplot = False\n self._universal_xlabel = False\n self._plotter = None\n self.Nsp = 0","def reset(self):\n for k,v in self.events.items():\n self.events[k] = None","def reset(self):\n if self.monotonic_energy is not None:\n self.monotonic_energy.reset()\n if self.chunk_energy is not None:\n self.chunk_energy.reset()\n self.bd_L_prev = 0\n self.key_tail = None","def _reset(self):\n self._values = {}","def reset(self):\n\n # Ending variables\n self.time_idle = 0\n self.time_episode = 0\n self.done_time_idle = False\n self.done_falling = False\n self.done_time_episode = False\n\n # hero variables\n self.last_location = None\n self.last_velocity = 0\n\n # Sensor stack\n self.prev_image_0 = None\n self.prev_image_1 = None\n self.prev_image_2 = None\n\n self.last_heading_deviation = 0","def reset(self):\n self.values.clear()\n\n self.on_reset()","def reset(self):\n\n self.results = []\n self._plot()","def reset(self):\n\t\tself._initial = None\n\t\tself._start = None\n\t\tself._time = 0\n\t\tself._total = 0\n\t\treturn self","def reset(self):\n self.m = normalize(self.m0)\n self.t = 0.0","def reset(self):\n self._stat = CardMeta()","def reset(self):\n self.mode = 0\n self.graphs = [[], [], []]\n self.coefficients = []\n self.sample = []","def clear(self):\r\n\r\n\t\tself.ITerm = 0.0\r\n\t\tself.DTerm = 0.0\r\n\t\tself.last_error = 0.0\r\n\r\n\t\tself.state_history = []\r\n\t\tself.error_history = []\r\n\t\tself.output_history = []\r\n\t\tself.sample_times = []\r\n\t\t\r\n\t\tself.OutputValue = 0.0","def clear(self):\r\n\r\n\t\tself.state_history = []\r\n\t\tself.error_history = []\r\n\t\tself.output_history = []\r\n\t\tself.sample_times = []\r\n\t\t\r\n\t\tself.OutputValue = 0.0","def reset(self):\n self._proportional = 0\n self._integral = 0\n self._derivative = 0\n\n self._last_time = self._current_time()\n self._last_output = None\n self._last_input = None","def clear(self):\r\n\r\n\t\tself.state_history = []\r\n\t\tself.error_history = []\r\n\t\tself.output_history = []\r\n\t\tself.sample_times = []\r\n\t\t\r\n\t\tself.LastOutputValue = 0.0\r\n\t\tself.OutputValue = 0.0","def clear(self):\r\n\r\n\t\tself.state_history = []\r\n\t\tself.error_history = []\r\n\t\tself.output_history = []\r\n\t\tself.sample_times = []\r\n\t\t\r\n\t\tself.LastOutputValue = 0.0\r\n\t\tself.OutputValue = 0.0","def reset_stats(self):\n print(\"Reseting stats\")\n self.player_lives = self.ai_stts.player_lives\n self.score = 0\n self.level = 1","def _reset(self):\n\n # Checking one attribute is enough, because they are all set together\n # in partial_fit\n if hasattr(self, 'scale_'):\n del self.scale_\n del self.n_samples_seen_\n del self.mean_\n del self.var_","def clearstats(self, clearstats) :\n\t\ttry :\n\t\t\tself._clearstats = clearstats\n\t\texcept Exception as e:\n\t\t\traise e","def clearstats(self, clearstats) :\n\t\ttry :\n\t\t\tself._clearstats = clearstats\n\t\texcept Exception as e:\n\t\t\traise e","def clearstats(self, clearstats) :\n\t\ttry :\n\t\t\tself._clearstats = clearstats\n\t\texcept Exception as e:\n\t\t\traise e","def clearstats(self, clearstats) :\n\t\ttry :\n\t\t\tself._clearstats = clearstats\n\t\texcept Exception as e:\n\t\t\traise e","def reset(self):\n self.score = None\n self.true = None\n self.meta = None","def reset(self):\n self.algo_state = {}\n self.actual_repetitions = 0\n self.next_session = -1\n self.last_session = -1\n self.past_quality = []","def reset(self):\n self._data = []","def reset(self):\n self.reserve.reset()\n self.revenue.reset()\n self.transfers.reset()\n self.missions.reset()\n self.debt.reset()\n self.genfund.reset()\n self.macro.reset(self.pop[self.start_yr],self.eco_first)\n self.summary = pd.DataFrame(index=self.names,columns=[t for t in range(self.start_yr,self.stop_yr)])\n self.year = self.start_yr","def reset(self):\n self.data = self._defaults","def reset(self):\n self.control_counter = 0\n self.last_position_error = np.zeros(3)\n self.integral_position_error = np.zeros(3)\n self.last_attitude_error = np.zeros(3)\n self.integral_attitude_error = np.zeros(3)","def reset(self):\n self.source_data = self.get_dict_from_range(None, None)\n self.selection_bounds = None\n self.selection = []\n for c in self.callbacks[\"reset_data\"]:\n c()\n if self.context is not None:\n self.context.doc.add_next_tick_callback(self.update_source)","def reset_states(self):\n self.mean_makespan_baseline.assign(0)\n self.mean_makespan_train.assign(0)\n self.step.assign(0)","def reset_metric_variables(self) -> None:\n with self._lock:\n self._reset_metric_variables()","def reset_global(self):\n self.T = 0\n self.ep = 0\n self.t = 0\n self.G = 0.0\n self._ep_starttime = time.time()","def reset(self):\n self.damage_dealt = 0\n self.kills = 0\n self.got_killed = False\n self.fitness = 0","def reset(self):\n self.game_status = game_status.GameStatus(copy.deepcopy(self.initial_rosters),\n self.game_status.game_date)\n self._current_event_ndx = 0","def reset_sgr(self):\n self.intensity = 0\n self.italic = False\n self.bold = False\n self.underline = False\n self.foreground_color = None\n self.background_color = None","def reset() -> None:\n Stat._cache = SortedDict()","def reset_metrics(self):\n self.metrics['loss'] = 0.0\n self.metrics['num_tokens'] = 0\n self.metrics['correct_tokens'] = 0\n self.metrics['correct_pred'] = 0\n self.metrics['pred_count'] = 0","def reset(self):\n self.N = self.N[0:2]\n self.t = 2\n self.harvest_available = 0\n self.harvest_record = [0,0]","def reset(self):\n self.x_prev = np.zeros_like(self.mu)","def reset_turn_stats(self):\n\n # Set the attribute value to 0\n self._current_score = 0","def reset(self):\n self._value_estimates[:] = self.prior\n self.action_attempts[:] = 0\n self.last_action = None\n self.t = 0","def __reset(self):\n\t\tself.__highest = -float('inf')\n\t\tself.__lowest = float('inf')\n\t\tself.__total = 0\n\t\tself.__steps = 0\n\t\tself.__cold_days = 0","def _reset(self):\n\n # Checking one attribute is enough, becase they are all set together\n # in partial_fit\n if hasattr(self, 'scale_'):\n del self.scale_\n del self.mean_\n del self.var_","def reset(self):\r\n self.myOutputs = list()\r\n self.myPlots = list()\r\n self.pause = 0\r\n self.doMPL = False\r\n self.graphLabelsX = []\r\n self.graphLabelsY = []\r\n for i in self.xData.iterkeys():\r\n self.xData[i] = []\r\n self.yData[i] = []\r\n self.xyData[i] = []\r\n self.graphs[i] = Gnuplot(debug=0)\r\n self.figures[i] = 0\r\n self.mplFigCount = 0","def reset_results_arrays(self):\n raw_dims = self._dataset.raw_dims\n raw_dims = list(raw_dims[::-1])\n \n if len(self.data) != raw_dims:\n self.freq_all = np.zeros(raw_dims, complex)\n self.time_all = np.zeros(raw_dims, complex)","def reinit(self):\n self.logger.info(\"Reinit called. Clear the population.\")\n self.set_init_population([], perf_name=None)\n self._gt_rollouts = []\n self._gt_scores = []","def reset(self) -> None:\n self.current = 0\n self.num_cuts = 0","def _reset_histories(self):\n self.train_loss_history = []\n self.train_pos_dist_history = []\n self.train_neg_dist_history = []\n self.val_loss_history = []\n self.val_pos_dist_history = []\n self.val_neg_dist_history = []","def reset(self):\n self.tracker.reset()\n self.total_max_q = 0.0\n self.episode_step = 0\n self.episode += 1","def reset(self):\n self.__sets = []\n self._computed = False","def reset_results_arrays(self):\n raw_dim0 = self._dataset.raw_dims[0]\n \n if len(self.data) != raw_dim0:\n self.freq_current = np.zeros(raw_dim0, complex)","def reset(self):\n\n self.elapsed_time = 0","def _reset_histories(self):\n\t\tself.train_loss_history = []\n\t\tself.train_acc_history = []\n\t\tself.val_acc_history = []\n\t\tself.val_loss_history = []","def reset_transaction_events(self):\n\n if self.__settings is not None:\n self._transaction_events = SampledDataSet(\n self.__settings.event_harvest_config.\n harvest_limits.analytic_event_data)\n else:\n self._transaction_events = SampledDataSet()","def reset():","def reset():","def reset():"],"string":"[\n \"def reset(self) -> None:\\n self.statistics = defaultdict(float)\",\n \"def stats_reset(self):\\n self.stats.reset()\",\n \"def stats_reset(self):\\n self.stats.reset()\",\n \"def reset(self) -> None:\\n self.statistics = defaultdict(int)\",\n \"def reset(self):\\n self.stats = {}\",\n \"def reset_stats() -> None:\\n STATS[\\\"cleaned\\\"] = 0\\n STATS[\\\"null\\\"] = 0\\n STATS[\\\"unknown\\\"] = 0\",\n \"def reset(self):\\n self.sum_metric = 0.\\n self.num_inst = 0.\\n self.metrics.reset_stats()\",\n \"def reset(self):\\n self.start_times = {}\\n self.stats = defaultdict(OnlineMeter) # float defaults to 0\",\n \"def reset_metric_stats(self):\\n\\n self.__stats_table = {}\",\n \"def reset_metric_stats(self):\\n self.__stats_table = {}\",\n \"def reset(self):\\n reset_system_health_series()\",\n \"def reset(self):\\n self.avg = 0\\n self.sum = 0\\n self.cnt = 0\",\n \"def clear_stats(self):\\n self._stats = None\",\n \"def reset(self):\\n self.sum = [0.] * len(self.topk)\\n self.data_num = 0\\n self.pfm = [0.] * len(self.topk)\",\n \"def reset(self) -> None:\\n self.statistics = defaultdict(self._mp_hack)\\n self._ddp_backend = get_backend()\",\n \"def reset(self) -> None:\\n self.statistics = defaultdict(self._mp_hack)\\n self._ddp_backend = get_backend()\",\n \"def reset(self) -> None:\\n self.statistics = defaultdict(self._mp_hack)\\n self._ddp_backend = get_backend()\",\n \"def reset(self):\\n self.num_inst = 0\\n self.sum_metric = 0.0\",\n \"def reset(self):\\n self.ref_value = 0.0\\n self._average = 0.0\\n self.num_samples = 0\",\n \"def reset_average(self):\\n self._total_time = 0\\n self._average_time = 0\\n self._calls = 0\",\n \"def reset(self):\\n self.val = 0\\n self.avg = 0\\n self.sum = 0\\n self.count = 0\",\n \"def reset_data_recorder(self):\\n\\n self.t_values = []\\n self.x_values = []\\n self.tau_values = []\",\n \"def reset(self) -> None:\\n self.statistics = defaultdict(self._mp_hack)\\n self._is_ddp = get_rank() > -1\",\n \"def reset(self):\\n super().reset()\\n self.m_n = 1\\n self.m_num_errors = 0\\n self.m_d = 0\\n self.m_lastd = 0\\n self.m_mean = 0.0\\n self.m_std_temp = 0.0\\n self.m_m2s_max = 0.0\\n self.estimation = 0.0\",\n \"def reset(self):\\n self.accumulation = None\",\n \"def reset(self):\\n self.accumulation = None\",\n \"def reset(self):\\n self._accumulated_time.clear()\\n self._hit_count.clear()\",\n \"def reset(self):\\n self.reset_count += 1\\n self._init_data()\",\n \"def reset(self):\\n super().reset()\\n self.sample_count = 1\\n self.miss_prob = 1.0\\n self.miss_std = 0.0\\n self.miss_prob_sd_min = float(\\\"inf\\\")\\n self.miss_prob_min = float(\\\"inf\\\")\\n self.miss_sd_min = float(\\\"inf\\\")\",\n \"def reset(self):\\n self.error_p = 0.0\\n self.error_i = 0.0\\n self.error_d = 0.0\\n self.errors = [ 0.0 ] * self.samples\\n if callable(self.debug_callback):\\n self.debug_callback(\\\"reset\\\")\",\n \"def reset(self):\\n self.current_exposure = None\\n self.scores = {}\",\n \"def reset_state(self):\\n for name in self.metrics:\\n self.metrics[name].reset_state()\",\n \"def reset(self):\\n self.work_state = work_state[\\\"Measuring\\\"]\\n self.report_mode = report_mode[\\\"Initiative\\\"]\\n self.duty_cycle = 0\\n self.logger.info(\\\"{}: sensor resetted.\\\".format(self.sensor_name))\",\n \"def reset_stats(self, settings, reset_stream=False):\\n\\n self.__settings = settings\\n self.__stats_table = {}\\n self.__sql_stats_table = {}\\n self.__slow_transaction = None\\n self.__slow_transaction_map = {}\\n self.__slow_transaction_old_duration = None\\n self.__transaction_errors = []\\n self.__synthetics_transactions = []\\n\\n self.reset_transaction_events()\\n self.reset_error_events()\\n self.reset_custom_events()\\n self.reset_span_events()\\n self.reset_synthetics_events()\\n # streams are never reset after instantiation\\n if reset_stream:\\n self._span_stream = StreamBuffer(\\n settings.infinite_tracing.span_queue_size)\",\n \"def resetSimulatedData(self):\\n self.space = 0*self.space # numpy array that represents the physical space\\n self.susceptible = self.space.size # the current number of healthy people in the population\\n self.infected = 0 # the current number of infected people in the population\\n self.recovered = 0 # the current number of infected people in the population\\n self.snapshots = [] # this will contain numpy arrays that represent each time step\\n self.SIR = np.zeros((3,1)) #this contains a time series of the number of susceptible, infected, recovered\\n self.time = np.zeros(1) # this array will be the time array\\n self.total_infections = 0 # set the number of total infections back to zero\",\n \"def reset(self):\\n for i in range(0, len(self.__counts)):\\n self.__counts[i] = 0\\n self.__overflow = 0\\n self.__total_count = 0\\n self.__total_values = 0\\n self.__min = None\\n self.__max = None\",\n \"def reset(self):\\n\\n self.scaler = None\\n self.isFitted = False\\n self.__create_scaler()\",\n \"def reset(self):\\n self._total_value = 0.0\\n self._count = 0\",\n \"def reset(self):\\n self.temp_data.clear()\",\n \"def reset(self,prepare=True):\\n if self.lowmem:\\n self.load(self.filename,prepare=prepare)\\n else:\\n self.data = np.copy(self.data_orig)\\n self.weights = np.copy(self.weights_orig)\\n self.durations = self.getSubintinfo('TSUBINT')\\n #if prepare:\\n # self.scrunch()\",\n \"def reset_state(self):\\n self.s = np.copy(self.s_i)\",\n \"def reset(self):\\n self.data = {}\\n self.is_bound = False\\n self._errors = None\",\n \"def reset(self):\\n self.baseline = None\\n self.cut = None\\n self.manual_push = 0\",\n \"def reset(self):\\n self.dims.clear()\\n self.xlabels.clear()\\n self.annotators.clear()\\n self._figTitle = None\\n self.tbmTitle = None\\n self._isSubplot = False\\n self._universal_xlabel = False\\n self._plotter = None\\n self.Nsp = 0\",\n \"def reset(self):\\n for k,v in self.events.items():\\n self.events[k] = None\",\n \"def reset(self):\\n if self.monotonic_energy is not None:\\n self.monotonic_energy.reset()\\n if self.chunk_energy is not None:\\n self.chunk_energy.reset()\\n self.bd_L_prev = 0\\n self.key_tail = None\",\n \"def _reset(self):\\n self._values = {}\",\n \"def reset(self):\\n\\n # Ending variables\\n self.time_idle = 0\\n self.time_episode = 0\\n self.done_time_idle = False\\n self.done_falling = False\\n self.done_time_episode = False\\n\\n # hero variables\\n self.last_location = None\\n self.last_velocity = 0\\n\\n # Sensor stack\\n self.prev_image_0 = None\\n self.prev_image_1 = None\\n self.prev_image_2 = None\\n\\n self.last_heading_deviation = 0\",\n \"def reset(self):\\n self.values.clear()\\n\\n self.on_reset()\",\n \"def reset(self):\\n\\n self.results = []\\n self._plot()\",\n \"def reset(self):\\n\\t\\tself._initial = None\\n\\t\\tself._start = None\\n\\t\\tself._time = 0\\n\\t\\tself._total = 0\\n\\t\\treturn self\",\n \"def reset(self):\\n self.m = normalize(self.m0)\\n self.t = 0.0\",\n \"def reset(self):\\n self._stat = CardMeta()\",\n \"def reset(self):\\n self.mode = 0\\n self.graphs = [[], [], []]\\n self.coefficients = []\\n self.sample = []\",\n \"def clear(self):\\r\\n\\r\\n\\t\\tself.ITerm = 0.0\\r\\n\\t\\tself.DTerm = 0.0\\r\\n\\t\\tself.last_error = 0.0\\r\\n\\r\\n\\t\\tself.state_history = []\\r\\n\\t\\tself.error_history = []\\r\\n\\t\\tself.output_history = []\\r\\n\\t\\tself.sample_times = []\\r\\n\\t\\t\\r\\n\\t\\tself.OutputValue = 0.0\",\n \"def clear(self):\\r\\n\\r\\n\\t\\tself.state_history = []\\r\\n\\t\\tself.error_history = []\\r\\n\\t\\tself.output_history = []\\r\\n\\t\\tself.sample_times = []\\r\\n\\t\\t\\r\\n\\t\\tself.OutputValue = 0.0\",\n \"def reset(self):\\n self._proportional = 0\\n self._integral = 0\\n self._derivative = 0\\n\\n self._last_time = self._current_time()\\n self._last_output = None\\n self._last_input = None\",\n \"def clear(self):\\r\\n\\r\\n\\t\\tself.state_history = []\\r\\n\\t\\tself.error_history = []\\r\\n\\t\\tself.output_history = []\\r\\n\\t\\tself.sample_times = []\\r\\n\\t\\t\\r\\n\\t\\tself.LastOutputValue = 0.0\\r\\n\\t\\tself.OutputValue = 0.0\",\n \"def clear(self):\\r\\n\\r\\n\\t\\tself.state_history = []\\r\\n\\t\\tself.error_history = []\\r\\n\\t\\tself.output_history = []\\r\\n\\t\\tself.sample_times = []\\r\\n\\t\\t\\r\\n\\t\\tself.LastOutputValue = 0.0\\r\\n\\t\\tself.OutputValue = 0.0\",\n \"def reset_stats(self):\\n print(\\\"Reseting stats\\\")\\n self.player_lives = self.ai_stts.player_lives\\n self.score = 0\\n self.level = 1\",\n \"def _reset(self):\\n\\n # Checking one attribute is enough, because they are all set together\\n # in partial_fit\\n if hasattr(self, 'scale_'):\\n del self.scale_\\n del self.n_samples_seen_\\n del self.mean_\\n del self.var_\",\n \"def clearstats(self, clearstats) :\\n\\t\\ttry :\\n\\t\\t\\tself._clearstats = clearstats\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"def clearstats(self, clearstats) :\\n\\t\\ttry :\\n\\t\\t\\tself._clearstats = clearstats\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"def clearstats(self, clearstats) :\\n\\t\\ttry :\\n\\t\\t\\tself._clearstats = clearstats\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"def clearstats(self, clearstats) :\\n\\t\\ttry :\\n\\t\\t\\tself._clearstats = clearstats\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"def reset(self):\\n self.score = None\\n self.true = None\\n self.meta = None\",\n \"def reset(self):\\n self.algo_state = {}\\n self.actual_repetitions = 0\\n self.next_session = -1\\n self.last_session = -1\\n self.past_quality = []\",\n \"def reset(self):\\n self._data = []\",\n \"def reset(self):\\n self.reserve.reset()\\n self.revenue.reset()\\n self.transfers.reset()\\n self.missions.reset()\\n self.debt.reset()\\n self.genfund.reset()\\n self.macro.reset(self.pop[self.start_yr],self.eco_first)\\n self.summary = pd.DataFrame(index=self.names,columns=[t for t in range(self.start_yr,self.stop_yr)])\\n self.year = self.start_yr\",\n \"def reset(self):\\n self.data = self._defaults\",\n \"def reset(self):\\n self.control_counter = 0\\n self.last_position_error = np.zeros(3)\\n self.integral_position_error = np.zeros(3)\\n self.last_attitude_error = np.zeros(3)\\n self.integral_attitude_error = np.zeros(3)\",\n \"def reset(self):\\n self.source_data = self.get_dict_from_range(None, None)\\n self.selection_bounds = None\\n self.selection = []\\n for c in self.callbacks[\\\"reset_data\\\"]:\\n c()\\n if self.context is not None:\\n self.context.doc.add_next_tick_callback(self.update_source)\",\n \"def reset_states(self):\\n self.mean_makespan_baseline.assign(0)\\n self.mean_makespan_train.assign(0)\\n self.step.assign(0)\",\n \"def reset_metric_variables(self) -> None:\\n with self._lock:\\n self._reset_metric_variables()\",\n \"def reset_global(self):\\n self.T = 0\\n self.ep = 0\\n self.t = 0\\n self.G = 0.0\\n self._ep_starttime = time.time()\",\n \"def reset(self):\\n self.damage_dealt = 0\\n self.kills = 0\\n self.got_killed = False\\n self.fitness = 0\",\n \"def reset(self):\\n self.game_status = game_status.GameStatus(copy.deepcopy(self.initial_rosters),\\n self.game_status.game_date)\\n self._current_event_ndx = 0\",\n \"def reset_sgr(self):\\n self.intensity = 0\\n self.italic = False\\n self.bold = False\\n self.underline = False\\n self.foreground_color = None\\n self.background_color = None\",\n \"def reset() -> None:\\n Stat._cache = SortedDict()\",\n \"def reset_metrics(self):\\n self.metrics['loss'] = 0.0\\n self.metrics['num_tokens'] = 0\\n self.metrics['correct_tokens'] = 0\\n self.metrics['correct_pred'] = 0\\n self.metrics['pred_count'] = 0\",\n \"def reset(self):\\n self.N = self.N[0:2]\\n self.t = 2\\n self.harvest_available = 0\\n self.harvest_record = [0,0]\",\n \"def reset(self):\\n self.x_prev = np.zeros_like(self.mu)\",\n \"def reset_turn_stats(self):\\n\\n # Set the attribute value to 0\\n self._current_score = 0\",\n \"def reset(self):\\n self._value_estimates[:] = self.prior\\n self.action_attempts[:] = 0\\n self.last_action = None\\n self.t = 0\",\n \"def __reset(self):\\n\\t\\tself.__highest = -float('inf')\\n\\t\\tself.__lowest = float('inf')\\n\\t\\tself.__total = 0\\n\\t\\tself.__steps = 0\\n\\t\\tself.__cold_days = 0\",\n \"def _reset(self):\\n\\n # Checking one attribute is enough, becase they are all set together\\n # in partial_fit\\n if hasattr(self, 'scale_'):\\n del self.scale_\\n del self.mean_\\n del self.var_\",\n \"def reset(self):\\r\\n self.myOutputs = list()\\r\\n self.myPlots = list()\\r\\n self.pause = 0\\r\\n self.doMPL = False\\r\\n self.graphLabelsX = []\\r\\n self.graphLabelsY = []\\r\\n for i in self.xData.iterkeys():\\r\\n self.xData[i] = []\\r\\n self.yData[i] = []\\r\\n self.xyData[i] = []\\r\\n self.graphs[i] = Gnuplot(debug=0)\\r\\n self.figures[i] = 0\\r\\n self.mplFigCount = 0\",\n \"def reset_results_arrays(self):\\n raw_dims = self._dataset.raw_dims\\n raw_dims = list(raw_dims[::-1])\\n \\n if len(self.data) != raw_dims:\\n self.freq_all = np.zeros(raw_dims, complex)\\n self.time_all = np.zeros(raw_dims, complex)\",\n \"def reinit(self):\\n self.logger.info(\\\"Reinit called. Clear the population.\\\")\\n self.set_init_population([], perf_name=None)\\n self._gt_rollouts = []\\n self._gt_scores = []\",\n \"def reset(self) -> None:\\n self.current = 0\\n self.num_cuts = 0\",\n \"def _reset_histories(self):\\n self.train_loss_history = []\\n self.train_pos_dist_history = []\\n self.train_neg_dist_history = []\\n self.val_loss_history = []\\n self.val_pos_dist_history = []\\n self.val_neg_dist_history = []\",\n \"def reset(self):\\n self.tracker.reset()\\n self.total_max_q = 0.0\\n self.episode_step = 0\\n self.episode += 1\",\n \"def reset(self):\\n self.__sets = []\\n self._computed = False\",\n \"def reset_results_arrays(self):\\n raw_dim0 = self._dataset.raw_dims[0]\\n \\n if len(self.data) != raw_dim0:\\n self.freq_current = np.zeros(raw_dim0, complex)\",\n \"def reset(self):\\n\\n self.elapsed_time = 0\",\n \"def _reset_histories(self):\\n\\t\\tself.train_loss_history = []\\n\\t\\tself.train_acc_history = []\\n\\t\\tself.val_acc_history = []\\n\\t\\tself.val_loss_history = []\",\n \"def reset_transaction_events(self):\\n\\n if self.__settings is not None:\\n self._transaction_events = SampledDataSet(\\n self.__settings.event_harvest_config.\\n harvest_limits.analytic_event_data)\\n else:\\n self._transaction_events = SampledDataSet()\",\n \"def reset():\",\n \"def reset():\",\n \"def reset():\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7742906","0.76873755","0.76873755","0.7634818","0.75161463","0.7410027","0.7406162","0.7288068","0.72181994","0.7202865","0.7152267","0.7146272","0.69828737","0.6976172","0.6946784","0.6946784","0.6946784","0.6941798","0.69132906","0.6901035","0.68861943","0.6865253","0.68582","0.68410826","0.68407196","0.68407196","0.68193007","0.6816973","0.68089724","0.67746085","0.67536366","0.6739397","0.67226845","0.67193407","0.6700165","0.6682633","0.66578156","0.665363","0.66438955","0.661865","0.6606193","0.65978545","0.6582929","0.65576357","0.6550307","0.6543814","0.6543017","0.65373474","0.6536049","0.653503","0.65348774","0.6529204","0.6526249","0.65160036","0.6513358","0.65102786","0.6509054","0.6502991","0.6502991","0.6495653","0.6491463","0.64601403","0.64601403","0.64601403","0.64601403","0.64558244","0.64521027","0.6446963","0.6431929","0.642925","0.6427681","0.64273185","0.64247394","0.6422794","0.6422269","0.64195305","0.6416748","0.6406664","0.6394978","0.63949555","0.63794404","0.6379068","0.63702387","0.63695616","0.6369141","0.6368345","0.6358609","0.6358318","0.6352675","0.63474905","0.63444775","0.63426757","0.634069","0.6338464","0.6336864","0.6329956","0.6321943","0.6317886","0.6317886","0.6317886"],"string":"[\n \"0.7742906\",\n \"0.76873755\",\n \"0.76873755\",\n \"0.7634818\",\n \"0.75161463\",\n \"0.7410027\",\n \"0.7406162\",\n \"0.7288068\",\n \"0.72181994\",\n \"0.7202865\",\n \"0.7152267\",\n \"0.7146272\",\n \"0.69828737\",\n \"0.6976172\",\n \"0.6946784\",\n \"0.6946784\",\n \"0.6946784\",\n \"0.6941798\",\n \"0.69132906\",\n \"0.6901035\",\n \"0.68861943\",\n \"0.6865253\",\n \"0.68582\",\n \"0.68410826\",\n \"0.68407196\",\n \"0.68407196\",\n \"0.68193007\",\n \"0.6816973\",\n \"0.68089724\",\n \"0.67746085\",\n \"0.67536366\",\n \"0.6739397\",\n \"0.67226845\",\n \"0.67193407\",\n \"0.6700165\",\n \"0.6682633\",\n \"0.66578156\",\n \"0.665363\",\n \"0.66438955\",\n \"0.661865\",\n \"0.6606193\",\n \"0.65978545\",\n \"0.6582929\",\n \"0.65576357\",\n \"0.6550307\",\n \"0.6543814\",\n \"0.6543017\",\n \"0.65373474\",\n \"0.6536049\",\n \"0.653503\",\n \"0.65348774\",\n \"0.6529204\",\n \"0.6526249\",\n \"0.65160036\",\n \"0.6513358\",\n \"0.65102786\",\n \"0.6509054\",\n \"0.6502991\",\n \"0.6502991\",\n \"0.6495653\",\n \"0.6491463\",\n \"0.64601403\",\n \"0.64601403\",\n \"0.64601403\",\n \"0.64601403\",\n \"0.64558244\",\n \"0.64521027\",\n \"0.6446963\",\n \"0.6431929\",\n \"0.642925\",\n \"0.6427681\",\n \"0.64273185\",\n \"0.64247394\",\n \"0.6422794\",\n \"0.6422269\",\n \"0.64195305\",\n \"0.6416748\",\n \"0.6406664\",\n \"0.6394978\",\n \"0.63949555\",\n \"0.63794404\",\n \"0.6379068\",\n \"0.63702387\",\n \"0.63695616\",\n \"0.6369141\",\n \"0.6368345\",\n \"0.6358609\",\n \"0.6358318\",\n \"0.6352675\",\n \"0.63474905\",\n \"0.63444775\",\n \"0.63426757\",\n \"0.634069\",\n \"0.6338464\",\n \"0.6336864\",\n \"0.6329956\",\n \"0.6321943\",\n \"0.6317886\",\n \"0.6317886\",\n \"0.6317886\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.71011585"},"document_rank":{"kind":"string","value":"12"}}},{"rowIdx":3698,"cells":{"query":{"kind":"string","value":"Creates a snapshot of the accumulated statistics, error details and slow transaction and returns it. This is a shallow copy, only copying the top level objects. The originals are then reset back to being empty, with the exception of the dictionary mapping metric (name, scope) to the integer identifiers received from the core application. The latter is retained as should carry forward to subsequent runs. This method would be called to snapshot the data when doing the harvest."},"document":{"kind":"string","value":"def harvest_snapshot(self, flexible=False):\n snapshot = self._snapshot()\n\n # Data types only appear in one place, so during a snapshot it must be\n # represented in either the snapshot or in the current stats object.\n #\n # If we're in flexible harvest, the goal is to have everything in the\n # whitelist appear in the snapshot. This means, we must remove the\n # whitelist data types from the current stats object.\n #\n # If we're not in flexible harvest, everything excluded from the\n # whitelist appears in the snapshot and is removed from the current\n # stats object.\n if flexible:\n whitelist_stats, other_stats = self, snapshot\n snapshot.reset_non_event_types()\n else:\n whitelist_stats, other_stats = snapshot, self\n self.reset_non_event_types()\n\n event_harvest_whitelist = \\\n self.__settings.event_harvest_config.whitelist\n\n # Iterate through harvest types. If they are in the list of types to\n # harvest reset them on stats_engine otherwise remove them from the\n # snapshot.\n for nr_method, stats_methods in EVENT_HARVEST_METHODS.items():\n for stats_method in stats_methods:\n if nr_method in event_harvest_whitelist:\n reset = getattr(whitelist_stats, stats_method)\n else:\n reset = getattr(other_stats, stats_method)\n\n reset()\n\n return snapshot"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_flat_results(self):\n test_results, error_dict, framestats = self.get_results()\n test_results = self._merge_test_results(test_results, error_dict)\n\n results = copy.deepcopy(test_results)\n results.update(framestats)\n\n return results","def __copy__(self) :\n return self.build(self.scope.copy(), self.scope_vids.copy(), np.array(self.table))","def copy(self):\n\n return BenchmarkObj(self.__df_timings.copy(), dtype=self.dtype, multivar=self.multivar, multiindex=self.multiindex)","def copy(self):\n\t\tassert ltrace_func(TRACE_BASE)\n\t\ttemp = self.__class__()\n\t\ttemp.copy_from(self)\n\t\treturn temp","def get_pure_data_copy(self):\n import copy\n data=copy.copy(self)\n data.xp = data.xp.get_pure_data_copy()\n data.timetable = data.timetable.get_pure_data_copy() \n return data","def get_snapshot(self):\n data = {\n \"t\": self.sim.t,\n \"time\": self.time,\n \"vehicles\": self.sim.vehicles,\n \"stations\": self.sim.stations,\n \"state\": self.state,\n \"done\": self.is_done}\n return copy.deepcopy(data)","def create_workarea(self):\n\n stats = copy.copy(self)\n stats.reset_stats(self.__settings)\n\n return stats","def snapshot(self) -> dict:\n snapshot: Dict[str, Any] = {\"status\": self.state.name, \"reals\": {}}\n for real in self.children:\n snapshot[\"reals\"][real.id_] = {\"status\": real.state.name, \"steps\": {}}\n if real.state.data:\n snapshot[\"reals\"][real.id_][\"data\"] = real.state.data\n\n for step in real.children:\n step_d: Dict[str, Any] = {\n \"status\": step.state.name,\n \"jobs\": {},\n }\n if step.state.data:\n step_d[\"data\"] = step.state.data\n snapshot[\"reals\"][real.id_][\"steps\"][step.id_] = step_d\n for job in step.children:\n job_d: Dict[str, Any] = {\"status\": job.state.name}\n if job.state.data:\n job_d[\"data\"] = job.state.data\n snapshot[\"reals\"][real.id_][\"steps\"][step.id_][\"jobs\"][\n job.id_\n ] = job_d\n return snapshot","def CreateSnapshot(self) -> Any:\r\n\r\n return (\r\n id(self),\r\n self.normalized_iter.Clone(),\r\n len(self.results),\r\n self._ignore_whitespace_ctr,\r\n )","def copy(self):\n copy = GeneSet(dict())\n\n copy.gene_sets = deepcopy(self.gene_sets)\n copy.gene_set_names = deepcopy(self.gene_set_names)\n copy.gene_set_size = deepcopy(self.gene_set_size)\n copy.interactors = deepcopy(self.interactors)\n copy.n_curated = deepcopy(self.n_curated)\n copy.n_interactors = deepcopy(self.n_interactors)\n\n return copy","def fresh_copy(self):\n return OrderedGraph()","def copy(self):\n return self.update({})","def copy(self):\n return self.as_dataframe(self.data.copy())","def __deepcopy__(self, memo):\n\t\tcopy_paster = Log()\n\t\tcopy_paster.__dict__.update(self.__dict__)\n\t\tcopy_paster.cur_tensor = self.cur_tensor.clone()\n\t\treturn copy_paster","def take_snapshot(self):\r\n self.snapshot = self.max_gain, self.__dup_array(), copy.copy(self.free_cell_list)","def __init__(self, aggregation_depth, include_bytes=True):\n\n self._prev_stats = {}\n self._aggregation_depth = aggregation_depth\n self._include_bytes = include_bytes\n\n self.init_cur_stats()","def fresh_copy(self):\n return OrderedMultiGraph()","def snapshot(self):\n pass","def copy(self):\n return self.from_dict(self.to_dict(True))","def copy(self):\n new = MetaResult(self.estimator,\n self.masker,\n copy.deepcopy(self.maps))\n return new","def __copy__(self):\n cls = self.__class__\n result = cls.__new__(cls)\n to_copy = {\"_cache\", \"_buffers\", \"_parameters\", \"_modules\"}\n result.__dict__.update(\n {k: v.copy() if k in to_copy else v for k, v in self.__dict__.items()}\n )\n return result","def copy(self):\n s = MemoryData(self.address, self.size, self.sort, self.irsb, self.irsb_addr, self.stmt, self.stmt_idx,\n pointer_addr=self.pointer_addr, max_size=self.max_size, insn_addr=self.insn_addr\n )\n s.refs = self.refs.copy()\n\n return s","def clean_copy(self) -> \"StorableObject\":\n return StorableObject(\n id=self.id, data=self.data, tags=self.tags, description=self.description\n )","def copy(self) -> \"SampleMetadata\":\n return type(self)(\n samples=self.__internal_samples.copy(),\n axis=0,\n metadata=self.metadata,\n name=self.name,\n )","def copy(self):\n return self.__class__(dict(self))","def _shallow_clone_dataset(self: TAvalancheDataset) -> TAvalancheDataset:\n dataset_copy = copy.copy(self)\n dataset_copy._flat_data = self._flat_data._shallow_clone_dataset()\n return dataset_copy","def reset(self):\n self.stats = {}","def deepcopy(self):\n return self.copy()","def copy(self):\n cls = self.__class__\n new_graph = cls.__new__(cls)\n new_graph._nodes = self._nodes[:]\n new_graph._node_wip = self._node_wip[:]\n new_graph._edges = self._edges[:]\n if self._sorted_nodes:\n new_graph._sorted_nodes = self._sorted_nodes[:]\n else:\n new_graph._sorted_nodes = None\n new_graph.predecessors = {}\n for key, val in self.predecessors.items():\n new_graph.predecessors[key] = self.predecessors[key][:]\n new_graph.successors = {}\n for key, val in self.successors.items():\n new_graph.successors[key] = self.successors[key][:]\n return new_graph","def deepcopy(self):\n return copy.deepcopy(self)","def copy(self) -> \"Memory\":\n return Memory(values={dataclasses.replace(k): v for k, v in self.values.items()})","def get_immutable_clone(self) -> Snapshot:\n return Snapshot(\n signers=frozenset(self.signers),\n block_hash=self.block_hash,\n votes=frozenset(self.votes),\n tallies=self.tallies.copy()\n )","def deep_copy(self):\n return self.__class__(self.inputs, self.outputs, self.middle)","def copy(self, copy_results: bool = True) -> \"ExperimentData\":\n new_instance = ExperimentData(\n backend=self.backend,\n service=self.service,\n parent_id=self.parent_id,\n job_ids=self.job_ids,\n child_data=list(self._child_data.values()),\n verbose=self.verbose,\n )\n new_instance._db_data = self._db_data.copy()\n new_instance._db_data.experiment_id = str(\n uuid.uuid4()\n ) # different id for copied experiment\n if self.experiment is None:\n new_instance._experiment = None\n else:\n new_instance._experiment = self.experiment.copy()\n\n LOG.debug(\n \"Copying experiment data [Experiment ID: %s]: %s\",\n self.experiment_id,\n new_instance.experiment_id,\n )\n\n # Copy basic properties and metadata\n\n new_instance._jobs = self._jobs.copy_object()\n new_instance._auto_save = self._auto_save\n new_instance._extra_data = self._extra_data\n\n # Copy circuit result data and jobs\n with self._result_data.lock: # Hold the lock so no new data can be added.\n new_instance._result_data = self._result_data.copy_object()\n for jid, fut in self._job_futures.items():\n if not fut.done():\n new_instance._add_job_future(new_instance._jobs[jid])\n\n # If not copying results return the object\n if not copy_results:\n return new_instance\n\n # Copy results and figures.\n # This requires analysis callbacks to finish\n self._wait_for_futures(self._analysis_futures.values(), name=\"analysis\")\n with self._analysis_results.lock:\n new_instance._analysis_results = ThreadSafeOrderedDict()\n new_instance.add_analysis_results([result.copy() for result in self.analysis_results()])\n with self._figures.lock:\n new_instance._figures = ThreadSafeOrderedDict()\n new_instance.add_figures(self._figures.values())\n\n # Recursively copy child data\n child_data = [data.copy(copy_results=copy_results) for data in self.child_data()]\n new_instance._set_child_data(child_data)\n return new_instance","def take_snapshot(self):\r\n self.snapshot = self.name, self.size, copy.copy(self.cells)\r\n self.bucket_array.take_snapshot()","def take_snapshot(self):\r\n self.snapshot = self.blockA, self.blockB, self.blockA_locked, self.blockB_locked, self.blockA_free, \\\r\n self.blockB_free, copy.copy(self.blockA_cells), copy.copy(self.blockB_cells), self.cut","def __copy__(self):\n\t\tcopy_paster = Log()\n\t\tcopy_paster.__dict__.update(self.__dict__)\n\t\tcopy_paster.cur_tensor = self.cur_tensor.clone()\n\t\treturn copy_paster","def fresh_copy(self):\n return OrderedMultiDiGraph()","def __deepcopy__(self, memo):\n copy = self.__class__()\n copy.wvalues = self.wvalues\n return copy","def __deepcopy__(self, memo):\n obj = self.__class__()\n for k, v in self.__dict__.items():\n if k == \"_result_cache\":\n obj.__dict__[k] = None\n else:\n obj.__dict__[k] = copy.deepcopy(v, memo)\n return obj","def __deepcopy__(self, memo):\n cls = self.__class__\n result = cls.__new__(cls)\n memo[id(self)] = result\n for k, v in self.__dict__.items():\n if k not in ['viewer', 'automatic_rendering_callback']:\n setattr(result, k, copy.deepcopy(v, memo))\n else:\n setattr(result, k, None)\n return result","def fresh_copy(self):\n return OrderedDiGraph()","def duplicate(self):\r\n duplicate = Profile()\r\n \r\n for i in self.__dict__:\r\n if type(getattr(self, i)) is dict:\r\n setattr(duplicate, i, getattr(self, i).copy())\r\n else:\r\n setattr(duplicate, i, getattr(self, i))\r\n\r\n return duplicate","def __deepcopy__(self, memo):\n obj = self.__class__()\n for k, v in self.__dict__.items():\n if k == '_result_cache':\n obj.__dict__[k] = None\n else:\n obj.__dict__[k] = copy.deepcopy(v, memo)\n return obj","def copy(self):\n return self.__class__(\n self.xs.copy(), self.ys.copy(),\n self.gauge_length,\n self.sample_width,\n self.sample_thickness,\n self.name\n )","def copy(self):\n\t\tassert ltrace_func(TRACE_BASE)\n\t\treturn NamedObject.copy(self)","def __deepcopy__(self, memo):\n obj = self.__class__()\n for k, v in self.__dict__.items():\n if k in ('_iter', '_result_cache'):\n obj.__dict__[k] = None\n else:\n obj.__dict__[k] = copy.deepcopy(v, memo)\n return obj","def deepcopy(self):\n return copymod.deepcopy(self)","def main_data(self) -> gpd.GeoDataFrame:\n if self._cached_data is None:\n data = self._get_main_data_dict()\n geometry = self.shot_geolocations\n\n self._cached_data = gpd.GeoDataFrame(data, geometry=geometry, crs=WGS84)\n\n return self._cached_data","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def copy(self):\n return copy.deepcopy(self)","def to_dataframe(self, copy=False):\n\n if not copy:\n return self.__df_timings\n else:\n return self.__df_timings.copy()","def Clone(self):\n st = PunterGameState()\n st.fullGraph = self.fullGraph\n st.score = self.score\n st.playerJustMoved = self.playerJustMoved\n st.pathes = copy.deepcopy(self.pathes)\n st.scores = copy.deepcopy(self.scores)\n st.endpoints = self.endpoints[:]\n return st","def __init__(self):\n self.stats = {}\n self.stats['hits'] = 0\n self.stats['operations'] = {}\n self.stats['operations']['GetCapabilities'] = {}\n self.stats['operations']['GetCapabilities']['hits'] = 0\n self.stats['operations']['POST'] = {}\n self.stats['operations']['POST']['hits'] = 0","def reset_metric_stats(self):\n self.__stats_table = {}","def reset_metric_stats(self):\n\n self.__stats_table = {}","def snapshot(self):\n return {\"unit_name\": self.unit_name}","def copy(self):\n return Dataset(self._data.copy(), self.gene_meta.copy(), self.n_genes)","def copy(self):\r\n return copy.deepcopy(self)","def __deepcopy__(self, memo):\n cls = self.__class__\n result = cls.__new__(cls)\n memo[id(self)] = result\n for k, v in self.__dict__.items():\n if k not in ['viewer', '_record_video_wrapper']:\n setattr(result, k, copy.deepcopy(v, memo))\n else:\n setattr(result, k, None)\n return result","def clone(self):\n memo = dict()\n c = self._clone(memo)\n c._clone_rip(memo)\n return c","def get_historical_data_copy(self):\n return copy.deepcopy(self._historical_data)","def copy(self):\n return self.__class__(\n self.kind, self.link_ids.copy(), self.included_nodes.copy(), self.mass,\n self.name, self.crossring_cleavages.copy(), self.composition.copy())","def init_cur_stats(self):\n self._cur_stats = defaultdict(lambda: defaultdict(int))\n self._cur_stats[\"writes\"][\"/\"] = 0\n self._cur_stats[\"reads\"][\"/\"] = 0\n self._cur_stats[\"total\"][\"/writes\"] = 0\n self._cur_stats[\"total\"][\"/reads\"] = 0\n\n if self._include_bytes:\n self._cur_stats[\"writesBytes\"][\"/\"] = 0\n self._cur_stats[\"readsBytes\"][\"/\"] = 0\n self._cur_stats[\"total\"][\"/writeBytes\"] = 0\n self._cur_stats[\"total\"][\"/readBytes\"] = 0","def _transfer(self, dfnew):\n newobj = copy.deepcopy(self) #This looks like None, but is it type (MetaPandasObject, just __union__ prints None\n newobj._frame = dfnew\n \n # THESE ARE NEVER TRANSFERED AT DF LEVEL, JUST CREATED NEW. TRY\n # df.loc\n # a = df*50\n # a._loc ---> Will be None\n #newobj._loc = self._loc\n #newobj._iloc = self._iloc\n #newobj._ix = self._ix \n return newobj","def derive_newrelic_qcache(self):\n # Query Cache\n vals = self.get_values([\"status/qcache_hits\", \"status/com_select\", \"status/qcache_free_blocks\",\n \"status/qcache_total_blocks\", \"status/qcache_inserts\", \"status/qcache_not_cached\"])\n if vals:\n qc_hits, reads, free, total, inserts, not_cached = vals\n\n self.update_metric(\"newrelic/query_cache_hits\", qc_hits)\n self.update_metric(\"newrelic/query_cache_misses\", inserts)\n self.update_metric(\"newrelic/query_cache_not_cached\", not_cached)\n\n pct_query_cache_hit_utilization = 0.0\n if (qc_hits + reads) > 0:\n pct_query_cache_hit_utilization = (qc_hits / (qc_hits + reads)) * 100.0\n\n self.update_metric(\"newrelic/pct_query_cache_hit_utilization\", pct_query_cache_hit_utilization)\n\n pct_query_cache_memory_in_use = 0.0\n if total > 0:\n pct_query_cache_memory_in_use = 100.0 - ((free / total) * 100.0)\n\n self.update_metric(\"newrelic/pct_query_cache_memory_in_use\", pct_query_cache_memory_in_use)\n\n # Temp Table\n vals = self.get_values([\"status/created_tmp_tables\", \"status/created_tmp_disk_tables\"])\n if vals:\n tmp_tables, tmp_tables_disk = vals\n\n pct_tmp_tables_written_to_disk = 0.0\n if tmp_tables > 0:\n pct_tmp_tables_written_to_disk = (tmp_tables_disk / tmp_tables) * 100.0\n\n self.update_metric(\"newrelic/pct_tmp_tables_written_to_disk\", pct_tmp_tables_written_to_disk)","def copy(self):\n return Level(repr=self.as_dict())","def copy(self):\n cpy = self._copyBase(MapEvalTree(self[:]))\n cpy.cachesize = self.cachesize # member specific to MapEvalTree\n return cpy","def copy(self):\n cls = type(self)\n new = cls()\n new.default = deepcopy(self.default)\n new.current = deepcopy(self.current)\n new.stepnames = deepcopy(self.stepnames)\n return new","def _copy_(self):\n return copy.copy(self)","def __deepcopy__(self, memo):\n return Quantity(copy.deepcopy(self._value, memo), self.unit)","def updated_object(self):\n o = deepcopy(self.object)\n o[\"name\"] += \"-copy\"\n return o","def copy(self):\n return super().copy()","def _shallow_clone_dataset(self: TDataWTransform) -> TDataWTransform:\n dataset_copy = copy.copy(self)\n dataset_copy._transform_groups = copy.copy(dataset_copy._transform_groups)\n dataset_copy._frozen_transform_groups = copy.copy(\n dataset_copy._frozen_transform_groups\n )\n return dataset_copy","def derive_newrelic_stats(self):\n self.logger.debug(\"Collecting stats for newrelic\")\n self.derive_newrelic_volume()\n self.derive_newrelic_throughput()\n self.derive_newrelic_innodb()\n self.derive_newrelic_qcache()\n self.derive_newrelic_slaves()","def take_snapshot(self):\r\n self.snapshot = self.gain, self.block, self.locked, self.bucket_num","def copy(self):\n import copy as pcopy\n return pcopy.deepcopy(self)","def copy(self):\n copy = TemporalGraph(self._start, self._end)\n copy.update(self)\n return copy","def copy(self):\n return Result(super().copy())","def get_state(self, deepcopy: bool = True):\n s = self.cache_ if hasattr(self, \"cache_\") else {}\n return copy.deepcopy(s) if deepcopy else s","def _clone(self):\n c = self.__class__(\n model=self.model,\n query=self.query.chain(),\n using=self._db,\n hints=self._hints,\n )\n c._sticky_filter = self._sticky_filter\n c._for_write = self._for_write\n c._prefetch_related_lookups = self._prefetch_related_lookups[:]\n c._known_related_objects = self._known_related_objects\n c._iterable_class = self._iterable_class\n c._fields = self._fields\n return c","def copy(self):\n return self.mutate().simple_copy()","def __deepcopy__(self, memo):\r\n new_inst = super().__deepcopy__(memo)\r\n new_inst.road_width = self.road_width\r\n new_inst.road_length = self.road_length\r\n new_inst.surface = self.surface\r\n \r\n return new_inst","def copy(self):\n new = self.__class__(integration=None, data=None)\n for attribute, value in self.__dict__.items():\n if attribute in self.referenced_attributes:\n setattr(new, attribute, value)\n elif hasattr(value, 'copy'):\n setattr(new, attribute, value.copy())\n else:\n setattr(new, attribute, deepcopy(value))\n return new","def copy(self):\n return pdict(dict.copy(self))","def __deepcopy__(self, memo: dict[Any, Any]) -> geom:\n cls = self.__class__\n result = cls.__new__(cls)\n memo[id(self)] = result\n old = self.__dict__\n new = result.__dict__\n\n # don't make a deepcopy of data, or environment\n shallow = {\"data\", \"_kwargs\", \"environment\"}\n for key, item in old.items():\n if key in shallow:\n new[key] = old[key]\n memo[id(new[key])] = new[key]\n else:\n new[key] = deepcopy(old[key], memo)\n\n return result","def snapshot(self):\n snapshot = super(VirtualMachineDAO, self).snapshot()\n for entry in snapshot:\n vm = entry.get(VirtualMachineDAO.INNER_OBJ)\n vm['network'] = VMNetworkDAO(self.session, vm.get(VirtualMachineDAO.FOREIGN_KEY)).snapshot()\n return snapshot"],"string":"[\n \"def get_flat_results(self):\\n test_results, error_dict, framestats = self.get_results()\\n test_results = self._merge_test_results(test_results, error_dict)\\n\\n results = copy.deepcopy(test_results)\\n results.update(framestats)\\n\\n return results\",\n \"def __copy__(self) :\\n return self.build(self.scope.copy(), self.scope_vids.copy(), np.array(self.table))\",\n \"def copy(self):\\n\\n return BenchmarkObj(self.__df_timings.copy(), dtype=self.dtype, multivar=self.multivar, multiindex=self.multiindex)\",\n \"def copy(self):\\n\\t\\tassert ltrace_func(TRACE_BASE)\\n\\t\\ttemp = self.__class__()\\n\\t\\ttemp.copy_from(self)\\n\\t\\treturn temp\",\n \"def get_pure_data_copy(self):\\n import copy\\n data=copy.copy(self)\\n data.xp = data.xp.get_pure_data_copy()\\n data.timetable = data.timetable.get_pure_data_copy() \\n return data\",\n \"def get_snapshot(self):\\n data = {\\n \\\"t\\\": self.sim.t,\\n \\\"time\\\": self.time,\\n \\\"vehicles\\\": self.sim.vehicles,\\n \\\"stations\\\": self.sim.stations,\\n \\\"state\\\": self.state,\\n \\\"done\\\": self.is_done}\\n return copy.deepcopy(data)\",\n \"def create_workarea(self):\\n\\n stats = copy.copy(self)\\n stats.reset_stats(self.__settings)\\n\\n return stats\",\n \"def snapshot(self) -> dict:\\n snapshot: Dict[str, Any] = {\\\"status\\\": self.state.name, \\\"reals\\\": {}}\\n for real in self.children:\\n snapshot[\\\"reals\\\"][real.id_] = {\\\"status\\\": real.state.name, \\\"steps\\\": {}}\\n if real.state.data:\\n snapshot[\\\"reals\\\"][real.id_][\\\"data\\\"] = real.state.data\\n\\n for step in real.children:\\n step_d: Dict[str, Any] = {\\n \\\"status\\\": step.state.name,\\n \\\"jobs\\\": {},\\n }\\n if step.state.data:\\n step_d[\\\"data\\\"] = step.state.data\\n snapshot[\\\"reals\\\"][real.id_][\\\"steps\\\"][step.id_] = step_d\\n for job in step.children:\\n job_d: Dict[str, Any] = {\\\"status\\\": job.state.name}\\n if job.state.data:\\n job_d[\\\"data\\\"] = job.state.data\\n snapshot[\\\"reals\\\"][real.id_][\\\"steps\\\"][step.id_][\\\"jobs\\\"][\\n job.id_\\n ] = job_d\\n return snapshot\",\n \"def CreateSnapshot(self) -> Any:\\r\\n\\r\\n return (\\r\\n id(self),\\r\\n self.normalized_iter.Clone(),\\r\\n len(self.results),\\r\\n self._ignore_whitespace_ctr,\\r\\n )\",\n \"def copy(self):\\n copy = GeneSet(dict())\\n\\n copy.gene_sets = deepcopy(self.gene_sets)\\n copy.gene_set_names = deepcopy(self.gene_set_names)\\n copy.gene_set_size = deepcopy(self.gene_set_size)\\n copy.interactors = deepcopy(self.interactors)\\n copy.n_curated = deepcopy(self.n_curated)\\n copy.n_interactors = deepcopy(self.n_interactors)\\n\\n return copy\",\n \"def fresh_copy(self):\\n return OrderedGraph()\",\n \"def copy(self):\\n return self.update({})\",\n \"def copy(self):\\n return self.as_dataframe(self.data.copy())\",\n \"def __deepcopy__(self, memo):\\n\\t\\tcopy_paster = Log()\\n\\t\\tcopy_paster.__dict__.update(self.__dict__)\\n\\t\\tcopy_paster.cur_tensor = self.cur_tensor.clone()\\n\\t\\treturn copy_paster\",\n \"def take_snapshot(self):\\r\\n self.snapshot = self.max_gain, self.__dup_array(), copy.copy(self.free_cell_list)\",\n \"def __init__(self, aggregation_depth, include_bytes=True):\\n\\n self._prev_stats = {}\\n self._aggregation_depth = aggregation_depth\\n self._include_bytes = include_bytes\\n\\n self.init_cur_stats()\",\n \"def fresh_copy(self):\\n return OrderedMultiGraph()\",\n \"def snapshot(self):\\n pass\",\n \"def copy(self):\\n return self.from_dict(self.to_dict(True))\",\n \"def copy(self):\\n new = MetaResult(self.estimator,\\n self.masker,\\n copy.deepcopy(self.maps))\\n return new\",\n \"def __copy__(self):\\n cls = self.__class__\\n result = cls.__new__(cls)\\n to_copy = {\\\"_cache\\\", \\\"_buffers\\\", \\\"_parameters\\\", \\\"_modules\\\"}\\n result.__dict__.update(\\n {k: v.copy() if k in to_copy else v for k, v in self.__dict__.items()}\\n )\\n return result\",\n \"def copy(self):\\n s = MemoryData(self.address, self.size, self.sort, self.irsb, self.irsb_addr, self.stmt, self.stmt_idx,\\n pointer_addr=self.pointer_addr, max_size=self.max_size, insn_addr=self.insn_addr\\n )\\n s.refs = self.refs.copy()\\n\\n return s\",\n \"def clean_copy(self) -> \\\"StorableObject\\\":\\n return StorableObject(\\n id=self.id, data=self.data, tags=self.tags, description=self.description\\n )\",\n \"def copy(self) -> \\\"SampleMetadata\\\":\\n return type(self)(\\n samples=self.__internal_samples.copy(),\\n axis=0,\\n metadata=self.metadata,\\n name=self.name,\\n )\",\n \"def copy(self):\\n return self.__class__(dict(self))\",\n \"def _shallow_clone_dataset(self: TAvalancheDataset) -> TAvalancheDataset:\\n dataset_copy = copy.copy(self)\\n dataset_copy._flat_data = self._flat_data._shallow_clone_dataset()\\n return dataset_copy\",\n \"def reset(self):\\n self.stats = {}\",\n \"def deepcopy(self):\\n return self.copy()\",\n \"def copy(self):\\n cls = self.__class__\\n new_graph = cls.__new__(cls)\\n new_graph._nodes = self._nodes[:]\\n new_graph._node_wip = self._node_wip[:]\\n new_graph._edges = self._edges[:]\\n if self._sorted_nodes:\\n new_graph._sorted_nodes = self._sorted_nodes[:]\\n else:\\n new_graph._sorted_nodes = None\\n new_graph.predecessors = {}\\n for key, val in self.predecessors.items():\\n new_graph.predecessors[key] = self.predecessors[key][:]\\n new_graph.successors = {}\\n for key, val in self.successors.items():\\n new_graph.successors[key] = self.successors[key][:]\\n return new_graph\",\n \"def deepcopy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self) -> \\\"Memory\\\":\\n return Memory(values={dataclasses.replace(k): v for k, v in self.values.items()})\",\n \"def get_immutable_clone(self) -> Snapshot:\\n return Snapshot(\\n signers=frozenset(self.signers),\\n block_hash=self.block_hash,\\n votes=frozenset(self.votes),\\n tallies=self.tallies.copy()\\n )\",\n \"def deep_copy(self):\\n return self.__class__(self.inputs, self.outputs, self.middle)\",\n \"def copy(self, copy_results: bool = True) -> \\\"ExperimentData\\\":\\n new_instance = ExperimentData(\\n backend=self.backend,\\n service=self.service,\\n parent_id=self.parent_id,\\n job_ids=self.job_ids,\\n child_data=list(self._child_data.values()),\\n verbose=self.verbose,\\n )\\n new_instance._db_data = self._db_data.copy()\\n new_instance._db_data.experiment_id = str(\\n uuid.uuid4()\\n ) # different id for copied experiment\\n if self.experiment is None:\\n new_instance._experiment = None\\n else:\\n new_instance._experiment = self.experiment.copy()\\n\\n LOG.debug(\\n \\\"Copying experiment data [Experiment ID: %s]: %s\\\",\\n self.experiment_id,\\n new_instance.experiment_id,\\n )\\n\\n # Copy basic properties and metadata\\n\\n new_instance._jobs = self._jobs.copy_object()\\n new_instance._auto_save = self._auto_save\\n new_instance._extra_data = self._extra_data\\n\\n # Copy circuit result data and jobs\\n with self._result_data.lock: # Hold the lock so no new data can be added.\\n new_instance._result_data = self._result_data.copy_object()\\n for jid, fut in self._job_futures.items():\\n if not fut.done():\\n new_instance._add_job_future(new_instance._jobs[jid])\\n\\n # If not copying results return the object\\n if not copy_results:\\n return new_instance\\n\\n # Copy results and figures.\\n # This requires analysis callbacks to finish\\n self._wait_for_futures(self._analysis_futures.values(), name=\\\"analysis\\\")\\n with self._analysis_results.lock:\\n new_instance._analysis_results = ThreadSafeOrderedDict()\\n new_instance.add_analysis_results([result.copy() for result in self.analysis_results()])\\n with self._figures.lock:\\n new_instance._figures = ThreadSafeOrderedDict()\\n new_instance.add_figures(self._figures.values())\\n\\n # Recursively copy child data\\n child_data = [data.copy(copy_results=copy_results) for data in self.child_data()]\\n new_instance._set_child_data(child_data)\\n return new_instance\",\n \"def take_snapshot(self):\\r\\n self.snapshot = self.name, self.size, copy.copy(self.cells)\\r\\n self.bucket_array.take_snapshot()\",\n \"def take_snapshot(self):\\r\\n self.snapshot = self.blockA, self.blockB, self.blockA_locked, self.blockB_locked, self.blockA_free, \\\\\\r\\n self.blockB_free, copy.copy(self.blockA_cells), copy.copy(self.blockB_cells), self.cut\",\n \"def __copy__(self):\\n\\t\\tcopy_paster = Log()\\n\\t\\tcopy_paster.__dict__.update(self.__dict__)\\n\\t\\tcopy_paster.cur_tensor = self.cur_tensor.clone()\\n\\t\\treturn copy_paster\",\n \"def fresh_copy(self):\\n return OrderedMultiDiGraph()\",\n \"def __deepcopy__(self, memo):\\n copy = self.__class__()\\n copy.wvalues = self.wvalues\\n return copy\",\n \"def __deepcopy__(self, memo):\\n obj = self.__class__()\\n for k, v in self.__dict__.items():\\n if k == \\\"_result_cache\\\":\\n obj.__dict__[k] = None\\n else:\\n obj.__dict__[k] = copy.deepcopy(v, memo)\\n return obj\",\n \"def __deepcopy__(self, memo):\\n cls = self.__class__\\n result = cls.__new__(cls)\\n memo[id(self)] = result\\n for k, v in self.__dict__.items():\\n if k not in ['viewer', 'automatic_rendering_callback']:\\n setattr(result, k, copy.deepcopy(v, memo))\\n else:\\n setattr(result, k, None)\\n return result\",\n \"def fresh_copy(self):\\n return OrderedDiGraph()\",\n \"def duplicate(self):\\r\\n duplicate = Profile()\\r\\n \\r\\n for i in self.__dict__:\\r\\n if type(getattr(self, i)) is dict:\\r\\n setattr(duplicate, i, getattr(self, i).copy())\\r\\n else:\\r\\n setattr(duplicate, i, getattr(self, i))\\r\\n\\r\\n return duplicate\",\n \"def __deepcopy__(self, memo):\\n obj = self.__class__()\\n for k, v in self.__dict__.items():\\n if k == '_result_cache':\\n obj.__dict__[k] = None\\n else:\\n obj.__dict__[k] = copy.deepcopy(v, memo)\\n return obj\",\n \"def copy(self):\\n return self.__class__(\\n self.xs.copy(), self.ys.copy(),\\n self.gauge_length,\\n self.sample_width,\\n self.sample_thickness,\\n self.name\\n )\",\n \"def copy(self):\\n\\t\\tassert ltrace_func(TRACE_BASE)\\n\\t\\treturn NamedObject.copy(self)\",\n \"def __deepcopy__(self, memo):\\n obj = self.__class__()\\n for k, v in self.__dict__.items():\\n if k in ('_iter', '_result_cache'):\\n obj.__dict__[k] = None\\n else:\\n obj.__dict__[k] = copy.deepcopy(v, memo)\\n return obj\",\n \"def deepcopy(self):\\n return copymod.deepcopy(self)\",\n \"def main_data(self) -> gpd.GeoDataFrame:\\n if self._cached_data is None:\\n data = self._get_main_data_dict()\\n geometry = self.shot_geolocations\\n\\n self._cached_data = gpd.GeoDataFrame(data, geometry=geometry, crs=WGS84)\\n\\n return self._cached_data\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def copy(self):\\n return copy.deepcopy(self)\",\n \"def to_dataframe(self, copy=False):\\n\\n if not copy:\\n return self.__df_timings\\n else:\\n return self.__df_timings.copy()\",\n \"def Clone(self):\\n st = PunterGameState()\\n st.fullGraph = self.fullGraph\\n st.score = self.score\\n st.playerJustMoved = self.playerJustMoved\\n st.pathes = copy.deepcopy(self.pathes)\\n st.scores = copy.deepcopy(self.scores)\\n st.endpoints = self.endpoints[:]\\n return st\",\n \"def __init__(self):\\n self.stats = {}\\n self.stats['hits'] = 0\\n self.stats['operations'] = {}\\n self.stats['operations']['GetCapabilities'] = {}\\n self.stats['operations']['GetCapabilities']['hits'] = 0\\n self.stats['operations']['POST'] = {}\\n self.stats['operations']['POST']['hits'] = 0\",\n \"def reset_metric_stats(self):\\n self.__stats_table = {}\",\n \"def reset_metric_stats(self):\\n\\n self.__stats_table = {}\",\n \"def snapshot(self):\\n return {\\\"unit_name\\\": self.unit_name}\",\n \"def copy(self):\\n return Dataset(self._data.copy(), self.gene_meta.copy(), self.n_genes)\",\n \"def copy(self):\\r\\n return copy.deepcopy(self)\",\n \"def __deepcopy__(self, memo):\\n cls = self.__class__\\n result = cls.__new__(cls)\\n memo[id(self)] = result\\n for k, v in self.__dict__.items():\\n if k not in ['viewer', '_record_video_wrapper']:\\n setattr(result, k, copy.deepcopy(v, memo))\\n else:\\n setattr(result, k, None)\\n return result\",\n \"def clone(self):\\n memo = dict()\\n c = self._clone(memo)\\n c._clone_rip(memo)\\n return c\",\n \"def get_historical_data_copy(self):\\n return copy.deepcopy(self._historical_data)\",\n \"def copy(self):\\n return self.__class__(\\n self.kind, self.link_ids.copy(), self.included_nodes.copy(), self.mass,\\n self.name, self.crossring_cleavages.copy(), self.composition.copy())\",\n \"def init_cur_stats(self):\\n self._cur_stats = defaultdict(lambda: defaultdict(int))\\n self._cur_stats[\\\"writes\\\"][\\\"/\\\"] = 0\\n self._cur_stats[\\\"reads\\\"][\\\"/\\\"] = 0\\n self._cur_stats[\\\"total\\\"][\\\"/writes\\\"] = 0\\n self._cur_stats[\\\"total\\\"][\\\"/reads\\\"] = 0\\n\\n if self._include_bytes:\\n self._cur_stats[\\\"writesBytes\\\"][\\\"/\\\"] = 0\\n self._cur_stats[\\\"readsBytes\\\"][\\\"/\\\"] = 0\\n self._cur_stats[\\\"total\\\"][\\\"/writeBytes\\\"] = 0\\n self._cur_stats[\\\"total\\\"][\\\"/readBytes\\\"] = 0\",\n \"def _transfer(self, dfnew):\\n newobj = copy.deepcopy(self) #This looks like None, but is it type (MetaPandasObject, just __union__ prints None\\n newobj._frame = dfnew\\n \\n # THESE ARE NEVER TRANSFERED AT DF LEVEL, JUST CREATED NEW. TRY\\n # df.loc\\n # a = df*50\\n # a._loc ---> Will be None\\n #newobj._loc = self._loc\\n #newobj._iloc = self._iloc\\n #newobj._ix = self._ix \\n return newobj\",\n \"def derive_newrelic_qcache(self):\\n # Query Cache\\n vals = self.get_values([\\\"status/qcache_hits\\\", \\\"status/com_select\\\", \\\"status/qcache_free_blocks\\\",\\n \\\"status/qcache_total_blocks\\\", \\\"status/qcache_inserts\\\", \\\"status/qcache_not_cached\\\"])\\n if vals:\\n qc_hits, reads, free, total, inserts, not_cached = vals\\n\\n self.update_metric(\\\"newrelic/query_cache_hits\\\", qc_hits)\\n self.update_metric(\\\"newrelic/query_cache_misses\\\", inserts)\\n self.update_metric(\\\"newrelic/query_cache_not_cached\\\", not_cached)\\n\\n pct_query_cache_hit_utilization = 0.0\\n if (qc_hits + reads) > 0:\\n pct_query_cache_hit_utilization = (qc_hits / (qc_hits + reads)) * 100.0\\n\\n self.update_metric(\\\"newrelic/pct_query_cache_hit_utilization\\\", pct_query_cache_hit_utilization)\\n\\n pct_query_cache_memory_in_use = 0.0\\n if total > 0:\\n pct_query_cache_memory_in_use = 100.0 - ((free / total) * 100.0)\\n\\n self.update_metric(\\\"newrelic/pct_query_cache_memory_in_use\\\", pct_query_cache_memory_in_use)\\n\\n # Temp Table\\n vals = self.get_values([\\\"status/created_tmp_tables\\\", \\\"status/created_tmp_disk_tables\\\"])\\n if vals:\\n tmp_tables, tmp_tables_disk = vals\\n\\n pct_tmp_tables_written_to_disk = 0.0\\n if tmp_tables > 0:\\n pct_tmp_tables_written_to_disk = (tmp_tables_disk / tmp_tables) * 100.0\\n\\n self.update_metric(\\\"newrelic/pct_tmp_tables_written_to_disk\\\", pct_tmp_tables_written_to_disk)\",\n \"def copy(self):\\n return Level(repr=self.as_dict())\",\n \"def copy(self):\\n cpy = self._copyBase(MapEvalTree(self[:]))\\n cpy.cachesize = self.cachesize # member specific to MapEvalTree\\n return cpy\",\n \"def copy(self):\\n cls = type(self)\\n new = cls()\\n new.default = deepcopy(self.default)\\n new.current = deepcopy(self.current)\\n new.stepnames = deepcopy(self.stepnames)\\n return new\",\n \"def _copy_(self):\\n return copy.copy(self)\",\n \"def __deepcopy__(self, memo):\\n return Quantity(copy.deepcopy(self._value, memo), self.unit)\",\n \"def updated_object(self):\\n o = deepcopy(self.object)\\n o[\\\"name\\\"] += \\\"-copy\\\"\\n return o\",\n \"def copy(self):\\n return super().copy()\",\n \"def _shallow_clone_dataset(self: TDataWTransform) -> TDataWTransform:\\n dataset_copy = copy.copy(self)\\n dataset_copy._transform_groups = copy.copy(dataset_copy._transform_groups)\\n dataset_copy._frozen_transform_groups = copy.copy(\\n dataset_copy._frozen_transform_groups\\n )\\n return dataset_copy\",\n \"def derive_newrelic_stats(self):\\n self.logger.debug(\\\"Collecting stats for newrelic\\\")\\n self.derive_newrelic_volume()\\n self.derive_newrelic_throughput()\\n self.derive_newrelic_innodb()\\n self.derive_newrelic_qcache()\\n self.derive_newrelic_slaves()\",\n \"def take_snapshot(self):\\r\\n self.snapshot = self.gain, self.block, self.locked, self.bucket_num\",\n \"def copy(self):\\n import copy as pcopy\\n return pcopy.deepcopy(self)\",\n \"def copy(self):\\n copy = TemporalGraph(self._start, self._end)\\n copy.update(self)\\n return copy\",\n \"def copy(self):\\n return Result(super().copy())\",\n \"def get_state(self, deepcopy: bool = True):\\n s = self.cache_ if hasattr(self, \\\"cache_\\\") else {}\\n return copy.deepcopy(s) if deepcopy else s\",\n \"def _clone(self):\\n c = self.__class__(\\n model=self.model,\\n query=self.query.chain(),\\n using=self._db,\\n hints=self._hints,\\n )\\n c._sticky_filter = self._sticky_filter\\n c._for_write = self._for_write\\n c._prefetch_related_lookups = self._prefetch_related_lookups[:]\\n c._known_related_objects = self._known_related_objects\\n c._iterable_class = self._iterable_class\\n c._fields = self._fields\\n return c\",\n \"def copy(self):\\n return self.mutate().simple_copy()\",\n \"def __deepcopy__(self, memo):\\r\\n new_inst = super().__deepcopy__(memo)\\r\\n new_inst.road_width = self.road_width\\r\\n new_inst.road_length = self.road_length\\r\\n new_inst.surface = self.surface\\r\\n \\r\\n return new_inst\",\n \"def copy(self):\\n new = self.__class__(integration=None, data=None)\\n for attribute, value in self.__dict__.items():\\n if attribute in self.referenced_attributes:\\n setattr(new, attribute, value)\\n elif hasattr(value, 'copy'):\\n setattr(new, attribute, value.copy())\\n else:\\n setattr(new, attribute, deepcopy(value))\\n return new\",\n \"def copy(self):\\n return pdict(dict.copy(self))\",\n \"def __deepcopy__(self, memo: dict[Any, Any]) -> geom:\\n cls = self.__class__\\n result = cls.__new__(cls)\\n memo[id(self)] = result\\n old = self.__dict__\\n new = result.__dict__\\n\\n # don't make a deepcopy of data, or environment\\n shallow = {\\\"data\\\", \\\"_kwargs\\\", \\\"environment\\\"}\\n for key, item in old.items():\\n if key in shallow:\\n new[key] = old[key]\\n memo[id(new[key])] = new[key]\\n else:\\n new[key] = deepcopy(old[key], memo)\\n\\n return result\",\n \"def snapshot(self):\\n snapshot = super(VirtualMachineDAO, self).snapshot()\\n for entry in snapshot:\\n vm = entry.get(VirtualMachineDAO.INNER_OBJ)\\n vm['network'] = VMNetworkDAO(self.session, vm.get(VirtualMachineDAO.FOREIGN_KEY)).snapshot()\\n return snapshot\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5934949","0.58573073","0.5802806","0.5787985","0.5758199","0.5758142","0.57447433","0.57192504","0.56812185","0.56527025","0.5649071","0.56446093","0.5642422","0.5546954","0.5542068","0.5531035","0.5524276","0.55112106","0.54917693","0.54811484","0.54652274","0.54537374","0.5438235","0.54345053","0.54282206","0.5424229","0.5424176","0.54232955","0.5418356","0.54171884","0.54165465","0.5414276","0.5410124","0.5392498","0.5387318","0.5377655","0.53765255","0.5370712","0.5362439","0.53606987","0.5359754","0.53553754","0.53531265","0.5347806","0.532736","0.5310748","0.5309094","0.53088975","0.5307617","0.5303066","0.5303066","0.5303066","0.5303066","0.5303066","0.5303066","0.5303066","0.5303066","0.5303066","0.5303066","0.5303066","0.5303066","0.5303066","0.5303066","0.5303066","0.5303066","0.5300905","0.5287608","0.52860683","0.5285083","0.5276645","0.52746713","0.52724516","0.5260488","0.5257124","0.52552867","0.52547115","0.5252767","0.52497095","0.5237924","0.52359235","0.5228307","0.52232224","0.5220752","0.5211702","0.5210698","0.52106315","0.5210178","0.5204689","0.51973915","0.5195693","0.51934046","0.5191894","0.51897335","0.5188902","0.5187907","0.5186611","0.5184845","0.51817137","0.51781636","0.5169009","0.51677275"],"string":"[\n \"0.5934949\",\n \"0.58573073\",\n \"0.5802806\",\n \"0.5787985\",\n \"0.5758199\",\n \"0.5758142\",\n \"0.57447433\",\n \"0.57192504\",\n \"0.56812185\",\n \"0.56527025\",\n \"0.5649071\",\n \"0.56446093\",\n \"0.5642422\",\n \"0.5546954\",\n \"0.5542068\",\n \"0.5531035\",\n \"0.5524276\",\n \"0.55112106\",\n \"0.54917693\",\n \"0.54811484\",\n \"0.54652274\",\n \"0.54537374\",\n \"0.5438235\",\n \"0.54345053\",\n \"0.54282206\",\n \"0.5424229\",\n \"0.5424176\",\n \"0.54232955\",\n \"0.5418356\",\n \"0.54171884\",\n \"0.54165465\",\n \"0.5414276\",\n \"0.5410124\",\n \"0.5392498\",\n \"0.5387318\",\n \"0.5377655\",\n \"0.53765255\",\n \"0.5370712\",\n \"0.5362439\",\n \"0.53606987\",\n \"0.5359754\",\n \"0.53553754\",\n \"0.53531265\",\n \"0.5347806\",\n \"0.532736\",\n \"0.5310748\",\n \"0.5309094\",\n \"0.53088975\",\n \"0.5307617\",\n \"0.5303066\",\n \"0.5303066\",\n \"0.5303066\",\n \"0.5303066\",\n \"0.5303066\",\n \"0.5303066\",\n \"0.5303066\",\n \"0.5303066\",\n \"0.5303066\",\n \"0.5303066\",\n \"0.5303066\",\n \"0.5303066\",\n \"0.5303066\",\n \"0.5303066\",\n \"0.5303066\",\n \"0.5303066\",\n \"0.5300905\",\n \"0.5287608\",\n \"0.52860683\",\n \"0.5285083\",\n \"0.5276645\",\n \"0.52746713\",\n \"0.52724516\",\n \"0.5260488\",\n \"0.5257124\",\n \"0.52552867\",\n \"0.52547115\",\n \"0.5252767\",\n \"0.52497095\",\n \"0.5237924\",\n \"0.52359235\",\n \"0.5228307\",\n \"0.52232224\",\n \"0.5220752\",\n \"0.5211702\",\n \"0.5210698\",\n \"0.52106315\",\n \"0.5210178\",\n \"0.5204689\",\n \"0.51973915\",\n \"0.5195693\",\n \"0.51934046\",\n \"0.5191894\",\n \"0.51897335\",\n \"0.5188902\",\n \"0.5187907\",\n \"0.5186611\",\n \"0.5184845\",\n \"0.51817137\",\n \"0.51781636\",\n \"0.5169009\",\n \"0.51677275\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":3699,"cells":{"query":{"kind":"string","value":"Creates and returns a new empty stats engine object. This would be used to distill stats from a single web transaction before then merging it back into the parent under a thread lock."},"document":{"kind":"string","value":"def create_workarea(self):\n\n stats = copy.copy(self)\n stats.reset_stats(self.__settings)\n\n return stats"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def create( ):\n \n uuid = generate_uuid( 10 )\n\n stats = Stats( key_name = uuid,\n uuid = uuid,\n )\n stats.put()\n return stats","def empty_instance():\n from weighted_graph import Graph\n return Graph()","def __init__(self, stats, keep=False):\n self.stats = stats if keep else None\n self.market_price_usd = stats.get('market_price_usd')\n self.hash_rate = stats.get('hash_rate')\n self.total_fees_btc = stats.get('total_fees_btc')\n self.n_btc_mined = stats.get('n_btc_mined')\n self.n_tx = stats.get('n_tx')\n self.n_blocks_mined = stats.get('n_blocks_mined')\n self.minutes_between_blocks = stats.get('minutes_between_blocks')\n self.total_bc = stats.get('totalbc')\n self.n_blocks_total = stats.get('n_blocks_total')\n self.estimated_transaction_volume_usd = stats.get('estimated_transaction_volume_usd')\n self.blocks_size = stats.get('blocks_size')\n self.miners_revenue_usd = stats.get('miners_revenue_usd')\n self.next_retarget = stats.get('nextretarget')\n self.difficulty = stats.get('difficulty')\n self.estimated_btc_sent = stats.get('estimated_btc_sent')\n self.miners_revenue_btc = stats.get('miners_revenue_btc')\n self.total_btc_sent = stats.get('total_btc_sent')\n self.trade_volume_btc = stats.get('trade_volume_btc')\n self.trade_volume_usd = stats.get('trade_volume_usd')\n self.timestamp = stats.get('timestamp')","def start(cls, stats, keep=False):\n return cls(stats, keep)","def Get() -> stats_collector.StatsCollector:\n if _stats_singleton is None:\n raise StatsNotInitializedError()\n return _stats_singleton","def newStatGraph():\n e = ET.Element('stats')\n n = newElement(e, 'ok')\n n = newElement(e, 'not_ok')\n return ET.ElementTree(e)","def __init__(self):\n self.stats = None\n self.ticker = None","def __init__(self):\n self.stats = {}\n self.stats['hits'] = 0\n self.stats['operations'] = {}\n self.stats['operations']['GetCapabilities'] = {}\n self.stats['operations']['GetCapabilities']['hits'] = 0\n self.stats['operations']['POST'] = {}\n self.stats['operations']['POST']['hits'] = 0","def __init__(self):\n self._workload = None\n self._engine = Engine()","def __init__(self, aggregation_depth, include_bytes=True):\n\n self._prev_stats = {}\n self._aggregation_depth = aggregation_depth\n self._include_bytes = include_bytes\n\n self.init_cur_stats()","def reset_stats(self, settings, reset_stream=False):\n\n self.__settings = settings\n self.__stats_table = {}\n self.__sql_stats_table = {}\n self.__slow_transaction = None\n self.__slow_transaction_map = {}\n self.__slow_transaction_old_duration = None\n self.__transaction_errors = []\n self.__synthetics_transactions = []\n\n self.reset_transaction_events()\n self.reset_error_events()\n self.reset_custom_events()\n self.reset_span_events()\n self.reset_synthetics_events()\n # streams are never reset after instantiation\n if reset_stream:\n self._span_stream = StreamBuffer(\n settings.infinite_tracing.span_queue_size)","def _create(self, context, values):\n # initialize load stats from existing instances:\n compute_node = db.compute_node_create(context, values)\n return compute_node","def __init__(self, settings):\n self._settings = settings\n self._stats = None","def __init__(self, stats):\n self._meta = stats['meta'].item()\n self._stats = stats['data'].item()\n self._moving_average_cache = {}","def __enter__(self):\r\n if not self._engine:\r\n self.restart()\r\n return self","def statsWorker():\n logger.info('STATS: Starting. Will report out every {0:.1g} hours'.format(\n config.STATS_HOURS))\n while True:\n gevent.sleep(timedelta(hours=config.STATS_HOURS).total_seconds())\n logger.info('STATS: {0}'.format(stats))\n stats.resetStats()\n\n return","def get_engine(settings: dict) -> sqlalchemy.engine.base.Engine:\n engine = create_engine(settings['sqlalchemy.url'], pool_recycle=3600)\n return engine","def __init__(self):\n self.ts = dict()\n self.cache = dict()","def _init_local_node_stats_publisher(self):\n stats = self._local_node_stats\n # Init cache\n stats.cache = StatsCache(stats.cache_size)\n # Init source\n stats_source = NodeStatsSource()\n # Configure stats publishing\n stats.publisher = StatsPublisher(stats_source, stats.update_interval)\n stats.publisher.subscribe(stats.cache)\n self._publishers.append(stats.publisher)\n # Configure handlers\n self._routes['/stats/local/node/cache'] = HandlerInfo(\n handler_class=CachedStatsHandler,\n init_kwargs=dict(stats_cache=stats.cache)\n )\n self._routes['/stats/local/node/current'] = HandlerInfo(\n handler_class=CurrentStatsHandler,\n init_kwargs=dict(stats_source=stats_source)\n )","def __init__(self):\n super(MultiProcessEngine, self).__init__()\n self._debug_output = False\n self._name = 'Main'\n self._last_worker_number = 0\n self._log_filename = None\n self._pid = os.getpid()\n self._process_information = process_info.ProcessInfo(self._pid)\n self._process_information_per_pid = {}\n self._processes_per_pid = {}\n self._quiet_mode = False\n self._rpc_clients_per_pid = {}\n self._rpc_errors_per_pid = {}\n self._status_update_active = False\n self._status_update_thread = None\n self._storage_writer = None\n self._worker_memory_limit = definitions.DEFAULT_WORKER_MEMORY_LIMIT","def __enter__(self):\n self.__init__()\n return self","def _new_instance(self):\n return self.__class__(self._vmodule)","def __init__(self, name='TemporaryStorage'):\n\n BaseStorage.__init__(self, name)\n\n self._index = {}\n self._referenceCount = {}\n self._oreferences = {}\n self._opickle = {}\n self._tmp = []\n self._conflict_cache = {}\n self._last_cache_gc = 0\n self._recently_gc_oids = [None for x in range(RECENTLY_GC_OIDS_LEN)]\n self._oid = z64\n self._ltid = z64\n\n # Alow overrides for testing.\n self._conflict_cache_gcevery = CONFLICT_CACHE_GCEVERY\n self._conflict_cache_maxage = CONFLICT_CACHE_MAXAGE","def __init__(self, router): # Promotion constructor :)\r\n # TODO: Use __bases__ to do this instead?\r\n self.__dict__ = router.__dict__\r\n self.reset()\r\n # StatsRouters should not be destroyed when Tor forgets about them\r\n # Give them an extra refcount:\r\n self.refcount += 1\r\n plog(\"DEBUG\", \"Stats refcount \"+str(self.refcount)+\" for \"+self.idhex)","def StartProfiling(self):\n hp = hpy()\n hp.setrelheap()\n return hp","def _create_empty(cls):\n self = object.__new__(cls)\n self.avatar_hash = 0\n self.avatar_type = IconType.none\n self.boosts_since = None\n self.flags = GuildProfileFlag()\n self.joined_at = None\n self.nick = None\n self.pending = False\n self.role_ids = None\n self.timed_out_until = None\n return self","def __init__(self):\n self.stats = Statblock(parent=self)\n self.compiled_circ = None\n self.backend = None\n self.job_id = None\n self.transpiler_config = None\n self.circuit = None\n\n # if isinstance(circuit, QuantumCircuit):\n # self.circuit = circuit\n # elif circuit is not None:\n # raise TypeError(f'Circuit must be a QuantumCircuit, or Premade. Was given type: {type(circuit)}')","def __new__(cls, *args, **kwargs):\n if GraphData._instance is None:\n with GraphData._instance_lock:\n if GraphData._instance is None:\n GraphData._instance = object.__new__(cls)\n return GraphData._instance","def __init__(self) -> None:\n self.metrics = {}\n self.current = None\n self.run = None","def __init__(self):\n self._maxTime = 0\n self._activeHeap = []\n self._activeDict = {}\n self._graph = _Graph()","def __enter__(self):\n self.initialize()\n return self","def _create_load_stats(self, context, instance=None):\n values = {}\n\n if instance:\n instances = [instance]\n else:\n self.stats.clear() # re-generating all, so clear old stats\n\n # grab all instances that are not yet DELETED\n filters = {'host': self.host, 'deleted': False}\n instances = db.instance_get_all_by_filters(context,\n {'host': self.host})\n\n for instance in instances:\n self.stats.add_stats_for_instance(instance)\n\n values['current_workload'] = self.stats.calculate_workload()\n values['running_vms'] = self.stats.num_instances\n values['vcpus_used'] = self.stats.num_vcpus_used\n values['stats'] = self.stats\n return values","def init_cur_stats(self):\n self._cur_stats = defaultdict(lambda: defaultdict(int))\n self._cur_stats[\"writes\"][\"/\"] = 0\n self._cur_stats[\"reads\"][\"/\"] = 0\n self._cur_stats[\"total\"][\"/writes\"] = 0\n self._cur_stats[\"total\"][\"/reads\"] = 0\n\n if self._include_bytes:\n self._cur_stats[\"writesBytes\"][\"/\"] = 0\n self._cur_stats[\"readsBytes\"][\"/\"] = 0\n self._cur_stats[\"total\"][\"/writeBytes\"] = 0\n self._cur_stats[\"total\"][\"/readBytes\"] = 0","def create_instance(min_bds, max_bds, debug=True):\n if not debug:\n return StatsTrackerDoNothing()\n\n try:\n return StatsTrackerBase(min_bds, max_bds)\n except:\n return StatsTrackerArray(min_bds, max_bds)","def get_statistics(self):\n\t\treturn Job(SDK.PrlSrv_GetStatistics(self.handle)[0])","def create_statistics(self):\n now = datetime.now()\n min_timestamp = Statistic.objects.all().aggregate(Max('timestamp_end'))[\"timestamp_end__max\"]\n max_timestamp = (now + ((datetime.min - now) % timedelta(minutes=60)) - timedelta(minutes=60)).replace(tzinfo=pytz.UTC)\n\n if min_timestamp is None:\n min_timestamp = datetime(2000, 1, 1, tzinfo=timezone('UTC'))\n\n aggregated_measurements = MeasurementService.get_aggregate_measurements(min_timestamp,max_timestamp)\n StatisticService.create_statistics(aggregated_measurements)","def __init__(self):\n # 保存用户推特数据\n self.user_pool = defaultdict(UserInfo)\n self.twitter_pool = defaultdict(list)\n self.time = 0","def __init__(self):\n OWSReport.__init__(self)\n self.stats['type'] = 'OGC:WMS'\n self.stats['operations']['GetMap'] = {}\n self.stats['operations']['GetMap']['hits'] = 0\n self.stats['operations']['GetMap']['resource'] = {}\n self.stats['operations']['GetMap']['resource']['param'] = 'layers'\n self.stats['operations']['GetMap']['resource']['list'] = {}\n self.stats['operations']['GetFeatureInfo'] = {}\n self.stats['operations']['GetFeatureInfo']['hits'] = 0\n self.stats['operations']['GetLegendGraphic'] = {}\n self.stats['operations']['GetLegendGraphic']['hits'] = 0\n self.stats['operations']['GetStyles'] = {}\n self.stats['operations']['GetStyles']['hits'] = 0\n self.stats['operations']['DescribeLayer'] = {}\n self.stats['operations']['DescribeLayer']['hits'] = 0","def __new__(cls):\n game_engine = get_gameengine()\n if game_engine is not None:\n return game_engine\n else:\n return super(GameEngine, cls).__new__(cls)\n # end if","def __init__(self):\n OWSReport.__init__(self)\n self.stats['type'] = 'OGC:WFS'\n self.stats['operations']['GetFeature'] = {}\n self.stats['operations']['GetFeature']['hits'] = 0\n self.stats['operations']['GetFeature']['resource'] = {}\n self.stats['operations']['GetFeature']['resource']['param'] = 'typename'\n self.stats['operations']['GetFeature']['resource']['list'] = {}\n self.stats['operations']['DescribeFeatureType'] = {}\n self.stats['operations']['DescribeFeatureType']['hits'] = 0","def get_stats(ns_profnum, clear=False, **kwargs):\n global SLOCK, STATS\n SLOCK.acquire()\n st = STATS\n if clear:\n STATS['ntotal'] = 0\n STATS['rtotal'] = 0\n STATS['oktotal'] = 0\n STATS['ertotal'] = 0\n STATS['ettotal'] = 0.0\n STATS['ethigh'] = 0.0\n STATS['etlow'] = 0.0\n SLOCK.release()\n #_LOGGER.info('get_stats(): %d %f %d', st['ntotal'], st['ettotal'], st['rtotal'])\n return st","def __enter__(self):\n self.new_session()\n return self","def __init__(self, accessor, settings, name=None):\n assert accessor\n self._lock = threading.Lock()\n self._accessor_lock = threading.Lock()\n self._accessor = accessor\n # _json_cache associates unparsed json to metadata instances.\n # The idea is that there are very few configs in use in a given\n # cluster so the few same strings will show up over and over.\n self._json_cache_lock = threading.Lock()\n self._json_cache = {}\n\n if name is None:\n name = str(hash(self))\n self.name = name\n\n self._size = CACHE_SIZE.labels(self.TYPE, name)\n self._size.set_function(lambda: self.stats()[\"size\"])\n self._max_size = CACHE_MAX_SIZE.labels(self.TYPE, name)\n self._hits = CACHE_HITS.labels(self.TYPE, name)\n self._misses = CACHE_MISSES.labels(self.TYPE, name)","def reset(self):\n self.stats = {}","def make_es_worker(search_conn, es_index, es_doc_type, class_name):\n new_esbase = copy.copy(search_conn)\n new_esbase.es_index = es_index\n new_esbase.doc_type = es_doc_type\n log.info(\"Indexing '%s' into ES index '%s' doctype '%s'\",\n class_name.pyuri,\n es_index,\n es_doc_type)\n return new_esbase","def __init__(self):\n OWSReport.__init__(self)\n self.stats['type'] = 'OGC:SOS'\n self.stats['operations']['GetObservation'] = {}\n self.stats['operations']['GetObservation']['hits'] = 0\n self.stats['operations']['GetObservation']['resource'] = {}\n self.stats['operations']['GetObservation']['resource']['param'] = 'observedproperty'\n self.stats['operations']['GetObservation']['resource']['list'] = {}\n self.stats['operations']['DescribeSensor'] = {}\n self.stats['operations']['DescribeSensor']['hits'] = 0","def __init__(self, name):\n super(Engine, self).__init__(name)\n self._outbuffer = []","def __init__(self):\n OWSReport.__init__(self)\n self.stats['type'] = 'OGC:WCS'\n self.stats['operations']['GetCoverage'] = {}\n self.stats['operations']['GetCoverage']['hits'] = 0\n self.stats['operations']['GetCoverage']['resource'] = {}\n self.stats['operations']['GetCoverage']['resource']['param'] = 'coverage'\n self.stats['operations']['GetCoverage']['resource']['list'] = {}\n self.stats['operations']['DescribeCoverage'] = {}\n self.stats['operations']['DescribeCoverage']['hits'] = 0","def tc_stat_wrapper():\n\n # Default, empty TcStatWrapper with some configuration values set\n # to /path/to:\n conf = metplus_config()\n return TcStatWrapper(conf, None)","def _new_instance(self):\n return self.__class__(self._vmodule, self._tensor_rank)","def generate_statistics():\r\n statistics = cache.get('statistics')\r\n if statistics is None:\r\n statistics = {}\r\n statistics['nr_hashtags'] = ('Number of Hashtags',\r\n get_number_hashtags())\r\n statistics['nr_tokens'] = ('Number of Tokens', get_token_count())\r\n statistics['media_storage_size'] = ('Storage Folder Size (MB)',\r\n str(get_folder_size(\r\n cfg['media_storage'])))\r\n\r\n cache.set('statistics', statistics,\r\n cfg['flask_cache_timeout'] * 60)\r\n\r\n return statistics","async def _get_connection_pool(self) -> aiopg.sa.Engine:\n if self._engine is None:\n self._engine = await aiopg.sa.create_engine(_CONNECTION_STRING)\n return self._engine","def __init__(self, name=None):\n super(TTSEngine, self).__init__()","def __init__(self):\n self.__engine = create_engine('mysql+mysqldb://{}:{}@{}/{}'\n .format(os.environ['HBNB_MYSQL_USER'],\n os.environ['HBNB_MYSQL_PWD'],\n os.environ['HBNB_MYSQL_HOST'],\n os.environ['HBNB_MYSQL_DB']),\n pool_pre_ping=True)\n try:\n if os.environ['HBNB_ENV'] == 'test':\n Base.metadata.drop_all(self.__engine)\n except KeyError:\n pass","def new(self, Entity_ID = None):\n if Entity_ID is None:\n tempID = len(self.pool[\"default\"])+1\n else:\n if self.exists(Entity_ID): return None\n tempID = Entity_ID\n\n tempent = Entity(tempID)\n self.pool[\"default\"].append(tempent)\n\n return self.pool[\"default\"][-1]","def __new__(cls):\n self = object.__new__(cls)\n self.acquired = False\n return self","def __init__(self, config, refresh='No'):\n logging.debug(\"Initializing Neostore object\")\n self.config = config\n self.graph = self._connect2db()\n if refresh == 'Yes':\n self._delete_all()\n self.calendar = GregorianCalendar(self.graph)\n return","def __new__(cls, *args, **kwargs):\n if cls.__instance is None:\n cls.__instance = super(CacheManagerSingleton, cls).__new__(cls)\n # Generate all ground truth data files from hard-coded data\n CacheManagerSingleton.export_all_ground_truth_data()\n return cls.__instance","def __init__(self):\n self._data = dict() # stores the data in the context\n self._last_context = None # stores previous context if the current one is used in a with block\n self._thread_id = self._get_thread_id() # the ID of the thread that context lives in","def get_cache_instance ( ):\n cache_strategy_instance = SimpleCacheStrategy ( )\n cache_strategy_instance.apply_cache_strategy ( threshold=50, default_timeout=100 )\n return cache_strategy_instance","def create():\n # for clean test cases, first the available databases will be flushed\n get_redis().flushdb()\n graph = FileStructureProcessor()\n return graph.get_graph()","def __init__(self, lifetime: int = 60):\n # maps transaction ID -> result, insertion time\n # can be made more efficient but we focus on that later\n # e.g. list ordered by insertion time, etc.\n self._lifetime = lifetime\n self._cache: dict[int, tuple[Union[bytes, Exception], float]] = {}","def new_session(self):\n return self.Session()","def __new__(cls, ctx):\n return cls.__run(cls, ctx)","def __init__(self):\r\n\t\t\r\n\t\tself.redis = redis.Redis()\r\n\t\tself.info_to_get = ['text', 'created_at', 'user']\r\n\t\tself.search_results = {}\r\n\t\tself.raw_data_directory_name = \"raw_mining_data\"\r\n\t\tself.filtered_data_directory_name = \"filtered_mining_data\"\r\n\t\tenglish_file = pjoin( sys.path[0], \"sentiment_word_files\", \"Nielsen2010Responsible_english.csv\")\r\n\t\tself.analyzeEnglish = dict(map(lambda (w,e): (w, int(e)), \\\r\n\t\t\t\t\t\t\t\t\t[ line.strip().lower().split('\\t') for line in open(english_file) ]))\r\n\t\tself.tweets_count = 0","def create(model: TModel, dataset: Dataset) -> aggregator.StatisticsAggregator:\n model_backend = get_backend(model)\n if model_backend == BackendType.ONNX:\n from nncf.onnx.statistics.aggregator import ONNXStatisticsAggregator\n\n return ONNXStatisticsAggregator(dataset)\n if model_backend == BackendType.OPENVINO:\n from nncf.openvino.statistics.aggregator import OVStatisticsAggregator\n\n return OVStatisticsAggregator(dataset)\n if model_backend == BackendType.TORCH:\n from nncf.torch.statistics.aggregator import PTStatisticsAggregator\n\n return PTStatisticsAggregator(dataset)\n raise RuntimeError(\n \"Cannot create backend-specific statistics aggregator because {} is not supported!\".format(model_backend)\n )","def __enter__(self):\n self.start = time.time()\n return self","def stat():\n from .Stat import Stat\n\n return Stat","def grid_stat_wrapper():\n\n conf = metplus_config()\n return GridStatWrapper(conf, None)","def __init__(self, **kwargs):\n creator = kwargs.pop(\"creator\", None)\n if not creator:\n import MySQLdb\n creator = MySQLdb\n mincached = kwargs.pop(\"mincached\", 2)\n maxcached = kwargs.pop(\"maxcached\", 10)\n maxshared = kwargs.pop(\"maxshared\", 10)\n maxconnections = kwargs.pop(\"maxconnections\", 20)\n blocking = kwargs.pop(\"blocking\", 0)\n reset = kwargs.pop(\"reset\", True)\n maxusage = kwargs.pop(\"maxusage\", 0)\n setsession = kwargs.pop(\"setsession\", [\"set autocommit = 0\"])\n ping = kwargs.pop(\"ping\", 1)\n\n self._pool = PooledDB(creator=creator, mincached=mincached, maxcached=maxcached,\n maxshared=maxshared, maxconnections=maxconnections,\n blocking=blocking, maxusage=maxusage,reset=reset,\n setsession=setsession, ping=ping, **kwargs)","def reset():\n global HEALTH_AGGREGATOR\n\n HEALTH_AGGREGATOR.cleanup()\n HEALTH_AGGREGATOR = HealthAggregator()\n\n return jsonify({}), 200","def __init__(self, publish_interval, health_stat_plugin):\n Thread.__init__(self)\n self.log = LogFactory().get_log(__name__)\n self.publish_interval = publish_interval\n \"\"\":type : int\"\"\"\n self.terminated = False\n self.publisher = HealthStatisticsPublisher()\n \"\"\":type : HealthStatisticsPublisher\"\"\"\n # If there are no health stat reader plugins, create the default reader instance\n self.stats_reader = health_stat_plugin if health_stat_plugin is not None else DefaultHealthStatisticsReader()","def Empty():\n return Container(name='(empty)',\n metadata={},\n section_sizes={},\n metrics_by_file={})","def single_threaded_session():\n return make_session(num_cpu=1)","def __enter__(self) -> \"AbstractMetricsPusher\":\n self.start()\n return self","def __init__(self, context, laststate=False):\n self.context = self.clone_context(context)\n self.global_filter = None\n self._rebalancetime = None\n\n self._swarm = None\n self._swarm_stats = None\n self._swarm_inposition = None\n\n self._picked_swarm = None\n self._picked_inposition = None\n self._picked_exposure = None\n\n self._last_date = None\n self._last_exoquote = None\n self._last_exposure = None\n self._last_members_list = None\n self._last_rebalance_date = None\n self._last_delta = None\n self._last_prev_exposure = None\n self._max_exposure = None\n\n self._swarm_series = None\n self._delta = None\n\n self._islast_state = laststate\n\n strategy_settings = self.context['strategy']\n # Initialize strategy class\n self.strategy = strategy_settings['class'](self.context)\n\n self._swarm_avg = None\n self._swarm = None\n self._swarm_exposure = None","def __enter__(self):\n if self._transaction_count == 0:\n self._db_copy = self.db._read()\n self._transaction_count += 1\n return self","def new(cls):\n return cls()","def __init__(self):\n self._inst = {}","def new_session(self):\n return self._SessionLocal()","def __new__(cls, *args, **kwargs):\n if not cls.__instances__:\n cls.__instances__ = super().__new__(cls, *args, **kwargs)\n cls._thread_runing = True\n cls._thread = cls._run_monitor_thread()\n return cls.__instances__","def __new__(cls):\n self = object.__new__(cls)\n self.synced = False\n self.value = None\n return self","def _generate_trading_instances(self):\n print(\"Creating DataHandler, Strategy, Portfolio, and ExecutionHandler for\")\n\n # Set internal data members equal to the classes we passed in earlier, along with necessary parameters.\n # https://softwareengineering.stackexchange.com/questions/131403/what-is-the-name-of-in-python/131415\n self.data_handler = self.data_handler_class(self.events, self.csv_dir, self.symbol_list)\n self.strategy = self.strategy_class(self.data_handler, self.events)\n self.portfolio = self.portfolio_class(self.data_handler, self.events, self.start_date, self.initial_capital)\n self.execution_handler = self.execution_handler_class(self.events) # The Event Queue sent to ExecutionHandler","def __enter__(self):\n\n self.create()\n return super().__enter__()","def _init_local_processes_stats_publisher(self):\n stats = self._local_processes_stats\n # Init cache\n stats.cache = StatsCache(stats.cache_size)\n # Init source\n stats_source = ProcessesStatsSource()\n # Configure stats publishing\n stats.publisher = StatsPublisher(stats_source, stats.update_interval)\n stats.publisher.subscribe(stats.cache)\n self._publishers.append(stats.publisher)\n # Configure handlers\n self._routes['/stats/local/processes/cache'] = HandlerInfo(\n handler_class=CachedStatsHandler,\n init_kwargs=dict(stats_cache=stats.cache)\n )\n self._routes['/stats/local/processes/current'] = HandlerInfo(\n handler_class=CurrentStatsHandler,\n init_kwargs=dict(stats_source=stats_source)\n )","def _init_pool(self, cfg: dict):\n pool = PyMysqlPoolBase(**cfg)\n return pool","def open(self):\n super(MemoryCache, self).open()\n\n def _timer():\n # Use a custom timer to try to spread expirations. Within one instance it\n # won't change anything but it will be better if you run multiple instances.\n return time.time() + self.__ttl * random.uniform(-0.25, 0.25)\n\n self.__cache = cachetools.TTLCache(\n maxsize=self.__size, ttl=self.__ttl, timer=_timer\n )","def _createModuleObj(self):\n ModuleTimeHistory.__init__(self)","def __init__(self, create_index=True, online=True):\n self.online = online\n index_exists = self.index_exists()\n if create_index and not index_exists:\n self.create_index()","def _new_instance(self):\n return self.__class__(self._fmodule, self._tensor_rank)","def stats(self):\r\n return {}","def __enter__(self):\n return self._get_storage().__enter__()","def get_table(base, engine):\n class w1_temp_table(base):\n __tablename__ = 'w1_temp'\n __table_args__ = {\"useexisting\": True}\n\n id = sa.Column(sa.types.Integer, primary_key=True)\n logger_id = sa.Column(sa.types.Integer)\n value = sa.Column(sa.types.String)\n datetime = sa.Column(sa.types.DateTime)\n return w1_temp_table","def __init__(self):\n super(_SerializedEventHeap, self).__init__()\n self._heap = []\n self.data_size = 0","def __initStats(self):\n players = self.teamparser.getPlayers()\n try:\n stats = players[(self.team, self.position)]\n except KeyError, err:\n stats = (0, 0, 0, 0, 0, 0)\n raise TypeError, \"Invalid Team/Position: \" + self.team\n self.max = int(stats[0]) #maximum\n self.ma = int(stats[1]) #movement\n self.st = int(stats[2]) #strength\n self.ag = int(stats[3]) #agility\n self.av = int(stats[4]) #armor value\n self.costs = int(stats[5]) #costs\n self.injury = 0 #injury\n self.ssp = 0 #starplayerpoints\n self.touchdowns = 0 #touchdown\n self.completions = 0 #completions\n self.interceptions = 0 #interceptions\n self.casualties = 0 #casualties\n self.mvpawards = 0 #most valuable player awards","def create_run_tracker(info_dir=None):\r\n # TODO(John Sirois): Rework uses around a context manager for cleanup of the info_dir in a more\r\n # disciplined manner\r\n info_dir = info_dir or safe_mkdtemp()\r\n run_tracker = RunTracker(info_dir)\r\n report = Report()\r\n run_tracker.start(report)\r\n return run_tracker","def initial_meter_statistics(apps, schema_editor):\n MeterStatistics = apps.get_model('dsmr_datalogger', 'MeterStatistics')\n DsmrReading = apps.get_model('dsmr_datalogger', 'DsmrReading')\n\n # We can't (and shouldn't) use Solo here.\n stats = MeterStatistics.objects.create() # All fields are NULL in database, by design.\n assert MeterStatistics.objects.exists()\n\n try:\n # Just use the latest DSMR reading, if any.\n latest_reading = DsmrReading.objects.all().order_by('-timestamp')[0]\n except IndexError:\n return\n\n stats.electricity_tariff = latest_reading.electricity_tariff\n stats.power_failure_count = latest_reading.power_failure_count\n stats.long_power_failure_count = latest_reading.long_power_failure_count\n stats.voltage_sag_count_l1 = latest_reading.voltage_sag_count_l1\n stats.voltage_sag_count_l2 = latest_reading.voltage_sag_count_l2\n stats.voltage_sag_count_l3 = latest_reading.voltage_sag_count_l3\n stats.voltage_swell_count_l1 = latest_reading.voltage_swell_count_l1\n stats.voltage_swell_count_l2 = latest_reading.voltage_swell_count_l2\n stats.voltage_swell_count_l3 = latest_reading.voltage_swell_count_l3\n stats.save()","def _new_instance(self):\n return self.__class__(self._fmodule)","def _new_instance(self):\n return self.__class__(self._fmodule)","def open(self):\r\n self._init_stats()\r\n return self.__trans.open()"],"string":"[\n \"def create( ):\\n \\n uuid = generate_uuid( 10 )\\n\\n stats = Stats( key_name = uuid,\\n uuid = uuid,\\n )\\n stats.put()\\n return stats\",\n \"def empty_instance():\\n from weighted_graph import Graph\\n return Graph()\",\n \"def __init__(self, stats, keep=False):\\n self.stats = stats if keep else None\\n self.market_price_usd = stats.get('market_price_usd')\\n self.hash_rate = stats.get('hash_rate')\\n self.total_fees_btc = stats.get('total_fees_btc')\\n self.n_btc_mined = stats.get('n_btc_mined')\\n self.n_tx = stats.get('n_tx')\\n self.n_blocks_mined = stats.get('n_blocks_mined')\\n self.minutes_between_blocks = stats.get('minutes_between_blocks')\\n self.total_bc = stats.get('totalbc')\\n self.n_blocks_total = stats.get('n_blocks_total')\\n self.estimated_transaction_volume_usd = stats.get('estimated_transaction_volume_usd')\\n self.blocks_size = stats.get('blocks_size')\\n self.miners_revenue_usd = stats.get('miners_revenue_usd')\\n self.next_retarget = stats.get('nextretarget')\\n self.difficulty = stats.get('difficulty')\\n self.estimated_btc_sent = stats.get('estimated_btc_sent')\\n self.miners_revenue_btc = stats.get('miners_revenue_btc')\\n self.total_btc_sent = stats.get('total_btc_sent')\\n self.trade_volume_btc = stats.get('trade_volume_btc')\\n self.trade_volume_usd = stats.get('trade_volume_usd')\\n self.timestamp = stats.get('timestamp')\",\n \"def start(cls, stats, keep=False):\\n return cls(stats, keep)\",\n \"def Get() -> stats_collector.StatsCollector:\\n if _stats_singleton is None:\\n raise StatsNotInitializedError()\\n return _stats_singleton\",\n \"def newStatGraph():\\n e = ET.Element('stats')\\n n = newElement(e, 'ok')\\n n = newElement(e, 'not_ok')\\n return ET.ElementTree(e)\",\n \"def __init__(self):\\n self.stats = None\\n self.ticker = None\",\n \"def __init__(self):\\n self.stats = {}\\n self.stats['hits'] = 0\\n self.stats['operations'] = {}\\n self.stats['operations']['GetCapabilities'] = {}\\n self.stats['operations']['GetCapabilities']['hits'] = 0\\n self.stats['operations']['POST'] = {}\\n self.stats['operations']['POST']['hits'] = 0\",\n \"def __init__(self):\\n self._workload = None\\n self._engine = Engine()\",\n \"def __init__(self, aggregation_depth, include_bytes=True):\\n\\n self._prev_stats = {}\\n self._aggregation_depth = aggregation_depth\\n self._include_bytes = include_bytes\\n\\n self.init_cur_stats()\",\n \"def reset_stats(self, settings, reset_stream=False):\\n\\n self.__settings = settings\\n self.__stats_table = {}\\n self.__sql_stats_table = {}\\n self.__slow_transaction = None\\n self.__slow_transaction_map = {}\\n self.__slow_transaction_old_duration = None\\n self.__transaction_errors = []\\n self.__synthetics_transactions = []\\n\\n self.reset_transaction_events()\\n self.reset_error_events()\\n self.reset_custom_events()\\n self.reset_span_events()\\n self.reset_synthetics_events()\\n # streams are never reset after instantiation\\n if reset_stream:\\n self._span_stream = StreamBuffer(\\n settings.infinite_tracing.span_queue_size)\",\n \"def _create(self, context, values):\\n # initialize load stats from existing instances:\\n compute_node = db.compute_node_create(context, values)\\n return compute_node\",\n \"def __init__(self, settings):\\n self._settings = settings\\n self._stats = None\",\n \"def __init__(self, stats):\\n self._meta = stats['meta'].item()\\n self._stats = stats['data'].item()\\n self._moving_average_cache = {}\",\n \"def __enter__(self):\\r\\n if not self._engine:\\r\\n self.restart()\\r\\n return self\",\n \"def statsWorker():\\n logger.info('STATS: Starting. Will report out every {0:.1g} hours'.format(\\n config.STATS_HOURS))\\n while True:\\n gevent.sleep(timedelta(hours=config.STATS_HOURS).total_seconds())\\n logger.info('STATS: {0}'.format(stats))\\n stats.resetStats()\\n\\n return\",\n \"def get_engine(settings: dict) -> sqlalchemy.engine.base.Engine:\\n engine = create_engine(settings['sqlalchemy.url'], pool_recycle=3600)\\n return engine\",\n \"def __init__(self):\\n self.ts = dict()\\n self.cache = dict()\",\n \"def _init_local_node_stats_publisher(self):\\n stats = self._local_node_stats\\n # Init cache\\n stats.cache = StatsCache(stats.cache_size)\\n # Init source\\n stats_source = NodeStatsSource()\\n # Configure stats publishing\\n stats.publisher = StatsPublisher(stats_source, stats.update_interval)\\n stats.publisher.subscribe(stats.cache)\\n self._publishers.append(stats.publisher)\\n # Configure handlers\\n self._routes['/stats/local/node/cache'] = HandlerInfo(\\n handler_class=CachedStatsHandler,\\n init_kwargs=dict(stats_cache=stats.cache)\\n )\\n self._routes['/stats/local/node/current'] = HandlerInfo(\\n handler_class=CurrentStatsHandler,\\n init_kwargs=dict(stats_source=stats_source)\\n )\",\n \"def __init__(self):\\n super(MultiProcessEngine, self).__init__()\\n self._debug_output = False\\n self._name = 'Main'\\n self._last_worker_number = 0\\n self._log_filename = None\\n self._pid = os.getpid()\\n self._process_information = process_info.ProcessInfo(self._pid)\\n self._process_information_per_pid = {}\\n self._processes_per_pid = {}\\n self._quiet_mode = False\\n self._rpc_clients_per_pid = {}\\n self._rpc_errors_per_pid = {}\\n self._status_update_active = False\\n self._status_update_thread = None\\n self._storage_writer = None\\n self._worker_memory_limit = definitions.DEFAULT_WORKER_MEMORY_LIMIT\",\n \"def __enter__(self):\\n self.__init__()\\n return self\",\n \"def _new_instance(self):\\n return self.__class__(self._vmodule)\",\n \"def __init__(self, name='TemporaryStorage'):\\n\\n BaseStorage.__init__(self, name)\\n\\n self._index = {}\\n self._referenceCount = {}\\n self._oreferences = {}\\n self._opickle = {}\\n self._tmp = []\\n self._conflict_cache = {}\\n self._last_cache_gc = 0\\n self._recently_gc_oids = [None for x in range(RECENTLY_GC_OIDS_LEN)]\\n self._oid = z64\\n self._ltid = z64\\n\\n # Alow overrides for testing.\\n self._conflict_cache_gcevery = CONFLICT_CACHE_GCEVERY\\n self._conflict_cache_maxage = CONFLICT_CACHE_MAXAGE\",\n \"def __init__(self, router): # Promotion constructor :)\\r\\n # TODO: Use __bases__ to do this instead?\\r\\n self.__dict__ = router.__dict__\\r\\n self.reset()\\r\\n # StatsRouters should not be destroyed when Tor forgets about them\\r\\n # Give them an extra refcount:\\r\\n self.refcount += 1\\r\\n plog(\\\"DEBUG\\\", \\\"Stats refcount \\\"+str(self.refcount)+\\\" for \\\"+self.idhex)\",\n \"def StartProfiling(self):\\n hp = hpy()\\n hp.setrelheap()\\n return hp\",\n \"def _create_empty(cls):\\n self = object.__new__(cls)\\n self.avatar_hash = 0\\n self.avatar_type = IconType.none\\n self.boosts_since = None\\n self.flags = GuildProfileFlag()\\n self.joined_at = None\\n self.nick = None\\n self.pending = False\\n self.role_ids = None\\n self.timed_out_until = None\\n return self\",\n \"def __init__(self):\\n self.stats = Statblock(parent=self)\\n self.compiled_circ = None\\n self.backend = None\\n self.job_id = None\\n self.transpiler_config = None\\n self.circuit = None\\n\\n # if isinstance(circuit, QuantumCircuit):\\n # self.circuit = circuit\\n # elif circuit is not None:\\n # raise TypeError(f'Circuit must be a QuantumCircuit, or Premade. Was given type: {type(circuit)}')\",\n \"def __new__(cls, *args, **kwargs):\\n if GraphData._instance is None:\\n with GraphData._instance_lock:\\n if GraphData._instance is None:\\n GraphData._instance = object.__new__(cls)\\n return GraphData._instance\",\n \"def __init__(self) -> None:\\n self.metrics = {}\\n self.current = None\\n self.run = None\",\n \"def __init__(self):\\n self._maxTime = 0\\n self._activeHeap = []\\n self._activeDict = {}\\n self._graph = _Graph()\",\n \"def __enter__(self):\\n self.initialize()\\n return self\",\n \"def _create_load_stats(self, context, instance=None):\\n values = {}\\n\\n if instance:\\n instances = [instance]\\n else:\\n self.stats.clear() # re-generating all, so clear old stats\\n\\n # grab all instances that are not yet DELETED\\n filters = {'host': self.host, 'deleted': False}\\n instances = db.instance_get_all_by_filters(context,\\n {'host': self.host})\\n\\n for instance in instances:\\n self.stats.add_stats_for_instance(instance)\\n\\n values['current_workload'] = self.stats.calculate_workload()\\n values['running_vms'] = self.stats.num_instances\\n values['vcpus_used'] = self.stats.num_vcpus_used\\n values['stats'] = self.stats\\n return values\",\n \"def init_cur_stats(self):\\n self._cur_stats = defaultdict(lambda: defaultdict(int))\\n self._cur_stats[\\\"writes\\\"][\\\"/\\\"] = 0\\n self._cur_stats[\\\"reads\\\"][\\\"/\\\"] = 0\\n self._cur_stats[\\\"total\\\"][\\\"/writes\\\"] = 0\\n self._cur_stats[\\\"total\\\"][\\\"/reads\\\"] = 0\\n\\n if self._include_bytes:\\n self._cur_stats[\\\"writesBytes\\\"][\\\"/\\\"] = 0\\n self._cur_stats[\\\"readsBytes\\\"][\\\"/\\\"] = 0\\n self._cur_stats[\\\"total\\\"][\\\"/writeBytes\\\"] = 0\\n self._cur_stats[\\\"total\\\"][\\\"/readBytes\\\"] = 0\",\n \"def create_instance(min_bds, max_bds, debug=True):\\n if not debug:\\n return StatsTrackerDoNothing()\\n\\n try:\\n return StatsTrackerBase(min_bds, max_bds)\\n except:\\n return StatsTrackerArray(min_bds, max_bds)\",\n \"def get_statistics(self):\\n\\t\\treturn Job(SDK.PrlSrv_GetStatistics(self.handle)[0])\",\n \"def create_statistics(self):\\n now = datetime.now()\\n min_timestamp = Statistic.objects.all().aggregate(Max('timestamp_end'))[\\\"timestamp_end__max\\\"]\\n max_timestamp = (now + ((datetime.min - now) % timedelta(minutes=60)) - timedelta(minutes=60)).replace(tzinfo=pytz.UTC)\\n\\n if min_timestamp is None:\\n min_timestamp = datetime(2000, 1, 1, tzinfo=timezone('UTC'))\\n\\n aggregated_measurements = MeasurementService.get_aggregate_measurements(min_timestamp,max_timestamp)\\n StatisticService.create_statistics(aggregated_measurements)\",\n \"def __init__(self):\\n # 保存用户推特数据\\n self.user_pool = defaultdict(UserInfo)\\n self.twitter_pool = defaultdict(list)\\n self.time = 0\",\n \"def __init__(self):\\n OWSReport.__init__(self)\\n self.stats['type'] = 'OGC:WMS'\\n self.stats['operations']['GetMap'] = {}\\n self.stats['operations']['GetMap']['hits'] = 0\\n self.stats['operations']['GetMap']['resource'] = {}\\n self.stats['operations']['GetMap']['resource']['param'] = 'layers'\\n self.stats['operations']['GetMap']['resource']['list'] = {}\\n self.stats['operations']['GetFeatureInfo'] = {}\\n self.stats['operations']['GetFeatureInfo']['hits'] = 0\\n self.stats['operations']['GetLegendGraphic'] = {}\\n self.stats['operations']['GetLegendGraphic']['hits'] = 0\\n self.stats['operations']['GetStyles'] = {}\\n self.stats['operations']['GetStyles']['hits'] = 0\\n self.stats['operations']['DescribeLayer'] = {}\\n self.stats['operations']['DescribeLayer']['hits'] = 0\",\n \"def __new__(cls):\\n game_engine = get_gameengine()\\n if game_engine is not None:\\n return game_engine\\n else:\\n return super(GameEngine, cls).__new__(cls)\\n # end if\",\n \"def __init__(self):\\n OWSReport.__init__(self)\\n self.stats['type'] = 'OGC:WFS'\\n self.stats['operations']['GetFeature'] = {}\\n self.stats['operations']['GetFeature']['hits'] = 0\\n self.stats['operations']['GetFeature']['resource'] = {}\\n self.stats['operations']['GetFeature']['resource']['param'] = 'typename'\\n self.stats['operations']['GetFeature']['resource']['list'] = {}\\n self.stats['operations']['DescribeFeatureType'] = {}\\n self.stats['operations']['DescribeFeatureType']['hits'] = 0\",\n \"def get_stats(ns_profnum, clear=False, **kwargs):\\n global SLOCK, STATS\\n SLOCK.acquire()\\n st = STATS\\n if clear:\\n STATS['ntotal'] = 0\\n STATS['rtotal'] = 0\\n STATS['oktotal'] = 0\\n STATS['ertotal'] = 0\\n STATS['ettotal'] = 0.0\\n STATS['ethigh'] = 0.0\\n STATS['etlow'] = 0.0\\n SLOCK.release()\\n #_LOGGER.info('get_stats(): %d %f %d', st['ntotal'], st['ettotal'], st['rtotal'])\\n return st\",\n \"def __enter__(self):\\n self.new_session()\\n return self\",\n \"def __init__(self, accessor, settings, name=None):\\n assert accessor\\n self._lock = threading.Lock()\\n self._accessor_lock = threading.Lock()\\n self._accessor = accessor\\n # _json_cache associates unparsed json to metadata instances.\\n # The idea is that there are very few configs in use in a given\\n # cluster so the few same strings will show up over and over.\\n self._json_cache_lock = threading.Lock()\\n self._json_cache = {}\\n\\n if name is None:\\n name = str(hash(self))\\n self.name = name\\n\\n self._size = CACHE_SIZE.labels(self.TYPE, name)\\n self._size.set_function(lambda: self.stats()[\\\"size\\\"])\\n self._max_size = CACHE_MAX_SIZE.labels(self.TYPE, name)\\n self._hits = CACHE_HITS.labels(self.TYPE, name)\\n self._misses = CACHE_MISSES.labels(self.TYPE, name)\",\n \"def reset(self):\\n self.stats = {}\",\n \"def make_es_worker(search_conn, es_index, es_doc_type, class_name):\\n new_esbase = copy.copy(search_conn)\\n new_esbase.es_index = es_index\\n new_esbase.doc_type = es_doc_type\\n log.info(\\\"Indexing '%s' into ES index '%s' doctype '%s'\\\",\\n class_name.pyuri,\\n es_index,\\n es_doc_type)\\n return new_esbase\",\n \"def __init__(self):\\n OWSReport.__init__(self)\\n self.stats['type'] = 'OGC:SOS'\\n self.stats['operations']['GetObservation'] = {}\\n self.stats['operations']['GetObservation']['hits'] = 0\\n self.stats['operations']['GetObservation']['resource'] = {}\\n self.stats['operations']['GetObservation']['resource']['param'] = 'observedproperty'\\n self.stats['operations']['GetObservation']['resource']['list'] = {}\\n self.stats['operations']['DescribeSensor'] = {}\\n self.stats['operations']['DescribeSensor']['hits'] = 0\",\n \"def __init__(self, name):\\n super(Engine, self).__init__(name)\\n self._outbuffer = []\",\n \"def __init__(self):\\n OWSReport.__init__(self)\\n self.stats['type'] = 'OGC:WCS'\\n self.stats['operations']['GetCoverage'] = {}\\n self.stats['operations']['GetCoverage']['hits'] = 0\\n self.stats['operations']['GetCoverage']['resource'] = {}\\n self.stats['operations']['GetCoverage']['resource']['param'] = 'coverage'\\n self.stats['operations']['GetCoverage']['resource']['list'] = {}\\n self.stats['operations']['DescribeCoverage'] = {}\\n self.stats['operations']['DescribeCoverage']['hits'] = 0\",\n \"def tc_stat_wrapper():\\n\\n # Default, empty TcStatWrapper with some configuration values set\\n # to /path/to:\\n conf = metplus_config()\\n return TcStatWrapper(conf, None)\",\n \"def _new_instance(self):\\n return self.__class__(self._vmodule, self._tensor_rank)\",\n \"def generate_statistics():\\r\\n statistics = cache.get('statistics')\\r\\n if statistics is None:\\r\\n statistics = {}\\r\\n statistics['nr_hashtags'] = ('Number of Hashtags',\\r\\n get_number_hashtags())\\r\\n statistics['nr_tokens'] = ('Number of Tokens', get_token_count())\\r\\n statistics['media_storage_size'] = ('Storage Folder Size (MB)',\\r\\n str(get_folder_size(\\r\\n cfg['media_storage'])))\\r\\n\\r\\n cache.set('statistics', statistics,\\r\\n cfg['flask_cache_timeout'] * 60)\\r\\n\\r\\n return statistics\",\n \"async def _get_connection_pool(self) -> aiopg.sa.Engine:\\n if self._engine is None:\\n self._engine = await aiopg.sa.create_engine(_CONNECTION_STRING)\\n return self._engine\",\n \"def __init__(self, name=None):\\n super(TTSEngine, self).__init__()\",\n \"def __init__(self):\\n self.__engine = create_engine('mysql+mysqldb://{}:{}@{}/{}'\\n .format(os.environ['HBNB_MYSQL_USER'],\\n os.environ['HBNB_MYSQL_PWD'],\\n os.environ['HBNB_MYSQL_HOST'],\\n os.environ['HBNB_MYSQL_DB']),\\n pool_pre_ping=True)\\n try:\\n if os.environ['HBNB_ENV'] == 'test':\\n Base.metadata.drop_all(self.__engine)\\n except KeyError:\\n pass\",\n \"def new(self, Entity_ID = None):\\n if Entity_ID is None:\\n tempID = len(self.pool[\\\"default\\\"])+1\\n else:\\n if self.exists(Entity_ID): return None\\n tempID = Entity_ID\\n\\n tempent = Entity(tempID)\\n self.pool[\\\"default\\\"].append(tempent)\\n\\n return self.pool[\\\"default\\\"][-1]\",\n \"def __new__(cls):\\n self = object.__new__(cls)\\n self.acquired = False\\n return self\",\n \"def __init__(self, config, refresh='No'):\\n logging.debug(\\\"Initializing Neostore object\\\")\\n self.config = config\\n self.graph = self._connect2db()\\n if refresh == 'Yes':\\n self._delete_all()\\n self.calendar = GregorianCalendar(self.graph)\\n return\",\n \"def __new__(cls, *args, **kwargs):\\n if cls.__instance is None:\\n cls.__instance = super(CacheManagerSingleton, cls).__new__(cls)\\n # Generate all ground truth data files from hard-coded data\\n CacheManagerSingleton.export_all_ground_truth_data()\\n return cls.__instance\",\n \"def __init__(self):\\n self._data = dict() # stores the data in the context\\n self._last_context = None # stores previous context if the current one is used in a with block\\n self._thread_id = self._get_thread_id() # the ID of the thread that context lives in\",\n \"def get_cache_instance ( ):\\n cache_strategy_instance = SimpleCacheStrategy ( )\\n cache_strategy_instance.apply_cache_strategy ( threshold=50, default_timeout=100 )\\n return cache_strategy_instance\",\n \"def create():\\n # for clean test cases, first the available databases will be flushed\\n get_redis().flushdb()\\n graph = FileStructureProcessor()\\n return graph.get_graph()\",\n \"def __init__(self, lifetime: int = 60):\\n # maps transaction ID -> result, insertion time\\n # can be made more efficient but we focus on that later\\n # e.g. list ordered by insertion time, etc.\\n self._lifetime = lifetime\\n self._cache: dict[int, tuple[Union[bytes, Exception], float]] = {}\",\n \"def new_session(self):\\n return self.Session()\",\n \"def __new__(cls, ctx):\\n return cls.__run(cls, ctx)\",\n \"def __init__(self):\\r\\n\\t\\t\\r\\n\\t\\tself.redis = redis.Redis()\\r\\n\\t\\tself.info_to_get = ['text', 'created_at', 'user']\\r\\n\\t\\tself.search_results = {}\\r\\n\\t\\tself.raw_data_directory_name = \\\"raw_mining_data\\\"\\r\\n\\t\\tself.filtered_data_directory_name = \\\"filtered_mining_data\\\"\\r\\n\\t\\tenglish_file = pjoin( sys.path[0], \\\"sentiment_word_files\\\", \\\"Nielsen2010Responsible_english.csv\\\")\\r\\n\\t\\tself.analyzeEnglish = dict(map(lambda (w,e): (w, int(e)), \\\\\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t[ line.strip().lower().split('\\\\t') for line in open(english_file) ]))\\r\\n\\t\\tself.tweets_count = 0\",\n \"def create(model: TModel, dataset: Dataset) -> aggregator.StatisticsAggregator:\\n model_backend = get_backend(model)\\n if model_backend == BackendType.ONNX:\\n from nncf.onnx.statistics.aggregator import ONNXStatisticsAggregator\\n\\n return ONNXStatisticsAggregator(dataset)\\n if model_backend == BackendType.OPENVINO:\\n from nncf.openvino.statistics.aggregator import OVStatisticsAggregator\\n\\n return OVStatisticsAggregator(dataset)\\n if model_backend == BackendType.TORCH:\\n from nncf.torch.statistics.aggregator import PTStatisticsAggregator\\n\\n return PTStatisticsAggregator(dataset)\\n raise RuntimeError(\\n \\\"Cannot create backend-specific statistics aggregator because {} is not supported!\\\".format(model_backend)\\n )\",\n \"def __enter__(self):\\n self.start = time.time()\\n return self\",\n \"def stat():\\n from .Stat import Stat\\n\\n return Stat\",\n \"def grid_stat_wrapper():\\n\\n conf = metplus_config()\\n return GridStatWrapper(conf, None)\",\n \"def __init__(self, **kwargs):\\n creator = kwargs.pop(\\\"creator\\\", None)\\n if not creator:\\n import MySQLdb\\n creator = MySQLdb\\n mincached = kwargs.pop(\\\"mincached\\\", 2)\\n maxcached = kwargs.pop(\\\"maxcached\\\", 10)\\n maxshared = kwargs.pop(\\\"maxshared\\\", 10)\\n maxconnections = kwargs.pop(\\\"maxconnections\\\", 20)\\n blocking = kwargs.pop(\\\"blocking\\\", 0)\\n reset = kwargs.pop(\\\"reset\\\", True)\\n maxusage = kwargs.pop(\\\"maxusage\\\", 0)\\n setsession = kwargs.pop(\\\"setsession\\\", [\\\"set autocommit = 0\\\"])\\n ping = kwargs.pop(\\\"ping\\\", 1)\\n\\n self._pool = PooledDB(creator=creator, mincached=mincached, maxcached=maxcached,\\n maxshared=maxshared, maxconnections=maxconnections,\\n blocking=blocking, maxusage=maxusage,reset=reset,\\n setsession=setsession, ping=ping, **kwargs)\",\n \"def reset():\\n global HEALTH_AGGREGATOR\\n\\n HEALTH_AGGREGATOR.cleanup()\\n HEALTH_AGGREGATOR = HealthAggregator()\\n\\n return jsonify({}), 200\",\n \"def __init__(self, publish_interval, health_stat_plugin):\\n Thread.__init__(self)\\n self.log = LogFactory().get_log(__name__)\\n self.publish_interval = publish_interval\\n \\\"\\\"\\\":type : int\\\"\\\"\\\"\\n self.terminated = False\\n self.publisher = HealthStatisticsPublisher()\\n \\\"\\\"\\\":type : HealthStatisticsPublisher\\\"\\\"\\\"\\n # If there are no health stat reader plugins, create the default reader instance\\n self.stats_reader = health_stat_plugin if health_stat_plugin is not None else DefaultHealthStatisticsReader()\",\n \"def Empty():\\n return Container(name='(empty)',\\n metadata={},\\n section_sizes={},\\n metrics_by_file={})\",\n \"def single_threaded_session():\\n return make_session(num_cpu=1)\",\n \"def __enter__(self) -> \\\"AbstractMetricsPusher\\\":\\n self.start()\\n return self\",\n \"def __init__(self, context, laststate=False):\\n self.context = self.clone_context(context)\\n self.global_filter = None\\n self._rebalancetime = None\\n\\n self._swarm = None\\n self._swarm_stats = None\\n self._swarm_inposition = None\\n\\n self._picked_swarm = None\\n self._picked_inposition = None\\n self._picked_exposure = None\\n\\n self._last_date = None\\n self._last_exoquote = None\\n self._last_exposure = None\\n self._last_members_list = None\\n self._last_rebalance_date = None\\n self._last_delta = None\\n self._last_prev_exposure = None\\n self._max_exposure = None\\n\\n self._swarm_series = None\\n self._delta = None\\n\\n self._islast_state = laststate\\n\\n strategy_settings = self.context['strategy']\\n # Initialize strategy class\\n self.strategy = strategy_settings['class'](self.context)\\n\\n self._swarm_avg = None\\n self._swarm = None\\n self._swarm_exposure = None\",\n \"def __enter__(self):\\n if self._transaction_count == 0:\\n self._db_copy = self.db._read()\\n self._transaction_count += 1\\n return self\",\n \"def new(cls):\\n return cls()\",\n \"def __init__(self):\\n self._inst = {}\",\n \"def new_session(self):\\n return self._SessionLocal()\",\n \"def __new__(cls, *args, **kwargs):\\n if not cls.__instances__:\\n cls.__instances__ = super().__new__(cls, *args, **kwargs)\\n cls._thread_runing = True\\n cls._thread = cls._run_monitor_thread()\\n return cls.__instances__\",\n \"def __new__(cls):\\n self = object.__new__(cls)\\n self.synced = False\\n self.value = None\\n return self\",\n \"def _generate_trading_instances(self):\\n print(\\\"Creating DataHandler, Strategy, Portfolio, and ExecutionHandler for\\\")\\n\\n # Set internal data members equal to the classes we passed in earlier, along with necessary parameters.\\n # https://softwareengineering.stackexchange.com/questions/131403/what-is-the-name-of-in-python/131415\\n self.data_handler = self.data_handler_class(self.events, self.csv_dir, self.symbol_list)\\n self.strategy = self.strategy_class(self.data_handler, self.events)\\n self.portfolio = self.portfolio_class(self.data_handler, self.events, self.start_date, self.initial_capital)\\n self.execution_handler = self.execution_handler_class(self.events) # The Event Queue sent to ExecutionHandler\",\n \"def __enter__(self):\\n\\n self.create()\\n return super().__enter__()\",\n \"def _init_local_processes_stats_publisher(self):\\n stats = self._local_processes_stats\\n # Init cache\\n stats.cache = StatsCache(stats.cache_size)\\n # Init source\\n stats_source = ProcessesStatsSource()\\n # Configure stats publishing\\n stats.publisher = StatsPublisher(stats_source, stats.update_interval)\\n stats.publisher.subscribe(stats.cache)\\n self._publishers.append(stats.publisher)\\n # Configure handlers\\n self._routes['/stats/local/processes/cache'] = HandlerInfo(\\n handler_class=CachedStatsHandler,\\n init_kwargs=dict(stats_cache=stats.cache)\\n )\\n self._routes['/stats/local/processes/current'] = HandlerInfo(\\n handler_class=CurrentStatsHandler,\\n init_kwargs=dict(stats_source=stats_source)\\n )\",\n \"def _init_pool(self, cfg: dict):\\n pool = PyMysqlPoolBase(**cfg)\\n return pool\",\n \"def open(self):\\n super(MemoryCache, self).open()\\n\\n def _timer():\\n # Use a custom timer to try to spread expirations. Within one instance it\\n # won't change anything but it will be better if you run multiple instances.\\n return time.time() + self.__ttl * random.uniform(-0.25, 0.25)\\n\\n self.__cache = cachetools.TTLCache(\\n maxsize=self.__size, ttl=self.__ttl, timer=_timer\\n )\",\n \"def _createModuleObj(self):\\n ModuleTimeHistory.__init__(self)\",\n \"def __init__(self, create_index=True, online=True):\\n self.online = online\\n index_exists = self.index_exists()\\n if create_index and not index_exists:\\n self.create_index()\",\n \"def _new_instance(self):\\n return self.__class__(self._fmodule, self._tensor_rank)\",\n \"def stats(self):\\r\\n return {}\",\n \"def __enter__(self):\\n return self._get_storage().__enter__()\",\n \"def get_table(base, engine):\\n class w1_temp_table(base):\\n __tablename__ = 'w1_temp'\\n __table_args__ = {\\\"useexisting\\\": True}\\n\\n id = sa.Column(sa.types.Integer, primary_key=True)\\n logger_id = sa.Column(sa.types.Integer)\\n value = sa.Column(sa.types.String)\\n datetime = sa.Column(sa.types.DateTime)\\n return w1_temp_table\",\n \"def __init__(self):\\n super(_SerializedEventHeap, self).__init__()\\n self._heap = []\\n self.data_size = 0\",\n \"def __initStats(self):\\n players = self.teamparser.getPlayers()\\n try:\\n stats = players[(self.team, self.position)]\\n except KeyError, err:\\n stats = (0, 0, 0, 0, 0, 0)\\n raise TypeError, \\\"Invalid Team/Position: \\\" + self.team\\n self.max = int(stats[0]) #maximum\\n self.ma = int(stats[1]) #movement\\n self.st = int(stats[2]) #strength\\n self.ag = int(stats[3]) #agility\\n self.av = int(stats[4]) #armor value\\n self.costs = int(stats[5]) #costs\\n self.injury = 0 #injury\\n self.ssp = 0 #starplayerpoints\\n self.touchdowns = 0 #touchdown\\n self.completions = 0 #completions\\n self.interceptions = 0 #interceptions\\n self.casualties = 0 #casualties\\n self.mvpawards = 0 #most valuable player awards\",\n \"def create_run_tracker(info_dir=None):\\r\\n # TODO(John Sirois): Rework uses around a context manager for cleanup of the info_dir in a more\\r\\n # disciplined manner\\r\\n info_dir = info_dir or safe_mkdtemp()\\r\\n run_tracker = RunTracker(info_dir)\\r\\n report = Report()\\r\\n run_tracker.start(report)\\r\\n return run_tracker\",\n \"def initial_meter_statistics(apps, schema_editor):\\n MeterStatistics = apps.get_model('dsmr_datalogger', 'MeterStatistics')\\n DsmrReading = apps.get_model('dsmr_datalogger', 'DsmrReading')\\n\\n # We can't (and shouldn't) use Solo here.\\n stats = MeterStatistics.objects.create() # All fields are NULL in database, by design.\\n assert MeterStatistics.objects.exists()\\n\\n try:\\n # Just use the latest DSMR reading, if any.\\n latest_reading = DsmrReading.objects.all().order_by('-timestamp')[0]\\n except IndexError:\\n return\\n\\n stats.electricity_tariff = latest_reading.electricity_tariff\\n stats.power_failure_count = latest_reading.power_failure_count\\n stats.long_power_failure_count = latest_reading.long_power_failure_count\\n stats.voltage_sag_count_l1 = latest_reading.voltage_sag_count_l1\\n stats.voltage_sag_count_l2 = latest_reading.voltage_sag_count_l2\\n stats.voltage_sag_count_l3 = latest_reading.voltage_sag_count_l3\\n stats.voltage_swell_count_l1 = latest_reading.voltage_swell_count_l1\\n stats.voltage_swell_count_l2 = latest_reading.voltage_swell_count_l2\\n stats.voltage_swell_count_l3 = latest_reading.voltage_swell_count_l3\\n stats.save()\",\n \"def _new_instance(self):\\n return self.__class__(self._fmodule)\",\n \"def _new_instance(self):\\n return self.__class__(self._fmodule)\",\n \"def open(self):\\r\\n self._init_stats()\\r\\n return self.__trans.open()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6012898","0.54610676","0.5434459","0.5365949","0.5345452","0.53324264","0.5254955","0.5204557","0.5093632","0.50626856","0.5004467","0.498278","0.49784213","0.4975857","0.49731496","0.49413025","0.49334928","0.49183187","0.49155334","0.48889562","0.48528484","0.48501238","0.48281246","0.48213986","0.48183307","0.4807528","0.48038778","0.47972417","0.47818562","0.47612146","0.47572654","0.4754027","0.4751779","0.4746518","0.4746509","0.47376403","0.47287735","0.4727484","0.47271544","0.47113094","0.4710821","0.47056285","0.46858755","0.46821922","0.46600673","0.46520022","0.46483654","0.46482888","0.4647542","0.46469098","0.46374536","0.4632999","0.4632792","0.46295866","0.46284005","0.46269456","0.46265864","0.46236548","0.46183944","0.46167243","0.46141404","0.46110973","0.46074614","0.459869","0.4594149","0.45720103","0.45667917","0.45557636","0.45546347","0.45536262","0.4550131","0.4544091","0.45414013","0.45355815","0.45200574","0.45192948","0.45170444","0.45016652","0.45014402","0.45014146","0.45009196","0.44961432","0.44957238","0.44913545","0.4489568","0.44846746","0.44842175","0.44842076","0.4479024","0.44771558","0.4476004","0.44755167","0.44703248","0.44579223","0.44554898","0.44524682","0.4448781","0.44460458","0.44460458","0.44418684"],"string":"[\n \"0.6012898\",\n \"0.54610676\",\n \"0.5434459\",\n \"0.5365949\",\n \"0.5345452\",\n \"0.53324264\",\n \"0.5254955\",\n \"0.5204557\",\n \"0.5093632\",\n \"0.50626856\",\n \"0.5004467\",\n \"0.498278\",\n \"0.49784213\",\n \"0.4975857\",\n \"0.49731496\",\n \"0.49413025\",\n \"0.49334928\",\n \"0.49183187\",\n \"0.49155334\",\n \"0.48889562\",\n \"0.48528484\",\n \"0.48501238\",\n \"0.48281246\",\n \"0.48213986\",\n \"0.48183307\",\n \"0.4807528\",\n \"0.48038778\",\n \"0.47972417\",\n \"0.47818562\",\n \"0.47612146\",\n \"0.47572654\",\n \"0.4754027\",\n \"0.4751779\",\n \"0.4746518\",\n \"0.4746509\",\n \"0.47376403\",\n \"0.47287735\",\n \"0.4727484\",\n \"0.47271544\",\n \"0.47113094\",\n \"0.4710821\",\n \"0.47056285\",\n \"0.46858755\",\n \"0.46821922\",\n \"0.46600673\",\n \"0.46520022\",\n \"0.46483654\",\n \"0.46482888\",\n \"0.4647542\",\n \"0.46469098\",\n \"0.46374536\",\n \"0.4632999\",\n \"0.4632792\",\n \"0.46295866\",\n \"0.46284005\",\n \"0.46269456\",\n \"0.46265864\",\n \"0.46236548\",\n \"0.46183944\",\n \"0.46167243\",\n \"0.46141404\",\n \"0.46110973\",\n \"0.46074614\",\n \"0.459869\",\n \"0.4594149\",\n \"0.45720103\",\n \"0.45667917\",\n \"0.45557636\",\n \"0.45546347\",\n \"0.45536262\",\n \"0.4550131\",\n \"0.4544091\",\n \"0.45414013\",\n \"0.45355815\",\n \"0.45200574\",\n \"0.45192948\",\n \"0.45170444\",\n \"0.45016652\",\n \"0.45014402\",\n \"0.45014146\",\n \"0.45009196\",\n \"0.44961432\",\n \"0.44957238\",\n \"0.44913545\",\n \"0.4489568\",\n \"0.44846746\",\n \"0.44842175\",\n \"0.44842076\",\n \"0.4479024\",\n \"0.44771558\",\n \"0.4476004\",\n \"0.44755167\",\n \"0.44703248\",\n \"0.44579223\",\n \"0.44554898\",\n \"0.44524682\",\n \"0.4448781\",\n \"0.44460458\",\n \"0.44460458\",\n \"0.44418684\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.53285325"},"document_rank":{"kind":"string","value":"6"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":36,"numItemsPerPage":100,"numTotalItems":95772,"offset":3600,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc3Mzc0Njg0Miwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3NzM3NTA0NDIsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.r_M-JBStMH-BdLh52ZFq_z71lOdcG5Ccp9FLCk5iwHPaVDQJbH2e3xi-pSHAXOsm-_TeJJEGTBx2UzQ6LHkeCw","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":95772,"numBytesParquet":1928375891}]},"discussionsStats":{"closed":1,"open":1,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102 values |
|---|---|---|---|---|---|---|
Example of entry point | def main(args=None):
if args is None:
args = sys.argv[1:]
print('This is the main function of pycharm')
"""Read the args"""
if len(args) > 0:
for i in args:
print(i)
if args[0] == 'create_setup':
remove = False
if len(args) > 2:
if args[2] == '-removeold':
remove = True
from pymake.tools.create_setup import create_setup
create_setup(args[1], remove)
else:
print('No parameters passed') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def entry_point():",
"def entry_point():",
"def entry_point():",
"def main():\n pass",
"def main():\n return",
"def main(self) -> None:\n pass",
"def main() -> None:",
"def main() -> None:",
"def main() -> None:",
"def main() -> None:",
"def main(args=None):",
"def main(args=... | [
"0.84055316",
"0.84055316",
"0.84055316",
"0.8176242",
"0.8088535",
"0.80879116",
"0.8034941",
"0.8034941",
"0.8034941",
"0.8034941",
"0.7982359",
"0.7982359",
"0.7979266",
"0.7979266",
"0.7979266",
"0.7979266",
"0.79315084",
"0.7893428",
"0.78562397",
"0.7752687",
"0.7752687... | 0.0 | -1 |
Converts an array with WCS to altitude and azimuth coordinates | def getAltAz(arr,header,time,location):
soln = wcs.WCS(header)
coords = cartesian([arange(arr.shape[1]),arange(arr.shape[0])])
world = soln.wcs_pix2world(coords,0)
radec = SkyCoord(ra=world[:,0],dec=world[:,1],frame='icrs',unit='deg')
altaz = radec.transform_to(AltAz(obstime=time,location=telescope))
return altaz.alt.deg,altaz.az.deg,coords[:,0],coords[:,1] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def offset_to_altaz(xoff, yoff, azimuth, altitude):\n #Deal with situations where offset = 0?\n\n d = sqrt(xoff*xoff+yoff*yoff)\n pos = np.where(d==0)\n d=1e-12 * u.deg # add a very small offset to prevent math errors\n\n q = arctan(d.to(u.rad).value)\n\n sq = sin(q)\n xp1 = xoff * (sq/d)\n ... | [
"0.55827683",
"0.544639",
"0.5397533",
"0.53178024",
"0.53166306",
"0.52501696",
"0.5224251",
"0.52204317",
"0.5159298",
"0.5143273",
"0.5093226",
"0.5093226",
"0.5093226",
"0.50815976",
"0.50683326",
"0.506293",
"0.50404876",
"0.5039063",
"0.501149",
"0.5002514",
"0.4954378"... | 0.6210394 | 0 |
Gets all immediate subdirectories of directory | def getsubdir(directory):
subdir=[name for name in os.listdir(directory)
if os.path.isdir(os.path.join(directory, name))]
subdir=['/'+name+'/' for name in subdir if '.' not in name]
return subdir | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getImmediateSubdirectories(dir):",
"def get_immediate_subdirectories(self, a_dir):\n return [name for name in os.listdir(a_dir)\n if os.path.isdir(os.path.join(a_dir, name))]",
"def all_subdirs_of(dir='.'):\n result = []\n for item in os.listdir(dir):\n path = os.path.joi... | [
"0.8456564",
"0.7849883",
"0.75609636",
"0.7355576",
"0.72475153",
"0.7239605",
"0.7224201",
"0.7196618",
"0.71935165",
"0.7171549",
"0.7157886",
"0.7133028",
"0.71121323",
"0.71053064",
"0.706358",
"0.7032585",
"0.6998959",
"0.69399023",
"0.692623",
"0.6925599",
"0.68778616"... | 0.6162301 | 81 |
Run source extractor on f, producing a catalogue, and object and background maps | def sexcall(f,ddi,odi,cdi,bdi):
# Split to make file name for catalogue,
# object map and background map filenames
fname = f.split('.fits')[0]
# Construct source extractor call
objsexcall = 'sex -CATALOG_TYPE ASCII_HEAD -PARAMETERS_NAME photo.param -CATALOG_NAME '+cdi+fname+'.cat'+' -CHECKIMAGE_TYPE OBJECTS -CHECKIMAGE_NAME '+odi+fname+'_objects.fits '+ddi+f
baksexcall = 'sex -CATALOG_TYPE ASCII_HEAD -PARAMETERS_NAME photo.param -CATALOG_NAME '+cdi+fname+'.cat'+' -CHECKIMAGE_TYPE BACKGROUND -CHECKIMAGE_NAME '+bdi+fname+'_background.fits '+ddi+f
os.system(objsexcall)
os.system(baksexcall) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, vcffiles):\n self.vcffilenames = vcffiles\n self.snpsites = {}\n self.snp_positions = {}",
"def sources_extraction(image,sextractor_pars):\n\n cat_name, detect_minarea, detect_thresh, analysis_thresh, phot_aperture, satur_level, ZP, gain, pixelScale,seeing,back_type,back... | [
"0.5765961",
"0.5687466",
"0.5643927",
"0.5641508",
"0.56028205",
"0.5564399",
"0.5480887",
"0.5477202",
"0.53990245",
"0.53960085",
"0.5374448",
"0.5361827",
"0.532035",
"0.53024715",
"0.5297381",
"0.52936006",
"0.52374905",
"0.52320105",
"0.5228987",
"0.5202783",
"0.5200074... | 0.51135045 | 25 |
Creates a 2D histogram from data given by numpy's histogram | def hist2d(x,y,nbins = 50 ,maskval = 0,saveloc = '',labels=[],slope = 1,sloperr = 0):
# Remove NANs and masked values
good = where((isnan(x) == False) & (isnan(y) == False) & (x != maskval) & (y != maskval))
x = x[good]
y = y[good]
# Create histogram
H,xedges,yedges = histogram2d(x,y,bins=nbins)
# Reorient appropriately
H = rot90(H)
H = flipud(H)
# Mask zero value bins
Hmasked = ma.masked_where(H==0,H)
# Find average values in y:
yavgs = []
ystds = []
xposs = []
for j in range(len(xedges)-1):
toavg = where((x > xedges[j]) & (x < xedges[j+1]))
xpos = np.mean(x[toavg])
yavg = np.median(y[toavg])
ystd = np.std(y[toavg])/len(y[toavg])
xposs.append(xpos)
yavgs.append(yavg)
ystds.append(ystd)
# Begin creating figure
plt.figure(figsize=(12,10))
# Make histogram pixels with logscale
plt.pcolormesh(xedges,yedges,Hmasked,
norm = LogNorm(vmin = Hmasked.min(),
vmax = Hmasked.max()),
cmap = plt.get_cmap('Spectral_r'))
# Create fit line x-array
uplim = nmax(x)+5
dolim = nmin(x)-5
x_range = arange(dolim,uplim)
# Plot fit line
plt.plot(x_range,slope*x_range,color = 'royalblue',linewidth = 3,label = 'Slope = {0}, Uncertainty = {1}'.format(slope,sloperr))
# Plot average points
plt.errorbar(xposs,yavgs,yerr = ystds,fmt = 'D',color='k',markersize = 5)
# Set plot limits
plt.xlim(dolim+5,uplim-5)
plt.ylim(nmin(y),nmax(y))
# Add colourbar
cbar = plt.colorbar()
# Add labels
if labels != []:
title,xlabel,ylabel,zlabel = labels
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.title(title)
cbar.ax.set_ylabel(zlabel)
plt.legend(loc = 'best',fontsize = 15)
# Save plot
if saveloc != '':
plt.savefig(saveloc)
plt.close()
# Return histogram
return xedges,yedges,Hmasked | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _fast_hist_2d(data, bin_edges):\n # Yes, I've tested this against histogramdd().\n xassign = np.digitize(data[:,0], bin_edges[1:-1]) \n yassign = np.digitize(data[:,1], bin_edges[1:-1])\n nbins = len(bin_edges) - 1\n flatcount = np.bincount(xassign + yassign * nbins, minlength=nbins*nbins)\n ... | [
"0.7927253",
"0.71789026",
"0.7173408",
"0.71437126",
"0.70881116",
"0.7070723",
"0.70457834",
"0.6966444",
"0.69234824",
"0.68995667",
"0.6850321",
"0.6839149",
"0.68292195",
"0.6809204",
"0.6803134",
"0.66806215",
"0.66756934",
"0.66571724",
"0.6654381",
"0.66289043",
"0.66... | 0.0 | -1 |
Convert a hexadecimal string with leading hash into a three item list of values between [0, 1]. E.g. 00ff00 > [0, 1, 0] | def convert_hex_to_rgb(hex_string):
hex_string = hex_string.lstrip('#')
return [int(hex_string[i:i + 2], 16) / 255.0 for i in (0, 2, 4)] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def hex_to_RGB(hex_code: str) -> list:\n\n hex_code = hex_code.lstrip('#')\n return [int(hex_code[i:i + 2], 16) for i in (0, 2, 4)]",
"def hex_list(self):\r\n return [''.join(['{:02X}'.format(b) for b in data]) for data in self.buffers()]",
"def _string_to_bitlist(self, data):\n l = len(dat... | [
"0.6689272",
"0.6465506",
"0.63749844",
"0.63519144",
"0.6346904",
"0.63243943",
"0.62050515",
"0.61755127",
"0.6154368",
"0.615235",
"0.61361694",
"0.60561454",
"0.603794",
"0.6002749",
"0.60014415",
"0.5985288",
"0.5950709",
"0.5918056",
"0.58994037",
"0.58917654",
"0.58843... | 0.625158 | 6 |
Return the element of an atom as defined in it's label. | def xd_element(name):
try:
name = name[:2]
except:
pass
try:
covalence_radius[name]
except:
name = name[0]
return name | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_item_from_label(self, label):\n idx = self.labels.index(label)\n item = self[idx][0]\n return item",
"def get_label(urs):\n return assign_term(urs)[1]",
"def findLabel(self, label):\n return self.root._findLabel(label)",
"def fromLabel(name):\n return Data.labels... | [
"0.64875007",
"0.6234612",
"0.6180619",
"0.60778284",
"0.6046715",
"0.60378295",
"0.6036052",
"0.59951407",
"0.59556556",
"0.5930658",
"0.59231937",
"0.58469766",
"0.5837059",
"0.57520235",
"0.5750243",
"0.564846",
"0.561729",
"0.56039554",
"0.55868757",
"0.5572615",
"0.55563... | 0.52610904 | 65 |
Reads a .FChk file and returns a list containing the charge of the compound, the number of electrons in the compound, the overall lengths of the dipole moment vector and the total HF energy. | def get_compound_properties(path):
filepointer = open(path)
charge = None
NE = None
E_HF = None
dipole = None
read_dipole = False
for line in filepointer:
if read_dipole:
read_dipole = False
dipole = [float(value) for value in line.split(' ') if '.' in value]
dipole = np.linalg.norm(dipole)
elif 'Charge' in line and not charge:
charge = line.split(' ')[-1].rstrip('\n')
elif 'Number of electrons' in line and not NE:
NE = line.split(' ')[-1].rstrip('\n')
elif 'Total Energy' in line and not E_HF:
E_HF = line.split(' ')[-1].rstrip('\n')
elif 'Dipole Moment' in line and not dipole:
read_dipole = True
if charge and NE and E_HF and dipole:
break
return [charge, NE, dipole, E_HF] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read_forces(self):\n #\"\"\"Read Forces from dftb output file (results.tag).\"\"\"\n\n from ase.units import Hartree, Bohr\n\n myfile = open(os.path.join(self.directory, 'detailed.out'), 'r')\n self.lines = myfile.readlines()\n myfile.close()\n\n # Force line indexes\n... | [
"0.61628425",
"0.6071108",
"0.5977757",
"0.5936052",
"0.5908581",
"0.5907671",
"0.5888301",
"0.5856573",
"0.5693422",
"0.5663114",
"0.56423414",
"0.5607148",
"0.55911905",
"0.550648",
"0.55034184",
"0.5458108",
"0.544729",
"0.54410064",
"0.54253936",
"0.5411036",
"0.538077",
... | 0.5689103 | 9 |
Returns the bond order between two atoms. | def bond_order(bondxi,
threshold_single_meso=0.0847,
# ================================================================
# threshold_meso_double=0.184,
#================================================================
threshold_meso_double=0.0847,
threshold_double_triple=0.27):
if bondxi < threshold_single_meso:
order = '1'
elif bondxi < threshold_meso_double:
order = '1.5'
elif bondxi < threshold_double_triple:
order = '2'
else:
order = '3'
return order | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_bond_order(molecule, bond_index):\n return molecule.GetBondOrder(bond_index)",
"def get_bond_order(bond_type):\n if bond_type == 'single':\n return 1\n elif bond_type == 'aromatic':\n return 1.5\n elif bond_type == 'double':\n return 2\n elif bond_type == 'triple':\n ... | [
"0.7267477",
"0.66433895",
"0.61120665",
"0.61120355",
"0.5785895",
"0.5775953",
"0.563049",
"0.5494583",
"0.5392209",
"0.5384048",
"0.5254621",
"0.52386093",
"0.5236131",
"0.5225658",
"0.52222705",
"0.51403373",
"0.51322603",
"0.5125893",
"0.508684",
"0.5077968",
"0.50548816... | 0.5774481 | 6 |
Rotates the ADP of 'atom' to match the orientation of 'source_atom. | def rotate_3D(atom, source_atom):
from lauescript.cryst.match import get_transform
lst2 = [np.array([0, 0, 0]), source_atom.orientation[0], source_atom.orientation[1]]
lst1 = [np.array([0, 0, 0]), atom.orientation[0], atom.orientation[1]]
matrix = get_transform(lst1, lst2, matrix=True)
adp = source_atom.adp['cart_int']
atom.adp['cart_int'] = rotate_adp(adp, matrix) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def rotation_alignment(referent_shape, current_shape):\n numerator = 0.\n denominator = 0.\n\n for i in range(len(referent_shape.points)):\n numerator += current_shape.points[i, 0] * referent_shape.points[i, 1] - current_shape.points[i, 1] * referent_shape.points[i, 0]\n denominator += curre... | [
"0.54740787",
"0.54063845",
"0.53933036",
"0.5287055",
"0.5184366",
"0.5183668",
"0.5156844",
"0.50909144",
"0.50790036",
"0.50740695",
"0.50218683",
"0.50043344",
"0.499874",
"0.49680153",
"0.49113643",
"0.49104938",
"0.4907334",
"0.489211",
"0.48913074",
"0.48873633",
"0.48... | 0.6927287 | 0 |
Calculates the bond distinguishing parameter Xi. | def xi(element1, element2, distance):
return (float(covalence_radius[element1]) + float(covalence_radius[element2]) -
(0.08 * float(abs(electro_negativ[element1] - electro_negativ[element2]))) - distance) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def Insurance(Md,X):\n u = X[iu]\n b = Md.b()\n return u/b - u/(1-u+u*b)",
"def Insurance(Md,X):\n if VERSION == 16:\n utemp = 0.0*X[iu]+1.0*Md.ubar\n elif VERSION == 31:\n utemp = 1.25*(X[iu]-Md.ubar)+1.0*Md.ubar\n else:\n utemp = X[iu]\... | [
"0.5771887",
"0.5497939",
"0.5485339",
"0.54537404",
"0.5412243",
"0.52982736",
"0.5246974",
"0.5218475",
"0.5195119",
"0.51869047",
"0.5173168",
"0.5159012",
"0.5122866",
"0.51090294",
"0.5108821",
"0.509033",
"0.5054854",
"0.5019761",
"0.50018966",
"0.4988922",
"0.49803066"... | 0.50392056 | 17 |
Function to identify atoms belonging to a previosly defined rigid group. | def framework_crawler(atom, direction, rigid_group_old=None):
if not rigid_group_old:
rigid_group = [atom, direction]
else:
rigid_group = rigid_group_old
for atom in get_framework_neighbours(direction):
if not atom in rigid_group and not atom.element == 'H':
rigid_group.append(atom)
framework_crawler(rigid_group[0], atom, rigid_group)
if not rigid_group_old:
#=======================================================================
# print ' Determined rigid group:', [i.name for i in rigid_group]
#=======================================================================
return rigid_group | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_group(group):\n # Get the true classification from the longest reads\n true_species = group[group['file'].eq(f'OG_reads_{sample_letter}')]['classification'].iloc[0]\n print(true_species)\n # return a 1 if it's true across the group and 0 if not\n group['positives']= np.where(group['classif... | [
"0.53478336",
"0.526648",
"0.52470756",
"0.5181944",
"0.51471263",
"0.51189107",
"0.50924",
"0.50924",
"0.5078987",
"0.5077038",
"0.505254",
"0.4997841",
"0.4995758",
"0.49540886",
"0.49455371",
"0.49252045",
"0.49165574",
"0.48953816",
"0.48634696",
"0.48486274",
"0.48388016... | 0.52450806 | 3 |
Returns the atom with the shortest distance to the given atom. | def get_closest_atom_of_element(element, atom, exclude=None):
for atom2 in atom.partner:
if (element == atom2.element or not element) and not atom2 == exclude:
return atom2 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def shortest_distance_to(self, pt):\n return self._nearest_to_point(pt)[0]",
"def _minimum_distance(self,arg):\n return min([abs(arg-e) for e in self if not e is arg])",
"def get_min_distance(self, node):\r\n if self.have_min_distance(node):\r\n return self.table[node][\"dist\"]... | [
"0.6927831",
"0.637707",
"0.6341551",
"0.6167055",
"0.61642516",
"0.60590255",
"0.59727126",
"0.595667",
"0.59553444",
"0.5929634",
"0.5880962",
"0.5837155",
"0.58368987",
"0.5720252",
"0.5698188",
"0.56682664",
"0.5647115",
"0.56391704",
"0.56333494",
"0.5632058",
"0.5616186... | 0.5314012 | 39 |
Needs a ATOM.atom instance as argument. Returns the names of the framework atoms bound to that atom. | def get_framework_neighbours(atom, useH=True):
neighbourlist = []
for atom2 in atom.partner[:5]:
#if not 'H(' in atom2.name and np.linalg.norm(atom.cart-atom2.cart)<=1.6:
if np.linalg.norm(atom.cart - atom2.cart) <= float(covalence_radius[atom.element]) + float(
covalence_radius[atom2.element]) + .1:
if not 'H' == atom2.element or useH:
neighbourlist.append(atom2)
return neighbourlist | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_atom_labels(self, full=False):\n import numpy\n\n labels = self.get_attr(\"atom_labels\")\n if full:\n return labels\n return numpy.array(labels)[self._get_equivalent_atom_list()].tolist()",
"def getAtomNames(self):\n return self._raw_data['ATOM_NAME']",
"d... | [
"0.5786468",
"0.5760434",
"0.5465059",
"0.54395527",
"0.5285232",
"0.52612984",
"0.52286106",
"0.5170458",
"0.5095022",
"0.5048037",
"0.5035679",
"0.5034012",
"0.49822837",
"0.49687123",
"0.4942803",
"0.49194297",
"0.49104977",
"0.49047342",
"0.4869437",
"0.48587328",
"0.4852... | 0.45080042 | 97 |
Reads the measured ADP from the xd.res file. The parameters are stored in atom.adp['frac_meas'] and atom.adp['cart_meas'] | def read_meas_adp(data, path='xd.res', use='meas'):
use2 = 'frac_' + use
switch = False
filepointer = open(path, 'r')
atomname = None
for line in filepointer:
if switch:
split = [i for i in line.split(' ') if len(i) > 0]
if not len(split) == 6:
print('WARNING!!! Inconsistend number of floats while\
reading measured ADP.')
data['exp'][atomname].adp[use2] = split
switch = False
if '(' in line:
split = [i for i in line.split(' ') if len(i) > 0]
if split[0][-1] == ')':
switch = True
atomname = split[0]
use = 'cart_' + use
for atom in data['exp'].atoms:
# if use == 'cart_neut': print(atom)
atom.adp[use] = rotate_adp2(atom.adp[use2],
atom.molecule.frac2cartmatrix,
atom.molecule.cell)
return data | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def R_adp(data):\n printer('S_adp = ?')\n printer('R_adp = | (U_iso_xxx - U_iso_obs) / U_iso_obs |')\n printer('mean = sum((U_iso_xxx - U_iso_obs) / U_iso_obs) / n')\n printer('abs = sum(R_adp) / n\\n')\n printer('(geometric mean is used)\\n')\n\n printer(' | ADP_calc / ADP_obs | APD_tl... | [
"0.58027154",
"0.546091",
"0.5428187",
"0.5426959",
"0.5236421",
"0.5206721",
"0.5204981",
"0.5189248",
"0.5178479",
"0.51778173",
"0.51361907",
"0.5132914",
"0.51140034",
"0.51137125",
"0.5094768",
"0.505379",
"0.5024168",
"0.49520048",
"0.49162722",
"0.4861708",
"0.48393014... | 0.78116286 | 0 |
Returns the ADP after reflection on the plane defined by its normal vector 'planev'. | def reflect_adp(adp, planev):
M = np.identity(4)
M[:3, :3] -= 2.0 * np.outer(planev, planev)
M[:3, 3] = (2.0 * np.dot(np.array([0, 0, 0]), planev)) * planev
return rotate_adp(adp, M[:3, :3]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def plane(self):\n return Plane(Point(0, self.evaluations.exposedWing.edges[2].point1.y, 0), Vector(0, 1, 0),\n hidden=True)",
"def GetPlane(plane):\r\n pass",
"def get_adp_from_calc(vx, vy, vz):\n ## lx=np.linalg.norm(vx)\n ## ly=np.linalg.norm(vy)\n ## lz=n... | [
"0.6131728",
"0.57741225",
"0.56889206",
"0.56324",
"0.55201805",
"0.55160964",
"0.5514133",
"0.5479282",
"0.5462305",
"0.5436542",
"0.52982074",
"0.52627504",
"0.52521896",
"0.5238149",
"0.52197284",
"0.52163255",
"0.51973253",
"0.5194445",
"0.5152425",
"0.5139117",
"0.51376... | 0.6869817 | 0 |
Calculates the tensor representation of ADP from its priciple axis. | def eigenv2tensor(axis):
vec = np.ones((3, 3))
vecval = np.ones((3, 3))
for i in xrange(len(axis)):
vmag = np.linalg.norm(axis[i])
v = axis[i] / vmag
#print v
vec[:, i] = v
vecval[:, i] = axis[i]
adp = np.linalg.solve(vec, vecval)
return adp | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ADP (self):",
"def get_axis(adp):\n adp = np.matrix([[float(adp[0]), float(adp[3]), float(adp[4])],\n [float(adp[3]), float(adp[1]), float(adp[5])],\n [float(adp[4]), float(adp[5]), float(adp[2])]])\n w, v = np.linalg.eig(adp)\n return [np.... | [
"0.6216488",
"0.60510916",
"0.60209346",
"0.58952725",
"0.5770266",
"0.5735157",
"0.55482674",
"0.5510566",
"0.53965545",
"0.5327899",
"0.5300603",
"0.5298402",
"0.52585346",
"0.5229013",
"0.520525",
"0.5193558",
"0.51586944",
"0.51218873",
"0.50987124",
"0.5092296",
"0.50793... | 0.56617206 | 6 |
Calculates an ADP in its matrix representation from the three principle axis representing the displacement ellipsoid. The three principle axis of the ellipsoid are needed as arguments. A Matrix representation of the ADP is returned. | def get_adp_from_calc(vx, vy, vz):
## lx=np.linalg.norm(vx)
## ly=np.linalg.norm(vy)
## lz=np.linalg.norm(vz)
lx = vx
ly = vy
lz = vz
L = np.matrix([[lx, 0, 0],
[0, ly, 0],
[0, 0, lz]])
## Vx=vx/lx
## Vy=vy/ly
## Vz=vz/lz
Vx = np.array([1, 0, 0])
Vy = np.array([0, 1, 0])
Vz = np.array([0, 0, 1])
V = np.matrix([[Vx[0], Vy[0], Vz[0]],
[Vx[1], Vy[1], Vz[1]],
[Vx[2], Vy[2], Vz[2]]])
Vinv = np.linalg.inv(V)
#print V,Vinv
M = np.dot(np.dot(Vinv, L), V)
#print M
return M | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def A_coefficients_ellipsoid(v, DD, bDDisDelta=False):\n #v can be given as an array with X/Y/Z cartesian dimensions being the last.\n #\"\"\"\n if bDDisDelta:\n delta=DD\n else:\n delta=Ddelta_ellipsoid(dd)\n #v=_sanitise_v(v)\n #v2=np.square(v)\n #v4=np.square(v2)\n #fact2=n... | [
"0.6180545",
"0.5795742",
"0.5749824",
"0.56150705",
"0.55909604",
"0.55889744",
"0.5540044",
"0.5520576",
"0.5389443",
"0.53017646",
"0.52529544",
"0.5236133",
"0.5234051",
"0.52170014",
"0.5192699",
"0.5171544",
"0.51684576",
"0.5142525",
"0.51380336",
"0.5134151",
"0.51299... | 0.59788334 | 1 |
Determines the the quaternion representing the best possible transformation of two coordinate systems into each other using a least sqare approach. This function is used by the get_refined_rotation() function. | def get_best_quaternion(coordlist1, coordlist2):
M = np.matrix([[0, 0, 0], [0, 0, 0], [0, 0, 0]])
if len(coordlist1) <= len(coordlist2):
number = len(coordlist1)
else:
number = len(coordlist2)
for i in xrange(number):
aaa = np.matrix(np.outer(coordlist1[i], coordlist2[i]))
M = M + aaa
N11 = float(M[0][:, 0] + M[1][:, 1] + M[2][:, 2])
N22 = float(M[0][:, 0] - M[1][:, 1] - M[2][:, 2])
N33 = float(-M[0][:, 0] + M[1][:, 1] - M[2][:, 2])
N44 = float(-M[0][:, 0] - M[1][:, 1] + M[2][:, 2])
N12 = float(M[1][:, 2] - M[2][:, 1])
N13 = float(M[2][:, 0] - M[0][:, 2])
N14 = float(M[0][:, 1] - M[1][:, 0])
N21 = float(N12)
N23 = float(M[0][:, 1] + M[1][:, 0])
N24 = float(M[2][:, 0] + M[0][:, 2])
N31 = float(N13)
N32 = float(N23)
N34 = float(M[1][:, 2] + M[2][:, 1])
N41 = float(N14)
N42 = float(N24)
N43 = float(N34)
N = np.matrix([[N11, N12, N13, N14],
[N21, N22, N23, N24],
[N31, N32, N33, N34],
[N41, N42, N43, N44]])
values, vectors = np.linalg.eig(N)
w = list(values)
quat = vectors[:, w.index(max(w))]
quat = np.array(quat).reshape(-1, ).tolist()
return quat, max(w) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def find_best_rotation(q1, q2, allow_reflection = False, only_xy = False):\n if q1.ndim != 2 or q2.ndim != 2:\n raise Exception(\"This only supports curves of shape (N,M) for N dimensions and M samples\")\n\n n = q1.shape[0]\n\n # if only_xy, strip everything but the x and y coordinates of q1 and q... | [
"0.71130097",
"0.6103423",
"0.60989857",
"0.6034236",
"0.5984659",
"0.5808696",
"0.5793859",
"0.5789661",
"0.57519877",
"0.57339966",
"0.5732701",
"0.56862444",
"0.56858575",
"0.56503344",
"0.563665",
"0.5622966",
"0.554343",
"0.5540692",
"0.5537474",
"0.55108356",
"0.5505729... | 0.6384881 | 1 |
Returns the rotation matrix equivalent of the given quaternion. This function is used by the get_refined_rotation() function. | def get_rotation_matrix_from_quaternion(q):
R = np.matrix([[q[0] * q[0] + q[1] * q[1] - q[2] * q[2] - q[3] * q[3],
2 * (q[1] * q[2] - q[0] * q[3]),
2 * (q[1] * q[3] + q[0] * q[2])],
[2 * (q[2] * q[1] + q[0] * q[3]),
q[0] * q[0] - q[1] * q[1] + q[2] * q[2] - q[3] * q[3],
2 * (q[2] * q[3] - q[0] * q[1])],
[2 * (q[3] * q[1] - q[0] * q[2]),
2 * (q[3] * q[2] + q[0] * q[1]),
q[0] * q[0] - q[1] * q[1] - q[2] * q[2] + q[3] * q[3]]])
return R | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def quaternion_to_rotation_matrix(quaternion):\n\n q_w, q_x, q_y, q_z = quaternion\n sqw, sqx, sqy, sqz = np.square(quaternion)\n norm = (sqx + sqy + sqz + sqw)\n rotation_matrix = np.zeros((3, 3))\n\n # division of square length if quaternion is not already normalized\n rotation_matrix[0, 0] = (... | [
"0.8077829",
"0.79751414",
"0.7973847",
"0.797261",
"0.79455817",
"0.79306656",
"0.79097867",
"0.780534",
"0.7727341",
"0.77201724",
"0.77022475",
"0.7419111",
"0.74067664",
"0.7311962",
"0.7208562",
"0.7142397",
"0.71323454",
"0.71113116",
"0.70910096",
"0.7048041",
"0.69940... | 0.8253612 | 0 |
Calculates the geometrical center of a set of points. | def get_geom_center(coordlist):
return sum(coordlist) / len(coordlist) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def centre_of_points(list_of_points):\n\n cp = np.average(list_of_points, axis=0)\n return cp",
"def pointcenter(x):\n return point(x)",
"def center(self):\n points = set()\n for face in self._points:\n points.update(face)\n x_points = [point[0] for point in points]\n ... | [
"0.7816311",
"0.7496035",
"0.74138576",
"0.7411869",
"0.735026",
"0.73026866",
"0.72750986",
"0.7227958",
"0.7207048",
"0.7157561",
"0.71556854",
"0.7141065",
"0.71169",
"0.70823437",
"0.7073579",
"0.70721674",
"0.7064834",
"0.7059459",
"0.70201606",
"0.7017879",
"0.7004704",... | 0.75747705 | 1 |
Moves the geometrical center of the atoms in atomlist to the given point. | def move_center_to_point(atomlist, point):
for atom in range(len(atomlist)):
atomlist[atom] = atomlist[atom] - point
return atomlist | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def recenter(self, point=(0, 0)):\n self.center = Point(*point)",
"def centerOn(self, point):\n rect = self.rect()\n x = point.x() - rect.width() / 2.0\n y = point.y() - rect.height() / 2.0\n \n self.setPos(x, y)",
"def centerOnPoint(self, point):\n\n inClass = ... | [
"0.70135695",
"0.6748774",
"0.6072873",
"0.60708416",
"0.60040206",
"0.59520334",
"0.5908273",
"0.5892884",
"0.57873654",
"0.5775457",
"0.57008934",
"0.56635845",
"0.5663209",
"0.5656083",
"0.5635438",
"0.5621651",
"0.5585128",
"0.5584832",
"0.5545488",
"0.55017734",
"0.54478... | 0.8722849 | 0 |
Rotates the adp with its corresponding rotation matrix. | def rotate_adp_reverse(adp, rotmat):
adp = np.matrix([[float(adp[0]), float(adp[3]), float(adp[4])],
[float(adp[3]), float(adp[1]), float(adp[5])],
[float(adp[4]), float(adp[5]), float(adp[2])]])
rotmatT = np.transpose(rotmat)
adp = np.dot(rotmat, adp)
adp = np.dot(adp, rotmatT)
adp = np.array(adp).flatten().tolist()
return [adp[0], adp[4], adp[8], adp[1], adp[2], adp[5]] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def rotate_adp(adp, rotmat):\n\n adp = np.matrix([[float(adp[0]), float(adp[3]), float(adp[4])],\n [float(adp[3]), float(adp[1]), float(adp[5])],\n [float(adp[4]), float(adp[5]), float(adp[2])]])\n rotmatT = np.transpose(rotmat)\n adp = np.dot(rotmatT, adp)\n adp... | [
"0.79376036",
"0.73167443",
"0.73074406",
"0.7229753",
"0.7113275",
"0.7051355",
"0.6992302",
"0.6686556",
"0.66458803",
"0.66007537",
"0.657009",
"0.6538239",
"0.6492567",
"0.64686793",
"0.6467943",
"0.6442462",
"0.64174455",
"0.63961196",
"0.6388264",
"0.63866466",
"0.63760... | 0.7127177 | 4 |
Rotates the adp with its corresponding rotation matrix. | def rotate_adp(adp, rotmat):
adp = np.matrix([[float(adp[0]), float(adp[3]), float(adp[4])],
[float(adp[3]), float(adp[1]), float(adp[5])],
[float(adp[4]), float(adp[5]), float(adp[2])]])
rotmatT = np.transpose(rotmat)
adp = np.dot(rotmatT, adp)
adp = np.dot(adp, rotmat)
# print '=\n',adp,'\n-------------------------------------------------\n\n\n\n\n\n'
adp = np.array(adp).flatten().tolist()
return [adp[0], adp[4], adp[8], adp[1], adp[2], adp[5]] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def rotate(mat,angle):\n return np.dot(Mueller.rotator(angle), np.dot(mat, Mueller.rotator(-angle)))",
"def rotate_adp2(adp, rotmat, cell):\n adp = np.matrix([[float(adp[0]), float(adp[3]), float(adp[4])],\n [float(adp[3]), float(adp[1]), float(adp[5])],\n [float... | [
"0.73167443",
"0.73074406",
"0.7229753",
"0.7127177",
"0.7113275",
"0.7051355",
"0.6992302",
"0.6686556",
"0.66458803",
"0.66007537",
"0.657009",
"0.6538239",
"0.6492567",
"0.64686793",
"0.6467943",
"0.6442462",
"0.64174455",
"0.63961196",
"0.6388264",
"0.63866466",
"0.637600... | 0.79376036 | 0 |
Rotates the adp with its corresponding rotation matrix. | def rotate_adp2(adp, rotmat, cell):
adp = np.matrix([[float(adp[0]), float(adp[3]), float(adp[4])],
[float(adp[3]), float(adp[1]), float(adp[5])],
[float(adp[4]), float(adp[5]), float(adp[2])]])
rotmat = np.linalg.inv(rotmat)
rotmatT = np.transpose(rotmat)
Nmat = np.matrix([[1 / cell[0], 0, 0],
[0, 1 / cell[1], 0],
[0, 0, 1 / cell[2]]])
Nmat = np.linalg.inv(Nmat)
NmatT = np.transpose(Nmat)
adp = np.dot(rotmat, adp)
adp = np.dot(adp, rotmatT)
adp = np.dot(Nmat, adp)
adp = np.dot(adp, NmatT)
adp = np.array(adp).flatten().tolist()
return [adp[0], adp[4], adp[8], adp[1], adp[2], adp[5]] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def rotate_adp(adp, rotmat):\n\n adp = np.matrix([[float(adp[0]), float(adp[3]), float(adp[4])],\n [float(adp[3]), float(adp[1]), float(adp[5])],\n [float(adp[4]), float(adp[5]), float(adp[2])]])\n rotmatT = np.transpose(rotmat)\n adp = np.dot(rotmatT, adp)\n adp... | [
"0.79376036",
"0.73167443",
"0.7229753",
"0.7127177",
"0.7113275",
"0.7051355",
"0.6992302",
"0.6686556",
"0.66458803",
"0.66007537",
"0.657009",
"0.6538239",
"0.6492567",
"0.64686793",
"0.6467943",
"0.6442462",
"0.64174455",
"0.63961196",
"0.6388264",
"0.63866466",
"0.637600... | 0.73074406 | 2 |
Rotates the adp with its corresponding rotation matrix. | def rotate_adp3(adp, rotmat, cell):
adp = np.matrix([[float(adp[0]), float(adp[3]), float(adp[4])],
[float(adp[3]), float(adp[1]), float(adp[5])],
[float(adp[4]), float(adp[5]), float(adp[2])]])
rotmati = np.matrix(rotmat)
rotmatiT = np.transpose(rotmati)
rotmat = np.linalg.inv(rotmat)
Nmat = np.matrix([[1 / cell[0], 0, 0],
[0, 1 / cell[1], 0],
[0, 0, 1 / cell[2]]])
Nmat = np.linalg.inv(Nmat)
NmatT = np.transpose(Nmat)
adp = np.dot(rotmati, adp)
adp = np.dot(adp, rotmatiT)
adp = np.dot(Nmat, adp)
adp = np.dot(adp, NmatT)
adp = np.array(adp).flatten().tolist()
return [adp[0], adp[4], adp[8], adp[1], adp[2], adp[5]] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def rotate_adp(adp, rotmat):\n\n adp = np.matrix([[float(adp[0]), float(adp[3]), float(adp[4])],\n [float(adp[3]), float(adp[1]), float(adp[5])],\n [float(adp[4]), float(adp[5]), float(adp[2])]])\n rotmatT = np.transpose(rotmat)\n adp = np.dot(rotmatT, adp)\n adp... | [
"0.79376036",
"0.73167443",
"0.73074406",
"0.7127177",
"0.7113275",
"0.7051355",
"0.6992302",
"0.6686556",
"0.66458803",
"0.66007537",
"0.657009",
"0.6538239",
"0.6492567",
"0.64686793",
"0.6467943",
"0.6442462",
"0.64174455",
"0.63961196",
"0.6388264",
"0.63866466",
"0.63760... | 0.7229753 | 3 |
Returns a list of coordinates where every position is rotated by the the rotation matrix 'R'. | def rotate_list_by(coordlist, R):
for coord in xrange(len(coordlist)):
value = np.dot(R, coordlist[coord])
value = np.array(value).reshape(-1, ).tolist()
coordlist[coord] = value
return coordlist | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _rotation_matrix_to_euler_angles(R):\n sy = math.sqrt(R[0, 0] * R[0, 0] + R[1, 0] * R[1, 0])\n\n singular = sy < 1e-6\n\n if not singular:\n x = math.atan2(R[2, 1], R[2, 2])\n y = math.atan2(-R[2, 0], sy)\n z = math.atan2(R[1, 0], R[0, 0])\n else:\n x = math.atan2(-R[1, ... | [
"0.72974885",
"0.7189403",
"0.7180745",
"0.6643963",
"0.6516557",
"0.65161246",
"0.6515442",
"0.65101385",
"0.64101136",
"0.6373137",
"0.63658345",
"0.63429064",
"0.6326192",
"0.6319467",
"0.6302939",
"0.6279795",
"0.62754506",
"0.6256248",
"0.622159",
"0.6209498",
"0.6200936... | 0.7185248 | 2 |
Returns the rotation matrix that rotates a vector around the given axis by the given angle using the "EulerRodrigues formula". | def get_3drotation_matrix(axis, angle):
angle = angle #*-1
norm = np.linalg.norm(np.array(axis))
if norm > 0:
axis /= norm
ax, ay, az = axis[0], axis[1], axis[2]
cos, sin = np.cos(angle), np.sin(angle)
rotmat = np.array([[cos + ax * ax * (1 - cos), ax * ay * (1 - cos) - az * sin, ax * az * (1 - cos) + ay * sin],
[ay * ax * (1 - cos) + az * sin, cos + ay * ay * (1 - cos), ay * az * (1 - cos) - ax * sin],
[az * ax * (1 - cos) - ay * sin, az * ay * (1 - cos) + ax * sin, cos + az * az * (1 - cos)]])
return rotmat | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def rotateEuler(axis, angle):\n if(axis == 'Z'):\n return np.array([[cos(angle), -sin(angle),0,0],[sin(angle), cos(angle),0,0],[0,0,1,0],[0,0,0,1]])\n if(axis == 'Y'):\n return np.array([[cos(angle),0,sin(angle),0],[0,1,0,0],[-sin(angle),0,cos(angle),0],[0,0,0,1]])\n if(axis == 'X'):\n ... | [
"0.7911063",
"0.788436",
"0.7837863",
"0.77911395",
"0.77726126",
"0.77274466",
"0.7598185",
"0.75942045",
"0.753307",
"0.7435765",
"0.7434122",
"0.7428991",
"0.74156046",
"0.7338308",
"0.732471",
"0.7321008",
"0.7264772",
"0.71720546",
"0.7159397",
"0.71509737",
"0.7136716",... | 0.7117221 | 21 |
Returns the normal vector of a plane defined by the points p1,p2 and p3. | def get_normal_vector_of_plane(p1, p2, p3):
v12 = np.array(p1) - np.array(p2)
v13 = np.array(p1) - np.array(p3)
nvec = np.cross(v12, v13)
## print 'norm: '+str(np.linalg.norm(nvec))
return nvec / np.linalg.norm(nvec) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def normal(self) -> Vec:\n # The three points are in clockwise order, so compute differences\n # in the clockwise direction, then cross to get the normal.\n point_1 = self.planes[1] - self.planes[0]\n point_2 = self.planes[2] - self.planes[1]\n\n return Vec.cross(point_1, point_2... | [
"0.7993883",
"0.75612503",
"0.7551127",
"0.75036335",
"0.73087484",
"0.70266545",
"0.6976466",
"0.69476783",
"0.69432104",
"0.68638694",
"0.6826915",
"0.6760271",
"0.67263806",
"0.670698",
"0.66756666",
"0.66190445",
"0.65483314",
"0.6541516",
"0.6535791",
"0.6529659",
"0.651... | 0.88373834 | 0 |
Returns a list where every element is a list of three atomnames. The second and third names are the closest neighbours of the first names. The argument is a list as returned by frac_to_cart and the number of neighbours to be returned. | def get_closest_neighbours(atomlist, neighbours=2):
print('atomlist', atomlist)
neighbourlist = []
for atom in atomlist:
listline = [atom[0][0]]
dists = []
distsc = []
for partner in atomlist:
dists.append(np.linalg.norm(atom[1] - partner[1]))
distsc.append(np.linalg.norm(atom[1] - partner[1]))
dists.remove(min(dists))
for _ in range(neighbours):
if min(dists) < 2.5:
listline.append(atomlist[distsc.index(min(dists))][0][0])
dists.remove(min(dists))
#listline.append(atomlist[distsc.index(min(dists))][0][0])
neighbourlist.append(listline)
return neighbourlist | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def find_CX_neighbours(list_of_atoms, atom_list):\n my_list = []\n atom_numbers = []\n for atom in list_of_atoms:\n for element in identify_bonds(atom, atom_list):\n if (((element[0].atom_name == \"CX\") or (element[0].atom_name == \"CY\")) and (element[0].atom_number not in atom_numbers... | [
"0.5994788",
"0.56360227",
"0.56093794",
"0.55970883",
"0.54962254",
"0.54728734",
"0.5409299",
"0.53933924",
"0.5324893",
"0.53109133",
"0.5306111",
"0.5260367",
"0.5244169",
"0.5237841",
"0.5218403",
"0.5203575",
"0.5183413",
"0.5180326",
"0.5171642",
"0.5169028",
"0.514798... | 0.67386407 | 0 |
Calculates for every atom the distances to all other atoms in atomlist. Returns a list where every element is a list of all distances. | def calculate_distance_matrix(atomlist):
distlist = []
for atom in atomlist:
atomdict = {}
for partner in atomlist:
if not str(int(partner[0][1])) in atomdict.keys():
atomdict[str(int(partner[0][1]))] = []
atomdict[str(int(partner[0][1]))].append(np.linalg.norm(atom[1] - partner[1]))
else:
atomdict[str(int(partner[0][1]))].append(np.linalg.norm(atom[1] - partner[1]))
atomdict[str(int(partner[0][1]))].sort()
distlist.append(atomdict)
return distlist | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _compute_distances(self, atoms: List[CellAtom]):\n muon = self._cell_atoms[self._muon_index]\n\n for atom in atoms:\n atom.distance_from_muon = np.linalg.norm(muon.position - atom.position)",
"def calcDistanceList(work_list):\n distance_list = []\n for swap in work_list... | [
"0.73647845",
"0.7127275",
"0.7080569",
"0.67939436",
"0.66801125",
"0.66210407",
"0.6395463",
"0.6352536",
"0.60293406",
"0.59641975",
"0.5953034",
"0.5883599",
"0.5874572",
"0.5861031",
"0.5844821",
"0.5841001",
"0.5830898",
"0.5818882",
"0.5806686",
"0.57949203",
"0.578432... | 0.78830993 | 0 |
The function is able to identify equal atoms of one molecule in different coordinate systems independent of the molecule's orientaion. | def link_atoms_by_distance(distlist1, atomlist1, distlist2, atomlist2, keys):
hitlist = []
for atom in distlist1:
atomtype = int(atomlist1[distlist1.index(atom)][0][1])
valuelist = []
for partner in distlist2:
partnertype = int(atomlist2[distlist2.index(partner)][0][1])
if atomtype == partnertype:
partnervalue = 0
keylist = partner.keys()
for key in keylist:
for element in xrange(len(atom[key])):
partnervalue += abs(atom[key][element] - partner[key][element])
else:
partnervalue = 9999999
valuelist.append(partnervalue)
minvalue = min(valuelist)
besthit = valuelist.index(minvalue)
hitlist.append(besthit) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_isomorphic_general(self):\n # check that hill formula fails are caught\n ethanol = create_ethanol()\n acetaldehyde = create_acetaldehyde()\n assert ethanol.is_isomorphic_with(acetaldehyde) is False\n assert acetaldehyde.is_isomorphic_with(ethanol) is False\n # che... | [
"0.6392542",
"0.63092023",
"0.6111575",
"0.6041727",
"0.6018316",
"0.6011343",
"0.60099506",
"0.6008797",
"0.5996518",
"0.59733444",
"0.5879561",
"0.58772373",
"0.586719",
"0.58652085",
"0.5841096",
"0.58306956",
"0.58160913",
"0.5800217",
"0.5774613",
"0.5731002",
"0.5731002... | 0.0 | -1 |
Determines the atoms defining the chemical enviroment of a given atom by checking their bonding partners. Only the first and second neighbours are considered. | def get_influence_atoms(atomlist):
enviromentlist = []
trunclist = []
neighbourlist = get_closest_neighbours(atomlist, 4)
for neighbours in neighbourlist:
if neighbours[0][0] == "H":
neighbours = neighbours[:2]
if neighbours[0][0] == "O":
neighbours = neighbours[:3]
trunclist.append(neighbours)
for atom in trunclist:
newatom = []
for atom1partner in atom[1:]:
for partner in trunclist:
if partner[0] == atom1partner:
counter = 0
for atomi in partner:
if atomi[0] == 'H':
counter += 1
if counter < 2 or (partner[0] in atom and atom[0][0] == 'H'):
newatom += atom + partner[1:]
newatom = make_list_unique(newatom)
newatom.sort()
enviromentlist.append(newatom)
return enviromentlist | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def identify_bonds(chosen_atom, atom_list):\n list_of_hydrogens = ['H15', 'H14', 'H13', 'H12', 'H11', 'H10', 'H9', 'H8', 'H7', 'H6', 'H5', 'H4', 'H3', 'H2', 'H1'] \n if ((chosen_atom.atom_name not in list_of_hydrogens) and (chosen_atom.residue_name != \"P1A\")):\n nearby_atoms_crude = [atom for ato... | [
"0.6319188",
"0.6093581",
"0.6006863",
"0.5989993",
"0.592804",
"0.59155035",
"0.58293885",
"0.56470066",
"0.5599455",
"0.5575535",
"0.5540133",
"0.54949856",
"0.5488254",
"0.54558873",
"0.54485685",
"0.5412311",
"0.54049903",
"0.5360718",
"0.5342761",
"0.5333482",
"0.5312497... | 0.60876876 | 2 |
The function is able to identify equivalent atoms in different molecules in different coordinate systems independent of the molecule's orientaion. | def link_atoms_by_distance_diff(distlist1, atomlist1, distlist2, atomlist2, keys):
hitlist = []
for atom in distlist1:
atomtype = int(atomlist1[distlist1.index(atom)][0][1])
valuelist = []
for partner in distlist2:
partnertype = int(atomlist2[distlist2.index(partner)][0][1])
if atomtype == partnertype:
partnervalue = 0
keylist = partner.keys()
for key in keylist:
for element in xrange(len(atom[key])):
value = abs(atom[key][element] - partner[key][element])
partnervalue += value
else:
partnervalue = 9999999
valuelist.append(partnervalue)
minvalue = min(valuelist)
besthit = valuelist.index(minvalue)
hitlist.append(besthit) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def assert_molecules_match_after_remap(self, mol1, mol2):\n for atoms in zip(mol1.atoms, mol2.atoms):\n assert atoms[0].to_dict() == atoms[1].to_dict()\n # bonds will not be in the same order in the molecule and the atom1 and atom2 indecies could be out of\n # order ... | [
"0.6269365",
"0.62579566",
"0.6199401",
"0.619143",
"0.6133642",
"0.6082986",
"0.6040619",
"0.59960866",
"0.59847355",
"0.5943765",
"0.59333813",
"0.58742005",
"0.5850207",
"0.5810267",
"0.5768853",
"0.5726057",
"0.5721131",
"0.5681214",
"0.5680995",
"0.5673212",
"0.5653497",... | 0.0 | -1 |
Calls read_coordinates and frac_to_cart for every path=name in fragmentnames and returns a dictionary where every returnvalue of frac_to_cart is keyed to its fragment name. | def read_multiple_coordinates(fragmentnames):
fragdict = {}
for name in fragmentnames:
path = name + '/'
cell, pos = read_coordinates(path)
atomlist = frac_to_cart(cell, pos)
atomdict = {}
for atom in atomlist:
atomdict[atom[0][0]] = atom[1]
fragdict[name] = atomlist
return fragdict | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read_files():\n with open(\"CvixLerC9.loc\") as loc, open(\"CvixLerC9.qua\") as qua:\n qua_file = (qua.read().split('\\n'))\n qua_file = qua_file[8:-1]\n new_qua = []\n for q in qua_file:\n new_qua.append(q.split('\\t')) # [['1', '1.279502474'], ['3', '0.303712231']..... | [
"0.5323081",
"0.511978",
"0.50651133",
"0.50311476",
"0.4981396",
"0.4981396",
"0.49392763",
"0.49328926",
"0.49008498",
"0.48904055",
"0.48638704",
"0.48584062",
"0.48568657",
"0.48379332",
"0.4833694",
"0.48149553",
"0.47972798",
"0.47770527",
"0.47520956",
"0.47463682",
"0... | 0.7859618 | 0 |
Returns the compound name and the cell parameters from a xd.mas style file specified by 'path'. | def read_xd_master_file(path, errorpointer):
filepointer = open(path, 'r')
for line in filepointer.readlines():
if 'TITLE' in line:
compound_name = line.partition('!')[2].lstrip().rstrip()
if 'CELL' in line:
cell = [float(i) for i in line.split(" ") if '.' in i]
break
filepointer.close()
try:
return compound_name, cell
except:
errorpointer.write(path + '\n')
return None, None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_compound_properties(path):\n filepointer = open(path)\n charge = None\n NE = None\n E_HF = None\n dipole = None\n read_dipole = False\n for line in filepointer:\n if read_dipole:\n read_dipole = False\n dipole = [float(value) for value in line.split(' ') if... | [
"0.6235567",
"0.61867106",
"0.5885496",
"0.5595712",
"0.5569049",
"0.5382577",
"0.5362518",
"0.53287804",
"0.5324073",
"0.53078204",
"0.52826846",
"0.52695364",
"0.52454114",
"0.51977813",
"0.5176313",
"0.5159904",
"0.51186925",
"0.50939804",
"0.5091242",
"0.5027156",
"0.4991... | 0.65298444 | 0 |
Reads the cell parameters from a 'xd.mas' file and the atomic positions from a 'xd.res' file. The function returns a list with the cell parameters and an dictionary which keys the atom name to its fractional coordinates. | def read_coordinates(path='', sort=True):
maspointer = open(path + 'xd.mas', 'r')
respointer = open(path + 'xd.res', 'r')
positions = {}
keylist = [] #Needed to keep the atomlist order. This is important for the frequency read function.
for line in maspointer.readlines():
if 'CELL ' in line:
cell = [float(i) for i in line.split(" ") if '.' in i]
break
for line in respointer.readlines():
if '(' in line and not '!' in line:
coords = [float(i) for i in line.split(" ") if '.' in i]
coords = coords[:-1]
key = line.split(" ")[0]
keylist.append(key)
positions[key] = coords
if sort:
sortkeylist = []
for i in xrange(len(keylist)):
j = i + 1
for key in keylist:
number = get_number(key)
if j == int(number):
sortkeylist.append(key)
else:
sortkeylist = keylist
return cell, positions, sortkeylist | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read_xyz(filename):\n\n config = {}\n\n with open(filename, 'r') as f:\n # number of atoms (spins)\n config['nat'] = int(re.findall('\\S+', f.readline())[0])\n\n # box parameters (type, dimension, shape, periodicity)\n sarr = re.findall('\\S+', f.readline())\n config['l... | [
"0.62354904",
"0.6009959",
"0.57815313",
"0.57030374",
"0.5664513",
"0.5592775",
"0.5527532",
"0.5496724",
"0.5496124",
"0.5451055",
"0.5448297",
"0.53548825",
"0.5279107",
"0.52720135",
"0.526696",
"0.5173193",
"0.5169754",
"0.5153554",
"0.5111259",
"0.51051176",
"0.5093884"... | 0.68994904 | 0 |
Returns the number in the brackets of an atomname. | def get_number(atomname):
switch = False
number = ''
for char in atomname:
if char == ')':
switch = False
if switch:
number += char
if char == '(':
switch = True
return number | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def atomic_number(name):\n try:\n return symbols.index(name.capitalize()) + 1\n except ValueError:\n return lower_names.index(name.lower()) + 1",
"def getNameIndex(name):\n try:\n location = len(name) - \"\".join(reversed(name)).index(\".\")\n index = int(name[location:])\n ... | [
"0.7131723",
"0.6602477",
"0.6242266",
"0.6130583",
"0.6037743",
"0.5923889",
"0.58768123",
"0.5873613",
"0.5861094",
"0.5821463",
"0.58060014",
"0.5772363",
"0.5768761",
"0.5733707",
"0.5732828",
"0.5719784",
"0.56835663",
"0.56269145",
"0.5624526",
"0.5605099",
"0.5601558",... | 0.78789806 | 0 |
Transforms a set of given fractional coordinates to cartesian coordinates. Needs a list containing the cell parameters as its first argument and the dictionary returned by read coordinates(). Returns a dictionary with cartesian coordinates analog to fractional dictionary. | def frac_to_cart(cell, positions):
atomlist = []
counter = 1
a, b, c = cell[0], cell[1], cell[2]
alpha, beta, gamma = cell[3] / 180 * np.pi, cell[4] / 180 * np.pi, cell[5] / 180 * np.pi
v = np.sqrt(1 - np.cos(alpha) * np.cos(alpha) - np.cos(beta) * np.cos(beta) - np.cos(gamma) * np.cos(gamma) \
+ 2 * np.cos(alpha) * np.cos(beta) * np.cos(gamma))
transmatrix = np.matrix([[a, b * np.cos(gamma), c * np.cos(beta)],
[0, b * np.sin(gamma), c * (np.cos(alpha) - np.cos(beta) * np.cos(gamma)) / np.sin(gamma)],
[0, 0, c * v / np.sin(gamma)]])
for atom in positions:
coordmatrix = np.dot(transmatrix, positions[str(atom)])
coordmatrix = np.array(coordmatrix).flatten().tolist()
atomlist.append([])
atomlist[-1].append([atom, atomtable[atom[0]]])
counter += 1
atomlist[-1].append(np.array(coordmatrix))
return atomlist | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def frac2cart_all(frac_coordinates, lattice_array):\n coordinates = deepcopy(frac_coordinates)\n for coord in range(coordinates.shape[0]):\n coordinates[coord] = cartisian_from_fractional(coordinates[coord],\n lattice_array)\n return coordinates... | [
"0.5961069",
"0.5903401",
"0.5892975",
"0.5871701",
"0.5760852",
"0.57279056",
"0.5697128",
"0.56781834",
"0.563091",
"0.56191087",
"0.5551498",
"0.54956114",
"0.5457087",
"0.5442877",
"0.5397851",
"0.5384462",
"0.5377413",
"0.53385407",
"0.53186697",
"0.5285104",
"0.52689",
... | 0.49380758 | 52 |
Keys the coordinates of the atoms read from xd.res to the numerical part of its name. | def list_to_dict(atomlist, full=False):
atomdict = {}
if full:
for atom in atomlist:
atomdict[atom[0]] = atom[1]
else:
for atom in atomlist:
atomdict[atom[0][0]] = atom[1]
return atomdict | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read_coordinates(path='', sort=True):\n maspointer = open(path + 'xd.mas', 'r')\n respointer = open(path + 'xd.res', 'r')\n\n positions = {}\n keylist = [] #Needed to keep the atomlist order. This is important for the frequency read function.\n for line in maspointer.readlines():\n if 'C... | [
"0.6195289",
"0.5910004",
"0.5743342",
"0.57411754",
"0.5721014",
"0.5695221",
"0.5674487",
"0.5631202",
"0.55479956",
"0.5417099",
"0.5406426",
"0.5400523",
"0.53862077",
"0.535987",
"0.53320104",
"0.52723056",
"0.5238446",
"0.5231852",
"0.522691",
"0.520619",
"0.52059865",
... | 0.0 | -1 |
Returns the angle between two vectors. | def get_angle(v1, v2):
return np.arccos(np.dot(v1, v2)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def angle_between_vectors(a, b):\n return math.acos(dot_product(a, b) / (length(a) * length(b)))",
"def angle_between_vectors(vec1, vec2):\n vec = vec1 - vec2\n vec = vec.perpendicular()\n return vec.angle",
"def angle(v1: Vector, v2: Vector) -> float:\n return math.degrees(math.acos((v1 * v2) /... | [
"0.8752359",
"0.8643369",
"0.8583145",
"0.85778534",
"0.8426674",
"0.83578736",
"0.8332106",
"0.825163",
"0.8241473",
"0.82317245",
"0.8202834",
"0.8093871",
"0.80870366",
"0.8080988",
"0.8072849",
"0.8057119",
"0.80453193",
"0.8017723",
"0.80015993",
"0.7964633",
"0.79558647... | 0.80805355 | 14 |
Create and init a conv1d layer with spectral normalization | def _conv1d_spect(ni, no, ks=1, stride=1, padding=0, bias=False):
conv = nn.Conv1d(ni, no, ks, stride=stride, padding=padding, bias=bias)
nn.init.kaiming_normal_(conv.weight)
if bias: conv.bias.data.zero_()
return spectral_norm(conv) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def V1_init(layer, size, spatial_freq, center=None, scale=1., bias=False, seed=None, tied=False):\n classname = layer.__class__.__name__\n assert classname.find('Conv2d') != -1, 'This init only works for Conv layers'\n\n out_channels, in_channels, xdim, ydim = layer.weight.shape\n data = layer.weight.d... | [
"0.6902533",
"0.6492409",
"0.6420767",
"0.6415407",
"0.63521636",
"0.62814677",
"0.62803644",
"0.6270748",
"0.6245793",
"0.6226488",
"0.607607",
"0.6073126",
"0.5975672",
"0.59554344",
"0.5911507",
"0.59033775",
"0.58964217",
"0.58664775",
"0.5841798",
"0.58339804",
"0.582184... | 0.74178046 | 0 |
Helper function that returns dedicated directory for Post media. This organizes user uploaded Post content and is used by `ministry.models.Post.attachment` to save uploaded content. Arguments ========= | def post_media_dir(instance, filename, prepend=settings.MEDIA_ROOT):
if instance.ministry:
_ministry = instance.ministry
elif instance.campaign:
_ministry = instance.campaign.ministry
else:
e = 'There was an unknown error finding a dir for %s' % instance.title
raise AttributeError(e)
return path.join(generic_media_dir(_ministry, prepend=prepend),
'post_media', filename) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def upload_dir(self):\n return os.path.join(settings.MEDIA_ROOT,self.upload_dir_rel())",
"def get_media_directory():\n\treturn _paths[_MEDIA_DIRECTORY_KEY]",
"def public_upload_dir(self):\n return os.path.join(settings.MEDIA_ROOT,\n self.public_upload_dir_rel())",
"de... | [
"0.6631069",
"0.6379418",
"0.63036174",
"0.61629796",
"0.6003821",
"0.5945193",
"0.58830917",
"0.58586687",
"0.5771684",
"0.5700648",
"0.5641766",
"0.56303585",
"0.55782616",
"0.5443347",
"0.543865",
"0.53991973",
"0.5380151",
"0.5379563",
"0.53412217",
"0.5281436",
"0.527181... | 0.6963208 | 0 |
Utility function that creates a dedicated directory for Post media. Arguments ========= | def create_news_post_dir(instance, prepend=settings.MEDIA_ROOT):
for _ in (post_media_dir,):
_path = path.split(_(instance, "", prepend=prepend))[0]
try:
mkdir(_path)
except FileExistsError:
pass
except FileNotFoundError:
if instance.ministry:
_ministry = instance.ministry
elif instance.campaign:
_campaign = instance.campaign
_ministry = _campaign.ministry
else:
e = 'There was an unknown error finding a dir for %s' % instance.name
raise AttributeError(e)
# NOTE: this is infinitely recursive if `prepend` does not lead to correct directory
create_news_post_dir(instance, prepend=prepend) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def post_media_dir(instance, filename, prepend=settings.MEDIA_ROOT):\n if instance.ministry:\n _ministry = instance.ministry\n elif instance.campaign:\n _ministry = instance.campaign.ministry\n else:\n e = 'There was an unknown error finding a dir for %s' % instance.title\n rai... | [
"0.7129349",
"0.6599995",
"0.65848666",
"0.6278641",
"0.6270782",
"0.62637275",
"0.621008",
"0.6079701",
"0.60781497",
"0.6029266",
"0.6018568",
"0.6013729",
"0.5994422",
"0.5988125",
"0.5982215",
"0.59778285",
"0.5950593",
"0.59488165",
"0.59399545",
"0.59338474",
"0.5907977... | 0.73629373 | 0 |
Roundtrip to check what we serialise is what we get back. | def test_serialises_and_deserialises_hs00_message_correctly_for_full_1d_data(self):
original_hist = {
"source": "some_source",
"timestamp": 123456,
"current_shape": [5],
"dim_metadata": [
{
"length": 5,
"unit": "m",
"label": "some_label",
"bin_boundaries": np.array([0.0, 1.0, 2.0, 3.0, 4.0, 5.0]),
}
],
"last_metadata_timestamp": 123456,
"data": np.array([1.0, 2.0, 3.0, 4.0, 5.0]),
"errors": np.array([5.0, 4.0, 3.0, 2.0, 1.0]),
"info": "info_string",
}
buf = serialise_hs00(original_hist)
hist = deserialise_hs00(buf)
assert hist["source"] == original_hist["source"]
assert hist["timestamp"] == original_hist["timestamp"]
assert hist["current_shape"] == original_hist["current_shape"]
self._check_metadata_for_one_dimension(
hist["dim_metadata"][0], original_hist["dim_metadata"][0]
)
assert np.array_equal(hist["data"], original_hist["data"])
assert np.array_equal(hist["errors"], original_hist["errors"])
assert hist["info"] == original_hist["info"]
assert (
hist["last_metadata_timestamp"] == original_hist["last_metadata_timestamp"]
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_dumps(self):\n result = self.mapper.dumps(self.serialization)\n self.mapper.to_dict.assert_called_once_with(\"custom\")\n self.serialization.assert_called_once_with(\n self.mapper.to_dict.return_value\n )\n self.assertIs(result, self.serialization.return_value... | [
"0.65497476",
"0.64250165",
"0.6318902",
"0.6110213",
"0.6092596",
"0.60524327",
"0.6047912",
"0.60349566",
"0.59723496",
"0.59635586",
"0.59397215",
"0.59294575",
"0.59294575",
"0.59081197",
"0.58982784",
"0.58918864",
"0.58906686",
"0.5878805",
"0.58623",
"0.5853447",
"0.58... | 0.0 | -1 |
Roundtrip to check what we serialise is what we get back. | def test_serialises_and_deserialises_hs00_message_correctly_for_minimal_1d_data(
self,
):
original_hist = {
"timestamp": 123456,
"current_shape": [5],
"dim_metadata": [
{
"length": 5,
"unit": "m",
"label": "some_label",
"bin_boundaries": np.array([0.0, 1.0, 2.0, 3.0, 4.0, 5.0]),
}
],
"data": np.array([1.0, 2.0, 3.0, 4.0, 5.0]),
}
buf = serialise_hs00(original_hist)
hist = deserialise_hs00(buf)
assert hist["source"] == ""
assert hist["timestamp"] == original_hist["timestamp"]
assert hist["current_shape"] == original_hist["current_shape"]
self._check_metadata_for_one_dimension(
hist["dim_metadata"][0], original_hist["dim_metadata"][0]
)
assert np.array_equal(hist["data"], original_hist["data"])
assert len(hist["errors"]) == 0
assert hist["info"] == "" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_dumps(self):\n result = self.mapper.dumps(self.serialization)\n self.mapper.to_dict.assert_called_once_with(\"custom\")\n self.serialization.assert_called_once_with(\n self.mapper.to_dict.return_value\n )\n self.assertIs(result, self.serialization.return_value... | [
"0.65497476",
"0.64250165",
"0.6318902",
"0.6110213",
"0.6092596",
"0.60524327",
"0.6047912",
"0.60349566",
"0.59723496",
"0.59635586",
"0.59397215",
"0.59294575",
"0.59294575",
"0.59081197",
"0.58982784",
"0.58918864",
"0.58906686",
"0.5878805",
"0.58623",
"0.5853447",
"0.58... | 0.0 | -1 |
Roundtrip to check what we serialise is what we get back. | def test_serialises_and_deserialises_hs00_message_correctly_for_full_2d_data(self):
original_hist = {
"source": "some_source",
"timestamp": 123456,
"current_shape": [2, 5],
"dim_metadata": [
{
"length": 2,
"unit": "b",
"label": "y",
"bin_boundaries": np.array([10.0, 11.0, 12.0]),
},
{
"length": 5,
"unit": "m",
"label": "x",
"bin_boundaries": np.array([0.0, 1.0, 2.0, 3.0, 4.0, 5.0]),
},
],
"last_metadata_timestamp": 123456,
"data": np.array([[1.0, 2.0, 3.0, 4.0, 5.0], [6.0, 7.0, 8.0, 9.0, 10.0]]),
"errors": np.array([[5.0, 4.0, 3.0, 2.0, 1.0], [10.0, 9.0, 8.0, 7.0, 6.0]]),
"info": "info_string",
}
buf = serialise_hs00(original_hist)
hist = deserialise_hs00(buf)
assert hist["source"] == original_hist["source"]
assert hist["timestamp"] == original_hist["timestamp"]
assert hist["current_shape"] == original_hist["current_shape"]
self._check_metadata_for_one_dimension(
hist["dim_metadata"][0], original_hist["dim_metadata"][0]
)
self._check_metadata_for_one_dimension(
hist["dim_metadata"][1], original_hist["dim_metadata"][1]
)
assert np.array_equal(hist["data"], original_hist["data"])
assert np.array_equal(hist["errors"], original_hist["errors"])
assert hist["info"] == original_hist["info"]
assert (
hist["last_metadata_timestamp"] == original_hist["last_metadata_timestamp"]
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_dumps(self):\n result = self.mapper.dumps(self.serialization)\n self.mapper.to_dict.assert_called_once_with(\"custom\")\n self.serialization.assert_called_once_with(\n self.mapper.to_dict.return_value\n )\n self.assertIs(result, self.serialization.return_value... | [
"0.65497476",
"0.64250165",
"0.6318902",
"0.6110213",
"0.6092596",
"0.60524327",
"0.6047912",
"0.60349566",
"0.59723496",
"0.59635586",
"0.59397215",
"0.59294575",
"0.59294575",
"0.59081197",
"0.58982784",
"0.58918864",
"0.58906686",
"0.5878805",
"0.58623",
"0.5853447",
"0.58... | 0.0 | -1 |
Roundtrip to check what we serialise is what we get back. | def test_serialises_and_deserialises_hs00_message_correctly_for_int_array_data(
self,
):
original_hist = {
"source": "some_source",
"timestamp": 123456,
"current_shape": [5],
"dim_metadata": [
{
"length": 5,
"unit": "m",
"label": "some_label",
"bin_boundaries": np.array([0, 1, 2, 3, 4, 5]),
}
],
"last_metadata_timestamp": 123456,
"data": np.array([1, 2, 3, 4, 5]),
"errors": np.array([5, 4, 3, 2, 1]),
"info": "info_string",
}
buf = serialise_hs00(original_hist)
hist = deserialise_hs00(buf)
assert hist["source"] == original_hist["source"]
assert hist["timestamp"] == original_hist["timestamp"]
assert hist["current_shape"] == original_hist["current_shape"]
self._check_metadata_for_one_dimension(
hist["dim_metadata"][0], original_hist["dim_metadata"][0]
)
assert np.array_equal(hist["data"], original_hist["data"])
assert np.array_equal(hist["errors"], original_hist["errors"])
assert hist["info"] == original_hist["info"]
assert (
hist["last_metadata_timestamp"] == original_hist["last_metadata_timestamp"]
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_dumps(self):\n result = self.mapper.dumps(self.serialization)\n self.mapper.to_dict.assert_called_once_with(\"custom\")\n self.serialization.assert_called_once_with(\n self.mapper.to_dict.return_value\n )\n self.assertIs(result, self.serialization.return_value... | [
"0.65497476",
"0.64250165",
"0.6318902",
"0.6110213",
"0.6092596",
"0.60524327",
"0.6047912",
"0.60349566",
"0.59723496",
"0.59635586",
"0.59397215",
"0.59294575",
"0.59294575",
"0.59081197",
"0.58982784",
"0.58918864",
"0.58906686",
"0.5878805",
"0.58623",
"0.5853447",
"0.58... | 0.0 | -1 |
Roundtrip to check what we serialise is what we get back. | def test_serialises_and_deserialises_hs00_message_correctly_when_float_input_is_not_ndarray(
self,
):
original_hist = {
"source": "some_source",
"timestamp": 123456,
"current_shape": [2, 5],
"dim_metadata": [
{
"length": 2,
"unit": "b",
"label": "y",
"bin_boundaries": [10.0, 11.0, 12.0],
},
{
"length": 5,
"unit": "m",
"label": "x",
"bin_boundaries": [0.0, 1.0, 2.0, 3.0, 4.0, 5.0],
},
],
"last_metadata_timestamp": 123456,
"data": [[1.0, 2.0, 3.0, 4.0, 5.0], [6.0, 7.0, 8.0, 9.0, 10.0]],
"errors": [[5.0, 4.0, 3.0, 2.0, 1.0], [10.0, 9.0, 8.0, 7.0, 6.0]],
"info": "info_string",
}
buf = serialise_hs00(original_hist)
hist = deserialise_hs00(buf)
assert hist["source"] == original_hist["source"]
assert hist["timestamp"] == original_hist["timestamp"]
assert hist["current_shape"] == original_hist["current_shape"]
self._check_metadata_for_one_dimension(
hist["dim_metadata"][0], original_hist["dim_metadata"][0]
)
self._check_metadata_for_one_dimension(
hist["dim_metadata"][1], original_hist["dim_metadata"][1]
)
assert np.array_equal(hist["data"], original_hist["data"])
assert np.array_equal(hist["errors"], original_hist["errors"])
assert hist["info"] == original_hist["info"]
assert (
hist["last_metadata_timestamp"] == original_hist["last_metadata_timestamp"]
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_dumps(self):\n result = self.mapper.dumps(self.serialization)\n self.mapper.to_dict.assert_called_once_with(\"custom\")\n self.serialization.assert_called_once_with(\n self.mapper.to_dict.return_value\n )\n self.assertIs(result, self.serialization.return_value... | [
"0.65497476",
"0.64250165",
"0.6318902",
"0.6110213",
"0.6092596",
"0.60524327",
"0.6047912",
"0.60349566",
"0.59723496",
"0.59635586",
"0.59397215",
"0.59294575",
"0.59294575",
"0.59081197",
"0.58982784",
"0.58918864",
"0.58906686",
"0.5878805",
"0.58623",
"0.5853447",
"0.58... | 0.0 | -1 |
Roundtrip to check what we serialise is what we get back. | def test_serialises_and_deserialises_hs00_message_correctly_when_int_input_is_not_ndarray(
self,
):
original_hist = {
"source": "some_source",
"timestamp": 123456,
"current_shape": [2, 5],
"dim_metadata": [
{
"length": 2,
"unit": "b",
"label": "y",
"bin_boundaries": [10, 11, 12],
},
{
"length": 5,
"unit": "m",
"label": "x",
"bin_boundaries": [0, 1, 2, 3, 4, 5],
},
],
"last_metadata_timestamp": 123456,
"data": [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]],
"errors": [[5, 4, 3, 2, 1], [10, 9, 8, 7, 6]],
"info": "info_string",
}
buf = serialise_hs00(original_hist)
hist = deserialise_hs00(buf)
assert hist["source"] == original_hist["source"]
assert hist["timestamp"] == original_hist["timestamp"]
assert hist["current_shape"] == original_hist["current_shape"]
self._check_metadata_for_one_dimension(
hist["dim_metadata"][0], original_hist["dim_metadata"][0]
)
self._check_metadata_for_one_dimension(
hist["dim_metadata"][1], original_hist["dim_metadata"][1]
)
assert np.array_equal(hist["data"], original_hist["data"])
assert np.array_equal(hist["errors"], original_hist["errors"])
assert hist["info"] == original_hist["info"]
assert (
hist["last_metadata_timestamp"] == original_hist["last_metadata_timestamp"]
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_dumps(self):\n result = self.mapper.dumps(self.serialization)\n self.mapper.to_dict.assert_called_once_with(\"custom\")\n self.serialization.assert_called_once_with(\n self.mapper.to_dict.return_value\n )\n self.assertIs(result, self.serialization.return_value... | [
"0.65497476",
"0.64250165",
"0.6318902",
"0.6110213",
"0.6092596",
"0.60524327",
"0.6047912",
"0.60349566",
"0.59723496",
"0.59635586",
"0.59397215",
"0.59294575",
"0.59294575",
"0.59081197",
"0.58982784",
"0.58918864",
"0.58906686",
"0.5878805",
"0.58623",
"0.5853447",
"0.58... | 0.0 | -1 |
In rare cases, pandas can produce broken datasets when writing to hdf5, this function can be used to delete them so they can be either downloaded again or discarded USE WITH UTTERMOST CARE | def del_sensordata(self):
organisation_id = '5af01e0210bac288dba249ad'
animal_id = '5b6419ff36b96c52808951b1'
with self.writefile() as file:
del file[f'data/{organisation_id}/{animal_id}/sensordata'] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def delete_ds(self, dt):\n\n for k in self.datasets_keys:\n for F in self.datasets[k]:\n if F not in data[k].keys():\n continue \n max_date = data[k][F]['max_date'] \n \"\"\" Deleting unec... | [
"0.65211636",
"0.65018326",
"0.6463773",
"0.6445838",
"0.6362268",
"0.63500494",
"0.6349943",
"0.63297856",
"0.6250076",
"0.6238",
"0.62326133",
"0.6211138",
"0.61991334",
"0.61900306",
"0.61059207",
"0.60499495",
"0.6021087",
"0.6016084",
"0.5999302",
"0.5968676",
"0.5964416... | 0.57039887 | 40 |
Fixture for client object passed as an argument to the test functions | def client():
client = Client()
return client | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_for_client():",
"def test_create_client(self):\n pass",
"def test_client_create(self):\n pass",
"def setUp(self):\n self.client = DummyClient()",
"def test_client_retrieve(self):\n pass",
"def setUp(self):\n self.client = Client()",
"def setUp(self):\n ... | [
"0.754755",
"0.74860317",
"0.73783517",
"0.7377558",
"0.73457175",
"0.73118144",
"0.73118144",
"0.73118144",
"0.73118144",
"0.73099387",
"0.7090056",
"0.6969668",
"0.69263554",
"0.69252616",
"0.6855763",
"0.67790884",
"0.6763768",
"0.66879356",
"0.66879356",
"0.66879356",
"0.... | 0.0 | -1 |
Fixture for user object passed as an argument to the user create view test function | def user():
user = User.objects.create(name='Janek', surname='Kowalski',
internal_id='PUHgjdJ', is_administrator=True,
is_payment_creator=True, is_payment_approver=False,
can_delete_payment=True)
return user | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_user_creation(self):\r\n \r\n self.assertIsInstance(self.user, User)",
"def test_create_user(self):\n pass",
"def test_create_user(self):\n pass",
"def test_create_user(self):\n pass",
"def test_user():\n user_data = {\n \"name\": \"Brad\",\n \"u... | [
"0.7581336",
"0.7469687",
"0.7469687",
"0.7469687",
"0.74045193",
"0.7388603",
"0.71562016",
"0.7155055",
"0.7099242",
"0.69998574",
"0.6995502",
"0.6981704",
"0.6978693",
"0.6965282",
"0.69559574",
"0.6923249",
"0.6908104",
"0.6863743",
"0.68313307",
"0.68313307",
"0.6827611... | 0.0 | -1 |
Fixture for company object passed as an argument to the company create view test function | def company():
company = Company.objects.create(name='Tre G.M.B.H.', country='Germany')
return company | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_website_companies_create(self):\n pass",
"def test_create_company_1(self):\n company_data = {\n \"_id\": \"sbucks\",\n \"headquarters\": \"Seattle\",\n \"name\": \"Starbucks Inc.\",\n }\n\n resp = self.app.post('/companies', data=json.dumps(co... | [
"0.7572055",
"0.6949927",
"0.6910108",
"0.6783742",
"0.6782509",
"0.6768939",
"0.6648319",
"0.64719594",
"0.62653613",
"0.62246877",
"0.61053544",
"0.60505855",
"0.604088",
"0.60324633",
"0.5979618",
"0.5958282",
"0.59542584",
"0.5907634",
"0.58789223",
"0.58328825",
"0.58327... | 0.662038 | 7 |
Fixture for admin object passed as an argument to the admin create view test function | def administrator():
administrator = Administrator.objects.create(name='Michał', surname='Paluch',
login='Udfsr43', password='Password_3',
password_repeat='Password_3')
return administrator | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_admin(self):\n assert(admin)",
"def test_an_admin_view(admin_client):\n response = admin_client.get('/admin/')\n assert status(response) == 'ok'",
"def test_add_admin(self):\n self.test_create_user()\n self.test_create_organization()\n url = reverse('MGA:add_admin')\n... | [
"0.7291503",
"0.6833036",
"0.6821154",
"0.6697566",
"0.668051",
"0.66122943",
"0.64742976",
"0.6402408",
"0.6336394",
"0.63025343",
"0.6262725",
"0.62581766",
"0.62279063",
"0.6192794",
"0.61778784",
"0.61756825",
"0.61681473",
"0.6146008",
"0.6134811",
"0.6131507",
"0.609376... | 0.0 | -1 |
Fixture for bank object passed as an argument to the admin bank view test function | def bank():
bank = Bank.objects.create(name='Random Bank')
return bank | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_client_bank_account_retrieve(self):\n pass",
"def test_client_bank_accounts_list(self):\n pass",
"def test_client_bank_account_create(self):\n pass",
"def test_create_virtual_account_beneficiary(self):\n pass",
"def test_edit_boat(self):\n pass",
"def test_hous... | [
"0.68055016",
"0.67185485",
"0.65409213",
"0.6355551",
"0.6263593",
"0.6235724",
"0.6129963",
"0.6102043",
"0.6074391",
"0.6055766",
"0.6043796",
"0.60177153",
"0.5949179",
"0.58791125",
"0.58271164",
"0.5814391",
"0.5790518",
"0.5788916",
"0.5783457",
"0.5749937",
"0.5742416... | 0.6220651 | 6 |
Fixture for account object passed as an argument to the account create view test function | def account():
bank_test = Bank.objects.create(name='R-Bank')
company_test = Company.objects.create(name='Tre Belarus', country='Belarus')
account = Account.objects.create(iban_number='TEEdddddddfs', swift_code='tertrefdsf',
bank=bank_test, company=company_test)
return account | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_create_account(self):\n url = reverse('account:accounts')\n data = {'name': 'Test Account 1'}\n response = self.client.post(url, data, format='json')\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n self.assertEqual(Account.objects.count(), 1)\n ... | [
"0.7160241",
"0.69238716",
"0.6886644",
"0.68595505",
"0.68389684",
"0.681058",
"0.6805492",
"0.66778797",
"0.66131806",
"0.66090906",
"0.6590591",
"0.6572082",
"0.6519173",
"0.6509788",
"0.6508603",
"0.6420267",
"0.6328433",
"0.62413573",
"0.62157965",
"0.6204515",
"0.619508... | 0.6343079 | 16 |
return a working WLAN(STA_IF) instance or None | def get_connection(ssid,password):
# First check if there already is any connection:
if wlan_sta.isconnected():
return wlan_sta
connected = False
try:
# ESP connecting to WiFi takes time, wait a bit and try again:
time.sleep(3)
if wlan_sta.isconnected():
return wlan_sta
# Search WiFis in range
wlan_sta.active(True)
connected = do_connect(ssid,password)
except OSError as e:
print("exception", str(e))
# start web server for connection manager:
# if not connected:
# connected = start()
return wlan_sta if connected else None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_wifi(self):\n return self._wifi",
"def init_wlan_sta():\n\n print('WLAN: STA mode')\n wlan.init(mode=WLAN.STA)\n if not wlan.isconnected():\n wlan.connect(WLAN_SSID, auth=WLAN_AUTH, timeout=5000)\n while not wlan.isconnected():\n machine.idle() # save power while... | [
"0.68340015",
"0.63972205",
"0.616661",
"0.5945816",
"0.5928931",
"0.5894898",
"0.5855707",
"0.58045965",
"0.56598383",
"0.5637595",
"0.5623451",
"0.5612626",
"0.55965465",
"0.5566321",
"0.5533868",
"0.5496373",
"0.544632",
"0.54028994",
"0.5389177",
"0.5349265",
"0.53430164"... | 0.61334884 | 3 |
Constructor for object DeleteRenewKeys that reads content from config.json | def __init__(self):
# open json config file that reads in information
config_path = open("config.json", "r")
config_json = config_path.read()
config_dict = json.loads(config_json)
# assign object variables
self.project_id = config_dict["project-id"]
self.bucket_name = config_dict["bucket-name"]
self.location_id = config_dict["key-location"]
self.key_ring_id = config_dict["key-ring-id"]
self.crypto_key_id = config_dict["crypto-key-id"]
self.service_account_email = config_dict["service-account-email"]
# close the file
config_path.close() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self):\n try:\n with open(os.path.expanduser(\"~/.dkeyrc\"), 'r') as f:\n self.__cfgdata = json.load(f)\n except Exception as e:\n print(\"Error: Unable to load config JSON at ~/.dkeyrc -- %s\" % (e))\n sys.exit(1)",
"def __init__(self, k... | [
"0.5615627",
"0.53399825",
"0.53382677",
"0.5235249",
"0.51820284",
"0.5108353",
"0.5022169",
"0.5007471",
"0.49882397",
"0.49877107",
"0.49747235",
"0.49552137",
"0.49455333",
"0.49418396",
"0.49205673",
"0.49144816",
"0.48787165",
"0.48667872",
"0.48570824",
"0.48304826",
"... | 0.52679664 | 3 |
Decrypts input ciphertext using a symmetric CryptoKey. | def decrypt_symmetric(self, ciphertext):
from google.cloud import kms_v1
# Creates an API client for the KMS API.
client = kms_v1.KeyManagementServiceClient()
# The resource name of the CryptoKey.
name = client.crypto_key_path_path(self.project_id, self.location_id, self.key_ring_id,
self.crypto_key_id)
# Use the KMS API to decrypt the data.
response = client.decrypt(name, ciphertext)
return response.plaintext | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def decrypt_symmetric(secret_key, ciphertext, ttl=None):\n f = Fernet(secret_key)\n # fernet requires the ciphertext to be bytes, it will raise an exception\n # if it is a string\n return f.decrypt(bytes(ciphertext), ttl)",
"def decrypt(priv_key, ciphertext):\n pk_encrypted_secret_key = ciphertext... | [
"0.78190506",
"0.76649165",
"0.73512477",
"0.7301102",
"0.72868747",
"0.7224076",
"0.7158116",
"0.7080847",
"0.7073697",
"0.70620877",
"0.7024779",
"0.70223695",
"0.70039576",
"0.7003598",
"0.6956859",
"0.6855221",
"0.6831517",
"0.68168175",
"0.6813381",
"0.679899",
"0.678872... | 0.8391707 | 0 |
Method that decrypts a file using the decrypt_symmetric method and writes the output of this decryption to a file named gcpkey.json | def decrypt_from_file(self, file_path):
# open and decrypt byte file
f = open(file_path, "rb").read()
decrypted = self.decrypt_symmetric(f)
json_string = decrypted.decode("utf-8")
# write string to json file
destination_file_name = Path("downloaded-key/gcp-key.json")
destination_file_name.touch(exist_ok=True) # creates file if it does not yet exist
destination_file_name.touch(exist_ok=True) # creates file if it does not yet exist
destination_file_name.write_text(json_string) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def decrypt_symmetric(self, ciphertext):\n from google.cloud import kms_v1\n\n # Creates an API client for the KMS API.\n client = kms_v1.KeyManagementServiceClient()\n\n # The resource name of the CryptoKey.\n name = client.crypto_key_path_path(self.project_id, self.location_id,... | [
"0.64325106",
"0.63213474",
"0.622638",
"0.61633515",
"0.61598235",
"0.6144704",
"0.608841",
"0.60840195",
"0.6062278",
"0.5996128",
"0.5985062",
"0.59499764",
"0.59409845",
"0.59369785",
"0.5880555",
"0.5879515",
"0.5877775",
"0.58735037",
"0.5852493",
"0.58381975",
"0.58349... | 0.801987 | 0 |
Downloads key for configured service account and stores it in the folder generatedkey/ | def download_key_from_blob(self):
source_blob_name = "generated-keys/{}".format(self.service_account_email)
destination_name = self.service_account_email
# generate destination folder and file if they do not yet exist
Path("downloaded-key/").mkdir(parents=True, exist_ok=True) # creates folder if not exists
folder = Path("downloaded-key/") # folder where all the newly generated keys go
destination_file_name = folder / "{}".format(destination_name) # file named after service-account name
destination_file_name.touch(exist_ok=True)
# download the file and store it locally
storage_client = storage.Client()
bucket = storage_client.get_bucket(self.bucket_name)
blob = bucket.blob(source_blob_name)
blob.download_to_filename(destination_file_name)
# prints source and destination indicating successful download
print('Encrypted key downloaded to -----> \n {}.'.format(
source_blob_name,
destination_file_name))
return destination_file_name | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def download_key():\n data = check_args(('cloudProvider', ))\n provider = jobs.init_provider(data, True)\n key = encrypt_key(provider.get_key(), data['username'])\n return make_response(keyName=provider.keyname, key=key)",
"def generate_key():\r\n # generating key\r\n key = Fernet.generate_key(... | [
"0.7312609",
"0.6830154",
"0.6603424",
"0.6562619",
"0.6516955",
"0.6498773",
"0.6481392",
"0.64763385",
"0.64518285",
"0.6347369",
"0.6296295",
"0.6269412",
"0.62465113",
"0.6237751",
"0.61916953",
"0.61603403",
"0.6143181",
"0.6117199",
"0.61056596",
"0.6093765",
"0.6090465... | 0.803482 | 0 |
main method that executes the workings of the script | def main():
print("Reading from config.json")
download_decrypt_store = DownloadDecryptStore()
print("Downloading key from storage-bucket")
file_path = download_decrypt_store.download_key_from_blob()
print("Decrypting downloaded file")
download_decrypt_store.decrypt_from_file(file_path)
print("Completed") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_script(self) -> None:\n main()",
"def run():\n main()",
"def main():\n run_program()",
"def run_script(self):\n pass",
"def main():\n pass",
"def main():\n return",
"def main():\n\n BASIC.run(PROGRAM)",
"def main(self) -> None:\n pass",
"def main():\... | [
"0.77707267",
"0.75500584",
"0.75495726",
"0.7460941",
"0.73748064",
"0.73399204",
"0.7278216",
"0.72609",
"0.72570956",
"0.72451305",
"0.72448987",
"0.72448987",
"0.72448987",
"0.72448987",
"0.72448987",
"0.72448987",
"0.72448987",
"0.72448987",
"0.72448987",
"0.72448987",
"... | 0.0 | -1 |
Calculate overlap among trajectories | def trajectory_overlap(gt_trajs, pred_traj):
max_overlap = 0
max_index = 0
for t, gt_traj in enumerate(gt_trajs):
s_viou = viou_sx(gt_traj['sub_traj'], gt_traj['duration'], pred_traj['sub_traj'], pred_traj['duration'])
o_viou = viou_sx(gt_traj['obj_traj'], gt_traj['duration'], pred_traj['obj_traj'], pred_traj['duration'])
so_viou = min(s_viou, o_viou)
if so_viou > max_overlap:
max_overlap = so_viou
max_index = t
return max_overlap, max_index | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def overlap_cost(track_a, track_b):\n return 1 - overlap(track_a.bbox, track_b.bbox)",
"def overlap_with(self, other):",
"def poverlap(t1, t2, size1, size2):\n x0 = t1[0]\n y0 = t1[1]\n x1 = t1[0] + size1[0]\n y1 = t1[1] + size1[1]\n\n x2 = t2[0]\n y2 = t2[1]\n x3 = t2[0] + size2[0]\n ... | [
"0.70222414",
"0.6725737",
"0.66626245",
"0.6478671",
"0.6425438",
"0.6360582",
"0.63547766",
"0.63308674",
"0.63305753",
"0.6300792",
"0.6285758",
"0.6278362",
"0.6266364",
"0.62542456",
"0.62484795",
"0.6247598",
"0.614467",
"0.613606",
"0.61254627",
"0.6118002",
"0.6102571... | 0.68951005 | 1 |
Encodes a list of strings to a single string. | def encode (self, strs):
if strs == []: return "null"
return chr(257).join(strs) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def encode_as_str(list_to_encode, sep = \"|\"):\n return sep.join([str(x) for x in list_to_encode])",
"def _encode_list(source: list) -> bytes:\n result_data = b\"l\"\n\n for item in source:\n result_data += encode(item)\n\n return result_data + b\"e\"",
"def stringer(list):\n\tstring = \"\"... | [
"0.7459969",
"0.74273384",
"0.7255417",
"0.6790711",
"0.67658377",
"0.66986156",
"0.6680612",
"0.6643305",
"0.6643305",
"0.65740526",
"0.6573746",
"0.6484516",
"0.6398631",
"0.6318684",
"0.63106364",
"0.628163",
"0.62610334",
"0.62113565",
"0.62003165",
"0.6199526",
"0.619224... | 0.7258633 | 2 |
Decodes a single string to a list of strings. | def decode (self, s):
if s == "null": return []
return s.split(chr(257)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def decode(self, s):\n lststr = s.split(',')\n if s=='': return []\n rst = []\n for i in range(len(lststr)):\n rst.append(lststr[i])\n return rst",
"def parse_string_list(data):\n txt = data.decode()\n x = ast.literal_eval(txt)\n return x",
"def _decode_li... | [
"0.7429849",
"0.67665714",
"0.6603133",
"0.6484686",
"0.64099807",
"0.63982195",
"0.63764083",
"0.63426924",
"0.63409954",
"0.6335773",
"0.62704915",
"0.62629604",
"0.6239383",
"0.6189524",
"0.6171236",
"0.6159162",
"0.6110307",
"0.6100137",
"0.6022035",
"0.60078084",
"0.5990... | 0.7317169 | 1 |
GameObjects by default don't have agency but they may still do things... | def update(self, dt):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def game_over(self):\n raise NotImplementedError(\"Abstract method\") # no mercy for stooges",
"def object_detection(self):\r\n pass",
"def playerdefeated(self):\n globalvalues.gameover_combat()",
"def is_actor():\n return False",
"def on_collision(self):",
"def __init__(self):\n... | [
"0.63586646",
"0.58945036",
"0.5857092",
"0.5510857",
"0.54797775",
"0.54752845",
"0.54633653",
"0.54481524",
"0.5443868",
"0.5428182",
"0.5414342",
"0.54023975",
"0.54023975",
"0.5400207",
"0.5384979",
"0.53770864",
"0.53669137",
"0.53261596",
"0.53248054",
"0.52967083",
"0.... | 0.0 | -1 |
sort list of objects randomly then update everything in this world | def update(self, dt):
random.shuffle(self.gameObjects)
for item in self.gameObjects:
description = item.update(dt) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def dispatch_items_randomly(self, level):\n for item in self.list:\n item.position = Item.define_random_position(item, level)",
"def populate_objects(self):\n if not self._random_object: # only populate the first object\n U.spawn_object(self.object_list[0], self.object_initia... | [
"0.6626622",
"0.6585694",
"0.6483848",
"0.6469369",
"0.6035206",
"0.6019736",
"0.60169244",
"0.5968468",
"0.5968468",
"0.5951621",
"0.5919026",
"0.59153605",
"0.5901477",
"0.5880968",
"0.58774793",
"0.5859918",
"0.5817854",
"0.58043724",
"0.5801076",
"0.5794008",
"0.5786912",... | 0.6854891 | 0 |
add this to the world | def add_to_world(self, thing):
thing.set_world_info(self.current_id, self)
self.gameObjects.append(thing)
self.current_id += 1 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_world(self):\n raise NotImplementedError()",
"def __init__(self, world):\n self.__init__(world, ArrayList())",
"def update_world(self):\n pass",
"def __init__(self, world, x, y, direction):\n self.ID = world.__register__(x, y, direction)",
"def world(self):\n ret... | [
"0.6970694",
"0.6964329",
"0.6806117",
"0.6598959",
"0.64999914",
"0.64821887",
"0.64505976",
"0.6299828",
"0.6281762",
"0.6269024",
"0.62657404",
"0.62023485",
"0.61991674",
"0.6084373",
"0.5940955",
"0.5922447",
"0.58853203",
"0.5820223",
"0.5784347",
"0.57677424",
"0.57647... | 0.74709004 | 0 |
Merge data from another instance of this object. | def merge_stats(self, other):
self[0] += other[0]
self[1] += other[1]
self[2] += other[2]
self[3] = ((self[0] or self[1] or self[2]) and
min(self[3], other[3]) or other[3])
self[4] = max(self[4], other[3]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def merge(self, other):\n from .dataset import Dataset\n\n if other is None:\n return self.to_dataset()\n else:\n other_vars = getattr(other, 'variables', other)\n coords = merge_coords_without_align([self.variables, other_vars])\n return Dataset._fr... | [
"0.69505",
"0.694683",
"0.6885921",
"0.68476945",
"0.68086684",
"0.6734038",
"0.67226946",
"0.66790885",
"0.66472447",
"0.66369003",
"0.6572359",
"0.6542647",
"0.6503335",
"0.65015614",
"0.6469766",
"0.64362967",
"0.64317465",
"0.6385473",
"0.6376822",
"0.6375423",
"0.6370342... | 0.0 | -1 |
Merge data from an apdex metric object. | def merge_apdex_metric(self, metric):
self[0] += metric.satisfying
self[1] += metric.tolerating
self[2] += metric.frustrating
self[3] = ((self[0] or self[1] or self[2]) and
min(self[3], metric.apdex_t) or metric.apdex_t)
self[4] = max(self[4], metric.apdex_t) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def merge_custom_metrics(self, metrics):\n\n if not self.__settings:\n return\n\n for name, other in metrics:\n key = (name, '')\n stats = self.__stats_table.get(key)\n if not stats:\n self.__stats_table[key] = other\n else:\n ... | [
"0.59747314",
"0.59122944",
"0.54553163",
"0.53186303",
"0.52896434",
"0.52513325",
"0.52353007",
"0.5187257",
"0.5186104",
"0.51808596",
"0.5174486",
"0.5171695",
"0.5140604",
"0.51390433",
"0.5108755",
"0.5096023",
"0.50914425",
"0.50677156",
"0.5052862",
"0.50481683",
"0.5... | 0.69163495 | 0 |
Merge data from another instance of this object. | def merge_stats(self, other):
self[1] += other[1]
self[2] += other[2]
self[3] = self[0] and min(self[3], other[3]) or other[3]
self[4] = max(self[4], other[4])
self[5] += other[5]
# Must update the call count last as update of the
# minimum call time is dependent on initial value.
self[0] += other[0] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def merge(self, other):\n from .dataset import Dataset\n\n if other is None:\n return self.to_dataset()\n else:\n other_vars = getattr(other, 'variables', other)\n coords = merge_coords_without_align([self.variables, other_vars])\n return Dataset._fr... | [
"0.69505",
"0.694683",
"0.6885921",
"0.68476945",
"0.68086684",
"0.6734038",
"0.67226946",
"0.66790885",
"0.66472447",
"0.66369003",
"0.6572359",
"0.6542647",
"0.6503335",
"0.65015614",
"0.6469766",
"0.64362967",
"0.64317465",
"0.6385473",
"0.6376822",
"0.6375423",
"0.6370342... | 0.0 | -1 |
Merge data from a time metric object. | def merge_time_metric(self, metric):
self.merge_raw_time_metric(metric.duration, metric.exclusive) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def combine(cls, date_obj, time_obj):\n return cls(date_obj.year, date_obj.month, date_obj.day,\n time_obj.hour, time_obj.minute, time_obj.second,\n time_obj.nanosecond)",
"def add_time(data, t):\n data['year'] = t.year\n data['month'] = t.month\n data['day'] = t.day\n ... | [
"0.6252038",
"0.5991166",
"0.5987996",
"0.59735656",
"0.5910383",
"0.5889225",
"0.58206046",
"0.5819968",
"0.5817195",
"0.56706285",
"0.5599506",
"0.55690825",
"0.5536235",
"0.54553676",
"0.5344387",
"0.532456",
"0.5291916",
"0.52320737",
"0.52283955",
"0.5221072",
"0.5202319... | 0.69977385 | 0 |
Record a single value metric, merging the data with any data from prior value metrics with the same name. | def record_custom_metric(self, name, value):
if isinstance(value, dict):
if len(value) == 1 and 'count' in value:
new_stats = CountStats(call_count=value['count'])
else:
new_stats = TimeStats(*c2t(**value))
else:
new_stats = TimeStats(1, value, value, value, value, value**2)
stats = self.__stats_table.get(name)
if stats is None:
self.__stats_table[name] = new_stats
else:
stats.merge_stats(new_stats) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def log(self, metric_name: str, value: float) -> None:\n if metric_name in self.metrics:\n self.metrics[metric_name].append(value)\n else:\n self.metrics[metric_name] = [value]",
"def log_metric(self, name: str, value):\n self.metrics[name] = value\n\n self._sync... | [
"0.72285455",
"0.6880584",
"0.6831178",
"0.67528003",
"0.64814395",
"0.6404036",
"0.6218233",
"0.6194534",
"0.6135706",
"0.60753894",
"0.6063926",
"0.60352796",
"0.59790224",
"0.59553665",
"0.59262705",
"0.589197",
"0.58506346",
"0.5779258",
"0.5770089",
"0.5764042",
"0.57204... | 0.69385666 | 1 |
Returns an iterator over the set of value metrics. The items returned are a tuple consisting of the metric name and accumulated stats for the metric. | def metrics(self):
return six.iteritems(self.__stats_table) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __iter__(self):\n prefix = len(META_NS) + 2\n for key, value in self.stats.items():\n yield (key[prefix:-6], int(value))",
"def get_val_iterator(self) -> Iterable[Batch]:\n if self._val_name not in self._datasets:\n raise ValueError(\"Val data not provided.\")\n ... | [
"0.61560905",
"0.6048202",
"0.6047815",
"0.6044677",
"0.6018319",
"0.6018319",
"0.5974941",
"0.59604144",
"0.5956948",
"0.595421",
"0.59379584",
"0.58816415",
"0.58809036",
"0.58738184",
"0.5869844",
"0.5865912",
"0.58568335",
"0.5852111",
"0.5831421",
"0.58237755",
"0.579164... | 0.66805625 | 0 |
Resets the accumulated statistics back to initial state for metric data. | def reset_metric_stats(self):
self.__stats_table = {} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def reset(self):\n self.sum_metric = 0.\n self.num_inst = 0.\n self.metrics.reset_stats()",
"def reset_metric_stats(self):\n\n self.__stats_table = {}",
"def stats_reset(self):\n self.stats.reset()",
"def stats_reset(self):\n self.stats.reset()",
"def reset(self) -... | [
"0.8448358",
"0.84143066",
"0.83299094",
"0.83299094",
"0.8270503",
"0.8153123",
"0.79735655",
"0.7772603",
"0.77270293",
"0.77003264",
"0.76665866",
"0.7614711",
"0.75709176",
"0.7540221",
"0.75036174",
"0.75036174",
"0.7490715",
"0.7471787",
"0.74488425",
"0.74488425",
"0.7... | 0.83412653 | 2 |
Merge data from another instance of this object. | def merge_stats(self, other):
self[1] += other[1]
self[2] = self[0] and min(self[2], other[2]) or other[2]
self[3] = max(self[3], other[3])
if self[3] == other[3]:
self[4] = other[4]
# Must update the call count last as update of the
# minimum call time is dependent on initial value.
self[0] += other[0] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def merge(self, other):\n from .dataset import Dataset\n\n if other is None:\n return self.to_dataset()\n else:\n other_vars = getattr(other, 'variables', other)\n coords = merge_coords_without_align([self.variables, other_vars])\n return Dataset._fr... | [
"0.69505",
"0.694683",
"0.6885921",
"0.68476945",
"0.68086684",
"0.6734038",
"0.67226946",
"0.66790885",
"0.66472447",
"0.66369003",
"0.6572359",
"0.6542647",
"0.6503335",
"0.65015614",
"0.6469766",
"0.64362967",
"0.64317465",
"0.6385473",
"0.6376822",
"0.6375423",
"0.6370342... | 0.0 | -1 |
Merge data from a slow sql node object. | def merge_slow_sql_node(self, node):
duration = node.duration
self[1] += duration
self[2] = self[0] and min(self[2], duration) or duration
self[3] = max(self[3], duration)
if self[3] == duration:
self[4] = node
# Must update the call count last as update of the
# minimum call time is dependent on initial value.
self[0] += 1 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def record_slow_sql_node(self, node):\n\n if not self.__settings:\n return\n\n key = node.identifier\n stats = self.__sql_stats_table.get(key)\n if stats is None:\n # Only record slow SQL if not already over the limit on\n # how many can be collected in ... | [
"0.5318473",
"0.52193975",
"0.5193449",
"0.515354",
"0.5137922",
"0.50185084",
"0.49987668",
"0.49883923",
"0.4975488",
"0.4966561",
"0.49638265",
"0.49379689",
"0.48661035",
"0.48568356",
"0.48503768",
"0.4833968",
"0.48199612",
"0.4812925",
"0.4802843",
"0.47878385",
"0.477... | 0.6787032 | 0 |
Returns a count of the number of unique metrics currently recorded for apdex, time and value metrics. | def metrics_count(self):
return len(self.__stats_table) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def number_of_running_metrics(self):\n try:\n return len(self.get_classads(\"OSGRSV==\\\"metrics\\\"\"))\n except TypeError:\n self.rsv.log(\"ERROR\", \"Classad parsing failed, unable to count running metrics\")",
"def metric_data_count(self):\n\n if not self.__settings... | [
"0.6411366",
"0.6283042",
"0.62296087",
"0.6220417",
"0.61111647",
"0.6047972",
"0.60304636",
"0.5969979",
"0.59513307",
"0.5878805",
"0.5827968",
"0.5753369",
"0.5703473",
"0.56953144",
"0.56933933",
"0.56933933",
"0.5690032",
"0.5668702",
"0.56455106",
"0.5636696",
"0.56353... | 0.6419223 | 0 |
Record a single apdex metric, merging the data with any data from prior apdex metrics with the same name. | def record_apdex_metric(self, metric):
if not self.__settings:
return
# Note that because we are using a scope here of an empty string
# we can potentially clash with an unscoped metric. Using None,
# although it may help to keep them separate in the agent will
# not make a difference to the data collector which treats None
# as an empty string anyway.
key = (metric.name, '')
stats = self.__stats_table.get(key)
if stats is None:
stats = ApdexStats(apdex_t=metric.apdex_t)
self.__stats_table[key] = stats
stats.merge_apdex_metric(metric)
return key | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def record_apdex_metrics(self, metrics):\n\n if not self.__settings:\n return\n\n for metric in metrics:\n self.record_apdex_metric(metric)",
"def merge_apdex_metric(self, metric):\n\n self[0] += metric.satisfying\n self[1] += metric.tolerating\n self[2] +... | [
"0.7010535",
"0.6634994",
"0.5894368",
"0.579922",
"0.57742566",
"0.5767345",
"0.5742764",
"0.5742764",
"0.56948036",
"0.5694517",
"0.56521934",
"0.564671",
"0.5620353",
"0.56121796",
"0.552094",
"0.53992915",
"0.5386446",
"0.52903515",
"0.5282726",
"0.52693814",
"0.5267927",... | 0.74796903 | 0 |
Record the apdex metrics supplied by the iterable for a single transaction, merging the data with any data from prior apdex metrics with the same name. | def record_apdex_metrics(self, metrics):
if not self.__settings:
return
for metric in metrics:
self.record_apdex_metric(metric) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def record_transaction(self, transaction):\n\n if not self.__settings:\n return\n\n settings = self.__settings\n\n # Record the apdex, value and time metrics generated from the\n # transaction. Whether time metrics are reported as distinct\n # metrics or into a rollup ... | [
"0.6160698",
"0.56689644",
"0.5492042",
"0.53796333",
"0.5373829",
"0.5285188",
"0.52515113",
"0.52226347",
"0.522238",
"0.52220243",
"0.5142877",
"0.51234",
"0.5087667",
"0.50747657",
"0.50630915",
"0.5057553",
"0.5049021",
"0.50429595",
"0.5004636",
"0.49941427",
"0.4935158... | 0.6819747 | 0 |
Record a single time metric, merging the data with any data from prior time metrics with the same name and scope. | def record_time_metric(self, metric):
if not self.__settings:
return
# Scope is forced to be empty string if None as
# scope of None is reserved for apdex metrics.
key = (metric.name, metric.scope or '')
stats = self.__stats_table.get(key)
if stats is None:
stats = TimeStats(call_count=1,
total_call_time=metric.duration,
total_exclusive_call_time=metric.exclusive,
min_call_time=metric.duration,
max_call_time=metric.duration,
sum_of_squares=metric.duration ** 2)
self.__stats_table[key] = stats
else:
stats.merge_time_metric(metric)
return key | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def record_time_metrics(self, metrics):\n\n if not self.__settings:\n return\n\n for metric in metrics:\n self.record_time_metric(metric)",
"def record_custom_metric(self, name, value):\n if isinstance(value, dict):\n if len(value) == 1 and 'count' in value:\... | [
"0.6278324",
"0.62258005",
"0.6140665",
"0.6003981",
"0.58658415",
"0.5658924",
"0.5652999",
"0.5612092",
"0.5606227",
"0.5472987",
"0.5470835",
"0.54608715",
"0.5427048",
"0.53969103",
"0.53553826",
"0.533382",
"0.52751833",
"0.5262168",
"0.52570385",
"0.52540565",
"0.522785... | 0.7506857 | 0 |
Record the time metrics supplied by the iterable for a single transaction, merging the data with any data from prior time metrics with the same name and scope. | def record_time_metrics(self, metrics):
if not self.__settings:
return
for metric in metrics:
self.record_time_metric(metric) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def flush(self):\n with self._lock:\n batch = self._batch\n timestamps = self._timestamps\n\n items = []\n for identity, value in batch.items():\n metric = {}\n typ, name, tags = identity\n metric[\"name\"] = name\n ... | [
"0.6077945",
"0.5785074",
"0.5715007",
"0.5492405",
"0.5340035",
"0.5287281",
"0.5276565",
"0.5246753",
"0.524385",
"0.5236754",
"0.5186013",
"0.5171279",
"0.5168675",
"0.51627535",
"0.51169103",
"0.51088405",
"0.5038848",
"0.5023717",
"0.49753776",
"0.49482045",
"0.4947367",... | 0.6196177 | 0 |
Record a single value metric, merging the data with any data from prior value metrics with the same name. | def record_custom_metric(self, name, value):
key = (name, '')
if isinstance(value, dict):
if len(value) == 1 and 'count' in value:
new_stats = CountStats(call_count=value['count'])
else:
new_stats = TimeStats(*c2t(**value))
else:
new_stats = TimeStats(1, value, value, value, value, value**2)
stats = self.__stats_table.get(key)
if stats is None:
self.__stats_table[key] = new_stats
else:
stats.merge_stats(new_stats)
return key | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def log(self, metric_name: str, value: float) -> None:\n if metric_name in self.metrics:\n self.metrics[metric_name].append(value)\n else:\n self.metrics[metric_name] = [value]",
"def record_custom_metric(self, name, value):\n if isinstance(value, dict):\n if... | [
"0.72274554",
"0.69380003",
"0.68785536",
"0.683132",
"0.6480525",
"0.6403055",
"0.6216645",
"0.61926883",
"0.61354136",
"0.6075729",
"0.6063117",
"0.6036601",
"0.59779376",
"0.5955456",
"0.5926401",
"0.5891051",
"0.5852286",
"0.5779409",
"0.5769437",
"0.576314",
"0.5720652",... | 0.6751963 | 4 |
Record the value metrics supplied by the iterable, merging the data with any data from prior value metrics with the same name. | def record_custom_metrics(self, metrics):
if not self.__settings:
return
for name, value in metrics:
self.record_custom_metric(name, value) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update(self, current_iter, *metrics, **named_metrics):\n\n # Same order as __init__() in python>=3.6\n if len(metrics) > 0:\n for key, metric in zip(self.metrics.keys(), metrics):\n self.metrics[key].append((current_iter, metric))\n \n # Random order wi... | [
"0.67049134",
"0.60828054",
"0.59038454",
"0.5789189",
"0.57760006",
"0.5761314",
"0.5761024",
"0.57179546",
"0.5702051",
"0.5690251",
"0.56831443",
"0.56656265",
"0.55119264",
"0.55036944",
"0.54828936",
"0.5461199",
"0.54519856",
"0.54256064",
"0.54026794",
"0.5388167",
"0.... | 0.59415793 | 2 |
Record a single sql metric, merging the data with any data from prior sql metrics for the same sql key. | def record_slow_sql_node(self, node):
if not self.__settings:
return
key = node.identifier
stats = self.__sql_stats_table.get(key)
if stats is None:
# Only record slow SQL if not already over the limit on
# how many can be collected in the harvest period.
settings = self.__settings
maximum = settings.agent_limits.slow_sql_data
if len(self.__sql_stats_table) < maximum:
stats = SlowSqlStats()
self.__sql_stats_table[key] = stats
if stats:
stats.merge_slow_sql_node(node)
return key | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def record_apdex_metric(self, metric):\n\n if not self.__settings:\n return\n\n # Note that because we are using a scope here of an empty string\n # we can potentially clash with an unscoped metric. Using None,\n # although it may help to keep them separate in the agent will\... | [
"0.6167947",
"0.59710056",
"0.59208757",
"0.5898851",
"0.58011967",
"0.5774532",
"0.5704721",
"0.5638924",
"0.5566081",
"0.5514177",
"0.5501307",
"0.54829776",
"0.5433093",
"0.5422249",
"0.5399425",
"0.53741735",
"0.53741735",
"0.5347321",
"0.5339919",
"0.533079",
"0.53289974... | 0.5720255 | 6 |
Check if transaction is the slowest transaction and update accordingly. | def _update_slow_transaction(self, transaction):
slowest = 0
name = transaction.path
if self.__slow_transaction:
slowest = self.__slow_transaction.duration
if name in self.__slow_transaction_map:
slowest = max(self.__slow_transaction_map[name], slowest)
if transaction.duration > slowest:
# We are going to replace the prior slow transaction.
# We need to be a bit tricky here. If we are overriding
# an existing slow transaction for a different name,
# then we need to restore in the transaction map what
# the previous slowest duration was for that, or remove
# it if there wasn't one. This is so we do not incorrectly
# suppress it given that it was never actually reported
# as the slowest transaction.
if self.__slow_transaction:
if self.__slow_transaction.path != name:
if self.__slow_transaction_old_duration:
self.__slow_transaction_map[
self.__slow_transaction.path] = (
self.__slow_transaction_old_duration)
else:
del self.__slow_transaction_map[
self.__slow_transaction.path]
if name in self.__slow_transaction_map:
self.__slow_transaction_old_duration = (
self.__slow_transaction_map[name])
else:
self.__slow_transaction_old_duration = None
self.__slow_transaction = transaction
self.__slow_transaction_map[name] = transaction.duration | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_database_with_block(self, block):\n\n txs = sorted(block['txs'], key=lambda x: x['count'] if 'count' in x else -1)\n\n for tx in txs:\n result = self.update_database_with_tx(tx, block['length'])\n if not result:\n return False\n\n return True",
... | [
"0.5796732",
"0.56013685",
"0.5567382",
"0.5438813",
"0.52951",
"0.527724",
"0.5276468",
"0.52692777",
"0.51808035",
"0.5144416",
"0.5136642",
"0.51107734",
"0.5102024",
"0.5087963",
"0.50874853",
"0.5072691",
"0.5062309",
"0.503474",
"0.49916357",
"0.49607188",
"0.4943801",
... | 0.78206486 | 0 |
Check if transaction is a synthetics trace and save it to __synthetics_transactions. | def _update_synthetics_transaction(self, transaction):
settings = self.__settings
if not transaction.synthetics_resource_id:
return
maximum = settings.agent_limits.synthetics_transactions
if len(self.__synthetics_transactions) < maximum:
self.__synthetics_transactions.append(transaction) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def isSStx(tx):\n try:\n checkSStx(tx)\n\n except Exception as e:\n log.debug(\"isSStx: {}\".format(e))\n\n else:\n return True",
"def save(self, trade: Trade) -> Trade:\n\n pass # pragma: no cover",
"def is_transaction(self) -> bool:\n return False",
"def isTx(se... | [
"0.54398495",
"0.5149518",
"0.51403934",
"0.51361316",
"0.5022585",
"0.48563206",
"0.48534927",
"0.48511583",
"0.4807828",
"0.47867486",
"0.46448067",
"0.46145433",
"0.45834467",
"0.4583097",
"0.45755658",
"0.4567124",
"0.45389777",
"0.45228583",
"0.45225585",
"0.45150757",
"... | 0.64152426 | 0 |
Record any apdex and time metrics for the transaction as well as any errors which occurred for the transaction. If the transaction qualifies to become the slow transaction remember it for later. | def record_transaction(self, transaction):
if not self.__settings:
return
settings = self.__settings
# Record the apdex, value and time metrics generated from the
# transaction. Whether time metrics are reported as distinct
# metrics or into a rollup is in part controlled via settings
# for minimum number of unique metrics to be reported and thence
# whether over a time threshold calculated as percentage of
# overall request time, up to a maximum number of unique
# metrics. This is intended to limit how many metrics are
# reported for each transaction and try and cut down on an
# explosion of unique metric names. The limits and thresholds
# are applied after the metrics are reverse sorted based on
# exclusive times for each metric. This ensures that the metrics
# with greatest exclusive time are retained over those with
# lesser time. Such metrics get reported into the performance
# breakdown tab for specific web transactions.
self.record_apdex_metrics(transaction.apdex_metrics(self))
self.merge_custom_metrics(transaction.custom_metrics.metrics())
self.record_time_metrics(transaction.time_metrics(self))
# Capture any errors if error collection is enabled.
# Only retain maximum number allowed per harvest.
error_collector = settings.error_collector
if (error_collector.enabled and settings.collect_errors and
len(self.__transaction_errors) <
settings.agent_limits.errors_per_harvest):
self.__transaction_errors.extend(transaction.error_details())
self.__transaction_errors = self.__transaction_errors[:
settings.agent_limits.errors_per_harvest]
if (error_collector.capture_events and
error_collector.enabled and
settings.collect_error_events):
events = transaction.error_events(self.__stats_table)
for event in events:
self._error_events.add(event, priority=transaction.priority)
# Capture any sql traces if transaction tracer enabled.
if settings.slow_sql.enabled and settings.collect_traces:
for node in transaction.slow_sql_nodes(self):
self.record_slow_sql_node(node)
# Remember as slowest transaction if transaction tracer
# is enabled, it is over the threshold and slower than
# any existing transaction seen for this period and in
# the historical snapshot of slow transactions, plus
# recording of transaction trace for this transaction
# has not been suppressed.
transaction_tracer = settings.transaction_tracer
if (not transaction.suppress_transaction_trace and
transaction_tracer.enabled and settings.collect_traces):
# Transactions saved for Synthetics transactions
# do not depend on the transaction threshold.
self._update_synthetics_transaction(transaction)
threshold = transaction_tracer.transaction_threshold
if threshold is None:
threshold = transaction.apdex_t * 4
if transaction.duration >= threshold:
self._update_slow_transaction(transaction)
# Create the transaction event and add it to the
# appropriate "bucket." Synthetic requests are saved in one,
# while transactions from regular requests are saved in another.
if transaction.synthetics_resource_id:
event = transaction.transaction_event(self.__stats_table)
self._synthetics_events.add(event)
elif (settings.collect_analytics_events and
settings.transaction_events.enabled):
event = transaction.transaction_event(self.__stats_table)
self._transaction_events.add(event, priority=transaction.priority)
# Merge in custom events
if (settings.collect_custom_events and
settings.custom_insights_events.enabled):
self.custom_events.merge(transaction.custom_events)
# Merge in span events
if (settings.distributed_tracing.enabled and
settings.span_events.enabled and settings.collect_span_events):
if settings.infinite_tracing.enabled:
for event in transaction.span_protos(settings):
self._span_stream.put(event)
elif transaction.sampled:
for event in transaction.span_events(self.__settings):
self._span_events.add(event, priority=transaction.priority) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _update_slow_transaction(self, transaction):\n\n slowest = 0\n name = transaction.path\n\n if self.__slow_transaction:\n slowest = self.__slow_transaction.duration\n if name in self.__slow_transaction_map:\n slowest = max(self.__slow_transaction_map[name], slow... | [
"0.57431966",
"0.56813395",
"0.5386816",
"0.5268158",
"0.52590114",
"0.5248265",
"0.51790476",
"0.51674783",
"0.51302767",
"0.5116144",
"0.5102775",
"0.5091443",
"0.50542235",
"0.5050009",
"0.5020564",
"0.50163",
"0.50032055",
"0.4916298",
"0.48824197",
"0.4858216",
"0.484667... | 0.7942365 | 0 |
Returns a list containing the low level metric data for sending to the core application pertaining to the reporting period. This consists of tuple pairs where first is dictionary with name and scope keys with corresponding values, or integer identifier if metric had an entry in dictionary mapping metric (name, scope) as supplied from core application. The second is the list of accumulated metric data, the list always being of length 6. | def metric_data(self, normalizer=None):
if not self.__settings:
return []
result = []
normalized_stats = {}
# Metric Renaming and Re-Aggregation. After applying the metric
# renaming rules, the metrics are re-aggregated to collapse the
# metrics with same names after the renaming.
if self.__settings.debug.log_raw_metric_data:
_logger.info('Raw metric data for harvest of %r is %r.',
self.__settings.app_name,
list(six.iteritems(self.__stats_table)))
if normalizer is not None:
for key, value in six.iteritems(self.__stats_table):
key = (normalizer(key[0])[0], key[1])
stats = normalized_stats.get(key)
if stats is None:
normalized_stats[key] = copy.copy(value)
else:
stats.merge_stats(value)
else:
normalized_stats = self.__stats_table
if self.__settings.debug.log_normalized_metric_data:
_logger.info('Normalized metric data for harvest of %r is %r.',
self.__settings.app_name,
list(six.iteritems(normalized_stats)))
for key, value in six.iteritems(normalized_stats):
key = dict(name=key[0], scope=key[1])
result.append((key, value))
return result | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_metric_list(self) -> List[str]:\n ...",
"def get_all_metrics(self):\n up_time = self.uptime()\n down_time = self.downtime()\n customer_sla = self.sla()\n objective = self.slo()\n indicator = self.sli()\n avail_percentage = self.availability()\n mt_b... | [
"0.63188374",
"0.61312807",
"0.59498423",
"0.59364164",
"0.59249073",
"0.58868825",
"0.5866983",
"0.5859181",
"0.57975364",
"0.5722001",
"0.57070863",
"0.57040983",
"0.5697402",
"0.5691452",
"0.5689769",
"0.5687561",
"0.5686129",
"0.5646755",
"0.56391305",
"0.5638463",
"0.563... | 0.5418441 | 37 |
Returns a count of the number of unique metrics. | def metric_data_count(self):
if not self.__settings:
return 0
return len(self.__stats_table) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def metrics_count(self):\n\n return len(self.__stats_table)",
"def get_count(cls):\n total = 0\n for counter in SimpleCounterShard.objects.all():\n total += counter.count\n return total",
"def count(self):\n return self._reduce_for_stat_function(F.count, only_numer... | [
"0.71416634",
"0.688916",
"0.6736414",
"0.65794754",
"0.6549508",
"0.6549508",
"0.6549508",
"0.6549508",
"0.6548206",
"0.65326285",
"0.64953864",
"0.64506423",
"0.64230627",
"0.64027196",
"0.6389793",
"0.6381835",
"0.6377822",
"0.6373317",
"0.6364511",
"0.63238037",
"0.629012... | 0.67860687 | 2 |
Returns a to a list containing any errors collected during the reporting period. | def error_data(self):
if not self.__settings:
return []
return self.__transaction_errors | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getErrorsList(self):\n return self.__errors",
"def errors(self) -> List[Error]:",
"def getErrors(self) -> java.util.Collection:\n ...",
"def get_errors(self):\n return [result for result in self.values() if result.outcome == Result.ERROR]",
"def errors(self) -> List[Error]:\n ... | [
"0.7021791",
"0.70215106",
"0.6847071",
"0.6754863",
"0.6702647",
"0.6692272",
"0.65891427",
"0.65589136",
"0.65341383",
"0.65235007",
"0.65032333",
"0.65032333",
"0.6477152",
"0.6440303",
"0.642983",
"0.6413375",
"0.6399626",
"0.6374042",
"0.6354887",
"0.6273033",
"0.6271302... | 0.59131587 | 38 |
Returns a list of slow transaction data collected during the reporting period. | def transaction_trace_data(self, connections):
_logger.debug('Generating transaction trace data.')
if not self.__settings:
return []
# Create a set 'traces' that is a union of slow transaction,
# and Synthetics transactions. This ensures we don't send
# duplicates of a transaction.
traces = set()
if self.__slow_transaction:
traces.add(self.__slow_transaction)
traces.update(self.__synthetics_transactions)
# Return an empty list if no transactions were captured.
if not traces:
return []
# We want to limit the number of explain plans we do across
# these. So work out what were the slowest and tag them.
# Later the explain plan will only be run on those which are
# tagged.
agent_limits = self.__settings.agent_limits
explain_plan_limit = agent_limits.sql_explain_plans_per_harvest
maximum_nodes = agent_limits.transaction_traces_nodes
database_nodes = []
if explain_plan_limit != 0:
for trace in traces:
for node in trace.slow_sql:
# Make sure we clear any flag for explain plans on
# the nodes in case a transaction trace was merged
# in from previous harvest period.
node.generate_explain_plan = False
# Node should be excluded if not for an operation
# that we can't do an explain plan on. Also should
# not be one which would not be included in the
# transaction trace because limit was reached.
if (node.node_count < maximum_nodes and
node.connect_params and node.statement.operation in
node.statement.database.explain_stmts):
database_nodes.append(node)
database_nodes = sorted(database_nodes,
key=lambda x: x.duration)[-explain_plan_limit:]
for node in database_nodes:
node.generate_explain_plan = True
else:
for trace in traces:
for node in trace.slow_sql:
node.generate_explain_plan = True
database_nodes.append(node)
# Now generate the transaction traces. We need to cap the
# number of nodes capture to the specified limit.
trace_data = []
for trace in traces:
transaction_trace = trace.transaction_trace(
self, maximum_nodes, connections)
data = [transaction_trace,
list(trace.string_table.values())]
if self.__settings.debug.log_transaction_trace_payload:
_logger.debug('Encoding slow transaction data where '
'payload=%r.', data)
json_data = json_encode(data)
level = self.__settings.agent_limits.data_compression_level
level = level or zlib.Z_DEFAULT_COMPRESSION
zlib_data = zlib.compress(six.b(json_data), level)
pack_data = base64.standard_b64encode(zlib_data)
if six.PY3:
pack_data = pack_data.decode('Latin-1')
root = transaction_trace.root
if trace.record_tt:
force_persist = True
else:
force_persist = False
if trace.include_transaction_trace_request_uri:
request_uri = trace.request_uri
else:
request_uri = None
trace_data.append([transaction_trace.start_time,
root.end_time - root.start_time,
trace.path,
request_uri,
pack_data,
trace.guid,
None,
force_persist,
None,
trace.synthetics_resource_id, ])
return trace_data | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def slow_transaction_data(self):\n\n # XXX This method no longer appears to be used. Being replaced\n # by the transaction_trace_data() method.\n\n if not self.__settings:\n return []\n\n if not self.__slow_transaction:\n return []\n\n maximum = self.__setti... | [
"0.7311632",
"0.5812507",
"0.571646",
"0.57079303",
"0.5684339",
"0.55696833",
"0.55493486",
"0.553089",
"0.5528401",
"0.5502346",
"0.548358",
"0.544936",
"0.544525",
"0.54149175",
"0.5411108",
"0.54054636",
"0.5394847",
"0.5392003",
"0.53741133",
"0.5357601",
"0.5319892",
... | 0.6642906 | 1 |
Returns a list containing any slow transaction data collected during the reporting period. NOTE Currently only the slowest transaction for the reporting period is retained. | def slow_transaction_data(self):
# XXX This method no longer appears to be used. Being replaced
# by the transaction_trace_data() method.
if not self.__settings:
return []
if not self.__slow_transaction:
return []
maximum = self.__settings.agent_limits.transaction_traces_nodes
transaction_trace = self.__slow_transaction.transaction_trace(
self, maximum)
data = [transaction_trace,
list(self.__slow_transaction.string_table.values())]
if self.__settings.debug.log_transaction_trace_payload:
_logger.debug('Encoding slow transaction data where '
'payload=%r.', data)
json_data = json_encode(data)
level = self.__settings.agent_limits.data_compression_level
level = level or zlib.Z_DEFAULT_COMPRESSION
zlib_data = zlib.compress(six.b(json_data), level)
pack_data = base64.standard_b64encode(zlib_data)
if six.PY3:
pack_data = pack_data.decode('Latin-1')
root = transaction_trace.root
trace_data = [[root.start_time,
root.end_time - root.start_time,
self.__slow_transaction.path,
self.__slow_transaction.request_uri,
pack_data]]
return trace_data | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def transaction_trace_data(self, connections):\n\n _logger.debug('Generating transaction trace data.')\n\n if not self.__settings:\n return []\n\n # Create a set 'traces' that is a union of slow transaction,\n # and Synthetics transactions. This ensures we don't send\n ... | [
"0.6520807",
"0.5953189",
"0.5941957",
"0.5698872",
"0.56704205",
"0.55845326",
"0.55000305",
"0.54975384",
"0.53581554",
"0.5356301",
"0.5327412",
"0.53010184",
"0.5276863",
"0.52720934",
"0.5257284",
"0.52468723",
"0.5164559",
"0.5162055",
"0.51554185",
"0.5149581",
"0.5126... | 0.72200763 | 0 |
Resets the accumulated statistics back to initial state and associates the application settings object with the stats engine. This should be called when application is first activated and combined application settings incorporating server side settings are available. Would also be called on any forced restart of agent or a reconnection due to loss of connection. | def reset_stats(self, settings, reset_stream=False):
self.__settings = settings
self.__stats_table = {}
self.__sql_stats_table = {}
self.__slow_transaction = None
self.__slow_transaction_map = {}
self.__slow_transaction_old_duration = None
self.__transaction_errors = []
self.__synthetics_transactions = []
self.reset_transaction_events()
self.reset_error_events()
self.reset_custom_events()
self.reset_span_events()
self.reset_synthetics_events()
# streams are never reset after instantiation
if reset_stream:
self._span_stream = StreamBuffer(
settings.infinite_tracing.span_queue_size) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def stats_reset(self):\n self.stats.reset()",
"def stats_reset(self):\n self.stats.reset()",
"def reset_settings():\n settings = Settings()\n settings.reset()\n settings.save()",
"def initialize(self):\n super(Stats, self).initialize()\n if not hasattr(self.application, '... | [
"0.5851397",
"0.5851397",
"0.5782384",
"0.57592905",
"0.56791097",
"0.5615239",
"0.5549352",
"0.5518156",
"0.5513039",
"0.54954475",
"0.54496795",
"0.54446083",
"0.54427564",
"0.54313743",
"0.5414981",
"0.5411828",
"0.5359997",
"0.5353914",
"0.5333628",
"0.53336173",
"0.53336... | 0.5501668 | 9 |
Resets the accumulated statistics back to initial state for metric data. | def reset_metric_stats(self):
self.__stats_table = {} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def reset(self):\n self.sum_metric = 0.\n self.num_inst = 0.\n self.metrics.reset_stats()",
"def reset_metric_stats(self):\n self.__stats_table = {}",
"def stats_reset(self):\n self.stats.reset()",
"def stats_reset(self):\n self.stats.reset()",
"def reset(self) -> ... | [
"0.8448358",
"0.83412653",
"0.83299094",
"0.83299094",
"0.8270503",
"0.8153123",
"0.79735655",
"0.7772603",
"0.77270293",
"0.77003264",
"0.76665866",
"0.7614711",
"0.75709176",
"0.7540221",
"0.75036174",
"0.75036174",
"0.7490715",
"0.7471787",
"0.74488425",
"0.74488425",
"0.7... | 0.84143066 | 1 |
Resets the accumulated statistics back to initial state for sample analytics data. | def reset_transaction_events(self):
if self.__settings is not None:
self._transaction_events = SampledDataSet(
self.__settings.event_harvest_config.
harvest_limits.analytic_event_data)
else:
self._transaction_events = SampledDataSet() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def stats_reset(self):\n self.stats.reset()",
"def stats_reset(self):\n self.stats.reset()",
"def reset(self) -> None:\n self.statistics = defaultdict(float)",
"def reset(self) -> None:\n self.statistics = defaultdict(int)",
"def reset(self):\n self.stats = {}",
"def re... | [
"0.8175733",
"0.8175733",
"0.81559753",
"0.81235486",
"0.78766066",
"0.78731924",
"0.77236235",
"0.76826066",
"0.7648331",
"0.7643053",
"0.75607705",
"0.7420023",
"0.73855245",
"0.735026",
"0.735026",
"0.735026",
"0.7306659",
"0.7305569",
"0.72605497",
"0.72255266",
"0.714615... | 0.0 | -1 |
Resets the accumulated statistics back to initial state for Synthetics events data. | def reset_synthetics_events(self):
if self.__settings is not None:
self._synthetics_events = LimitedDataSet(
self.__settings.agent_limits.synthetics_events)
else:
self._synthetics_events = LimitedDataSet() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def reset(self) -> None:\n self.statistics = defaultdict(float)",
"def stats_reset(self):\n self.stats.reset()",
"def stats_reset(self):\n self.stats.reset()",
"def reset(self) -> None:\n self.statistics = defaultdict(int)",
"def reset(self):\n self.stats = {}",
"def re... | [
"0.7742906",
"0.76873755",
"0.76873755",
"0.7634818",
"0.75161463",
"0.7410027",
"0.7406162",
"0.7288068",
"0.72181994",
"0.7202865",
"0.7152267",
"0.7146272",
"0.69828737",
"0.6976172",
"0.6946784",
"0.6946784",
"0.6946784",
"0.6941798",
"0.69132906",
"0.6901035",
"0.6886194... | 0.71011585 | 12 |
Creates a snapshot of the accumulated statistics, error details and slow transaction and returns it. This is a shallow copy, only copying the top level objects. The originals are then reset back to being empty, with the exception of the dictionary mapping metric (name, scope) to the integer identifiers received from the core application. The latter is retained as should carry forward to subsequent runs. This method would be called to snapshot the data when doing the harvest. | def harvest_snapshot(self, flexible=False):
snapshot = self._snapshot()
# Data types only appear in one place, so during a snapshot it must be
# represented in either the snapshot or in the current stats object.
#
# If we're in flexible harvest, the goal is to have everything in the
# whitelist appear in the snapshot. This means, we must remove the
# whitelist data types from the current stats object.
#
# If we're not in flexible harvest, everything excluded from the
# whitelist appears in the snapshot and is removed from the current
# stats object.
if flexible:
whitelist_stats, other_stats = self, snapshot
snapshot.reset_non_event_types()
else:
whitelist_stats, other_stats = snapshot, self
self.reset_non_event_types()
event_harvest_whitelist = \
self.__settings.event_harvest_config.whitelist
# Iterate through harvest types. If they are in the list of types to
# harvest reset them on stats_engine otherwise remove them from the
# snapshot.
for nr_method, stats_methods in EVENT_HARVEST_METHODS.items():
for stats_method in stats_methods:
if nr_method in event_harvest_whitelist:
reset = getattr(whitelist_stats, stats_method)
else:
reset = getattr(other_stats, stats_method)
reset()
return snapshot | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_flat_results(self):\n test_results, error_dict, framestats = self.get_results()\n test_results = self._merge_test_results(test_results, error_dict)\n\n results = copy.deepcopy(test_results)\n results.update(framestats)\n\n return results",
"def __copy__(self) :\n ... | [
"0.5934949",
"0.58573073",
"0.5802806",
"0.5787985",
"0.5758199",
"0.5758142",
"0.57447433",
"0.57192504",
"0.56812185",
"0.56527025",
"0.5649071",
"0.56446093",
"0.5642422",
"0.5546954",
"0.5542068",
"0.5531035",
"0.5524276",
"0.55112106",
"0.54917693",
"0.54811484",
"0.5465... | 0.0 | -1 |
Creates and returns a new empty stats engine object. This would be used to distill stats from a single web transaction before then merging it back into the parent under a thread lock. | def create_workarea(self):
stats = copy.copy(self)
stats.reset_stats(self.__settings)
return stats | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create( ):\n \n uuid = generate_uuid( 10 )\n\n stats = Stats( key_name = uuid,\n uuid = uuid,\n )\n stats.put()\n return stats",
"def empty_instance():\n from weighted_graph import Graph\n return Graph()",
"def __init__(se... | [
"0.6012898",
"0.54610676",
"0.5434459",
"0.5365949",
"0.5345452",
"0.53324264",
"0.5254955",
"0.5204557",
"0.5093632",
"0.50626856",
"0.5004467",
"0.498278",
"0.49784213",
"0.4975857",
"0.49731496",
"0.49413025",
"0.49334928",
"0.49183187",
"0.49155334",
"0.48889562",
"0.4852... | 0.53285325 | 6 |
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