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values | func_name stringlengths 1 134 | docstring stringlengths 1 46.9k | path stringlengths 4 223 | original_string stringlengths 75 104k | code stringlengths 75 104k | docstring_tokens listlengths 1 1.97k | repo stringlengths 7 55 | language stringclasses 1
value | url stringlengths 87 315 | code_tokens listlengths 19 28.4k | sha stringlengths 40 40 |
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train | Superset.sync_druid_source | Syncs the druid datasource in main db with the provided config.
The endpoint takes 3 arguments:
user - user name to perform the operation as
cluster - name of the druid cluster
config - configuration stored in json that contains:
name: druid datasource name
... | superset/views/core.py | def sync_druid_source(self):
"""Syncs the druid datasource in main db with the provided config.
The endpoint takes 3 arguments:
user - user name to perform the operation as
cluster - name of the druid cluster
config - configuration stored in json that contains:
... | def sync_druid_source(self):
"""Syncs the druid datasource in main db with the provided config.
The endpoint takes 3 arguments:
user - user name to perform the operation as
cluster - name of the druid cluster
config - configuration stored in json that contains:
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train | Superset.cache_key_exist | Returns if a key from cache exist | superset/views/core.py | def cache_key_exist(self, key):
"""Returns if a key from cache exist"""
key_exist = True if cache.get(key) else False
status = 200 if key_exist else 404
return json_success(json.dumps({'key_exist': key_exist}),
status=status) | def cache_key_exist(self, key):
"""Returns if a key from cache exist"""
key_exist = True if cache.get(key) else False
status = 200 if key_exist else 404
return json_success(json.dumps({'key_exist': key_exist}),
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train | Superset.results | Serves a key off of the results backend | superset/views/core.py | def results(self, key):
"""Serves a key off of the results backend"""
if not results_backend:
return json_error_response("Results backend isn't configured")
read_from_results_backend_start = now_as_float()
blob = results_backend.get(key)
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"""Serves a key off of the results backend"""
if not results_backend:
return json_error_response("Results backend isn't configured")
read_from_results_backend_start = now_as_float()
blob = results_backend.get(key)
stats_logger.timing(
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train | Superset.sql_json | Runs arbitrary sql and returns and json | superset/views/core.py | def sql_json(self):
"""Runs arbitrary sql and returns and json"""
async_ = request.form.get('runAsync') == 'true'
sql = request.form.get('sql')
database_id = request.form.get('database_id')
schema = request.form.get('schema') or None
template_params = json.loads(
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"""Runs arbitrary sql and returns and json"""
async_ = request.form.get('runAsync') == 'true'
sql = request.form.get('sql')
database_id = request.form.get('database_id')
schema = request.form.get('schema') or None
template_params = json.loads(
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train | Superset.csv | Download the query results as csv. | superset/views/core.py | def csv(self, client_id):
"""Download the query results as csv."""
logging.info('Exporting CSV file [{}]'.format(client_id))
query = (
db.session.query(Query)
.filter_by(client_id=client_id)
.one()
)
rejected_tables = security_manager.rejected... | def csv(self, client_id):
"""Download the query results as csv."""
logging.info('Exporting CSV file [{}]'.format(client_id))
query = (
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train | Superset.queries | Get the updated queries. | superset/views/core.py | def queries(self, last_updated_ms):
"""Get the updated queries."""
stats_logger.incr('queries')
if not g.user.get_id():
return json_error_response(
'Please login to access the queries.', status=403)
# Unix time, milliseconds.
last_updated_ms_int = int... | def queries(self, last_updated_ms):
"""Get the updated queries."""
stats_logger.incr('queries')
if not g.user.get_id():
return json_error_response(
'Please login to access the queries.', status=403)
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train | Superset.search_queries | Search for previously run sqllab queries. Used for Sqllab Query Search
page /superset/sqllab#search.
Custom permission can_only_search_queries_owned restricts queries
to only queries run by current user.
:returns: Response with list of sql query dicts | superset/views/core.py | def search_queries(self) -> Response:
"""
Search for previously run sqllab queries. Used for Sqllab Query Search
page /superset/sqllab#search.
Custom permission can_only_search_queries_owned restricts queries
to only queries run by current user.
:returns: Response with ... | def search_queries(self) -> Response:
"""
Search for previously run sqllab queries. Used for Sqllab Query Search
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Custom permission can_only_search_queries_owned restricts queries
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train | Superset.welcome | Personalized welcome page | superset/views/core.py | def welcome(self):
"""Personalized welcome page"""
if not g.user or not g.user.get_id():
return redirect(appbuilder.get_url_for_login)
welcome_dashboard_id = (
db.session
.query(UserAttribute.welcome_dashboard_id)
.filter_by(user_id=g.user.get_id(... | def welcome(self):
"""Personalized welcome page"""
if not g.user or not g.user.get_id():
return redirect(appbuilder.get_url_for_login)
welcome_dashboard_id = (
db.session
.query(UserAttribute.welcome_dashboard_id)
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train | Superset.profile | User profile page | superset/views/core.py | def profile(self, username):
"""User profile page"""
if not username and g.user:
username = g.user.username
payload = {
'user': bootstrap_user_data(username, include_perms=True),
'common': self.common_bootsrap_payload(),
}
return self.render_... | def profile(self, username):
"""User profile page"""
if not username and g.user:
username = g.user.username
payload = {
'user': bootstrap_user_data(username, include_perms=True),
'common': self.common_bootsrap_payload(),
}
return self.render_... | [
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train | Superset.sqllab | SQL Editor | superset/views/core.py | def sqllab(self):
"""SQL Editor"""
d = {
'defaultDbId': config.get('SQLLAB_DEFAULT_DBID'),
'common': self.common_bootsrap_payload(),
}
return self.render_template(
'superset/basic.html',
entry='sqllab',
bootstrap_data=json.dumps... | def sqllab(self):
"""SQL Editor"""
d = {
'defaultDbId': config.get('SQLLAB_DEFAULT_DBID'),
'common': self.common_bootsrap_payload(),
}
return self.render_template(
'superset/basic.html',
entry='sqllab',
bootstrap_data=json.dumps... | [
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train | Superset.slice_query | This method exposes an API endpoint to
get the database query string for this slice | superset/views/core.py | def slice_query(self, slice_id):
"""
This method exposes an API endpoint to
get the database query string for this slice
"""
viz_obj = get_viz(slice_id)
security_manager.assert_datasource_permission(viz_obj.datasource)
return self.get_query_string_response(viz_obj... | def slice_query(self, slice_id):
"""
This method exposes an API endpoint to
get the database query string for this slice
"""
viz_obj = get_viz(slice_id)
security_manager.assert_datasource_permission(viz_obj.datasource)
return self.get_query_string_response(viz_obj... | [
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train | Superset.schemas_access_for_csv_upload | This method exposes an API endpoint to
get the schema access control settings for csv upload in this database | superset/views/core.py | def schemas_access_for_csv_upload(self):
"""
This method exposes an API endpoint to
get the schema access control settings for csv upload in this database
"""
if not request.args.get('db_id'):
return json_error_response(
'No database is allowed for you... | def schemas_access_for_csv_upload(self):
"""
This method exposes an API endpoint to
get the schema access control settings for csv upload in this database
"""
if not request.args.get('db_id'):
return json_error_response(
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train | stats_timing | Provide a transactional scope around a series of operations. | superset/utils/decorators.py | def stats_timing(stats_key, stats_logger):
"""Provide a transactional scope around a series of operations."""
start_ts = now_as_float()
try:
yield start_ts
except Exception as e:
raise e
finally:
stats_logger.timing(stats_key, now_as_float() - start_ts) | def stats_timing(stats_key, stats_logger):
"""Provide a transactional scope around a series of operations."""
start_ts = now_as_float()
try:
yield start_ts
except Exception as e:
raise e
finally:
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train | etag_cache | A decorator for caching views and handling etag conditional requests.
The decorator adds headers to GET requests that help with caching: Last-
Modified, Expires and ETag. It also handles conditional requests, when the
client send an If-Matches header.
If a cache is set, the decorator will cache GET re... | superset/utils/decorators.py | def etag_cache(max_age, check_perms=bool):
"""
A decorator for caching views and handling etag conditional requests.
The decorator adds headers to GET requests that help with caching: Last-
Modified, Expires and ETag. It also handles conditional requests, when the
client send an If-Matches header.
... | def etag_cache(max_age, check_perms=bool):
"""
A decorator for caching views and handling etag conditional requests.
The decorator adds headers to GET requests that help with caching: Last-
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train | BaseEngineSpec.apply_limit_to_sql | Alters the SQL statement to apply a LIMIT clause | superset/db_engine_specs.py | def apply_limit_to_sql(cls, sql, limit, database):
"""Alters the SQL statement to apply a LIMIT clause"""
if cls.limit_method == LimitMethod.WRAP_SQL:
sql = sql.strip('\t\n ;')
qry = (
select('*')
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TextAsFrom(text(s... | def apply_limit_to_sql(cls, sql, limit, database):
"""Alters the SQL statement to apply a LIMIT clause"""
if cls.limit_method == LimitMethod.WRAP_SQL:
sql = sql.strip('\t\n ;')
qry = (
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.select_from(
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train | BaseEngineSpec.modify_url_for_impersonation | Modify the SQL Alchemy URL object with the user to impersonate if applicable.
:param url: SQLAlchemy URL object
:param impersonate_user: Bool indicating if impersonation is enabled
:param username: Effective username | superset/db_engine_specs.py | def modify_url_for_impersonation(cls, url, impersonate_user, username):
"""
Modify the SQL Alchemy URL object with the user to impersonate if applicable.
:param url: SQLAlchemy URL object
:param impersonate_user: Bool indicating if impersonation is enabled
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Modify the SQL Alchemy URL object with the user to impersonate if applicable.
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train | BaseEngineSpec.make_label_compatible | Conditionally mutate and/or quote a sql column/expression label. If
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"""
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Conditionally mutate and/or quote a sql column/expression label. If
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train | BaseEngineSpec.truncate_label | In the case that a label exceeds the max length supported by the engine,
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"""
label = hashlib.md5(label.encode('utf-8')).hexdigest()
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In the case that a label exceeds the max length supported by the engine,
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train | PostgresEngineSpec.get_table_names | Need to consider foreign tables for PostgreSQL | superset/db_engine_specs.py | def get_table_names(cls, inspector, schema):
"""Need to consider foreign tables for PostgreSQL"""
tables = inspector.get_table_names(schema)
tables.extend(inspector.get_foreign_table_names(schema))
return sorted(tables) | def get_table_names(cls, inspector, schema):
"""Need to consider foreign tables for PostgreSQL"""
tables = inspector.get_table_names(schema)
tables.extend(inspector.get_foreign_table_names(schema))
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train | PostgresEngineSpec.get_timestamp_column | Postgres is unable to identify mixed case column names unless they
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"""Postgres is unable to identify mixed case column names unless they
are quoted."""
if expression:
return expression
elif column_name.lower() != column_name:
return f'"{column_name}"'
return column_name | def get_timestamp_column(expression, column_name):
"""Postgres is unable to identify mixed case column names unless they
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if expression:
return expression
elif column_name.lower() != column_name:
return f'"{column_name}"'
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train | MySQLEngineSpec.extract_error_message | Extract error message for queries | superset/db_engine_specs.py | def extract_error_message(cls, e):
"""Extract error message for queries"""
message = str(e)
try:
if isinstance(e.args, tuple) and len(e.args) > 1:
message = e.args[1]
except Exception:
pass
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"""Extract error message for queries"""
message = str(e)
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message = e.args[1]
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train | PrestoEngineSpec.fetch_result_sets | Returns a list of tables [schema1.table1, schema2.table2, ...]
Datasource_type can be 'table' or 'view'.
Empty schema corresponds to the list of full names of the all
tables or views: <schema>.<result_set_name>. | superset/db_engine_specs.py | def fetch_result_sets(cls, db, datasource_type):
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Datasource_type can be 'table' or 'view'.
Empty schema corresponds to the list of full names of the all
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Datasource_type can be 'table' or 'view'.
Empty schema corresponds to the list of full names of the all
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train | PrestoEngineSpec.handle_cursor | Updates progress information | superset/db_engine_specs.py | def handle_cursor(cls, cursor, query, session):
"""Updates progress information"""
logging.info('Polling the cursor for progress')
polled = cursor.poll()
# poll returns dict -- JSON status information or ``None``
# if the query is done
# https://github.com/dropbox/PyHive/... | def handle_cursor(cls, cursor, query, session):
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logging.info('Polling the cursor for progress')
polled = cursor.poll()
# poll returns dict -- JSON status information or ``None``
# if the query is done
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train | PrestoEngineSpec._partition_query | Returns a partition query
:param table_name: the name of the table to get partitions from
:type table_name: str
:param limit: the number of partitions to be returned
:type limit: int
:param order_by: a list of tuples of field name and a boolean
that determines if tha... | superset/db_engine_specs.py | def _partition_query(
cls, table_name, limit=0, order_by=None, filters=None):
"""Returns a partition query
:param table_name: the name of the table to get partitions from
:type table_name: str
:param limit: the number of partitions to be returned
:type limit: int
... | def _partition_query(
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:param table_name: the name of the table to get partitions from
:type table_name: str
:param limit: the number of partitions to be returned
:type limit: int
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train | HiveEngineSpec.create_table_from_csv | Uploads a csv file and creates a superset datasource in Hive. | superset/db_engine_specs.py | def create_table_from_csv(form, table):
"""Uploads a csv file and creates a superset datasource in Hive."""
def convert_to_hive_type(col_type):
"""maps tableschema's types to hive types"""
tableschema_to_hive_types = {
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'intege... | def create_table_from_csv(form, table):
"""Uploads a csv file and creates a superset datasource in Hive."""
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"""maps tableschema's types to hive types"""
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train | HiveEngineSpec.handle_cursor | Updates progress information | superset/db_engine_specs.py | def handle_cursor(cls, cursor, query, session):
"""Updates progress information"""
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unfinished_states = (
hive.ttypes.TOperationState.INITIALIZED_STATE,
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hive.ttypes.TOperationState.INITIALIZED_STATE,
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train | HiveEngineSpec.get_configuration_for_impersonation | Return a configuration dictionary that can be merged with other configs
that can set the correct properties for impersonating users
:param uri: URI string
:param impersonate_user: Bool indicating if impersonation is enabled
:param username: Effective username
:return: Dictionary ... | superset/db_engine_specs.py | def get_configuration_for_impersonation(cls, uri, impersonate_user, username):
"""
Return a configuration dictionary that can be merged with other configs
that can set the correct properties for impersonating users
:param uri: URI string
:param impersonate_user: Bool indicating i... | def get_configuration_for_impersonation(cls, uri, impersonate_user, username):
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Return a configuration dictionary that can be merged with other configs
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train | BQEngineSpec.mutate_label | BigQuery field_name should start with a letter or underscore and contain only
alphanumeric characters. Labels that start with a number are prefixed with an
underscore. Any unsupported characters are replaced with underscores and an
md5 hash is added to the end of the label to avoid possible coll... | superset/db_engine_specs.py | def mutate_label(label):
"""
BigQuery field_name should start with a letter or underscore and contain only
alphanumeric characters. Labels that start with a number are prefixed with an
underscore. Any unsupported characters are replaced with underscores and an
md5 hash is added t... | def mutate_label(label):
"""
BigQuery field_name should start with a letter or underscore and contain only
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Using literal_column handles that issue.
https://docs.sqlalchemy.org/en/latest/core/tutorial.html#using-more-specific-text-with-table-literal-column-and-column
Also explicility specifying column names so ... | superset/db_engine_specs.py | def _get_fields(cls, cols):
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BigQuery dialect requires us to not use backtick in the fieldname which are
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Using literal_column handles that issue.
https://docs.sqlalchemy.org/en/latest/core/tutorial.html#using-more-specific-text-with-table-literal-column-and-column
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https://docs.sqlalchemy.org/en/latest/core/tutorial.html#using-more-specific-text-with-table-literal-column-and-column
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train | load_multiformat_time_series | Loading time series data from a zip file in the repo | superset/data/multiformat_time_series.py | def load_multiformat_time_series():
"""Loading time series data from a zip file in the repo"""
data = get_example_data('multiformat_time_series.json.gz')
pdf = pd.read_json(data)
pdf.ds = pd.to_datetime(pdf.ds, unit='s')
pdf.ds2 = pd.to_datetime(pdf.ds2, unit='s')
pdf.to_sql(
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"""Loading time series data from a zip file in the repo"""
data = get_example_data('multiformat_time_series.json.gz')
pdf = pd.read_json(data)
pdf.ds = pd.to_datetime(pdf.ds, unit='s')
pdf.ds2 = pd.to_datetime(pdf.ds2, unit='s')
pdf.to_sql(
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train | import_dashboards | Imports dashboards from a stream to databases | superset/utils/dashboard_import_export.py | def import_dashboards(session, data_stream, import_time=None):
"""Imports dashboards from a stream to databases"""
current_tt = int(time.time())
import_time = current_tt if import_time is None else import_time
data = json.loads(data_stream.read(), object_hook=decode_dashboards)
# TODO: import DRUID ... | def import_dashboards(session, data_stream, import_time=None):
"""Imports dashboards from a stream to databases"""
current_tt = int(time.time())
import_time = current_tt if import_time is None else import_time
data = json.loads(data_stream.read(), object_hook=decode_dashboards)
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train | export_dashboards | Returns all dashboards metadata as a json dump | superset/utils/dashboard_import_export.py | def export_dashboards(session):
"""Returns all dashboards metadata as a json dump"""
logging.info('Starting export')
dashboards = session.query(Dashboard)
dashboard_ids = []
for dashboard in dashboards:
dashboard_ids.append(dashboard.id)
data = Dashboard.export_dashboards(dashboard_ids)
... | def export_dashboards(session):
"""Returns all dashboards metadata as a json dump"""
logging.info('Starting export')
dashboards = session.query(Dashboard)
dashboard_ids = []
for dashboard in dashboards:
dashboard_ids.append(dashboard.id)
data = Dashboard.export_dashboards(dashboard_ids)
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train | QueryObject.cache_key | The cache key is made out of the key/values in `query_obj`, plus any
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We remove datetime bounds that are hard values, and replace them with
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"""
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train | handle_query_error | Local method handling error while processing the SQL | superset/sql_lab.py | def handle_query_error(msg, query, session, payload=None):
"""Local method handling error while processing the SQL"""
payload = payload or {}
troubleshooting_link = config['TROUBLESHOOTING_LINK']
query.error_message = msg
query.status = QueryStatus.FAILED
query.tmp_table_name = None
session.... | def handle_query_error(msg, query, session, payload=None):
"""Local method handling error while processing the SQL"""
payload = payload or {}
troubleshooting_link = config['TROUBLESHOOTING_LINK']
query.error_message = msg
query.status = QueryStatus.FAILED
query.tmp_table_name = None
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train | get_query | attemps to get the query and retry if it cannot | superset/sql_lab.py | def get_query(query_id, session, retry_count=5):
"""attemps to get the query and retry if it cannot"""
query = None
attempt = 0
while not query and attempt < retry_count:
try:
query = session.query(Query).filter_by(id=query_id).one()
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train | session_scope | Provide a transactional scope around a series of operations. | superset/sql_lab.py | def session_scope(nullpool):
"""Provide a transactional scope around a series of operations."""
if nullpool:
engine = sqlalchemy.create_engine(
app.config.get('SQLALCHEMY_DATABASE_URI'), poolclass=NullPool)
session_class = sessionmaker()
session_class.configure(bind=engine)
... | def session_scope(nullpool):
"""Provide a transactional scope around a series of operations."""
if nullpool:
engine = sqlalchemy.create_engine(
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session_class = sessionmaker()
session_class.configure(bind=engine)
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train | get_sql_results | Executes the sql query returns the results. | superset/sql_lab.py | def get_sql_results(
ctask, query_id, rendered_query, return_results=True, store_results=False,
user_name=None, start_time=None):
"""Executes the sql query returns the results."""
with session_scope(not ctask.request.called_directly) as session:
try:
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ctask, query_id, rendered_query, return_results=True, store_results=False,
user_name=None, start_time=None):
"""Executes the sql query returns the results."""
with session_scope(not ctask.request.called_directly) as session:
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train | execute_sql_statement | Executes a single SQL statement | superset/sql_lab.py | def execute_sql_statement(sql_statement, query, user_name, session, cursor):
"""Executes a single SQL statement"""
database = query.database
db_engine_spec = database.db_engine_spec
parsed_query = ParsedQuery(sql_statement)
sql = parsed_query.stripped()
SQL_MAX_ROWS = app.config.get('SQL_MAX_ROW... | def execute_sql_statement(sql_statement, query, user_name, session, cursor):
"""Executes a single SQL statement"""
database = query.database
db_engine_spec = database.db_engine_spec
parsed_query = ParsedQuery(sql_statement)
sql = parsed_query.stripped()
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train | execute_sql_statements | Executes the sql query returns the results. | superset/sql_lab.py | def execute_sql_statements(
ctask, query_id, rendered_query, return_results=True, store_results=False,
user_name=None, session=None, start_time=None,
):
"""Executes the sql query returns the results."""
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# only asynchronous queries
stats_logger.timing(
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ctask, query_id, rendered_query, return_results=True, store_results=False,
user_name=None, session=None, start_time=None,
):
"""Executes the sql query returns the results."""
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# only asynchronous queries
stats_logger.timing(
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train | flasher | Flask's flash if available, logging call if not | superset/utils/core.py | def flasher(msg, severity=None):
"""Flask's flash if available, logging call if not"""
try:
flash(msg, severity)
except RuntimeError:
if severity == 'danger':
logging.error(msg)
else:
logging.info(msg) | def flasher(msg, severity=None):
"""Flask's flash if available, logging call if not"""
try:
flash(msg, severity)
except RuntimeError:
if severity == 'danger':
logging.error(msg)
else:
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train | string_to_num | Converts a string to an int/float
Returns ``None`` if it can't be converted
>>> string_to_num('5')
5
>>> string_to_num('5.2')
5.2
>>> string_to_num(10)
10
>>> string_to_num(10.1)
10.1
>>> string_to_num('this is not a string') is None
True | superset/utils/core.py | def string_to_num(s: str):
"""Converts a string to an int/float
Returns ``None`` if it can't be converted
>>> string_to_num('5')
5
>>> string_to_num('5.2')
5.2
>>> string_to_num(10)
10
>>> string_to_num(10.1)
10.1
>>> string_to_num('this is not a string') is None
True
... | def string_to_num(s: str):
"""Converts a string to an int/float
Returns ``None`` if it can't be converted
>>> string_to_num('5')
5
>>> string_to_num('5.2')
5.2
>>> string_to_num(10)
10
>>> string_to_num(10.1)
10.1
>>> string_to_num('this is not a string') is None
True
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train | list_minus | Returns l without what is in minus
>>> list_minus([1, 2, 3], [2])
[1, 3] | superset/utils/core.py | def list_minus(l: List, minus: List) -> List:
"""Returns l without what is in minus
>>> list_minus([1, 2, 3], [2])
[1, 3]
"""
return [o for o in l if o not in minus] | def list_minus(l: List, minus: List) -> List:
"""Returns l without what is in minus
>>> list_minus([1, 2, 3], [2])
[1, 3]
"""
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train | parse_human_datetime | Returns ``datetime.datetime`` from human readable strings
>>> from datetime import date, timedelta
>>> from dateutil.relativedelta import relativedelta
>>> parse_human_datetime('2015-04-03')
datetime.datetime(2015, 4, 3, 0, 0)
>>> parse_human_datetime('2/3/1969')
datetime.datetime(1969, 2, 3, 0... | superset/utils/core.py | def parse_human_datetime(s):
"""
Returns ``datetime.datetime`` from human readable strings
>>> from datetime import date, timedelta
>>> from dateutil.relativedelta import relativedelta
>>> parse_human_datetime('2015-04-03')
datetime.datetime(2015, 4, 3, 0, 0)
>>> parse_human_datetime('2/3/1... | def parse_human_datetime(s):
"""
Returns ``datetime.datetime`` from human readable strings
>>> from datetime import date, timedelta
>>> from dateutil.relativedelta import relativedelta
>>> parse_human_datetime('2015-04-03')
datetime.datetime(2015, 4, 3, 0, 0)
>>> parse_human_datetime('2/3/1... | [
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train | decode_dashboards | Function to be passed into json.loads obj_hook parameter
Recreates the dashboard object from a json representation. | superset/utils/core.py | def decode_dashboards(o):
"""
Function to be passed into json.loads obj_hook parameter
Recreates the dashboard object from a json representation.
"""
import superset.models.core as models
from superset.connectors.sqla.models import (
SqlaTable, SqlMetric, TableColumn,
)
if '__Da... | def decode_dashboards(o):
"""
Function to be passed into json.loads obj_hook parameter
Recreates the dashboard object from a json representation.
"""
import superset.models.core as models
from superset.connectors.sqla.models import (
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train | parse_human_timedelta | Returns ``datetime.datetime`` from natural language time deltas
>>> parse_human_datetime('now') <= datetime.now()
True | superset/utils/core.py | def parse_human_timedelta(s: str):
"""
Returns ``datetime.datetime`` from natural language time deltas
>>> parse_human_datetime('now') <= datetime.now()
True
"""
cal = parsedatetime.Calendar()
dttm = dttm_from_timtuple(datetime.now().timetuple())
d = cal.parse(s or '', dttm)[0]
d = ... | def parse_human_timedelta(s: str):
"""
Returns ``datetime.datetime`` from natural language time deltas
>>> parse_human_datetime('now') <= datetime.now()
True
"""
cal = parsedatetime.Calendar()
dttm = dttm_from_timtuple(datetime.now().timetuple())
d = cal.parse(s or '', dttm)[0]
d = ... | [
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train | datetime_f | Formats datetime to take less room when it is recent | superset/utils/core.py | def datetime_f(dttm):
"""Formats datetime to take less room when it is recent"""
if dttm:
dttm = dttm.isoformat()
now_iso = datetime.now().isoformat()
if now_iso[:10] == dttm[:10]:
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"""Formats datetime to take less room when it is recent"""
if dttm:
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if now_iso[:10] == dttm[:10]:
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train | json_iso_dttm_ser | json serializer that deals with dates
>>> dttm = datetime(1970, 1, 1)
>>> json.dumps({'dttm': dttm}, default=json_iso_dttm_ser)
'{"dttm": "1970-01-01T00:00:00"}' | superset/utils/core.py | def json_iso_dttm_ser(obj, pessimistic: Optional[bool] = False):
"""
json serializer that deals with dates
>>> dttm = datetime(1970, 1, 1)
>>> json.dumps({'dttm': dttm}, default=json_iso_dttm_ser)
'{"dttm": "1970-01-01T00:00:00"}'
"""
val = base_json_conv(obj)
if val is not None:
... | def json_iso_dttm_ser(obj, pessimistic: Optional[bool] = False):
"""
json serializer that deals with dates
>>> dttm = datetime(1970, 1, 1)
>>> json.dumps({'dttm': dttm}, default=json_iso_dttm_ser)
'{"dttm": "1970-01-01T00:00:00"}'
"""
val = base_json_conv(obj)
if val is not None:
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train | json_int_dttm_ser | json serializer that deals with dates | superset/utils/core.py | def json_int_dttm_ser(obj):
"""json serializer that deals with dates"""
val = base_json_conv(obj)
if val is not None:
return val
if isinstance(obj, (datetime, pd.Timestamp)):
obj = datetime_to_epoch(obj)
elif isinstance(obj, date):
obj = (obj - EPOCH.date()).total_seconds() *... | def json_int_dttm_ser(obj):
"""json serializer that deals with dates"""
val = base_json_conv(obj)
if val is not None:
return val
if isinstance(obj, (datetime, pd.Timestamp)):
obj = datetime_to_epoch(obj)
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train | error_msg_from_exception | Translate exception into error message
Database have different ways to handle exception. This function attempts
to make sense of the exception object and construct a human readable
sentence.
TODO(bkyryliuk): parse the Presto error message from the connection
created via create_eng... | superset/utils/core.py | def error_msg_from_exception(e):
"""Translate exception into error message
Database have different ways to handle exception. This function attempts
to make sense of the exception object and construct a human readable
sentence.
TODO(bkyryliuk): parse the Presto error message from the connection
... | def error_msg_from_exception(e):
"""Translate exception into error message
Database have different ways to handle exception. This function attempts
to make sense of the exception object and construct a human readable
sentence.
TODO(bkyryliuk): parse the Presto error message from the connection
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train | generic_find_constraint_name | Utility to find a constraint name in alembic migrations | superset/utils/core.py | def generic_find_constraint_name(table, columns, referenced, db):
"""Utility to find a constraint name in alembic migrations"""
t = sa.Table(table, db.metadata, autoload=True, autoload_with=db.engine)
for fk in t.foreign_key_constraints:
if fk.referred_table.name == referenced and set(fk.column_key... | def generic_find_constraint_name(table, columns, referenced, db):
"""Utility to find a constraint name in alembic migrations"""
t = sa.Table(table, db.metadata, autoload=True, autoload_with=db.engine)
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train | generic_find_fk_constraint_name | Utility to find a foreign-key constraint name in alembic migrations | superset/utils/core.py | def generic_find_fk_constraint_name(table, columns, referenced, insp):
"""Utility to find a foreign-key constraint name in alembic migrations"""
for fk in insp.get_foreign_keys(table):
if fk['referred_table'] == referenced and set(fk['referred_columns']) == columns:
return fk['name'] | def generic_find_fk_constraint_name(table, columns, referenced, insp):
"""Utility to find a foreign-key constraint name in alembic migrations"""
for fk in insp.get_foreign_keys(table):
if fk['referred_table'] == referenced and set(fk['referred_columns']) == columns:
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train | generic_find_fk_constraint_names | Utility to find foreign-key constraint names in alembic migrations | superset/utils/core.py | def generic_find_fk_constraint_names(table, columns, referenced, insp):
"""Utility to find foreign-key constraint names in alembic migrations"""
names = set()
for fk in insp.get_foreign_keys(table):
if fk['referred_table'] == referenced and set(fk['referred_columns']) == columns:
names.... | def generic_find_fk_constraint_names(table, columns, referenced, insp):
"""Utility to find foreign-key constraint names in alembic migrations"""
names = set()
for fk in insp.get_foreign_keys(table):
if fk['referred_table'] == referenced and set(fk['referred_columns']) == columns:
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train | generic_find_uq_constraint_name | Utility to find a unique constraint name in alembic migrations | superset/utils/core.py | def generic_find_uq_constraint_name(table, columns, insp):
"""Utility to find a unique constraint name in alembic migrations"""
for uq in insp.get_unique_constraints(table):
if columns == set(uq['column_names']):
return uq['name'] | def generic_find_uq_constraint_name(table, columns, insp):
"""Utility to find a unique constraint name in alembic migrations"""
for uq in insp.get_unique_constraints(table):
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train | table_has_constraint | Utility to find a constraint name in alembic migrations | superset/utils/core.py | def table_has_constraint(table, name, db):
"""Utility to find a constraint name in alembic migrations"""
t = sa.Table(table, db.metadata, autoload=True, autoload_with=db.engine)
for c in t.constraints:
if c.name == name:
return True
return False | def table_has_constraint(table, name, db):
"""Utility to find a constraint name in alembic migrations"""
t = sa.Table(table, db.metadata, autoload=True, autoload_with=db.engine)
for c in t.constraints:
if c.name == name:
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train | send_email_smtp | Send an email with html content, eg:
send_email_smtp(
'test@example.com', 'foo', '<b>Foo</b> bar',['/dev/null'], dryrun=True) | superset/utils/core.py | def send_email_smtp(to, subject, html_content, config,
files=None, data=None, images=None, dryrun=False,
cc=None, bcc=None, mime_subtype='mixed'):
"""
Send an email with html content, eg:
send_email_smtp(
'test@example.com', 'foo', '<b>Foo</b> bar',['/dev/null... | def send_email_smtp(to, subject, html_content, config,
files=None, data=None, images=None, dryrun=False,
cc=None, bcc=None, mime_subtype='mixed'):
"""
Send an email with html content, eg:
send_email_smtp(
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train | setup_cache | Setup the flask-cache on a flask app | superset/utils/core.py | def setup_cache(app: Flask, cache_config) -> Optional[Cache]:
"""Setup the flask-cache on a flask app"""
if cache_config and cache_config.get('CACHE_TYPE') != 'null':
return Cache(app, config=cache_config)
return None | def setup_cache(app: Flask, cache_config) -> Optional[Cache]:
"""Setup the flask-cache on a flask app"""
if cache_config and cache_config.get('CACHE_TYPE') != 'null':
return Cache(app, config=cache_config)
return None | [
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train | zlib_compress | Compress things in a py2/3 safe fashion
>>> json_str = '{"test": 1}'
>>> blob = zlib_compress(json_str) | superset/utils/core.py | def zlib_compress(data):
"""
Compress things in a py2/3 safe fashion
>>> json_str = '{"test": 1}'
>>> blob = zlib_compress(json_str)
"""
if PY3K:
if isinstance(data, str):
return zlib.compress(bytes(data, 'utf-8'))
return zlib.compress(data)
return zlib.compress(d... | def zlib_compress(data):
"""
Compress things in a py2/3 safe fashion
>>> json_str = '{"test": 1}'
>>> blob = zlib_compress(json_str)
"""
if PY3K:
if isinstance(data, str):
return zlib.compress(bytes(data, 'utf-8'))
return zlib.compress(data)
return zlib.compress(d... | [
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train | zlib_decompress_to_string | Decompress things to a string in a py2/3 safe fashion
>>> json_str = '{"test": 1}'
>>> blob = zlib_compress(json_str)
>>> got_str = zlib_decompress_to_string(blob)
>>> got_str == json_str
True | superset/utils/core.py | def zlib_decompress_to_string(blob):
"""
Decompress things to a string in a py2/3 safe fashion
>>> json_str = '{"test": 1}'
>>> blob = zlib_compress(json_str)
>>> got_str = zlib_decompress_to_string(blob)
>>> got_str == json_str
True
"""
if PY3K:
if isinstance(blob, bytes):
... | def zlib_decompress_to_string(blob):
"""
Decompress things to a string in a py2/3 safe fashion
>>> json_str = '{"test": 1}'
>>> blob = zlib_compress(json_str)
>>> got_str = zlib_decompress_to_string(blob)
>>> got_str == json_str
True
"""
if PY3K:
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train | user_label | Given a user ORM FAB object, returns a label | superset/utils/core.py | def user_label(user: User) -> Optional[str]:
"""Given a user ORM FAB object, returns a label"""
if user:
if user.first_name and user.last_name:
return user.first_name + ' ' + user.last_name
else:
return user.username
return None | def user_label(user: User) -> Optional[str]:
"""Given a user ORM FAB object, returns a label"""
if user:
if user.first_name and user.last_name:
return user.first_name + ' ' + user.last_name
else:
return user.username
return None | [
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train | get_since_until | Return `since` and `until` date time tuple from string representations of
time_range, since, until and time_shift.
This functiom supports both reading the keys separately (from `since` and
`until`), as well as the new `time_range` key. Valid formats are:
- ISO 8601
- X days/years/hours/day... | superset/utils/core.py | def get_since_until(time_range: Optional[str] = None,
since: Optional[str] = None,
until: Optional[str] = None,
time_shift: Optional[str] = None,
relative_end: Optional[str] = None) -> Tuple[datetime, datetime]:
"""Return `since` and `u... | def get_since_until(time_range: Optional[str] = None,
since: Optional[str] = None,
until: Optional[str] = None,
time_shift: Optional[str] = None,
relative_end: Optional[str] = None) -> Tuple[datetime, datetime]:
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train | add_ago_to_since | Backwards compatibility hack. Without this slices with since: 7 days will
be treated as 7 days in the future.
:param str since:
:returns: Since with ago added if necessary
:rtype: str | superset/utils/core.py | def add_ago_to_since(since: str) -> str:
"""
Backwards compatibility hack. Without this slices with since: 7 days will
be treated as 7 days in the future.
:param str since:
:returns: Since with ago added if necessary
:rtype: str
"""
since_words = since.split(' ')
grains = ['days', '... | def add_ago_to_since(since: str) -> str:
"""
Backwards compatibility hack. Without this slices with since: 7 days will
be treated as 7 days in the future.
:param str since:
:returns: Since with ago added if necessary
:rtype: str
"""
since_words = since.split(' ')
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train | split_adhoc_filters_into_base_filters | Mutates form data to restructure the adhoc filters in the form of the four base
filters, `where`, `having`, `filters`, and `having_filters` which represent
free form where sql, free form having sql, structured where clauses and structured
having clauses. | superset/utils/core.py | def split_adhoc_filters_into_base_filters(fd):
"""
Mutates form data to restructure the adhoc filters in the form of the four base
filters, `where`, `having`, `filters`, and `having_filters` which represent
free form where sql, free form having sql, structured where clauses and structured
having cla... | def split_adhoc_filters_into_base_filters(fd):
"""
Mutates form data to restructure the adhoc filters in the form of the four base
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train | load_energy | Loads an energy related dataset to use with sankey and graphs | superset/data/energy.py | def load_energy():
"""Loads an energy related dataset to use with sankey and graphs"""
tbl_name = 'energy_usage'
data = get_example_data('energy.json.gz')
pdf = pd.read_json(data)
pdf.to_sql(
tbl_name,
db.engine,
if_exists='replace',
chunksize=500,
dtype={
... | def load_energy():
"""Loads an energy related dataset to use with sankey and graphs"""
tbl_name = 'energy_usage'
data = get_example_data('energy.json.gz')
pdf = pd.read_json(data)
pdf.to_sql(
tbl_name,
db.engine,
if_exists='replace',
chunksize=500,
dtype={
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] | apache/incubator-superset | python | https://github.com/apache/incubator-superset/blob/ca2996c78f679260eb79c6008e276733df5fb653/superset/data/energy.py#L32-L140 | [
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train | load_random_time_series_data | Loading random time series data from a zip file in the repo | superset/data/random_time_series.py | def load_random_time_series_data():
"""Loading random time series data from a zip file in the repo"""
data = get_example_data('random_time_series.json.gz')
pdf = pd.read_json(data)
pdf.ds = pd.to_datetime(pdf.ds, unit='s')
pdf.to_sql(
'random_time_series',
db.engine,
if_exist... | def load_random_time_series_data():
"""Loading random time series data from a zip file in the repo"""
data = get_example_data('random_time_series.json.gz')
pdf = pd.read_json(data)
pdf.ds = pd.to_datetime(pdf.ds, unit='s')
pdf.to_sql(
'random_time_series',
db.engine,
if_exist... | [
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train | runserver | Starts a Superset web server. | superset/cli.py | def runserver(debug, console_log, use_reloader, address, port, timeout, workers, socket):
"""Starts a Superset web server."""
debug = debug or config.get('DEBUG') or console_log
if debug:
print(Fore.BLUE + '-=' * 20)
print(
Fore.YELLOW + 'Starting Superset server in ' +
... | def runserver(debug, console_log, use_reloader, address, port, timeout, workers, socket):
"""Starts a Superset web server."""
debug = debug or config.get('DEBUG') or console_log
if debug:
print(Fore.BLUE + '-=' * 20)
print(
Fore.YELLOW + 'Starting Superset server in ' +
... | [
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] | apache/incubator-superset | python | https://github.com/apache/incubator-superset/blob/ca2996c78f679260eb79c6008e276733df5fb653/superset/cli.py#L112-L144 | [
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train | version | Prints the current version number | superset/cli.py | def version(verbose):
"""Prints the current version number"""
print(Fore.BLUE + '-=' * 15)
print(Fore.YELLOW + 'Superset ' + Fore.CYAN + '{version}'.format(
version=config.get('VERSION_STRING')))
print(Fore.BLUE + '-=' * 15)
if verbose:
print('[DB] : ' + '{}'.format(db.engine))
p... | def version(verbose):
"""Prints the current version number"""
print(Fore.BLUE + '-=' * 15)
print(Fore.YELLOW + 'Superset ' + Fore.CYAN + '{version}'.format(
version=config.get('VERSION_STRING')))
print(Fore.BLUE + '-=' * 15)
if verbose:
print('[DB] : ' + '{}'.format(db.engine))
p... | [
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train | refresh_druid | Refresh druid datasources | superset/cli.py | def refresh_druid(datasource, merge):
"""Refresh druid datasources"""
session = db.session()
from superset.connectors.druid.models import DruidCluster
for cluster in session.query(DruidCluster).all():
try:
cluster.refresh_datasources(datasource_name=datasource,
... | def refresh_druid(datasource, merge):
"""Refresh druid datasources"""
session = db.session()
from superset.connectors.druid.models import DruidCluster
for cluster in session.query(DruidCluster).all():
try:
cluster.refresh_datasources(datasource_name=datasource,
... | [
"Refresh",
"druid",
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train | import_dashboards | Import dashboards from JSON | superset/cli.py | def import_dashboards(path, recursive):
"""Import dashboards from JSON"""
p = Path(path)
files = []
if p.is_file():
files.append(p)
elif p.exists() and not recursive:
files.extend(p.glob('*.json'))
elif p.exists() and recursive:
files.extend(p.rglob('*.json'))
for f i... | def import_dashboards(path, recursive):
"""Import dashboards from JSON"""
p = Path(path)
files = []
if p.is_file():
files.append(p)
elif p.exists() and not recursive:
files.extend(p.glob('*.json'))
elif p.exists() and recursive:
files.extend(p.rglob('*.json'))
for f i... | [
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train | export_dashboards | Export dashboards to JSON | superset/cli.py | def export_dashboards(print_stdout, dashboard_file):
"""Export dashboards to JSON"""
data = dashboard_import_export.export_dashboards(db.session)
if print_stdout or not dashboard_file:
print(data)
if dashboard_file:
logging.info('Exporting dashboards to %s', dashboard_file)
with ... | def export_dashboards(print_stdout, dashboard_file):
"""Export dashboards to JSON"""
data = dashboard_import_export.export_dashboards(db.session)
if print_stdout or not dashboard_file:
print(data)
if dashboard_file:
logging.info('Exporting dashboards to %s', dashboard_file)
with ... | [
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train | import_datasources | Import datasources from YAML | superset/cli.py | def import_datasources(path, sync, recursive):
"""Import datasources from YAML"""
sync_array = sync.split(',')
p = Path(path)
files = []
if p.is_file():
files.append(p)
elif p.exists() and not recursive:
files.extend(p.glob('*.yaml'))
files.extend(p.glob('*.yml'))
eli... | def import_datasources(path, sync, recursive):
"""Import datasources from YAML"""
sync_array = sync.split(',')
p = Path(path)
files = []
if p.is_file():
files.append(p)
elif p.exists() and not recursive:
files.extend(p.glob('*.yaml'))
files.extend(p.glob('*.yml'))
eli... | [
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train | export_datasources | Export datasources to YAML | superset/cli.py | def export_datasources(print_stdout, datasource_file,
back_references, include_defaults):
"""Export datasources to YAML"""
data = dict_import_export.export_to_dict(
session=db.session,
recursive=True,
back_references=back_references,
include_defaults=includ... | def export_datasources(print_stdout, datasource_file,
back_references, include_defaults):
"""Export datasources to YAML"""
data = dict_import_export.export_to_dict(
session=db.session,
recursive=True,
back_references=back_references,
include_defaults=includ... | [
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train | export_datasource_schema | Export datasource YAML schema to stdout | superset/cli.py | def export_datasource_schema(back_references):
"""Export datasource YAML schema to stdout"""
data = dict_import_export.export_schema_to_dict(
back_references=back_references)
yaml.safe_dump(data, stdout, default_flow_style=False) | def export_datasource_schema(back_references):
"""Export datasource YAML schema to stdout"""
data = dict_import_export.export_schema_to_dict(
back_references=back_references)
yaml.safe_dump(data, stdout, default_flow_style=False) | [
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train | update_datasources_cache | Refresh sqllab datasources cache | superset/cli.py | def update_datasources_cache():
"""Refresh sqllab datasources cache"""
from superset.models.core import Database
for database in db.session.query(Database).all():
if database.allow_multi_schema_metadata_fetch:
print('Fetching {} datasources ...'.format(database.name))
try:
... | def update_datasources_cache():
"""Refresh sqllab datasources cache"""
from superset.models.core import Database
for database in db.session.query(Database).all():
if database.allow_multi_schema_metadata_fetch:
print('Fetching {} datasources ...'.format(database.name))
try:
... | [
"Refresh",
"sqllab",
"datasources",
"cache"
] | apache/incubator-superset | python | https://github.com/apache/incubator-superset/blob/ca2996c78f679260eb79c6008e276733df5fb653/superset/cli.py#L372-L384 | [
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train | worker | Starts a Superset worker for async SQL query execution. | superset/cli.py | def worker(workers):
"""Starts a Superset worker for async SQL query execution."""
logging.info(
"The 'superset worker' command is deprecated. Please use the 'celery "
"worker' command instead.")
if workers:
celery_app.conf.update(CELERYD_CONCURRENCY=workers)
elif config.get('SUP... | def worker(workers):
"""Starts a Superset worker for async SQL query execution."""
logging.info(
"The 'superset worker' command is deprecated. Please use the 'celery "
"worker' command instead.")
if workers:
celery_app.conf.update(CELERYD_CONCURRENCY=workers)
elif config.get('SUP... | [
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train | flower | Runs a Celery Flower web server
Celery Flower is a UI to monitor the Celery operation on a given
broker | superset/cli.py | def flower(port, address):
"""Runs a Celery Flower web server
Celery Flower is a UI to monitor the Celery operation on a given
broker"""
BROKER_URL = celery_app.conf.BROKER_URL
cmd = (
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"""Runs a Celery Flower web server
Celery Flower is a UI to monitor the Celery operation on a given
broker"""
BROKER_URL = celery_app.conf.BROKER_URL
cmd = (
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train | load_flights | Loading random time series data from a zip file in the repo | superset/data/flights.py | def load_flights():
"""Loading random time series data from a zip file in the repo"""
tbl_name = 'flights'
data = get_example_data('flight_data.csv.gz', make_bytes=True)
pdf = pd.read_csv(data, encoding='latin-1')
# Loading airports info to join and get lat/long
airports_bytes = get_example_dat... | def load_flights():
"""Loading random time series data from a zip file in the repo"""
tbl_name = 'flights'
data = get_example_data('flight_data.csv.gz', make_bytes=True)
pdf = pd.read_csv(data, encoding='latin-1')
# Loading airports info to join and get lat/long
airports_bytes = get_example_dat... | [
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train | load_birth_names | Loading birth name dataset from a zip file in the repo | superset/data/birth_names.py | def load_birth_names():
"""Loading birth name dataset from a zip file in the repo"""
data = get_example_data('birth_names.json.gz')
pdf = pd.read_json(data)
pdf.ds = pd.to_datetime(pdf.ds, unit='ms')
pdf.to_sql(
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db.engine,
if_exists='replace',
chunksize... | def load_birth_names():
"""Loading birth name dataset from a zip file in the repo"""
data = get_example_data('birth_names.json.gz')
pdf = pd.read_json(data)
pdf.ds = pd.to_datetime(pdf.ds, unit='ms')
pdf.to_sql(
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chunksize... | [
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train | Druid.refresh_datasources | endpoint that refreshes druid datasources metadata | superset/connectors/druid/views.py | def refresh_datasources(self, refreshAll=True):
"""endpoint that refreshes druid datasources metadata"""
session = db.session()
DruidCluster = ConnectorRegistry.sources['druid'].cluster_class
for cluster in session.query(DruidCluster).all():
cluster_name = cluster.cluster_nam... | def refresh_datasources(self, refreshAll=True):
"""endpoint that refreshes druid datasources metadata"""
session = db.session()
DruidCluster = ConnectorRegistry.sources['druid'].cluster_class
for cluster in session.query(DruidCluster).all():
cluster_name = cluster.cluster_nam... | [
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train | convert_to_list | converts a positive integer into a (reversed) linked list.
for example: give 112
result 2 -> 1 -> 1 | algorithms/linkedlist/add_two_numbers.py | def convert_to_list(number: int) -> Node:
"""
converts a positive integer into a (reversed) linked list.
for example: give 112
result 2 -> 1 -> 1
"""
if number >= 0:
head = Node(0)
current = head
remainder = number % 10
quotient = number // 10
... | def convert_to_list(number: int) -> Node:
"""
converts a positive integer into a (reversed) linked list.
for example: give 112
result 2 -> 1 -> 1
"""
if number >= 0:
head = Node(0)
current = head
remainder = number % 10
quotient = number // 10
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train | convert_to_str | converts the non-negative number list into a string. | algorithms/linkedlist/add_two_numbers.py | def convert_to_str(l: Node) -> str:
"""
converts the non-negative number list into a string.
"""
result = ""
while l:
result += str(l.val)
l = l.next
return result | def convert_to_str(l: Node) -> str:
"""
converts the non-negative number list into a string.
"""
result = ""
while l:
result += str(l.val)
l = l.next
return result | [
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train | longest_consecutive | :type root: TreeNode
:rtype: int | algorithms/tree/longest_consecutive.py | def longest_consecutive(root):
"""
:type root: TreeNode
:rtype: int
"""
if root is None:
return 0
max_len = 0
dfs(root, 0, root.val, max_len)
return max_len | def longest_consecutive(root):
"""
:type root: TreeNode
:rtype: int
"""
if root is None:
return 0
max_len = 0
dfs(root, 0, root.val, max_len)
return max_len | [
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train | three_sum | :param array: List[int]
:return: Set[ Tuple[int, int, int] ] | algorithms/arrays/three_sum.py | def three_sum(array):
"""
:param array: List[int]
:return: Set[ Tuple[int, int, int] ]
"""
res = set()
array.sort()
for i in range(len(array) - 2):
if i > 0 and array[i] == array[i - 1]:
continue
l, r = i + 1, len(array) - 1
while l < r:
s = ar... | def three_sum(array):
"""
:param array: List[int]
:return: Set[ Tuple[int, int, int] ]
"""
res = set()
array.sort()
for i in range(len(array) - 2):
if i > 0 and array[i] == array[i - 1]:
continue
l, r = i + 1, len(array) - 1
while l < r:
s = ar... | [
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train | top_sort_recursive | Time complexity is the same as DFS, which is O(V + E)
Space complexity: O(V) | algorithms/sort/top_sort.py | def top_sort_recursive(graph):
""" Time complexity is the same as DFS, which is O(V + E)
Space complexity: O(V)
"""
order, enter, state = [], set(graph), {}
def dfs(node):
state[node] = GRAY
#print(node)
for k in graph.get(node, ()):
sk = state.get(k, Non... | def top_sort_recursive(graph):
""" Time complexity is the same as DFS, which is O(V + E)
Space complexity: O(V)
"""
order, enter, state = [], set(graph), {}
def dfs(node):
state[node] = GRAY
#print(node)
for k in graph.get(node, ()):
sk = state.get(k, Non... | [
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train | top_sort | Time complexity is the same as DFS, which is O(V + E)
Space complexity: O(V) | algorithms/sort/top_sort.py | def top_sort(graph):
""" Time complexity is the same as DFS, which is O(V + E)
Space complexity: O(V)
"""
order, enter, state = [], set(graph), {}
def is_ready(node):
lst = graph.get(node, ())
if len(lst) == 0:
return True
for k in lst:
sk = s... | def top_sort(graph):
""" Time complexity is the same as DFS, which is O(V + E)
Space complexity: O(V)
"""
order, enter, state = [], set(graph), {}
def is_ready(node):
lst = graph.get(node, ())
if len(lst) == 0:
return True
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train | max_product | :type nums: List[int]
:rtype: int | algorithms/dp/max_product_subarray.py | def max_product(nums):
"""
:type nums: List[int]
:rtype: int
"""
lmin = lmax = gmax = nums[0]
for i in range(len(nums)):
t1 = nums[i] * lmax
t2 = nums[i] * lmin
lmax = max(max(t1, t2), nums[i])
lmin = min(min(t1, t2), nums[i])
gmax = max(gmax, lmax) | def max_product(nums):
"""
:type nums: List[int]
:rtype: int
"""
lmin = lmax = gmax = nums[0]
for i in range(len(nums)):
t1 = nums[i] * lmax
t2 = nums[i] * lmin
lmax = max(max(t1, t2), nums[i])
lmin = min(min(t1, t2), nums[i])
gmax = max(gmax, lmax) | [
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train | subarray_with_max_product | arr is list of positive/negative numbers | algorithms/dp/max_product_subarray.py | def subarray_with_max_product(arr):
''' arr is list of positive/negative numbers '''
l = len(arr)
product_so_far = max_product_end = 1
max_start_i = 0
so_far_start_i = so_far_end_i = 0
all_negative_flag = True
for i in range(l):
max_product_end *= arr[i]
if arr[i] > 0:
... | def subarray_with_max_product(arr):
''' arr is list of positive/negative numbers '''
l = len(arr)
product_so_far = max_product_end = 1
max_start_i = 0
so_far_start_i = so_far_end_i = 0
all_negative_flag = True
for i in range(l):
max_product_end *= arr[i]
if arr[i] > 0:
... | [
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train | text_justification | :type words: list
:type max_width: int
:rtype: list | algorithms/strings/text_justification.py | def text_justification(words, max_width):
'''
:type words: list
:type max_width: int
:rtype: list
'''
ret = [] # return value
row_len = 0 # current length of strs in a row
row_words = [] # current words in a row
index = 0 # the index of current word in words
is_first_word = T... | def text_justification(words, max_width):
'''
:type words: list
:type max_width: int
:rtype: list
'''
ret = [] # return value
row_len = 0 # current length of strs in a row
row_words = [] # current words in a row
index = 0 # the index of current word in words
is_first_word = T... | [
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train | insertion_sort | Insertion Sort
Complexity: O(n^2) | algorithms/sort/insertion_sort.py | def insertion_sort(arr, simulation=False):
""" Insertion Sort
Complexity: O(n^2)
"""
iteration = 0
if simulation:
print("iteration",iteration,":",*arr)
for i in range(len(arr)):
cursor = arr[i]
pos = i
while pos > 0 and arr[pos - 1] > cu... | def insertion_sort(arr, simulation=False):
""" Insertion Sort
Complexity: O(n^2)
"""
iteration = 0
if simulation:
print("iteration",iteration,":",*arr)
for i in range(len(arr)):
cursor = arr[i]
pos = i
while pos > 0 and arr[pos - 1] > cu... | [
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train | cycle_sort | cycle_sort
This is based on the idea that the permutations to be sorted
can be decomposed into cycles,
and the results can be individually sorted by cycling.
reference: https://en.wikipedia.org/wiki/Cycle_sort
Average time complexity : O(N^2)
Worst case time complexity : O(N^2) | algorithms/sort/cycle_sort.py | def cycle_sort(arr):
"""
cycle_sort
This is based on the idea that the permutations to be sorted
can be decomposed into cycles,
and the results can be individually sorted by cycling.
reference: https://en.wikipedia.org/wiki/Cycle_sort
Average time complexity : O(N^2)
Worst case... | def cycle_sort(arr):
"""
cycle_sort
This is based on the idea that the permutations to be sorted
can be decomposed into cycles,
and the results can be individually sorted by cycling.
reference: https://en.wikipedia.org/wiki/Cycle_sort
Average time complexity : O(N^2)
Worst case... | [
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train | cocktail_shaker_sort | Cocktail_shaker_sort
Sorting a given array
mutation of bubble sort
reference: https://en.wikipedia.org/wiki/Cocktail_shaker_sort
Worst-case performance: O(N^2) | algorithms/sort/cocktail_shaker_sort.py | def cocktail_shaker_sort(arr):
"""
Cocktail_shaker_sort
Sorting a given array
mutation of bubble sort
reference: https://en.wikipedia.org/wiki/Cocktail_shaker_sort
Worst-case performance: O(N^2)
"""
def swap(i, j):
arr[i], arr[j] = arr[j], arr[i]
n = len(arr)
swap... | def cocktail_shaker_sort(arr):
"""
Cocktail_shaker_sort
Sorting a given array
mutation of bubble sort
reference: https://en.wikipedia.org/wiki/Cocktail_shaker_sort
Worst-case performance: O(N^2)
"""
def swap(i, j):
arr[i], arr[j] = arr[j], arr[i]
n = len(arr)
swap... | [
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train | reconstruct_queue | :type people: List[List[int]]
:rtype: List[List[int]] | algorithms/queues/reconstruct_queue.py | def reconstruct_queue(people):
"""
:type people: List[List[int]]
:rtype: List[List[int]]
"""
queue = []
people.sort(key=lambda x: (-x[0], x[1]))
for h, k in people:
queue.insert(k, [h, k])
return queue | def reconstruct_queue(people):
"""
:type people: List[List[int]]
:rtype: List[List[int]]
"""
queue = []
people.sort(key=lambda x: (-x[0], x[1]))
for h, k in people:
queue.insert(k, [h, k])
return queue | [
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train | min_depth | :type root: TreeNode
:rtype: int | algorithms/tree/min_height.py | def min_depth(self, root):
"""
:type root: TreeNode
:rtype: int
"""
if root is None:
return 0
if root.left is not None or root.right is not None:
return max(self.minDepth(root.left), self.minDepth(root.right))+1
return min(self.minDepth(root.left), self.minDepth(root.right)) ... | def min_depth(self, root):
"""
:type root: TreeNode
:rtype: int
"""
if root is None:
return 0
if root.left is not None or root.right is not None:
return max(self.minDepth(root.left), self.minDepth(root.right))+1
return min(self.minDepth(root.left), self.minDepth(root.right)) ... | [
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train | is_one_edit | :type s: str
:type t: str
:rtype: bool | algorithms/strings/one_edit_distance.py | def is_one_edit(s, t):
"""
:type s: str
:type t: str
:rtype: bool
"""
if len(s) > len(t):
return is_one_edit(t, s)
if len(t) - len(s) > 1 or t == s:
return False
for i in range(len(s)):
if s[i] != t[i]:
return s[i+1:] == t[i+1:] or s[i:] == t[i+1:]
... | def is_one_edit(s, t):
"""
:type s: str
:type t: str
:rtype: bool
"""
if len(s) > len(t):
return is_one_edit(t, s)
if len(t) - len(s) > 1 or t == s:
return False
for i in range(len(s)):
if s[i] != t[i]:
return s[i+1:] == t[i+1:] or s[i:] == t[i+1:]
... | [
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train | shell_sort | Shell Sort
Complexity: O(n^2) | algorithms/sort/shell_sort.py | def shell_sort(arr):
''' Shell Sort
Complexity: O(n^2)
'''
n = len(arr)
# Initialize size of the gap
gap = n//2
while gap > 0:
y_index = gap
while y_index < len(arr):
y = arr[y_index]
x_index = y_index - gap
while x_index >= 0 and ... | def shell_sort(arr):
''' Shell Sort
Complexity: O(n^2)
'''
n = len(arr)
# Initialize size of the gap
gap = n//2
while gap > 0:
y_index = gap
while y_index < len(arr):
y = arr[y_index]
x_index = y_index - gap
while x_index >= 0 and ... | [
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train | common_prefix | Return prefix common of 2 strings | algorithms/strings/longest_common_prefix.py | def common_prefix(s1, s2):
"Return prefix common of 2 strings"
if not s1 or not s2:
return ""
k = 0
while s1[k] == s2[k]:
k = k + 1
if k >= len(s1) or k >= len(s2):
return s1[0:k]
return s1[0:k] | def common_prefix(s1, s2):
"Return prefix common of 2 strings"
if not s1 or not s2:
return ""
k = 0
while s1[k] == s2[k]:
k = k + 1
if k >= len(s1) or k >= len(s2):
return s1[0:k]
return s1[0:k] | [
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train | euler_totient | Euler's totient function or Phi function.
Time Complexity: O(sqrt(n)). | algorithms/maths/euler_totient.py | def euler_totient(n):
"""Euler's totient function or Phi function.
Time Complexity: O(sqrt(n))."""
result = n;
for i in range(2, int(n ** 0.5) + 1):
if n % i == 0:
while n % i == 0:
n //= i
result -= result // i
if n > 1:
result -= result // n;... | def euler_totient(n):
"""Euler's totient function or Phi function.
Time Complexity: O(sqrt(n))."""
result = n;
for i in range(2, int(n ** 0.5) + 1):
if n % i == 0:
while n % i == 0:
n //= i
result -= result // i
if n > 1:
result -= result // n;... | [
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train | is_palindrome_dict | This function builds up a dictionary where the keys are the values of the list,
and the values are the positions at which these values occur in the list.
We then iterate over the dict and if there is more than one key with an odd
number of occurrences, bail out and return False.
Otherwise, we want to en... | algorithms/linkedlist/is_palindrome.py | def is_palindrome_dict(head):
"""
This function builds up a dictionary where the keys are the values of the list,
and the values are the positions at which these values occur in the list.
We then iterate over the dict and if there is more than one key with an odd
number of occurrences, bail out and ... | def is_palindrome_dict(head):
"""
This function builds up a dictionary where the keys are the values of the list,
and the values are the positions at which these values occur in the list.
We then iterate over the dict and if there is more than one key with an odd
number of occurrences, bail out and ... | [
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... | 4d6569464a62a75c1357acc97e2dd32ee2f9f4a3 |
train | fib_list | [summary]
This algorithm computes the n-th fibbonacci number
very quick. approximate O(n)
The algorithm use dynamic programming.
Arguments:
n {[int]} -- [description]
Returns:
[int] -- [description] | algorithms/dp/fib.py | def fib_list(n):
"""[summary]
This algorithm computes the n-th fibbonacci number
very quick. approximate O(n)
The algorithm use dynamic programming.
Arguments:
n {[int]} -- [description]
Returns:
[int] -- [description]
"""
# precondition
assert n >= 0, 'n m... | def fib_list(n):
"""[summary]
This algorithm computes the n-th fibbonacci number
very quick. approximate O(n)
The algorithm use dynamic programming.
Arguments:
n {[int]} -- [description]
Returns:
[int] -- [description]
"""
# precondition
assert n >= 0, 'n m... | [
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... | 4d6569464a62a75c1357acc97e2dd32ee2f9f4a3 |
train | fib_iter | [summary]
Works iterative approximate O(n)
Arguments:
n {[int]} -- [description]
Returns:
[int] -- [description] | algorithms/dp/fib.py | def fib_iter(n):
"""[summary]
Works iterative approximate O(n)
Arguments:
n {[int]} -- [description]
Returns:
[int] -- [description]
"""
# precondition
assert n >= 0, 'n must be positive integer'
fib_1 = 0
fib_2 = 1
sum = 0
if n <= 1:
return n
... | def fib_iter(n):
"""[summary]
Works iterative approximate O(n)
Arguments:
n {[int]} -- [description]
Returns:
[int] -- [description]
"""
# precondition
assert n >= 0, 'n must be positive integer'
fib_1 = 0
fib_2 = 1
sum = 0
if n <= 1:
return n
... | [
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train | subsets | :param nums: List[int]
:return: Set[tuple] | algorithms/bit/subsets.py | def subsets(nums):
"""
:param nums: List[int]
:return: Set[tuple]
"""
n = len(nums)
total = 1 << n
res = set()
for i in range(total):
subset = tuple(num for j, num in enumerate(nums) if i & 1 << j)
res.add(subset)
return res | def subsets(nums):
"""
:param nums: List[int]
:return: Set[tuple]
"""
n = len(nums)
total = 1 << n
res = set()
for i in range(total):
subset = tuple(num for j, num in enumerate(nums) if i & 1 << j)
res.add(subset)
return res | [
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"... | 4d6569464a62a75c1357acc97e2dd32ee2f9f4a3 |
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