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237,600 | douban/libmc | misc/runbench.py | benchmark_method | def benchmark_method(f):
"decorator to turn f into a factory of benchmarks"
@wraps(f)
def inner(name, *args, **kwargs):
return Benchmark(name, f, args, kwargs)
return inner | python | def benchmark_method(f):
"decorator to turn f into a factory of benchmarks"
@wraps(f)
def inner(name, *args, **kwargs):
return Benchmark(name, f, args, kwargs)
return inner | [
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237,601 | douban/libmc | misc/runbench.py | bench | def bench(participants=participants, benchmarks=benchmarks,
bench_time=BENCH_TIME):
"""Do you even lift?"""
mcs = [p.factory() for p in participants]
means = [[] for p in participants]
stddevs = [[] for p in participants]
# Have each lifter do one benchmark each
last_fn = None
fo... | python | def bench(participants=participants, benchmarks=benchmarks,
bench_time=BENCH_TIME):
"""Do you even lift?"""
mcs = [p.factory() for p in participants]
means = [[] for p in participants]
stddevs = [[] for p in participants]
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last_fn = None
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237,602 | liampauling/betfair | betfairlightweight/resources/baseresource.py | BaseResource.strip_datetime | def strip_datetime(value):
"""
Converts value to datetime if string or int.
"""
if isinstance(value, basestring):
try:
return parse_datetime(value)
except ValueError:
return
elif isinstance(value, integer_types):
... | python | def strip_datetime(value):
"""
Converts value to datetime if string or int.
"""
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try:
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except ValueError:
return
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237,603 | liampauling/betfair | betfairlightweight/baseclient.py | BaseClient.set_session_token | def set_session_token(self, session_token):
"""
Sets session token and new login time.
:param str session_token: Session token from request.
"""
self.session_token = session_token
self._login_time = datetime.datetime.now() | python | def set_session_token(self, session_token):
"""
Sets session token and new login time.
:param str session_token: Session token from request.
"""
self.session_token = session_token
self._login_time = datetime.datetime.now() | [
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237,604 | liampauling/betfair | betfairlightweight/baseclient.py | BaseClient.get_password | def get_password(self):
"""
If password is not provided will look in environment variables
for username+'password'.
"""
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if os.environ.get(self.username+'password'):
self.password = os.environ.get(self.username+'password')
... | python | def get_password(self):
"""
If password is not provided will look in environment variables
for username+'password'.
"""
if self.password is None:
if os.environ.get(self.username+'password'):
self.password = os.environ.get(self.username+'password')
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237,605 | liampauling/betfair | betfairlightweight/baseclient.py | BaseClient.get_app_key | def get_app_key(self):
"""
If app_key is not provided will look in environment
variables for username.
"""
if self.app_key is None:
if os.environ.get(self.username):
self.app_key = os.environ.get(self.username)
else:
raise A... | python | def get_app_key(self):
"""
If app_key is not provided will look in environment
variables for username.
"""
if self.app_key is None:
if os.environ.get(self.username):
self.app_key = os.environ.get(self.username)
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237,606 | liampauling/betfair | betfairlightweight/baseclient.py | BaseClient.session_expired | def session_expired(self):
"""
Returns True if login_time not set or seconds since
login time is greater than 200 mins.
"""
if not self._login_time or (datetime.datetime.now()-self._login_time).total_seconds() > 12000:
return True | python | def session_expired(self):
"""
Returns True if login_time not set or seconds since
login time is greater than 200 mins.
"""
if not self._login_time or (datetime.datetime.now()-self._login_time).total_seconds() > 12000:
return True | [
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237,607 | liampauling/betfair | betfairlightweight/utils.py | check_status_code | def check_status_code(response, codes=None):
"""
Checks response.status_code is in codes.
:param requests.request response: Requests response
:param list codes: List of accepted codes or callable
:raises: StatusCodeError if code invalid
"""
codes = codes or [200]
if response.status_code... | python | def check_status_code(response, codes=None):
"""
Checks response.status_code is in codes.
:param requests.request response: Requests response
:param list codes: List of accepted codes or callable
:raises: StatusCodeError if code invalid
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237,608 | liampauling/betfair | betfairlightweight/endpoints/betting.py | Betting.list_runner_book | def list_runner_book(self, market_id, selection_id, handicap=None, price_projection=None, order_projection=None,
match_projection=None, include_overall_position=None, partition_matched_by_strategy_ref=None,
customer_strategy_refs=None, currency_code=None, matched_since=... | python | def list_runner_book(self, market_id, selection_id, handicap=None, price_projection=None, order_projection=None,
match_projection=None, include_overall_position=None, partition_matched_by_strategy_ref=None,
customer_strategy_refs=None, currency_code=None, matched_since=... | [
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:param unicode market_id: The unique id for the market
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237,609 | liampauling/betfair | betfairlightweight/endpoints/betting.py | Betting.list_current_orders | def list_current_orders(self, bet_ids=None, market_ids=None, order_projection=None, customer_order_refs=None,
customer_strategy_refs=None, date_range=time_range(), order_by=None, sort_dir=None,
from_record=None, record_count=None, session=None, lightweight=None):
... | python | def list_current_orders(self, bet_ids=None, market_ids=None, order_projection=None, customer_order_refs=None,
customer_strategy_refs=None, date_range=time_range(), order_by=None, sort_dir=None,
from_record=None, record_count=None, session=None, lightweight=None):
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237,610 | liampauling/betfair | betfairlightweight/endpoints/betting.py | Betting.list_cleared_orders | def list_cleared_orders(self, bet_status='SETTLED', event_type_ids=None, event_ids=None, market_ids=None,
runner_ids=None, bet_ids=None, customer_order_refs=None, customer_strategy_refs=None,
side=None, settled_date_range=time_range(), group_by=None, include_item_... | python | def list_cleared_orders(self, bet_status='SETTLED', event_type_ids=None, event_ids=None, market_ids=None,
runner_ids=None, bet_ids=None, customer_order_refs=None, customer_strategy_refs=None,
side=None, settled_date_range=time_range(), group_by=None, include_item_... | [
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237,611 | liampauling/betfair | betfairlightweight/endpoints/betting.py | Betting.list_market_profit_and_loss | def list_market_profit_and_loss(self, market_ids, include_settled_bets=None, include_bsp_bets=None,
net_of_commission=None, session=None, lightweight=None):
"""
Retrieve profit and loss for a given list of OPEN markets.
:param list market_ids: List of markets... | python | def list_market_profit_and_loss(self, market_ids, include_settled_bets=None, include_bsp_bets=None,
net_of_commission=None, session=None, lightweight=None):
"""
Retrieve profit and loss for a given list of OPEN markets.
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237,612 | liampauling/betfair | betfairlightweight/endpoints/betting.py | Betting.place_orders | def place_orders(self, market_id, instructions, customer_ref=None, market_version=None,
customer_strategy_ref=None, async_=None, session=None, lightweight=None):
"""
Place new orders into market.
:param str market_id: The market id these orders are to be placed on
:... | python | def place_orders(self, market_id, instructions, customer_ref=None, market_version=None,
customer_strategy_ref=None, async_=None, session=None, lightweight=None):
"""
Place new orders into market.
:param str market_id: The market id these orders are to be placed on
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237,613 | liampauling/betfair | betfairlightweight/streaming/cache.py | MarketBookCache.serialise | def serialise(self):
"""Creates standard market book json response,
will error if EX_MARKET_DEF not incl.
"""
return {
'marketId': self.market_id,
'totalAvailable': None,
'isMarketDataDelayed': None,
'lastMatchTime': None,
'betD... | python | def serialise(self):
"""Creates standard market book json response,
will error if EX_MARKET_DEF not incl.
"""
return {
'marketId': self.market_id,
'totalAvailable': None,
'isMarketDataDelayed': None,
'lastMatchTime': None,
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237,614 | liampauling/betfair | betfairlightweight/endpoints/scores.py | Scores.list_race_details | def list_race_details(self, meeting_ids=None, race_ids=None, session=None, lightweight=None):
"""
Search for races to get their details.
:param dict meeting_ids: Optionally restricts the results to the specified meeting IDs.
The unique Id for the meeting equivalent to the eventId for th... | python | def list_race_details(self, meeting_ids=None, race_ids=None, session=None, lightweight=None):
"""
Search for races to get their details.
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237,615 | liampauling/betfair | betfairlightweight/endpoints/scores.py | Scores.list_available_events | def list_available_events(self, event_ids=None, event_type_ids=None, event_status=None, session=None,
lightweight=None):
"""
Search for events that have live score data available.
:param list event_ids: Optionally restricts the results to the specified event IDs
... | python | def list_available_events(self, event_ids=None, event_type_ids=None, event_status=None, session=None,
lightweight=None):
"""
Search for events that have live score data available.
:param list event_ids: Optionally restricts the results to the specified event IDs
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237,616 | liampauling/betfair | betfairlightweight/endpoints/scores.py | Scores.list_scores | def list_scores(self, update_keys, session=None, lightweight=None):
"""
Returns a list of current scores for the given events.
:param list update_keys: The filter to select desired markets. All markets that match
the criteria in the filter are selected e.g. [{'eventId': '28205674', 'las... | python | def list_scores(self, update_keys, session=None, lightweight=None):
"""
Returns a list of current scores for the given events.
:param list update_keys: The filter to select desired markets. All markets that match
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237,617 | liampauling/betfair | betfairlightweight/endpoints/scores.py | Scores.list_incidents | def list_incidents(self, update_keys, session=None, lightweight=None):
"""
Returns a list of incidents for the given events.
:param dict update_keys: The filter to select desired markets. All markets that match
the criteria in the filter are selected e.g. [{'eventId': '28205674', 'lastU... | python | def list_incidents(self, update_keys, session=None, lightweight=None):
"""
Returns a list of incidents for the given events.
:param dict update_keys: The filter to select desired markets. All markets that match
the criteria in the filter are selected e.g. [{'eventId': '28205674', 'lastU... | [
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237,618 | liampauling/betfair | betfairlightweight/endpoints/inplayservice.py | InPlayService.get_event_timeline | def get_event_timeline(self, event_id, session=None, lightweight=None):
"""
Returns event timeline for event id provided.
:param int event_id: Event id to return
:param requests.session session: Requests session object
:param bool lightweight: If True will return dict not a reso... | python | def get_event_timeline(self, event_id, session=None, lightweight=None):
"""
Returns event timeline for event id provided.
:param int event_id: Event id to return
:param requests.session session: Requests session object
:param bool lightweight: If True will return dict not a reso... | [
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237,619 | liampauling/betfair | betfairlightweight/endpoints/inplayservice.py | InPlayService.get_event_timelines | def get_event_timelines(self, event_ids, session=None, lightweight=None):
"""
Returns a list of event timelines based on event id's
supplied.
:param list event_ids: List of event id's to return
:param requests.session session: Requests session object
:param bool lightwei... | python | def get_event_timelines(self, event_ids, session=None, lightweight=None):
"""
Returns a list of event timelines based on event id's
supplied.
:param list event_ids: List of event id's to return
:param requests.session session: Requests session object
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237,620 | liampauling/betfair | betfairlightweight/endpoints/inplayservice.py | InPlayService.get_scores | def get_scores(self, event_ids, session=None, lightweight=None):
"""
Returns a list of scores based on event id's
supplied.
:param list event_ids: List of event id's to return
:param requests.session session: Requests session object
:param bool lightweight: If True will ... | python | def get_scores(self, event_ids, session=None, lightweight=None):
"""
Returns a list of scores based on event id's
supplied.
:param list event_ids: List of event id's to return
:param requests.session session: Requests session object
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237,621 | liampauling/betfair | betfairlightweight/endpoints/streaming.py | Streaming.create_stream | def create_stream(self, unique_id=0, listener=None, timeout=11, buffer_size=1024, description='BetfairSocket',
host=None):
"""
Creates BetfairStream.
:param dict unique_id: Id used to start unique id's of the stream (+1 before every request)
:param resources.Listen... | python | def create_stream(self, unique_id=0, listener=None, timeout=11, buffer_size=1024, description='BetfairSocket',
host=None):
"""
Creates BetfairStream.
:param dict unique_id: Id used to start unique id's of the stream (+1 before every request)
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237,622 | liampauling/betfair | betfairlightweight/endpoints/historic.py | Historic.get_my_data | def get_my_data(self, session=None):
"""
Returns a list of data descriptions for data which has been purchased by the signed in user.
:param requests.session session: Requests session object
:rtype: dict
"""
params = clean_locals(locals())
method = 'GetMyData'
... | python | def get_my_data(self, session=None):
"""
Returns a list of data descriptions for data which has been purchased by the signed in user.
:param requests.session session: Requests session object
:rtype: dict
"""
params = clean_locals(locals())
method = 'GetMyData'
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237,623 | liampauling/betfair | betfairlightweight/endpoints/historic.py | Historic.get_data_size | def get_data_size(self, sport, plan, from_day, from_month, from_year, to_day, to_month, to_year, event_id=None,
event_name=None, market_types_collection=None, countries_collection=None,
file_type_collection=None, session=None):
"""
Returns a dictionary of file... | python | def get_data_size(self, sport, plan, from_day, from_month, from_year, to_day, to_month, to_year, event_id=None,
event_name=None, market_types_collection=None, countries_collection=None,
file_type_collection=None, session=None):
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237,624 | liampauling/betfair | betfairlightweight/endpoints/racecard.py | RaceCard.login | def login(self, session=None):
"""
Parses app key from betfair exchange site.
:param requests.session session: Requests session object
"""
session = session or self.client.session
try:
response = session.get(self.login_url)
except ConnectionError:
... | python | def login(self, session=None):
"""
Parses app key from betfair exchange site.
:param requests.session session: Requests session object
"""
session = session or self.client.session
try:
response = session.get(self.login_url)
except ConnectionError:
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237,625 | liampauling/betfair | betfairlightweight/endpoints/racecard.py | RaceCard.get_race_card | def get_race_card(self, market_ids, data_entries=None, session=None, lightweight=None):
"""
Returns a list of race cards based on market ids provided.
:param list market_ids: The filter to select desired markets
:param str data_entries: Data to be returned
:param requests.sessio... | python | def get_race_card(self, market_ids, data_entries=None, session=None, lightweight=None):
"""
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:param list market_ids: The filter to select desired markets
:param str data_entries: Data to be returned
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237,626 | liampauling/betfair | betfairlightweight/streaming/listener.py | StreamListener.on_data | def on_data(self, raw_data):
"""Called when raw data is received from connection.
Override this method if you wish to manually handle
the stream data
:param raw_data: Received raw data
:return: Return False to stop stream and close connection
"""
try:
... | python | def on_data(self, raw_data):
"""Called when raw data is received from connection.
Override this method if you wish to manually handle
the stream data
:param raw_data: Received raw data
:return: Return False to stop stream and close connection
"""
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237,627 | liampauling/betfair | betfairlightweight/streaming/listener.py | StreamListener._on_connection | def _on_connection(self, data, unique_id):
"""Called on collection operation
:param data: Received data
"""
if unique_id is None:
unique_id = self.stream_unique_id
self.connection_id = data.get('connectionId')
logger.info('[Connect: %s]: connection_id: %s' % ... | python | def _on_connection(self, data, unique_id):
"""Called on collection operation
:param data: Received data
"""
if unique_id is None:
unique_id = self.stream_unique_id
self.connection_id = data.get('connectionId')
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237,628 | liampauling/betfair | betfairlightweight/streaming/listener.py | StreamListener._on_status | def _on_status(data, unique_id):
"""Called on status operation
:param data: Received data
"""
status_code = data.get('statusCode')
logger.info('[Subscription: %s]: %s' % (unique_id, status_code)) | python | def _on_status(data, unique_id):
"""Called on status operation
:param data: Received data
"""
status_code = data.get('statusCode')
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237,629 | liampauling/betfair | betfairlightweight/streaming/listener.py | StreamListener._error_handler | def _error_handler(data, unique_id):
"""Called when data first received
:param data: Received data
:param unique_id: Unique id
:return: True if error present
"""
if data.get('statusCode') == 'FAILURE':
logger.error('[Subscription: %s] %s: %s' % (unique_id, da... | python | def _error_handler(data, unique_id):
"""Called when data first received
:param data: Received data
:param unique_id: Unique id
:return: True if error present
"""
if data.get('statusCode') == 'FAILURE':
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:param data: Received data
:param unique_id: Unique id
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237,630 | liampauling/betfair | betfairlightweight/streaming/betfairstream.py | BetfairStream.stop | def stop(self):
"""Stops read loop and closes socket if it has been created.
"""
self._running = False
if self._socket is None:
return
try:
self._socket.shutdown(socket.SHUT_RDWR)
self._socket.close()
except socket.error:
p... | python | def stop(self):
"""Stops read loop and closes socket if it has been created.
"""
self._running = False
if self._socket is None:
return
try:
self._socket.shutdown(socket.SHUT_RDWR)
self._socket.close()
except socket.error:
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237,631 | liampauling/betfair | betfairlightweight/streaming/betfairstream.py | BetfairStream.authenticate | def authenticate(self):
"""Authentication request.
"""
unique_id = self.new_unique_id()
message = {
'op': 'authentication',
'id': unique_id,
'appKey': self.app_key,
'session': self.session_token,
}
self._send(message)
... | python | def authenticate(self):
"""Authentication request.
"""
unique_id = self.new_unique_id()
message = {
'op': 'authentication',
'id': unique_id,
'appKey': self.app_key,
'session': self.session_token,
}
self._send(message)
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237,632 | liampauling/betfair | betfairlightweight/streaming/betfairstream.py | BetfairStream.heartbeat | def heartbeat(self):
"""Heartbeat request to keep session alive.
"""
unique_id = self.new_unique_id()
message = {
'op': 'heartbeat',
'id': unique_id,
}
self._send(message)
return unique_id | python | def heartbeat(self):
"""Heartbeat request to keep session alive.
"""
unique_id = self.new_unique_id()
message = {
'op': 'heartbeat',
'id': unique_id,
}
self._send(message)
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237,633 | liampauling/betfair | betfairlightweight/streaming/betfairstream.py | BetfairStream.subscribe_to_markets | def subscribe_to_markets(self, market_filter, market_data_filter, initial_clk=None, clk=None,
conflate_ms=None, heartbeat_ms=None, segmentation_enabled=True):
"""
Market subscription request.
:param dict market_filter: Market filter
:param dict market_data_f... | python | def subscribe_to_markets(self, market_filter, market_data_filter, initial_clk=None, clk=None,
conflate_ms=None, heartbeat_ms=None, segmentation_enabled=True):
"""
Market subscription request.
:param dict market_filter: Market filter
:param dict market_data_f... | [
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237,634 | liampauling/betfair | betfairlightweight/streaming/betfairstream.py | BetfairStream.subscribe_to_orders | def subscribe_to_orders(self, order_filter=None, initial_clk=None, clk=None, conflate_ms=None,
heartbeat_ms=None, segmentation_enabled=True):
"""
Order subscription request.
:param dict order_filter: Order filter to be applied
:param str initial_clk: Sequence... | python | def subscribe_to_orders(self, order_filter=None, initial_clk=None, clk=None, conflate_ms=None,
heartbeat_ms=None, segmentation_enabled=True):
"""
Order subscription request.
:param dict order_filter: Order filter to be applied
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237,635 | liampauling/betfair | betfairlightweight/streaming/betfairstream.py | BetfairStream._create_socket | def _create_socket(self):
"""Creates ssl socket, connects to stream api and
sets timeout.
"""
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s = ssl.wrap_socket(s)
s.connect((self.host, self.__port))
s.settimeout(self.timeout)
return s | python | def _create_socket(self):
"""Creates ssl socket, connects to stream api and
sets timeout.
"""
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s = ssl.wrap_socket(s)
s.connect((self.host, self.__port))
s.settimeout(self.timeout)
return s | [
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237,636 | liampauling/betfair | betfairlightweight/streaming/betfairstream.py | BetfairStream._read_loop | def _read_loop(self):
"""Read loop, splits by CRLF and pushes received data
to _data.
"""
while self._running:
received_data_raw = self._receive_all()
if self._running:
self.receive_count += 1
self.datetime_last_received = datetime.... | python | def _read_loop(self):
"""Read loop, splits by CRLF and pushes received data
to _data.
"""
while self._running:
received_data_raw = self._receive_all()
if self._running:
self.receive_count += 1
self.datetime_last_received = datetime.... | [
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237,637 | liampauling/betfair | betfairlightweight/streaming/betfairstream.py | BetfairStream._receive_all | def _receive_all(self):
"""Whilst socket is running receives data from socket,
till CRLF is detected.
"""
(data, part) = ('', '')
if is_py3:
crlf_bytes = bytes(self.__CRLF, encoding=self.__encoding)
else:
crlf_bytes = self.__CRLF
while sel... | python | def _receive_all(self):
"""Whilst socket is running receives data from socket,
till CRLF is detected.
"""
(data, part) = ('', '')
if is_py3:
crlf_bytes = bytes(self.__CRLF, encoding=self.__encoding)
else:
crlf_bytes = self.__CRLF
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237,638 | liampauling/betfair | betfairlightweight/streaming/betfairstream.py | BetfairStream._data | def _data(self, received_data):
"""Sends data to listener, if False is returned; socket
is closed.
:param received_data: Decoded data received from socket.
"""
if self.listener.on_data(received_data) is False:
self.stop()
raise ListenerError(self.listener... | python | def _data(self, received_data):
"""Sends data to listener, if False is returned; socket
is closed.
:param received_data: Decoded data received from socket.
"""
if self.listener.on_data(received_data) is False:
self.stop()
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237,639 | liampauling/betfair | betfairlightweight/streaming/betfairstream.py | BetfairStream._send | def _send(self, message):
"""If not running connects socket and
authenticates. Adds CRLF and sends message
to Betfair.
:param message: Data to be sent to Betfair.
"""
if not self._running:
self._connect()
self.authenticate()
message_dumped... | python | def _send(self, message):
"""If not running connects socket and
authenticates. Adds CRLF and sends message
to Betfair.
:param message: Data to be sent to Betfair.
"""
if not self._running:
self._connect()
self.authenticate()
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237,640 | dmbee/seglearn | seglearn/pipe.py | Pype.fit_transform | def fit_transform(self, X, y=None, **fit_params):
"""
Fit the model and transform with the final estimator
Fits all the transforms one after the other and transforms the
data, then uses fit_transform on transformed data with the final
estimator.
Parameters
------... | python | def fit_transform(self, X, y=None, **fit_params):
"""
Fit the model and transform with the final estimator
Fits all the transforms one after the other and transforms the
data, then uses fit_transform on transformed data with the final
estimator.
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237,641 | dmbee/seglearn | seglearn/pipe.py | Pype.predict | def predict(self, X):
"""
Apply transforms to the data, and predict with the final estimator
Parameters
----------
X : iterable
Data to predict on. Must fulfill input requirements of first step
of the pipeline.
Returns
-------
yp ... | python | def predict(self, X):
"""
Apply transforms to the data, and predict with the final estimator
Parameters
----------
X : iterable
Data to predict on. Must fulfill input requirements of first step
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Returns
-------
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237,642 | dmbee/seglearn | seglearn/pipe.py | Pype.transform_predict | def transform_predict(self, X, y):
"""
Apply transforms to the data, and predict with the final estimator.
Unlike predict, this also returns the transformed target
Parameters
----------
X : iterable
Data to predict on. Must fulfill input requirements of first... | python | def transform_predict(self, X, y):
"""
Apply transforms to the data, and predict with the final estimator.
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237,643 | dmbee/seglearn | seglearn/pipe.py | Pype.score | def score(self, X, y=None, sample_weight=None):
"""
Apply transforms, and score with the final estimator
Parameters
----------
X : iterable
Data to predict on. Must fulfill input requirements of first step
of the pipeline.
y : iterable, default=No... | python | def score(self, X, y=None, sample_weight=None):
"""
Apply transforms, and score with the final estimator
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X : iterable
Data to predict on. Must fulfill input requirements of first step
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237,644 | dmbee/seglearn | seglearn/pipe.py | Pype.predict_proba | def predict_proba(self, X):
"""
Apply transforms, and predict_proba of the final estimator
Parameters
----------
X : iterable
Data to predict on. Must fulfill input requirements of first step
of the pipeline.
Returns
-------
y_pro... | python | def predict_proba(self, X):
"""
Apply transforms, and predict_proba of the final estimator
Parameters
----------
X : iterable
Data to predict on. Must fulfill input requirements of first step
of the pipeline.
Returns
-------
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237,645 | dmbee/seglearn | seglearn/pipe.py | Pype.decision_function | def decision_function(self, X):
"""
Apply transforms, and decision_function of the final estimator
Parameters
----------
X : iterable
Data to predict on. Must fulfill input requirements of first step
of the pipeline.
Returns
-------
... | python | def decision_function(self, X):
"""
Apply transforms, and decision_function of the final estimator
Parameters
----------
X : iterable
Data to predict on. Must fulfill input requirements of first step
of the pipeline.
Returns
-------
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237,646 | dmbee/seglearn | seglearn/pipe.py | Pype.predict_log_proba | def predict_log_proba(self, X):
"""
Apply transforms, and predict_log_proba of the final estimator
Parameters
----------
X : iterable
Data to predict on. Must fulfill input requirements of first step
of the pipeline.
Returns
-------
... | python | def predict_log_proba(self, X):
"""
Apply transforms, and predict_log_proba of the final estimator
Parameters
----------
X : iterable
Data to predict on. Must fulfill input requirements of first step
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237,647 | dmbee/seglearn | seglearn/feature_functions.py | base_features | def base_features():
''' Returns dictionary of some basic features that can be calculated for segmented time
series data '''
features = {'mean': mean,
'median': median,
'abs_energy': abs_energy,
'std': std,
'var': var,
'min': mi... | python | def base_features():
''' Returns dictionary of some basic features that can be calculated for segmented time
series data '''
features = {'mean': mean,
'median': median,
'abs_energy': abs_energy,
'std': std,
'var': var,
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237,648 | dmbee/seglearn | seglearn/feature_functions.py | all_features | def all_features():
''' Returns dictionary of all features in the module
.. note:: Some of the features (hist4, corr) are relatively expensive to compute
'''
features = {'mean': mean,
'median': median,
'gmean': gmean,
'hmean': hmean,
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''' Returns dictionary of all features in the module
.. note:: Some of the features (hist4, corr) are relatively expensive to compute
'''
features = {'mean': mean,
'median': median,
'gmean': gmean,
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237,649 | dmbee/seglearn | seglearn/feature_functions.py | emg_features | def emg_features(threshold=0):
'''Return a dictionary of popular features used for EMG time series classification.'''
return {
'mean_abs_value': mean_abs,
'zero_crossings': zero_crossing(threshold),
'slope_sign_changes': slope_sign_changes(threshold),
'waveform_length': waveform_... | python | def emg_features(threshold=0):
'''Return a dictionary of popular features used for EMG time series classification.'''
return {
'mean_abs_value': mean_abs,
'zero_crossings': zero_crossing(threshold),
'slope_sign_changes': slope_sign_changes(threshold),
'waveform_length': waveform_... | [
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237,650 | dmbee/seglearn | seglearn/feature_functions.py | means_abs_diff | def means_abs_diff(X):
''' mean absolute temporal derivative '''
return np.mean(np.abs(np.diff(X, axis=1)), axis=1) | python | def means_abs_diff(X):
''' mean absolute temporal derivative '''
return np.mean(np.abs(np.diff(X, axis=1)), axis=1) | [
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237,651 | dmbee/seglearn | seglearn/feature_functions.py | mse | def mse(X):
''' computes mean spectral energy for each variable in a segmented time series '''
return np.mean(np.square(np.abs(np.fft.fft(X, axis=1))), axis=1) | python | def mse(X):
''' computes mean spectral energy for each variable in a segmented time series '''
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237,652 | dmbee/seglearn | seglearn/feature_functions.py | mean_crossings | def mean_crossings(X):
''' Computes number of mean crossings for each variable in a segmented time series '''
X = np.atleast_3d(X)
N = X.shape[0]
D = X.shape[2]
mnx = np.zeros((N, D))
for i in range(D):
pos = X[:, :, i] > 0
npos = ~pos
c = (pos[:, :-1] & npos[:, 1:]) | (n... | python | def mean_crossings(X):
''' Computes number of mean crossings for each variable in a segmented time series '''
X = np.atleast_3d(X)
N = X.shape[0]
D = X.shape[2]
mnx = np.zeros((N, D))
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237,653 | dmbee/seglearn | seglearn/feature_functions.py | corr2 | def corr2(X):
''' computes correlations between all variable pairs in a segmented time series
.. note:: this feature is expensive to compute with the current implementation, and cannot be
used with univariate time series
'''
X = np.atleast_3d(X)
N = X.shape[0]
D = X.shape[2]
if D == 1:... | python | def corr2(X):
''' computes correlations between all variable pairs in a segmented time series
.. note:: this feature is expensive to compute with the current implementation, and cannot be
used with univariate time series
'''
X = np.atleast_3d(X)
N = X.shape[0]
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237,654 | dmbee/seglearn | seglearn/feature_functions.py | waveform_length | def waveform_length(X):
''' cumulative length of the waveform over a segment for each variable in the segmented time
series '''
return np.sum(np.abs(np.diff(X, axis=1)), axis=1) | python | def waveform_length(X):
''' cumulative length of the waveform over a segment for each variable in the segmented time
series '''
return np.sum(np.abs(np.diff(X, axis=1)), axis=1) | [
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237,655 | dmbee/seglearn | seglearn/feature_functions.py | root_mean_square | def root_mean_square(X):
''' root mean square for each variable in the segmented time series '''
segment_width = X.shape[1]
return np.sqrt(np.sum(X * X, axis=1) / segment_width) | python | def root_mean_square(X):
''' root mean square for each variable in the segmented time series '''
segment_width = X.shape[1]
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237,656 | dmbee/seglearn | seglearn/split.py | TemporalKFold.split | def split(self, X, y):
'''
Splits time series data and target arrays, and generates splitting indices
Parameters
----------
X : array-like, shape [n_series, ...]
Time series data and (optionally) contextual data
y : array-like shape [n_series, ]
ta... | python | def split(self, X, y):
'''
Splits time series data and target arrays, and generates splitting indices
Parameters
----------
X : array-like, shape [n_series, ...]
Time series data and (optionally) contextual data
y : array-like shape [n_series, ]
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237,657 | dmbee/seglearn | seglearn/split.py | TemporalKFold._ts_slice | def _ts_slice(self, Xt, y):
''' takes time series data, and splits each series into temporal folds '''
Ns = len(Xt)
Xt_new = []
for i in range(self.n_splits):
for j in range(Ns):
Njs = int(len(Xt[j]) / self.n_splits)
Xt_new.append(Xt[j][(Njs * ... | python | def _ts_slice(self, Xt, y):
''' takes time series data, and splits each series into temporal folds '''
Ns = len(Xt)
Xt_new = []
for i in range(self.n_splits):
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237,658 | dmbee/seglearn | seglearn/split.py | TemporalKFold._make_indices | def _make_indices(self, Ns):
''' makes indices for cross validation '''
N_new = int(Ns * self.n_splits)
test = [np.full(N_new, False) for i in range(self.n_splits)]
for i in range(self.n_splits):
test[i][np.arange(Ns * i, Ns * (i + 1))] = True
train = [np.logical_not... | python | def _make_indices(self, Ns):
''' makes indices for cross validation '''
N_new = int(Ns * self.n_splits)
test = [np.full(N_new, False) for i in range(self.n_splits)]
for i in range(self.n_splits):
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237,659 | dmbee/seglearn | seglearn/preprocessing.py | TargetRunLengthEncoder.transform | def transform(self, X, y, sample_weight=None):
'''
Transforms the time series data with run length encoding of the target variable
Note this transformation changes the number of samples in the data
If sample_weight is provided, it is transformed to align to the new target encoding
... | python | def transform(self, X, y, sample_weight=None):
'''
Transforms the time series data with run length encoding of the target variable
Note this transformation changes the number of samples in the data
If sample_weight is provided, it is transformed to align to the new target encoding
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237,660 | dmbee/seglearn | seglearn/preprocessing.py | TargetRunLengthEncoder._rle | def _rle(self, a):
'''
rle implementation credit to Thomas Browne from his SOF post Sept 2015
Parameters
----------
a : array, shape[n,]
input vector
Returns
-------
z : array, shape[nt,]
run lengths
p : array, shape[nt,]
... | python | def _rle(self, a):
'''
rle implementation credit to Thomas Browne from his SOF post Sept 2015
Parameters
----------
a : array, shape[n,]
input vector
Returns
-------
z : array, shape[nt,]
run lengths
p : array, shape[nt,]
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237,661 | dmbee/seglearn | seglearn/preprocessing.py | TargetRunLengthEncoder._transform | def _transform(self, X, y):
'''
Transforms single series
'''
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p = np.append(p, len(y))
big_enough = p[1:] - p[:-1] >= self.min_length
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for i in range(len(y_rle)):
if (big_enough[i]):
Xt.append(X... | python | def _transform(self, X, y):
'''
Transforms single series
'''
z, p, y_rle = self._rle(y)
p = np.append(p, len(y))
big_enough = p[1:] - p[:-1] >= self.min_length
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237,662 | dmbee/seglearn | seglearn/util.py | get_ts_data_parts | def get_ts_data_parts(X):
'''
Separates time series data object into time series variables and contextual variables
Parameters
----------
X : array-like, shape [n_series, ...]
Time series data and (optionally) contextual data
Returns
-------
Xt : array-like, shape [n_series, ]
... | python | def get_ts_data_parts(X):
'''
Separates time series data object into time series variables and contextual variables
Parameters
----------
X : array-like, shape [n_series, ...]
Time series data and (optionally) contextual data
Returns
-------
Xt : array-like, shape [n_series, ]
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237,663 | dmbee/seglearn | seglearn/util.py | check_ts_data_with_ts_target | def check_ts_data_with_ts_target(X, y=None):
'''
Checks time series data with time series target is good. If not raises value error.
Parameters
----------
X : array-like, shape [n_series, ...]
Time series data and (optionally) contextual data
y : array-like, shape [n_series, ...]
... | python | def check_ts_data_with_ts_target(X, y=None):
'''
Checks time series data with time series target is good. If not raises value error.
Parameters
----------
X : array-like, shape [n_series, ...]
Time series data and (optionally) contextual data
y : array-like, shape [n_series, ...]
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237,664 | dmbee/seglearn | seglearn/util.py | ts_stats | def ts_stats(Xt, y, fs=1.0, class_labels=None):
'''
Generates some helpful statistics about the data X
Parameters
----------
X : array-like, shape [n_series, ...]
Time series data and (optionally) contextual data
y : array-like, shape [n_series]
target data
fs : float
... | python | def ts_stats(Xt, y, fs=1.0, class_labels=None):
'''
Generates some helpful statistics about the data X
Parameters
----------
X : array-like, shape [n_series, ...]
Time series data and (optionally) contextual data
y : array-like, shape [n_series]
target data
fs : float
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237,665 | dmbee/seglearn | seglearn/datasets.py | load_watch | def load_watch():
'''
Loads some of the 6-axis inertial sensor data from my smartwatch project. The sensor data was
recorded as study subjects performed sets of 20 shoulder exercise repetitions while wearing a
smartwatch. It is a multivariate time series.
The study can be found here: https://arxiv.... | python | def load_watch():
'''
Loads some of the 6-axis inertial sensor data from my smartwatch project. The sensor data was
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smartwatch. It is a multivariate time series.
The study can be found here: https://arxiv.... | [
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237,666 | dmbee/seglearn | seglearn/transform.py | shuffle_data | def shuffle_data(X, y=None, sample_weight=None):
''' Shuffles indices X, y, and sample_weight together'''
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ind = np.arange(len(X), dtype=np.int)
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Xt = X[ind]
yt = y
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yt = yt[ind... | python | def shuffle_data(X, y=None, sample_weight=None):
''' Shuffles indices X, y, and sample_weight together'''
if len(X) > 1:
ind = np.arange(len(X), dtype=np.int)
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237,667 | dmbee/seglearn | seglearn/transform.py | expand_variables_to_segments | def expand_variables_to_segments(v, Nt):
''' expands contextual variables v, by repeating each instance as specified in Nt '''
N_v = len(np.atleast_1d(v[0]))
return np.concatenate([np.full((Nt[i], N_v), v[i]) for i in np.arange(len(v))]) | python | def expand_variables_to_segments(v, Nt):
''' expands contextual variables v, by repeating each instance as specified in Nt '''
N_v = len(np.atleast_1d(v[0]))
return np.concatenate([np.full((Nt[i], N_v), v[i]) for i in np.arange(len(v))]) | [
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237,668 | dmbee/seglearn | seglearn/transform.py | sliding_window | def sliding_window(time_series, width, step, order='F'):
'''
Segments univariate time series with sliding window
Parameters
----------
time_series : array like shape [n_samples]
time series or sequence
width : int > 0
segment width in samples
step : int > 0
stepsize ... | python | def sliding_window(time_series, width, step, order='F'):
'''
Segments univariate time series with sliding window
Parameters
----------
time_series : array like shape [n_samples]
time series or sequence
width : int > 0
segment width in samples
step : int > 0
stepsize ... | [
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237,669 | dmbee/seglearn | seglearn/transform.py | sliding_tensor | def sliding_tensor(mv_time_series, width, step, order='F'):
'''
segments multivariate time series with sliding window
Parameters
----------
mv_time_series : array like shape [n_samples, n_variables]
multivariate time series or sequence
width : int > 0
segment width in samples
... | python | def sliding_tensor(mv_time_series, width, step, order='F'):
'''
segments multivariate time series with sliding window
Parameters
----------
mv_time_series : array like shape [n_samples, n_variables]
multivariate time series or sequence
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segment width in samples
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237,670 | dmbee/seglearn | seglearn/transform.py | SegmentXY.transform | def transform(self, X, y=None, sample_weight=None):
'''
Transforms the time series data into segments
Note this transformation changes the number of samples in the data
If y is provided, it is segmented and transformed to align to the new samples as per
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Current... | python | def transform(self, X, y=None, sample_weight=None):
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Transforms the time series data into segments
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237,671 | dmbee/seglearn | seglearn/transform.py | PadTrunc.transform | def transform(self, X, y=None, sample_weight=None):
'''
Transforms the time series data into fixed length segments using padding and or truncation
If y is a time series and passed, it will be transformed as well
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----------
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'''
Transforms the time series data into fixed length segments using padding and or truncation
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237,672 | dmbee/seglearn | seglearn/transform.py | InterpLongToWide._check_data | def _check_data(self, X):
'''
Checks that unique identifiers vaf_types are consistent between time series.
Parameters
----------
X : array-like, shape [n_series, ...]
Time series data and (optionally) contextual data
'''
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sv... | python | def _check_data(self, X):
'''
Checks that unique identifiers vaf_types are consistent between time series.
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X : array-like, shape [n_series, ...]
Time series data and (optionally) contextual data
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237,673 | dmbee/seglearn | seglearn/transform.py | FeatureRep._check_features | def _check_features(self, features, Xti):
'''
tests output of each feature against a segmented time series X
Parameters
----------
features : dict
feature function dictionary
Xti : array-like, shape [n_samples, segment_width, n_variables]
segmente... | python | def _check_features(self, features, Xti):
'''
tests output of each feature against a segmented time series X
Parameters
----------
features : dict
feature function dictionary
Xti : array-like, shape [n_samples, segment_width, n_variables]
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237,674 | dmbee/seglearn | seglearn/transform.py | FeatureRep._generate_feature_labels | def _generate_feature_labels(self, X):
'''
Generates string feature labels
'''
Xt, Xc = get_ts_data_parts(X)
ftr_sizes = self._check_features(self.features, Xt[0:3])
f_labels = []
# calculated features
for key in ftr_sizes:
for i in range(ftr... | python | def _generate_feature_labels(self, X):
'''
Generates string feature labels
'''
Xt, Xc = get_ts_data_parts(X)
ftr_sizes = self._check_features(self.features, Xt[0:3])
f_labels = []
# calculated features
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237,675 | dmbee/seglearn | seglearn/transform.py | FeatureRepMix._retrieve_indices | def _retrieve_indices(cols):
'''
Retrieve a list of indices corresponding to the provided column specification.
'''
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return [cols]
elif isinstance(cols, slice):
start = cols.start if cols.start else 0
stop = cols.stop
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'''
Retrieve a list of indices corresponding to the provided column specification.
'''
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return [cols]
elif isinstance(cols, slice):
start = cols.start if cols.start else 0
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237,676 | dmbee/seglearn | seglearn/transform.py | FeatureRepMix._validate | def _validate(self):
'''
Internal function to validate the transformer before applying all internal transformers.
'''
if self.f_labels is None:
raise NotFittedError('FeatureRepMix')
if not self.transformers:
return
names, transformers, _ = zip(*s... | python | def _validate(self):
'''
Internal function to validate the transformer before applying all internal transformers.
'''
if self.f_labels is None:
raise NotFittedError('FeatureRepMix')
if not self.transformers:
return
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237,677 | dmbee/seglearn | seglearn/transform.py | FunctionTransformer.transform | def transform(self, X):
'''
Transforms the time series data based on the provided function. Note this transformation
must not change the number of samples in the data.
Parameters
----------
X : array-like, shape [n_samples, ...]
time series data and (optional... | python | def transform(self, X):
'''
Transforms the time series data based on the provided function. Note this transformation
must not change the number of samples in the data.
Parameters
----------
X : array-like, shape [n_samples, ...]
time series data and (optional... | [
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237,678 | SAP/PyHDB | pyhdb/protocol/segments.py | RequestSegment.build_payload | def build_payload(self, payload):
"""Build payload of all parts and write them into the payload buffer"""
remaining_size = self.MAX_SEGMENT_PAYLOAD_SIZE
for part in self.parts:
part_payload = part.pack(remaining_size)
payload.write(part_payload)
remaining_siz... | python | def build_payload(self, payload):
"""Build payload of all parts and write them into the payload buffer"""
remaining_size = self.MAX_SEGMENT_PAYLOAD_SIZE
for part in self.parts:
part_payload = part.pack(remaining_size)
payload.write(part_payload)
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237,679 | SAP/PyHDB | pyhdb/protocol/types.py | escape | def escape(value):
"""
Escape a single value.
"""
if isinstance(value, (tuple, list)):
return "(" + ", ".join([escape(arg) for arg in value]) + ")"
else:
typ = by_python_type.get(value.__class__)
if typ is None:
raise InterfaceError(
"Unsupported ... | python | def escape(value):
"""
Escape a single value.
"""
if isinstance(value, (tuple, list)):
return "(" + ", ".join([escape(arg) for arg in value]) + ")"
else:
typ = by_python_type.get(value.__class__)
if typ is None:
raise InterfaceError(
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237,680 | SAP/PyHDB | pyhdb/protocol/types.py | escape_values | def escape_values(values):
"""
Escape multiple values from a list, tuple or dict.
"""
if isinstance(values, (tuple, list)):
return tuple([escape(value) for value in values])
elif isinstance(values, dict):
return dict([
(key, escape(value)) for (key, value) in values.items... | python | def escape_values(values):
"""
Escape multiple values from a list, tuple or dict.
"""
if isinstance(values, (tuple, list)):
return tuple([escape(value) for value in values])
elif isinstance(values, dict):
return dict([
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237,681 | SAP/PyHDB | pyhdb/protocol/types.py | Date.prepare | def prepare(cls, value):
"""Pack datetime value into proper binary format"""
pfield = struct.pack('b', cls.type_code)
if isinstance(value, string_types):
value = datetime.datetime.strptime(value, "%Y-%m-%d")
year = value.year | 0x8000 # for some unknown reasons year has to b... | python | def prepare(cls, value):
"""Pack datetime value into proper binary format"""
pfield = struct.pack('b', cls.type_code)
if isinstance(value, string_types):
value = datetime.datetime.strptime(value, "%Y-%m-%d")
year = value.year | 0x8000 # for some unknown reasons year has to b... | [
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237,682 | SAP/PyHDB | pyhdb/protocol/types.py | Time.prepare | def prepare(cls, value):
"""Pack time value into proper binary format"""
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237,683 | SAP/PyHDB | pyhdb/protocol/types.py | MixinLobType.prepare | def prepare(cls, value, length=0, position=0, is_last_data=True):
"""Prepare Lob header.
Note that the actual lob data is NOT written here but appended after the parameter block for each row!
"""
hstruct = WriteLobHeader.header_struct
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"""Prepare Lob header.
Note that the actual lob data is NOT written here but appended after the parameter block for each row!
"""
hstruct = WriteLobHeader.header_struct
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237,684 | SAP/PyHDB | pyhdb/protocol/lobs.py | Lob.seek | def seek(self, offset, whence=SEEK_SET):
"""Seek pointer in lob data buffer to requested position.
Might trigger further loading of data from the database if the pointer is beyond currently read data.
"""
# A nice trick is to (ab)use BytesIO.seek() to go to the desired position for easie... | python | def seek(self, offset, whence=SEEK_SET):
"""Seek pointer in lob data buffer to requested position.
Might trigger further loading of data from the database if the pointer is beyond currently read data.
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237,685 | SAP/PyHDB | pyhdb/protocol/lobs.py | Lob._read_missing_lob_data_from_db | def _read_missing_lob_data_from_db(self, readoffset, readlength):
"""Read LOB request part from database"""
logger.debug('Reading missing lob data from db. Offset: %d, readlength: %d' % (readoffset, readlength))
lob_data = self._make_read_lob_request(readoffset, readlength)
# make sure ... | python | def _read_missing_lob_data_from_db(self, readoffset, readlength):
"""Read LOB request part from database"""
logger.debug('Reading missing lob data from db. Offset: %d, readlength: %d' % (readoffset, readlength))
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237,686 | SAP/PyHDB | pyhdb/protocol/lobs.py | Clob._init_io_container | def _init_io_container(self, init_value):
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For CLobs ensure that an initial unicode value only contains valid ascii chars.
"""
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237,687 | SAP/PyHDB | pyhdb/cursor.py | Cursor._handle_upsert | def _handle_upsert(self, parts, unwritten_lobs=()):
"""Handle reply messages from INSERT or UPDATE statements"""
self.description = None
self._received_last_resultset_part = True # set to 'True' so that cursor.fetch*() returns just empty list
for part in parts:
if part.kind... | python | def _handle_upsert(self, parts, unwritten_lobs=()):
"""Handle reply messages from INSERT or UPDATE statements"""
self.description = None
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237,688 | SAP/PyHDB | pyhdb/cursor.py | Cursor._handle_select | def _handle_select(self, parts, result_metadata=None):
"""Handle reply messages from SELECT statements"""
self.rowcount = -1
if result_metadata is not None:
# Select was prepared and we can use the already received metadata
self.description, self._column_types = self._han... | python | def _handle_select(self, parts, result_metadata=None):
"""Handle reply messages from SELECT statements"""
self.rowcount = -1
if result_metadata is not None:
# Select was prepared and we can use the already received metadata
self.description, self._column_types = self._han... | [
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237,689 | SAP/PyHDB | pyhdb/cursor.py | Cursor._handle_dbproc_call | def _handle_dbproc_call(self, parts, parameters_metadata):
"""Handle reply messages from STORED PROCEDURE statements"""
for part in parts:
if part.kind == part_kinds.ROWSAFFECTED:
self.rowcount = part.values[0]
elif part.kind == part_kinds.TRANSACTIONFLAGS:
... | python | def _handle_dbproc_call(self, parts, parameters_metadata):
"""Handle reply messages from STORED PROCEDURE statements"""
for part in parts:
if part.kind == part_kinds.ROWSAFFECTED:
self.rowcount = part.values[0]
elif part.kind == part_kinds.TRANSACTIONFLAGS:
... | [
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237,690 | SAP/PyHDB | pyhdb/lib/stringlib.py | allhexlify | def allhexlify(data):
"""Hexlify given data into a string representation with hex values for all chars
Input like
'ab\x04ce'
becomes
'\x61\x62\x04\x63\x65'
"""
hx = binascii.hexlify(data)
return b''.join([b'\\x' + o for o in re.findall(b'..', hx)]) | python | def allhexlify(data):
"""Hexlify given data into a string representation with hex values for all chars
Input like
'ab\x04ce'
becomes
'\x61\x62\x04\x63\x65'
"""
hx = binascii.hexlify(data)
return b''.join([b'\\x' + o for o in re.findall(b'..', hx)]) | [
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237,691 | SAP/PyHDB | pyhdb/protocol/parts.py | Part.pack | def pack(self, remaining_size):
"""Pack data of part into binary format"""
arguments_count, payload = self.pack_data(remaining_size - self.header_size)
payload_length = len(payload)
# align payload length to multiple of 8
if payload_length % 8 != 0:
payload += b"\x00... | python | def pack(self, remaining_size):
"""Pack data of part into binary format"""
arguments_count, payload = self.pack_data(remaining_size - self.header_size)
payload_length = len(payload)
# align payload length to multiple of 8
if payload_length % 8 != 0:
payload += b"\x00... | [
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237,692 | SAP/PyHDB | pyhdb/protocol/parts.py | Part.unpack_from | def unpack_from(cls, payload, expected_parts):
"""Unpack parts from payload"""
for num_part in iter_range(expected_parts):
hdr = payload.read(cls.header_size)
try:
part_header = PartHeader(*cls.header_struct.unpack(hdr))
except struct.error:
... | python | def unpack_from(cls, payload, expected_parts):
"""Unpack parts from payload"""
for num_part in iter_range(expected_parts):
hdr = payload.read(cls.header_size)
try:
part_header = PartHeader(*cls.header_struct.unpack(hdr))
except struct.error:
... | [
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237,693 | SAP/PyHDB | pyhdb/protocol/parts.py | ReadLobRequest.pack_data | def pack_data(self, remaining_size):
"""Pack data. readoffset has to be increased by one, seems like HANA starts from 1, not zero."""
payload = self.part_struct.pack(self.locator_id, self.readoffset + 1, self.readlength, b' ')
return 4, payload | python | def pack_data(self, remaining_size):
"""Pack data. readoffset has to be increased by one, seems like HANA starts from 1, not zero."""
payload = self.part_struct.pack(self.locator_id, self.readoffset + 1, self.readlength, b' ')
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237,694 | SAP/PyHDB | pyhdb/protocol/message.py | RequestMessage.build_payload | def build_payload(self, payload):
""" Build payload of message. """
for segment in self.segments:
segment.pack(payload, commit=self.autocommit) | python | def build_payload(self, payload):
""" Build payload of message. """
for segment in self.segments:
segment.pack(payload, commit=self.autocommit) | [
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] | 826539d06b8bcef74fe755e7489b8a8255628f12 | https://github.com/SAP/PyHDB/blob/826539d06b8bcef74fe755e7489b8a8255628f12/pyhdb/protocol/message.py#L42-L45 |
237,695 | SAP/PyHDB | pyhdb/protocol/message.py | RequestMessage.pack | def pack(self):
""" Pack message to binary stream. """
payload = io.BytesIO()
# Advance num bytes equal to header size - the header is written later
# after the payload of all segments and parts has been written:
payload.seek(self.header_size, io.SEEK_CUR)
# Write out pa... | python | def pack(self):
""" Pack message to binary stream. """
payload = io.BytesIO()
# Advance num bytes equal to header size - the header is written later
# after the payload of all segments and parts has been written:
payload.seek(self.header_size, io.SEEK_CUR)
# Write out pa... | [
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237,696 | serge-sans-paille/pythran | pythran/syntax.py | check_specs | def check_specs(specs, renamings, types):
'''
Does nothing but raising PythranSyntaxError if specs
are incompatible with the actual code
'''
from pythran.types.tog import unify, clone, tr
from pythran.types.tog import Function, TypeVariable, InferenceError
functions = {renamings.get(k, k): ... | python | def check_specs(specs, renamings, types):
'''
Does nothing but raising PythranSyntaxError if specs
are incompatible with the actual code
'''
from pythran.types.tog import unify, clone, tr
from pythran.types.tog import Function, TypeVariable, InferenceError
functions = {renamings.get(k, k): ... | [
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237,697 | serge-sans-paille/pythran | pythran/syntax.py | check_exports | def check_exports(mod, specs, renamings):
'''
Does nothing but raising PythranSyntaxError if specs
references an undefined global
'''
functions = {renamings.get(k, k): v for k, v in specs.functions.items()}
mod_functions = {node.name: node for node in mod.body
if isinstance... | python | def check_exports(mod, specs, renamings):
'''
Does nothing but raising PythranSyntaxError if specs
references an undefined global
'''
functions = {renamings.get(k, k): v for k, v in specs.functions.items()}
mod_functions = {node.name: node for node in mod.body
if isinstance... | [
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237,698 | serge-sans-paille/pythran | pythran/syntax.py | SyntaxChecker.visit_Import | def visit_Import(self, node):
""" Check if imported module exists in MODULES. """
for alias in node.names:
current_module = MODULES
# Recursive check for submodules
for path in alias.name.split('.'):
if path not in current_module:
r... | python | def visit_Import(self, node):
""" Check if imported module exists in MODULES. """
for alias in node.names:
current_module = MODULES
# Recursive check for submodules
for path in alias.name.split('.'):
if path not in current_module:
r... | [
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237,699 | serge-sans-paille/pythran | pythran/syntax.py | SyntaxChecker.visit_ImportFrom | def visit_ImportFrom(self, node):
"""
Check validity of imported functions.
Check:
- no level specific value are provided.
- a module is provided
- module/submodule exists in MODULES
- imported function exists in the given ... | python | def visit_ImportFrom(self, node):
"""
Check validity of imported functions.
Check:
- no level specific value are provided.
- a module is provided
- module/submodule exists in MODULES
- imported function exists in the given ... | [
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