body stringlengths 26 98.2k | body_hash int64 -9,222,864,604,528,158,000 9,221,803,474B | docstring stringlengths 1 16.8k | path stringlengths 5 230 | name stringlengths 1 96 | repository_name stringlengths 7 89 | lang stringclasses 1
value | body_without_docstring stringlengths 20 98.2k |
|---|---|---|---|---|---|---|---|
def __init__(self, l2_norm_clip, stddev):
'Initializes the GaussianSumQuery.\n\n Args:\n l2_norm_clip: The clipping norm to apply to the global norm of each\n record.\n stddev: The stddev of the noise added to the sum.\n '
self._l2_norm_clip = l2_norm_clip
self._stddev = stddev
se... | 830,702,645,456,667,900 | Initializes the GaussianSumQuery.
Args:
l2_norm_clip: The clipping norm to apply to the global norm of each
record.
stddev: The stddev of the noise added to the sum. | tensorflow_privacy/privacy/dp_query/gaussian_query.py | __init__ | Juspem1980/privacy | python | def __init__(self, l2_norm_clip, stddev):
'Initializes the GaussianSumQuery.\n\n Args:\n l2_norm_clip: The clipping norm to apply to the global norm of each\n record.\n stddev: The stddev of the noise added to the sum.\n '
self._l2_norm_clip = l2_norm_clip
self._stddev = stddev
se... |
def make_global_state(self, l2_norm_clip, stddev):
'Creates a global state from the given parameters.'
return self._GlobalState(tf.cast(l2_norm_clip, tf.float32), tf.cast(stddev, tf.float32)) | 7,631,861,765,875,769,000 | Creates a global state from the given parameters. | tensorflow_privacy/privacy/dp_query/gaussian_query.py | make_global_state | Juspem1980/privacy | python | def make_global_state(self, l2_norm_clip, stddev):
return self._GlobalState(tf.cast(l2_norm_clip, tf.float32), tf.cast(stddev, tf.float32)) |
def preprocess_record_impl(self, params, record):
'Clips the l2 norm, returning the clipped record and the l2 norm.\n\n Args:\n params: The parameters for the sample.\n record: The record to be processed.\n\n Returns:\n A tuple (preprocessed_records, l2_norm) where `preprocessed_records` is\n ... | -7,945,747,213,345,504,000 | Clips the l2 norm, returning the clipped record and the l2 norm.
Args:
params: The parameters for the sample.
record: The record to be processed.
Returns:
A tuple (preprocessed_records, l2_norm) where `preprocessed_records` is
the structure of preprocessed tensors, and l2_norm is the total l2 norm
befor... | tensorflow_privacy/privacy/dp_query/gaussian_query.py | preprocess_record_impl | Juspem1980/privacy | python | def preprocess_record_impl(self, params, record):
'Clips the l2 norm, returning the clipped record and the l2 norm.\n\n Args:\n params: The parameters for the sample.\n record: The record to be processed.\n\n Returns:\n A tuple (preprocessed_records, l2_norm) where `preprocessed_records` is\n ... |
def get_noised_result(self, sample_state, global_state):
'See base class.'
if (LooseVersion(tf.__version__) < LooseVersion('2.0.0')):
def add_noise(v):
return (v + tf.random.normal(tf.shape(input=v), stddev=global_state.stddev))
else:
random_normal = tf.random_normal_initializer... | -6,189,862,732,280,817,000 | See base class. | tensorflow_privacy/privacy/dp_query/gaussian_query.py | get_noised_result | Juspem1980/privacy | python | def get_noised_result(self, sample_state, global_state):
if (LooseVersion(tf.__version__) < LooseVersion('2.0.0')):
def add_noise(v):
return (v + tf.random.normal(tf.shape(input=v), stddev=global_state.stddev))
else:
random_normal = tf.random_normal_initializer(stddev=global_st... |
def __init__(self, l2_norm_clip, sum_stddev, denominator):
'Initializes the GaussianAverageQuery.\n\n Args:\n l2_norm_clip: The clipping norm to apply to the global norm of each\n record.\n sum_stddev: The stddev of the noise added to the sum (before\n normalization).\n denominator: ... | -4,537,654,262,657,255,000 | Initializes the GaussianAverageQuery.
Args:
l2_norm_clip: The clipping norm to apply to the global norm of each
record.
sum_stddev: The stddev of the noise added to the sum (before
normalization).
denominator: The normalization constant (applied after noise is added to
the sum). | tensorflow_privacy/privacy/dp_query/gaussian_query.py | __init__ | Juspem1980/privacy | python | def __init__(self, l2_norm_clip, sum_stddev, denominator):
'Initializes the GaussianAverageQuery.\n\n Args:\n l2_norm_clip: The clipping norm to apply to the global norm of each\n record.\n sum_stddev: The stddev of the noise added to the sum (before\n normalization).\n denominator: ... |
def _assert_community_contribution_stats_is_in_default_state(self):
'Checks if the community contribution stats is in its default\n state.\n '
community_contribution_stats = suggestion_services.get_community_contribution_stats()
self.assertEqual(community_contribution_stats.translation_reviewe... | -8,273,821,425,724,012,000 | Checks if the community contribution stats is in its default
state. | core/domain/suggestion_registry_test.py | _assert_community_contribution_stats_is_in_default_state | AdityaDubey0/oppia | python | def _assert_community_contribution_stats_is_in_default_state(self):
'Checks if the community contribution stats is in its default\n state.\n '
community_contribution_stats = suggestion_services.get_community_contribution_stats()
self.assertEqual(community_contribution_stats.translation_reviewe... |
def conversion_fn():
'Temporary function.'
pass | -7,532,713,194,701,360,000 | Temporary function. | core/domain/suggestion_registry_test.py | conversion_fn | AdityaDubey0/oppia | python | def conversion_fn():
pass |
def __init__(self, feats_extract: Optional[AbsFeatsExtract], normalize: Optional[(AbsNormalize and InversibleInterface)], tts: AbsGANTTS):
'Initialize ESPnetGANTTSModel module.'
assert check_argument_types()
super().__init__()
self.feats_extract = feats_extract
self.normalize = normalize
self.tt... | -4,964,261,881,008,749,000 | Initialize ESPnetGANTTSModel module. | espnet2/gan_tts/espnet_model.py | __init__ | actboy/espnet | python | def __init__(self, feats_extract: Optional[AbsFeatsExtract], normalize: Optional[(AbsNormalize and InversibleInterface)], tts: AbsGANTTS):
assert check_argument_types()
super().__init__()
self.feats_extract = feats_extract
self.normalize = normalize
self.tts = tts
assert hasattr(tts, 'gener... |
def forward(self, text: torch.Tensor, text_lengths: torch.Tensor, speech: torch.Tensor, speech_lengths: torch.Tensor, spembs: Optional[torch.Tensor]=None, sids: Optional[torch.Tensor]=None, lids: Optional[torch.Tensor]=None, forward_generator: bool=True) -> Dict[(str, Any)]:
'Return generator or discriminator loss ... | -8,931,493,892,899,705,000 | Return generator or discriminator loss with dict format.
Args:
text (Tensor): Text index tensor (B, T_text).
text_lengths (Tensor): Text length tensor (B,).
speech (Tensor): Speech waveform tensor (B, T_wav).
speech_lengths (Tensor): Speech length tensor (B,).
spembs (Optional[Tensor]): Speaker emb... | espnet2/gan_tts/espnet_model.py | forward | actboy/espnet | python | def forward(self, text: torch.Tensor, text_lengths: torch.Tensor, speech: torch.Tensor, speech_lengths: torch.Tensor, spembs: Optional[torch.Tensor]=None, sids: Optional[torch.Tensor]=None, lids: Optional[torch.Tensor]=None, forward_generator: bool=True) -> Dict[(str, Any)]:
'Return generator or discriminator loss ... |
def collect_feats(self, text: torch.Tensor, text_lengths: torch.Tensor, speech: torch.Tensor, speech_lengths: torch.Tensor, spembs: Optional[torch.Tensor]=None, sids: Optional[torch.Tensor]=None, lids: Optional[torch.Tensor]=None) -> Dict[(str, torch.Tensor)]:
'Calculate features and return them as a dict.\n\n ... | -8,469,345,931,603,927,000 | Calculate features and return them as a dict.
Args:
text (Tensor): Text index tensor (B, T_text).
text_lengths (Tensor): Text length tensor (B,).
speech (Tensor): Speech waveform tensor (B, T_wav).
speech_lengths (Tensor): Speech length tensor (B, 1).
spembs (Optional[Tensor]): Speaker embedding te... | espnet2/gan_tts/espnet_model.py | collect_feats | actboy/espnet | python | def collect_feats(self, text: torch.Tensor, text_lengths: torch.Tensor, speech: torch.Tensor, speech_lengths: torch.Tensor, spembs: Optional[torch.Tensor]=None, sids: Optional[torch.Tensor]=None, lids: Optional[torch.Tensor]=None) -> Dict[(str, torch.Tensor)]:
'Calculate features and return them as a dict.\n\n ... |
@staticmethod
def factory(sql_connection_type: SqlDialect, **kwargs) -> 'SqlConnectionOptions':
'\n Function signatures for factory method\n\n Postgres: (dialect: SqlDialects, host: str, port: int, username: str, password: str,\n database_name: str, timeout: int = None)\n '
return Sq... | -2,160,951,259,533,789,000 | Function signatures for factory method
Postgres: (dialect: SqlDialects, host: str, port: int, username: str, password: str,
database_name: str, timeout: int = None) | flask_app/utilities/DataInterfaces/SqlInterface.py | factory | cliftbar/flask_app_template | python | @staticmethod
def factory(sql_connection_type: SqlDialect, **kwargs) -> 'SqlConnectionOptions':
'\n Function signatures for factory method\n\n Postgres: (dialect: SqlDialects, host: str, port: int, username: str, password: str,\n database_name: str, timeout: int = None)\n '
return Sq... |
def get_latest_version() -> str:
'\n :return: The latest available version in the format "vX.Y.Z"\n '
subdomain = ('staging.' if config.is_staging.value else '')
credentials = config.staging_credentials()
url = BRAINFRAME_LATEST_TAG_URL.format(subdomain=subdomain)
response = requests.get(url, ... | -3,277,660,655,089,626,000 | :return: The latest available version in the format "vX.Y.Z" | brainframe/cli/docker_compose.py | get_latest_version | aotuai/brainframe-cli | python | def get_latest_version() -> str:
'\n \n '
subdomain = ('staging.' if config.is_staging.value else )
credentials = config.staging_credentials()
url = BRAINFRAME_LATEST_TAG_URL.format(subdomain=subdomain)
response = requests.get(url, auth=credentials)
return response.text |
def _assert_has_docker_permissions() -> None:
'Fails if the user does not have permissions to interact with Docker'
if (not (os_utils.is_root() or os_utils.currently_in_group('docker'))):
error_message = ((i18n.t('general.docker-bad-permissions') + '\n') + _group_recommendation_message('docker'))
... | 3,725,551,011,273,241,600 | Fails if the user does not have permissions to interact with Docker | brainframe/cli/docker_compose.py | _assert_has_docker_permissions | aotuai/brainframe-cli | python | def _assert_has_docker_permissions() -> None:
if (not (os_utils.is_root() or os_utils.currently_in_group('docker'))):
error_message = ((i18n.t('general.docker-bad-permissions') + '\n') + _group_recommendation_message('docker'))
print_utils.fail(error_message) |
def _assert_has_write_permissions(path: Path) -> None:
'Fails if the user does not have write access to the given path.'
if os.access(path, os.W_OK):
return
error_message = i18n.t('general.file-bad-write-permissions', path=path)
error_message += '\n'
if (path.stat().st_gid == os_utils.BRAINF... | -1,048,546,519,596,421,600 | Fails if the user does not have write access to the given path. | brainframe/cli/docker_compose.py | _assert_has_write_permissions | aotuai/brainframe-cli | python | def _assert_has_write_permissions(path: Path) -> None:
if os.access(path, os.W_OK):
return
error_message = i18n.t('general.file-bad-write-permissions', path=path)
error_message += '\n'
if (path.stat().st_gid == os_utils.BRAINFRAME_GROUP_ID):
error_message += (' ' + _group_recommenda... |
def configure_logger(path: pathlib.Path, verbose: bool=False) -> logging.Logger:
'Configure logger\n\n Args:\n path (pathlib.Path): Path where the persistent logger should write to.\n verbose (bool, optional): Use verbose logging. Defaults to False.\n\n Returns:\n logging.Logger: Created ... | 664,404,265,283,654,300 | Configure logger
Args:
path (pathlib.Path): Path where the persistent logger should write to.
verbose (bool, optional): Use verbose logging. Defaults to False.
Returns:
logging.Logger: Created logger | c8ylp/cli/core.py | configure_logger | SoftwareAG/cumulocity-remote-access-local-proxy | python | def configure_logger(path: pathlib.Path, verbose: bool=False) -> logging.Logger:
'Configure logger\n\n Args:\n path (pathlib.Path): Path where the persistent logger should write to.\n verbose (bool, optional): Use verbose logging. Defaults to False.\n\n Returns:\n logging.Logger: Created ... |
def signal_handler(_signal, _frame):
'Signal handler'
sys.exit(ExitCodes.TERMINATE) | 1,730,254,604,786,225,000 | Signal handler | c8ylp/cli/core.py | signal_handler | SoftwareAG/cumulocity-remote-access-local-proxy | python | def signal_handler(_signal, _frame):
sys.exit(ExitCodes.TERMINATE) |
def register_signals():
'Register signal handlers'
signal.signal(signal.SIGINT, signal_handler) | 7,165,289,326,244,733,000 | Register signal handlers | c8ylp/cli/core.py | register_signals | SoftwareAG/cumulocity-remote-access-local-proxy | python | def register_signals():
signal.signal(signal.SIGINT, signal_handler) |
def create_client(ctx: click.Context, opts: ProxyContext) -> CumulocityClient:
'Create Cumulocity client and prompt for missing credentials\n if necessary.\n\n Args:\n ctx (click.Context): Click context\n opts (ProxyContext): Proxy options\n\n Returns:\n CumulocityClient: Configured Cu... | 180,156,177,567,948,860 | Create Cumulocity client and prompt for missing credentials
if necessary.
Args:
ctx (click.Context): Click context
opts (ProxyContext): Proxy options
Returns:
CumulocityClient: Configured Cumulocity client | c8ylp/cli/core.py | create_client | SoftwareAG/cumulocity-remote-access-local-proxy | python | def create_client(ctx: click.Context, opts: ProxyContext) -> CumulocityClient:
'Create Cumulocity client and prompt for missing credentials\n if necessary.\n\n Args:\n ctx (click.Context): Click context\n opts (ProxyContext): Proxy options\n\n Returns:\n CumulocityClient: Configured Cu... |
def store_credentials(opts: ProxyContext, client: CumulocityClient):
'Store credentials to the environment file. It creates\n the file if it does not already exist.\n\n The file will only be written to if it has changed.\n\n Args:\n opts (ProxyContext): Proxy options\n client (CumulocityClien... | -3,190,738,371,489,343,000 | Store credentials to the environment file. It creates
the file if it does not already exist.
The file will only be written to if it has changed.
Args:
opts (ProxyContext): Proxy options
client (CumulocityClient): Cumulocity client containing valid
credentials | c8ylp/cli/core.py | store_credentials | SoftwareAG/cumulocity-remote-access-local-proxy | python | def store_credentials(opts: ProxyContext, client: CumulocityClient):
'Store credentials to the environment file. It creates\n the file if it does not already exist.\n\n The file will only be written to if it has changed.\n\n Args:\n opts (ProxyContext): Proxy options\n client (CumulocityClien... |
def get_config_id(ctx: click.Context, mor: Dict[(str, Any)], config: str) -> str:
'Get the remote access configuration id matching a specific type\n from a device managed object\n\n Args:\n mor (Dict[str, Any]): Device managed object\n config (str): Expected configuration type\n\n Returns:\n ... | 6,785,673,878,745,491,000 | Get the remote access configuration id matching a specific type
from a device managed object
Args:
mor (Dict[str, Any]): Device managed object
config (str): Expected configuration type
Returns:
str: Remote access configuration id | c8ylp/cli/core.py | get_config_id | SoftwareAG/cumulocity-remote-access-local-proxy | python | def get_config_id(ctx: click.Context, mor: Dict[(str, Any)], config: str) -> str:
'Get the remote access configuration id matching a specific type\n from a device managed object\n\n Args:\n mor (Dict[str, Any]): Device managed object\n config (str): Expected configuration type\n\n Returns:\n ... |
def run_proxy_in_background(ctx: click.Context, opts: ProxyContext, connection_data: RemoteAccessConnectionData, ready_signal: threading.Event=None):
'Run the proxy in a background thread\n\n Args:\n ctx (click.Context): Click context\n opts (ProxyContext): Proxy options\n connection_data (R... | -36,272,710,872,368,430 | Run the proxy in a background thread
Args:
ctx (click.Context): Click context
opts (ProxyContext): Proxy options
connection_data (RemoteAccessConnectionData): Remote access connection data | c8ylp/cli/core.py | run_proxy_in_background | SoftwareAG/cumulocity-remote-access-local-proxy | python | def run_proxy_in_background(ctx: click.Context, opts: ProxyContext, connection_data: RemoteAccessConnectionData, ready_signal: threading.Event=None):
'Run the proxy in a background thread\n\n Args:\n ctx (click.Context): Click context\n opts (ProxyContext): Proxy options\n connection_data (R... |
def pre_start_checks(ctx: click.Context, opts: ProxyContext) -> Optional[RemoteAccessConnectionData]:
'Run prestart checks before starting the local proxy\n\n Args:\n ctx (click.Context): Click context\n opts (ProxyContext): Proxy options\n\n Returns:\n Optional[RemoteAccessConnectionData... | 3,013,459,128,331,044,000 | Run prestart checks before starting the local proxy
Args:
ctx (click.Context): Click context
opts (ProxyContext): Proxy options
Returns:
Optional[RemoteAccessConnectionData]: Remote access connection data | c8ylp/cli/core.py | pre_start_checks | SoftwareAG/cumulocity-remote-access-local-proxy | python | def pre_start_checks(ctx: click.Context, opts: ProxyContext) -> Optional[RemoteAccessConnectionData]:
'Run prestart checks before starting the local proxy\n\n Args:\n ctx (click.Context): Click context\n opts (ProxyContext): Proxy options\n\n Returns:\n Optional[RemoteAccessConnectionData... |
def start_proxy(ctx: click.Context, opts: ProxyContext, connection_data: RemoteAccessConnectionData, stop_signal: threading.Event=None, ready_signal: threading.Event=None) -> NoReturn:
'Start the local proxy\n\n Args:\n ctx (click.Context): Click context\n opts (ProxyContext): Proxy options\n '
... | 6,606,672,606,113,287,000 | Start the local proxy
Args:
ctx (click.Context): Click context
opts (ProxyContext): Proxy options | c8ylp/cli/core.py | start_proxy | SoftwareAG/cumulocity-remote-access-local-proxy | python | def start_proxy(ctx: click.Context, opts: ProxyContext, connection_data: RemoteAccessConnectionData, stop_signal: threading.Event=None, ready_signal: threading.Event=None) -> NoReturn:
'Start the local proxy\n\n Args:\n ctx (click.Context): Click context\n opts (ProxyContext): Proxy options\n '
... |
@property
def used_port(self) -> int:
'Get the port used by the local proxy\n\n Returns:\n int: Port number\n '
return self._root_context.get('used_port', self.port) | 8,691,517,613,530,922,000 | Get the port used by the local proxy
Returns:
int: Port number | c8ylp/cli/core.py | used_port | SoftwareAG/cumulocity-remote-access-local-proxy | python | @property
def used_port(self) -> int:
'Get the port used by the local proxy\n\n Returns:\n int: Port number\n '
return self._root_context.get('used_port', self.port) |
@used_port.setter
def used_port(self, value: int):
'Store the port used by the local proxy for later reference\n\n Args:\n value (int): Port number\n '
self._root_context['used_port'] = value | -7,539,411,045,327,996,000 | Store the port used by the local proxy for later reference
Args:
value (int): Port number | c8ylp/cli/core.py | used_port | SoftwareAG/cumulocity-remote-access-local-proxy | python | @used_port.setter
def used_port(self, value: int):
'Store the port used by the local proxy for later reference\n\n Args:\n value (int): Port number\n '
self._root_context['used_port'] = value |
def exit_server_not_ready(self) -> NoReturn:
'Exit with a server not ready error\n\n Returns:\n NoReturn: The function does not return\n '
self.show_error(f'Timed out waiting for local port to open: port={self.used_port}, timeout={self.wait_port_timeout}s')
self._ctx.exit(ExitCodes.... | -3,488,656,533,846,449,700 | Exit with a server not ready error
Returns:
NoReturn: The function does not return | c8ylp/cli/core.py | exit_server_not_ready | SoftwareAG/cumulocity-remote-access-local-proxy | python | def exit_server_not_ready(self) -> NoReturn:
'Exit with a server not ready error\n\n Returns:\n NoReturn: The function does not return\n '
self.show_error(f'Timed out waiting for local port to open: port={self.used_port}, timeout={self.wait_port_timeout}s')
self._ctx.exit(ExitCodes.... |
def fromdict(self, src_dict: Dict[(str, Any)]) -> 'ProxyContext':
'Load proxy settings from a dictionary\n\n Args:\n src_dict (Dict[str, Any]): [description]\n\n Returns:\n ProxyContext: Proxy options after the values have been set\n via the dictionary\n '
... | 6,400,380,737,886,094,000 | Load proxy settings from a dictionary
Args:
src_dict (Dict[str, Any]): [description]
Returns:
ProxyContext: Proxy options after the values have been set
via the dictionary | c8ylp/cli/core.py | fromdict | SoftwareAG/cumulocity-remote-access-local-proxy | python | def fromdict(self, src_dict: Dict[(str, Any)]) -> 'ProxyContext':
'Load proxy settings from a dictionary\n\n Args:\n src_dict (Dict[str, Any]): [description]\n\n Returns:\n ProxyContext: Proxy options after the values have been set\n via the dictionary\n '
... |
def start_background(self, ctx: click.Context=None) -> 'ProxyContext':
'Start the local proxy in the background\n\n Returns:\n ProxyContext: Reference to the proxy context so it can be chained\n with other commands or used after the initialization of the class.\n '
cur_ct... | 7,925,858,526,088,870,000 | Start the local proxy in the background
Returns:
ProxyContext: Reference to the proxy context so it can be chained
with other commands or used after the initialization of the class. | c8ylp/cli/core.py | start_background | SoftwareAG/cumulocity-remote-access-local-proxy | python | def start_background(self, ctx: click.Context=None) -> 'ProxyContext':
'Start the local proxy in the background\n\n Returns:\n ProxyContext: Reference to the proxy context so it can be chained\n with other commands or used after the initialization of the class.\n '
cur_ct... |
def start(self, ctx: click.Context=None) -> None:
'Start the local proxy in the background\n\n Returns:\n ProxyContext: Reference to the proxy context so it can be chained\n with other commands or used after the initialization of the class.\n '
cur_ctx = (ctx or self._ctx... | -3,952,982,397,710,996,500 | Start the local proxy in the background
Returns:
ProxyContext: Reference to the proxy context so it can be chained
with other commands or used after the initialization of the class. | c8ylp/cli/core.py | start | SoftwareAG/cumulocity-remote-access-local-proxy | python | def start(self, ctx: click.Context=None) -> None:
'Start the local proxy in the background\n\n Returns:\n ProxyContext: Reference to the proxy context so it can be chained\n with other commands or used after the initialization of the class.\n '
cur_ctx = (ctx or self._ctx... |
@classmethod
def show_message(cls, msg: str, *args, **kwargs):
'Show an message to the user and log it\n\n Args:\n msg (str): User message to print on the console\n '
click.secho(msg, fg='green')
logging.info(msg, *args, **kwargs) | -635,830,249,380,264,400 | Show an message to the user and log it
Args:
msg (str): User message to print on the console | c8ylp/cli/core.py | show_message | SoftwareAG/cumulocity-remote-access-local-proxy | python | @classmethod
def show_message(cls, msg: str, *args, **kwargs):
'Show an message to the user and log it\n\n Args:\n msg (str): User message to print on the console\n '
click.secho(msg, fg='green')
logging.info(msg, *args, **kwargs) |
def show_error(self, msg: str, *args, **kwargs):
'Show an error to the user and log it\n\n Args:\n msg (str): User message to print on the console\n '
if (not self.verbose):
click.secho(msg, fg='red')
logging.warning(msg, *args, **kwargs) | 6,178,511,922,028,288,000 | Show an error to the user and log it
Args:
msg (str): User message to print on the console | c8ylp/cli/core.py | show_error | SoftwareAG/cumulocity-remote-access-local-proxy | python | def show_error(self, msg: str, *args, **kwargs):
'Show an error to the user and log it\n\n Args:\n msg (str): User message to print on the console\n '
if (not self.verbose):
click.secho(msg, fg='red')
logging.warning(msg, *args, **kwargs) |
def show_info(self, msg: str, *args, **kwargs):
'Show an info message to the user and log it\n\n Args:\n msg (str): User message to print on the console\n '
if (not self.verbose):
click.secho(msg)
logging.warning(msg, *args, **kwargs) | 3,718,197,683,893,955,600 | Show an info message to the user and log it
Args:
msg (str): User message to print on the console | c8ylp/cli/core.py | show_info | SoftwareAG/cumulocity-remote-access-local-proxy | python | def show_info(self, msg: str, *args, **kwargs):
'Show an info message to the user and log it\n\n Args:\n msg (str): User message to print on the console\n '
if (not self.verbose):
click.secho(msg)
logging.warning(msg, *args, **kwargs) |
def show_warning(self, msg: str, *args, **kwargs):
'Show a warning to the user and log it\n\n Args:\n msg (str): User message to print on the console\n '
if (not self.verbose):
click.secho(msg, fg='yellow')
logging.warning(msg, *args, **kwargs) | -5,328,920,099,859,709,000 | Show a warning to the user and log it
Args:
msg (str): User message to print on the console | c8ylp/cli/core.py | show_warning | SoftwareAG/cumulocity-remote-access-local-proxy | python | def show_warning(self, msg: str, *args, **kwargs):
'Show a warning to the user and log it\n\n Args:\n msg (str): User message to print on the console\n '
if (not self.verbose):
click.secho(msg, fg='yellow')
logging.warning(msg, *args, **kwargs) |
def set_env(self):
'Set environment variables so information about the proxy can\n be access by plugins\n '
os.environ['C8Y_HOST'] = str(self.host)
os.environ['PORT'] = str(self.used_port)
os.environ['DEVICE'] = self.device
os.environ['WSLENV'] = 'PORT/u:DEVICE/u:C8Y_HOST/u' | -3,005,475,876,850,048,500 | Set environment variables so information about the proxy can
be access by plugins | c8ylp/cli/core.py | set_env | SoftwareAG/cumulocity-remote-access-local-proxy | python | def set_env(self):
'Set environment variables so information about the proxy can\n be access by plugins\n '
os.environ['C8Y_HOST'] = str(self.host)
os.environ['PORT'] = str(self.used_port)
os.environ['DEVICE'] = self.device
os.environ['WSLENV'] = 'PORT/u:DEVICE/u:C8Y_HOST/u' |
@classmethod
def log_path(cls) -> pathlib.Path:
'Get the log path'
return (pathlib.Path(os.getenv('C8YLP_LOG_DIR', '~/.c8ylp/')).expanduser() / 'localproxy.log') | -4,331,468,252,760,386,000 | Get the log path | c8ylp/cli/core.py | log_path | SoftwareAG/cumulocity-remote-access-local-proxy | python | @classmethod
def log_path(cls) -> pathlib.Path:
return (pathlib.Path(os.getenv('C8YLP_LOG_DIR', '~/.c8ylp/')).expanduser() / 'localproxy.log') |
def get_ffme_returns():
'\n Load the Fama-French Dataset for the returns of the Top and Bottom Deciles by MarketCap\n '
me_m = pd.read_csv('data/Portfolios_Formed_on_ME_monthly_EW.csv', header=0, index_col=0, na_values=(- 99.99))
rets = me_m[['Lo 10', 'Hi 10']]
rets.columns = ['SmallCap', 'LargeCa... | -8,356,439,846,472,406,000 | Load the Fama-French Dataset for the returns of the Top and Bottom Deciles by MarketCap | kit.py | get_ffme_returns | jaimeaguilera/Investing-projects | python | def get_ffme_returns():
'\n \n '
me_m = pd.read_csv('data/Portfolios_Formed_on_ME_monthly_EW.csv', header=0, index_col=0, na_values=(- 99.99))
rets = me_m[['Lo 10', 'Hi 10']]
rets.columns = ['SmallCap', 'LargeCap']
rets = (rets / 100)
rets.index = pd.to_datetime(rets.index, format='%Y%m').... |
def get_fff_returns():
'\n Load the Fama-French Research Factor Monthly Dataset\n '
rets = (pd.read_csv('data/F-F_Research_Data_Factors_m.csv', header=0, index_col=0, na_values=(- 99.99)) / 100)
rets.index = pd.to_datetime(rets.index, format='%Y%m').to_period('M')
return rets | -7,927,721,906,992,404,000 | Load the Fama-French Research Factor Monthly Dataset | kit.py | get_fff_returns | jaimeaguilera/Investing-projects | python | def get_fff_returns():
'\n \n '
rets = (pd.read_csv('data/F-F_Research_Data_Factors_m.csv', header=0, index_col=0, na_values=(- 99.99)) / 100)
rets.index = pd.to_datetime(rets.index, format='%Y%m').to_period('M')
return rets |
def get_hfi_returns():
'\n Load and format the EDHEC Hedge Fund Index Returns\n '
hfi = pd.read_csv('data/edhec-hedgefundindices.csv', header=0, index_col=0, parse_dates=True)
hfi = (hfi / 100)
hfi.index = hfi.index.to_period('M')
return hfi | -2,043,821,786,576,152,000 | Load and format the EDHEC Hedge Fund Index Returns | kit.py | get_hfi_returns | jaimeaguilera/Investing-projects | python | def get_hfi_returns():
'\n \n '
hfi = pd.read_csv('data/edhec-hedgefundindices.csv', header=0, index_col=0, parse_dates=True)
hfi = (hfi / 100)
hfi.index = hfi.index.to_period('M')
return hfi |
def get_ind_file(filetype, weighting='vw', n_inds=30):
'\n Load and format the Ken French Industry Portfolios files\n Variant is a tuple of (weighting, size) where:\n weighting is one of "ew", "vw"\n number of inds is 30 or 49\n '
if (filetype is 'returns'):
name = f'{weighting}_r... | -1,716,315,270,824,754,000 | Load and format the Ken French Industry Portfolios files
Variant is a tuple of (weighting, size) where:
weighting is one of "ew", "vw"
number of inds is 30 or 49 | kit.py | get_ind_file | jaimeaguilera/Investing-projects | python | def get_ind_file(filetype, weighting='vw', n_inds=30):
'\n Load and format the Ken French Industry Portfolios files\n Variant is a tuple of (weighting, size) where:\n weighting is one of "ew", "vw"\n number of inds is 30 or 49\n '
if (filetype is 'returns'):
name = f'{weighting}_r... |
def get_ind_returns(weighting='vw', n_inds=30):
'\n Load and format the Ken French Industry Portfolios Monthly Returns\n '
return get_ind_file('returns', weighting=weighting, n_inds=n_inds) | 8,765,230,633,926,116,000 | Load and format the Ken French Industry Portfolios Monthly Returns | kit.py | get_ind_returns | jaimeaguilera/Investing-projects | python | def get_ind_returns(weighting='vw', n_inds=30):
'\n \n '
return get_ind_file('returns', weighting=weighting, n_inds=n_inds) |
def get_ind_nfirms(n_inds=30):
'\n Load and format the Ken French 30 Industry Portfolios Average number of Firms\n '
return get_ind_file('nfirms', n_inds=n_inds) | -3,638,385,353,629,806,600 | Load and format the Ken French 30 Industry Portfolios Average number of Firms | kit.py | get_ind_nfirms | jaimeaguilera/Investing-projects | python | def get_ind_nfirms(n_inds=30):
'\n \n '
return get_ind_file('nfirms', n_inds=n_inds) |
def get_ind_size(n_inds=30):
'\n Load and format the Ken French 30 Industry Portfolios Average size (market cap)\n '
return get_ind_file('size', n_inds=n_inds) | -8,120,265,645,189,793,000 | Load and format the Ken French 30 Industry Portfolios Average size (market cap) | kit.py | get_ind_size | jaimeaguilera/Investing-projects | python | def get_ind_size(n_inds=30):
'\n \n '
return get_ind_file('size', n_inds=n_inds) |
def get_ind_market_caps(n_inds=30, weights=False):
'\n Load the industry portfolio data and derive the market caps\n '
ind_nfirms = get_ind_nfirms(n_inds=n_inds)
ind_size = get_ind_size(n_inds=n_inds)
ind_mktcap = (ind_nfirms * ind_size)
if weights:
total_mktcap = ind_mktcap.sum(axis=1... | -2,019,311,730,886,692,000 | Load the industry portfolio data and derive the market caps | kit.py | get_ind_market_caps | jaimeaguilera/Investing-projects | python | def get_ind_market_caps(n_inds=30, weights=False):
'\n \n '
ind_nfirms = get_ind_nfirms(n_inds=n_inds)
ind_size = get_ind_size(n_inds=n_inds)
ind_mktcap = (ind_nfirms * ind_size)
if weights:
total_mktcap = ind_mktcap.sum(axis=1)
ind_capweight = ind_mktcap.divide(total_mktcap, a... |
def get_total_market_index_returns(n_inds=30):
'\n Load the 30 industry portfolio data and derive the returns of a capweighted total market index\n '
ind_capweight = get_ind_market_caps(n_inds=n_inds)
ind_return = get_ind_returns(weighting='vw', n_inds=n_inds)
total_market_return = (ind_capweight ... | -2,314,792,494,135,799,300 | Load the 30 industry portfolio data and derive the returns of a capweighted total market index | kit.py | get_total_market_index_returns | jaimeaguilera/Investing-projects | python | def get_total_market_index_returns(n_inds=30):
'\n \n '
ind_capweight = get_ind_market_caps(n_inds=n_inds)
ind_return = get_ind_returns(weighting='vw', n_inds=n_inds)
total_market_return = (ind_capweight * ind_return).sum(axis='columns')
return total_market_return |
def skewness(r):
'\n Alternative to scipy.stats.skew()\n Computes the skewness of the supplied Series or DataFrame\n Returns a float or a Series\n '
r = r[((r != 0) & r.notnull())]
demeaned_r = (r - r.mean())
sigma_r = r.std(ddof=0)
exp = (demeaned_r ** 3).mean()
return (exp / (sigma... | 7,755,191,297,281,593,000 | Alternative to scipy.stats.skew()
Computes the skewness of the supplied Series or DataFrame
Returns a float or a Series | kit.py | skewness | jaimeaguilera/Investing-projects | python | def skewness(r):
'\n Alternative to scipy.stats.skew()\n Computes the skewness of the supplied Series or DataFrame\n Returns a float or a Series\n '
r = r[((r != 0) & r.notnull())]
demeaned_r = (r - r.mean())
sigma_r = r.std(ddof=0)
exp = (demeaned_r ** 3).mean()
return (exp / (sigma... |
def kurtosis(r):
'\n Alternative to scipy.stats.kurtosis()\n Computes the kurtosis of the supplied Series or DataFrame\n Returns a float or a Series\n '
r = r[((r != 0) & r.notnull())]
demeaned_r = (r - r.mean())
sigma_r = r.std(ddof=0)
exp = (demeaned_r ** 4).mean()
return (exp / (s... | -8,853,748,143,048,105,000 | Alternative to scipy.stats.kurtosis()
Computes the kurtosis of the supplied Series or DataFrame
Returns a float or a Series | kit.py | kurtosis | jaimeaguilera/Investing-projects | python | def kurtosis(r):
'\n Alternative to scipy.stats.kurtosis()\n Computes the kurtosis of the supplied Series or DataFrame\n Returns a float or a Series\n '
r = r[((r != 0) & r.notnull())]
demeaned_r = (r - r.mean())
sigma_r = r.std(ddof=0)
exp = (demeaned_r ** 4).mean()
return (exp / (s... |
def compound(r):
'\n returns the result of compounding the set of returns in r\n '
return np.expm1(np.log1p(r).sum()) | -5,716,126,220,503,370,000 | returns the result of compounding the set of returns in r | kit.py | compound | jaimeaguilera/Investing-projects | python | def compound(r):
'\n \n '
return np.expm1(np.log1p(r).sum()) |
def annualize_rets(r):
'\n Annualizes a set of returns\n We should infer the periods per year\n but that is currently left as an exercise\n to the reader :-)\n '
r_valid = r[((r != 0) & r.notnull())]
date_beg = r_valid.agg((lambda x: x.first_valid_index()))
date_end = r_valid.agg((lambda ... | 444,043,545,948,035,400 | Annualizes a set of returns
We should infer the periods per year
but that is currently left as an exercise
to the reader :-) | kit.py | annualize_rets | jaimeaguilera/Investing-projects | python | def annualize_rets(r):
'\n Annualizes a set of returns\n We should infer the periods per year\n but that is currently left as an exercise\n to the reader :-)\n '
r_valid = r[((r != 0) & r.notnull())]
date_beg = r_valid.agg((lambda x: x.first_valid_index()))
date_end = r_valid.agg((lambda ... |
def annualize_vol(r):
'\n Annualizes the vol of a set of returns\n We should infer the periods per year\n but that is currently left as an exercise\n to the reader :-)\n '
r_valid = r[((r != 0) & r.notnull())]
total_num_periods = r_valid.count()
date_beg = r_valid.agg((lambda x: x.first_v... | 4,409,763,613,660,388,000 | Annualizes the vol of a set of returns
We should infer the periods per year
but that is currently left as an exercise
to the reader :-) | kit.py | annualize_vol | jaimeaguilera/Investing-projects | python | def annualize_vol(r):
'\n Annualizes the vol of a set of returns\n We should infer the periods per year\n but that is currently left as an exercise\n to the reader :-)\n '
r_valid = r[((r != 0) & r.notnull())]
total_num_periods = r_valid.count()
date_beg = r_valid.agg((lambda x: x.first_v... |
def sharpe_ratio(r, riskfree_rate):
'\n Computes the annualized sharpe ratio of a set of returns\n '
r_valid = r[((r != 0) & r.notnull())]
total_num_periods = r_valid.count()
date_beg = r_valid.agg((lambda x: x.first_valid_index()))
date_end = r_valid.agg((lambda x: x.last_valid_index()))
... | 2,533,955,272,572,073,000 | Computes the annualized sharpe ratio of a set of returns | kit.py | sharpe_ratio | jaimeaguilera/Investing-projects | python | def sharpe_ratio(r, riskfree_rate):
'\n \n '
r_valid = r[((r != 0) & r.notnull())]
total_num_periods = r_valid.count()
date_beg = r_valid.agg((lambda x: x.first_valid_index()))
date_end = r_valid.agg((lambda x: x.last_valid_index()))
try:
years_fraction = ((date_end - date_beg).dt.... |
def is_normal(r, level=0.01):
'\n Applies the Jarque-Bera test to determine if a Series is normal or not\n Test is applied at the 1% level by default\n Returns True if the hypothesis of normality is accepted, False otherwise\n '
if isinstance(r, pd.DataFrame):
return r.aggregate(is_normal)
... | 4,814,920,000,768,772,000 | Applies the Jarque-Bera test to determine if a Series is normal or not
Test is applied at the 1% level by default
Returns True if the hypothesis of normality is accepted, False otherwise | kit.py | is_normal | jaimeaguilera/Investing-projects | python | def is_normal(r, level=0.01):
'\n Applies the Jarque-Bera test to determine if a Series is normal or not\n Test is applied at the 1% level by default\n Returns True if the hypothesis of normality is accepted, False otherwise\n '
if isinstance(r, pd.DataFrame):
return r.aggregate(is_normal)
... |
def drawdown(return_series: pd.Series):
'Takes a time series of asset returns.\n returns a DataFrame with columns for\n the wealth index, \n the previous peaks, and \n the percentage drawdown\n '
wealth_index = (1000 * (1 + return_series).cumprod())
previous_peaks = wealth_index.c... | 1,925,900,058,201,987,300 | Takes a time series of asset returns.
returns a DataFrame with columns for
the wealth index,
the previous peaks, and
the percentage drawdown | kit.py | drawdown | jaimeaguilera/Investing-projects | python | def drawdown(return_series: pd.Series):
'Takes a time series of asset returns.\n returns a DataFrame with columns for\n the wealth index, \n the previous peaks, and \n the percentage drawdown\n '
wealth_index = (1000 * (1 + return_series).cumprod())
previous_peaks = wealth_index.c... |
def semideviation(r):
'\n Returns the semideviation aka negative semideviation of r\n r must be a Series or a DataFrame, else raises a TypeError\n '
if isinstance(r, pd.Series):
is_negative = (r < 0)
return r[is_negative].std(ddof=0)
elif isinstance(r, pd.DataFrame):
return ... | -1,785,064,964,747,070,700 | Returns the semideviation aka negative semideviation of r
r must be a Series or a DataFrame, else raises a TypeError | kit.py | semideviation | jaimeaguilera/Investing-projects | python | def semideviation(r):
'\n Returns the semideviation aka negative semideviation of r\n r must be a Series or a DataFrame, else raises a TypeError\n '
if isinstance(r, pd.Series):
is_negative = (r < 0)
return r[is_negative].std(ddof=0)
elif isinstance(r, pd.DataFrame):
return ... |
def var_historic(r, level=5):
'\n Returns the historic Value at Risk at a specified level\n i.e. returns the number such that "level" percent of the returns\n fall below that number, and the (100-level) percent are above\n '
r = r[((r != 0) & r.notnull())]
if isinstance(r, pd.DataFrame):
... | 8,531,678,315,750,730,000 | Returns the historic Value at Risk at a specified level
i.e. returns the number such that "level" percent of the returns
fall below that number, and the (100-level) percent are above | kit.py | var_historic | jaimeaguilera/Investing-projects | python | def var_historic(r, level=5):
'\n Returns the historic Value at Risk at a specified level\n i.e. returns the number such that "level" percent of the returns\n fall below that number, and the (100-level) percent are above\n '
r = r[((r != 0) & r.notnull())]
if isinstance(r, pd.DataFrame):
... |
def cvar_historic(r, level=5):
'\n Computes the Conditional VaR of Series or DataFrame\n '
r = r[((r != 0) & r.notnull())]
if isinstance(r, pd.Series):
is_beyond = (r <= (- var_historic(r, level=level)))
return (- r[is_beyond].mean())
elif isinstance(r, pd.DataFrame):
retur... | -3,315,997,735,787,485,000 | Computes the Conditional VaR of Series or DataFrame | kit.py | cvar_historic | jaimeaguilera/Investing-projects | python | def cvar_historic(r, level=5):
'\n \n '
r = r[((r != 0) & r.notnull())]
if isinstance(r, pd.Series):
is_beyond = (r <= (- var_historic(r, level=level)))
return (- r[is_beyond].mean())
elif isinstance(r, pd.DataFrame):
return r.aggregate(cvar_historic, level=level)
else:... |
def var_gaussian(r, level=5, modified=False):
'\n Returns the Parametric Gauusian VaR of a Series or DataFrame\n If "modified" is True, then the modified VaR is returned,\n using the Cornish-Fisher modification\n '
r = r[((r != 0) & r.notnull())]
z = norm.ppf((level / 100))
if modified:
... | 4,522,035,123,193,760,300 | Returns the Parametric Gauusian VaR of a Series or DataFrame
If "modified" is True, then the modified VaR is returned,
using the Cornish-Fisher modification | kit.py | var_gaussian | jaimeaguilera/Investing-projects | python | def var_gaussian(r, level=5, modified=False):
'\n Returns the Parametric Gauusian VaR of a Series or DataFrame\n If "modified" is True, then the modified VaR is returned,\n using the Cornish-Fisher modification\n '
r = r[((r != 0) & r.notnull())]
z = norm.ppf((level / 100))
if modified:
... |
def portfolio_return(weights, returns):
'\n Computes the return on a portfolio from constituent returns and weights\n weights are a numpy array or Nx1 matrix and returns are a numpy array or Nx1 matrix\n '
return (weights.T @ returns) | -1,847,745,247,972,241,200 | Computes the return on a portfolio from constituent returns and weights
weights are a numpy array or Nx1 matrix and returns are a numpy array or Nx1 matrix | kit.py | portfolio_return | jaimeaguilera/Investing-projects | python | def portfolio_return(weights, returns):
'\n Computes the return on a portfolio from constituent returns and weights\n weights are a numpy array or Nx1 matrix and returns are a numpy array or Nx1 matrix\n '
return (weights.T @ returns) |
def portfolio_vol(weights, covmat):
'\n Computes the vol of a portfolio from a covariance matrix and constituent weights\n weights are a numpy array or N x 1 maxtrix and covmat is an N x N matrix\n '
vol = (((weights.T @ covmat) @ weights) ** 0.5)
return vol | -3,955,678,057,595,877,400 | Computes the vol of a portfolio from a covariance matrix and constituent weights
weights are a numpy array or N x 1 maxtrix and covmat is an N x N matrix | kit.py | portfolio_vol | jaimeaguilera/Investing-projects | python | def portfolio_vol(weights, covmat):
'\n Computes the vol of a portfolio from a covariance matrix and constituent weights\n weights are a numpy array or N x 1 maxtrix and covmat is an N x N matrix\n '
vol = (((weights.T @ covmat) @ weights) ** 0.5)
return vol |
def plot_ef2(n_points, er, cov):
'\n Plots the 2-asset efficient frontier\n '
if ((er.shape[0] != 2) or (er.shape[0] != 2)):
raise ValueError('plot_ef2 can only plot 2-asset frontiers')
weights = [np.array([w, (1 - w)]) for w in np.linspace(0, 1, n_points)]
rets = [portfolio_return(w, er) ... | 6,113,814,886,680,996,000 | Plots the 2-asset efficient frontier | kit.py | plot_ef2 | jaimeaguilera/Investing-projects | python | def plot_ef2(n_points, er, cov):
'\n \n '
if ((er.shape[0] != 2) or (er.shape[0] != 2)):
raise ValueError('plot_ef2 can only plot 2-asset frontiers')
weights = [np.array([w, (1 - w)]) for w in np.linspace(0, 1, n_points)]
rets = [portfolio_return(w, er) for w in weights]
vols = [portfo... |
def minimize_vol(target_return, er, cov):
'\n Returns the optimal weights that achieve the target return\n given a set of expected returns and a covariance matrix\n '
n = er.shape[0]
init_guess = np.repeat((1 / n), n)
bounds = (((0.0, 1.0),) * n)
weights_sum_to_1 = {'type': 'eq', 'fun': (la... | 7,147,474,082,142,541,000 | Returns the optimal weights that achieve the target return
given a set of expected returns and a covariance matrix | kit.py | minimize_vol | jaimeaguilera/Investing-projects | python | def minimize_vol(target_return, er, cov):
'\n Returns the optimal weights that achieve the target return\n given a set of expected returns and a covariance matrix\n '
n = er.shape[0]
init_guess = np.repeat((1 / n), n)
bounds = (((0.0, 1.0),) * n)
weights_sum_to_1 = {'type': 'eq', 'fun': (la... |
def tracking_error(r_a, r_b):
'\n Returns the Tracking Error between the two return series\n '
return np.sqrt(((r_a - r_b) ** 2).sum()) | -6,295,209,354,440,144,000 | Returns the Tracking Error between the two return series | kit.py | tracking_error | jaimeaguilera/Investing-projects | python | def tracking_error(r_a, r_b):
'\n \n '
return np.sqrt(((r_a - r_b) ** 2).sum()) |
def msr(riskfree_rate, er, cov):
'\n Returns the weights of the portfolio that gives you the maximum sharpe ratio\n given the riskfree rate and expected returns and a covariance matrix\n '
n = er.shape[0]
init_guess = np.repeat((1 / n), n)
bounds = (((0.0, 1.0),) * n)
weights_sum_to_1 = {'t... | 3,631,754,018,628,217,300 | Returns the weights of the portfolio that gives you the maximum sharpe ratio
given the riskfree rate and expected returns and a covariance matrix | kit.py | msr | jaimeaguilera/Investing-projects | python | def msr(riskfree_rate, er, cov):
'\n Returns the weights of the portfolio that gives you the maximum sharpe ratio\n given the riskfree rate and expected returns and a covariance matrix\n '
n = er.shape[0]
init_guess = np.repeat((1 / n), n)
bounds = (((0.0, 1.0),) * n)
weights_sum_to_1 = {'t... |
def gmv(cov):
'\n Returns the weights of the Global Minimum Volatility portfolio\n given a covariance matrix\n '
n = cov.shape[0]
return msr(0, np.repeat(1, n), cov) | 239,850,363,951,659,140 | Returns the weights of the Global Minimum Volatility portfolio
given a covariance matrix | kit.py | gmv | jaimeaguilera/Investing-projects | python | def gmv(cov):
'\n Returns the weights of the Global Minimum Volatility portfolio\n given a covariance matrix\n '
n = cov.shape[0]
return msr(0, np.repeat(1, n), cov) |
def optimal_weights(n_points, er, cov):
'\n Returns a list of weights that represent a grid of n_points on the efficient frontier\n '
target_rs = np.linspace(er.min(), er.max(), n_points)
weights = [minimize_vol(target_return, er, cov) for target_return in target_rs]
return weights | -8,565,410,215,275,897,000 | Returns a list of weights that represent a grid of n_points on the efficient frontier | kit.py | optimal_weights | jaimeaguilera/Investing-projects | python | def optimal_weights(n_points, er, cov):
'\n \n '
target_rs = np.linspace(er.min(), er.max(), n_points)
weights = [minimize_vol(target_return, er, cov) for target_return in target_rs]
return weights |
def plot_ef(n_points, er, cov, style='.-', legend=False, show_cml=False, riskfree_rate=0, show_ew=False, show_gmv=False):
'\n Plots the multi-asset efficient frontier\n '
weights = optimal_weights(n_points, er, cov)
rets = [portfolio_return(w, er) for w in weights]
vols = [portfolio_vol(w, cov) fo... | -588,327,934,387,513,900 | Plots the multi-asset efficient frontier | kit.py | plot_ef | jaimeaguilera/Investing-projects | python | def plot_ef(n_points, er, cov, style='.-', legend=False, show_cml=False, riskfree_rate=0, show_ew=False, show_gmv=False):
'\n \n '
weights = optimal_weights(n_points, er, cov)
rets = [portfolio_return(w, er) for w in weights]
vols = [portfolio_vol(w, cov) for w in weights]
ef = pd.DataFrame({'... |
def run_cppi(risky_r, safe_r=None, m=3, start=1000, floor=0.8, riskfree_rate=0.03, drawdown=None):
'\n Run a backtest of the CPPI strategy, given a set of returns for the risky asset\n Returns a dictionary containing: Asset Value History, Risk Budget History, Risky Weight History\n '
dates = risky_r.in... | 1,973,406,296,599,866,400 | Run a backtest of the CPPI strategy, given a set of returns for the risky asset
Returns a dictionary containing: Asset Value History, Risk Budget History, Risky Weight History | kit.py | run_cppi | jaimeaguilera/Investing-projects | python | def run_cppi(risky_r, safe_r=None, m=3, start=1000, floor=0.8, riskfree_rate=0.03, drawdown=None):
'\n Run a backtest of the CPPI strategy, given a set of returns for the risky asset\n Returns a dictionary containing: Asset Value History, Risk Budget History, Risky Weight History\n '
dates = risky_r.in... |
def summary_stats(r, riskfree_rate=0.03):
'\n Return a DataFrame that contains aggregated summary stats for the returns in the columns of r\n '
ann_r = annualize_rets(r)
ann_vol = annualize_vol(r)
ann_sr = sharpe_ratio(r, riskfree_rate=riskfree_rate)
dd = r.aggregate((lambda r: drawdown(r).Dra... | -3,695,334,458,142,436,400 | Return a DataFrame that contains aggregated summary stats for the returns in the columns of r | kit.py | summary_stats | jaimeaguilera/Investing-projects | python | def summary_stats(r, riskfree_rate=0.03):
'\n \n '
ann_r = annualize_rets(r)
ann_vol = annualize_vol(r)
ann_sr = sharpe_ratio(r, riskfree_rate=riskfree_rate)
dd = r.aggregate((lambda r: drawdown(r).Drawdown.min()))
skew = r.aggregate(skewness)
kurt = r.aggregate(kurtosis)
cf_var5 =... |
def gbm(n_years=10, n_scenarios=1000, mu=0.07, sigma=0.15, steps_per_year=12, s_0=100.0, prices=True):
'\n Evolution of Geometric Brownian Motion trajectories, such as for Stock Prices through Monte Carlo\n :param n_years: The number of years to generate data for\n :param n_paths: The number of scenarios/... | -5,941,378,999,358,949,000 | Evolution of Geometric Brownian Motion trajectories, such as for Stock Prices through Monte Carlo
:param n_years: The number of years to generate data for
:param n_paths: The number of scenarios/trajectories
:param mu: Annualized Drift, e.g. Market Return
:param sigma: Annualized Volatility
:param steps_per_year: gran... | kit.py | gbm | jaimeaguilera/Investing-projects | python | def gbm(n_years=10, n_scenarios=1000, mu=0.07, sigma=0.15, steps_per_year=12, s_0=100.0, prices=True):
'\n Evolution of Geometric Brownian Motion trajectories, such as for Stock Prices through Monte Carlo\n :param n_years: The number of years to generate data for\n :param n_paths: The number of scenarios/... |
def regress(dependent_variable, explanatory_variables, alpha=True):
"\n Runs a linear regression to decompose the dependent variable into the explanatory variables\n returns an object of type statsmodel's RegressionResults on which you can call\n .summary() to print a full summary\n .params for th... | -2,629,055,977,376,220,700 | Runs a linear regression to decompose the dependent variable into the explanatory variables
returns an object of type statsmodel's RegressionResults on which you can call
.summary() to print a full summary
.params for the coefficients
.tvalues and .pvalues for the significance levels
.rsquared_adj and .rsqu... | kit.py | regress | jaimeaguilera/Investing-projects | python | def regress(dependent_variable, explanatory_variables, alpha=True):
"\n Runs a linear regression to decompose the dependent variable into the explanatory variables\n returns an object of type statsmodel's RegressionResults on which you can call\n .summary() to print a full summary\n .params for th... |
def portfolio_tracking_error(weights, ref_r, bb_r):
'\n returns the tracking error between the reference returns\n and a portfolio of building block returns held with given weights\n '
return tracking_error(ref_r, (weights * bb_r).sum(axis=1)) | -5,504,051,318,243,047,000 | returns the tracking error between the reference returns
and a portfolio of building block returns held with given weights | kit.py | portfolio_tracking_error | jaimeaguilera/Investing-projects | python | def portfolio_tracking_error(weights, ref_r, bb_r):
'\n returns the tracking error between the reference returns\n and a portfolio of building block returns held with given weights\n '
return tracking_error(ref_r, (weights * bb_r).sum(axis=1)) |
def style_analysis(dependent_variable, explanatory_variables):
'\n Returns the optimal weights that minimizes the Tracking error between\n a portfolio of the explanatory variables and the dependent variable\n '
n = explanatory_variables.shape[1]
init_guess = np.repeat((1 / n), n)
bounds = (((0.... | -7,954,644,322,848,577,000 | Returns the optimal weights that minimizes the Tracking error between
a portfolio of the explanatory variables and the dependent variable | kit.py | style_analysis | jaimeaguilera/Investing-projects | python | def style_analysis(dependent_variable, explanatory_variables):
'\n Returns the optimal weights that minimizes the Tracking error between\n a portfolio of the explanatory variables and the dependent variable\n '
n = explanatory_variables.shape[1]
init_guess = np.repeat((1 / n), n)
bounds = (((0.... |
def ff_analysis(r, factors):
'\n Returns the loadings of r on the Fama French Factors\n which can be read in using get_fff_returns()\n the index of r must be a (not necessarily proper) subset of the index of factors\n r is either a Series or a DataFrame\n '
if isinstance(r, pd.Series):
d... | 5,331,380,417,579,062,000 | Returns the loadings of r on the Fama French Factors
which can be read in using get_fff_returns()
the index of r must be a (not necessarily proper) subset of the index of factors
r is either a Series or a DataFrame | kit.py | ff_analysis | jaimeaguilera/Investing-projects | python | def ff_analysis(r, factors):
'\n Returns the loadings of r on the Fama French Factors\n which can be read in using get_fff_returns()\n the index of r must be a (not necessarily proper) subset of the index of factors\n r is either a Series or a DataFrame\n '
if isinstance(r, pd.Series):
d... |
def weight_ew(r, cap_weights=None, max_cw_mult=None, microcap_threshold=None, **kwargs):
'\n Returns the weights of the EW portfolio based on the asset returns "r" as a DataFrame\n If supplied a set of capweights and a capweight tether, it is applied and reweighted \n '
n = len(r.columns)
ew = pd.S... | 8,770,652,885,608,347,000 | Returns the weights of the EW portfolio based on the asset returns "r" as a DataFrame
If supplied a set of capweights and a capweight tether, it is applied and reweighted | kit.py | weight_ew | jaimeaguilera/Investing-projects | python | def weight_ew(r, cap_weights=None, max_cw_mult=None, microcap_threshold=None, **kwargs):
'\n Returns the weights of the EW portfolio based on the asset returns "r" as a DataFrame\n If supplied a set of capweights and a capweight tether, it is applied and reweighted \n '
n = len(r.columns)
ew = pd.S... |
def weight_cw(r, cap_weights, **kwargs):
'\n Returns the weights of the CW portfolio based on the time series of capweights\n '
w = cap_weights.loc[r.index[1]]
return (w / w.sum()) | 345,298,233,300,992,060 | Returns the weights of the CW portfolio based on the time series of capweights | kit.py | weight_cw | jaimeaguilera/Investing-projects | python | def weight_cw(r, cap_weights, **kwargs):
'\n \n '
w = cap_weights.loc[r.index[1]]
return (w / w.sum()) |
def backtest_ws(r, estimation_window=60, weighting=weight_ew, verbose=False, **kwargs):
'\n Backtests a given weighting scheme, given some parameters:\n r : asset returns to use to build the portfolio\n estimation_window: the window to use to estimate parameters\n weighting: the weighting scheme to use,... | 4,342,074,272,339,228,000 | Backtests a given weighting scheme, given some parameters:
r : asset returns to use to build the portfolio
estimation_window: the window to use to estimate parameters
weighting: the weighting scheme to use, must be a function that takes "r", and a variable number of keyword-value arguments | kit.py | backtest_ws | jaimeaguilera/Investing-projects | python | def backtest_ws(r, estimation_window=60, weighting=weight_ew, verbose=False, **kwargs):
'\n Backtests a given weighting scheme, given some parameters:\n r : asset returns to use to build the portfolio\n estimation_window: the window to use to estimate parameters\n weighting: the weighting scheme to use,... |
def sample_cov(r, **kwargs):
'\n Returns the sample covariance of the supplied returns\n '
return r.cov() | 3,398,896,989,843,861,500 | Returns the sample covariance of the supplied returns | kit.py | sample_cov | jaimeaguilera/Investing-projects | python | def sample_cov(r, **kwargs):
'\n \n '
return r.cov() |
def weight_gmv(r, cov_estimator=sample_cov, **kwargs):
'\n Produces the weights of the GMV portfolio given a covariance matrix of the returns \n '
est_cov = cov_estimator(r, **kwargs)
return gmv(est_cov) | -4,044,872,262,371,711,500 | Produces the weights of the GMV portfolio given a covariance matrix of the returns | kit.py | weight_gmv | jaimeaguilera/Investing-projects | python | def weight_gmv(r, cov_estimator=sample_cov, **kwargs):
'\n \n '
est_cov = cov_estimator(r, **kwargs)
return gmv(est_cov) |
def cc_cov(r, **kwargs):
'\n Estimates a covariance matrix by using the Elton/Gruber Constant Correlation model\n '
rhos = r.corr()
n = rhos.shape[0]
rho_bar = ((rhos.values.sum() - n) / (n * (n - 1)))
ccor = np.full_like(rhos, rho_bar)
np.fill_diagonal(ccor, 1.0)
sd = r.std()
retu... | -2,987,201,178,320,407,000 | Estimates a covariance matrix by using the Elton/Gruber Constant Correlation model | kit.py | cc_cov | jaimeaguilera/Investing-projects | python | def cc_cov(r, **kwargs):
'\n \n '
rhos = r.corr()
n = rhos.shape[0]
rho_bar = ((rhos.values.sum() - n) / (n * (n - 1)))
ccor = np.full_like(rhos, rho_bar)
np.fill_diagonal(ccor, 1.0)
sd = r.std()
return pd.DataFrame((ccor * np.outer(sd, sd)), index=r.columns, columns=r.columns) |
def shrinkage_cov(r, delta=0.5, **kwargs):
'\n Covariance estimator that shrinks between the Sample Covariance and the Constant Correlation Estimators\n '
prior = cc_cov(r, **kwargs)
sample = sample_cov(r, **kwargs)
return ((delta * prior) + ((1 - delta) * sample)) | 7,209,318,898,781,462,000 | Covariance estimator that shrinks between the Sample Covariance and the Constant Correlation Estimators | kit.py | shrinkage_cov | jaimeaguilera/Investing-projects | python | def shrinkage_cov(r, delta=0.5, **kwargs):
'\n \n '
prior = cc_cov(r, **kwargs)
sample = sample_cov(r, **kwargs)
return ((delta * prior) + ((1 - delta) * sample)) |
def risk_contribution(w, cov):
'\n Compute the contributions to risk of the constituents of a portfolio, given a set of portfolio weights and a covariance matrix\n '
total_portfolio_var = (portfolio_vol(w, cov) ** 2)
marginal_contrib = (cov @ w)
risk_contrib = (np.multiply(marginal_contrib, w.T) /... | 499,167,234,481,604,100 | Compute the contributions to risk of the constituents of a portfolio, given a set of portfolio weights and a covariance matrix | kit.py | risk_contribution | jaimeaguilera/Investing-projects | python | def risk_contribution(w, cov):
'\n \n '
total_portfolio_var = (portfolio_vol(w, cov) ** 2)
marginal_contrib = (cov @ w)
risk_contrib = (np.multiply(marginal_contrib, w.T) / total_portfolio_var)
return risk_contrib |
def target_risk_contributions(target_risk, cov):
'\n Returns the weights of the portfolio that gives you the weights such\n that the contributions to portfolio risk are as close as possible to\n the target_risk, given the covariance matrix\n '
n = cov.shape[0]
init_guess = np.repeat((1 / n), n)
... | -6,586,840,443,301,521,000 | Returns the weights of the portfolio that gives you the weights such
that the contributions to portfolio risk are as close as possible to
the target_risk, given the covariance matrix | kit.py | target_risk_contributions | jaimeaguilera/Investing-projects | python | def target_risk_contributions(target_risk, cov):
'\n Returns the weights of the portfolio that gives you the weights such\n that the contributions to portfolio risk are as close as possible to\n the target_risk, given the covariance matrix\n '
n = cov.shape[0]
init_guess = np.repeat((1 / n), n)
... |
def equal_risk_contributions(cov):
'\n Returns the weights of the portfolio that equalizes the contributions\n of the constituents based on the given covariance matrix\n '
n = cov.shape[0]
return target_risk_contributions(target_risk=np.repeat((1 / n), n), cov=cov) | -4,143,323,565,454,961,700 | Returns the weights of the portfolio that equalizes the contributions
of the constituents based on the given covariance matrix | kit.py | equal_risk_contributions | jaimeaguilera/Investing-projects | python | def equal_risk_contributions(cov):
'\n Returns the weights of the portfolio that equalizes the contributions\n of the constituents based on the given covariance matrix\n '
n = cov.shape[0]
return target_risk_contributions(target_risk=np.repeat((1 / n), n), cov=cov) |
def weight_erc(r, cov_estimator=sample_cov, **kwargs):
'\n Produces the weights of the ERC portfolio given a covariance matrix of the returns \n '
est_cov = cov_estimator(r, **kwargs)
return equal_risk_contributions(est_cov) | 1,028,696,242,090,410,500 | Produces the weights of the ERC portfolio given a covariance matrix of the returns | kit.py | weight_erc | jaimeaguilera/Investing-projects | python | def weight_erc(r, cov_estimator=sample_cov, **kwargs):
'\n \n '
est_cov = cov_estimator(r, **kwargs)
return equal_risk_contributions(est_cov) |
def implied_returns(delta, sigma, w):
'\nObtain the implied expected returns by reverse engineering the weights\nInputs:\ndelta: Risk Aversion Coefficient (scalar)\nsigma: Variance-Covariance Matrix (N x N) as DataFrame\n w: Portfolio weights (N x 1) as Series\nReturns an N x 1 vector of Returns as Series\n '... | -2,040,071,156,509,215,700 | Obtain the implied expected returns by reverse engineering the weights
Inputs:
delta: Risk Aversion Coefficient (scalar)
sigma: Variance-Covariance Matrix (N x N) as DataFrame
w: Portfolio weights (N x 1) as Series
Returns an N x 1 vector of Returns as Series | kit.py | implied_returns | jaimeaguilera/Investing-projects | python | def implied_returns(delta, sigma, w):
'\nObtain the implied expected returns by reverse engineering the weights\nInputs:\ndelta: Risk Aversion Coefficient (scalar)\nsigma: Variance-Covariance Matrix (N x N) as DataFrame\n w: Portfolio weights (N x 1) as Series\nReturns an N x 1 vector of Returns as Series\n '... |
def proportional_prior(sigma, tau, p):
'\n Returns the He-Litterman simplified Omega\n Inputs:\n sigma: N x N Covariance Matrix as DataFrame\n tau: a scalar\n p: a K x N DataFrame linking Q and Assets\n returns a P x P DataFrame, a Matrix representing Prior Uncertainties\n '
helit_omega = p... | -3,693,253,497,267,448,300 | Returns the He-Litterman simplified Omega
Inputs:
sigma: N x N Covariance Matrix as DataFrame
tau: a scalar
p: a K x N DataFrame linking Q and Assets
returns a P x P DataFrame, a Matrix representing Prior Uncertainties | kit.py | proportional_prior | jaimeaguilera/Investing-projects | python | def proportional_prior(sigma, tau, p):
'\n Returns the He-Litterman simplified Omega\n Inputs:\n sigma: N x N Covariance Matrix as DataFrame\n tau: a scalar\n p: a K x N DataFrame linking Q and Assets\n returns a P x P DataFrame, a Matrix representing Prior Uncertainties\n '
helit_omega = p... |
def bl(w_prior, sigma_prior, p, q, omega=None, delta=2.5, tau=0.02):
'\n# Computes the posterior expected returns based on \n# the original black litterman reference model\n#\n# W.prior must be an N x 1 vector of weights, a Series\n# Sigma.prior is an N x N covariance matrix, a DataFrame\n# P must be a K x N matrix... | -185,691,400,770,407,680 | # Computes the posterior expected returns based on
# the original black litterman reference model
#
# W.prior must be an N x 1 vector of weights, a Series
# Sigma.prior is an N x N covariance matrix, a DataFrame
# P must be a K x N matrix linking Q and the Assets, a DataFrame
# Q must be an K x 1 vector of views, a Se... | kit.py | bl | jaimeaguilera/Investing-projects | python | def bl(w_prior, sigma_prior, p, q, omega=None, delta=2.5, tau=0.02):
'\n# Computes the posterior expected returns based on \n# the original black litterman reference model\n#\n# W.prior must be an N x 1 vector of weights, a Series\n# Sigma.prior is an N x N covariance matrix, a DataFrame\n# P must be a K x N matrix... |
def inverse(d):
'\n Invert the dataframe by inverting the underlying matrix\n '
return pd.DataFrame(inv(d.values), index=d.columns, columns=d.index) | -3,257,535,559,224,084,500 | Invert the dataframe by inverting the underlying matrix | kit.py | inverse | jaimeaguilera/Investing-projects | python | def inverse(d):
'\n \n '
return pd.DataFrame(inv(d.values), index=d.columns, columns=d.index) |
def weight_msr(sigma, mu, scale=True):
'\n Optimal (Tangent/Max Sharpe Ratio) Portfolio weights\n by using the Markowitz Optimization Procedure\n Mu is the vector of Excess expected Returns\n Sigma must be an N x N matrix as a DataFrame and Mu a column vector as a Series\n This implements page 188 Eq... | 1,760,906,879,167,205,600 | Optimal (Tangent/Max Sharpe Ratio) Portfolio weights
by using the Markowitz Optimization Procedure
Mu is the vector of Excess expected Returns
Sigma must be an N x N matrix as a DataFrame and Mu a column vector as a Series
This implements page 188 Equation 5.2.28 of
"The econometrics of financial markets" Campbell, Lo ... | kit.py | weight_msr | jaimeaguilera/Investing-projects | python | def weight_msr(sigma, mu, scale=True):
'\n Optimal (Tangent/Max Sharpe Ratio) Portfolio weights\n by using the Markowitz Optimization Procedure\n Mu is the vector of Excess expected Returns\n Sigma must be an N x N matrix as a DataFrame and Mu a column vector as a Series\n This implements page 188 Eq... |
def neg_sharpe(weights, riskfree_rate, er, cov):
'\n Returns the negative of the sharpe ratio\n of the given portfolio\n '
r = portfolio_return(weights, er)
vol = portfolio_vol(weights, cov)
return ((- (r - riskfree_rate)) / vol) | -5,140,026,034,260,675,000 | Returns the negative of the sharpe ratio
of the given portfolio | kit.py | neg_sharpe | jaimeaguilera/Investing-projects | python | def neg_sharpe(weights, riskfree_rate, er, cov):
'\n Returns the negative of the sharpe ratio\n of the given portfolio\n '
r = portfolio_return(weights, er)
vol = portfolio_vol(weights, cov)
return ((- (r - riskfree_rate)) / vol) |
def msd_risk(weights, target_risk, cov):
'\n Returns the Mean Squared Difference in risk contributions\n between weights and target_risk\n '
w_contribs = risk_contribution(weights, cov)
return ((w_contribs - target_risk) ** 2).sum() | -1,417,795,964,434,329,000 | Returns the Mean Squared Difference in risk contributions
between weights and target_risk | kit.py | msd_risk | jaimeaguilera/Investing-projects | python | def msd_risk(weights, target_risk, cov):
'\n Returns the Mean Squared Difference in risk contributions\n between weights and target_risk\n '
w_contribs = risk_contribution(weights, cov)
return ((w_contribs - target_risk) ** 2).sum() |
def __init__(self, datasetPathList, nodeList, group, level='dataset', priority='normal', move='n', static='n', custodial='n', request_only='y', blocks=None, subscriptionId=(- 1), comments=''):
'\n Initialize PhEDEx subscription with default value\n '
if isinstance(datasetPathList, basestring):
... | -6,030,256,932,251,470,000 | Initialize PhEDEx subscription with default value | src/python/WMCore/Services/PhEDEx/DataStructs/SubscriptionList.py | __init__ | cbbrainerd/WMCore | python | def __init__(self, datasetPathList, nodeList, group, level='dataset', priority='normal', move='n', static='n', custodial='n', request_only='y', blocks=None, subscriptionId=(- 1), comments=):
'\n \n '
if isinstance(datasetPathList, basestring):
datasetPathList = [datasetPathList]
if isi... |
def __str__(self):
'\n Write out useful information for this object\n :return:\n '
res = {'datasetPaths': self.datasetPaths, 'nodes': self.nodes, 'priority': self.priority, 'move': self.move, 'group': self.group, 'custodial': self.custodial, 'request_only': self.request_only, 'blocks': self... | -1,481,429,788,718,934,000 | Write out useful information for this object
:return: | src/python/WMCore/Services/PhEDEx/DataStructs/SubscriptionList.py | __str__ | cbbrainerd/WMCore | python | def __str__(self):
'\n Write out useful information for this object\n :return:\n '
res = {'datasetPaths': self.datasetPaths, 'nodes': self.nodes, 'priority': self.priority, 'move': self.move, 'group': self.group, 'custodial': self.custodial, 'request_only': self.request_only, 'blocks': self... |
def getDatasetsAndBlocks(self):
'\n _getDatasetsAndBlocks_\n\n Get the block structure\n with datasets and blocks\n '
return self.blocks | 7,009,327,071,037,417,000 | _getDatasetsAndBlocks_
Get the block structure
with datasets and blocks | src/python/WMCore/Services/PhEDEx/DataStructs/SubscriptionList.py | getDatasetsAndBlocks | cbbrainerd/WMCore | python | def getDatasetsAndBlocks(self):
'\n _getDatasetsAndBlocks_\n\n Get the block structure\n with datasets and blocks\n '
return self.blocks |
def matchesExistingTransferRequest(self, phedexDataSvc):
'\n _matchesExistingTransferRequest_\n\n Check the given phedex data service to verify if an unapproved\n transfer request equal to this subscription is already in the system.\n '
if ((len(self.datasetPaths) != 1) or (len(self.... | 6,305,325,705,504,872,000 | _matchesExistingTransferRequest_
Check the given phedex data service to verify if an unapproved
transfer request equal to this subscription is already in the system. | src/python/WMCore/Services/PhEDEx/DataStructs/SubscriptionList.py | matchesExistingTransferRequest | cbbrainerd/WMCore | python | def matchesExistingTransferRequest(self, phedexDataSvc):
'\n _matchesExistingTransferRequest_\n\n Check the given phedex data service to verify if an unapproved\n transfer request equal to this subscription is already in the system.\n '
if ((len(self.datasetPaths) != 1) or (len(self.... |
def matchesExistingSubscription(self, phedexDataSvc):
'\n _matchesExistingSubscription_\n\n Check the given phedex data service to verify if a PhEDEx subscription\n equal to this subscription is already in the system.\n '
if ((len(self.datasetPaths) != 1) or (len(self.nodes) != 1)):
... | 1,436,007,145,053,543,200 | _matchesExistingSubscription_
Check the given phedex data service to verify if a PhEDEx subscription
equal to this subscription is already in the system. | src/python/WMCore/Services/PhEDEx/DataStructs/SubscriptionList.py | matchesExistingSubscription | cbbrainerd/WMCore | python | def matchesExistingSubscription(self, phedexDataSvc):
'\n _matchesExistingSubscription_\n\n Check the given phedex data service to verify if a PhEDEx subscription\n equal to this subscription is already in the system.\n '
if ((len(self.datasetPaths) != 1) or (len(self.nodes) != 1)):
... |
def addSubscription(self, subObj):
'\n _addSubscription_\n Add a new subscription to the subscription policy.\n If the same subscription key exist just add the node list\n '
for subscription in self._subList:
if subscription.isEqualOptions(subObj):
if subscription... | 5,781,099,127,773,631,000 | _addSubscription_
Add a new subscription to the subscription policy.
If the same subscription key exist just add the node list | src/python/WMCore/Services/PhEDEx/DataStructs/SubscriptionList.py | addSubscription | cbbrainerd/WMCore | python | def addSubscription(self, subObj):
'\n _addSubscription_\n Add a new subscription to the subscription policy.\n If the same subscription key exist just add the node list\n '
for subscription in self._subList:
if subscription.isEqualOptions(subObj):
if subscription... |
def compact(self):
'\n _compact_\n\n Compact the subscription list by aggregating the subscriptions where the nodes\n share a list of dataset paths.\n '
bags = []
baggedIndexes = set()
for (i, subscriptionA) in enumerate(self._subList):
if (i in baggedIndexes):
... | 2,831,347,083,717,089,300 | _compact_
Compact the subscription list by aggregating the subscriptions where the nodes
share a list of dataset paths. | src/python/WMCore/Services/PhEDEx/DataStructs/SubscriptionList.py | compact | cbbrainerd/WMCore | python | def compact(self):
'\n _compact_\n\n Compact the subscription list by aggregating the subscriptions where the nodes\n share a list of dataset paths.\n '
bags = []
baggedIndexes = set()
for (i, subscriptionA) in enumerate(self._subList):
if (i in baggedIndexes):
... |
def get_source(category):
'\n function that gets the json response to our url request\n '
get_source_url = NEWS_API_BASE_URL.format(category, api_key)
print(get_source_url)
with urllib.request.urlopen(get_source_url) as url:
get_source_data = url.read()
get_source_response = json.l... | 2,996,768,572,009,040,400 | function that gets the json response to our url request | app/requests.py | get_source | ClarisseU/newsHighlight | python | def get_source(category):
'\n \n '
get_source_url = NEWS_API_BASE_URL.format(category, api_key)
print(get_source_url)
with urllib.request.urlopen(get_source_url) as url:
get_source_data = url.read()
get_source_response = json.loads(get_source_data)
sources_result = None
... |
def process_sources(sources_list):
'\n Function that checks the news results and turn them into objects\n \n Args:\n sources_list: A list of dictionaries that contain sources details\n '
sources_result = []
for source_item in sources_list:
author = source_item.get('author')
... | 5,628,184,623,351,458,000 | Function that checks the news results and turn them into objects
Args:
sources_list: A list of dictionaries that contain sources details | app/requests.py | process_sources | ClarisseU/newsHighlight | python | def process_sources(sources_list):
'\n Function that checks the news results and turn them into objects\n \n Args:\n sources_list: A list of dictionaries that contain sources details\n '
sources_result = []
for source_item in sources_list:
author = source_item.get('author')
... |
def get_articles(id):
'\n Function that processes the articles and returns a list of articles objects\n '
get_articles_url = ARTICLE.format(id, api_key)
print(get_articles_url)
with urllib.request.urlopen(get_articles_url) as url:
article_data = url.read()
articles_response = json.... | -5,579,780,585,189,511,000 | Function that processes the articles and returns a list of articles objects | app/requests.py | get_articles | ClarisseU/newsHighlight | python | def get_articles(id):
'\n \n '
get_articles_url = ARTICLE.format(id, api_key)
print(get_articles_url)
with urllib.request.urlopen(get_articles_url) as url:
article_data = url.read()
articles_response = json.loads(article_data)
articles_object = None
if articles_resp... |
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