nannyml.config module

class nannyml.config.CalculatorConfig(*, type: str, name: Optional[str] = None, enabled: Optional[bool] = True, outputs: Optional[List[nannyml.config.WriterConfig]] = None, store: Optional[nannyml.config.StoreConfig] = None, params: Dict[str, Any])[source]

Bases: pydantic.main.BaseModel

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

enabled: Optional[bool]
name: Optional[str]
outputs: Optional[List[nannyml.config.WriterConfig]]
params: Dict[str, Any]
store: Optional[nannyml.config.StoreConfig]
type: str
class nannyml.config.ChunkerConfig(*, chunk_size: Optional[int] = None, chunk_period: Optional[str] = None, chunk_count: Optional[int] = None)[source]

Bases: pydantic.main.BaseModel

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

chunk_count: Optional[int]
chunk_period: Optional[str]
chunk_size: Optional[int]
class nannyml.config.Config(*, input: nannyml.config.InputConfig, calculators: List[nannyml.config.CalculatorConfig], scheduling: Optional[nannyml.config.SchedulingConfig] = None, ignore_errors: Optional[bool] = None)[source]

Bases: pydantic.main.BaseModel

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

calculators: List[nannyml.config.CalculatorConfig]
ignore_errors: Optional[bool]
input: nannyml.config.InputConfig
classmethod load(config_path: Optional[str] = None)[source]
classmethod parse(config: str)[source]
scheduling: Optional[nannyml.config.SchedulingConfig]
class nannyml.config.CronSchedulingConfig(*, crontab: str)[source]

Bases: pydantic.main.BaseModel

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

crontab: str
class nannyml.config.InputConfig(*, reference_data: nannyml.config.InputDataConfig, analysis_data: nannyml.config.InputDataConfig, target_data: Optional[nannyml.config.TargetDataConfig] = None)[source]

Bases: pydantic.main.BaseModel

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

analysis_data: nannyml.config.InputDataConfig
reference_data: nannyml.config.InputDataConfig
target_data: Optional[nannyml.config.TargetDataConfig]
class nannyml.config.InputDataConfig(*, path: str, credentials: Optional[Dict[str, Any]] = None, read_args: Optional[Dict[str, Any]] = None)[source]

Bases: pydantic.main.BaseModel

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

credentials: Optional[Dict[str, Any]]
path: str
read_args: Optional[Dict[str, Any]]
class nannyml.config.IntervalSchedulingConfig(*, weeks: Optional[int] = None, days: Optional[int] = None, hours: Optional[int] = None, minutes: Optional[int] = None)[source]

Bases: pydantic.main.BaseModel

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

days: Optional[int]
hours: Optional[int]
minutes: Optional[int]
weeks: Optional[int]
class nannyml.config.SchedulingConfig(*, interval: Optional[nannyml.config.IntervalSchedulingConfig] = None, cron: Optional[nannyml.config.CronSchedulingConfig] = None)[source]

Bases: pydantic.main.BaseModel

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

cron: Optional[nannyml.config.CronSchedulingConfig]
interval: Optional[nannyml.config.IntervalSchedulingConfig]
class nannyml.config.StoreConfig(*, path: str, credentials: Optional[Dict[str, Any]] = None, filename: Optional[str] = None)[source]

Bases: pydantic.main.BaseModel

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

credentials: Optional[Dict[str, Any]]
filename: Optional[str]
path: str
class nannyml.config.TargetDataConfig(*, path: str, credentials: Optional[Dict[str, Any]] = None, read_args: Optional[Dict[str, Any]] = None, join_column: Optional[str] = None)[source]

Bases: nannyml.config.InputDataConfig

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

join_column: Optional[str]
class nannyml.config.WriterConfig(*, type: str, params: Optional[Dict[str, Any]] = None, write_args: Optional[Dict[str, Any]] = None)[source]

Bases: pydantic.main.BaseModel

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

params: Optional[Dict[str, Any]]
type: str
write_args: Optional[Dict[str, Any]]
nannyml.config.get_config_path(custom_config_path: Optional[str] = None) pathlib.Path[source]