Signatures

class Signatures

Define and customize signature metrics (single-field statistics).

Notes

Signatures operate on a single field (typically primary_value) to characterize timeseries properties. Available signatures:

Basic Statistics: - Count, Minimum, Maximum, Average, Sum, Variance

Time-Based: - MaxValueTime

Hydrologic: - FlowDurationCurveSlope

Example

>>> from teehr import Signatures
>>> avg = Signatures.Average()
>>> fdc = Signatures.FlowDurationCurveSlope(lower_quantile=0.33, upper_quantile=0.66)

Methods

class Average(*, return_type: str | ~pyspark.sql.types.ArrayType | ~pyspark.sql.types.MapType = 'float', unpack_results: bool = False, unpack_function: ~typing.Callable = <function unpack_sdf_dict_columns>, reference_configuration: str = None, bootstrap: ~typing.Any = None, add_epsilon: bool = False, transform: ~typing.Any = None, output_field_name: str = None, func: ~typing.Callable = None, input_field_names: str | ~teehr.models.str_enum.StrEnum | ~typing.List[str | ~teehr.models.str_enum.StrEnum] = None, attrs: ~typing.Dict = None)

Average: arithmetic mean of the series.

default_func() → Callable

Create average metric function.

class Count(*, return_type: str | ~pyspark.sql.types.ArrayType | ~pyspark.sql.types.MapType = 'float', unpack_results: bool = False, unpack_function: ~typing.Callable = <function unpack_sdf_dict_columns>, reference_configuration: str = None, bootstrap: ~typing.Any = None, add_epsilon: bool = False, transform: ~typing.Any = None, output_field_name: str = None, func: ~typing.Callable = None, input_field_names: str | ~teehr.models.str_enum.StrEnum | ~typing.List[str | ~teehr.models.str_enum.StrEnum] = None, attrs: ~typing.Dict = None)

Count: number of non-null values in the series.

default_func() → Callable

Create count metric function.

class FlowDurationCurveSlope(*, return_type: str | ~pyspark.sql.types.ArrayType | ~pyspark.sql.types.MapType = 'float', unpack_results: bool = False, unpack_function: ~typing.Callable = <function unpack_sdf_dict_columns>, reference_configuration: str = None, bootstrap: ~typing.Any = None, add_epsilon: bool = False, transform: ~typing.Any = None, output_field_name: str = None, func: ~typing.Callable = None, input_field_names: str | ~teehr.models.str_enum.StrEnum | ~typing.List[str | ~teehr.models.str_enum.StrEnum] = None, attrs: ~typing.Dict = None, lower_quantile: float = 0.25, upper_quantile: float = 0.85, as_percentile: bool = False)

Flow Duration Curve Slope: slope of the FDC between quantiles.

Additional Parameters

lower_quantilefloat

The lower exceedance probability quantile, by default 0.25.

upper_quantilefloat

The upper exceedance probability quantile, by default 0.85.

as_percentilebool

Whether to express exceedance probability as percentile (0-100) or fraction (0-1), by default False.

default_func() → Callable

Create flow duration curve slope metric function.

class MaxValueTime(*, return_type: str = 'timestamp', unpack_results: bool = False, unpack_function: ~typing.Callable = <function unpack_sdf_dict_columns>, reference_configuration: str = None, bootstrap: ~typing.Any = None, add_epsilon: bool = False, transform: ~typing.Any = None, output_field_name: str = None, func: ~typing.Callable = None, input_field_names: str | ~teehr.models.str_enum.StrEnum | ~typing.List[str | ~teehr.models.str_enum.StrEnum] = None, attrs: ~typing.Dict = None)

Max Value Time: timestamp when the maximum value occurs.

This signature requires both primary_value and value_time fields.

default_func() → Callable

Create max_value_time metric function.

class Maximum(*, return_type: str | ~pyspark.sql.types.ArrayType | ~pyspark.sql.types.MapType = 'float', unpack_results: bool = False, unpack_function: ~typing.Callable = <function unpack_sdf_dict_columns>, reference_configuration: str = None, bootstrap: ~typing.Any = None, add_epsilon: bool = False, transform: ~typing.Any = None, output_field_name: str = None, func: ~typing.Callable = None, input_field_names: str | ~teehr.models.str_enum.StrEnum | ~typing.List[str | ~teehr.models.str_enum.StrEnum] = None, attrs: ~typing.Dict = None)

Maximum: largest value in the series.

default_func() → Callable

Create maximum metric function.

class Minimum(*, return_type: str | ~pyspark.sql.types.ArrayType | ~pyspark.sql.types.MapType = 'float', unpack_results: bool = False, unpack_function: ~typing.Callable = <function unpack_sdf_dict_columns>, reference_configuration: str = None, bootstrap: ~typing.Any = None, add_epsilon: bool = False, transform: ~typing.Any = None, output_field_name: str = None, func: ~typing.Callable = None, input_field_names: str | ~teehr.models.str_enum.StrEnum | ~typing.List[str | ~teehr.models.str_enum.StrEnum] = None, attrs: ~typing.Dict = None)

Minimum: smallest value in the series.

default_func() → Callable

Create minimum metric function.

class Sum(*, return_type: str | ~pyspark.sql.types.ArrayType | ~pyspark.sql.types.MapType = 'float', unpack_results: bool = False, unpack_function: ~typing.Callable = <function unpack_sdf_dict_columns>, reference_configuration: str = None, bootstrap: ~typing.Any = None, add_epsilon: bool = False, transform: ~typing.Any = None, output_field_name: str = None, func: ~typing.Callable = None, input_field_names: str | ~teehr.models.str_enum.StrEnum | ~typing.List[str | ~teehr.models.str_enum.StrEnum] = None, attrs: ~typing.Dict = None)

Sum: total of all values in the series.

default_func() → Callable

Create sum metric function.

class Variance(*, return_type: str | ~pyspark.sql.types.ArrayType | ~pyspark.sql.types.MapType = 'float', unpack_results: bool = False, unpack_function: ~typing.Callable = <function unpack_sdf_dict_columns>, reference_configuration: str = None, bootstrap: ~typing.Any = None, add_epsilon: bool = False, transform: ~typing.Any = None, output_field_name: str = None, func: ~typing.Callable = None, input_field_names: str | ~teehr.models.str_enum.StrEnum | ~typing.List[str | ~teehr.models.str_enum.StrEnum] = None, attrs: ~typing.Dict = None)

Variance: statistical variance of the series.

default_func() → Callable

Create variance metric function.