Normals

class Normals(*, table_name: TimeseriesTableNamesEnum = TimeseriesTableNamesEnum.primary_timeseries, filters: str | dict | TableFilter | List[str | dict | TableFilter] = None, temporal_resolution: NormalsResolutionEnum = NormalsResolutionEnum.day_of_year, summary_statistic: NormalsStatisticEnum = NormalsStatisticEnum.mean, df: DataFrame = None)

Model for generating synthetic normals timeseries.

Parameters:

• temporal_resolution (NormalsResolutionEnum) – The temporal resolution for the normals timeseries.

• summary_statistic (NormalsStatisticEnum) – The summary statistic to use for the normals timeseries.

• df (ps.DataFrame) – The DataFrame containing the timeseries data.

Methods

generate

Generate synthetic normals timeseries.

Attributes

model_computed_fields
model_config	Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
model_extra	Get extra fields set during validation.
model_fields
model_fields_set	Returns the set of fields that have been explicitly set on this model instance.
temporal_resolution
summary_statistic
df
table_name
filters

generate(input_dataframe: DataFrame, output_dataframe: DataFrame, fillna: bool, dropna: bool) → DataFrame

Generate synthetic normals timeseries.

Parameters:

• input_dataframe (ps.DataFrame) – The input DataFrame containing the timeseries data.

• output_dataframe (ps.DataFrame) – The output DataFrame to join the normals data to.

• fillna (bool) – Whether to fill NaN values using forward and backward fill.

• dropna (bool) – Whether to drop rows with NaN values.

Returns:

ps.DataFrame – The DataFrame containing the generated normals timeseries.