noether.core.schemas.normalizers¶

Attributes¶

`TorchTensor`
`FloatOrArray`
`SequenceOrTensor`
`AnyNormalizer`

Classes¶

`NormalizerConfig`	Base configuration for normalizers. All normalizer configs should inherit from this class.
`MeanStdNormalizerConfig`	Base configuration for normalizers. All normalizer configs should inherit from this class.
`PositionNormalizerConfig`	Base configuration for normalizers. All normalizer configs should inherit from this class.
`ShiftAndScaleNormalizerConfig`	Base configuration for normalizers. All normalizer configs should inherit from this class.
`FieldNormalizerConfig`	Declarative normalizer config that references dataset statistics by convention.

Functions¶

validate_tensor(v)

Module Contents¶

noether.core.schemas.normalizers.validate_tensor(v)¶

Parameters:: v (Any)
Return type:: torch.Tensor

noether.core.schemas.normalizers.TorchTensor¶

noether.core.schemas.normalizers.FloatOrArray¶

noether.core.schemas.normalizers.SequenceOrTensor¶

class noether.core.schemas.normalizers.NormalizerConfig(/, **data)¶

Bases: noether.core.schemas.lib._RegistryBase

Base configuration for normalizers. All normalizer configs should inherit from this class.

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

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:: data (Any)

kind: str | None = None¶: Kind of normalizer to use, i.e. class path

model_config¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class noether.core.schemas.normalizers.MeanStdNormalizerConfig(/, **data)¶

Bases: NormalizerConfig

Base configuration for normalizers. All normalizer configs should inherit from this class.

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

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:: data (Any)

kind: str | None = 'noether.data.preprocessors.normalizers.MeanStdNormalization'¶: Kind of normalizer to use, i.e. class path

mean: TorchTensor¶: mean to subtract from the input data. Can be a single value or a Sequence if we want to apply a different mean per dimension.

std: TorchTensor¶: standard deviation to divide the input data by. Can be a single value or a Sequence if we want to apply a different std per dimension.

logscale: bool = False¶: If true, the input data is assumed to be in log scale.

class noether.core.schemas.normalizers.PositionNormalizerConfig(/, **data)¶

Bases: NormalizerConfig

Base configuration for normalizers. All normalizer configs should inherit from this class.

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

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:: data (Any)

kind: str | None = 'noether.data.preprocessors.normalizers.PositionNormalizer'¶: Kind of normalizer to use, i.e. class path

raw_pos_min: TorchTensor¶: Minimum raw position values of the entire simulation mesh. Can be a single value or a sequence of values.

raw_pos_max: TorchTensor¶: Maximum raw position values of the entire simulation mesh. Can be a single value or a sequence of values.

scale: float = None¶: Scaling factor, the coordinates will be scaled linearly between [0, scale]. Defaults to 1000.

check_min_max()¶

Return type:: Self

class noether.core.schemas.normalizers.ShiftAndScaleNormalizerConfig(/, **data)¶

Bases: NormalizerConfig

Base configuration for normalizers. All normalizer configs should inherit from this class.

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

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:: data (Any)

kind: str | None = 'noether.data.preprocessors.normalizers.ShiftAndScaleNormalizer'¶: Kind of normalizer to use, i.e. class path

shift: TorchTensor¶: Value to subtract from the input data. Can be a single value or a Sequence if we want to apply a different shift per dimension. Assumed in log scale if logscale is True.

scale: TorchTensor¶: Value to divide the input data by. Can be a single value or a Sequence if we want to apply a different scale per dimension. Assumed in log scale if logscale is True.

logscale: bool = False¶: If true, the input data is assumed to be in log scale.

check_shift_scale()¶

Return type:: Self

class noether.core.schemas.normalizers.FieldNormalizerConfig(/, **data)¶

Bases: NormalizerConfig

Declarative normalizer config that references dataset statistics by convention.

Instead of embedding numeric values (mean, std, etc.) directly, this config declares how to normalize a field. The actual statistics are resolved at runtime from the dataset’s statistics file.

For "mean_std" normalization, the builder looks up {field}_mean and {field}_std in the dataset statistics (customizable via stat_keys).

For "position" normalization, the builder looks up raw_pos_min and raw_pos_max (customizable via stat_keys).

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

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:: data (Any)

kind: str | None = 'noether.data.preprocessors.normalizers.FieldNormalizer'¶: Kind of normalizer to use, i.e. class path

strategy: Literal['mean_std', 'position'] = 'mean_std'¶: Normalization strategy. "mean_std" for mean/std normalization, "position" for position normalization.

logscale: bool = False¶: If true, the input data is converted to log scale before normalization. Only used for "mean_std".

stat_keys: dict[str, str] | None = None¶

{"mean": "custom_mean_key", "std": "custom_std_key"}. For "position": {"min": "custom_min_key", "max": "custom_max_key"}.

Type:: Optional overrides for statistic key lookup. For "mean_std"

scale: float = None¶: Scaling factor for position normalization. Coordinates are scaled to [0, scale]. Only used for "position".

noether.core.schemas.normalizers.AnyNormalizer¶