Normalization Config

Pydantic models for normalization strategies.

`MeanStdConfig`

Bases: BaseModel

Mean and standard deviation normalization configuration.

Holds mean and standard deviation statistics for input and target, used to normalize data. Each statistic can be a single float (applied globally to all channels) or a list of floats (one per channel). If not provided, statistics can be computed automatically.

Attributes:

Name	Type	Description
`name`	`Literal['mean_std']`	Identifier for the mean-std normalization scheme.
`input_means`	`float \| list[float] \| None`	Means for input normalization. None for automatic computation.
`input_stds`	`float \| list[float] \| None`	Standard deviations for input normalization. None for automatic computation.
`target_means`	`float \| list[float] \| None`	Means for target normalization. None for automatic computation.
`target_stds`	`float \| list[float] \| None`	Standard deviations for target normalization. None for automatic computation.
`per_channel`	`bool`	When True (default), statistics are computed independently for each channel. When False, a single statistic is computed across all channels.

`needs_computation()`

Check if statistics need to be computed.

Returns:

Type	Description
`bool`	True if input statistics are missing, False otherwise.

`set_input_stats(means, stds)`

Set input means and stds together to avoid validation errors.

Parameters:

Name	Type	Description	Default
`means`	`list[float]`	Mean values per channel.	required
`stds`	`list[float]`	Standard deviation values per channel.	required

`set_target_stats(means, stds)`

Set target means and stds together to avoid validation errors.

Parameters:

Name	Type	Description	Default
`means`	`list[float]`	Mean values per channel.	required
`stds`	`list[float]`	Standard deviation values per channel.	required

`validate_means_stds()`

Validate that means and stds are provided in pairs or set to None.

Returns:

Type	Description
`Self`	The validated model instance.

Raises:

Type	Description
`ValueError`	If only one of means or stds is provided for input or target, or if paired lists have mismatched lengths.

`MinMaxConfig`

Bases: BaseModel

Min-max normalization configuration.

Stores minimum and maximum statistics for scaling data into a desired range. Each statistic can be a single float (applied globally to all channels) or a list of floats (one per channel). If not provided, statistics can be computed automatically.

Attributes:

Name	Type	Description
`name`	`Literal['min_max']`	Identifier for min-max normalization.
`input_mins`	`float \| list[float] \| None`	Minimum values for input normalization. None for automatic computation.
`input_maxes`	`float \| list[float] \| None`	Maximum values for input normalization. None for automatic computation.
`target_mins`	`float \| list[float] \| None`	Minimum values for target normalization. None for automatic computation.
`target_maxes`	`float \| list[float] \| None`	Maximum values for target normalization. None for automatic computation.
`per_channel`	`bool`	When True (default), statistics are computed independently for each channel. When False, a single statistic is computed across all channels.

`needs_computation()`

Check if min/max values need to be computed.

Returns:

Type	Description
`bool`	True if input statistics are missing, False otherwise.

`set_input_range(mins, maxes)`

Set input mins and maxes together to avoid validation errors.

Parameters:

Name	Type	Description	Default
`mins`	`list[float]`	Minimum values per channel.	required
`maxes`	`list[float]`	Maximum values per channel.	required

`set_target_range(mins, maxes)`

Set target mins and maxes together to avoid validation errors.

Parameters:

Name	Type	Description	Default
`mins`	`list[float]`	Minimum values per channel.	required
`maxes`	`list[float]`	Maximum values per channel.	required

`validate_mins_maxes()`

Validate that mins and maxes are provided in pairs or both None.

Returns:

Type	Description
`Self`	The validated model instance.

Raises:

Type	Description
`ValueError`	If only one of mins or maxes is provided for input or target, or if paired lists have mismatched lengths.

`NoNormConfig`

Bases: BaseModel

No normalization configuration.

Indicates that no normalization should be applied.

Attributes:

Name	Type	Description
`name`	`Literal['none']`	Identifier for no normalization scheme.

`needs_computation()`

Check if statistics need to be computed.

Returns:

Type	Description
`bool`	Always False, as no statistics are required.

`QuantileConfig`

Bases: BaseModel

Quantile normalization configuration.

Normalizes data using quantile-based range scaling. Quantile levels can be specified as a single value (applied to all channels) or a list (one per channel). If not provided, quantile values can be computed automatically.

Attributes:

Name	Type	Description
`name`	`Literal['quantile']`	Identifier for quantile normalization.
`lower_quantiles`	`float \| list[float]`	Lower quantile level(s). Values must be in [0, 1).
`upper_quantiles`	`float \| list[float]`	Upper quantile level(s). Values must be in (0, 1].
`input_lower_quantile_values`	`float \| list[float] \| None`	Computed lower quantile values for input.
`input_upper_quantile_values`	`float \| list[float] \| None`	Computed upper quantile values for input.
`target_lower_quantile_values`	`float \| list[float] \| None`	Computed lower quantile values for target.
`target_upper_quantile_values`	`float \| list[float] \| None`	Computed upper quantile values for target.
`per_channel`	`bool`	When True (default), quantile values are computed independently for each channel. When False, a single quantile is computed across all channels.

`needs_computation()`

Check if quantile values need to be computed.

Returns:

Type	Description
`bool`	True if quantile values need to be computed.

`set_input_quantile_values(lower, upper)`

Set input quantile values together to avoid validation errors.

Parameters:

Name	Type	Description	Default
`lower`	`list[float]`	Lower quantile values per channel.	required
`upper`	`list[float]`	Upper quantile values per channel.	required

`set_target_quantile_values(lower, upper)`

Set target quantile values together to avoid validation errors.

Parameters:

Name	Type	Description	Default
`lower`	`list[float]`	Lower quantile values per channel.	required
`upper`	`list[float]`	Upper quantile values per channel.	required

`validate_per_channel()`

Validate quantile levels match per_channel setting.

Returns:

Type	Description
`Self`	The validated model instance.

Raises:

Type	Description
`ValueError`	If per_channel=False but quantile levels are not length 1.

`validate_quantile_levels()`

Validate quantile levels are in valid range and properly ordered.

Returns:

Type	Description
`Self`	The validated model instance.

`validate_quantile_values()`

Validate that computed quantile value lists are provided in pairs.

Returns:

Type	Description
`Self`	The validated model instance.