match_sampling_rate

MatchSamplingRate(reference_feature_name, feature_interpolation_map=None, fill_strategy='both_ways')

Bases: Transform

Matches the sampling rate of all time series in the DataFrame.

Interpolates the time series to match the sampling rate of the reference time series. The feature_interpolation_map parameter is a dictionary that specifies the interpolation method for each feature. The keys are the feature names, and the values are the interpolation methods. The interpolation method can be 'linear' or 'nearest'. If the feature_interpolation_map parameter is not provided, all features except the reference feature will be interpolated using the 'nearest' method. The fill_strategy parameter specifies the strategy to fill missing values after interpolation. The default value is 'both_ways', which means that missing values will be filled using both forward and backward filling. Other options include 'forward', 'backward', 'min', 'max', 'mean', 'zero', and 'one'.The below example shows the usage of a MatchSamplingRate transform in a run.py file. Assuming the loaded data is represented by the table:

| feature_a                   | feature_b                   | const |
| ---                         | ---                         | ---   |
| list[struct[time,struct[]]] | list[struct[time,struct[]]] | int   |
| --------------------------- | --------------------------- | ----- |
| [{12:26:01.0, {1.2}},       | [{12:26:00.0, {1.0}},       | 1     |
|  {12:26:02.0, {2.4}},       |  {12:26:05.0, {2.0}}]       |       |
|  {12:26:03.0, {3.6}},       |                             |       |
|  {12:26:04.0, {4.8}}]       |                             |       |

The following transform can be used to match the sampling rate of the time series feature_b to the sampling rate of the time series feature_a.

    ...
    environment.load()
    data = environment.get_data()
    transform = MatchSamplingRate(
        reference_feature_name="feature_a",
        feature_interpolation_map={
            "feature_b": "linear",
        },
    )
    transformed_data = transform.transform(data)
    ...

The resulting Dataframe after the transform is:

| feature_a                   | feature_b                   | const |
| ---                         | ---                         | ---   |
| list[struct[time,struct[]]] | list[struct[time,struct[]]] | int   |
| --------------------------- | --------------------------- | ----- |
| [{12:26:00.0, {1.2}},       | [{12:26:00.0, {1.2}},       | 1     |
|  {12:26:01.0, {2.4}},       |  {12:26:01.0, {1.4}},       |       |
|  {12:26:02.0, {3.6}},       |  {12:26:02.0, {1.6}},       |       |
|  {12:26:03.0, {4.8}}]       |  {12:26:03.0, {1.8}}]       |       |

Initialize the transform.

Parameters:

Name	Type	Description	Default
reference_feature_name	str	Reference timeseries feature.	required
feature_interpolation_map	dict[str, MatchSamplingRateMethod] | None	Key-value pairs of the timeseries features that are targeted in interpolation columns and the interpolation method to use. The interpolation method can be 'linear' or 'nearest'.	None
fill_strategy	FillStrategy	Strategy to fill missing values after interpolation.	'both_ways'

Source code in src/flowcean/polars/transforms/match_sampling_rate.py

def __init__(
    self,
    reference_feature_name: str,
    feature_interpolation_map: dict[str, MatchSamplingRateMethod]
    | None = None,
    fill_strategy: FillStrategy = "both_ways",
) -> None:
    """Initialize the transform.

    Args:
        reference_feature_name: Reference timeseries feature.
        feature_interpolation_map: Key-value pairs of the timeseries
            features that are targeted in interpolation columns and the
            interpolation method to use. The interpolation
            method can be 'linear' or 'nearest'.
        fill_strategy: Strategy to fill missing values after interpolation.
    """
    self.reference_feature_name = reference_feature_name
    self.feature_interpolation_map = feature_interpolation_map
    self.fill_strategy = fill_strategy

apply(data)

Transform the input DataFrame.

Parameters:

Name	Type	Description	Default
data	LazyFrame	Input DataFrame.	required

Returns:

Type	Description
LazyFrame	Transformed DataFrame.

Source code in src/flowcean/polars/transforms/match_sampling_rate.py

def apply(self, data: pl.LazyFrame) -> pl.LazyFrame:
    """Transform the input DataFrame.

    Args:
        data: Input DataFrame.

    Returns:
        Transformed DataFrame.

    """
    # preserve all constant columns that are not timeseries data
    transformed_data = pl.DataFrame()
    collected_data = data.collect()
    for i in range(len(collected_data.rows())):
        transformed_data_slice = self._transform_row(
            collected_data.slice(i, 1),
        )
        transformed_data = transformed_data.vstack(transformed_data_slice)
    return transformed_data.lazy()

FeatureNotFoundError(feature)

Bases: Exception

Feature not found in the DataFrame.

This exception is raised when a feature is not found in the DataFrame.

Source code in src/flowcean/polars/transforms/match_sampling_rate.py

def __init__(self, feature: str) -> None:
    super().__init__(f"{feature} not found")

interpolate_feature(target_feature_name, data, reference_feature, interpolation_method='linear', fill_strategy=None)

Interpolate a single time series feature using Polars expressions.

Source code in src/flowcean/polars/transforms/match_sampling_rate.py

def interpolate_feature(
    target_feature_name: str,
    data: pl.DataFrame,
    reference_feature: pl.DataFrame,
    interpolation_method: Literal["linear", "nearest"] = "linear",
    fill_strategy: FillStrategy | None = None,
) -> pl.DataFrame:
    """Interpolate a single time series feature using Polars expressions."""
    logger.debug("Interpolating feature %s", target_feature_name)

    # Extract and unnest feature dataframe
    feature_df = data.select(pl.col(target_feature_name).explode()).unnest(
        cs.all(),
    )
    # Get the original time type from the reference feature
    original_time_type = reference_feature.schema["time"]

    # Cast time to Float64 for interpolation
    feature_df = feature_df.with_columns(pl.col("time").cast(pl.Float64))

    # Handle 'value' field
    if "value" in feature_df.columns:
        value_schema = feature_df.schema["value"]
        if isinstance(value_schema, pl.Struct):
            # Get the schema of 'value' and extract field names for structs
            original_field_names = [
                field.name for field in value_schema.fields
            ]
            feature_df = feature_df.unnest("value")
            value_is_struct = True
        else:
            # Rename non-struct 'value' to a temporary name to avoid conflicts
            feature_df = feature_df.rename(
                {"value": f"{target_feature_name}_value"},
            )
            original_field_names = [f"{target_feature_name}_value"]
            value_is_struct = False
    else:
        msg = f"Feature {target_feature_name} is missing 'value' field."
        raise ValueError(msg)

    # Store column names after unnesting (or renaming for non-struct 'value')
    value_columns = [col for col in feature_df.columns if col != "time"]

    # Get reference times and feature times, cast to Float64 for interpolation
    reference_times = reference_feature.get_column("time").cast(pl.Float64)
    feature_times = feature_df.get_column("time").cast(pl.Float64)

    # Combine all unique times and sort
    all_times = (
        pl.concat([reference_times, feature_times])
        .unique()
        .sort()
        .to_frame("time")
        .with_columns(pl.col("time").cast(pl.Float64))
    )

    # Join with feature data
    joined_df = all_times.join(feature_df, on="time", how="left")

    # Interpolate missing values
    interpolated = joined_df.with_columns(
        [
            pl.col(col).interpolate(method=interpolation_method)
            for col in value_columns
        ],
    )
    if fill_strategy == "both_ways":
        fill_strategy = "backward"
        interpolated = interpolated.with_columns(
            [
                pl.col(col).fill_null(strategy=fill_strategy)
                for col in value_columns
            ],
        )
        fill_strategy = "forward"
        interpolated = interpolated.with_columns(
            [
                pl.col(col).fill_null(strategy=fill_strategy)
                for col in value_columns
            ],
        )
    elif fill_strategy:
        interpolated = interpolated.with_columns(
            [
                pl.col(col).fill_null(strategy=fill_strategy)
                for col in value_columns
            ],
        )

    # Filter to only include reference times
    interpolated = interpolated.filter(pl.col("time").is_in(reference_times))

    # Restore original time type
    interpolated = interpolated.with_columns(
        pl.col("time").cast(original_time_type),
    )

    # Restructure to nested format
    if value_is_struct:
        # Struct case: map original field names to their respective columns
        restructure_value = pl.struct(
            {
                name: pl.col(col)
                for name, col in zip(
                    original_field_names,
                    value_columns,
                    strict=False,
                )
            },
        ).alias("value")
    else:
        # Scalar case: restore the original scalar 'value' field
        restructure_value = pl.col(value_columns[0]).alias("value")

    restructure = pl.struct(pl.col("time"), restructure_value).alias(
        target_feature_name,
    )

    return interpolated.select(restructure).select(pl.all().implode())