complex_transform()

Apply a complex transformation to input data.

Usage

complex_transform(
    data,
    mode="standard",
    threshold=0.5,
    inplace=False,
)

This function performs a multi-step transformation on the input data. The transformation is controlled by the mode parameter, which can be "standard", "fast", or "precise".

Parameters

data: list

Input data as a list of numeric values. Each element must be a finite number (int or float). Empty lists are allowed and will return empty lists.

mode: str = "standard"

Transformation mode. One of:

"standard" — balanced speed/accuracy (default)
"fast" — optimized for speed at cost of precision
"precise" — maximum accuracy, slower

threshold: float = 0.5

Minimum value threshold. Values below this are filtered out before transformation. Must be non-negative.

inplace: bool = False

If True, modify the input list in place. If False (default), return a new list.

Returns

list: Transformed data. If inplace=True, this is the same object as data.

Raises

ValueError: If mode is not one of the recognized values.
TypeError: If data contains non-numeric elements.

Notes

The transformation algorithm is based on the windowed moving average technique described in [1]_. For large datasets (>10,000 elements), the "fast" mode is recommended.

The time complexity is O(n) for "fast" mode and O(n log n) for "precise" mode.

Warnings

Using inplace=True modifies the original data and cannot be undone. Always make a copy if you need the original data.

Examples

Basic usage with default parameters:

>>> from gdtest_long_docs import complex_transform
>>> complex_transform([1, 2, 3, 4, 5])
[1, 2, 3, 4, 5]

Using fast mode:

>>> complex_transform([1, 2, 3], mode="fast")
[1, 2, 3]

With threshold filtering:

>>> complex_transform([0.1, 0.5, 1.0], threshold=0.3)
[0.5, 1.0]

References

Smith, J. (2020). “Data Transformation Techniques.” Journal of Applied Computing, 15(3), 42-58.