Class SampleOperations

monai/auto3dseg/operations.py:44–103

Apply statistical operation to a sample (image/ndarray/tensor). Notes: Percentile operation uses a partial function that embeds different kwargs (q). In order to print the result nicely, data_addon is added to map the numbers generated by percentile to different key

class SampleOperations(Operations):
    """
    Apply statistical operation to a sample (image/ndarray/tensor).

    Notes:
        Percentile operation uses a partial function that embeds different kwargs (q).
        In order to print the result nicely, data_addon is added to map the numbers
        generated by percentile to different keys ("percentile_00_5" for example).
        Annotation of the postfix means the percentage for percentile computation.
        For example, _00_5 means 0.5% and _99_5 means 99.5%.

    Example:

        .. code-block:: python

            # use the existing operations
            import numpy as np
            op = SampleOperations()
            data_np = np.random.rand(10, 10).astype(np.float64)
            print(op.evaluate(data_np))

            # add a new operation
            op.update({"sum": np.sum})
            print(op.evaluate(data_np))
    """

    def __init__(self) -> None:
        self.data = {
            "max": max,
            "mean": mean,
            "median": median,
            "min": min,
            "stdev": std,
            "percentile": partial(percentile, q=[0.5, 10, 90, 99.5]),
        }
        self.data_addon = {
            "percentile_00_5": ("percentile", 0),
            "percentile_10_0": ("percentile", 1),
            "percentile_90_0": ("percentile", 2),
            "percentile_99_5": ("percentile", 3),
        }

    def evaluate(self, data: Any, **kwargs: Any) -> dict:
        """
        Applies the callables to the data, and convert the
        numerics to list or Python numeric types (int/float).

        Args:
            data: input data
        """
        ret = super().evaluate(data, **kwargs)
        for k, v in self.data_addon.items():
            cache = v[0]
            idx = v[1]
            if isinstance(v, tuple) and cache in ret:
                ret.update({k: ret[cache][idx]})

        for k, v in ret.items():