gem5/src/python/m5/ext/pystats/statistic.py

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from abc import ABC
from typing import Any, Optional, Union, List

from .jsonserializable import JsonSerializable
from .storagetype import StorageType

class Statistic(ABC, JsonSerializable):
    """
    The abstract base class for all Python statistics.
    """

    value: Any
    type: Optional[str]
    unit: Optional[str]
    description: Optional[str]
    datatype: Optional[StorageType]

    def __init__(self, value: Any, type: Optional[str] = None,
                 unit: Optional[str] = None,
                 description: Optional[str] = None,
                 datatype: Optional[StorageType] = None):
        self.value = value
        self.type = type
        self.unit = unit
        self.description = description
        self.datatype = datatype

class Scalar(Statistic):
    """
    A scalar Python statistic type.
    """

    value: Union[float, int]

    def __init__(self, value: Any,
                 unit: Optional[str] = None,
                 description: Optional[str] = None,
                 datatype: Optional[StorageType] = None):
        super(Scalar, self).__init__(
                                     value=value,
                                     type="Scalar",
                                     unit=unit,
                                     description=description,
                                     datatype=datatype,
                                    )

class BaseScalarVector(Statistic):
    """
    An abstract base class for classes containing a vector of Scalar values.
    """
    value: List[Union[int,float]]

    def __init__(self, value: List[Union[int,float]],
                 type: Optional[str] = None,
                 unit: Optional[str] = None,
                 description: Optional[str] = None,
                 datatype: Optional[StorageType] = None):
        super(BaseScalarVector, self).__init__(
                                           value=value,
                                           type=type,
                                           unit=unit,
                                           description=description,
                                           datatype=datatype,
                                           )

    def mean(self) -> float:
        """
        Returns the mean of the value vector.

        Returns
        -------
        float
            The mean value across all bins.
        """
        assert(self.value != None)
        assert(isinstance(self.value, List))

        from statistics import mean as statistics_mean
        return statistics_mean(self.value)

    def count(self) -> int:
        """
        Returns the count across all the bins.

        Returns
        -------
        float
            The sum of all bin values.
        """
        assert(self.value != None)
        assert(isinstance(self.value, List))
        return sum(self.value)


class Distribution(BaseScalarVector):
    """
    A statistic type that stores information relating to distributions. Each
    distribution has a number of bins (>=1)
    between this range. The values correspond to the value of each bin.
    E.g., value[3]` is the value of the 4th bin.

    It is assumed each bucket is of equal size.
    """

    value: List[int]
    min: Union[float, int]
    max: Union[float, int]
    num_bins: int
    bin_size: Union[float, int]
    sum: Optional[int]
    sum_squared: Optional[int]
    underflow: Optional[int]
    overflow: Optional[int]
    logs: Optional[float]

    def __init__(self, value: List[int],
                 min: Union[float, int],
                 max: Union[float, int],
                 num_bins: int,
                 bin_size: Union[float, int],
                 sum: Optional[int] = None,
                 sum_squared: Optional[int] = None,
                 underflow: Optional[int] = None,
                 overflow: Optional[int] = None,
                 logs: Optional[float] = None,
                 unit: Optional[str] = None,
                 description: Optional[str] = None,
                 datatype: Optional[StorageType] = None):
        super(Distribution, self).__init__(
                                           value=value,
                                           type="Distribution",
                                           unit=unit,
                                           description=description,
                                           datatype=datatype,
                                           )

        self.min = min
        self.max = max
        self.num_bins = num_bins
        self.bin_size = bin_size
        self.sum = sum
        self.underflow = underflow
        self.overflow = overflow
        self.logs = logs
        self.sum_squared = sum_squared

        # These check some basic conditions of a distribution.
        assert(self.bin_size >= 0)
        assert(self.num_bins >= 1)

class Accumulator(BaseScalarVector):
    """
    A statistical type representing an accumulator.
    """

    count: int
    min: Union[int, float]
    max: Union[int, float]
    sum_squared: Optional[int]

    def __init__(self, value: List[Union[int,float]],
                 count: int,
                 min: Union[int, float],
                 max: Union[int, float],
                 sum_squared: Optional[int] = None,
                 unit: Optional[str] = None,
                 description: Optional[str] = None,
                 datatype: Optional[StorageType] = None):
        super(Accumulator, self).__init__(
                                     value=value,
                                     type="Accumulator",
                                     unit=unit,
                                     description=description,
                                     datatype=datatype,
                                    )

        self.count = count
        self.min = min
        self.max = max
        self.sum_squared = sum_squared