# Apheleia Verification Library Hierachical component
# Copyright 2024 Apheleia
#
# Description:
# Apheleia Verification Library Coverpoint
from math import sqrt
from typing import Any
import pandas as pd
from .component import Component
from .var import Var
[docs]
class Coverbin(Component):
[docs]
def __init__(self, name: str, parent: Any, *args: list[Any], **kwargs: list[Any]) -> None:
"""
Initialize an instance of the Coverbin class.
:param name: The name of the AVL cover bin.
:type name: str
:param parent: The parent component of the AVL cover bin.
:type parent: object
:param args: Additional arguments that can be values (int, float, Var), ranges, or lambda functions.
:type args: int, float, Var, range, callable
:param kwargs: Additional keyword arguments for configuration.
:type kwargs: dict
:raises ValueError: If an unknown bin condition is provided in args.
"""
super().__init__(name, parent)
if "illegal" in kwargs:
self.illegal = kwargs["illegal"]
else:
self.illegal = False
if "stats" in kwargs:
self.stats = kwargs["stats"]
else:
self.stats = False
self._values_ = []
self._ranges_ = []
self._funcs_ = []
self._count_ = 0
# Stats
if self.stats:
self.min = None
self.max = None
self.mean = 0.0
self._m2_ = 0.0
for arg in args:
if isinstance(arg, (int | float | list | tuple | set | Var)):
self._values_.append(arg)
elif isinstance(arg, range):
self._ranges_.append(arg)
elif callable(arg) and (arg.__name__ == "<lambda>"):
self._funcs_.append(arg)
else:
raise ValueError(f"Unknown bin condition {arg}")
[docs]
def check(self, var: Any) -> bool:
"""
Check if the given variable matches any of the bin's conditions.
:param var: The variable to be checked.
:type var: Any
:return: True if the variable matches any condition, False otherwise.
:rtype: bool
"""
if var is None:
return False
hit = False
for v in self._values_:
if v == var:
hit = True
for r in self._ranges_:
if var in r:
hit = True
for f in self._funcs_:
if f(var):
hit = True
return hit
[docs]
def sample(self, var: Any) -> None:
"""
Check if the given variable matches any of the bin's conditions and increment the count.
:param var: The variable to be checked.
:type var: Any
"""
if self.check(var):
if self.illegal:
raise ValueError(f"Illegal value {var} found in coverbin {self.name}")
self._count_ += 1
if self.stats:
var_float = float(var)
if self.min is None or var_float < self.min:
self.min = var_float
if self.max is None or var_float > self.max:
self.max = var_float
delta = float(var) - self.mean
self.mean += delta / self._count_
delta2 = float(var) - self.mean
self._m2_ += delta * delta2
[docs]
def get_mean(self) -> float:
"""
Return the mean value.
:return: The mean value.
:rtype: float
"""
if self.stats:
return self.mean if self._count_ > 0 else None
else:
return None
[docs]
def get_variance(self) -> float:
"""
Return the variance value.
:return: The variance value.
:rtype: float
"""
if self.stats:
return self._m2_ / (self._count_ - 1) if self._count_ > 1 else None
else:
return None
[docs]
def get_stddev(self) -> float:
"""
Return the standard deviation value.
:return: The standard deviation value.
:rtype: float
"""
if self.stats:
return sqrt(self.get_variance()) if self._count_ > 1 else None
else:
return None
[docs]
def report(self) -> pd.DataFrame:
"""
Generate a report of the coverage data.
:return: A DataFrame containing the coverage data. The structure of the DataFrame depends on the value of the `full` parameter:
- If `full` is True, the DataFrame contains columns: 'covergroup', 'coverpoint', 'at_least', 'count'.
- If `full` is False, the DataFrame contains columns: 'covergroup', 'coverpoint', 'coverage'.
:rtype: pandas.DataFrame
"""
if self.stats:
return pd.DataFrame(
{
"bin": [self.name],
"count": [int(self._count_)],
"min": [self.min],
"max": [self.max],
"mean": [self.get_mean()],
"variance": [self.get_variance()],
"stddev": [self.get_stddev()],
}
)
else:
return pd.DataFrame({"bin": [self.name], "count": [int(self._count_)]})
__all__ = ["Coverbin"]