Source code for hermespy.modem.bits_source

# -*- coding: utf-8 -*-

from __future__ import annotations
from abc import ABC, abstractmethod
from math import ceil
from typing import Optional
from io import BufferedReader

import numpy as np

from hermespy.core.factory import Serializable
from hermespy.core.random_node import RandomNode

__author__ = "Andre Noll Barreto"
__copyright__ = "Copyright 2024, Barkhausen Institut gGmbH"
__credits__ = ["Andre Noll Barreto", "Tobias Kronauer", "Jan Adler"]
__license__ = "AGPLv3"
__version__ = "1.4.0"
__maintainer__ = "Jan Adler"
__email__ = "jan.adler@barkhauseninstitut.org"
__status__ = "Prototype"


[docs] class BitsSource(ABC, RandomNode): """Base Class for Arbitrary Streams of Communication Bits. Inheriting classes are required to implement the :meth:`.generate_bits` routine. """ def __init__(self, seed: Optional[int] = None) -> None: """ Args: seed (int, optional): Seed used to initialize the pseudo-random number generator. """ RandomNode.__init__(self, seed=seed)
[docs] @abstractmethod def generate_bits(self, num_bits: int) -> np.ndarray: """Generate a new sequence of bits. Args: num_bits (int): Number of bits to be generated. Returns: np.ndarray: A numpy vector of `num_bits` generated bits. """ ... # pragma: no cover
[docs] class RandomBitsSource(BitsSource, Serializable): """Bit stream generator for pseudo-random sequences of bits.""" yaml_tag = "RandomBits" def __init__(self, seed: Optional[int] = None) -> None: """ Args: seed (int, optional): Seed used to initialize the pseudo-random number generator. """ # Initialize base classes BitsSource.__init__(self, seed=seed)
[docs] def generate_bits(self, num_bits: int) -> np.ndarray: return self._rng.integers(0, 2, size=num_bits, dtype=int)
[docs] class StreamBitsSource(BitsSource, Serializable): """Bit-stream generator mapping representing file system streams as bit sources.""" __stream: BufferedReader def __init__(self, path: str) -> None: """ Args: path (str): Path to the stream bits source. """ BitsSource.__init__(self) self.__stream = open(path, mode="rb") def __del__(self) -> None: self.__stream.close()
[docs] def generate_bits(self, num_bits: int) -> np.ndarray: num_bytes = int(ceil(num_bits / 8)) bit_overflow = num_bytes * 8 - num_bits if bit_overflow > 0: raise RuntimeError("Bit caching not yet supported") byte_string = self.__stream.read(num_bytes) array = np.unpackbits(np.frombuffer(byte_string, dtype=np.uint8)) return array