Removed/commented references to `numpy.typing`.

That module isn't available yet on the numpy version installed on Debian
`oldstable`.
This commit is contained in:
Fabio Manganiello 2023-10-23 22:24:49 +02:00
parent 193314fcd0
commit 23e53f13f0
7 changed files with 17 additions and 29 deletions

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@ -2,7 +2,6 @@ from abc import ABC, abstractmethod
from typing import Optional, Tuple
import numpy as np
from numpy.typing import NDArray
from ._parser import SoundParser
@ -31,7 +30,7 @@ class SoundBase(SoundParser, ABC):
t_start: float = 0,
t_end: float = 0,
**_,
) -> NDArray[np.floating]:
): # -> NDArray[np.floating]:
"""
Get the wave binary data associated to this sound
@ -50,7 +49,7 @@ class SoundBase(SoundParser, ABC):
t_end: float = 0.0,
freq_range: Optional[Tuple[float, float]] = None,
freq_buckets: Optional[int] = None,
) -> NDArray[np.floating]:
): # -> NDArray[np.floating]:
"""
Get the real part of the Fourier transform associated to a time-bounded
sample of this sound.

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@ -4,9 +4,6 @@ from threading import Thread
from time import time
from typing import Any, Callable, Optional
import numpy as np
from numpy.typing import NDArray
from ._mix import Mix
@ -20,7 +17,7 @@ class AudioGenerator(Thread):
def __init__(
self,
*args,
audio_queue: Queue[NDArray[np.number]],
audio_queue: Queue, # Queue[NDArray[np.number]],
mix: Mix,
blocksize: int,
sample_rate: int,

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@ -3,7 +3,6 @@ import logging
from typing import List, Tuple, Union
import numpy as np
from numpy.typing import DTypeLike, NDArray
from ...._utils import convert_nd_array
from ._base import SoundBase
@ -16,7 +15,7 @@ class Mix(SoundBase):
through an audio stream to an audio device
"""
def __init__(self, *sounds, channels: int, dtype: DTypeLike, **kwargs):
def __init__(self, *sounds, channels: int, dtype, **kwargs):
super().__init__(**kwargs)
self._sounds: List[Sound] = []
self.logger = logging.getLogger(__name__)
@ -64,7 +63,7 @@ class Mix(SoundBase):
normalize_range: Tuple[float, float] = (-1.0, 1.0),
on_clip: str = 'scale',
**_,
) -> NDArray[np.number]:
): # -> NDArray[np.number]:
wave = None
for sound in self._sounds:

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@ -4,9 +4,6 @@ from typing import Callable, Optional
import sounddevice as sd
import numpy as np
from numpy.typing import NDArray
# pylint: disable=too-few-public-methods
class AudioOutputCallback:
@ -18,7 +15,7 @@ class AudioOutputCallback:
def __init__(
self,
*args,
audio_queue: Queue[NDArray[np.number]],
audio_queue: Queue, # Queue[NDArray[np.number]],
channels: int,
blocksize: int,
should_stop: Callable[[], bool] = lambda: False,
@ -50,7 +47,8 @@ class AudioOutputCallback:
assert not status.output_underflow, 'Output underflow: increase blocksize?'
assert not status, f'Audio callback failed: {status}'
def _audio_callback(self, outdata: NDArray[np.number], frames: int, status):
# outdata: NDArray[np.number]
def _audio_callback(self, outdata, frames: int, status):
if self._is_paused():
return
@ -72,7 +70,8 @@ class AudioOutputCallback:
outdata[:audio_length] = data[:audio_length]
# _ = time
def __call__(self, outdata: NDArray[np.number], frames: int, _, status):
# outdata: NDArray[np.number]
def __call__(self, outdata, frames: int, _, status):
try:
self._audio_callback(outdata, frames, status)
except AssertionError as e:

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@ -3,9 +3,7 @@ from queue import Queue
from threading import Event
from typing import Any, Generator, Iterable, Optional, Type
import numpy as np
import sounddevice as sd
from numpy.typing import DTypeLike, NDArray
from ...._model import AudioState
from ..._player import AudioPlayer
@ -26,7 +24,7 @@ class AudioSynthPlayer(AudioPlayer):
*args,
volume: float,
channels: int,
dtype: DTypeLike,
dtype, # : DTypeLike,
sounds: Optional[Iterable[Sound]] = None,
**kwargs
):
@ -36,7 +34,7 @@ class AudioSynthPlayer(AudioPlayer):
super().__init__(*args, volume=volume, channels=channels, dtype=dtype, **kwargs)
self._generator_stopped = Event()
self._completed_callback_event = Event()
self._audio_queue: Queue[NDArray[np.number]] = Queue(
self._audio_queue = Queue( # Queue[NDArray[np.number]]
maxsize=self.queue_size or 0
)

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@ -1,9 +1,8 @@
from enum import Enum
import json
from typing import Final, Optional, Tuple, Union
from typing import Final, Optional, Union
import numpy as np
from numpy.typing import NDArray
from ._base import SoundBase
@ -103,7 +102,7 @@ class Sound(SoundBase):
def _get_audio_pad(
self, sample_rate: float, t_start: float, t_end: float
) -> Tuple[NDArray[np.floating], NDArray[np.floating]]:
): # -> Tuple[NDArray[np.floating], NDArray[np.floating]]:
"""
Return the left and right audio pads for a given audio length as a
``(left, right)`` tuple of numpy zero-filled arrays.
@ -120,7 +119,7 @@ class Sound(SoundBase):
)
)
def _generate_wave(self, x: NDArray[np.floating]):
def _generate_wave(self, x):
"""
Generate a raw audio wave as a numpy array of floating between -1 and 1
given ``x`` as a set of timestamp samples.
@ -149,7 +148,7 @@ class Sound(SoundBase):
t_start: float = 0,
t_end: float = 0,
**_,
) -> NDArray[np.floating]:
): # -> NDArray[np.floating]:
"""
Get the wave binary data associated to this sound

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@ -1,10 +1,7 @@
import numpy as np
from numpy.typing import DTypeLike, NDArray
def convert_nd_array( # pylint: disable=too-many-return-statements
wave: NDArray[np.floating], dtype: DTypeLike
) -> NDArray[np.number]:
def convert_nd_array(wave, dtype): # pylint: disable=too-many-return-statements
"""
Given a wave as a series of floating point numbers, convert them to the
appropriate data type.