kopia lustrzana https://github.com/corrscope/corrscope
Add custom stereo downmixing and GUI
nyanpasu64
rodzic
267d1387be
commit
0ed61ff7a4
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@ -832,6 +832,7 @@ class ChannelModel(qc.QAbstractTableModel):
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Column("wav_path", path_strip_quotes, "", "WAV Path"),
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Column("amplification", float, None, "Amplification\n(override)"),
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Column("line_color", str, None, "Line Color"),
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Column("render_stereo", str, None, "Render Stereo\nDownmix"),
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Column("trigger_width", int, None, "Trigger Width ×"),
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Column("render_width", int, None, "Render Width ×"),
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Column("trigger__buffer_strength", float, None),
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@ -871,7 +872,7 @@ class ChannelModel(qc.QAbstractTableModel):
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# data
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TRIGGER = "trigger__"
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def data(self, index: QModelIndex, role=Qt.DisplayRole) -> qc.QVariant:
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def data(self, index: QModelIndex, role=Qt.DisplayRole) -> Any:
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col = index.column()
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row = index.row()
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@ -174,9 +174,15 @@ class MainWindow(QWidget):
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with add_row(s, "", BoundComboBox) as self.trigger_stereo:
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pass
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with add_row(s, tr("Downmix"), BoundLineEdit, name="trigger_stereo"):
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pass
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with add_row(s, "", BoundComboBox) as self.render_stereo:
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pass
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with add_row(s, tr("Downmix"), BoundLineEdit, name="render_stereo"):
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pass
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with append_widget(s, QGroupBox) as self.dockStereo_2:
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set_layout(s, QFormLayout)
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@ -1,55 +1,114 @@
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import copy
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import enum
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import warnings
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from enum import auto
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from typing import Union, List
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import numpy as np
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import corrscope.utils.scipy.wavfile as wavfile
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from corrscope.config import CorrError, CorrWarning, TypedEnumDump
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from corrscope.config import CorrError, TypedEnumDump
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FLOAT = np.single
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# Depends on FLOAT
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from corrscope.utils.windows import rightpad
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@enum.unique
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class Flatten(TypedEnumDump):
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class Flatten(str, TypedEnumDump):
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""" How to flatten a stereo signal. (Channels beyond first 2 are ignored.)
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Flatten(0) == Flatten.Stereo == Flatten['Stereo']
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"""
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# Keep both channels.
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Stereo = 0
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Stereo = "stereo"
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# Mono
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Mono = auto() # NOT publicly exposed
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Mono = "1" # NOT publicly exposed
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# Take sum or difference.
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SumAvg = auto()
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DiffAvg = auto()
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SumAvg = "1 1"
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DiffAvg = "1, -1"
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def __str__(self):
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return self.value
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# Both our app and GUI treat:
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# - Flatten.SumAvg -> "sum of all channels"
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# - "1 1" -> "assert nchan == 2, left + right".
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# - "1 0" -> "assert nchan == 2, left".
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def __eq__(self, other):
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return self is other
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def __hash__(self):
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return hash(self.value)
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modes: List["Flatten"]
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assert "1" == str(Flatten.Mono)
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assert not "1" == Flatten.Mono
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assert not Flatten.Mono == "1"
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FlattenOrStr = Union[Flatten, str]
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def calc_flatten_matrix(flatten: FlattenOrStr, stereo_nchan: int) -> np.ndarray:
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""" Raises CorrError on invalid input.
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If flatten is Flatten.Stereo, returns shape=(nchan,nchan) identity matrix.
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- (N,nchan) @ (nchan,nchan) = (N,nchan).
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Otherwise, returns shape=(nchan) flattening matrix.
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- (N,nchan) @ (nchan) = (N)
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https://docs.scipy.org/doc/numpy/reference/generated/numpy.matmul.html#numpy.matmul
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'''
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If the second argument is 1-D,
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it is promoted to a matrix by appending a 1 to its dimensions.
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After matrix multiplication the appended 1 is removed."
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'''
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"""
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if flatten is Flatten.Stereo:
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# 2D identity (results in 2-dim data)
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flatten_matrix = np.eye(stereo_nchan, dtype=FLOAT)
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# 1D (results in 1-dim data)
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elif flatten is Flatten.SumAvg:
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flatten_matrix = np.ones(stereo_nchan, dtype=FLOAT) / stereo_nchan
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elif flatten is Flatten.DiffAvg:
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flatten_matrix = calc_flatten_matrix(str(flatten), stereo_nchan)
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flatten_matrix = rightpad(flatten_matrix, stereo_nchan, 0)
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else:
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words = flatten.replace(",", " ").split()
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try:
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flatten_matrix = np.array([FLOAT(word) for word in words])
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except ValueError as e:
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raise CorrError("Invalid stereo flattening matrix") from e
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flatten_abs_sum = np.sum(np.abs(flatten_matrix))
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if flatten_abs_sum == 0:
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raise CorrError("Stereo flattening matrix must have nonzero elements")
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flatten_matrix /= flatten_abs_sum
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assert flatten_matrix.dtype == FLOAT, flatten_matrix.dtype
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return flatten_matrix
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_rejected_modes = {Flatten.Mono}
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Flatten.modes = [f for f in Flatten.__members__.values() if f not in _rejected_modes]
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class Wave:
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__slots__ = """
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wave_path
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amplification offset
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smp_s data return_channels _flatten is_mono
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nsamp dtype
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center max_val
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""".split()
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smp_s: int
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data: "np.ndarray"
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"""2-D array of shape (nsamp, nchan)"""
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data: np.ndarray
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_flatten: Flatten
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_flatten: FlattenOrStr
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flatten_matrix: np.ndarray
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@property
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def flatten(self) -> Flatten:
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@ -64,13 +123,13 @@ class Wave:
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return self._flatten
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@flatten.setter
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def flatten(self, flatten: Flatten) -> None:
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def flatten(self, flatten: FlattenOrStr) -> None:
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# Reject invalid modes (including Mono).
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if flatten not in Flatten.modes: # type: ignore
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if flatten in _rejected_modes:
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# Flatten.Mono not in Flatten.modes.
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raise CorrError(
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f"Wave {self.wave_path} has invalid flatten mode {flatten} "
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f"not in {Flatten.modes}"
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f"not a numeric string, nor in {Flatten.modes}"
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)
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# If self.is_mono, converts all non-Stereo modes to Mono.
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@ -78,6 +137,8 @@ class Wave:
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if self.is_mono and flatten != Flatten.Stereo:
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self._flatten = Flatten.Mono
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self.flatten_matrix = calc_flatten_matrix(self._flatten, self.stereo_nchan)
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def __init__(
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self,
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wave_path: str,
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@ -87,28 +148,26 @@ class Wave:
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self.wave_path = wave_path
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self.amplification = amplification
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self.offset = 0
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# self.data: 2-D array of shape (nsamp, nchan)
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self.smp_s, self.data = wavfile.read(wave_path, mmap=True)
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assert self.data.ndim in [1, 2]
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self.is_mono = self.data.ndim == 1
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self.flatten = flatten
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self.return_channels = False
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# Cast self.data to stereo (nsamp, nchan)
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if self.is_mono:
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self.data.shape = (-1, 1)
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self.nsamp, stereo_nchan = self.data.shape
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if stereo_nchan > 2:
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warnings.warn(
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f"File {wave_path} has {stereo_nchan} channels, "
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f"only first 2 will be used",
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CorrWarning,
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)
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self.nsamp, self.stereo_nchan = self.data.shape
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dtype = self.data.dtype
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# Depends on self.stereo_nchan
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self.flatten = flatten
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# Calculate scaling factor.
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dtype = self.data.dtype
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def is_type(parent: type) -> bool:
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return np.issubdtype(dtype, parent)
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@ -152,16 +211,7 @@ class Wave:
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data: np.ndarray = self.data[index].astype(FLOAT, subok=False, copy=True)
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# Flatten stereo to mono.
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flatten = self._flatten # Potentially faster than property getter.
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if flatten == Flatten.Mono:
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data = data.reshape(-1) # ndarray.flatten() creates copy, is slow.
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elif flatten != Flatten.Stereo:
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# data.strides = (4,), so data == contiguous float32
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if flatten == Flatten.SumAvg:
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data = data[..., 0] + data[..., 1]
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else:
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data = data[..., 0] - data[..., 1]
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data /= 2
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data = data @ self.flatten_matrix
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data -= self.center
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data *= self.amplification / self.max_val
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@ -145,6 +145,7 @@ def test_config_channel_width_stride(
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[Flatten.Stereo, None],
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[Flatten.SumAvg, Flatten.Stereo],
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[Flatten.Stereo, Flatten.SumAvg],
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[Flatten.Stereo, "1 0"],
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],
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)
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def test_per_channel_stereo(
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@ -8,7 +8,7 @@ from delayed_assert import expect, assert_expectations
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from corrscope.config import CorrError
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from corrscope.utils.scipy.wavfile import WavFileWarning
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from corrscope.wave import Wave, Flatten
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from corrscope.wave import Wave, Flatten, calc_flatten_matrix
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prefix = "tests/wav-formats/"
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wave_paths = [
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@ -43,6 +43,55 @@ def test_wave(wave_path):
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# Stereo tests
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def arr(*args):
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return np.array(args)
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def test_calc_flatten_matrix():
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nchan = 3
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# Test Stereo
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np.testing.assert_equal(calc_flatten_matrix(Flatten.Stereo, nchan), np.eye(nchan))
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# Test SumAvg on various channel counts
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np.testing.assert_equal(calc_flatten_matrix(Flatten.SumAvg, 1), [1])
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np.testing.assert_equal(calc_flatten_matrix(Flatten.SumAvg, 2), [0.5, 0.5])
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np.testing.assert_equal(calc_flatten_matrix(Flatten.SumAvg, 4), [0.25] * 4)
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# Test DiffAvg on various channel counts
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# (Wave will use Mono instead of DiffAvg, on mono audio signals.
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# But ensure it doesn't crash anyway.)
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np.testing.assert_equal(calc_flatten_matrix(Flatten.DiffAvg, 1), [0.5])
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np.testing.assert_equal(calc_flatten_matrix(Flatten.DiffAvg, 2), [0.5, -0.5])
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np.testing.assert_equal(calc_flatten_matrix(Flatten.DiffAvg, 4), [0.5, -0.5, 0, 0])
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# Test Mono
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np.testing.assert_equal(calc_flatten_matrix(Flatten.Mono, 1), [1])
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# Test custom strings and delimiters
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out = arr(1, 2, 1)
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nchan = 3
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np.testing.assert_equal(calc_flatten_matrix(",1,2,1,", nchan), out / sum(out))
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np.testing.assert_equal(calc_flatten_matrix(" 1, 2, 1 ", nchan), out / sum(out))
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np.testing.assert_equal(calc_flatten_matrix("1 2 1", nchan), out / sum(out))
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# Test negative values
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nchan = 2
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np.testing.assert_equal(calc_flatten_matrix("1, -1", nchan), arr(1, -1) / 2)
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np.testing.assert_equal(calc_flatten_matrix("-1, 1", nchan), arr(-1, 1) / 2)
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np.testing.assert_equal(calc_flatten_matrix("-1, -1", nchan), arr(-1, -1) / 2)
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# Test invalid inputs
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with pytest.raises(CorrError):
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calc_flatten_matrix("", 0)
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with pytest.raises(CorrError):
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calc_flatten_matrix("1 -1 uwu", 3)
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with pytest.raises(CorrError):
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calc_flatten_matrix("0 0", 2)
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def test_stereo_merge():
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"""Test indexing Wave by slices *or* ints. Flatten using default SumAvg mode."""
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@ -75,7 +124,7 @@ def test_stereo_merge():
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check_bound(wave[:])
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AllFlattens = Flatten.__members__.values()
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AllFlattens = [*Flatten.__members__.values(), "1 1", "1 0", "1 -1"]
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@pytest.mark.parametrize("flatten", AllFlattens)
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@ -107,7 +156,7 @@ def test_stereo_flatten_modes(
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assert nchan == len(peaks)
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wave = Wave(path)
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if flatten not in Flatten.modes:
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if flatten is Flatten.Mono:
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with pytest.raises(CorrError):
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wave.with_flatten(flatten, return_channels)
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return
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@ -135,9 +184,9 @@ def test_stereo_flatten_modes(
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else:
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pass
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# If SumAvg, check average.
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else:
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assert flatten == Flatten.SumAvg
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elif flatten == Flatten.SumAvg:
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assert_full_scale(data, np.mean(peaks))
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# Don't test custom string modes for now.
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def assert_full_scale(data, peak):
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