kopia lustrzana https://github.com/corrscope/corrscope
Merge pull request #162 from nyanpasu64/pitch-invariant-trigger
Add pitch-tracking trigger, set trigger_diameter=None (improves bass)pull/357/head
nyanpasu64
GitHub
commit
bf4a13b329
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@ -20,6 +20,7 @@ from corrscope.triggers import (
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CorrelationTriggerConfig,
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PerFrameCache,
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CorrelationTrigger,
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SpectrumConfig,
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)
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from corrscope.util import pushd, coalesce
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from corrscope.wave import Wave, Flatten
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@ -85,7 +86,7 @@ class Config(
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trigger_stereo: Flatten = Flatten.SumAvg
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render_stereo: Flatten = Flatten.SumAvg
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trigger: ITriggerConfig # Can be overriden per Wave
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trigger: CorrelationTriggerConfig # Can be overriden per Wave
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# Multiplies by trigger_width, render_width. Can override trigger.
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channels: List[ChannelConfig]
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@ -118,6 +119,7 @@ def default_config(**kwargs) -> Config:
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responsiveness=0.5,
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buffer_falloff=0.5,
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use_edge_trigger=False,
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pitch_tracking=SpectrumConfig()
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# Removed due to speed hit.
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# post=LocalPostTriggerConfig(strength=0.1),
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),
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@ -263,13 +265,9 @@ class CorrScope:
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self.renderer.render_frame(datas)
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self.output.write_frame(self.renderer.get_frame())
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extra_outputs.window = None
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if "window" in internals:
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extra_outputs.window = RenderOutput()
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extra_outputs.buffer = None
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if "buffer" in internals:
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extra_outputs.buffer = RenderOutput()
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extra_outputs.window = RenderOutput() if "window" in internals else None
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extra_outputs.buffer = RenderOutput() if "buffer" in internals else None
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extra_outputs.spectrum = RenderOutput() if "spectrum" in internals else None
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# endregion
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if PRINT_TIMESTAMP:
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@ -331,17 +329,22 @@ class CorrScope:
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continue
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# region Display buffers, for debugging purposes.
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triggers = cast(List[CorrelationTrigger], self.triggers)
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if extra_outputs.window:
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triggers = cast(List[CorrelationTrigger], self.triggers)
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extra_outputs.window.render_frame(
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[trigger._prev_window for trigger in triggers]
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)
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if extra_outputs.buffer:
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triggers = cast(List[CorrelationTrigger], self.triggers)
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extra_outputs.buffer.render_frame(
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[trigger._buffer for trigger in triggers]
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)
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if extra_outputs.spectrum:
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extra_outputs.spectrum.render_frame(
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[trigger._spectrum - 0.99 for trigger in triggers]
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)
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# endregion
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if not_benchmarking or benchmark_mode >= BenchmarkMode.RENDER:
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@ -38,7 +38,7 @@ from corrscope.gui.util import color2hex, Locked, find_ranges, TracebackDialog
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from corrscope.layout import Orientation, StereoOrientation
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from corrscope.outputs import IOutputConfig, FFplayOutputConfig, FFmpegOutputConfig
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from corrscope.settings import paths
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from corrscope.triggers import CorrelationTriggerConfig, ITriggerConfig
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from corrscope.triggers import CorrelationTriggerConfig, ITriggerConfig, SpectrumConfig
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from corrscope.util import obj_name
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from corrscope.wave import Flatten
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@ -676,6 +676,17 @@ class ConfigModel(PresentationModel):
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render__line_width = default_property("render__line_width", 1.5)
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@property
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def trigger__pitch_tracking(self) -> bool:
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scfg = self.cfg.trigger.pitch_tracking
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gui = scfg is not None
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return gui
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@trigger__pitch_tracking.setter
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def trigger__pitch_tracking(self, gui: bool):
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scfg = SpectrumConfig() if gui else None
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self.cfg.trigger.pitch_tracking = scfg
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# End ConfigModel
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@ -550,6 +550,13 @@
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</property>
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</widget>
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</item>
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<item row="0" column="3" rowspan="2">
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<widget class="BoundCheckBox" name="trigger__pitch_tracking">
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<property name="text">
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<string>Pitch Tracking</string>
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</property>
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</widget>
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</item>
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</layout>
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</widget>
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</item>
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@ -1,6 +1,18 @@
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import warnings
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from abc import ABC, abstractmethod
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from typing import TYPE_CHECKING, Type, Tuple, Optional, ClassVar, Callable, Union
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from typing import (
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TYPE_CHECKING,
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Type,
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Tuple,
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Optional,
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ClassVar,
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Callable,
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Union,
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NewType,
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Sequence,
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List,
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Any,
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)
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import attr
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import numpy as np
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@ -105,10 +117,183 @@ class PerFrameCache:
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# CorrelationTrigger
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class CorrelationTriggerConfig(ITriggerConfig):
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class SpectrumConfig(KeywordAttrs):
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"""
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# Rationale:
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If no basal frequency note-bands are to be truncated,
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the spectrum must have freq resolution
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`min_hz * (2 ** 1/notes_per_octave - 1)`.
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At 20hz, 10 octaves, 12 notes/octave, this is 1.19Hz fft freqs.
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Our highest band must be
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`min_hz * 2**octaves`,
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leading to nearly 20K freqs, which produces an somewhat slow FFT.
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So increase min_hz and decrease octaves and notes_per_octave.
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--------
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Using a Constant-Q transform may eliminate performance concerns?
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"""
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# Spectrum X density
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min_hz: float = 20
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octaves: int = 8
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notes_per_octave: int = 6
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# Spectrum Y power
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exponent: float = 1
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divide_by_freq: bool = True
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# Spectral alignment and resampling
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pitch_estimate_boost: float = 1.2
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max_octaves_to_resample: float = 1.0
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@property
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def max_notes_to_resample(self) -> int:
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return round(self.notes_per_octave * self.max_octaves_to_resample)
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# Time-domain history parameters
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min_frames_between_recompute: int = 6
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frames_to_lookbehind: int = 2
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class DummySpectrum:
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# noinspection PyMethodMayBeStatic,PyUnusedLocal
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def calc_spectrum(self, data: np.ndarray) -> np.ndarray:
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return np.array([])
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# Indices are linearly spaced in FFT. Notes are exponentially spaced.
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# FFT is grouped into notes.
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FFTIndex = NewType("FFTIndex", int)
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# Very hacky and weird. Maybe it's not worth getting mypy to pass.
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if TYPE_CHECKING:
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FFTIndexArray = Any # mypy
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else:
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FFTIndexArray = "np.ndarray[FFTIndex]" # pycharm
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class LogFreqSpectrum(DummySpectrum):
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"""
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Invariants:
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- len(note_fenceposts) == n_fencepost
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- rfft()[ : note_fenceposts[0]] is NOT used.
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- rfft()[note_fenceposts[-1] : ] is NOT used.
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- rfft()[note_fenceposts[0] : note_fenceposts[1]] becomes a note.
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"""
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n_fftindex: FFTIndex # Determines frequency resolution, not range.
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note_fenceposts: FFTIndexArray
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n_fencepost: int
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def __init__(self, scfg: SpectrumConfig, subsmp_s: float, dummy_data: np.ndarray):
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self.scfg = scfg
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n_fftindex: FFTIndex = signal.next_fast_len(len(dummy_data))
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# Increase n_fftindex until every note has nonzero width.
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while True:
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# Compute parameters
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self.min_hz = scfg.min_hz
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self.max_hz = self.min_hz * 2 ** scfg.octaves
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n_fencepost = scfg.notes_per_octave * scfg.octaves + 1
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note_fenceposts_hz = np.geomspace(
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self.min_hz, self.max_hz, n_fencepost, dtype=FLOAT
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)
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# Convert fenceposts to FFTIndex
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fft_from_hertz = n_fftindex / subsmp_s
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note_fenceposts: FFTIndexArray = (
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fft_from_hertz * note_fenceposts_hz
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).astype(np.int32)
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note_widths = np.diff(note_fenceposts)
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if np.any(note_widths == 0):
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n_fftindex = signal.next_fast_len(n_fftindex + n_fftindex // 5 + 1)
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continue
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else:
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break
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self.n_fftindex = n_fftindex # Passed to rfft() to automatically zero-pad data.
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self.note_fenceposts = note_fenceposts
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self.n_fencepost = len(note_fenceposts)
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def calc_spectrum(self, data: np.ndarray) -> np.ndarray:
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""" Unfortunately converting to FLOAT (single) adds too much overhead.
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Input: Time-domain signal to be analyzed.
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Output: Frequency-domain spectrum with exponentially-spaced notes.
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- ret[note] = nonnegative float.
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"""
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scfg = self.scfg
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# Compute FFT spectrum[freq]
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spectrum = np.fft.rfft(data, self.n_fftindex)
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spectrum = abs(spectrum)
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if scfg.exponent != 1:
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spectrum **= scfg.exponent
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# Compute energy of each note
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# spectrum_per_note[note] = np.ndarray[float]
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spectrum_per_note: List[np.ndarray] = split(spectrum, self.note_fenceposts)
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# energy_per_note[note] = float
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energy_per_note: np.ndarray
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# np.add.reduce is much faster than np.sum/mean.
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if scfg.divide_by_freq:
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energy_per_note = np.array(
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[np.add.reduce(region) / len(region) for region in spectrum_per_note]
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)
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else:
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energy_per_note = np.array(
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[np.add.reduce(region) for region in spectrum_per_note]
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)
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assert len(energy_per_note) == self.n_fencepost - 1
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return energy_per_note
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def split(data: np.ndarray, fenceposts: Sequence[FFTIndex]) -> List[np.ndarray]:
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""" Based off np.split(), but faster.
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Unlike np.split, does not include data before fenceposts[0] or after fenceposts[-1].
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"""
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sub_arys = []
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ndata = len(data)
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for i in range(len(fenceposts) - 1):
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st = fenceposts[i]
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end = fenceposts[i + 1]
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if not st < ndata:
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break
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region = data[st:end]
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sub_arys.append(region)
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return sub_arys
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class CircularArray:
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def __init__(self, size: int, *dims: int):
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self.size = size
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self.buf = np.zeros((size, *dims))
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self.index = 0
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def push(self, arr: np.ndarray) -> None:
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if self.size == 0:
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return
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self.buf[self.index] = arr
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self.index = (self.index + 1) % self.size
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def peek(self) -> np.ndarray:
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"""Return is borrowed from self.buf.
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Do NOT push to self while borrow is alive."""
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return self.buf[self.index]
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class CorrelationTriggerConfig(ITriggerConfig, always_dump="pitch_tracking"):
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# get_trigger
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edge_strength: float
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trigger_diameter: float = 0.5
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trigger_diameter: Optional[float] = None
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trigger_falloff: Tuple[float, float] = (4.0, 1.0)
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recalc_semitones: float = 1.0
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@ -118,6 +303,9 @@ class CorrelationTriggerConfig(ITriggerConfig):
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responsiveness: float
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buffer_falloff: float # Gaussian std = wave_period * buffer_falloff
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# Pitch tracking = compute spectrum.
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pitch_tracking: Optional["SpectrumConfig"] = None
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# region Legacy Aliases
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trigger_strength = Alias("edge_strength")
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falloff_width = Alias("buffer_falloff")
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@ -152,6 +340,10 @@ class CorrelationTriggerConfig(ITriggerConfig):
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class CorrelationTrigger(Trigger):
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cfg: CorrelationTriggerConfig
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@property
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def scfg(self) -> SpectrumConfig:
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return self.cfg.pitch_tracking
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def __init__(self, *args, **kwargs):
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"""
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Correlation-based trigger which looks at a window of `trigger_tsamp` samples.
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@ -181,6 +373,24 @@ class CorrelationTrigger(Trigger):
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self._prev_period: Optional[int] = None
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self._prev_window: Optional[np.ndarray] = None
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# (mutable) Log-scaled spectrum
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self.frames_since_spectrum = 0
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if self.scfg:
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self._spectrum_calc = LogFreqSpectrum(
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scfg=self.scfg,
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subsmp_s=self._wave.smp_s / self._stride,
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dummy_data=self._buffer,
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)
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self._spectrum = self._spectrum_calc.calc_spectrum(self._buffer)
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self.history = CircularArray(
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self.scfg.frames_to_lookbehind, self._buffer_nsamp
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)
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else:
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self._spectrum_calc = DummySpectrum()
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self._spectrum = np.array([0])
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self.history = CircularArray(0, self._buffer_nsamp)
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def _calc_data_taper(self) -> np.ndarray:
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""" Input data window. Zeroes out all data older than 1 frame old.
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See https://github.com/nyanpasu64/corrscope/wiki/Correlation-Trigger
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@ -242,6 +452,7 @@ class CorrelationTrigger(Trigger):
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# begin per-frame
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def get_trigger(self, index: int, cache: "PerFrameCache") -> int:
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N = self._buffer_nsamp
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cfg = self.cfg
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# Get data
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stride = self._stride
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@ -253,50 +464,39 @@ class CorrelationTrigger(Trigger):
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period = get_period(data)
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cache.period = period * stride
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if self._is_window_invalid(period):
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diameter, falloff = [round(period * x) for x in self.cfg.trigger_falloff]
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semitones = self._is_window_invalid(period)
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# If pitch changed...
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if semitones:
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diameter, falloff = [round(period * x) for x in cfg.trigger_falloff]
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falloff_window = cosine_flat(N, diameter, falloff)
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window = np.minimum(falloff_window, self._data_taper)
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# If pitch tracking enabled, rescale buffer to match data's pitch.
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if self.scfg and (data != 0).any():
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if isinstance(semitones, float):
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peak_semitones = semitones
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else:
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peak_semitones = None
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self.spectrum_rescale_buffer(data, peak_semitones)
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self._prev_period = period
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self._prev_window = window
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else:
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window = self._prev_window
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self.history.push(data)
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data *= window
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# prev_buffer
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prev_buffer = self._windowed_step + self._buffer
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prev_buffer: np.ndarray = self._buffer.copy()
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prev_buffer += self._windowed_step
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# Calculate correlation
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"""
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If offset < optimal, we need to `offset += positive`.
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- The peak will appear near the right of `data`.
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if self.cfg.trigger_diameter is not None:
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radius = round(N * self.cfg.trigger_diameter / 2)
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else:
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radius = None
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Either we must slide prev_buffer to the right:
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- correlate(data, prev_buffer)
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- trigger = offset + peak_offset
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Or we must slide data to the left (by sliding offset to the right):
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- correlate(prev_buffer, data)
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- trigger = offset - peak_offset
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"""
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corr = signal.correlate(data, prev_buffer) # returns double, not single/FLOAT
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assert len(corr) == 2 * N - 1
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# Find optimal offset (within trigger_diameter, default=±N/4)
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mid = N - 1
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radius = round(N * self.cfg.trigger_diameter / 2)
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left = mid - radius
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right = mid + radius + 1
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corr = corr[left:right]
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mid = mid - left
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# argmax(corr) == mid + peak_offset == (data >> peak_offset)
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# peak_offset == argmax(corr) - mid
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peak_offset = np.argmax(corr) - mid # type: int
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peak_offset = self.correlate_offset(data, prev_buffer, radius)
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trigger = index + (stride * peak_offset)
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# Apply post trigger (before updating correlation buffer)
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@ -306,11 +506,107 @@ class CorrelationTrigger(Trigger):
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# Update correlation buffer (distinct from visible area)
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aligned = self._wave.get_around(trigger, self._buffer_nsamp, stride)
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self._update_buffer(aligned, cache)
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self.frames_since_spectrum += 1
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return trigger
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def _is_window_invalid(self, period: int) -> bool:
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""" Returns True if pitch has changed more than `recalc_semitones`. """
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def spectrum_rescale_buffer(
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self, data: np.ndarray, peak_semitones: Optional[float]
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) -> None:
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"""Rewrites self._spectrum, and possibly rescales self._buffer."""
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scfg = self.scfg
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N = self._buffer_nsamp
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if self.frames_since_spectrum < self.scfg.min_frames_between_recompute:
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return
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self.frames_since_spectrum = 0
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spectrum = self._spectrum_calc.calc_spectrum(data)
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normalize_buffer(spectrum)
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# Don't normalize self._spectrum. It was already normalized when being assigned.
|
||||
prev_spectrum = self._spectrum_calc.calc_spectrum(self.history.peek())
|
||||
|
||||
# rewrite spectrum
|
||||
self._spectrum = spectrum
|
||||
|
||||
assert not np.any(np.isnan(spectrum))
|
||||
|
||||
# Find spectral correlation peak,
|
||||
# but prioritize "changing pitch by ???".
|
||||
if peak_semitones is not None:
|
||||
boost_x = int(round(peak_semitones / 12 * scfg.notes_per_octave))
|
||||
boost_y: float = scfg.pitch_estimate_boost
|
||||
else:
|
||||
boost_x = 0
|
||||
boost_y = 1.0
|
||||
|
||||
# If we want to double pitch...
|
||||
resample_notes = self.correlate_offset(
|
||||
spectrum,
|
||||
prev_spectrum,
|
||||
scfg.max_notes_to_resample,
|
||||
boost_x=boost_x,
|
||||
boost_y=boost_y,
|
||||
)
|
||||
if resample_notes != 0:
|
||||
# we must divide sampling rate by 2.
|
||||
new_len = int(round(N / 2 ** (resample_notes / scfg.notes_per_octave)))
|
||||
|
||||
# Copy+resample self._buffer.
|
||||
self._buffer = np.interp(
|
||||
np.linspace(0, 1, new_len), np.linspace(0, 1, N), self._buffer
|
||||
)
|
||||
# assert len(self._buffer) == new_len
|
||||
self._buffer = midpad(self._buffer, N)
|
||||
|
||||
@staticmethod
|
||||
def correlate_offset(
|
||||
data: np.ndarray,
|
||||
prev_buffer: np.ndarray,
|
||||
radius: Optional[int],
|
||||
boost_x: int = 0,
|
||||
boost_y: float = 1.0,
|
||||
) -> int:
|
||||
"""
|
||||
This is confusing.
|
||||
|
||||
If data index < optimal, data will be too far to the right,
|
||||
and we need to `index += positive`.
|
||||
- The peak will appear near the right of `data`.
|
||||
|
||||
Either we must slide prev_buffer to the right,
|
||||
or we must slide data to the left (by sliding index to the right):
|
||||
- correlate(data, prev_buffer)
|
||||
- trigger = index + peak_offset
|
||||
"""
|
||||
N = len(data)
|
||||
corr = signal.correlate(data, prev_buffer) # returns double, not single/FLOAT
|
||||
Ncorr = 2 * N - 1
|
||||
assert len(corr) == Ncorr
|
||||
|
||||
# Find optimal offset
|
||||
mid = N - 1
|
||||
|
||||
if radius is not None:
|
||||
left = max(mid - radius, 0)
|
||||
right = min(mid + radius + 1, Ncorr)
|
||||
|
||||
corr = corr[left:right]
|
||||
mid = mid - left
|
||||
|
||||
# Prioritize part of it.
|
||||
corr[mid + boost_x : mid + boost_x + 1] *= boost_y
|
||||
|
||||
# argmax(corr) == mid + peak_offset == (data >> peak_offset)
|
||||
# peak_offset == argmax(corr) - mid
|
||||
peak_offset = np.argmax(corr) - mid # type: int
|
||||
return peak_offset
|
||||
|
||||
def _is_window_invalid(self, period: int) -> Union[bool, float]:
|
||||
""" Returns number of semitones,
|
||||
if pitch has changed more than `recalc_semitones`. """
|
||||
|
||||
prev = self._prev_period
|
||||
|
||||
|
|
@ -319,12 +615,12 @@ class CorrelationTrigger(Trigger):
|
|||
elif prev * period == 0:
|
||||
return prev != period
|
||||
else:
|
||||
semitones = abs(np.log(period / prev) / np.log(2) * 12)
|
||||
|
||||
# If period doubles, semitones are -12.
|
||||
semitones = np.log(period / prev) / np.log(2) * -12
|
||||
# If semitones == recalc_semitones == 0, do NOT recalc.
|
||||
if semitones <= self.cfg.recalc_semitones:
|
||||
if abs(semitones) <= self.cfg.recalc_semitones:
|
||||
return False
|
||||
return True
|
||||
return semitones
|
||||
|
||||
def _update_buffer(self, data: np.ndarray, cache: PerFrameCache) -> None:
|
||||
"""
|
||||
|
|
|
|||
|
|
@ -94,6 +94,14 @@ version = "0.10.0"
|
|||
[package.dependencies]
|
||||
six = "*"
|
||||
|
||||
[[package]]
|
||||
category = "dev"
|
||||
description = "Better living through Python with decorators"
|
||||
name = "decorator"
|
||||
optional = false
|
||||
python-versions = ">=2.6, !=3.0.*, !=3.1.*"
|
||||
version = "4.3.2"
|
||||
|
||||
[[package]]
|
||||
category = "dev"
|
||||
description = "Delayed/soft assertions for python"
|
||||
|
|
@ -268,6 +276,17 @@ py = ">=1.5.0"
|
|||
setuptools = "*"
|
||||
six = ">=1.10.0"
|
||||
|
||||
[[package]]
|
||||
category = "dev"
|
||||
description = "Separate test code from test cases in pytest."
|
||||
name = "pytest-cases"
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
version = "1.2.2"
|
||||
|
||||
[package.dependencies]
|
||||
decorator = "*"
|
||||
|
||||
[[package]]
|
||||
category = "dev"
|
||||
description = "Pytest plugin for measuring coverage."
|
||||
|
|
@ -350,7 +369,7 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, <4"
|
|||
version = "1.24.1"
|
||||
|
||||
[metadata]
|
||||
content-hash = "5f805ebc68759139df36aae10a903fd9d347b2f23248eb32182c5b48b48e2a1c"
|
||||
content-hash = "ba2ed55facdab2c93972f4a3cbf732de4ed2b709eb376a204ab563aa0d264272"
|
||||
python-versions = "^3.6"
|
||||
|
||||
[metadata.hashes]
|
||||
|
|
@ -365,6 +384,7 @@ codecov = ["8ed8b7c6791010d359baed66f84f061bba5bd41174bf324c31311e8737602788", "
|
|||
colorama = ["05eed71e2e327246ad6b38c540c4a3117230b19679b875190486ddd2d721422d", "f8ac84de7840f5b9c4e3347b3c1eaa50f7e49c2b07596221daec5edaabbd7c48"]
|
||||
coverage = ["06123b58a1410873e22134ca2d88bd36680479fe354955b3579fb8ff150e4d27", "09e47c529ff77bf042ecfe858fb55c3e3eb97aac2c87f0349ab5a7efd6b3939f", "0a1f9b0eb3aa15c990c328535655847b3420231af299386cfe5efc98f9c250fe", "0cc941b37b8c2ececfed341444a456912e740ecf515d560de58b9a76562d966d", "0d34245f824cc3140150ab7848d08b7e2ba67ada959d77619c986f2062e1f0e8", "10e8af18d1315de936d67775d3a814cc81d0747a1a0312d84e27ae5610e313b0", "1b4276550b86caa60606bd3572b52769860a81a70754a54acc8ba789ce74d607", "1e8a2627c48266c7b813975335cfdea58c706fe36f607c97d9392e61502dc79d", "258b21c5cafb0c3768861a6df3ab0cfb4d8b495eee5ec660e16f928bf7385390", "2b224052bfd801beb7478b03e8a66f3f25ea56ea488922e98903914ac9ac930b", "3ad59c84c502cd134b0088ca9038d100e8fb5081bbd5ccca4863f3804d81f61d", "447c450a093766744ab53bf1e7063ec82866f27bcb4f4c907da25ad293bba7e3", "46101fc20c6f6568561cdd15a54018bb42980954b79aa46da8ae6f008066a30e", "4710dc676bb4b779c4361b54eb308bc84d64a2fa3d78e5f7228921eccce5d815", "510986f9a280cd05189b42eee2b69fecdf5bf9651d4cd315ea21d24a964a3c36", "5535dda5739257effef56e49a1c51c71f1d37a6e5607bb25a5eee507c59580d1", "5a7524042014642b39b1fcae85fb37556c200e64ec90824ae9ecf7b667ccfc14", "5f55028169ef85e1fa8e4b8b1b91c0b3b0fa3297c4fb22990d46ff01d22c2d6c", "6694d5573e7790a0e8d3d177d7a416ca5f5c150742ee703f3c18df76260de794", "6831e1ac20ac52634da606b658b0b2712d26984999c9d93f0c6e59fe62ca741b", "71afc1f5cd72ab97330126b566bbf4e8661aab7449f08895d21a5d08c6b051ff", "7349c27128334f787ae63ab49d90bf6d47c7288c63a0a5dfaa319d4b4541dd2c", "77f0d9fa5e10d03aa4528436e33423bfa3718b86c646615f04616294c935f840", "828ad813c7cdc2e71dcf141912c685bfe4b548c0e6d9540db6418b807c345ddd", "859714036274a75e6e57c7bab0c47a4602d2a8cfaaa33bbdb68c8359b2ed4f5c", "85a06c61598b14b015d4df233d249cd5abfa61084ef5b9f64a48e997fd829a82", "869ef4a19f6e4c6987e18b315721b8b971f7048e6eaea29c066854242b4e98d9", "8cb4febad0f0b26c6f62e1628f2053954ad2c555d67660f28dfb1b0496711952", "977e2d9a646773cc7428cdd9a34b069d6ee254fadfb4d09b3f430e95472f3cf3", "99bd767c49c775b79fdcd2eabff405f1063d9d959039c0bdd720527a7738748a", "a5c58664b23b248b16b96253880b2868fb34358911400a7ba39d7f6399935389", "aaa0f296e503cda4bc07566f592cd7a28779d433f3a23c48082af425d6d5a78f", "ab235d9fe64833f12d1334d29b558aacedfbca2356dfb9691f2d0d38a8a7bfb4", "b3b0c8f660fae65eac74fbf003f3103769b90012ae7a460863010539bb7a80da", "bab8e6d510d2ea0f1d14f12642e3f35cefa47a9b2e4c7cea1852b52bc9c49647", "c45297bbdbc8bb79b02cf41417d63352b70bcb76f1bbb1ee7d47b3e89e42f95d", "d19bca47c8a01b92640c614a9147b081a1974f69168ecd494687c827109e8f42", "d64b4340a0c488a9e79b66ec9f9d77d02b99b772c8b8afd46c1294c1d39ca478", "da969da069a82bbb5300b59161d8d7c8d423bc4ccd3b410a9b4d8932aeefc14b", "ed02c7539705696ecb7dc9d476d861f3904a8d2b7e894bd418994920935d36bb", "ee5b8abc35b549012e03a7b1e86c09491457dba6c94112a2482b18589cc2bdb9"]
|
||||
cycler = ["1d8a5ae1ff6c5cf9b93e8811e581232ad8920aeec647c37316ceac982b08cb2d", "cd7b2d1018258d7247a71425e9f26463dfb444d411c39569972f4ce586b0c9d8"]
|
||||
decorator = ["33cd704aea07b4c28b3eb2c97d288a06918275dac0ecebdaf1bc8a48d98adb9e", "cabb249f4710888a2fc0e13e9a16c343d932033718ff62e1e9bc93a9d3a9122b"]
|
||||
delayed-assert = ["02eae58d56b9b8e3a72f890b85c1eb530184e25a65689e43889d4e9995cd42e4", "ecfffa0eba4980606739475480c4722330eb44a828e3b9cfa9a4deed4104b7d1"]
|
||||
future = ["67045236dcfd6816dc439556d009594abf643e5eb48992e36beac09c2ca659b8"]
|
||||
hypothesis = ["25023a6610a97e48bae511e71227a42293853432906e5a13403015e51b69e27c", "2b1ec609a1e357fd89e98dc59864f78f112dd58893c976e6b744caae83e6c494", "b0f0f3cca1954847a96b54d73345b3e5590739e235baa5470033d84f99dcd62d"]
|
||||
|
|
@ -382,6 +402,7 @@ pyparsing = ["40856e74d4987de5d01761a22d1621ae1c7f8774585acae358aa5c5936c6c90b",
|
|||
pyqt5 = ["517e4339135c4874b799af0d484bc2e8c27b54850113a68eec40a0b56534f450", "ac1eb5a114b6e7788e8be378be41c5e54b17d5158994504e85e43b5fca006a39", "d2309296a5a79d0a1c0e6c387c30f0398b65523a6dcc8a19cc172e46b949e00d", "e85936bae1581bcb908847d2038e5b34237a5e6acc03130099a78930770e7ead"]
|
||||
pyqt5-sip = ["125f77c087572c9272219cda030a63c2f996b8507592b2a54d7ef9b75f9f054d", "14c37b06e3fb7c2234cb208fa461ec4e62b4ba6d8b32ca3753c0b2cfd61b00e3", "1cb2cf52979f9085fc0eab7e0b2438eb4430d4aea8edec89762527e17317175b", "4babef08bccbf223ec34464e1ed0a23caeaeea390ca9a3529227d9a57f0d6ee4", "53cb9c1208511cda0b9ed11cffee992a5a2f5d96eb88722569b2ce65ecf6b960", "549449d9461d6c665cbe8af4a3808805c5e6e037cd2ce4fd93308d44a049bfac", "5f5b3089b200ff33de3f636b398e7199b57a6b5c1bb724bdb884580a072a14b5", "a4d9bf6e1fa2dd6e73f1873f1a47cee11a6ba0cf9ba8cf7002b28c76823600d0", "a4ee6026216f1fbe25c8847f9e0fbce907df5b908f84816e21af16ec7666e6fe", "a91a308a5e0cc99de1e97afd8f09f46dd7ca20cfaa5890ef254113eebaa1adff", "b0342540da479d2713edc68fb21f307473f68da896ad5c04215dae97630e0069", "f997e21b4e26a3397cb7b255b8d1db5b9772c8e0c94b6d870a5a0ab5c27eacaa"]
|
||||
pytest = ["3e65a22eb0d4f1bdbc1eacccf4a3198bf8d4049dea5112d70a0c61b00e748d02", "5924060b374f62608a078494b909d341720a050b5224ff87e17e12377486a71d"]
|
||||
pytest-cases = ["7f466da8058b1fcb371010ccad8e17efbd62c934c4a4ece5fee0defad270ce5a", "8baef91b47482d0febc3fd39a034d6add4ac5cfa2314a1dfb6ac847736412d1d"]
|
||||
pytest-cov = ["0ab664b25c6aa9716cbf203b17ddb301932383046082c081b9848a0edf5add33", "230ef817450ab0699c6cc3c9c8f7a829c34674456f2ed8df1fe1d39780f7c87f"]
|
||||
pytest-mock = ["53801e621223d34724926a5c98bd90e8e417ce35264365d39d6c896388dcc928", "d89a8209d722b8307b5e351496830d5cc5e192336003a485443ae9adeb7dd4c0"]
|
||||
python-dateutil = ["063df5763652e21de43de7d9e00ccf239f953a832941e37be541614732cdfc93", "88f9287c0174266bb0d8cedd395cfba9c58e87e5ad86b2ce58859bc11be3cf02"]
|
||||
|
|
|
|||
|
|
@ -31,6 +31,7 @@ pywin32-ctypes = {version = "^0.2.0",platform = "win32"}
|
|||
coverage = "^4.5"
|
||||
pytest-cov = "^2.6"
|
||||
codecov = "^2.0"
|
||||
pytest_cases = "^1.2"
|
||||
|
||||
[tool.poetry.scripts]
|
||||
corr = 'corrscope.cli:main'
|
||||
|
|
|
|||
|
|
@ -1,8 +1,10 @@
|
|||
import attr
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
import pytest
|
||||
from matplotlib.axes import Axes
|
||||
from matplotlib.figure import Figure
|
||||
from pytest_cases import pytest_fixture_plus
|
||||
|
||||
from corrscope import triggers
|
||||
from corrscope.triggers import (
|
||||
|
|
@ -11,6 +13,7 @@ from corrscope.triggers import (
|
|||
PerFrameCache,
|
||||
ZeroCrossingTriggerConfig,
|
||||
LocalPostTriggerConfig,
|
||||
SpectrumConfig,
|
||||
)
|
||||
from corrscope.wave import Wave
|
||||
|
||||
|
|
@ -25,10 +28,16 @@ def cfg_template(**kwargs) -> CorrelationTriggerConfig:
|
|||
return attr.evolve(cfg, **kwargs)
|
||||
|
||||
|
||||
@pytest.fixture(scope="session", params=[False, True])
|
||||
def cfg(request):
|
||||
use_edge_trigger = request.param
|
||||
return cfg_template(use_edge_trigger=use_edge_trigger)
|
||||
@pytest_fixture_plus
|
||||
@pytest.mark.parametrize("use_edge_trigger", [False, True])
|
||||
@pytest.mark.parametrize("trigger_diameter", [None, 0.5])
|
||||
@pytest.mark.parametrize("pitch_tracking", [None, SpectrumConfig()])
|
||||
def cfg(use_edge_trigger, trigger_diameter, pitch_tracking):
|
||||
return cfg_template(
|
||||
use_edge_trigger=use_edge_trigger,
|
||||
trigger_diameter=trigger_diameter,
|
||||
pitch_tracking=pitch_tracking,
|
||||
)
|
||||
|
||||
|
||||
@pytest.fixture(
|
||||
|
|
@ -177,6 +186,43 @@ def test_trigger_should_recalc_window():
|
|||
assert trigger._is_window_invalid(x), x
|
||||
|
||||
|
||||
# Test pitch-invariant triggering using spectrum
|
||||
def test_correlate_offset():
|
||||
"""
|
||||
Catches bug where writing N instead of Ncorr
|
||||
prevented function from returning positive numbers.
|
||||
"""
|
||||
|
||||
np.random.seed(31337)
|
||||
correlate_offset = CorrelationTrigger.correlate_offset
|
||||
|
||||
# Ensure autocorrelation on random data returns peak at 0.
|
||||
N = 100
|
||||
spectrum = np.random.random(N)
|
||||
assert correlate_offset(spectrum, spectrum, 12) == 0
|
||||
|
||||
# Ensure cross-correlation of time-shifted impulses works.
|
||||
# Assume wave where y=[i==99].
|
||||
wave = np.eye(N)[::-1]
|
||||
# Taking a slice beginning at index i will produce an impulse at 99-i.
|
||||
left = wave[30]
|
||||
right = wave[40]
|
||||
|
||||
# We need to slide `left` to the right by 10 samples, and vice versa.
|
||||
for radius in [None, 12]:
|
||||
assert correlate_offset(data=left, prev_buffer=right, radius=radius) == 10
|
||||
assert correlate_offset(data=right, prev_buffer=left, radius=radius) == -10
|
||||
|
||||
# The correlation peak at zero-offset is small enough for boost_x to be returned.
|
||||
boost_y = 1.5
|
||||
ones = np.ones(N)
|
||||
for boost_x in [6, -6]:
|
||||
assert (
|
||||
correlate_offset(ones, ones, radius=9, boost_x=boost_x, boost_y=boost_y)
|
||||
== boost_x
|
||||
)
|
||||
|
||||
|
||||
# Test the ability to load legacy TriggerConfig
|
||||
|
||||
|
||||
|
|
|
|||
Ładowanie…
Reference in New Issue