Mikkeli Matlock
b400551321
Replace the fire-and-forget matplotlib pipeline (render() -> throwaway Figure -> canvas teardown) with a three-stage architecture that supports zoom/pan, lin/log toggling, and multi-file overlay: compute(audio_file) -> data # heavy, worker thread, backend-neutral build_spec(data, view) -> PlotSpec # cheap, GUI thread, view-aware show_specs([(label, spec, color)]) # pyqtgraph, persistent PlotItem, overlay - plotspec.py: backend-agnostic descriptors (Curve, Band, HLine, Heatmap, AxisSpec, PlotSpec) + ViewState (recompute-free lin/log) - audio_visualization_widget.py: persistent pyqtgraph plot, never torn down; per-dataset colours for overlay; spectrogram log-freq via row resample (ImageItem is affine-only); ColorBarItem at a fixed cell - Compare/overlay driven by file-list checkboxes; stable per-song colour by row - Custom draggable reference lines (add/clear), persist across redraws - Axis-constrained scroll zoom: Ctrl=time, Shift=value (_AxisZoomViewBox) - RMS render no longer per-segment fill_between (was the slow path) Fixes found in review/testing: - FillBetweenItem needs penned child curves or it fills nothing (RMS/Waveform were blank); band fill verified by pixel count - band overlay alpha was a no-op (QBrush.color() returns a copy) - colorbar could stack across renders; now added/removed at a fixed layout cell Deferred (per scope): stereo retention, deep perf rewrites (eager beat_track, true-peak/crest loops, shared LUFS), per-song colour picker UI. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
125 lines
3.9 KiB
Python
125 lines
3.9 KiB
Python
"""
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Backend-agnostic plot descriptors.
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A metric's `build_spec` turns precomputed data into a `PlotSpec`: a declarative
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description of *what* to draw (curves, reference lines, an optional heatmap) and
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*how the axes should behave* (labels, default scale, which lin/log toggles are
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legal). It says nothing about the plotting library, colours, or widget layout —
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that is the renderer's job.
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This seam is what makes overlay/compare cheap: drawing N datasets on one axis is
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"render N specs," and the renderer owns the colour cycle so overlaid curves stay
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distinct. It is also what makes lin/log a pure view toggle — `build_spec` takes a
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`ViewState`, so switching scale never touches `compute`.
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"""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from typing import Optional
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import numpy as np
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@dataclass
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class Curve:
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"""A single x/y line. Colour is assigned by the renderer for overlay distinctness."""
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x: np.ndarray
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y: np.ndarray
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label: str = ""
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width: float = 1.4
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# Explicit colour overrides the dataset colour cycle. Leave None for overlay.
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color: Optional[str] = None
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@dataclass
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class HLine:
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"""A horizontal reference line with an attached label.
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The label rides on the line itself (renderer places it), so reference markers
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no longer need anchoring at `times[-1]` — overlaid tracks of different lengths
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stop fighting over label position.
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"""
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y: float
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label: str = ""
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color: str = "#888888"
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style: str = "dot" # 'solid' | 'dash' | 'dot'
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width: float = 0.8
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@dataclass
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class Band:
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"""A filled envelope between `lo` and `hi` over `x` (RMS area, waveform min/max).
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One drawn primitive instead of thousands of per-segment fills, and overlay-safe:
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the renderer gives each dataset's band a translucent dataset colour.
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"""
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x: np.ndarray
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lo: np.ndarray # scalar-broadcast or per-x lower edge
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hi: np.ndarray # per-x upper edge
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label: str = ""
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color: Optional[str] = None
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@dataclass
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class Heatmap:
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"""A 2-D field (e.g. a spectrogram). Heatmaps do not overlay — at most one."""
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x: np.ndarray # column axis (time)
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y: np.ndarray # row axis (frequency), linear; renderer handles log
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z: np.ndarray # shape (len(y), len(x))
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z_min: float
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z_max: float
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cmap: str = "magma"
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label: str = "" # colourbar label
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@dataclass
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class AxisSpec:
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x_label: str = ""
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y_label: str = ""
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y_log: bool = False # this metric's natural default scale
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x_log: bool = False
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y_range: Optional[tuple[float, float]] = None
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x_range: Optional[tuple[float, float]] = None
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y_log_allowed: bool = False # is a lin/log toggle meaningful on this axis?
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x_log_allowed: bool = False
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@dataclass
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class PlotSpec:
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"""Everything the renderer needs to draw one dataset of one metric."""
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title: str = ""
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axes: AxisSpec = field(default_factory=AxisSpec)
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curves: list[Curve] = field(default_factory=list)
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bands: list[Band] = field(default_factory=list)
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hlines: list[HLine] = field(default_factory=list)
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heatmap: Optional[Heatmap] = None
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# Scalar readouts (integrated LUFS, LRA, max dBTP) surfaced in the legend.
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annotations: list[str] = field(default_factory=list)
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@property
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def is_heatmap(self) -> bool:
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return self.heatmap is not None
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@dataclass
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class ViewState:
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"""User-controlled, recompute-free view options.
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`None` means "use the metric's default for this axis." `build_spec` resolves
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the concrete scale via `resolve_*`, so a metric never has to special-case the
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unset state.
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"""
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y_log: Optional[bool] = None
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x_log: Optional[bool] = None
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def resolve_y_log(self, default: bool) -> bool:
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return self.y_log if self.y_log is not None else default
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def resolve_x_log(self, default: bool) -> bool:
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return self.x_log if self.x_log is not None else default
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# A neutral default reused wherever a caller hasn't supplied view options.
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DEFAULT_VIEW = ViewState()
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