2f7b6cf298
The renderer kept a braille canvas, char-field scene, star-glyph/orbital helpers, and seed/links params from earlier visual iterations that the final timeline bar chart never uses. Remove them (~190 lines), simplify the empty-state placeholder, and refresh the module + RPC docstrings to describe what actually ships.
656 lines
24 KiB
Python
656 lines
24 KiB
Python
"""Terminal renderer for the learning timeline (learned skills + memories).
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The desktop app (``apps/desktop/src/app/starmap``) paints a GPU radial
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constellation; a terminal can't, so this is a *rendition* of the same data as a
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timeline bar chart — date rows, proportional skill/memory bars colored by the
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day's dominant category, and a cumulative trajectory sparkline — plus per-slice
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bucket metadata the TUI walks as a tree. The age gradient and complementary
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memory ink are ported from the desktop source, not guessed.
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Grids are emitted as style runs — ``[text, style, alpha, hex?]`` — so each
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consumer maps the semantic style + brightness onto its own palette; the
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optional 4th element overrides the base color (category heatmap). Pure,
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stdlib-only.
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"""
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from __future__ import annotations
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import math
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from datetime import datetime, timezone
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from typing import Any, Iterable, Optional
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# time-axis.ts LEAD_IN: the oldest node sits just off recency 0.
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LEAD_IN = 0.06
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# constants.ts AGE_GRADIENT — old quiet, recent bright.
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AGE_OLD_INK = 0.42
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AGE_MID_INK = 0.74
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AGE_NEW_INK = 0.95
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AGE_MID = 0.52
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# Style keys consumers map to base colors (brightness = the run alpha).
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STYLE_BG = "bg"
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STYLE_SKILL = "skill"
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STYLE_MEMORY = "memory"
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STYLE_LABEL = "label"
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STYLE_DIM = "dim"
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# Legend glyphs mirror NODE_SHAPE (skill = circle, memory = diamond).
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SKILL_GLYPH = "●"
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MEMORY_GLYPH = "◆"
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_LABEL_KEYS = tuple("123456789abc")
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Run = list # [text, style, alpha, hex?]
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Row = list # list[Run]
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Grid = list # list[Row]
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def _to_ts(value: Any) -> Optional[float]:
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try:
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return None if value is None else float(value)
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except (TypeError, ValueError):
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return None
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def _clamp(v: float, lo: float, hi: float) -> float:
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return lo if v < lo else hi if v > hi else v
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def _smoothstep(p: float) -> float:
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p = _clamp(p, 0.0, 1.0)
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return p * p * (3 - 2 * p)
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def recency_ink(rec: float) -> float:
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"""Port of geometry.ts ``recencyInk`` — smoothstep age → ink alpha."""
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t = _clamp(rec, 0.0, 1.0)
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if t <= AGE_MID:
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return AGE_OLD_INK + (AGE_MID_INK - AGE_OLD_INK) * _smoothstep(t / AGE_MID)
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return AGE_MID_INK + (AGE_NEW_INK - AGE_MID_INK) * _smoothstep((t - AGE_MID) / (1 - AGE_MID))
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def format_date(ts: Optional[float]) -> str:
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if not ts:
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return "unknown"
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try:
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return datetime.fromtimestamp(float(ts), tz=timezone.utc).strftime("%-d %b %Y")
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except (ValueError, OSError, OverflowError):
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return "unknown"
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def compute_recency(nodes: list[dict[str, Any]]) -> dict[str, Any]:
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"""Port of time-axis.ts ``computeRecency`` (id → recency ratio, timed flag)."""
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known = [t for t in (_to_ts(n.get("timestamp")) for n in nodes) if t is not None]
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min_ts = min(known) if known else None
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max_ts = max(known) if known else None
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timed = min_ts is not None and max_ts is not None and max_ts > min_ts
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ordered = sorted(
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nodes,
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key=lambda n: (
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_to_ts(n.get("timestamp")) if _to_ts(n.get("timestamp")) is not None else math.inf,
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str(n.get("id", "")),
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),
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)
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last = max(len(ordered) - 1, 1)
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ord_ratio = {str(n.get("id", "")): (i / last if len(ordered) > 1 else 0.0) for i, n in enumerate(ordered)}
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rec: dict[str, float] = {}
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for n in nodes:
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nid = str(n.get("id", ""))
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ts = _to_ts(n.get("timestamp"))
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if timed and ts is not None and min_ts is not None and max_ts is not None:
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ratio = (ts - min_ts) / (max_ts - min_ts)
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else:
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ratio = ord_ratio.get(nid, 0.0)
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rec[nid] = LEAD_IN + (1 - LEAD_IN) * _clamp(ratio, 0.0, 1.0)
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return {"rec": rec, "timed": timed, "minTs": min_ts, "maxTs": max_ts}
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def _date_at(rec: dict[str, Any], reveal: float) -> Optional[float]:
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if not rec.get("timed"):
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return None
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lo, hi = rec.get("minTs"), rec.get("maxTs")
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if lo is None or hi is None:
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return None
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return round(lo + _clamp(reveal, 0, 1) * (hi - lo))
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# ── Color: ported from color.ts so memory ink + age fade match the desktop ──
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def hex_to_rgb(s: str) -> tuple[int, int, int]:
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s = s.strip().lstrip("#")
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if len(s) == 3:
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s = "".join(c * 2 for c in s)
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try:
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return int(s[0:2], 16), int(s[2:4], 16), int(s[4:6], 16)
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except (ValueError, IndexError):
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return 255, 215, 0
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def rgb_to_hex(c: tuple) -> str:
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return "#{:02X}{:02X}{:02X}".format(*(int(_clamp(v, 0, 255)) for v in c))
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def mix_rgb(a: tuple, b: tuple, t: float) -> tuple[int, int, int]:
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p = _clamp(t, 0.0, 1.0)
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return tuple(round(a[i] + (b[i] - a[i]) * p) for i in range(3)) # type: ignore[return-value]
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def _rgb_to_hsl(c: tuple) -> tuple[float, float, float]:
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r, g, b = (x / 255 for x in c)
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mx, mn = max(r, g, b), min(r, g, b)
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light = (mx + mn) / 2
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d = mx - mn
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if not d:
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return 0.0, 0.0, light
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s = d / (2 - mx - mn) if light > 0.5 else d / (mx + mn)
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if mx == r:
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h = (g - b) / d + (6 if g < b else 0)
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elif mx == g:
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h = (b - r) / d + 2
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else:
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h = (r - g) / d + 4
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return h * 60, s, light
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def _hsl_to_rgb(h: float, s: float, light: float) -> tuple[int, int, int]:
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hue = ((h % 360) + 360) % 360
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c = (1 - abs(2 * light - 1)) * s
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x = c * (1 - abs(((hue / 60) % 2) - 1))
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m = light - c / 2
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if hue < 60:
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r, g, b = c, x, 0.0
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elif hue < 120:
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r, g, b = x, c, 0.0
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elif hue < 180:
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r, g, b = 0.0, c, x
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elif hue < 240:
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r, g, b = 0.0, x, c
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elif hue < 300:
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r, g, b = x, 0.0, c
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else:
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r, g, b = c, 0.0, x
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return round((r + m) * 255), round((g + m) * 255), round((b + m) * 255)
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def _complementary_ink(c: tuple) -> tuple[int, int, int]:
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h, s, light = _rgb_to_hsl(c)
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return _hsl_to_rgb(h + 165, max(s, 0.5), _clamp(light, 0.5, 0.7))
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def derive_palette(primary_hex: str, *, dark: bool = True) -> dict[str, str]:
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"""Port of color.ts ``computePalette`` (the bits a terminal needs)."""
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primary = hex_to_rgb(primary_hex)
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base = (255, 255, 255) if dark else (0, 0, 0)
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bg = (8, 8, 12) if dark else (250, 250, 250)
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return {
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"primary": primary_hex,
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"skill": rgb_to_hex(mix_rgb(primary, base, 0.12 if dark else 0.18)),
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"memory": rgb_to_hex(mix_rgb(_complementary_ink(primary), bg, 0.45)),
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"label": rgb_to_hex(mix_rgb(base, bg, 0.35)),
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"dim": rgb_to_hex(mix_rgb(base, bg, 0.7)),
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"bg": rgb_to_hex(bg),
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}
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def _node_score(node: dict[str, Any], rec: float) -> float:
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"""Pick which visible objects deserve map markers + label rows."""
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if node.get("kind") == "memory":
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return 3.5 + rec
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use = float(node.get("useCount", 0) or 0)
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return rec * 2 + math.sqrt(max(0.0, use)) + (2.0 if node.get("pinned") else 0.0)
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def _node_label(node: dict[str, Any]) -> str:
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text = str(node.get("label") or node.get("id") or "unknown").strip()
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return text if len(text) <= 26 else text[:23].rstrip() + "…"
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def _node_meta(node: dict[str, Any]) -> str:
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if node.get("kind") == "memory":
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source = "profile memory" if node.get("memorySource") == "profile" else "memory"
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return f"{source} · {format_date(_to_ts(node.get('timestamp')))}"
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bits = [str(node.get("category") or "skill"), format_date(_to_ts(node.get("timestamp")))]
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count = int(node.get("useCount", 0) or 0)
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if count:
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bits.append(f"x{count}")
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if node.get("pinned"):
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bits.append("pinned")
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return " · ".join(bits)
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# ── Timeline chart frame ─────────────────────────────────────────────────────
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class _ChartBucket:
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__slots__ = ("label", "ts", "skills", "memories", "nodes", "rec")
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def __init__(self, label: str, ts: float):
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self.label = label
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self.ts = ts
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self.skills = 0
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self.memories = 0
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self.nodes: list[dict[str, Any]] = []
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self.rec = 1.0
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@property
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def total(self) -> int:
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return self.skills + self.memories
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def _period_key(ts: float, granularity: str) -> tuple[int, ...]:
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dt = datetime.fromtimestamp(ts, tz=timezone.utc)
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if granularity == "day":
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return (dt.year, dt.month, dt.day)
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if granularity == "month":
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return (dt.year, dt.month)
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return (dt.year,)
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def _period_label(ts: float, granularity: str) -> str:
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dt = datetime.fromtimestamp(ts, tz=timezone.utc)
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if granularity == "day":
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return dt.strftime("%-d %b")
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if granularity == "month":
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return dt.strftime("%b %Y")
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return dt.strftime("%Y")
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def _build_chart_buckets(nodes: list[dict[str, Any]], rec: dict[str, Any], max_rows: int) -> list[_ChartBucket]:
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"""Timeline rows: finest date granularity that fits, oldest → newest."""
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if not nodes:
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return []
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if not rec["timed"]:
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ordered = sorted(nodes, key=lambda n: rec["rec"].get(str(n.get("id", "")), 0.0))
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n_bins = min(max_rows, max(1, len(ordered)))
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buckets = [_ChartBucket(f"#{i + 1}", float(i)) for i in range(n_bins)]
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for node in ordered:
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idx = int(_clamp(math.floor(rec["rec"].get(str(node.get("id", "")), 0.0) * n_bins), 0, n_bins - 1))
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b = buckets[idx]
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b.nodes.append(node)
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if node.get("kind") == "memory":
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b.memories += 1
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else:
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b.skills += 1
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return buckets
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chosen: Optional[list[_ChartBucket]] = None
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for granularity in ("day", "month", "year"):
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groups: dict[tuple[int, ...], _ChartBucket] = {}
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for node in nodes:
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ts = _to_ts(node.get("timestamp"))
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if ts is None:
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continue
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key = _period_key(ts, granularity)
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bucket = groups.get(key)
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if bucket is None:
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bucket = _ChartBucket(_period_label(ts, granularity), ts)
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groups[key] = bucket
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bucket.nodes.append(node)
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if node.get("kind") == "memory":
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bucket.memories += 1
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else:
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bucket.skills += 1
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# For short spans, keep the useful day-by-day graph even when the caller
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# asked for fewer rows; terminal scrollback is better than collapsing a
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# month of activity into one unreadable bar.
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if len(groups) <= max_rows or (granularity == "day" and len(groups) <= 32):
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chosen = [groups[key] for key in sorted(groups)]
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break
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if chosen is None:
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# If even yearly buckets overflow, fall back to even time bins.
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min_ts, max_ts = rec.get("minTs"), rec.get("maxTs")
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n_bins = max(1, max_rows)
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chosen = []
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for i in range(n_bins):
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ts = min_ts + (i / max(1, n_bins - 1)) * (max_ts - min_ts) if min_ts and max_ts else float(i)
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chosen.append(_ChartBucket(format_date(ts), ts))
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for node in nodes:
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r = rec["rec"].get(str(node.get("id", "")), 0.0)
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idx = int(_clamp(math.floor(r * n_bins), 0, n_bins - 1))
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b = chosen[idx]
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b.nodes.append(node)
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if node.get("kind") == "memory":
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b.memories += 1
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else:
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b.skills += 1
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min_ts, max_ts = rec.get("minTs"), rec.get("maxTs")
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span = (max_ts - min_ts) if min_ts is not None and max_ts is not None and max_ts > min_ts else 0
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for bucket in chosen:
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bucket.rec = LEAD_IN + (1 - LEAD_IN) * ((bucket.ts - min_ts) / span) if span else 1.0
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return chosen
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def _bucket_label_node(bucket: _ChartBucket) -> Optional[dict[str, Any]]:
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if not bucket.nodes:
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return None
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return max(bucket.nodes, key=lambda node: _node_score(node, _to_ts(node.get("timestamp")) or bucket.ts))
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def _bucket_nodes(bucket: _ChartBucket, memory_lookup: Optional[dict[str, dict[str, Any]]] = None) -> list[dict[str, Any]]:
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out: list[dict[str, Any]] = []
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# Chronological within the slice so the TUI tree reads oldest → newest.
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ordered = sorted(bucket.nodes, key=lambda n: _to_ts(n.get("timestamp")) or bucket.ts)
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for node in ordered:
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style = STYLE_MEMORY if node.get("kind") == "memory" else STYLE_SKILL
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raw_label = str(node.get("label") or node.get("id") or "unknown").strip()
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memory = (memory_lookup or {}).get(str(node.get("id", "")))
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out.append(
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{
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"id": str(node.get("id", "")),
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"glyph": MEMORY_GLYPH if node.get("kind") == "memory" else SKILL_GLYPH,
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"label": _node_label(node),
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"fullLabel": raw_label,
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"meta": _node_meta(node),
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"body": str(memory.get("body", "")) if memory else "",
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"style": style,
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}
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)
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return out
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def _bucket_rows(buckets: list[_ChartBucket], payload: dict[str, Any]) -> list[dict[str, Any]]:
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cmap = category_color_map(payload)
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memory_lookup = {
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f"memory:{card.get('source')}:{idx}": card
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for idx, card in enumerate(payload.get("memory", []) or [])
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if isinstance(card, dict)
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}
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rows: list[dict[str, Any]] = []
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for idx, bucket in enumerate(buckets):
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cat = _bucket_category(bucket)
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rows.append(
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{
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"index": idx,
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"label": bucket.label,
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"date": format_date(bucket.ts),
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"skills": bucket.skills,
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"memories": bucket.memories,
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"total": bucket.total,
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"category": cat,
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"color": cmap.get(cat) if cat else None,
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"nodes": _bucket_nodes(bucket, memory_lookup),
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}
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)
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return rows
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def _category_counts(payload: dict[str, Any]) -> list[tuple[str, int]]:
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clusters = [
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(str(c.get("category")), int(c.get("count", 0)))
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for c in payload.get("clusters", []) or []
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if c.get("category") and c.get("category") != "memory"
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]
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if clusters:
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return clusters
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counts: dict[str, int] = {}
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for node in payload.get("nodes", []):
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if node.get("kind") == "memory":
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continue
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cat = str(node.get("category") or "skill")
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counts[cat] = counts.get(cat, 0) + 1
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return sorted(counts.items(), key=lambda kv: (-kv[1], kv[0]))
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def category_color_map(payload: dict[str, Any]) -> dict[str, str]:
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"""Deterministic, evenly-spread hue per skill category (theme-independent)."""
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clusters = _category_counts(payload)
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n = max(1, len(clusters))
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# Golden-angle hue spacing so adjacent categories never collide in color.
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return {cat: rgb_to_hex(_hsl_to_rgb((i * 137.508) % 360, 0.55, 0.62)) for i, (cat, _c) in enumerate(clusters)}
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def category_legend(payload: dict[str, Any], limit: int = 4) -> list[dict[str, Any]]:
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cmap = category_color_map(payload)
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cats = _category_counts(payload)
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shown = cats[:limit]
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hidden = max(0, len(cats) - len(shown))
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return [
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{"glyph": "●", "color": cmap.get(cat, ""), "label": f"{cat} ({count})"}
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for cat, count in shown
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] + ([{"glyph": "·", "color": "", "label": f"+{hidden}"}] if hidden else [])
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def _bucket_category(bucket: _ChartBucket) -> Optional[str]:
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counts: dict[str, int] = {}
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for node in bucket.nodes:
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if node.get("kind") == "memory":
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continue
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cat = str(node.get("category") or "skill")
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counts[cat] = counts.get(cat, 0) + 1
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return max(counts, key=lambda k: counts[k]) if counts else None
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def _trajectory_row(buckets: list[_ChartBucket], width: int, reveal: float) -> Row:
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"""Cumulative learning curve as a compact star-path sparkline."""
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if not buckets:
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return []
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total = sum(b.total for b in buckets) or 1
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visible = int(_clamp(math.ceil(reveal * len(buckets)), 0, len(buckets)))
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acc = 0
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points: list[int] = []
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for b in buckets[:visible]:
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acc += b.total
|
|
points.append(round((acc / total) * (width - 1)))
|
|
cells = [" "] * width
|
|
last = 0
|
|
for p in points:
|
|
for x in range(min(last, p), max(last, p) + 1):
|
|
if 0 <= x < width and cells[x] == " ":
|
|
cells[x] = "·"
|
|
if 0 <= p < width:
|
|
cells[p] = "✦"
|
|
last = p
|
|
return [["trajectory ", STYLE_LABEL, 0.55], ["".join(cells), STYLE_SKILL, 0.48]]
|
|
|
|
|
|
def render_graph(payload: dict[str, Any], *, cols: int = 80, rows: int = 16, reveal: float = 1.0) -> dict[str, Any]:
|
|
"""Render one timeline frame at ``reveal`` (0→1).
|
|
|
|
Date rows with proportional skill/memory bars colored by the day's dominant
|
|
category, numbered markers tied to label rows, and a cumulative trajectory
|
|
sparkline underneath.
|
|
"""
|
|
reveal = _clamp(reveal, 0.0, 1.0)
|
|
cols = max(44, cols)
|
|
rows = max(14, rows)
|
|
nodes = list(payload.get("nodes", []))
|
|
if not nodes:
|
|
placeholder = [["no learning yet — keep using Hermes and it maps out here", STYLE_DIM, 0.7]]
|
|
return {"grid": [placeholder], "date": "", "reveal": reveal, "visible": 0}
|
|
|
|
rec = compute_recency(nodes)
|
|
cmap = category_color_map(payload)
|
|
buckets = _build_chart_buckets(nodes, rec, max_rows=max(4, rows - 3))
|
|
n_buckets = len(buckets)
|
|
visible_bucket_count = int(_clamp(math.ceil(reveal * n_buckets), 0, n_buckets))
|
|
max_total = max((b.total for b in buckets), default=1) or 1
|
|
label_w = min(9, max(len(b.label) for b in buckets))
|
|
bar_w = max(14, cols - label_w - 16)
|
|
|
|
grid: Grid = []
|
|
labels: list[dict[str, Any]] = []
|
|
visible = 0
|
|
for i, bucket in enumerate(buckets):
|
|
if i >= visible_bucket_count:
|
|
grid.append([])
|
|
continue
|
|
visible += bucket.total
|
|
ink = recency_ink(bucket.rec)
|
|
bar_len = max(1, round((bucket.total / max_total) * bar_w)) if bucket.total else 0
|
|
skill_len = round((bucket.skills / bucket.total) * bar_len) if bucket.total else 0
|
|
if bucket.skills and skill_len == 0:
|
|
skill_len = 1
|
|
memory_len = bar_len - skill_len
|
|
if bucket.memories and memory_len == 0 and bar_len > 1:
|
|
memory_len = 1
|
|
skill_len = bar_len - 1
|
|
|
|
node = _bucket_label_node(bucket)
|
|
marker = ""
|
|
if node and len(labels) < 6:
|
|
marker = _LABEL_KEYS[len(labels)]
|
|
style = STYLE_MEMORY if node.get("kind") == "memory" else STYLE_SKILL
|
|
labels.append(
|
|
{
|
|
"key": marker,
|
|
"glyph": MEMORY_GLYPH if node.get("kind") == "memory" else SKILL_GLYPH,
|
|
"label": _node_label(node),
|
|
"meta": _node_meta(node),
|
|
"style": style,
|
|
"alpha": round(ink, 3),
|
|
}
|
|
)
|
|
|
|
cat = _bucket_category(bucket)
|
|
cat_hex = cmap.get(cat) if cat else None
|
|
|
|
row: Row = [[f"{bucket.label:>{label_w}} ", STYLE_LABEL, ink], ["│ ", STYLE_DIM, 0.55]]
|
|
if marker:
|
|
row.append([marker, STYLE_LABEL, 0.95])
|
|
elif bucket.total:
|
|
head_hex = cat_hex if bucket.skills else None
|
|
row.append(["✦" if bucket.skills else "◆", STYLE_SKILL if bucket.skills else STYLE_MEMORY, ink, head_hex])
|
|
if skill_len:
|
|
# Bar colored by the day's dominant category — a learning heatmap.
|
|
row.append(["━" * skill_len, STYLE_SKILL, ink, cat_hex])
|
|
if memory_len:
|
|
if memory_len == 1:
|
|
mem_trail = "◆"
|
|
else:
|
|
mem_trail = "◆" + ("━" * (memory_len - 2)) + "◆"
|
|
row.append([mem_trail, STYLE_MEMORY, max(0.65, ink)])
|
|
if bar_len < bar_w:
|
|
# Empty space keeps counts aligned; starmap texture lives in the
|
|
# trajectory row below, where it reads as signal rather than noise.
|
|
row.append([" " * (bar_w - bar_len), STYLE_BG, 1.0])
|
|
row.append([" ", STYLE_BG, 1.0])
|
|
row.append([str(bucket.skills), STYLE_SKILL, max(0.72, ink)])
|
|
if bucket.memories:
|
|
row.append(["+", STYLE_DIM, 0.6])
|
|
row.append([str(bucket.memories), STYLE_MEMORY, max(0.72, ink)])
|
|
if i == visible_bucket_count - 1:
|
|
row.append([" ◀ now", STYLE_LABEL, 0.9])
|
|
elif bucket.total == max_total and max_total > 1:
|
|
row.append([" ☄ peak", STYLE_LABEL, 0.75])
|
|
grid.append(row)
|
|
|
|
# Cumulative learning trajectory underneath the rows.
|
|
grid.append([[(" " * (label_w + 2)), STYLE_BG, 1.0], *_trajectory_row(buckets, max(12, cols - label_w - 13), reveal)])
|
|
|
|
return {
|
|
"grid": grid,
|
|
"date": format_date(_date_at(rec, reveal)),
|
|
"reveal": reveal,
|
|
"visible": visible,
|
|
"labels": labels,
|
|
}
|
|
|
|
|
|
# ── Trimmings ──────────────────────────────────────────────────────────────
|
|
|
|
|
|
def build_legend(payload: dict[str, Any]) -> list[dict[str, Any]]:
|
|
nodes = payload.get("nodes", [])
|
|
skills = sum(1 for n in nodes if n.get("kind") != "memory")
|
|
memories = sum(1 for n in nodes if n.get("kind") == "memory")
|
|
return [
|
|
{"glyph": SKILL_GLYPH, "style": STYLE_SKILL, "label": f"skills ({skills})"},
|
|
{"glyph": MEMORY_GLYPH, "style": STYLE_MEMORY, "label": f"memories ({memories})"},
|
|
]
|
|
|
|
|
|
def axis_labels(payload: dict[str, Any]) -> dict[str, str]:
|
|
rec = compute_recency(list(payload.get("nodes", [])))
|
|
if not rec["timed"]:
|
|
return {"start": "oldest", "end": "now"}
|
|
return {"start": format_date(rec.get("minTs")), "end": format_date(rec.get("maxTs"))}
|
|
|
|
|
|
def _peak_day(payload: dict[str, Any]) -> Optional[str]:
|
|
counts: dict[tuple[int, ...], int] = {}
|
|
reps: dict[tuple[int, ...], float] = {}
|
|
for node in payload.get("nodes", []):
|
|
ts = _to_ts(node.get("timestamp"))
|
|
if ts is None:
|
|
continue
|
|
key = _period_key(ts, "day")
|
|
counts[key] = counts.get(key, 0) + 1
|
|
reps[key] = ts
|
|
if not counts:
|
|
return None
|
|
best = max(counts, key=lambda k: counts[k])
|
|
return f"busiest day {_period_label(reps[best], 'day')} · {counts[best]} learned"
|
|
|
|
|
|
def build_summary(payload: dict[str, Any]) -> list[str]:
|
|
stats = payload.get("stats", {}) or {}
|
|
lines: list[str] = []
|
|
learned = stats.get("learned_skills", stats.get("nodes", 0))
|
|
mem = stats.get("memory_nodes", 0)
|
|
edges = stats.get("related_edges", 0)
|
|
lines.append(f"{learned} learned skills · {mem} memories · {edges} skill links")
|
|
extra = []
|
|
if stats.get("memory_skill_edges"):
|
|
extra.append(f"{stats['memory_skill_edges']} memory↔skill links")
|
|
peak = _peak_day(payload)
|
|
if peak:
|
|
extra.append(peak)
|
|
if extra:
|
|
lines.append(" · ".join(extra))
|
|
return lines
|
|
|
|
|
|
def _merge_runs(cells: Iterable[Run]) -> Row:
|
|
out: Row = []
|
|
for run in cells:
|
|
text, style, alpha = run[0], run[1], (run[2] if len(run) > 2 else 1.0)
|
|
hex_override = run[3] if len(run) > 3 else None
|
|
prev_hex = out[-1][3] if out and len(out[-1]) > 3 else None
|
|
if out and out[-1][1] == style and abs(out[-1][2] - alpha) < 1e-6 and prev_hex == hex_override:
|
|
out[-1][0] += text
|
|
else:
|
|
merged: Run = [text, style, alpha]
|
|
if hex_override:
|
|
merged.append(hex_override)
|
|
out.append(merged)
|
|
return out
|
|
|
|
|
|
def render_frames(payload: dict[str, Any], *, cols: int = 80, rows: int = 16, frames: int = 48) -> dict[str, Any]:
|
|
"""Pre-render a full play-through (reveal 0→1) plus static legend/summary."""
|
|
frames = max(2, min(frames, 240))
|
|
nodes = list(payload.get("nodes", []))
|
|
rec = compute_recency(nodes)
|
|
# Mirror render_graph's bucketing so the interactive row list lines up with
|
|
# what the user sees.
|
|
buckets = _build_chart_buckets(nodes, rec, max_rows=max(4, rows - 3)) if nodes else []
|
|
out_frames = []
|
|
for i in range(frames):
|
|
reveal = i / (frames - 1)
|
|
frame = render_graph(payload, cols=cols, rows=rows, reveal=reveal)
|
|
out_frames.append(
|
|
{
|
|
"reveal": frame["reveal"],
|
|
"date": frame["date"],
|
|
"visible": frame["visible"],
|
|
"grid": frame["grid"],
|
|
"labels": frame.get("labels", []),
|
|
}
|
|
)
|
|
return {
|
|
"frames": out_frames,
|
|
"legend": build_legend(payload),
|
|
"categories": category_legend(payload),
|
|
"buckets": _bucket_rows(buckets, payload),
|
|
"summary": build_summary(payload),
|
|
"axis": axis_labels(payload),
|
|
"count": len(payload.get("nodes", [])),
|
|
"cols": cols,
|
|
"rows": rows,
|
|
}
|