update ascii-video skill: design patterns, local time, examples
- New references/design-patterns.md: layer hierarchy (bg/content/accent), directional parameter arcs, scene concepts and visual metaphors, counter-rotating systems, wave collision, progressive fragmentation, entropy/consumption, staggered crescendo buildup, scene ordering - New references/examples.md: copy-paste-ready scenes at every complexity - Update scenes.md: local time convention (t=0 at scene start) - Update SKILL.md: add design-patterns.md to reference table - Add README.md to hermes-agent copy - Sync all reference docs with canonical source (SHL0MS/ascii-video)
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# Architecture Reference
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**Cross-references:**
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- Effect building blocks (value fields, noise, SDFs, particles): `effects.md`
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- `_render_vf()`, blend modes, tonemap, masking: `composition.md`
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- Scene protocol, render_clip, SCENES table: `scenes.md`
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- Shader pipeline, feedback buffer, output encoding: `shaders.md`
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- Complete scene examples: `examples.md`
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- Input sources (audio analysis, video, TTS): `inputs.md`
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- Performance tuning, hardware detection: `optimization.md`
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- Common bugs (broadcasting, font, encoding): `troubleshooting.md`
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## Grid System
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### Resolution Presets
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```python
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RESOLUTION_PRESETS = {
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"landscape": (1920, 1080), # 16:9 — YouTube, default
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"portrait": (1080, 1920), # 9:16 — TikTok, Reels, Stories
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"square": (1080, 1080), # 1:1 — Instagram feed
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"ultrawide": (2560, 1080), # 21:9 — cinematic
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"landscape4k":(3840, 2160), # 16:9 — 4K
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"portrait4k": (2160, 3840), # 9:16 — 4K portrait
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}
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def get_resolution(preset="landscape", custom=None):
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"""Returns (VW, VH) tuple."""
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if custom:
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return custom
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return RESOLUTION_PRESETS.get(preset, RESOLUTION_PRESETS["landscape"])
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```
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### Multi-Density Grids
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Pre-initialize multiple grid sizes. Switch per section for visual variety.
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Pre-initialize multiple grid sizes. Switch per section for visual variety. Grid dimensions auto-compute from resolution:
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| Key | Font Size | Grid (1920x1080) | Use |
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**Landscape (1920x1080):**
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| Key | Font Size | Grid (cols x rows) | Use |
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|-----|-----------|-------------------|-----|
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| xs | 8 | 400x108 | Ultra-dense data fields |
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| sm | 10 | 320x83 | Dense detail, rain, starfields |
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@ -15,7 +46,34 @@ Pre-initialize multiple grid sizes. Switch per section for visual variety.
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| xl | 24 | 137x37 | Short quotes, large titles |
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| xxl | 40 | 80x22 | Giant text, minimal |
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**Grid sizing for text-heavy content**: When displaying readable text (quotes, lyrics, testimonials), use 20px (`lg`) as the primary grid. This gives 160 columns -- plenty for lines up to ~50 chars centered. For very short quotes (< 60 chars, <= 3 lines), 24px (`xl`) makes them more impactful. Only init the grids you actually use -- each grid pre-rasterizes all characters which costs ~0.3-0.5s.
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**Portrait (1080x1920):**
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| Key | Font Size | Grid (cols x rows) | Use |
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|-----|-----------|-------------------|-----|
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| xs | 8 | 225x192 | Ultra-dense, tall data columns |
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| sm | 10 | 180x148 | Dense detail, vertical rain |
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| md | 16 | 112x100 | Default balanced |
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| lg | 20 | 90x80 | Readable text (~30 chars/line centered) |
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| xl | 24 | 75x66 | Short quotes, stacked |
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| xxl | 40 | 45x39 | Giant text, minimal |
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**Square (1080x1080):**
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| Key | Font Size | Grid (cols x rows) | Use |
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|-----|-----------|-------------------|-----|
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| sm | 10 | 180x83 | Dense detail |
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| md | 16 | 112x56 | Default balanced |
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| lg | 20 | 90x45 | Readable text |
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**Key differences in portrait mode:**
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- Fewer columns (90 at `lg` vs 160) — lines must be shorter or wrap
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- Many more rows (80 at `lg` vs 45) — vertical stacking is natural
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- Aspect ratio correction flips: `asp = cw / ch` still works but the visual emphasis is vertical
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- Radial effects appear as tall ellipses unless corrected
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- Vertical effects (rain, embers, fire columns) are naturally enhanced
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- Horizontal effects (spectrum bars, waveforms) need rotation or compression
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**Grid sizing for text in portrait**: Use `lg` (20px) for 2-3 word lines. Max comfortable line length is ~25-30 chars. For longer quotes, break aggressively into many short lines stacked vertically — portrait has vertical space to spare. `xl` (24px) works for single words or very short phrases.
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Grid dimensions: `cols = VW // cell_width`, `rows = VH // cell_height`.
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@ -59,7 +117,23 @@ FONT_PREFS_LINUX = [
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("Noto Sans Mono", "/usr/share/fonts/truetype/noto/NotoSansMono-Regular.ttf"),
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("Ubuntu Mono", "/usr/share/fonts/truetype/ubuntu/UbuntuMono-R.ttf"),
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]
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FONT_PREFS = FONT_PREFS_MACOS if platform.system() == "Darwin" else FONT_PREFS_LINUX
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FONT_PREFS_WINDOWS = [
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("Consolas", r"C:\Windows\Fonts\consola.ttf"),
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("Courier New", r"C:\Windows\Fonts\cour.ttf"),
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("Lucida Console", r"C:\Windows\Fonts\lucon.ttf"),
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("Cascadia Code", os.path.expandvars(r"%LOCALAPPDATA%\Microsoft\Windows\Fonts\CascadiaCode.ttf")),
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("Cascadia Mono", os.path.expandvars(r"%LOCALAPPDATA%\Microsoft\Windows\Fonts\CascadiaMono.ttf")),
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]
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def _get_font_prefs():
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s = platform.system()
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if s == "Darwin":
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return FONT_PREFS_MACOS
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elif s == "Windows":
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return FONT_PREFS_WINDOWS
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return FONT_PREFS_LINUX
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FONT_PREFS = _get_font_prefs()
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```
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**Multi-font rendering**: use different fonts for different layers (e.g., monospace for background, a bolder variant for overlay text). Each GridLayer owns its own font:
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@ -77,8 +151,8 @@ Before initializing grids, gather all characters that need bitmap pre-rasterizat
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all_chars = set()
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for pal in [PAL_DEFAULT, PAL_DENSE, PAL_BLOCKS, PAL_RUNE, PAL_KATA,
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PAL_GREEK, PAL_MATH, PAL_DOTS, PAL_BRAILLE, PAL_STARS,
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PAL_BINARY, PAL_MUSIC, PAL_BOX, PAL_CIRCUIT, PAL_ARROWS,
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PAL_HERMES]: # ... all palettes used in project
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PAL_HALFFILL, PAL_HATCH, PAL_BINARY, PAL_MUSIC, PAL_BOX,
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PAL_CIRCUIT, PAL_ARROWS, PAL_HERMES]: # ... all palettes used in project
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all_chars.update(pal)
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# Add any overlay text characters
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all_chars.update("ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789 .,-:;!?/|")
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### GridLayer Initialization
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Each grid pre-computes coordinate arrays for vectorized effect math:
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Each grid pre-computes coordinate arrays for vectorized effect math. The grid automatically adapts to any resolution (landscape, portrait, square):
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```python
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class GridLayer:
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def __init__(self, font_path, font_size):
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def __init__(self, font_path, font_size, vw=None, vh=None):
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"""Initialize grid for any resolution.
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vw, vh: video width/height in pixels. Defaults to global VW, VH."""
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vw = vw or VW; vh = vh or VH
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self.vw = vw; self.vh = vh
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self.font = ImageFont.truetype(font_path, font_size)
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asc, desc = self.font.getmetrics()
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bbox = self.font.getbbox("M")
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self.cw = bbox[2] - bbox[0] # character cell width
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self.ch = asc + desc # CRITICAL: not textbbox height
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self.cols = VW // self.cw
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self.rows = VH // self.ch
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self.ox = (VW - self.cols * self.cw) // 2 # centering
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self.oy = (VH - self.rows * self.ch) // 2
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self.cols = vw // self.cw
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self.rows = vh // self.ch
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self.ox = (vw - self.cols * self.cw) // 2 # centering
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self.oy = (vh - self.rows * self.ch) // 2
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# Aspect ratio metadata
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self.aspect = vw / vh # >1 = landscape, <1 = portrait, 1 = square
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self.is_portrait = vw < vh
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self.is_landscape = vw > vh
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# Index arrays
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self.rr = np.arange(self.rows, dtype=np.float32)[:, None]
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#### Dot / Point Progressions
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```python
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PAL_DOTS = " \u22c5\u2218\u2219\u25cf\u25c9\u25ce\u25c6\u2726\u2605" # dot size progression
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PAL_BRAILLE = " \u2801\u2802\u2803\u2804\u2805\u2806\u2807\u2808\u2809\u280a\u280b\u280c\u280d\u280e\u280f\u2810\u2811\u2812\u2813\u2814\u2815\u2816\u2817\u2818\u2819\u281a\u281b\u281c\u281d\u281e\u281f\u283f" # braille patterns
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PAL_STARS = " \u00b7\u2727\u2726\u2729\u2728\u2605\u2736\u2733\u2738" # star progression
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PAL_DOTS = " ⋅∘∙●◉◎◆✦★" # dot size progression
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PAL_BRAILLE = " ⠁⠂⠃⠄⠅⠆⠇⠈⠉⠊⠋⠌⠍⠎⠏⠐⠑⠒⠓⠔⠕⠖⠗⠘⠙⠚⠛⠜⠝⠞⠟⠿" # braille patterns
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PAL_STARS = " ·✧✦✩✨★✶✳✸" # star progression
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PAL_HALFFILL = " ◔◑◕◐◒◓◖◗◙" # directional half-fill progression
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PAL_HATCH = " ▣▤▥▦▧▨▩" # crosshatch density ramp
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```
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#### Project-Specific (examples -- invent new ones per project)
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return R, G, B
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```
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### OKLAB Color Space (Perceptually Uniform)
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HSV hue is perceptually non-uniform: green occupies far more visual range than blue. OKLAB / OKLCH provide perceptually even color steps — hue increments of 0.1 look equally different regardless of starting hue. Use OKLAB for:
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- Gradient interpolation (no unwanted intermediate hues)
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- Color harmony generation (perceptually balanced palettes)
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- Smooth color transitions over time
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```python
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# --- sRGB <-> Linear sRGB ---
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def srgb_to_linear(c):
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"""Convert sRGB [0,1] to linear light. c: float32 array."""
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return np.where(c <= 0.04045, c / 12.92, ((c + 0.055) / 1.055) ** 2.4)
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def linear_to_srgb(c):
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"""Convert linear light to sRGB [0,1]."""
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return np.where(c <= 0.0031308, c * 12.92, 1.055 * np.power(np.maximum(c, 0), 1/2.4) - 0.055)
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# --- Linear sRGB <-> OKLAB ---
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def linear_rgb_to_oklab(r, g, b):
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"""Linear sRGB to OKLAB. r,g,b: float32 arrays [0,1].
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Returns (L, a, b) where L=[0,1], a,b=[-0.4, 0.4] approx."""
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l_ = 0.4122214708 * r + 0.5363325363 * g + 0.0514459929 * b
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m_ = 0.2119034982 * r + 0.6806995451 * g + 0.1073969566 * b
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s_ = 0.0883024619 * r + 0.2817188376 * g + 0.6299787005 * b
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l_c = np.cbrt(l_); m_c = np.cbrt(m_); s_c = np.cbrt(s_)
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L = 0.2104542553 * l_c + 0.7936177850 * m_c - 0.0040720468 * s_c
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a = 1.9779984951 * l_c - 2.4285922050 * m_c + 0.4505937099 * s_c
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b_ = 0.0259040371 * l_c + 0.7827717662 * m_c - 0.8086757660 * s_c
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return L, a, b_
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def oklab_to_linear_rgb(L, a, b):
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"""OKLAB to linear sRGB. Returns (r, g, b) float32 arrays [0,1]."""
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l_ = L + 0.3963377774 * a + 0.2158037573 * b
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m_ = L - 0.1055613458 * a - 0.0638541728 * b
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s_ = L - 0.0894841775 * a - 1.2914855480 * b
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l_c = l_ ** 3; m_c = m_ ** 3; s_c = s_ ** 3
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r = +4.0767416621 * l_c - 3.3077115913 * m_c + 0.2309699292 * s_c
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g = -1.2684380046 * l_c + 2.6097574011 * m_c - 0.3413193965 * s_c
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b_ = -0.0041960863 * l_c - 0.7034186147 * m_c + 1.7076147010 * s_c
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return np.clip(r, 0, 1), np.clip(g, 0, 1), np.clip(b_, 0, 1)
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# --- Convenience: sRGB uint8 <-> OKLAB ---
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def rgb_to_oklab(R, G, B):
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"""sRGB uint8 arrays to OKLAB."""
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r = srgb_to_linear(R.astype(np.float32) / 255.0)
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g = srgb_to_linear(G.astype(np.float32) / 255.0)
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b = srgb_to_linear(B.astype(np.float32) / 255.0)
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return linear_rgb_to_oklab(r, g, b)
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def oklab_to_rgb(L, a, b):
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"""OKLAB to sRGB uint8 arrays."""
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r, g, b_ = oklab_to_linear_rgb(L, a, b)
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R = np.clip(linear_to_srgb(r) * 255, 0, 255).astype(np.uint8)
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G = np.clip(linear_to_srgb(g) * 255, 0, 255).astype(np.uint8)
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B = np.clip(linear_to_srgb(b_) * 255, 0, 255).astype(np.uint8)
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return R, G, B
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# --- OKLCH (cylindrical form of OKLAB) ---
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def oklab_to_oklch(L, a, b):
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"""OKLAB to OKLCH. Returns (L, C, H) where H is in [0, 1] (normalized)."""
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C = np.sqrt(a**2 + b**2)
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H = (np.arctan2(b, a) / (2 * np.pi)) % 1.0
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return L, C, H
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def oklch_to_oklab(L, C, H):
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"""OKLCH to OKLAB. H in [0, 1]."""
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angle = H * 2 * np.pi
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a = C * np.cos(angle)
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b = C * np.sin(angle)
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return L, a, b
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```
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### Gradient Interpolation (OKLAB vs HSV)
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Interpolating colors through OKLAB avoids the hue detours that HSV produces:
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```python
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def lerp_oklab(color_a, color_b, t_array):
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"""Interpolate between two sRGB colors through OKLAB.
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color_a, color_b: (R, G, B) tuples 0-255
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t_array: float32 array [0,1] — interpolation parameter per pixel.
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Returns (R, G, B) uint8 arrays."""
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La, aa, ba = rgb_to_oklab(
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np.full_like(t_array, color_a[0], dtype=np.uint8),
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np.full_like(t_array, color_a[1], dtype=np.uint8),
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np.full_like(t_array, color_a[2], dtype=np.uint8))
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Lb, ab, bb = rgb_to_oklab(
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np.full_like(t_array, color_b[0], dtype=np.uint8),
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np.full_like(t_array, color_b[1], dtype=np.uint8),
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np.full_like(t_array, color_b[2], dtype=np.uint8))
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L = La + (Lb - La) * t_array
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a = aa + (ab - aa) * t_array
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b = ba + (bb - ba) * t_array
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return oklab_to_rgb(L, a, b)
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def lerp_oklch(color_a, color_b, t_array, short_path=True):
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"""Interpolate through OKLCH (preserves chroma, smooth hue path).
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short_path: take the shorter arc around the hue wheel."""
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La, aa, ba = rgb_to_oklab(
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np.full_like(t_array, color_a[0], dtype=np.uint8),
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np.full_like(t_array, color_a[1], dtype=np.uint8),
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np.full_like(t_array, color_a[2], dtype=np.uint8))
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Lb, ab, bb = rgb_to_oklab(
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np.full_like(t_array, color_b[0], dtype=np.uint8),
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np.full_like(t_array, color_b[1], dtype=np.uint8),
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np.full_like(t_array, color_b[2], dtype=np.uint8))
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L1, C1, H1 = oklab_to_oklch(La, aa, ba)
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L2, C2, H2 = oklab_to_oklch(Lb, ab, bb)
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# Shortest hue path
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if short_path:
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dh = H2 - H1
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dh = np.where(dh > 0.5, dh - 1.0, np.where(dh < -0.5, dh + 1.0, dh))
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H = (H1 + dh * t_array) % 1.0
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else:
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H = H1 + (H2 - H1) * t_array
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L = L1 + (L2 - L1) * t_array
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C = C1 + (C2 - C1) * t_array
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Lout, aout, bout = oklch_to_oklab(L, C, H)
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return oklab_to_rgb(Lout, aout, bout)
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```
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### Color Harmony Generation
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Auto-generate harmonious palettes from a seed color:
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```python
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def harmony_complementary(seed_rgb):
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"""Two colors: seed + opposite hue."""
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L, a, b = rgb_to_oklab(np.array([seed_rgb[0]]), np.array([seed_rgb[1]]), np.array([seed_rgb[2]]))
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_, C, H = oklab_to_oklch(L, a, b)
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return [seed_rgb, _oklch_to_srgb_tuple(L[0], C[0], (H[0] + 0.5) % 1.0)]
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def harmony_triadic(seed_rgb):
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"""Three colors: seed + two at 120-degree offsets."""
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L, a, b = rgb_to_oklab(np.array([seed_rgb[0]]), np.array([seed_rgb[1]]), np.array([seed_rgb[2]]))
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_, C, H = oklab_to_oklch(L, a, b)
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return [seed_rgb,
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_oklch_to_srgb_tuple(L[0], C[0], (H[0] + 0.333) % 1.0),
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_oklch_to_srgb_tuple(L[0], C[0], (H[0] + 0.667) % 1.0)]
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def harmony_analogous(seed_rgb, spread=0.08, n=5):
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"""N colors spread evenly around seed hue."""
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L, a, b = rgb_to_oklab(np.array([seed_rgb[0]]), np.array([seed_rgb[1]]), np.array([seed_rgb[2]]))
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_, C, H = oklab_to_oklch(L, a, b)
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offsets = np.linspace(-spread * (n-1)/2, spread * (n-1)/2, n)
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return [_oklch_to_srgb_tuple(L[0], C[0], (H[0] + off) % 1.0) for off in offsets]
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def harmony_split_complementary(seed_rgb, split=0.08):
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"""Three colors: seed + two flanking the complement."""
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L, a, b = rgb_to_oklab(np.array([seed_rgb[0]]), np.array([seed_rgb[1]]), np.array([seed_rgb[2]]))
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_, C, H = oklab_to_oklch(L, a, b)
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comp = (H[0] + 0.5) % 1.0
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return [seed_rgb,
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_oklch_to_srgb_tuple(L[0], C[0], (comp - split) % 1.0),
|
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_oklch_to_srgb_tuple(L[0], C[0], (comp + split) % 1.0)]
|
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|
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def harmony_tetradic(seed_rgb):
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"""Four colors: two complementary pairs at 90-degree offset."""
|
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L, a, b = rgb_to_oklab(np.array([seed_rgb[0]]), np.array([seed_rgb[1]]), np.array([seed_rgb[2]]))
|
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_, C, H = oklab_to_oklch(L, a, b)
|
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return [seed_rgb,
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_oklch_to_srgb_tuple(L[0], C[0], (H[0] + 0.25) % 1.0),
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_oklch_to_srgb_tuple(L[0], C[0], (H[0] + 0.5) % 1.0),
|
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_oklch_to_srgb_tuple(L[0], C[0], (H[0] + 0.75) % 1.0)]
|
||||
|
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def _oklch_to_srgb_tuple(L, C, H):
|
||||
"""Helper: single OKLCH -> sRGB (R,G,B) int tuple."""
|
||||
La = np.array([L]); Ca = np.array([C]); Ha = np.array([H])
|
||||
Lo, ao, bo = oklch_to_oklab(La, Ca, Ha)
|
||||
R, G, B = oklab_to_rgb(Lo, ao, bo)
|
||||
return (int(R[0]), int(G[0]), int(B[0]))
|
||||
```
|
||||
|
||||
### OKLAB Hue Fields
|
||||
|
||||
Drop-in replacements for `hf_*` generators that produce perceptually uniform hue variation:
|
||||
|
||||
```python
|
||||
def hf_oklch_angle(offset=0.0, chroma=0.12, lightness=0.7):
|
||||
"""OKLCH hue mapped to angle from center. Perceptually uniform rainbow.
|
||||
Returns (R, G, B) uint8 color array instead of a float hue.
|
||||
NOTE: Use with _render_vf_rgb() variant, not standard _render_vf()."""
|
||||
def fn(g, f, t, S):
|
||||
H = (g.angle / (2 * np.pi) + offset + t * 0.05) % 1.0
|
||||
L = np.full_like(H, lightness)
|
||||
C = np.full_like(H, chroma)
|
||||
Lo, ao, bo = oklch_to_oklab(L, C, H)
|
||||
R, G, B = oklab_to_rgb(Lo, ao, bo)
|
||||
return mkc(R, G, B, g.rows, g.cols)
|
||||
return fn
|
||||
```
|
||||
|
||||
### Compositing Helpers
|
||||
|
||||
```python
|
||||
|
|
@ -458,7 +740,7 @@ subprocess.run(["ffmpeg", "-y", "-f", "concat", "-safe", "0", "-i", concat_path,
|
|||
|
||||
### v2 Protocol (Current)
|
||||
|
||||
Every scene function: `(renderer, features_dict, time_float, state_dict) -> canvas_uint8`
|
||||
Every scene function: `(r, f, t, S) -> canvas_uint8` — where `r` = Renderer, `f` = features dict, `t` = time float, `S` = persistent state dict
|
||||
|
||||
```python
|
||||
def fx_example(r, f, t, S):
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue