bigmerge
4bbfdcceff
audio-surface.el / image-surface.el: own-core additive-synthesis WAV and
raster-PNG renderers (integer-only DSP, since EL has no floats), rendered
from learned engram signatures via a pluggable surface-profile
abstraction (surface-profile.el). audio-demo.el / image-demo.el are
drivers. NOTE: demo files hardcode absolute paths to this worktree's own
directory — will need a path fixup before landing.
elp/projector/ is a Python package the author's own README marks as
"STAGING/PROOF-OF-SHAPE — not the deliverable", superseded by the native
.el surface-profile work above; kept as a validated architecture proof.
Generated output (elp/faculty/{out,sig}, elp/projector/out,
__pycache__) intentionally excluded.
66 lines
4.1 KiB
EmacsLisp
66 lines
4.1 KiB
EmacsLisp
// image-demo.el - Drive the native PNG surface: plan a scene from a small
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// meaning phrase (incl. a NEG frame) and emit a byte-valid 64x64 PNG whose
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// palette is read from elp/faculty/sig/scene.basis.
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fn img_frame(relation: String, polarity: String, confidence: String, importance: String, salience: String, subj_id: String) -> [String] {
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let f: [String] = native_list_empty()
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let f: [String] = native_list_append(f, "relation")
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let f: [String] = native_list_append(f, relation)
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let f: [String] = native_list_append(f, "polarity")
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let f: [String] = native_list_append(f, polarity)
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let f: [String] = native_list_append(f, "confidence")
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let f: [String] = native_list_append(f, confidence)
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let f: [String] = native_list_append(f, "importance")
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let f: [String] = native_list_append(f, importance)
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let f: [String] = native_list_append(f, "salience")
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let f: [String] = native_list_append(f, salience)
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let f: [String] = native_list_append(f, "subj_id")
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let f: [String] = native_list_append(f, subj_id)
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return f
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}
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fn rgb_str(c: [Int]) -> String {
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return int_to_str(native_list_get(c, 0)) + "," + int_to_str(native_list_get(c, 1)) + "," + int_to_str(native_list_get(c, 2))
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}
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fn run_image() -> Int {
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fs_mkdir("/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-aaf04b0a9714c4070/elp/faculty/out")
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let table: [Int] = crc_table()
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println("crc_table[1]=" + int_to_str(native_list_get(table, 1)) + " (expect 1996959894 / 0x77073096)")
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let basis: [String] = basis_load("/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-aaf04b0a9714c4070/elp/faculty/sig/scene.basis")
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let warm: [Int] = parse_rgb(basis_field(basis, "warm"))
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let cool: [Int] = parse_rgb(basis_field(basis, "cool"))
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let bg: [Int] = parse_rgb(basis_field(basis, "bg"))
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println("basis warm=" + rgb_str(warm) + " cool=" + rgb_str(cool) + " bg=" + rgb_str(bg) + " (read from scene.basis)")
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let frames: [[String]] = native_list_empty()
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let frames: [[String]] = native_list_append(frames, img_frame("agent", "aff", "0.9", "0.8", "0", "s1"))
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let frames: [[String]] = native_list_append(frames, img_frame("theme", "aff", "0.7", "0.6", "1", "s2"))
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let frames: [[String]] = native_list_append(frames, img_frame("cause", "aff", "0.8", "0.9", "0", "s3"))
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let frames: [[String]] = native_list_append(frames, img_frame("negation", "neg", "0.85", "0.7", "1", "s4"))
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let frames: [[String]] = native_list_append(frames, img_frame("goal", "aff", "0.6", "0.5", "0", "s5"))
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let frames: [[String]] = native_list_append(frames, img_frame("result", "aff", "0.95", "1.0", "1", "s6"))
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let shapes: [[Int]] = plan_scene(frames, warm, cool)
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let ns: Int = native_list_len(shapes)
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println("planned " + int_to_str(ns) + " shapes:")
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let si: Int = 0
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while si < ns {
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let sh: [Int] = native_list_get(shapes, si)
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let pol: String = surface_get(native_list_get(frames, si), "polarity")
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println(" shape " + int_to_str(si) + " type=" + int_to_str(native_list_get(sh, 0)) + " x=" + int_to_str(native_list_get(sh, 1)) + " y=" + int_to_str(native_list_get(sh, 2)) + " size=" + int_to_str(native_list_get(sh, 3)) + " rgb=" + int_to_str(native_list_get(sh, 4)) + "," + int_to_str(native_list_get(sh, 5)) + "," + int_to_str(native_list_get(sh, 6)) + " polarity=" + pol)
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let si: Int = si + 1
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}
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let raw: [Int] = rasterize(64, 64, shapes, bg)
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println("rasterized raw (filtered scanlines) bytes=" + int_to_str(native_list_len(raw)) + " (expect 12352)")
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let png: [Int] = png_build(64, 64, raw, table)
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let plen: Int = native_list_len(png)
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let ok: Int = png_write("/Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-aaf04b0a9714c4070/elp/faculty/out/scene.png", png)
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println("PNG bytes=" + int_to_str(plen) + " -> /Users/will/Development/neuron-technologies/foundation/el/.claude/worktrees/agent-aaf04b0a9714c4070/elp/faculty/out/scene.png (write_ok=" + int_to_str(ok) + ")")
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return plen
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}
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println("image-demo returned png_bytes=" + int_to_str(run_image()))
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