kalpesh77/Multiverse-field-coordinates
Multiverse-field-coordinates Vedic Neural Geometry – Multiverse Field Dataset एक सार्वत्रिक, शुद्ध-संख्यात्मक, बहुआयामी कॉऑर्डिनेट सिस्टीम.ओलंपिक मैदानाप्रमाणे एकच मैदान — ज्यातून वेगवेगळ्या विषयांवर कॉऑर्डिनेशन / mapping करता येते. Core Concept केंद्र (Bindu / Brahma): सर्व आयामांमध्ये केंद्रबिंदू Angular Grid: 0° ते 360° (1° स्टेप) ID: 1 ते 108 (108 Divisions) Layers (Avaran): 7 थर Dimensions: 3D, 8D, 16D, 32D, 64D Math Engine: Orthogonal bases + Rotation… See the full description on the dataset page: https://huggingface.co/datasets/kalpesh77/Multiverse-field-coordinates.
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1 2"""3Generate all layers for given dimensions.4"""5import numpy as np6import pandas as pd7from pathlib import Path8from utils.orthogonal_bases import generate_orthogonal_matrix9from utils.hypersphere import point_on_hypersphere10 11def generate_coords(n_dim, layer=1, ids=range(1, 109), angles=np.arange(0, 361)):12 radius = 0.2 + (layer - 1) * 0.1513 rows = []14 for id_num in ids:15 Q = generate_orthogonal_matrix(n_dim, id_num)16 row = {"ID": id_num}17 for angle_deg in angles:18 theta = np.deg2rad(angle_deg)19 base = point_on_hypersphere(n_dim, theta)20 final = (Q @ base) * radius21 row[str(angle_deg)] = ",".join(f"{v:.6f}" for v in final)22 rows.append(row)23 df = pd.DataFrame(rows)24 return df[["ID"] + [str(a) for a in angles]]25 26if __name__ == "__main__":27 dims = [3, 8, 16, 32, 64]28 base = Path("data")29 for layer in range(1, 8):30 for dim in dims:31 print(f"Generating layer_{layer:02d} | {dim}D ...")32 df = generate_coords(dim, layer=layer)33 out = base / f"layer_{layer:02d}" / f"coords_{dim}d.csv"34 out.parent.mkdir(parents=True, exist_ok=True)35 df.to_csv(out, index=False)36 print(f" → {out}")37 