unsig_00000

Metadata:

{
  "files": [
    {
      "env": {
        "src": [
          "name: unsig",
          "channels:",
          "  - defaults",
          "dependencies:",
          "  - python=3.8.10=hdb3f193_7",
          "  - numpy=1.20.2=py38h2d18471_0",
          "  - pillow=8.2.0=py38he98fc37_0"
        ],
        "file": "unsig.yml",
        "mediatype": "text/conda"
      },
      "code": {
        "src": [
          "import numpy as np",
          "from PIL import Image",
          "",
          "",
          "#pixel dimension",
          "dim = 4096",
          "",
          "#replace the content inside {} with your unsig's properties",
          "unsig = {'index': 0,",
          "         'num_props': 0,",
          "         'properties': {",
          "             'multipliers'   : [],",
          "             'colors'        : [],",
          "             'distributions' : [],",
          "             'rotations'     : []}}",
          "",
          "def norm(x , mean , std):",
          "    p = (np.pi*std) * np.exp(-0.5*((x-mean)/std)**2)",
          "    return p",
          "",
          "def scale_make2d(s):",
          "    scaled = np.interp(s, (s.min(), s.max()), (0, u_range))",
          "    two_d = np.tile(scaled, (dim, 1))",
          "    return two_d",
          "",
          "def gen_nft(nft):",
          "    idx = unsig['index']",
          "    props = unsig['properties']",
          "    props['multipliers']=list(map(float, props['multipliers']))",
          "    props['rotations']=list(map(int, props['rotations']))",
          "",
          "    n = np.zeros((dim, dim, 3)).astype(np.uint32)",
          "",
          "    for i in range(unsig['num_props']):",
          "        mult = props['multipliers'][i]",
          "        col = props['colors'][i]",
          "        dist = props['distributions'][i]",
          "        rot = props['rotations'][i]",
          "        c = channels[col]",
          "        buffer =  mult * np.rot90(dists[dist], k=(rot / 90))",
          "        n[ :, :, c ] = n[ :, :, c ] + buffer",
          "",
          "    n = np.interp(n, (0, u_range), (0, 255)).astype(np.uint8)",
          "",
          "    return (idx, n)",
          "",
          "if __name__ == '__main__':",
          "    #setup",
          "    x = list(range(dim))",
          "    u_range = 4294967293",
          "    mean = np.mean(x)",
          "    std = dim/6",
          "",
          "    #probability and cumulative distribution",
          "    p_1d = np.array(norm(x, mean, std)).astype(np.uint32)",
          "    c_1d = np.cumsum(p_1d)",
          "",
          "    #2d arrays",
          "    p_2d = scale_make2d(p_1d)",
          "    c_2d = scale_make2d(c_1d)",
          "",
          "    #dicts for retrieving values",
          "    dists = {'Normal': p_2d, 'CDF': c_2d}",
          "    channels = {'Red': 0, 'Green': 1, 'Blue': 2}",
          "",
          "    #make your nft",
          "    i, nft = gen_nft(unsig)",
          "",
          "    img = Image.fromarray(nft)",
          "    img.save(f'unsig_{i:05d}.png')"
        ],
        "file": "unsig.py",
        "mediatype": "text/python"
      }
    }
  ],
  "image": "ipfs://QmTBKj1y1CXNaZzBWHtSiAWbuUTHUgT1h7NEUDSmaCxBdJ",
  "title": "unsig_00000",
  "series": "unsigned_algorithms"
}