ISPITIVANJE_CVRSTOCE_FDM_3D_PRINTANOG_DIJELA/software/GRID_OSI.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "206a681f-a3b4-430d-8e88-d4b11a9f32d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==== Grid Infill 80.0% ====\n",
      "XY Total Area         = 87.0324 mm²\n",
      "  Shell Area          = 31.1879 mm²\n",
      "  Infill Area         = 55.8445 mm²\n",
      "I_x                   = 764.6175\n",
      "I_y                   = 764.6175\n",
      "I_xy                  = 0.0000\n",
      "Polar moment, J       = 1529.2350\n",
      "-- Cross-sections through Z (pattern constant over Z) --\n",
      "Length along X @ y=1.000 mm: 7.4593 mm\n",
      "Length along Y @ x=-2.000 mm: 8.6108 mm\n",
      "Area of XZ plane @ y=1.000: 149.1865 mm²  (Z=20.000 mm)\n",
      "Area of YZ plane @ x=-2.000: 172.2153 mm²  (Z=20.000 mm)\n",
      "\n",
      "A_xz(y=1mm) = 149.18648310388022 mm^2\n",
      "A_yz(x=-2mm) = 172.21526908635838 mm^2\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# ------------------------------\n",
    "# Geometry utilities\n",
    "# ------------------------------\n",
    "\n",
    "def _dist_mod(u, spacing):\n",
    "    \"\"\"Distance to nearest line center in a periodic family with pitch=spacing.\"\"\"\n",
    "    r = np.mod(u, spacing)\n",
    "    return np.minimum(r, spacing - r)\n",
    "\n",
    "def _rectilinear_mask(XX, YY, spacing, line_width, angle_deg=0.0, phase=0.0):\n",
    "    \"\"\"\n",
    "    Rectilinear (parallel lines) mask at angle_deg, with true line width.\n",
    "    Lines are centered where (x cos + y sin + phase) is a multiple of spacing.\n",
    "    \"\"\"\n",
    "    th = np.deg2rad(angle_deg)\n",
    "    # Coordinate along the line-normal direction (u-axis)\n",
    "    u =  XX * np.cos(th) + YY * np.sin(th)\n",
    "    dist = _dist_mod(u + phase, spacing)\n",
    "    # Render a line of width 'line_width' around the center => half width threshold\n",
    "    return dist <= (line_width / 2.0)\n",
    "\n",
    "def _spacing_for_grid_density(line_width, f):\n",
    "    \"\"\"\n",
    "    For a two-axis grid (two orthogonal families of rectilinear lines with true width 'w'),\n",
    "    the area fraction is f = 2r - r^2 where r = w/s.\n",
    "    Solve for s: r = 1 - sqrt(1 - f)  =>  s = w / r.\n",
    "    \"\"\"\n",
    "    f = float(np.clip(f, 0.0, 1.0))\n",
    "    if f <= 0.0:\n",
    "        return np.inf\n",
    "    if f >= 1.0:\n",
    "        return line_width  # centers are one line_width apart for a \"solid\" raster\n",
    "    r = 1.0 - np.sqrt(1.0 - f)\n",
    "    return line_width / r\n",
    "\n",
    "# ------------------------------\n",
    "# Area & inertia on a raster mask\n",
    "# ------------------------------\n",
    "\n",
    "def compute_area_moments(XX, YY, mask):\n",
    "    x_vals = XX[mask]\n",
    "    y_vals = YY[mask]\n",
    "\n",
    "    if x_vals.size == 0:\n",
    "        return {\"A\": 0.0, \"x_c\": 0.0, \"y_c\": 0.0, \"I_x\": 0.0, \"I_y\": 0.0, \"I_xy\": 0.0, \"J\": 0.0}\n",
    "\n",
    "    dx = XX[0, 1] - XX[0, 0]\n",
    "    dy = YY[1, 0] - YY[0, 0]\n",
    "    dA = dx * dy\n",
    "\n",
    "    A   = x_vals.size * dA\n",
    "    x_c = float(np.mean(x_vals))\n",
    "    y_c = float(np.mean(y_vals))\n",
    "\n",
    "    x_shift = x_vals - x_c\n",
    "    y_shift = y_vals - y_c\n",
    "\n",
    "    I_x = float(np.sum(y_shift**2) * dA)\n",
    "    I_y = float(np.sum(x_shift**2) * dA)\n",
    "    I_xy = float(np.sum(x_shift * y_shift) * dA)\n",
    "    J   = I_x + I_y\n",
    "\n",
    "    return {\"A\": A, \"x_c\": x_c, \"y_c\": y_c, \"I_x\": I_x, \"I_y\": I_y, \"I_xy\": I_xy, \"J\": J}\n",
    "\n",
    "# ------------------------------\n",
    "# Main: Prusa-style rectilinear/grid with shells\n",
    "# ------------------------------\n",
    "\n",
    "def prusa_style_grid_or_rectilinear(\n",
    "    width, height,\n",
    "    infill_fraction,\n",
    "    line_width=0.42,\n",
    "    shell_layers=2,\n",
    "    base_infill_angle_deg=45.0,\n",
    "    grid=True,\n",
    "    z_height=0.0,              # keep for optional phase control\n",
    "    phase_per_mm=0.0,\n",
    "    # --- NEW: cross-sections extruded through Z ---\n",
    "    z_object_height=None,      # mm (required to report XZ / YZ areas)\n",
    "    y_plane=0.0,               # mm, plane parallel to XZ at y = y_plane\n",
    "    x_plane=0.0,               # mm, plane parallel to YZ at x = x_plane\n",
    "    N=800,\n",
    "    plot=True,\n",
    "    verbose=True\n",
    "):\n",
    "    # ----- Raster grid\n",
    "    xs = np.linspace(-width/2,  width/2,  N)\n",
    "    ys = np.linspace(-height/2, height/2, N)\n",
    "    XX, YY = np.meshgrid(xs, ys)\n",
    "\n",
    "    # ----- Shells (perimeters)\n",
    "    shell_mask = np.zeros_like(XX, dtype=bool)\n",
    "    for i in range(shell_layers):\n",
    "        off = (i + 0.5) * line_width\n",
    "        shell_mask |= np.abs(XX - (-width/2  + off)) <= (line_width / 2)\n",
    "        shell_mask |= np.abs(XX - ( +width/2  - off)) <= (line_width / 2)\n",
    "        shell_mask |= np.abs(YY - (-height/2 + off)) <= (line_width / 2)\n",
    "        shell_mask |= np.abs(YY - ( +height/2 - off)) <= (line_width / 2)\n",
    "\n",
    "    # ----- Infill region (inside shells)\n",
    "    inner_offset = shell_layers * line_width\n",
    "    inner_rect = (\n",
    "        (np.abs(XX) <= (width/2  - inner_offset)) &\n",
    "        (np.abs(YY) <= (height/2 - inner_offset))\n",
    "    )\n",
    "\n",
    "    if infill_fraction <= 0.0:\n",
    "        infill_mask = np.zeros_like(XX, dtype=bool)\n",
    "    elif infill_fraction >= 1.0:\n",
    "        spacing = line_width\n",
    "        masks = []\n",
    "        angles = [base_infill_angle_deg] + ([base_infill_angle_deg + 90] if grid else [])\n",
    "        phase = phase_per_mm * z_height\n",
    "        for a in angles:\n",
    "            masks.append(_rectilinear_mask(XX, YY, spacing, line_width, angle_deg=a, phase=phase))\n",
    "        infill_mask = np.logical_or.reduce(masks) & inner_rect\n",
    "    else:\n",
    "        spacing = _spacing_for_grid_density(line_width, infill_fraction) if grid \\\n",
    "                  else line_width / infill_fraction\n",
    "        masks = []\n",
    "        angles = [base_infill_angle_deg] + ([base_infill_angle_deg + 90] if grid else [])\n",
    "        phase = phase_per_mm * z_height\n",
    "        for a in angles:\n",
    "            masks.append(_rectilinear_mask(XX, YY, spacing, line_width, angle_deg=a, phase=phase))\n",
    "        infill_mask = np.logical_or.reduce(masks) & inner_rect\n",
    "\n",
    "    # ----- Final mask (material present)\n",
    "    final_mask = shell_mask | infill_mask\n",
    "\n",
    "    # ----- Plot\n",
    "    if plot:\n",
    "        plt.figure(figsize=(6, 6))\n",
    "        img = np.where(final_mask, 1.0, np.nan)\n",
    "        plt.imshow(img, origin='lower',\n",
    "                   extent=[-width/2, width/2, -height/2, height/2],\n",
    "                   interpolation='nearest')\n",
    "        title = \"Grid\" if grid else \"Rectilinear\"\n",
    "        plt.title(f\"{title} @ {infill_fraction*100:.1f}%  | shells={shell_layers}×{line_width:.2f} angle={base_infill_angle_deg:.0f}°\")\n",
    "        plt.xlabel(\"X (mm)\")\n",
    "        plt.ylabel(\"Y (mm)\")\n",
    "        plt.gca().set_aspect('equal', 'box')\n",
    "        plt.grid(True)\n",
    "\n",
    "        # guide lines\n",
    "        plt.hlines(y_plane, -width/2, width/2, linestyles='--')\n",
    "        plt.vlines(x_plane, -height/2, height/2, linestyles='--')\n",
    "\n",
    "        plt.show()\n",
    "\n",
    "    # ----- XY numbers\n",
    "    total = compute_area_moments(XX, YY, final_mask)\n",
    "    shells = compute_area_moments(XX, YY, shell_mask)\n",
    "    A_infill = total[\"A\"] - shells[\"A\"]\n",
    "\n",
    "    # ----- NEW: cross-sections parallel to XZ and YZ\n",
    "    dx = XX[0, 1] - XX[0, 0]\n",
    "    dy = YY[1, 0] - YY[0, 0]\n",
    "\n",
    "    ys = YY[:, 0]\n",
    "    xs = XX[0, :]\n",
    "    row = int(np.argmin(np.abs(ys - y_plane)))\n",
    "    col = int(np.argmin(np.abs(xs - x_plane)))\n",
    "\n",
    "    length_x_at_y = float(np.count_nonzero(final_mask[row, :]) * dx)\n",
    "    length_y_at_x = float(np.count_nonzero(final_mask[:, col]) * dy)\n",
    "\n",
    "    area_xz_at_y = None\n",
    "    area_yz_at_x = None\n",
    "    if z_object_height is not None and z_object_height > 0:\n",
    "        area_xz_at_y = length_x_at_y * z_object_height\n",
    "        area_yz_at_x = length_y_at_x * z_object_height\n",
    "\n",
    "    if verbose:\n",
    "        print(f\"==== {('Grid' if grid else 'Rectilinear')} Infill {infill_fraction*100:.1f}% ====\")\n",
    "        print(f\"XY Total Area         = {total['A']:.4f} mm²\")\n",
    "        print(f\"  Shell Area          = {shells['A']:.4f} mm²\")\n",
    "        print(f\"  Infill Area         = {A_infill:.4f} mm²\")\n",
    "        print(f\"I_x                   = {total['I_x']:.4f}\")\n",
    "        print(f\"I_y                   = {total['I_y']:.4f}\")\n",
    "        print(f\"I_xy                  = {total['I_xy']:.4f}\")\n",
    "        print(f\"Polar moment, J       = {total['J']:.4f}\")\n",
    "        print(f\"-- Cross-sections through Z (pattern constant over Z) --\")\n",
    "        print(f\"Length along X @ y={y_plane:.3f} mm: {length_x_at_y:.4f} mm\")\n",
    "        print(f\"Length along Y @ x={x_plane:.3f} mm: {length_y_at_x:.4f} mm\")\n",
    "        if area_xz_at_y is not None:\n",
    "            print(f\"Area of XZ plane @ y={y_plane:.3f}: {area_xz_at_y:.4f} mm²  (Z={z_object_height:.3f} mm)\")\n",
    "        if area_yz_at_x is not None:\n",
    "            print(f\"Area of YZ plane @ x={x_plane:.3f}: {area_yz_at_x:.4f} mm²  (Z={z_object_height:.3f} mm)\")\n",
    "        print()\n",
    "\n",
    "    return {\n",
    "        \"mask\": final_mask,\n",
    "        \"XX\": XX, \"YY\": YY,\n",
    "        \"dx\": dx, \"dy\": dy,\n",
    "        \"area_total_xy\": total[\"A\"],\n",
    "        \"area_shells_xy\": shells[\"A\"],\n",
    "        \"area_infill_xy\": A_infill,\n",
    "        \"moments_xy\": total,\n",
    "        \"length_x_at_y\": length_x_at_y,\n",
    "        \"length_y_at_x\": length_y_at_x,\n",
    "        \"area_xz_at_y\": area_xz_at_y,\n",
    "        \"area_yz_at_x\": area_yz_at_x,\n",
    "    }\n",
    "\n",
    "# ------------------------------\n",
    "# Demo\n",
    "# ------------------------------\n",
    "if __name__ == \"__main__\":\n",
    "    W, H = 10.0, 10.0\n",
    "    Z = 20.0  # object height in Z\n",
    "    res = prusa_style_grid_or_rectilinear(\n",
    "        width=W, height=H,\n",
    "        infill_fraction=0.8,\n",
    "        line_width=0.42,\n",
    "        shell_layers=2,\n",
    "        base_infill_angle_deg=45.0,\n",
    "        grid=True,\n",
    "        z_object_height=Z,  # << set Z height\n",
    "        y_plane=+1.0,       # XZ cross-section at y = +1 mm\n",
    "        x_plane=-2.0,       # YZ cross-section at x = -2 mm\n",
    "        plot=True, verbose=True\n",
    "    )\n",
    "\n",
    "    print(\"A_xz(y=1mm) =\", res[\"area_xz_at_y\"], \"mm^2\")\n",
    "    print(\"A_yz(x=-2mm) =\", res[\"area_yz_at_x\"], \"mm^2\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c04da43a-0894-4100-9457-0160dd2b54d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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TJkzA0KFDde5YYAnU72+BmSwREZHFEB59sTONLcmoiezx48fRvXt3zWV13eqIESOwbNkyfPTRR8jJycG4ceOQmZmJTp06YefOnbCxKW6RtWrVKkyYMAE9e/aEWCzGwIEDMX/+/Gp/LqZA/QYXOCdLRERkMYpnZI0ahkkyaiLbrVs3za+M0ohEIsyaNQuzZs0q8zaurq5YvXq1IcIzO5yRJSIiskCPvteZx5ZksjWypDsRa2SJiIgsjvpIK2tkS2Iia0GKZ2SZyhIREVkK1aMFvcTMY0tgImtBxI8yWeaxRERElkNz7gtnZEtgImuBeLIXERGR5RBYI1smJrIWhCd7ERERWR52LSgbE1kLwpO9iIiILE/xjCwz2ScxkbUgnJElIiKyROquBUYOwwQxkbUgXBCBiIjI8rBGtmxMZC2IqDiTJSIiIgvBGtmyMZG1IOpGySrWFhAREVkM9fc6a2RLYiJrQVgjS0REZHkEtpEtExNZC8KuBURERJanuLSAmeyTmMhaEM7IEhERWR5BU1pAT2Iia0HYtYCIiMhycUK2JCayFoQzskRERJaHNbJlYyJrQXg2IxERkeVRH2nl93xJTGQtSPGMLKdkiYiILAVnZMvGRNaCcD0EIiIiy8P5qbIxkbUkXBCBiIjI4qi/18Wcki2BiawFEfNkLyIiIovDJWrLxkTWgnBBBCIiIgukrpHlyV4lmHwim52djcmTJ8PPzw+2trbo0KEDYmNjNdcLgoAZM2bAy8sLtra2CAsLw5UrV4wYsfGw/RYREZHl0XQtYB5bgsknsmPHjkVUVBRWrlyJs2fPolevXggLC0NSUhIAYO7cuZg/fz4WL16MmJgY2Nvbo3fv3sjLyzNy5NWv+P3NTJaIiMhSaLoWGDcMk2TSiezDhw+xceNGzJ07F126dEGDBg3wxRdfoEGDBli0aBEEQcC8efPw2WefoX///mjevDlWrFiBO3fuYPPmzcYOv9pxRpaIiMjysEa2bCadyBYWFkKpVMLGxkZru62tLQ4dOoSEhAQkJycjLCxMc52zszNCQkIQHR1d3eEanejRO5yJLBERkeUo7g/PTPZJVsYOoDyOjo4IDQ3Fl19+icaNG8PDwwNr1qxBdHQ0GjRogOTkZACAh4eH1v08PDw015UmPz8f+fn5mstyuRwAoFAooFAoDPBMqodKqQQAKFUqs34ehqB+Pfi6WC6OseXjGFs+jnHp1O23VEql2b82+o7fpBNZAFi5ciVGjx6N2rVrQyKRoHXr1nj11VcRFxdX6X3OmTMHM2fOLLF97969sLOzq0q4RhWfJAIgQeKtW9i+/aaxwzFJUVFRxg6BDIxjbPk4xpaPY6wtI0MCQIQTJ+KguGHeh11zc3P1uj+TT2Tr16+P/fv3IycnB3K5HF5eXhgyZAjq1asHT09PAEBKSgq8vLw090lJSUHLli3L3Oe0adMQERGhuSyXy+Hj44Pu3bvDzc3NYM/F0G4dSMC2xCuoXacO+vZtauxwTIpCoUBUVBTCw8MhlUqNHQ4ZAMfY8nGMLR/HuHRLE48CD+QIDm6DnoG1jB1OlaSlpel1fyafyKrZ29vD3t4eGRkZ2LVrF+bOnQt/f394enpi9+7dmsRVLpcjJiYG77zzTpn7kslkkMlkJbZLpVKz/uBIrSQAimplzfl5GJK5jzE9HcfY8nGMLR/H+AmPzoGRWlmZ/eui7/hNPpHdtWsXBEFAQEAArl69ig8//BCBgYEYNWoURCIRJk+ejNmzZ6Nhw4bw9/fH9OnT4e3tjQEDBhg79GqnOZvRvI86EBER0WM07bd4rlcJJp/IZmVlYdq0abh9+zZcXV0xcOBAfPXVV5qM/qOPPkJOTg7GjRuHzMxMdOrUCTt37izR6eBZwJW9iIiILI9mQQR2LSjB5BPZwYMHY/DgwWVeLxKJMGvWLMyaNasaozJNxX1kmcoSERFZCnbfKlulEtnExETcvHkTubm5cHd3R1BQUKk1p2QcTGOJiIgsB1f2KluFE9kbN25g0aJFWLt2LW7fvq0162dtbY3OnTtj3LhxGDhwIMRik15nwWJxQQQiIiLLU7yyF1PZJ1Uo45w4cSJatGiBhIQEzJ49GxcuXEBWVhYKCgqQnJyM7du3o1OnTpgxYwaaN2+O2NhYQ8dNpeC5XkRERJZHPXnINLakCs3I2tvb4/r166X2WK1VqxZ69OiBHj164PPPP8fOnTtx69YttG3bVu/BUvnUP9RUnJIlIiKyGOqvdTFnZEuoUCI7Z86cCu/wueeeq3QwVDWatzfzWCIiIouh6VrAPLaEChezKhQKxMfHay5HR0cbJCCqPLFY3X6LmSwREZGl4MleZatwIjtixAi88MIL+OSTTwAA77//vsGCosrR1MgyjyUiIrIYmq91ZrIlVDiRPXfuHC5fvgypVIqFCxcaMiaqLHYtICIisjjFJ3sxk31ShRNZLy8vAMDMmTNx+PBhJCQkGCwoqpzirgXMZImIiCxFcfsto4ZhkiqcyHbs2BGFhYUAgMWLFyMkJMRgQVHlFK/sZdw4iIiISI9YI1umCi+IMGPGDM3/Ozk5YfPmzYaIh6pAfciBeSwREZHl4IIIZavUErV37tzBoUOHkJqaCpVKpXXdxIkT9RIY6a54RpapLBERkaVQ94cXM48tQedEdtmyZXjrrbdgbW0NNzc3rV8HIpGIiawRsWsBERGR5dG032IiW4LOiez06dMxY8YMTJs2DWJxhUtsqRpoZmSNGwYRERHpUfFJ3Mxkn6RzJpqbm4uhQ4cyiTVBIk37LaayREREloIzsmXTORsdM2YMNmzYYIhYqIqK228RERGRpeDKXmXTubRgzpw56NevH3bu3IlmzZpBKpVqXf/999/rLTjSjYgLIhAREVksdi0oqVKJ7K5duxAQEAAAJU72IuPhjCwREZHlKV7Zi56kcyL73Xff4ffff8fIkSMNEA5VBdtvERERWR6u7FU2nWtkZTIZOnbsaIhYqIr4BiciIrI8xTWy/KJ/ks6J7KRJk7BgwQJDxEJVpH6DqzgjS0REZDHU3+ucsCpJ59KCY8eOYc+ePdi2bRuCgoJKnOy1adMmvQVHuikuLTBuHERERKQ/LC0om84zsi4uLnj55ZfRtWtX1KxZE87Ozlr/9EmpVGL69Onw9/eHra0t6tevjy+//FKrBlQQBMyYMQNeXl6wtbVFWFgYrly5otc4zAW7FhAREVkelhaUTecZ2cjISEPEUapvv/0WixYtwvLlyxEUFITjx49j1KhRcHZ21iyFO3fuXMyfPx/Lly+Hv78/pk+fjt69e+PChQuwsbGptlhNQXHXAmayREREloOlBWXROZGtTkeOHEH//v3x/PPPAwDq1q2LNWvW4NixYwCKZmPnzZuHzz77DP379wcArFixAh4eHti8eTOGDh1qtNiNgaUFRERElocre5VN50Q2LS0NM2bMwN69e5GamgqVSqV1fXp6ut6C69ChA5YsWYLLly+jUaNGOH36NA4dOqRZdCEhIQHJyckICwvT3MfZ2RkhISGIjo4uM5HNz89Hfn6+5rJcLgcAKBQKKBQKvcVf3VRKZdF/BcGsn4chqF8Pvi6Wi2Ns+TjGlo9jXDr1yV7KwkKzf230Hb/Oiezw4cNx9epVjBkzBh4eHgZdBGHq1KmQy+UIDAyERCKBUqnEV199hWHDhgEAkpOTAQAeHh5a9/Pw8NBcV5o5c+Zg5syZJbbv3bsXdnZ2enwG1etUmgiABOlp6di+fbuxwzFJUVFRxg6BDIxjbPk4xpaPY6ytoEACQISDBw/iqvmmKQCA3Nxcve5P50T24MGDOHToEFq0aKHXQEqzfv16rFq1CqtXr0ZQUBBOnTqFyZMnw9vbGyNGjKj0fqdNm4aIiAjNZblcDh8fH3Tv3h1ubm76CN0oxOdTEHn5NGq41kDfvu2MHY5JUSgUiIqKQnh4eIlOG2QZOMaWj2Ns+TjGpfv81F6gUIEuXbqgYS0HY4dTJWlpaXrdn86JbGBgIB4+fKjXIMry4YcfYurUqZoSgWbNmuHmzZuYM2cORowYAU9PTwBASkoKvLy8NPdLSUlBy5Yty9yvTCaDTCYrsV0qlZr1B0dqJQEACBCZ9fMwJHMfY3o6jrHl4xhbPo6xNnVpgbUFvC76jl/n9ls///wzPv30U+zfvx9paWmQy+Va//QpNzcXYrF2iBKJRFOX6+/vD09PT+zevVtzvVwuR0xMDEJDQ/Uai3lQt9/i2V5ERESWgn1ky6bzjKyLiwvkcjl69OihtV0QBIhEIigfnXCkDy+88AK++uor+Pr6IigoCCdPnsT333+P0aNHAyjqmzp58mTMnj0bDRs21LTf8vb2xoABA/QWh7nQdC0wbhhERESkT5o+svQknRPZYcOGQSqVYvXq1QY/2WvBggWYPn063n33XaSmpsLb2xtvvfUWZsyYobnNRx99hJycHIwbNw6ZmZno1KkTdu7c+cz1kAUAMRdEICIisjjFM7JMZZ+kcyJ77tw5nDx5EgEBAYaIR4ujoyPmzZuHefPmlXkbkUiEWbNmYdasWQaPx9QVL4hARERElkJdMsg0tiSda2SDg4Nx69YtQ8RCVaT5ocYpWSIiIovBGtmy6Twj+95772HSpEn48MMP0axZsxJnnzVv3lxvwZFuWCNLRERkeTQre3FOtgSdE9khQ4YAgOaEK6Do8L4hTvYi3YjAGlkiIiJLIzyaouKMbEk6J7IJCQmGiIP0QTMjy0yWiIjIUmhmZJnIlqBzIuvn52eIOEgP1O/vR212iYiIyAIUJ7LMZJ9UoZO9jh49WuEd5ubm4vz585UOiCpP/QbnfCwREZHl0JQWGDkOU1ShRHb48OHo3bs3NmzYgJycnFJvc+HCBXzyySeoX78+4uLi9BokVUxx0wKmskRERJaCpQVlq1BpwYULF7Bo0SJ89tlneO2119CoUSN4e3vDxsYGGRkZuHTpEh48eICXXnoJ//77L5o1a2bouKkUYr7DiYiILI6m/RbnZEuoUCIrlUoxceJETJw4EcePH8ehQ4dw8+ZNPHz4EC1atMCUKVPQvXt3uLq6GjpeKoem/RYnZImIiCyGZkEE5rEl6HyyV3BwMIKDgw0RC1VR8cpezGSJiIgsRfGMLD1J55W9yIRxRpaIiMjiCMxky8RE1oJoFkQwchxERESkH4+fwM0a2ZKYyFoQde2MilOyREREFuHxr3Qx89gSmMhaEM37m3ksERGRRXj8K50LIpRUpUQ2Ly9PX3GQHnBBBCIiIsuiXVpAT9I5kVWpVPjyyy9Ru3ZtODg44Pr16wCA6dOnY+nSpXoPkCquuP0WU1kiIiJLoD0ja7QwTJbOiezs2bOxbNkyzJ07F9bW1prtTZs2xW+//abX4Eg36toZprFERESW4fG5KZ7sVZLOieyKFSuwZMkSDBs2DBKJRLO9RYsWuHTpkl6DI109Ki1gJktERGQRtHrDM48tQedENikpCQ0aNCixXaVSQaFQ6CUoqhxNaQHnZImIiCyC1owsE9kSdE5kmzRpgoMHD5bY/ueff6JVq1Z6CYoqR7OyF/NYIiIii8M8tiSdl6idMWMGRowYgaSkJKhUKmzatAnx8fFYsWIFtm3bZogYqYI0XQuYyBIREVkE7T6yTGWfpPOMbP/+/bF161b8999/sLe3x4wZM3Dx4kVs3boV4eHheg+wbt26EIlEJf6NHz8eQFELsPHjx8PNzQ0ODg4YOHAgUlJS9B6HOSiekWUmS0REZAkeX+SIeWxJOs/IAkDnzp0RFRWl71hKFRsbC6VSqbl87tw5hIeHY9CgQQCAKVOm4J9//sGGDRvg7OyMCRMm4OWXX8bhw4erJT5TImLXAiIiIoui1X6LxQUl6JzIxsbGQqVSISQkRGt7TEwMJBIJgoOD9RYcALi7u2td/uabb1C/fn107doVWVlZWLp0KVavXo0ePXoAACIjI9G4cWMcPXoU7du312sspk7ErgVEREQWReCMbLl0Li0YP348bt26VWJ7UlKS5nC/oRQUFOCPP/7A6NGjIRKJEBcXB4VCgbCwMM1tAgMD4evri+joaIPGYorYtYCIiMiy8Bu9fDrPyF64cAGtW7cusb1Vq1a4cOGCXoIqy+bNm5GZmYmRI0cCAJKTk2FtbQ0XFxet23l4eCA5ObnM/eTn5yM/P19zWS6XAwAUCoVZtxBTKgsBFM3ImvPzMAT168HXxXJxjC0fx9jycYxLUhQUvxaFhYVQQGXEaKpO32OrcyIrk8mQkpKCevXqaW2/e/curKwqVXJbYUuXLkWfPn3g7e1dpf3MmTMHM2fOLLF97969sLOzq9K+jSkpBwCskJeXh+3btxs7HJNUXbXdZDwcY8vHMbZ8HONiuYWAOl3btXMnrHQ+lm5acnNz9bo/nTPPXr16Ydq0afj777/h7OwMAMjMzMQnn3xikK4Fajdv3sR///2HTZs2abZ5enqioKAAmZmZWrOyKSkp8PT0LHNf06ZNQ0REhOayXC6Hj48PunfvDjc3N4PEXx0uJWdj7ploWMtk6Nu3m7HDMSkKhQJRUVEIDw+HVCo1djhkABxjy8cxtnwc45IycxWYFrsXANCnz3OQSsw7k01LS9Pr/nROZP/3v/+hS5cu8PPz0yyAcOrUKXh4eGDlypV6De5xkZGRqFWrFp5//nnNtjZt2kAqlWL37t0YOHAgACA+Ph6JiYkIDQ0tc18ymQwymazEdqlUatYfHKnU6rH/N9/nYUjmPsb0dBxjy8cxtnwc42JWVsVVsjJra0jE5n3Gl77HVedEtnbt2jhz5gxWrVqF06dPw9bWFqNGjcKrr75qsDedSqVCZGQkRowYoVW+4OzsjDFjxiAiIgKurq5wcnLCe++9h9DQ0GeuYwFQ3LVAxcpwIiIii6DVR9aIcZiqShW12tvbY9y4cfqOpUz//fcfEhMTMXr06BLX/fDDDxCLxRg4cCDy8/PRu3dv/Pzzz9UWmynRdC1g/y0iIiKLoNVHlplsCRVKZLds2YI+ffpAKpViy5Yt5d72xRdf1Etgj+vVq1eZyZmNjQ0WLlyIhQsX6v1xzY1mZS+jRkFERET68nj6I2ImW0KFEtkBAwYgOTkZtWrVwoABA8q8nUgk0lqFi6pX8YysceMgIiIi/WBv+PJVKJFVqVSl/j+ZFvUvNZYWEBERWYhHX+mcjC2defdwIC0sLSAiIrIs6u905rGlq9CM7Pz58yu8w4kTJ1Y6GKoaUfEatURERGQBBM2MLFPZ0lQokf3hhx8qtDORSMRE1og4I0tERGRZ1DWyTGNLV6FENiEhwdBxkB6of6ypWCNLRERkEdS94cWckS1VlWtklUolTp06hYyMDH3EQ1WgXhCBeSwREZFl0JzAzTy2VDonspMnT8bSpUsBFCWxXbp0QevWreHj44N9+/bpOz7SQXGJLDNZIiIiS8A8tnw6J7J//vknWrRoAQDYunUrbty4gUuXLmHKlCn49NNP9R4g6Y4zskRERJaFlQWl0zmRvX//Pjw9PQEA27dvx6BBg9CoUSOMHj0aZ8+e1XuAVHFsWkBERGRZimdkmcmWRudE1sPDAxcuXIBSqcTOnTsRHh4OAMjNzYVEItF7gFRxYmayREREFkXTtYB5bKkq1LXgcaNGjcLgwYPh5eUFkUiEsLAwAEBMTAwCAwP1HiBVHGtkiYiILAtrZMuncyL7xRdfoGnTprh16xYGDRoEmUwGAJBIJJg6dareA6SKY9cCIiIiy6JZ2YtTsqXSOZEFgFdeeaXEthEjRlQ5GKoaVhYQERFZFnX7LeaxpatyH1kyHer3OBdEICIisgwqlhaUi4msJVHPyDKPJSIishDqGVmmsqVhImtB2JqDiIjIsmhO9uJXfKmYyFqQx9/kAqdliYiIzJ7mZC+jRmG6KpzIjhkzBjExMWVen5GRgR49euglKKqcx9/kzGOJiIjMX/GMLFPZ0lQ4kY2MjES3bt0QGRlZ6vUFBQXYv3+/3gIj3Ykfe5MzjyUiIjJ/mgURjByHqdKptOCjjz7CW2+9hUmTJkGlUhkqJqoklhYQERFZFtbIlk+nRHb8+PGIiorC2rVr0atXL6SnpxsqLqqEx0/2YhpLRERk/ornpZjJlkbnk726du2KY8eOIS0tDW3btsW5c+cMEZdGUlISXn/9dbi5ucHW1hbNmjXD8ePHNdcLgoAZM2bAy8sLtra2CAsLw5UrVwwak8l67D3OXrJERETmT/19LmYeW6pKdS3w8/PDkSNH0K5dO3To0AGbNm3Sd1wAik4g69ixI6RSKXbs2IELFy7gu+++Q40aNTS3mTt3LubPn4/FixcjJiYG9vb26N27N/Ly8gwSkynTLi0wXhxERESkXywtKF2Fl6h98mw5W1tbrFmzBt9++y2GDh2KsWPH6j24b7/9Fj4+PlonmPn7+2v+XxAEzJs3D5999hn69+8PAFixYgU8PDywefNmDB06VO8xmTK+x4mIiCyLpkaW3/KlqnAiW9bJQx9//DGaN2+OYcOG6S0otS1btqB3794YNGgQ9u/fj9q1a+Pdd9/Fm2++CQBISEhAcnIywsLCNPdxdnZGSEgIoqOjy0xk8/PzkZ+fr7ksl8sBAAqFAgqFQu/Po7oUFhZq/r+gQAEJeEKemnpczXl8qXwcY8vHMbZ8HOOSFIXq10KwiNdF38+hwolsZGQknJ2dS72uT58+iImJwZo1a/QWGABcv34dixYtQkREBD755BPExsZi4sSJsLa2xogRI5CcnAwA8PDw0Lqfh4eH5rrSzJkzBzNnziyxfe/evbCzs9Prc6hO+UpAPaQ7d+2CTGLUcExSVFSUsUMgA+MYWz6OseXjGBe7+QAArJCXl4ft27cbO5wqy83N1ev+REIF+zTl5eXBxsam3NtcuXIFDRs21EtgAGBtbY3g4GAcOXJEs23ixImIjY1FdHQ0jhw5go4dO+LOnTvw8vLS3Gbw4MEQiURYt25dqfstbUbWx8cHd+/ehZubm97ir265BYVo8eUeAMCpz3rAXlbh3ykWT6FQICoqCuHh4ZBKpcYOhwyAY2z5OMaWj2Nc0unbWXjllxh4O9tg/wddjB1OlaWlpcHLywtZWVlwcnKq8v4qnOm0bNkSy5cvR0hISKnXf//995g+fTpycnKqHJSal5cXmjRporWtcePG2LhxIwDA09MTAJCSkqKVyKakpKBly5Zl7lcmk0Emk5XYLpVKzfqDI3vs3D0rqRRSKRPZJ5n7GNPTcYwtH8fY8nGMi0kkRYdXRSKRRbwm+n4OFe5aEB4ejs6dO2PatGla9Q1XrlxBx44dMWfOHPz22296Da5jx46Ij4/X2nb58mX4+fkBKDrxy9PTE7t379ZcL5fLERMTg9DQUL3GYm64IAIREZH5U3+bs2tB6SqcyC5YsAA7duzAmjVr0Lp1axw/fhw//PADWrRogZo1a+Ls2bN49dVX9RrclClTcPToUXz99de4evUqVq9ejSVLlmD8+PEAin6dTJ48GbNnz8aWLVtw9uxZvPHGG/D29saAAQP0Gos50Gq/ZbwwiIiISE/U81JiZrKl0unYc8+ePXH27Fm8/vrrCAkJgZ2dHX755RcMHz7cIMG1bdsWf/31F6ZNm4ZZs2bB398f8+bN0+qQ8NFHHyEnJwfjxo1DZmYmOnXqhJ07dz61ntcSaa3sxYYFREREZk99hJV5bOl0LqJcs2YN9u7di5CQEJw4cQIHDhzASy+9BAcHB0PEh379+qFfv35lXi8SiTBr1izMmjXLII9vTrRnZDknS0REZO40pQVGjcJ0Vbi0ICkpCb1798bHH3+M+fPn48iRI4iJiUFsbCyCgoK06lTJOB5/k7NEloiIyPxpFkTglGypKpzINm3aFCKRCGfPnsXIkSMBAC1atEBsbCyGDx+OPn364J133jFUnFQBj7/JmccSERGZP01pgZHjMFUVTmTnzJmDnTt3ok6dOlrbpVIpZs+ejcOHD+PAgQN6D5AqTntGlqksERGRudN8mzOTLVWFE9m333673Ovbtm2LkydPVjkgqjx2LSAiIrIsmtIC44ZhsiqcyFaEtbW1PndHOtIqLWAmS0REZPbUJ2+zRrZ0ek1kyfjU73N2LSAiIrIAmj6yxg3DVDGRtTDq9zlnZImIiMyfSlNawEy2NBVKZM+cOQOVih32zYH60AMTWSIiIvNXXFpg5EBMVIUS2VatWuH+/fsAgHr16iEtLc2gQVHlaWZkWVpARERk9jgxVb4KJbIuLi5ISEgAANy4cYOzsyZMUyPLNz4REZHZ06zsxSnZUlVoidqBAweia9eu8PLygkgkQnBwMCQSSam3vX79ul4DJN0U1dBwPpaIiMgScEGE8lUokV2yZAlefvllXL16FRMnTsSbb74JR0dHQ8dGlaGZkWUqS0REZO6KZ2SNGobJqlAiCwDPPfccACAuLg6TJk1iImui2LWAiIjIgqi7FjCRLVWFE1m1yMhIzf/fvn0bAEosW0vGI+Y7nYiIyGJouhawuKBUOveRValUmDVrFpydneHn5wc/Pz+4uLjgyy+/5ElgJoAnexEREVkOgQsilEvnGdlPP/0US5cuxTfffIOOHTsCAA4dOoQvvvgCeXl5+Oqrr/QeJFWc+n2uYiZLRERk9lQski2Xzons8uXL8dtvv+HFF1/UbGvevDlq166Nd999l4mskWkWRDByHERERFR17FpQPp1LC9LT0xEYGFhie2BgINLT0/USFFVe8cleTGWJiIjMHSdky6dzItuiRQv89NNPJbb/9NNPaNGihV6CoipQ18gaNwoiIiLSA/W8FPPY0ulcWjB37lw8//zz+O+//xAaGgoAiI6Oxq1bt7B9+3a9B0i6YfstIiIiS/KotIBTsqXSeUa2a9euuHz5Ml566SVkZmYiMzMTL7/8MuLj49G5c2dDxEg6KH6jM5MlIiIyd5yRLZ/OiSwAeHt746uvvsLGjRuxceNGzJ49G97e3vqODV988QVEIpHWv8frc/Py8jB+/Hi4ubnBwcEBAwcOREpKit7jMCfiSrTfOnD5Hv4+lWQydbV3sx7i90MJSM8pMHYoRHqlUglYH3sLc3deQmJarrHDAQCcvpWJOdsvIurCs/23kyxTYlouIg8nQJ6nMHYoAACFUoU/jt7EuaSsCt+HNbLl07m0oLoFBQXhv//+01y2sioOecqUKfjnn3+wYcMGODs7Y8KECXj55Zdx+PBhY4RqEnTtWrDpxG1ErD8NALid8RDjuzcwUGQVk/YgH68sikZS5kNsiLuNtePaw9lWatSYiPRBEATM3Hoey6NvAgCO38jA+rdDjRpTfqES41YeR4o8H78cuI4v+wdheGhdo8ZEpC8303LwyuJo3MvOx7Yzd7HmzfawtqrU/J3efPznGWw6mQQbqRgrx4SgbV3Xp95HMyPLTLZUxh3RCrCysoKnp6fmX82aNQEAWVlZWLp0Kb7//nv06NEDbdq0QWRkJI4cOYKjR48aOWrj0aWPbNSFFHz45xnN5R93X0Fqdp6BIquYxfuvISnzIQDg4l05xiyLxcMCpVFjItKHH/67okliAeDYjXTE3cwwYkTA5pNJSJHnay7P2HIef59KMmJERPqRIs/D60tjcC+76P0ddzMDm08a970ddzMdmx7FkKdQYfSyWJy/8/SZWRXbb5XL5BPZK1euwNvbG/Xq1cOwYcOQmJgIAIiLi4NCoUBYWJjmtoGBgfD19UV0dLSxwjW6iq7sFX0tDeNXn4BSJWBg6zpo5euCgkIVIg/fMHiMZcnKVWB1TNH4fvRcAJxsrHD8Zgbe/iMOBYVcNY7M19JDCZi/+woA4Mv+QRgcXLSs9+L914wWk0ol4JcD1wEAn/ZtjBGhfhAE4P31p7HnEssMyHxl5hZg+NIY3Ep/CD83O7zZ2R8AsPjANahUxiuhW7Sv6PPWv6U32tV1RXZeIUb8fgwJ93PKvR9LC8pn0qUFISEhWLZsGQICAnD37l3MnDkTnTt3xrlz55CcnAxra2u4uLho3cfDwwPJycnl7jc/Px/5+cWzEHK5HACgUCigUJhGHU1VKRSFZT6Xs0lZGLv8OAoKVQgLdMfsFwOx7/J9vLP6FFZG38SbHX3haFP9h/OXHb6OnAIlAjwcMLaDL9r4OGPEsuPYf/kepqw9ie8GNYOkkmv0qV8LSxlfKslUx3jjiSR8ue0CAGBKzwYYGlwbbf1csCHuNqIupOBiUgYa1HKo9rj+vZCC6/dy4GRjhVdae8FOKkFGTgG2nLmLd/44gd9HtEa7Chz2rE6mOsakP1Ud45z8QoxYFofLKQ/g4ShD5IjWcLG1xtrYW7h+Lwc7ziahVxMPfYZcIVdSHuC/iykQiYB3u/jD3dEaw5Yex8XkbAz79SjWvtkOXs42pd63sLCw6H8EwSLe+/p+DiLBVM7wqYDMzEz4+fnh+++/h62tLUaNGqWVkAJAu3bt0L17d3z77bdl7ueLL77AzJkzS2xfvXo17Ozs9B53dZp+XAK5QoQPmxeijn3J65NzgfnnJcgpFKGhkwpvNVZBKi5aAu+b0xKkPBThRV8letau3rdFgRKYeUKCB4UiDG+gRLB70eNfzBTh10tiKAUROnioMNhfxV+lZDbOpIvwe7wYAkTo5qXCAL/i9+9vl8Q4myFGO3cVhjWo3iMOggD8cE6Cmw9ECK+tQj/fosdXqoCll8U4nyGGjUTAhCZK+FR/jk1UKYUq4JdLYlzOEsPOSsDEICW8Hn2lb0sUIypJDD8HAVOaKqv9e2TVVTGO3ROjuasKYwKKPm/ZCmD+OQlS80TwsC2K16GUOaTj90RYeVWChk4qTAgy/6OTubm5eO2115CVlQUnJ6cq769SM7J//vkn1q9fj8TERBQUaJ9ZfuLEiSoHVRYXFxc0atQIV69eRXh4OAoKCpCZmak1K5uSkgJPT89y9zNt2jRERERoLsvlcvj4+KB79+5wc3MzVPjV4qtz+yFX5KNjx04I8tZ+gyRlPsTXvx5DTmE+mtd2wvJRwXCQFb8F8r2SMPWv8ziaYYevR3WGrBqL4lcdu4UHhRdR28UGn7zeCVaSosfuC6Dx2WRM3nAGR1LEaB5QH++HN9R5/wqFAlFRUQgPD4dUypPHLJGpjfGRa2lYsfIEBAgY2NobcwYEaZ2s4dUsE4OXHMPJdAn+17FbmbMxhhCTkI6bR4/D2kqMWa93RU0Hmea6cIUSo1ecQOyNDPx+3RZrxrRDPfdSfhUbgamNMelfZce4UKnCpPVncDkrFXbWEqwYFYwWdZw117d7kI/93x3EzQcq1GzSHiH+1Xe04W5WHt6POQhAwIxBoVpxder6EEN/PYZkeT7W3nXF8pHBcLTRTs0Up+9i5dWzcK9ZE337Bldb3IaSlpam1/3pnMjOnz8fn376KUaOHIm///4bo0aNwrVr1xAbG4vx48frNbgnPXjwANeuXcPw4cPRpk0bSKVS7N69GwMHDgQAxMfHIzExUbNQQ1lkMhlkMlmJ7VKp1Oz/OKq/J62srLSey73sfIxafgIp8nw0rOWAZaNDUMPeWuu+L7fxxY97ruFuVh62nU3B0Ha+1RJzoVKF3w8XnQTzZud6sLXRHpv+rX2QWyhg2qazWHwgATXsZXira/1KPZYljDGVzxTG+NStTLyz+hQUSgHPBXni24EtND/O1NrVc0eIvytiEtKx4ugtfNavSbXF9+uhos/b4OA68KqhPeUqlUrx+8i2ePXXoziXJMeo5XHY8E4H1Haxrbb4nsYUxpgMS5cxFgQBn2w+g38vpMJaIsavbwQj2L+m1m28akgxqE0drIpJxG+Hb6JTo+orL1h+9AoKVQLa13MtEVdddyn+GNseg3+JxtkkOd5dcwrLRrWDjVSiuY3k0d8OsVhsEe97fT8Hnafcfv75ZyxZsgQLFiyAtbU1PvroI0RFRWHixInIyqp4X7SK+OCDD7B//37cuHEDR44cwUsvvQSJRIJXX30Vzs7OGDNmDCIiIrB3717ExcVh1KhRCA0NRfv27fUahzkRPTqv8fGCkayHCk1BeW0XW6wcEwLXJ5JYALC2EmNMp6Ki+CUHrkNZTUXxO84lIzE9FzXspBjc1qfU27zazhdT+xT1EJ6z4xLWHEusltiIdHU5JRsjI48ht0CJjg3c8OOrLUsksWpvdyv6QbbmWCKycqun9u3CHTn2X74HsQgY17n0H4SONlIsH1U0E3snKw/Df4vB/Qf5pd6WyJgEQcBX/1zEhrjbEIuABa+1QscGNUu97bgu9SAWAfvi7+HiXXm1xJeZW6D5vnq7jAmYBrUcsHxUOzjIrHD0ejomrD6JQmVxCUFx+y2Dh2uWdE5kExMT0aFDBwCAra0tsrOzAQDDhw/HmjVr9Brc7du38eqrryIgIACDBw+Gm5sbjh49Cnd3dwDADz/8gH79+mHgwIHo0qULPD09sWnTJr3GYG40CyI8Os/xYYESY5fH4sJdOWo6yLBqbAg8yzmEObSdL5xsrHD9fg6iLpR/0pw+CIKgOXN7RIe6sLMu+yDB213ra/4QfPLXWfxz5q7B4yPSxa30XAxfGoPMXAVa+rhgyfBgyKwkZd6+WyN3BHo6IqdAiZVHb1RLjL8cKPq89W3mBV+3ss8JcHOQ4Y8xIfB2tsH1+zkY8fsxk2kqT6S2cO9V/HYoAQDw7cDm6B1Udmmhn5s9+jTzAgD8Uk0dQ1ZG30RugRKNvZzQtZF7mbdrVscZv40IhrWVGP9dTMFHf57RdFhgH9ny6ZzIenp6Ij09HQDg6+ur6dmakJCg95Wh1q5dizt37iA/Px+3b9/G2rVrUb9+8S8aGxsbLFy4EOnp6cjJycGmTZueWh9r6TQLIghAQaEK76yKQ+yNDDjaWGHF6HaoW7P8WjcHmRXeeNQQfVWM4Wc9zyZl4fwdOWykYoyoQCP2j58LwKvtfCAIwOR1J3Hg8j2Dx0hUEanZRX0rU+T5aOThgMiRbWEvK796SyQSaX6cRR6+gTyFYXsm30rPxbZHPwDLmh16nLeLLVaODYGbvTXO35Fj7PLjBo+RqKJWRt/A//69DACY3q8JBgWXfkTvce88et9vO3MXWQ8N+8NMEATNbOxbXeo9NRFtX88NP7/WGhKxCJtOJmHWtgsQBKG4/ZZBozVfOieyPXr0wJYtWwAAo0aNwpQpUxAeHo4hQ4bgpZde0nuAVDmFKgER609hX/w92EjFiBzZFk28K3Z24EutawMAjl5PM/gMzL/ni/pV9gisVaJmtzQikQizBzTD8829oFAKeGtlHOJuphs0RqKnycpV4I2lx3AzLRc+rkXlOxV5PwNAv+Ze8Ha2QVpOAfbFG/aH2d+nkqBUCehQ3w1Nazs//Q4A6rs7YPnodnCUWeFYQjreXXUCCqX5nzlN5u3vU0mYseU8AGBijwaasrinaVrbGQ1rOaBQJWBffKohQ8T5O3LcycqDrVSC55pWbJItrIkH/jeoOQBg2ZEb+HH3leIFEZjJlkrnRHbJkiX49NNPAQDjx4/H77//jsaNG2PWrFlYtGiR3gMk3ajf6F9vv4htZ+5CKhHhl+HBCNahH2R9dwfUd7eHQikY/Iv130flC72aVHwmXSIW4YfBLdG1kTseKpQYFRlbbfVORE/KLSjEqGXHcCk5G+6ORYfjPZwq3oHASiLWHO7818DlPP9eKPrh+GILb53u17S2M5aObAuZlRh7LqXigw2njdpYnp5tey6l4P31pyEIwIhQP0wJb6TT/XsFFZ3opZ5IMZR/zxd9nrs2ctc6eetpXmpVBzNfDAIAzPvvCtbF3gLAGdmy6JzIisViWFkVHy4bOnQo5s+fj/feew/W1hWbgSDDUSeycTczIBIBPwxpWW5dTlnCHyWWURcM90G/mZaDyykPIBGL0D2glk73tbYSY9HrrdHGrwbkeYUYvvQYbjxldRQifcsvVOKtlXE4kZgJJxsrrBzTDn5uureqCn/UoH3PpVStkzz06W7WQ5y5nQWRCOjZWPczttv5u2LR661hJRbh71N38MXW83ovJyN6mpjraXjnjxMoVAkY0NIbn78QpHPtqPr7bV98KvILDVcqo/7hGF6JBRhGdKiLiEcJunopa9bIlq5SjUIPHjyI119/HaGhoUhKKlo3eOXKlTh06JBegyPdiR77zfbVgGbo11y3mRc19S/WfZdSDbY8rDpJbl/PFc52urfjsLO2wu8j2iLQ0xH3H+Tj9aUxSM7K03eYRKVSqgRErDuNg1fuw1YqQeSodgj0rFxz72C/GqhhJ0VmrgKxNzL0HGmR/x593lr71oC7Y8n2gxXRI9AD3w1uAZEIWBF9E99HXdZniETlOvdoVcr8QhV6BtbC/w1qAXElVntsXtsZHk4y5BQoEX1Nvz1N1W6l5+JScjYkYhF6BOo2UaP2Xo8GGN2xuGSCaWzpdE5kN27ciN69e8PW1hYnT57UrKyVlZWFr7/+Wu8Bkm7UX1AfPxeI10Iq3we2ZR0XuDvKkJ1fiKPXDfNBVx/WCa/E7JCas50UK8a0Q103O9zOeIjhS2OQkVPw9DsSVYEgCPhs81n8c7aofGfJG23Qxq9GpfdnJRGjR2DR58BQR0GqMjv0uP4ta2NW/6YAgAV7ruK3g9erHBvR01y79wAjfj+G7PxChPi7YuGw1pCW0dbuacRiEcIefe/8a+DPW9u6NSpcL/8kkUiEz55vjIGt6wBApX+AWjqd3wWzZ8/G4sWL8euvv2o1te3YsaNBV/WiivnptVZY/1Yo3ulWuQUD1Io+6EW/Ig3xxZr2IB/HH52kFVbFL9ZajjZYOSYEHk4yXEl9gJHLYvEgv1AfYRKV6pudl7Dm2C2IRcCPQ1uhc0Pdy3eepD4KEnUxWe+H7OV5Cs0PUn2sMz+8vR8+7B0AAJj9z0WsP36ryvskKktS5kMM/y0GaTkFaFrbCb+NCNap5rQ06h90/11IMUi9t7p9ZbgO53+URiwWYe4rzbFyTDtM69NYH6FZHJ0T2fj4eHTp0qXEdmdnZ2RmZuojJqoCL2dbtNPT0nu9HquT1fcX6+5LqVAJQJC3E+rUKLuXZUX5uNrhjzEhcLGT4vStTIxbwTZBZBiL9l3DL/uLZiHnvNwMfR+dqFVVnRvWhMxKjFvpD3EpOVsv+1TbF38PCqWA+u72qOfu8PQ7VMC73erjzc5Fhz2nbjyDnecM33eanj33H+Rj+G8xuJOVh3ru9lg+qh0cbaq+MlRofTc4yKyQmp2P07czqx7oYzJyCnAsoWiiRh8/HCViETo3dK9UCd6zoFJ9ZK9evVpi+6FDh1CvXj29BEWmIbS+G+ysJUiW5+Fskn5XbYvS02HOxzX0cMSyUe1gby3BkWtpmLjmpMFOnKFn0+qYRHy78xIA4JO+gRjSVn/LONtZW6Fzw6IVifR9FKT486a/PtsikQif9G2MwcF1oBKAiWtO4tCV+3rbP5E8r2hVyuv3c+DtbIM/xoTAzUE/h9dlVhJ0DSg6kqLvz9ueRxM1gZ6O8HGt+kQNlU/nRPbNN9/EpEmTEBMTA5FIhDt37mDVqlX44IMP8M477xgiRjISG6kE3R590PXZpuRhgRIHrxS19dKl7VZFtPRxwa9vFK2O8u+FFEzddJZtgkgvtp6+g083nwVQNBs5rkvVyndKo/486LMNV0GhCvsuFfXLVJcv6ItIJMLXLzXDc0GeKFCqMG7lcZxMNMzJavRsyVMoMXb5cZy/I4ebvTX+GBsCbxdbvT6GerZU33Wy6sS4VzmrjJH+6JzITp06Fa+99hp69uyJBw8eoEuXLhg7dizeeustvPfee4aIkYxIPWOqz1+sB6/cQ55Chdoutmjs5ai3/ap1aFATP73aChKxCH/G3cbsfy6yTRBVyb74VESsPwVBAIaF+GrqQ/WtR+NaEImAc0ly3Ml8qJd9Hr2ehuz8Qrg7ytCyjote9vk4K4kYP77aEp0a1ERugRIjI2MRr+fSCHq2KFXAe2tP41hCOhxlVlg+up3eSmIe1y2gFqzEIlxNfYAEPbVvzFMosf+yeqJGvz8cqXQ6JbJKpRIHDx7E+PHjkZ6ejnPnzuHo0aO4d+8evvzyS0PFSEbUPaAWJGIR4lOycTNNPx/0x8sKDNUXr1eQJ74dWLQ6yu+HE/Dz/gSDPA5ZvuM30vH2H3FQKAW80MIbs/o3Ndj7tqaDDMGPuh/8d1E/Px7Vn7ewxh6ValVUETIrCX4Z3gYtfVyQ9VCB4UtjcCs91yCPRZZNpRKw6poY+y7fh8xKjKUj21Z4FTpdOdtK0b6eG4Dik7Oq6vDV+3ioUMLb2QZBFVxNk6pGp0RWIpGgV69eyMjIgLW1NZo0aYJ27drBwUH/v5TINLjYWSPk0clj+piVVaoE7DbQYc4nvdKmDmb0awIAmLf7Kg7cZRc+0s2FO3KMWhaLPIUK3QLc8d2gFpAYKBlUUx8F0Uc5jyAIxYc5DTw7ZC+zwrJRbRHg4YjU7HwM+y0GqXL2daaKEwQBX26/hLj7YliJRVj0emu9nbxcFn2v8qVpK2nAiRrSpnNpQdOmTXH9OvsGPkvC9VhHFHczA+k5BXC2laKdDsvmVtboTv6Y2LMhAGDjDQn+Pn3X4I9JliHhfg7e+P0YsvMKEexXA4uGtYG1VeX6VupCfULW0etpyHqoqNK+ziZlIVmeB3trCULru+kjvHK52FljxZh28HG1RWJ6LoYvPYbMXPZ1por5Ieoy/oi5BREEzB3YVNNb2ZDU/WTjEjNw/0F+lfZVNFGj/xMrqXyV6iP7wQcfYNu2bbh79y7kcrnWP7I86kT2+I10pFdxsQH14ZuegbVgVclm1rqaEtYQw9sXnV3+8aZzmhWOiMpyN+shXv8tBvcf5KOJlxOWjmwLW+uq9a2sKP+a9mhYywGFKgH74lOrtC/17FDXAN3Weq8KD6eis8vdHWWIT8nGqGWxyC1gX2cq328Hr2P+nqKOSK/4q/BCc/20tXsabxdbNKvtDEEAdlexnOfUrQzcf1AARxsrhNQz/EQNFdE5k+jbty9Onz6NF198EXXq1EGNGjVQo0YNuLi4oEaNyq9sQ6arTg07NPFygqqKH3RBEPS2upAuRCIRPusTgOCaKihVAsavPmGw1crI/KXnFGD40mNIynwI/5r2WD66HZxtq7d/o76OghiizV1F+LnZ448xIXC2leJkYibeWhln0DXtybytP34Ls/+5CACICGuATp7Ve3Kuvk5qVv9w7BFYq9KrjpHudH6l9+7dq/m3Z88ezT/1ZbJMmjqiKnzQr6Q+wM20XFhbidGlUdVXQtKFWCzCa/VV6BHgjvxCFcYuP46zt/XbG5fM34P8QoyMPIarqQ/g6WSDlWPaGWVZSHXbnn2XUiudAN5My0F8yqO13gOq/+zpAE9HRI5qC1upBAev3MfktaegZCs8esLOc8mYuvEMAODNzv54u4t/tceg/n47eOV+pY8ePD5Ro++2klQ+nRPZrl27lvnPzc3wNVhkHOpfrAev3MPDgsp9sf57vqisoFODmrCXWekttoqSiIEfhzRHiL8rHuQXYsSjhIUIKGqb8+by4zhzOws17KT4Y2w7vaw6VxnNazujlqMMOQVKRF+r3NED9exSiL+r0VYEau1bA0veaAOpRIQd55LxyaazbIVHGoev3sfENSehEoDBwXXwSd/GRjlBKsDDET6utsgvVOHA5cot6nHtXlELL2uJWLPQAlWPKs99Z2dnY8mSJWjXrh1atGihj5jIBDXxckJtF1vkKVQ4dLVyH3RjHeZ8nI1Ugt9GBKNZbedHh5BjkKSnfp1kvgqVKry35iSir6fB4VHfyga19N/juKLEYlGVD3f+W03dCp6mc0N3zB/aCmIRsO74LczZcYnJLOFkYgbeXHEcBUoVngvyxNcvNTPaWf4ikQjhjYuXZK8M9edNvfQtVZ9KJ7IHDhzAiBEj4OXlhf/973/o0aMHjh49qs/YyISIRKLH2gLp3m8vOSsPp29nQSQCejaupe/wdOJoI8WyUW1R390ed7PyMPzRST30bFKpBHy08QyiLqTA2kqMX98IRnMDLBygq8cTWV1Xp0vPKcDxG0VrvYeZQFP2Ps288M3LRX2dlxy4jp/3XTNyRGRM8cnZGBkZi9wCJTo1qIkfX21ZbSf/lkVdXrD7UkqlljYvXs3L+J+3Z41O75zk5GR88803aNiwIQYNGgQnJyfk5+dj8+bN+Oabb9C2bVtDxUkmQD2zs/tSqs61blGPThJr5eOCWo42eo9NV24OMqwcE4LaLra4fj8Hbyw9Bnle1VodkfkRBAGztl3AphNJkIhFWPha62ppU1UR6pmd1Ox8nEnSrZ5798UUqISiIynGKo940uC2Pvi0b2MAwP/tiscfR28aOSIyhlvpuRi+NAZZDxVo6eOCX4a3gcyqejpqlCfYrwZc7KTIzFXg+E3dlllOlefhZGImgOJ2XlR9KpzIvvDCCwgICMCZM2cwb9483LlzBwsWLDBkbGRi2vq7wtlWivScAsTp+EE3xbWnvV1ssXJMO7jZW+PCXTnGLjte6fpfMk/zd1/FsiM3AAD/90pzo5a9PElmJdHU2ul6FORfE50derNLPYzvXh8AMP3vc9hy+o6RI6LqlCrPK1ooIzsfAR6OWDaqrVHOlyiNlUSMnoGVWxzhv4tFbfJa+rjAw8n4EzXPmgonsjt27MCYMWMwc+ZMPP/885BIjP8LiqqXVCJGj8CisgBdlvOT5ykQfa2ortaUEgUAqOfugOWj28FRZoVjN9Lx7qo4KCpxWInMz7LDCfjhv8sAgC9eaIKXW9cxckQl9apEnezDAiUOXila693UPm8A8EGvALze3heCAESsO4W9l6rWK5fMQ1auAm/8fgyJ6bnwdbXDijHt4GJnbeywtGjKeS4m61TH/e+j70NT/Lw9Cyr8U+jQoUNYunQp2rRpg8aNG2P48OEYOnSoIWMr4ZtvvsG0adMwadIkzJs3DwCQl5eH999/H2vXrkV+fj569+6Nn3/+GR4efEMZQngTD/x1Mgn/Xkip8Bmm++PvQaEUUM/dHvXdTW8546a1nfH7qLYYvjQGe+Pv4f31pzWrgZk6X1e7alltqiJUKgGJ6bkoNFKLpcLCQqQ8BK7dy4GVVfl/2mIS0vDF1gsAgClhjTCyY/W3/KmIbgG1YCUW4Upq0RnR/jXtn3qfQ1fvI0+hQm0XWzTxMr213kUiEWa92BTyh4XYcvoO3lkVh59ebY26FXhuuoyxIdhIxajtYmsyS48+yC9EcpbpLwOsEgRM3XgGl5KzUctRhj/GhJjkzGWXRjUhsxLjVvpDXErORuMKfH4e5BfiyNWiziLGPrHyWVXhvwTt27dH+/btMW/ePKxbtw6///47IiIioFKpEBUVBR8fHzg6Gu4s39jYWPzyyy9o3ry51vYpU6bgn3/+wYYNG+Ds7IwJEybg5ZdfxuHDhw0Wy7OsSyN3WFuJcTMtF1dSH6CRx9PHPMoMeuu1reuKRcPa4M0Vx7Hl9B2zOeRZ28UWa8e1h4+r8esgp/99DqtiEo0chRW+PlXxz/6ojnUxsWcDA8ZTNc62UrSv54ZDV+8j6kIyxnWp/9T7qMsQTHmtd7FYhO8Gt0B2ngJ74+9h7IrjOtxbtzHWt89faIJRJvDDJ+5mOkZGxiI7z3xWTXO2lWLlmBD4uhn/71Vp7Kyt0LlhTfx3MRVRF1IqlMgeuHwPBUoV/Gvao0Et05uoeRbo/JPW3t4eo0ePxujRoxEfH4+lS5fim2++wdSpUxEeHo4tW7boPcgHDx5g2LBh+PXXXzF79mzN9qysLCxduhSrV69Gjx49AACRkZFo3Lgxjh49ivbt2+s9lmedg8wKHeu7YW/8PURdSHlqIltQqNIcOjT1wy7dA2th/qut8PX2i2bx5ZD1UIGkzIeYvO4U/nw71KhJS1LmQ6yLvQUAcLKxMlIsAhQFCkitpQDKf3yxCBjY2nh9K3XRK8jjUSKb8tREtmit91TN/UyZVCLGz8Pa4IM/T+PQlYq29Kv4GOubSiUgO78QC/dew6vtfKttyd/S5CmUeOePE5q/U9W98lxl1HKU4dtXmiPA03ht7SoivImHJpGtyJE5c/jhaOmqdGwmICAAc+fOxZw5c7B161b8/vvv+opLy/jx4/H8888jLCxMK5GNi4uDQqFAWFiYZltgYCB8fX0RHR1dZiKbn5+P/PzidktyuRwAoFAooFDwzPWn6Rnojr3x97Dr3F2M6+RX7m0PX01Ddn4hajpYo6mnvdFeX/XjPu3xwwNrIjywc3WEVGV3s/IQNu8Q4m5mIPrqPbSta7wlopfsv4pClYDQeq5YMSrYKDEoFApERUUhPLwHpNKKfbErlYVQmvj5fd0aFnVROH4zA8kZD+DmUPZKY7E3MpCeUwBnWyu0qu1o8n/PrETAvEHNKnz7yoyxviiUKvT84RDuZuVhQ2wihrY1Xk31+thbSM3Oh5ezDXZN7Ahba/M5Z+Vp78mK/q02lK4NXCESAWeTspB4PxtezmWXQCiUKux59MOxRyM3k/+8mQp9v056KTKSSCQYMGAABgwYoI/daVm7di1OnDiB2NjYEtclJyfD2toaLi4uWts9PDyQnFz2yUhz5szBzJkzS2zfu3cv7OxM85CHKREKABEkOJMkx+q/tsOlnBU8N1wXAxCjoV0edu7cUW0xliUqKsrYIehVsKsYR1LFmL0xBm81Ns5JajkKYM0JCQARWsruYfv27UaJQ83SxhgA6thLcDtHhB//3IP2tcquQd5849Hnzb4A/+7aWX0BVjNjjXH7GiL8lSXBj/+eh0PqGYiNMAGnEoD5J4s+b+1r5GDvf7uqP4hqYMzPcV0HCRKyRZi/cS86e5b9eYvPEkGeJ4GDlYC756Kx/Xw1BmnGcnNz9bo/0+h7UYZbt25h0qRJiIqKgo2N/grDp02bhoiICM1luVwOHx8fdO/encvsVtCm1BicvJUFoXYz9G3nU+ptBEHAnP8dAJCP0b3boFsj4y3bVzyTE17tMzmG1CQtB71+PIwLmWLUb93RKIftFuy9hgLVNTTxcsSUV9sb7fCapY4xAFy3vYYf91xDipUn+vZtVeptBEHAd/MOAXiIEWGt8JyJlxZUhrHHuGt+IfZ8dwD3HxZC4tcafZpWf93/9rPJuH/0DFxspfhieA/YWZv017jOjD3GAHDH6Qa+3XUZd8Tu6Nu37CNMx/+5BCARfVrUQb/ng6ovQDOXlla5ZbfLYtKfgLi4OKSmpqJ169aabUqlEgcOHMBPP/2EXbt2oaCgAJmZmVqzsikpKfD0LPsPjEwmg0xWchpRKpVa3BegofQK8sLJW1nYE38fIzvWK/U2Z29nIVmeDztrCTo38oDUiDVlapY2xg09XdC3qRf+OXsXS48k4ochLav18XMLCrHyaNEJXm93awBra+O307G0MQaA55p548c913D4WhoUgqjU5OVySjYS0x/C2kqMHo09IZWa9J/3KjHWGLtIpRgRWhfz91zFr4du4oWWdar1h5sgCPj18A0AwIgOdeFsb1ttj13djPk5fq6ZN77ddRnHEjKQW1h6DbIgCNhzqajNXe8gL4v7m2NI+n6tTKNvTxl69uyJs2fP4tSpU5p/wcHBGDZsmOb/pVIpdu/erblPfHw8EhMTERoaasTILZ/6xK3oa/fLXBFL3VuvayN3o54YYene7lp0AtCW03dwO0O/h2yeZn3sLWTkKuDraoe+RpidelYEejrCx9UW+YUqHCzjxCh1d5BODWqaTJN5SzSiQ13YSMU4m5SFI9f0O7P0NIevpuFckhw2UjFGdKhbrY/9LFF3IChUCdgXX3qf4/N35EjKfAhbqQSdGtas5gjpcSadyDo6OqJp06Za/+zt7eHm5oamTZvC2dkZY8aMQUREBPbu3Yu4uDiMGjUKoaGh7FhgYA1qOaCeuz0USgH74++VehuuPV09mtVxRscGblCqBPx2MKHaHlehVOHXR4/3Zpd6Rl8r3ZKJRCKENy76oVDWqkOPnz1NhuPmIMOQ4KJyqsX7r1XrY6sfb2hbX7jaG//ohyVT94T9t4zFSNTfb10a1eREjZGZ/TfPDz/8gH79+mHgwIHo0qULPD09sWnTJmOH9UwIL2fVocS0XFxKzoZELEL3gFrVHdozRz0ruy72FjJyCqrlMf85cxdJmQ9R08Eag9qY3qpYlkb9g3DPpRQUPrH6XHJWHk7fzoJIBPRszM+boY3tXA8SsQgHr9zHuaSsannMs7ezcOjqfUjEIozpZPw+tpZO/f22P/4e8gtLtjZRJ7jhJtwf/Vlhdonsvn37NKt6AYCNjQ0WLlyI9PR05OTkYNOmTeXWx5L+qBc42HspFQWF2l+s6rKCEH9Xk1uG0BJ1alATQd5OeKhQYnn0DYM/niAImtmhkR3qckaiGgT71YCLnRQZuQocv5mhdV3UxaIv1VY+LqjlaHorJlkaH1c79GvuBaD6ZmUXHyh6nBeae5nEAiiWrkUdF9RylOFBfiGinyghuZWei4t35RCLgJ6B/OFobGaXyJLpaOXjgpoOMmTnFyImQfuDHqX5tcrDnNVBJBJpZmWXH7mB3ALDLuiw7/I9XErOhr21BMPb1zXoY1ERK4kYPR59aT55FCSKs0PV7q1Hi1NsP3sXN9NyDPpYN+7nYMfZu0WP2/Xpq7tR1YnFIoSVcdTxv0c/HNvWdUUNlngYHRNZqjSxWITwJkVfrI/X7aXnFCD2RjoAJrLVqU9TT/i62iEjV6FZZctQFu8rmh16tZ0vnO14tm51UR8FibqQAkEo6m+ZnadA9LWiE8BYj159mng7oWsjd6gE4NeD1w36WEsOXodKALoFuFdo2VTSj16PJbIqVXE/WfX3Xa8g/nA0BUxkqUoer5NVf7HuuZQKlQA08XJCnRo8BFZdrCRijOtS1Artt4MJUCgNs0DCicQMxCSkQyoRYUxn1upVpy6NakJmJUZiei7iU7IBAPvi70GhFFDP3R713bnWe3VSHwVZf/w27mXnP+XWlZOanYc/424DAN7hbGy1Cq3vBgeZFVKz83HmUS10Zm4Bjj2aqOnFiRqTwESWqqRD/Zqws5YgWZ6Hs48+6Dx72nheaVMHNR2skZT5ENvO3DHIY6hnYwe0rA0vZ8vtY2mK7Kyt0PlRq5+oR7NCmu4gLCuodu3ruaKljwsKClVYdsQwHUOWHb6BgkIVWvm6oJ2/q0Eeg0ons5Kg66OFfNTfa3supUKpEh61xONEjSlgIktVYiMt/qBHXUjBwwIlDlwpasfFw5zVz0YqwaiORbOkv+y/rpkl15erqQ80Jxa91bX0hTDIsMIfawtUUKjC3kdrvfOHY/V7vDZ9ZfRNPMjXb216dp4CK4/eBFA0+2usVfOeZervMfUPxuIfjvy8mQomslRlj5cXHLp6H3kKFWq72KIJa7mM4vUQP9hbS3ApORv7yujxW1lLDlyDIBSNeYNa1b8cLgE9G3tAJALOJmVh88kkZOcXoqaDDK18XIwd2jOpVxMP1HO3hzyvEGtiEvW67zXHEpGdV4j67vYIb8zEyRi6BdSClViEK6kPcClZjv2Xi/6m8sRK08FElqqsR2AtSMQiXErOxu+Hig6vhTfx4OyBkTjbSfFaiC8AYJEeWwMlZ+Xhr5NJAIprA6n61XSQoY1vDQDARxvPAADCm9SCWMzPmzGIxSK89ag2femhhBKtCCsrv1CJpY/+nr7VpT7H10icbaVoX88NADBzywXkFijh5WyDprU5UWMqmMhSlbnYWaNd3aLarejrRW24eNjFuMZ0qgepRIRjCek4kZjx9DtUwO+HE6BQCmhX1xVt/GroZZ9UOU+WEbCswLgGtKoNDycZkuV52HwqSS/7/PvkHaTI8+HhJEP/Vt562SdVjmZJ9kffb5yoMS1MZEkvHv8idbKxQluelGBUns42GNCyNoDik7OqIuuhAqsfHTZ9uxtrY43t8bY/dtYSdKjPtd6NSWYlwWhNbfo1rVZNlaFSCZoFEMZ08ofMiguOGFMYfziaNCaypBePf7B7NvaAVMK3lrG91bUeRKKik4KupmZXaV9/HC06kSXAw5FLDpsA/5r2GNmhLuq72yMivBFXVjMBr4X4wtHGCtfu5Wga5lfWvxdScP1eDpxsrPBqO189RUiVVdvFVlNK4GhjhRB/NyNHRI9jtkF64eNqh+Z1nAEUNeYn42tQy1Fzgsgv+yvfsD1PoUTk4aJavbe71eMhNRPxxYtB2P1+N4ztzBlyU+BoI8Xw9n4AimrTK9sx5PHln4eH+sHRhguOmII+TYuWJA5v4gFrK6ZOpoSjQXqz8LXWWDSsNQ+7mJC3uxWdlLX5VBLuZj2s1D7+jLuN+w8KUNvFFv2as1aPqCyjOvrD2kqMk4mZOJaQXql9xCSk49StTFhbiTGyAxccMRXjutTDd4Na4PN+QcYOhZ7ARJb0xsfVDn2aeXHGzoS09q2Bdv6uUCgFTUcJXShVgmb5zbGd/VkyQlQOd0cZXmlTBwA0s6q6Ut9vUJs6cHeU6S02qhqpRIyBbepwSW4TxG8lIgunXtZydUwisnIVOt13x7m7uJmWixp2Ugxp62OI8IgsyrjO9SAWAXvj7+FSslyn+168K8e++HsQi6BZbpqIysdElsjCdQtwR6CnI3IKlPgj5maF7/d4rd4boXVhZ21lqBCJLEbdmvaaekpda9N/efR569PMC35u9nqPjcgSMZElsnAikUiznGzk4QTkKZQVut/hq2k4lySHjVSMER3qGjBCIsuiXjBky+k7uJ2RW6H73ErPxdYzdwEUH0UhoqdjIkv0DOjX3Bu1XWxx/0EB/oy7XaH7qGdjh7b1hau9tSHDI7Iozeo4o2MDNyhVAn47WLHa9KWHEqBUCejcsCaa1nY2cIREloOJLNEzQCoR483ORWdALzlwHYXK8pfRPHs7C4eu3odELMLYzjxzmkhX73RtAABYF3sL6TkF5d427UE+1sY+WnCEs7FEOmEiS/SMGNzWBzXspEhMz8WOc8nl3lY9G/tiC2/UqWFXHeERWZSODdzQtLYTHiqUWH7kRrm3XR59E3kKFZrVdkaH+my2T6QLJrJEzwg7aytNrevichq237ifgx3nimr11LW1RKQbkUikmV1dHn0DuQWFpd4ut6AQK6JvACiajWX7QiLdMJEleoaMCK0LW6kE5+/Icejq/VJvs+TgdagEoHuAOwI9nao5QiLL0aepF/zc7JCZq8C62Ful3mbtsVvIzFWgrpsdnuOqiEQ6YyJL9AypYW+t6QdbWsP21Ow8zclgrNUjqhqJWIQ3Hy0h/NvBBCieqE1XKFVY+mihkje71INEzNlYIl2ZdCK7aNEiNG/eHE5OTnByckJoaCh27NihuT4vLw/jx4+Hm5sbHBwcMHDgQKSkpBgxYiLTN7azPyRiEQ5fTcPZ21la1y07fAMFhSq08nVBO39XI0VIZDleaVMHNR2skZT5ENvO3NG6buvpO0jKfIiaDjIMbF3HSBESmTeTTmTr1KmDb775BnFxcTh+/Dh69OiB/v374/z58wCAKVOmYOvWrdiwYQP279+PO3fu4OWXXzZy1ESmrU4NO/Rv4Q1Ae1Y2O0+BlUeLFkxgrR6RfthIJRjVsajzxy/7r2tq0wVB0CyYMLpTXdhIJUaLkcicmXQi+8ILL6Bv375o2LAhGjVqhK+++goODg44evQosrKysHTpUnz//ffo0aMH2rRpg8jISBw5cgRHjx41duhEJu2tR2UDO87dRcL9HADAmmOJyM4rRH13e4Q39jBmeEQW5fX2fnCQWeFScjb2xd8DAOyNT0V8SjYcZFYYFuJn5AiJzJfZrDmpVCqxYcMG5OTkIDQ0FHFxcVAoFAgLC9PcJjAwEL6+voiOjkb79u3L3Fd+fj7y8/M1l+XyovWwFQoFFArd1qIn86AeV45vkXpuNujWqCb2Xb6PxfuuYvrzgVj6qHH72E51oVQWQlmxBcBMBsfY8pnrGNtZAUOCa2Pp4Zv4ed9VdKpfAz/vvQoAGNq2DuyszO85GYq5jjFVnL7H1uQT2bNnzyI0NBR5eXlwcHDAX3/9hSZNmuDUqVOwtraGi4uL1u09PDyQnFx+j8w5c+Zg5syZJbbv3bsXdnbsmWnJoqKijB2CyWguBfbBChvjbuFByk2kZEvgbC3A+s5pbE8+bezwKo1jbPnMcYx98wGJSILYGxn44LcdOH5TAolIgO/Dq9i+/aqxwzM55jjGVDG5uRVbtrmiTD6RDQgIwKlTp5CVlYU///wTI0aMwP79+6u0z2nTpiEiIkJzWS6Xw8fHB927d4ebG5tRWyKFQoGoqCiEh4dDKpUaOxyTIAgCDv56DCdvZeGfW0X1ee/0CMCLHesaN7BK4hhbPnMf47M4jz9PJOHvm0Wft5db18GrA4KMHJVpMfcxpqdLS0vT6/5MPpG1trZGgwZFS/21adMGsbGx+PHHHzFkyBAUFBQgMzNTa1Y2JSUFnp7l9+KTyWSQyWQltkulUn5wLBzHWNs73Rpg3Mo4AICjjRVeD/WHVGryfxbKxTG2fOY6xm93a4A/TyRpXTbH51EdzHWM6en0Pa4mfbJXaVQqFfLz89GmTRtIpVLs3r1bc118fDwSExMRGhpqxAiJzEdYYw80q+0MaysxFrzaCg4y805iiUxZg1oOmNU/CCIRMKClN+q7Oxg7JCKzZ9LfWtOmTUOfPn3g6+uL7OxsrF69Gvv27cOuXbvg7OyMMWPGICIiAq6urnBycsJ7772H0NDQck/0IqJiYrEI698KRU5BIWo6lDxKQUT69UZoXfRq4gk3B2tjh0JkEUw6kU1NTcUbb7yBu3fvwtnZGc2bN8euXbsQHh4OAPjhhx8gFosxcOBA5Ofno3fv3vj555+NHDWRebG1lsDWmj0siaqLp7ONsUMgshgmncguXbq03OttbGywcOFCLFy4sJoiIiIiIiJTYXY1skREREREABNZIiIiIjJTTGSJiIiIyCwxkSUiIiIis8REloiIiIjMEhNZIiIiIjJLTGSJiIiIyCyZdB/Z6iIIAgAgOzubaztbKIVCgdzcXMjlco6xheIYWz6OseXjGFu+7OxsAMW5V1UxkQWQlpYGAPD39zdyJERERESWLy0tDc7OzlXeDxNZAK6urgCAxMREvbyoZHrkcjl8fHxw69YtODk5GTscMgCOseXjGFs+jrHly8rKgq+vryb3qiomsgDE4qJSYWdnZ35wLJyTkxPH2MJxjC0fx9jycYwtnzr3qvJ+9LIXIiIiIqJqxkSWiIiIiMwSE1kAMpkMn3/+OWQymbFDIQPhGFs+jrHl4xhbPo6x5dP3GIsEffU/ICIiIiKqRpyRJSIiIiKzxESWiIiIiMwSE1kiIiIiMkvPfCK7cOFC1K1bFzY2NggJCcGxY8eMHRLpyZw5c9C2bVs4OjqiVq1aGDBgAOLj440dFhnQN998A5FIhMmTJxs7FNKzpKQkvP7663Bzc4OtrS2aNWuG48ePGzss0hOlUonp06fD398ftra2qF+/Pr788ku9LWNK1e/AgQN44YUX4O3tDZFIhM2bN2tdLwgCZsyYAS8vL9ja2iIsLAxXrlzR+XGe6UR23bp1iIiIwOeff44TJ06gRYsW6N27N1JTU40dGunB/v37MX78eBw9ehRRUVFQKBTo1asXcnJyjB0aGUBsbCx++eUXNG/e3NihkJ5lZGSgY8eOkEql2LFjBy5cuIDvvvsONWrUMHZopCfffvstFi1ahJ9++gkXL17Et99+i7lz52LBggXGDo0qKScnBy1atMDChQtLvX7u3LmYP38+Fi9ejJiYGNjb26N3797Iy8vT6XGe6a4FISEhaNu2LX766ScAgEqlgo+PD9577z1MnTrVyNGRvt27dw+1atXC/v370aVLF2OHQ3r04MEDtG7dGj///DNmz56Nli1bYt68ecYOi/Rk6tSpOHz4MA4ePGjsUMhA+vXrBw8PDyxdulSzbeDAgbC1tcUff/xhxMhIH0QiEf766y8MGDAAQNFsrLe3N95//3188MEHAIqWrvXw8MCyZcswdOjQCu/7mZ2RLSgoQFxcHMLCwjTbxGIxwsLCEB0dbcTIyFCysrIAQG/rO5PpGD9+PJ5//nmtzzNZji1btiA4OBiDBg1CrVq10KpVK/z666/GDov0qEOHDti9ezcuX74MADh9+jQOHTqEPn36GDkyMoSEhAQkJydr/c12dnZGSEiIzjmYlb6DMxf379+HUqmEh4eH1nYPDw9cunTJSFGRoahUKkyePBkdO3ZE06ZNjR0O6dHatWtx4sQJxMbGGjsUMpDr169j0aJFiIiIwCeffILY2FhMnDgR1tbWGDFihLHDIz2YOnUq5HI5AgMDIZFIoFQq8dVXX2HYsGHGDo0MIDk5GQBKzcHU11XUM5vI0rNl/PjxOHfuHA4dOmTsUEiPbt26hUmTJiEqKgo2NjbGDocMRKVSITg4GF9//TUAoFWrVjh37hwWL17MRNZCrF+/HqtWrcLq1asRFBSEU6dOYfLkyfD29uYYU7me2dKCmjVrQiKRICUlRWt7SkoKPD09jRQVGcKECROwbds27N27F3Xq1DF2OKRHcXFxSE1NRevWrWFlZQUrKyvs378f8+fPh5WVFZRKpbFDJD3w8vJCkyZNtLY1btwYiYmJRoqI9O3DDz/E1KlTMXToUDRr1gzDhw/HlClTMGfOHGOHRgagzrP0kYM9s4mstbU12rRpg927d2u2qVQq7N69G6GhoUaMjPRFEARMmDABf/31F/bs2QN/f39jh0R61rNnT5w9exanTp3S/AsODsawYcNw6tQpSCQSY4dIetCxY8cSrfMuX74MPz8/I0VE+pabmwuxWDslkUgkUKlURoqIDMnf3x+enp5aOZhcLkdMTIzOOdgzXVoQERGBESNGIDg4GO3atcO8efOQk5ODUaNGGTs00oPx48dj9erV+Pvvv+Ho6Kipu3F2doatra2RoyN9cHR0LFHzbG9vDzc3N9ZCW5ApU6agQ4cO+PrrrzF48GAcO3YMS5YswZIlS4wdGunJCy+8gK+++gq+vr4ICgrCyZMn8f3332P06NHGDo0q6cGDB7h69armckJCAk6dOgVXV1f4+vpi8uTJmD17Nho2bAh/f39Mnz4d3t7ems4GFSY84xYsWCD4+voK1tbWQrt27YSjR48aOyTSEwCl/ouMjDR2aGRAXbt2FSZNmmTsMEjPtm7dKjRt2lSQyWRCYGCgsGTJEmOHRHokl8uFSZMmCb6+voKNjY1Qr1494dNPPxXy8/ONHRpV0t69e0v9Dh4xYoQgCIKgUqmE6dOnCx4eHoJMJhN69uwpxMfH6/w4z3QfWSIiIiIyX89sjSwRERERmTcmskRERERklpjIEhEREZFZYiJLRERERGaJiSwRERERmSUmskRERERklpjIEhEREZFZYiJLRERERGaJiSwRmaRLly6hffv2sLGxQcuWLY0dDvbt2weRSITMzExjh2LyvvjiC60xGzlyZIWWnRw+fDi+/vprwwVWjvbt22Pjxo1GeWwiqjwmskRkkj7//HPY29sjPj4eu3fvNnY4elO3bl3MmzdPa9uyZcvg4uJilHhMxenTp7F9+3ZMnDjRKI//2WefYerUqVCpVEZ5fCKqHCayRGSSrl27hk6dOsHPzw9ubm6l3kahUFRzVGQoCxYswKBBg+Dg4GCUx+/Tpw+ys7OxY8cOozw+EVUOE1kiMqgVK1bAzc0N+fn5WtsHDBiA4cOHl3ofkUiEuLg4zJo1CyKRCF988QVu3LgBkUiEdevWoWvXrrCxscGqVaugUqkwa9Ys1KlTBzKZDC1btsTOnTs1+1Lfb/369ejcuTNsbW3Rtm1bXL58GbGxsQgODoaDgwP69OmDe/fuPfX5HD58GM2bN4eNjQ3at2+Pc+fOaV2/ceNGBAUFQSaToW7duvjuu+8013Xr1g03b97ElClTIBKJIBKJsG/fPowaNQpZWVmabV988QUAICMjA2+88QZq1KgBOzs79OnTB1euXNHsTz2Tu23bNgQEBMDOzg6vvPIKcnNzsXz5ctStWxc1atTAxIkToVQqS30+WVlZkEgkOH78OABApVLB1dUV7du319zmjz/+gI+Pj+byxx9/jEaNGsHOzg716tXD9OnTq/SjQqlU4s8//8QLL7ygtb1u3bqYPXs23njjDTg4OMDPzw9btmzBvXv30L9/fzg4OKB58+aa2KvymkgkEvTt2xdr166t9PMgIiMQiIgMKDc3V3B2dhbWr1+v2ZaSkiJYWVkJe/bsKfU+d+/eFYKCgoT3339fuHv3rpCdnS0kJCQIAIS6desKGzduFK5fvy7cuXNH+P777wUnJydhzZo1wqVLl4SPPvpIkEqlwuXLlwVBEDT3CwwMFHbu3ClcuHBBaN++vdCmTRuhW7duwqFDh4QTJ04IDRo0EN5+++0yn8fevXsFAELjxo2Ff//9Vzhz5ozQr18/oW7dukJBQYEgCIJw/PhxQSwWC7NmzRLi4+OFyMhIwdbWVoiMjBQEQRDS0tKEOnXqCLNmzRLu3r0r3L17V8jPzxfmzZsnODk5abZlZ2cLgiAIL774otC4cWPhwIEDwqlTp4TevXsLDRo00DxeZGSkIJVKhfDwcOHEiRPC/v37BTc3N6FXr17C4MGDhfPnzwtbt24VrK2thbVr15b53Fq3bi383//9nyAIgnDq1CnB1dVVsLa21sQxduxYYdiwYZrbf/nll8Lhw4eFhIQEYcuWLYKHh4fw7bffaq7//PPPhRYtWmgujxgxQujfv3+Zj3/ixAkBgJCcnKy13c/PT3B1dRUWL14sXL58WXjnnXcEJycn4bnnnhPWr18vxMfHCwMGDBAaN24sqFSqKr8mixYtEvz8/MqMk4hMDxNZIjK4d955R+jTp4/m8nfffSfUq1dPk3yUpkWLFsLnn3+uuaxOSOfNm6d1O29vb+Grr77S2ta2bVvh3Xff1brfb7/9prl+zZo1AgBh9+7dmm1z5swRAgICyoxHncg+nvykpaUJtra2wrp16wRBEITXXntNCA8P17rfhx9+KDRp0kRz2c/PT/jhhx+0bhMZGSk4Oztrbbt8+bIAQDh8+LBm2/379wVbW1vNj4LIyEgBgHD16lXNbd566y3Bzs5Ok4QKgiD07t1beOutt8p8bhEREcLzzz8vCIIgzJs3TxgyZIjQokULYceOHYIgCEKDBg2EJUuWlHn///u//xPatGmjuaxrIvvXX38JEomkxPvBz89PeP311zWX7969KwAQpk+frtkWHR0tABDu3r0rCELVXpO///5bEIvFglKpLDNWIjItLC0gIoN788038e+//yIpKQlA0eHfkSNHQiQS6byv4OBgzf/L5XLcuXMHHTt21LpNx44dcfHiRa1tzZs31/y/h4cHAKBZs2Za21JTU5/6+KGhoZr/d3V1RUBAgOaxLl68WGosV65cKfPQflkuXrwIKysrhISEaLa5ublpPR4A2NnZoX79+lrPo27dulq1pk97bl27dsWhQ4egVCqxf/9+dOvWDd26dcO+fftw584dXL16Fd26ddPcft26dejYsSM8PT3h4OCAzz77DImJiTo9v8c9fPgQMpms1PdDRcYNgNbzq+xrYmtrC5VKVaIMhohMFxNZIjK4Vq1aoUWLFlixYgXi4uJw/vx5jBw5slL7sre3r9T9pFKp5v/VCdOT28zxjPXHnwNQ9DxK21bec+vSpQuys7Nx4sQJHDhwQCuR3b9/P7y9vdGwYUMAQHR0NIYNG4a+ffti27ZtOHnyJD799FMUFBRU+jnUrFkTubm5pe6jIuMGQOv5VfY1SU9Ph729PWxtbSv5TIioujGRJaJqMXbsWCxbtgyRkZEICwvTOnmospycnODt7Y3Dhw9rbT98+DCaNGlS5f2X5ujRo5r/z8jIwOXLl9G4cWMAQOPGjUuNpVGjRpBIJAAAa2vrErOzpW1r3LgxCgsLERMTo9mWlpaG+Ph4vT83FxcXNG/eHD/99BOkUikCAwPRpUsXnDx5Etu2bUPXrl01tz1y5Aj8/Pzw6aefIjg4GA0bNsTNmzer9PjqnrMXLlyo0n6q6ty5c2jVqpVRYyAi3TCRJaJq8dprr+H27dv49ddfMXr0aL3t98MPP8S3336LdevWIT4+HlOnTsWpU6cwadIkvT3G42bNmoXdu3fj3LlzGDlyJGrWrKlp9v/+++9j9+7d+PLLL3H58mUsX74cP/30Ez744APN/evWrYsDBw4gKSkJ9+/f12x78OABdu/ejfv37yM3NxcNGzZE//798eabb+LQoUM4ffo0Xn/9ddSuXRv9+/fX+/Pq1q0bVq1apUlaXV1d0bhxY02XCLWGDRsiMTERa9euxbVr1zB//nz89ddfVXpsd3d3tG7dGocOHarSfqrq4MGD6NWrl1FjICLdMJElomrh7OyMgQMHwsHBoUKrPFXUxIkTERERgffffx/NmjXDzp07sWXLFs2hcH375ptvMGnSJLRp0wbJycnYunUrrK2tAQCtW7fG+vXrsXbtWjRt2hQzZszArFmztMooZs2ahRs3bqB+/fpwd3cHAHTo0AFvv/02hgwZAnd3d8ydOxcAEBkZiTZt2qBfv34IDQ2FIAjYvn17icPk+tC1a1colUqtWthu3bqV2Pbiiy9iypQpmDBhAlq2bIkjR45g+vTpVX78sWPHYtWqVVXeT2UlJSXhyJEjGDVqlNFiICLdiQRBEIwdBBE9G3r27ImgoCDMnz/f2KGQiXn48CECAgKwbt06rRPqqsvHH3+MjIwMLFmypNofm4gqz8rYARCR5cvIyMC+ffuwb98+/Pzzz8YOh0yQra0tVqxYoSm3qG61atVCRESEUR6biCqPM7JEZHB169ZFRkYGpk+frlUvSkREVBVMZImIiIjILPFkLyIiIiIyS0xkiYiIiMgsMZElIiIiIrPERJaIiIiIzBITWSIiIiIyS0xkiYiIiMgsMZElIiIiIrPERJaIiIiIzBITWSIiIiIyS/8PE5qFxvnzTbQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 700x350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==== Grid Infill 30.0% ====\n",
      "XY Total Area         = 52.1754 mm²\n",
      "  Shell Area          = 31.1879 mm²\n",
      "  Infill Area         = 20.9874 mm²\n",
      "I_x                   = 567.1050\n",
      "I_y                   = 567.1050\n",
      "I_xy                  = 0.0000\n",
      "Polar moment, J       = 1134.2101\n",
      "-- Cross-sections through Z (pattern constant over Z) --\n",
      "Length along X @ y=1.000 mm: 4.0801 mm\n",
      "Length along Y @ x=-2.000 mm: 3.6045 mm\n",
      "Area of XZ plane @ y=1.000: 40.8010 mm²  (Z=10.000 mm)\n",
      "Area of YZ plane @ x=-2.000: 36.0451 mm²  (Z=10.000 mm)\n",
      "\n",
      "A_xz(y=1mm) = 40.80100125156456 mm^2\n",
      "A_yz(x=-2mm) = 36.04505632040059 mm^2\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# ------------------------------\n",
    "# Geometry utilities\n",
    "# ------------------------------\n",
    "\n",
    "def _dist_mod(u, spacing):\n",
    "    r = np.mod(u, spacing)\n",
    "    return np.minimum(r, spacing - r)\n",
    "\n",
    "def _rectilinear_mask(XX, YY, spacing, line_width, angle_deg=0.0, phase=0.0):\n",
    "    th = np.deg2rad(angle_deg)\n",
    "    u =  XX * np.cos(th) + YY * np.sin(th)\n",
    "    dist = _dist_mod(u + phase, spacing)\n",
    "    return dist <= (line_width / 2.0)\n",
    "\n",
    "def _spacing_for_grid_density(line_width, f):\n",
    "    f = float(np.clip(f, 0.0, 1.0))\n",
    "    if f <= 0.0:\n",
    "        return np.inf\n",
    "    if f >= 1.0:\n",
    "        return line_width\n",
    "    r = 1.0 - np.sqrt(1.0 - f)\n",
    "    return line_width / r\n",
    "\n",
    "# ------------------------------\n",
    "# Area & inertia on a raster mask\n",
    "# ------------------------------\n",
    "\n",
    "def compute_area_moments(XX, YY, mask):\n",
    "    x_vals = XX[mask]\n",
    "    y_vals = YY[mask]\n",
    "\n",
    "    if x_vals.size == 0:\n",
    "        return {\"A\": 0.0, \"x_c\": 0.0, \"y_c\": 0.0, \"I_x\": 0.0, \"I_y\": 0.0, \"I_xy\": 0.0, \"J\": 0.0}\n",
    "\n",
    "    dx = XX[0, 1] - XX[0, 0]\n",
    "    dy = YY[1, 0] - YY[0, 0]\n",
    "    dA = dx * dy\n",
    "\n",
    "    A   = x_vals.size * dA\n",
    "    x_c = float(np.mean(x_vals))\n",
    "    y_c = float(np.mean(y_vals))\n",
    "\n",
    "    x_shift = x_vals - x_c\n",
    "    y_shift = y_vals - y_c\n",
    "\n",
    "    I_x = float(np.sum(y_shift**2) * dA)\n",
    "    I_y = float(np.sum(x_shift**2) * dA)\n",
    "    I_xy = float(np.sum(x_shift * y_shift) * dA)\n",
    "    J   = I_x + I_y\n",
    "\n",
    "    return {\"A\": A, \"x_c\": x_c, \"y_c\": y_c, \"I_x\": I_x, \"I_y\": I_y, \"I_xy\": I_xy, \"J\": J}\n",
    "\n",
    "# ------------------------------\n",
    "# Main: Prusa-style rectilinear/grid with shells\n",
    "# ------------------------------\n",
    "\n",
    "def prusa_style_grid_or_rectilinear(\n",
    "    width, height,\n",
    "    infill_fraction,\n",
    "    line_width=0.42,\n",
    "    shell_layers=2,\n",
    "    base_infill_angle_deg=45.0,\n",
    "    grid=True,\n",
    "    z_height=0.0,              # optional phase control\n",
    "    phase_per_mm=0.0,\n",
    "    # Cross-sections extruded through Z\n",
    "    z_object_height=None,      # mm (if set => areas in mm²; else lengths in mm)\n",
    "    y_plane=0.0,               # mm, plane parallel to XZ at y = y_plane\n",
    "    x_plane=0.0,               # mm, plane parallel to YZ at x = x_plane\n",
    "    N=800,\n",
    "    plot=True,\n",
    "    verbose=True\n",
    "):\n",
    "    # ----- Raster grid (centered at origin)\n",
    "    xs = np.linspace(-width/2,  width/2,  N)\n",
    "    ys = np.linspace(-height/2, height/2, N)\n",
    "    XX, YY = np.meshgrid(xs, ys)\n",
    "\n",
    "    # ----- Shells (perimeters)\n",
    "    shell_mask = np.zeros_like(XX, dtype=bool)\n",
    "    for i in range(shell_layers):\n",
    "        off = (i + 0.5) * line_width\n",
    "        shell_mask |= np.abs(XX - (-width/2  + off)) <= (line_width / 2)\n",
    "        shell_mask |= np.abs(XX - ( +width/2  - off)) <= (line_width / 2)\n",
    "        shell_mask |= np.abs(YY - (-height/2 + off)) <= (line_width / 2)\n",
    "        shell_mask |= np.abs(YY - ( +height/2 - off)) <= (line_width / 2)\n",
    "\n",
    "    # ----- Infill region (inside shells)\n",
    "    inner_offset = shell_layers * line_width\n",
    "    inner_rect = (\n",
    "        (np.abs(XX) <= (width/2  - inner_offset)) &\n",
    "        (np.abs(YY) <= (height/2 - inner_offset))\n",
    "    )\n",
    "\n",
    "    if infill_fraction <= 0.0:\n",
    "        infill_mask = np.zeros_like(XX, dtype=bool)\n",
    "    elif infill_fraction >= 1.0:\n",
    "        spacing = line_width\n",
    "        masks = []\n",
    "        angles = [base_infill_angle_deg] + ([base_infill_angle_deg + 90] if grid else [])\n",
    "        phase = phase_per_mm * z_height\n",
    "        for a in angles:\n",
    "            masks.append(_rectilinear_mask(XX, YY, spacing, line_width, angle_deg=a, phase=phase))\n",
    "        infill_mask = np.logical_or.reduce(masks) & inner_rect\n",
    "    else:\n",
    "        spacing = _spacing_for_grid_density(line_width, infill_fraction) if grid \\\n",
    "                  else line_width / infill_fraction\n",
    "        masks = []\n",
    "        angles = [base_infill_angle_deg] + ([base_infill_angle_deg + 90] if grid else [])\n",
    "        phase = phase_per_mm * z_height\n",
    "        for a in angles:\n",
    "            masks.append(_rectilinear_mask(XX, YY, spacing, line_width, angle_deg=a, phase=phase))\n",
    "        infill_mask = np.logical_or.reduce(masks) & inner_rect\n",
    "\n",
    "    # ----- Final mask (material present)\n",
    "    final_mask = shell_mask | infill_mask\n",
    "\n",
    "    # ----- Plot: XY bitmap preview\n",
    "    if plot:\n",
    "        plt.figure(figsize=(6, 6))\n",
    "        img = np.where(final_mask, 1.0, np.nan)\n",
    "        plt.imshow(img, origin='lower',\n",
    "                   extent=[-width/2, width/2, -height/2, height/2],\n",
    "                   interpolation='nearest')\n",
    "        title = \"Grid\" if grid else \"Rectilinear\"\n",
    "        plt.title(f\"{title} @ {infill_fraction*100:.1f}%  | shells={shell_layers}×{line_width:.2f} angle={base_infill_angle_deg:.0f}°\")\n",
    "        plt.xlabel(\"X (mm)\")\n",
    "        plt.ylabel(\"Y (mm)\")\n",
    "        plt.gca().set_aspect('equal', 'box')\n",
    "        plt.grid(True)\n",
    "\n",
    "        # guide lines\n",
    "        plt.hlines(y_plane, -width/2, width/2, linestyles='--')\n",
    "        plt.vlines(x_plane, -height/2, height/2, linestyles='--')\n",
    "\n",
    "        plt.show()\n",
    "\n",
    "    # ----- XY numbers\n",
    "    total = compute_area_moments(XX, YY, final_mask)\n",
    "    shells = compute_area_moments(XX, YY, shell_mask)\n",
    "    A_infill = total[\"A\"] - shells[\"A\"]\n",
    "\n",
    "    # ----- Cross-sections parallel to XZ and YZ at specific planes\n",
    "    dx = XX[0, 1] - XX[0, 0]\n",
    "    dy = YY[1, 0] - YY[0, 0]\n",
    "    ys_centered = YY[:, 0]\n",
    "    xs_centered = XX[0, :]\n",
    "    row = int(np.argmin(np.abs(ys_centered - y_plane)))\n",
    "    col = int(np.argmin(np.abs(xs_centered - x_plane)))\n",
    "\n",
    "    length_x_at_y = float(np.count_nonzero(final_mask[row, :]) * dx)\n",
    "    length_y_at_x = float(np.count_nonzero(final_mask[:, col]) * dy)\n",
    "\n",
    "    area_xz_at_y = None\n",
    "    area_yz_at_x = None\n",
    "    if z_object_height is not None and z_object_height > 0:\n",
    "        area_xz_at_y = length_x_at_y * z_object_height\n",
    "        area_yz_at_x = length_y_at_x * z_object_height\n",
    "\n",
    "    # ----- NEW: Variation curves over full width/height\n",
    "    # lengths per row/column\n",
    "    length_x_vs_y = np.count_nonzero(final_mask, axis=1) * dx   # for each y-row, length along X\n",
    "    length_y_vs_x = np.count_nonzero(final_mask, axis=0) * dy   # for each x-col, length along Y\n",
    "\n",
    "    if z_object_height is not None and z_object_height > 0:\n",
    "        area_xz_vs_y = length_x_vs_y * z_object_height\n",
    "        area_yz_vs_x = length_y_vs_x * z_object_height\n",
    "        y_label_curves = \"Area of XZ slice (mm²)\"\n",
    "        x_label_curves = \"Area of YZ slice (mm²)\"\n",
    "    else:\n",
    "        area_xz_vs_y = length_x_vs_y\n",
    "        area_yz_vs_x = length_y_vs_x\n",
    "        y_label_curves = \"Length along X (mm)  [set z_object_height for area]\"\n",
    "        x_label_curves = \"Length along Y (mm)  [set z_object_height for area]\"\n",
    "\n",
    "    # axes remapped to start at 0 (wall) → up to width/height\n",
    "    y_axis_0_to_H = ys_centered + height/2.0\n",
    "    x_axis_0_to_W = xs_centered + width/2.0\n",
    "\n",
    "    if plot:\n",
    "        # --- Area vs y (XZ plane area as y varies)\n",
    "        plt.figure(figsize=(7, 3.5))\n",
    "        plt.plot(y_axis_0_to_H, area_xz_vs_y)\n",
    "        plt.xlabel(\"y from bottom wall (mm)\")\n",
    "        plt.ylabel(y_label_curves)\n",
    "        plt.title(\"Variation vs y\")\n",
    "        plt.grid(True)\n",
    "        plt.xlim(0, height)\n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "\n",
    "        # --- Area vs x (YZ plane area as x varies)\n",
    "        plt.figure(figsize=(7, 3.5))\n",
    "        plt.plot(x_axis_0_to_W, area_yz_vs_x)\n",
    "        plt.xlabel(\"x from left wall (mm)\")\n",
    "        plt.ylabel(x_label_curves)\n",
    "        plt.title(\"Variation vs x\")\n",
    "        plt.grid(True)\n",
    "        plt.xlim(0, width)\n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "\n",
    "    if verbose:\n",
    "        print(f\"==== {('Grid' if grid else 'Rectilinear')} Infill {infill_fraction*100:.1f}% ====\")\n",
    "        print(f\"XY Total Area         = {total['A']:.4f} mm²\")\n",
    "        print(f\"  Shell Area          = {shells['A']:.4f} mm²\")\n",
    "        print(f\"  Infill Area         = {A_infill:.4f} mm²\")\n",
    "        print(f\"I_x                   = {total['I_x']:.4f}\")\n",
    "        print(f\"I_y                   = {total['I_y']:.4f}\")\n",
    "        print(f\"I_xy                  = {total['I_xy']:.4f}\")\n",
    "        print(f\"Polar moment, J       = {total['J']:.4f}\")\n",
    "        print(f\"-- Cross-sections through Z (pattern constant over Z) --\")\n",
    "        print(f\"Length along X @ y={y_plane:.3f} mm: {length_x_at_y:.4f} mm\")\n",
    "        print(f\"Length along Y @ x={x_plane:.3f} mm: {length_y_at_x:.4f} mm\")\n",
    "        if area_xz_at_y is not None:\n",
    "            print(f\"Area of XZ plane @ y={y_plane:.3f}: {area_xz_at_y:.4f} mm²  (Z={z_object_height:.3f} mm)\")\n",
    "        if area_yz_at_x is not None:\n",
    "            print(f\"Area of YZ plane @ x={x_plane:.3f}: {area_yz_at_x:.4f} mm²  (Z={z_object_height:.3f} mm)\")\n",
    "        print()\n",
    "\n",
    "    return {\n",
    "        \"mask\": final_mask,\n",
    "        \"XX\": XX, \"YY\": YY,\n",
    "        \"dx\": dx, \"dy\": dy,\n",
    "        \"area_total_xy\": total[\"A\"],\n",
    "        \"area_shells_xy\": shells[\"A\"],\n",
    "        \"area_infill_xy\": A_infill,\n",
    "        \"moments_xy\": total,\n",
    "        \"length_x_at_y\": length_x_at_y,\n",
    "        \"length_y_at_x\": length_y_at_x,\n",
    "        \"area_xz_at_y\": area_xz_at_y,\n",
    "        \"area_yz_at_x\": area_yz_at_x,\n",
    "        # NEW: full variation arrays and their axes (0→W/H)\n",
    "        \"y_axis_mm\": y_axis_0_to_H,\n",
    "        \"x_axis_mm\": x_axis_0_to_W,\n",
    "        \"area_xz_vs_y\": area_xz_vs_y,\n",
    "        \"area_yz_vs_x\": area_yz_vs_x,\n",
    "    }\n",
    "\n",
    "# ------------------------------\n",
    "# Demo\n",
    "# ------------------------------\n",
    "if __name__ == \"__main__\":\n",
    "    W, H = 10.0, 10.0\n",
    "    Z = 10.0  # object height in Z\n",
    "    res = prusa_style_grid_or_rectilinear(\n",
    "        width=W, height=H,\n",
    "        infill_fraction=0.3,\n",
    "        line_width=0.42,\n",
    "        shell_layers=2,\n",
    "        base_infill_angle_deg=45.0,\n",
    "        grid=True,\n",
    "        z_object_height=Z,  # set Z height for true areas\n",
    "        y_plane=+1.0,       # XZ cross-section at y = +1 mm\n",
    "        x_plane=-2.0,       # YZ cross-section at x = -2 mm (centered coords)\n",
    "        N=800,\n",
    "        plot=True, verbose=True\n",
    "    )\n",
    "\n",
    "    print(\"A_xz(y=1mm) =\", res[\"area_xz_at_y\"], \"mm^2\")\n",
    "    print(\"A_yz(x=-2mm) =\", res[\"area_yz_at_x\"], \"mm^2\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "adbb08c3-31f7-4b32-b37b-f6fde39033fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==== Grid ispuna 20.0% ====\n",
      "XY ukupna povrsina     = 245.4207 mm²\n",
      "  Povrsina ljuski      = 132.4033 mm²\n",
      "  Povrsina ispune      = 113.0174 mm²\n",
      "-- Presjeci kroz Z (uzorak konstantan po Z) --\n",
      "Duzina po X @ y=1.000 mm: 2.8786 mm\n",
      "Duzina po Y @ x=-2.000 mm: 15.9449 mm\n",
      "Povrsina XZ @ y=1.000: 28.7860 mm²  (Z=10.000 mm)\n",
      "Povrsina YZ @ x=-2.000: 159.4493 mm²  (Z=10.000 mm)\n",
      "\n",
      "A_xz(y=1mm) = 28.785982478097694 mm^2\n",
      "A_yz(x=-2mm) = 159.44931163954337 mm^2\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# ------------------------------\n",
    "# Geometrijske pomocne funkcije\n",
    "# ------------------------------\n",
    "\n",
    "def _udaljenost_mod(u, razmak):\n",
    "    \"\"\"Najmanja udaljenost do najblize paralelne linije s periodom 'razmak'.\"\"\"\n",
    "    r = np.mod(u, razmak)\n",
    "    return np.minimum(r, razmak - r)\n",
    "\n",
    "def _pravocrtna_maska(XX, YY, razmak, sirina_linije, kut_stupnjevi=0.0, faza=0.0):\n",
    "    \"\"\"\n",
    "    Generira pravocrtni uzorak (paralelne linije) kao booleovu masku.\n",
    "    XX, YY: mreza koordinata; razmak: period linija; sirina_linije: debljina trake.\n",
    "    kut_stupnjevi: orijentacija linija; faza: pomak uzorka.\n",
    "    \"\"\"\n",
    "    th = np.deg2rad(kut_stupnjevi)\n",
    "    u =  XX * np.cos(th) + YY * np.sin(th)\n",
    "    dist = _udaljenost_mod(u + faza, razmak)\n",
    "    return dist <= (sirina_linije / 2.0)\n",
    "\n",
    "def _razmak_za_gustocu_mreze(sirina_linije, f):\n",
    "    \"\"\"\n",
    "    Za zadani udio ispune f (0..1) vrati razmak linija koji daje istu gustocu\n",
    "    kod 'grid' (dvije orijentacije pod 90°). Za cistu pravocrtnu (jedna orijentacija)\n",
    "    koristi se sirina_linije / f (vidi dolje).\n",
    "    \"\"\"\n",
    "    f = float(np.clip(f, 0.0, 1.0))\n",
    "    if f <= 0.0:\n",
    "        return np.inf\n",
    "    if f >= 1.0:\n",
    "        return sirina_linije\n",
    "    r = 1.0 - np.sqrt(1.0 - f)\n",
    "    return sirina_linije / r\n",
    "\n",
    "# ------------------------------\n",
    "# Povrsina i momenti na raster maski\n",
    "# ------------------------------\n",
    "\n",
    "def izracun_povrsine_i_momenata(XX, YY, maska):\n",
    "    \"\"\"\n",
    "    Racuna povrsinu A, te geometrijske momente inercije oko tezista (I_x, I_y, I_xy)\n",
    "    na raster reprezentaciji (True piksli su 'materijal').\n",
    "    \"\"\"\n",
    "    x_vals = XX[maska]\n",
    "    y_vals = YY[maska]\n",
    "\n",
    "    if x_vals.size == 0:\n",
    "        return {\"A\": 0.0, \"x_c\": 0.0, \"y_c\": 0.0, \"I_x\": 0.0, \"I_y\": 0.0, \"I_xy\": 0.0, \"J\": 0.0}\n",
    "\n",
    "    dx = XX[0, 1] - XX[0, 0]\n",
    "    dy = YY[1, 0] - YY[0, 0]\n",
    "    dA = dx * dy\n",
    "\n",
    "    A   = x_vals.size * dA\n",
    "    x_c = float(np.mean(x_vals))\n",
    "    y_c = float(np.mean(y_vals))\n",
    "\n",
    "    x_p = x_vals - x_c\n",
    "    y_p = y_vals - y_c\n",
    "\n",
    "    I_x = float(np.sum(y_p**2) * dA)\n",
    "    I_y = float(np.sum(x_p**2) * dA)\n",
    "    I_xy = float(np.sum(x_p * y_p) * dA)\n",
    "    J   = I_x + I_y\n",
    "\n",
    "    return {\"A\": A, \"x_c\": x_c, \"y_c\": y_c, \"I_x\": I_x, \"I_y\": I_y, \"I_xy\": I_xy, \"J\": J}\n",
    "\n",
    "# ------------------------------\n",
    "# Glavno: Prusa-stil pravocrtni/grid s ljuskama (perimetrima)\n",
    "# ------------------------------\n",
    "\n",
    "def prusa_mreza_ili_pravocrtna(\n",
    "    sirina, visina,\n",
    "    udio_ispune,\n",
    "    sirina_linije=0.42,\n",
    "    slojevi_ljuske=2,\n",
    "    osnovni_kut_ispune_stupnjevi=45.0,\n",
    "    mreza=True,\n",
    "    z_visina=0.0,         # opcionalna faza po Z\n",
    "    faza_po_mm=0.0,\n",
    "    # Poprecni presjeci kroz Z\n",
    "    z_visina_objekta=None,  # mm (ako je postavljeno -> povrsine u mm²; inace duzine u mm)\n",
    "    y_ravnina=0.0,          # mm, ravnina paralelna s XZ na y = y_ravnina\n",
    "    x_ravnina=0.0,          # mm, ravnina paralelna s YZ na x = x_ravnina\n",
    "    N=800,\n",
    "    graficki_prikaz=True,\n",
    "    detaljno=True\n",
    "):\n",
    "    # ----- Raster mreza (centar u ishodištu)\n",
    "    xs = np.linspace(-sirina/2,  sirina/2,  N)\n",
    "    ys = np.linspace(-visina/2, visina/2, N)\n",
    "    XX, YY = np.meshgrid(xs, ys)\n",
    "\n",
    "    # ----- Ljuske (perimetri)\n",
    "    shell_mask = np.zeros_like(XX, dtype=bool)\n",
    "    for i in range(slojevi_ljuske):\n",
    "        off = (i + 0.5) * sirina_linije\n",
    "        shell_mask |= np.abs(XX - (-sirina/2  + off)) <= (sirina_linije / 2)\n",
    "        shell_mask |= np.abs(XX - ( +sirina/2  - off)) <= (sirina_linije / 2)\n",
    "        shell_mask |= np.abs(YY - (-visina/2 + off)) <= (sirina_linije / 2)\n",
    "        shell_mask |= np.abs(YY - ( +visina/2 - off)) <= (sirina_linije / 2)\n",
    "\n",
    "    # ----- Unutarnji pravokutnik (podrucje ispune)\n",
    "    unutarnji_pomak = slojevi_ljuske * sirina_linije\n",
    "    unutarnji_pravokutnik = (\n",
    "        (np.abs(XX) <= (sirina/2  - unutarnji_pomak)) &\n",
    "        (np.abs(YY) <= (visina/2 - unutarnji_pomak))\n",
    "    )\n",
    "\n",
    "    # ----- Ispuna\n",
    "    if udio_ispune <= 0.0:\n",
    "        infill_mask = np.zeros_like(XX, dtype=bool)\n",
    "    elif udio_ispune >= 1.0:\n",
    "        razmak = sirina_linije\n",
    "        maske = []\n",
    "        kutevi = [osnovni_kut_ispune_stupnjevi] + ([osnovni_kut_ispune_stupnjevi + 90] if mreza else [])\n",
    "        faza = faza_po_mm * z_visina\n",
    "        for a in kutevi:\n",
    "            maske.append(_pravocrtna_maska(XX, YY, razmak, sirina_linije, kut_stupnjevi=a, faza=faza))\n",
    "        infill_mask = np.logical_or.reduce(maske) & unutarnji_pravokutnik\n",
    "    else:\n",
    "        razmak = _razmak_za_gustocu_mreze(sirina_linije, udio_ispune) if mreza \\\n",
    "                 else sirina_linije / udio_ispune\n",
    "        maske = []\n",
    "        kutevi = [osnovni_kut_ispune_stupnjevi] + ([osnovni_kut_ispune_stupnjevi + 90] if mreza else [])\n",
    "        faza = faza_po_mm * z_visina\n",
    "        for a in kutevi:\n",
    "            maske.append(_pravocrtna_maska(XX, YY, razmak, sirina_linije, kut_stupnjevi=a, faza=faza))\n",
    "        infill_mask = np.logical_or.reduce(maske) & unutarnji_pravokutnik\n",
    "\n",
    "    # ----- Konacna maska (materijal prisutan)\n",
    "    konacna_maska = shell_mask | infill_mask\n",
    "\n",
    "    # ----- Graficki pregled XY\n",
    "    if graficki_prikaz:\n",
    "        plt.figure(figsize=(6, 6))\n",
    "        img = np.where(konacna_maska, 1.0, np.nan)\n",
    "        plt.imshow(img, origin='lower',\n",
    "                   extent=[-sirina/2, sirina/2, -visina/2, visina/2],\n",
    "                   interpolation='nearest')\n",
    "        naslov = \"Grid\" if mreza else \"Pravocrtna\"\n",
    "        plt.title(f\"{naslov} @ {udio_ispune*100:.1f}%  | ljuske={slojevi_ljuske}×{sirina_linije:.2f} kut={osnovni_kut_ispune_stupnjevi:.0f}°\")\n",
    "        plt.xlabel(\"X (mm)\")\n",
    "        plt.ylabel(\"Y (mm)\")\n",
    "        plt.gca().set_aspect('equal', 'box')\n",
    "        plt.grid(True)\n",
    "        # vodilice\n",
    "        plt.hlines(y_ravnina, -sirina/2, sirina/2, linestyles='--')\n",
    "        plt.vlines(x_ravnina, -visina/2, visina/2, linestyles='--')\n",
    "        plt.show()\n",
    "\n",
    "    # ----- XY brojke\n",
    "    total = izracun_povrsine_i_momenata(XX, YY, konacna_maska)\n",
    "    ljuske = izracun_povrsine_i_momenata(XX, YY, shell_mask)\n",
    "    A_ispuna = total[\"A\"] - ljuske[\"A\"]\n",
    "\n",
    "    # ----- Presjeci paralelni s XZ i YZ kroz zadane ravnine\n",
    "    dx = XX[0, 1] - XX[0, 0]\n",
    "    dy = YY[1, 0] - YY[0, 0]\n",
    "    ys_centered = YY[:, 0]\n",
    "    xs_centered = XX[0, :]\n",
    "    row = int(np.argmin(np.abs(ys_centered - y_ravnina)))\n",
    "    col = int(np.argmin(np.abs(xs_centered - x_ravnina)))\n",
    "\n",
    "    duzina_x_na_y = float(np.count_nonzero(konacna_maska[row, :]) * dx)\n",
    "    duzina_y_na_x = float(np.count_nonzero(konacna_maska[:, col]) * dy)\n",
    "\n",
    "    povrsina_xz_na_y = None\n",
    "    povrsina_yz_na_x = None\n",
    "    if z_visina_objekta is not None and z_visina_objekta > 0:\n",
    "        povrsina_xz_na_y = duzina_x_na_y * z_visina_objekta\n",
    "        povrsina_yz_na_x = duzina_y_na_x * z_visina_objekta\n",
    "\n",
    "    # ----- Krivulje varijacije preko cijele visine/sirine\n",
    "    duzina_x_vs_y = np.count_nonzero(konacna_maska, axis=1) * dx\n",
    "    duzina_y_vs_x = np.count_nonzero(konacna_maska, axis=0) * dy\n",
    "\n",
    "    if z_visina_objekta is not None and z_visina_objekta > 0:\n",
    "        povrsina_xz_vs_y = duzina_x_vs_y * z_visina_objekta\n",
    "        povrsina_yz_vs_x = duzina_y_vs_x * z_visina_objekta\n",
    "        y_oznaka = \"Povrsina XZ presjeka (mm²)\"\n",
    "        x_oznaka = \"Povrsina YZ presjeka (mm²)\"\n",
    "    else:\n",
    "        povrsina_xz_vs_y = duzina_x_vs_y\n",
    "        povrsina_yz_vs_x = duzina_y_vs_x\n",
    "        y_oznaka = \"Duzina po X (mm)  [postavi z_visina_objekta za povrsinu]\"\n",
    "        x_oznaka = \"Duzina po Y (mm)  [postavi z_visina_objekta za povrsinu]\"\n",
    "\n",
    "    # osi od 0 (lijevi/donji zid) do sirina/visina\n",
    "    y_os_0_do_H = ys_centered + visina/2.0\n",
    "    x_os_0_do_W = xs_centered + sirina/2.0\n",
    "\n",
    "    if graficki_prikaz:\n",
    "        # --- Povrsina naspram y (XZ presjek kako y varira)\n",
    "        plt.figure(figsize=(7, 3.5))\n",
    "        plt.plot(y_os_0_do_H, povrsina_xz_vs_y)\n",
    "        plt.xlabel(\"y od donjeg zida (mm)\")\n",
    "        plt.ylabel(y_oznaka)\n",
    "        plt.title(\"Varijacija prema y\")\n",
    "        plt.grid(True)\n",
    "        plt.xlim(0, visina)\n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "\n",
    "        # --- Povrsina naspram x (YZ presjek kako x varira)\n",
    "        plt.figure(figsize=(7, 3.5))\n",
    "        plt.plot(x_os_0_do_W, povrsina_yz_vs_x)\n",
    "        plt.xlabel(\"x od lijevog zida (mm)\")\n",
    "        plt.ylabel(x_oznaka)\n",
    "        plt.title(\"Varijacija prema x\")\n",
    "        plt.grid(True)\n",
    "        plt.xlim(0, sirina)\n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "\n",
    "    if detaljno:\n",
    "        print(f\"==== {('Grid' if mreza else 'Pravocrtna')} ispuna {udio_ispune*100:.1f}% ====\")\n",
    "        print(f\"XY ukupna povrsina     = {total['A']:.4f} mm²\")\n",
    "        print(f\"  Povrsina ljuski      = {ljuske['A']:.4f} mm²\")\n",
    "        print(f\"  Povrsina ispune      = {A_ispuna:.4f} mm²\")\n",
    " #       print(f\"I_x                    = {total['I_x']:.4f}\")\n",
    " #       print(f\"I_y                    = {total['I_y']:.4f}\")\n",
    " #       print(f\"I_xy                   = {total['I_xy']:.4f}\")\n",
    " #       print(f\"Polarni moment, J      = {total['J']:.4f}\")\n",
    "        print(f\"-- Presjeci kroz Z (uzorak konstantan po Z) --\")\n",
    "        print(f\"Duzina po X @ y={y_ravnina:.3f} mm: {duzina_x_na_y:.4f} mm\")\n",
    "        print(f\"Duzina po Y @ x={x_ravnina:.3f} mm: {duzina_y_na_x:.4f} mm\")\n",
    "        if povrsina_xz_na_y is not None:\n",
    "            print(f\"Povrsina XZ @ y={y_ravnina:.3f}: {povrsina_xz_na_y:.4f} mm²  (Z={z_visina_objekta:.3f} mm)\")\n",
    "        if povrsina_yz_na_x is not None:\n",
    "            print(f\"Povrsina YZ @ x={x_ravnina:.3f}: {povrsina_yz_na_x:.4f} mm²  (Z={z_visina_objekta:.3f} mm)\")\n",
    "        print()\n",
    "\n",
    "    return {\n",
    "        \"maska\": konacna_maska,\n",
    "        \"XX\": XX, \"YY\": YY,\n",
    "        \"dx\": dx, \"dy\": dy,\n",
    "        \"povrsina_ukupno_xy\": total[\"A\"],\n",
    "        \"povrsina_ljuske_xy\": ljuske[\"A\"],\n",
    "        \"povrsina_ispune_xy\": A_ispuna,\n",
    "        \"momenti_xy\": total,\n",
    "        \"duzina_x_na_y\": duzina_x_na_y,\n",
    "        \"duzina_y_na_x\": duzina_y_na_x,\n",
    "        \"povrsina_xz_na_y\": povrsina_xz_na_y,\n",
    "        \"povrsina_yz_na_x\": povrsina_yz_na_x,\n",
    "        # pune krivulje varijacije i njihove osi (0→sirina/visina)\n",
    "        \"y_os_mm\": y_os_0_do_H,\n",
    "        \"x_os_mm\": x_os_0_do_W,\n",
    "        \"povrsina_xz_vs_y\": povrsina_xz_vs_y,\n",
    "        \"povrsina_yz_vs_x\": povrsina_yz_vs_x,\n",
    "    }\n",
    "\n",
    "# ------------------------------\n",
    "# Demo\n",
    "# ------------------------------\n",
    "if __name__ == \"__main__\":\n",
    "    W, H = 10.0, 70.0\n",
    "    Z = 10.0  # visina objekta u Z\n",
    "    res = prusa_mreza_ili_pravocrtna(\n",
    "        sirina=W, visina=H,\n",
    "        udio_ispune=0.2,\n",
    "        sirina_linije=0.42,\n",
    "        slojevi_ljuske=2,\n",
    "        osnovni_kut_ispune_stupnjevi=45.0,\n",
    "        mreza=True,\n",
    "        z_visina_objekta=Z,  # postavi Z za prave povrsine\n",
    "        y_ravnina=+1.0,      # XZ presjek na y = +1 mm\n",
    "        x_ravnina=-2.0,      # YZ presjek na x = -2 mm (cent. koord.)\n",
    "        N=800,\n",
    "        graficki_prikaz=True, detaljno=True\n",
    "    )\n",
    "\n",
    "    print(\"A_xz(y=1mm) =\", res[\"povrsina_xz_na_y\"], \"mm^2\")\n",
    "    print(\"A_yz(x=-2mm) =\", res[\"povrsina_yz_na_x\"], \"mm^2\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60473fab-724f-43a9-919b-09c571734009",
   "metadata": {},
   "outputs": [],
   "source": []
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   "cell_type": "code",
   "execution_count": null,
   "id": "2363b1b8-e9f0-47f5-b74b-4827e3ab6f91",
   "metadata": {},
   "outputs": [],
   "source": []
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