Puzzles/SimpleLoop.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple Loop (简单回路)\n",
    "\n",
    "- 用一条连续线路串联起每个白色格子。必须形成一个环路。\n",
    "- 线只能从上下左右格子走，不能对角线走；\n",
    "\n",
    "-------\n",
    "\n",
    "- Draw a single loop through all unshaded cells, avoiding all shaded cells.\n",
    "- Can't move diagonally. Only horizontally or vertically.\n",
    "\n",
    "> 💡 Hamiltonian cycle\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import networkx as nx\n",
    "import numpy as np\n",
    "import math\n",
    "import gurobipy as grb\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import MultipleLocator\n",
    "from matplotlib.patches import Rectangle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "READING FILE ../assets/data/Simpleloop/20x20easy1.txt\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0]\n",
      "[0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0]\n",
      "[1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1]\n",
      "[1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1]\n",
      "[1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1]\n",
      "[1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1]\n",
      "[1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0]\n",
      "[1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0]\n",
      "[1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
      "[1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1]\n",
      "[1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1]\n",
      "[1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1]\n",
      "[1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1]\n",
      "[1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1]\n",
      "[1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1]\n",
      "[1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1]\n",
      "[0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0]\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
      "[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n"
     ]
    }
   ],
   "source": [
    "def readInstance(path):\n",
    "\n",
    "    file_name = f\"../assets/data/Simpleloop/{path}.txt\"\n",
    "\n",
    "    m = 0\n",
    "    n = 0\n",
    "    grids = None\n",
    "    print( f\"READING FILE {file_name}\" )\n",
    "    if not os.path.isfile(file_name):\n",
    "        print('File does not exist')\n",
    "    else:\n",
    "        \n",
    "        with open(file_name) as file:\n",
    "            firstline = file.readline().strip().split(\" \")\n",
    "            m = int(firstline[0])\n",
    "            n = int(firstline[1])\n",
    "            grids = [[-1 for _ in range(n)] for _ in range(m)]\n",
    "\n",
    "            for idx in range(m):\n",
    "                line = file.readline().strip().split(\" \")\n",
    "                assert len(line) == n\n",
    "                \n",
    "                for j in range(n):\n",
    "                    if line[j] != '.':\n",
    "                        grids[idx][j] = int(line[j])\n",
    "    return m, n, grids\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    m, n, grids = readInstance(\"20x20easy1\")\n",
    "    # print(grids)\n",
    "    for line in grids:\n",
    "        print(line)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "READING FILE ../assets/data/Simpleloop/20x20easy1.txt\n",
      "Model Status: optimal\n",
      "Total arcs: 254.0\n",
      "Running time: 0.138 seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/wp/zfkgqzvj2lq6fk2gy0rbmq6m0000gn/T/ipykernel_68914/4050741418.py:30: UserWarning: linked SCIP 9.02 is not recommended for this version of PySCIPOpt - use version 9.2.1\n",
      "  model = Model(\"SimpleLoop\")\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pyscipopt import Model, quicksum\n",
    "import time\n",
    "import networkx\n",
    "\n",
    "def cell2Arcs(m, n, i, j):\n",
    "    arcs = []\n",
    "    if i != m - 1:\n",
    "        arcs.append((i * n + j, (i + 1) * n + j))\n",
    "    if j != n - 1:\n",
    "        arcs.append((i * n + j, i * n + j + 1))\n",
    "    return arcs\n",
    "\n",
    "def cell2Arcs2(m, n, i, j):\n",
    "    arcs = []\n",
    "    if i != 0:\n",
    "        arcs.append(((i - 1) * n + j ,i * n + j))\n",
    "    if i != m - 1:\n",
    "        arcs.append((i * n + j, (i + 1) * n + j))\n",
    "    if j != 0:\n",
    "        arcs.append((i * n + j - 1, i * n + j))\n",
    "    if j != n - 1:\n",
    "        arcs.append((i * n + j, i * n + j + 1))\n",
    "    return arcs\n",
    "\n",
    "def SimpleLoopSolverWithSCIP(m, n, grids):\n",
    "    \n",
    "    starttime = time.time()\n",
    "    model = Model(\"SimpleLoop\")\n",
    "    x = dict()\n",
    "    \n",
    "    all_arcs = []\n",
    "    for i in range(m):\n",
    "        for j in range(n):\n",
    "            cur_arc = cell2Arcs(m, n, i, j)\n",
    "            for (a, b) in cur_arc:\n",
    "                x[a, b] = model.addVar(\n",
    "                    vtype = \"B\", \n",
    "                    name = f\"x[{a},{b}]\"\n",
    "                )\n",
    "                all_arcs.append((a, b))\n",
    "                \n",
    "    for i in range(m):\n",
    "        for j in range(n):\n",
    "            if grids[i][j] == 1:\n",
    "                cur_arc = cell2Arcs2(m, n, i, j)\n",
    "                model.addCons(quicksum(x[a, b] for (a, b) in cur_arc) == 0, name = f\"0_cell_{i}_{j}\")\n",
    "            elif grids[i][j] == 0:\n",
    "                cur_arc = cell2Arcs2(m, n, i, j)\n",
    "                model.addCons(quicksum(x[a, b] for (a, b) in cur_arc) == 2, name = f\"1_cell_{i}_{j}\")\n",
    "            \n",
    "    model.setObjective(quicksum(x[a, b] for (a, b) in all_arcs), 'minimize')\n",
    "    model.hideOutput()\n",
    "    def addCut(edges):\n",
    "        G = networkx.Graph()\n",
    "        G.add_edges_from(edges)\n",
    "        components = list( list(c) for c in networkx.connected_components(G))\n",
    "        # print(f\"Totally: {len(components)} subtours!\")\n",
    "        if len(components) == 1:\n",
    "            return False\n",
    "        else:\n",
    "            for component in components:\n",
    "                prev = -1\n",
    "                curr = component[0]\n",
    "                cntt = len(component)\n",
    "                nodeSet = set(component)\n",
    "                temp = list()\n",
    "                while cntt > 0:\n",
    "                    for direct in [- n , n , -1, 1]:\n",
    "                        target = direct + curr \n",
    "                        curredge = (curr, target) if curr < target else (target, curr)\n",
    "                        if target in nodeSet and target != prev and curredge in edges:\n",
    "                            temp.append(curredge)\n",
    "                            prev = curr \n",
    "                            curr = target \n",
    "                            break \n",
    "                    cntt -= 1\n",
    "                model.freeTransform()\n",
    "                model.addCons(quicksum(x[a, b] for (a, b) in temp) <= len(temp) - 1)\n",
    "            return True\n",
    "    \n",
    "    arcs = []\n",
    "    while True:\n",
    "        model.optimize()\n",
    "        edges = []\n",
    "        for (a, b) in x.keys():\n",
    "            if model.getVal( x[a, b]) > 1e-1:\n",
    "                edges.append((a, b))\n",
    "        if not addCut(edges):\n",
    "            print(f\"Model Status: {model.getStatus()}\")\n",
    "            print(f\"Total arcs: {model.getObjVal()}\")\n",
    "            sol = model.getBestSol()\n",
    "            for (a, b) in all_arcs:\n",
    "                if sol[x[a, b]] > 1e-1:\n",
    "                    arcs.append([a, b])\n",
    "            break\n",
    "    endtime = time.time()\n",
    "    print(f\"Running time: {(round(endtime - starttime, 3)) } seconds\")\n",
    "    return arcs\n",
    "\n",
    "    \n",
    "if __name__ == \"__main__\":\n",
    "    m, n, grids = readInstance(\"20x20easy1\")\n",
    "    result = SimpleLoopSolverWithSCIP(m, n, grids)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def coordinatesParser(m, n, idx):\n",
    "    \"\"\"_summary_\n",
    "        进行坐标转换 \n",
    "    Args:\n",
    "        m (_type_): _description_\n",
    "        n (_type_): _description_\n",
    "        idx (_type_): _description_\n",
    "\n",
    "    Returns:\n",
    "        _type_: _description_\n",
    "    \"\"\"\n",
    "    ori_x , ori_y = idx // n, idx % n \n",
    "    return (0.5 + ori_y, 0.5 + ( m - ori_x - 1) )\n",
    "\n",
    "def Vis(m, n, grid, result):\n",
    "    plt.figure(figsize=(8,8))\n",
    "\n",
    "    ax = plt.gca()\n",
    "    x_major_locator = MultipleLocator(1)\n",
    "    y_major_locator = MultipleLocator(1)\n",
    "    ax.xaxis.set_major_locator(x_major_locator)\n",
    "    # 把x/y轴的主刻度设置为1的倍数\n",
    "    ax.yaxis.set_major_locator(y_major_locator)\n",
    "\n",
    "    plt.xlim(0,n)\n",
    "    plt.ylim(0,m)\n",
    "\n",
    "    plt.grid(linestyle = \"--\")\n",
    "\n",
    "    # 绘制无法被访问的方块\n",
    "    for i in range(m):\n",
    "        for j in range(n):\n",
    "            if grids[i][j] == 1:    \n",
    "                new_coor = coordinatesParser(m, n, i * n + j)\n",
    "                ax.add_patch(Rectangle((new_coor[0] - 0.5, new_coor[1] - 0.5), 1, 1, color = \"black\"))\n",
    "\n",
    "    # 绘制结果连线\n",
    "\n",
    "    for line in result[\"arcs\"]:\n",
    "        dot1 = coordinatesParser(m, n, line[0])\n",
    "        dot2 = coordinatesParser(m, n, line[1])\n",
    "        plt.plot([dot1[0], dot2[0]], [dot1[1], dot2[1]], linestyle=\"-\", color = \"red\") \n",
    "\n",
    "    plt.show()\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    Vis(m, n, grids, {\"arcs\": result})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gurobi Solver (Verified!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Set parameter Username\n",
      "Academic license - for non-commercial use only - expires 2024-09-11\n",
      "All Nodes need to be filled is 254\n",
      "Set parameter LazyConstraints to value 1\n",
      "Gurobi Optimizer version 10.0.3 build v10.0.3rc0 (mac64[rosetta2])\n",
      "\n",
      "CPU model: Apple M2\n",
      "Thread count: 8 physical cores, 8 logical processors, using up to 8 threads\n",
      "\n",
      "Optimize a model with 508 rows, 766 columns and 1532 nonzeros\n",
      "Model fingerprint: 0x10fee560\n",
      "Variable types: 0 continuous, 766 integer (766 binary)\n",
      "Coefficient statistics:\n",
      "  Matrix range     [1e+00, 1e+00]\n",
      "  Objective range  [1e+00, 1e+00]\n",
      "  Bounds range     [1e+00, 1e+00]\n",
      "  RHS range        [1e+00, 1e+00]\n",
      "Presolve removed 226 rows and 228 columns\n",
      "Presolve time: 0.01s\n",
      "Presolved: 282 rows, 538 columns, 1030 nonzeros\n",
      "Variable types: 0 continuous, 538 integer (538 binary)\n",
      "The length is 78\n",
      "\n",
      "Root relaxation: objective 2.540000e+02, 339 iterations, 0.00 seconds (0.00 work units)\n",
      "The length is 64\n",
      "\n",
      "    Nodes    |    Current Node    |     Objective Bounds      |     Work\n",
      " Expl Unexpl |  Obj  Depth IntInf | Incumbent    BestBd   Gap | It/Node Time\n",
      "\n",
      "     0     0  254.00000    0    -          -  254.00000      -     -    0s\n",
      "The length is 30\n",
      "     0     0  254.00000    0    -          -  254.00000      -     -    0s\n",
      "     0     0  254.00000    0   34          -  254.00000      -     -    0s\n",
      "The length is 19\n",
      "     0     0  254.00000    0   47          -  254.00000      -     -    0s\n",
      "The length is 15\n",
      "     0     0  254.00000    0    -          -  254.00000      -     -    0s\n",
      "     0     0  254.00000    0   50          -  254.00000      -     -    0s\n",
      "The length is 15\n",
      "     0     0  254.00000    0    -          -  254.00000      -     -    0s\n",
      "     0     0  254.00000    0   22          -  254.00000      -     -    0s\n",
      "The length is 8\n",
      "The length is 8\n",
      "The length is 6\n",
      "The length is 5\n",
      "The length is 2\n",
      "The length is 4\n",
      "The length is 1\n",
      "*    0     0               0     254.0000000  254.00000  0.00%     -    0s\n",
      "\n",
      "Cutting planes:\n",
      "  MIR: 1\n",
      "  Lazy constraints: 254\n",
      "\n",
      "Explored 1 nodes (789 simplex iterations) in 0.18 seconds (0.02 work units)\n",
      "Thread count was 8 (of 8 available processors)\n",
      "\n",
      "Solution count 1: 254 \n",
      "\n",
      "Optimal solution found (tolerance 1.00e-04)\n",
      "Best objective 2.540000000000e+02, best bound 2.540000000000e+02, gap 0.0000%\n",
      "\n",
      "User-callback calls 706, time in user-callback 0.08 sec\n",
      "Gurobi Optimizer version 10.0.3 build v10.0.3rc0 (mac64[rosetta2])\n",
      "\n",
      "CPU model: Apple M2\n",
      "Thread count: 8 physical cores, 8 logical processors, using up to 8 threads\n",
      "\n",
      "Optimize a model with 508 rows, 766 columns and 1532 nonzeros\n",
      "Model fingerprint: 0x10fee560\n",
      "Variable types: 0 continuous, 766 integer (766 binary)\n",
      "Coefficient statistics:\n",
      "  Matrix range     [1e+00, 1e+00]\n",
      "  Objective range  [1e+00, 1e+00]\n",
      "  Bounds range     [1e+00, 1e+00]\n",
      "  RHS range        [1e+00, 1e+00]\n",
      "Presolved: 282 rows, 538 columns, 1030 nonzeros\n",
      "\n",
      "Continuing optimization...\n",
      "\n",
      "\n",
      "Cutting planes:\n",
      "  MIR: 1\n",
      "  Lazy constraints: 254\n",
      "\n",
      "Explored 1 nodes (789 simplex iterations) in 0.01 seconds (0.00 work units)\n",
      "Thread count was 8 (of 8 available processors)\n",
      "\n",
      "Solution count 1: 254 \n",
      "\n",
      "Optimal solution found (tolerance 1.00e-04)\n",
      "Best objective 2.540000000000e+02, best bound 2.540000000000e+02, gap 0.0000%\n",
      "254.0\n"
     ]
    }
   ],
   "source": [
    "def SimpleLoopwithLazyCuts(m, n, grids):\n",
    "    \"\"\"_summary_\n",
    "\n",
    "    Args:\n",
    "        m (_type_): _description_\n",
    "        n (_type_): _description_\n",
    "        grids (_type_): _description_\n",
    "    \"\"\"\n",
    "    SimpleLoop = grb.Model(\"SimpleLoop\")\n",
    "    x = {}\n",
    "    to_dict = {}\n",
    "    for i in range(m):\n",
    "        for j in range(n):\n",
    "            if grids[i][j] == 1:\n",
    "                continue\n",
    "            cur_idx = i * n + j\n",
    "            to_dict[cur_idx] = []\n",
    "            directions = [\n",
    "                (0, -1),\n",
    "                (0, 1),\n",
    "                (1, 0),\n",
    "                (-1, 0)\n",
    "            ]\n",
    "            for (xx, yy) in directions:\n",
    "                newX = i + xx\n",
    "                newY = j + yy\n",
    "                if newX < 0 or newX >= m or newY < 0 or newY >= n or grids[newX][newY] == 1:\n",
    "                    continue\n",
    "                tar_idx = newX * m + newY\n",
    "                x[cur_idx, tar_idx] = SimpleLoop.addVar(\n",
    "                    vtype = grb.GRB.BINARY,\n",
    "                    obj = 1,\n",
    "                    name = f\"x[{cur_idx},{tar_idx}]\"\n",
    "                )\n",
    "                to_dict[cur_idx].append(tar_idx)\n",
    "\n",
    "    print(f\"All Nodes need to be filled is {len(to_dict.keys())}\")\n",
    "    SimpleLoop.modelSense = grb.GRB.MINIMIZE\n",
    "    SimpleLoop.Params.lazyConstraints = 1\n",
    "    SimpleLoop.update()\n",
    "    \n",
    "    for i in to_dict.keys():\n",
    "        SimpleLoop.addConstr(grb.quicksum(x[i, j] for j in to_dict[i]) == 1, name = f\"Out_Balance_Node_{i}\")\n",
    "        SimpleLoop.addConstr(grb.quicksum(x[j, i] for j in to_dict[i]) == 1, name = f\"In_Balance_Node_{i}\")\n",
    "    \n",
    "    def subtourelim(model, where):\n",
    "        if (where == grb.GRB.Callback.MIPSOL):\n",
    "            x_sol = model.cbGetSolution(model._x)\n",
    "            # 从新的 MIP 解决方案中检索值。\n",
    "            G = nx.Graph()\n",
    "            nodes_appear = []\n",
    "            for (i, j) in x.keys():\n",
    "                if (x_sol[i, j] > 0.9):\n",
    "                    if i not in nodes_appear:\n",
    "                        nodes_appear.append(i)\n",
    "                    if j not in nodes_appear:\n",
    "                        nodes_appear.append(j)\n",
    "                    G.add_edge(i, j, weight = 1)\n",
    "            components = [list(c) for c in nx.connected_components(G)]\n",
    "            print(\"The length is \" + str(len(components)))\n",
    "            for component in components:\n",
    "                if (len(component) < len(nodes_appear)):\n",
    "                    # print(\"Add Cut!\")\n",
    "                    model.cbLazy(grb.quicksum(x[i,j] for i in component for j in component if j in to_dict[i]) <= len(component) - 1)\n",
    "    SimpleLoop._x = x\n",
    "    SimpleLoop.optimize(subtourelim)\n",
    "    SimpleLoop.optimize()\n",
    "    # SimpleLoop.write(\"SimpleLoop.lp\")\n",
    "\n",
    "    ofv = None\n",
    "    seq = []\n",
    "    arcs = []\n",
    "    solType = None\n",
    "    gap = None\n",
    "    lb = None\n",
    "    ub = None\n",
    "    runtime = None\n",
    "    if (SimpleLoop.status == grb.GRB.status.OPTIMAL):\n",
    "        solType = 'IP_Optimal'\n",
    "        ofv = SimpleLoop.getObjective().getValue()\n",
    "        print(ofv)\n",
    "        for i, j in x:\n",
    "            if (x[i, j].x > 0.5):\n",
    "                arcs.append([i, j])\n",
    "        # assert len(arcs) == len(nodes_pos) - 1\n",
    "        # currentNode = nodeIDs[0]\n",
    "        # seq.append(currentNode)\n",
    "        # while (len(arcs) > 0):\n",
    "        #     for i in range(len(arcs)):\n",
    "        #         if (arcs[i][0] == currentNode):\n",
    "        #             currentNode = arcs[i][1]\n",
    "        #             seq.append(currentNode)\n",
    "        #             arcs.pop(i)\n",
    "        #             break\n",
    "        gap = 0\n",
    "        lb = ofv\n",
    "        ub = ofv\n",
    "        runtime = SimpleLoop.Runtime\n",
    "    elif (SimpleLoop.status == grb.GRB.status.TIME_LIMIT):\n",
    "        solType = 'IP_TimeLimit'\n",
    "        ofv = None\n",
    "        seq = []\n",
    "        gap = SimpleLoop.MIPGap\n",
    "        lb = SimpleLoop.ObjBoundC\n",
    "        ub = SimpleLoop.ObjVal\n",
    "        runtime = SimpleLoop.Runtime\n",
    "\n",
    "    return {\n",
    "        \"TotalCost\": ofv,\n",
    "        \"runtime\": runtime,\n",
    "        \"arcs\": arcs\n",
    "    }\n",
    "result = SimpleLoopwithLazyCuts(m, n, grids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py310_arm",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}