{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d6cb560a-707c-4c1e-b50f-2b9c8037a3c7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-06-01T17:57:32.767132702Z",
     "start_time": "2026-06-01T17:57:30.130629821Z"
    },
    "editable": true,
    "execution": {
     "iopub.execute_input": "2026-06-01T18:05:07.380499Z",
     "iopub.status.busy": "2026-06-01T18:05:07.380324Z",
     "iopub.status.idle": "2026-06-01T18:05:11.030587Z",
     "shell.execute_reply": "2026-06-01T18:05:11.029614Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "import matplotlib.pyplot as plt\n",
    "from rbm.model import Model\n",
    "from rbm.entity import Entity, EntityParams, TrainingParams\n",
    "from rbm.matrix import Mat, np, rms_error_accu, sample_gaussian\n",
    "from rbm.torch import Optimizer\n",
    "from rbm.image import SubImage, normalize\n",
    "import math\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4065302d-862f-4639-99d7-e396d055434c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-06-01T17:57:32.821169932Z",
     "start_time": "2026-06-01T17:57:32.769072543Z"
    },
    "editable": true,
    "execution": {
     "iopub.execute_input": "2026-06-01T18:05:11.032871Z",
     "iopub.status.busy": "2026-06-01T18:05:11.032546Z",
     "iopub.status.idle": "2026-06-01T18:05:11.036109Z",
     "shell.execute_reply": "2026-06-01T18:05:11.035245Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "transform = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))                            \n",
    "])                              "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ffe48fc6-0f57-48a0-b5e9-aa68cbc8edf1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-06-01T17:57:34.110409687Z",
     "start_time": "2026-06-01T17:57:32.822368553Z"
    },
    "editable": true,
    "execution": {
     "iopub.execute_input": "2026-06-01T18:05:11.037686Z",
     "iopub.status.busy": "2026-06-01T18:05:11.037526Z",
     "iopub.status.idle": "2026-06-01T18:05:12.466249Z",
     "shell.execute_reply": "2026-06-01T18:05:12.465235Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_data = torchvision.datasets.CIFAR10(root='./data', train=True, transform=transform, download=True)\n",
    "test_data = torchvision.datasets.CIFAR10(root='./data', train=False, transform=transform, download=True)\n",
    "\n",
    "train_loader = torch.utils.data.DataLoader(train_data, batch_size=32, shuffle=True, num_workers=2)\n",
    "test_loader = torch.utils.data.DataLoader(test_data, batch_size=32, shuffle=True, num_workers=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "33501c0f-6d78-43e9-b2ce-adbe40b6d95a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-06-01T17:57:34.177330992Z",
     "start_time": "2026-06-01T17:57:34.116314258Z"
    },
    "editable": true,
    "execution": {
     "iopub.execute_input": "2026-06-01T18:05:12.468794Z",
     "iopub.status.busy": "2026-06-01T18:05:12.468559Z",
     "iopub.status.idle": "2026-06-01T18:05:12.472474Z",
     "shell.execute_reply": "2026-06-01T18:05:12.471545Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset CIFAR10\n",
      "    Number of datapoints: 50000\n",
      "    Root location: ./data\n",
      "    Split: Train\n",
      "    StandardTransform\n",
      "Transform: Compose(\n",
      "               ToTensor()\n",
      "               Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))\n",
      "           )\n"
     ]
    }
   ],
   "source": [
    "print(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "aedf601a-9084-4047-98e8-481f4d41a2d9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-06-01T17:57:34.398042393Z",
     "start_time": "2026-06-01T17:57:34.180536787Z"
    },
    "editable": true,
    "execution": {
     "iopub.execute_input": "2026-06-01T18:05:12.474580Z",
     "iopub.status.busy": "2026-06-01T18:05:12.474238Z",
     "iopub.status.idle": "2026-06-01T18:05:12.748650Z",
     "shell.execute_reply": "2026-06-01T18:05:12.747714Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([3, 32, 32])\n"
     ]
    }
   ],
   "source": [
    "N = 500\n",
    "n_side = 10\n",
    "PATCH  = 8\n",
    "STRIDE = 6\n",
    "N_VIS  = 3 * PATCH * PATCH\n",
    "\n",
    "image, label = train_data[0]\n",
    "size = len(torch.flatten(image))\n",
    "train_images = np.zeros(shape=(N, size))\n",
    "train_images_tensor = torch.Tensor(N, size)\n",
    "for i in range(N):\n",
    "    image, label = train_data[i]\n",
    "    flat_array = torch.flatten(image)\n",
    "    train_images[i, :] = np.asarray(flat_array.numpy())\n",
    "    train_images_tensor[i, :] = flat_array\n",
    "print(image.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5ee865c3-2af4-487f-a184-1291f99839b1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-06-01T17:57:36.333225542Z",
     "start_time": "2026-06-01T17:57:34.399658692Z"
    },
    "editable": true,
    "execution": {
     "iopub.execute_input": "2026-06-01T18:05:12.750649Z",
     "iopub.status.busy": "2026-06-01T18:05:12.750448Z",
     "iopub.status.idle": "2026-06-01T18:05:14.002882Z",
     "shell.execute_reply": "2026-06-01T18:05:14.002007Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_images.shape:(500, 3072)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12500, 3, 8, 8)\n",
      "(12500, 192)\n"
     ]
    }
   ],
   "source": [
    "image_indices = np.array(random.sample(range(min(N, n_side*n_side)), n_side*n_side))\n",
    "print(f\"train_images.shape:{train_images.shape}\")\n",
    "mean_training_batch = np.reshape(np.repeat(np.mean(train_images, axis=1), 3072, axis=0), train_images.shape)\n",
    "var_training_batch  = np.reshape(np.repeat(np.std(train_images,  axis=1), 3072, axis=0), train_images.shape)\n",
    "train_images_norm = (train_images - mean_training_batch) / var_training_batch\n",
    "sub_generator = SubImage(PATCH, PATCH, STRIDE, STRIDE)\n",
    "train_subimages = sub_generator(np.reshape(train_images, (N, 3, 32, 32)), 3)\n",
    "print(train_subimages.shape)\n",
    "train_subimages = np.reshape(train_subimages, (train_subimages.shape[0], N_VIS))\n",
    "print(train_subimages.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b4310933-d850-4128-a2dd-27d88c004846",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-06-01T17:57:37.735621606Z",
     "start_time": "2026-06-01T17:57:36.334036265Z"
    },
    "execution": {
     "iopub.execute_input": "2026-06-01T18:05:14.004890Z",
     "iopub.status.busy": "2026-06-01T18:05:14.004617Z",
     "iopub.status.idle": "2026-06-01T18:05:15.450743Z",
     "shell.execute_reply": "2026-06-01T18:05:15.449902Z"
    }
   },
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 3000x3000 with 100 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(n_side, n_side, figsize=(30,30))\n",
    "index = 0             \n",
    "for x in range(n_side):\n",
    "    for y in range(n_side):\n",
    "        inp = train_subimages[image_indices[index]]\n",
    "        img = (1+np.reshape(inp, (3, PATCH, PATCH)))/2\n",
    "        img = img.transpose((1,2,0))\n",
    "        axes[x,y].imshow(np.asnumpy(img))\n",
    "        axes[x,y].axis('off')\n",
    "        index += 1\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "08143a53-12a1-4896-a4b2-166dedf0e021",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-06-01T17:57:37.786193706Z",
     "start_time": "2026-06-01T17:57:37.737050952Z"
    },
    "editable": true,
    "execution": {
     "iopub.execute_input": "2026-06-01T18:05:15.452497Z",
     "iopub.status.busy": "2026-06-01T18:05:15.452325Z",
     "iopub.status.idle": "2026-06-01T18:05:15.456017Z",
     "shell.execute_reply": "2026-06-01T18:05:15.455418Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class TestModel(Model):\n",
    "    def __init__(self, name: str, work_dir: str = '.'):\n",
    "        super().__init__(name, work_dir)\n",
    "        self.unit1 = Entity((N_VIS, 200), EntityParams(do_gaussian_visible=True, do_gaussian_hidden=False), TrainingParams(learning_rate=0.005, momentum=0.9, num_epochs=1000, mini_batch_size=1000, weight_decay=0.0, l1_lambda=0.02))\n",
    "    \n",
    "    def forward(self, x: Mat):\n",
    "        x = self.unit1.forward(x)\n",
    "        return x\n",
    "    \n",
    "    def backward(self, x: Mat):\n",
    "        x = self.unit1.reconstruct(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6b3be9f6-38e1-442d-a7bd-1f3c164ec8d4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-06-01T17:58:19.592702084Z",
     "start_time": "2026-06-01T17:57:37.787975671Z"
    },
    "editable": true,
    "execution": {
     "iopub.execute_input": "2026-06-01T18:05:15.457849Z",
     "iopub.status.busy": "2026-06-01T18:05:15.457672Z",
     "iopub.status.idle": "2026-06-01T18:05:40.787667Z",
     "shell.execute_reply": "2026-06-01T18:05:40.786713Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------\n",
      "Entity-192x200: progress              : 0%\n",
      "Entity-192x200: err_rms              : 0.025838447616614947\n",
      "Entity-192x200: l2_norm              : 0.3199044091714827\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------\n",
      "Entity-192x200: progress              : 10%\n",
      "Entity-192x200: err_rms              : 0.025831811280644126\n",
      "Entity-192x200: l2_norm              : 0.315564451149696\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------\n",
      "Entity-192x200: progress              : 20%\n",
      "Entity-192x200: err_rms              : 0.025911035266241764\n",
      "Entity-192x200: l2_norm              : 0.31571057476532316\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------\n",
      "Entity-192x200: progress              : 30%\n",
      "Entity-192x200: err_rms              : 0.02590101076840089\n",
      "Entity-192x200: l2_norm              : 0.31632907820940576\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------\n",
      "Entity-192x200: progress              : 40%\n",
      "Entity-192x200: err_rms              : 0.026241090192976175\n",
      "Entity-192x200: l2_norm              : 0.3161115791596577\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------\n",
      "Entity-192x200: progress              : 50%\n",
      "Entity-192x200: err_rms              : 0.025513378728358904\n",
      "Entity-192x200: l2_norm              : 0.3160825991470746\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------\n",
      "Entity-192x200: progress              : 60%\n",
      "Entity-192x200: err_rms              : 0.025746118590822126\n",
      "Entity-192x200: l2_norm              : 0.31645051288108683\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------\n",
      "Entity-192x200: progress              : 70%\n",
      "Entity-192x200: err_rms              : 0.025845673803099854\n",
      "Entity-192x200: l2_norm              : 0.3161200059309051\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------\n",
      "Entity-192x200: progress              : 80%\n",
      "Entity-192x200: err_rms              : 0.025871357051162402\n",
      "Entity-192x200: l2_norm              : 0.315978706204221\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------\n",
      "Entity-192x200: progress              : 90%\n",
      "Entity-192x200: err_rms              : 0.026120424786220082\n",
      "Entity-192x200: l2_norm              : 0.31644273994809974\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------\n",
      "Entity-192x200: progress              : 100%\n",
      "Entity-192x200: err_rms              : 0.025589037563306043\n",
      "Entity-192x200: l2_norm              : 0.3167808254022048\n",
      "-------------------------------------------\n",
      "Entity-192x200: progress              : 100%\n",
      "Entity-192x200: err_rms_total              : 0.025522490420863866\n",
      "Entity-192x200: l2_norm              : 0.31626854660711595\n"
     ]
    }
   ],
   "source": [
    "work_dir = \"results\"\n",
    "prj_name = \"cifar_test\"\n",
    "prj_root = \"/home/jens/work/repos/Rbm\"\n",
    "\n",
    "# Create model\n",
    "model = TestModel(prj_name, \"results\")\n",
    "\n",
    "# Init state\n",
    "model.init(0.01)\n",
    "\n",
    "# load state\n",
    "model.load()\n",
    "\n",
    "# Train\n",
    "#model.train(normalize(train_subimages))\n",
    "model.train(train_subimages)\n",
    "\n",
    "# save state\n",
    "model.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d036b106-77a3-4993-bc2a-5fb164b8ec46",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-06-01T17:58:20.910856706Z",
     "start_time": "2026-06-01T17:58:19.594628552Z"
    },
    "execution": {
     "iopub.execute_input": "2026-06-01T18:05:40.789519Z",
     "iopub.status.busy": "2026-06-01T18:05:40.789280Z",
     "iopub.status.idle": "2026-06-01T18:05:42.168103Z",
     "shell.execute_reply": "2026-06-01T18:05:42.167459Z"
    }
   },
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 3000x3000 with 100 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot reconstructions\n",
    "fig, axes = plt.subplots(n_side, n_side, figsize=(30,30))\n",
    "index = 0             \n",
    "for x in range(n_side):\n",
    "    for y in range(n_side):\n",
    "        inp = train_subimages[image_indices[index]]\n",
    "        recon = model.backward(model.forward(inp))\n",
    "        recon -= np.min(recon)\n",
    "        recon = recon / np.max(recon)\n",
    "        img = np.reshape(recon, (3, PATCH, PATCH))\n",
    "        img = img.transpose((1,2,0))\n",
    "        axes[x,y].imshow(np.asnumpy(img))\n",
    "        axes[x,y].axis('off')\n",
    "        index += 1\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "41430b5f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-06-01T17:58:21.585629663Z",
     "start_time": "2026-06-01T17:58:20.912692545Z"
    },
    "execution": {
     "iopub.execute_input": "2026-06-01T18:05:42.169881Z",
     "iopub.status.busy": "2026-06-01T18:05:42.169722Z",
     "iopub.status.idle": "2026-06-01T18:05:42.691947Z",
     "shell.execute_reply": "2026-06-01T18:05:42.691290Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x3000 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy\n",
    "\n",
    "# Reconstruct full 32x32 images by assembling reconstructed sub-images (overlap-averaged)\n",
    "nx_steps = (32 - PATCH) // STRIDE + 1\n",
    "ny_steps = (32 - PATCH) // STRIDE + 1\n",
    "n_images = n_side\n",
    "\n",
    "fig, axes = plt.subplots(n_images, 2, figsize=(6, n_images * 3))\n",
    "\n",
    "for img_idx in range(n_images):\n",
    "    orig = np.reshape(train_images[img_idx], (3, 32, 32))\n",
    "    orig_display = np.asnumpy((orig - np.min(orig)) / (np.max(orig) - np.min(orig)))\n",
    "\n",
    "    accum = numpy.zeros((3, 32, 32), dtype=numpy.float64)\n",
    "    count = numpy.zeros((1, 32, 32), dtype=numpy.float64)\n",
    "    for ix in range(nx_steps):\n",
    "        for iy in range(ny_steps):\n",
    "            sub_idx = img_idx * nx_steps * ny_steps + ix * ny_steps + iy\n",
    "            inp = train_subimages[sub_idx]\n",
    "            recon = model.backward(model.forward(inp))\n",
    "            arr = np.asnumpy(np.reshape(recon, (3, PATCH, PATCH)))\n",
    "            x0, y0 = ix * STRIDE, iy * STRIDE\n",
    "            accum[:, x0:x0+PATCH, y0:y0+PATCH] += arr\n",
    "            count[0, x0:x0+PATCH, y0:y0+PATCH] += 1.0\n",
    "\n",
    "    recon_full = (accum / numpy.maximum(count, 1)).transpose(1, 2, 0)\n",
    "    lo, hi = recon_full.min(), recon_full.max()\n",
    "    recon_full = numpy.clip((recon_full - lo) / (hi - lo + 1e-8), 0, 1)\n",
    "\n",
    "    axes[img_idx, 0].imshow(orig_display.transpose(1, 2, 0))\n",
    "    axes[img_idx, 0].axis('off')\n",
    "    if img_idx == 0:\n",
    "        axes[img_idx, 0].set_title('Original')\n",
    "\n",
    "    axes[img_idx, 1].imshow(recon_full)\n",
    "    axes[img_idx, 1].axis('off')\n",
    "    if img_idx == 0:\n",
    "        axes[img_idx, 1].set_title('Reconstructed')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9adce292-cae6-455c-9409-c57ae71a36d5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-06-01T17:58:23.320011034Z",
     "start_time": "2026-06-01T17:58:21.588136821Z"
    },
    "execution": {
     "iopub.execute_input": "2026-06-01T18:05:42.694095Z",
     "iopub.status.busy": "2026-06-01T18:05:42.693875Z",
     "iopub.status.idle": "2026-06-01T18:05:44.224681Z",
     "shell.execute_reply": "2026-06-01T18:05:44.223717Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 8, 8, 200)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x3000 with 100 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot weights\n",
    "weights = model.unit1.state.w_hv\n",
    "n, n_hid = weights.shape\n",
    "weight_indices = np.array(random.sample(range(n_hid), n_side*n_side))\n",
    "w = np.reshape(weights, (3, PATCH, PATCH, n_hid))\n",
    "print(w.shape)\n",
    "fig, axes = plt.subplots(n_side, n_side, figsize=(30,30))\n",
    "\n",
    "index = 0\n",
    "for x in range(n_side):\n",
    "    for y in range(n_side):\n",
    "        inp = np.asnumpy(w[:,:,:, weight_indices[index]])\n",
    "        inp -= np.min(inp)\n",
    "        inp = inp / np.max(inp)\n",
    "        img = inp.transpose((1,2,0))\n",
    "        axes[x,y].imshow(np.asnumpy(img))\n",
    "        axes[x,y].axis('off')\n",
    "        index += 1\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c976fed5-162c-4a70-8219-fc6c68c1fb66",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-06-01T17:58:23.367461323Z",
     "start_time": "2026-06-01T17:58:23.322515679Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "661dfc9d-e58a-4994-a5fa-c377d7f743e5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-06-01T17:58:23.416025632Z",
     "start_time": "2026-06-01T17:58:23.370109432Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c1a67046-17e4-4924-8067-da23481eb6d3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-06-01T17:58:23.463714862Z",
     "start_time": "2026-06-01T17:58:23.418192823Z"
    }
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}