{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4e11d459-5b89-4059-8728-86d06dba35f5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-20T10:47:35.087272Z",
     "iopub.status.busy": "2023-11-20T10:47:35.086842Z",
     "iopub.status.idle": "2023-11-20T10:47:39.634060Z",
     "shell.execute_reply": "2023-11-20T10:47:39.632570Z",
     "shell.execute_reply.started": "2023-11-20T10:47:35.087187Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from fastai import *\n",
    "from fastai.vision.all import *\n",
    "\n",
    "\n",
    "import sys\n",
    "sys.path.append('..')\n",
    "from src.band_plotters import *\n",
    "from src.Tiff32Image import TiffImage, load_tiff_uint16_image\n",
    "\n",
    "sys.path.append('/notebooks/Beta-VAE/')\n",
    "from model import BetaVAE_B\n",
    "\n",
    "latent_length=64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0e8e7e64-feb0-4e7d-a5ea-b18ee3976611",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-20T10:47:43.527714Z",
     "iopub.status.busy": "2023-11-20T10:47:43.527110Z",
     "iopub.status.idle": "2023-11-20T10:47:46.938664Z",
     "shell.execute_reply": "2023-11-20T10:47:46.937436Z",
     "shell.execute_reply.started": "2023-11-20T10:47:43.527666Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/notebooks/Beta-VAE/model.py:191: UserWarning: nn.init.kaiming_normal is now deprecated in favor of nn.init.kaiming_normal_.\n",
      "  init.kaiming_normal(m.weight)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "BetaVAE_B(\n",
       "  (encoder): Sequential(\n",
       "    (0): Conv2d(3, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
       "    (1): ReLU(inplace=True)\n",
       "    (2): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
       "    (3): ReLU(inplace=True)\n",
       "    (4): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
       "    (5): ReLU(inplace=True)\n",
       "    (6): Conv2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
       "    (7): ReLU(inplace=True)\n",
       "    (8): View()\n",
       "    (9): Linear(in_features=512, out_features=256, bias=True)\n",
       "    (10): ReLU(inplace=True)\n",
       "    (11): Linear(in_features=256, out_features=256, bias=True)\n",
       "    (12): ReLU(inplace=True)\n",
       "    (13): Linear(in_features=256, out_features=128, bias=True)\n",
       "  )\n",
       "  (decoder): Sequential(\n",
       "    (0): Linear(in_features=64, out_features=256, bias=True)\n",
       "    (1): ReLU(inplace=True)\n",
       "    (2): Linear(in_features=256, out_features=256, bias=True)\n",
       "    (3): ReLU(inplace=True)\n",
       "    (4): Linear(in_features=256, out_features=512, bias=True)\n",
       "    (5): ReLU(inplace=True)\n",
       "    (6): View()\n",
       "    (7): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
       "    (8): ReLU(inplace=True)\n",
       "    (9): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
       "    (10): ReLU(inplace=True)\n",
       "    (11): ConvTranspose2d(32, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
       "    (12): ReLU(inplace=True)\n",
       "    (13): ConvTranspose2d(32, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = BetaVAE_B(z_dim=latent_length, nc=3)\n",
    "\n",
    "checkpoint = torch.load(\"/notebooks/Beta-VAE/checkpoints/BW_B_gamma100_z{0}/last\".format(latent_length))\n",
    "model.load_state_dict(checkpoint['model_states']['net'])\n",
    "\n",
    "model.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae3aea8d-7661-44be-97cf-72933d8415c1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "68755b68-8870-4d66-b187-32ebce8a2ee2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-19T23:57:18.166403Z",
     "iopub.status.busy": "2023-11-19T23:57:18.166041Z",
     "iopub.status.idle": "2023-11-19T23:57:18.498639Z",
     "shell.execute_reply": "2023-11-19T23:57:18.497263Z",
     "shell.execute_reply.started": "2023-11-19T23:57:18.166383Z"
    }
   },
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '/storage/2dmatpedia/images/no_dos_bw/low_dpi_bands/2dm-1.tiff'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Input \u001b[0;32mIn [4]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mview_prediction\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m2dm-1\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m7\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m7\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mimage_directory\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m../../../storage/2dmatpedia/images/no_dos_bw/low_dpi_bands\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwidth\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m64\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mheight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m64\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mheight_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mpad\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mact_func\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msigmoid\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/notebooks/band-fingerprint/misc/../src/band_plotters.py:151\u001b[0m, in \u001b[0;36mview_prediction\u001b[0;34m(material_id, model, min_energy_minus_efermi, max_energy_minus_efermi, data_directory, image_directory, device, e_bounds, verbose, width, height, height_mode, act_func)\u001b[0m\n\u001b[1;32m    148\u001b[0m fig, ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39msubplots(\u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m    150\u001b[0m image_filename \u001b[38;5;241m=\u001b[39m data_directory\u001b[38;5;241m/\u001b[39m\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mimages/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mimage_directory\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmaterial_id\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.tiff\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 151\u001b[0m input_numpy \u001b[38;5;241m=\u001b[39m \u001b[43mload_tiff_uint16_image\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimage_filename\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mastype(np\u001b[38;5;241m.\u001b[39mfloat64)\n\u001b[1;32m    153\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m width:\n\u001b[1;32m    154\u001b[0m     input_numpy \u001b[38;5;241m=\u001b[39m resize(input_numpy, (input_numpy\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m], width))\n",
      "File \u001b[0;32m/notebooks/band-fingerprint/misc/../src/Tiff32Image.py:7\u001b[0m, in \u001b[0;36mload_tiff_uint16_image\u001b[0;34m(fn)\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mload_tiff_uint16_image\u001b[39m(fn):\n\u001b[1;32m      6\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOpen and load a uint16 as a float32 numpy array\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m----> 7\u001b[0m     arr \u001b[38;5;241m=\u001b[39m \u001b[43mio\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mimread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfn\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mastype(np\u001b[38;5;241m.\u001b[39mfloat32)\n\u001b[1;32m      8\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m arr\n",
      "File \u001b[0;32m/usr/local/lib/python3.9/dist-packages/skimage/io/_io.py:53\u001b[0m, in \u001b[0;36mimread\u001b[0;34m(fname, as_gray, plugin, **plugin_args)\u001b[0m\n\u001b[1;32m     50\u001b[0m         plugin \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtifffile\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m     52\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m file_or_url_context(fname) \u001b[38;5;28;01mas\u001b[39;00m fname:\n\u001b[0;32m---> 53\u001b[0m     img \u001b[38;5;241m=\u001b[39m \u001b[43mcall_plugin\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mimread\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mplugin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mplugin\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mplugin_args\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     55\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(img, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mndim\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m     56\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m img\n",
      "File \u001b[0;32m/usr/local/lib/python3.9/dist-packages/skimage/io/manage_plugins.py:207\u001b[0m, in \u001b[0;36mcall_plugin\u001b[0;34m(kind, *args, **kwargs)\u001b[0m\n\u001b[1;32m    203\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mIndexError\u001b[39;00m:\n\u001b[1;32m    204\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCould not find the plugin \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m for \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m%\u001b[39m\n\u001b[1;32m    205\u001b[0m                            (plugin, kind))\n\u001b[0;32m--> 207\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/usr/local/lib/python3.9/dist-packages/skimage/io/_plugins/tifffile_plugin.py:30\u001b[0m, in \u001b[0;36mimread\u001b[0;34m(fname, **kwargs)\u001b[0m\n\u001b[1;32m     27\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mimg_num\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m kwargs:\n\u001b[1;32m     28\u001b[0m     kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mkey\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mimg_num\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m---> 30\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtifffile_imread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/usr/local/lib/python3.9/dist-packages/tifffile/tifffile.py:973\u001b[0m, in \u001b[0;36mimread\u001b[0;34m(files, aszarr, key, series, level, squeeze, maxworkers, name, offset, size, pattern, axesorder, categories, imread, sort, container, axestiled, ioworkers, chunkmode, fillvalue, zattrs, _multifile, _useframes, **kwargs)\u001b[0m\n\u001b[1;32m    968\u001b[0m     files \u001b[38;5;241m=\u001b[39m files[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m    970\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(files, \u001b[38;5;28mstr\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\n\u001b[1;32m    971\u001b[0m     files, collections\u001b[38;5;241m.\u001b[39mabc\u001b[38;5;241m.\u001b[39mSequence\n\u001b[1;32m    972\u001b[0m ):\n\u001b[0;32m--> 973\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mTiffFile\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    974\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfiles\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    975\u001b[0m \u001b[43m        \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    976\u001b[0m \u001b[43m        \u001b[49m\u001b[43moffset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moffset\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    977\u001b[0m \u001b[43m        \u001b[49m\u001b[43msize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msize\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    978\u001b[0m \u001b[43m        \u001b[49m\u001b[43m_multifile\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_multifile\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    979\u001b[0m \u001b[43m        \u001b[49m\u001b[43m_useframes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_useframes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    980\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mis_flags\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    981\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m tif:\n\u001b[1;32m    982\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m aszarr:\n\u001b[1;32m    983\u001b[0m             \u001b[38;5;28;01massert\u001b[39;00m key \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, \u001b[38;5;28mint\u001b[39m)\n",
      "File \u001b[0;32m/usr/local/lib/python3.9/dist-packages/tifffile/tifffile.py:3598\u001b[0m, in \u001b[0;36mTiffFile.__init__\u001b[0;34m(self, file, mode, name, offset, size, _multifile, _useframes, _parent, **kwargs)\u001b[0m\n\u001b[1;32m   3595\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mode \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m (\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrb\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mr+b\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m   3596\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124minvalid mode \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmode\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m-> 3598\u001b[0m fh \u001b[38;5;241m=\u001b[39m \u001b[43mFileHandle\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moffset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moffset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msize\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3599\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fh \u001b[38;5;241m=\u001b[39m fh\n\u001b[1;32m   3600\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_multifile \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m \u001b[38;5;28;01mif\u001b[39;00m _multifile \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mbool\u001b[39m(_multifile)\n",
      "File \u001b[0;32m/usr/local/lib/python3.9/dist-packages/tifffile/tifffile.py:11829\u001b[0m, in \u001b[0;36mFileHandle.__init__\u001b[0;34m(self, file, mode, name, offset, size)\u001b[0m\n\u001b[1;32m  11827\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_close \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m  11828\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock \u001b[38;5;241m=\u001b[39m NullContext()\n\u001b[0;32m> 11829\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m  11830\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fh \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m  11831\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n",
      "File \u001b[0;32m/usr/local/lib/python3.9/dist-packages/tifffile/tifffile.py:11848\u001b[0m, in \u001b[0;36mFileHandle.open\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m  11846\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_file \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mrealpath(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_file)\n\u001b[1;32m  11847\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dir, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_name \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39msplit(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_file)\n\u001b[0;32m> 11848\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fh \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_file\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mode\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# type: ignore\u001b[39;00m\n\u001b[1;32m  11849\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_close \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m  11850\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_offset \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m0\u001b[39m:\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/storage/2dmatpedia/images/no_dos_bw/low_dpi_bands/2dm-1.tiff'"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "view_prediction(\"2dm-1\", model, -7, 7, image_directory=\"../../../storage/2dmatpedia/images/no_dos_bw/low_dpi_bands\", width=64, height=64, height_mode=\"pad\", act_func=F.sigmoid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ebc3af8-9060-404c-a520-33be249a145b",
   "metadata": {},
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}