{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9d0165eb-4a50-4600-826f-3277cbd8d84d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install pymatgen -q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4e11d459-5b89-4059-8728-86d06dba35f5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from fastai import *\n",
    "from fastai.vision.all import *\n",
    "\n",
    "import torch\n",
    "#import visdom\n",
    "\n",
    "#from torchvision import transforms\n",
    "#from PIL import Image\n",
    "\n",
    "import sys\n",
    "\n",
    "sys.path.append('..')\n",
    "sys.path.append('../autoencoder')\n",
    "# sys.path.append('/notebooks/Beta-VAE/')\n",
    "from models import*\n",
    "\n",
    "from src.band_plotters import*\n",
    "from src.Tiff32Image import*\n",
    "from src.TensorImageNoised import *\n",
    "from src.cluster_plotters import *\n",
    "sys.path.append('/notebooks/band-fingerprint/autoencoder/resnet_autoencoder')\n",
    "sys.path.append('/notebooks/band-fingerprint/src')\n",
    "\n",
    "from model import *\n",
    "\n",
    "from ae_misc 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=98"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4a184653-5138-4a5c-8fbb-fa94e54ec825",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_image(z, image_shape=(64, 64)):\n",
    "    x_recon = model._decode(z).view(image_shape)\n",
    "    return torch.sigmoid(x_recon)\n",
    "\n",
    "def generate_and_plot_image(z, ax, image_shape=(64, 64)):\n",
    "    x_recon = generate_image(z, image_shape=image_shape)\n",
    "    ax.imshow(x_recon.detach().numpy(), vmin=0, vmax=1, aspect='auto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4a3c5ebd-ee8a-4ba6-8739-1463de63b144",
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_resnet_one_mat(material_id, CNN_model, input_band_image_type, data_directory=DATA_DIRECTORY):\n",
    "        image_filename = data_directory/f\"images/{input_band_image_type}/{material_id}.png\"\n",
    "        \n",
    "        # Use the dataloaders to preprocess the input image\n",
    "        dl = CNN_model.dls.test_dl([image_filename])\n",
    "        # Forward pass through the encoder\n",
    "        with torch.no_grad():\n",
    "            encoded_representation = CNN_model.model.encoder(dl.one_batch()[0])\n",
    "        #out = trained_model.encoder(DATA_DIRECTORY/f\"images/grayscale_4ev_linewidth3/2dm-4.png\")\n",
    "\n",
    "        \n",
    "        # flatten encoded 2d array representation of the band structure as the fingerprint\n",
    "        return encoded_representation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "b9b89667-0a6d-4d84-af92-61a1e79796b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TensorImageNoised([[[[0.0661, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0929],\n",
      "                     [2.1475, 1.7247, 0.5420, 0.3226, 1.1750, 1.8739, 2.0061],\n",
      "                     [2.6441, 1.4996, 1.6050, 1.6669, 1.5835, 1.6155, 2.5809],\n",
      "                     [2.0378, 2.5651, 2.0525, 2.5929, 1.8671, 1.9867, 2.0279],\n",
      "                     [2.2301, 2.0941, 1.7493, 2.7838, 2.8140, 2.2413, 2.2298],\n",
      "                     [2.3347, 2.3168, 1.9505, 2.1215, 2.2314, 2.0834, 2.3036],\n",
      "                     [1.4896, 1.5920, 1.6643, 1.5785, 1.4815, 1.6817, 1.4483]],\n",
      "\n",
      "                    [[1.9013, 2.1252, 0.0459, 0.0000, 0.0292, 2.4361, 1.7268],\n",
      "                     [2.3427, 1.7886, 0.2263, 0.0205, 0.1674, 1.8391, 2.3060],\n",
      "                     [2.4858, 2.0722, 1.4835, 1.8307, 2.4699, 2.5515, 2.4150],\n",
      "                     [1.9606, 2.2804, 2.7395, 1.9667, 1.8662, 2.6096, 1.8336],\n",
      "                     [2.0716, 2.7010, 2.5283, 2.2991, 2.3263, 2.6256, 1.9678],\n",
      "                     [1.8486, 1.7781, 1.7486, 1.7463, 1.8509, 1.7042, 1.8004],\n",
      "                     [1.7834, 1.7512, 1.6574, 1.6992, 1.7571, 1.6486, 1.7509]]]])\n",
      "1\n",
      "3\n",
      "2\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "NUM_TRAVERSE_STEPS = 6\n",
    "MAX_VARIATION = 0.7\n",
    "z_dim = 98 # latent length \n",
    "\n",
    "# Assuming you have a trained beta-VAE model with encoder and decoder\n",
    "# model is an instance of your beta-VAE model\n",
    "# input_data is the input for which you want to visualize the latent space\n",
    "\n",
    "\n",
    "resnet_input_file_type = \"grayscale_4ev_linewidth3\"\n",
    "# change path to the location of the desired trained model\n",
    "model_path = \"../autoencoder/trained_models/resnet18_size224_lossbce_channels2.pkl\"\n",
    "resnet_model = load_learner(model_path)\n",
    "#.format(FINGERPRINT_LENGTH)\n",
    "\n",
    "latent_representation = run_resnet_one_mat(\"2dm-1\", resnet_model, resnet_input_file_type)\n",
    "print(latent_representation)\n",
    "print(len(latent_representation))\n",
    "\n",
    "# fig, ax  = plt.subplots()\n",
    "# plt.title(\"Input\")\n",
    "# plt.imshow(input_data)\n",
    "# plt.show()\n",
    "\n",
    "latent_section = (3,3)\n",
    "\n",
    "\n",
    "variation_from_mean = np.linspace(-MAX_VARIATION, MAX_VARIATION, NUM_TRAVERSE_STEPS)\n",
    "mu = latent_representation\n",
    "\n",
    "#FOR NORMAL AE\n",
    "with torch.no_grad():\n",
    "    out = resnet_model.decoder(mu)\n",
    "#print(out[0,0])\n",
    "#show_image(torch.sigmoid(out[0,0]))\n",
    "#show_image(out)\n",
    "\n",
    "iters = 2\n",
    "\n",
    "fig, ax = plt.subplots(iters, NUM_TRAVERSE_STEPS, figsize=(20,6))\n",
    "\n",
    "#print(mu[0, 1][latent_section_2])\n",
    "\n",
    "\n",
    "# for j in range(iters):\n",
    "#     ax[j, 0].set_ylabel(j)\n",
    "\n",
    "for dim in range(iters):\n",
    "    if dim==0:\n",
    "        print(\"3\")\n",
    "        latent_section_2 = (2,2)\n",
    "        variation_from_mean = np.linspace(0.5, 0.9, NUM_TRAVERSE_STEPS)\n",
    "        ax[dim,0].set_ylabel(\"dimension=[2,2]\")\n",
    "\n",
    "    if dim==1:\n",
    "        print(\"2\")\n",
    "        latent_section_2 = (3,0)\n",
    "        #variation_from_mean = np.linspace(0.4, 1.8, NUM_TRAVERSE_STEPS)\n",
    "        variation_from_mean = np.linspace(-0.25, 1.1, NUM_TRAVERSE_STEPS)\n",
    "        ax[dim,0].set_ylabel(\"dimension=[3,0]\")\n",
    "    for j, variation in enumerate(variation_from_mean):\n",
    "\n",
    "\n",
    "        mu_plus_variation = mu.clone()\n",
    "        #latent_section[]\n",
    "        mu_plus_variation[0, 1][latent_section_2] += variation*torch.ones(mu[0,1][latent_section_2].size())\n",
    "        #mu_plus_variation[0, 0][latent_section_2] += variation*torch.ones(mu[0,0][latent_section_2].size())\n",
    "        \n",
    "        \n",
    "        # FOR NORMAL AE\n",
    "        with torch.no_grad():\n",
    "            out = resnet_model.decoder(mu_plus_variation)\n",
    "        #print(out[0,0])\n",
    "        map_ = torch.sigmoid(out[0,0])\n",
    "        show_image(1-map_, ax=ax[dim,j], cmap='Greys')\n",
    "        #show_image(out)\n",
    "    \n",
    "        \n",
    "    for i, variation in enumerate(variation_from_mean):\n",
    "        ax[dim, i].set_title(r\"$\\Delta=${0}\".format(round(variation, 2)))\n",
    "       \n",
    "fig.savefig(\"out1.png\", bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
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   "execution_count": 42,
   "id": "5a7edd3b-0e91-471a-a231-d4c14233442c",
   "metadata": {},
   "outputs": [
    {
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       "\u001b[0;31mSignature:\u001b[0m\n",
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       "\u001b[0;34m\u001b[0m    \u001b[0mim\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mctx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mnorm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0maspect\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0minterpolation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mvmin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mvmax\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0morigin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mextent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0minterpolation_stage\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mfilternorm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mfilterrad\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4.0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mresample\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m Show a PIL or PyTorch image on `ax`.\n",
       "\u001b[0;31mFile:\u001b[0m      /usr/local/lib/python3.9/dist-packages/fastai/torch_core.py\n",
       "\u001b[0;31mType:\u001b[0m      function\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_image?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4f575f3-c6ce-4e72-aa51-7c18f1773b81",
   "metadata": {},
   "outputs": [],
   "source": []
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   "cell_type": "code",
   "execution_count": null,
   "id": "45237a23-c716-4958-ae70-2d890f82585a",
   "metadata": {},
   "outputs": [],
   "source": []
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