{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "32f62046-ef8f-422b-a66d-fab3bd4a85d3",
   "metadata": {
    "tags": []
   },
   "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[0m\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[0m"
     ]
    }
   ],
   "source": [
    "!pip install hdbscan -q\n",
    "!pip install pymatgen -q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bc8c6ef5-5010-46b3-b89b-eab6dc0c00b3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import manifold\n",
    "import hdbscan\n",
    "import os\n",
    "import json\n",
    "\n",
    "import sys\n",
    "sys.path.append('..')\n",
    "sys.path.append('../autoencoder')\n",
    "\n",
    "\n",
    "from fastai import *\n",
    "from fastai.vision.all import *\n",
    "\n",
    "from src.band_plotters import *\n",
    "from src.TensorImageNoised import *\n",
    "from src.transforms import Binarize\n",
    "\n",
    "sys.path.append('/notebooks/band-fingerprint/autoencoder/resnet_autoencoder')\n",
    "from model import *"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e486923d-2b4e-47fa-83e2-3a00e78cf965",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Select Fingerprint Name and Length Here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "09af96eb-50f6-41dd-b1fe-c320ca91575f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "224_2channel_resnet_L=98\n"
     ]
    }
   ],
   "source": [
    "FINGERPRINT_LENGTH = 98\n",
    "# fingerprint length for old all_k_paths fingerprint, no need to change for auto-encoded prints!\n",
    "FINGERPRINT_LENGTH_old = 60\n",
    "\n",
    "#FINGERPRINT_NAME = \"128x128_random_erase_resnet18_VAE_L={0}\".format(FINGERPRINT_LENGTH)\n",
    "FINGERPRINT_NAME = \"224_2channel_resnet_L={0}\".format(FINGERPRINT_LENGTH)\n",
    "\n",
    "WIDTH=128\n",
    "PERPLEXITY = 30\n",
    "OUTPUT_NAME = f\"{FINGERPRINT_NAME}_perplexity_{PERPLEXITY}_length_{FINGERPRINT_LENGTH}.csv\"\n",
    "print(FINGERPRINT_NAME)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfcdda2e-41e4-4556-9799-c1a171759edc",
   "metadata": {
    "tags": []
   },
   "source": [
    "To make a new fingerprint all you need to change is the constants above and/or the calc_fingerprint function below."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1a859a5-ce17-434f-823b-16c2e9d3ea58",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Start with Anupam's list of materials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d485efe0-a52e-408a-9f08-dd49c228554f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>formula</th>\n",
       "      <th>gen_formula</th>\n",
       "      <th>space_group</th>\n",
       "      <th>segments</th>\n",
       "      <th>flat_segments</th>\n",
       "      <th>flatness_score</th>\n",
       "      <th>discovery</th>\n",
       "      <th>binary_flatness</th>\n",
       "      <th>horz_flat_seg</th>\n",
       "      <th>exfoliation_eg</th>\n",
       "      <th>...</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "      <th>radio</th>\n",
       "      <th>f_orb</th>\n",
       "      <th>sg_sto_group</th>\n",
       "      <th>percentage_flat</th>\n",
       "      <th>relative_id</th>\n",
       "      <th>crystal_system</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2dm-1</th>\n",
       "      <td>IrF2</td>\n",
       "      <td>AB2</td>\n",
       "      <td>164</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.095102</td>\n",
       "      <td>bottom-up</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.234620</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>non-radioactive</td>\n",
       "      <td>no-f-in-valence</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2dm-4963</td>\n",
       "      <td>trigonal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2dm-2</th>\n",
       "      <td>Ba2Sb</td>\n",
       "      <td>AB2</td>\n",
       "      <td>164</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.387410</td>\n",
       "      <td>bottom-up</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.210650</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>non-radioactive</td>\n",
       "      <td>no-f-in-valence</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2dm-3279</td>\n",
       "      <td>trigonal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2dm-3</th>\n",
       "      <td>TlS</td>\n",
       "      <td>AB</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0.846460</td>\n",
       "      <td>bottom-up</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.095794</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>non-radioactive</td>\n",
       "      <td>no-f-in-valence</td>\n",
       "      <td>276.0</td>\n",
       "      <td>24.2</td>\n",
       "      <td>2dm-5155</td>\n",
       "      <td>triclinic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2dm-4</th>\n",
       "      <td>MoCl2</td>\n",
       "      <td>AB2</td>\n",
       "      <td>166</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0.713760</td>\n",
       "      <td>bottom-up</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.055818</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>non-radioactive</td>\n",
       "      <td>no-f-in-valence</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2dm-4342</td>\n",
       "      <td>trigonal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2dm-6</th>\n",
       "      <td>RuI2</td>\n",
       "      <td>AB2</td>\n",
       "      <td>164</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.264930</td>\n",
       "      <td>bottom-up</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.084831</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>non-radioactive</td>\n",
       "      <td>no-f-in-valence</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2dm-3574</td>\n",
       "      <td>trigonal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      formula gen_formula  space_group  segments  flat_segments  \\\n",
       "ID                                                                \n",
       "2dm-1    IrF2         AB2          164         3              0   \n",
       "2dm-2   Ba2Sb         AB2          164         3              1   \n",
       "2dm-3     TlS          AB            2         4              4   \n",
       "2dm-4   MoCl2         AB2          166         5              4   \n",
       "2dm-6    RuI2         AB2          164         3              1   \n",
       "\n",
       "       flatness_score  discovery  binary_flatness  horz_flat_seg  \\\n",
       "ID                                                                 \n",
       "2dm-1        0.095102  bottom-up                0              0   \n",
       "2dm-2        0.387410  bottom-up                0              0   \n",
       "2dm-3        0.846460  bottom-up                1              3   \n",
       "2dm-4        0.713760  bottom-up                0              0   \n",
       "2dm-6        0.264930  bottom-up                0              0   \n",
       "\n",
       "       exfoliation_eg  ...    C    D   E   F            radio  \\\n",
       "ID                     ...                                      \n",
       "2dm-1        0.234620  ...  NaN  NaN NaN NaN  non-radioactive   \n",
       "2dm-2        0.210650  ...  NaN  NaN NaN NaN  non-radioactive   \n",
       "2dm-3        0.095794  ...  NaN  NaN NaN NaN  non-radioactive   \n",
       "2dm-4       -0.055818  ...  NaN  NaN NaN NaN  non-radioactive   \n",
       "2dm-6        0.084831  ...  NaN  NaN NaN NaN  non-radioactive   \n",
       "\n",
       "                 f_orb sg_sto_group percentage_flat  relative_id  \\\n",
       "ID                                                                 \n",
       "2dm-1  no-f-in-valence          NaN             NaN     2dm-4963   \n",
       "2dm-2  no-f-in-valence          NaN             NaN     2dm-3279   \n",
       "2dm-3  no-f-in-valence        276.0            24.2     2dm-5155   \n",
       "2dm-4  no-f-in-valence          NaN             NaN     2dm-4342   \n",
       "2dm-6  no-f-in-valence          NaN             NaN     2dm-3574   \n",
       "\n",
       "       crystal_system  \n",
       "ID                     \n",
       "2dm-1        trigonal  \n",
       "2dm-2        trigonal  \n",
       "2dm-3       triclinic  \n",
       "2dm-4        trigonal  \n",
       "2dm-6        trigonal  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_material = pd.read_csv(\"../fingerprints/template.csv\", index_col=\"ID\")\n",
    "df_material.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1e11653-5de9-412d-a54f-022eefac5870",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Fingerprint Functions Here\n",
    "Fingerprint functions take material ID as only input"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa86ae22-934c-4284-a1ac-c38c2d335b94",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Resnet AE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "321355ce-ef91-497f-810f-6aa4798b2cf4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.856085   1.5035467  0.46254492 0.5073596  0.47224814 0.36940938\n",
      " 0.97784513 0.35026598 0.27631813 0.27631813 0.27631813 0.27631813\n",
      " 0.27631813 0.27631813 0.27631813 0.27631813 0.27631813 0.27631813\n",
      " 0.27631813 0.27631813 0.27631813 1.0918787  1.4173777  1.3991685\n",
      " 0.76038766 0.27631813 0.8318017  1.1461501  0.34052575 0.3679015\n",
      " 0.39192075 0.7668956  1.1656171  1.1901555  0.5543515  0.3033347\n",
      " 0.27631813 0.27631813 0.27631813 0.3163302  0.32624865 0.42067116\n",
      " 0.76466095 1.0245469  0.7751954  1.5793316  0.64609486 1.1456921\n",
      " 1.0896502  0.9489341  0.9831291  1.106703   0.94399565 0.73465115\n",
      " 1.0107518  0.90015006 0.76373124 0.44497964 0.43757144 0.46055087\n",
      " 0.46683878 0.4515661  0.7350662  0.53630626 0.5075112  0.4852231\n",
      " 0.50032115 0.47637194 0.49495393 0.54523504 0.5036707  0.67664886\n",
      " 0.79348046 0.5147604  0.45597887 0.4056238  0.6647309  0.66431123\n",
      " 0.84090626 0.8971062  1.3663919  0.5930507  1.2105727  0.8578156\n",
      " 0.5355583  0.45899767 0.4115734  0.4570094  0.55945194 0.561468\n",
      " 0.38556436 1.3199701  0.59152496 0.42841786 0.5924424  1.103667\n",
      " 1.1078688  1.0893543 ]\n",
      "98\n"
     ]
    }
   ],
   "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.flatten().detach().numpy()\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",
    "fingerprint = run_resnet_one_mat(\"2dm-11\", resnet_model, resnet_input_file_type)\n",
    "print(fingerprint)\n",
    "print(len(fingerprint))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa3c58a6-c2d8-472d-a517-8fb84a9fdae2",
   "metadata": {
    "tags": []
   },
   "source": [
    "## PDOS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b6cb8369-5d4b-4cfe-8070-06fe91ac608a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def calc_all_k_branches_histogram_fingerprint(material_id, fingerprint_length=FINGERPRINT_LENGTH_old, e_range=None, normalize=False, shifted=True):\n",
    "    filename_bands=DATA_DIRECTORY/f\"bands/{material_id}.json\"\n",
    "    if not filename_bands.exists():\n",
    "        print(f\"No such file {filename_bands}\")\n",
    "        \n",
    "    bands_dict=json.load(open(filename_bands))\n",
    "\n",
    "    fingerprint = np.zeros(fingerprint_length)\n",
    "    \n",
    "    k_branches = bands_dict[\"branches\"]\n",
    "    length_per_branch = fingerprint_length // len(k_branches)\n",
    "\n",
    "    energies = np.array(bands_dict[\"bands\"][\"1\"])\n",
    "\n",
    "    if shifted:\n",
    "        energies -= bands_dict[\"efermi\"]\n",
    "\n",
    "    for i, branch in enumerate(k_branches):\n",
    "        branch_energies = energies[:, branch[\"start_index\"]:branch[\"end_index\"]+1]\n",
    "\n",
    "        counts, edges = np.histogram(branch_energies, bins=length_per_branch, range=e_range, density=normalize)\n",
    "\n",
    "        fingerprint[i*length_per_branch : (i+1)*length_per_branch] = counts\n",
    "    \n",
    "    return fingerprint"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8643f486-e857-4c79-97bd-35ae7b7d2602",
   "metadata": {
    "tags": []
   },
   "source": [
    "## View All Fingerprint Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "dc03c6f7-0657-4867-a3d1-2b9a70000636",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fingerprint_functions = {\n",
    "    \"all_k_branches_histogram_-8_to_8\": lambda x: calc_all_k_branches_histogram_fingerprint(x, e_range=[-8, 8], normalize=False),\n",
    "    \"all_k_branches_histogram_all_energies\": lambda x: calc_all_k_branches_histogram_fingerprint(x, normalize=False),\n",
    "    \"all_k_branches_histogram_-8_to_8_normed\": lambda x: calc_all_k_branches_histogram_fingerprint(x, e_range=[-8, 8], normalize=True),\n",
    "    \"all_k_branches_histogram_all_energies_normed\": lambda x: calc_all_k_branches_histogram_fingerprint(x, normalize=True),\n",
    "    \"224_2channel_resnet_L={0}\".format(FINGERPRINT_LENGTH): lambda x: run_resnet_one_mat(x, resnet_model, resnet_input_file_type, data_directory=DATA_DIRECTORY),    \n",
    "}\n",
    "\n",
    "\n",
    "for fingerprint_name in fingerprint_functions: \n",
    "    fp = fingerprint_functions[fingerprint_name](\"2dm-11\")\n",
    "    plt.plot(fp)\n",
    "    plt.title(fingerprint_name)\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a16ea3b7-486c-4a58-aec0-747c75cc4c94",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "fingerprint_array = np.zeros([len(df_material), FINGERPRINT_LENGTH])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "43963cd5-6031-4e5f-b221-3b3c18639c4a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 / 5270\n",
      "100 / 5270\n",
      "200 / 5270\n",
      "300 / 5270\n",
      "400 / 5270\n",
      "500 / 5270\n",
      "600 / 5270\n",
      "700 / 5270\n",
      "800 / 5270\n",
      "900 / 5270\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Input \u001b[0;32mIn [21]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m i \u001b[38;5;241m%\u001b[39m \u001b[38;5;241m100\u001b[39m \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m      3\u001b[0m         \u001b[38;5;28mprint\u001b[39m(i, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mlen\u001b[39m(df_material))\n\u001b[0;32m----> 5\u001b[0m     fingerprint_array[i, :] \u001b[38;5;241m=\u001b[39m \u001b[43mfingerprint_functions\u001b[49m\u001b[43m[\u001b[49m\u001b[43mFINGERPRINT_NAME\u001b[49m\u001b[43m]\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmaterial_id\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28mprint\u001b[39m(fingerprint_array)\n",
      "Input \u001b[0;32mIn [19]\u001b[0m, in \u001b[0;36m<lambda>\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m      1\u001b[0m fingerprint_functions \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m      2\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mall_k_branches_histogram_-8_to_8\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28;01mlambda\u001b[39;00m x: calc_all_k_branches_histogram_fingerprint(x, e_range\u001b[38;5;241m=\u001b[39m[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m8\u001b[39m, \u001b[38;5;241m8\u001b[39m], normalize\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m),\n\u001b[1;32m      3\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mall_k_branches_histogram_all_energies\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28;01mlambda\u001b[39;00m x: calc_all_k_branches_histogram_fingerprint(x, normalize\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m),\n\u001b[1;32m      4\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mall_k_branches_histogram_-8_to_8_normed\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28;01mlambda\u001b[39;00m x: calc_all_k_branches_histogram_fingerprint(x, e_range\u001b[38;5;241m=\u001b[39m[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m8\u001b[39m, \u001b[38;5;241m8\u001b[39m], normalize\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m),\n\u001b[1;32m      5\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mall_k_branches_histogram_all_energies_normed\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28;01mlambda\u001b[39;00m x: calc_all_k_branches_histogram_fingerprint(x, normalize\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m),\n\u001b[0;32m----> 6\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m224_2channel_resnet_L=\u001b[39m\u001b[38;5;132;01m{0}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(FINGERPRINT_LENGTH): \u001b[38;5;28;01mlambda\u001b[39;00m x: \u001b[43mrun_resnet_one_mat\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresnet_model\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresnet_input_file_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata_directory\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mDATA_DIRECTORY\u001b[49m\u001b[43m)\u001b[49m,    \n\u001b[1;32m      7\u001b[0m }\n\u001b[1;32m     10\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m fingerprint_name \u001b[38;5;129;01min\u001b[39;00m fingerprint_functions: \n\u001b[1;32m     11\u001b[0m     fp \u001b[38;5;241m=\u001b[39m fingerprint_functions[fingerprint_name](\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m2dm-11\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "Input \u001b[0;32mIn [17]\u001b[0m, in \u001b[0;36mrun_resnet_one_mat\u001b[0;34m(material_id, CNN_model, input_band_image_type, data_directory)\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;66;03m# Forward pass through the encoder\u001b[39;00m\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mno_grad():\n\u001b[0;32m----> 8\u001b[0m     encoded_representation \u001b[38;5;241m=\u001b[39m \u001b[43mCNN_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoder\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdl\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mone_batch\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      9\u001b[0m \u001b[38;5;66;03m#out = trained_model.encoder(DATA_DIRECTORY/f\"images/grayscale_4ev_linewidth3/2dm-4.png\")\u001b[39;00m\n\u001b[1;32m     10\u001b[0m \n\u001b[1;32m     11\u001b[0m \n\u001b[1;32m     12\u001b[0m \u001b[38;5;66;03m# flatten encoded 2d array representation of the band structure as the fingerprint\u001b[39;00m\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m encoded_representation\u001b[38;5;241m.\u001b[39mflatten()\u001b[38;5;241m.\u001b[39mdetach()\u001b[38;5;241m.\u001b[39mnumpy()\n",
      "File \u001b[0;32m/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py:1130\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1126\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1127\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1128\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1129\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1130\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;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\u001b[1;32m   1131\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1132\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
      "File \u001b[0;32m/notebooks/band-fingerprint/fingerprint_creation/../autoencoder/model.py:164\u001b[0m, in \u001b[0;36mResNetEncoder.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    162\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconv1(x)\n\u001b[1;32m    163\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconv2(x)\n\u001b[0;32m--> 164\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconv3\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    165\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconv4(x)\n\u001b[1;32m    166\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconv5(x)\n",
      "File \u001b[0;32m/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py:1130\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1126\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1127\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1128\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1129\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1130\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;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\u001b[1;32m   1131\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1132\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
      "File \u001b[0;32m/notebooks/band-fingerprint/fingerprint_creation/../autoencoder/model.py:254\u001b[0m, in \u001b[0;36mEncoderResidualBlock.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    250\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x):\n\u001b[1;32m    252\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m name, layer \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnamed_children():\n\u001b[0;32m--> 254\u001b[0m         x \u001b[38;5;241m=\u001b[39m \u001b[43mlayer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    256\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m x\n",
      "File \u001b[0;32m/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py:1130\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1126\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1127\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1128\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1129\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1130\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;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\u001b[1;32m   1131\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1132\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
      "File \u001b[0;32m/notebooks/band-fingerprint/fingerprint_creation/../autoencoder/model.py:387\u001b[0m, in \u001b[0;36mEncoderResidualLayer.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    384\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdownsample \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    385\u001b[0m     identity \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdownsample(identity)\n\u001b[0;32m--> 387\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[43mx\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43midentity\u001b[49m\n\u001b[1;32m    389\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrelu(x)\n\u001b[1;32m    391\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m x\n",
      "File \u001b[0;32m/usr/local/lib/python3.9/dist-packages/fastai/torch_core.py:376\u001b[0m, in \u001b[0;36mTensorBase.__torch_function__\u001b[0;34m(cls, func, types, args, kwargs)\u001b[0m\n\u001b[1;32m    374\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mdebug \u001b[38;5;129;01mand\u001b[39;00m func\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m (\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m__str__\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m__repr__\u001b[39m\u001b[38;5;124m'\u001b[39m): \u001b[38;5;28mprint\u001b[39m(func, types, args, kwargs)\n\u001b[1;32m    375\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _torch_handled(args, \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_opt, func): types \u001b[38;5;241m=\u001b[39m (torch\u001b[38;5;241m.\u001b[39mTensor,)\n\u001b[0;32m--> 376\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__torch_function__\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtypes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mifnone\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    377\u001b[0m dict_objs \u001b[38;5;241m=\u001b[39m _find_args(args) \u001b[38;5;28;01mif\u001b[39;00m args \u001b[38;5;28;01melse\u001b[39;00m _find_args(\u001b[38;5;28mlist\u001b[39m(kwargs\u001b[38;5;241m.\u001b[39mvalues()))\n\u001b[1;32m    378\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28missubclass\u001b[39m(\u001b[38;5;28mtype\u001b[39m(res),TensorBase) \u001b[38;5;129;01mand\u001b[39;00m dict_objs: res\u001b[38;5;241m.\u001b[39mset_meta(dict_objs[\u001b[38;5;241m0\u001b[39m],as_copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
      "File \u001b[0;32m/usr/local/lib/python3.9/dist-packages/torch/_tensor.py:1121\u001b[0m, in \u001b[0;36mTensor.__torch_function__\u001b[0;34m(cls, func, types, args, kwargs)\u001b[0m\n\u001b[1;32m   1118\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mNotImplemented\u001b[39m\n\u001b[1;32m   1120\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _C\u001b[38;5;241m.\u001b[39mDisableTorchFunction():\n\u001b[0;32m-> 1121\u001b[0m     ret \u001b[38;5;241m=\u001b[39m \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\u001b[1;32m   1122\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m func \u001b[38;5;129;01min\u001b[39;00m get_default_nowrap_functions():\n\u001b[1;32m   1123\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m ret\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "for i, material_id in enumerate(df_material.index):\n",
    "    if i % 100 == 0:\n",
    "        print(i, \"/\", len(df_material))\n",
    "\n",
    "    fingerprint_array[i, :] = fingerprint_functions[FINGERPRINT_NAME](material_id)\n",
    "\n",
    "print(fingerprint_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9f767343-a44e-4374-bdfc-6850e31df18a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5270\n"
     ]
    }
   ],
   "source": [
    "#check for NaNs \n",
    "np.isnan(fingerprint_array).sum()\n",
    "print(len(fingerprint_array))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec1bca9b-6c55-4012-8ee8-ca7398c2db9b",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "# Create T-SNE Reduced Dimension Fingerprints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7fe8854c-c3cf-443a-94ad-41d9c59c07de",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[t-SNE] Computing 46 nearest neighbors...\n",
      "[t-SNE] Indexed 5270 samples in 0.001s...\n",
      "[t-SNE] Computed neighbors for 5270 samples in 0.765s...\n",
      "[t-SNE] Computed conditional probabilities for sample 1000 / 5270\n",
      "[t-SNE] Computed conditional probabilities for sample 2000 / 5270\n",
      "[t-SNE] Computed conditional probabilities for sample 3000 / 5270\n",
      "[t-SNE] Computed conditional probabilities for sample 4000 / 5270\n",
      "[t-SNE] Computed conditional probabilities for sample 5000 / 5270\n",
      "[t-SNE] Computed conditional probabilities for sample 5270 / 5270\n",
      "[t-SNE] Mean sigma: 1.088484\n",
      "[t-SNE] Computed conditional probabilities in 0.120s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.9/dist-packages/sklearn/manifold/_t_sne.py:991: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[t-SNE] Iteration 50: error = 1011.3580933, gradient norm = 1.0931278 (50 iterations in 2.867s)\n",
      "[t-SNE] Iteration 100: error = 1022.9692383, gradient norm = 0.9706119 (50 iterations in 3.324s)\n",
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      "[t-SNE] Iteration 250: error = 1039.4141846, gradient norm = 0.9507743 (50 iterations in 3.098s)\n",
      "[t-SNE] KL divergence after 250 iterations with early exaggeration: 1039.414185\n",
      "[t-SNE] Iteration 300: error = 3.4382002, gradient norm = 0.0027696 (50 iterations in 3.033s)\n",
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      "[t-SNE] KL divergence after 10000 iterations: 1.619395\n"
     ]
    }
   ],
   "source": [
    "tsne = manifold.TSNE(n_components=2, early_exaggeration=100.0, init=\"pca\",learning_rate=100, random_state=0, perplexity=15 ,n_iter=10000, verbose=2)\n",
    "fingerprint_2d = tsne.fit_transform(fingerprint_array)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90cbcb1c-2485-4b9a-bb82-11f70c9ca51e",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "# Quick cluster (not saved) and plots to check fingerprint and T-SNE worked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7926919e-bc65-4e17-b1be-67852d033100",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "clusterer = hdbscan.HDBSCAN(algorithm='best', alpha=1.0, approx_min_span_tree=True,\\\n",
    "                        gen_min_span_tree=False, leaf_size=40, metric='manhattan', cluster_selection_method='leaf', min_cluster_size=4, min_samples=2, p=0.2)\n",
    "clusterer.fit(fingerprint_array)\n",
    "labels = clusterer.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0e4da6a5-32a1-4beb-be78-13a30d8871e6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'fingerprint_length=98')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax  = plt.subplots(figsize=(8,8))\n",
    "size=np.ones((len(labels),1))*5\n",
    "size[labels==-1]=0.2\n",
    "\n",
    "ax.scatter(fingerprint_2d[:,0], fingerprint_2d[:,1],s=size, c=labels*5, cmap=\"turbo\")\n",
    "plt.title(\"fingerprint_length={0}\".format(FINGERPRINT_LENGTH))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5905704e-e2c6-4abc-9e50-33f1b990f16f",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "# Add fingerprints, T-SNE to dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1c076682-eb36-4392-96ec-eb0f50664a54",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>formula</th>\n",
       "      <th>gen_formula</th>\n",
       "      <th>space_group</th>\n",
       "      <th>segments</th>\n",
       "      <th>flat_segments</th>\n",
       "      <th>flatness_score</th>\n",
       "      <th>discovery</th>\n",
       "      <th>binary_flatness</th>\n",
       "      <th>horz_flat_seg</th>\n",
       "      <th>exfoliation_eg</th>\n",
       "      <th>...</th>\n",
       "      <th>90</th>\n",
       "      <th>91</th>\n",
       "      <th>92</th>\n",
       "      <th>93</th>\n",
       "      <th>94</th>\n",
       "      <th>95</th>\n",
       "      <th>96</th>\n",
       "      <th>97</th>\n",
       "      <th>fx</th>\n",
       "      <th>fy</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2dm-1</th>\n",
       "      <td>IrF2</td>\n",
       "      <td>AB2</td>\n",
       "      <td>164</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.095102</td>\n",
       "      <td>bottom-up</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.234620</td>\n",
       "      <td>...</td>\n",
       "      <td>1.800380</td>\n",
       "      <td>1.783413</td>\n",
       "      <td>1.751181</td>\n",
       "      <td>1.657435</td>\n",
       "      <td>1.699241</td>\n",
       "      <td>1.757099</td>\n",
       "      <td>1.648585</td>\n",
       "      <td>1.750903</td>\n",
       "      <td>80.652298</td>\n",
       "      <td>33.975147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2dm-2</th>\n",
       "      <td>Ba2Sb</td>\n",
       "      <td>AB2</td>\n",
       "      <td>164</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.387410</td>\n",
       "      <td>bottom-up</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.210650</td>\n",
       "      <td>...</td>\n",
       "      <td>2.065734</td>\n",
       "      <td>1.790841</td>\n",
       "      <td>1.797779</td>\n",
       "      <td>1.940001</td>\n",
       "      <td>1.778363</td>\n",
       "      <td>1.646309</td>\n",
       "      <td>1.777840</td>\n",
       "      <td>1.804859</td>\n",
       "      <td>-102.511086</td>\n",
       "      <td>76.040359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2dm-3</th>\n",
       "      <td>TlS</td>\n",
       "      <td>AB</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0.846460</td>\n",
       "      <td>bottom-up</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.095794</td>\n",
       "      <td>...</td>\n",
       "      <td>2.325100</td>\n",
       "      <td>1.733577</td>\n",
       "      <td>1.816098</td>\n",
       "      <td>1.953408</td>\n",
       "      <td>1.904952</td>\n",
       "      <td>1.718350</td>\n",
       "      <td>1.920829</td>\n",
       "      <td>1.940830</td>\n",
       "      <td>-17.031164</td>\n",
       "      <td>-22.583645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2dm-4</th>\n",
       "      <td>MoCl2</td>\n",
       "      <td>AB2</td>\n",
       "      <td>166</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0.713760</td>\n",
       "      <td>bottom-up</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.055818</td>\n",
       "      <td>...</td>\n",
       "      <td>2.374648</td>\n",
       "      <td>2.153072</td>\n",
       "      <td>2.535491</td>\n",
       "      <td>1.912330</td>\n",
       "      <td>2.053569</td>\n",
       "      <td>2.283617</td>\n",
       "      <td>2.490727</td>\n",
       "      <td>2.131947</td>\n",
       "      <td>-74.647247</td>\n",
       "      <td>38.610275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2dm-6</th>\n",
       "      <td>RuI2</td>\n",
       "      <td>AB2</td>\n",
       "      <td>164</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.264930</td>\n",
       "      <td>bottom-up</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.084831</td>\n",
       "      <td>...</td>\n",
       "      <td>2.629308</td>\n",
       "      <td>2.182670</td>\n",
       "      <td>2.361369</td>\n",
       "      <td>2.758861</td>\n",
       "      <td>2.421932</td>\n",
       "      <td>2.233414</td>\n",
       "      <td>2.194449</td>\n",
       "      <td>2.162071</td>\n",
       "      <td>74.347855</td>\n",
       "      <td>47.444942</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 126 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      formula gen_formula  space_group  segments  flat_segments  \\\n",
       "ID                                                                \n",
       "2dm-1    IrF2         AB2          164         3              0   \n",
       "2dm-2   Ba2Sb         AB2          164         3              1   \n",
       "2dm-3     TlS          AB            2         4              4   \n",
       "2dm-4   MoCl2         AB2          166         5              4   \n",
       "2dm-6    RuI2         AB2          164         3              1   \n",
       "\n",
       "       flatness_score  discovery  binary_flatness  horz_flat_seg  \\\n",
       "ID                                                                 \n",
       "2dm-1        0.095102  bottom-up                0              0   \n",
       "2dm-2        0.387410  bottom-up                0              0   \n",
       "2dm-3        0.846460  bottom-up                1              3   \n",
       "2dm-4        0.713760  bottom-up                0              0   \n",
       "2dm-6        0.264930  bottom-up                0              0   \n",
       "\n",
       "       exfoliation_eg  ...        90        91        92        93        94  \\\n",
       "ID                     ...                                                     \n",
       "2dm-1        0.234620  ...  1.800380  1.783413  1.751181  1.657435  1.699241   \n",
       "2dm-2        0.210650  ...  2.065734  1.790841  1.797779  1.940001  1.778363   \n",
       "2dm-3        0.095794  ...  2.325100  1.733577  1.816098  1.953408  1.904952   \n",
       "2dm-4       -0.055818  ...  2.374648  2.153072  2.535491  1.912330  2.053569   \n",
       "2dm-6        0.084831  ...  2.629308  2.182670  2.361369  2.758861  2.421932   \n",
       "\n",
       "             95        96        97          fx         fy  \n",
       "ID                                                          \n",
       "2dm-1  1.757099  1.648585  1.750903   80.652298  33.975147  \n",
       "2dm-2  1.646309  1.777840  1.804859 -102.511086  76.040359  \n",
       "2dm-3  1.718350  1.920829  1.940830  -17.031164 -22.583645  \n",
       "2dm-4  2.283617  2.490727  2.131947  -74.647247  38.610275  \n",
       "2dm-6  2.233414  2.194449  2.162071   74.347855  47.444942  \n",
       "\n",
       "[5 rows x 126 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df_material.join(pd.DataFrame(fingerprint_array, index=df_material.index))\n",
    "df[\"fx\"] = fingerprint_2d[:, 0]\n",
    "df[\"fy\"] = fingerprint_2d[:, 1]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "16f03c59",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df.to_csv(\"../fingerprints/\"+OUTPUT_NAME)"
   ]
  }
 ],
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