diff --git "a/analysis/compare_anupam_clusters/comparing_with_anupam's_clusters.ipynb" "b/analysis/compare_anupam_clusters/comparing_with_anupam's_clusters.ipynb"
new file mode 100644--- /dev/null
+++ "b/analysis/compare_anupam_clusters/comparing_with_anupam's_clusters.ipynb"
@@ -0,0 +1,3861 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "bc8c6ef5-5010-46b3-b89b-eab6dc0c00b3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import hdbscan\n",
+ "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 os\n",
+ "import json\n",
+ "from matplotlib.colors import LinearSegmentedColormap\n",
+ "\n",
+ "pd.set_option('display.max_rows', 100)\n",
+ "pd.set_option('display.max_columns', 15)\n",
+ "pd.set_option('display.width', 1000)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "09af96eb-50f6-41dd-b1fe-c320ca91575f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "MATERIAL_PATH = \"../../All_mat_new_test_score_with_horz_flat_index.csv\"\n",
+ "#DATA_PATH = \"../../data\"\n",
+ "\n",
+ "# \"henry's local data path\"\n",
+ "DATA_PATH = \"../../../MPhys_Project/data extraction+fingerprinting/FULL_MATPEDIA_DATA\""
+ ]
+ },
+ {
+ "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": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true,
+ "source_hidden": true
+ },
+ "scrolled": true,
+ "tags": []
+ },
+ "outputs": [
+ {
+ "ename": "FileNotFoundError",
+ "evalue": "[Errno 2] No such file or directory: '../../All_mat_new_test_score_with_horz_flat_index.csv'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
+ "Input \u001b[0;32mIn [3]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m material_df \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m../../All_mat_new_test_score_with_horz_flat_index.csv\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindex_col\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mID\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(material_df\u001b[38;5;241m.\u001b[39mshape)\n\u001b[1;32m 3\u001b[0m material_df\u001b[38;5;241m.\u001b[39mhead()\n",
+ "File \u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/util/_decorators.py:311\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 305\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[1;32m 306\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m 307\u001b[0m msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39marguments),\n\u001b[1;32m 308\u001b[0m \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[1;32m 309\u001b[0m stacklevel\u001b[38;5;241m=\u001b[39mstacklevel,\n\u001b[1;32m 310\u001b[0m )\n\u001b[0;32m--> 311\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/pandas/io/parsers/readers.py:680\u001b[0m, in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m 665\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[1;32m 666\u001b[0m dialect,\n\u001b[1;32m 667\u001b[0m delimiter,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 676\u001b[0m defaults\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdelimiter\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m,\u001b[39m\u001b[38;5;124m\"\u001b[39m},\n\u001b[1;32m 677\u001b[0m )\n\u001b[1;32m 678\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[0;32m--> 680\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/io/parsers/readers.py:575\u001b[0m, in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 572\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[1;32m 574\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[0;32m--> 575\u001b[0m parser \u001b[38;5;241m=\u001b[39m \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\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[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 577\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[1;32m 578\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
+ "File \u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/io/parsers/readers.py:934\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m 931\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 933\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 934\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\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[43mengine\u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/io/parsers/readers.py:1218\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m 1214\u001b[0m mode \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1215\u001b[0m \u001b[38;5;66;03m# error: No overload variant of \"get_handle\" matches argument types\u001b[39;00m\n\u001b[1;32m 1216\u001b[0m \u001b[38;5;66;03m# \"Union[str, PathLike[str], ReadCsvBuffer[bytes], ReadCsvBuffer[str]]\"\u001b[39;00m\n\u001b[1;32m 1217\u001b[0m \u001b[38;5;66;03m# , \"str\", \"bool\", \"Any\", \"Any\", \"Any\", \"Any\", \"Any\"\u001b[39;00m\n\u001b[0;32m-> 1218\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# type: ignore[call-overload]\u001b[39;49;00m\n\u001b[1;32m 1219\u001b[0m \u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1220\u001b[0m \u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1221\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1222\u001b[0m \u001b[43m \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcompression\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1223\u001b[0m \u001b[43m \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmemory_map\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1224\u001b[0m \u001b[43m \u001b[49m\u001b[43mis_text\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1225\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding_errors\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstrict\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1226\u001b[0m \u001b[43m \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstorage_options\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1227\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1228\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \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 1229\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n",
+ "File \u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/io/common.py:786\u001b[0m, in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m 781\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m 782\u001b[0m \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[1;32m 783\u001b[0m \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[1;32m 784\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[1;32m 785\u001b[0m \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[0;32m--> 786\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m 787\u001b[0m \u001b[43m \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 788\u001b[0m \u001b[43m \u001b[49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 789\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 790\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 791\u001b[0m \u001b[43m \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 792\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 793\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 794\u001b[0m \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[1;32m 795\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n",
+ "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '../../All_mat_new_test_score_with_horz_flat_index.csv'"
+ ]
+ }
+ ],
+ "source": [
+ "material_df = pd.read_csv(\"../../All_mat_new_test_score_with_horz_flat_index.csv\", index_col=\"ID\")\n",
+ "print(material_df.shape)\n",
+ "material_df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "99ae04e3-cddc-4283-87ab-ad4451071f9a",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(2005, 24)"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Select flat materials only\n",
+ "flat_df = material_df[material_df.horz_flat_seg > 0]\n",
+ "flat_df.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e1e11653-5de9-412d-a54f-022eefac5870",
+ "metadata": {
+ "jp-MarkdownHeadingCollapsed": true,
+ "tags": []
+ },
+ "source": [
+ "## Read in anupam's fingerprints here\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "651d08bb",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[['0.00095621588570265' '0.00120049635912853' '0.00265397411836174' ...\n",
+ " '0' '0' '0']\n",
+ " ['0' '0' '0' ... '0' '0' '0']\n",
+ " ['0' '0' '0' ... '0' '0' '0']\n",
+ " ...\n",
+ " ['0' '0' '0' ... '0' '0' '0']\n",
+ " ['0.0556027744938957' '0.0336091542804928' '0.0906179808572843' ... '0'\n",
+ " '0' '0']\n",
+ " ['0' '0' '0' ... '0' '0' '0']]\n",
+ "['2dm-3' '2dm-21' '2dm-22' ... '2dm-6447' '2dm-6449' '2dm-6450']\n"
+ ]
+ }
+ ],
+ "source": [
+ "import csv\n",
+ "Inp=[]\n",
+ "with open('input_flat_materials_244 (2).csv','r') as file:\n",
+ " csvreader = csv.reader(file)\n",
+ " for row in csvreader:\n",
+ " Inp.append(row)\n",
+ "Input=np.array(Inp)\n",
+ "finger_print_array = Input\n",
+ "print(finger_print_array)\n",
+ "\n",
+ "\n",
+ "Inp2=[]\n",
+ "with open('input_indices_244.csv','r') as file:\n",
+ " csvreader = csv.reader(file)\n",
+ " for row in csvreader:\n",
+ " Inp2.append(\"2dm-\"+str(row[0]))\n",
+ "#print(Inp2)\n",
+ "Input2=np.array(Inp2)\n",
+ "print(Input2)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "1c076682-eb36-4392-96ec-eb0f50664a54",
+ "metadata": {},
+ "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>...</th>\n",
+ " <th>237</th>\n",
+ " <th>238</th>\n",
+ " <th>239</th>\n",
+ " <th>240</th>\n",
+ " <th>241</th>\n",
+ " <th>242</th>\n",
+ " <th>243</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",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\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>0.84646</td>\n",
+ " <td>bottom-up</td>\n",
+ " <td>...</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2dm-21</th>\n",
+ " <td>TaI3</td>\n",
+ " <td>AB3</td>\n",
+ " <td>162</td>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
+ " <td>0.88201</td>\n",
+ " <td>bottom-up</td>\n",
+ " <td>...</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2dm-22</th>\n",
+ " <td>Li2O</td>\n",
+ " <td>AB2</td>\n",
+ " <td>164</td>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
+ " <td>0.96678</td>\n",
+ " <td>bottom-up</td>\n",
+ " <td>...</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2dm-25</th>\n",
+ " <td>VBr4</td>\n",
+ " <td>AB4</td>\n",
+ " <td>123</td>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
+ " <td>0.97834</td>\n",
+ " <td>bottom-up</td>\n",
+ " <td>...</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2dm-29</th>\n",
+ " <td>SBr</td>\n",
+ " <td>AB</td>\n",
+ " <td>2</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.82037</td>\n",
+ " <td>bottom-up</td>\n",
+ " <td>...</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>5 rows × 268 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " formula gen_formula space_group segments flat_segments flatness_score discovery ... 237 238 239 240 241 242 243\n",
+ "ID ... \n",
+ "2dm-3 TlS AB 2 4 4 0.84646 bottom-up ... 0 0 0 0 0 0 0\n",
+ "2dm-21 TaI3 AB3 162 3 3 0.88201 bottom-up ... 0 0 0 0 0 0 0\n",
+ "2dm-22 Li2O AB2 164 3 3 0.96678 bottom-up ... 0 0 0 0 0 0 0\n",
+ "2dm-25 VBr4 AB4 123 3 3 0.97834 bottom-up ... 0 0 0 0 0 0 0\n",
+ "2dm-29 SBr AB 2 4 4 0.82037 bottom-up ... 0 0 0 0 0 0 0\n",
+ "\n",
+ "[5 rows x 268 columns]"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df = flat_df.join(pd.DataFrame(Input, index=Input2))\n",
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "4b61cdfe",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "columns = list(range(244))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "48b86de6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = df[ df[columns].isna().sum(axis=1) == 0 ]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "9f767343-a44e-4374-bdfc-6850e31df18a",
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "TypeError",
+ "evalue": "ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[1;32mIn[19], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;66;03m#check for NaNs \u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m np\u001b[38;5;241m.\u001b[39misnan(finger_print_array)\u001b[38;5;241m.\u001b[39msum()\n",
+ "\u001b[1;31mTypeError\u001b[0m: ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''"
+ ]
+ }
+ ],
+ "source": [
+ "#check for NaNs \n",
+ "np.isnan(finger_print_array).sum()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "d30eb3d5-f7dd-44aa-a095-2dbb5aa00739",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "exfoliation_eg 603\n",
+ "decomp_eng 46\n",
+ "B 10\n",
+ "C 1116\n",
+ "D 1741\n",
+ "E 1993\n",
+ "F 1993\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.isna().sum()[ df.isna().sum() != 0 ]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "d4d149a8-e867-4acc-af35-11eae6ebcafd",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
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+ " .dataframe tbody tr th {\n",
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+ " .dataframe thead th {\n",
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+ " }\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>...</th>\n",
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+ " <th>241</th>\n",
+ " <th>242</th>\n",
+ " <th>243</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",
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+ " <th></th>\n",
+ " <th></th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\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>0.84646</td>\n",
+ " <td>bottom-up</td>\n",
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+ " <tr>\n",
+ " <th>2dm-21</th>\n",
+ " <td>TaI3</td>\n",
+ " <td>AB3</td>\n",
+ " <td>162</td>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
+ " <td>0.88201</td>\n",
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+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2dm-22</th>\n",
+ " <td>Li2O</td>\n",
+ " <td>AB2</td>\n",
+ " <td>164</td>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
+ " <td>0.96678</td>\n",
+ " <td>bottom-up</td>\n",
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+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2dm-25</th>\n",
+ " <td>VBr4</td>\n",
+ " <td>AB4</td>\n",
+ " <td>123</td>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
+ " <td>0.97834</td>\n",
+ " <td>bottom-up</td>\n",
+ " <td>...</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2dm-29</th>\n",
+ " <td>SBr</td>\n",
+ " <td>AB</td>\n",
+ " <td>2</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.82037</td>\n",
+ " <td>bottom-up</td>\n",
+ " <td>...</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2dm-6436</th>\n",
+ " <td>Sn2O5</td>\n",
+ " <td>A2B5</td>\n",
+ " <td>30</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.84160</td>\n",
+ " <td>bottom-up</td>\n",
+ " <td>...</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2dm-6444</th>\n",
+ " <td>Ge2O5</td>\n",
+ " <td>A2B5</td>\n",
+ " <td>30</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.95138</td>\n",
+ " <td>bottom-up</td>\n",
+ " <td>...</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2dm-6447</th>\n",
+ " <td>CeS2</td>\n",
+ " <td>AB2</td>\n",
+ " <td>160</td>\n",
+ " <td>5</td>\n",
+ " <td>5</td>\n",
+ " <td>0.87940</td>\n",
+ " <td>bottom-up</td>\n",
+ " <td>...</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2dm-6449</th>\n",
+ " <td>C2O5</td>\n",
+ " <td>A2B5</td>\n",
+ " <td>30</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.95280</td>\n",
+ " <td>bottom-up</td>\n",
+ " <td>...</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2dm-6450</th>\n",
+ " <td>CeTe2</td>\n",
+ " <td>AB2</td>\n",
+ " <td>42</td>\n",
+ " <td>5</td>\n",
+ " <td>5</td>\n",
+ " <td>0.91995</td>\n",
+ " <td>bottom-up</td>\n",
+ " <td>...</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>1993 rows × 268 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " formula gen_formula space_group segments flat_segments flatness_score discovery ... 237 238 239 240 241 242 243\n",
+ "ID ... \n",
+ "2dm-3 TlS AB 2 4 4 0.84646 bottom-up ... 0 0 0 0 0 0 0\n",
+ "2dm-21 TaI3 AB3 162 3 3 0.88201 bottom-up ... 0 0 0 0 0 0 0\n",
+ "2dm-22 Li2O AB2 164 3 3 0.96678 bottom-up ... 0 0 0 0 0 0 0\n",
+ "2dm-25 VBr4 AB4 123 3 3 0.97834 bottom-up ... 0 0 0 0 0 0 0\n",
+ "2dm-29 SBr AB 2 4 4 0.82037 bottom-up ... 0 0 0 0 0 0 0\n",
+ "... ... ... ... ... ... ... ... ... ... ... ... ... ... .. ..\n",
+ "2dm-6436 Sn2O5 A2B5 30 4 4 0.84160 bottom-up ... 0 0 0 0 0 0 0\n",
+ "2dm-6444 Ge2O5 A2B5 30 4 4 0.95138 bottom-up ... 0 0 0 0 0 0 0\n",
+ "2dm-6447 CeS2 AB2 160 5 5 0.87940 bottom-up ... 0 0 0 0 0 0 0\n",
+ "2dm-6449 C2O5 A2B5 30 4 4 0.95280 bottom-up ... 0 0 0 0 0 0 0\n",
+ "2dm-6450 CeTe2 AB2 42 5 5 0.91995 bottom-up ... 0 0 0 0 0 0 0\n",
+ "\n",
+ "[1993 rows x 268 columns]"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# save df\n",
+ "df.to_csv(\"materials_data_anupam's_fingerprints.csv\")\n",
+ "df"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "87bf976b-7f73-45d3-96e5-72587eee28a6",
+ "metadata": {},
+ "source": [
+ "## Clustering"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "b93d104f-3a61-4ae0-96a5-af55bfddb77c",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(array([-1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,\n",
+ " 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32,\n",
+ " 33, 34, 35, 36, 37, 38, 39, 40, 41, 42]), array([1362, 10, 9, 9, 6, 57, 7, 8, 12, 16, 9,\n",
+ " 14, 11, 8, 9, 11, 6, 12, 7, 10, 13, 9,\n",
+ " 9, 81, 7, 29, 11, 15, 9, 8, 33, 7, 7,\n",
+ " 7, 10, 9, 6, 13, 13, 8, 46, 16, 24, 10]))\n"
+ ]
+ },
+ {
+ "data": {
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+ " <th></th>\n",
+ " <th>ID</th>\n",
+ " <th>formula</th>\n",
+ " <th>gen_formula</th>\n",
+ " <th>space_group</th>\n",
+ " <th>segments</th>\n",
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+ " <tbody>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>2dm-25</td>\n",
+ " <td>VBr4</td>\n",
+ " <td>AB4</td>\n",
+ " <td>123</td>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
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+ " </tr>\n",
+ " <tr>\n",
+ " <th>179</th>\n",
+ " <td>2dm-723</td>\n",
+ " <td>OsCl4</td>\n",
+ " <td>AB4</td>\n",
+ " <td>14</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.87758</td>\n",
+ " <td>...</td>\n",
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+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>29</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>280</th>\n",
+ " <td>2dm-1063</td>\n",
+ " <td>CeSi2</td>\n",
+ " <td>AB2</td>\n",
+ " <td>123</td>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
+ " <td>0.97122</td>\n",
+ " <td>...</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>29</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>299</th>\n",
+ " <td>2dm-1115</td>\n",
+ " <td>NiSe2</td>\n",
+ " <td>AB2</td>\n",
+ " <td>14</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.83425</td>\n",
+ " <td>...</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>29</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>306</th>\n",
+ " <td>2dm-1129</td>\n",
+ " <td>Sr2Co</td>\n",
+ " <td>AB2</td>\n",
+ " <td>123</td>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
+ " <td>0.69227</td>\n",
+ " <td>...</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>29</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>312</th>\n",
+ " <td>2dm-1151</td>\n",
+ " <td>CeC2</td>\n",
+ " <td>AB2</td>\n",
+ " <td>123</td>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
+ " <td>0.95186</td>\n",
+ " <td>...</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>29</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>314</th>\n",
+ " <td>2dm-1156</td>\n",
+ " <td>CeSn2</td>\n",
+ " <td>AB2</td>\n",
+ " <td>123</td>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
+ " <td>0.97447</td>\n",
+ " <td>...</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>29</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>337</th>\n",
+ " <td>2dm-1243</td>\n",
+ " <td>CePb2</td>\n",
+ " <td>AB2</td>\n",
+ " <td>123</td>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
+ " <td>0.97444</td>\n",
+ " <td>...</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>29</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>338</th>\n",
+ " <td>2dm-1245</td>\n",
+ " <td>NiPb4</td>\n",
+ " <td>AB4</td>\n",
+ " <td>125</td>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
+ " <td>0.83687</td>\n",
+ " <td>...</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>29</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>347</th>\n",
+ " <td>2dm-1273</td>\n",
+ " <td>VI4</td>\n",
+ " <td>AB4</td>\n",
+ " <td>123</td>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
+ " <td>0.91735</td>\n",
+ " <td>...</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>29</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>396</th>\n",
+ " <td>2dm-1379</td>\n",
+ " <td>OsI4</td>\n",
+ " <td>AB4</td>\n",
+ " <td>14</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.88741</td>\n",
+ " <td>...</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>29</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>481</th>\n",
+ " <td>2dm-1662</td>\n",
+ " <td>RuCl4</td>\n",
+ " <td>AB4</td>\n",
+ " <td>14</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.95021</td>\n",
+ " <td>...</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>29</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>505</th>\n",
+ " <td>2dm-1744</td>\n",
+ " <td>NbI4</td>\n",
+ " <td>AB4</td>\n",
+ " <td>123</td>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
+ " <td>0.88739</td>\n",
+ " <td>...</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>29</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>687</th>\n",
+ " <td>2dm-2313</td>\n",
+ " <td>TiI4</td>\n",
+ " <td>AB4</td>\n",
+ " <td>123</td>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
+ " <td>0.88554</td>\n",
+ " <td>...</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>29</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>795</th>\n",
+ " <td>2dm-2679</td>\n",
+ " <td>NbCl5</td>\n",
+ " <td>AB5</td>\n",
+ " <td>51</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.95056</td>\n",
+ " <td>...</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>29</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>822</th>\n",
+ " <td>2dm-2777</td>\n",
+ " <td>CrI5</td>\n",
+ " <td>AB5</td>\n",
+ " <td>13</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.88556</td>\n",
+ " <td>...</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>29</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>874</th>\n",
+ " <td>2dm-2899</td>\n",
+ " <td>VBr5</td>\n",
+ " <td>AB5</td>\n",
+ " <td>47</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.93733</td>\n",
+ " <td>...</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>29</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>958</th>\n",
+ " <td>2dm-3367</td>\n",
+ " <td>LaNb2O7</td>\n",
+ " <td>AB2C7</td>\n",
+ " <td>123</td>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
+ " <td>0.61528</td>\n",
+ " <td>...</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>29</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>964</th>\n",
+ " <td>2dm-3411</td>\n",
+ " <td>TiCuF6</td>\n",
+ " <td>ABC6</td>\n",
+ " <td>2</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.97042</td>\n",
+ " <td>...</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>29</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>995</th>\n",
+ " <td>2dm-3517</td>\n",
+ " <td>CuBr</td>\n",
+ " <td>AB</td>\n",
+ " <td>129</td>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
+ " <td>0.70637</td>\n",
+ " <td>...</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>29</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1037</th>\n",
+ " <td>2dm-3652</td>\n",
+ " <td>CeGe2</td>\n",
+ " <td>AB2</td>\n",
+ " <td>123</td>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
+ " <td>0.82630</td>\n",
+ " <td>...</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>29</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1077</th>\n",
+ " <td>2dm-3770</td>\n",
+ " <td>Xe(OF)2</td>\n",
+ " <td>AB2C2</td>\n",
+ " <td>38</td>\n",
+ " <td>5</td>\n",
+ " <td>5</td>\n",
+ " <td>0.83779</td>\n",
+ " <td>...</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>29</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1078</th>\n",
+ " <td>2dm-3775</td>\n",
+ " <td>TlF</td>\n",
+ " <td>AB</td>\n",
+ " <td>99</td>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
+ " <td>0.83820</td>\n",
+ " <td>...</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>29</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1087</th>\n",
+ " <td>2dm-3798</td>\n",
+ " <td>Bi(PdO2)2</td>\n",
+ " <td>AB2C4</td>\n",
+ " <td>129</td>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
+ " <td>0.98217</td>\n",
+ " <td>...</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>29</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1206</th>\n",
+ " <td>2dm-4062</td>\n",
+ " <td>NiP2</td>\n",
+ " <td>AB2</td>\n",
+ " <td>127</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.74983</td>\n",
+ " <td>...</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>29</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1295</th>\n",
+ " <td>2dm-4288</td>\n",
+ " <td>Ca(ClO2)2</td>\n",
+ " <td>AB2C4</td>\n",
+ " <td>125</td>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
+ " <td>0.98820</td>\n",
+ " <td>...</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>29</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1346</th>\n",
+ " <td>2dm-4420</td>\n",
+ " <td>Co(IO3)2</td>\n",
+ " <td>AB2C6</td>\n",
+ " <td>1</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.81151</td>\n",
+ " <td>...</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>29</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1475</th>\n",
+ " <td>2dm-4781</td>\n",
+ " <td>CuBiSCl2</td>\n",
+ " <td>ABCD2</td>\n",
+ " <td>51</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.98936</td>\n",
+ " <td>...</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>29</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1868</th>\n",
+ " <td>2dm-5625</td>\n",
+ " <td>PdSeO3</td>\n",
+ " <td>ABC3</td>\n",
+ " <td>11</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.96607</td>\n",
+ " <td>...</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>29</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1969</th>\n",
+ " <td>2dm-5953</td>\n",
+ " <td>N2</td>\n",
+ " <td>A</td>\n",
+ " <td>123</td>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
+ " <td>0.78245</td>\n",
+ " <td>...</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>29</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>30 rows × 270 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " ID formula gen_formula space_group segments flat_segments flatness_score ... 238 239 240 241 242 243 labels\n",
+ "3 2dm-25 VBr4 AB4 123 3 3 0.97834 ... 0.0 0 0.0 0.0 0.0 0 29\n",
+ "179 2dm-723 OsCl4 AB4 14 4 4 0.87758 ... 0.0 0 0.0 0.0 0.0 0 29\n",
+ "280 2dm-1063 CeSi2 AB2 123 3 3 0.97122 ... 0.0 0 0.0 0.0 0.0 0 29\n",
+ "299 2dm-1115 NiSe2 AB2 14 4 4 0.83425 ... 0.0 0 0.0 0.0 0.0 0 29\n",
+ "306 2dm-1129 Sr2Co AB2 123 3 3 0.69227 ... 0.0 0 0.0 0.0 0.0 0 29\n",
+ "312 2dm-1151 CeC2 AB2 123 3 3 0.95186 ... 0.0 0 0.0 0.0 0.0 0 29\n",
+ "314 2dm-1156 CeSn2 AB2 123 3 3 0.97447 ... 0.0 0 0.0 0.0 0.0 0 29\n",
+ "337 2dm-1243 CePb2 AB2 123 3 3 0.97444 ... 0.0 0 0.0 0.0 0.0 0 29\n",
+ "338 2dm-1245 NiPb4 AB4 125 3 3 0.83687 ... 0.0 0 0.0 0.0 0.0 0 29\n",
+ "347 2dm-1273 VI4 AB4 123 3 3 0.91735 ... 0.0 0 0.0 0.0 0.0 0 29\n",
+ "396 2dm-1379 OsI4 AB4 14 4 4 0.88741 ... 0.0 0 0.0 0.0 0.0 0 29\n",
+ "481 2dm-1662 RuCl4 AB4 14 4 4 0.95021 ... 0.0 0 0.0 0.0 0.0 0 29\n",
+ "505 2dm-1744 NbI4 AB4 123 3 3 0.88739 ... 0.0 0 0.0 0.0 0.0 0 29\n",
+ "687 2dm-2313 TiI4 AB4 123 3 3 0.88554 ... 0.0 0 0.0 0.0 0.0 0 29\n",
+ "795 2dm-2679 NbCl5 AB5 51 4 4 0.95056 ... 0.0 0 0.0 0.0 0.0 0 29\n",
+ "822 2dm-2777 CrI5 AB5 13 4 4 0.88556 ... 0.0 0 0.0 0.0 0.0 0 29\n",
+ "874 2dm-2899 VBr5 AB5 47 4 4 0.93733 ... 0.0 0 0.0 0.0 0.0 0 29\n",
+ "958 2dm-3367 LaNb2O7 AB2C7 123 3 3 0.61528 ... 0.0 0 0.0 0.0 0.0 0 29\n",
+ "964 2dm-3411 TiCuF6 ABC6 2 4 4 0.97042 ... 0.0 0 0.0 0.0 0.0 0 29\n",
+ "995 2dm-3517 CuBr AB 129 3 3 0.70637 ... 0.0 0 0.0 0.0 0.0 0 29\n",
+ "1037 2dm-3652 CeGe2 AB2 123 3 3 0.82630 ... 0.0 0 0.0 0.0 0.0 0 29\n",
+ "1077 2dm-3770 Xe(OF)2 AB2C2 38 5 5 0.83779 ... 0.0 0 0.0 0.0 0.0 0 29\n",
+ "1078 2dm-3775 TlF AB 99 3 3 0.83820 ... 0.0 0 0.0 0.0 0.0 0 29\n",
+ "1087 2dm-3798 Bi(PdO2)2 AB2C4 129 3 3 0.98217 ... 0.0 0 0.0 0.0 0.0 0 29\n",
+ "1206 2dm-4062 NiP2 AB2 127 4 4 0.74983 ... 0.0 0 0.0 0.0 0.0 0 29\n",
+ "1295 2dm-4288 Ca(ClO2)2 AB2C4 125 3 3 0.98820 ... 0.0 0 0.0 0.0 0.0 0 29\n",
+ "1346 2dm-4420 Co(IO3)2 AB2C6 1 4 4 0.81151 ... 0.0 0 0.0 0.0 0.0 0 29\n",
+ "1475 2dm-4781 CuBiSCl2 ABCD2 51 4 4 0.98936 ... 0.0 0 0.0 0.0 0.0 0 29\n",
+ "1868 2dm-5625 PdSeO3 ABC3 11 4 4 0.96607 ... 0.0 0 0.0 0.0 0.0 0 29\n",
+ "1969 2dm-5953 N2 A 123 3 3 0.78245 ... 0.0 0 0.0 0.0 0.0 0 29\n",
+ "\n",
+ "[30 rows x 270 columns]"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df = pd.read_csv(\"materials_data_anupam's_fingerprints.csv\")\n",
+ "fingerprint_length = 244\n",
+ "fingerprint_cols = [str(i) for i in range(fingerprint_length)]\n",
+ "fingerprint_array = df[fingerprint_cols]\n",
+ "\n",
+ "clusterer = hdbscan.HDBSCAN(algorithm='best', alpha=1.0, approx_min_span_tree=True,\\\n",
+ " gen_min_span_tree=False, leaf_size=40, metric='minkowski', cluster_selection_method='eom', min_cluster_size=6, min_samples=6, p=0.2)\n",
+ "clusterer.fit(fingerprint_array)\n",
+ "\n",
+ "labels = clusterer.labels_\n",
+ "df[\"labels\"] = labels\n",
+ "print(np.unique(labels, return_counts=True))\n",
+ "df[df.labels==29].head(30)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "01d506b3-0607-4027-9f1a-459c4ff6f0fa",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "1113822f220d490186f19c22394011ea",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "interactive(children=(IntSlider(value=20, description='label', max=42, min=-1), Output()), _dom_classes=('widg…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "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>ID</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>...</th>\n",
+ " <th>238</th>\n",
+ " <th>239</th>\n",
+ " <th>240</th>\n",
+ " <th>241</th>\n",
+ " <th>242</th>\n",
+ " <th>243</th>\n",
+ " <th>labels</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>760</th>\n",
+ " <td>2dm-2600</td>\n",
+ " <td>Ga2S3</td>\n",
+ " <td>A2B3</td>\n",
+ " <td>1</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.96402</td>\n",
+ " <td>...</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>-1</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>1 rows × 270 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " ID formula gen_formula space_group segments flat_segments flatness_score ... 238 239 240 241 242 243 labels\n",
+ "760 2dm-2600 Ga2S3 A2B3 1 4 4 0.96402 ... 0.0 0 0.0 0.0 0.0 0 -1\n",
+ "\n",
+ "[1 rows x 270 columns]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from ipywidgets import interact\n",
+ "\n",
+ "def view_cluster(label):\n",
+ " display(df[df.labels==label].head(10))\n",
+ "\n",
+ " num_plots = len(df[df.labels==label])\n",
+ "\n",
+ " fig, ax = plt.subplots(num_plots, 1, figsize=(4, 1*num_plots))\n",
+ "\n",
+ " for i, index in enumerate(df[df.labels==label].index):\n",
+ " # ax[i].bar(edges[:-1], df.loc[index][fingerprint_cols], align=\"edge\", width=edges[1]-edges[0])?\n",
+ " ax[i].plot(np.linspace(-8, 8, fingerprint_length), df.loc[index][fingerprint_cols])\n",
+ "\n",
+ "\n",
+ "interact(view_cluster, label=(-1, len(np.unique(labels))-2, 1))\n",
+ "display(df[df.ID==\"2dm-2600\"])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "3d2fde56",
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "1ffcc6f15aec4bbcbba8c33b01b91e13",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "interactive(children=(IntSlider(value=20, description='label', max=42, min=-1), Output()), _dom_classes=('widg…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<function __main__.view_cluster(label)>"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "interact(view_cluster, label=(-1, len(np.unique(labels))-2, 1))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "755470ff",
+ "metadata": {},
+ "source": [
+ "# OUR CLUSTERS"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "9408b2c6-94cc-474a-95ce-25aea41d589d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "FINGERPRINT_LENGTH = 98\n",
+ "\n",
+ "#FINGERPRINT_NAME = \"functional_10dpi_bernoulli_VAE_L={0}\".format(FINGERPRINT_LENGTH)\n",
+ "FINGERPRINT_NAME = \"224_2channel_resnet_L={0}\".format(FINGERPRINT_LENGTH)\n",
+ "# FINGERPRINT_NAME = \"all_k_branches_histogram_-8_to_8\".format(FINGERPRINT_LENGTH)\n",
+ "# FINGERPRINT_NAME = \"128x128_random_erase_resnet18_VAE_L={0}\".format(FINGERPRINT_LENGTH)\n",
+ "\n",
+ "PERPLEXITY = 30\n",
+ "FLAT_ONLY = True\n",
+ "BORING_COLUMNS = [\"flat_segments\", \"flatness_score\", \"binary_flatness\", \"horz_flat_seg\", \"exfoliation_eg\", \"A\", \"B\", \"C\", \"D\", \"E\", \"F\"]\n",
+ "INPUT_NAME = f\"{FINGERPRINT_NAME}_perplexity_{PERPLEXITY}_length_{FINGERPRINT_LENGTH}.csv\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "b3da9ef7-d96b-48ca-908f-0397f71be7cb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_2 = pd.read_csv(f\"../../fingerprints/{INPUT_NAME}\")\n",
+ "if FLAT_ONLY:\n",
+ " df_2 = df_2[df_2.horz_flat_seg>0]\n",
+ "df_2.head()\n",
+ "\n",
+ "fingerprint_cols_2 = [str(i) for i in range(FINGERPRINT_LENGTH)]\n",
+ "BORING_COLUMNS += fingerprint_cols_2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "224132ed-1217-4ea4-adb9-351e3ca62caa",
+ "metadata": {},
+ "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>ID</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>...</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",
+ " <th>labels</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>376</th>\n",
+ " <td>2dm-401</td>\n",
+ " <td>MoS3</td>\n",
+ " <td>AB3</td>\n",
+ " <td>14</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.83684</td>\n",
+ " <td>...</td>\n",
+ " <td>2.992969</td>\n",
+ " <td>2.704516</td>\n",
+ " <td>2.647496</td>\n",
+ " <td>2.823139</td>\n",
+ " <td>-125.28775</td>\n",
+ " <td>-52.162083</td>\n",
+ " <td>38</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>640</th>\n",
+ " <td>2dm-684</td>\n",
+ " <td>NbTe2</td>\n",
+ " <td>AB2</td>\n",
+ " <td>2</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.99236</td>\n",
+ " <td>...</td>\n",
+ " <td>2.701447</td>\n",
+ " <td>2.675659</td>\n",
+ " <td>2.714955</td>\n",
+ " <td>2.779525</td>\n",
+ " <td>-138.98457</td>\n",
+ " <td>-64.181440</td>\n",
+ " <td>38</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1280</th>\n",
+ " <td>2dm-1390</td>\n",
+ " <td>WS3</td>\n",
+ " <td>AB3</td>\n",
+ " <td>14</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.89014</td>\n",
+ " <td>...</td>\n",
+ " <td>2.786536</td>\n",
+ " <td>2.558453</td>\n",
+ " <td>2.732575</td>\n",
+ " <td>2.716396</td>\n",
+ " <td>-125.54242</td>\n",
+ " <td>-52.416164</td>\n",
+ " <td>38</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1532</th>\n",
+ " <td>2dm-1661</td>\n",
+ " <td>NbTe2</td>\n",
+ " <td>AB2</td>\n",
+ " <td>12</td>\n",
+ " <td>5</td>\n",
+ " <td>5</td>\n",
+ " <td>0.95441</td>\n",
+ " <td>...</td>\n",
+ " <td>2.547163</td>\n",
+ " <td>2.646659</td>\n",
+ " <td>2.528497</td>\n",
+ " <td>2.700752</td>\n",
+ " <td>-137.31633</td>\n",
+ " <td>-64.275310</td>\n",
+ " <td>38</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1644</th>\n",
+ " <td>2dm-1781</td>\n",
+ " <td>TaTe2</td>\n",
+ " <td>AB2</td>\n",
+ " <td>2</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.98765</td>\n",
+ " <td>...</td>\n",
+ " <td>2.655947</td>\n",
+ " <td>2.651948</td>\n",
+ " <td>2.666260</td>\n",
+ " <td>2.678349</td>\n",
+ " <td>-139.39467</td>\n",
+ " <td>-64.029010</td>\n",
+ " <td>38</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1689</th>\n",
+ " <td>2dm-1829</td>\n",
+ " <td>Te3W</td>\n",
+ " <td>AB3</td>\n",
+ " <td>14</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.99261</td>\n",
+ " <td>...</td>\n",
+ " <td>2.414061</td>\n",
+ " <td>2.450804</td>\n",
+ " <td>2.369822</td>\n",
+ " <td>2.876198</td>\n",
+ " <td>-127.02869</td>\n",
+ " <td>-54.423717</td>\n",
+ " <td>38</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1830</th>\n",
+ " <td>2dm-1988</td>\n",
+ " <td>Si3As4</td>\n",
+ " <td>A3B4</td>\n",
+ " <td>1</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.75412</td>\n",
+ " <td>...</td>\n",
+ " <td>2.525611</td>\n",
+ " <td>2.447950</td>\n",
+ " <td>2.431037</td>\n",
+ " <td>2.262234</td>\n",
+ " <td>-107.29348</td>\n",
+ " <td>-27.590958</td>\n",
+ " <td>38</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1844</th>\n",
+ " <td>2dm-2007</td>\n",
+ " <td>MoSe3</td>\n",
+ " <td>AB3</td>\n",
+ " <td>14</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.96063</td>\n",
+ " <td>...</td>\n",
+ " <td>2.302569</td>\n",
+ " <td>2.529596</td>\n",
+ " <td>2.557750</td>\n",
+ " <td>2.301805</td>\n",
+ " <td>-126.39939</td>\n",
+ " <td>-53.930386</td>\n",
+ " <td>38</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2155</th>\n",
+ " <td>2dm-2352</td>\n",
+ " <td>Al2Te3</td>\n",
+ " <td>A2B3</td>\n",
+ " <td>8</td>\n",
+ " <td>5</td>\n",
+ " <td>5</td>\n",
+ " <td>0.84034</td>\n",
+ " <td>...</td>\n",
+ " <td>2.544886</td>\n",
+ " <td>2.431981</td>\n",
+ " <td>2.317205</td>\n",
+ " <td>2.600342</td>\n",
+ " <td>-90.73142</td>\n",
+ " <td>-11.822838</td>\n",
+ " <td>38</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2329</th>\n",
+ " <td>2dm-2551</td>\n",
+ " <td>Al2Se3</td>\n",
+ " <td>A2B3</td>\n",
+ " <td>1</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.95854</td>\n",
+ " <td>...</td>\n",
+ " <td>2.866658</td>\n",
+ " <td>2.513502</td>\n",
+ " <td>2.684730</td>\n",
+ " <td>2.533837</td>\n",
+ " <td>-143.11590</td>\n",
+ " <td>-55.788418</td>\n",
+ " <td>38</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2375</th>\n",
+ " <td>2dm-2600</td>\n",
+ " <td>Ga2S3</td>\n",
+ " <td>A2B3</td>\n",
+ " <td>1</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.96402</td>\n",
+ " <td>...</td>\n",
+ " <td>2.839315</td>\n",
+ " <td>2.699904</td>\n",
+ " <td>2.250316</td>\n",
+ " <td>2.594157</td>\n",
+ " <td>-143.81593</td>\n",
+ " <td>-59.647770</td>\n",
+ " <td>38</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2391</th>\n",
+ " <td>2dm-2616</td>\n",
+ " <td>B2Te3</td>\n",
+ " <td>A2B3</td>\n",
+ " <td>1</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.86829</td>\n",
+ " <td>...</td>\n",
+ " <td>2.416384</td>\n",
+ " <td>2.536661</td>\n",
+ " <td>2.715641</td>\n",
+ " <td>2.618738</td>\n",
+ " <td>-139.09589</td>\n",
+ " <td>-59.881985</td>\n",
+ " <td>38</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2422</th>\n",
+ " <td>2dm-2649</td>\n",
+ " <td>In2S3</td>\n",
+ " <td>A2B3</td>\n",
+ " <td>1</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.98343</td>\n",
+ " <td>...</td>\n",
+ " <td>2.518386</td>\n",
+ " <td>2.524114</td>\n",
+ " <td>2.547173</td>\n",
+ " <td>2.574234</td>\n",
+ " <td>-142.79400</td>\n",
+ " <td>-58.896390</td>\n",
+ " <td>38</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2428</th>\n",
+ " <td>2dm-2655</td>\n",
+ " <td>Al2Te3</td>\n",
+ " <td>A2B3</td>\n",
+ " <td>1</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.88849</td>\n",
+ " <td>...</td>\n",
+ " <td>2.628780</td>\n",
+ " <td>2.649371</td>\n",
+ " <td>2.609238</td>\n",
+ " <td>2.733802</td>\n",
+ " <td>-137.92350</td>\n",
+ " <td>-62.516080</td>\n",
+ " <td>38</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2431</th>\n",
+ " <td>2dm-2658</td>\n",
+ " <td>Ga2Se3</td>\n",
+ " <td>A2B3</td>\n",
+ " <td>1</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.97087</td>\n",
+ " <td>...</td>\n",
+ " <td>2.556134</td>\n",
+ " <td>2.522672</td>\n",
+ " <td>2.514445</td>\n",
+ " <td>2.521293</td>\n",
+ " <td>-139.93678</td>\n",
+ " <td>-59.541210</td>\n",
+ " <td>38</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2469</th>\n",
+ " <td>2dm-2699</td>\n",
+ " <td>In2Se3</td>\n",
+ " <td>A2B3</td>\n",
+ " <td>1</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.92713</td>\n",
+ " <td>...</td>\n",
+ " <td>2.814635</td>\n",
+ " <td>2.352931</td>\n",
+ " <td>2.460795</td>\n",
+ " <td>2.389499</td>\n",
+ " <td>-140.40262</td>\n",
+ " <td>-60.129936</td>\n",
+ " <td>38</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2592</th>\n",
+ " <td>2dm-2833</td>\n",
+ " <td>Tl2Te3</td>\n",
+ " <td>A2B3</td>\n",
+ " <td>1</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.84085</td>\n",
+ " <td>...</td>\n",
+ " <td>2.248686</td>\n",
+ " <td>2.615065</td>\n",
+ " <td>2.557053</td>\n",
+ " <td>2.564985</td>\n",
+ " <td>-139.78183</td>\n",
+ " <td>-65.260010</td>\n",
+ " <td>38</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2597</th>\n",
+ " <td>2dm-2838</td>\n",
+ " <td>Tl2Se3</td>\n",
+ " <td>A2B3</td>\n",
+ " <td>1</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.89736</td>\n",
+ " <td>...</td>\n",
+ " <td>2.480628</td>\n",
+ " <td>2.566813</td>\n",
+ " <td>2.644517</td>\n",
+ " <td>2.288446</td>\n",
+ " <td>-142.02257</td>\n",
+ " <td>-64.413635</td>\n",
+ " <td>38</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2598</th>\n",
+ " <td>2dm-2839</td>\n",
+ " <td>Al2Se3</td>\n",
+ " <td>A2B3</td>\n",
+ " <td>1</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.94066</td>\n",
+ " <td>...</td>\n",
+ " <td>2.177766</td>\n",
+ " <td>2.731873</td>\n",
+ " <td>2.887099</td>\n",
+ " <td>2.734977</td>\n",
+ " <td>-143.28668</td>\n",
+ " <td>-56.002666</td>\n",
+ " <td>38</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>19 rows × 128 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " ID formula gen_formula space_group segments flat_segments flatness_score ... 94 95 96 97 fx fy labels\n",
+ "376 2dm-401 MoS3 AB3 14 4 4 0.83684 ... 2.992969 2.704516 2.647496 2.823139 -125.28775 -52.162083 38\n",
+ "640 2dm-684 NbTe2 AB2 2 4 4 0.99236 ... 2.701447 2.675659 2.714955 2.779525 -138.98457 -64.181440 38\n",
+ "1280 2dm-1390 WS3 AB3 14 4 4 0.89014 ... 2.786536 2.558453 2.732575 2.716396 -125.54242 -52.416164 38\n",
+ "1532 2dm-1661 NbTe2 AB2 12 5 5 0.95441 ... 2.547163 2.646659 2.528497 2.700752 -137.31633 -64.275310 38\n",
+ "1644 2dm-1781 TaTe2 AB2 2 4 4 0.98765 ... 2.655947 2.651948 2.666260 2.678349 -139.39467 -64.029010 38\n",
+ "1689 2dm-1829 Te3W AB3 14 4 4 0.99261 ... 2.414061 2.450804 2.369822 2.876198 -127.02869 -54.423717 38\n",
+ "1830 2dm-1988 Si3As4 A3B4 1 4 4 0.75412 ... 2.525611 2.447950 2.431037 2.262234 -107.29348 -27.590958 38\n",
+ "1844 2dm-2007 MoSe3 AB3 14 4 4 0.96063 ... 2.302569 2.529596 2.557750 2.301805 -126.39939 -53.930386 38\n",
+ "2155 2dm-2352 Al2Te3 A2B3 8 5 5 0.84034 ... 2.544886 2.431981 2.317205 2.600342 -90.73142 -11.822838 38\n",
+ "2329 2dm-2551 Al2Se3 A2B3 1 4 4 0.95854 ... 2.866658 2.513502 2.684730 2.533837 -143.11590 -55.788418 38\n",
+ "2375 2dm-2600 Ga2S3 A2B3 1 4 4 0.96402 ... 2.839315 2.699904 2.250316 2.594157 -143.81593 -59.647770 38\n",
+ "2391 2dm-2616 B2Te3 A2B3 1 4 4 0.86829 ... 2.416384 2.536661 2.715641 2.618738 -139.09589 -59.881985 38\n",
+ "2422 2dm-2649 In2S3 A2B3 1 4 4 0.98343 ... 2.518386 2.524114 2.547173 2.574234 -142.79400 -58.896390 38\n",
+ "2428 2dm-2655 Al2Te3 A2B3 1 4 4 0.88849 ... 2.628780 2.649371 2.609238 2.733802 -137.92350 -62.516080 38\n",
+ "2431 2dm-2658 Ga2Se3 A2B3 1 4 4 0.97087 ... 2.556134 2.522672 2.514445 2.521293 -139.93678 -59.541210 38\n",
+ "2469 2dm-2699 In2Se3 A2B3 1 4 4 0.92713 ... 2.814635 2.352931 2.460795 2.389499 -140.40262 -60.129936 38\n",
+ "2592 2dm-2833 Tl2Te3 A2B3 1 4 4 0.84085 ... 2.248686 2.615065 2.557053 2.564985 -139.78183 -65.260010 38\n",
+ "2597 2dm-2838 Tl2Se3 A2B3 1 4 4 0.89736 ... 2.480628 2.566813 2.644517 2.288446 -142.02257 -64.413635 38\n",
+ "2598 2dm-2839 Al2Se3 A2B3 1 4 4 0.94066 ... 2.177766 2.731873 2.887099 2.734977 -143.28668 -56.002666 38\n",
+ "\n",
+ "[19 rows x 128 columns]"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# clusterer = hdbscan.HDBSCAN(algorithm='best', alpha=1.0, approx_min_span_tree=True,\\\n",
+ "# gen_min_span_tree=True, leaf_size=40, metric='minkowski', cluster_selection_method='leaf', min_cluster_size=6, min_samples=2, p=0.2)\n",
+ "\n",
+ "clusterer_2 = hdbscan.HDBSCAN(algorithm='best', alpha=0.8, approx_min_span_tree=True,\\\n",
+ "gen_min_span_tree=False, leaf_size=40, metric='minkowski', cluster_selection_method='leaf', min_cluster_size=6, min_samples=2, p=0.2)\n",
+ "\n",
+ "clusterer_2.fit(df_2[fingerprint_cols_2])\n",
+ "\n",
+ "labels_2 = clusterer_2.labels_\n",
+ "df_2[\"labels\"] = labels_2\n",
+ "#df_2[\"member_strength\"] = clusterer_2.probabilities_\n",
+ "df_2[df_2.labels==38].head(19)\n",
+ "#print(df_2.index[1])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d8faffe0-a9ba-4c58-adb9-0f5a7ae35f56",
+ "metadata": {},
+ "source": [
+ "# OLD:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "id": "52e6e59f",
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true,
+ "source_hidden": true
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(array([-1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,\n",
+ " 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32,\n",
+ " 33, 34, 35, 36, 37, 38, 39, 40], dtype=int64), array([1710, 8, 5, 14, 7, 6, 5, 7, 4, 6, 6,\n",
+ " 5, 4, 5, 6, 7, 5, 9, 5, 5, 7, 8,\n",
+ " 9, 6, 11, 9, 4, 5, 5, 6, 4, 4, 8,\n",
+ " 8, 12, 4, 14, 5, 4, 6, 29, 8], dtype=int64))\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
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+ " .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>ID</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>...</th>\n",
+ " <th>54</th>\n",
+ " <th>55</th>\n",
+ " <th>56</th>\n",
+ " <th>57</th>\n",
+ " <th>58</th>\n",
+ " <th>59</th>\n",
+ " <th>labels</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>233</th>\n",
+ " <td>2dm-900</td>\n",
+ " <td>Ga2Te</td>\n",
+ " <td>AB2</td>\n",
+ " <td>143</td>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
+ " <td>0.78315</td>\n",
+ " <td>...</td>\n",
+ " <td>398.0</td>\n",
+ " <td>224.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>236</th>\n",
+ " <td>2dm-903</td>\n",
+ " <td>Ga2Te</td>\n",
+ " <td>AB2</td>\n",
+ " <td>143</td>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
+ " <td>0.81656</td>\n",
+ " <td>...</td>\n",
+ " <td>390.0</td>\n",
+ " <td>219.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>240</th>\n",
+ " <td>2dm-910</td>\n",
+ " <td>Ga2Se</td>\n",
+ " <td>AB2</td>\n",
+ " <td>143</td>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
+ " <td>0.87920</td>\n",
+ " <td>...</td>\n",
+ " <td>343.0</td>\n",
+ " <td>276.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>522</th>\n",
+ " <td>2dm-1777</td>\n",
+ " <td>Ga2S</td>\n",
+ " <td>AB2</td>\n",
+ " <td>143</td>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
+ " <td>0.87920</td>\n",
+ " <td>...</td>\n",
+ " <td>319.0</td>\n",
+ " <td>309.0</td>\n",
+ " <td>91.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>531</th>\n",
+ " <td>2dm-1794</td>\n",
+ " <td>Ga2S</td>\n",
+ " <td>AB2</td>\n",
+ " <td>143</td>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
+ " <td>0.84263</td>\n",
+ " <td>...</td>\n",
+ " <td>331.0</td>\n",
+ " <td>305.0</td>\n",
+ " <td>70.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>5 rows × 86 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " ID formula gen_formula space_group segments flat_segments flatness_score ... 54 55 56 57 58 59 labels\n",
+ "233 2dm-900 Ga2Te AB2 143 3 3 0.78315 ... 398.0 224.0 0.0 0.0 0.0 0.0 1\n",
+ "236 2dm-903 Ga2Te AB2 143 3 3 0.81656 ... 390.0 219.0 0.0 0.0 0.0 0.0 1\n",
+ "240 2dm-910 Ga2Se AB2 143 3 3 0.87920 ... 343.0 276.0 0.0 0.0 0.0 0.0 1\n",
+ "522 2dm-1777 Ga2S AB2 143 3 3 0.87920 ... 319.0 309.0 91.0 0.0 0.0 0.0 1\n",
+ "531 2dm-1794 Ga2S AB2 143 3 3 0.84263 ... 331.0 305.0 70.0 0.0 0.0 0.0 1\n",
+ "\n",
+ "[5 rows x 86 columns]"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_2 = pd.read_csv(\"../materials_data_all_k_branches_fingerprint_60.csv\")\n",
+ "fingerprint_length_2 = 60\n",
+ "fingerprint_cols_2 = [str(i) for i in range(fingerprint_length_2)]\n",
+ "fingerprint_array_2 = df_2[fingerprint_cols_2]\n",
+ "\n",
+ "clusterer_2 = hdbscan.HDBSCAN(algorithm='best', alpha=1.0, approx_min_span_tree=True,\\\n",
+ " gen_min_span_tree=False, leaf_size=40, metric='minkowski', cluster_selection_method='leaf', min_cluster_size=4, min_samples=4, p=0.2)\n",
+ "clusterer_2.fit(fingerprint_array_2)\n",
+ "\n",
+ "labels_2 = clusterer_2.labels_\n",
+ "df_2[\"labels\"] = labels_2\n",
+ "print(np.unique(labels_2, return_counts=True))\n",
+ "df_2[df_2.labels==1].head(19)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "id": "c22cd62b",
+ "metadata": {
+ "jupyter": {
+ "source_hidden": true
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "c83c5d3c152a4e588e326539a54fa5bf",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "interactive(children=(IntSlider(value=19, description='label', max=40, min=-1), Output()), _dom_classes=('widg…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "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>ID</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>...</th>\n",
+ " <th>54</th>\n",
+ " <th>55</th>\n",
+ " <th>56</th>\n",
+ " <th>57</th>\n",
+ " <th>58</th>\n",
+ " <th>59</th>\n",
+ " <th>labels</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>768</th>\n",
+ " <td>2dm-2600</td>\n",
+ " <td>Ga2S3</td>\n",
+ " <td>A2B3</td>\n",
+ " <td>1</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.96402</td>\n",
+ " <td>...</td>\n",
+ " <td>171.0</td>\n",
+ " <td>220.0</td>\n",
+ " <td>296.0</td>\n",
+ " <td>329.0</td>\n",
+ " <td>142.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>1 rows × 86 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " ID formula gen_formula space_group segments flat_segments flatness_score ... 54 55 56 57 58 59 labels\n",
+ "768 2dm-2600 Ga2S3 A2B3 1 4 4 0.96402 ... 171.0 220.0 296.0 329.0 142.0 0.0 2\n",
+ "\n",
+ "[1 rows x 86 columns]"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "def view_cluster_2(label):\n",
+ " display(df_2[df_2.labels==label].head(100))\n",
+ "\n",
+ " num_plots = len(df_2[df_2.labels==label])\n",
+ "\n",
+ " fig, ax = plt.subplots(num_plots, 1, figsize=(4, 1*num_plots))\n",
+ "\n",
+ " for i, index in enumerate(df_2[df_2.labels==label].index):\n",
+ " # ax[i].bar(edges[:-1], df.loc[index][fingerprint_cols], align=\"edge\", width=edges[1]-edges[0])?\n",
+ " ax[i].plot(np.linspace(-8, 8, fingerprint_length_2), df_2.loc[index][fingerprint_cols_2])\n",
+ "\n",
+ "\n",
+ " \n",
+ " \n",
+ "interact(view_cluster_2, label=(-1, len(np.unique(labels_2))-2, 1))\n",
+ "df_2[df_2.ID==\"2dm-2600\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0bbf571b",
+ "metadata": {},
+ "source": [
+ "# Pick out one material to see which cluster it's in in both cluster sets"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "350d8060",
+ "metadata": {
+ "collapsed": true,
+ "jupyter": {
+ "outputs_hidden": true
+ },
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "anupam:\n",
+ "UNCLUSTERED\n",
+ "HP+Tom:\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
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+ " .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>ID</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>...</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",
+ " <th>labels</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>376</th>\n",
+ " <td>2dm-401</td>\n",
+ " <td>MoS3</td>\n",
+ " <td>AB3</td>\n",
+ " <td>14</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.83684</td>\n",
+ " <td>...</td>\n",
+ " <td>2.992969</td>\n",
+ " <td>2.704516</td>\n",
+ " <td>2.647496</td>\n",
+ " <td>2.823139</td>\n",
+ " <td>-125.28775</td>\n",
+ " <td>-52.162083</td>\n",
+ " <td>38</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>640</th>\n",
+ " <td>2dm-684</td>\n",
+ " <td>NbTe2</td>\n",
+ " <td>AB2</td>\n",
+ " <td>2</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.99236</td>\n",
+ " <td>...</td>\n",
+ " <td>2.701447</td>\n",
+ " <td>2.675659</td>\n",
+ " <td>2.714955</td>\n",
+ " <td>2.779525</td>\n",
+ " <td>-138.98457</td>\n",
+ " <td>-64.181440</td>\n",
+ " <td>38</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1280</th>\n",
+ " <td>2dm-1390</td>\n",
+ " <td>WS3</td>\n",
+ " <td>AB3</td>\n",
+ " <td>14</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.89014</td>\n",
+ " <td>...</td>\n",
+ " <td>2.786536</td>\n",
+ " <td>2.558453</td>\n",
+ " <td>2.732575</td>\n",
+ " <td>2.716396</td>\n",
+ " <td>-125.54242</td>\n",
+ " <td>-52.416164</td>\n",
+ " <td>38</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1532</th>\n",
+ " <td>2dm-1661</td>\n",
+ " <td>NbTe2</td>\n",
+ " <td>AB2</td>\n",
+ " <td>12</td>\n",
+ " <td>5</td>\n",
+ " <td>5</td>\n",
+ " <td>0.95441</td>\n",
+ " <td>...</td>\n",
+ " <td>2.547163</td>\n",
+ " <td>2.646659</td>\n",
+ " <td>2.528497</td>\n",
+ " <td>2.700752</td>\n",
+ " <td>-137.31633</td>\n",
+ " <td>-64.275310</td>\n",
+ " <td>38</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1644</th>\n",
+ " <td>2dm-1781</td>\n",
+ " <td>TaTe2</td>\n",
+ " <td>AB2</td>\n",
+ " <td>2</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.98765</td>\n",
+ " <td>...</td>\n",
+ " <td>2.655947</td>\n",
+ " <td>2.651948</td>\n",
+ " <td>2.666260</td>\n",
+ " <td>2.678349</td>\n",
+ " <td>-139.39467</td>\n",
+ " <td>-64.029010</td>\n",
+ " <td>38</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1689</th>\n",
+ " <td>2dm-1829</td>\n",
+ " <td>Te3W</td>\n",
+ " <td>AB3</td>\n",
+ " <td>14</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.99261</td>\n",
+ " <td>...</td>\n",
+ " <td>2.414061</td>\n",
+ " <td>2.450804</td>\n",
+ " <td>2.369822</td>\n",
+ " <td>2.876198</td>\n",
+ " <td>-127.02869</td>\n",
+ " <td>-54.423717</td>\n",
+ " <td>38</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1830</th>\n",
+ " <td>2dm-1988</td>\n",
+ " <td>Si3As4</td>\n",
+ " <td>A3B4</td>\n",
+ " <td>1</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.75412</td>\n",
+ " <td>...</td>\n",
+ " <td>2.525611</td>\n",
+ " <td>2.447950</td>\n",
+ " <td>2.431037</td>\n",
+ " <td>2.262234</td>\n",
+ " <td>-107.29348</td>\n",
+ " <td>-27.590958</td>\n",
+ " <td>38</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1844</th>\n",
+ " <td>2dm-2007</td>\n",
+ " <td>MoSe3</td>\n",
+ " <td>AB3</td>\n",
+ " <td>14</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.96063</td>\n",
+ " <td>...</td>\n",
+ " <td>2.302569</td>\n",
+ " <td>2.529596</td>\n",
+ " <td>2.557750</td>\n",
+ " <td>2.301805</td>\n",
+ " <td>-126.39939</td>\n",
+ " <td>-53.930386</td>\n",
+ " <td>38</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2155</th>\n",
+ " <td>2dm-2352</td>\n",
+ " <td>Al2Te3</td>\n",
+ " <td>A2B3</td>\n",
+ " <td>8</td>\n",
+ " <td>5</td>\n",
+ " <td>5</td>\n",
+ " <td>0.84034</td>\n",
+ " <td>...</td>\n",
+ " <td>2.544886</td>\n",
+ " <td>2.431981</td>\n",
+ " <td>2.317205</td>\n",
+ " <td>2.600342</td>\n",
+ " <td>-90.73142</td>\n",
+ " <td>-11.822838</td>\n",
+ " <td>38</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2329</th>\n",
+ " <td>2dm-2551</td>\n",
+ " <td>Al2Se3</td>\n",
+ " <td>A2B3</td>\n",
+ " <td>1</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.95854</td>\n",
+ " <td>...</td>\n",
+ " <td>2.866658</td>\n",
+ " <td>2.513502</td>\n",
+ " <td>2.684730</td>\n",
+ " <td>2.533837</td>\n",
+ " <td>-143.11590</td>\n",
+ " <td>-55.788418</td>\n",
+ " <td>38</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2375</th>\n",
+ " <td>2dm-2600</td>\n",
+ " <td>Ga2S3</td>\n",
+ " <td>A2B3</td>\n",
+ " <td>1</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.96402</td>\n",
+ " <td>...</td>\n",
+ " <td>2.839315</td>\n",
+ " <td>2.699904</td>\n",
+ " <td>2.250316</td>\n",
+ " <td>2.594157</td>\n",
+ " <td>-143.81593</td>\n",
+ " <td>-59.647770</td>\n",
+ " <td>38</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2391</th>\n",
+ " <td>2dm-2616</td>\n",
+ " <td>B2Te3</td>\n",
+ " <td>A2B3</td>\n",
+ " <td>1</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.86829</td>\n",
+ " <td>...</td>\n",
+ " <td>2.416384</td>\n",
+ " <td>2.536661</td>\n",
+ " <td>2.715641</td>\n",
+ " <td>2.618738</td>\n",
+ " <td>-139.09589</td>\n",
+ " <td>-59.881985</td>\n",
+ " <td>38</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2422</th>\n",
+ " <td>2dm-2649</td>\n",
+ " <td>In2S3</td>\n",
+ " <td>A2B3</td>\n",
+ " <td>1</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.98343</td>\n",
+ " <td>...</td>\n",
+ " <td>2.518386</td>\n",
+ " <td>2.524114</td>\n",
+ " <td>2.547173</td>\n",
+ " <td>2.574234</td>\n",
+ " <td>-142.79400</td>\n",
+ " <td>-58.896390</td>\n",
+ " <td>38</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2428</th>\n",
+ " <td>2dm-2655</td>\n",
+ " <td>Al2Te3</td>\n",
+ " <td>A2B3</td>\n",
+ " <td>1</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.88849</td>\n",
+ " <td>...</td>\n",
+ " <td>2.628780</td>\n",
+ " <td>2.649371</td>\n",
+ " <td>2.609238</td>\n",
+ " <td>2.733802</td>\n",
+ " <td>-137.92350</td>\n",
+ " <td>-62.516080</td>\n",
+ " <td>38</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2431</th>\n",
+ " <td>2dm-2658</td>\n",
+ " <td>Ga2Se3</td>\n",
+ " <td>A2B3</td>\n",
+ " <td>1</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.97087</td>\n",
+ " <td>...</td>\n",
+ " <td>2.556134</td>\n",
+ " <td>2.522672</td>\n",
+ " <td>2.514445</td>\n",
+ " <td>2.521293</td>\n",
+ " <td>-139.93678</td>\n",
+ " <td>-59.541210</td>\n",
+ " <td>38</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2469</th>\n",
+ " <td>2dm-2699</td>\n",
+ " <td>In2Se3</td>\n",
+ " <td>A2B3</td>\n",
+ " <td>1</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.92713</td>\n",
+ " <td>...</td>\n",
+ " <td>2.814635</td>\n",
+ " <td>2.352931</td>\n",
+ " <td>2.460795</td>\n",
+ " <td>2.389499</td>\n",
+ " <td>-140.40262</td>\n",
+ " <td>-60.129936</td>\n",
+ " <td>38</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2592</th>\n",
+ " <td>2dm-2833</td>\n",
+ " <td>Tl2Te3</td>\n",
+ " <td>A2B3</td>\n",
+ " <td>1</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.84085</td>\n",
+ " <td>...</td>\n",
+ " <td>2.248686</td>\n",
+ " <td>2.615065</td>\n",
+ " <td>2.557053</td>\n",
+ " <td>2.564985</td>\n",
+ " <td>-139.78183</td>\n",
+ " <td>-65.260010</td>\n",
+ " <td>38</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2597</th>\n",
+ " <td>2dm-2838</td>\n",
+ " <td>Tl2Se3</td>\n",
+ " <td>A2B3</td>\n",
+ " <td>1</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.89736</td>\n",
+ " <td>...</td>\n",
+ " <td>2.480628</td>\n",
+ " <td>2.566813</td>\n",
+ " <td>2.644517</td>\n",
+ " <td>2.288446</td>\n",
+ " <td>-142.02257</td>\n",
+ " <td>-64.413635</td>\n",
+ " <td>38</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2598</th>\n",
+ " <td>2dm-2839</td>\n",
+ " <td>Al2Se3</td>\n",
+ " <td>A2B3</td>\n",
+ " <td>1</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.94066</td>\n",
+ " <td>...</td>\n",
+ " <td>2.177766</td>\n",
+ " <td>2.731873</td>\n",
+ " <td>2.887099</td>\n",
+ " <td>2.734977</td>\n",
+ " <td>-143.28668</td>\n",
+ " <td>-56.002666</td>\n",
+ " <td>38</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2599</th>\n",
+ " <td>2dm-2840</td>\n",
+ " <td>Ga2Se3</td>\n",
+ " <td>A2B3</td>\n",
+ " <td>1</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.98954</td>\n",
+ " <td>...</td>\n",
+ " <td>2.143645</td>\n",
+ " <td>2.733167</td>\n",
+ " <td>2.634298</td>\n",
+ " <td>2.597434</td>\n",
+ " <td>-143.01650</td>\n",
+ " <td>-57.902336</td>\n",
+ " <td>38</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2613</th>\n",
+ " <td>2dm-2856</td>\n",
+ " <td>Al2Te3</td>\n",
+ " <td>A2B3</td>\n",
+ " <td>1</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.96840</td>\n",
+ " <td>...</td>\n",
+ " <td>2.759936</td>\n",
+ " <td>2.543243</td>\n",
+ " <td>2.561956</td>\n",
+ " <td>2.501265</td>\n",
+ " <td>-139.54509</td>\n",
+ " <td>-62.198387</td>\n",
+ " <td>38</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2614</th>\n",
+ " <td>2dm-2857</td>\n",
+ " <td>In2Te3</td>\n",
+ " <td>A2B3</td>\n",
+ " <td>1</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.94763</td>\n",
+ " <td>...</td>\n",
+ " <td>2.518876</td>\n",
+ " <td>2.373713</td>\n",
+ " <td>2.553932</td>\n",
+ " <td>2.321163</td>\n",
+ " <td>-141.27708</td>\n",
+ " <td>-64.012740</td>\n",
+ " <td>38</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2617</th>\n",
+ " <td>2dm-2860</td>\n",
+ " <td>In2Se3</td>\n",
+ " <td>A2B3</td>\n",
+ " <td>1</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.95714</td>\n",
+ " <td>...</td>\n",
+ " <td>2.698484</td>\n",
+ " <td>2.453103</td>\n",
+ " <td>2.576481</td>\n",
+ " <td>2.595978</td>\n",
+ " <td>-141.54002</td>\n",
+ " <td>-61.731476</td>\n",
+ " <td>38</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2653</th>\n",
+ " <td>2dm-2898</td>\n",
+ " <td>Ga2Te3</td>\n",
+ " <td>A2B3</td>\n",
+ " <td>1</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.92116</td>\n",
+ " <td>...</td>\n",
+ " <td>2.578041</td>\n",
+ " <td>2.559579</td>\n",
+ " <td>2.308213</td>\n",
+ " <td>2.553730</td>\n",
+ " <td>-140.67873</td>\n",
+ " <td>-62.343860</td>\n",
+ " <td>38</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2656</th>\n",
+ " <td>2dm-2901</td>\n",
+ " <td>B2Te3</td>\n",
+ " <td>A2B3</td>\n",
+ " <td>1</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.83736</td>\n",
+ " <td>...</td>\n",
+ " <td>2.887425</td>\n",
+ " <td>2.626384</td>\n",
+ " <td>2.674782</td>\n",
+ " <td>2.785355</td>\n",
+ " <td>-139.81282</td>\n",
+ " <td>-58.207360</td>\n",
+ " <td>38</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2670</th>\n",
+ " <td>2dm-2917</td>\n",
+ " <td>Tl2Te3</td>\n",
+ " <td>A2B3</td>\n",
+ " <td>1</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.94174</td>\n",
+ " <td>...</td>\n",
+ " <td>2.268583</td>\n",
+ " <td>2.650200</td>\n",
+ " <td>2.432583</td>\n",
+ " <td>2.693635</td>\n",
+ " <td>-140.38884</td>\n",
+ " <td>-64.695755</td>\n",
+ " <td>38</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2671</th>\n",
+ " <td>2dm-2918</td>\n",
+ " <td>Ga2Te3</td>\n",
+ " <td>A2B3</td>\n",
+ " <td>1</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.92796</td>\n",
+ " <td>...</td>\n",
+ " <td>2.636732</td>\n",
+ " <td>2.779189</td>\n",
+ " <td>2.228261</td>\n",
+ " <td>2.664211</td>\n",
+ " <td>-141.17853</td>\n",
+ " <td>-63.170937</td>\n",
+ " <td>38</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2679</th>\n",
+ " <td>2dm-2928</td>\n",
+ " <td>In2Te3</td>\n",
+ " <td>A2B3</td>\n",
+ " <td>1</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.95349</td>\n",
+ " <td>...</td>\n",
+ " <td>2.717375</td>\n",
+ " <td>2.567620</td>\n",
+ " <td>2.709185</td>\n",
+ " <td>2.563196</td>\n",
+ " <td>-138.78062</td>\n",
+ " <td>-62.685190</td>\n",
+ " <td>38</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2680</th>\n",
+ " <td>2dm-2931</td>\n",
+ " <td>Tl2Se3</td>\n",
+ " <td>A2B3</td>\n",
+ " <td>1</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.93519</td>\n",
+ " <td>...</td>\n",
+ " <td>2.298888</td>\n",
+ " <td>2.677713</td>\n",
+ " <td>2.762901</td>\n",
+ " <td>2.718805</td>\n",
+ " <td>-141.32210</td>\n",
+ " <td>-65.816810</td>\n",
+ " <td>38</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>29 rows × 128 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " ID formula gen_formula space_group segments flat_segments flatness_score ... 94 95 96 97 fx fy labels\n",
+ "376 2dm-401 MoS3 AB3 14 4 4 0.83684 ... 2.992969 2.704516 2.647496 2.823139 -125.28775 -52.162083 38\n",
+ "640 2dm-684 NbTe2 AB2 2 4 4 0.99236 ... 2.701447 2.675659 2.714955 2.779525 -138.98457 -64.181440 38\n",
+ "1280 2dm-1390 WS3 AB3 14 4 4 0.89014 ... 2.786536 2.558453 2.732575 2.716396 -125.54242 -52.416164 38\n",
+ "1532 2dm-1661 NbTe2 AB2 12 5 5 0.95441 ... 2.547163 2.646659 2.528497 2.700752 -137.31633 -64.275310 38\n",
+ "1644 2dm-1781 TaTe2 AB2 2 4 4 0.98765 ... 2.655947 2.651948 2.666260 2.678349 -139.39467 -64.029010 38\n",
+ "1689 2dm-1829 Te3W AB3 14 4 4 0.99261 ... 2.414061 2.450804 2.369822 2.876198 -127.02869 -54.423717 38\n",
+ "1830 2dm-1988 Si3As4 A3B4 1 4 4 0.75412 ... 2.525611 2.447950 2.431037 2.262234 -107.29348 -27.590958 38\n",
+ "1844 2dm-2007 MoSe3 AB3 14 4 4 0.96063 ... 2.302569 2.529596 2.557750 2.301805 -126.39939 -53.930386 38\n",
+ "2155 2dm-2352 Al2Te3 A2B3 8 5 5 0.84034 ... 2.544886 2.431981 2.317205 2.600342 -90.73142 -11.822838 38\n",
+ "2329 2dm-2551 Al2Se3 A2B3 1 4 4 0.95854 ... 2.866658 2.513502 2.684730 2.533837 -143.11590 -55.788418 38\n",
+ "2375 2dm-2600 Ga2S3 A2B3 1 4 4 0.96402 ... 2.839315 2.699904 2.250316 2.594157 -143.81593 -59.647770 38\n",
+ "2391 2dm-2616 B2Te3 A2B3 1 4 4 0.86829 ... 2.416384 2.536661 2.715641 2.618738 -139.09589 -59.881985 38\n",
+ "2422 2dm-2649 In2S3 A2B3 1 4 4 0.98343 ... 2.518386 2.524114 2.547173 2.574234 -142.79400 -58.896390 38\n",
+ "2428 2dm-2655 Al2Te3 A2B3 1 4 4 0.88849 ... 2.628780 2.649371 2.609238 2.733802 -137.92350 -62.516080 38\n",
+ "2431 2dm-2658 Ga2Se3 A2B3 1 4 4 0.97087 ... 2.556134 2.522672 2.514445 2.521293 -139.93678 -59.541210 38\n",
+ "2469 2dm-2699 In2Se3 A2B3 1 4 4 0.92713 ... 2.814635 2.352931 2.460795 2.389499 -140.40262 -60.129936 38\n",
+ "2592 2dm-2833 Tl2Te3 A2B3 1 4 4 0.84085 ... 2.248686 2.615065 2.557053 2.564985 -139.78183 -65.260010 38\n",
+ "2597 2dm-2838 Tl2Se3 A2B3 1 4 4 0.89736 ... 2.480628 2.566813 2.644517 2.288446 -142.02257 -64.413635 38\n",
+ "2598 2dm-2839 Al2Se3 A2B3 1 4 4 0.94066 ... 2.177766 2.731873 2.887099 2.734977 -143.28668 -56.002666 38\n",
+ "2599 2dm-2840 Ga2Se3 A2B3 1 4 4 0.98954 ... 2.143645 2.733167 2.634298 2.597434 -143.01650 -57.902336 38\n",
+ "2613 2dm-2856 Al2Te3 A2B3 1 4 4 0.96840 ... 2.759936 2.543243 2.561956 2.501265 -139.54509 -62.198387 38\n",
+ "2614 2dm-2857 In2Te3 A2B3 1 4 4 0.94763 ... 2.518876 2.373713 2.553932 2.321163 -141.27708 -64.012740 38\n",
+ "2617 2dm-2860 In2Se3 A2B3 1 4 4 0.95714 ... 2.698484 2.453103 2.576481 2.595978 -141.54002 -61.731476 38\n",
+ "2653 2dm-2898 Ga2Te3 A2B3 1 4 4 0.92116 ... 2.578041 2.559579 2.308213 2.553730 -140.67873 -62.343860 38\n",
+ "2656 2dm-2901 B2Te3 A2B3 1 4 4 0.83736 ... 2.887425 2.626384 2.674782 2.785355 -139.81282 -58.207360 38\n",
+ "2670 2dm-2917 Tl2Te3 A2B3 1 4 4 0.94174 ... 2.268583 2.650200 2.432583 2.693635 -140.38884 -64.695755 38\n",
+ "2671 2dm-2918 Ga2Te3 A2B3 1 4 4 0.92796 ... 2.636732 2.779189 2.228261 2.664211 -141.17853 -63.170937 38\n",
+ "2679 2dm-2928 In2Te3 A2B3 1 4 4 0.95349 ... 2.717375 2.567620 2.709185 2.563196 -138.78062 -62.685190 38\n",
+ "2680 2dm-2931 Tl2Se3 A2B3 1 4 4 0.93519 ... 2.298888 2.677713 2.762901 2.718805 -141.32210 -65.816810 38\n",
+ "\n",
+ "[29 rows x 128 columns]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "def compare_cluster_of_one_material(formula):\n",
+ " print(\"anupam:\")\n",
+ " df_one_chemical = df[df.formula==formula]\n",
+ " cluster_ids = np.array(df_one_chemical.labels)\n",
+ " for id_ in cluster_ids:\n",
+ " if id_==-1:\n",
+ " print(\"UNCLUSTERED\")\n",
+ " else:\n",
+ " display(df[df.labels==id_])\n",
+ " \n",
+ " \n",
+ " #print(df[df.labels==df[df.formula==formula].labels])\n",
+ " print(\"HP+Tom:\")\n",
+ " df_one_chemical_2 = df_2[df_2.formula==formula]\n",
+ " cluster_ids_2 = np.array(df_one_chemical_2.labels)\n",
+ " for id_ in cluster_ids_2:\n",
+ " if id_==-1:\n",
+ " print(\"UNCLUSTERED\")\n",
+ " else:\n",
+ " display(df_2[df_2.labels==id_])\n",
+ " \n",
+ "def compare_cluster_of_one_material(mat_ID):\n",
+ " print(\"anupam:\")\n",
+ " df_one_chemical = df[df.ID==mat_ID]\n",
+ " cluster_ids = np.array(df_one_chemical.labels)\n",
+ " for id_ in cluster_ids:\n",
+ " if id_==-1:\n",
+ " print(\"UNCLUSTERED\")\n",
+ " else:\n",
+ " display(df[df.labels==id_])\n",
+ " \n",
+ " \n",
+ " #print(df[df.labels==df[df.formula==formula].labels])\n",
+ " print(\"HP+Tom:\")\n",
+ " df_one_chemical_2 = df_2[df_2.ID==mat_ID]\n",
+ " cluster_ids_2 = np.array(df_one_chemical_2.labels)\n",
+ " for id_ in cluster_ids_2:\n",
+ " if id_==-1:\n",
+ " print(\"UNCLUSTERED\")\n",
+ " else:\n",
+ " display(df_2[df_2.labels==id_])\n",
+ "\n",
+ "#compare_cluster_of_one_material(\"AsBr3\")\n",
+ "compare_cluster_of_one_material(\"2dm-2600\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "75c43611",
+ "metadata": {},
+ "source": [
+ "# BEST COMPARISON METHOD\n",
+ "this shows a certain cluster from our run along with the anupam cluster ids (sorted) from his run\n",
+ "it also does the reverse of this for a given pam cluster id"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "ad73f537",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "5c427dac04cf4b7ba8f448d712289e9e",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "interactive(children=(IntSlider(value=23, description='label', max=47, min=-1), Output()), _dom_classes=('widg…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "5fae1e8049e44834a8d208f678969638",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "interactive(children=(IntSlider(value=20, description='label', max=42, min=-1), Output()), _dom_classes=('widg…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<function __main__.view_cluster_combined_pam(label)>"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "\n",
+ "df_rename = df.rename(columns={'labels': 'pam_labels'})\n",
+ "df_2_rename = df_2.rename(columns={'labels':'th_labels'})\n",
+ "\n",
+ "df_cut = df_rename[[\"ID\",\"pam_labels\"]]\n",
+ "df_all_labels = pd.merge(df_2_rename, df_cut, on='ID', how='inner')\n",
+ "\n",
+ "# display(df_all_labels)\n",
+ "# display(df_all_labels[df_all_labels.th_labels==21])\n",
+ "\n",
+ "def view_cluster_combined_th(label):\n",
+ " sorted_subset = df_all_labels[df_all_labels.th_labels==label].sort_values(by='pam_labels')\n",
+ " display(sorted_subset.head(8))\n",
+ "def view_cluster_combined_pam(label):\n",
+ " sorted_subset = df_all_labels[df_all_labels.pam_labels==label].sort_values(by='th_labels')\n",
+ " display(sorted_subset.head(8))\n",
+ "\n",
+ "interact(view_cluster_combined_th, label=(-1, len(np.unique(labels_2))-2, 1))\n",
+ "interact(view_cluster_combined_pam, label=(-1, len(np.unique(labels))-2, 1))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "dfce8b7e",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "TH\n"
+ ]
+ },
+ {
+ "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>ID</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>...</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",
+ " <th>labels</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>1334</th>\n",
+ " <td>2dm-1446</td>\n",
+ " <td>VBr3</td>\n",
+ " <td>AB3</td>\n",
+ " <td>162</td>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
+ " <td>0.97137</td>\n",
+ " <td>...</td>\n",
+ " <td>2.256537</td>\n",
+ " <td>2.770921</td>\n",
+ " <td>2.491376</td>\n",
+ " <td>2.701213</td>\n",
+ " <td>80.77287</td>\n",
+ " <td>73.65113</td>\n",
+ " <td>22</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>1 rows × 128 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " ID formula gen_formula space_group segments flat_segments flatness_score ... 94 95 96 97 fx fy labels\n",
+ "1334 2dm-1446 VBr3 AB3 162 3 3 0.97137 ... 2.256537 2.770921 2.491376 2.701213 80.77287 73.65113 22\n",
+ "\n",
+ "[1 rows x 128 columns]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "ANUPAM\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
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+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
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+ "\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>ID</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>...</th>\n",
+ " <th>238</th>\n",
+ " <th>239</th>\n",
+ " <th>240</th>\n",
+ " <th>241</th>\n",
+ " <th>242</th>\n",
+ " <th>243</th>\n",
+ " <th>labels</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>412</th>\n",
+ " <td>2dm-1446</td>\n",
+ " <td>VBr3</td>\n",
+ " <td>AB3</td>\n",
+ " <td>162</td>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
+ " <td>0.97137</td>\n",
+ " <td>...</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>4</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>1 rows × 270 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " ID formula gen_formula space_group segments flat_segments flatness_score ... 238 239 240 241 242 243 labels\n",
+ "412 2dm-1446 VBr3 AB3 162 3 3 0.97137 ... 0.0 0 0.0 0.0 0.0 0 4\n",
+ "\n",
+ "[1 rows x 270 columns]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "ID_test = \"2dm-1446\"\n",
+ "print(\"TH\")\n",
+ "display(df_2[df_2.ID==ID_test])\n",
+ "print(\"ANUPAM\")\n",
+ "display(df[df.ID==ID_test])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "d984bf34-2cd4-4126-b1be-3c6257ee35a9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_all_labels.to_csv(\"TH_and_Anupam_cluster_labels.csv\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "4f302127-c62d-4175-85c6-5d4b1996d196",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>ID</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>...</th>\n",
+ " <th>95</th>\n",
+ " <th>96</th>\n",
+ " <th>97</th>\n",
+ " <th>fx</th>\n",
+ " <th>fy</th>\n",
+ " <th>th_labels</th>\n",
+ " <th>pam_labels</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>2dm-3</td>\n",
+ " <td>TlS</td>\n",
+ " <td>AB</td>\n",
+ " <td>2</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.84646</td>\n",
+ " <td>...</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",
+ " <td>-1</td>\n",
+ " <td>-1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>2dm-21</td>\n",
+ " <td>TaI3</td>\n",
+ " <td>AB3</td>\n",
+ " <td>162</td>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
+ " <td>0.88201</td>\n",
+ " <td>...</td>\n",
+ " <td>2.217409</td>\n",
+ " <td>2.636327</td>\n",
+ " <td>2.281451</td>\n",
+ " <td>-85.320190</td>\n",
+ " <td>19.231680</td>\n",
+ " <td>-1</td>\n",
+ " <td>4</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>2dm-22</td>\n",
+ " <td>Li2O</td>\n",
+ " <td>AB2</td>\n",
+ " <td>164</td>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
+ " <td>0.96678</td>\n",
+ " <td>...</td>\n",
+ " <td>1.716548</td>\n",
+ " <td>1.714002</td>\n",
+ " <td>1.817789</td>\n",
+ " <td>74.049220</td>\n",
+ " <td>-91.258260</td>\n",
+ " <td>-1</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>2dm-25</td>\n",
+ " <td>VBr4</td>\n",
+ " <td>AB4</td>\n",
+ " <td>123</td>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
+ " <td>0.97834</td>\n",
+ " <td>...</td>\n",
+ " <td>2.584419</td>\n",
+ " <td>2.809708</td>\n",
+ " <td>2.114330</td>\n",
+ " <td>36.688946</td>\n",
+ " <td>55.726463</td>\n",
+ " <td>-1</td>\n",
+ " <td>29</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>2dm-29</td>\n",
+ " <td>SBr</td>\n",
+ " <td>AB</td>\n",
+ " <td>2</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.82037</td>\n",
+ " <td>...</td>\n",
+ " <td>2.280239</td>\n",
+ " <td>2.296243</td>\n",
+ " <td>2.235125</td>\n",
+ " <td>11.995536</td>\n",
+ " <td>-106.832634</td>\n",
+ " <td>31</td>\n",
+ " <td>-1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5</th>\n",
+ " <td>2dm-31</td>\n",
+ " <td>AlF3</td>\n",
+ " <td>AB3</td>\n",
+ " <td>12</td>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
+ " <td>0.91302</td>\n",
+ " <td>...</td>\n",
+ " <td>2.198094</td>\n",
+ " <td>2.495989</td>\n",
+ " <td>2.491156</td>\n",
+ " <td>190.981610</td>\n",
+ " <td>14.114123</td>\n",
+ " <td>30</td>\n",
+ " <td>39</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6</th>\n",
+ " <td>2dm-32</td>\n",
+ " <td>MoBr3</td>\n",
+ " <td>AB3</td>\n",
+ " <td>12</td>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
+ " <td>0.97822</td>\n",
+ " <td>...</td>\n",
+ " <td>2.545511</td>\n",
+ " <td>2.544856</td>\n",
+ " <td>2.491382</td>\n",
+ " <td>78.335640</td>\n",
+ " <td>68.976230</td>\n",
+ " <td>22</td>\n",
+ " <td>4</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7</th>\n",
+ " <td>2dm-34</td>\n",
+ " <td>GaSb3</td>\n",
+ " <td>AB3</td>\n",
+ " <td>164</td>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
+ " <td>0.73300</td>\n",
+ " <td>...</td>\n",
+ " <td>2.063323</td>\n",
+ " <td>1.905523</td>\n",
+ " <td>1.790170</td>\n",
+ " <td>-123.318930</td>\n",
+ " <td>57.101960</td>\n",
+ " <td>-1</td>\n",
+ " <td>-1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>8</th>\n",
+ " <td>2dm-56</td>\n",
+ " <td>YbF2</td>\n",
+ " <td>AB2</td>\n",
+ " <td>164</td>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
+ " <td>0.78940</td>\n",
+ " <td>...</td>\n",
+ " <td>1.793689</td>\n",
+ " <td>1.798209</td>\n",
+ " <td>1.732302</td>\n",
+ " <td>164.964750</td>\n",
+ " <td>27.765863</td>\n",
+ " <td>-1</td>\n",
+ " <td>-1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>9</th>\n",
+ " <td>2dm-79</td>\n",
+ " <td>NCl4</td>\n",
+ " <td>AB4</td>\n",
+ " <td>1</td>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>0.96742</td>\n",
+ " <td>...</td>\n",
+ " <td>2.539618</td>\n",
+ " <td>2.534813</td>\n",
+ " <td>2.522714</td>\n",
+ " <td>43.405037</td>\n",
+ " <td>84.966225</td>\n",
+ " <td>-1</td>\n",
+ " <td>-1</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>10 rows × 129 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " ID formula gen_formula space_group segments flat_segments flatness_score ... 95 96 97 fx fy th_labels pam_labels\n",
+ "0 2dm-3 TlS AB 2 4 4 0.84646 ... 1.718350 1.920829 1.940830 -17.031164 -22.583645 -1 -1\n",
+ "1 2dm-21 TaI3 AB3 162 3 3 0.88201 ... 2.217409 2.636327 2.281451 -85.320190 19.231680 -1 4\n",
+ "2 2dm-22 Li2O AB2 164 3 3 0.96678 ... 1.716548 1.714002 1.817789 74.049220 -91.258260 -1 0\n",
+ "3 2dm-25 VBr4 AB4 123 3 3 0.97834 ... 2.584419 2.809708 2.114330 36.688946 55.726463 -1 29\n",
+ "4 2dm-29 SBr AB 2 4 4 0.82037 ... 2.280239 2.296243 2.235125 11.995536 -106.832634 31 -1\n",
+ "5 2dm-31 AlF3 AB3 12 3 3 0.91302 ... 2.198094 2.495989 2.491156 190.981610 14.114123 30 39\n",
+ "6 2dm-32 MoBr3 AB3 12 3 3 0.97822 ... 2.545511 2.544856 2.491382 78.335640 68.976230 22 4\n",
+ "7 2dm-34 GaSb3 AB3 164 3 3 0.73300 ... 2.063323 1.905523 1.790170 -123.318930 57.101960 -1 -1\n",
+ "8 2dm-56 YbF2 AB2 164 3 3 0.78940 ... 1.793689 1.798209 1.732302 164.964750 27.765863 -1 -1\n",
+ "9 2dm-79 NCl4 AB4 1 4 4 0.96742 ... 2.539618 2.534813 2.522714 43.405037 84.966225 -1 -1\n",
+ "\n",
+ "[10 rows x 129 columns]"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_all_labels.head(10)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "db70c33f-5277-4af3-86c1-8c5684ee1ff1",
+ "metadata": {},
+ "source": [
+ "## T-SNE Plot"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 101,
+ "id": "a99da80d-248d-4e60-af04-af88bad563d6",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[t-SNE] Computing 91 nearest neighbors...\n",
+ "[t-SNE] Indexed 1993 samples in 0.003s...\n",
+ "[t-SNE] Computed neighbors for 1993 samples in 0.067s...\n",
+ "[t-SNE] Computed conditional probabilities for sample 1000 / 1993\n",
+ "[t-SNE] Computed conditional probabilities for sample 1993 / 1993\n",
+ "[t-SNE] Mean sigma: 0.110213\n",
+ "[t-SNE] Computed conditional probabilities in 0.084s\n",
+ "[t-SNE] Iteration 50: error = 72.2479706, gradient norm = 0.0112694 (50 iterations in 0.474s)\n",
+ "[t-SNE] Iteration 100: error = 71.5108185, gradient norm = 0.0024503 (50 iterations in 0.395s)\n",
+ "[t-SNE] Iteration 150: error = 71.4420471, gradient norm = 0.0014011 (50 iterations in 0.367s)\n",
+ "[t-SNE] Iteration 200: error = 71.4200592, gradient norm = 0.0005456 (50 iterations in 0.451s)\n",
+ "[t-SNE] Iteration 250: error = 71.4066238, gradient norm = 0.0005375 (50 iterations in 0.611s)\n",
+ "[t-SNE] KL divergence after 250 iterations with early exaggeration: 71.406624\n",
+ "[t-SNE] Iteration 300: error = 1.3937706, gradient norm = 0.0140555 (50 iterations in 0.523s)\n",
+ "[t-SNE] Iteration 350: error = 1.1260365, gradient norm = 0.0102305 (50 iterations in 0.567s)\n",
+ "[t-SNE] Iteration 400: error = 1.0342742, gradient norm = 0.0079844 (50 iterations in 0.525s)\n",
+ "[t-SNE] Iteration 450: error = 0.9941890, gradient norm = 0.0066004 (50 iterations in 0.537s)\n",
+ "[t-SNE] Iteration 500: error = 0.9740797, gradient norm = 0.0050340 (50 iterations in 0.564s)\n",
+ "[t-SNE] Iteration 550: error = 0.9627084, gradient norm = 0.0035135 (50 iterations in 0.508s)\n",
+ "[t-SNE] Iteration 600: error = 0.9566687, gradient norm = 0.0019627 (50 iterations in 0.571s)\n",
+ "[t-SNE] Iteration 650: error = 0.9534255, gradient norm = 0.0017037 (50 iterations in 0.616s)\n",
+ "[t-SNE] Iteration 700: error = 0.9507077, gradient norm = 0.0015651 (50 iterations in 0.666s)\n",
+ "[t-SNE] Iteration 750: error = 0.9486960, gradient norm = 0.0014639 (50 iterations in 0.633s)\n",
+ "[t-SNE] Iteration 800: error = 0.9467834, gradient norm = 0.0013940 (50 iterations in 0.601s)\n",
+ "[t-SNE] Iteration 850: error = 0.9452586, gradient norm = 0.0011081 (50 iterations in 0.551s)\n",
+ "[t-SNE] Iteration 900: error = 0.9441024, gradient norm = 0.0010685 (50 iterations in 0.572s)\n",
+ "[t-SNE] Iteration 950: error = 0.9429975, gradient norm = 0.0011559 (50 iterations in 0.593s)\n",
+ "[t-SNE] Iteration 1000: error = 0.9420382, gradient norm = 0.0008742 (50 iterations in 0.658s)\n",
+ "[t-SNE] Iteration 1050: error = 0.9410946, gradient norm = 0.0011086 (50 iterations in 0.528s)\n",
+ "[t-SNE] Iteration 1100: error = 0.9401885, gradient norm = 0.0010169 (50 iterations in 0.581s)\n",
+ "[t-SNE] Iteration 1150: error = 0.9395473, gradient norm = 0.0006255 (50 iterations in 0.596s)\n",
+ "[t-SNE] Iteration 1200: error = 0.9390716, gradient norm = 0.0006360 (50 iterations in 0.570s)\n",
+ "[t-SNE] Iteration 1250: error = 0.9386777, gradient norm = 0.0007707 (50 iterations in 0.525s)\n",
+ "[t-SNE] Iteration 1300: error = 0.9382787, gradient norm = 0.0005771 (50 iterations in 0.525s)\n",
+ "[t-SNE] Iteration 1350: error = 0.9378866, gradient norm = 0.0006203 (50 iterations in 0.525s)\n",
+ "[t-SNE] Iteration 1400: error = 0.9373749, gradient norm = 0.0005261 (50 iterations in 0.588s)\n",
+ "[t-SNE] Iteration 1450: error = 0.9370693, gradient norm = 0.0005027 (50 iterations in 0.587s)\n",
+ "[t-SNE] Iteration 1500: error = 0.9367257, gradient norm = 0.0005992 (50 iterations in 0.550s)\n",
+ "[t-SNE] Iteration 1550: error = 0.9362627, gradient norm = 0.0006709 (50 iterations in 0.533s)\n",
+ "[t-SNE] Iteration 1600: error = 0.9360057, gradient norm = 0.0004849 (50 iterations in 0.548s)\n",
+ "[t-SNE] Iteration 1650: error = 0.9357244, gradient norm = 0.0005934 (50 iterations in 0.591s)\n",
+ "[t-SNE] Iteration 1700: error = 0.9353707, gradient norm = 0.0005250 (50 iterations in 0.575s)\n",
+ "[t-SNE] Iteration 1750: error = 0.9352429, gradient norm = 0.0005206 (50 iterations in 0.545s)\n",
+ "[t-SNE] Iteration 1800: error = 0.9350183, gradient norm = 0.0005363 (50 iterations in 0.550s)\n",
+ "[t-SNE] Iteration 1850: error = 0.9347786, gradient norm = 0.0006004 (50 iterations in 0.572s)\n",
+ "[t-SNE] Iteration 1900: error = 0.9346865, gradient norm = 0.0003501 (50 iterations in 0.496s)\n",
+ "[t-SNE] Iteration 1950: error = 0.9344808, gradient norm = 0.0004283 (50 iterations in 0.561s)\n",
+ "[t-SNE] Iteration 2000: error = 0.9342691, gradient norm = 0.0003689 (50 iterations in 0.511s)\n",
+ "[t-SNE] Iteration 2050: error = 0.9342403, gradient norm = 0.0002415 (50 iterations in 0.513s)\n",
+ "[t-SNE] Iteration 2100: error = 0.9340649, gradient norm = 0.0003218 (50 iterations in 0.523s)\n",
+ "[t-SNE] Iteration 2150: error = 0.9339377, gradient norm = 0.0003213 (50 iterations in 0.526s)\n",
+ "[t-SNE] Iteration 2200: error = 0.9338155, gradient norm = 0.0003517 (50 iterations in 0.534s)\n",
+ "[t-SNE] Iteration 2250: error = 0.9337827, gradient norm = 0.0003917 (50 iterations in 0.544s)\n",
+ "[t-SNE] Iteration 2300: error = 0.9336966, gradient norm = 0.0003650 (50 iterations in 0.575s)\n",
+ "[t-SNE] Iteration 2350: error = 0.9334977, gradient norm = 0.0002841 (50 iterations in 0.634s)\n",
+ "[t-SNE] Iteration 2400: error = 0.9333682, gradient norm = 0.0003717 (50 iterations in 0.614s)\n",
+ "[t-SNE] Iteration 2450: error = 0.9334833, gradient norm = 0.0003372 (50 iterations in 0.592s)\n",
+ "[t-SNE] Iteration 2500: error = 0.9334196, gradient norm = 0.0004493 (50 iterations in 0.621s)\n",
+ "[t-SNE] Iteration 2550: error = 0.9333242, gradient norm = 0.0003501 (50 iterations in 0.666s)\n",
+ "[t-SNE] Iteration 2600: error = 0.9330519, gradient norm = 0.0004588 (50 iterations in 0.667s)\n",
+ "[t-SNE] Iteration 2650: error = 0.9330550, gradient norm = 0.0003869 (50 iterations in 0.639s)\n",
+ "[t-SNE] Iteration 2700: error = 0.9330163, gradient norm = 0.0003956 (50 iterations in 0.555s)\n",
+ "[t-SNE] Iteration 2750: error = 0.9329599, gradient norm = 0.0002723 (50 iterations in 0.567s)\n",
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+ "[t-SNE] Iteration 2950: error = 0.9323311, gradient norm = 0.0003409 (50 iterations in 0.509s)\n",
+ "[t-SNE] Iteration 3000: error = 0.9322901, gradient norm = 0.0003258 (50 iterations in 0.538s)\n",
+ "[t-SNE] Iteration 3050: error = 0.9322026, gradient norm = 0.0003482 (50 iterations in 0.524s)\n",
+ "[t-SNE] Iteration 3100: error = 0.9321430, gradient norm = 0.0003260 (50 iterations in 0.491s)\n",
+ "[t-SNE] Iteration 3150: error = 0.9320239, gradient norm = 0.0002436 (50 iterations in 0.496s)\n",
+ "[t-SNE] Iteration 3200: error = 0.9319804, gradient norm = 0.0003435 (50 iterations in 0.509s)\n",
+ "[t-SNE] Iteration 3250: error = 0.9319482, gradient norm = 0.0003290 (50 iterations in 0.585s)\n",
+ "[t-SNE] Iteration 3300: error = 0.9318272, gradient norm = 0.0003050 (50 iterations in 0.568s)\n",
+ "[t-SNE] Iteration 3350: error = 0.9319005, gradient norm = 0.0002685 (50 iterations in 0.592s)\n",
+ "[t-SNE] Iteration 3400: error = 0.9317706, gradient norm = 0.0002363 (50 iterations in 0.618s)\n",
+ "[t-SNE] Iteration 3450: error = 0.9317455, gradient norm = 0.0002941 (50 iterations in 0.595s)\n",
+ "[t-SNE] Iteration 3500: error = 0.9316670, gradient norm = 0.0003024 (50 iterations in 0.580s)\n",
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+ "[t-SNE] Iteration 3600: error = 0.9315575, gradient norm = 0.0002916 (50 iterations in 0.492s)\n",
+ "[t-SNE] Iteration 3650: error = 0.9314196, gradient norm = 0.0003059 (50 iterations in 0.539s)\n",
+ "[t-SNE] Iteration 3700: error = 0.9313557, gradient norm = 0.0003932 (50 iterations in 0.492s)\n",
+ "[t-SNE] Iteration 3750: error = 0.9312595, gradient norm = 0.0003836 (50 iterations in 0.526s)\n",
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+ "[t-SNE] Iteration 3900: error = 0.9311436, gradient norm = 0.0002339 (50 iterations in 0.542s)\n",
+ "[t-SNE] Iteration 3950: error = 0.9310212, gradient norm = 0.0002955 (50 iterations in 0.511s)\n",
+ "[t-SNE] Iteration 4000: error = 0.9309894, gradient norm = 0.0002785 (50 iterations in 0.526s)\n",
+ "[t-SNE] Iteration 4050: error = 0.9309398, gradient norm = 0.0003360 (50 iterations in 0.525s)\n",
+ "[t-SNE] Iteration 4100: error = 0.9309331, gradient norm = 0.0004377 (50 iterations in 0.556s)\n",
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+ "[t-SNE] Iteration 4250: error = 0.9308573, gradient norm = 0.0002294 (50 iterations in 0.555s)\n",
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+ "[t-SNE] Iteration 4350: error = 0.9307033, gradient norm = 0.0002384 (50 iterations in 0.542s)\n",
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+ "[t-SNE] Iteration 4950: error = 0.9300811, gradient norm = 0.0002111 (50 iterations in 0.537s)\n",
+ "[t-SNE] Iteration 5000: error = 0.9300954, gradient norm = 0.0003395 (50 iterations in 0.511s)\n",
+ "[t-SNE] Iteration 5050: error = 0.9300765, gradient norm = 0.0003889 (50 iterations in 0.524s)\n",
+ "[t-SNE] Iteration 5100: error = 0.9300311, gradient norm = 0.0003401 (50 iterations in 0.542s)\n",
+ "[t-SNE] Iteration 5150: error = 0.9300241, gradient norm = 0.0001948 (50 iterations in 0.555s)\n",
+ "[t-SNE] Iteration 5200: error = 0.9300673, gradient norm = 0.0003210 (50 iterations in 0.494s)\n",
+ "[t-SNE] Iteration 5250: error = 0.9300022, gradient norm = 0.0003608 (50 iterations in 0.525s)\n",
+ "[t-SNE] Iteration 5300: error = 0.9298401, gradient norm = 0.0003229 (50 iterations in 0.524s)\n",
+ "[t-SNE] Iteration 5350: error = 0.9298301, gradient norm = 0.0004337 (50 iterations in 0.566s)\n",
+ "[t-SNE] Iteration 5400: error = 0.9299204, gradient norm = 0.0003237 (50 iterations in 0.493s)\n",
+ "[t-SNE] Iteration 5450: error = 0.9299589, gradient norm = 0.0003049 (50 iterations in 0.523s)\n",
+ "[t-SNE] Iteration 5500: error = 0.9298639, gradient norm = 0.0001567 (50 iterations in 0.525s)\n",
+ "[t-SNE] Iteration 5550: error = 0.9298998, gradient norm = 0.0002510 (50 iterations in 0.542s)\n",
+ "[t-SNE] Iteration 5600: error = 0.9299159, gradient norm = 0.0002267 (50 iterations in 0.508s)\n",
+ "[t-SNE] Iteration 5650: error = 0.9298988, gradient norm = 0.0001991 (50 iterations in 0.546s)\n",
+ "[t-SNE] Iteration 5700: error = 0.9298679, gradient norm = 0.0002502 (50 iterations in 0.657s)\n",
+ "[t-SNE] Iteration 5700: did not make any progress during the last 300 episodes. Finished.\n",
+ "[t-SNE] KL divergence after 5700 iterations: 0.929868\n"
+ ]
+ }
+ ],
+ "source": [
+ "n_components=2\n",
+ "tsne = manifold.TSNE(n_components=n_components, early_exaggeration=12.0, init=\"pca\",learning_rate=100, random_state=0, perplexity=30 ,n_iter=10000, verbose=2)\n",
+ "fingerprints_2d = tsne.fit_transform(fingerprint_array)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 107,
+ "id": "5087c29f-a7f8-4564-8611-075788048622",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\hkpen\\AppData\\Local\\Temp\\ipykernel_11156\\3318175735.py:6: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
+ " cmap = plt.cm.get_cmap('turbo')\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.collections.PathCollection at 0x25468965c90>"
+ ]
+ },
+ "execution_count": 107,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 800x800 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig = plt.figure(figsize=(8,8))\n",
+ "size=np.ones((len(labels),1))*5\n",
+ "size[labels==-1]=0.2\n",
+ " \n",
+ "unique_label,cluster_rep_index, counts = np.unique(labels, return_index=True, return_counts=True)\n",
+ "cmap = plt.cm.get_cmap('turbo')\n",
+ "norm = matplotlib.colors.Normalize(vmin=min(labels), vmax=max(labels))\n",
+ " \n",
+ "for rep_id in cluster_rep_index:\n",
+ " col=cmap(norm(labels[rep_id]))\n",
+ " plt.annotate(labels[rep_id],fingerprints_2d[rep_id,:]+[-4.5,-1],color=col,alpha=1, weight='normal', ha='center', va='center', size=9).draggable()\n",
+ "\n",
+ "plt.scatter(fingerprints_2d[:,0], fingerprints_2d[:,1],s=size, c=labels*5, cmap=\"turbo\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "id": "a7d24aa9",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Axes: ylabel='$\\\\lambda$ value'>"
+ ]
+ },
+ "execution_count": 30,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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Ttb6nLbEZMWIE8vPzW2zzqaeewlNPPdXs+yNHjoSJS2wRERG1iwboN4n8zuvbS0pKCgICAvDggw+2GnvmzBnU1tbCzc0NQOM0HpVKhe+++w4PP/wwAODo0aNQqVQmncrTIZIpIiIi6tx0mT5TWVmJ//u//8M///lPjet//vln7Ny5E6NHj4aTkxPOnj2LWbNm4aGHHsKQIUMANBb1Dg8PR0xMjFgyYdq0aRg7dqzJVvIBTKaI0AD2UBKRedG38Oad17eVLtNnUlNTIQgCnn32WY3rra2t8dVXX+HDDz/EzZs34eHhgTFjxmDRokWwsPhvNfedO3ciLi5OLM49btw4k28NZ/LtZDozbidjHm782nqtsF7ul41wJ0RE0jR9L63JC0L3e/TvL6m6WYfXA47y+01HJt/omIiIiKgz4zAfERGRmWmADA2QMgHddGUGOiMmU0RERGamXuiGegl1pqRc2xXxt0VEREQkAXumqMur52o+IjIz0ot2sq+lLZhMERERmZkGQYYGKUU7JVzbFTGZoi6PdaaIiEgKJlNERERmpkHiMJ+Ugp9dEZMp6vLqWbeWiMxMg9ANDRJW5Em5titiMkVdXntu6ElEZAr1kKFeQq0oKdd2RUw9iYiIiCRgzxR1eSyNQETmhsN8xsVkirq8euZSRGRm6iFtqK7ecLfSJTD1JCIiIpKAPVNERERmhsN8xsVkiro8ruYjInPDjY6Ni8kUdXlcAkxERFIwmaIur4ET0InIzAiQoUHCH4oC/8hsEyZTREREZobDfMbFZIq6PA7zERGRFEymOqg//vgD586dM/VtdAkXSmrFn6tvC/j1Sh3cPSwhl/83ybp9Pd8Ut0YA+vfvjx49epj6Nog6lQZBhgZB/z8UpVzbFTGZ6qDOnTuHgIAAU98GifjPwlTy8vLg7+9v6tsg6lTq0Q31EkpJSrm2K2Iy1UH1798feXl5pr6NLqeqqgoXL17Efffdh+7du5v6dsxeQfHoFt/3cctE//79jXQ3ROaDPVPGxWSqg+rRowf/GjeRIUOGmPoWuozaSy0nrP6e/G+AiDo+JlNERERmpgHd0CBhqE7KtV0RkykiIiIzUy/IUC9hqE7KtV0RkykiMhmWpSAic8BkioiIyMxwArpxMZkiIiIyM4LQDQ0SqpgLrIDeJh3it7Vu3Tp4eXnBxsYGAQEBOHToUIvxBw8eREBAAGxsbNC3b19s2LBBIyY9PR2+vr6Qy+Xw9fVFRkaG2vvJycn4+9//DltbWzg7O2PChAk4f/68QZ+LiFrWtOVFcwcRUWdg8v9bpaWlIT4+HgsWLMDx48cxbNgwREREoKioSGt8YWEhRo8ejWHDhuH48eOYP38+4uLikJ6eLsYolUpERUUhOjoaJ0+eRHR0NCIjI3H06FEx5uDBg3jttddw5MgRZGVloa6uDqGhobh161a7PzMRNWpacdTcQUT6qYdM8kG6kwmCIJjyBoKCguDv74/169eL53x8fDBhwgQkJydrxM+ZMwd79uxBQUGBeC42NhYnT56EUqkEAERFRaGyshJ79+4VY8LDw2Fvb49du3ZpvY/r16/D2dkZBw8exPDhw3W698rKSigUCqhUKtjZ2el0DRH919cXH2jx/UfuY28xUVs0fS+99E0krO+x1rudmps12DLyf/n9piOT/ulXU1ODvLw8hIaGqp0PDQ1Fbm6u1muUSqVGfFhYGI4dO4ba2toWY5prEwBUKhUAwMHBodmY6upqVFZWqh1EpD8O8xGROTDpBPSysjLU19fDxcVF7byLiwtKSkq0XlNSUqI1vq6uDmVlZXBzc2s2prk2BUFAYmIihg4dCj8/v2bvNzk5GW+99ZYuj9Ym2jY1bm5bE276SkRErWmQOAFdyrVdUYdYzSeTqY/NCoKgca61+LvPt6XN119/HT/88ANycnJavM958+YhMTFRfF1ZWQkPD48Wr9FFWzY15qavZE5qYWHqWyAySw2QoUHCvCcp13ZFJk09nZycYGFhodFjVFpaqtGz1MTV1VVrvKWlJRwdHVuM0dbmjBkzsGfPHhw4cAD33ntvi/crl8thZ2endhhC06bGdx45OTnYsWMHcnJy1M5z01ciImpNUwV0KUdbffvtt3jiiSfg7u4OmUyGzz77TO39F198ETKZTO0YPHiwWkx1dTVmzJgBJycn9OzZE+PGjcOVK1fUYioqKhAdHQ2FQgGFQoHo6GjcuHGjzfdrSCbtmbK2tkZAQACysrIwceJE8XxWVhbGjx+v9Zrg4GB8/vnnauf279+PwMBAWFlZiTFZWVlISEhQiwkJCRFfC4KAGTNmICMjA9988w28vLwM+Wht0tymxobYcFfbECJRR3HuSo34c011A0qv1MD5Xjms5Y3/I3etyDfVrRFp4DSLlt26dQsPPvggXnrpJUyaNElrTHh4OLZs2SK+trZWnyQfHx+Pzz//HKmpqXB0dMSsWbMwduxY5OXlwcKisSf7ueeew5UrV7Bv3z4AwLRp0xAdHa2RGxiTyYf5EhMTER0djcDAQAQHB2PTpk0oKipCbGwsgMahtatXr2L79u0AGlfurVmzBomJiYiJiYFSqURKSoraKr2ZM2di+PDhWL58OcaPH4/du3cjOztbbRjvtddewyeffILdu3fD1tZW7MlSKBRqc5Q6E22JU0FBASZPnmyiOyKSSrfhbyJj6EzTLEwxZyoiIgIREREtxsjlcri6ump9T6VSISUlBR9//DEee+wxAMCOHTvg4eGB7OxshIWFoaCgAPv27cORI0cQFBQEAPjoo48QHByM8+fP44EHWl4h3F5MnkxFRUWhvLwcS5YsQXFxMfz8/JCZmQlPT08AQHFxsVrNKS8vL2RmZiIhIQFr166Fu7s7Vq1apZYFh4SEIDU1FQsXLkRSUhK8vb2RlpYm/uIBiKUYRo4cqXY/W7ZswYsvvth+D9yO2jL3qr3s2LEDPj4+Jr0H6pyaW3RB1FF0pmkWDZC4ncyfc6buXrUul8shl8v1bvebb76Bs7MzevXqhREjRuCdd96Bs7MzgMZktba2Vm01vru7O/z8/JCbm4uwsDAolUooFAq17/PBgwdDoVAgNze36yZTADB9+nRMnz5d63tbt27VODdixAjk57fc/f/UU0/hqaeeavZ9E5fXahdNc69aUlFRAaVSCXd392b/g6iursavv/6qEXPaYjms5c3/tTLeczO7wUkSQwxtE5Hh3L3IatGiRVi8eLFebUVERODpp5+Gp6cnCgsLkZSUhEceeQR5eXmQy+UoKSmBtbU17O3t1a67czV+SUmJmHzdydnZudkV+8bQIZIpMozm5l7d7dFHH9Wr/X/9qLltz5387+8c3d9EROZOkLiaT/jz2suXL6sttpLSKxUVFSX+7Ofnh8DAQHh6euKLL77Ak08+2fy93LUaX9vK/NaqALQ3JlOksxqB/7oQEXUGDYLEYb4/rzXkyvW7ubm5wdPTExcuXADQuBK/pqYGFRUVar1TpaWl4gIyV1dXXLt2TaOt69evN1sFwBhYlYt0Nv2BA+J/oNoOIiIiXZWXl+Py5ctwc3MDAAQEBMDKygpZWVliTHFxMU6fPi0mU8HBwVCpVPjuu+/EmKNHj0KlUqmt2Dc2djVQm9Qz/yYi6vBMsZrv5s2b+Omnn8TXhYWFOHHiBBwcHODg4IDFixdj0qRJcHNzw8WLFzF//nw4OTmJpZEUCgWmTp2KWbNmwdHREQ4ODpg9ezYGDhworu7z8fFBeHg4YmJisHHjRgCNpRHGjh1rssnnAJMpaqNagRWriYg6OkMN87XFsWPHMGrUKPF1044hU6ZMwfr163Hq1Cls374dN27cgJubG0aNGoW0tDTY2tqK13zwwQewtLREZGQkqqqq8Oijj2Lr1q1ijSkA2LlzJ+Li4sRVf+PGjcOaNWv0fVSDkAnmuKzNSJp25+5Ku2onnx3d7HvzfDONeCdERHS3pu+l8ftfhlVP69YvaEbtrRrsDt3cpb7fpGDPFLUJN78kIur4uDefcTGZIg0tbUFz+afGAm611fW4cbUKvf7SHVbyxu7X/Grdtv5gLSoiovZlimG+rozJFGnQt5L6Rh23/uhMWzIQEXVGTKaMi8kUadClknpLW39s/CmhmauAV//6QafakoGIiKg1TKZIg66V1Jvb+sPJ0rHZa/z/xh4pIqL2xp4p42IyRQbH8glERKbFZMq4mEyRwTGZIiKiroTJFBkc/6IhIjItAdLKG7AAZdswmSKDYy0qIiLT4jCfcTGZIoOrZTJFRERdCJMpMjj2TBERmRZ7poyLyRQZHHumiIhMi8mUcTGZIoNjzxQREXUlTKZILy3t33e9oBwAUF9dh5vFN3GPuy0srP/cv8+S+/cREbU39kwZF5Mp0ou++/ftxW6d4rh/HxGR/gRBBkFCQiTl2q6IyRTpRZf9+yoqKqBUKuHu7g65XK723ocXNgMA6mvrcbvsD3R37IFuf/Zezez3MqqqqpCf33IvFnuviIi0a4BMUp0pKdd2RUymSC+67N+Xn5+PpKSkNrc9GV/rFMfeKyIi6giYTFG70aX3qqqqChcvXsR9992H7t27q70Xn7+82etW+s9B//79DXKfRETmhnOmjIvJFLUbXXqvAGDIkCFaz/e86drsNeyRIiJqHudMGReTKTKpllYF3jh/HUDjqsDbJZWwcbWDhbzxX9n8e7gqkIiIOgYmU2RS+q4KDMAuneI4r4qIuiIO8xkXkykyKanzql75bmWz1/3r4XjOqyKiLonDfMbFZIpMSuq8qh6/cV4VERGZFpMp6tTq6i20nj8SlmzkOyEi6jgEicN87JlqGyZT1OG1NEn99wvXAAANNXWovnYDcude6Ca3RH5vTlAnoq5LACAI0q4n3TGZog5Pn0nqAdiiU9yOHTvg4+PTahyTLiIiag6TKerwdJmkfuLECUydOrXNbU+ePFmnOK4KJKLOpAEyyLidjNF0iGRq3bp1eP/991FcXIwBAwZg5cqVGDZsWLPxBw8eRGJiIs6cOQN3d3e88cYbiI2NVYtJT09HUlISfv75Z3h7e+Odd97BxIkTxffXr1+P9evX4+LFiwCAAQMG4M0330RERES7PCPpT5dJ6v3798egQYNabaullYFPfbNR6zX/HvkqVwUSUafC1Xzabdu2DU5OThgzZgwA4I033sCmTZvg6+uLXbt2wdPTU692TZ5MpaWlIT4+HuvWrcOQIUOwceNGRERE4OzZs+jTp49GfGFhIUaPHo2YmBjs2LEDhw8fxvTp09G7d29MmjQJAKBUKhEVFYW3334bEydOREZGBiIjI5GTk4OgoCAAwL333ot3330Xf/3rXwE0/oLHjx+P48ePY8CAAcb7BZBB6LoqEGh+ZaBNkZvW8+yRIqLOpkGQQcY6UxqWLVuG9evXA2jMFdasWYOVK1fiP//5DxISEvDpp5/q1a5MEKRMUZMuKCgI/v7+4sMBgI+PDyZMmIDkZM0VWXPmzMGePXtQUFAgnouNjcXJkyehVCoBAFFRUaisrMTevXvFmPDwcNjb22PXruaLPTo4OOD999/XebiosrISCoUCKpUKdnZ2Ol1DHZdPxltazxdMXGTkOyEi0k/T95Lf//4DFj3kerdT/0c1Tke+b3bfbz169MC5c+fQp08fzJkzB8XFxdi+fTvOnDmDkSNH4vr163q1283A99kmNTU1yMvLQ2hoqNr50NBQ5Obmar1GqVRqxIeFheHYsWOora1tMaa5Nuvr65Gamopbt24hODi42futrq5GZWWl2kHmo6li8N0HEVFnIwjSD3N0zz33oLy8HACwf/9+PPbYYwAAGxsbVFVV6d2uSYf5ysrKUF9fDxcXF7XzLi4uKCkp0XpNSUmJ1vi6ujqUlZXBzc2t2Zi72zx16hSCg4Nx+/Zt3HPPPcjIyICvr2+z95ucnIy33tLee0Gdn7nOESCirodzprR7/PHH8corr+Chhx7Cjz/+KM6dOnPmDO677z692zVpz1QTmUz9H5ogCBrnWou/+7wubT7wwAM4ceIEjhw5gv/3//4fpkyZgrNnzzb7ufPmzYNKpRKPy5cvt/xg1LkImsePk5JMe09ERGQwa9euRXBwMK5fv4709HQ4OjoCaFyx/eyzz+rdrkl7ppycnGBhYaHRY1RaWqrRs9TE1dVVa7ylpaX4S2ku5u42ra2txQnogYGB+P777/Hhhx9i40btq7rkcjnkcv3HoKlja2gwz7/EiKjrYc+UdpWVlVi1ahW6dVPvS1q8eLGkDhKT9kxZW1sjICAAWVlZauezsrIQEhKi9Zrg4GCN+P379yMwMBBWVlYtxjTXZhNBEFBdXd3WxyAzITR00ziIiDqj5uaAtuUwR15eXigrK9M4/9tvv8HLy0vvdk3+bZGYmIh//etf2Lx5MwoKCpCQkICioiKxbtS8efPwwgsviPGxsbG4dOkSEhMTUVBQgM2bNyMlJQWzZ88WY2bOnIn9+/dj+fLlOHfuHJYvX47s7GzEx8eLMfPnz8ehQ4dw8eJFnDp1CgsWLMA333yD559/3mjPTh1LV5mASUTUHr799ls88cQTcHd3h0wmw2effSa+V1tbizlz5mDgwIHo2bMn3N3d8cILL+DXX39Va2PkyJGQyWRqxzPPPKMWU1FRgejoaCgUCigUCkRHR+PGjRs63WNzBQxu3rwJGxubNj3vnUxeZyoqKgrl5eVYsmQJiouL4efnh8zMTLFwVnFxMYqKisR4Ly8vZGZmIiEhAWvXroW7uztWrVol1pgCgJCQEKSmpmLhwoVISkqCt7c30tLSxBpTAHDt2jVER0ejuLgYCoUCf/vb37Bv3z48/vjjxnt46lAEDvMRkZmQ+gehPtfeunULDz74IF566SW172SgcY/V/Px8JCUl4cEHH0RFRQXi4+Mxbtw4HDt2TC02JiYGS5YsEV/fXWD5ueeew5UrV7Bv3z4AwLRp0xAdHY3PP/+82XtLTEwE0Dif+s0331TbHqy+vh5Hjx7VqfBzc0xeZ6ozY52pzqWlDZMBYMwXjfv5CTW1qCurgKWTPTInTtO5fe7fR0Sm1vS91G/HXFj00L+npf6P27gw+V29v99kMhkyMjIwYcKEZmO+//57PPzww7h06ZJYpHvkyJEYNGgQVq5cqfWagoIC+Pr64siRI2IHyZEjRxAcHIxz587hgQce0HrdqFGjADTuoBIcHAxra2vxPWtra9x3332YPXs2+vXr1+ZnBTpAzxSRsei1YfJS7YsRtOH+fURkbu6up2jIhVgqlQoymQy9evVSO79z507s2LEDLi4uiIiIwKJFi2BrawugsY6kQqFQG2kaPHgwFAoFcnNzm02mDhw4AAB46aWX8OGHHxq8A4TJFHUZumyYDLS8fx8AjNmzTePcF+OmcP8+IuowDLWaz8PDQ+38okWLsHjxYim3BgC4ffs25s6di+eee04tsXn++efh5eUFV1dXnD59GvPmzcPJkyfFRWUlJSVwdnbWaM/Z2bnZ+pR32rJli+R714bJFHUZhti/DwCsj2drnGOPFBF1JE3l8qRcDwCXL19WS3YM0StVW1uLZ555Bg0NDVi3bp3aezExMeLPfn5+6NevHwIDA5Gfny/+f1ZbHcrW6lM2uXXrFt5991189dVXKC0tRUNDg9r7v/zyiz6PxGSKqK1knKhORB2coXqm7OzsDDokVltbi8jISBQWFuLrr79utW1/f39YWVnhwoUL8Pf3h6urK65du6YRd/369WbrU97plVdewcGDBxEdHQ03NzedEjBdMJkiaisu2SAiarOmROrChQs4cOCAWGi7JWfOnEFtbS3c3NwANNaRVKlU+O677/Dwww8DAI4ePQqVStVqLUkA2Lt3L7744osWRx/0wWSKqK3u6pm6GDu7mUAiIhMx1DhfG9y8eRM//fST+LqwsBAnTpyAg4MD3N3d8dRTTyE/Px//+c9/UF9fL85xcnBwgLW1NX7++Wfs3LkTo0ePhpOTE86ePYtZs2bhoYceEpMfHx8fhIeHIyYmRtytZNq0aRg7dmyzk8/vZG9vDwcHh7Y/XCuYTBHdobXyCQBQffEKhNpa1JVXwNLRHvn5+Tq3z/IJRGQUEof5oMe1x44dE0sQAP+t7TRlyhQsXrwYe/bsAQCNek4HDhzAyJEjYW1tja+++goffvghbt68CQ8PD4wZMwaLFi2ChYWFGL9z507ExcUhNDQUADBu3DisWbNGp3t8++238eabb2Lbtm0G/X8x60xJwDpT5ic/P7/N5RPa4u2339a6ZUF1dTV+/fVXuLu7Qy6XN7uSsDlM0ogI+O/3Ut+tC9BNQp2phj9u45cX3zG777eHHnoIP//8MwRBwH333SduQ9ekLX8c34k9U0R30KV8QkFBASZPnqxX+0lJSXpd1xrWuCKiO5miAnpn0FIRUSmYTBHdQZfyCbokXE21qu7ucbpT03tA48TM69evo3fv3rCystIa3yR+XyaEujrUqyphobDDh2PHoaqqSq+/qNijRWSeDLWaz9wsWrSoXdplMkXURrrWq2pttUh+fj6mTp0q+X4mp/6v3teyR4uISDomU0QmomtFdm1aq9KuzbjtO9Re73lhMqu2E5krQabXJHK1682Eg4MDfvzxRzg5OcHe3r7F2lK//fabXp/BZIrIRNpSkV2bttZJsfnqG7XX7JEiMl+cM/VfH3zwgbi3X3MbKEvFZIqok9GlfIM21ZevoqG2FnUVv8HS3gGHDx/GxYsXdb/+zzlejo6OKC8vb3Fe193X6LNKkfO5iCQwQZ2pjmrKlClafzYkJlNEncy5c+cMUr5h6IbVBrib9sP5XETUHurr6/HZZ5+hoKAAMpkMvr6+GDdunFotq7ZiMkXUyRhqrhWADtMzNXHTTrXXGdOe53wuIgm4mk+7n376CaNHj8bVq1fxwAMPQBAE/Pjjj/Dw8MAXX3wBb29vvdplMkXUyRhyrpWh96fSV3fXg2qv2SNFZABmNFRnKHFxcfD29saRI0fEbWXKy8sxefJkxMXF4YsvvtCrXSZTRGRysgZT3wERdQUHDx5US6QAwNHREe+++66kPy6ZTBGR6fEvaCKD4jCfdnK5HL///rvG+Zs3b8La2lrvdrtJuSkiIkOQNagfRCSRYIDDDI0dOxbTpk3D0aNHIQgCBEHAkSNHEBsbi3HjxundLpMpIjK5OxOps+8kmPp2iMhMrVq1Ct7e3ggODoaNjQ1sbGwwZMgQ/PWvf8WHH36od7sc5iMig2trLayqX6+goa4WtarfcPjwYZ1rUd2NtamImsj+PKRcb3569eqF3bt348KFCygoKAAA+Pr64q9//aukdplMEZHBSamFNfSLna0HNYO1qYj+xKKdLerXr5+YQLW0vYyumEwRkcHpUwurrfsNRq5QT7r+N5G1qYiodSkpKfjggw9w4cIFAI2JVXx8PF555RW929Q7mTp06BA2btyIn3/+Gf/+97/xl7/8BR9//DG8vLwwdOhQvW+IiAxP27BbS8mL1OEyfWthtWVpcndn1qYiahZ7prRKSkrCBx98gBkzZiA4OBgAoFQqkZCQgIsXL2Lp0qV6tatXMpWeno7o6Gg8//zzOH78OKqrqwEAv//+O5YtW4bMzEy9boaI2kdbh906w3DZnR3zP6zgpHUiNYKs8ZByvRlav349PvroIzz77LPiuXHjxuFvf/sbZsyYYdxkaunSpdiwYQNeeOEFpKamiudDQkKwZMkSvW6EiNpP//798TAexfq898Rz2nqm/l/gXKw/9m6nGC5jCQWi5glC4yHlenNUX1+PwMBAjfMBAQGoq6vTu129kqnz589j+PDhGuft7Oxw48YNvW+GiNpHjx49YCez1+htuntYTVtMe2nrij+N6681rgCsqZS2AvBuXBFIZL4mT56M9evXY8WKFWrnN23ahOeff17vdvVKptzc3PDTTz+Jm6U2ycnJQd++ffW+GSLqOqSs+Lvb0P36rwC8W2cY4iRqFedMNSslJQX79+/H4MGDAQBHjhzB5cuX8cILLyAxMVGMuzvhaoleydSrr76KmTNnYvPmzZDJZPj111+hVCoxe/ZsvPnmm/o0SURdjD4r/u7W1hWA2jyz/L+JWOocrggkM8E5U1qdPn1a/GPp559/BgD07t0bvXv3xunTp8W4tpZL0CuZeuONN6BSqTBq1Cjcvn0bw4cPh1wux+zZs/H666/r0yQRdTH6rvi7m5TNSQGgp+N/VwWyR4rIvB04cKBd2tW7NMI777yDBQsW4OzZs2hoaICvry/uueceQ94bEVG7k5nxcAZ1XTJB2r/b/O+ibSQV7ezRo4fWWfFE1EkJXXCJXBd8ZOoCOGfKqPTa6HjJkiUtHm21bt06eHl5wcbGBgEBATh06FCL8QcPHkRAQABsbGzQt29fbNiwQSMmPT0dvr6+kMvl8PX1RUZGRrPtJScnQyaTIT4+vs33TkSdW7d6QTyIiPShV8/U3YlJbW0tCgsLYWlpCW9v7zZNQk9LS0N8fDzWrVuHIUOGYOPGjYiIiMDZs2fRp08fjfjCwkKMHj0aMTEx2LFjBw4fPozp06ejd+/emDRpEoDGaqZRUVF4++23MXHiRGRkZCAyMhI5OTkICgpSa+/777/Hpk2b8Le//U2P3wQRtSep5RN0cbO4ENU3f8OOZdORn5/frp/VhOUXqN1xArpRyQTBMKW5Kisr8eKLL2LixImIjo7W+bqgoCD4+/tj/fr14jkfHx9MmDABycnJGvFz5szBnj17xN2eASA2NhYnT56EUqkEAERFRaGyshJ79+4VY8LDw2Fvb49du3aJ527evAl/f3+sW7cOS5cuxaBBg7By5co2PbNCoYBKpYKdnZ3O1xGZwuPdnkZWw/9JjjGm/Px8g5VP6EhYfoHaS9P3kseKt9Gtu43e7TRU3cblxCSz+H7z9/fHV199BXt7eyxZsgSzZ882+B8zBtvo2M7ODkuWLMHYsWN1TqZqamqQl5eHuXPnqp0PDQ1Fbm6u1muUSiVCQ0PVzoWFhSElJQW1tbWwsrIS99m5O+buROm1117DmDFj8Nhjj+lUQr66ulrcOgdo/JeWiNqPIcontMYQ5RVaE71oh/jzx29NZvkFIiMqKCjArVu3YG9vj7feeguxsbEdN5kCgBs3bkClUukcX1ZWhvr6eri4uKidd3FxQUlJidZrSkpKtMbX1dWhrKwMbm5uzcbc2WZqairy8/Px/fff63y/ycnJeOutt3SOJyJpDFU+oTVSyyu0pqfDN+LP7JEio+AEdNGgQYPw0ksvYejQoRAEAf/zP//TbPUBfWtl6pVMrVq1Su21IAgoLi7Gxx9/jPDw8Da3d3dxLEEQWiyYpS3+7vMttXn58mXMnDkT+/fvh42N7t2g8+bNU6uOWllZCQ8PD52vJ6KuicvMyeiYTIm2bt2KRYsW4T//+Q9kMhn27t0LS0vN9Ecmkxk3mfrggw/UXnfr1g29e/fGlClTMG/ePJ3bcXJygoWFhUYvVGlpqUbPUhNXV1et8ZaWlnB0dGwxpqnNvLw8lJaWqs3FqK+vx7fffos1a9aguroaFhYWGp8tl8shl8t1fj4iIoCbMpMJcAK66IEHHkBqaiqAxnzlq6++grOzs0E/Q69kqrCw0CAfbm1tjYCAAGRlZWHixIni+aysLIwfP17rNcHBwfj888/Vzu3fvx+BgYGwsrISY7KystTmTe3fvx8hISEAgEcffRSnTp1Sa+Oll15C//79MWfOHK2JFBEREXVuDQ3t85eNQedM6SMxMRHR0dEIDAxEcHAwNm3ahKKiIsTGxgJoHFq7evUqtm/fDqBx5d6aNWuQmJiImJgYKJVKpKSkqK3SmzlzJoYPH47ly5dj/Pjx2L17N7Kzs5GTkwMAsLW1hZ+fn9p99OzZE46OjhrniYgkM8yiaSKdsQJ6837++WesXLkSBQUFkMlk8PHxwcyZM+Ht7a13mzonU3fOFWpNW3ZajoqKQnl5OZYsWYLi4mL4+fkhMzMTnp6eAIDi4mIUFRWJ8V5eXsjMzERCQgLWrl0Ld3d3rFq1SqwxBQAhISFITU3FwoULkZSUBG9vb6SlpWnUmCLq6LTVWWpu9RlrF3Vc5vzFRB0U50xp9eWXX2LcuHEYNGgQhgwZAkEQkJubiwEDBuDzzz/H448/rle7OteZGjVqlG4NymT4+uuv9bqZzoZ1pqi9taXOUmu1izpjnSlzERL1T/Hn3LRZJrwTMndN30t9li+VXGeqaM7CNn2/ffvtt3j//feRl5eH4uJiZGRkYMKECeL7giDgrbfewqZNm1BRUYGgoCCsXbsWAwYMEGOqq6sxe/Zs7Nq1C1VVVXj00Uexbt063HvvvWJMRUUF4uLisGfPHgDAuHHjsHr1avTq1avVe3zooYcQFhaGd999V+383LlzsX//fr0L9+rcM9VeOy0TUfO01VlqqWeKOigz/Suf6E63bt3Cgw8+iJdeeklttKjJe++9hxUrVmDr1q24//77sXTpUjz++OM4f/48bG1tAQDx8fH4/PPPkZqaCkdHR8yaNQtjx45FXl6eOJ/5ueeew5UrV7Bv3z4AwLRp0xAdHa0xn1qbgoIC/O///q/G+ZdffrlNRbvvJmnO1NmzZ1FUVISamhrxnEwmwxNPPCGlWSL6U3N1ltq7LhIZFof5yNhkkDhnSo9rIiIiEBERofU9QRCwcuVKLFiwAE8++SQAYNu2bXBxccEnn3yCV199FSqVCikpKfj444/x2GOPAQB27NgBDw8PZGdnIywsDAUFBdi3bx+OHDkiTt356KOPEBwcjPPnz+OBBx5o8R579+6NEydOoF+/fmrnT5w4IWmFn17J1C+//IKJEyfi1KlTkMlkGnWe6uvr9b4hIiIi6hju3ulD3xJBhYWFKCkpUdvBRC6XY8SIEcjNzcWrr76KvLw81NbWqsW4u7vDz88Pubm5CAsLg1KphEKhUJsDPXjwYCgUCuTm5raaTMXExGDatGn45ZdfEBISAplMhpycHCxfvhyzZuk/BN9Nn4tmzpwJLy8vXLt2DT169MCZM2fw7bffIjAwEN98843eN0NEZJYE4b8HkTE01ZmScgDw8PCAQqEQD2175uqiqfZjS7uTlJSUwNraGvb29i3GaOtBcnZ2bnbnlDslJSXhzTffxOrVqzFixAgMHz4ca9asweLFi7FgwQK9ng3Qs2dKqVTi66+/Ru/evdGtWzd069YNQ4cORXJyMuLi4nD8+HG9b4iIiIgkMtBqvsuXL6tNQJdauLqtO55oi9EWr0s7TdcmJCQgISEBv//+OwCI87Wk0Ktnqr6+XtzXxsnJCb/++isAwNPTE+fPn5d8U0RE5kTW8N+DqDOxs7NTO/RNplxdXQGgxd1JXF1dUVNTg4qKihZjrl27ptH+9evXm905pTm2trYGSaQAPZMpPz8//PDDDwCAoKAgvPfeezh8+DCWLFmCvn37GuTGiIjMRVMBRU5EJ6MRDHAYkJeXF1xdXZGVlSWeq6mpwcGDB8XdSQICAmBlZaUWU1xcjNOnT4sxwcHBUKlU+O6778SYo0ePQqVSiTGmoNcw38KFC3Hr1i0AwNKlSzF27FgMGzYMjo6OSEtLM+gNEhERUduYogL6zZs38dNPP4mvCwsLceLECTg4OKBPnz6Ij4/HsmXL0K9fP/Tr1w/Lli1Djx498NxzzwEAFAoFpk6dilmzZsHR0REODg6YPXs2Bg4cKK7u8/HxQXh4OGJiYrBx40YAjaURxo4d2+rk8/akVzIVFhYm/ty3b1+cPXsWv/32G+zt7XUasySijokFO9vHoYzZGDbhfVPfBnUlJqiAfuzYMbUC3007p0yZMgVbt27FG2+8gaqqKkyfPl0s2rl//361obYPPvgAlpaWiIyMFIt2bt26VW3P3J07dyIuLk5c9Tdu3DisWbNGzwc1DJ0roN/ppZdewuTJk/HII4906eSJFdCpM2F1c9MaPq4xmfp2zz9MfCdkzpq+l+5b+g662UiogH77Ni4uXGBW329NZRc2btyI+++/36Bt6zVnqry8HGPGjMG9996LWbNmcfUeEVErOGeKjKqDzZnqCKysrHD69Ol26QTSK5nas2cPSkpKsGjRIuTl5SEwMBC+vr5YtmwZLl68aOBbJCIyAw1C40FkBHcuetD3MEcvvPACUlJSDN6u3tvJ9OrVC9OmTcO0adNw5coV7Nq1C5s3b8abb76Juro6Q94jEVGz/vjjD5w7d87Ut9EqVflF3K6qwOHDh9X2VOyo+vfvjx49epj6NogMqqamBv/617+QlZWFwMBA9OzZU+39FStW6NWupL35gMYxyGPHjuHo0aO4ePFim+s8EBFJce7cOQQEBJj6NnQ2dGiqqW9BJykpKRg0aJCpb8NozC55vKOKud7Xm6HTp0+L+53++OOPau9JGf7TO5k6cOAAPvnkE6Snp6O+vh5PPvkkPv/8czzyyCN63wwRUVv1798feXl5Bm+3oKAAkydPNni7ncXUqVNNfQtGlZeXp3VT8U7LBKv5OoMDBw60S7t6JVP33nsvysvLERYWho0bN+KJJ56AjYRVA0RE+urRo0e7fAn2798ffw98DQCwYePLBm+/o6qqqsLFixdx3333dYrhSEPp37+/qW+BjOinn37Czz//jOHDh6N79+46b0fTHL2SqTfffBNPP/20xmaERETmokePHrC95y8AYF49FjoYMmSIqW+BJDJF0c7OoLy8HJGRkThw4ABkMhkuXLiAvn374pVXXkGvXr3wz3/+U6929Uqmpk2bpteHERGZgr6T1G9UFOL27RuSJ42b3Xwc6vg4zKdVQkICrKysUFRUBB8fH/F8VFQUEhISjJtMERF1JlInqQ8dKq3YqdnNxyHqpPbv348vv/wS9957r9r5fv364dKlS3q3y2SKiMyevpPUpcwf+n8v/QsAsH7LK5yPQ8YntVaUmfZM3bp1S2svcVlZGeRyud7tMpkiIrMnZZK6vvOH7Hq6A+h6862og+Awn1bDhw/H9u3b8fbbbwNoLIfQ0NCA999/X21fwbZiMkVE1A5kbd/2lMhwmExp9f7772PkyJE4duwYampq8MYbb+DMmTP47bffcPjwYb3b1Ws7GSIiaoUgNB5E1GH4+vrihx9+wMMPP4zHH38ct27dwpNPPonjx4/D29tb73Yl90zV1dXh/PnzOH36tHhkZGRIbZaIqHNrMPUNUFfG0gjNc3V1xVtvvWXQNtuUTP3yyy84deqUWuL0448/oq6uDtbW1vDx8cHAgQMNeoNERMZg6D3+blRewu1q6WUVtGGpBSL9VVRUICUlBQUFBZDJZPDx8cFLL70EBwcHvdvUOZmaPHkydu3aBZlMhh49euDWrVsYM2YM3nzzTQwcOBD9+vWDhYWF3jdCRGRK7bXH39Chnxq8TZZaINLPwYMHMX78eNjZ2SEwMBAAsGrVKixZsgR79uzBiBEj9GpX52Tq3//+N1avXo2XX34ZdXV1WLBgATZu3Ij+/ftj7NixTKSIOrisBmm1ksydLuUTmkol6KK6uhq//vor3N3dJS25vrO95IU7MG/pZFRVVSE/P19ym/pgr1gnwQnoWr322muIjIzE+vXrxbylvr4e06dPx2uvvYbTp0/r1a5MEHSbIZmUlIQ5c+bgnnvuEc/l5eUhNjYWZWVlWL9+PcLDw/W6ic6qsrISCoUCKpUKdnZ2pr4dImpn+fn57dJ71ZmwV6xja/pe+uvcZbCQsGdu/e3b+Ond+Wb3/da9e3ecOHECDzzwgNr58+fPY9CgQaiqqtKrXZ17pppqMtwpICAA3333HVatWoWoqCiMGTMGH374IXr37q3XzRARdWT6Fv80BFNvQPz6M+uxJvX/sQApdWr+/v4oKCjQSKYKCgowaNAgvduVvJpPJpNh5syZeOqppxAXF4f+/fujvLxcarNERB2OlOKfhmDKDYjturuyR6qzMdOhurb64YcfxJ/j4uIwc+ZM/PTTTxg8eDAA4MiRI1i7di3effddvT/DYEU7//KXvyA9PR1ffPGFoZokIup0WlsVWFVVhR9++AGnTp1C7969YWVlpTWupqYGZWVl+Nvf/gZbW9sWP9MYvVUVN4uwc+dOk/WMSdEl53lxzpRo0KBBkMlkuHNW0xtvvKER99xzzyEqKkqvzzB4BfQxY8YYukkiok6jvVYFdgSTJ2839S3ohfO8urbCwsJ2/wxuJ0NEZECtzasyZM/U8n+kYc77UUbpLTL1nC0puuI8Lxbt/C9PT892/4wOkUytW7cO77//PoqLizFgwACsXLkSw4YNazb+4MGDSExMxJkzZ+Du7o433ngDsbGxajHp6elISkrCzz//DG9vb7zzzjuYOHGi+P7ixYs1KqC6uLigpKTEsA9HRF2KLvOqDDX3acfS03j++ecN0pYuTDlni9qIw3zNunr1Kg4fPozS0lI0NKhvVRAXF6dXmyZPptLS0hAfH49169ZhyJAh2LhxIyIiInD27Fn06dNHI76wsBCjR49GTEwMduzYgcOHD2P69Ono3bs3Jk2aBABQKpWIiorC22+/jYkTJyIjIwORkZHIyclBUFCQ2NaAAQOQnZ0tvmatLCLqVBrM+BuPJGHPlHZbtmxBbGwsrK2t4ejoCJlMJr4nk8n0TqZ0rjPVXoKCguDv74/169eL53x8fDBhwgQkJydrxM+ZMwd79uxBQUGBeC42NhYnT56EUqkEAERFRaGyshJ79+4VY8LDw2Fvb49du3YBaOyZ+uyzz3DixAmd77W6uhrV1dXi68rKSnh4eJhdHQ4i6hwi7p+DvT8uN/VtUAfSVGfq/tnLYCGXUGeq+jZ+/B/zqzPl4eGB2NhYzJs3D926dTNYu4ZrSQ81NTXIy8tDaGio2vnQ0FDk5uZqvUapVGrEh4WF4dixY6itrW0x5u42L1y4AHd3d3h5eeGZZ57BL7/80uL9JicnQ6FQiIeHh4dOz0lERGRUggEOM/THH3/gmWeeMWgiBZh4mK+srAz19fVwcXFRO9/S3KWSkhKt8XV1dSgrK4Obm1uzMXe2GRQUhO3bt+P+++/HtWvXsHTpUoSEhODMmTNwdHTU+tnz5s1DYmKi+LqpZ4qIyNB02Xj5t1tXdC5X0CXLA3RlnDOl1dSpU/F///d/mDt3rkHbNfmcKQBqY5YAIAiCxrnW4u8+31qbERER4s8DBw5EcHAwvL29sW3bNrWE6U5yudwge2wREbVG1xILkyd/olN7KSkprVZ4ZsJF5i45ORljx47Fvn37MHDgQI3VtCtWrNCrXZMmU05OTrCwsNDohSotLdXoWWri6uqqNd7S0lLsUWouprk2AaBnz54YOHAgLly4oM+jEBEZlC5b15w4cQJTp07VqT1d4liPyXxwArp2y5Ytw5dffiluJ9NSJ0xbmDSZsra2RkBAALKystTKFmRlZWH8+PFarwkODsbnn3+udm7//v0IDAwUM8zg4GBkZWUhISFBLSYkJKTZe6murkZBQUGLJRmIiIxFlxIL/fv3b7W3qaKiAkqlEu7u7s32rCfP2IpnEkegoqIC+fn5rX4me686AQ7zabVixQps3rwZL774okHbNfkwX2JiIqKjoxEYGIjg4GBs2rQJRUVFYt2oefPm4erVq9i+vbHybmxsLNasWYPExETExMRAqVQiJSVFXKUHADNnzsTw4cOxfPlyjB8/Hrt370Z2djZycnLEmNmzZ+OJJ55Anz59UFpaiqVLl6KyshJTpkwx7i+AiEhPuiRc+fn5SEpKarWtpKTsVmMA9l5R5yaXy9ulXprJk6moqCiUl5djyZIlKC4uhp+fHzIzM8WKpcXFxSgqKhLjvby8kJmZiYSEBKxduxbu7u5YtWqVWGMKAEJCQpCamoqFCxciKSkJ3t7eSEtLU6sxdeXKFTz77LMoKytD7969MXjwYBw5csQolVKJiIxFl+FCXaqbvx7xPtbs/UeXrCbeKbFnSquZM2di9erVWLVqlUHbNXmdqc6sqZ6HudXhICK6W4THTOy9/KGpb4Na0fS91D9Oep2pc6vMr87UxIkT8fXXX8PR0REDBgzQmID+6aef6tWuyXumiIioE+Df3WQGevXqhSeffNLg7TKZIiIiMjdGHua77777cOnSJY3z06dPx9q1a/Hiiy9i27Ztau8FBQXhyJEj4uvq6mrMnj0bu3btQlVVFR599FGsW7cO9957r16PoM2WLVsM1tadTFoBnYiIiAyvqTSClKMtvv/+exQXF4tHVlYWAODpp58WY8LDw9ViMjMz1dqIj49HRkYGUlNTkZOTg5s3b2Ls2LGor6+X/Ptob+yZIiIiMjdG7pnq3bu32ut3330X3t7eGDFihHhOLpfD1dVV6/UqlQopKSn4+OOP8dhjjwEAduzYAQ8PD2RnZyMsLKxtN9QMLy+vFutJtbatXHOYTBERdXGtbV1TVVWFq7fO4fDhw9y2pouprKxUe63LTiA1NTXYsWMHEhMT1RKXb775Bs7OzujVqxdGjBiBd955B87OzgAaS27U1taq7avr7u4OPz8/5ObmGiyZio+PV3tdW1uL48ePY9++ffjHP/6hd7tMpoiIujhdt64ZOjSr1RjWoeogDNQzdff+s4sWLcLixYtbvPSzzz7DjRs31ApjRkRE4Omnn4anpycKCwuRlJSERx55BHl5eZDL5SgpKYG1tTXs7e3V2mppr159zJw5U+v5tWvX4tixY3q3y2SKiKiLa60WlS51qO5si0xP9uch5XoAuHz5slppBF32p01JSUFERATc3d3Fc1FRUeLPfn5+CAwMhKenJ7744osWV9e1tlevoURERGDevHl6T1BnMkVE1MXpUkn9oYceanUo8OLFi6iqquJQoBmxs7NrU52pS5cuITs7u9V6TW5ubvD09BT3w3V1dUVNTQ0qKirUeqdKS0tb3ArOUP7973/DwcFB7+uZTBERUat0HQrUBYcCjcBEFdC3bNkCZ2dnjBkzpsW48vJyXL58GW5ubgCAgIAAWFlZISsrC5GRkQAad0A5ffo03nvvPf1uRouHHnpIradLEASUlJTg+vXrWLdund7tMpkiIqJWGWIo8PVH38GarxZwKNAI9ClvcPf1bdXQ0IAtW7ZgypQpsLT8b3px8+ZNLF68GJMmTYKbmxsuXryI+fPnw8nJCRMnTgQAKBQKTJ06FbNmzYKjoyMcHBwwe/ZsDBw4UFzdZwgTJkxQe92tWzf07t0bI0eOlPTvJZMpIiJqlS5Dga1tIKuw7M0eKTOWnZ2NoqIivPzyy2rnLSwscOrUKWzfvh03btyAm5sbRo0ahbS0NNja2opxH3zwASwtLREZGSkW7dy6dSssLCwMdo+LFi0yWFt3YjJFRERkbkwwzBcaGgpt2/12794dX375ZavX29jYYPXq1Vi9enXbP9zEmEwRERGZI26nKOrWrVurqwJlMhnq6ur0ap/JFBERGURrxT9/qy3Gzp07Wy2xwNV+ZGgZGRnNvpebm4vVq1dr7VXTFZMpIiIyCF1W/E2evKfVdrjaTzpTTEDvyMaPH69x7ty5c5g3bx4+//xzPP/883j77bf1bp/JFBERGYTUFX+vP7IUa75eyNV+hmCi0gidwa+//opFixZh27ZtCAsLw4kTJ+Dn5yepTSZTRERkEFJX/NlZOrFHykDYM6VJpVJh2bJlWL16NQYNGoSvvvoKw4YNM0jbTKaIiKhjaDDDb3DqEN577z0sX74crq6u2LVrl9ZhPymYTBEREZkbDvOpmTt3Lrp3746//vWv2LZtG7Zt26Y1rrVtcJrDZIqIiMjMcJhP3QsvvNCuGyYzmSIioo5BwtJ0opZs3bq1XdtnMkVERGRuOMxnVEymiIiIzA2TKaPqZuobICIiIurM2DNFRERkZjgB3biYTBERkdG0tH9fRW1Ji3v3cc++NuAwn1ExmSIiIqNpbf++yZO/aPY97tlHHRWTKSIiMpqW9u9rbe8+7tmnO5kgQCah1ISUa7siJlNERGQ0re3f19LefdQGHOYzKiZTREREZoYT0I2LpRGIiIiIJGDPFBERkbnhMJ9RdYieqXXr1sHLyws2NjYICAjAoUOHWow/ePAgAgICYGNjg759+2LDhg0aMenp6fD19YVcLoevry8yMjI0Yq5evYrJkyfD0dERPXr0wKBBg5qdGElERNRZNA3zSTlIdyZPptLS0hAfH48FCxbg+PHjGDZsGCIiIlBUVKQ1vrCwEKNHj8awYcNw/PhxzJ8/H3FxcUhPTxdjlEoloqKiEB0djZMnTyI6OhqRkZE4evSoGFNRUYEhQ4bAysoKe/fuxdmzZ/HPf/4TvXr1au9HJiIiIjMiEwTTrn8MCgqCv78/1q9fL57z8fHBhAkTkJycrBE/Z84c7NmzBwUFBeK52NhYnDx5EkqlEgAQFRWFyspK7N27V4wJDw+Hvb09du3aBQCYO3cuDh8+3GovWEsqKyuhUCigUqlgZ2endztERESG0PS95P/MO7CwttG7nfqa28hPXcDvNx2ZtGeqpqYGeXl5CA0NVTsfGhqK3NxcrdcolUqN+LCwMBw7dgy1tbUtxtzZ5p49exAYGIinn34azs7OeOihh/DRRx+1eL/V1dWorKxUO4iIiDoaDvMZl0mTqbKyMtTX18PFxUXtvIuLC0pKSrReU1JSojW+rq4OZWVlLcbc2eYvv/yC9evXo1+/fvjyyy8RGxuLuLg4bN++vdn7TU5OhkKhEA8PD482PS8RERGZH5PPmQIAmUym9loQBI1zrcXffb61NhsaGuDv749ly5bhoYcewquvvoqYmBi14ca7zZs3DyqVSjwuX77c+sMREREZm2CAg3Rm0tIITk5OsLCw0OiFKi0t1ehZauLq6qo13tLSEo6Oji3G3Nmmm5sbfH191WJ8fHzUJrLfTS6XQy6Xt/5gREREJsahOuMxac+UtbU1AgICkJWVpXY+KysLISEhWq8JDg7WiN+/fz8CAwNhZWXVYsydbQ4ZMgTnz59Xi/nxxx/h6emp9/MQERFR12Pyop2JiYmIjo5GYGAggoODsWnTJhQVFSE2NhZA49Da1atXxblMsbGxWLNmDRITExETEwOlUomUlBRxlR4AzJw5E8OHD8fy5csxfvx47N69G9nZ2cjJyRFjEhISEBISgmXLliEyMhLfffcdNm3ahE2bNhn3F0BERGRogtB4SLmedGbyZCoqKgrl5eVYsmQJiouL4efnh8zMTLGHqLi4WK3mlJeXFzIzM5GQkIC1a9fC3d0dq1atwqRJk8SYkJAQpKamYuHChUhKSoK3tzfS0tIQFBQkxvz9739HRkYG5s2bhyVLlsDLywsrV67E888/b7yHJyIiagfcm8+4TF5nqjNjnSkiIupImr6XAicthaWV/nWm6mpv41j6Qn6/6ahDrOYjIiIi6qxMPsxHREREhiVraDykXE+6YzJFRERkbqTWiuIEoDbhMB8RERGRBEymiIiIzIyx9+ZbvHgxZDKZ2uHq6iq+LwgCFi9eDHd3d3Tv3h0jR47EmTNn1Nqorq7GjBkz4OTkhJ49e2LcuHG4cuWKIX4d7Y7JFBERkblpqjMl5WijAQMGoLi4WDxOnTolvvfee+9hxYoVWLNmDb7//nu4urri8ccfx++//y7GxMfHIyMjA6mpqcjJycHNmzcxduxY1NfXG+RX0p44Z4qIiIgks7S0VOuNaiIIAlauXIkFCxbgySefBABs27YNLi4u+OSTT/Dqq69CpVIhJSUFH3/8MR577DEAwI4dO+Dh4YHs7GyEhYUZ9Vnaij1TREREZsZQw3yVlZVqR3V1dbOfeeHCBbi7u8PLywvPPPMMfvnlFwBAYWEhSkpKEBoaKsbK5XKMGDECubm5AIC8vDzU1taqxbi7u8PPz0+M6ciYTBEREZkbwQAHAA8PDygUCvFITk7W+nFBQUHYvn07vvzyS3z00UcoKSlBSEgIysvLUVJSAgBwcXFRu8bFxUV8r6SkBNbW1rC3t282piPjMB8RERFpdfnyZbUK6HK5XGtcRESE+PPAgQMRHBwMb29vbNu2DYMHDwYAyGQytWsEQdA4dzddYjoC9kwRERGZGUMN89nZ2akdzSVTd+vZsycGDhyICxcuiPOo7u5hKi0tFXurXF1dUVNTg4qKimZjOjImU0RERObGBKv57lRdXY2CggK4ubnBy8sLrq6uyMrKEt+vqanBwYMHERISAgAICAiAlZWVWkxxcTFOnz4txnRkHOYjIiIyM/rUirr7+raYPXs2nnjiCfTp0welpaVYunQpKisrMWXKFMhkMsTHx2PZsmXo168f+vXrh2XLlqFHjx547rnnAAAKhQJTp07FrFmz4OjoCAcHB8yePRsDBw4UV/d1ZEymiIiISJIrV67g2WefRVlZGXr37o3BgwfjyJEj8PT0BAC88cYbqKqqwvTp01FRUYGgoCDs378ftra2YhsffPABLC0tERkZiaqqKjz66KPYunUrLCwsTPVYOpMJgsS+vC6ssrISCoUCKpVKbYIeERGRKTR9LwWHL4GllY3e7dTV3oZy35v8ftMRe6aIiIjMjLGH+bo6TkAnIiIikoA9U0REROamQWg8pFxPOmMyRUREZG7uqGKu9/WkMw7zEREREUnAnikiIiIzI4PECegGu5OugckUERGRuZFaxZxVk9qEw3xEREREErBnioiIyMywzpRxMZkiIiIyN1zNZ1RMpoiIiMyMTBAgkzDvScq1XRHnTBERERFJwJ4pIiLqUP744w+cO3dOfF1VVYWLFy/ivvvuQ/fu3dVi+/fvjx49ehj7Fju+hj8PKdeTzphMERFRh3Lu3DkEBAToFJuXlwd/f/92vqPOh8N8xsVkioiIOpT+/fsjLy9PfN1azxSRqTGZIiKiDqVHjx4avU1Dhgwx0d10UlzNZ1RMpoiIiMwNK6AbFVfzEREREUnQIZKpdevWwcvLCzY2NggICMChQ4dajD948CACAgJgY2ODvn37YsOGDRox6enp8PX1hVwuh6+vLzIyMtTev++++yCTyTSO1157zaDPRkREZGxNFdClHKQ7kydTaWlpiI+Px4IFC3D8+HEMGzYMERERKCoq0hpfWFiI0aNHY9iwYTh+/Djmz5+PuLg4pKenizFKpRJRUVGIjo7GyZMnER0djcjISBw9elSM+f7771FcXCweWVlZAICnn366fR+YiIiovTUN80k5SGcyQTDtbywoKAj+/v5Yv369eM7HxwcTJkxAcnKyRvycOXOwZ88eFBQUiOdiY2Nx8uRJKJVKAEBUVBQqKyuxd+9eMSY8PBz29vbYtWuX1vuIj4/Hf/7zH1y4cAEymUyne6+srIRCoYBKpYKdnZ1O1xAREbWXpu+lEcELYWlpo3c7dXW3cVC5lN9vOjJpz1RNTQ3y8vIQGhqqdj40NBS5ublar1EqlRrxYWFhOHbsGGpra1uMaa7Nmpoa7NixAy+//HKLiVR1dTUqKyvVDiIioo5G1iD9IN2ZNJkqKytDfX09XFxc1M67uLigpKRE6zUlJSVa4+vq6lBWVtZiTHNtfvbZZ7hx4wZefPHFFu83OTkZCoVCPDw8PFqMJyIiMgkO8xmVyedMAdDoDRIEocUeIm3xd59vS5spKSmIiIiAu7t7i/c5b948qFQq8bh8+XKL8URERCYhGOAgnZm0zpSTkxMsLCw0eoxKS0s1epaauLq6ao23tLSEo6NjizHa2rx06RKys7Px6aeftnq/crkccrm81TgiIiLqOkzaM2VtbY2AgABxJV2TrKwshISEaL0mODhYI37//v0IDAyElZVVizHa2tyyZQucnZ0xZswYKY9CRETUYTTtzSflIN2ZvAJ6YmIioqOjERgYiODgYGzatAlFRUWIjY0F0Di0dvXqVWzfvh1A48q9NWvWIDExETExMVAqlUhJSVFbpTdz5kwMHz4cy5cvx/jx47F7925kZ2cjJydH7bMbGhqwZcsWTJkyBZaWJv9VEBERGQYroBuVyTOIqKgolJeXY8mSJSguLoafnx8yMzPh6ekJACguLlarOeXl5YXMzEwkJCRg7dq1cHd3x6pVqzBp0iQxJiQkBKmpqVi4cCGSkpLg7e2NtLQ0BAUFqX12dnY2ioqK8PLLLxvnYYmIiMjsmLzOVGfGOlNERNSRNH0vjfKfB0sLCXWm6m/jQH4yv990ZPKeKSIiIjIsqfOeOGeqbTpEaQQiIiKizoo9U0REROZGgMQJ6Aa7ky6ByRQREZG54Wo+o+IwHxEREZEE7JkiIiIyNw0Amt+VTbfrSWdMpoiIiMwMV/MZF4f5iIiIzE3TnCkpRxskJyfj73//O2xtbeHs7IwJEybg/PnzajEvvvgiZDKZ2jF48GC1mOrqasyYMQNOTk7o2bMnxo0bhytXrkj+dbQ3JlNEREQkycGDB/Haa6/hyJEjyMrKQl1dHUJDQ3Hr1i21uPDwcBQXF4tHZmam2vvx8fHIyMhAamoqcnJycPPmTYwdOxb19fXGfJw24zAfERGRuTHQar7Kykq103K5HHK5XCN83759aq+3bNkCZ2dn5OXlYfjw4WrXu7q6av1IlUqFlJQUfPzxx3jssccAADt27ICHhweys7MRFham//O0M/ZMERERmRsDDfN5eHhAoVCIR3Jysk4fr1KpAAAODg5q57/55hs4Ozvj/vvvR0xMDEpLS8X38vLyUFtbi9DQUPGcu7s7/Pz8kJubK/U30q7YM0VERERaXb58WW1vPm29UncTBAGJiYkYOnQo/Pz8xPMRERF4+umn4enpicLCQiQlJeGRRx5BXl4e5HI5SkpKYG1tDXt7e7X2XFxcUFJSYriHagdMpoiIiMyNgUoj2NnZtXmj49dffx0//PADcnJy1M5HRUWJP/v5+SEwMBCenp744osv8OSTTzbbniAIkMmkPEz74zAfERGRmWkqjSDl0MeMGTOwZ88eHDhwAPfee2+LsW5ubvD09MSFCxcAAK6urqipqUFFRYVaXGlpKVxcXPS6H2NhMkVERESSCIKA119/HZ9++im+/vpreHl5tXpNeXk5Ll++DDc3NwBAQEAArKyskJWVJcYUFxfj9OnTCAkJabd7NwQO8xEREZkbI+/N99prr+GTTz7B7t27YWtrK85xUigU6N69O27evInFixdj0qRJcHNzw8WLFzF//nw4OTlh4sSJYuzUqVMxa9YsODo6wsHBAbNnz8bAgQPF1X0dFZMpIiIic9MgADIJyVRD265dv349AGDkyJFq57ds2YIXX3wRFhYWOHXqFLZv344bN27Azc0No0aNQlpaGmxtbcX4Dz74AJaWloiMjERVVRUeffRRbN26FRYWFvo/ixHIBIE14/VVWVkJhUIBlUrV5gl6REREhtb0vfSYdzwsLVpfedecuvpqZP+8kt9vOmLPFBERkbkx8jBfV8dkioiIyOxITKbAZKotmEwRERGZG/ZMGRVLIxARERFJwJ4pIiIic9MgQNJQXRtX83V1TKaIiIjMjdDQeEi5nnTGYT4iIiIiCdgzRUREZG44Ad2omEwRERGZG86ZMioO8xERERFJwJ4pIiIic8NhPqNiMkVERGRuBEhMpgx2J10Ch/mIiIiIJGDPFBERkbnhMJ9RMZkiIiIyNw0NACQU3mxg0c626BDDfOvWrYOXlxdsbGwQEBCAQ4cOtRh/8OBBBAQEwMbGBn379sWGDRs0YtLT0+Hr6wu5XA5fX19kZGSovV9XV4eFCxfCy8sL3bt3R9++fbFkyRI08F8gIiLq7Jp6pqQcpDOTJ1NpaWmIj4/HggULcPz4cQwbNgwREREoKirSGl9YWIjRo0dj2LBhOH78OObPn4+4uDikp6eLMUqlElFRUYiOjsbJkycRHR2NyMhIHD16VIxZvnw5NmzYgDVr1qCgoADvvfce3n//faxevbrdn5mIiIjMh0wQTJt+BgUFwd/fH+vXrxfP+fj4YMKECUhOTtaInzNnDvbs2YOCggLxXGxsLE6ePAmlUgkAiIqKQmVlJfbu3SvGhIeHw97eHrt27QIAjB07Fi4uLkhJSRFjJk2ahB49euDjjz/W6d4rKyuhUCigUqlgZ2fXtgcnIiIysKbvpcecXoZlN2u926lrqEF22WZ+v+nIpD1TNTU1yMvLQ2hoqNr50NBQ5Obmar1GqVRqxIeFheHYsWOora1tMebONocOHYqvvvoKP/74IwDg5MmTyMnJwejRo5u93+rqalRWVqodREREHU6DIP0gnZl0AnpZWRnq6+vh4uKidt7FxQUlJSVarykpKdEaX1dXh7KyMri5uTUbc2ebc+bMgUqlQv/+/WFhYYH6+nq88847ePbZZ5u93+TkZLz11lttfUwiIiIyYyafMwUAMplM7bUgCBrnWou/+3xrbaalpWHHjh345JNPkJ+fj23btuF//ud/sG3btmY/d968eVCpVOJx+fLl1h+OiIjIyAShQfJBujNpz5STkxMsLCw0eqFKS0s1epaauLq6ao23tLSEo6NjizF3tvmPf/wDc+fOxTPPPAMAGDhwIC5duoTk5GRMmTJF62fL5XLI5fK2PSQREZGxCRKH6riar01M2jNlbW2NgIAAZGVlqZ3PyspCSEiI1muCg4M14vfv34/AwEBYWVm1GHNnm3/88Qe6dVN/fAsLC5ZGICIiojYxedHOxMREREdHIzAwEMHBwdi0aROKiooQGxsLoHFo7erVq9i+fTuAxpV7a9asQWJiImJiYqBUKpGSkiKu0gOAmTNnYvjw4Vi+fDnGjx+P3bt3Izs7Gzk5OWLME088gXfeeQd9+vTBgAEDcPz4caxYsQIvv/yycX8BREREhiYIkLTBHnum2sTkyVRUVBTKy8uxZMkSFBcXw8/PD5mZmfD09AQAFBcXq9Wc8vLyQmZmJhISErB27Vq4u7tj1apVmDRpkhgTEhKC1NRULFy4EElJSfD29kZaWhqCgoLEmNWrVyMpKQnTp09HaWkp3N3d8eqrr+LNN9803sMTERG1h4YGQCZhpIVzptrE5HWmOjPWmSIioo6k6XvpUdvnYSmTUGdKqMFXv+/k95uOTN4zRURERAbGYT6jYjJFRERkZoSGBggShvlYGqFtmEwRERGZG/ZMGVWHKNpJRERE1FmxZ4qIiMjcNAiAjD1TxsJkioiIyNwIAgAppRGYTLUFh/mIiIiIJGDPFBERkZkRGgQIEob5WIKybZhMERERmRuhAdKG+VgaoS04zEdEREQGsW7dOnh5ecHGxgYBAQE4dOiQqW/JKJhMERERmRmhQZB8tFVaWhri4+OxYMECHD9+HMOGDUNERITa/rrmiskUERGRuREapB9ttGLFCkydOhWvvPIKfHx8sHLlSnh4eGD9+vXt8IAdC+dMERERmZk61EoqgF6HWgCNGyffSS6XQy6Xa8TX1NQgLy8Pc+fOVTsfGhqK3Nxc/W+kk2AyRUREZCasra3h6uqKnJJMyW3dc8898PDwUDu3aNEiLF68WCO2rKwM9fX1cHFxUTvv4uKCkpISyffS0TGZksDW1hYqlQq2tramvhUiIiLY2NigsLAQNTU1ktsSBAEymUztnLZeqTvdHa+tDXPEZEoCmUwGOzs7U98GERGRyMbGBjY2Nkb9TCcnJ1hYWGj0QpWWlmr0VpkjTkAnIiIiSaytrREQEICsrCy181lZWQgJCTHRXRkPe6aIiIhIssTERERHRyMwMBDBwcHYtGkTioqKEBsba+pba3dMpoiIiEiyqKgolJeXY8mSJSguLoafnx8yMzPh6elp6ltrdzKBG/AQERER6Y1zpoiIiIgkYDJFREREJAGTKSIiIiIJmEwRERERScBkioiIiEgCJlNEREREEjCZIiIiIpKAyRQRERGRBEymiIiIiCRgMkVEREQkAZMpIiIiIgn+P9aOqWjKTeTeAAAAAElFTkSuQmCC",
+ "text/plain": [
+ "<Figure size 640x480 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "clusterer.condensed_tree_\n",
+ "clusterer.condensed_tree_.plot()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "3fcf298c-ebf4-4ce7-b303-46e7e117e81b",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[t-SNE] Computing 151 nearest neighbors...\n",
+ "[t-SNE] Indexed 2005 samples in 0.001s...\n",
+ "[t-SNE] Computed neighbors for 2005 samples in 0.069s...\n",
+ "[t-SNE] Computed conditional probabilities for sample 1000 / 2005\n",
+ "[t-SNE] Computed conditional probabilities for sample 2000 / 2005\n",
+ "[t-SNE] Computed conditional probabilities for sample 2005 / 2005\n",
+ "[t-SNE] Mean sigma: 165.883147\n",
+ "[t-SNE] KL divergence after 250 iterations with early exaggeration: 68.255951\n",
+ "[t-SNE] KL divergence after 300 iterations: 1.839144\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1200x1200 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "###################################\n",
+ "######### tSNE visualization 3D \n",
+ "min_size=4\n",
+ "min_samp=4\n",
+ "\n",
+ "tsne=manifold.TSNE(n_components=3, verbose=1, perplexity=50, n_iter=300)\n",
+ "ts= tsne.fit_transform(fingerprint_array)\n",
+ "fig = plt.figure(figsize=(12, 12))\n",
+ "ax = fig.add_subplot(projection='3d')\n",
+ "########\n",
+ "##### for different eps\n",
+ "# for i in np.arange(0.025,0.31,0.025):\n",
+ "# eps=round(i, 3)\n",
+ "# label=db.single_linkage_tree_.get_clusters(eps, min_cluster_size=3)\n",
+ "# n_clusters_ = label.max()\n",
+ "# n_noise_ = list(label).count(-1)\n",
+ "# print(str(eps)+' '+str(len(labels))+' '+str(n_clusters_)+' '+str(n_noise_)+' '+str(metrics.silhouette_score(X, label)))\n",
+ "# ax.scatter(ts[:,0], ts[:,1], ts[:,2], c=labels+1, cmap=\"hsv\")\n",
+ "# fname=\"t-SNE result_at_eps\"+str(eps)\n",
+ "# plt.title(fname)\n",
+ "# plt.savefig(fname+'.png', bbox_inches='tight')\n",
+ "# plt.show()\n",
+ "###########\n",
+ "###### for current minsize and minsamples\n",
+ "ax.scatter(ts[:,0], ts[:,1], ts[:,2], c=labels+1, cmap=\"hsv\")\n",
+ "fname=\"t-SNE result_at_minsize_\"+str(min_size)+\"_minsamp_\"+str(min_samp)\n",
+ "plt.title(fname)\n",
+ "plt.savefig(fname+'.png', bbox_inches='tight')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "409199d6",
+ "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
+}