clustering_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5999607c-a61d-4251-9f0e-628b909ac2f9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "  var py_version = '3.1.1'.replace('rc', '-rc.');\n",
       "  var is_dev = py_version.indexOf(\"+\") !== -1 || py_version.indexOf(\"-\") !== -1;\n",
       "  var reloading = false;\n",
       "  var Bokeh = root.Bokeh;\n",
       "  var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n",
       "\n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
       "        if (callback != null)\n",
       "          callback();\n",
       "      });\n",
       "    } finally {\n",
       "      delete root._bokeh_onload_callbacks;\n",
       "    }\n",
       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n",
       "    if (css_urls == null) css_urls = [];\n",
       "    if (js_urls == null) js_urls = [];\n",
       "    if (js_modules == null) js_modules = [];\n",
       "    if (js_exports == null) js_exports = {};\n",
       "\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    if (!reloading) {\n",
       "      console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    }\n",
       "\n",
       "    function on_load() {\n",
       "      root._bokeh_is_loading--;\n",
       "      if (root._bokeh_is_loading === 0) {\n",
       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
       "        run_callbacks()\n",
       "      }\n",
       "    }\n",
       "    window._bokeh_on_load = on_load\n",
       "\n",
       "    function on_error() {\n",
       "      console.error(\"failed to load \" + url);\n",
       "    }\n",
       "\n",
       "    var skip = [];\n",
       "    if (window.requirejs) {\n",
       "      window.requirejs.config({'packages': {}, 'paths': {'jspanel': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu', 'jspanel-dock': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock', 'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@7.2.3/dist/gridstack-all', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'jspanel': {'exports': 'jsPanel'}, 'gridstack': {'exports': 'GridStack'}}});\n",
       "      require([\"jspanel\"], function(jsPanel) {\n",
       "\twindow.jsPanel = jsPanel\n",
       "\ton_load()\n",
       "      })\n",
       "      require([\"jspanel-modal\"], function() {\n",
       "\ton_load()\n",
       "      })\n",
       "      require([\"jspanel-tooltip\"], function() {\n",
       "\ton_load()\n",
       "      })\n",
       "      require([\"jspanel-hint\"], function() {\n",
       "\ton_load()\n",
       "      })\n",
       "      require([\"jspanel-layout\"], function() {\n",
       "\ton_load()\n",
       "      })\n",
       "      require([\"jspanel-contextmenu\"], function() {\n",
       "\ton_load()\n",
       "      })\n",
       "      require([\"jspanel-dock\"], function() {\n",
       "\ton_load()\n",
       "      })\n",
       "      require([\"gridstack\"], function(GridStack) {\n",
       "\twindow.GridStack = GridStack\n",
       "\ton_load()\n",
       "      })\n",
       "      require([\"notyf\"], function() {\n",
       "\ton_load()\n",
       "      })\n",
       "      root._bokeh_is_loading = css_urls.length + 9;\n",
       "    } else {\n",
       "      root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n",
       "    }\n",
       "\n",
       "    var existing_stylesheets = []\n",
       "    var links = document.getElementsByTagName('link')\n",
       "    for (var i = 0; i < links.length; i++) {\n",
       "      var link = links[i]\n",
       "      if (link.href != null) {\n",
       "\texisting_stylesheets.push(link.href)\n",
       "      }\n",
       "    }\n",
       "    for (var i = 0; i < css_urls.length; i++) {\n",
       "      var url = css_urls[i];\n",
       "      if (existing_stylesheets.indexOf(url) !== -1) {\n",
       "\ton_load()\n",
       "\tcontinue;\n",
       "      }\n",
       "      const element = document.createElement(\"link\");\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.rel = \"stylesheet\";\n",
       "      element.type = \"text/css\";\n",
       "      element.href = url;\n",
       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
       "      document.body.appendChild(element);\n",
       "    }    if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n",
       "      var urls = ['https://cdn.holoviz.org/panel/1.1.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.1.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.1.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.1.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.1.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.1.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.1.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock.js'];\n",
       "      for (var i = 0; i < urls.length; i++) {\n",
       "        skip.push(urls[i])\n",
       "      }\n",
       "    }    if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n",
       "      var urls = ['https://cdn.holoviz.org/panel/1.1.0/dist/bundled/gridstack/gridstack@7.2.3/dist/gridstack-all.js'];\n",
       "      for (var i = 0; i < urls.length; i++) {\n",
       "        skip.push(urls[i])\n",
       "      }\n",
       "    }    if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n",
       "      var urls = ['https://cdn.holoviz.org/panel/1.1.0/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n",
       "      for (var i = 0; i < urls.length; i++) {\n",
       "        skip.push(urls[i])\n",
       "      }\n",
       "    }    var existing_scripts = []\n",
       "    var scripts = document.getElementsByTagName('script')\n",
       "    for (var i = 0; i < scripts.length; i++) {\n",
       "      var script = scripts[i]\n",
       "      if (script.src != null) {\n",
       "\texisting_scripts.push(script.src)\n",
       "      }\n",
       "    }\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n",
       "\tif (!window.requirejs) {\n",
       "\t  on_load();\n",
       "\t}\n",
       "\tcontinue;\n",
       "      }\n",
       "      var element = document.createElement('script');\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.async = false;\n",
       "      element.src = url;\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "    for (var i = 0; i < js_modules.length; i++) {\n",
       "      var url = js_modules[i];\n",
       "      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n",
       "\tif (!window.requirejs) {\n",
       "\t  on_load();\n",
       "\t}\n",
       "\tcontinue;\n",
       "      }\n",
       "      var element = document.createElement('script');\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.async = false;\n",
       "      element.src = url;\n",
       "      element.type = \"module\";\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "    for (const name in js_exports) {\n",
       "      var url = js_exports[name];\n",
       "      if (skip.indexOf(url) >= 0 || root[name] != null) {\n",
       "\tif (!window.requirejs) {\n",
       "\t  on_load();\n",
       "\t}\n",
       "\tcontinue;\n",
       "      }\n",
       "      var element = document.createElement('script');\n",
       "      element.onerror = on_error;\n",
       "      element.async = false;\n",
       "      element.type = \"module\";\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      element.textContent = `\n",
       "      import ${name} from \"${url}\"\n",
       "      window.${name} = ${name}\n",
       "      window._bokeh_on_load()\n",
       "      `\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "    if (!js_urls.length && !js_modules.length) {\n",
       "      on_load()\n",
       "    }\n",
       "  };\n",
       "\n",
       "  function inject_raw_css(css) {\n",
       "    const element = document.createElement(\"style\");\n",
       "    element.appendChild(document.createTextNode(css));\n",
       "    document.body.appendChild(element);\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.holoviz.org/panel/1.1.0/dist/panel.min.js\"];\n",
       "  var js_modules = [];\n",
       "  var js_exports = {};\n",
       "  var css_urls = [];\n",
       "  var inline_js = [    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "function(Bokeh) {} // ensure no trailing comma for IE\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i].call(root, root.Bokeh);\n",
       "      }\n",
       "      // Cache old bokeh versions\n",
       "      if (Bokeh != undefined && !reloading) {\n",
       "\tvar NewBokeh = root.Bokeh;\n",
       "\tif (Bokeh.versions === undefined) {\n",
       "\t  Bokeh.versions = new Map();\n",
       "\t}\n",
       "\tif (NewBokeh.version !== Bokeh.version) {\n",
       "\t  Bokeh.versions.set(NewBokeh.version, NewBokeh)\n",
       "\t}\n",
       "\troot.Bokeh = Bokeh;\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    }\n",
       "    root._bokeh_is_initializing = false\n",
       "  }\n",
       "\n",
       "  function load_or_wait() {\n",
       "    // Implement a backoff loop that tries to ensure we do not load multiple\n",
       "    // versions of Bokeh and its dependencies at the same time.\n",
       "    // In recent versions we use the root._bokeh_is_initializing flag\n",
       "    // to determine whether there is an ongoing attempt to initialize\n",
       "    // bokeh, however for backward compatibility we also try to ensure\n",
       "    // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n",
       "    // before older versions are fully initialized.\n",
       "    if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n",
       "      root._bokeh_is_initializing = false;\n",
       "      root._bokeh_onload_callbacks = undefined;\n",
       "      console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n",
       "      load_or_wait();\n",
       "    } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n",
       "      setTimeout(load_or_wait, 100);\n",
       "    } else {\n",
       "      Bokeh = root.Bokeh;\n",
       "      bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n",
       "      root._bokeh_is_initializing = true\n",
       "      root._bokeh_onload_callbacks = []\n",
       "      if (!reloading && (!bokeh_loaded || is_dev)) {\n",
       "\troot.Bokeh = undefined;\n",
       "      }\n",
       "      load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n",
       "\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "\trun_inline_js();\n",
       "      });\n",
       "    }\n",
       "  }\n",
       "  // Give older versions of the autoload script a head-start to ensure\n",
       "  // they initialize before we start loading newer version.\n",
       "  setTimeout(load_or_wait, 100)\n",
       "}(window));"
      ],
      "application/vnd.holoviews_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n  var py_version = '3.1.1'.replace('rc', '-rc.');\n  var is_dev = py_version.indexOf(\"+\") !== -1 || py_version.indexOf(\"-\") !== -1;\n  var reloading = false;\n  var Bokeh = root.Bokeh;\n  var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks;\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n    if (js_modules == null) js_modules = [];\n    if (js_exports == null) js_exports = {};\n\n    root._bokeh_onload_callbacks.push(callback);\n\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n      run_callbacks();\n      return null;\n    }\n    if (!reloading) {\n      console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    }\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n    window._bokeh_on_load = on_load\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    var skip = [];\n    if (window.requirejs) {\n      window.requirejs.config({'packages': {}, 'paths': {'jspanel': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu', 'jspanel-dock': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock', 'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@7.2.3/dist/gridstack-all', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'jspanel': {'exports': 'jsPanel'}, 'gridstack': {'exports': 'GridStack'}}});\n      require([\"jspanel\"], function(jsPanel) {\n\twindow.jsPanel = jsPanel\n\ton_load()\n      })\n      require([\"jspanel-modal\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-tooltip\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-hint\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-layout\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-contextmenu\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-dock\"], function() {\n\ton_load()\n      })\n      require([\"gridstack\"], function(GridStack) {\n\twindow.GridStack = GridStack\n\ton_load()\n      })\n      require([\"notyf\"], function() {\n\ton_load()\n      })\n      root._bokeh_is_loading = css_urls.length + 9;\n    } else {\n      root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n    }\n\n    var existing_stylesheets = []\n    var links = document.getElementsByTagName('link')\n    for (var i = 0; i < links.length; i++) {\n      var link = links[i]\n      if (link.href != null) {\n\texisting_stylesheets.push(link.href)\n      }\n    }\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      if (existing_stylesheets.indexOf(url) !== -1) {\n\ton_load()\n\tcontinue;\n      }\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }    if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.1.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.1.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.1.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.1.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.1.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.1.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.1.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.1.0/dist/bundled/gridstack/gridstack@7.2.3/dist/gridstack-all.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.1.0/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    var existing_scripts = []\n    var scripts = document.getElementsByTagName('script')\n    for (var i = 0; i < scripts.length; i++) {\n      var script = scripts[i]\n      if (script.src != null) {\n\texisting_scripts.push(script.src)\n      }\n    }\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (var i = 0; i < js_modules.length; i++) {\n      var url = js_modules[i];\n      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (const name in js_exports) {\n      var url = js_exports[name];\n      if (skip.indexOf(url) >= 0 || root[name] != null) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onerror = on_error;\n      element.async = false;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      element.textContent = `\n      import ${name} from \"${url}\"\n      window.${name} = ${name}\n      window._bokeh_on_load()\n      `\n      document.head.appendChild(element);\n    }\n    if (!js_urls.length && !js_modules.length) {\n      on_load()\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.holoviz.org/panel/1.1.0/dist/panel.min.js\"];\n  var js_modules = [];\n  var js_exports = {};\n  var css_urls = [];\n  var inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n  ];\n\n  function run_inline_js() {\n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }\n      // Cache old bokeh versions\n      if (Bokeh != undefined && !reloading) {\n\tvar NewBokeh = root.Bokeh;\n\tif (Bokeh.versions === undefined) {\n\t  Bokeh.versions = new Map();\n\t}\n\tif (NewBokeh.version !== Bokeh.version) {\n\t  Bokeh.versions.set(NewBokeh.version, NewBokeh)\n\t}\n\troot.Bokeh = Bokeh;\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    }\n    root._bokeh_is_initializing = false\n  }\n\n  function load_or_wait() {\n    // Implement a backoff loop that tries to ensure we do not load multiple\n    // versions of Bokeh and its dependencies at the same time.\n    // In recent versions we use the root._bokeh_is_initializing flag\n    // to determine whether there is an ongoing attempt to initialize\n    // bokeh, however for backward compatibility we also try to ensure\n    // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n    // before older versions are fully initialized.\n    if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n      root._bokeh_is_initializing = false;\n      root._bokeh_onload_callbacks = undefined;\n      console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n      load_or_wait();\n    } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n      setTimeout(load_or_wait, 100);\n    } else {\n      Bokeh = root.Bokeh;\n      bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n      root._bokeh_is_initializing = true\n      root._bokeh_onload_callbacks = []\n      if (!reloading && (!bokeh_loaded || is_dev)) {\n\troot.Bokeh = undefined;\n      }\n      load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n\trun_inline_js();\n      });\n    }\n  }\n  // Give older versions of the autoload script a head-start to ensure\n  // they initialize before we start loading newer version.\n  setTimeout(load_or_wait, 100)\n}(window));"
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     "output_type": "display_data"
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     "data": {
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       "  var output = handle.output;\n",
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       "  var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "  var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
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       "    var html_node = toinsert[nchildren-1].children[0];\n",
       "    html_node.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "    var scripts = [];\n",
       "    var nodelist = html_node.querySelectorAll(\"script\");\n",
       "    for (var i in nodelist) {\n",
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      "application/vnd.holoviews_load.v0+json": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n  window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n    function JupyterCommManager() {\n    }\n\n    JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n      if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n        comm_manager.register_target(comm_id, function(comm) {\n          comm.on_msg(msg_handler);\n        });\n      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n        window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n          comm.onMsg = msg_handler;\n        });\n      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n        google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n          var messages = comm.messages[Symbol.asyncIterator]();\n          function processIteratorResult(result) {\n            var message = result.value;\n            console.log(message)\n            var content = {data: message.data, comm_id};\n            var buffers = []\n            for (var buffer of message.buffers || []) {\n              buffers.push(new DataView(buffer))\n            }\n            var metadata = message.metadata || {};\n            var msg = {content, buffers, metadata}\n            msg_handler(msg);\n            return messages.next().then(processIteratorResult);\n          }\n          return messages.next().then(processIteratorResult);\n        })\n      }\n    }\n\n    JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n      if (comm_id in window.PyViz.comms) {\n        return window.PyViz.comms[comm_id];\n      } else if (window.comm_manager || 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       "<style>*[data-root-id],\n",
       "*[data-root-id] > * {\n",
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       "}\n",
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       "}\n",
       "</style>"
      ]
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import dependencies\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import re\n",
    "\n",
    "import hvplot.pandas\n",
    "import holoviews as hv\n",
    "\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b626c360-0dfc-41ab-b4ee-4aaeb222ddb1",
   "metadata": {
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>school</th>\n",
       "      <th>conference</th>\n",
       "      <th>wins</th>\n",
       "      <th>losses</th>\n",
       "      <th>winning_pct</th>\n",
       "      <th>conf_wins</th>\n",
       "      <th>conf_losses</th>\n",
       "      <th>conf_winning_pct</th>\n",
       "      <th>ppg_offense</th>\n",
       "      <th>ppg_defense</th>\n",
       "      <th>SRS</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000</td>\n",
       "      <td>Florida State</td>\n",
       "      <td>ACC</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.846</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000</td>\n",
       "      <td>Clemson</td>\n",
       "      <td>ACC</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.750</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000</td>\n",
       "      <td>Georgia Tech</td>\n",
       "      <td>ACC</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>32.2</td>\n",
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       "      <td>11.30</td>\n",
       "      <td>1.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>ACC</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.500</td>\n",
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       "      <td>2000</td>\n",
       "      <td>North Carolina State</td>\n",
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       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.667</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.500</td>\n",
       "      <td>31.6</td>\n",
       "      <td>28.2</td>\n",
       "      <td>3.92</td>\n",
       "      <td>-0.33</td>\n",
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      ],
      "text/plain": [
       "   year                school conference  wins  losses  winning_pct  \\\n",
       "0  2000         Florida State        ACC  11.0     2.0        0.846   \n",
       "1  2000               Clemson        ACC   9.0     3.0        0.750   \n",
       "2  2000          Georgia Tech        ACC   9.0     3.0        0.750   \n",
       "3  2000              Virginia        ACC   6.0     6.0        0.500   \n",
       "4  2000  North Carolina State        ACC   8.0     4.0        0.667   \n",
       "\n",
       "   conf_wins  conf_losses  conf_winning_pct  ppg_offense  ppg_defense    SRS  \\\n",
       "0        8.0          0.0             1.000         39.3         10.5  23.13   \n",
       "1        6.0          2.0             0.750         34.7         21.1  11.54   \n",
       "2        6.0          2.0             0.750         32.2         19.8  11.30   \n",
       "3        5.0          3.0             0.625         20.2         24.3   0.10   \n",
       "4        4.0          4.0             0.500         31.6         28.2   3.92   \n",
       "\n",
       "    SOS  \n",
       "0  5.59  \n",
       "1  2.04  \n",
       "2  1.21  \n",
       "3  3.18  \n",
       "4 -0.33  "
      ]
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     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the CSV file from the Resources folder into a Pandas DataFrame\n",
    "df_data = pd.read_csv(\"output/standings.csv\")\n",
    "\n",
    "# Display the data\n",
    "df_data.head()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 3,
   "id": "e4a94a3e-4dda-4fe5-b9a6-bbfd5eaefc96",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2835 entries, 0 to 2834\n",
      "Data columns (total 13 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   year              2835 non-null   int64  \n",
      " 1   school            2835 non-null   object \n",
      " 2   conference        2835 non-null   object \n",
      " 3   wins              2835 non-null   float64\n",
      " 4   losses            2835 non-null   float64\n",
      " 5   winning_pct       2835 non-null   float64\n",
      " 6   conf_wins         2724 non-null   float64\n",
      " 7   conf_losses       2724 non-null   float64\n",
      " 8   conf_winning_pct  2724 non-null   float64\n",
      " 9   ppg_offense       2835 non-null   float64\n",
      " 10  ppg_defense       2835 non-null   float64\n",
      " 11  SRS               2835 non-null   float64\n",
      " 12  SOS               2835 non-null   float64\n",
      "dtypes: float64(10), int64(1), object(2)\n",
      "memory usage: 288.1+ KB\n"
     ]
    }
   ],
   "source": [
    "# Check the dataset to determine initial processing steps\n",
    "df_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c983417-3492-4a7c-a803-0c0f3f7467c4",
   "metadata": {},
   "source": [
    "# Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "843cb62f-6670-46ad-b0f7-bf663ed29d4a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Fill all the non-null values with zero\n",
    "df_data = df_data.fillna(0)\n",
    "\n",
    "# Create a UID of year and school (handle LA-Monroe and UNLV formatting)\n",
    "df_data['team'] = '(' + df_data['school'] + ')' + '-' + df_data['year'].astype(str)\n",
    "df_data['team'] = df_data['team'].str.replace('Louisiana-Monroe', \"Louisiana Monroe\")\n",
    "df_data['team'] = df_data['team'].str.replace('Nevada-Las Vegas', \"UNLV\")\n",
    "\n",
    "# Set the index to the UID\n",
    "df_data = df_data.set_index('team')\n",
    "\n",
    "# Drop the unnecessary columns and set the team column to the index\n",
    "columns_to_drop = ['year', 'school', 'conference']\n",
    "df_data = df_data.drop(columns_to_drop, axis=1)\n",
    "\n",
    "# Use the `StandardScaler()` module from scikit-learn to normalize the data\n",
    "data_scaled = StandardScaler().fit_transform(df_data)\n",
    "\n",
    "# Creating a DataFrame with with the scaled data\n",
    "df_scaled = pd.DataFrame(data_scaled, columns=list(df_data.columns))\n",
    "\n",
    "# Set the index to the team value\n",
    "df_scaled['team'] = df_data.index\n",
    "df_scaled = df_scaled.set_index('team')"
   ]
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  {
   "cell_type": "markdown",
   "id": "4d657f5c-0033-4570-b20a-fb11a223f854",
   "metadata": {},
   "source": [
    "# Determine k and Model Dataset"
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   "cell_type": "code",
   "execution_count": 5,
   "id": "c2c0460d-263c-4058-93ef-902e93eb2270",
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      "C:\\Users\\grant\\anaconda3\\envs\\dev\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\grant\\anaconda3\\envs\\dev\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\grant\\anaconda3\\envs\\dev\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\grant\\anaconda3\\envs\\dev\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\grant\\anaconda3\\envs\\dev\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\grant\\anaconda3\\envs\\dev\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\grant\\anaconda3\\envs\\dev\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\grant\\anaconda3\\envs\\dev\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\grant\\anaconda3\\envs\\dev\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\grant\\anaconda3\\envs\\dev\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n"
     ]
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      "text/plain": [
       ":Curve   [k]   (inertia)"
      ]
     },
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   "source": [
    "# Create a a list to store inertia values\n",
    "inertia = []\n",
    "\n",
    "# Create a a list to store the values of k\n",
    "k = list(range(1, 11))\n",
    "\n",
    "# Create a for loop where each value of k is evaluated using the K-means algorithm\n",
    "for i in k:\n",
    "    k_model = KMeans(n_clusters=i, random_state=1)\n",
    "    k_model.fit(df_scaled)\n",
    "    inertia.append(k_model.inertia_)\n",
    "\n",
    "# Create a dictionary that holds the list values for k and inertia\n",
    "elbow_data = {\"k\": k, \"inertia\": inertia}\n",
    "\n",
    "# Create a DataFrame using the elbow_data dictionary\n",
    "df_elbow_data = pd.DataFrame(elbow_data)\n",
    "\n",
    "# Plot the DataFrame\n",
    "df_elbow_data.hvplot.line(\n",
    "    x=\"k\", \n",
    "    y=\"inertia\", \n",
    "    title=\"Elbow Curve\", \n",
    "    xticks=k\n",
    ")"
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  {
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   "execution_count": 6,
   "id": "5909218e-5df7-43eb-9b67-8343449416fc",
   "metadata": {
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\grant\\anaconda3\\envs\\dev\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Input value for k from Elbox Curve\n",
    "k = 3\n",
    "\n",
    "# Initialize the K-Means model\n",
    "model = KMeans(n_clusters=int(k), random_state=1)\n",
    "\n",
    "# Fit the K-Means model using the scaled data\n",
    "model.fit(df_scaled)\n",
    "\n",
    "# Predict the clusters to group\n",
    "groups = model.predict(df_scaled)\n",
    "\n",
    "# Create a copy of the Dataframe and update with the predicted groups\n",
    "df_predictions = df_scaled.copy()\n",
    "df_predictions['group'] = groups"
   ]
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  {
   "cell_type": "markdown",
   "id": "02a9a5b0-ae34-408d-9299-de58e51a9a37",
   "metadata": {},
   "source": [
    "# Generate User Input Graphs"
   ]
  },
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   "id": "caeb3cfc-dc5e-40ba-a727-a12d811ed0a2",
   "metadata": {},
   "source": [
    "#### Available input values: \n",
    "* wins\n",
    "* losses\n",
    "* winning_pct\n",
    "* ppg_offense\n",
    "* ppg_defense\n",
    "* SRS\n",
    "* SOS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cc4c19a0-f4bc-4648-a74d-43f7b706c7ea",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Select your independent variable (x axis value) from available inputs list SOS\n",
      "Select your dependent variable (y axis value) from available inputs list SRS\n",
      "Select the team to analyze Nebraska\n"
     ]
    },
    {
     "data": {},
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     "output_type": "display_data"
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\"},\"shape\":[1209],\"dtype\":\"float64\",\"order\":\"little\"}],[\"SRS\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"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       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
       "\treturn null\n",
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       "      return root.Bokeh.versions.get(py_version);\n",
       "    } else if (root.Bokeh.version === py_version) {\n",
       "      return root.Bokeh\n",
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       "    var Bokeh = get_bokeh(root)\n",
       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
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      ],
      "text/plain": [
       ":Overlay\n",
       "   .NdOverlay.I  :NdOverlay   [group]\n",
       "      :Scatter   [SOS]   (SRS,team)\n",
       "   .NdOverlay.II :NdOverlay   [group]\n",
       "      :Scatter   [SOS]   (SRS,team)"
      ]
     },
     "execution_count": 7,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "p1148"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate the Variables\n",
    "x = input('Select your independent variable (x axis value) from available inputs list')\n",
    "y = input('Select your dependent variable (y axis value) from available inputs list')\n",
    "team = input('Select the team to analyze')\n",
    "title = f'Clustering Analysis for {team} comparing {y} vs {x}'\n",
    "\n",
    "# Generate the main scatter plot\n",
    "scatter_plot = df_predictions.reset_index().hvplot.scatter(\n",
    "    x=x,\n",
    "    y=y,\n",
    "    by=\"group\",\n",
    "    hover_cols=\"team\"\n",
    ").opts(title=title, yformatter=\"%.0f\", width=750, height=500)\n",
    "\n",
    "# Generate the filtered plot\n",
    "filter_condition = df_predictions.index.str.startswith(f'({team})-')\n",
    "\n",
    "# Generate a scatter plot with stars for selected team\n",
    "filtered_points = df_predictions.loc[filter_condition].reset_index().hvplot.scatter(\n",
    "    x=x,\n",
    "    y=y,\n",
    "    by=\"group\",\n",
    "    hover_cols=\"team\",\n",
    "    marker='s',\n",
    "    color='black',\n",
    "    size=50\n",
    ").opts(show_legend=False, width=750, height=500)\n",
    "\n",
    "# Overlay the two plots to show the original scatter plot and the filtered points with stars\n",
    "final_plot = scatter_plot * filtered_points\n",
    "\n",
    "# Render the final plot\n",
    "final_plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c6be100-701d-41e4-aec4-10ef45302d8b",
   "metadata": {},
   "source": [
    "# Export Center"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3d118f3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Determine the frequency of teams in each group\n",
    "df_team_count = df_predictions.copy().reset_index()\n",
    "\n",
    "# Splitting the 'team' column into 'school' and 'season' and dropping scaled data\n",
    "df_team_count = df_team_count['team'].str.split('-', expand=True)\n",
    "\n",
    "# Renaming columns using the rename method and fill in cluster group values\n",
    "df_team_count = df_team_count.rename(columns={0: 'team', 1: 'year', 2: 'group'})\n",
    "df_team_count['team'] = df_team_count['team'].str.replace(r\"\\(\", \"\", regex=True)\n",
    "df_team_count['team'] = df_team_count['team'].str.replace(r\"\\)\", \"\", regex=True)\n",
    "df_team_count['group'] = list(df_predictions['group'])\n",
    "\n",
    "# CONCAT original dataset with cluster values\n",
    "df1 = df_data.copy().reset_index().drop('team', axis=1)\n",
    "df_output = pd.concat([df_team_count, df1], axis=1)\n",
    "\n",
    "# Output file\n",
    "df_output.to_csv('output/clustering_analysis.csv', index=False)"
   ]
  }
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