docs/thicket_rajaperf_clustering.ipynb

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    "# Clustering RAJA Performance Suite Dataset: Thicket Tutorial\n",
    "\n",
    "Thicket is a python-based toolkit for Exploratory Data Analysis (EDA) of parallel performance data that enables performance optimization and understanding of applications’ performance on supercomputers. It bridges the performance tool gap between being able to consider only a single instance of a simulation run (e.g., single platform, single measurement tool, or single scale) and finding actionable insights in multi-dimensional, multi-scale, multi-architecture, and multi-tool performance datasets.\n",
    "\n",
    "**NOTE: An interactive version of this notebook is available in the Binder environment.**\n",
    "\n",
    "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/llnl/thicket-tutorial/develop)\n",
    "\n",
    "***\n",
    "\n",
    "## 1. Import Necessary Packages"
   ]
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       "var Roundtrip_Obj = {};\n",
       "var refresh_cycle = false;\n",
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       "var cached_cells = Jupyter.notebook.get_cell_elements();\n",
       "\n",
       "/**\n",
       " * @name unindentPyCode\n",
       " * @description Removes leading indentations from a python code string.\n",
       " * \n",
       " * @param {string} code Python code in string form\n",
       " * @returns Passed code string but with no leading indentations\n",
       " */\n",
       "function unindentPyCode(code){\n",
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       "\n",
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       "    return uicode;\n",
       "}\n",
       "\n",
       "/**\n",
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       " * @description Builds up a python code string which assigns javascript data back into jypyter notebook namespace\n",
       " * \n",
       " * @param {string} val This is data assigned back to the python code\n",
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       " * @param {string} converter This is a definition of a python function which translates data back to the desired format\n",
       " * @returns The python code to be run in the jupyter shell\n",
       " */\n",
       "function buildPythonAssignment(val, py_var, converter){\n",
       "    // console.log(val, py_var, converter);\n",
       "    var holder = `'${val}'`;\n",
       "    var code = `${unindentPyCode(converter.code)}`\n",
       "    code += `\\ntmp = ${holder}`;\n",
       "    code += `\\n${py_var} = ${converter.name}(tmp)`\n",
       "\n",
       "    return code\n",
       "}\n",
       "\n",
       "/**\n",
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       " * \n",
       " * @description  Increments all two way bound cell ids by the number of new cells which proceed them. \n",
       " *  Ex. Adding one cell at position 2 will increment a bound cell at position 3 from 3->4. \n",
       " * \n",
       " * @param {array} newCells A list of our current cells in the notebook to be compared against cached cells\n",
       " * @param {} obj The current roundtrip object containing all data bindings\n",
       " */\n",
       "function manageNewCell(newCells, obj){\n",
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       "\n",
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       "    });\n",
       "\n",
       "    //increment all bindings past each new id\n",
       "    for(let js_var in obj){\n",
       "        for(let id of newIds){\n",
       "            for(let key in obj[js_var][\"two_way\"]){\n",
       "                obj[js_var][\"two_way\"][key].forEach((two_way_id, i) => {\n",
       "                    if(two_way_id > id){\n",
       "                        obj[js_var][\"two_way\"][key][i] += 1;\n",
       "                    }\n",
       "                });\n",
       "            }\n",
       "        }   \n",
       "    }\n",
       "\n",
       "    cached_cells = newCells;\n",
       "}\n",
       "\n",
       "function manageDeletedCell(newCells, obj){\n",
       "    let deletedId = null;\n",
       "    \n",
       "    for(i of Object.keys(cachedCells)){\n",
       "        if (cached_cells[i] !== newCells[i]){\n",
       "            deletedId = i;\n",
       "            break;\n",
       "        }\n",
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       "\n",
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       "\n",
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       "\n",
       "        if(cell !== undefined){\n",
       "            cell.addEventListener('mousedown', () => {\n",
       "                    clicked_cell = i;\n",
       "                }, true)\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "bindClickDetectToCells();\n",
       "\n",
       "/**\n",
       " * @name RT_Handler\n",
       " * @description A wrapper for our roundtrip object. It is called as a proxy for the\n",
       " *      roundtrip object defined above. This enables us to define custom call backs for\n",
       " *      gets and sets on the roundtrip object. The custom set handles necessary data conversion,\n",
       " *      the registering of two-way bound variables and automatic updating of watched cells. The get\n",
       " *      allows users to interact with the underlying object without worrying about the proxy.\n",
       " */\n",
       "var RT_Handler = {\n",
       "    set(obj, prop, value){\n",
       "        //Do cell housekeeping\n",
       "\n",
       "\n",
       "        //Initial pass of value into roundtrip object\n",
       "        // from python code; there may be multiple different\n",
       "        // visualizations of the same type we need to catch\n",
       "        if (typeof value === 'object' && value.hasOwnProperty('origin') && value.origin == 'INIT'){\n",
       "            \n",
       "            /**\n",
       "             * In this code block we need to check if there is already a \n",
       "             * an array of id's which are two way bound already defined and \n",
       "             * add to it or remove from it\n",
       "             */\n",
       "            let ida = Jupyter.notebook.get_selected_index()-1;\n",
       "            value.id = ida;\n",
       "            let new_val = value;\n",
       "\n",
       "            // Block updating bindings while jupyter is running\n",
       "            if(refresh_cycle){\n",
       "                new_val = obj[prop];\n",
       "                new_val.data = value.data;\n",
       "                return Reflect.set(obj, prop, new_val);\n",
       "            }\n",
       "\n",
       "            /**\n",
       "             * The broad case where we are updating bindings \n",
       "             * on existing data\n",
       "             */\n",
       "            if(obj[prop] != undefined){\n",
       "                new_val = obj[prop];\n",
       "                new_val.data = value.data;\n",
       "                new_val.converter = value.converter;\n",
       "\n",
       "                // If there is no two way array, create one\n",
       "                // Else push on our new id\n",
       "                if(value.two_way === true){\n",
       "                    if(!Object.keys(new_val.two_way).includes(value['python_var'])){\n",
       "                        new_val.two_way[value['python_var']] = [];\n",
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       "\n",
       "                    let pybinding = new_val.two_way[value['python_var']];\n",
       "\n",
       "                    if(!pybinding.includes(value.id)){\n",
       "                        pybinding.push(value.id);\n",
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       "\n",
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       "\n",
       "                //Deregister a cell id from being two-way bound now\n",
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       "                    let pybinding = new_val.two_way[value['python_var']];\n",
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       "                    \n",
       "                    if (index > -1) {\n",
       "                        pybinding.splice(index, 1);\n",
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       "\n",
       "            //Initalize a new two-way object if\n",
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       "                if(new_val.two_way == true){\n",
       "                    new_val.two_way = {};\n",
       "                    new_val.two_way[value['python_var']] = [value.id];\n",
       "                }\n",
       "                else{\n",
       "                    new_val.two_way = {};\n",
       "                }\n",
       "                delete new_val.id;\n",
       "                delete new_val.from_py;\n",
       "                delete new_val.python_var;\n",
       "            }\n",
       "\n",
       "            return Reflect.set(obj, prop, new_val);\n",
       "        }\n",
       "        //Assignment from javascript code\n",
       "        else {\n",
       "            // TODO: make the py/js data identification object a\n",
       "            // formal class\n",
       "            if(obj[prop] === undefined){\n",
       "                obj[prop] = {\n",
       "                    two_way: {},\n",
       "                    origin: \"JS\",\n",
       "                    data: null,\n",
       "                    python_var: \"\",\n",
       "                    converter: null,\n",
       "                    type: typeof(value)\n",
       "                }\n",
       "            }\n",
       "\n",
       "            var execable_cells = [];\n",
       "            let origin = 'STANDARD';\n",
       "            let python_var = '';\n",
       "\n",
       "            if (typeof value === 'object' && \n",
       "                value.hasOwnProperty('origin') && \n",
       "                value.origin == 'PYASSIGN'){\n",
       "\n",
       "                origin = value.origin;\n",
       "                python_var = value.python_var;\n",
       "                value = value.data;\n",
       "            }\n",
       "\n",
       "            //TODO: Replace with imported, webpacked D3\n",
       "            require(['https://d3js.org/d3.v4.min.js'], function(d3) {\n",
       "\n",
       "                // When 2 way bound this calls automatically when something changes\n",
       "                if (obj[prop] !== undefined && Object.keys(obj[prop][\"two_way\"]).length > 0){\n",
       "\n",
       "                    let current_cell = Number(clicked_cell);\n",
       "                    let py_var = '';\n",
       "\n",
       "                    //ust set the data without updating if our current cell is not two way bound\n",
       "                    if(origin == 'STANDARD'){\n",
       "                        let found = false;\n",
       "                        for(let key in obj[prop][\"two_way\"]){\n",
       "                            if (obj[prop][\"two_way\"][key].includes(current_cell)){\n",
       "                                found = true;\n",
       "                                py_var = key;\n",
       "                            }\n",
       "                        }\n",
       "\n",
       "                        if(!found){\n",
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       "\n",
       "\n",
       "                    if(origin == 'PYASSIGN'){\n",
       "                        py_var = python_var;\n",
       "                    }\n",
       "\n",
       "\n",
       "                    /**\n",
       "                     * We now have a list of registered cells we can execute.\n",
       "                     * So we look through our javascript variables to see if they\n",
       "                     * are bound to the same py variable as our current assignment\n",
       "                     * TODO: Make this list update when cells are moved up or down\n",
       "                     */\n",
       "\n",
       "                    for(let js_var in obj){\n",
       "                        let boundpyvars =  Object.keys(obj[js_var][\"two_way\"]);\n",
       "\n",
       "                        if(boundpyvars.includes(py_var)){\n",
       "                            let clls = obj[js_var][\"two_way\"][py_var].filter(x => x != current_cell );\n",
       "                            execable_cells = execable_cells.concat(clls);\n",
       "                        }\n",
       "                    }\n",
       "\n",
       "                    if(origin == 'STANDARD'){\n",
       "                        // TODO:THROW AN ERROR IF CONVERTER == NONE\n",
       "                        const code = buildPythonAssignment(value, py_var, obj[prop][\"converter\"]);\n",
       "                        \n",
       "                        //TODO: Turn this into a function that manages error reporting and printing\n",
       "                        Jupyter.notebook.kernel.execute(code, { \n",
       "                                                            shell:{\n",
       "                                                                reply: function(r){\n",
       "                                                                    //consider putting this in a reserved jupyter variable\n",
       "                                                                    if(r.content.status == 'error'){\n",
       "                                                                        console.error(`${r.content.ename} in JS->Python coversion:\\n ${r.content.evalue}`)\n",
       "                                                                    }\n",
       "                                                                }\n",
       "                                                            }\n",
       "                                                        });\n",
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       "\n",
       "                    refresh_cycle = true;\n",
       "                    Jupyter.notebook.execute_cells(execable_cells);\n",
       "\n",
       "                    /**\n",
       "                     * Test every half second to see if some of the\n",
       "                     * jupyter cells are still running. Avoids a race condition\n",
       "                     * where incorrect ids were stored in our roundtrip object.\n",
       "                     */\n",
       "                    const test_running = function(){\n",
       "                        let runtest = d3.selectAll(\".running\");\n",
       "                        if(runtest.empty()){\n",
       "                            refresh_cycle = false;\n",
       "                            return;\n",
       "                        }\n",
       "                        else{\n",
       "                            setTimeout(test_running, 500);\n",
       "                        }\n",
       "                    }\n",
       "\n",
       "                    test_running();\n",
       "                }\n",
       "\n",
       "            });\n",
       "        }   \n",
       "\n",
       "        return Reflect.set(obj[prop], \"data\", value);\n",
       "    },\n",
       "    get(obj, prop, reciever){\n",
       "        let ret = obj[prop].data\n",
       "        return ret;  \n",
       "    }\n",
       "}\n",
       "\n",
       "window.Roundtrip = new Proxy(Roundtrip_Obj, RT_Handler);\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
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    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "import re\n",
    "from itertools import product\n",
    "\n",
    "from IPython.display import display\n",
    "from IPython.display import HTML\n",
    "from IPython.display import Markdown\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import matplotlib.cm as cm\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import silhouette_samples, silhouette_score\n",
    "import hatchet as ht\n",
    "from hatchet import QueryMatcher\n",
    "\n",
    "import thicket as th\n",
    "from thicket import Thicket"
   ]
  },
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   "source": [
    "# Disable the absurd number of warnings that NumPy will produce\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning) "
   ]
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   "source": [
    "## 2. Utility Functions for Ingesting and Manipulating Performance Data"
   ]
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   "source": [
    "def get_perf_leaves(thicket):\n",
    "    \"\"\"Get data associated with only the leaf nodes of the thicket.\n",
    "    \n",
    "    Keyword Arguments:\n",
    "    thicket -- thicket object\n",
    "    \"\"\"\n",
    "    query = QueryMatcher().match(\n",
    "        \".\",\n",
    "        lambda row: len(row.index.get_level_values(\"node\")[0].children) == 0\n",
    "    )\n",
    "    new_thicket = thicket.copy()\n",
    "    matches = query.apply(thicket)\n",
    "    idx_names = new_thicket.dataframe.index.names\n",
    "    new_thicket.dataframe.reset_index(inplace=True)\n",
    "    new_thicket.dataframe = new_thicket.dataframe.loc[new_thicket.dataframe[\"node\"].isin(matches)]\n",
    "    new_thicket.dataframe.set_index(idx_names, inplace=True)\n",
    "    squashed_gf = new_thicket.squash()\n",
    "    new_thicket.graph = squashed_gf.graph\n",
    "    new_thicket.statsframe.graph = squashed_gf.graph\n",
    "    \n",
    "    # NEW: need to update the performance data and the statsframe with the remaining (re-indexed) nodes.\n",
    "    # The dataframe is internally updated in squash(), so we can easily just save it to our thicket perfdata.\n",
    "    # For the statsframe, we'll have to come up with a better way eventually, but for now, we'll just create\n",
    "    #    a new statsframe the same way we do when we create a new thicket. \n",
    "    new_thicket.dataframe = squashed_gf.dataframe    \n",
    "    subset_df = new_thicket.dataframe[\"name\"].reset_index().drop_duplicates(subset=[\"node\"])\n",
    "    new_thicket.statsframe = ht.GraphFrame(\n",
    "        graph=squashed_gf.graph,\n",
    "        dataframe=pd.DataFrame(\n",
    "            index=subset_df[\"node\"],\n",
    "            data={\"name\": subset_df[\"name\"].values},\n",
    "        ),\n",
    "    )\n",
    "    return new_thicket\n",
    "\n",
    "def apply_query_to_thicket(thicket, query):\n",
    "    \"\"\"Applies query to thicket object.\n",
    "    \n",
    "    Keyword Arguments:\n",
    "    thicket -- thicket object\n",
    "    query -- thicket query\n",
    "    \"\"\"\n",
    "    new_thicket = thicket.copy()\n",
    "    matches = query.apply(thicket)\n",
    "    idx_names = new_thicket.dataframe.index.names\n",
    "    new_thicket.dataframe.reset_index(inplace=True)\n",
    "    new_thicket.dataframe = new_thicket.dataframe.loc[new_thicket.dataframe[\"node\"].isin(matches)]\n",
    "    new_thicket.dataframe.set_index(idx_names, inplace=True)\n",
    "    squashed_gf = new_thicket.squash()\n",
    "    new_thicket.graph = squashed_gf.graph\n",
    "    new_thicket.statsframe.graph = squashed_gf.graph\n",
    "    \n",
    "    # NEW: need to update the performance data and the statsframe with the remaining (re-indexed) nodes.\n",
    "    # The dataframe is internally updated in squash(), so we can easily just save it to our thicket perfdata.\n",
    "    # For the statsframe, we'll have to come up with a better way eventually, but for now, we'll just create\n",
    "    #    a new statsframe the same way we do when we create a new thicket. \n",
    "    new_thicket.dataframe = squashed_gf.dataframe    \n",
    "    subset_df = new_thicket.dataframe[\"name\"].reset_index().drop_duplicates(subset=[\"node\"])\n",
    "    new_thicket.statsframe = ht.GraphFrame(\n",
    "        graph=squashed_gf.graph,\n",
    "        dataframe=pd.DataFrame(\n",
    "            index=subset_df[\"node\"],\n",
    "            data={\"name\": subset_df[\"name\"].values},\n",
    "        ),\n",
    "    )\n",
    "    return new_thicket\n",
    "\n",
    "def get_node(thicket, node_name):\n",
    "    \"\"\"Get data associated with only the node corresponding to node_name.\n",
    "    \n",
    "    Keyword Arguments:\n",
    "    thicket -- thicket object\n",
    "    node_name -- (str) name of a node\n",
    "    \"\"\"\n",
    "    query = QueryMatcher().match(\n",
    "        \".\",\n",
    "        lambda row: row.index.get_level_values(\"node\")[0].frame[\"name\"] == node_name\n",
    "    )        \n",
    "\n",
    "    return apply_query_to_thicket(thicket, query)\n",
    "\n",
    "def get_nodes_in_group(thicket, group_prefix):\n",
    "    \"\"\"Get data associated with kernels with the specified name prefix\n",
    "    \n",
    "    Keyword Arguments:\n",
    "    thicket -- thicket object\n",
    "    group_prefix -- prefix to satisfy startswith condition\n",
    "    \"\"\"\n",
    "    nodes_thicket = get_perf_leaves(thicket)\n",
    "    query = QueryMatcher().match(\n",
    "        \".\",\n",
    "        lambda row: row.index.get_level_values(\"node\")[0].frame[\"name\"].startswith(group_prefix)\n",
    "    )\n",
    "    return apply_query_to_thicket(nodes_thicket, query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cc7affa3",
   "metadata": {
    "execution": {
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   "source": [
    "def check_for_optimization_level(val):\n",
    "    match = re.search(r\"(?P<opt_level>-O[0123]).*\", val)\n",
    "    if match is None:\n",
    "        raise ValueError(\"Could not find opt level in {}\".format(val))\n",
    "    return match.group(\"opt_level\")\n",
    "\n",
    "def check_for_optimization_level_int(val):\n",
    "    match = re.search(r\"-O(?P<opt_level>[0-3]).*\", val)\n",
    "    if match is None:\n",
    "        raise ValueError(\"Could not find opt level in {}\".format(val))\n",
    "    return match.group(\"opt_level\")\n",
    "\n",
    "def add_metadata_to_thicket(thicket):\n",
    "    \"\"\"Insert additional metadata columns to the performance data table.\n",
    "    \n",
    "    Keyword Arguments:\n",
    "    thicket -- thicket object\n",
    "    \"\"\"\n",
    "    thicket.metadata[\"opt_level\"] = thicket.metadata[\"rajaperf_compiler_options\"].apply(\n",
    "        check_for_optimization_level\n",
    "    )\n",
    "    thicket.metadata[\"opt_level_int\"] = thicket.metadata[\"rajaperf_compiler_options\"].apply(\n",
    "        check_for_optimization_level_int\n",
    "    )\n",
    "    thicket.metadata_column_to_perfdata(\"opt_level\")\n",
    "    thicket.metadata_column_to_perfdata(\"opt_level_int\")\n",
    "    thicket.metadata_column_to_perfdata(\"compiler\")\n",
    "    return [\"opt_level\", \"opt_level_int\", \"compiler\"], thicket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5d602c2d",
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   "source": [
    "def collect_ensemble_data(directories, metrics, exc_time_metric=\"time (exc)\", profile_level_name=\"profile\"):\n",
    "    \"\"\"Create a thicket from multiple profiles with specified metric columns.\n",
    "    \n",
    "    Keyword Arguments:\n",
    "    directories -- list of paths to profiles\n",
    "    metrics -- perf metrics\n",
    "    \"\"\"\n",
    "    real_metrics = metrics.copy()\n",
    "    if \"name\" not in real_metrics:\n",
    "        real_metrics.append(\"name\")\n",
    "    if \"nid\" not in real_metrics:\n",
    "        real_metrics.append(\"nid\")\n",
    "    if exc_time_metric not in real_metrics:\n",
    "        real_metrics.append(exc_time_metric)\n",
    "    subthickets = []\n",
    "    for directory in directories:\n",
    "        thicket = Thicket.from_caliperreader(directory)\n",
    "        thicket.dataframe = thicket.dataframe[[*real_metrics]]\n",
    "        thicket.exc_metrics = [m for m in thicket.dataframe.columns if m in thicket.exc_metrics]\n",
    "        thicket.inc_metrics = [m for m in thicket.dataframe.columns if m in thicket.inc_metrics]\n",
    "        if thicket.default_metric not in thicket.dataframe.columns:\n",
    "            if exc_time_metric in thicket.dataframe.columns:\n",
    "                thicket.default_metric = exc_time_metric\n",
    "            else:\n",
    "                raise ValueError(\"Could not set default metric\")\n",
    "        subthickets.append(thicket)\n",
    "    return Thicket.concat_thickets(axis=\"index\", thickets=subthickets)"
   ]
  },
  {
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   "execution_count": 6,
   "id": "d1880c5e",
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   "source": [
    "def calc_speedup_by_opt_level(df, opt_level_metric=\"opt_level\", name_metric=\"name\",\n",
    "                              exc_time_metric=\"time (exc)\", min_opt_level=\"-O0\"):\n",
    "    \"\"\"Calculate speedup based on optimization level (e.g., O0/O1, O0/O2, etc.).\n",
    "    \n",
    "    Keyword Arguments:\n",
    "    df -- performace data table\n",
    "    \"\"\"\n",
    "    index_names = df.index.names\n",
    "    df.reset_index(inplace=True)\n",
    "    min_opt_groups = df.groupby(\n",
    "        by=name_metric\n",
    "    )\n",
    "    min_opt_times = {}\n",
    "    for group_name, group in min_opt_groups:\n",
    "        min_time = group.loc[group[opt_level_metric] == min_opt_level, exc_time_metric].iloc[0]\n",
    "        min_opt_times[group_name] = min_time\n",
    "        \n",
    "    def calc_speedup(row):\n",
    "        base_time = min_opt_times[row[name_metric]]\n",
    "        ret_val = base_time / row[exc_time_metric]\n",
    "        return ret_val\n",
    "    \n",
    "    speedup_col = df.apply(calc_speedup, axis=\"columns\")\n",
    "    df[\"speedup\"] = speedup_col\n",
    "    \n",
    "    df.set_index(index_names, inplace=True)\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a811cb0a",
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 3. Utility Functions for KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0534d891",
   "metadata": {
    "execution": {
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   "source": [
    "def build_clustering_df(gf, metrics):\n",
    "    tmp_df = gf.dataframe.reset_index()\n",
    "    real_metrics = metrics\n",
    "    if \"name\" in tmp_df.columns and \"name\" not in metrics:\n",
    "        real_metrics.append(\"name\")\n",
    "    return tmp_df[[*metrics]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cddd1f09",
   "metadata": {
    "execution": {
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   "source": [
    "def normalize_data(df, metrics, **kwargs):\n",
    "    \"\"\"Creates normalized versions of the specified metrics.\n",
    "    \n",
    "    Keyword Arguments:\n",
    "    df -- performace data table\n",
    "    metrics -- perf metrics\n",
    "    \"\"\"\n",
    "    scaler = StandardScaler()\n",
    "    data = df[[*metrics]].to_numpy()\n",
    "    new_data = scaler.fit_transform(data)\n",
    "    for col in range(new_data.shape[1]):\n",
    "        df[\"{}_norm\".format(metrics[col])] = new_data[:, col]\n",
    "    return df\n",
    "\n",
    "\n",
    "def cluster_data(df, x_metric, y_metric, n_clusters=8, use_y_for_label=True):\n",
    "    \"\"\"Conducts a KMeans clustering of data from the DataFrame.\n",
    "    \n",
    "    Keyword Arguments:\n",
    "    df -- performace data table\n",
    "    n_clusters -- no. of clusters\n",
    "    \"\"\"\n",
    "    print(x_metric, y_metric)\n",
    "    km = KMeans(n_clusters=n_clusters)\n",
    "    pts = df[[x_metric, y_metric]].to_numpy()\n",
    "    cluster_labels = km.fit_predict(pts)\n",
    "    label_metric = y_metric if use_y_for_label else x_metric\n",
    "    if label_metric.endswith(\"_norm\"):\n",
    "        label_metric = label_metric[:-len(\"_norm\")]\n",
    "    cluster_label = \"clusters\" if label_metric.startswith(\"pca\") else \"{}_cluster\".format(label_metric)\n",
    "    df[\"{}_id\".format(cluster_label)] = cluster_labels\n",
    "    df[cluster_label] = [\"Cluster {}\".format(l) for l in cluster_labels]\n",
    "    return cluster_label, km.cluster_centers_, df"
   ]
  },
  {
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   "execution_count": 9,
   "id": "06bcf566",
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   "source": [
    "# Modified version of the code from\n",
    "# https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_silhouette_analysis.html\n",
    "def silhouette_test(df, x_metric, y_metric):\n",
    "    \"\"\"Silhouette analysis on KMeans clustering.\n",
    "    \n",
    "    Keyword Arguments:\n",
    "    df -- performace data table\n",
    "    \"\"\"\n",
    "    range_n_clusters = [2, 3, 4, 5, 6]\n",
    "    \n",
    "    pts = df[[x_metric, y_metric]].to_numpy()\n",
    "\n",
    "    for n_clusters in range_n_clusters:\n",
    "        # Create a subplot with 1 row and 2 columns\n",
    "        fig, (ax1, ax2) = plt.subplots(1, 2)\n",
    "        fig.set_size_inches(18, 7)\n",
    "\n",
    "        # The 1st subplot is the silhouette plot\n",
    "        # The silhouette coefficient can range from -1, 1 but in this example all\n",
    "        # lie within [-0.1, 1]\n",
    "        ax1.set_xlim([-0.1, 1])\n",
    "        # The (n_clusters+1)*10 is for inserting blank space between silhouette\n",
    "        # plots of individual clusters, to demarcate them clearly.\n",
    "        ax1.set_ylim([0, len(pts) + (n_clusters + 1) * 10])\n",
    "\n",
    "        # Initialize the clusterer with n_clusters value and a random generator\n",
    "        # seed of 10 for reproducibility.\n",
    "        clusterer = KMeans(n_clusters=n_clusters, random_state=10)\n",
    "        cluster_labels = clusterer.fit_predict(pts)\n",
    "\n",
    "        # The silhouette_score gives the average value for all the samples.\n",
    "        # This gives a perspective into the density and separation of the formed\n",
    "        # clusters\n",
    "        silhouette_avg = silhouette_score(pts, cluster_labels)\n",
    "        print(\n",
    "            \"For n_clusters =\",\n",
    "            n_clusters,\n",
    "            \"The average silhouette_score is :\",\n",
    "            silhouette_avg,\n",
    "        )\n",
    "\n",
    "        # Compute the silhouette scores for each sample\n",
    "        sample_silhouette_values = silhouette_samples(pts, cluster_labels)\n",
    "        \n",
    "        y_lower = 10\n",
    "        for i in range(n_clusters):\n",
    "            # Aggregate the silhouette scores for samples belonging to\n",
    "            # cluster i, and sort them\n",
    "            ith_cluster_silhouette_values = sample_silhouette_values[cluster_labels == i]\n",
    "\n",
    "            ith_cluster_silhouette_values.sort()\n",
    "\n",
    "            size_cluster_i = ith_cluster_silhouette_values.shape[0]\n",
    "            y_upper = y_lower + size_cluster_i\n",
    "\n",
    "            color = cm.nipy_spectral(float(i) / n_clusters)\n",
    "            ax1.fill_betweenx(\n",
    "                np.arange(y_lower, y_upper),\n",
    "                0,\n",
    "                ith_cluster_silhouette_values,\n",
    "                facecolor=color,\n",
    "                edgecolor=color,\n",
    "                alpha=0.7,\n",
    "            )\n",
    "\n",
    "            # Label the silhouette plots with their cluster numbers at the middle\n",
    "            ax1.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\n",
    "\n",
    "            # Compute the new y_lower for next plot\n",
    "            y_lower = y_upper + 10  # 10 for the 0 samples\n",
    "\n",
    "        ax1.set_title(\"The silhouette plot for the various clusters.\")\n",
    "        ax1.set_xlabel(\"The silhouette coefficient values\")\n",
    "        ax1.set_ylabel(\"Cluster label\")\n",
    "\n",
    "        # The vertical line for average silhouette score of all the values\n",
    "        ax1.axvline(x=silhouette_avg, color=\"red\", linestyle=\"--\")\n",
    "\n",
    "        ax1.set_yticks([])  # Clear the yaxis labels / ticks\n",
    "        ax1.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])\n",
    "\n",
    "        # 2nd Plot showing the actual clusters formed\n",
    "        colors = cm.nipy_spectral(cluster_labels.astype(float) / n_clusters)\n",
    "        ax2.scatter(\n",
    "            pts[:, 0],\n",
    "            pts[:, 1],\n",
    "            marker=\".\",\n",
    "            s=30,\n",
    "            lw=0,\n",
    "            alpha=0.7,\n",
    "            c=colors,\n",
    "            edgecolor=\"k\"\n",
    "        )\n",
    "\n",
    "        # Labeling the clusters\n",
    "        centers = clusterer.cluster_centers_\n",
    "        # Draw white circles at cluster centers\n",
    "        ax2.scatter(\n",
    "            centers[:, 0],\n",
    "            centers[:, 1],\n",
    "            marker=\"o\",\n",
    "            c=\"white\",\n",
    "            alpha=1,\n",
    "            s=200,\n",
    "            edgecolor=\"k\",\n",
    "        )\n",
    "\n",
    "        for i, c in enumerate(centers):\n",
    "            ax2.scatter(\n",
    "                 c[0],\n",
    "                 c[1],\n",
    "                marker=\"$%d$\" % i,\n",
    "                alpha=1,\n",
    "                s=50,\n",
    "                edgecolor=\"k\"\n",
    "            )\n",
    "\n",
    "        ax2.set_title(\"The visualization of the clustered data.\")\n",
    "        ax2.set_xlabel(\"Feature space for the 1st feature\")\n",
    "        ax2.set_ylabel(\"Feature space for the 2nd feature\")\n",
    "\n",
    "        plt.suptitle(\n",
    "            \"Silhouette analysis for KMeans clustering on sample data with n_clusters = %d\"\n",
    "            % n_clusters,\n",
    "            fontsize=14,\n",
    "            fontweight=\"bold\",\n",
    "        )\n",
    "\n",
    "    plt.show()"
   ]
  },
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   "source": [
    "def plot_clusters(df, x_metric, y_metric, hue_metric, marker_style_metric=None, \n",
    "                  swap_marker_and_hue=False, cluster_centers=None, use_y_for_label=True,\n",
    "                  **kwargs):\n",
    "    \"\"\"Plot cluster with specific configuration.\n",
    "    \n",
    "    Keyword Arguments:\n",
    "    df -- performace data table\n",
    "    \"\"\"\n",
    "    hue = hue_metric\n",
    "    df = df.sort_values(\n",
    "        [\n",
    "            \"opt_level_int\",\n",
    "            hue\n",
    "        ],\n",
    "    )\n",
    "    ax = None\n",
    "    if marker_style_metric is not None:\n",
    "        if swap_marker_and_hue:\n",
    "            hue_order = sorted(df[marker_style_metric].unique().tolist())\n",
    "            style_order = sorted(df[hue].unique().tolist())\n",
    "            ax = sns.scatterplot(\n",
    "                data=df,\n",
    "                x=x_metric,\n",
    "                y=y_metric,\n",
    "                hue=marker_style_metric,\n",
    "                style=hue,\n",
    "                legend=\"auto\",\n",
    "                hue_order=hue_order,\n",
    "                style_order=style_order,\n",
    "                **kwargs\n",
    "            )\n",
    "        else:\n",
    "            hue_order = sorted(df[hue].unique().tolist())\n",
    "            style_order = sorted(df[marker_style_metric].unique().tolist())\n",
    "            ax = sns.scatterplot(\n",
    "                data=df,\n",
    "                x=x_metric,\n",
    "                y=y_metric,\n",
    "                hue=hue,\n",
    "                style=marker_style_metric,\n",
    "                legend=\"auto\",\n",
    "                hue_order=hue_order,\n",
    "                style_order=style_order,\n",
    "                **kwargs\n",
    "            )\n",
    "    else:\n",
    "        hue_order = sorted(df[hue].unique().tolist())\n",
    "        ax = sns.scatterplot(\n",
    "            data=df,\n",
    "            x=x_metric,\n",
    "            y=y_metric,\n",
    "            hue=hue,\n",
    "            legend=\"auto\",\n",
    "            hue_order=hue_order,\n",
    "            **kwargs\n",
    "        )\n",
    "    return ax"
   ]
  },
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   "source": [
    "def plot_lines_by_profile(df, x_metric, y_metric, grouping_metric, axes, legend=\"auto\"):\n",
    "    \"\"\"Plot line with specific configuration.\"\"\"\n",
    "    sns.lineplot(\n",
    "        data=df,\n",
    "        x=x_metric,\n",
    "        y=y_metric,\n",
    "        style=grouping_metric,\n",
    "        ax=axes,\n",
    "        legend=legend,\n",
    "        color=\"#c0c0c0\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c922e597",
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   "source": [
    "## 4. Set Variables to be Used for All Clustering\n",
    "\n",
    "Set `directories` to be a list of the directories containing the `.cali` files to load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a25f953f",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "root = \"../data/quartz\"\n",
    "dirs_ordered = [\n",
    "    \"GCC_10.3.1_BaseSeq_08388608/O0/GCC_1031_BaseSeq_O0_8388608_01.cali\",\n",
    "    \"GCC_10.3.1_BaseSeq_08388608/O1/GCC_1031_BaseSeq_O1_8388608_01.cali\",\n",
    "    \"GCC_10.3.1_BaseSeq_08388608/O2/GCC_1031_BaseSeq_O2_8388608_01.cali\",\n",
    "    \"GCC_10.3.1_BaseSeq_08388608/O3/GCC_1031_BaseSeq_O3_8388608_01.cali\"\n",
    "]\n",
    "directories = [\"{}/{}\".format(root, d) for d in dirs_ordered]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f345aff7",
   "metadata": {
    "papermill": {
     "duration": 0.002828,
     "end_time": "2024-04-05T18:05:41.323430",
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     "start_time": "2024-04-05T18:05:41.320602",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Set `topdown_cols` to be a list containing the Topdown metrics to be used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9c32973c",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "topdown_cols=[\n",
    "    \"Retiring\",\n",
    "    \"Frontend bound\",\n",
    "    \"Backend bound\",\n",
    "    \"Bad speculation\",\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e479a3a6",
   "metadata": {
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 5. Thicket Clustering\n",
    "\n",
    "Collect the performance data from Quartz. Then, normalize and cluster the 4 high-level topdown metrics (i.e., retiring, frontend bound, backend bound, and bad speculation) with respect to time. Finally, plot the clusters and produce a dataframe containing the node name, metrics, cluster ID, and optimization level.\n",
    "\n",
    "Set variables used for clustering:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "10cfa497",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-05T18:05:41.344518Z",
     "iopub.status.busy": "2024-04-05T18:05:41.344421Z",
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    },
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   },
   "outputs": [],
   "source": [
    "# If desired, set max_time_variance_node to the node to focus on for the clustering\n",
    "max_time_variance_node = \"Stream\"\n",
    "# Specify the number of clusters to create for each topdown metric\n",
    "n_clusters_retiring = 3\n",
    "n_clusters_frontend_bound = 4\n",
    "n_clusters_backend_bound = 3\n",
    "n_clusters_bad_speculation = 4\n",
    "# If desired, scale the topdown metrics by this factor before clustering\n",
    "topdown_scaling_factor = 1\n",
    "# If true, use sklearn's StandardScaler to create normalized metrics\n",
    "use_normalization = True"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9315af2a",
   "metadata": {
    "papermill": {
     "duration": 0.002846,
     "end_time": "2024-04-05T18:05:41.353576",
     "exception": false,
     "start_time": "2024-04-05T18:05:41.350730",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Read data into a single Thicket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e439c1a1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-05T18:05:41.359705Z",
     "iopub.status.busy": "2024-04-05T18:05:41.359609Z",
     "iopub.status.idle": "2024-04-05T18:05:41.483189Z",
     "shell.execute_reply": "2024-04-05T18:05:41.482847Z"
    },
    "papermill": {
     "duration": 0.12737,
     "end_time": "2024-04-05T18:05:41.483801",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.9/site-packages/thicket/ensemble.py:319: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  perfdata[col].replace({fill_value: None}, inplace=True)\n"
     ]
    }
   ],
   "source": [
    "thicket = collect_ensemble_data(directories, topdown_cols)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4ea7b97",
   "metadata": {
    "papermill": {
     "duration": 0.002887,
     "end_time": "2024-04-05T18:05:41.489898",
     "exception": false,
     "start_time": "2024-04-05T18:05:41.487011",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**NOTE:** Only run one of the next three cells\n",
    "\n",
    "Cell 1: Query the Thicket object to get data associated with only the node corresponding to `max_time_variance_node`\n",
    "\n",
    "Cell 2: Query the Thicket object to get data associated with only the leaf nodes\n",
    "\n",
    "Cell 3: Query the Thicket object to get data associated with kernels with the specified name prefix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "aea168dc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-05T18:05:41.496217Z",
     "iopub.status.busy": "2024-04-05T18:05:41.496124Z",
     "iopub.status.idle": "2024-04-05T18:05:41.519410Z",
     "shell.execute_reply": "2024-04-05T18:05:41.518912Z"
    },
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     "end_time": "2024-04-05T18:05:41.520609",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "single_node_thicket = get_node(thicket, max_time_variance_node)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "11be2ef5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-05T18:05:41.527303Z",
     "iopub.status.busy": "2024-04-05T18:05:41.527185Z",
     "iopub.status.idle": "2024-04-05T18:05:41.904114Z",
     "shell.execute_reply": "2024-04-05T18:05:41.903760Z"
    },
    "papermill": {
     "duration": 0.381142,
     "end_time": "2024-04-05T18:05:41.904942",
     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "single_node_thicket = get_perf_leaves(thicket)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cf9b54ef",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-05T18:05:41.912018Z",
     "iopub.status.busy": "2024-04-05T18:05:41.911924Z",
     "iopub.status.idle": "2024-04-05T18:05:42.366200Z",
     "shell.execute_reply": "2024-04-05T18:05:42.365791Z"
    },
    "papermill": {
     "duration": 0.458586,
     "end_time": "2024-04-05T18:05:42.367013",
     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "single_node_thicket = get_nodes_in_group(thicket, \"Stream\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20d3e2cb",
   "metadata": {
    "papermill": {
     "duration": 0.003575,
     "end_time": "2024-04-05T18:05:42.373859",
     "exception": false,
     "start_time": "2024-04-05T18:05:42.370284",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Add important metadata columns into the performance data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5fdffa2b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-05T18:05:42.380691Z",
     "iopub.status.busy": "2024-04-05T18:05:42.380594Z",
     "iopub.status.idle": "2024-04-05T18:05:42.386108Z",
     "shell.execute_reply": "2024-04-05T18:05:42.385817Z"
    },
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     "exception": false,
     "start_time": "2024-04-05T18:05:42.377160",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "metadata_cols, single_node_thicket = add_metadata_to_thicket(single_node_thicket)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba80ae38",
   "metadata": {
    "papermill": {
     "duration": 0.003017,
     "end_time": "2024-04-05T18:05:42.392748",
     "exception": false,
     "start_time": "2024-04-05T18:05:42.389731",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Calculate speedup based on optimization level (e.g., O0/O1, O0/O2, etc.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1703fcef",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-05T18:05:42.399071Z",
     "iopub.status.busy": "2024-04-05T18:05:42.398978Z",
     "iopub.status.idle": "2024-04-05T18:05:42.404246Z",
     "shell.execute_reply": "2024-04-05T18:05:42.403913Z"
    },
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     "end_time": "2024-04-05T18:05:42.404851",
     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "single_node_thicket.dataframe = calc_speedup_by_opt_level(single_node_thicket.dataframe)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "337a8cfa",
   "metadata": {
    "papermill": {
     "duration": 0.00292,
     "end_time": "2024-04-05T18:05:42.410846",
     "exception": false,
     "start_time": "2024-04-05T18:05:42.407926",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Extract the important data into a DataFrame better formatted for clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bc244ead",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-05T18:05:42.417059Z",
     "iopub.status.busy": "2024-04-05T18:05:42.416964Z",
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    },
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     "status": "completed"
    },
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   },
   "outputs": [
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       "      <td>0.948870</td>\n",
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       "      <td>0.000229</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Stream_MUL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-O2</td>\n",
       "      <td>2</td>\n",
       "      <td>g++-10.3.1</td>\n",
       "      <td>0.066528</td>\n",
       "      <td>0.007375</td>\n",
       "      <td>0.926011</td>\n",
       "      <td>0.000086</td>\n",
       "      <td>1.023686</td>\n",
       "      <td>Stream_MUL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-O1</td>\n",
       "      <td>1</td>\n",
       "      <td>g++-10.3.1</td>\n",
       "      <td>0.066369</td>\n",
       "      <td>0.007401</td>\n",
       "      <td>0.926359</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.005575</td>\n",
       "      <td>Stream_MUL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-O3</td>\n",
       "      <td>3</td>\n",
       "      <td>g++-10.3.1</td>\n",
       "      <td>0.067028</td>\n",
       "      <td>0.007162</td>\n",
       "      <td>0.925704</td>\n",
       "      <td>0.000105</td>\n",
       "      <td>0.998667</td>\n",
       "      <td>Stream_MUL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-O0</td>\n",
       "      <td>0</td>\n",
       "      <td>g++-10.3.1</td>\n",
       "      <td>0.057387</td>\n",
       "      <td>0.007198</td>\n",
       "      <td>0.935109</td>\n",
       "      <td>0.000307</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Stream_TRIAD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-O2</td>\n",
       "      <td>2</td>\n",
       "      <td>g++-10.3.1</td>\n",
       "      <td>0.057509</td>\n",
       "      <td>0.006734</td>\n",
       "      <td>0.935501</td>\n",
       "      <td>0.000256</td>\n",
       "      <td>1.029841</td>\n",
       "      <td>Stream_TRIAD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-O1</td>\n",
       "      <td>1</td>\n",
       "      <td>g++-10.3.1</td>\n",
       "      <td>0.056760</td>\n",
       "      <td>0.006755</td>\n",
       "      <td>0.936456</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>1.010570</td>\n",
       "      <td>Stream_TRIAD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-O3</td>\n",
       "      <td>3</td>\n",
       "      <td>g++-10.3.1</td>\n",
       "      <td>0.057196</td>\n",
       "      <td>0.006985</td>\n",
       "      <td>0.935524</td>\n",
       "      <td>0.000295</td>\n",
       "      <td>1.000759</td>\n",
       "      <td>Stream_TRIAD</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   opt_level opt_level_int    compiler  Retiring  Frontend bound  \\\n",
       "0        -O0             0  g++-10.3.1  0.057756        0.007283   \n",
       "1        -O2             2  g++-10.3.1  0.056447        0.006957   \n",
       "2        -O1             1  g++-10.3.1  0.056757        0.007064   \n",
       "3        -O3             3  g++-10.3.1  0.056902        0.006809   \n",
       "4        -O0             0  g++-10.3.1  0.050230        0.000932   \n",
       "5        -O2             2  g++-10.3.1  0.050511        0.000783   \n",
       "6        -O1             1  g++-10.3.1  0.050061        0.000932   \n",
       "7        -O3             3  g++-10.3.1  0.050100        0.000739   \n",
       "8        -O0             0  g++-10.3.1  0.085235        0.009152   \n",
       "9        -O2             2  g++-10.3.1  0.083024        0.009174   \n",
       "10       -O1             1  g++-10.3.1  0.083823        0.009310   \n",
       "11       -O3             3  g++-10.3.1  0.084316        0.009045   \n",
       "12       -O0             0  g++-10.3.1  0.067740        0.007602   \n",
       "13       -O2             2  g++-10.3.1  0.066528        0.007375   \n",
       "14       -O1             1  g++-10.3.1  0.066369        0.007401   \n",
       "15       -O3             3  g++-10.3.1  0.067028        0.007162   \n",
       "16       -O0             0  g++-10.3.1  0.057387        0.007198   \n",
       "17       -O2             2  g++-10.3.1  0.057509        0.006734   \n",
       "18       -O1             1  g++-10.3.1  0.056760        0.006755   \n",
       "19       -O3             3  g++-10.3.1  0.057196        0.006985   \n",
       "\n",
       "    Backend bound  Bad speculation   speedup          name  \n",
       "0        0.934745         0.000215  1.000000    Stream_ADD  \n",
       "1        0.936493         0.000103  1.015401    Stream_ADD  \n",
       "2        0.936231         0.000000  1.007056    Stream_ADD  \n",
       "3        0.935997         0.000292  0.994071    Stream_ADD  \n",
       "4        0.948421         0.000417  1.000000   Stream_COPY  \n",
       "5        0.948439         0.000267  1.022479   Stream_COPY  \n",
       "6        0.948870         0.000137  1.004292   Stream_COPY  \n",
       "7        0.949018         0.000143  0.998211   Stream_COPY  \n",
       "8        0.905275         0.000337  1.000000    Stream_DOT  \n",
       "9        0.907857         0.000000  1.005844    Stream_DOT  \n",
       "10       0.906485         0.000381  1.004005    Stream_DOT  \n",
       "11       0.906415         0.000224  0.991317    Stream_DOT  \n",
       "12       0.924428         0.000229  1.000000    Stream_MUL  \n",
       "13       0.926011         0.000086  1.023686    Stream_MUL  \n",
       "14       0.926359         0.000000  1.005575    Stream_MUL  \n",
       "15       0.925704         0.000105  0.998667    Stream_MUL  \n",
       "16       0.935109         0.000307  1.000000  Stream_TRIAD  \n",
       "17       0.935501         0.000256  1.029841  Stream_TRIAD  \n",
       "18       0.936456         0.000030  1.010570  Stream_TRIAD  \n",
       "19       0.935524         0.000295  1.000759  Stream_TRIAD  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = build_clustering_df(single_node_thicket, metadata_cols + topdown_cols + [\"speedup\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f91e0e6d",
   "metadata": {
    "papermill": {
     "duration": 0.003044,
     "end_time": "2024-04-05T18:05:42.431040",
     "exception": false,
     "start_time": "2024-04-05T18:05:42.427996",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**This cell is optional!**\n",
    "\n",
    "If desired, run this cell to create normalized versions of the specified metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1ea23bf3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-05T18:05:42.437615Z",
     "iopub.status.busy": "2024-04-05T18:05:42.437507Z",
     "iopub.status.idle": "2024-04-05T18:05:42.446297Z",
     "shell.execute_reply": "2024-04-05T18:05:42.445961Z"
    },
    "papermill": {
     "duration": 0.012804,
     "end_time": "2024-04-05T18:05:42.446895",
     "exception": false,
     "start_time": "2024-04-05T18:05:42.434091",
     "status": "completed"
    },
    "tags": []
   },
   "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>opt_level</th>\n",
       "      <th>opt_level_int</th>\n",
       "      <th>compiler</th>\n",
       "      <th>Retiring</th>\n",
       "      <th>Frontend bound</th>\n",
       "      <th>Backend bound</th>\n",
       "      <th>Bad speculation</th>\n",
       "      <th>speedup</th>\n",
       "      <th>name</th>\n",
       "      <th>Retiring_norm</th>\n",
       "      <th>Frontend bound_norm</th>\n",
       "      <th>Backend bound_norm</th>\n",
       "      <th>Bad speculation_norm</th>\n",
       "      <th>speedup_norm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-O0</td>\n",
       "      <td>0</td>\n",
       "      <td>g++-10.3.1</td>\n",
       "      <td>0.057756</td>\n",
       "      <td>0.007283</td>\n",
       "      <td>0.934745</td>\n",
       "      <td>0.000215</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Stream_ADD</td>\n",
       "      <td>-0.451957</td>\n",
       "      <td>0.357531</td>\n",
       "      <td>0.304406</td>\n",
       "      <td>0.187331</td>\n",
       "      <td>-0.566466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-O2</td>\n",
       "      <td>2</td>\n",
       "      <td>g++-10.3.1</td>\n",
       "      <td>0.056447</td>\n",
       "      <td>0.006957</td>\n",
       "      <td>0.936493</td>\n",
       "      <td>0.000103</td>\n",
       "      <td>1.015401</td>\n",
       "      <td>Stream_ADD</td>\n",
       "      <td>-0.562996</td>\n",
       "      <td>0.242517</td>\n",
       "      <td>0.428783</td>\n",
       "      <td>-0.694227</td>\n",
       "      <td>0.994632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-O1</td>\n",
       "      <td>1</td>\n",
       "      <td>g++-10.3.1</td>\n",
       "      <td>0.056757</td>\n",
       "      <td>0.007064</td>\n",
       "      <td>0.936231</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.007056</td>\n",
       "      <td>Stream_ADD</td>\n",
       "      <td>-0.536700</td>\n",
       "      <td>0.280267</td>\n",
       "      <td>0.410141</td>\n",
       "      <td>-1.504945</td>\n",
       "      <td>0.148736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-O3</td>\n",
       "      <td>3</td>\n",
       "      <td>g++-10.3.1</td>\n",
       "      <td>0.056902</td>\n",
       "      <td>0.006809</td>\n",
       "      <td>0.935997</td>\n",
       "      <td>0.000292</td>\n",
       "      <td>0.994071</td>\n",
       "      <td>Stream_ADD</td>\n",
       "      <td>-0.524400</td>\n",
       "      <td>0.190302</td>\n",
       "      <td>0.393491</td>\n",
       "      <td>0.793402</td>\n",
       "      <td>-1.167452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-O0</td>\n",
       "      <td>0</td>\n",
       "      <td>g++-10.3.1</td>\n",
       "      <td>0.050230</td>\n",
       "      <td>0.000932</td>\n",
       "      <td>0.948421</td>\n",
       "      <td>0.000417</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Stream_COPY</td>\n",
       "      <td>-1.090369</td>\n",
       "      <td>-1.883123</td>\n",
       "      <td>1.277513</td>\n",
       "      <td>1.777283</td>\n",
       "      <td>-0.566466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-O2</td>\n",
       "      <td>2</td>\n",
       "      <td>g++-10.3.1</td>\n",
       "      <td>0.050511</td>\n",
       "      <td>0.000783</td>\n",
       "      <td>0.948439</td>\n",
       "      <td>0.000267</td>\n",
       "      <td>1.022479</td>\n",
       "      <td>Stream_COPY</td>\n",
       "      <td>-1.066533</td>\n",
       "      <td>-1.935691</td>\n",
       "      <td>1.278794</td>\n",
       "      <td>0.596626</td>\n",
       "      <td>1.711995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-O1</td>\n",
       "      <td>1</td>\n",
       "      <td>g++-10.3.1</td>\n",
       "      <td>0.050061</td>\n",
       "      <td>0.000932</td>\n",
       "      <td>0.948870</td>\n",
       "      <td>0.000137</td>\n",
       "      <td>1.004292</td>\n",
       "      <td>Stream_COPY</td>\n",
       "      <td>-1.104705</td>\n",
       "      <td>-1.883123</td>\n",
       "      <td>1.309461</td>\n",
       "      <td>-0.426611</td>\n",
       "      <td>-0.131445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-O3</td>\n",
       "      <td>3</td>\n",
       "      <td>g++-10.3.1</td>\n",
       "      <td>0.050100</td>\n",
       "      <td>0.000739</td>\n",
       "      <td>0.949018</td>\n",
       "      <td>0.000143</td>\n",
       "      <td>0.998211</td>\n",
       "      <td>Stream_COPY</td>\n",
       "      <td>-1.101397</td>\n",
       "      <td>-1.951214</td>\n",
       "      <td>1.319992</td>\n",
       "      <td>-0.379385</td>\n",
       "      <td>-0.747844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-O0</td>\n",
       "      <td>0</td>\n",
       "      <td>g++-10.3.1</td>\n",
       "      <td>0.085235</td>\n",
       "      <td>0.009152</td>\n",
       "      <td>0.905275</td>\n",
       "      <td>0.000337</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Stream_DOT</td>\n",
       "      <td>1.879020</td>\n",
       "      <td>1.016920</td>\n",
       "      <td>-1.792514</td>\n",
       "      <td>1.147599</td>\n",
       "      <td>-0.566466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-O2</td>\n",
       "      <td>2</td>\n",
       "      <td>g++-10.3.1</td>\n",
       "      <td>0.083024</td>\n",
       "      <td>0.009174</td>\n",
       "      <td>0.907857</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.005844</td>\n",
       "      <td>Stream_DOT</td>\n",
       "      <td>1.691466</td>\n",
       "      <td>1.024682</td>\n",
       "      <td>-1.608793</td>\n",
       "      <td>-1.504945</td>\n",
       "      <td>0.025889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-O1</td>\n",
       "      <td>1</td>\n",
       "      <td>g++-10.3.1</td>\n",
       "      <td>0.083823</td>\n",
       "      <td>0.009310</td>\n",
       "      <td>0.906485</td>\n",
       "      <td>0.000381</td>\n",
       "      <td>1.004005</td>\n",
       "      <td>Stream_DOT</td>\n",
       "      <td>1.759243</td>\n",
       "      <td>1.072663</td>\n",
       "      <td>-1.706417</td>\n",
       "      <td>1.493925</td>\n",
       "      <td>-0.160496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-O3</td>\n",
       "      <td>3</td>\n",
       "      <td>g++-10.3.1</td>\n",
       "      <td>0.084316</td>\n",
       "      <td>0.009045</td>\n",
       "      <td>0.906415</td>\n",
       "      <td>0.000224</td>\n",
       "      <td>0.991317</td>\n",
       "      <td>Stream_DOT</td>\n",
       "      <td>1.801063</td>\n",
       "      <td>0.979170</td>\n",
       "      <td>-1.711398</td>\n",
       "      <td>0.258170</td>\n",
       "      <td>-1.446604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-O0</td>\n",
       "      <td>0</td>\n",
       "      <td>g++-10.3.1</td>\n",
       "      <td>0.067740</td>\n",
       "      <td>0.007602</td>\n",
       "      <td>0.924428</td>\n",
       "      <td>0.000229</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Stream_MUL</td>\n",
       "      <td>0.394961</td>\n",
       "      <td>0.470075</td>\n",
       "      <td>-0.429694</td>\n",
       "      <td>0.297526</td>\n",
       "      <td>-0.566466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-O2</td>\n",
       "      <td>2</td>\n",
       "      <td>g++-10.3.1</td>\n",
       "      <td>0.066528</td>\n",
       "      <td>0.007375</td>\n",
       "      <td>0.926011</td>\n",
       "      <td>0.000086</td>\n",
       "      <td>1.023686</td>\n",
       "      <td>Stream_MUL</td>\n",
       "      <td>0.292150</td>\n",
       "      <td>0.389989</td>\n",
       "      <td>-0.317057</td>\n",
       "      <td>-0.828034</td>\n",
       "      <td>1.834334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-O1</td>\n",
       "      <td>1</td>\n",
       "      <td>g++-10.3.1</td>\n",
       "      <td>0.066369</td>\n",
       "      <td>0.007401</td>\n",
       "      <td>0.926359</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.005575</td>\n",
       "      <td>Stream_MUL</td>\n",
       "      <td>0.278663</td>\n",
       "      <td>0.399162</td>\n",
       "      <td>-0.292295</td>\n",
       "      <td>-1.504945</td>\n",
       "      <td>-0.001373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-O3</td>\n",
       "      <td>3</td>\n",
       "      <td>g++-10.3.1</td>\n",
       "      <td>0.067028</td>\n",
       "      <td>0.007162</td>\n",
       "      <td>0.925704</td>\n",
       "      <td>0.000105</td>\n",
       "      <td>0.998667</td>\n",
       "      <td>Stream_MUL</td>\n",
       "      <td>0.334564</td>\n",
       "      <td>0.314842</td>\n",
       "      <td>-0.338901</td>\n",
       "      <td>-0.678484</td>\n",
       "      <td>-0.701590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-O0</td>\n",
       "      <td>0</td>\n",
       "      <td>g++-10.3.1</td>\n",
       "      <td>0.057387</td>\n",
       "      <td>0.007198</td>\n",
       "      <td>0.935109</td>\n",
       "      <td>0.000307</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Stream_TRIAD</td>\n",
       "      <td>-0.483258</td>\n",
       "      <td>0.327543</td>\n",
       "      <td>0.330306</td>\n",
       "      <td>0.911468</td>\n",
       "      <td>-0.566466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-O2</td>\n",
       "      <td>2</td>\n",
       "      <td>g++-10.3.1</td>\n",
       "      <td>0.057509</td>\n",
       "      <td>0.006734</td>\n",
       "      <td>0.935501</td>\n",
       "      <td>0.000256</td>\n",
       "      <td>1.029841</td>\n",
       "      <td>Stream_TRIAD</td>\n",
       "      <td>-0.472909</td>\n",
       "      <td>0.163842</td>\n",
       "      <td>0.358198</td>\n",
       "      <td>0.510044</td>\n",
       "      <td>2.458264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-O1</td>\n",
       "      <td>1</td>\n",
       "      <td>g++-10.3.1</td>\n",
       "      <td>0.056760</td>\n",
       "      <td>0.006755</td>\n",
       "      <td>0.936456</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>1.010570</td>\n",
       "      <td>Stream_TRIAD</td>\n",
       "      <td>-0.536445</td>\n",
       "      <td>0.171251</td>\n",
       "      <td>0.426151</td>\n",
       "      <td>-1.268813</td>\n",
       "      <td>0.504867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-O3</td>\n",
       "      <td>3</td>\n",
       "      <td>g++-10.3.1</td>\n",
       "      <td>0.057196</td>\n",
       "      <td>0.006985</td>\n",
       "      <td>0.935524</td>\n",
       "      <td>0.000295</td>\n",
       "      <td>1.000759</td>\n",
       "      <td>Stream_TRIAD</td>\n",
       "      <td>-0.499460</td>\n",
       "      <td>0.252395</td>\n",
       "      <td>0.359835</td>\n",
       "      <td>0.817015</td>\n",
       "      <td>-0.489583</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   opt_level opt_level_int    compiler  Retiring  Frontend bound  \\\n",
       "0        -O0             0  g++-10.3.1  0.057756        0.007283   \n",
       "1        -O2             2  g++-10.3.1  0.056447        0.006957   \n",
       "2        -O1             1  g++-10.3.1  0.056757        0.007064   \n",
       "3        -O3             3  g++-10.3.1  0.056902        0.006809   \n",
       "4        -O0             0  g++-10.3.1  0.050230        0.000932   \n",
       "5        -O2             2  g++-10.3.1  0.050511        0.000783   \n",
       "6        -O1             1  g++-10.3.1  0.050061        0.000932   \n",
       "7        -O3             3  g++-10.3.1  0.050100        0.000739   \n",
       "8        -O0             0  g++-10.3.1  0.085235        0.009152   \n",
       "9        -O2             2  g++-10.3.1  0.083024        0.009174   \n",
       "10       -O1             1  g++-10.3.1  0.083823        0.009310   \n",
       "11       -O3             3  g++-10.3.1  0.084316        0.009045   \n",
       "12       -O0             0  g++-10.3.1  0.067740        0.007602   \n",
       "13       -O2             2  g++-10.3.1  0.066528        0.007375   \n",
       "14       -O1             1  g++-10.3.1  0.066369        0.007401   \n",
       "15       -O3             3  g++-10.3.1  0.067028        0.007162   \n",
       "16       -O0             0  g++-10.3.1  0.057387        0.007198   \n",
       "17       -O2             2  g++-10.3.1  0.057509        0.006734   \n",
       "18       -O1             1  g++-10.3.1  0.056760        0.006755   \n",
       "19       -O3             3  g++-10.3.1  0.057196        0.006985   \n",
       "\n",
       "    Backend bound  Bad speculation   speedup          name  Retiring_norm  \\\n",
       "0        0.934745         0.000215  1.000000    Stream_ADD      -0.451957   \n",
       "1        0.936493         0.000103  1.015401    Stream_ADD      -0.562996   \n",
       "2        0.936231         0.000000  1.007056    Stream_ADD      -0.536700   \n",
       "3        0.935997         0.000292  0.994071    Stream_ADD      -0.524400   \n",
       "4        0.948421         0.000417  1.000000   Stream_COPY      -1.090369   \n",
       "5        0.948439         0.000267  1.022479   Stream_COPY      -1.066533   \n",
       "6        0.948870         0.000137  1.004292   Stream_COPY      -1.104705   \n",
       "7        0.949018         0.000143  0.998211   Stream_COPY      -1.101397   \n",
       "8        0.905275         0.000337  1.000000    Stream_DOT       1.879020   \n",
       "9        0.907857         0.000000  1.005844    Stream_DOT       1.691466   \n",
       "10       0.906485         0.000381  1.004005    Stream_DOT       1.759243   \n",
       "11       0.906415         0.000224  0.991317    Stream_DOT       1.801063   \n",
       "12       0.924428         0.000229  1.000000    Stream_MUL       0.394961   \n",
       "13       0.926011         0.000086  1.023686    Stream_MUL       0.292150   \n",
       "14       0.926359         0.000000  1.005575    Stream_MUL       0.278663   \n",
       "15       0.925704         0.000105  0.998667    Stream_MUL       0.334564   \n",
       "16       0.935109         0.000307  1.000000  Stream_TRIAD      -0.483258   \n",
       "17       0.935501         0.000256  1.029841  Stream_TRIAD      -0.472909   \n",
       "18       0.936456         0.000030  1.010570  Stream_TRIAD      -0.536445   \n",
       "19       0.935524         0.000295  1.000759  Stream_TRIAD      -0.499460   \n",
       "\n",
       "    Frontend bound_norm  Backend bound_norm  Bad speculation_norm  \\\n",
       "0              0.357531            0.304406              0.187331   \n",
       "1              0.242517            0.428783             -0.694227   \n",
       "2              0.280267            0.410141             -1.504945   \n",
       "3              0.190302            0.393491              0.793402   \n",
       "4             -1.883123            1.277513              1.777283   \n",
       "5             -1.935691            1.278794              0.596626   \n",
       "6             -1.883123            1.309461             -0.426611   \n",
       "7             -1.951214            1.319992             -0.379385   \n",
       "8              1.016920           -1.792514              1.147599   \n",
       "9              1.024682           -1.608793             -1.504945   \n",
       "10             1.072663           -1.706417              1.493925   \n",
       "11             0.979170           -1.711398              0.258170   \n",
       "12             0.470075           -0.429694              0.297526   \n",
       "13             0.389989           -0.317057             -0.828034   \n",
       "14             0.399162           -0.292295             -1.504945   \n",
       "15             0.314842           -0.338901             -0.678484   \n",
       "16             0.327543            0.330306              0.911468   \n",
       "17             0.163842            0.358198              0.510044   \n",
       "18             0.171251            0.426151             -1.268813   \n",
       "19             0.252395            0.359835              0.817015   \n",
       "\n",
       "    speedup_norm  \n",
       "0      -0.566466  \n",
       "1       0.994632  \n",
       "2       0.148736  \n",
       "3      -1.167452  \n",
       "4      -0.566466  \n",
       "5       1.711995  \n",
       "6      -0.131445  \n",
       "7      -0.747844  \n",
       "8      -0.566466  \n",
       "9       0.025889  \n",
       "10     -0.160496  \n",
       "11     -1.446604  \n",
       "12     -0.566466  \n",
       "13      1.834334  \n",
       "14     -0.001373  \n",
       "15     -0.701590  \n",
       "16     -0.566466  \n",
       "17      2.458264  \n",
       "18      0.504867  \n",
       "19     -0.489583  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = normalize_data(df, topdown_cols + [\"speedup\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc0832fc",
   "metadata": {
    "papermill": {
     "duration": 0.00329,
     "end_time": "2024-04-05T18:05:42.453628",
     "exception": false,
     "start_time": "2024-04-05T18:05:42.450338",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Cluster the topdown metrics with respect to exclusive time.\n",
    "\n",
    "If `topdown_scaling_factor` is set to any value besides 1, the topdown metrics will be multiplied by that value before clustering and divided by that value after clustering.\n",
    "\n",
    "If `use_normalization` is true, use the normalized metrics calculated by `normalize_data` for clustering instead of the vanilla versions.\n",
    "\n",
    "If `silhouette_mode` (in the next cell) is true, don't cluster. Instead run a Silhouette analysis on the data to find the correct K values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8e8f0f53",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-05T18:05:42.460740Z",
     "iopub.status.busy": "2024-04-05T18:05:42.460642Z",
     "iopub.status.idle": "2024-04-05T18:05:42.462964Z",
     "shell.execute_reply": "2024-04-05T18:05:42.462670Z"
    },
    "papermill": {
     "duration": 0.006584,
     "end_time": "2024-04-05T18:05:42.463537",
     "exception": false,
     "start_time": "2024-04-05T18:05:42.456953",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "silhouette_mode = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9c1f94a1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-05T18:05:42.470626Z",
     "iopub.status.busy": "2024-04-05T18:05:42.470538Z",
     "iopub.status.idle": "2024-04-05T18:05:42.581296Z",
     "shell.execute_reply": "2024-04-05T18:05:42.580817Z"
    },
    "papermill": {
     "duration": 0.11517,
     "end_time": "2024-04-05T18:05:42.581928",
     "exception": false,
     "start_time": "2024-04-05T18:05:42.466758",
     "status": "completed"
    },
    "scrolled": false,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "speedup_norm Retiring_norm\n",
      "speedup_norm Frontend bound_norm\n",
      "speedup_norm Backend bound_norm\n",
      "speedup_norm Bad speculation_norm\n"
     ]
    }
   ],
   "source": [
    "cluster_centers = []\n",
    "cluster_labels = []\n",
    "pairs = list(product([\"speedup\"], topdown_cols))\n",
    "for x, y in pairs:\n",
    "    nc = 8\n",
    "    if y == \"Retiring\":\n",
    "        nc = n_clusters_retiring\n",
    "    elif y == \"Frontend bound\":\n",
    "        nc = n_clusters_frontend_bound\n",
    "    elif y == \"Backend bound\":\n",
    "        nc = n_clusters_backend_bound\n",
    "    elif y == \"Bad speculation\":\n",
    "        nc = n_clusters_bad_speculation\n",
    "    df[y] = df[y] * topdown_scaling_factor\n",
    "    if silhouette_mode:\n",
    "        x_met = x\n",
    "        y_met = y\n",
    "        if use_normalization:\n",
    "            x_met = \"{}_norm\".format(x_met)\n",
    "            y_met = \"{}_norm\".format(y_met)\n",
    "        print(\"Printing Silhouette Test for: {} vs {}\".format(x_met, y_met), end=\"\\n\\n\")\n",
    "        silhouette_test(df, x_met, y_met)\n",
    "    else:\n",
    "        if use_normalization:\n",
    "            clab, cc, df = cluster_data(df, \"{}_norm\".format(x), \"{}_norm\".format(y), n_clusters=nc)\n",
    "        else:\n",
    "            clab, cc, df = cluster_data(df, x, y, n_clusters=nc)\n",
    "        df[y] = df[y] / topdown_scaling_factor\n",
    "        cc = [(sc[0], sc[1]/topdown_scaling_factor) for sc in cc]\n",
    "        cluster_centers.append(cc)\n",
    "        cluster_labels.append(clab)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95d78248",
   "metadata": {
    "papermill": {
     "duration": 0.003884,
     "end_time": "2024-04-05T18:05:42.589877",
     "exception": false,
     "start_time": "2024-04-05T18:05:42.585993",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "These two cells simply print out the post-clustering DataFrame and the centroids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a062f169",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-05T18:05:42.597445Z",
     "iopub.status.busy": "2024-04-05T18:05:42.597330Z",
     "iopub.status.idle": "2024-04-05T18:05:42.607911Z",
     "shell.execute_reply": "2024-04-05T18:05:42.607622Z"
    },
    "papermill": {
     "duration": 0.015071,
     "end_time": "2024-04-05T18:05:42.608485",
     "exception": false,
     "start_time": "2024-04-05T18:05:42.593414",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>opt_level</th>\n",
       "      <th>opt_level_int</th>\n",
       "      <th>compiler</th>\n",
       "      <th>Retiring</th>\n",
       "      <th>Frontend bound</th>\n",
       "      <th>Backend bound</th>\n",
       "      <th>Bad speculation</th>\n",
       "      <th>speedup</th>\n",
       "      <th>name</th>\n",
       "      <th>Retiring_norm</th>\n",
       "      <th>...</th>\n",
       "      <th>Bad speculation_norm</th>\n",
       "      <th>speedup_norm</th>\n",
       "      <th>Retiring_cluster_id</th>\n",
       "      <th>Retiring_cluster</th>\n",
       "      <th>Frontend bound_cluster_id</th>\n",
       "      <th>Frontend bound_cluster</th>\n",
       "      <th>Backend bound_cluster_id</th>\n",
       "      <th>Backend bound_cluster</th>\n",
       "      <th>Bad speculation_cluster_id</th>\n",
       "      <th>Bad speculation_cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-O3</td>\n",
       "      <td>3</td>\n",
       "      <td>g++-10.3.1</td>\n",
       "      <td>0.056902</td>\n",
       "      <td>0.006809</td>\n",
       "      <td>0.935997</td>\n",
       "      <td>0.000292</td>\n",
       "      <td>0.994071</td>\n",
       "      <td>Stream_ADD</td>\n",
       "      <td>-0.524400</td>\n",
       "      <td>...</td>\n",
       "      <td>0.793402</td>\n",
       "      <td>-1.167452</td>\n",
       "      <td>2</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>2</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>0</td>\n",
       "      <td>Cluster 0</td>\n",
       "      <td>1</td>\n",
       "      <td>Cluster 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-O0</td>\n",
       "      <td>0</td>\n",
       "      <td>g++-10.3.1</td>\n",
       "      <td>0.057756</td>\n",
       "      <td>0.007283</td>\n",
       "      <td>0.934745</td>\n",
       "      <td>0.000215</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Stream_ADD</td>\n",
       "      <td>-0.451957</td>\n",
       "      <td>...</td>\n",
       "      <td>0.187331</td>\n",
       "      <td>-0.566466</td>\n",
       "      <td>2</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>2</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>0</td>\n",
       "      <td>Cluster 0</td>\n",
       "      <td>3</td>\n",
       "      <td>Cluster 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-O1</td>\n",
       "      <td>1</td>\n",
       "      <td>g++-10.3.1</td>\n",
       "      <td>0.056757</td>\n",
       "      <td>0.007064</td>\n",
       "      <td>0.936231</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.007056</td>\n",
       "      <td>Stream_ADD</td>\n",
       "      <td>-0.536700</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.504945</td>\n",
       "      <td>0.148736</td>\n",
       "      <td>2</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>1</td>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>0</td>\n",
       "      <td>Cluster 0</td>\n",
       "      <td>0</td>\n",
       "      <td>Cluster 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-O2</td>\n",
       "      <td>2</td>\n",
       "      <td>g++-10.3.1</td>\n",
       "      <td>0.056447</td>\n",
       "      <td>0.006957</td>\n",
       "      <td>0.936493</td>\n",
       "      <td>0.000103</td>\n",
       "      <td>1.015401</td>\n",
       "      <td>Stream_ADD</td>\n",
       "      <td>-0.562996</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.694227</td>\n",
       "      <td>0.994632</td>\n",
       "      <td>1</td>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>0</td>\n",
       "      <td>Cluster 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-O3</td>\n",
       "      <td>3</td>\n",
       "      <td>g++-10.3.1</td>\n",
       "      <td>0.050100</td>\n",
       "      <td>0.000739</td>\n",
       "      <td>0.949018</td>\n",
       "      <td>0.000143</td>\n",
       "      <td>0.998211</td>\n",
       "      <td>Stream_COPY</td>\n",
       "      <td>-1.101397</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.379385</td>\n",
       "      <td>-0.747844</td>\n",
       "      <td>2</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>0</td>\n",
       "      <td>Cluster 0</td>\n",
       "      <td>0</td>\n",
       "      <td>Cluster 0</td>\n",
       "      <td>3</td>\n",
       "      <td>Cluster 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-O0</td>\n",
       "      <td>0</td>\n",
       "      <td>g++-10.3.1</td>\n",
       "      <td>0.050230</td>\n",
       "      <td>0.000932</td>\n",
       "      <td>0.948421</td>\n",
       "      <td>0.000417</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Stream_COPY</td>\n",
       "      <td>-1.090369</td>\n",
       "      <td>...</td>\n",
       "      <td>1.777283</td>\n",
       "      <td>-0.566466</td>\n",
       "      <td>2</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>0</td>\n",
       "      <td>Cluster 0</td>\n",
       "      <td>0</td>\n",
       "      <td>Cluster 0</td>\n",
       "      <td>1</td>\n",
       "      <td>Cluster 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-O1</td>\n",
       "      <td>1</td>\n",
       "      <td>g++-10.3.1</td>\n",
       "      <td>0.050061</td>\n",
       "      <td>0.000932</td>\n",
       "      <td>0.948870</td>\n",
       "      <td>0.000137</td>\n",
       "      <td>1.004292</td>\n",
       "      <td>Stream_COPY</td>\n",
       "      <td>-1.104705</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.426611</td>\n",
       "      <td>-0.131445</td>\n",
       "      <td>2</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>0</td>\n",
       "      <td>Cluster 0</td>\n",
       "      <td>0</td>\n",
       "      <td>Cluster 0</td>\n",
       "      <td>3</td>\n",
       "      <td>Cluster 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-O2</td>\n",
       "      <td>2</td>\n",
       "      <td>g++-10.3.1</td>\n",
       "      <td>0.050511</td>\n",
       "      <td>0.000783</td>\n",
       "      <td>0.948439</td>\n",
       "      <td>0.000267</td>\n",
       "      <td>1.022479</td>\n",
       "      <td>Stream_COPY</td>\n",
       "      <td>-1.066533</td>\n",
       "      <td>...</td>\n",
       "      <td>0.596626</td>\n",
       "      <td>1.711995</td>\n",
       "      <td>1</td>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>0</td>\n",
       "      <td>Cluster 0</td>\n",
       "      <td>1</td>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>2</td>\n",
       "      <td>Cluster 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-O3</td>\n",
       "      <td>3</td>\n",
       "      <td>g++-10.3.1</td>\n",
       "      <td>0.084316</td>\n",
       "      <td>0.009045</td>\n",
       "      <td>0.906415</td>\n",
       "      <td>0.000224</td>\n",
       "      <td>0.991317</td>\n",
       "      <td>Stream_DOT</td>\n",
       "      <td>1.801063</td>\n",
       "      <td>...</td>\n",
       "      <td>0.258170</td>\n",
       "      <td>-1.446604</td>\n",
       "      <td>0</td>\n",
       "      <td>Cluster 0</td>\n",
       "      <td>2</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>2</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>3</td>\n",
       "      <td>Cluster 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-O0</td>\n",
       "      <td>0</td>\n",
       "      <td>g++-10.3.1</td>\n",
       "      <td>0.085235</td>\n",
       "      <td>0.009152</td>\n",
       "      <td>0.905275</td>\n",
       "      <td>0.000337</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Stream_DOT</td>\n",
       "      <td>1.879020</td>\n",
       "      <td>...</td>\n",
       "      <td>1.147599</td>\n",
       "      <td>-0.566466</td>\n",
       "      <td>0</td>\n",
       "      <td>Cluster 0</td>\n",
       "      <td>2</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>2</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>1</td>\n",
       "      <td>Cluster 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-O1</td>\n",
       "      <td>1</td>\n",
       "      <td>g++-10.3.1</td>\n",
       "      <td>0.083823</td>\n",
       "      <td>0.009310</td>\n",
       "      <td>0.906485</td>\n",
       "      <td>0.000381</td>\n",
       "      <td>1.004005</td>\n",
       "      <td>Stream_DOT</td>\n",
       "      <td>1.759243</td>\n",
       "      <td>...</td>\n",
       "      <td>1.493925</td>\n",
       "      <td>-0.160496</td>\n",
       "      <td>0</td>\n",
       "      <td>Cluster 0</td>\n",
       "      <td>1</td>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>2</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>1</td>\n",
       "      <td>Cluster 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-O2</td>\n",
       "      <td>2</td>\n",
       "      <td>g++-10.3.1</td>\n",
       "      <td>0.083024</td>\n",
       "      <td>0.009174</td>\n",
       "      <td>0.907857</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.005844</td>\n",
       "      <td>Stream_DOT</td>\n",
       "      <td>1.691466</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.504945</td>\n",
       "      <td>0.025889</td>\n",
       "      <td>0</td>\n",
       "      <td>Cluster 0</td>\n",
       "      <td>1</td>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>2</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>0</td>\n",
       "      <td>Cluster 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-O3</td>\n",
       "      <td>3</td>\n",
       "      <td>g++-10.3.1</td>\n",
       "      <td>0.067028</td>\n",
       "      <td>0.007162</td>\n",
       "      <td>0.925704</td>\n",
       "      <td>0.000105</td>\n",
       "      <td>0.998667</td>\n",
       "      <td>Stream_MUL</td>\n",
       "      <td>0.334564</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.678484</td>\n",
       "      <td>-0.701590</td>\n",
       "      <td>0</td>\n",
       "      <td>Cluster 0</td>\n",
       "      <td>2</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>0</td>\n",
       "      <td>Cluster 0</td>\n",
       "      <td>3</td>\n",
       "      <td>Cluster 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-O0</td>\n",
       "      <td>0</td>\n",
       "      <td>g++-10.3.1</td>\n",
       "      <td>0.067740</td>\n",
       "      <td>0.007602</td>\n",
       "      <td>0.924428</td>\n",
       "      <td>0.000229</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Stream_MUL</td>\n",
       "      <td>0.394961</td>\n",
       "      <td>...</td>\n",
       "      <td>0.297526</td>\n",
       "      <td>-0.566466</td>\n",
       "      <td>0</td>\n",
       "      <td>Cluster 0</td>\n",
       "      <td>2</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>0</td>\n",
       "      <td>Cluster 0</td>\n",
       "      <td>3</td>\n",
       "      <td>Cluster 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-O1</td>\n",
       "      <td>1</td>\n",
       "      <td>g++-10.3.1</td>\n",
       "      <td>0.066369</td>\n",
       "      <td>0.007401</td>\n",
       "      <td>0.926359</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.005575</td>\n",
       "      <td>Stream_MUL</td>\n",
       "      <td>0.278663</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.504945</td>\n",
       "      <td>-0.001373</td>\n",
       "      <td>0</td>\n",
       "      <td>Cluster 0</td>\n",
       "      <td>1</td>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>0</td>\n",
       "      <td>Cluster 0</td>\n",
       "      <td>0</td>\n",
       "      <td>Cluster 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-O2</td>\n",
       "      <td>2</td>\n",
       "      <td>g++-10.3.1</td>\n",
       "      <td>0.066528</td>\n",
       "      <td>0.007375</td>\n",
       "      <td>0.926011</td>\n",
       "      <td>0.000086</td>\n",
       "      <td>1.023686</td>\n",
       "      <td>Stream_MUL</td>\n",
       "      <td>0.292150</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.828034</td>\n",
       "      <td>1.834334</td>\n",
       "      <td>1</td>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>3</td>\n",
       "      <td>Cluster 3</td>\n",
       "      <td>1</td>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>2</td>\n",
       "      <td>Cluster 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-O0</td>\n",
       "      <td>0</td>\n",
       "      <td>g++-10.3.1</td>\n",
       "      <td>0.057387</td>\n",
       "      <td>0.007198</td>\n",
       "      <td>0.935109</td>\n",
       "      <td>0.000307</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Stream_TRIAD</td>\n",
       "      <td>-0.483258</td>\n",
       "      <td>...</td>\n",
       "      <td>0.911468</td>\n",
       "      <td>-0.566466</td>\n",
       "      <td>2</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>2</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>0</td>\n",
       "      <td>Cluster 0</td>\n",
       "      <td>1</td>\n",
       "      <td>Cluster 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-O3</td>\n",
       "      <td>3</td>\n",
       "      <td>g++-10.3.1</td>\n",
       "      <td>0.057196</td>\n",
       "      <td>0.006985</td>\n",
       "      <td>0.935524</td>\n",
       "      <td>0.000295</td>\n",
       "      <td>1.000759</td>\n",
       "      <td>Stream_TRIAD</td>\n",
       "      <td>-0.499460</td>\n",
       "      <td>...</td>\n",
       "      <td>0.817015</td>\n",
       "      <td>-0.489583</td>\n",
       "      <td>2</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>2</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>0</td>\n",
       "      <td>Cluster 0</td>\n",
       "      <td>1</td>\n",
       "      <td>Cluster 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-O1</td>\n",
       "      <td>1</td>\n",
       "      <td>g++-10.3.1</td>\n",
       "      <td>0.056760</td>\n",
       "      <td>0.006755</td>\n",
       "      <td>0.936456</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>1.010570</td>\n",
       "      <td>Stream_TRIAD</td>\n",
       "      <td>-0.536445</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.268813</td>\n",
       "      <td>0.504867</td>\n",
       "      <td>2</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>1</td>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>0</td>\n",
       "      <td>Cluster 0</td>\n",
       "      <td>0</td>\n",
       "      <td>Cluster 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-O2</td>\n",
       "      <td>2</td>\n",
       "      <td>g++-10.3.1</td>\n",
       "      <td>0.057509</td>\n",
       "      <td>0.006734</td>\n",
       "      <td>0.935501</td>\n",
       "      <td>0.000256</td>\n",
       "      <td>1.029841</td>\n",
       "      <td>Stream_TRIAD</td>\n",
       "      <td>-0.472909</td>\n",
       "      <td>...</td>\n",
       "      <td>0.510044</td>\n",
       "      <td>2.458264</td>\n",
       "      <td>1</td>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>3</td>\n",
       "      <td>Cluster 3</td>\n",
       "      <td>1</td>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>2</td>\n",
       "      <td>Cluster 2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   opt_level opt_level_int    compiler  Retiring  Frontend bound  \\\n",
       "3        -O3             3  g++-10.3.1  0.056902        0.006809   \n",
       "0        -O0             0  g++-10.3.1  0.057756        0.007283   \n",
       "2        -O1             1  g++-10.3.1  0.056757        0.007064   \n",
       "1        -O2             2  g++-10.3.1  0.056447        0.006957   \n",
       "7        -O3             3  g++-10.3.1  0.050100        0.000739   \n",
       "4        -O0             0  g++-10.3.1  0.050230        0.000932   \n",
       "6        -O1             1  g++-10.3.1  0.050061        0.000932   \n",
       "5        -O2             2  g++-10.3.1  0.050511        0.000783   \n",
       "11       -O3             3  g++-10.3.1  0.084316        0.009045   \n",
       "8        -O0             0  g++-10.3.1  0.085235        0.009152   \n",
       "10       -O1             1  g++-10.3.1  0.083823        0.009310   \n",
       "9        -O2             2  g++-10.3.1  0.083024        0.009174   \n",
       "15       -O3             3  g++-10.3.1  0.067028        0.007162   \n",
       "12       -O0             0  g++-10.3.1  0.067740        0.007602   \n",
       "14       -O1             1  g++-10.3.1  0.066369        0.007401   \n",
       "13       -O2             2  g++-10.3.1  0.066528        0.007375   \n",
       "16       -O0             0  g++-10.3.1  0.057387        0.007198   \n",
       "19       -O3             3  g++-10.3.1  0.057196        0.006985   \n",
       "18       -O1             1  g++-10.3.1  0.056760        0.006755   \n",
       "17       -O2             2  g++-10.3.1  0.057509        0.006734   \n",
       "\n",
       "    Backend bound  Bad speculation   speedup          name  Retiring_norm  \\\n",
       "3        0.935997         0.000292  0.994071    Stream_ADD      -0.524400   \n",
       "0        0.934745         0.000215  1.000000    Stream_ADD      -0.451957   \n",
       "2        0.936231         0.000000  1.007056    Stream_ADD      -0.536700   \n",
       "1        0.936493         0.000103  1.015401    Stream_ADD      -0.562996   \n",
       "7        0.949018         0.000143  0.998211   Stream_COPY      -1.101397   \n",
       "4        0.948421         0.000417  1.000000   Stream_COPY      -1.090369   \n",
       "6        0.948870         0.000137  1.004292   Stream_COPY      -1.104705   \n",
       "5        0.948439         0.000267  1.022479   Stream_COPY      -1.066533   \n",
       "11       0.906415         0.000224  0.991317    Stream_DOT       1.801063   \n",
       "8        0.905275         0.000337  1.000000    Stream_DOT       1.879020   \n",
       "10       0.906485         0.000381  1.004005    Stream_DOT       1.759243   \n",
       "9        0.907857         0.000000  1.005844    Stream_DOT       1.691466   \n",
       "15       0.925704         0.000105  0.998667    Stream_MUL       0.334564   \n",
       "12       0.924428         0.000229  1.000000    Stream_MUL       0.394961   \n",
       "14       0.926359         0.000000  1.005575    Stream_MUL       0.278663   \n",
       "13       0.926011         0.000086  1.023686    Stream_MUL       0.292150   \n",
       "16       0.935109         0.000307  1.000000  Stream_TRIAD      -0.483258   \n",
       "19       0.935524         0.000295  1.000759  Stream_TRIAD      -0.499460   \n",
       "18       0.936456         0.000030  1.010570  Stream_TRIAD      -0.536445   \n",
       "17       0.935501         0.000256  1.029841  Stream_TRIAD      -0.472909   \n",
       "\n",
       "    ...  Bad speculation_norm  speedup_norm  Retiring_cluster_id  \\\n",
       "3   ...              0.793402     -1.167452                    2   \n",
       "0   ...              0.187331     -0.566466                    2   \n",
       "2   ...             -1.504945      0.148736                    2   \n",
       "1   ...             -0.694227      0.994632                    1   \n",
       "7   ...             -0.379385     -0.747844                    2   \n",
       "4   ...              1.777283     -0.566466                    2   \n",
       "6   ...             -0.426611     -0.131445                    2   \n",
       "5   ...              0.596626      1.711995                    1   \n",
       "11  ...              0.258170     -1.446604                    0   \n",
       "8   ...              1.147599     -0.566466                    0   \n",
       "10  ...              1.493925     -0.160496                    0   \n",
       "9   ...             -1.504945      0.025889                    0   \n",
       "15  ...             -0.678484     -0.701590                    0   \n",
       "12  ...              0.297526     -0.566466                    0   \n",
       "14  ...             -1.504945     -0.001373                    0   \n",
       "13  ...             -0.828034      1.834334                    1   \n",
       "16  ...              0.911468     -0.566466                    2   \n",
       "19  ...              0.817015     -0.489583                    2   \n",
       "18  ...             -1.268813      0.504867                    2   \n",
       "17  ...              0.510044      2.458264                    1   \n",
       "\n",
       "    Retiring_cluster  Frontend bound_cluster_id Frontend bound_cluster  \\\n",
       "3          Cluster 2                          2              Cluster 2   \n",
       "0          Cluster 2                          2              Cluster 2   \n",
       "2          Cluster 2                          1              Cluster 1   \n",
       "1          Cluster 1                          1              Cluster 1   \n",
       "7          Cluster 2                          0              Cluster 0   \n",
       "4          Cluster 2                          0              Cluster 0   \n",
       "6          Cluster 2                          0              Cluster 0   \n",
       "5          Cluster 1                          0              Cluster 0   \n",
       "11         Cluster 0                          2              Cluster 2   \n",
       "8          Cluster 0                          2              Cluster 2   \n",
       "10         Cluster 0                          1              Cluster 1   \n",
       "9          Cluster 0                          1              Cluster 1   \n",
       "15         Cluster 0                          2              Cluster 2   \n",
       "12         Cluster 0                          2              Cluster 2   \n",
       "14         Cluster 0                          1              Cluster 1   \n",
       "13         Cluster 1                          3              Cluster 3   \n",
       "16         Cluster 2                          2              Cluster 2   \n",
       "19         Cluster 2                          2              Cluster 2   \n",
       "18         Cluster 2                          1              Cluster 1   \n",
       "17         Cluster 1                          3              Cluster 3   \n",
       "\n",
       "    Backend bound_cluster_id Backend bound_cluster  \\\n",
       "3                          0             Cluster 0   \n",
       "0                          0             Cluster 0   \n",
       "2                          0             Cluster 0   \n",
       "1                          1             Cluster 1   \n",
       "7                          0             Cluster 0   \n",
       "4                          0             Cluster 0   \n",
       "6                          0             Cluster 0   \n",
       "5                          1             Cluster 1   \n",
       "11                         2             Cluster 2   \n",
       "8                          2             Cluster 2   \n",
       "10                         2             Cluster 2   \n",
       "9                          2             Cluster 2   \n",
       "15                         0             Cluster 0   \n",
       "12                         0             Cluster 0   \n",
       "14                         0             Cluster 0   \n",
       "13                         1             Cluster 1   \n",
       "16                         0             Cluster 0   \n",
       "19                         0             Cluster 0   \n",
       "18                         0             Cluster 0   \n",
       "17                         1             Cluster 1   \n",
       "\n",
       "    Bad speculation_cluster_id Bad speculation_cluster  \n",
       "3                            1               Cluster 1  \n",
       "0                            3               Cluster 3  \n",
       "2                            0               Cluster 0  \n",
       "1                            0               Cluster 0  \n",
       "7                            3               Cluster 3  \n",
       "4                            1               Cluster 1  \n",
       "6                            3               Cluster 3  \n",
       "5                            2               Cluster 2  \n",
       "11                           3               Cluster 3  \n",
       "8                            1               Cluster 1  \n",
       "10                           1               Cluster 1  \n",
       "9                            0               Cluster 0  \n",
       "15                           3               Cluster 3  \n",
       "12                           3               Cluster 3  \n",
       "14                           0               Cluster 0  \n",
       "13                           2               Cluster 2  \n",
       "16                           1               Cluster 1  \n",
       "19                           1               Cluster 1  \n",
       "18                           0               Cluster 0  \n",
       "17                           2               Cluster 2  \n",
       "\n",
       "[20 rows x 22 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by=[\"name\", \"speedup\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2cb3f41b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-05T18:05:42.616346Z",
     "iopub.status.busy": "2024-04-05T18:05:42.616254Z",
     "iopub.status.idle": "2024-04-05T18:05:42.619017Z",
     "shell.execute_reply": "2024-04-05T18:05:42.618768Z"
    },
    "papermill": {
     "duration": 0.007521,
     "end_time": "2024-04-05T18:05:42.619592",
     "exception": false,
     "start_time": "2024-04-05T18:05:42.612071",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[(-0.4881578350674403, 1.1627113660348523),\n",
       "  (1.749806261598802, -0.4525719966261081),\n",
       "  (-0.3980133556581501, -0.7031879528599482)],\n",
       " [(0.06655968097162471, -1.913287530716048),\n",
       "  (0.2520426338192505, 0.531756881220382),\n",
       "  (-0.7588865867499884, 0.4885972785470779),\n",
       "  (2.1462990835988407, 0.27691530358264266)],\n",
       " [(-0.4042957384725225, 0.42253368260272484),\n",
       "  (1.749806261598802, 0.4371796258076567),\n",
       "  (-0.5369190461812909, -1.704780673615863)],\n",
       " [(0.33455027736619464, -1.295574703324575),\n",
       "  (-0.586154751651631, 1.1567818685617761),\n",
       "  (2.0015309188686548, 0.09287838091877257),\n",
       "  (-0.6934026055878959, -0.12357547291735016)]]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_centers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0965157",
   "metadata": {
    "papermill": {
     "duration": 0.003751,
     "end_time": "2024-04-05T18:05:42.627158",
     "exception": false,
     "start_time": "2024-04-05T18:05:42.623407",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 6. Plot the Clusters as Scatterplots\n",
    "\n",
    "The legends will not be printed, but they will be dumped to file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "27ce46ff",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-05T18:05:42.634970Z",
     "iopub.status.busy": "2024-04-05T18:05:42.634867Z",
     "iopub.status.idle": "2024-04-05T18:05:43.001342Z",
     "shell.execute_reply": "2024-04-05T18:05:43.000873Z"
    },
    "papermill": {
     "duration": 0.371192,
     "end_time": "2024-04-05T18:05:43.002063",
     "exception": false,
     "start_time": "2024-04-05T18:05:42.630871",
     "status": "completed"
    },
    "scrolled": false,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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zszrgYWA5cDowB/guQaL82l6anhXWvQJYBbwB+Eb4/b05DFlERAokrwkK+CRQBpzh7k3AQ2ZWDVxqZleGZdl8y913pH1+xMw6gB+a2Ux3fyXHcYuISJ7le4hvEfBARiK6mSBpHTtYo4zk1OvZ8Pu00QtPRESKRb4T1FxgRXqBu78KtIXrRuKtQApYMzqhiYhIMcl3gqoDdmcpbwzXDYuZTSG4ZvUzd982OqGJiEgxGXPTzM0sAdwCtAAX7qXe+Wa2xMyWbN++PW/xiYjI6Mh3gmoEarKU14Xr9srMDPgpMB84xd0HbePu17v7AndfMGnSpH2NV0RECiTfs/hWkHGtycxmAOVkXJsaxPcIpqef5O7DqS8iImNUvs+g7gMWmllVWtlZQDvw6N4amtlXgAuAc9z9sdyFKCIixSDfCeoHQCdwu5mdaGbnA5cCV6VPPQ+fGHFj2ucPAv9NMLy30cyOTvvS+J2IyAEor0N87t5oZicA3wfuJpjRdzVBksqMK/3xRyeH388Lv9J9GLhpVAMVEZGCy/c1KNx9OXD8EHVmZXw+j4GJSUREDmBjbpq5iIiMD0pQIiJSlJSgRESkKClBiYhIUVKCEhGRoqQEJSIiRUkJSkREipISlIiIFCUlKBERKUpKUCIiUpSUoEREpCjl/Vl8xSbZthtPJYMP7sSqJhY2oBxq6+qhvTvFno5u4tEIZfEodWUxopHc/J3Sk0rR2NUGDmbGxNKKIdt4KkmqqwlwIvEqLBrPSWwiUvzGbYJKdrTQvX0d237zNVqeuxdSSUpnvZmJ7/oaFa87jmhFbaFDHFU7W7v4r8Uvc8NfXqWlM0jIs+rLuOzk1/Ku+VOoLRvdRLCzo5VfrH2GH654gu0dLRxSNYEvHH4sJ007jLqS8gH1PdlNqmsPratup3XV7eApymYtpGreh7BENZFY6ajGJyLFz9y90DHk3IIFC3zJkiV9n1OdbbS88CAbrnkveGpA/drjP8nkM/+baEVdPsPMmV1tXfzzjU/x5CuNWdd/452v5YK3zaamdHSS1Oa2Jo659/usa9k1YN07pszh1uPPpT4tSXmym+7GlWy+9XhSnbv71bdYGZPfcw8lDQuIxMtGJT4RGT1mttTdF+Ri2+PyGlSqq42N130ga3IC2P37H9C5cXmeo8oNd+fBldv7ktM/zarnY0cdzLkLDmJ2fZAkLnlgJW1dyVHZ367ONj7yp5uzJieAR7as4Zrlj9GZ7OkrS3U1sfnWEwYkJwDvaWfr7aeS6tozKvGJyNgxLhNU09O34d2d/crcorgF70j0SJzt9/wfkq2NJJNJUqkgkSWTSZLJ4Bd5T09P1uXu7m56enoGLHd1dfVb7u7uBqCzs7PfcldXFwAdHR1Zl9vb2+nsDGJva2ujs7MTT6Vo2r6RPev+yp4nb2b7Xx+idecmkp1tbNy5m5ueXMO75k3mqU+9hc++dTpVJTEOrYlx0/vmc89Hj+SoaeXcvPSVAftKjzn9GNP7xN1JPwtv7+nmoU2r9tr/1770Z5rD/ndP0bb2HlKd2c/uADzZQfPzN5BKdg5aR8aOZOceUp39/+BIdbeR6mlnPIzoyPCNuwSV6umiffUTA8p3nflzmt92UbD83p+yddLReLKbJUuW8PLLLwOwZMkSVq0KfvkuXbo06/IzzzzD6tWrByw/++yz/ZbXrFkDwF//+td+y2vXrgXgueeey7q8bNky1q1bB8Dzzz/P2jWr6dz4AsuWPMlLi3/NxuvOZsWGnbxw1w9ofPj7bFj1EmcfEueL75jDupdX8OxLq7j6j2uZww7u/8vzXPGH1XzrqHImJxvpTqb67Ss95vRjTO+Tp556ipUrV/ZbdpwbDjqWz098PQDXTjuGz0w4HICrp/4jZ1bMpDuVZNmyZaxatYK2NXeyffal7JlyDgA7Zv0nqUj/a05t6+7Fu5qH948sRSvZuYfmF37CljtOI9m+EwiSU+eWp9j4szeTbNmoJCV9xt0kCYtEiZTXDCgvf+EWIi1bg+XnbyZRkgCMGTNmUFJSAsDBBx9MIpHoW+4tnzlzZl95+vKsWbOyLs+ePTvr8iGHHEI8Hu9b7i2fM2dOX/mcOXP6yg899FBo2cG6y4+kouFwIh3BX6VVj1+FdTSxbedKJn/gWqa/4TRef91THFrp7OkK/vN/57l2mrudVXs6+El5LZ899lDi0Ui//c6ePbtvv+nHld4n06dPp7Q0SCZTpkxhXWcTAI+0bGJTdxsAS9q3sbG7FYCVnbvZ0N1K1IxoIs7OnjZqgEiyBUu2//0fZJDh1wNdqqsFSBFJVP+9LNkFniQSG9vX4FKde2h56ec0/ulLAGy96wymvPsuOrc9y5bfngqpbjbdcizTPvBnYhVTChwtpLrbwZNg0b7rn55K4t0tOBAtGfh7REbXuJwk0bHhBdZ+9fV7bdNw1hXUL7yQSBFPc062N7H5f/+Npid+OWgdS5Qz7VtrabjyabqTg/9bP3Ph2/mH6fv/H25nRyuzb/0vWnu6Bq3zxvppPLjwE6Tc+fyTv+XbtW10PfqZvW639qivU3Pkl4hES/Y7xmKV6mqh/dXFNC//KZNOvoFoaR2pZBc9u1ex46FP0HDa7cTKGwod5pCSnU1YrJRINNFXlupux1Nd9DS9wuZbjsXDP1hi1TPpadkEqWCYu2zOaUw66UdESws7QSnV3U7XtmfYcudpTD79TkoajsCiCXpaNrD55rdR85YvUznvHCUpcjtJYtydQQHEaqdRPu942pb/Puv6aHUDtcd8eNjJKdnehHd3DLre4qVEy6oHXb+vPNlN89O39Y+laipdBx0VXE+LxCASpXHF4zxz4Qm07tiMe4qS6npiJWWUt2yhp6sTDKzpb2zq2krDhFqaW9tpbGqhpLSM6TMPoa2tjR07dmBmwfGY0dDQQCKRYPPmzbg7dXV1lJWV0bGnmW+95h3c/bflOLC1p51lHTs5KF7BnEQ1zaluLn/zIiqJctvKJWzcvo32Q06hu/4dJHY/RSTVRkflG8EixNvXEu3ZQ1fVG0nO/CCbdu6mK9VDLBajtKKc7s5OOjs6iUaj1NXUUkqElpaWfv1RXV1NLBajsbERd6eyspJEIsGePXv6rqkBJBIJUokYzW0tdHZ0EolEKK+qpKe7m47WdhLRKOWxxIi2GY/HqaqqoqOjg7a2NqLRKDU1NXR1dQ2Is7I0QvfmR3nl0SvAjJ4HL2DawmvZtXUd2x+5CO9xmu/5LFOOu4KaSTNpb2+nra2NWCzWt83m5v5DoDU1NcRiMXbt2oW7U1VVRSKRYPfu3QOOvaqqqm+b0WiU2trafdpmqruNzs1PUD7xMGqnzaOzK0lLUyPe8goVvoXI5GMoXfQQux77CvR0EEm2UFJkyck9Rc+e1Wy+fSEku9hy+zuZcsb9xKsOZvPNbyPZtpVdj15IpLSWikPfTSQ+9P19sm/GZ4KqrOegT9/Cphs+Qsuzd/Vbl5g6l4O/cC/Rivphb8+7O3j5M5MHXX/YNVthlBNUsr0Jkj14xplKT/0cWt76+X5l1W3rqEhEWbN5E2VR49L71/PE1h4eP3MSna1NYAYWIfHqn9n9+0tom38mrUddQEVZSV+C6r3u1au2tpZEIsGaNWtIJpPMnz+fsrIyNv9tA/M7S5jf8CYAHmvdzLKOnRxdPplPTZhPewzePPEgtjXuYsaeJJdPXsDG9dtg5leZ1vPvRJqWsf2Qy/BoBRPXXkxFy7Psmfcdtqza0Lfvx1o3c/HWJZxZcwifmjCfVZ17OO2Y4+hq7eTFF1/sF+cRRxxBZWUlL774Yl+cEydOZNWqVbS2tvbVmzBhAg1zZvKF39/St81PbPwjbyufwuVT3rLP2zz88MPZsWMHa9asobKykiOOOIKmpqYBcb7pjfOJl9SxY/bX8GgFvvZi7LaTeGXip+me+bW+el2bd/L6STPZuXPnkNvsjXP58uX94ly9enXWOEdvm5Mp2/Ag/1AWY0drBWvXv0q8fQ1TV36K+HG3saaxCsJjKml5gcmrLgSMhoU/7je0WShmEaKVB1F28Im0r/tdkKRuOwmLluA9wbB1vO4wyg4+Uckpx8blEF+vntZdeEcLzX+9F+/uoHzuscQnHEy0ckLf2cJw9DRt60tQ3RNeQ6psQvBLH8AiTPv4/xItrxlw8Tf9897WZaub6mgm2bKTXYuvJdXREu7PSEViEE2AA6TAnfJjP0lX9XSu/MMaDEhEYHZ1lFNfU0v383eDOxbWBceat2DdHVQsvIiK6joikQitra2YWV8cZkYkEiGZTFJSUkIikaC9vZ1YLEYkEmF3SzM9qSSrmnfSlOykJlbKzPJa1rfs4tebl/P6+qkcUTGZeza9RKenOKb+II6cNA3bvYqe5q3gTrRyIrGJ84nFSilNlNHY0sT/fflxGpNdVEXilEdiVEXidHuSTxx+DKUWpbu7m5KSEqLRKO3t7VRXVxOPx9m1K5j2Ho/HiUajwezH8FgSiQTxeJw9ne184em7qYjEmBgrZUeygxKLUhct4fzDjqY8HpxB9Z417Nixg5KSEsrLy+no6Oi3TYBIJEIikSAWi5FMJuno6KC2thZ3p7Gxsd/PWCKRAO+hvfFvdCz/IfG29XSXHkSkp5FodyOpySdQdfh5xEqqKSkpobu7m+7ubiKRCBMnBk8/2bZtG+Xl5USjUZqbmykpKSESidDe3t63j2g0SjQaxcxobm6mvLyckpISOjo66Onpoauri0gkQklJCclkkq6uLioqKohGozQ1NTFx4kRKSkrYuDGYzNC7zc7OTlI9HTQv/ymRbX8k2rWNnqrXUTv7HTSv+BWdpa8h1r2DsnkfIVUyiZblPwdPYd5Fxe4/Eulponvmucx4+5cpqZrEjh07Bv3/VlNTQzweZ/v27dTW1vYtD6a3zrZt2/r+sNq2bdte6ycSCbZsXE9q2TfpXn8nrXXHU7HrISKpDuJ1hzHlzMXEKgb/o3Q8yeUQ37hOUKMlPUHtOf5yuma9PWf7kvGldM+T1G38AZvn3TRonblz51JVVcXTTz/db7lQ9Sc33kJk8+/YPO8mJqz/PyTaVu41/gl/u4pE8zI2z7uJybtv46C3f4Wlywa/VSGfx3vo9EpaHzqTzfNuYtrzHyDWs5MJJ1xL5evO0dNNQkpQ+ymfCaqnejqeqAK874xk5pceJlbZf8gw/a/nzLO1vX3uXe5p2cH6b/wTMz5/F2sfvJHm6f+U3gKA0rUP8dqFH6at9lBWr1rF1ubO3lVEgKk1ZZT/6mycCHtO+u/gzAvCGXROrGYKr3/DG4nH47z88sv9rpv0/tzMmDGD+vp6du7cycaNG/vi611fWlXJ55bfz+RYOf9S+xoAkjgOTC2tYrf38KVX/si0eAWfqJ9HPO1YY5EotYkyDnvNa4IY1qxh056dOBDj7/UebNnIBW88llRbB7t27eoXg5lRX19PVVUVnZ2dbN68mUylpaXMnj2bbXsaeXHdauIWIYphafuYNXMm1YlStm3b1ndGkm7SpEmUl5ezZ88edu/ePWB9bwypVIpXX311wPpEIsHUKQ0k23fwt42bcYsF/0reg3kSi5Ywf/584mW1rFu3jqampgHbmD17Ng0NDWzdupX169dnjXH69Ol0d3fzwgsvDFhfXl7O3LlzgeDWhvTrVL2OPPJI4vE4zz//fNYY5syZw4TqEl5+4GJ21Z1GzZZfUL39NmzyMUxc+GM6Nz3Jrke/CKQoPfgEJhxzBda+ka2/OZEeK6ekaipT33MXydjgkw/i8ThmRldXV7/lXpn/f3rP6ru6uvotp0tvE41GMZz23a+y/fYTSbVuIhWrJtLTFIw0RBNMOeN+ShreTCQ+8LFd440mSYwhsaaNA8oqy0uJVVaO6n6inXGs8RW2X382U879Gb52OV3bVvfdgByvP4gpJ3+KypmvJeFGZXUtd6/diHs4+gecO72cyO71AJRseBIyJoVUz/ggFRUVRCIR6uvr+6aZp6uvr6eyspLOzk7q6wdet4tWlvF42zamxsqZW9L/4vehk6czsbSUr009la8/cz9Pt28jbhEiFmF2ZT1vnjiZ0miciRMnEolEqJ5Qyx1bVgzYx4Mtf+PiyZOItgXDU9libGho6BvGylRSUsKECRNIlid4/IU/E8syvHvElLcwpaKmb7gz07Rp06isrKSioiJrP9XV1TFp0qS+ocABMSTiTKlsY9O9i6ic9EHc4li8MrjmEU6573jiBmoW/oiJEydSVjZwynnvUFZFRQVTpgycpl1bW0tlZSUdHR1MnTo1az9UVATXVKZMmdJ3M3a6eDxOJBKhoaGByiw/01WVpXjzKuJ7nqUqaZS0rcC8B9v5NCXJRsoPPZHS2BW0vnwrE0/8DtHSelIV1Ux7/2J2Lv4Uk0/7LdHyBoYzPan39obM5cH03hqRuZzJPUVP8wZ2/uZYvG0rBpRUTiRafjidmx7rmzgx9Yz7SShJ5ZTOoEZB+hlUNodds5VY9ehOD07fZ6S8ltpjzqPmHz9EJF5KtGoSFosTLa/tq7+lqYNplz/UbxsbvvAP7P7KwTmNe1t7M1Nvvizrus0fuISGsiqSqRSNXe00d3fSmexhYmkFJdEoVfHSEW8r17HmSrJzD60rf83O318AQGLyAqac8Tu6ti9jy+2LINVNtGIa085+gljlwORSDFI9HXRtf44tt52IZ3nqhyWqmPb+R4lWzQD630eU6ukETxXN8xZ7Wrey5bYT6W5c2XfNyaIlbH/gPNrX/Y5ISS1Tz/oj8Zo54/6J+xri20+5TlCFmGa+t6SYLbHsautiU1P/GA+pSBIJf5FEzIhkDi2OQtyj+Uu/qauDjmR31nWl0TjVif27JpDr7Q8l2bmHluU/o2XFL5ny7ruIlk0Mpm1vXcKOBz/G1PctJlo5HbPifACM93Sw4WdvomdP8GSU0oNPpOGUn7P1jtPo3PIUAImGNzH1/Y+MiZuOe1q3snPxp5hwwrV9EyKSHY3s/MPnqT3qK0pOIQ3xFbloWfWoTyMfbT1J5w3feXTQ9VsuOZmGquK+CbY6UUo1uUsSud7+UKIlNVTO+xBV8/6VSEnw8xSJl1MyeQHT/3UZFk0UbXICIJpg6pkPsunXbyde/zoaTvk50dJ6Jp9+J1vvPJ1kxy4mn37nmEhOALGKyUx6501EEn//IypaWsfE46/BYmVKTnmgM6gxam9nbdnOfJo6uunoHvzxQaXxCNWj9LqNfvst8FmJ5Jd7imTrVixWQrT079ckk+078VR3UTzCSEbXAXUGZWbzgGuAtwK7gRuAy9x9r+97MLMa4HvAuwkmod0DfNbdd+Yw3KI10rO26tI41QXIBYU+K5H8MotkvUYWLZtQgGhkrMtrgjKzOuBhYDlwOjAH+C5BwvnaXpoC3AIcBnwMSAFXAHcAx+QoXBERKaB8n0F9EigDznD3JuAhM6sGLjWzK8OyAczsrcDJwLHu/sewbCPwFzM70d0fzlP8IiKSJ/m+4roIeCAjEd1MkLSOHaLd1t7kBODuTwHrwnUiInKAyXeCmgv0u9PS3V8F2sJ1w24XemmIdiIiMkblO0HVEUyMyNQYrhu1dmZ2vpktMbMle3uQpIiIFKcivqli/7j79e6+wN0XTJo0qdDhiIjICOU7QTUC2Z4CWReuG+12IiIyRuU7Qa0g45qRmc0Aysl+jWnQdqHBrk2JiMgYl+8EdR+w0MzSH8B2FtAODP4cnqDdFDN7W2+BmS0ADgnXiYjIASbfCeoHQCdwu5mdaGbnA5cCV6VPPTez1WZ2Y+9nd38CeBD4qZmdYWbvBn4BPKZ7oEREDkx5TVDu3gicAESBu4HLgKuBSzKqxsI66c4iOMv6MfBTYCnwnlzGKyIihZP3Z/G5+3Lg+CHqzMpSthv4cPglIiIHuAN2mrmIiIxtSlAiIlKUlKBERKQojYsXFppZM7Cy0HEUmYnAjkIHUWTUJ/2pPwZSnwz0WnevGrrayI2XV76vzNUbH8cqM1uiPulPfdKf+mMg9clAZpaz15VriE9ERIqSEpSIiBSl8ZKgri90AEVIfTKQ+qQ/9cdA6pOBctYn42KShIiIjD3j5QxKRETGGCUoEREpSmMmQZnZPDNbbGZtZrbJzC43s8wHymZrN9/MHgzb7TCz68ysMqOOmdlXzexVM+sws2fMbGHujmZ0mNmhZvZDM1tmZkkze2SY7WrM7Cdm1mhme8zsF2Y2IUu9083s+bBPlpvZWaN+EKMsl31iZieZ2a/MbL2ZuZldmotjGE256g8zi5rZf5jZn8xsZ/j1oJm9JWcHM0py/DNyWfh/psnMms1syXj/f5NR//Tw/86wpqaPiQRlZnXAw4ADpwOXA/9O8DT0vbWrAX4PlBE8Df2LwHuBn2dU/TJwMfA/4fZfBO4eA//Z5gOnENyE/PII2t0CvAP4GHAe8BbgjvQK4bu3fgP8AVgE3Av8ysxO3s+Ycy1nfQK8E3gDsBho278w8yZX/VFG8P/maeBDwDlAN/CYmR2xnzHnWi5/RqqBmwh+37wXeAa42czO3I948yGXfQKAmZUSvL1i67C37u5F/wV8heDV7tVpZV8i+CVRPUS7JqA2rexdBIluQfg5Edb5RkbbpcA9hT72IfolkrZ8G/DIMNq8NTz+t6eVHRmWnZhW9gDw+4y2vyN4B1fBj71AfZK+7R3ApYU+3kL1B8HrcOoy2iWA9cBPCn3chfoZGaTtn4G7Cn3che4T4OvAnwgS+JLhxDUmzqAI/oJ/wNNeagjcTPBX3LF7afcPBB2xO63sIYIOPDX8PAeoCsvTPQicZGaJfQ87t9w9tQ/NFgFb3f2Padt5ClgXrsPMSoDjCP46Sncz8NbwzLQo5apP9mPbBZWr/nD3pAfvd0vfVxfB6MO0fY8493L5MzKInQTJu2jluk/M7GCCk4rPjWQHYyVBzQVWpBe4+6sEZ1Bz99KuFOjKKOsBUsDr0uqQpV4XwQ/VIfsQbzEb0Jehl/h7X84B4lnqvUTwM3NYzqIrjOH0yXiyT/0R/mHzZkY2RDRWjKhPzCxmZrVm9i/AyQRvEz/QjKRPvgvc4u7PjGQHYyVB1QG7s5Q3husGsxp4o5nF08qOIBieqA8/ryU4o8q83nRk+L2eA8tw+rL3e2a9xoz1B4p9/fk6UO1rf3yV4P/L93MQU6ENu0/M7GiC63GNBMNZn3P3O3IbXkEMq0/M7HiCJP2fI93BWElQ++pHwCTgGjObYmbzgWuBJMFZFO6+B/gV8FUzO87M6s3sM8CJ4TbG3LCOSL6Z2akECeo/3H28vzngeYI/eE8iSNbfN7OzCxtSYZhZDPh/wH+5+/AnR4TGSoJqBLJd96jj73/VD+DuK4DzgbOBzcAy4Cngr8CWtKqfB5YTzPjbCVwEfDNcl17vQDCcvuz9nlmvLmP9gWKffr4OYCPqj3C266+BH7j793IbWsEMu0/cvdXdl7j7w+5+IfAz4Io8xJhvw+mTj4d1bgqHPGsJLp1Ew8/xLO37jJUEtYKMMU0zmwGUk30MtI+7/xiYTDA9eBpwAXAo8GRane3ufjwwAzic4LpTK7DF3deP2lEUhwF9GUofT15DMESRWW8uwRnlgXaNYTh9Mp4Muz/M7DCCWxAWA5/NfWgFsz8/I88AM8KziQPJcPrktcBBBFPLG8OvswkmsDUSTMcf1FhJUPcBC80s/aVYZwHtwKNDNXb3Dnd/PjzFPIfguDNnqOHuG9z9RYL3ZH0E+PFoBF9k7gOmhPc5AWBmCwiS8n0A7t5JcP/T+zLangU8EQ6LHkiG7JNxZlj9YWZTCW5HWAOc7e7JfAeaR/vzM/JPwAZ378lhfIUwnD75PsGM4PSvBwj+yD2OgbOn+yv0/PthztGvIxiie4jg2tD5QAvwzYx6q4Eb0z5XE5xanwosBL5FcGZwXka7DxEkpHcA/wo8SzCOXFnoYx+iX8qBM8OvJwim+PZ+Ls/WJ2HZAwSTQ84A3k1wc96fMuq8jWDG4/fCfrmS4Ozp5EIfdwH7ZGbatpoI/sg5E1hU6OPOd38Q3OLxV4KL5KcCR6d9vanQx12gPplJcCb5ceB44DTgJwSTsD5Z6OMuRJ8Msq+bGOZ9UAXvmBF04DyCa0TtBMnqG0A0o8564Ka0zxUE9zPtCts9Dbw7y7bPDTu2g+BU9IfAhEIf8zD6ZFb4w5/ta1a2PgnLasP/OLsJftH+EpiYZfvvBl4AOglO2T9Q6GMuZJ8Q3CmfbbvrC33c+e6PIbZbtP2R4z6pIbjetC78XbKF4HfWKYU+5kL1ySD7uolhJii9bkNERIrSWLkGJSIi44wSlIiIFCUlKBERKUpKUCIiUpSUoEREpCgpQYmISFFSghLJwszOM7Ol4Wu7G83sWTO7qtBxZTKz75jZ+kLHIZILSlAiGczsK8ANBHfJn0HwdJE7CZ4MICJ5oht1RTKY2UbgDnf/dEa5eZH9hzGz7wBnuvusQsciMtp0BiUyUC1ZXrOSnpzMbJaZuZl90Mx+Fg4FbjOzSzLbmdnhZnZvWKfZzG41sykZderN7Hoz22pmHWb2uJkdlVGn1sx+aWYtZrbZzL6aZV+XmtmOLOVuZhekfV4fDg9+3cy2hNv8hZlle32CSEEoQYkM9AzwGTM718wmDFH320AbwUM1fwRcYmZ9Z15mdijwZ6CU4En65wHzgbvNzMI6JcDDBA9CvojgGYjbgYczEtlPgEXAhQQPTD4Z+MB+HOfZ4T4/DnyB4KGvN+zH9kRG1YH2fhKR0fBp4A6Ch1q6mb0E/Ab4jrs3ZdR90d0/ES4/YGYNwH+a2XXungIuITgbW+TuXQBmtozg4bunELxL6RyC95DNd/dVYZ2HCR5g/O/AReHboN9N8MDeX4d1/gC8SvCQzn1RBpzq7i3h9lqBn5nZ69z9pX3cpsio0RmUSAZ3Xwa8jmBSxLWAAV8HlphZZUb132Z8vp3gxZgHhZ9PDOukzCwWvrRuHcGToRek1VkKrEurA8G7znrrvCX8fmdanC0M9T6dvXuoNzmlHYul7UukoJSgRLJw9053v9vdL3D3ecDHgNcAH82oum2Qz1PD7xOB/yB4D1n61yEEb3DurXN0ljofTqszBWh2944h9j8S/dq6exvBe9amZq8ukl8a4hMZBne/0cyuZOArrhsG+bw5/L6L4Mwk27WdHWl1lgD/lqVOZ/h9C1BlZqUZSSpz/x1AIr3AzOqybHdAWzMrByrTYhcpKCUokQxm1uDu2zLKJhG8kG5rRvX3ANelfT6D4Bf8hvDzYoJJEUv3MkV9McGEh1cz95vm6fD76UDvNahK4CT6X4PaQJDIprv7xrDs5EG2eZKZVaYN872H4AV1SwapL5JXSlAiAz1vZncSvI15G8GrvL9IMFvvfzPqzjezHxJMong7wRDg58IJEgCXAk8B95rZjwnOmqYTJJab3P0R4KfAJ4FHwvua1gITgCOBLe5+tbu/aGZ3AdeZWTVBErwojCnd/QRvj/6xmX0XmB1uO5v2MK5vEwzrfRv4rbsvH3ZPieSQEpTIQJcTnKn8P6CeYHjtceAsd1+XUfdLwD8TJKgO4BvA93tXuvvLZnY08E3geoKZcxsJzppWh3U6zOy4cL+XAZMJEuNTwF1p+zqP4GztewTXiv6H4MzqzLT97TCz9wLfIZiJuBT4IJAt6dwMNAM3Egzt3UX2YUaRgtCTJET2gZnNIpiN9y53v6fA4YxY+Py+29z9i4WORWQwmsUnIiJFSQlKRESKkob4RESkKOkMSkREipISlIiIFCUlKBERKUpKUCIiUpSUoEREpCj9f11ePLR+Ayg/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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y0eu3mtkrwGozWxB1lojjGsIMvL8ws8PM7HRCJ4tvFXc9N7MXzOw6AHfPufvDxQ/gz1HVp9390Zj7FhGRUSRuL77PEiYm7PE/wGvAB6NtfDPORty9hTDYbBK4i3CT7hXA10qqpqI6IiIyRsU9xbcj8HcAM5sCvBM41N0fNrNu4Oq4O3T3Zwk3/vZXZ8YAy5cAFnefIiIy+sQ9guoCMtHzgwk31v4uev0GMGFowxIRkbEu7hHUY8Cno+tOnwN+5e75aNlMwuk+ERGRIRP3COpLwB7A08B0Nh6s9RTC+HgiIiJDJu59UM8Cu5jZJOANdy8emeEMNkwDLyIiMiQGO6Pu6jJlTw9dOCIiIsGgZtQVEREZLkpQIiJSlZSgRESkKilBiYhIVVKCEhGRqtRnLz4zWwzEnujP3WcOSUQiIiL03838NjZOUO8jzNv0ABtm1D0caCPMyyQiIjJk+kxQ7n5Gz3MzO5swMeAx7t5WVN4A3M3GI52LiIhssbjXoD4NXFqcnADcvRW4LFouIiIyZOImqCZgmz6WTQMahiYcERGRIO5QR3cBl5rZOuBOd+82swxwHHBxtFxERGTIxE1QnwJuAG4F3MzWA42ESQPvjJaLiIgMmbijma8FTjCz3YF9CKf1Xgcej0Y6FxERGVKDHc38WUAJSUREKm5QCcrMdgB2A2pLl7n7vUMVlIiISKwEZWaNhOtPR/QURT+Lb+RNDmFcIiIyxsXtZv7fwI7AuwjJ6QTgIOA6YDGwXyWCExGRsStugjoauAh4NHr9mrv/1t1PB34JnFmJ4EREZOyKm6C2AV529zxh7L2JRcvuZcOpPxERkSERN0G9DEyOnj8PHFu0bF+gcyiDEhERiduL7wHgMOB24Argx2a2N9AFvBu4vDLhiYjIWBU3QZ1FmGoDd7/RzFqBk4E64DPA9ysTnoiIjFVxR5JoB9qLXt9OOJoSERGpiM2a8t3MJpjZ3mY2dTPW3d3M5plZu5m9ZmYXmlm/91CZ2T5mdr2ZvRCt93cz+5qZbXLDsIiIbB36TVBm9j4zu9nMbjOzD0Zl5wHLgMeAZdGycXF2ZmbNwIOEG3yPAy4EvgRcMMCqpwC7EEZOPxr4DvBF4KY4+xURkdGnz1N8ZvYJwrWlx4H1wPVmNgc4DTgbeA54C3BO9Dg7xv4+SbhudaK7rwMeMLMm4HwzuyQqK+eb7r6q6PXDZtYJfN/MdnL3l2LsW0RERpH+jqA+C1zp7vu6+2HAR4HPAee5+xXu/it3v5Rw9HNSzP3NBe4vSUQ3E5LWgX2tVJKcejwZ/dwu5r5FRGQU6S9B7cLGExH+kjDM0YKSevOBnWLubzawsLjA3ZcSOmDMjrmNHvsDBWDRINcTEZFRoL8EVUcYNaJHTy++rpJ63UA65v6agTVlyluiZbGY2TTgXOBGd18Rdz0RERk9BurF5zHLhk001fytQCvwhX7qnW5m881s/sqVK4ctPhERGRoD3Qd1v5nlSsrmlZQNZk6pFmB8mfLmaFm/zMyAnwB7AO909z7XcfdrgWsB5syZM6JJVUREBq+/5DJQ1+/NsZCSa01mNp0wSsXCsmts7EpC9/TD3T1OfRERGaX6TFDuXokEdR9wppk1uvv6qOwUoAN4pL8VzeyrhGGV/tXdf1+B2EREpIps1kgSW+AaQieLX5jZYWZ2OnA+8K3irufRiBHXFb3+APBfhNN7r5rZfkWPKcP7FkREZDgM5vrRFnP3FjM7FLia0IV9DWF09PPLxFU8/FHPfFOnRY9iHwFuGNJARURkxA1rggJw92eBQwaoM6Pk9WlsmphERGQrNtyn+ERERGJRghIRkaqkBCUiIlVJCUpERKqSEpSIiFQlJSgREalKSlAiIlKVlKBERKQqKUGJiEhVUoISEZGqpAQlIiJVSQlKRESqkhKUiIhUJSUoERGpSkpQIiJSlZSgRESkKilBiYhIVVKCEhGRqqQEJSIiVUkJSkREqpISlIiIVCUlKBERqUpKUCIiUpWUoEREpCopQYmISFVSghIRkaqkBCUiIlVJCUpERKqSEpSIiFSl1HDv0Mx2B64C9gfWAD8ELnD3/ADrjQeuBI4nJNa7gc+5++otiSffsQ7Pdm7YT7qWZF3ToLZR6GzF81ksVUOipj6U5bIUOtdhiRTJ+vFbEmK/iuO3ZBrPZ8Pzkvexqq0Ld6hJJmjPhqZusE5qE06XpxjX0Dhkcbs7KzvbSCeSNNfUAbC+u4vOKLbaZJrGTE1v/dZsF935PO0dq5iYSpJb/jjmBeqm7Yul60jWTgSgM5elLdfN0y3LWNnZyq5NU5g+bgLNmVqSieRmxVosV8iztruT59etYknrG2xX38QeE6bRkM5Qk0xv8fbjcHcKnavJt6+ge8WTWLqB2u3egaVqSWQahyUGkWoxrAnKzJqBB4FngeOAXYDLCQnn3AFWvxXYDfg4UAAuBu4A3rUlMXm2k398dpve17tdtRwGkaAKna20LXyYV65+Lzt+6V7qZu4LyTTZFYtY/PX9mXzceTS/+2MVS1LF8c/69jKe//y2m7yPVW1dXPLQIn6y4GX+8NkD+Oztf+OCg7Zn5ponePmaU9juC/fSttMc6mprya54gcVff8dmx+3uvNK+lgPuuYojt5vNxfscS3NNHZ35LMfPux6AOw79CI2EBNXS1c73Fv6R/zd9V1J/Poflz98GXoi2ZtTNOIIpR1xHe7qJWxY/xVcev4eW7o7e/c1snMT/vvsD7DVxW+pSmc1ux9ZsF4+ufImP/O4WXm1f21s+uWYc397veObu8GbGZ2o3e/txeD5Hbv1LLL/7X8muenrDgmSGpr3+jQn7nkuytrmiMYhUk+E+xfdJoA440d0fcPdrgAuAL5pZn1nBzPYHjgA+7O63ufvtwKnAAWZ22HAEXo4XCnS99iwvf/t4PNvJS5ceSceLj5Jd+SKLv74/hfY1rPjZl1j/1F0Uiv6oDqc32rr48eOvcNkji1jR2s07r/o93z9xD97kr7D8Oyfi2U5evfxICkvnR3G/oyjuuwcdd3suy7vuuZpX2tZy3fOPctbjd9OW7eL4edfz55Uv8eeVL3Hqb2+iNdvFmq4Ozl5wL0dP2Rab91E6/vHzouQE4HQsuZ9ltx2Jd67hk3+4baPkBPDi+tUcdN93WdzaskXt9EzL6xz16x9slJwAVnW18cFHbmLea8+TKxT6WHto5DtX89rNB2ycnADy3ax78ipa/nAu+c41FY1hKBS61pFvX1H2UehaN+DyatFfnNUW69ZquE/xzQXud/fi/9mbCUdDBwJ39bPecnf/bU+Buz9mZoujZQ9WKN5+WSJBZptZNLz1WFqf+CXks7x08aGQSEHP6ayd/pmGtxxFIlM3EiEycVwNH3jb9vzw0aX8fWUrK1q7ecfVf+Kh095M3V7H0vFUiPvlS0rinvE2Gt5y5KDjLlDgsn3ew/se/l8c57rnH+XHLzxOLko8DakaLtnnPWQSSdZmO3l85cucu91kul/9fZ/bzK5+htaXH+bI7d/Er15duMny7kKeMx67k5sO/CDN0SnWwVjd2cbnH72Dgnufdb702J0cOG0mk2rHDXr7cRRynax78tsUOvs+Y73+6R8yYd9zgQkViWGoeL6TpdfuUHbZjqe/AjDA8sGdYq+U/t4HVFesWyvzfn4ph3xnZiuA77r7+SXlbcD57n5pH+vdCkx194NKyu8BcPdj+tvvnDlzfP78+b2vi6/b5NevYtHZe9Ax62gSHavY/d+vZ1V3ikwqxcTJk1mxpo1MJsOkSZNYtmxZ2eevLnmB9Q9dTeGRb7N+v//Ak2lSa5dS2Gl/Gnc/iIbxE1m3bh2pVIr6+voter62ZTVJg/raGta3d5IwSBa6WdveTSJhdL/4KLkJO5Fu2oaamgx5d2rSKZwES1aupTvvLO8oML0hSXNdimTbCvIF8FQNlu2ARJKEF0g3b0c2X2DChAnU1dWxbNkyABKJBPl8nlQqRSqVIpvNkslkGDduHGvWrAn/L8kEHZ2drM92sTrfRQEnidGQTDMlU0+6JkNNOkPLunXkKDAlt5T2fC3mBRKFdnLpKVihG8u3k/Asya7XSNVPpqV+d17uWEdbIUdNIkmNJai1FGvyXbyabeeYbXcj350lmUxiZuRyOerq6hg3bhzr1q0L13cKBRKJBLlcjt7PfjLBba8vpD6RYlKyloZEmvpEitZClrQlqE+kaMl3sWfzNFJu1NTUkEwmaWtrI5FIkMlkSCaT5HI5stks+Xy+t62SySSJRAIzA6CxsZHW1lY6OjY+EkwnDZY/THeyGbcaEtk38PR4LN9GIt9KPjOVVNer1E/dk/SkPeno6CCXy2FmTJgwAXenvb2dpqYmcrkca9asIZPJkEgk6OwMn/WGhgYaGxvJZrOsX7+erq4u0uk0mUyGfD6Pu/fGm8/nyeVydHd3k8lkMDO6urqYNWsWDQ0NPP300+Ryud733tXVRaFQoKamhoa6BGsfv5jsuDeTafsbOGTrdibT8QKN+5xHe2cX7S/eG9oo307TiltJ5lponXg4M4+6jNqmaSxfvrzP3+fm5mYymQzLly/vff7666/3WX/ixIm9dYqfD1T/tZdfoOv+95DMvUHrpKNoWHU3icKG69U7nv4KyfqpfW5nrDCzBe4+pxLbHu4jqGZCx4hSLdGyzVlvZrkVzOx04HSAHXfccaNlpdedANrfeirp5U+z6Ow9WP3en5Je/jTN7/0Pli5dyvjx45k0aVKfz195fRXj9jyJxCPfpnPWkZCuoyvabsfqNaxYXS70Ctk+fE668wW628MvU3tHiGZCTTijO7U+dCjwQoFc3eTeVb0mXITPA/nucCS1evXG3+h7/vh2d3fT3d0NQDabpa2tbaN6CWB8qobxqZqNyik42Y4ush1dpDHSJGlN7dz7SezpKdPztSkPZMfNAiBdgJk1m14T2yZdz5tqm+lobdtkWXd3N2vXrt2kvNR7xs/od/k26XpyHV3koPcPfo/SZNOfdevKnxbqBmjav6hk503qZOtn0ZEFSv64tra29j5vadlwqrP0/6StrY3ly5eTTqfJZrOxY25vb99o+5lMpvd9FC/red3SAslJR5KvnU4uPbX3/6+dI1izLPo8Nb+7d53a1ifJtP+dNTv8O63tnSTrsixatKjPeGbPnk1jYyOLFi3qff7iiy/2WT+TyfTWKX4+UP0lL69gUv2uIbbt/436N36zUYKSyhvuI6gscKa7X1lS/grwE3c/u4/1HgDa3P34kvL/BWa6+zv622/pEVRu3YpNElQhVYd5Hst39z5/05UvY+MmAZBMJnv/OBc/T+B0LPsHL33zYHz9Cgo1TeCOFbJ4poEdPnMr43aeE45QzHq/nQ7mec835UQiQffa5Sz6yu5YIctO5/2ZJRe9C8wopMcR/qw7hUxjeC+5TsAg286OFzzFP3/vLxQc2nNw4h4T+eo/19J6/YfId7VDMo3lusDzeCLN9h+7gdT2b6amvrH3W7i7k0gkKBQKJJNJkskk2Wy293nPUcma7g7OefweFq5ZQXshR3shR40laEpmOHL73Th8+u78dPETHDBxB5a1reW9+Sdpf+F+KGRJFjrozmxLotBBMt+GeRb3ArUzDuWRycdy9T8W0BWlsYwlqbUknYU8nebcc+hHGWep3iOWfD5PJpMhlUrR1dXV85khkUiQzWZ7j6Ba81mOfOiHFApOxhKkSfQeQaVIUJdI0kmenx10KpPS9b1HDR0dHSQSCdLp0MMvn8/3Hon07CuVCpm3p6ympoZcLtcbT49EoY21D32KjvXRkWquFU/UgOdIFLopJMdhhQ6mHHQZ43Y+ilwu1/sZqa2txd3p7u4mnU73Pk+lQlv0fJHoaYueOPP5fO9RHkAhusZmZpgZhUKht05Pe9bV1ZFKpWhra8Pde/fR056JRAK6W3jtpr3BElDIgSVxS5MstLLdh/5GLl9g2S2hb5N5jmTuDfACnqxjx48sJDVuau/vVznF8RQ/H+r63a2v8+p1M4ECnqjFCp0YG/5e6ggq2JqOoFqAct3CmqNl/a03ZTPWK8vStaGXGxtO8SVy4VvwLv/1DMnGyb31en55gU2eeyFP94oXWXrRO/H2NQDUTZ1BsnEK7c/Ow3KdvHb5kex01oPU7bwPiXRd2e0M5rnVN/Lmy58PLxIpZl/yHPm2N6Klzovn7AW8zsyL/kaqcRIFjGy+wOJ1BZZGX6i/dtgufHq2s+riA0i2ryEJ1Ezfi2TjTrQ/Ow+Ald8+gp3Omkdt0xwSmVpqa+P1YGvpaucDv/sJf175EhCuOX1o1t5cs/BPeM554sU/sSpV4KJ3Ho/j7HfX//Dhg06BJy/q3UZNxz822W79nj/issd+x3Ndb2yyDODfZ7+DKc2TGJcu35Nv3Li+rx3VdHcyZ7uZ3LL4qT7rHLDNzkxunrTRNaj+tjmQxsaNu4y7TyL9T6ey6v7T+lzHMk1MmPEukn38X9TUbDharaurK/sc6E2oW6KhoaHPfeetjVSu/K9lJpMmA6S7X9tkmeXbSCRso8Ten+I6FamfTGLRlyErjEwnp7FuuHvxLQRmFxeY2XSgPloWe73I7AHWKytZ10SqaSqppqm9yah3WePkDcsG6G5uiSTJ+gmkm7cDQoeInc6ax/TP/JyGtx0XttcwkfSkHbEt6ALdV+ypholYMsWL57yFF895C8mGDTk81TSF1Php5Gsn8VK2gXdfF3qGfXjOdE74p+1pmDBpQ9wz3sb0Lz9YJu7pg47bMN65TTg91ZCq4XfHfJqL5xzLzw76IBb9O3S7WdQkkzSkMlyyz7E8sHIZNW8/p89tNr7lE9RN2JmlbeX/6M1snMR5bz2iz+Q0kKZMLZe9/V/Yvo8u9RMydfzogFMq1kECwlFL/c5zqZ1+SB8VkkydeyOWrlwMItVmuE/xfRU4E9jJ3ddHZWcAFwLTSnr3Fa+3P/BH4F3u/vuobA7wOHC4u/fbi6/0FF+x0tN9u121nFTT4A7bc+tW8PpPv8C0D36bVJTw8m1vsPyWs5j8L+eQnjgdG4IbSfvad1/3QfW8j47uHItbOvjeH5fwn4fvxq7//RBHvWkKP/6XHVh185eY+oEryTRNGbK413R1cMUzj3DSjL2YPX4qmWSKFR2t3P3yMwAcO30PptaFb+Drs508t2Y5iWwr2619Bn/iUrpX/gWAdPNujN/nLOpnvodcahyvdqzjq/Pv4Y6X/kbOCzSla/norLdz1l6H9G5vcxW8wMrONr7x1AP8+IX5tOW6qUmm+NcZ/8TX957LtNpG0snK/B8Wy3e20Prcjax94tvk178MGHU7HU7zOy8iPWEXEpkte5/DodC1Ds+Xv1ZjyXD019/yRE119Izr731AdcU6kip5im+4E1Qz4SbdvxG6ls8EvgVc6e7nFtV7AXjE3T9WVHY/MAs4gw036q5w9wFv1K10goLQM7D0iCvfvpZEbUPFkhPES1AAHV052nIF8vkC2174AADv3WtbbjxpFplxGx85DEXca7o7qE+mySTDqZQVHeu5/vnHAfjIrH2YWrfxKa43OttJJhLUZNeRNDDALEGidiJmGw7013Z3kCsUyHuBhCUYl0pv0Q26pdpz3XTksuS9QNISZBKpjUa9GA5eyFHoWgPu4TqOJXSDrlStrSZBQe9QR1ez8VBH5xcPdWRmS4CH3f20orIJwBXACWw81NGqgfbZX4IaiqGORlLcoY56tLR305XbcMNpTSpBc/3Q/YHvy7ruzo2GOmqq8KgMIjI8tqoENRL6S1AiIrL5KpmgNJq5iIhUJSUoERGpSkpQIiJSlcbENSgzWw/8faTjqDKTgQE7mIwxapONqT02pTbZ1JvcvSKTlQ37hIUj5O+Vuog3WpnZfLXJxtQmG1N7bEptsikzq1gPNJ3iExGRqqQEJSIiVWmsJKhrRzqAKqQ22ZTaZGNqj02pTTZVsTYZE50kRERk9BkrR1AiIjLKKEGJiEhVGjUJysx2N7N5ZtZuZq+Z2YVmNuBw22a2h5n9OlpvlZl9z8waSuqYmZ1jZkvNrNPMnjCzIyv3boaGme1qZt83s7+aWd7MHo653ngzu97MWsxsrZndZGaTytQ7zsyejtrkWTM7ZcjfxBCrZJuY2eFm9jMzW2JmbmbnV+I9DKVKtYeZJc3sLDP7nZmtjh6/NrN9KvZmhkiFPyMXRL8z68xsvZnNH+u/NyX1j4t+d2J1TR8VCSqapuNBwpzmxxHmj/oScMEA640HHgLqgFMIU3WcBPxvSdWvAP8JfCfa/jPAXaPgl20P4GjCTcibTkPbt1uBg4CPA6cB+wB3FFcwswOA24DfAHOBe4CfmdkRWxhzpVWsTYCjgL2AeUD7loU5bCrVHnWE35vHgQ8BpwJZ4PdmtvcWxlxplfyMNAE3EP7enAQ8AdxsZidvQbzDoZJtAoCZ1RJmpFgee+vuXvUP4KuEqd2bisq+TPgj0TTAeuuACUVl7yEkujnR60xU5+sl6y4A7h7p9z5AuySKnv8fYYqSgdbZP3r/7y4qe3tUdlhR2f3AQyXr3gv8fqTf9wi2SfG2VxGmiRnx9zwS7QEkgeaS9TLAEuD6kX7fI/UZ6WPdPwB3jvT7Huk2Ac4DfkdI4PPjxDUqjqAI3+Dv941n3L2Z8C3uwH7WeyuhIdYUlT1AaMBjote7AI1RebFfA4ebWeUnS9pM7l4YuNYm5gLL3f23Rdt5DFgcLcPMaoCDCd+Oit0M7B8dmValSrXJFmx7RFWqPdw97+4tJfvqJpx92G7zI668Sn5G+rCakLyrVqXbxMx2JBxUfH4wOxgtCWo2sLC4wN2XEo6gZvezXi3QXVKWI8zI++aiOpSp1034UM3cjHir2SZtGXmODW25C5AuU+85wmdmt4pFNzLitMlYslntEX2xeRuDO0U0WgyqTcwsZWYTzOyDwBHANRWObyQMpk0uB2519ycGs4PRkqCaCbPvlmqJlvXlBeCfzCxdVLY34fTExOj1i4QjqtLrTW+Pfk5k6xKnLXt+ltZrKVm+tdjcz9fWanPb4xzC78vVFYhppMVuEzPbj3A9roVwOuvz7n5HZcMbEbHaxMwOISTpswe7g9GSoDbXD4ApwFVmNs3M9gC+C+QJR1G4+1rgZ8A5ZnawmU00s88Ch0XbGHWndUSGm5kdQ0hQZ7n7WJ854GnCF97DCcn6ajN7/8iGNDLMLAX8D3CRu8fvHBEZLQmqBSh33aOZDd/qN+HuC4HTgfcDy4C/Ao8BTwGvF1X9D+BZQo+/1cCZwDeiZcX1tgZx2rLnZ2m95pLlW4vN+nxtxQbVHlFv11uAa9z9ysqGNmJit4m7t7n7fHd/0N2/ANwIXDwMMQ63OG3yiajODdEpzwmESyfJ6HW6zPq9RkuCWkjJOU0zmw7UU/4caC93/xGwDaF78HbAZ4BdgT8X1Vnp7ocA04E9Cded2oDX3X3JkL2L6rBJW0aKzycvIpyiKK03m3BEubVdY4jTJmNJ7PYws90ItyDMAz5X+dBGzJZ8Rp4ApkdHE1uTOG3yJmAHQtfylujxfkIHthZCd/w+jZYEdR9wpJkVT4p1CtABPDLQyu7e6e5PR4eYpxLed2kPNdz9FXd/hjBP1keBHw1F8FXmPmBadJ8TAGY2h5CU7wNw9y7C/U/vLVn3FOBP0WnRrcmAbTLGxGoPM9uWcDvCIuD97p4f7kCH0ZZ8Rt4JvOLuuQrGNxLitMnVhB7BxY/7CV9yD2bT3tMbG+n+9zH76DcTTtE9QLg2dDrQCnyjpN4LwHVFr5sIh9bHAEcC3yQcGZxWst6HCAnpIOD/AU8SziM3jPR7H6Bd6oGTo8efCF18e17Xl2uTqOx+QueQE4HjCTfn/a6kzgGEHo9XRu1yCeHo6YiRft8j2CY7FW1rHeFLzsnA3JF+38PdHoRbPJ4iXCQ/Btiv6PHPI/2+R6hNdiIcSX4COAT4F+B6QiesT470+x6JNuljXzcQ8z6oEW+YQTTg7oRrRB2EZPV1IFlSZwlwQ9HrcYT7md6I1nscOL7Mtj8cNWwn4VD0+8CkkX7PMdpkRvThL/eYUa5NorIJ0S/OGsIf2p8Ck8ts/3jgb0AX4ZD9fSP9nkeyTQh3ypfb7pKRft/D3R4DbLdq26PCbTKecL1pcfS35HXC36yjR/o9j1Sb9LGvG4iZoDTdhoiIVKXRcg1KRETGGCUoERGpSkpQIiJSlZSgRESkKilBiYhIVVKCEhGRqqQEJVKGmZ1mZguiabtbzOxJM/vWSMdVyswuM7MlIx2HSCUoQYmUMLOvAj8k3CV/ImF0kV8SRgYQkWGiG3VFSpjZq8Ad7v7pknLzKvuFMbPLgJPdfcZIxyIy1HQEJbKpCZSZZqU4OZnZDDNzM/uAmd0YnQpcYWZfK13PzPY0s3uiOuvN7OdmNq2kzkQzu9bMlptZp5n90cz2Lakzwcx+amatZrbMzM4ps6/zzWxVmXI3s88UvV4SnR48z8xej7Z5k5mVmz5BZEQoQYls6gngs2b2YTObNEDdS4F2wqCaPwC+Zma9R15mtivwB6CWMJL+acAewF1mZlGdGuBBwkDIZxLGQFwJPFiSyK4H5gJfIAyYfATwvi14n++P9vkJ4IuEQV9/uAXbExlSW9v8JCJD4dPAHYRBLd3MngNuAy5z93UldZ9x93+Lnt9vZlOBs83se+5eAL5GOBqb6+7dAGb2V8Lgu0cT5lI6lTAP2R7u/nxU50HCAMZfAs6MZoM+njBg7y1Rnd8ASwmDdG6OOuAYd2+NttcG3Ghmb3b35zZzmyJDRkdQIiXc/a/AmwmdIr4LGHAeMN/MGkqq317y+heEiTF3iF4fFtUpmFkqmrRuMWFk6DlFdRYAi4vqQJjrrKfOPtHPXxbF2cpA8+n074Ge5FT0XqxoXyIjSglKpAx373L3u9z9M+6+O/BxYBbwsZKqK/p4vW30czJwFmEesuLHTMIMzj119itT5yNFdaYB6929c4D9D8ZG67p7O2GetW3LVxcZXjrFJxKDu19nZpew6RTXU/t4vSz6+QbhyKTctZ1VRXXmA58qU6cr+vk60GhmtSVJqnT/nUCmuMDMmstsd5N1zaweaCiKXWREKUGJlDCzqe6+oqRsCmFCuuUl1U8Avlf0+kTCH/hXotfzCJ0iFvTTRX0eocPD0tL9Fnk8+nkc0HMNqgE4nI2vQb1CSGTbu/urUdkRfWzzcDNrKDrNdwJhgrr5fdQXGVZKUCKbetrMfkmYjXkFYSrvMwi99X5cUncPM/s+oRPFuwmnAD8fdZAAOB94DLjHzH5EOGranpBYbnD3h4GfAJ8EHo7ua3oRmAS8HXjd3a9w92fM7E7ge2bWREiCZ0YxFfsVYfboH5nZ5cDO0bbL6YjiupRwWu9S4HZ3fzZ2S4lUkBKUyKYuJByp/A8wkXB67Y/AKe6+uKTul4FjCQmqE/g6cHXPQnf/h5ntB3wDuJbQc+5VwlHTC1GdTjM7ONrvBcA2hMT4GHBn0b5OIxytXUm4VvQdwpHVyUX7W2VmJwGXEXoiLgA+AJRLOjcD64HrCKf27qT8aUaREaGRJEQ2g5nNIPTGe4+73z3C4QxaNH7f/7n7GSMdi0hf1ItPRESqkhKUiIhUJZ3iExGRqqQjKBERqUpKUCIiUpWUoEREpCopQYmISFVSghIRkar0/wENLLKbW81CPAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns_handles = None\n",
    "sns_labels = None\n",
    "\n",
    "is_first = True\n",
    "\n",
    "for clab, mets, ccs in zip(cluster_labels, pairs, cluster_centers):\n",
    "    scatter_palette = sns.color_palette(\"colorblind\", n_colors=4).as_hex()\n",
    "    tmp = scatter_palette[1]\n",
    "    scatter_palette[1] = scatter_palette[2]\n",
    "    scatter_palette[2] = tmp\n",
    "    ax = plot_clusters(\n",
    "        df,\n",
    "        mets[0],\n",
    "        mets[1],\n",
    "        hue_metric=\"opt_level\",\n",
    "        marker_style_metric=\"{}_cluster\".format(mets[1]),\n",
    "        swap_marker_and_hue=False,\n",
    "        palette=scatter_palette,\n",
    "        s=3*mpl.rcParams[\"lines.markersize\"]**2\n",
    "    )\n",
    "    ax.tick_params(\n",
    "        axis=\"both\",\n",
    "        which=\"major\",\n",
    "        labelsize=1.5*ax.get_xticklabels()[0].get_fontsize()\n",
    "    )\n",
    "    xlab = ax.xaxis.get_label()\n",
    "    ax.set_xlabel(\n",
    "        \"Speedup\",#xlab.get_text(),\n",
    "        fontfamily=xlab.get_fontfamily(),\n",
    "        fontsize=1.5*xlab.get_fontsize(),\n",
    "        fontstyle=xlab.get_fontstyle(),\n",
    "        fontvariant=xlab.get_fontvariant(),\n",
    "        fontweight=xlab.get_fontweight(),\n",
    "    )\n",
    "    ylab = ax.yaxis.get_label()\n",
    "    ax.set_ylabel(\n",
    "        ylab.get_text(),\n",
    "        fontfamily=ylab.get_fontfamily(),\n",
    "        fontsize=1.5*ylab.get_fontsize(),\n",
    "        fontstyle=ylab.get_fontstyle(),\n",
    "        fontvariant=ylab.get_fontvariant(),\n",
    "        fontweight=ylab.get_fontweight(),\n",
    "    )\n",
    "    ax.set_xlim(0.99, 1.04)\n",
    "    ax.set_ylim(-0.05, 1.0)\n",
    "    plot_lines_by_profile(df, mets[0], mets[1], \"name\", ax)\n",
    "    legend = ax.legend(loc=\"center left\", bbox_to_anchor=(1.25, 0.5), ncol=2)\n",
    "    legend.get_texts()[0].set_text(\"Optimization Level\")\n",
    "    legend.get_texts()[5].set_text(\"Clusters\")\n",
    "    legend_fig = legend.figure\n",
    "    legend_fig.canvas.draw()\n",
    "    bbox = legend.get_window_extent().transformed(legend_fig.dpi_scale_trans.inverted())\n",
    "    legend.remove()\n",
    "    plt.tight_layout()\n",
    "    if is_first:\n",
    "        sns_handles, sns_labels = ax.get_legend_handles_labels()\n",
    "        is_first = False\n",
    "    plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.9.12"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 3.573767,
   "end_time": "2024-04-05T18:05:43.326424",
   "environment_variables": {},
   "exception": null,
   "input_path": "02_thicket_rajaperf_clustering.ipynb",
   "output_path": "02_thicket_rajaperf_clustering.ipynb",
   "parameters": {},
   "start_time": "2024-04-05T18:05:39.752657",
   "version": "2.5.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}