From 145d8bb7c4398f05fc41ae340e337c656c10f1e5 Mon Sep 17 00:00:00 2001
From: anthonyc <acorreia@lpnhe.in2p3.fr>
Date: Thu, 23 Mar 2023 04:27:49 +0100
Subject: [PATCH 01/30] load parquet files

---
 LHCb_Pipeline/Processing/utils/preprocessing.py | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/LHCb_Pipeline/Processing/utils/preprocessing.py b/LHCb_Pipeline/Processing/utils/preprocessing.py
index f19baa09..34fa374e 100644
--- a/LHCb_Pipeline/Processing/utils/preprocessing.py
+++ b/LHCb_Pipeline/Processing/utils/preprocessing.py
@@ -27,8 +27,10 @@ def preprocess(input_dir, output_dir, output_num, clear_directories=True, num_tr
             except Exception as e:
                 print('Failed to delete %s. Reason: %s' % (file_path, e))
 
-    hits = pd.read_csv(f'{input_dir}/hits_velo.csv') # Read hits
-    particles = pd.read_csv(f'{input_dir}/mc_particles.csv') # Read MC particles
+    # hits = pd.read_csv(f'{input_dir}/hits_velo.csv') # Read hits
+    # particles = pd.read_csv(f'{input_dir}/mc_particles.csv') # Read MC particles
+    hits = pd.read_parquet(f'{input_dir}/hits_velo.parquet.lz4')
+    particles = pd.read_parquet(f'{input_dir}/mc_particles.parquet.lz4')
     hits = hits.merge(particles, on=['event', 'mcid'], how='left') # Merge
     # NB: left join!: keep fake hits
 
@@ -47,7 +49,8 @@ def preprocess(input_dir, output_dir, output_num, clear_directories=True, num_tr
         hits_csv.to_csv(f'{output_dir}/event{num}-hits.csv', index=False)
 
         # -particles.csv, use event_particles
-        particles_csv = event_particles[['mcid','vx', 'vy', 'vz', 'p', 'pt', 'eta', 'phi', 'charge', 'nhits_velo']]
+        # particles_csv = event_particles[['mcid','vx', 'vy', 'vz', 'p', 'pt', 'eta', 'phi', 'charge', 'nhits_velo']]
+        particles_csv = event_particles  # save everything
         particles_csv.rename(columns={'mcid': 'particle_id', 'charge': 'q', 'nhits_velo': 'nhits'}, inplace=True)
         particles_csv['particle_id'] = particles_csv['particle_id'] + 1
         pt = event_particles['pt']
-- 
GitLab


From e33e9580625971fea677e714fbc8c52b0344f2a0 Mon Sep 17 00:00:00 2001
From: anthonyc <acorreia@lpnhe.in2p3.fr>
Date: Thu, 23 Mar 2023 04:27:59 +0100
Subject: [PATCH 02/30] playing around

---
 LHCb_Pipeline/full_pipeline_anthony.ipynb | 4400 +++++++++++++++++++++
 1 file changed, 4400 insertions(+)
 create mode 100644 LHCb_Pipeline/full_pipeline_anthony.ipynb

diff --git a/LHCb_Pipeline/full_pipeline_anthony.ipynb b/LHCb_Pipeline/full_pipeline_anthony.ipynb
new file mode 100644
index 00000000..77585245
--- /dev/null
+++ b/LHCb_Pipeline/full_pipeline_anthony.ipynb
@@ -0,0 +1,4400 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 0. Setup"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/acorreia/softwares/mambaforge/envs/etx4velo/lib/python3.8/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       "        .bk-notebook-logo {\n",
+       "            display: block;\n",
+       "            width: 20px;\n",
+       "            height: 20px;\n",
+       "            background-image: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABQAAAAUCAYAAACNiR0NAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAABx0RVh0U29mdHdhcmUAQWRvYmUgRmlyZXdvcmtzIENTNui8sowAAAOkSURBVDiNjZRtaJVlGMd/1/08zzln5zjP1LWcU9N0NkN8m2CYjpgQYQXqSs0I84OLIC0hkEKoPtiH3gmKoiJDU7QpLgoLjLIQCpEsNJ1vqUOdO7ppbuec5+V+rj4ctwzd8IIbbi6u+8f1539dt3A78eXC7QizUF7gyV1fD1Yqg4JWz84yffhm0qkFqBogB9rM8tZdtwVsPUhWhGcFJngGeWrPzHm5oaMmkfEg1usvLFyc8jLRqDOMru7AyC8saQr7GG7f5fvDeH7Ej8CM66nIF+8yngt6HWaKh7k49Soy9nXurCi1o3qUbS3zWfrYeQDTB/Qj6kX6Ybhw4B+bOYoLKCC9H3Nu/leUTZ1JdRWkkn2ldcCamzrcf47KKXdAJllSlxAOkRgyHsGC/zRday5Qld9DyoM4/q/rUoy/CXh3jzOu3bHUVZeU+DEn8FInkPBFlu3+nW3Nw0mk6vCDiWg8CeJaxEwuHS3+z5RgY+YBR6V1Z1nxSOfoaPa4LASWxxdNp+VWTk7+4vzaou8v8PN+xo+KY2xsw6une2frhw05CTYOmQvsEhjhWjn0bmXPjpE1+kplmmkP3suftwTubK9Vq22qKmrBhpY4jvd5afdRA3wGjFAgcnTK2s4hY0/GPNIb0nErGMCRxWOOX64Z8RAC4oCXdklmEvcL8o0BfkNK4lUg9HTl+oPlQxdNo3Mg4Nv175e/1LDGzZen30MEjRUtmXSfiTVu1kK8W4txyV6BMKlbgk3lMwYCiusNy9fVfvvwMxv8Ynl6vxoByANLTWplvuj/nF9m2+PDtt1eiHPBr1oIfhCChQMBw6Aw0UulqTKZdfVvfG7VcfIqLG9bcldL/+pdWTLxLUy8Qq38heUIjh4XlzZxzQm19lLFlr8vdQ97rjZVOLf8nclzckbcD4wxXMidpX30sFd37Fv/GtwwhzhxGVAprjbg0gCAEeIgwCZyTV2Z1REEW8O4py0wsjeloKoMr6iCY6dP92H6Vw/oTyICIthibxjm/DfN9lVz8IqtqKYLUXfoKVMVQVVJOElGjrnnUt9T9wbgp8AyYKaGlqingHZU/uG2NTZSVqwHQTWkx9hxjkpWDaCg6Ckj5qebgBVbT3V3NNXMSiWSDdGV3hrtzla7J+duwPOToIg42ChPQOQjspnSlp1V+Gjdged7+8UN5CRAV7a5EdFNwCjEaBR27b3W890TE7g24NAP/mMDXRWrGoFPQI9ls/MWO2dWFAar/xcOIImbbpA3zgAAAABJRU5ErkJggg==);\n",
+       "        }\n",
+       "    </style>\n",
+       "    <div>\n",
+       "        <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-notebook-logo\"></a>\n",
+       "        <span id=\"p1001\">Loading BokehJS ...</span>\n",
+       "    </div>\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/javascript": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\nconst JS_MIME_TYPE = 'application/javascript';\n  const HTML_MIME_TYPE = 'text/html';\n  const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n  const CLASS_NAME = 'output_bokeh rendered_html';\n\n  /**\n   * Render data to the DOM node\n   */\n  function render(props, node) {\n    const script = document.createElement(\"script\");\n    node.appendChild(script);\n  }\n\n  /**\n   * Handle when an output is cleared or removed\n   */\n  function handleClearOutput(event, handle) {\n    const cell = handle.cell;\n\n    const id = cell.output_area._bokeh_element_id;\n    const server_id = cell.output_area._bokeh_server_id;\n    // Clean up Bokeh references\n    if (id != null && id in Bokeh.index) {\n      Bokeh.index[id].model.document.clear();\n      delete Bokeh.index[id];\n    }\n\n    if (server_id !== undefined) {\n      // Clean up Bokeh references\n      const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n      cell.notebook.kernel.execute(cmd_clean, {\n        iopub: {\n          output: function(msg) {\n            const id = msg.content.text.trim();\n            if (id in Bokeh.index) {\n              Bokeh.index[id].model.document.clear();\n              delete Bokeh.index[id];\n            }\n          }\n        }\n      });\n      // Destroy server and session\n      const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n      cell.notebook.kernel.execute(cmd_destroy);\n    }\n  }\n\n  /**\n   * Handle when a new output is added\n   */\n  function handleAddOutput(event, handle) {\n    const output_area = handle.output_area;\n    const output = handle.output;\n\n    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n    if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n      return\n    }\n\n    const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n\n    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n      // store reference to embed id on output_area\n      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n    }\n    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n      const bk_div = document.createElement(\"div\");\n      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n      const script_attrs = bk_div.children[0].attributes;\n      for (let i = 0; i < script_attrs.length; i++) {\n        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n      }\n      // store reference to server id on output_area\n      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n    }\n  }\n\n  function register_renderer(events, OutputArea) {\n\n    function append_mime(data, metadata, element) {\n      // create a DOM node to render to\n      const toinsert = this.create_output_subarea(\n        metadata,\n        CLASS_NAME,\n        EXEC_MIME_TYPE\n      );\n      this.keyboard_manager.register_events(toinsert);\n      // Render to node\n      const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n      render(props, toinsert[toinsert.length - 1]);\n      element.append(toinsert);\n      return toinsert\n    }\n\n    /* Handle when an output is cleared or removed */\n    events.on('clear_output.CodeCell', handleClearOutput);\n    events.on('delete.Cell', handleClearOutput);\n\n    /* Handle when a new output is added */\n    events.on('output_added.OutputArea', handleAddOutput);\n\n    /**\n     * Register the mime type and append_mime function with output_area\n     */\n    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n      /* Is output safe? */\n      safe: true,\n      /* Index of renderer in `output_area.display_order` */\n      index: 0\n    });\n  }\n\n  // register the mime type if in Jupyter Notebook environment and previously unregistered\n  if (root.Jupyter !== undefined) {\n    const events = require('base/js/events');\n    const OutputArea = require('notebook/js/outputarea').OutputArea;\n\n    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n      register_renderer(events, OutputArea);\n    }\n  }\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"p1001\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.0.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.0.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.0.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.0.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.0.3.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"p1001\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));",
+      "application/vnd.bokehjs_load.v0+json": ""
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "\n",
+    "from Scripts.utils.convenience_utils import *\n",
+    "from Scripts.utils.plotting_utils import plot_observable_performance\n",
+    "from Scripts.Step_1_Train_Metric_Learning import train as train_metric_learning\n",
+    "from Scripts.Step_2_Run_Metric_Learning import train as run_metric_learning_inference\n",
+    "from Scripts.Step_3_Train_GNN import train as train_gnn\n",
+    "from Scripts.Step_4_Run_GNN import train as run_gnn_inference\n",
+    "from Scripts.Step_5_Build_Track_Candidates import train as build_track_candidates\n",
+    "from Scripts.Step_6_Evaluate_Reconstruction import evaluate as evaluate_candidates\n",
+    "\n",
+    "import sys\n",
+    "from Scripts.utils.convenience_utils import get_example_data, plot_true_graph, get_training_metrics, plot_training_metrics, plot_neighbor_performance, plot_predicted_graph, plot_track_lengths, plot_edge_performance, plot_graph_sizes\n",
+    "import yaml\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "\n",
+    "CONFIG = 'pipeline_config.yaml'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Pipeline configurations\n",
+    "\n",
+    "The configurations for the entire pipeline are defined under pipeline_config.yml."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(CONFIG, 'r') as f:\n",
+    "    configs = yaml.load(f, Loader=yaml.FullLoader)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Download data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ! xrdcp -r root://eoslhcb.cern.ch//eos/lhcb/user/a/anthonyc/tracking/data/csv/v2/minbias-sim10b-xdigi/0 data/input/"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Telegram notification bot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "import json\n",
+    "# from datetime import datetime\n",
+    "\n",
+    "# def send_telegram_message(message: str,\n",
+    "#                           chat_id: str,\n",
+    "#                           api_key: str,\n",
+    "#                          ):\n",
+    "#     responses = {}\n",
+    "\n",
+    "#     url = f'https://api.telegram.org/bot{api_key}/sendMessage?chat_id={chat_id}&text={message}'\n",
+    "    \n",
+    "#     response = requests.post(url)\n",
+    "    \n",
+    "#     return response\n",
+    "\n",
+    "def send_telegram_message(*args, **kwargs):\n",
+    "    pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# chat_id = \"5027012918\"\n",
+    "# api_key = \"6268687426:AAE1P7WQofCBuQPiYZlYaKU-p1GNn6OvAxM\"\n",
+    "\n",
+    "# send_telegram_message(\"======================\", chat_id, api_key)\n",
+    "\n",
+    "# send_telegram_message(\"Starting.\", chat_id, api_key)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Delete contents of `bokeh-plots`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clear_contents('bokeh-plots')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Saving data into `torch` tensor form"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving event 004206889, 1/100, contains 814 true hits.\n",
+      "Saving event 004206890, 2/100, contains 935 true hits.\n",
+      "Saving event 004206891, 3/100, contains 1508 true hits.\n",
+      "Saving event 004206892, 4/100, contains 691 true hits.\n",
+      "Saving event 004206893, 5/100, contains 1584 true hits.\n",
+      "Saving event 004206894, 6/100, contains 1527 true hits.\n",
+      "Saving event 004206895, 7/100, contains 834 true hits.\n",
+      "Saving event 004206896, 8/100, contains 1012 true hits.\n",
+      "Saving event 004206897, 9/100, contains 1343 true hits.\n",
+      "Saving event 004206898, 10/100, contains 1185 true hits.\n",
+      "Saving event 004206899, 11/100, contains 841 true hits.\n",
+      "Saving event 004206900, 12/100, contains 1000 true hits.\n",
+      "Saving event 004206901, 13/100, contains 2350 true hits.\n",
+      "Saving event 004206902, 14/100, contains 2537 true hits.\n",
+      "Saving event 004206903, 15/100, contains 1938 true hits.\n",
+      "Saving event 004206904, 16/100, contains 3223 true hits.\n",
+      "Saving event 004206905, 17/100, contains 3772 true hits.\n",
+      "Discarding event 004206906, contains only fake hits.\n",
+      "Saving event 004206907, 18/100, contains 1071 true hits.\n",
+      "Saving event 004206908, 19/100, contains 911 true hits.\n",
+      "Saving event 004206909, 20/100, contains 891 true hits.\n",
+      "Saving event 004206910, 21/100, contains 287 true hits.\n",
+      "Saving event 004206911, 22/100, contains 1154 true hits.\n",
+      "Saving event 004206912, 23/100, contains 1430 true hits.\n",
+      "Saving event 004206913, 24/100, contains 3562 true hits.\n",
+      "Saving event 004206914, 25/100, contains 616 true hits.\n",
+      "Saving event 004206915, 26/100, contains 739 true hits.\n",
+      "Saving event 004206916, 27/100, contains 1019 true hits.\n",
+      "Saving event 004206917, 28/100, contains 2024 true hits.\n",
+      "Saving event 004206918, 29/100, contains 1679 true hits.\n",
+      "Saving event 004206919, 30/100, contains 1747 true hits.\n",
+      "Saving event 004206920, 31/100, contains 1254 true hits.\n",
+      "Saving event 004206921, 32/100, contains 2582 true hits.\n",
+      "Saving event 004206922, 33/100, contains 627 true hits.\n",
+      "Saving event 004206923, 34/100, contains 1168 true hits.\n",
+      "Saving event 004206924, 35/100, contains 1606 true hits.\n",
+      "Saving event 004206925, 36/100, contains 2457 true hits.\n",
+      "Saving event 004206926, 37/100, contains 1325 true hits.\n",
+      "Saving event 004206927, 38/100, contains 2473 true hits.\n",
+      "Saving event 004206928, 39/100, contains 3194 true hits.\n",
+      "Saving event 004206929, 40/100, contains 2108 true hits.\n",
+      "Saving event 004206930, 41/100, contains 3139 true hits.\n",
+      "Saving event 004206931, 42/100, contains 2060 true hits.\n",
+      "Discarding event 004206932, contains only fake hits.\n",
+      "Saving event 004206933, 43/100, contains 1013 true hits.\n",
+      "Saving event 004206934, 44/100, contains 397 true hits.\n",
+      "Saving event 004206935, 45/100, contains 709 true hits.\n",
+      "Saving event 004206936, 46/100, contains 772 true hits.\n",
+      "Saving event 004206937, 47/100, contains 872 true hits.\n",
+      "Saving event 004206938, 48/100, contains 1117 true hits.\n",
+      "Saving event 004206939, 49/100, contains 397 true hits.\n",
+      "Saving event 004206940, 50/100, contains 1636 true hits.\n",
+      "Saving event 004206941, 51/100, contains 3559 true hits.\n",
+      "Saving event 004206942, 52/100, contains 1822 true hits.\n",
+      "Saving event 004206943, 53/100, contains 344 true hits.\n",
+      "Saving event 004206944, 54/100, contains 1340 true hits.\n",
+      "Saving event 004206945, 55/100, contains 2038 true hits.\n",
+      "Saving event 004206946, 56/100, contains 3145 true hits.\n",
+      "Saving event 004206947, 57/100, contains 4457 true hits.\n",
+      "Saving event 004206948, 58/100, contains 1102 true hits.\n",
+      "Saving event 004206949, 59/100, contains 955 true hits.\n",
+      "Saving event 004206950, 60/100, contains 1426 true hits.\n",
+      "Saving event 004206951, 61/100, contains 428 true hits.\n",
+      "Saving event 004206952, 62/100, contains 1250 true hits.\n",
+      "Saving event 004206953, 63/100, contains 1591 true hits.\n",
+      "Saving event 004206954, 64/100, contains 2626 true hits.\n",
+      "Saving event 004206955, 65/100, contains 293 true hits.\n",
+      "Saving event 004206956, 66/100, contains 1312 true hits.\n",
+      "Saving event 004206957, 67/100, contains 983 true hits.\n",
+      "Saving event 004206958, 68/100, contains 1659 true hits.\n",
+      "Saving event 004206959, 69/100, contains 2843 true hits.\n",
+      "Saving event 004206960, 70/100, contains 816 true hits.\n",
+      "Saving event 004206961, 71/100, contains 211 true hits.\n",
+      "Saving event 004206962, 72/100, contains 1821 true hits.\n",
+      "Saving event 004206963, 73/100, contains 1017 true hits.\n",
+      "Saving event 004206964, 74/100, contains 2710 true hits.\n",
+      "Saving event 004206965, 75/100, contains 2393 true hits.\n",
+      "Saving event 004206966, 76/100, contains 3461 true hits.\n",
+      "Saving event 007212913, 77/100, contains 1855 true hits.\n",
+      "Saving event 007212914, 78/100, contains 923 true hits.\n",
+      "Saving event 007212915, 79/100, contains 1247 true hits.\n",
+      "Saving event 007212916, 80/100, contains 2129 true hits.\n",
+      "Saving event 007212917, 81/100, contains 2372 true hits.\n",
+      "Saving event 007212918, 82/100, contains 1742 true hits.\n",
+      "Saving event 007212919, 83/100, contains 2170 true hits.\n",
+      "Saving event 007212920, 84/100, contains 768 true hits.\n",
+      "Saving event 007212921, 85/100, contains 2265 true hits.\n",
+      "Saving event 007212922, 86/100, contains 3096 true hits.\n",
+      "Saving event 007212923, 87/100, contains 1829 true hits.\n",
+      "Saving event 007212924, 88/100, contains 833 true hits.\n",
+      "Saving event 007212925, 89/100, contains 2592 true hits.\n",
+      "Saving event 007212926, 90/100, contains 1433 true hits.\n",
+      "Saving event 007212927, 91/100, contains 1745 true hits.\n",
+      "Saving event 007212928, 92/100, contains 2669 true hits.\n",
+      "Saving event 007212929, 93/100, contains 982 true hits.\n",
+      "Saving event 007212930, 94/100, contains 1134 true hits.\n",
+      "Saving event 007212931, 95/100, contains 2717 true hits.\n",
+      "Saving event 007212932, 96/100, contains 2973 true hits.\n",
+      "Saving event 007212933, 97/100, contains 970 true hits.\n",
+      "Saving event 007212934, 98/100, contains 1286 true hits.\n",
+      "Saving event 007212935, 99/100, contains 420 true hits.\n",
+      "Saving event 007212936, 100/100, contains 3010 true hits.\n",
+      "Writing outputs to data/processed\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|          | 0/100 [00:00<?, ?it/s]INFO:Preparing event 004206889\n",
+      "INFO:Preparing event 004206896\n",
+      "INFO:Preparing event 004206891\n",
+      "INFO:Preparing event 004206890\n",
+      "INFO:Preparing event 004206892\n",
+      "INFO:Preparing event 004206893\n",
+      "INFO:Preparing event 004206894\n",
+      "INFO:Preparing event 004206895\n",
+      "INFO:Preparing event 004206897\n",
+      "INFO:Preparing event 004206901\n",
+      "INFO:Preparing event 004206900\n",
+      "INFO:Preparing event 004206902\n",
+      "INFO:Preparing event 004206904\n",
+      "INFO:Preparing event 004206905\n",
+      "INFO:Preparing event 004206907\n",
+      "INFO:Preparing event 004206908\n",
+      "INFO:Preparing event 004206909\n",
+      "INFO:Preparing event 004206910\n",
+      "INFO:Preparing event 004206913\n",
+      "INFO:Preparing event 004206911\n",
+      "INFO:Preparing event 004206916\n",
+      "INFO:Preparing event 004206903\n",
+      "INFO:Preparing event 004206921\n",
+      "INFO:Preparing event 004206917\n",
+      "INFO:Preparing event 004206915\n",
+      "INFO:Preparing event 004206914\n",
+      "INFO:Preparing event 004206912\n",
+      "INFO:Preparing event 004206919\n",
+      "INFO:Preparing event 004206898\n",
+      "INFO:Preparing event 004206920\n",
+      "INFO:Preparing event 004206918\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206890 with size (2, 742)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206908 with size (2, 691)\n",
+      "INFO:Preparing event 004206922\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206907 with size (2, 803)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206892 with size (2, 545)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206902 with size (2, 1860)\n",
+      "INFO:Preparing event 004206899\n",
+      "INFO:Preparing event 004206923\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206895 with size (2, 625)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206913 with size (2, 2754)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206910 with size (2, 228)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206904 with size (2, 2437)\n",
+      "INFO:Preparing event 004206926\n",
+      "INFO:Preparing event 004206925\n",
+      "INFO:Preparing event 004206928\n",
+      "INFO:Preparing event 004206924\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206921 with size (2, 2020)\n",
+      "INFO:Preparing event 004206927\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206903 with size (2, 1492)\n",
+      "INFO:Preparing event 004206931\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206916 with size (2, 736)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206915 with size (2, 597)\n",
+      "INFO:Preparing event 004206930\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206917 with size (2, 1507)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206897 with size (2, 983)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206909 with size (2, 708)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206891 with size (2, 1109)\n",
+      "INFO:Preparing event 004206933\n",
+      "INFO:Preparing event 004206934\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206914 with size (2, 488)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206889 with size (2, 626)\n",
+      "INFO:Preparing event 004206929\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206911 with size (2, 875)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206900 with size (2, 729)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206901 with size (2, 1779)\n",
+      "INFO:Preparing event 004206937\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206896 with size (2, 782)\n",
+      "INFO:Preparing event 004206936\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206905 with size (2, 2741)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206920 with size (2, 954)\n",
+      "INFO:Preparing event 004206940\n",
+      "INFO:Preparing event 004206939\n",
+      "INFO:Preparing event 004206942\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206893 with size (2, 1163)\n",
+      "INFO:Preparing event 004206943\n",
+      "  1%|          | 1/100 [00:00<00:16,  5.84it/s]INFO:Preparing event 004206941\n",
+      "INFO:Preparing event 004206935\n",
+      "INFO:Preparing event 004206938\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206912 with size (2, 1089)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206919 with size (2, 1329)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206894 with size (2, 1145)\n",
+      "INFO:Preparing event 004206944\n",
+      "INFO:Preparing event 004206947\n",
+      "INFO:Preparing event 004206949\n",
+      "INFO:Preparing event 004206945\n",
+      "INFO:Preparing event 004206948\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206923 with size (2, 848)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206899 with size (2, 650)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206898 with size (2, 935)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206922 with size (2, 505)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206933 with size (2, 757)\n",
+      "INFO:Preparing event 004206951\n",
+      "INFO:Preparing event 004206953\n",
+      "INFO:Preparing event 004206950\n",
+      "INFO:Preparing event 004206946\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206934 with size (2, 310)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206931 with size (2, 1546)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206939 with size (2, 288)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206918 with size (2, 1355)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206928 with size (2, 2391)\n",
+      "INFO:Preparing event 004206956\n",
+      "INFO:Preparing event 004206958\n",
+      "INFO:Preparing event 004206957\n",
+      "INFO:Preparing event 004206955\n",
+      "INFO:Preparing event 004206952\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206940 with size (2, 1241)\n",
+      "INFO:Preparing event 004206963\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206935 with size (2, 542)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206927 with size (2, 1847)\n",
+      "INFO:Preparing event 004206962\n",
+      "INFO:Preparing event 004206961\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206938 with size (2, 836)\n",
+      "INFO:Preparing event 004206960\n",
+      "INFO:Preparing event 004206954\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206926 with size (2, 1027)\n",
+      "INFO:Preparing event 004206964\n",
+      "INFO:Preparing event 004206965\n",
+      "INFO:Preparing event 004206959\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206930 with size (2, 2426)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206944 with size (2, 1020)\n",
+      "INFO:Preparing event 007212913\n",
+      "INFO:Preparing event 004206966\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206948 with size (2, 849)\n",
+      "INFO:Preparing event 007212914\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206943 with size (2, 258)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206942 with size (2, 1367)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206929 with size (2, 1550)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206936 with size (2, 584)\n",
+      "INFO:Preparing event 007212917\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206956 with size (2, 1019)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206949 with size (2, 734)\n",
+      "INFO:Preparing event 007212918\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206937 with size (2, 690)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206941 with size (2, 2737)\n",
+      "INFO:Preparing event 007212919\n",
+      "INFO:Preparing event 007212921\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206958 with size (2, 1260)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206924 with size (2, 1197)\n",
+      "INFO:Preparing event 007212916\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206961 with size (2, 169)\n",
+      "INFO:Preparing event 007212922\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206945 with size (2, 1431)\n",
+      "INFO:Preparing event 007212920\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206947 with size (2, 3438)\n",
+      "INFO:Preparing event 007212915\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206960 with size (2, 627)\n",
+      "INFO:Preparing event 007212923\n",
+      "INFO:Preparing event 007212924\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206963 with size (2, 776)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206962 with size (2, 1329)\n",
+      " 35%|███▌      | 35/100 [00:00<00:00, 124.40it/s]INFO:Preparing event 007212925\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206959 with size (2, 2069)\n",
+      "INFO:Preparing event 007212927\n",
+      "INFO:Preparing event 007212929\n",
+      "INFO:Preparing event 007212928\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206946 with size (2, 2389)\n",
+      "INFO:Preparing event 007212930\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206957 with size (2, 728)\n",
+      "INFO:Preparing event 007212926\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206925 with size (2, 1898)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206953 with size (2, 1235)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206965 with size (2, 1832)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206955 with size (2, 212)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event007212913 with size (2, 1389)\n",
+      "INFO:Preparing event 007212932\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206951 with size (2, 322)\n",
+      "INFO:Preparing event 007212934\n",
+      "INFO:Preparing event 007212935\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206966 with size (2, 2581)\n",
+      "INFO:Preparing event 007212933\n",
+      "INFO:Preparing event 007212936\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206952 with size (2, 976)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206950 with size (2, 1073)\n",
+      "INFO:Preparing event 007212931\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event007212915 with size (2, 982)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206964 with size (2, 2053)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206954 with size (2, 1990)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event007212922 with size (2, 2242)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event007212921 with size (2, 1661)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event007212914 with size (2, 681)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event007212923 with size (2, 1412)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event007212920 with size (2, 605)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event007212929 with size (2, 706)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event007212927 with size (2, 1295)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event007212916 with size (2, 1597)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event007212935 with size (2, 282)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event007212930 with size (2, 856)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event007212917 with size (2, 1753)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event007212919 with size (2, 1630)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event007212934 with size (2, 1002)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event007212932 with size (2, 2200)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event007212924 with size (2, 626)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event007212926 with size (2, 1111)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event007212925 with size (2, 1990)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event007212928 with size (2, 2023)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event007212918 with size (2, 1298)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event007212931 with size (2, 2041)\n",
+      " 82%|████████▏ | 82/100 [00:00<00:00, 235.08it/s]INFO:Modulewise truth graph built for data/preprocessed/event007212933 with size (2, 722)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event007212936 with size (2, 2279)\n",
+      "100%|██████████| 100/100 [00:00<00:00, 222.39it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from Processing.Models.feature_construction import FeatureStore\n",
+    "\n",
+    "fs = FeatureStore(CONFIG)\n",
+    "fs.prepare_data()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 1. Train Metric Learning"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## What it does\n",
+    "Broadly speaking, the first stage of our pipeline is embedding the space points on to graphs, in a way that is efficient, i.e. we miss as few points on a graph as possible. We train a MLP to transform the input feature vector of each space point $\\mathbf{u}_i$ into an N-dimensional latent space $\\mathbf{v}_i$. The graph is then constructed by connecting the space points whose Euclidean distance between the latent space points $$d_{ij} = \\left| \\mathbf{v}_i - \\mathbf{v}_j \\right| < r_{embedding}$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training data\n",
+    "Let us take a look at the data before training. In this example pipeline, we have preprocessed the TrackML data into a more convenient form. We calculated directional information and summary statistics from the charge deposited in each spacepoints, and append them to its cyclidrical coordinates. Let us load an example data file and inspect the content."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "    .dataframe thead th {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.465221</td>\n",
+       "      <td>-0.462661</td>\n",
+       "      <td>-1.440705</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.122540</td>\n",
+       "      <td>-0.611363</td>\n",
+       "      <td>-1.440705</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.513063</td>\n",
+       "      <td>-0.629230</td>\n",
+       "      <td>-1.434295</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.498745</td>\n",
+       "      <td>-0.508192</td>\n",
+       "      <td>-1.440705</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.249448</td>\n",
+       "      <td>-0.532812</td>\n",
+       "      <td>-1.440705</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>673</th>\n",
+       "      <td>0.726360</td>\n",
+       "      <td>0.467563</td>\n",
+       "      <td>3.753205</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>674</th>\n",
+       "      <td>0.244590</td>\n",
+       "      <td>0.593452</td>\n",
+       "      <td>3.746795</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>675</th>\n",
+       "      <td>0.722281</td>\n",
+       "      <td>0.704765</td>\n",
+       "      <td>3.746795</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>676</th>\n",
+       "      <td>0.128899</td>\n",
+       "      <td>-0.142000</td>\n",
+       "      <td>3.753205</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>677</th>\n",
+       "      <td>0.340476</td>\n",
+       "      <td>-0.061835</td>\n",
+       "      <td>3.753205</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>678 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            0         1         2\n",
+       "0    0.465221 -0.462661 -1.440705\n",
+       "1    0.122540 -0.611363 -1.440705\n",
+       "2    0.513063 -0.629230 -1.434295\n",
+       "3    0.498745 -0.508192 -1.440705\n",
+       "4    0.249448 -0.532812 -1.440705\n",
+       "..        ...       ...       ...\n",
+       "673  0.726360  0.467563  3.753205\n",
+       "674  0.244590  0.593452  3.746795\n",
+       "675  0.722281  0.704765  3.746795\n",
+       "676  0.128899 -0.142000  3.753205\n",
+       "677  0.340476 -0.061835  3.753205\n",
+       "\n",
+       "[678 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "example_data_df, example_data_pyg = get_example_data(configs)\n",
+    "example_data_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_true_graph(example_data_pyg, num_tracks=20)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train metric learning model\n",
+    "\n",
+    "Finally we come to model training. By default, we train the MLP for 30 epochs, which takes approximately 15 minutes on an NVidia V100. Feel free to adjust the epoch number in pipeline_config.yml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/bin/bash: ligne 1: nvcc : commande introuvable\n"
+     ]
+    }
+   ],
+   "source": [
+    "! nvcc --version"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tue Mar 14 11:58:25 2023       \n",
+      "+-----------------------------------------------------------------------------+\n",
+      "| NVIDIA-SMI 520.61.05    Driver Version: 520.61.05    CUDA Version: 11.8     |\n",
+      "|-------------------------------+----------------------+----------------------+\n",
+      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
+      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
+      "|                               |                      |               MIG M. |\n",
+      "|===============================+======================+======================|\n",
+      "|   0  NVIDIA RTX A200...  On   | 00000000:01:00.0 Off |                  N/A |\n",
+      "| N/A   48C    P8     5W /  N/A |      7MiB /  8192MiB |      0%      Default |\n",
+      "|                               |                      |                  N/A |\n",
+      "+-------------------------------+----------------------+----------------------+\n",
+      "                                                                               \n",
+      "+-----------------------------------------------------------------------------+\n",
+      "| Processes:                                                                  |\n",
+      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
+      "|        ID   ID                                                   Usage      |\n",
+      "|=============================================================================|\n",
+      "|    0   N/A  N/A      2427      G   /usr/lib/xorg/Xorg                  4MiB |\n",
+      "+-----------------------------------------------------------------------------+\n"
+     ]
+    }
+   ],
+   "source": [
+    "! nvidia-smi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:-------------------- Step 1: Running metric learning training --------------------\n",
+      "INFO:----------------------------- a) Initialising model -----------------------------\n",
+      "INFO:------------------------------ b) Running training ------------------------------\n",
+      "GPU available: True (cuda), used: True\n",
+      "TPU available: False, using: 0 TPU cores\n",
+      "IPU available: False, using: 0 IPUs\n",
+      "HPU available: False, using: 0 HPUs\n",
+      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
+      "\n",
+      "  | Name    | Type       | Params\n",
+      "---------------------------------------\n",
+      "0 | network | Sequential | 201 K \n",
+      "---------------------------------------\n",
+      "201 K     Trainable params\n",
+      "0         Non-trainable params\n",
+      "201 K     Total params\n",
+      "0.805     Total estimated model params size (MB)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sanity Checking DataLoader 0:   0%|          | 0/2 [00:00<?, ?it/s]eff=  tensor(0.0206)\n",
+      "pur=  tensor(0.0267)\n",
+      "torch.Size([2, 1048])\n",
+      "torch.Size([1048])\n",
+      "torch.Size([2, 1360])\n",
+      "Sanity Checking DataLoader 0:  50%|█████     | 1/2 [00:02<00:02,  2.17s/it]eff=  tensor(0.0162)\n",
+      "pur=  tensor(0.0090)\n",
+      "torch.Size([2, 4662])\n",
+      "torch.Size([4662])\n",
+      "torch.Size([2, 2586])\n",
+      "Epoch 0:  89%|████████▉ | 80/90 [00:15<00:01,  5.03it/s, loss=0.00784, v_num=2]eff=  tensor(0.6765)\n",
+      "pur=  tensor(0.1892)\n",
+      "torch.Size([2, 4862])\n",
+      "torch.Size([4862])\n",
+      "torch.Size([2, 1360])\n",
+      "Epoch 0:  90%|█████████ | 81/90 [00:17<00:01,  4.75it/s, loss=0.00784, v_num=2]eff=  tensor(0.6411)\n",
+      "pur=  tensor(0.1287)\n",
+      "torch.Size([2, 12884])\n",
+      "torch.Size([12884])\n",
+      "torch.Size([2, 2586])\n",
+      "Epoch 0:  91%|█████████ | 82/90 [00:17<00:01,  4.76it/s, loss=0.00784, v_num=2]eff=  tensor(0.6829)\n",
+      "pur=  tensor(0.1819)\n",
+      "torch.Size([2, 6322])\n",
+      "torch.Size([6322])\n",
+      "torch.Size([2, 1684])\n",
+      "Epoch 0:  92%|█████████▏| 83/90 [00:17<00:01,  4.77it/s, loss=0.00784, v_num=2]eff=  tensor(0.6341)\n",
+      "pur=  tensor(0.2081)\n",
+      "torch.Size([2, 3364])\n",
+      "torch.Size([3364])\n",
+      "torch.Size([2, 1104])\n",
+      "Epoch 0:  93%|█████████▎| 84/90 [00:17<00:01,  4.78it/s, loss=0.00784, v_num=2]eff=  tensor(0.7185)\n",
+      "pur=  tensor(0.1371)\n",
+      "torch.Size([2, 11800])\n",
+      "torch.Size([11800])\n",
+      "torch.Size([2, 2252])\n",
+      "Epoch 0:  94%|█████████▍| 85/90 [00:17<00:01,  4.79it/s, loss=0.00784, v_num=2]eff=  tensor(0.6482)\n",
+      "pur=  tensor(0.1134)\n",
+      "torch.Size([2, 12612])\n",
+      "torch.Size([12612])\n",
+      "torch.Size([2, 2206])\n",
+      "Epoch 0:  96%|█████████▌| 86/90 [00:17<00:00,  4.80it/s, loss=0.00784, v_num=2]eff=  tensor(0.7132)\n",
+      "pur=  tensor(0.1076)\n",
+      "torch.Size([2, 24304])\n",
+      "torch.Size([24304])\n",
+      "torch.Size([2, 3668])\n",
+      "Epoch 0:  97%|█████████▋| 87/90 [00:18<00:00,  4.81it/s, loss=0.00784, v_num=2]eff=  tensor(0.6621)\n",
+      "pur=  tensor(0.1176)\n",
+      "torch.Size([2, 15498])\n",
+      "torch.Size([15498])\n",
+      "torch.Size([2, 2752])\n",
+      "Epoch 0:  98%|█████████▊| 88/90 [00:18<00:00,  4.82it/s, loss=0.00784, v_num=2]eff=  tensor(0.5985)\n",
+      "pur=  tensor(0.3096)\n",
+      "torch.Size([2, 1040])\n",
+      "torch.Size([1040])\n",
+      "torch.Size([2, 538])\n",
+      "Epoch 0:  99%|█████████▉| 89/90 [00:18<00:00,  4.83it/s, loss=0.00784, v_num=2]eff=  tensor(0.6323)\n",
+      "pur=  tensor(0.1270)\n",
+      "torch.Size([2, 12350])\n",
+      "torch.Size([12350])\n",
+      "torch.Size([2, 2480])\n",
+      "Epoch 1:  89%|████████▉ | 80/90 [00:15<00:01,  5.18it/s, loss=0.00768, v_num=2]eff=  tensor(0.7368)\n",
+      "pur=  tensor(0.1854)\n",
+      "torch.Size([2, 5404])\n",
+      "torch.Size([5404])\n",
+      "torch.Size([2, 1360])\n",
+      "Epoch 1:  90%|█████████ | 81/90 [00:16<00:01,  4.86it/s, loss=0.00768, v_num=2]eff=  tensor(0.7007)\n",
+      "pur=  tensor(0.1137)\n",
+      "torch.Size([2, 15934])\n",
+      "torch.Size([15934])\n",
+      "torch.Size([2, 2586])\n",
+      "Epoch 1:  91%|█████████ | 82/90 [00:16<00:01,  4.87it/s, loss=0.00768, v_num=2]eff=  tensor(0.7838)\n",
+      "pur=  tensor(0.1828)\n",
+      "torch.Size([2, 7222])\n",
+      "torch.Size([7222])\n",
+      "torch.Size([2, 1684])\n",
+      "Epoch 1:  92%|█████████▏| 83/90 [00:17<00:01,  4.87it/s, loss=0.00768, v_num=2]eff=  tensor(0.6848)\n",
+      "pur=  tensor(0.2026)\n",
+      "torch.Size([2, 3732])\n",
+      "torch.Size([3732])\n",
+      "torch.Size([2, 1104])\n",
+      "Epoch 1:  93%|█████████▎| 84/90 [00:17<00:01,  4.88it/s, loss=0.00768, v_num=2]eff=  tensor(0.7726)\n",
+      "pur=  tensor(0.1297)\n",
+      "torch.Size([2, 13412])\n",
+      "torch.Size([13412])\n",
+      "torch.Size([2, 2252])\n",
+      "Epoch 1:  94%|█████████▍| 85/90 [00:17<00:01,  4.89it/s, loss=0.00768, v_num=2]eff=  tensor(0.7063)\n",
+      "pur=  tensor(0.1015)\n",
+      "torch.Size([2, 15349])\n",
+      "torch.Size([15349])\n",
+      "torch.Size([2, 2206])\n",
+      "Epoch 1:  96%|█████████▌| 86/90 [00:17<00:00,  4.90it/s, loss=0.00768, v_num=2]eff=  tensor(0.8092)\n",
+      "pur=  tensor(0.1020)\n",
+      "torch.Size([2, 29090])\n",
+      "torch.Size([29090])\n",
+      "torch.Size([2, 3668])\n",
+      "Epoch 1:  97%|█████████▋| 87/90 [00:17<00:00,  4.91it/s, loss=0.00768, v_num=2]eff=  tensor(0.7464)\n",
+      "pur=  tensor(0.1092)\n",
+      "torch.Size([2, 18804])\n",
+      "torch.Size([18804])\n",
+      "torch.Size([2, 2752])\n",
+      "Epoch 1:  98%|█████████▊| 88/90 [00:17<00:00,  4.92it/s, loss=0.00768, v_num=2]eff=  tensor(0.6543)\n",
+      "pur=  tensor(0.2798)\n",
+      "torch.Size([2, 1258])\n",
+      "torch.Size([1258])\n",
+      "torch.Size([2, 538])\n",
+      "Epoch 1:  99%|█████████▉| 89/90 [00:18<00:00,  4.93it/s, loss=0.00768, v_num=2]eff=  tensor(0.7387)\n",
+      "pur=  tensor(0.1173)\n",
+      "torch.Size([2, 15616])\n",
+      "torch.Size([15616])\n",
+      "torch.Size([2, 2480])\n",
+      "Epoch 2:  89%|████████▉ | 80/90 [00:16<00:02,  5.00it/s, loss=0.00758, v_num=2]eff=  tensor(0.7721)\n",
+      "pur=  tensor(0.1799)\n",
+      "torch.Size([2, 5838])\n",
+      "torch.Size([5838])\n",
+      "torch.Size([2, 1360])\n",
+      "Epoch 2:  90%|█████████ | 81/90 [00:17<00:01,  4.67it/s, loss=0.00758, v_num=2]eff=  tensor(0.7463)\n",
+      "pur=  tensor(0.1141)\n",
+      "torch.Size([2, 16910])\n",
+      "torch.Size([16910])\n",
+      "torch.Size([2, 2586])\n",
+      "Epoch 2:  91%|█████████ | 82/90 [00:17<00:01,  4.68it/s, loss=0.00758, v_num=2]eff=  tensor(0.8207)\n",
+      "pur=  tensor(0.1752)\n",
+      "torch.Size([2, 7886])\n",
+      "torch.Size([7886])\n",
+      "torch.Size([2, 1684])\n",
+      "Epoch 2:  92%|█████████▏| 83/90 [00:17<00:01,  4.69it/s, loss=0.00758, v_num=2]eff=  tensor(0.7138)\n",
+      "pur=  tensor(0.2079)\n",
+      "torch.Size([2, 3790])\n",
+      "torch.Size([3790])\n",
+      "torch.Size([2, 1104])\n",
+      "Epoch 2:  93%|█████████▎| 84/90 [00:17<00:01,  4.70it/s, loss=0.00758, v_num=2]eff=  tensor(0.8002)\n",
+      "pur=  tensor(0.1276)\n",
+      "torch.Size([2, 14124])\n",
+      "torch.Size([14124])\n",
+      "torch.Size([2, 2252])\n",
+      "Epoch 2:  94%|█████████▍| 85/90 [00:18<00:01,  4.71it/s, loss=0.00758, v_num=2]eff=  tensor(0.7185)\n",
+      "pur=  tensor(0.1008)\n",
+      "torch.Size([2, 15731])\n",
+      "torch.Size([15731])\n",
+      "torch.Size([2, 2206])\n",
+      "Epoch 2:  96%|█████████▌| 86/90 [00:18<00:00,  4.72it/s, loss=0.00758, v_num=2]eff=  tensor(0.8381)\n",
+      "pur=  tensor(0.0998)\n",
+      "torch.Size([2, 30800])\n",
+      "torch.Size([30800])\n",
+      "torch.Size([2, 3668])\n",
+      "Epoch 2:  97%|█████████▋| 87/90 [00:18<00:00,  4.73it/s, loss=0.00758, v_num=2]eff=  tensor(0.7762)\n",
+      "pur=  tensor(0.1084)\n",
+      "torch.Size([2, 19696])\n",
+      "torch.Size([19696])\n",
+      "torch.Size([2, 2752])\n",
+      "Epoch 2:  98%|█████████▊| 88/90 [00:18<00:00,  4.74it/s, loss=0.00758, v_num=2]eff=  tensor(0.7026)\n",
+      "pur=  tensor(0.3024)\n",
+      "torch.Size([2, 1250])\n",
+      "torch.Size([1250])\n",
+      "torch.Size([2, 538])\n",
+      "Epoch 2:  99%|█████████▉| 89/90 [00:18<00:00,  4.76it/s, loss=0.00758, v_num=2]eff=  tensor(0.7718)\n",
+      "pur=  tensor(0.1152)\n",
+      "torch.Size([2, 16620])\n",
+      "torch.Size([16620])\n",
+      "torch.Size([2, 2480])\n",
+      "Epoch 2: 100%|██████████| 90/90 [00:18<00:00,  4.75it/s, loss=0.00758, v_num=2]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "`Trainer.fit` stopped: `max_epochs=3` reached.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 2: 100%|██████████| 90/90 [00:18<00:00,  4.74it/s, loss=0.00758, v_num=2]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:-------------------------------- c) Saving model --------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "# send_telegram_message('Started metric learning training.', chat_id, api_key)\n",
+    "\n",
+    "metric_learning_trainer, metric_learning_model = train_metric_learning(CONFIG)\n",
+    "\n",
+    "# send_telegram_message('Finished metric learning training.', chat_id, api_key)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### From checkpoint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# from Embedding.Models.layerless_embedding import LayerlessEmbedding\n",
+    "# from pytorch_lightning import Trainer\n",
+    "# from pytorch_lightning.loggers import CSVLogger\n",
+    "\n",
+    "# version_number = 6\n",
+    "\n",
+    "# HPARAMS_PATH = f'/home/fgias/velo-gnn/LHCb_Pipeline/artifacts/metric_learning/velo-minbias-sim10b-xdigi/version_1/hparams.yaml'\n",
+    "# CKPT_PATH = f'/home/fgias/velo-gnn/LHCb_Pipeline/artifacts/metric_learning/velo-minbias-sim10b-xdigi/version_1/checkpoints/epoch=19-step=1600.ckpt'\n",
+    "\n",
+    "# load_configs = {}\n",
+    "# with open(HPARAMS_PATH, 'r') as f:\n",
+    "#     load_configs = yaml.load(f, Loader=yaml.FullLoader)\n",
+    "\n",
+    "# metric_learning_model = LayerlessEmbedding(load_configs)\n",
+    "\n",
+    "# logger = CSVLogger('artifacts', name='metric_learning/velo_data')\n",
+    "\n",
+    "# metric_learning_trainer = Trainer(\n",
+    "#         accelerator='gpu' if torch.cuda.is_available() else 'cpu',\n",
+    "#         gpus=1,\n",
+    "#         max_epochs=40,\n",
+    "#         logger=logger,\n",
+    "#         # callbacks=[EarlyStopping(monitor=\"val_loss\", mode=\"min\")]\n",
+    "#     )\n",
+    "\n",
+    "# metric_learning_trainer.fit(metric_learning_model, ckpt_path=CKPT_PATH)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot training metrics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can examine how the training went. This is stored in a simple dataframe:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>epoch</th>\n",
+       "      <th>train_loss</th>\n",
+       "      <th>val_loss</th>\n",
+       "      <th>eff</th>\n",
+       "      <th>pur</th>\n",
+       "      <th>current_lr</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.007978</td>\n",
+       "      <td>0.008570</td>\n",
+       "      <td>0.669372</td>\n",
+       "      <td>0.139480</td>\n",
+       "      <td>0.000125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.007980</td>\n",
+       "      <td>0.008654</td>\n",
+       "      <td>0.746478</td>\n",
+       "      <td>0.131017</td>\n",
+       "      <td>0.000250</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   epoch  train_loss  val_loss       eff       pur  current_lr\n",
+       "0      0    0.007978  0.008570  0.669372  0.139480    0.000125\n",
+       "1      1    0.007980  0.008654  0.746478  0.131017    0.000250"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "embedding_metrics = get_training_metrics(metric_learning_trainer) \n",
+    "\n",
+    "# embedding_metrics = get_training_metrics(metric_learning_trainer, METRICS_PATH) # Use when loading from checkpoint\n",
+    "\n",
+    "embedding_metrics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_training_metrics(embedding_metrics, 'Metric Learning')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Evaluate model performance on sample test data\n",
+    "\n",
+    "Here we evaluate the model performace on one sample test data. We look at how the efficiency and purity change with the embedding radius."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 596])\n",
+      "torch.Size([596])\n",
+      "torch.Size([2, 2370])\n",
+      "torch.Size([2, 6664])\n",
+      "torch.Size([6664])\n",
+      "torch.Size([2, 2370])\n",
+      "torch.Size([2, 15730])\n",
+      "torch.Size([15730])\n",
+      "torch.Size([2, 2370])\n",
+      "torch.Size([2, 28012])\n",
+      "torch.Size([28012])\n",
+      "torch.Size([2, 2370])\n",
+      "torch.Size([2, 41682])\n",
+      "torch.Size([41682])\n",
+      "torch.Size([2, 2370])\n",
+      "torch.Size([2, 55179])\n",
+      "torch.Size([55179])\n",
+      "torch.Size([2, 2370])\n",
+      "torch.Size([2, 65439])\n",
+      "torch.Size([65439])\n",
+      "torch.Size([2, 2370])\n",
+      "torch.Size([2, 70761])\n",
+      "torch.Size([70761])\n",
+      "torch.Size([2, 2370])\n",
+      "torch.Size([2, 73106])\n",
+      "torch.Size([73106])\n",
+      "torch.Size([2, 2370])\n",
+      "torch.Size([2, 74294])\n",
+      "torch.Size([74294])\n",
+      "torch.Size([2, 2370])\n",
+      "torch.Size([2, 75154])\n",
+      "torch.Size([75154])\n",
+      "torch.Size([2, 2370])\n",
+      "torch.Size([2, 75588])\n",
+      "torch.Size([75588])\n",
+      "torch.Size([2, 2370])\n",
+      "torch.Size([2, 75877])\n",
+      "torch.Size([75877])\n",
+      "torch.Size([2, 2370])\n",
+      "torch.Size([2, 76123])\n",
+      "torch.Size([76123])\n",
+      "torch.Size([2, 2370])\n",
+      "torch.Size([2, 76231])\n",
+      "torch.Size([76231])\n",
+      "torch.Size([2, 2370])\n",
+      "torch.Size([2, 76287])\n",
+      "torch.Size([76287])\n",
+      "torch.Size([2, 2370])\n",
+      "torch.Size([2, 76328])\n",
+      "torch.Size([76328])\n",
+      "torch.Size([2, 2370])\n",
+      "torch.Size([2, 76342])\n",
+      "torch.Size([76342])\n",
+      "torch.Size([2, 2370])\n",
+      "torch.Size([2, 76342])\n",
+      "torch.Size([76342])\n",
+      "torch.Size([2, 2370])\n",
+      "torch.Size([2, 76342])\n",
+      "torch.Size([76342])\n",
+      "torch.Size([2, 2370])\n",
+      "torch.Size([2, 76342])\n",
+      "torch.Size([76342])\n",
+      "torch.Size([2, 2370])\n",
+      "torch.Size([2, 76342])\n",
+      "torch.Size([76342])\n",
+      "torch.Size([2, 2370])\n",
+      "torch.Size([2, 76342])\n",
+      "torch.Size([76342])\n",
+      "torch.Size([2, 2370])\n",
+      "torch.Size([2, 76342])\n",
+      "torch.Size([76342])\n",
+      "torch.Size([2, 2370])\n",
+      "torch.Size([2, 76342])\n",
+      "torch.Size([76342])\n",
+      "torch.Size([2, 2370])\n",
+      "torch.Size([2, 76342])\n",
+      "torch.Size([76342])\n",
+      "torch.Size([2, 2370])\n",
+      "torch.Size([2, 76342])\n",
+      "torch.Size([76342])\n",
+      "torch.Size([2, 2370])\n",
+      "torch.Size([2, 76342])\n",
+      "torch.Size([76342])\n",
+      "torch.Size([2, 2370])\n",
+      "torch.Size([2, 76342])\n",
+      "torch.Size([76342])\n",
+      "torch.Size([2, 2370])\n",
+      "torch.Size([2, 76342])\n",
+      "torch.Size([76342])\n",
+      "torch.Size([2, 2370])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_neighbor_performance(metric_learning_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot example truth and predicted graphs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 13588])\n",
+      "torch.Size([13588])\n",
+      "torch.Size([2, 2370])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_predicted_graph(metric_learning_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Track lengths"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 13588])\n",
+      "torch.Size([13588])\n",
+      "torch.Size([2, 2370])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_track_lengths(metric_learning_model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  2%|â–Ž         | 2/80 [00:00<00:13,  5.91it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 33177])\n",
+      "torch.Size([33177])\n",
+      "torch.Size([2, 1008])\n",
+      "torch.Size([2, 145236])\n",
+      "torch.Size([145236])\n",
+      "torch.Size([2, 4474])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  5%|▌         | 4/80 [00:00<00:12,  6.03it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 77371])\n",
+      "torch.Size([77371])\n",
+      "torch.Size([2, 2310])\n",
+      "torch.Size([2, 23698])\n",
+      "torch.Size([23698])\n",
+      "torch.Size([2, 770])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  8%|â–Š         | 6/80 [00:01<00:12,  5.97it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 106526])\n",
+      "torch.Size([106526])\n",
+      "torch.Size([2, 3388])\n",
+      "torch.Size([2, 118727])\n",
+      "torch.Size([118727])\n",
+      "torch.Size([2, 3588])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 10%|â–ˆ         | 8/80 [00:01<00:11,  6.05it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 45129])\n",
+      "torch.Size([45129])\n",
+      "torch.Size([2, 1364])\n",
+      "torch.Size([2, 45374])\n",
+      "torch.Size([45374])\n",
+      "torch.Size([2, 1392])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 12%|█▎        | 10/80 [00:01<00:12,  5.80it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 93982])\n",
+      "torch.Size([93982])\n",
+      "torch.Size([2, 2878])\n",
+      "torch.Size([2, 40327])\n",
+      "torch.Size([40327])\n",
+      "torch.Size([2, 1212])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 15%|█▌        | 12/80 [00:02<00:11,  6.00it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 18314])\n",
+      "torch.Size([18314])\n",
+      "torch.Size([2, 556])\n",
+      "torch.Size([2, 58898])\n",
+      "torch.Size([58898])\n",
+      "torch.Size([2, 1870])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 18%|█▊        | 14/80 [00:02<00:11,  5.54it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 165032])\n",
+      "torch.Size([165032])\n",
+      "torch.Size([2, 4848])\n",
+      "torch.Size([2, 41258])\n",
+      "torch.Size([41258])\n",
+      "torch.Size([2, 1204])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 20%|██        | 16/80 [00:02<00:11,  5.68it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 157927])\n",
+      "torch.Size([157927])\n",
+      "torch.Size([2, 4940])\n",
+      "torch.Size([2, 106722])\n",
+      "torch.Size([106722])\n",
+      "torch.Size([2, 3244])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 22%|██▎       | 18/80 [00:03<00:10,  5.78it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 64141])\n",
+      "torch.Size([64141])\n",
+      "torch.Size([2, 1992])\n",
+      "torch.Size([2, 42679])\n",
+      "torch.Size([42679])\n",
+      "torch.Size([2, 1342])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 25%|██▌       | 20/80 [00:03<00:10,  5.95it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 30539])\n",
+      "torch.Size([30539])\n",
+      "torch.Size([2, 992])\n",
+      "torch.Size([2, 66983])\n",
+      "torch.Size([66983])\n",
+      "torch.Size([2, 2102])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 28%|██▊       | 22/80 [00:03<00:09,  5.89it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 146559])\n",
+      "torch.Size([146559])\n",
+      "torch.Size([2, 4544])\n",
+      "torch.Size([2, 49735])\n",
+      "torch.Size([49735])\n",
+      "torch.Size([2, 1452])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 30%|███       | 24/80 [00:04<00:09,  5.92it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 41307])\n",
+      "torch.Size([41307])\n",
+      "torch.Size([2, 1208])\n",
+      "torch.Size([2, 130830])\n",
+      "torch.Size([130830])\n",
+      "torch.Size([2, 4044])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 32%|███▎      | 26/80 [00:04<00:08,  6.04it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 17636])\n",
+      "torch.Size([17636])\n",
+      "torch.Size([2, 544])\n",
+      "torch.Size([2, 81389])\n",
+      "torch.Size([81389])\n",
+      "torch.Size([2, 2488])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 35%|███▌      | 28/80 [00:04<00:08,  6.11it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 42225])\n",
+      "torch.Size([42225])\n",
+      "torch.Size([2, 1340])\n",
+      "torch.Size([2, 48295])\n",
+      "torch.Size([48295])\n",
+      "torch.Size([2, 1494])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 38%|███▊      | 30/80 [00:05<00:08,  6.05it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 72177])\n",
+      "torch.Size([72177])\n",
+      "torch.Size([2, 2196])\n",
+      "torch.Size([2, 94619])\n",
+      "torch.Size([94619])\n",
+      "torch.Size([2, 2852])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 40%|████      | 32/80 [00:05<00:07,  6.09it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 38122])\n",
+      "torch.Size([38122])\n",
+      "torch.Size([2, 1166])\n",
+      "torch.Size([2, 36015])\n",
+      "torch.Size([36015])\n",
+      "torch.Size([2, 1126])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 42%|████▎     | 34/80 [00:05<00:07,  6.18it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 27868])\n",
+      "torch.Size([27868])\n",
+      "torch.Size([2, 900])\n",
+      "torch.Size([2, 16184])\n",
+      "torch.Size([16184])\n",
+      "torch.Size([2, 496])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 45%|████▌     | 36/80 [00:06<00:07,  5.84it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 131810])\n",
+      "torch.Size([131810])\n",
+      "torch.Size([2, 3964])\n",
+      "torch.Size([2, 120491])\n",
+      "torch.Size([120491])\n",
+      "torch.Size([2, 3702])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 48%|████▊     | 38/80 [00:06<00:07,  5.65it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 114268])\n",
+      "torch.Size([114268])\n",
+      "torch.Size([2, 3516])\n",
+      "torch.Size([2, 50176])\n",
+      "torch.Size([50176])\n",
+      "torch.Size([2, 1546])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 50%|█████     | 40/80 [00:06<00:07,  5.66it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 139258])\n",
+      "torch.Size([139258])\n",
+      "torch.Size([2, 4212])\n",
+      "torch.Size([2, 113876])\n",
+      "torch.Size([113876])\n",
+      "torch.Size([2, 3600])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 51%|█████▏    | 41/80 [00:06<00:06,  5.65it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 132790])\n",
+      "torch.Size([132790])\n",
+      "torch.Size([2, 3960])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 54%|█████▍    | 43/80 [00:07<00:06,  5.30it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 63651])\n",
+      "torch.Size([63651])\n",
+      "torch.Size([2, 1922])\n",
+      "torch.Size([2, 112798])\n",
+      "torch.Size([112798])\n",
+      "torch.Size([2, 3376])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 56%|█████▋    | 45/80 [00:07<00:06,  5.63it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 35462])\n",
+      "torch.Size([35462])\n",
+      "torch.Size([2, 1122])\n",
+      "torch.Size([2, 156800])\n",
+      "torch.Size([156800])\n",
+      "torch.Size([2, 4768])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 59%|█████▉    | 47/80 [00:08<00:05,  5.99it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 9371])\n",
+      "torch.Size([9371])\n",
+      "torch.Size([2, 326])\n",
+      "torch.Size([2, 12197])\n",
+      "torch.Size([12197])\n",
+      "torch.Size([2, 380])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 61%|██████▏   | 49/80 [00:08<00:05,  5.86it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 58555])\n",
+      "torch.Size([58555])\n",
+      "torch.Size([2, 1710])\n",
+      "torch.Size([2, 40082])\n",
+      "torch.Size([40082])\n",
+      "torch.Size([2, 1192])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 64%|██████▍   | 51/80 [00:08<00:04,  5.96it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 71883])\n",
+      "torch.Size([71883])\n",
+      "torch.Size([2, 2208])\n",
+      "torch.Size([2, 37779])\n",
+      "torch.Size([37779])\n",
+      "torch.Size([2, 1210])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 66%|██████▋   | 53/80 [00:09<00:04,  6.03it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 93884])\n",
+      "torch.Size([93884])\n",
+      "torch.Size([2, 2802])\n",
+      "torch.Size([2, 44443])\n",
+      "torch.Size([44443])\n",
+      "torch.Size([2, 1288])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 69%|██████▉   | 55/80 [00:09<00:04,  5.88it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 36064])\n",
+      "torch.Size([36064])\n",
+      "torch.Size([2, 1136])\n",
+      "torch.Size([2, 31939])\n",
+      "torch.Size([31939])\n",
+      "torch.Size([2, 994])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 71%|███████▏  | 57/80 [00:09<00:03,  6.01it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 18326])\n",
+      "torch.Size([18326])\n",
+      "torch.Size([2, 480])\n",
+      "torch.Size([2, 71050])\n",
+      "torch.Size([71050])\n",
+      "torch.Size([2, 2190])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 72%|███████▎  | 58/80 [00:09<00:03,  6.03it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 49196])\n",
+      "torch.Size([49196])\n",
+      "torch.Size([2, 1548])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 74%|███████▍  | 59/80 [00:10<00:03,  5.43it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 67424])\n",
+      "torch.Size([67424])\n",
+      "torch.Size([2, 2008])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 76%|███████▋  | 61/80 [00:10<00:03,  5.41it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 53062])\n",
+      "torch.Size([53062])\n",
+      "torch.Size([2, 1682])\n",
+      "torch.Size([2, 102606])\n",
+      "torch.Size([102606])\n",
+      "torch.Size([2, 3176])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 79%|███████▉  | 63/80 [00:10<00:02,  5.78it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 33730])\n",
+      "torch.Size([33730])\n",
+      "torch.Size([2, 1098])\n",
+      "torch.Size([2, 74578])\n",
+      "torch.Size([74578])\n",
+      "torch.Size([2, 2448])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 81%|████████▏ | 65/80 [00:11<00:02,  5.88it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 54733])\n",
+      "torch.Size([54733])\n",
+      "torch.Size([2, 1728])\n",
+      "torch.Size([2, 139013])\n",
+      "torch.Size([139013])\n",
+      "torch.Size([2, 4380])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 84%|████████▍ | 67/80 [00:11<00:02,  5.94it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 115493])\n",
+      "torch.Size([115493])\n",
+      "torch.Size([2, 3588])\n",
+      "torch.Size([2, 93149])\n",
+      "torch.Size([93149])\n",
+      "torch.Size([2, 2656])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 86%|████████▋ | 69/80 [00:11<00:01,  6.05it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 67130])\n",
+      "torch.Size([67130])\n",
+      "torch.Size([2, 2044])\n",
+      "torch.Size([2, 43267])\n",
+      "torch.Size([43267])\n",
+      "torch.Size([2, 1306])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 89%|████████▉ | 71/80 [00:12<00:01,  6.05it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 89229])\n",
+      "torch.Size([89229])\n",
+      "torch.Size([2, 2710])\n",
+      "torch.Size([2, 62475])\n",
+      "torch.Size([62475])\n",
+      "torch.Size([2, 1922])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 91%|█████████▏| 73/80 [00:12<00:01,  6.13it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 11883])\n",
+      "torch.Size([11883])\n",
+      "torch.Size([2, 394])\n",
+      "torch.Size([2, 96922])\n",
+      "torch.Size([96922])\n",
+      "torch.Size([2, 2852])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 94%|█████████▍| 75/80 [00:12<00:00,  6.15it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 65464])\n",
+      "torch.Size([65464])\n",
+      "torch.Size([2, 1946])\n",
+      "torch.Size([2, 40276])\n",
+      "torch.Size([40276])\n",
+      "torch.Size([2, 1284])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 96%|█████████▋| 77/80 [00:13<00:00,  6.14it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 137396])\n",
+      "torch.Size([137396])\n",
+      "torch.Size([2, 4262])\n",
+      "torch.Size([2, 38857])\n",
+      "torch.Size([38857])\n",
+      "torch.Size([2, 1252])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 99%|█████████▉| 79/80 [00:13<00:00,  6.14it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 104958])\n",
+      "torch.Size([104958])\n",
+      "torch.Size([2, 3320])\n",
+      "torch.Size([2, 31802])\n",
+      "torch.Size([31802])\n",
+      "torch.Size([2, 1036])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 80/80 [00:13<00:00,  5.87it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 106085])\n",
+      "torch.Size([106085])\n",
+      "torch.Size([2, 3240])\n"
+     ]
+    },
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_graph_sizes(metric_learning_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 2. Construct graphs from metric learning inference\n",
+    "\n",
+    "This step performs model inference on the entire input datasets (train, validation and test), to obtain input graphs to the graph neural network. Optionally, we also clear the directory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:------------- Step 2: Constructing graphs from metric learning model -------------\n",
+      "INFO:---------------------------- a) Loading trained model ----------------------------\n",
+      "INFO:----------------------------- b) Running inferencing -----------------------------\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training finished, running inference to build graphs...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  1%|▏         | 1/80 [00:00<00:14,  5.33it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 24720])\n",
+      "torch.Size([24720])\n",
+      "torch.Size([2, 2480])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  2%|â–Ž         | 2/80 [00:00<00:13,  5.62it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 22072])\n",
+      "torch.Size([22072])\n",
+      "torch.Size([2, 2710])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  4%|▍         | 3/80 [00:00<00:13,  5.80it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 8778])\n",
+      "torch.Size([8778])\n",
+      "torch.Size([2, 1036])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  5%|▌         | 4/80 [00:00<00:13,  5.81it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 22034])\n",
+      "torch.Size([22034])\n",
+      "torch.Size([2, 2252])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  6%|â–‹         | 5/80 [00:00<00:12,  5.86it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 5860])\n",
+      "torch.Size([5860])\n",
+      "torch.Size([2, 1122])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  8%|â–Š         | 6/80 [00:01<00:12,  5.91it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 8212])\n",
+      "torch.Size([8212])\n",
+      "torch.Size([2, 900])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  9%|â–‰         | 7/80 [00:01<00:12,  5.92it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 14606])\n",
+      "torch.Size([14606])\n",
+      "torch.Size([2, 1832])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 10%|â–ˆ         | 8/80 [00:01<00:12,  5.93it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 28448])\n",
+      "torch.Size([28448])\n",
+      "torch.Size([2, 3176])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 11%|█▏        | 9/80 [00:01<00:11,  5.94it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 27961])\n",
+      "torch.Size([27961])\n",
+      "torch.Size([2, 2310])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 12%|█▎        | 10/80 [00:01<00:11,  6.01it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 7526])\n",
+      "torch.Size([7526])\n",
+      "torch.Size([2, 1104])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 14%|█▍        | 11/80 [00:01<00:11,  5.95it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 32078])\n",
+      "torch.Size([32078])\n",
+      "torch.Size([2, 2656])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 15%|█▌        | 12/80 [00:02<00:11,  5.93it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 41196])\n",
+      "torch.Size([41196])\n",
+      "torch.Size([2, 3588])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 16%|█▋        | 13/80 [00:02<00:11,  5.95it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 1594])\n",
+      "torch.Size([1594])\n",
+      "torch.Size([2, 380])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 18%|█▊        | 14/80 [00:02<00:11,  5.75it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 42760])\n",
+      "torch.Size([42760])\n",
+      "torch.Size([2, 3702])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 19%|█▉        | 15/80 [00:02<00:11,  5.71it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 58912])\n",
+      "torch.Size([58912])\n",
+      "torch.Size([2, 4262])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 20%|██        | 16/80 [00:02<00:10,  5.84it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 10370])\n",
+      "torch.Size([10370])\n",
+      "torch.Size([2, 1098])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 21%|██▏       | 17/80 [00:02<00:10,  5.84it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 30992])\n",
+      "torch.Size([30992])\n",
+      "torch.Size([2, 2852])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 22%|██▎       | 18/80 [00:03<00:10,  5.82it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 50589])\n",
+      "torch.Size([50589])\n",
+      "torch.Size([2, 3960])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 24%|██▍       | 19/80 [00:03<00:10,  5.86it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 20262])\n",
+      "torch.Size([20262])\n",
+      "torch.Size([2, 2008])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 25%|██▌       | 20/80 [00:03<00:10,  5.88it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 11828])\n",
+      "torch.Size([11828])\n",
+      "torch.Size([2, 1340])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 26%|██▋       | 21/80 [00:03<00:09,  5.96it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 8052])\n",
+      "torch.Size([8052])\n",
+      "torch.Size([2, 1192])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 28%|██▊       | 22/80 [00:03<00:09,  5.91it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 49886])\n",
+      "torch.Size([49886])\n",
+      "torch.Size([2, 4044])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 29%|██▉       | 23/80 [00:03<00:09,  5.95it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 1132])\n",
+      "torch.Size([1132])\n",
+      "torch.Size([2, 326])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 30%|███       | 24/80 [00:04<00:09,  5.95it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 29434])\n",
+      "torch.Size([29434])\n",
+      "torch.Size([2, 3320])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 31%|███▏      | 25/80 [00:04<00:09,  5.73it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 79962])\n",
+      "torch.Size([79962])\n",
+      "torch.Size([2, 4848])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 32%|███▎      | 26/80 [00:04<00:09,  5.87it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 7098])\n",
+      "torch.Size([7098])\n",
+      "torch.Size([2, 1136])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 34%|███▍      | 27/80 [00:04<00:09,  5.67it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 17254])\n",
+      "torch.Size([17254])\n",
+      "torch.Size([2, 2044])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 35%|███▌      | 28/80 [00:04<00:09,  5.75it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 18100])\n",
+      "torch.Size([18100])\n",
+      "torch.Size([2, 1870])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 36%|███▋      | 29/80 [00:04<00:08,  5.75it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 45740])\n",
+      "torch.Size([45740])\n",
+      "torch.Size([2, 3600])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 38%|███▊      | 30/80 [00:05<00:08,  5.83it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 7286])\n",
+      "torch.Size([7286])\n",
+      "torch.Size([2, 1208])\n",
+      "torch.Size([2, 23350])\n",
+      "torch.Size([23350])\n",
+      "torch.Size([2, 2448])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 39%|███▉      | 31/80 [00:05<00:08,  5.57it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 15212])\n",
+      "torch.Size([15212])\n",
+      "torch.Size([2, 1946])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 41%|████▏     | 33/80 [00:05<00:08,  5.78it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 12366])\n",
+      "torch.Size([12366])\n",
+      "torch.Size([2, 1684])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 42%|████▎     | 34/80 [00:05<00:08,  5.64it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 61640])\n",
+      "torch.Size([61640])\n",
+      "torch.Size([2, 4768])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 44%|████▍     | 35/80 [00:06<00:07,  5.79it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 5338])\n",
+      "torch.Size([5338])\n",
+      "torch.Size([2, 1126])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 45%|████▌     | 36/80 [00:06<00:07,  5.88it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 9758])\n",
+      "torch.Size([9758])\n",
+      "torch.Size([2, 1306])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 46%|████▋     | 37/80 [00:06<00:07,  5.90it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 19674])\n",
+      "torch.Size([19674])\n",
+      "torch.Size([2, 2208])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 48%|████▊     | 38/80 [00:06<00:07,  5.95it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 9620])\n",
+      "torch.Size([9620])\n",
+      "torch.Size([2, 1548])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 49%|████▉     | 39/80 [00:06<00:06,  5.86it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 65041])\n",
+      "torch.Size([65041])\n",
+      "torch.Size([2, 4212])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 50%|█████     | 40/80 [00:06<00:06,  5.72it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 47670])\n",
+      "torch.Size([47670])\n",
+      "torch.Size([2, 4380])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 51%|█████▏    | 41/80 [00:07<00:06,  5.76it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 39053])\n",
+      "torch.Size([39053])\n",
+      "torch.Size([2, 3478])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 52%|█████▎    | 42/80 [00:07<00:06,  5.84it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 2146])\n",
+      "torch.Size([2146])\n",
+      "torch.Size([2, 556])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 54%|█████▍    | 43/80 [00:07<00:06,  5.80it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 26480])\n",
+      "torch.Size([26480])\n",
+      "torch.Size([2, 2196])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 55%|█████▌    | 44/80 [00:07<00:06,  5.90it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 4498])\n",
+      "torch.Size([4498])\n",
+      "torch.Size([2, 394])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 56%|█████▋    | 45/80 [00:07<00:05,  5.98it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 7242])\n",
+      "torch.Size([7242])\n",
+      "torch.Size([2, 1342])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 57%|█████▊    | 46/80 [00:07<00:05,  6.03it/s]"
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+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 6792])\n",
+      "torch.Size([6792])\n",
+      "torch.Size([2, 1284])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 59%|█████▉    | 47/80 [00:08<00:05,  6.06it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 8062])\n",
+      "torch.Size([8062])\n",
+      "torch.Size([2, 1136])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 60%|██████    | 48/80 [00:08<00:05,  6.04it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 13442])\n",
+      "torch.Size([13442])\n",
+      "torch.Size([2, 1546])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 62%|██████▎   | 50/80 [00:08<00:05,  5.51it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 18458])\n",
+      "torch.Size([18458])\n",
+      "torch.Size([2, 2190])\n",
+      "torch.Size([2, 9836])\n",
+      "torch.Size([9836])\n",
+      "torch.Size([2, 1392])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 65%|██████▌   | 52/80 [00:08<00:04,  5.75it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 25132])\n",
+      "torch.Size([25132])\n",
+      "torch.Size([2, 2522])\n",
+      "torch.Size([2, 12338])\n",
+      "torch.Size([12338])\n",
+      "torch.Size([2, 1728])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 68%|██████▊   | 54/80 [00:09<00:04,  5.91it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 5718])\n",
+      "torch.Size([5718])\n",
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+      "torch.Size([16898])\n",
+      "torch.Size([2, 1758])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 70%|███████   | 56/80 [00:09<00:04,  5.85it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 8258])\n",
+      "torch.Size([8258])\n",
+      "torch.Size([2, 480])\n",
+      "torch.Size([2, 39463])\n",
+      "torch.Size([39463])\n",
+      "torch.Size([2, 3668])\n"
+     ]
+    },
+    {
+     "name": "stderr",
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+     "text": [
+      " 72%|███████▎  | 58/80 [00:09<00:03,  5.96it/s]"
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+    },
+    {
+     "name": "stdout",
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+      "torch.Size([2, 18288])\n",
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+    {
+     "name": "stderr",
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+     "text": [
+      " 75%|███████▌  | 60/80 [00:10<00:03,  5.95it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
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+      "torch.Size([2, 1970])\n",
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+    },
+    {
+     "name": "stderr",
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+     "text": [
+      " 76%|███████▋  | 61/80 [00:10<00:03,  5.27it/s]"
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+     ]
+    },
+    {
+     "name": "stderr",
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+     "text": [
+      " 80%|████████  | 64/80 [00:11<00:02,  5.67it/s]"
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+    },
+    {
+     "name": "stdout",
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+      "torch.Size([2, 6534])\n",
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+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 82%|████████▎ | 66/80 [00:11<00:02,  5.75it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 66162])\n",
+      "torch.Size([66162])\n",
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+      "torch.Size([15754])\n",
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+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 85%|████████▌ | 68/80 [00:11<00:02,  5.85it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
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+      "torch.Size([2, 43104])\n",
+      "torch.Size([43104])\n",
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+      "torch.Size([13614])\n",
+      "torch.Size([2, 1364])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 88%|████████▊ | 70/80 [00:12<00:01,  5.97it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 2210])\n",
+      "torch.Size([2210])\n",
+      "torch.Size([2, 538])\n",
+      "torch.Size([2, 27512])\n",
+      "torch.Size([27512])\n",
+      "torch.Size([2, 2586])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 90%|█████████ | 72/80 [00:12<00:01,  6.00it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 27678])\n",
+      "torch.Size([27678])\n",
+      "torch.Size([2, 2878])\n",
+      "torch.Size([2, 18042])\n",
+      "torch.Size([18042])\n",
+      "torch.Size([2, 1494])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 92%|█████████▎| 74/80 [00:12<00:00,  6.04it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 41784])\n",
+      "torch.Size([41784])\n",
+      "torch.Size([2, 3240])\n",
+      "torch.Size([2, 4340])\n",
+      "torch.Size([4340])\n",
+      "torch.Size([2, 992])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 95%|█████████▌| 76/80 [00:13<00:00,  5.89it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 28100])\n",
+      "torch.Size([28100])\n",
+      "torch.Size([2, 2488])\n",
+      "torch.Size([2, 50622])\n",
+      "torch.Size([50622])\n",
+      "torch.Size([2, 4474])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 98%|█████████▊| 78/80 [00:13<00:00,  5.78it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 72144])\n",
+      "torch.Size([72144])\n",
+      "torch.Size([2, 4544])\n",
+      "torch.Size([2, 37952])\n",
+      "torch.Size([37952])\n",
+      "torch.Size([2, 3244])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 80/80 [00:13<00:00,  5.83it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 34502])\n",
+      "torch.Size([34502])\n",
+      "torch.Size([2, 3376])\n",
+      "torch.Size([2, 7566])\n",
+      "torch.Size([7566])\n",
+      "torch.Size([2, 1204])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 20%|██        | 2/10 [00:00<00:01,  6.14it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 17506])\n",
+      "torch.Size([17506])\n",
+      "torch.Size([2, 1922])\n",
+      "torch.Size([2, 7544])\n",
+      "torch.Size([7544])\n",
+      "torch.Size([2, 1210])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 40%|████      | 4/10 [00:00<00:01,  5.76it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 24664])\n",
+      "torch.Size([24664])\n",
+      "torch.Size([2, 2802])\n",
+      "torch.Size([2, 14966])\n",
+      "torch.Size([14966])\n",
+      "torch.Size([2, 1682])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 60%|██████    | 6/10 [00:01<00:00,  5.70it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 12184])\n",
+      "torch.Size([12184])\n",
+      "torch.Size([2, 1552])\n",
+      "torch.Size([2, 29416])\n",
+      "torch.Size([29416])\n",
+      "torch.Size([2, 2206])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 80%|████████  | 8/10 [00:01<00:00,  5.85it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 13216])\n",
+      "torch.Size([13216])\n",
+      "torch.Size([2, 1212])\n",
+      "torch.Size([2, 25590])\n",
+      "torch.Size([25590])\n",
+      "torch.Size([2, 2852])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 10/10 [00:01<00:00,  5.84it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 6970])\n",
+      "torch.Size([6970])\n",
+      "torch.Size([2, 1008])\n",
+      "torch.Size([2, 44808])\n",
+      "torch.Size([44808])\n",
+      "torch.Size([2, 3388])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 20%|██        | 2/10 [00:00<00:01,  5.58it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 9266])\n",
+      "torch.Size([9266])\n",
+      "torch.Size([2, 1496])\n",
+      "torch.Size([2, 23218])\n",
+      "torch.Size([23218])\n",
+      "torch.Size([2, 2370])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 40%|████      | 4/10 [00:00<00:01,  5.90it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 4230])\n",
+      "torch.Size([4230])\n",
+      "torch.Size([2, 770])\n",
+      "torch.Size([2, 14336])\n",
+      "torch.Size([14336])\n",
+      "torch.Size([2, 1992])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 60%|██████    | 6/10 [00:01<00:00,  5.73it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 109148])\n",
+      "torch.Size([109148])\n",
+      "torch.Size([2, 5882])\n",
+      "torch.Size([2, 9952])\n",
+      "torch.Size([9952])\n",
+      "torch.Size([2, 1452])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 80%|████████  | 8/10 [00:01<00:00,  5.95it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 18698])\n",
+      "torch.Size([18698])\n",
+      "torch.Size([2, 1710])\n",
+      "torch.Size([2, 2114])\n",
+      "torch.Size([2114])\n",
+      "torch.Size([2, 496])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 10/10 [00:01<00:00,  5.83it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 55732])\n",
+      "torch.Size([55732])\n",
+      "torch.Size([2, 3516])\n",
+      "torch.Size([2, 7484])\n",
+      "torch.Size([7484])\n",
+      "torch.Size([2, 1252])\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "graph_builder = run_metric_learning_inference(CONFIG)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 3. Train graph neural networks"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We have a set of graphs constructed. We now train a GNN to classify edges as either \"true\" (belonging to the same track) or \"false\" (not belonging to the same track). We train for 30 epochs, which should take around 10 minutes on a V100 GPU. Your mileage may vary."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:-------------------------  Step 3: Running GNN training  -------------------------\n",
+      "INFO:----------------------------- a) Initialising model -----------------------------\n",
+      "INFO:------------------------------ b) Running training ------------------------------\n",
+      "GPU available: True (cuda), used: True\n",
+      "TPU available: False, using: 0 TPU cores\n",
+      "IPU available: False, using: 0 IPUs\n",
+      "HPU available: False, using: 0 HPUs\n",
+      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
+      "\n",
+      "  | Name                   | Type       | Params\n",
+      "------------------------------------------------------\n",
+      "0 | node_encoder           | Sequential | 34.0 K\n",
+      "1 | edge_encoder           | Sequential | 66.4 K\n",
+      "2 | edge_network           | Sequential | 82.8 K\n",
+      "3 | node_network           | Sequential | 82.8 K\n",
+      "4 | output_edge_classifier | Sequential | 83.2 K\n",
+      "------------------------------------------------------\n",
+      "349 K     Trainable params\n",
+      "0         Non-trainable params\n",
+      "349 K     Total params\n",
+      "1.397     Total estimated model params size (MB)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 2: 100%|██████████| 90/90 [00:14<00:00,  6.43it/s, loss=0.728, v_num=1]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "`Trainer.fit` stopped: `max_epochs=3` reached.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 2: 100%|██████████| 90/90 [00:14<00:00,  6.41it/s, loss=0.728, v_num=1]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:-------------------------------- c) Saving model --------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "# send_telegram_message('Started GNN training.', chat_id, api_key)\n",
+    "\n",
+    "gnn_trainer, gnn_model = train_gnn(CONFIG)\n",
+    "\n",
+    "# send_telegram_message('Finished GNN training.', chat_id, api_key)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### From checkpoint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# from GNN.Models.interaction_gnn import InteractionGNN\n",
+    "# from pytorch_lightning import Trainer\n",
+    "# from pytorch_lightning.loggers import CSVLogger\n",
+    "\n",
+    "# version_number = 1\n",
+    "\n",
+    "# HPARAMS_PATH = f'/home/fgias/velo-gnn/LHCb_Pipeline/artifacts/metric_learning/velo-minbias-sim10b-xdigi/version_1/hparams.yaml'\n",
+    "# CKPT_PATH = f'/home/fgias/velo-gnn/LHCb_Pipeline/artifacts/metric_learning/velo-minbias-sim10b-xdigi/version_1/checkpoints/epoch=19-step=1600.ckpt'\n",
+    "# METRICS_PATH = f'/home/fgias/velo-gnn/LHCb_Pipeline/artifacts/gnn/velo_data/version_{version_number}/metrics.csv'\n",
+    "\n",
+    "# load_configs = {}\n",
+    "# with open(HPARAMS_PATH, 'r') as f:\n",
+    "#     load_configs = yaml.load(f, Loader=yaml.FullLoader)\n",
+    "    \n",
+    "# gnn_model = InteractionGNN(load_configs)\n",
+    "\n",
+    "# logger = CSVLogger('artifacts', name='gnn/velo_data')\n",
+    "\n",
+    "# gnn_trainer = Trainer(\n",
+    "#         gpus=1,\n",
+    "#         max_epochs=40,\n",
+    "#         logger=logger,\n",
+    "#         # callbacks=[EarlyStopping(monitor=\"val_loss\", mode=\"min\")]\n",
+    "# )\n",
+    "\n",
+    "# gnn_trainer.fit(gnn_model, ckpt_path=CKPT_PATH)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot training metrics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>epoch</th>\n",
+       "      <th>train_loss</th>\n",
+       "      <th>val_loss</th>\n",
+       "      <th>eff</th>\n",
+       "      <th>pur</th>\n",
+       "      <th>current_lr</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.808832</td>\n",
+       "      <td>0.788465</td>\n",
+       "      <td>0.538224</td>\n",
+       "      <td>0.419893</td>\n",
+       "      <td>0.0002</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.760482</td>\n",
+       "      <td>0.759609</td>\n",
+       "      <td>0.497386</td>\n",
+       "      <td>0.483790</td>\n",
+       "      <td>0.0004</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   epoch  train_loss  val_loss       eff       pur  current_lr\n",
+       "0      0    0.808832  0.788465  0.538224  0.419893      0.0002\n",
+       "1      1    0.760482  0.759609  0.497386  0.483790      0.0004"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gnn_metrics = get_training_metrics(gnn_trainer)\n",
+    "\n",
+    "# gnn_metrics = get_training_metrics(gnn_trainer, METRICS_PATH) # Use when loading from checkpoint\n",
+    "\n",
+    "gnn_metrics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_training_metrics(gnn_metrics, 'GNN')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Evaluate model performance on sample test data\n",
+    "\n",
+    "Here we evaluate the model performace on one sample test data. We look at how the efficiency and purity change with the embedding radius."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_edge_performance(gnn_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step 4: GNN inference "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:--------------------- Step 4: Scoring graph edges using GNN  ---------------------\n",
+      "INFO:---------------------------- a) Loading trained model ----------------------------\n",
+      "INFO:----------------------------- b) Running inferencing -----------------------------\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training finished, running inference to filter graphs...\n",
+      "Building train\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 80/80 [00:03<00:00, 23.62it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Building val\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 10/10 [00:00<00:00, 28.12it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Building test\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 10/10 [00:00<00:00, 22.84it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "run_gnn_inference(CONFIG)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step 5: Build track candidates from GNN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:-----------  Step 5: Building track candidates from the scored graph  -----------\n",
+      "INFO:---------------------------- a) Loading scored graphs ----------------------------\n",
+      "INFO:---------------------------- b) Labelling graph nodes ----------------------------\n",
+      "100%|██████████| 100/100 [00:00<00:00, 698.73it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "build_track_candidates(CONFIG)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step 6: Evaluate track candidates"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can control the matching style in the pipeline config file. The following all require at least a majority of hits to match in each scheme (i.e. matching fraction = 50%).\n",
+    "A discussion of each matching style and some worked examples can be found in the [Documentation](https://hsf-reco-and-software-triggers.github.io/Tracking-ML-Exa.TrkX/performance/matching_definitions/)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "ATLAS style matching is the default."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:------------ Step 6: Evaluating the track reconstruction performance ------------\n",
+      "INFO:--------------------------- a) Loading labelled graphs ---------------------------\n",
+      "100%|██████████| 100/100 [00:02<00:00, 43.86it/s]\n",
+      "INFO:--------------------- b) Calculating the performance metrics ---------------------\n",
+      "INFO:Number of reconstructed particles: 0\n",
+      "INFO:Number of particles: 23405\n",
+      "INFO:Number of matched tracks: 0\n",
+      "INFO:Number of tracks: 0\n",
+      "INFO:Number of duplicate reconstructed particles: 0\n"
+     ]
+    },
+    {
+     "ename": "ZeroDivisionError",
+     "evalue": "division by zero",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[3], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m evaluated_events, reconstructed_particles, particles, matched_tracks, tracks \u001b[39m=\u001b[39m evaluate_candidates(CONFIG)\n\u001b[1;32m      3\u001b[0m \u001b[39m# send_telegram_message('Finished evaluation.', chat_id, api_key)\u001b[39;00m\n\u001b[1;32m      4\u001b[0m \n\u001b[1;32m      5\u001b[0m \u001b[39m# send_telegram_message(\"======================\", chat_id, api_key)\u001b[39;00m\n",
+      "File \u001b[0;32m~/Documents/PhD/tracking/etx4velo/LHCb_Pipeline/Scripts/Step_6_Evaluate_Reconstruction.py:163\u001b[0m, in \u001b[0;36mevaluate\u001b[0;34m(config_file)\u001b[0m\n\u001b[1;32m    161\u001b[0m \u001b[39m# Plot the results across pT and eta\u001b[39;00m\n\u001b[1;32m    162\u001b[0m eff \u001b[39m=\u001b[39m n_reconstructed_particles \u001b[39m/\u001b[39m n_particles\n\u001b[0;32m--> 163\u001b[0m fake_rate \u001b[39m=\u001b[39m \u001b[39m1\u001b[39m \u001b[39m-\u001b[39m (n_matched_tracks \u001b[39m/\u001b[39;49m n_tracks)\n\u001b[1;32m    164\u001b[0m dup_rate \u001b[39m=\u001b[39m n_dup_reconstructed_particles \u001b[39m/\u001b[39m n_reconstructed_particles\n\u001b[1;32m    166\u001b[0m logging\u001b[39m.\u001b[39minfo(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mEfficiency: \u001b[39m\u001b[39m{\u001b[39;00meff\u001b[39m:\u001b[39;00m\u001b[39m.3f\u001b[39m\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n",
+      "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero"
+     ]
+    }
+   ],
+   "source": [
+    "evaluated_events, reconstructed_particles, particles, matched_tracks, tracks = evaluate_candidates(CONFIG)\n",
+    "\n",
+    "# send_telegram_message('Finished evaluation.', chat_id, api_key)\n",
+    "\n",
+    "# send_telegram_message(\"======================\", chat_id, api_key)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get all graph files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import montetracko as mt\n",
+    "from Scripts import Step_6_Evaluate_Reconstruction as evalreco\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(CONFIG) as file:\n",
+    "    all_configs = yaml.load(file, Loader=yaml.FullLoader)\n",
+    "\n",
+    "common_configs = all_configs[\"common_configs\"]\n",
+    "track_building_configs = all_configs[\"track_building_configs\"]\n",
+    "evaluation_configs = all_configs[\"evaluation_configs\"]\n",
+    "\n",
+    "\n",
+    "input_dir = track_building_configs[\"output_dir\"]\n",
+    "output_dir = evaluation_configs[\"output_dir\"]\n",
+    "os.makedirs(output_dir, exist_ok=True)\n",
+    "\n",
+    "all_graph_files = os.listdir(input_dir)\n",
+    "all_graph_files = [os.path.join(input_dir, graph) for graph in all_graph_files]"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get tracks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import os.path as op\n",
+    "import yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "graph_file = all_graph_files[0]\n",
+    "reconstruction_df = evalreco.load_reconstruction_df(graph_file)\n",
+    "particles_df = evalreco.load_particles_df(graph_file)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(CONFIG) as file:\n",
+    "    all_configs = yaml.load(file, Loader=yaml.FullLoader)\n",
+    "preprocessed_dir = all_configs[\"processing_configs\"][\"preprocessed_dir\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pyarrow as pa\n",
+    "import pyarrow.csv as pac"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "event_id_str = op.basename(graph_file)\n",
+    "path_particle_file = op.join(\n",
+    "    preprocessed_dir, f\"event{event_id_str}-particles.csv\"\n",
+    ")\n",
+    "path_hits_file = op.join(\n",
+    "    preprocessed_dir, f\"event{event_id_str}-hits.csv\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'data/track_building_processed/007212930'"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "graph_file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_particles = pac.read_csv(path_particle_file).to_pandas()\n",
+    "df_hits = pac.read_csv(\n",
+    "    path_hits_file\n",
+    ").to_pandas()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>x</th>\n",
+       "      <th>y</th>\n",
+       "      <th>z</th>\n",
+       "      <th>module_id</th>\n",
+       "      <th>hit_id</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>17.90920</td>\n",
+       "      <td>-32.687800</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>179847</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>6.16420</td>\n",
+       "      <td>-17.403700</td>\n",
+       "      <td>-288.141</td>\n",
+       "      <td>0</td>\n",
+       "      <td>179848</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-16.64530</td>\n",
+       "      <td>-17.423100</td>\n",
+       "      <td>-286.859</td>\n",
+       "      <td>0</td>\n",
+       "      <td>179849</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-19.79550</td>\n",
+       "      <td>-17.384200</td>\n",
+       "      <td>-286.859</td>\n",
+       "      <td>0</td>\n",
+       "      <td>179850</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>13.88400</td>\n",
+       "      <td>-27.340300</td>\n",
+       "      <td>-288.141</td>\n",
+       "      <td>0</td>\n",
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+       "    <tr>\n",
+       "      <th>...</th>\n",
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+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
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+       "    <tr>\n",
+       "      <th>1554</th>\n",
+       "      <td>4.70580</td>\n",
+       "      <td>-2.839030</td>\n",
+       "      <td>750.641</td>\n",
+       "      <td>25</td>\n",
+       "      <td>181401</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1555</th>\n",
+       "      <td>4.88080</td>\n",
+       "      <td>-2.625130</td>\n",
+       "      <td>750.641</td>\n",
+       "      <td>25</td>\n",
+       "      <td>181402</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1556</th>\n",
+       "      <td>18.20090</td>\n",
+       "      <td>-4.355780</td>\n",
+       "      <td>750.641</td>\n",
+       "      <td>25</td>\n",
+       "      <td>181403</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1557</th>\n",
+       "      <td>7.42816</td>\n",
+       "      <td>-0.194454</td>\n",
+       "      <td>750.641</td>\n",
+       "      <td>25</td>\n",
+       "      <td>181404</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1558</th>\n",
+       "      <td>7.38926</td>\n",
+       "      <td>-0.155563</td>\n",
+       "      <td>749.359</td>\n",
+       "      <td>25</td>\n",
+       "      <td>181405</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1559 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             x          y        z  module_id  hit_id\n",
+       "0     17.90920 -32.687800 -288.141          0  179847\n",
+       "1      6.16420 -17.403700 -288.141          0  179848\n",
+       "2    -16.64530 -17.423100 -286.859          0  179849\n",
+       "3    -19.79550 -17.384200 -286.859          0  179850\n",
+       "4     13.88400 -27.340300 -288.141          0  179851\n",
+       "...        ...        ...      ...        ...     ...\n",
+       "1554   4.70580  -2.839030  750.641         25  181401\n",
+       "1555   4.88080  -2.625130  750.641         25  181402\n",
+       "1556  18.20090  -4.355780  750.641         25  181403\n",
+       "1557   7.42816  -0.194454  750.641         25  181404\n",
+       "1558   7.38926  -0.155563  749.359         25  181405\n",
+       "\n",
+       "[1559 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_hits"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>particle_id</th>\n",
+       "      <th>vx</th>\n",
+       "      <th>vy</th>\n",
+       "      <th>vz</th>\n",
+       "      <th>q</th>\n",
+       "      <th>nhits</th>\n",
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+       "      <th>pz</th>\n",
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+       "  </thead>\n",
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+       "    <tr>\n",
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+       "      <td>-0.0602</td>\n",
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+       "      <td>-10.240854</td>\n",
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+       "    <tr>\n",
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+       "      <td>-0.0041</td>\n",
+       "      <td>-0.0602</td>\n",
+       "      <td>10.4652</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0.213420</td>\n",
+       "      <td>-0.242910</td>\n",
+       "      <td>-3.359714</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>366</td>\n",
+       "      <td>13.2756</td>\n",
+       "      <td>-4.5004</td>\n",
+       "      <td>-70.5277</td>\n",
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+       "      <td>0.343090</td>\n",
+       "      <td>-0.106520</td>\n",
+       "      <td>-2.660824</td>\n",
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+       "      <td>-0.0041</td>\n",
+       "      <td>-0.0602</td>\n",
+       "      <td>10.4652</td>\n",
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+       "      <td>-0.638633</td>\n",
+       "      <td>0.837760</td>\n",
+       "      <td>-19.624627</td>\n",
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+       "    <tr>\n",
+       "      <th>4</th>\n",
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+       "      <td>-0.0602</td>\n",
+       "      <td>10.4652</td>\n",
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+       "      <td>0.450230</td>\n",
+       "      <td>-5.729871</td>\n",
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+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
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+       "    <tr>\n",
+       "      <th>468</th>\n",
+       "      <td>2977</td>\n",
+       "      <td>0.0058</td>\n",
+       "      <td>-0.0229</td>\n",
+       "      <td>-49.6429</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.296190</td>\n",
+       "      <td>0.188880</td>\n",
+       "      <td>-1.590305</td>\n",
+       "    </tr>\n",
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+       "      <td>2982</td>\n",
+       "      <td>0.0058</td>\n",
+       "      <td>-0.0229</td>\n",
+       "      <td>-49.6429</td>\n",
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+       "      <td>5</td>\n",
+       "      <td>0.099371</td>\n",
+       "      <td>0.733550</td>\n",
+       "      <td>-5.143205</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>470</th>\n",
+       "      <td>2999</td>\n",
+       "      <td>0.0058</td>\n",
+       "      <td>-0.0229</td>\n",
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+       "      <td>3</td>\n",
+       "      <td>-0.322370</td>\n",
+       "      <td>-0.106259</td>\n",
+       "      <td>0.536697</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>471</th>\n",
+       "      <td>3002</td>\n",
+       "      <td>0.0058</td>\n",
+       "      <td>-0.0229</td>\n",
+       "      <td>-49.6429</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>8</td>\n",
+       "      <td>-0.320081</td>\n",
+       "      <td>0.144409</td>\n",
+       "      <td>2.521247</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>472</th>\n",
+       "      <td>3003</td>\n",
+       "      <td>-20.5282</td>\n",
+       "      <td>8.9883</td>\n",
+       "      <td>112.1490</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-0.001380</td>\n",
+       "      <td>-0.000800</td>\n",
+       "      <td>0.002110</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>473 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     particle_id       vx      vy        vz  q  nhits        px        py  \\\n",
+       "0            356  -0.0041 -0.0602   10.4652  1      3 -0.053259 -0.267060   \n",
+       "1            363  -0.0041 -0.0602   10.4652 -1      6  0.213420 -0.242910   \n",
+       "2            366  13.2756 -4.5004  -70.5277  1      4  0.343090 -0.106520   \n",
+       "3            368  -0.0041 -0.0602   10.4652  1      5 -0.638633  0.837760   \n",
+       "4            369  -0.0041 -0.0602   10.4652 -1      5  0.109862  0.450230   \n",
+       "..           ...      ...     ...       ... ..    ...       ...       ...   \n",
+       "468         2977   0.0058 -0.0229  -49.6429 -1      2  0.296190  0.188880   \n",
+       "469         2982   0.0058 -0.0229  -49.6429  1      5  0.099371  0.733550   \n",
+       "470         2999   0.0058 -0.0229  -49.6429  1      3 -0.322370 -0.106259   \n",
+       "471         3002   0.0058 -0.0229  -49.6429 -1      8 -0.320081  0.144409   \n",
+       "472         3003 -20.5282  8.9883  112.1490 -1      1 -0.001380 -0.000800   \n",
+       "\n",
+       "            pz  \n",
+       "0   -10.240854  \n",
+       "1    -3.359714  \n",
+       "2    -2.660824  \n",
+       "3   -19.624627  \n",
+       "4    -5.729871  \n",
+       "..         ...  \n",
+       "468  -1.590305  \n",
+       "469  -5.143205  \n",
+       "470   0.536697  \n",
+       "471   2.521247  \n",
+       "472   0.002110  \n",
+       "\n",
+       "[473 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_particles.to_pandas()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'data/track_building_processed/007212930'"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "graph_file"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.15"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
-- 
GitLab


From 10a62a85d8c62b46473eca79f7d4245302e379d1 Mon Sep 17 00:00:00 2001
From: anthonyc <acorreia@lpnhe.in2p3.fr>
Date: Thu, 23 Mar 2023 15:04:13 +0100
Subject: [PATCH 03/30] define get_all_graph_files

I need this function for my script
---
 .../Scripts/Step_6_Evaluate_Reconstruction.py | 24 ++++++++++++++-----
 1 file changed, 18 insertions(+), 6 deletions(-)

diff --git a/LHCb_Pipeline/Scripts/Step_6_Evaluate_Reconstruction.py b/LHCb_Pipeline/Scripts/Step_6_Evaluate_Reconstruction.py
index 2c01c4d0..bd9cc152 100644
--- a/LHCb_Pipeline/Scripts/Step_6_Evaluate_Reconstruction.py
+++ b/LHCb_Pipeline/Scripts/Step_6_Evaluate_Reconstruction.py
@@ -1,7 +1,6 @@
 """
 This script runs step 6 of the TrackML Quickstart example: Evaluating the track reconstruction performance.
 """
-
 import sys
 import os
 import yaml
@@ -19,6 +18,7 @@ from functools import partial
 from Scripts.utils.convenience_utils import headline
 from Scripts.utils.plotting_utils import plot_pt_eff
 
+
 def parse_args():
     """Parse command line arguments."""
     parser = argparse.ArgumentParser("5_Build_Track_Candidates.py")
@@ -109,6 +109,22 @@ def evaluate_labelled_graph(graph_file, matching_fraction=0.5, matching_style="A
 
     return matching_df
 
+
+def get_all_graph_files(config: dict) -> typing.List[str]:
+    """Obtain the list of all the graph files.
+    
+    Args:
+        config: dictionnary representing the configuration
+    
+    Returns:
+        List of paths to the graph files
+    """
+    track_building_configs = config["track_building_configs"]
+    input_dir = track_building_configs["output_dir"]
+    all_graph_filenames = os.listdir(input_dir)
+    all_graph_files = [os.path.join(input_dir, graph) for graph in all_graph_filenames]
+    return all_graph_files
+
 def evaluate(config_file="pipeline_config.yaml"):
 
     logging.info(headline("Step 6: Evaluating the track reconstruction performance"))
@@ -116,18 +132,14 @@ def evaluate(config_file="pipeline_config.yaml"):
     with open(config_file) as file:
         all_configs = yaml.load(file, Loader=yaml.FullLoader)
 
-    common_configs = all_configs["common_configs"]
-    track_building_configs = all_configs["track_building_configs"]
     evaluation_configs = all_configs["evaluation_configs"]
 
     logging.info(headline("a) Loading labelled graphs"))
 
-    input_dir = track_building_configs["output_dir"]
     output_dir = evaluation_configs["output_dir"]
     os.makedirs(output_dir, exist_ok=True)
 
-    all_graph_files = os.listdir(input_dir)
-    all_graph_files = [os.path.join(input_dir, graph) for graph in all_graph_files]
+    all_graph_files = get_all_graph_files(config=all_configs)
 
     evaluated_events = []
     for graph_file in tqdm(all_graph_files):
-- 
GitLab


From e4a774f602587390316a4ac62db320893760a6e2 Mon Sep 17 00:00:00 2001
From: anthonyc <acorreia@lpnhe.in2p3.fr>
Date: Thu, 23 Mar 2023 15:04:28 +0100
Subject: [PATCH 04/30] remove unused libraries

---
 LHCb_Pipeline/Scripts/Step_6_Evaluate_Reconstruction.py | 7 +------
 1 file changed, 1 insertion(+), 6 deletions(-)

diff --git a/LHCb_Pipeline/Scripts/Step_6_Evaluate_Reconstruction.py b/LHCb_Pipeline/Scripts/Step_6_Evaluate_Reconstruction.py
index bd9cc152..fc15634a 100644
--- a/LHCb_Pipeline/Scripts/Step_6_Evaluate_Reconstruction.py
+++ b/LHCb_Pipeline/Scripts/Step_6_Evaluate_Reconstruction.py
@@ -1,7 +1,7 @@
 """
 This script runs step 6 of the TrackML Quickstart example: Evaluating the track reconstruction performance.
 """
-import sys
+import typing
 import os
 import yaml
 import argparse
@@ -9,15 +9,10 @@ import logging
 import torch
 import numpy as np
 import pandas as pd
-import scipy.sparse as sps
 import matplotlib.pyplot as plt
 
-from tqdm.contrib.concurrent import process_map
 from tqdm import tqdm
-from functools import partial
 from Scripts.utils.convenience_utils import headline
-from Scripts.utils.plotting_utils import plot_pt_eff
-
 
 def parse_args():
     """Parse command line arguments."""
-- 
GitLab


From df15f0c807a7cfb8bbf80480530c32f6d8f651cd Mon Sep 17 00:00:00 2001
From: anthonyc <acorreia@lpnhe.in2p3.fr>
Date: Thu, 23 Mar 2023 15:04:54 +0100
Subject: [PATCH 05/30] formatting with black

---
 .../Scripts/Step_6_Evaluate_Reconstruction.py | 199 ++++++++++++------
 1 file changed, 135 insertions(+), 64 deletions(-)

diff --git a/LHCb_Pipeline/Scripts/Step_6_Evaluate_Reconstruction.py b/LHCb_Pipeline/Scripts/Step_6_Evaluate_Reconstruction.py
index fc15634a..9ed27d98 100644
--- a/LHCb_Pipeline/Scripts/Step_6_Evaluate_Reconstruction.py
+++ b/LHCb_Pipeline/Scripts/Step_6_Evaluate_Reconstruction.py
@@ -14,6 +14,7 @@ import matplotlib.pyplot as plt
 from tqdm import tqdm
 from Scripts.utils.convenience_utils import headline
 
+
 def parse_args():
     """Parse command line arguments."""
     parser = argparse.ArgumentParser("5_Build_Track_Candidates.py")
@@ -21,12 +22,16 @@ def parse_args():
     add_arg("config", nargs="?", default="pipeline_config.yaml")
     return parser.parse_args()
 
+
 def load_reconstruction_df(file):
     """Load the reconstructed tracks from a file."""
     graph = torch.load(file, map_location="cpu")
-    reconstruction_df = pd.DataFrame({"hit_id": graph.hid, "track_id": graph.labels, "particle_id": graph.pid})
+    reconstruction_df = pd.DataFrame(
+        {"hit_id": graph.hid, "track_id": graph.labels, "particle_id": graph.pid}
+    )
     return reconstruction_df
 
+
 def load_particles_df(file):
     """Load the particles from a file."""
     graph = torch.load(file, map_location="cpu")
@@ -35,45 +40,79 @@ def load_particles_df(file):
     particles_df = pd.DataFrame({"particle_id": graph.pid, "pt": graph.pt})
 
     # Reduce to only unique particle_ids
-    particles_df = particles_df.drop_duplicates(subset=['particle_id'])
+    particles_df = particles_df.drop_duplicates(subset=["particle_id"])
 
     return particles_df
 
-def get_matching_df(reconstruction_df, particles_df, min_track_length=1, min_particle_length=1):
-    
+
+def get_matching_df(
+    reconstruction_df, particles_df, min_track_length=1, min_particle_length=1
+):
     # Get track lengths
-    candidate_lengths = reconstruction_df.track_id.value_counts(sort=False)\
-        .reset_index().rename(
-            columns={"index":"track_id", "track_id": "n_reco_hits"})
+    candidate_lengths = (
+        reconstruction_df.track_id.value_counts(sort=False)
+        .reset_index()
+        .rename(columns={"index": "track_id", "track_id": "n_reco_hits"})
+    )
 
     # Get true track lengths
-    particle_lengths = reconstruction_df.drop_duplicates(subset=['hit_id']).particle_id.value_counts(sort=False)\
-        .reset_index().rename(
-            columns={"index":"particle_id", "particle_id": "n_true_hits"})
-
-    spacepoint_matching = reconstruction_df.groupby(['track_id', 'particle_id']).size()\
-        .reset_index().rename(columns={0:"n_shared"})
-
-    spacepoint_matching = spacepoint_matching.merge(candidate_lengths, on=['track_id'], how='left')
-    spacepoint_matching = spacepoint_matching.merge(particle_lengths, on=['particle_id'], how='left')
-    spacepoint_matching = spacepoint_matching.merge(particles_df, on=['particle_id'], how='left')
+    particle_lengths = (
+        reconstruction_df.drop_duplicates(subset=["hit_id"])
+        .particle_id.value_counts(sort=False)
+        .reset_index()
+        .rename(columns={"index": "particle_id", "particle_id": "n_true_hits"})
+    )
+
+    spacepoint_matching = (
+        reconstruction_df.groupby(["track_id", "particle_id"])
+        .size()
+        .reset_index()
+        .rename(columns={0: "n_shared"})
+    )
+
+    spacepoint_matching = spacepoint_matching.merge(
+        candidate_lengths, on=["track_id"], how="left"
+    )
+    spacepoint_matching = spacepoint_matching.merge(
+        particle_lengths, on=["particle_id"], how="left"
+    )
+    spacepoint_matching = spacepoint_matching.merge(
+        particles_df, on=["particle_id"], how="left"
+    )
 
     # Filter out tracks with too few shared spacepoints
-    spacepoint_matching["is_matchable"] = spacepoint_matching.n_reco_hits >= min_track_length
-    spacepoint_matching["is_reconstructable"] = spacepoint_matching.n_true_hits >= min_particle_length
+    spacepoint_matching["is_matchable"] = (
+        spacepoint_matching.n_reco_hits >= min_track_length
+    )
+    spacepoint_matching["is_reconstructable"] = (
+        spacepoint_matching.n_true_hits >= min_particle_length
+    )
 
     return spacepoint_matching
 
+
 def calculate_matching_fraction(spacepoint_matching_df):
     spacepoint_matching_df = spacepoint_matching_df.assign(
-        purity_reco=np.true_divide(spacepoint_matching_df.n_shared, spacepoint_matching_df.n_reco_hits))
+        purity_reco=np.true_divide(
+            spacepoint_matching_df.n_shared, spacepoint_matching_df.n_reco_hits
+        )
+    )
     spacepoint_matching_df = spacepoint_matching_df.assign(
-        eff_true = np.true_divide(spacepoint_matching_df.n_shared, spacepoint_matching_df.n_true_hits))
+        eff_true=np.true_divide(
+            spacepoint_matching_df.n_shared, spacepoint_matching_df.n_true_hits
+        )
+    )
 
     return spacepoint_matching_df
 
-def evaluate_labelled_graph(graph_file, matching_fraction=0.5, matching_style="ATLAS", min_track_length=1, min_particle_length=1):
 
+def evaluate_labelled_graph(
+    graph_file,
+    matching_fraction=0.5,
+    matching_style="ATLAS",
+    min_track_length=1,
+    min_particle_length=1,
+):
     if matching_fraction < 0.5:
         raise ValueError("Matching fraction must be >= 0.5")
 
@@ -86,31 +125,42 @@ def evaluate_labelled_graph(graph_file, matching_fraction=0.5, matching_style="A
     particles_df = load_particles_df(graph_file)
 
     # Get matching dataframe
-    matching_df = get_matching_df(reconstruction_df, particles_df, min_track_length=min_track_length, min_particle_length=min_particle_length) 
+    matching_df = get_matching_df(
+        reconstruction_df,
+        particles_df,
+        min_track_length=min_track_length,
+        min_particle_length=min_particle_length,
+    )
     matching_df["event_id"] = int(graph_file.split("/")[-1])
 
     # Calculate matching fraction
     matching_df = calculate_matching_fraction(matching_df)
-    matching_df = matching_df[matching_df['particle_id'] != 0] # Drop particles with particle_id == 0
+    matching_df = matching_df[
+        matching_df["particle_id"] != 0
+    ]  # Drop particles with particle_id == 0
 
     # Run matching depending on the matching style
     if matching_style == "ATLAS":
-        matching_df["is_matched"] = matching_df["is_reconstructed"] = matching_df.purity_reco >= matching_fraction
+        matching_df["is_matched"] = matching_df["is_reconstructed"] = (
+            matching_df.purity_reco >= matching_fraction
+        )
     elif matching_style == "one_way":
         matching_df["is_matched"] = matching_df.purity_reco >= matching_fraction
         matching_df["is_reconstructed"] = matching_df.eff_true >= matching_fraction
     elif matching_style == "two_way":
-        matching_df["is_matched"] = matching_df["is_reconstructed"] = (matching_df.purity_reco >= matching_fraction) & (matching_df.eff_true >= matching_fraction)
+        matching_df["is_matched"] = matching_df["is_reconstructed"] = (
+            matching_df.purity_reco >= matching_fraction
+        ) & (matching_df.eff_true >= matching_fraction)
 
     return matching_df
 
 
 def get_all_graph_files(config: dict) -> typing.List[str]:
     """Obtain the list of all the graph files.
-    
+
     Args:
         config: dictionnary representing the configuration
-    
+
     Returns:
         List of paths to the graph files
     """
@@ -120,8 +170,8 @@ def get_all_graph_files(config: dict) -> typing.List[str]:
     all_graph_files = [os.path.join(input_dir, graph) for graph in all_graph_filenames]
     return all_graph_files
 
-def evaluate(config_file="pipeline_config.yaml"):
 
+def evaluate(config_file="pipeline_config.yaml"):
     logging.info(headline("Step 6: Evaluating the track reconstruction performance"))
 
     with open(config_file) as file:
@@ -138,42 +188,56 @@ def evaluate(config_file="pipeline_config.yaml"):
 
     evaluated_events = []
     for graph_file in tqdm(all_graph_files):
-        evaluated_events.append(evaluate_labelled_graph(graph_file, 
-                                matching_fraction=evaluation_configs["matching_fraction"], 
-                                matching_style=evaluation_configs["matching_style"],
-                                min_track_length=evaluation_configs["min_track_length"],
-                                min_particle_length=evaluation_configs["min_particle_length"]))
+        evaluated_events.append(
+            evaluate_labelled_graph(
+                graph_file,
+                matching_fraction=evaluation_configs["matching_fraction"],
+                matching_style=evaluation_configs["matching_style"],
+                min_track_length=evaluation_configs["min_track_length"],
+                min_particle_length=evaluation_configs["min_particle_length"],
+            )
+        )
     evaluated_events = pd.concat(evaluated_events)
 
     particles = evaluated_events[evaluated_events["is_reconstructable"]]
-    reconstructed_particles = particles[particles["is_reconstructed"] & particles["is_matchable"]]    
+    reconstructed_particles = particles[
+        particles["is_reconstructed"] & particles["is_matchable"]
+    ]
     tracks = evaluated_events[evaluated_events["is_matchable"]]
     matched_tracks = tracks[tracks["is_matched"]]
 
-    n_particles = len(particles.drop_duplicates(subset=['event_id', 'particle_id']))
-    n_reconstructed_particles = len(reconstructed_particles.drop_duplicates(subset=['event_id', 'particle_id']))
-    
-    n_tracks = len(tracks.drop_duplicates(subset=['event_id', 'track_id']))
-    n_matched_tracks = len(matched_tracks.drop_duplicates(subset=['event_id', 'track_id']))
+    n_particles = len(particles.drop_duplicates(subset=["event_id", "particle_id"]))
+    n_reconstructed_particles = len(
+        reconstructed_particles.drop_duplicates(subset=["event_id", "particle_id"])
+    )
+
+    n_tracks = len(tracks.drop_duplicates(subset=["event_id", "track_id"]))
+    n_matched_tracks = len(
+        matched_tracks.drop_duplicates(subset=["event_id", "track_id"])
+    )
 
-    n_dup_reconstructed_particles = len(reconstructed_particles) - n_reconstructed_particles
+    n_dup_reconstructed_particles = (
+        len(reconstructed_particles) - n_reconstructed_particles
+    )
 
     logging.info(headline("b) Calculating the performance metrics"))
     logging.info(f"Number of reconstructed particles: {n_reconstructed_particles}")
     logging.info(f"Number of particles: {n_particles}")
     logging.info(f"Number of matched tracks: {n_matched_tracks}")
     logging.info(f"Number of tracks: {n_tracks}")
-    logging.info(f"Number of duplicate reconstructed particles: {n_dup_reconstructed_particles}")   
+    logging.info(
+        f"Number of duplicate reconstructed particles: {n_dup_reconstructed_particles}"
+    )
 
     eff = n_reconstructed_particles / n_particles
 
-    if n_tracks!=0 and n_reconstructed_particles!=0:
+    if n_tracks != 0 and n_reconstructed_particles != 0:
         fake_rate = 1 - (n_matched_tracks / n_tracks)
         dup_rate = n_dup_reconstructed_particles / n_reconstructed_particles
     else:
         fake_rate = np.nan
         dup_rate = np.nan
-    
+
     logging.info(f"Efficiency: {eff:.3f}")
     logging.info(f"Fake rate: {fake_rate:.3f}")
     logging.info(f"Duplication rate: {dup_rate:.3f}")
@@ -182,39 +246,46 @@ def evaluate(config_file="pipeline_config.yaml"):
 
     num_bins = 30
     f1, ax1 = plt.subplots()
-    n1 = ax1.hist(particles.drop_duplicates(subset=['event_id', 'particle_id']).pt, bins=num_bins, range=(0,3))
-    n2 = ax1.hist(reconstructed_particles.drop_duplicates(subset=['event_id', 'particle_id']).pt, bins=num_bins, range=(0,3))            
-    ax1.legend(['reconstructible', 'reconstructed'])
-    ax1.set_title('Tranverse momentum distribution')
-    ax1.set_xlabel('$p_T$ [GeV]')
-    f1.savefig('reconstructible_vs_reconstructed_pt.png')
-
-    x = [(n1[1][i]+n1[1][i+1])/2 for i in range(len(n1[1]) - 1)]
+    n1 = ax1.hist(
+        particles.drop_duplicates(subset=["event_id", "particle_id"]).pt,
+        bins=num_bins,
+        range=(0, 3),
+    )
+    n2 = ax1.hist(
+        reconstructed_particles.drop_duplicates(subset=["event_id", "particle_id"]).pt,
+        bins=num_bins,
+        range=(0, 3),
+    )
+    ax1.legend(["reconstructible", "reconstructed"])
+    ax1.set_title("Tranverse momentum distribution")
+    ax1.set_xlabel("$p_T$ [GeV]")
+    f1.savefig("reconstructible_vs_reconstructed_pt.png")
+
+    x = [(n1[1][i] + n1[1][i + 1]) / 2 for i in range(len(n1[1]) - 1)]
 
     f2, ax2 = plt.subplots()
-    ax2.scatter(x, n2[0]/n1[0], marker='+')
-    ax2.set_title('Efficiency vs transverse momentum')
-    ax2.set_ylabel('Efficiency')
-    ax2.set_xlabel('$p_T$ [GeV]')
-    f2.savefig('pt_efficiency.png')
+    ax2.scatter(x, n2[0] / n1[0], marker="+")
+    ax2.set_title("Efficiency vs transverse momentum")
+    ax2.set_ylabel("Efficiency")
+    ax2.set_xlabel("$p_T$ [GeV]")
+    f2.savefig("pt_efficiency.png")
 
     # First get the list of particles without duplicates
-    grouped_reco_particles = particles.groupby('particle_id')["is_reconstructed"].any()
-    particles["is_reconstructed"] = particles["particle_id"].isin(grouped_reco_particles[grouped_reco_particles].index.values)
-    particles = particles.drop_duplicates(subset=['particle_id'])
+    grouped_reco_particles = particles.groupby("particle_id")["is_reconstructed"].any()
+    particles["is_reconstructed"] = particles["particle_id"].isin(
+        grouped_reco_particles[grouped_reco_particles].index.values
+    )
+    particles = particles.drop_duplicates(subset=["particle_id"])
 
     # Plot the results across pT and eta
     # plot_pt_eff(particles)
 
     # TODO: Plot the results
     return evaluated_events, reconstructed_particles, particles, matched_tracks, tracks
-    
-
 
 
 if __name__ == "__main__":
-
     args = parse_args()
     config_file = args.config
 
-    evaluate(config_file) 
\ No newline at end of file
+    evaluate(config_file)
-- 
GitLab


From df344080258b2482d4cd5581a4371ace54569beb Mon Sep 17 00:00:00 2001
From: anthonyc <acorreia@lpnhe.in2p3.fr>
Date: Thu, 23 Mar 2023 15:45:31 +0100
Subject: [PATCH 06/30] formatting

---
 .../Processing/utils/preprocessing.py         | 158 +++++++++++-------
 1 file changed, 97 insertions(+), 61 deletions(-)

diff --git a/LHCb_Pipeline/Processing/utils/preprocessing.py b/LHCb_Pipeline/Processing/utils/preprocessing.py
index c7cc0ce0..e78bd795 100644
--- a/LHCb_Pipeline/Processing/utils/preprocessing.py
+++ b/LHCb_Pipeline/Processing/utils/preprocessing.py
@@ -1,20 +1,25 @@
 import os, shutil
 import numpy as np
 import pandas as pd
-import matplotlib.pyplot as plt
 
 
-def preprocess(input_dir, output_dir, output_num, clear_directories=True, num_true_hits_threshold=0):
+def preprocess(
+    input_dir: str,
+    output_dir: str,
+    output_num: str,
+    clear_directories: bool = True,
+    num_true_hits_threshold: int = 0,
+):
     """
-    Preprocess the first `output_num` events in the input files, into the form of the TrackML dataset.
+    Preprocess the first `output_num` events in the input files,
+    into the form of the TrackML dataset.
     Remove any events that contain only fake hits.
     """
 
     os.makedirs(input_dir, exist_ok=True)
     os.makedirs(output_dir, exist_ok=True)
-    
 
-    # Clear contents 
+    # Clear contents
     if clear_directories:
         folder = output_dir
         for filename in os.listdir(folder):
@@ -25,93 +30,124 @@ def preprocess(input_dir, output_dir, output_num, clear_directories=True, num_tr
                 elif os.path.isdir(file_path):
                     shutil.rmtree(file_path)
             except Exception as e:
-                print('Failed to delete %s. Reason: %s' % (file_path, e))
+                print("Failed to delete %s. Reason: %s" % (file_path, e))
 
     # hits = pd.read_csv(f'{input_dir}/hits_velo.csv') # Read hits
     # particles = pd.read_csv(f'{input_dir}/mc_particles.csv') # Read MC particles
-    hits = pd.read_parquet(f'{input_dir}/hits_velo.parquet.lz4')
-    particles = pd.read_parquet(f'{input_dir}/mc_particles.parquet.lz4')
-    hits = hits.merge(particles, on=['event', 'mcid'], how='left') # Merge
+    hits = pd.read_parquet(f"{input_dir}/hits_velo.parquet.lz4")
+    particles = pd.read_parquet(f"{input_dir}/mc_particles.parquet.lz4")
+    hits = hits.merge(particles, on=["event", "mcid"], how="left")  # Merge
     # NB: left join!: keep fake hits
 
     # Remove hits with has_velo == 0
-    hits = hits[hits['has_velo'] == 1]
+    # hits = hits[hits["has_velo"] == 1]
 
     # Remove electrons and positrons ?
-    hits = hits[np.abs(hits['pid']) != 11]
+    # hits = hits[np.abs(hits["pid"]) != 11]
 
-    event_list = hits['event'].unique() # The order is not mixed
+    event_list = hits["event"].unique()  # The order is not mixed
 
-    i = 0 # Count the number of events outputed
+    i = 0  # Count the number of events outputed
     for event_num in event_list:
-        event_hits = hits[hits['event'] == event_num]
-        event_particles = particles[particles['event'] == event_num]
-        
+        event_hits = hits[hits["event"] == event_num]
+        event_particles = particles[particles["event"] == event_num]
+
         # -hits.csv, use event_hits
-        hits_csv = event_hits[['x', 'y', 'z', 'plane']]
-        hits_csv.rename(columns={'plane': 'module_id'}, inplace=True)
-        hits_csv['hit_id'] = hits_csv.index + 1
+        hits_csv = event_hits[["x", "y", "z", "plane"]]
+        hits_csv.rename(columns={"plane": "module_id"}, inplace=True)
+        hits_csv["hit_id"] = hits_csv.index + 1  # TODO: this is not accurate 
         num = str(event_num).zfill(9)
-        hits_csv.to_csv(f'{output_dir}/event{num}-hits.csv', index=False)
+        hits_csv.to_csv(f"{output_dir}/event{num}-hits.csv", index=False)
 
         # -particles.csv, use event_particles
-        # particles_csv = event_particles[['mcid','vx', 'vy', 'vz', 'p', 'pt', 'eta', 'phi', 'charge', 'nhits_velo']]
+        # particles_csv = event_particles[
+        #     [
+        #         "mcid",
+        #         "vx",
+        #         "vy",
+        #         "vz",
+        #         "p",
+        #         "pt",
+        #         "eta",
+        #         "phi",
+        #         "charge",
+        #         "nhits_velo",
+        #     ]
+        # ]
         particles_csv = event_particles  # save everything
-        particles_csv.rename(columns={'mcid': 'particle_id', 'charge': 'q', 'nhits_velo': 'nhits'}, inplace=True)
-        particles_csv['particle_id'] = particles_csv['particle_id'] + 1
-        pt = event_particles['pt']
-        eta = event_particles['eta']
-        phi = event_particles['phi']
-        px = pt * np.cos(phi) # Correctly transform coordinates
-        py = pt * np.sin(phi) # https://thespectrumofriemannium.wordpress.com/tag/pseudorapidity/
+        particles_csv.rename(
+            columns={"mcid": "particle_id", "charge": "q", "nhits_velo": "nhits"},
+            inplace=True,
+        )
+        particles_csv["particle_id"] = particles_csv["particle_id"] + 1
+        pt = event_particles["pt"]
+        eta = event_particles["eta"]
+        phi = event_particles["phi"]
+        px = pt * np.cos(phi)  # Correctly transform coordinates
+        py = pt * np.sin(
+            phi
+        )  # https://thespectrumofriemannium.wordpress.com/tag/pseudorapidity/
         pz = pt * np.sinh(eta)
-        particles_csv['px'] = px / 1000 # Convert momentum from MeV to GeV
-        particles_csv['py'] = py / 1000
-        particles_csv['pz'] = pz / 1000
-        particles_csv.drop(['p', 'pt', 'eta', 'phi'], axis=1, inplace=True)
+        particles_csv["px"] = px / 1000  # Convert momentum from MeV to GeV
+        particles_csv["py"] = py / 1000
+        particles_csv["pz"] = pz / 1000
+        # particles_csv.drop(["p", "pt", "eta", "phi"], axis=1, inplace=True)  # let's keep them
         num = str(event_num).zfill(9)
-        particles_csv.to_csv(f'{output_dir}/event{num}-particles.csv', index=False)
+        particles_csv.to_csv(f"{output_dir}/event{num}-particles.csv", index=False)
 
         # -truth.csv, use event_hits
-        truth_csv = event_hits[['mcid','x', 'y', 'z', 'p', 'pt', 'eta', 'phi', 'lhcbid']]
-        truth_csv.rename(columns={'mcid': 'particle_id', 'x': 'tx', 'y': 'ty', 'z': 'tz'}, inplace=True)
-        truth_csv['particle_id'] = truth_csv['particle_id'] + 1
-        pt = event_hits['pt']
-        eta = event_hits['eta']
-        phi = event_hits['phi']
-        px = pt * np.cos(phi) # Correctly transform coordinates
+        truth_csv = event_hits[
+            ["mcid", "x", "y", "z", "p", "pt", "eta", "phi", "lhcbid"]
+        ]
+        truth_csv.rename(
+            columns={"mcid": "particle_id", "x": "tx", "y": "ty", "z": "tz"},
+            inplace=True,
+        )
+        truth_csv["particle_id"] = truth_csv["particle_id"] + 1
+        pt = event_hits["pt"]
+        eta = event_hits["eta"]
+        phi = event_hits["phi"]
+        px = pt * np.cos(phi)  # Correctly transform coordinates
         py = pt * np.sin(phi)
         pz = pt * np.sinh(eta)
-        truth_csv['tpx'] = px / 1000 # Convert momentum from MeV to GeV
-        truth_csv['tpy'] = py / 1000
-        truth_csv['tpz'] = pz / 1000
-        truth_csv.drop(['p', 'pt', 'eta', 'phi'], axis=1, inplace=True)
+        truth_csv["tpx"] = px / 1000  # Convert momentum from MeV to GeV
+        truth_csv["tpy"] = py / 1000
+        truth_csv["tpz"] = pz / 1000
+        truth_csv.drop(["p", "pt", "eta", "phi"], axis=1, inplace=True)
         num = str(event_num).zfill(9)
-        truth_csv['hit_id'] = hits_csv.index + 1
-        truth_csv.to_csv(f'{output_dir}/event{num}-truth.csv', index=False)
+        truth_csv["hit_id"] = hits_csv.index + 1
+        truth_csv.to_csv(f"{output_dir}/event{num}-truth.csv", index=False)
 
-        num_true_hits = len(truth_csv[truth_csv['particle_id'] != 0].drop_duplicates(subset=['lhcbid']))
-        if (len(truth_csv['particle_id'].value_counts()) == 1 and truth_csv['particle_id'].value_counts().iloc[0] != 0):
+        num_true_hits = len(
+            truth_csv[truth_csv["particle_id"] != 0].drop_duplicates(subset=["lhcbid"])
+        )
+        if (
+            len(truth_csv["particle_id"].value_counts()) == 1
+            and truth_csv["particle_id"].value_counts().iloc[0] != 0
+        ):
             # Discard "fake" events
-            print(f'Discarding event {num}, contains only fake hits.')
+            print(f"Discarding event {num}, contains only fake hits.")
             # Discard events that only contain fake hits
-            os.remove(f'{output_dir}/event{num}-hits.csv')
-            os.remove(f'{output_dir}/event{num}-particles.csv')
-            os.remove(f'{output_dir}/event{num}-truth.csv')
+            os.remove(f"{output_dir}/event{num}-hits.csv")
+            os.remove(f"{output_dir}/event{num}-particles.csv")
+            os.remove(f"{output_dir}/event{num}-truth.csv")
         elif num_true_hits < num_true_hits_threshold:
             # Check the number of hits
-            print(f'Discarding event {num}, contains only {num_true_hits} true hits.')
+            print(f"Discarding event {num}, contains only {num_true_hits} true hits.")
             # Discard events
-            os.remove(f'{output_dir}/event{num}-hits.csv')
-            os.remove(f'{output_dir}/event{num}-particles.csv')
-            os.remove(f'{output_dir}/event{num}-truth.csv')
+            os.remove(f"{output_dir}/event{num}-hits.csv")
+            os.remove(f"{output_dir}/event{num}-particles.csv")
+            os.remove(f"{output_dir}/event{num}-truth.csv")
         else:
             i += 1  # If not discarded, count
-            print(f'Saving event {num}, {i}/{output_num}, contains {num_true_hits} true hits.')
+            print(
+                f"Saving event {num}, {i}/{output_num}, contains {num_true_hits} true hits."
+            )
 
         if i == output_num:
-            break   # If output number reached, break loop
-    
-    if i < output_num:
-        raise Exception(f'Not enough events found with more than {num_true_hits} true hits')
+            break  # If output number reached, break loop
 
+    if i < output_num:
+        raise Exception(
+            f"Not enough events found with more than {num_true_hits} true hits"
+        )
-- 
GitLab


From b605dce3a0a36986b51692dd5802a64d001c9283 Mon Sep 17 00:00:00 2001
From: anthonyc <acorreia@lpnhe.in2p3.fr>
Date: Thu, 23 Mar 2023 16:05:21 +0100
Subject: [PATCH 07/30] temporary pipeline for testing

---
 LHCb_Pipeline/full_pipeline_anthony.ipynb | 1110 +++++++++++----------
 1 file changed, 570 insertions(+), 540 deletions(-)

diff --git a/LHCb_Pipeline/full_pipeline_anthony.ipynb b/LHCb_Pipeline/full_pipeline_anthony.ipynb
index 77585245..13bcc803 100644
--- a/LHCb_Pipeline/full_pipeline_anthony.ipynb
+++ b/LHCb_Pipeline/full_pipeline_anthony.ipynb
@@ -19,14 +19,6 @@
    "execution_count": 1,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/acorreia/softwares/mambaforge/envs/etx4velo/lib/python3.8/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
@@ -186,7 +178,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -302,207 +294,226 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "  0%|          | 0/100 [00:00<?, ?it/s]INFO:Preparing event 004206889\n",
-      "INFO:Preparing event 004206896\n",
-      "INFO:Preparing event 004206891\n",
       "INFO:Preparing event 004206890\n",
+      "INFO:Preparing event 004206889\n",
+      "INFO:Preparing event 004206891\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a467fbebb2da45359ca424b4e5aa2f07",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/100 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "INFO:Preparing event 004206892\n",
       "INFO:Preparing event 004206893\n",
-      "INFO:Preparing event 004206894\n",
       "INFO:Preparing event 004206895\n",
+      "INFO:Preparing event 004206894\n",
+      "INFO:Preparing event 004206899\n",
       "INFO:Preparing event 004206897\n",
-      "INFO:Preparing event 004206901\n",
       "INFO:Preparing event 004206900\n",
-      "INFO:Preparing event 004206902\n",
+      "INFO:Preparing event 004206901\n",
+      "INFO:Preparing event 004206903\n",
       "INFO:Preparing event 004206904\n",
+      "INFO:Preparing event 004206902\n",
       "INFO:Preparing event 004206905\n",
+      "INFO:Preparing event 004206911\n",
       "INFO:Preparing event 004206907\n",
+      "INFO:Preparing event 004206912\n",
+      "INFO:Preparing event 004206914\n",
+      "INFO:Preparing event 004206913\n",
+      "INFO:Preparing event 004206910\n",
       "INFO:Preparing event 004206908\n",
+      "INFO:Preparing event 004206915\n",
       "INFO:Preparing event 004206909\n",
-      "INFO:Preparing event 004206910\n",
-      "INFO:Preparing event 004206913\n",
-      "INFO:Preparing event 004206911\n",
       "INFO:Preparing event 004206916\n",
-      "INFO:Preparing event 004206903\n",
-      "INFO:Preparing event 004206921\n",
       "INFO:Preparing event 004206917\n",
-      "INFO:Preparing event 004206915\n",
-      "INFO:Preparing event 004206914\n",
-      "INFO:Preparing event 004206912\n",
+      "INFO:Preparing event 004206918\n",
       "INFO:Preparing event 004206919\n",
-      "INFO:Preparing event 004206898\n",
       "INFO:Preparing event 004206920\n",
-      "INFO:Preparing event 004206918\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206890 with size (2, 742)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206908 with size (2, 691)\n",
+      "INFO:Preparing event 004206921\n",
+      "INFO:Preparing event 004206896\n",
+      "INFO:Preparing event 004206898\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206897 with size (2, 983)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206912 with size (2, 1089)\n",
       "INFO:Preparing event 004206922\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206907 with size (2, 803)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206892 with size (2, 545)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206902 with size (2, 1860)\n",
-      "INFO:Preparing event 004206899\n",
       "INFO:Preparing event 004206923\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206895 with size (2, 625)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206913 with size (2, 2754)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206910 with size (2, 228)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206899 with size (2, 650)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206914 with size (2, 488)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206891 with size (2, 1109)\n",
       "INFO:Modulewise truth graph built for data/preprocessed/event004206904 with size (2, 2437)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206893 with size (2, 1163)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206890 with size (2, 742)\n",
       "INFO:Preparing event 004206926\n",
       "INFO:Preparing event 004206925\n",
-      "INFO:Preparing event 004206928\n",
-      "INFO:Preparing event 004206924\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206921 with size (2, 2020)\n",
       "INFO:Preparing event 004206927\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206903 with size (2, 1492)\n",
-      "INFO:Preparing event 004206931\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206916 with size (2, 736)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206915 with size (2, 597)\n",
+      "INFO:Preparing event 004206924\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206895 with size (2, 625)\n",
+      "INFO:Preparing event 004206928\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206900 with size (2, 729)\n",
+      "INFO:Preparing event 004206929\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206894 with size (2, 1145)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206909 with size (2, 708)\n",
       "INFO:Preparing event 004206930\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206896 with size (2, 782)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206901 with size (2, 1779)\n",
       "INFO:Modulewise truth graph built for data/preprocessed/event004206917 with size (2, 1507)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206897 with size (2, 983)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206909 with size (2, 708)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206891 with size (2, 1109)\n",
-      "INFO:Preparing event 004206933\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206918 with size (2, 1355)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206913 with size (2, 2754)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206922 with size (2, 505)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206892 with size (2, 545)\n",
+      "INFO:Preparing event 004206935\n",
       "INFO:Preparing event 004206934\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206914 with size (2, 488)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206889 with size (2, 626)\n",
-      "INFO:Preparing event 004206929\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206911 with size (2, 875)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206900 with size (2, 729)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206901 with size (2, 1779)\n",
-      "INFO:Preparing event 004206937\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206896 with size (2, 782)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206923 with size (2, 848)\n",
       "INFO:Preparing event 004206936\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206905 with size (2, 2741)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206920 with size (2, 954)\n",
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-      "INFO:Modulewise truth graph built for data/preprocessed/event004206893 with size (2, 1163)\n",
-      "INFO:Preparing event 004206943\n",
-      "  1%|          | 1/100 [00:00<00:16,  5.84it/s]INFO:Preparing event 004206941\n",
-      "INFO:Preparing event 004206935\n",
+      "INFO:Preparing event 004206933\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206902 with size (2, 1860)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206915 with size (2, 597)\n",
+      "INFO:Preparing event 004206931\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206910 with size (2, 228)\n",
       "INFO:Preparing event 004206938\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206912 with size (2, 1089)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206919 with size (2, 1329)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206894 with size (2, 1145)\n",
+      "INFO:Preparing event 004206937\n",
+      "INFO:Preparing event 004206939\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206908 with size (2, 691)\n",
+      "INFO:Preparing event 004206940\n",
+      "INFO:Preparing event 004206941\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206907 with size (2, 803)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206916 with size (2, 736)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206911 with size (2, 875)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206889 with size (2, 626)\n",
+      "INFO:Preparing event 004206946\n",
       "INFO:Preparing event 004206944\n",
-      "INFO:Preparing event 004206947\n",
-      "INFO:Preparing event 004206949\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206919 with size (2, 1329)\n",
+      "INFO:Preparing event 004206942\n",
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-      "INFO:Preparing event 004206962\n",
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+      "INFO:Modulewise truth graph built for data/preprocessed/event004206925 with size (2, 1898)\n",
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+      "INFO:Modulewise truth graph built for data/preprocessed/event004206921 with size (2, 2020)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206924 with size (2, 1197)\n",
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+      "INFO:Modulewise truth graph built for data/preprocessed/event004206938 with size (2, 836)\n",
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-      "INFO:Modulewise truth graph built for data/preprocessed/event004206937 with size (2, 690)\n",
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-      "INFO:Modulewise truth graph built for data/preprocessed/event004206960 with size (2, 627)\n",
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+      "INFO:Preparing event 007212917\n",
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+      "INFO:Modulewise truth graph built for data/preprocessed/event004206941 with size (2, 2737)\n",
+      "INFO:Preparing event 007212916\n",
+      "INFO:Preparing event 007212919\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206960 with size (2, 627)\n",
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-      "INFO:Modulewise truth graph built for data/preprocessed/event004206963 with size (2, 776)\n",
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-      " 35%|███▌      | 35/100 [00:00<00:00, 124.40it/s]INFO:Preparing event 007212925\n",
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+      "INFO:Modulewise truth graph built for data/preprocessed/event004206949 with size (2, 734)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206947 with size (2, 3438)\n",
+      "INFO:Preparing event 007212926\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206940 with size (2, 1241)\n",
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+      "INFO:Modulewise truth graph built for data/preprocessed/event004206957 with size (2, 728)\n",
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-      "INFO:Modulewise truth graph built for data/preprocessed/event004206946 with size (2, 2389)\n",
+      "INFO:Preparing event 007212925\n",
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-      "INFO:Modulewise truth graph built for data/preprocessed/event004206957 with size (2, 728)\n",
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-      "INFO:Modulewise truth graph built for data/preprocessed/event004206925 with size (2, 1898)\n",
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-      "INFO:Modulewise truth graph built for data/preprocessed/event004206955 with size (2, 212)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event007212913 with size (2, 1389)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206958 with size (2, 1260)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206956 with size (2, 1019)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206946 with size (2, 2389)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206952 with size (2, 976)\n",
+      "INFO:Preparing event 007212931\n",
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-      "INFO:Modulewise truth graph built for data/preprocessed/event004206951 with size (2, 322)\n",
+      "INFO:Preparing event 007212928\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event007212915 with size (2, 982)\n",
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+      "INFO:Modulewise truth graph built for data/preprocessed/event004206962 with size (2, 1329)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206950 with size (2, 1073)\n",
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-      "INFO:Modulewise truth graph built for data/preprocessed/event004206952 with size (2, 976)\n",
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-      "INFO:Preparing event 007212931\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event007212915 with size (2, 982)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206963 with size (2, 776)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206965 with size (2, 1832)\n",
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+      "INFO:Modulewise truth graph built for data/preprocessed/event004206961 with size (2, 169)\n",
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+      "INFO:Modulewise truth graph built for data/preprocessed/event004206959 with size (2, 2069)\n",
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+      "INFO:Modulewise truth graph built for data/preprocessed/event007212933 with size (2, 722)\n",
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+      "INFO:Modulewise truth graph built for data/preprocessed/event007212913 with size (2, 1389)\n",
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+      "INFO:Modulewise truth graph built for data/preprocessed/event007212920 with size (2, 605)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event007212927 with size (2, 1295)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event007212934 with size (2, 1002)\n",
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+      "INFO:Modulewise truth graph built for data/preprocessed/event007212922 with size (2, 2242)\n",
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-      " 82%|████████▏ | 82/100 [00:00<00:00, 235.08it/s]INFO:Modulewise truth graph built for data/preprocessed/event007212933 with size (2, 722)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event007212917 with size (2, 1753)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event007212919 with size (2, 1630)\n",
       "INFO:Modulewise truth graph built for data/preprocessed/event007212936 with size (2, 2279)\n",
-      "100%|██████████| 100/100 [00:00<00:00, 222.39it/s]\n"
+      "INFO:Modulewise truth graph built for data/preprocessed/event007212925 with size (2, 1990)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event007212932 with size (2, 2200)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event007212928 with size (2, 2023)\n"
      ]
     }
    ],
@@ -3561,70 +3572,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>epoch</th>\n",
-       "      <th>train_loss</th>\n",
-       "      <th>val_loss</th>\n",
-       "      <th>eff</th>\n",
-       "      <th>pur</th>\n",
-       "      <th>current_lr</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0.808832</td>\n",
-       "      <td>0.788465</td>\n",
-       "      <td>0.538224</td>\n",
-       "      <td>0.419893</td>\n",
-       "      <td>0.0002</td>\n",
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-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0.760482</td>\n",
-       "      <td>0.759609</td>\n",
-       "      <td>0.497386</td>\n",
-       "      <td>0.483790</td>\n",
-       "      <td>0.0004</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   epoch  train_loss  val_loss       eff       pur  current_lr\n",
-       "0      0    0.808832  0.788465  0.538224  0.419893      0.0002\n",
-       "1      1    0.760482  0.759609  0.497386  0.483790      0.0004"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "NameError",
+     "evalue": "name 'gnn_trainer' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[3], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m gnn_metrics \u001b[39m=\u001b[39m get_training_metrics(gnn_trainer)\n\u001b[1;32m      3\u001b[0m \u001b[39m# gnn_metrics = get_training_metrics(gnn_trainer, METRICS_PATH) # Use when loading from checkpoint\u001b[39;00m\n\u001b[1;32m      5\u001b[0m gnn_metrics\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'gnn_trainer' is not defined"
+     ]
     }
    ],
    "source": [
@@ -3804,7 +3764,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {
     "scrolled": true
    },
@@ -3815,26 +3775,38 @@
      "text": [
       "INFO:------------ Step 6: Evaluating the track reconstruction performance ------------\n",
       "INFO:--------------------------- a) Loading labelled graphs ---------------------------\n",
-      "100%|██████████| 100/100 [00:02<00:00, 43.86it/s]\n",
+      "100%|██████████| 100/100 [00:01<00:00, 57.16it/s]\n",
       "INFO:--------------------- b) Calculating the performance metrics ---------------------\n",
-      "INFO:Number of reconstructed particles: 0\n",
+      "INFO:Number of reconstructed particles: 17074\n",
       "INFO:Number of particles: 23405\n",
-      "INFO:Number of matched tracks: 0\n",
-      "INFO:Number of tracks: 0\n",
-      "INFO:Number of duplicate reconstructed particles: 0\n"
+      "INFO:Number of matched tracks: 17157\n",
+      "INFO:Number of tracks: 17651\n",
+      "INFO:Number of duplicate reconstructed particles: 83\n",
+      "INFO:Efficiency: 0.730\n",
+      "INFO:Fake rate: 0.028\n",
+      "INFO:Duplication rate: 0.005\n",
+      "INFO:------------------------------ c) Plotting results ------------------------------\n"
      ]
     },
     {
-     "ename": "ZeroDivisionError",
-     "evalue": "division by zero",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[3], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m evaluated_events, reconstructed_particles, particles, matched_tracks, tracks \u001b[39m=\u001b[39m evaluate_candidates(CONFIG)\n\u001b[1;32m      3\u001b[0m \u001b[39m# send_telegram_message('Finished evaluation.', chat_id, api_key)\u001b[39;00m\n\u001b[1;32m      4\u001b[0m \n\u001b[1;32m      5\u001b[0m \u001b[39m# send_telegram_message(\"======================\", chat_id, api_key)\u001b[39;00m\n",
-      "File \u001b[0;32m~/Documents/PhD/tracking/etx4velo/LHCb_Pipeline/Scripts/Step_6_Evaluate_Reconstruction.py:163\u001b[0m, in \u001b[0;36mevaluate\u001b[0;34m(config_file)\u001b[0m\n\u001b[1;32m    161\u001b[0m \u001b[39m# Plot the results across pT and eta\u001b[39;00m\n\u001b[1;32m    162\u001b[0m eff \u001b[39m=\u001b[39m n_reconstructed_particles \u001b[39m/\u001b[39m n_particles\n\u001b[0;32m--> 163\u001b[0m fake_rate \u001b[39m=\u001b[39m \u001b[39m1\u001b[39m \u001b[39m-\u001b[39m (n_matched_tracks \u001b[39m/\u001b[39;49m n_tracks)\n\u001b[1;32m    164\u001b[0m dup_rate \u001b[39m=\u001b[39m n_dup_reconstructed_particles \u001b[39m/\u001b[39m n_reconstructed_particles\n\u001b[1;32m    166\u001b[0m logging\u001b[39m.\u001b[39minfo(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mEfficiency: \u001b[39m\u001b[39m{\u001b[39;00meff\u001b[39m:\u001b[39;00m\u001b[39m.3f\u001b[39m\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n",
-      "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero"
-     ]
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -3855,12 +3827,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
     "import montetracko as mt\n",
-    "from Scripts import Step_6_Evaluate_Reconstruction as evalreco\n"
+    "import montetracko.lhcb as mtb\n",
+    "from Scripts import Step_6_Evaluate_Reconstruction as evalreco\n",
+    "mt_config = {\n",
+    "    \"aliases\": {\n",
+    "        \"n_hits\": \"nhits\",\n",
+    "        \"hit_id\": \"lhcbid\",\n",
+    "    },\n",
+    "    \"fake_particle_id\": 0,\n",
+    "}"
    ]
   },
   {
@@ -3895,7 +3875,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3906,18 +3886,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
     "graph_file = all_graph_files[0]\n",
-    "reconstruction_df = evalreco.load_reconstruction_df(graph_file)\n",
-    "particles_df = evalreco.load_particles_df(graph_file)\n"
+    "\n",
+    "# particles_df = evalreco.load_particles_df(graph_file)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3928,64 +3908,169 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pyarrow as pa\n",
-    "import pyarrow.csv as pac"
+    "import pyarrow.csv as pac\n",
+    "from tqdm.notebook import tqdm"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
-    "event_id_str = op.basename(graph_file)\n",
-    "path_particle_file = op.join(\n",
-    "    preprocessed_dir, f\"event{event_id_str}-particles.csv\"\n",
-    ")\n",
-    "path_hits_file = op.join(\n",
-    "    preprocessed_dir, f\"event{event_id_str}-hits.csv\"\n",
-    ")"
+    "# %%timeit\n",
+    "\n",
+    "# dict_paths = {\n",
+    "#     \"particles\": op.join(preprocessed_dir, \"event{event_id_str}-particles.csv\"),\n",
+    "#     \"hits_particles\": op.join(preprocessed_dir, \"event{event_id_str}-truth.csv\"),\n",
+    "# }\n",
+    "# dict_convert_options = {\n",
+    "#     \"hits_particles\": pac.ConvertOptions(include_columns=[\"particle_id\", \"lhcbid\"]),\n",
+    "# }\n",
+    "# dict_columns = {\n",
+    "#     \"hits_particles\": [\"particle_id\", \"lhcbid\"],\n",
+    "# }\n",
+    "\n",
+    "# dict_list_dataframes = {\n",
+    "#     \"particles\": [],\n",
+    "#     \"hits_particles\": [],\n",
+    "#     \"tracks\": [],\n",
+    "# }\n",
+    "\n",
+    "# first_iteration: bool = True\n",
+    "# for graph_file in tqdm(all_graph_files):\n",
+    "#     # Event ID from graph file name\n",
+    "#     event_id_str = op.basename(graph_file)\n",
+    "#     event_id = int(event_id_str)\n",
+    "\n",
+    "#     # Load dataframe of tracks\n",
+    "#     reconstruction_df = evalreco.load_reconstruction_df(graph_file)\n",
+    "#     df_tracks = reconstruction_df[[\"hit_id\", \"track_id\"]].drop_duplicates()\n",
+    "\n",
+    "#     # Particles and hit-particle truth association\n",
+    "#     tables_event = {}\n",
+    "#     for name, path in dict_paths.items():\n",
+    "#         tables_event[name] = pac.read_csv(\n",
+    "#             path.format(event_id_str=event_id_str),\n",
+    "#             convert_options=dict_convert_options.get(name),\n",
+    "#         )\n",
+    "\n",
+    "#     if first_iteration:\n",
+    "#         first_iteration = False\n",
+    "#         for name, table in tables_event.items():\n",
+    "#             dict_convert_options[name] = pac.ConvertOptions(\n",
+    "#                 column_types=table.schema,\n",
+    "#                 include_columns=table.column_names,\n",
+    "#             )\n",
+    "\n",
+    "#     # Retrieve read options\n",
+    "#     if not dict_convert_options:\n",
+    "#         for name, table in tabtables_eventles.items():\n",
+    "#             dict_convert_options[name] = pac.ConvertOptions()\n",
+    "\n",
+    "#     # Convert to pandas DataFrame\n",
+    "#     dataframes_event = {name: table.to_pandas() for name, table in tables_event.items()}\n",
+    "#     dataframes_event[\"tracks\"] = df_tracks\n",
+    "\n",
+    "#     # Rename column\n",
+    "#     dataframes_event[\"hits_particles\"].rename(columns={\"lhcbid\": \"hit_id\"}, inplace=True)\n",
+    "\n",
+    "#     # Add event ID column\n",
+    "#     for dataframe in dataframes_event.values():\n",
+    "#         dataframe[\"event_id\"] = event_id\n",
+    "\n",
+    "#     # Append to list for concatenation\n",
+    "#     for name, dataframe in dataframes_event.items():\n",
+    "#         dict_list_dataframes[name].append(dataframe)\n",
+    "\n",
+    "# dataframes = {\n",
+    "#     name: pd.concat(list_dataframes)\n",
+    "#     for name, list_dataframes in dict_list_dataframes.items()\n",
+    "# }"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b8d68af13f204cb6b52b2244a0fef3f3",
+       "version_major": 2,
+       "version_minor": 0
+      },
       "text/plain": [
-       "'data/track_building_processed/007212930'"
+       "  0%|          | 0/100 [00:00<?, ?it/s]"
       ]
      },
-     "execution_count": 46,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "graph_file"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_particles = pac.read_csv(path_particle_file).to_pandas()\n",
-    "df_hits = pac.read_csv(\n",
-    "    path_hits_file\n",
-    ").to_pandas()\n"
+    "dict_paths = {\n",
+    "    \"particles\": op.join(preprocessed_dir, \"event{event_id_str}-particles.csv\"),\n",
+    "    \"hits_particles\": op.join(preprocessed_dir, \"event{event_id_str}-truth.csv\"),\n",
+    "}\n",
+    "dict_list_dataframes = {\n",
+    "    \"particles\": [],\n",
+    "    \"hits_particles\": [],\n",
+    "    \"tracks\": [],\n",
+    "}\n",
+    "dict_convert_options = {\n",
+    "    \"hits_particles\": pac.ConvertOptions(\n",
+    "        include_columns=[\"particle_id\", \"lhcbid\", \"hit_id\"]\n",
+    "    ),\n",
+    "}\n",
+    "\n",
+    "for graph_file in tqdm(all_graph_files):\n",
+    "    # Event ID from graph file name\n",
+    "    event_id_str = op.basename(graph_file)\n",
+    "    event_id = int(event_id_str)\n",
+    "\n",
+    "    # Load dataframe of tracks\n",
+    "    reconstruction_df = evalreco.load_reconstruction_df(graph_file)\n",
+    "    df_tracks = reconstruction_df[[\"hit_id\", \"track_id\"]].drop_duplicates()\n",
+    "\n",
+    "    # Particles and hit-particle truth association\n",
+    "    dataframes_event = {}\n",
+    "    for name, path in dict_paths.items():\n",
+    "        dataframes_event[name] = pac.read_csv(\n",
+    "            path.format(event_id_str=event_id_str),\n",
+    "            convert_options=dict_convert_options.get(name),\n",
+    "        ).to_pandas()\n",
+    "    dataframes_event[\"tracks\"] = df_tracks\n",
+    "\n",
+    "    # Add event ID column\n",
+    "    for dataframe in dataframes_event.values():\n",
+    "        dataframe[\"event_id\"] = event_id\n",
+    "\n",
+    "    dataframes_event[\"tracks\"][\"lhcbid\"] = mt.array_utils.transform.replace(\n",
+    "        array=np.asarray(dataframes_event[\"tracks\"][\"hit_id\"]),\n",
+    "        values_to_replace=np.asarray(dataframes_event[\"hits_particles\"][\"hit_id\"]),\n",
+    "        replacement_values=np.asarray(dataframes_event[\"hits_particles\"][\"lhcbid\"]),\n",
+    "    )\n",
+    "\n",
+    "    # Append to list for concatenation\n",
+    "    for name, dataframe in dataframes_event.items():\n",
+    "        dict_list_dataframes[name].append(dataframe)\n",
+    "\n",
+    "dataframes = {\n",
+    "    name: pd.concat(list_dataframes)\n",
+    "    for name, list_dataframes in dict_list_dataframes.items()\n",
+    "}\n",
+    "dataframes[\"hits_particles\"].drop(\"hit_id\", axis=1, inplace=True)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -4009,366 +4094,311 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>x</th>\n",
-       "      <th>y</th>\n",
-       "      <th>z</th>\n",
-       "      <th>module_id</th>\n",
-       "      <th>hit_id</th>\n",
+       "      <th>particle_id</th>\n",
+       "      <th>lhcbid</th>\n",
+       "      <th>event_id</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>17.90920</td>\n",
-       "      <td>-32.687800</td>\n",
-       "      <td>-288.141</td>\n",
-       "      <td>0</td>\n",
-       "      <td>179847</td>\n",
+       "      <td>2829</td>\n",
+       "      <td>671094942</td>\n",
+       "      <td>7212930</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>6.16420</td>\n",
-       "      <td>-17.403700</td>\n",
-       "      <td>-288.141</td>\n",
-       "      <td>0</td>\n",
-       "      <td>179848</td>\n",
+       "      <td>2259</td>\n",
+       "      <td>604074443</td>\n",
+       "      <td>7212930</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>-16.64530</td>\n",
-       "      <td>-17.423100</td>\n",
-       "      <td>-286.859</td>\n",
        "      <td>0</td>\n",
-       "      <td>179849</td>\n",
+       "      <td>537154464</td>\n",
+       "      <td>7212930</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>-19.79550</td>\n",
-       "      <td>-17.384200</td>\n",
-       "      <td>-286.859</td>\n",
-       "      <td>0</td>\n",
-       "      <td>179850</td>\n",
+       "      <td>501</td>\n",
+       "      <td>537143928</td>\n",
+       "      <td>7212930</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>13.88400</td>\n",
-       "      <td>-27.340300</td>\n",
-       "      <td>-288.141</td>\n",
-       "      <td>0</td>\n",
-       "      <td>179851</td>\n",
+       "      <td>2828</td>\n",
+       "      <td>536908207</td>\n",
+       "      <td>7212930</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1554</th>\n",
-       "      <td>4.70580</td>\n",
-       "      <td>-2.839030</td>\n",
-       "      <td>750.641</td>\n",
-       "      <td>25</td>\n",
-       "      <td>181401</td>\n",
+       "      <th>2265</th>\n",
+       "      <td>1858</td>\n",
+       "      <td>591324376</td>\n",
+       "      <td>4206903</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1555</th>\n",
-       "      <td>4.88080</td>\n",
-       "      <td>-2.625130</td>\n",
-       "      <td>750.641</td>\n",
-       "      <td>25</td>\n",
-       "      <td>181402</td>\n",
+       "      <th>2266</th>\n",
+       "      <td>2236</td>\n",
+       "      <td>590901436</td>\n",
+       "      <td>4206903</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1556</th>\n",
-       "      <td>18.20090</td>\n",
-       "      <td>-4.355780</td>\n",
-       "      <td>750.641</td>\n",
-       "      <td>25</td>\n",
-       "      <td>181403</td>\n",
+       "      <th>2267</th>\n",
+       "      <td>601</td>\n",
+       "      <td>658027554</td>\n",
+       "      <td>4206903</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1557</th>\n",
-       "      <td>7.42816</td>\n",
-       "      <td>-0.194454</td>\n",
-       "      <td>750.641</td>\n",
-       "      <td>25</td>\n",
-       "      <td>181404</td>\n",
+       "      <th>2268</th>\n",
+       "      <td>3974</td>\n",
+       "      <td>591239709</td>\n",
+       "      <td>4206903</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1558</th>\n",
-       "      <td>7.38926</td>\n",
-       "      <td>-0.155563</td>\n",
-       "      <td>749.359</td>\n",
-       "      <td>25</td>\n",
-       "      <td>181405</td>\n",
+       "      <th>2269</th>\n",
+       "      <td>3250</td>\n",
+       "      <td>591260403</td>\n",
+       "      <td>4206903</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>1559 rows × 5 columns</p>\n",
+       "<p>194437 rows × 3 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "             x          y        z  module_id  hit_id\n",
-       "0     17.90920 -32.687800 -288.141          0  179847\n",
-       "1      6.16420 -17.403700 -288.141          0  179848\n",
-       "2    -16.64530 -17.423100 -286.859          0  179849\n",
-       "3    -19.79550 -17.384200 -286.859          0  179850\n",
-       "4     13.88400 -27.340300 -288.141          0  179851\n",
-       "...        ...        ...      ...        ...     ...\n",
-       "1554   4.70580  -2.839030  750.641         25  181401\n",
-       "1555   4.88080  -2.625130  750.641         25  181402\n",
-       "1556  18.20090  -4.355780  750.641         25  181403\n",
-       "1557   7.42816  -0.194454  750.641         25  181404\n",
-       "1558   7.38926  -0.155563  749.359         25  181405\n",
+       "      particle_id     lhcbid  event_id\n",
+       "0            2829  671094942   7212930\n",
+       "1            2259  604074443   7212930\n",
+       "2               0  537154464   7212930\n",
+       "3             501  537143928   7212930\n",
+       "4            2828  536908207   7212930\n",
+       "...           ...        ...       ...\n",
+       "2265         1858  591324376   4206903\n",
+       "2266         2236  590901436   4206903\n",
+       "2267          601  658027554   4206903\n",
+       "2268         3974  591239709   4206903\n",
+       "2269         3250  591260403   4206903\n",
        "\n",
-       "[1559 rows x 5 columns]"
+       "[194437 rows x 3 columns]"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df_hits"
+    "dataframes[\"hits_particles\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trackEvaluator = mt.TrackMatcher(config=mt_config).full_matching(\n",
+    "    df_events_hits_particles=dataframes[\"hits_particles\"],\n",
+    "    df_events_particles=dataframes[\"particles\"],\n",
+    "    df_events_tracks_hits=dataframes[\"tracks\"],\n",
+    "    matching_condition=mt.matchcond.MinMatchingFraction(0.7),\n",
+    "    track_condition=mt.matchcond.MinLengthTrack(2),\n",
+    ")\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>particle_id</th>\n",
-       "      <th>vx</th>\n",
-       "      <th>vy</th>\n",
-       "      <th>vz</th>\n",
-       "      <th>q</th>\n",
-       "      <th>nhits</th>\n",
-       "      <th>px</th>\n",
-       "      <th>py</th>\n",
-       "      <th>pz</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>356</td>\n",
-       "      <td>-0.0041</td>\n",
-       "      <td>-0.0602</td>\n",
-       "      <td>10.4652</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>-0.053259</td>\n",
-       "      <td>-0.267060</td>\n",
-       "      <td>-10.240854</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>363</td>\n",
-       "      <td>-0.0041</td>\n",
-       "      <td>-0.0602</td>\n",
-       "      <td>10.4652</td>\n",
-       "      <td>-1</td>\n",
-       "      <td>6</td>\n",
-       "      <td>0.213420</td>\n",
-       "      <td>-0.242910</td>\n",
-       "      <td>-3.359714</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>366</td>\n",
-       "      <td>13.2756</td>\n",
-       "      <td>-4.5004</td>\n",
-       "      <td>-70.5277</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0.343090</td>\n",
-       "      <td>-0.106520</td>\n",
-       "      <td>-2.660824</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>368</td>\n",
-       "      <td>-0.0041</td>\n",
-       "      <td>-0.0602</td>\n",
-       "      <td>10.4652</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>-0.638633</td>\n",
-       "      <td>0.837760</td>\n",
-       "      <td>-19.624627</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>369</td>\n",
-       "      <td>-0.0041</td>\n",
-       "      <td>-0.0602</td>\n",
-       "      <td>10.4652</td>\n",
-       "      <td>-1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>0.109862</td>\n",
-       "      <td>0.450230</td>\n",
-       "      <td>-5.729871</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>468</th>\n",
-       "      <td>2977</td>\n",
-       "      <td>0.0058</td>\n",
-       "      <td>-0.0229</td>\n",
-       "      <td>-49.6429</td>\n",
-       "      <td>-1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0.296190</td>\n",
-       "      <td>0.188880</td>\n",
-       "      <td>-1.590305</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>469</th>\n",
-       "      <td>2982</td>\n",
-       "      <td>0.0058</td>\n",
-       "      <td>-0.0229</td>\n",
-       "      <td>-49.6429</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>0.099371</td>\n",
-       "      <td>0.733550</td>\n",
-       "      <td>-5.143205</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>470</th>\n",
-       "      <td>2999</td>\n",
-       "      <td>0.0058</td>\n",
-       "      <td>-0.0229</td>\n",
-       "      <td>-49.6429</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>-0.322370</td>\n",
-       "      <td>-0.106259</td>\n",
-       "      <td>0.536697</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>471</th>\n",
-       "      <td>3002</td>\n",
-       "      <td>0.0058</td>\n",
-       "      <td>-0.0229</td>\n",
-       "      <td>-49.6429</td>\n",
-       "      <td>-1</td>\n",
-       "      <td>8</td>\n",
-       "      <td>-0.320081</td>\n",
-       "      <td>0.144409</td>\n",
-       "      <td>2.521247</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>472</th>\n",
-       "      <td>3003</td>\n",
-       "      <td>-20.5282</td>\n",
-       "      <td>8.9883</td>\n",
-       "      <td>112.1490</td>\n",
-       "      <td>-1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>-0.001380</td>\n",
-       "      <td>-0.000800</td>\n",
-       "      <td>0.002110</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>473 rows × 9 columns</p>\n",
-       "</div>"
-      ],
       "text/plain": [
-       "     particle_id       vx      vy        vz  q  nhits        px        py  \\\n",
-       "0            356  -0.0041 -0.0602   10.4652  1      3 -0.053259 -0.267060   \n",
-       "1            363  -0.0041 -0.0602   10.4652 -1      6  0.213420 -0.242910   \n",
-       "2            366  13.2756 -4.5004  -70.5277  1      4  0.343090 -0.106520   \n",
-       "3            368  -0.0041 -0.0602   10.4652  1      5 -0.638633  0.837760   \n",
-       "4            369  -0.0041 -0.0602   10.4652 -1      5  0.109862  0.450230   \n",
-       "..           ...      ...     ...       ... ..    ...       ...       ...   \n",
-       "468         2977   0.0058 -0.0229  -49.6429 -1      2  0.296190  0.188880   \n",
-       "469         2982   0.0058 -0.0229  -49.6429  1      5  0.099371  0.733550   \n",
-       "470         2999   0.0058 -0.0229  -49.6429  1      3 -0.322370 -0.106259   \n",
-       "471         3002   0.0058 -0.0229  -49.6429 -1      8 -0.320081  0.144409   \n",
-       "472         3003 -20.5282  8.9883  112.1490 -1      1 -0.001380 -0.000800   \n",
-       "\n",
-       "            pz  \n",
-       "0   -10.240854  \n",
-       "1    -3.359714  \n",
-       "2    -2.660824  \n",
-       "3   -19.624627  \n",
-       "4    -5.729871  \n",
-       "..         ...  \n",
-       "468  -1.590305  \n",
-       "469  -5.143205  \n",
-       "470   0.536697  \n",
-       "471   2.521247  \n",
-       "472   0.002110  \n",
-       "\n",
-       "[473 rows x 9 columns]"
+       "0.9149435564993853"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df_particles.to_pandas()"
+    "trackEvaluator.compute_metric(\n",
+    "    \"efficiency\",\n",
+    "    category=mtb.category.allen_categories[0],\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TrackChecker output                               :       678/    18902   3.59% ghosts\n",
+      "01_velo                                           :      8186/     8947  91.49% ( 92.65%),        86 (  1.04%) clones, pur  99.19%, hit eff  94.86% \n",
+      "02_long                                           :      4846/     5143  94.23% ( 95.52%),        47 (  0.96%) clones, pur  99.28%, hit eff  95.94% \n",
+      "03_long_P>5GeV                                    :      3161/     3330  94.92% ( 96.32%),        34 (  1.06%) clones, pur  99.32%, hit eff  96.42% \n",
+      "04_long_strange                                   :         0/        0    nan% (   nan%),         0 (   nan%) clones, pur    nan%, hit eff    nan% \n",
+      "05_long_strange_P>5GeV                            :         0/        0    nan% (   nan%),         0 (   nan%) clones, pur    nan%, hit eff    nan% \n",
+      "06_long_fromB                                     :         0/        0    nan% (   nan%),         0 (   nan%) clones, pur    nan%, hit eff    nan% \n",
+      "07_long_fromB_P>5GeV                              :         0/        0    nan% (   nan%),         0 (   nan%) clones, pur    nan%, hit eff    nan% \n",
+      "08_long_electrons                                 :       253/      381  66.40% ( 70.75%),         6 (  2.32%) clones, pur  98.26%, hit eff  74.89% \n",
+      "09_long_fromB_electrons                           :         0/        0    nan% (   nan%),         0 (   nan%) clones, pur    nan%, hit eff    nan% \n",
+      "10_long_fromB_electrons_P>5GeV                    :         0/        0    nan% (   nan%),         0 (   nan%) clones, pur    nan%, hit eff    nan% \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "trackEvaluator.print_report(\n",
+    "    reporter=mt.AllenReporter(),\n",
+    "    categories=mtb.category.allen_categories,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+----------------------+--------------+----------------------+------------+--------------------------+----------------------+\n",
+      "| Categories           | Efficiency   | Average efficiency   | % clones   | Average hit efficiency   | Average hit purity   |\n",
+      "+======================+==============+======================+============+==========================+======================+\n",
+      "| Velo, only electrons | 60.08%       | 63.28%               | 3.84%      | 98.22%                   | 72.04%               |\n",
+      "+----------------------+--------------+----------------------+------------+--------------------------+----------------------+\n",
+      "| Velo, no electrons   | 91.49%       | 92.65%               | 1.04%      | 99.19%                   | 94.86%               |\n",
+      "+----------------------+--------------+----------------------+------------+--------------------------+----------------------+\n",
+      "+--------------+------------+------------+------------+\n",
+      "| Categories   |   # ghosts | # tracks   | % ghosts   |\n",
+      "+==============+============+============+============+\n",
+      "| Everything   |        678 | 18,902     | 3.59%      |\n",
+      "+--------------+------------+------------+------------+\n"
+     ]
+    }
+   ],
+   "source": [
+    "trackEvaluator.print_report(\n",
+    "    reporter=mt.TabReporter(\n",
+    "        [\n",
+    "            \"efficiency\",\n",
+    "            \"efficiency_per_event\",\n",
+    "            \"clone_rate\",\n",
+    "            \"hit_purity_per_candidate\",\n",
+    "            \"hit_efficiency_per_candidate\",\n",
+    "        ],\n",
+    "        mode=\"markdown\",\n",
+    "        tablefmt=\"grid\",\n",
+    "    ),\n",
+    "    categories=mtb.category.velo_categories,\n",
+    ")\n",
+    "trackEvaluator.print_report(\n",
+    "    reporter=mt.TabReporter(\n",
+    "        metric_names=[\"n_ghosts\", \"n_tracks\", \"ghost_rate\"],\n",
+    "        mode=\"markdown\",\n",
+    "        tablefmt=\"grid\",\n",
+    "    ),\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "data": {
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",
       "text/plain": [
-       "'data/track_building_processed/007212930'"
+       "<Figure size 3200x2400 with 32 Axes>"
       ]
      },
-     "execution_count": 14,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "graph_file"
+    "import matplotlib as mpl\n",
+    "# mpl.rcParams.update(**mpl.rcParamsDefault)\n",
+    "mpl.rcParams.update(\n",
+    "    **{\n",
+    "        # Font\n",
+    "        'font.family': 'serif',\n",
+    "        'font.serif': 'Computer Modern Roman',\n",
+    "        # Latex\n",
+    "        'text.latex.preamble': r'\\usepackage{amsmath}',\n",
+    "        'text.usetex': True,  # Put to False to disable Latex\n",
+    "        # Fontsizes    \n",
+    "        'legend.fontsize': 20,\n",
+    "        'axes.titlesize': 24,\n",
+    "        'xtick.labelsize': 20,\n",
+    "        'ytick.labelsize': 20,\n",
+    "        'axes.labelsize': 25,\n",
+    "        'font.size': 20,    \n",
+    "        # Lines\n",
+    "        'lines.markersize': 6.,\n",
+    "        'lines.linestyle': '-',\n",
+    "        'lines.linewidth': 1.5,\n",
+    "        # Figure\n",
+    "        \"figure.figsize\": (8, 6),\n",
+    "        # \"figure.dpi\": 200,\n",
+    "        # Ticks\n",
+    "        # Ticks settings\n",
+    "        \"xtick.direction\": \"in\",\n",
+    "        \"ytick.direction\": \"in\",\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "columns = [\"pt\", \"p\", \"eta\", \"vz\"]\n",
+    "\n",
+    "metric_names = [\n",
+    "    \"efficiency\",\n",
+    "    # \"ghost_rate\",\n",
+    "    \"clone_rate\",\n",
+    "    \"hit_purity_per_candidate\",\n",
+    "    \"hit_efficiency_per_candidate\",\n",
+    "]\n",
+    "\n",
+    "\n",
+    "# _ = trackEvaluator.plot_histogram(\n",
+    "#     column=\"vz\",\n",
+    "#     metric_name=\"efficiency\",\n",
+    "#     column_label=column_labels[\"vz\"],\n",
+    "#     range=column_ranges[\"vz\"],\n",
+    "#     category=mtb.category.velo_category,\n",
+    "#     bins=\"auto\",\n",
+    "# )\n",
+    "\n",
+    "_ = trackEvaluator.plot_histograms(\n",
+    "    columns=columns,\n",
+    "    metric_names=metric_names,\n",
+    "    column_labels=column_labels,\n",
+    "    column_ranges=column_ranges,\n",
+    "    category=mtb.category.velo_category,\n",
+    "    bins=20,\n",
+    ")\n",
+    "\n"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
-- 
GitLab


From 4609f65e9fb26732af9150f22df69e42a08c710d Mon Sep 17 00:00:00 2001
From: anthonyc <acorreia@lpnhe.in2p3.fr>
Date: Thu, 23 Mar 2023 17:16:22 +0100
Subject: [PATCH 08/30] tidy up pre-processing

---
 .../Processing/utils/preprocessing.py         | 293 +++++++++++-------
 1 file changed, 180 insertions(+), 113 deletions(-)

diff --git a/LHCb_Pipeline/Processing/utils/preprocessing.py b/LHCb_Pipeline/Processing/utils/preprocessing.py
index e78bd795..524d089d 100644
--- a/LHCb_Pipeline/Processing/utils/preprocessing.py
+++ b/LHCb_Pipeline/Processing/utils/preprocessing.py
@@ -1,8 +1,101 @@
-import os, shutil
+import typing
+import os
+import shutil
 import numpy as np
 import pandas as pd
 
 
+def clear_existing_directories(directory: str):
+    """Delete all files and directories within a given directory.
+
+    Args:
+        directory: The directory to clear.
+    """
+    for filename in os.listdir(directory):
+        file_path = os.path.join(directory, filename)
+        try:
+            if os.path.isfile(file_path) or os.path.islink(file_path):
+                os.unlink(file_path)
+            elif os.path.isdir(file_path):
+                shutil.rmtree(file_path)
+        except Exception as e:
+            print("Failed to delete %s. Reason: %s" % (file_path, e))
+
+
+def load_dataframes(indir: str) -> typing.Tuple[pd.DataFrame, pd.DataFrame]:
+    """Load the dataframes of hits and particles. This function is also used
+    in the validation step.
+
+    Args:
+        indir: directory where the dataframes are saved
+
+    Returns:
+        A 2-tuple containing the dataframe of hits and the dataframes of particles
+
+    Notes:
+        The function also defines the column ``particle_id = mcid + 1``
+        in both dataframes.
+    """
+    particles = pd.read_parquet(f"{indir}/mc_particles.parquet.lz4")
+    hits = pd.read_parquet(f"{indir}/hits_velo.parquet.lz4")
+
+    # Define `particle_id = mcid + 1` directly in the original dataframes
+    particles["particle_id"] = particles["mcid"] + 1
+    hits["particle_id"] = hits["mcid"] + 1
+
+    return particles, hits
+
+
+def enough_true_hits(
+    event_hits: pd.DataFrame,
+    num_true_hits_threshold: int,
+    event_id_str: str,
+    num_events: int,
+    required_num_events: int,
+) -> bool:
+    """Check whether an event has enough true hits to be saved.
+
+    Args:
+        event_hits: DataFrame of all hits for an event.
+        num_true_hits_threshold: Minimum number of true hits required for the event
+            to be saved.
+        event_id_str: String representation of the event ID.
+        num_events: The current number of saved events.
+        required_num_events: The desired number of saved events.
+
+    Returns:
+        ``True`` if the event has enough true hits to be saved, ``False`` otherwise.
+
+    Notes:
+        The function checks the number of true hits in an event and compares it
+        with the given threshold.
+        If the number of true hits is equal to 0, the event is discarded as
+        it contains only fake hits.
+        If the number of true hits is below the given threshold,
+        the event is discarded as it does not contain enough true hits.
+        Otherwise, the event is saved as the function returns ``True``.
+    """
+    #: Number of true hits for this event
+    num_true_hits = len(
+        event_hits[event_hits["particle_id"] != 0].drop_duplicates(subset=["lhcbid"])
+    )
+    if num_true_hits == 0:
+        print(f"Discarding event {event_id_str}, contains only fake hits.")
+        return False
+    elif num_true_hits < num_true_hits_threshold:
+        print(
+            f"Discarding event {event_id_str}, contains only {num_true_hits} true hits",
+            f"below {num_true_hits_threshold} threshold",
+        )
+        return False
+    else:
+        print(
+            f"Saving event {event_id_str}, {num_events+1}/{required_num_events},",
+            f"contains {num_true_hits} true hits.",
+        )
+        return True
+
+
 def preprocess(
     input_dir: str,
     output_dir: str,
@@ -10,144 +103,118 @@ def preprocess(
     clear_directories: bool = True,
     num_true_hits_threshold: int = 0,
 ):
-    """
-    Preprocess the first `output_num` events in the input files,
+    """Preprocess the first `output_num` events in the input files,
     into the form of the TrackML dataset.
     Remove any events that contain only fake hits.
     """
-
     os.makedirs(input_dir, exist_ok=True)
     os.makedirs(output_dir, exist_ok=True)
 
     # Clear contents
     if clear_directories:
-        folder = output_dir
-        for filename in os.listdir(folder):
-            file_path = os.path.join(folder, filename)
-            try:
-                if os.path.isfile(file_path) or os.path.islink(file_path):
-                    os.unlink(file_path)
-                elif os.path.isdir(file_path):
-                    shutil.rmtree(file_path)
-            except Exception as e:
-                print("Failed to delete %s. Reason: %s" % (file_path, e))
-
-    # hits = pd.read_csv(f'{input_dir}/hits_velo.csv') # Read hits
-    # particles = pd.read_csv(f'{input_dir}/mc_particles.csv') # Read MC particles
-    hits = pd.read_parquet(f"{input_dir}/hits_velo.parquet.lz4")
-    particles = pd.read_parquet(f"{input_dir}/mc_particles.parquet.lz4")
-    hits = hits.merge(particles, on=["event", "mcid"], how="left")  # Merge
+        clear_existing_directories(output_dir)
+
+    # Load dataframes
+    particles, hits = load_dataframes(input_dir)
+
+    # Correctly transform coordinates
+    # source: https://thespectrumofriemannium.wordpress.com/tag/pseudorapidity/
+    pt, eta, phi = particles["pt"], particles["eta"], particles["phi"]
+    px = pt * np.cos(phi)
+    py = pt * np.sin(phi)
+    pz = pt * np.sinh(eta)
+    # Convert momentum from MeV to GeV
+    particles["px"] = px / 1000
+    particles["py"] = py / 1000
+    particles["pz"] = pz / 1000
+
+    # Add truth particle information to the dataframe of hits
+    hits = hits.merge(particles, on=["event", "particle_id"], how="left")  # Merge
     # NB: left join!: keep fake hits
 
+    # Filter
     # Remove hits with has_velo == 0
     # hits = hits[hits["has_velo"] == 1]
-
     # Remove electrons and positrons ?
     # hits = hits[np.abs(hits["pid"]) != 11]
 
     event_list = hits["event"].unique()  # The order is not mixed
-
     i = 0  # Count the number of events outputed
     for event_num in event_list:
+        if i == output_num:
+            break  # If output number reached, break loop
+
         event_hits = hits[hits["event"] == event_num]
         event_particles = particles[particles["event"] == event_num]
+        #: String representation of the event ID
+        event_id_str = str(event_num).zfill(9)
 
-        # -hits.csv, use event_hits
-        hits_csv = event_hits[["x", "y", "z", "plane"]]
-        hits_csv.rename(columns={"plane": "module_id"}, inplace=True)
-        hits_csv["hit_id"] = hits_csv.index + 1  # TODO: this is not accurate 
-        num = str(event_num).zfill(9)
-        hits_csv.to_csv(f"{output_dir}/event{num}-hits.csv", index=False)
-
-        # -particles.csv, use event_particles
-        # particles_csv = event_particles[
-        #     [
-        #         "mcid",
-        #         "vx",
-        #         "vy",
-        #         "vz",
-        #         "p",
-        #         "pt",
-        #         "eta",
-        #         "phi",
-        #         "charge",
-        #         "nhits_velo",
-        #     ]
-        # ]
-        particles_csv = event_particles  # save everything
-        particles_csv.rename(
-            columns={"mcid": "particle_id", "charge": "q", "nhits_velo": "nhits"},
-            inplace=True,
-        )
-        particles_csv["particle_id"] = particles_csv["particle_id"] + 1
-        pt = event_particles["pt"]
-        eta = event_particles["eta"]
-        phi = event_particles["phi"]
-        px = pt * np.cos(phi)  # Correctly transform coordinates
-        py = pt * np.sin(
-            phi
-        )  # https://thespectrumofriemannium.wordpress.com/tag/pseudorapidity/
-        pz = pt * np.sinh(eta)
-        particles_csv["px"] = px / 1000  # Convert momentum from MeV to GeV
-        particles_csv["py"] = py / 1000
-        particles_csv["pz"] = pz / 1000
-        # particles_csv.drop(["p", "pt", "eta", "phi"], axis=1, inplace=True)  # let's keep them
-        num = str(event_num).zfill(9)
-        particles_csv.to_csv(f"{output_dir}/event{num}-particles.csv", index=False)
-
-        # -truth.csv, use event_hits
-        truth_csv = event_hits[
-            ["mcid", "x", "y", "z", "p", "pt", "eta", "phi", "lhcbid"]
-        ]
-        truth_csv.rename(
-            columns={"mcid": "particle_id", "x": "tx", "y": "ty", "z": "tz"},
-            inplace=True,
-        )
-        truth_csv["particle_id"] = truth_csv["particle_id"] + 1
-        pt = event_hits["pt"]
-        eta = event_hits["eta"]
-        phi = event_hits["phi"]
-        px = pt * np.cos(phi)  # Correctly transform coordinates
-        py = pt * np.sin(phi)
-        pz = pt * np.sinh(eta)
-        truth_csv["tpx"] = px / 1000  # Convert momentum from MeV to GeV
-        truth_csv["tpy"] = py / 1000
-        truth_csv["tpz"] = pz / 1000
-        truth_csv.drop(["p", "pt", "eta", "phi"], axis=1, inplace=True)
-        num = str(event_num).zfill(9)
-        truth_csv["hit_id"] = hits_csv.index + 1
-        truth_csv.to_csv(f"{output_dir}/event{num}-truth.csv", index=False)
-
-        num_true_hits = len(
-            truth_csv[truth_csv["particle_id"] != 0].drop_duplicates(subset=["lhcbid"])
-        )
-        if (
-            len(truth_csv["particle_id"].value_counts()) == 1
-            and truth_csv["particle_id"].value_counts().iloc[0] != 0
+        if enough_true_hits(
+            event_hits=event_hits,
+            num_true_hits_threshold=num_true_hits_threshold,
+            event_id_str=event_id_str,
+            num_events=i,
+            required_num_events=output_num,
         ):
-            # Discard "fake" events
-            print(f"Discarding event {num}, contains only fake hits.")
-            # Discard events that only contain fake hits
-            os.remove(f"{output_dir}/event{num}-hits.csv")
-            os.remove(f"{output_dir}/event{num}-particles.csv")
-            os.remove(f"{output_dir}/event{num}-truth.csv")
-        elif num_true_hits < num_true_hits_threshold:
-            # Check the number of hits
-            print(f"Discarding event {num}, contains only {num_true_hits} true hits.")
-            # Discard events
-            os.remove(f"{output_dir}/event{num}-hits.csv")
-            os.remove(f"{output_dir}/event{num}-particles.csv")
-            os.remove(f"{output_dir}/event{num}-truth.csv")
-        else:
             i += 1  # If not discarded, count
-            print(
-                f"Saving event {num}, {i}/{output_num}, contains {num_true_hits} true hits."
+            # Define `hit_id` directly in `event_hits`
+            event_hits["hit_id"] = event_hits.index + 1
+            # NB: `event_hits` may have duplicated `particle_id`, in the case where
+            #     one hit is associated with several MC particles
+            #     but by setting `hit_id` this way, you virtually consider that
+            #     `(lcbid, particle_id1)` and `(lcbid, particle_id2)` are two different
+            #     hits, although they have the same `lhcbid`
+            # TODO: keep LHCbID, or drop duplicated `lhcbid`
+
+            # hits.csv, use event_hits ----------------------------------------------
+            hits_csv = event_hits[["x", "y", "z", "plane", "hit_id"]]
+            hits_csv.rename(columns={"plane": "module_id"}, inplace=True)
+
+            # particles.csv, use event_particles ------------------------------------
+            particles_csv = event_particles[
+                [
+                    "particle_id",
+                    "vx",
+                    "vy",
+                    "vz",
+                    "px",
+                    "py",
+                    "pz",
+                    "charge",
+                    "nhits_velo",
+                ]
+            ]
+
+            particles_csv.rename(
+                columns={"charge": "q", "nhits_velo": "nhits"},
+                inplace=True,
             )
 
-        if i == output_num:
-            break  # If output number reached, break loop
+            # -truth.csv, use event_hits -------------------------------------------
+            truth_csv = event_hits[
+                ["particle_id", "hit_id", "x", "y", "z", "px", "py", "pz", "lhcbid"]
+            ]
+            truth_csv.rename(
+                columns={
+                    "x": "tx",
+                    "y": "ty",
+                    "z": "tz",
+                    "px": "tpx",
+                    "py": "tpy",
+                    "pz": "tpz",
+                },
+                inplace=True,
+            )
+
+            # Save -----------------------------------------------------------------
+            hits_csv.to_csv(f"{output_dir}/event{event_id_str}-hits.csv", index=False)
+            particles_csv.to_csv(
+                f"{output_dir}/event{event_id_str}-particles.csv", index=False
+            )
+            truth_csv.to_csv(f"{output_dir}/event{event_id_str}-truth.csv", index=False)
 
     if i < output_num:
         raise Exception(
-            f"Not enough events found with more than {num_true_hits} true hits"
+            "Not enough events found with more than "
+            f"{num_true_hits_threshold} true hits"
         )
-- 
GitLab


From b369907ba4820effb89696a1c10f7d5f3cd82d73 Mon Sep 17 00:00:00 2001
From: anthonyc <acorreia@lpnhe.in2p3.fr>
Date: Thu, 23 Mar 2023 17:28:05 +0100
Subject: [PATCH 09/30] check that preprocessing is the EXACT same as main

---
 LHCb_Pipeline/Processing/utils/preprocessing.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/LHCb_Pipeline/Processing/utils/preprocessing.py b/LHCb_Pipeline/Processing/utils/preprocessing.py
index 524d089d..b59374fe 100644
--- a/LHCb_Pipeline/Processing/utils/preprocessing.py
+++ b/LHCb_Pipeline/Processing/utils/preprocessing.py
@@ -177,11 +177,11 @@ def preprocess(
                     "vx",
                     "vy",
                     "vz",
+                    "charge",
+                    "nhits_velo",
                     "px",
                     "py",
                     "pz",
-                    "charge",
-                    "nhits_velo",
                 ]
             ]
 
@@ -192,7 +192,7 @@ def preprocess(
 
             # -truth.csv, use event_hits -------------------------------------------
             truth_csv = event_hits[
-                ["particle_id", "hit_id", "x", "y", "z", "px", "py", "pz", "lhcbid"]
+                ["particle_id", "x", "y", "z", "lhcbid", "px", "py", "pz", "hit_id"]
             ]
             truth_csv.rename(
                 columns={
-- 
GitLab


From f61416f11f70fbe2f6f217ddfbb98e833f9b8ac3 Mon Sep 17 00:00:00 2001
From: anthonyc <acorreia@lpnhe.in2p3.fr>
Date: Thu, 23 Mar 2023 19:38:31 +0100
Subject: [PATCH 10/30] column labels, range and configure_matplotlib

---
 .../utils/plotting_utils_validation.py        | 50 +++++++++++++++++++
 1 file changed, 50 insertions(+)
 create mode 100644 LHCb_Pipeline/Scripts/utils/plotting_utils_validation.py

diff --git a/LHCb_Pipeline/Scripts/utils/plotting_utils_validation.py b/LHCb_Pipeline/Scripts/utils/plotting_utils_validation.py
new file mode 100644
index 00000000..9797a469
--- /dev/null
+++ b/LHCb_Pipeline/Scripts/utils/plotting_utils_validation.py
@@ -0,0 +1,50 @@
+import matplotlib as mpl
+
+#: Associates a column name with its label for the plots
+column_labels = {
+    "pt": "$p_T$ [MeV/c]",
+    "p": "$p$ [MeV/c]",
+    "eta": "$\eta$",
+    "vz": r"$\text{ovtx}_{z}$ [mm]",
+}
+
+#: Associates a column name with its range for the plots
+column_ranges = {
+    "pt": (0, 2000),
+    "p": (0, 50000),
+    "eta": None,
+    "vz": (0, 200),
+}
+
+
+
+def configure_matplotlib():
+    """Set up ``rcParams`` matplotlib object to configure font, fontsizes, etc."""
+    mpl.rcParams.update(
+        **{
+            # Font
+            "font.family": "serif",
+            "font.serif": "Computer Modern Roman",
+            # Latex
+            "text.latex.preamble": r"\usepackage{amsmath}",
+            "text.usetex": True,  # Put to False to disable Latex
+            # Fontsizes
+            "legend.fontsize": 20,
+            "axes.titlesize": 24,
+            "xtick.labelsize": 20,
+            "ytick.labelsize": 20,
+            "axes.labelsize": 25,
+            "font.size": 20,
+            # Lines
+            "lines.markersize": 6.0,
+            "lines.linestyle": "-",
+            "lines.linewidth": 1.5,
+            # Figure
+            "figure.figsize": (8, 6),
+            # "figure.dpi": 200,
+            # Ticks
+            # Ticks settings
+            "xtick.direction": "in",
+            "ytick.direction": "in",
+        }
+    )
-- 
GitLab


From b189f98bb77b7f1b810979fe0992a6991287698c Mon Sep 17 00:00:00 2001
From: anthonyc <acorreia@lpnhe.in2p3.fr>
Date: Thu, 23 Mar 2023 19:38:42 +0100
Subject: [PATCH 11/30] working evaluation script using montetracko

---
 ...p_6_Evaluate_Reconstruction_MonteTracko.py | 282 ++++++++++++++++++
 1 file changed, 282 insertions(+)
 create mode 100644 LHCb_Pipeline/Scripts/Step_6_Evaluate_Reconstruction_MonteTracko.py

diff --git a/LHCb_Pipeline/Scripts/Step_6_Evaluate_Reconstruction_MonteTracko.py b/LHCb_Pipeline/Scripts/Step_6_Evaluate_Reconstruction_MonteTracko.py
new file mode 100644
index 00000000..9112f111
--- /dev/null
+++ b/LHCb_Pipeline/Scripts/Step_6_Evaluate_Reconstruction_MonteTracko.py
@@ -0,0 +1,282 @@
+"""This script runs the performance evaluation of the tracking inference, using
+MonteTracko.
+"""
+import os.path as op
+import typing
+import yaml
+import logging
+
+import numpy as np
+import numpy.typing as npt
+import pandas as pd
+import pyarrow.csv as pac
+
+import montetracko as mt
+import montetracko.lhcb as mtb
+from montetracko.utils.libs import get_correct_tqdm
+
+from Scripts.utils.convenience_utils import headline
+import Scripts.utils.plotting_utils_validation as plotutils
+from Processing.utils.preprocessing import load_dataframes
+import Scripts.Step_6_Evaluate_Reconstruction as evalreco
+
+
+def filter_event_ids(dataframe: pd.DataFrame, event_ids: npt.ArrayLike) -> pd.DataFrame:
+    """Filters a Pandas DataFrame to keep only rows corresponding to given event IDs.
+
+    Args:
+        dataframe: The input DataFrame to filter.
+        event_ids: A list-like object of event IDs to keep.
+
+    Returns:
+        A new DataFrame containing only rows corresponding to the given event IDs.
+
+    Raises:
+        ValueError: If any of the given event IDs are not found in the input DataFrame.
+    """
+    dataframe = dataframe[dataframe["event"].isin(event_ids)]
+
+    # Sanity check: check that all the event IDs are in the dataframe
+    event_ids_not_in_dataframe = event_ids[~np.isin(event_ids, dataframe["event"])]
+    if event_ids_not_in_dataframe.size:
+        raise ValueError(
+            "The following event IDs were not found in the dataframe: "
+            + ", ".join(event_ids)
+        )
+    # Return filtered dataframe
+    return dataframe
+
+
+def load_tracks(
+    all_graph_files: typing.List[str],
+    indir: str,
+) -> typing.Tuple[pd.DataFrame, np.ndarray]:
+    """Load the tracks from graphs.
+
+    Args:
+        all_graph_files: A list of strings representing the paths to the graphs.
+        indir: A string representing the directory where the hit-particle truth
+            association CSV files are located.
+            The CSV files are expected to have names following the format
+            ``"event{event_id_str}-truth.csv"``, where ``{event_id_str}``
+            is the ID of the corresponding event.
+
+    Returns:
+        : A tuple containing:
+
+            * A concatenated Pandas DataFrame containing all the tracks, \
+            with the following columns: ``event`` (event ID), ``track_id`` (track ID), \
+            and ``lhcbid`` (equivalent to a hit ID).
+            * A NumPy array of the event IDs
+
+    Notes:
+        This function also converts the ``hit_id`` column to the corresponding
+        ``lhcbid``.
+        Each track DataFrame is augmented with an ``event`` column containing
+        the corresponding event ID.
+    """
+    path_df_hits_particles = op.join(indir, "event{event_id_str}-truth.csv")
+
+    #: List of dataframes of tracks (one dataframe = one event)
+    list_df_tracks = []
+    #: List of corresponding event IDs
+    event_ids = []
+
+    tqdm = get_correct_tqdm()
+    # Loop over the graphs (one graph = one event)
+    for graph_file in tqdm(all_graph_files):
+        # Obtain the Event ID from the graph file name
+        event_id_str = op.basename(graph_file)
+        event_id = int(event_id_str)
+        event_ids.append(event_id)
+
+        # Load the dataframe of tracks
+        reconstruction_df = evalreco.load_reconstruction_df(graph_file)
+        df_tracks_event = reconstruction_df[["hit_id", "track_id"]].drop_duplicates()
+
+        # Load hit-particle truth association in order to convert `hit_id` to `lhcbid`
+        df_hits_particles_event = pac.read_csv(
+            path_df_hits_particles.format(event_id_str=event_id_str),
+            convert_options=pac.ConvertOptions(include_columns=["lhcbid", "hit_id"]),
+        ).to_pandas()
+
+        # Deduce `lhcbid` from `hit_id`
+        df_tracks_event["lhcbid"] = mt.array_utils.transform.replace(
+            array=np.asarray(df_tracks_event["hit_id"]),
+            values_to_replace=np.asarray(df_hits_particles_event["hit_id"]),
+            replacement_values=np.asarray(df_hits_particles_event["lhcbid"]),
+        )
+
+        # Drop the `hit_id` column to avoid possible confusions
+        df_tracks_event.drop(["hit_id"], axis=1, inplace=True)
+
+        # Add event ID column
+        df_tracks_event["event"] = event_id
+        list_df_tracks.append(df_tracks_event)
+
+    # Return concatenated dataframe and array of event IDs
+    return pd.concat(list_df_tracks), np.asarray(event_ids)
+
+
+def report(
+    trackEvaluator: mt.TrackEvaluator,
+    allen_report: bool = False,
+):
+    """Generate a report of the track evaluation.
+
+    The function prints out the track evaluation report using two different reporters.
+    If `allen_report` is True, the function uses the Allen reporter.
+
+    In any cases, it uses the `TabReporter` with the  following metrics:
+
+    * ``efficiency``: the fraction of MC particles with a matching reconstructed track
+    * ``efficiency_per_event``: the fraction of MC particles with a matching
+        reconstructed track, averaged over all events
+    * ``clone_rate``: proportion of reconstructed tracks that are matched to
+        the same MC particle
+    * ``hit_purity_per_candidate``: Average fraction of correctly matched hits
+    on matched tracks, out of all matched track-particle associations.
+    * ``hit_efficiency_per_candidate``: Average fraction of correctly matched hits
+    on matched particles, out of all matched track-particle associations.
+
+    Additionally, a separate ``TabReporter`` is used to report the global
+    number of ghosts andtracks, and the ghost rate.
+
+    Args:
+        trackEvaluator: A ``TrackEvaluator`` instance containing the results
+            of the track matching.
+        allen_report: whether to report in Allen categories using the Allen reporter
+    """
+    # Report
+    logging.info("3) Reporting")
+    if allen_report:
+        trackEvaluator.print_report(
+            reporter=mt.AllenReporter(),
+            categories=mtb.category.allen_categories,
+        )
+    trackEvaluator.print_report(
+        reporter=mt.TabReporter(
+            [
+                "efficiency",
+                "efficiency_per_event",
+                "clone_rate",
+                "hit_purity_per_candidate",
+                "hit_efficiency_per_candidate",
+            ],
+            mode="markdown",
+            tablefmt="grid",
+        ),
+        categories=mtb.category.velo_categories,
+    )
+    trackEvaluator.print_report(
+        reporter=mt.TabReporter(
+            metric_names=["n_ghosts", "n_tracks", "ghost_rate"],
+            mode="markdown",
+            tablefmt="grid",
+        ),
+    )
+
+
+def plot(trackEvaluator):
+    """Generate and display histograms of track evaluation metrics in specified
+    particle-related columns (`pt`, `p`, `eta` and `vz`)
+
+    Args:
+        trackEvaluator: A ``TrackEvaluator`` instance containing the results
+            of the track matching.
+    """
+    logging.info("4) Plotting")
+    plotutils.configure_matplotlib()
+    fig, _, _ = trackEvaluator.plot_histograms(
+        columns=["pt", "p", "eta", "vz"],
+        metric_names=[
+            "efficiency",
+            "clone_rate",
+            "hit_purity_per_candidate",
+            "hit_efficiency_per_candidate",
+        ],
+        column_labels=plotutils.column_labels,
+        column_ranges=plotutils.column_ranges,
+        category=mtb.category.velo_category,
+        bins=20,
+    )
+    fig.savefig("track_evaluation.pdf", dpi=200, bbox_inches="tight")
+
+
+def evaluate(
+    config_file: str = "pipeline_config.yaml",
+    min_track_length: int = 2,
+    whether_to_plot: bool = True,
+    allen_report: bool = False,
+):
+    """Runs truth-based tracking evaluation.
+
+    Args:
+        config_file: path to the Exa.TrkX configuration file.
+        min_track_length: minimum length of a track to be considered in the evaluation.
+        whether_to_plot: whether to plot histograms.
+        allen_report: whether to report in Allen categories using the Allen reporter
+
+    Returns:
+        mt.TrackEvaluator: object containing the results of the evaluation.
+    """
+    logging.info(headline("Step 6: Evaluation using MonteTracko"))
+
+    # Load the configuration
+    with open(config_file) as file:
+        all_configs = yaml.load(file, Loader=yaml.FullLoader)
+
+    # Get some directories
+    #: Directory where the pre-processed CSV files are
+    preprocessed_dir = all_configs["processing_configs"]["preprocessed_dir"]
+    #: Directory where the original parquet files are
+    input_dir = all_configs["processing_configs"]["input_dir"]
+
+    # Get path to the graph files, which contain the tracks for each event
+    all_graph_files = evalreco.get_all_graph_files(config=all_configs)
+
+    logging.info("1) Load the dataframes")
+    # Load the dataframe of tracks
+    df_tracks, event_ids = load_tracks(
+        all_graph_files=all_graph_files, indir=preprocessed_dir
+    )
+    # Load the dataframes of hits and particles
+    df_particles, df_hits_particles = load_dataframes(indir=input_dir)
+    df_particles = filter_event_ids(df_particles, event_ids)
+    df_hits_particles = filter_event_ids(df_hits_particles, event_ids)
+
+    logging.info("2) Matching")
+    # Filter tracks and match them to particles
+    mt_config = {
+        # Associates a column name used in MonteTracko with the actual column names
+        "aliases": {
+            "n_hits": "nhits_velo",
+            "hit_id": "lhcbid",
+            "event_id": "event",
+        },
+        # Value of `particle_id` when the hit is fake
+        "fake_particle_id": 0,
+    }
+    trackEvaluator = mt.TrackMatcher(config=mt_config).full_matching(
+        df_events_hits_particles=df_hits_particles,
+        df_events_particles=df_particles,
+        df_events_tracks_hits=df_tracks,
+        matching_condition=mt.matchcond.MinMatchingFraction(0.7),
+        # Min length of a track
+        # default is 2.
+        track_condition=mt.matchcond.MinLengthTrack(min_track_length),
+    )
+
+    # Report
+    report(trackEvaluator=trackEvaluator, allen_report=allen_report)
+
+    # Plot
+    if whether_to_plot:
+        plot(trackEvaluator=trackEvaluator)
+    return trackEvaluator
+
+
+if __name__ == "__main__":
+    args = evalreco.parse_args()
+    config_file = args.config
+    evalreco.evaluate(config_file)
-- 
GitLab


From cc483d831e9a21e6d5fea807be3305ae4923f57a Mon Sep 17 00:00:00 2001
From: anthonyc <acorreia@lpnhe.in2p3.fr>
Date: Thu, 23 Mar 2023 19:39:12 +0100
Subject: [PATCH 12/30] remove test notebooks

---
 LHCb_Pipeline/full_pipeline_anthony.ipynb | 4430 ---------------------
 1 file changed, 4430 deletions(-)
 delete mode 100644 LHCb_Pipeline/full_pipeline_anthony.ipynb

diff --git a/LHCb_Pipeline/full_pipeline_anthony.ipynb b/LHCb_Pipeline/full_pipeline_anthony.ipynb
deleted file mode 100644
index 13bcc803..00000000
--- a/LHCb_Pipeline/full_pipeline_anthony.ipynb
+++ /dev/null
@@ -1,4430 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 0. Setup"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Imports"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>\n",
-       "        .bk-notebook-logo {\n",
-       "            display: block;\n",
-       "            width: 20px;\n",
-       "            height: 20px;\n",
-       "            background-image: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABQAAAAUCAYAAACNiR0NAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAABx0RVh0U29mdHdhcmUAQWRvYmUgRmlyZXdvcmtzIENTNui8sowAAAOkSURBVDiNjZRtaJVlGMd/1/08zzln5zjP1LWcU9N0NkN8m2CYjpgQYQXqSs0I84OLIC0hkEKoPtiH3gmKoiJDU7QpLgoLjLIQCpEsNJ1vqUOdO7ppbuec5+V+rj4ctwzd8IIbbi6u+8f1539dt3A78eXC7QizUF7gyV1fD1Yqg4JWz84yffhm0qkFqBogB9rM8tZdtwVsPUhWhGcFJngGeWrPzHm5oaMmkfEg1usvLFyc8jLRqDOMru7AyC8saQr7GG7f5fvDeH7Ej8CM66nIF+8yngt6HWaKh7k49Soy9nXurCi1o3qUbS3zWfrYeQDTB/Qj6kX6Ybhw4B+bOYoLKCC9H3Nu/leUTZ1JdRWkkn2ldcCamzrcf47KKXdAJllSlxAOkRgyHsGC/zRday5Qld9DyoM4/q/rUoy/CXh3jzOu3bHUVZeU+DEn8FInkPBFlu3+nW3Nw0mk6vCDiWg8CeJaxEwuHS3+z5RgY+YBR6V1Z1nxSOfoaPa4LASWxxdNp+VWTk7+4vzaou8v8PN+xo+KY2xsw6une2frhw05CTYOmQvsEhjhWjn0bmXPjpE1+kplmmkP3suftwTubK9Vq22qKmrBhpY4jvd5afdRA3wGjFAgcnTK2s4hY0/GPNIb0nErGMCRxWOOX64Z8RAC4oCXdklmEvcL8o0BfkNK4lUg9HTl+oPlQxdNo3Mg4Nv175e/1LDGzZen30MEjRUtmXSfiTVu1kK8W4txyV6BMKlbgk3lMwYCiusNy9fVfvvwMxv8Ynl6vxoByANLTWplvuj/nF9m2+PDtt1eiHPBr1oIfhCChQMBw6Aw0UulqTKZdfVvfG7VcfIqLG9bcldL/+pdWTLxLUy8Qq38heUIjh4XlzZxzQm19lLFlr8vdQ97rjZVOLf8nclzckbcD4wxXMidpX30sFd37Fv/GtwwhzhxGVAprjbg0gCAEeIgwCZyTV2Z1REEW8O4py0wsjeloKoMr6iCY6dP92H6Vw/oTyICIthibxjm/DfN9lVz8IqtqKYLUXfoKVMVQVVJOElGjrnnUt9T9wbgp8AyYKaGlqingHZU/uG2NTZSVqwHQTWkx9hxjkpWDaCg6Ckj5qebgBVbT3V3NNXMSiWSDdGV3hrtzla7J+duwPOToIg42ChPQOQjspnSlp1V+Gjdged7+8UN5CRAV7a5EdFNwCjEaBR27b3W890TE7g24NAP/mMDXRWrGoFPQI9ls/MWO2dWFAar/xcOIImbbpA3zgAAAABJRU5ErkJggg==);\n",
-       "        }\n",
-       "    </style>\n",
-       "    <div>\n",
-       "        <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-notebook-logo\"></a>\n",
-       "        <span id=\"p1001\">Loading BokehJS ...</span>\n",
-       "    </div>\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/javascript": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\nconst JS_MIME_TYPE = 'application/javascript';\n  const HTML_MIME_TYPE = 'text/html';\n  const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n  const CLASS_NAME = 'output_bokeh rendered_html';\n\n  /**\n   * Render data to the DOM node\n   */\n  function render(props, node) {\n    const script = document.createElement(\"script\");\n    node.appendChild(script);\n  }\n\n  /**\n   * Handle when an output is cleared or removed\n   */\n  function handleClearOutput(event, handle) {\n    const cell = handle.cell;\n\n    const id = cell.output_area._bokeh_element_id;\n    const server_id = cell.output_area._bokeh_server_id;\n    // Clean up Bokeh references\n    if (id != null && id in Bokeh.index) {\n      Bokeh.index[id].model.document.clear();\n      delete Bokeh.index[id];\n    }\n\n    if (server_id !== undefined) {\n      // Clean up Bokeh references\n      const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n      cell.notebook.kernel.execute(cmd_clean, {\n        iopub: {\n          output: function(msg) {\n            const id = msg.content.text.trim();\n            if (id in Bokeh.index) {\n              Bokeh.index[id].model.document.clear();\n              delete Bokeh.index[id];\n            }\n          }\n        }\n      });\n      // Destroy server and session\n      const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n      cell.notebook.kernel.execute(cmd_destroy);\n    }\n  }\n\n  /**\n   * Handle when a new output is added\n   */\n  function handleAddOutput(event, handle) {\n    const output_area = handle.output_area;\n    const output = handle.output;\n\n    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n    if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n      return\n    }\n\n    const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n\n    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n      // store reference to embed id on output_area\n      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n    }\n    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n      const bk_div = document.createElement(\"div\");\n      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n      const script_attrs = bk_div.children[0].attributes;\n      for (let i = 0; i < script_attrs.length; i++) {\n        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n      }\n      // store reference to server id on output_area\n      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n    }\n  }\n\n  function register_renderer(events, OutputArea) {\n\n    function append_mime(data, metadata, element) {\n      // create a DOM node to render to\n      const toinsert = this.create_output_subarea(\n        metadata,\n        CLASS_NAME,\n        EXEC_MIME_TYPE\n      );\n      this.keyboard_manager.register_events(toinsert);\n      // Render to node\n      const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n      render(props, toinsert[toinsert.length - 1]);\n      element.append(toinsert);\n      return toinsert\n    }\n\n    /* Handle when an output is cleared or removed */\n    events.on('clear_output.CodeCell', handleClearOutput);\n    events.on('delete.Cell', handleClearOutput);\n\n    /* Handle when a new output is added */\n    events.on('output_added.OutputArea', handleAddOutput);\n\n    /**\n     * Register the mime type and append_mime function with output_area\n     */\n    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n      /* Is output safe? */\n      safe: true,\n      /* Index of renderer in `output_area.display_order` */\n      index: 0\n    });\n  }\n\n  // register the mime type if in Jupyter Notebook environment and previously unregistered\n  if (root.Jupyter !== undefined) {\n    const events = require('base/js/events');\n    const OutputArea = require('notebook/js/outputarea').OutputArea;\n\n    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n      register_renderer(events, OutputArea);\n    }\n  }\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"p1001\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.0.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.0.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.0.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.0.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.0.3.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"p1001\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));",
-      "application/vnd.bokehjs_load.v0+json": ""
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2\n",
-    "\n",
-    "from Scripts.utils.convenience_utils import *\n",
-    "from Scripts.utils.plotting_utils import plot_observable_performance\n",
-    "from Scripts.Step_1_Train_Metric_Learning import train as train_metric_learning\n",
-    "from Scripts.Step_2_Run_Metric_Learning import train as run_metric_learning_inference\n",
-    "from Scripts.Step_3_Train_GNN import train as train_gnn\n",
-    "from Scripts.Step_4_Run_GNN import train as run_gnn_inference\n",
-    "from Scripts.Step_5_Build_Track_Candidates import train as build_track_candidates\n",
-    "from Scripts.Step_6_Evaluate_Reconstruction import evaluate as evaluate_candidates\n",
-    "\n",
-    "import sys\n",
-    "from Scripts.utils.convenience_utils import get_example_data, plot_true_graph, get_training_metrics, plot_training_metrics, plot_neighbor_performance, plot_predicted_graph, plot_track_lengths, plot_edge_performance, plot_graph_sizes\n",
-    "import yaml\n",
-    "\n",
-    "import warnings\n",
-    "warnings.filterwarnings(\"ignore\")\n",
-    "\n",
-    "CONFIG = 'pipeline_config.yaml'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Pipeline configurations\n",
-    "\n",
-    "The configurations for the entire pipeline are defined under pipeline_config.yml."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open(CONFIG, 'r') as f:\n",
-    "    configs = yaml.load(f, Loader=yaml.FullLoader)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Download data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# ! xrdcp -r root://eoslhcb.cern.ch//eos/lhcb/user/a/anthonyc/tracking/data/csv/v2/minbias-sim10b-xdigi/0 data/input/"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Telegram notification bot"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import requests\n",
-    "import json\n",
-    "# from datetime import datetime\n",
-    "\n",
-    "# def send_telegram_message(message: str,\n",
-    "#                           chat_id: str,\n",
-    "#                           api_key: str,\n",
-    "#                          ):\n",
-    "#     responses = {}\n",
-    "\n",
-    "#     url = f'https://api.telegram.org/bot{api_key}/sendMessage?chat_id={chat_id}&text={message}'\n",
-    "    \n",
-    "#     response = requests.post(url)\n",
-    "    \n",
-    "#     return response\n",
-    "\n",
-    "def send_telegram_message(*args, **kwargs):\n",
-    "    pass"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# chat_id = \"5027012918\"\n",
-    "# api_key = \"6268687426:AAE1P7WQofCBuQPiYZlYaKU-p1GNn6OvAxM\"\n",
-    "\n",
-    "# send_telegram_message(\"======================\", chat_id, api_key)\n",
-    "\n",
-    "# send_telegram_message(\"Starting.\", chat_id, api_key)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Delete contents of `bokeh-plots`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "clear_contents('bokeh-plots')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Saving data into `torch` tensor form"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saving event 004206889, 1/100, contains 814 true hits.\n",
-      "Saving event 004206890, 2/100, contains 935 true hits.\n",
-      "Saving event 004206891, 3/100, contains 1508 true hits.\n",
-      "Saving event 004206892, 4/100, contains 691 true hits.\n",
-      "Saving event 004206893, 5/100, contains 1584 true hits.\n",
-      "Saving event 004206894, 6/100, contains 1527 true hits.\n",
-      "Saving event 004206895, 7/100, contains 834 true hits.\n",
-      "Saving event 004206896, 8/100, contains 1012 true hits.\n",
-      "Saving event 004206897, 9/100, contains 1343 true hits.\n",
-      "Saving event 004206898, 10/100, contains 1185 true hits.\n",
-      "Saving event 004206899, 11/100, contains 841 true hits.\n",
-      "Saving event 004206900, 12/100, contains 1000 true hits.\n",
-      "Saving event 004206901, 13/100, contains 2350 true hits.\n",
-      "Saving event 004206902, 14/100, contains 2537 true hits.\n",
-      "Saving event 004206903, 15/100, contains 1938 true hits.\n",
-      "Saving event 004206904, 16/100, contains 3223 true hits.\n",
-      "Saving event 004206905, 17/100, contains 3772 true hits.\n",
-      "Discarding event 004206906, contains only fake hits.\n",
-      "Saving event 004206907, 18/100, contains 1071 true hits.\n",
-      "Saving event 004206908, 19/100, contains 911 true hits.\n",
-      "Saving event 004206909, 20/100, contains 891 true hits.\n",
-      "Saving event 004206910, 21/100, contains 287 true hits.\n",
-      "Saving event 004206911, 22/100, contains 1154 true hits.\n",
-      "Saving event 004206912, 23/100, contains 1430 true hits.\n",
-      "Saving event 004206913, 24/100, contains 3562 true hits.\n",
-      "Saving event 004206914, 25/100, contains 616 true hits.\n",
-      "Saving event 004206915, 26/100, contains 739 true hits.\n",
-      "Saving event 004206916, 27/100, contains 1019 true hits.\n",
-      "Saving event 004206917, 28/100, contains 2024 true hits.\n",
-      "Saving event 004206918, 29/100, contains 1679 true hits.\n",
-      "Saving event 004206919, 30/100, contains 1747 true hits.\n",
-      "Saving event 004206920, 31/100, contains 1254 true hits.\n",
-      "Saving event 004206921, 32/100, contains 2582 true hits.\n",
-      "Saving event 004206922, 33/100, contains 627 true hits.\n",
-      "Saving event 004206923, 34/100, contains 1168 true hits.\n",
-      "Saving event 004206924, 35/100, contains 1606 true hits.\n",
-      "Saving event 004206925, 36/100, contains 2457 true hits.\n",
-      "Saving event 004206926, 37/100, contains 1325 true hits.\n",
-      "Saving event 004206927, 38/100, contains 2473 true hits.\n",
-      "Saving event 004206928, 39/100, contains 3194 true hits.\n",
-      "Saving event 004206929, 40/100, contains 2108 true hits.\n",
-      "Saving event 004206930, 41/100, contains 3139 true hits.\n",
-      "Saving event 004206931, 42/100, contains 2060 true hits.\n",
-      "Discarding event 004206932, contains only fake hits.\n",
-      "Saving event 004206933, 43/100, contains 1013 true hits.\n",
-      "Saving event 004206934, 44/100, contains 397 true hits.\n",
-      "Saving event 004206935, 45/100, contains 709 true hits.\n",
-      "Saving event 004206936, 46/100, contains 772 true hits.\n",
-      "Saving event 004206937, 47/100, contains 872 true hits.\n",
-      "Saving event 004206938, 48/100, contains 1117 true hits.\n",
-      "Saving event 004206939, 49/100, contains 397 true hits.\n",
-      "Saving event 004206940, 50/100, contains 1636 true hits.\n",
-      "Saving event 004206941, 51/100, contains 3559 true hits.\n",
-      "Saving event 004206942, 52/100, contains 1822 true hits.\n",
-      "Saving event 004206943, 53/100, contains 344 true hits.\n",
-      "Saving event 004206944, 54/100, contains 1340 true hits.\n",
-      "Saving event 004206945, 55/100, contains 2038 true hits.\n",
-      "Saving event 004206946, 56/100, contains 3145 true hits.\n",
-      "Saving event 004206947, 57/100, contains 4457 true hits.\n",
-      "Saving event 004206948, 58/100, contains 1102 true hits.\n",
-      "Saving event 004206949, 59/100, contains 955 true hits.\n",
-      "Saving event 004206950, 60/100, contains 1426 true hits.\n",
-      "Saving event 004206951, 61/100, contains 428 true hits.\n",
-      "Saving event 004206952, 62/100, contains 1250 true hits.\n",
-      "Saving event 004206953, 63/100, contains 1591 true hits.\n",
-      "Saving event 004206954, 64/100, contains 2626 true hits.\n",
-      "Saving event 004206955, 65/100, contains 293 true hits.\n",
-      "Saving event 004206956, 66/100, contains 1312 true hits.\n",
-      "Saving event 004206957, 67/100, contains 983 true hits.\n",
-      "Saving event 004206958, 68/100, contains 1659 true hits.\n",
-      "Saving event 004206959, 69/100, contains 2843 true hits.\n",
-      "Saving event 004206960, 70/100, contains 816 true hits.\n",
-      "Saving event 004206961, 71/100, contains 211 true hits.\n",
-      "Saving event 004206962, 72/100, contains 1821 true hits.\n",
-      "Saving event 004206963, 73/100, contains 1017 true hits.\n",
-      "Saving event 004206964, 74/100, contains 2710 true hits.\n",
-      "Saving event 004206965, 75/100, contains 2393 true hits.\n",
-      "Saving event 004206966, 76/100, contains 3461 true hits.\n",
-      "Saving event 007212913, 77/100, contains 1855 true hits.\n",
-      "Saving event 007212914, 78/100, contains 923 true hits.\n",
-      "Saving event 007212915, 79/100, contains 1247 true hits.\n",
-      "Saving event 007212916, 80/100, contains 2129 true hits.\n",
-      "Saving event 007212917, 81/100, contains 2372 true hits.\n",
-      "Saving event 007212918, 82/100, contains 1742 true hits.\n",
-      "Saving event 007212919, 83/100, contains 2170 true hits.\n",
-      "Saving event 007212920, 84/100, contains 768 true hits.\n",
-      "Saving event 007212921, 85/100, contains 2265 true hits.\n",
-      "Saving event 007212922, 86/100, contains 3096 true hits.\n",
-      "Saving event 007212923, 87/100, contains 1829 true hits.\n",
-      "Saving event 007212924, 88/100, contains 833 true hits.\n",
-      "Saving event 007212925, 89/100, contains 2592 true hits.\n",
-      "Saving event 007212926, 90/100, contains 1433 true hits.\n",
-      "Saving event 007212927, 91/100, contains 1745 true hits.\n",
-      "Saving event 007212928, 92/100, contains 2669 true hits.\n",
-      "Saving event 007212929, 93/100, contains 982 true hits.\n",
-      "Saving event 007212930, 94/100, contains 1134 true hits.\n",
-      "Saving event 007212931, 95/100, contains 2717 true hits.\n",
-      "Saving event 007212932, 96/100, contains 2973 true hits.\n",
-      "Saving event 007212933, 97/100, contains 970 true hits.\n",
-      "Saving event 007212934, 98/100, contains 1286 true hits.\n",
-      "Saving event 007212935, 99/100, contains 420 true hits.\n",
-      "Saving event 007212936, 100/100, contains 3010 true hits.\n",
-      "Writing outputs to data/processed\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:Preparing event 004206890\n",
-      "INFO:Preparing event 004206889\n",
-      "INFO:Preparing event 004206891\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a467fbebb2da45359ca424b4e5aa2f07",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/100 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:Preparing event 004206892\n",
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-      "INFO:Modulewise truth graph built for data/preprocessed/event004206897 with size (2, 983)\n",
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-      "INFO:Modulewise truth graph built for data/preprocessed/event007212931 with size (2, 2041)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event007212922 with size (2, 2242)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event007212918 with size (2, 1298)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event007212917 with size (2, 1753)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event007212919 with size (2, 1630)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event007212936 with size (2, 2279)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event007212925 with size (2, 1990)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event007212932 with size (2, 2200)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event007212928 with size (2, 2023)\n"
-     ]
-    }
-   ],
-   "source": [
-    "from Processing.Models.feature_construction import FeatureStore\n",
-    "\n",
-    "fs = FeatureStore(CONFIG)\n",
-    "fs.prepare_data()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 1. Train Metric Learning"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## What it does\n",
-    "Broadly speaking, the first stage of our pipeline is embedding the space points on to graphs, in a way that is efficient, i.e. we miss as few points on a graph as possible. We train a MLP to transform the input feature vector of each space point $\\mathbf{u}_i$ into an N-dimensional latent space $\\mathbf{v}_i$. The graph is then constructed by connecting the space points whose Euclidean distance between the latent space points $$d_{ij} = \\left| \\mathbf{v}_i - \\mathbf{v}_j \\right| < r_{embedding}$$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Training data\n",
-    "Let us take a look at the data before training. In this example pipeline, we have preprocessed the TrackML data into a more convenient form. We calculated directional information and summary statistics from the charge deposited in each spacepoints, and append them to its cyclidrical coordinates. Let us load an example data file and inspect the content."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "      <td>-0.532812</td>\n",
-       "      <td>-1.440705</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>673</th>\n",
-       "      <td>0.726360</td>\n",
-       "      <td>0.467563</td>\n",
-       "      <td>3.753205</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>674</th>\n",
-       "      <td>0.244590</td>\n",
-       "      <td>0.593452</td>\n",
-       "      <td>3.746795</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>675</th>\n",
-       "      <td>0.722281</td>\n",
-       "      <td>0.704765</td>\n",
-       "      <td>3.746795</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>676</th>\n",
-       "      <td>0.128899</td>\n",
-       "      <td>-0.142000</td>\n",
-       "      <td>3.753205</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>677</th>\n",
-       "      <td>0.340476</td>\n",
-       "      <td>-0.061835</td>\n",
-       "      <td>3.753205</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>678 rows × 3 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            0         1         2\n",
-       "0    0.465221 -0.462661 -1.440705\n",
-       "1    0.122540 -0.611363 -1.440705\n",
-       "2    0.513063 -0.629230 -1.434295\n",
-       "3    0.498745 -0.508192 -1.440705\n",
-       "4    0.249448 -0.532812 -1.440705\n",
-       "..        ...       ...       ...\n",
-       "673  0.726360  0.467563  3.753205\n",
-       "674  0.244590  0.593452  3.746795\n",
-       "675  0.722281  0.704765  3.746795\n",
-       "676  0.128899 -0.142000  3.753205\n",
-       "677  0.340476 -0.061835  3.753205\n",
-       "\n",
-       "[678 rows x 3 columns]"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example_data_df, example_data_pyg = get_example_data(configs)\n",
-    "example_data_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_true_graph(example_data_pyg, num_tracks=20)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Train metric learning model\n",
-    "\n",
-    "Finally we come to model training. By default, we train the MLP for 30 epochs, which takes approximately 15 minutes on an NVidia V100. Feel free to adjust the epoch number in pipeline_config.yml"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "/bin/bash: ligne 1: nvcc : commande introuvable\n"
-     ]
-    }
-   ],
-   "source": [
-    "! nvcc --version"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Tue Mar 14 11:58:25 2023       \n",
-      "+-----------------------------------------------------------------------------+\n",
-      "| NVIDIA-SMI 520.61.05    Driver Version: 520.61.05    CUDA Version: 11.8     |\n",
-      "|-------------------------------+----------------------+----------------------+\n",
-      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
-      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
-      "|                               |                      |               MIG M. |\n",
-      "|===============================+======================+======================|\n",
-      "|   0  NVIDIA RTX A200...  On   | 00000000:01:00.0 Off |                  N/A |\n",
-      "| N/A   48C    P8     5W /  N/A |      7MiB /  8192MiB |      0%      Default |\n",
-      "|                               |                      |                  N/A |\n",
-      "+-------------------------------+----------------------+----------------------+\n",
-      "                                                                               \n",
-      "+-----------------------------------------------------------------------------+\n",
-      "| Processes:                                                                  |\n",
-      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
-      "|        ID   ID                                                   Usage      |\n",
-      "|=============================================================================|\n",
-      "|    0   N/A  N/A      2427      G   /usr/lib/xorg/Xorg                  4MiB |\n",
-      "+-----------------------------------------------------------------------------+\n"
-     ]
-    }
-   ],
-   "source": [
-    "! nvidia-smi"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:-------------------- Step 1: Running metric learning training --------------------\n",
-      "INFO:----------------------------- a) Initialising model -----------------------------\n",
-      "INFO:------------------------------ b) Running training ------------------------------\n",
-      "GPU available: True (cuda), used: True\n",
-      "TPU available: False, using: 0 TPU cores\n",
-      "IPU available: False, using: 0 IPUs\n",
-      "HPU available: False, using: 0 HPUs\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
-      "\n",
-      "  | Name    | Type       | Params\n",
-      "---------------------------------------\n",
-      "0 | network | Sequential | 201 K \n",
-      "---------------------------------------\n",
-      "201 K     Trainable params\n",
-      "0         Non-trainable params\n",
-      "201 K     Total params\n",
-      "0.805     Total estimated model params size (MB)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Sanity Checking DataLoader 0:   0%|          | 0/2 [00:00<?, ?it/s]eff=  tensor(0.0206)\n",
-      "pur=  tensor(0.0267)\n",
-      "torch.Size([2, 1048])\n",
-      "torch.Size([1048])\n",
-      "torch.Size([2, 1360])\n",
-      "Sanity Checking DataLoader 0:  50%|█████     | 1/2 [00:02<00:02,  2.17s/it]eff=  tensor(0.0162)\n",
-      "pur=  tensor(0.0090)\n",
-      "torch.Size([2, 4662])\n",
-      "torch.Size([4662])\n",
-      "torch.Size([2, 2586])\n",
-      "Epoch 0:  89%|████████▉ | 80/90 [00:15<00:01,  5.03it/s, loss=0.00784, v_num=2]eff=  tensor(0.6765)\n",
-      "pur=  tensor(0.1892)\n",
-      "torch.Size([2, 4862])\n",
-      "torch.Size([4862])\n",
-      "torch.Size([2, 1360])\n",
-      "Epoch 0:  90%|█████████ | 81/90 [00:17<00:01,  4.75it/s, loss=0.00784, v_num=2]eff=  tensor(0.6411)\n",
-      "pur=  tensor(0.1287)\n",
-      "torch.Size([2, 12884])\n",
-      "torch.Size([12884])\n",
-      "torch.Size([2, 2586])\n",
-      "Epoch 0:  91%|█████████ | 82/90 [00:17<00:01,  4.76it/s, loss=0.00784, v_num=2]eff=  tensor(0.6829)\n",
-      "pur=  tensor(0.1819)\n",
-      "torch.Size([2, 6322])\n",
-      "torch.Size([6322])\n",
-      "torch.Size([2, 1684])\n",
-      "Epoch 0:  92%|█████████▏| 83/90 [00:17<00:01,  4.77it/s, loss=0.00784, v_num=2]eff=  tensor(0.6341)\n",
-      "pur=  tensor(0.2081)\n",
-      "torch.Size([2, 3364])\n",
-      "torch.Size([3364])\n",
-      "torch.Size([2, 1104])\n",
-      "Epoch 0:  93%|█████████▎| 84/90 [00:17<00:01,  4.78it/s, loss=0.00784, v_num=2]eff=  tensor(0.7185)\n",
-      "pur=  tensor(0.1371)\n",
-      "torch.Size([2, 11800])\n",
-      "torch.Size([11800])\n",
-      "torch.Size([2, 2252])\n",
-      "Epoch 0:  94%|█████████▍| 85/90 [00:17<00:01,  4.79it/s, loss=0.00784, v_num=2]eff=  tensor(0.6482)\n",
-      "pur=  tensor(0.1134)\n",
-      "torch.Size([2, 12612])\n",
-      "torch.Size([12612])\n",
-      "torch.Size([2, 2206])\n",
-      "Epoch 0:  96%|█████████▌| 86/90 [00:17<00:00,  4.80it/s, loss=0.00784, v_num=2]eff=  tensor(0.7132)\n",
-      "pur=  tensor(0.1076)\n",
-      "torch.Size([2, 24304])\n",
-      "torch.Size([24304])\n",
-      "torch.Size([2, 3668])\n",
-      "Epoch 0:  97%|█████████▋| 87/90 [00:18<00:00,  4.81it/s, loss=0.00784, v_num=2]eff=  tensor(0.6621)\n",
-      "pur=  tensor(0.1176)\n",
-      "torch.Size([2, 15498])\n",
-      "torch.Size([15498])\n",
-      "torch.Size([2, 2752])\n",
-      "Epoch 0:  98%|█████████▊| 88/90 [00:18<00:00,  4.82it/s, loss=0.00784, v_num=2]eff=  tensor(0.5985)\n",
-      "pur=  tensor(0.3096)\n",
-      "torch.Size([2, 1040])\n",
-      "torch.Size([1040])\n",
-      "torch.Size([2, 538])\n",
-      "Epoch 0:  99%|█████████▉| 89/90 [00:18<00:00,  4.83it/s, loss=0.00784, v_num=2]eff=  tensor(0.6323)\n",
-      "pur=  tensor(0.1270)\n",
-      "torch.Size([2, 12350])\n",
-      "torch.Size([12350])\n",
-      "torch.Size([2, 2480])\n",
-      "Epoch 1:  89%|████████▉ | 80/90 [00:15<00:01,  5.18it/s, loss=0.00768, v_num=2]eff=  tensor(0.7368)\n",
-      "pur=  tensor(0.1854)\n",
-      "torch.Size([2, 5404])\n",
-      "torch.Size([5404])\n",
-      "torch.Size([2, 1360])\n",
-      "Epoch 1:  90%|█████████ | 81/90 [00:16<00:01,  4.86it/s, loss=0.00768, v_num=2]eff=  tensor(0.7007)\n",
-      "pur=  tensor(0.1137)\n",
-      "torch.Size([2, 15934])\n",
-      "torch.Size([15934])\n",
-      "torch.Size([2, 2586])\n",
-      "Epoch 1:  91%|█████████ | 82/90 [00:16<00:01,  4.87it/s, loss=0.00768, v_num=2]eff=  tensor(0.7838)\n",
-      "pur=  tensor(0.1828)\n",
-      "torch.Size([2, 7222])\n",
-      "torch.Size([7222])\n",
-      "torch.Size([2, 1684])\n",
-      "Epoch 1:  92%|█████████▏| 83/90 [00:17<00:01,  4.87it/s, loss=0.00768, v_num=2]eff=  tensor(0.6848)\n",
-      "pur=  tensor(0.2026)\n",
-      "torch.Size([2, 3732])\n",
-      "torch.Size([3732])\n",
-      "torch.Size([2, 1104])\n",
-      "Epoch 1:  93%|█████████▎| 84/90 [00:17<00:01,  4.88it/s, loss=0.00768, v_num=2]eff=  tensor(0.7726)\n",
-      "pur=  tensor(0.1297)\n",
-      "torch.Size([2, 13412])\n",
-      "torch.Size([13412])\n",
-      "torch.Size([2, 2252])\n",
-      "Epoch 1:  94%|█████████▍| 85/90 [00:17<00:01,  4.89it/s, loss=0.00768, v_num=2]eff=  tensor(0.7063)\n",
-      "pur=  tensor(0.1015)\n",
-      "torch.Size([2, 15349])\n",
-      "torch.Size([15349])\n",
-      "torch.Size([2, 2206])\n",
-      "Epoch 1:  96%|█████████▌| 86/90 [00:17<00:00,  4.90it/s, loss=0.00768, v_num=2]eff=  tensor(0.8092)\n",
-      "pur=  tensor(0.1020)\n",
-      "torch.Size([2, 29090])\n",
-      "torch.Size([29090])\n",
-      "torch.Size([2, 3668])\n",
-      "Epoch 1:  97%|█████████▋| 87/90 [00:17<00:00,  4.91it/s, loss=0.00768, v_num=2]eff=  tensor(0.7464)\n",
-      "pur=  tensor(0.1092)\n",
-      "torch.Size([2, 18804])\n",
-      "torch.Size([18804])\n",
-      "torch.Size([2, 2752])\n",
-      "Epoch 1:  98%|█████████▊| 88/90 [00:17<00:00,  4.92it/s, loss=0.00768, v_num=2]eff=  tensor(0.6543)\n",
-      "pur=  tensor(0.2798)\n",
-      "torch.Size([2, 1258])\n",
-      "torch.Size([1258])\n",
-      "torch.Size([2, 538])\n",
-      "Epoch 1:  99%|█████████▉| 89/90 [00:18<00:00,  4.93it/s, loss=0.00768, v_num=2]eff=  tensor(0.7387)\n",
-      "pur=  tensor(0.1173)\n",
-      "torch.Size([2, 15616])\n",
-      "torch.Size([15616])\n",
-      "torch.Size([2, 2480])\n",
-      "Epoch 2:  89%|████████▉ | 80/90 [00:16<00:02,  5.00it/s, loss=0.00758, v_num=2]eff=  tensor(0.7721)\n",
-      "pur=  tensor(0.1799)\n",
-      "torch.Size([2, 5838])\n",
-      "torch.Size([5838])\n",
-      "torch.Size([2, 1360])\n",
-      "Epoch 2:  90%|█████████ | 81/90 [00:17<00:01,  4.67it/s, loss=0.00758, v_num=2]eff=  tensor(0.7463)\n",
-      "pur=  tensor(0.1141)\n",
-      "torch.Size([2, 16910])\n",
-      "torch.Size([16910])\n",
-      "torch.Size([2, 2586])\n",
-      "Epoch 2:  91%|█████████ | 82/90 [00:17<00:01,  4.68it/s, loss=0.00758, v_num=2]eff=  tensor(0.8207)\n",
-      "pur=  tensor(0.1752)\n",
-      "torch.Size([2, 7886])\n",
-      "torch.Size([7886])\n",
-      "torch.Size([2, 1684])\n",
-      "Epoch 2:  92%|█████████▏| 83/90 [00:17<00:01,  4.69it/s, loss=0.00758, v_num=2]eff=  tensor(0.7138)\n",
-      "pur=  tensor(0.2079)\n",
-      "torch.Size([2, 3790])\n",
-      "torch.Size([3790])\n",
-      "torch.Size([2, 1104])\n",
-      "Epoch 2:  93%|█████████▎| 84/90 [00:17<00:01,  4.70it/s, loss=0.00758, v_num=2]eff=  tensor(0.8002)\n",
-      "pur=  tensor(0.1276)\n",
-      "torch.Size([2, 14124])\n",
-      "torch.Size([14124])\n",
-      "torch.Size([2, 2252])\n",
-      "Epoch 2:  94%|█████████▍| 85/90 [00:18<00:01,  4.71it/s, loss=0.00758, v_num=2]eff=  tensor(0.7185)\n",
-      "pur=  tensor(0.1008)\n",
-      "torch.Size([2, 15731])\n",
-      "torch.Size([15731])\n",
-      "torch.Size([2, 2206])\n",
-      "Epoch 2:  96%|█████████▌| 86/90 [00:18<00:00,  4.72it/s, loss=0.00758, v_num=2]eff=  tensor(0.8381)\n",
-      "pur=  tensor(0.0998)\n",
-      "torch.Size([2, 30800])\n",
-      "torch.Size([30800])\n",
-      "torch.Size([2, 3668])\n",
-      "Epoch 2:  97%|█████████▋| 87/90 [00:18<00:00,  4.73it/s, loss=0.00758, v_num=2]eff=  tensor(0.7762)\n",
-      "pur=  tensor(0.1084)\n",
-      "torch.Size([2, 19696])\n",
-      "torch.Size([19696])\n",
-      "torch.Size([2, 2752])\n",
-      "Epoch 2:  98%|█████████▊| 88/90 [00:18<00:00,  4.74it/s, loss=0.00758, v_num=2]eff=  tensor(0.7026)\n",
-      "pur=  tensor(0.3024)\n",
-      "torch.Size([2, 1250])\n",
-      "torch.Size([1250])\n",
-      "torch.Size([2, 538])\n",
-      "Epoch 2:  99%|█████████▉| 89/90 [00:18<00:00,  4.76it/s, loss=0.00758, v_num=2]eff=  tensor(0.7718)\n",
-      "pur=  tensor(0.1152)\n",
-      "torch.Size([2, 16620])\n",
-      "torch.Size([16620])\n",
-      "torch.Size([2, 2480])\n",
-      "Epoch 2: 100%|██████████| 90/90 [00:18<00:00,  4.75it/s, loss=0.00758, v_num=2]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "`Trainer.fit` stopped: `max_epochs=3` reached.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 2: 100%|██████████| 90/90 [00:18<00:00,  4.74it/s, loss=0.00758, v_num=2]\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:-------------------------------- c) Saving model --------------------------------\n"
-     ]
-    }
-   ],
-   "source": [
-    "# send_telegram_message('Started metric learning training.', chat_id, api_key)\n",
-    "\n",
-    "metric_learning_trainer, metric_learning_model = train_metric_learning(CONFIG)\n",
-    "\n",
-    "# send_telegram_message('Finished metric learning training.', chat_id, api_key)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### From checkpoint"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# from Embedding.Models.layerless_embedding import LayerlessEmbedding\n",
-    "# from pytorch_lightning import Trainer\n",
-    "# from pytorch_lightning.loggers import CSVLogger\n",
-    "\n",
-    "# version_number = 6\n",
-    "\n",
-    "# HPARAMS_PATH = f'/home/fgias/velo-gnn/LHCb_Pipeline/artifacts/metric_learning/velo-minbias-sim10b-xdigi/version_1/hparams.yaml'\n",
-    "# CKPT_PATH = f'/home/fgias/velo-gnn/LHCb_Pipeline/artifacts/metric_learning/velo-minbias-sim10b-xdigi/version_1/checkpoints/epoch=19-step=1600.ckpt'\n",
-    "\n",
-    "# load_configs = {}\n",
-    "# with open(HPARAMS_PATH, 'r') as f:\n",
-    "#     load_configs = yaml.load(f, Loader=yaml.FullLoader)\n",
-    "\n",
-    "# metric_learning_model = LayerlessEmbedding(load_configs)\n",
-    "\n",
-    "# logger = CSVLogger('artifacts', name='metric_learning/velo_data')\n",
-    "\n",
-    "# metric_learning_trainer = Trainer(\n",
-    "#         accelerator='gpu' if torch.cuda.is_available() else 'cpu',\n",
-    "#         gpus=1,\n",
-    "#         max_epochs=40,\n",
-    "#         logger=logger,\n",
-    "#         # callbacks=[EarlyStopping(monitor=\"val_loss\", mode=\"min\")]\n",
-    "#     )\n",
-    "\n",
-    "# metric_learning_trainer.fit(metric_learning_model, ckpt_path=CKPT_PATH)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Plot training metrics"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can examine how the training went. This is stored in a simple dataframe:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>epoch</th>\n",
-       "      <th>train_loss</th>\n",
-       "      <th>val_loss</th>\n",
-       "      <th>eff</th>\n",
-       "      <th>pur</th>\n",
-       "      <th>current_lr</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0.007978</td>\n",
-       "      <td>0.008570</td>\n",
-       "      <td>0.669372</td>\n",
-       "      <td>0.139480</td>\n",
-       "      <td>0.000125</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0.007980</td>\n",
-       "      <td>0.008654</td>\n",
-       "      <td>0.746478</td>\n",
-       "      <td>0.131017</td>\n",
-       "      <td>0.000250</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   epoch  train_loss  val_loss       eff       pur  current_lr\n",
-       "0      0    0.007978  0.008570  0.669372  0.139480    0.000125\n",
-       "1      1    0.007980  0.008654  0.746478  0.131017    0.000250"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "embedding_metrics = get_training_metrics(metric_learning_trainer) \n",
-    "\n",
-    "# embedding_metrics = get_training_metrics(metric_learning_trainer, METRICS_PATH) # Use when loading from checkpoint\n",
-    "\n",
-    "embedding_metrics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_training_metrics(embedding_metrics, 'Metric Learning')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Evaluate model performance on sample test data\n",
-    "\n",
-    "Here we evaluate the model performace on one sample test data. We look at how the efficiency and purity change with the embedding radius."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 596])\n",
-      "torch.Size([596])\n",
-      "torch.Size([2, 2370])\n",
-      "torch.Size([2, 6664])\n",
-      "torch.Size([6664])\n",
-      "torch.Size([2, 2370])\n",
-      "torch.Size([2, 15730])\n",
-      "torch.Size([15730])\n",
-      "torch.Size([2, 2370])\n",
-      "torch.Size([2, 28012])\n",
-      "torch.Size([28012])\n",
-      "torch.Size([2, 2370])\n",
-      "torch.Size([2, 41682])\n",
-      "torch.Size([41682])\n",
-      "torch.Size([2, 2370])\n",
-      "torch.Size([2, 55179])\n",
-      "torch.Size([55179])\n",
-      "torch.Size([2, 2370])\n",
-      "torch.Size([2, 65439])\n",
-      "torch.Size([65439])\n",
-      "torch.Size([2, 2370])\n",
-      "torch.Size([2, 70761])\n",
-      "torch.Size([70761])\n",
-      "torch.Size([2, 2370])\n",
-      "torch.Size([2, 73106])\n",
-      "torch.Size([73106])\n",
-      "torch.Size([2, 2370])\n",
-      "torch.Size([2, 74294])\n",
-      "torch.Size([74294])\n",
-      "torch.Size([2, 2370])\n",
-      "torch.Size([2, 75154])\n",
-      "torch.Size([75154])\n",
-      "torch.Size([2, 2370])\n",
-      "torch.Size([2, 75588])\n",
-      "torch.Size([75588])\n",
-      "torch.Size([2, 2370])\n",
-      "torch.Size([2, 75877])\n",
-      "torch.Size([75877])\n",
-      "torch.Size([2, 2370])\n",
-      "torch.Size([2, 76123])\n",
-      "torch.Size([76123])\n",
-      "torch.Size([2, 2370])\n",
-      "torch.Size([2, 76231])\n",
-      "torch.Size([76231])\n",
-      "torch.Size([2, 2370])\n",
-      "torch.Size([2, 76287])\n",
-      "torch.Size([76287])\n",
-      "torch.Size([2, 2370])\n",
-      "torch.Size([2, 76328])\n",
-      "torch.Size([76328])\n",
-      "torch.Size([2, 2370])\n",
-      "torch.Size([2, 76342])\n",
-      "torch.Size([76342])\n",
-      "torch.Size([2, 2370])\n",
-      "torch.Size([2, 76342])\n",
-      "torch.Size([76342])\n",
-      "torch.Size([2, 2370])\n",
-      "torch.Size([2, 76342])\n",
-      "torch.Size([76342])\n",
-      "torch.Size([2, 2370])\n",
-      "torch.Size([2, 76342])\n",
-      "torch.Size([76342])\n",
-      "torch.Size([2, 2370])\n",
-      "torch.Size([2, 76342])\n",
-      "torch.Size([76342])\n",
-      "torch.Size([2, 2370])\n",
-      "torch.Size([2, 76342])\n",
-      "torch.Size([76342])\n",
-      "torch.Size([2, 2370])\n",
-      "torch.Size([2, 76342])\n",
-      "torch.Size([76342])\n",
-      "torch.Size([2, 2370])\n",
-      "torch.Size([2, 76342])\n",
-      "torch.Size([76342])\n",
-      "torch.Size([2, 2370])\n",
-      "torch.Size([2, 76342])\n",
-      "torch.Size([76342])\n",
-      "torch.Size([2, 2370])\n",
-      "torch.Size([2, 76342])\n",
-      "torch.Size([76342])\n",
-      "torch.Size([2, 2370])\n",
-      "torch.Size([2, 76342])\n",
-      "torch.Size([76342])\n",
-      "torch.Size([2, 2370])\n",
-      "torch.Size([2, 76342])\n",
-      "torch.Size([76342])\n",
-      "torch.Size([2, 2370])\n",
-      "torch.Size([2, 76342])\n",
-      "torch.Size([76342])\n",
-      "torch.Size([2, 2370])\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_neighbor_performance(metric_learning_model)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Plot example truth and predicted graphs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 13588])\n",
-      "torch.Size([13588])\n",
-      "torch.Size([2, 2370])\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_predicted_graph(metric_learning_model)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Track lengths"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 13588])\n",
-      "torch.Size([13588])\n",
-      "torch.Size([2, 2370])\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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0s7ZW22tqtb22Tt1jOEgOANEqmPnDstmlqKhIhmF4HwCAM5lHVii7+B5dZLtM+2skm02Kjz9Hd174dY1JHixJSo2PU3avBF11Vj+lufur7sgAJR4dooyyofp4U09V1DnDvBYAgFALdv6wbAHCHF4AaJ1Zvlru978nt7NGk2tduqjPfB1vlBK7D2u1zZcGdtP6E06VGFJjYoLquvfUbSuOqsbpCWHkAIBwCkX+sGwBAgBonfvD2TIdlTIluWq2aWx5uS455zXFx/Zrtc3ZCbF65MuZkiEZMVJcz1htqXfrlpVHQxc4ACCsQpE/KEAAoAsy+uScnLRrSKYhOfc8pXMqTig+JqPVNs4Gpwat3qbvjE6TDEOm5+QR5k2VTaELHAAQVqHIHxQgANAFxYz7k4z0KTIdnpOXLzEk56ofSFWbW23jqG3U0z9/Q+e/9J6yByTKtBnqEWvTQ2P7hDByAEA4hSJ/UIAAQFcU002xlz8ps2m4PNUZUtwYGWkT5DnwcqtNqkqrFNe9pzb+ZY3Oe/p1de8Wpwdy0zRtQI8QBg4ACKsQ5A8KEADoqnokyky+Tk3b09RYMkDOgzlylmbKtfE9eQ7vkVlXJUny7Dkq9z3PqmJfpTwetxLSMuR4dYP+7+hO5WVSfABA1Aly/uA+IJJ2VzpUbdT43C41IV79Elu/zTwAhJ3rhDy1x6TatXLvWHtymc2Q6XKq57wFMsqHquHHjyuuv0tHbzg5v9c0TaUmZ2hQz/gwBm4NuysdiktsVHqvbuEOBQACK4j5w7IFSFFRkebPn9/p91m6rUy3/3uH4mJ3+ty2V/c4vfOzSerVzbLDCKCLs/UZ8PmTz+6Z5HE1Kf6KH8n9nOT4ywIZMtRoa1TFzqMyZUimqfiYbkrt3zdMUVvDqfyRmrBXH/4iL9zhAEBABTN/WHYKVqDuA7L3aL2cbo9qHS6fH5W1DjW5uD4+gMgVN/kGxQweJUkynW45q+pkxI2X56kecvzlDUmGTNOpmtKDOrzx8Mkrn0hKGpCogTkDWn9jePOH/bgj3KEAQMAFM3+w6x4AujAjIVG97nlZ7u3FcpUdlMdercafbZSraadkmDKMGLldDXJ1s2nkVSNkylBS394ac+1oxXUnRQBAtApm/iC7AEAUiBlxqWJGXCpJissqUf0ti+Q5XC0ZUuzEERq0+FYN7df6Nd4BANEpGPnDslOwAAD+iZ2UrYS/zFLMoHTFXjFcSS/+n2KiqPgoKiqSYRjeBwCgYwKVP0J+BOTpp5/W008/7X3u8Xh08cUX65577tHy5cu1ZMkSmaapGTNmaNq0aaEODwCiQuzEbCVuvCfcYYRFYWGhCgsLvc8pQgCg4wKRP0JegOTn5ys/P9/7/P7779fkyZNVXV2tRYsWaeHChbLZbJo9e7ZycnKUlpYW6hABAAAABElYp2Bt2LBBhmEoJydHxcXFysnJUUpKipKSkjRu3DitXr06nOEBAAAACLCwnYTu8Xi0ePFiFRUVSZIqKiqUkfH5HLL09HRVVlY2a9fW/T8Mw1BBQYHmzp3b4Thqany/AeHpysrK5OjR9c/lt9vt4Q4h4jFG7WOM2ubv+DCFCABgJWH7y3nlypUaOnSod4pVS/fy8Hia32Pji3N3TzEMw6/7gSQmnpB02Od2p/Tr10+pCV37bsF2u12ZmZnhDiOiMUbtY4za1pnxobADAFhJ2KZgLV++XJdddpn3eVpamioqKrzPKyoq1KdPn3CEBgCWMnmVS9n/KQ13GAAAiwlX/ghLAeJwOLRlyxZdeOGF3mW5ubkqLi5WfX29GhoatGbNGo0dOzYc4QEAAAAIkrBMwVq/fr1Gjhyp+PjPpy6lpKRo5syZKigokGmays/PV3p6ejjCAwAAABAkYSlAJkyYoAkTJjRbnpeXp7y8vDBEBAAAACAUuBM6AAAAgJChAAEAAAAQMpYtQIqKimQYhvcBAAAAIPJZtgApLCyUaZreBwAAAIDIZ9kCBADQeR+uXafijzaGOwwAgMV0Jn+E7U7oAIDw2r1nn3bt2StJSktJ0dAhg8IbEADAEjqbPzgCAgBRaPeefVq1tliJib2VmNhbq9YWa/eefeEOKyQ4hxAA/BeI/EEBAgBR6rLcS9Wje3f16N5dl+VeGu5wQoZzCAGgczqbP5iCBQBR5NReqlOHy3ft3XvG8y/+HgAAKbD5gyMgABAlTh02b080TccCALQv0PmDIyAAEAU+XLtOu/bsVWJib+3au9e75+poRaUk6a1l73lfe2pOb2VVlS69eExY4gUARIZg5A/LHgHhJEIACC6XyxXuEAAAFtRe/rDsEZDCwkIVFhZ6n1OEAEDrxudeor7p6Vq1tlijR4zwztE9tedqWt5kSZ8fZr8s91LOAwEABCV/WLYAAQD4pqMFBcUHAOB0gc4fFCAAEEW+eLWSU7j6FQCgLYHMH5Y9BwQA0Dmr1hbrhMOhEw5Hh65uAgCA1Pn8wREQAIhCp/ZUnUocTLsCAHREIPIHBQgARKmhQwbpyNGj3p8BAOiIzuYPChAAiGLjcy8JdwgAAAvqTP6w7Dkg3AcEAAAAsB7LFiCFhYUyTdP7AAAAABD5LFuAAAAAALAeChAAQFRhCi8AhBcFCAAgqjCFFwDCiwIEAAAAQMhQgAAAAAAIGe4DAgAW995lscrMzAx3GAAAiwlX/rDsERBOIgQAAACsx7IFCCcRAgAAANZj2QIEAAAAgPVQgAAAAAAIGQoQAAAAACETlqtgbd++XQsXLlRlZaUmT56sm2++WZK0fPlyLVmyRKZpasaMGZo2bVo4wgMAAAAQJCEvQBwOh37729/qvvvuU3p6ugoKCrRjxw5lZGRo0aJFWrhwoWw2m2bPnq2cnBylpaWFOkQAAAAAQRLyKVhr167V6NGj1b9/f8XHx+vBBx/UkCFDVFxcrJycHKWkpCgpKUnjxo3T6tWrQx0eAHRJTU6ndu/dp/dWrFLpwUOSpFVrirVuwyZJ0u49J3+3e+8+NTmdYYwUABBJgpE/Qn4EpKysTA0NDbrllltUVVWl3NxczZkzRxUVFcrIyPC+Lj09XZWVlc3aFxUVaf78+S2+t2EYKigo0Ny5czscT01Njc/rcLqysjI5enT9+zna7fZwhxDxGKP2MUZt83d8OnIvpAMHDql4w0alpiTL2XR6gvj8MuZNTU0q/mijZEpDhwzyK5ZgYwovAIRWMPJHyP9ybmxs1M6dO/Xwww+rZ8+eKiws1Ouvv97ivTw8Hk+zZYWFhSosLGy23DAMv+4Hkph4QtJhn9ud0q9fP6UmxPvd3grsdjt3WW4HY9Q+xqhtnRmfjhQupQcPKjUlWdPyJnuXXTb2Uu/PQ4cM0tAhg/TWsvdUevBgRBYgTOEFgNALRv4IeQGSkpKiiy++WOnp6ZKksWPHat++fcrOzlZJSYn3dRUVFcrKygp1eADQJQ0cMKBDrztn8OAgR+K/06fwStKDDz4owzC0fPly7xReSd4pvNOnTw9nuADQJQQjf4S8ALn00kv1zDPPqLy8XAkJCVq5cqWuuuoq5eTkaMmSJaqvr5dhGFqzZo2uu+66UIcHAF1SR49oROKRj1M6O4UXAOC7YOSPkBcgffv21U033aR58+bJ7Xbr8ssv15e//GUZhqGZM2eqoKBApmkqPz/fe5QEANA5q9YUSzrzsLkk1dXXa92GTSo7Ui6n06m+Gem6YPRI9TvtD/pI0dkpvME8h5Dzm1rH2LSPMWofY9S2YJ5DGIz8EZazp6dMmaIpU6Y0W56Xl6e8vLwwRAQAXVt8fFyzZXX19Xr1zbfVK6GnLhw9UpJUdqRcby97X1PzLo+4IqSzU3iDeQ4h5ze1jHO/2scYtY8xaluwzyEMRv7o+pdvAgDokovGNFu27qON6pXQU1d/9fMrRo3IHqa3lr2nj7dsU7+8yCpAmMILAKEXjPxBAQIAUWD3nn2Ki4/TwAH9vcvKyo9691ydbuCA/tq0ZVsow+sQpvACQOgFI39YtgBpay4vAOBMpQcPqsnpPCOBWBFTeAEgtIKRP0J+J/RAKSwslGma3gcAoHUDBwzQsarqz67TfvJOtv0y0rVrz75mry09eEipKcmhDRAAEJGCkT8sewQEANBxWVkn91yVHjzovZPtJReP0atvvq1X33xb53x2+cTSg4d0pPyoRo0YHq5QAQARJBj5gwIEAKJAfFyc9261p/RKSNDVX52qdR9t1KYt27yXURw1Yri2bv9USYmJEX1fEABA8AUjf1CAAEAU65WQoMmTJjRbnpSYqFVri5WSksx0LABAM53JHxQgAIBmhg4ZpJSUZPXqlRDuUAAAFtKR/EEBAgBoEUc+AAD+aC9/WPYqWAAAAACshyMgFlNW49DG0mq/2o4bkqbknnGBDQgAAADwgWULkGi9EeFNf1un0mMNshm+tXN7TF0yKEVPzcwNTmAAAABAB1h2Cla03ojweINT9Q6X6nx8NDS6dbzBFe7wAQAAEOUsW4AAAAAAsB4KEABAVCkqKpJhGN4HACC0LHsOCADAN1u3bu3Q60aNGhXkSMKrsLBQhYWF3ucUIQDQtkDnDwoQAIgSXb2wAAAER6DzB1OwAAAAAIQMBQgAAACAkLFsAcJJhAAAAID1WLYAidb7gAAAAABWZtkCBAAAAID1UIAAAAAACBkKEABAi5jeCgDwR3v5g/uAAEAXZ0o6dakOt9utmJiYk8tN84yLeFQeq9JBu10xNpuy+vdXUlJi6IMFAESMYOUPChAA6OIMSbW1ddq0dZtKSw9qYFZ/XTB6pBJ795YkuT0efbx5q0p27ZZpmvKYHm3etl2jRmRr9MjzuNIgAESpYOUPChAA6OJqamu17IOVanI6lZyUKHtZuSqrqjV54mVKSuytbds/lb28QqPPG6Hhw86V2+PW1k+2a98Bu7r36KFhQ4eEexUi3tJtR3TzU+t12Tl99M8f5YY7HAAIiGDlD8ueA8J9QACgYzZs2iyHo1ETx16qq74yRRPH56rJ6dS27SUyTVPbS3aqR3ysRp03XLGxMeoWH6+LL7xA3eJjtWXb9nCHbwlr9lRKhqnNB6vCHQoABEyw8odlCxDuAwIAHVNeUamzs/or86x+Omi3K7NfX2X2y1DpwQNqbGqSx+NRnz59JJ2c4+t2uyVJKcnJsn023xcAEH2ClT8sW4AAADqmW7duSklO1u69+7R+42bt3rtPqcnJcrs9MiTFxcbooN0uj8dUTEyMYmJi5HK5VHbkiNwuZ7jDBwCESbDyBwUIAHRxbo9bTqdTSYm9Nf7SHCUl9pbT5ZJh2NStWzcNGTRIpmx6778rdPCQXaUHD2rZByskI0Yjhp0b7vADjim8ANAxwcofFCAA0MXZDJtssTYdrajUuo2bVV5RqRhbjAzj5BVMRp03Qt3jY1VT16DijR9r05ZPVFffoD6pyRreBQsQpvACQMcEK3+E5SpYN954oyoqKrzP77//fo0cOVLLly/XkiVLZJqmZsyYoWnTpoUjPADoUgzDJpnS8Oxz5XS6NGLYudq6vUQyDHncHsXHxykpsbcqj1Vp8NlZiouNU9++6crs1y/coQMAwihY+SPkBYjH41FTU5PeeOONM5ZXV1dr0aJFWrhwoWw2m2bPnq2cnBylpaWFOkQA6FIcjhM6frxGhmHo/FHnSZKO1xyXaZqKjY2R0+nSrj17lZyYpEsvvijM0QIAIkWw8kfIp2BVVFS0WFQUFxcrJydHKSkpSkpK0rhx47R69epQhwcAXU7f9D46aD+ssvJySVJZebnsZeXKyjxLhmHocFmZunXvoUFnD5RpmnK73UxNAgAELX+E/AhIWVmZjh8/rjvuuEP79u3TpEmTdNttt6miokIZGRne16Wnp6uysrJZ+6KiIs2fP7/F9zYMQwUFBZo7d26H46mpqfF5HU5XVlYmR4/QDaPH4/a7rdPplN1u96utv+2iCWPUPsaobf6OT3snUo85f5Te/WClln+wShl9UlVeUanu3bt792btO3BAzqYmDRzQX4ZhyGazcXI2ACBo+SPkBUjv3r117bXX6mtf+5pqa2t111136fXXX2+xWvJ4PM2WFRYWqrCwsNlywzD82mOXmHhC0mGf253Sr18/pSbE+93eVzbbp363jYuLU2Zmps/t7Ha7X+2iCWPUPsaobZ0Zn/YKl6SkJE2eeJk+3rpN9rIyZfbtqwtGj1RSYqKampp0yF6mPmmpSkjoKan9ggYAEB2ClT9CXoCcffbZGjRokAzDUFJSkiZOnKi9e/dq2LBhKikp8b6uoqJCWVlZoQ4PALoc0zSVmpKsyRMvk9vtVsxnN4cyTVNxcXGadNk4de/WLcxRAgAiTbDyR8jPAXnhhRdUVFQkl8uluro6ffjhhxo5cqRyc3NVXFys+vp6NTQ0aM2aNRo7dmyowwOALufUEWLTPHmjqFM/n7oPRv+z+iktNSXcYQIAIkyw8kfIj4B8/etf19GjRzVr1iy5XC5NnTpVeXl5kqSZM2eqoKBApmkqPz9f6enpoQ4PALqk0w+LM8UKANBRwcgfIS9A4uLidOutt+rWW29t9ru8vDxvMQIAAACg6+FO6AAAAABChgIEAAAAQMhQgAAAAAAIGcsWIEVFRd4z8DmhEgAAALAGyxYghYWF3kuB+XMDQgAAAAChF/KrYAEAwmPr1q0det2oUaOCHEl4FRUVaf78+eEOAwAsI9D5gwIEAKJEVy8sOqqwsFCFhYXe50zjBYC2BTp/WHYKFgAAAADroQABAAAAEDIUIAAAAABChgIEAAAAQMhYtgDhPiAAAACA9Vi2AOE+IAAAAID1WLYAAQAAAGA9FCAAAAAAQoYCBACi2Ko1xVq1pjjcYQAALKYz+YM7oQNAFKurrw93CAAAC+pM/uAICAAAAICQoQABAAAAEDKWLUC4DwgA+OdYVbWanM5my5ucTh2rqg59QAAASwhU/rBsAcJ9QADAP28te09vvfveGUmkyenUW+++p7eWvRfGyAAAkSxQ+cOyBQgAwD/T8iarrr5eb737njweUx6PqbfefU919fWaljc53OEBACJUoPIHBQgARJnUlGRvEqmqrlZVdbU3eaSmJIc7vKBjCi8A+CdQ+YMCBACixOlzd08lEbfbLbfbfUby6OrngjCFFwB8E+j8QQECAFHitaVv61hVlff5qSTyxT1Xx6qq9NrSt8MQIQAgEgU6f3AjQgCIIk1NZ169JCO9T7uvAQAgkPmDAgQAokRW/0yt27BR20t2tPm6uvp6ZfXPDFFUAIBIF+j8YdkpWJxECAC+ueTiMRo4oH+7rxs4oL8uuXhMCCICAFhBoPOHZY+AFBYWqrCw0PucIgQA2tYrIUGXXERhAQDwTaDzh2WPgAAA0BXsOFKrpVvLwh0GAISMZY+AAADQFXzr0Q/lcpuqcYzU9TlZ4Q4HAIIu7EdAPB5PuEMAACBsah0uNbk9qm7g6mMAokNYj4AsXrxYjY2NuvXWWyVJy5cv15IlS2SapmbMmKFp06aFMzwAAAAAAdbhIyB//vOfA9rx5s2b9corr3ifV1dXa9GiRXrwwQf1xz/+UUuWLFFlZWVA+wQAhF6g88cpHEEHAGtqtwCZM2eO7rrrLi1btkwbNmxQfX39Gb9fuXKlz53W19friSee0HXXXeddVlxcrJycHKWkpCgpKUnjxo3T6tWrfX5vAEBkCEb+OGXx4sX6y1/+4n2+fPly/eAHP9BNN92kt956y+/3BQAEX7tTsObNm6etW7dqw4YNeuyxx7R//34NGDBAI0aM0Nlnn62nnnpKEyZM8KnTBQsW6Hvf+57Ky8tVU1MjSaqoqFBGRob3Nenp6RwBAQALC0b+kD4/gn5qmu6pI+gLFy6UzWbT7NmzlZOTo7S0tECvEgAgANotQAYOHKiBAweqvr5e1113nU6cOKGSkhJ9+umn+uSTT3T99df71OG7776rhIQEXXrppXrttde8y03TbPbalg6vFxUVaf78+S2+t2EYKigo0Ny5czscz6kCyF9lZWVy9AjdqTQej9vvtk6nU3a73a+2/raLJoxR+xijtvk7PpF6H6RA5w/pzCPop76/Tz+CLsl7BH369OkBXR8AQGB0+C/nU9OlevTooQsvvFAXXnihXx2+//772rNnjz766CPV19fL5XKpvr5eo0aNUklJifd1FRUVyspqfjnCL96A8BTDMFosYtqTmHhC0mGf253Sr18/pSbE+93eVzbbp363jYuLU2Zmps/t7Ha7X+2iCWPUPsaobZ0Zn0gv7AKVPySOoANAV9DhAqSmpkYffvihevXqpeHDh6tPnz5+dXj33Xd7f37ttdd04MAB3XrrraqqqtKSJUtUX18vwzC0Zs2aM84RAQBYU6DyRyQfQa+vrzvZr2n6VRCan71fpBeT/uiK6xRojFH7GKO2We0IeocLkLvuuktHjx5Vnz59tGvXLiUmJmrEiBEaPny4brjhhk4HkpKSopkzZ6qgoECmaSo/P1/p6emdft9gMiW9seWwenXzfQrW1JH91DM+JvBBAUCECVT+iOQj6AkJvSSVy2YYfhzJ2ihDUmJiYpc7SsiRz/YxRu1jjNpmxSPoHf7Lef/+/fr73/+upKQkuVwu7dmzR59++qk+/dT/KUFfnJ+bl5envLw8v98v1Jqcpn77xqfytXh0eUyt3FWpB647PziBAUAECVT+4Ag6AHQNHS5AzjnnHLndJ0+Ajo2N1bBhwzRs2DB97WtfC1pwEc8wVd/o9PnwlWlKtQ7ueAsgOgQ7f1jxCDoARLMOFyC5ubn67W9/q9tvv10DBgwIZkwAgC4kGPnD6kfQASCadbgA+fDDD7Vr1y7NmjVLOTk5ys7O1rBhw3TuuedyrXUAQKvIHwCA03W4AHnooYfk8XhUWlqqkpIS7dixQ//4xz+0Z88evfHGG8GMsUVtXc0EABA5Ii1/AADCq8MFyB/+8AdlZ2crOztbeXl53jvQulyuoAXXli9ezSRSb8QFAJHiWFW1evVKUFNTk7aX7FTZkXJVVVdLkvpmpGvggP4aOmSw4uPiAtpvpOUPAIBvAp0/OlyAnHXWWVq3bp3+8Y9/qKamRkOHDvUmlClTpvi1MgCA0Hlt6du65KILtWnLNknSOUMG6YLRI+VscqqsvFybtmzTJyU7NHniBKWmJAesX/IHAFhboPNHhwuQ73znO96fKysr9frrr+vZZ5/VkSNHSCAAYBHrNmxSSnKSpn35ijP2VA0dMkiXOJ16778r9d6Klbr6q9MCdiSE/AEA1hfI/GHzJ4C0tDTdeOONuvvuu7nUIQBYjqGPP9uLdbr4uDhNnjRBTU1O7d6zNyg9kz8AwMoCkz86XICsX79etbW1Zyy74IILtG7duo6+BQAgjIYOHqS+GemKj4+T1PKdv+Pj4nTOkEHatWdfwPolfwCAtQU6f3R4CtZf//pX7d27V5mZmRoxYoSGDx+uY8eONUsqAIDIdNnYSzv0uqwB/QNagJA/AMDaAp0/OlyAPPbYY3I4HNqxY4c++eQTbdiwQXa7XbfccktH3wIAEEb/eu5FTZ50mfplZLT5un4ZGZpx7TcC1i/5AwCsLdD5o8MFiCTt3LlTu3fv1tChQ3XNNdeoe/fuvjQPKO4DAgC+6ZXQUwcOHmo1gaxaU6y6+npJUmpKii656MKA9R1J+QMA4JtA548OnwPyzDPP6K677tL69ev18MMPKz8/P6w3kCosLJRpmt4HAKBtAwcM0K49+9TkdLb6mqamJh0pP6rtJTsC1m+k5Q8AgG8CnT86XIC8+OKLuueee3TPPffo6aef1h/+8Ae9+OKL+uCDDzr6FgCAMBoxfJji4+L03n9XtphELrl4jKTA39Q10vJHUVGRDMPwPgAAbQt0/uhwAeJyuXTWWWd5nw8aNEjz5s3TCy+80OHOAADhc+oyiXV19Xr+5de0ak2xdu/dp91792ndhk16/uXXVFdfH9CpV1Lk5Q+OoAOAbwKdPzp8DsiYMWP0yiuv6KabbvIuy8zM1N69wblWPAAg8FJTknX1ldO0e89elR48pFVriiVJKclJOi97mIYOGaT4+Hj1bedEQ1+QPwDA+gKZPzpcgNx88836+c9/rv3792v8+PEaMGCA3nzzTQ0ePNj/NQEAhFx8XJxGZA/TiOxhrb4mNSU5YP2RPwCgawhU/uhwAZKenq5HHnlEr7zyil588UUdPnxYAwcOVEFBQUffAgAQhcgfAIDTtVmAOBwOPfnkk/rggw/08MMPq2/fvvr2t7+tc845R+eff77i4+NDFScAwELIHwCA1rR5EvqiRYu0Zs0a3XTTTUpJSfEuf/XVV3XTTTdpy5YtQQ+wNVzFBAAiVyTnDwBAeLVZgCxfvlx33HGHpk2bdsbeqsLCQn3nO9/Rr3/9a5WXlwc9yJZwFRMAiFyRnD8AAOHVZgFimqb69OnTvJHNpunTp+trX/uaFi9eHLTgAADWRP4AALSmzQJkyJAh2r59e6u/v/zyy9v8PQAgOpE/AACtafMk9G9/+9t68MEHNXLkyBb3ZBmGoaqqqqAF11WZprTpQLV+8OR6n9seP9EUhIgAILDIHwCA1rRZgOTk5Oiqq67SLbfcopkzZ+qKK65Q9+7dJUlOp1N///vfNXTo0JAE2qUY0pEah8pqHOGOBACCgvwBAGhNu/cByc/PV3Z2tv7617/qkUce0YABA9SjRw/t3LlTsbGxuvfee0MRZ5djMwx5/Dh5ntPtAVgF+QMA0JIO3YgwJydHOTk52rt3rzZt2qTq6mpdccUVmjhxopKTk4McIgDAqsgfAIAv6vCd0CVp8ODBGjx4cLBi8UlRUZHmz58f7jAAAB0QSfkDABBebV4FK5JxHxAAAADAeixbgAAA4I+ioiIZhuF9AABCiwIEABBVOIIOAOFFAQIAAAAgZHw6CT1QHn30Ua1cuVJut1vTp0/Xd7/7XUnS8uXLtWTJEpmmqRkzZmjatGnhCA8AAABAkIS8AFm3bp1KSkr05JNPyuFw6Ic//KHGjRuntLQ0LVq0SAsXLpTNZtPs2bOVk5OjtLS0UIcIAF3esapqVVVX61hVtSQpNSVZKcnJSk1JDmtcAIDIFoj8EfICpGfPnvre976nuLg4xcXFacCAAWpsbFRxcbFycnKUkpIiSRo3bpxWr16t6dOnhzpEAOjS1m3YqO0lOyVJfTPSJUnbS3ZIki4YPVIXjBoZttgAAJErUPkj5AXIyJEnA1u+fLneeOMNpaena/jw4dq0aZMyMjK8r0tPT1dlZWWow+vS6htdevuTIz63czfUKTMzCAEBCLlX33xLdfUNunziZRo4oP8Zvys9eEir1hSr9MAhXf3VqWGKEAAQiQKZP8JyDogknX/++ZKkp59+Wjt37mzxSiQej6fZsrZuQGgYhgoKCjR37twOx1FTU9Ph10YCQ5Jf12wxTe2pqNfPntngc9MYw1CMzdCofgn+9Bw17HZ7uEOIeIxR2/wdn45eSnbdho2qq2/QtLzJSk1J1u49+1S8YaMk6cLRIzUie5h65U3WW8ve08dbtumC0RwJAQAEPn+EvABZu3atUlJSNGzYMF1xxRXatWuXiouLlZ6erpKSEu/rKioqlJWV1ax9YWGhCgsLmy03DMOvyykmJp6QdNjnduHi7wUjTcOQTFP1Tb6/Q0J8jHompigzs4+fvXd9drtdmRwmahNj1LbOjE9HCpdjVdXaXrJTl0+8zDtPt3jDRp2XPUzx8XFat2GT+mZkKDUlWZeNvVTvr1ilrAH9OScEAKJcMPJHyC/DW1lZqb///e9yOByqq6vTxx9/rKysLOXm5qq4uFj19fVqaGjQmjVrNHbs2FCHBwBdUtVnJwuefth8xrXf0AWf7blq6TWnngMAolcw8kfIj4B85Stf0a5du/TDH/5Qpmlq8uTJ+tKXviTDMDRz5kwVFBTINE3l5+crPT091OEBQJd0rLrKe8Lg6ZqcTq1aUyxJ6tv389/3zUjXseoqDdWgUIUIAIhAwcgfIS9AbDab5syZ0+Lv8vLylJeXF+KIACB6fbxlq8qOlGv6V6aqVwLneQEAOqYz+YM7oQNAFEhNTtGR8qMtLr9s7KXN5uoeKT+q1OSUEEUHAIhUwcgfFCAAEAVSPksQpQcPnbG8b9/0Zsnj1GtSOAEdAKJeMPIHBQgARIHUlGSNyD5Xq9YUe+9eK528gdSpm0hJJ692smpNsS4YNZIrYAEAgpI/LFuAFBUVyTAM7wMA0LZLLhqjXgk99day97x7qS65aIwuuWiMpJN7rt5a9p56JfTkHiAAAK9A54+w3Yiws754PxCKEABo39VfnaZ1Gzbq/RWrJMl7ZZNT83svGDWS4gMA0Ewg84dlCxAAgH8uuWiMhg4erKqqah2rrpIknTN4sFJSkqNi2lVRUZHmz58f7jAAwHIClT8oQAAgCqV+liyi8T4fHEEHAP8FIn9Y9hwQAAAAANZDAQIAQJAcqXFo8aq9OlrbGO5QACBiMAULAIAg+fFT67XlUI1e/fiwXrxtfLjDAYCIwBEQAACCxCNDbo8pj8cMdygAEDEsW4BwHxAAAADAeixbgBQWFso0Te8DAAAAQOSzbAECAAAAwHooQAAAAACEDAUIAAAAgJChAAEAAAAQMhQgAAAAAEKGAgQAAABAyFi2AOE+IAAAAID1WLYA4T4gAAAAgPVYtgABAMAfHEEHgPCiAAEARBWOoANAeFGAAAAAAAgZChAAAAAAIUMBAgAAACBkKEAAAAAAhAwFCAAAAICQsWwBwmUUAQAAAOuxbAHCZRQBAAAA67FsAQIAAADAeihAAAAAAIRMbDg6ffHFF/XKK6+ooaFBY8eO1Zw5cxQTE6Ply5dryZIlMk1TM2bM0LRp08IRHgAAAIAgCXkB8sknn+ill17SwoULZbPZ9H//939aunSpLrvsMi1atMi7fPbs2crJyVFaWlqoQwQARCh2YAGA9YV8CtaxY8d01VVXqXfv3kpISNDYsWNVVlam4uJi5eTkKCUlRUlJSRo3bpxWr14d6vAAABHq1A6sBQsWaPHixSotLdXSpUtVXV2tRYsW6cEHH9Qf//hHLVmyRJWVleEOFwDQipAXIBMmTND1118vSaqsrNQ777yjcePGqaKiQhkZGd7Xpaenk0AAAF7swAKAriEs54BI0tKlS/X000/r1ltv1XnnnaeNGzc2e43H42m2rKioSPPnz2/xPQ3DUEFBgebOndvhOGpqajr82khgSPL/osP+t66srJS9Z5PfPUcDu90e7hAiHmPUNn/HJ1ruhTRhwgTvz6d2YM2dO1ebNm1iBxYAWEjICxCPx6N7771Xbrdbf/rTn5ScnCxJSktLU0lJifd1FRUVysrKata+sLBQhYWFzZYbhuHX/UASE09IOuxzu3Dp3B1P/G+dlpamzMw+neq9K7Pb7crMzAx3GBGNMWpbZ8Yn2gq7SNyBVV9fd7Jf0zzj83A2ndxx43Q62/yczM/eryt+ll1xnQKNMWofY9Q2q+3ACnkBsmLFCjU0NOiee+45Y3lubq6WLFmi+vp6GYahNWvW6Lrrrgt1eACACBXJO7ASEnpJKpfNMM4oJOPi90pqUFxcXBsF5kYZkhITE7tckc6Oh/YxRu1jjNpmxR1YIS9Atm7dqo0bN+qaa67xLrv66qs1a9YszZw5UwUFBTJNU/n5+UpPTw91eACACMUOLADoGkJegMyePVuzZ89u8Xd5eXnKy8sLcUQAACtgBxYAdA1hOwkdAABfsAMLALqGkF+GFwAAAED0ogABAAAAEDKWLUCKiopkGIb3AQAAACDyWbYAKSwslGma3gcAAJHs/17apvN+/Zb+89HBcIcCAGFl2QIEAAAreXd7meocLr2z7Ui4QwGAsKIAAQAgBAydnC7MrGEA0Y4CBAAAAEDIUIAAAAAACBkKEAAAAAAhQwECAAAAIGRiwx2Av4qKijR//vxwhxEVnB5Tv3ltu9IS4nxq5zFNfeviLF138YAgRQYAAACrsWwBUlhYqMLCQu9zbkYYPE6XqU/LanxuZ8rU/sp6ChAAEYUdWAAQXkzBQvuMz//x9WGzURgCiCzcyBYAwosCBAAAAEDIUIAAAAAACBkKEAAAAAAhQwECAAAAIGQoQAAAAACEDAUIAAAAgJCxbAFSVFQkwzC8DwAAurpah0uz/7lBD7yzU24PlxAGYE2WLUC4jjsAINq8ta1M72w7osfe26HjJ5zhDgcA/GLZAgQAgGgUG2OTjSP/ACyMAgQAAABAyFCAAAAQYi9vOqzb/rlJVfVMowIQfWLDHQAAANHmf5/bpIYmt/r2jg93KAAQchwBAQAgxAzbyXM4OJcDQDSiAAEAAAAQMpYtQLgPCADAH+QPAAgvyxYg3AcEAOAP8gcAhJdlCxAAAAAA1sNVsBBUHlMqq3GEtM+kHnHqERcT0j4BAADQMRQgCBrTlI4cb9Tk+9/3ua3bNOUxpTibb/OzTVPKPquXXp49wec+AQAAEHxhK0AcDod+/etf6/e//7132fLly7VkyRKZpqkZM2Zo2rRp4QoPgWBIpkydcLp9bmqzGXJ7TLl8bGrK1O7yOp/7AwAAQGiEpQB588039eabb6qpqcm7rLq6WosWLdLChQtls9k0e/Zs5eTkKC0tLRwhAgAAAAiCsJyEnpSUpEmTJp2xrLi4WDk5OUpJSVFSUpLGjRun1atXhyM8AAAAAEESliMg48eP14EDB/Tuu+96l1VUVCgjI8P7PD09XZWVlc3aFhUVaf78+S2+r2EYKigo0Ny5czscS01NTccDjwCGJP8vGtm51v4xP+vXx1adCNNjmrLb7f6/gZ/C0afVMEZt83d8uJcFAMBKIuYk9Jauxe7xeJotKywsVGFhYbPlhmH4dT33xMQTkg773C5cOlc+hON69/79YWR0olayGYYyMzP9a+wnu90e8j6thjFqW2fGh8IOAGAlEXMfkLS0NFVUVHifV1RUqE+fPmGMCAAAAECgRUwBkpubq+LiYtXX16uhoUFr1qzR2LFjwx0WAAAAgACKmClYKSkpmjlzpgoKCmSapvLz85Wenh7usBBFVu+p1M1PrVeMj/cekaTs9B565lamFwGIXPZqh4r3VmrqyH7qGc/NWgGET9gKkKysLD322GNnLMvLy1NeXl6YIkK0e2vbER13uOTPeStr9lvrYgYAos/3F6/V3sp6XbOrUg9cd364wwEQxSLmCAgQKYywnKwPAMFV1+hWk9NUrcMZ7lAARLmIOQcEAAAAQNdn2SMgbd0PBAg1j0e68o8rfG7ndJt69LsX65yMhCBEBaCr+Gh/lbrFdmyf4QmnW6t3V2r0gCSl9+oW5MgAwHeWLUC+eD8QbsSFcLLZpG2H/TgPxJT+WVyqX08fEfigALTIajuwNpRWa9bf18swpJsnDWn39UWvfqIXNh5Sdt9eevV/JoQgQgDwDVOwgAAx/HhI/t6qEYC/CgsLZZqm9xHpah1Oud2mmlweudztx1tZ16RGp0vH6htDEB0A+I4CBAAAAEDIWHYKFtAS0zTlcJn60ZKPfG77aVltECKKPHWNLv3qxa1qcLp9bjs4LUHzpg1TfAz7LgArO1Lj0N6KBuUOThUzmAGEGgUIuhZDcrrcemdbmc9NPTJlRMGEqKVby/TmlsNqcnt8bts9Nkbfuqi/svv1DkJkAELlhsfX6PBxh/73K8M187JB4Q4HQJRhNybwmWi6kEGsn0cwYmKiZ4yAruxIjUMnGl06VHUi3KEAiEIUIAAAAABCxrIFSFFRkQzD8D4AAIgETW6PPjlco1qHy+e2ZTUO7TpaF4SoACByWLYAsdplFAEA0eH3S0v0jUc+1I+eWu9TO4fTo2/8eZW++ciHWrPnWJCiA4Dws2wBAgBAJNpf2SCH06UDVfU+tXOZHh2rb5TT7dHh45ybAaDrogABAAAAEDJchhcIsx1HavX02lKf2/WMi/HrXh4bSqv8nrboMU29vqVM6/dX+dz2gjQp069eAbTmUPUJ9e4ep8TupHMA1sE3FhBWpj7cVaF1+yp9bnnC6VH32BgZhm/FhNMtvwsQR5NHj36wS75ejddtSiMzeuqlOQP96hdAcyt3HtX/Pr9FGYnd9cHPLw93OADQYRQgQFgZcpum3E4/CgLTlMPp8vkqcB5TirX5d+U4j0w1OU2f75xsmlKTm4tFAIG0q7xeTS6P9lf6dq4JAIQb54AAAAAACBmOgAAIiQanR8s/Lfe5XXKPOF10dkoQIgK6lhqHS93jgr9fsa7RpbgYm7rFsg8TgH8sW4AUFRVp/vz54Q4DQAeYkkqrHPrpMxt9bhtrs+lvP7hEF2YlBzwuRKeumD9e2mTX3Gc3aUxWslITugWtn48PVuu7f12jPgnd9fpPJ6pnfEzQ+gLQdVl29wU3IgQsxpRqHS6fH063R3WNvt9RGmhNV8wfmw5Uy+3xqKSsJqj97DxSJ7fH0KHqBjn8uAofAEgWLkAAAAAAWA8FCAAAAICQoQABACCKffWPKzT0l29o1a5KffkPH2jU/Le0/XCtT++xdNsRnf2/r2vG42uCFCWAroQCBACAKLb98MnzRtbuq9SBqnq5PaY+9fFckjV7KiXD1JZD1UGIEEBXQwECAAAAIGQoQAAAAACEjGULkKKiIhmG4X0AANBV1DW6NPmB9zX+vmWqrG8Kdzgt2na4Rtn/96a+8edVAX/vyvom7/ofqj4R8PcHEF6WLUC64nXcAQCQpOoTTpXXOlTjcOvw8cj8A3zdnmNyuaWNB6oD/t5Hahze9d9bUR/w9wcQXpYtQAAA6MoMw5Aifgdb8OKzxvoD8AcFCAAAAICQiQ13AADQlka3qR8sXq8Ym297Qk1J3xgzQPd9a3RwAgMsYMHyXXr8g12afcW5PrVraHTrqgUr1Ogy9dANF+inz2xUQnysrr8ky6f3+du6Mv1rw2bNvuJclVU79J+PSnX3Nefrmxf1b/baRpdH331ijQ4ed+jua0b51E84bD9cqxlPrNbo/slaMuvScIcDWEpEFSDLly/XkiVLZJqmZsyYoWnTpoU7JABh5nJ75DFNGR7f2pkytWLX0eAEhYhD/mjZ29vKVOtw680tZT61O3aiSfsqGyRJ/915VEeON8pUo443OH16n//uPu7tv7zWoTqHR0u3lrVYgNQ4nNp86LgMw9Anh327D0k4vF9SrlqHWyt28j0D+CpiCpDq6motWrRICxculM1m0+zZs5WTk6O0tLRwhwbAspg/Hg3IH607dZVIf64VaRjy/heyGYY8fvx/Mk771/jsWdsXrjStdWVLzlEB/BIx54AUFxcrJydHKSkpSkpK0rhx47R69epwhwUAiHDkDwCwFsOMkGvY/vOf/5RpmsrPz5ckPfvsszpx4oS+//3vn/G6oqIizZ8/v9X3KSgo0Ny5czvc73/3HNf8pXtlM3yvxRrdHsXapBgf2za5TRmGR3G2GJ/7bPJ4FCNDMTbf9hA5PR7JNBQX4/ueJafbI8MwFOtjny6PR25J3Wy+j22Txy2ZhuJjfGvrNk25PKa6+dhOOvm5SKbPfUrSCZdbPWL9+DzdJ+cV+dMn21/bPObJz9PtxzdctxibGt0+zvn6TGqPOB074ds0FUmKMQwtv+0Cv/o0DENnnXWWX227gnDnD9Owye02ZRimYm02uT0emaZ08r+1IY8p2YyTe/5dnpN7/mw2Q263Rx5JMTZDNtnU6HarW+zJ7bzRZap7nE2GIZ1o+uz7xSadaPKoe6whjww1uTyKi5EUYf3HGCe/nxK7xai+ydNi/4k94lR9okk2w1BSz1jVNHjk9LgVH9O8/0anRwnxMYqLten4Cae6xdjUIz62WXu36VZqjzjVN7m9/XtkqK7RpZ5xgW9f2dCkONvJ9TdMI+T90572n7d3WS5/RMwUrJbqII+n+R8AhYWFKiwsbLbcMAy/7gfy7cxMZSV1U5/0dJ/bxnz2Re8Pw2bI9Pger82wyWP616fNMD77o8w3FRXl6tMnw78+bYY8fqynYZP8XE3F2Ay5/enTz21Iko4erVB6eh+/2hoyZPoxtcFq219lRYXS+vg+RjZD8iNUSVJFfZPqHC6f23Vm+4s1JJcf8dZUVykzM9OvPu12u1/tuopw54+4XqmqbXRqQHJ3VTU0qcHpVv+kHqqob5Lb7VbfxB46UtOoGJtNfXrF6eBxh3rFxSi5Z5xKK08oqWe8EnvEan9lvTJ6xSu+m00HKx3ql9xdMZIOVTvUP7mHXG5TR2odGpDSU00uj8prHRqUmqDjDldE9V/T6JLpkWwxhuqrjmnE4P7N+jfdptymKcM0JJvxWUHkbrX/GJshmYbcpkcyDMVILbaPNSS3IW//humR22MErf2p9a9pdPnd/7HqKvXpkxaW+K3S/nh1tVJTUi0bf7DbH6+qtFz+iJgCJC0tTSUlJd7nFRUVysry7Wob/hqc1l2Z/XqHpC8r6u2pZXzawRi1zx6GMcoOaW+dY7e7wx2CZYU9f2Qmt/i707e/7NO2/dZ+Ht6vl/fnkf2SvD+P6v/5z1JrP0de/5Jkt7uU2a93q322pvX+ux673aXMTP92YEULu92tzEzfdxRHC7vd96Pu4RYx54Dk5uaquLhY9fX1amho0Jo1azR27NhwhwUAiHDkDwCwlog5ApKSkqKZM2eqoKDAO5c33Y9pUQCA6EL+AABriZgCRJLy8vKUl5cX7jAAABZD/gAA64iYKVgAAAAAuj4KEAAAAAAhY9kCpKioSIZheB8AAAAAIp9lC5DCwkKZpul9AAAAAIh8li1AAAAAAFgPBQgAAACAkKEAAQAAABAyFCCSHnzwwXCHENEYn/YxRu1jjNrG+FgTn1vbGJ/2MUbtY4zaZsXxMcwucga3YRh+n4zembbRgPFpH2PUPsaobZ0ZH7vdrszMzABHFD3IH8HD+LSPMWofY9Q2K+aPiLoTemcMHDiwU5fj5VK+bWN82scYtY8xapu/43PZZZdp5cqVAY4mepA/govxaR9j1D7GqG1Wyx8cAelEW/qkz3C2pU/6DERbRM/nTZ9dq8/OtKVP+gxE287gHBAAAAAAIUMBAgAAACBkukwBMn/+fEv16W/bcPTZGVZaz3CMT2f6jZYxstp6Wm2MYL3PjO06OO3C1WdnMEbB69NKY9sZ4dp2u8w5IJ3B/Om2MT7tY4zaxxi1jfGxJj63tjE+7WOM2scYtc2K49NljoB0BnsP28b4tI8xah9j1DbGx5r43NrG+LSPMWofY9Q2K44PR0AAAAAAhAxHQAAAAACEDAUIAAAAgJChAAEAAAAQMhQgAAAAAEImNtwBhNPy5cu1ZMkSmaapGTNmaNq0aeEOKeLceOONqqio8D6///77NXLkyDBGFDkcDod+/etf6/e//713GdvU51oaH7anz7344ot65ZVX1NDQoLFjx2rOnDmKiYlhG7IIPqeWtfZ/nPHyLWdE43j5kjOicXx8zRkRP0ZmlKqqqjK/853vmMeOHTOrq6vN/Px8s6KiItxhRRS3223ecMMN4Q4jIr3xxhvmT37yE/Pmm2/2LmOb+lxL48P29Llt27aZN954o1lTU2PW1dWZt99+u/naa6+xDVkEn1PLWvs/znj5ljOicbx8yRnROD6+5gwrjFHUTsEqLi5WTk6OUlJSlJSUpHHjxmn16tXhDiuiVFRUKC0tLdxhRKSkpCRNmjTpjGVsU59raXzYnj537NgxXXXVVerdu7cSEhI0duxYlZWVsQ1ZBJ9Ty1r7P854+ZYzonG8fMkZ0Tg+vuYMK4xR1BYgFRUVysjI8D5PT09XZWVlGCOKPGVlZTp+/LjuuOMOXX/99Vq4cKE8Hk+4w4oI48ePV25u7hnL2KY+19L4sD19bsKECbr++uslSZWVlXrnnXc0btw4tiGL4HNqWWv/xxkv33JGNI6XLzkjGsfH15xhhTGK2gLEbOH+i9H6x1BrevfurWuvvVb33XefnnjiCe3YsUOvv/56uMOKWGxTbWN7am7p0qW6/fbbNXPmTJ133nlsQxbB59Sy1v6PM14ta21cGK+T2J6a62jOsMIYRe1J6GlpaSopKfE+r6ioUFZWVhgjijxnn322Bg0aJMMwlJSUpIkTJ2rv3r3hDitisU21je3pcx6PR/fee6/cbrf+9Kc/KTk5WRLbkFXwObWstf/jw4YNY7xa0Np2FBcXx3iJ7el0vuYMK2xDUXsEJDc3V8XFxaqvr1dDQ4PWrFmjsWPHhjusiPLCCy+oqKhILpdLdXV1+vDDD6P2ikUdwTbVNranz61YsUINDQ369a9/7U0kEtuQVfA5tay1/+OMV8taGxfG6yS2p8/5mjOsMEZRewQkJSVFM2fOVEFBgUzTVH5+vtLT08MdVkT5+te/rqNHj2rWrFlyuVyaOnWq8vLywh1WxGKbahvb0+e2bt2qjRs36pprrvEuu/rqqzVr1iy2IQvg/3rL2vo/zng119Z2xHixPZ3On5wR6WNkmC1NFAMAAACAIIjaKVgAAAAAQo8CBAAAAEDIUIAAAAAACBkKEAAAAAAhQwECAAAAIGQoQAAAAACEDAUIAAAAgJChAAEAAAAQMhQgAAAAAEKGAgQAAABAyMSGOwAgknzzm99UbW1ts+VXXHGFfvGLXzRbPm/ePF199dX60pe+FIrwAAARivwBdBwFCPCZsrIy1dbW6sknn1Tv3r3P+F18fHyLbXbv3q3s7OxQhAcAiFDkD8A3FCDAZ0pKSpSZman+/ft36PXl5eWSpH79+p2x3Ol0Ki4uLuDxAQAiE/kD8A0FCPCZnTt3asSIEa3+3m6369FHH9XWrVuVnp6uqVOnatiwYZKkqqoq/fnPf9aGDRvkcrk0ffp0lZaW6u6775bD4dBf//pXrV27Vg0NDfrqV7+qWbNmyTAMbdiwQYsWLdKBAwd0zjnn6Ac/+IFGjx4dqlUGAAQA+QPwDSehA5/ZsWOHli1bpilTppzxePTRR1VeXq45c+ZowoQJeuqpp/Stb31LTzzxhIYNGyaHw6E5c+YoOztbixcv1pw5c/Sf//xHw4cPl8fj0dy5c+V0OvXHP/5RRUVFevnll/XBBx/Ibrfr//2//6f8/Hw9/fTTGjRokJ599tlwDwMAwEfkD8A3HAEBPrNjxw7dc889Gj58+BnLu3fvrgULFuhLX/qSpk2bJkmaNm2aN4E8//zzuvDCC3XddddJkr785S9rwYIFGj58uN599105nU795Cc/UWxsrFJTUzVu3Djt379fcXFx6tatm1JSUtSrVy/9z//8jzweT8jXGwDQOeQPwDcUIIBOHh6vr6/XyJEjlZCQ0Oz369ev19y5c89Y1tjYqGHDhmnJkiW66aabvMtPnDghh8Oh7Oxs3XfffSorK9O11157RtvZs2fr0ksv1aRJk1RUVKSmpibl5eXphz/8oWJj+W8JAFZB/gB8x5YK6OTeq379+rWYPJxOpyorK5WVleVdtnnzZsXFxSk9PV0HDhzQgAEDvL/74IMP1L9/f/Xu3VulpaX61a9+pbFjx0qSHA6H7Ha7MjMztX37ds2ePVuzZ8/W/v37deedd+qCCy7QhAkTgr/CAICAIH8AvuMcEEAnE0hWVpbq6+ubPWJiYpSRkaFXX31VdXV12rRpkx566CFlZ2fLZrMpJSVFb7/9tpxOpzZs2KDHHnvMezLiueeeq9dee01HjhzR/v379Ytf/EJvv/22qqqqVFBQoA0bNsjpdOrgwYNyOBwaNGhQeAcCAOAT8gfgO8M0TTPcQQDhNm/ePH388cct/u6FF17Q7t279dBDD8nhcCg3N1eGYSgpKUkzZ87U8uXL9bvf/U6JiYnKzc1VWVmZpk2bpilTpqiqqkr333+/Nm/erJSUFF111VW6/vrrZbPZtGTJEr300ktqamrS2Wefre9///u65JJLQrzmAIDOIH8AvqMAATph9+7dstlsGjx4sCSpvr5e+fn5WrRokdLS0sIcHQAgUpE/EM2YggV0wtq1a/W73/1O5eXlqqys1AMPPKArrriC5AEAaBP5A9GMIyBAJ1RXV2vBggXatm2bkpOTNX78eN1www3q3r17uEMDAEQw8geiGQUIAAAAgJBhChYAAACAkPn/tEhY9NTZNNkAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_track_lengths(metric_learning_model)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  2%|â–Ž         | 2/80 [00:00<00:13,  5.91it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 33177])\n",
-      "torch.Size([33177])\n",
-      "torch.Size([2, 1008])\n",
-      "torch.Size([2, 145236])\n",
-      "torch.Size([145236])\n",
-      "torch.Size([2, 4474])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  5%|▌         | 4/80 [00:00<00:12,  6.03it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 77371])\n",
-      "torch.Size([77371])\n",
-      "torch.Size([2, 2310])\n",
-      "torch.Size([2, 23698])\n",
-      "torch.Size([23698])\n",
-      "torch.Size([2, 770])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  8%|â–Š         | 6/80 [00:01<00:12,  5.97it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 106526])\n",
-      "torch.Size([106526])\n",
-      "torch.Size([2, 3388])\n",
-      "torch.Size([2, 118727])\n",
-      "torch.Size([118727])\n",
-      "torch.Size([2, 3588])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 10%|â–ˆ         | 8/80 [00:01<00:11,  6.05it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 45129])\n",
-      "torch.Size([45129])\n",
-      "torch.Size([2, 1364])\n",
-      "torch.Size([2, 45374])\n",
-      "torch.Size([45374])\n",
-      "torch.Size([2, 1392])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 12%|█▎        | 10/80 [00:01<00:12,  5.80it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 93982])\n",
-      "torch.Size([93982])\n",
-      "torch.Size([2, 2878])\n",
-      "torch.Size([2, 40327])\n",
-      "torch.Size([40327])\n",
-      "torch.Size([2, 1212])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 15%|█▌        | 12/80 [00:02<00:11,  6.00it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 18314])\n",
-      "torch.Size([18314])\n",
-      "torch.Size([2, 556])\n",
-      "torch.Size([2, 58898])\n",
-      "torch.Size([58898])\n",
-      "torch.Size([2, 1870])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 18%|█▊        | 14/80 [00:02<00:11,  5.54it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 165032])\n",
-      "torch.Size([165032])\n",
-      "torch.Size([2, 4848])\n",
-      "torch.Size([2, 41258])\n",
-      "torch.Size([41258])\n",
-      "torch.Size([2, 1204])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 20%|██        | 16/80 [00:02<00:11,  5.68it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 157927])\n",
-      "torch.Size([157927])\n",
-      "torch.Size([2, 4940])\n",
-      "torch.Size([2, 106722])\n",
-      "torch.Size([106722])\n",
-      "torch.Size([2, 3244])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 22%|██▎       | 18/80 [00:03<00:10,  5.78it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 64141])\n",
-      "torch.Size([64141])\n",
-      "torch.Size([2, 1992])\n",
-      "torch.Size([2, 42679])\n",
-      "torch.Size([42679])\n",
-      "torch.Size([2, 1342])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 25%|██▌       | 20/80 [00:03<00:10,  5.95it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 30539])\n",
-      "torch.Size([30539])\n",
-      "torch.Size([2, 992])\n",
-      "torch.Size([2, 66983])\n",
-      "torch.Size([66983])\n",
-      "torch.Size([2, 2102])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 28%|██▊       | 22/80 [00:03<00:09,  5.89it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 146559])\n",
-      "torch.Size([146559])\n",
-      "torch.Size([2, 4544])\n",
-      "torch.Size([2, 49735])\n",
-      "torch.Size([49735])\n",
-      "torch.Size([2, 1452])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 30%|███       | 24/80 [00:04<00:09,  5.92it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 41307])\n",
-      "torch.Size([41307])\n",
-      "torch.Size([2, 1208])\n",
-      "torch.Size([2, 130830])\n",
-      "torch.Size([130830])\n",
-      "torch.Size([2, 4044])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 32%|███▎      | 26/80 [00:04<00:08,  6.04it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 17636])\n",
-      "torch.Size([17636])\n",
-      "torch.Size([2, 544])\n",
-      "torch.Size([2, 81389])\n",
-      "torch.Size([81389])\n",
-      "torch.Size([2, 2488])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 35%|███▌      | 28/80 [00:04<00:08,  6.11it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 42225])\n",
-      "torch.Size([42225])\n",
-      "torch.Size([2, 1340])\n",
-      "torch.Size([2, 48295])\n",
-      "torch.Size([48295])\n",
-      "torch.Size([2, 1494])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 38%|███▊      | 30/80 [00:05<00:08,  6.05it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 72177])\n",
-      "torch.Size([72177])\n",
-      "torch.Size([2, 2196])\n",
-      "torch.Size([2, 94619])\n",
-      "torch.Size([94619])\n",
-      "torch.Size([2, 2852])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 40%|████      | 32/80 [00:05<00:07,  6.09it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 38122])\n",
-      "torch.Size([38122])\n",
-      "torch.Size([2, 1166])\n",
-      "torch.Size([2, 36015])\n",
-      "torch.Size([36015])\n",
-      "torch.Size([2, 1126])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 42%|████▎     | 34/80 [00:05<00:07,  6.18it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 27868])\n",
-      "torch.Size([27868])\n",
-      "torch.Size([2, 900])\n",
-      "torch.Size([2, 16184])\n",
-      "torch.Size([16184])\n",
-      "torch.Size([2, 496])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 45%|████▌     | 36/80 [00:06<00:07,  5.84it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 131810])\n",
-      "torch.Size([131810])\n",
-      "torch.Size([2, 3964])\n",
-      "torch.Size([2, 120491])\n",
-      "torch.Size([120491])\n",
-      "torch.Size([2, 3702])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 48%|████▊     | 38/80 [00:06<00:07,  5.65it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 114268])\n",
-      "torch.Size([114268])\n",
-      "torch.Size([2, 3516])\n",
-      "torch.Size([2, 50176])\n",
-      "torch.Size([50176])\n",
-      "torch.Size([2, 1546])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 50%|█████     | 40/80 [00:06<00:07,  5.66it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 139258])\n",
-      "torch.Size([139258])\n",
-      "torch.Size([2, 4212])\n",
-      "torch.Size([2, 113876])\n",
-      "torch.Size([113876])\n",
-      "torch.Size([2, 3600])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 51%|█████▏    | 41/80 [00:06<00:06,  5.65it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 132790])\n",
-      "torch.Size([132790])\n",
-      "torch.Size([2, 3960])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 54%|█████▍    | 43/80 [00:07<00:06,  5.30it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 63651])\n",
-      "torch.Size([63651])\n",
-      "torch.Size([2, 1922])\n",
-      "torch.Size([2, 112798])\n",
-      "torch.Size([112798])\n",
-      "torch.Size([2, 3376])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 56%|█████▋    | 45/80 [00:07<00:06,  5.63it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 35462])\n",
-      "torch.Size([35462])\n",
-      "torch.Size([2, 1122])\n",
-      "torch.Size([2, 156800])\n",
-      "torch.Size([156800])\n",
-      "torch.Size([2, 4768])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 59%|█████▉    | 47/80 [00:08<00:05,  5.99it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 9371])\n",
-      "torch.Size([9371])\n",
-      "torch.Size([2, 326])\n",
-      "torch.Size([2, 12197])\n",
-      "torch.Size([12197])\n",
-      "torch.Size([2, 380])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 61%|██████▏   | 49/80 [00:08<00:05,  5.86it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 58555])\n",
-      "torch.Size([58555])\n",
-      "torch.Size([2, 1710])\n",
-      "torch.Size([2, 40082])\n",
-      "torch.Size([40082])\n",
-      "torch.Size([2, 1192])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 64%|██████▍   | 51/80 [00:08<00:04,  5.96it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 71883])\n",
-      "torch.Size([71883])\n",
-      "torch.Size([2, 2208])\n",
-      "torch.Size([2, 37779])\n",
-      "torch.Size([37779])\n",
-      "torch.Size([2, 1210])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 66%|██████▋   | 53/80 [00:09<00:04,  6.03it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 93884])\n",
-      "torch.Size([93884])\n",
-      "torch.Size([2, 2802])\n",
-      "torch.Size([2, 44443])\n",
-      "torch.Size([44443])\n",
-      "torch.Size([2, 1288])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 69%|██████▉   | 55/80 [00:09<00:04,  5.88it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 36064])\n",
-      "torch.Size([36064])\n",
-      "torch.Size([2, 1136])\n",
-      "torch.Size([2, 31939])\n",
-      "torch.Size([31939])\n",
-      "torch.Size([2, 994])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 71%|███████▏  | 57/80 [00:09<00:03,  6.01it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 18326])\n",
-      "torch.Size([18326])\n",
-      "torch.Size([2, 480])\n",
-      "torch.Size([2, 71050])\n",
-      "torch.Size([71050])\n",
-      "torch.Size([2, 2190])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 72%|███████▎  | 58/80 [00:09<00:03,  6.03it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 49196])\n",
-      "torch.Size([49196])\n",
-      "torch.Size([2, 1548])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 74%|███████▍  | 59/80 [00:10<00:03,  5.43it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 67424])\n",
-      "torch.Size([67424])\n",
-      "torch.Size([2, 2008])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 76%|███████▋  | 61/80 [00:10<00:03,  5.41it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 53062])\n",
-      "torch.Size([53062])\n",
-      "torch.Size([2, 1682])\n",
-      "torch.Size([2, 102606])\n",
-      "torch.Size([102606])\n",
-      "torch.Size([2, 3176])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 79%|███████▉  | 63/80 [00:10<00:02,  5.78it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 33730])\n",
-      "torch.Size([33730])\n",
-      "torch.Size([2, 1098])\n",
-      "torch.Size([2, 74578])\n",
-      "torch.Size([74578])\n",
-      "torch.Size([2, 2448])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 81%|████████▏ | 65/80 [00:11<00:02,  5.88it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 54733])\n",
-      "torch.Size([54733])\n",
-      "torch.Size([2, 1728])\n",
-      "torch.Size([2, 139013])\n",
-      "torch.Size([139013])\n",
-      "torch.Size([2, 4380])\n"
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-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 84%|████████▍ | 67/80 [00:11<00:02,  5.94it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 115493])\n",
-      "torch.Size([115493])\n",
-      "torch.Size([2, 3588])\n",
-      "torch.Size([2, 93149])\n",
-      "torch.Size([93149])\n",
-      "torch.Size([2, 2656])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 86%|████████▋ | 69/80 [00:11<00:01,  6.05it/s]"
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-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 67130])\n",
-      "torch.Size([67130])\n",
-      "torch.Size([2, 2044])\n",
-      "torch.Size([2, 43267])\n",
-      "torch.Size([43267])\n",
-      "torch.Size([2, 1306])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 89%|████████▉ | 71/80 [00:12<00:01,  6.05it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 89229])\n",
-      "torch.Size([89229])\n",
-      "torch.Size([2, 2710])\n",
-      "torch.Size([2, 62475])\n",
-      "torch.Size([62475])\n",
-      "torch.Size([2, 1922])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 91%|█████████▏| 73/80 [00:12<00:01,  6.13it/s]"
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-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 11883])\n",
-      "torch.Size([11883])\n",
-      "torch.Size([2, 394])\n",
-      "torch.Size([2, 96922])\n",
-      "torch.Size([96922])\n",
-      "torch.Size([2, 2852])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 94%|█████████▍| 75/80 [00:12<00:00,  6.15it/s]"
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-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 65464])\n",
-      "torch.Size([65464])\n",
-      "torch.Size([2, 1946])\n",
-      "torch.Size([2, 40276])\n",
-      "torch.Size([40276])\n",
-      "torch.Size([2, 1284])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 96%|█████████▋| 77/80 [00:13<00:00,  6.14it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 137396])\n",
-      "torch.Size([137396])\n",
-      "torch.Size([2, 4262])\n",
-      "torch.Size([2, 38857])\n",
-      "torch.Size([38857])\n",
-      "torch.Size([2, 1252])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 99%|█████████▉| 79/80 [00:13<00:00,  6.14it/s]"
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-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 104958])\n",
-      "torch.Size([104958])\n",
-      "torch.Size([2, 3320])\n",
-      "torch.Size([2, 31802])\n",
-      "torch.Size([31802])\n",
-      "torch.Size([2, 1036])\n"
-     ]
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-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 80/80 [00:13<00:00,  5.87it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 106085])\n",
-      "torch.Size([106085])\n",
-      "torch.Size([2, 3240])\n"
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",
-      "text/plain": [
-       "<Figure size 1000x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_graph_sizes(metric_learning_model)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 2. Construct graphs from metric learning inference\n",
-    "\n",
-    "This step performs model inference on the entire input datasets (train, validation and test), to obtain input graphs to the graph neural network. Optionally, we also clear the directory."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:------------- Step 2: Constructing graphs from metric learning model -------------\n",
-      "INFO:---------------------------- a) Loading trained model ----------------------------\n",
-      "INFO:----------------------------- b) Running inferencing -----------------------------\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training finished, running inference to build graphs...\n"
-     ]
-    },
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-      "torch.Size([2, 24720])\n",
-      "torch.Size([24720])\n",
-      "torch.Size([2, 2480])\n"
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-      "torch.Size([2, 22072])\n",
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-      "torch.Size([2, 2710])\n"
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-      "torch.Size([2, 8778])\n",
-      "torch.Size([8778])\n",
-      "torch.Size([2, 1036])\n"
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-      "torch.Size([2, 22034])\n",
-      "torch.Size([22034])\n",
-      "torch.Size([2, 2252])\n"
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-      "torch.Size([2, 5860])\n",
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-      "torch.Size([2, 8212])\n",
-      "torch.Size([8212])\n",
-      "torch.Size([2, 900])\n"
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-      "torch.Size([2, 14606])\n",
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-      "torch.Size([2, 28448])\n",
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-      "torch.Size([2, 27961])\n",
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-      "torch.Size([2, 7526])\n",
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-      "torch.Size([2, 32078])\n",
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-      "torch.Size([2, 41196])\n",
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-      "torch.Size([2, 1594])\n",
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-      "torch.Size([2, 42760])\n",
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-      "torch.Size([2, 58912])\n",
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-      "torch.Size([2, 10370])\n",
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-      "torch.Size([2, 1098])\n"
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-      "torch.Size([2, 30992])\n",
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-      "torch.Size([2, 50589])\n",
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-      "torch.Size([2, 20262])\n",
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-      "torch.Size([2, 2008])\n"
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-      "torch.Size([2, 11828])\n",
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-      "torch.Size([2, 8052])\n",
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-      "torch.Size([2, 49886])\n",
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-      "torch.Size([2, 4044])\n"
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-      "torch.Size([2, 1132])\n",
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-      "torch.Size([2, 29434])\n",
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-      "torch.Size([2, 79962])\n",
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-      "torch.Size([2, 7098])\n",
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-      "torch.Size([2, 1136])\n"
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-      "torch.Size([2, 17254])\n",
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-      "torch.Size([2, 2044])\n"
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-      "torch.Size([2, 18100])\n",
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-      "torch.Size([2, 1870])\n"
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-      "torch.Size([2, 45740])\n",
-      "torch.Size([45740])\n",
-      "torch.Size([2, 3600])\n"
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-      "torch.Size([2, 7286])\n",
-      "torch.Size([7286])\n",
-      "torch.Size([2, 1208])\n",
-      "torch.Size([2, 23350])\n",
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-      "torch.Size([2, 2448])\n"
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-      "torch.Size([2, 15212])\n",
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-      "torch.Size([2, 1946])\n"
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-      "torch.Size([2, 12366])\n",
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-      "torch.Size([2, 1684])\n"
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-      "torch.Size([2, 61640])\n",
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-      "torch.Size([2, 4768])\n"
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-      "torch.Size([2, 5338])\n",
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-      "torch.Size([2, 1126])\n"
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-      "torch.Size([2, 9758])\n",
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-      "torch.Size([2, 19674])\n",
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-      "torch.Size([2, 2208])\n"
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-      "torch.Size([2, 9620])\n",
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-      "torch.Size([2, 65041])\n",
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-      "torch.Size([2, 4212])\n"
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-      "torch.Size([2, 47670])\n",
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-      "torch.Size([2, 4380])\n"
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-      "torch.Size([2, 39053])\n",
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-      "torch.Size([2, 3478])\n"
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-      "torch.Size([2, 2146])\n",
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-      "torch.Size([2, 556])\n"
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-      "torch.Size([2, 26480])\n",
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-      "torch.Size([2, 2196])\n"
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-      "torch.Size([2, 4498])\n",
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-      "torch.Size([2, 6792])\n",
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-      "torch.Size([2, 8062])\n",
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-      "torch.Size([2, 13442])\n",
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-      "torch.Size([2, 18458])\n",
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-      "torch.Size([2, 28100])\n",
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-      "torch.Size([2, 72144])\n",
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-      "torch.Size([2, 6970])\n",
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-      "torch.Size([2, 9266])\n",
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-      "torch.Size([2, 4230])\n",
-      "torch.Size([4230])\n",
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-      "torch.Size([2, 1992])\n"
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-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 109148])\n",
-      "torch.Size([109148])\n",
-      "torch.Size([2, 5882])\n",
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-      "torch.Size([2, 1452])\n"
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-    },
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 18698])\n",
-      "torch.Size([18698])\n",
-      "torch.Size([2, 1710])\n",
-      "torch.Size([2, 2114])\n",
-      "torch.Size([2114])\n",
-      "torch.Size([2, 496])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 55732])\n",
-      "torch.Size([55732])\n",
-      "torch.Size([2, 3516])\n",
-      "torch.Size([2, 7484])\n",
-      "torch.Size([7484])\n",
-      "torch.Size([2, 1252])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "graph_builder = run_metric_learning_inference(CONFIG)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 3. Train graph neural networks"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We have a set of graphs constructed. We now train a GNN to classify edges as either \"true\" (belonging to the same track) or \"false\" (not belonging to the same track). We train for 30 epochs, which should take around 10 minutes on a V100 GPU. Your mileage may vary."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:-------------------------  Step 3: Running GNN training  -------------------------\n",
-      "INFO:----------------------------- a) Initialising model -----------------------------\n",
-      "INFO:------------------------------ b) Running training ------------------------------\n",
-      "GPU available: True (cuda), used: True\n",
-      "TPU available: False, using: 0 TPU cores\n",
-      "IPU available: False, using: 0 IPUs\n",
-      "HPU available: False, using: 0 HPUs\n",
-      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
-      "\n",
-      "  | Name                   | Type       | Params\n",
-      "------------------------------------------------------\n",
-      "0 | node_encoder           | Sequential | 34.0 K\n",
-      "1 | edge_encoder           | Sequential | 66.4 K\n",
-      "2 | edge_network           | Sequential | 82.8 K\n",
-      "3 | node_network           | Sequential | 82.8 K\n",
-      "4 | output_edge_classifier | Sequential | 83.2 K\n",
-      "------------------------------------------------------\n",
-      "349 K     Trainable params\n",
-      "0         Non-trainable params\n",
-      "349 K     Total params\n",
-      "1.397     Total estimated model params size (MB)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 2: 100%|██████████| 90/90 [00:14<00:00,  6.43it/s, loss=0.728, v_num=1]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "`Trainer.fit` stopped: `max_epochs=3` reached.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 2: 100%|██████████| 90/90 [00:14<00:00,  6.41it/s, loss=0.728, v_num=1]\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:-------------------------------- c) Saving model --------------------------------\n"
-     ]
-    }
-   ],
-   "source": [
-    "# send_telegram_message('Started GNN training.', chat_id, api_key)\n",
-    "\n",
-    "gnn_trainer, gnn_model = train_gnn(CONFIG)\n",
-    "\n",
-    "# send_telegram_message('Finished GNN training.', chat_id, api_key)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### From checkpoint"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# from GNN.Models.interaction_gnn import InteractionGNN\n",
-    "# from pytorch_lightning import Trainer\n",
-    "# from pytorch_lightning.loggers import CSVLogger\n",
-    "\n",
-    "# version_number = 1\n",
-    "\n",
-    "# HPARAMS_PATH = f'/home/fgias/velo-gnn/LHCb_Pipeline/artifacts/metric_learning/velo-minbias-sim10b-xdigi/version_1/hparams.yaml'\n",
-    "# CKPT_PATH = f'/home/fgias/velo-gnn/LHCb_Pipeline/artifacts/metric_learning/velo-minbias-sim10b-xdigi/version_1/checkpoints/epoch=19-step=1600.ckpt'\n",
-    "# METRICS_PATH = f'/home/fgias/velo-gnn/LHCb_Pipeline/artifacts/gnn/velo_data/version_{version_number}/metrics.csv'\n",
-    "\n",
-    "# load_configs = {}\n",
-    "# with open(HPARAMS_PATH, 'r') as f:\n",
-    "#     load_configs = yaml.load(f, Loader=yaml.FullLoader)\n",
-    "    \n",
-    "# gnn_model = InteractionGNN(load_configs)\n",
-    "\n",
-    "# logger = CSVLogger('artifacts', name='gnn/velo_data')\n",
-    "\n",
-    "# gnn_trainer = Trainer(\n",
-    "#         gpus=1,\n",
-    "#         max_epochs=40,\n",
-    "#         logger=logger,\n",
-    "#         # callbacks=[EarlyStopping(monitor=\"val_loss\", mode=\"min\")]\n",
-    "# )\n",
-    "\n",
-    "# gnn_trainer.fit(gnn_model, ckpt_path=CKPT_PATH)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Plot training metrics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'gnn_trainer' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[3], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m gnn_metrics \u001b[39m=\u001b[39m get_training_metrics(gnn_trainer)\n\u001b[1;32m      3\u001b[0m \u001b[39m# gnn_metrics = get_training_metrics(gnn_trainer, METRICS_PATH) # Use when loading from checkpoint\u001b[39;00m\n\u001b[1;32m      5\u001b[0m gnn_metrics\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'gnn_trainer' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "gnn_metrics = get_training_metrics(gnn_trainer)\n",
-    "\n",
-    "# gnn_metrics = get_training_metrics(gnn_trainer, METRICS_PATH) # Use when loading from checkpoint\n",
-    "\n",
-    "gnn_metrics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_training_metrics(gnn_metrics, 'GNN')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Evaluate model performance on sample test data\n",
-    "\n",
-    "Here we evaluate the model performace on one sample test data. We look at how the efficiency and purity change with the embedding radius."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_edge_performance(gnn_model)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Step 4: GNN inference "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:--------------------- Step 4: Scoring graph edges using GNN  ---------------------\n",
-      "INFO:---------------------------- a) Loading trained model ----------------------------\n",
-      "INFO:----------------------------- b) Running inferencing -----------------------------\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training finished, running inference to filter graphs...\n",
-      "Building train\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 80/80 [00:03<00:00, 23.62it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Building val\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 10/10 [00:00<00:00, 28.12it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Building test\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 10/10 [00:00<00:00, 22.84it/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "run_gnn_inference(CONFIG)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Step 5: Build track candidates from GNN"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:-----------  Step 5: Building track candidates from the scored graph  -----------\n",
-      "INFO:---------------------------- a) Loading scored graphs ----------------------------\n",
-      "INFO:---------------------------- b) Labelling graph nodes ----------------------------\n",
-      "100%|██████████| 100/100 [00:00<00:00, 698.73it/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "build_track_candidates(CONFIG)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Step 6: Evaluate track candidates"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can control the matching style in the pipeline config file. The following all require at least a majority of hits to match in each scheme (i.e. matching fraction = 50%).\n",
-    "A discussion of each matching style and some worked examples can be found in the [Documentation](https://hsf-reco-and-software-triggers.github.io/Tracking-ML-Exa.TrkX/performance/matching_definitions/)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "ATLAS style matching is the default."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:------------ Step 6: Evaluating the track reconstruction performance ------------\n",
-      "INFO:--------------------------- a) Loading labelled graphs ---------------------------\n",
-      "100%|██████████| 100/100 [00:01<00:00, 57.16it/s]\n",
-      "INFO:--------------------- b) Calculating the performance metrics ---------------------\n",
-      "INFO:Number of reconstructed particles: 17074\n",
-      "INFO:Number of particles: 23405\n",
-      "INFO:Number of matched tracks: 17157\n",
-      "INFO:Number of tracks: 17651\n",
-      "INFO:Number of duplicate reconstructed particles: 83\n",
-      "INFO:Efficiency: 0.730\n",
-      "INFO:Fake rate: 0.028\n",
-      "INFO:Duplication rate: 0.005\n",
-      "INFO:------------------------------ c) Plotting results ------------------------------\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "evaluated_events, reconstructed_particles, particles, matched_tracks, tracks = evaluate_candidates(CONFIG)\n",
-    "\n",
-    "# send_telegram_message('Finished evaluation.', chat_id, api_key)\n",
-    "\n",
-    "# send_telegram_message(\"======================\", chat_id, api_key)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Get all graph files"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import montetracko as mt\n",
-    "import montetracko.lhcb as mtb\n",
-    "from Scripts import Step_6_Evaluate_Reconstruction as evalreco\n",
-    "mt_config = {\n",
-    "    \"aliases\": {\n",
-    "        \"n_hits\": \"nhits\",\n",
-    "        \"hit_id\": \"lhcbid\",\n",
-    "    },\n",
-    "    \"fake_particle_id\": 0,\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open(CONFIG) as file:\n",
-    "    all_configs = yaml.load(file, Loader=yaml.FullLoader)\n",
-    "\n",
-    "common_configs = all_configs[\"common_configs\"]\n",
-    "track_building_configs = all_configs[\"track_building_configs\"]\n",
-    "evaluation_configs = all_configs[\"evaluation_configs\"]\n",
-    "\n",
-    "\n",
-    "input_dir = track_building_configs[\"output_dir\"]\n",
-    "output_dir = evaluation_configs[\"output_dir\"]\n",
-    "os.makedirs(output_dir, exist_ok=True)\n",
-    "\n",
-    "all_graph_files = os.listdir(input_dir)\n",
-    "all_graph_files = [os.path.join(input_dir, graph) for graph in all_graph_files]"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Get tracks"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import os.path as op\n",
-    "import yaml"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "graph_file = all_graph_files[0]\n",
-    "\n",
-    "# particles_df = evalreco.load_particles_df(graph_file)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open(CONFIG) as file:\n",
-    "    all_configs = yaml.load(file, Loader=yaml.FullLoader)\n",
-    "preprocessed_dir = all_configs[\"processing_configs\"][\"preprocessed_dir\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pyarrow.csv as pac\n",
-    "from tqdm.notebook import tqdm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# %%timeit\n",
-    "\n",
-    "# dict_paths = {\n",
-    "#     \"particles\": op.join(preprocessed_dir, \"event{event_id_str}-particles.csv\"),\n",
-    "#     \"hits_particles\": op.join(preprocessed_dir, \"event{event_id_str}-truth.csv\"),\n",
-    "# }\n",
-    "# dict_convert_options = {\n",
-    "#     \"hits_particles\": pac.ConvertOptions(include_columns=[\"particle_id\", \"lhcbid\"]),\n",
-    "# }\n",
-    "# dict_columns = {\n",
-    "#     \"hits_particles\": [\"particle_id\", \"lhcbid\"],\n",
-    "# }\n",
-    "\n",
-    "# dict_list_dataframes = {\n",
-    "#     \"particles\": [],\n",
-    "#     \"hits_particles\": [],\n",
-    "#     \"tracks\": [],\n",
-    "# }\n",
-    "\n",
-    "# first_iteration: bool = True\n",
-    "# for graph_file in tqdm(all_graph_files):\n",
-    "#     # Event ID from graph file name\n",
-    "#     event_id_str = op.basename(graph_file)\n",
-    "#     event_id = int(event_id_str)\n",
-    "\n",
-    "#     # Load dataframe of tracks\n",
-    "#     reconstruction_df = evalreco.load_reconstruction_df(graph_file)\n",
-    "#     df_tracks = reconstruction_df[[\"hit_id\", \"track_id\"]].drop_duplicates()\n",
-    "\n",
-    "#     # Particles and hit-particle truth association\n",
-    "#     tables_event = {}\n",
-    "#     for name, path in dict_paths.items():\n",
-    "#         tables_event[name] = pac.read_csv(\n",
-    "#             path.format(event_id_str=event_id_str),\n",
-    "#             convert_options=dict_convert_options.get(name),\n",
-    "#         )\n",
-    "\n",
-    "#     if first_iteration:\n",
-    "#         first_iteration = False\n",
-    "#         for name, table in tables_event.items():\n",
-    "#             dict_convert_options[name] = pac.ConvertOptions(\n",
-    "#                 column_types=table.schema,\n",
-    "#                 include_columns=table.column_names,\n",
-    "#             )\n",
-    "\n",
-    "#     # Retrieve read options\n",
-    "#     if not dict_convert_options:\n",
-    "#         for name, table in tabtables_eventles.items():\n",
-    "#             dict_convert_options[name] = pac.ConvertOptions()\n",
-    "\n",
-    "#     # Convert to pandas DataFrame\n",
-    "#     dataframes_event = {name: table.to_pandas() for name, table in tables_event.items()}\n",
-    "#     dataframes_event[\"tracks\"] = df_tracks\n",
-    "\n",
-    "#     # Rename column\n",
-    "#     dataframes_event[\"hits_particles\"].rename(columns={\"lhcbid\": \"hit_id\"}, inplace=True)\n",
-    "\n",
-    "#     # Add event ID column\n",
-    "#     for dataframe in dataframes_event.values():\n",
-    "#         dataframe[\"event_id\"] = event_id\n",
-    "\n",
-    "#     # Append to list for concatenation\n",
-    "#     for name, dataframe in dataframes_event.items():\n",
-    "#         dict_list_dataframes[name].append(dataframe)\n",
-    "\n",
-    "# dataframes = {\n",
-    "#     name: pd.concat(list_dataframes)\n",
-    "#     for name, list_dataframes in dict_list_dataframes.items()\n",
-    "# }"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b8d68af13f204cb6b52b2244a0fef3f3",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/100 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "dict_paths = {\n",
-    "    \"particles\": op.join(preprocessed_dir, \"event{event_id_str}-particles.csv\"),\n",
-    "    \"hits_particles\": op.join(preprocessed_dir, \"event{event_id_str}-truth.csv\"),\n",
-    "}\n",
-    "dict_list_dataframes = {\n",
-    "    \"particles\": [],\n",
-    "    \"hits_particles\": [],\n",
-    "    \"tracks\": [],\n",
-    "}\n",
-    "dict_convert_options = {\n",
-    "    \"hits_particles\": pac.ConvertOptions(\n",
-    "        include_columns=[\"particle_id\", \"lhcbid\", \"hit_id\"]\n",
-    "    ),\n",
-    "}\n",
-    "\n",
-    "for graph_file in tqdm(all_graph_files):\n",
-    "    # Event ID from graph file name\n",
-    "    event_id_str = op.basename(graph_file)\n",
-    "    event_id = int(event_id_str)\n",
-    "\n",
-    "    # Load dataframe of tracks\n",
-    "    reconstruction_df = evalreco.load_reconstruction_df(graph_file)\n",
-    "    df_tracks = reconstruction_df[[\"hit_id\", \"track_id\"]].drop_duplicates()\n",
-    "\n",
-    "    # Particles and hit-particle truth association\n",
-    "    dataframes_event = {}\n",
-    "    for name, path in dict_paths.items():\n",
-    "        dataframes_event[name] = pac.read_csv(\n",
-    "            path.format(event_id_str=event_id_str),\n",
-    "            convert_options=dict_convert_options.get(name),\n",
-    "        ).to_pandas()\n",
-    "    dataframes_event[\"tracks\"] = df_tracks\n",
-    "\n",
-    "    # Add event ID column\n",
-    "    for dataframe in dataframes_event.values():\n",
-    "        dataframe[\"event_id\"] = event_id\n",
-    "\n",
-    "    dataframes_event[\"tracks\"][\"lhcbid\"] = mt.array_utils.transform.replace(\n",
-    "        array=np.asarray(dataframes_event[\"tracks\"][\"hit_id\"]),\n",
-    "        values_to_replace=np.asarray(dataframes_event[\"hits_particles\"][\"hit_id\"]),\n",
-    "        replacement_values=np.asarray(dataframes_event[\"hits_particles\"][\"lhcbid\"]),\n",
-    "    )\n",
-    "\n",
-    "    # Append to list for concatenation\n",
-    "    for name, dataframe in dataframes_event.items():\n",
-    "        dict_list_dataframes[name].append(dataframe)\n",
-    "\n",
-    "dataframes = {\n",
-    "    name: pd.concat(list_dataframes)\n",
-    "    for name, list_dataframes in dict_list_dataframes.items()\n",
-    "}\n",
-    "dataframes[\"hits_particles\"].drop(\"hit_id\", axis=1, inplace=True)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>particle_id</th>\n",
-       "      <th>lhcbid</th>\n",
-       "      <th>event_id</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2829</td>\n",
-       "      <td>671094942</td>\n",
-       "      <td>7212930</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2259</td>\n",
-       "      <td>604074443</td>\n",
-       "      <td>7212930</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0</td>\n",
-       "      <td>537154464</td>\n",
-       "      <td>7212930</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>501</td>\n",
-       "      <td>537143928</td>\n",
-       "      <td>7212930</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2828</td>\n",
-       "      <td>536908207</td>\n",
-       "      <td>7212930</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2265</th>\n",
-       "      <td>1858</td>\n",
-       "      <td>591324376</td>\n",
-       "      <td>4206903</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2266</th>\n",
-       "      <td>2236</td>\n",
-       "      <td>590901436</td>\n",
-       "      <td>4206903</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2267</th>\n",
-       "      <td>601</td>\n",
-       "      <td>658027554</td>\n",
-       "      <td>4206903</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2268</th>\n",
-       "      <td>3974</td>\n",
-       "      <td>591239709</td>\n",
-       "      <td>4206903</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2269</th>\n",
-       "      <td>3250</td>\n",
-       "      <td>591260403</td>\n",
-       "      <td>4206903</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>194437 rows × 3 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      particle_id     lhcbid  event_id\n",
-       "0            2829  671094942   7212930\n",
-       "1            2259  604074443   7212930\n",
-       "2               0  537154464   7212930\n",
-       "3             501  537143928   7212930\n",
-       "4            2828  536908207   7212930\n",
-       "...           ...        ...       ...\n",
-       "2265         1858  591324376   4206903\n",
-       "2266         2236  590901436   4206903\n",
-       "2267          601  658027554   4206903\n",
-       "2268         3974  591239709   4206903\n",
-       "2269         3250  591260403   4206903\n",
-       "\n",
-       "[194437 rows x 3 columns]"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dataframes[\"hits_particles\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trackEvaluator = mt.TrackMatcher(config=mt_config).full_matching(\n",
-    "    df_events_hits_particles=dataframes[\"hits_particles\"],\n",
-    "    df_events_particles=dataframes[\"particles\"],\n",
-    "    df_events_tracks_hits=dataframes[\"tracks\"],\n",
-    "    matching_condition=mt.matchcond.MinMatchingFraction(0.7),\n",
-    "    track_condition=mt.matchcond.MinLengthTrack(2),\n",
-    ")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.9149435564993853"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "trackEvaluator.compute_metric(\n",
-    "    \"efficiency\",\n",
-    "    category=mtb.category.allen_categories[0],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "TrackChecker output                               :       678/    18902   3.59% ghosts\n",
-      "01_velo                                           :      8186/     8947  91.49% ( 92.65%),        86 (  1.04%) clones, pur  99.19%, hit eff  94.86% \n",
-      "02_long                                           :      4846/     5143  94.23% ( 95.52%),        47 (  0.96%) clones, pur  99.28%, hit eff  95.94% \n",
-      "03_long_P>5GeV                                    :      3161/     3330  94.92% ( 96.32%),        34 (  1.06%) clones, pur  99.32%, hit eff  96.42% \n",
-      "04_long_strange                                   :         0/        0    nan% (   nan%),         0 (   nan%) clones, pur    nan%, hit eff    nan% \n",
-      "05_long_strange_P>5GeV                            :         0/        0    nan% (   nan%),         0 (   nan%) clones, pur    nan%, hit eff    nan% \n",
-      "06_long_fromB                                     :         0/        0    nan% (   nan%),         0 (   nan%) clones, pur    nan%, hit eff    nan% \n",
-      "07_long_fromB_P>5GeV                              :         0/        0    nan% (   nan%),         0 (   nan%) clones, pur    nan%, hit eff    nan% \n",
-      "08_long_electrons                                 :       253/      381  66.40% ( 70.75%),         6 (  2.32%) clones, pur  98.26%, hit eff  74.89% \n",
-      "09_long_fromB_electrons                           :         0/        0    nan% (   nan%),         0 (   nan%) clones, pur    nan%, hit eff    nan% \n",
-      "10_long_fromB_electrons_P>5GeV                    :         0/        0    nan% (   nan%),         0 (   nan%) clones, pur    nan%, hit eff    nan% \n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "trackEvaluator.print_report(\n",
-    "    reporter=mt.AllenReporter(),\n",
-    "    categories=mtb.category.allen_categories,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "+----------------------+--------------+----------------------+------------+--------------------------+----------------------+\n",
-      "| Categories           | Efficiency   | Average efficiency   | % clones   | Average hit efficiency   | Average hit purity   |\n",
-      "+======================+==============+======================+============+==========================+======================+\n",
-      "| Velo, only electrons | 60.08%       | 63.28%               | 3.84%      | 98.22%                   | 72.04%               |\n",
-      "+----------------------+--------------+----------------------+------------+--------------------------+----------------------+\n",
-      "| Velo, no electrons   | 91.49%       | 92.65%               | 1.04%      | 99.19%                   | 94.86%               |\n",
-      "+----------------------+--------------+----------------------+------------+--------------------------+----------------------+\n",
-      "+--------------+------------+------------+------------+\n",
-      "| Categories   |   # ghosts | # tracks   | % ghosts   |\n",
-      "+==============+============+============+============+\n",
-      "| Everything   |        678 | 18,902     | 3.59%      |\n",
-      "+--------------+------------+------------+------------+\n"
-     ]
-    }
-   ],
-   "source": [
-    "trackEvaluator.print_report(\n",
-    "    reporter=mt.TabReporter(\n",
-    "        [\n",
-    "            \"efficiency\",\n",
-    "            \"efficiency_per_event\",\n",
-    "            \"clone_rate\",\n",
-    "            \"hit_purity_per_candidate\",\n",
-    "            \"hit_efficiency_per_candidate\",\n",
-    "        ],\n",
-    "        mode=\"markdown\",\n",
-    "        tablefmt=\"grid\",\n",
-    "    ),\n",
-    "    categories=mtb.category.velo_categories,\n",
-    ")\n",
-    "trackEvaluator.print_report(\n",
-    "    reporter=mt.TabReporter(\n",
-    "        metric_names=[\"n_ghosts\", \"n_tracks\", \"ghost_rate\"],\n",
-    "        mode=\"markdown\",\n",
-    "        tablefmt=\"grid\",\n",
-    "    ),\n",
-    ")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 3200x2400 with 32 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib as mpl\n",
-    "# mpl.rcParams.update(**mpl.rcParamsDefault)\n",
-    "mpl.rcParams.update(\n",
-    "    **{\n",
-    "        # Font\n",
-    "        'font.family': 'serif',\n",
-    "        'font.serif': 'Computer Modern Roman',\n",
-    "        # Latex\n",
-    "        'text.latex.preamble': r'\\usepackage{amsmath}',\n",
-    "        'text.usetex': True,  # Put to False to disable Latex\n",
-    "        # Fontsizes    \n",
-    "        'legend.fontsize': 20,\n",
-    "        'axes.titlesize': 24,\n",
-    "        'xtick.labelsize': 20,\n",
-    "        'ytick.labelsize': 20,\n",
-    "        'axes.labelsize': 25,\n",
-    "        'font.size': 20,    \n",
-    "        # Lines\n",
-    "        'lines.markersize': 6.,\n",
-    "        'lines.linestyle': '-',\n",
-    "        'lines.linewidth': 1.5,\n",
-    "        # Figure\n",
-    "        \"figure.figsize\": (8, 6),\n",
-    "        # \"figure.dpi\": 200,\n",
-    "        # Ticks\n",
-    "        # Ticks settings\n",
-    "        \"xtick.direction\": \"in\",\n",
-    "        \"ytick.direction\": \"in\",\n",
-    "    }\n",
-    ")\n",
-    "\n",
-    "columns = [\"pt\", \"p\", \"eta\", \"vz\"]\n",
-    "\n",
-    "metric_names = [\n",
-    "    \"efficiency\",\n",
-    "    # \"ghost_rate\",\n",
-    "    \"clone_rate\",\n",
-    "    \"hit_purity_per_candidate\",\n",
-    "    \"hit_efficiency_per_candidate\",\n",
-    "]\n",
-    "\n",
-    "\n",
-    "# _ = trackEvaluator.plot_histogram(\n",
-    "#     column=\"vz\",\n",
-    "#     metric_name=\"efficiency\",\n",
-    "#     column_label=column_labels[\"vz\"],\n",
-    "#     range=column_ranges[\"vz\"],\n",
-    "#     category=mtb.category.velo_category,\n",
-    "#     bins=\"auto\",\n",
-    "# )\n",
-    "\n",
-    "_ = trackEvaluator.plot_histograms(\n",
-    "    columns=columns,\n",
-    "    metric_names=metric_names,\n",
-    "    column_labels=column_labels,\n",
-    "    column_ranges=column_ranges,\n",
-    "    category=mtb.category.velo_category,\n",
-    "    bins=20,\n",
-    ")\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.15"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
-- 
GitLab


From 73d1b7baa4ee814de81cc4a6d9c794112cc8ec99 Mon Sep 17 00:00:00 2001
From: anthonyc <acorreia@lpnhe.in2p3.fr>
Date: Thu, 23 Mar 2023 19:42:22 +0100
Subject: [PATCH 13/30] add MonteTracko evaluation script to full pipeline

---
 LHCb_Pipeline/full_pipeline.ipynb | 108 +++++++++++++++++++++++++++---
 1 file changed, 100 insertions(+), 8 deletions(-)

diff --git a/LHCb_Pipeline/full_pipeline.ipynb b/LHCb_Pipeline/full_pipeline.ipynb
index 943dc542..bc08ec5c 100644
--- a/LHCb_Pipeline/full_pipeline.ipynb
+++ b/LHCb_Pipeline/full_pipeline.ipynb
@@ -60,6 +60,9 @@
     "from Scripts.Step_4_Run_GNN import train as run_gnn_inference\n",
     "from Scripts.Step_5_Build_Track_Candidates import train as build_track_candidates\n",
     "from Scripts.Step_6_Evaluate_Reconstruction import evaluate as evaluate_candidates\n",
+    "from Scripts.Step_6_Evaluate_Reconstruction_MonteTracko import (\n",
+    "    evaluate as evaluate_candidates_montetracko\n",
+    ")\n",
     "\n",
     "import os\n",
     "import sys\n",
@@ -127,17 +130,20 @@
     "import json\n",
     "# from datetime import datetime\n",
     "\n",
-    "def send_telegram_message(message: str,\n",
-    "                          chat_id: str,\n",
-    "                          api_key: str,\n",
-    "                         ):\n",
-    "    responses = {}\n",
+    "# def send_telegram_message(message: str,\n",
+    "#                           chat_id: str,\n",
+    "#                           api_key: str,\n",
+    "#                          ):\n",
+    "#     responses = {}\n",
     "\n",
-    "    url = f'https://api.telegram.org/bot{api_key}/sendMessage?chat_id={chat_id}&text={message}'\n",
+    "#     url = f'https://api.telegram.org/bot{api_key}/sendMessage?chat_id={chat_id}&text={message}'\n",
     "    \n",
-    "    response = requests.post(url)\n",
+    "#     response = requests.post(url)\n",
     "    \n",
-    "    return response"
+    "#     return response\n",
+    "\n",
+    "def send_telegram_message(*args, **kwargs):\n",
+    "    pass"
    ]
   },
   {
@@ -9801,6 +9807,92 @@
     "ATLAS style matching is the default."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:---------------------- Step 6: Evaluation using MonteTracko ----------------------\n",
+      "INFO:1) Load the dataframes\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7d4c05583c584ea9aeaf706af0d8ea70",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/100 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:2) Matching\n",
+      "INFO:3) Reporting\n",
+      "INFO:4) Plotting\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TrackChecker output                               :       678/    18902   3.59% ghosts\n",
+      "01_velo                                           :      8186/     8947  91.49% ( 92.65%),        86 (  1.04%) clones, pur  99.19%, hit eff  94.86% \n",
+      "02_long                                           :      4846/     5143  94.23% ( 95.52%),        47 (  0.96%) clones, pur  99.28%, hit eff  95.94% \n",
+      "03_long_P>5GeV                                    :      3161/     3330  94.92% ( 96.32%),        34 (  1.06%) clones, pur  99.32%, hit eff  96.42% \n",
+      "04_long_strange                                   :         0/        0    nan% (   nan%),         0 (   nan%) clones, pur    nan%, hit eff    nan% \n",
+      "05_long_strange_P>5GeV                            :         0/        0    nan% (   nan%),         0 (   nan%) clones, pur    nan%, hit eff    nan% \n",
+      "06_long_fromB                                     :         0/        0    nan% (   nan%),         0 (   nan%) clones, pur    nan%, hit eff    nan% \n",
+      "07_long_fromB_P>5GeV                              :         0/        0    nan% (   nan%),         0 (   nan%) clones, pur    nan%, hit eff    nan% \n",
+      "08_long_electrons                                 :       253/      381  66.40% ( 70.75%),         6 (  2.32%) clones, pur  98.26%, hit eff  74.89% \n",
+      "09_long_fromB_electrons                           :         0/        0    nan% (   nan%),         0 (   nan%) clones, pur    nan%, hit eff    nan% \n",
+      "10_long_fromB_electrons_P>5GeV                    :         0/        0    nan% (   nan%),         0 (   nan%) clones, pur    nan%, hit eff    nan% \n",
+      "\n",
+      "+----------------------+--------------+----------------------+------------+----------------------+--------------------------+\n",
+      "| Categories           | Efficiency   | Average efficiency   | % clones   | Average hit purity   | Average hit efficiency   |\n",
+      "+======================+==============+======================+============+======================+==========================+\n",
+      "| Velo, only electrons | 60.08%       | 63.28%               | 3.84%      | 98.22%               | 72.04%                   |\n",
+      "+----------------------+--------------+----------------------+------------+----------------------+--------------------------+\n",
+      "| Velo, no electrons   | 91.49%       | 92.65%               | 1.04%      | 99.19%               | 94.86%                   |\n",
+      "+----------------------+--------------+----------------------+------------+----------------------+--------------------------+\n",
+      "+--------------+------------+------------+------------+\n",
+      "| Categories   |   # ghosts | # tracks   | % ghosts   |\n",
+      "+==============+============+============+============+\n",
+      "| Everything   |        678 | 18,902     | 3.59%      |\n",
+      "+--------------+------------+------------+------------+\n"
+     ]
+    },
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 3200x2400 with 32 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "trackEvaluator = evaluate_candidates_montetracko(\n",
+    "    CONFIG,\n",
+    "    min_track_length=2,\n",
+    "    whether_to_plot=True,\n",
+    "    allen_report=True,\n",
+    ")"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 28,
-- 
GitLab


From 764b8c71af2a12745c6b7655ef4d65f729e7f76d Mon Sep 17 00:00:00 2001
From: anthonyc <acorreia@lpnhe.in2p3.fr>
Date: Fri, 24 Mar 2023 14:25:24 +0100
Subject: [PATCH 14/30] fix range ovtxz

---
 LHCb_Pipeline/Embedding/embedding_base.py              | 10 +++++-----
 .../Scripts/utils/plotting_utils_validation.py         |  2 +-
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/LHCb_Pipeline/Embedding/embedding_base.py b/LHCb_Pipeline/Embedding/embedding_base.py
index d662c98e..ac95056a 100644
--- a/LHCb_Pipeline/Embedding/embedding_base.py
+++ b/LHCb_Pipeline/Embedding/embedding_base.py
@@ -304,8 +304,8 @@ class EmbeddingBase(LightningModule):
                 on_epoch=True,
                 on_step=False
             )
-            print("eff= ", eff)
-            print("pur= ", pur)
+            # print("eff= ", eff)
+            # print("pur= ", pur)
 
         if verbose:
             logging.info("Efficiency: {}".format(eff))
@@ -313,9 +313,9 @@ class EmbeddingBase(LightningModule):
             logging.info(batch.event_file)
 
         
-        print(e_spatial.shape)
-        print(y_cluster.shape)
-        print(e_bidir.shape)
+        # print(e_spatial.shape)
+        # print(y_cluster.shape)
+        # print(e_bidir.shape)
 
         return {
             "loss": loss,
diff --git a/LHCb_Pipeline/Scripts/utils/plotting_utils_validation.py b/LHCb_Pipeline/Scripts/utils/plotting_utils_validation.py
index 9797a469..b4468337 100644
--- a/LHCb_Pipeline/Scripts/utils/plotting_utils_validation.py
+++ b/LHCb_Pipeline/Scripts/utils/plotting_utils_validation.py
@@ -13,7 +13,7 @@ column_ranges = {
     "pt": (0, 2000),
     "p": (0, 50000),
     "eta": None,
-    "vz": (0, 200),
+    "vz": (-200, 700),
 }
 
 
-- 
GitLab


From 9dd52537b37083615a664026f8ada72c1643de27 Mon Sep 17 00:00:00 2001
From: anthonyc <acorreia@lpnhe.in2p3.fr>
Date: Fri, 24 Mar 2023 14:38:33 +0100
Subject: [PATCH 15/30] comment out print statements + formatting

---
 LHCb_Pipeline/Embedding/Models/inference.py | 17 ++---------------
 1 file changed, 2 insertions(+), 15 deletions(-)

diff --git a/LHCb_Pipeline/Embedding/Models/inference.py b/LHCb_Pipeline/Embedding/Models/inference.py
index 7cf17e54..4ff31111 100644
--- a/LHCb_Pipeline/Embedding/Models/inference.py
+++ b/LHCb_Pipeline/Embedding/Models/inference.py
@@ -31,7 +31,6 @@ class EmbeddingTelemetry(Callback):
         logging.info("Constructing telemetry callback")
 
     def on_test_start(self, trainer, pl_module):
-
         """
         This hook is automatically called when the model is tested after training. The best checkpoint is automatically loaded
         """
@@ -47,7 +46,6 @@ class EmbeddingTelemetry(Callback):
     def on_test_batch_end(
         self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx
     ):
-
         """
         Get the relevant outputs from each batch
         """
@@ -65,7 +63,6 @@ class EmbeddingTelemetry(Callback):
         self.truth_graph.append(outputs["truth_graph"].cpu())
 
     def on_test_end(self, trainer, pl_module):
-
         """
         1. Aggregate all outputs,
         2. Calculate the ROC curve,
@@ -80,7 +77,6 @@ class EmbeddingTelemetry(Callback):
         self.save_metrics(metrics_plots, pl_module.hparams.output_dir)
 
     def get_pt_metrics(self):
-
         pt_true_pos = np.concatenate(self.pt_true_pos, axis=1)
         pt_true = np.concatenate(self.pt_true, axis=1)
 
@@ -99,28 +95,26 @@ class EmbeddingTelemetry(Callback):
         return centers, ratio_hist
 
     def get_eff_pur_metrics(self):
-
         self.distances = torch.cat(self.distances)
         self.truth = torch.cat(self.truth)
         self.truth_graph = torch.cat(self.truth_graph, axis=1)
 
         r_cuts = np.arange(0.01, self.hparams["r_test"], 0.01)
 
-        print(r_cuts)
+        # print(r_cuts)
 
         positives = np.array([(self.distances < r_cut**2).sum() for r_cut in r_cuts])
         true_positives = np.array(
             [self.truth[self.distances < r_cut**2].sum() for r_cut in r_cuts]
         )
 
-        print(positives, true_positives)
+        # print(positives, true_positives)
         eff = true_positives / self.truth_graph.shape[1]
         pur = true_positives / positives
 
         return eff, pur, r_cuts
 
     def calculate_metrics(self):
-
         centers, ratio_hist = self.get_pt_metrics()
 
         eff, pur, r_cuts = self.get_eff_pur_metrics()
@@ -132,7 +126,6 @@ class EmbeddingTelemetry(Callback):
         }
 
     def make_plot(self, x_val, y_val, x_lab, y_lab, title):
-
         # Update this to dynamically adapt to number of metrics
         fig, axs = plt.subplots(nrows=1, ncols=1, figsize=(20, 20))
         axs = axs.flatten() if type(axs) is list else [axs]
@@ -146,7 +139,6 @@ class EmbeddingTelemetry(Callback):
         return fig, axs
 
     def plot_metrics(self, metrics):
-
         centers, ratio_hist = (
             metrics["pt_plot"]["centers"],
             metrics["pt_plot"]["ratio_hist"],
@@ -185,7 +177,6 @@ class EmbeddingTelemetry(Callback):
         }
 
     def save_metrics(self, metrics_plots, output_dir):
-
         os.makedirs(output_dir, exist_ok=True)
 
         for metric, (fig, axs) in metrics_plots.items():
@@ -208,7 +199,6 @@ class EmbeddingBuilder(Callback):
         self.overwrite = False
 
     def on_test_end(self, trainer, pl_module):
-
         print("Testing finished, running inference to build graphs...")
 
         datasets = self.prepare_datastructure(pl_module)
@@ -264,7 +254,6 @@ class EmbeddingBuilder(Callback):
         return datasets
 
     def get_truth(self, batch, e_spatial, e_bidir):
-
         e_spatial_easy_fake = e_spatial[
             :, batch.pid[e_spatial[0]] != batch.pid[e_spatial[1]]
         ]
@@ -283,7 +272,6 @@ class EmbeddingBuilder(Callback):
         return e_spatial, y_cluster
 
     def construct_downstream(self, batch, pl_module, datatype):
-
         input_data = pl_module.get_input_data(batch)
 
         spatial = pl_module(input_data)
@@ -319,7 +307,6 @@ class EmbeddingBuilder(Callback):
         self.save_downstream(batch, pl_module, datatype)
 
     def save_downstream(self, batch, pl_module, datatype):
-
         with open(
             os.path.join(self.output_dir, datatype, batch.event_file[-4:]), "wb"
         ) as pickle_file:
-- 
GitLab


From a6b0a32fd492c45486abfb878b6d779f62204dbd Mon Sep 17 00:00:00 2001
From: anthonyc <acorreia@lpnhe.in2p3.fr>
Date: Fri, 24 Mar 2023 14:39:29 +0100
Subject: [PATCH 16/30] formatting with black

---
 LHCb_Pipeline/GNN/Models/interaction_gnn.py   | 12 ++---
 LHCb_Pipeline/GNN/gnn_base.py                 |  5 +-
 .../Scripts/Step_1_Train_Metric_Learning.py   | 25 +++++-----
 .../Scripts/Step_2_Run_Metric_Learning.py     | 40 +++++++++++-----
 LHCb_Pipeline/Scripts/Step_3_Train_GNN.py     | 25 +++++-----
 LHCb_Pipeline/Scripts/Step_4_Run_GNN.py       | 34 +++++++++-----
 .../Scripts/Step_5_Build_Track_Candidates.py  | 38 ++++++++-------
 ...p_6_Evaluate_Reconstruction_MonteTracko.py | 22 +++++----
 LHCb_Pipeline/notebooks/build_embedding.py    | 47 ++++++++-----------
 9 files changed, 138 insertions(+), 110 deletions(-)

diff --git a/LHCb_Pipeline/GNN/Models/interaction_gnn.py b/LHCb_Pipeline/GNN/Models/interaction_gnn.py
index 7537d468..ee9ead7f 100644
--- a/LHCb_Pipeline/GNN/Models/interaction_gnn.py
+++ b/LHCb_Pipeline/GNN/Models/interaction_gnn.py
@@ -11,15 +11,13 @@ from ..utils import make_mlp
 
 
 class InteractionGNN(GNNBase):
-
-    """
-    An interaction network class
+    """An interaction network class
     """
-
     def __init__(self, hparams):
         super().__init__(hparams)
         """
-        Initialise the Lightning Module that can scan over different GNN training regimes
+        Initialise the Lightning Module that can scan over different GNN training
+        regimes
         """
 
         concatenation_factor = (
@@ -72,14 +70,12 @@ class InteractionGNN(GNNBase):
         )
 
     def reset_parameters(self):
-
         for layer in self.modules():
             if type(layer) is Linear:
                 eval(self.hparams["initialization"])(layer.weight)
                 layer.bias.data.fill_(0)
 
     def message_step(self, x, start, end, e):
-
         # Compute new node features
         if self.hparams["aggregation"] == "sum":
             edge_messages = scatter_add(e, end, dim=0, dim_size=x.shape[0])
@@ -110,13 +106,11 @@ class InteractionGNN(GNNBase):
         return x_out, e_out
 
     def output_step(self, x, start, end, e):
-
         classifier_inputs = torch.cat([x[start], x[end], e], dim=1)
 
         return self.output_edge_classifier(classifier_inputs).squeeze(-1)
 
     def forward(self, x, edge_index):
-
         start, end = edge_index
 
         # Encode the graph features into the hidden space
diff --git a/LHCb_Pipeline/GNN/gnn_base.py b/LHCb_Pipeline/GNN/gnn_base.py
index 8b2b419a..dcd8e08e 100644
--- a/LHCb_Pipeline/GNN/gnn_base.py
+++ b/LHCb_Pipeline/GNN/gnn_base.py
@@ -23,8 +23,8 @@ class GNNBase(LightningModule):
         self.save_hyperparameters(hparams)
 
     def setup(self, stage):
-        # Handle any subset of [train, val, test] data split, assuming that ordering
-
+        """Handle any subset of [train, val, test] data split, assuming that ordering
+        """
         input_subdirs = [None, None, None]
         input_subdirs[: len(self.hparams["datatype_names"])] = [
             os.path.join(self.hparams["input_dir"], datatype)
@@ -40,7 +40,6 @@ class GNNBase(LightningModule):
         ]
 
     def setup_data(self):
-
         self.setup(stage="fit")
 
     def train_dataloader(self):
diff --git a/LHCb_Pipeline/Scripts/Step_1_Train_Metric_Learning.py b/LHCb_Pipeline/Scripts/Step_1_Train_Metric_Learning.py
index 6f3d2865..1cc819f7 100644
--- a/LHCb_Pipeline/Scripts/Step_1_Train_Metric_Learning.py
+++ b/LHCb_Pipeline/Scripts/Step_1_Train_Metric_Learning.py
@@ -7,7 +7,8 @@ import os
 import yaml
 import argparse
 import logging
-logging.basicConfig(level=logging.INFO, format='%(levelname)s:%(message)s')
+
+logging.basicConfig(level=logging.INFO, format="%(levelname)s:%(message)s")
 
 from pytorch_lightning import Trainer
 from pytorch_lightning.loggers import CSVLogger
@@ -19,6 +20,7 @@ sys.path.append("../../")
 from Embedding.Models.layerless_embedding import LayerlessEmbedding
 from Scripts.utils.convenience_utils import headline
 
+
 def parse_args():
     """Parse command line arguments."""
     parser = argparse.ArgumentParser("1_Train_Metric_Learning.py")
@@ -28,12 +30,11 @@ def parse_args():
 
 
 def train(config_file="pipeline_config.yaml"):
-
     logging.info(headline("Step 1: Running metric learning training"))
 
     with open(config_file) as file:
         all_configs = yaml.load(file, Loader=yaml.FullLoader)
-    
+
     common_configs = all_configs["common_configs"]
     metric_learning_configs = all_configs["metric_learning_configs"]
 
@@ -41,13 +42,15 @@ def train(config_file="pipeline_config.yaml"):
 
     model = LayerlessEmbedding(metric_learning_configs)
 
-    logging.info(headline("b) Running training" ))
+    logging.info(headline("b) Running training"))
 
-    save_directory = os.path.join(common_configs["artifact_directory"], "metric_learning")
+    save_directory = os.path.join(
+        common_configs["artifact_directory"], "metric_learning"
+    )
     logger = CSVLogger(save_directory, name=common_configs["experiment_name"])
 
     trainer = Trainer(
-        accelerator='gpu' if torch.cuda.is_available() else 'cpu',
+        accelerator="gpu" if torch.cuda.is_available() else "cpu",
         gpus=common_configs["gpus"],
         max_epochs=metric_learning_configs["max_epochs"],
         logger=logger,
@@ -56,18 +59,18 @@ def train(config_file="pipeline_config.yaml"):
 
     trainer.fit(model)
 
-    logging.info(headline("c) Saving model") )
+    logging.info(headline("c) Saving model"))
 
     os.makedirs(save_directory, exist_ok=True)
-    trainer.save_checkpoint(os.path.join(save_directory, common_configs["experiment_name"]+".ckpt"))
+    trainer.save_checkpoint(
+        os.path.join(save_directory, common_configs["experiment_name"] + ".ckpt")
+    )
 
     return trainer, model
 
 
 if __name__ == "__main__":
-
     args = parse_args()
     config_file = args.config
 
-    trainer, model = train(config_file)    
-
+    trainer, model = train(config_file)
diff --git a/LHCb_Pipeline/Scripts/Step_2_Run_Metric_Learning.py b/LHCb_Pipeline/Scripts/Step_2_Run_Metric_Learning.py
index c2df6d27..46601a9d 100644
--- a/LHCb_Pipeline/Scripts/Step_2_Run_Metric_Learning.py
+++ b/LHCb_Pipeline/Scripts/Step_2_Run_Metric_Learning.py
@@ -7,7 +7,8 @@ import os
 import yaml
 import argparse
 import logging
-logging.basicConfig(level=logging.INFO, format='%(levelname)s:%(message)s')
+
+logging.basicConfig(level=logging.INFO, format="%(levelname)s:%(message)s")
 import torch
 
 sys.path.append("../../")
@@ -16,6 +17,7 @@ from Embedding.Models.layerless_embedding import LayerlessEmbedding
 from Scripts.utils.convenience_utils import headline, delete_directory
 from notebooks.build_embedding import EmbeddingInferenceBuilder
 
+
 def parse_args():
     """Parse command line arguments."""
     parser = argparse.ArgumentParser("2_Run_Metric_Learning.py")
@@ -23,40 +25,54 @@ def parse_args():
     add_arg("config", nargs="?", default="pipeline_config.yaml")
     return parser.parse_args()
 
-def train(config_file="pipeline_config.yaml", checkpoint=''):
 
+def train(config_file="pipeline_config.yaml", checkpoint=""):
     logging.info(headline("Step 2: Constructing graphs from metric learning model"))
 
     with open(config_file) as file:
         all_configs = yaml.load(file, Loader=yaml.FullLoader)
-    
+
     common_configs = all_configs["common_configs"]
     metric_learning_configs = all_configs["metric_learning_configs"]
 
     logging.info(headline("a) Loading trained model"))
 
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    if checkpoint=='':
-        model = LayerlessEmbedding.load_from_checkpoint(os.path.join(common_configs["artifact_directory"], "metric_learning", common_configs["experiment_name"]+".ckpt")).to(device)
+    if checkpoint == "":
+        model = LayerlessEmbedding.load_from_checkpoint(
+            os.path.join(
+                common_configs["artifact_directory"],
+                "metric_learning",
+                common_configs["experiment_name"] + ".ckpt",
+            )
+        ).to(device)
     else:
-        model = LayerlessEmbedding.load_from_checkpoint(checkpoint_path=checkpoint).to(device)
-
-
-
+        model = LayerlessEmbedding.load_from_checkpoint(checkpoint_path=checkpoint).to(
+            device
+        )
 
     logging.info(headline("b) Running inferencing"))
     if common_configs["clear_directories"]:
         delete_directory(metric_learning_configs["output_dir"])
 
-    graph_builder = EmbeddingInferenceBuilder(model, metric_learning_configs["train_split"], overwrite=True, knn_max=metric_learning_configs["knn"], radius=metric_learning_configs["r_test"])
+    model.hparams["input_dir"] = metric_learning_configs["input_dir"]
+    model.hparams["output_dir"] = metric_learning_configs["output_dir"]
+    
+    graph_builder = EmbeddingInferenceBuilder(
+        model,
+        metric_learning_configs["train_split"],
+        overwrite=True,
+        knn_max=metric_learning_configs["knn"],
+        radius=metric_learning_configs["r_test"],
+    )
+    
     graph_builder.build()
 
     return graph_builder
 
 
 if __name__ == "__main__":
-
     args = parse_args()
     config_file = args.config
 
-    gb = train(config_file) 
\ No newline at end of file
+    gb = train(config_file)
diff --git a/LHCb_Pipeline/Scripts/Step_3_Train_GNN.py b/LHCb_Pipeline/Scripts/Step_3_Train_GNN.py
index 530f8168..df1f6cec 100644
--- a/LHCb_Pipeline/Scripts/Step_3_Train_GNN.py
+++ b/LHCb_Pipeline/Scripts/Step_3_Train_GNN.py
@@ -7,7 +7,8 @@ import os
 import yaml
 import argparse
 import logging
-logging.basicConfig(level=logging.INFO, format='%(levelname)s:%(message)s')
+
+logging.basicConfig(level=logging.INFO, format="%(levelname)s:%(message)s")
 
 import torch
 from pytorch_lightning import Trainer
@@ -19,6 +20,7 @@ sys.path.append("../../")
 from GNN.Models.interaction_gnn import InteractionGNN
 from Scripts.utils.convenience_utils import headline
 
+
 def parse_args():
     """Parse command line arguments."""
     parser = argparse.ArgumentParser("3_Train_GNN.py")
@@ -28,26 +30,25 @@ def parse_args():
 
 
 def train(config_file="pipeline_config.yaml"):
-
     logging.info(headline(" Step 3: Running GNN training "))
 
     with open(config_file) as file:
         all_configs = yaml.load(file, Loader=yaml.FullLoader)
-    
+
     common_configs = all_configs["common_configs"]
     gnn_configs = all_configs["gnn_configs"]
 
-    logging.info(headline("a) Initialising model" ))
+    logging.info(headline("a) Initialising model"))
 
     model = InteractionGNN(gnn_configs)
 
-    logging.info(headline( "b) Running training" ))
+    logging.info(headline("b) Running training"))
 
     save_directory = os.path.join(common_configs["artifact_directory"], "gnn")
     logger = CSVLogger(save_directory, name=common_configs["experiment_name"])
 
     trainer = Trainer(
-        accelerator='gpu' if torch.cuda.is_available() else 'cpu',
+        accelerator="gpu" if torch.cuda.is_available() else "cpu",
         gpus=common_configs["gpus"],
         max_epochs=gnn_configs["max_epochs"],
         logger=logger,
@@ -56,18 +57,18 @@ def train(config_file="pipeline_config.yaml"):
 
     trainer.fit(model)
 
-    logging.info(headline( "c) Saving model" ))
-    
+    logging.info(headline("c) Saving model"))
+
     os.makedirs(save_directory, exist_ok=True)
-    trainer.save_checkpoint(os.path.join(save_directory, common_configs["experiment_name"]+".ckpt"))
+    trainer.save_checkpoint(
+        os.path.join(save_directory, common_configs["experiment_name"] + ".ckpt")
+    )
 
     return trainer, model
 
 
 if __name__ == "__main__":
-
     args = parse_args()
     config_file = args.config
 
-    train(config_file)    
-
+    train(config_file)
diff --git a/LHCb_Pipeline/Scripts/Step_4_Run_GNN.py b/LHCb_Pipeline/Scripts/Step_4_Run_GNN.py
index 8f73b525..da667d99 100644
--- a/LHCb_Pipeline/Scripts/Step_4_Run_GNN.py
+++ b/LHCb_Pipeline/Scripts/Step_4_Run_GNN.py
@@ -1,5 +1,6 @@
 """
-This script runs step 4 of the TrackML Quickstart example: Inferencing the GNN to score edges in the event graphs.
+This script runs step 4 of the TrackML Quickstart example: Inferencing the GNN
+to score edges in the event graphs.
 """
 
 import sys
@@ -7,7 +8,8 @@ import os
 import yaml
 import argparse
 import logging
-logging.basicConfig(level=logging.INFO, format='%(levelname)s:%(message)s')
+
+logging.basicConfig(level=logging.INFO, format="%(levelname)s:%(message)s")
 import torch
 
 sys.path.append("../../")
@@ -24,36 +26,44 @@ def parse_args():
     add_arg("config", nargs="?", default="pipeline_config.yaml")
     return parser.parse_args()
 
-def train(config_file="pipeline_config.yaml", checkpoint=''):
 
-    logging.info(headline( "Step 4: Scoring graph edges using GNN " ))
+def train(config_file="pipeline_config.yaml", checkpoint=""):
+    logging.info(headline("Step 4: Scoring graph edges using GNN "))
 
     with open(config_file) as file:
         all_configs = yaml.load(file, Loader=yaml.FullLoader)
-    
+
     common_configs = all_configs["common_configs"]
     gnn_configs = all_configs["gnn_configs"]
 
-    logging.info(headline( "a) Loading trained model" ))
+    logging.info(headline("a) Loading trained model"))
 
     if common_configs["clear_directories"]:
         delete_directory(gnn_configs["output_dir"])
 
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    if checkpoint=='':
-        model = InteractionGNN.load_from_checkpoint(os.path.join(common_configs["artifact_directory"], "gnn", common_configs["experiment_name"]+".ckpt")).to(device)
+    if checkpoint == "":
+        model = InteractionGNN.load_from_checkpoint(
+            os.path.join(
+                common_configs["artifact_directory"],
+                "gnn",
+                common_configs["experiment_name"] + ".ckpt",
+            )
+        ).to(device)
     else:
-        model = InteractionGNN.load_from_checkpoint(checkpoint_path=checkpoint).to(device)
+        model = InteractionGNN.load_from_checkpoint(checkpoint_path=checkpoint).to(
+            device
+        )
 
     model.setup_data()
 
-    logging.info(headline( "b) Running inferencing" ))
+    logging.info(headline("b) Running inferencing"))
     graph_scorer = GNNInferenceBuilder(model)
     graph_scorer.infer()
 
-if __name__ == "__main__":
 
+if __name__ == "__main__":
     args = parse_args()
     config_file = args.config
 
-    train(config_file) 
\ No newline at end of file
+    train(config_file)
diff --git a/LHCb_Pipeline/Scripts/Step_5_Build_Track_Candidates.py b/LHCb_Pipeline/Scripts/Step_5_Build_Track_Candidates.py
index 2b981049..57de0958 100644
--- a/LHCb_Pipeline/Scripts/Step_5_Build_Track_Candidates.py
+++ b/LHCb_Pipeline/Scripts/Step_5_Build_Track_Candidates.py
@@ -7,19 +7,19 @@ import os
 import yaml
 import argparse
 import logging
-logging.basicConfig(level=logging.INFO, format='%(levelname)s:%(message)s')
+
+logging.basicConfig(level=logging.INFO, format="%(levelname)s:%(message)s")
 import torch
 import numpy as np
 import scipy.sparse as sps
 
-from tqdm.contrib.concurrent import process_map
 from tqdm import tqdm
-from functools import partial
 
 from Scripts.utils.convenience_utils import headline, delete_directory
 
 sys.path.append("../../")
 
+
 def parse_args():
     """Parse command line arguments."""
     parser = argparse.ArgumentParser("5_Build_Track_Candidates.py")
@@ -28,8 +28,9 @@ def parse_args():
     return parser.parse_args()
 
 
-def label_graph(graph, score_cut=0.8, save_dir="datasets/quickstart_track_building_processed"):
-
+def label_graph(
+    graph, score_cut=0.8, save_dir="datasets/quickstart_track_building_processed"
+):
     os.makedirs(save_dir, exist_ok=True)
 
     edge_mask = graph.scores > score_cut
@@ -40,35 +41,42 @@ def label_graph(graph, score_cut=0.8, save_dir="datasets/quickstart_track_buildi
     N = graph.x.size(0)
     sparse_edges = sps.coo_matrix((edge_attr, (row.numpy(), col.numpy())), (N, N))
 
-    _, candidate_labels = sps.csgraph.connected_components(sparse_edges, directed=False, return_labels=True)  
+    _, candidate_labels = sps.csgraph.connected_components(
+        sparse_edges, directed=False, return_labels=True
+    )
     graph.labels = torch.from_numpy(candidate_labels).long()
 
     torch.save(graph, os.path.join(save_dir, graph.event_file[-9:]))
 
 
 def train(config_file="pipeline_config.yaml"):
-
-    logging.info(headline( " Step 5: Building track candidates from the scored graph " ))
+    logging.info(headline(" Step 5: Building track candidates from the scored graph "))
 
     with open(config_file) as file:
         all_configs = yaml.load(file, Loader=yaml.FullLoader)
-    
+
     common_configs = all_configs["common_configs"]
     gnn_configs = all_configs["gnn_configs"]
     track_building_configs = all_configs["track_building_configs"]
 
-    logging.info(headline("a) Loading scored graphs" ))
+    logging.info(headline("a) Loading scored graphs"))
 
     all_graphs = []
     for subdir in ["train", "val", "test"]:
         subdir_graphs = os.listdir(os.path.join(gnn_configs["output_dir"], subdir))
-        all_graphs += [torch.load(os.path.join(gnn_configs["output_dir"], subdir, graph), map_location="cpu") for graph in subdir_graphs]
+        all_graphs += [
+            torch.load(
+                os.path.join(gnn_configs["output_dir"], subdir, graph),
+                map_location="cpu",
+            )
+            for graph in subdir_graphs
+        ]
 
-    logging.info(headline( "b) Labelling graph nodes" ) )
+    logging.info(headline("b) Labelling graph nodes"))
 
     score_cut = track_building_configs["score_cut"]
     save_dir = track_building_configs["output_dir"]
-    
+
     if common_configs["clear_directories"]:
         delete_directory(track_building_configs["output_dir"])
 
@@ -77,10 +85,8 @@ def train(config_file="pipeline_config.yaml"):
         label_graph(graph, score_cut=score_cut, save_dir=save_dir)
 
 
-
 if __name__ == "__main__":
-
     args = parse_args()
     config_file = args.config
 
-    train(config_file) 
\ No newline at end of file
+    train(config_file)
diff --git a/LHCb_Pipeline/Scripts/Step_6_Evaluate_Reconstruction_MonteTracko.py b/LHCb_Pipeline/Scripts/Step_6_Evaluate_Reconstruction_MonteTracko.py
index 9112f111..1b82137e 100644
--- a/LHCb_Pipeline/Scripts/Step_6_Evaluate_Reconstruction_MonteTracko.py
+++ b/LHCb_Pipeline/Scripts/Step_6_Evaluate_Reconstruction_MonteTracko.py
@@ -164,7 +164,7 @@ def report(
                 "hit_efficiency_per_candidate",
             ],
             mode="markdown",
-            tablefmt="grid",
+            # tablefmt="grid",
         ),
         categories=mtb.category.velo_categories,
     )
@@ -172,12 +172,12 @@ def report(
         reporter=mt.TabReporter(
             metric_names=["n_ghosts", "n_tracks", "ghost_rate"],
             mode="markdown",
-            tablefmt="grid",
+            # tablefmt="grid",
         ),
     )
 
 
-def plot(trackEvaluator):
+def plot(trackEvaluator, category: mt.requirement.CategoryBase):
     """Generate and display histograms of track evaluation metrics in specified
     particle-related columns (`pt`, `p`, `eta` and `vz`)
 
@@ -197,10 +197,10 @@ def plot(trackEvaluator):
         ],
         column_labels=plotutils.column_labels,
         column_ranges=plotutils.column_ranges,
-        category=mtb.category.velo_category,
+        category=category,
         bins=20,
     )
-    fig.savefig("track_evaluation.pdf", dpi=200, bbox_inches="tight")
+    fig.savefig(f"eval_{category.name}.pdf", dpi=200, bbox_inches="tight")
 
 
 def evaluate(
@@ -208,7 +208,7 @@ def evaluate(
     min_track_length: int = 2,
     whether_to_plot: bool = True,
     allen_report: bool = False,
-):
+) -> mt.TrackEvaluator:
     """Runs truth-based tracking evaluation.
 
     Args:
@@ -218,7 +218,7 @@ def evaluate(
         allen_report: whether to report in Allen categories using the Allen reporter
 
     Returns:
-        mt.TrackEvaluator: object containing the results of the evaluation.
+        object containing the evaluation.
     """
     logging.info(headline("Step 6: Evaluation using MonteTracko"))
 
@@ -272,7 +272,13 @@ def evaluate(
 
     # Plot
     if whether_to_plot:
-        plot(trackEvaluator=trackEvaluator)
+        for category in [
+            mtb.category.category_velo,
+            mtb.category.category_long,
+            mtb.category.category_long_only_electrons,
+        ]:
+            plot(trackEvaluator=trackEvaluator, category=category)
+
     return trackEvaluator
 
 
diff --git a/LHCb_Pipeline/notebooks/build_embedding.py b/LHCb_Pipeline/notebooks/build_embedding.py
index 49f6ea86..f2c6d1e8 100644
--- a/LHCb_Pipeline/notebooks/build_embedding.py
+++ b/LHCb_Pipeline/notebooks/build_embedding.py
@@ -9,9 +9,11 @@ from tqdm import tqdm
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
+
 class EmbeddingInferenceBuilder:
-    def __init__(self, model, split = [80, 10, 10], overwrite=False, knn_max = 1000, radius = 0.1):
-        
+    def __init__(
+        self, model, split=[80, 10, 10], overwrite=False, knn_max=1000, radius=0.1
+    ):
         self.model = model
         self.output_dir = self.model.hparams["output_dir"]
         self.input_dir = self.model.hparams["input_dir"]
@@ -19,29 +21,25 @@ class EmbeddingInferenceBuilder:
         self.split = split
         self.knn_max = knn_max
         self.radius = radius
-        
+
         single_file_split = [1, 1, 1]
         model.hparams["train_split"] = single_file_split
         model.setup(stage="fit")
-        
 
     def build(self):
         print("Training finished, running inference to build graphs...")
 
         # By default, the set of examples propagated through the pipeline will be train+val+test set
         datasets = self.prepare_datastructure()
-        
+
         self.model.eval()
         with torch.no_grad():
             for datatype, dataset in datasets.items():
                 for event in tqdm(dataset):
-                    
                     event_file = os.path.join(self.input_dir, event)
                     if (
                         not os.path.exists(
-                            os.path.join(
-                                self.output_dir, datatype, event_file[-4:]
-                            )
+                            os.path.join(self.output_dir, datatype, event_file[-4:])
                         )
                     ) or self.overwrite:
                         batch = torch.load(event_file).to(self.model.device)
@@ -61,27 +59,26 @@ class EmbeddingInferenceBuilder:
         all_events = os.listdir(self.model.hparams["input_dir"])
         random.shuffle(all_events)
         self.dataset_list = np.split(np.array(all_events), np.cumsum(self.split))
-        
+
         # By default, the set of examples propagated through the pipeline will be train+val+test set
         datasets = {
             "train": list(self.dataset_list[0]),
             "val": list(self.dataset_list[1]),
             "test": list(self.dataset_list[2]),
         }
-        
+
         return datasets
-                        
-    def construct_downstream(self, batch, datatype):
 
+    def construct_downstream(self, batch, datatype):
         batch = self.select_data(batch)
-        
+
         y_cluster, e_spatial, e_bidir = self.get_performance(
             batch=batch, r_max=self.radius, k_max=self.knn_max
         )
-        
+
         module_mask = batch.modules[e_spatial[0]] != batch.modules[e_spatial[1]]
         y_cluster, e_spatial = y_cluster[module_mask], e_spatial[:, module_mask]
-        
+
         # Arbitrary ordering to remove half of the duplicate edges
         R_dist = torch.sqrt(batch.x[:, 0] ** 2 + batch.x[:, 2] ** 2)
         e_spatial = e_spatial[:, (R_dist[e_spatial[0]] <= R_dist[e_spatial[1]])]
@@ -107,23 +104,19 @@ class EmbeddingInferenceBuilder:
         return results["truth"], results["preds"], results["truth_graph"]
 
     def save_downstream(self, batch, datatype):
-
         with open(
             os.path.join(self.output_dir, datatype, batch.event_file[-9:]), "wb"
         ) as pickle_file:
             torch.save(batch, pickle_file)
-            
+
     def select_data(self, event):
-    
         event.signal_true_edges = event.modulewise_true_edges
-        
-        if (
-            ("pt" in event.keys and self.model.hparams["pt_signal_cut"] > 0)
-        ):
+
+        if "pt" in event.keys and self.model.hparams["pt_signal_cut"] > 0:
             edge_subset = (
-                (event.pt[event.signal_true_edges] > self.model.hparams["pt_signal_cut"]).all(0)
-            )
+                event.pt[event.signal_true_edges] > self.model.hparams["pt_signal_cut"]
+            ).all(0)
 
             event.signal_true_edges = event.signal_true_edges[:, edge_subset]
-        
-        return event
\ No newline at end of file
+
+        return event
-- 
GitLab


From d731e335a7a4f63b54bb0ac5e788f3f497e99a5d Mon Sep 17 00:00:00 2001
From: anthonyc <acorreia@lpnhe.in2p3.fr>
Date: Fri, 24 Mar 2023 14:39:51 +0100
Subject: [PATCH 17/30] add apply_selection flag

---
 LHCb_Pipeline/Processing/utils/preprocessing.py | 13 +++++++------
 1 file changed, 7 insertions(+), 6 deletions(-)

diff --git a/LHCb_Pipeline/Processing/utils/preprocessing.py b/LHCb_Pipeline/Processing/utils/preprocessing.py
index b59374fe..6c43583d 100644
--- a/LHCb_Pipeline/Processing/utils/preprocessing.py
+++ b/LHCb_Pipeline/Processing/utils/preprocessing.py
@@ -102,6 +102,7 @@ def preprocess(
     output_num: str,
     clear_directories: bool = True,
     num_true_hits_threshold: int = 0,
+    apply_selection: bool = True,
 ):
     """Preprocess the first `output_num` events in the input files,
     into the form of the TrackML dataset.
@@ -132,11 +133,11 @@ def preprocess(
     hits = hits.merge(particles, on=["event", "particle_id"], how="left")  # Merge
     # NB: left join!: keep fake hits
 
-    # Filter
-    # Remove hits with has_velo == 0
-    # hits = hits[hits["has_velo"] == 1]
-    # Remove electrons and positrons ?
-    # hits = hits[np.abs(hits["pid"]) != 11]
+    # if apply_selection:
+    #     # Remove hits with has_velo == 0
+    #     hits = hits[hits["has_velo"] == 1]
+    #     # Remove electrons and positrons ?
+    #     hits = hits[np.abs(hits["pid"]) != 11]
 
     event_list = hits["event"].unique()  # The order is not mixed
     i = 0  # Count the number of events outputed
@@ -149,7 +150,7 @@ def preprocess(
         #: String representation of the event ID
         event_id_str = str(event_num).zfill(9)
 
-        if enough_true_hits(
+        if (not apply_selection) or enough_true_hits(
             event_hits=event_hits,
             num_true_hits_threshold=num_true_hits_threshold,
             event_id_str=event_id_str,
-- 
GitLab


From 7cf396ed538c6a083120bf3cf17bf1f86545a107 Mon Sep 17 00:00:00 2001
From: anthonyc <acorreia@lpnhe.in2p3.fr>
Date: Fri, 24 Mar 2023 15:43:28 +0100
Subject: [PATCH 18/30] formatting with black

---
 LHCb_Pipeline/Embedding/Models/inference.py | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/LHCb_Pipeline/Embedding/Models/inference.py b/LHCb_Pipeline/Embedding/Models/inference.py
index 4ff31111..5e9b2706 100644
--- a/LHCb_Pipeline/Embedding/Models/inference.py
+++ b/LHCb_Pipeline/Embedding/Models/inference.py
@@ -1,3 +1,5 @@
+"""Class-based Callback inference for integration with Lightning
+"""
 import sys
 import os
 import copy
@@ -15,10 +17,6 @@ import numpy as np
 
 from ..utils import build_edges, graph_intersection
 
-"""
-Class-based Callback inference for integration with Lightning
-"""
-
 
 class EmbeddingTelemetry(Callback):
 
@@ -32,7 +30,8 @@ class EmbeddingTelemetry(Callback):
 
     def on_test_start(self, trainer, pl_module):
         """
-        This hook is automatically called when the model is tested after training. The best checkpoint is automatically loaded
+        This hook is automatically called when the model is tested after training.
+        The best checkpoint is automatically loaded
         """
         self.preds = []
         self.truth = []
-- 
GitLab


From 369c713937dd80f696a158d57b1288156d29e7ae Mon Sep 17 00:00:00 2001
From: anthonyc <acorreia@lpnhe.in2p3.fr>
Date: Fri, 24 Mar 2023 15:43:48 +0100
Subject: [PATCH 19/30] add apply selection argument to prepare_data method

---
 .../Processing/Models/feature_construction.py | 19 ++++++++++++++-----
 1 file changed, 14 insertions(+), 5 deletions(-)

diff --git a/LHCb_Pipeline/Processing/Models/feature_construction.py b/LHCb_Pipeline/Processing/Models/feature_construction.py
index 20899c21..ab4c20e1 100644
--- a/LHCb_Pipeline/Processing/Models/feature_construction.py
+++ b/LHCb_Pipeline/Processing/Models/feature_construction.py
@@ -11,21 +11,23 @@ from ..feature_store_base import FeatureStoreBase
 from ..utils.event_utils import prepare_event
 from ..utils.preprocessing import preprocess
 
+
 class FeatureStore(FeatureStoreBase):
     def __init__(self, hparams):
         super().__init__(hparams)
 
-    def prepare_data(self):
+    def prepare_data(self, apply_selection: bool = True):
         # Preprocess in the needed form
         # Split calculation in separate file
         # To be able to run on trackml and velo at the same time
         if self.needs_preprocessing:
             preprocess(
-                input_dir=self.input_dir, 
+                input_dir=self.input_dir,
                 output_dir=self.preprocessed_dir,
                 output_num=self.n_files,
                 clear_directories=self.clear_directories,
-                num_true_hits_threshold=self.num_true_hits_threshold
+                num_true_hits_threshold=self.num_true_hits_threshold,
+                apply_selection=apply_selection,
             )
 
         # Find the input files
@@ -33,12 +35,19 @@ class FeatureStore(FeatureStoreBase):
         if self.needs_preprocessing:
             all_files = os.listdir(self.preprocessed_dir)
             all_events = sorted(
-                np.unique([os.path.join(self.preprocessed_dir, event[:14]) for event in all_files])
+                np.unique(
+                    [
+                        os.path.join(self.preprocessed_dir, event[:14])
+                        for event in all_files
+                    ]
+                )
             )[: self.n_files]
         else:
             all_files = os.listdir(self.input_dir)
             all_events = sorted(
-                np.unique([os.path.join(self.input_dir, event[:14]) for event in all_files])
+                np.unique(
+                    [os.path.join(self.input_dir, event[:14]) for event in all_files]
+                )
             )[: self.n_files]
 
         # Define the cell features to be added to the dataset
-- 
GitLab


From bf32cf8d88860a44cea2f5732ce2043b178b42ac Mon Sep 17 00:00:00 2001
From: anthonyc <acorreia@lpnhe.in2p3.fr>
Date: Fri, 24 Mar 2023 16:26:51 +0100
Subject: [PATCH 20/30] set up new module

---
 .gitmodules | 3 +++
 montetracko | 1 +
 2 files changed, 4 insertions(+)
 create mode 100644 .gitmodules
 create mode 160000 montetracko

diff --git a/.gitmodules b/.gitmodules
new file mode 100644
index 00000000..cf5c44ea
--- /dev/null
+++ b/.gitmodules
@@ -0,0 +1,3 @@
+[submodule "montetracko"]
+	path = montetracko
+	url = ssh://git@gitlab.cern.ch:7999/gdl4hep/montetracko.git
diff --git a/montetracko b/montetracko
new file mode 160000
index 00000000..d59d51b6
--- /dev/null
+++ b/montetracko
@@ -0,0 +1 @@
+Subproject commit d59d51b6637f9cf496d203d3fe608a7a3f6c265e
-- 
GitLab


From f67963590418c200baa0b4eee2095690da278e40 Mon Sep 17 00:00:00 2001
From: anthonyc <acorreia@lpnhe.in2p3.fr>
Date: Fri, 24 Mar 2023 16:27:00 +0100
Subject: [PATCH 21/30] set up instruction to run with montetracko

---
 montetracko-setup.sh | 32 ++++++++++++++++++++++++++++++++
 1 file changed, 32 insertions(+)
 create mode 100755 montetracko-setup.sh

diff --git a/montetracko-setup.sh b/montetracko-setup.sh
new file mode 100755
index 00000000..b61baa3f
--- /dev/null
+++ b/montetracko-setup.sh
@@ -0,0 +1,32 @@
+# Some instruction to be able to use MonteTracko
+
+# 1. Initialise the MonteTracko git sub-module
+# git submodule init
+# git submodule update
+
+# # 2. Install new required packages
+# conda activate ext4velo # don't forget to activate your environment!
+# mamba install -c conda-forge ipywidgets tabular black
+# jupyter nbextension enable --py widgetsnbextension  # activate jupyter widgets
+
+# # 3. Add MonteTracko to your PYTHONPATH
+currentdir=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )
+montetracko_dir="$currentdir/montetracko"
+
+cd $CONDA_PREFIX
+mkdir -p ./etc/conda/activate.d
+mkdir -p ./etc/conda/deactivate.d
+touch ./etc/conda/activate.d/env_vars.sh
+touch ./etc/conda/deactivate.d/env_vars.sh
+
+cat <<EOF > ./etc/conda/activate.d/env_vars.sh
+#!/bin/sh
+
+export PYTHONPATH="$PYTHONPATH:$montetracko_dir"
+EOF
+
+cat <<EOF > ./etc/conda/deactivate.d/env_vars.sh
+#!/bin/sh
+
+unset PYTHONPATH
+EOF
\ No newline at end of file
-- 
GitLab


From d36c0eb7a52cc031d80659959ae8c997fbb54aff Mon Sep 17 00:00:00 2001
From: anthonyc <acorreia@lpnhe.in2p3.fr>
Date: Fri, 24 Mar 2023 16:27:44 +0100
Subject: [PATCH 22/30] Use config to set up path to artifacts

---
 LHCb_Pipeline/evaluation_pipeline.ipynb | 2788 ++++-------------------
 1 file changed, 445 insertions(+), 2343 deletions(-)

diff --git a/LHCb_Pipeline/evaluation_pipeline.ipynb b/LHCb_Pipeline/evaluation_pipeline.ipynb
index bb794c5b..40c31c99 100644
--- a/LHCb_Pipeline/evaluation_pipeline.ipynb
+++ b/LHCb_Pipeline/evaluation_pipeline.ipynb
@@ -9,327 +9,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:Loading faiss with AVX2 support.\n",
-      "INFO:Successfully loaded faiss with AVX2 support.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<style>\n",
-       "        .bk-notebook-logo {\n",
-       "            display: block;\n",
-       "            width: 20px;\n",
-       "            height: 20px;\n",
-       "            background-image: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABQAAAAUCAYAAACNiR0NAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAABx0RVh0U29mdHdhcmUAQWRvYmUgRmlyZXdvcmtzIENTNui8sowAAAOkSURBVDiNjZRtaJVlGMd/1/08zzln5zjP1LWcU9N0NkN8m2CYjpgQYQXqSs0I84OLIC0hkEKoPtiH3gmKoiJDU7QpLgoLjLIQCpEsNJ1vqUOdO7ppbuec5+V+rj4ctwzd8IIbbi6u+8f1539dt3A78eXC7QizUF7gyV1fD1Yqg4JWz84yffhm0qkFqBogB9rM8tZdtwVsPUhWhGcFJngGeWrPzHm5oaMmkfEg1usvLFyc8jLRqDOMru7AyC8saQr7GG7f5fvDeH7Ej8CM66nIF+8yngt6HWaKh7k49Soy9nXurCi1o3qUbS3zWfrYeQDTB/Qj6kX6Ybhw4B+bOYoLKCC9H3Nu/leUTZ1JdRWkkn2ldcCamzrcf47KKXdAJllSlxAOkRgyHsGC/zRday5Qld9DyoM4/q/rUoy/CXh3jzOu3bHUVZeU+DEn8FInkPBFlu3+nW3Nw0mk6vCDiWg8CeJaxEwuHS3+z5RgY+YBR6V1Z1nxSOfoaPa4LASWxxdNp+VWTk7+4vzaou8v8PN+xo+KY2xsw6une2frhw05CTYOmQvsEhjhWjn0bmXPjpE1+kplmmkP3suftwTubK9Vq22qKmrBhpY4jvd5afdRA3wGjFAgcnTK2s4hY0/GPNIb0nErGMCRxWOOX64Z8RAC4oCXdklmEvcL8o0BfkNK4lUg9HTl+oPlQxdNo3Mg4Nv175e/1LDGzZen30MEjRUtmXSfiTVu1kK8W4txyV6BMKlbgk3lMwYCiusNy9fVfvvwMxv8Ynl6vxoByANLTWplvuj/nF9m2+PDtt1eiHPBr1oIfhCChQMBw6Aw0UulqTKZdfVvfG7VcfIqLG9bcldL/+pdWTLxLUy8Qq38heUIjh4XlzZxzQm19lLFlr8vdQ97rjZVOLf8nclzckbcD4wxXMidpX30sFd37Fv/GtwwhzhxGVAprjbg0gCAEeIgwCZyTV2Z1REEW8O4py0wsjeloKoMr6iCY6dP92H6Vw/oTyICIthibxjm/DfN9lVz8IqtqKYLUXfoKVMVQVVJOElGjrnnUt9T9wbgp8AyYKaGlqingHZU/uG2NTZSVqwHQTWkx9hxjkpWDaCg6Ckj5qebgBVbT3V3NNXMSiWSDdGV3hrtzla7J+duwPOToIg42ChPQOQjspnSlp1V+Gjdged7+8UN5CRAV7a5EdFNwCjEaBR27b3W890TE7g24NAP/mMDXRWrGoFPQI9ls/MWO2dWFAar/xcOIImbbpA3zgAAAABJRU5ErkJggg==);\n",
-       "        }\n",
-       "    </style>\n",
-       "    <div>\n",
-       "        <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-notebook-logo\"></a>\n",
-       "        <span id=\"p1001\">Loading BokehJS ...</span>\n",
-       "    </div>\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/javascript": [
-       "(function(root) {\n",
-       "  function now() {\n",
-       "    return new Date();\n",
-       "  }\n",
-       "\n",
-       "  const force = true;\n",
-       "\n",
-       "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
-       "    root._bokeh_onload_callbacks = [];\n",
-       "    root._bokeh_is_loading = undefined;\n",
-       "  }\n",
-       "\n",
-       "const JS_MIME_TYPE = 'application/javascript';\n",
-       "  const HTML_MIME_TYPE = 'text/html';\n",
-       "  const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
-       "  const CLASS_NAME = 'output_bokeh rendered_html';\n",
-       "\n",
-       "  /**\n",
-       "   * Render data to the DOM node\n",
-       "   */\n",
-       "  function render(props, node) {\n",
-       "    const script = document.createElement(\"script\");\n",
-       "    node.appendChild(script);\n",
-       "  }\n",
-       "\n",
-       "  /**\n",
-       "   * Handle when an output is cleared or removed\n",
-       "   */\n",
-       "  function handleClearOutput(event, handle) {\n",
-       "    const cell = handle.cell;\n",
-       "\n",
-       "    const id = cell.output_area._bokeh_element_id;\n",
-       "    const server_id = cell.output_area._bokeh_server_id;\n",
-       "    // Clean up Bokeh references\n",
-       "    if (id != null && id in Bokeh.index) {\n",
-       "      Bokeh.index[id].model.document.clear();\n",
-       "      delete Bokeh.index[id];\n",
-       "    }\n",
-       "\n",
-       "    if (server_id !== undefined) {\n",
-       "      // Clean up Bokeh references\n",
-       "      const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
-       "      cell.notebook.kernel.execute(cmd_clean, {\n",
-       "        iopub: {\n",
-       "          output: function(msg) {\n",
-       "            const id = msg.content.text.trim();\n",
-       "            if (id in Bokeh.index) {\n",
-       "              Bokeh.index[id].model.document.clear();\n",
-       "              delete Bokeh.index[id];\n",
-       "            }\n",
-       "          }\n",
-       "        }\n",
-       "      });\n",
-       "      // Destroy server and session\n",
-       "      const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
-       "      cell.notebook.kernel.execute(cmd_destroy);\n",
-       "    }\n",
-       "  }\n",
-       "\n",
-       "  /**\n",
-       "   * Handle when a new output is added\n",
-       "   */\n",
-       "  function handleAddOutput(event, handle) {\n",
-       "    const output_area = handle.output_area;\n",
-       "    const output = handle.output;\n",
-       "\n",
-       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
-       "    if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n",
-       "      return\n",
-       "    }\n",
-       "\n",
-       "    const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
-       "\n",
-       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
-       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
-       "      // store reference to embed id on output_area\n",
-       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
-       "    }\n",
-       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
-       "      const bk_div = document.createElement(\"div\");\n",
-       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
-       "      const script_attrs = bk_div.children[0].attributes;\n",
-       "      for (let i = 0; i < script_attrs.length; i++) {\n",
-       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
-       "        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n",
-       "      }\n",
-       "      // store reference to server id on output_area\n",
-       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
-       "    }\n",
-       "  }\n",
-       "\n",
-       "  function register_renderer(events, OutputArea) {\n",
-       "\n",
-       "    function append_mime(data, metadata, element) {\n",
-       "      // create a DOM node to render to\n",
-       "      const toinsert = this.create_output_subarea(\n",
-       "        metadata,\n",
-       "        CLASS_NAME,\n",
-       "        EXEC_MIME_TYPE\n",
-       "      );\n",
-       "      this.keyboard_manager.register_events(toinsert);\n",
-       "      // Render to node\n",
-       "      const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
-       "      render(props, toinsert[toinsert.length - 1]);\n",
-       "      element.append(toinsert);\n",
-       "      return toinsert\n",
-       "    }\n",
-       "\n",
-       "    /* Handle when an output is cleared or removed */\n",
-       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
-       "    events.on('delete.Cell', handleClearOutput);\n",
-       "\n",
-       "    /* Handle when a new output is added */\n",
-       "    events.on('output_added.OutputArea', handleAddOutput);\n",
-       "\n",
-       "    /**\n",
-       "     * Register the mime type and append_mime function with output_area\n",
-       "     */\n",
-       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
-       "      /* Is output safe? */\n",
-       "      safe: true,\n",
-       "      /* Index of renderer in `output_area.display_order` */\n",
-       "      index: 0\n",
-       "    });\n",
-       "  }\n",
-       "\n",
-       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
-       "  if (root.Jupyter !== undefined) {\n",
-       "    const events = require('base/js/events');\n",
-       "    const OutputArea = require('notebook/js/outputarea').OutputArea;\n",
-       "\n",
-       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
-       "      register_renderer(events, OutputArea);\n",
-       "    }\n",
-       "  }\n",
-       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
-       "    root._bokeh_timeout = Date.now() + 5000;\n",
-       "    root._bokeh_failed_load = false;\n",
-       "  }\n",
-       "\n",
-       "  const NB_LOAD_WARNING = {'data': {'text/html':\n",
-       "     \"<div style='background-color: #fdd'>\\n\"+\n",
-       "     \"<p>\\n\"+\n",
-       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
-       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
-       "     \"</p>\\n\"+\n",
-       "     \"<ul>\\n\"+\n",
-       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
-       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
-       "     \"</ul>\\n\"+\n",
-       "     \"<code>\\n\"+\n",
-       "     \"from bokeh.resources import INLINE\\n\"+\n",
-       "     \"output_notebook(resources=INLINE)\\n\"+\n",
-       "     \"</code>\\n\"+\n",
-       "     \"</div>\"}};\n",
-       "\n",
-       "  function display_loaded() {\n",
-       "    const el = document.getElementById(\"p1001\");\n",
-       "    if (el != null) {\n",
-       "      el.textContent = \"BokehJS is loading...\";\n",
-       "    }\n",
-       "    if (root.Bokeh !== undefined) {\n",
-       "      if (el != null) {\n",
-       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
-       "      }\n",
-       "    } else if (Date.now() < root._bokeh_timeout) {\n",
-       "      setTimeout(display_loaded, 100)\n",
-       "    }\n",
-       "  }\n",
-       "\n",
-       "  function run_callbacks() {\n",
-       "    try {\n",
-       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
-       "        if (callback != null)\n",
-       "          callback();\n",
-       "      });\n",
-       "    } finally {\n",
-       "      delete root._bokeh_onload_callbacks\n",
-       "    }\n",
-       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
-       "  }\n",
-       "\n",
-       "  function load_libs(css_urls, js_urls, callback) {\n",
-       "    if (css_urls == null) css_urls = [];\n",
-       "    if (js_urls == null) js_urls = [];\n",
-       "\n",
-       "    root._bokeh_onload_callbacks.push(callback);\n",
-       "    if (root._bokeh_is_loading > 0) {\n",
-       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
-       "      return null;\n",
-       "    }\n",
-       "    if (js_urls == null || js_urls.length === 0) {\n",
-       "      run_callbacks();\n",
-       "      return null;\n",
-       "    }\n",
-       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
-       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
-       "\n",
-       "    function on_load() {\n",
-       "      root._bokeh_is_loading--;\n",
-       "      if (root._bokeh_is_loading === 0) {\n",
-       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
-       "        run_callbacks()\n",
-       "      }\n",
-       "    }\n",
-       "\n",
-       "    function on_error(url) {\n",
-       "      console.error(\"failed to load \" + url);\n",
-       "    }\n",
-       "\n",
-       "    for (let i = 0; i < css_urls.length; i++) {\n",
-       "      const url = css_urls[i];\n",
-       "      const element = document.createElement(\"link\");\n",
-       "      element.onload = on_load;\n",
-       "      element.onerror = on_error.bind(null, url);\n",
-       "      element.rel = \"stylesheet\";\n",
-       "      element.type = \"text/css\";\n",
-       "      element.href = url;\n",
-       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
-       "      document.body.appendChild(element);\n",
-       "    }\n",
-       "\n",
-       "    for (let i = 0; i < js_urls.length; i++) {\n",
-       "      const url = js_urls[i];\n",
-       "      const element = document.createElement('script');\n",
-       "      element.onload = on_load;\n",
-       "      element.onerror = on_error.bind(null, url);\n",
-       "      element.async = false;\n",
-       "      element.src = url;\n",
-       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
-       "      document.head.appendChild(element);\n",
-       "    }\n",
-       "  };\n",
-       "\n",
-       "  function inject_raw_css(css) {\n",
-       "    const element = document.createElement(\"style\");\n",
-       "    element.appendChild(document.createTextNode(css));\n",
-       "    document.body.appendChild(element);\n",
-       "  }\n",
-       "\n",
-       "  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.0.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.0.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.0.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.0.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.0.3.min.js\"];\n",
-       "  const css_urls = [];\n",
-       "\n",
-       "  const inline_js = [    function(Bokeh) {\n",
-       "      Bokeh.set_log_level(\"info\");\n",
-       "    },\n",
-       "function(Bokeh) {\n",
-       "    }\n",
-       "  ];\n",
-       "\n",
-       "  function run_inline_js() {\n",
-       "    if (root.Bokeh !== undefined || force === true) {\n",
-       "          for (let i = 0; i < inline_js.length; i++) {\n",
-       "      inline_js[i].call(root, root.Bokeh);\n",
-       "    }\n",
-       "if (force === true) {\n",
-       "        display_loaded();\n",
-       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
-       "      setTimeout(run_inline_js, 100);\n",
-       "    } else if (!root._bokeh_failed_load) {\n",
-       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
-       "      root._bokeh_failed_load = true;\n",
-       "    } else if (force !== true) {\n",
-       "      const cell = $(document.getElementById(\"p1001\")).parents('.cell').data().cell;\n",
-       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
-       "    }\n",
-       "  }\n",
-       "\n",
-       "  if (root._bokeh_is_loading === 0) {\n",
-       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
-       "    run_inline_js();\n",
-       "  } else {\n",
-       "    load_libs(css_urls, js_urls, function() {\n",
-       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
-       "      run_inline_js();\n",
-       "    });\n",
-       "  }\n",
-       "}(window));"
-      ],
-      "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"p1001\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.0.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.0.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.0.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.0.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.0.3.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"p1001\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import os\n",
+    "import os.path as op\n",
     "import yaml\n",
     "import torch\n",
     "\n",
@@ -337,2116 +22,451 @@
     "from Scripts.Step_4_Run_GNN import train as run_gnn_inference\n",
     "from Scripts.Step_5_Build_Track_Candidates import train as build_track_candidates\n",
     "from Scripts.Step_6_Evaluate_Reconstruction import evaluate as evaluate_candidates\n",
+    "from Scripts.Step_6_Evaluate_Reconstruction_MonteTracko import (\n",
+    "    evaluate as evaluate_candidates_montetracko\n",
+    ")\n",
     "\n",
     "import warnings\n",
-    "warnings.filterwarnings(\"ignore\")\n",
-    "\n",
-    "CONFIG = 'evaluation_config.yaml'\n",
-    "\n",
-    "ML_CKPT_PATH = '/home/fgias/etx4velo/LHCb_Pipeline/artifacts/metric_learning/velo-minbias-sim10b-xdigi/version_0/checkpoints/epoch=49-step=4000.ckpt'\n",
-    "\n",
-    "GNN_CKPT_PATH = '/home/fgias/etx4velo/LHCb_Pipeline/artifacts/gnn/velo-minbias-sim10b-xdigi/version_0/checkpoints/epoch=49-step=4000.ckpt'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 1. Download data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# path = 'data/input/2'\n",
-    "# os.makedirs(path, exist_ok=True)\n",
-    "# ! xrdcp -r root://eoslhcb.cern.ch//eos/lhcb/user/a/anthonyc/tracking/data/csv/v2/minbias-sim10b-xdigi/2 data/input  --parallel 4"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saving event 003324721, 1/100, contains 511 true hits.\n",
-      "Saving event 003324722, 2/100, contains 3132 true hits.\n",
-      "Saving event 003324723, 3/100, contains 993 true hits.\n",
-      "Saving event 003324724, 4/100, contains 1528 true hits.\n",
-      "Saving event 003324725, 5/100, contains 2849 true hits.\n",
-      "Saving event 003324726, 6/100, contains 923 true hits.\n",
-      "Saving event 003324727, 7/100, contains 926 true hits.\n",
-      "Saving event 003324728, 8/100, contains 907 true hits.\n",
-      "Saving event 003324729, 9/100, contains 647 true hits.\n",
-      "Saving event 003324730, 10/100, contains 1769 true hits.\n",
-      "Saving event 003324731, 11/100, contains 507 true hits.\n",
-      "Saving event 003324732, 12/100, contains 1170 true hits.\n",
-      "Saving event 003324733, 13/100, contains 1098 true hits.\n",
-      "Saving event 003324734, 14/100, contains 2107 true hits.\n",
-      "Saving event 003324735, 15/100, contains 2493 true hits.\n",
-      "Saving event 003324736, 16/100, contains 1103 true hits.\n",
-      "Saving event 003324737, 17/100, contains 2431 true hits.\n",
-      "Saving event 003324738, 18/100, contains 1232 true hits.\n",
-      "Saving event 003324739, 19/100, contains 1923 true hits.\n",
-      "Saving event 003324740, 20/100, contains 534 true hits.\n",
-      "Saving event 003324741, 21/100, contains 1418 true hits.\n",
-      "Saving event 003324742, 22/100, contains 1850 true hits.\n",
-      "Saving event 003324743, 23/100, contains 2080 true hits.\n",
-      "Saving event 003324744, 24/100, contains 1494 true hits.\n",
-      "Saving event 003324745, 25/100, contains 2428 true hits.\n",
-      "Saving event 003324746, 26/100, contains 2623 true hits.\n",
-      "Saving event 003324747, 27/100, contains 372 true hits.\n",
-      "Saving event 003324748, 28/100, contains 2356 true hits.\n",
-      "Saving event 003324749, 29/100, contains 1631 true hits.\n",
-      "Saving event 003324750, 30/100, contains 1056 true hits.\n",
-      "Saving event 003324751, 31/100, contains 2199 true hits.\n",
-      "Saving event 003324752, 32/100, contains 1834 true hits.\n",
-      "Saving event 003324753, 33/100, contains 421 true hits.\n",
-      "Saving event 003324754, 34/100, contains 982 true hits.\n",
-      "Saving event 003324755, 35/100, contains 1844 true hits.\n",
-      "Saving event 003324756, 36/100, contains 1619 true hits.\n",
-      "Saving event 003324757, 37/100, contains 1206 true hits.\n",
-      "Saving event 003324758, 38/100, contains 761 true hits.\n",
-      "Saving event 003324759, 39/100, contains 1573 true hits.\n",
-      "Saving event 003324760, 40/100, contains 1145 true hits.\n",
-      "Saving event 003324761, 41/100, contains 1763 true hits.\n",
-      "Saving event 003324762, 42/100, contains 2743 true hits.\n",
-      "Saving event 003324763, 43/100, contains 2700 true hits.\n",
-      "Saving event 003324764, 44/100, contains 921 true hits.\n",
-      "Saving event 003324765, 45/100, contains 2131 true hits.\n",
-      "Saving event 003324766, 46/100, contains 3022 true hits.\n",
-      "Saving event 003324767, 47/100, contains 1743 true hits.\n",
-      "Saving event 003324768, 48/100, contains 1867 true hits.\n",
-      "Saving event 003324769, 49/100, contains 271 true hits.\n",
-      "Saving event 003324770, 50/100, contains 907 true hits.\n",
-      "Saving event 003324771, 51/100, contains 1132 true hits.\n",
-      "Saving event 003324772, 52/100, contains 2625 true hits.\n",
-      "Saving event 003324773, 53/100, contains 1103 true hits.\n",
-      "Saving event 003324774, 54/100, contains 303 true hits.\n",
-      "Saving event 003324775, 55/100, contains 3346 true hits.\n",
-      "Saving event 003324776, 56/100, contains 1760 true hits.\n",
-      "Saving event 003324777, 57/100, contains 603 true hits.\n",
-      "Saving event 003324778, 58/100, contains 649 true hits.\n",
-      "Saving event 003324779, 59/100, contains 1464 true hits.\n",
-      "Saving event 003324780, 60/100, contains 2651 true hits.\n",
-      "Saving event 003324781, 61/100, contains 1455 true hits.\n",
-      "Saving event 003324782, 62/100, contains 1246 true hits.\n",
-      "Saving event 003324783, 63/100, contains 1566 true hits.\n",
-      "Saving event 003324784, 64/100, contains 1030 true hits.\n",
-      "Saving event 003324785, 65/100, contains 2739 true hits.\n",
-      "Saving event 003324786, 66/100, contains 1510 true hits.\n",
-      "Saving event 003324787, 67/100, contains 808 true hits.\n",
-      "Saving event 003324788, 68/100, contains 786 true hits.\n",
-      "Saving event 003324789, 69/100, contains 1061 true hits.\n",
-      "Saving event 003324790, 70/100, contains 2266 true hits.\n",
-      "Saving event 003324791, 71/100, contains 1171 true hits.\n",
-      "Saving event 003324792, 72/100, contains 2426 true hits.\n",
-      "Saving event 003324793, 73/100, contains 829 true hits.\n",
-      "Saving event 003324794, 74/100, contains 1381 true hits.\n",
-      "Saving event 003324795, 75/100, contains 3826 true hits.\n",
-      "Saving event 003324796, 76/100, contains 87 true hits.\n",
-      "Saving event 003324797, 77/100, contains 837 true hits.\n",
-      "Saving event 003324798, 78/100, contains 2052 true hits.\n",
-      "Saving event 003324799, 79/100, contains 1069 true hits.\n",
-      "Saving event 003324800, 80/100, contains 4375 true hits.\n",
-      "Saving event 003324801, 81/100, contains 1420 true hits.\n",
-      "Saving event 003324802, 82/100, contains 2193 true hits.\n",
-      "Saving event 003324803, 83/100, contains 2032 true hits.\n",
-      "Saving event 003324804, 84/100, contains 988 true hits.\n",
-      "Saving event 003324805, 85/100, contains 158 true hits.\n",
-      "Saving event 003324806, 86/100, contains 1983 true hits.\n",
-      "Saving event 003324807, 87/100, contains 1137 true hits.\n",
-      "Saving event 003324808, 88/100, contains 1293 true hits.\n",
-      "Saving event 003324809, 89/100, contains 968 true hits.\n",
-      "Saving event 003324810, 90/100, contains 200 true hits.\n",
-      "Saving event 003324811, 91/100, contains 2669 true hits.\n",
-      "Saving event 003324812, 92/100, contains 3045 true hits.\n",
-      "Saving event 003324813, 93/100, contains 1283 true hits.\n",
-      "Saving event 003324815, 94/100, contains 1685 true hits.\n",
-      "Saving event 003324816, 95/100, contains 538 true hits.\n",
-      "Saving event 003324817, 96/100, contains 167 true hits.\n",
-      "Saving event 003324818, 97/100, contains 763 true hits.\n",
-      "Saving event 003324819, 98/100, contains 1097 true hits.\n",
-      "Saving event 003324820, 99/100, contains 578 true hits.\n",
-      "Saving event 003324821, 100/100, contains 881 true hits.\n",
-      "Writing outputs to data/processed\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:Preparing event 003324722\n",
-      "INFO:Preparing event 003324723\n",
-      "INFO:Preparing event 003324721\n",
-      "INFO:Preparing event 003324727\n",
-      "INFO:Preparing event 003324730\n",
-      "INFO:Preparing event 003324729\n",
-      "INFO:Preparing event 003324724\n",
-      "INFO:Preparing event 003324726\n",
-      "INFO:Preparing event 003324725\n",
-      "INFO:Preparing event 003324728\n",
-      "INFO:Preparing event 003324731\n",
-      "INFO:Preparing event 003324732\n",
-      "INFO:Preparing event 003324733\n",
-      "INFO:Preparing event 003324735\n",
-      "INFO:Preparing event 003324742\n",
-      "INFO:Preparing event 003324740\n",
-      "INFO:Preparing event 003324743\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a4abcb6a1ec3490788133edc53593089",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/100 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:Preparing event 003324738\n",
-      "INFO:Preparing event 003324737\n",
-      "INFO:Preparing event 003324739\n",
-      "INFO:Preparing event 003324744\n",
-      "INFO:Preparing event 003324752\n",
-      "INFO:Preparing event 003324751\n",
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-      "INFO:Preparing event 003324741\n",
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-      "INFO:Preparing event 003324745\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event003324727 with size (2, 755)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event003324724 with size (2, 1275)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event003324721 with size (2, 422)\n",
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-      "INFO:Preparing event 003324755\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event003324729 with size (2, 535)\n",
-      "INFO:Preparing event 003324754\n",
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-      "INFO:Preparing event 003324756\n",
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-      "INFO:Modulewise truth graph built for data/preprocessed/event003324731 with size (2, 417)\n",
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-      "INFO:Modulewise truth graph built for data/preprocessed/event003324740 with size (2, 434)\n",
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-      "INFO:Preparing event 003324758\n",
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-      "INFO:Preparing event 003324760\n",
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-      "INFO:Preparing event 003324757\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event003324753 with size (2, 338)\n",
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-      "INFO:Preparing event 003324762\n",
-      "INFO:Preparing event 003324763\n",
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-      "INFO:Modulewise truth graph built for data/preprocessed/event003324754 with size (2, 802)\n",
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-      "INFO:Modulewise truth graph built for data/preprocessed/event003324751 with size (2, 1788)\n",
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-      "INFO:Preparing event 003324787\n",
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-      "INFO:Modulewise truth graph built for data/preprocessed/event003324764 with size (2, 787)\n",
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-      "INFO:Preparing event 003324791\n",
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-      "INFO:Preparing event 003324805\n",
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-      "INFO:Modulewise truth graph built for data/preprocessed/event003324791 with size (2, 949)\n",
-      "INFO:Preparing event 003324806\n",
-      "INFO:Preparing event 003324808\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event003324776 with size (2, 1454)\n",
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-      "INFO:Modulewise truth graph built for data/preprocessed/event003324779 with size (2, 1217)\n",
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-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:Preparing event 003324811\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event003324778 with size (2, 540)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event003324785 with size (2, 2274)\n",
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-      "INFO:Preparing event 003324816\n",
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-      "INFO:Modulewise truth graph built for data/preprocessed/event003324805 with size (2, 128)\n",
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-      "INFO:Modulewise truth graph built for data/preprocessed/event003324803 with size (2, 1657)\n",
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-      "INFO:Preparing event 003324819\n",
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-      "INFO:Modulewise truth graph built for data/preprocessed/event003324808 with size (2, 1078)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event003324809 with size (2, 808)\n",
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-      "INFO:Modulewise truth graph built for data/preprocessed/event003324802 with size (2, 1820)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event003324817 with size (2, 143)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event003324818 with size (2, 633)\n",
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-      "INFO:Modulewise truth graph built for data/preprocessed/event003324795 with size (2, 3141)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event003324800 with size (2, 3617)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event003324813 with size (2, 1062)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event003324815 with size (2, 1373)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event003324810 with size (2, 161)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event003324806 with size (2, 1671)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event003324807 with size (2, 950)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event003324819 with size (2, 887)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event003324821 with size (2, 743)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event003324820 with size (2, 484)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event003324816 with size (2, 447)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event003324811 with size (2, 2172)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event003324812 with size (2, 2542)\n"
-     ]
-    }
-   ],
-   "source": [
-    "from Processing.Models.feature_construction import FeatureStore\n",
-    "\n",
-    "fs = FeatureStore(CONFIG)\n",
-    "fs.prepare_data()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 2. Load models from artifacts and run\n",
     "\n",
-    "## Metric learning model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:------------- Step 2: Constructing graphs from metric learning model -------------\n",
-      "INFO:---------------------------- a) Loading trained model ----------------------------\n",
-      "INFO:----------------------------- b) Running inferencing -----------------------------\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training finished, running inference to build graphs...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 5142])\n",
-      "torch.Size([5142])\n",
-      "torch.Size([2, 1456])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "  2%|â–Ž         | 2/80 [00:01<00:36,  2.12it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 16426])\n",
-      "torch.Size([16426])\n",
-      "torch.Size([2, 3128])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
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-      "\r",
-      "  4%|▍         | 3/80 [00:01<00:27,  2.80it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 6324])\n",
-      "torch.Size([6324])\n",
-      "torch.Size([2, 1696])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
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-      "\r",
-      "  5%|▌         | 4/80 [00:01<00:23,  3.30it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 5352])\n",
-      "torch.Size([5352])\n",
-      "torch.Size([2, 1642])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "  6%|â–‹         | 5/80 [00:01<00:20,  3.67it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 3742])\n",
-      "torch.Size([3742])\n",
-      "torch.Size([2, 1060])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "  8%|â–Š         | 6/80 [00:01<00:18,  3.94it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 6060])\n",
-      "torch.Size([6060])\n",
-      "torch.Size([2, 1704])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
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-      "\r",
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-     ]
-    },
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 5470])\n",
-      "torch.Size([5470])\n",
-      "torch.Size([2, 1542])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
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-      "torch.Size([2, 11242])\n",
-      "torch.Size([11242])\n",
-      "torch.Size([2, 2426])\n"
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-      "torch.Size([2, 5446])\n",
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-      "torch.Size([2, 10616])\n",
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-      "torch.Size([2, 9410])\n",
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-      "torch.Size([2, 2028])\n"
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-      "torch.Size([2, 2802])\n",
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-      "torch.Size([2, 918])\n"
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-      "torch.Size([2, 264])\n",
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-      "torch.Size([2, 31716])\n",
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-      "torch.Size([2, 18440])\n",
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-      "torch.Size([2, 21988])\n",
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-      "torch.Size([2, 3750])\n"
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-      "torch.Size([2, 25832])\n",
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-      "torch.Size([2, 4284])\n"
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-      "torch.Size([2, 27162])\n",
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-      "torch.Size([2, 4068])\n"
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-      "torch.Size([2, 23016])\n",
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-      "torch.Size([2, 3712])\n"
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-      "torch.Size([2, 12592])\n",
-      "torch.Size([12592])\n",
-      "torch.Size([2, 2574])\n"
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-      "torch.Size([2, 13254])\n",
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-      "torch.Size([2, 2708])\n"
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-      "torch.Size([2, 10868])\n",
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-      "torch.Size([2, 2464])\n"
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-      "torch.Size([2, 14746])\n",
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-      "torch.Size([2, 2948])\n"
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-      "torch.Size([2, 2674])\n",
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-      "torch.Size([2, 816])\n"
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-      "torch.Size([2, 2532])\n",
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-      "torch.Size([2, 844])\n"
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-      "torch.Size([2, 5606])\n",
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-      "torch.Size([2, 5064])\n",
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-      "torch.Size([2, 1530])\n"
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-      "torch.Size([2, 2336])\n",
-      "torch.Size([2336])\n",
-      "torch.Size([2, 788])\n"
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-      "torch.Size([2, 49022])\n",
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-      "torch.Size([2, 5912])\n"
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-      "torch.Size([2, 14932])\n",
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-      "torch.Size([2, 2796])\n"
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-      "torch.Size([2, 13008])\n",
-      "torch.Size([13008])\n",
-      "torch.Size([2, 2578])\n"
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-     "name": "stdout",
-     "output_type": "stream",
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-      "torch.Size([2, 1206])\n",
-      "torch.Size([1206])\n",
-      "torch.Size([2, 482])\n"
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-      "torch.Size([2, 37536])\n",
-      "torch.Size([37536])\n",
-      "torch.Size([2, 4918])\n"
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-      "torch.Size([2, 26440])\n",
-      "torch.Size([26440])\n",
-      "torch.Size([2, 4372])\n"
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-     "name": "stdout",
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-      "torch.Size([2, 13396])\n",
-      "torch.Size([13396])\n",
-      "torch.Size([2, 2720])\n"
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-     "name": "stdout",
-     "output_type": "stream",
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-      "torch.Size([2, 16908])\n",
-      "torch.Size([16908])\n",
-      "torch.Size([2, 3264])\n"
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-     "name": "stdout",
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-      "torch.Size([2, 20602])\n",
-      "torch.Size([20602])\n",
-      "torch.Size([2, 3318])\n"
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-      "torch.Size([2, 7324])\n",
-      "torch.Size([7324])\n",
-      "torch.Size([2, 1796])\n"
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-     "name": "stdout",
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-      "torch.Size([2, 8348])\n",
-      "torch.Size([8348])\n",
-      "torch.Size([2, 1876])\n"
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-     "name": "stdout",
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-      "torch.Size([2, 18080])\n",
-      "torch.Size([18080])\n",
-      "torch.Size([2, 3202])\n"
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-     "name": "stdout",
-     "output_type": "stream",
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-      "torch.Size([2, 19512])\n",
-      "torch.Size([19512])\n",
-      "torch.Size([2, 3452])\n"
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-     "name": "stdout",
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-      "torch.Size([2, 6708])\n",
-      "torch.Size([6708])\n",
-      "torch.Size([2, 1748])\n"
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-     "name": "stdout",
-     "output_type": "stream",
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-      "torch.Size([2, 9876])\n",
-      "torch.Size([9876])\n",
-      "torch.Size([2, 2216])\n"
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-     "name": "stdout",
-     "output_type": "stream",
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-      "torch.Size([2, 9496])\n",
-      "torch.Size([9496])\n",
-      "torch.Size([2, 2164])\n"
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-     "name": "stdout",
-     "output_type": "stream",
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-      "torch.Size([2, 1284])\n",
-      "torch.Size([1284])\n",
-      "torch.Size([2, 438])\n"
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-     "name": "stdout",
-     "output_type": "stream",
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-      "torch.Size([2, 5700])\n",
-      "torch.Size([5700])\n",
-      "torch.Size([2, 1560])\n"
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-     "name": "stdout",
-     "output_type": "stream",
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-      "torch.Size([2, 6108])\n",
-      "torch.Size([6108])\n",
-      "torch.Size([2, 1748])\n"
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-     "name": "stdout",
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-      "torch.Size([2, 34780])\n",
-      "torch.Size([34780])\n",
-      "torch.Size([2, 4742])\n"
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-     "name": "stdout",
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-      "torch.Size([2, 13538])\n",
-      "torch.Size([13538])\n",
-      "torch.Size([2, 2682])\n"
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-      "torch.Size([2, 8190])\n",
-      "torch.Size([8190])\n",
-      "torch.Size([2, 1932])\n"
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-      "torch.Size([2, 5652])\n",
-      "torch.Size([5652])\n",
-      "torch.Size([2, 1252])\n"
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-      "torch.Size([2, 7654])\n",
-      "torch.Size([7654])\n",
-      "torch.Size([2, 1956])\n"
-     ]
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-    {
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-     "text": [
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-     ]
-    },
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 5586])\n",
-      "torch.Size([5586])\n",
-      "torch.Size([2, 1568])\n"
-     ]
-    },
-    {
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-    },
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 35366])\n",
-      "torch.Size([35366])\n",
-      "torch.Size([2, 4792])\n"
-     ]
-    },
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-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 584])\n",
-      "torch.Size([584])\n",
-      "torch.Size([2, 216])\n"
-     ]
-    },
-    {
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-     "name": "stdout",
-     "output_type": "stream",
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-      "torch.Size([2, 14508])\n",
-      "torch.Size([14508])\n",
-      "torch.Size([2, 2782])\n"
-     ]
-    },
-    {
-     "name": "stderr",
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 62114])\n",
-      "torch.Size([62114])\n",
-      "torch.Size([2, 6836])\n"
-     ]
-    },
-    {
-     "name": "stderr",
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-     ]
-    },
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 20394])\n",
-      "torch.Size([20394])\n",
-      "torch.Size([2, 3866])\n"
-     ]
-    },
-    {
-     "name": "stderr",
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 7198])\n",
-      "torch.Size([7198])\n",
-      "torch.Size([2, 1700])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
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-      "\r",
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 25146])\n",
-      "torch.Size([25146])\n",
-      "torch.Size([2, 4218])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
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-      "\r",
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 2582])\n",
-      "torch.Size([2582])\n",
-      "torch.Size([2, 772])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 3446])\n",
-      "torch.Size([3446])\n",
-      "torch.Size([2, 998])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      " 79%|███████▉  | 63/80 [00:14<00:03,  4.51it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 4232])\n",
-      "torch.Size([4232])\n",
-      "torch.Size([2, 1274])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 12142])\n",
-      "torch.Size([12142])\n",
-      "torch.Size([2, 2418])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
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-    },
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 2074])\n",
-      "torch.Size([2074])\n",
-      "torch.Size([2, 610])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
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-     ]
-    },
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 4406])\n",
-      "torch.Size([4406])\n",
-      "torch.Size([2, 1240])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      " 84%|████████▍ | 67/80 [00:15<00:02,  4.56it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 2948])\n",
-      "torch.Size([2948])\n",
-      "torch.Size([2, 1018])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      " 85%|████████▌ | 68/80 [00:15<00:02,  4.57it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 638])\n",
-      "torch.Size([638])\n",
-      "torch.Size([2, 262])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      " 86%|████████▋ | 69/80 [00:15<00:02,  4.52it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 21366])\n",
-      "torch.Size([21366])\n",
-      "torch.Size([2, 3498])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      " 88%|████████▊ | 70/80 [00:16<00:02,  4.49it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 23234])\n",
-      "torch.Size([23234])\n",
-      "torch.Size([2, 3716])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      " 89%|████████▉ | 71/80 [00:16<00:02,  4.44it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 6084])\n",
-      "torch.Size([6084])\n",
-      "torch.Size([2, 1688])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      " 90%|█████████ | 72/80 [00:16<00:01,  4.42it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 4894])\n",
-      "torch.Size([4894])\n",
-      "torch.Size([2, 1512])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      " 91%|█████████▏| 73/80 [00:16<00:01,  4.44it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 1634])\n",
-      "torch.Size([1634])\n",
-      "torch.Size([2, 630])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
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-      " 92%|█████████▎| 74/80 [00:17<00:01,  4.42it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 26672])\n",
-      "torch.Size([26672])\n",
-      "torch.Size([2, 4086])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      " 94%|█████████▍| 75/80 [00:17<00:01,  4.46it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 10676])\n",
-      "torch.Size([10676])\n",
-      "torch.Size([2, 2240])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      " 95%|█████████▌| 76/80 [00:17<00:00,  4.48it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 12438])\n",
-      "torch.Size([12438])\n",
-      "torch.Size([2, 2526])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 13934])\n",
-      "torch.Size([13934])\n",
-      "torch.Size([2, 2786])\n"
-     ]
-    },
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-     "name": "stderr",
-     "output_type": "stream",
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 7108])\n",
-      "torch.Size([7108])\n",
-      "torch.Size([2, 1636])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      " 99%|█████████▉| 79/80 [00:18<00:00,  4.51it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 15404])\n",
-      "torch.Size([15404])\n",
-      "torch.Size([2, 2848])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 7480])\n",
-      "torch.Size([7480])\n",
-      "torch.Size([2, 1814])\n"
-     ]
-    },
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 14632])\n",
-      "torch.Size([14632])\n",
-      "torch.Size([2, 2956])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      " 20%|██        | 2/10 [00:00<00:01,  4.45it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 37500])\n",
-      "torch.Size([37500])\n",
-      "torch.Size([2, 5244])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      " 30%|███       | 3/10 [00:00<00:01,  4.54it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 4004])\n",
-      "torch.Size([4004])\n",
-      "torch.Size([2, 1182])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      " 40%|████      | 4/10 [00:00<00:01,  4.56it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 7468])\n",
-      "torch.Size([7468])\n",
-      "torch.Size([2, 1696])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      " 50%|█████     | 5/10 [00:01<00:01,  4.58it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 6318])\n",
-      "torch.Size([6318])\n",
-      "torch.Size([2, 1806])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      " 60%|██████    | 6/10 [00:01<00:00,  4.57it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 11022])\n",
-      "torch.Size([11022])\n",
-      "torch.Size([2, 2318])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      " 70%|███████   | 7/10 [00:01<00:00,  4.58it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 4944])\n",
-      "torch.Size([4944])\n",
-      "torch.Size([2, 1470])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      " 80%|████████  | 8/10 [00:01<00:00,  4.53it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 26252])\n",
-      "torch.Size([26252])\n",
-      "torch.Size([2, 4192])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      " 90%|█████████ | 9/10 [00:01<00:00,  4.55it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 4628])\n",
-      "torch.Size([4628])\n",
-      "torch.Size([2, 1222])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 10/10 [00:02<00:00,  4.54it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 29436])\n",
-      "torch.Size([29436])\n",
-      "torch.Size([2, 4348])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 10%|â–ˆ         | 1/10 [00:00<00:01,  4.56it/s]"
-     ]
-    },
+    "warnings.filterwarnings(\"ignore\")\n",
+    "\n",
+    "CONFIG = \"evaluation_config.yaml\"\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(CONFIG, 'r') as f:\n",
+    "    configs = yaml.load(f, Loader=yaml.FullLoader)\n",
+    "\n",
+    "experiment_name = configs[\"common_configs\"][\"experiment_name\"]\n",
+    "artifact_directory = configs[\"common_configs\"][\"artifact_directory\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ML_CKPT_PATH = op.abspath(\n",
+    "    op.join(\n",
+    "        artifact_directory,\n",
+    "        \"metric_learning\",\n",
+    "        experiment_name,\n",
+    "        \"version_4\",\n",
+    "        \"checkpoints\",\n",
+    "        \"epoch=49-step=4000.ckpt\",\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "GNN_CKPT_PATH = op.abspath(\n",
+    "    op.join(\n",
+    "        artifact_directory,\n",
+    "        \"gnn\",\n",
+    "        experiment_name,\n",
+    "        \"version_2\",\n",
+    "        \"checkpoints\",\n",
+    "        \"epoch=49-step=4000.ckpt\",\n",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 1. Download data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# path = 'data/input/2'\n",
+    "# os.makedirs(path, exist_ok=True)\n",
+    "# ! xrdcp -r root://eoslhcb.cern.ch//eos/lhcb/user/a/anthonyc/tracking/data/csv/v2/minbias-sim10b-xdigi/2 data/input  --parallel 4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "torch.Size([2, 15026])\n",
-      "torch.Size([15026])\n",
-      "torch.Size([2, 2676])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      " 20%|██        | 2/10 [00:00<00:01,  4.56it/s]"
+      "Writing outputs to data/processed\n"
      ]
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 6990])\n",
-      "torch.Size([6990])\n",
-      "torch.Size([2, 1778])\n"
-     ]
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7b42d359b5a54d55b454b8c05a1bfa35",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/100 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\r",
-      " 30%|███       | 3/10 [00:00<00:01,  4.59it/s]"
+      "INFO:Preparing event 003324722\n",
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+      "INFO:Preparing event 003324735\n",
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+      "INFO:Preparing event 003324731\n",
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+      "INFO:Preparing event 003324745\n",
+      "INFO:Preparing event 003324749\n",
+      "INFO:Preparing event 003324752\n",
+      "INFO:Preparing event 003324746\n",
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+      "INFO:Preparing event 003324751\n",
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+      "INFO:Preparing event 003324723\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324729 with size (2, 671)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324745 with size (2, 2307)\n",
+      "INFO:Preparing event 003324754\n",
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+      "INFO:Modulewise truth graph built for data/preprocessed/event003324738 with size (2, 1168)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324721 with size (2, 455)\n",
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+      "INFO:Preparing event 003324756\n",
+      "INFO:Preparing event 003324755\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324735 with size (2, 2340)\n",
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+      "INFO:Modulewise truth graph built for data/preprocessed/event003324733 with size (2, 982)\n",
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+      "INFO:Preparing event 003324758\n",
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+      "INFO:Preparing event 003324760\n",
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+      "INFO:Modulewise truth graph built for data/preprocessed/event003324741 with size (2, 1354)\n",
+      "INFO:Preparing event 003324771\n",
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+      "INFO:Preparing event 003324772\n",
+      "INFO:Preparing event 003324774\n",
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+      "INFO:Preparing event 003324777\n",
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+      "INFO:Preparing event 003324778\n",
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+      "INFO:Preparing event 003324776\n",
+      "INFO:Preparing event 003324773\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324730 with size (2, 1698)\n",
+      "INFO:Preparing event 003324779\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324757 with size (2, 1174)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324758 with size (2, 769)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324740 with size (2, 493)\n",
+      "INFO:Preparing event 003324783\n",
+      "INFO:Preparing event 003324782\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324764 with size (2, 886)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324762 with size (2, 2704)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324759 with size (2, 1570)\n",
+      "INFO:Preparing event 003324781\n",
+      "INFO:Preparing event 003324786\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324751 with size (2, 2086)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324755 with size (2, 1765)\n",
+      "INFO:Preparing event 003324784\n",
+      "INFO:Preparing event 003324788\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324734 with size (2, 2003)\n",
+      "INFO:Preparing event 003324785\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324763 with size (2, 2569)\n",
+      "INFO:Preparing event 003324791\n",
+      "INFO:Preparing event 003324789\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324774 with size (2, 283)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324722 with size (2, 3041)\n",
+      "INFO:Preparing event 003324793\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324777 with size (2, 602)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324739 with size (2, 1851)\n",
+      "INFO:Preparing event 003324794\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324768 with size (2, 1796)\n",
+      "INFO:Preparing event 003324790\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324769 with size (2, 254)\n",
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+      "INFO:Preparing event 003324787\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324770 with size (2, 874)\n",
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+      "INFO:Modulewise truth graph built for data/preprocessed/event003324771 with size (2, 1179)\n",
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+      "INFO:Modulewise truth graph built for data/preprocessed/event003324772 with size (2, 2484)\n",
+      "INFO:Preparing event 003324795\n",
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+      "INFO:Modulewise truth graph built for data/preprocessed/event003324737 with size (2, 2349)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324778 with size (2, 626)\n",
+      "INFO:Preparing event 003324799\n",
+      "INFO:Preparing event 003324803\n",
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+      "INFO:Modulewise truth graph built for data/preprocessed/event003324779 with size (2, 1397)\n",
+      "INFO:Preparing event 003324800\n",
+      "INFO:Preparing event 003324804\n",
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+      "INFO:Preparing event 003324805\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324765 with size (2, 2103)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324784 with size (2, 932)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324773 with size (2, 1051)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324794 with size (2, 1426)\n",
+      "INFO:Preparing event 003324806\n",
+      "INFO:Preparing event 003324807\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324788 with size (2, 800)\n",
+      "INFO:Preparing event 003324808\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324776 with size (2, 1841)\n",
+      "INFO:Preparing event 003324811\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324780 with size (2, 2586)\n",
+      "INFO:Preparing event 003324809\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324789 with size (2, 1002)\n",
+      "INFO:Preparing event 003324814\n",
+      "INFO:Preparing event 003324813\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324783 with size (2, 1546)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324790 with size (2, 2185)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324785 with size (2, 2564)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324781 with size (2, 1347)\n",
+      "INFO:Preparing event 003324817\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324798 with size (2, 2013)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324803 with size (2, 1964)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324793 with size (2, 844)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324792 with size (2, 2556)\n",
+      "INFO:Preparing event 003324812\n",
+      "INFO:Preparing event 003324810\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324787 with size (2, 784)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324775 with size (2, 3208)\n",
+      "INFO:Preparing event 003324815\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324797 with size (2, 797)\n",
+      "INFO:Preparing event 003324820\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324804 with size (2, 998)\n",
+      "INFO:Preparing event 003324816\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324766 with size (2, 3053)\n",
+      "INFO:Preparing event 003324818\n",
+      "INFO:Preparing event 003324819\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324799 with size (2, 1057)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324796 with size (2, 97)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324805 with size (2, 142)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324795 with size (2, 3625)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324786 with size (2, 1434)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324807 with size (2, 1086)\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "torch.Size([2, 6102])\n",
-      "torch.Size([6102])\n",
-      "torch.Size([2, 1616])\n"
+      "Exception with file: "
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\r",
-      " 40%|████      | 4/10 [00:00<00:01,  4.57it/s]"
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324791 with size (2, 1098)\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "torch.Size([2, 11416])\n",
-      "torch.Size([11416])\n",
-      "torch.Size([2, 2034])\n"
+      "data/preprocessed/event003324814"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\r",
-      " 50%|█████     | 5/10 [00:01<00:01,  4.60it/s]"
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324819 with size (2, 1098)\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "torch.Size([2, 672])\n",
-      "torch.Size([672])\n",
-      "torch.Size([2, 316])\n"
+      " "
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\r",
-      " 60%|██████    | 6/10 [00:01<00:00,  4.57it/s]"
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324811 with size (2, 2446)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324817 with size (2, 172)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324820 with size (2, 542)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324815 with size (2, 1599)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324810 with size (2, 182)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324800 with size (2, 4314)\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "torch.Size([2, 15546])\n",
-      "torch.Size([15546])\n",
-      "torch.Size([2, 3150])\n"
+      "Exception: "
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\r",
-      " 70%|███████   | 7/10 [00:01<00:00,  4.58it/s]"
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324812 with size (2, 3062)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324813 with size (2, 1267)\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "torch.Size([2, 4462])\n",
-      "torch.Size([4462])\n",
-      "torch.Size([2, 1342])\n"
+      "arrays used as indices must be of integer (or boolean) type"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\r",
-      " 80%|████████  | 8/10 [00:01<00:00,  4.58it/s]"
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324809 with size (2, 928)\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "torch.Size([2, 11494])\n",
-      "torch.Size([11494])\n",
-      "torch.Size([2, 2346])\n"
+      "\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\r",
-      " 90%|█████████ | 9/10 [00:01<00:00,  4.57it/s]"
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324818 with size (2, 703)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324806 with size (2, 1987)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324802 with size (2, 2137)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324816 with size (2, 508)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324801 with size (2, 1367)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event003324808 with size (2, 1271)\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "from Processing.Models.feature_construction import FeatureStore\n",
+    "\n",
+    "fs = FeatureStore(CONFIG)\n",
+    "fs.prepare_data(apply_selection=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 2. Load models from artifacts and run\n",
+    "\n",
+    "## Metric learning model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([2, 16640])\n",
-      "torch.Size([16640])\n",
-      "torch.Size([2, 3222])\n"
-     ]
-    },
+     "data": {
+      "text/plain": [
+       "'/home/acorreia/Documents/PhD/tracking/etx4velo/LHCb_Pipeline/artifacts/metric_learning/velo-minbias-sim10b-xdigi/version_4/checkpoints/epoch=49-step=4000.ckpt'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ML_CKPT_PATH"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 10/10 [00:02<00:00,  4.56it/s]"
+      "INFO:------------- Step 2: Constructing graphs from metric learning model -------------\n",
+      "INFO:---------------------------- a) Loading trained model ----------------------------\n",
+      "INFO:----------------------------- b) Running inferencing -----------------------------\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "torch.Size([2, 23886])\n",
-      "torch.Size([23886])\n",
-      "torch.Size([2, 3834])\n"
+      "Training finished, running inference to build graphs...\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\n"
+      "100%|██████████| 80/80 [00:13<00:00,  5.81it/s]\n",
+      "100%|██████████| 10/10 [00:01<00:00,  5.69it/s]\n",
+      "100%|██████████| 9/9 [00:01<00:00,  5.82it/s]\n"
      ]
     }
    ],
@@ -2463,7 +483,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -2487,7 +507,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 80/80 [00:02<00:00, 39.87it/s]\n"
+      "100%|██████████| 80/80 [00:07<00:00, 10.26it/s]\n"
      ]
     },
     {
@@ -2501,7 +521,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 10/10 [00:00<00:00, 38.00it/s]\n"
+      "100%|██████████| 10/10 [00:01<00:00,  8.98it/s]\n"
      ]
     },
     {
@@ -2515,7 +535,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 10/10 [00:00<00:00, 45.19it/s]\n"
+      "100%|██████████| 9/9 [00:00<00:00,  9.28it/s]\n"
      ]
     }
    ],
@@ -2525,7 +545,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -2535,7 +555,7 @@
       "INFO:-----------  Step 5: Building track candidates from the scored graph  -----------\n",
       "INFO:---------------------------- a) Loading scored graphs ----------------------------\n",
       "INFO:---------------------------- b) Labelling graph nodes ----------------------------\n",
-      "100%|██████████| 100/100 [00:00<00:00, 160.70it/s]\n"
+      "100%|██████████| 99/99 [00:00<00:00, 258.43it/s]\n"
      ]
     }
    ],
@@ -2552,7 +572,89 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:---------------------- Step 6: Evaluation using MonteTracko ----------------------\n",
+      "INFO:1) Load the dataframes\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "63f9c2b1151745fbae4906d00678460c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/99 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:2) Matching\n",
+      "INFO:3) Reporting\n",
+      "INFO:4) Plotting\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TrackChecker output                               :      1031/    21885   4.71% ghosts\n",
+      "01_velo                                           :      9391/    10690  87.85% ( 89.86%),       115 (  1.21%) clones, pur  98.95%, hit eff  93.89% \n",
+      "02_long                                           :      5512/     6094  90.45% ( 92.19%),        66 (  1.18%) clones, pur  99.07%, hit eff  94.80% \n",
+      "03_long_P>5GeV                                    :      3589/     3903  91.95% ( 93.65%),        49 (  1.35%) clones, pur  99.13%, hit eff  95.35% \n",
+      "04_long_strange                                   :         0/        0    nan% (   nan%),         0 (   nan%) clones, pur    nan%, hit eff    nan% \n",
+      "05_long_strange_P>5GeV                            :         0/        0    nan% (   nan%),         0 (   nan%) clones, pur    nan%, hit eff    nan% \n",
+      "06_long_fromB                                     :         0/        0    nan% (   nan%),         0 (   nan%) clones, pur    nan%, hit eff    nan% \n",
+      "07_long_fromB_P>5GeV                              :         0/        0    nan% (   nan%),         0 (   nan%) clones, pur    nan%, hit eff    nan% \n",
+      "08_long_electrons                                 :       336/      493  68.15% ( 70.98%),         9 (  2.61%) clones, pur  97.86%, hit eff  74.20% \n",
+      "09_long_fromB_electrons                           :         0/        0    nan% (   nan%),         0 (   nan%) clones, pur    nan%, hit eff    nan% \n",
+      "10_long_fromB_electrons_P>5GeV                    :         0/        0    nan% (   nan%),         0 (   nan%) clones, pur    nan%, hit eff    nan% \n",
+      "\n",
+      "| Categories           | Efficiency   | Average efficiency   | % clones   | Average hit purity   | Average hit efficiency   |\n",
+      "|:---------------------|:-------------|:---------------------|:-----------|:---------------------|:-------------------------|\n",
+      "| Velo, no electrons   | 87.85%       | 89.86%               | 1.21%      | 98.95%               | 93.89%                   |\n",
+      "| Long, only electrons | 68.15%       | 70.98%               | 2.61%      | 97.86%               | 74.20%                   |\n",
+      "| Velo, only electrons | 59.01%       | 60.84%               | 3.88%      | 98.14%               | 70.97%                   |\n",
+      "| Categories   | # ghosts   | # tracks   | % ghosts   |\n",
+      "|:-------------|:-----------|:-----------|:-----------|\n",
+      "| Everything   | 1,031      | 21,885     | 4.71%      |\n"
+     ]
+    },
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 3200x2400 with 32 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "trackEvaluator = evaluate_candidates_montetracko(\n",
+    "    CONFIG,\n",
+    "    min_track_length=3,\n",
+    "    whether_to_plot=True,\n",
+    "    allen_report=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -2561,24 +663,24 @@
      "text": [
       "INFO:------------ Step 6: Evaluating the track reconstruction performance ------------\n",
       "INFO:--------------------------- a) Loading labelled graphs ---------------------------\n",
-      "100%|██████████| 100/100 [00:01<00:00, 53.44it/s]\n",
+      "100%|██████████| 99/99 [00:01<00:00, 52.46it/s]\n",
       "INFO:--------------------- b) Calculating the performance metrics ---------------------\n",
-      "INFO:Number of reconstructed particles: 20401\n",
-      "INFO:Number of particles: 23205\n",
-      "INFO:Number of matched tracks: 20519\n",
-      "INFO:Number of tracks: 21116\n",
-      "INFO:Number of duplicate reconstructed particles: 118\n",
-      "INFO:Efficiency: 0.879\n",
-      "INFO:Fake rate: 0.028\n",
-      "INFO:Duplication rate: 0.006\n",
+      "INFO:Number of reconstructed particles: 19447\n",
+      "INFO:Number of particles: 27380\n",
+      "INFO:Number of matched tracks: 19534\n",
+      "INFO:Number of tracks: 20330\n",
+      "INFO:Number of duplicate reconstructed particles: 87\n",
+      "INFO:Efficiency: 0.710\n",
+      "INFO:Fake rate: 0.039\n",
+      "INFO:Duplication rate: 0.004\n",
       "INFO:------------------------------ c) Plotting results ------------------------------\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 800x600 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -2586,9 +688,9 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 800x600 with 1 Axes>"
       ]
      },
      "metadata": {},
-- 
GitLab


From 61a7835e18033b0afdeee769ffd5fabc2fb4905f Mon Sep 17 00:00:00 2001
From: anthonyc <acorreia@lpnhe.in2p3.fr>
Date: Fri, 24 Mar 2023 16:27:59 +0100
Subject: [PATCH 23/30] new training

---
 LHCb_Pipeline/full_pipeline.ipynb | 628 +++++++++++++++---------------
 1 file changed, 312 insertions(+), 316 deletions(-)

diff --git a/LHCb_Pipeline/full_pipeline.ipynb b/LHCb_Pipeline/full_pipeline.ipynb
index bc08ec5c..de143439 100644
--- a/LHCb_Pipeline/full_pipeline.ipynb
+++ b/LHCb_Pipeline/full_pipeline.ipynb
@@ -196,113 +196,115 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Saving event 004206889, 1/100, contains 690 true hits.\n",
-      "Saving event 004206890, 2/100, contains 693 true hits.\n",
-      "Saving event 004206891, 3/100, contains 1132 true hits.\n",
-      "Saving event 004206892, 4/100, contains 564 true hits.\n",
-      "Saving event 004206893, 5/100, contains 1210 true hits.\n",
-      "Saving event 004206894, 6/100, contains 1246 true hits.\n",
-      "Saving event 004206895, 7/100, contains 696 true hits.\n",
-      "Saving event 004206896, 8/100, contains 817 true hits.\n",
-      "Saving event 004206897, 9/100, contains 1090 true hits.\n",
-      "Saving event 004206898, 10/100, contains 854 true hits.\n",
-      "Saving event 004206899, 11/100, contains 646 true hits.\n",
-      "Saving event 004206900, 12/100, contains 743 true hits.\n",
-      "Saving event 004206901, 13/100, contains 1874 true hits.\n",
-      "Saving event 004206902, 14/100, contains 1985 true hits.\n",
-      "Saving event 004206903, 15/100, contains 1526 true hits.\n",
-      "Saving event 004206904, 16/100, contains 2655 true hits.\n",
-      "Saving event 004206905, 17/100, contains 2895 true hits.\n",
-      "Saving event 004206907, 18/100, contains 835 true hits.\n",
-      "Saving event 004206908, 19/100, contains 631 true hits.\n",
-      "Saving event 004206909, 20/100, contains 709 true hits.\n",
-      "Saving event 004206910, 21/100, contains 220 true hits.\n",
-      "Saving event 004206911, 22/100, contains 881 true hits.\n",
-      "Saving event 004206912, 23/100, contains 1191 true hits.\n",
-      "Saving event 004206913, 24/100, contains 2875 true hits.\n",
-      "Saving event 004206914, 25/100, contains 514 true hits.\n",
-      "Saving event 004206915, 26/100, contains 629 true hits.\n",
-      "Saving event 004206916, 27/100, contains 790 true hits.\n",
-      "Saving event 004206917, 28/100, contains 1546 true hits.\n",
-      "Saving event 004206918, 29/100, contains 1309 true hits.\n",
-      "Saving event 004206919, 30/100, contains 1387 true hits.\n",
-      "Saving event 004206920, 31/100, contains 920 true hits.\n",
-      "Saving event 004206921, 32/100, contains 2010 true hits.\n",
-      "Saving event 004206922, 33/100, contains 438 true hits.\n",
-      "Saving event 004206923, 34/100, contains 872 true hits.\n",
-      "Saving event 004206924, 35/100, contains 1260 true hits.\n",
-      "Saving event 004206925, 36/100, contains 1941 true hits.\n",
-      "Saving event 004206926, 37/100, contains 1123 true hits.\n",
-      "Saving event 004206927, 38/100, contains 1989 true hits.\n",
-      "Saving event 004206928, 39/100, contains 2414 true hits.\n",
-      "Saving event 004206929, 40/100, contains 1645 true hits.\n",
-      "Saving event 004206930, 41/100, contains 2557 true hits.\n",
-      "Saving event 004206931, 42/100, contains 1590 true hits.\n",
-      "Saving event 004206933, 43/100, contains 821 true hits.\n",
-      "Saving event 004206934, 44/100, contains 286 true hits.\n",
-      "Saving event 004206935, 45/100, contains 601 true hits.\n",
-      "Saving event 004206936, 46/100, contains 556 true hits.\n",
-      "Saving event 004206937, 47/100, contains 669 true hits.\n",
-      "Saving event 004206938, 48/100, contains 897 true hits.\n",
-      "Saving event 004206939, 49/100, contains 336 true hits.\n",
-      "Saving event 004206940, 50/100, contains 1270 true hits.\n",
-      "Saving event 004206941, 51/100, contains 2678 true hits.\n",
-      "Saving event 004206942, 52/100, contains 1501 true hits.\n",
-      "Saving event 004206943, 53/100, contains 279 true hits.\n",
-      "Saving event 004206944, 54/100, contains 1052 true hits.\n",
-      "Saving event 004206945, 55/100, contains 1538 true hits.\n",
-      "Saving event 004206946, 56/100, contains 2534 true hits.\n",
-      "Saving event 004206947, 57/100, contains 3339 true hits.\n",
-      "Saving event 004206948, 58/100, contains 891 true hits.\n",
-      "Saving event 004206949, 59/100, contains 775 true hits.\n",
-      "Saving event 004206950, 60/100, contains 1159 true hits.\n",
-      "Saving event 004206951, 61/100, contains 302 true hits.\n",
-      "Saving event 004206952, 62/100, contains 970 true hits.\n",
-      "Saving event 004206953, 63/100, contains 1149 true hits.\n",
-      "Saving event 004206954, 64/100, contains 2092 true hits.\n",
-      "Saving event 004206955, 65/100, contains 227 true hits.\n",
-      "Saving event 004206956, 66/100, contains 1018 true hits.\n",
-      "Saving event 004206957, 67/100, contains 723 true hits.\n",
-      "Saving event 004206958, 68/100, contains 1374 true hits.\n",
-      "Saving event 004206959, 69/100, contains 2123 true hits.\n",
-      "Saving event 004206960, 70/100, contains 703 true hits.\n",
-      "Saving event 004206961, 71/100, contains 191 true hits.\n",
-      "Saving event 004206962, 72/100, contains 1292 true hits.\n",
-      "Saving event 004206963, 73/100, contains 795 true hits.\n",
-      "Saving event 004206964, 74/100, contains 2119 true hits.\n",
-      "Saving event 004206965, 75/100, contains 1925 true hits.\n",
-      "Saving event 004206966, 76/100, contains 2772 true hits.\n",
-      "Saving event 007212913, 77/100, contains 1519 true hits.\n",
-      "Saving event 007212914, 78/100, contains 708 true hits.\n",
-      "Saving event 007212915, 79/100, contains 1026 true hits.\n",
-      "Saving event 007212916, 80/100, contains 1684 true hits.\n",
-      "Saving event 007212917, 81/100, contains 1917 true hits.\n",
-      "Saving event 007212918, 82/100, contains 1303 true hits.\n",
-      "Saving event 007212919, 83/100, contains 1714 true hits.\n",
-      "Saving event 007212920, 84/100, contains 645 true hits.\n",
-      "Saving event 007212921, 85/100, contains 1610 true hits.\n",
-      "Saving event 007212922, 86/100, contains 2287 true hits.\n",
-      "Saving event 007212923, 87/100, contains 1438 true hits.\n",
-      "Saving event 007212924, 88/100, contains 666 true hits.\n",
-      "Saving event 007212925, 89/100, contains 2108 true hits.\n",
-      "Saving event 007212926, 90/100, contains 1163 true hits.\n",
-      "Saving event 007212927, 91/100, contains 1389 true hits.\n",
-      "Saving event 007212928, 92/100, contains 2102 true hits.\n",
-      "Saving event 007212929, 93/100, contains 771 true hits.\n",
-      "Saving event 007212930, 94/100, contains 934 true hits.\n",
-      "Saving event 007212931, 95/100, contains 2019 true hits.\n",
-      "Saving event 007212932, 96/100, contains 2317 true hits.\n",
-      "Saving event 007212933, 97/100, contains 666 true hits.\n",
-      "Saving event 007212934, 98/100, contains 970 true hits.\n",
-      "Saving event 007212935, 99/100, contains 275 true hits.\n",
-      "Saving event 007212936, 100/100, contains 2436 true hits.\n",
+      "Saving event 004206889, 1/100, contains 814 true hits.\n",
+      "Saving event 004206890, 2/100, contains 935 true hits.\n",
+      "Saving event 004206891, 3/100, contains 1508 true hits.\n",
+      "Saving event 004206892, 4/100, contains 691 true hits.\n",
+      "Saving event 004206893, 5/100, contains 1584 true hits.\n",
+      "Saving event 004206894, 6/100, contains 1527 true hits.\n",
+      "Saving event 004206895, 7/100, contains 834 true hits.\n",
+      "Saving event 004206896, 8/100, contains 1012 true hits.\n",
+      "Saving event 004206897, 9/100, contains 1343 true hits.\n",
+      "Saving event 004206898, 10/100, contains 1185 true hits.\n",
+      "Saving event 004206899, 11/100, contains 841 true hits.\n",
+      "Saving event 004206900, 12/100, contains 1000 true hits.\n",
+      "Saving event 004206901, 13/100, contains 2350 true hits.\n",
+      "Saving event 004206902, 14/100, contains 2537 true hits.\n",
+      "Saving event 004206903, 15/100, contains 1938 true hits.\n",
+      "Saving event 004206904, 16/100, contains 3223 true hits.\n",
+      "Saving event 004206905, 17/100, contains 3772 true hits.\n",
+      "Discarding event 004206906, contains only fake hits.\n",
+      "Saving event 004206907, 18/100, contains 1071 true hits.\n",
+      "Saving event 004206908, 19/100, contains 911 true hits.\n",
+      "Saving event 004206909, 20/100, contains 891 true hits.\n",
+      "Saving event 004206910, 21/100, contains 287 true hits.\n",
+      "Saving event 004206911, 22/100, contains 1154 true hits.\n",
+      "Saving event 004206912, 23/100, contains 1430 true hits.\n",
+      "Saving event 004206913, 24/100, contains 3562 true hits.\n",
+      "Saving event 004206914, 25/100, contains 616 true hits.\n",
+      "Saving event 004206915, 26/100, contains 739 true hits.\n",
+      "Saving event 004206916, 27/100, contains 1019 true hits.\n",
+      "Saving event 004206917, 28/100, contains 2024 true hits.\n",
+      "Saving event 004206918, 29/100, contains 1679 true hits.\n",
+      "Saving event 004206919, 30/100, contains 1747 true hits.\n",
+      "Saving event 004206920, 31/100, contains 1254 true hits.\n",
+      "Saving event 004206921, 32/100, contains 2582 true hits.\n",
+      "Saving event 004206922, 33/100, contains 627 true hits.\n",
+      "Saving event 004206923, 34/100, contains 1168 true hits.\n",
+      "Saving event 004206924, 35/100, contains 1606 true hits.\n",
+      "Saving event 004206925, 36/100, contains 2457 true hits.\n",
+      "Saving event 004206926, 37/100, contains 1325 true hits.\n",
+      "Saving event 004206927, 38/100, contains 2473 true hits.\n",
+      "Saving event 004206928, 39/100, contains 3194 true hits.\n",
+      "Saving event 004206929, 40/100, contains 2108 true hits.\n",
+      "Saving event 004206930, 41/100, contains 3139 true hits.\n",
+      "Saving event 004206931, 42/100, contains 2060 true hits.\n",
+      "Discarding event 004206932, contains only fake hits.\n",
+      "Saving event 004206933, 43/100, contains 1013 true hits.\n",
+      "Saving event 004206934, 44/100, contains 397 true hits.\n",
+      "Saving event 004206935, 45/100, contains 709 true hits.\n",
+      "Saving event 004206936, 46/100, contains 772 true hits.\n",
+      "Saving event 004206937, 47/100, contains 872 true hits.\n",
+      "Saving event 004206938, 48/100, contains 1117 true hits.\n",
+      "Saving event 004206939, 49/100, contains 397 true hits.\n",
+      "Saving event 004206940, 50/100, contains 1636 true hits.\n",
+      "Saving event 004206941, 51/100, contains 3559 true hits.\n",
+      "Saving event 004206942, 52/100, contains 1822 true hits.\n",
+      "Saving event 004206943, 53/100, contains 344 true hits.\n",
+      "Saving event 004206944, 54/100, contains 1340 true hits.\n",
+      "Saving event 004206945, 55/100, contains 2038 true hits.\n",
+      "Saving event 004206946, 56/100, contains 3145 true hits.\n",
+      "Saving event 004206947, 57/100, contains 4457 true hits.\n",
+      "Saving event 004206948, 58/100, contains 1102 true hits.\n",
+      "Saving event 004206949, 59/100, contains 955 true hits.\n",
+      "Saving event 004206950, 60/100, contains 1426 true hits.\n",
+      "Saving event 004206951, 61/100, contains 428 true hits.\n",
+      "Saving event 004206952, 62/100, contains 1250 true hits.\n",
+      "Saving event 004206953, 63/100, contains 1591 true hits.\n",
+      "Saving event 004206954, 64/100, contains 2626 true hits.\n",
+      "Saving event 004206955, 65/100, contains 293 true hits.\n",
+      "Saving event 004206956, 66/100, contains 1312 true hits.\n",
+      "Saving event 004206957, 67/100, contains 983 true hits.\n",
+      "Saving event 004206958, 68/100, contains 1659 true hits.\n",
+      "Saving event 004206959, 69/100, contains 2843 true hits.\n",
+      "Saving event 004206960, 70/100, contains 816 true hits.\n",
+      "Saving event 004206961, 71/100, contains 211 true hits.\n",
+      "Saving event 004206962, 72/100, contains 1821 true hits.\n",
+      "Saving event 004206963, 73/100, contains 1017 true hits.\n",
+      "Saving event 004206964, 74/100, contains 2710 true hits.\n",
+      "Saving event 004206965, 75/100, contains 2393 true hits.\n",
+      "Saving event 004206966, 76/100, contains 3461 true hits.\n",
+      "Saving event 007212913, 77/100, contains 1855 true hits.\n",
+      "Saving event 007212914, 78/100, contains 923 true hits.\n",
+      "Saving event 007212915, 79/100, contains 1247 true hits.\n",
+      "Saving event 007212916, 80/100, contains 2129 true hits.\n",
+      "Saving event 007212917, 81/100, contains 2372 true hits.\n",
+      "Saving event 007212918, 82/100, contains 1742 true hits.\n",
+      "Saving event 007212919, 83/100, contains 2170 true hits.\n",
+      "Saving event 007212920, 84/100, contains 768 true hits.\n",
+      "Saving event 007212921, 85/100, contains 2265 true hits.\n",
+      "Saving event 007212922, 86/100, contains 3096 true hits.\n",
+      "Saving event 007212923, 87/100, contains 1829 true hits.\n",
+      "Saving event 007212924, 88/100, contains 833 true hits.\n",
+      "Saving event 007212925, 89/100, contains 2592 true hits.\n",
+      "Saving event 007212926, 90/100, contains 1433 true hits.\n",
+      "Saving event 007212927, 91/100, contains 1745 true hits.\n",
+      "Saving event 007212928, 92/100, contains 2669 true hits.\n",
+      "Saving event 007212929, 93/100, contains 982 true hits.\n",
+      "Saving event 007212930, 94/100, contains 1134 true hits.\n",
+      "Saving event 007212931, 95/100, contains 2717 true hits.\n",
+      "Saving event 007212932, 96/100, contains 2973 true hits.\n",
+      "Saving event 007212933, 97/100, contains 970 true hits.\n",
+      "Saving event 007212934, 98/100, contains 1286 true hits.\n",
+      "Saving event 007212935, 99/100, contains 420 true hits.\n",
+      "Saving event 007212936, 100/100, contains 3010 true hits.\n",
       "Writing outputs to data/processed\n"
      ]
     },
@@ -310,16 +312,41 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO:Preparing event 004206889\n",
       "INFO:Preparing event 004206894\n",
       "INFO:Preparing event 004206890\n",
-      "INFO:Preparing event 004206896\n"
+      "INFO:Preparing event 004206901\n",
+      "INFO:Preparing event 004206900\n",
+      "INFO:Preparing event 004206899\n",
+      "INFO:Preparing event 004206889\n",
+      "INFO:Preparing event 004206902\n",
+      "INFO:Preparing event 004206905\n",
+      "INFO:Preparing event 004206895\n",
+      "INFO:Preparing event 004206903\n",
+      "INFO:Preparing event 004206893\n",
+      "INFO:Preparing event 004206896\n",
+      "INFO:Preparing event 004206891\n",
+      "INFO:Preparing event 004206892\n",
+      "INFO:Preparing event 004206904\n",
+      "INFO:Preparing event 004206907\n",
+      "INFO:Preparing event 004206911\n",
+      "INFO:Preparing event 004206913\n",
+      "INFO:Preparing event 004206915\n",
+      "INFO:Preparing event 004206917\n",
+      "INFO:Preparing event 004206918\n",
+      "INFO:Preparing event 004206909\n",
+      "INFO:Preparing event 004206916\n",
+      "INFO:Preparing event 004206912\n",
+      "INFO:Preparing event 004206897\n",
+      "INFO:Preparing event 004206908\n",
+      "INFO:Preparing event 004206914\n",
+      "INFO:Preparing event 004206910\n",
+      "INFO:Preparing event 004206919\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7aa224c9d25347abbc55b2b3b570554f",
+       "model_id": "89d0e44bd4534b309c6f3b966ac04da2",
        "version_major": 2,
        "version_minor": 0
       },
@@ -334,208 +361,177 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO:Preparing event 004206891\n",
-      "INFO:Preparing event 004206898\n",
-      "INFO:Preparing event 004206893\n",
-      "INFO:Preparing event 004206897\n",
-      "INFO:Preparing event 004206895\n",
-      "INFO:Preparing event 004206901\n",
-      "INFO:Preparing event 004206900\n",
-      "INFO:Preparing event 004206904\n",
-      "INFO:Preparing event 004206892\n",
-      "INFO:Preparing event 004206902\n",
-      "INFO:Preparing event 004206908\n",
-      "INFO:Preparing event 004206907\n",
-      "INFO:Preparing event 004206899\n",
-      "INFO:Preparing event 004206909\n",
-      "INFO:Preparing event 004206912\n",
-      "INFO:Preparing event 004206910\n",
-      "INFO:Preparing event 004206903\n",
-      "INFO:Preparing event 004206918\n",
-      "INFO:Preparing event 004206921\n",
-      "INFO:Preparing event 004206913\n",
-      "INFO:Preparing event 004206915\n",
-      "INFO:Preparing event 004206914\n",
-      "INFO:Preparing event 004206919\n",
-      "INFO:Preparing event 004206916\n",
       "INFO:Preparing event 004206920\n",
-      "INFO:Preparing event 004206917\n",
-      "INFO:Preparing event 004206905\n",
-      "INFO:Preparing event 004206911\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206890 with size (2, 590)\n",
-      "INFO:Preparing event 004206922\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206892 with size (2, 484)\n",
+      "INFO:Preparing event 004206921\n",
+      "INFO:Preparing event 004206898\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206899 with size (2, 650)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206894 with size (2, 1145)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206911 with size (2, 875)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206903 with size (2, 1492)\n",
       "INFO:Preparing event 004206923\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206922 with size (2, 372)\n",
       "INFO:Preparing event 004206924\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206923 with size (2, 711)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206918 with size (2, 1119)\n",
       "INFO:Preparing event 004206925\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206924 with size (2, 1047)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206914 with size (2, 435)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206910 with size (2, 180)\n",
-      "INFO:Preparing event 004206927\n",
+      "INFO:Preparing event 004206922\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206896 with size (2, 782)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206905 with size (2, 2741)\n",
       "INFO:Preparing event 004206926\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206901 with size (2, 1536)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206902 with size (2, 1632)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206893 with size (2, 984)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206895 with size (2, 578)\n",
-      "INFO:Preparing event 004206930\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206908 with size (2, 517)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206918 with size (2, 1355)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206892 with size (2, 545)\n",
+      "INFO:Preparing event 004206927\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206902 with size (2, 1860)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206901 with size (2, 1779)\n",
       "INFO:Preparing event 004206929\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206904 with size (2, 2437)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206895 with size (2, 625)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206920 with size (2, 954)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206922 with size (2, 505)\n",
       "INFO:Preparing event 004206928\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206889 with size (2, 571)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206925 with size (2, 1626)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206912 with size (2, 969)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206909 with size (2, 601)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206915 with size (2, 597)\n",
+      "INFO:Preparing event 004206930\n",
+      "INFO:Preparing event 004206931\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206917 with size (2, 1507)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206898 with size (2, 935)\n",
+      "INFO:Preparing event 004206933\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206908 with size (2, 691)\n",
+      "INFO:Preparing event 004206936\n",
       "INFO:Preparing event 004206934\n",
       "INFO:Preparing event 004206935\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206897 with size (2, 883)\n",
-      "INFO:Preparing event 004206933\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206921 with size (2, 2020)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206914 with size (2, 488)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206925 with size (2, 1898)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206923 with size (2, 848)\n",
+      "INFO:Preparing event 004206939\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206907 with size (2, 803)\n",
       "INFO:Preparing event 004206938\n",
-      "INFO:Preparing event 004206931\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206898 with size (2, 708)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206919 with size (2, 1153)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206890 with size (2, 742)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206926 with size (2, 1027)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206891 with size (2, 1109)\n",
       "INFO:Preparing event 004206937\n",
-      "INFO:Preparing event 004206936\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206921 with size (2, 1674)\n",
-      "INFO:Preparing event 004206939\n",
-      "INFO:Preparing event 004206940\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206920 with size (2, 767)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206899 with size (2, 541)\n",
       "INFO:Preparing event 004206941\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206926 with size (2, 923)\n",
-      "INFO:Preparing event 004206942\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206916 with size (2, 652)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206928 with size (2, 1990)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206927 with size (2, 1661)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206907 with size (2, 678)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206896 with size (2, 682)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206930 with size (2, 2147)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206924 with size (2, 1197)\n",
       "INFO:Preparing event 004206945\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206900 with size (2, 609)\n",
-      "INFO:Preparing event 004206947\n",
       "INFO:Preparing event 004206943\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206889 with size (2, 626)\n",
       "INFO:Preparing event 004206944\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206933 with size (2, 677)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206900 with size (2, 729)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206912 with size (2, 1089)\n",
+      "INFO:Preparing event 004206947\n",
+      "INFO:Preparing event 004206940\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206897 with size (2, 983)\n",
+      "INFO:Preparing event 004206948\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206893 with size (2, 1163)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206909 with size (2, 708)\n",
+      "INFO:Preparing event 004206951\n",
+      "INFO:Preparing event 004206952\n",
       "INFO:Preparing event 004206946\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206891 with size (2, 936)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206935 with size (2, 496)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206911 with size (2, 728)\n",
+      "INFO:Preparing event 004206953\n",
       "INFO:Preparing event 004206949\n",
-      "INFO:Preparing event 004206948\n",
       "INFO:Preparing event 004206950\n",
-      "INFO:Preparing event 004206952\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206929 with size (2, 1346)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206936 with size (2, 462)\n",
-      "INFO:Preparing event 004206951\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206917 with size (2, 1290)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206940 with size (2, 1040)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206905 with size (2, 2412)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206939 with size (2, 276)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206934 with size (2, 236)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206929 with size (2, 1550)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206939 with size (2, 288)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206919 with size (2, 1329)\n",
+      "INFO:Preparing event 004206942\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206916 with size (2, 736)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206910 with size (2, 228)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206913 with size (2, 2754)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206927 with size (2, 1847)\n",
       "INFO:Preparing event 004206954\n",
-      "INFO:Preparing event 004206953\n",
-      "INFO:Preparing event 004206955\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206915 with size (2, 537)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206937 with size (2, 554)\n",
-      "INFO:Preparing event 004206958\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206941 with size (2, 2234)\n",
       "INFO:Preparing event 004206957\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206931 with size (2, 1301)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206943 with size (2, 228)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206942 with size (2, 1248)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206938 with size (2, 741)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206903 with size (2, 1267)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206936 with size (2, 584)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206938 with size (2, 836)\n",
+      "INFO:Preparing event 004206958\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206928 with size (2, 2391)\n",
+      "INFO:Preparing event 004206959\n",
+      "INFO:Preparing event 004206955\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206930 with size (2, 2426)\n",
       "INFO:Preparing event 004206960\n",
       "INFO:Preparing event 004206956\n",
+      "INFO:Preparing event 004206966\n",
       "INFO:Preparing event 004206961\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206894 with size (2, 1033)\n",
-      "INFO:Preparing event 004206963\n",
-      "INFO:Preparing event 004206959\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206949 with size (2, 665)\n",
+      "INFO:Preparing event 004206964\n",
       "INFO:Preparing event 004206962\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206944 with size (2, 870)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206950 with size (2, 949)\n",
-      "INFO:Preparing event 007212913\n",
+      "INFO:Preparing event 004206963\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206952 with size (2, 976)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206944 with size (2, 1020)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206953 with size (2, 1235)\n",
+      "INFO:Preparing event 004206965\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206934 with size (2, 310)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206937 with size (2, 690)\n",
       "INFO:Preparing event 007212914\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206945 with size (2, 1260)\n",
+      "INFO:Preparing event 007212913\n",
       "INFO:Preparing event 007212915\n",
-      "INFO:Preparing event 004206964\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206948 with size (2, 743)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206904 with size (2, 2192)\n",
-      "INFO:Preparing event 004206966\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206941 with size (2, 2737)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206945 with size (2, 1431)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206951 with size (2, 322)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206942 with size (2, 1367)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206931 with size (2, 1546)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206935 with size (2, 542)\n",
+      "INFO:Preparing event 007212918\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206940 with size (2, 1241)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206933 with size (2, 757)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206957 with size (2, 728)\n",
       "INFO:Preparing event 007212916\n",
-      "INFO:Preparing event 007212919\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206955 with size (2, 212)\n",
       "INFO:Preparing event 007212917\n",
-      "INFO:Preparing event 004206965\n",
-      "INFO:Preparing event 007212920\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206913 with size (2, 2373)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206952 with size (2, 818)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206955 with size (2, 182)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206947 with size (2, 2819)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206950 with size (2, 1073)\n",
       "INFO:Preparing event 007212924\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206954 with size (2, 1747)\n",
-      "INFO:Preparing event 007212921\n",
-      "INFO:Preparing event 007212922\n",
-      "INFO:Preparing event 007212923\n",
-      "INFO:Preparing event 007212926\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
       "INFO:Preparing event 007212925\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206960 with size (2, 577)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206961 with size (2, 161)\n",
-      "INFO:Preparing event 007212927\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206951 with size (2, 252)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206946 with size (2, 2117)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206954 with size (2, 1990)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206956 with size (2, 1019)\n",
+      "INFO:Preparing event 007212920\n",
+      "INFO:Preparing event 007212926\n",
+      "INFO:Preparing event 007212922\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206959 with size (2, 2069)\n",
       "INFO:Preparing event 007212928\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206962 with size (2, 1070)\n",
-      "INFO:Preparing event 007212918\n",
-      "INFO:Preparing event 007212931\n",
+      "INFO:Preparing event 007212923\n",
       "INFO:Preparing event 007212930\n",
-      "INFO:Preparing event 007212932\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event007212913 with size (2, 1252)\n",
-      "INFO:Preparing event 007212934\n",
+      "INFO:Preparing event 007212931\n",
+      "INFO:Preparing event 007212919\n",
+      "INFO:Preparing event 007212921\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206949 with size (2, 734)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206960 with size (2, 627)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206947 with size (2, 3438)\n",
+      "INFO:Preparing event 007212927\n",
       "INFO:Preparing event 007212929\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206956 with size (2, 830)\n",
-      "INFO:Preparing event 007212935\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206943 with size (2, 258)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206948 with size (2, 849)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206962 with size (2, 1329)\n",
+      "INFO:Preparing event 007212934\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206958 with size (2, 1260)\n",
+      "INFO:Preparing event 007212932\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206966 with size (2, 2581)\n",
       "INFO:Preparing event 007212933\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206953 with size (2, 971)\n",
       "INFO:Preparing event 007212936\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event007212915 with size (2, 854)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event007212923 with size (2, 1174)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206963 with size (2, 649)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event007212927 with size (2, 1164)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event007212917 with size (2, 1575)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event007212924 with size (2, 548)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206958 with size (2, 1113)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206965 with size (2, 1612)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event007212920 with size (2, 549)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206957 with size (2, 597)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event007212916 with size (2, 1384)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206959 with size (2, 1729)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206964 with size (2, 1738)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event007212929 with size (2, 623)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event007212926 with size (2, 973)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event007212930 with size (2, 775)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event004206966 with size (2, 2266)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event007212922 with size (2, 1879)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event007212921 with size (2, 1303)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event007212914 with size (2, 578)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event007212931 with size (2, 1660)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event007212928 with size (2, 1727)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event007212919 with size (2, 1422)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event007212925 with size (2, 1766)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event007212934 with size (2, 812)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event007212918 with size (2, 1085)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event007212933 with size (2, 548)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event007212935 with size (2, 215)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event007212936 with size (2, 2010)\n",
-      "INFO:Modulewise truth graph built for data/preprocessed/event007212932 with size (2, 1901)\n"
+      "INFO:Modulewise truth graph built for data/preprocessed/event007212924 with size (2, 626)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event007212914 with size (2, 681)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206946 with size (2, 2389)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206961 with size (2, 169)\n",
+      "INFO:Preparing event 007212935\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206963 with size (2, 776)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event007212918 with size (2, 1298)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event007212917 with size (2, 1753)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event007212915 with size (2, 982)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event007212928 with size (2, 2023)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event007212922 with size (2, 2242)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event007212926 with size (2, 1111)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206964 with size (2, 2053)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event007212930 with size (2, 856)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event007212923 with size (2, 1412)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event004206965 with size (2, 1832)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event007212921 with size (2, 1661)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event007212916 with size (2, 1597)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event007212929 with size (2, 706)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event007212934 with size (2, 1002)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event007212919 with size (2, 1630)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event007212933 with size (2, 722)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event007212913 with size (2, 1389)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event007212927 with size (2, 1295)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event007212932 with size (2, 2200)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event007212920 with size (2, 605)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event007212931 with size (2, 2041)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event007212935 with size (2, 282)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event007212936 with size (2, 2279)\n",
+      "INFO:Modulewise truth graph built for data/preprocessed/event007212925 with size (2, 1990)\n"
      ]
     }
    ],
@@ -571,7 +567,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -603,32 +599,32 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0.465221</td>\n",
-       "      <td>-0.462661</td>\n",
+       "      <td>0.215308</td>\n",
+       "      <td>-0.707593</td>\n",
        "      <td>-1.440705</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.513063</td>\n",
-       "      <td>-0.629230</td>\n",
-       "      <td>-1.434295</td>\n",
+       "      <td>0.701576</td>\n",
+       "      <td>-0.335364</td>\n",
+       "      <td>-1.440705</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0.498745</td>\n",
-       "      <td>-0.508192</td>\n",
-       "      <td>-1.440705</td>\n",
+       "      <td>0.848610</td>\n",
+       "      <td>-0.509776</td>\n",
+       "      <td>-1.434295</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0.249448</td>\n",
-       "      <td>-0.532812</td>\n",
+       "      <td>0.162432</td>\n",
+       "      <td>-0.924610</td>\n",
        "      <td>-1.440705</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>0.730112</td>\n",
-       "      <td>-0.392865</td>\n",
+       "      <td>0.148984</td>\n",
+       "      <td>-0.659478</td>\n",
        "      <td>-1.440705</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -638,58 +634,58 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>516</th>\n",
-       "      <td>0.726360</td>\n",
-       "      <td>0.467563</td>\n",
+       "      <th>1849</th>\n",
+       "      <td>0.366591</td>\n",
+       "      <td>-0.193348</td>\n",
        "      <td>3.753205</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>517</th>\n",
-       "      <td>0.244590</td>\n",
-       "      <td>0.593452</td>\n",
+       "      <th>1850</th>\n",
+       "      <td>0.403005</td>\n",
+       "      <td>-0.157476</td>\n",
        "      <td>3.746795</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>518</th>\n",
-       "      <td>0.722281</td>\n",
-       "      <td>0.704765</td>\n",
-       "      <td>3.746795</td>\n",
+       "      <th>1851</th>\n",
+       "      <td>0.165458</td>\n",
+       "      <td>-0.130846</td>\n",
+       "      <td>3.753205</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>519</th>\n",
-       "      <td>0.128899</td>\n",
-       "      <td>-0.142000</td>\n",
+       "      <th>1852</th>\n",
+       "      <td>0.179799</td>\n",
+       "      <td>-0.073637</td>\n",
        "      <td>3.753205</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>520</th>\n",
-       "      <td>0.340476</td>\n",
-       "      <td>-0.061835</td>\n",
-       "      <td>3.753205</td>\n",
+       "      <th>1853</th>\n",
+       "      <td>0.732901</td>\n",
+       "      <td>-0.067736</td>\n",
+       "      <td>3.746795</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>521 rows × 3 columns</p>\n",
+       "<p>1854 rows × 3 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "            0         1         2\n",
-       "0    0.465221 -0.462661 -1.440705\n",
-       "1    0.513063 -0.629230 -1.434295\n",
-       "2    0.498745 -0.508192 -1.440705\n",
-       "3    0.249448 -0.532812 -1.440705\n",
-       "4    0.730112 -0.392865 -1.440705\n",
-       "..        ...       ...       ...\n",
-       "516  0.726360  0.467563  3.753205\n",
-       "517  0.244590  0.593452  3.746795\n",
-       "518  0.722281  0.704765  3.746795\n",
-       "519  0.128899 -0.142000  3.753205\n",
-       "520  0.340476 -0.061835  3.753205\n",
+       "             0         1         2\n",
+       "0     0.215308 -0.707593 -1.440705\n",
+       "1     0.701576 -0.335364 -1.440705\n",
+       "2     0.848610 -0.509776 -1.434295\n",
+       "3     0.162432 -0.924610 -1.440705\n",
+       "4     0.148984 -0.659478 -1.440705\n",
+       "...        ...       ...       ...\n",
+       "1849  0.366591 -0.193348  3.753205\n",
+       "1850  0.403005 -0.157476  3.746795\n",
+       "1851  0.165458 -0.130846  3.753205\n",
+       "1852  0.179799 -0.073637  3.753205\n",
+       "1853  0.732901 -0.067736  3.746795\n",
        "\n",
-       "[521 rows x 3 columns]"
+       "[1854 rows x 3 columns]"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
-- 
GitLab


From 74c78448c360c2b67841b32b1f18dc793b8da11f Mon Sep 17 00:00:00 2001
From: Fotis Giasemis <Fotis.Giasemis@cern.ch>
Date: Fri, 24 Mar 2023 17:43:10 +0100
Subject: [PATCH 24/30] Prepare to merge with main

---
 .../utils/plotting_utils_validation.py        |   6 +-
 LHCb_Pipeline/evaluation_pipeline.ipynb       | 431 ++++++++++++++++--
 LHCb_Pipeline/testing.py                      |   4 +
 gpu_environment.yaml                          |   4 +-
 installation.txt                              |   8 +-
 montetracko-setup.sh                          |  32 --
 6 files changed, 410 insertions(+), 75 deletions(-)
 delete mode 100755 montetracko-setup.sh

diff --git a/LHCb_Pipeline/Scripts/utils/plotting_utils_validation.py b/LHCb_Pipeline/Scripts/utils/plotting_utils_validation.py
index b4468337..7f8de8f9 100644
--- a/LHCb_Pipeline/Scripts/utils/plotting_utils_validation.py
+++ b/LHCb_Pipeline/Scripts/utils/plotting_utils_validation.py
@@ -5,7 +5,7 @@ column_labels = {
     "pt": "$p_T$ [MeV/c]",
     "p": "$p$ [MeV/c]",
     "eta": "$\eta$",
-    "vz": r"$\text{ovtx}_{z}$ [mm]",
+    "vz": r"$ovtxz$ [mm]",
 }
 
 #: Associates a column name with its range for the plots
@@ -24,10 +24,10 @@ def configure_matplotlib():
         **{
             # Font
             "font.family": "serif",
-            "font.serif": "Computer Modern Roman",
+            # "font.serif": "Computer Modern Roman",
             # Latex
             "text.latex.preamble": r"\usepackage{amsmath}",
-            "text.usetex": True,  # Put to False to disable Latex
+            "text.usetex": False,  # Put to False to disable Latex
             # Fontsizes
             "legend.fontsize": 20,
             "axes.titlesize": 24,
diff --git a/LHCb_Pipeline/evaluation_pipeline.ipynb b/LHCb_Pipeline/evaluation_pipeline.ipynb
index 40c31c99..9b7f168d 100644
--- a/LHCb_Pipeline/evaluation_pipeline.ipynb
+++ b/LHCb_Pipeline/evaluation_pipeline.ipynb
@@ -9,15 +9,334 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:Loading faiss with AVX2 support.\n",
+      "INFO:Successfully loaded faiss with AVX2 support.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>\n",
+       "        .bk-notebook-logo {\n",
+       "            display: block;\n",
+       "            width: 20px;\n",
+       "            height: 20px;\n",
+       "            background-image: url(data:image/png;base64,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);\n",
+       "        }\n",
+       "    </style>\n",
+       "    <div>\n",
+       "        <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-notebook-logo\"></a>\n",
+       "        <span id=\"p1001\">Loading BokehJS ...</span>\n",
+       "    </div>\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/javascript": [
+       "(function(root) {\n",
+       "  function now() {\n",
+       "    return new Date();\n",
+       "  }\n",
+       "\n",
+       "  const force = true;\n",
+       "\n",
+       "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
+       "    root._bokeh_onload_callbacks = [];\n",
+       "    root._bokeh_is_loading = undefined;\n",
+       "  }\n",
+       "\n",
+       "const JS_MIME_TYPE = 'application/javascript';\n",
+       "  const HTML_MIME_TYPE = 'text/html';\n",
+       "  const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
+       "  const CLASS_NAME = 'output_bokeh rendered_html';\n",
+       "\n",
+       "  /**\n",
+       "   * Render data to the DOM node\n",
+       "   */\n",
+       "  function render(props, node) {\n",
+       "    const script = document.createElement(\"script\");\n",
+       "    node.appendChild(script);\n",
+       "  }\n",
+       "\n",
+       "  /**\n",
+       "   * Handle when an output is cleared or removed\n",
+       "   */\n",
+       "  function handleClearOutput(event, handle) {\n",
+       "    const cell = handle.cell;\n",
+       "\n",
+       "    const id = cell.output_area._bokeh_element_id;\n",
+       "    const server_id = cell.output_area._bokeh_server_id;\n",
+       "    // Clean up Bokeh references\n",
+       "    if (id != null && id in Bokeh.index) {\n",
+       "      Bokeh.index[id].model.document.clear();\n",
+       "      delete Bokeh.index[id];\n",
+       "    }\n",
+       "\n",
+       "    if (server_id !== undefined) {\n",
+       "      // Clean up Bokeh references\n",
+       "      const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
+       "      cell.notebook.kernel.execute(cmd_clean, {\n",
+       "        iopub: {\n",
+       "          output: function(msg) {\n",
+       "            const id = msg.content.text.trim();\n",
+       "            if (id in Bokeh.index) {\n",
+       "              Bokeh.index[id].model.document.clear();\n",
+       "              delete Bokeh.index[id];\n",
+       "            }\n",
+       "          }\n",
+       "        }\n",
+       "      });\n",
+       "      // Destroy server and session\n",
+       "      const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
+       "      cell.notebook.kernel.execute(cmd_destroy);\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  /**\n",
+       "   * Handle when a new output is added\n",
+       "   */\n",
+       "  function handleAddOutput(event, handle) {\n",
+       "    const output_area = handle.output_area;\n",
+       "    const output = handle.output;\n",
+       "\n",
+       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
+       "    if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n",
+       "      return\n",
+       "    }\n",
+       "\n",
+       "    const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
+       "\n",
+       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
+       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
+       "      // store reference to embed id on output_area\n",
+       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
+       "    }\n",
+       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
+       "      const bk_div = document.createElement(\"div\");\n",
+       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
+       "      const script_attrs = bk_div.children[0].attributes;\n",
+       "      for (let i = 0; i < script_attrs.length; i++) {\n",
+       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
+       "        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n",
+       "      }\n",
+       "      // store reference to server id on output_area\n",
+       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  function register_renderer(events, OutputArea) {\n",
+       "\n",
+       "    function append_mime(data, metadata, element) {\n",
+       "      // create a DOM node to render to\n",
+       "      const toinsert = this.create_output_subarea(\n",
+       "        metadata,\n",
+       "        CLASS_NAME,\n",
+       "        EXEC_MIME_TYPE\n",
+       "      );\n",
+       "      this.keyboard_manager.register_events(toinsert);\n",
+       "      // Render to node\n",
+       "      const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
+       "      render(props, toinsert[toinsert.length - 1]);\n",
+       "      element.append(toinsert);\n",
+       "      return toinsert\n",
+       "    }\n",
+       "\n",
+       "    /* Handle when an output is cleared or removed */\n",
+       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
+       "    events.on('delete.Cell', handleClearOutput);\n",
+       "\n",
+       "    /* Handle when a new output is added */\n",
+       "    events.on('output_added.OutputArea', handleAddOutput);\n",
+       "\n",
+       "    /**\n",
+       "     * Register the mime type and append_mime function with output_area\n",
+       "     */\n",
+       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
+       "      /* Is output safe? */\n",
+       "      safe: true,\n",
+       "      /* Index of renderer in `output_area.display_order` */\n",
+       "      index: 0\n",
+       "    });\n",
+       "  }\n",
+       "\n",
+       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
+       "  if (root.Jupyter !== undefined) {\n",
+       "    const events = require('base/js/events');\n",
+       "    const OutputArea = require('notebook/js/outputarea').OutputArea;\n",
+       "\n",
+       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
+       "      register_renderer(events, OutputArea);\n",
+       "    }\n",
+       "  }\n",
+       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
+       "    root._bokeh_timeout = Date.now() + 5000;\n",
+       "    root._bokeh_failed_load = false;\n",
+       "  }\n",
+       "\n",
+       "  const NB_LOAD_WARNING = {'data': {'text/html':\n",
+       "     \"<div style='background-color: #fdd'>\\n\"+\n",
+       "     \"<p>\\n\"+\n",
+       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
+       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
+       "     \"</p>\\n\"+\n",
+       "     \"<ul>\\n\"+\n",
+       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
+       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
+       "     \"</ul>\\n\"+\n",
+       "     \"<code>\\n\"+\n",
+       "     \"from bokeh.resources import INLINE\\n\"+\n",
+       "     \"output_notebook(resources=INLINE)\\n\"+\n",
+       "     \"</code>\\n\"+\n",
+       "     \"</div>\"}};\n",
+       "\n",
+       "  function display_loaded() {\n",
+       "    const el = document.getElementById(\"p1001\");\n",
+       "    if (el != null) {\n",
+       "      el.textContent = \"BokehJS is loading...\";\n",
+       "    }\n",
+       "    if (root.Bokeh !== undefined) {\n",
+       "      if (el != null) {\n",
+       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
+       "      }\n",
+       "    } else if (Date.now() < root._bokeh_timeout) {\n",
+       "      setTimeout(display_loaded, 100)\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  function run_callbacks() {\n",
+       "    try {\n",
+       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
+       "        if (callback != null)\n",
+       "          callback();\n",
+       "      });\n",
+       "    } finally {\n",
+       "      delete root._bokeh_onload_callbacks\n",
+       "    }\n",
+       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
+       "  }\n",
+       "\n",
+       "  function load_libs(css_urls, js_urls, callback) {\n",
+       "    if (css_urls == null) css_urls = [];\n",
+       "    if (js_urls == null) js_urls = [];\n",
+       "\n",
+       "    root._bokeh_onload_callbacks.push(callback);\n",
+       "    if (root._bokeh_is_loading > 0) {\n",
+       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
+       "      return null;\n",
+       "    }\n",
+       "    if (js_urls == null || js_urls.length === 0) {\n",
+       "      run_callbacks();\n",
+       "      return null;\n",
+       "    }\n",
+       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
+       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
+       "\n",
+       "    function on_load() {\n",
+       "      root._bokeh_is_loading--;\n",
+       "      if (root._bokeh_is_loading === 0) {\n",
+       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
+       "        run_callbacks()\n",
+       "      }\n",
+       "    }\n",
+       "\n",
+       "    function on_error(url) {\n",
+       "      console.error(\"failed to load \" + url);\n",
+       "    }\n",
+       "\n",
+       "    for (let i = 0; i < css_urls.length; i++) {\n",
+       "      const url = css_urls[i];\n",
+       "      const element = document.createElement(\"link\");\n",
+       "      element.onload = on_load;\n",
+       "      element.onerror = on_error.bind(null, url);\n",
+       "      element.rel = \"stylesheet\";\n",
+       "      element.type = \"text/css\";\n",
+       "      element.href = url;\n",
+       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
+       "      document.body.appendChild(element);\n",
+       "    }\n",
+       "\n",
+       "    for (let i = 0; i < js_urls.length; i++) {\n",
+       "      const url = js_urls[i];\n",
+       "      const element = document.createElement('script');\n",
+       "      element.onload = on_load;\n",
+       "      element.onerror = on_error.bind(null, url);\n",
+       "      element.async = false;\n",
+       "      element.src = url;\n",
+       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
+       "      document.head.appendChild(element);\n",
+       "    }\n",
+       "  };\n",
+       "\n",
+       "  function inject_raw_css(css) {\n",
+       "    const element = document.createElement(\"style\");\n",
+       "    element.appendChild(document.createTextNode(css));\n",
+       "    document.body.appendChild(element);\n",
+       "  }\n",
+       "\n",
+       "  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.0.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.0.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.0.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.0.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.0.3.min.js\"];\n",
+       "  const css_urls = [];\n",
+       "\n",
+       "  const inline_js = [    function(Bokeh) {\n",
+       "      Bokeh.set_log_level(\"info\");\n",
+       "    },\n",
+       "function(Bokeh) {\n",
+       "    }\n",
+       "  ];\n",
+       "\n",
+       "  function run_inline_js() {\n",
+       "    if (root.Bokeh !== undefined || force === true) {\n",
+       "          for (let i = 0; i < inline_js.length; i++) {\n",
+       "      inline_js[i].call(root, root.Bokeh);\n",
+       "    }\n",
+       "if (force === true) {\n",
+       "        display_loaded();\n",
+       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
+       "      setTimeout(run_inline_js, 100);\n",
+       "    } else if (!root._bokeh_failed_load) {\n",
+       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
+       "      root._bokeh_failed_load = true;\n",
+       "    } else if (force !== true) {\n",
+       "      const cell = $(document.getElementById(\"p1001\")).parents('.cell').data().cell;\n",
+       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
+       "    }\n",
+       "  }\n",
+       "\n",
+       "  if (root._bokeh_is_loading === 0) {\n",
+       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
+       "    run_inline_js();\n",
+       "  } else {\n",
+       "    load_libs(css_urls, js_urls, function() {\n",
+       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
+       "      run_inline_js();\n",
+       "    });\n",
+       "  }\n",
+       "}(window));"
+      ],
+      "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"p1001\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.0.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.0.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.0.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.0.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.0.3.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"p1001\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import os\n",
+    "import sys\n",
     "import os.path as op\n",
     "import yaml\n",
     "import torch\n",
     "\n",
+    "sys.path.append('../montetracko')\n",
+    "\n",
     "from Scripts.Step_2_Run_Metric_Learning import train as run_metric_learning_inference\n",
     "from Scripts.Step_4_Run_GNN import train as run_gnn_inference\n",
     "from Scripts.Step_5_Build_Track_Candidates import train as build_track_candidates\n",
@@ -26,6 +345,8 @@
     "    evaluate as evaluate_candidates_montetracko\n",
     ")\n",
     "\n",
+    "\n",
+    "\n",
     "import warnings\n",
     "\n",
     "warnings.filterwarnings(\"ignore\")\n",
@@ -35,7 +356,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -572,7 +893,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -586,12 +907,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "63f9c2b1151745fbae4906d00678460c",
+       "model_id": "fcae7933d1a24005a6b24a1ddaf14fd9",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "  0%|          | 0/99 [00:00<?, ?it/s]"
+       "  0%|          | 0/100 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -610,31 +931,49 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "TrackChecker output                               :      1031/    21885   4.71% ghosts\n",
-      "01_velo                                           :      9391/    10690  87.85% ( 89.86%),       115 (  1.21%) clones, pur  98.95%, hit eff  93.89% \n",
-      "02_long                                           :      5512/     6094  90.45% ( 92.19%),        66 (  1.18%) clones, pur  99.07%, hit eff  94.80% \n",
-      "03_long_P>5GeV                                    :      3589/     3903  91.95% ( 93.65%),        49 (  1.35%) clones, pur  99.13%, hit eff  95.35% \n",
-      "04_long_strange                                   :         0/        0    nan% (   nan%),         0 (   nan%) clones, pur    nan%, hit eff    nan% \n",
-      "05_long_strange_P>5GeV                            :         0/        0    nan% (   nan%),         0 (   nan%) clones, pur    nan%, hit eff    nan% \n",
-      "06_long_fromB                                     :         0/        0    nan% (   nan%),         0 (   nan%) clones, pur    nan%, hit eff    nan% \n",
-      "07_long_fromB_P>5GeV                              :         0/        0    nan% (   nan%),         0 (   nan%) clones, pur    nan%, hit eff    nan% \n",
-      "08_long_electrons                                 :       336/      493  68.15% ( 70.98%),         9 (  2.61%) clones, pur  97.86%, hit eff  74.20% \n",
-      "09_long_fromB_electrons                           :         0/        0    nan% (   nan%),         0 (   nan%) clones, pur    nan%, hit eff    nan% \n",
-      "10_long_fromB_electrons_P>5GeV                    :         0/        0    nan% (   nan%),         0 (   nan%) clones, pur    nan%, hit eff    nan% \n",
-      "\n",
       "| Categories           | Efficiency   | Average efficiency   | % clones   | Average hit purity   | Average hit efficiency   |\n",
       "|:---------------------|:-------------|:---------------------|:-----------|:---------------------|:-------------------------|\n",
-      "| Velo, no electrons   | 87.85%       | 89.86%               | 1.21%      | 98.95%               | 93.89%                   |\n",
-      "| Long, only electrons | 68.15%       | 70.98%               | 2.61%      | 97.86%               | 74.20%                   |\n",
-      "| Velo, only electrons | 59.01%       | 60.84%               | 3.88%      | 98.14%               | 70.97%                   |\n",
-      "| Categories   | # ghosts   | # tracks   | % ghosts   |\n",
-      "|:-------------|:-----------|:-----------|:-----------|\n",
-      "| Everything   | 1,031      | 21,885     | 4.71%      |\n"
+      "| Velo                 | 75.54%       | 76.82%               | 1.13%      | 99.33%               | 95.63%                   |\n",
+      "| Long                 | 86.08%       | 87.43%               | 1.26%      | 99.40%               | 96.01%                   |\n",
+      "| Velo, no electrons   | 87.86%       | 89.77%               | 0.59%      | 99.36%               | 96.51%                   |\n",
+      "| Velo, only electrons | 0.00%        | 0.00%                | nan%       | nan%                 | nan%                     |\n",
+      "| Long, only electrons | 0.00%        | 0.00%                | nan%       | nan%                 | nan%                     |\n",
+      "| Categories   |   # ghosts | # tracks   | % ghosts   |\n",
+      "|:-------------|-----------:|:-----------|:-----------|\n",
+      "| Everything   |        585 | 21,116     | 2.77%      |\n"
      ]
     },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:4) Plotting\n",
+      "INFO:4) Plotting\n"
+     ]
+    },
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 3200x2400 with 32 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 3200x2400 with 32 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
        "<Figure size 3200x2400 with 32 Axes>"
       ]
@@ -648,13 +987,13 @@
     "    CONFIG,\n",
     "    min_track_length=3,\n",
     "    whether_to_plot=True,\n",
-    "    allen_report=True,\n",
+    "    allen_report=False,\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -663,22 +1002,22 @@
      "text": [
       "INFO:------------ Step 6: Evaluating the track reconstruction performance ------------\n",
       "INFO:--------------------------- a) Loading labelled graphs ---------------------------\n",
-      "100%|██████████| 99/99 [00:01<00:00, 52.46it/s]\n",
+      "100%|██████████| 100/100 [00:01<00:00, 63.52it/s]\n",
       "INFO:--------------------- b) Calculating the performance metrics ---------------------\n",
-      "INFO:Number of reconstructed particles: 19447\n",
-      "INFO:Number of particles: 27380\n",
-      "INFO:Number of matched tracks: 19534\n",
-      "INFO:Number of tracks: 20330\n",
-      "INFO:Number of duplicate reconstructed particles: 87\n",
-      "INFO:Efficiency: 0.710\n",
-      "INFO:Fake rate: 0.039\n",
-      "INFO:Duplication rate: 0.004\n",
+      "INFO:Number of reconstructed particles: 20401\n",
+      "INFO:Number of particles: 23205\n",
+      "INFO:Number of matched tracks: 20519\n",
+      "INFO:Number of tracks: 21116\n",
+      "INFO:Number of duplicate reconstructed particles: 118\n",
+      "INFO:Efficiency: 0.879\n",
+      "INFO:Fake rate: 0.028\n",
+      "INFO:Duplication rate: 0.006\n",
       "INFO:------------------------------ c) Plotting results ------------------------------\n"
      ]
     },
     {
      "data": {
-      "image/png": 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zm9/oj3/8o/7t3/5N//zP/7yyEVxuKAMArMz19bU+++wzgi0A7ezs6PPPP9cnn3yi9+/fr+w8hFsAwErElyL/7u/+btNVAbAldnZ2tLu7qw8fPiiKopWcg3ALAFiJn3/+WVEU6dNPP910VQBskc8++0x//etf9Ze//GUl5RNuAQAr8csvv0i6WQYIAGIff/yxpF+/I/LGNw4AYKVYGQHAuFV/J7BaggG++Oa7XMp59+3XuZQDAACwKYzcAgAAwBiEWwAAABiDaQkAgK2Q1xSrbcS0L2B9CLcbYvKXOAAAwKYwLQEAAMAAYRiqXC6rUChoZ2dHYRhmPjY+bn9/X77vr66Sa0C4BQAAeGCq1aoKhcLEY5ZlqdPpqFKpLF1er9dTvV5fKhBvK8ItAAAwVrvdVhAEm67GUrLU2fd9BUEwFUYty1K5XL7TeUul0p2O2zaEWwAAYKzBYLDpKiwtS50Hg4FGo5Esy1p9hR4Ywi0AADCW53mbrsLSstaZYJuOcAsAAIwUBMGDuznqIdZ52xBuAQCAceKVAx6Sh1jnbcQ6twAAPGBhGKparSoIAgVBoCiK5HmefN9P5m66rjtxjO/7Oj8/V6FQUBiGGgwGqtfrchxnqvwgCNRqtSYugRcKBdVqtdS6nJycJOVeXV2lvna8zsPhUP1+X5LU7XYl3cwnDYJAnU4n9dL76elp8nh8Q5XjOAqCQLVaTe12e+LYarWqg4OD5Pher7dU292u79u3byfaqlqtyvd9DYdDtVqt1LZZ1I5Z6yzdLNs1qy63xe/Hsqzks75dj6yW6TebRLgFAOABsyxLvV5PzWZTp6en8jxPYRiq0WioXq+r3W5PhNv473GglG5CXrFYVKvVmlhGqtvtqtlsqtfrybbtidefnp6q0Wgkj3mep2azqbdv304Ep3a7rXK5PBHcbtc5vhQ/Xl6z2VSxWJy6uep2WbFisaijoyNJUq1WU61Wk+d5yevH679s28VLbJ2cnOj09HSqnE6no263q2q1mvYRZWrHrHWWboLu6empms1m6vOxdrutUqk00a5BEKhYLMp13aVWR1im32wa0xIAADBAfDm71+slQaPVak2M+Pm+r3q9rk6nM3GsZVlqNps6Pj5OHguCQNVqVa1WaypktdttNZvNZLmq+HJ6Wuis1WpyHGei7FgcRl3XnQhgklSv1xUEwcTNVfFc1LRRx1arNd0oGWVpO8uykvqmmRUUl2nHZWQJppZlTY2q2ratVqularWaeU3bZfrNNiDcAgBgkPGF/S3LmghBx8fHKpVKqSOCr169UhiGydSAZrMpy7JmjshZlpVcNj8+PpbjODNHGuv1urrd7swbpdLmmcZl3w5gnuelhsE81mid13aLzLrMv0w75m1W/SuVisIwXDjyG1um32wDwi0AAAZ58eLFzOd8358ZQONwdnFxkbx2VlmNRmNijdVutzv3vPE5z8/P5z6fVp/hcJg8FgfoYrGYBObb9bqPee/hrpZpx3VyHCfzkmPL9JttwJxbAAAMMisoxaOmQRCo3W6nvsZ13SSIBUGQ6UaheBR1UUCzLGvmyO0yI5f9fl/Hx8dqt9vJ+3AcR2dnZ/e+sWkVITNrO67bwcFBpiXHlu0324BwCwCAQWYFxTi4OY6Tejd/mjy3rQ3DcGIU9q7im7ukX1cCOD8/T26SyvLewjBMDbKrmh6QRzvOqvOq3aXfbBrTEgAAeATiy8pZg1a8tFbWcrPcnHTf0b04zMbilQD6/b4ajUbmOaRv3ry5Vz3SzHr/WdtxkbzrPBwOM40oL9tvtgHhFgCAR6JSqSy8FB3PwyyVSgrDcG5oHX/tvPmb8XOzlsrKKgzDmfN2W63WwvrGZdzVvJHTWeFvmXac5T51nsX3/cw3zC3Tb7YB4RYAgEfi7Oxsanmtcd1uN7k0Hy/0P2s0tN1uJ6N6ruvO3TY2XlM1jxUNZs37lG5GGccDaNqoYxAEM2+OWiRum7TpFbPadJl2zLvOs+rUbrdlWVbm5dOW6TfbgDm3SHzxzXe5lfXu269zKwsAsFg8ujccDmeOMMabFlSr1amdreIdtsaXrHr79q2++uorlcvlicfj4BUHLtu21el0VK1WpzYq6Ha7CoJAb9++napPHBLTwuK80crbG0hIN4GtXq9PPGbbthzHUafTSYK153lTx2ZpO+nX5cF6vd5EUB9fTWB8k4NY1nZcps6L5i+XSiUdHBxMBWPf99VqteZ+Hmnve5l+s2k7URRFm67Epl1fX2tvb0/v37/X7u7uWs6ZZ5DcRoRbAD/++KO+//57ffnll/r00083XR1jxVvDXl5eKgxD2bYt27bVbDZnjpSOr3FaKBRkWZZs2059/e3XxkEpLczEW8zG68VeXV3p8PAwNUyOb3sb1zneBCLezjYIAlmWpRcvXiSjw2EYynEcdbvdZEtZ6SYUptUpPpdt2yoUCiqVSkk4y7vtdnZ2kr/fXr1hmXacV2fp1+1347YrlUoTu9DFgbNUKiX/uIhdXV3p9evXUyH+dpm2bU9sYrHovS/jrt8NWfMa4VaE21Ug3AIg3AJIs+pwm9u0hHq9rmazOXNOSBiGOjk5kSQdHh5qMBhMDc9v6hgAAACYIZdw6/t+6lyXWBiGKhaL6nQ6E8Pq9XpdFxcXqROa13UMAAAAzJFLuF20rly1WlWlUplaT811Xe3v76tcLk/N11jXMQAAADDHvZcCa7fbc9eti5eOmDWq++rVq6kR1XUdAwAAALPcK9ymLWFxW3z33qzXFAoFeZ43seTHuo4BAACAWe4VbrPs4ez7/tw14+Iwenl5ufZjAAAAYJY7z7ntdrszpwCMC4Jg7q4VcSC9vRPHOo657fr6euLvT58+1dOnT2e+HgAAANvlTiO3YRhm3gpu0W4fcSAdny6wrmNue/78ufb29pI/8ZJiAAAAeBjuNHJ7cnKS+easrHNcr66u1n7MbT/88MPEosCM2gIAADwsS4dbz/NULpdXUZeN293dXdsOZQAAAMjf0tMSer3eUmvFju/7PM/h4eHajwEAAIBZlgq3p6enev369VInmHeTl3QzV1bSxHzZdR0DAAAAs2QOt0EQyLKspcOhbdtJsEwTj7aO35y2rmMAAABglsxzbn3fV6fTUafTmXouXl7r+Pg4GUHt9XqSJMdx5HnezHLjY8enOqzrGAAAAJgl88htpVJRr9dL/ROvnHB2dpY8Fjs6OpJ0E47TXFxcTAXOdR0DAAAAs9xrh7IsHMdRqVTS+fl56vPdblfNZnMjxwAAAMAsuYTb+JL/rN2/Op2Out3u1KhqvV5Xo9FIHVFd1zEAAAAwx52335VuQmMQBLq8vJR0M+fWdV05jjOxyYNlWer3+2o2m7IsS4eHhxoMBiqXy6pUKqllr+sYAMCW+P3epmuwOr9/v+kaAI/GvcKt67qZX2tZ1lKvX+cxAAAAMMPK59wCAABg+5TLZRUKBe3v789dceqhIdwCAAA8Qr1eT81mM9MOrw8J4RYAABir3W7PvOF9W62zzq9evVrLedaJcAsAAIw1GAw2XYWlPcQ6bxPCLQAAMNZDnEv6EOu8TQi3AADASEEQzNy5dFs9xDpvG8ItAAAwThiGKpfLm67GUh5inbfRvda5BQAAmxWGoarVqoIgUBAEiqJInufJ9/1k7ubt9d9939f5+bkKhYLCMNRgMFC9XpfjOFPlB0GgVqsly7KSxwqFgmq1WmpdTk5OknKvrq5SXzte5+FwqH6/L0nqdruSbuacBkGgTqczcd7Y6elp8nh8p7/jOAqCQLVaTe12e+LYarWqg4OD5Pher7dU292u79u3byfaqlqtyvd9DYdDtVqt1LZZ1I5Z6zxumc9RkprNpg4PDyVJV1dXevnypZG7txJuAQB4wCzLSpZ0Oj09led5CsNQjUZD9Xpd7XZ7ItzGf48DpXQT8orFolqt1sSOnt1uV81mU71eT7ZtT7z+9PRUjUYjeczzPDWbTb19+3YiwLXbbZXL5YngdrvO8aX48fKazaaKxeLUzVW3y4oVi0UdHR1Jkmq1mmq1mjzPS14/Xv9l286yLHU6HZ2cnOj09HSqnE6no263q2q1mvYRZWrHrHUeb9esn6Pv+6pWq+p0OhPB1/d9NZvNmed4qJiWAACAAeLL2b1eLwk2rVZrYsTP933V63V1Op2JYy3LUrPZ1PHxcfJYEASqVqtqtVpTIavdbqvZbCbLVcWX09NCZ61Wk+M4E2XH4jDquu5EsJWker2uIAgmbq6K56Kmjea2Wq3pRskoS9tZlpXUN82sEdBl2jGrZT5HSfrqq6/UbDanRnQdxzFyGgThFgAAgxQKheT/LcuaCF3Hx8cqlUqpI4KvXr1SGIbJ1IBmsynLsiZGAMdZlpVcNj8+PpbjODNHGuv1urrd7swbpdICVlz27Q0GPM9LDYN5XF6f13aLpAVuabl2zGrZzzEMw9SpEpJmTmF4yAi3AAAY5MWLFzOf831/ZgCNw9nFxUXy2lllNRoNjUaj5Jhutzv3vPE5z8/P5z6fVp/hcJg8FgfoYrGYBObb9bqPee/hrpZpx2XKzPo5ep5nZICdhzm3AAAYZFZQikdNgyBQu91OfY3rukkQC4IgUyiKR1EXBTTLsmaO3C4zctnv93V8fKx2u528D8dxdHZ2du8Qt2zIzCJrO2a17Ofo+76RN43NQ7gFAMAgs4JiHNwcx5l5ifq2PLeADcNwYhT2ruKbuyQlKxucn5+rWCzKdd1M7y0Mw9Qgu+z0gKzyaMe4znf5HB8bpiUAAPAIxJexswateGmtrOXenhub5r6X/eMwGyuVSmo0Gur3+2o0Gpnv/H/z5s296pFm1vvP2o6LxHVe9nO0bTuXf1Q8JIRbAAAeiUqlsnD3q3h1glKppDAM54bW8dfO2zI2fm7WUllZhWE4c95uq9VaWN+4jLuaN21hVthcph1nuX3ssp/jvNfepz22FeEWAIBH4uzsbGp5rXHdbje5NB9vODBrNLTdbiejiK7rzt021nVdlUqlXOZ+zppnKt2MUo4H0LRRziAI5q4fO0/cNmkjobPadJl2zFrnu3yOt2++W1Tvh4xwCwCAAeIRuHmXoONNC+IdtcbFO2yN3/z09u1bvXnzZioYxcErDly2bavT6SS7eI3rdrvJbmO3xXVNq/O8EcW0jRTa7bbq9frEY7Zty3GciXN7njcVsrO0nfTr8mC3dwsbX71gfFOFWNZ2zFrnZT7HeI5y2nq6nufp6uoqOc4UO1EURZuuxKZdX19rb29P79+/1+7u7lrO+cU3363lPJvy7tuvN10FABv2448/6vvvv9eXX36pTz/9dNPVMVa8Nezl5aXCMJRt27JtW81mc+ZIaRiGyUhioVCQZVmybTv19bdfGwextHVb4y1m4/Vir66udHh4OLVE1+1tb+M6x5tAxKEtCAJZlqUXL14ko8NhGMpxHHW7XVmWlQRT27ZT6xSfy7ZtFQoFlUqlJPjl3XY7OzvJ32+v3rBMO86rc9a6zHpt/Lq4HeNzxMfe3mFuFe763ZA1rxFuRbhdBcItAMItgDSrDrdMSwAAAIAxCLcAAAAwBuEWAAAAxiDcAgAAwBiEWwAAABiDcAsAAABjEG4BAABgDMItAAAAjEG4BQAAgDEItwCAlWIjTADjVv2dQLgFAKzERx/d/Ir561//uuGaANgmP//8syTp448/Xkn5hFsAwEp88skn+uSTT/SnP/1p01UBsEXev3+vp0+f6smTJyspn3ALAFiJnZ0dPXv2TO/fv9ef//znTVcHwBYYjUb68OGDLMvSzs7OSs6xmsgMAICkv//7v9ef//xn/eu//qt2d3f17Nkzffzxxyv7pQZgu0RRpF9++UU//vij/vSnP+m///u/tb+/r/39/ZWdk3ALAFiZjz/+WM+fP9d//ud/6sOHDwrDcNNVArABH330kT777DN9/vnn2tvbW+m5CLcAgJX6+OOP9Y//+I/6h3/4B/3lL3/RL7/8sukqAVijjz76SE+ePEluMl01wi0AYC12dnb0N3/zN5uuBgDDEW6xEl98811uZb379uvcygIAAGZjtQQAAAAYg3ALAAAAYxBuAQAAYAzCLQAAAIxBuAUAAIAxCLcAAAAwBuEWAAAAxiDcAgAAwBiEWwAAABiDcAsAAABjEG4BAABgDMItAAAAjEG4BQAAgDEItwAAADAG4RYAAADGINwCAADAGIRbAAAAGINwCwAAAGMQbgEAAGAMwi0AAACMQbgFAACAMQi3AAAAMAbhFgAAAMYg3AIAAMAYhFsAAAAYg3ALAAAAYxBuAQAAYAzCLQAAAIxBuAUAAIAxCLcAAAAwBuEWAAAAxiDcAgAAwBiEWwAAABiDcAsAAABjEG4BAABgDMItAAAAjEG4BQAAgDEItwAAADAG4RYAAADGINwCAADAGIRbAAAAGINwCwAAAGMQbgEAAGAMwi0AAACMQbgFAACAMZ7c5aAgCNRqtSRJw+FQYRiqXC6r0WjMPCYMQ52cnEiSDg8PNRgMVC6XValUNn7MQ/fu09/lUs4XP/4hl3IAAAA2Zelw2+12dXFxIdd1Jx4vFotyXVeDwWDqmDAMVSwW1el05DhO8ni9XtfFxUUSlDdxDAAAAMyx1LSEMAx1fn6eGhLPzs4UBIHq9frUc9VqVZVKZSJwSpLrumq32/I8b2PHAAAAwBxLhdvLy0t1u101m82p5+JAeTtABkEgz/NSQ68kvXr1aiosr+sYAAAAmGWpcHtwcCDLsnR4eDjzNZZlTfw9nr5g23bq6wuFgjzPUxiGaz8GAAAAZlkq3DqOo9FolHrjmO/7kqRSqTT1+O3AOy4Oo5eXl2s/BgAAAGa502oJaZrNpmzbTp0ucHBwMPO4OJAGQbD2Y267vr6e+PvTp0/19OnTma8HAADAdrn3OrfxTWS2baeulDAcDueOqMaBdHy6wLqOue358+fa29tL/sRLigEAAOBhuPPIbbwkWBAEsm1b5XI59XVZ57heXV2t/ZjbfvjhB+3u7iZ/Z9QWAADgYblzuK1UKhMbI5TLZbmuq7Ozs7kjqNtsd3d3Itw+NnltBiGxIQQAANiM3Obcdjod7e/vKwxD9Xq95HHLsjKNqo6vwLCuYwAAAGCWe8+5jVmWpUqlIs/zJta6nXeTl3QzVzY+ft3HAAAAwCxL71A2b7WBeLmt8ZFb27aTYDmrzPFj13kMAAAAzLJUuN3f31ehUJh5+T++5D/+vOM4c6cLxGF5fH3cdR0DAAAAsywVbi3Lkm3bMy/tx0uBFYvF5LGjoyNJv27ycNvFxcVU4FzXMQAAADDLUuG2VqtNTDm47c2bN7IsS69evUoecxxHpVJJ5+fnqcd0u101m82Jx9Z1DAAAAMyyVLhttVpqtVoTN4zFqtWqJOnt27dTI7udTkfdbndqVLVer6vRaKSOqK7rGAAAAJhj6aXAXNdVt9tVtVrVwcGBhsOhwjCU4zj6/vvvU6csWJalfr+vZrMpy7J0eHiowWCgcrk8sVbuJo4BAACAOXaiKIo2XYlNu76+1t7ent6/f7+2TRy++Oa73MrKc/OFvOS5icO7b7/OrSwAAPAwZc1rua1zCwAAAGwa4RYAAADGINwCAADAGIRbAAAAGINwCwAAAGMQbgEAAGAMwi0AAACMQbgFAACAMQi3AAAAMAbhFgAAAMYg3AIAAMAYhFsAAAAYg3ALAAAAYxBuAQAAYAzCLQAAAIxBuAUAAIAxCLcAAAAwBuEWAAAAxiDcAgAAwBiEWwAAABiDcAsAAABjEG4BAABgDMItAAAAjPFk0xWAmd59+rscS3ufY1kAAMBkjNwCAADAGIRbAAAAGINwCwAAAGMQbgEAAGAMwi0AAACMQbgFAACAMQi3AAAAMAbhFgAAAMYg3AIAAMAYhFsAAAAYg3ALAAAAYxBuAQAAYAzCLQAAAIzxZNMVABb54pvvcivr3bdf51YWAADYPozcAgAAwBiEWwAAABiDcAsAAABjEG4BAABgDMItAAAAjEG4BQAAgDEItwAAADAG4RYAAADGINwCAADAGIRbAAAAGINwCwAAAGMQbgEAAGAMwi0AAACMQbgFAACAMQi3AAAAMAbhFgAAAMYg3AIAAMAYhFsAAAAYg3ALAAAAYxBuAQAAYAzCLQAAAIxBuAUAAIAxCLcAAAAwBuEWAAAAxiDcAgAAwBiEWwAAABiDcAsAAABjEG4BAABgDMItAAAAjEG4BQAAgDGebLoCj9W7T3+36SoAAAAYh5FbAAAAGINwCwAAAGMQbgEAAGAMwi0AAACMQbgFAACAMQi3AAAAMAbhFgAAAMYg3AIAAMAYhFsAAAAYg3ALAAAAYxBuAQAAYAzCLQAAAIxBuAUAAIAxCLcAAAAwxpNlD/B9X67rajgcyvd9WZaler2uWq0285gwDHVyciJJOjw81GAwULlcVqVS2fgxAAAAMMdS4bbdbkuSXNdNHvM8T9VqVa1WS/1+X5ZlTRwThqGKxaI6nY4cx0ker9fruri4UKvVmjrPuo4BAACAWTJPSwiCQGEYTo3QlkolvX37VkEQqFqtTh1XrVZVqVQmAqd0E5Db7bY8z9vYMQAAADBL5nDruu7MqQeO46hUKsnzPAVBkDweBIE8z1O9Xk897tWrV1Mjqus6BgAAAObJHG49z9OXX36pMAxTn49HTH3fTx6Lpy/Ytp16TKFQkOd5E2Wu6xgAAACYJ3O4PTg4UBiGEyOzi8Q3nM0Sh9HLy8u1HwMAAADzZL6hrNfrKQiCmaOjcegdn/MaBIEODg5mlhkH0ttTGdZxTJrr6+uJvz99+lRPnz6dewwAAAC2x1Lr3M4KtpLU7XblOM7Ea4bD4dwR1TiQjk8XWNcxaZ4/f669vb3kT7ysGAAAAB6Gpde5TXN6eipJOjs7m3g86xzXq6urtR+T5ocfftDu7m7yd0ZtAQAAHpZ7h1vf99VsNqfWl32Idnd3J8ItAAAAHpZ7b79brVblum7qLmCWZWUaVT08PFz7MQAAADDPvcJttVqdu/XuvJu8pJu5spIm5suu6xgAAACY587httls6uXLl2o0GjNfY9t2EizTxKOt4zehresYAAAAmOdOc27b7bYKhULqiG0YhskIqeM4c7e9jZfmKpVKyWPrOgYPx7tPf5djae9zLAsAAGybpUduu92uJKUG23gb3NjR0ZGkyV3Lxl1cXEwFznUdAwAAAPMsFW5939dwOJw5x9bzvIkVExzHUalU0vn5eerru92ums3mxGPrOgYAAADm2YmiKMrywiAIVC6XZ46ADodDeZ6n0Wg08XgYhioWi1NLhdXrdVmWpVarNVXWuo6JXV9fa29vT+/fv1/fUmC/31vPeTDp90xLAADgIcqa1zKH20KhsHD7Wtu2NRgMph4Pw1DNZlOWZenw8FCDwUDlcjl1+bB1HyMRbh8Vwi0AAA9S7uHWZITbR4RwCwDAg5Q1r917EwcAAABgWxBuAQAAYAzCLQAAAIxxp00cgIfqi2++y62sd99+nVtZAAAgH4zcAgAAwBiEWwAAABiDcAsAAABjEG4BAABgDMItAAAAjEG4BQAAgDEItwAAADAG4RYAAADGINwCAADAGIRbAAAAGINwCwAAAGMQbgEAAGAMwi0AAACMQbgFAACAMQi3AAAAMAbhFgAAAMYg3AIAAMAYhFsAAAAYg3ALAAAAYxBuAQAAYAzCLQAAAIxBuAUAAIAxCLcAAAAwBuEWAAAAxiDcAgAAwBiEWwAAABiDcAsAAABjEG4BAABgDMItAAAAjEG4BQAAgDEItwAAADAG4RYAAADGINwCAADAGIRbAAAAGINwCwAAAGMQbgEAAGAMwi0AAACMQbgFAACAMZ5sugLAQ/XFN9/lUs67b7/OpRwAAMDILQAAAAxCuAUAAIAxmJaAR+Xdp7/LrawvfvxDbmUBAIB8MHILAAAAYxBuAQAAYAzCLQAAAIxBuAUAAIAxCLcAAAAwBuEWAAAAxiDcAgAAwBiEWwAAABiDcAsAAABjEG4BAABgDMItAAAAjEG4BQAAgDEItwAAADAG4RYAAADGINwCAADAGIRbAAAAGINwCwAAAGMQbgEAAGAMwi0AAACMQbgFAACAMQi3AAAAMAbhFgAAAMYg3AIAAMAYhFsAAAAYg3ALAAAAYxBuAQAAYAzCLQAAAIxBuAUAAIAxCLcAAAAwBuEWAAAAxiDcAgAAwBiEWwAAABiDcAsAAABjPNl0BYCH6t2nv8uppPc5lQMAABi5BQAAgDEItwAAADAG4RYAAADGuPOc23q9rmq1qlKpNPd1YRjq5OREknR4eKjBYKByuaxKpbLxYwAAAGCWpcJtEATyPE+u68r3fVWr1bmvD8NQxWJRnU5HjuMkj9frdV1cXKjVam3sGAAAAJgn87SEdrutZrMpSZnDYrVaVaVSmQickuS6rtrttjzP29gxAAAAMM9OFEXRsgf5vq9isaherzdzWkIQBCoUChoMBrJte+r5er2uIAjU6/XWfsxt19fX2tvb0/v377W7uzv3vefm93vrOQ+23hc//iG3st59+3VuZQEAsE2y5rWV3VDmuq4kpQZOSSoUCvI8T2EYrv0YAAAAmGll4db3fVmWNfP5OIxeXl6u/RgAAACYaWU7lAVBoIODg5nPx4E0CIK1HzPL9fX1xN+fPn2qp0+fLjwOAAAA22FlI7fD4XDuiGocSMenC6zrmFmeP3+uvb295E+8tBgAAAAehpWN3Gad43p1dbX2Y2b54YcfJiYoM2oLAADwsKws3D5Eu7u761stAQAAALlb2bQEy7IyjaoeHh6u/RgAAACYaWXhdt5NXtLNXFlJE/Nl13UMAAAAzLSycGvbdhIs08SjrePr067rGAAAAJhpZeHWcZy50wXipbnGdzhb1zEAAAAw08rC7dHRkaSbTRbSXFxcTAXOdR0DAAAAM6105LZUKun8/Dz1+W63q2azuZFjAAAAYKY7hdv4Uv+iVQo6nY663e7UqGq9Xlej0UgdUV3XMQAAADBP5nVuu92uXNeVJF1eXkqSjo+Pk8eq1apqtdrEMZZlqd/vq9lsyrIsHR4eajAYqFwuq1KppJ5nXccAAADAPDtRFEWbrsSmXV9fa29vT+/fv1/fJg6/31vPebD1vvjxD7mV9e7br3MrCwCAbZI1r61szi0AAACwboRbAAAAGINwCwAAAGMQbgEAAGAMwi0AAACMQbgFAACAMQi3AAAAMAbhFgAAAMbIvEMZgO33xTff5VYWG0IAAB4iRm4BAABgDMItAAAAjMG0BGDD3n36u9zK+uLHP+RWFgAADxEjtwAAADAG4RYAAADGINwCAADAGIRbAAAAGINwCwAAAGMQbgEAAGAMwi0AAACMQbgFAACAMQi3AAAAMAbhFgAAAMYg3AIAAMAYhFsAAAAYg3ALAAAAYxBuAQAAYIwnm64AgO30xTff5VbWu2+/zq0sAADmIdwCBnn36e9yK+uLH/+QW1kAAKwL0xIAAABgDMItAAAAjEG4BQAAgDEItwAAADAG4RYAAADGINwCAADAGIRbAAAAGINwCwAAAGMQbgEAAGAMwi0AAACMwfa7AFbui2++y6Wcd99+nUs5AABzEW4BpHr36e9yK+uLH/+QW1kAAMzDtAQAAAAYg3ALAAAAYxBuAQAAYAzCLQAAAIxBuAUAAIAxCLcAAAAwBkuBAXgw8lovV2LNXAAwFSO3AAAAMAbhFgAAAMYg3AIAAMAYzLkFsHJ5beXLNr4AgEUYuQUAAIAxCLcAAAAwBuEWAAAAxiDcAgAAwBiEWwAAABiDcAsAAABjEG4BAABgDNa5BfBg5LVe7o33OZYFANgWhFsAj9IX33yXW1nvvv06t7IAAPfDtAQAAAAYg3ALAAAAYxBuAQAAYAzCLQAAAIxBuAUAAIAxWC0BAO6JlRcAYHsQbgE8SnmumfvFj3/IrSwAwP0wLQEAAADGINwCAADAGIRbAAAAGIM5twCwRbg5DQDuh3ALAPfEzWkAsD2YlgAAAABjEG4BAABgDKYlAICh8pq/y9xdAA8JI7cAAAAwBiO3ALBFtvHmNFZwAPCQMHILAAAAYxBuAQAAYAymJQCAofKc4pCf95uuAADDEW4BAGvD/F0Aq0a4BQA8SARlAGkItwCAtdnG1SAAmMWYcBuGoU5OTiRJh4eHGgwGKpfLqlQqG64ZAGDbMQoMmMOIcBuGoYrFojqdjhzHSR6v1+u6uLhQq9XaYO0AAKuwraPAeQblvBC48ZgYEW6r1aoqlcpEsJUk13W1v7+vcrmsUqm0odpN++mnn3Ty//6o1//jqZ4+2dl0dR6Nn36OdPL//US7rxntvhm0+3LyCsp5tjvTLrL76aefdHJyotevX+vp06ebrs6jsM1tvhNFUbTpStxHEAQqFAoaDAaybXvq+Xq9riAI1Ov1ZpZxfX2tvb09vX//Xru7u6us7uT5vnmm3af80lmX658i7X37gXZfM9p9M2j3zdjWdjc9KP/y03/rh//nlZ7/rzf66Oln9yqLUe5s1p2dljnngx+5dV1XklKDrSQVCgW1222FYSjLstZYMwAAtsO2TuHIq17XO5H2JP2fT//nvf9R8cU3Ob4/gvJGPPhw6/v+3NAah97Ly8utmpoAAMBDtJ2bg+Qn1/f3+/yK0u/z2wAlj3nhv/z03znUZDUefLgNgkAHBwczn4+DbxAEM18Tz8z493//d11fXyePP336dCXzSOJzXP/0oGeEPDhxe9Pu60W7bwbtvhm0+2Y8hnb/v/7vTm5l/Z9P/+e9y7jeifRcmshNqxafa9GM2gcfbofD4cwpCZKS4BuG4czXfPjwQZL0L//yL7nWbZHn//tPaz0fbtDum0G7bwbtvhm0+2aY3e6vcitpL7eSpOfPn+dYWjYfPnzQ3t7sd/Hgw+280Dru6upq5nOff/65BoOBPvnkE+3s/DpXZ1UjtwAAAFhOFEX68OGDPv/887mve/DhNg8fffTR3NFfAAAAbN68EdvYR2uox0pZlpVp9Pbw8HD1lQEAAMBGPfhwO+9mMulmTq4klgEDAAB4BB58uLVtOwmwaeJRXaYdAAAAmO/Bh1vHceZOS4iXAGONWwAAAPM9+O13fd9XsVhUv9+X4zhTz1erVYVhOHf73bsKw1AnJyeSbub0DgYDlctlVSqVrSrTNHm3UbvdVqfTUb1eV6lUkmVZCoJAvu/r/Pxcr1+/Tu1bj1W9Xle1Wr33Pxjp68vJo93p69n5vi/XdTUcDpPNgur1umq12p3LpM8vlne70+ezC4JArVZL0s2UzjAMVS6X1Wg07lzmxvp8ZIBSqRQ1Go3U5yRFvV4v93OORqPItu2o3+9PPF6r1WbWZRNlmmYVbdRqtSJJU38sy1pJ33mIBoNB5Lpu5DhOLj9T9PVs8m53+no2rutGrutOPNbr9SLLsiLbtqPRaLR0mfT5xVbR7vT5bDqdTmo/dBwnsm37TmVuss8bEW430YDzAvVdf2hWUaZpVtFGrVYrcl03ajQaUaVSiWq12tQX7GPmum5UqVQi13WjXq+XS8iiry+2inanry82GAyiVquV+ly/348kRaVSaely6fPzrard6fOLjUajqFKppD4Xt32tVlu63E32eSPCbRTdfDhxmG21WlGtVos6nc5KzjUYDCJJ0WAwSH2+Vqst/UO4ijJNs6o2arVadxoReIziL7r7fCnR15eXR7tHEX09i0ajMbeNSqXS3P6bhj6/2CraPYro81nE/3iedwV82dHbTff5B39DWcyyLLmuq1arpUajIdd1Vzanw3VdSbNXYCgUCvI8L/Puaasq0zS0kRn4HLHNPM/Tl19+ObP/xfMzfd/PXCZ9frFVtDuyOTg4kGVZc/cDWHY51U33eWPC7TrFk9xniT/My8vLjZZpGtrIDHyO2GYHBwcKwzBZaScP9PnFVtHuyMZxHI1Go9Qbx+J/TCx7I+um+zzb795BEARzN4+IP9BlfkhXUaZp1tFGvu/r8vJSL1684A7aFaGvbwf6erper6cgCGaOOMX9cpk2o88vtop2v40+v7xmsynbtpNVFLLadJ9n5PYOhsPh3H+RxB/oMsPtqyjTNKtsI8/zdHp6KknJkjPlclme5y1dFuajr28WfX2xeZv+dLtdOY6z1MZA9Pls8m73GH1+eUEQqF6vy7ZtDQaDpY/fdJ9n5PYOsn4YV1dXGy3TNKtqo/jLcvySjOM46nQ62t/fn7mGMu6Gvr459PX7iQPS2dnZUsfR5+/nru0u0eeX1e12dXFxkYyil8vlO5Wz6T5PuMWjN+vGQ8uyVKlUVK1W7/QvV2Db0Nfvzvd9NZtNdTodwtAa3bfd6fPLqVQqE21WLpfluq7Ozs6Wvqlsk5iWcAeWZWX6V8m8Ow/XUaZpNtFGL1++VBAEj3ouXN7o69uJvj5ftVq98yo89Pm7u0+7L0KfX6zT6ajb7aparS513Kb7POH2DuZNkpZu5ppIyy2dsYoyTbOJNorLYvmZ/NDXtxN9fbZqtXqvLWDp83dz33ZfhD6/WDzC7XneUnOUN93nCbd3YNt28sGkif+1sszE91WUaZpVtFG9XlehULhv1bAE+vpm0Nfvptls6uXLl6nLJGVFn19eHu1On89m0RJscb/s9XqZy9x0nyfc3oHjOHOH2+NOssy6cKso0zSraKPLy8tMP4DMscsPfX0z6OvLa7fbKhQKqQFrmbu86fPLyavd6fPZ7O/vq1AozGzbeOrAQ+rzhNs7ODo6kjT7UsbFxcXSH9gqyjTNKtqoVCppNBrNfP7i4kKWZTGikiP6+mbQ15fT7XYlKfWSeBAES12ipc9nl2e70+ezidtg1hSB+Ia7YrGYucyN9/mVbexruFKpNHcf5rQ94EejUdRoNGbuD3+XMh+bvNu93+9Hruumlhfvjd3pdO5XaYP0+/1MfZG+nq882p2+nt28toqiKHJdNxoMBhOP0efvL+92p89n02g0ptp1nGVZkWVZ0Wg0mnh8m/s84faORqNRZNt21O/3Jx6v1WozP8xWqxVJiizLyq3Mx2YV7d5qtaJWqzXx2GAwiCzLot1v6XQ6mX4h0NfzlVe709cXGwwGkW3bUa1WS/1TqVRS25c+fz+ranf6fDa1Wi01bMbtfrvfRtF29/mdKIqi1Y0Lmy0MQzWbTVmWpcPDQw0GA5XL5ZlLlvi+r6+++kqvXr2S67q5lPkYraLdPc9Tp9PRcDhUGIayLEuvX79+9HOxpJvLhHG7XV5eJu3z4sULSTd3NN++hEhfv79VtTt9fb5CobBwaai0XZvo8/ezynanz2fT7XZ1fn6ug4ODpK0cx9Hr169Tpyxsc58n3AIAAMAY3FAGAAAAYxBuAQAAYAzCLQAAAIxBuAUAAIAxCLcAAAAwBuEWAAAAxiDcAgAAwBiEWwAAABiDcAsAAABjEG4BAABgDMItADwA3W5XOzs7E3/CMNx0tTaq2WxOtMf+/v6mqwRgCzzZdAUAANlYlqW3b99O/P0xe/36tY6OjiRJnufp5ORkwzUCsA0YuQWAB+Lg4ECO4yR/sgjDUO12W+VyWYVCQfv7+yoUCioWizo9PZ0a/fU8T91uN5f61ut17e/vT/3Z2dnR6elp5vrHo7Ljx7fbbVmWtXR7ADAf4RYADNVsNrW/v69Wq6Vqtap+v6/RaKTBYKB+vy/btvXVV19NhNl6vZ7b+V3X1Wg0UqfTURiGCsNQpVJJURSp0WhkKsOyLI1GI7148UJhGKpWq2k0GqlWq+VWTwBmYVoCABgmCAKVy2UFQaBOp6NKpZL6ukqlokqlonq9riAIZFmWgiDIvT6lUkmlUkme593peMuyVK1WZVmWWq1WzrUDYBpGbgHAIL7vq1AoaDgcajAYzAy241zX1WAwyHXU9ra47LtOeej3+wRbAJkQbgHAEGEY6quvvpIkdTod2bad+VjXdVd6g1qlUknKb7fbSx8fBMFS7wfA40W4BQBDHB8fKwxDVSoVlUqlpY8/OztbQa1+9erVK0k3QXoZ7XZb1Wp1FVUCYCDCLQAYwPf95JL/XS/fZ5nCcB/x1ATf95ea29vpdLiBDEBmhFsAMEC8xqtt2/e6fH+XEd+sHMdJ6pZ19JbpCACWRbgFAAPEKxHcd/R11Zf/m82mpOzzbl3XXemNbgDMQ7gF8OjU6/VkU4Pxu/fDMFS9Xle1WlW5XFa1Wl3J0lh5830/2Yzh5cuX9yqrVqstDMhBECTtFLdl1raK592GYZhpaTDP89igAcBSCLcAHpU4lPV6PVUqFVWrVfm+L8/zVK1W1Ww21el01Ov19PLlSxUKBfm+v+lqzzUcDpP/X/WWvJ7nJWG20+nIdV31ej2Vy2UVi8WFgdWyrCQ8L5ob7Hlesr0uAGRFuAXwaIRhqCAIknmlhUJB0s2l+Dikjc/vbDQayQYC22x8xPTg4GBl5+l2uyqXy3Jdd2pubq1W06tXr5LNI+aJpxl4nje1/e8413W5kQzA0gi3AB6N20tKDQYDSTcjn51OJ/UY27YVBMHE6G08paFQKKhYLKpcLs98rFgsJo/H803zNh5ox0dx83Z8fCzHcWbedBa/v0UjsqVSaeGat3HoXfVINADzEG4BPBq9Xi+Z8yn9ehPWvDAWh6zLy0tJN6Okl5eXarVaGgwG6vf76vV66vV6yfa18bSGXq+nfr+vfr8vy7J0eHi4kvc1HgDnjYTex+npqcIwnDtNIF6pIcvNYvGI7KxVE9rtNlMSANwJ4RbAo9FqtSaCYDwaO2/5q/gSexwau92uzs7OUm+68n0/dSkuy7J0dHS0siWtXrx4kfz/MiO38YjyrD/jI83n5+eStPDmrvg9LgrZ8dSE26Pi4+db9bq7AMz0ZNMVAIB1GQ9m8aitZVkzQ+f43NH4NYPBQI1GY+q1cXmzgrJlWSu76z9+D0EQqNfrZZ6nGk/LiFcuiKds1Gq1mf8QiEelZ4nbbDgczp1SYNu2SqWSPM+T67oTI7i+7690vV0AZiPcAniU4oA2Pk3htvE7/x3HURAEKhaLc8srl8upzw8Gg5XeHFWpVHR6epppea3b4hUMLMtSGIYql8szg2mz2Zw7Ar3M7mj1el2e56ndbk+EW9d1VzY/GYD5mJYA4FGKQ+CsMCr9GljjqQZhGM4Mw/HI5qzR2XnnycPr168lZV8/9q7ynNM7Pu1gfL1hdiUDcB+EWwCPUpb5tnHgikcRHceZOaLped7cKQ6rvsxuWVYyXWIVo57x+8p7NYZ4NDvePvj2ihYAsCzCLYBHJx7ZtG17ZliN7/i3bXvhdIIsQXkdWq2WHMeR7/s6PT1d+vh5o7LxDWBZNrRYZle38XLjlSZY2xbAfRBuATw649MN0oRhmIx+zlr/dlyWKQ7r8vbtW9m2rWazOXGpf5FFUxkajYZs2565dNe4ZUaOHcdJPodF83kBIAvCLYBHJw5y8dq1t1WrVYVhqE6nk2mFgzgsb3rkVrqZntDv9+U4TrKd8CJBEKjZbC4Mrp1OR0EQzB0VjrfnXUY8etvtdpP/B4C7ItwCeHTiS+tnZ2dT8zvr9bouLy/V6/Uyr7O6aL7tusUBt9Vq6fT0VPv7+zo9PZ2aLhAH1Wq1qrOzM9VqtbkB3XEc9fv9masZdLtd+b6/9LSC+PW2ba9suTQAjwdLgQF4VMbn28bhtVgs6uDgQMPhUKVSSd9//33mbV+3Zb5tmkajoVqtpjdv3qjT6ch1XQ2HQx0cHCRh/OjoSP1+Pzmm1+vNnXvrOI4Gg4GazaaKxaJs2062/61Wq6lrAC8SL0X28uXLpY8FgNsItwAeldtTCCqVyr12woqnNmxrMLMsS7VabanR1CzBfpn1bLPIMrcZALJgWgKARyXvm7+2ab4tAIBwC+CRWbTZwrLisMxcUQDYDoRbAI9GHETzuvnL932FYUiwBYAtwpxbAI9GHlMIut2uzs/PFYZhMt82CAKVy2VZlqVyucwmBACwQYRbAI9CGIZLbWowy31vQAMArBbTEgAYb39/X/v7+xoOh5JuRl93dna0v7+/4ZoBAPJGuAVgvNFopCiKkv/Gf0aj0aartpQgCLSzs5P8mbce7WPQbDaTttiGrY8BbIedKIqiTVcCADBfGIZTO4w99hvZaBMAaQi3AAAAMAbTEgAAAGAMwi0AAACMQbgFAACAMQi3AAAAMAbhFgAAAMYg3AIAAMAYhFsAAAAYg3ALAAAAY/z/es2GaQD3vksAAAAASUVORK5CYII=",
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\n",
       "text/plain": [
        "<Figure size 800x600 with 1 Axes>"
       ]
@@ -688,7 +1027,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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\n",
       "text/plain": [
        "<Figure size 800x600 with 1 Axes>"
       ]
@@ -700,6 +1039,22 @@
    "source": [
     "evaluated_events, reconstructed_particles, particles, matched_tracks, tracks = evaluate_candidates(CONFIG)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "00"
+   ]
   }
  ],
  "metadata": {
diff --git a/LHCb_Pipeline/testing.py b/LHCb_Pipeline/testing.py
index 39a0430b..5b0934c0 100644
--- a/LHCb_Pipeline/testing.py
+++ b/LHCb_Pipeline/testing.py
@@ -6,6 +6,9 @@ from Scripts.Step_3_Train_GNN import train as train_gnn
 from Scripts.Step_4_Run_GNN import train as run_gnn_inference
 from Scripts.Step_5_Build_Track_Candidates import train as build_track_candidates
 from Scripts.Step_6_Evaluate_Reconstruction import evaluate as evaluate_candidates
+from Scripts.Step_6_Evaluate_Reconstruction_MonteTracko import (
+    evaluate as evaluate_candidates_montetracko
+)
 
 import sys
 from Scripts.utils.convenience_utils import get_example_data, plot_true_graph, get_training_metrics, plot_training_metrics, plot_neighbor_performance, plot_predicted_graph, plot_track_lengths, plot_edge_performance, plot_graph_sizes
@@ -39,3 +42,4 @@ run_gnn_inference(CONFIG)
 build_track_candidates(CONFIG)
 
 evaluated_events, reconstructed_particles, particles, matched_tracks, tracks = evaluate_candidates(CONFIG)
+_ = evaluate_candidates_montetracko(CONFIG)
diff --git a/gpu_environment.yaml b/gpu_environment.yaml
index 892c82e4..91cd24ff 100644
--- a/gpu_environment.yaml
+++ b/gpu_environment.yaml
@@ -25,4 +25,6 @@ dependencies:
     - selenium
     - geckodriver
     - firefox
-    
+    - ipywidgets
+    - tabulate
+    - black
diff --git a/installation.txt b/installation.txt
index a9c1fd11..7681f617 100644
--- a/installation.txt
+++ b/installation.txt
@@ -1,5 +1,11 @@
 - Install dependencies
 conda env create -f gpu_environment.yaml
 
+jupyter nbextension enable --py widgetsnbextension  # activate jupyter widgets
+
 - Export
-conda list --export > package-list.txt
\ No newline at end of file
+conda list --export > package-list.txt
+
+- Submodules
+git submodule init
+git submodule update
\ No newline at end of file
diff --git a/montetracko-setup.sh b/montetracko-setup.sh
deleted file mode 100755
index b61baa3f..00000000
--- a/montetracko-setup.sh
+++ /dev/null
@@ -1,32 +0,0 @@
-# Some instruction to be able to use MonteTracko
-
-# 1. Initialise the MonteTracko git sub-module
-# git submodule init
-# git submodule update
-
-# # 2. Install new required packages
-# conda activate ext4velo # don't forget to activate your environment!
-# mamba install -c conda-forge ipywidgets tabular black
-# jupyter nbextension enable --py widgetsnbextension  # activate jupyter widgets
-
-# # 3. Add MonteTracko to your PYTHONPATH
-currentdir=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )
-montetracko_dir="$currentdir/montetracko"
-
-cd $CONDA_PREFIX
-mkdir -p ./etc/conda/activate.d
-mkdir -p ./etc/conda/deactivate.d
-touch ./etc/conda/activate.d/env_vars.sh
-touch ./etc/conda/deactivate.d/env_vars.sh
-
-cat <<EOF > ./etc/conda/activate.d/env_vars.sh
-#!/bin/sh
-
-export PYTHONPATH="$PYTHONPATH:$montetracko_dir"
-EOF
-
-cat <<EOF > ./etc/conda/deactivate.d/env_vars.sh
-#!/bin/sh
-
-unset PYTHONPATH
-EOF
\ No newline at end of file
-- 
GitLab


From 8364e1b12c4947baae1a84f04d9c1e8e97778afd Mon Sep 17 00:00:00 2001
From: Fotis Giasemis <Fotis.Giasemis@cern.ch>
Date: Fri, 24 Mar 2023 17:56:22 +0100
Subject: [PATCH 25/30] Prepare for merge with main

---
 LHCb_Pipeline/testing.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/LHCb_Pipeline/testing.py b/LHCb_Pipeline/testing.py
index 5b0934c0..9799c168 100644
--- a/LHCb_Pipeline/testing.py
+++ b/LHCb_Pipeline/testing.py
@@ -1,3 +1,6 @@
+import sys
+sys.path.append('../montetracko')
+
 from Scripts.utils.convenience_utils import *
 from Scripts.utils.plotting_utils import plot_observable_performance
 from Scripts.Step_1_Train_Metric_Learning import train as train_metric_learning
@@ -10,7 +13,6 @@ from Scripts.Step_6_Evaluate_Reconstruction_MonteTracko import (
     evaluate as evaluate_candidates_montetracko
 )
 
-import sys
 from Scripts.utils.convenience_utils import get_example_data, plot_true_graph, get_training_metrics, plot_training_metrics, plot_neighbor_performance, plot_predicted_graph, plot_track_lengths, plot_edge_performance, plot_graph_sizes
 import yaml
 
-- 
GitLab


From e3623877f6545011ac244a5774f07b213b6a9916 Mon Sep 17 00:00:00 2001
From: Fotis Giasemis <Fotis.Giasemis@cern.ch>
Date: Fri, 24 Mar 2023 18:07:09 +0100
Subject: [PATCH 26/30] Import montetracko correctly

---
 .gitlab-ci.yml | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index 6f9dd393..fa70209b 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -6,6 +6,8 @@ stages:
 build-job:
   stage: build
   script:
+      - git submodule init
+      - git submodule update
       - eval "$(micromamba shell hook --shell=bash)"
       - micromamba env create -f gpu_environment.yaml
       - micromamba activate etx4velo
-- 
GitLab


From 203bf158c825e71964825b691afcdaf4f03317cd Mon Sep 17 00:00:00 2001
From: Fotis Giasemis <Fotis.Giasemis@cern.ch>
Date: Fri, 24 Mar 2023 18:11:23 +0100
Subject: [PATCH 27/30] Import montetracko correctly

---
 .gitlab-ci.yml | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index fa70209b..207fedd6 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -3,11 +3,12 @@ image: mambaorg/micromamba:git-bd1178c-focal-cuda-11.7.1
 stages:
   - build
 
+before_script:
+    - git submodule update --init
+
 build-job:
   stage: build
   script:
-      - git submodule init
-      - git submodule update
       - eval "$(micromamba shell hook --shell=bash)"
       - micromamba env create -f gpu_environment.yaml
       - micromamba activate etx4velo
-- 
GitLab


From be6a909af40413292794c4f11f9104608b934b8c Mon Sep 17 00:00:00 2001
From: Fotis Giasemis <Fotis.Giasemis@cern.ch>
Date: Fri, 24 Mar 2023 18:14:58 +0100
Subject: [PATCH 28/30] Import montetracko correctly

---
 .gitlab-ci.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index 207fedd6..aca4ad19 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -4,6 +4,7 @@ stages:
   - build
 
 before_script:
+    - apt-get update && apt-get install -y git
     - git submodule update --init
 
 build-job:
-- 
GitLab


From 99d31ff182d948c3e4871dd1ea4cb59c26611504 Mon Sep 17 00:00:00 2001
From: anthonyc <acorreia@lpnhe.in2p3.fr>
Date: Fri, 24 Mar 2023 18:18:51 +0100
Subject: [PATCH 29/30] use relative path for submodule

---
 .gitmodules | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.gitmodules b/.gitmodules
index cf5c44ea..65ce140b 100644
--- a/.gitmodules
+++ b/.gitmodules
@@ -1,3 +1,3 @@
 [submodule "montetracko"]
 	path = montetracko
-	url = ssh://git@gitlab.cern.ch:7999/gdl4hep/montetracko.git
+	url = ../montetracko.git
-- 
GitLab


From 544f43436b7ff5750a577cbbd8294e63413e2a5b Mon Sep 17 00:00:00 2001
From: anthonyc <acorreia@lpnhe.in2p3.fr>
Date: Fri, 24 Mar 2023 18:19:29 +0100
Subject: [PATCH 30/30] use GIT_SUBMODULE_STRATEGY

---
 .gitlab-ci.yml | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index aca4ad19..6eac78fd 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -3,9 +3,8 @@ image: mambaorg/micromamba:git-bd1178c-focal-cuda-11.7.1
 stages:
   - build
 
-before_script:
-    - apt-get update && apt-get install -y git
-    - git submodule update --init
+variables:
+  GIT_SUBMODULE_STRATEGY: recursive
 
 build-job:
   stage: build
-- 
GitLab